Docformatter

.github/workflows/workflow.yml

@@ -19,6 +19,10 @@ jobs:
       run: |
         pip install black
         black --check .
+    - name: Docformatter
+      run: |
+        pip install docformatter
+        docformatter --check -r blobmodel/
 
   pytest:
 

blobmodel/blobs.py

@@ -5,9 +5,7 @@
 
 
 class Blob:
-    """
-    A single blob
-    """
+    """A single blob."""
 
     def __init__(
         self,

blobmodel/geometry.py

@@ -4,9 +4,7 @@
 
 
 class Geometry:
-    """
-    Define grid for Model
-    """
+    """Define grid for Model."""
 
     def __init__(
         self,
@@ -50,9 +48,7 @@ def __init__(
         )
 
     def __str__(self) -> str:
-        """
-        string representation of Geometry
-        """
+        """string representation of Geometry."""
         return (
             f"Geometry parameters:  Nx:{self.Nx},  Ny:{self.Ny}, Lx:{self.Lx}, Ly:{self.Ly}, "
             + f"dt:{self.dt}, T:{self.T}, y-periodicity:{self.periodic_y}"

blobmodel/model.py

@@ -7,9 +7,7 @@
 
 
 class Model:
-    """
-    2D Model of propagating blobs
-    """
+    """2D Model of propagating blobs."""
 
     def __init__(
         self,
@@ -64,16 +62,13 @@ def __init__(
         self.__blob_factory = blob_factory
 
     def __str__(self) -> str:
-        """
-        string representation of Model
-        """
+        """string representation of Model."""
         return f"2d Blob Model with blob shape:{self.blob_shape}, num_blobs:{self.num_blobs} and t_drain:{self.t_drain}"
 
     def make_realization(
         self, file_name: str = None, speed_up: bool = False, error: float = 1e-10
     ) -> xr.Dataset:
-        """
-        Integrate Model over time and write out data as xarray dataset
+        """Integrate Model over time and write out data as xarray dataset.
 
         Parameters
         ----------
@@ -164,7 +159,9 @@ def make_realization(
         return ds
 
     def get_blobs(self) -> list[Blob]:
-        """
-        Returns blobs list. Note that if Model.sample_blobs has not been called, the list will be empty
+        """Returns blobs list.
+
+        Note that if Model.sample_blobs has not been called, the list
+        will be empty
         """
         return self.__blobs

blobmodel/plotting.py

@@ -12,8 +12,7 @@ def show_model(
     gif_name: str = "2d_blobs.gif",
     fps: int = 10,
 ) -> None:
-    """
-    Show animation of Model output
+    """Show animation of Model output.
 
     Parameters
     ----------

blobmodel/stochasticality.py

@@ -6,25 +6,22 @@
 
 
 class BlobFactory(ABC):
-    """
-    Abstract class used by 2d propagating blob model to specify blob parameters.
-    """
+    """Abstract class used by 2d propagating blob model to specify blob
+    parameters."""
 
     @abstractmethod
     def sample_blobs(
         self, Ly: float, T: float, num_blobs: int, blob_shape: str, t_drain: float
     ) -> list[Blob]:
-        """
-        creates list of Blobs used in Model
-        """
+        """creates list of Blobs used in Model."""
         raise NotImplementedError
 
 
 class DefaultBlobFactory(BlobFactory):
-    """
-    Default implementation of BlobFactory.
-    Generates blob parameters for different possible random distributions.
-    All random variables are independent from each other
+    """Default implementation of BlobFactory.
+
+    Generates blob parameters for different possible random
+    distributions. All random variables are independent from each other
     """
 
     def __init__(
@@ -38,15 +35,15 @@ def __init__(
         vx_parameter: float = 1.0,
         vy_parameter: float = 1.0,
     ) -> None:
-        """
-        The following distributions are implemented:
-            exp: exponential distribution with mean 1
-            gamma: gamma distribution with `free_parameter` as shape parameter and mean 1
-            normal: normal distribution with zero mean and `free_parameter` as scale parameter
-            uniform: uniorm distribution with mean 1 and `free_parameter` as width
-            ray: rayleight distribution with mean 1
-            deg: array on ones
-            zeros: array of zeros
+        """The following distributions are implemented:
+
+        exp: exponential distribution with mean 1
+        gamma: gamma distribution with `free_parameter` as shape parameter and mean 1
+        normal: normal distribution with zero mean and `free_parameter` as scale parameter
+        uniform: uniorm distribution with mean 1 and `free_parameter` as width
+        ray: rayleight distribution with mean 1
+        deg: array on ones
+        zeros: array of zeros
         """
         self.A_dist = A_dist
         self.W_dist = W_dist