FormaK: User Interface - Building


Parent Design: designs/


FormaK aims to combine symbolic modeling for fast, efficient system modelling with code generation to create performant code that is easy to use.

The values (in order) are:

  • Easy to use
  • Performant

In line with those values, the intended user experience is as follows. The user provides:

  • Model that describes the physics of the system
  • Execution criteria (e.g. memory usage, execution time)
  • Time series data for the system

And the user gets a performant model that satisfies their execution criteria and optimally fits the data.

The Five Key Elements the library provides to achieve this user experience are: 1. Python Interface to define models 2. Python implementation of the model and supporting tooling 3. Integration to scikit-learn to leverage the model selection and parameter tuning functions 4. C++ and Python to C++ interoperability for performance 5. C++ interfaces to support a variety of model uses

This feature provides the first of the Five Keys: the Python Interface to define models.

Feature Preview

The user interface is designed to be easy to use and familiar to folks that work with Python. Setting up a simple physics model for a rocket looks like:

vp = vehicle_properties = {k: Symbol(k) for k in ["m", "x", "v", "a"]}
fuel_burn_rate = Symbol("fuel_burn_rate")

state = set(vehicle_properties.values())

control = set([fuel_burn_rate])  # kg/sec

# momentum = mv
# dmomentum / dt = F = d(mv)/dt
# F = m dv/dt + dm/dt v
# a = dv / dt = (F - dm/dt * v) / m

F = -gravitational_force(vp["m"], Earth_Mass, vp["x"] + Earth_Equatorial_Radius)

state_model = {
    vp["m"]: vp["m"] - fuel_burn_rate * dt,
    vp["x"]: vp["x"] + (vp["v"] * dt) + (1 / 2 * vp["a"] * dt * dt),
    vp["v"]: vp["v"] + (vp["a"] * dt),
    vp["a"]: (F - (fuel_burn_rate * vp["v"])) / vp["m"],

orbital_model = Model(dt=dt, state=state, control=control, state_model=state_model)

Based on

One of the areas that I'm working on improving further is the way to set up symbols. In a complicated system, having to specify everything in a verbose way isn't necessarily sustainable.

Solution Approach

To start, the user interface leans on the sympy package for symbolic math. Sympy shares the value of being easy to use. In addition to the values match, leaning on sympy instead of a proprietary interface enables a lot of flexibility and future progress for things like code generation based on the model.

This feature is ultimately simple because it leaned on sympy quite a bit. The primary change from the first plan was that there was already demo code to borrow from for feature tests which made writing them much quicker.

Roadmap and Milestones

The development process for this feature and future features follows:

  1. Design Doc
  2. Write feature tests. When the feature tests pass, the feature is nominally working at an alpha level
  3. Build a simple prototype
  4. Implement the feature, including additional testing
  5. Code Review, Refactor
  6. Merge via PR
  7. Write up successes, retro of what changed (to include that feedback in future designs)