update

docs/source/_templates/class_template.rst

@@ -4,7 +4,7 @@
 
 .. autoclass:: {{ objname }}
    :members:
-   :special-members: '__init__, __matmul__, __add__, __mul__, __or__, __and__, __radd__, __rmul__, __rmatmul__, __ror__, __rand__, __truediv__, __eq__, __pow__'
+   :special-members: '__init__, __call__, __matmul__, __add__, __mul__, __or__, __and__, __radd__, __rmul__, __rmatmul__, __ror__, __rand__, __truediv__, __eq__, __pow__'
    :inherited-members: Module
    :show-inheritance:
 

docs/source/conf.py

@@ -38,6 +38,7 @@
     'sphinx.ext.intersphinx',
     'sphinx_autodoc_typehints',
     'sphinx.ext.autosectionlabel',
+    'sphinx-copybutton'
 ]
 
 

docs/source/contributor_guide.rst

@@ -84,13 +84,13 @@ You can use VSCode's test panel to discover and run tests. All tests must pass b
 
 Building the Documentation
 ==========================
-You can build the documentation locally via running ```make html``` in the docs folder. The documentation will also be build in each PR and can be viewed online.
+You can build the documentation locally via running ``make html`` in the docs folder. The documentation will also be build in each PR and can be viewed online.
 Please check how your new additions render in the documentation before requesting a PR review.
 
 
 Adding new Examples
 ===================
-New exciting applications of MRpro can be added in ```examples``` as only ```.py``` files with code-cells. These can, for example, be used in VSCode with the python extension, or in JupyterLab with the `jupytext <https://jupytext.readthedocs.io/en/latest/>`_ extension.
+New exciting applications of MRpro can be added in ``examples`` as only ``.py`` files with code-cells. These can, for example, be used in VSCode with the python extension, or in JupyterLab with the `jupytext <https://jupytext.readthedocs.io/en/latest/>`_ extension.
 A pre-commit action will convert the scripts to notebooks. Our documetantion build will pick up these notebooks, run them, and include them with outputs in the documentation.
 The data to run the examples should be publicly available and hosted externally, for example at zenodo.
 Please be careful not to add any binary files to your commits.

docs/source/examples.rst

@@ -5,7 +5,7 @@ Examples
     :target: https://colab.research.google.com/github/PTB-MR/mrpro
 
 Notebooks with examples of how you can use MRpro.
-Each notebook can be launched in Colab |colab-badge|
+Each notebook can be launched in Colab: |colab-badge|
 
 .. toctree::
    :maxdepth: 1

docs/source/user_guide.rst

@@ -39,8 +39,7 @@ A basic pipeline would contain the following steps:
 
 The following provides some basic information about these steps.
 For more detailed information please have a look at the :doc:`examples`.
-You can easily launch notebooks via the |colab-badge| badge and give the notebooks a try without having to
-install anything.
+You can easily launch notebooks via the |colab-badge| badge and give the notebooks a try -
 
 Reading in raw data
 -------------------

src/mrpro/algorithms/optimizers/adam.py

@@ -35,13 +35,13 @@ def adam(
         \hat{m}_t &= \frac{m_t}{1 - \beta_1^t}, \quad \hat{v}_t = \frac{v_t}{1 - \beta_2^t} \\
         \theta_{t+1} &= \theta_t - \frac{\eta}{\sqrt{\hat{v}_t} + \epsilon} \hat{m}_t
 
-    where:
-    - :math:`g_t` is the gradient at step :math:`t`,
-    - :math:`m_t` and :math:`v_t` are biased estimates of the first and second moments,
-    - :math:`\hat{m}_t` and :math:`\hat{v}_t` are bias-corrected estimates,
-    - :math:`\eta` is the learning rate,
-    - :math:`\epsilon` is a small constant for numerical stability,
-    - :math:`\beta_1` and :math:`\beta_2` are decay rates for the moment estimates.
+    where
+    :math:`g_t` is the gradient at step :math:`t`,
+    :math:`m_t` and :math:`v_t` are biased estimates of the first and second moments,
+    :math:`\hat{m}_t` and :math:`\hat{v}_t` are bias-corrected estimates,
+    :math:`\eta` is the learning rate,
+    :math:`\epsilon` is a small constant for numerical stability,
+    :math:`\beta_1` and :math:`\beta_2` are decay rates for the moment estimates.
 
