📚 API Reference

This reference provides a detailed overview of the functions and classes in the Archimedes library.

Modules

observers

State estimation and Kalman filtering

optimize

Numerical optimization algorithms

simulate

spatial

Spatial representations and kinematics/dynamics models.

sysid

System identification and parameter estimation functionality

tree

Utilities for working with hierarchical tree-structured data.

Core Functions

array(x[, dtype])

Create an array supporting both numeric and symbolic computation.

callback(func, result_shape_dtypes, *args)

Execute an arbitrary Python function within an symbolic computational graph.

codegen(func, args[, static_argnums, ...])

Generate C/C++ code from a compiled function.

compile([func, static_argnums, ...])

Create a "compiled" function from a Python function.

discretize([func, dt, method, n_steps, name])

Convert continuous-time dynamics to discrete-time using numerical integration.

eye(N[, M, k, dtype, order, device, kind])

Construct a symbolic identity matrix of size n with the given dtype.

field([static, metadata])

Create a field specification with struct-related metadata.

grad(func[, argnums, name, static_argnums, ...])

Create a function that evaluates the gradient of func.

hess(func[, argnums, name, static_argnums, ...])

Create a function that evaluates the Hessian of func.

implicit(func[, static_argnames, solver, name])

Construct an explicit function from an implicit relation.

integrator(func[, method, atol, rtol, ...])

Create an ODE solver function from a dynamics function.

interpolant(grid, data[, method, arg_names, ...])

Create a callable N-dimensional interpolant function.

jac(func[, argnums, name, static_argnums, ...])

Create a function that evaluates the Jacobian of func.

jvp(func[, name, static_argnums, ...])

Create a function that evaluates the Jacobian-vector product of func.

minimize(obj, x0[, args, static_argnames, ...])

Minimize a scalar function with optional constraints and tree support.

nlp_solver(obj[, constr, static_argnames, ...])

Create a reusable solver for a nonlinear optimization problem.

odeint(func, t_span, x0[, method, t_eval, ...])

Integrate a system of ordinary differential equations.

ones(shape[, dtype, kind])

Construct a symbolic array of ones with the given shape and dtype.

ones_like(x[, dtype, kind])

Create a symbolic array of ones with the same shape and dtype as an input array.

qpsol(obj, constr, x0[, lba, uba, lam_a0, ...])

Solve a quadratic programming problem

root(func, x0[, args, static_argnames, ...])

Find a root of a nonlinear function.

scan(func, init_carry[, xs, length])

Apply a function repeatedly while carrying state between iterations.

struct([cls])

Decorator to convert a class into a tree-compatible frozen dataclass.

StructConfig()

Base class for creating configuration objects with automatic type discrimination.

switch(index, branches, *args[, name, kind])

Selectively apply one of several functions based on an index.

sym(name[, shape, dtype, kind])

Create a symbolic array for use in symbolic computations.

sym_like(x, name[, dtype, kind])

Create a symbolic array with the same shape and dtype as an existing array.

UnionConfig()

Discriminated union of StructConfig subclasses.

vjp(func[, name, static_argnums, ...])

Create a function that evaluates the vector-Jacobian product of func.

vmap(func[, in_axes, out_axes, name])

Vectorize a function along specified argument axes.

zeros(shape[, dtype, sparse, kind])

Construct a symbolic array of zeros with the given shape and dtype.

zeros_like(x[, dtype, sparse, kind])

Create a symbolic array of zeros with the same shape and dtype as an input array.