--- a/pylearn/io/image_tiling.py	Wed Mar 16 20:20:02 2011 -0400
+++ b/pylearn/io/image_tiling.py	Thu Mar 31 18:29:11 2011 -0400
@@ -11,9 +11,9 @@
     ndar *= 1.0 / max(ndar.max(),eps)
     return ndar
 
-def tile_raster_images(X, img_shape, 
+def tile_raster_images(X, img_shape,
         tile_shape=None, tile_spacing=(1,1),
-        scale_rows_to_unit_interval=True, 
+        scale_rows_to_unit_interval=True,
         output_pixel_vals=True,
         min_dynamic_range=1e-4,
         ):
@@ -40,7 +40,19 @@
     :rtype: a 2-d array with same dtype as X.
 
     """
-    if isinstance(X, tuple): 
+    if len(img_shape)==3 and img_shape[2]==3:
+        # make this save an rgb image
+
+        return tile_raster_images(
+                (X[:,0::3], X[:,1::3], X[:,2::3], None),
+                img_shape=img_shape[:2],
+                tile_shape=tile_shape,
+                tile_spacing=tile_spacing,
+                scale_rows_to_unit_interval=scale_rows_to_unit_interval,
+                output_pixel_vals=output_pixel_vals,
+                min_dynamic_range=min_dynamic_range)
+
+    if isinstance(X, tuple):
         n_images_in_x = X[0].shape[0]
     else:
         n_images_in_x = X.shape[0]

--- a/pylearn/preprocessing/pca.py	Wed Mar 16 20:20:02 2011 -0400
+++ b/pylearn/preprocessing/pca.py	Thu Mar 31 18:29:11 2011 -0400
@@ -72,7 +72,8 @@
     return w,v
 
 
-def pca_from_examples(X, max_components=None, max_energy_fraction=None, x_centered=False):
+def pca_from_examples(X, max_components=None, max_energy_fraction=None,
+        x_centered=False, inplace=False):
     """Return ((eigvals, eigvecs), centered_X) of observations `X` (1-per-row)
 
     This function exists to wrap several algorithms for getting the principle components.
@@ -90,10 +91,11 @@
     :returns: ((eigvals, eigvecs), centered_X) of PCA decomposition
 
     """
-    if x_centered:
-        centered_X = X
-    else:
-        centered_X = X - numpy.mean(X, axis=0)
+    if not inplace:
+        X = X.copy()
+    centered_X = X
+    if not x_centered:
+        centered_X -= numpy.mean(centered_X, axis=0)
     cov_X = numpy.dot(centered_X.T, centered_X) / (len(X)- 1)
     evals, evecs = pca_from_cov(cov_X, max_components=max_components,
             max_energy_fraction=max_energy_fraction)