diff --git a/gallery/tutorials/configuration.py b/gallery/tutorials/configuration.py
index 89dde5ed97..819ff9b675 100644
--- a/gallery/tutorials/configuration.py
+++ b/gallery/tutorials/configuration.py
@@ -122,7 +122,7 @@
 
 # %%
 # Logging preferences
-# ---------
+# -------------------
 # The "Logging" section contains options for controlling the verbosity and destination of log messages generated by ASPIRE.
 #
 # By default, the ``log_dir`` is a directory called ``logs`` within the current working directory from which ASPIRE was invoked, but
@@ -162,7 +162,7 @@
 
 # %%
 # Advanced logging options
-# ---------
+# ------------------------
 # Users with sophisticated knowledge of Python's ``logging`` library are able to directly work with the ``logging.conf`` file stored in
 # ``src/aspire`` in the source.
 #
diff --git a/gallery/tutorials/pipeline_demo.py b/gallery/tutorials/pipeline_demo.py
index 3f38ae8597..31146317bb 100644
--- a/gallery/tutorials/pipeline_demo.py
+++ b/gallery/tutorials/pipeline_demo.py
@@ -141,11 +141,13 @@
 # We use ``RIRClass2D`` object to classify the images via the
 # rotationally invariant representation (RIR) algorithm. Class
 # selection is customizable. The classification module also includes a
-# set of protocols for selecting a set of images to be used for
-# classification.  Here we're using ``TopClassSelector``, which
-# selects the first ``n_classes`` images from the source.  In
-# practice, the selection is done by sorting class averages based on
-# some configurable notion of quality.
+# set of protocols for selecting a set of images to be used after
+# classification.  Here we're using the simplest
+# ``DebugClassAvgSource`` which internally uses the
+# ``TopClassSelector`` to select the first ``n_classes`` images from
+# the source.  In practice, the selection is done by sorting class
+# averages based on some configurable notion of quality (contrast,
+# neighbor distance etc).
 
 from aspire.classification import RIRClass2D
 
@@ -250,15 +252,13 @@
 # the z-axis.  These estimated projections should align with the
 # original projection images.
 
-from aspire.source import ArrayImageSource
-
-# Get projections from the estimated volume using the estimated
-# orientations.  We instantiate the projections as an
-# ``ArrayImageSource`` to access the ``Image.show()`` method.
-projections_est = ArrayImageSource(estimated_volume.project(oriented_src.rotations))
+# Get the first 10 projections from the estimated volume using the
+# estimated orientations.  Recall that ``project`` returns an
+# ``Image`` instance, which we can peek at with ``show``.
+projections_est = estimated_volume.project(oriented_src.rotations[0:10])
 
 # We view the first 10 projections of the estimated volume.
-projections_est.images[0:10].show()
+projections_est.show()
 
 # %%
 
diff --git a/gallery/tutorials/tutorials/ctf.py b/gallery/tutorials/tutorials/ctf.py
index b26bc637c2..a06ce51d87 100644
--- a/gallery/tutorials/tutorials/ctf.py
+++ b/gallery/tutorials/tutorials/ctf.py
@@ -206,7 +206,7 @@ def generate_example_image(L, noise_variance=0.1):
 
 # %%
 # Validating ``CTFFilter``
-# --------------------
+# ------------------------
 # The forward modeling of CTF can be validated by passing a corrupted image
 # through CTF estimators and comparing the resulting defocus value(s).
 
diff --git a/src/aspire/source/image.py b/src/aspire/source/image.py
index dfed0825f7..4f4a499a0a 100644
--- a/src/aspire/source/image.py
+++ b/src/aspire/source/image.py
@@ -1,4 +1,5 @@
 import copy
+import functools
 import logging
 import os.path
 import types
@@ -102,7 +103,7 @@ def __getitem__(self, indices):
         return self.fun(indices)
 
 
-def as_copy(func):
+def _as_copy(func):
     """
     Method decorator that invokes the decorated method on a deepcopy of the object,
     and returns it on exit. The original object is unmodified.
@@ -114,6 +115,7 @@ def as_copy(func):
     is best used on methods that mutate the object but don't return anything.
     """
 
+    @functools.wraps(func)  # Pass metadata (eg name and doctrings) from `func`
     def wrapper(self, *args, **kwargs):
         obj_copy = copy.deepcopy(self)
         func_copy = copy.deepcopy(func)
@@ -757,7 +759,7 @@ def _apply_source_filters(self, im_orig, indices):
             self.filter_indices[indices],
         )
 
-    @as_copy
+    @_as_copy
     def cache(self):
         """
         Computes all queued pipeline transformations and stores the
@@ -784,7 +786,7 @@ def _images(self, indices):
         Subclasses handle cached image check as well as applying transforms in the generation pipeline.
         """
 
-    @as_copy
+    @_as_copy
     def downsample(self, L):
         if L > self.L:
             raise ValueError(
@@ -800,7 +802,7 @@ def downsample(self, L):
 
         self.L = L
 
-    @as_copy
+    @_as_copy
     def whiten(self, noise_estimate=None):
         """
         Modify the `ImageSource` in-place by appending a whitening filter to the generation pipeline.
@@ -840,7 +842,7 @@ def whiten(self, noise_estimate=None):
         logger.info("Adding Whitening Filter Xform to end of generation pipeline")
         self.generation_pipeline.add_xform(FilterXform(whiten_filter))
 
-    @as_copy
+    @_as_copy
     def phase_flip(self):
         """
         Perform phase flip on images in the source object using CTF information.
@@ -865,7 +867,7 @@ def phase_flip(self):
                 "  Confirm you have correctly populated CTFFilters."
             )
 
-    @as_copy
+    @_as_copy
     def invert_contrast(self, batch_size=512):
         """
         invert the global contrast of images
@@ -915,7 +917,7 @@ def invert_contrast(self, batch_size=512):
         logger.info("Adding Scaling Xform to end of generation pipeline")
         self.generation_pipeline.add_xform(Multiply(scale_factor))
 
-    @as_copy
+    @_as_copy
     def normalize_background(self, bg_radius=1.0, do_ramp=True):
         """
         Normalize the images by the noise background