diff --git a/.gitignore b/.gitignore
index 1ecb708..b995bce 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,3 +16,4 @@ docs/.DS_Store
 **/.ipynb_checkpoints
 docs/build
 **/generated_outputs
+.nova
diff --git a/pyscses/calculation.py b/pyscses/calculation.py
index a162755..eecdd2b 100644
--- a/pyscses/calculation.py
+++ b/pyscses/calculation.py
@@ -1,27 +1,28 @@
 from __future__ import annotations
-import numpy as np 
+import numpy as np
 import mpmath # type: ignore
 from bisect import bisect_left, bisect_right
-from pyscses.set_of_sites import Set_of_Sites
+from pyscses.set_of_sites import SetOfSites
 from pyscses.matrix_solver import MatrixSolver
 from pyscses.set_up_calculation import calculate_grid_offsets
 from pyscses.constants import *
 from pyscses.grid import delta_x_from_grid, Grid, phi_at_x
 from scipy.optimize import minimize # type: ignore
+from typing import Tuple, List
 
 class Calculation:
-    """The Calculation class contains methods for calculating the relevant space charge properties for any given system, such as electrostatic potential, charge density, defect mole fractions and parallel and perpendicular grain boundary resistivities. 
+    """The Calculation class contains methods for calculating the relevant space charge properties for any given system, such as electrostatic potential, charge density, defect mole fractions and parallel and perpendicular grain boundary resistivities.
 
     Args:
         grid (:obj:`pyscses.Grid`): A pyscses.Grid object. This contains properties of the grid including the x coordinates and the volumes.
         bulk_x_min (float): The minimum x coordinate defining a region of bulk.
         bulk_x_max (float): The maximum x coordinate defining a region of bulk.
         alpha (float): A damping parameter for updating the potential at each iteration.
-        convergence (float): The convergence limit. The difference between the updated phi and the phi from the previous iteration must be below this for the calculation to be sufficiently converged. 
+        convergence (float): The convergence limit. The difference between the updated phi and the phi from the previous iteration must be below this for the calculation to be sufficiently converged.
         dielectric (float): The dielectric constant for the studied material.
-        temp (float): The temperature that the calculation is run. 
+        temp (float): The temperature that the calculation is run.
         boundary_conditions (str): Specified boundary conditions for the matrix solver. Allowed values are `dirichlet` and `periodic`. Default = `dirichlet`.
- 
+
     """
 
     def __init__(self,
@@ -38,8 +39,8 @@ def __init__(self,
         self.bulk_x_max = bulk_x_max
         self.alpha = alpha
         self.convergence = convergence
-        self.dielectric = dielectric 
-        self.temp = temp 
+        self.dielectric = dielectric
+        self.temp = temp
         self.boundary_conditions = boundary_conditions
         # self.mf: Dict[str, np.ndarray]
 
@@ -53,10 +54,10 @@ def mole_fraction_error(self,
 
         Args:
             input_mole_fractions (list): Mole fractions for each of the species used in the iterative Poisson-Boltzmann solver.
-            target_mole_fractions  (list): The value that the mole fractions should be in the bulk. 
+            target_mole_fractions  (list): The value that the mole fractions should be in the bulk.
 
         Returns:
-            float: Sum of squares error between output and target mole fractions. 
+            float: Sum of squares error between output and target mole fractions.
 
         """
         input_mole_fractions = np.array([input_mole_fractions])
@@ -69,13 +70,15 @@ def mole_fraction_error(self,
                 squares.append(( output - target )**2)
         return sum( squares )
 
-    def mole_fraction_output(self, input_mole_fractions, approximation):
+    def mole_fraction_output(self,
+                             input_mole_fractions: np.ndarray,
+                             approximation: str) -> np.ndarray:
         """Calculates the output mole fraction for a given input mole fraction when solving the Poisson-Boltzmann equation.
 
         Args:
             input_mole_fractions (list): Mole fractions for each of the species used in the iterative Poisson-Boltzmann solver.
-            approximation (str): The defect mobility approximation. Either 'mott-schottky' to enforce only a single mobile defect, or 'gouy-chapman' to allow all defect species to redistribute. 
-   
+            approximation (str): The defect mobility approximation. Either 'mott-schottky' to enforce only a single mobile defect, or 'gouy-chapman' to allow all defect species to redistribute.
+
         Returns:
             list: Mole fractions that are calculated from the iterative Poisson-Boltzmann solver.
         """
@@ -86,7 +89,7 @@ def mole_fraction_output(self, input_mole_fractions, approximation):
                         defect.mole_fraction = input_mole_fractions[0,i]
                     for defect in site.defects:
                         defect.mole_fraction = input_mole_fractions[0,i]
-        
+
         self.solve(approximation)
         species = []
         for mf in input_mole_fractions:
@@ -99,18 +102,21 @@ def mole_fraction_output(self, input_mole_fractions, approximation):
             for i in range(len(mf)):
                 self.mf[self.site_labels[i]] = [mf for mf in self.mf[self.site_labels[i]] if mf != 0.0 ]
                 average_mf = self.calculate_average(self.subgrids[self.site_labels[i]], self.bulk_x_min, self.bulk_x_max, self.mf[self.site_labels[i]])
-                average_mole_fractions.append(average_mf) 
+                average_mole_fractions.append(average_mf)
         output_mole_fractions = np.array( [ average_mole_fractions ] )
         return output_mole_fractions
 
-        
-    def mole_fraction_correction( self, target_mole_fractions, approximation, initial_guess ):
-        """Starting from an initial guess for the appropriate input mole fractions, minimises the error between the target bulk mole fraction and the output mole fraction from the iterative Poisson-Boltzmann solver. The output is stored as a Calculation attribute. Calculation.initial_guess (list): The optimum values to be used as the input mole fractions for the iterative Poisson-Boltzmann solver so that the output bulk mole fractions are the target bulk mole fractions. 
+
+    def mole_fraction_correction(self,
+                                 target_mole_fractions: np.ndarray,
+                                 approximation: str,
+                                 initial_guess: np.ndarray) -> None:
+        """Starting from an initial guess for the appropriate input mole fractions, minimises the error between the target bulk mole fraction and the output mole fraction from the iterative Poisson-Boltzmann solver. The output is stored as a Calculation attribute. Calculation.initial_guess (list): The optimum values to be used as the input mole fractions for the iterative Poisson-Boltzmann solver so that the output bulk mole fractions are the target bulk mole fractions.
 
         Args:
-            target_mole_fractions (list): The value that the mole fractions should be in the bulk. 
-            approximation (str): The defect mobility approximation. Either 'mott-schottky' to enforce only a single mobile defect, or 'gouy-chapman' to allow all defect species to redistribute. 
-            initial_guess (list): Values for an initial guess for the defect mole fractions used in the error minimisation. 
+            target_mole_fractions (list): The value that the mole fractions should be in the bulk.
+            approximation (str): The defect mobility approximation. Either 'mott-schottky' to enforce only a single mobile defect, or 'gouy-chapman' to allow all defect species to redistribute.
+            initial_guess (list): Values for an initial guess for the defect mole fractions used in the error minimisation.
 
         """
         self.initial_guess = initial_guess
@@ -127,12 +133,15 @@ def mole_fraction_correction( self, target_mole_fractions, approximation, initia
                         defect.mole_fraction = opt_mole_fractions.x[0,i]
         self.initial_guess = opt_mole_fractions.x
 
-    def find_index( self, grid, min_cutoff, max_cutoff ):
+    def find_index(self,
+                   grid: Grid,
+                   min_cutoff: float,
+                   max_cutoff: float) -> Tuple[int, int]:
         """Calculates the indices of the grid positions closest to a minimum and maximum value.
 
         Args:
 	    grid (:obj:`pyscses.Grid`): pyscses.Grid object. This contains properties of the grid including the x coordinates and the volumes. Used to access the x coordinates.
-	    min_cutoff (float): Minimum x coordinate value defining the calculation region. 
+	    min_cutoff (float): Minimum x coordinate value defining the calculation region.
 	    max_cutoff (float): Maximum x coordinate value defining the calculation region.
 
 	Returns:
@@ -143,12 +152,15 @@ def find_index( self, grid, min_cutoff, max_cutoff ):
         max_index = bisect_left( grid.x, max_cutoff )
         return min_index, max_index
 
-    def calculate_offset( self, grid, min_cutoff, max_cutoff ):
+    def calculate_offset(self,
+                         grid: Grid,
+                         min_cutoff: float,
+                         max_cutoff: float) -> Tuple[float, float]:
         """Calculate the offset between the midpoint of the last x coordinate in the calculation region and the x coordinate outside of the calulation region.
 
         Args:
             grid (:obj:`pyscses.Grid`): Contains properties of the grid including the x coordinates and the volumes. Used to access the x coordinates.
-            min_cutoff (float): Minimum x coordinate value defining the calculation region. 
+            min_cutoff (float): Minimum x coordinate value defining the calculation region.
 	    max_cutoff (float): Maximum x coordinate value defining the calculation region.
 
         Returns:
@@ -161,33 +173,40 @@ def calculate_offset( self, grid, min_cutoff, max_cutoff ):
         max_offset = ( ( grid.x[ max_index ] - grid.x[ max_index - 1 ] ) / 2 ) + ( ( grid.x[ max_index - 1 ] - grid.x[ max_index -2 ] ) / 2 )
         return min_offset, max_offset
 
-    def calculate_delta_x( self, grid, min_cutoff, max_cutoff ):
-        """Calculates the distance between the midpoints of each consecutive site. Inserts the calculated distance to the next grid point outside of the calculation region to the first and last position as the delta_x value for the endmost sites. 
+    def calculate_delta_x(self,
+                          grid: Grid,
+                          min_cutoff: float,
+                          max_cutoff: float) -> np.ndarray:
+        """Calculates the distance between the midpoints of each consecutive site. Inserts the calculated distance to the next grid point outside of the calculation region to the first and last position as the delta_x value for the endmost sites.
 
         Args:
             grid (:obj:`pyscses.Grid`): Contains properties of the grid including the x coordinates and the volumes. Used to access the x coordinates.
-            min_cutoff (float): Minimum x coordinate value defining the calculation region. 
+            min_cutoff (float): Minimum x coordinate value defining the calculation region.
 	    max_cutoff (float): Maximum x coordinate value defining the calculation region.
 
 	Returns:
-	    list: Distance between consecutive sites. 
+	    list: Distance between consecutive sites.
 
 	"""
         min_index, max_index = self.find_index( grid, min_cutoff, max_cutoff )
         min_offset, max_offset = self.calculate_offset( grid, min_cutoff, max_cutoff )
         return delta_x_from_grid( grid.x[ min_index+1 : max_index ], [min_offset, max_offset] )
 
-    def calculate_average( self, grid, min_cutoff, max_cutoff, sc_property ):
+    def calculate_average(self,
+                          grid: Grid,
+                          min_cutoff: float,
+                          max_cutoff: float,
+                          sc_property: np.ndarray) -> float:
         """Calculate the average of a given space chage property over a given region.
 
         Args:
             grid (:obj:`pyscses.Grid`): Contains properties of the grid including the x coordinates and the volumes. Used to access the x coordinates.
-            min_cutoff (float): Minimum x coordinate value defining the calculation region. 
+            min_cutoff (float): Minimum x coordinate value defining the calculation region.
             max_cutoff (float): Maximum x coordinate value defining the calculation region.
             sc_property (list): Value of space charge property at all sites.
 
         Returns:
-            float: The average value for the property over the given sites. 
+            float: The average value for the property over the given sites.
 
 	"""
         min_index, max_index = self.find_index(grid, min_cutoff, max_cutoff)
@@ -199,7 +218,7 @@ def solve(self,
               verbose: bool = False) -> None:
         """
         Self-consistent solving of the Poisson-Boltzmann equation. Iterates until the convergence is less than the convergence limit. The outputs are stored as Calculation attributes.
-        Calculation.phi (array): Electrostatic potential on a one-dimensional grid. 
+        Calculation.phi (array): Electrostatic potential on a one-dimensional grid.
         Calculation.rho (array): Charge density on a one-dimensional grid.
         Calculation.niter (int): Number of iterations performed to reach convergence.
 
