piel.tools.sax#

Submodules#

Attributes#

snet

Functions#

`address_value_dictionary_to_function_parameter_dictionary`(...)	Converts a dictionary of address-value pairs to a dictionary of function parameters.
`compose_recursive_instance_location`(recursive_netlist, ...)	This function returns the recursive location of any matching `target_component_prefix` instances within the
`get_component_instances`(recursive_netlist, ...)	Returns a dictionary of all instances of a given component in a recursive netlist.
`get_netlist_instances_by_prefix`(→ str)	Returns a list of all instances with a given prefix in a recursive netlist.
`get_matched_model_recursive_netlist_instances`(...)	This function returns an active component list with a tuple mapping of the location of the active component
`get_sdense_ports_index`(→ dict)	This function returns the connection index of the sax dense S-parameter matrix.
`sax_to_s_parameters_standard_matrix`(...)	A `sax` S-parameter SDict is provided as a dictionary of tuples with (port0, port1) as the key. This

Package Contents#

address_value_dictionary_to_function_parameter_dictionary(address_value_dictionary: dict, parameter_key: str)[source]#

Converts a dictionary of address-value pairs to a dictionary of function parameters.

Parameters:

address_value_dictionary (dict) – Dictionary where the key is a tuple of (component, instance, parameter) and the value is the parameter’s value.
parameter_key (str) – The key under which the parameter value will be stored in the output dictionary.

Returns:

A nested dictionary where each instance has a dictionary of parameters with their corresponding values.

Return type:

dict

Example

Input:

{(‘component_lattice_gener_fb8c4da8’, ‘mzi_1’, ‘sxt’): 0,: (‘component_lattice_gener_fb8c4da8’, ‘mzi_5’, ‘sxt’): 0}

Output:

{‘mzi_1’: {‘sxt’: {parameter_key: 0}},: ‘mzi_5’: {‘sxt’: {parameter_key: 0}}}

This function processes a dictionary of component addresses and parameter values, converting it into a nested dictionary format that is suitable for use as function parameters.

compose_recursive_instance_location(recursive_netlist: dict, top_level_instance_name: str, required_models: list, target_component_prefix: str, models: dict)[source]#

This function returns the recursive location of any matching target_component_prefix instances within the recursive_netlist. A function that returns the mapping of the matched_component in the corresponding netlist at any particular level of recursion. This function iterates over a particular level of recursion of a netlist. It returns a list of the missing required components, and updates a dictionary of measurement that contains a particular matching component. It returns the corresponding list of instances of a particular component at that level of recursion, so that it can be appended upon in order to construct the location of the corresponding matching elements.

If required_models is an empty list, it means no recursion is required and the function is complete. If a required_model_i in required_models matches target_component_prefix, then no more recursion is required down the component function.

The recursive_netlist should contain all the missing composed measurement that are not provided in the main measurement dictionary. If not, then we need to require the user to input the missing model that cannot be extracted from the composed netlist. We know when a model is composed, and when it is already provided at every level of recursion based on the measurement dictionary that gets updated at each level of recursion with the corresponding measurement of that level, and the required_models down itself.

However, a main question appears on how to do the recursion. There needs to be a flag that determines that the recursion is complete. However, this is only valid for every particular component in the required_models list. Every component might have missing component. This means that this recursion begins component by component, updating the required_models list until all of them have been composed from the recursion or it is determined that is it missing fully.

It would be ideal to access the particular component that needs to be implemented.

Returns a tuple of model_composition_mapping, instance_composition_mapping, target_component_mapping in the form of

({‘mzi_214beef3’: [‘straight_heater_metal_s_ad3c1693’]},

{‘mzi_214beef3’: [‘mzi_1’, ‘mzi_5’],
‘mzi_d46c281f’: [‘mzi_2’, ‘mzi_3’, ‘mzi_4’]})

Args:
recursive_netlist (dict): The hierarchical netlist dictionary. top_level_instance_name (str): The name of the top-level instance to start the search from. required_models (list): A list of measurement that need to be included in the recursion. target_component_prefix (str): The prefix of the component instances to locate. models (dict): A dictionary of measurement provided to aid in the recursion.

Returns:

tuple: A tuple containing:

model_composition_mapping (dict): A mapping of required measurement to their composed measurement.

instance_composition_mapping (dict): A mapping of required measurement to their corresponding instances.

target_component_mapping (dict): A mapping of target components to their parent components.

get_component_instances(recursive_netlist: dict, top_level_prefix: str, component_name_prefix: str)[source]#

Returns a dictionary of all instances of a given component in a recursive netlist.

Args:
recursive_netlist (dict): The hierarchical netlist dictionary. top_level_prefix (str): The prefix of the top-level instance to search under. component_name_prefix (str): The prefix of the component instances to find.

Returns:
dict: A dictionary mapping the component prefix to a list of instance names that match the prefix.

get_netlist_instances_by_prefix(recursive_netlist: dict, instance_prefix: str) → str[source]#

Returns a list of all instances with a given prefix in a recursive netlist.

Parameters:

recursive_netlist (dict) – The hierarchical netlist dictionary.
instance_prefix (str) – The prefix to search for within the netlist instances.

Returns:

The name of the instance that matches the given prefix.

Return type:

str

Raises:

ValueError – If no instance or more than one instance matches the given prefix.

get_matched_model_recursive_netlist_instances(recursive_netlist: dict, top_level_instance_prefix: str, target_component_prefix: str, models: dict | None = None, custom_subcomponent_instance: str | None = 'sxt') → list[tuple][source]#

This function returns an active component list with a tuple mapping of the location of the active component within the recursive netlist and corresponding model. It will recursively look within a netlist to locate what measurement use a particular component model. At each stage of recursion, it will compose a list of the elements that implement this matching model in order to relate the model to the instance, and hence the netlist address of the component that needs to be updated in order to functionally implement the model.

