improving docstrings for object functions

src/hyvr/objects/channel.py

@@ -27,8 +27,8 @@ def channel(
     trajectory of the channel and a parabola, which defines the cross section.
     Besides, it may have internal structure (Not currently implemented).
 
-    params:
-    ---
+    Parameters
+    ----------
     f_array: ndarray(int32) of the facies values at the coordinates (x,y,z)
     dip_array: ndarray(float32) of the dip (positive value) of the internal structure at (x,y,z)
     dip_dir_array: ndarray(float32) of the dip-direction of the internal structure
@@ -43,8 +43,9 @@ def channel(
     layer_dist: perpendicular to dip distance between layers
     facies: np.array(int32) with the facies code (1 in case no layering or more in case of layering)
 
-    Modified arrays:
-    ---
+    Returns
+    -------
+    Modifies the following arrays in place:
     f_array: ndarray(int32) of the facies values at the coordinates (x,y,z)
     dip_array: ndarray(float32) of the dip (positive value) of the internal structure at (x,y,z)
     dip_dir_array: ndarray(float32) of the dip-direction of the internal structure

src/hyvr/objects/sheet.py

@@ -33,8 +33,9 @@ def sheet(
     Assigns a sheet to the grid points x,y,z.
     The sheet is a layer is defined by bounding x and y coordinates and top and bottom contacts.
     It may have internal layering (inclined or planar)
-    params:
-    ---
+    
+    Parameters
+    ----------
     f_array: ndarray(int32) of the facies values at the coordinates (x,y,z)
     dip_array: ndarray(float32) of the dip (positive value) of the internal structure at (x,y,z)
     dip_dir_array: ndarray(float32) of the dip-direction of the internal structure
@@ -51,8 +52,9 @@ def sheet(
     follows the mathematical convention, anticlockwise from east
     layer_dist: perpendicular to dip distance between layers
 
-    Modified arrays:
-    ---
+    Returns
+    -------
+    Modifies the following arrays in place:
     f_array: ndarray(int32) of the facies values at the coordinates (x,y,z)
     dip_array: ndarray(float32) of the dip (positive value) of the internal structure at (x,y,z)
     dip_dir_array: ndarray(float32) of the dip-direction of the internal structure

src/hyvr/objects/trough.py

@@ -34,8 +34,8 @@ def half_ellipsoid(
     Half ellipsoid is the lower half of an ellipsoid, defined by its center, dimensions and azimuth.
     It can be used to model discrete lenses, trough structure, scour pool fills, etc.
 
-    params:
-    ---
+    Parameters
+    ----------
     f_array: ndarray(int32) of the facies values at the coordinates (x,y,z)
     dip_array: ndarray(float32) of the dip (positive value) of the internal structure at (x,y,z)
     dip_dir_array: ndarray(float32) of the dip-direction of the internal structure
@@ -53,8 +53,9 @@ def half_ellipsoid(
     follows the mathematical convention, anticlockwise from east
     layer_dist: perpendicular to dip distance between layers
 
-    Modified arrays:
-    ---
+    Returns
+    -------
+    Modifies the following arrays in place:
     f_array: ndarray(int32) of the facies values at the coordinates (x,y,z)
     dip_array: ndarray(float32) of the dip (positive value) of the internal structure at (x,y,z)
     dip_dir_array: ndarray(float32) of the dip-direction of the internal structure

src/hyvr/tools.py

@@ -103,14 +103,16 @@ def specsim_surface(
     """
     Creates gaussian random surface with mean value and variance input
     with the spectral method from  Dietrich & Newsam (1993).
-    Input:
+    Parameters:
     -------------
     x,y: 2D grid of x and y points
     mean: mean value
     var: variance
     corl: correlation lenghts (same unit as x and y) in x and y directions
     mask: mask array (same dimensions as x and y)
-    Returns:
+
+    Returns
+    -------
     Z: output np.array with same dimensions as x and y and with Z values corrensponding to the surface
     """
     M = np.diag(1 / corl ** 2)
@@ -137,14 +139,17 @@ def contact_surface(
     """
     Creates gaussian random contact surface with mean value and variance input
     with the spectral method from  Dietrich & Newsam (1993).
-    Input:
+
+    Parameters:
     -------------
     x,y: 2D grid of x and y points
     mean: mean value
     var: variance
     corl: correlation lenghts (same unit as x and y) in x and y directions
     mask: mask array (same dimensions as x and y)
-    Returns:
+
+    Returns
+    -------
     Z: output np.array with same dimensions as x and y and with Z values corrensponding to the surface
     """
     return None
@@ -162,10 +167,12 @@ def surface_gauss_regression(
     """
     Performs surface gaussian regression on input x,y data with given dataset (x,y,z) and error
     Based on the algorithm from Rasmussen & Williams (2006) Gaussian Processes for Machine Learning
-    Input:
+    Parameters:
     -------------
     x,y: 2D grid of x and y points
-    Returns:
+    
+    Returns
+    -------
     Z: output np.array with same dimensions as x and y and with Z values corrensponding to the surface
     """
     # Calculating distances: