API Reference

class control_systems.converterTool.app.ConverterApp(level: 'int | str' = 20)

class ConverterEngine

Pure conversion and normalization logic (no plotting, no I/O).

static coeffs_from_tf(G) → tuple[ndarray, ndarray]

static normalize(num, den) → tuple[ndarray, ndarray]

ss_to_tf(ssm: SSModel)

tf_to_ss(tfm: TFModel) → SSModel

eng: ConverterEngine

field(*, default_factory=<dataclasses._MISSING_TYPE object>, init=True, repr=True, hash=None, compare=True, metadata=None, kw_only=<dataclasses._MISSING_TYPE object>)

Return an object to identify dataclass fields.

default is the default value of the field. default_factory is a 0-argument function called to initialize a field’s value. If init is true, the field will be a parameter to the class’s __init__() function. If repr is true, the field will be included in the object’s repr(). If hash is true, the field will be included in the object’s hash(). If compare is true, the field will be used in comparison functions. metadata, if specified, must be a mapping which is stored but not otherwise examined by dataclass. If kw_only is true, the field will become a keyword-only parameter to __init__().

It is an error to specify both default and default_factory.

level: int | str

run(cfg: ConverterConfig) → ConverterResult

class control_systems.converterTool.apis.ConverterConfig(num: 'Optional[str]' = None, den: 'Optional[str]' = None, A: 'Optional[str]' = None, B: 'Optional[str]' = None, C: 'Optional[str]' = None, D: 'Optional[str]' = None, iu: 'int' = 0, tfinal: 'float' = 8.0, dt: 'float' = 0.001, sympy: 'bool' = False, no_plot: 'bool' = False, save_json: 'Optional[str]' = None)

A: str | None

B: str | None

C: str | None

D: str | None

den: str | None

dt: float

iu: int

no_plot: bool

num: str | None

save_json: str | None

sympy: bool

tfinal: float

class control_systems.converterTool.apis.ConverterResult(mode: 'str', tf: 'Optional[ct.TransferFunction]' = None, ss: 'Optional[ct.StateSpace]' = None, pretty_tf: 'Optional[str]' = None, pretty_sympy: 'Optional[str]' = None)

mode: str

pretty_sympy: str | None

pretty_tf: str | None

ss: StateSpace | None

tf: TransferFunction | None

control_systems.converterTool.apis.convert_ss_to_tf(A: ndarray, B: ndarray, C: ndarray, D: ndarray)

control_systems.converterTool.apis.convert_tf_to_ss(num: ndarray, den: ndarray) → SSModel

class control_systems.converterTool.core.ConverterEngine

Pure conversion and normalization logic (no plotting, no I/O).

static coeffs_from_tf(G) → tuple[ndarray, ndarray]

static normalize(num, den) → tuple[ndarray, ndarray]

ss_to_tf(ssm: SSModel)

tf_to_ss(tfm: TFModel) → SSModel

class control_systems.converterTool.core.SSModel(A: 'np.ndarray', B: 'np.ndarray', C: 'np.ndarray', D: 'np.ndarray')

A: ndarray

B: ndarray

C: ndarray

D: ndarray

to_ct() → StateSpace

class control_systems.converterTool.core.TFModel(num: 'np.ndarray', den: 'np.ndarray')

den: ndarray

num: ndarray

to_ct() → TransferFunction

control_systems.converterTool.io.ensure_out_path(path: Path | None, default_name: str, suffix: str = '.json') → Path

control_systems.converterTool.io.parse_matrix(s: str | None) → ndarray | None

control_systems.converterTool.io.parse_vector(s: str | None) → ndarray | None

control_systems.converterTool.io.read_json(path: Path)

control_systems.converterTool.io.write_json(obj, out_path: Path) → Path

class control_systems.converterTool.design.ConverterPretty

static coeffs(G)

static sympy_rat(G) → str

control_systems.converterTool.design.plot_step_ss(ssys, iu=0, tfinal=8.0, dt=0.001, title='Step response (SS)') → None

control_systems.converterTool.design.plot_step_tf(G, tfinal=8.0, dt=0.001, title='Step response (TF)') → None

control_systems.converterTool.design.pretty_tf_any(G) → str