add multi group curve analysis class and update hamtomo analysis

Qiskit-Extensions · Jun 9, 2022 · a1be2e5 · a1be2e5
1 parent d91ce05
commit a1be2e5
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Showing 8 changed files with 496 additions and 139 deletions.
diff --git a/qiskit_experiments/curve_analysis/__init__.py b/qiskit_experiments/curve_analysis/__init__.py
@@ -467,6 +467,7 @@ def _create_analysis_results(self, fit_data, quality, **metadata):
 
     BaseCurveAnalysis
     CurveAnalysis
+    MultiGroupCurveAnalysis
 
 Data Classes
 ============
@@ -548,6 +549,7 @@ def _create_analysis_results(self, fit_data, quality, **metadata):
 """
 from .base_curve_analysis import BaseCurveAnalysis
 from .curve_analysis import CurveAnalysis
+from .grouped_curve_analysis import MultiGroupCurveAnalysis
 from .curve_data import (
     CurveData,
     CurveFitResult,

diff --git a/qiskit_experiments/curve_analysis/base_curve_analysis.py b/qiskit_experiments/curve_analysis/base_curve_analysis.py
@@ -86,8 +86,6 @@ class BaseCurveAnalysis(BaseAnalysis, ABC):
 
     This method to creates analysis results for important fit parameters
     that might be defined by analysis options ``result_parameters``.
-    In addition, another entry for all fit parameters is created when
-    the analysis option ``return_fit_parameters`` is ``True``.
 
     .. rubric:: _create_curve_data
 
@@ -391,13 +389,16 @@ def _run_curve_fit(
         self,
         curve_data: CurveData,
         models: List[lmfit.Model],
+        init_guesses: Dict[str, float],
     ) -> CurveFitResult:
         """Perform curve fitting on given data collection and fit models.
 
         Args:
             curve_data: Formatted data to fit.
             models: A list of LMFIT models that are used to build a cost function
                 for the LMFIT minimizer.
+            init_guesses: Dictionary of fit parameter initial guesses keyed on the
+                fit parameter name.
 
         Returns:
             The best fitting outcome with minimum reduced chi-squared value.
@@ -419,10 +420,11 @@ def _run_curve_fit(
 
         default_fit_opt = FitOptions(
             parameters=unite_parameter_names,
-            default_p0=self.options.p0,
+            default_p0=init_guesses,
             default_bounds=self.options.bounds,
             **self.options.lmfit_options,
         )
+
         # Bind fixed parameters if not empty
         if self.options.fixed_parameters:
             fixed_parameters = {

diff --git a/qiskit_experiments/curve_analysis/curve_analysis.py b/qiskit_experiments/curve_analysis/curve_analysis.py
@@ -193,6 +193,7 @@ def _run_analysis(
         fit_data = self._run_curve_fit(
             curve_data=formatted_data,
             models=self._models,
+            init_guesses=self.options.p0,
         )
 
         if fit_data.success:
@@ -201,15 +202,15 @@ def _run_analysis(
             quality = "bad"
 
         if self.options.return_fit_parameters:
-            # Store fit status entry regardless of success.
+            # Store fit status overview entry regardless of success.
             # This is sometime useful when debugging the fitting code.
-            fit_parameters = AnalysisResultData(
+            overview = AnalysisResultData(
                 name=PARAMS_ENTRY_PREFIX + self.__class__.__name__,
                 value=fit_data,
                 quality=quality,
                 extra=self.options.extra,
             )
-            analysis_results.append(fit_parameters)
+            analysis_results.append(overview)
 
         # Create figure and result data
         if fit_data.success: