horizon: add apparent horizon equations

2 files changed, 624 insertions, 0 deletions

diff --git a/horizon.py b/horizon.py
index 739714f..a1ca300 100644
--- a/horizon.py
+++ b/horizon.py
@@ -2,9 +2,12 @@
 
 from . import curvature
 from . import diff
+from . import interp
 from . import utils
 
+import itertools as it
 import numpy as np
+import scipy
 
 def calc_expansion(x, z, metric, curv, surf, direction = 1, diff_op = diff.fd4):
     """
@@ -68,3 +71,606 @@ def calc_expansion(x, z, metric, curv, surf, direction = 1, diff_op = diff.fd4):
     H = Div_s_u - trK + np.einsum('ij...,i...,j...', curv, s_u, s_u)
 
     return H
+
+def _tensor_transform_cart_spherical_d2(theta, r, x, z, tens):
+    zero = np.zeros_like(theta)
+    st   = np.sin(theta)
+    ct   = np.cos(theta)
+    jac  = np.array([[st,    r * ct,   zero ],
+                     [zero,    zero, r * st ],
+                     [ct,   -r * st,   zero ]])
+    return np.einsum('ai...,bj...,ab...->ij...', jac, jac, tens)
+
+def _tensor_transform_cart_spherical_u2(theta, r, x, z, tens):
+    zero = np.zeros_like(theta)
+    st = np.sin(theta)
+    ct = np.cos(theta)
+    r2 = r * r
+    # ∂ spherical / ∂ cart
+    jac_inv = np.array([[st,       zero,      ct ],
+                        [z / r2,   zero, -x / r2 ],
+                        [zero,   1. / x,     zero ]])
+    return np.einsum('ai...,bj...,ij...->ab...', jac_inv, jac_inv, tens)
+
+def _dtensor_transform_cart_spherical_d2(theta, r, x, z, tens, dtens):
+    zero = np.zeros_like(theta)
+    st = np.sin(theta)
+    ct = np.cos(theta)
+    jac = np.array([[st,    r * ct,   zero ],
+                    [zero,    zero, r * st ],
+                    [ct,   -r * st,   zero ]])
+    djac = np.array([[ # ∂^2 x
+                        [ zero, ct,    zero ], # / ∂r
+                        [ ct, -r * st, zero ], # / ∂θ
+                        [ zero, zero,    -x ], # / ∂φ
+                     ],
+                     [ # ∂^2 y
+                         [ zero, zero,     st ], # / ∂r
+                         [ zero, zero, r * ct ], # / ∂θ
+                         [ st, r * ct, zero   ], # / ∂φ
+                     ],
+                     [ # ∂^2 z
+                         [ zero,     -st, zero ], # / ∂r
+                         [ -st,  -r * ct, zero ], # / ∂θ
+                         [ zero,    zero, zero ], # / ∂φ
+                     ]])
+
+    return np.einsum('bij...,ck...,bc...->ijk...', djac, jac, tens) +\
+           np.einsum('bj...,cik...,bc...->ijk...', jac, djac, tens) +\
+           np.einsum('bj...,ck...,ai...,abc...->ijk...', jac, jac, jac, dtens)
+
+def _christoffel_transform_cart_spherical(theta, r, x, z, Gamma_cart):
+    zero = np.zeros_like(theta)
+    st = np.sin(theta)
+    ct = np.cos(theta)
+    r2 = r * r
+    # ∂ cart / ∂ spherical
+    jac = np.array([[st,    r * ct,   zero ],
+                    [zero,    zero, r * st ],
+                    [ct,   -r * st,   zero ]])
+    # ∂ spherical / ∂ cart
+    jac_inv = np.array([[st,       zero,      ct ],
+                        [z / r2,   zero, -x / r2 ],
+                        [zero,   1. / x,     zero ]])
+    djac = np.array([[ # ∂^2 x
+                        [ zero, ct,    zero ], # / ∂r
+                        [ ct, -r * st, zero ], # / ∂θ
+                        [ zero, zero,    -x ], # / ∂φ
+                     ],
+                     [ # ∂^2 y
+                         [ zero, zero,     st ], # / ∂r
+                         [ zero, zero, r * ct ], # / ∂θ
+                         [ st, r * ct, zero   ], # / ∂φ
+                     ],
+                     [ # ∂^2 z
+                         [ zero,     -st, zero ], # / ∂r
+                         [ -st,  -r * ct, zero ], # / ∂θ
+                         [ zero,    zero, zero ], # / ∂φ
+                     ]])
+
+    return np.einsum('ai...,jb...,kc...,ijk...->abc...', jac_inv, jac, jac, Gamma_cart) +\
+           np.einsum('ak...,kbc...->abc...', jac_inv, djac)
+
+def _dchristoffel_transform_cart_spherical(theta, r, x, z, Gamma_cart, dGamma_cart):
+    zero = np.zeros_like(theta)
+    st = np.sin(theta)
+    ct = np.cos(theta)
+
+    r2 = r * r
+    r3 = r2 * r
+    r4 = r3 * r
+
+    x2 = x * x
+    x4 = x2 * x2
+
+    z2 = z * z
+    # ∂ cart / ∂ spherical
+    jac = np.array([[st,    r * ct,   zero ],
+                    [zero,    zero, r * st ],
+                    [ct,   -r * st,   zero ]])
+    # ∂ spherical / ∂ cart
+    jac_inv = np.array([[st,       zero,      ct ],
+                        [z / r2,   zero, -x / r2 ],
+                        [zero,   1. / x,     zero ]])
+    djac = np.array([[ # ∂^2 x
+                        [ zero, ct,    zero ], # / ∂r
+                        [ ct, -r * st, zero ], # / ∂θ
+                        [ zero, zero,    -x ], # / ∂φ
+                     ],
+                     [ # ∂^2 y
+                         [ zero, zero,     st ], # / ∂r
+                         [ zero, zero, r * ct ], # / ∂θ
+                         [ st, r * ct, zero   ], # / ∂φ
+                     ],
+                     [ # ∂^2 z
+                         [ zero,     -st, zero ], # / ∂r
+                         [ -st,  -r * ct, zero ], # / ∂θ
+                         [ zero,    zero, zero ], # / ∂φ
+                     ]])
+    djac_inv = np.array([[ # ∂^2 r
+                             [ z2 / r3,     zero,   -x * z / r3 ], # / ∂x
+                             [ zero,      1. / r,          zero ], # / ∂y
+                             [ -x * z / r3, zero,       x2 / r3 ], # / ∂z
+                         ],
+                         [ # ∂^2 θ
+                             [     -2 * z * np.abs(x) / r4,                                       zero, np.sign(x) * (x2 - z2) / r4], # / ∂x
+                             [                        zero, z * (x4 + x2 * z2) * np.abs(x) / (r4 * x4),                       zero ], # / ∂y
+                             [ np.sign(x) * (x2 - z2) / r4,                                       zero,   2 * z * np.abs(x) / r4   ], # / ∂z
+                         ],
+                         [ # ∂^2 φ
+                             [ zero,     -1 / x2, zero ], # / ∂x
+                             [ -1 / x2,     zero, zero ], # / ∂y
+                             [ zero,        zero, zero ], # / ∂z
+                         ]])
+
+    d2jac = np.array([[ # ∂^3 x
+                         [ # / ∂r
+                             [ zero,   zero,    zero ], # / ∂r
+                             [ zero,    -st,    zero ], # / ∂θ
+                             [ zero,   zero,     -st ], # / ∂φ
+                         ],
+                         [ # / ∂θ
+                             [ zero,    -st,    zero ], # / ∂r
+                             [  -st,    -ct,    zero ], # / ∂θ
+                             [ zero,   zero, -r * ct ], # / ∂φ
+                         ],
+                         [ # / ∂φ
+                             [ zero,   zero,     -st ], # / ∂r
+                             [ zero,   zero, -r * ct ], # / ∂θ
+                             [ -st, -r * ct,    zero ], # / ∂φ
+                         ],
+                      ],
