Clean up all docstrings.

src/pg_rad/isotope.py

@@ -8,9 +8,9 @@ class Isotope:
         """_Represents the essential information of an isotope._
 
         Args:
-            name (str): _Full name (e.g. Caesium-137)._
-            E (float): _Energy of the primary gamma in keV._
-            b (float): _Branching ratio for the gamma at energy E._
+            name (str): Full name (e.g. Caesium-137).
+            E (float): Energy of the primary gamma in keV.
+            b (float): Branching ratio for the gamma at energy E.
         """        
         
         if E <= 0:

src/pg_rad/landscape.py

@@ -15,12 +15,9 @@ class Landscape:
         """_A generic Landscape that can contain a Path and sources._
 
         Args:
-            air_density (float, optional): _Air density in kg / m^3_. Defaults to 1.243.
-            size (int | tuple[int, int, int], optional): _Size of the world_. Defaults to 500.
-            scale (str, optional): _The scale of the size argument passed_. Defaults to 'meters'.
-
-        Raises:
-            TypeError: _description_
+            air_density (float, optional): Air density in kg / m^3. Defaults to 1.243.
+            size (int | tuple[int, int, int], optional): Size of the world. Defaults to 500.
+            scale (str, optional): The scale of the size argument passed. Defaults to 'meters'.
         """        
         
         if isinstance(size, int):
@@ -103,11 +100,11 @@ def create_landscape_from_path(path: Path, max_z = 500):
     the size of the Landscape._
 
     Args:
-        path (Path): _A Path object describing the trajectory._
-        max_z (int, optional): _Height of the world_. Defaults to 500 meters.
+        path (Path): A Path object describing the trajectory.
+        max_z (int, optional): Height of the world. Defaults to 500 meters.
 
     Returns:
-        _type_: _A Landscape with dimensions based on the provided Path._
+        Landscape: A Landscape with dimensions based on the provided Path.
     """    
     max_x = np.ceil(max(path.x_list))
     max_y = np.ceil(max(path.y_list))

src/pg_rad/objects.py

@@ -38,15 +38,15 @@ class Source(Object):
         """_A point source._
 
         Args:
-            x (float): _X coordinate._
-            y (float):  _Y coordinate._
-            z (float):  _Z coordinate._
-            activity (int): _Activity A in MBq._
-            isotope (Isotope): _The isotope._
-            name (str | None, optional): _Can give the source a unique name_.
-            Defaults to None, making the name sequential
+            x (float): X coordinate.
+            y (float): Y coordinate.
+            z (float): Z coordinate.
+            activity (int): Activity A in MBq.
+            isotope (Isotope): The isotope.
+            name (str | None, optional): Can give the source a unique name.
+            Defaults to None, making the name sequential.
             (Source-1, Source-2, etc.).
-            color (str, optional): _Matplotlib compatible color string_. Defaults to "red".
+            color (str, optional): Matplotlib compatible color string. Defaults to "red".
         """        
         self.id = Source._id_counter
         Source._id_counter += 1

src/pg_rad/path.py

@@ -45,14 +45,11 @@ class Path:
             z: float = 0,
             simplify_path = False
                  ):
-        """Construct a path of sequences based on a list of coordinates.
+        """_Construct a path of sequences based on a list of coordinates._
 
         Args:
-            coord_list (Sequence[tuple[float, float]]): _description_
-            z (float, optional): _description_. Defaults to 0.
-
-        Raises:
-            ValueError: _description_
+            coord_list (Sequence[tuple[float, float]]): List of x,y coordinates.
+            z (float, optional): Height of the path. Defaults to 0.
         """        
         
         if len(coord_list) < 2:
@@ -114,12 +111,12 @@ def piecewise_regression_on_path(
     order to find better global optima."
 
     Args:
-        x (Sequence[float]): _Full list of x coordinates._
-        y (Sequence[float]): _Full list of y coordinates._
-        keep_endpoints_equal (bool, optional): _Whether or not to force start
+        x (Sequence[float]): Full list of x coordinates.
+        y (Sequence[float]): Full list of y coordinates.
+        keep_endpoints_equal (bool, optional): Whether or not to force start
         and end to be exactly equal to the original. This will worsen the linear
-        approximation at the beginning and end of path. Defaults to False._
-        n_breakpoints (int, optional): _Number of breakpoints. Defaults to 3._
+        approximation at the beginning and end of path. Defaults to False.
+        n_breakpoints (int, optional): Number of breakpoints. Defaults to 3.
 
     Returns:
         x (Sequence[float]): _Reduced list of x coordinates._
@@ -170,12 +167,12 @@ def path_from_RT90(
     """_Construct a path from East and North formatted coordinates (RT90) in a Pandas DataFrame._
 
     Args:
-        df (pd.DataFrame): _DataFrame containing at least the two columns noted in the cols argument._
-        east_col (str): _The column name for the East coordinates._
-        north_col (str): _The column name for the North coordinates._
+        df (pd.DataFrame): DataFrame containing at least the two columns noted in the cols argument.
+        east_col (str): The column name for the East coordinates.
+        north_col (str): The column name for the North coordinates.
 
     Returns:
-        Path: _A Path object built from the aquisition coordinates in the DataFrame._
+        Path: A Path object built from the aquisition coordinates in the DataFrame.
     """    
     
     east_arr = np.array(df[east_col]) - min(df[east_col])