From c5f7e7ba38dbae7590047b9210691c268c6d17e0 Mon Sep 17 00:00:00 2001
From: Daniel Egger <deg@zurich.ibm.com>
Date: Fri, 29 Oct 2021 10:26:38 +0200
Subject: [PATCH 01/11] * Moved calibration.DragCal ->
 characterization.RoughDrag. * New cal class RoughDragCal. * Added test and
 refactored tests. * Adjusted inits.

---
 .../base_calibration_experiment.py            |  17 +++
 qiskit_experiments/library/__init__.py        |   6 +-
 .../library/calibration/__init__.py           |   4 +-
 .../library/calibration/fine_amplitude.py     |   4 -
 .../library/calibration/rough_drag_cal.py     | 115 ++++++++++++++++++
 .../library/characterization/__init__.py      |   2 +
 .../{calibration => characterization}/drag.py |  90 +++++++-------
 test/calibration/experiments/test_drag.py     |  64 ++++++----
 8 files changed, 221 insertions(+), 81 deletions(-)
 create mode 100644 qiskit_experiments/library/calibration/rough_drag_cal.py
 rename qiskit_experiments/library/{calibration => characterization}/drag.py (76%)

diff --git a/qiskit_experiments/calibration_management/base_calibration_experiment.py b/qiskit_experiments/calibration_management/base_calibration_experiment.py
index 899f83da58..50eac604e6 100644
--- a/qiskit_experiments/calibration_management/base_calibration_experiment.py
+++ b/qiskit_experiments/calibration_management/base_calibration_experiment.py
@@ -139,6 +139,23 @@ def calibrations(self) -> Calibrations:
         """Return the calibrations."""
         return self._cals
 
+    @classmethod
+    def _default_experiment_options(cls):
+        """Default values for the fine amplitude calibration experiment.
+
+        Experiment Options:
+            result_index (int): The index of the result from which to update the calibrations.
+            group (str): The calibration group to which the parameter belongs. This will default
+                to the value "default".
+
+        """
+        options = super()._default_experiment_options()
+
+        options.result_index = -1
+        options.group = "default"
+
+        return options
+
     def update_calibrations(self, experiment_data: ExperimentData):
         """Update parameter values in the :class:`Calibrations` instance.
 
diff --git a/qiskit_experiments/library/__init__.py b/qiskit_experiments/library/__init__.py
index c0767f31a9..4962d4b440 100644
--- a/qiskit_experiments/library/__init__.py
+++ b/qiskit_experiments/library/__init__.py
@@ -62,6 +62,7 @@
     ~characterization.EFSpectroscopy
     ~characterization.CrossResonanceHamiltonian
     ~characterization.EchoedCrossResonanceHamiltonian
+    ~characterization.RoughDrag
     ~characterization.FineAmplitude
     ~characterization.FineXAmplitude
     ~characterization.FineSXAmplitude
@@ -82,7 +83,7 @@ class instance to manage parameters and pulse schedules.
     :template: autosummary/experiment.rst
 
     ~calibration.RoughFrequencyCal
-    ~calibration.DragCal
+    ~calibration.RoughDragCal
     ~calibration.FineDrag
     ~calibration.FineXDrag
     ~calibration.FineSXDrag
@@ -95,7 +96,7 @@ class instance to manage parameters and pulse schedules.
 
 """
 from .calibration import (
-    DragCal,
+    RoughDragCal,
     FineDrag,
     FineXDrag,
     FineSXDrag,
@@ -114,6 +115,7 @@ class instance to manage parameters and pulse schedules.
     EFSpectroscopy,
     CrossResonanceHamiltonian,
     EchoedCrossResonanceHamiltonian,
+    RoughDrag,
     FineAmplitude,
     FineXAmplitude,
     FineSXAmplitude,
diff --git a/qiskit_experiments/library/calibration/__init__.py b/qiskit_experiments/library/calibration/__init__.py
index 139c575207..26bcb6b744 100644
--- a/qiskit_experiments/library/calibration/__init__.py
+++ b/qiskit_experiments/library/calibration/__init__.py
@@ -40,7 +40,7 @@
     :template: autosummary/experiment.rst
 
     RoughFrequencyCal
-    DragCal
+    RoughDragCal
     FineDrag
     FineXDrag
     FineSXDrag
@@ -68,7 +68,7 @@
 """
 
 from .rough_frequency import RoughFrequencyCal
-from .drag import DragCal
+from .rough_drag_cal import RoughDragCal
 from .fine_drag import FineDrag, FineXDrag, FineSXDrag
 from .fine_amplitude import FineAmplitudeCal, FineXAmplitudeCal, FineSXAmplitudeCal
 from .rabi import Rabi, EFRabi
diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py
index 63538736df..cf0bca5d86 100644
--- a/qiskit_experiments/library/calibration/fine_amplitude.py
+++ b/qiskit_experiments/library/calibration/fine_amplitude.py
@@ -82,11 +82,7 @@ def _default_experiment_options(cls):
 
         """
         options = super()._default_experiment_options()
-
-        options.result_index = -1
         options.target_angle = np.pi
-        options.group = "default"
-
         return options
 
     def _add_cal_metadata(self, circuits: List[QuantumCircuit]):
diff --git a/qiskit_experiments/library/calibration/rough_drag_cal.py b/qiskit_experiments/library/calibration/rough_drag_cal.py
new file mode 100644
index 0000000000..275485bef9
--- /dev/null
+++ b/qiskit_experiments/library/calibration/rough_drag_cal.py
@@ -0,0 +1,115 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Rough drag calibration experiment."""
+
+from typing import Iterable, List, Optional
+
+from qiskit import QuantumCircuit
+from qiskit.circuit import Parameter
+from qiskit.providers.backend import Backend
+
+from qiskit_experiments.framework import ExperimentData
+from qiskit_experiments.calibration_management import (
+    BaseCalibrationExperiment,
+    BackendCalibrations,
+)
+from qiskit_experiments.calibration_management.update_library import BaseUpdater
+from qiskit_experiments.library.characterization.drag import RoughDrag
+
+
+class RoughDragCal(BaseCalibrationExperiment, RoughDrag):
+    """A calibration version of the Drag experiment.
+
+    # section: see_also
+        qiskit_experiments.library.characterization.rough_drag.RoughDrag
+    """
+
+    def __init__(
+        self,
+        qubit: int,
+        calibrations: BackendCalibrations,
+        schedule_name: str = "x",
+        betas: Iterable[float] = None,
+        cal_parameter_name: Optional[str] = "β",
+        auto_update: bool = True,
+        group: str = "default",
+        backend: Optional[Backend] = None,
+    ):
+        r"""see class :class:`RoughDrag` for details.
+
+        Args:
+            qubit: The qubit for which to run the rough drag calibration.
+            calibrations: The calibrations instance with the schedules.
+            schedule_name: The name of the schedule to calibrate. Defaults to "x".
+            betas: A list of drag parameter values to scan. If None is given 51 betas ranging
+                from -5 to 5 will be scanned.
+            cal_parameter_name: The name of the parameter in the schedule to update.
+                Defaults to "β".
+            auto_update: Whether or not to automatically update the calibrations. By
+                default this variable is set to True.
+            group: The group of calibration parameters to use. The default value is "default".
+            backend: Optional, the backend to run the experiment on.
+        """
+        schedule = calibrations.get_schedule(
+            schedule_name, qubit, assign_params={cal_parameter_name: Parameter("β")}, group=group
+        )
+
+        super().__init__(
+            calibrations,
+            qubit,
+            schedule=schedule,
+            betas=betas,
+            backend=backend,
+            schedule_name=schedule_name,
+            cal_parameter_name=cal_parameter_name,
+            auto_update=auto_update,
+        )
+
+    def _add_cal_metadata(self, circuits: List[QuantumCircuit]):
+        """Add metadata to the circuit to make the experiment data more self contained.
+
+        The following keys are added to each circuit's metadata:
+            cal_param_value: The value of the previous calibrated beta.
+            cal_param_name: The name of the parameter in the calibrations.
+            cal_schedule: The name of the schedule in the calibrations.
+            cal_group: The calibration group to which the parameter belongs.
+        """
+
+        prev_beta = self._cals.get_parameter_value(
+            self._param_name, self.physical_qubits, self._sched_name, self.experiment_options.group
+        )
+
+        for circuit in circuits:
+            circuit.metadata["cal_param_value"] = prev_beta
+            circuit.metadata["cal_param_name"] = self._param_name
+            circuit.metadata["cal_schedule"] = self._sched_name
+            circuit.metadata["cal_group"] = self.experiment_options.group
+
+    def update_calibrations(self, experiment_data: ExperimentData):
+        """Update the beta using the value directly reported from the fit.
+
+        See :class:`DragCalAnalysis` for details on the fit.
+        """
+
+        new_beta = BaseUpdater.get_value(
+            experiment_data, "beta", self.experiment_options.result_index
+        )
+
+        BaseUpdater.add_parameter_value(
+            self._cals,
+            experiment_data,
+            new_beta,
+            self._param_name,
+            self._sched_name,
+            self.experiment_options.group,
+        )
diff --git a/qiskit_experiments/library/characterization/__init__.py b/qiskit_experiments/library/characterization/__init__.py
index 25e2f473d9..71449917d6 100644
--- a/qiskit_experiments/library/characterization/__init__.py
+++ b/qiskit_experiments/library/characterization/__init__.py
@@ -31,6 +31,7 @@
     FineAmplitude
     FineXAmplitude
     FineSXAmplitude
+    RoughDrag
 
 
 Analysis
@@ -55,3 +56,4 @@
 from .cr_hamiltonian import CrossResonanceHamiltonian, EchoedCrossResonanceHamiltonian
 from .cr_hamiltonian_analysis import CrossResonanceHamiltonianAnalysis
 from .fine_amplitude import FineAmplitude, FineXAmplitude, FineSXAmplitude
+from .drag import RoughDrag
diff --git a/qiskit_experiments/library/calibration/drag.py b/qiskit_experiments/library/characterization/drag.py
similarity index 76%
rename from qiskit_experiments/library/calibration/drag.py
rename to qiskit_experiments/library/characterization/drag.py
index 055c4b9e10..a88f80aec9 100644
--- a/qiskit_experiments/library/calibration/drag.py
+++ b/qiskit_experiments/library/characterization/drag.py
@@ -10,22 +10,23 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-"""Rough drag pulse calibration experiment."""
+"""Rough drag experiment."""
 
-from typing import List, Optional
+from typing import Iterable, List, Optional
 import numpy as np
 
 from qiskit import QuantumCircuit
-from qiskit.circuit import Gate, Parameter
+from qiskit.circuit import Gate
+from qiskit.exceptions import QiskitError
 from qiskit.providers.backend import Backend
-import qiskit.pulse as pulse
+from qiskit.pulse import ScheduleBlock
 
 from qiskit_experiments.framework import BaseExperiment, Options
 from qiskit_experiments.exceptions import CalibrationError
 from qiskit_experiments.library.calibration.analysis.drag_analysis import DragCalAnalysis
 
 
-class DragCal(BaseExperiment):
+class RoughDrag(BaseExperiment):
     r"""An experiment that scans the DRAG parameter to find the optimal value.
 
     # section: overview
@@ -74,30 +75,17 @@ class DragCal(BaseExperiment):
 
     @classmethod
     def _default_experiment_options(cls) -> Options:
-        r"""Default values for the pulse if no schedule is given.
-        Users can set the positive and negative rotation schedules with
-
-        .. code-block::
-
-            drag.set_experiment_options(schedule=xp_schedule)
+        r"""Default values for the rough drag experiment.
 
         Experiment Options:
             schedule (ScheduleBlock): The schedule of the rotation.
-            amp (complex): The amplitude for the default Drag pulse. Must have a magnitude
-                smaller than one.
-            duration (int): The duration of the default pulse in samples.
-            sigma (float): The standard deviation of the default pulse.
             reps (List[int]): The number of times the Rp - Rm gate sequence is repeated in
                 each series. Note that this list must always have a length of three as
                 otherwise the analysis class will not run.
             betas (Iterable): the values of the DRAG parameter to scan.
         """
         options = super()._default_experiment_options()
-
         options.schedule = None
-        options.amp = 0.2
-        options.duration = 160
-        options.sigma = 40
         options.reps = [1, 3, 5]
         options.betas = np.linspace(-5, 5, 51)
 
@@ -132,15 +120,44 @@ def set_experiment_options(self, reps: Optional[List] = None, **fields):
 
         super().set_experiment_options(reps=reps, **fields)
 
-    def __init__(self, qubit: int, backend: Optional[Backend] = None):
-        """
+    def __init__(
+        self,
+        qubit: int,
+        schedule: ScheduleBlock,
+        betas: Optional[Iterable[float]] = None,
+        backend: Optional[Backend] = None,
+    ):
+        """Initialize a Drag experiment in the given qubit.
+
         Args:
             qubit: The qubit for which to run the Drag calibration.
+            schedule: The schedule to run. This schedule should have one free parameter
+                corresponding to a DRAG parameter.
+            betas: The values of the DRAG parameter to scan. If None is given the default range
+                :code:`linspace(-5, 5, 51)` is used.
             backend: Optional, the backend to run the experiment on.
+
+        Raises:
+            QiskitError: if the schedule does not have a free parameter.
         """
 
         super().__init__([qubit], backend=backend)
 
+        if betas is not None:
+            self.experiment_options.betas = betas
+
+        if len(schedule.parameters) != 1:
+            raise QiskitError(
+                f"Schedule {schedule} for {self.__class__.__name__} experiment must have "
+                f"exactly one free parameter, found {schedule.parameters} parameters."
+            )
+
+        self.experiment_options.schedule = schedule
+
+    def _pre_circuit(self) -> QuantumCircuit:
+        """A circuit with operations to perform before the Drag."""
+        return QuantumCircuit(1)
+
     def circuits(self) -> List[QuantumCircuit]:
         """Create the circuits for the Drag calibration.
 
@@ -148,47 +165,24 @@ def circuits(self) -> List[QuantumCircuit]:
             circuits: The circuits that will run the Drag calibration.
 
         Raises:
-            CalibrationError:
-                - If the beta parameters in the xp and xm pulses are not the same.
-                - If either the xp or xm pulse do not have at least one Drag pulse.
-                - If the number of different repetition series is not three.
+            QiskitError: if the number of different repetition series is not three.
         """
         schedule = self.experiment_options.schedule
 
-        if schedule is None:
-            beta = Parameter("β")
-            with pulse.build(backend=self.backend, name="drag") as schedule:
-                pulse.play(
-                    pulse.Drag(
-                        duration=self.experiment_options.duration,
-                        amp=self.experiment_options.amp,
-                        sigma=self.experiment_options.sigma,
-                        beta=beta,
-                    ),
-                    pulse.DriveChannel(self.physical_qubits[0]),
-                )
-
-        if len(schedule.parameters) != 1:
-            raise CalibrationError(
-                "The schedule for Drag calibration must have one free parameter."
-                f"Found {len(schedule.parameters)}."
-            )
-
         beta = next(iter(schedule.parameters))
-
         drag_gate = Gate(name=schedule.name, num_qubits=1, params=[beta])
 
         reps = self.experiment_options.reps
         if len(reps) != 3:
-            raise CalibrationError(
-                f"{self.__class__.__name__} must use exactly three repetition numbers. "
+            raise QiskitError(
+                f"{self.__class__.__name__} uses exactly three repetitions. "
                 f"Received {reps} with length {len(reps)} != 3."
             )
 
         circuits = []
 
         for idx, rep in enumerate(reps):
-            circuit = QuantumCircuit(1)
+            circuit = self._pre_circuit()
             for _ in range(rep):
                 circuit.append(drag_gate, (0,))
                 circuit.rz(np.pi, 0)
diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py
index 48d79d9126..e1471fddc2 100644
--- a/test/calibration/experiments/test_drag.py
+++ b/test/calibration/experiments/test_drag.py
@@ -16,14 +16,17 @@
 
 from qiskit.test import QiskitTestCase
 from qiskit.circuit import Parameter
+from qiskit.exceptions import QiskitError
 from qiskit.pulse import DriveChannel, Drag
 import qiskit.pulse as pulse
 from qiskit.qobj.utils import MeasLevel
 from qiskit import transpile
 
 from qiskit_experiments.exceptions import CalibrationError
-from qiskit_experiments.library import DragCal
+from qiskit_experiments.library import RoughDrag, RoughDragCal
 from qiskit_experiments.test.mock_iq_backend import DragBackend
+from qiskit_experiments.calibration_management.basis_gate_library import FixedFrequencyTransmon
+from qiskit_experiments.calibration_management import BackendCalibrations
 
 
 class TestDragEndToEnd(QiskitTestCase):
@@ -41,14 +44,21 @@ def setUp(self):
         self.x_plus = xp
         self.test_tol = 0.05
 
+    def test_reps(self):
+        """Test that setting reps raises and error if reps is not of length three."""
+
+        drag = RoughDrag(0, self.x_plus)
+
+        with self.assertRaises(CalibrationError):
+            drag.set_experiment_options(reps=[1, 2, 3, 4])
+
     def test_end_to_end(self):
         """Test the drag experiment end to end."""
 
         backend = DragBackend(gate_name="xp")
 
-        drag = DragCal(1)
+        drag = RoughDrag(1, self.x_plus)
 
-        drag.set_experiment_options(schedule=self.x_plus)
         expdata = drag.run(backend).block_for_results()
         result = expdata.analysis_results(1)
 
@@ -59,25 +69,20 @@ def test_end_to_end(self):
         # rather increase beta.
         backend = DragBackend(error=0.0051, gate_name="xp")
 
-        drag = DragCal(0)
+        drag = RoughDrag(0, self.x_plus)
         drag.set_analysis_options(p0={"beta": 1.2})
-        drag.set_experiment_options(schedule=self.x_plus)
         exp_data = drag.run(backend).block_for_results()
         result = exp_data.analysis_results(1)
 
-        meas_level = exp_data.metadata["job_metadata"][-1]["run_options"]["meas_level"]
-
         self.assertTrue(abs(result.value.value - backend.ideal_beta) < self.test_tol)
         self.assertEqual(result.quality, "good")
 
         # Large leakage will make the curves oscillate quickly.
         backend = DragBackend(error=0.05, gate_name="xp")
 
-        drag = DragCal(1)
+        drag = RoughDrag(1, self.x_plus, betas=np.linspace(-4, 4, 31))
         drag.set_run_options(shots=200)
-        drag.set_experiment_options(betas=np.linspace(-4, 4, 31))
         drag.set_analysis_options(p0={"beta": 1.8, "freq0": 0.08, "freq1": 0.16, "freq2": 0.32})
-        drag.set_experiment_options(schedule=self.x_plus)
         exp_data = drag.run(backend).block_for_results()
         result = exp_data.analysis_results(1)
 
@@ -107,8 +112,8 @@ def test_default_circuits(self):
 
         backend = DragBackend(error=0.005, gate_name="xp")
 
-        drag = DragCal(0)
-        drag.set_experiment_options(reps=[2, 4, 8], schedule=self.x_plus)
+        drag = RoughDrag(0, self.x_plus)
+        drag.set_experiment_options(reps=[2, 4, 8])
         drag.backend = DragBackend(gate_name="xp")
         circuits = drag.circuits()
 
@@ -125,23 +130,32 @@ def test_raise_multiple_parameter(self):
         with pulse.build(name="xp") as xp:
             pulse.play(Drag(duration=160, amp=amp, sigma=40, beta=beta), DriveChannel(0))
 
-        backend = DragBackend(error=0.05, gate_name="xp")
+        with self.assertRaises(QiskitError):
+            RoughDrag(1, xp, betas=np.linspace(-3, 3, 21))
 
-        drag = DragCal(1)
-        drag.set_experiment_options(betas=np.linspace(-3, 3, 21))
-        drag.set_experiment_options(schedule=xp)
 
-        with self.assertRaises(CalibrationError):
-            drag.run(backend).analysis_results(0)
+class TestRoughDragCalUpdate(QiskitTestCase):
+    """Test that a Drag calibration experiment properly updates the calibrations."""
 
+    def setUp(self):
+        """Setup the tests"""
+        super().setUp()
 
-class TestDragOptions(QiskitTestCase):
-    """Test non-trivial options."""
+        library = FixedFrequencyTransmon()
 
-    def test_reps(self):
-        """Test that setting reps raises and error if reps is not of length three."""
+        self.backend = DragBackend(gate_name="x")
+        self.cals = BackendCalibrations(self.backend, library)
+        self.test_tol = 0.05
 
-        drag = DragCal(0)
+    def test_update(self):
+        """Test that running RoughDragCal updates the calibrations."""
 
-        with self.assertRaises(CalibrationError):
-            drag.set_experiment_options(reps=[1, 2, 3, 4])
+        qubit = 0
+        prev_beta = self.cals.get_parameter_value("β", (0, ), "x")
+        self.assertEqual(prev_beta, 0)
+
+        RoughDragCal(qubit, self.cals, backend=self.backend).run().block_for_results()
+
+        new_beta = self.cals.get_parameter_value("β", (0,), "x")
+        self.assertTrue(abs(new_beta - self.backend.ideal_beta) < self.test_tol)
+        self.assertTrue(abs(new_beta) > self.test_tol)

From a1150626df0d9c9daedfef7a8c565509a7a582a0 Mon Sep 17 00:00:00 2001
From: Daniel Egger <deg@zurich.ibm.com>
Date: Tue, 2 Nov 2021 17:00:10 +0100
Subject: [PATCH 02/11] * Default options.

---
 .../library/calibration/half_angle_cal.py       | 17 -----------------
 .../library/calibration/rough_amplitude_cal.py  |  2 --
 2 files changed, 19 deletions(-)

diff --git a/qiskit_experiments/library/calibration/half_angle_cal.py b/qiskit_experiments/library/calibration/half_angle_cal.py
index 56970a2a43..58bbabf1a6 100644
--- a/qiskit_experiments/library/calibration/half_angle_cal.py
+++ b/qiskit_experiments/library/calibration/half_angle_cal.py
@@ -63,23 +63,6 @@ def __init__(
 
         self.set_transpile_options(inst_map=calibrations.default_inst_map)
 
-    @classmethod
-    def _default_experiment_options(cls):
-        """Default values for the half angle calibration experiment.
-
-        Experiment Options:
-            result_index (int): The index of the result from which to update the calibrations.
-            group (str): The calibration group to which the parameter belongs. This will default
-                to the value "default".
-
-        """
-        options = super()._default_experiment_options()
-
-        options.result_index = -1
-        options.group = "default"
-
-        return options
-
     def _add_cal_metadata(self, circuits: List[QuantumCircuit]):
         """Add metadata to the circuit to make the experiment data more self contained.
 
diff --git a/qiskit_experiments/library/calibration/rough_amplitude_cal.py b/qiskit_experiments/library/calibration/rough_amplitude_cal.py
index 9538425431..4f5344abaa 100644
--- a/qiskit_experiments/library/calibration/rough_amplitude_cal.py
+++ b/qiskit_experiments/library/calibration/rough_amplitude_cal.py
@@ -115,11 +115,9 @@ def _default_experiment_options(cls):
         """
         options = super()._default_experiment_options()
 
-        options.result_index = -1
         options.angles_schedules = [
             AnglesSchedules(target_angle=np.pi, parameter="amp", schedule="x", previous_value=None)
         ]
-        options.group = "default"
 
         return options
 

From 886b73a975fa1f1336fd47fbe99126e5470eab42 Mon Sep 17 00:00:00 2001
From: Daniel Egger <deg@zurich.ibm.com>
Date: Wed, 3 Nov 2021 09:52:41 +0100
Subject: [PATCH 03/11] * Black. * Amp. update fix.

---
 .../library/calibration/fine_amplitude.py              |  4 ++++
 .../library/calibration/rough_drag_cal.py              |  3 ++-
 qiskit_experiments/library/characterization/drag.py    | 10 ++++++++--
 test/calibration/experiments/test_drag.py              |  2 +-
 4 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/qiskit_experiments/library/calibration/fine_amplitude.py b/qiskit_experiments/library/calibration/fine_amplitude.py
index baab59bed9..7762e015e2 100644
--- a/qiskit_experiments/library/calibration/fine_amplitude.py
+++ b/qiskit_experiments/library/calibration/fine_amplitude.py
@@ -137,6 +137,10 @@ def update_calibrations(self, experiment_data: ExperimentData):
             target_angle = data[0]["metadata"]["target_angle"]
             prev_amp = data[0]["metadata"]["cal_param_value"]
 
+            # Protect against cases where the complex amplitude was converted to a list.
+            if isinstance(prev_amp, list) and len(prev_amp) == 2:
+                prev_amp = prev_amp[0] + 1.0j * prev_amp[1]
+
             d_theta = BaseUpdater.get_value(experiment_data, "d_theta", result_index)
 
             BaseUpdater.add_parameter_value(
diff --git a/qiskit_experiments/library/calibration/rough_drag_cal.py b/qiskit_experiments/library/calibration/rough_drag_cal.py
index 275485bef9..22272817b3 100644
--- a/qiskit_experiments/library/calibration/rough_drag_cal.py
+++ b/qiskit_experiments/library/calibration/rough_drag_cal.py
@@ -18,7 +18,7 @@
 from qiskit.circuit import Parameter
 from qiskit.providers.backend import Backend
 
-from qiskit_experiments.framework import ExperimentData
+from qiskit_experiments.framework import ExperimentData, fix_class_docs
 from qiskit_experiments.calibration_management import (
     BaseCalibrationExperiment,
     BackendCalibrations,
@@ -27,6 +27,7 @@
 from qiskit_experiments.library.characterization.drag import RoughDrag
 
 
+@fix_class_docs
 class RoughDragCal(BaseCalibrationExperiment, RoughDrag):
     """A calibration version of the Drag experiment.
 
diff --git a/qiskit_experiments/library/characterization/drag.py b/qiskit_experiments/library/characterization/drag.py
index 174835cff2..cda21afb9d 100644
--- a/qiskit_experiments/library/characterization/drag.py
+++ b/qiskit_experiments/library/characterization/drag.py
@@ -171,7 +171,11 @@ def circuits(self) -> List[QuantumCircuit]:
         schedule = self.experiment_options.schedule
 
         beta = next(iter(schedule.parameters))
-        drag_gate = Gate(name=schedule.name, num_qubits=1, params=[beta])
+
+        # Note: if the pulse has a reserved name, e.g. x, which does not have parameters
+        # then we cannot directly call the gate x and attach a schedule to it. Doing so
+        # would results in QObj errors.
+        drag_gate = Gate(name="Drag(" + schedule.name + ")", num_qubits=1, params=[beta])
 
         reps = self.experiment_options.reps
         if len(reps) != 3:
@@ -192,7 +196,9 @@ def circuits(self) -> List[QuantumCircuit]:
 
             circuit.measure_active()
 
-            circuit.add_calibration(schedule.name, self.physical_qubits, schedule, params=[beta])
+            circuit.add_calibration(
+                "Drag(" + schedule.name + ")", self.physical_qubits, schedule, params=[beta]
+            )
 
             for beta_val in self.experiment_options.betas:
                 beta_val = np.round(beta_val, decimals=6)
diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py
index f0ee576a1d..9b027f0b9d 100644
--- a/test/calibration/experiments/test_drag.py
+++ b/test/calibration/experiments/test_drag.py
@@ -151,7 +151,7 @@ def test_update(self):
         """Test that running RoughDragCal updates the calibrations."""
 
         qubit = 0
-        prev_beta = self.cals.get_parameter_value("β", (0, ), "x")
+        prev_beta = self.cals.get_parameter_value("β", (0,), "x")
         self.assertEqual(prev_beta, 0)
 
         RoughDragCal(qubit, self.cals, backend=self.backend).run().block_for_results()

From 19cc4ea318dc26018c930ef6c50d3e4f28332f3b Mon Sep 17 00:00:00 2001
From: Daniel Egger <deg@zurich.ibm.com>
Date: Wed, 3 Nov 2021 09:57:49 +0100
Subject: [PATCH 04/11] * Removed Drag update library test as this is now
 redundent with RoughDragCal test.

---
 test/calibration/test_update_library.py | 62 +------------------------
 1 file changed, 1 insertion(+), 61 deletions(-)

diff --git a/test/calibration/test_update_library.py b/test/calibration/test_update_library.py
index c0c8fbca93..15002bc044 100644
--- a/test/calibration/test_update_library.py
+++ b/test/calibration/test_update_library.py
@@ -21,7 +21,7 @@
 import qiskit.pulse as pulse
 from qiskit.test.mock import FakeAthens
 
-from qiskit_experiments.library import FineXDrag, DragCal, QubitSpectroscopy
+from qiskit_experiments.library import FineXDrag, QubitSpectroscopy
 from qiskit_experiments.calibration_management.calibrations import Calibrations
 from qiskit_experiments.calibration_management.update_library import (
     Frequency,
@@ -29,7 +29,6 @@
     FineDragUpdater,
 )
 from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations
-from qiskit_experiments.test.mock_iq_backend import DragBackend
 from .experiments.test_fine_drag import FineDragTestBackend
 
 
@@ -88,65 +87,6 @@ def test_frequency(self):
         self.assertEqual(cals.get_qubit_frequencies()[qubit], value)
 
 
-class TestDragUpdate(QiskitTestCase):
-    """Test the frequency update function in the update library."""
-
-    def test_drag(self):
-        """Test calibrations update from drag."""
-
-        backend = DragBackend(gate_name="xp")
-        beta = Parameter("β")
-        qubit = 1
-        test_tol = 0.02
-        chan = Parameter("ch0")
-
-        with pulse.build(backend=backend, name="xp") as x_plus:
-            pulse.play(
-                pulse.Drag(duration=160, amp=0.208519, sigma=40, beta=beta),
-                pulse.DriveChannel(chan),
-            )
-
-        # Setup the calibrations
-        cals = BackendCalibrations(backend)
-
-        cals.add_schedule(x_plus, num_qubits=1)
-
-        cals.add_parameter_value(0.2, "β", qubit, x_plus)
-        cals.inst_map_add("xp", (qubit,))
-
-        # Check that the inst_map has the default beta
-        beta_val = cals.default_inst_map.get("xp", (qubit,)).blocks[0].pulse.beta
-        self.assertEqual(beta_val, 0.2)
-
-        # Run a Drag calibration experiment.
-        drag = DragCal(qubit)
-        drag.set_experiment_options(
-            schedule=cals.get_schedule("xp", qubit, assign_params={"β": beta}),
-        )
-
-        exp_data = drag.run(backend)
-        exp_data.block_for_results()
-        result = exp_data.analysis_results(1)
-
-        # Test the fit for good measure.
-        self.assertTrue(abs(result.value.value - backend.ideal_beta) < test_tol)
-        self.assertEqual(result.quality, "good")
-
-        # Check schedules pre-update
-        expected = x_plus.assign_parameters({beta: 0.2, chan: 1}, inplace=False)
-        self.assertEqual(cals.get_schedule("xp", qubit), expected)
-
-        Drag.update(cals, exp_data, parameter="β", schedule="xp")
-
-        # Check schedules post-update
-        expected = x_plus.assign_parameters({beta: result.value.value, chan: 1}, inplace=False)
-        self.assertEqual(cals.get_schedule("xp", qubit), expected)
-
-        # Check the inst map post update
-        beta_val = cals.default_inst_map.get("xp", (qubit,)).blocks[0].pulse.beta
-        self.assertTrue(np.allclose(beta_val, result.value.value))
-
-
 class TestFineDragUpdate(QiskitTestCase):
     """A class to test fine DRAG updates."""
 

From 34ec72231f5fba251bd185e7c08f099ce6726d1b Mon Sep 17 00:00:00 2001
From: Daniel Egger <deg@zurich.ibm.com>
Date: Wed, 3 Nov 2021 10:00:07 +0100
Subject: [PATCH 05/11] * Removed Drag updater.

---
 qiskit_experiments/calibration_management/__init__.py       | 3 +--
 qiskit_experiments/calibration_management/update_library.py | 6 ------
 2 files changed, 1 insertion(+), 8 deletions(-)

diff --git a/qiskit_experiments/calibration_management/__init__.py b/qiskit_experiments/calibration_management/__init__.py
index 0a5f6ed86e..e2ed834877 100644
--- a/qiskit_experiments/calibration_management/__init__.py
+++ b/qiskit_experiments/calibration_management/__init__.py
@@ -39,7 +39,6 @@
     BackendCalibrations
     Calibrations
     Frequency
-    Drag
 
 Managing Calibration Data
 =========================
@@ -149,4 +148,4 @@
 from .backend_calibrations import BackendCalibrations
 from .base_calibration_experiment import BaseCalibrationExperiment
 
-from .update_library import Frequency, Drag, FineDragUpdater
+from .update_library import Frequency, FineDragUpdater
diff --git a/qiskit_experiments/calibration_management/update_library.py b/qiskit_experiments/calibration_management/update_library.py
index c699d1e25b..7fbd09452a 100644
--- a/qiskit_experiments/calibration_management/update_library.py
+++ b/qiskit_experiments/calibration_management/update_library.py
@@ -184,12 +184,6 @@ def update(
         )
 
 
-class Drag(BaseUpdater):
-    """Update drag parameters."""
-
-    __fit_parameter__ = "beta"
-
-
 class FineDragUpdater(BaseUpdater):
     """Updater for the fine drag calibration."""
 

From 1f5a7f7814ff1a4db29420755da62ff5fbfe47f4 Mon Sep 17 00:00:00 2001
From: Daniel Egger <deg@zurich.ibm.com>
Date: Wed, 3 Nov 2021 10:05:54 +0100
Subject: [PATCH 06/11] * Removed unused import.

---
 test/calibration/test_update_library.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/test/calibration/test_update_library.py b/test/calibration/test_update_library.py
index 15002bc044..3db5d0e7da 100644
--- a/test/calibration/test_update_library.py
+++ b/test/calibration/test_update_library.py
@@ -25,7 +25,6 @@
 from qiskit_experiments.calibration_management.calibrations import Calibrations
 from qiskit_experiments.calibration_management.update_library import (
     Frequency,
-    Drag,
     FineDragUpdater,
 )
 from qiskit_experiments.calibration_management.backend_calibrations import BackendCalibrations

From 6fcb63d298e679dbb29beb601ea51e4782047ab6 Mon Sep 17 00:00:00 2001
From: Daniel Egger <deg@zurich.ibm.com>
Date: Wed, 3 Nov 2021 11:19:38 +0100
Subject: [PATCH 07/11] * updated NB.