@@ -218,7 +237,7 @@ def solve(self,
 
         phi = np.zeros_like(self.grid.x)
         rho = np.zeros_like(self.grid.x)
-        
+
         conv = 1.0
         niter = 0
         while conv > self.convergence:
@@ -232,7 +251,7 @@ def solve(self,
             elif approximation == 'mott-schottky':
                 subgrid = self.grid.subgrid(self.site_labels[0])
                 predicted_phi_subgrid = np.array([phi_at_x(phi=predicted_phi,
-                                                   coordinates=self.grid.x, 
+                                                   coordinates=self.grid.x,
                                                    x=x)
                                             for x in subgrid.x])
                 average_predicted_phi = self.calculate_average(grid=subgrid,
@@ -246,24 +265,25 @@ def solve(self,
             niter += 1
             if verbose:
                 if niter % 500 == 0:
-                    print(f'Iteration: {niter} -> Convergence: {conv} / {self.convergence}') 
+                    print(f'Iteration: {niter} -> Convergence: {conv} / {self.convergence}')
         if verbose:
             print('Converged at iteration {niter} -> Convergence: {conv} / {self.convergence}')
         self.phi = phi
         self.rho = self.grid.rho( phi, self.temp )
         self.niter = niter
 
-    def form_subgrids( self, site_labels ):
-        """Creates a `pysces.Grid` object for each species in the system. The output is a dictionary of separate Grid classes for the different site species and is stored as Calculation.subgrids. 
+    def form_subgrids(self,
+                      site_labels: List[str]) -> None:
+        """Creates a `pysces.Grid` object for each species in the system. The output is a dictionary of separate Grid classes for the different site species and is stored as Calculation.subgrids.
 
         Args:
             site_labels (list): List of strings for the different site species.
 
         """
         self.site_labels = site_labels
-        subgrids = {} 
+        subgrids = {}
         for label in site_labels:
-            name = '{}'.format( label ) 
+            name = '{}'.format( label )
             subgrids[name] = self.grid.subgrid( label )
             subgrids[name].delta_x[0] = subgrids[name].delta_x[1]
             subgrids[name].delta_x[-1] = subgrids[name].delta_x[1]
@@ -272,11 +292,11 @@ def form_subgrids( self, site_labels ):
         self.subgrids = subgrids
 
     def create_subregion_sites( self, grid, min_cutoff, max_cutoff ):
-        """Creates a `pyscses.Set_of_Sites` object for a defined region of the grid.
+        """Creates a `pyscses.SetOfSites` object for a defined region of the grid.
 
         Args:
             grid (object): Grid object - contains properties of the grid including the x coordinates and the volumes. Used to access the x coordinates.
-            min_cutoff (float): Minimum x coordinate value defining the calculation region. 
+            min_cutoff (float): Minimum x coordinate value defining the calculation region.
             max_cutoff (float): Maximum x coordinate value defining the calculation region.
 
         Returns:
@@ -287,7 +307,7 @@ def create_subregion_sites( self, grid, min_cutoff, max_cutoff ):
         for site in grid.set_of_sites:
             if site.x > min_cutoff and site.x < max_cutoff:
                 sites.append( site )
-        sites = Set_of_Sites( sites )
+        sites = SetOfSites( sites )
         return sites
 
     def create_space_charge_region( self, grid, pos_or_neg_scr, scr_limit ):
@@ -302,15 +322,15 @@ def create_space_charge_region( self, grid, pos_or_neg_scr, scr_limit ):
 	Returns:
 	    list: List of x coordinates for sites within the space charge region.
 
-	"""	
+	"""
         space_charge_region = []
-        self.phi_on_mobile_defect_grid = [ phi_at_x( self.phi, self.grid.x, x ) for x in grid.x ] 
+        self.phi_on_mobile_defect_grid = [ phi_at_x( self.phi, self.grid.x, x ) for x in grid.x ]
         x_and_phi = np.column_stack( ( grid.x, self.phi_on_mobile_defect_grid ) )
         for i in range( len( x_and_phi ) ):
             if pos_or_neg_scr == 'positive':
                 if x_and_phi[i, 1]-x_and_phi[0,1] > scr_limit:
                     space_charge_region.append( x_and_phi[i,0] )
-            if pos_or_neg_scr == 'negative': 
+            if pos_or_neg_scr == 'negative':
                 if x_and_phi[i,1]-x_and_phi[0,1] < scr_limit:
                     space_charge_region.append( x_and_phi[i,0] )
         return space_charge_region
@@ -318,7 +338,7 @@ def create_space_charge_region( self, grid, pos_or_neg_scr, scr_limit ):
     def calculate_mobile_defect_conductivities( self, pos_or_neg_scr, scr_limit, species, mobility_scaling=False ):
         """Calculate the conductivity ratio between the space charge region and the bulk both perpendicular and parallel to the grain boundary.
 
-        A `Set_of_Sites` object is created for the sites in the space charge region, and the defect distributions calculated. The width of the space charge region is calculated and a bulk region of the same width is defined. A Set_of_Sites object for the bulk region is created and the defect distributions calculated. Taking each site as a resistor in series or parallel respectively, the conductivity is calculated and the ratio between the space charge region and the bulk is taken. 
+        A `SetOfSites` object is created for the sites in the space charge region, and the defect distributions calculated. The width of the space charge region is calculated and a bulk region of the same width is defined. A SetOfSites object for the bulk region is created and the defect distributions calculated. Taking each site as a resistor in series or parallel respectively, the conductivity is calculated and the ratio between the space charge region and the bulk is taken.
 
         Args:
             pos_or_neg_scr (str): 'positive' - for a positive space charge potential.
@@ -329,7 +349,7 @@ def calculate_mobile_defect_conductivities( self, pos_or_neg_scr, scr_limit, spe
 
         Returns:
             float: The perpendicular conductivity ratio. The conductivity ratio between the bulk and the space charge region perpendicular to the grain boundary.
-            float: The parallel conductivity ratio. The conductivity ratio between the bulk and the space charge region parallel to the grain boundary. 
+            float: The parallel conductivity ratio. The conductivity ratio between the bulk and the space charge region parallel to the grain boundary.
 
