Condor is a new mathematical modeling framework for Python, developed at NASA's Ames Research Center. Initial development began in April 2023 to address model implementation challenges for aircraft synthesis and robust orbital trajectory design. The goal is for Condor to help evaluate numerical models and then get out of the way.

One key aspect to achieve this goal was to create an API that looked as much like the mathematical description as possible with as little distraction from programming cruft as possible. To best understand this approach, we can consider a simple benchmark problem which consists of a set of coupled algebraic expressions. This can be represented as a system of algebraic equations:

import condor as co

class Coupling(co.AlgebraicSystem):
    x = parameter(shape=3)
    y1 = variable(initializer=1.)
    y2 = variable(initializer=1.)

    residual(y1 == x[0] ** 2 + x[1] + x[2] - 0.2 * y2)
    residual(y2 == y1**0.5 + x[0] + x[1])

This parametric model can be evaluated by providing the values for the parameters; the resulting object has values for its inputs and outputs bound, so the solved values for y1 and y2 can be accessed easily:

coupling = Coupling([5., 2., 1]) # evaluate the model numerically
print(coupling.y1, coupling.y2) # individual elements are bound numerically
print(coupling.variable) # fields are bound as a dataclass

Models can also be seamlessly built-up, with parent models accessing any input or output of models embedded within them. For example, we can optimize this system of algebraic equations by embedding it within an optimization problem:

from condor.backend import operators as ops

class Sellar(co.OptimizationProblem):
    x = variable(shape=3, lower_bound=0, upper_bound=10)
    coupling = Coupling(x)
    y1, y2 = coupling

    objective = x[2]**2 + x[1] + y1 + ops.exp(-y2)
    constraint(y1 >= 3.16)
    constraint(24. >= y2)

After the model is solved, the embedded model can be accessed directly:

Sellar.set_initial(x=[5,2,1])
sellar = Sellar()
print(sellar.objective) # scalar value
print(sellar.constraint) # field
print(sellar.coupling.y1) # sub-model element

NASA's Condor is a framework for mathematical modeling of engineering systems in Python, written for engineers with deadlines.

Condor is available on PyPI, so you can install with pip:

pip install condor

Optionally, you can include the dependencies to run the examples:

pip install condor[examples]

Please feel free to share any thoughts or opinions about the design and implementation of this software by opening an issue on GitHub. Constructive feedback is welcomed and appreciated.

Bug fix pull requests are always welcome. For feature additions, breaking changes, etc. check if there is an open issue discussing the change and reference it in the pull request. If there isn't one, it is recommended to open one with your rationale for the change before spending significant time preparing the pull request.

Ideally, new/changed functionality should come with tests and documentation. If you are new to contributing, it is perfectly fine to open a work-in-progress pull request and have it iteratively reviewed.

For pull requests to be accepted, all contributors must have a contributor's agreement on file with NASA. We will provide contributors with additional information during the review process.

Clone the repository and install it in editable mode along with the dev dependency group in a virtual environment:

pip install --upgrade pip
pip install -e . --group dev

A pre-commit configuration is available to automatically format and fix linting issues when creating a commit. Install pre-commit either in the virtual environment created above or as a system-wide tool (e.g. pipx or uv tool), then install the hooks:

pre-commit install

The first run of the hooks will take a few seconds as the isolated environments are created. Subsequent runs will generally be much faster.

To run the tests, run:

pytest

This software is released under the NASA Open Source Agreement Version 1.3.

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