cods build docstring complaints

src/mpol/crossval.py

@@ -46,9 +46,9 @@ class CrossValidate:
         strength ('lambda', float), whether to guess an initial value for lambda 
         ('guess', bool), and other quantities needed to compute their loss term.
         Example:
-            {"sparsity":{"lambda":1e-3, "guess":False},
-             "entropy": {"lambda":1e-3, "guess":True, "prior_intensity":1e-10}
-            }
+        {"sparsity":{"lambda":1e-3, "guess":False},
+        "entropy": {"lambda":1e-3, "guess":True, "prior_intensity":1e-10}
+        }
     train_diag_step : int, default=None
         Interval at which training diagnostics are output. If None, no
         diagnostics will be generated.

src/mpol/fourier.py

@@ -48,8 +48,9 @@ def __init__(self, coords: GridCoords, persistent_vis: bool = False):
     def from_image_properties(
         cls, cell_size: float, npix: int, persistent_vis: bool = False
     ) -> FourierCube:
-        r"""Alternative method for instantiating a FourierCube from ``cell_size``
-         and ``npix``
+        r"""
+        Alternative method for instantiating a FourierCube from ``cell_size`` and ``npix``
+
         Args:
             cell_size (float): the width of an image-plane pixel [arcseconds]
             npix (int): the number of pixels per image side
@@ -298,6 +299,7 @@ def from_image_properties(
         nchan: int = 1,
         sparse_matrices: bool = True,
     ) -> NuFFT:
+
         coords = GridCoords(cell_size, npix)
         return cls(coords, uu, vv, nchan, sparse_matrices)
 

src/mpol/plot.py

@@ -272,7 +272,7 @@ def split_diagnostics_fig(splitter, channel=0, save_prefix=None):
     Parameters
     ----------
     splitter : `mpol.crossval.RandomCellSplitGridded` object
-        Iterator that returns a `(train, test)` pair of `GriddedDataset`s 
+        Iterator that returns a `(train, test)` pair of `GriddedDataset` 
         for each iteration.
     channel : int, default=0
         Channel (of the datasets in `splitter`) to use to generate figure
@@ -339,9 +339,9 @@ def train_diagnostics_fig(model, losses=[], train_state=None, channel=0,
     """
     Figure for model diagnostics during an optimization loop. For a `model` in 
     a given state, plots the current: 
-        - model image (both linear and arcsinh colormap normalization)
-        - gradient image
-        - loss function
+    - model image (both linear and arcsinh colormap normalization)
+    - gradient image
+    - loss function
 
     Parameters
     ----------