twpasolver.twoport module#

Two-port network module.

This module provides classes and methods for representing and manipulating two-port RF networks. It includes functionality for handling frequency-dependent network parameters, converting between different representations (such as ABCD matrices and S-parameters), and performing common operations on two-port networks.

Examples

Create a TwoPortCell from ABCD parameters:

freqs = np.array([1e9, 2e9, 3e9])
abcd = np.array([
    [[1, 2], [3, 4]],
    [[5, 6], [7, 8]],
    [[9, 10], [11, 12]]
])
cell = TwoPortCell(freqs, abcd)

Convert to S-parameters:

s_params = cell.s

Interpolate to new frequencies:

new_freqs = np.array([1.5e9, 2.5e9])
interpolated_cell = cell.interpolate(new_freqs)

Save and load from file:

cell.dump_to_file('cell_data.hdf5')
loaded_cell = TwoPortCell.from_file('cell_data.hdf5')

class twpasolver.twoport.TwoPortCell(freqs: ndarray, abcd: ndarray | ABCDArray, Z0: complex | float = 50)[source]#

Bases: object

Class representing a two-port RF cell.

classmethod from_s(freqs: ndarray, s_mat: ndarray, Z0: float | int = 50) → TwoPortCell[source]#

Instantiate from array of S-parameters.

Parameters:

freqs (numpy.ndarray) – Frequencies of the network.
s_mat (numpy.ndarray) – S-parameter matrix.
Z0 (float | int) – Reference line impedance.

Returns:

Instance of TwoPortCell.

Return type:

TwoPortCell

classmethod from_z(freqs: ndarray, z_mat: ndarray, Z0: float | int = 50) → TwoPortCell[source]#

Instantiate from array of Z-parameters.

Parameters:

freqs (numpy.ndarray) – Frequencies of the network.
z_mat (numpy.ndarray) – Z-parameter matrix.
Z0 (float | int) – Reference line impedance.

Returns:

Instance of TwoPortCell.

Return type:

TwoPortCell

classmethod from_file(filename: str, writer='hdf5') → TwoPortCell[source]#

Load model from file.

Parameters:

filename (str) – Name of the file.
writer (str) – File format.

Returns:

Instance of TwoPortCell.

Return type:

TwoPortCell

property freqs: ndarray#

Get the frequencies array.

Returns:: Frequencies array.
Return type:: numpy.ndarray

property Z0: complex | float#

Get the reference line impedance.

Returns:: Reference line impedance.
Return type:: Impedance

to_network() → Network[source]#

Convert to scikit-rf Network.

Returns:: scikit-rf Network.
Return type:: rf.Network

property s: SMatrixArray#

Get the S-parameter matrix.

Returns:: S-parameter matrix.
Return type:: SMatrixArray

property z: ZMatrixArray#

Get the Z-parameter matrix.

Returns:: Z-parameter matrix.
Return type:: ZMatrixArray

as_dict() → dict[source]#

Return cell contents as dictionary.

Returns:: Dictionary containing cell contents.
Return type:: dict

dump_to_file(filename: str, writer='hdf5') → None[source]#

Dump cell to file.

Parameters:

filename (str) – Name of the file.
writer (str) – File format.

interpolate(freqs: ndarray, polar: bool = True) → TwoPortCell[source]#

Return abcd matrix of internal cell interpolating the given frequencies.

Parameters:

freqs (numpy.ndarray) – Frequencies array to interpolate.
polar (bool) – Interpolate magnitude and phase instead of real and imaginary part.

Returns:

Interpolated instance of TwoPortCell.

Return type:

TwoPortCell

pydantic model twpasolver.twoport.TwoPortModel[source]#

Bases: BaseModel, ABC

Base class for models of two-port networks.

Show JSON schema

{
   "title": "TwoPortModel",
   "description": "Base class for models of two-port networks.",
   "type": "object",
   "properties": {
      "Z0_ref": {
         "default": 50.0,
         "description": "Reference line impedance of the two-port component.",
         "title": "Z0 Ref",
         "type": "number"
      },
      "N": {
         "default": 1,
         "description": "Number of repetitions of the model in the computed abcd matrix.",
         "minimum": 0,
         "title": "N",
         "type": "integer"
      }
   }
}

Fields:

N (int)
Z0_ref (complex | float)

field Z0_ref: Impedance = 50.0#

Reference line impedance of the two-port component.

Constraints:

__module__ = twpasolver.bonus_types
__get_pydantic_core_schema__ = <bound method _Impedance2PydanticAnnotation.__get_pydantic_core_schema__ of <class ‘twpasolver.bonus_types._Impedance2PydanticAnnotation’>>
__get_pydantic_json_schema__ = <bound method _Impedance2PydanticAnnotation.__get_pydantic_json_schema__ of <class ‘twpasolver.bonus_types._Impedance2PydanticAnnotation’>>
__dict__ = {‘__module__’: ‘twpasolver.bonus_types’, ‘__get_pydantic_core_schema__’: <classmethod(<function _Impedance2PydanticAnnotation.__get_pydantic_core_schema__ at 0x7f42bdabe5f0>)>, ‘__get_pydantic_json_schema__’: <classmethod(<function _Impedance2PydanticAnnotation.__get_pydantic_json_schema__ at 0x7f42bdabe680>)>, ‘__dict__’: <attribute ‘__dict__’ of ‘_Impedance2PydanticAnnotation’ objects>, ‘__weakref__’: <attribute ‘__weakref__’ of ‘_Impedance2PydanticAnnotation’ objects>, ‘__doc__’: None}
__weakref__ = <attribute ‘__weakref__’ of ‘_Impedance2PydanticAnnotation’ objects>
func = <function validate_impedance at 0x7f42bdabe4d0>
json_schema_input_type = PydanticUndefined

field N: NonNegativeInt = 1#

Number of repetitions of the model in the computed abcd matrix.

Constraints:

ge = 0

update(**kwargs) → None[source]#

Update multiple attributes of the model.

Parameters:: **kwargs – Attributes to update.

abstract single_abcd(freqs: ndarray) → ABCDArray[source]#

Compute the abcd matrix of a single iteration of the model.

Parameters:: freqs (numpy.ndarray) – Frequencies array.
Returns:: ABCD matrix for a single iteration.
Return type:: ABCDArray

get_abcd(freqs: ndarray) → ABCDArray[source]#

Compute the abcd matrix of the model.

Parameters:: freqs (numpy.ndarray) – Frequencies array.
Returns:: Computed ABCD matrix of the model.
Return type:: ABCDArray

get_cell(freqs: ndarray) → TwoPortCell[source]#

Return the two-port cell of the model.

Parameters:: freqs (numpy.ndarray) – Frequencies array.
Returns:: Two-port cell of the model.
Return type:: TwoPortCell

get_network(freqs: ndarray) → Network[source]#

Return the two-port cell of the model as a scikit-rf Network.

Parameters:: freqs (numpy.ndarray) – Frequencies array.
Returns:: scikit-rf Network representation of the model.
Return type:: rf.Network