Revert "optional index cleanup commit"

This reverts commit 27d89b4.

Author: garrettwrong

src/aspire/covariance/covar.py

@@ -179,7 +179,7 @@ def src_backward(self, mean_vol, noise_variance, shrink_method=None):
                 (batch_n, self.src.L, self.src.L, self.src.L), dtype=self.dtype
             )
             for j in range(batch_n):
-                im_centered_b[j] = self.src.im_backward([i + j], im_centered[j])
+                im_centered_b[j] = self.src.im_backward(im_centered[j], i + j)
             im_centered_b = Volume(im_centered_b).to_vec()
 
             covar_b += (im_centered_b.T @ im_centered_b).reshape(

src/aspire/reconstruction/mean.py

@@ -175,12 +175,12 @@ def src_backward(self):
         )
 
         for i in range(0, self.src.n, self.batch_size):
-            idx = np.arange(i, min(self.src.n, i + self.batch_size), dtype=int)
-            im = self.src.images[idx]
             for k in range(self.r):
+                im = self.src.images[i : i + self.batch_size]
+
                 batch_vol_rhs = self.src.im_backward(
-                    idx,
                     im,
+                    i,
                     self.weights[:, k],
                     symmetry_group=symmetry_group,
                 ) / (self.src.n * sym_order)

src/aspire/source/image.py

@@ -937,38 +937,31 @@ def normalize_background(self, bg_radius=1.0, do_ramp=True):
             LambdaXform(normalize_bg, bg_radius=bg_radius, do_ramp=do_ramp)
         )
 
-    def im_backward(self, idx, im=None, weights=None, symmetry_group=None):
+    def im_backward(self, im, start, weights=None, symmetry_group=None):
         """
-        Apply adjoint mapping to set of images identified by indices `idx`.
+        Apply adjoint mapping to set of images
 
-        :param idx: Source indices corresponding to a set of images.
-        :param im: An Image instance to which we wish to apply the
-            adjoint of the forward model.  When `None`, infers
-            `im=self.images[idx]`.
+        :param im: An Image instance to which we wish to apply the adjoint of the forward model.
+        :param start: Start index of image to consider
         :param weights: Optional vector of weights to apply to images.
             Weights should be length `self.n`.
         :param symmetry_group: A SymmetryGroup instance. If supplied, uses symmetry to increase
              number of images used in back-projectioon.
         :return: An L-by-L-by-L volume containing the sum of the adjoint mappings applied to the start+num-1 images.
         """
+        num = im.n_images
 
-        if im is None:
-            im = self.images[idx]
-        elif im.n_images != len(idx):
-            raise RuntimeError(
-                f"`im_backward` n_images != len(idx): {im.n_images} != {len(idx)}"
-            )
-
-        im *= self.amplitudes[idx, np.newaxis, np.newaxis]
-        im = im.shift(-self.offsets[idx, :])
-        im = self._apply_source_filters(im, idx)
+        all_idx = np.arange(start, min(start + num, self.n))
+        im *= self.amplitudes[all_idx, np.newaxis, np.newaxis]
+        im = im.shift(-self.offsets[all_idx, :])
+        im = self._apply_source_filters(im, all_idx)
 
         if weights is not None:
-            im *= weights[idx, np.newaxis, np.newaxis]
+            im *= weights[all_idx, np.newaxis, np.newaxis]
 
-        vol = im.backproject(self.rotations[idx, :, :], symmetry_group=symmetry_group)[
-            0
-        ]
+        vol = im.backproject(
+            self.rotations[start : start + num, :, :], symmetry_group=symmetry_group
+        )[0]
 
         return vol