forked from nilearn/nilearn.github.io
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathwhats_new.html
2401 lines (2365 loc) · 182 KB
/
whats_new.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 xmlns="http://www.w3.org/1999/xhtml" lang="en">
<head>
<meta http-equiv="X-UA-Compatible" content="IE=Edge" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>Nilearn: Statistical Analysis for NeuroImaging in Python — Machine learning for NeuroImaging</title>
<link rel="stylesheet" href="_static/nature.css" type="text/css" />
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" type="text/css" href="_static/gallery.css" />
<link rel="stylesheet" type="text/css" href="_static/gallery-binder.css" />
<link rel="stylesheet" type="text/css" href="_static/gallery-dataframe.css" />
<script type="text/javascript" id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
<script type="text/javascript" src="_static/jquery.js"></script>
<script type="text/javascript" src="_static/underscore.js"></script>
<script type="text/javascript" src="_static/doctools.js"></script>
<script type="text/javascript" src="_static/language_data.js"></script>
<script type="text/javascript" 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="Nilearn development process" href="development.html" />
<link rel="prev" title="9.8.10. Advanced decoding using scikit learn" href="auto_examples/07_advanced/plot_advanced_decoding_scikit.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="modules/reference.html#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="development.html" title="Nilearn development process"
accesskey="N">next</a> |</li>
<li class="right" >
<a href="auto_examples/07_advanced/plot_advanced_decoding_scikit.html" title="9.8.10. Advanced decoding using scikit learn"
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="modules/reference.html">Reference</a> | </li>
<li id="navbar-about"><a href="authors.html">About</a>| </li>
<li id="navbar-ecosystem"><a href="http://www.nipy.org/">Nipy ecosystem</a></li>
</ul>
</div>
</div>
<div class="document">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body" role="main">
<div class="section" id="v0-7-0">
<span id="id1"></span><h1>0.7.0<a class="headerlink" href="#v0-7-0" title="Permalink to this headline">¶</a></h1>
<p><strong>Released November 2020</strong></p>
<div class="section" id="highlights">
<h2>HIGHLIGHTS<a class="headerlink" href="#highlights" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Nilearn now includes the functionality of <a class="reference external" href="https://nistats.github.io">Nistats</a> as <a class="reference internal" href="modules/reference.html#module-nilearn.glm" title="nilearn.glm"><code class="xref py py-mod docutils literal notranslate"><span class="pre">nilearn.glm</span></code></a>. This module is experimental, hence subject to change in any future release.
<a class="reference internal" href="nistats_migration.html#nistats-migration"><span class="std std-ref">Here’s a guide to replacing Nistats imports to work in Nilearn.</span></a></li>
<li>New decoder object
<a class="reference internal" href="modules/generated/nilearn.decoding.Decoder.html#nilearn.decoding.Decoder" title="nilearn.decoding.Decoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.Decoder</span></code></a> (for classification) and
<a class="reference internal" href="modules/generated/nilearn.decoding.DecoderRegressor.html#nilearn.decoding.DecoderRegressor" title="nilearn.decoding.DecoderRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.DecoderRegressor</span></code></a> (for regression) implement a model
selection scheme that averages the best models within a cross validation loop.</li>
<li>New FREM object
<a class="reference internal" href="modules/generated/nilearn.decoding.FREMClassifier.html#nilearn.decoding.FREMClassifier" title="nilearn.decoding.FREMClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.FREMClassifier</span></code></a> (for classification) and
<a class="reference internal" href="modules/generated/nilearn.decoding.FREMRegressor.html#nilearn.decoding.FREMRegressor" title="nilearn.decoding.FREMRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.FREMRegressor</span></code></a> (for regression) extend the decoder
object with one fast clustering step at the beginning and aggregates a high number of estimators trained on various splits of the training set.</li>
<li>New plotting functions:<ul>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_event.html#nilearn.plotting.plot_event" title="nilearn.plotting.plot_event"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_event</span></code></a> to visualize events file.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_roi.html#nilearn.plotting.plot_roi" title="nilearn.plotting.plot_roi"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_roi</span></code></a> can now plot ROIs in contours with <cite>view_type</cite> argument.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_carpet.html#nilearn.plotting.plot_carpet" title="nilearn.plotting.plot_carpet"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_carpet</span></code></a> generates a “carpet plot” (also known
as a “Power plot” or a “grayplot”)</li>
<li><a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_img_on_surf</span></code></a> generates multiple views of
<a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_surf_stat_map</span></code></a> in a single figure.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_markers.html#nilearn.plotting.plot_markers" title="nilearn.plotting.plot_markers"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_markers</span></code></a> shows network nodes (markers) on a glass
brain template</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_surf_contours.html#nilearn.plotting.plot_surf_contours" title="nilearn.plotting.plot_surf_contours"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_surf_contours</span></code></a> plots the contours of regions of
interest on the surface</li>
</ul>
</li>
</ul>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">Minimum required version of Joblib is now 0.12.</p>
</div>
</div>
<div class="section" id="new">
<h2>NEW<a class="headerlink" href="#new" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Nilearn now includes the functionality of <a class="reference external" href="https://nistats.github.io">Nistats</a>.
<a class="reference internal" href="nistats_migration.html#nistats-migration"><span class="std std-ref">Here’s a guide to replacing Nistats imports to work in Nilearn.</span></a></li>
<li>New decoder object
<a class="reference internal" href="modules/generated/nilearn.decoding.Decoder.html#nilearn.decoding.Decoder" title="nilearn.decoding.Decoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.Decoder</span></code></a> (for classification) and
<a class="reference internal" href="modules/generated/nilearn.decoding.DecoderRegressor.html#nilearn.decoding.DecoderRegressor" title="nilearn.decoding.DecoderRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.DecoderRegressor</span></code></a> (for regression) implement a model
selection scheme that averages the best models within a cross validation loop.
The resulting average model is the one used as a classifier or a regressor.
These two objects also leverage the <cite>NiftiMaskers</cite> to provide a direct
interface with the Nifti files on disk.</li>
<li>New FREM object
<a class="reference internal" href="modules/generated/nilearn.decoding.FREMClassifier.html#nilearn.decoding.FREMClassifier" title="nilearn.decoding.FREMClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.FREMClassifier</span></code></a> (for classification) and
<a class="reference internal" href="modules/generated/nilearn.decoding.FREMRegressor.html#nilearn.decoding.FREMRegressor" title="nilearn.decoding.FREMRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.FREMRegressor</span></code></a> (for regression) extend the decoder
object pipeline with one fast clustering step at the beginning (yielding an
implicit spatial regularization) and aggregates a high number of estimators
trained on various splits of the training set. This returns a state-of-the-art
decoding pipeline at a low computational cost.
These two objects also leverage the <cite>NiftiMaskers</cite> to provide a direct
interface with the Nifti files on disk.</li>
<li>Plot events file
Use <a class="reference internal" href="modules/generated/nilearn.plotting.plot_event.html#nilearn.plotting.plot_event" title="nilearn.plotting.plot_event"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_event</span></code></a> to visualize events file.
