removed changes to reconstruction functions for pull request

openmc/data/grid.py

@@ -112,65 +112,3 @@ def thin(x, y, tolerance=0.001):
         y_out[i_remove] = np.nan
 
     return x_out[np.isfinite(x_out)], y_out[np.isfinite(y_out)]
-
-def linearizeIter(x, f, tolerance=0.001, unified=True):
-    """Return a tabulated representation of multiple functions of one
-    variable.
-
-    Parameters
-    ----------
-    x : Iterable of float
-        Initial x values at which the function should be evaluated
-    f : Callable
-        Function of a single variable that returns a dictionary
-    tolerance : float
-        Tolerance on the interpolation error
-    unified : boolean
-        Flag to indicate usage of a unified grid for all functions
-        if True, or independent grids if False
-
-    Returns
-    -------
-    numpy.ndarray
-        Tabulated values of the independent variable
-    dictionary of numpy.ndarray's
-        Tabulated values of the dependent variable
-
-    """
-    if unified==True:
-        # Initialize dictionary of output
-        y_dict = f(x[0])
-    
-        for item in y_dict:
-            #Initialize output
-            x_out = []
-
-            #Initialize stacks
-            x_stack = [x[0]]
-            y_stack = [y_dict[item]]
-            for i in range(x.shape[0] - 1):
-                x_stack.insert(0, x[i + 1])
-                y_stack.insert(0, f(x[i + 1])[item])
-        
-                while True:
-                    x_high, x_low = x_stack[-2:]
-                    y_high, y_low = y_stack[-2:]
-                    x_mid = 0.5*(x_low + x_high)
-                    y_mid = f(x_mid)[item]
-        
-                    y_interp = y_low + (y_high - y_low)/(x_high - x_low)*(x_mid - x_low)
-                    error = abs((y_interp - y_mid)/y_mid)
-                    if error > tolerance:
-                        x_stack.insert(-1, x_mid)
-                        y_stack.insert(-1, y_mid)
-                    else:
-                        x_out.append(x_stack.pop())
-                        y_stack.pop()
-                        if len(x_stack) == 1:
-                            break
-        
-            x_out.append(x_stack.pop())
-            x=np.array(x_out) #Use x_out for initial x values in next item
-
-        y_dict_out = f(np.array(x_out))
-        return np.array(x_out), y_dict_out