`_.
diff --git a/doc/install_doc_component.html b/doc/install_doc_component.html
index c6584d3961..d1e97934b9 100644
--- a/doc/install_doc_component.html
+++ b/doc/install_doc_component.html
@@ -53,7 +53,8 @@
as an alternative.
Nilearn requires a Python installation and the following
- dependencies: ipython, scipy, scikit-learn, matplotlib and nibabel.
+ dependencies: ipython, scipy, scikit-learn, joblib, matplotlib
+ and nibabel.
Second: open a Command Prompt
(Press "Win-R", type "cmd" and press "Enter". This will open
@@ -83,7 +84,8 @@
it will save you time and trouble.
Nilearn requires a Python installation and the following
- dependencies: ipython, scipy, scikit-learn, matplotlib and nibabel.
+ dependencies: ipython, scipy, scikit-learn, joblib,
+ matplotlib and nibabel.
Second: open a Terminal
(Navigate to /Applications/Utilities and double-click on
@@ -114,7 +116,8 @@
Install or ask your system administrator to install the following
packages using the distribution package manager: ipython
, scipy, scikit-learn (sometimes called sklearn,
- or python-sklearn), matplotlib (sometimes
+ or python-sklearn), joblib,
+ matplotlib (sometimes
called python-matplotlib) and nibabel
(sometimes called python-nibabel).
diff --git a/doc/logos/dataia.png b/doc/logos/dataia.png
new file mode 100644
index 0000000000..fae26199d3
Binary files /dev/null and b/doc/logos/dataia.png differ
diff --git a/doc/manipulating_images/manipulating_images.rst b/doc/manipulating_images/manipulating_images.rst
index 57993de8ad..8c388ff426 100644
--- a/doc/manipulating_images/manipulating_images.rst
+++ b/doc/manipulating_images/manipulating_images.rst
@@ -8,7 +8,7 @@ This chapter discusses how nilearn can be used to do simple operations on
brain images.
-.. contents:: **Chapters contents**
+.. contents:: **Chapter contents**
:local:
:depth: 1
@@ -198,7 +198,7 @@ brain. It is thus convenient to apply a brain mask in order to convert the
:width: 100%
Note that in an analysis pipeline, this operation is best done using the
-:ref:`masker objects `. For completness, we give code to
+:ref:`masker objects `. For completeness, we give the code to
do it manually below:
.. literalinclude:: ../../examples/01_plotting/plot_visualization.py
@@ -219,8 +219,8 @@ statistical test. This requires a chain of image
operations on the input data. Here is a possible recipe for computing an
ROI mask:
- * **Smoothing**: Before a statistical test, it is often use to smooth a bit
- the image using :func:`nilearn.image.smooth_img`, typically fwhm=6 for
+ * **Smoothing**: Before a statistical test, it is often useful to smooth the image a bit
+ using :func:`nilearn.image.smooth_img`, typically fwhm=6 for
fMRI.
* **Selecting voxels**: Given the smoothed data, we can select voxels
diff --git a/doc/manipulating_images/masker_objects.rst b/doc/manipulating_images/masker_objects.rst
index 5e2a2c2330..dc2f06dafd 100644
--- a/doc/manipulating_images/masker_objects.rst
+++ b/doc/manipulating_images/masker_objects.rst
@@ -136,12 +136,25 @@ mask computation parameters.
The mask can be retrieved and visualized from the `mask_img_` attribute
of the masker:
+.. literalinclude:: ../../examples/04_manipulating_images/plot_mask_computation.py
+ :start-after: # A NiftiMasker with the default strategy
+ :end-before: # Plot the generated mask using the .generate_report method
+
+.. figure:: ../auto_examples/04_manipulating_images/images/sphx_glr_plot_mask_computation_002.png
+ :target: ../auto_examples/04_manipulating_images/plot_mask_computation.html
+ :align: center
+ :scale: 40
+
+Alternatively, the mask can be visualized using the `generate_report`
+method of the masker. The generated report can be viewed in a Jupyter notebook,
+opened in a new browser tab using `report.open_in_browser()`,
+or saved as a portable HTML file `report.save_as_html(output_filepath)`.
+
.. literalinclude:: ../../examples/04_manipulating_images/plot_mask_computation.py
:start-after: # We need to specify an 'epi' mask_strategy, as this is raw EPI data
:end-before: # Generate mask with strong opening
-
-.. figure:: ../auto_examples/04_manipulating_images/images/sphx_glr_plot_mask_computation_004.png
+.. figure:: /images/niftimasker_report.png
:target: ../auto_examples/04_manipulating_images/plot_mask_computation.html
:scale: 50%
@@ -163,7 +176,7 @@ Controling these arguments set the fine aspects of the mask. See the
functions documentation, or :doc:`the NiftiMasker example
<../auto_examples/04_manipulating_images/plot_mask_computation>`.
-.. figure:: ../auto_examples/04_manipulating_images/images/sphx_glr_plot_mask_computation_005.png
+.. figure:: /images/niftimasker_report_params.png
:target: ../auto_examples/04_manipulating_images/plot_mask_computation.html
:scale: 50%
@@ -177,12 +190,13 @@ preparation::
>>> from nilearn import input_data
>>> masker = input_data.NiftiMasker()
- >>> masker # doctest: +ELLIPSIS
+ >>> masker # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
NiftiMasker(detrend=False, dtype=None, high_pass=None, low_pass=None,
mask_args=None, mask_img=None, mask_strategy='background',
- memory=Memory(...), memory_level=1, sample_mask=None,
- sessions=None, smoothing_fwhm=None, standardize=False, t_r=None,
- target_affine=None, target_shape=None, verbose=0)
+ memory=Memory(...), memory_level=1, reports=True,
+ sample_mask=None, sessions=None, smoothing_fwhm=None,
+ standardize=False, t_r=None, target_affine=None, target_shape=None,
+ verbose=0)
.. note::
@@ -234,7 +248,7 @@ Temporal Filtering and confound removal
properties, before conversion to voxel signals.
- **Standardization**. Parameter ``standardize``: Signals can be
- standardized (scaled to unit variance).
+ standardized (scaled to unit variance).
- **Frequency filtering**. Low-pass and high-pass filters can be used to
remove artifacts. Parameters: ``high_pass`` and ``low_pass``, specified
@@ -242,7 +256,7 @@ properties, before conversion to voxel signals.
the ``t_r`` parameter: ``loss_pass=.5, t_r=2.1``).
- **Confound removal**. Two ways of removing confounds are provided: simple
- detrending or using prespecified confounds, such as behavioral or movement
+ detrending or using prespecified confounds, such as behavioral or movement
information.
* Linear trends can be removed by activating the `detrend` parameter.
@@ -251,7 +265,7 @@ properties, before conversion to voxel signals.
signal of interest (e.g., the neural correlates of cognitive tasks).
It is not activated by default in :class:`NiftiMasker` but is recommended
in almost all scenarios.
-
+
* More complex confounds, measured during the acquision, can be removed
by passing them to :meth:`NiftiMasker.transform`. If the dataset
provides a confounds file, just pass its path to the masker.
diff --git a/doc/modules/reference.rst b/doc/modules/reference.rst
index 8ea986a79f..6f43d06135 100644
--- a/doc/modules/reference.rst
+++ b/doc/modules/reference.rst
@@ -71,6 +71,7 @@ uses.
fetch_atlas_harvard_oxford
fetch_atlas_msdl
fetch_coords_power_2011
+ fetch_coords_seitzman_2018
fetch_atlas_smith_2009
fetch_atlas_yeo_2011
fetch_atlas_aal
@@ -269,6 +270,7 @@ uses.
RegionExtractor
Parcellations
+ ReNA
:mod:`nilearn.mass_univariate`: Mass-univariate analysis
diff --git a/doc/themes/nilearn/layout.html b/doc/themes/nilearn/layout.html
index 60d25c1665..ae52a6df74 100644
--- a/doc/themes/nilearn/layout.html
+++ b/doc/themes/nilearn/layout.html
@@ -261,7 +261,7 @@ Machine learning for Neuro-Imaging in Python
News
- April 12th 2019: Nilearn 0.5.2 released
+
April 17th 2019: Nilearn 0.5.2 released
April 12th 2019: Nilearn 0.5.1 released
Development
href="https://github.com/nilearn/nilearn">Nilearn on GitHub
All material Free Software: BSD license (3 clause).
Authors
- Contributing
+ Contributing
{% endif %}
diff --git a/doc/themes/nilearn/static/nature.css_t b/doc/themes/nilearn/static/nature.css_t
index e6e3b39a2b..7397860e39 100644
--- a/doc/themes/nilearn/static/nature.css_t
+++ b/doc/themes/nilearn/static/nature.css_t
@@ -420,6 +420,12 @@ div.body p.sphx-glr-script-out {
margin: -.9ex 0ex;
}
+div.body div.sphx-glr-download-link-note {
+ max-width: 8em;
+ float: right;
+ margin-right: -1em;
+}
+
div.bodywrapper {
margin: 0 246px 0 auto;
/*border-right: 1px solid #EEE;*/
@@ -1683,3 +1689,22 @@ ul#tab li div.contents p{
p.sphx-glr-horizontal {
margin-top: 2em;
}
+
+
+/* Sphinx-gallery Report embedding */
+div.sg-report {
+ padding: 0pt;
+ transform: scale(.95);
+}
+
+div.sg-report iframe {
+ display: block;
+ border-style: none;
+ transform: scale(.85);
+ height: 470px;
+ margin-left: -12%; /* Negative because of .8 scaling */
+ margin-top: -4%;
+ padding: 0pt;
+ margin-bottom: 0pt;
+ width: 126%; /* More than 100% because of .8 scaling */
+}
diff --git a/doc/themes/nilearn/static/sphinxdoc.css b/doc/themes/nilearn/static/sphinxdoc.css
deleted file mode 100644
index b680a95710..0000000000
--- a/doc/themes/nilearn/static/sphinxdoc.css
+++ /dev/null
@@ -1,339 +0,0 @@
-/*
- * sphinxdoc.css_t
- * ~~~~~~~~~~~~~~~
- *
- * Sphinx stylesheet -- sphinxdoc theme. Originally created by
- * Armin Ronacher for Werkzeug.
- *
- * :copyright: Copyright 2007-2011 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-@import url("basic.css");
-
-/* -- page layout ----------------------------------------------------------- */
-
-body {
- font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
- 'Verdana', sans-serif;
- font-size: 14px;
- letter-spacing: -0.01em;
- line-height: 150%;
- text-align: center;
- background-color: #BFD1D4;
- color: black;
- padding: 0;
- border: 1px solid #aaa;
-
- margin: 0px 80px 0px 80px;
- min-width: 740px;
-}
-
-div.document {
- background-color: white;
- text-align: left;
- background-image: url(contents.png);
- background-repeat: repeat-x;
-}
-
-div.bodywrapper {
- margin: 0 240px 0 0;
- border-right: 1px solid #ccc;
-}
-
-div.body {
- margin: 0;
- padding: 0.5em 20px 20px 20px;
-}
-
-div.related {
- font-size: 1em;
-}
-
-div.related ul {
- background-image: url(navigation.png);
- height: 2em;
- border-top: 1px solid #ddd;
- border-bottom: 1px solid #ddd;
-}
-
-div.related ul li {
- margin: 0;
- padding: 0;
- height: 2em;
- float: left;
-}
-
-div.related ul li.right {
- float: right;
- margin-right: 5px;
-}
-
-div.related ul li a {
- margin: 0;
- padding: 0 5px 0 5px;
- line-height: 1.75em;
- color: #EE9816;
-}
-
-div.related ul li a:hover {
- color: #3CA8E7;
-}
-
-div.sphinxsidebarwrapper {
- padding: 0;
-}
-
-div.sphinxsidebar {
- margin: 0;
- padding: 0.5em 15px 15px 0;
- width: 210px;
- float: right;
- font-size: 1em;
- text-align: left;
-}
-
-div.sphinxsidebar h3, div.sphinxsidebar h4 {
- margin: 1em 0 0.5em 0;
- font-size: 1em;
- padding: 0.1em 0 0.1em 0.5em;
- color: white;
- border: 1px solid #86989B;
- background-color: #AFC1C4;
-}
-
-div.sphinxsidebar h3 a {
- color: white;
-}
-
-div.sphinxsidebar ul {
- padding-left: 1.5em;
- margin-top: 7px;
- padding: 0;
- line-height: 130%;
-}
-
-div.sphinxsidebar ul ul {
- margin-left: 20px;
-}
-
-div.footer {
- background-color: #E3EFF1;
- color: #86989B;
- padding: 3px 8px 3px 0;
- clear: both;
- font-size: 0.8em;
- text-align: right;
-}
-
-div.footer a {
- color: #86989B;
- text-decoration: underline;
-}
-
-/* -- body styles ----------------------------------------------------------- */
-
-p {
- margin: 0.8em 0 0.5em 0;
-}
-
-a {
- color: #CA7900;
- text-decoration: none;
-}
-
-a:hover {
- color: #2491CF;
-}
-
-div.body a {
- text-decoration: underline;
-}
-
-h1 {
- margin: 0;
- padding: 0.7em 0 0.3em 0;
- font-size: 1.5em;
- color: #11557C;
-}
-
-h2 {
- margin: 1.3em 0 0.2em 0;
- font-size: 1.35em;
- padding: 0;
-}
-
-h3 {
- margin: 1em 0 -0.3em 0;
- font-size: 1.2em;
-}
-
-div.body h1 a, div.body h2 a, div.body h3 a, div.body h4 a, div.body h5 a, div.body h6 a {
- color: black!important;
-}
-
-h1 a.anchor, h2 a.anchor, h3 a.anchor, h4 a.anchor, h5 a.anchor, h6 a.anchor {
- display: none;
- margin: 0 0 0 0.3em;
- padding: 0 0.2em 0 0.2em;
- color: #aaa!important;
-}
-
-h1:hover a.anchor, h2:hover a.anchor, h3:hover a.anchor, h4:hover a.anchor,
-h5:hover a.anchor, h6:hover a.anchor {
- display: inline;
-}
-
-h1 a.anchor:hover, h2 a.anchor:hover, h3 a.anchor:hover, h4 a.anchor:hover,
-h5 a.anchor:hover, h6 a.anchor:hover {
- color: #777;
- background-color: #eee;
-}
-
-a.headerlink {
- color: #c60f0f!important;
- font-size: 1em;
- margin-left: 6px;
- padding: 0 4px 0 4px;
- text-decoration: none!important;
-}
-
-a.headerlink:hover {
- background-color: #ccc;
- color: white!important;
-}
-
-cite, code, tt {
- font-family: 'Consolas', 'Deja Vu Sans Mono',
- 'Bitstream Vera Sans Mono', monospace;
- font-size: 0.95em;
- letter-spacing: 0.01em;
-}
-
-tt {
- background-color: #f2f2f2;
- border-bottom: 1px solid #ddd;
- color: #333;
-}
-
-tt.descname, tt.descclassname, tt.xref {
- border: 0;
-}
-
-hr {
- border: 1px solid #abc;
- margin: 2em;
-}
-
-a tt {
- border: 0;
- color: #CA7900;
-}
-
-a tt:hover {
- color: #2491CF;
-}
-
-pre {
- font-family: 'Consolas', 'Deja Vu Sans Mono',
- 'Bitstream Vera Sans Mono', monospace;
- font-size: 0.95em;
- letter-spacing: 0.015em;
- line-height: 120%;
- padding: 0.5em;
- border: 1px solid #ccc;
- background-color: #f8f8f8;
-}
-
-pre a {
- color: inherit;
- text-decoration: underline;
-}
-
-td.linenos pre {
- padding: 0.5em 0;
-}
-
-div.quotebar {
- background-color: #f8f8f8;
- max-width: 250px;
- float: right;
- padding: 2px 7px;
- border: 1px solid #ccc;
-}
-
-div.topic {
- background-color: #f8f8f8;
-}
-
-table {
- border-collapse: collapse;
- margin: 0 -0.5em 0 -0.5em;
-}
-
-table td, table th {
- padding: 0.2em 0.5em 0.2em 0.5em;
-}
-
-div.admonition, div.warning {
- font-size: 0.9em;
- margin: 1em 0 1em 0;
- border: 1px solid #86989B;
- background-color: #f7f7f7;
- padding: 0;
-}
-
-div.admonition p, div.warning p {
- margin: 0.5em 1em 0.5em 1em;
- padding: 0;
-}
-
-div.admonition pre, div.warning pre {
- margin: 0.4em 1em 0.4em 1em;
-}
-
-div.admonition p.admonition-title,
-div.warning p.admonition-title {
- margin: 0;
- padding: 0.1em 0 0.1em 0.5em;
- color: white;
- border-bottom: 1px solid #86989B;
- font-weight: bold;
- background-color: #AFC1C4;
-}
-
-div.warning {
- border: 1px solid #940000;
-}
-
-div.warning p.admonition-title {
- background-color: #CF0000;
- border-bottom-color: #940000;
-}
-
-div.admonition ul, div.admonition ol,
-div.warning ul, div.warning ol {
- margin: 0.1em 0.5em 0.5em 3em;
- padding: 0;
-}
-
-div.versioninfo {
- margin: 1em 0 0 0;
- border: 1px solid #ccc;
- background-color: #DDEAF0;
- padding: 8px;
- line-height: 1.3em;
- font-size: 0.9em;
-}
-
-.viewcode-back {
- font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
- 'Verdana', sans-serif;
-}
-
-div.viewcode-block:target {
- background-color: #f4debf;
- border-top: 1px solid #ac9;
- border-bottom: 1px solid #ac9;
-}
\ No newline at end of file
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 66add0cda8..a96d2dd40f 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -1,13 +1,44 @@
-0.6.0
-=====
+0.6.0a
+======
NEW
---
+.. warning::
+
+ | **Python2 and 3.4 are no longer supported. We recommend upgrading to Python 3.6 minimum.**
+ |
+ | **Minimum supported versions of packages have been bumped up.**
+ | - Matplotlib -- v2.0
+ | - Scikit-learn -- v0.19
+ | - Scipy -- v0.19
+
+- A new method for :class:`nilearn.input_data.NiftiMasker` instances
+ for generating reports viewable in a web browser, Jupyter Notebook, or VSCode.
+
+- joblib is now a dependency
+
+- Parcellation method ReNA: Fast agglomerative clustering based on recursive
+ nearest neighbor grouping.
+ Yields very fast & accurate models, without creation of giant
+ clusters.
+ :class:`nilearn.regions.ReNA`
+
+- Optimization to image resampling
+ :func:`nilearn.image.resample_img` 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 :class:`nilearn.input_data.NiftiMasker`
+ when using the `mask_strategy="template"` option for brains in MNI space.
- New brain development fMRI dataset fetcher
:func:`nilearn.datasets.fetch_development_fmri` can be used to download
- movie-watching data in children and adults. A light-weight dataset implemented
- for teaching and usage in the examples.
+ movie-watching data in children and adults. A light-weight dataset
+ implemented for teaching and usage in the examples.
+- New example in `examples/05_advanced/plot_age_group_prediction_cross_val.py`
+ to compare methods for classifying subjects into age groups based on
+ functional connectivity. Similar example in
+ `examples/03_connectivity/plot_group_level_connectivity.py` simplified.
+
+- the Localizer dataset now follows the BIDS organization.
Changes
-------
@@ -15,6 +46,22 @@ Changes
- All the connectivity examples are changed from ADHD to brain development
fmri dataset.
+- :func:`nilearn.plotting.view_img_on_surf`, :func:`nilearn.plotting.view_surf`
+ and :func:`nilearn.plotting.view_connectome` now allow disabling the colorbar,
+ and setting its height and the fontsize of its ticklabels.
+
+- :func:`nilearn.plotting.view_img_on_surf`, :func:`nilearn.plotting.view_surf`
+ and :func:`nilearn.plotting.view_connectome` can now display a title.
+
+- Rework of the standardize-options of :func:`nilearn.signal.clean` and the various Maskers
+ in `nilearn.input_data`. You can now set `standardize` to `zscore` or `psc`. `psc` stands
+ for `Percent Signal Change`, which can be a meaningful metric for BOLD.
+
+- :func:`nilearn.plotting.plot_img` now has explicit keyword arguments `bg_img`,
+ `vmin` and `vmax` to control the background image and the bounds of the
+ colormap. These arguments were already accepted in `kwargs` but not documented
+ before.
+
Fixes
-----
@@ -28,6 +75,19 @@ Fixes
- When :func:`nilearn.plotting.plot_surf_stat_map` 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.
+- :func:`nilearn.plotting.view_surf` now accepts surface data provided as a file
+ path.
+- :func:`nilearn.plotting.plot_glass_brain` now correctly displays the left 'l' orientation even when
+ the given images are completely masked (empty images).
+- :func:`nilearn.plotting.plot_matrix` providing labels=None, False, or an empty list now correctly disables labels.
+- :func:`nilearn.plotting.plot_surf_roi` now takes vmin, vmax parameters
+- :func:`nilearn.datasets.fetch_surf_nki_enhanced` is now downloading the correct
+ left and right functional surface data for each subject
+- :func:`nilearn.datasets.fetch_atlas_schaefer_2018` 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.
+- Colormap creation functions have been updated to avoid matplotlib deprecation warnings
+ about colormap reversal
0.5.2
=====
@@ -75,6 +135,7 @@ NEW
- 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.
- New function :func:`nilearn.datasets.fetch_atlas_schaefer_2018`
+- New function :func:`nilearn.datasets.fetch_coords_seitzman_2018`
Changes
-------
@@ -98,6 +159,13 @@ Changes
- coords is now marker_coords
- colors is now marker_color
+- :func:`nilearn.plotting.view_img_on_surf` now accepts a `symmetric_cmap`
+ argument to control whether the colormap is centered around 0 and a `vmin`
+ argument.
+
+- Users can now control the size and fontsize of colorbars in interactive
+ surface and connectome plots, or disable the colorbar.
+
Fixes
-----
diff --git a/examples/01_plotting/plot_3d_map_to_surface_projection.py b/examples/01_plotting/plot_3d_map_to_surface_projection.py
index c8d08a0e6e..0b01f4197e 100644
--- a/examples/01_plotting/plot_3d_map_to_surface_projection.py
+++ b/examples/01_plotting/plot_3d_map_to_surface_projection.py
@@ -85,15 +85,16 @@
view = plotting.view_surf(fsaverage.infl_right, texture, threshold='90%',
bg_map=fsaverage.sulc_right)
-# uncomment this to open the plot in a web browser:
-# view.open_in_browser()
-##############################################################################
# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
-
view
+##############################################################################
+
+# uncomment this to open the plot in a web browser:
+# view.open_in_browser()
+
##############################################################################
# We don't need to do the projection ourselves, we can use view_img_on_surf:
diff --git a/examples/01_plotting/plot_demo_plotting.py b/examples/01_plotting/plot_demo_plotting.py
index 629864a913..9123648522 100644
--- a/examples/01_plotting/plot_demo_plotting.py
+++ b/examples/01_plotting/plot_demo_plotting.py
@@ -33,6 +33,7 @@
motor_images = datasets.fetch_neurovault_motor_task()
stat_img = motor_images.images[0]
+
###############################################################################
# Plotting statistical maps with function `plot_stat_map`
# --------------------------------------------------------
@@ -54,15 +55,15 @@
# for more details.
view = plotting.view_img(stat_img, threshold=3)
+# In a Jupyter notebook, if ``view`` is the output of a cell, it will
+# be displayed below the cell
+view
+
+##############################################################################
# uncomment this to open the plot in a web browser:
# view.open_in_browser()
-##############################################################################
-# In a Jupyter notebook, if ``view`` is the output of a cell, it will
-# be displayed below the cell
-
-view
###############################################################################
# Plotting statistical maps in a glass brain with function `plot_glass_brain`
diff --git a/examples/01_plotting/plot_dim_plotting.py b/examples/01_plotting/plot_dim_plotting.py
index afecc379c3..11253a33ad 100644
--- a/examples/01_plotting/plot_dim_plotting.py
+++ b/examples/01_plotting/plot_dim_plotting.py
@@ -21,9 +21,9 @@
localizer_dataset = datasets.fetch_localizer_button_task()
# Contrast map of motor task
-localizer_tmap_filename = localizer_dataset.tmap
+localizer_tmap_filename = localizer_dataset.tmaps[0]
# Subject specific anatomical image
-localizer_anat_filename = localizer_dataset.anat
+localizer_anat_filename = localizer_dataset.anats[0]
###########################################################################
# Plotting with enhancement of background image with dim=-.5
# --------------------------------------------------------------------------
diff --git a/examples/01_plotting/plot_surf_atlas.py b/examples/01_plotting/plot_surf_atlas.py
index 90fb6996dd..85b35a3dab 100644
--- a/examples/01_plotting/plot_surf_atlas.py
+++ b/examples/01_plotting/plot_surf_atlas.py
@@ -124,14 +124,14 @@
view = plotting.view_surf(fsaverage.infl_left, parcellation,
cmap='gist_ncar', symmetric_cmap=False)
-# uncomment this to open the plot in a web browser:
-# view.open_in_browser()
-
-##############################################################################
# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
view
+##############################################################################
+
+# uncomment this to open the plot in a web browser:
+# view.open_in_browser()
##############################################################################
# you can also use :func:`nilearn.plotting.view_connectome` to open an
diff --git a/examples/01_plotting/plot_surface_projection_strategies.py b/examples/01_plotting/plot_surface_projection_strategies.py
index 06d87f7596..15b614a5cf 100644
--- a/examples/01_plotting/plot_surface_projection_strategies.py
+++ b/examples/01_plotting/plot_surface_projection_strategies.py
@@ -61,7 +61,6 @@
for sample_points in [line_sample_points, ball_sample_points]:
fig = plt.figure()
ax = plt.subplot(projection='3d')
- ax.set_aspect(1)
ax.plot_trisurf(x, y, z, triangles=triangulation.triangles)
diff --git a/examples/02_decoding/plot_haxby_stimuli.py b/examples/02_decoding/plot_haxby_stimuli.py
index ba63648a27..ac72643b92 100644
--- a/examples/02_decoding/plot_haxby_stimuli.py
+++ b/examples/02_decoding/plot_haxby_stimuli.py
@@ -7,7 +7,6 @@
Cortex" (Science 2001)
"""
-from scipy.misc import imread
import matplotlib.pyplot as plt
from nilearn import datasets
@@ -16,22 +15,19 @@
haxby_dataset = datasets.fetch_haxby(subjects=[], fetch_stimuli=True)
stimulus_information = haxby_dataset.stimuli
-for stim_type in sorted(stimulus_information.keys()):
- if stim_type == b'controls':
- # skip control images, there are too many
- continue
-
- file_names = stimulus_information[stim_type]
-
- plt.figure()
- for i in range(48):
- plt.subplot(6, 8, i + 1)
- try:
- plt.imshow(imread(file_names[i]), cmap=plt.cm.gray)
- except:
- # just go to the next one if the file is not present
- pass
- plt.axis("off")
- plt.suptitle(stim_type)
+for stim_type in stimulus_information:
+ # skip control images, there are too many
+ if stim_type != 'controls':
+
+ file_names = stimulus_information[stim_type]
+
+ fig, axes = plt.subplots(6, 8)
+ fig.suptitle(stim_type)
+
+ for img_path, ax in zip(file_names, axes.ravel()):
+ ax.imshow(plt.imread(img_path), cmap=plt.cm.gray)
+
+ for ax in axes.ravel():
+ ax.axis("off")
show()
diff --git a/examples/02_decoding/plot_miyawaki_encoding.py b/examples/02_decoding/plot_miyawaki_encoding.py
index 8520076823..e0e3b3697b 100644
--- a/examples/02_decoding/plot_miyawaki_encoding.py
+++ b/examples/02_decoding/plot_miyawaki_encoding.py
@@ -152,7 +152,7 @@
# bring the scores into the shape of the background brain
score_map_img = masker.inverse_transform(cut_score)
-thresholded_score_map_img = threshold_img(score_map_img, threshold=1e-6)
+thresholded_score_map_img = threshold_img(score_map_img, threshold=1e-6, copy=False)
##############################################################################
# Plotting the statistical map on a background brain, we mark four voxels
diff --git a/examples/03_connectivity/plot_compare_decomposition.py b/examples/03_connectivity/plot_compare_decomposition.py
index ff93baa186..29d0949a95 100644
--- a/examples/03_connectivity/plot_compare_decomposition.py
+++ b/examples/03_connectivity/plot_compare_decomposition.py
@@ -16,13 +16,11 @@
CanICA is an ICA method for group-level analysis of fMRI data. Compared
to other strategies, it brings a well-controlled group model, as well as a
thresholding algorithm controlling for specificity and sensitivity with
-an explicit model of the signal. The reference papers are:
+an explicit model of the signal. The reference paper is:
* G. Varoquaux et al. "A group model for stable multi-subject ICA on
fMRI datasets", NeuroImage Vol 51 (2010), p. 288-299
-
-Pre-prints for both papers are available on hal
-(http://hal.archives-ouvertes.fr)
+ `preprint `_
"""
###############################################################################
@@ -39,7 +37,7 @@
####################################################################
-# Here we apply CanICA on the data
+# Apply CanICA on the data
# ---------------------------------
# We use as "template" as a strategy to compute the mask, as this leads
# to slightly faster and more reproducible results. However, the images
@@ -64,7 +62,6 @@
####################################################################
# To visualize we plot the outline of all components on one figure
-# -----------------------------------------------------------------
from nilearn.plotting import plot_prob_atlas
# Plot all ICA components together
@@ -73,7 +70,6 @@
####################################################################
# Finally, we plot the map for each ICA component separately
-# -----------------------------------------------------------
from nilearn.image import iter_img
from nilearn.plotting import plot_stat_map, show
@@ -98,7 +94,6 @@
###############################################################################
# Create a dictionary learning estimator
-# ---------------------------------------------------------------
from nilearn.decomposition import DictLearning
dict_learning = DictLearning(n_components=20,
@@ -120,28 +115,21 @@
###############################################################################
# Visualize the results
-# ----------------------
-from nilearn.plotting import find_xyz_cut_coords
-from nilearn.image import index_img
-
-names = {dict_learning: 'DictionaryLearning', canica: 'CanICA'}
-estimators = [canica, dict_learning]
-components_imgs = [canica_components_img, dictlearning_components_img]
-
-# Selecting specific maps to display: maps were manually chosen to be similar
-indices = {dict_learning: 8, canica: 14}
-# We select relevant cut coordinates for displaying
-cut_component = index_img(components_imgs[0], indices[dict_learning])
-cut_coords = find_xyz_cut_coords(cut_component)
-for estimator, components in zip(estimators, components_imgs):
- # 4D plotting
- plot_prob_atlas(components, view_type="filled_contours",
- title="%s" % names[estimator],
- cut_coords=cut_coords, colorbar=False)
- # 3D plotting
- plot_stat_map(index_img(components, indices[estimator]),
- title="%s" % names[estimator],
- cut_coords=cut_coords, colorbar=False)
+#
+# First plot all DictLearning components together
+plot_prob_atlas(dictlearning_components_img,
+ title='All DictLearning components')
+
+
+
+###############################################################################
+# One plot of each component
+
+for i, cur_img in enumerate(iter_img(dictlearning_components_img)):
+ plot_stat_map(cur_img, display_mode="z", title="Comp %d" % i,
+ cut_coords=1, colorbar=False)
+
+
show()
################################################################################
# .. note::
diff --git a/examples/03_connectivity/plot_data_driven_parcellations.py b/examples/03_connectivity/plot_data_driven_parcellations.py
index 336095b9ce..552230d765 100644
--- a/examples/03_connectivity/plot_data_driven_parcellations.py
+++ b/examples/03_connectivity/plot_data_driven_parcellations.py
@@ -2,8 +2,8 @@
Clustering methods to learn a brain parcellation from fMRI
==========================================================
-We use spatially-constrained Ward-clustering and KMeans to create a set
-of parcels.
+We use spatially-constrained Ward-clustering, KMeans, and Recursive Neighbor
+Agglomeration (ReNA) to create a set of parcels.
In a high dimensional regime, these methods can be interesting
to create a 'compressed' representation of the data, replacing the data
@@ -92,7 +92,7 @@
###########################################################################
# Visualize: Brain parcellations (Ward)
-# -------------------------------------
+# .....................................
#
# First, we display the parcellations of the brain image stored in attribute
# `labels_img_`
@@ -110,9 +110,10 @@
# Grab cut coordinates from this plot to use as a common for all plots
cut_coords = first_plot.cut_coords
+
###########################################################################
# Compressed representation of Ward clustering
-# --------------------------------------------
+# ............................................
#
# Second, we illustrate the effect that the clustering has on the signal.
# We show the original data, and the approximation provided by the
@@ -165,16 +166,17 @@
# This object uses method='kmeans' for KMeans clustering with 10mm smoothing
# and standardization ON
+start = time.time()
kmeans = Parcellations(method='kmeans', n_parcels=50,
standardize=True, smoothing_fwhm=10.,
memory='nilearn_cache', memory_level=1,
verbose=1)
# Call fit on functional dataset: single subject (less samples)
kmeans.fit(dataset.func)
-print("KMeans 50 clusters: %.2fs" % (time.time() - start))
+
###########################################################################
# Visualize: Brain parcellations (KMeans)
-# ---------------------------------------
+# .......................................
#
# Grab parcellations of brain image stored in attribute `labels_img_`
kmeans_labels_img = kmeans.labels_img_
@@ -188,6 +190,81 @@
kmeans_labels_img.to_filename('kmeans_parcellation.nii.gz')
##################################################################
-# Finally show them
+# Brain parcellations with ReNA Clustering
+# ----------------------------------------
+#
+# One interesting algorithmic property of ReNA (see References) is that
+# it is very fast for a large number of parcels (notably faster than Ward).
+# As before, the parcellation is done with a Parcellations object.
+# The spatial constraints are implemented inside the Parcellations object.
+#
+# References
+# ..........
+#
+# More about ReNA clustering algorithm in the original paper
+#
+# * A. Hoyos-Idrobo, G. Varoquaux, J. Kahn and B. Thirion, "Recursive
+# Nearest Agglomeration (ReNA): Fast Clustering for Approximation of
+# Structured Signals," in IEEE Transactions on Pattern Analysis and
+# Machine Intelligence, vol. 41, no. 3, pp. 669-681, 1 March 2019.
+# https://hal.archives-ouvertes.fr/hal-01366651/
+start = time.time()
+rena = Parcellations(method='rena', n_parcels=5000, standardize=False,
+ smoothing_fwhm=2., scaling=True)
+
+rena.fit_transform(dataset.func)
+print("ReNA 5000 clusters: %.2fs" % (time.time() - start))
+
+##################################################################
+# Visualize: Brain parcellations (ReNA)
+# .....................................
+#
+# First, we display the parcellations of the brain image stored in attribute
+# `labels_img_`
+rena_labels_img = rena.labels_img_
+
+# Now, rena_labels_img are Nifti1Image object, it can be saved to file
+# with the following code:
+rena_labels_img.to_filename('rena_parcellation.nii.gz')
+
+plotting.plot_roi(ward_labels_img, title="ReNA parcellation",
+ display_mode='xz', cut_coords=cut_coords)
+
+##################################################################
+# Compressed representation of ReNA clustering
+# ............................................
+#
+# We illustrate the effect that the clustering has on the signal.
+# We show the original data, and the approximation provided by
+# the clustering by averaging the signal on each parcel.
+#
+# We can then compare the results with the compressed representation
+# obtained with Ward.
+
+# Display the original data
+plotting.plot_epi(mean_func_img, cut_coords=cut_coords,
+ title='Original (%i voxels)' % original_voxels,
+ vmax=vmax, vmin=vmin, display_mode='xz')
+
+# A reduced data can be created by taking the parcel-level average:
+# Note that, as many scikit-learn objects, the ReNA object exposes
+# a transform method that modifies input features. Here it reduces their
+# dimension.
+# However, the data are in one single large 4D image, we need to use
+# index_img to do the split easily:
+fmri_reduced_rena = rena.transform(dataset.func)
+
+# Display the corresponding data compression using the parcellation
+compressed_img_rena = rena.inverse_transform(fmri_reduced_rena)
+
+plotting.plot_epi(index_img(compressed_img_rena, 0), cut_coords=cut_coords,
+ title='ReNA compressed representation (5000 parcels)',
+ vmin=vmin, vmax=vmax, display_mode='xz')
-plotting.show()
+####################################################################
+# Even if the compressed signal is relatively close
+# to the original signal, we can notice that Ward Clustering
+# gives a slightly more accurate compressed representation.
+# However, as said in the previous section, the computation time is
+# reduced which could still make ReNA more relevant than Ward in
+# some cases.
diff --git a/examples/03_connectivity/plot_group_level_connectivity.py b/examples/03_connectivity/plot_group_level_connectivity.py
index cf15f87899..daf21f8f70 100644
--- a/examples/03_connectivity/plot_group_level_connectivity.py
+++ b/examples/03_connectivity/plot_group_level_connectivity.py
@@ -1,35 +1,17 @@
"""
-Functional connectivity matrices for group analysis of connectomes
-==================================================================
+Classification of age groups using functional connectivity
+==========================================================
This example compares different kinds of functional connectivity between
-regions of interest : correlation, partial correlation, as well as a kind
-called **tangent**.
+regions of interest : correlation, partial correlation, and tangent space
+embedding.
The resulting connectivity coefficients can be used to
-discriminate children from adults. In general, the **tangent kind**
+discriminate children from adults. In general, the tangent space embedding
**outperforms** the standard correlations: see `Dadi et al 2019
`_
for a careful study.
"""
-# Matrix plotting from Nilearn: nilearn.plotting.plot_matrix
-import numpy as np
-import matplotlib.pylab as plt
-
-
-def plot_matrices(matrices, matrix_kind):
- n_matrices = len(matrices)
- fig = plt.figure(figsize=(n_matrices * 4, 4))
- for n_subject, matrix in enumerate(matrices):
- plt.subplot(1, n_matrices, n_subject + 1)
- matrix = matrix.copy() # avoid side effects
- # Set diagonal to zero, for better visualization
- np.fill_diagonal(matrix, 0)
- vmax = np.max(np.abs(matrix))
- title = '{0}, subject {1}'.format(matrix_kind, n_subject)
- plotting.plot_matrix(matrix, vmin=-vmax, vmax=vmax, cmap='RdBu_r',
- title=title, figure=fig, colorbar=False)
-
###############################################################################
# Load brain development fMRI dataset and MSDL atlas
@@ -37,7 +19,7 @@ def plot_matrices(matrices, matrix_kind):
# We study only 30 subjects from the dataset, to save computation time.
from nilearn import datasets
-rest_data = datasets.fetch_development_fmri(n_subjects=30)
+development_dataset = datasets.fetch_development_fmri(n_subjects=30)
###############################################################################
# We use probabilistic regions of interest (ROIs) from the MSDL atlas.
@@ -57,7 +39,7 @@ def plot_matrices(matrices, matrix_kind):
masker = input_data.NiftiMapsMasker(
msdl_data.maps, resampling_target="data", t_r=2, detrend=True,
- low_pass=.1, high_pass=.01, memory='nilearn_cache', memory_level=1)
+ low_pass=.1, high_pass=.01, memory='nilearn_cache', memory_level=1).fit()
###############################################################################
# Then we compute region signals and extract useful phenotypic informations.
@@ -65,13 +47,13 @@ def plot_matrices(matrices, matrix_kind):
pooled_subjects = []
groups = [] # child or adult
for func_file, confound_file, phenotypic in zip(
- rest_data.func, rest_data.confounds, rest_data.phenotypic):
- time_series = masker.fit_transform(func_file, confounds=confound_file)
+ development_dataset.func,
+ development_dataset.confounds,
+ development_dataset.phenotypic):
+ time_series = masker.transform(func_file, confounds=confound_file)
pooled_subjects.append(time_series)
- is_child = phenotypic['Child_Adult'] == 'child'
- if is_child:
+ if phenotypic['Child_Adult'] == 'child':
children.append(time_series)
-
groups.append(phenotypic['Child_Adult'])
print('Data has {0} children.'.format(len(children)))
@@ -101,10 +83,14 @@ def plot_matrices(matrices, matrix_kind):
print('Mean correlation has shape {0}.'.format(mean_correlation_matrix.shape))
###############################################################################
-# We display the connectome matrices of the first 4 children
+# We display the connectome matrices of the first 3 children
from nilearn import plotting
+from matplotlib import pyplot as plt
-plot_matrices(correlation_matrices[:4], 'correlation')
+_, axes = plt.subplots(1, 3, figsize=(15, 5))
+for i, (matrix, ax) in enumerate(zip(correlation_matrices, axes)):
+ plotting.plot_matrix(matrix, tri='lower', colorbar=False, axes=ax,
+ title='correlation, child {}'.format(i))
###############################################################################
# The blocks structure that reflect functional networks are visible.
@@ -119,19 +105,17 @@ def plot_matrices(matrices, matrix_kind):
# We can also study **direct connections**, revealed by partial correlation
# coefficients. We just change the `ConnectivityMeasure` kind
partial_correlation_measure = ConnectivityMeasure(kind='partial correlation')
-
-###############################################################################
-# and repeat the previous operation.
partial_correlation_matrices = partial_correlation_measure.fit_transform(
children)
###############################################################################
-# Most of direct connections are weaker than full connections,
-plot_matrices(partial_correlation_matrices[:4], 'partial')
+# Most of direct connections are weaker than full connections.
+_, axes = plt.subplots(1, 3, figsize=(15, 5))
+for i, (matrix, ax) in enumerate(zip(partial_correlation_matrices, axes)):
+ plotting.plot_matrix(matrix, tri='lower', colorbar=False, axes=ax,
+ title='partial correlation, child {}'.format(i))
###############################################################################
-# Compared to a connectome computed on correlations, the connectome graph
-# with partial correlations is more sparse:
plotting.plot_connectome(
partial_correlation_measure.mean_, msdl_coords,
title='mean partial correlation over all children')
@@ -140,8 +124,8 @@ def plot_matrices(matrices, matrix_kind):
# Extract subjects variabilities around a group connectivity
# ----------------------------------------------------------
# We can use **both** correlations and partial correlations to capture
-# reproducible connectivity patterns at the group-level and build a **robust**
-# **group connectivity matrix**. This is done by the **tangent** kind.
+# reproducible connectivity patterns at the group-level.
+# This is done by the tangent space embedding.
tangent_measure = ConnectivityMeasure(kind='tangent')
###############################################################################
@@ -154,84 +138,80 @@ def plot_matrices(matrices, matrix_kind):
# `tangent_matrices` model individual connectivities as
# **perturbations** of the group connectivity matrix `tangent_measure.mean_`.
# Keep in mind that these subjects-to-group variability matrices do not
-# straight reflect individual brain connections. For instance negative
+# directly reflect individual brain connections. For instance negative
# coefficients can not be interpreted as anticorrelated regions.
-plot_matrices(tangent_matrices[:4], 'tangent variability')
+_, axes = plt.subplots(1, 3, figsize=(15, 5))
+for i, (matrix, ax) in enumerate(zip(tangent_matrices, axes)):
+ plotting.plot_matrix(matrix, tri='lower', colorbar=False, axes=ax,
+ title='tangent offset, child {}'.format(i))
+
###############################################################################
-# The average tangent matrix cannot be interpreted, as the average
-# variation is expected to be zero
+# The average tangent matrix cannot be interpreted, as individual matrices
+# represent deviations from the mean, which is set to 0.
###############################################################################
# What kind of connectivity is most powerful for classification?
# --------------------------------------------------------------
-# *ConnectivityMeasure* can output the estimated subjects coefficients
-# as a 1D arrays through the parameter *vectorize*.
-connectivity_biomarkers = {}
-kinds = ['correlation', 'partial correlation', 'tangent']
-for kind in kinds:
- conn_measure = ConnectivityMeasure(kind=kind, vectorize=True)
- connectivity_biomarkers[kind] = conn_measure.fit_transform(pooled_subjects)
-
-# For each kind, all individual coefficients are stacked in a unique 2D matrix.
-print('{0} correlation biomarkers for each subject.'.format(
- connectivity_biomarkers['correlation'].shape[1]))
-
-###############################################################################
-# Note that we use the **pooled groups**. This is crucial for **tangent** kind,
-# to get the displacements from a **unique** `mean_` of all subjects.
-
-###############################################################################
-# We stratify the dataset into homogeneous classes according to phenotypic
-# and scan site. We then split the subjects into 3 folds with the same
-# proportion of each class as in the whole cohort
-from sklearn.model_selection import StratifiedKFold
+# We will use connectivity matrices as features to distinguish children from
+# adults. We use cross-validation and measure classification accuracy to
+# compare the different kinds of connectivity matrices.
+# We use random splits of the subjects into training/testing sets.
+# StratifiedShuffleSplit allows preserving the proportion of children in the
+# test set.
+from sklearn.svm import LinearSVC
+from sklearn.model_selection import StratifiedShuffleSplit
+from sklearn.metrics import accuracy_score
+import numpy as np
+kinds = ['correlation', 'partial correlation', 'tangent']
_, classes = np.unique(groups, return_inverse=True)
-cv = StratifiedKFold(n_splits=3)
-###############################################################################
-# and use the connectivity coefficients to classify children vs adults.
+cv = StratifiedShuffleSplit(n_splits=15, random_state=0, test_size=5)
+pooled_subjects = np.asarray(pooled_subjects)
-# Note that in cv.split(X, y),
-# providing y is sufficient to generate the splits and
-# hence np.zeros(n_samples) may be used as a placeholder for X
-# instead of actual training data.
-from sklearn.svm import LinearSVC
-from sklearn.model_selection import cross_val_score
-
-mean_scores = []
+scores = {}
for kind in kinds:
- svc = LinearSVC(random_state=0)
- cv_scores = cross_val_score(svc,
- connectivity_biomarkers[kind],
- y=classes,
- cv=cv,
- groups=groups,
- scoring='accuracy',
- )
- mean_scores.append(cv_scores.mean())
-
-###############################################################################
-# Finally, we can display the classification scores.
-
-###############################################################################
-# Finally, we can display the classification scores.
-from nilearn.plotting import show
+ scores[kind] = []
+ for train, test in cv.split(pooled_subjects, classes):
+ # *ConnectivityMeasure* can output the estimated subjects coefficients
+ # as a 1D arrays through the parameter *vectorize*.
+ connectivity = ConnectivityMeasure(kind=kind, vectorize=True)
+ # build vectorized connectomes for subjects in the train set
+ connectomes = connectivity.fit_transform(pooled_subjects[train])
+ # fit the classifier
+ classifier = LinearSVC().fit(connectomes, classes[train])
+ # make predictions for the left-out test subjects
+ predictions = classifier.predict(
+ connectivity.transform(pooled_subjects[test]))
+ # store the accuracy for this cross-validation fold
+ scores[kind].append(accuracy_score(classes[test], predictions))
+
+
+######################################################################
+# display the results
+
+mean_scores = [np.mean(scores[kind]) for kind in kinds]
+scores_std = [np.std(scores[kind]) for kind in kinds]
plt.figure(figsize=(6, 4))
positions = np.arange(len(kinds)) * .1 + .1
-plt.barh(positions, mean_scores, align='center', height=.05)
-yticks = [kind.replace(' ', '\n') for kind in kinds]
+plt.barh(positions, mean_scores, align='center', height=.05, xerr=scores_std)
+yticks = [k.replace(' ', '\n') for k in kinds]
plt.yticks(positions, yticks)
-plt.xlabel('Classification accuracy')
-plt.grid(True)
+plt.gca().grid(True)
+plt.gca().set_axisbelow(True)
+plt.gca().axvline(.8, color='red', linestyle='--')
+plt.xlabel('Classification accuracy\n(red line = chance level)')
plt.tight_layout()
+
###############################################################################
-# While the comparison is not fully conclusive on this small dataset,
+# This is a small example to showcase nilearn features. In practice such
+# comparisons need to be performed on much larger cohorts and several
+# datasets.
# `Dadi et al 2019
# `_
# Showed that across many cohorts and clinical questions, the tangent
# kind should be preferred.
-show()
+plotting.show()
diff --git a/examples/03_connectivity/plot_inverse_covariance_connectome.py b/examples/03_connectivity/plot_inverse_covariance_connectome.py
index 5ee1cef823..88c430659b 100644
--- a/examples/03_connectivity/plot_inverse_covariance_connectome.py
+++ b/examples/03_connectivity/plot_inverse_covariance_connectome.py
@@ -51,9 +51,13 @@
##############################################################################
# Compute the sparse inverse covariance
# --------------------------------------
-from sklearn.covariance import GraphLassoCV
-estimator = GraphLassoCV()
+try:
+ from sklearn.covariance import GraphicalLassoCV
+except ImportError:
+ # for Scitkit-Learn < v0.20.0
+ from sklearn.covariance import GraphLassoCV as GraphicalLassoCV
+estimator = GraphicalLassoCV()
estimator.fit(time_series)
##############################################################################
@@ -103,11 +107,12 @@
view = plotting.view_connectome(-estimator.precision_, coords)
-# uncomment this to open the plot in a web browser:
-# view.open_in_browser()
-
-##############################################################################
# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
-
view
+
+##############################################################################
+
+# uncomment this to open the plot in a web browser:
+# view.open_in_browser()
+
diff --git a/examples/03_connectivity/plot_multi_subject_connectome.py b/examples/03_connectivity/plot_multi_subject_connectome.py
index c7f45f0ce4..78dc2ce2b6 100644
--- a/examples/03_connectivity/plot_multi_subject_connectome.py
+++ b/examples/03_connectivity/plot_multi_subject_connectome.py
@@ -52,7 +52,7 @@ def plot_matrices(cov, prec, title, labels):
from nilearn import input_data
# A "memory" to avoid recomputation
-from sklearn.externals.joblib import Memory
+from nilearn._utils.compat import Memory
mem = Memory('nilearn_cache')
masker = input_data.NiftiMapsMasker(
@@ -84,8 +84,13 @@ def plot_matrices(cov, prec, title, labels):
gsc = GroupSparseCovarianceCV(verbose=2)
gsc.fit(subject_time_series)
-from sklearn import covariance
-gl = covariance.GraphLassoCV(verbose=2)
+try:
+ from sklearn.covariance import GraphicalLassoCV
+except ImportError:
+ # for Scitkit-Learn < v0.20.0
+ from sklearn.covariance import GraphLassoCV as GraphicalLassoCV
+
+gl = GraphicalLassoCV(verbose=2)
gl.fit(np.concatenate(subject_time_series))
@@ -102,10 +107,10 @@ def plot_matrices(cov, prec, title, labels):
display_mode="lzr")
plotting.plot_connectome(-gl.precision_, atlas_region_coords,
edge_threshold='90%',
- title="Sparse inverse covariance (GraphLasso)",
+ title="Sparse inverse covariance (GraphicalLasso)",
display_mode="lzr",
edge_vmax=.5, edge_vmin=-.5)
-plot_matrices(gl.covariance_, gl.precision_, "GraphLasso", labels)
+plot_matrices(gl.covariance_, gl.precision_, "GraphicalLasso", labels)
title = "GroupSparseCovariance"
plotting.plot_connectome(-gsc.precisions_[..., 0],
diff --git a/examples/03_connectivity/plot_probabilistic_atlas_extraction.py b/examples/03_connectivity/plot_probabilistic_atlas_extraction.py
index 62293fb4f0..21fb2153b6 100644
--- a/examples/03_connectivity/plot_probabilistic_atlas_extraction.py
+++ b/examples/03_connectivity/plot_probabilistic_atlas_extraction.py
@@ -1,6 +1,6 @@
"""
-Extracting signals of a probabilistic atlas of movie watching functional regions
-================================================================================
+Extracting signals of a probabilistic atlas of functional regions
+=================================================================
This example extracts the signal on regions defined via a probabilistic
atlas, to construct a functional connectome.
@@ -88,11 +88,12 @@
view = plotting.view_connectome(correlation_matrix, coords, threshold='80%')
-# uncomment this to open the plot in a web browser:
-# view.open_in_browser()
-
-##############################################################################
# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
-
view
+
+##############################################################################
+
+# uncomment this to open the plot in a web browser:
+# view.open_in_browser()
+
diff --git a/examples/03_connectivity/plot_simulated_connectome.py b/examples/03_connectivity/plot_simulated_connectome.py
index 385676c08d..3df491300c 100644
--- a/examples/03_connectivity/plot_simulated_connectome.py
+++ b/examples/03_connectivity/plot_simulated_connectome.py
@@ -49,8 +49,13 @@
# Fit one graph lasso per subject
-from sklearn.covariance import GraphLassoCV
-gl = GraphLassoCV(verbose=1)
+try:
+ from sklearn.covariance import GraphicalLassoCV
+except ImportError:
+ # for Scitkit-Learn < v0.20.0
+ from sklearn.covariance import GraphLassoCV as GraphicalLassoCV
+
+gl = GraphicalLassoCV(verbose=1)
for n, subject in enumerate(subjects[:n_displayed]):
gl.fit(subject)
diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
index aa69f2d4f4..1559bb760d 100644
--- a/examples/03_connectivity/plot_sphere_based_connectome.py
+++ b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -1,10 +1,11 @@
"""
Extract signals on spheres and plot a connectome
-================================================
+==============================================================
This example shows how to extract signals from spherical regions.
We show how to build spheres around user-defined coordinates, as well as
-centered on coordinates from Power-264 atlas [1] and Dosenbach-160 [2].
+centered on coordinates from the Power-264 atlas [1], the Dosenbach-160
+atlas [2], and the Seitzman-300 atlas [3].
**References**
@@ -14,6 +15,11 @@
[2] Dosenbach N.U., Nardos B., et al. "Prediction of individual brain maturity
using fMRI.", 2010, Science 329, 1358-1361.
+[3] `Seitzman, B. A., et al. "A set of functionally-defined brain regions with
+improved representation of the subcortex and cerebellum.", 2018, bioRxiv,
+450452
+`_
+
We estimate connectomes using two different methods: **sparse inverse
covariance** and **partial_correlation**, to recover the functional brain
**networks structure**.
@@ -64,10 +70,10 @@
masker = input_data.NiftiSpheresMasker(
dmn_coords, radius=8,
detrend=True, standardize=True,
- low_pass=0.1, high_pass=0.01, t_r=2.5,
+ low_pass=0.1, high_pass=0.01, t_r=2,
memory='nilearn_cache', memory_level=1, verbose=2)
-# Additionally, we pass confound information so ensure our extracted
+# Additionally, we pass confound information to ensure our extracted
# signal is cleaned from confounds.
func_filename = dataset.func[16]
@@ -138,25 +144,24 @@
view = plotting.view_connectome(partial_correlation_matrix, dmn_coords)
-# uncomment this to open the plot in a web browser:
-# view.open_in_browser()
-
-
-##############################################################################
# In a Jupyter notebook, if ``view`` is the output of a cell, it will
# be displayed below the cell
-
view
+##############################################################################
+
+# uncomment this to open the plot in a web browser:
+# view.open_in_browser()
+
##########################################################################
# Extract signals on spheres from an atlas
# ----------------------------------------
-#
+#
# Next, instead of supplying our own coordinates, we will use coordinates
# generated at the center of mass of regions from two different atlases.
# This time, we'll use a different correlation measure.
-#
+#
# First we fetch the coordinates of the Power atlas
power = datasets.fetch_coords_power_2011()
@@ -188,7 +193,7 @@
# and define spheres masker, with small enough radius to avoid regions overlap.
spheres_masker = input_data.NiftiSpheresMasker(
- seeds=coords, smoothing_fwhm=4, radius=5.,
+ seeds=coords, smoothing_fwhm=6, radius=5.,
detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2)
timeseries = spheres_masker.fit_transform(func_filename,
@@ -207,13 +212,17 @@
###############################################################################
# in which situation the graphical lasso **sparse inverse covariance**
# estimator captures well the covariance **structure**.
-from sklearn.covariance import GraphLassoCV
+try:
+ from sklearn.covariance import GraphicalLassoCV
+except ImportError:
+ # for Scitkit-Learn < v0.20.0
+ from sklearn.covariance import GraphLassoCV as GraphicalLassoCV
-covariance_estimator = GraphLassoCV(cv=3, verbose=1)
+covariance_estimator = GraphicalLassoCV(cv=3, verbose=1)
###############################################################################
-# We just fit our regions signals into the `GraphLassoCV` object
+# We just fit our regions signals into the `GraphicalLassoCV` object
covariance_estimator.fit(timeseries)
@@ -289,13 +298,13 @@
)).T
spheres_masker = input_data.NiftiSpheresMasker(
- seeds=coords, smoothing_fwhm=4, radius=4.5,
+ seeds=coords, smoothing_fwhm=6, radius=4.5,
detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2)
timeseries = spheres_masker.fit_transform(func_filename,
confounds=confounds_filename)
-covariance_estimator = GraphLassoCV()
+covariance_estimator = GraphicalLassoCV()
covariance_estimator.fit(timeseries)
matrix = covariance_estimator.covariance_
@@ -323,12 +332,58 @@
plotting.show()
+
+###############################################################################
+# Connectome extracted from Seitzman's atlas
+# -----------------------------------------------------
+# We repeat the same steps for Seitzman's atlas.
+seitzman = datasets.fetch_coords_seitzman_2018()
+
+coords = np.vstack((
+ seitzman.rois['x'],
+ seitzman.rois['y'],
+ seitzman.rois['z'],
+)).T
+
+###############################################################################
+# Before calculating the connectivity matrix, let's look at the distribution
+# of the regions of the default mode network.
+dmn_rois = seitzman.networks == "DefaultMode"
+dmn_coords = coords[dmn_rois]
+zero_matrix = np.zeros((len(dmn_coords), len(dmn_coords)))
+plotting.plot_connectome(zero_matrix, dmn_coords,
+ title='Seitzman default mode network',
+ node_color='darkred', node_size=20)
+
+###############################################################################
+# Now let's calculate connectivity for the Seitzman atlas.
+spheres_masker = input_data.NiftiSpheresMasker(
+ seeds=coords, smoothing_fwhm=6, radius=4,
+ detrend=True, standardize=True, low_pass=0.1, high_pass=0.01, t_r=2,
+ allow_overlap=True)
+
+timeseries = spheres_masker.fit_transform(func_filename,
+ confounds=confounds_filename)
+
+covariance_estimator = GraphicalLassoCV()
+covariance_estimator.fit(timeseries)
+matrix = covariance_estimator.covariance_
+
+plotting.plot_matrix(matrix, vmin=-1., vmax=1., colorbar=True,
+ title='Seitzman correlation matrix')
+
+plotting.plot_connectome(matrix, coords, title='Seitzman correlation graph',
+ edge_threshold="99.7%", node_size=20, colorbar=True)
+
+
+###############################################################################
+# We can easily identify the networks from the matrix blocks.
+print('Seitzman networks names are {0}'.format(np.unique(seitzman.networks)))
+plotting.show()
+
###############################################################################
# .. seealso::
#
-# :ref:`sphx_glr_auto_examples_03_connectivity_plot_atlas_comparison.py`
+# * :ref:`sphx_glr_auto_examples_03_connectivity_plot_atlas_comparison.py`
#
-# .. seealso::
-#
-# :ref:`sphx_glr_auto_examples_03_connectivity_plot_multi_subject_connectome.py`
-
+# * :ref:`sphx_glr_auto_examples_03_connectivity_plot_multi_subject_connectome.py`
diff --git a/examples/04_manipulating_images/plot_extract_rois_statistical_maps.py b/examples/04_manipulating_images/plot_extract_rois_statistical_maps.py
index 72773ff068..d63f7518c6 100644
--- a/examples/04_manipulating_images/plot_extract_rois_statistical_maps.py
+++ b/examples/04_manipulating_images/plot_extract_rois_statistical_maps.py
@@ -28,12 +28,12 @@
# Two types of strategies can be used from this threshold function
# Type 1: strategy used will be based on scoreatpercentile
-threshold_percentile_img = threshold_img(tmap_filename, threshold='97%')
+threshold_percentile_img = threshold_img(tmap_filename, threshold='97%', copy=False)
# Type 2: threshold strategy used will be based on image intensity
# Here, threshold value should be within the limits i.e. less than max value.
-threshold_value_img = threshold_img(tmap_filename, threshold=3.0)
+threshold_value_img = threshold_img(tmap_filename, threshold=3.0, copy=False)
################################################################################
# Visualization
diff --git a/examples/04_manipulating_images/plot_mask_computation.py b/examples/04_manipulating_images/plot_mask_computation.py
index d6b76db8e2..470a2506c4 100644
--- a/examples/04_manipulating_images/plot_mask_computation.py
+++ b/examples/04_manipulating_images/plot_mask_computation.py
@@ -17,7 +17,6 @@
"""
-
from nilearn.input_data import NiftiMasker
import nilearn.image as image
from nilearn.plotting import plot_roi, plot_epi, show
@@ -48,10 +47,15 @@
masker = NiftiMasker()
masker.fit(miyawaki_filename)
-# Plot the generated mask
+# Plot the generated mask using the mask_img_ attribute
plot_roi(masker.mask_img_, miyawaki_mean_img,
title="Mask from already masked data")
+###############################################################################
+# Plot the generated mask using the .generate_report method
+report = masker.generate_report()
+report
+
###############################################################################
# Computing a mask from raw EPI data
@@ -77,7 +81,8 @@
# We need to specify an 'epi' mask_strategy, as this is raw EPI data
masker = NiftiMasker(mask_strategy='epi')
masker.fit(epi_img)
-plot_roi(masker.mask_img_, mean_img, title='EPI automatic mask')
+report = masker.generate_report()
+report
###############################################################################
# Generate mask with strong opening
@@ -90,7 +95,8 @@
# skull parts in the image.
masker = NiftiMasker(mask_strategy='epi', mask_args=dict(opening=10))
masker.fit(epi_img)
-plot_roi(masker.mask_img_, mean_img, title='EPI Mask with strong opening')
+report = masker.generate_report()
+report
###############################################################################
# Generate mask with a high lower cutoff
@@ -107,8 +113,8 @@
mask_args=dict(upper_cutoff=.9, lower_cutoff=.8,
opening=False))
masker.fit(epi_img)
-plot_roi(masker.mask_img_, mean_img,
- title='EPI Mask: high lower_cutoff')
+report = masker.generate_report()
+report
###############################################################################
# Computing the mask from the MNI template
@@ -119,9 +125,27 @@
masker = NiftiMasker(mask_strategy='template')
masker.fit(epi_img)
-plot_roi(masker.mask_img_, mean_img,
- title='Mask from template')
+report = masker.generate_report()
+report
+
+###############################################################################
+# Compute and resample a mask
+###############################################################################
+#
+# NiftiMasker also allows passing parameters directly to `image.resample_img`.
+# We can specify a `target_affine`, a `target_shape`, or both.
+# For more information on these arguments,
+# see :doc:`plot_affine_transformation`.
+#
+# The NiftiMasker report allows us to see the mask before and after resampling.
+# Simply hover over the report to see the mask from the original image.
+
+import numpy as np
+masker = NiftiMasker(mask_strategy='epi', target_affine=np.eye(3) * 8)
+masker.fit(epi_img)
+report = masker.generate_report()
+report
###############################################################################
# After mask computation: extracting time series
@@ -136,7 +160,6 @@
detrended_data = detrended.fit_transform(epi_img)
# The timeseries are numpy arrays, so we can manipulate them with numpy
-import numpy as np
print("Trended: mean %.2f, std %.2f" %
(np.mean(trended_data), np.std(trended_data)))
diff --git a/examples/04_manipulating_images/plot_nifti_simple.py b/examples/04_manipulating_images/plot_nifti_simple.py
index 6f3045f4c9..b24122490c 100644
--- a/examples/04_manipulating_images/plot_nifti_simple.py
+++ b/examples/04_manipulating_images/plot_nifti_simple.py
@@ -10,7 +10,7 @@
# Retrieve the brain development functional dataset
from nilearn import datasets
-dataset = datasets.fetch_haxby()
+dataset = datasets.fetch_development_fmri(n_subjects=1)
func_filename = dataset.func[0]
# print basic information on the dataset
@@ -30,7 +30,7 @@
mask_img = nifti_masker.mask_img_
###########################################################################
-# Visualize the mask
+# Visualize the mask using the plot_roi method
from nilearn.plotting import plot_roi
from nilearn.image.image import mean_img
@@ -39,6 +39,13 @@
plot_roi(mask_img, mean_func_img, display_mode='y', cut_coords=4, title="Mask")
+###########################################################################
+# Visualize the mask using the 'generate_report' method
+# This report can be displayed in a Jupyter Notebook,
+# opened in-browser using the .open_in_browser() method,
+# or saved to a file using the .save_as_html(output_filepath) method.
+report = nifti_masker.generate_report()
+report
###########################################################################
# Preprocess data with the NiftiMasker
@@ -60,6 +67,10 @@
# Visualize results
from nilearn.plotting import plot_stat_map, show
from nilearn.image import index_img
+from nilearn.image.image import mean_img
+
+# calculate mean image for the background
+mean_func_img = mean_img(func_filename)
plot_stat_map(index_img(components, 0), mean_func_img,
display_mode='y', cut_coords=4, title="Component 0")
diff --git a/examples/05_advanced/plot_age_group_prediction_cross_val.py b/examples/05_advanced/plot_age_group_prediction_cross_val.py
new file mode 100644
index 0000000000..8a4f193efe
--- /dev/null
+++ b/examples/05_advanced/plot_age_group_prediction_cross_val.py
@@ -0,0 +1,105 @@
+"""
+Functional connectivity predicts age group
+==========================================
+
+This example compares different kinds of functional connectivity between
+regions of interest : correlation, partial correlation, and tangent space
+embedding.
+
+The resulting connectivity coefficients can be used to
+discriminate children from adults. In general, the tangent space embedding
+**outperforms** the standard correlations: see `Dadi et al 2019
+`_
+for a careful study.
+"""
+
+###############################################################################
+# Load brain development fMRI dataset and MSDL atlas
+# -------------------------------------------------------------------
+# We study only 60 subjects from the dataset, to save computation time.
+from nilearn import datasets
+
+development_dataset = datasets.fetch_development_fmri(n_subjects=60)
+
+###############################################################################
+# We use probabilistic regions of interest (ROIs) from the MSDL atlas.
+from nilearn.input_data import NiftiMapsMasker
+
+msdl_data = datasets.fetch_atlas_msdl()
+msdl_coords = msdl_data.region_coords
+
+masker = NiftiMapsMasker(
+ msdl_data.maps, resampling_target="data", t_r=2, detrend=True,
+ low_pass=.1, high_pass=.01, memory='nilearn_cache', memory_level=1).fit()
+masked_data = [masker.transform(func, confounds) for
+ (func, confounds) in zip(
+ development_dataset.func, development_dataset.confounds)]
+
+###############################################################################
+# What kind of connectivity is most powerful for classification?
+# --------------------------------------------------------------
+# we will use connectivity matrices as features to distinguish children from
+# adults. We use cross-validation and measure classification accuracy to
+# compare the different kinds of connectivity matrices.
+
+# prepare the classification pipeline
+from sklearn.pipeline import Pipeline
+from nilearn.connectome import ConnectivityMeasure
+from sklearn.svm import LinearSVC
+from sklearn.dummy import DummyClassifier
+from sklearn.model_selection import GridSearchCV
+
+kinds = ['correlation', 'partial correlation', 'tangent']
+
+pipe = Pipeline(
+ [('connectivity', ConnectivityMeasure(vectorize=True)),
+ ('classifier', GridSearchCV(LinearSVC(), {'C': [.1, 1., 10.]}, cv=5))])
+
+param_grid = [
+ {'classifier': [DummyClassifier('most_frequent')]},
+ {'connectivity__kind': kinds}
+]
+
+######################################################################
+# We use random splits of the subjects into training/testing sets.
+# StratifiedShuffleSplit allows preserving the proportion of children in the
+# test set.
+from sklearn.model_selection import GridSearchCV, StratifiedShuffleSplit
+from sklearn.preprocessing import LabelEncoder
+
+groups = [pheno['Child_Adult'] for pheno in development_dataset.phenotypic]
+classes = LabelEncoder().fit_transform(groups)
+
+cv = StratifiedShuffleSplit(n_splits=30, random_state=0, test_size=10)
+gs = GridSearchCV(pipe, param_grid, scoring='accuracy', cv=cv, verbose=1,
+ refit=False, n_jobs=8)
+gs.fit(masked_data, classes)
+mean_scores = gs.cv_results_['mean_test_score']
+scores_std = gs.cv_results_['std_test_score']
+
+######################################################################
+# display the results
+from matplotlib import pyplot as plt
+
+plt.figure(figsize=(6, 4))
+positions = [.1, .2, .3, .4]
+plt.barh(positions, mean_scores, align='center', height=.05, xerr=scores_std)
+yticks = ['dummy'] + list(gs.cv_results_['param_connectivity__kind'].data[1:])
+yticks = [t.replace(' ', '\n') for t in yticks]
+plt.yticks(positions, yticks)
+plt.xlabel('Classification accuracy')
+plt.gca().grid(True)
+plt.gca().set_axisbelow(True)
+plt.tight_layout()
+
+
+###############################################################################
+# This is a small example to showcase nilearn features. In practice such
+# comparisons need to be performed on much larger cohorts and several
+# datasets.