The function accepts the BIDS events file read using <cite>pandas</cite>
utilities.</li>
<li>Plotting function <a class="reference internal" href="modules/generated/nilearn.plotting.plot_roi.html#nilearn.plotting.plot_roi" title="nilearn.plotting.plot_roi"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_roi</span></code></a> can now plot ROIs
in contours with <cite>view_type</cite> argument.</li>
<li>New plotting function
<a class="reference internal" href="modules/generated/nilearn.plotting.plot_carpet.html#nilearn.plotting.plot_carpet" title="nilearn.plotting.plot_carpet"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_carpet</span></code></a> generates a “carpet plot” (also known
as a “Power plot” or a “grayplot”), for visualizing global patterns in
4D functional data over time.</li>
<li>New plotting function
<a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_img_on_surf</span></code></a> generates multiple views of
<a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_surf_stat_map</span></code></a> in a single figure.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_markers.html#nilearn.plotting.plot_markers" title="nilearn.plotting.plot_markers"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_markers</span></code></a> shows network nodes (markers) on a glass
brain template and color code them according to provided nodal measure (i.e.
connection strength). This function will replace
<a class="reference internal" href="modules/generated/nilearn.plotting.plot_connectome_strength.html#nilearn.plotting.plot_connectome_strength" title="nilearn.plotting.plot_connectome_strength"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_connectome_strength</span></code></a>.</li>
<li>New plotting function
<a class="reference internal" href="modules/generated/nilearn.plotting.plot_surf_contours.html#nilearn.plotting.plot_surf_contours" title="nilearn.plotting.plot_surf_contours"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_surf_contours</span></code></a> plots the contours of regions of
interest on the surface, optionally overlayed on top of a statistical map.</li>
<li>The position annotation on the plot methods now implements the <cite>decimals</cite> option
to enable annotation of a slice coordinate position with the float.</li>
<li>New example in
<a class="reference internal" href="auto_examples/02_decoding/plot_haxby_searchlight_surface.html#sphx-glr-auto-examples-02-decoding-plot-haxby-searchlight-surface-py"><span class="std std-ref">Cortical surface-based searchlight decoding</span></a>
to demo how to do cortical surface-based searchlight decoding with Nilearn.</li>
<li>confounds or additional regressors for design matrix can be specified as
numpy arrays or pandas DataFrames interchangeably</li>
<li>The decomposition estimators will now accept argument <cite>per_component</cite>
with <cite>score</cite> method to explain the variance for each component.</li>
</ul>
</div>
<div class="section" id="fixes">
<h2>Fixes<a class="headerlink" href="#fixes" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference internal" href="modules/generated/nilearn.input_data.NiftiLabelsMasker.html#nilearn.input_data.NiftiLabelsMasker" title="nilearn.input_data.NiftiLabelsMasker"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.input_data.NiftiLabelsMasker</span></code></a> no longer ignores its <cite>mask_img</cite></li>
<li><a class="reference internal" href="modules/generated/nilearn.masking.compute_brain_mask.html#nilearn.masking.compute_brain_mask" title="nilearn.masking.compute_brain_mask"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.masking.compute_brain_mask</span></code></a> has replaced
nilearn.masking.compute_gray_matter_mask. Features remained the same but
some corrections regarding its description were made in the docstring.</li>
<li>the default background (MNI template) in plotting functions now has the
correct orientation; before left and right were inverted.</li>
<li><a class="reference internal" href="modules/generated/nilearn.mass_univariate.permuted_ols.html#nilearn.mass_univariate.permuted_ols" title="nilearn.mass_univariate.permuted_ols"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.mass_univariate.permuted_ols</span></code></a> no longer returns transposed
t-statistic arrays when no permutations are performed.</li>
<li>Fix decomposition estimators returning explained variance score as 0.
based on all components i.e., when per_component=False.</li>
<li>Fix readme file of the Destrieux 2009 atlas.</li>
</ul>
</div>
<div class="section" id="changes">
<h2>Changes<a class="headerlink" href="#changes" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference internal" href="modules/generated/nilearn.datasets.fetch_cobre.html#nilearn.datasets.fetch_cobre" title="nilearn.datasets.fetch_cobre"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_cobre</span></code></a> has been deprecated and will be
removed in release 0.9 .</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_connectome_strength.html#nilearn.plotting.plot_connectome_strength" title="nilearn.plotting.plot_connectome_strength"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_connectome_strength</span></code></a> has been deprecated and will
be removed in release 0.9 .</li>
<li><a class="reference internal" href="modules/generated/nilearn.connectome.ConnectivityMeasure.html#nilearn.connectome.ConnectivityMeasure" title="nilearn.connectome.ConnectivityMeasure"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.connectome.ConnectivityMeasure</span></code></a> can now remove
confounds in its transform step.</li>
<li><a class="reference internal" href="modules/generated/nilearn.surface.vol_to_surf.html#nilearn.surface.vol_to_surf" title="nilearn.surface.vol_to_surf"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.surface.vol_to_surf</span></code></a> can now sample between two nested surfaces
(eg white matter and pial surfaces) at specific cortical depths</li>
<li><a class="reference internal" href="modules/generated/nilearn.datasets.fetch_surf_fsaverage.html#nilearn.datasets.fetch_surf_fsaverage" title="nilearn.datasets.fetch_surf_fsaverage"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_surf_fsaverage</span></code></a> now also downloads white matter
surfaces.</li>
</ul>
</div>
</div>
<div class="section" id="v0-6-2">
<span id="id3"></span><h1>0.6.2<a class="headerlink" href="#v0-6-2" title="Permalink to this headline">¶</a></h1>
<div class="section" id="enhancements">
<h2>ENHANCEMENTS<a class="headerlink" href="#enhancements" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Generated documentation now includes Binder links to launch examples interactively
in the browser</li>
<li><a class="reference internal" href="modules/generated/nilearn.input_data.NiftiSpheresMasker.html#nilearn.input_data.NiftiSpheresMasker" title="nilearn.input_data.NiftiSpheresMasker"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.input_data.NiftiSpheresMasker</span></code></a> now has an inverse transform,
projecting spheres to the corresponding mask_img.</li>
</ul>
</div>
<div class="section" id="id4">
<h2>Fixes<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>More robust matplotlib backend selection</li>
<li>Typo in example fixed</li>
</ul>
</div>
<div class="section" id="id5">
<h2>Changes<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Atlas <cite>nilearn.datasets.fetch_nyu_rest</cite> has been deprecated and wil be removed in Nilearn 0.8.0 .</li>
</ul>
</div>
<div class="section" id="contributors">
<h2>Contributors<a class="headerlink" href="#contributors" title="Permalink to this headline">¶</a></h2>
