From 5aa50ee515830f2aa51db92dc4e1bac2a6c36884 Mon Sep 17 00:00:00 2001
From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com>
Date: Thu, 4 Jul 2024 14:18:17 +0400
Subject: [PATCH] Revert "fix: Missing qubit key because of identation"

This reverts commit 4ea33940b3d4e857e8bd6b01f3701c91e87a2568.
---
 .../twpa_calibration/frequency_power.py       | 70 +++++++++----------
 1 file changed, 35 insertions(+), 35 deletions(-)

diff --git a/src/qibocal/protocols/readout_optimization/twpa_calibration/frequency_power.py b/src/qibocal/protocols/readout_optimization/twpa_calibration/frequency_power.py
index e9a9a89a6..4bc359892 100644
--- a/src/qibocal/protocols/readout_optimization/twpa_calibration/frequency_power.py
+++ b/src/qibocal/protocols/readout_optimization/twpa_calibration/frequency_power.py
@@ -100,46 +100,46 @@ def _acquisition(
         ].twpa.local_oscillator.frequency
         initial_twpa_power[qubit] = platform.qubits[qubit].twpa.local_oscillator.power
 
-    for freq in freq_range:
-        platform.qubits[qubit].twpa.local_oscillator.frequency = (
-            initial_twpa_freq[qubit] + freq
-        )
-
-        for power in power_range:
-            for qubit in targets:
-                platform.qubits[qubit].twpa.local_oscillator.power = (
-                    initial_twpa_power[qubit] + power
-                )
-
-            classification_data = classification._acquisition(
-                classification.SingleShotClassificationParameters.load(
-                    {"nshots": params.nshots}
-                ),
-                platform,
-                targets,
+        for freq in freq_range:
+            platform.qubits[qubit].twpa.local_oscillator.frequency = (
+                initial_twpa_freq[qubit] + freq
             )
 
-            classification_result = classification._fit(classification_data)
+            for power in power_range:
+                for qubit in targets:
+                    platform.qubits[qubit].twpa.local_oscillator.power = (
+                        initial_twpa_power[qubit] + power
+                    )
 
-            data.register_qubit(
-                TwpaFrequencyPowerType,
-                (qubit),
-                dict(
-                    freq=np.array(
-                        [platform.qubits[qubit].twpa.local_oscillator.frequency],
-                        dtype=np.float64,
-                    ),
-                    power=np.array(
-                        [platform.qubits[qubit].twpa.local_oscillator.power],
-                        dtype=np.float64,
+                classification_data = classification._acquisition(
+                    classification.SingleShotClassificationParameters.load(
+                        {"nshots": params.nshots}
                     ),
-                    assignment_fidelity=np.array(
-                        [classification_result.assignment_fidelity[qubit]],
+                    platform,
+                    targets,
+                )
+
+                classification_result = classification._fit(classification_data)
+
+                data.register_qubit(
+                    TwpaFrequencyPowerType,
+                    (qubit),
+                    dict(
+                        freq=np.array(
+                            [platform.qubits[qubit].twpa.local_oscillator.frequency],
+                            dtype=np.float64,
+                        ),
+                        power=np.array(
+                            [platform.qubits[qubit].twpa.local_oscillator.power],
+                            dtype=np.float64,
+                        ),
+                        assignment_fidelity=np.array(
+                            [classification_result.assignment_fidelity[qubit]],
+                        ),
+                        angle=np.array([classification_result.rotation_angle[qubit]]),
+                        threshold=np.array([classification_result.threshold[qubit]]),
                     ),
-                    angle=np.array([classification_result.rotation_angle[qubit]]),
-                    threshold=np.array([classification_result.threshold[qubit]]),
-                ),
-            )
+                )
     return data