adding back files to be reviewed

openmc/data/grid.py

@@ -0,0 +1,114 @@
+import numpy as np
+
+
+def linearize(x, f, tolerance=0.001):
+    """Return a tabulated representation of a function 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
+    tolerance : float
+        Tolerance on the interpolation error
+
+    Returns
+    -------
+    numpy.ndarray
+        Tabulated values of the independent variable
+    numpy.ndarray
+        Tabulated values of the dependent variable
+
+    """
+    # Make sure x is a numpy array
+    x = np.asarray(x)
+
+    # Initialize output arrays
+    x_out = []
+    y_out = []
+
+    # Initialize stack
+    x_stack = [x[0]]
+    y_stack = [f(x[0])]
+
+    for i in range(x.shape[0] - 1):
+        x_stack.insert(0, x[i + 1])
+        y_stack.insert(0, f(x[i + 1]))
+
+        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)
+
+            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_out.append(y_stack.pop())
+                if len(x_stack) == 1:
+                    break
+
+    x_out.append(x_stack.pop())
+    y_out.append(y_stack.pop())
+
+    return np.array(x_out), np.array(y_out)
+
+def thin(x, y, tolerance=0.001):
+    """Check for (x,y) points that can be removed.
+
+    Parameters
+    ----------
+    x : numpy.ndarray
+        Independent variable
+    y : numpy.ndarray
+        Dependent variable
+    tolerance : float
+        Tolerance on interpolation error
+
+    Returns
+    -------
+    numpy.ndarray
+        Tabulated values of the independent variable
+    numpy.ndarray
+        Tabulated values of the dependent variable
+
+    """
+    # Initialize output arrays
+    x_out = x.copy()
+    y_out = y.copy()
+
+    N = x.shape[0]
+    i_left = 0
+    i_right = 2
+
+    while i_left < N - 2 and i_right < N:
+        m = (y[i_right] - y[i_left])/(x[i_right] - x[i_left])
+
+        for i in range(i_left + 1, i_right):
+            # Determine error in interpolated point
+            y_interp = y[i_left] + m*(x[i] - x[i_left])
+            if abs(y[i]) > 0.:
+                error = abs((y_interp - y[i])/y[i])
+            else:
+                error = 2*tolerance
+
+            if error > tolerance:
+                for i_remove in range(i_left + 1, i_right - 1):
+                    x_out[i_remove] = np.nan
+                    y_out[i_remove] = np.nan
+                i_left = i_right - 1
+                i_right = i_left + 1
+                break
+
+        i_right += 1
+
+    for i_remove in range(i_left + 1, i_right - 1):
+        x_out[i_remove] = np.nan
+        y_out[i_remove] = np.nan
+
+    return x_out[np.isfinite(x_out)], y_out[np.isfinite(y_out)]