+# `Dadi et al 2019
+# `_
+# Showed that across many cohorts and clinical questions, the tangent
+# kind should be preferred.
+
+plt.show()
diff --git a/examples/05_advanced/plot_localizer_mass_univariate_methods.py b/examples/05_advanced/plot_localizer_mass_univariate_methods.py
index 9c8f4d5e2a..12531abf6c 100644
--- a/examples/05_advanced/plot_localizer_mass_univariate_methods.py
+++ b/examples/05_advanced/plot_localizer_mass_univariate_methods.py
@@ -35,7 +35,7 @@
tested_var = localizer_dataset.ext_vars['pseudo']
# Quality check / Remove subjects with bad tested variate
-mask_quality_check = np.where(tested_var != b'None')[0]
+mask_quality_check = np.where(tested_var != b'n/a')[0]
n_samples = mask_quality_check.size
contrast_map_filenames = [localizer_dataset.cmaps[i]
for i in mask_quality_check]
diff --git a/nilearn/__init__.py b/nilearn/__init__.py
index 7bfee2f735..5d39246d7b 100644
--- a/nilearn/__init__.py
+++ b/nilearn/__init__.py
@@ -35,11 +35,17 @@
import gzip
import sys
import warnings
+import os
from distutils.version import LooseVersion
from .version import _check_module_dependencies, __version__
+# Workaround issue discovered in intel-openmp 2019.5:
+# https://github.com/ContinuumIO/anaconda-issues/issues/11294
+#
+# see also https://github.com/scikit-learn/scikit-learn/pull/15020
+os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")
def _py2_deprecation_warning():
py2_warning = ('Python2 support is deprecated and will be removed in '
diff --git a/nilearn/_utils/cache_mixin.py b/nilearn/_utils/cache_mixin.py
index 30bde14440..3f82def65f 100644
--- a/nilearn/_utils/cache_mixin.py
+++ b/nilearn/_utils/cache_mixin.py
@@ -13,16 +13,10 @@
import nibabel
import sklearn
-from sklearn.externals.joblib import Memory
+from nilearn._utils.compat import Memory
MEMORY_CLASSES = (Memory, )
-try:
- from joblib import Memory as JoblibMemory
- MEMORY_CLASSES = (Memory, JoblibMemory)
-except ImportError:
- pass
-
import nilearn
from .compat import _basestring
diff --git a/nilearn/_utils/compat.py b/nilearn/_utils/compat.py
index f76de77cae..27b648fdae 100644
--- a/nilearn/_utils/compat.py
+++ b/nilearn/_utils/compat.py
@@ -7,6 +7,7 @@
from distutils.version import LooseVersion
import nibabel
+import sklearn
if sys.version_info[0] == 3:
@@ -66,3 +67,15 @@ def md5_hash(string):
m = hashlib.md5()
m.update(string)
return m.hexdigest()
+
+
+if sklearn.__version__ < '0.21':
+ from sklearn.externals import joblib
+else:
+ import joblib
+
+Memory = joblib.Memory
+Parallel = joblib.Parallel
+hash = joblib.hash
+delayed = joblib.delayed
+cpu_count = joblib.cpu_count
diff --git a/nilearn/_utils/extmath.py b/nilearn/_utils/extmath.py
index 6cbc8c6daf..615464f1eb 100644
--- a/nilearn/_utils/extmath.py
+++ b/nilearn/_utils/extmath.py
@@ -6,8 +6,6 @@
import numpy as np
-from numpy import partition
-
def fast_abs_percentile(data, percentile=80):
""" A fast version of the percentile of the absolute value.
@@ -36,11 +34,8 @@ def fast_abs_percentile(data, percentile=80):
data = np.abs(data)
data = data.ravel()
index = int(data.size * .01 * percentile)
- if partition is not None:
- # Partial sort: faster than sort
- data = partition(data, index)
- else:
- data.sort()
+ # Partial sort: faster than sort
+ data = np.partition(data, index)
return data[index]
diff --git a/nilearn/_utils/niimg_conversions.py b/nilearn/_utils/niimg_conversions.py
index efdfad485c..3ecf93a159 100644
--- a/nilearn/_utils/niimg_conversions.py
+++ b/nilearn/_utils/niimg_conversions.py
@@ -10,7 +10,7 @@
import nilearn as ni
import numpy as np
import itertools
-from sklearn.externals.joblib import Memory
+from nilearn._utils.compat import Memory
from .cache_mixin import cache
from .niimg import _safe_get_data, load_niimg
diff --git a/nilearn/conftest.py b/nilearn/conftest.py
new file mode 100644
index 0000000000..7d2d5abfe6
--- /dev/null
+++ b/nilearn/conftest.py
@@ -0,0 +1,30 @@
+from distutils.version import LooseVersion
+
+import numpy as np
+import pytest
+
+from _pytest.doctest import DoctestItem
+
+try:
+ import matplotlib
+except ImportError:
+ collect_ignore = ['plotting']
+else:
+ matplotlib # Prevents flake8 erring due to unused entities.
+
+
+def pytest_collection_modifyitems(items):
+
+ # numpy changed the str/repr formatting of numpy arrays in 1.14. We want to
+ # run doctests only for numpy >= 1.14.Adapted from scikit-learn
+ if LooseVersion(np.__version__) < LooseVersion('1.14'):
+ reason = 'doctests are only run for numpy >= 1.14'
+ skip_doctests = True
+ else:
+ skip_doctests = False
+
+ if skip_doctests:
+ skip_marker = pytest.mark.skip(reason=reason)
+ for item in items:
+ if isinstance(item, DoctestItem):
+ item.add_marker(skip_marker)
diff --git a/nilearn/connectome/__init__.py b/nilearn/connectome/__init__.py
index 51262f8196..701675768b 100644
--- a/nilearn/connectome/__init__.py
+++ b/nilearn/connectome/__init__.py
@@ -9,7 +9,8 @@
cov_to_corr, prec_to_partial)
from .group_sparse_cov import (GroupSparseCovariance,
- GroupSparseCovarianceCV, group_sparse_covariance)
+ GroupSparseCovarianceCV,
+ group_sparse_covariance)
__all__ = ['sym_matrix_to_vec', 'vec_to_sym_matrix', 'sym_to_vec',
'ConnectivityMeasure', 'cov_to_corr', 'prec_to_partial',
diff --git a/nilearn/connectome/connectivity_matrices.py b/nilearn/connectome/connectivity_matrices.py
index fce8fd064a..e2988d0632 100644
--- a/nilearn/connectome/connectivity_matrices.py
+++ b/nilearn/connectome/connectivity_matrices.py
@@ -486,7 +486,7 @@ def _fit_transform(self, X, do_transform=False, do_fit=False):
# Compute all the matrices, stored in "connectivities"
if self.kind == 'correlation':
covariances_std = [self.cov_estimator_.fit(
- signal._standardize(x, detrend=False, normalize=True)
+ signal._standardize(x, detrend=False, standardize=True)
).covariance_ for x in X]
connectivities = [cov_to_corr(cov) for cov in covariances_std]
else:
diff --git a/nilearn/connectome/group_sparse_cov.py b/nilearn/connectome/group_sparse_cov.py
index f43076b992..06317097aa 100644
--- a/nilearn/connectome/group_sparse_cov.py
+++ b/nilearn/connectome/group_sparse_cov.py
@@ -15,7 +15,7 @@
from sklearn.base import BaseEstimator
from sklearn.covariance import empirical_covariance
-from sklearn.externals.joblib import Memory, delayed, Parallel
+from nilearn._utils.compat import Memory, delayed, Parallel
from sklearn.model_selection import check_cv
from sklearn.utils.extmath import fast_logdet
@@ -891,7 +891,7 @@ class GroupSparseCovarianceCV(BaseEstimator, CacheMixin):
See also
--------
GroupSparseCovariance,
- sklearn.covariance.GraphLassoCV
+ sklearn.covariance.GraphicalLassoCV
Notes
-----
diff --git a/nilearn/datasets/__init__.py b/nilearn/datasets/__init__.py
index 3c383da582..280689e9b4 100644
--- a/nilearn/datasets/__init__.py
+++ b/nilearn/datasets/__init__.py
@@ -17,6 +17,7 @@
from .atlas import (fetch_atlas_craddock_2012, fetch_atlas_destrieux_2009,
fetch_atlas_harvard_oxford, fetch_atlas_msdl,
fetch_coords_power_2011,
+ fetch_coords_seitzman_2018,
fetch_atlas_smith_2009,
fetch_atlas_yeo_2011, fetch_atlas_aal,
fetch_atlas_basc_multiscale_2015,
@@ -44,6 +45,7 @@
'fetch_atlas_harvard_oxford', 'fetch_atlas_msdl',
'fetch_atlas_schaefer_2018',
'fetch_coords_power_2011',
+ 'fetch_coords_seitzman_2018',
'fetch_atlas_smith_2009',
'fetch_atlas_allen_2011',
'fetch_atlas_yeo_2011', 'fetch_mixed_gambles', 'fetch_atlas_aal',
diff --git a/nilearn/datasets/atlas.py b/nilearn/datasets/atlas.py
index 36689b8105..3897f95110 100644
--- a/nilearn/datasets/atlas.py
+++ b/nilearn/datasets/atlas.py
@@ -10,6 +10,7 @@
import nibabel as nb
import numpy as np
+from numpy.lib import recfunctions
from sklearn.datasets.base import Bunch
from .utils import _get_dataset_dir, _fetch_files, _get_dataset_descr
@@ -808,6 +809,79 @@ def fetch_coords_dosenbach_2010(ordered_regions=True):
return Bunch(**params)
+def fetch_coords_seitzman_2018(ordered_regions=True):
+ """Load the Seitzman et al. 300 ROIs. These ROIs cover cortical,
+ subcortical and cerebellar regions and are assigned to one of 13
+ networks (Auditory, CinguloOpercular, DefaultMode, DorsalAttention,
+ FrontoParietal, MedialTemporalLobe, ParietoMedial, Reward, Salience,
+ SomatomotorDorsal, SomatomotorLateral, VentralAttention, Visual) and
+ have a regional label (cortexL, cortexR, cerebellum, thalamus, hippocampus,
+ basalGanglia, amygdala, cortexMid).
+
+ .. versionadded:: 0.5.1
+
+ Parameters
+ ----------
+ ordered_regions : bool, optional
+ ROIs from same networks are grouped together and ordered with respect
+ to their locations (anterior to posterior).
+
+ Returns
+ -------
+ data: sklearn.datasets.base.Bunch
+ dictionary-like object, contains:
+ - "rois": Coordinates of 300 ROIs in MNI space
+ - "radius": Radius of each ROI in mm
+ - "networks": Network names
+ - "regions": Region names
+
+ References
+ ----------
+ Seitzman, B. A., Gratton, C., Marek, S., Raut, R. V., Dosenbach, N. U.,
+ Schlaggar, B. L., et al. (2018). A set of functionally-defined brain
+ regions with improved representation of the subcortex and cerebellum.
+ bioRxiv, 450452. http://doi.org/10.1101/450452
+ """
+ dataset_name = 'seitzman_2018'
+ fdescr = _get_dataset_descr(dataset_name)
+ package_directory = os.path.dirname(os.path.abspath(__file__))
+ roi_file = os.path.join(package_directory, "data",
+ "seitzman_2018_ROIs_300inVol_MNI_allInfo.txt")
+ anatomical_file = os.path.join(package_directory, "data",
+ "seitzman_2018_ROIs_anatomicalLabels.txt")
+
+ rois = np.recfromcsv(roi_file, delimiter=" ")
+ rois = recfunctions.rename_fields(rois, {"netname": "network",
+ "radiusmm": "radius"})
+ rois.network = rois.network.astype(str)
+
+ # get integer regional labels and convert to text labels with mapping
+ # from header line
+ with open(anatomical_file, 'r') as fi:
+ header = fi.readline()
+ region_mapping = {}
+ for r in header.strip().split(","):
+ i, region = r.split("=")
+ region_mapping[int(i)] = region
+
+ anatomical = np.genfromtxt(anatomical_file, skip_header=1)
+ anatomical_names = np.array([region_mapping[a] for a in anatomical])
+
+ rois = recfunctions.merge_arrays((rois, anatomical_names),
+ asrecarray=True, flatten=True)
+ rois.dtype.names = rois.dtype.names[:-1] + ("region",)
+
+ if ordered_regions:
+ rois = np.sort(rois, order=['network', 'y'])
+
+ params = dict(rois=rois[['x', 'y', 'z']],
+ radius=rois['radius'],
+ networks=rois['network'].astype(str),
+ regions=rois['region'], description=fdescr)
+
+ return Bunch(**params)
+
+
def fetch_atlas_allen_2011(data_dir=None, url=None, resume=True, verbose=1):
"""Download and return file names for the Allen and MIALAB ICA atlas
(dated 2011).
@@ -1201,7 +1275,7 @@ def fetch_atlas_schaefer_2018(n_rois=400, yeo_networks=7, resolution_mm=1,
----------
n_rois: int
number of regions of interest {100, 200, 300, 400 (default), 500, 600,
- 800, 1000}
+ 700, 800, 900, 1000}
yeo_networks: int
ROI annotation according to yeo networks {7 (default), 17}
@@ -1233,7 +1307,7 @@ def fetch_atlas_schaefer_2018(n_rois=400, yeo_networks=7, resolution_mm=1,
References
----------
For more information on this dataset, see
- https://github.com/ThomasYeoLab/CBIG/tree/v0.8.1-Schaefer2018_LocalGlobal/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal
+ https://github.com/ThomasYeoLab/CBIG/tree/v0.14.3-Update_Yeo2011_Schaefer2018_labelname/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations
Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo XN, Holmes AJ,
Eickhoff SB, Yeo BTT. Local-Global parcellation of the human
@@ -1246,8 +1320,15 @@ def fetch_atlas_schaefer_2018(n_rois=400, yeo_networks=7, resolution_mm=1,
intrinsic functional connectivity. J Neurophysiol 106(3):1125-65, 2011.
Licence: MIT.
+
+ Notes
+ -----
+ Release v0.14.3 of the Schaefer 2018 parcellation is used by
+ default. Versions prior to v0.14.3 are known to contain erroneous region
+ label names. For more details, see
+ https://github.com/ThomasYeoLab/CBIG/blob/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations/Updates/Update_20190916_README.md
"""
- valid_n_rois = [100, 200, 300, 400, 500, 600, 800, 1000]
+ valid_n_rois = list(range(100, 1100, 100))
valid_yeo_networks = [7, 17]
valid_resolution_mm = [1, 2]
if n_rois not in valid_n_rois:
@@ -1264,9 +1345,9 @@ def fetch_atlas_schaefer_2018(n_rois=400, yeo_networks=7, resolution_mm=1,
if base_url is None:
base_url = ('https://raw.githubusercontent.com/ThomasYeoLab/CBIG/'
- 'v0.8.1-Schaefer2018_LocalGlobal/stable_projects/'
- 'brain_parcellation/Schaefer2018_LocalGlobal/'
- 'Parcellations/MNI/'
+ 'v0.14.3-Update_Yeo2011_Schaefer2018_labelname/'
+ 'stable_projects/brain_parcellation/'
+ 'Schaefer2018_LocalGlobal/Parcellations/MNI/'
)
files = []
diff --git a/nilearn/datasets/data/seitzman_2018_ROIs_300inVol_MNI_allInfo.txt b/nilearn/datasets/data/seitzman_2018_ROIs_300inVol_MNI_allInfo.txt
new file mode 100644
index 0000000000..6e73eb8025
--- /dev/null
+++ b/nilearn/datasets/data/seitzman_2018_ROIs_300inVol_MNI_allInfo.txt
@@ -0,0 +1,301 @@
+x y z radius(mm) netWorkbenchLabel netName
+-56.16 -44.76 -24.23 5 0 unassigned
+-24.66 -97.84 -12.33 5 0 unassigned
+8.13 41.12 -24.31 5 0 unassigned
+26.68 -97.3 -13.49 5 0 unassigned
+48.52 -2.85 -38.49 5 0 unassigned
+51.79 -34.17 -27.23 5 0 unassigned
+55.18 -30.8 -16.93 5 0 unassigned
+-57.97 -25.69 -14.73 5 0 unassigned
+64.6 -24.41 -18.57 5 0 unassigned
+-50.06 -7.09 -39.24 5 0 unassigned
+33.55 38.46 -12.03 5 0 unassigned
+-46.68 -50.91 -20.91 5 0 unassigned
+-7.12 -52.22 60.71 5 10 SomatomotorDorsal
+0.05 -14.53 46.74 5 10 SomatomotorDorsal
+-53.52 -22.54 43.1 5 10 SomatomotorDorsal
+-45.1 -31.85 46.63 5 10 SomatomotorDorsal
+-39.63 -19.04 54.21 5 10 SomatomotorDorsal
+-38.28 -27.17 69.45 5 10 SomatomotorDorsal
+-38.24 -14.57 68.72 5 10 SomatomotorDorsal
+-29.1 -43 60.66 5 10 SomatomotorDorsal
+-22.5 -30.1 72.44 5 10 SomatomotorDorsal
+-20.66 -31.33 60.85 5 10 SomatomotorDorsal
+-16.25 -45.8 73.22 5 10 SomatomotorDorsal
+-13.74 -17.95 39.84 5 10 SomatomotorDorsal
+-12.96 -17.34 74.66 5 10 SomatomotorDorsal
+-6.9 -20.59 65.21 5 10 SomatomotorDorsal
+-6.79 -33.09 72.27 5 10 SomatomotorDorsal
+2.4 -27.94 60.15 5 10 SomatomotorDorsal
+3.45 -17.44 58.45 5 10 SomatomotorDorsal
+9.5 -1.84 44.73 5 10 SomatomotorDorsal
+9.94 -45.52 72.63 5 10 SomatomotorDorsal
+10.09 -17.1 74.14 5 10 SomatomotorDorsal
+13.19 -32.82 74.98 5 10 SomatomotorDorsal
+20.21 -28.8 59.8 5 10 SomatomotorDorsal
+22.45 -42.29 68.99 5 10 SomatomotorDorsal
+28.88 -16.95 70.55 5 10 SomatomotorDorsal
+28.54 -39.24 59.17 5 10 SomatomotorDorsal
+37.74 -17.3 45.01 5 10 SomatomotorDorsal
+42.14 -20.24 54.59 5 10 SomatomotorDorsal
+44.34 -7.55 56.98 5 10 SomatomotorDorsal
+47.21 -29.75 48.7 5 10 SomatomotorDorsal
+50.24 -20.37 41.74 5 10 SomatomotorDorsal
+19.33 -7.71 63.88 5 10 SomatomotorDorsal
+54.22 -27.83 33.64 5 10 SomatomotorDorsal
+-52.84 -10.23 24.41 5 11 SomatomotorLateral
+-49.47 -11.06 34.95 5 11 SomatomotorLateral
+36.04 -9.44 13.95 5 11 SomatomotorLateral
+51.14 -5.8 32.42 5 11 SomatomotorLateral
+65.64 -7.88 24.83 5 11 SomatomotorLateral
+-51.26 8.26 -2.06 5 9 CinguloOpercular
+-44.76 0.1 8.83 5 9 CinguloOpercular
+-34.37 3.29 4.19 5 9 CinguloOpercular
+-16.14 -4.82 70.83 5 9 CinguloOpercular
+-10.48 -2.1 42.02 5 9 CinguloOpercular
+-5.33 17.8 34.41 5 9 CinguloOpercular
+-2.88 2.38 53.21 5 9 CinguloOpercular
+6.52 7.69 50.58 5 9 CinguloOpercular
+13.21 -1.36 69.98 5 9 CinguloOpercular
+35.83 10.32 1.18 5 9 CinguloOpercular
+36.73 0.78 -3.57 5 9 CinguloOpercular
+49.4 8.32 -1.12 5 9 CinguloOpercular
+-30.12 -27.02 12.2 5 9 CinguloOpercular
+42.05 -0.39 47.1 5 9 CinguloOpercular
+35.91 21.91 2.62 5 9 CinguloOpercular
+-39.12 50.79 17.38 5 9 CinguloOpercular
+31.24 32.79 26.39 5 9 CinguloOpercular
+31.07 55.71 14.49 5 9 CinguloOpercular
+36.89 32.35 -2.24 5 9 CinguloOpercular
+-52.92 -21.83 22.97 5 12 Auditory
+43.45 -22.93 19.85 5 12 Auditory
+59.4 -17.34 28.69 5 12 Auditory
+-60.48 -25.22 13.82 5 12 Auditory
+-55.22 -9.42 11.73 5 12 Auditory
+-49.77 -34.36 25.74 5 12 Auditory
+-49.14 -26.3 5.18 5 12 Auditory
+-38.43 -33.34 16.98 5 12 Auditory
+31.75 -26.33 12.91 5 12 Auditory
+55.96 -5.03 13.25 5 12 Auditory
+57.88 -15.62 7.49 5 12 Auditory
+65.43 -33.2 19.97 5 12 Auditory
+-68.47 -22.66 -15.74 5 1 DefaultMode
+-68.3 -41.41 -5.14 5 1 DefaultMode
+-57.75 -29.7 -3.94 5 1 DefaultMode
+-55.72 -12.96 -10.24 5 1 DefaultMode
+-52.89 2.55 -27.06 5 1 DefaultMode
+-49.3 -42.15 0.83 5 1 DefaultMode
+-45.79 -60.69 20.85 5 1 DefaultMode
+-46.17 31.26 -13.03 5 1 DefaultMode
+-44.45 -64.64 34.78 5 1 DefaultMode
+-43.58 11.99 -34.15 5 1 DefaultMode
+-40.5 -75.27 25.8 5 1 DefaultMode
+-39.05 -74.95 43.72 5 1 DefaultMode
+-35.36 19.86 50.8 5 1 DefaultMode
+-19.78 45.07 39.48 5 1 DefaultMode
+-20.16 63.65 19.39 5 1 DefaultMode
+-17.65 63.19 -9.17 5 1 DefaultMode
+-16.4 28.52 53.05 5 1 DefaultMode
+-12.6 -39.64 0.93 5 1 DefaultMode
+-11.29 -56.2 15.6 5 1 DefaultMode
+-10.09 39.09 52.29 5 1 DefaultMode
+-10.33 54.63 38.71 5 1 DefaultMode
+-11.06 44.62 7.61 5 1 DefaultMode
+-7.55 48.08 23.18 5 1 DefaultMode
+-6.84 -54.9 27.05 5 1 DefaultMode
+-7.04 50.82 -1.29 5 1 DefaultMode
+-2.94 -48.79 12.87 5 1 DefaultMode
+-2.2 -36.68 43.85 5 1 DefaultMode
+-3.06 44.41 -9.46 5 1 DefaultMode
+-2.06 37.85 36.34 5 1 DefaultMode
+-2.5 41.7 16.05 5 1 DefaultMode
+5.55 66.69 -3.55 5 1 DefaultMode
+5.91 -58.82 35.45 5 1 DefaultMode
+5.94 54.42 16.18 5 1 DefaultMode
+6.11 63.98 21.96 5 1 DefaultMode
+7.94 -48.37 30.57 5 1 DefaultMode
+7.51 42.49 -5.35 5 1 DefaultMode
+8.36 47.59 -15.18 5 1 DefaultMode
+8.8 54.23 3.45 5 1 DefaultMode
+10.77 -53.83 17.09 5 1 DefaultMode
+12.73 54.87 38.19 5 1 DefaultMode
+13.08 29.99 58.65 5 1 DefaultMode
+12.25 35.63 20.3 5 1 DefaultMode
+15.12 -63.09 25.98 5 1 DefaultMode
+22.11 39.21 38.9 5 1 DefaultMode
+23.33 33.07 47.68 5 1 DefaultMode
+43.43 -72.21 28 5 1 DefaultMode
+45.64 16.2 -30.02 5 1 DefaultMode
+46.68 -50.08 28.76 5 1 DefaultMode
+49.26 35.47 -12.2 5 1 DefaultMode
+52.04 -59.37 35.52 5 1 DefaultMode
+52.16 -2.43 -16.4 5 1 DefaultMode
+51.9 6.81 -29.61 5 1 DefaultMode
+64.64 -11.8 -19.3 5 1 DefaultMode
+64.8 -30.55 -8.7 5 1 DefaultMode
+-33.93 -38.06 -15.6 5 1 DefaultMode
+4.2 -48.06 50.71 5 15 ParietoMedial
+-6.58 -71.47 41.74 5 15 ParietoMedial
+-2.47 -34.8 31.07 5 15 ParietoMedial
+1.75 -24.25 30.36 5 15 ParietoMedial
+11.27 -66.01 42.09 5 15 ParietoMedial
+-12.08 -94.56 -12.8 5 2 Visual
+7.98 -91.08 -7.1 5 2 Visual
+17.27 -91.09 -13.64 5 2 Visual
+-46.54 -75.95 -9.95 5 2 Visual
+-42.1 -73.62 0.38 5 2 Visual
+-40.21 -88.44 -6.19 5 2 Visual
+-33 -79.02 -13.24 5 2 Visual
+-28.07 -79.45 19.43 5 2 Visual
+-26.39 -90.23 3.12 5 2 Visual
+-23.94 -90.98 18.96 5 2 Visual
+-17.87 -68.03 4.81 5 2 Visual
+-16.21 -76.97 33.82 5 2 Visual
+-15.85 -52.34 -1.43 5 2 Visual
+-15.02 -72.42 -7.68 5 2 Visual
+-14.22 -90.66 31.4 5 2 Visual
+-8.43 -80.5 7.44 5 2 Visual
+-2.88 -81.25 21.1 5 2 Visual
+5.59 -71.65 23.52 5 2 Visual
+6.21 -81.41 6.11 5 2 Visual
+8.45 -71.84 10.79 5 2 Visual
+15.27 -87.09 36.89 5 2 Visual
+15.18 -76.68 31 5 2 Visual
+17.53 -46.86 -9.88 5 2 Visual
+19.81 -65.56 1.72 5 2 Visual
+19.64 -85.62 -2.39 5 2 Visual
+24.41 -87.21 24.01 5 2 Visual
+25.66 -79.47 -15.56 5 2 Visual
+26.93 -59.37 -9.36 5 2 Visual
+28.68 -76.62 25.42 5 2 Visual
+36.76 -84.11 12.99 5 2 Visual
+36.51 -81.16 1.2 5 2 Visual
+39.98 -72.49 14.36 5 2 Visual
+41.6 -65.5 -8.27 5 2 Visual
+42.52 -78.17 -11.78 5 2 Visual
+-52.6 -48.83 42.5 5 3 FrontoParietal
+-46.5 10.85 23.04 5 3 FrontoParietal
+-43.93 1.8 45.7 5 3 FrontoParietal
+-42.23 38.21 21.35 5 3 FrontoParietal
+-42.09 -54.98 44.74 5 3 FrontoParietal
+-42.1 24.68 29.53 5 3 FrontoParietal
+-41.06 5.81 32.72 5 3 FrontoParietal
+-41.68 45.16 -2.31 5 3 FrontoParietal
+-34.16 54.83 4.36 5 3 FrontoParietal
+-28.4 -57.93 47.78 5 3 FrontoParietal
+-22.53 10.76 63.73 5 3 FrontoParietal
+-2.98 26.41 44.42 5 3 FrontoParietal
+24.07 44.61 -15.35 5 3 FrontoParietal
+31.83 14.37 55.98 5 3 FrontoParietal
+33.38 -53.12 44.02 5 3 FrontoParietal
+33.6 54.22 -12.95 5 3 FrontoParietal
+37.45 -64.7 40.38 5 3 FrontoParietal
+38.37 43.18 15.06 5 3 FrontoParietal
+39.87 18.39 39.72 5 3 FrontoParietal
+43.93 -52.95 46.95 5 3 FrontoParietal
+43.25 49.25 -2.31 5 3 FrontoParietal
+47.01 9.93 32.66 5 3 FrontoParietal
+47.98 24.56 26.5 5 3 FrontoParietal
+49.18 -42.41 45.16 5 3 FrontoParietal
+58.31 -52.79 -13.61 5 3 FrontoParietal
+47.6 22.16 9.74 5 3 FrontoParietal
+55.27 -44.59 36.7 5 3 FrontoParietal
+-35.44 20.03 0.07 5 8 Salience
+-27.5 52.04 21.28 5 8 Salience
+-10.76 25.99 24.54 5 8 Salience
+-0.94 14.86 43.99 5 8 Salience
+-0.2 30.35 27.22 5 8 Salience
+5.23 23.22 37.03 5 8 Salience
+10.26 22.06 27.48 5 8 Salience
+26.07 49.56 26.58 5 8 Salience
+33.56 16.45 -7.58 5 8 Salience
+51.28 -28.52 -4.3 5 7 VentralAttention
+-56.47 -50.48 9.92 5 7 VentralAttention
+-55.3 -39.89 13.51 5 7 VentralAttention
+51.52 -32.52 7.55 5 7 VentralAttention
+55.75 -46.07 11.42 5 7 VentralAttention
+-49.07 25.13 -0.98 5 7 VentralAttention
+-9.88 10.95 66.61 5 7 VentralAttention
+52.68 32.58 0.57 5 7 VentralAttention
+53.9 -42.76 21.83 5 7 VentralAttention
+-26.6 -70.72 36.86 5 5 DorsalAttention
+-37.26 -28.8 -25.58 5 5 DorsalAttention
+45.68 -46.67 -16.85 5 5 DorsalAttention
+10.51 -38.54 50.02 5 5 DorsalAttention
+-52.44 -63.14 5.29 5 5 DorsalAttention
+-42.26 -60.12 -8.85 5 5 DorsalAttention
+-32.56 -46.42 47.2 5 5 DorsalAttention
+-32.23 -1.08 54.06 5 5 DorsalAttention
+-16.5 -58.57 64.46 5 5 DorsalAttention
+9.61 -61.5 60.88 5 5 DorsalAttention
+21.9 -64.74 48.12 5 5 DorsalAttention
+25.34 -58.18 60.34 5 5 DorsalAttention
+28.56 -4.62 53.99 5 5 DorsalAttention
+46.09 -58.93 3.93 5 5 DorsalAttention
+-31.13 -9.99 -36.32 5 17 MedialTemporalLobe
+32.85 -12.41 -34.41 5 17 MedialTemporalLobe
+-25.57 -11.78 -21.54 4 17 MedialTemporalLobe
+24.73 -11.25 -22.68 4 17 MedialTemporalLobe
+-25.24 -38.78 -2.01 4 1 DefaultMode
+25.08 -37.18 -2.16 4 1 DefaultMode
+-30.63 18.71 -18.98 5 4 Reward
+-21.14 40.87 -20.48 5 4 Reward
+23.96 31.94 -17.78 5 4 Reward
+27.06 16.22 -16.93 5 4 Reward
+-20.3 -2.27 -22.21 4 4 Reward
+19.51 -1.85 -23.11 4 4 Reward
+12.66 17.32 -5.06 4 4 Reward
+-12.49 17.05 -4.49 4 4 Reward
+11.87 17.51 6.57 4 1 DefaultMode
+-13.28 17.24 7.14 4 1 DefaultMode
+14.15 -1.19 18.18 4 3 FrontoParietal
+-15.19 -1.5 18.84 4 3 FrontoParietal
+28.56 -7.53 7.66 4 10 SomatomotorDorsal
+-28.02 -10.23 9.07 4 10 SomatomotorDorsal
+28.28 -6.7 -5.06 4 10 SomatomotorDorsal
+-28.31 -10.45 -3.6 4 10 SomatomotorDorsal
+25.26 1.75 -1.26 4 10 SomatomotorDorsal
+-28.26 -0.97 -3.09 4 10 SomatomotorDorsal
+25.46 5.05 7 4 10 SomatomotorDorsal
+-24.83 7.67 7.95 4 10 SomatomotorDorsal
+18.88 -4.68 -3.91 4 10 SomatomotorDorsal
+-18.84 -5.08 -3.06 4 10 SomatomotorDorsal
+3.42 -7.79 8.23 4 1 DefaultMode
+-2.88 -9.96 8.5 4 1 DefaultMode
+18.89 -28.79 0.66 4 2 Visual
+-18.84 -29.18 1.51 4 2 Visual
+12.9 -13.94 11.55 4 9 CinguloOpercular
+-15.39 -14.24 12.19 4 9 CinguloOpercular
+9.56 -7.84 1.76 4 9 CinguloOpercular
+-9.35 -10.17 0.2 4 9 CinguloOpercular
+13.67 -19.39 0.23 4 10 SomatomotorDorsal
+-13.57 -19.67 0.84 4 10 SomatomotorDorsal
+15.95 -22.37 8.8 4 10 SomatomotorDorsal
+-18.63 -22.73 9.59 4 10 SomatomotorDorsal
+32 -49 -51 4 0 unassigned
+-13 -52 -50 4 0 unassigned
+14 -48 -52 4 0 unassigned
+-32 -78 -38 4 1 DefaultMode
+32 -81 -38 4 1 DefaultMode
+-24 -76 -28 4 1 DefaultMode
+24 -76 -28.01 4 1 DefaultMode
+-5.72 -50.8 -40.84 4 1 DefaultMode
+8 -50 -40 4 1 DefaultMode
+0 -74 -25 4 2 Visual
+-10 -78 -28 4 3 FrontoParietal
+10 -78 -28 4 3 FrontoParietal
+-34 -72.01 -48 4 3 FrontoParietal
+34 -72 -48 4 3 FrontoParietal
+-30.5 -66 -30 4 3 FrontoParietal
+31.68 -62.83 -30.4 4 3 FrontoParietal
+40 -44 -38 4 3 FrontoParietal
+43.5 -60 -30 4 9 CinguloOpercular
+-43.5 -60 -30 4 9 CinguloOpercular
+-34 -42 -44 4 9 CinguloOpercular
+-33 -51 -50 4 10 SomatomotorDorsal
+-6 -74 -42 4 10 SomatomotorDorsal
+7.5 -72 -39 4 10 SomatomotorDorsal
+-10 -62 -18 4 10 SomatomotorDorsal
+10 -62 -18 4 10 SomatomotorDorsal
+-12 -44 -18 4 10 SomatomotorDorsal
+12 -44 -18 4 10 SomatomotorDorsal
diff --git a/nilearn/datasets/data/seitzman_2018_ROIs_anatomicalLabels.txt b/nilearn/datasets/data/seitzman_2018_ROIs_anatomicalLabels.txt
new file mode 100644
index 0000000000..9b41c0b51a
--- /dev/null
+++ b/nilearn/datasets/data/seitzman_2018_ROIs_anatomicalLabels.txt
@@ -0,0 +1,301 @@
+0=cortexMid,1=cortexL,2=cortexR,3=hippocampus,4=amygdala,5=basalGanglia,6=thalamus,7=cerebellum
+1
+1
+2
+2
+2
+2
+2
+1
+2
+1
+2
+1
+1
+0
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+1
+1
+2
+2
+2
+1
+1
+1
+1
+1
+1
+1
+2
+2
+2
+2
+2
+1
+2
+2
+1
+2
+2
+2
+1
+2
+2
+1
+1
+1
+1
+1
+2
+2
+2
+2
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
+1
+2
+1
+1
+2
+2
+1
+2
+2
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+2
+2
+2
+2
+2
+2
+2
+2
+2
+2
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+2
+2
+2
+2
+2
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+1
+2
+2
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+2
+2
+2
+2
+2
+2
+2
+2
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+2
+2
+2
+1
+1
+1
+1
+1
+2
+2
+2
+2
+2
+1
+1
+2
+2
+1
+1
+2
+2
+1
+1
+2
+2
+1
+1
+1
+1
+1
+2
+2
+2
+2
+2
+1
+2
+3
+3
+3
+3
+1
+1
+2
+2
+4
+4
+5
+5
+5
+5
+5
+5
+5
+5
+5
+5
+5
+5
+5
+5
+5
+5
+6
+6
+6
+6
+6
+6
+6
+6
+6
+6
+6
+6
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
+7
diff --git a/nilearn/datasets/description/fsaverage5_sphere.rst b/nilearn/datasets/description/fsaverage5_sphere.rst
new file mode 100644
index 0000000000..8b2aba6ea8
--- /dev/null
+++ b/nilearn/datasets/description/fsaverage5_sphere.rst
@@ -0,0 +1,16 @@
+fsaverage5
+
+
+Notes
+-----
+Fsaverage5 standard spheres as distributed with Freesurfer (Fischl et al, 1999)
+
+Content
+-------
+ :'sphere_left': Gifti file, left hemisphere spherical surface mesh
+ :'sphere_right': Gifti file, right hemisphere spherical surface mesh
+
+References
+----------
+Fischl et al, (1999). High-resolution intersubject averaging and a
+coordinate system for the cortical surface. Hum Brain Mapp 8, 272-284.
diff --git a/nilearn/datasets/description/seitzman_2018.rst b/nilearn/datasets/description/seitzman_2018.rst
new file mode 100644
index 0000000000..26c9e62686
--- /dev/null
+++ b/nilearn/datasets/description/seitzman_2018.rst
@@ -0,0 +1,35 @@
+Seitzman 2018 atlas
+
+
+Notes
+-----
+300 ROI coordinates in cortical, subcortical and cerebellar regions.
+
+"Here, we apply a winner-take-all partitioning method to resting-state fMRI data and careful consideration of
+anatomy to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and
+cerebellum. We validate these ROIs in three datasets via several anatomical and functional criteria, including known
+anatomical divisions and functions, as well as agreement with existing literature.
+Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends
+previously described functional network organization." (Seitzman et. al, 2018)
+
+
+
+Content
+-------
+ :"rois": Coordinates of 300 ROIs in MNI space
+ :"radius": Radius of each ROI in mm
+ :"networks": Network names
+ :"regions": Region names
+
+References
+----------
+For more information see:
+https://greenelab.wustl.edu/data_software
+
+ROI coordinates downloaded from:
+https://wustl.box.com/s/twpyb1pflj6vrlxgh3rohyqanxbdpelw
+
+
+Seitzman, B. A., Gratton, C., Marek, S., Raut, R. V., Dosenbach, N. U., Schlaggar, B. L., et al. (2018).
+A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum.
+bioRxiv, 450452. http://doi.org/10.1101/450452
\ No newline at end of file
diff --git a/nilearn/datasets/func.py b/nilearn/datasets/func.py
index 64281abfdd..38844a93b1 100644
--- a/nilearn/datasets/func.py
+++ b/nilearn/datasets/func.py
@@ -13,7 +13,7 @@
from sklearn.utils import deprecated
from .utils import (_get_dataset_dir, _fetch_files, _get_dataset_descr,
- _read_md5_sum_file, _tree, _filter_columns)
+ _read_md5_sum_file, _tree, _filter_columns, _fetch_file)
from .._utils import check_niimg
from .._utils.compat import BytesIO, _basestring, _urllib
from .._utils.numpy_conversions import csv_to_array
@@ -824,7 +824,7 @@ def fetch_localizer_contrasts(contrasts, n_subjects=None, get_tmaps=False,
if n_subjects is None:
n_subjects = 94 # 94 subjects available
if (isinstance(n_subjects, numbers.Number) and
- ((n_subjects > 94) or (n_subjects < 1))):
+ ((n_subjects > 94) or (n_subjects < 1))):
warnings.warn("Wrong value for \'n_subjects\' (%d). The maximum "
"value will be used instead (\'n_subjects=94\')")
n_subjects = 94 # 94 subjects available
@@ -886,6 +886,7 @@ def fetch_localizer_contrasts(contrasts, n_subjects=None, get_tmaps=False,
"button press vs calculation and sentence listening/reading":
"auditory&visual motor vs cognitive processing"}
allowed_contrasts = list(contrast_name_wrapper.values())
+
# convert contrast names
contrasts_wrapped = []
# get a unique ID for each contrast. It is used to give a unique name to
@@ -893,21 +894,27 @@ def fetch_localizer_contrasts(contrasts, n_subjects=None, get_tmaps=False,
contrasts_indices = []
for contrast in contrasts:
if contrast in allowed_contrasts:
- contrasts_wrapped.append(contrast)
+ contrasts_wrapped.append(contrast.title().replace(" ", ""))
contrasts_indices.append(allowed_contrasts.index(contrast))
elif contrast in contrast_name_wrapper:
name = contrast_name_wrapper[contrast]
- contrasts_wrapped.append(name)
+ contrasts_wrapped.append(name.title().replace(" ", ""))
contrasts_indices.append(allowed_contrasts.index(name))
else:
raise ValueError("Contrast \'%s\' is not available" % contrast)
- # It is better to perform several small requests than a big one because:
- # - Brainomics server has no cache (can lead to timeout while the archive
- # is generated on the remote server)
- # - Local (cached) version of the files can be checked for each contrast
- opts = {'uncompress': True}
+ # Get the dataset OSF index
+ dataset_name = "brainomics_localizer"
+ index_url = "https://osf.io/hwbm2/download"
+ data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
+ verbose=verbose)
+ index_file = _fetch_file(index_url, data_dir, verbose=verbose)
+ with open(index_file, "rt") as of:
+ index = json.load(of)
+ # Build data URLs that will be fetched
+ files = {}
+ root_url = "https://osf.io/download/{0}"
if isinstance(n_subjects, numbers.Number):
subject_mask = np.arange(1, n_subjects + 1)
subject_id_max = "S%02d" % n_subjects
@@ -916,26 +923,18 @@ def fetch_localizer_contrasts(contrasts, n_subjects=None, get_tmaps=False,
subject_id_max = "S%02d" % np.max(n_subjects)
n_subjects = len(n_subjects)
subject_ids = ["S%02d" % s for s in subject_mask]
- data_types = ["c map"]
+ data_types = ["cmaps"]
if get_tmaps:
- data_types.append("t map")
- rql_types = str.join(", ", ["\"%s\"" % x for x in data_types])
- root_url = "http://brainomics.cea.fr/localizer/"
-
- base_query = ("Any X,XT,XL,XI,XF,XD WHERE X is Scan, X type XT, "
- "X concerns S, "
- "X label XL, X identifier XI, "
- "X format XF, X description XD, "
- 'S identifier <= "%s", ' % (subject_id_max, ) +
- 'X type IN(%(types)s), X label "%(label)s"')
-
- urls = ["%sbrainomics_data_%d.zip?rql=%s&vid=data-zip"
- % (root_url, i,
- _urllib.parse.quote(base_query % {"types": rql_types,
- "label": c},
- safe=',()'))
- for c, i in zip(contrasts_wrapped, contrasts_indices)]
+ data_types.append("tmaps")
filenames = []
+
+ def _is_valid_path(path, index, verbose):
+ if path not in index:
+ if verbose > 0:
+ print("Skiping path '{0}'...".format(path))
+ return False
+ return True
+
for subject_id in subject_ids:
for data_type in data_types:
for contrast_id, contrast in enumerate(contrasts_wrapped):
@@ -943,80 +942,87 @@ def fetch_localizer_contrasts(contrasts, n_subjects=None, get_tmaps=False,
str.join('_', [data_type, contrast]), ' ', '_')
file_path = os.path.join(
"brainomics_data", subject_id, "%s.nii.gz" % name_aux)
- file_tarball_url = urls[contrast_id]
- filenames.append((file_path, file_tarball_url, opts))
+ path = "/".join([
+ "/localizer", "derivatives", "spm_1st_level",
+ "sub-%s" % subject_id,
+ "sub-%s_task-localizer_acq-%s_%s.nii.gz" % (
+ subject_id, contrast, data_type)])
+ if _is_valid_path(path, index, verbose=verbose):
+ file_url = root_url.format(index[path][1:])
+ opts = {"move": file_path}
+ filenames.append((file_path, file_url, opts))
+ files.setdefault(data_type, []).append(file_path)
+
# Fetch masks if asked by user
if get_masks:
- urls.append("%sbrainomics_data_masks.zip?rql=%s&vid=data-zip"
- % (root_url,
- _urllib.parse.quote(base_query % {"types": '"boolean mask"',
- "label": "mask"},
- safe=',()')))
for subject_id in subject_ids:
file_path = os.path.join(
"brainomics_data", subject_id, "boolean_mask_mask.nii.gz")
- file_tarball_url = urls[-1]
- filenames.append((file_path, file_tarball_url, opts))
+ path = "/".join([
+ "/localizer", "derivatives", "spm_1st_level",
+ "sub-%s" % subject_id, "sub-%s_mask.nii.gz" % subject_id])
+ if _is_valid_path(path, index, verbose=verbose):
+ file_url = root_url.format(index[path][1:])
+ opts = {"move": file_path}
+ filenames.append((file_path, file_url, opts))
+ files.setdefault("masks", []).append(file_path)
+
# Fetch anats if asked by user
if get_anats:
- urls.append("%sbrainomics_data_anats.zip?rql=%s&vid=data-zip"
- % (root_url,
- _urllib.parse.quote(base_query % {"types": '"normalized T1"',
- "label": "anatomy"},
- safe=',()')))
for subject_id in subject_ids:
file_path = os.path.join(
"brainomics_data", subject_id,
"normalized_T1_anat_defaced.nii.gz")
- file_tarball_url = urls[-1]
- filenames.append((file_path, file_tarball_url, opts))
- # Fetch subject characteristics (separated in two files)
- if url is None:
- url_csv = ("%sdataset/cubicwebexport.csv?rql=%s&vid=csvexport"
- % (root_url, _urllib.parse.quote("Any X WHERE X is Subject")))
- url_csv2 = ("%sdataset/cubicwebexport2.csv?rql=%s&vid=csvexport"
- % (root_url,
- _urllib.parse.quote("Any X,XI,XD WHERE X is QuestionnaireRun, "
- "X identifier XI, X datetime "
- "XD", safe=',')
- ))
- else:
- url_csv = "%s/cubicwebexport.csv" % url
- url_csv2 = "%s/cubicwebexport2.csv" % url
- filenames += [("cubicwebexport.csv", url_csv, {}),
- ("cubicwebexport2.csv", url_csv2, {})]
+ path = "/".join([
+ "/localizer", "derivatives", "spm_preprocessing",
+ "sub-%s" % subject_id, "sub-%s_T1w.nii.gz" % subject_id])
+ if _is_valid_path(path, index, verbose=verbose):
+ file_url = root_url.format(index[path][1:])
+ opts = {"move": file_path}
+ filenames.append((file_path, file_url, opts))
+ files.setdefault("anats", []).append(file_path)
+
+ # Fetch subject characteristics
+ participants_file = os.path.join("brainomics_data", "participants.tsv")
+ path = "/localizer/participants.tsv"
+ if _is_valid_path(path, index, verbose=verbose):
+ file_url = root_url.format(index[path][1:])
+ opts = {"move": participants_file}
+ filenames.append((participants_file, file_url, opts))
+
+ # Fetch behavioural
+ behavioural_file = os.path.join(
+ "brainomics_data", "phenotype", "behavioural.tsv")
+ path = "/localizer/phenotype/behavioural.tsv"
+ if _is_valid_path(path, index, verbose=verbose):
+ file_url = root_url.format(index[path][1:])
+ opts = {"move": behavioural_file}
+ filenames.append((behavioural_file, file_url, opts))
# Actual data fetching
- dataset_name = 'brainomics_localizer'
- data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
- verbose=verbose)
fdescr = _get_dataset_descr(dataset_name)
- files = _fetch_files(data_dir, filenames, verbose=verbose)
- anats = None
- masks = None
- tmaps = None
- # combine data from both covariates files into one single recarray
+ _fetch_files(data_dir, filenames, verbose=verbose)
+ for key, value in files.items():
+ files[key] = [os.path.join(data_dir, val) for val in value]
+
+ # Load covariates file
from numpy.lib.recfunctions import join_by
- ext_vars_file2 = files[-1]
- csv_data2 = np.recfromcsv(ext_vars_file2, delimiter=';')
- files = files[:-1]
- ext_vars_file = files[-1]
- csv_data = np.recfromcsv(ext_vars_file, delimiter=';')
- files = files[:-1]
- # join_by sorts the output along the key
- csv_data = join_by('subject_id', csv_data, csv_data2,
- usemask=False, asrecarray=True)[subject_mask - 1]
- if get_anats:
- anats = files[-n_subjects:]
- files = files[:-n_subjects]
- if get_masks:
- masks = files[-n_subjects:]
- files = files[:-n_subjects]
- if get_tmaps:
- tmaps = files[1::2]
- files = files[::2]
- return Bunch(cmaps=files, tmaps=tmaps, masks=masks, anats=anats,
- ext_vars=csv_data, description=fdescr)
+ participants_file = os.path.join(data_dir, participants_file)
+ csv_data = np.recfromcsv(participants_file, delimiter='\t')
+ behavioural_file = os.path.join(data_dir, behavioural_file)
+ csv_data2 = np.recfromcsv(behavioural_file, delimiter='\t')
+ csv_data = join_by(
+ "participant_id", csv_data, csv_data2, usemask=False, asrecarray=True)
+ subject_names = csv_data["participant_id"].tolist()
+ subjects_indices = []
+ for name in subject_ids:
+ name = name.encode("utf8")
+ if name not in subject_names:
+ continue
+ subjects_indices.append(subject_names.index(name))
+ csv_data = csv_data[subjects_indices]
+
+ return Bunch(ext_vars=csv_data, description=fdescr, **files)
def fetch_localizer_calculation_task(n_subjects=1, data_dir=None, url=None,
@@ -1064,20 +1070,20 @@ def fetch_localizer_calculation_task(n_subjects=1, data_dir=None, url=None,
get_tmaps=False, get_masks=False,
get_anats=False, data_dir=data_dir,
url=url, resume=True, verbose=verbose)
- data.pop('tmaps')
- data.pop('masks')
- data.pop('anats')
return data
-def fetch_localizer_button_task(data_dir=None, url=None, verbose=1):
+def fetch_localizer_button_task(n_subjects=None, data_dir=None, url=None,
+ verbose=1):
"""Fetch left vs right button press contrast maps from the localizer.
- This function ships only 2nd subject (S02) specific tmap and
- its normalized T1 image.
-
Parameters
----------
+ n_subjects: int, optional
+ The number of subjects to load. If None is given,
+ this function ships only 2nd subject (S02) specific tmap and
+ its normalized T1 image.
+
data_dir: string, optional
Path of the data directory. Used to force data storage in a specified
location.
@@ -1093,6 +1099,7 @@ def fetch_localizer_button_task(data_dir=None, url=None, verbose=1):
-------
data: Bunch
Dictionary-like object, the interest attributes are :
+ 'cmaps': string list, giving paths to nifti contrast maps
'tmap': string, giving paths to nifti contrast maps
'anat': string, giving paths to normalized anatomical image
@@ -1109,30 +1116,19 @@ def fetch_localizer_button_task(data_dir=None, url=None, verbose=1):
nilearn.datasets.fetch_localizer_contrasts
"""
- # The URL can be retrieved from the nilearn account on OSF (Open
- # Science Framework). Uploaded files specific to S02 from
- # fetch_localizer_contrasts ['left vs right button press']
- if url is None:
- url = 'https://osf.io/dx9jn/download'
-
- tmap = "t_map_left_auditory_&_visual_click_vs_right_auditory&visual_click.nii.gz"
- anat = "normalized_T1_anat_defaced.nii.gz"
-
- opts = {'uncompress': True}
-
- options = ('tmap', 'anat')
- filenames = [(os.path.join('localizer_button_task', name), url, opts)
- for name in (tmap, anat)]
-
- dataset_name = 'brainomics'
- data_dir = _get_dataset_dir(dataset_name, data_dir=data_dir,
- verbose=verbose)
- files = _fetch_files(data_dir, filenames, verbose=verbose)
-
- fdescr = _get_dataset_descr('brainomics_localizer')
-
- params = dict([('description', fdescr)] + list(zip(options, files)))
- return Bunch(**params)
+ if n_subjects is None:
+ n_subjects = [2]
+ data = fetch_localizer_contrasts(["left vs right button press"],
+ n_subjects=n_subjects,
+ get_tmaps=True, get_masks=False,
+ get_anats=True, data_dir=data_dir,
+ url=url, resume=True, verbose=verbose)
+ # TODO: remove -> only here for compatibility
+ if len(data["tmaps"]) == 1:
+ setattr(data, "tmap", data["tmaps"][0])
+ if len(data["anats"]) == 1:
+ setattr(data, "anat", data["anats"][0])
+ return data
def fetch_abide_pcp(data_dir=None, n_subjects=None, pipeline='cpac',
@@ -1360,7 +1356,7 @@ def _load_mixed_gambles(zmap_imgs):
def fetch_mixed_gambles(n_subjects=1, data_dir=None, url=None, resume=True,
- return_raw_data=False, verbose=0):
+ return_raw_data=False, verbose=1):
"""Fetch Jimura "mixed gambles" dataset.
Parameters
@@ -1380,7 +1376,7 @@ def fetch_mixed_gambles(n_subjects=1, data_dir=None, url=None, resume=True,
resume: bool, optional (default True)
If true, try resuming download if possible.
- verbose: int, optional (default 0)
+ verbose: int, optional (default 1)
Defines the level of verbosity of the output.
return_raw_data: bool, optional (default True)
@@ -1808,7 +1804,7 @@ def fetch_surf_nki_enhanced(n_subjects=10, data_dir=None,
'A00056097', 'A00056098', 'A00056164', 'A00056372', 'A00056452',
'A00056489', 'A00056949']
- nitrc_ids = range(8260, 8470)
+ nitrc_ids = range(8260, 8464)
max_subjects = len(ids)
if n_subjects is None:
n_subjects = max_subjects
@@ -1816,7 +1812,6 @@ def fetch_surf_nki_enhanced(n_subjects=10, data_dir=None,
warnings.warn('Warning: there are only %d subjects' % max_subjects)
n_subjects = max_subjects
ids = ids[:n_subjects]
- nitrc_ids = nitrc_ids[:n_subjects]
# Dataset description
fdescr = _get_dataset_descr(dataset_name)
@@ -1850,13 +1845,13 @@ def fetch_surf_nki_enhanced(n_subjects=10, data_dir=None,
func = os.path.join('%s', '%s_%s_preprocessed_fwhm6.gii')
rh = _fetch_files(data_dir,
[(func % (ids[i], ids[i], 'right'),
- archive % (nitrc_ids[i], ids[i], 'rh'),
+ archive % (nitrc_ids[2*i+1], ids[i], 'rh'),
{'move': func % (ids[i], ids[i], 'right')}
)],
resume=resume, verbose=verbose)
lh = _fetch_files(data_dir,
[(func % (ids[i], ids[i], 'left'),
- archive % (nitrc_ids[i], ids[i], 'lh'),
+ archive % (nitrc_ids[2*i], ids[i], 'lh'),
{'move': func % (ids[i], ids[i], 'left')}
)],
resume=resume, verbose=verbose)
@@ -1995,7 +1990,7 @@ def _fetch_development_fmri_functional(participants, data_dir, url, verbose):
def fetch_development_fmri(n_subjects=None, reduce_confounds=True,
- data_dir=None, resume=True, verbose=0):
+ data_dir=None, resume=True, verbose=1):
"""Fetch movie watching based brain development dataset (fMRI)
The data is downsampled to 4mm resolution for convenience. The origin of
@@ -2024,7 +2019,7 @@ def fetch_development_fmri(n_subjects=None, reduce_confounds=True,
resume: bool, optional (default True)
Whether to resume download of a partly-downloaded file.
- verbose: int, optional (default 0)
+ verbose: int, optional (default 1)
Defines the level of verbosity of the output.
Returns
diff --git a/nilearn/datasets/neurovault.py b/nilearn/datasets/neurovault.py
index 0f51b319ac..7843ddcd3e 100644
--- a/nilearn/datasets/neurovault.py
+++ b/nilearn/datasets/neurovault.py
@@ -2629,8 +2629,6 @@ def fetch_neurovault_motor_task(data_dir=None, verbose=1):
Notes
------
- This function is only a caller for the fetch_localizer_contrasts in order
- to simplify examples reading and understanding.
The 'left vs right button press' contrast is used:
https://neurovault.org/images/10426/
@@ -2673,8 +2671,6 @@ def fetch_neurovault_auditory_computation_task(data_dir=None, verbose=1):
Notes
------
- This function is only a caller for the fetch_localizer_contrasts in order
- to simplify examples reading and understanding.
The 'auditory_calculation_vs_baseline' contrast is used:
https://neurovault.org/images/32980/
diff --git a/nilearn/datasets/struct.py b/nilearn/datasets/struct.py
index 8393e44bd4..b452d589bc 100644
--- a/nilearn/datasets/struct.py
+++ b/nilearn/datasets/struct.py
@@ -146,7 +146,7 @@ def load_mni152_brain_mask():
See Also
--------
- nilearn.datasets.load_mni152_template for details about version of the
+ nilearn.datasets.load_mni152_template : for details about version of the
MNI152 T1 template and related.
"""
# Load MNI template
@@ -440,6 +440,7 @@ def fetch_surf_fsaverage(mesh='fsaverage5', data_dir=None):
mesh: str, optional (default='fsaverage5')
Which mesh to fetch.
'fsaverage5': the low-resolution fsaverage5 mesh (10242 nodes)
+ 'fsaverage5_sphere': the low-resolution fsaverage5 spheres (10242 nodes)
'fsaverage': the high-resolution fsaverage mesh (163842 nodes)
(high-resolution fsaverage will result in
more computation time and memory usage)
@@ -467,6 +468,7 @@ def fetch_surf_fsaverage(mesh='fsaverage5', data_dir=None):
"""
meshes = {'fsaverage5': _fetch_surf_fsaverage5,
+ 'fsaverage5_sphere': _fetch_surf_fsaverage5_sphere,
'fsaverage': _fetch_surf_fsaverage}
if mesh not in meshes:
raise ValueError(
@@ -476,6 +478,11 @@ def fetch_surf_fsaverage(mesh='fsaverage5', data_dir=None):
def _fetch_surf_fsaverage(data_dir=None):
+ """Helper function to ship fsaverage (highest resolution) surfaces
+ and sulcal information with Nilearn.
+
+ The source of the data is downloaded from nitrc.
+ """
dataset_dir = _get_dataset_dir('fsaverage', data_dir=data_dir)
url = 'https://www.nitrc.org/frs/download.php/10846/fsaverage.tar.gz'
if not os.path.isdir(os.path.join(dataset_dir, 'fsaverage')):
@@ -571,3 +578,27 @@ def _fetch_surf_fsaverage5(data_dir=None, url=None, resume=True, verbose=1):
sulc_left=sulcs[0],
sulc_right=sulcs[1],
description=fdescr)
+
+def _fetch_surf_fsaverage5_sphere(data_dir=None):
+ """Helper function to ship fsaverage5 spherical meshes.
+
+ These meshes can be used for visualization purposes, but also to run
+ cortical surface-based searchlight decoding.
+
+ The source of the data is downloaded from OSF.
+ """
+
+ fsaverage_dir = _get_dataset_dir('fsaverage', data_dir=data_dir)
+ dataset_dir = _get_dataset_dir('fsaverage5_sphere', data_dir=fsaverage_dir)
+ url = 'https://osf.io/b79fy/download'
+ opts = {'uncompress': True}
+ names = ['sphere_right', 'sphere_left']
+ filenames = [('{}.gii'.format(name), url, opts)
+ for name in names]
+ _fetch_files(dataset_dir, filenames)
+ result = {
+ name: os.path.join(dataset_dir, '{}.gii'.format(name))
+ for name in names}
+
+ result['description'] = str(_get_dataset_descr('fsaverage5_sphere'))
+ return Bunch(**result)
diff --git a/nilearn/datasets/tests/data/localizer_behavioural.tsv b/nilearn/datasets/tests/data/localizer_behavioural.tsv
new file mode 100644
index 0000000000..d7c18f0a3c
--- /dev/null
+++ b/nilearn/datasets/tests/data/localizer_behavioural.tsv
@@ -0,0 +1,95 @@
+participant_id nuage2 pente_dots_lin couleur nom_des_gens pb_phono lin_vs_log aspect pb_express facilite_addition espece_plante visu_vb_pour_calcul tv r2_dots_log ratio_pw_w pb_allocentrique pb_rotation details score_cal_complexe activites_pratiques_nb bruxe espece_animales mot pb_lecture moyenne_pw_w trajet score_3d nuage10 nuage11 nuage12 pb_ecriture gaucherie score_addition probleme_ecole facilite_soustraction pente_dots_log arc perspective score_multiplication r2_dots_lin pair1 pair3 pair2 pair5 pair4 itineraire_plan score_soustraction erreur_w imagerie type_pb_langage decomposition niveau_etude francais1erl geometrie coef_bisec pente_sujet_estim_lin pb_prononc seine parle_tard pb_a_l_ecole autoestimation_calcul lettre_miroir rapport_pente_lin_groupe comment nuage3 nuage1 nuage6 nuage7 nuage4 nuage5 strategie nuage8 nuage9 score_tableau_langage pb_binaire erreur_langage droitier erreur_ps_w pb_consonne facilite_multiplication dyslexie visage coef_estim_qt pb_nonlangage bilingue dessin_fini bus billet pente_sujet_estim_log autoevaluation_cx pb_reconnaissance difference_pw_w score_pb_g_d car synaesthete error difficult_apprend_lire pente_bisec orthophoniste lieux orientation normalized_pw autoevaluation_dessin bonne_reconnaissance lycee avion id difficulte_calc_localizer dessin_note_sur_20 rapport_pente_log_groupe score_imageur visuel_verbal algebre pain edimburgh arbre g_contrarie lecture_ok pb_en_3d pb_2eme_langue oiseau pb_egocentrique pseudo pente_estim_qt autoevaluation_rotation pb_orthogr
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diff --git a/nilearn/datasets/tests/data/localizer_index.json b/nilearn/datasets/tests/data/localizer_index.json
new file mode 100755
index 0000000000..2d0bb9fb2f
--- /dev/null
+++ b/nilearn/datasets/tests/data/localizer_index.json
@@ -0,0 +1,114 @@
+[
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+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualCalculation_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualCalculation_tmaps.nii.gz",
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+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Auditory&VisualMotorVsCognitiveProcessing_contrasts.json",
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+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryClickVsAuditorySentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryClickVsAuditorySentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryClickVsAuditorySentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryProcessingVsVisualProcessing_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryProcessingVsVisualProcessing_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryProcessingVsVisualProcessing_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryProcessing_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryProcessing_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditoryProcessing_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditorySentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditorySentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-AuditorySentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Checkerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Checkerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Checkerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-CognitiveProcessingVsMotor_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-CognitiveProcessingVsMotor_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-CognitiveProcessingVsMotor_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-EffectsOfInterest_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-EffectsOfInterest_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-EffectsOfInterest_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-HorizontalCheckerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-HorizontalCheckerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-HorizontalCheckerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-HorizontalVsVerticalCheckerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-HorizontalVsVerticalCheckerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-HorizontalVsVerticalCheckerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditory&VisualClickVsRightAuditory&VisualClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditory&VisualClickVsRightAuditory&VisualClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditory&VisualClickVsRightAuditory&VisualClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditory&VisualClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditory&VisualClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditory&VisualClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditoryClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditoryClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftAuditoryClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftVisualClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftVisualClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-LeftVisualClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Motor_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Motor_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-Motor_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditory&VisualClickVsLeftAuditory&VisualClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditory&VisualClickVsLeftAuditory&VisualClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditory&VisualClickVsLeftAuditory&VisualClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditory&VisualClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditory&VisualClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditory&VisualClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditoryClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditoryClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightAuditoryClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightVisualClick_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightVisualClick_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-RightVisualClick_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VerticalCheckerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VerticalCheckerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VerticalCheckerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VerticalVsHorizontalCheckerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VerticalVsHorizontalCheckerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VerticalVsHorizontalCheckerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualCalculationVsSentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualCalculationVsSentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualCalculationVsSentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualCalculation_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualCalculation_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualCalculation_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualClickVsVisualSentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualClickVsVisualSentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualClickVsVisualSentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessingVsAuditoryProcessing_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessingVsAuditoryProcessing_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessingVsAuditoryProcessing_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessingVsCheckerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessingVsCheckerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessingVsCheckerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessing_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessing_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualProcessing_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualSentencesVsCheckerboard_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualSentencesVsCheckerboard_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualSentencesVsCheckerboard_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualSentences_cmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualSentences_contrasts.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_acq-VisualSentences_tmaps.nii.gz",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_design.json",
+ "/localizer/derivatives/spm_1st_level/sub-{0}/sub-{0}_task-localizer_design.npy",
+ "/localizer/derivatives/spm_preprocessing/sub-{0}/sub-{0}_T1w.nii.gz",
+ "/localizer/derivatives/spm_preprocessing/sub-{0}/sub-{0}_task-localizer_bold.nii.gz",
+ "/localizer/sub-{0}/anat/sub-{0}_T1w.nii.gz",
+ "/localizer/sub-{0}/func/sub-{0}_task-localizer_bold.json",
+ "/localizer/sub-{0}/func/sub-{0}_task-localizer_bold.nii.gz"
+]
+
diff --git a/nilearn/datasets/tests/data/localizer_participants.tsv b/nilearn/datasets/tests/data/localizer_participants.tsv
new file mode 100644
index 0000000000..a029d440d6
--- /dev/null
+++ b/nilearn/datasets/tests/data/localizer_participants.tsv
@@ -0,0 +1,95 @@
+participant_id age sex site family language
+S01 24 M SHFJ F01 French
+S02 20 M SHFJ F02 French
+S03 22 F SHFJ F03 French
+S04 20 M Neurospin F04 French
+S05 23 F Neurospin F05 French
+S06 19 M Neurospin F06 French
+S07 26 F Neurospin F07 French
+S08 27 M Neurospin F89 French
+S09 21 F Neurospin F09 French
+S10 24 F SHFJ F10 French
+S11 47 F SHFJ F11 French
+S12 23 F SHFJ F12 French
+S13 19 M Neurospin F13 French
+S14 21 F SHFJ F14 French
+S15 22 F SHFJ F15 French
+S16 24 F SHFJ F16 French
+S17 22 M SHFJ F17 French
+S18 20 F Neurospin F18 French
+S19 18 M SHFJ F52 French
+S20 25 F SHFJ F20 French
+S21 21 M SHFJ F21 French
+S22 40 M SHFJ F22 French
+S23 24 F Neurospin F23 French
+S24 18 F Neurospin F24 French
+S25 24 F SHFJ F25 French
+S26 24 F Neurospin F26 French
+S27 30 M Neurospin F27 French
+S28 18 F Neurospin F28 French
+S29 21 F Neurospin F29 French
+S30 34 F SHFJ F30 French
+S31 22 F Neurospin F31 French
+S32 26 F SHFJ F32 French
+S33 21 F SHFJ F33 French
+S34 21 M SHFJ F34 French
+S35 24 F SHFJ F35 French
+S36 25 F SHFJ F36 French
+S37 22 M Neurospin F37 French
+S38 n/a M SHFJ F38 French
+S39 22 M SHFJ F39 French
+S40 24 F SHFJ F40 French
+S41 18 M Neurospin F41 French
+S42 26 M Neurospin F42 French
+S43 49 M Neurospin F43 French
+S44 34 F SHFJ F44 French
+S45 21 M SHFJ F45 French
+S46 24 F Neurospin F46 French
+S47 19 M SHFJ F47 French
+S48 21 F SHFJ F48 French
+S49 22 F SHFJ F49 French
+S50 32 F SHFJ F50 French
+S51 30 M SHFJ F51 French
+S52 22 F SHFJ F52 French
+S53 18 F Neurospin F53 French
+S54 20 F SHFJ F54 French
+S55 21 F Neurospin F55 French
+S56 23 M Neurospin F56 French
+S57 20 F SHFJ F57 French
+S58 34 M SHFJ F58 French
+S59 30 M SHFJ F59 French
+S60 21 M Neurospin F60 French
+S61 23 M SHFJ F61 French
+S62 n/a F SHFJ F62 French
+S63 19 F Neurospin F63 French
+S64 21 F Neurospin F64 French
+S65 19 M Neurospin F65 French
+S66 26 M Neurospin F66 French
+S67 44 F SHFJ F67 French
+S68 20 M SHFJ F68 French
+S69 21 F SHFJ F69 French
+S70 25 M SHFJ F59 French
+S71 21 M SHFJ F71 French
+S72 37 F Neurospin F72 French
+S73 19 M Neurospin F73 French
+S74 21 M SHFJ F74 French
+S75 20 M Neurospin F75 French
+S76 28 F SHFJ F76 French
+S77 23 F SHFJ F77 French
+S78 29 M Neurospin F78 French
+S79 43 F SHFJ F79 French
+S80 19 F SHFJ F80 French
+S81 n/a M SHFJ F81 French
+S82 20 F SHFJ F82 French
+S83 20 F Neurospin F83 French
+S84 23 F SHFJ F84 French
+S85 21 M SHFJ F85 French
+S86 31 M Neurospin F86 French
+S87 n/a M SHFJ F87 French
+S88 19 M SHFJ F88 French
+S89 29 M Neurospin F89 French
+S90 21 M SHFJ F90 French
+S91 23 M Neurospin F91 French
+S92 26 F SHFJ F92 French
+S93 36 M Neurospin F93 French
+S94 41 M SHFJ F94 French
diff --git a/nilearn/datasets/tests/test_atlas.py b/nilearn/datasets/tests/test_atlas.py
index f6cffc48d1..a2ce5b8cf1 100644
--- a/nilearn/datasets/tests/test_atlas.py
+++ b/nilearn/datasets/tests/test_atlas.py
@@ -308,6 +308,24 @@ def test_fetch_coords_power_2011():
assert_not_equal(bunch.description, '')
+def test_fetch_coords_seitzman_2018():
+ bunch = atlas.fetch_coords_seitzman_2018()
+ assert_equal(len(bunch.rois), 300)
+ assert_equal(len(bunch.radius), 300)
+ assert_equal(len(bunch.networks), 300)
+ assert_equal(len(bunch.regions), 300)
+ assert_equal(len(np.unique(bunch.networks)), 14)
+ assert_equal(len(np.unique(bunch.regions)), 8)
+ np.testing.assert_array_equal(bunch.networks, np.sort(bunch.networks))
+ assert_not_equal(bunch.description, '')
+
+ assert bunch.regions[0] == "cortexL"
+
+ bunch = atlas.fetch_coords_seitzman_2018(ordered_regions=False)
+ assert_true(np.any(bunch.networks != np.sort(bunch.networks)))
+
+
+
@with_setup(setup_mock, teardown_mock)
@with_setup(tst.setup_tmpdata, tst.teardown_tmpdata)
def test_fetch_atlas_destrieux_2009():
@@ -547,7 +565,7 @@ def test_fetch_atlas_pauli_2017():
@with_setup(tst.setup_tmpdata, tst.teardown_tmpdata)
def test_fetch_atlas_schaefer_2018():
- valid_n_rois = [100, 200, 300, 400, 500, 600, 800, 1000]
+ valid_n_rois = list(range(100, 1100, 100))
valid_yeo_networks = [7, 17]
valid_resolution_mm = [1, 2]
diff --git a/nilearn/datasets/tests/test_func.py b/nilearn/datasets/tests/test_func.py
index 27193e9ac0..9c52e3dbca 100644
--- a/nilearn/datasets/tests/test_func.py
+++ b/nilearn/datasets/tests/test_func.py
@@ -5,6 +5,7 @@
# License: simplified BSD
import os
+import uuid
import numpy as np
import json
import nibabel
@@ -12,7 +13,8 @@
from sklearn.utils import check_random_state
from nose import with_setup
-from nose.tools import assert_true, assert_equal, assert_not_equal
+from nose.tools import (
+ assert_true, assert_false, assert_equal, assert_not_equal)
from . import test_utils as tst
from nilearn.datasets import utils, func
@@ -146,193 +148,145 @@ def test_miyawaki2008():
assert_not_equal(dataset.description, '')
+with open(os.path.join(tst.datadir, 'localizer_index.json')) as of:
+ localizer_template = json.load(of)
+LOCALIZER_INDEX = {}
+for idx in range(1, 95):
+ idx = str(idx).zfill(2)
+ sid = 'S{0}'.format(idx)
+ LOCALIZER_INDEX.update(dict(
+ (key.format(sid), uuid.uuid4().hex)
+ for key in localizer_template))
+LOCALIZER_INDEX['/localizer/phenotype/behavioural.tsv'] = uuid.uuid4().hex
+LOCALIZER_PARTICIPANTS = np.recfromcsv(
+ os.path.join(tst.datadir, 'localizer_participants.tsv'), delimiter='\t')
+LOCALIZER_BEHAVIOURAL = np.recfromcsv(
+ os.path.join(tst.datadir, 'localizer_behavioural.tsv'), delimiter='\t')
+
+
+def mock_localizer_index(*args, **kwargs):
+ return LOCALIZER_INDEX
+
+
+def mock_np_recfromcsv(*args, **kwargs):
+ if args[0].endswith('participants.tsv'):
+ return LOCALIZER_PARTICIPANTS
+ elif args[0].endswith('behavioural.tsv'):
+ return LOCALIZER_BEHAVIOURAL
+ else:
+ raise ValueError('Unexpected args!')
+
+
+def setup_localizer():
+ global original_json_load
+ global mock_json_load
+ mock_json_load = mock_localizer_index
+ original_json_load = json.load
+ json.load = mock_json_load
+
+ global original_np_recfromcsv
+ global mock_np_recfromcsv
+ mock_np_recfromcsv = mock_np_recfromcsv
+ original_np_recfromcsv = np.recfromcsv
+ np.recfromcsv = mock_np_recfromcsv
+
+
+def teardown_localizer():
+ global original_json_load
+ json.load = original_json_load
+
+ global original_np_recfromcsv
+ np.recfromcsv = original_np_recfromcsv
+
+
@with_setup(setup_mock, teardown_mock)
@with_setup(tst.setup_tmpdata, tst.teardown_tmpdata)
+@with_setup(setup_localizer, teardown_localizer)
def test_fetch_localizer_contrasts():
- local_url = "file://" + tst.datadir
- ids = np.asarray([('S%2d' % i).encode() for i in range(94)])
- ids = ids.view(dtype=[('subject_id', 'S3')])
- tst.mock_fetch_files.add_csv('cubicwebexport.csv', ids)
- tst.mock_fetch_files.add_csv('cubicwebexport2.csv', ids)
-
- # Disabled: cannot be tested without actually fetching covariates CSV file
- # All subjects
- dataset = func.fetch_localizer_contrasts(["checkerboard"],
- data_dir=tst.tmpdir,
- url=local_url,
- verbose=0)
- assert_true(dataset.anats is None)
- assert_true(dataset.tmaps is None)
- assert_true(dataset.masks is None)
- assert_true(isinstance(dataset.ext_vars, np.recarray))
- assert_true(isinstance(dataset.cmaps[0], _basestring))
- assert_equal(dataset.ext_vars.size, 94)
- assert_equal(len(dataset.cmaps), 94)
-
- # 20 subjects
- dataset = func.fetch_localizer_contrasts(["checkerboard"],
- n_subjects=20,
- data_dir=tst.tmpdir,
- url=local_url,
- verbose=0)
- assert_true(dataset.anats is None)
- assert_true(dataset.tmaps is None)
- assert_true(dataset.masks is None)
+ # 2 subjects
+ dataset = func.fetch_localizer_contrasts(
+ ['checkerboard'],
+ n_subjects=2,
+ data_dir=tst.tmpdir,
+ verbose=1)
+ assert_false(hasattr(dataset, 'anats'))
+ assert_false(hasattr(dataset, 'tmaps'))
+ assert_false(hasattr(dataset, 'masks'))
assert_true(isinstance(dataset.cmaps[0], _basestring))
assert_true(isinstance(dataset.ext_vars, np.recarray))
- assert_equal(len(dataset.cmaps), 20)
- assert_equal(dataset.ext_vars.size, 20)
+ assert_equal(len(dataset.cmaps), 2)
+ assert_equal(dataset.ext_vars.size, 2)
# Multiple contrasts
dataset = func.fetch_localizer_contrasts(
- ["checkerboard", "horizontal checkerboard"],
- n_subjects=20, data_dir=tst.tmpdir,
- verbose=0)
- assert_true(dataset.anats is None)
- assert_true(dataset.tmaps is None)
- assert_true(dataset.masks is None)
+ ['checkerboard', 'horizontal checkerboard'],
+ n_subjects=2,
+ data_dir=tst.tmpdir,
+ verbose=1)
assert_true(isinstance(dataset.ext_vars, np.recarray))
assert_true(isinstance(dataset.cmaps[0], _basestring))
- assert_equal(len(dataset.cmaps), 20 * 2) # two contrasts are fetched
- assert_equal(dataset.ext_vars.size, 20)
-
- # get_anats=True
- dataset = func.fetch_localizer_contrasts(["checkerboard"],
- data_dir=tst.tmpdir,
- url=local_url,
- get_anats=True,
- verbose=0)
- assert_true(dataset.masks is None)
- assert_true(dataset.tmaps is None)
- assert_true(isinstance(dataset.ext_vars, np.recarray))
- assert_true(isinstance(dataset.anats[0], _basestring))
- assert_true(isinstance(dataset.cmaps[0], _basestring))
- assert_equal(dataset.ext_vars.size, 94)
- assert_equal(len(dataset.anats), 94)
- assert_equal(len(dataset.cmaps), 94)
-
- # get_masks=True
- dataset = func.fetch_localizer_contrasts(["checkerboard"],
- data_dir=tst.tmpdir,
- url=local_url,
- get_masks=True,
- verbose=0)
- assert_true(dataset.anats is None)
- assert_true(dataset.tmaps is None)
- assert_true(isinstance(dataset.ext_vars, np.recarray))
- assert_true(isinstance(dataset.cmaps[0], _basestring))
- assert_true(isinstance(dataset.masks[0], _basestring))
- assert_equal(dataset.ext_vars.size, 94)
- assert_equal(len(dataset.cmaps), 94)
- assert_equal(len(dataset.masks), 94)
-
- # get_tmaps=True
- dataset = func.fetch_localizer_contrasts(["checkerboard"],
- data_dir=tst.tmpdir,
- url=local_url,
- get_tmaps=True,
- verbose=0)
- assert_true(dataset.anats is None)
- assert_true(dataset.masks is None)
- assert_true(isinstance(dataset.ext_vars, np.recarray))
- assert_true(isinstance(dataset.cmaps[0], _basestring))
- assert_true(isinstance(dataset.tmaps[0], _basestring))
- assert_equal(dataset.ext_vars.size, 94)
- assert_equal(len(dataset.cmaps), 94)
- assert_equal(len(dataset.tmaps), 94)
+ assert_equal(len(dataset.cmaps), 2 * 2) # two contrasts are fetched
+ assert_equal(dataset.ext_vars.size, 2)
# all get_*=True
- dataset = func.fetch_localizer_contrasts(["checkerboard"],
- data_dir=tst.tmpdir,
- url=local_url,
- get_anats=True,
- get_masks=True,
- get_tmaps=True,
- verbose=0)
-
+ dataset = func.fetch_localizer_contrasts(
+ ['checkerboard'],
+ n_subjects=1,
+ data_dir=tst.tmpdir,
+ get_anats=True,
+ get_masks=True,
+ get_tmaps=True,
+ verbose=1)
assert_true(isinstance(dataset.ext_vars, np.recarray))
assert_true(isinstance(dataset.anats[0], _basestring))
assert_true(isinstance(dataset.cmaps[0], _basestring))
assert_true(isinstance(dataset.masks[0], _basestring))
assert_true(isinstance(dataset.tmaps[0], _basestring))
- assert_equal(dataset.ext_vars.size, 94)
- assert_equal(len(dataset.anats), 94)
- assert_equal(len(dataset.cmaps), 94)
- assert_equal(len(dataset.masks), 94)
- assert_equal(len(dataset.tmaps), 94)
+ assert_equal(dataset.ext_vars.size, 1)
+ assert_equal(len(dataset.anats), 1)
+ assert_equal(len(dataset.cmaps), 1)
+ assert_equal(len(dataset.masks), 1)
+ assert_equal(len(dataset.tmaps), 1)
assert_not_equal(dataset.description, '')
# grab a given list of subjects
- dataset2 = func.fetch_localizer_contrasts(["checkerboard"],
- n_subjects=[2, 3, 5],
- data_dir=tst.tmpdir,
- url=local_url,
- get_anats=True,
- get_masks=True,
- get_tmaps=True,
- verbose=0)
-
- # Check that we are getting only 3 subjects
+ dataset2 = func.fetch_localizer_contrasts(
+ ['checkerboard'],
+ n_subjects=[2, 3, 5],
+ data_dir=tst.tmpdir,
+ verbose=1)
assert_equal(dataset2.ext_vars.size, 3)
- assert_equal(len(dataset2.anats), 3)
assert_equal(len(dataset2.cmaps), 3)
- assert_equal(len(dataset2.masks), 3)
- assert_equal(len(dataset2.tmaps), 3)
- np.testing.assert_array_equal(dataset2.ext_vars,
- dataset.ext_vars[[1, 2, 4]])
- np.testing.assert_array_equal(dataset2.anats,
- np.array(dataset.anats)[[1, 2, 4]])
- np.testing.assert_array_equal(dataset2.cmaps,
- np.array(dataset.cmaps)[[1, 2, 4]])
- np.testing.assert_array_equal(dataset2.masks,
- np.array(dataset.masks)[[1, 2, 4]])
- np.testing.assert_array_equal(dataset2.tmaps,
- np.array(dataset.tmaps)[[1, 2, 4]])
+ assert_equal([row[0] for row in dataset2.ext_vars],
+ [b'S02', b'S03', b'S05'])
@with_setup(setup_mock, teardown_mock)
@with_setup(tst.setup_tmpdata, tst.teardown_tmpdata)
+@with_setup(setup_localizer, teardown_localizer)
def test_fetch_localizer_calculation_task():
- local_url = "file://" + tst.datadir
- ids = np.asarray(['S%2d' % i for i in range(94)])
- ids = ids.view(dtype=[('subject_id', 'S3')])
- tst.mock_fetch_files.add_csv('cubicwebexport.csv', ids)
- tst.mock_fetch_files.add_csv('cubicwebexport2.csv', ids)
-
- # Disabled: cannot be tested without actually fetching covariates CSV file
- # All subjects
- dataset = func.fetch_localizer_calculation_task(data_dir=tst.tmpdir,
- url=local_url,
- verbose=0)
- assert_true(isinstance(dataset.ext_vars, np.recarray))
- assert_true(isinstance(dataset.cmaps[0], _basestring))
- assert_equal(dataset.ext_vars.size, 1)
- assert_equal(len(dataset.cmaps), 1)
-
- # 20 subjects
- dataset = func.fetch_localizer_calculation_task(n_subjects=20,
- data_dir=tst.tmpdir,
- url=local_url,
- verbose=0)
+ # 2 subjects
+ dataset = func.fetch_localizer_calculation_task(
+ n_subjects=2,
+ data_dir=tst.tmpdir,
+ verbose=1)
assert_true(isinstance(dataset.ext_vars, np.recarray))
assert_true(isinstance(dataset.cmaps[0], _basestring))
- assert_equal(dataset.ext_vars.size, 20)
- assert_equal(len(dataset.cmaps), 20)
+ assert_equal(dataset.ext_vars.size, 2)
+ assert_equal(len(dataset.cmaps), 2)
assert_not_equal(dataset.description, '')
@with_setup(setup_mock, teardown_mock)
@with_setup(tst.setup_tmpdata, tst.teardown_tmpdata)
+@with_setup(setup_localizer, teardown_localizer)
def test_fetch_localizer_button_task():
- local_url = "file://" + tst.datadir
-
- # Disabled: cannot be tested without actually fetching covariates CSV file
- # Only one subject
- dataset = func.fetch_localizer_button_task(data_dir=tst.tmpdir,
- url=local_url,
- verbose=0)
- assert_true(isinstance(dataset.tmap, _basestring))
- assert_true(isinstance(dataset.anat, _basestring))
+ # 2 subjects
+ dataset = func.fetch_localizer_button_task(
+ data_dir=tst.tmpdir,
+ verbose=1)
+ assert_true(isinstance(dataset.tmaps[0], _basestring))
+ assert_true(isinstance(dataset.anats[0], _basestring))
assert_not_equal(dataset.description, '')
diff --git a/nilearn/datasets/tests/test_struct.py b/nilearn/datasets/tests/test_struct.py
index f94a99833e..fbcce318f4 100644
--- a/nilearn/datasets/tests/test_struct.py
+++ b/nilearn/datasets/tests/test_struct.py
@@ -175,3 +175,14 @@ def test_fetch_surf_fsaverage():
assert keys.issubset(set(dataset.keys()))
assert_not_equal(dataset.description, '')
+
+def test_fetch_surf_fsaverage5_sphere():
+ for mesh in ['fsaverage5_sphere']:
+
+ dataset = struct.fetch_surf_fsaverage(
+ mesh, data_dir=tst.tmpdir)
+
+ keys = {'sphere_left', 'sphere_right'}
+
+ assert keys.issubset(set(dataset.keys()))
+ assert_not_equal(dataset.description, '')
diff --git a/nilearn/datasets/utils.py b/nilearn/datasets/utils.py
index 446cae359e..d9aade43fa 100644
--- a/nilearn/datasets/utils.py
+++ b/nilearn/datasets/utils.py
@@ -336,9 +336,13 @@ def _uncompress_file(file_, delete_archive=True, verbose=1):
processed = True
elif ext == '.gz' or header.startswith(b'\x1f\x8b'):
import gzip
- gz = gzip.open(file_)
if ext == '.tgz':
filename = filename + '.tar'
+ elif ext == '':
+ # For gzip file, we rely on the assumption that there is an extenstion
+ shutil.move(file_, file_ + '.gz')
+ file_ = file_ + '.gz'
+ gz = gzip.open(file_)
out = open(filename, 'wb')
shutil.copyfileobj(gz, out, 8192)
gz.close()
diff --git a/nilearn/decoding/searchlight.py b/nilearn/decoding/searchlight.py
index 5829b1ff5b..c853f9e0de 100644
--- a/nilearn/decoding/searchlight.py
+++ b/nilearn/decoding/searchlight.py
@@ -16,7 +16,7 @@
import numpy as np
-from sklearn.externals.joblib import Parallel, delayed, cpu_count
+from nilearn._utils.compat import Parallel, delayed, cpu_count
from sklearn import svm
from sklearn.base import BaseEstimator
from sklearn.exceptions import ConvergenceWarning
diff --git a/nilearn/decoding/space_net.py b/nilearn/decoding/space_net.py
index 9867b3ca21..368513a8ef 100644
--- a/nilearn/decoding/space_net.py
+++ b/nilearn/decoding/space_net.py
@@ -24,7 +24,7 @@
from sklearn.linear_model.base import LinearModel
from sklearn.feature_selection import (SelectPercentile, f_regression,
f_classif)
-from sklearn.externals.joblib import Memory, Parallel, delayed
+from nilearn._utils.compat import Memory, Parallel, delayed
from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import accuracy_score
from ..input_data.masker_validation import check_embedded_nifti_masker
diff --git a/nilearn/decoding/tests/test_graph_net.py b/nilearn/decoding/tests/test_graph_net.py
index 9a7ddb1a65..45cf046ffc 100644
--- a/nilearn/decoding/tests/test_graph_net.py
+++ b/nilearn/decoding/tests/test_graph_net.py
@@ -2,7 +2,7 @@
import numpy as np
import scipy as sp
from numpy.testing import assert_almost_equal
-from sklearn.utils import extmath
+from scipy import linalg
from sklearn.utils import check_random_state
from nilearn.decoding.objective_functions import _gradient, _div
from nilearn.decoding.space_net_solvers import (
@@ -167,12 +167,12 @@ def test__squared_loss_derivative_lipschitz_constant():
for _ in range(20):
x_1 = rng.rand(*w.shape) * rng.randint(1000)
x_2 = rng.rand(*w.shape) * rng.randint(1000)
- gradient_difference = extmath.norm(
+ gradient_difference = linalg.norm(
_squared_loss_and_spatial_grad_derivative(X, y, x_1, mask,
grad_weight)
- _squared_loss_and_spatial_grad_derivative(X, y, x_2, mask,
grad_weight))
- point_difference = extmath.norm(x_1 - x_2)
+ point_difference = linalg.norm(x_1 - x_2)
assert_true(
gradient_difference <= lipschitz_constant * point_difference)
@@ -186,12 +186,12 @@ def test_logistic_derivative_lipschitz_constant():
for _ in range(20):
x_1 = rng.rand((w.shape[0] + 1)) * rng.randint(1000)
x_2 = rng.rand((w.shape[0] + 1)) * rng.randint(1000)
- gradient_difference = extmath.norm(
+ gradient_difference = linalg.norm(
_logistic_data_loss_and_spatial_grad_derivative(
X, y, x_1, mask, grad_weight)
- _logistic_data_loss_and_spatial_grad_derivative(
X, y, x_2, mask, grad_weight))
- point_difference = extmath.norm(x_1 - x_2)
+ point_difference = linalg.norm(x_1 - x_2)
assert_true(
gradient_difference <= lipschitz_constant * point_difference)
@@ -224,12 +224,12 @@ def test_tikhonov_regularization_vs_graph_net():
screening_percentile=100., standardize=False)
graph_net.fit(X_, y.copy())
coef_ = graph_net.coef_[0]
- graph_net_perf = 0.5 / y.size * extmath.norm(
+ graph_net_perf = 0.5 / y.size * linalg.norm(
np.dot(X, coef_) - y) ** 2\
- + 0.5 * extmath.norm(np.dot(G, coef_)) ** 2
- optimal_model_perf = 0.5 / y.size * extmath.norm(
+ + 0.5 * linalg.norm(np.dot(G, coef_)) ** 2
+ optimal_model_perf = 0.5 / y.size * linalg.norm(
np.dot(X, optimal_model) - y) ** 2\
- + 0.5 * extmath.norm(np.dot(G, optimal_model)) ** 2
+ + 0.5 * linalg.norm(np.dot(G, optimal_model)) ** 2
assert_almost_equal(graph_net_perf, optimal_model_perf, decimal=1)
diff --git a/nilearn/decoding/tests/test_searchlight.py b/nilearn/decoding/tests/test_searchlight.py
index c731ad92e0..fc7b42f9df 100644
--- a/nilearn/decoding/tests/test_searchlight.py
+++ b/nilearn/decoding/tests/test_searchlight.py
@@ -100,10 +100,10 @@ def test_searchlight():
rand = np.random.RandomState(0)
data = rand.rand(5, 5, 5)
data_img = nibabel.Nifti1Image(data, affine=np.eye(4))
- imgs = [data_img, data_img, data_img, data_img, data_img, data_img]
+ imgs = [data_img] * 12
# labels
- y = [0, 1, 0, 1, 0, 1]
+ y = [0, 1] * 6
# run searchlight on list of 3D images
sl = searchlight.SearchLight(mask_img)
diff --git a/nilearn/decoding/tests/test_space_net.py b/nilearn/decoding/tests/test_space_net.py
index f75b23ebd0..bd18818bb9 100644
--- a/nilearn/decoding/tests/test_space_net.py
+++ b/nilearn/decoding/tests/test_space_net.py
@@ -4,8 +4,8 @@
from nose.tools import (assert_equal, assert_true, assert_false,
assert_raises)
import numpy as np
+from scipy import linalg
from sklearn.datasets import load_iris
-from sklearn.utils import extmath
from sklearn.linear_model import Lasso
from sklearn.utils import check_random_state
from sklearn.linear_model import LogisticRegression
@@ -241,7 +241,7 @@ def test_lasso_vs_graph_net():
penalty="graph-net", max_iter=100)
lasso.fit(X_, y)
graph_net.fit(X, y)
- lasso_perf = 0.5 / y.size * extmath.norm(np.dot(
+ lasso_perf = 0.5 / y.size * linalg.norm(np.dot(
X_, lasso.coef_) - y) ** 2 + np.sum(np.abs(lasso.coef_))
graph_net_perf = 0.5 * ((graph_net.predict(X) - y) ** 2).mean()
np.testing.assert_almost_equal(graph_net_perf, lasso_perf, decimal=3)
diff --git a/nilearn/decomposition/base.py b/nilearn/decomposition/base.py
index aef70a7219..d479dd2078 100644
--- a/nilearn/decomposition/base.py
+++ b/nilearn/decomposition/base.py
@@ -14,7 +14,7 @@
import sklearn
import nilearn
from sklearn.base import BaseEstimator, TransformerMixin
-from sklearn.externals.joblib import Memory, Parallel, delayed
+from nilearn._utils.compat import Memory, Parallel, delayed
from sklearn.linear_model import LinearRegression
from sklearn.utils import check_random_state
from sklearn.utils.extmath import randomized_svd, svd_flip
@@ -84,7 +84,6 @@ def fast_svd(X, n_components, random_state=None):
return U, S, V
-
def mask_and_reduce(masker, imgs,
confounds=None,
reduction_ratio='auto',
@@ -218,10 +217,10 @@ def _mask_and_reduce_single(masker,
n_samples = int(ceil(data_n_samples * reduction_ratio))
U, S, V = cache(fast_svd, memory,
- memory_level=memory_level,
- func_memory_level=3)(this_data.T,
- n_samples,
- random_state=random_state)
+ memory_level=memory_level,
+ func_memory_level=3)(this_data.T,
+ n_samples,
+ random_state=random_state)
U = U.T.copy()
U = U * S[:, np.newaxis]
return U
diff --git a/nilearn/decomposition/canica.py b/nilearn/decomposition/canica.py
index ef6d77d63a..2a4ad45aac 100644
--- a/nilearn/decomposition/canica.py
+++ b/nilearn/decomposition/canica.py
@@ -10,7 +10,7 @@
import numpy as np
from scipy.stats import scoreatpercentile
from sklearn.decomposition import fastica
-from sklearn.externals.joblib import Memory, delayed, Parallel
+from nilearn._utils.compat import Memory, delayed, Parallel
from sklearn.utils import check_random_state
from .multi_pca import MultiPCA
diff --git a/nilearn/decomposition/dict_learning.py b/nilearn/decomposition/dict_learning.py
index e31b2e734f..25c79a435f 100644
--- a/nilearn/decomposition/dict_learning.py
+++ b/nilearn/decomposition/dict_learning.py
@@ -15,7 +15,7 @@
import numpy as np
import sklearn
from sklearn.decomposition import dict_learning_online
-from sklearn.externals.joblib import Memory
+from nilearn._utils.compat import Memory
from sklearn.linear_model import Ridge
from .base import BaseDecomposition
diff --git a/nilearn/decomposition/multi_pca.py b/nilearn/decomposition/multi_pca.py
index 1cc60f6229..84b6daeb45 100644
--- a/nilearn/decomposition/multi_pca.py
+++ b/nilearn/decomposition/multi_pca.py
@@ -3,7 +3,7 @@
This is a good initialization method for ICA.
"""
import numpy as np
-from sklearn.externals.joblib import Memory
+from nilearn._utils.compat import Memory
from sklearn.utils.extmath import randomized_svd
from .base import BaseDecomposition
@@ -32,7 +32,8 @@ class MultiPCA(BaseDecomposition):
If smoothing_fwhm is not None, it gives the size in millimeters of the
spatial smoothing to apply to the signal.
- mask: Niimg-like object, instance of NiftiMasker or MultiNiftiMasker, optional
+ mask: Niimg-like object, instance of NiftiMasker or MultiNiftiMasker,
+ optional
Mask to be used on data. If an instance of masker is passed,
then its mask will be used. If no mask is given,
it will be computed automatically by a MultiNiftiMasker with default
diff --git a/nilearn/externals/README.md b/nilearn/externals/README.md
new file mode 100644
index 0000000000..0e6519bbed
--- /dev/null
+++ b/nilearn/externals/README.md
@@ -0,0 +1,5 @@
+This directory contains bundled external dependencies.
+
+Note for distribution packagers: if you want to remove the duplicated
+code and depend on a packaged version, we suggest that you simply do a
+symbolic link in this directory.
diff --git a/nilearn/externals/__init__.py b/nilearn/externals/__init__.py
new file mode 100644
index 0000000000..c861213890
--- /dev/null
+++ b/nilearn/externals/__init__.py
@@ -0,0 +1,7 @@
+"""
+External, bundled dependencies for Nilearn.
+
+To ignore linting on these files, at the top define:
+
+# flake8: noqa
+"""
diff --git a/nilearn/externals/conftest.py b/nilearn/externals/conftest.py
new file mode 100644
index 0000000000..f3bb9d9e9a
--- /dev/null
+++ b/nilearn/externals/conftest.py
@@ -0,0 +1,8 @@
+# Do not collect any tests in externals. This is more robust than using
+# --ignore because --ignore needs a path and it is not convenient to pass in
+# the externals path (very long install-dependent path in site-packages) when
+# using --pyargs
+
+
+def pytest_ignore_collect(path, config):
+ return True
diff --git a/nilearn/externals/install_tempita.sh b/nilearn/externals/install_tempita.sh
new file mode 100644
index 0000000000..9e6c1d4425
--- /dev/null
+++ b/nilearn/externals/install_tempita.sh
@@ -0,0 +1,22 @@
+#!/bin/sh
+# Script to do a local install of tempita
+set +x
+export LC_ALL=C
+INSTALL_FOLDER=tmp/tempita_install
+rm -rf tempita $INSTALL_FOLDER
+if [ -z "$1" ]
+then
+ TEMPITA=tempita
+else
+ TEMPITA=$1
+fi
+
+pip install --no-cache $TEMPITA --target $INSTALL_FOLDER
+cp -r $INSTALL_FOLDER/tempita tempita
+rm -rf $INSTALL_FOLDER
+
+# Needed to rewrite the doctests
+# Note: BSD sed -i needs an argument unders OSX
+# so first renaming to .bak and then deleting backup files
+find tempita -name "*.py" | xargs sed -i.bak "s/from tempita/from nilearn.externals.tempita/"
+find tempita -name "*.bak" | xargs rm
diff --git a/nilearn/externals/tempita/__init__.py b/nilearn/externals/tempita/__init__.py
new file mode 100644
index 0000000000..91f4091672
--- /dev/null
+++ b/nilearn/externals/tempita/__init__.py
@@ -0,0 +1,1311 @@
+# flake8: noqa
+"""
+A small templating language
+
+This implements a small templating language. This language implements
+if/elif/else, for/continue/break, expressions, and blocks of Python
+code. The syntax is::
+
+ {{any expression (function calls etc)}}
+ {{any expression | filter}}
+ {{for x in y}}...{{endfor}}
+ {{if x}}x{{elif y}}y{{else}}z{{endif}}
+ {{py:x=1}}
+ {{py:
+ def foo(bar):
+ return 'baz'
+ }}
+ {{default var = default_value}}
+ {{# comment}}
+
+You use this with the ``Template`` class or the ``sub`` shortcut.
+The ``Template`` class takes the template string and the name of
+the template (for errors) and a default namespace. Then (like
+``string.Template``) you can call the ``tmpl.substitute(**kw)``
+method to make a substitution (or ``tmpl.substitute(a_dict)``).
+
+``sub(content, **kw)`` substitutes the template immediately. You
+can use ``__name='tmpl.html'`` to set the name of the template.
+
+If there are syntax errors ``TemplateError`` will be raised.
+"""
+from __future__ import absolute_import, division, print_function
+
+import re
+import sys
+try:
+ from urllib.parse import quote as url_quote
+ from io import StringIO
+ from html import escape as html_escape
+except ImportError:
+ from urllib import quote as url_quote
+ from cStringIO import StringIO
+ from cgi import escape as html_escape
+import os
+import tokenize
+from ._looper import looper
+from .compat3 import (
+ PY3, bytes, basestring_, next, is_unicode, coerce_text, iteritems)
+
+__all__ = ['TemplateError', 'Template', 'sub', 'HTMLTemplate',
+ 'sub_html', 'html', 'bunch']
+
+in_re = re.compile(r'\s+in\s+')
+var_re = re.compile(r'^[a-z_][a-z0-9_]*$', re.I)
+
+
+class TemplateError(Exception):
+ """Exception raised while parsing a template
+ """
+
+ def __init__(self, message, position, name=None):
+ Exception.__init__(self, message)
+ self.position = position
+ self.name = name
+
+ def __str__(self):
+ msg = ' '.join(self.args)
+ if self.position:
+ msg = '%s at line %s column %s' % (
+ msg, self.position[0], self.position[1])
+ if self.name:
+ msg += ' in %s' % self.name
+ return msg
+
+
+class _TemplateContinue(Exception):
+ pass
+
+
+class _TemplateBreak(Exception):
+ pass
+
+
+def get_file_template(name, from_template):
+ path = os.path.join(os.path.dirname(from_template.name), name)
+ return from_template.__class__.from_filename(
+ path, namespace=from_template.namespace,
+ get_template=from_template.get_template)
+
+
+class Template(object):
+
+ default_namespace = {
+ 'start_braces': '{{',
+ 'end_braces': '}}',
+ 'looper': looper,
+ }
+
+ default_encoding = 'utf8'
+ default_inherit = None
+
+ def __init__(self, content, name=None, namespace=None, stacklevel=None,
+ get_template=None, default_inherit=None, line_offset=0,
+ delimeters=None):
+ self.content = content
+
+ # set delimeters
+ if delimeters is None:
+ delimeters = (self.default_namespace['start_braces'],
+ self.default_namespace['end_braces'])
+ else:
+ assert len(delimeters) == 2 and all(
+ [isinstance(delimeter, basestring_)
+ for delimeter in delimeters])
+ self.default_namespace = self.__class__.default_namespace.copy()
+ self.default_namespace['start_braces'] = delimeters[0]
+ self.default_namespace['end_braces'] = delimeters[1]
+ self.delimeters = delimeters
+
+ self._unicode = is_unicode(content)
+ if name is None and stacklevel is not None:
+ try:
+ caller = sys._getframe(stacklevel)
+ except ValueError:
+ pass
+ else:
+ globals = caller.f_globals
+ lineno = caller.f_lineno
+ if '__file__' in globals:
+ name = globals['__file__']
+ if name.endswith('.pyc') or name.endswith('.pyo'):
+ name = name[:-1]
+ elif '__name__' in globals:
+ name = globals['__name__']
+ else:
+ name = ''
+ if lineno:
+ name += ':%s' % lineno
+ self.name = name
+ self._parsed = parse(
+ content, name=name, line_offset=line_offset,
+ delimeters=self.delimeters)
+ if namespace is None:
+ namespace = {}
+ self.namespace = namespace
+ self.get_template = get_template
+ if default_inherit is not None:
+ self.default_inherit = default_inherit
+
+ def from_filename(cls, filename, namespace=None, encoding=None,
+ default_inherit=None, get_template=get_file_template):
+ f = open(filename, 'rb')
+ c = f.read()
+ f.close()
+ if encoding:
+ c = c.decode(encoding)
+ elif PY3:
+ c = c.decode('latin-1')
+ return cls(content=c, name=filename, namespace=namespace,
+ default_inherit=default_inherit, get_template=get_template)
+
+ from_filename = classmethod(from_filename)
+
+ def __repr__(self):
+ return '<%s %s name=%r>' % (
+ self.__class__.__name__,
+ hex(id(self))[2:], self.name)
+
+ def substitute(self, *args, **kw):
+ if args:
+ if kw:
+ raise TypeError(
+ "You can only give positional *or* keyword arguments")
+ if len(args) > 1:
+ raise TypeError(
+ "You can only give one positional argument")
+ if not hasattr(args[0], 'items'):
+ raise TypeError(
+ ("If you pass in a single argument, you must pass in a ",
+ "dict-like object (with a .items() method); you gave %r")
+ % (args[0],))
+ kw = args[0]
+ ns = kw
+ ns['__template_name__'] = self.name
+ if self.namespace:
+ ns.update(self.namespace)
+ result, defs, inherit = self._interpret(ns)
+ if not inherit:
+ inherit = self.default_inherit
+ if inherit:
+ result = self._interpret_inherit(result, defs, inherit, ns)
+ return result
+
+ def _interpret(self, ns):
+ # __traceback_hide__ = True
+ parts = []
+ defs = {}
+ self._interpret_codes(self._parsed, ns, out=parts, defs=defs)
+ if '__inherit__' in defs:
+ inherit = defs.pop('__inherit__')
+ else:
+ inherit = None
+ return ''.join(parts), defs, inherit
+
+ def _interpret_inherit(self, body, defs, inherit_template, ns):
+ # __traceback_hide__ = True
+ if not self.get_template:
+ raise TemplateError(
+ 'You cannot use inheritance without passing in get_template',
+ position=None, name=self.name)
+ templ = self.get_template(inherit_template, self)
+ self_ = TemplateObject(self.name)
+ for name, value in iteritems(defs):
+ setattr(self_, name, value)
+ self_.body = body
+ ns = ns.copy()
+ ns['self'] = self_
+ return templ.substitute(ns)
+
+ def _interpret_codes(self, codes, ns, out, defs):
+ # __traceback_hide__ = True
+ for item in codes:
+ if isinstance(item, basestring_):
+ out.append(item)
+ else:
+ self._interpret_code(item, ns, out, defs)
+
+ def _interpret_code(self, code, ns, out, defs):
+ # __traceback_hide__ = True
+ name, pos = code[0], code[1]
+ if name == 'py':
+ self._exec(code[2], ns, pos)
+ elif name == 'continue':
+ raise _TemplateContinue()
+ elif name == 'break':
+ raise _TemplateBreak()
+ elif name == 'for':
+ vars, expr, content = code[2], code[3], code[4]
+ expr = self._eval(expr, ns, pos)
+ self._interpret_for(vars, expr, content, ns, out, defs)
+ elif name == 'cond':
+ parts = code[2:]
+ self._interpret_if(parts, ns, out, defs)
+ elif name == 'expr':
+ parts = code[2].split('|')
+ base = self._eval(parts[0], ns, pos)
+ for part in parts[1:]:
+ func = self._eval(part, ns, pos)
+ base = func(base)
+ out.append(self._repr(base, pos))
+ elif name == 'default':
+ var, expr = code[2], code[3]
+ if var not in ns:
+ result = self._eval(expr, ns, pos)
+ ns[var] = result
+ elif name == 'inherit':
+ expr = code[2]
+ value = self._eval(expr, ns, pos)
+ defs['__inherit__'] = value
+ elif name == 'def':
+ name = code[2]
+ signature = code[3]
+ parts = code[4]
+ ns[name] = defs[name] = TemplateDef(
+ self, name, signature, body=parts, ns=ns, pos=pos)
+ elif name == 'comment':
+ return
+ else:
+ assert 0, "Unknown code: %r" % name
+
+ def _interpret_for(self, vars, expr, content, ns, out, defs):
+ # __traceback_hide__ = True
+ for item in expr:
+ if len(vars) == 1:
+ ns[vars[0]] = item
+ else:
+ if len(vars) != len(item):
+ raise ValueError(
+ 'Need %i items to unpack (got %i items)'
+ % (len(vars), len(item)))
+ for name, value in zip(vars, item):
+ ns[name] = value
+ try:
+ self._interpret_codes(content, ns, out, defs)
+ except _TemplateContinue:
+ continue
+ except _TemplateBreak:
+ break
+
+ def _interpret_if(self, parts, ns, out, defs):
+ # __traceback_hide__ = True
+ # @@: if/else/else gets through
+ for part in parts:
+ assert not isinstance(part, basestring_)
+ name, pos = part[0], part[1]
+ if name == 'else':
+ result = True
+ else:
+ result = self._eval(part[2], ns, pos)
+ if result:
+ self._interpret_codes(part[3], ns, out, defs)
+ break
+
+ def _eval(self, code, ns, pos):
+ # __traceback_hide__ = True
+ try:
+ try:
+ value = eval(code, self.default_namespace, ns)
+ except SyntaxError as e:
+ raise SyntaxError(
+ 'invalid syntax in expression: %s' % code)
+ return value
+ except:
+ exc_info = sys.exc_info()
+ e = exc_info[1]
+ if getattr(e, 'args', None):
+ arg0 = e.args[0]
+ else:
+ arg0 = coerce_text(e)
+ e.args = (self._add_line_info(arg0, pos),)
+ if PY3:
+ raise(e)
+ else:
+ raise (exc_info[1], e, exc_info[2])
+
+ def _exec(self, code, ns, pos):
+ # __traceback_hide__ = True
+ try:
+ exec(code, self.default_namespace, ns)
+ except:
+ exc_info = sys.exc_info()
+ e = exc_info[1]
+ if e.args:
+ e.args = (self._add_line_info(e.args[0], pos),)
+ else:
+ e.args = (self._add_line_info(None, pos),)
+ if PY3:
+ raise(e)
+ else:
+ raise (exc_info[1], e, exc_info[2])
+
+ def _repr(self, value, pos):
+ # __traceback_hide__ = True
+ try:
+ if value is None:
+ return ''
+ if self._unicode:
+ value = str(value)
+ if not is_unicode(value):
+ value = value.decode('utf-8')
+ else:
+ if not isinstance(value, basestring_):
+ value = coerce_text(value)
+ if (is_unicode(value) and self.default_encoding):
+ value = value.encode(self.default_encoding)
+ except:
+ exc_info = sys.exc_info()
+ e = exc_info[1]
+ e.args = (self._add_line_info(e.args[0], pos),)
+ if PY3:
+ raise(e)
+ else:
+ raise (exc_info[1], e, exc_info[2])
+ else:
+ if self._unicode and isinstance(value, bytes):
+ if not self.default_encoding:
+ raise UnicodeDecodeError(
+ 'Cannot decode bytes value %r into unicode '
+ '(no default_encoding provided)' % value)
+ try:
+ value = value.decode(self.default_encoding)
+ except UnicodeDecodeError as e:
+ raise UnicodeDecodeError(
+ e.encoding,
+ e.object,
+ e.start,
+ e.end,
+ e.reason + ' in string %r' % value)
+ elif not self._unicode and is_unicode(value):
+ if not self.default_encoding:
+ raise UnicodeEncodeError(
+ 'Cannot encode unicode value %r into bytes '
+ '(no default_encoding provided)' % value)
+ value = value.encode(self.default_encoding)
+ return value
+
+ def _add_line_info(self, msg, pos):
+ msg = "%s at line %s column %s" % (
+ msg, pos[0], pos[1])
+ if self.name:
+ msg += " in file %s" % self.name
+ return msg
+
+
+def sub(content, delimeters=None, **kw):
+ name = kw.get('__name')
+ tmpl = Template(content, name=name, delimeters=delimeters)
+ return tmpl.substitute(kw)
+
+
+def paste_script_template_renderer(content, vars, filename=None):
+ tmpl = Template(content, name=filename)
+ return tmpl.substitute(vars)
+
+
+class bunch(dict):
+
+ def __init__(self, **kw):
+ for name, value in iteritems(kw):
+ setattr(self, name, value)
+
+ def __setattr__(self, name, value):
+ self[name] = value
+
+ def __getattr__(self, name):
+ try:
+ return self[name]
+ except KeyError:
+ raise AttributeError(name)
+
+ def __getitem__(self, key):
+ if 'default' in self:
+ try:
+ return dict.__getitem__(self, key)
+ except KeyError:
+ return dict.__getitem__(self, 'default')
+ else:
+ return dict.__getitem__(self, key)
+
+ def __repr__(self):
+ items = [
+ (k, v) for k, v in iteritems(self)]
+ items.sort()
+ return '<%s %s>' % (
+ self.__class__.__name__,
+ ' '.join(['%s=%r' % (k, v) for k, v in items]))
+
+############################################################
+# HTML Templating
+############################################################
+
+
+class html(object):
+
+ def __init__(self, value):
+ self.value = value
+
+ def __str__(self):
+ return self.value
+
+ def __html__(self):
+ return self.value
+
+ def __repr__(self):
+ return '<%s %r>' % (
+ self.__class__.__name__, self.value)
+
+
+def html_quote(value, force=True):
+ if not force and hasattr(value, '__html__'):
+ return value.__html__()
+ if value is None:
+ return ''
+ if not isinstance(value, basestring_):
+ value = coerce_text(value)
+ if sys.version >= "3" and isinstance(value, bytes):
+ value = html_escape(value.decode('latin1'), 1)
+ value = value.encode('latin1')
+ else:
+ value = html_escape(value, 1)
+ if sys.version < "3":
+ if is_unicode(value):
+ value = value.encode('ascii', 'xmlcharrefreplace')
+ return value
+
+
+def url(v):
+ v = coerce_text(v)
+ if is_unicode(v):
+ v = v.encode('utf8')
+ return url_quote(v)
+
+
+def attr(**kw):
+ kw = list(iteritems(kw))
+ kw.sort()
+ parts = []
+ for name, value in kw:
+ if value is None:
+ continue
+ if name.endswith('_'):
+ name = name[:-1]
+ parts.append('%s="%s"' % (html_quote(name), html_quote(value)))
+ return html(' '.join(parts))
+
+
+class HTMLTemplate(Template):
+
+ default_namespace = Template.default_namespace.copy()
+ default_namespace.update(dict(
+ html=html,
+ attr=attr,
+ url=url,
+ html_quote=html_quote))
+
+ def _repr(self, value, pos):
+ if hasattr(value, '__html__'):
+ value = value.__html__()
+ quote = False
+ else:
+ quote = True
+ plain = Template._repr(self, value, pos)
+ if quote:
+ return html_quote(plain)
+ else:
+ return plain
+
+
+def sub_html(content, **kw):
+ name = kw.get('__name')
+ tmpl = HTMLTemplate(content, name=name)
+ return tmpl.substitute(kw)
+
+
+class TemplateDef(object):
+ def __init__(self, template, func_name, func_signature,
+ body, ns, pos, bound_self=None):
+ self._template = template
+ self._func_name = func_name
+ self._func_signature = func_signature
+ self._body = body
+ self._ns = ns
+ self._pos = pos
+ self._bound_self = bound_self
+
+ def __repr__(self):
+ return '' % (
+ self._func_name, self._func_signature,
+ self._template.name, self._pos)
+
+ def __str__(self):
+ return self()
+
+ def __call__(self, *args, **kw):
+ values = self._parse_signature(args, kw)
+ ns = self._ns.copy()
+ ns.update(values)
+ if self._bound_self is not None:
+ ns['self'] = self._bound_self
+ out = []
+ subdefs = {}
+ self._template._interpret_codes(self._body, ns, out, subdefs)
+ return ''.join(out)
+
+ def __get__(self, obj, type=None):
+ if obj is None:
+ return self
+ return self.__class__(
+ self._template, self._func_name, self._func_signature,
+ self._body, self._ns, self._pos, bound_self=obj)
+
+ def _parse_signature(self, args, kw):
+ values = {}
+ sig_args, var_args, var_kw, defaults = self._func_signature
+ extra_kw = {}
+ for name, value in iteritems(kw):
+ if not var_kw and name not in sig_args:
+ raise TypeError(
+ 'Unexpected argument %s' % name)
+ if name in sig_args:
+ values[sig_args] = value
+ else:
+ extra_kw[name] = value
+ args = list(args)
+ sig_args = list(sig_args)
+ while args:
+ while sig_args and sig_args[0] in values:
+ sig_args.pop(0)
+ if sig_args:
+ name = sig_args.pop(0)
+ values[name] = args.pop(0)
+ elif var_args:
+ values[var_args] = tuple(args)
+ break
+ else:
+ raise TypeError(
+ 'Extra position arguments: %s'
+ % ', '.join(repr(v) for v in args))
+ for name, value_expr in iteritems(defaults):
+ if name not in values:
+ values[name] = self._template._eval(
+ value_expr, self._ns, self._pos)
+ for name in sig_args:
+ if name not in values:
+ raise TypeError(
+ 'Missing argument: %s' % name)
+ if var_kw:
+ values[var_kw] = extra_kw
+ return values
+
+
+class TemplateObject(object):
+
+ def __init__(self, name):
+ self.__name = name
+ self.get = TemplateObjectGetter(self)
+
+ def __repr__(self):
+ return '<%s %s>' % (self.__class__.__name__, self.__name)
+
+
+class TemplateObjectGetter(object):
+
+ def __init__(self, template_obj):
+ self.__template_obj = template_obj
+
+ def __getattr__(self, attr):
+ return getattr(self.__template_obj, attr, Empty)
+
+ def __repr__(self):
+ return '<%s around %r>' % (
+ self.__class__.__name__, self.__template_obj)
+
+
+class _Empty(object):
+ def __call__(self, *args, **kw):
+ return self
+
+ def __str__(self):
+ return ''
+
+ def __repr__(self):
+ return 'Empty'
+
+ def __unicode__(self):
+ return '' if PY3 else u''
+
+ def __iter__(self):
+ return iter(())
+
+ def __bool__(self):
+ return False
+
+ if sys.version < "3":
+ __nonzero__ = __bool__
+
+Empty = _Empty()
+del _Empty
+
+############################################################
+# Lexing and Parsing
+############################################################
+
+
+def lex(s, name=None, trim_whitespace=True, line_offset=0, delimeters=None):
+ if delimeters is None:
+ delimeters = (Template.default_namespace['start_braces'],
+ Template.default_namespace['end_braces'])
+ in_expr = False
+ chunks = []
+ last = 0
+ last_pos = (line_offset + 1, 1)
+ token_re = re.compile(r'%s|%s' % (re.escape(delimeters[0]),
+ re.escape(delimeters[1])))
+ for match in token_re.finditer(s):
+ expr = match.group(0)
+ pos = find_position(s, match.end(), last, last_pos)
+ if expr == delimeters[0] and in_expr:
+ raise TemplateError('%s inside expression' % delimeters[0],
+ position=pos,
+ name=name)
+ elif expr == delimeters[1] and not in_expr:
+ raise TemplateError('%s outside expression' % delimeters[1],
+ position=pos,
+ name=name)
+ if expr == delimeters[0]:
+ part = s[last:match.start()]
+ if part:
+ chunks.append(part)
+ in_expr = True
+ else:
+ chunks.append((s[last:match.start()], last_pos))
+ in_expr = False
+ last = match.end()
+ last_pos = pos
+ if in_expr:
+ raise TemplateError('No %s to finish last expression' % delimeters[1],
+ name=name, position=last_pos)
+ part = s[last:]
+ if part:
+ chunks.append(part)
+ if trim_whitespace:
+ chunks = trim_lex(chunks)
+ return chunks
+
+lex.__doc__ = """
+Lex a string into chunks:
+
+ >>> lex('hey')
+ ['hey']
+ >>> lex('hey {{you}}')
+ ['hey ', ('you', (1, 7))]
+ >>> lex('hey {{')
+ Traceback (most recent call last):
+ ...
+ tempita.TemplateError: No }} to finish last expression at line 1 column 7
+ >>> lex('hey }}')
+ Traceback (most recent call last):
+ ...
+ tempita.TemplateError: }} outside expression at line 1 column 7
+ >>> lex('hey {{ {{')
+ Traceback (most recent call last):
+ ...
+ tempita.TemplateError: {{ inside expression at line 1 column 10
+
+""" if PY3 else """
+Lex a string into chunks:
+
+ >>> lex('hey')
+ ['hey']
+ >>> lex('hey {{you}}')
+ ['hey ', ('you', (1, 7))]
+ >>> lex('hey {{')
+ Traceback (most recent call last):
+ ...
+ TemplateError: No }} to finish last expression at line 1 column 7
+ >>> lex('hey }}')
+ Traceback (most recent call last):
+ ...
+ TemplateError: }} outside expression at line 1 column 7
+ >>> lex('hey {{ {{')
+ Traceback (most recent call last):
+ ...
+ TemplateError: {{ inside expression at line 1 column 10
+
+"""
+
+statement_re = re.compile(r'^(?:if |elif |for |def |inherit |default |py:)')
+single_statements = ['else', 'endif', 'endfor', 'enddef', 'continue', 'break']
+trail_whitespace_re = re.compile(r'\n\r?[\t ]*$')
+lead_whitespace_re = re.compile(r'^[\t ]*\n')
+
+
+def trim_lex(tokens):
+ last_trim = None
+ for i in range(len(tokens)):
+ current = tokens[i]
+ if isinstance(tokens[i], basestring_):
+ # we don't trim this
+ continue
+ item = current[0]
+ if not statement_re.search(item) and item not in single_statements:
+ continue
+ if not i:
+ prev = ''
+ else:
+ prev = tokens[i - 1]
+ if i + 1 >= len(tokens):
+ next_chunk = ''
+ else:
+ next_chunk = tokens[i + 1]
+ if (not
+ isinstance(next_chunk, basestring_) or
+ not isinstance(prev, basestring_)):
+ continue
+ prev_ok = not prev or trail_whitespace_re.search(prev)
+ if i == 1 and not prev.strip():
+ prev_ok = True
+ if last_trim is not None and last_trim + 2 == i and not prev.strip():
+ prev_ok = 'last'
+ if (prev_ok and (not next_chunk or lead_whitespace_re.search(
+ next_chunk) or (
+ i == len(tokens) - 2 and not next_chunk.strip()))):
+ if prev:
+ if ((i == 1 and not prev.strip()) or prev_ok == 'last'):
+ tokens[i - 1] = ''
+ else:
+ m = trail_whitespace_re.search(prev)
+ # +1 to leave the leading \n on:
+ prev = prev[:m.start() + 1]
+ tokens[i - 1] = prev
+ if next_chunk:
+ last_trim = i
+ if i == len(tokens) - 2 and not next_chunk.strip():
+ tokens[i + 1] = ''
+ else:
+ m = lead_whitespace_re.search(next_chunk)
+ next_chunk = next_chunk[m.end():]
+ tokens[i + 1] = next_chunk
+ return tokens
+
+trim_lex.__doc__ = r"""
+ Takes a lexed set of tokens, and removes whitespace when there is
+ a directive on a line by itself:
+
+ >>> tokens = lex('{{if x}}\nx\n{{endif}}\ny', trim_whitespace=False)
+ >>> tokens
+ [('if x', (1, 3)), '\nx\n', ('endif', (3, 3)), '\ny']
+ >>> trim_lex(tokens)
+ [('if x', (1, 3)), 'x\n', ('endif', (3, 3)), 'y']
+ """ if PY3 else r"""
+ Takes a lexed set of tokens, and removes whitespace when there is
+ a directive on a line by itself:
+
+ >>> tokens = lex('{{if x}}\nx\n{{endif}}\ny', trim_whitespace=False)
+ >>> tokens
+ [('if x', (1, 3)), '\nx\n', ('endif', (3, 3)), '\ny']
+ >>> trim_lex(tokens)
+ [('if x', (1, 3)), 'x\n', ('endif', (3, 3)), 'y']
+ """
+
+
+def find_position(string, index, last_index, last_pos):
+ """
+ Given a string and index, return (line, column)
+ """
+ lines = string.count('\n', last_index, index)
+ if lines > 0:
+ column = index - string.rfind('\n', last_index, index)
+ else:
+ column = last_pos[1] + (index - last_index)
+ return (last_pos[0] + lines, column)
+
+
+def parse(s, name=None, line_offset=0, delimeters=None):
+
+ if delimeters is None:
+ delimeters = (Template.default_namespace['start_braces'],
+ Template.default_namespace['end_braces'])
+ tokens = lex(s, name=name, line_offset=line_offset, delimeters=delimeters)
+ result = []
+ while tokens:
+ next_chunk, tokens = parse_expr(tokens, name)
+ result.append(next_chunk)
+ return result
+
+parse.__doc__ = r"""
+ Parses a string into a kind of AST
+
+ >>> parse('{{x}}')
+ [('expr', (1, 3), 'x')]
+ >>> parse('foo')
+ ['foo']
+ >>> parse('{{if x}}test{{endif}}')
+ [('cond', (1, 3), ('if', (1, 3), 'x', ['test']))]
+ >>> parse(
+ ... 'series->{{for x in y}}x={{x}}{{endfor}}'
+ ... ) #doctest: +NORMALIZE_WHITESPACE
+ ['series->',
+ ('for', (1, 11), ('x',), 'y', ['x=', ('expr', (1, 27), 'x')])]
+ >>> parse('{{for x, y in z:}}{{continue}}{{endfor}}')
+ [('for', (1, 3), ('x', 'y'), 'z', [('continue', (1, 21))])]
+ >>> parse('{{py:x=1}}')
+ [('py', (1, 3), 'x=1')]
+ >>> parse(
+ ... '{{if x}}a{{elif y}}b{{else}}c{{endif}}'
+ ... ) #doctest: +NORMALIZE_WHITESPACE
+ [('cond', (1, 3), ('if', (1, 3), 'x', ['a']),
+ ('elif', (1, 12), 'y', ['b']), ('else', (1, 23), None, ['c']))]
+
+ Some exceptions::
+
+ >>> parse('{{continue}}')
+ Traceback (most recent call last):
+ ...
+ tempita.TemplateError: continue outside of for loop at line 1 column 3
+ >>> parse('{{if x}}foo')
+ Traceback (most recent call last):
+ ...
+ tempita.TemplateError: No {{endif}} at line 1 column 3
+ >>> parse('{{else}}')
+ Traceback (most recent call last):
+ ...
+ tempita.TemplateError: else outside of an if block at line 1 column 3
+ >>> parse('{{if x}}{{for x in y}}{{endif}}{{endfor}}')
+ Traceback (most recent call last):
+ ...
+ tempita.TemplateError: Unexpected endif at line 1 column 25
+ >>> parse('{{if}}{{endif}}')
+ Traceback (most recent call last):
+ ...
+ tempita.TemplateError: if with no expression at line 1 column 3
+ >>> parse('{{for x y}}{{endfor}}')
+ Traceback (most recent call last):
+ ...
+ tempita.TemplateError: Bad for (no "in") in 'x y' at line 1 column 3
+ >>> parse('{{py:x=1\ny=2}}') #doctest: +NORMALIZE_WHITESPACE
+ Traceback (most recent call last):
+ ...
+ tempita.TemplateError: Multi-line py blocks must start
+ with a newline at line 1 column 3
+ """ if PY3 else r"""
+ Parses a string into a kind of AST
+
+ >>> parse('{{x}}')
+ [('expr', (1, 3), 'x')]
+ >>> parse('foo')
+ ['foo']
+ >>> parse('{{if x}}test{{endif}}')
+ [('cond', (1, 3), ('if', (1, 3), 'x', ['test']))]
+ >>> parse(
+ ... 'series->{{for x in y}}x={{x}}{{endfor}}'
+ ... ) #doctest: +NORMALIZE_WHITESPACE
+ ['series->',
+ ('for', (1, 11), ('x',), 'y', ['x=', ('expr', (1, 27), 'x')])]
+ >>> parse('{{for x, y in z:}}{{continue}}{{endfor}}')
+ [('for', (1, 3), ('x', 'y'), 'z', [('continue', (1, 21))])]
+ >>> parse('{{py:x=1}}')
+ [('py', (1, 3), 'x=1')]
+ >>> parse(
+ ... '{{if x}}a{{elif y}}b{{else}}c{{endif}}'
+ ... ) #doctest: +NORMALIZE_WHITESPACE
+ [('cond', (1, 3), ('if', (1, 3), 'x', ['a']),
+ ('elif', (1, 12), 'y', ['b']), ('else', (1, 23), None, ['c']))]
+
+ Some exceptions::
+
+ >>> parse('{{continue}}')
+ Traceback (most recent call last):
+ ...
+ TemplateError: continue outside of for loop at line 1 column 3
+ >>> parse('{{if x}}foo')
+ Traceback (most recent call last):
+ ...
+ TemplateError: No {{endif}} at line 1 column 3
+ >>> parse('{{else}}')
+ Traceback (most recent call last):
+ ...
+ TemplateError: else outside of an if block at line 1 column 3
+ >>> parse('{{if x}}{{for x in y}}{{endif}}{{endfor}}')
+ Traceback (most recent call last):
+ ...
+ TemplateError: Unexpected endif at line 1 column 25
+ >>> parse('{{if}}{{endif}}')
+ Traceback (most recent call last):
+ ...
+ TemplateError: if with no expression at line 1 column 3
+ >>> parse('{{for x y}}{{endfor}}')
+ Traceback (most recent call last):
+ ...
+ TemplateError: Bad for (no "in") in 'x y' at line 1 column 3
+ >>> parse('{{py:x=1\ny=2}}') #doctest: +NORMALIZE_WHITESPACE
+ Traceback (most recent call last):
+ ...
+ TemplateError: Multi-line py blocks must start
+ with a newline at line 1 column 3
+ """
+
+
+def parse_expr(tokens, name, context=()):
+ if isinstance(tokens[0], basestring_):
+ return tokens[0], tokens[1:]
+ expr, pos = tokens[0]
+ expr = expr.strip()
+ if expr.startswith('py:'):
+ expr = expr[3:].lstrip(' \t')
+ if expr.startswith('\n') or expr.startswith('\r'):
+ expr = expr.lstrip('\r\n')
+ if '\r' in expr:
+ expr = expr.replace('\r\n', '\n')
+ expr = expr.replace('\r', '')
+ expr += '\n'
+ else:
+ if '\n' in expr:
+ raise TemplateError(
+ 'Multi-line py blocks must start with a newline',
+ position=pos, name=name)
+ return ('py', pos, expr), tokens[1:]
+ elif expr in ('continue', 'break'):
+ if 'for' not in context:
+ raise TemplateError(
+ 'continue outside of for loop',
+ position=pos, name=name)
+ return (expr, pos), tokens[1:]
+ elif expr.startswith('if '):
+ return parse_cond(tokens, name, context)
+ elif (expr.startswith('elif ') or expr == 'else'):
+ raise TemplateError(
+ '%s outside of an if block' % expr.split()[0],
+ position=pos, name=name)
+ elif expr in ('if', 'elif', 'for'):
+ raise TemplateError(
+ '%s with no expression' % expr,
+ position=pos, name=name)
+ elif expr in ('endif', 'endfor', 'enddef'):
+ raise TemplateError(
+ 'Unexpected %s' % expr,
+ position=pos, name=name)
+ elif expr.startswith('for '):
+ return parse_for(tokens, name, context)
+ elif expr.startswith('default '):
+ return parse_default(tokens, name, context)
+ elif expr.startswith('inherit '):
+ return parse_inherit(tokens, name, context)
+ elif expr.startswith('def '):
+ return parse_def(tokens, name, context)
+ elif expr.startswith('#'):
+ return ('comment', pos, tokens[0][0]), tokens[1:]
+ return ('expr', pos, tokens[0][0]), tokens[1:]
+
+
+def parse_cond(tokens, name, context):
+ start = tokens[0][1]
+ pieces = []
+ context = context + ('if',)
+ while 1:
+ if not tokens:
+ raise TemplateError(
+ 'Missing {{endif}}',
+ position=start, name=name)
+ if (isinstance(tokens[0], tuple) and tokens[0][0] == 'endif'):
+ return ('cond', start) + tuple(pieces), tokens[1:]
+ next_chunk, tokens = parse_one_cond(tokens, name, context)
+ pieces.append(next_chunk)
+
+
+def parse_one_cond(tokens, name, context):
+ (first, pos), tokens = tokens[0], tokens[1:]
+ content = []
+ if first.endswith(':'):
+ first = first[:-1]
+ if first.startswith('if '):
+ part = ('if', pos, first[3:].lstrip(), content)
+ elif first.startswith('elif '):
+ part = ('elif', pos, first[5:].lstrip(), content)
+ elif first == 'else':
+ part = ('else', pos, None, content)
+ else:
+ assert 0, "Unexpected token %r at %s" % (first, pos)
+ while 1:
+ if not tokens:
+ raise TemplateError(
+ 'No {{endif}}',
+ position=pos, name=name)
+ if (isinstance(tokens[0], tuple) and (
+ tokens[0][0] == 'endif' or tokens[0][0].startswith(
+ 'elif ') or tokens[0][0] == 'else')):
+ return part, tokens
+ next_chunk, tokens = parse_expr(tokens, name, context)
+ content.append(next_chunk)
+
+
+def parse_for(tokens, name, context):
+ first, pos = tokens[0]
+ tokens = tokens[1:]
+ context = ('for',) + context
+ content = []
+ assert first.startswith('for ')
+ if first.endswith(':'):
+ first = first[:-1]
+ first = first[3:].strip()
+ match = in_re.search(first)
+ if not match:
+ raise TemplateError(
+ 'Bad for (no "in") in %r' % first,
+ position=pos, name=name)
+ vars = first[:match.start()]
+ if '(' in vars:
+ raise TemplateError(
+ 'You cannot have () in the variable section of a for loop (%r)'
+ % vars, position=pos, name=name)
+ vars = tuple([
+ v.strip() for v in first[:match.start()].split(',')
+ if v.strip()])
+ expr = first[match.end():]
+ while 1:
+ if not tokens:
+ raise TemplateError(
+ 'No {{endfor}}',
+ position=pos, name=name)
+ if (isinstance(tokens[0], tuple) and tokens[0][0] == 'endfor'):
+ return ('for', pos, vars, expr, content), tokens[1:]
+ next_chunk, tokens = parse_expr(tokens, name, context)
+ content.append(next_chunk)
+
+
+def parse_default(tokens, name, context):
+ first, pos = tokens[0]
+ assert first.startswith('default ')
+ first = first.split(None, 1)[1]
+ parts = first.split('=', 1)
+ if len(parts) == 1:
+ raise TemplateError(
+ "Expression must be {{default var=value}}; no = found in %r" %
+ first, position=pos, name=name)
+ var = parts[0].strip()
+ if ',' in var:
+ raise TemplateError(
+ "{{default x, y = ...}} is not supported",
+ position=pos, name=name)
+ if not var_re.search(var):
+ raise TemplateError(
+ "Not a valid variable name for {{default}}: %r"
+ % var, position=pos, name=name)
+ expr = parts[1].strip()
+ return ('default', pos, var, expr), tokens[1:]
+
+
+def parse_inherit(tokens, name, context):
+ first, pos = tokens[0]
+ assert first.startswith('inherit ')
+ expr = first.split(None, 1)[1]
+ return ('inherit', pos, expr), tokens[1:]
+
+
+def parse_def(tokens, name, context):
+ first, start = tokens[0]
+ tokens = tokens[1:]
+ assert first.startswith('def ')
+ first = first.split(None, 1)[1]
+ if first.endswith(':'):
+ first = first[:-1]
+ if '(' not in first:
+ func_name = first
+ sig = ((), None, None, {})
+ elif not first.endswith(')'):
+ raise TemplateError("Function definition doesn't end with ): %s" %
+ first, position=start, name=name)
+ else:
+ first = first[:-1]
+ func_name, sig_text = first.split('(', 1)
+ sig = parse_signature(sig_text, name, start)
+ context = context + ('def',)
+ content = []
+ while 1:
+ if not tokens:
+ raise TemplateError(
+ 'Missing {{enddef}}',
+ position=start, name=name)
+ if (isinstance(tokens[0], tuple) and tokens[0][0] == 'enddef'):
+ return ('def', start, func_name, sig, content), tokens[1:]
+ next_chunk, tokens = parse_expr(tokens, name, context)
+ content.append(next_chunk)
+
+
+def parse_signature(sig_text, name, pos):
+ tokens = tokenize.generate_tokens(StringIO(sig_text).readline)
+ sig_args = []
+ var_arg = None
+ var_kw = None
+ defaults = {}
+
+ def get_token(pos=False):
+ try:
+ tok_type, tok_string, (srow, scol), (erow, ecol), line = next(
+ tokens)
+ except StopIteration:
+ return tokenize.ENDMARKER, ''
+ if pos:
+ return tok_type, tok_string, (srow, scol), (erow, ecol)
+ else:
+ return tok_type, tok_string
+ while 1:
+ var_arg_type = None
+ tok_type, tok_string = get_token()
+ if tok_type == tokenize.ENDMARKER:
+ break
+ if tok_type == tokenize.OP and (
+ tok_string == '*' or tok_string == '**'):
+ var_arg_type = tok_string
+ tok_type, tok_string = get_token()
+ if tok_type != tokenize.NAME:
+ raise TemplateError('Invalid signature: (%s)' % sig_text,
+ position=pos, name=name)
+ var_name = tok_string
+ tok_type, tok_string = get_token()
+ if tok_type == tokenize.ENDMARKER or (
+ tok_type == tokenize.OP and tok_string == ','):
+ if var_arg_type == '*':
+ var_arg = var_name
+ elif var_arg_type == '**':
+ var_kw = var_name
+ else:
+ sig_args.append(var_name)
+ if tok_type == tokenize.ENDMARKER:
+ break
+ continue
+ if var_arg_type is not None:
+ raise TemplateError('Invalid signature: (%s)' % sig_text,
+ position=pos, name=name)
+ if tok_type == tokenize.OP and tok_string == '=':
+ nest_type = None
+ unnest_type = None
+ nest_count = 0
+ start_pos = end_pos = None
+ parts = []
+ while 1:
+ tok_type, tok_string, s, e = get_token(True)
+ if start_pos is None:
+ start_pos = s
+ end_pos = e
+ if tok_type == tokenize.ENDMARKER and nest_count:
+ raise TemplateError('Invalid signature: (%s)' % sig_text,
+ position=pos, name=name)
+ if (not nest_count and
+ (tok_type == tokenize.ENDMARKER or
+ (tok_type == tokenize.OP and tok_string == ','))):
+ default_expr = isolate_expression(
+ sig_text, start_pos, end_pos)
+ defaults[var_name] = default_expr
+ sig_args.append(var_name)
+ break
+ parts.append((tok_type, tok_string))
+ if nest_count \
+ and tok_type == tokenize.OP \
+ and tok_string == nest_type:
+ nest_count += 1
+ elif nest_count \
+ and tok_type == tokenize.OP \
+ and tok_string == unnest_type:
+ nest_count -= 1
+ if not nest_count:
+ nest_type = unnest_type = None
+ elif not nest_count \
+ and tok_type == tokenize.OP \
+ and tok_string in ('(', '[', '{'):
+ nest_type = tok_string
+ nest_count = 1
+ unnest_type = {'(': ')', '[': ']', '{': '}'}[nest_type]
+ return sig_args, var_arg, var_kw, defaults
+
+
+def isolate_expression(string, start_pos, end_pos):
+ srow, scol = start_pos
+ srow -= 1
+ erow, ecol = end_pos
+ erow -= 1
+ lines = string.splitlines(True)
+ if srow == erow:
+ return lines[srow][scol:ecol]
+ parts = [lines[srow][scol:]]
+ parts.extend(lines[srow + 1:erow])
+ if erow < len(lines):
+ # It'll sometimes give (end_row_past_finish, 0)
+ parts.append(lines[erow][:ecol])
+ return ''.join(parts)
+
+_fill_command_usage = """\
+%prog [OPTIONS] TEMPLATE arg=value
+
+Use py:arg=value to set a Python value; otherwise all values are
+strings.
+"""
+
+
+def fill_command(args=None):
+ import sys
+ import optparse
+ import pkg_resources
+ import os
+ if args is None:
+ args = sys.argv[1:]
+ dist = pkg_resources.get_distribution('Paste')
+ parser = optparse.OptionParser(
+ version=coerce_text(dist),
+ usage=_fill_command_usage)
+ parser.add_option(
+ '-o', '--output',
+ dest='output',
+ metavar="FILENAME",
+ help="File to write output to (default stdout)")
+ parser.add_option(
+ '--html',
+ dest='use_html',
+ action='store_true',
+ help="Use HTML style filling (including automatic HTML quoting)")
+ parser.add_option(
+ '--env',
+ dest='use_env',
+ action='store_true',
+ help="Put the environment in as top-level variables")
+ options, args = parser.parse_args(args)
+ if len(args) < 1:
+ print('You must give a template filename')
+ sys.exit(2)
+ template_name = args[0]
+ args = args[1:]
+ vars = {}
+ if options.use_env:
+ vars.update(os.environ)
+ for value in args:
+ if '=' not in value:
+ print('Bad argument: %r' % value)
+ sys.exit(2)
+ name, value = value.split('=', 1)
+ if name.startswith('py:'):
+ name = name[:3]
+ value = eval(value)
+ vars[name] = value
+ if template_name == '-':
+ template_content = sys.stdin.read()
+ template_name = ''
+ else:
+ f = open(template_name, 'rb', encoding="latin-1")
+ template_content = f.read()
+ f.close()
+ if options.use_html:
+ TemplateClass = HTMLTemplate
+ else:
+ TemplateClass = Template
+ template = TemplateClass(template_content, name=template_name)
+ result = template.substitute(vars)
+ if options.output:
+ f = open(options.output, 'wb')
+ f.write(result)
+ f.close()
+ else:
+ sys.stdout.write(result)
+
+if __name__ == '__main__':
+ fill_command()
diff --git a/nilearn/externals/tempita/_looper.py b/nilearn/externals/tempita/_looper.py
new file mode 100644
index 0000000000..a2a67800d8
--- /dev/null
+++ b/nilearn/externals/tempita/_looper.py
@@ -0,0 +1,163 @@
+"""
+Helper for looping over sequences, particular in templates.
+
+Often in a loop in a template it's handy to know what's next up,
+previously up, if this is the first or last item in the sequence, etc.
+These can be awkward to manage in a normal Python loop, but using the
+looper you can get a better sense of the context. Use like::
+
+ >>> for loop, item in looper(['a', 'b', 'c']):
+ ... print(loop.number, item)
+ ... if not loop.last:
+ ... print('---')
+ 1 a
+ ---
+ 2 b
+ ---
+ 3 c
+
+"""
+from __future__ import absolute_import, division, print_function
+
+import sys
+from .compat3 import basestring_
+
+__all__ = ['looper']
+
+
+class looper(object):
+ """
+ Helper for looping (particularly in templates)
+
+ Use this like::
+
+ for loop, item in looper(seq):
+ if loop.first:
+ ...
+ """
+
+ def __init__(self, seq):
+ self.seq = seq
+
+ def __iter__(self):
+ return looper_iter(self.seq)
+
+ def __repr__(self):
+ return '<%s for %r>' % (
+ self.__class__.__name__, self.seq)
+
+
+class looper_iter(object):
+
+ def __init__(self, seq):
+ self.seq = list(seq)
+ self.pos = 0
+
+ def __iter__(self):
+ return self
+
+ def __next__(self):
+ if self.pos >= len(self.seq):
+ raise StopIteration
+ result = loop_pos(self.seq, self.pos), self.seq[self.pos]
+ self.pos += 1
+ return result
+
+ if sys.version < "3":
+ next = __next__
+
+
+class loop_pos(object):
+
+ def __init__(self, seq, pos):
+ self.seq = seq
+ self.pos = pos
+
+ def __repr__(self):
+ return '' % (
+ self.seq[self.pos], self.pos)
+
+ def index(self):
+ return self.pos
+ index = property(index)
+
+ def number(self):
+ return self.pos + 1
+ number = property(number)
+
+ def item(self):
+ return self.seq[self.pos]
+ item = property(item)
+
+ def __next__(self):
+ try:
+ return self.seq[self.pos + 1]
+ except IndexError:
+ return None
+ __next__ = property(__next__)
+
+ if sys.version < "3":
+ next = __next__
+
+ def previous(self):
+ if self.pos == 0:
+ return None
+ return self.seq[self.pos - 1]
+ previous = property(previous)
+
+ def odd(self):
+ return not self.pos % 2
+ odd = property(odd)
+
+ def even(self):
+ return self.pos % 2
+ even = property(even)
+
+ def first(self):
+ return self.pos == 0
+ first = property(first)
+
+ def last(self):
+ return self.pos == len(self.seq) - 1
+ last = property(last)
+
+ def length(self):
+ return len(self.seq)
+ length = property(length)
+
+ def first_group(self, getter=None):
+ """
+ Returns true if this item is the start of a new group,
+ where groups mean that some attribute has changed. The getter
+ can be None (the item itself changes), an attribute name like
+ ``'.attr'``, a function, or a dict key or list index.
+ """
+ if self.first:
+ return True
+ return self._compare_group(self.item, self.previous, getter)
+
+ def last_group(self, getter=None):
+ """
+ Returns true if this item is the end of a new group,
+ where groups mean that some attribute has changed. The getter
+ can be None (the item itself changes), an attribute name like
+ ``'.attr'``, a function, or a dict key or list index.
+ """
+ if self.last:
+ return True
+ return self._compare_group(self.item, self.__next__, getter)
+
+ def _compare_group(self, item, other, getter):
+ if getter is None:
+ return item != other
+ elif (isinstance(getter, basestring_) and getter.startswith('.')):
+ getter = getter[1:]
+ if getter.endswith('()'):
+ getter = getter[:-2]
+ return getattr(item, getter)() != getattr(other, getter)()
+ else:
+ return getattr(item, getter) != getattr(other, getter)
+ elif hasattr(getter, '__call__'):
+ return getter(item) != getter(other)
+ else:
+ return item[getter] != other[getter]
diff --git a/nilearn/externals/tempita/compat3.py b/nilearn/externals/tempita/compat3.py
new file mode 100644
index 0000000000..861f5aaf1a
--- /dev/null
+++ b/nilearn/externals/tempita/compat3.py
@@ -0,0 +1,56 @@
+# flake8: noqa
+from __future__ import absolute_import, division, print_function
+
+import sys
+
+__all__ = ['PY3', 'b', 'basestring_', 'bytes', 'next', 'is_unicode',
+ 'iteritems']
+
+PY3 = True if sys.version_info[0] == 3 else False
+
+if sys.version_info[0] < 3:
+
+ def next(obj):
+ return obj.next()
+
+ def iteritems(d, **kw):
+ return d.iteritems(**kw)
+
+ b = bytes = str
+ basestring_ = basestring
+
+else:
+
+ def b(s):
+ if isinstance(s, str):
+ return s.encode('latin1')
+ return bytes(s)
+
+ def iteritems(d, **kw):
+ return iter(d.items(**kw))
+
+ next = next
+ basestring_ = (bytes, str)
+ bytes = bytes
+
+text = str
+
+
+def is_unicode(obj):
+ if sys.version_info[0] < 3:
+ return isinstance(obj, unicode)
+ else:
+ return isinstance(obj, str)
+
+
+def coerce_text(v):
+ if not isinstance(v, basestring_):
+ if sys.version_info[0] < 3:
+ attr = '__unicode__'
+ else:
+ attr = '__str__'
+ if hasattr(v, attr):
+ return unicode(v)
+ else:
+ return bytes(v)
+ return v
diff --git a/nilearn/image/image.py b/nilearn/image/image.py
index e51a29bc06..61623200df 100644
--- a/nilearn/image/image.py
+++ b/nilearn/image/image.py
@@ -14,7 +14,7 @@
from scipy.stats import scoreatpercentile
import copy
import nibabel
-from sklearn.externals.joblib import Parallel, delayed
+from nilearn._utils.compat import Parallel, delayed
from .. import signal
from .._utils import (check_niimg_4d, check_niimg_3d, check_niimg, as_ndarray,
@@ -172,8 +172,8 @@ def _smooth_array(arr, affine, fwhm=None, ensure_finite=True, copy=True):
filtering.
copy: bool
- if True, input array is not modified. False by default: the filtering
- is performed in-place.
+ if True, input array is not modified. True by default: the filtering
+ is not performed in-place.
Returns
-------
@@ -327,7 +327,7 @@ def _crop_img_to(img, slices, copy=True):
return new_img_like(img, cropped_data, new_affine)
-def crop_img(img, rtol=1e-8, copy=True):
+def crop_img(img, rtol=1e-8, copy=True, pad=True, return_offset=False):
"""Crops img as much as possible
Will crop img, removing as many zero entries as possible
@@ -349,10 +349,22 @@ def crop_img(img, rtol=1e-8, copy=True):
copy: boolean
Specifies whether cropped data is copied or not.
+ pad: boolean
+ Toggles adding 1-voxel of 0s around the border. Recommended.
+
+ return_offset: boolean
+ Specifies whether to return a tuple of the removed padding.
+
Returns
-------
cropped_img: image
Cropped version of the input image
+
+ offset: list (optional)
+ List of tuples representing the number of voxels removed (before, after)
+ the cropped volumes, i.e.:
+ [(x1_pre, x1_post), (x2_pre, x2_post), ..., (xN_pre, xN_post)]
+
"""
img = check_niimg(img)
@@ -364,16 +376,53 @@ def crop_img(img, rtol=1e-8, copy=True):
if data.ndim == 4:
passes_threshold = np.any(passes_threshold, axis=-1)
coords = np.array(np.where(passes_threshold))
- start = coords.min(axis=1)
- end = coords.max(axis=1) + 1
+
+ # Sets full range if no data are found along the axis
+ if coords.shape[1] == 0:
+ start, end = [0, 0, 0], list(data.shape)
+ else:
+ start = coords.min(axis=1)
+ end = coords.max(axis=1) + 1
# pad with one voxel to avoid resampling problems
- start = np.maximum(start - 1, 0)
- end = np.minimum(end + 1, data.shape[:3])
+ if pad:
+ start = np.maximum(start - 1, 0)
+ end = np.minimum(end + 1, data.shape[:3])
+
+ slices = [slice(s, e) for s, e in zip(start, end)][:3]
+ cropped_im = _crop_img_to(img, slices, copy=copy)
+ return cropped_im if not return_offset else (cropped_im, tuple(slices))
+
- slices = [slice(s, e) for s, e in zip(start, end)]
+def _pad_array(array, pad_sizes):
+ """Pad an ndarray with zeros of quantity specified
+ as follows pad_sizes = [x1minpad, x1maxpad, x2minpad,
+ x2maxpad, x3minpad, ...]
+ """
+
+ if len(pad_sizes) % 2 != 0:
+ raise ValueError("Please specify as many max paddings as min"
+ " paddings. You have specified %d arguments" %
+ len(pad_sizes))
+
+ all_paddings = np.zeros([array.ndim, 2], dtype=np.int64)
+ all_paddings[:len(pad_sizes) // 2] = np.array(pad_sizes).reshape(-1, 2)
+
+ lower_paddings, upper_paddings = all_paddings.T
+ new_shape = np.array(array.shape) + upper_paddings + lower_paddings
- return _crop_img_to(img, slices, copy=copy)
+ padded = np.zeros(new_shape, dtype=array.dtype)
+ source_slices = [slice(max(-lp, 0), min(s + up, s))
+ for lp, up, s in zip(lower_paddings,
+ upper_paddings,
+ array.shape)]
+ target_slices = [slice(max(lp, 0), min(s - up, s))
+ for lp, up, s in zip(lower_paddings,
+ upper_paddings,
+ new_shape)]
+
+ padded[tuple(target_slices)] = array[source_slices].copy()
+ return padded
def _compute_mean(imgs, target_affine=None,
@@ -656,7 +705,7 @@ def new_img_like(ref_niimg, data, affine=None, copy_header=False):
return klass(data, affine, header=header)
-def threshold_img(img, threshold, mask_img=None):
+def threshold_img(img, threshold, mask_img=None, copy=True):
""" Threshold the given input image, mostly statistical or atlas images.
Thresholding can be done based on direct image intensities or selection
@@ -683,6 +732,10 @@ def threshold_img(img, threshold, mask_img=None):
Mask image applied to mask the input data.
If None, no masking will be applied.
+ copy: bool
+ if True, input array is not modified. True by default: the filtering
+ is not performed in-place.
+
Returns
-------
threshold_img: Nifti1Image
@@ -693,6 +746,8 @@ def threshold_img(img, threshold, mask_img=None):
img = check_niimg(img)
img_data = _safe_get_data(img, ensure_finite=True)
+ if copy:
+ img_data = img_data.copy()
affine = img.affine
if mask_img is not None:
diff --git a/nilearn/image/resampling.py b/nilearn/image/resampling.py
index 27dede7712..a699351341 100644
--- a/nilearn/image/resampling.py
+++ b/nilearn/image/resampling.py
@@ -13,6 +13,7 @@
import scipy
from scipy import ndimage, linalg
+from .image import crop_img
from .. import _utils
from .._utils.compat import _basestring
@@ -289,7 +290,7 @@ def _resample_one_img(data, A, b, target_shape,
def resample_img(img, target_affine=None, target_shape=None,
interpolation='continuous', copy=True, order="F",
- clip=True, fill_value=0):
+ clip=True, fill_value=0, force_resample=False):
"""Resample a Niimg-like object
Parameters
@@ -331,6 +332,9 @@ def resample_img(img, target_affine=None, target_shape=None,
fill_value: float, optional
Use a fill value for points outside of input volume (default 0).
+ force_resample: bool, optional
+ Intended for testing, this prevents the use of a padding optimzation
+
Returns
-------
resampled: nibabel.Nifti1Image
@@ -418,18 +422,19 @@ def resample_img(img, target_affine=None, target_shape=None,
input_img_is_string = False
img = _utils.check_niimg(img)
+ shape = img.shape
+ affine = img.affine
# noop cases
if target_affine is None and target_shape is None:
if copy and not input_img_is_string:
img = _utils.copy_img(img)
return img
+ if target_affine is affine and target_shape is shape:
+ return img
if target_affine is not None:
target_affine = np.asarray(target_affine)
- shape = img.shape
- affine = img.affine
-
if (np.all(np.array(target_shape) == shape[:3]) and
np.allclose(target_affine, affine)):
if copy and not input_img_is_string:
@@ -486,14 +491,6 @@ def resample_img(img, target_affine=None, target_shape=None,
else:
transform_affine = np.dot(linalg.inv(affine), target_affine)
A, b = to_matrix_vector(transform_affine)
- # If A is diagonal, ndimage.affine_transform is clever enough to use a
- # better algorithm.
- if np.all(np.diag(np.diag(A)) == A):
- if LooseVersion(scipy.__version__) < LooseVersion('0.18'):
- # Before scipy 0.18, ndimage.affine_transform was applying a
- # different logic to the offset for diagonal affine
- b = np.dot(linalg.inv(A), b)
- A = np.diag(A)
data_shape = list(data.shape)
# Make sure that we have a list here
@@ -501,6 +498,11 @@ def resample_img(img, target_affine=None, target_shape=None,
target_shape = target_shape.tolist()
target_shape = tuple(target_shape)
+ if LooseVersion(scipy.__version__) < LooseVersion('0.20'):
+ # Before scipy 0.20, force native data types due to endian issues
+ # that caused instability.
+ data = data.astype(data.dtype.newbyteorder('N'))
+
if interpolation == 'continuous' and data.dtype.kind == 'i':
# cast unsupported data types to closest support dtype
aux = data.dtype.name.replace('int', 'float')
@@ -527,20 +529,58 @@ def resample_img(img, target_affine=None, target_shape=None,
# Code is generic enough to work for both 3D and 4D images
other_shape = data_shape[3:]
- resampled_data = np.empty(list(target_shape) + other_shape,
+ resampled_data = np.zeros(list(target_shape) + other_shape,
order=order, dtype=resampled_data_dtype)
all_img = (slice(None), ) * 3
- # Iterate over a set of 3D volumes, as the interpolation problem is
- # separable in the extra dimensions. This reduces the
- # computational cost
- for ind in np.ndindex(*other_shape):
- _resample_one_img(data[all_img + ind], A, b, target_shape,
- interpolation_order,
- out=resampled_data[all_img + ind],
- copy=not input_img_is_string,
- fill_value=fill_value)
+ # if (A == I OR some combination of permutation(I) and sign-flipped(I)) AND
+ # all(b == integers):
+ if (np.all(np.eye(3) == A) and all(bt == np.round(bt) for bt in b) and
+ not force_resample):
+ # TODO: also check for sign flips
+ # TODO: also check for permutations of I
+
+ # ... special case: can be solved with padding alone
+ # crop source image and keep N voxels offset before/after volume
+ cropped_img, offsets = crop_img(img, pad=False, return_offset=True)
+
+ # TODO: flip axes that are flipped
+ # TODO: un-shuffle permuted dimensions
+
+ # offset the original un-cropped image indices by the relative
+ # translation, b.
+ indices = [(int(off.start - dim_b), int(off.stop - dim_b))
+ for off, dim_b in zip(offsets[:3], b[:3])]
+
+ # If image are not fully overlapping, place only portion of image.
+ slices = []
+ for dimsize, index in zip(resampled_data.shape, indices):
+ slices.append(slice(np.max((0, index[0])),
+ np.min((dimsize, index[1]))))
+ slices = tuple(slices)
+
+ # ensure the source image being placed isn't larger than the dest
+ subset_indices = tuple(slice(0, s.stop-s.start) for s in slices)
+ resampled_data[slices] = cropped_img.get_data()[subset_indices]
+ else:
+ # If A is diagonal, ndimage.affine_transform is clever enough to use a
+ # better algorithm.
+ if np.all(np.diag(np.diag(A)) == A):
+ if LooseVersion(scipy.__version__) < LooseVersion('0.18'):
+ # Before scipy 0.18, ndimage.affine_transform was applying a
+ # different logic to the offset for diagonal affine
+ b = np.dot(linalg.inv(A), b)
+ A = np.diag(A)
+ # Iterate over a set of 3D volumes, as the interpolation problem is
+ # separable in the extra dimensions. This reduces the
+ # computational cost
+ for ind in np.ndindex(*other_shape):
+ _resample_one_img(data[all_img + ind], A, b, target_shape,
+ interpolation_order,
+ out=resampled_data[all_img + ind],
+ copy=not input_img_is_string,
+ fill_value=fill_value)
if clip:
# force resampled data to have a range contained in the original data
@@ -556,7 +596,7 @@ def resample_img(img, target_affine=None, target_shape=None,
def resample_to_img(source_img, target_img,
interpolation='continuous', copy=True, order='F',
- clip=False, fill_value=0):
+ clip=False, fill_value=0, force_resample=False):
"""Resample a Niimg-like source image on a target Niimg-like image
(no registration is performed: the image should already be aligned).
@@ -593,6 +633,9 @@ def resample_to_img(source_img, target_img,
fill_value: float, optional
Use a fill value for points outside of input volume (default 0).
+ force_resample: bool, optional
+ Intended for testing, this prevents the use of a padding optimzation
+
Returns
-------
resampled: nibabel.Nifti1Image
@@ -616,7 +659,8 @@ def resample_to_img(source_img, target_img,
target_affine=target.affine,
target_shape=target_shape,
interpolation=interpolation, copy=copy, order=order,
- clip=clip, fill_value=fill_value)
+ clip=clip, fill_value=fill_value,
+ force_resample=force_resample)
def reorder_img(img, resample=None):
diff --git a/nilearn/image/tests/test_image.py b/nilearn/image/tests/test_image.py
index 92232c75e2..fe78003965 100644
--- a/nilearn/image/tests/test_image.py
+++ b/nilearn/image/tests/test_image.py
@@ -499,6 +499,27 @@ def test_threshold_img():
# when threshold is a percentile
thr_maps_percent2 = threshold_img(img, threshold='2%')
+def test_threshold_img_copy():
+
+ img_zeros = Nifti1Image(np.zeros((10, 10, 10, 10)), np.eye(4))
+ img_ones = Nifti1Image(np.ones((10, 10, 10, 10)), np.eye(4))
+
+ # Check that copy does not mutate. It returns modified copy.
+ thresholded = threshold_img(img_ones, 2) # threshold 2 > 1
+
+ # Original img_ones should have all ones.
+ assert_array_equal(img_ones.get_data(), np.ones((10, 10, 10, 10)))
+ # Thresholded should have all zeros.
+ assert_array_equal(thresholded.get_data(), np.zeros((10, 10, 10, 10)))
+
+ # Check that not copying does mutate.
+ img_to_mutate = Nifti1Image(np.ones((10, 10, 10, 10)), np.eye(4))
+ thresholded = threshold_img(img_to_mutate, 2, copy=False)
+ # Check that original mutates
+ assert_array_equal(img_to_mutate.get_data(), np.zeros((10, 10, 10, 10)))
+ # And that returned value is also thresholded.
+ assert_array_equal(img_to_mutate.get_data(), thresholded.get_data())
+
def test_isnan_threshold_img_data():
shape = (10, 10, 10)
diff --git a/nilearn/image/tests/test_resampling.py b/nilearn/image/tests/test_resampling.py
index a0b66d551d..9cff7ed923 100644
--- a/nilearn/image/tests/test_resampling.py
+++ b/nilearn/image/tests/test_resampling.py
@@ -18,6 +18,7 @@
from nilearn.image.resampling import from_matrix_vector, coord_transform
from nilearn.image.resampling import get_bounds
from nilearn.image.resampling import BoundingBoxError
+from nilearn.image.image import _pad_array, crop_img
from nilearn._utils import testing
@@ -37,37 +38,6 @@ def rotation(theta, phi):
return np.dot(a1, a2)
-def pad(array, *args):
- """Pad an ndarray with zeros of quantity specified
- in args as follows args = (x1minpad, x1maxpad, x2minpad,
- x2maxpad, x3minpad, ...)
- """
-
- if len(args) % 2 != 0:
- raise ValueError("Please specify as many max paddings as min"
- " paddings. You have specified %d arguments" %
- len(args))
-
- all_paddings = np.zeros([array.ndim, 2], dtype=np.int64)
- all_paddings[:len(args) // 2] = np.array(args).reshape(-1, 2)
-
- lower_paddings, upper_paddings = all_paddings.T
- new_shape = np.array(array.shape) + upper_paddings + lower_paddings
-
- padded = np.zeros(new_shape, dtype=array.dtype)
- source_slices = [slice(max(-lp, 0), min(s + up, s))
- for lp, up, s in zip(lower_paddings,
- upper_paddings,
- array.shape)]
- target_slices = [slice(max(lp, 0), min(s - up, s))
- for lp, up, s in zip(lower_paddings,
- upper_paddings,
- new_shape)]
-
- padded[target_slices] = array[source_slices].copy()
- return padded
-
-
###############################################################################
# Tests
def test_identity_resample():
@@ -121,6 +91,11 @@ def test_downsample():
np.testing.assert_almost_equal(downsampled,
rot_img.get_data()[:x, :y, :z, ...])
+ rot_img_2 = resample_img(Nifti1Image(data, affine),
+ target_affine=2 * affine, interpolation='nearest',
+ force_resample=True)
+ np.testing.assert_almost_equal(rot_img_2.get_data(),
+ rot_img.get_data())
# Test with non native endian data
# Test to check that if giving non native endian data as input should
@@ -471,8 +446,8 @@ def test_resampling_result_axis_permutation():
offset_cropping = np.vstack([-offset[ap][np.newaxis, :],
np.zeros([1, 3])]
).T.ravel().astype(int)
- what_resampled_data_should_be = pad(full_data.transpose(ap),
- *list(offset_cropping))
+ what_resampled_data_should_be = _pad_array(full_data.transpose(ap),
+ list(offset_cropping))
assert_array_almost_equal(resampled_data,
what_resampled_data_should_be)
@@ -558,6 +533,56 @@ def test_resample_to_img():
np.testing.assert_almost_equal(downsampled,
result_img.get_data()[:x, :y, :z, ...])
+def test_crop():
+ # Testing that padding of arrays and cropping of images work symmetrically
+ shape = (4, 6, 2)
+ data = np.ones(shape)
+ padded = _pad_array(data, [3, 2, 4, 4, 5, 7])
+ padd_nii = Nifti1Image(padded, np.eye(4))
+
+ cropped = crop_img(padd_nii, pad=False)
+ np.testing.assert_equal(cropped.get_data(), data)
+
+
+def test_resample_identify_affine_int_translation():
+ # Testing resample to img function
+ rand_gen = np.random.RandomState(0)
+
+ source_shape = (6, 4, 6)
+ source_affine = np.eye(4)
+ source_affine[:, 3] = np.append(np.random.randint(0, 4, 3), 1)
+ source_data = rand_gen.random_sample(source_shape)
+ source_img = Nifti1Image(source_data, source_affine)
+
+ target_shape = (11, 10, 9)
+ target_data = np.zeros(target_shape)
+ target_affine = source_affine
+ target_affine[:3, 3] -= 3 # add an offset of 3 in x, y, z
+ target_data[3:9, 3:7, 3:9] = source_data # put the data at the offset location
+ target_img = Nifti1Image(target_data, target_affine)
+
+ result_img = resample_to_img(source_img, target_img,
+ interpolation='nearest')
+ np.testing.assert_almost_equal(target_img.get_data(),
+ result_img.get_data())
+
+ result_img_2 = resample_to_img(result_img, source_img,
+ interpolation='nearest')
+ np.testing.assert_almost_equal(source_img.get_data(),
+ result_img_2.get_data())
+
+ result_img_3 = resample_to_img(result_img, source_img,
+ interpolation='nearest',
+ force_resample=True)
+ np.testing.assert_almost_equal(result_img_2.get_data(),
+ result_img_3.get_data())
+
+ result_img_4 = resample_to_img(source_img, target_img,
+ interpolation='nearest',
+ force_resample=True)
+ np.testing.assert_almost_equal(target_img.get_data(),
+ result_img_4.get_data())
+
def test_resample_clip():
# Resample and image and get larger and smaller
# value than in the original. Use clip to get rid of these images
diff --git a/nilearn/input_data/base_masker.py b/nilearn/input_data/base_masker.py
index f90b54785f..af604b581e 100644
--- a/nilearn/input_data/base_masker.py
+++ b/nilearn/input_data/base_masker.py
@@ -10,7 +10,7 @@
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
-from sklearn.externals.joblib import Memory
+from nilearn._utils.compat import Memory
from .. import masking
from .. import image
diff --git a/nilearn/input_data/multi_nifti_masker.py b/nilearn/input_data/multi_nifti_masker.py
index f0f5737684..b70f44297e 100644
--- a/nilearn/input_data/multi_nifti_masker.py
+++ b/nilearn/input_data/multi_nifti_masker.py
@@ -8,7 +8,7 @@
import itertools
import warnings
-from sklearn.externals.joblib import Memory, Parallel, delayed
+from nilearn._utils.compat import Memory, Parallel, delayed
from .. import _utils
from .. import image
@@ -36,12 +36,18 @@ class MultiNiftiMasker(NiftiMasker, CacheMixin):
fine tune the mask extraction.
smoothing_fwhm: float, optional
- If smoothing_fwhm is not None, it gives the size in millimeters of the
- spatial smoothing to apply to the signal.
-
- standardize: boolean, optional
- If standardize is True, the time-series are centered and normed:
- their mean is put to 0 and their variance to 1 in the time dimension.
+ If smoothing_fwhm is not None, it gives the size in millimeters of
+ the spatial smoothing to apply to the signal.
+
+ standardize: {'zscore', 'psc', True, False}, default is 'zscore'
+ Strategy to standardize the signal.
+ 'zscore': the signal is z-scored. Timeseries are shifted
+ to zero mean and scaled to unit variance.
+ 'psc': Timeseries are shifted to zero mean value and scaled
+ to percent signal change (as compared to original mean signal).
+ True : the signal is z-scored. Timeseries are shifted
+ to zero mean and scaled to unit variance.
+ False : Do not standardize the data.
detrend: boolean, optional
This parameter is passed to signal.clean. Please see the related
@@ -121,13 +127,11 @@ class MultiNiftiMasker(NiftiMasker, CacheMixin):
"""
def __init__(self, mask_img=None, smoothing_fwhm=None,
- standardize=False, detrend=False,
- low_pass=None, high_pass=None, t_r=None,
- target_affine=None, target_shape=None,
- mask_strategy='background', mask_args=None, dtype=None,
- memory=Memory(cachedir=None), memory_level=0,
- n_jobs=1, verbose=0
- ):
+ standardize=False, detrend=False, low_pass=None,
+ high_pass=None, t_r=None, target_affine=None,
+ target_shape=None, mask_strategy='background',
+ mask_args=None, dtype=None, memory=Memory(cachedir=None),
+ memory_level=0, n_jobs=1, verbose=0):
# Mask is provided or computed
self.mask_img = mask_img
diff --git a/nilearn/input_data/nifti_labels_masker.py b/nilearn/input_data/nifti_labels_masker.py
index f4e6dca9ea..22223e6b52 100644
--- a/nilearn/input_data/nifti_labels_masker.py
+++ b/nilearn/input_data/nifti_labels_masker.py
@@ -4,7 +4,7 @@
import numpy as np
-from sklearn.externals.joblib import Memory
+from nilearn._utils.compat import Memory
from .. import _utils
from .._utils import logger, CacheMixin, _compose_err_msg
@@ -56,9 +56,15 @@ class NiftiLabelsMasker(BaseMasker, CacheMixin):
If smoothing_fwhm is not None, it gives the full-width half maximum in
millimeters of the spatial smoothing to apply to the signal.
- standardize: boolean, optional
- If standardize is True, the time-series are centered and normed:
- their mean is put to 0 and their variance to 1 in the time dimension.
+ standardize: {'zscore', 'psc', True, False}, default is 'zscore'
+ Strategy to standardize the signal.
+ 'zscore': the signal is z-scored. Timeseries are shifted
+ to zero mean and scaled to unit variance.
+ 'psc': Timeseries are shifted to zero mean value and scaled
+ to percent signal change (as compared to original mean signal).
+ True : the signal is z-scored. Timeseries are shifted
+ to zero mean and scaled to unit variance.
+ False : Do not standardize the data.
detrend: boolean, optional
This parameter is passed to signal.clean. Please see the related
diff --git a/nilearn/input_data/nifti_maps_masker.py b/nilearn/input_data/nifti_maps_masker.py
index 0cb57567f9..c9ff8525ab 100644
--- a/nilearn/input_data/nifti_maps_masker.py
+++ b/nilearn/input_data/nifti_maps_masker.py
@@ -3,7 +3,7 @@
"""
import numpy as np
-from sklearn.externals.joblib import Memory
+from nilearn._utils.compat import Memory
from .. import _utils
from .._utils import logger, CacheMixin
@@ -22,11 +22,11 @@ def __init__(self, _resampled_maps_img_, _resampled_mask_img_):
self._resampled_mask_img_ = _resampled_mask_img_
def __call__(self, imgs):
- from ..regions import signal_extraction
+ from ..regions import signal_extraction
- return signal_extraction.img_to_signals_maps(
- imgs, self._resampled_maps_img_,
- mask_img=self._resampled_mask_img_)
+ return signal_extraction.img_to_signals_maps(
+ imgs, self._resampled_maps_img_,
+ mask_img=self._resampled_mask_img_)
class NiftiMapsMasker(BaseMasker, CacheMixin):
@@ -58,9 +58,15 @@ class NiftiMapsMasker(BaseMasker, CacheMixin):
If smoothing_fwhm is not None, it gives the full-width half maximum in
millimeters of the spatial smoothing to apply to the signal.
- standardize: boolean, optional
- If standardize is True, the time-series are centered and normed:
- their mean is put to 0 and their variance to 1 in the time dimension.
+ standardize: {'zscore', 'psc', True, False}, default is 'zscore'
+ Strategy to standardize the signal.
+ 'zscore': the signal is z-scored. Timeseries are shifted
+ to zero mean and scaled to unit variance.
+ 'psc': Timeseries are shifted to zero mean value and scaled
+ to percent signal change (as compared to original mean signal).
+ True : the signal is z-scored. Timeseries are shifted
+ to zero mean and scaled to unit variance.
+ False : Do not standardize the data.
detrend: boolean, optional
This parameter is passed to signal.clean. Please see the related
@@ -116,10 +122,9 @@ class NiftiMapsMasker(BaseMasker, CacheMixin):
# memory and memory_level are used by CacheMixin.
def __init__(self, maps_img, mask_img=None,
- allow_overlap=True,
- smoothing_fwhm=None, standardize=False, detrend=False,
- low_pass=None, high_pass=None, t_r=None, dtype=None,
- resampling_target="data",
+ allow_overlap=True, smoothing_fwhm=None, standardize=False,
+ detrend=False, low_pass=None, high_pass=None, t_r=None,
+ dtype=None, resampling_target="data",
memory=Memory(cachedir=None, verbose=0), memory_level=0,
verbose=0):
self.maps_img = maps_img
diff --git a/nilearn/input_data/nifti_masker.py b/nilearn/input_data/nifti_masker.py
index 9a752b7c89..cd1a29fa36 100644
--- a/nilearn/input_data/nifti_masker.py
+++ b/nilearn/input_data/nifti_masker.py
@@ -4,14 +4,16 @@
# Author: Gael Varoquaux, Alexandre Abraham
# License: simplified BSD
+import warnings
from copy import copy as copy_object
-from sklearn.externals.joblib import Memory
+from nilearn._utils.compat import Memory
from .base_masker import BaseMasker, filter_and_extract
from .. import _utils
from .. import image
from .. import masking
+from nilearn.reporting import ReportMixin
from .._utils import CacheMixin
from .._utils.class_inspect import get_params
from .._utils.niimg import img_data_dtype
@@ -63,7 +65,7 @@ def filter_and_mask(imgs, mask_img_, parameters,
return data
-class NiftiMasker(BaseMasker, CacheMixin):
+class NiftiMasker(BaseMasker, CacheMixin, ReportMixin):
"""Applying a mask to extract time-series from Niimg-like objects.
NiftiMasker is useful when preprocessing (detrending, standardization,
@@ -86,25 +88,31 @@ class NiftiMasker(BaseMasker, CacheMixin):
If smoothing_fwhm is not None, it gives the full-width half maximum in
millimeters of the spatial smoothing to apply to the signal.
- standardize : boolean, optional
- If standardize is True, the time-series are centered and normed:
- their mean is put to 0 and their variance to 1 in the time dimension.
+ standardize: {'zscore', 'psc', True, False}, default is 'zscore'
+ Strategy to standardize the signal.
+ 'zscore': the signal is z-scored. Timeseries are shifted
+ to zero mean and scaled to unit variance.
+ 'psc': Timeseries are shifted to zero mean value and scaled
+ to percent signal change (as compared to original mean signal).
+ True : the signal is z-scored. Timeseries are shifted
+ to zero mean and scaled to unit variance.
+ False : Do not standardize the data.
detrend : boolean, optional
This parameter is passed to signal.clean. Please see the related
- documentation for details
+ documentation for details: :func:`nilearn.signal.clean`.
low_pass: None or float, optional
This parameter is passed to signal.clean. Please see the related
- documentation for details
+ documentation for details: :func:`nilearn.signal.clean`.
high_pass: None or float, optional
This parameter is passed to signal.clean. Please see the related
- documentation for details
+ documentation for details: :func:`nilearn.signal.clean`.
t_r : float, optional
This parameter is passed to signal.clean. Please see the related
- documentation for details
+ documentation for details: :func:`nilearn.signal.clean`.
target_affine : 3x3 or 4x4 matrix, optional
This parameter is passed to image.resample_img. Please see the
@@ -178,7 +186,7 @@ def __init__(self, mask_img=None, sessions=None, smoothing_fwhm=None,
mask_strategy='background',
mask_args=None, sample_mask=None, dtype=None,
memory_level=1, memory=Memory(cachedir=None),
- verbose=0
+ verbose=0, reports=True,
):
# Mask is provided or computed
self.mask_img = mask_img
@@ -200,9 +208,78 @@ def __init__(self, mask_img=None, sessions=None, smoothing_fwhm=None,
self.memory = memory
self.memory_level = memory_level
self.verbose = verbose
+ self.reports = reports
+ self._report_description = ('This report shows the input Nifti '
+ 'image overlaid with the outlines of the '
+ 'mask (in green). We recommend to inspect '
+ 'the report for the overlap between the '
+ 'mask and its input image. ')
+ self._overlay_text = ('\n To see the input Nifti image before '
+ 'resampling, hover over the displayed image.')
self._shelving = False
+ def _reporting(self):
+ """
+ Returns
+ -------
+ displays : list
+ A list of all displays to be rendered.
+ """
+ try:
+ from nilearn import plotting
+ except ImportError:
+ with warnings.catch_warnings():
+ mpl_unavail_msg = ('Matplotlib is not imported! '
+ 'No reports will be generated.')
+ warnings.filterwarnings('always', message=mpl_unavail_msg)
+ warnings.warn(category=ImportWarning,
+ message=mpl_unavail_msg)
+ return [None]
+
+ img = self._reporting_data['images']
+ mask = self._reporting_data['mask']
+ if img is not None:
+ dim = image.load_img(img).shape
+ if len(dim) == 4:
+ # compute middle image from 4D series for plotting
+ img = image.index_img(img, dim[-1] // 2)
+ else: # images were not provided to fit
+ img = mask
+
+ # create display of retained input mask, image
+ # for visual comparison
+ init_display = plotting.plot_img(img,
+ black_bg=False,
+ cmap='CMRmap_r')
+ init_display.add_contours(mask, levels=[.5], colors='g',
+ linewidths=2.5)
+
+ if 'transform' not in self._reporting_data:
+ return [init_display]
+
+ else: # if resampling was performed
+ self._report_description = (self._report_description +
+ self._overlay_text)
+
+ # create display of resampled NiftiImage and mask
+ # assuming that resampl_img has same dim as img
+ resampl_img, resampl_mask = self._reporting_data['transform']
+ if resampl_img is not None:
+ if len(dim) == 4:
+ # compute middle image from 4D series for plotting
+ resampl_img = image.index_img(resampl_img, dim[-1] // 2)
+ else: # images were not provided to fit
+ resampl_img = resampl_mask
+
+ final_display = plotting.plot_img(resampl_img,
+ black_bg=False,
+ cmap='CMRmap_r')
+ final_display.add_contours(resampl_mask, levels=[.5],
+ colors='g', linewidths=2.5)
+
+ return [init_display, final_display]
+
def _check_fitted(self):
if not hasattr(self, 'mask_img_'):
raise ValueError('It seems that %s has not been fitted. '
@@ -248,6 +325,11 @@ def fit(self, imgs=None, y=None):
else:
self.mask_img_ = _utils.check_niimg_3d(self.mask_img)
+ if self.reports: # save inputs for reporting
+ self._reporting_data = {'images': imgs, 'mask': self.mask_img_}
+ else:
+ self._reporting_data = None
+
# If resampling is requested, resample also the mask
# Resampling: allows the user to change the affine, the shape or both
if self.verbose > 0:
@@ -257,14 +339,25 @@ def fit(self, imgs=None, y=None):
target_affine=self.target_affine,
target_shape=self.target_shape,
copy=False, interpolation='nearest')
- if self.target_affine is not None:
+ if self.target_affine is not None: # resample image to target affine
self.affine_ = self.target_affine
- else:
+ else: # resample image to mask affine
self.affine_ = self.mask_img_.affine
# Load data in memory
self.mask_img_.get_data()
if self.verbose > 10:
print("[%s.fit] Finished fit" % self.__class__.__name__)
+
+ if (self.target_shape is not None) or (self.target_affine is not None):
+ if self.reports:
+ if imgs is not None:
+ resampl_imgs = self._cache(image.resample_img)(
+ imgs, target_affine=self.affine_,
+ copy=False, interpolation='nearest')
+ else: # imgs not provided to fit
+ resampl_imgs = None
+ self._reporting_data['transform'] = [resampl_imgs, self.mask_img_]
+
return self
def transform_single_imgs(self, imgs, confounds=None, copy=True):
@@ -279,7 +372,7 @@ def transform_single_imgs(self, imgs, confounds=None, copy=True):
confounds: CSV file or array-like, optional
This parameter is passed to signal.clean. Please see the related
- documentation for details.
+ documentation for details: :func:`nilearn.signal.clean`.
shape: (number of scans, number of confounds)
Returns
diff --git a/nilearn/input_data/nifti_spheres_masker.py b/nilearn/input_data/nifti_spheres_masker.py
index fc7e829d58..9eb8aef701 100644
--- a/nilearn/input_data/nifti_spheres_masker.py
+++ b/nilearn/input_data/nifti_spheres_masker.py
@@ -7,7 +7,7 @@
import numpy as np
import warnings
from sklearn import neighbors
-from sklearn.externals.joblib import Memory
+from nilearn._utils.compat import Memory
from ..image.resampling import coord_transform
from .._utils.niimg_conversions import _safe_get_data
@@ -167,9 +167,15 @@ class NiftiSpheresMasker(BaseMasker, CacheMixin):
If smoothing_fwhm is not None, it gives the full-width half maximum in
millimeters of the spatial smoothing to apply to the signal.
- standardize: boolean, optional
- If standardize is True, the time-series are centered and normed:
- their mean is set to 0 and their variance to 1 in the time dimension.
+ standardize: {'zscore', 'psc', True, False}, default is 'zscore'
+ Strategy to standardize the signal.
+ 'zscore': the signal is z-scored. Timeseries are shifted
+ to zero mean and scaled to unit variance.
+ 'psc': Timeseries are shifted to zero mean value and scaled
+ to percent signal change (as compared to original mean signal).
+ True : the signal is z-scored. Timeseries are shifted
+ to zero mean and scaled to unit variance.
+ False : Do not standardize the data.
detrend: boolean, optional
This parameter is passed to signal.clean. Please see the related
diff --git a/nilearn/input_data/tests/test_base_masker.py b/nilearn/input_data/tests/test_base_masker.py
index 3903e9935b..967b6d03c2 100644
--- a/nilearn/input_data/tests/test_base_masker.py
+++ b/nilearn/input_data/tests/test_base_masker.py
@@ -37,7 +37,7 @@ def test_cropping_code_paths():
"low_pass": None,
"t_r": None,
"detrend": None,
- "standardize": None
+ "standardize": 'zscore',
}
# Now do the two maskings
diff --git a/nilearn/input_data/tests/test_masker_validation.py b/nilearn/input_data/tests/test_masker_validation.py
index 7972769919..4522d7edb3 100644
--- a/nilearn/input_data/tests/test_masker_validation.py
+++ b/nilearn/input_data/tests/test_masker_validation.py
@@ -3,7 +3,7 @@
import numpy as np
from sklearn.base import BaseEstimator
-from sklearn.externals.joblib import Memory
+from nilearn._utils.compat import Memory
from nilearn._utils.testing import assert_warns
from nilearn.input_data.masker_validation import check_embedded_nifti_masker
@@ -54,10 +54,10 @@ def test_check_embedded_nifti_masker():
if param_key not in ['memory', 'memory_level', 'n_jobs',
'verbose']:
assert_equal(getattr(masker, param_key),
- getattr(mask, param_key))
+ getattr(mask, param_key))
else:
assert_equal(getattr(masker, param_key),
- getattr(owner, param_key))
+ getattr(owner, param_key))
# Check use of mask as mask_img
shape = (6, 8, 10, 5)
diff --git a/nilearn/input_data/tests/test_multi_nifti_masker.py b/nilearn/input_data/tests/test_multi_nifti_masker.py
index 620864d10c..ef46fe0814 100644
--- a/nilearn/input_data/tests/test_multi_nifti_masker.py
+++ b/nilearn/input_data/tests/test_multi_nifti_masker.py
@@ -14,7 +14,7 @@
from nose import SkipTest
from nose.tools import assert_true, assert_false, assert_raises, assert_equal
from numpy.testing import assert_array_equal
-from sklearn.externals.joblib import Memory
+from nilearn._utils.compat import Memory
from nilearn._utils.exceptions import DimensionError
from nilearn._utils.testing import assert_raises_regex, write_tmp_imgs
@@ -116,7 +116,7 @@ def test_3d_images():
def test_joblib_cache():
- from sklearn.externals.joblib import hash
+ from nilearn._utils.compat import hash
# Dummy mask
mask = np.zeros((40, 40, 40))
mask[20, 20, 20] = 1
@@ -196,3 +196,37 @@ def test_dtype():
masked_img = masker.transform([[img]])
assert(masked_img[0].dtype == np.float32)
+
+
+def test_standardization():
+ data_shape = (9, 9, 5)
+ n_samples = 500
+
+ signals = np.random.randn(2, np.prod(data_shape), n_samples)
+ means = np.random.randn(2, np.prod(data_shape), 1) * 50 + 1000
+ signals += means
+
+ img1 = Nifti1Image(signals[0].reshape(data_shape + (n_samples,)),
+ np.eye(4))
+ img2 = Nifti1Image(signals[1].reshape(data_shape + (n_samples,)),
+ np.eye(4))
+
+ mask = Nifti1Image(np.ones(data_shape), np.eye(4))
+
+ # z-score
+ masker = MultiNiftiMasker(mask, standardize='zscore')
+ trans_signals = masker.fit_transform([img1, img2])
+
+ for ts in trans_signals:
+ np.testing.assert_almost_equal(ts.mean(0), 0)
+ np.testing.assert_almost_equal(ts.std(0), 1)
+
+ # psc
+ masker = MultiNiftiMasker(mask, standardize='psc')
+ trans_signals = masker.fit_transform([img1, img2])
+
+ for ts, s in zip(trans_signals, signals):
+ np.testing.assert_almost_equal(ts.mean(0), 0)
+ np.testing.assert_almost_equal(ts,
+ (s / s.mean(1)[:, np.newaxis] *
+ 100 - 100).T)
diff --git a/nilearn/input_data/tests/test_nifti_labels_masker.py b/nilearn/input_data/tests/test_nifti_labels_masker.py
index 18e7148e3b..fe936028a6 100644
--- a/nilearn/input_data/tests/test_nifti_labels_masker.py
+++ b/nilearn/input_data/tests/test_nifti_labels_masker.py
@@ -266,3 +266,38 @@ def test_nifti_labels_masker_resampling():
compressed_img2 = masker.inverse_transform(transformed2)
np.testing.assert_array_equal(compressed_img.get_data(),
compressed_img2.get_data())
+
+
+def test_standardization():
+ data_shape = (9, 9, 5)
+ n_samples = 500
+
+ signals = np.random.randn(np.prod(data_shape), n_samples)
+ means = np.random.randn(np.prod(data_shape), 1) * 50 + 1000
+ signals += means
+ img = nibabel.Nifti1Image(
+ signals.reshape(data_shape + (n_samples,)), np.eye(4)
+ )
+
+ labels = data_gen.generate_labeled_regions((9, 9, 5), 10)
+
+ # Unstandarized
+ masker = NiftiLabelsMasker(labels, standardize=False)
+ unstandarized_label_signals = masker.fit_transform(img)
+
+ # z-score
+ masker = NiftiLabelsMasker(labels, standardize='zscore')
+ trans_signals = masker.fit_transform(img)
+
+ np.testing.assert_almost_equal(trans_signals.mean(0), 0)
+ np.testing.assert_almost_equal(trans_signals.std(0), 1)
+
+ # psc
+ masker = NiftiLabelsMasker(labels, standardize='psc')
+ trans_signals = masker.fit_transform(img)
+
+ np.testing.assert_almost_equal(trans_signals.mean(0), 0)
+ np.testing.assert_almost_equal(trans_signals,
+ (unstandarized_label_signals /
+ unstandarized_label_signals.mean(0) *
+ 100 - 100))
diff --git a/nilearn/input_data/tests/test_nifti_maps_masker.py b/nilearn/input_data/tests/test_nifti_maps_masker.py
index 82c09b9b76..e931220999 100644
--- a/nilearn/input_data/tests/test_nifti_maps_masker.py
+++ b/nilearn/input_data/tests/test_nifti_maps_masker.py
@@ -298,3 +298,39 @@ def test_nifti_maps_masker_overlap():
allow_overlap=False)
assert_raises_regex(ValueError, 'Overlap detected',
non_overlapping_masker.fit_transform, fmri_img)
+
+
+def test_standardization():
+ data_shape = (9, 9, 5)
+ n_samples = 500
+
+ signals = np.random.randn(np.prod(data_shape), n_samples)
+ means = np.random.randn(np.prod(data_shape), 1) * 50 + 1000
+ signals += means
+ img = nibabel.Nifti1Image(signals.reshape(data_shape + (n_samples,)),
+ np.eye(4))
+
+ maps, _ = data_gen.generate_maps((9, 9, 5), 10)
+
+ # Unstandarized
+ masker = NiftiMapsMasker(maps, standardize=False)
+ unstandarized_label_signals = masker.fit_transform(img)
+
+ # z-score
+ masker = NiftiMapsMasker(maps,
+ standardize='zscore')
+ trans_signals = masker.fit_transform(img)
+
+ np.testing.assert_almost_equal(trans_signals.mean(0), 0)
+ np.testing.assert_almost_equal(trans_signals.std(0), 1)
+
+ # psc
+ masker = NiftiMapsMasker(maps, standardize='psc')
+ trans_signals = masker.fit_transform(img)
+
+ np.testing.assert_almost_equal(trans_signals.mean(0), 0)
+ np.testing.assert_almost_equal(
+ trans_signals,
+ unstandarized_label_signals /
+ unstandarized_label_signals.mean(0) * 100 - 100,
+ )
diff --git a/nilearn/input_data/tests/test_nifti_masker.py b/nilearn/input_data/tests/test_nifti_masker.py
index 4c857ff820..94eb09aa54 100644
--- a/nilearn/input_data/tests/test_nifti_masker.py
+++ b/nilearn/input_data/tests/test_nifti_masker.py
@@ -233,7 +233,7 @@ def test_sessions():
def test_joblib_cache():
- from sklearn.externals.joblib import hash, Memory
+ from nilearn._utils.compat import hash, Memory
mask = np.zeros((40, 40, 40))
mask[20, 20, 20] = 1
mask_img = Nifti1Image(mask, np.eye(4))
@@ -390,3 +390,31 @@ def test_dtype():
masker_2 = NiftiMasker(dtype='float64')
assert(masker_2.fit_transform(img_32).dtype == np.float64)
assert(masker_2.fit_transform(img_64).dtype == np.float64)
+
+
+def test_standardization():
+ data_shape = (9, 9, 5)
+ n_samples = 500
+
+ signals = np.random.randn(np.prod(data_shape), n_samples)
+ means = np.random.randn(np.prod(data_shape), 1) * 50 + 1000
+ signals += means
+ img = Nifti1Image(signals.reshape(data_shape + (n_samples,)), np.eye(4))
+
+ mask = Nifti1Image(np.ones(data_shape), np.eye(4))
+
+ # z-score
+ masker = NiftiMasker(mask, standardize='zscore')
+ trans_signals = masker.fit_transform(img)
+
+ np.testing.assert_almost_equal(trans_signals.mean(0), 0)
+ np.testing.assert_almost_equal(trans_signals.std(0), 1)
+
+ # psc
+ masker = NiftiMasker(mask, standardize='psc')
+ trans_signals = masker.fit_transform(img)
+
+ np.testing.assert_almost_equal(trans_signals.mean(0), 0)
+ np.testing.assert_almost_equal(trans_signals,
+ (signals / signals.mean(1)[:, np.newaxis] *
+ 100 - 100).T)
diff --git a/nilearn/input_data/tests/test_nifti_spheres_masker.py b/nilearn/input_data/tests/test_nifti_spheres_masker.py
index 21d1c2cef7..0df537ec1c 100644
--- a/nilearn/input_data/tests/test_nifti_spheres_masker.py
+++ b/nilearn/input_data/tests/test_nifti_spheres_masker.py
@@ -5,6 +5,7 @@
from nilearn._utils.testing import assert_raises_regex
from nose.tools import assert_false
+
def test_seed_extraction():
data = np.random.random((3, 3, 3, 5))
img = nibabel.Nifti1Image(data, np.eye(4))
@@ -62,6 +63,7 @@ def test_anisotropic_sphere_extraction():
affine_2[0, 0] = 4
mask_img = nibabel.Nifti1Image(mask_img, affine=affine_2)
masker = NiftiSpheresMasker([(2, 1, 2)], radius=1, mask_img=mask_img)
+
masker.fit()
s = masker.transform(img)
assert_array_equal(s[:, 0], data[1, 0, 1])
@@ -151,3 +153,26 @@ def test_is_nifti_spheres_masker_give_nans():
# When mask_img is provided, the seed interacts within the brain, so no nan
masker = NiftiSpheresMasker(seeds=seed, radius=2., mask_img=mask_img)
assert_false(np.isnan(np.sum(masker.fit_transform(img))))
+
+
+def test_standardization():
+ data = np.random.random((3, 3, 3, 5))
+ img = nibabel.Nifti1Image(data, np.eye(4))
+
+ # test zscore
+ masker = NiftiSpheresMasker([(1, 1, 1)], standardize='zscore')
+ # Test the fit
+ s = masker.fit_transform(img)
+
+ np.testing.assert_almost_equal(s.mean(), 0)
+ np.testing.assert_almost_equal(s.std(), 1)
+
+ # test psc
+ masker = NiftiSpheresMasker([(1, 1, 1)], standardize='psc')
+ # Test the fit
+ s = masker.fit_transform(img)
+
+ np.testing.assert_almost_equal(s.mean(), 0)
+ np.testing.assert_almost_equal(s.ravel(), data[1, 1, 1] /
+ data[1, 1, 1].mean() * 100 - 100,
+ )
diff --git a/nilearn/masking.py b/nilearn/masking.py
index 711d005f36..6708151537 100644
--- a/nilearn/masking.py
+++ b/nilearn/masking.py
@@ -8,7 +8,7 @@
import numpy as np
from scipy import ndimage
-from sklearn.externals.joblib import Parallel, delayed
+from nilearn._utils.compat import Parallel, delayed
from . import _utils
from .image import new_img_like
@@ -52,7 +52,7 @@ def _load_mask_img(mask_img, allow_empty=False):
'The mask is invalid as it is empty: it masks all data.')
elif len(values) == 2:
# If there are 2 different values, one of them must be 0 (background)
- if not 0 in values:
+ if 0 not in values:
raise ValueError('Background of the mask must be represented with'
'0. Given mask contains: %s.' % values)
elif len(values) != 2:
@@ -91,8 +91,8 @@ def _extrapolate_out_mask(data, mask, iterations=1):
extrapolation.append(masked_data[this_x, this_y, this_z])
extrapolation = np.array(extrapolation)
- extrapolation = (np.nansum(extrapolation, axis=0)
- / np.sum(np.isfinite(extrapolation), axis=0))
+ extrapolation = (np.nansum(extrapolation, axis=0) /
+ np.sum(np.isfinite(extrapolation), axis=0))
extrapolation[np.logical_not(np.isfinite(extrapolation))] = 0
new_data = np.zeros_like(masked_data)
new_data[outer_shell] = extrapolation
@@ -162,7 +162,8 @@ def intersect_masks(mask_imgs, threshold=0.5, connected=True):
if np.any(grp_mask > 0) and connected:
grp_mask = largest_connected_component(grp_mask)
grp_mask = _utils.as_ndarray(grp_mask, dtype=np.int8)
- return new_img_like(_utils.check_niimg_3d(mask_imgs[0]), grp_mask, ref_affine)
+ return new_img_like(_utils.check_niimg_3d(mask_imgs[0]), grp_mask,
+ ref_affine)
def _post_process_mask(mask, affine, opening=2, connected=True,
@@ -258,10 +259,10 @@ def compute_epi_mask(epi_img, lower_cutoff=0.2, upper_cutoff=0.85,
# Delayed import to avoid circular imports
from .image.image import _compute_mean
- mean_epi, affine = cache(_compute_mean, memory)(epi_img,
- target_affine=target_affine,
- target_shape=target_shape,
- smooth=(1 if opening else False))
+ mean_epi, affine = \
+ cache(_compute_mean, memory)(epi_img, target_affine=target_affine,
+ target_shape=target_shape,
+ smooth=(1 if opening else False))
if ensure_finite:
# Get rid of memmapping
@@ -278,13 +279,14 @@ def compute_epi_mask(epi_img, lower_cutoff=0.2, upper_cutoff=0.85,
delta = sorted_input[lower_cutoff + 1:upper_cutoff + 1] \
- sorted_input[lower_cutoff:upper_cutoff]
ia = delta.argmax()
- threshold = 0.5 * (sorted_input[ia + lower_cutoff]
- + sorted_input[ia + lower_cutoff + 1])
+ threshold = 0.5 * (sorted_input[ia + lower_cutoff] +
+ sorted_input[ia + lower_cutoff + 1])
mask = mean_epi >= threshold
mask, affine = _post_process_mask(mask, affine, opening=opening,
- connected=connected, warning_msg="Are you sure that input "
+ connected=connected,
+ warning_msg="Are you sure that input "
"data are EPI images not detrended. ")
return new_img_like(epi_img, mask, affine)
@@ -426,7 +428,8 @@ def compute_background_mask(data_imgs, border_size=2,
# Delayed import to avoid circular imports
from .image.image import _compute_mean
data, affine = cache(_compute_mean, memory)(data_imgs,
- target_affine=target_affine, target_shape=target_shape,
+ target_affine=target_affine,
+ target_shape=target_shape,
smooth=False)
background = np.median(get_border_data(data, border_size))
@@ -438,7 +441,8 @@ def compute_background_mask(data_imgs, border_size=2,
mask = data != background
mask, affine = _post_process_mask(mask, affine, opening=opening,
- connected=connected, warning_msg="Are you sure that input "
+ connected=connected,
+ warning_msg="Are you sure that input "
"images have a homogeneous background.")
return new_img_like(data_imgs, mask, affine)
@@ -866,9 +870,9 @@ def unmask(X, mask_img, order="F"):
def _unmask_from_to_3d_array(w, mask):
- """Unmask an image into whole brain, with off-mask voxels set to 0. Used as
- a stand-alone function in low-level decoding (SpaceNet) and clustering (ReNA)
- functions.
+ """Unmask an image into whole brain, with off-mask voxels set to 0.
+ Used as a stand-alone function in low-level decoding (SpaceNet) and
+ clustering (ReNA) functions.
Parameters
----------
diff --git a/nilearn/mass_univariate/permuted_least_squares.py b/nilearn/mass_univariate/permuted_least_squares.py
index bf4e616bf6..91d3dda2bf 100644
--- a/nilearn/mass_univariate/permuted_least_squares.py
+++ b/nilearn/mass_univariate/permuted_least_squares.py
@@ -8,7 +8,7 @@
import numpy as np
from scipy import linalg
from sklearn.utils import check_random_state
-import sklearn.externals.joblib as joblib
+from nilearn._utils.compat import joblib
def normalize_matrix_on_axis(m, axis=0):
@@ -34,11 +34,11 @@ def normalize_matrix_on_axis(m, axis=0):
... normalize_matrix_on_axis)
>>> X = np.array([[0, 4], [1, 0]])
>>> normalize_matrix_on_axis(X)
- array([[ 0., 1.],
- [ 1., 0.]])
+ array([[0., 1.],
+ [1., 0.]])
>>> normalize_matrix_on_axis(X, axis=1)
- array([[ 0., 1.],
- [ 1., 0.]])
+ array([[0., 1.],
+ [1., 0.]])
"""
if m.ndim > 2:
diff --git a/nilearn/plotting/__init__.py b/nilearn/plotting/__init__.py
index 9db06858cd..9fab7632d0 100644
--- a/nilearn/plotting/__init__.py
+++ b/nilearn/plotting/__init__.py
@@ -53,5 +53,5 @@ def _set_mpl_backend():
'show', 'plot_matrix', 'view_surf', 'view_img_on_surf',
'view_img', 'view_connectome', 'view_markers',
'find_parcellation_cut_coords', 'find_probabilistic_atlas_cut_coords',
- 'plot_surf', 'plot_surf_stat_map', 'plot_surf_roi',
+ 'plot_surf', 'plot_surf_stat_map', 'plot_surf_roi'
]
diff --git a/nilearn/plotting/cm.py b/nilearn/plotting/cm.py
index 1c989f4caa..9ee553e664 100644
--- a/nilearn/plotting/cm.py
+++ b/nilearn/plotting/cm.py
@@ -38,16 +38,16 @@ def _pigtailed_cmap(cmap, swap_order=('green', 'red', 'blue')):
orig_cdict = cmap._segmentdata.copy()
cdict = dict()
- cdict['green'] = [(0.5*(1-p), c1, c2)
- for (p, c1, c2) in reversed(orig_cdict[swap_order[0]])]
- cdict['blue'] = [(0.5*(1-p), c1, c2)
- for (p, c1, c2) in reversed(orig_cdict[swap_order[1]])]
- cdict['red'] = [(0.5*(1-p), c1, c2)
- for (p, c1, c2) in reversed(orig_cdict[swap_order[2]])]
+ cdict['green'] = [(0.5 * (1 - p), c1, c2)
+ for (p, c1, c2) in reversed(orig_cdict[swap_order[0]])]
+ cdict['blue'] = [(0.5 * (1 - p), c1, c2)
+ for (p, c1, c2) in reversed(orig_cdict[swap_order[1]])]
+ cdict['red'] = [(0.5 * (1 - p), c1, c2)
+ for (p, c1, c2) in reversed(orig_cdict[swap_order[2]])]
for color in ('red', 'green', 'blue'):
- cdict[color].extend([(0.5*(1+p), c1, c2)
- for (p, c1, c2) in orig_cdict[color]])
+ cdict[color].extend([(0.5 * (1 + p), c1, c2)
+ for (p, c1, c2) in orig_cdict[color]])
return cdict
@@ -149,19 +149,28 @@ def alpha_cmap(color, name='', alpha_min=0.5, alpha_max=1.):
_cmaps_data['bwr'] = _colors.LinearSegmentedColormap.from_list(
'bwr', _bwr_data)._segmentdata.copy()
+
################################################################################
# Build colormaps and their reverse.
+
+# backported and adapted from matplotlib since it's deprecated in 3.2
+def _revcmap(data):
+ data_r = {}
+ for key, val in data.items():
+ data_r[key] = [(1.0 - x, y1, y0) for x, y0, y1 in reversed(val)]
+ return data_r
+
+
_cmap_d = dict()
for _cmapname in list(_cmaps_data.keys()): # needed as dict changes within loop
_cmapname_r = _cmapname + '_r'
_cmapspec = _cmaps_data[_cmapname]
- _cmaps_data[_cmapname_r] = _cm.revcmap(_cmapspec)
+ _cmaps_data[_cmapname_r] = _revcmap(_cmapspec)
_cmap_d[_cmapname] = _colors.LinearSegmentedColormap(
- _cmapname, _cmapspec, _cm.LUTSIZE)
+ _cmapname, _cmapspec, _cm.LUTSIZE)
_cmap_d[_cmapname_r] = _colors.LinearSegmentedColormap(
- _cmapname_r, _cmaps_data[_cmapname_r],
- _cm.LUTSIZE)
+ _cmapname_r, _cmaps_data[_cmapname_r], _cm.LUTSIZE)
################################################################################
# A few transparent colormaps
@@ -212,13 +221,15 @@ def alpha_cmap(color, name='', alpha_min=0.5, alpha_max=1.):
def dim_cmap(cmap, factor=.3, to_white=True):
""" Dim a colormap to white, or to black.
"""
- assert factor >= 0 and factor <=1, ValueError(
- 'Dimming factor must be larger than 0 and smaller than 1, %s was passed.'
- % factor)
+ assert 0 <= factor <= 1, ValueError(
+ 'Dimming factor must be larger than 0 and smaller than 1, %s was '
+ 'passed.' % factor)
if to_white:
- dimmer = lambda c: 1 - factor*(1-c)
+ def dimmer(c):
+ return 1 - factor * (1 - c)
else:
- dimmer = lambda c: factor*c
+ def dimmer(c):
+ return factor * c
cdict = cmap._segmentdata.copy()
for c_index, color in enumerate(('red', 'green', 'blue')):
color_lst = list()
@@ -227,19 +238,17 @@ def dim_cmap(cmap, factor=.3, to_white=True):
cdict[color] = color_lst
return _colors.LinearSegmentedColormap(
- '%s_dimmed' % cmap.name,
- cdict,
- _cm.LUTSIZE)
+ '%s_dimmed' % cmap.name, cdict, _cm.LUTSIZE)
def replace_inside(outer_cmap, inner_cmap, vmin, vmax):
""" Replace a colormap by another inside a pair of values.
"""
assert vmin < vmax, ValueError('vmin must be smaller than vmax')
- assert vmin >= 0, ValueError('vmin must be larger than 0, %s was passed.'
- % vmin)
- assert vmax <= 1, ValueError('vmax must be smaller than 1, %s was passed.'
- % vmax)
+ assert vmin >= 0, ValueError('vmin must be larger than 0, %s was passed.'
+ % vmin)
+ assert vmax <= 1, ValueError('vmax must be smaller than 1, %s was passed.'
+ % vmax)
outer_cdict = outer_cmap._segmentdata.copy()
inner_cdict = inner_cmap._segmentdata.copy()
@@ -257,7 +266,6 @@ def replace_inside(outer_cmap, inner_cmap, vmin, vmax):
this_cdict['green'].append((p, g, g))
this_cdict['blue'].append((p, b, b))
-
for c_index, color in enumerate(('red', 'green', 'blue')):
color_lst = list()
@@ -266,8 +274,9 @@ def replace_inside(outer_cmap, inner_cmap, vmin, vmax):
break
color_lst.append((value, c1, c2))
- color_lst.append((vmin, outer_cmap(vmin)[c_index],
- inner_cmap(vmin)[c_index]))
+ color_lst.append((vmin,
+ outer_cmap(vmin)[c_index],
+ inner_cmap(vmin)[c_index]))
for value, c1, c2 in inner_cdict[color]:
if value <= vmin:
@@ -276,8 +285,9 @@ def replace_inside(outer_cmap, inner_cmap, vmin, vmax):
break
color_lst.append((value, c1, c2))
- color_lst.append((vmax, inner_cmap(vmax)[c_index],
- outer_cmap(vmax)[c_index]))
+ color_lst.append((vmax,
+ inner_cmap(vmax)[c_index],
+ outer_cmap(vmax)[c_index]))
for value, c1, c2 in outer_cdict[color]:
if value <= vmax:
@@ -287,8 +297,5 @@ def replace_inside(outer_cmap, inner_cmap, vmin, vmax):
cdict[color] = color_lst
return _colors.LinearSegmentedColormap(
- '%s_inside_%s' % (inner_cmap.name, outer_cmap.name),
- cdict,
- _cm.LUTSIZE)
-
-
+ '%s_inside_%s' % (inner_cmap.name, outer_cmap.name),
+ cdict, _cm.LUTSIZE)
diff --git a/nilearn/plotting/data/html/connectome_plot_template.html b/nilearn/plotting/data/html/connectome_plot_template.html
index 92a72a5ddc..11e592a173 100644
--- a/nilearn/plotting/data/html/connectome_plot_template.html
+++ b/nilearn/plotting/data/html/connectome_plot_template.html
@@ -3,7 +3,8 @@
connectome plot
- $INSERT_JS_LIBRARIES_HERE
+
+ $INSERT_JS_LIBRARIES_HERE
-
diff --git a/nilearn/plotting/data/html/surface_plot_template.html b/nilearn/plotting/data/html/surface_plot_template.html
index ef3d1487f4..ba56e986fe 100644
--- a/nilearn/plotting/data/html/surface_plot_template.html
+++ b/nilearn/plotting/data/html/surface_plot_template.html
@@ -9,6 +9,7 @@