📚 API Reference

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

Modules

measure

Weight functions and reference domains for classical measures.

observers

State estimation and Kalman filtering

optimize

Numerical optimization algorithms

quadrature

Numerical quadrature methods for approximating integrals.

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.