<p>The following people contributed to this release:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Elizabeth</span> <span class="n">DuPre</span>
<span class="n">Franz</span> <span class="n">Liem</span>
<span class="n">Gael</span> <span class="n">Varoquaux</span>
<span class="n">Jon</span> <span class="n">Haitz</span> <span class="n">Legarreta</span> <span class="n">Gorroño</span>
<span class="n">Joshua</span> <span class="n">Teves</span>
<span class="n">Kshitij</span> <span class="n">Chawla</span> <span class="p">(</span><span class="n">kchawla</span><span class="o">-</span><span class="n">pi</span><span class="p">)</span>
<span class="n">Zvi</span> <span class="n">Baratz</span>
<span class="n">Simon</span> <span class="n">R</span><span class="o">.</span> <span class="n">Steinkamp</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="v0-6-1">
<span id="id6"></span><h1>0.6.1<a class="headerlink" href="#v0-6-1" title="Permalink to this headline">¶</a></h1>
<div class="section" id="id7">
<h2>ENHANCEMENTS<a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>html pages use the user-provided plot title, if any, as their title</li>
</ul>
</div>
<div class="section" id="id8">
<h2>Fixes<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Fetchers for developmental_fmri and localizer datasets resolve URLs correctly.</li>
</ul>
</div>
<div class="section" id="id9">
<h2>Contributors<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h2>
<p>The following people contributed to this release:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Elizabeth</span> <span class="n">DuPre</span>
<span class="n">Jerome</span> <span class="n">Dockes</span>
<span class="n">Kshitij</span> <span class="n">Chawla</span> <span class="p">(</span><span class="n">kchawla</span><span class="o">-</span><span class="n">pi</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="id10">
<h1>0.6.0<a class="headerlink" href="#id10" title="Permalink to this headline">¶</a></h1>
<p><strong>Released December 2019</strong></p>
<div class="section" id="id11">
<h2>HIGHLIGHTS<a class="headerlink" href="#id11" title="Permalink to this headline">¶</a></h2>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<div class="last line-block">
<div class="line"><strong>Python2 and 3.4 are no longer supported. We recommend upgrading to Python 3.6 minimum.</strong></div>
<div class="line"><br /></div>
<div class="line"><strong>Support for Python3.5 wil be removed in the 0.7.x release.</strong></div>
<div class="line">Users with a Python3.5 environment will be warned at their first Nilearn import.</div>
<div class="line"><br /></div>
<div class="line"><strong>joblib is now a dependency</strong></div>
<div class="line"><br /></div>
<div class="line"><strong>Minimum supported versions of packages have been bumped up.</strong></div>
<div class="line">- Matplotlib – v2.0</div>
<div class="line">- Scikit-learn – v0.19</div>
<div class="line">- Scipy – v0.19</div>
</div>
</div>
</div>
<div class="section" id="id12">
<h2>NEW<a class="headerlink" href="#id12" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>A new method for <a class="reference internal" href="modules/generated/nilearn.input_data.NiftiMasker.html#nilearn.input_data.NiftiMasker" title="nilearn.input_data.NiftiMasker"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.input_data.NiftiMasker</span></code></a> instances
for generating reports viewable in a web browser, Jupyter Notebook, or VSCode.</li>
<li>A new function <a class="reference internal" href="modules/generated/nilearn.image.get_data.html#nilearn.image.get_data" title="nilearn.image.get_data"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.image.get_data</span></code></a> to replace the deprecated
nibabel method <cite>Nifti1Image.get_data</cite>. Now use <cite>nilearn.image.get_data(img)</cite>
rather than <cite>img.get_data()</cite>. This is because Nibabel is removing the
<cite>get_data</cite> method. You may also consider using the Nibabel
<cite>Nifti1Image.get_fdata</cite>, which returns the data cast to floating-point.
See <a class="reference external" href="https://github.com/nipy/nibabel/wiki/BIAP8">https://github.com/nipy/nibabel/wiki/BIAP8</a> .
As a benefit, the <cite>get_data</cite> function works on niimg-like objects such as
filenames (see <a class="reference external" href="http://nilearn.github.io/manipulating_images/input_output.html">http://nilearn.github.io/manipulating_images/input_output.html</a> ).</li>
<li>Parcellation method ReNA: Fast agglomerative clustering based on recursive
nearest neighbor grouping.
Yields very fast & accurate models, without creation of giant
clusters.
<a class="reference internal" href="modules/generated/nilearn.regions.ReNA.html#nilearn.regions.ReNA" title="nilearn.regions.ReNA"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.regions.ReNA</span></code></a></li>
<li>Plot connectome strength
Use <a class="reference internal" href="modules/generated/nilearn.plotting.plot_connectome_strength.html#nilearn.plotting.plot_connectome_strength" title="nilearn.plotting.plot_connectome_strength"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_connectome_strength</span></code></a> to plot the strength of a
connectome on a glass brain. Strength is absolute sum of the edges at a node.</li>
<li>Optimization to image resampling</li>
<li>New brain development fMRI dataset fetcher
<a class="reference internal" href="modules/generated/nilearn.datasets.fetch_development_fmri.html#nilearn.datasets.fetch_development_fmri" title="nilearn.datasets.fetch_development_fmri"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_development_fmri</span></code></a> can be used to download
movie-watching data in children and adults. A light-weight dataset
implemented for teaching and usage in the examples. All the connectivity examples
are changed from ADHD to brain development fmri dataset.</li>
</ul>
</div>
<div class="section" id="id13">
<h2>ENHANCEMENTS<a class="headerlink" href="#id13" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_img_on_surf</span></code></a>, <a class="reference internal" href="modules/generated/nilearn.plotting.view_surf.html#nilearn.plotting.view_surf" title="nilearn.plotting.view_surf"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_surf</span></code></a>
and <a class="reference internal" href="modules/generated/nilearn.plotting.view_connectome.html#nilearn.plotting.view_connectome" title="nilearn.plotting.view_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_connectome</span></code></a> can display a title, and allow
disabling the colorbar, and setting its height and the fontsize of its ticklabels.</li>
<li>Rework of the standardize-options of <a class="reference internal" href="modules/generated/nilearn.signal.clean.html#nilearn.signal.clean" title="nilearn.signal.clean"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.signal.clean</span></code></a> and the various Maskers
in <cite>nilearn.input_data</cite>. You can now set <cite>standardize</cite> to <cite>zscore</cite> or <cite>psc</cite>. <cite>psc</cite> stands
for <cite>Percent Signal Change</cite>, which can be a meaningful metric for BOLD.</li>
<li>Class <a class="reference internal" href="modules/generated/nilearn.input_data.NiftiLabelsMasker.html#nilearn.input_data.NiftiLabelsMasker" title="nilearn.input_data.NiftiLabelsMasker"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.input_data.NiftiLabelsMasker</span></code></a> now accepts an optional
<cite>strategy</cite> parameter which allows it to change the function used to reduce
values within each labelled ROI. Available functions include mean, median,
minimum, maximum, standard_deviation and variance.
This change is also introduced in <a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.regions.img_to_signals_labels</span></code></a>.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.view_surf.html#nilearn.plotting.view_surf" title="nilearn.plotting.view_surf"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_surf</span></code></a> now accepts surface data provided as a file
path.</li>
</ul>
</div>
<div class="section" id="id14">
<h2>CHANGES<a class="headerlink" href="#id14" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_img.html#nilearn.plotting.plot_img" title="nilearn.plotting.plot_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_img</span></code></a> now has explicit keyword arguments <cite>bg_img</cite>,
<cite>vmin</cite> and <cite>vmax</cite> to control the background image and the bounds of the
colormap. These arguments were already accepted in <cite>kwargs</cite> but not documented
before.</li>
</ul>
</div>
<div class="section" id="id15">
<h2>FIXES<a class="headerlink" href="#id15" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference internal" href="modules/generated/nilearn.input_data.NiftiLabelsMasker.html#nilearn.input_data.NiftiLabelsMasker" title="nilearn.input_data.NiftiLabelsMasker"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.input_data.NiftiLabelsMasker</span></code></a> no longer truncates region means to their integral part
when input images are of integer type.</li>
<li>The arg <cite>version=’det’</cite> in <a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_atlas_pauli_2017</span></code></a> now works as expected.</li>
<li><cite>pip install nilearn</cite> now installs the necessary dependencies.</li>
</ul>
<p><strong>Lots of other fixes in documentation and examples.</strong> More detailed change list follows:</p>
<p>0.6.0rc
NEW
—
.. warning:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>- :func:`nilearn.plotting.view_connectome` no longer accepts old parameter names.
Instead of `coords`, `threshold`, `cmap`, and `marker_size`,
use `node_coords`, `edge_threshold`, `edge_cmap`, `node_size` respectively.
- :func:`nilearn.plotting.view_markers` no longer accepts old parameter names.
Instead of `coord` and `color`, use `marker_coords` and `marker_color` respectively.
</pre></div>
</div>
<ul class="simple">
<li><strong>Support for Python3.5 wil be removed in the 0.7.x release.</strong>
Users with a Python3.5 environment will be warned
at their first Nilearn import.</li>
</ul>
</div>
<div class="section" id="id16">
<h2>Changes<a class="headerlink" href="#id16" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Add a warning to <a class="reference internal" href="modules/generated/nilearn.regions.Parcellations.html#nilearn.regions.Parcellations" title="nilearn.regions.Parcellations"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.regions.Parcellations</span></code></a>
if the generated number of parcels does not match the requested number
of parcels.</li>
<li>Class <a class="reference internal" href="modules/generated/nilearn.input_data.NiftiLabelsMasker.html#nilearn.input_data.NiftiLabelsMasker" title="nilearn.input_data.NiftiLabelsMasker"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.input_data.NiftiLabelsMasker</span></code></a> now accepts an optional
<cite>strategy</cite> parameter which allows it to change the function used to reduce
values within each labelled ROI. Available functions include mean, median,
minimum, maximum, standard_deviation and variance.
This change is also introduced in <a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.regions.img_to_signals_labels</span></code></a>.</li>
</ul>
</div>
<div class="section" id="id17">
<h2>Fixes<a class="headerlink" href="#id17" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference internal" href="modules/generated/nilearn.input_data.NiftiLabelsMasker.html#nilearn.input_data.NiftiLabelsMasker" title="nilearn.input_data.NiftiLabelsMasker"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.input_data.NiftiLabelsMasker</span></code></a> no longer truncates region means to their integral part
when input images are of integer type.</li>
<li><table class="first docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">func:</th><td class="field-body"><cite>nilearn.image.smooth_image</cite> no longer fails if <cite>fwhm</cite> is a <cite>numpy.ndarray</cite>.</td>
</tr>
</tbody>
</table>
</li>
<li><cite>pip install nilearn</cite> now installs the necessary dependencies.</li>
<li><a class="reference internal" href="modules/generated/nilearn.image.new_img_like.html#nilearn.image.new_img_like" title="nilearn.image.new_img_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.image.new_img_like</span></code></a> no longer attempts to copy non-iterable headers. (PR #2212)</li>
<li>Nilearn no longer raises ImportError for nose when Matplotlib is not installed.</li>
<li>The arg <cite>version=’det’</cite> in <a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_atlas_pauli_2017</span></code></a> now works as expected.</li>
<li><a class="reference internal" href="modules/generated/nilearn.input_data.NiftiLabelsMasker.html#nilearn.input_data.NiftiLabelsMasker.inverse_transform" title="nilearn.input_data.NiftiLabelsMasker.inverse_transform"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.input_data.NiftiLabelsMasker.inverse_transform</span></code></a> now works without the need to call
transform first.</li>
</ul>
</div>
<div class="section" id="id18">
<h2>Contributors<a class="headerlink" href="#id18" title="Permalink to this headline">¶</a></h2>
<p>The following people contributed to this release (in alphabetical order):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Chris</span> <span class="n">Markiewicz</span>
<span class="n">Dan</span> <span class="n">Gale</span>
<span class="n">Daniel</span> <span class="n">Gomez</span>
<span class="n">Derek</span> <span class="n">Pisner</span>
<span class="n">Elizabeth</span> <span class="n">DuPre</span>
<span class="n">Eric</span> <span class="n">Larson</span>
<span class="n">Gael</span> <span class="n">Varoquaux</span>
<span class="n">Jerome</span> <span class="n">Dockes</span>
<span class="n">JohannesWiesner</span>
<span class="n">Kshitij</span> <span class="n">Chawla</span> <span class="p">(</span><span class="n">kchawla</span><span class="o">-</span><span class="n">pi</span><span class="p">)</span>
<span class="n">Paula</span> <span class="n">Sanz</span><span class="o">-</span><span class="n">Leon</span>
<span class="n">ltetrel</span>
<span class="n">ryanhammonds</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="b0">
<h1>0.6.0b0<a class="headerlink" href="#b0" title="Permalink to this headline">¶</a></h1>
<p><strong>Released November 2019</strong></p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<div class="last line-block">
<div class="line"><strong>Python2 and 3.4 are no longer supported. Pip will raise an error in these environments.</strong></div>
<div class="line"><strong>Minimum supported version of Python is now 3.5 .</strong></div>
<div class="line"><strong>We recommend upgrading to Python 3.6 .</strong></div>
</div>
</div>
<div class="section" id="id19">
<h2>NEW<a class="headerlink" href="#id19" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>A new function <a class="reference internal" href="modules/generated/nilearn.image.get_data.html#nilearn.image.get_data" title="nilearn.image.get_data"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.image.get_data</span></code></a> to replace the deprecated
nibabel method <cite>Nifti1Image.get_data</cite>. Now use <cite>nilearn.image.get_data(img)</cite>
rather than <cite>img.get_data()</cite>. This is because Nibabel is removing the
<cite>get_data</cite> method. You may also consider using the Nibabel
<cite>Nifti1Image.get_fdata</cite>, which returns the data cast to floating-point.
See <a class="reference external" href="https://github.com/nipy/nibabel/wiki/BIAP8">https://github.com/nipy/nibabel/wiki/BIAP8</a> .
As a benefit, the <cite>get_data</cite> function works on niimg-like objects such as
filenames (see <a class="reference external" href="http://nilearn.github.io/manipulating_images/input_output.html">http://nilearn.github.io/manipulating_images/input_output.html</a> ).</li>
</ul>
</div>
<div class="section" id="id20">
<h2>Changes<a class="headerlink" href="#id20" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>All functions and examples now use <cite>nilearn.image.get_data</cite> rather than the
deprecated method <cite>nibabel.Nifti1Image.get_data</cite>.</li>
<li><a class="reference internal" href="modules/generated/nilearn.datasets.fetch_neurovault.html#nilearn.datasets.fetch_neurovault" title="nilearn.datasets.fetch_neurovault"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_neurovault</span></code></a> now does not filter out images that
have their metadata field <cite>is_valid</cite> cleared by default.</li>
<li>Users can now specify fetching data for adults, children, or both from
<a class="reference internal" href="modules/generated/nilearn.datasets.fetch_development_fmri.html#nilearn.datasets.fetch_development_fmri" title="nilearn.datasets.fetch_development_fmri"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_development_fmri</span></code></a> .</li>
</ul>
</div>
<div class="section" id="id21">
<h2>Fixes<a class="headerlink" href="#id21" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_connectome.html#nilearn.plotting.plot_connectome" title="nilearn.plotting.plot_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_connectome</span></code></a> now correctly displays marker size on ‘l’
and ‘r’ orientations, if an array or a list is passed to the function.</li>
</ul>
</div>
<div class="section" id="id22">
<h2>Contributors<a class="headerlink" href="#id22" title="Permalink to this headline">¶</a></h2>
<p>The following people contributed to this release (in alphabetical order):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Jake</span> <span class="n">Vogel</span>
<span class="n">Jerome</span> <span class="n">Dockes</span>
<span class="n">Kshitij</span> <span class="n">Chawla</span> <span class="p">(</span><span class="n">kchawla</span><span class="o">-</span><span class="n">pi</span><span class="p">)</span>
<span class="n">Roberto</span> <span class="n">Guidotti</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="a0">
<h1>0.6.0a0<a class="headerlink" href="#a0" title="Permalink to this headline">¶</a></h1>
<p><strong>Released October 2019</strong></p>
<div class="section" id="id23">
<h2>NEW<a class="headerlink" href="#id23" title="Permalink to this headline">¶</a></h2>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<div class="last line-block">
<div class="line"><strong>Python2 and 3.4 are no longer supported. We recommend upgrading to Python 3.6 minimum.</strong></div>
<div class="line"><br /></div>
<div class="line"><strong>Minimum supported versions of packages have been bumped up.</strong></div>
<div class="line">- Matplotlib – v2.0</div>
<div class="line">- Scikit-learn – v0.19</div>
<div class="line">- Scipy – v0.19</div>
</div>
</div>
<ul class="simple">
<li>A new method for <a class="reference internal" href="modules/generated/nilearn.input_data.NiftiMasker.html#nilearn.input_data.NiftiMasker" title="nilearn.input_data.NiftiMasker"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.input_data.NiftiMasker</span></code></a> instances
for generating reports viewable in a web browser, Jupyter Notebook, or VSCode.</li>
<li>joblib is now a dependency</li>
<li>Parcellation method ReNA: Fast agglomerative clustering based on recursive
nearest neighbor grouping.
Yields very fast & accurate models, without creation of giant
clusters.
<a class="reference internal" href="modules/generated/nilearn.regions.ReNA.html#nilearn.regions.ReNA" title="nilearn.regions.ReNA"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.regions.ReNA</span></code></a></li>
<li>Plot connectome strength
Use <a class="reference internal" href="modules/generated/nilearn.plotting.plot_connectome_strength.html#nilearn.plotting.plot_connectome_strength" title="nilearn.plotting.plot_connectome_strength"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_connectome_strength</span></code></a> to plot the strength of a
connectome on a glass brain. Strength is absolute sum of the edges at a node.</li>
<li>Optimization to image resampling
<a class="reference internal" href="modules/generated/nilearn.image.resample_img.html#nilearn.image.resample_img" title="nilearn.image.resample_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.image.resample_img</span></code></a> has been optimized to pad rather than
resample images in the special case when there is only a translation
between two spaces. This is a common case in <a class="reference internal" href="modules/generated/nilearn.input_data.NiftiMasker.html#nilearn.input_data.NiftiMasker" title="nilearn.input_data.NiftiMasker"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.input_data.NiftiMasker</span></code></a>
when using the <cite>mask_strategy=”template”</cite> option for brains in MNI space.</li>
<li>New brain development fMRI dataset fetcher
<a class="reference internal" href="modules/generated/nilearn.datasets.fetch_development_fmri.html#nilearn.datasets.fetch_development_fmri" title="nilearn.datasets.fetch_development_fmri"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_development_fmri</span></code></a> can be used to download
movie-watching data in children and adults; a light-weight dataset
implemented for teaching and usage in the examples.</li>
<li>New example in <cite>examples/05_advanced/plot_age_group_prediction_cross_val.py</cite>
to compare methods for classifying subjects into age groups based on
functional connectivity. Similar example in
<cite>examples/03_connectivity/plot_group_level_connectivity.py</cite> simplified.</li>
<li>Merged <cite>examples/03_connectivity/plot_adhd_spheres.py</cite> and
<cite>examples/03_connectivity/plot_sphere_based_connectome.py</cite> to remove
duplication across examples. The improved
<cite>examples/03_connectivity/plot_sphere_based_connectome.py</cite> contains
concepts previously reviewed in both examples.</li>
<li>Merged <cite>examples/03_connectivity/plot_compare_decomposition.py</cite>
and <cite>examples/03_connectivity/plot_canica_analysis.py</cite> into an improved
<cite>examples/03_connectivity/plot_compare_decomposition.py</cite>.</li>
<li>The Localizer dataset now follows the BIDS organization.</li>
</ul>
</div>
<div class="section" id="id24">
<h2>Changes<a class="headerlink" href="#id24" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>All the connectivity examples are changed from ADHD to brain development
fmri dataset.</li>
<li>Examples plot_decoding_tutorial, plot_haxby_decoder,
plot_haxby_different_estimators, plot_haxby_full_analysis, plot_oasis_vbm now
use <a class="reference internal" href="modules/generated/nilearn.decoding.Decoder.html#nilearn.decoding.Decoder" title="nilearn.decoding.Decoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.Decoder</span></code></a> and <a class="reference internal" href="modules/generated/nilearn.decoding.DecoderRegressor.html#nilearn.decoding.DecoderRegressor" title="nilearn.decoding.DecoderRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">nilearn.decoding.DecoderRegressor</span></code></a>
instead of sklearn SVC and SVR.</li>
<li><a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_img_on_surf</span></code></a>, <a class="reference internal" href="modules/generated/nilearn.plotting.view_surf.html#nilearn.plotting.view_surf" title="nilearn.plotting.view_surf"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_surf</span></code></a>
and <a class="reference internal" href="modules/generated/nilearn.plotting.view_connectome.html#nilearn.plotting.view_connectome" title="nilearn.plotting.view_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_connectome</span></code></a> now allow disabling the colorbar,
and setting its height and the fontsize of its ticklabels.</li>
<li><a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_img_on_surf</span></code></a>, <a class="reference internal" href="modules/generated/nilearn.plotting.view_surf.html#nilearn.plotting.view_surf" title="nilearn.plotting.view_surf"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_surf</span></code></a>
and <a class="reference internal" href="modules/generated/nilearn.plotting.view_connectome.html#nilearn.plotting.view_connectome" title="nilearn.plotting.view_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_connectome</span></code></a> can now display a title.</li>
<li>Rework of the standardize-options of <a class="reference internal" href="modules/generated/nilearn.signal.clean.html#nilearn.signal.clean" title="nilearn.signal.clean"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.signal.clean</span></code></a> and the various Maskers
in <cite>nilearn.input_data</cite>. You can now set <cite>standardize</cite> to <cite>zscore</cite> or <cite>psc</cite>. <cite>psc</cite> stands
for <cite>Percent Signal Change</cite>, which can be a meaningful metric for BOLD.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_img.html#nilearn.plotting.plot_img" title="nilearn.plotting.plot_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_img</span></code></a> now has explicit keyword arguments <cite>bg_img</cite>,
<cite>vmin</cite> and <cite>vmax</cite> to control the background image and the bounds of the
colormap. These arguments were already accepted in <cite>kwargs</cite> but not documented
before.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.view_connectome.html#nilearn.plotting.view_connectome" title="nilearn.plotting.view_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_connectome</span></code></a> now converts NaNs in the adjacency
matrix to 0.</li>
<li>Removed the plotting connectomes example which used the Seitzman atlas
from <cite>examples/03_connectivity/plot_sphere_based_connectome.py</cite>.
The atlas data is unsuitable for the method & the example is redundant.</li>
</ul>
</div>
<div class="section" id="id25">
<h2>Fixes<a class="headerlink" href="#id25" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference internal" href="modules/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">nilearn.plotting.plot_glass_brain</span></code></a> with colorbar=True does not crash when
images have NaNs.</li>
<li>add_contours now accepts <cite>threshold</cite> argument for filled=False. Now
<cite>threshold</cite> is equally applied when asked for fillings in the contours.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_surf.html#nilearn.plotting.plot_surf" title="nilearn.plotting.plot_surf"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_surf</span></code></a> and
<a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_surf_stat_map</span></code></a> no longer threshold zero values
when no threshold is given.</li>
<li>When <a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_surf_stat_map</span></code></a> is used with a thresholded map
but without a background map, the surface mesh is displayed in
half-transparent grey to maintain a 3D perception.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.view_surf.html#nilearn.plotting.view_surf" title="nilearn.plotting.view_surf"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_surf</span></code></a> now accepts surface data provided as a file
path.</li>
<li><a class="reference internal" href="modules/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">nilearn.plotting.plot_glass_brain</span></code></a> now correctly displays the left ‘l’ orientation even when
the given images are completely masked (empty images).</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_matrix.html#nilearn.plotting.plot_matrix" title="nilearn.plotting.plot_matrix"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_matrix</span></code></a> providing labels=None, False, or an empty list now correctly disables labels.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.plot_surf_roi.html#nilearn.plotting.plot_surf_roi" title="nilearn.plotting.plot_surf_roi"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_surf_roi</span></code></a> now takes vmin, vmax parameters</li>
<li><a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_surf_nki_enhanced</span></code></a> is now downloading the correct
left and right functional surface data for each subject</li>
<li><a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_atlas_schaefer_2018</span></code></a> now downloads from release
version 0.14.3 (instead of 0.8.1) by default, which includes corrected region label
names along with 700 and 900 region parcelations.</li>
<li>Colormap creation functions have been updated to avoid matplotlib deprecation warnings
about colormap reversal.</li>
<li>Neurovault fetcher no longer fails if unable to update dataset metadata file due to faulty permissions.</li>
</ul>
</div>
<div class="section" id="id26">
<h2>Contributors<a class="headerlink" href="#id26" title="Permalink to this headline">¶</a></h2>
<p>The following people contributed to this release (in alphabetical order):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Alexandre</span> <span class="n">Abraham</span>
<span class="n">Alexandre</span> <span class="n">Gramfort</span>
<span class="n">Ana</span> <span class="n">Luisa</span>
<span class="n">Ana</span> <span class="n">Luisa</span> <span class="n">Pinho</span>
<span class="n">Andrés</span> <span class="n">Hoyos</span> <span class="n">Idrobo</span>
<span class="n">Antoine</span> <span class="n">Grigis</span>
<span class="n">BAZEILLE</span> <span class="n">Thomas</span>
<span class="n">Bertrand</span> <span class="n">Thirion</span>
<span class="n">Colin</span> <span class="n">Reininger</span>
<span class="n">Céline</span> <span class="n">Delettre</span>
<span class="n">Dan</span> <span class="n">Gale</span>
<span class="n">Daniel</span> <span class="n">Gomez</span>
<span class="n">Elizabeth</span> <span class="n">DuPre</span>
<span class="n">Eric</span> <span class="n">Larson</span>
<span class="n">Franz</span> <span class="n">Liem</span>
<span class="n">Gael</span> <span class="n">Varoquaux</span>
<span class="n">Gilles</span> <span class="n">de</span> <span class="n">Hollander</span>
<span class="n">Greg</span> <span class="n">Kiar</span>
<span class="n">Guillaume</span> <span class="n">Lemaitre</span>
<span class="n">Ian</span> <span class="n">Abenes</span>
<span class="n">Jake</span> <span class="n">Vogel</span>
<span class="n">Jerome</span> <span class="n">Dockes</span>
<span class="n">Jerome</span><span class="o">-</span><span class="n">Alexis</span> <span class="n">Chevalier</span>
<span class="n">Julia</span> <span class="n">Huntenburg</span>
<span class="n">Kamalakar</span> <span class="n">Daddy</span>
<span class="n">Kshitij</span> <span class="n">Chawla</span> <span class="p">(</span><span class="n">kchawla</span><span class="o">-</span><span class="n">pi</span><span class="p">)</span>
<span class="n">Mehdi</span> <span class="n">Rahim</span>
<span class="n">Moritz</span> <span class="n">Boos</span>
<span class="n">Sylvain</span> <span class="n">Takerkart</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="id27">
<h1>0.5.2<a class="headerlink" href="#id27" title="Permalink to this headline">¶</a></h1>
<p><strong>Released April 2019</strong></p>
<div class="section" id="id28">
<h2>NEW<a class="headerlink" href="#id28" title="Permalink to this headline">¶</a></h2>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<div class="last line-block">
<div class="line">This is the <strong>last</strong> release supporting Python2 and 3.4 .</div>
<div class="line">The lowest Python version supported is now Python3.5.</div>
<div class="line">We recommend switching to Python3.6 .</div>
</div>
</div>
</div>
<div class="section" id="id29">
<h2>Fixes<a class="headerlink" href="#id29" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Plotting <code class="docutils literal notranslate"><span class="pre">.mgz</span></code> files in MNE broke in <code class="docutils literal notranslate"><span class="pre">0.5.1</span></code> and has been fixed.</li>
</ul>
</div>
<div class="section" id="id30">
<h2>Contributors<a class="headerlink" href="#id30" title="Permalink to this headline">¶</a></h2>
<p>The following people contributed to this release:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">11</span> <span class="n">Kshitij</span> <span class="n">Chawla</span> <span class="p">(</span><span class="n">kchawla</span><span class="o">-</span><span class="n">pi</span><span class="p">)</span>
<span class="mi">3</span> <span class="n">Gael</span> <span class="n">Varoquaux</span>
<span class="mi">2</span> <span class="n">Alexandre</span> <span class="n">Gramfort</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="id31">
<h1>0.5.1<a class="headerlink" href="#id31" title="Permalink to this headline">¶</a></h1>
<p><strong>Released April 2019</strong></p>
<div class="section" id="id32">
<h2>NEW<a class="headerlink" href="#id32" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><strong>Support for Python2 & Python3.4 wil be removed in the next release.</strong>
We recommend Python 3.6 and up.
