Designer

class fird.designer.FIRDesigner(filter_order: int, objective: Objective, constraints: Constraint | Sequence[Constraint] | None = None, *, fractional_bits: int | None = None, even_symmetry: bool = True, cascade_filter: Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | None = None, period: int = 1)

Bases: object

Base class for designing FIR filters.

Parameters:

filter_orderint

Order of the filter to be designed.

objectiveObjective

The objective function to optimize for.

constraintsConstraint | Sequence[Constraint] | None, optional

The constraints to consider during optimization.

fractional_bitsint | None, optional

Number of fractional bits for fixed-point coefficients. If not provided, continuous (floating-point) coefficients are used.

even_symmetrybool, default: True

If the filter to be designed is of odd or even symmetry.

cascade_filter1D array, optional

The optional filter in cascade with the designed.

periodint, default: 1

The period of the designed filter in relation to the specification.

get_impulse_response() → ndarray[tuple[int, ...], dtype[float64]]

Return the impulse response of the optimized FIR filter.

property objective_value: float

The objective value of the optimized problem.

solve(solver: LpSolver | None = None) → None

Optimize FIR filter using solver.

Parameters:

solverLpSolver, optional

The solver to use. To see which solvers are available:

import pulp

print(pulp.listSolvers(onlyAvailable=True))