+                      [ # ∂^3 y
+                         [ # / ∂r
+                             [ zero,   zero,    zero ], # / ∂r
+                             [ zero,   zero,      ct ], # / ∂θ
+                             [ zero,     ct,    zero ], # / ∂φ
+                         ],
+                         [ # / ∂θ
+                             [ zero,   zero,      ct ], # / ∂r
+                             [ zero,   zero, -r * st ], # / ∂θ
+                             [  ct, -r * st,    zero ], # / ∂φ
+                         ],
+                         [ # / ∂φ
+                             [ zero,     ct,    zero ], # / ∂r
+                             [  ct, -r * st,    zero ], # / ∂θ
+                             [ zero,   zero, -r * st ], # / ∂φ
+                         ],
+                      ],
+                      [ # ∂^3 z
+                         [ # / ∂r
+                             [ zero,   zero,    zero ], # / ∂r
+                             [ zero,    -ct,    zero ], # / ∂θ
+                             [ zero,   zero,    zero ], # / ∂φ
+                         ],
+                         [ # / ∂θ
+                             [ zero,    -ct,    zero ], # / ∂r
+                             [  -ct, r * st,    zero ], # / ∂θ
+                             [ zero,   zero,    zero ], # / ∂φ
+                         ],
+                         [ # / ∂φ
+                             [ zero,   zero,    zero ], # / ∂r
+                             [ zero,   zero,    zero ], # / ∂θ
+                             [ zero,   zero,    zero ], # / ∂φ
+                         ],
+                      ]])
+
+    for i, j, k, l in it.product(range(3), repeat = 4):
+        assert(np.all(d2jac[i, j, k, l] == d2jac[i, l, j, k]))
+        assert(np.all(d2jac[i, j, k, l] == d2jac[i, k, l, j]))
+        assert(np.all(d2jac[i, j, k, l] == d2jac[i, l, k, j]))
+
+    # this is broken (probably borked index somewhere)
+    #return np.einsum('ia...,bj...,ck...,dl...,dabc...->lijk...', jac_inv,  jac, jac,  jac, dGamma_cart) +\
+    #       np.einsum('iad...,dl...,bj...,ck...,abc...->lijk...', djac_inv, jac, jac,  jac,  Gamma_cart) +\
+    #       np.einsum('ia...,bjl...,ck...,abc...->lijk...',       jac_inv,      djac,  jac,  Gamma_cart) +\
+    #       np.einsum('ia...,bj...,ckl...,abc...->lijk...',       jac_inv,       jac, djac,  Gamma_cart) +\
+    #       np.einsum('ajkl...,ia...->lijk...', d2jac, jac_inv)                                          +\
+    #       np.einsum('ajk...,iab...,bl...->lijk...', djac, djac_inv, jac_inv)
+
+    ret = np.empty((3,) + Gamma_cart.shape)
+    for a, b, c, d in it.product(range(3), repeat = 4):
+        val = np.zeros_like(theta)
+        for i, j, k, l in it.product(range(3), repeat = 4):
+            val += jac[l, d] * jac_inv[a, i] * jac[j, b] * jac[k, c] * dGamma_cart[l, i, j, k]
+
+        for i, j, k in it.product(range(3), repeat = 3):
+            val += (np.einsum('i...,i...', djac_inv[a, i], jac[:, d]) * jac[j, b] * jac[k, c] +
+                    jac_inv[a, i] * djac[j, b, d] * jac[k, c] +
+                    jac_inv[a, i] * jac[j, b] * djac[k, c, d]) * Gamma_cart[i, j, k]
+
+        for i, j in it.product(range(3), repeat = 2):
+            val += djac[i, b, c] * djac_inv[a, i, j] * jac[j, d]
+
+        for i in range(3):
+            val += d2jac[i, b, c, d] * jac_inv[a, i]
+
+        ret[d, a, b, c] = val
+
+    return ret
+
+class AHCalc(object):
+    """
+    Object encapsulating an axisymmetric apparent horizon calculation, intended
+    to be used with the nonlin_solve_1d solver.