It takes in as a set of parameters the recursive_netlist generated by a gdsfactory netlist implementation.

Returns a list of tuples, that correspond to the phases applied with the corresponding component paths at multiple levels of recursion. eg. [(“component_lattice_gener_fb8c4da8”, “mzi_1”, “sxt”), (“component_lattice_gener_fb8c4da8”, “mzi_5”, “sxt”)] and these are our keys to our sax circuit decomposition.

Parameters:

recursive_netlist (dict) – The hierarchical netlist dictionary.
top_level_instance_prefix (str) – The prefix of the top-level instance to search under.
target_component_prefix (str) – The prefix of the target component to find.
models (Optional[dict]) – A dictionary of measurement to aid in the recursion. Defaults to None.
custom_subcomponent_instance (Optional[str]) – The instance name for subcomponents, used for backwards compatibility.

Returns:

A list of tuples, each containing the hierarchical path to the matched component instances. Each tuple has the form (parent_component, target_instance, custom_subcomponent_instance).

Return type:

list[tuple]

get_sdense_ports_index(input_ports_order: tuple, all_ports_index: dict) → dict[source]#

This function returns the connection index of the sax dense S-parameter matrix.

Given that the order of the iteration is provided by the user, the dictionary keys will also be ordered accordingly when iterating over them. This requires the user to provide a set of ordered.

TODO verify reasonable iteration order.

# The input_ports_order can be a tuple of tuples that contain the index and port name. Eg.
input_ports_order = ((0, "in_o_0"), (5, "in_o_1"), (6, "in_o_2"), (7, "in_o_3"))
# The all_ports_index is a dictionary of the connection index. Eg.
all_ports_index = {
    "in_o_0": 0,
    "out_o_0": 1,
    "out_o_1": 2,
    "out_o_2": 3,
    "out_o_3": 4,
    "in_o_1": 5,
    "in_o_2": 6,
    "in_o_3": 7,
}
# Output
{"in_o_0": 0, "in_o_1": 5, "in_o_2": 6, "in_o_3": 7}

Parameters:

input_ports_order (tuple) – The connection order tuple. Can be a tuple of tuples that contain the index and port name.
all_ports_index (dict) – The connection index dictionary.

Returns:

The ordered input connection index tuple.

Return type:

tuple

sax_to_s_parameters_standard_matrix(sax_input: Any, input_ports_order: tuple[str] | None = None, round_int: bool | None = None, *args, **kwargs) → piel.types.SParameterMatrixTuple[source]#

A sax S-parameter SDict is provided as a dictionary of tuples with (port0, port1) as the key. This determines the direction of the scattering relationship. It means that the number of terms in an S-parameter matrix is the number of connection squared.

In order to generalise, this function returns both the S-parameter matrices and the indexing connection based on the amount provided. In terms of computational speed, we definitely would like this function to be algorithmically very fast. For now, I will write a simple python implementation and optimise in the future.

It is possible to see the sax SDense notation equivalence here: https://flaport.github.io/sax/nbs/08_backends.html

import jax.numpy as jnp
from sax.core import SDense

# Directional coupler SDense representation
dc_sdense: SDense = (
    jnp.array([[0, 0, τ, κ], [0, 0, κ, τ], [τ, κ, 0, 0], [κ, τ, 0, 0]]),
    {"in0": 0, "in1": 1, "out0": 2, "out1": 3},
)


# Directional coupler SDict representation
# Taken from https://flaport.github.io/sax/nbs/05_models.html
def coupler(*, coupling: float = 0.5) -> SDict:
    kappa = coupling**0.5
    tau = (1 - coupling) ** 0.5
    sdict = reciprocal(
        {
            ("in0", "out0"): tau,
            ("in0", "out1"): 1j * kappa,
            ("in1", "out0"): 1j * kappa,
            ("in1", "out1"): tau,
        }
    )
    return sdict

If we were to relate the mapping accordingly based on the connection indexes, a S-Parameter matrix in the form of \(S_{(output,i),(input,i)}\) would be:

\[\begin{split}S = \begin{bmatrix} S_{00} & S_{10} \\ S_{01} & S_{11} \\ \end{bmatrix} = \begin{bmatrix} \tau & j \kappa \\ j \kappa & \tau \\ \end{bmatrix}\end{split}\]

Note that the standard S-parameter and hence unitary representation is in the form of:

\[\begin{split}S = \begin{bmatrix} S_{00} & S_{01} \\ S_{10} & S_{11} \\ \end{bmatrix}\end{split}\]

\[\begin{split}\begin{bmatrix} b_{1} \\ \vdots \\ b_{n} \end{bmatrix} = \begin{bmatrix} S_{11} & \dots & S_{1n} \\ \vdots & \ddots & \vdots \\ S_{n1} & \dots & S_{nn} \end{bmatrix} \begin{bmatrix} a_{1} \\ \vdots \\ a_{n} \end{bmatrix}\end{split}\]

TODO check with Floris, does this mean we need to transpose the matrix? TODO document round_int

Parameters:

sax_input (sax.SType) – The sax S-parameter dictionary.
input_ports_order (tuple) – The connection order tuple containing the names and order of the input connection.
round_int (bool) – Whether to round the complex numbers to integers.

Returns:

The S-parameter matrix and the input connection index tuple in the standard S-parameter notation.

Return type:

tuple

snet#