---
 docs/tutorials/calibrating_armonk.ipynb | 938 ++++++++++++------------
 1 file changed, 473 insertions(+), 465 deletions(-)

diff --git a/docs/tutorials/calibrating_armonk.ipynb b/docs/tutorials/calibrating_armonk.ipynb
index 742412c9ee..fbf59fad74 100644
--- a/docs/tutorials/calibrating_armonk.ipynb
+++ b/docs/tutorials/calibrating_armonk.ipynb
@@ -23,6 +23,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import pandas as pd\n",
     "import numpy as np\n",
     "\n",
     "import qiskit.pulse as pulse\n",
@@ -183,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "id": "fa22b8a4",
    "metadata": {},
    "outputs": [
@@ -223,132 +224,110 @@
        "      <th>0</th>\n",
        "      <td>σ</td>\n",
        "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>8.000000e+01+0.000000e+00j</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>8.000000e+01</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953702+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785441+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>qubit_lo_freq</td>\n",
-       "      <td>(0,)</td>\n",
-       "      <td>None</td>\n",
-       "      <td>4.971589e+09+0.000000e+00j</td>\n",
+       "      <td>duration</td>\n",
+       "      <td>()</td>\n",
+       "      <td>x</td>\n",
+       "      <td>3.200000e+02</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953446+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785387+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>β</td>\n",
+       "      <td>duration</td>\n",
        "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.000000e+00+0.000000e+00j</td>\n",
+       "      <td>3.200000e+02</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953730+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785416+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>duration</td>\n",
+       "      <td>β</td>\n",
        "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>3.200000e+02+0.000000e+00j</td>\n",
+       "      <td>0.000000e+00</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953694+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785345+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>duration</td>\n",
+       "      <td>β</td>\n",
        "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>3.200000e+02+0.000000e+00j</td>\n",
+       "      <td>0.000000e+00</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953716+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785429+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>amp</td>\n",
-       "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>2.500000e-01+0.000000e+00j</td>\n",
+       "      <td>qubit_lo_freq</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4.971589e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953709+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.784983+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>β</td>\n",
+       "      <td>amp</td>\n",
        "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.000000e+00+0.000000e+00j</td>\n",
+       "      <td>5.000000e-01</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953685+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785369+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
-       "      <td>x</td>\n",
-       "      <td>8.578134e-01+0.000000e+00j</td>\n",
-       "      <td>default</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2021-10-28 10:37:56.254000+0200</td>\n",
-       "      <td>aa8b9513-a1d8-48b5-82ed-2e3538860ad3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>meas_lo_freq</td>\n",
-       "      <td>(0,)</td>\n",
-       "      <td>None</td>\n",
-       "      <td>6.993371e+09+0.000000e+00j</td>\n",
+       "      <td>()</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>2.500000e-01</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953469+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785453+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>8</th>\n",
        "      <td>σ</td>\n",
        "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>8.000000e+01+0.000000e+00j</td>\n",
+       "      <td>x</td>\n",
+       "      <td>8.000000e+01</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953723+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785402+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>amp</td>\n",
+       "      <th>9</th>\n",
+       "      <td>meas_lo_freq</td>\n",
        "      <td>(0,)</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>4.289067e-01+0.000000e+00j</td>\n",
-       "      <td>default</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2021-10-28 10:37:56.254000+0200</td>\n",
-       "      <td>aa8b9513-a1d8-48b5-82ed-2e3538860ad3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>amp</td>\n",
-       "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>5.000000e-01+0.000000e+00j</td>\n",
+       "      <td>None</td>\n",
+       "      <td>6.993371e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953672+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785055+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -356,43 +335,37 @@
        "</div>"
       ],
       "text/plain": [
-       "        parameter qubits schedule                       value    group  valid  \\\n",
-       "0               σ     ()        x  8.000000e+01+0.000000e+00j  default   True   \n",
-       "1   qubit_lo_freq   (0,)     None  4.971589e+09+0.000000e+00j  default   True   \n",
-       "2               β     ()       sx  0.000000e+00+0.000000e+00j  default   True   \n",
-       "3        duration     ()        x  3.200000e+02+0.000000e+00j  default   True   \n",
-       "4        duration     ()       sx  3.200000e+02+0.000000e+00j  default   True   \n",
-       "5             amp     ()       sx  2.500000e-01+0.000000e+00j  default   True   \n",
-       "6               β     ()        x  0.000000e+00+0.000000e+00j  default   True   \n",
-       "7             amp   (0,)        x  8.578134e-01+0.000000e+00j  default   True   \n",
-       "8    meas_lo_freq   (0,)     None  6.993371e+09+0.000000e+00j  default   True   \n",
-       "9               σ     ()       sx  8.000000e+01+0.000000e+00j  default   True   \n",
-       "10            amp   (0,)       sx  4.289067e-01+0.000000e+00j  default   True   \n",
-       "11            amp     ()        x  5.000000e-01+0.000000e+00j  default   True   \n",
+       "       parameter qubits schedule         value    group  valid  \\\n",
+       "0              σ     ()       sx  8.000000e+01  default   True   \n",
+       "1       duration     ()        x  3.200000e+02  default   True   \n",
+       "2       duration     ()       sx  3.200000e+02  default   True   \n",
+       "3              β     ()        x  0.000000e+00  default   True   \n",
+       "4              β     ()       sx  0.000000e+00  default   True   \n",
+       "5  qubit_lo_freq   (0,)     None  4.971589e+09  default   True   \n",
+       "6            amp     ()        x  5.000000e-01  default   True   \n",
+       "7            amp     ()       sx  2.500000e-01  default   True   \n",
+       "8              σ     ()        x  8.000000e+01  default   True   \n",
+       "9   meas_lo_freq   (0,)     None  6.993371e+09  default   True   \n",
        "\n",
-       "                          date_time                                exp_id  \n",
-       "0   2021-10-28 10:27:44.953702+0200                                  None  \n",
-       "1   2021-10-28 10:27:44.953446+0200                                  None  \n",
-       "2   2021-10-28 10:27:44.953730+0200                                  None  \n",
-       "3   2021-10-28 10:27:44.953694+0200                                  None  \n",
-       "4   2021-10-28 10:27:44.953716+0200                                  None  \n",
-       "5   2021-10-28 10:27:44.953709+0200                                  None  \n",
-       "6   2021-10-28 10:27:44.953685+0200                                  None  \n",
-       "7   2021-10-28 10:37:56.254000+0200  aa8b9513-a1d8-48b5-82ed-2e3538860ad3  \n",
-       "8   2021-10-28 10:27:44.953469+0200                                  None  \n",
-       "9   2021-10-28 10:27:44.953723+0200                                  None  \n",
-       "10  2021-10-28 10:37:56.254000+0200  aa8b9513-a1d8-48b5-82ed-2e3538860ad3  \n",
-       "11  2021-10-28 10:27:44.953672+0200                                  None  "
+       "                         date_time exp_id  \n",
+       "0  2021-11-03 10:23:20.785441+0100   None  \n",
+       "1  2021-11-03 10:23:20.785387+0100   None  \n",
+       "2  2021-11-03 10:23:20.785416+0100   None  \n",
+       "3  2021-11-03 10:23:20.785345+0100   None  \n",
+       "4  2021-11-03 10:23:20.785429+0100   None  \n",
+       "5  2021-11-03 10:23:20.784983+0100   None  \n",
+       "6  2021-11-03 10:23:20.785369+0100   None  \n",
+       "7  2021-11-03 10:23:20.785453+0100   None  \n",
+       "8  2021-11-03 10:23:20.785402+0100   None  \n",
+       "9  2021-11-03 10:23:20.785055+0100   None  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "\n",
     "pd.DataFrame(**cals.parameters_table(qubit_list=[qubit, ()]))"
    ]
   },
@@ -411,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "91184061",
    "metadata": {},
    "outputs": [
@@ -422,7 +395,7 @@
        "<Figure size 392.294x144.48 with 1 Axes>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -434,18 +407,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "32a49399",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x237.6 with 1 Axes>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -456,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "1e24ce2a",
    "metadata": {},
    "outputs": [],
@@ -466,18 +439,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "e880af97",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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rtiaw1A5UjL/fd999ad26NQDDhw/nkEMO4YILLmDGjBkAXHXVVRx22GEVM9B9+OGHnHvuuTzxxBP079+fb775hmeffZbhw4fTpk0b1q5dyx133EGjRo044YQTAHj//ff5+OOPGTx4MC1btuSzzz7jz3/+M126dGHw4MEx096tWzcWLlxIQUEBF198MQ8//HC1gv6UKVN4//33eeONN/jxx2KKilzAbNJkDzp0aEKMSp+0IOIKMBMmBDdhjh7tSu5LllQWeMLJzXWd95IiXMN+XdnSqdNeModgxMOrTnvVGaaSKqF5UV7uOso0bqyalVWZpsaN3f6A0V3VEk+nPb+ZM2dq586dNScnRw855JAqnfhuvvnmuIcrVVd1h+UFCtdpb+jQoTp06NCKx8uWLdODDjpImzRpoi1atNCTTz457DCwb7/9VkVE582bV600vPXWWzpy5Ejdc889tUGDBtq2bVs96aST9IUXXqg4J95heZHyOdqwvKFDh4a9JnCrjUhpX79+vY4aNUqbNWumbdq00bPOOks3bNhQ5Tr/533NmjV67LHHaps2bbRhw4basWNHPeuss3T58uUV1/zvf//TgoICbdWqlTZq1Eg7d+6s48aN0++//z5qGkOH5X3zzTfasWNHHTt2rJaXl4cdlldaqrp4sRuW9+9/L9TS0sh5efPNj+qSJapr11b+P6Zjp71YNm923yvRvg8bN1YtLq5dOonQac/zoJzMLdEfiKFD3VYdiQomtf3B4FXAnzpVVST6B1zEnZcqoXkxaZLrHZvoXrPp9IMzmtoE/LqoLuRHaanqZ5+pfvqp6g8/uMc1kYy8KC93gXvJEtWPPnLb0qVaEdDXrnWP/ccCt6VL3XHVzAz4qsn7vgkUKeBnQqe9tOCv7l292lX3FhfHd11tp1nUDO/w1r595A4sftWpwkr0zFTVnRnLmHSmCuvWwaefuu+YXbvg++/d43Xr0uP7Yv162LgxOC3l5e7xxo2u/bo8whys5eXu+O7dyU9ncbHrO9C1a/W+82OZOtVV9zduDP4uLE2buscTJlT2AUgGC/gx1CbgJiKYJGpe5rKy5Hx4Yxk1yr12rLQF9FeLqKY/uqKZP9/1io0mmb1mjUmkWMF0/fr4n2vFish9b2pq9+7YAT3WjxKR5H5/RfvOX78+OH01KYD42/jXr4fu3aFLF7jnHvfdduut7niyWMCPoToBN/SPX9tgkoh5mf0f3k8/jf2DJRnzOscappKb645H67CXyFqOggLw9XsD3JdPpPz1S2avWWMSJZ5gGm/pePduN168vBx+/DFxJeri4toHtPLy6GPZayvad/7GjZXf+bUtgOTluR9VK1fC2LGp6bRsAT+K6gTccH/82gaT6vxgiPTh8394VaP/YElG6dmvtlVY8f7oilUF53+PO3dWHk90k4MxXoknmMYqHYc2CagmtknA/yOiNrKyYo9lj1doISfWd355Odx9N1xzTWY2s1rAjyKegCsCZ54J+fnw9dfuj3/VVe7xu+9CkybRr2/SxP3SKympuq1ZE334BrjjTzwR/vWvvNJ9OEtKoLS06p+6pATuust9WMNdf/31Ljju2uX+UXfvdtXv1f1Ax1uFFS5gx/OjK9Y/YHl5cA3Brl2Vx1esSFyTgzFeiieYxiodJ6pJYPdu+Pxz+H//L7iGoGHDyh/9NaUKe+zhbn/9NXgL9z0aaVu3zm0rV8IDD7j7Tz8dPX27dmVRVgb33hu+APKXv7jvzeqkw30/1y5P4iWazj9Haqlfv366ZMmSGl9/661w883p/YvNSyJuy8pyW3Z2ZZtfkyauZNywodufkxO8NW7szvFvK1bAZ59VBt+cHJfv/frBJ5+4f+ZIGjZ06QgYCl0hNxcOOQQ+/rjyR8O0aYVMnFgQ8Xjo9RMm1GxioH79+lGbz1+qbN26Na7x+vVFpuRH6KQ0P/7oSuPRgn5WFuy9N4RbX6e01AXowO+7li138PPPjYPOa9myssbQ/2PA/1jVBfdIaRBJ3Pfpccf1Y9Om9P//iseMGa6AligislRV+4Xuz4BpDLzjr+6NVcqujQYNKqun/BNZBS7mFS3QVe91yti9u3qTkFReG/xP7Rdun5//l2tN+YP3++/HPjfW3NTRZgQtKYGPPoLLL3e/8kNnxkp2r1ljaiK0jT0vz20xZtVF1f3Y/ukn9+PcX4O3c2f4/6PQYO/21TzdiQj2/hpBf2EDXAEh9Hgk/trKmmjYsIzS0tjfow0bUq1JglI1oZBV6UcRTw/zWHJzYeTI8O3Xkya5fzZ/9VL37tCpk6suWrfO7Z80qXbzMvvdccfbNUp/06Ywc6arpsrJqXwPubnuh8kNN7hjkZouGjeGyy5zzQXLlrnS+n//C4sWwX/+45ojEtUeV1MNGkDPnt70mjU18/3331NQUMB+++3HgQceWDGzXl0Xbdjdxo2ukBKtSlq1sqNYURFs3uyWZo30o7lp06rVZnl57v+kZ0/Yd1/o3Rv228/dxtOHoE8f12QYeG5Wlnucnw99+7qavYMOct8fOTnQubN73K+fO963L3TsWPmded997v83VrX+unW1C65//nPs71ER14RYnSr9Sy+teZqqw0r4Ufh7mE+fHr602rBh7LaXX3918yc/+WTVaRb9VWM33RQ87/L48W7FpYkTK9dWrum8zLVVUgJ//7srBQcO0fHnxz33RP/FvGMHPPKIC5zheqHOmeP+oVPVhhWOv+Okv9esSX8NGjRgxowZHHTQQWzYsIG+ffty/PHH07RpU6+TllSR2tjB7W/b1v2fbd4c+TkaNapsSmvUyP3/NWoEv/wCa9cG19jl5e1k+/bK4nNWFrRo4V4jXJNCrCp7EVdDsNde0K4dfPmle738fPf/FxiMGzSAAw6o+hyqLh/WrnU1FaHfmVOnVv6YCF0bI55+WdGaCEVif++ncydfC/gxRFsI4cgjXUk1nnmRIwWTeBeWqem8zP4Pbzj+dvJoH94mTeC99yKfE0+Tg38kQbj1VeIZyQCR/8lyctzfpDbDhtL5H7QuGDNmDJs2beKll15K2HPm5+eTn58PuHnqW7duzebNm+t0wPcPu4sUUP3D7kL5Owa3aOHuRwp4rVq52oJoVN1zhWtSqG6nwUgBPZbAHz2BneYg+Dsz3NoY8XzflJbC0KGuJjL0O79tW5d/0b4z07mTr1XpxxDYw/yhh9wPAH9171NP1a6Hd3WG/YUbsxlPk0NWlvvlG9je5W9SuPzy2L92d+2qfZV7tKGH8Q6LGzEifLPI5ZdXv4rupZeCF2ZJ53/QTDBmzBhEpMr2ySefAHDvvffy1FNPAW6t+Msuuyyhr7906VLKysrYe++9E/q8sSxevJiTTjqJvfbaCxHhsccei+u6WbNm0bVrVxo3bkzfvn15++3gauJbbrmlSl62b9++yrC7TZuKuOWW8zj66DYMGtSY007bj6VLF9Gggfu/6t3bVX3vv78bkdKsWfT/d/91kZoEsrLc8Y0bwzcpbN8euwd+bYfUxZprINaonQ8+iP1907QpnH12+Ca+jh1rP6+Il6yEH6e8vPAl1GhV/v4e3pH++NUZZx/utWM1OQT2MH/zTffhDW1SaNIk+vWHHlr7ZoNoJehRo9wPkmjKy92PK9XwzSKx3kOfPrB0aWUVXWFhp4rjTZqkx0p9mW748OE8+eSTQfv8K7tFWmUuETZv3sy5557LnDlzkvYakWzbto0DDjiAc889t2KFwFjmzZvHlVdeyaxZsxg8eDCzZs3iuOOOY9myZXTqVPm57NWrF4UBazRnZ2cHlaC3bv2Z3/9+EAcdNJgZM16mZcs2rFv3Ha1ataVtWxfcasJ/XWBNQlaWu+9fSTlSk8LWrbE75flrCGrK/6Mn2uuUl7s2/cAqeX8NwMKFsZsP/QWAwGaLQNFqfdO9k6+V8GupNpPKJGKWt3hfPzs7/KxOsa4/66zYv4hjiVaCrs5MfJFmpor1Ht55B/74R9dOKQKHHLIxKG1NmiRuNER91ahRI9q3bx+0NfBVvYwZM4YTTjiBMWPGsGjRImbOnFlRcl21alXY5zv99NPZc889K5Z+BVi+fDm5ubnMnTsXcOuz//a3v+W6665j4MCByX6LVRx//PHcfvvtjBo1iqw4B5dPnz6dMWPGMHbsWHr37s39999Pfn4+Dz74YNB5DRo0CMrLNm3aBI1hf+KJu2jdOp8pU55g//37s9deXenf/yj22ad33CXoFSuqBjQR177ep4/7/xFxw/j69HEBP55pcWPVENSm01w8zQalpeHb38H9n6vWroTu5dS4tWUBv5Zq88dPxCxvtf3wxbp+9OjYzQbZ2bX7B6rtTHyx3kNWlrstKoKHH4aJE5czZQoce6z7YrjxRtfb+LXXor+OqZ17772Xww8/nPPPP5+ioiKKiooiVsPPmDGDs846iym+Xqs7d+7kzDPPZNSoUZxxxhmoKmPGjOHII4/knHPOifnat99+O82aNYu6hVatJ9quXbtYunQpI0aMCNo/YsQI3nvvvaB93333HR06dKBr166cccYZfP31d/z6a2WwW7ToOQ444DCuv/50Roxoy1lnHcQ///kA5eUaVwna3wa/c2f4qXP97eu5uW7MfoMG8c3kl5UFzZuH74Hfrl3Nax78EjFxT6NGMGxY7Rev8WJq3NqyKv0EqUkP73iqs+NtX65tD/NI18fTbDB+vPuHrmkVlz9gh+uYWJ1/olh54G+WKSx0syOCa+q4+mrXBnnssa4m4LbbIrczhvb6Nc5//vMfmjVrVvH4iCOO4NVXXw06Z4899iAnJ4fc3Fzax+glmZ+fz9VXX80DDzzA6tWrmTFjBlu2bGHmzJkAvPvuu8ybN48DDzyQ5557DoAnn3yS3/zmN2Gfb9y4cZx22mlRX3OvvfaK9TZrZdOmTZSVldHOXzfu065dO954442Kx4cddhiPPfYY++67Lz/88ANTptzG4YcPZO7cL2jZck9EYN2675g/fxZnnjmeMWOuY8WKT5g27XJatIB+/SL3kfD3cA+ssv/+ezd+v317F5AjBfV4O+U1bepmy4zWA7+m4plrIJbAkVPz57u8aN+++t83mcgCvofibYP3+kMYT5uVSPIDdjIcdZRr37/jDjdi4q67XJ+Ff/zD1RSY+AwZMoTZs2dXPG4Sa07pOHTp0oWWLVty1113MXv2bBYvXlwxA97gwYMpr8ak7K1ataJVq1a1TlMqHHfccRX3f/wRbr99ACef3I0FCx5n8uQJ/PILlJeX07t3Py677M9kZcG++x5McfHXPP30TCZNihzwYw3rA1elH46/dB1rJj//pDM16YEfi79j4caN4Y/HM/IocORUuL5RdZlV6XvMy7WR4xVvs0EmVnGBa5K48UY3xLJjR9eT96CD3MRAgZK5wFCmy83NpXv37hVbokrLffr0YdasWUyaNInDDz+8xs+TDlX6rVu3Jjs7m40h0Wrjxo1VajzKy93nbPVqaNKkGb167c+WLV/TrJkLyB065NOjx37k5FS2sffv35s1UYq/tV1NLy8v+Z3y4tGhg2seqOnIo/o8KsdK+B5LVHV2KtT1iWkGD3ZV+xdcAM8/Dyed5BbTGDXKlf4jTY4UONGHiS4nJ4eyakxfqarsv//+TJo0qVavmw5V+jk5OfTt25fXX3+d0QER5/XXX+fUU0+teFxaCt9+62bAc23fO1i16kuOO25YxTmDBg3i++9XcOCBlc//1Vdf0blz54ivH08Pd/9qeuHm2g8sXYf70ZCV5QJxsqeJ9Xcs7NjRfSdVd9ROOtSaesUCfpqo68E0U7RqBf/+t2vLnzYNzjjDTY385puxJ0fyC1xu0wTr0qULH374IatWraJZs2a0atUqYg/3mTNnsnjxYnr16kV2rGJbDMmo0t+2bRvffPMN4KrY16xZwyeffEKrVq0qhtg98MADPPDAA3z55ZcATJgwgXPOOYf+/fszaNAgHnroIdavX8+4ceMAV7oeO3Yihx9+Ivn5nSgp+YGbb76V7du3c95551W89vjx4xk4cCB/+tOfOP300/nf//7Hfffdx+233x4xvYlYTS/WsL3adsqrjqysujdsLulUtc5uffv2VeMsXLjQ6ySkjXjyorxcdcoU//JA0bfGjVWLi911mzer9uyp2qWLaq9efbW0NKlvJSG2bNlSq+vPO+88HTlyZFzHV6xYoQMGDNAmTZoooCtXrgx7zRdffKFNmjTRSy+9VLOysnT79u21SmN1xJsfCxcuVKDKdt5551Wcc/PNN6v7mq00c+ZM7dy5szZsmKP77XeILlq0SFVVS0tVv/hC9eijT9c2bfK1QYOG2qZNBx058hT99NMvqrz+Sy+9pAceeKA2atRIe/Tooffee6+Wl5dHTO8PP6guXar60UeRt6VL3Xmx8qK0VPWzz1Q//dSd78XnPNb3e+D/4uzZlf+jtZEp36PAEg0TE2153HqisLCQAit6AtXLi9NOcxMfRdO0qSvpf/99cKmiTZt+vPrqkpi9n72WbsvB7ty5k8MOO4z99tuPRx55hObNm/Puu+8yYMCAlLx+KvJj9+7gXuwtWsB337mao+zsyjHtUFmCru3naPdu12QVq0q/T5/Kavl0+2wE8mL56Uz5HrXlcY2pgd/8JnbAj7TAkL8OIFbvZxPsuuuu45dffuHBBx8kNzeXHj16cO+999KhQ4eg2egyUaRhcf6q9tBgD/H3oo8lXdrgjXesl74xUbRv70rw0fgXGIo0a2Ks3s+m0oIFC3jggQd46qmnKqbkvfHGG3nrrbeC2rAzVbRhceDamWMtjlObz1FgD3e/RE6MY9KbBXxjoohngaJ4Fhjy93420Y0YMYLS0lIGDRpUse+cc85h48aNLFy40MOU1V6sYXHxqO3nKHTq3MBhfXvtlb7NTiYxLOAbE0U8c/0PGhR7Lv5YvZ9N3RfP1LSxJOpz5J8Y58ADK6fONXWf/ZmNiSFwmM/OnZVVrtnZbpjP3nvDkiWVQ/XCqe2yoCbzxTMsLhb7HJnasBK+MTEEzjTYo4frVd2woavqP/TQ+BYYSsUMZCa9JWLhF/scmdqwgG9MnPyTI61fD3fe6fZdfLErtUWr9k/EsqAm88UzNS0kd3lZU79ZwDemBq64wk3Fu2GDux9uTQQR6/1sKjVoAHvuGfl4VparPbJe9CZZ7LeiMTWQnQ2PPup6N//973DqqVXXRGjUKHgSE1O/qYbv3Bk6Na0/uBcXu3b/hg0Tt7ysqd+shG9MDXXvXlm1P26cW8o0L8+V0jp3hmbN7EvaVPrxR9exs2FDN6FTtGFxDRq43vMdOlgvepM49jEyphYuuQSefRYKC93SnHPnuvsAJ56YT79+VWa3TDs7duygcePGXicjbSQjP8rKXN8PN+Vy5P4e6SadPxv5+fleJyHjWMA3phaysuBvf3NjmufNg8suc237AC+++KK3iYtTpswPnirJyI/Ro+GTT9ySy889lzkT3Nhno26xKn1jaqlrV7j6anf/mmvi64lt6o+XXoL5890UzfffnznB3tQ9ngR8EblERFaKyA4RWSoiR8Q4/ywR+URESkRkg4g8JSLtU5VeY2K55hrX0eqDD9yXuzHgOt1deaW7f+utkOFr/5gMl/KALyKnA/cCtwMHA+8Br4pI2H8FERkEPAk8DuwP/BbYD3g6Fek1Jh7Nm8OUKe7+dde5GfmMefRRt+xtr16uj4cxXvKihD8BeExV56jqclW9HCgC/hDh/MOBtap6j6quVNUPgPuBw1KUXmPi8vvfQ+/e7gv+wQe9To3xUkEBDBniSvXg5mmwnvbGaykN+CKSA/QFFoQcWgAMjHDZu0C+iJwoTmvgDOCV5KXUmOpr0ADuusvdnzrVVser79avh7Vr3QI1o0Z5nRpjUl/Cbw1kAxtD9m8EwrbJq+r7uAD/NLAL+BEQIPMXxzZ1zsiRrnRXXAy33+51aowXioth3TpYudI9vvba2s+hb0wiiKawS7GIdADWAUNVdXHA/snA2araK8w1+wGvAzOA14B84G7gE1U9N8z5FwEXAbRr167v3Llzk/BOMs+2bdto1qyZ18lIC8nOixUrmjFuXD8aNixn7twPaNVqV9JeKxHssxGsNvmxfr2bbvnNN/fmlVf2oVOnLVxxxcfk52fmtLj22QiWKfkxbNiwpapadRIQVU3ZBuQAu4HRIftnAosiXPMk8O+QfYMBBTpGe72+ffuqcRYuXOh1EtJGKvLit79VBdXrr0/6S9WafTaC1TQ/Jk1Szc11f/fQLTfXHc809tkIlin5ASzRMDExpRVNqroLWAocHXLoaFxv/XBygdDFR/2PraLMpKVrr3W3s2bB1q3epsUkX3ExTJsGJSXhj5eUuOM//5zSZBkTxIuAOR0YIyIXikhvEbkX6AA8BCAiT4jIEwHnvwicLCJ/EJFuvmF69wEfq+qalKfemDgMGABHHAG//AKzZ3udGpNs8+e7BZWiyc6GZ55JTXqMCSflAV9V5wFXAZOAT3DV88er6mrfKZ18m//8x3BD+S4DPgfmA18BJ6cqzcbUhL+Uf889bvU8U3dt2BC5dO9XUuLOM8YrEUeGiki3Gj7n96paGu0EVZ0FzIpwrCDMvvtxY++NyRjHHQf77w9ffOGW0B0zxusUmWRp3x6aNIke9HNz3XnGeCVaCf8b4OsabL9JYnqNyRhZWfDHP7r7d9/tVkozddOoUW4a3WjKytwiOsZ4JdbcT38Cvo3zubKBObVLjjF1yxlnwI03wrJl8PLLcOKJXqfIJENenlvPftWq8Mdzc2HCBGjZMpWpMiZYrID/kqp+GM8TiUg28Ejtk2RM3ZGT477oJ0xws/BZwK+bPv/cBfuGDV3NTmmpq9Fp2tSV7CdMcLMvGuOlaFX6RwBfxPtEqlrmu+bL2ibKmLrkwgtdye6dd+Djj71OjUmGe+91txddBEVF0L07dOniOmwWFbk59W1ZXOO1iAFfVd9V1e3VeTLfNTH6qhpTvzRvDuf65oScY41edc6mTfDUU+7+FVe46v0VK9zUumPHWjW+SR9xDcsTke9EpE+EYweIyHeJTZYxdcvYse726adhe7V+Rpt0N3s27Njh1lHo2dPr1BgTWbzj8LsAjSIcawx0TkhqjKmjDjgADj/czbr3z396nRqTKKWlMHOmu3/lld6mxZhYqjPxTqRVdvoBP9c+KcbUbf5S/lVXuRX1TOZ77TW3YE6vXjB8uNepMSa6iAFfRMaLyBoRWYML9i/6HwdsP+IWvvlPqhJsTKY67TRo0QK2bLFq/briscfc7fnnW6c8k/6iDcv7DnjTd/88YAluLfpAO4Fl2HA8Y2Jq2hROPRUefdR16pozx03YkpfndcpMTfz0E7zwghuG97vfeZ0aY2KLGPBV9XngeQBxP12nqurKFKXLmDpFFSZPdp32wLXlX3WV69U9caIbo20lxMzyj3+4Nvxjj3WT7hiT7mJNvAOAqp6f7IQYU5dNngzTpwcvouOfd336dHd7662pT5epOX91vq2RYDJFtMVzJgOPqOp63/1oVFXt68qYMPxrpe/YEf64f630q6+2MduZ4tBDYelS2GMPONnW7TQZIloJ/xZcZ7z1vvvRKGAB35gwqrNWur8nv0lv/mVuzzwTGjf2Ni3GxCtaG35WuPvGmOqxtdLrlh9/dNPlgquRKS62jpcmM1ggNybJ2rd3q6VFY2ulpz9VuOkm10GvrMztu+8+6NDB7ddIM5UYkyaqHfBFpK2IdArdkpE4Y+qCUaMqA0QktlZ6+vN3vAxc976kxPXNmD7dHTcmncU7l34LEXlUREqAImBlmM0YE0Zenht6F6mU36CBO24d9tKXv+NlpKYZf8fLn39OabKMqZa4huXhZtM7Ffgr8Bluwh1jTJz8a6FPm+aG5pWXu85eO3ZAs2YwZYq36TPRWcdLUxfEG/CPBa5R1ZnJTIwxdZWIG2c/YQIMGOCC/nXXwc03w8aNbojXoYd6nUoTiXW8NHVBddrwVyQtFcbUE3l5kJ8PnTvDxRfD6ae7/XPnepsuE1379tCkSfRzrOOlSXfxBvy5wInJTIgx9UVhodsAzjjD3c6b56r5TXoaNSq4s1441vHSpLt4q/QXADNEpDnwCrA59ARVfSuRCTOmPhgwwJX2V6+Gd9+FI47wOkUmnLw86NIFvv46/PHcXNdcYx0vTTqLN+A/77vtCowJ2K+A+G5jdGkxxoQScaX8O+90i7FYwE9Pv/zifpQBNGrkSvvl5W4FxLIyF+z9HTONSVfxBvxhSU2FMfWYP+A/84ybyKVBvP+VJmVeesl1tBwyBJ57rrLj5Q03uGp8K9mbTBDvanmLkp0QY+qrPn2gVy9YsQIWLoSjj/Y6RSbU/PnudvToyo6XYEPwTGaxqXWN8ZgInHqqu//CC96mxVS1dSu8+qq7f8op7jaw46UxmSKuEr6IxOqQp6p6VALSY0y9dPLJcPvt8PzzrlpfxOsUGb+XX4adO2HQIDdvvjGZKt4Sfhauc17g1hoYBPT0PTbG1FC/fq6a+Pvv4dNPvU6NCRRYnW9MJosr4KtqgaoOC9kOBPYDioHbk5pKY+q4rCw40TfTxfPPRz/XpM727fDKK+6+vzrfmExVqzZ8Vf0WuAO4OzHJMab+Oukkd2vt+OljwQL49Vc47DDYe2+vU2NM7SSi096PuGp9Y0wtHHmkm8Dl449h7VqvU2PADceDyh9jxmSyWgV8EdkTmAB8m5jkGFN/NWkCxxzj7lsp31sFBTB0aGV1/gkneJocYxIiroAvIitF5LuQbS2wATgKmJTUVBpTT1i1fvrYts2tftexI/zmN16nxpjai3dOr0W46XMD7QBWA8/42vKNMbU0cqTrwPfWW7BlC7Ro4XWK6p+yMigqcssWg5sIyYZJmrog3pn2xiQ5HcYYoE0bGDgQ3nkHXnvNhoKlkipMnuxm0vvmm8rVC596Cvbay82Vb4HfZDJPZtoTkUt8zQQ7RGSpiERdMkREckRkqu+anSKyRkSuSFV6jUmlk092t1atn1qTJ8P06S7wBy5VXFrq9k+e7F3ajEmElAd8ETkduBc3dv9g4D3gVRHpFOWyucCxwEVAL2A08P+SnFRjPOFvx3/55