MRG+1: allowing transparency in divergent colormaps

examples/plot_meg_inverse_solution.py

@@ -23,7 +23,7 @@
 create Brain object for visualization
 """
 brain = Brain(subject_id, hemi, surf, size=(400, 400),
-              interaction='terrain')
+              interaction='terrain', cortex='bone')
 
 """
 label for time annotation in milliseconds

examples/plot_resting_correlations.py

@@ -45,15 +45,27 @@
 """
 We want to use an appropriate color map for these data: a divergent map that
 is centered on 0, which is a meaningful transition-point as it marks the change
-from negative correlations to positive correlations.
+from negative correlations to positive correlations. By providing the 'center'
+argument the add_data function automatically chooses a divergent colormap.
+"""
+brain.add_data(surf_data_lh, 0, .7, center=0, hemi='lh')
+brain.add_data(surf_data_rh, 0, .7, center=0, hemi='rh')
 
-We'll also plot the map with some transparency so that we can see through to
-the underlying anatomy.
 """
-brain.add_data(surf_data_lh, -.7, .7, colormap="vlag", alpha=.75,
-               hemi='lh')
-brain.add_data(surf_data_rh, -.7, .7, colormap="vlag", alpha=.75,
-               hemi='rh')
+You can tune the data display by shifting the colormap around interesting
+regions. For example, you can ignore small correlation up to a magnitude of 0.2
+and let colors become gradually less transparent from 0.2 to 0.5 by re-scaling
+the colormap as follows. For more information see the help string of this
+function.
+"""
+brain.scale_data_colormap(.2, .5, .7, transparent=True, center=0)
+
+"""
+You can also set the overall opacity of the displayed data while maintaining
+the transparency of the small values.
+"""
+brain.scale_data_colormap(0, .35, .7, transparent=True, center=0,
+                          alpha=0.75)
 
 """
 This overlay represents resting-state correlations with a

surfer/utils.py

@@ -464,7 +464,7 @@ def mesh_edges(faces):
     return edges
 
 
-def create_color_lut(cmap, n_colors=256):
+def create_color_lut(cmap, n_colors=256, center=None):
     """Return a colormap suitable for setting as a Mayavi LUT.
 
     Parameters
@@ -473,9 +473,16 @@ def create_color_lut(cmap, n_colors=256):
         Input colormap definition. This can be the name of a matplotlib
         colormap, a list of valid matplotlib colors, or a suitable
         mayavi LUT (possibly missing the alpha channel).
+
+        if value is "auto", a default sequential or divergent colormap is
+        returned
     n_colors : int, optional
         Number of colors in the resulting LUT. This is ignored if cmap
         is a 2d array.
+    center : double, optional
+        indicates whether desired colormap should be for divergent values,
+        currently only used to select default colormap for cmap='auto'
+
     Returns
     -------
     lut : n_colors x 4 integer array
@@ -493,6 +500,14 @@ def create_color_lut(cmap, n_colors=256):
 
             return lut
 
+    # choose default colormaps (REMEMBER to change doc, e.g., in
+    # Brain.add_data, when changing these defaults)
+    if isinstance(cmap, string_types) and cmap == "auto":
+        if center is None:
+            cmap = "rocket"
+        else:
+            cmap = "icefire"
+
     surfer_cmaps = ["rocket", "mako", "icefire", "vlag"]
     surfer_cmaps += [name + "_r" for name in surfer_cmaps]