Added resampling to NiftiMapsMasker

Resampling is performed on maps or mask and images, depending on the "target"
parameter, which gives the image whose shape/affine is the reference. Two
targets have been implemented so far: "maps" and "mask".

Tests have been written, they only check for correct shape/affine, not for
the content.
Renamed "smooth" parameters to "fwhm"

Everywhere "smooth" was used as a parameter, is was renamed in "fwhm", to
be consistent with image.smooth().
Add an example using MSDL atlas

plot_msdl_covariance_advanced.py demonstrates usage of an atlas without
the same shape/affine as the data. Two different methods are implemented
(with/without NiftiMapsMasker).
"smooth" renamed in "smoothing_fwhm"

The "smooth" parameter has been renamed in "smoothing_fwhm" for clarity.
The original name has been kept in _smooth_array() and image.smooth().

Some minor fixes have been performed as well.
Renamed "target" into "resampling_target"
Simplified examples.
Added resampling to NiftiLabelsMasker

resampling_target parameter has been added to NiftiLabelsMasker. Only one
target has been implemented: labels, because resampling labels is considered
dangerous right now.

Corrected a bug in NiftiMapsMasker: the interpolation method used for "maps"
and "mask" targets were swapped.
Renamed example, remove duplicate.
Changed output of region signal extracting functions

img_to_signals_labels() and img_to_signals_maps() now always return a
number of signals equal to the number of regions passed as parameter. In
the previous state, only regions not fully outside the mask were
considered, the number of output signals thus could vary. This is not
the case anymore.

Tests have been adapted.
Added user messages to Nifti*Masker

Renamed plot_msdl_covariance.py co plot_adhd_covariance.py.
Documentation fixes
Changed default value for resampling_target