finalize

- small docs fix
- lint
- reno

qiskit_experiments/curve_analysis/base_curve_analysis.py

@@ -135,10 +135,13 @@ def _default_options(cls) -> Options:
                 instance that defines the `self.__call__` method.
             normalization (bool) : Set ``True`` to normalize y values within range [-1, 1].
                 Default to ``False``.
-            p0 (Dict[str, float]): Array-like or dictionary
-                of initial parameters.
-            bounds (Dict[str, Tuple[float, float]]): Array-like or dictionary
-                of (min, max) tuple of fit parameter boundaries.
+            p0 (Dict[str, float]): Initial guesses for the fit parameters.
+                The dictionary is keyed on the fit parameter names.
+            bounds (Dict[str, Tuple[float, float]]): Boundary of fit parameters.
+                The dictionary is keyed on the fit parameter names and
+                values are the tuples of (min, max) of each parameter.
+            curve_fitter_options (Dict[str, Any]) Options that are passed to the
+                scipy curve fit which performs the least square fitting on the experiment results.
             x_key (str): Circuit metadata key representing a scanned value.
             result_parameters (List[Union[str, ParameterRepr]): Parameters reported in the
                 database as a dedicated entry. This is a list of parameter representation
@@ -149,8 +152,6 @@ def _default_options(cls) -> Options:
                 Representation should be printable in standard output, i.e. no latex syntax.
             extra (Dict[str, Any]): A dictionary that is appended to all database entries
                 as extra information.
-            curve_fitter_options (Dict[str, Any]) Options that are passed to the
-                specified curve fitting function.
             fixed_parameters (Dict[str, Any]): Fitting model parameters that are fixed
                 during the curve fitting. This should be provided with default value
                 keyed on one of the parameter names in the series definition.

qiskit_experiments/curve_analysis/curve_analysis.py

@@ -59,9 +59,6 @@ def __init__(self):
         #: List[CurveData]: Processed experiment data set. For backward compatibility.
         self.__processed_data_set = {}
 
-        #: List[int]: Index of physical qubits
-        self._physical_qubits = None
-
     @classmethod
     def _fit_params(cls) -> List[str]:
         """Return a list of fitting parameters.
@@ -200,9 +197,7 @@ def _run_analysis(
             quality = self._evaluate_quality(fit_data)
 
             # Create analysis results
-            analysis_results.extend(
-                self._create_analysis_results(fit_data, quality, **metadata)
-            )
+            analysis_results.extend(self._create_analysis_results(fit_data, quality, **metadata))
             # calling old extra entry method for backward compatibility
             if hasattr(self, "_extra_database_entry"):
                 warnings.warn(
@@ -331,6 +326,9 @@ def analysis_result_to_repr(result: AnalysisResultData) -> str:
 
     Returns:
         String representation of the data.
+
+    Raises:
+        AnalysisError: When the result data is not likely fit parameter.
     """
     if not isinstance(result.value, (float, UFloat)):
         raise AnalysisError(f"Result data {result.name} is not a valid fit parameter data type.")

qiskit_experiments/curve_analysis/standard_analysis/gaussian.py

@@ -127,10 +127,10 @@ def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
         fit_a = fit_data.fitval("a")
         fit_b = fit_data.fitval("b")
         fit_freq = fit_data.fitval("freq")
-        fit_kappa = fit_data.fitval("kappa")
+        fit_sigma = fit_data.fitval("sigma")
 
         snr = abs(fit_a.n) / np.sqrt(abs(np.median(fit_data.y_data) - fit_b.n))
-        fit_width_ratio = fit_kappa.n / np.ptp(fit_data.x_data)
+        fit_width_ratio = fit_sigma.n / np.ptp(fit_data.x_data)
 
         criteria = [
             fit_data.x_range[0] <= fit_freq.n <= fit_data.x_range[1],

qiskit_experiments/library/characterization/analysis/cr_hamiltonian_analysis.py

@@ -21,7 +21,6 @@
 import qiskit_experiments.curve_analysis as curve
 
 import qiskit_experiments.data_processing as dp
-from qiskit_experiments.database_service.device_component import Qubit
 from qiskit_experiments.framework import AnalysisResultData
 
 

releasenotes/notes/cleanup-curve-analysis-96d7ff706cae5b4e.yaml

@@ -0,0 +1,31 @@
+---
+developer:
+  - |
+    :class:`CurveAnalysis` is now a subclass of :class:`BaseCurveAnalysis`.
+    This change has been made to introduce multi-grouped curve analysis,
+    see qiskit-experiments/#737 for details.
+    
+    According to this update, several protected methods that are
+    originally designed to be overridden by subclass have been deprecated or upgraded.
+    Check the following list of changes you may need to upgrade your analysis subclass.
+    
+    - :meth:`CurveAnalysis._generate_fit_guesses`
+    
+    The signature of method has been upgraded. Now this method should be called with
+    ``curve_data`` and calling ``self._data()`` to get curve data has been deprecated.
+    
+    - :meth:`CurveAnalysis._data`
+    
+    This method has been deprecated. 
+    
+    - :meth:`CurveAnalysis._extra_database_entry`
+    
+    This method has been deprecated. You can directly override 
+    new method :meth:`CurveAnalysis._create_analysis_results`.
+    
+    - :class:`FitData`
+    
+    Fit data dataclass is now created with :attr:`x_data` and :attr:`y_data`
+    so that you can retrieve the data used for the fitting along with the outcome parameters.
+
+    See :ref:`curve_analysis_overview` for the overview of new curve analysis base class.

test/curve_analysis/test_baseclass.py

@@ -81,6 +81,7 @@ class TestCurveAnalysisUnit(QiskitExperimentsTestCase):
 
     class TestAnalysis(CurveAnalysis):
         """Fake analysis class for unittest."""
+
         __series__ = [
             SeriesDef(
                 name="curve1",
@@ -136,9 +137,7 @@ def test_data_extraction(self):
         xvalues = np.linspace(1.0, 5.0, 10)
 
         analysis = self.TestAnalysis()
-        analysis.set_options(
-            data_processor=DataProcessor("counts", [Probability("1")])
-        )
+        analysis.set_options(data_processor=DataProcessor("counts", [Probability("1")]))
 
         # data to analyze
         test_data0 = simulate_output_data(
@@ -171,9 +170,7 @@ def test_data_extraction_with_subset(self):
         xvalues = np.linspace(1.0, 5.0, 10)
 
         analysis = self.TestAnalysis()
-        analysis.set_options(
-            data_processor=DataProcessor("counts", [Probability("1")])
-        )
+        analysis.set_options(data_processor=DataProcessor("counts", [Probability("1")]))
 
         # data to analyze
         test_data0 = simulate_output_data(
@@ -257,6 +254,7 @@ def test_invalid_options(self):
 
         class InvalidClass:
             """Dummy class."""
+
             pass
 
         with self.assertRaises(TypeError):