def get_processor(experiment_data: ExperimentData,
analysis_options: Options) -> DataProcessor:
"""Get a DataProcessor that produces a continuous signal given the options.
Args:
experiment_data: The experiment data that holds all the data and metadata needed
to determine the data processor to use to process the data for analysis.
analysis_options: The analysis options with which to analyze the data. The options that
are relevant for the configuration of a data processor are:
- normalization (bool): A boolean to specify if the data should be normalized to
the interval [0, 1]. The default is True. This option is only relevant if
kerneled data is used.
- dimensionality_reduction: An optional string or instance of :class:`ProjectorType`
to represent the dimensionality reduction node for Kerneled data. For the
supported nodes, see :class:`ProjectorType`. Typically, these nodes convert
complex IQ data to real data, for example by performing a singular-value
decomposition. This argument is only needed for Kerneled data (i.e. level 1)
and can thus be ignored if Classified data (the default) is used.
- outcome (string): The measurement outcome that will be passed to a Probability node.
The default value is a string of 1's where the length of the string is the number of
qubits, e.g. '111' for three qubits.
Returns:
An instance of DataProcessor capable of processing the data for the corresponding job.
Notes:
The `physical_qubits` argument is extracted from the `experiment_data`
metadata and is used to determine the default `outcome` to extract from
classified data if it was not given in the analysis options.
Raises:
DataProcessorError: if the measurement level is not supported.
"""
metadata = experiment_data.metadata
if "job_metadata" in metadata:
# Backwards compatibility for old experiment data
# remove job metadata and add required fields to new location in metadata
job_meta = metadata.pop("job_metadata")
run_options = job_meta[-1].get("run_options", {})
for opt in ["meas_level", "meas_return"]:
if opt in run_options:
metadata[opt] = run_options[opt]
warnings.warn(
"The analyzed ExperimentData contains deprecated data processor "
" job_metadata which has been been updated to current metadat format. "
"If this data was loaded from a database servide you should re-save it "
"to update the metadata in the database.",
DeprecationWarning,
)
meas_level = metadata.get("meas_level", MeasLevel.CLASSIFIED)
meas_return = metadata.get("meas_return", MeasReturnType.AVERAGE)
normalize = analysis_options.get("normalization", True)
dimensionality_reduction = analysis_options.get("dimensionality_reduction",
ProjectorType.SVD)
if meas_level == MeasLevel.CLASSIFIED:
num_qubits = len(metadata.get("physical_qubits", [0]))
outcome = analysis_options.get("outcome", "1" * num_qubits)
return DataProcessor("counts", [nodes.Probability(outcome)])
if meas_level == MeasLevel.KERNELED:
return get_kerneled_processor(dimensionality_reduction, meas_return,
normalize)
raise DataProcessorError(f"Unsupported measurement level {meas_level}.")
def get_processor(
experiment_data: ExperimentData,
analysis_options: Options,
index: int = -1,
) -> DataProcessor:
"""Get a DataProcessor that produces a continuous signal given the options.
Args:
experiment_data: The experiment data that holds all the data and metadata needed
to determine the data processor to use to process the data for analysis.
analysis_options: The analysis options with which to analyze the data. The options that
are relevant for the configuration of a data processor are:
- normalization (bool): A boolean to specify if the data should be normalized to
the interval [0, 1]. The default is True. This option is only relevant if
kerneled data is used.
- dimensionality_reduction: An optional string or instance of :class:`ProjectorType`
to represent the dimensionality reduction node for Kerneled data. For the
supported nodes, see :class:`ProjectorType`. Typically, these nodes convert
complex IQ data to real data, for example by performing a singular-value
decomposition. This argument is only needed for Kerneled data (i.e. level 1)
and can thus be ignored if Classified data (the default) is used.
- outcome (string): The measurement outcome that will be passed to a Probability node.
The default value is a string of 1's where the length of the string is the number of
qubits, e.g. '111' for three qubits.
index: The index of the job for which to get a data processor. The default value is -1.
Returns:
An instance of DataProcessor capable of processing the data for the corresponding job.
Notes:
The `num_qubits` argument is extracted from the `experiment_data` metadata and is used
to determine the default `outcome` to extract from classified data if it was not given in the
analysis options.
Raises:
DataProcessorError: if the measurement level is not supported.
DataProcessorError: if the wrong dimensionality reduction for kerneled data
is specified.
"""
run_options = experiment_data.metadata["job_metadata"][index].get(
"run_options", {})
meas_level = run_options.get("meas_level", MeasLevel.CLASSIFIED)
meas_return = run_options.get("meas_return", MeasReturnType.AVERAGE)
normalize = analysis_options.get("normalization", True)
dimensionality_reduction = analysis_options.get("dimensionality_reduction",
ProjectorType.SVD)
if meas_level == MeasLevel.CLASSIFIED:
num_qubits = experiment_data.metadata.get("num_qubits", 1)
outcome = analysis_options.get("outcome", "1" * num_qubits)
return DataProcessor("counts", [nodes.Probability(outcome)])
if meas_level == MeasLevel.KERNELED:
try:
if isinstance(dimensionality_reduction, ProjectorType):
projector_name = dimensionality_reduction.name
else:
projector_name = dimensionality_reduction
projector = ProjectorType[projector_name].value
except KeyError as error:
raise DataProcessorError(
f"Invalid dimensionality reduction: {dimensionality_reduction}."
) from error
if meas_return == "single":
processor = DataProcessor("memory",
[nodes.AverageData(axis=1),
projector()])
else:
processor = DataProcessor("memory", [projector()])
if normalize:
processor.append(nodes.MinMaxNormalize())
return processor
raise DataProcessorError(f"Unsupported measurement level {meas_level}.")