 	"""
         space_charge_region = self.create_space_charge_region( self.subgrids[species], pos_or_neg_scr, scr_limit )
@@ -340,19 +360,19 @@ def calculate_mobile_defect_conductivities( self, pos_or_neg_scr, scr_limit, spe
             mobilities = site.defects[0].mobility
         space_charge_region_grid = Grid.grid_from_set_of_sites( space_charge_region_sites, space_charge_region_limits, space_charge_region_limits, self.grid.b, self.grid.c )
         space_charge_region_width = space_charge_region_grid.x[-1] - space_charge_region_grid.x[0]
-        mobile_defect_density = Set_of_Sites( self.subgrids[species].set_of_sites ).subgrid_calculate_defect_density( self.subgrids[species], self.grid, self.phi, self.temp )
+        mobile_defect_density = SetOfSites( self.subgrids[species].set_of_sites ).subgrid_calculate_defect_density( self.subgrids[species], self.grid, self.phi, self.temp )
         space_charge_region_mobile_defect_mf = space_charge_region_sites.calculate_probabilities( space_charge_region_grid, self.phi, self.temp )
         space_charge_region_mobile_defect_density = space_charge_region_sites.subgrid_calculate_defect_density( space_charge_region_grid, self.grid, self.phi, self.temp )
         if mobility_scaling:
-             mobile_defect_conductivity = space_charge_region_mobile_defect_density * ( 1 - space_charge_region_mobile_defect_mf ) * charge * mobilities 
+             mobile_defect_conductivity = space_charge_region_mobile_defect_density * ( 1 - space_charge_region_mobile_defect_mf ) * charge * mobilities
         else:
             mobile_defect_conductivity = space_charge_region_mobile_defect_density * charge * mobilities
         bulk_x_max = self.bulk_x_min + space_charge_region_width
         min_bulk_index, max_bulk_index = self.find_index( self.subgrids[species], self.bulk_x_min, bulk_x_max )
-        self.bulk_limits = self.calculate_offset( self.subgrids[species], self.bulk_x_min, bulk_x_max ) 
+        self.bulk_limits = self.calculate_offset( self.subgrids[species], self.bulk_x_min, bulk_x_max )
         bulk_mobile_defect_sites = self.create_subregion_sites( self.subgrids[species], self.bulk_x_min, bulk_x_max )
         bulk_mobile_defect_grid = Grid.grid_from_set_of_sites( bulk_mobile_defect_sites, self.bulk_limits, self.bulk_limits, self.grid.b, self.grid.c )
-        bulk_mobile_defect_density = Set_of_Sites( bulk_mobile_defect_grid.set_of_sites ).subgrid_calculate_defect_density( bulk_mobile_defect_grid, self.grid, self.phi, self.temp )  
+        bulk_mobile_defect_density = SetOfSites( bulk_mobile_defect_grid.set_of_sites ).subgrid_calculate_defect_density( bulk_mobile_defect_grid, self.grid, self.phi, self.temp )
         bulk_region_mobile_defect_mf = bulk_mobile_defect_sites.calculate_probabilities( bulk_mobile_defect_grid, self.phi, self.temp )
         if mobility_scaling:
             bulk_mobile_defect_conductivity = bulk_mobile_defect_density * charge * mobilities
@@ -370,7 +390,7 @@ def calculate_mobile_defect_conductivities( self, pos_or_neg_scr, scr_limit, spe
             parallel_conductivity_ratio = space_charge_parallel * bulk_parallel
         else:
             perpendicular_conductivity_ratio = 0.0
-            parallel_conductivity_ratio = 0.0 
+            parallel_conductivity_ratio = 0.0
 #        self.depletion_factor = 1 - ( mobile_defect_density / average_bulk )
         return perpendicular_conductivity_ratio, parallel_conductivity_ratio
 
@@ -392,13 +412,13 @@ def calculate_resistivity_ratio( self, pos_or_neg_scr, scr_limit, mobility_scali
             full_perpendicular_conductivity_data.append(c_per)
         self.perpendicular_resistivity_ratio = 1 / sum(full_perpendicular_conductivity_data)
         self.parallel_resistivity_ratio = 1 / sum(full_parallel_conductivity_data)
-            
+
     def solve_MS_approx_for_phi( self, valence ):
         """Calculate the space-charge potential, :math:`\phi_0`, from the grain-boundary resistivity ratio, within the Mott-Schottky approximation.
         Within the Mott-Schottky approximation the grain boundary resistivity is related to the space-charge potential (the electrostatic potential at the grain boundary core, compared to the bulk value) according to
-        
+
         .. math:: r_\mathrm{gb} = \frac{\rho_{i,\mathrm{gb}}}{\rho_{i,\infty}} = \frac{\exp(z_i\phi_0 / V_\mathrm{th})}{2z_i\phi_0/V_\mathrm{th}}
-       
+
         where
 
         .. math:: V_\mathrm{th} = \frac{k_\mathrm{B}T}{q}.
@@ -416,8 +436,8 @@ def solve_MS_approx_for_phi( self, valence ):
 
 
         Args:
-            valence( float ): Charge of the mobile defect species. 
-   
+            valence( float ): Charge of the mobile defect species.
+
         Raises:
             ValueError: If the calculated resistivity ratio is less than 1.36, the LambertW function returns a complex, non-physical value.
 
@@ -451,20 +471,20 @@ def calculate_space_charge_width( self, valence ):
 
 
     def mole_fractions( self ):
-        """Calculate the mole fractions (probability of defects occupation) for each site on the subgrid for each species. The output is stored as a Calculation attribute. Calculation.mf (dict): A dictionary of the defect species mole fractions for each site on the subgrid for each site species. 
+        """Calculate the mole fractions (probability of defects occupation) for each site on the subgrid for each species. The output is stored as a Calculation attribute. Calculation.mf (dict): A dictionary of the defect species mole fractions for each site on the subgrid for each site species.
         Args:
             None
- 
+
         """
         mole_fractions = {}
         for label in self.site_labels:
             name = '{}'.format(label)
-            mole_fractions[name] = Set_of_Sites(self.subgrids[name].set_of_sites).calculate_probabilities( self.grid, self.phi, self.temp )
+            mole_fractions[name] = SetOfSites(self.subgrids[name].set_of_sites).calculate_probabilities( self.grid, self.phi, self.temp )
         self.mf = mole_fractions
 
 def diff_central(x, y):
     """Calculate the numerical derivative of x,y data using a central difference approximation.
-   
+
     Args:
         x (numpy.array): x values.
         y (numpy.array): y values.
@@ -480,19 +500,19 @@ def diff_central(x, y):
     y1 = y[1:-1]
     y2 = y[2:]
     f = (x2 - x1)/(x2 - x0)
-    return (1-f)*(y2 - y1)/(x2 - x1) + f*(y1 - y0)/(x1 - x0) 
+    return (1-f)*(y2 - y1)/(x2 - x1) + f*(y1 - y0)/(x1 - x0)
 
 def calculate_activation_energies( ratios, temp ):
     """Solves the Arrhenius equation using the calculated resistivity ratio for a series of temperatures to calculate the activation energy for single defect segregation.
 
     Uses a central difference approach so endpoints filled in with NaN to create an array with the same length as the args.
-   
+
     Args:
         ratios (list): Resistivity ratios calculated for a range of temperatures.
         temp (list): Temperature (values used to calculate resistivity ratio values).
 
     Returns:
-        numpy.array: The activation energy calculated for different temperatures.  
+        numpy.array: The activation energy calculated for different temperatures.
 
     """
     temp = np.array( temp )
@@ -504,4 +524,4 @@ def calculate_activation_energies( ratios, temp ):
     Ea = np.append( Ea, 0 )
     Ea = np.insert( Ea, [0], 0 )
     Ea2 = np.where(Ea == 0, np.nan, Ea )
-    return Ea2           
+    return Ea2
diff --git a/pyscses/grid.py b/pyscses/grid.py
index fa285cd..b8e08d3 100644
--- a/pyscses/grid.py
+++ b/pyscses/grid.py
@@ -269,10 +269,10 @@ def subgrid( self, subset_species ):
     @classmethod
     def grid_from_set_of_sites( cls, set_of_sites, limits, limits_for_laplacian, b, c ):
         """
-        Creates a grid from a given Set_of_Sites object.
+        Creates a grid from a given SetOfSites object.
 
         Args:
-            set_of_sites (object): Set_of_Sites object containing a set of all Site objects. 
+            set_of_sites (object): SetOfSites object containing a set of all Site objects. 
             limits (list): distance between the midpoint of the endmost sites and the midpoint of the next site outside of the calculation region for the first and last sites respectively. 
             limits_for_laplacian (list): distance between the endmost sites and the next site outside of the calculation region for the first and last sites respectively.
             b (float):                b dimension for every grid-point.
diff --git a/pyscses/set_of_sites.py b/pyscses/set_of_sites.py
index f8e4425..01c1c50 100644
--- a/pyscses/set_of_sites.py
+++ b/pyscses/set_of_sites.py
@@ -8,16 +8,16 @@
 from pyscses.constants import boltzmann_eV
 from bisect import bisect_left
 
-class Set_of_Sites:
-    """The Set_of_Sites object groups together all of the Site objects into one object and contains functions for the calculations that provide properties of all of the sites together rather than individually. """
+class SetOfSites:
+    """The SetOfSites object groups together all of the Site objects into one object and contains functions for the calculations that provide properties of all of the sites together rather than individually. """
     def __init__( self, sites ):
         self.sites = sites
     
     def __add__( self, other ):
-        """ Allows the concatenation of multiple Set_of_Sites objects"""
-        if type( other ) is not Set_of_Sites:
+        """ Allows the concatenation of multiple SetOfSites objects"""
+        if type( other ) is not SetOfSites:
             raise TypeError
-        return Set_of_Sites( self.sites + other.sites )
+        return SetOfSites( self.sites + other.sites )
 
     def __getitem__( self, index ):
         """ Returns the site corresponding to a given index """
@@ -131,10 +131,10 @@ def subgrid_calculate_defect_density( self, sub_grid, full_grid, phi, temp ):
     def form_continuum_sites( all_sites, x_min, x_max, n_points, b, c, defect_species, limits_for_laplacian, site_labels, defect_labels ):
   
         """
-        Creates a Set_of_Sites object for sites interpolated onto a regular grid, this is equivalent to assuming a continuum approximation.
+        Creates a SetOfSites object for sites interpolated onto a regular grid, this is equivalent to assuming a continuum approximation.
 
         Args:
-            all_sites (object): Orginal Set_of_Sites object from full data.
+            all_sites (object): Orginal SetOfSites object from full data.
             x_min (float): Minimum x coordinate value defining the calculation region.
             x_max (float): Maximum x coordinate value defining the calculation region. 
             n_points (int): Number of points that the data should be interpolated on to.
@@ -146,7 +146,7 @@ def form_continuum_sites( all_sites, x_min, x_max, n_points, b, c, defect_specie
             defect_labels (list): List of strings for the different defect species.
 
         Returns:
-            :obj:`Set_of_Sites`: Sites interpolated onto a regular grid.
+            :obj:`SetOfSites`: Sites interpolated onto a regular grid.
 
 	"""	    
   
@@ -160,12 +160,12 @@ def form_continuum_sites( all_sites, x_min, x_max, n_points, b, c, defect_specie
             new_energies = griddata( ( [ site.x for site in all_sites.subset( label ) ] ), average_energies, grid, method = 'nearest' )
             for x, e in zip( grid, new_energies):
                 sites.append( Site( label, x, [ defect_species[ d_label ] ], [e], scaling = np.array( scaling ) ) )
-        return Set_of_Sites( sites ), limits    
+        return SetOfSites( sites ), limits    
 
     @ classmethod
     def set_of_sites_from_input_data( cls, input_data, limits, defect_species, site_charge, core, temperature, offset=0.0 ):
         """
-        Takes the data from the input file and creates a Set_of_Sites object for those sites. 
+        Takes the data from the input file and creates a SetOfSites object for those sites. 
 	The input data file is a .txt file where each line in the file corresponds to a site. The values in each line are formatted and separated into the corresponding properties before creating a Site object for each site.
 
         Args:
@@ -177,7 +177,7 @@ def set_of_sites_from_input_data( cls, input_data, limits, defect_species, site_
 	    temperature (float): Temperature that the calculation is being run at.
 
 	Returns:
-	    :obj:`Set_of_Sites`: `Set_of_Sites` object for the input data.
+	    :obj:`SetOfSites`: `SetOfSites` object for the input data.
 