+    """
+
+    """
+    A list of callables calculating the right-hand side of the axisymmetric AH
+    equation and its variational derivatives.
+    Intended to be passed to nonlin_solve_1d.
+    """
+    Fs = None
+
+    _diff_op        = None
+    _diff2_op       = None
+    _interp_stencil = None
+
+    _X              = None
+    _Z              = None
+    _metric_cart    = None
+    _curv_cart      = None
+    _metric_u_cart  = None
+    _dmetric_cart   = None
+    _dmetric_u_cart = None
+    _d2metric_cart  = None
+    _Gamma_cart     = None
+    _dGamma_cart    = None
+    _dcurv_cart     = None
+
+    def __init__(self, X, Z, metric_cart, curv_cart):
+        self._diff_op      = diff.fd8
+        self._diff2_op     = diff.fd28
+        self._interp_order = 6
+
+        self._X            = X
+        self._Z            = Z
+        self._metric_cart  = metric_cart
+        self._curv_cart    = curv_cart
+
+        self.Fs = [self._F, self._dF_r_fd, self._dF_R_fd]
+
+    @property
+    def X(self):
+        return self._X
+    @property
+    def Z(self):
+        return self._Z
+    @property
+    def metric_cart(self):
+        return self._metric_cart
+    @property
+    def curv_cart(self):
+        return self._curv_cart
+
+    @property
+    def metric_u_cart(self):
+        if self._metric_u_cart is None:
+            self._metric_u_cart = utils.matrix_invert(self.metric_cart)
+        return self._metric_u_cart
+    @property
+    def dmetric_cart(self):
+        if self._dmetric_cart is None:
+            self._dmetric_cart = curvature._calc_dmetric(self.X, self.Z, self.metric_cart, self._diff_op)
+        return self._dmetric_cart
+    @property
+    def d2metric_cart(self):
+        if self._d2metric_cart is None:
+            self._d2metric_cart = curvature._calc_d2metric(self.X, self.Z, self.metric_cart,
+                                                           self.dmetric_cart, self._diff_op, self._diff2_op)
+        return self._d2metric_cart
+    @property
+    def dmetric_u_cart(self):
+        if self._dmetric_u_cart is None:
+            self._dmetric_u_cart = -np.einsum('ij...,km...,ljk...->lim...',
+                                              self.metric_u_cart, self.metric_u_cart,
+                                              self.dmetric_cart)
+        return self._dmetric_u_cart
+    @property
+    def Gamma_cart(self):
+        if self._Gamma_cart is None:
+            self._Gamma_cart = curvature._calc_christoffel(self.metric_cart, self.metric_u_cart,
+                                                           self.dmetric_cart)
+        return self._Gamma_cart
+    @property
+    def dGamma_cart(self):
+        if self._dGamma_cart is None:
+            self._dGamma_cart = curvature._calc_dchristoffel(self.metric_cart,  self.metric_u_cart,
+                                                             self.dmetric_cart, self.dmetric_u_cart,
+                                                             self.d2metric_cart)
+        return self._dGamma_cart
+    @property
+    def dcurv_cart(self):
+        if self._dcurv_cart is None:
+            self._dcurv_cart = curvature._calc_dmetric(self.X, self.Z, self.curv_cart, self._diff_op)
+        return self._dcurv_cart
+
+    def _interp_var(self, theta, r, x, z, var):
+        origin = (self.Z[0, 0], self.X[0, 0])
+        step   = (self.Z[1, 0] - self.Z[0, 0], self.X[0, 1] - self.X[0, 0])
+        return interp.interp2d(origin, step, var, (z, x), self._interp_order)
