Multivariate Pattern Analysis in Python

# mvpa2.misc.plot.lightbox.plot_lightbox

`mvpa2.misc.plot.lightbox.``plot_lightbox`(background=None, background_mask=None, cmap_bg='gray', overlay=None, overlay_mask=None, cmap_overlay='autumn', vlim=(0.0, None), vlim_type=None, do_stretch_colors=False, add_info=True, add_hist=True, add_colorbar=True, fig=None, interactive=None, nrows=None, ncolumns=None, slices=None, slice_title='k=%(islice)s')

  Very basic plotting of 3D data with interactive thresholding.

  `background`/`overlay` and corresponding masks could be nifti files names or `SpatialImage` objects, or 3D `ndarrays`. If no mask is provided, only non-0 elements are plotted.

  Parameters:

  do_stretch_colors : bool, optional

  > Stratch color range to the data (not just to visible data)

  vlim : tuple, optional

  > 2 element tuple of low/upper bounds of values to plot

  vlim_type : None or ‘symneg_z’

  > If not None, then vlim would be treated accordingly:
  >   symneg_z

  >     z-score values of symmetric normal around 0, estimated by symmetrizing negative part of the distribution, which often could be assumed when total distribution is a mixture of by-chance performance normal around 0, and some other in the positive tail

  ncolumns : int or None

  > Explicit starting number of columns into which position the slice renderings. If None, square arrangement would be used

  nrows : int or None

  > Explicit starting number of rows into which position the slice renderings. If None, square arrangement would be used

  add_hist : bool or tuple (int, int)

  > If True, add histogram and position automagically. If a tuple – use as (row, column)

  add_info : bool or tuple (int, int)

  > If True, add information and position automagically. If a tuple – use as (row, column).

  slices : None or list of int

  > If not to plot whole volume, what slices to plot.

  slice_title : None or str

  > Desired title of slices. Use string comprehension and assume `islice` variable present with current slice index.

  Available colormaps are presented nicely on

  > http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps

  TODO:

  > - Make interface more attractive/usable
  > - allow multiple overlays… or just unify for them all to be just a list of entries
  > - handle cases properly when there is only one - background/overlay

  Notes

  No care is taken to deduce the orientation (e.g. Left-to-Right, Posterior-to-Anterior) of fMRI volumes. Therefore all input volumes should be in the same orientation.