  	"""
         site_data = load_site_data( input_data, limits[0], limits[1], site_charge, offset )
@@ -191,7 +191,7 @@ def set_of_sites_from_input_data( cls, input_data, limits, defect_species, site_
             for line in site_data:
                 if ( -boltzmann_eV * temperature) <= line[4] <= ( boltzmann_eV * temperature ):
                     line[4] = 0.0
-        return Set_of_Sites( [ site_from_input_file( line, defect_species, site_charge, core, temperature ) for line in site_data ] )
+        return SetOfSites( [ site_from_input_file( line, defect_species, site_charge, core, temperature ) for line in site_data ] )
 
     @ classmethod
     def core_width_analysis( cls, input_data, limits, defect_species, site_charge, core, temperature ):
diff --git a/tests/test_grid.py b/tests/test_grid.py
index 3091c27..98eb792 100644
--- a/tests/test_grid.py
+++ b/tests/test_grid.py
@@ -1,6 +1,6 @@
 import unittest
 from pyscses.grid import Grid, Grid_Point, delta_x_from_grid
-from pyscses.set_of_sites import Set_of_Sites
+from pyscses.set_of_sites import SetOfSites
 from pyscses.site import Site
 from pyscses.defect_species import DefectSpecies
 from unittest.mock import Mock, MagicMock, patch, call
@@ -22,7 +22,7 @@ def test_grid_instance_is_initialised( self, mock_Grid_Point, mock_index ):
         c = 0.1
         limits = [ 1.0, 1.0 ]
         limits_for_laplacian = [1.0, 1.0]
-        set_of_sites = MagicMock( spec=Set_of_Sites )
+        set_of_sites = MagicMock( spec=SetOfSites )
         sites = [ Mock( spec=Site ), Mock( spec=Site ) ]
         sites[0].x = 1.0
         sites[1].x = 3.0
diff --git a/tests/test_notebooks/test_1/test_notebook_1.ipynb b/tests/test_notebooks/test_1/test_notebook_1.ipynb
index c9049f2..61f4616 100644
--- a/tests/test_notebooks/test_1/test_notebook_1.ipynb
+++ b/tests/test_notebooks/test_1/test_notebook_1.ipynb
@@ -20,7 +20,7 @@
    "outputs": [],
    "source": [
     "from pyscses.defect_species import DefectSpecies\n",
-    "from pyscses.set_of_sites import Set_of_Sites\n",
+    "from pyscses.set_of_sites import SetOfSites\n",
     "from pyscses.constants import boltzmann_eV\n",
     "from pyscses.calculation import Calculation, calculate_activation_energies\n",
     "from pyscses.set_up_calculation import calculate_grid_offsets\n",
@@ -64,7 +64,7 @@
    "outputs": [],
    "source": [
     "alpha = 0.0005\n",
-    "conv = 1e-8\n",
+    "conv = 1e-6\n",
     "grid_x_min = -6.094e-9\n",
     "grid_x_max = 5.16e-9\n",
     "bulk_x_min = -5.783e-9\n",
@@ -117,7 +117,25 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration: 500 -> Convergence: 0.007447986294976805 / 1e-06\n",
+      "Iteration: 1000 -> Convergence: 0.0007754893152833815 / 1e-06\n",
+      "Iteration: 1500 -> Convergence: 0.00018699456659750744 / 1e-06\n",
+      "Iteration: 2000 -> Convergence: 6.407008310764769e-05 / 1e-06\n",
+      "Iteration: 2500 -> Convergence: 2.641345352581461e-05 / 1e-06\n",
+      "Iteration: 3000 -> Convergence: 1.2241838100617005e-05 / 1e-06\n",
+      "Iteration: 3500 -> Convergence: 6.144657281250713e-06 / 1e-06\n",
+      "Iteration: 4000 -> Convergence: 3.2588703205936678e-06 / 1e-06\n",
+      "Iteration: 4500 -> Convergence: 1.7952087978299646e-06 / 1e-06\n",
+      "Iteration: 5000 -> Convergence: 1.015070044794803e-06 / 1e-06\n",
+      "Converged at iteration {niter} -> Convergence: {conv} / {self.convergence}\n"
+     ]
+    }
+   ],
    "source": [
     "limits, laplacian_limits = calculate_grid_offsets( data, grid_x_min, grid_x_max, 'single' )\n",
     "\n",
@@ -126,9 +144,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'Ce' ):\n",
     "                site.defect_with_label('Gd').fixed = True\n",
@@ -141,7 +159,7 @@
     "        c_o.form_subgrids( site_labels )\n",
     "        if systems == 'gouy-chapman':\n",
     "            c_o.mole_fraction_correction( m, systems, initial_guess )\n",
-    "        c_o.solve(systems)\n",
+    "        c_o.solve(systems, verbose=True)\n",
     "        c_o.mole_fractions()\n",
     "        c_o.calculate_resistivity_ratio( 'positive', 2e-2 )\n",
     "        c_o.solve_MS_approx_for_phi( valence[0] )\n",
@@ -161,7 +179,7 @@
     "        output_data.to_csv(filename, index=False)\n",
     "        \n",
     "        key_data = np.column_stack(( max(c_o.phi), c_o.perpendicular_resistivity_ratio, c_o.parallel_resistivity_ratio ))\n",
-    "        np.savetxt( 'generated_outputs/key_data.dat', key_data )     "
+    "        np.savetxt('generated_outputs/key_data.dat', key_data)"
    ]
   },
   {
@@ -171,46 +189,51 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11cad4b70>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
+      "image/png": {
+       "height": 264,
+       "width": 393
+      },
       "needs_background": "light"
      },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11f063550>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
+      "image/png": {
+       "height": 272,
+       "width": 387
+      },
       "needs_background": "light"
      },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11f91bbe0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
+      "image/png": {
+       "height": 261,
+       "width": 392
+      },
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "104.2860275390558\n"
-     ]
     }
    ],
    "source": [
@@ -243,6 +266,13 @@
     "plt.savefig('generated_outputs/x_vs_molefractions.pdf')\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -261,7 +291,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/tests/test_notebooks/test_2/test_notebook_2.ipynb b/tests/test_notebooks/test_2/test_notebook_2.ipynb
index 0b6626a..e615d1a 100644
--- a/tests/test_notebooks/test_2/test_notebook_2.ipynb
+++ b/tests/test_notebooks/test_2/test_notebook_2.ipynb
@@ -20,7 +20,7 @@
    "outputs": [],
    "source": [
     "from pyscses.defect_species import DefectSpecies\n",
-    "from pyscses.set_of_sites import Set_of_Sites\n",
+    "from pyscses.set_of_sites import SetOfSites\n",
     "from pyscses.constants import boltzmann_eV\n",
     "from pyscses.calculation import Calculation, calculate_activation_energies\n",
     "from pyscses.set_up_calculation import calculate_grid_offsets\n",
@@ -126,9 +126,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'Ce' ):\n",
     "                site.defect_with_label('Gd').fixed = True\n",
diff --git a/tests/test_notebooks/test_3/test_notebook_3.ipynb b/tests/test_notebooks/test_3/test_notebook_3.ipynb
index b084244..6c837e1 100644
--- a/tests/test_notebooks/test_3/test_notebook_3.ipynb
+++ b/tests/test_notebooks/test_3/test_notebook_3.ipynb
@@ -20,7 +20,7 @@
    "outputs": [],
    "source": [
     "from pyscses.defect_species import DefectSpecies\n",
-    "from pyscses.set_of_sites import Set_of_Sites\n",
+    "from pyscses.set_of_sites import SetOfSites\n",
     "from pyscses.constants import boltzmann_eV\n",
     "from pyscses.calculation import Calculation, calculate_activation_energies\n",
     "from pyscses.set_up_calculation import calculate_grid_offsets\n",
@@ -128,9 +128,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'Ce' ):\n",
     "                site.defect_with_label('Gd').fixed = True\n",
diff --git a/tests/test_notebooks/test_4/test_notebook_4.ipynb b/tests/test_notebooks/test_4/test_notebook_4.ipynb
index 0f356d4..74ffe04 100644
--- a/tests/test_notebooks/test_4/test_notebook_4.ipynb
+++ b/tests/test_notebooks/test_4/test_notebook_4.ipynb
@@ -20,7 +20,7 @@
    "outputs": [],
    "source": [
     "from pyscses.defect_species import DefectSpecies\n",
-    "from pyscses.set_of_sites import Set_of_Sites\n",
+    "from pyscses.set_of_sites import SetOfSites\n",
     "from pyscses.constants import boltzmann_eV\n",
     "from pyscses.calculation import Calculation, calculate_activation_energies\n",
     "from pyscses.set_up_calculation import calculate_grid_offsets\n",
@@ -126,9 +126,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'Ce' ):\n",
     "                site.defect_with_label('Gd').fixed = True\n",
diff --git a/tests/test_set_of_sites.py b/tests/test_set_of_sites.py
index 537d95e..a0777ed 100644
--- a/tests/test_set_of_sites.py
+++ b/tests/test_set_of_sites.py
@@ -1,18 +1,18 @@
 import unittest
-from pyscses.set_of_sites import Set_of_Sites
+from pyscses.set_of_sites import SetOfSites
 
 class TestSetOfSites( unittest.TestCase ):
 
     def test_set_of_sites_is_initialised( self ):
         sites = [ 1,2,3,4 ]
-        set_of_sites = Set_of_Sites( sites )
+        set_of_sites = SetOfSites( sites )
         self.assertEqual( set_of_sites.sites, sites )
 
     def test_set_of_sites_addition( self ):
         sites1 = [ 1, 2, 3, 4 ]
         sites2 = [ 5, 6, 7, 8 ]
-        set_of_sites_1 = Set_of_Sites( sites1 )
-        set_of_sites_2 = Set_of_Sites( sites2 )
+        set_of_sites_1 = SetOfSites( sites1 )
+        set_of_sites_2 = SetOfSites( sites2 )
         combined_set_of_sites = set_of_sites_1 + set_of_sites_2
         self.assertEqual( combined_set_of_sites.sites, sites1 + sites2 )
 
diff --git a/userguides/notebooks/Ex_1_BC.ipynb b/userguides/notebooks/Ex_1_BC.ipynb
index 6e93763..f0afe76 100644
--- a/userguides/notebooks/Ex_1_BC.ipynb
+++ b/userguides/notebooks/Ex_1_BC.ipynb
@@ -23,7 +23,7 @@
    "outputs": [],
    "source": [
     "from pyscses.defect_species import DefectSpecies\n",
-    "from pyscses.set_of_sites import Set_of_Sites\n",
+    "from pyscses.set_of_sites import SetOfSites\n",
     "from pyscses.constants import boltzmann_eV\n",
     "from pyscses.calculation import Calculation, calculate_activation_energies\n",
     "from pyscses.set_up_calculation import calculate_grid_offsets\n",
@@ -131,9 +131,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'site_2' ):\n",
     "                site.defect_with_label('defect_2').fixed = True\n",
@@ -260,9 +260,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data_1, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data_1, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'site_2' ):\n",
     "                site.defect_with_label('defect_2').fixed = True\n",
@@ -379,9 +379,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data_1, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data_1, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'site_2' ):\n",
     "                site.defect_with_label('defect_2').fixed = True\n",
@@ -501,9 +501,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data_2, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data_2, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'site_2' ):\n",
     "                site.defect_with_label('defect_2').fixed = True\n",
diff --git a/userguides/notebooks/Ex_2_MSGC.ipynb b/userguides/notebooks/Ex_2_MSGC.ipynb
index 1632c68..c4ddf3a 100644
--- a/userguides/notebooks/Ex_2_MSGC.ipynb
+++ b/userguides/notebooks/Ex_2_MSGC.ipynb
@@ -18,7 +18,7 @@
    "outputs": [],
    "source": [
     "from pyscses.defect_species import DefectSpecies\n",
-    "from pyscses.set_of_sites import Set_of_Sites\n",
+    "from pyscses.set_of_sites import SetOfSites\n",
     "from pyscses.constants import boltzmann_eV\n",
     "from pyscses.calculation import Calculation, calculate_activation_energies\n",
     "from pyscses.set_up_calculation import calculate_grid_offsets\n",
@@ -124,9 +124,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'site_2' ):\n",
     "                site.defect_with_label('defect_2').fixed = True\n",
@@ -246,9 +246,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'site_2' ):\n",
     "                site.defect_with_label('defect_2').fixed = True\n",
diff --git a/userguides/notebooks/Ex_3_Res.ipynb b/userguides/notebooks/Ex_3_Res.ipynb
index 65eeeca..be406af 100644
--- a/userguides/notebooks/Ex_3_Res.ipynb
+++ b/userguides/notebooks/Ex_3_Res.ipynb
@@ -23,7 +23,7 @@
    "outputs": [],
    "source": [
     "from pyscses.defect_species import DefectSpecies\n",
-    "from pyscses.set_of_sites import Set_of_Sites\n",
+    "from pyscses.set_of_sites import SetOfSites\n",
     "from pyscses.constants import boltzmann_eV\n",
     "from pyscses.calculation import Calculation, calculate_activation_energies\n",
     "from pyscses.set_up_calculation import calculate_grid_offsets\n",
@@ -119,9 +119,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'site_2' ):\n",
     "                site.defect_with_label('defect_2').fixed = True\n",
diff --git a/userguides/notebooks/Ex_4_MSapp.ipynb b/userguides/notebooks/Ex_4_MSapp.ipynb
index dba61a9..a3aa094 100644
--- a/userguides/notebooks/Ex_4_MSapp.ipynb
+++ b/userguides/notebooks/Ex_4_MSapp.ipynb
@@ -27,7 +27,7 @@
    "outputs": [],
    "source": [
     "from pyscses.defect_species import DefectSpecies\n",
-    "from pyscses.set_of_sites import Set_of_Sites\n",
+    "from pyscses.set_of_sites import SetOfSites\n",
     "from pyscses.constants import boltzmann_eV\n",
     "from pyscses.calculation import Calculation, calculate_activation_energies\n",
     "from pyscses.set_up_calculation import calculate_grid_offsets\n",
@@ -123,9 +123,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'site_2' ):\n",
     "                site.defect_with_label('defect_2').fixed = True\n",
diff --git a/userguides/notebooks/Ex_5_real_data.ipynb b/userguides/notebooks/Ex_5_real_data.ipynb
index 5002f79..b6fa798 100644
--- a/userguides/notebooks/Ex_5_real_data.ipynb
+++ b/userguides/notebooks/Ex_5_real_data.ipynb
@@ -18,7 +18,7 @@
    "outputs": [],
    "source": [
     "from pyscses.defect_species import DefectSpecies\n",
-    "from pyscses.set_of_sites import Set_of_Sites\n",
+    "from pyscses.set_of_sites import SetOfSites\n",