+
+    def _spherical_metric(self, theta, r, x, z):
+        metric_cart_dst = np.empty((3, 3) + theta.shape)
+        for idx in it.product(range(3), repeat = 2):
+            metric_cart_dst[idx] = self._interp_var(theta, r, x, z, self.metric_cart[idx])
+        return _tensor_transform_cart_spherical_d2(theta, r, x, z, metric_cart_dst)
+
+    def _spherical_metric_u(self, theta, r, x, z):
+        metric_u_cart_dst = np.empty((3, 3) + theta.shape)
+        for idx in it.product(range(3), repeat = 2):
+            metric_u_cart_dst[idx] = self._interp_var(theta, r, x, z, self.metric_u_cart[idx])
+        return _tensor_transform_cart_spherical_u2(theta, r, x, z, metric_u_cart_dst)
+
+    def _spherical_dmetric_u(self, theta, r, x, z, metric, metric_u, Gamma):
+        # extract the derivatives of the metric from the Christoffel symbols
+        Gamma_l = np.einsum('ij...,ikl...->jkl...', metric, Gamma)
+        dmetric = np.empty_like(Gamma_l)
+        for i, j, k in it.product(range(3), repeat = 3):
+            dmetric[i, j, k] = Gamma_l[j, i, k] + Gamma_l[k, i, j]
+
+        return -np.einsum('ik...,jl...,mkl...->mij...', metric_u, metric_u, dmetric)
+
+    def _spherical_curv(self, theta, r, x, z):
+        curv_cart_dst = np.empty((3, 3) + theta.shape)
+        for idx in it.product(range(3), repeat = 2):
+            curv_cart_dst[idx] = self._interp_var(theta, r, x, z, self.curv_cart[idx])
+        return _tensor_transform_cart_spherical_d2(theta, r, x, z, curv_cart_dst)
+
+    def _spherical_dcurv(self, theta, r, x, z):
+        curv_cart_dst  = np.empty(   (3, 3) + theta.shape)
+        dcurv_cart_dst = np.empty((3, 3, 3) + theta.shape)
+        for idx in it.product(range(3), repeat = 2):
+            curv_cart_dst[idx] = self._interp_var(theta, r, x, z, self.curv_cart[idx])
+        for idx in it.product(range(3), repeat = 3):
+            dcurv_cart_dst[idx] = self._interp_var(theta, r, x, z, self.dcurv_cart[idx])
+        return _dtensor_transform_cart_spherical_d2(theta, r, x, z, curv_cart_dst, dcurv_cart_dst)
+
+    def _spherical_Gamma(self, theta, r, x, z):
+        Gamma_cart_dst = np.empty((3, 3, 3) + theta.shape)
+        for idx in it.product(range(3), repeat = 3):
+            Gamma_cart_dst[idx] = self._interp_var(theta, r, x, z, self.Gamma_cart[idx])
+        return _christoffel_transform_cart_spherical(theta, r, x, z, Gamma_cart_dst)
+
+    def _spherical_dGamma(self, theta, r, x, z):
+        Gamma_cart_dst  = np.empty(   (3, 3, 3) + theta.shape)
+        dGamma_cart_dst = np.empty((3, 3, 3, 3) + theta.shape)
+        for idx in it.product(range(3), repeat = 3):
+            Gamma_cart_dst[idx] = self._interp_var(theta, r, x, z, self.Gamma_cart[idx])
+        for idx in it.product(range(3), repeat = 4):
+            dGamma_cart_dst[idx] = self._interp_var(theta, r, x, z, self.dGamma_cart[idx])
+
+        return _dchristoffel_transform_cart_spherical(theta, r, x, z, Gamma_cart_dst, dGamma_cart_dst)
+
+    def _F(self, theta, H):
+        r, dr = H
+
+        x = r * np.sin(theta)
+        z = r * np.cos(theta)
+
+        metric_u = self._spherical_metric_u(theta, r, x, z)
+        curv     = self._spherical_curv    (theta, r, x, z)
+        Gamma    = self._spherical_Gamma   (theta, r, x, z)
+
+        u2 = metric_u[1, 1] * dr * dr - 2 * metric_u[0, 1] * dr + metric_u[0, 0]
+        u  = np.sqrt(u2)
+
+        df_ij = np.empty_like(metric_u)
+        for i, j in it.product(range(3), repeat = 2):
+            df_ij[i, j] = (metric_u[0, i] - metric_u[1, i] * dr) * (metric_u[0, j] - metric_u[1, j] * dr)
+
+        term1 = u2 * metric_u - df_ij
+        term2 = Gamma[0] - dr * Gamma[1] + u * curv
+
+        ret = -np.einsum('ij...,ij...', term1, term2) / (metric_u[0, 0] * metric_u[1, 1] - metric_u[0, 1] * metric_u[0, 1])
+
+        # handle the symmetry axis
+        g = theta / np.pi
+        for idx in np.where(np.abs(g.round() - g) < 1e-12)[0]:
+            stencil    = 8
+
+            coords     = np.empty(stencil * 2)
+            vals       = np.empty(stencil * 2)
+            fact       = np.empty(stencil * 2)
+            if idx + stencil < ret.shape[0]:
+                for i in range(stencil):
+                    coords[2 * i]     = theta[idx + i + 1]
+                    coords[2 * i + 1] = 2 * theta[idx] - coords[2 * i]
+
+                    vals[2 * i]       = ret[idx + i + 1]
+                    vals[2 * i + 1]   = ret[idx + i + 1]
+            elif idx - stencil >= 0:
+                for i in range(stencil):
+                    coords[2 * i]     = theta[idx - i - 1]
+                    coords[2 * i + 1] = 2 * theta[idx] - coords[2 * i]
+
+                    vals[2 * i]       = ret[idx - i - 1]
+                    vals[2 * i + 1]   = ret[idx - i - 1]
+            else:
+                continue
+
+            for i in range(len(fact)):
+                f = 1.0
+                for j in range(len(fact)):
+                    if i == j:
+                        continue
+                    f *= (theta[idx] - coords[j]) / (coords[i] - coords[j])
+                fact[i] = f
+
+            ret[idx] = np.dot(fact, vals)
+
+        return ret
+
+    def _dF_r_fd(self, theta, H):
+        eps = 1e-8
+        r, R = H
+
+        rp = r + eps
+        rm = r - eps
+        Fp = self._F(theta, (rp, R))
+        Fm = self._F(theta, (rm, R))
+        return (Fp - Fm) / (2 * eps)
+
+    def _dF_r_exact(self, theta, H):
+        r, dr = H
+
+        x = r * np.sin(theta)
+        z = r * np.cos(theta)
+
+        metric    = self._spherical_metric   (theta, r, x, z)
+        metric_u  = self._spherical_metric_u (theta, r, x, z)
+        curv      = self._spherical_curv     (theta, r, x, z)
+        dcurv     = self._spherical_dcurv    (theta, r, x, z)
+        Gamma     = self._spherical_Gamma    (theta, r, x, z)
+        dmetric_u = self._spherical_dmetric_u(theta, r, x, z, metric, metric_u, Gamma)
+        dGamma    = self._spherical_dGamma   (theta, r, x, z)
+
+        u2     = metric_u[1, 1] * dr * dr - 2 * metric_u[0, 1] * dr + metric_u[0, 0]
+        u      = np.sqrt(u2)
+        var_u2 = dmetric_u[0, 1, 1] * dr * dr - 2 * dmetric_u[0, 0, 1] * dr + dmetric_u[0, 0, 0]
+        var_u  = var_u2 / (2 * u)
+
+        df_ij     = np.empty_like(metric_u)
+        var_df_ij = np.empty_like(metric_u)
+        for i, j in it.product(range(3), repeat = 2):
+            df_ij[i, j]     =  ( metric_u   [0, i] -  metric_u   [1, i] * dr) * (metric_u[0, j] - metric_u[1, j] * dr)
+            var_df_ij[i, j] = ((dmetric_u[0, 0, i] - dmetric_u[0, 1, i] * dr) * (metric_u[0, j] - metric_u[1, j] * dr) +
+                               (dmetric_u[0, 0, j] - dmetric_u[0, 1, j] * dr) * (metric_u[0, i] - metric_u[1, i] * dr))
+
+        term1     = u2 * metric_u                         - df_ij
+        var_term1 = var_u2 * metric_u + u2 * dmetric_u[0] - var_df_ij
+        term2     = Gamma[0] - dr * Gamma[1] + u * curv
