update

docs/source/conf.py

@@ -94,7 +94,7 @@
     'github_version': 'main',
 }
 linkcode_blob = html_context['github_version']
-default_role = 'any'
+default_role = 'py:obj'
 
 
 def get_lambda_source(obj):

examples/notebooks/cartesian_reconstruction.ipynb

@@ -654,7 +654,7 @@
     "As you might have guessed, these are also included in MRpro:\n",
     "Instead of the `~mrpro.algorithms.reconstruction.DirectReconstruction`,\n",
     "we can use `mrpro.algorithms.reconstruction.IterativeSENSEReconstruction`, which is explained in\n",
-    "the example <project:iterative_sense_reconstruction.ipynb>."
+    "the example <project:iterative_sense_reconstruction_radial2D.ipynb>."
    ]
   },
   {

examples/notebooks/iterative_sense_reconstruction_radial2D.ipynb

@@ -330,7 +330,7 @@
     "show_images(\n",
     "    img_direct.rss()[0, 0],\n",
     "    img.rss()[0, 0],\n",
-    "    img_manual.abs()[0, 0],\n",
+    "    img_manual.abs()[0, 0, 0],\n",
     "    titles=['Direct', 'Iterative SENSE', 'Manual Iterative SENSE'],\n",
     ")"
    ]
@@ -364,7 +364,7 @@
    "source": [
     "### Next steps\n",
     "We can also reconstruct undeersampled data or use a regularization term in the optimization problem.\n",
-    "For the latter, see the example in <project:regularized_iterative_sense_reconstruction.ipynb>."
+    "For the latter, see the example in <project:iterative_sense_reconstruction_with_regularization.ipynb>."
    ]
   }
  ],

examples/notebooks/iterative_sense_reconstruction_with_regularization.ipynb

@@ -292,7 +292,7 @@
    "source": [
     "##### Set-up the density compensation operator $W$ and acquisition model $A$\n",
     "\n",
-    "This is very similar to <project:iterative_sense_reconstruction.ipynb> .\n",
+    "This is very similar to <project:iterative_sense_reconstruction_radial2D.ipynb> .\n",
     "For more details, please refer to that notebook."
    ]
   },

examples/scripts/cartesian_reconstruction.py

@@ -336,6 +336,6 @@ def show_images(*images: torch.Tensor, titles: list[str] | None = None) -> None:
 # As you might have guessed, these are also included in MRpro:
 # Instead of the `~mrpro.algorithms.reconstruction.DirectReconstruction`,
 # we can use `mrpro.algorithms.reconstruction.IterativeSENSEReconstruction`, which is explained in
-# the example <project:iterative_sense_reconstruction.ipynb>.
+# the example <project:iterative_sense_reconstruction_radial2D.ipynb>.
 
 # %%

examples/scripts/iterative_sense_reconstruction_with_regularization.py

@@ -154,7 +154,7 @@ def show_images(*images: torch.Tensor, titles: list[str] | None = None) -> None:
 # %% [markdown]
 # ##### Set-up the density compensation operator $W$ and acquisition model $A$
 #
-# This is very similar to <project:iterative_sense_reconstruction.ipynb> .
+# This is very similar to <project:iterative_sense_reconstruction_radial2D.ipynb> .
 # For more details, please refer to that notebook.
 # %%
 dcf_operator = mrpro.data.DcfData.from_traj_voronoi(kdata_undersampled.traj).as_operator()