Demo files and new Quam state

qm_id-41.txt

@@ -0,0 +1 @@
+QM-538f590a-f5b0-42fa-994a-e274ed5e5723

qualibration_graphs/quantum_dots/calibration_utils/T1_parity_diff/analysis.py

@@ -38,6 +38,10 @@
 from scipy.optimize import differential_evolution
 
 from qualibrate import QualibrationNode
+from calibration_utils.common_utils.parity_dataset import (
+    get_pdiff_trace,
+    get_qubit_names_from_pdiff,
+)
 
 _logger = logging.getLogger(__name__)
 
@@ -147,7 +151,7 @@ def _profile_residual(t1_arr):
         result["offset"] = offset
         result["decay_rate"] = decay_rate
         result["fitted_curve"] = fitted_curve
-        result["success"] = np.isfinite(t1_best) and t1_best > 0 and abs(amplitude) > 1e-6
+        result["success"] = bool(np.isfinite(t1_best) and t1_best > 0 and abs(amplitude) > 1e-6)
 
         _logger.debug(
             "T1 fit: T1=%.1f ns, A=%.4f, offset=%.4f, γ=%.6f 1/ns",
@@ -191,16 +195,13 @@ def fit_raw_data(
     qubits = node.namespace["qubits"]
     tau_ns = np.asarray(ds.tau.values, dtype=float)
 
-    pdiff_vars = [v for v in ds.data_vars if v.startswith("pdiff_")]
-    qubit_names = [v.replace("pdiff_", "") for v in sorted(pdiff_vars)]
-    if not qubit_names:
-        qubit_names = [getattr(q, "name", f"Q{i}") for i, q in enumerate(qubits)]
+    qubit_names = get_qubit_names_from_pdiff(ds, fallback_qubits=qubits)
 
     fit_results: Dict[str, Dict[str, Any]] = {}
 
     for qname in qubit_names:
-        pdiff_var = f"pdiff_{qname}"
-        if pdiff_var not in ds.data_vars:
+        pdiff = get_pdiff_trace(ds, qname)
+        if pdiff is None:
             fp = FitParameters(
                 T1=np.nan,
                 amplitude=0.0,
@@ -211,7 +212,6 @@ def fit_raw_data(
             fit_results[qname] = asdict(fp)
             continue
 
-        pdiff = np.asarray(ds[pdiff_var].values, dtype=float)
         raw = _fit_single_qubit(pdiff, tau_ns)
 
         fp = FitParameters(

qualibration_graphs/quantum_dots/calibration_utils/T1_parity_diff/plotting.py

@@ -15,11 +15,14 @@
 import numpy as np
 import xarray as xr
 
+from calibration_utils.common_utils.parity_dataset import (
+    get_pdiff_trace,
+    get_qubit_names_from_pdiff,
+)
+
 
 def _get_qubit_names_from_ds(ds: xr.Dataset) -> List[str]:
-    """Extract qubit names from ``pdiff_<name>`` data-variable keys."""
-    pdiff_vars = [v for v in ds.data_vars if v.startswith("pdiff_") and not v.endswith("_fit")]
-    return [v.replace("pdiff_", "") for v in sorted(pdiff_vars)]
+    return get_qubit_names_from_pdiff(ds)
 
 
 def plot_raw_data_with_fit(
@@ -74,10 +77,8 @@ def plot_raw_data_with_fit(
         offset = fr.get("offset", np.nan)
         success = fr.get("success", False)
 
-        pdiff_var = f"pdiff_{qname}"
-        if pdiff_var in ds.data_vars:
-            pdiff = np.asarray(ds[pdiff_var].values, dtype=float)
-        else:
+        pdiff = get_pdiff_trace(ds, qname)
+        if pdiff is None:
             pdiff = np.full_like(tau_ns, np.nan)
 
         # Data

qualibration_graphs/quantum_dots/calibration_utils/charge_stability/analysis.py

@@ -186,7 +186,7 @@ def fit_raw_data(ds: xr.Dataset, node: QualibrationNode) -> Tuple[xr.Dataset, di
     ds_fit = ds_fit.assign_coords(success=("sensors", success_list))
 
     # Set node outcomes
-    node.outcomes = {sensor.id: "successful" if fit_results[sensor.id].success else "fail" for sensor in sensors}
+    node.outcomes = {sensor.id: "successful" if fit_results[sensor.id].success else "failed" for sensor in sensors}
 
     return ds_fit, fit_results
 

qualibration_graphs/quantum_dots/calibration_utils/common_utils/parity_dataset.py

@@ -0,0 +1,36 @@
+"""Helpers for parity-difference datasets across fetch conventions."""
+
+from __future__ import annotations
+
+from typing import Iterable, List
+
+import numpy as np
+import xarray as xr
+
+
+def get_qubit_names_from_pdiff(ds: xr.Dataset, fallback_qubits: Iterable | None = None) -> List[str]:
+    """Resolve qubit names from grouped or per-variable parity-diff datasets."""
+    if "pdiff" in ds.data_vars and "qubit" in ds["pdiff"].dims:
+        return [str(name) for name in np.asarray(ds["pdiff"].coords["qubit"].values)]
+
+    pdiff_vars = [v for v in ds.data_vars if v.startswith("pdiff_") and not v.endswith("_fit")]
+    if pdiff_vars:
+        return [v.replace("pdiff_", "") for v in sorted(pdiff_vars)]
+
+    if fallback_qubits is None:
+        return []
+    return [getattr(q, "name", f"Q{i}") for i, q in enumerate(fallback_qubits)]
+
+
+def get_pdiff_trace(ds: xr.Dataset, qname: str) -> np.ndarray | None:
+    """Return 1D/2D parity-difference trace for one qubit name if available."""
+    if "pdiff" in ds.data_vars and "qubit" in ds["pdiff"].dims:
+        qubit_coords = [str(name) for name in np.asarray(ds["pdiff"].coords["qubit"].values)]
+        if qname not in qubit_coords:
+            return None
+        return np.asarray(ds["pdiff"].sel(qubit=qname).values, dtype=float)
+
+    pdiff_var = f"pdiff_{qname}"
+    if pdiff_var not in ds.data_vars:
+        return None
+    return np.asarray(ds[pdiff_var].values, dtype=float)

qualibration_graphs/quantum_dots/calibration_utils/hahn_echo_parity_diff/analysis.py

@@ -29,6 +29,7 @@
 from scipy.optimize import differential_evolution
 
 import xarray as xr
+from calibration_utils.common_utils.parity_dataset import get_pdiff_trace
 
 logger = logging.getLogger(__name__)
 
@@ -117,11 +118,7 @@ def _cost(params: np.ndarray) -> float:
 
     # Sanity: T2 should be positive and finite, amplitude should be non-zero
     success = bool(
-        result.success
-        and np.isfinite(t2_best)
-        and t2_best > 0
-        and np.isfinite(amp_best)
-        and abs(amp_best) > 1e-6
+        result.success and np.isfinite(t2_best) and t2_best > 0 and np.isfinite(amp_best) and abs(amp_best) > 1e-6
     )
 
     decay_rate = 2.0 / t2_best if t2_best > 0 else 0.0
@@ -166,14 +163,13 @@ def fit_raw_data(
 
     for qubit in qubits:
         qname = qubit.name
-        var_name = f"pdiff_{qname}"
-        if var_name not in ds_raw.data_vars:
+        pdiff = get_pdiff_trace(ds_raw, qname)
+        if pdiff is None:
             logger.warning("No pdiff variable for qubit %s — skipping.", qname)
             results[qname] = dict(FitParameters().__dict__, fitted_curve=np.array([]))
             continue
 
-        pdiff = ds_raw[var_name].values.astype(np.float64)
-        results[qname] = _fit_single_qubit(tau_ns, pdiff)
+        results[qname] = _fit_single_qubit(tau_ns, pdiff.astype(np.float64))
 
     return results
 

qualibration_graphs/quantum_dots/calibration_utils/hahn_echo_parity_diff/plotting.py

@@ -16,6 +16,8 @@
 import numpy as np
 import xarray as xr
 
+from calibration_utils.common_utils.parity_dataset import get_pdiff_trace
+
 
 def plot_raw_data_with_fit(
     ds_raw: xr.Dataset,
@@ -53,13 +55,12 @@ def plot_raw_data_with_fit(
     for idx, qubit in enumerate(qubits):
         ax = axes[idx, 0]
         qname = qubit.name
-        var_name = f"pdiff_{qname}"
-
-        if var_name not in ds_raw.data_vars:
+        pdiff = get_pdiff_trace(ds_raw, qname)
+        if pdiff is None:
             ax.set_title(f"{qname} — no data")
             continue
 