+        var_term2 = dGamma[0, 0] - dr * dGamma[0, 1] + dcurv[0] * u + curv * var_u
+
+        denom     =  metric_u   [0, 0] * metric_u[1, 1]                                       -      metric_u   [0, 1] * metric_u[0, 1]
+        var_denom = dmetric_u[0, 0, 0] * metric_u[1, 1] + metric_u[0, 0] * dmetric_u[0, 1, 1] - 2 * dmetric_u[0, 0, 1] * metric_u[0, 1]
+
+        ret = -(np.einsum('ij...,ij...', var_term1, term2) + np.einsum('ij...,ij...', term1, var_term2) - np.einsum('ij...,ij...', term1, term2) * var_denom / denom) / denom
+        return ret
+
+    def _dF_R_fd(self, theta, H):
+        eps = 1e-8
+        r, R = H
+
+        Rp = R + eps
+        Rm = R - eps
+        Fp = self._F(theta, (r, Rp))
+        Fm = self._F(theta, (r, Rm))
+        return (Fp - Fm) / (2 * eps)
+
+    def _dF_R_exact(self, theta, H):
+        r, dr = H
+
+        x = r * np.sin(theta)
+        z = r * np.cos(theta)
+
+        metric_u = self._spherical_metric_u(theta, r, x, z)
+        curv     = self._spherical_curv    (theta, r, x, z)
+        Gamma    = self._spherical_Gamma   (theta, r, x, z)
+
+        u2     = metric_u[1, 1] * dr * dr - 2 * metric_u[0, 1] * dr + metric_u[0, 0]
+        u      = np.sqrt(u2)
+        var_u2 = metric_u[1, 1] * dr * 2  - 2 * metric_u[0, 1]
+        var_u  = var_u2 / (2 * u)
+
+        df_ij     = np.empty_like(metric_u)
+        var_df_ij = np.empty_like(metric_u)
+
+        for i, j in it.product(range(3), repeat = 2):
+            df_ij[i, j]     = (metric_u[0, i] - metric_u[1, i] * dr) * (metric_u[0, j] - metric_u[1, j] * dr)
+            var_df_ij[i, j] = -metric_u[1, i] * (metric_u[0, j] - metric_u[1, j] * dr) - metric_u[1, j] * (metric_u[0, i] - metric_u[1, i] * dr)
+
+        term1     = u2 * metric_u - df_ij
+        var_term1 = var_u2 * metric_u - var_df_ij
+        term2     = Gamma[0] - dr * Gamma[1] + u * curv
+        var_term2 = -Gamma[1] + curv * var_u
+
+        ret = -(np.einsum('ij...,ij...', var_term1, term2) + np.einsum('ij...,ij...', term1, var_term2)) / (metric_u[0, 0] * metric_u[1, 1] - metric_u[0, 1] * metric_u[0, 1])
+        return ret
+
+    def hor_area(self, hor, log2points = 10):
+        theta = np.linspace(0, np.pi, (1 << log2points) + 1)
+        r     = hor.eval(theta)
+        dr    = hor.eval(theta, 1)
+
+        st    = np.sin(theta)
+        ct    = np.cos(theta)
+
+        x     = r * st
+        z     = r * ct
+
+        r2    = r * r
+        z2    = z * z
+
+        metric_u = np.empty((3, 3) + theta.shape)
+        for i, j in it.product(range(3), repeat = 2):
+            metric_u[i, j] = self._interp_var(theta, r, x, z, self.metric_u_cart[i, j])
+
+        metric_u_det = utils.matrix_det(metric_u)
+
+        ds = np.empty((3,) + theta.shape)
+        ds[0] = x / r - dr * x * z / (r2 * np.sqrt(r2 - z2))
+        ds[1] = 0.0
+        ds[2] = z / r + dr * np.sqrt(r2 - z2) / r2
+        lm2 = np.einsum('ij...,i...,j...', metric_u, ds, ds)
+
+        dA = np.sqrt(lm2 / metric_u_det) * r2 * st
+        dA[0]  = 0.0
+        dA[-1] = 0.0
+
+        return scipy.integrate.romb(dA, theta[1] - theta[0]) * 2. * np.pi
+
+    def hor_mass(self, hor, *args, **kwargs):
+        A = self.hor_area(hor, *args, **kwargs)
+        return np.sqrt(A / (16. * np.pi))
+
+    def calc_expansion(self, surf, direction):
+        """
+        Calculate expansion of null geodesics on a sequence of surfaces [1]. The
+        surfaces are specified as level surfaces of F(r, θ) = r - h(θ).