     Steps of the Adam algorithm:
 

src/mrpro/data/Data.py

@@ -14,7 +14,7 @@ class Data(MoveDataMixin, ABC):
     """A general data class with field data and header."""
 
     data: torch.Tensor
-    """Data. Shape (...other coils k2 k1 k0)"""
+    """Data. Shape `(...other coils k2 k1 k0)`"""
 
     header: Any
     """Header information for data."""

src/mrpro/data/IData.py

@@ -76,9 +76,9 @@ def from_tensor_and_kheader(cls, data: torch.Tensor, kheader: KHeader) -> Self:
         Parameters
         ----------
         data
-            torch.Tensor containing image data with dimensions (broadcastable to) (other, coils, z, y, x).
+            torch.Tensor containing image data with dimensions (broadcastable to) `(other, coils, z, y, x)`.
         kheader
-            MR raw data header (KHeader) containing required meta data for the image header (IHeader).
+            MR raw data header containing required meta data for the image header (`mrpro.data.IHeader`).
         """
         header = IHeader.from_kheader(kheader)
         return cls(header=header, data=data)

src/mrpro/operators/models/WASABI.py

@@ -82,13 +82,13 @@ def forward(
             signal with shape `(offsets *other, coils, z, y, x)`
         """
         offsets = unsqueeze_right(self.offsets, b0_shift.ndim - (self.offsets.ndim - 1))  # -1 for offset
-        delta_x = offsets - b0_shift
+        delta_b = offsets - b0_shift
         b1 = self.b1_nominal * relative_b1
 
         signal = (
             c
             - d
-            * (torch.pi * b1 * self.gamma * self.tp) ** 2
-            * torch.sinc(self.tp * torch.sqrt((b1 * self.gamma) ** 2 + delta_x**2)) ** 2
+            * (torch.pi * b1 * self.gamma * self.rf_duration) ** 2
+            * torch.sinc(self.rf_duration * torch.sqrt((b1 * self.gamma) ** 2 + delta_b**2)) ** 2
         )
         return (signal,)

src/mrpro/operators/models/WASABITI.py

@@ -17,7 +17,7 @@ def __init__(
         rf_duration: float | torch.Tensor = 0.005,
         b1_nominal: float | torch.Tensor = 3.75,
         gamma: float | torch.Tensor = 42.5764,
-        lamor_frequency: float | torch.Tensor = 127.7292,
+        larmor_frequency: float | torch.Tensor = 127.7292,
     ) -> None:
         """Initialize WASABITI signal model for mapping of B0, B1 and T1 [SCH2023]_.
 
@@ -33,7 +33,7 @@ def __init__(
             nominal B1 amplitude [µT]
         gamma
             gyromagnetic ratio [MHz/T]
-        lamor_frequency
+        larmor_frequency
             larmor frequency [MHz]
 
         References
@@ -47,7 +47,7 @@ def __init__(
         rf_duration = torch.as_tensor(rf_duration)
         b1_nominal = torch.as_tensor(b1_nominal)
         gamma = torch.as_tensor(gamma)
-        lamor_frequency = torch.as_tensor(lamor_frequency)
+        larmor_frequency = torch.as_tensor(larmor_frequency)
 
         if recovery_time.shape != offsets.shape:
             raise ValueError(
@@ -60,7 +60,7 @@ def __init__(
         self.rf_duration = nn.Parameter(rf_duration, requires_grad=rf_duration.requires_grad)
         self.b1_nominal = nn.Parameter(b1_nominal, requires_grad=b1_nominal.requires_grad)
         self.gamma = nn.Parameter(gamma, requires_grad=gamma.requires_grad)
-        self.lamor_frequency = nn.Parameter(lamor_frequency, requires_grad=lamor_frequency.requires_grad)
+        self.larmor_frequency = nn.Parameter(larmor_frequency, requires_grad=larmor_frequency.requires_grad)
 
     def forward(self, b0_shift: torch.Tensor, relative_b1: torch.Tensor, t1: torch.Tensor) -> tuple[torch.Tensor,]:
         """Apply WASABITI signal model.