dhrsJvEKC6GadOgpCT88ZISd/znn1OaLGMSyosS/gTgMVWdo6rLVfVyoAj4Q7iTRWQErmPg8ar6uqquUtX/qmph6pJsTOpcdJEbnldc7Kr2TfLNnw/ZMRb4zs52Kxoak6lSGvBFJAfoCywIObQAGBjhst8CHwETRGStiHwtIveJSLPkpdQYb7Vq5W79i7aY5NqwobJ0v3t3+Ib6khJ3njGZSlRDO98n8cVEOgDrgKGqujhg/2TgbFXtFeaa/wAFwJvAVKAlcD/w/1R1VJjzL8JV/dOuXbu+c+fOTfwbyUDbtm2jWTP7jQTpnxdffQXLl7dkxoyD6NZtG3/965Kkvl6650cqbNrk1jEoL4fNmxtz++0DyM/fxtVXV+Z9Vpabba91aw8TmmL22QiWKfkxbNiwparaL3R/JgT8BcARQHtV/cW3bwTwmm/fxkiv169fP12yJLlflpmisLCQgoICr5ORFtI5L4qLYcAAtzpbURHs2gXr1iV3lbZ0zo9UKS52ebxjBwwevJZ33ulY5ZzGjd3fpGXL1KfPK/bZCJYp+SEiYQN+rav0RWTvGB3uAm0CyoB2Ifvb4SbxCacIWOcP9j7Lfbfxvq4xaU0VbrrJBZ1vvoHVq914cIBx49xxkzx5eTBxopvtcPnyPascz811x+tTsDd1TyLa8L/zbTGp6i5gKXB0yKGjcb31w3kX6BDSZu+fu391NdJpTNryDwnbsaNySJg/4L/yig0JS4WpU+G88+Cnn5pU7Gva1JXsJ0xwx43JZIkI+Lf6tnhNB8aIyIUi0ltE7gU6AA8BiMgTIvJEwPl/B34CHhWR/UVkEG5Y33xV/SEB6TfGU7GGhJWVwd1325CwZBOBnr6iRPPm0KUL3HOPq8a/9VabdMdkvnin1o1IVav1u1dV5/kW3ZkE5AOf44bc+UvrnULO3yYiw3Ed9T4CioHngOtqmXRj0kI8Q8JE3JCwsWNTk6b66vXX3W2HDtC+veW3qVtqHfBrQlVnAbMiHCsIs28FMCLJyTLGE4FDwiLZscOGhCXbzp2waJG7n5fnbVqMSYa4A76ItATGA4cDe+F6278HzFDVn5OROGPqg/btXaew7dsjn5OV5c4zyfPee+6HV9eu23j//fQfemVMdcW7PG4f4GvgeqAxsMx3ewPwlYjY4pHG1NCoUZUd9CIpL3ertpnk8Vfn9+tX7G1CjEmSeDvt3YfrONdDVYeo6mhVHYLrLb8Z175ujKkB/5Cw3Nzwx7N8/6X+KSUKCtxmEmuBb/5PC/imroo34B8K3BTQsQ4AVV0F3Az0T3C6jKlXpk51Q78aN64M8P4hYUOHusevveZd+uq6n36Cjz+GnBw48MCfvU6OMUkRb8D/CdgZ4dgO33FjTA2JuKFf69dD9+7BQ8L+8hd3zmuvwebNbt/q1TBnjhvSZ2rvzTfd5EaDB0PjxuWxLzAmA8Ub8B8ErhGRxoE7RaQJMBGYmeiEGVMf5eVBfj507uyGhLVsCX36QNu2bq73/Hw3E9+qVTB+vBs+dtNNNhNfbfmr862fhKnLIvbSF5HA8fUCdAbWiMgrwEbcdLjHA78CEVofjTHVVVgY/DgrywX8H35wc+v7+Xv1T5/ubm+tzvRXpoJqZYe9ESNgyxZv02NMskQbljcpwv5zw+y7EbDJP41JguJiWLEi8vGSEjdT39VX21zvNfHVV7BmjVsF76CDYPHimJcYk5EiVumralY1thjzhBljamr+fGgQY8aM7Gw3E5+pPn/p/qijKjtMGlMXxfx4i0iOiFwpIgekIkHGmGAbNriZ9qIpKbGZ+GrK334/wubyNHVczIDvW+HuDqBV8pNjjAnln4kvmtxcm4mvJkpLK/tMWIc9U9fFW4G1HOiWzIQYY8KLZya+sjIYPTo16alL/vtf2LoV9t0X9t7b69QYk1zxBvzJwE02ha4xqeefia9Jk/DHc3PdceuwV31vvOFuhw/3Nh3GpEK8i+dcCzQD/iciq4AiIHDkr6rq0ASnzRjjM9U3SPZPf6occ9+0qSvZT5hQedxUz1tvudujjvI2HcakQrwBvwy3YI4xxgP+mfiKi2HmTNhjD7j7bleNbyX7mikpgQ8+cHk71Iorph6IK+CHW6PeGJN6I0e6gF9e7mbiMzX33nuu097BB7tmE2PqOht1akwGOeIIVyLduhV++cXr1GS2hQvd7bBh3qbDmFSpVsAXkTwR6S8iQ0K3ZCXQGFOpWTMYONDdtxnhascf8I880tt0GJMqcVXp+xbN+RtwGm5e/XBstj1jUuDII+Hdd12HsxNP9Do1mWnrVvjwQzdD4RFHeJ0aY1Ij3hL+TUABcB4u4F8GXAi8A3wLnJCMxBljqvL3KH/zTW/TkcneeceNcOjbF1q08Do1xqRGvAH/VGAqMNf3+L+q+qhvKN6nwLHJSJwxpqoBA9yY/M8+cyvomeqz9ntTH8Ub8DsBX6hqGVAKNA049jfg9EQnzBgTXqNGMHiwu+8PXKZ6rP3e1EfxBvyfcBPvAHwP9Ak41hqIMAeYMSYZ/IHKP3GMid/PP8OSJW60w6BBXqfGmNSJd+KdD4CDgVeBZ4FbRaQ5sBu4GteWb4xJkYICd7tokafJyEj+0Q3Nm7vZCo2pL+IN+HfiqvUBbgO649r0s3E/Bv6Q+KQZYyLp29cFqxUroKgI8vO9TlHm8Ffn2wyFpr6Jq0pfVZeo6r9897eq6qm4Kv6WqjpQVdckM5HGmGANG1a249t4/OrxL5izcyfMmeOmKzamPqjxTHuqulNVtyQyMcaY+Pnnf/ev526iU4Wrr4bPP3ePN26E8eOhQwe46abKRYmMqasiBnwROVdE9qzOk/musVmpjUkBa8evnsmT4YEHgvdt3w47dsD06e64MXVZtBL+o0C3eJ9IRLJ913StbaKMMbH16we5ubB8uSutmsiKi2HaNNi1K/zxkhJ3/OefU5osY1IqWqc9AS4UkePifC5biMeYFGrY0A0re/11144/erTXKUpf8+e7aXSjyc6GZ56BHj1SkyZjUi1WL31bgNOYNDZ0qAv4hYUW8KPZsMFV30dTUuLOs4Bv6qqIAV9VrcRuTJrzt+Nbx73o2rd3MxTu3Bn5nNxcd54xdZUFdWMy2KGHunn1ly2zefWjGTUKdu+Ofk5ZmdWSmLrNAr4xGSwnBwYOdPdtPH5keXnQpk3k47m5MHGiTcZj6jYL+MZkOP94fBueF9kvv7iRDFlZrmo/y/fN17QpNG4MEybA1KneptGYZPMk4IvIJSKyUkR2iMhSETkizusGi8huEfk82Wk0JlNYO35s77zjJtYZMMBNRdy9O3TpAvfc4x7feqtbTMeYuizlAV9ETgfuBW7HLcjzHvCqiHSKcV0e8ATwZtITaUwG6d/flVI//xw2bfI6NenJX/sxdKir3s/Ph86dYexYq8Y39YcXJfwJwGOqOkdVl6vq5UARsRfg+SvwOPB+shNoTCZp1AgOP9zdt3b88AIDvjH1VUoDvojkAH2BBSGHFgADo1x3CdAOt1KfMSaEVetHtnUrLF3qJtbxd3AsLLS8MvVPxHH4IlIOxLuchKpqPEvttsYtqRs6EehGYHiEdPwGuBkYoKplEqOhTUQuAi4CaNeuHYX2Xw3Atm3bLC986mJetGixB3Awr7yyjVNOWVKta+tifgT68MM8ysr6sO++W1i69OOY59f1/KgOy4tgmZ4f0YL0VOIP+EkhIo2AecBEVV0ZzzWqOhuYDdCvXz8t8Bd96rnCwkIsL5y6mBeHHQbXXgvffdeMPn0KyKvGElZ1MT8CLfDVJ554You43mddz4/qsLwIlun5EW2mvVuS8HqbgDJc9XygdsCGMOfnA72BR0XkUd++LEBEZDdwvKqGNg8YU+80aeKC/ttvwxFHQOvWVmXtZ+33xjgpbcNX1V3AUuDokENH43rrh1oH/AY4KGB7CPjGdz/cNcbUS0OGuNtffvE2HemkpAQ++sgNuRs0yOvUGOOteNrdgYoOd8cBvYDGIYdVVW+N86mmA0+KyIfAu8A4oAMukCMiT/ie8FxVLQWCxtyLyA/ATlW1sfjGBBgyBP70Jwv4gT74AEpL4eCDbfidMXEFfBHpALwDdMG16/t7zgW28ccV8FV1nojsCUzCVdl/jquaX+07Jep4fGNMeAMHup7oW7fCypUwZ46bQ7467fl1jVXnG1Mp3ir9u4EfccFYgMOAbsCfcNXr3arzoqo6S1W7qGojVe2rqosDjhWoakGUa29R1QOq83rG1HWqcOedUF7uHq9ZA+PHQ4cOcNNN7nh9ZAHfmErxVukfAUwE1vsel6vqKmCyiGQD9wEnJz55xph4TJ4M06cHB3b/+u/Tp7vbW+NtdKsjdu50VfrgOjIaU9/FW8LfE1ivquXAdiCwkvAtoCDB6TLGxKm4GKZNcx3Uwikpccd//jmlyfJUQYEbtbBzJxxwAOy5p9cpMsZ78Qb8tbhJcwC+BUYEHOsP7Ehkoowx8Zs/37XdR5OdDc88k5r0pAt/50WrzjfGibdKfyEwFHgOeBiYKSIHAaXAMb59xhgPbNgQuXTvV1LizqtP/AHfP1zRmPou3oA/CWgFoKoPikgD4HQgF7gLNyufMcYD7dtDbm5lm304ubnuvPqivNwCvjGh4qrSV9VNqvpVwOP7VXWwqh6iqjeoqlXpG+ORUaOgrCz6OWVlMHp0atLjteJiWLXKBf127dxqgsYYb5bHNcYkUF4eTJzoSvHh5Oa643V94hlVNwSxQwdYu9bt++knG5pojF91ZtobCpyJG4sfbqa9oxKZMGNM/Kb6GtWmTYNduyrH4+fkwIQJlcfrMv/QxB0B9Y27d7utvg5NNCZQXCV8EbkY13FvFNASN/lO4GY1BcZ4SMQFs/XroXt3aNbM7b/2Wrc/xqrSGc+GJhoTW7wl/KuBvwMX+BbAMcakobw8yM93w/CWL4cPP/Q6RalRnaGJY8emJk3GpJt4S+Z7AY9asDcmM+yxh7t9911XpV3X2dBEY2KLN+AvpZrz5RtjvNOoEeyzD2zbBv/7n9epST7/0MRo6tvQRGNCxRvwrwCuEhEb0WpMmissdFtBQeXjus6GJhoTW7wB/0WgI7BQRLaKyJqQbXWsJzDGpJZ/Sln/inF1mX9oYuPQ8UM+9WVoojHRxNtp703ARrEak0H8Af/tt13pNlantkwSrvZi6lRYuhRefbVyX9Om7r3Xl6GJxkQTV8BX1TFJTocxJsE6dYIuXdysc59+Cocc4nWKkkuksrNi27auVH/DDa4a30r2xtj4eWPqtPpUra9a+T47dYLOnd0QPAv2xjhxlfBF5Nwoh8uBX4D/qerahKTKGJMQQ4fC44+7QDh+vNepSYziYigqcjMKzpnjOuzl5cHXX7v9/tK9MSZYvG34j1HZhh84Z1fgvnIRmQecb+P1jUkP/rbuxYvddLtZGVynp+qmzw2cPnj8eLjiCtchr2NHd15BAWzc6GlSjUlL8Qb8QcDTuN7684GNQDvgNOAE4BJgf9wyuauBGxKeUmNMtXXpAnvvDd9/D599Bn36eJ2imgs3V75/SeDp06FrV3e/oAD+8IeUJ8+YtBfv7/2JwFxVvVJV31bVr3y3lwP/AC5S1WnAX4AzkpVYY0z1iNSNdvx45sr/4gt331+rYYwJFm/AH4EbmhfOW4B/pbzFuGl4jTFpoi4E/Hjmygdo3hz23Tf56TEmE8Ub8HcCfSMc6wv42+yzgO21TZQxJnH8AX/x4sxdEz6eufLB9cyv6ysDGlNT8Qb8Z4ApInK1iHQWkSa+24nALcA833kHASsSn0xjTE117+5W0Nu0CZYt8zo1NRPPXPkABx6Y/LQYk6niDfgTgGeBu4DvgG2+2ztxnfiu9p33OXBtgtNojKmFutCOH89c+QCXX578tBiTqeIK+Kr6q6r+DugNjAGu993up6rnqOoO33kvq+riJKXVGFNDmb6Qjn+u/Gil/NxcOOyw1KXJmEwT77A8AFT1K+CrJKXFGJMkgSV81cxs5/bPhR84Dr9pU9i5E3bvhpEjM/N9GZMqEUv4ItJJRBoG3I+6pS7Jxpjq6tXLteP/8EPmtuOLwK23wvr1rl9Cly5wzz3w29+64zYcz5joolXprwQO9t1f5XscbTPGpCkROPJId/+tt7xNS23l5bkfL507w4UXwnvvuf0W8I2JLlqV/gXAtwH3M3RAjzEGXMB/+ml4882607ntm29cib9NG+jd2+vUGJPeIgZ8VX084P5jKUmNMSZp/CX8wkLX4z2eiWzSnb8TYkGBtd8bE0uNltIQkT1EpJ+IdEx0gowxydGli5tv/pdfoH9/r1NTO4WFlRtYdb4x8YjWae8YEbkjzP4bgB+A/wKrReTvIlKt3v7GGG8c5ZsEu7jY23QkgiosXOjuW8A3JrZoJfxxQM/AHSJyNHAb8CVwFfAwcDpwZZLSZ4xJIH+1/s8/e5qMhFi2DIqKXAc+a783JrZoJfODgVtD9p0P7ACOUdUNAOIazs7CrZRnjEljw4a5219+cWPZc3K8TU9tvPGGuz3qKGu/NyYe0Ur4banspe93NPCOP9j7vExITYAxJj01auSCfHk5TJqU2VX7/oA/fLi36TAmU0QL+FuBpv4HItID2BP4IOS8LUC1+vuKyCUislJEdojIUhE5Isq5p4jIAhH5UUS2ish/ReSk6ryeMfWdKtx0E3To4Er2ADNmuMfr12feKnqlpZXrAvj7JRhjoosW8L8ETg54fDJuLP6CkPO6AhvjfUEROR24F7gd12zwHvBqlNn6hgJvASN9578C/DvajwRjTLDJk2H6dNixo3Jfaal7vHGjO55JPvoItm6FffeFjjZWyJi4RAv49wAXish8EZkJTAE+A94NOe944NNqvOYE4DFVnaOqy1X1cqAI+EO4k1X1SlW9Q1U/VNVvVHUKsBT4bTVe05h6q7jYzT8faT358nJ3PJM68ll1vjHVFzHgq+pzuJ74hwLn4qryR6tWVv6JSHtgOK7UHZOI5AB9qVpLsAAYWI10NwcyuPXRmNSZPz/2JDvZ2fDMM6lJTyJYwDem+kRT2HgnIh2AdcDQwGV0RWQycLaq9orjOS4F7gAOUNXVYY5fBFwE0K5du75z585NVPIz2rZt22jWrJnXyUgL9S0viopcO32gl17qRmFhJ448cjUXXfQZa9c2o0MHN8Qt3f36azYnnjgIVeH559+hWbOyhD5/fft8RGN5ESxT8mPYsGFLVbVflQOqmrIN6IDrBzAkZP9kYEUc158KlAAnxvN6ffv2VeMsXLjQ6ySkjfqWF7NnqzZtquq65lXdpk1bqE2buvMywcsvu3QPGJCc569vn49oLC+CZUp+AEs0TEys0dS6tbAJKAPahexvB2yoenolERkFPAmcq6ovJid5xtQ9o0a5ufMjKSlpQFkZjB6dujTVhlXnG1MzKQ34qroL1+Hu6JBDR+N664clIqfhgv0YVZ2fvBQaU/fk5cHEiZCbG/7411/nMXEitGyZ0mTVmAV8Y2rGiznwpwNPisiHuB7/43BV/Q8BiMgTAKp6ru/xGbhgPxFY7OsoCLBLVTenOO3GZKSpU93ttGluHH55OTRs6IbmrV7discfj359utiwAT77zP14GTDA69QYk1lSXaWPqs7D9f6fBHwCDAaO18oOeJ18m9843A+TGbjhe/7tXylJsDF1gAjceqvrvNe9u1s57/rr3bHPP2/ladqq46233O2QIW7WQGNM/DxZ5U5VZwGzIhwriPbYGFNzeXmVPfFvuQX++ldYt64Rn34KBx3kZcriY9X5xtRcykv4xpj0IALHHuvu/+c/3qYlHqrw+uvuvgV8Y6rPAr4x9VgmBfzPPoO1a6F9e/jNb7xOjTGZx5MqfWOMdwoLK+8PHw5ZWcq77wpbtkCLFp4lK6aXXnK3I0dClhVVjKk2+7cxph5r2RL23/8Xdu+GN9/0OjXRvfyyux050tt0GJOpLOAbU8/17+9Gt6Zztf6mTfD++5CTY+33xtSUBXxj6rlDD3UB/9VXXce4dORPW0EBNG/udWqMyUwW8I2p53r02EbbtvD997B8udepCc9fnX/CCd6mw5hMZgHfmHouKwuOOcbdT8dq/dLSynRZ+70xNWcB3xhTMTzv1Ve9TUc4774Lv/wCvXtDt25ep8aYzGUB3xjDiBFuIp7Fi2HbNq9TE8yq841JDAv4xhhat4bDD3cL67zyitepCRY4/t4YU3MW8I0xAJx6qrt99llv0xHo22/hyy/dfAEDB3qdGmMymwV8YwwAp5zibl9+GX791du0+Pmr84891i3na4ypOZta1xgDuCVz+/WDJUvgtdfgt7/1Li0FBe42J8fdWvu9MbVnJXxjTIV0qtbfvdvN+5+VVTmKwBhTcxbwjTEV/AH/hRdg505v07JpkxuDX1AAe+7pbVqMqQss4BtjKvToAQceCFu2eL+Yzg8/uNszz/Q2HcbUFRbwjTFBRo1yt/Pne/P6xcVumt/iYsjOhqOO8iYdxtQ1FvCNMUH81frPP++q1FNFFW66CTp0gJUrK/fvt5/bn64L+xiTKSzgG2OC7Lcf7LsvbN4Mixal7nUnT4bp02HHjsrgXlbmHk+f7o4bY2rOAr4xpopUV+sXF8O0aVBSEv54SYk7/vPPqUmPMXWRBXxjTBX+av1//9uVspNt/nzXXh9NdjY880zy02JMXWUB3xhTRZ8+sM8+rqf8G28k//U2bIhcuvcrKXHnGWNqxgK+MaYKEbjgAnf/4YeT/3rt20NubvRzcnPdecaYmrGAb4wJ64ILoEEDNwnP+vWV+wsKKqe+TZRRo2I3HZSVwejRiX1dY+oTC/jGmLDat3fz6ZeVwV//mtzXysuDq692NQvh5ObCxIlu1TxjTM1YwDfGBAkswY8b525nz3Zz2yfT0KGVw/H8gb9pU2jcGCZMgKlTk/v6xtR1FvCNMRENGwbdu8PatfDqq4l73nDNAtOnu9sbbnBT/HbpAvfcA0VFcOutkUv/xpj4WMA3xkSUlQUXX+zuP/ywGy9fVASrV8OcOe5xInzxBfznP9CkiSvN5+dD584wdqxV4xuTKBbwjTFRjRnj1qV/+WUXiL/5BlatgvHj3TS4iZj21l+6HzPGVsYzJlks4BtjKoQrwbdu7arYwS2ZW17u7m/fHnna2+r05N+4EZ56ylXZjx+fqHdijAllAd8YE7RwTWgJfuJE+PrryNfWdtrbmTNh1y446aTKHxaFhW4zxiSOBXxjTNDCNaEl+Pvvr9wXSXWmvQ2sRXjgARfwwQ3LM8YkjwV8Y+q5srLoC9fs2hV7SF48096Gq0W4+mq3Kl+HDjBoUI2Sb4yJkwV8Y+q54uLYC9fEEjjtbaSe/OFqEXbtcrebNsHNN9cuDcaY6DwJ+CJyiYisFJEdIrJURI6Icf5Q33k7ROQ7ERmXqrQaU9eVlsZeuCaWsjI3PW60fgCxahFs+VtjkivlAV9ETgfuBW4HDgbeA14VkU4Rzu8KvOI772Dgz8D9InJqalJsTN3WsGHshWsaNnRD88LxT3s7fXr0fgCx5sq35W+NSS4vSvgTgMdUdY6qLlfVy4Ei4A8Rzh8HrFfVy33nzwEeByamKL3G1Gl5ebGDcVYWXHGFm+Y2cMa7nBw3Uc748bFL8KWl0V/Dlr81JrlSGvBFJAfoCywIObQAGBjhssPDnP8a0E9EGiY2hcbUP9nZroQeqZSfmwvXXAN33+1WzevRA/bYwx3r08fNcf/ss4ntB2CMSTzR2k6RVZ0XE+kArAOGqurigP2TgbNVtVeYa74CnlLVqQH7hgCLgA6qWhRy/kXARQDt2rXrO3fu3KS8l0yzbds2mjVr5nUy0oLlRTB/fqxf70rY/q+ErCx3v3171w7v99VXsGNHNlOm9Gfz5kaMGbOSESNWBy2hG8lPPzVm2rRDKS3N5txzP+fAAzdVHBNxPyBq+8OhtuzzUcnyIlim5MewYcOWqmq/KgdUNWUb0AFQYEjI/snAigjXfAVMDtk3xPc8+dFer2/fvmqchQsXep2EtGF5ESwwPzZvVu3ZU7VLF9XZs1WLi6ueP3So2+bPVxVRBdUTTlDNzXX3I20NGlSeH7rl5qpOmpSiNxyDfT4qWV4Ey5T8AJZomJiY6jb8TUAZ0C5kfzsgUuvdhgjn7/Y9nzEmQfLy4l+45tRT3ZS4WVnw0ktu2t1oysps+VtjvJTSgK+qu4ClwNEhh47G9cIP5/0I5y9R1RjdgIwxiRY47e1ZZ8E//uGq4cvKoEGDyNepQrt27seBLX9rTOp50Ut/OjBGRC4Ukd4ici+uqv8hABF5QkSeCDj/IWAvEZnhO/9CYAwwLdUJN8ZUddppMG+eC/ahM/JlBXzDjBoFn38OI0fa8rfGeCHK7/HkUNV5IrInMAnIBz4HjlfV1b5TOoWcv1JEjgfuwQ3dWw9coarPpjDZxtQbNVm05tRT4bnn4I9/hBUrKof5lZe7Hv2zZsGZZ1op3hgvpTzgA6jqLGBWhGMFYfYtAg5JcrKMMbUwcqTbdu2CwYPduPorroATTgju5W+M8YYnAd8YU3fl5Lgx9bm5cNFF4c+xpW+NST1bPMcYY4ypB6yEb4xJOCvBG5N+rIRvjDHG1AMW8I0xxph6wAK+McYYUw9YwDfGGGPqAQv4xhhjTD1gAd8YY4ypByzgG2OMMfWABXxjjDGmHrCAb4wxxtQDFvCNMcaYesACvjHGGFMPWMA3xhhj6gFRVa/TkDQi8iOw2ut0pInWwCavE5EmLC+CWX4Es/yoZHkRLFPyo7OqtgndWacDvqkkIktUtZ/X6UgHlhfBLD+CWX5UsrwIlun5YVX6xhhjTD1gAd8YY4ypByzg1x+zvU5AGrG8CGb5Eczyo5LlRbCMzg9rwzfGGGPqASvhG2OMMfWABXxjjDGmHrCAn+FEpLWIrBMRFZHWMc5tJyKPich6ESkRkf+ISI8w5/UXkddFZJuIbBWR92I9d7pIZH6ISBff84Tbrkn+u6kdy4tgif5fEZH2IvKkiGzwnfOpiJyd3HeRGJYXwZKQH/uIyL9F5EcR2SIi/xSRdsl9F7FZwM98jwKfxDpJRAR4DugB/BY4GDcp0Rsi0jTgvMOABUAhMADoC0wDShOa6uRJZH58D+SHbJcACsxPbLKTwvIiWEL/V4AngN7AycABvsdPisiQRCY6SSwvgiUsP3y3CwABjgQGATnAiyLibcxVVdsydAOuBN7EfagUaB3l3J6+c/oE7MsCfgAuDNj3HvAnr99buuRHmOteBxZ4/V4tL7zPD2AbcH7ItauBiV6/X8sL7/IDGAGUA3kB5+zh2zfcy/dqJfwMJSIHA9cC5+I+SLE08t3u8O9Q1XJgJzDY95xtgcOBIhF5R0R+EJG3ReSohCY+CZKRH2FeoxtwFGk+NMfyIlgS8+Md4DQR2VNEskTkZKAN8EZCEp4ElhfBkpQfjXA/CnYEXLfD9/xh/59SxQJ+BvJVGc0FLlfVdXFe9iWwBrhdRFqJSI6IXAt0xFXPAnTz3U4B/gYcA7wNvCYifRL2BhIsifkR6kLgR+D52qY5WSwvgiU5P07DfbFvwn3hPw2cqaqfJCr9iWR5ESyJ+fEBrsbjbhFp6nudaUA2kf+fUsICfma6D3hHVZ+N9wJVLQVOAfYBfgJKgGHAq1T+svV/Hh5W1b+p6v9U9QbgI2BcohKfBMnKjwoi0gA4H3jcd226srwIlsz8uA23mMpwoB9wN/BEGv84trwIlpT8UNUfgdHAccBW4BegJfAx8dUiJI/X7Se2VX8DVgFlwG7fVob7db2bONrfce1JbXz3/wvM9N3v6nue34Wc/1fgZa/fd6rzI+Sc//M9Z0+v36/lhff5gfvCD2rL9e1/A3jE6/dteeFdfoSc0xpo6bu/AbjGy/fcAJOJRuB6ffodiquCLwC+jnWxqv4C4BtK0g+4yXdoFbAe6BVySU/gs9okOMmSlR+BxgKLVPWr2iY2ySwvgiUrP3J9t2Uhl5SRvjWnlhfBkv6/oqqbfOccCbQFXqhtomvF619ZttV+w31Ag3qXAnvh2pv+L2DfaFz1Uzfc8JlVwLMhz3UVrgpqNNAduAE3JK+P1+/Ti/zwndcJ9+V1ttfvzfIiPfIDaIgLCouB/rhS7tW4KtsTvX6flhfe5YfvnPNxHaD3AX6Hq/7/i9fv0Ur4dVdDXEl9j4B9+cB0oB1QhBsre2vgRao6Q0QaAX8B9gS+AI5T1U9TkegkqlF++Pwe9yMo7ra+NGd5Eaza+aGqpSJyPHAH8CLQDPgGNzTtxRSlOxksL4LV9H+lF/BnoBXuB8GfgHuSnNaYbPEcY4wxph5I5/YVY4wxxiSIBXxjjDGmHrCAb4wxxtQDFvCNMcaYesACvjHGGFMPWMA3xhhj6gEL+MYkmIiMERGNsA33On2ZTkQKQvK0S8jxhiLyB99Kj8UiUioiRSLykoic41sLwH+u/2/VPczrNPAdu6Wa6dsdkLYLa/o+jUk0m3jHmOQZDawN2bfMi4TUUZfiFiQp8u8Qkea4hUz6AnNwi7j8jFvN7CTgUWAXMC+J6RoEdAD+lcTXMKbaLOAbkzyfqOo38ZwoIo1UdWeyE1THLFPVD0L23Y+b13yoqv435NjffeufN0lmolT1v6G1DsakA6vSNybFAqqRh4jIMyLyM261LX818vUi8qWI7BSR9SLyFxFpHPIc3UTkZREpEZEfReReEbk4tIo7XJW0iHTx7R8Tsn+oiLwpIltFZLuIvCYiB4ScUygi74jIcBH52Pf6n4vI/4V5n31E5N8i8pOI/CoiK0Tket+x+0Vko4g0DLmmue/176hBvu6Fm7f84TDBHgB1Sz6/V93n9j2/P9/CbYU1eU5jUslK+MYkT3ZgezGgqhq4otjTwD+AUVT+Lz4FnAjcCbwH9MbN090FOBVARHKA13El1UuBH4CLcet014iIjASeB17GBU2Aa4G3ReRAVf0+4PR9gHtxc4Vvwi2U8oyI7Ouv0RCR/kAhbk718bimjR7Agb7neBC4DLfU7j8DnvssoCnwcA3eRgGQDbxUg2tD/1b4nitQEW5BlED7A7OB5TV4TWNSygK+McnzZcjjd4HBAY/nq+of/Q9E5AjgdOA8VX3Ct/sNEdkMPCUiB6nqJ8B5uJW6DvdXaYvIq9RuCeN7cUvenhyQnoXAd7iAflXAua2BIar6te88fzv6acDtvnOm4VYIG6CqJb59b/mfQFWXicgi3A+VwIB/MbBAVVfW4D109N2uCdwpIkJw8C5X1fKQa0P/VlX4mlwqmhBEpA3wd1ztzPgapNeYlLIqfWOS5/9wa2z7t9+HHP93yONjcR3K5vuq9hv4Sp0LfMeH+G4PB74PbL/2BbB/UgO+9bz3AZ4Oed0S4P2A1/X72h/sfa/9A66WoZPv+XJxHdeeDgj24cwChvleHxE5FDiYmpXuo7kWt8Szf3sizDmhf6tDgQGRntBXy+L/+52sqjsSmWBjksFK+MYkz+cxOu0VhTxuC+QA2yOcv6fvNh/YGOZ4uH3xaOu7/atvC7Um5PHmMOfsBPz9DPJwhYnQEQqh/g1swJXqJwLjgPW4JVZrwv96nYAVAfsfA97w3X8hwrVV/lZhqvgDPQIcgKtl+bH6STUm9SzgG+Od0LWpfwJ2AEdEOH+977YI13Ycql2YfTtxPyIC7Rny+Cff7fVUBsZAuyKkJ5JioBzYK9pJvnXUHwEuEZG7gDOAv6jq7mq+nt8i3+uegOvj4H+dDbgfFohIdd9LFSJyA3AmcJyqWtu9yRhWpW9M+vgPrpS8h6ouCbP5A/77wN4iUlHlLCJZuDb0UKtxJdFAI0MerwBWAftHeN3/V5034avGfwf4nYjEGgL3MNASeAZohBs7XyOquhbXEfJiETmsps8TjYicCtwGXKqq4X4cGZO2rIRvTJpQ1UIR+QeuDX868CGuxNoFOB64VlW/Ah4HrgP+5Stt/oCrDm8R5mnnApNE5EZch7MjcKXTwNdVEbkUeN7XNv1PXO/7dsBAYI2qTq/m25mIK3G/LyJ/wVW3dwMOUtXLA157nYi8gGtDfzFkNEBNXIYbDbBQRObgaix+xjUzDAHaA1tr8sQi0g3X/r8A+H+BP7iALapqkyqZtGYB35j08jvgcuAC4EZclfwq4DV8bfSquktEjgYewHV8247rLf4y8FDI8/0ZV4K+DPcj4RXgHHzj/v1U9RURGeJ7zUdwQ/424H4kVHtWOlX9SEQGAVNxk+E0wtU2PBrm9GdwAb/WnfVUdYuIDAXG4ob4nYcb5rcJWIrrODm3hk/fCcgFjvFtgRbhhgUak7ZENbQZ0RiTiXwT6TwKdFXVVd6mJn4i8jSuV3+3MMPlwp1fACwEhuOGEta0zT8pRCQbVyvzDTBWVR/xNkXGOFbCN8Z4wlclfhBu7oEJ8QT7EG/4nifdfuDspOqkPcZ4zgK+McYr7wPbcH0SZlXjuqW4cfJ+6yOd6JHDAPHdX+VhOowJYlX6xhhjTD1gw/KMMcaYesACvjHGGFMPWMA3xhhj6gEL+MYYY0w9YAHfGGOMqQcs4BtjjDH1wP8H3hSs5wBJQ6UAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -488,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "6e8e067c",
    "metadata": {},
    "outputs": [
@@ -498,8 +471,8 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: f01\n",
-      "- value: 4971657997.745945 ± 45050.505720374684 Hz\n",
-      "- χ²: 0.7650116404414524\n",
+      "- value: 4971644046.603253 ± 46329.06674455518 Hz\n",
+      "- χ²: 1.056178573009652\n",
       "- quality: good\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
@@ -520,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "6937956d",
    "metadata": {},
    "outputs": [
@@ -564,7 +537,7 @@
        "      <td>4.971589e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 09:22:53.219031+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.784983+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -572,11 +545,11 @@
        "      <td>qubit_lo_freq</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>None</td>\n",
-       "      <td>4.971658e+09</td>\n",
+       "      <td>4.971644e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 09:27:39.803000+0200</td>\n",
-       "      <td>595c08f2-ed79-4c25-8843-803c569b4e90</td>\n",
+       "      <td>2021-11-03 10:24:42.182000+0100</td>\n",
+       "      <td>f808753b-c40e-4376-9ce8-548275a4b548</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -586,7 +559,7 @@
        "      <td>6.993371e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 09:22:53.219055+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785055+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -596,16 +569,16 @@
       "text/plain": [
        "       parameter qubits schedule         value    group  valid  \\\n",
        "0  qubit_lo_freq   (0,)     None  4.971589e+09  default   True   \n",
-       "1  qubit_lo_freq   (0,)     None  4.971658e+09  default   True   \n",
+       "1  qubit_lo_freq   (0,)     None  4.971644e+09  default   True   \n",
        "2   meas_lo_freq   (0,)     None  6.993371e+09  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-10-28 09:22:53.219031+0200                                  None  \n",
-       "1  2021-10-28 09:27:39.803000+0200  595c08f2-ed79-4c25-8843-803c569b4e90  \n",
-       "2  2021-10-28 09:22:53.219055+0200                                  None  "
+       "0  2021-11-03 10:23:20.784983+0100                                  None  \n",
+       "1  2021-11-03 10:24:42.182000+0100  f808753b-c40e-4376-9ce8-548275a4b548  \n",
+       "2  2021-11-03 10:23:20.785055+0100                                  None  "
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -634,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
    "id": "ed4a5f77",
    "metadata": {},
    "outputs": [],
@@ -644,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "id": "8227b8ba",
    "metadata": {},
    "outputs": [],
@@ -662,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 19,
    "id": "b82cf6dc",
    "metadata": {},
    "outputs": [
@@ -673,7 +646,7 @@
        "<Figure size 392.294x144.48 with 1 Axes>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -692,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 20,
    "id": "68d32b29",
    "metadata": {},
    "outputs": [],
@@ -702,18 +675,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 21,
    "id": "9eefc00c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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m2PDDeMrJyanwftWqVZx55plceeWVjBw5kj322IPPP/+cCy+8sKxJ3iuagTL98wFw1lln0apVK/7zn/+w9957k56eTocOHSrlw1dpaSkADz/8cMhe5/7mz5/P6aefHjLNkCFDGDJkSET7a9asGfXq1WP9+vUVlq9fv54WLVpEnC+Arl27MmPGjLL3Q4cO5cILL+SKK64AoGPHjmzbto0rrriiUi/0AQMGMHPmTObMmcN+fjXtzp07s2TJEjZv3kxxcTF77rknXbt2DTl+3Vth8tprr70qLauK6v6dmjdvHnCb9PR0mjVrhqpGvN+CgoKy2ypvvPFG0EBF06dPp169elx88cURl8/XwIEDGTBgAGvXrqVJkyasXLmS2267jf322w9VWLPGnY9/+82dQ70/+N27u6Hawb7227e77bKzq3fOzs52F40igX8Tbr4Z/vUvuP56eOstl4/WreGyy9x28Rwy6CsZhg/WChdf7GIBDBsG11xTHl/A22IwcGDF4BK9e7uD6r0gCPSlGDjQDU8cPjzw+q5dYd680PlSdXlQLf9yb9vm3icqOIVxFi1aRHFxMffdd1/Z1eMbb7xRKV1paSkLFy4s+7FdvXo1a9asoX379lHJx59//sl3333HlClT6NmzJwCff/45u3yaqjIzMwE35MqrefPm5Obm8tNPP3HppZdG/HneK92CgoKyIXj+qnKlm5mZSefOnXnvvffo7XPz9L333uO8886LeD8AS5YsoWXLlmXvCwsLy46NV7169Spdyd944408//zzvPHGGxx88MFB99+oUSMAli9fzqJFixgVx3++6v6djj76aN58880Ky9577z26dOlCRkYGQET73bp1a1kryDvvvBP02AM8+uij9O7du+zvVR0iUtaq9txzz9G6dWuOPPJI1qyB9etdnw3/BpkFC8L3q6pfH3r2hPffr/o523tFLxL8NwHgjTfgpZdcvJhffnEXe4mqBADx7yMQz0c0+gj42rEjomQBbdzoeoKOHOmefTqOB11fk+GL11zzhYK77+X/WcmsNvcRuO666yos+/LLLxXQ8ePH688//6zPPvustm7dWgFdsWKFqrr7/+np6XrUUUfpxx9/rF988YX26NFDO3bsWHafujqjBnyVlJRos2bN9MILLywbbnXUUUdpenq6Tps2TVXdMMMGDRroiBEjdN26dbpp0yZVVZ06dapmZWXphAkT9LvvvtOlS5fqk08+qaNHjw6bl2gPH8zIyNCpU6fqsmXL9IYbbtCcnBxduXJlWZpLLrlEL7nkkrL39913n77yyiv6ww8/6Ndff62DBw9WQF966aWyNMOHD9fddttNn3vuOf3555911qxZuv/+++u5555blqZfv36622676fvvv6/Lly8vG/rmO+TthRde0A8++EB/+uknffXVV3WfffapsI9AAg2n83/4fkas/k7e4YM33nijLlu2TKdOnaoZGRmVhg+G2u+WLVv0mGOO0Q4dOugPP/xQoQz+Q2nnz59fqV+Mr1WrVukXX3yh48aNU0C/+OIL/eKLLyr8LcaOHatfffWVfv311zpy5EjNyMjQV155RXfuVF20SPWzzyo+mjXrHPG50zt8rzrn7KratMn1Cdi2LbL0KTl8MNaPaFcE4q0mwxfT0krLOp8EG4qSjGrDsYq0IqCqOmnSJM3NzdWsrCz9y1/+os8//3ylikBOTo6++uqr2q5dO83MzNQTTzxRly9fXraPmlYEVFXff/99PeSQQ7R+/fp6yCGH6DvvvKM5OTllFQFV96PfunVrTUtLqzCM7dlnn9VOnTpp/fr1tXHjxnrcccfpc889FzYv0awIqKpOnjxZ99lnH83MzNQjjzyyUue17t27V8j3Pffco+3atdOsrCxt0qSJHn/88frmm29W2Gbnzp16xx13lKVr1aqVXnvttbrRZ3wuEPAxfPjwsjSTJk3SVq1aaUZGhrZp00aHDh0aNpaEd3hjqIfvZ8Tq77RlyxadM2eOdurUSTMzM7Vt27b60EMPVWm/ocri/z976aWXhuxs2qdPn7D76dmzpzZq1EizsrK0a9eu+tZbb6mq6qpV5T/+X3yxq1oVgVDD9xItVhUBcdukpi5dumgkMeHnzJlDjx49Yp+hahg2LHgfg4yMyDq25OVVL0JibVQbjlWXLl1sroEIpGK5rEy1k6prgv/tt/JlublbWbPGlev007vwxx+R/c9mZcHatQluqg8i0vOfiCxW1YgnWEiGOAJ12siR7l5TVpbrBCjinrOy4JRT3OtALrqoPA7m889DDKPTGmNMwqjCr7+WVwK8/W6DxXnJyHAdqwPJznYd+mpjJSCWrCJQy3k7I65ZA/fd567s77vP1Viffro81rW/I4/8vez199+7SsPGjXHKtImZQw45JGiQHt9hZ8bUBaWlsHKl6xwoAvvsE36btDTXSS/QxVWihu8lmo0aSBJNmsCVV1ZefvPNoYcn9u0Lb74Jn34KPXrArFmuB6tJTm+99RY7g9wP8o8dYEwy27XLTfW7c6e7im/SxI3D9youdrMAbt/ufsz328+lKS52FYNAsrPdj/2oUTBkSPBe/XWNVQSSnLf26j+UxTt8ZeRIuO02OPlkWLoUTjzRzWzVpk1i822qZ59ILnmMSWLqiQOwbp07j5WWuqv41avdD3ZurrsNsG5dxe1+/rl8vVdamttepPIVf7CLq7rIKgJJLlgcg/32g5NOcmlatYLXXnOVgeXLXUCMuXPhkEMSm3djjPHnjQPg7ccP7scc3PKtW11kQF/edN6WgL33dnOztG7tWhSaNoUffqi7V/zhWEUgRfjXbr1TVqu6UQPjx5d3nvnzT+jY0UWyGj/eVSby8ytWJHr1cvs0xph42bXLnYOCDWYrLa1cCfBfv24dNG/uzmt77umWN2xolYBQrCKQ4vLyXB+CHTsqLleFiRNd5SArq/KthRtucP0PLESxMSZe8vPLI6VWl/fCJkh0YxOAjRpIYfn57gc+UEdCcLXn8ePh3ntdRcEbmnjbNvd+wgRXkTCmtvnll1/o0aMHHTp04LDDDuPFF19MdJZMFOzcWX4boLpKS2s+cVBdYxWBFDZzZvmcCKFs3x54eWGhqyh4Z1s0prZIT09n4sSJLFu2jFmzZnHTTTexzTuFnElaGRnBx/9HKi3N7cdEzioCKWzduuCtAZGqVw/sYsvURN++fTnrrLOius+WLVtyxBFHANCiRQuaNWvGRguUkfSaNKnZbQFw21v/pqqxikAKa9HC3e+vicLCysN0jPHVt29fRKTS46uvvgJg0qRJPP300wD06NGD66+/Pqqfv3jxYkpKSmjdunVU9xvOvHnz+L//+z/23ntvRIQnnngiou3Wrl1Lnz592HPPPcnKyqJDhw7MnTu3bP3kyZM57LDD2H333dl9993p1q1bpdkBfY0ZMwYRifrfNRHS0915K1irQFqa6/gXan2LFhXjDZjwrCKQwnr1Ch55MFLZ2RaAyIR38skns3bt2gqPDh06AG5q3sYx6rK9ceNGLr30Uh555JGY7D+UgoICDj30UCZNmkSDBg0i2mbTpk0cd9xxqCpvvvkm3377LQ888AB77bVXWZpWrVpxzz338Pnnn7No0SL+8pe/cM4555RVrHx98sknPPLIIxx22GFRK1ei5eaW9/r3EnGP5s3hoIPK13srBGlp5et94wiYyFhFIIU1aeJ6/gdrFWjQIHzNuaTERdwyJpT69evTokWLCo90z5fLe2ugb9++zJ07l8mTJ5e1GqxcuTLg/s4//3z22GMPJk6cWLbs22+/JTs7mxkzZgBQVFTEOeecw+DBgzn22GNjXcRKzjjjDEaPHk2vXr1Ii/DG9tixY2nZsiXTp0/n6KOPZt999+Wkk06iffv2ZWn+9re/cfrpp9OuXTsOPPBA7rrrLnbbbTcWLFhQYV+bN2/m4osv5vHHH6dJCrWFi7iLj/r13fv69V08gMMPd/EBRNzz4Ye75bm5ldebqrGKQIoLNWnRoEEweHDwikJdnYDDxMakSZPo1q0bl112WVmrQbDm/IkTJ3LRRRcxwjNtZlFRERdeeCG9evXiggsuQFXp27cvf/nLX7jkkkvCfvbo0aODztHgfcyfPz+q5Q3k1VdfpWvXrpx//vnstddeHHHEETz44IMEmwW2pKSEGTNmUFBQUKmyc9VVV9GrVy969uwZ83zHk6qbP2DHDnex0qED7LVX5YuW9HQXJyA31z3b7YDqsz9digsWedAbV9t7/vEGHPJ2LkxLcwGH6uIEHKbq3nnnHRo2bFj2/oQTTuCFF16okKZRo0ZkZmaSnZ1NizD3m1q2bMmgQYN48MEHWbVqFRMnTmTLli1MnjwZgI8++ojnn3+eww47jFdffRWAp556io4dOwbc3zXXXMM//vGPkJ+59957hytmjf38889MmTKFAQMGMHjwYJYsWUL//v0BKtzjX7p0Kd26dWPHjh00bNiQV155pULZpk6dyo8//ljW9yKVrF/vhj7Xqwf77x/ZyCdTM1YRqCOCxdX2ryh8/TU88oirje+1lzWzmciceOKJFe7TR3rPPJS2bdvSuHFjxo4dyyOPPMK8efPYbTc3v/zxxx9PaRUGnDdt2pSmTZvWOE81VVpaSpcuXRgzZgwAnTp1Yvny5UyePLlCReCggw5iyZIlbN68mZkzZ9KnTx/mzJnDoYceyvfff8+QIUP48MMPyUjicXKBJhUqLHRTCgPsu68FBYoXqwgYoGJF4fjj4R//cLcOjjoKunVLbN5M7ZednU27du0qLNu6dWuN93v44YczZcoU7rjjDrrV4Is4evRoRo8eHTLN22+/zQknnFDtz4hEy5YtyzpRerVv355JkyZVWJaZmVn29+zcuTOfffYZ9913H4899hgLFizgjz/+4BCfyUJKSkqYN28eDz/8MNu2baO+9wZ7LRRqUiHvhUfLlnZLMp6sImAq6d0bbrwRJk1yFYLPP3f34Gw+AlNTmZmZlFRhKIuqcsghhzB06NAafW5tuTVw3HHH8f3331dY9sMPP4SdVbK0tJSioiIAzjnnHLp06VJh/WWXXcYBBxzAkCFDyMzMjG6moyzUpEKqrnOg9fyPL6sImIDGjoWFC2HBArj4Yjj6aBeKuLjY/dPm5Nh8BKbq2rZty8KFC1m5ciUNGzakadOmQXvcT548mXnz5nHQQQdRr4Y3imNxa6CgoIAff/wRcD/Uq1evZsmSJTRt2pQ2nnm+H3zwQR588EG+++47AAYMGMCxxx7LXXfdxfnnn88XX3zB/fffX6G1YvDgwZx55pm0bt2arVu38uyzzzJnzpyyWAKNGzeuNBwzJyeHpk2bcuihh0a1jNEWblIhcOeYkhLr/BdPNmrABJSZCS+8AM2awXvvuYrBjh3lNXebj8BUx80330xmZiYdOnRgzz33ZPXq1QHTLVu2jFtuuYXrrruO5cuXU1jTEJkxsGjRIjp16kSnTp3Yvn07w4cPp1OnTuT5/EP88ccfFVoAjjrqKF599VVeeOEFDj30UG6//XZGjRpFv379ytKsW7eOf/7znxx00EGcdNJJfPbZZ7z99tucfvrpcS1fLHgnFQrFO2mQiR8JNmwlFXTp0kUXLVoUNt2cOXPo0aNH7DMUR9Eq04wZcOGFodNkZcHatfG5p1cbjlWXLl2I5HsVqa1bt5Z1gksl1S1XUVERXbt2pUOHDjz66KPstttufPTRRxxzzDExyGXVpOKximeZ1qxxj3Byc2t+e8C3XNH+n02USM9/IrJYVbuETehhLQImpK1bwzfR2XwEJpoGDx7M5s2beeihh8jOzuaAAw5g0qRJQVsPTPKIZFIhmzQo/qwiYEJat87d1wvF5iMw0TJr1iwefPBBnn76aRo1agTA7bffzgcffECfPn0SnDtTU5FMKmSTBsWfdccwIbVo4ToGhprh1eYjMNFy6qmnstNvMvlLLrkkouiBpvbzTioUrMNgWpqbL8A6CsaXtQiYkCKZuMjmIzDGRCo3t3LTv00alFhWETAhhZu4yOYjMMZUxfr1bohgRoZNGlRbWAOMCcs738D48e6fdPt29z4jw4UmtvkIjDGR2L4dfvvNvW7bFjzdQEyCWYuACcs7H8GaNS7a4JlnuuWNG8NNN1kN3hgTnndWQVUXn8QqAbWHVQRMxLzzEbz2Gpx4ImzY4EIRG2NMOOvXu07HGRnQqlWic2N8WUXAVFlaGjz2mJsr/JlnXMXAGGOC8b8lYKMCaherCJhqadcOvOHRr7nGhQTNz4epU91thKlTLUyoMcbdCli1ym4J1GZWLzPV1r+/iyj48cdw0knw7bd1Y1Kili1bVpr9rSZ27NhBVgpOvJ6K5bIyVV1BAfz5p4tAmpsbPrJgtPiWq2XLlvH50CRlFQFTbfXquSv/jh3hiy8qrvMGIJowwT2PGhXfvMXS66+/HtX91Yb5E2IhFctlZaqa9evh4INh0yZ49tnw85ZEUyoeq1hJyK0BEeknIitEZIeILBaRE8Kkv0hElohIoYisE5GnRcRi2dUCLVuGvtovLHTDDjdtiluWjDG1xIAB7n//tNPgggsSnRsTTNwrAiJyPjAJGA10Aj4G3haRNkHSHwc8BTwJHAKcA3QAnolHfk1oM2dC/fqh09ikRMbUPe+8A8895zoVP/RQat0eTDWJaBEYCDyhqlNV9VtV7Q+sBa4Nkr4b8Kuq3qeqK1T1E+ABoGuc8mtCWLeuPMBQMDYpkTF1S2EhXOs5o48YAfvum9j8mNDiWhEQkUygMzDLb9Us4Nggm30EtBSRs8VpBlwAvBW7nJpItWgRPPywl01KZEzdctddLnjQ4Ye7oGOmdot3i0AzoB6w3m/5eiDgT4WqLsD98D8DFAMbAAFsTtJawCYlMsb4Wr7c9QsCOOooeOIJG0pc24mGmxw6mh8mkgv8BnRX1Xk+y/OAi1X1oADbdADeAyYC7wItgXHAElW9NED6q4CrAJo3b955xowZYfNVUFBAw4YNq1OkWiueZVqzxvUOLi2Fjz/O5eWXD6Rx4x3ceutCsrJKozqjmB2r5JGK5bIyhaYKN93Uka++2oOjjlrL+ed/T1qaW96iRXxnFqzLx6pnz56LVTXyMc6qGrcHkAnsAnr7LZ8MzA2yzVPAK37LjgcUaBXq8zp37qyRmD17dkTpkkk8y1Raqjp0qGpWlmp2tqr7t1etV88tLy2N3mfZsUoeqVguK1NovXuX///7P7Kz3fkgXurysQIWaRV+myO+NSAimSJyjIicKyIXi8hpItI24hqHq3QUA4uBU/xWnYIbPRBINuDf+Ox9b5ERawHfSYkmToTLL3fL09Phssust7AxdcGaNaFHB9lQ4tor5A+piNQTkV4i8g6wGddxbybuKv1t4CcRWS0i94hIuwg/cwLQV0SuEJH2IjIJyAUe9nzmdBGZ7pP+deBvInKtiOznGU54P/C5qq6uSmFNbHknJXr0UfjnP6GoyI0jNsakvquvDp/GhhLXTkErAiLSC/gOeBooAobirtwPBw4EjgEuwlUM/g58KyJTRaR5qA9U1eeBmzz7W4Jr5j9DVVd5krTxPLzpn8ANObwe+NrzeT8Af6tKQU18jR0LDRu6CYneeSfRuTHGxNLPP0f2f25DiWunUCGG7wfG4sb8bwqSZiHwPDBQRLoC/8Z11AsZUFZVpwBTgqzrEWDZA7jYASZJtGwJw4fDLbe4OQeWLg0feMgYkxzy810wsXXrXCfA116DXbvc7cBdu4JvZ0OJa6dQFYH9VHVHpDtS1U+Bc0UktWbkMNV2ww3uNsH338OkSXDrrYnOkTGmJlQhL8/d6/dOMJaVBTt2QEaGm1AoVEXAhhLXTkFvDVSlEhCN7Uzqycx0FQBwAUZ+/z2x+THG1ExenptIbMcOVwkA99rrqKOCBxjLznazkTZuHPNsmiqKqNe9iBwoIkf7vG8gImNE5HURuT522TPJ7rTT4PTTYcsWGDzYzVY4apR7tiAjxiSP/HzXElBYGHj9zp3w2WfQr59rJcjJcSOGcnLc+4ED3ZTkpvaJdBriB3Ed+xZ63t+F67y3FLhPRFRVJ0c/eyYVjBvnOhJNmwYzZrgriOxsd+vg5pvdycGGGBpTu82c6Xr9h5KeDgce6IYS+vYh6N3bWgJqs0grAofjgv4gImnApcC/VfU+ERmO6yBoFQET0IwZ7gSya1f5BEXbtrnnCRPc86iQ3UuNMYm2bl3w1gAv76gA71BikxwiDcjTCPjT87oT0AQ3jA9gDrBfdLNlUoW3OTFYByILMmJMcrAJxlJXpBWB9YA3YNCpwE+q+ovnfUNc2GBjKomkOdGCjBhT+9kEY6kr0orAa8AYERkPDAJ8T9sdgZ+jnTGTGqrSnGiMqb2aNHF9eoLFA7FRAckr0orAYOAN4DRcpeAun3X/h5sd0JhKrDnRmNQxYgTstZd7nZFhowJSRUSdBVV1GxCw64eqHhvVHJmU0quXGx0QijUnGpMcXn8dfvkF9tgDhg1zw4JtVEDyi6giICI/A39X1S8DrDsUeE1VrcOgqcTbnDhhQuBbBA0awKBBdhIxprbbtQv+/W/3evhw6N8/sfkx0RPp8MG2QLBI8VnAPlHJjUlJ3ubC8eNdx8DCQheKtKQE2re35kRjksFjj7lw4e3aRTbToEkekVYEADTI8i7ApppnxaQqERcnYODA8iAjqq4C8OWX7uRy8MGJzqUxJpitW10rAMCYMS58uEkdQSsCIjIA8M4mr8DrIlLsl6wB0BSYEZvsmVTiH2Tkt9/gkUfgttvglVcSly9jTGj33gvr18Mxx8B55yU6NybaQrUI/Ay873ndB1gEbPBLUwQsAx6NftZMqrvjDnj6aXj1VfjoIzjuuETnyBjj7/ffXUUAYOxYCweeioJWBFT1v8B/AcQd+ZGquiJO+TJ1QMuW7nbBnXe6Tkjz59tJxpjaID+//Dbe3LlQUABnnQUnnJDonJlYiHT44GWxzoipm265BR56yLUIvPUWnHlmonNkTN2l6qYa9nbs9c4JAq7irmqV9VQUqo9AHvCoqq7xvA5FVdWmjTFVtvvuMGSIG0J4++1uyuK0SMNcGWOiKi/PDfXdsaPyumeegebNbYKwVBSqReAO4B1gjed1KArY18NUS79+cN99bgTB1VdDmzYuSEmvXq6DoTEm9rwThAWqBED5BGEW9yP1BL32UtU0VV3o8zrUI8y0MsYEV78+HH64e/3oo+6qZMAAyM110cs02MBVY0zU2ARhdVdV4ggYExN5efDBBxWXee9NTpjgnq050pjYsgnC6q4q340Vkb1EpI3/IxaZM6nP2xy5fXvg9d7myE2b4potY+ocmyCs7oqoIiAiu4vINBEpBNYCKwI8jKkya440Jv7y82HqVNfS9scf7n2vXi7sdyg2QVhqivTWwGTgPOAxYCkukJAxNWbNkcbEj+/wwOJiKC11t99yc93kYIMGuRDCpaWVt83OdnE/rKNg6om0IvBX4BZVnRzLzJi6x9sc6Tte2Z81RxoTHYGGB5aWuvcTJsDZZ5dXArKz3S277GzXEjBwoE0Qlqqq0lnw+5jlwtRZvXrBDTeETmPNkcbUXCTDA194wb0eNcrFDFi3zlXCe/e2loBUFmlFYAZwNvC/GObF1EFNmrgmyQkTAt8iSE936+0kZEzNRNIfR9X9rw0aBA0axCVbphaItCIwC5goIrsBbwEb/ROo6geVtjImAt7mRm9Y08JCF1tgxw4XZbB//8Tmz5hUEEl/HIDjj7dKQF0TaUXgv57nfYG+PssVEM+zBRUy1SLimiIHDiyf6KRFC9dM+b//wbhx7pGf73o4jxplkQeNqapI+uOIwBlnxC9PpnaItCLQM6a5MAb3o37lleXvjzwSunSByZNh5074z3/cTIV5eZCT4/oW3Hyza1GwiVCMCS1Yf5ySkvJ/nnr14MIL45gpUytEOvvg3FhnxBh/nTvDOefAq6+6ysCuXeXrLPKgMVUTrD/O5583B1xl+tZbrT9OXWTzvJla7eab3bNvJcCXRR40JnIjR7pbcFlZrlUN4H//2wdwQwfvvDOBmTMJE1GLgIiE6wioqnpSFPJjTAXLlrnmylARz7yRB31vKxhjKvPvj/PWW/Dqqw1o1w5eeslusdVVkbYIpOE6Bfo+mgHHAQd63hsTdevWVawEbNpUv1IaizxoTNU0aQJ9+sCSJe79HXe4obqmboq0j0CPQMtFZH/gVWB09LJkTLkWLVwTprdPwPvvV57fyiIPGlN1Tz4JK1dCmzbbuOCCnERnxyRQjfoIqOpPwN3AuOhkx5iK/CdCWbiwZaU0FnnQmKopLi7vD9Cnz6qwgYZMaotGZ8ENuNsDxkSdt6ezd3rUkpKKX9nsbIs8aExVTZsGq1dDhw7Qvfvvic6OSbAa3RUSkT2AgcBP0cmOMZV5Iw+OHQs7dyqqQoMGLhyqTYRiTNUUFcFdd7nXw4eHDztsUl9ELQIiskJEfvZ7/AqsA04ChlblQ0Wkn2efO0RksYicECZ9poiM9GxTJCKrRSTMVDUmVXh7Oq9bB8cd565eunSBtWvdcuvpbEzkpk2DX36BQw5xt96MibRFYC4ujLCvHcAq4EVPX4GIiMj5wCSgH/Ch5/ltEemgqquDbDYDaAVcBSwHmgMWDbuOcZEHV/Lxx81ZsAA2b7ZbAsZURXExjBnjXuflubk8jIl01EDfKH7mQOAJVZ3qed9fRP4KXAvc5p9YRE7FtTrsr6p/eBavjGJ+TBJp02Y7F14IzzzjTmgPP5zoHBmTPJ54orxvgLUGGK+41gdFJBPojJvN0Ncs4Nggm50DfAYMFJFfRWS5iNwvIg1jl1NTmw0d6m4HPP64O6kZY8IrLobRnoHe1hpgfImqf4t/DD9MJBf4DeiuqvN8lucBF6vqQQG2eQfoAbwPjAQaAw8AX6lqpTqtiFyFu4VA8+bNO8+YMSNsvgoKCmjYMLXqFalYJigv16hR7fngg+b83//9xoAByxOdrRpJ9WOVSpK5TG++2ZLx4w9in3228dhjn5V1EkzmMoWSiuWKtEw9e/ZcrKpdIt6xqsbtAeTi+hqc6Lc8D/g+yDazgO1AI59lp3r20zzU53Xu3FkjMXv27IjSJZNULJNqebm++UZVRDUzU/WXXxKbp5pK9WOVSpK1TMXFqm3bqoLqs89WXJesZQonFcsVaZmARVqF3+Z4Nw79AZTgOvv5ao4bgRDIWuA3Vd3ss+xbz3PlMHOmTujQwQURKi6Ge+5JdG6Mqd2mT3dRBA8+GP7xj0TnxtQ2ca0IqGoxsBg4xW/VKcDHQTb7CMj16xPgDWC0Kro5NMlk2DD3PHUqrFmT2LwYU1vt3FkeN2DoUIsbYCpLRHeRCUBfEblCRNqLyCTcLYOHAURkuohM90n/LPAnME1EDhGR43DDD2eqqoXEqsMOPdT1fC4qslYBY4J55hlYsQIOPBAuuCDRuTG1UY0rAiLSWkQibqJX1eeBm3BBiJYAxwNnqKr36r4NPk3+qloAnAw0wo0eeAEX1+BfNc27SX55ee75kUdcgCFjTLldu8pHClhrgAkmGhNP/oybhjjifanqFGBKkHU9Aiz7HtdB0JgKOnaEc8+Fl192k6gccYSLQNiihWstaNIk0Tk0JnGefx6WL4f994cLL0x0bkxtFY2KwChcRcCYhLj9dlcRmDLFxRdQdVMX33CDm5Bo5EgLQ2zqnpKS8hkGhwyB9Gic7U1KqvFXQ1VtyheTUK+84po8S0pcJQBg2zb3PGGCex41KjF5MyZRZs6E776DffaBSy5JdG5MbWaxpUxSy8+H8eNdJSCQwkK3ftOmuGbLmIQqLS2v/A4ZAhkZic2Pqd0irgiISGMRGSEis0TkG8/zHSLSOIb5MyakmTPDd4CqVw9efDE++TGmNnj6afjmG2jUyHUYzM9PdI5MbRbpNMSH42b9uw3IApZ5nocAP4hIx5jl0JgQ1q1zV/2hFBa6dMakOlU3OqBvX/d+82a49VbIzXVxN+IYUd4kkUj7CNyPG8vfxWeYHyLSFngHF/u/R7QzZ0w4LVpAdnZ5n4BAsrNdOmNSXV4ejBtX8Qff+suYcCK9NXAUMMy3EgCgqiuB4cDRUc6XMRHp1St4/wCvkhIXjtiYVJaf7yoBxcWB11t/GRNMpBWBP4GiIOt2eNYbE3dNmrghgtnZgddnZ7v1jRvHNVvGxN3MmeHTWH8ZE0ikFYGHgFtEJMt3oYg0AG4GJkc7Y8ZEauRIGDgQsrJc/ABf11/v1huT6taudeG2Q7H+MiaQoH0ERMT39CnAPsBqEXkLWI+bMfAM3BTBQa7HjIk9EXffc+BAd1W0di08+ST8/DPstZcFEzJ1w+bN4dNYfxkTSKjOgkODLL80wLLbgbyaZ8eY6mvSBK680r3u3BnOOsvdM+3XDxo0cPdQZ860EMQmNX32Wfg01l/GBBK0IqCqFmzIJK0zzoAjj4TPP3fTFG/Y4DpKFRe7YCsWgtikko8+gvnzoX59SEuD7dsrp8nOdq1m1l/G+As7fFBEMoFrgfdV9evYZ8mYmhNx46nPPdc979oFO3aUr7chVSaVeL+/t9zinsePdx0DCwtdBaCkxFUCrL+MCSRsRUBVi0XkbuC0OOTHmKj529+gfXv49tvgabxDqgYNsislk5w++wzefRcaNoQBA6Bp0/L+Mt7bYL172/fbBBdpQKFvgf2AeTHMizFRlZYGxx8fuiIA5UOqvP0LjEkm3taA665zlQCo2F/GmHAi7QeQBwyzUMIm2ey9d/g0NqTKJKslS+D1111n2IEDE50bk6wibRH4N9AQ+EJEVgJrAd+o1aqq3aOcN2NqLDcXMjODR1sDG1Jlktedd7rna65xQ2WNqY5IWwRKcBMNzQd+AXZ5lnkfpTHJnTE11KtX+BEBNqTKJJv8fBgxAl56CdLT4YorEp0jk8wiahFQ1R4xzocxMdGkietJPXZs4FYBG1Jlkomqm1ho/PiKo2A6d7ahsKb6Ir01YEzSGjnSXfWPGVO+LCfHhlSZ5JOX54a8+lYCdu1yDxsKa6qrShUBEWkCHABk+a9TVRtRYGolERg92vWovuUW1x9gxAj4xz+sJcAkj/z8yi0BvmworKmuiPoIiEiWiDwLbAAWALMDPIyp1a6/Hlq2LB9bbSdLk0xmznRDXUOx2QVNdUTaWXAY0APog5uA6HrgCuBD4CfgrFhkzphoysqCW291r0eNcvdbjamN8vNdaOxRo9xzfr6rwBYWht7OhsKa6oi0InAeMBKY4Xn/qapO8wwZ/BL4aywyZ0y0XXWVG2a1aJGLxmZMbaIKw4a5Ya/XXOP6BAwY4N5/8omrzIZiQ2FNdURaEWgDfKOqJcBOwHfW98eB86OdMWNiITvb3UMF10nQWgVMbeLbGbDUMyh72zb3fvbs4P0DvGworKmOSCsCf+ICCoGLI3C4z7pmQINoZsqYWOrXD/bYAxYsgA8+SHRujHG8nQGDNf9v3x664pqd7YYQWt8XU1WRVgQ+ATp5Xr8EjBKR20TkFmAcrq+AMUnBOzkL2FArU3tE0hkQXNN/VpYbAivinrOybCisqb5Ihw/eg7s9AHAn0A7XZ6AerpJwbfSzZkzsXH+9u/qaOxfmzYMTT0x0jkxdF0lnQIC//x3uustmFzTRE2lkwUXAIs/rrcB5IlIfqK+qW2KYP2NiolEjuPFGF09g1Ch4771E58jUdS1auOb9bduCp6lXDzp1stkFTXRFemugElUtskqASWY33gi77Qb/+5/rL2BMIvXq5Tr7hVKvnnUGNNEXtCIgIudWdWci0lJEjqlZloyJjyZNoH9/99r6CphEa9LEdfbLzg68Pi3NxcGwWwAm2kK1CDwgIktE5BoRaRpqJyJygog8AvwIHBbVHBoTQwMGuM5Wb7/tYgsYk0gjR7pOf97OgL4uucQ6A5rYCFUROAB4GdcpcL2IfCUiT4nIBBEZIyIPi8gsEdkIzPGkP0VVH4l9to2JjmbN3HBCsFYBk3gi7nu4Zg3cdx/06OGW9+gBTzxhMwua2AhaEVDVQlUdCbQC/onrLNgZ+BcwADgbN2pgEnCIqvZU1Y9jn2VjomvQIGjQAF57Db74ItG5McbdJujdu/z7aC0BJpbCdhZU1WJVfV5V/6WqHVS1sapmqereqnqSqo5Q1e/ikVljYqF5cxfOFeyEa2qP+++HzZuhZ0844YRE58aksmqPGjAmldx6q7sv++qr8OWXic6Nqeu2bHG3BsCFHTYmlqwiYAxuDPfVV7vX1lfAJNqDD8KmTa4loHv3ROfGpLqEVAREpJ+IrBCRHSKyWEQiavgSkeNFZJeIfB3rPJq659ZboX59eOklWLo00bkxddXWrXDvve718OHWQdDEXtwrAiJyPq6D4Wjc/AUfA2+LSJsw2zUBpgPvxzyTpk7KzS2P1nbnnYnNi6m7pkyBjRvh2GPhL39JdG5MXZCIFoGBwBOqOlVVv1XV/sBaws9X8BjwJGAx4ExM5OdD69YuetuLL1q0QRN/BQUwbpx7nZdnrQEmPuJaERCRTNwQxFl+q2YBx4bYrh/QHDfhkTFRpQrDhrkWgdtuc2FeVeH4493yUFO/GhNNkyfDn39Ct25w6qmJzo2pK0TjeJYTkVzgN6C7qs7zWZ4HXKyqBwXYpiPwP+AYVV0hIncAvVT10CCfcRVwFUDz5s07z5gxI2y+CgoKaNiwYTVKVHulYpkgNuVaswbWr4fSUvd+06b6jBnTldJS4ZZbPuPwwwvJzY3qR1Zgxyp5xLJM27fX44ILjmHLlgzGjv2So47Kj8nn+EvF4wSpWa5Iy9SzZ8/Fqtol4h2rasAHUAqURPjYFWw/fvvMBRQ40W95HvB9gPT1gWXAJT7L7gC+juTzOnfurJGYPXt2ROmSSSqWSTX65dq4UTUrS9Vd9wd+ZGWp5udH9WMrsGOVPGJZprvvdt+3bt1US0tj9jGVpOJxUk3NckVaJmCRRvAb6X2EmoZ4pOdHO5r+8FQcmvstbw6sC5C+JdAemCYi0zzL0gARkV3AGarqf5vBmIjNnOn6BIQi4voM9OpVcQ74Xr1cBDhjasq3b8Add1jfABNfQSsCqnpHtD9MVYtFZDFwCvCiz6pTgJcCbPIb0NFvWT9P+r8DK6OdR1O3rFsHhYWh02zfDs88AzfcAMXF7hZCTo57f/PNLhqhnbhNTXj7Bhx7LJxySqJzY+qaUC0CsTIBeEpEFgIfAdfgbhk8DCAi0wFU9VJV3QlUiBkgIr8DRapqsQRMjbVo4aZ93bYtdLqPPoJdu8rfe9NPmOCeLQiRqYr8/PLWpcaNYexYt9xaA0wiRFwR8PT4Px04CMjyW62qGtGpUFWfF5E9gKG4pv+vcU38qzxJQsYTMCaaevVyV/bh+FYCfBUWwvjxbuIimyfehKPqhgWOH1/eupSRATt3QqtWcNJJic6hqYsiqgh4evt/CLTF9Rvw1ll9+xBEfE2kqlOAKUHW9Qiz7R24DoPG1FiTJq55f8KEwLcIvCfpULxxB7zBiIwJJi/Pfdd27Chf5v1+bdjgIgla65KJt0jjCIwDNuCu1gXoCuwH3AX86HltTFIaORIGDnSTDuXkuKbZnBz3/tig0S3KFRa6Jl5jQsnPdy0BwfqkFBW59Zs2xTVbxkRcETgBuBdY43lfqqorVTUPmAncH4vMGRMPIu4qbM0aN+PbiBHuee1auPhiaNAg9PbZ2a6vgTGhRDJCxdu6ZEw8RdpHYA9gjaqWisg2wHfQ1AfA9VHPmTFx1qRJ5eb9SPoQlJRA796xy5dJDZGMULHWJZMIkbYI/Ao087z+CfANfnk0sKPSFsakAG8fgmCtAtnZbr11FDTheEeohGKtSyYRIq0IzAa8s2L/B7hZRGaJyJu4ToIzY5E5Y2qDkSPdqADfZl1vH4KBA916Y8Lp1cu1HoVirUsmESKtCAwFHgJQ1YeAG4Fs3PC/scCgmOTOmFrA24fg++/djz9Av36uD8GoUTbu20TGWpdMbRVRRUBV/1DVH3zeP6Cqx6vqkao6RFXt1oBJefvvD7fe6l5/9pmdsE3VjRwJhx3mXterV3GEirUumURJRGRBY5LWgAFw//0wZw588AH85S+JzpFJJmvWwJdfuteDB0P9+q5PQO/eVrE0iVOVyILdgQtxsQQCRRa0mFgm5TVuDLfcArff7h4ff2y3Bkzk7rzTBRPq1cu9NqY2iOjWgIhcjesw2AtojAsq5PuItK+BMUnvhhtgzz3hk0/gjTcSnRtT2+Tnw9Sprv/I1KnuPcBPP8Gjj0Jamt0CMLVLpC0Cg4BngX+panEM82NMrdewoWsNuOkm93zmme7kbuq2QPMI+M5SuXKlm7Oib19o3z7RuTWmXKSnr72BaVYJMMa5+mpo3RqWLoUZMxKdG1Mb+M4jUFrqlm3b5t6PHw9PP+3mrhg+PLH5NMZfpBWBxdh8AsaUycpyU8aC+wEINzGRSW3h5hHwTjLUty+0bRuvXBkTmUgrAjcAN4nIibHMjDHJ5NJL4aCD3L3fxx9PdG5MIkUyjwDAgQfGPi/GVFWkFYHXgVbAbBHZKiKr/R6rYphHY2ql9PTyKWNHjoTt2xObH5M4kcwjAPYdMbVTpJ0F3wc0lhkxJhmddx4ceSR8/jlMnuw6hZm6xzuPwLZtwdPYPAKmtoqoIqCqfWOcD2OSUloa3HUXnH46jBkDV1xhgWHqokhmqSwttXkETO1kg56MqaHTToMePWDjRrj77kTnxiSCdx6BYLMLNmhg8wiY2iuiFgERuTTE6lJgM/CFqv4alVwZk0RE4J57oGtXmDQJrrvODS00dYs3SND48a6lyNtnID3dzV5pQYRMbRVpH4EnKO8j4BtQ1XdZqYg8D1xm8QZMXXP00a7Z98UX3bDCxx5LdI5MvHlnqRw40M1OOWOG6xOwdCk0a5bo3BkTXKS3Bo4DVgEPAt2Bgz3PU4DVwJnAYODvwB1Rz6UxSWD0aHf198QT8M03ic6NSZT0dHj/fff6oYesEmBqv0grAjcDM1T1RlWdr6o/eJ77A88BV6nqeOBe4IJYZdaY2qxdOxcwprQUzj23Ypx5U3fcey9s2ADdusHf/pbo3BgTXqQVgVNxQwgD+QDwzjw4DxeO2Jg6RRWGDYOnnnLvf/jB9SLPzXXL1Qbf1glr17o+AuD6jdjMlCYZRNpHoAjoTODKQGfA2ycgDQgxktaY1OSNM19UVL7MG1Z2wgT3PGqUayGYOdMFoGnRwg07M6kjL8/FEvjb3+CEExKdG2MiE2lF4EVghIiUADOB34G9gN64PgHeAKtHAN9HN4vG1G7eOPPeH35/hYUwbpxb/+CDlWeme+wx12JgV4/JbelSF2o6PR3Gjk10boyJXKS3BgYCLwFjgZ+BAs/zPbiKwSBPuq+Bf0c5j8bUapHEmS8thfvvDzwz3fr17krSJLebb3bH9pprbE4Bk1wijSy4HfiniIwEugItgbXAQlX93ifdmzHJpTG1WCRx5kPNTlha6loUBg2ygDPJ6t13YdYsaNTIphk2ySfSWwMAqOoPwA8xyosxSSmSOPPh1KvnYhBceWX08mXio6SkfI6J22+34YIm+QS9NSAibUQkw+d1yEf8smxM7dKrl/sxqInCQteyYJLP44/D119D27bQv3+ic2NM1YXqI7AC6OR5vdLzPtTDmDopXJz5SNjMdMlpyxY3PBTcPBNZWYnNjzHVEerWwL+An3xe20hoY4LwjTNfr567ws/Odi0FBx8MS5aE3r6kxGamS0Z33uk6ex5zDPzjH4nOjTHVE7QioKpP+rx+Ii65MSZJ+caZ940T0Ls37NoFrVpVjDHgKy3NZqZLRj/8ABMnutf332/DP03yqlJnQS8RaQQcAKyzGQeNKdekSeAOf3ffDQMGuB+LBg1g+/byFoPmzeGmm+KeVVNDAwe60SCXXQZHHZXo3BhTfaE6C54mIpVmVxeRIbiAQp8Cq0TkWRGpVoXCmLriuuugQwcXOOjUU2HECLjvPheSNjfXriaTzVtvwZtvwu67w5gxic6NMTUT6gf8Gvz6BYjIKcCdwFLgUaA9cDWwGDfhkDEmgIwM13x88sluvPn990Pr1onOlamO4mLXugMuEFTz5onNjzE1FWrUQCfAP0DQZcAO4DRVfUBV+wH/AS6KUf6MSRknnQTnnec6Et56a6JzY6rr7rtd/4BmzdztHZth0iS7UBWBvSgfNeB1CvChqvqOeH4TsICaxkTg3nvdELMZM2Du3ETnxlSFKnzzTWZZ5MA//oBbbrEZJk3yC1UR2ArkeN+IyAHAHsAnfum2AGEirVckIv1EZIWI7BCRxSISdJ4uETlXRGaJyAYR2Soin4rI/1Xl84ypLfbZBwYPdq/793cjCkxyyMuDxx7bv8Iy73wREybYfBEmeYWqCHwH/M3n/d9wfQZm+aXbF1gf6QeKyPnAJGA07vbDx8DbIaITdgc+AM70pH8LeCVU5cGY2uzWW10UuqVL3WyEpvbLz4d77oEvvgjcIaCw0MWQ2LQpvvkyJhpCVQTuA64QkZkiMhkYgesk+JFfujOAL6vwmQOBJ1R1qqp+q6r9cRMYXRsosareqKp3q+pCVf1RVUfgOieeU4XPNKbWaNDAdRYE16S8YUP9xGbIVJCfD1OnurgQU6e69889F771xjtfhDHJJlRAoVdF5CbcFMNNcbcErlEtvxMmIi2Ak4EhkXyYiGQCnYHxfqtmAcdWId+7AdZFxySts8+Gv/8dXnkFHnignUUVrAVUXfO+f3TIG25wwaFUYa+9tvH77zkBt7f5IkyyEo1jDxcRyQV+A7qr6jyf5XnAxap6UAT7uA64GzhUVVcFWH8VcBVA8+bNO8+YMSNsvgoKCmjYsGHE5UgGqVgmSK1y/f57ffr2PYrt29MZPXop3br9megsRVWyHas1a1y44NLSisv//DOLceOOYteuegwbtoBGjQKHiExLc0NCk232wWQ7TpFKxXJFWqaePXsuVtUuEe9YVeP2AHJx/QxO9FueB3wfwfbnAYXA2ZF8XufOnTUSs2fPjihdMknFMqmmXrkmTFAF1TZtVAsKEp2b6EqmY7Vxo2pWljsWoR7jx88Oui4rSzU/P9ElqbpkOk5VkYrlirRMwCKtwm9zqD4CsfAHUAL497hpDoRsVBORXsBTwKWq+npssmdMfPXvD+3abWX16vKJi0z8zZzpbgeEkp4ePAJkdrbNF2GSV1wrAqpajOvod4rfqlNwowcCEpF/4CoBfVV1ZuxyaEx8pafDwIE/IOKGoH1ZlW63JmrWrXP3+EPZtQsaNnRxIHJyXKUgJ8e9HzjQKnImeSVijoAJwFMishA3AuEa3C2DhwFEZDqAql7qeX8BrhJwMzDP00ERoFhVN8Y578ZEXfv2W+nXDyZPhn/9Cz791FUQTPy0aOGu6rdtC54mJweaNnV9CfxnmLSWAJPM4n66UdXnRWQPYCjQEvgaOEPLO/75xxO4BpfPiZ6H11ygRyzzaky8jBkDr78On3/u5rXv1Mn9yPTq5WY0NLHVq5cbHRBKSYk7FsFmmDQmWcW7jwAAqjpFVduqan1V7aw+IwhUtYeq9vB7LwEePQLt25hk1LAhHHece/3KK24Y24ABFr42Xpo0cff4s7MDr/f2AQjXj8CYZJSQioAxpqK8PPjvfysus/C18TVypLvX7/tjn51tfQBM6rOKgDEJVlLigtgE66xm4WvjQwROOcXFEUhLg379YOJEWLvWRRkMNmLAmGRnFQFjEiw/P3yTs4Wvjb0tW6BPH3cb5vbbXefNK6+0joAm9VlFwJgE27kz/NA1C18bezfdBCtXwpFHun4ZxtQVVhEwJsEyMoJ3UvPKznajCExs/Pe/MG2a6w/w1FPumBhTV1hFwJgEa9LE9RMIZedObGKiGFm/vnw44N13Q4cOic2PMfFmFQFjEqxevdBD18AFs7EgQ9Gn6ioBGzbASSe5kM/G1DV2ajGmFvAOTfOfAnfXLmjUCH7/3Q1hu+eeilHtLOBQzUyd6gI5NWrkbg2k2aWRqYOsImBMLSDihqgNHFg5fO2vv0KXLu5H64knIDOzvKJwww2uNWHkSBveVlVfflkeTXDKFDeFsDF1kVUEjKlFAoWvbdwYuneHWbNcX4GdO91yb1z8CRPc86hRcctm0tu61YVyLiqCK66Aiy5KdI6MSRxrCDOmlsvPh7lzg6+3gENVs3EjnHwy/PCDC+F8xx2JzpExiWUVAWNquZkzw3cUtIBD4am6+ADNm8PChW5Zfj60a2fzOZi6zW4NGFPLrVtnAYeiIS/PtZzs2lW+bPt292y3V0xdZi0CxtRyLVpYwKGays+HcePcJE6B2O0VU5dZRcCYWq5Xr/ABh0pKLOBQKDNmlHeyDMZur5i6yioCxtRyTZqEDjiUlgYDBtjkOKE8+aSbVTAUu71i6iqrCBiTBEaOdDEGsrJclEERaNDArSsthT//tM5uwTz2GHz6afh0dnvF1FXWWdCYJBAs4NC++8JZZ8Ejj8Chh1qIXH8ffQTXXutep6dX7Cjoz26vmLrKKgLGJJFAAYcefxwuvthNo3vggXDaaQnJWq2Rn+8qS1995f42O3e6CIK77+5GBwQagZGd7SpZdnvF1EVWETAmyV10ESxbBnfdBeedB++/D127JjpX8adaPkRQpHxoYFoaNGwII0a49/7zOZSUuEqAd74HY+oaqwgYkwJGjoTVq+Gpp+CMM2DePDjkkETnKr7y8twVv/8QwdJSmDjRVQiCzedgLQGmLrOKgDEpIC3NdYrbtMnNpnfqqe7+eNu2ic5ZfOTnuyv9cHECBg0KfHvFmLrMRg0YkyIyMuD55+GEE2DNGjjlFFi/vmKa/Hw3i+GoUe45Pz8xeY22mTPDTyFscQKMCcwqAsakkB07XD+BFi3gxx+hZ0/XBO6Ns5+b62IODB/unnNzUyPO/i+/WBhmY6rLbg0YkwJ8O8oVF7v74iLw7beur8DFF7tbB75N56kyjXFhYWRX+hYnwJjArEXAmBTg21HOG0HPe5W/cSM88EDwK+ZkjrNfUOA6R373Xfi0FifAmMCsImBMkvN2lAvXNB5KMt4/37DB9YOYOxdatoRrrgkehjk724VpttEBxlRmtwaMSXIzZ7of8ppIpvvn+fmuhWPiRPd6771h9mxo1w6aNbM4AcZUlVUEjEly69bVrDUAkuP+ubcfxD33lM8kmJYGf/wB06e7H3qLE2BM1VlFwJgk16KF+yH3dv6rjtp0/9wbItj7Q96rlxv7P2wYjB1bcTrh0lIoKqrY4dHiBBhTNdZHwJgk16uX+yEPpV694PfPs7Jqx/3zUEMcr78exoypWAnwlcwdHo1JNKsIGJPkmjRxP+ShOsoNHlxxGmNwwwvB/QC3axe9WALeoEVr11YtaJHvyIdt21x+tm1z7ydPLh8NEUwydng0pjawioAxKWDkyIo/9CLuOSvLLR81yj3WrIH77nPpx4+HM890Tet9+7qWhR9/rH7kQf8r+jVrIg9aFI2RD8nU4dGY2sT6CBiTAkQi6yjnf/98wAB48kk3Te/LL7tHZqYLSpST45bffLOrOHhbEIIJNOlPpEGLojHyIRk6PBpTG1lFwJgUUtWOciKuNWDxYpgyxTW/Fxe7dVWJPFiVSX8C9UWIxsiH2tTh0ZhkYrcGjKnj8vPh0UeD34MvLIRx40J3xIvkit57D99/4qONG92+w00alJHhWisCsYBBxlSftQgYU8dF8iNeXAx9+sBDD7l7/v4iuaLftg2eecbdbqhXz73PzISrr46so2JaGvTvDw8+aAGDjImmhLQIiEg/EVkhIjtEZLGInBAmfXdPuh0i8rOIXBOvvBqT6iL5EVeF116DVq2gbVvo3t1dgXunOfbGMgglPR0+/rh8VAC4Coa3EtCmDTRoEHjb7Gy45RbXMuHt8DhihHteu9a1LoTrw2CMCSzuLQIicj4wCegHfOh5fltEOqjq6gDp9wXeAh4H/gkcD0wRkQ2q+lL8cm5MaookIFH9+q7/wbp1sGqVe8ybB/fe68L6dupUuX/Ae+/tU+H9rl2h87F+fWRX/BYwyJjoSsStgYHAE6o61fO+v4j8FbgWuC1A+muANara3/P+WxHpCtwMWEXAmBrq1cs114eyaxds2RJ43R9/wHvvVV7+7rv7Vikf6elw4IHuit9CBBsTP3G9NSAimUBnYJbfqlnAsUE26xYg/btAFxHJiG4Ojal7wgUkatDANbuHun2QkQF33gldu7qr+YwMOOmkVWRkuNedOoXPhzcOgPeKf9gw92yVAGNiK959BJoB9YD1fsvXA8FGALcIkj7dsz9jTA2FCkjUs6e7NRBKZibstRd88ombHnjyZLj88hVMngy//w7XXlse0TAYiwNgTGKIRiuuaCQfJpIL/AZ0V9V5PsvzgItV9aAA2/wAPK2qI32WnQjMBXJVda1f+quAqwCaN2/eecaMGWHzVVBQQMOGDatXqFoqFcsEqVmu2lSmkhI3vG/nTncl36SJ+yFfsyb8trm50LJl+XvfcpWUwJdfhh4dIAKHH17zwEKxVJuOVbSkYpkgNcsVaZl69uy5WFW7RLxjVY3bA8gEdgG9/ZZPBuYG2WYeMNlvWW9gJ5AR6vM6d+6skZg9e3ZE6ZJJKpZJNTXLVdvL9Mgjqjk5qu5nPPAjJ8el8+VfrqFDVbOzA2+fne3W13a1/VhVRyqWSTU1yxVpmYBFWoXf5rjeGlDVYmAxcIrfqlOAj4NstiBI+kWqGmQuMmNMtEQyu2EkUf3CzYdgcQCMSYxEjBqYADwlIguBj3CjAnKBhwFEZDqAql7qSf8wcL2ITAT+AxwH9AUujGuujamjvJ0JJ0wI3GEwO9v9kIfr1BfpfAjGmPiKe0VAVZ8XkT2AoUBL4GvgDFVd5UnSxi/9ChE5A7gPN8RwDXCDWgwBY+LGe7U+fnzNo/pZHABjapeEhBhW1SnAlCDregRYNhc4MsbZMsYEYVfzxqQum2vAGBMxu5o3JvXY7IPGGGNMHWYVAWOMMaYOs4qAMcYYU4dZRcAYY4ypw6wiYIwxxtRhVhEwxhhj6jCrCBhjjDF1mFUEjDHGmDosrtMQx5uIbABWhU0IzYA/YpydeEvFMkFqlisVywSpWS4rU/JIxXJFWqZ9VHXPSHea0hWBSInIIq3K3M1JIBXLBKlZrlQsE6RmuaxMySMVyxWrMtmtAWOMMaYOs4qAMcYYU4dZRcB5JNEZiIFULBOkZrlSsUyQmuWyMiWPVCxXTMpkfQSMMcaYOsxaBIwxxpg6zCoCxhhjTB2W8hUBEblKRGaLyCYRURFpG+F254nIMhEp8jz/3W+9iMgdIrJGRLaLyBwROSQmhaict/oi8oCI/CEi20TkNRFpFWablZ7y+z/e9ElzR4D162JforLPr065wuY5CY/VbSLymYhsEZENIvK6iBzql+aJAOX+JIbl6CciK0Rkh4gsFpETwqTv7km3Q0R+FpFrarrPaKvK54vIuSIyy3M8torIpyLyf35p+gb5H8uKfWkq5KMq5eoRJM8H+6ULeT6MtSqWKdD/horINp80EZU7huU50XMu+M3zuX0j2KajiMz1nMN+E5E8ERG/NNU7Tqqa0g/gJuA2z7MCbSPYphuwC7gdaO953gV09Unzb2ArcB5wKPACsAbYLQ5lesjzWacARwJzgCVAvRDb7Am08Hl0AkqBPj5p7gC+80u3ZxyPVXXKFTbPSXis3gUu8+S1I/AKsA5o6pPmCeA9v3I3jVEZzgd2Ald6/h8eAAqANkHS7wts86Rr79luJ3BedfdZC8o0CRgMHA20A4YDJcAJPmn6esrte0xaxOv/p5rl6oE7L3bwy3c9nzRhz4e1rEyN/I8B8BMwrSrljnGZzgBGA72AQqBvmPS7e84BL3jOC71w57RB0ThOcfuCJvoBdCHyisDzwHt+y/4HPOd5LcBa4Haf9Q08B+bqGJejEVAMXOyzrDXuR/20KuzndmAT0MBn2R3A1wk6PtUqV7g8p8ixaoj70TnbZ9kTwBtxOjafAlP9li0HxgRJfw+w3G/Zo8CC6u4z0WUKso+FwL0+7/sCBfHIfxSPVQ/PebFZiH2GPB/WtjIF2P44TxmPrUq543jMCghfEbgW2OJ3vh4K/EZ5p/9qH6eUvzVQTd2AWX7L3gWO9bzeF1d7LEujqtuBeT5pYqUzkOH32b8A30b62Z7mpMuBpz359rWfpwl9hYjMEJH9opTvcGpSrlB5Tupj