refactor: Replace feature with boolean find_min

Author: andrea-pasquale

src/qibocal/protocols/flux_dependence/qubit_flux_dependence.py

@@ -96,17 +96,17 @@ def register_qubit(self, qubit, freq, bias, signal, phase):
         )
 
     @property
-    def feature(self) -> str:
-        """Get feature (either min or max) depending on resonator type."""
-        return "max" if self.resonator_type == "2D" else "min"
+    def find_min(self) -> bool:
+        """Returns True if resonator_type is 2D else False otherwise."""
+        return self.resonator_type != "2D"
 
     def filtered_data(self, qubit: QubitId) -> np.ndarray:
         """Apply mask to specific qubit data."""
         return extract_feature(
             self.data[qubit].freq,
             self.data[qubit].bias,
             self.data[qubit].signal,
-            self.feature,
+            self.find_min,
         )
 
 

src/qibocal/protocols/flux_dependence/resonator_flux_dependence.py

@@ -89,17 +89,17 @@ def register_qubit(self, qubit, freq, bias, signal, phase):
         )
 
     @property
-    def feature(self) -> str:
-        """Get feature (either min or max) depending on resonator type."""
-        return "min" if self.resonator_type == "2D" else "max"
+    def find_min(self) -> bool:
+        """Returns True if resonator_type is 2D else False otherwise."""
+        return self.resonator_type == "2D"
 
     def filtered_data(self, qubit: QubitId) -> np.ndarray:
         """Apply mask to specific qubit data."""
         return extract_feature(
             self.data[qubit].freq,
             self.data[qubit].bias,
             self.data[qubit].signal,
-            self.feature,
+            self.find_min,
         )
 
 

src/qibocal/protocols/flux_dependence/utils.py

@@ -266,36 +266,20 @@ def transmon_readout_frequency(
      Returns:
          (float): resonator frequency as a function of bias.
     """
+
+    qubit_frequency = transmon_frequency(
+        xi=xi,
+        xj=xj,
+        w_max=w_max,
+        d=d,
+        normalization=normalization,
+        offset=offset,
+        crosstalk_element=crosstalk_element,
+        charging_energy=charging_energy,
+    )
     return resonator_freq + g**2 * (
-        1
-        / (
-            resonator_freq
-            - transmon_frequency(
-                xi=xi,
-                xj=xj,
-                w_max=w_max,
-                d=d,
-                normalization=normalization,
-                offset=offset,
-                crosstalk_element=crosstalk_element,
-                charging_energy=charging_energy,
-            )
-        )
-        - 1
-        / (
-            resonator_freq
-            - transmon_frequency(
-                xi=xi,
-                xj=xj,
-                w_max=w_max,
-                d=d,
-                normalization=normalization,
-                offset=offset,
-                crosstalk_element=crosstalk_element,
-                charging_energy=charging_energy,
-            )
-            + charging_energy
-        )
+        1 / (resonator_freq - qubit_frequency)
+        - 1 / (resonator_freq - qubit_frequency + charging_energy)
     )
 
 

src/qibocal/protocols/utils.py

@@ -639,9 +639,7 @@ def extract_feature(
     x: np.ndarray,
     y: np.ndarray,
     z: np.ndarray,
-    feat: str,
-    ci_first_mask: float = CONFIDENCE_INTERVAL_FIRST_MASK,
-    ci_second_mask: float = CONFIDENCE_INTERVAL_SECOND_MASK,
+    find_min: bool,
 ):
     """Extract feature using confidence intervals.
 
@@ -656,7 +654,7 @@ def extract_feature(
 
     """
 
-    # background remove over y axis
+    # background removed over y axis
     z_ = z.reshape(len(np.unique(y)), len(np.unique(x)))
     z_ = z_ / np.mean(z, axis=0)
     normalized_z = z_.reshape(z.shape)
@@ -669,7 +667,7 @@ def extract_feature(
     for i in unique_bias:
         signal_fixed_y = normalized_z[y == i]
         peak, _ = find_peaks(
-            -signal_fixed_y if feat == "min" else signal_fixed_y, prominence=0.3
+            -signal_fixed_y if find_min else signal_fixed_y, prominence=0.3
         )
         if len(peak) > 0:
             for j in peak: