  Our long term goal is to execute General Purpose computation on homogeneous
computing media consisting of millions of small identical Processing Elements
(PE) communicating locally. We proceed by simulating the Self-Development of a
Network (SDN) of membranes, and this implies a medium able to implement
artificial physics laws that simulates simplified membrane-agents, dividing and
homogenizing. This is a difficult challenge: our current version of SDN-media
uses PEs with 77 bits of state and 13878 gates. This high level of complexity
forced us to work out an efficient and expressive scheme for programming the
medium, the goal of this paper it to present it. The PE's communication graph
has to be a maximal planar graph. Fields of bits are spread in 2D, over three
locus: the vertices, edges and faces of this planar graph. They constitute
three data types, which abstract away the ensemble of PEs. The simplicial
proximity between bits is used to define operations on fields, thus
implementing "{\it spatial type}". Expression combining operations can be
translated in logical circuits.
  The efficiency is achieved because fields of different locus are combined
using reduction operation. This allows to factorize computation by exploiting
the symmetries always present when simulating physics.
  The expressiveness is achieved by allowing a modular procedural programming:
Instead of directly focusing on a specific target update function, we develop a
library or reusable functions mapping fields to other fields.
  We consider two kinds of maximal planar graph: with isotropic distribution of
PEs or with the hexagonal lattice structure. The first compares to amorphous
computing medium and has a better potential for hardware scalability, the
second compares with cellular automata computing medium, it is more efficient.
