pydsm.NTFdesign.weighting.ntf_fir_from_q0¶
- pydsm.NTFdesign.weighting.ntf_fir_from_q0(q0, H_inf=1.5, normalize='auto', **options)¶
Synthesize FIR NTF from quadratic form expressing noise weighting.
Parameters: q0 : ndarray
first row of the Toeplitz symmetric matrix defining the quadratic form
H_inf : real, optional
Max peak NTF gain, defaults to 1.5, used to enforce the Lee criterion
normalize : string or real, optional
Normalization to apply to the quadratic form used in the NTF selection. Defaults to ‘auto’ which means setting the top left entry in the matrix Q defining the quadratic form to 1.
Returns: ntf : ndarray
FIR NTF in zpk form
Other Parameters: show_progress : bool, optional
provide extended output.
fix_pos : bool, optional
fix quadratic form for positive definiteness. Numerical noise may make it not positive definite leading to errors.
modeler : string, optional
modeling backend for the optimization problem. Currently, the cvxpy_old, cvxpy and picos backends are supported. Default is cvxpy_old.
cvxpy_opts : dictionary, optional
A dictionary of options to use with the cvxpy modeling library. Allowed options include:
- override_kktsolver (bool)
Whether to override the default cvxopt kkt solver using the chol kkt solver. Leave this at the default True setting, to avoid paying a performance price.
- solver (string)
The solver backend to use. Either cvxopt or scs
cvxopt_opts : dict, optional
A dictionary of options for the cvxopt optimizer. Allowed options include:
- maxiters (int)
Maximum number of iterations
- abstol (real)
Absolute accuracy
- reltol (real)
Relative accuracy
- feastol (real)
Tolerance for feasibility conditions (defaults to 1e-6)
Do not use other options since they could break cvxopt in unexpected ways. These options can be passed when using the cvxpy_old modeler, the picos modeler or the cvxpy modeler with the cvxopt backend.
scs_opts : dict, optional
A dictionary of options for the scs optimizer. Allowed options include:
- max_iters (int)
Maximum number of iterations
- eps (real)
Convergence tolerance
- alpha (real)
Relaxation parameter
- normalize (bool)
Whether to precondition data matrices
- use_indirect (bool)
Whether to use indirect solver for KKT sytem (instead of direct)
Do not use other options since they could break scs in unexpected ways. These options can be passed when using the cvxpy modeler with the scs backend.
See also
- cvxopt
- for the optimizer parameters
- scs
- for the optimizer parameters
- cvxpy
- for the modeler parameters
Notes
Default values for the options not directly documented in the function call signature can be checked and updated by changing the function default_options attribute.