[WIP] Integrate T1 experiment with result database

qiskit_experiments/__init__.py

@@ -14,9 +14,6 @@
 
 from .version import __version__
 
-# Base result classes
-from .experiment_data import ExperimentData, AnalysisResult
-
 # Experiment modules
 from . import composite
 from . import characterization

qiskit_experiments/analysis/curve_fitting.py

@@ -19,7 +19,6 @@
 import numpy as np
 import scipy.optimize as opt
 from qiskit.exceptions import QiskitError
-from qiskit_experiments.base_analysis import AnalysisResult
 from qiskit_experiments.analysis.data_processing import filter_data
 
 
@@ -31,7 +30,7 @@ def curve_fit(
     sigma: Optional[np.ndarray] = None,
     bounds: Optional[Union[Dict[str, Tuple[float, float]], Tuple[np.ndarray, np.ndarray]]] = None,
     **kwargs,
-) -> AnalysisResult:
+) -> Dict:
     r"""Perform a non-linear least squares to fit
 
     This solves the optimization problem
@@ -110,8 +109,8 @@ def fit_func(x, *params):
         kwargs["absolute_sigma"] = True
 
     # Run curve fit
-    # TODO: Add error handling so if fitting fails we can return an analysis
-    #       result containing this information
+    # TODO: Add error handling so if fitting fails we can return a
+    #       dictionary containing this information
     # pylint: disable = unbalanced-tuple-unpacking
     popt, pcov = opt.curve_fit(
         fit_func, xdata, ydata, sigma=sigma, p0=param_p0, bounds=param_bounds, **kwargs
@@ -138,7 +137,7 @@ def fit_func(x, *params):
         "xrange": xdata_range,
     }
 
-    return AnalysisResult(result)
+    return result
 
 
 def multi_curve_fit(
@@ -151,7 +150,7 @@ def multi_curve_fit(
     weights: Optional[np.ndarray] = None,
     bounds: Optional[Union[Dict[str, Tuple[float, float]], Tuple[np.ndarray, np.ndarray]]] = None,
     **kwargs,
-) -> AnalysisResult:
+) -> Dict:
     r"""Perform a linearized multi-objective non-linear least squares fit.
 
     This solves the optimization problem
@@ -230,9 +229,7 @@ def f(x, *params):
         return y
 
     # Run linearized curve_fit
-    analysis_result = curve_fit(f, xdata, ydata, p0, sigma=wsigma, bounds=bounds, **kwargs)
-
-    return analysis_result
+    return curve_fit(f, xdata, ydata, p0, sigma=wsigma, bounds=bounds, **kwargs)
 
 
 def process_curve_data(

qiskit_experiments/analysis/plotting.py

@@ -13,11 +13,9 @@
 Plotting functions for experiment analysis
 """
 import functools
-from typing import Callable, Optional
+from typing import Callable, Optional, Dict
 import numpy as np
 
-from qiskit_experiments.base_analysis import AnalysisResult
-
 try:
     from matplotlib import pyplot as plt
 
@@ -44,21 +42,21 @@ def wrapped(*args, **kwargs):
 @requires_matplotlib
 def plot_curve_fit(
     func: Callable,
-    result: AnalysisResult,
+    result: Dict,
     confidence_interval: bool = True,
     ax: Optional["AxesSubplot"] = None,
     num_fit_points: int = 100,
     labelsize: int = 14,
     grid: bool = True,
     **kwargs,
 ) -> "AxesSubplot":
-    """Generate plot of a curve fit analysis result.
+    """Generate plot of a curve fitresult.
 
     Wraps ``matplotlib.pyplot.plot``.
 
     Args:
         func: the fit funcion for curve_fit.
-        result: an AnalysisResult from curve_fit.
+        result: a result dictionary from curve_fit.
         confidence_interval: if True plot the confidence interval from popt_err.
         ax: Optional, a matplotlib axes to add the plot to.
         num_fit_points: the number of points to plot for xrange.

qiskit_experiments/base_analysis.py

@@ -16,7 +16,7 @@
 from abc import ABC, abstractmethod
 
 from qiskit.exceptions import QiskitError
-from .experiment_data import ExperimentData, AnalysisResult
+from qiskit.providers.experiment import AnalysisResult, ExperimentData
 
 
 class BaseAnalysis(ABC):

qiskit_experiments/base_experiment.py

@@ -19,7 +19,7 @@
 from qiskit import transpile, assemble
 from qiskit.exceptions import QiskitError
 
-from .experiment_data import ExperimentData
+from qiskit.providers.experiment import ExperimentDataV1
 
 
 _TRANSPILE_OPTIONS = {
@@ -57,7 +57,7 @@ class BaseExperiment(ABC):
     __analysis_class__ = None
 
     # ExperimentData class for experiment
-    __experiment_data__ = ExperimentData
+    __experiment_data__ = ExperimentDataV1
 
     # Custom default transpiler options for experiment subclasses
     __transpile_defaults__ = {"optimization_level": 0}
@@ -89,7 +89,6 @@ def __init__(self, qubits, experiment_type=None, circuit_options=None):
             self._num_qubits = len(qubits)
             self._physical_qubits = tuple(qubits)
             if self._num_qubits != len(set(self._physical_qubits)):
-                print(self._num_qubits, self._physical_qubits)
                 raise QiskitError("Duplicate qubits in physical qubits list.")
 
