fix: rst cleanup

Author: paquiteau

README.rst

@@ -76,7 +76,7 @@ For improved image quality, embed these steps in a more complex reconstruction p
 
 Want to see more ?
 
-- Check the `Documentation <https://mind-inria.github.io/mri-nufft/>`_
+- Check the `Documentation <https://mind-inria.github.io/mri-nufft/>`__
 
 - Or go visit the `Examples <https://mind-inria.github.io/mri-nufft/generated/autoexamples/index.html>`_
 
@@ -88,7 +88,7 @@ MRI-nufft is available on `PyPi <https://pypi.org/project/mri-nufft>`__ and can
 
   pip install mri-nufft
 
-Additionally, you will have to install at least one NUFFT computation backend. See the `Documentation <https://mind-inria.github.io/mri-nufft/getting_started.html#choosing-a-nufft-backend>`_ for more guidance.
+Additionally, you will have to install at least one NUFFT computation backend. See the `Documentation <https://mind-inria.github.io/mri-nufft/getting_started.html#choosing-a-nufft-backend>`__ for more guidance.
 Typically we recommend:: 
 
   pip install mri-nufft[finufft] 

docs/explanations/mrinufft_convention.rst

@@ -1,5 +1,6 @@
 .. include:: <isonum.txt>
 .. _mri-nufft-interface:
+
 ===============================
 MRI-NUFFT Interfaces Convention
 ===============================

docs/install.rst

@@ -41,17 +41,17 @@ If you are using ``uv`` as your package installer you will need to do
 Development Version
 ~~~~~~~~~~~~~~~~~~~
 
-If you want to modify the mri-nufft code base
+If you want to modify the mri-nufft code base:
 
 .. code-block:: sh
 
     git clone https://github.com:mind-inria/mri-nufft
     pip install -e ./mri-nufft[dev,doc,extra,io,autodiff,tests,cufinufft,gpunufft,finufft]
 
-or using ``uv``
+or using ``uv``: 
 
 .. code-block:: sh
 
     git clone https://github.com:mind-inria/mri-nufft
     uv venv 
-    uv sync --all-extras --no-build-isolation --no-extra <backend-you-don't-need>
+    uv sync --all-extras --no-build-isolation --no-extra <backend-you-dont-need>

src/mrinufft/extras/data.py

@@ -107,7 +107,7 @@ def fse_simulation(
     Returns
     -------
     signal : np.ndarray
-        Simulated signal of shape (nTE*nTR, *M0).
+        Simulated signal of shape ``(nTE*nTR, *M0)``.
 
     """
     # preprocess sequence parameters

src/mrinufft/extras/field_map.py

@@ -85,7 +85,7 @@ def make_t2smap(shape, t2svalue=15.0, mask=None):
     Returns
     -------
     np.ndarray
-        T2* map of shape (*shape) in [ms].
+        T2* map of shape ``(*shape)`` in [ms].
     np.ndarray, optional
         Spatial support binary mask.
 

src/mrinufft/operators/autodiff.py

@@ -125,9 +125,9 @@ class MRINufftAutoGrad(torch.nn.Module):
         k-space trajectory (samples locations).
     paired_batch: bool, default False
         If True, an extra dimension for batchs is considered for the data.
-        Data dimensions for inputs and output will be `(batch_size,
-        nufft_op.n_batchs, nufft_op.n_coils, nufft_op.n_samples)` for k-space
-        and `(batch_Size, nufft_op.n_batchs, 1, *nufft_op.shape)` for image
+        Data dimensions for inputs and output will be ``(batch_size,
+        nufft_op.n_batchs, nufft_op.n_coils, nufft_op.n_samples)`` for k-space
+        and ``(batch_Size, nufft_op.n_batchs, 1, *nufft_op.shape)`` for image
         (with smaps support).
         The NUFFT operator will support different processing different k-space /
         image data and corresponding sensitivity map pairs. This is particularly
@@ -136,10 +136,10 @@ class MRINufftAutoGrad(torch.nn.Module):
     Notes
     -----
         - Even with batch_size, the data is processed sequentially.
-        - Unlike the `n_batchs` argument of the base nufft_operator,
-          `batch_size` allows for batching on other data than images and
+        - Unlike the ``n_batchs`` argument of the base nufft_operator,
+          ``batch_size`` allows for batching on other data than images and
           k-space. By using :meth:`op_batched` and :mod:`adj_op_batched`. In
-          this context `n_batchs` of the NUFFT operator can be seen
+          this context ``n_batchs`` of the NUFFT operator can be seen
           as a number of MR echos.
     """
 
@@ -176,16 +176,17 @@ def op(self, x, smaps=None, samples=None):
             Image data of shape ``(batch_size, nufft_op.n_batchs, (1 if smaps
             else nufft_op.n_coils), *nufft_op.shape)``
         smaps: Tensor, optional
-            Sensitivity maps of shape ``(batch_size, nufft_op.n_coils, *nufft_op.shape)
+            Sensitivity maps
+            with shape ``(batch_size, nufft_op.n_coils, *nufft_op.shape)``
         samples: Tensor, optional
             Samples for the batches of shape ``(batch_size, nufft_op.n_samples,
             nufft_op.ndim)``
 
         Returns
         -------
         Tensor:
-             with shape `(batch_size, nufft_op.n_batchs, nufft_op.n_coils,
-             nufft_op.n_samples)`
+             with shape ``(batch_size, nufft_op.n_batchs, nufft_op.n_coils,
+             nufft_op.n_samples)``
 
         Notes
         -----
@@ -205,7 +206,8 @@ def adj_op(self, kspace, smaps=None, samples=None):
              with shape
              ``(batch_size, nufft_op.n_batchs, nufft_op.n_coils, nufft_op.n_samples)``
         smaps: Tensor, optional
-            Sensitivity maps of shape ``(batch_size, nufft_op.n_coils, *nufft_op.shape)
+            Sensitivity maps with shape
+            ``(batch_size, nufft_op.n_coils, *nufft_op.shape)``
         samples: Tensor, optional
             Samples for the batches of shape
             ``(batch_size, nufft_op.n_samples, nufft_op.ndim)``

src/mrinufft/operators/base.py

@@ -434,7 +434,7 @@ def compute_density(self, method: Callable[..., NDArray] = None):
             - If a string, the method should be registered in the density registry.
             - If a callable, it should take the samples and the shape as input.
             - If a dict, it should have a key 'name', to determine which method to use.
-            other items will be used as kwargs.
+              other items will be used as kwargs.
             - If an array, it should be of shape (Nsamples,) and will be used as is.
             - If `True`, the method `pipe` is chosen as default estimation method.