-        pdiff = ds_raw[var_name].values.astype(np.float64)
+        pdiff = pdiff.astype(np.float64)
         r = fit_results.get(qname, {})
 
         # Raw data

qualibration_graphs/quantum_dots/calibration_utils/power_rabi/analysis.py

@@ -17,6 +17,10 @@
 from scipy.optimize import curve_fit
 
 from qualibrate import QualibrationNode
+from calibration_utils.common_utils.parity_dataset import (
+    get_pdiff_trace,
+    get_qubit_names_from_pdiff,
+)
 
 _logger = logging.getLogger(__name__)
 
@@ -252,7 +256,7 @@ def _analyse_single_qubit(
             gamma,
         )
 
-    success = np.isfinite(a_pi) and a_pi > 0
+    success = bool(np.isfinite(a_pi) and a_pi > 0)
 
     return {
         "opt_amp": float(a_pi),
@@ -275,18 +279,15 @@ def fit_raw_data(
 ) -> Tuple[xr.Dataset, Dict[str, Dict[str, Any]]]:
     """Fit optimal amplitude per qubit from power-Rabi data."""
     qubits = node.namespace["qubits"]
-    pdiff_vars = [v for v in ds.data_vars if v.startswith("pdiff_")]
-    qubit_names = [v.replace("pdiff_", "") for v in sorted(pdiff_vars)]
-    if not qubit_names:
-        qubit_names = [getattr(q, "name", f"Q{i}") for i, q in enumerate(qubits)]
+    qubit_names = get_qubit_names_from_pdiff(ds, fallback_qubits=qubits)
 
     amps = np.asarray(ds.amp_prefactor.values, dtype=float)
 
     fit_results: Dict[str, Dict[str, Any]] = {}
 
     for qname in qubit_names:
-        pdiff_var = f"pdiff_{qname}"
-        if pdiff_var not in ds.data_vars:
+        pdiff = get_pdiff_trace(ds, qname)
+        if pdiff is None:
             fp = FitParameters(
                 opt_amp=np.nan,
                 rabi_frequency=np.nan,
@@ -296,7 +297,6 @@ def fit_raw_data(
             fit_results[qname] = asdict(fp)
             continue
 
-        pdiff = np.asarray(ds[pdiff_var].values, dtype=float)
         result = _analyse_single_qubit(pdiff, amps)
 
         fp = FitParameters(

qualibration_graphs/quantum_dots/calibration_utils/power_rabi/plotting.py

@@ -9,11 +9,14 @@
 import xarray as xr
 
 from calibration_utils.power_rabi.analysis import FFT_FREQ_MIN, FFT_FREQ_MAX
+from calibration_utils.common_utils.parity_dataset import (
+    get_pdiff_trace,
+    get_qubit_names_from_pdiff,
+)
 
 
 def _get_qubit_names_from_ds(ds: xr.Dataset) -> List[str]:
-    pdiff_vars = [v for v in ds.data_vars if v.startswith("pdiff_") and not v.endswith("_fit")]
-    return [v.replace("pdiff_", "") for v in sorted(pdiff_vars)]
+    return get_qubit_names_from_pdiff(ds)
 
 
 def _plot_rabi_trace_ax(
@@ -103,18 +106,16 @@ def plot_raw_data_with_fit(
 
     for i, qname in enumerate(qubit_names):
         ax_trace, ax_fft = axes[i, 0], axes[i, 1]
-        pdiff_var = f"pdiff_{qname}"
         fr = fit_results.get(qname, {})
 
         amps = np.asarray(ds.amp_prefactor.values, dtype=float)
 
-        if pdiff_var not in ds.data_vars:
+        pdiff = get_pdiff_trace(ds, qname)
+        if pdiff is None:
             ax_trace.text(0.5, 0.5, f"No data for {qname}", transform=ax_trace.transAxes, ha="center")
             ax_fft.text(0.5, 0.5, f"No data for {qname}", transform=ax_fft.transAxes, ha="center")
             continue
 