Users with a Python2 or Python3.4 environment will be warned
at their first Nilearn import.</li>
<li>Calculate image data dtype from header information</li>
<li>New display mode ‘tiled’ which allows 2x2 plot arrangement when plotting three cuts
(see <a class="reference internal" href="plotting/index.html#plotting"><span class="std std-ref">Plotting brain images</span></a>).</li>
<li>NiftiLabelsMasker now consumes less memory when extracting the signal from a 3D/4D
image. This is especially noteworthy when extracting signals from large 4D images.</li>
<li>New function <a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_atlas_schaefer_2018</span></code></a></li>
<li>New function <a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_coords_seitzman_2018</span></code></a></li>
</ul>
</div>
<div class="section" id="id33">
<h2>Changes<a class="headerlink" href="#id33" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Lighting used for interactive surface plots changed; plots may look a bit
different.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.view_connectome.html#nilearn.plotting.view_connectome" title="nilearn.plotting.view_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_connectome</span></code></a> default colormap is <cite>bwr</cite>, consistent with plot_connectome.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.view_connectome.html#nilearn.plotting.view_connectome" title="nilearn.plotting.view_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_connectome</span></code></a> parameter names are consistent with plot_connectome:<ul>
<li>coords is now node_coord</li>
<li>marker_size is noe node_size</li>
<li>cmap is now edge_cmap</li>
<li>threshold is now edge_threshold</li>
</ul>
</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.view_markers.html#nilearn.plotting.view_markers" title="nilearn.plotting.view_markers"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_markers</span></code></a> and <a class="reference internal" href="modules/generated/nilearn.plotting.view_connectome.html#nilearn.plotting.view_connectome" title="nilearn.plotting.view_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_connectome</span></code></a> can accept different marker
sizes for each node / marker.</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.view_markers.html#nilearn.plotting.view_markers" title="nilearn.plotting.view_markers"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_markers</span></code></a> default marker color is now ‘red’, consistent with add_markers().</li>
<li><a class="reference internal" href="modules/generated/nilearn.plotting.view_markers.html#nilearn.plotting.view_markers" title="nilearn.plotting.view_markers"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_markers</span></code></a> parameter names are consistent with add_markers():<ul>
<li>coords is now marker_coords</li>
<li>colors is now marker_color</li>
</ul>
</li>
<li><a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_img_on_surf</span></code></a> now accepts a <cite>symmetric_cmap</cite>
argument to control whether the colormap is centered around 0 and a <cite>vmin</cite>
argument.</li>
<li>Users can now control the size and fontsize of colorbars in interactive
surface and connectome plots, or disable the colorbar.</li>
</ul>
</div>
<div class="section" id="id34">
<h2>Fixes<a class="headerlink" href="#id34" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Example plot_seed_to_voxel_correlation now really saves z-transformed maps.</li>
<li>region_extractor.connected_regions and regions.RegionExtractor now correctly
use the provided mask_img.</li>
<li>load_niimg no longer drops header if dtype is changed.</li>
<li>NiftiSpheresMasker no longer silently ignores voxels if no <cite>mask_img</cite> is specified.</li>
<li>Interactive brainsprites generated from <cite>view_img</cite> are correctly rendered in Jupyter Book.</li>
</ul>
</div>
<div class="section" id="known-issues">
<h2>Known Issues<a class="headerlink" href="#known-issues" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>On Python2, <a class="reference internal" href="modules/generated/nilearn.plotting.view_connectome.html#nilearn.plotting.view_connectome" title="nilearn.plotting.view_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_connectome</span></code></a> &
<a class="reference internal" href="modules/generated/nilearn.plotting.view_markers.html#nilearn.plotting.view_markers" title="nilearn.plotting.view_markers"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_markers</span></code></a>
do not show parameters names in function signature
when using help() and similar features.
Please refer to their docstrings for this information.</li>
<li>Plotting <code class="docutils literal notranslate"><span class="pre">.mgz</span></code> files in MNE is broken.</li>
</ul>
</div>
<div class="section" id="id35">
<h2>Contributors<a class="headerlink" href="#id35" title="Permalink to this headline">¶</a></h2>
<p>The following people contributed to this release:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">2</span> <span class="n">Bertrand</span> <span class="n">Thirion</span>
<span class="mi">90</span> <span class="n">Kshitij</span> <span class="n">Chawla</span> <span class="p">(</span><span class="n">kchawla</span><span class="o">-</span><span class="n">pi</span><span class="p">)</span>
<span class="mi">22</span> <span class="n">fliem</span>
<span class="mi">16</span> <span class="n">Jerome</span> <span class="n">Dockes</span>
<span class="mi">11</span> <span class="n">Gael</span> <span class="n">Varoquaux</span>
<span class="mi">8</span> <span class="n">Salma</span> <span class="n">Bougacha</span>
<span class="mi">7</span> <span class="n">himanshupathak21061998</span>
<span class="mi">2</span> <span class="n">Elizabeth</span> <span class="n">DuPre</span>
<span class="mi">1</span> <span class="n">Eric</span> <span class="n">Larson</span>
<span class="mi">1</span> <span class="n">Pierre</span> <span class="n">Bellec</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="id36">
<h1>0.5.0<a class="headerlink" href="#id36" title="Permalink to this headline">¶</a></h1>
<p><strong>Released November 2018</strong></p>
<div class="section" id="id37">
<h2>NEW<a class="headerlink" href="#id37" title="Permalink to this headline">¶</a></h2>
<blockquote>
<div><a class="reference internal" href="plotting/index.html#interactive-plotting"><span class="std std-ref">interactive plotting functions</span></a>,
eg for use in a notebook.</div></blockquote>
<ul class="simple">
<li>New functions <a class="reference internal" href="modules/generated/nilearn.plotting.view_surf.html#nilearn.plotting.view_surf" title="nilearn.plotting.view_surf"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_surf</span></code></a> and
<a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_img_on_surf</span></code></a> for interactive visualization of
maps on the cortical surface in a web browser.</li>
<li>New functions <a class="reference internal" href="modules/generated/nilearn.plotting.view_connectome.html#nilearn.plotting.view_connectome" title="nilearn.plotting.view_connectome"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_connectome</span></code></a> and
<a class="reference internal" href="modules/generated/nilearn.plotting.view_markers.html#nilearn.plotting.view_markers" title="nilearn.plotting.view_markers"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_markers</span></code></a> for interactive visualization of
connectomes and seed locations in 3D</li>
<li>New function <a class="reference internal" href="modules/generated/nilearn.plotting.view_img.html#nilearn.plotting.view_img" title="nilearn.plotting.view_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_img</span></code></a> for interactive
visualization of volumes with 3 orthogonal cuts.</li>
</ul>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Note:</th><td class="field-body"><a class="reference internal" href="modules/generated/nilearn.plotting.view_img.html#nilearn.plotting.view_img" title="nilearn.plotting.view_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_img</span></code></a> was <cite>nilearn.plotting.view_stat_map</cite> in alpha and beta releases.</td>
</tr>
</tbody>
</table>
<ul class="simple">
<li><a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.find_parcellation_cut_coords</span></code></a> for
extraction of coordinates on brain parcellations denoted as labels.</li>
<li>Added <a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.plotting.find_probabilistic_atlas_cut_coords</span></code></a> for
extraction of coordinates on brain probabilistic maps.</li>
</ul>
<dl class="docutils">
<dt><strong>Minimum supported versions of packages have been bumped up.</strong></dt>
<dd><ul class="first last simple">
<li>scikit-learn – v0.18</li>
<li>scipy – v0.17</li>
<li>pandas – v0.18</li>
<li>numpy – v1.11</li>
<li>matplotlib – v1.5.1</li>
</ul>
</dd>
<dt><strong>Nilearn Python2 support is being removed in the near future.</strong></dt>
<dd>Users with a Python2 environment will be warned
at their first Nilearn import.</dd>
</dl>
<p><strong>Additional dataset downloaders for examples and tutorials.</strong></p>
<ul class="simple">
<li><a class="reference internal" href="modules/generated/nilearn.datasets.fetch_surf_fsaverage.html#nilearn.datasets.fetch_surf_fsaverage" title="nilearn.datasets.fetch_surf_fsaverage"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_surf_fsaverage</span></code></a></li>
<li><a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_atlas_pauli_2017</span></code></a></li>
<li><a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_neurovault_auditory_computation_task</span></code></a></li>
<li><a class="reference internal" href="modules/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-func docutils literal notranslate"><span class="pre">nilearn.datasets.fetch_neurovault_motor_task</span></code></a></li>
</ul>
</div>
<div class="section" id="id38">
<h2>ENHANCEMENTS<a class="headerlink" href="#id38" title="Permalink to this headline">¶</a></h2>
<blockquote>
<div><p><a class="reference internal" href="modules/generated/nilearn.image.clean_img.html#nilearn.image.clean_img" title="nilearn.image.clean_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.image.clean_img</span></code></a> now accepts a mask to restrict
the cleaning of the image, reducing memory load and computation time.</p>
<p>NiftiMaskers now have a <cite>dtype</cite> parameter, by default keeping the same data type as the input data.</p>
<p>Displays by plotting functions can now add a scale bar (see <a class="reference internal" href="plotting/index.html#plotting"><span class="std std-ref">Plotting brain images</span></a>)</p>
</div></blockquote>
</div>
<div class="section" id="improvements">
<h2>IMPROVEMENTS<a class="headerlink" href="#improvements" title="Permalink to this headline">¶</a></h2>
<blockquote>
<div><ul class="simple">
<li>Lots of other fixes in documentation and examples.</li>
<li>A cleaner layout and improved navigation for the website, with a better introduction.</li>
<li>Dataset fetchers are now more reliable, less verbose.</li>
<li>Searchlight().fit() now accepts 4D niimgs.</li>
<li>Anaconda link in the installation documentation updated.</li>
<li>Scipy is listed as a dependency for Nilearn installation.</li>
</ul>
</div></blockquote>
</div>
<div class="section" id="notable-changes">
<h2>Notable Changes<a class="headerlink" href="#notable-changes" title="Permalink to this headline">¶</a></h2>
<blockquote>
<div>Default value of <cite>t_r</cite> in <a class="reference internal" href="modules/generated/nilearn.signal.clean.html#nilearn.signal.clean" title="nilearn.signal.clean"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.signal.clean</span></code></a> and
<a class="reference internal" href="modules/generated/nilearn.image.clean_img.html#nilearn.image.clean_img" title="nilearn.image.clean_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.image.clean_img</span></code></a> is None
and cannot be None if <cite>low_pass</cite> or <cite>high_pass</cite> is specified.</div></blockquote>
<p>Lots of changes and improvements. Detailed change list for each release follows.</p>
</div>
</div>
<div class="section" id="rc">
<h1>0.5.0 rc<a class="headerlink" href="#rc" title="Permalink to this headline">¶</a></h1>
<div class="section" id="id39">
<h2>Highlights<a class="headerlink" href="#id39" title="Permalink to this headline">¶</a></h2>
<p><a class="reference internal" href="modules/generated/nilearn.plotting.view_img.html#nilearn.plotting.view_img" title="nilearn.plotting.view_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_img</span></code></a> (formerly <cite>nilearn.plotting.view_stat_map</cite> in
Nilearn 0.5.0 pre-release versions) generates significantly smaller notebooks
and HTML pages while getting a more consistent look and feel with Nilearn’s
plotting functions. Huge shout out to Pierre Bellec (pbellec) for
making a great feature awesome and for sportingly accommodating all our feedback.</p>
<dl class="docutils">
<dt><a class="reference internal" href="modules/generated/nilearn.image.clean_img.html#nilearn.image.clean_img" title="nilearn.image.clean_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.image.clean_img</span></code></a> now accepts a mask to restrict the cleaning of</dt>
<dd>the image. This approach can help to reduce the memory load and computation time.
Big thanks to Michael Notter (miykael).</dd>
</dl>
</div>
<div class="section" id="id40">
<h2>Enhancements<a class="headerlink" href="#id40" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference internal" href="modules/generated/nilearn.plotting.view_img.html#nilearn.plotting.view_img" title="nilearn.plotting.view_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.view_img</span></code></a> is now using the brainsprite.js library,
which results in much smaller notebooks or html pages. The interactive viewer
also looks more similar to the plots generated by
<a class="reference internal" href="modules/generated/nilearn.plotting.plot_stat_map.html#nilearn.plotting.plot_stat_map" title="nilearn.plotting.plot_stat_map"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.plotting.plot_stat_map</span></code></a>, and most parameters found in
<cite>plot_stat_map</cite> are now supported in <cite>view_img</cite>.</li>
<li><a class="reference internal" href="modules/generated/nilearn.image.clean_img.html#nilearn.image.clean_img" title="nilearn.image.clean_img"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.image.clean_img</span></code></a> now accepts a mask to restrict the cleaning of
the image. This approach can help to reduce the memory load and computation time.</li>
<li><a class="reference internal" href="modules/generated/nilearn.decoding.SpaceNetRegressor.html#nilearn.decoding.SpaceNetRegressor.fit" title="nilearn.decoding.SpaceNetRegressor.fit"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.decoding.SpaceNetRegressor.fit</span></code></a> raises a meaningful error in regression tasks
if the target Y contains all 1s.</li>
</ul>
</div>
<div class="section" id="id41">
<h2>Changes<a class="headerlink" href="#id41" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li>Default value of <cite>t_r</cite> in <a class="reference internal" href="modules/generated/nilearn.signal.clean.html#nilearn.signal.clean" title="nilearn.signal.clean"><code class="xref py py-func docutils literal notranslate"><span class="pre">nilearn.signal.clean</span></code></a> and