         # Store options and values
@@ -113,7 +112,7 @@ def run(self, backend, experiment_data=None, **kwargs):
 
         # Create new experiment data
         if experiment_data is None:
-            experiment_data = self.__experiment_data__(self)
+            experiment_data = self.__experiment_data__(backend, self._type)
 
         # Filter kwargs
         run_options = self.__run_defaults__.copy()

qiskit_experiments/characterization/t1_experiment.py

@@ -21,9 +21,10 @@
 
 from qiskit_experiments.base_experiment import BaseExperiment
 from qiskit_experiments.base_analysis import BaseAnalysis
+from qiskit.providers.experiment import AnalysisResultV1
+from qiskit.providers.experiment.device_component import Qubit
 from qiskit_experiments.analysis.curve_fitting import process_curve_data, curve_fit
 from qiskit_experiments.analysis.data_processing import level2_probability
-from qiskit_experiments import AnalysisResult
 
 
 class T1Analysis(BaseAnalysis):
@@ -40,12 +41,12 @@ def _run_analysis(
         amplitude_bounds=None,
         offset_bounds=None,
         **kwargs,
-    ) -> Tuple[AnalysisResult, None]:
+    ) -> Tuple[AnalysisResultV1, None]:
         """
         Calculate T1
 
         Args:
-            experiment_data (ExperimentData): the experiment data to analyze
+            experiment_data (ExperimentDataV1): the experiment data to analyze
             t1_guess (float): Optional, an initial guess of T1
             amplitude_guess (float): Optional, an initial guess of the coefficient of the exponent
             offset_guess (float): Optional, an initial guess of the offset
@@ -58,13 +59,14 @@ def _run_analysis(
             The analysis result with the estimated T1
         """
 
-        unit = experiment_data._data[0]["metadata"]["unit"]
-        conversion_factor = experiment_data._data[0]["metadata"].get("dt_factor", None)
+        unit = experiment_data.data(0)["metadata"]["unit"]
+        conversion_factor = experiment_data.data(0)["metadata"].get("dt_factor", None)
+
         if conversion_factor is None:
             conversion_factor = 1 if unit == "s" else apply_prefix(1, unit)
 
         xdata, ydata, sigma = process_curve_data(
-            experiment_data._data, lambda datum: level2_probability(datum, "1")
+            experiment_data.data(), lambda datum: level2_probability(datum, "1")
         )
         xdata *= conversion_factor
 
@@ -95,22 +97,29 @@ def _run_analysis(
             ),
         )
 
-        analysis_result = AnalysisResult(
-            {
-                "value": fit_result["popt"][1],
-                "stderr": fit_result["popt_err"][1],
-                "unit": "s",
-                "label": "T1",
-                "fit": fit_result,
-                "quality": self._fit_quality(
-                    fit_result["popt"], fit_result["popt_err"], fit_result["reduced_chisq"]
-                ),
-            }
-        )
+        result_data = {
+            "value": fit_result["popt"][1],
+            "stderr": fit_result["popt_err"][1],
+            "unit": "s",
+            "label": "T1",
+            "fit": fit_result,
+            "quality": self._fit_quality(
+                fit_result["popt"], fit_result["popt_err"], fit_result["reduced_chisq"]
+            ),
+        }
 
-        analysis_result["fit"]["circuit_unit"] = unit
+        result_data["fit"]["circuit_unit"] = unit
         if unit == "dt":
-            analysis_result["fit"]["dt"] = conversion_factor
+            result_data["fit"]["dt"] = conversion_factor
+
+        analysis_result = AnalysisResultV1(
+            result_data,
+            "T1",
+            [Qubit(experiment_data.data(0)["metadata"]["qubit"])],
+            experiment_data.id,
+            quality=result_data["quality"],
+            verified=True,
+        )
 
         return analysis_result, None
 
@@ -125,9 +134,9 @@ def _fit_quality(fit_out, fit_err, reduced_chisq):
             and (fit_err[1] is None or fit_err[1] < fit_out[1])
             and (fit_err[2] is None or fit_err[2] < 0.1)
         ):
-            return "computer_good"
+            return "good"
         else:
-            return "computer_bad"
+            return "bad"
 
 
 class T1Experiment(BaseExperiment):

qiskit_experiments/composite/composite_analysis.py

@@ -14,8 +14,9 @@
 """
 
 from qiskit.exceptions import QiskitError
-from qiskit_experiments.base_analysis import BaseAnalysis, AnalysisResult
+from qiskit_experiments.base_analysis import BaseAnalysis
 from .composite_experiment_data import CompositeExperimentData
+from qiskit.providers.experiment import AnalysisResult
 
 
 class CompositeAnalysis(BaseAnalysis):

qiskit_experiments/composite/composite_experiment_data.py

@@ -14,7 +14,7 @@
 """
 
 from qiskit.result import marginal_counts
-from qiskit_experiments.experiment_data import ExperimentData
+from qiskit.providers.experiment import ExperimentData
 
 
 class CompositeExperimentData(ExperimentData):

qiskit_experiments/composite/parallel_experiment.py

@@ -22,7 +22,7 @@ class ParallelExperiment(CompositeExperiment):
     """Parallel Experiment class"""
 
     def __init__(self, experiments):
-        """Initialize the analysis object.
+        """Initialize the parallel experiments object.
 
         Args:
             experiments (List[BaseExperiment]): a list of experiments.