Big documentation and structure updates

- Split math into fdmath package
- Rename waveguide into _2d _3d and _cyl variants
- pdoc-based documentation

meanas/fdtd/__init__.py

@@ -1,5 +1,5 @@
 """
-Basic FDTD functionality
+Utilities for running finite-difference time-domain (FDTD) simulations
 """
 
 from .base import maxwell_e, maxwell_h

meanas/fdtd/base.py

@@ -5,70 +5,14 @@ from typing import List, Callable, Tuple, Dict
 import numpy
 
 from .. import dx_lists_t, field_t, field_updater
+from ..fdmath.functional import curl_forward, curl_back
+
 
 __author__ = 'Jan Petykiewicz'
 
 
-def curl_h(dxes: dx_lists_t = None) -> field_updater:
-    """
-    Curl operator for use with the H field.
-
-    :param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types
-    :return: Function for taking the discretized curl of the H-field, F(H) -> curlH
-    """
-    if dxes:
-        dxyz_b = numpy.meshgrid(*dxes[1], indexing='ij')
-
-        def dh(f, ax):
-            return (f - numpy.roll(f, 1, axis=ax)) / dxyz_b[ax]
-    else:
-        def dh(f, ax):
-            return f - numpy.roll(f, 1, axis=ax)
-
-    def ch_fun(h: field_t) -> field_t:
-        output = numpy.empty_like(h)
-        output[0] = dh(h[2], 1)
-        output[1] = dh(h[0], 2)
-        output[2] = dh(h[1], 0)
-        output[0] -= dh(h[1], 2)
-        output[1] -= dh(h[2], 0)
-        output[2] -= dh(h[0], 1)
-        return output
-
-    return ch_fun
-
-
-def curl_e(dxes: dx_lists_t = None) -> field_updater:
-    """
-    Curl operator for use with the E field.
-
-    :param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types
-    :return: Function for taking the discretized curl of the E-field, F(E) -> curlE
-    """
-    if dxes is not None:
-        dxyz_a = numpy.meshgrid(*dxes[0], indexing='ij')
-
-        def de(f, ax):
-            return (numpy.roll(f, -1, axis=ax) - f) / dxyz_a[ax]
-    else:
-        def de(f, ax):
-            return numpy.roll(f, -1, axis=ax) - f
-
-    def ce_fun(e: field_t) -> field_t:
-        output = numpy.empty_like(e)
-        output[0] = de(e[2], 1)
-        output[1] = de(e[0], 2)
-        output[2] = de(e[1], 0)
-        output[0] -= de(e[1], 2)
-        output[1] -= de(e[2], 0)
-        output[2] -= de(e[0], 1)
-        return output
-
-    return ce_fun
-
-
 def maxwell_e(dt: float, dxes: dx_lists_t = None) -> field_updater:
-    curl_h_fun = curl_h(dxes)
+    curl_h_fun = curl_back(dxes[1])
 
     def me_fun(e: field_t, h: field_t, epsilon: field_t):
         e += dt * curl_h_fun(h) / epsilon
@@ -78,7 +22,7 @@ def maxwell_e(dt: float, dxes: dx_lists_t = None) -> field_updater:
 
 
 def maxwell_h(dt: float, dxes: dx_lists_t = None) -> field_updater:
-    curl_e_fun = curl_e(dxes)
+    curl_e_fun = curl_forward(dxes[0])
 
     def mh_fun(e: field_t, h: field_t):
         h -= dt * curl_e_fun(e)

meanas/fdtd/energy.py

@@ -35,6 +35,7 @@ def poynting_divergence(s: field_t = None,
     if s is None:
         s = poynting(e, h, dxes=dxes)
 
+    #TODO use deriv operators
     ds = ((s[0] - numpy.roll(s[0], 1, axis=0)) +
           (s[1] - numpy.roll(s[1], 1, axis=1)) +
           (s[2] - numpy.roll(s[2], 1, axis=2)))