-        pdiff = np.asarray(ds[pdiff_var].values, dtype=float)
-
         _plot_rabi_trace_ax(ax_trace, pdiff, amps, qname, fit_result=fr)
         _plot_fft_ax(ax_fft, qname, fit_result=fr)
 

qualibration_graphs/quantum_dots/calibration_utils/ramsey_chevron_parity_diff/analysis.py

@@ -34,6 +34,10 @@
 from scipy.optimize import curve_fit, differential_evolution
 
 from qualibrate import QualibrationNode
+from calibration_utils.common_utils.parity_dataset import (
+    get_pdiff_trace,
+    get_qubit_names_from_pdiff,
+)
 
 _logger = logging.getLogger(__name__)
 
@@ -357,7 +361,7 @@ def _objective(params):
         t2_star,
     )
 
-    success = np.isfinite(t2_star) and t2_star > 0
+    success = bool(np.isfinite(t2_star) and t2_star > 0)
 
     return {
         "freq_offset": freq_offset,
@@ -401,19 +405,16 @@ def fit_raw_data(
         ``_diag`` key with diagnostic arrays for plotting.
     """
     qubits = node.namespace["qubits"]
-    pdiff_vars = [v for v in ds.data_vars if v.startswith("pdiff_")]
-    qubit_names = [v.replace("pdiff_", "") for v in sorted(pdiff_vars)]
-    if not qubit_names:
-        qubit_names = [getattr(q, "name", f"Q{i}") for i, q in enumerate(qubits)]
+    qubit_names = get_qubit_names_from_pdiff(ds, fallback_qubits=qubits)
 
     detuning_hz = np.asarray(ds.detuning.values, dtype=float)
     tau_ns = np.asarray(ds.tau.values, dtype=float)
 
     fit_results: Dict[str, Dict[str, Any]] = {}
 
     for qname in qubit_names:
-        pdiff_var = f"pdiff_{qname}"
-        if pdiff_var not in ds.data_vars:
+        pdiff = get_pdiff_trace(ds, qname)
+        if pdiff is None:
             fp = FitParameters(
                 freq_offset=0.0,
                 t2_star=np.nan,
@@ -424,7 +425,6 @@ def fit_raw_data(
             fit_results[qname] = asdict(fp)
             continue
 
-        pdiff = np.asarray(ds[pdiff_var].values, dtype=float)
         result = _analyse_single_qubit(pdiff, detuning_hz, tau_ns)
 
         fp = FitParameters(

qualibration_graphs/quantum_dots/calibration_utils/ramsey_chevron_parity_diff/plotting.py

@@ -16,11 +16,14 @@
 import numpy as np
 import xarray as xr
 
+from calibration_utils.common_utils.parity_dataset import (
+    get_pdiff_trace,
+    get_qubit_names_from_pdiff,
+)
+
 
 def _get_qubit_names_from_ds(ds: xr.Dataset) -> List[str]:
-    """Extract qubit names from ``pdiff_<name>`` data-variable keys."""
-    pdiff_vars = [v for v in ds.data_vars if v.startswith("pdiff_") and not v.endswith("_fit")]
-    return [v.replace("pdiff_", "") for v in sorted(pdiff_vars)]
+    return get_qubit_names_from_pdiff(ds)
 
 
 def _plot_chevron_ax(
@@ -127,13 +130,13 @@ def plot_raw_data_with_fit(
     fig, axes = plt.subplots(n, ncol, figsize=(6 * ncol, 4 * n), squeeze=False)
 
     for i, qname in enumerate(qubit_names):
-        pdiff_var = f"pdiff_{qname}"
         fr = fit_results.get(qname, {})
 
         tau_ns = np.asarray(ds.tau.values, dtype=float)
         detuning_mhz = np.asarray(ds.detuning.values, dtype=float) * 1e-6
+        pdiff = get_pdiff_trace(ds, qname)
 
-        if pdiff_var not in ds.data_vars:
+        if pdiff is None:
             for j in range(ncol):
                 axes[i, j].text(
                     0.5,
@@ -144,8 +147,6 @@ def plot_raw_data_with_fit(
                 )
             continue
 
-        pdiff = np.asarray(ds[pdiff_var].values, dtype=float)
-
         _plot_chevron_ax(axes[i, 0], pdiff, tau_ns, detuning_mhz, qname, fr)
         _plot_resonance_ax(axes[i, 1], detuning_mhz, qname, fr)