remove outdated comments, delete hpars, update docs

src/qibocal/protocols/characterization/classification.py

@@ -379,9 +379,9 @@ def _update(
     update.threshold(results.threshold[qubit], platform, qubit)
     update.mean_gnd_states(results.mean_gnd_states[qubit], platform, qubit)
     update.mean_exc_states(results.mean_exc_states[qubit], platform, qubit)
-    update.classifiers_hpars(results.classifiers_hpars[qubit], platform, qubit)
     update.readout_fidelity(results.fidelity[qubit], platform, qubit)
     update.assignment_fidelity(results.assignment_fidelity[qubit], platform, qubit)
 
 
 single_shot_classification = Routine(_acquisition, _fit, _plot, _update)
+"""Qubit classification routine object."""

src/qibocal/protocols/characterization/qutrit_classification.py

@@ -5,9 +5,7 @@
 from qibolab import AcquisitionType, ExecutionParameters
 from qibolab.platform import Platform
 from qibolab.pulses import PulseSequence
-from qibolab.qubits import QubitId
 
-from qibocal import update
 from qibocal.auto.operation import Qubits, Routine
 from qibocal.fitting.classifier import run
 from qibocal.protocols.characterization.classification import (
@@ -53,6 +51,9 @@ def _acquisition(
     qubits: Qubits,
 ) -> QutritClassificationData:
     """
+    This Routine prepares the qubits in 0,1 and 2 states and measures their
+    respective I, Q values.
+
     Args:
         nshots (int): number of times the pulse sequence will be repeated.
         classifiers (list): list of classifiers, the available ones are:
@@ -66,10 +67,6 @@ def _acquisition(
         relaxation_time (float): Relaxation time.
     """
 
-    # create two sequences of pulses:
-    # state0_sequence: I  - MZ
-    # state1_sequence: RX - MZ
-
     # taking advantage of multiplexing, apply the same set of gates to all qubits in parallel
     states_sequences = [PulseSequence() for _ in range(3)]
     ro_pulses = {}
@@ -88,7 +85,6 @@ def _acquisition(
             )
             sequence.add(ro_pulses[qubit][-1])
 
-    # create a DataUnits object to store the results
     data = QutritClassificationData(
         nshots=params.nshots,
         classifiers_list=params.classifiers_list,
@@ -178,12 +174,5 @@ def _plot(data: QutritClassificationData, qubit, fit: SingleShotClassificationRe
     return figures, fitting_report
 
 
-def _update(
-    results: SingleShotClassificationResults, platform: Platform, qubit: QubitId
-):
-    update.qutrit_classifiers_hpars(
-        results.classifiers_hpars[qubit], platform, qubit
-    )  # TODO: implement a qutrit classifiers hpars (?)
-
-
-qutrit_classification = Routine(_acquisition, _fit, _plot, _update)
+qutrit_classification = Routine(_acquisition, _fit, _plot)
+"""Qutrit classification Routine object."""

src/qibocal/update.py

@@ -93,16 +93,6 @@ def assignment_fidelity(fidelity: float, platform: Platform, qubit: QubitId):
     platform.qubits[qubit].assignment_fidelity = float(fidelity)
 
 
-def classifiers_hpars(hpars: list, platform: Platform, qubit: QubitId):
-    """Update classifier hyperparameters in platform for specific qubit."""
-    platform.qubits[qubit].classifiers_hpars = hpars
-
-
-def qutrit_classifiers_hpars(hpars: list, platform: Platform, qubit: QubitId):
-    """Update qutrit classifier hyperparameters in platform for specific qubit."""
-    platform.qubits[qubit].qutrit_classifiers_hpars = hpars
-
-
 def virtual_phases(phases: dict[QubitId, float], platform: Platform, pair: QubitPairId):
     """Update virtual phases for given qubits in pair in results."""
     virtual_z_pulses = {