gplugins.sax.read.model_from_csv

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gplugins.sax.read.model_from_csv#

gplugins.sax.read.model_from_csv(filepath: str | Path | DataFrame, xkey: str = 'wavelengths', xunits: float = 1, prefix: str = 's') → Annotated[Annotated[Callable[[...], dict[tuple[Annotated[str, PlainValidator(func=val_port, json_schema_input_type=Any)], Annotated[str, PlainValidator(func=val_port, json_schema_input_type=Any)]], Annotated[Array, complexfloating, PlainValidator(func=val_complex_array, json_schema_input_type=Any)]]], PlainValidator(func=val_model, json_schema_input_type=Any)] | Annotated[Callable[[...], tuple[Annotated[Array, complexfloating, PlainValidator(func=val_complex_array, json_schema_input_type=Any)], dict[Annotated[str, PlainValidator(func=val_port, json_schema_input_type=Any)], int]]], PlainValidator(func=val_model, json_schema_input_type=Any)] | Annotated[Callable[[...], tuple[Annotated[Array | ndarray, signedinteger, 1, PlainValidator(func=val_int_array_1d, json_schema_input_type=Any)], Annotated[Array | ndarray, signedinteger, 1, PlainValidator(func=val_int_array_1d, json_schema_input_type=Any)], Annotated[Array, complexfloating, PlainValidator(func=val_complex_array, json_schema_input_type=Any)], dict[Annotated[str, PlainValidator(func=val_port, json_schema_input_type=Any)], int]]], PlainValidator(func=val_model, json_schema_input_type=Any)], PlainValidator(func=val_model, json_schema_input_type=Any)] | Annotated[Annotated[Callable[[...], tuple[Annotated[Array, complexfloating, PlainValidator(func=val_complex_array, json_schema_input_type=Any)], dict[Annotated[str, PlainValidator(func=val_port_mode, json_schema_input_type=Any)], int]]], PlainValidator(func=val_model, json_schema_input_type=Any)] | Annotated[Callable[[...], tuple[Annotated[Array | ndarray, signedinteger, 1, PlainValidator(func=val_int_array_1d, json_schema_input_type=Any)], Annotated[Array | ndarray, signedinteger, 1, PlainValidator(func=val_int_array_1d, json_schema_input_type=Any)], Annotated[Array, complexfloating, PlainValidator(func=val_complex_array, json_schema_input_type=Any)], dict[Annotated[str, PlainValidator(func=val_port_mode, json_schema_input_type=Any)], int]]], PlainValidator(func=val_model, json_schema_input_type=Any)] | Annotated[Callable[[...], dict[tuple[Annotated[str, PlainValidator(func=val_port_mode, json_schema_input_type=Any)], Annotated[str, PlainValidator(func=val_port_mode, json_schema_input_type=Any)]], Annotated[Array, complexfloating, PlainValidator(func=val_complex_array, json_schema_input_type=Any)]]], PlainValidator(func=val_model, json_schema_input_type=Any)], PlainValidator(func=val_model, json_schema_input_type=Any)][source]#

Returns a SAX Sparameters Model from a CSV file.

The SAX Model is a function that returns a SAX SDict interpolated over wavelength.

Parameters:

  • filepath – CSV Sparameters path or pandas DataFrame.

  • xkey – key for wavelengths in file.

  • xunits – x units in um from the loaded file (um). 1 means 1um.

  • prefix – for the sparameters column names in file.

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