0.3.0

In addition, more details of this release are listed below. Please checkout in 0.3.0 beta release section for minimum version support of dependencies, latest updates, highlights, changelog and enhancements.

Changelog

  • Function nilearn.plotting.find_cut_slices now supports to accept Nifti1Image as an input for argument img.
  • Helper functions _get_mask_volume and _adjust_screening_percentile are now moved to param_validation file in utilties module to be used in common with Decoder object.

Bug fix

  • Fix bug uncompressing tar files with datasets fetcher.
  • Fixed bunch of CircleCI documentation build failures.
  • Fixed deprecations set_axis_bgcolor related to matplotlib in plotting functions.
  • Fixed bug related to not accepting a list of arrays as an input to unmask, in masking module.

Enhancements

  • ANOVA SVM example on Haxby datasets plot_haxby_anova_svm in Decoding section now uses SelectPercentile to select voxels rather than SelectKBest.
  • New function fast_svd implementation in base decomposition module to Automatically switch between randomized and lapack SVD (heuristic of scikit-learn).

0.3.0 beta

To install the beta version, use:

pip install --upgrade --pre nilearn

Highlights

  • Simple surface plotting
  • A function to break a parcellation into its connected components
  • Dropped support for scikit-learn older than 0.14.1 Minimum supported version is now 0.14.1.
  • Dropped support for Python 2.6
  • Minimum required version of NiBabel is now 1.2.0, to support loading annoted data with freesurfer.

Changelog

Bug fix

  • Fix colormap issue with colorbar=True when using qualitative colormaps Fixed in according with changes of matplotlib 2.0 fixes.
  • Fix plotting functions to work with NAN values in the images.
  • Fix bug related get dtype of the images with nibabel get_data().
  • Fix bug in nilearn clean_img

Enhancements

  • A new function nilearn.regions.connected_label_regions to extract the connected components represented as same label to regions apart with each region labelled as unique label.
  • New plotting modules for surface plotting visualization. Matplotlib with version higher 1.3.1 is required for plotting surface data using these functions.
  • Function nilearn.plotting.plot_surf can be used for plotting surfaces mesh data with optional background.
  • A function nilearn.plotting.plot_surf_stat_map can be used for plotting statistical maps on a brain surface with optional background.
  • A function nilearn.plotting.plot_surf_roi can be used for plotting statistical maps rois onto brain surface.
  • A function nilearn.datasets.fetch_surf_fsaverage5 can be used for surface data object to be as background map for the above plotting functions.
  • A new data fetcher function nilearn.datasets.fetch_atlas_surf_destrieux can give you Destrieux et. al 2010 cortical atlas in fsaverage5 surface space.
  • A new functional data fetcher function nilearn.datasets.fetch_surf_nki_enhanced gives you resting state data preprocessed and projected to fsaverage5 surface space.
  • Two good examples in plotting gallery shows how to fetch atlas and NKI data and used for plotting on brain surface.
  • Helper function load_surf_mesh in surf_plotting module for loading surface mesh data into two arrays, containing (x, y, z) coordinates for mesh vertices and indices of mesh faces.
  • Helper function load_surf_data in surf_plotting module for loading data of numpy array to represented on a surface mesh.
  • Add fetcher for Allen et al. 2011 RSN atlas in nilearn.datasets.fetch_atlas_allen_2011.
  • A function nilearn.datasets.fetch_cobre is now updated to new light release of COBRE data (schizophrenia)
  • A new example to show how to extract regions on labels image in example section manipulating images.
  • coveralls is replaces with codecov
  • Upgraded to Sphinx version 0.1.7
  • Extensive plotting example shows how to use contours and filled contours on glass brain.

0.2.6

Changelog

This release enhances usage of several functions by fine tuning their parameters. It allows to select which Haxby subject to fetch. It also refactors documentation to make it easier to understand. Sphinx-gallery has been updated and nilearn is ready for new nibabel 2.1 version. Several bugs related to masks in Searchlight and ABIDE fetching have been resolved.

Bug fix

  • Change default dtype in nilearn.image.concat_imgs to be the original type of the data (see #1238).
  • Fix SearchLight that did not run without process_mask or with one voxel mask.
  • Fix flipping of left hemisphere when plotting glass brain.
  • Fix bug when downloading ABIDE timeseries

Enhancements

API changes summary

  • The parameter ‘n_subjects’ is deprecated and will be removed in future release. Use ‘subjects’ instead in nilearn.datasets.fetch_haxby.
  • The function nilearn.datasets.fetch_haxby will now fetch the data accepting input given in ‘subjects’ as a list than integer.
  • Replace get_affine by affine with recent versions of nibabel.

0.2.5.1

Changelog

This is a bugfix release. The new minimum required version of scikit-learn is 0.14.1

API changes summary

  • default option for dim argument in plotting functions which uses MNI template as a background image is now changed to ‘auto’ mode. Meaning that an automatic contrast setting on background image is applied by default.
  • Scikit-learn validation tools have been imported and are now used to check consistency of input data, in SpaceNet for example.

New features

  • Add an option to select only off-diagonal elements in sym_to_vec. Also, the scaling of matrices is modified: we divide the diagonal by sqrt(2) instead of multiplying the off-diagonal elements.
  • Connectivity examples rely on nilearn.connectome.ConnectivityMeasure

Bug fix

  • Scipy 0.18 introduces a bug in a corner-case of resampling. Nilearn 0.2.5 can give wrong results with scipy 0.18, but this is fixed in 0.2.6.
  • Broken links and references fixed in docs

0.2.5

Changelog

The 0.2.5 release includes plotting for connectomes and glass brain with hemisphere-specific projection, as well as more didactic examples and improved documentation.

New features

Contributors

Contributors (from git shortlog -ns 0.2.4..0.2.5):

55  Gael Varoquaux
39  Alexandre Abraham
26  Martin Perez-Guevara
20  Kamalakar Daddy
 8  amadeuskanaan
 3  Alexandre Abadie
 3  Arthur Mensch
 3  Elvis Dohmatob
 3  Loïc Estève
 2  Jerome Dockes
 1  Alexandre M. S
 1  Bertrand Thirion
 1  Ivan Gonzalez
 1  robbisg

0.2.4

Changelog

The 0.2.4 is a small release focused on documentation for teaching.

New features

  • The path given to the “memory” argument of object now have their “~” expanded to the homedir
  • Display object created by plotting now uniformely expose an “add_markers” method.
  • plotting plot_connectome with colorbar is now implemented in function nilearn.plotting.plot_connectome
  • New function nilearn.image.resample_to_img to resample one img on another one (just resampling / interpolation, no coregistration)

API changes summary

  • Atlas fetcher nilearn.datasets.fetch_atlas_msdl now returns directly labels of the regions in output variable ‘labels’ and its coordinates in output variable ‘region_coords’ and its type of network in ‘networks’.
  • The output variable name ‘regions’ is now changed to ‘maps’ in AAL atlas fetcher in nilearn.datasets.fetch_atlas_aal.
  • AAL atlas now returns directly its labels in variable ‘labels’ and its index values in variable ‘indices’.

0.2.3

Changelog

The 0.2.3 is a small feature release for BrainHack 2016.

New features

Bug fixes

  • Better dimming on white background for plotting

0.2.2

Changelog

The 0.2.2 is a bugfix + dependency update release (for sphinx gallery). It aims at preparing a renewal of the tutorials.

New features

  • Fetcher for Megatrawl Netmats dataset.

Enhancements

  • Flake8 is now run on pull requests.
  • Reworking of the documentation organization.
  • Sphinx-gallery updated to version 0.1.1
  • The default n_subjects=None in nilearn.datasets.fetch_adhd is now changed to n_subjects=30.

Bug fixes

API changes summary

  • Deprecated dataset downloading function have been removed.
  • Download progression message refreshing rate has been lowered to sparsify CircleCI logs.

Contributors

Contributors (from git shortlog -ns 0.2.1..0.2.2):

39  Kamalakar Daddy
22  Alexandre Abraham
21  Loïc Estève
19  Gael Varoquaux
12  Alexandre Abadie
 7  Salma
 3  Danilo Bzdok
 1  Arthur Mensch
 1  Ben Cipollini
 1  Elvis Dohmatob
 1  Óscar Nájera

0.2.1

Changelog

Small bugfix for more flexible input types (targetter in particular at making code easier in nistats).

0.2

Changelog

The new minimum required version of scikit-learn is 0.13

New features

Enhancements

  • Making website a bit elaborated & modernise by using sphinx-gallery.
  • Documentation enhancement by integrating sphinx-gallery notebook style examples.
  • Documentation about nilearn.input_data.NiftiSpheresMasker.

Bug fixes

API changes summary

Contributors

Contributors (from git shortlog -ns 0.1.4..0.2.0):

822  Elvis Dohmatob
142  Gael Varoquaux
119  Alexandre Abraham
 90  Loïc Estève
 85  Kamalakar Daddy
 65  Alexandre Abadie
 43  Chris Filo Gorgolewski
 39  Salma BOUGACHA
 29  Danilo Bzdok
 20  Martin Perez-Guevara
 19  Mehdi Rahim
 19  Óscar Nájera
 17  martin
  8  Arthur Mensch
  8  Ben Cipollini
  4  ainafp
  4  juhuntenburg
  2  Martin_Perez_Guevara
  2  Michael Hanke
  2  arokem
  1  Bertrand Thirion
  1  Dimitri Papadopoulos Orfanos

0.1.4

Changelog

Highlights:

  • NiftiSpheresMasker: extract signals from balls specified by their coordinates
  • Obey Debian packaging rules
  • Add the Destrieux 2009 and Power 2011 atlas
  • Better caching in maskers

Contributors (from git shortlog -ns 0.1.3..0.1.4):

141  Alexandre Abraham
 15  Gael Varoquaux
 10  Loïc Estève
  2  Arthur Mensch
  2  Danilo Bzdok
  2  Michael Hanke
  1  Mehdi Rahim

0.1.3

Changelog

The 0.1.3 release is a bugfix release that fixes a lot of minor bugs. It also includes a full rewamp of the documentation, and support for Python 3.

Minimum version of supported packages are now:

  • numpy 1.6.1
  • scipy 0.9.0
  • scikit-learn 0.12.1
  • Matplotlib 1.1.1 (optional)

A non exhaustive list of issues fixed:

  • Dealing with NaNs in plot_connectome
  • Fix extreme values in colorbar were sometimes brok
  • Fix confounds removal with single confounds
  • Fix frequency filtering
  • Keep header information in images
  • add_overlay finds vmin and vmax automatically
  • vmin and vmax support in plot_connectome
  • detrending 3D images no longer puts them to zero

Contributors (from git shortlog -ns 0.1.2..0.1.3):

129  Alexandre Abraham
 67  Loïc Estève
 57  Gael Varoquaux
 44  Ben Cipollini
 37  Danilo Bzdok
 20  Elvis Dohmatob
 14  Óscar Nájera
  9  Salma BOUGACHA
  8  Alexandre Gramfort
  7  Kamalakar Daddy
  3  Demian Wassermann
  1  Bertrand Thirion

0.1.2

Changelog

The 0.1.2 release is a bugfix release, specifically to fix the NiftiMapsMasker.

0.1.1

Changelog

The main change compared to 0.1 is the addition of connectome plotting via the nilearn.plotting.plot_connectome function. See the plotting documentation for more details.

Contributors (from git shortlog -ns 0.1..0.1.1):

81  Loïc Estève
18  Alexandre Abraham
18  Danilo Bzdok
14  Ben Cipollini
 2  Gaël Varoquaux

0.1

Changelog

First release of nilearn.

Contributors (from git shortlog -ns 0.1):

600  Gaël Varoquaux
483  Alexandre Abraham
302  Loïc Estève
254  Philippe Gervais
122  Virgile Fritsch
 83  Michael Eickenberg
 59  Jean Kossaifi
 57  Jaques Grobler
 46  Danilo Bzdok
 35  Chris Filo Gorgolewski
 28  Ronald Phlypo
 25  Ben Cipollini
 15  Bertrand Thirion
 13  Alexandre Gramfort
 12  Fabian Pedregosa
 11  Yannick Schwartz
  9  Mehdi Rahim
  7  Óscar Nájera
  6  Elvis Dohmatob
  4  Konstantin Shmelkov
  3  Jason Gors
  3  Salma Bougacha
  1  Alexandre Savio
  1  Jan Margeta
  1  Matthias Ekman
  1  Michael Waskom
  1  Vincent Michel