     "from pyscses.constants import boltzmann_eV\n",
     "from pyscses.calculation import Calculation, calculate_activation_energies\n",
     "from pyscses.set_up_calculation import calculate_grid_offsets\n",
@@ -109,9 +109,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'Ce' ):\n",
     "                site.defect_with_label('Gd').fixed = True\n",
diff --git a/userguides/notebooks/Ex_6_store_data.ipynb b/userguides/notebooks/Ex_6_store_data.ipynb
index 960f5aa..73a266a 100644
--- a/userguides/notebooks/Ex_6_store_data.ipynb
+++ b/userguides/notebooks/Ex_6_store_data.ipynb
@@ -18,7 +18,7 @@
    "outputs": [],
    "source": [
     "from pyscses.defect_species import DefectSpecies\n",
-    "from pyscses.set_of_sites import Set_of_Sites\n",
+    "from pyscses.set_of_sites import SetOfSites\n",
     "from pyscses.constants import boltzmann_eV\n",
     "from pyscses.calculation import Calculation, calculate_activation_energies\n",
     "from pyscses.set_up_calculation import calculate_grid_offsets\n",
@@ -151,9 +151,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charge, core_model, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charge, core_model, t )\n",
     "        if site_model == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if system == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'Ce' ):\n",
     "                site.defect_with_label('Gd').fixed = True\n",
diff --git a/userguides/notebooks/Running.ipynb b/userguides/notebooks/Running.ipynb
index 443e180..d36c4da 100644
--- a/userguides/notebooks/Running.ipynb
+++ b/userguides/notebooks/Running.ipynb
@@ -17,7 +17,7 @@
    "outputs": [],
    "source": [
     "from pyscses.defect_species import DefectSpecies\n",
-    "from pyscses.set_of_sites import Set_of_Sites\n",
+    "from pyscses.set_of_sites import SetOfSites\n",
     "from pyscses.constants import boltzmann_eV\n",
     "from pyscses.calculation import Calculation, calculate_activation_energies\n",
     "from pyscses.set_up_calculation import calculate_grid_offsets\n",
@@ -117,17 +117,17 @@
     "defect_species = { l : Defect_Species( l, v, m ) for l, v, m in zip( defect_labels, valence, m) }\n",
     "```\n",
     "\n",
-    "The input data file and system information are passed into ```set_of_sites_from_input_data```, which formats and splits wach line of the input file to create ```Site``` objects for the individual sites, which are then grouped together into a ```Set_of_Sites``` object. \n",
+    "The input data file and system information are passed into ```set_of_sites_from_input_data```, which formats and splits wach line of the input file to create ```Site``` objects for the individual sites, which are then grouped together into a ```SetOfSites``` object. \n",
     "\n",
     "```\n",
-    "all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "```\n",
     "\n",
     "The code checks whether the model being used is continuum, if so, the sites are passed into ```form_continuum_sites``` which interpolates the defect segregation energies onto a regular grid.\n",
     "\n",
     "```\n",
     "if site_models == 'continuum':\n",
-    "    all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "    all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "```\n",
     "\n",
     "ace charge models typically consider dopant ions as either mobile or immobile. If the dopant ions are considered immobile, the model follows a Mott-Schottky approximation. If the dopant ions are considered mobile, the model follows a Gouy-Chapman approximation. The code checks whether a Mott-Schottky or Gouy-Chapman approximation is being applied and fixes the mole fractions of the defects accordingly. \n",
@@ -216,9 +216,9 @@
     "    \n",
     "        defect_species = { l : DefectSpecies( l, v, m, mob ) for l, v, m, mob in zip( defect_labels, valence, m, mobilities ) }\n",
     "\n",
-    "        all_sites = Set_of_Sites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
+    "        all_sites = SetOfSites.set_of_sites_from_input_data( data, [grid_x_min, grid_x_max], defect_species, site_charges, core_models, t )\n",
     "        if site_models == 'continuum':\n",
-    "            all_sites, limits = Set_of_Sites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
+    "            all_sites, limits = SetOfSites.form_continuum_sites( all_sites, grid_x_min, grid_x_max, 1000, b, c, defect_species, laplacian_limits, site_labels, defect_labels )\n",
     "        if systems == 'mott-schottky':\n",
     "            for site in all_sites.subset( 'site_2' ):\n",
     "                site.defect_with_label('defect_2').fixed = True\n",
diff --git a/userguides/notebooks/Setting_up.ipynb b/userguides/notebooks/Setting_up.ipynb
index f51ec36..5f0b997 100644
--- a/userguides/notebooks/Setting_up.ipynb
+++ b/userguides/notebooks/Setting_up.ipynb
@@ -17,7 +17,7 @@
    "outputs": [],
    "source": [
     "from pyscses.defect_species import DefectSpecies\n",
-    "from pyscses.set_of_sites import Set_of_Sites\n",
+    "from pyscses.set_of_sites import SetOfSites\n",
     "from pyscses.constants import boltzmann_eV\n",
     "from pyscses.calculation import Calculation, calculate_activation_energies\n",
     "from pyscses.set_up_calculation import calculate_grid_offsets\n",