[WIP] Mock experiment

qiskit_experiments/test/__init__.py

@@ -0,0 +1,15 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""
+Test tools for experiment.
+"""

qiskit_experiments/test/mock_experiment.py

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+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+"""
+Standard RB analysis class.
+"""
+
+from qiskit_experiments.base_experiment import BaseExperiment
+from qiskit_experiments.experiment_data import ExperimentData
+from qiskit_experiments.analysis import CurveAnalysis, SeriesDef
+from typing import Tuple, Optional
+import numpy as np
+
+
+class FakeExperiment(BaseExperiment):
+    """A fake experiment class."""
+
+    def __init__(self, qubits=(0,)):
+        super().__init__(qubits=qubits, experiment_type="fake_experiment")
+
+    def circuits(self, backend=None, **circuit_options):
+        return []
+
+
+def curve_model_based_level2_probability_experiment(
+        experiment: BaseExperiment,
+        xvals: np.ndarray,
+        target_params: np.ndarray,
+        outcome_labels: Tuple[str, str],
+        shots: Optional[int] = 1024
+) -> ExperimentData:
+    """Simulate fake experiment data with curve analysis fit model.
+
+    Args:
+        experiment: Target experiment class.
+        xvals: Values to scan.
+        target_params: Parameters used to generate sampling data with the fit function.
+        outcome_labels: Two strings tuple of out come label in count dictionary.
+            The first and second label should be assigned to success and failure, respectively.
+        shots: Number of shot to generate count data.
+
+    Returns:
+        A fake experiment data.
+    """
+    analysis = experiment.__analysis_class__
+
+    if isinstance(analysis, CurveAnalysis):
+        raise Exception("The attached analysis is not CurveAnalysis subclass. "
+                        "This function cannot simulate output data.")
+
+    x_key = analysis.__x_key__
+    series = analysis.__series__
+    fit_funcs = analysis.__fit_funcs__
+    param_names = analysis.__param_names__
+
+    if series is None:
+        series = [
+            SeriesDef(
+                name='default',
+                param_names=[f"p{idx}" for idx in range(len(target_params))],
+                fit_func_index=0,
+                filter_kwargs=dict()
+            )
+        ]
+
+    data = []
+    for curve_properties in series:
+        fit_func = fit_funcs[curve_properties.fit_func_index]
+        params = []
+        for param_name in curve_properties.param_names:
+            param_index = param_names.index(param_name)
+            params.append(param_index)
+        y_values = fit_func(xvals, *params)
+        counts = np.asarray(y_values * shots, dtype=int)
+
+        for xi, count in zip(xvals, counts):
+            metadata = {
+                x_key: xi,
+                "qubits": experiment._physical_qubits,
+                "experiment_type": experiment._type
+            }
+            metadata.update(**series.filter_kwargs)
+
+            data.append(
+                {
+                    "counts": {outcome_labels[0]: shots - count, outcome_labels[1]: count},
+                    "metadata": metadata
+                }
+            )
+
+    expdata = ExperimentData(experiment=experiment)
+    for datum in data:
+        expdata.add_data(datum)
+
+    return expdata