Merge branch 'master' of github.com:nilearn/nilearn into immigrate-ni…

…stats

* 'master' of github.com:nilearn/nilearn:
  Expose bg_img, vmin and vmax in plot_img signature (nilearn#2157)
  Replac conda with pip in TravisCI setup (nilearn#2141)
  Core devs doc and add @emdupre (nilearn#2151)
  Add check for vmin, vmax in plot_surf_roi (nilearn#2052)

.travis.yml

@@ -2,47 +2,54 @@ sudo: required
 dist: xenial
 
 language: python
-python: "3.5"
-
-virtualenv:
-  system_site_packages: true
 
 env:
   global:
-    - TEST_RUN_FOLDER="/tmp" # folder where the tests are run from
+    - TEST_RUN_FOLDER="/tmp"
 
 matrix:
   # Do not wait for the allowed_failures entry to finish before
   # setting the status
   fast_finish: true
   allow_failures:
-    # allow_failures seems to be keyed on the python version.
-    - python: 3.5
+    # allow_failures keyed to skipping tests.
+    - env: SKIP_TESTS="true"
   include:
     # without matplotlib
-    - env: DISTRIB="conda" PYTHON_VERSION="3.5"
+    - name: "Python 3.5 minimum package versions"
+      python: "3.5"
+      env: DISTRIB="travisci" PYTHON_VERSION="3.5"
            NUMPY_VERSION="1.11" SCIPY_VERSION="0.19" PANDAS_VERSION="*"
-           SCIKIT_LEARN_VERSION="0.19" COVERAGE="true" JOBLIB_VERSION="*"
+           SCIKIT_LEARN_VERSION="0.19" COVERAGE="true" JOBLIB_VERSION="0.11"
            LXML_VERSION="*"
-    - env: DISTRIB="conda" PYTHON_VERSION="3.5"
+    - name: "Python 3.5 latest package versions"
+      python: "3.5"
+      env: DISTRIB="travisci" PYTHON_VERSION="3.5"
            NUMPY_VERSION="*" SCIPY_VERSION="*" PANDAS_VERSION="*"
            SCIKIT_LEARN_VERSION="*" MATPLOTLIB_VERSION="*" COVERAGE="true"
            JOBLIB_VERSION="0.11"
            LXML_VERSION="*"
-    - env: DISTRIB="conda" PYTHON_VERSION="3.6"
+    - name: "Python 3.6 latest package versions"
+      python: "3.6"
+      env: DISTRIB="travisci" PYTHON_VERSION="3.6"
            NUMPY_VERSION="*" SCIPY_VERSION="*" PANDAS_VERSION="*"
            SCIKIT_LEARN_VERSION="*" MATPLOTLIB_VERSION="*" COVERAGE="true"
-           JOBLIB_VERSION="*"
-           LXML_VERSION="*"
-    - env: DISTRIB="conda" PYTHON_VERSION="3.7"
+           JOBLIB_VERSION="0.12" LXML_VERSION="*"
+      # joblib.Memory switches from keyword cachedir to location in version 0.12
+      # Making sure we get the deprecation warning.
+
+    - name: "Python 3.7 latest package versions"
+      python: "3.7"
+      env: DISTRIB="travisci" PYTHON_VERSION="3.7"
            NUMPY_VERSION="*" SCIPY_VERSION="*" PANDAS_VERSION="*"
            SCIKIT_LEARN_VERSION="*" MATPLOTLIB_VERSION="*" COVERAGE="true"
-           JOBLIB_VERSION="0.12" LXML_VERSION="*"
+           JOBLIB_VERSION="*" LXML_VERSION="*"
 
   # FLAKE8 linting on diff wrt common ancestor with upstream/master
     # Note: the python value is only there to trigger allow_failures
-    - python: 3.5
-      env: DISTRIB="conda" PYTHON_VERSION="3.5" FLAKE8_VERSION="*" SKIP_TESTS="true"
+    - name: Python 3.5 Flake8 no tests
+      python: 3.5
+      env: DISTRIB="travisci" PYTHON_VERSION="3.5" FLAKE8_VERSION="*" SKIP_TESTS="true"
 
 install: source continuous_integration/install.sh
 

AUTHORS.rst

@@ -3,38 +3,36 @@
 People
 ------
 
-This work is made available by a community of people, amongst which
+This work is made available by a community of people, which
+originated from
 the `INRIA Parietal Project Team <https://team.inria.fr/parietal/>`_
-and the `scikit-learn <http://scikit-learn.org/>`_ folks, in
-particular:
-
-* Alexandre Abraham
-* `Alexandre Gramfort <http://alexandre.gramfort.net>`_
-* Vincent Michel
-* Bertrand Thirion
-* `Fabian Pedregosa <http://fa.bianp.net/>`_
-* `Gael Varoquaux <http://gael-varoquaux.info/>`_
-* Philippe Gervais
-* Michael Eickenberg
-* Danilo Bzdok
-* Loïc Estève
-* Kamalakar Reddy Daddy
-* Elvis Dohmatob
-* Alexandre Abadie
-* Andres Hoyos Idrobo
-* Salma Bougacha
-* Mehdi Rahim
-* Sylvain Lanuzel
-* `Kshitij Chawla <https://github.com/kchawla-pi>`_
-
-Many of also contributed outside of Parietal, notably:
-
-* `Chris Filo Gorgolewski <http://multiplecomparisons.blogspot.fr/>`_
-* `Ben Cipollini <http://cseweb.ucsd.edu/~bcipolli/>`_
-* Julia Huntenburg
-* Martin Perez-Guevara
-
-Thanks to M. Hanke and Y. Halchenko for data and packaging.
+and the `scikit-learn <http://scikit-learn.org/>`_ but grew much further.
+
+An up-to-date list of contributors can be seen in on `gitub
+<https://github.com/nilearn/nilearn/graphs/contributors>`_
+
+Additional credit goes to M. Hanke and Y. Halchenko for data and packaging.
+
+.. _core_devs:
+
+Core developers
+.................
+
+The nilearn core developers are:
+
+* Alexandre Gramfort https://github.com/agramfort
+* Ben Cipollini https://github.com/bcipolli
+* Bertrand Thirion https://github.com/bthirion
+* Chris Gorgolewski https://github.com/chrisgorgo
+* Danilo Bzdok https://github.com/banilo
+* Elizabeth DuPre https://github.com/emdupre
+* Gael Varoquaux https://github.com/GaelVaroquaux
+* Jerome Dockes https://github.com/jeromedockes
+* Julia Huntenburg https://github.com/juhuntenburg
+* KamalakerDadi https://github.com/KamalakerDadi
+* Kshitij Chawla https://github.com/kchawla-pi
+* Medhi Rahim https://github.com/mrahim
+* Salma Bougacha https://github.com/salma1601
 
 Funding
 ........
@@ -45,7 +43,8 @@ Mehdi Rahim, Philippe Gervais where payed by the `NiConnect
 project, funded by the French `Investissement d'Avenir
 <http://www.gouvernement.fr/investissements-d-avenir-cgi>`_.
 
-NiLearn is also supported by `DigiCosme <https://digicosme.lri.fr>`_ |digicomse logo|
+NiLearn is also supported by `DigiCosme <https://digicosme.lri.fr>`_
+|digicosme logo| and `DataIA <https://dataia.eu/en>`_ |dataia_logo|.
 
 .. _citing:
 
@@ -74,6 +73,10 @@ See the scikit-learn documentation on `how to cite
 <http://scikit-learn.org/stable/about.html#citing-scikit-learn>`_.
 
 
-.. |digicomse logo| image:: logos/digi-saclay-logo-small.png
+.. |digicosme logo| image:: logos/digi-saclay-logo-small.png
+    :height: 25
+    :alt: DigiComse Logo
+
+.. |dataia_logo| image:: logos/dataia.png
     :height: 25
-    :alt: DigiComse Logo
+    :alt: DataIA Logo

continuous_integration/install.sh

@@ -25,8 +25,8 @@ create_new_venv() {
     pip install nose pytest
 }
 
-print_conda_requirements() {
-    # Echo a conda requirement string for example
+echo_requirements_string() {
+    # Echo a requirement string for example
     # "pip nose python='2.7.3 scikit-learn=*". It has a hardcoded
     # list of possible packages to install and looks at _VERSION
     # environment variables to know whether to install a given package and
@@ -35,8 +35,7 @@ print_conda_requirements() {
     #   - for scikit-learn, SCIKIT_LEARN_VERSION is used
     TO_INSTALL_ALWAYS="pip nose pytest"
     REQUIREMENTS="$TO_INSTALL_ALWAYS"
-    TO_INSTALL_MAYBE="python numpy scipy matplotlib scikit-learn pandas \
-flake8 lxml joblib"
+    TO_INSTALL_MAYBE="numpy scipy matplotlib scikit-learn pandas flake8 lxml joblib"
     for PACKAGE in $TO_INSTALL_MAYBE; do
         # Capitalize package name and add _VERSION
         PACKAGE_VERSION_VARNAME="${PACKAGE^^}_VERSION"
@@ -45,45 +44,25 @@ flake8 lxml joblib"
         # dereference $PACKAGE_VERSION_VARNAME to figure out the
         # version to install
         PACKAGE_VERSION="${!PACKAGE_VERSION_VARNAME}"
-        if [ -n "$PACKAGE_VERSION" ]; then
-            REQUIREMENTS="$REQUIREMENTS $PACKAGE=$PACKAGE_VERSION"
+        if [[ -n "$PACKAGE_VERSION" ]]; then
+            if [[ "$PACKAGE_VERSION" == "*" ]]; then
+                REQUIREMENTS="$REQUIREMENTS $PACKAGE"
+            else
+                REQUIREMENTS="$REQUIREMENTS $PACKAGE==$PACKAGE_VERSION"
+            fi
         fi
     done
     echo $REQUIREMENTS
 }
 
-create_new_conda_env() {
-    # Skip Travis related code on circle ci.
-    if [ -z $CIRCLECI ]; then
-        # Deactivate the travis-provided virtual environment and setup a
-        # conda-based environment instead
-        deactivate
-    fi
-
-    # Use the miniconda installer for faster download / install of conda
-    # itself
-    wget https://repo.continuum.io/miniconda/Miniconda3-4.6.14-Linux-x86_64.sh \
-        -O ~/miniconda.sh
-    chmod +x ~/miniconda.sh && ~/miniconda.sh -b
-    export PATH=$HOME/miniconda3/bin:$PATH
-    echo $PATH
-
-    # Configure the conda environment and put it in the path using the
-    # provided versions
-    REQUIREMENTS=$(print_conda_requirements)
-    echo "conda requirements string: $REQUIREMENTS"
-    conda create -n testenv --quiet --yes $REQUIREMENTS
-    source activate testenv
-    conda install pytest pytest-cov --yes
+create_new_travisci_env() {
+    REQUIREMENTS=$(echo_requirements_string)
+    pip install ${REQUIREMENTS}
+    pip install pytest pytest-cov
 
     if [[ "$INSTALL_MKL" == "true" ]]; then
         # Make sure that MKL is used
-        conda install --quiet --yes mkl
-    elif [[ -z $CIRCLECI ]]; then
-        # Travis doesn't use MKL but circle ci does for speeding up examples
-        # generation in the html documentation.
-        # Make sure that MKL is not used
-        conda remove --yes --features mkl || echo "MKL not installed"
+        pip install mkl
     fi
 }
 
@@ -93,14 +72,14 @@ if [[ "$DISTRIB" == "neurodebian" ]]; then
     bash <(wget -q -O- http://neuro.debian.net/_files/neurodebian-travis.sh)
     sudo apt-get install -qq python-scipy python-nose python-nibabel python-sklearn python-joblib
 
-elif [[ "$DISTRIB" == "conda" ]]; then
-    create_new_conda_env
+elif [[ "$DISTRIB" == "travisci" ]]; then
+    create_new_travisci_env
     pip install nose-timer
     # Note: nibabel is in setup.py install_requires so nibabel will
     # always be installed eventually. Defining NIBABEL_VERSION is only
     # useful if you happen to want a specific nibabel version rather
     # than the latest available one.
-    if [ -n "$NIBABEL_VERSION" ]; then
+    if [[ -n "$NIBABEL_VERSION" ]]; then
         pip install nibabel=="$NIBABEL_VERSION"
     fi
 

doc/development.rst

@@ -122,12 +122,9 @@ Decisions are made public, through discussion on issues and pull requests
 in Github.
 
 The decisions are made by the core-contributors, ie people with write
-access to the repository, as listed `here
-<https://github.com/orgs/nilearn/people>`__
+access to the repository, as listed :ref:`here <core_devs>`
 
 If there are open questions, final decisions are made by the Temporary
 Benevolent Dictator, currently Gaël Varoquaux.
 
-
-
 .. include:: ../CONTRIBUTING.rst

doc/whats_new.rst

@@ -51,6 +51,11 @@ Changes
   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
 -----
 
@@ -67,6 +72,7 @@ Fixes
 - :func:`nilearn.plotting.view_surf` now accepts surface data provided as a file
   path.
 - :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

nilearn/plotting/img_plotting.py

@@ -232,7 +232,8 @@ def _crop_colorbar(cbar, cbar_vmin, cbar_vmax):
 def plot_img(img, cut_coords=None, output_file=None, display_mode='ortho',
              figure=None, axes=None, title=None, threshold=None,
              annotate=True, draw_cross=True, black_bg=False, colorbar=False,
-             resampling_interpolation='continuous', **kwargs):
+             resampling_interpolation='continuous',
+             bg_img=None, vmin=None, vmax=None, **kwargs):
     """ Plot cuts of a given image (by default Frontal, Axial, and Lateral)
 
         Parameters
@@ -290,6 +291,14 @@ def plot_img(img, cut_coords=None, output_file=None, display_mode='ortho',
             space. Can be "continuous" (default) to use 3rd-order spline
             interpolation, or "nearest" to use nearest-neighbor mapping.
             "nearest" is faster but can be noisier in some cases.
+        bg_img : Niimg-like object, optional
+            See http://nilearn.github.io/manipulating_images/input_output.html
+            The background image that the ROI/mask will be plotted on top of.
+            If nothing is specified, no background image is plotted.
+        vmin : float, optional
+            lower bound of the colormap. If `None`, the min of the image is used.
+        vmax : float, optional
+            upper bound of the colormap. If `None`, the max of the image is used.
         kwargs: extra keyword arguments, optional
             Extra keyword arguments passed to matplotlib.pyplot.imshow
     """  # noqa: E501
@@ -300,7 +309,8 @@ def plot_img(img, cut_coords=None, output_file=None, display_mode='ortho',
         threshold=threshold, annotate=annotate,
         draw_cross=draw_cross,
         resampling_interpolation=resampling_interpolation,
-        black_bg=black_bg, colorbar=colorbar, **kwargs)
+        black_bg=black_bg, colorbar=colorbar,
+        bg_img=bg_img, vmin=vmin, vmax=vmax, **kwargs)
 
     return display
 

nilearn/plotting/surf_plotting.py

@@ -514,7 +514,10 @@ def plot_surf_roi(surf_mesh, roi_map, bg_map=None,
     # messages in case of wrong inputs
 
     roi = load_surf_data(roi_map)
-    vmin, vmax = np.min(roi), 1 + np.max(roi)
+    if vmin is None:
+        vmin = np.min(roi)
+    if vmax is None:
+        vmax = 1 + np.max(roi)
 
     mesh = load_surf_mesh(surf_mesh)
 

nilearn/plotting/tests/test_surf_plotting.py

@@ -162,6 +162,14 @@ def test_plot_surf_roi():
     # plot roi
     plot_surf_roi(mesh, roi_map=roi_map)
     plot_surf_roi(mesh, roi_map=roi_map, colorbar=True)
+    # change vmin, vmax
+    img = plot_surf_roi(mesh, roi_map=roi_map,
+						vmin=1.2, vmax=8.9, colorbar=True)
+    cbar = img.axes[-1]
+    cbar_vmin = float(cbar.get_yticklabels()[0].get_text())
+    cbar_vmax = float(cbar.get_yticklabels()[-1].get_text())
+    assert cbar_vmin == 1.2
+    assert cbar_vmax == 8.9
 
     # plot parcellation
     plot_surf_roi(mesh, roi_map=parcellation)