+
+        [1] Alcubierre (2008): Introduction to 3+1 Numerical Relativity, chapter
+            6.7, specifically equation (6.7.13).
+
+        :param callable surf: A callable that specifies the surfaces. Accepts an
+                              array of θ and returns the array of correponding h.
+        :param int direction: Values of 1/-1 specify that the expansion of outgoing
+                              or ingoing geodesics is to be computed.
+        :rtype: array_like, shape (self.Z.shape[0], self.X.shape[0])
+        :return: Expansion values at the grid formed from X and Z.
+        """
+        X, Z = self.X, self.Z
+        dX = [X[0, 1] - X[0, 0], 0, Z[1, 0] - Z[0, 0]]
+
+        R     = np.sqrt(X ** 2 + Z ** 2)
+        Theta = np.where(R > 1e-12, np.arccos(Z / R), 0.0)
+
+        trK = np.einsum('ij...,ij...', self.metric_u_cart, self.curv_cart)
+
+        F = R[:]
+        for i in range(Theta.shape[0]):
+            F[i] -= surf.eval(Theta[i])
+
+        dF = np.empty((3,) + F.shape)
+        dF[0] = self._diff_op(F, 1, dX[0])
+        dF[1] = 0.0
+        dF[2] = self._diff_op(F, 0, dX[2])
+
+        s_l  = direction * dF[:]
+        s_u  = np.einsum('ij...,j...->i...', self.metric_u_cart, s_l)
+        s_u /= np.sqrt(np.einsum('ij...,i...,j...', self.metric_cart, s_u, s_u))
+
+        ds_u = np.zeros((3,) + s_u.shape)
+        for i in range(3):
+            for j in range(3):
+                if i == 1 or j == 1:
+                    continue
+                diff_dir = 1 if (i == 0) else 0
+                ds_u[i, j] = self._diff_op(s_u[j], diff_dir, dX[i])
+        ds_u[1, 1] = np.where(np.abs(X) > 1e-8, s_u[0] / X, ds_u[0, 0])
+
+        Div_s_u = np.einsum('ii...', ds_u) + np.einsum('iki...,k...', self.Gamma_cart, s_u)
+
+        H = Div_s_u - trK + np.einsum('ij...,i...,j...', self.curv_cart, s_u, s_u)
+
+        return H
diff --git a/utils.py b/utils.py
index 7440919..f0ba2f4 100644
--- a/utils.py
+++ b/utils.py
@@ -19,6 +19,24 @@ def matrix_invert(mat):
     inv = np.linalg.inv(mat)
     return inv.transpose((1, 2, 0)).reshape(oldshape)
 
+def matrix_det(mat):
+    """
+    Pointwise matrix determinant.
+
+    Given an array that stores many square matrices (e.g. the values of the
+    metric tensor at spacetime points), compute the array of corresponding
+    determinants.
+
+    :param array_like mat: N-D array with N>2 and each mat[i0, ..., :, :] a
+                           square matrix.
+    :return: Array of determinants.
+    """
+    oldshape = mat.shape
+    newshape = oldshape[:2] + (np.product(mat.shape[2:]),)
+
+    mat = mat.reshape(newshape).transpose((2, 0, 1))
+    return np.linalg.det(mat).reshape(oldshape[2:])
+
 def array_reflect(data, parity = 1.0, axis = -1):
     """
     Reflect an N-D array with respect to the specified axis. E.g. input