documentation updates

meanas/fdmath/__init__.py

@@ -0,0 +1,94 @@
+"""
+Basic discrete calculus for finite difference (fd) simulations.
+
+This documentation and approach is roughly based on W.C. Chew's excellent
+"Electromagnetic Theory on a Lattice" (doi:10.1063/1.355770),
+which covers a superset of this material with similar notation and more detail.
+
+
+Define the discrete forward derivative as
+
+    Dx_forward(f)[i] = (f[i + 1] - f[i]) / dx[i]
+
+or
+    $$ [\\tilde{\\partial}_x f ]_{m + \\frac{1}{2}} = \\frac{1}{\\Delta_{x, m}} (f_{m + 1} - f_m) $$
+
+Likewise, discrete reverse derivative is
+
+    Dx_back(f)[i] = (f[i] - f[i - 1]) / dx[i]
+
+or
+    $$ [\\hat{\\partial}_x f ]_{m - \\frac{1}{2}} = \\frac{1}{\\Delta_{x, m}} (f_{m} - f_{m - 1}) $$
+
+
+The derivatives are shifted by a half-cell relative to the original function:
+
+     _________________________
+     |     |     |     |     |
+     |  f0 |  f1 |  f2 |  f3 |
+     |_____|_____|_____|_____|
+        |     |     |     |
+        | Df0 | Df1 | Df2 | Df3
+     ___|_____|_____|_____|____
+
+Periodic boundaries are used unless otherwise noted.
+
+
+Expanding to three dimensions, we can define two gradients
+  $$ [\\tilde{\\nabla} f]_{n,m,p} = \\vec{x} [\\tilde{\\partial}_x f]_{m + \\frac{1}{2},n,p} +
+                                    \\vec{y} [\\tilde{\\partial}_y f]_{m,n + \\frac{1}{2},p} +
+                                    \\vec{z} [\\tilde{\\partial}_z f]_{m,n,p + \\frac{1}{2}}  $$
+  $$ [\\hat{\\nabla} f]_{m,n,p} = \\vec{x} [\\hat{\\partial}_x f]_{m + \\frac{1}{2},n,p} +
+                                  \\vec{y} [\\hat{\\partial}_y f]_{m,n + \\frac{1}{2},p} +
+                                  \\vec{z} [\\hat{\\partial}_z f]_{m,n,p + \\frac{1}{2}}  $$
+
+The three derivatives in the gradient cause shifts in different
+directions, so the x/y/z components of the resulting "vector" are defined
+at different points: the x-component is shifted in the x-direction,
+y in y, and z in z.
+
+We call the resulting object a "fore-vector" or "back-vector", depending
+on the direction of the shift. We write it as
+  $$ \\tilde{g}_{m,n,p} = \\vec{x} g^x_{m + \\frac{1}{2},n,p} +
+                          \\vec{y} g^y_{m,n + \\frac{1}{2},p} +
+                          \\vec{z} g^z_{m,n,p + \\frac{1}{2}} $$
+  $$ \\hat{g}_{m,n,p} = \\vec{x} g^x_{m - \\frac{1}{2},n,p} +
+                        \\vec{y} g^y_{m,n - \\frac{1}{2},p} +
+                        \\vec{z} g^z_{m,n,p - \\frac{1}{2}} $$
+
+
+There are also two divergences,
+
+  $$ d_{n,m,p} = [\\tilde{\\nabla} \\cdot \\hat{g}]_{n,m,p}
+               = [\\tilde{\\partial}_x g^x]_{m,n,p} +
+                 [\\tilde{\\partial}_y g^y]_{m,n,p} +
+                 [\\tilde{\\partial}_z g^z]_{m,n,p}   $$
+
+  $$ d_{n,m,p} = [\\hat{\\nabla} \\cdot \\tilde{g}]_{n,m,p}
+               = [\\hat{\\partial}_x g^x]_{m,n,p} +
+                 [\\hat{\\partial}_y g^y]_{m,n,p} +
+                 [\\hat{\\partial}_z g^z]_{m,n,p}  $$
+
+Since we applied the forward divergence to the back-vector (and vice-versa), the resulting scalar value
+is defined at the back-vector's (fore-vectors) location \\( (m,n,p) \\) and not at the locations of its components
+\\( (m \\pm \\frac{1}{2},n,p) \\) etc.
+
+
+The two curls are then
+  $$ \\begin{align}
+     \\hat{h}_{m + \\frac{1}{2}, n + \\frac{1}{2}, p + \\frac{1}{2}} &= \\\\
+     [\\tilde{\\nabla} \\times \\tilde{g}]_{m + \\frac{1}{2}, n + \\frac{1}{2}, p + \\frac{1}{2}} &=
+        \\vec{x} (\\tilde{\\partial}_y g^z_{m,n,p + \\frac{1}{2}} - \\tilde{\\partial}_z g^y_{m,n + \\frac{1}{2},p}) \\\\
+     &+ \\vec{y} (\\tilde{\\partial}_z g^x_{m + \\frac{1}{2},n,p} - \\tilde{\\partial}_x g^z_{m,n,p + \\frac{1}{2}}) \\\\
+     &+ \\vec{z} (\\tilde{\\partial}_x g^y_{m,n + \\frac{1}{2},p} - \\tilde{\\partial}_x g^z_{m + \\frac{1}{2},n,p})
+     \\end{align}$$
+and
+  $$ \\tilde{h}_{m - \\frac{1}{2}, n - \\frac{1}{2}, p - \\frac{1}{2}} =
+     [\\hat{\\nabla} \\times \\hat{g}]_{m - \\frac{1}{2}, n - \\frac{1}{2}, p - \\frac{1}{2}} $$
+
+  where \\( \\hat{g} \\) and \\( \\tilde{g} \\) are located at \\((m,n,p)\\)
+  with components at  \\( (m \\pm \\frac{1}{2},n,p) \\) etc.,
+  while \\( \\hat{h} \\) and \\( \\tilde{h} \\) are located at \\((m \pm \\frac{1}{2}, n \\pm \\frac{1}{2}, p \\pm \\frac{1}{2})\\)
+  with components at \\((m, n \\pm \\frac{1}{2}, p \\pm \\frac{1}{2})\\) etc.
+
+"""

meanas/types.py

@@ -6,19 +6,34 @@ from typing import List, Callable
 
 
 # Field types
-field_t = numpy.ndarray
-"""vector field with shape (3, X, Y, Z) (e.g. `[E_x, E_y, E_z]`)"""
+# TODO: figure out a better way to set the docstrings without creating actual subclasses?
+#   Probably not a big issue since they're only used for type hinting
+class field_t(numpy.ndarray):
+    """
+    Vector field with shape (3, X, Y, Z) (e.g. `[E_x, E_y, E_z]`)
+
+    This is actually is just an unaltered `numpy.ndarray`
+    """
+    pass
+
+class vfield_t(numpy.ndarray):
+    """
+    Linearized vector field (single vector of length 3*X*Y*Z)
+
+    This is actually just an unaltered `numpy.ndarray`
+    """
+    pass
 
-vfield_t = numpy.ndarray
-"""Linearized vector field (vector of length 3*X*Y*Z)"""
 
 dx_lists_t = List[List[numpy.ndarray]]
 '''
  'dxes' datastructure which contains grid cell width information in the following format:
- `[[[dx_e_0, dx_e_1, ...], [dy_e_0, ...], [dz_e_0, ...]],
-   [[dx_h_0, dx_h_1, ...], [dy_h_0, ...], [dz_h_0, ...]]]`
- where `dx_e_0` is the x-width of the `x=0` cells, as used when calculating dE/dx,
- and `dy_h_0` is  the y-width of the `y=0` cells, as used when calculating dH/dy, etc.
+
+     [[[dx_e_0, dx_e_1, ...], [dy_e_0, ...], [dz_e_0, ...]],
+      [[dx_h_0, dx_h_1, ...], [dy_h_0, ...], [dz_h_0, ...]]]
+
+   where `dx_e_0` is the x-width of the `x=0` cells, as used when calculating dE/dx,
+   and `dy_h_0` is  the y-width of the `y=0` cells, as used when calculating dH/dy, etc.
 '''