5bEb7jZevt/y40XkdxH5QUSmisheNc2wPxHJxJXD//9hFsHLEOz/p4uIZFRzn1ETxc/fjcrHpIGIrBKRX0XkDRHpVIOsVkkNy7VIRNaKyPsi0tNvXbjzYcxE6VhdCXyjqh8HWBeq3LVJN2C+3/n6XSAXaOuTplrHySoCgbUA1vstW+9Zjs9zqDSx0gJ3degfb7oqn30K7gdyqt/yT3FXNX/F/fO0AD4WkT2qm9kqqG65wuU52Y8VuGbpJcACn2XvAJcCJwGDcE3WH4hI/epmNohmQD2q9vcL9v+T7tlfdfYZTTX+fBG5DmgFPOWz+HvgX8DfgAuBHcBHInJATTMcoeqUay3uavM84FxcGd73uwcf7nwYSzU6ViLSCPgHlc91kZS7Ngl2DLzrQqUJ+3dKr1HWEkRE7sQ1bYfSU1XnxCE7URFpmaL0cVcCn6nql74LVfVtvzx9AvwM9AEmVOeDYl2uWOQ5nHgeKxGZABwPHK+qJd7lqjrDJ9lSEVmMm2DrTODlaHy2CUxEzgPGAeeratmkZqq6AJ/Kmoh8jKvA9QduiHM2I6Kq3+N+BL0WiOtQfQswPyGZiq5/4i54fStsdaHcVZKUFQFgIvB0mDSra7D/dUBzv2XNPcvxeW7u9zm+aapqIpGV6RhcDbkZsMHvs8N+gT3Nx38DrguXVlULROQboCZXNBOJQ7m8AuQ5mY/VfcAFuErtz6HSquoaEfmVmh2rQP7AtWqE+n/wF+z/Z5dnf1KNfUZTdcoEgIj0AqYDl6rq66HSqmqJiCwi+sckmGqXy8+nuO+dV7jzYSzVtExXAi+p6sYI0vqXuzYJdgy860KlCft3SspbA6r6h6p+F+ZRWIOPWIBrPvd1CuC9x7QC98ctS+MZInSCT5oqqUKZFuN60Pp+ditcL9FIPrsvUAQ8Fy6hp0wH45rRqiWO5QqW56Q8ViIyCde8/BdV/S5cnkSkGbA3NThWgahqMa4cof4f/AX7/1mkqjuruc+oqe7ni8g/cFeWfVV1ZrjP8fTFOYwoH5Ngovh3PYKKeQ53PoyZmpRJRI4GDqfybYFgjiBOx6oaFgAn+A1FPQU3ImmlT5rqHadE95iM9QN3f+QI4CJcL9EzPO99h2K9j08PVFznil244UIH44Yf7qTy8MHNuPtLhwIziO+QtF+Bk3HDAGfjNyQNN6Tuer/tBPgBvx64PuvHA91x/Qe6Am/geqruE6djVeVyRZLnZDtWwGRPGf5CxaFNDT3rG3rK3Q3XUagH7iTwayzKhBu+VQxcgavETML1dN7Hs346MN0nvXf44ERP+is82/sPHwy6zzgcl6qW6QLcOeBGv2Piex4ZDpwG7Ic7xzzu2eboeJSpmuW6CTgH12pxCDAGd5481ydN2PNhbSqTz3aPAj8E2WfYcse4TA0935EjcMMH8zyv23jWjwHe90nfCHdBMwN3DjsXd47wHT5Y7eMUly9nIh+44WUa4NHXJ81K4Am/7XrhTtDFuF7e5/qtF8++1+I6Bc0FDo1TmerjxtL+6fkSvQ609kujwB1+y3p6lgc8MVH+A1mMG5byEtAhjseqyuWKJM/JdqyCfF/L0uCGP74L/O4p9yrccMLWMSxHP8//SRHuCu1En3VzgDl+6bsDn3vSrwCuqco+43RsIi6T532gY+Kb5j7PsSjyHJt3gW7xLFM1ynUrbijedmAj7pbVGQH2GfJ8WJvK5Fm2G66ycGuQ/UVU7hiWp0eQ79MTnvVPACv9tumIG+20A3cuG45n6GBNj5NNOmSMMcbUYUnZR8AYY4wx0WEVAWOMMaYOs4qAMcYYU4dZRcAYY4ypw6wiYIwxxtRhVhEwxhhj6jCrCBiT5DwzD6onJHGi8vCEiKz0ed/Wk6e+Psv6isi/YvDZfT2f1Tba+zamLrCKgDFJTEQa4GZXA7hIRGrL/CFrcZEP3/RZ1hc3O58xphaxioAxye0cYHfgLWAv3HTMCaeqRar6iapuCJ/aGJNIVhEwJrn1AfJxV9vbPe/LiMgdnmbzg0XkXRHZJiKrReQyz/pLROQ7ESkQkdkisr/f9itF5GkRuVJEfhSRHSLyuYiEnGbZ/9aAiMzBhR0+zrNcPcvK8hhgHxVuN3iW7Scib4pIoYhs8EzOVD9IHq4SkS89ef5DRB4Tkaah8m1MXVRbmhGNMVUkIrm4yYymquoGEXkVOFdEmqhqvl/yF3GzsI3HxW1/XEQOwMU8Hwxk4CZzeRY3eZOvHkBn4HZcrPd/A2+LyOHq5nWPRD/c1M31gKs9y7ZEuC0AIpIJvIeba+E6XDz/q3ETsPinvRsYBNyPm2N+b+BO4FAROVZVS6ry2cakMqsIGJO8/on7YZ3uef8kburi84GH/dKOU9XpACKyCDgb9yO6r6pu8SxvCUwSkX1UdZXPtnvhJs/5xZPufdzkOkOBSyLJqKouE5EtQLqqflLlkjp9cDP7dfPuQ0TeBpb6JvJ0GrwFGKGqI32W/wB8iCv7q9XMgzEpx24NGJO8+gDLVXWB5/3/cDMx9gmQ9m3vC09rwe/AJ95KgMd3nufWftt+4q0EeLbfiusE2K1m2a+ybsAvvhUJVS0FXvBLdwru3PaMiKR7H8CnwFbgxHhl2JhkYBUBY5KQiHQBOgAvi0hjEWmMm3r1ZeAYETnQbxP/WwXFQZYBZPktXx8gC+txze3x1DJEXnzt5Xn+ETcfu+9jN2CPWGXQmGRktwaMSU7eq/5/ex7+LsU13UdD8yDLfovS/neA6wOgqsU+y/1/sNcChwTJi68/Pc+nUrmy47veGINVBIxJOp5OcxfimroHB0hyH3CJiAyL0kceIyKtffoI7AacScUYAZEowl2R+/P2RzgU+NzzGY2BY3FN+V4LgMtE5BifPgJplMdR8HoPKAXaqOp7VcyjMXWOVQSMST5n4q6WB6nqHP+VIvIf4CFcb/9oWA/MEpE7KB81kAOMquJ+lgH9ROR84Cdgq2fUwdvAZmCqiAzHDQe8FSjw2/5JXMXnZREZguvncA0ujkIZVf1JRO4BHhSRg4C5uFaH1rj+A4+q6uwq5t2YlGV9BIxJPn1wV8ovBln/HAFiCtTAXOBeYDTwPK4Pwemq+kMV93MP8D7wKPAZ8B8AVd0EnIW7in8BGAM8AFT4sfbcNjgFWAJMwVUMVuCGBeKXdghwFa5j4AvAf3EVmHxgeRXzbUxKE9VKcTyMMQZwAYWAD1X1n4nOizEmNqxFwBhjjKnDrCJgjDHG1GF2a8AYY4ypw6xFwBhjjKnDrCJgjDHG1GFWETDGGGPqMKsIGGOMMXWYVQSMMcaYOswqAsYYY0wd9v8z4lUlBujymAAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -724,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 22,
    "id": "444d829c",
    "metadata": {},
    "outputs": [
@@ -734,8 +707,8 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: rabi_rate\n",
-      "- value: 0.5828773180419505 ± 0.0027186379727953116\n",
-      "- χ²: 1.6337913847682912\n",
+      "- value: 0.5810497529891635 ± 0.0027460368083858143\n",
+      "- χ²: 1.2517635384635177\n",
       "- quality: good\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
@@ -748,7 +721,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "id": "7fa0e4b4",
    "metadata": {},
    "outputs": [
@@ -787,23 +760,23 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.857813+0.000000j</td>\n",
+       "      <td>()</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:37:56.254000+0200</td>\n",
-       "      <td>aa8b9513-a1d8-48b5-82ed-2e3538860ad3</td>\n",
+       "      <td>2021-11-03 10:23:20.785453+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>amp</td>\n",
        "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953709+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785369+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -811,22 +784,22 @@
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.428907+0.000000j</td>\n",
+       "      <td>0.430256+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:37:56.254000+0200</td>\n",
-       "      <td>aa8b9513-a1d8-48b5-82ed-2e3538860ad3</td>\n",
+       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
+       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
+       "      <td>(0,)</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.500000+0.000000j</td>\n",
+       "      <td>0.860512+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-28 10:27:44.953672+0200</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
+       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -834,19 +807,19 @@
       ],
       "text/plain": [
        "  parameter qubits schedule               value    group  valid  \\\n",
-       "0       amp   (0,)        x  0.857813+0.000000j  default   True   \n",
-       "1       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "2       amp   (0,)       sx  0.428907+0.000000j  default   True   \n",
-       "3       amp     ()        x  0.500000+0.000000j  default   True   \n",
+       "0       amp     ()       sx  0.250000+0.000000j  default   True   \n",
+       "1       amp     ()        x  0.500000+0.000000j  default   True   \n",
+       "2       amp   (0,)       sx  0.430256+0.000000j  default   True   \n",
+       "3       amp   (0,)        x  0.860512+0.000000j  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-10-28 10:37:56.254000+0200  aa8b9513-a1d8-48b5-82ed-2e3538860ad3  \n",
-       "1  2021-10-28 10:27:44.953709+0200                                  None  \n",
-       "2  2021-10-28 10:37:56.254000+0200  aa8b9513-a1d8-48b5-82ed-2e3538860ad3  \n",
-       "3  2021-10-28 10:27:44.953672+0200                                  None  "
+       "0  2021-11-03 10:23:20.785453+0100                                  None  \n",
+       "1  2021-11-03 10:23:20.785369+0100                                  None  \n",
+       "2  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  \n",
+       "3  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  "
       ]
      },
-     "execution_count": 17,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -872,7 +845,7 @@
     {
      "data": {
       "text/plain": [
-       "ScheduleBlock(Play(Drag(duration=320, amp=(0.43300531+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())"
+       "ScheduleBlock(Play(Drag(duration=320, amp=(0.43025575+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())"
       ]
      },
      "execution_count": 24,
@@ -893,7 +866,7 @@
     {
      "data": {
       "text/plain": [
-       "ScheduleBlock(Play(Drag(duration=320, amp=(0.86601063+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())"
+       "ScheduleBlock(Play(Drag(duration=320, amp=(0.86051151+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())"
       ]
      },
      "execution_count": 25,
@@ -914,7 +887,7 @@
     {
      "data": {
       "text/plain": [
-       "ScheduleBlock(Play(Drag(duration=320, amp=0.86601063j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())"
+       "ScheduleBlock(Play(Drag(duration=320, amp=0.86051151j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())"
       ]
      },
      "execution_count": 26,
@@ -1015,13 +988,13 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.860512+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:40:51.746391+0200</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
+       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1031,19 +1004,19 @@
        "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496333+0200</td>\n",
-       "      <td></td>\n",
+       "      <td>2021-11-03 10:25:55.416335+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.433005+0.000000j</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:39:49.487000+0200</td>\n",
-       "      <td>1b5c7f5c-2a93-4beb-a3cd-037e3f18c397</td>\n",
+       "      <td>2021-11-03 10:23:20.785453+0100</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1053,7 +1026,7 @@
        "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:40:51.746354+0200</td>\n",
+       "      <td>2021-11-03 10:25:55.416203+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1064,19 +1037,19 @@
        "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496299+0200</td>\n",
+       "      <td>2021-11-03 10:23:20.785369+0100</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.866011+0.000000j</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.430256+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:39:49.487000+0200</td>\n",
-       "      <td>1b5c7f5c-2a93-4beb-a3cd-037e3f18c397</td>\n",
+       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
+       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1084,20 +1057,20 @@
       ],
       "text/plain": [
        "  parameter qubits schedule               value    group  valid  \\\n",
-       "0       amp     ()       sx  0.250000+0.000000j  default   True   \n",
+       "0       amp   (0,)        x  0.860512+0.000000j  default   True   \n",
        "1       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "2       amp   (0,)       sx  0.433005+0.000000j  default   True   \n",
+       "2       amp     ()       sx  0.250000+0.000000j  default   True   \n",
        "3       amp     ()        x  0.500000+0.000000j  default   True   \n",
        "4       amp     ()        x  0.500000+0.000000j  default   True   \n",
-       "5       amp   (0,)        x  0.866011+0.000000j  default   True   \n",
+       "5       amp   (0,)       sx  0.430256+0.000000j  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-10-21 14:40:51.746391+0200                                  None  \n",
-       "1  2021-10-21 14:21:13.496333+0200                                        \n",
-       "2  2021-10-21 14:39:49.487000+0200  1b5c7f5c-2a93-4beb-a3cd-037e3f18c397  \n",
-       "3  2021-10-21 14:40:51.746354+0200                                  None  \n",
-       "4  2021-10-21 14:21:13.496299+0200                                        \n",
-       "5  2021-10-21 14:39:49.487000+0200  1b5c7f5c-2a93-4beb-a3cd-037e3f18c397  "
+       "0  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  \n",
+       "1  2021-11-03 10:25:55.416335+0100                                  None  \n",
+       "2  2021-11-03 10:23:20.785453+0100                                        \n",
+       "3  2021-11-03 10:25:55.416203+0100                                  None  \n",
+       "4  2021-11-03 10:23:20.785369+0100                                        \n",
+       "5  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  "
       ]
      },
      "execution_count": 29,
@@ -1134,89 +1107,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "id": "74edd0ee",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit_experiments.library.calibration.drag import DragCal\n",
-    "from qiskit_experiments.calibration_management.update_library import Drag"
+    "from qiskit_experiments.library import RoughDragCal"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "id": "fac11c93",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cal_drag = DragCal(qubit)"
+    "cal_drag = RoughDragCal(qubit, cals, backend=backend, betas=np.linspace(-20, 20, 25))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "id": "3a337cf4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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       "text/plain": [
-       "<Figure size 994.294x144.48 with 1 Axes>"
+       "<Figure size 1656.49x325.08 with 1 Axes>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "cal_drag.set_experiment_options(\n",
-    "    rp=cals.get_schedule(\"x\", qubit, assign_params={\"β\": Parameter(\"β\")}),\n",
-    "    betas=np.linspace(-20, 20, 25),\n",
-    "    reps=[3, 5, 7]\n",
-    ")\n",
+    "cal_drag.set_experiment_options(reps=[3, 5, 7])\n",
     "\n",
-    "cal_drag.circuits(backend)[1].draw(output='mpl')"
+    "cal_drag.circuits()[5].draw(output='mpl')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "id": "c3958dff",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ExperimentData(DragCal, 56de17e6-ed83-4280-9df3-b53c14154952, backend=ibmq_armonk, job_ids=['61043d401e71b07cf7bfc3d1'])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
    "source": [
-    "drag_data = cal_drag.run(backend)\n",
-    "drag_data.block_for_results()"
+    "drag_data = cal_drag.run().block_for_results()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "id": "3a6430f4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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9de/LkCkhkknIuLfiZNCkIMkkLC6Rr0bK5xc/bzp+//n9cuB3AyWTkDGvx8hlR5Y5di8Gg5T9+6svqnp1KRcuVOtvukl9ceZfZEiI+kG1z02a6PxtlI8tL/2YmBj59NNPy3379slff/1VVqlSRb799tumfe6++27ZoEED+euvv8rDhw/LTZs2ybffflv+9ttvUkopT506JYUQ8ssvv5Tnzp2TGRkZ0mAwyLi4OHnnnXfKgwcPyhUrVshu3brJkJAQ+fXXX7t0L8546W/dulVu3bpV9unTRw4dOlRu3bpV7t6927T9999/ly1btpSnTp0yrbv22mtlu3bt5Nq1a+XatWtlu3bt5PXXX1/m3FOnTpWVK1eWWVlZFq+9e/duuXXrVnn77bfLLl26mNpi9T6MRnlhwwaZuHChTFy1SiYeOyYTExPVcvCgTPznH5m4cKH6Q5deNm+W8uTJMqcsLCyU7dq1k1deeaVcu2GtnPHzDFmjdg152wO3yU2nN8lNpzfJb+Z9Ixs2bSi/m/+dLDQUSimlfOyxx2TdunXlkiVL5JYtW2T//v1lx44dZWFhoTQYDXLLmS3y9zW/yx/++UHeNfIuGVcrTq5av0pu3bpV5uXl2f09JScnyxkzZsg9e/bIrVu3yqefflpGRETIDRs2mPZ577335B9//CEPHDggd+3aJceNGycB03NYGnv76TNnpOzeXZbpf0NDpRw3rvyIqc8/L79PjY6W8r33pHz/fSlbty5eX6eOlDNnSllQYFdTHcKTXvpeF8ruXPQIyxs/XsqoKMsPR2Sk2u4u0tOlvOGG8gV+dLR6mB3BUwL/hcUvmIT5s4uele0+bmdV2GvLZZ9dVuIc+YX58q7f7pJMQoZPDZd/7v3TtO1s5ln56ZxP5YnUE6UvrZg5U31JNWtKefRo8fqVK6WMibH9xVapImV2tv5fig3KC8t74oknZJUqVWTVqlXlmDFjZGFhoWmf/Px8+corr8jGjRvL0NBQWatWLTl06FC5efNm0z5TpkyRtWvXlkIIU9jbv//+K9u2bSvDw8Nl27Zt5aJFi2R0dLRXBD6lQgeBEiFkX3/9tQTkUbPf8uLFi/Luu++WlSpVkpUqVZJ33323TCkVn2U0GmWjRo3k448/bvXaDRs2tHh9q/eRlCT7XXaZxWPMF4sCf9MmKfPzLZ72+PHjcsiQITIiMkJWqVpF3v7g7fK/I/+ZBP6nv34qAfnZnM/kyTQ1aMjNzZVPPvmkrF69uoyMjJTXX3+9KbQvNSdVJpxJkJf1tNxW8++yvO8pOTlZ9ujRQ0ZHR8uoqCh59dVXy/WlZjxvvvmmbNasmYyIiJDVqlWTvXv3lvPnz7f6vZfXTxsMUn72mZSVKxf/NTt3lnLatI3y888dC8VzJETaaJRy6VIpu3Yt3t6mjZSlIh1dJiDwfUjga3H4YWHq27ruusMyKsr+OHxXuXhRyvBw+x9Se/GEwJ+ZMFMyCRkyJUT+susXl85lMBrkE/OfkExCBk8Olt9u+1ZKKWWD9xrId2a9I2/75bayByUmSlm1qvqSfvqp5DajUcp69ax/qUFB6vWVV1xqt6NYE/j+iCtx+L6ExfvIzlaz9E2brAdxJyZKmZBgXeCfPm3zurvO7jIJeWvL7nO7bZ5DSin3Ju817X88+XiJ45Mykuz5CtyKref9wAEp+/Ur/lted52U27apbc70YbYmcFFRlidwBoPqPho3Lt535EgpT5zQJ5Y/EIfvQwihPOCTkuDjj+Gee07w/vvK4W7qVPeHw1WrBs8/bz2OPzLS/jh+T7Ls6DIen/84AJ8M+YRb297q0vmCRBAfDf6ICX0nYJAGHvzrQZYeWcrpdBUSMffAXLYmbi150DPPKDvq4MFw++0ltwmhDHOWvtjoaLUNlNvu4cMutT1ABcNohKNHVd8fF6f+pJaIi1P7lEbzC0lKUj4AVmhdozXRocpwHBMWw2V1LqNrfNcSS5satp0PcwpyyC4o9jwOEyUd3U6ln8JgNJQ+zOtIqez1HTrAypVQo4ZyuZk3Dzp2dP68U6bAmDHKfSI6WnUD0dHq85gxantpgoLgjjuUU/Ubbyhfy5kzlavG00/7Vyx/QODbSbVqMGqUiqcfOdKzAtb8IS0tn8LClKOgL7Hv/D5u+eUWCo2FPH/F8zx8mWVPbUcRQjDlyik8f8XzGKSB2369zRTLXyZ5z7x5ygMyKgpmzLA8MhsxAgwWOrvYWHjzTeXol5en/tW+/C8O4FnOnFGeY+HhyuHTGiEhKkTHnKAgaNRIdSBGozqXBaSUHE89TlZBFmHBYTSt1pQg4Xh3nZiZaPqPWLwOksMpvjWgTU9XoXFa9NO99yphe/vtrk+wtAncmTPKKXryZPVqzwQuPBxefFF1G0KoLiE3V736S4rfgMD3A8wf0vffVwOASZNUyElaGvTvDz/+6N02apzPPs/1s64nNTeVG1vdyBsD3tD9Gq9d9Rq96vciJTcFicQojUgke5L3sODgAhUCNWqU2vnVV1UHa4nYWLjuupLroqPVACEkRLn8Vq4MCxbA33/rfh8B/JCcnOJKV40bl5+oo1QSHSIj1SBAC/s8f16dsxRns85yIecCQSKIZtWbERrsXGxYTkHZc5cmPS+9TPY+b7F3L3TvDn/8of56f/6pAmZiY/W9TrVqauI2YYJjE7iUFNUea+N/LUy6dICGrxAQ+H6E+UP6yiuwcSM8/riahN5zD/zf/5XMIOVppJQ89PdDHE45zGV1LuOHm35walZSHqHBoTzW9THT5xUpKwDIKshi1PxRGCdOUAG5XbqomBpbjB5dHEMpBLRrVzwIqFVLDRhAmQfKS8oQoOJz9qx6rVFDJdMpj5gYpYYD9Zw1bKies4gIdQ6AUlXt0nLTTOGrjas2JirU+YyFbWu2LWECiA6LLvE5LkplXDycctiuDHzu5PfflbDfv1/9DTdvhhtu8GqTyuDv5c4DAt+PCQ1Vk9GPP1YP2bRpcPPNKo+MN/ht72/8vf9vKodX5s/b/yQ6zH2Bq59t/sz0ftH5Rab38sJ5jJ/MUB9mziw/jr5PH2V8A9UJf/JJSb3e448rVcrx4/DWWzq1PoBfkp9fnKGx9MzdGkKotM2gZvbmNrn4eDUISEtTemxUJsrjaSonfXyleKpFWvEP0IkGVRoQFRpFniGPoylHi01iHuajj5QaPzNT2cvXr7cvB4mnsTcZm6+WOw8I/ArAqFEqSVzVqiqfzIABnlcppeSk8NRCNZt+c8CbrmXFK4fd53aTkJhg+mykWK1x54ZsQvIKKBg0UGXiKA/NeU8IGDq07DEhIVBUZIbp022n6QpQsTl3Tulyq1ZVg0N7iYtTqd5K2/tDQ5VTEKhZvlRpdPMN+USFRlEnxvlENfYSJIJoWq0pwSKYtLw0LuZcdPs1zZFSjXe0LHrTpqlU5b6a5MaecucQEPgB3MyAAbBhgzJXb9gAV12lzIOe4oUlL5CUmUTvBr15pMsjbr3WtDXTyDcUezc3iFDFYoIN8MRGte7bvlUsHWqZESNUqt2iIiBl6NMHLrtMze58VVcXQD/27FH573ftUtVW8vKUV9e5c2p7bKxjTpwhIdCyZbFq35xatdT67Gzyzp/lbJYyGTSs0tBiFjx3EB4Sbhqge9JrX0pleUtNVYqOL7+EceN8uxDY8OGW/XxL8/nnvtlVBAR+BaJFC5Uvunlz1V/17++ZkebKYyv5YusXhAWH8fn1n7vFbm/OjrM7MMjif93NNW8G4Ib90CAd9sfCp9Ud8DyOjYXly63nzxeiuKThxx872+wAOnLy5En69+9PmzZt6NChA7/q2buGhalePTdXqdoLC1VFLM1B5sgRNSjQg6AgNW0ECs6qIjg1o2u61RxmidjIWKJDoykwFpQp1uMOpFSRjefOqb/Xr7/Cgw+6/bIuU16588hI6NlTPTJ33AE//ODZ9pVHQOBXMOrXV3GrbdrA7t1q4lrKJ0hXcgtzeWSemtH/X+//o3WN1u67WBE7Ht+BfEWalhsa38BDnR/i6Q1qe8tJH7H5sS36XvSOO5Qqd8MGVfgggFcJCQnh/fffZ8+ePSxevJjRo0eTZauUmiPUqmW7GIYQZcPtXKF6daQQxOQaiJah1K1UV79z24kQggZVlKbsXNY5u7z7naGwEJKTlTf+xYvqa65ZU/ke+Qu2Yvmfew7WrFGheUajKoL21Vc2TrZ3L7WWLLFdBlBHAgK/AlKnjirE07Gj0khefbX6k7mDaaunceDCAdrUaMO43uPccxE7eDPuTvodh/Qw+KdX2SppLhMVBQ88oN5/8on+56/AjBgxguuvv17Xc9apU4dORTVMa9euTVxcHBcv6mR/jokp4ewZUtpLS4hi27sO5AsjF4sKGjYqjCE4qBw3cDcRHRZNjagaSCQn00/q6sAnpVKSbN+u/F+1r9Q8lt1fKC+WPyhIrXvtNXVfDz1kY6b/6ae0fv11FXrlAQICv4JSowYsW6YyVR04oJLz6O29fy7rHO+seweAz67/jPCQcH0v4ACxX84C4KvO8PR/L5ew8evG4ypzILNmqYDcAIAS6EKIMsuOHTsA+OCDD/ihqMfr378/Tz75pK7XT0hIwGAwUN9WEhxH0Dzri2b5YRkZxduCgqBuXQgOZtWqVQwbNoy6desihOCbb76x6/QzZsygcePGRERE0KVLF/745w+SI5XEi0jNxFBYyIQJE0z7NG7cmPHjx5eoYOjstcujbqW6hASFkJ6XTmpuqi7nBCUcz54tK9ilVFYTX05WY43yYvn/7/9U/i5Qc4WFC0udoKAAfvpJvS+qcuhuAgK/AlO9uvLeb9wYNm1SajMbmTwd5o01b5BdkM2wlsPo3aC3fid2kNC0NPjxR6QQLBjUmAMXDvDRho/0v1Dz5qpQdk4O6NTBVhQGDBhAYmJiiUWrpV6lShWquik15cWLF7nvvvv4/PPP9T2xWVrcYK2uPaiZf1H8fGZmJu3ateODDz4gMjLSrtPOnj2bZ555hv/7v/9j69atdO/RnQdufYDD55MwhocjCgp4c/JkPv74Yz788EP27dvHBx98wMcff8y0adNM53Hm2vYQEhxCfCUVpnoy/aQuDnyFhcqXyFqOECl9O1mNK7zwgkqNXlioHP42bAC6dlWjhSZNIDmZLE0lO3s2rF0LJ06oA9xAQOBXcOrUgX/+UROWJUvg/vv1Sc5zOv00MzapePcp/S0koPYgdebNg7w8xHXX8ew9yqlu8srJJGe5wY6hZfD75BPvZjnyMcLDw6ldu3aJJaRILa6p9EeMGMHKlSv5+OOPTVqAY8eOWTzf7bffTmxsLO+//75p3d69e4mKiuLnn38GIC8vjxtvvJFx48ZxxRVX6HtD1tLiNmhgciO/7rrreP311xk+fDhBtmz+Zrz77ruMGDGCkSNH0rp1a55/9XniasYxf9YCgooGEmvXrGHo0KEMHTqURo0aMWzYMIYNG8aGDRtM53Hm2vZSI6oGUaFR5BvySc52/T9kjzLMl5PVuMqbb6p+Nztb5fTaW7WnikUscq4626mTUgeMHKl2aNlSRQW5gYDAvwRo3lypkypVUgUorrlG2ZtmznReM/3a6tfIM+Rxa5tb6VjbhWoWzqCNkFu3hoEDif/jD7W+QwcGJ1fh2rr9yMjP4L317+l/7euvV53+wYOwdKn+56/AfPDBB/Ts2ZMHHnjApAWwpoZ///33ueuuu5g8eTKghPudd97J8OHDueOOO5BSMmLECK666iruvffecq/9+uuvExMTY3NZvXp1yYO0hDkaWlpcJ8nPzychIYFrrrkGUGlvL+ZcpEe/HuxM2KmiRYSgd9u2LF++nH379gGwZ88eli1bxnWl00C7CSGEaZZ/NvMsRrOB7d7kvWxN3Ep+of2qwqys8m302dm+G7vuKkKovnbIEOWoOGjn25yJaGzafrZzZ6Xez8hQAwEhVMEvNxAQ+JcInTsXp6n891/444/aTld4OppylC+2fEGQCGJy/8nuabAtGjRQf4x9+2DpUiK07GcffQTXXcfEaesA+HjTx7raIQE1FXn0UfU+4LxnYtGiRSWE52ALHVaVKlUICwsjKirKpAUItpKntE6dOjz33HOkpqZy/Phxxo0bR3p6Oh8XhUX+999/zJ49mz///JNOnTrRqVMndu7cabV9jz32GNu2bbO5dO3ateRBRX8KQ0iI6oS1tLhOcv78eQwGA7WKsvRp4W/14utx7uw5lYinalVevP9+7r3xRtq0aUNoaCht27bl/vvvZ5SmXfIAVcKrEBUaRYGxgPM5xQk9CowFGKSBk+kn7TpPbq4ScuURFWWKTqyQhIaqWl5XXAEnz0UwM7d4kJpXvXqJfWVYGIwf75Z2lJN31D0IIUYBzwN1gN3AaCnlahv73wW8ALQA0oGlwFgpZQUdE+rPxIkqV7XG77+3MCWQePdd9Tp1qn3nmrpqKgXGAu7tcK9HwvDKMHq0sk+U9kIs+twzP5KrDA1YlneC6RunM76vzn+eBx9Uf8gFC5THUeXK+p7fD+nbt28JO7oeduVGjRpRtWpV3nrrLT7//HNWrVpFpUqVAOjdu3eJmWd5VK9eneqlOtZyKZJUhVFRamBiT4o1O8kuyCYlNwWBoFJYpeINcXHMnj2b7375hVk//kjbdu3Ytm0bzzzzDI0bN+ahhx7SrQ22EEJQJ6YOh1MOk5iRaMq5rznDpuWlkZ2fTVSY9e+ksBAOHbLP8mUwwK2uVdD2eaKiVDGg5s2hT9oqAErPszKJZtkV0xhWqVKZ4/XA4zN8IcTtwAfA60BnYC2wUAjRwMr+vYDvgW+BtsCNQBvAR+rD+T4pKcopxjy6yGAo/ukdqfB04MIBvt3+LcEimFf6vaJ/Y+2hTx/b5a3Cwnj5djUTfH/9+2Tm6xyeULs29O6tPCDnz9f33H5KVFQUzZo1My116+oTS96xY0dmzJjB+PHj6dmzp9PncVilbzSa7F0FMTG2y+DaSVxcHMHBwZw9e9Y0u68RXYPzyeeprU1vK1fm+Q8/ZOzdd3PHkCG0b9+ee++9lzFjxpRw2vMEVSOqEhkSSYGxgAvZF0qk3TVKI8fTjlsN3TMaVX6i3FxlCald23pqAyFUMhtPlhz3FiEhEJt9kv6sIJdwPqak1uYCsdy1bKTbHBi9odIfA3wjpZwppdwrpXwKSAQet7J/T+CUlPI9KeVRKeV64CPgcg+11++xVOGpefOSejZ7nWYmr5yMURp5sPODNK3eVMdWOoCW/z5chQHmmdtVo6Nh2jSubDOEHvV6cCHnQolCO7pxyy3q9bff9D93BSYsLAyDPblJi5BS0rZtW8a7qOJ0WKWflqakVlQUslIly2lxHSQsLIwuXbqwcNFCUnNTEUJQO6Y2S5YsKXY6FILsvDylUTDThQcHBzuk0dADIQR1Kql8A4mZiWUy8OUU5pCWl2bx2NOnlfIrJASaNVORjLVqqb+uJviDgtTnypVVMptLgTlz4G5+JAjJ3wzjGT5k4/6GgJrdj0KV5naXA6NHBb4QIgzoAiwutWkxYM3N9j+gjhBiqFDEAXcAC9zX0oqFpQpP995bMjWoPRWeTqSdYPau2YQEhZBwJoGqb1TlZJp9tjzdGTHCFLqS3KFD8frYWBg5EiEE4/soIfHOunfILdS56I2WGmzhwkDZXAdo1KgRGzdu5NixY5w/f96mEPv4449ZtWoVhYWFVm399lK9evUSGghLSwkzhCZsbZgBMjMzTYMFo9HIiRMn2LZtGydOnDDtM336dFq1amX6PGbMGH74/gf+nPUnaSfTeH7M85w5c4bHHisu9zx0yBDe+PZb5s+dy7GjR/njjz949913uemmmxy6th5Ui6hGREgE+YZ8CgwFJbYZpZETaSfKzPJTUlTMvRDQtKkalwuhhH7HjkpZEh+vXjt2VDN7X86frydJiZLbC74HYBGDMBLMlO+vZy8t2EU7FnCdWx0YPT3DjwOCgbOl1p8FLLpsSCnXoQT8j0A+kAwI4H73NbNiYanCU1RUyTjPsLDynWY+3vgxBmng1ja3cj7nPGl5aYxdPFbn1tpJ9eqmWdc5TeBHR6t6wUXhYNc1v45OtTuRlJnE11u/1vf69eur4t3Z2SrZQQC7GDt2LGFhYbRp04YaNWpYFVB79uzh+eef54knnuDgwYNke3JQZTAU27dsCPzNmzfTuXNnOnfuTE5ODq+88gqdO3dmolkWmfPnz7N//37T5xtvuZFnJz3LVx98xeA+g1mzZg0LFiygYcOGpn0+mjGD4QMHMur112ndpg3PPfccI0eO5LXXXnPo2nqg2fIBZBmLMxQaC0uE7uXmghZpWa+eigwyR0tjEB+vXsurXl3RaJu/lbbsIZk4vudermUhWbkRfMTTPM4ngHCrA6PwZP1jIUQ8cBroJ6VcZbZ+InC3lLKlhWPaAEuA94F/UI5+bwPbpJT3Wdj/EeARgFq1anXRYnb1IjMzk5iYGF3P6W4MBpXS0vynrlcvk1OnYti6tSY//tiGkBAj06dvoWVLy/buHEMOt62/jczCTD7u/DE5WSrXdpAIolVsKyJD9Uv+YQ+V9uyhyxNPkFelCks++YSYM2eUwDebTQGsSF7B5D2TqRVeix+6/0BIkH49TP2ffqLp559z9uqr2auTV21mZibjxo1j3bp1upzPmxgMBqdm5nl5eVx11VW0bNmS6dOnEx8fz+LFi+nevbsbWlmWkLQ0IpOSKIyMJKdBA6fvwxLncs+RUpBC5ZDK1Im0np43/Nw5wlJSyKtWjfzS4YEu4My9GKSBY1nHKJSFVA+tTlRQydmDEILIkEikFJw4EUVeXjAxMQXEx+faNXPv2bMn06dPd6hN4J99cZPpH9Pgtzls7XUTy256mip5F9iwvwmD2m3jeJAK1RNCaT5ceeSuvPLKBCll1zIbpJQeW4AwoBC4tdT6j4GVVo75Hvij1LreKAfHerau16VLF6k3y5cv1/2cnmD8eCmjoqRUYl/Kd95ZbnofEqJemzeXMj3d8vEfb/xYMgnZ84ue8sstX8qgyUGSSUgxSchun3eTRqPRszf03HOq0UFBcvk770gZGSnlli1ldis0FMpW01tJJiF/3PGjvm04dEi1oXJlKXNzdTnl8uXLpTueW2+Qbu1hKofRo0fLRo0aydTUVCmllC1btpR33HGHPH78uJ7Ns86BA1Ju2iTl2bNSSufvozSFhkK55cwWuen0JpmZl2l75/R01YYdO6TU8b/lzL0cvnhYbjq9yeqScCZBnkw7KY8eLW5yQYH953f2efe7vtholLJJEylBXhW+RoKU1Tkv/37nM1mXkxJUHz1+vOuXAjZLCzLRoyp9KWU+kAAMLLVpIMpb3xJRQGkvH+1zII+AnZSu8AQlKzx16KByyTz6aNmYfKM08sGGDwAY3WM0U1ZOwSiV7VUi2ZO8hwUHPehSIaXyfoHi/PZDh6pkA6UIDgpm9OWjAUyZAXWjaVM1FE9PDyTh0YnFixczffp0fvjhB6oUOWO+/PLLLFu2jPvv94AVr6BA/Z6gkjvpyIWcCxikgZiwmPLL32oFfPLylJ7ci5RXOc8ojVy8KDh/vthuf6mp6u1i/344cgQZG0uv53oQEQF50bEcoAWp0fWIiFB9tDsdGL0hMN8FRgghHhZCtBZCfADEA58CCCG+E0J8Z7b/XOAGIcTjQogmRWF6HwJbpJT6eqhUYEpXeIqPL67w9MYbKilEdLSq5fDFFyWPXXRoEQcuHKB+5fpUCqvEhZwLJbZnFWQxav4oCo3uyf9chk2bVMmt+Hh45RVlKPzf/6zufneHu6kcXpn/Tv7H9qTt+rYl4K2vK9dccw0FBQX06tXLtO7ee+/l7NmzLF++3P0NSEtTA8rKlVW2FJ2QUnIu6xyg6t2XixDFcWpeTjLftmZbusZ3NbU7LiqOrvFdTUv72K4UXlRhmA0a6JquoGIxbx4AYvBgprwWbLEvnjrVvQ6MHhf4UsrZwGhgPLANpZ6/Tkp5vGiXBkWLtv83qFC+J4FdwBzgAHCDp9pckdAqPNWpU7LCU8uW8Omn6v3TT6skdhpaitqnuj/F66tftxjXfiHnAjO3zHRz64vQYlZuuUV5/rRooTyErBATFsP9HdXsUPdZvibw//pLzQ4D+DdpRWFmOgeFp+elk1uYS1hwGNUi7NQcaG3wkcqMmsC/mHPRNLiXEo4eVRGM1aqpmkMBrKDl7CgqFW2tL3YnXlGJSylnSCkbSSnDpZRdpJkDn5Syv5Syf6n9P5JStpVSRkkp60gp75ZSnvJ4wys499wD992nNIj33afk165zu1h6ZCnRodH0atCLhMQEi8dmFWTx0tKXSM9Ld28jzdX5DqTmeryrUv3/sPMHfdPttmmjHAUvXoSVK/U7bwDPYzQWq/NdyJlvibNZKjCpRlQNhL1TuMqVVbB6dra+ZS6dJCIkgsrhlTFKI+ezVbrdpCSV4DI01OXMwxWb1FRYvVp54g0a5LVmBGzgAUrw4Ycq4mzTJpg2DT5Yr2z3IzqNYMamGTbrzOcb8nl11avubeDevSrup2ZNMFP7lkfrGq25qvFVZBdk893278o/wB60Ij7JRWFJo0cr04IHylwGcANZWSqkJSLClNRJD3IKckjPSydIBFEjuob9BwYFFadt9pHasdosPzkrmcwsyZmiXDyNGwfs9jZZvFg9W717ezWlYEDgByhBlSrFpd6nvJXG99tVBuOnL3+aHWd3YJDWs6TlFObw79F/3dvAf/5Rr9dcYz1XpxVGdVVpLGdsmmE1JahDaEV8tOI9u3fDuHEeKXMZwA1o6nydZ/dJmSqLStWIqo6HhfqIHV+jSngVwoLDyDPkcfhUOlKqDHqBchLlUGS/Z8gQrzYjMCYLUIarroJnnoEP/vsJgzGH/g2uokVsC3Y8vsPbTSsW+E6oxW5odQPxleLZf2E/y44u4+omV7vWFktFfAoLVZlLUEnE3VTmMoAb0AS+jtLLYDSYnFydcmrVBH5Ghnq2vDyNFkJQM7omp9JPURB6jsjIKuhUNqHiYjCojJxgst97i8AMP4BFpk2DiJ5fAhCx1zMVusolJ6fYTl5UU9wRQoJCeLSLKm378aaPXW+PHUV83FXmMoDO5Oer5ysoqGx6OBcwLziTmZ9Jdr6DGQNDQlR7pCwekHiZCGMcSAERadRtmOuoou3SY+NGOH9e2T1KJQbzNIGfKoBFDqbvIDd2M+RUZdF7N7FmjbdbhHJ6yc1V8fZOZh8bedlIQoJC+Gv/X5xKd9HvUyviYykOqaiIj57CI4Ab0Zz1KlVy2FRkC81ZD8qvMGcVbVDpAwLfYICTx0MgR6UczjSe93KL/ABz73wvezUGVPoBLPLlFjW77xp+N5sLI3n4Ydi2TfkzeQ0X1PkadSrV4ebWN/PL7l/4autXTOznYu7xESPg5ZfLri8q4uNUG+vUKVm5zU/Jzc0lwqsPjAMkJytv+OrVywzSnL2PAmNBmQpzAkGNqBqOpaIuKFAJNIKDbYaf2oOrv0lKSlEVvIg8CiOSCBbB1KvsWpvq1LGeYrhC4CP2ewgI/AAWyCvM44edPwDw0YMPMWK2ShL12msqMYTX0EHgAzzY6UF+2f0L323/jgl9J9gfJmWJ2FjloPfHH8XrIiJKFPFxlLlz5zrfHh9ixYoV9O/f39vNKJ/CQhVAnpam1K+NG5fY7Ox93PnbnezYVdbvJbJyJNuf2V6+A1/XrnD4sPKKy8hQWffuuQe6dVOhNPXqqawtDjxnrvwmS5YU//XGvSSZXaMFR9IO8c3d33Bts2udOmeF59QpVcgkOhr69fN2awIq/QBl+XPfn1zMuUjn2p3p0bCzKfPeG2/Azp1eatSpU8oLPiYGrrBWSdk+BjQZQHyleA6nHOa/k/+53rbRo0uqgatVU4OAAP7BunVK2LdsWUbYO4vBaOD3vb9b3GZ3kiotCmT/fiXsQaVk83AUiJTw0ktK2GvVOD78QHDi7xEAfLPtG7e3wW/R1PkDBnhZPaoICPwAZfhiq5LwD3VWznq9e8OoUWoi9PDDyo7ncRYvVq9XXmkqi+sswUHB3NP+HgC+3fatqy1Tznvms6xKlbxuqwvgAJoHtY4RFTMTZlrNWWF3kqrRo4sLX2gYjWq2n5amnjEPRIFMnAjvvFOyxkZWFhRuuRekYM6uP0nJ8Y1sgD7HgqIaI172ztcICPwAJTiWeoylR5YSHhzOXe3vMq2fNk1pEDduhI8+8kLDNIGvU5aq+zupVLu/7PmFy7+4nKpvVOVk2knnTiYEvP66eh8cDAcOwNmzto8J4Du4QeBPWzPN5na7klT5QBRISgq89ZaV/FFpDeDI1RhEHl9vmu3WdvglhYWg1X/wYnY9cwICP0AJvt76NQC3tLmFapHFOb8rV4ZPPlHvX35ZqfenToWZMz2Q6ttgUAZEcCoczxJtarSha3xX0vPSOXzxMGl5aYxdPNb5E44YAf37K3UIBKrn+QuJicobNSoK+vbV5ZTns89zMt324NGuJFU+EAXy66/lJIvcNgKA6Wu+cWs7/JItW5Q2plkz5XPhAwQEfgATRmnkm+3fAMXqfHOGDFGp47OzlU1v4kR49lnlNzRhQtmyurqRkKBy1TdurP48OqEV1NESo8w9MJetiVudO1lsrBrNDxumPmsaiQC+jeYIeuWVutlYf9zxIxLJdc2vQ74irS4Jj1iuS1GCESMs29BciAJxhMWLlRXBKvtugrxKHC3YwN7kvW5vj1+hze6vvNK77TAjIPADmFhzYg0n0k7QsEpD+jfqX2b7xIlw5EjJdVlZKjT+3XfVdrdg7p2vo238znZ3EiyCTZ9zC3N5dN6jrqXd1TQQixe7cQQUQDe0TnngQN1O+dW2rwAVDeIyWhSIOUFBLkWB2EtKih3j1oIoQvbdDsC323Xwh6lIBAR+AF9m1s5ZANzV/i6CRMlHIyVFOe7k5lo+NjtbbXdLym+dwvFKUz2yOmHBxQ6AEsme5D0sOLjA+ZO2bavqXSYlwa5dOrQygNuQElasUO916pR3ndvFjrM7qBZRjaEth+pyzjJRIEZjsenIjbz0ktJIlzfGDtoxAoDvd3yPwegNj14fJD9fJQoDZerzEewW+EKIMCFEDyHEzUKIu4UQg4QQjdzYtgAeJN+Qz697VJ35O9vdWWb7nDnKH80WwcHFpep1Iy0N1q9Xs5mrrtL11EuOLEFSchaeVZDFqPmjnMt7Dqp31GaLmt9BAN/k2DFV0bB6dWjXTpdT/rTzJwCGtxleYjDpEuZRIJrg14SJm1i/Hj7/XF32kUcsuxGAWv/8nVfQrHozzmScYcmRwDMPqHKj2dkqla4PJRayKfCFEMFCiOFCiEVAGvAfMAf4HlgIHBZCnBBCvCmE0M+4GsDjLD68mIs5F2lXsx3ta7Uvsz0pST2/tsjOVvvpyn//KRtm9+66l+SaunIquYVlVRZ2x0lbw1ytH8B30eoy9O2rSzpdKSU/7VIC39Kg2Wm0KBAhinOxu9Ep1GBQYbhSwtixyll3zBjl4hAdrZoRHa0+jxkDU6cIkz/M9zu+d1u7/AofVOeDDYEvhBgO7AN+APKA8cBAoCPQAugB3IUaANwE7BVCzBRC1HJ3owPoj9ZR3dXuLovba9e2PsrXiIpS++nKqlXqVecsVbvP7SYh0bLTlN1x0tYYMEC9rlxZnDAlgO+hqfN1UrluOL2Bo6lHqRNTh74N9fH4NzFihPoPTJ6sPrtR4H/5JWzdqhzLx49XAn7qVJXd9733VBPee08FOEydqrZrIbx/7/+bnIIct7XNb9AEvs5aSVexNaz9EPgYqC2lvEFK+T8p5TIp5U4p5SEp5UYp5Wwp5RgpZQugNxALPOKJhgfQj6z8LP7c9ycAd7S7w+I+w4eXn3DHYIBbb9W5ceazMB2Ztmaa1cQoYGectDVq1VLhDLm5sHmzky0M4HY0ga/TYFJT59/R7g6Cg8qxfzmKeRRIVJTKOqm7Ok356milId5+u2Ten2rVVGDAhAnq1TxFQJNqTegW343M/EzXfGAqArm5sHateu9D9nuwLfCbSCnfl1Km2nMiKeUGKeXNwNu6tCyAx/h7/99kF2TTs15PGleznFq0WjWl3rM2y9dChm3lCXGYrCwlMIOCXE6nW5odZ3dgkNZHMHbFSdtCG6C42dYawEmOHYPjx9WD3aGDy6czGA38sucXQGd1fmnCwoqfrX9deD6tMHmyquTaty/cdptjx97eVnnr/7z7Z93b5VesX6+Efvv2qkaDD2FV4Esprfhj28bZ4wJ4j1m7ir3zbTFlSllbXlSUepVSZ2EPKsd5YaHKF66z/X7H4ztKxES/cMULADzR7QnH4qStoXXKmkkigG+hs/1+xbEVJGUm0bRaU7rGu7nSoWYy0lmtv3s3TJ+uvo4PP3Q8Ava2tmqEMP/AfDLyMnRtm1/ho/Z7sNNLXwjRQgjR3exzpBBimhBirhDiSfc1L4C7uZB9gUWHFhEsgrm1jW19vCVb3vvvKw9+gFdf1VnLqAlLndX5lri9nZqdzNkzR5/Qoj591KvmdBjAt9BbnW/mrOdS9UV70NTEOmqPpIRnnlGP6iOPQMeOjp+jfpX69Krfi5zCHOYeqBgVH53C3wU+MB0Ybvb5NeA5IB54TwjxhN4NC+AZftv7G4XGQgY0GUCtGPv8LUvb8m6+GYYOhcxMy6XhncaDAr9z7c40rdaUs1lnWXl8pesnrFdPZQZMT4cdZUukBvAyOjrs5RXm8dve3wC4s70b1fkaHTuqqpGHDyvPOR34809lIahWzbUS2JoP0Ozdl2hu/exspdIXwifK4ZbGXoHfERWShxAiCLgPeFFK2QV4lYCjnt+iJdtx1e74v/9BaCh8/bVKIe0yubnqjwPFs2U3IoQw2SBn79Kpswqo9X2T48eVDb9qVV3s9/8c/ofU3FQ61OpAmxptXD5fuYSEQM+e6v2aNS6fLi9P+eeAMtu5YnYe3mY4QSKIhQcXkpqb6nLb/I61a6GgADp3VqMnH8NegV8FuFD0vjNQDRWOB7ACaKJvswJ4gtPpp1l1fBXhweHc1Poml87VvDk8/XSxatDlrLKbNqmeqH17lRjFA2hq/d/2/kaBocD1EwYEvm+i2e/79Ck/m5QduCX2vjy0QbAOav3p01XK7DZt4LHHXDtX7Zja9G/UnwJjgSny55LCh9X5YL/APwtoiXWuAQ5LKbVyUDGAk2nJAniTP/b9gUQyuPlgKoe77hQ3YQLUqKEmHS5n3POgOl+jfc32tIprxYWcCyw7usz1E5p3yoG8+r6Djur87IJs/t7/N2A9pNUtaKl1XZzhX7igfG9AheHpkZ7f5K2/6xL01q8gAv9vYJoQ4h2U7d68O28PHLF4VACfZs4epaQZ3np4OXvaR5UqxZ3HSy+5mHNGm4V50A5WQq2vhw2yWTOViSg5Gfbvd/18AfRBR4G/6NAisguyubzu5TSq2sjl89nN5Zcr6bx9u/ITcZKpU1X9iwEDYPBgfZp2c+ubCQkKYemRpZzPPq/PSf2BnBwVRiyER2odOIO9An8cMA8YhBL+r5ltGwYEEij7GSn5Kaw+sZrQoFCub3G9bud98EFo3VqpCD/5xMmTFBQUJ67wgP3eHE3g/7HvD5uJeexCiIBa39c4cQKOHlWjU2dc0UuhOevd0voWl8/lEFFR0KWLKqSzbp1Tpzh4ED7+WD2m77yjXyHKuKg4BjQZgEEa+G3Pb/qc1B/YtEn1Xe3bq+fLB7FL4Esps6SUI6WU7aWUD0ops822XSGlHOe+JgZwB2vOr8EojQxsOpAqEfo9nCEh8NZb6r02e3CYLVtU0p0WLdyQq9c2rWu0pn3N9qTmprL4sA658LUBS0Dg+wY62u/zCvOYd2AeoGa1HsdFO/6LL6o0Fw88oMvYpwTawFkryHVJ8N9/6rVXL++2wwb2xuEfEUJYfCSEEO2EEAGVvp+x6rwSQO6YmQwZorSlFy+qmh8O46b8+faia2cVyLjnW2i/gw7P1tIjS0nPS6djrY40rd7U5fM5jAt2/FWr4I8/lKLAlTA8awxrOYyQoBBWHFvBxZyL+l/AF6koAh9oBIRb2RYBNNSlNQE8wsWci2xN3UqwCOaGljfofn5NRQgqY9fx4w6ewAsOe+bc0kYNgubun+u6t367dir868QJJ76IALqjmYp06JR/3/s74AV1voZ2Dxs2OOQwo1XBA3jhBYiP179p1SOr079RfwzSwNz9l0ASHnPTSgUQ+ADW3Iy7AqmuNyWAp/h7/98YpIErG19JbFSsW67RpQvcdZfqhxxKxmMwFM/CvCTwW8W1olVcK1JyU1xPwhMUVDwTC6j1vUtqqsofGxam0jW7QKGxkL/2/wUUDxA9TlyccpjJzXUo+cWcOcrcXKdOseB3Bze3UmaO3/f97r6L+Ar79yuVZnw8NPTd+a+t8rjPFtW6P4ES9nO1z2ZLMqqi3iJPNTiA63jK0ei111Tf+uOPkGBvWvpduyAtTf1pGjRwa/tsoXVWf+z9w/WTBdT6voGWyKlrVwi3prC0j5XHVnIh5wItY1vSOq61Do1zEjvt+Ckp0LIlbNsmeLIoGforr5Sshqc3N7a6EYHgn0P/kJmf6b4L+QLm6nx3p1Z2AVsz/CPAv0WLADabfdaW34BngZHubWYAvUjPS2f+gfkIBDe1ci3ZTnk0aqSS8YAK07MLTeXq5bAWLRHRn/v/xCiNrp0s4KnvG2jPlg6VF80HzW7PnW8LTeBbseNLqfJjxMfDoUOwenUdzp1TMun4cfemh6hTqQ496/ckz5DHokMVfE7oB/Z7sF0t7y8p5QNSygeAb4GntM9my2NSyg/NvfYD+DbzDsxDImkS2cT1sDM7eOklFaGyZImd1Ty1TllLHeolutTpQv3K9TmTcYZNpze5drLLLlPeUfv3w7lz+jQwgOPoJPCN0sgf+5Tmx2vqfA1zxz1j2YHpxInw7rtK6280wpIlSt0sJXzwgdruTrRJhebvUGHxd4FvTpFwP+ruxgRwP9rMpF1MO8YudqMBr4jq1ZVjEMD//Z8dMwrN8cXLAl8IwY2tbgQwde5OExoK3YuKTWpq5QCepbBQObeBy8/WupPrSMpMolHVRnSu3VmHxrlAw4aqUFNKCuzdW2JTSopyns02m45lZBSbMrKz1XanQmftRBP48w7MI6/QlUxcPsy5cyqpQVSU/vGNOmPLhj9RCBFv9t7WMsFzTQ7gLFn5WSw8uBCA9jHtmXtgLlsTt7r9us88A7VqwcaNqiqXVc6dUxXAoqJ0KWriKuazE+mq7rNHD/XqZJKUAC6ya5cq59ikicu5HbRB882tbvauOh9KZnUrZcefM6f8VAPBwTqkwbZB0+pN6VirIxn5Gfqkq/ZFNM3R5Zerwb0PY2uGPwmoZ/a+vCWAj7P48GJyCnMAqBpaldzCXB6d96jrwqwcoqOLVYcvv6wmWykpMHOmigGeOVN9Ns1+u3XTJ6m3i/Rp2IfYyFgOXjzInuQ9rp1Mm1UGBL530EmdL6UsDsfztjpfw4rjXlJSydk9QIsWJWPis7PVfu6kwqv1dfQNcTe2bPhBUsqNZu9tLa6XnArgdrQwIg2JZE/yHhYcXOD2az/8sJpc7d0LN92knIgee0wNBJ59Vn1e9WaRMPSRP05IUAjDWg4DdFDrazP8TZvUiCeAZ9GpU96atJXjacepE1OHHvV66NAwHdDsxqUGk7VrK2WZOUOGlMyRFhXl/mSWWhbCP/f/icFocO/FvIGf2O/BsTj8AH6MwWiwKLSyCrIYNX8UhUb3CqGwsOKMXvPnFzsRgcqim5sLrPcN+7052uzEZYFfsyY0baqmVDt36tCyAA6hk8DXKuPd0PIGgoSPdJ/t2ik12tGjcPasafXw4SqthTl165YMjzMY4NZb3dy8mu1oVr0Z57PPs+aEa9X9fI7c3OKCOT7Ub1nD4SdWCFFTCNGg9OKOxgXQj7Un15KeZ7mq1oWcC8zcMtPtbRg0SP0vLFkQQiigq3EjAGmtfWTmBAxsOpDo0Gi2JG7heKqLmfICdnzvkJiohGGlSko4uoAm8DXNj08QHFzsFKo5JgLVqqliVtaIilKJd6pWdW/zhBAVV62fkAD5+dC2rfu/SB2wN5d+ZSHE10KIbCAROGphCeDDfLn1S6vbsgqyeGnpS1YHBHrx++9qpm+JDuwgihwOiWb8sryGW9vhCBEhEVzX/DpAh1l+wI7vHbTZfY8eLhXMOZF2gq1JW4kOjebKxj5W71wbTJaKAklLU6/BwcVJdqKjISICxoyBKVM80zxN4P+1/y+3+wy5na5d1WiqdWsYOFCti4mB2bPVs3bihM+a7ez1jPoYuAX4EtgJVND4ioqJuaORNfIN+by66lXeGviW29qRlGQ95XdPlBD8T17hdiciR7mp1U38uudX/tz3J6N7jHb+RJrAD4TmeRad1PlaTvhrm11LREiEq63SFwsCf9cumDVLOY5v2qSiZGJi4L33lBrfkxPSy+tdTs3omhxPO06nzzpxPPU4Ox/fSf0q9T3XCL1o0EClMjaPZ9y0CUaOVKm08/KgeXPYscNrTbSGvSr9a4HnpZRPSSk/l1J+W3pxZyMDuMa+8/vIyM+wuU9OYQ7/HrUnM47z1K5tPZWnJvC3hPX0dEXcchncfDAhQSGsObGGlJwU50/UoQNERqqUZ8nJ+jUwgG30st8f8EF1vsbll6vXjRtNhvuJE5X57JFHVHj4yJEqf/7IkZ7XPgeJIIa2GArA8dTjpOWleSQPiFsYPbpsR2YwQEaGUqkIAYMHe6Vp5eGIDX+/21oRwK1o3vn3d7wf+YpEviLpUqeL6b22JDxib8J757DkRKShCfx19HS7E5GjVI2oSt+GfTFIAwsPLXT+RCEhKuQQArN8T5Gbq+ysQhQLRSdIy01j+dHlBIkgk4nHp6hVCxo3Vh6wu3ezebMqfxsZ6WDxKjeiCfy0PGVn8FQeEN3p08f2iCksDMaP91hzHMFegf8zMNSdDQngPnzF0ahaNeUkVDpUqBZJNOEoGcRw7dh2Pun7onVWcw+4WOozYMf3LAkJUFCgnPWqVHH6NP8c/ocCYwG9G/QmLipOxwbqiJlaX5M3Tz6pZvW+wIAmAwgNKk5M46k8ILojhJrlW3JIio6GadOUg6gPYq/AXwxcL4T4SggxXAhxVenFnY0M4DxnM8+y/tR6woPDuabpNd5uDlOmKGehCDMTaJ9gJfzONerO5Fd9M6WDJvAXHlxIgaHA+RMF7PieRedwvGEtfFCdr1Ek8M/+tZ5//lEy58UXvdwmM6JCowgJKnYb82QeEN0ZMcKyujI2VtlMfBR7Bf5fQGNgBPALsLRoWWL2GsAHmXtgLhLJgCYDiAmL8XZzEELF4585o3LrA/SQSuA3vecKn60s2bR6U9rUaENaXhqrT7hQ5labhW3c6LOevBUKHWozFBoLmX9wPuB9LZlNiu4xd4UaTI4ereSPr7DkyJIylSc9lQdEd2JjoXLlkuuio2HGDJ/IEmoNewX+lVaWq8xe7UYIMUoIcVQIkSuESBBC9Cln/zAhxJSiY/KEECeEEE87cs1LFc1+72sdVbVq8NprcOON0N3oewl3LKHN8rXZnlOY21p37dKpZQGsosWlu2C/35m2k9TcVFrHtaZ5bHOdGuYGOnbEGBpOw+y9NKicypgx3m5QSaaunEqeoWyYjqfygOhKXp6qzaAhhDIbXeeD/h1m2Fstb2V5i70XFELcDnwAvA50BtYCC8tJ3vMzKlLgEaAlcCvgezEPPkZWfhZLjywFioWVrzF1Qj5d2QzAmQa+k3DHEtqgae6Bua7ZHQN2fPdhHiPdt69SJUVEwNatTsdIr72gzAK+NmgujQwNY3dkFwBeu2GjT/nC7D63m4REy07BnsoDoivbtinfEE0lGREBn3yCz6ooi/BGbsgxwDdSyplSyr1SyqdQyXwet7SzEOIa4GrgOinlEinlMSnlBinlCs812T9ZemQpuYW5dK/bnTqVfMRzpxTtDNuJJJd9tGTaZ9W93RybXF73cuKi4jiScoS95/eWf4A1AnZ899GggQqN2revuJhMXh48+qiafbVsCZddZvfppJT8d0HlSvd1gb98OSxOV4Pm4XV9azA5bc008g35VrdreUD8Bk1z1LWrEvJDh0JnL5dKtgN7M+0tK2exK4BbCBEGdEE5AZqzGLDmVXMjsAkYI4Q4JYQ4KIT4UAjhfYO0jzPvwDzAxx2Niv446+nB55/D6dNebo8NgoOCub7F9YCLav1Ail33YSlGWkqnY6R3J+8mMTeRmtE1ubyu82YBdyOlirtfj3q2Irb51mByx9kdGKT1wjmeyAOiK5rAv/126NcP/vc/77bHTuyd4QcBotQSB/QCWhR9toc4IBg4W2r9WcBaupUmQG+gIyrb35Mo9f43dl7zkkRKaXI00oSUT1L0xym87HLy81VEiy+jS3hex44qQPrgQTh/XqeWBQB0j5HWBs1Dmg8hOMg3I0gAli5VRdv2VSkaTG7YUFydygfY8fgOU74P40QjDaooC+6Ghzd4LA+IrmgC/+qrlWqlXj3b+/sIdrkTSin7W1ovhGgK/Imyx7uLIEACd0kp04qu+yTwjxCilpSyxOBBCPEIytZPrVq1WLFiha6NyczM1P2c7uBAxgESMxOpEV6Di3svsmLfihLbfeU+uq9cSRRQ8/ooxFbJ559L+vbdQM2a9mdv9uS9RBoiCRWhrDu5jj8X/0nVsKpOnadTs2ZU3bmTHV98wcUexb4LvvK76IHX7uWdd+DUKXq//DIheXn898orFFSqpNKe1qunYvPtZNbWWQA0ym/ks7+LlDB6dGegCj1vzSfvzzjCz59nww8/kNOgpGuUrzxfXaK7cCLtBNMXT+fBxjYq/NjAW/cSmpZGr8OHMUREsObCBaSLbfDofUgpXVqAu4Gtdu4bBhQCt5Za/zGw0sox3wKHSq2rjxoEdLN1vS5duki9Wb58ue7ndAeTV0yWTEI+OvdRi9t94j4uXpQSpAwPlzI/X95+u/o4apRjp/H0vVz7w7WSScivt37t/Emee07d7CuvlFjtE7+LTnjtXs6flzI0VH2/5kuDBlIWFNh/mqzzMmhykAyZHCLTc9Pd2GDXWLRI3V5cnJQZGVLKm29WK775psy+vvJ8/XPoH8kkZIdPOjh9Dq/dy7x56vvt00eX07njPoDN0oJM1MNpLxml1rdncJEPJAADS20aiPLWt8R/QHwpm712PRfrlVZcNFWkz6nzzb2oBwxQ62rXht9/Z+LQrQgh+eILycmT3m2mLXRR62vlTDdu1KFFAUoQG1u2DK4TMdKLDi3CKI10rNKRSuG+mTlNSpg0Sb1//nlVHMda5Txfol/DfsSExbDj7A5Opvnwn90SOoR6eguXBL4QIhbldX/YgcPeBUYIIR4WQrQWQnwAxAOfFp3zOyHEd2b7zwIuAF8LIdoKIXqhwvrmSCnPudL+ikpSZhKbzmwiIiSCqxr7WBJEcy/qLVvUupMnYeRI2jxxJbeLX8nPF7zuTiORi2gCf/HhxeQVOlk40lzg+1tqUX+gbt3i907GSGs+MPVD6/usUFqyRMn1uDgYNapopR+EfYaHhDOgiRrw+12mvYou8IsS3hwptZwCklAhc3Z7wUgpZwOji47ZhnLIu05Kqc3WGxQt2v6ZwACgCspb/xdgJeCc4ecSYOFBVeDlqsZXERUaVc7eHsaSF7XRaPKinhg6DYGRL7/EZ2f59avUp33N9mTmZzqfda9hQ6hRAy5cgKNH9W1gABVvr+FEjHShsdBUKKlVdCufrOwmJUyerN4/91zR7B5U2GFwsErslJ3ttfaVx/XNlfZRG1j5BUZjsVauh2/nDbGEvTP8lRaWucAEoJWU0qEYJSnlDCllIylluJSyi5Ryldm2/rKUk6CUcr+U8hopZZSUsq6U8gkppe16r5cw8w4WqfOb+5g6H8r1om4dcZTbbzFQUABvvum5ZjnKkOZDAJh/wMnOSoiAWt9dlM5i6ESM9LqT60jNTQUgLizOJyu7/fuvyiVUvTo88YTZhqgopdEwGFTCIR9Fqzq49MhScgpyvNwaOzl4EFJTIT7ebzzzzbE3094IKeUDpZbHpZRvSCkdUecHcDN5hXksPqzSHAxpMcTLrbGAVmnKvHqORlGlqfGTQxECZs703bh87btdcMgFdWRA4LuHhAQ1E2vfHvr3dypGWvOB0fC1ym7ms/uxYy0UZ/ODZ6tOpTpcVucycgpzWHFshbebYx9+rM4H72TaC+BGVh1fRWZ+Jh1qdTDFuvocI0ZYTm9aVGmqbVsYPhzy8+GttzzeOrvoUa8H1SKqceDCAQ5dPOTcSfygU/ZLtE75iiucjpEurWb2tcpuy5fDmjVqdv/kkxZ26NZNvW7a5NF2OYpJU+Yvan3NEdJPBb7vlvUJ4BTaH0f7I/kksbHQqBEcMhOUpbyoJ0yAX3+Fzz+HceN8p6a3RkhQCNc2u5afdv3E/APzmb17NnuS97Dz8Z3Ur1LfvpNonfKWLSovd2io7f0D2Ic2gHKyUz6WeozdybvLrNcqux1udrhEmVdvoM3ux4yxUnrdTwaTQ5oPYeqqqcw/OJ+P5EcIISgoKODUqVPk5ubaPLZKlSrs3etCimtnGD4chg1TRbB0urYr9xEREUG9evUItbPvCAj8CoSU0hQq5nPheKUxd9yz4EXdvj3cfDP8/ju8/Ta8+64X2lgOQ5oPUQL/4HxOZ5wmLS+NsYvHMvvW2fadIDYWmjaFw4dh927o1Mmt7b1kcFHtassvQ6vs9nhXi6U/PMLKlbBqlYpufeopKzu1bauyOR4+rBxDfalOrhnd6najRlQNjqUeY+/5vbSp0YZTp05RqVIlGjVqhLDhaJmRkUEli6MdN2E0KidIKaFDB+UYqQPO3oeUkgsXLnDq1CkaN25s1zEBlX4FYv+F/RxJOUJsZKxP5/2msFA5v2hY8aKeMEG9fvopnC2djNkHGNRsEALByuMrOZV2CsBx5y4/mYn5DYmJKryjUiVVKMcJft71s9VtvlDZbcoU9Tp6dNmS7CZCQqCLqpzH5s2eaJZTBIkgBjdXtQ20gVZubi6xsbE2hb1X0IR9ZKRuwt4VhBDExsaWqwkxJyDwKxDaH2Zw88E+nfebPXvUn6d6dZuVpjp1UtqznByVLdXXiIuKo0e9HuQb8pEoZy6HnbsCAl9ftO+xWzenOuWs/CzWnrKWA0zhzcpu//0Hy5YpQf/00+XsrJmMfPzZsmTHd0TYp6SosV3jxsrRNyVF9yYqsrLUa+mwYi/i6KAoIPArEJrHuE/b76G4A+rbt9xKUxMnqtcZM3yzzowWWqQJfIeduwICX19cVOf/e/RfjNJ20RlPV3YzF2iPPqrWPf207RpBgN88W9c0vYZgEcyaE2tMoZD2IKXSAsbHK3egY8fg2WfV5wkT3JDPShP4UbZzm3z66ad89913Nvexh4sXLzJw4ECaN2/OwIEDSdFhJOOywBdC1BdC+Kg7+KVDel46q4+vJkgEcU3Ta7zdHNuYC/xyvKi7dFGm/exseO89D7XPAWIjy9pGNeeuQqOFSITSdO6sZqK7d0NmphtaeImhPVuasHMQbaA2uf9kUxW3LnW6mN57srKbJYG2u8iXUNMu28RPsjlWjahK7wa9MUiDKaTYHl59NYx334Xc3OLCgFlZ6vO77xZPFnRDS2JkY4ZfWFjIY489xn333efy5d544w2uvvpqDh48yNVXX80bb7zh8jn1mOEfKVoqNunpsGwZ0Ud881b/PfIvBcYCetbrSfXI6t5ujm0c7JQ1W/5HH7lRXeckP+740eJ6zbmrXCIjlQOQ0VicajiAcxiNxWFoTgh8KaVJ4PuClmziRMoINI1PP7VDoDVurJz1zp3z3bSVRWjfd+n8B9ZISYGPPgqzmkgwO1uZAVNTnWtPVlYWQ4YMoWPHjrRr147Zs2aRsHUr/R59lC59+zJo0CASExMB6N+/P6NHj6Zr16588MEHTJo0iXeKbJCHDx/m2muvpUuXLvTp04d9+/YB8Ouvv9KuXTs6duzItddea7ENf/31F/fffz8A999/P3/++adzN2OGHgJ/atFSsfn0U7j6auL/+svbLbGI1lFpKmafJSsLdu5Us1o7s5/16KHq7GRkwIcfurl9DrD73G62JFkW0g45d/mJ6tXnOXhQDczj49XiIHuS93Ay/SS1omvRuY5jmfn0JiVFCSyXBJoQ/mPHL0pktfDQQpN5zBZz5qhqx7YIDlahvc6waNEi4uPj2b59O7t27eLaPn146u23mfPhhyQkJPDggw/y8ssvm/bPz89n8+bNPPfccyXO88gjj/DRRx+RkJDAO++8w6iiggdTpkzhn3/+Yfv27fz8s2Un0bNnz1KnKB65du3anNXBc9llgS+lnCKlnOxyS3wVrbrb9OkAVNq5U9mcZ89WeS1PnLCcRMaDSClN9nufF/hbthRnQSvHFmaONsv/4APVp/sC09ZMI9+Qb3W73c5dAYGvD9rsXhNyDqINmgc3H0yQ8K5705w55fsc2iXQ/OTZah3XmkZVG3E++zz5hdb/UxpJScqZ1xbZ2Wo/Z2jfvj1LlizhxRdfZPXq1Zw8dIhdR44w8JFH6NSpE6+++iqnTp0y7X/77beXOUdmZiZr167l1ltvpVOnTjz66KMmrUCvXr0YMWIEM2fOxGAwlNseIYQuUQuBOPzyaNBACamioXTMiRMqE0xkpBpi5uVB8+awY4fXmrjj7A7OZJyhTkwdOtbq6LV22IWTNta+fdWyapVy4Bs3zg1tc5AdZ3dgkNb/rHY7d5l3yqaSZwEcxlWBrw2am3l/0JyUVH7dG7sEmp9k3BNCMLjZYD7Z/Ak5heXn1a9dW3XBtr6jqCi1nzO0aNGCLVu2sGDBAsaPH89VnTrRtkkT1i1fbjGnQbQFu77RaKRq1aps27atzLZPP/2UDRs2MH/+fPr168eWLVsYO3YsW7duJT4+ngULFlCrVi0SExOpU6cOiYmJ1KxZ07mbMcPuYawQoqoQYrIQYrEQYnfR6yQhRFWXW+HLlKruFmQwqBl9UXU3hIDBg73XPkqq830udrU0LnTK44tqMv7vf8UOs95kx+M7TE5cA5sMBODHm3903LmrdWv1jB0/TujFi25udQXGBft9Wm4aa06sIVgEM7DpQJ0b5ji1a5evALNLoGn/s82bVTEdH0bTTtpTSGf48LJ+DaUxGODWW51ry5kzZ4iKiuKee+7h+eefZ0NCAskpKazbuROAgoICdu8um43RnMqVK9O4cWN+LVLDSCnZvn07oGz7l19+OVOmTCE2NpaTJ0/y9ddfs23bNhYsUP35sGHD+PbbbwH49ttvueGGG5y7GTPsLY/bETgIvAREAHuKXv8POCCEaO9yS3yVcqq7ERZWLIm8hN+o88ElgT9ggIq2On8ePvtM53a5yOBmatDnVK714GBlOgIqFzn1BHCQgoLiynBF36UjLD2ylEJjIb0a9KJqRFV92+YEw4eXL5/tEmi1aqlSzJmZ4OPP1pWNriQ8OJx8Qz4FhgKb+6osg/lWB0VRUaqoULmhi1bYuXMn3bt3p1OnTkyeNIkpjzzCnLff5sWJE+nYsSOdOnVi7Vrb+RoAfvzxR7788ks6duxI27Zt+avIB+z555+nffv2tGvXjssvv5yOHctqZseNG8eSJUto3rw5S5cuZZweak0pZbkLqhzuPqBhqfWNitavsOc8nl66dOkideGdd6SMipJSBbcUL9HRUs6Yoc81nORi9kUZNDlIhk4JlWm5aXYft3z5cvc1yhrnz6vvLTJSyoICp04xb546Re3aUubkqHVeuZdS7EveJ5mEjH0zVhYaCu0/sEsXKatWlbJ6dSlBHh00SD1vP/8s5X//SXn8uNPflbfx6O+ydat6MJo1c+rwB/98UDIJ+cbqN8ps89bzNX68lMHBZbsdUN3R+PF2nujWW9VBX33lE/8VWwz6fpBcuHahPJ91vtx909LS5fjxUkZESBkUVNwlR0So78Zo1KlRKSlSbtok5b59Op2wJOnp6S4dv2fPnjLrgM3Sgky0V6XfDZggpTxearBwDHgFcC7o1V8YMcLycLuoups3+efwPxilkT4N+1A53FqeTR9BS/HZubOpSI6jXHedOjwpCb76Sse2uUiL2BY0qdaECzkX2HzGgVSmDRoo01CRKr/SsWPw0kvqubruOpVx5bLL3NPoioR5hj0HkT7q9Prgg8Xh85GRynoYHa0yUY8ZU5xi1yZdu8JcVV+Dxx9XvkY+5nRsjvb9p+WllbuvEDB1Kpw5o4KopkxRuToSE9V63aybPphhz1ns7XUvAHlWtuUWba+4xMaqzvePPzAGBRFkNCqdkVl1N29hst/7gKNRubjoVAXqTzx+PNxyC0ybBu+/rwqIHDyo1KDVqunTVMfbJbiu2XVM3zSdBQcXcHk9OzO9jR4NS5aYku5UPnlSqacLilSakZFe9xHxC1x4trYlbSMpM4l6levRrmY7nRvmPG+/rezUt92mzFlJScpmf+utDqiqGzSAhCI/krw89Vy99JIaNfiI07E51zW/jkP7D5GWm4aU0i6fpGrV3DzvqkAC394Z/ifA80KICPOVQohIYCzwsd4N8zlGj4agILK0Oq0NG5ao7uYNjNLIwkMLAd+amVhFB4EPcMMNEBcHp06pDGT5+W5OqWknWhEQbbZoF6V8REJLux37gI+IX+DCs2U+aPYVp9fTp+HLL9UA95VXlECbMEG9OmSXLu10rA0mfcjp2Jxm1ZsREhSCQRrIyvcBz1wpK5TAtzo9FUKYK4wE0BA4IYRYAJwFagHXATmA/QHV/kqfPhASQkb9+lQ6fRoGDdJRZ+Qcm89s5nz2eRpVbUSruFZebUu5SOmS2tWcSZOKY/GlBINBmP6TWhndqV5IBdW/UX8iQiLYfGYzZzPPUiumVvkHCaE65YkTy8YYRUcrNYYnS4D6I9nZsGuXmrHamczJHG2Apg3YfIF33lED2eHDoU0bF07Up4+aAhf9QaLPnCm53QcHlJGhkYBS68eEx3i3MXl5ypwbGqoWP8fWDH+82fIyUA+IA+4Dni96jQPqF22v2AgB9eqRXr+++lz6j+MFTIlCmg32mZmJVU6fVjrJKlWgWTOnT6NlIMs3y82xfXsN03tXU2q6QlRoFFc2uhJQvhV248M+In7Btm3q+2vb1uFZ2IXsC6w/tZ7QoFCubny1e9rnIOfOFUehuCyLtQFlURafyuYpdn10QBkZUizwvY55wRxf72PtwKrAl1IGObD4cC1WHdm4kYxWRTNpH0hk4bfq/PJyYtrAUgayf/9tWOKzKyk1XcWp8DzNR8Sc6Gif8BHxC1yIv198eDFGaaRvw75UCvcNwffeeyqL3NChYCFay3FGjDAJq0rmAt9HB5QRIREEiSCyC7JtZrI0p/83/en/TX/9G2NHwRx/otyeVwgRJoR4RgjhO94s3iI2lux+/ZQj1dGjXq3XmpyVzKbTmwgPDjfNKn0anez3ljKQnT1b8s/oSkpNV9HUwv8c/se+ankao0eXnEG0bet1HxG/wRX7/aFiLZkvkJICHxd5RL2sl940NhZ69QLMBL4PDyiFEFQKU4Mvu2pRoNJYb0vaxsk0nYsEOWG/16s87q+//krbtm0JCgpi82YHIn9sUK7Al1LmA28APl6CzTPI4ODiMCmdfgRn+OfwP0gk/Rr1IzrMD0afOgl83TKQuYlm1ZvRIrYFqbmpbDi1wf4D+/SB0FByNY+sF16oECpEj+Dks2WURhYdWgT4jpbso4+UP52WZEo3imwDUcnJ6nO7dj49oKwSUQVQGRDt4XTGadLy0hi7eKx+jZDS4Rm+nuVx27Vrx++//07fvn1dPpeGvbrVvUAT3a7q7/hAfmq/CseTsnhw5KLAt5SBrFKlkhGjrqTU1AOn1PpCwOuvk6H5iBSF6QUoh9RUOHAAwsNVQSYH8DWn14wMFWYKbvCju/pqEAIhpXLU++QTnx5QVglXAj89Lx2jtJ1DN7cwl9PppwGYe2AuWxO3unz9rKwshgweTMc77qDdHXcw+7ffSEhIoF+/fnTp0sUj5XFbt25Ny5YtXb4Xc+wV+BOBCRU6ha4jeLnkpMFoMDmF+ZJnsVUOHVIdc+3aULeuS6eqVk2lzDSf5ffrV6zGi4x0LaWmHmizxfkH5zt24IgRZGgOjT7gI+IXaAPJTp0c9qL2NafXTz9VKv1evVShKF0RQtWZBmjVyqloBk8SHhJOREiEXeF5s3bOMpXUzS3M5dF5j2qZYJ1m0aJFxNeowfZZs9j1zz9ce+21PPXUU8yZM8dj5XHdgb0GnBeBGGCrEOIYkAglihZLKWU/ndvmu5jP8KX0+Eh54+mNXMy5SNNqTWlevblHr+0U5ipXHb4rLcPYO+8oB72ePROZN08JyltusTMDmRvp27AvUaFRbD+7nTMZZ4ivZGdt9thY0nv1gj/+CAh8e3HBVORLTq85Oep5BjW7d0uXcscdsG6dSsbjB1QJr0JuYS5peWlWHSqllExZOcWkBZBI9iTvYcHBBQxpMcTpa7dv357nnnmGF4OCuP6mm6jWrBm7du1i4EBVWMlgMJhq1UP55XE18vKUNlIrj3vbbbeZzukJ7J3hG1AFc1YDJ4HConXaUk7dogpGs2ZqCnn2rMr+4mG0jspXZiblopP9XsM8peZ770Hjxga0QlJnznhfUxkREsFVja8CMNmI7SVTU+Ft21Yy9jCAZZx8tjSn17DgMJ9wev3iCxWO16WLSvHhFq4sus9du9x0AX2xx46/5MgSLuSUTPSaVZDFqPmjHHOaLUWLFi3Y8ssvtG/alPHvvMNvv/1G27Zt2bZtG9u2bWPnzp0sXrzYtH955XG1Ze/evYBy7Hv11Vc5efIk/fr148KFCzzwwAN06tSJ69zoW2GXwJdS9pdSXmlrcVsLfREhiityeXgm1v+b/ry3/j3AN2YmdqGzwNfQUmrWqQPffAOVK8OyZWoS42003wpHq+cVxsRAixZK2BeV4gxgAyefLc3ptX+j/l53es3Ph7feUu9fftmNA9bWrTFERMCxY6A57/kwMWExBIkgcgpzyC+0PPidunIqmfll/V0u5Fxg5paZTl/7zKlTRAH3XHedKo+7YQPJycmsK+pcPFEe1x04HxB9qaPF/HpY4GfmZ5KZn0l4cDj9G/X36LWdorAQtmxR750oW2ovVavCk0+q96+95rbL2I3mW7HkyJJyS32WwQecQv2CpCSlYatcWRUZcgBz+723+e47dRtt24IOJc+tExJCRvMiE6AXI4zsJUgEmQqCWUrCs/f8XhISEywem1WQxUtLX7I7rK80Ozdvpvv999PpnnuY/OqrTJkyhTlz5vDiiy96rDzuH3/8Qb169Vi3bh1DhgxhkA6qH4eCMIUQ1YDmQETpbVLKVS63xp/wUqd8NPUoANUiq5lSUPo0e/YoA2XjxioBvhsZPVp5Oc+fr0qje9MvqVHVRrSOa83e83tZe3It/Ro54OLSrRv8+KN6th57zH2N9He0/16XLg4lczJ3evW2lqywUCW7A/i//3MpJ5VdZLRsSdWdO9V350M59K1RJbwKqbmppOWlUSO6Rolt/9v4P5uJefIN+by66lXeGviWw9cd1LMng376SfVZjRqZ1q9aVVbMrVixosTnSZMmmd43btyYRYvKmvV+//130/uMjAyLptmbbrqJm266yeG228Kux0sIESGEmAUkA+uA5RaWSwtN4G/erEpaeYDcwlxSclIAlRJUj/ATt+Mmdb4latSARx9V719/3e2XKxdNmDiq1g/M8O3EydoMmtNrk2pNvO70+vPPcOSIKlhnwe9Ld3wpU6g92ArP231+NwZpISV1ETmFOfx79F/nLqzF35eX9MPPsHc8OQHoD9yPKqTzJPAwsAY4DFzvjsb5NHXrKuNxWpqqzeoBvt/+vSn8pMBYoEv4idvxoMAHFZIXFga//aaUC95EUxdrTpZ206mTCj/Yvbs401eAsjj5bJm8871cHc9oLDY/vfRS2ZTR7qCEwPf1vgMICwkjMiQSozSWsdWvu28d8hVpc0l4xLLKv1wqUIU8c+wV+LcAUwAtYHCDlPLrolC87YDlzAEVHQ/a8aWUTFg+ocQ6LfzEp9FmYU7kOXeG+Hh46CHVl2mqUm/Ru0FvYsJi2Hlup2MpP6OiVCY0o1HZJgKURUqnc+ib7PdezmHx+++wb5+KkrvnHs9cMyc+Xnm7nj2rClr5AY5m3XOZwkLIzVXek5F+YDZ1AHsFfgNgt5TSABQA5sOerwAPKKN8EA+qXpccWcLFnIsl1ukRfuJWcnKUp3lQUHE6Yg/wwgsqRfisWXD4sMcuW4bwkHBTBTaHZ/kBtb5tjh6FixehZk3QshPawdnMsyQkJhAREuFVp1cp4dVX1ftx4zxYedWLEUbOoqn1PVY9z1yd726nCg9j791cQCXeARWHb+5SGAdUrGGQvXgw497UlVMpMJb19nY1/MStbNumRsutW0OM5+paN2oE996rJshvvOGxy1pEs+M7LPC9FAXiN5jb7x1Qy2t5Efo36k9UqPfss/Pnw/btyir4wAMevrifDSajw6IJEkHkFuaSV5hX/gGuYl4St4Jhr8BfD2g+z78BU4UQLwkhngfeRtnyLz20kfK2bVDgYOiVA+w+t5vNZyyH0bgafuJWXChb6irjxqnB+bffwokTHr+8Cc2Ov/TIUrtLfQJeT9/s87hov/dmOJ757P755yGiTMyTm/EzgW8tPC+qb19lnmjdGqpXV/a8//0PZs+GtWvVH7/QCe1nBSuJa469Av9NYF/R+1eBZSib/pvAEeBx/ZvmB1SvrrLu5ea6NXvVtDXTyDNYH9lq4Sc+h4cd9sxp0UJ5PRcUwNtve/zyJupXqU+7mu3IzM9kzQkHxsVt2yr74eHDSnUdoCROPFuFxkKPh+OlpKgUAY0bw8yZ6vO//8KGDSri65FHPNKMknghwshVTGp9Mzu+sV495TS9b5/6YhMTlffjyJGqEmDLls6ZEl102NOrPO7zzz9Pq1at6NChAzfddBOpqakun9PeTHubpZS/F73PkFLeglLxV5VSXiGl9OIcyst4YCa24+wOk3e+JVwKP3EnHnbYK83//Z96nTlT5WjxFk5l3QsNLU4k4CczMY9hMBQnc3JA4K87uY7U3FRaxLagWfVmbmqcQkqYMEFNOg8dUsntnn222KkUYMwYL00itQij1FTvOrk4gOa4l5GfYQrPK3jiibJfYEGBKjuYlqZMPY7mGigoUKkPg4KcUr3oWR534MCB7Nq1ix07dtCiRQum6eCF7LRHgpQyT0rpg3pkD+MBW+vmRzZTKUwVjzj2zDF9w0/chVa2NCzM4bKletGuHdx8M+TlKU2ft9C8wR2OqAjY8S2zd6+ahTVqpJIv2In2/Q9p7nxRFXuZOBHefVcp/7RJdFaW+nzihJIlRYXTvIP2bPmJySgsuDg8LyMvAwDDFVfYLosZFuZ4neGi2X0WMOT66+nYsSPt2rVj9uzZHi+Pe8011xASonLj9ejRg1M61G2xKvCFEDc7ejIhRB0hRA/XmuRneMAe9t+J/8jIz6BtjbY0rNrQbdfRFS11Z+fO6o/nJbQKlp98AufPe6cNver3onJ4Zfae38ux1GP2Hxiw41vGyYQ7Cw4pge9udX5Kiqp8p5mCLVFQ4OUweD8T+GAWnqfZ8YVQ6TUtOddFR6u43EqWq+xZpehHW7RpE/Hx8Wzfvp1du3Z5vTzuV199xWAdMiPamuF/JITYJoR4TAhR3dZJhBB9hBCfA4eADi63yp/o3FllzNi1y21JUrSZibfTgDqEF+335lx2mTLnZWWpynreIDQ4lIFNVAlMh2b55p2yHyRJ8RhOPFun0k+x4+wOokOj6dOgj5sappgzp/wkOuHhUFRTxTv4ocCvGl4VKBWPP2KEMvGUJjZW2fIdpagPb9+5M0uWLOHFF19k9erVnDx50lQet1OnTrz66qslZtzllcft1KkTjz76qEkroJXHnTlzJgZL7TfjtddeIyQkhLvvvtvx+ymFLYHfHPgd5Zx3VgixQwjxvRDiXSHENCHEp0KIxUKIi8CKov0HSik/d7lV/oQHkqR4amaiK16235ujafU++kjNvryBpkaef3C+/Qc1bVqcJMULZZh9FicEvjbQGtBkAOEh4e5olYmkJNuze1ApKrzpV2KKMNq61W/KMEeHRRMsgskz5JFbmKtWxsaqEX2JHaNhxgyVjMMRpDQJ/BYdO7Jlyxbat2/P+PHjvVYe95tvvmHevHn8+OOPumSFtCrwpZTZUsopQD3gHmAz0AV4EHgWGAoEAx8AbYvK5JZfPqgi4ka1/rHUY+xJ3kPl8Mr0qt9L9/O7DR+Z4QP07AkDBihfng8/9E4bNDv+sqPLyC4oRxpoCBFQ65cmNxd27FDfTZcudh/mSft97drlh3BHRan9vEbVqsqLPS/Pb8owCyEsZ90bPbo4QY4QagLmTE35/HwVxhcSwpnz54mKiuKee+7xWnncRYsW8dZbb/H3338TpVNOgHKd9qSU+VLK2VLKB6WUbaSUVaWUEVLKulLKq6WUk6WU+8o7T4XGjeoxraO6puk1hAZ7Kh2Xi5w5o9J2Vq6s4uN8gAlFWYnffx/SveBqWjumNl3qdCG3MJcVx1bYf6CfxUy7ne3blQG8dWu77bN5hXksPbIU8Ew63eHDLWuZzTEY4NZb3d4U2/ihWt9i1r0+fYpn8xERymHHmdmwWcKdnbt20b17dzp16sTkyZO9Uh73ySefJCMjw2RGeEyHypkO6jwCWMSNnbLJft/Mj9T52vfQtavPpKbs21ctq1bB9OnFIXueZEjzISQkJjD/wHz7zTN+2Cm7FSc0R6tPrCarIIsOtTpQr3I9NzWsmGrVVBGnd9+1rNqPilIhebYczD1C9+7w/ffqO33cP1KpaAl4MvIyqBVaS60UAurVU2UHhw51vi62Wfz9oEGDLNaf92R53EOHDtnZcPvxjd7Y32nbVo0sdU6SklOQw7KjywC4tpkf1SfyIXW+Odos/913ITPT9r7uQBPy8w/Ot7/KoR8mSXErTjxb8w8ovwlPDpqnTFFCPSKi2IEvJER9HjNGbfc6fmguCg0OJTo0Gokkq9DMSXrjRujf37X4W03gezANuKcJCHw9CA0tzuik4yx/5fGV5BTmcFmdy6hTqY5u53U7PuSwZ87VVyt7/oUL8Omnnr9+t7rdqBFVg+Npx9l7fq99B9Wpo2YvGRmwf797G+gPOCDw+3/Tn/7f9Dc5vQ5p4X77vYYQMHUqrFihxmnBwepzYqJ69WJV3mI6dlR915496vnyEzQ7fpbBTODHxsLy5eq/4gxSliyaU0EJCHy9cINa3y/V+eZlS31shi9E8Sy/vDhpdxAkgkw2ZG3WaReBBDwKLY1qWJgSVuWQb8gnITGBAxcOUDWiKj3qeT5FyPTp6i/x0EOqvoPX1fjmRESo71FKSPCxxF020MLzMgsz7deUlUdOjhqZhYd7sHSh5/GKwBdCjBJCHBVC5AohEoQQdgXGCiF6CyEKhRDuS1zvLDqrx6SUzDswD/CzcLxDh1SWvdq1nR9tu5Frr1WuBWfPqpS7nkYbvDkUnueHqle3sHmzEk6dOqmOuRxOZ5wmM1/ZbgY1HURIkGddlg4eVCWaQ0JUinefxEd9RGwJ8sjQSAQCgzToVzTMxfz53sLRAY/HBb4Q4nZUKN/rqAp8a4GFQogG5RxXDfgO8MGk8eieJGXf+X0cTT1KXFQc3ev6lmrcJuaze5/QW5bEfJb/5psqysuTDGo2iGARzJoTa0qGFtkiMMNXbNigXi+/vNxdcwtzOZ1+2vS5bY227mqVVV5/XU0a779fZQH2SXxQ4EdERHDhwgWrwkwIYXJyO5WuU34KPxT4UkouXLhAhAM5/73hpT8G+EZKqc2vnhJCXIuquGdrHPwl8C0ggOHubaITNGumquedPauSZTd0LQWu+ew+OKictF2+hI+q883RHHm3blWz/Kee8ty1q0ZUpVeDXqw6vorFhxdza1s7YrO0ePNt21SssBdTFXsVBwT+rJ2zTEVWAH7f+zvj+47XJXmJPRw+rBzgg4O9ExFiNz44mKxXrx6nTp0iOTnZ4nYpJefSzpk+F54rJCzYxf/EmTMq3DMkxOPVKXNzcx0S2uZERERQzwFNqlWBL4Qwgo0SbSWRUspyBw9CiDBU8p53Sm1aDFxh47hRQC1Uad4JdrbJswih/jyLFqmOyVWBf1AJ/OubX69H6zzH+vXq1ccc9swRQhU3uekmlW47OFg58tWurWKoq1Vz7/WHNB/CquOrmH9wvn0Cv0oVaNVK2a937CjOknYpIaXdAl9KyZSVU0pUmDx48SALDi7wmOPetGkq1n7ECGjSxCOXdI6WLVU+gxMnVOo/r2YDUoSGhtK4cWOr27/a+hUPL36YIIIwYKBz7c4kPJLg/GAuM1Ml6wkKUkk6IiOdbLlzrFixgs7OhhI6iC0hPQX7Bb69xKGy850ttf4sMMDSAUKI9sArQA8ppaG8H1UI8QjwCECtWrXKxEi6SmZmptVzNqpVi0bAyTlzOFyzptPXSC9IZ83xNQSLYCITI1mRbPl6rmDrPpxFFBTQJyGBIGBNQQGFOp/fGs7cS5Uq0KBBV06ciGHp0gP06nWGzEz4+mvV58XHu6etADWyVIW3v/f8zbIqywgSxZY1a/fSqn59au/bx4Hvv+eMN2IKnUDPZyw8KYmeZ89SULky/508aTPVcHpeOs/Ufoaf5c9sTN/I4LjBXF39ao5sO8KKM463x9H7SEqK4JtvuhMUJLj66o2sWJHj8DXdhaV76disGdW2bmXnV19x4Qqr8y6fIetcFm+3eJuvT3/N7qzdtBFt+Hvx36akPI5SZds2OhuNZDRtSoI2qPQg7uiLrSKl9NgCxKMGEX1LrZ8I7LewfziwB7jXbN0kYJc91+vSpYvUm+XLl1vfuHChlCBlr14uXWPWjlmSScirvr3KpfPYwuZ9OMvGjer+W7XS/9w2cOZexo+XMixMNbf0EhWltrsLo9EoG7zXQDIJueHUhhLbrN7L9Omqcffd576G6Yyuz9js2er+r7223F17f9lbMokyS/Rr0XLGphkOX9rR+3j0UdXUe+91+FJux+K9vPiiarA7H3qd+OfQPzLm9RjJJOSwj4eZftsG7zaQBYYC50761lvq/h97TN/G2ok7+mJgs7QgEz3ttHceMKDU8+bUAiyVkqgDtAa+LvLOL0QNDtoWfb7Gra11FE2NnZCg7EFO4vfqfDtsrN5EK19qrWZIdrbanprqnusLIUy/rearUS49ikLKtO/4UsNOdf7uc7tJSLQcYpZVkMVLS1/Sz7PbAsePw1dfKbORWfVU38YHHfesMXXlVFPkRZvoNqb157PPM3OLk2E3DviG+Dt2C3whRJgQ4gYhxAtCiImlFrvs6lLKfCABGFhq00CUt35pTgPtgU5my6eoMrydrBzjPapXh+bNiwt8OEGhsZCFBxcCcH0LPxP42h+nh+fjnR3BnvKlwcHuLV86tOVQAOYemGvfAR06qLjpAweUw8Glhp2d8rQ108g3WK/+lm/I59VVr+rZshK8/roa6991lzKP+wV+Uoa59GAuLizO9D67MNv5wZyPJgpzB3YJfCFEPLAP+AOYhlKrT0LZ1l8pem8v7wIjhBAPCyFaCyE+QKn6Py261ndCiO8ApJQFUspd5gtwDsgr+ux7xkytQ3LSFrT+1HpSclNoEduC5rHNdWyYB/CTGb495Uuzs91bvrR/o/5Eh0azLWkbJ9NOln9AaGixt74fzMR0paCgODFMOZ3yjrM7MEjrlWtyCnP496h7InuPHVOz+6Cg4tBPv6BuXZXRMTVVJQ/wUdwymEtMhJMnleNiq1YuttD3sXeG/zaQDDRAhcVdDjQBXkPNtu32Q5VSzgZGA+OBbUBv4Dop5fGiXRoULf6JiwJfU/F6ooynrpw/r2KRIiOhfXtvt8YmvlC+NCIkgmuaKouUw2p9LzgWeZWdO5XWrFkzlULVBjse38GpZ5VDX1RoFDkv5yBfkSWWhEfck1XutddUdVW/mt2Dsj+42G95ArcM5rTBc7duPlPoy53Ye4d9gP8BZ4o+G6WUx6SUE4E5gENVxqWUM6SUjaSU4VLKLlLKVWbb+ksp+9s4dpKUsp0j1/MoLnbKWufvt+r8rl2LS1X6KL5SvlT7je1W61+qdnwHbaxaSuoBTQYQEeJcfLOjHD0K33zjh7N7jZ491WtRvXdfZMfjO0oM3LrU6ULBhAKqRag42v1P7nd8MKcJfB/XSuqFvQI/FjgjpTQCWYB5pPIyoL/O7fJfOnRQaT/371feYQ5wNOUou5N3Uzm8Mr0b9HZTA92EHzm+aOVLrc3yIyPVdnfnPR/SfAgCwbKjy8jKzyr/APPBZEWsnNe1q/pxWrdW/jDx8ar62c8/q+3Nm6spdDl4w+lVm93ffTe0aOGxy+qHnw4mQ4JCTPUp5u63c+BsjtZvXQL2e7Bf4J9CxdADHAbMveO7Ax5OUOrDhIUVV85z0Naq5Vcf1HSQ65mjPI3WUfi4w56GeflSTZOnvfbt65nypbViatG9bnfyDHksObKk/APq1VNCMDVVOe9VNBo0KC6Qk5Ki7KsvvQRr1qjtr79e/N+yQk5BDksOq+/SUzUojhxRs/vgYD+d3YPyDwkOVs7GWXYMPn2IoS2UA6w20LOJ+aBy4EBYvVqtT0qCtWtVAiI7BpX+ir0CfznQr+j9Z8BYIcRiIcR8YCpKrR9Aw0l7mN+q841Gv1ONaeVLz5xRpXKnTIHHHlPbtm/3XI59rbOye3ZSke34o0eXzWVeUFCszQgKgsGDbZ5i+bHlppLSdSvXdU87S/Hqq8oEdM89Sgnhl0RHq8p5BoMqUuRHXNvsWoJFMKuPryYlpxytqvmgcunS4tjcF16A665TzhflDCr9GXsF/njgEwAp5SfAM0AUKk7+LeA5t7TOX3FC4KfnpbPsqMq6NriZ7U7N5zhwQP2J6tb1yQp5tqhWDUaOVDOz6dPVfz0pSQ0CPIEWnjf/4PwSud+t4qeqV7vo08e2HSU8HMaPt3kK06DZQ+r8Q4fgu+/U5Licpvk+fvpsVY2oSp+GfTBIA4sOLbK9s6VBJUBGhurDhCh3UOnP2CXwpZTnpZQHzD5/JKXsLaW8TEr5f1LKgErfHPNZmJ1xrYsOLaLAWMAV9a+gRnQNNzbODfhJOF55CAGTJ6v3b7zhGc1m+5rtaVClAWezzrLptB0FTPy0U7YLIVSHbMm5IiREJaivVMnq4VJK/t7/N+A5LdnkyWpSfN99KojAr/EDxz1r2K3WL29QGRZWAUZu1qn4cQjeoGFDqFlTJUg5fNiuQ/7a/xcAN7S8wZ0tcw9+5LBXHkOGqAidc+dgxgz3X08IUazWt8db349trXYxYoTlEIoqVZQqxgYJiQmczjhN3Up16RLfxT3tM2PPHvjxR5UiYeJEt1/O/ZgPJn04AY8ltP/QwoMLKTTasMHbGlRGR5c7qPR3HMm0108I8akQYoEQYlmpxTdr1HsLB+NaCwwFplAivxT4fuawZwshih323nxTFc9yN1pn9d7698pPxBMVpWytRqPf2VrtIjZW2VJLM21aueGef+1Tg+ZhLYeVKEjkLl55RcnFhx/24Xr3jtC0KcTFqRLfx455uzUO0Ty2OS1iW5CSm8J/J/6zvfOIEZYd82Jjyx1U+jv2Ztp7FOW4Nxyoikq+Y74ENAWlcUDgrz6xmtTcVFrHtfa/7HpZWSoxSnBwcSY4P2fQIOjVSylo3n/f/dfTsu5lF2STnJ/M2MVjbR/gB0lSXGL06JJJUEJC4KGHyj3Mk1qyrVtViubwcD/KmV8eQvi1yWhYi2FA8XNgldjYsiWmo6OVSs/Hc4i4ir2C+jlgFhAvpbxCSnll6cWNbfRPHOiUtZmJX87uExKUCrZ9e8vOMH6IECquGlQY+MWL7r1eeEg4A5qo6tB7Mvcw98BctiZutX6AH3fKdtGnT8mO94orys2CdiTlCDvP7aRyeGWubOz+7khT4Y8apXxVKwzas+WHdvwbW90IwJ/7/tQqq1rHPFmCENCunWXNUgXDXoFfF/i6qPhNAHvo3l09SFu3Qo71ethSyuKZSSs/FPgVyH5vTr9+Kkw3PR3eftv914uLUmkudmXuIrcwl0fnPWq90zLvlP3M1moXQqiYew07OmJt0Dy42WC357BYvx7mzVPWlXHj3Hopz6M57vnhYLJHvR7UjK7J0dSj7Dy30/bOZ84Uv4+IgE8+Uc9dBcdegZ+AA/nyAwCVK6tZb0GBTVvrjrM7OJ52nNoxtele1w+zPVUQD31LvFpUh+PDD91bSEdKyT+H/wHgSM4RJJI9yXtMfh1laN5cxRMmJanCHxWRESOKNUZXXFHu7tqgWZvluRNtdv/MM8o3t0LRrZtdExVfJDgo2KTW/3Pfn9Z3NBhKDmiGDoXOnd3bOB/BXoH/NDBaCNHXnY2pcPTqpV7/s+5EonVUQ1sM9Yijka5IWXxvdnTK/kb37nDDDapy3rRp7rvOkiNLSM1NBcCIisXPKshi1PxRlj2O/dzWahchIeqLDw0ta28txYXsC6w+sZrQoFC357BYsQKWLFHj+bHluFr4JZUqKfV2YSFs2eLt1jjMTa1vAuCPfX9Y32nPHqW6q1cP+vdXdrtLBHslzFygHrBcCJEhhDhRajle3gkuSRwQ+H5pvz9yRHn0xsX5aQLx8pkyRcnXTz9VWTfdwdSVU8nML1vp+ULOBWZumWn5IE2jUlEFvpbD4rLLVHEDG2hJi/o36k+ViCpua5KUxSr8555T6f4rJH6s1r+q8VXEhMWwLWkbx1KPWd5J80/o3RuWL/e7ZGGuYK/A/xf4HfgOlUb331LLMre0zt/RBP7atRaLnZxMO8mWxC1EhUZxdZOrPdw4JzDPQ33NNfDII2p9o0bqT1QB81B36AC3364ycLojv/7uc7tJSLRc4SurIIuXlr5Eep6F2EA7BpN+jXZf2n3aQFPfulud//ffahxSo4aqw1Bh8WPHvYiQCJOWR/PrKMPateq1Amoly8PeTHsjpJQP2Frc3VC/pGFDVezk4kVVPa8UWlawQU0HeayMp0uY56FesgSWFY3zdu6s0Hmop0xRUYdffw179+p77mlrppFvKPaFrR9ev8T2fEM+r656teyBl1+uPNe3bKmYCXjs7JRzCnJM/g/DWg5zW3MMBvi//1PvJ0yAmBi3Xcr7+PEMH8y89ff/aXkH7dnS7vMSws+Mxn6GEDZnYn6nzreWhzovr0LnoW7eXCVXMRr1j7necXYHBlmcWa5dpXYltucU5vDvUQt5rSpVgk6dlEbFwaqMPk9hYbGwKUfg/3v0X7ILsulSpwv1KrtPNfvDD8r026hRsWKrwtKihUo/e/q0XzqFXtf8OkKCQlh1fBUXsi+U3Hj+PBw8qMxEHTt6p4FexN7EO/fZWO4RQgwVQlw6hhBH0DqsUgI/JSeF5ceWEyyCGdJiiBca5gSXcB7qiRNVH/HHH/pOfHY8vgP5ijQttze6HYDqkdUpmFCAfEWS8IhllT+9e6tXrXxsRWHXLsjMhMaNoU4dm7tqalt3qvPz84XJM3/KFJVsp0ITFFSs1vdDk1HViKpc1fgqjNJoKqZkQjNTdOumHEIvMeyd4X8DfF20fGO2fA18C/wFHBNC/CiE8LNC7m7Gygz/7/1/U2gspH+j/qYYbJ/nEs5DHR8Pzz6r3r/4ovvC3xtGN6RlbEsu5lxk1fFVtneuqALfTvu9wWhwq5YsJQVmzoQffojnxAnlunLXXbpfxjfRni2tXryfcWPLGwEL3vqawL8E7fdgv8DvBRwHpgP9gFZFrzOAE8AQYBxwEzBJ91b6M506KQF58KCqyFLEnL1zABjeZriXGuYk1oqbXAJ5qF94QXlmr1oFCxe67zo3tVKhRTZjiaGkU2hFcpa0036/6vgqkrOTaV69Oe1qtrO5ryNIqez08fHw6KPw++8NAVUKd9KkipnrqAx9+qhXPxX4mj/H4sOLyS7ILt5wCTvsgf0Cfyzws5TyGSnlainlgaLXp4CfgEeklO8A/wPucFdj/ZLQUBXQDaaHLT0vncWHFyMQHkkUoiuWiptcInmoq1QpdtwaN87yuEcP7E4RGh8PTZoo9ffOcjKL+RNap1zODH/OHjVovrXNrQgds6RNnAjvvgu5uUq4Z2UppWVBgVpfISrjlUf37spEt2uXUnX4GXUr16V73e7kFObwzyHl1ElBQbG/SwUo9OUM9gr8a1Dhd5ZYBmgxZatQaXgDmFNKrT/vwDzyDfn0adiH2jG1vdgwJxk1quTnSyQPNcATT0D9+kq+zprlnmt0q9uN+ErxnEw/aTVkz0RFU+ufOaMqtVWuDG3bWt3NYDTw+77fAX21ZCkp8M47KuePJbKz1fbUVN0u6ZtERCihb55cy8/QNGW/7f1NrdixQ2UPbN5cxVZegtgr8PMAa6XQugBaXFEQUAFjhFyklMDXHsBbWt/irRa5hnkilMjISyYPNah+UIvHHz9ezQJTUlREYuPGyubr6oQoSASZbJC/7fnN9s4VTeBrs/sePVQspLXdTq4lKTOJJtWa0Kl2J90uP2dO2cu2b59c4nNwMPz6q26X9F38XK1/a5tbAeUvlVuYWzxwuQTD8TTsFfi/ApOFEM8JIRoKISKLXseibPazi/brBJQNOL/Ueekl9bp+PZkD+7Fgj3I0uvlgiOrg/C1hjdYpwyWVh1rj3ntVmYQTJ2DIEKVZP3RITUyffVZ9njDBNVvvrW1VZ/Xrnl9tq/XNBX5FMC7bmapZU+cPbz1cV3V+UlLZ2f111x0p8Tk72721FXwGPxf4Tas35bI6l5GRn6HU+quKnGC1/8wliL0CfwzwG/AWcATILHp9E5V577mi/XYBL+rcRv+nSVHdISnZeGgVucJAj1OCeqPG+WfCGq1TbtXqkspDrREcrNS6oHIP5eYWJ1LMylKfXbX19mnQh1rRtTiccpitSTZK5bZqpfwqNFW4v2OH/d4ojSYtmd5Or7Vrlw1CqVGjZBGZqCi1X4XniiuU5m7zZr8rpKOhzfJ/3f1LscDv18+LLfIu9mbay5FS3gO0BkYALxW9tpFS3iulzC3ab76UspxYokuQ0aNNDm1JRRm6hu+WkJHhfwlrzG168+ZdUnmozenWzXaJdldtvcFBwSZh9svuX6zvaJ7cyd/V+tnZKnNgUJDN6osbTm3gdMZpGlRpQNd424V1HGX4cJVG2RYGA9x6q66X9U2qVFHJaQoKistg+xmawN+/+k9ITlYjtebNvdsoL+JQpr0i7/zvpZRvFb0G1Pf20KePKT69Sp5adYt5ilZ/Slhz4IDKVlWrVrHm4hJkzpzyE7C4auu9re1tgBL4NtX6CUWOfU8/rWocPPGE0rzMnu1fJqP161U7O3Swmc/BXep8UDIuNtb69qgoVSXPVv6pCkUFUet3PVRkp+nf/5LxN7KE1TgqIUQDIFFKWVD03iZSSjfVEqsACAGPPQbTpnH5KehyGhqlFm3zt4Q12iyyd+9L+o+TlKRU97Zw1dbbq34v6sTU4WjqURISE6zPZps2VWlQU1NVjYMlS1Q4aESEmi3n5alZzY4dzjfGE6xYoV6vvNLqLlJKt+aw+P579ZtVqqRm+lqkaXS0mtmPGeOeIko+S58+8NFHfivwQc3yG31YVOr3Elbng+0Z/lFA88Y6VvTZ1hLAFmPGcDEC4nLgsc1m6/0tYY0DVcwqMpZsvaVx1dZrt1p/woSy6woK/M9ktHy5erUh8Ded2cSJtBPUrVSXy+tZV/s7Q2ZmsX/txx9DYiK8955ywnzvPfV56tRLbJyrzfDXrfMPLZEFbm09nH5FBdzzel2a8fcatgT+g8Bhs/flLQFskFs1hiXN1PthRYaQ7LAg5Mcf+1fCGm2kf4kL/OHDy0+8o4et1y61/tVXK7OQNfzBZJSdrezEQhQLGQto6vxbWt9CkNC39te0aUqod+8Od9+tKkGPHKnS+Y8ceQmp8c2pXRuaNVOjoW3bvN0ap2h6wUidTDgbDf+EHvd2c7yK1X+MlPJbKeWFovffFH22uniuyf7JgoML+KfI5F0zGwzA7pqwwJ/8R06cUPFnlSv7V1SBG6hWTdlyrc3y9bL1XlH/CuIrxXM87TibzmyyvJMQ1kON/MVktHat0kp07mz1S5NSFtvvdVbnHz1aHHDywQe2HTIvOfzcjs/KlQCsagi/7LkUEihYx6nHWghRRQjRNVAhz37eW/8eyxsXf84LhkeGGBm14AkKjX6iKtNUrv36+ZdWwk1MmaJsuhERJR34wsP1s/UGiSCz0CIbnZW1mq3+YjKyw36//tR6jqYeJb5SPFfU1zcX+gsvKFeHu+++ZLOuWqeCCPyVDc2S8FyiWBX4QohBQog3LKz/P+AcsAE4LoSYJYQI9P42SMtN478T/3GsGpwvSlK3uiFsqwMXci4wc8tM7zbQXpYtU69XXeXddvgIQiib7pkz8OGH0FDVWOHBB/W19WoC/5c9NtT6Q4aUvaA/1TjQBpP9+1vd5cedPwJwZ7s7CQ6ynoXPUVauVFEXUVHwRpkeL4BJ4PtjcicpTQI/uVtrMvIzWHx4sZcb5T1szfAfA1qYrxBCDAReBfYBo4HPgNuBZ9zUvgrB9I3Tkag/yj9FdvwNRbqRrIIsXlr6Eul56V5qnZ1IGRD4VqhWTU2w//xTqYJnzoQ9e/Q7f8/6PalbqS4n0k6w8fRGyzvFxKiaBhpC+E+Ng6wsVdQkKMiq/b7AUMDs3Sqh593t79bt0gYDPFPUe40bd8mmlbBN06bKlp+cDPv9LBL76FE4dQqqV6fz1fcA8NOun7zcKO9hS+B3BuaXWvcAkAsMklJ+JKUchRL6l0qVaKeYvmm66f2iIoF/WWLx9nxDPq+uetXDrXKQQ4fUHycurqRgCWCiUyelPS8sVLmW9JoMmav1Z+20UbHnxhuL30dE+E+Ng//+U1/aZZepQHgLLDmyhPPZ52kd11rX3Plffgnbt0ODBsrnIoAFhIC+fdV7zfTiLxTN7unblzs7qIHin/v+9P0JlpuwJfBrUuylrzEQWCOlNI8unk8pTUCAYpIyk0jKLP66ljdSr32PQ3CRl3dOYQ7/HrVWjNBH0Gb3V14Z8GiywauvKp+zJUvg77/1O+89HYpnJwWGAss7DRhQ/N6fahzYYb/X1Pl3t79bt2Q7588Xh+G9/XbJmlABSnF1UUHUJUu82w5H0Z6tfv1oWLUh/Rr2I7cwt/yiVBUUWz13BhCtfRBCNAdigfWl9ksH9DOoVTBm71JqyGEthyFfkZx6V0Lz5lTOh8Ih65GvSOQrkoRHyimD6m0C6ny7iIsrdtZ79ln9UpBfVucyWse1Jjk72boNskeP4rCBF/2opIXWKVux32fmZ/Lnvj8BuKu9fsrEcePg4kU1TrokUuW6wsCB6nXZsvLjUX0JbYZflHDn3g73AvDDzh+44ssrqPpGVU6mnfRW6zyOLYG/D7jB7PMNgARK9zaNgbM6t6vCMGuXUsGWsDtqMxnNUcnXMRqL2xoQ+OXy+OOqmt7RoyoiTg+EENzX8T4AvtvxneWdwsKKbeD79ulzYXeTmQmbNqk8xFZCC//a9xfZBdlcUf8KGldrbHEfR1m3TqnzQ0Nh+nT/sHx4lcaNlS0/NbU4lbOvc/y4WqpUUemagVva3EJ4cDjLjy7neNpx0vLSGLv40rHl2BL47wEPCyHmCCE+BiYDO4H/Su13HbDdTe3zaw5dPMTG0xuJCYvh+hbXF2/QhKa/CPzdu5XDTt26l3ThCXsJCVHmc1Be33r5Od3d/m4Egr/2/UVqbqrlnTTV678+biLS0Oz3Xbqo/A4WMFfn60FhIYwapd6PHauKVQawQdeuyjP1wgX1+amn/KNWgza779NHDSiBqhFVlbYVSWKGcqSae2AuWxNtVKSsQNhKvPMnyhO/G3AfSpV/qzSLCxJC1AYGAAvc2ko/5aedyhv0plY3ERVqlqFFU12uWVN+aS5fwFydH5gK2UWvXvDQQyqXzKhR+jjw1a9SnysbX0meIc+UgKYM5gLfH0Koykmney7rHIsPLyYkKMSUddBVPvlEJY1r0ABeflmXU1ZsGjRQKZq10o8bNyrnh5Ejfbu8t9Zvlcqfr6n1tcip3MJcHp33qO0CVRUEm95XUsoPpZQNpZSVpJRXSykPltqeJKWMk1J+7t5m+h9SStPMpIzdsVYtaNNGpRPdaCXMypcI2O+d4s03Vd6bZctglg3nekfQOqvvtltR63fqBNWrK1XmkSP6XNSdWLHfa/bVzzZ/hkEaGNR0EHFRcS5fLimpOMvwBx+oVAUBymH06LJflK/XapAS/vlHvR80qMSmQU0HlUjLLJHsSd7DgoMVf94acLd2ExtPb2T/hf3UiKrBgCYDyu7gL2r9wkK7vKgDlCU2Ft55R70fMwZSUlw/5y2tbyEyJJLVJ1ZzNMVCzaqgoOLfydfV+hkZsHmzUreWqs1wOuM0aXlpfLDhA0A/df6YMZCeriamN9xQ/v4BUCpxWzmifbFWw44danRXt26ZMOIVx1cQLEr6mWcVZDFq/ij/yXrqJAGB7ya+3PolAPd1vI+QIAuZzvzFcW/rVtVDNm1anEougN3cf7/qL8+d00d9XCm8Eje1vgmAH3b8YHknf7Hjr1ihPL67dSuR6z+3MJfT6acBlYkyMiSSYS2HuXy5+fPhp59U+N1HHwWsU3YjhJrlWyoc4au1GhYtUq/XXlvmh566cioFxrKhrX6V9dRJAgLfDWTmZ5qyOT3U+SHLO/Xrpx7EtWv1i91yBwF1vksIoWzGISHw6afF1YVd4b4Oxd76Fu2OmsBftkxFWPgqC4pUqKXUwbN2zjLZVwGiw6JL+sA4QUaGip4AlSuhSROXTnfpMWKE5XA8X63VYC7wzdh9bjcJiZajDPwm66kLBAS+G/hl9y9k5mfSq34vWtdobXmn2FiVGCUvr9ib1BfRBL4mRAI4TNu2KixeSpVn39Xx3dVNrqZ2TG0OXTzEhtMbyu7QvLnKEXv+POzc6drF3IWUxQLfLP2vlJIpK6dglMUDlcy8TJftq//3f3DypHI4f/ppl051aRIbWzZNc2Skb9ZqSE9XDtHBwSWTUQHT1kwj32DdUdovsp66QEDguwFNnW91dq9xfVGo3rx5bm6Rk2Rnw6pV6r2NoiYBymfCBGjdGg4cgMmTXTtXSFCIyab99davy+4ghO+r9XfvVuFctWqV8PBecmQJF3IulNg115DrsH01JUXVNJg6VSXY+fhjJZe++ML35JPfMHp0ySyb8fG+Wath2TLle9SjRxnfgx1nd2CQ1hMH+UXWUxcICHyd2ZO8h7Un11IprBK3ti0nfdeQIep13jzfDKFauhRyc5WNtVYtb7fGrwkPh6+/Vv3lO+8oXzVXeLDzg4CKUbeogtQE/tKlrl3IXZir882EyNSVU8nMzyyzu732VSnV4Co+XmU6nDgR3npLrb/8clP+lQDO0KeP+mI1Wrf2TUcIK+p8gB2P7zBlN5WvSK5rrgYsbw9823+ynrpAQODrzJdb1Oz+znZ3EhMWY3vnrl2hZk0VQqVneTW90DQPQ4d6tx0VhMsvV5Mkg0Gp9l1JwdCmRhv6NuxLVkEWP+74sewOWirU5ctVNTpfw4I6Xw/76sSJ8O67apyq3bY2lt6yRW0P4CSa855GQoLvTVSktCnwS/NYl8cA+Dzh8xJmpIqKVwS+EGKUEOKoECJXCJEghLBcE1Pte7MQYrEQIlkIkSGE2CCEcN1l1w3kG/JNaU8fvuzh8g8ICio5y/cljMaAwHcDU6eqgIedO12vvf54V+WF9snmT8o679WurUYYubm+V/AkLa3YxqoNTHDdvpqSorQn2dmWt+fkqO1a/pgATjBihHI4rlkTEhN9b6Kyf7+aQMXF2ZUMaHDzwdSrXI+DFw+y/KiPR0zpgMcFvhDiduAD4HVUCd61wEIhRAMrh/QDlgFDivZfAPxha5DgLf7e/zfns8/TvmZ7usZ3te8gXxX4W7aoP3T9+tCxo7dbU2GIilI53EF5i2/b5vy5bmp1EzWiarDz3E7WnVpXdodhReNiPcv26cHixUrN0bt3CRurq/bVOXNMGVStEhwMv/7qaIMDmIiNVeGU11yjPmvJbXwFbXY/aJBdVT1DgkJ4uLOanH2W8Jk7W+YTeGOGPwb4Rko5U0q5V0r5FJAIPG5pZynlM1LKN6SUG6WUh6SUk4EE4EbPNdk+vtjyBaBm93aX8Bw4UFXwWLu2OFe1LzB3rnq9/nrftNP5Mf36wZNPqmRld9/tvNd+eEi4yTH0082flt1Byywzb55vVTizoM4HZV/9+w41OGlevTnGicYS9tby7KtJSdZn9xrZ2Wq/AC6iTVT+/NOrzSiDA+p8jYcue4ggEcQf+/7gbGbFrgPnUYEvhAgDulC24t5i4AoHTlUJ0CFvmX4cSz3G4sOLCQ8ON9Uut4vKlZUEMBp9a7RsLvAD6M6bb0KrVkojOm6c8+d5pMsjCAS/7P6FC9mlBoxt2qiA8+RkWF+6qrWXMBph4UL13oKH9xdbnRg0F1G7tuXcMOZERan9ArjIddepDHtr1sBZHxGSOTnFIc6aBsIO6lWux9AWQyk0FvL1NgtRLxUITweoxAHBlC2nexZVhKdchBBPAPWA761sfwR4BKBWrVqs0NLC6kRmZqbFc844PAOJpE9sH3Zs2OHQOeu2bEnzpUs5++WX7DX3gnUj1u4DIDw5mZ5bt2KIiOC/kBCMOn+HemPrXnyZZ5+NYdSoy/jwwyDq1t1O9+4pTt1L9+rd2XBxAxN+m8Bt9UsWmGl62WXUP3KEE9Onc6SgbHYxd2LpXirt30+Xs2fJrVWL9cnJxWmbgeS8ZObtn0ewCKZZVjOHv4cmTeCll0J4++2upKVFcO21Rxkw4HiJfYRQ+zlyan99viyh572079yZ2A0b2P/22yR6YWJQ+l6qb9xIh9xcMlq0IGHPHof8C3qG9uQv/uLD/z6ke0H3Ern23Y1Hny8ppccWIB6QQN9S6ycC++04/hYgGxhqz/W6dOki9Wb58uVl1qXnpsvK0ypLJiE3n97s+EkPHpQSpKxaVcqCAtcbaQeW7sPEJ5+o9txwg0fa4io278XHmTZNfdW1akn5v/9JOWvWcvn551JevGj/Of7a95dkErLZh82kwWgouXH5cnWBli11bbc9WPxdJk9W7XnssTKbnl/8vGQS8vZfb3fqekajlO3aqdNbWqKipBw/3vHz+vPzVRpd72XmTPXFDh6s3zkdoMy9PP20as/LLzt8rkJDoWz4XkPJJOSig4v0aaCduOP5AjZLCzLR0zb884ABKB3UXQuwaVkTQgxHzervk1LOdU/znOOrrV+RnpdO34Z96RLfxfETNGumSkympipbvrfR1PkB73y3M3as8os8exaeew5On1bx4/HxKp7cnqinIc2HUL9yfQ5dPMSyo8tKbuzdW9Uy379fLd7Giv0+Iy+DzxNU0c3nej7n1Km//hp27VIuMWFhKs27EOo1IkIVzpkyxaXWBzBn2DDlGPfvvyrywpsYjfD77+q95l/gAMFBwYy8TKUInrF5hp4t8yk8KvCllPkoh7uBpTYNRHnrW0QIcRtK2I+QUlopBO4dDEaDqaLXsz2edf5Emkps/nwdWuUCWVnF2dmc+OMEcIzJk5WJXWPduniyslQ03bvv2hc3bt5ZfbTxo5IbQ0KKf0dve+snJ6ty0OHhZWozfLn1S9Ly0ujToA/d6nZz+NS7dytHSFDZ9JKS4L331Pf73nsq4GTq1ID/qa7UrKkGlPn5xQM5b7FuHZw6BQ0aqAx7TvDwZQ8THhzO3P1z2Xd+n84N9A284aX/LjBCCPGwEKK1EOIDlKr/UwAhxHdCCFOxbyHEHcCPwDhglRCidtFS3QttL8Nf+//iaOpRmlRrwtAWLsyIfSU8799/VX7/7t0D3k1uRosbz80tXvfXX81M77Oz7Y8bf6TLI4QHh/P3/r/Zk1zKdukr4Xlz5yqVRf/+JeqrFxoLTYPmMT3HOHzarCy49VblszViBNx3n1JqjByptCQjR9qu7hrABW5SlRv54w/vtmP2bPV6221Oj+pqxdTi/o73I5G8s/YdHRvnO3hc4EspZwOjgfHANqA3cJ2UUvOuaVC0aDyGci58HxW+py2/e6TB5fDe+vcAGH35aIKDygkCtkXv3spjf88eOHxYp9Y5QUCd7zEsxY0bDCX/kvbGjdeKqWVKt9vp006cTDtZvHHQoOLQT3N1gqf5sSgj4K0lU07/sfcPjqUeo1n1Zk4Nmp96CvbuVZlep0/Xo6EB7EYT+AsWlBy5ehKDofhPcvvtLp1q7BVjEQi+3/E9ZzLO6NA438IrmfaklDOklI2klOFSyi5SylVm2/pLKfuX+iwsLP0tndudnMs6x/4L+02d6abTm1hzYg1VwqvwQOcHXDt5aGixkJ01y8WWOonBUKxhCITjuR1LceP165dMHetI3PjYK8YCUGAs4PH5ZmktKldWKnSj0Xsmo9OnVZrfsDC45RbTaikl/1v3P0CZxBwdNH//vbLdR0bCL7+UUBwE8AQNG6qMdllZ3svouHq1+pM0aQJdnPChMqN5bHNubn0z+YZ8Plj/gU4N9B0CufQdoNvMbmTmZzJ2sepYtdn9yMtGlp833x7uKYrf//577+SoXrZM/XEaNw5k1/MAluLG7713d4nPjsSNx1eKR6DUmYsOLWJr4tbijZpa/6+/nG2ua8yerZ7p668voV9fd2odG05voHpkdUZ0GuHQKRMS4NFH1fuPPoJ27fRrbgAHuPlm9eottb4O6nxzXuz1IgCfJnxKWq6XnRF1JiDw7SS3MJfT6acBmHtgLgsPLuTXPb8SLIJ56vKn9LnIgAGqdz94UDk3eZpvv1Wv990X8G7yAMOHl02AV716XonP+fllNOBWmbWzWDNkkAYe/OvB4hz7msBfuFA5D3gaTZ1/990lVmuz+8e7Pk5UaDlZc8xISoIbb1R2+4ceUsWIAngJTa3/99+qLK0nKSyE335T711U52t0q9uN/o36k56XXuHS7QYEvp3M2jkLieo8c/+/vTMPj6q8GvjvJJElEVkCyK6igoAsZVGhBbEKblQBoQouRK2o0FbrgoAoslRqXQBFCiLWFkRoFVq1boAG/FBQWQVEQUAUgiyiEMg+7/fHmSHJMEkmySwJc37PM89k7r1z73lz79xz37PmZjLkP0PI9eQysM1AmtUsqg1AKUlIgMGD9e85AesKhY/Dh/PTWm65JbLHjlFq19a0vOKqw8XFBWfSd84xftn449cowKb9m3h7qzd6ukkTbZmblQXz55dT8lKyZYv2ZqhZs1A63taDW1n05SKqxFdheJfhQe8uK0u9At9/D926aa97ez6NIq1aQYsWWhr8o48ie+zUVI1LadEipFZJ3yx/ysopZOVmlbB15cEUfhD4bqa+9okOx/5j+4mTOMZePDa0B7v5Zn1/9dXy9U8tLa+9ptOlHj3UF2ZEhPHjNT+8WrV8/3NSkmautWypyq1Pn5LbLCzevpiDGYU3yvHkcNdbd5Hr8c66fNPgl14K8ShKwDe7v+46HaiXR1MfxeG4pd0tNKzRMKhdOQfDhmn8YZMm+oxatWo4hDaCRiTfrB/ph0mfOf/660P61Hf52ZfT7vR2pKWnMXfD3JDtN9qYwg+CQDdTgOoJ1TmnzjkBvlEO2rdXZ+SPP+bXHI8EPnP+kCGRO6aBiOaH79mj+eKNGun73r3qo+7YUZM2+vcv/vlvwrIJpGenn7B839F9zFozSz/066ez7M8/1/68kcC5/CDUAub8tWlrmb9xPlXjq/LoxcE3qX/uOX1eqVZN+7ac7l/Cy4gOvonKvHlw5Eh4jtG5s5rFWrWC3r2R7dvzr60WLWDXrpC5FESEEd1GACW3ba5MmMIPgoI307SstOPLPc6TfzMNFSL5JvVImfW3b4flyzXUecCAyBzTKIQvb7xhw/y88aQkdYs2aqSn5667Asdybtq3idVpgbvIZXuyGbl4JIezDuv59bmM/h6hJiErV+r11aiRNonyMvqD0QAM7zKcpjWbBrWrhQu1CiGo+OUMyDZCSevW0L07pKerdTIcNGumFf22bIHFi6n98ceaxhIXp1WXWrbUJ+QQ8ds2v6Vlcku+OfQNMz8/OXz5pvBLwP9m+t7B/I52GbkZjFoySm+moWTwYFX8b74ZmQCrf3rrHPXvrylcRoWhcWNV+tWrq5KbMOHEbUqagRzLOcbE5RP1g8+sP2dOZFxGvhnYoEHHiw6k7kzl3W3vUqNKDUZ1H3XCVw4dglmzdKyzZunn1FTdhcejbpAbbgi/6EYp8aVMzJgRniyje+8tlHdZf906/cPj0QcBEbjyypAd7pT4U3jisicAGLdsHD9l/hSyfUcLU/gl4H8z3Zi+sdD67Lzs/JtpqGjcWAOssrM1uTiceDz5Ct/M+RWSTp3UDR4XB2PHwlS/9OANP2wgzxXd7z7X5eYH73XqBG3bwoEDoa/qGMjk6rMktGkDu3bhcnIYtVSV/IPdHqRuYt3jX3dOK+M1aqQz+bFj9b1BA21vnp0Nw4fDmDGhFdsIEdddB8nJsHatuo1CTffuhVI6669fX3h9lSohvziuaXkNPc7owcGMg0z6aFJI9x0NTOGXQEk304zcDJbuWBr6A/t8YuE26//f/8GOHRoB5Vff3Kg49Ouns13QiU7BuLsNd2/AjXUBX4Pbqgn//PreJHWR8AXvBTK5Hj2qTyr33gstW/JGn3NZ+f1K6iXW409dC/eeePRR7R+Qmalfc07fs7M1eLF1a33YsYj8Ckq1alrbGGBmGEzgInodedNa4gr665OSYNIkqFEjxIcUnuqlZXanrprKtz99W8I3Kjam8EvAdzNNHZIKQFJ8EgcePFDoprp6aGD/abno318v7BUrwltq1xesd/PNJ9Z5NSoUt90GU6bo33fcEVzJ3UmXTqJaQjUWbFrAJ999ogtvvFErO77zjkYLhgo/k2vT5d4Cmh4PHD5MXhyMvkhjYR7p8UihYlW+vgL+lQcL8s034YsHM0LE0KH6/uqr4emgl5ISODAvOVl/FGGgS+MuDDp/EFl5WTz8wcNhOUakMIUfJBv3baRKfBUGNhlIcmJy+A946qn5FVeefrr8+/Mzt7JrF/z5z/kBNr16Rb5ohlFq7rlHfdgej4Z6FOyHE8j33axms+PtZv/03p+0EE+9elqIp6A7JxT4mVxrb91aaPWMzrA57iBn1DyDoZ2GFloXqK+APwkJwT3kGFGkRQu45BJ9cpsbhnS25OTjJRXTGzXSZUlJMH26XiBh4vFLH6dKfBVe+eIVPt8TBndFhDCFHyTDLxjO1j9sZWCTIMuehYIRI9SMNXu2VhkpD37mVvbvVxtqRobeaa+6KqQRrkb4GDNGC/bk5qohaM6cwL7vRo10+YhuD3F60ums2r2K+Ru9edI+s/7s2SeW+ysrfibXguxoVJ2HLtVArqd7P03VhMLJ84H6CvhTmr4CRhTxBe/NnBn64D3njreP/K5HD73mzj+/UEGncHBmrTO558J7ALjvvfuO12SpbJjCLwXNajYjMSH48p/lpnVrneVnZ8Nf/1q+ffmZW+MzMnSGB3rDD3GEqxE+RPRyGD1aT90tt8ATT5zo+87MVJ/4k3+uwcRfa2DpA4sf4OCxg2rlOfNM2LYtvzBOKEhJgZwcADzeGZcD7ujj4ajLYmDrgVzX+roTvhaor4A/pekrYESRfv3UivTFF5qWGUqWLtU0z7g49nXooHEDf/tbRAI7RncfTb3Eeny06yOeXfVs2I8XDkzhV3R8UafPPQfnnqs36uHD1cy/YIGWHAum4ISfubWJfwnMMES4GuFDRD0y48frZ6+OPYFjx9Q33u/MW+napCt7juxh6FtDcfHxMG6cbjR2rEbFhYLk5ONaOa1LFwBmda3C0kZZ1E2sy7SrAvevHTCg6DH4yMsLvq+AEUWqVMm3ID3zTGj3PVkbltG7Ny4hQTuM/uIXoT1GEdSqVotZv9HI2ZFLRrJx38YSvlHxMIVf0WnbVm2zoLOxxYvVXzVqlAapXHVVcAUn/MytxwOqIGwRrkb4adBA76/FER8PC1+P55X+r1CjSg0WfrmQF9e8qMF7bdrAzp3wwguhEWjnzuOBgN/17MmumvDApfowOu3KadRPqh/wa6+9Vvwza2KiujEKPLMaFZnf/16LR7z2Wujq63/5Jbz9ts7qp07V+1Uo4ptKwbXnXcvtv7idrLwsblp4E9/9/B0Xv3zx8ZbpFR1T+JUB3zSuIDk5GrJcmoITKSnH/bUJmZn5y8MY4WqEl717S54Z+3zfZ9U+ixl9ZgBwz7v3sOXQVnj8cd1owoTQhMA/9ZReY4mJZNSpwx194ziS4KHfef34bZvfnrB5bq4+hw4dqh6miy7S2vhJSXpZJyXp/f2++wL/DIwKSpMmGoMEGmkaijgRX4rKkCEaHNiihR4nwky+fDLNazdn/Q/raTO9Dcu/XX68ZXpFxxR+ZeC22wo1HTmBYM3xycla0KcgEYhwNcJHaX3fg9sO5uZ2N5ORm8Hg1weTdWVv6NpVgzjr18/P4iiL2+iHHzQIEODuu/l3zjLeP8tDnep1mH71dMTPz7p/vzYGmjpVswRnz4ZPPoG0NLXcjhun72lp+jxi+feVjBEjVCGvXQsvv1y+fX32Gbz4Yr6lMorUqFqDuf3mEidxHMnWh+Q3v36TtWlroypXMJjCrwyI5Ee++lNac7zXJnqwZcuIRbga4WPAgJInT7m5hX3f066aRvPazVm7dy2jPhit1w9olJ8vi2P6dPIeGoX7XSncRmPG6D6uuYaZfRryt2yt5Dfj6hk0OLVwtN0bb+il9957ULeuxmL53L6+vgKPPJLfV8CohCQm5gcbjx6tLbjLQmamWic9HjX1nHdeyEQsK12bduWqc/Pvmxm5Gdz51p2a9lqBMYVfWXjkEa1Y5k9pzPE7dmiLMWDn5ZdHNMLVCA+1a6tvu7hZfu3a6lr3cVrV03il/yvESzyTV07m8bgVuAsuOOF78Xk5SLq6jVxJbqP583UGVrUqK+/qw7Dlas5tV78dA9vkP20cPgy33w7XXgv79mnK9urVGlNqnITccAN066Yne2IZS5CPGwebN+tDZ6BmElHAOceGvRsKLdu4b2N+CesKiin8ykJ5zfGHD2tE67Fj0LYtR5o1i2iEqxE+xo/XiU+1aoV936ecor2Q9u7VukujR+tkCeCiJhcx+5rZCMLDHzzMHe2Kn71neYpxG23devyh87tx99Nr7f14nIcetXqwed/XLPtqLc7B669r9+eXXlI//eTJsGSJlogwTlJE8n3vU6Zo4HFpWLVKrQRxcdqXoXr1UEtYJhZvX8yPmT8WWpaRm8Gdb95JrqfiFjAzhV+ZGDOm8Gy8SZPgzPG5ufqkvWmT+mj/+9+oRLga4UFEJz579hT2fe/bB7t3wx//qNbQSZPUGjprlgb6DekwhH/0/QeCMLvJDBY0Dqx500niwbxJ/JQXwG2UmQnXXw/p6Ry+5gpaH5tNes4RyIunT70+5Hqy6DliOk2bOgYMUEtDx46wZo26YgMZrYyTjC5ddMKSkwPt2mnPjmBiRPxN+V27RkX8QBRsmV6QPel7mLxychQkCg77uVUmunfXTno+du8Ors7+Aw9o3fTkZG25e9ZZUYtwNcJHIN/3qadqUNyKFeoz//ZbjYhv0UIt8De0vpnbas0BTxyDb9/FhgBZcwdJ5p9V7ghc1vbBB2HtWg43rkvzVstIj/sBcqpBXB47v2oKCxbCG7PYvVuOG6RWrtSaUkYM4XMZZWTAhx+WnFrsHIwcqTElLVtWqBQN/5bpBXE4Ri4ZycrvQ1xwKESYwq9MFIxQbdwY0tOhb18tml4UM2fmh0EvWgRnnx0JSY0KRteusG6dtk447zydad9xB5xxBmxecCMsnIuHOLoMhVUFninTSWQY0zmSkVC4rK2v+uO0aeTEC7++6gAHq2fAN5fCZ3fB85uZPv4G+KovnHIULpxMTl4ugwbppWjEGCNHnhhoUlRqcVoaXH213rfi4jTCv4KY8uHElun+eJyHX770S0YsHsHR7KMRlKxkTOFXNlJSoGdPdX62aqVm+mbNtHj6jh26TU6Omu379oVhw3TZCy9YZFSMEx+vnp2NG2HePL180tI0FY6Ng2De/8g73IzeN8Nab1B9TryHJuyiRbVdmtrn8eB5ZS5ZLZrDQw8BcF9vx/rTayKvz4NX3ob3J8OBVtSolQ6/ehz+eA5c+giu4yxrfhOrdO8OdeoUvd6XWvzaa1ps7J131GT1r39pcYYKREkt00GV/pMfP0mr51sxf+N8Dhw7cMI2B44d4D9b/sP0b6azfu/6cIlbCEu+rmwkJ6tJDOCtt3Sa9sEHGhDz7LNw2WWa97p/v24TH6/mMF+faiPmiY+HQYNU+X/6qQbRvfACsO0K8p7/msMXTeXKgeNYMfcYZx/KZCbDIAO+Gn0aXz0stNz/M1WBzXVh1GXwRtVL4Nl/wOGmgAfOeQc6zWTM1Rfz0Pb8dqI5PUaxM+1G4LQojdyIGj7r5KOPBu6SlJenZqhNm/Rz7956YRZ0YVYQNty9ocRtPt39KXf/727WpK1h0OuDAGhcozEdGnSgflJ9Vn6/ki8PfHl8+07bOtG+QfuwyezDFH5lpnlzTWBet05rVr/6Krz/vq5r3RpuvRVuusk6jhgBEYELL9RX3boaQ5VVawvNNnfip/Wr6fzLSQxwi7hy1xF6bYeWBzSP+rvTYOyFdfjnKf3JW9uHtlV/Q73OcaRmTMHTbRLU2AdAfIKfRSk+m9WJE4FyNoIyKicpKfBwEf3k09NV2VevDk8+qZbJSpwufEHjC/j0d58y4/MZzNs4j/V717P7yG52H9l9fJtqCdW4sPGFNHPN6HV2r4jIZQr/ZKBDB+1rPmmS1ppu314jYyvxD8aILBMngsQ5/nz0WtJP+ZaFC+DGhufwYmZPNtS6ny+GV6HKF29S9ch+5OLh3HhOe0Y0Fho31oSPQ4cgecxLx5V9QE7JYG/i0sgNyqhYJCdrgN6iRfnLkpJ0otKpk0aUnnXWSTNBiY+LZ/gFwxl+wXA8zsO2H7exbu869qbvpXOjznRq2Imfs35m0buLqJdYLyIymcI/mWjc2GriG2VCBHqkLGbKgoP8mAOX3QqwDdjG3tPW8/Cwb0iIKzotqnZteLjOBp75awGL7VOp8JhWHktM1MyqCcPCPBCjYnPvvRpf5PHkV/rs00f/9jUJOwmJkzhaJLegRXKLQsunfTSNmtk1eeD9B1gwcEH45Qj7EQzDqBRMWDaBozkn5hYfzDjIrDWzSvy+fwEgsOY3hh/du+cr9hiv9JmZm8njH2nzqkjV4jeFbxhGsbnFR3OOMmrJKA5nFV8L3b8AUKNG1vzG8MMXvCcS85U+530xD4dawDJzMyNSi98UvmEYJeYWZ+dlM3F5cLXQfQWAGja05jdGAFJS4OKLY7rSp3OO8cvG43Ee/Yxj8/7NYa/FbwrfMIwSc4szcjNYusMC7owQ4EstjuFKn4u3L+ZgxsFCy47mHGXY/4aFtRa/Be0ZhhFUbrFhGKGhqFr8vniZuzvfHZbj2gzfMAzDMCJEKOJlyoopfMMwDMOIEKGMlyktpvANwzAMI0JEM17GfPiGYRiGESH842VSU1Nxg8KbjufDZviGYRiGEQOYwjcMwzCMGMAUvmEYhmHEAKbwDcMwDCMGMIVvGIZhGDGAKXzDMAzDiAFM4RuGYRhGDGAK3zAMwzBiAAl3/91oIiL7gW9DvNu6wIEQ7zManCzjABtLReVkGcvJMg6wsVREwjGOM5xz9fwXntQKPxyIyOfOuc7RlqO8nCzjABtLReVkGcvJMg6wsVREIjkOM+kbhmEYRgxgCt8wDMMwYgBT+KXnhWgLECJOlnGAjaWicrKM5WQZB9hYKiIRG4f58A3DMAwjBrAZvmEYhmHEAKbwDcMwDCMGMIVfAiJSR0SeE5EtIpIhIt+JyN9EJNlvu9oiMkdEfva+5ohIrSiJXSQiMlREPhSRn0TEiciZAbbZ6V1X8PWXKIhbLEGOpVKcF39EJDXAOZgfbbmCQUSGicgOEckUkdUi0j3aMpUWEXkswP9/b7TlCgYR6SEib4jIbq/cKX7rxTu+Pd57WqqItImSuEUSxDheDnCOVkZJ3GIRkVEi8pmIHBaR/SLypoic77dN2M+LKfySaQQ0BkYAbYGbgB7Aq37bzQM6Ald4Xx2BOZETM2gSgfeBx0rYbjzQsMBrYnjFKhPBjKWynJdA/J3C5+DO6IpTMiJyPTAVeBz4BfAx8I6INIuqYGXjKwr//9tGV5ygORXYCNwDZARYPwK4H/gD0AXYBywWkRoRkzA4ShoHwBIKn6OrIiNaqekJTAe6Ab8GcoElIlKnwDbhPy/OOXuV8oVeVB7gNO/nVoADfllgm195l7WMtrxFjKGzV74zA6zbCTwQbRnLO5bKeF4KyJkKTIu2HGWQexUwy2/ZVmBStGUr5TgeAzZGW44QjCMdSCnwWYA04OECy6oDR4A7oy1vsOPwLnsZeCvaspVxPKcCecBvInlebIZfNk4DsoBj3s9d0Qvy4wLbrACOok90lZEHROSgiKwTkYdFpEq0BSoDlf283CAiB0Rkk4g8VQFnYIXwXiOdUKtLQd6ncvy//WnuNa/uEJH5ItI82gKFgLOABhQ4R865DGA5lfMc/UpE9onI1yIyS0TqR1ugIKmBWtgPeT9H5LwkhGpHsYLX/zsBncXkehc3APY772MZgHPOicg+77rKxrPAWuAgcAHwF/SC/F00hSoDlfm8zEP7QOwB2gCTgHZA72gKVQJ1gXjgB7/lPwCXRV6ccrEKSAG2APWBMcDHItLGOXcwmoKVE991H+gcNY6wLOXlXWAhsAM4E3U7fiAinZxzWdEULAimAuuAT7yfI3JeYnaGLyITAwR8+L96+n3nVOBNYDfqb6kQlGUsxeGce8Y596FzboNz7kVgGHC7+AUqhoNQj6UiUZqxOedecM6955z7wjk3H7ge6CUiHaM5hljBOfeOc+5f3t/AEqAPer8cEmXRDC/OufnOuTe8v5E3gSuBlsDVURatWETkGdS1eJ1zLi+Sx47lGf4UYG4J2+zy/eFV9m97P/ZxzmUW2G4vUE9ExDebFBFBZwaRiOydQinGUgZWed/PQWf94WQKoRtLtM+LP1Mo+9g+R31+5wJrQihTKDmAyni63/LTic7/O2Q459JFZBP6/6/M+M7D6RS+1k6Gc7RHRL6nAp8jEZkM3ABc4pzbXmBVRM5LzCp859wBgmxJ6PWdvoMGVlzhnEv32+QTNAijK/n+4q5AEoX9x2GhNGMpIx2872lhPAYQ8rFE9bz4U86xtUXN5WE/B2XFOZctIquBXsC/C6zqBbweHalCg4hUA84DPoy2LOVkB6pAegGfwfGxdQcejKJc5UZE6qLm7wr5GxGRqail7hLn3Ba/1RE5LzGr8IPFq+zfRwP1+gJJIpLkXf2jcy7bOfeliLwLzBSRod51M9EI0q8iLnQxiEgD1F/UwruotTcuYZdz7kcR6QpchN7YfkbTQyYDbzjnymMlCDkljaUynZeCiMjZwI2oRekA0Bp4Go2rWBFF0YLhGWCOiHyKynoXmto6I6pSlRIReQp13+1CLUKPoA+K/4imXMHgtUae4/0YBzQTkQ7o/WqXiEwBRovIFuBrND4hHY0bqTAUNw7v6zH0QTIN9eFPQlPZFkVY1BIRkeeBm1Edcsh77wJId86le2OLphDu8xLt9ISK/kLzJ10Rr54FtquNmmsPe19zgVrRlj/AeB4rYiwp3vUdgZXAT2ju6xbvdxKjLXtpx1KZzovfuJoCy1D3SRawDQ3yqRNt2YKUfxia2pkFrAZ6RFumMoxhPhowmY3G7LwOtI62XEHKXtQ962XvevH+dtKATO+1dn605S7NONCUtfdQBZ+NBri+DDSNttxFjKUoHfJYgW3Cfl6seY5hGIZhxAAxG6VvGIZhGLGEKXzDMAzDiAFM4RuGYRhGDGAK3zAMwzBiAFP4hmEYhhEDmMI3DMMwjBjAFL5hGCcgIil+Nf7zRGS3iPxLRFqWcX+3hUNWwzCCwyrtGYZRHAOB79GyvmejFeeWervG/VyK/aSg95uXQi6hYRhBYQrfMIziWOec2+b9e4WI7AEWoz2634meWIZhlBYz6RuGURoOe99P8S0QkfYi8oaIHBKRDBFZISLdC6xPBS4GflnARZDqXVdPRGaKyNcickxEvhOReSJS2XqzG0aFx2b4hmEUR7yIJKAm/ebA42j98lQAEekIfIQ29rkDOIY2zFkiIt2cc6vR2vpzvfu407tf34NDHbRu+ChgP9po537UmnCeK9yG2jCMcmC19A3DOAERSQH+HmDVHqCvc87XwnMpqqTbO+eyvcvigY3AV865vt5lqUCCc+5XJRw33ru/XUB/51yF63xmGJUVM+kbhlEc/dAWyRegrT03A2+LSCsRqY6a6v8NeEQkwWsNEGAJ0COYA4jI3SKyXkTSgVxU2QOUOhvAMIyiMYVvGEZxbHTOfe6c+8w591/gGvLbeNZBzfSPADl+r98DtUWk2HuMiPwBmI4+IPRHHywu8q6uFvLRGEYMYz58wzCCxjmXISLbgXbAT4AHeB74ZxHbe0rY5Q3AUufc/b4FInJWaKQ1DKMgpvANwwgaEUlE8/E3OeeOishHQHtgTQnKPQuoEWB5IvkBfD5uDYmwhmEUwhS+YRjF0UFE6qJm/Iaoqb4O8Jx3/X3AcuA9EZkNpAF1gY5AvHNupHe7zcAwEbke+AY44pz7CngXeEhERgOfAr8GBkRkZIYRY5jCNwyjOP5d4O/9aPT9Fc659wCcc2tEpAswFngWqOndbg0wo8B3n0CD8F4ETgWWAT2B8UAt4E+oz34ZcDmwPVwDMoxYxdLyDMMwDCMGsCh9wzAMw4gBTOEbhmEYRgxgCt8wDMMwYgBT+IZhGIYRA5jCNwzDMIwYwBS+YRiGYcQApvANwzAMIwYwhW8YhmEYMYApfMMwDMOIAf4fUeDigi0nxDgAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1227,7 +1185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 35,
    "id": "dc39db70",
    "metadata": {},
    "outputs": [
@@ -1237,8 +1195,8 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: beta\n",
-      "- value: -0.8424663551657885 ± 0.016291164278910576\n",
-      "- χ²: 1.0897174737821766\n",
+      "- value: -1.071187039504064 ± 0.017950959722479044\n",
+      "- χ²: 1.0808645373835433\n",
       "- quality: good\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
@@ -1251,17 +1209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "df67fefe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Drag.update(cals, drag_data, parameter=\"β\", schedule=\"x\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 36,
    "id": "f02bd7a6",
    "metadata": {},
    "outputs": [
@@ -1286,93 +1234,93 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>parameter</th>\n",
+       "      <th>qubits</th>\n",
+       "      <th>schedule</th>\n",
        "      <th>value</th>\n",
-       "      <th>date_time</th>\n",
+       "      <th>group</th>\n",
        "      <th>valid</th>\n",
+       "      <th>date_time</th>\n",
        "      <th>exp_id</th>\n",
-       "      <th>group</th>\n",
-       "      <th>qubits</th>\n",
-       "      <th>parameter</th>\n",
-       "      <th>schedule</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2021-07-30 17:56:11.297365+0000</td>\n",
-       "      <td>True</td>\n",
-       "      <td>None</td>\n",
-       "      <td>default</td>\n",
-       "      <td>()</td>\n",
        "      <td>β</td>\n",
+       "      <td>(0,)</td>\n",
        "      <td>x</td>\n",
+       "      <td>-1.071187</td>\n",
+       "      <td>default</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2021-11-03 10:27:29.247000+0100</td>\n",
+       "      <td>c395b263-bd79-47c1-8832-1353ce51ce58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>β</td>\n",
+       "      <td>()</td>\n",
+       "      <td>sx</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>2021-07-30 17:53:14.422964+0000</td>\n",
-       "      <td>True</td>\n",
-       "      <td></td>\n",
        "      <td>default</td>\n",
-       "      <td>()</td>\n",
-       "      <td>β</td>\n",
-       "      <td>x</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2021-11-03 10:25:55.416292+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.842466</td>\n",
-       "      <td>2021-07-31 02:57:58.051000+0900</td>\n",
-       "      <td>True</td>\n",
-       "      <td>56de17e6-ed83-4280-9df3-b53c14154952</td>\n",
-       "      <td>default</td>\n",
-       "      <td>(0,)</td>\n",
        "      <td>β</td>\n",
-       "      <td>x</td>\n",
+       "      <td>()</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>default</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2021-11-03 10:23:20.785429+0100</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
+       "      <td>β</td>\n",
+       "      <td>()</td>\n",
+       "      <td>x</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>2021-07-30 17:56:11.297420+0000</td>\n",
+       "      <td>default</td>\n",
        "      <td>True</td>\n",
+       "      <td>2021-11-03 10:25:55.416175+0100</td>\n",
        "      <td>None</td>\n",
-       "      <td>default</td>\n",
-       "      <td>()</td>\n",
-       "      <td>β</td>\n",
-       "      <td>sx</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
+       "      <td>β</td>\n",
+       "      <td>()</td>\n",
+       "      <td>x</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>2021-07-30 17:53:14.423004+0000</td>\n",
+       "      <td>default</td>\n",
        "      <td>True</td>\n",
+       "      <td>2021-11-03 10:23:20.785345+0100</td>\n",
        "      <td></td>\n",
-       "      <td>default</td>\n",
-       "      <td>()</td>\n",
-       "      <td>β</td>\n",
-       "      <td>sx</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "      value                        date_time  valid  \\\n",
-       "0  0.000000  2021-07-30 17:56:11.297365+0000   True   \n",
-       "1  0.000000  2021-07-30 17:53:14.422964+0000   True   \n",
-       "2 -0.842466  2021-07-31 02:57:58.051000+0900   True   \n",
-       "3  0.000000  2021-07-30 17:56:11.297420+0000   True   \n",
-       "4  0.000000  2021-07-30 17:53:14.423004+0000   True   \n",
+       "  parameter qubits schedule     value    group  valid  \\\n",
+       "0         β   (0,)        x -1.071187  default   True   \n",
+       "1         β     ()       sx  0.000000  default   True   \n",
+       "2         β     ()       sx  0.000000  default   True   \n",
+       "3         β     ()        x  0.000000  default   True   \n",
+       "4         β     ()        x  0.000000  default   True   \n",
        "\n",
-       "                                 exp_id    group qubits parameter schedule  \n",
-       "0                                  None  default     ()         β        x  \n",
-       "1                                        default     ()         β        x  \n",
-       "2  56de17e6-ed83-4280-9df3-b53c14154952  default   (0,)         β        x  \n",
-       "3                                  None  default     ()         β       sx  \n",
-       "4                                        default     ()         β       sx  "
+       "                         date_time                                exp_id  \n",
+       "0  2021-11-03 10:27:29.247000+0100  c395b263-bd79-47c1-8832-1353ce51ce58  \n",
+       "1  2021-11-03 10:25:55.416292+0100                                  None  \n",
+       "2  2021-11-03 10:23:20.785429+0100                                        \n",
+       "3  2021-11-03 10:25:55.416175+0100                                  None  \n",
+       "4  2021-11-03 10:23:20.785345+0100                                        "
       ]
      },
-     "execution_count": 39,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1396,7 +1344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 37,
    "id": "20ab91f2",
    "metadata": {},
    "outputs": [],
@@ -1406,17 +1354,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 38,
    "id": "bfb3124b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "amp_x_cal = FineXAmplitudeCal(qubit, cals, schedule_name=\"x\")"
+    "amp_x_cal = FineXAmplitudeCal(qubit, cals, backend=backend, schedule_name=\"x\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 39,
    "id": "c6127e65",
    "metadata": {},
    "outputs": [
@@ -1427,39 +1375,39 @@
        "<Figure size 538.279x144.48 with 1 Axes>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "amp_x_cal.circuits(backend)[5].draw(output=\"mpl\")"
+    "amp_x_cal.circuits()[5].draw(output=\"mpl\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 40,
    "id": "24067164",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_fine = amp_x_cal.run(backend).block_for_results()"
+    "data_fine = amp_x_cal.run().block_for_results()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 41,
    "id": "076bed0c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1470,7 +1418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 42,
    "id": "cf2cc09a",
    "metadata": {},
    "outputs": [
@@ -1480,9 +1428,9 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: d_theta\n",
-      "- value: -0.10585059452665099 ± 0.0036016923711092905\n",
-      "- χ²: 1.2761864922591215\n",
-      "- quality: good\n",
+      "- value: -0.08077045198832339 ± 0.0010627568898399157\n",
+      "- χ²: 15.2839558816469\n",
+      "- quality: bad\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
      ]
@@ -1502,7 +1450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 43,
    "id": "81adf659",
    "metadata": {},
    "outputs": [
@@ -1510,8 +1458,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The ideal angle is 3.14 rad. We measured a deviation of -0.106 rad.\n",
-      "Thus, scale the 0.8962+0.0000j pulse amplitude by 1.035 to obtain 0.92746+0.00000j.\n"
+      "The ideal angle is 3.14 rad. We measured a deviation of -0.081 rad.\n",
+      "Thus, scale the 0.8832+0.0000j pulse amplitude by 1.026 to obtain 0.90653+0.00000j.\n"
      ]
     }
    ],
@@ -1534,7 +1482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 44,
    "id": "81e7f3de",
    "metadata": {},
    "outputs": [
@@ -1573,46 +1521,46 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.860512+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:40:51.746391+0200</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
+       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.883219+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496333+0200</td>\n",
-       "      <td></td>\n",
+       "      <td>2021-11-03 10:28:02.751000+0100</td>\n",
+       "      <td>84f3109a-924e-400d-853c-ab5f3a7a8fbf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.433005+0.000000j</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:39:49.487000+0200</td>\n",
-       "      <td>1b5c7f5c-2a93-4beb-a3cd-037e3f18c397</td>\n",
+       "      <td>2021-11-03 10:25:55.416335+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>amp</td>\n",
        "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.500000+0.000000j</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:40:51.746354+0200</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 10:23:20.785453+0100</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1622,30 +1570,30 @@
        "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496299+0200</td>\n",
-       "      <td></td>\n",
+       "      <td>2021-11-03 10:25:55.416203+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.866011+0.000000j</td>\n",
+       "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:39:49.487000+0200</td>\n",
-       "      <td>1b5c7f5c-2a93-4beb-a3cd-037e3f18c397</td>\n",
+       "      <td>2021-11-03 10:23:20.785369+0100</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.896207+0.000000j</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.430256+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:42:13.806000+0200</td>\n",
-       "      <td>1507e843-ca7f-4670-a2a8-ad3bbc51fe0c</td>\n",
+       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
+       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1653,25 +1601,25 @@
       ],
       "text/plain": [
        "  parameter qubits schedule               value    group  valid  \\\n",
-       "0       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "1       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "2       amp   (0,)       sx  0.433005+0.000000j  default   True   \n",
-       "3       amp     ()        x  0.500000+0.000000j  default   True   \n",
+       "0       amp   (0,)        x  0.860512+0.000000j  default   True   \n",
+       "1       amp   (0,)        x  0.883219+0.000000j  default   True   \n",
+       "2       amp     ()       sx  0.250000+0.000000j  default   True   \n",
+       "3       amp     ()       sx  0.250000+0.000000j  default   True   \n",
        "4       amp     ()        x  0.500000+0.000000j  default   True   \n",
-       "5       amp   (0,)        x  0.866011+0.000000j  default   True   \n",
-       "6       amp   (0,)        x  0.896207+0.000000j  default   True   \n",
+       "5       amp     ()        x  0.500000+0.000000j  default   True   \n",
+       "6       amp   (0,)       sx  0.430256+0.000000j  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-10-21 14:40:51.746391+0200                                  None  \n",
-       "1  2021-10-21 14:21:13.496333+0200                                        \n",
-       "2  2021-10-21 14:39:49.487000+0200  1b5c7f5c-2a93-4beb-a3cd-037e3f18c397  \n",
-       "3  2021-10-21 14:40:51.746354+0200                                  None  \n",
-       "4  2021-10-21 14:21:13.496299+0200                                        \n",
-       "5  2021-10-21 14:39:49.487000+0200  1b5c7f5c-2a93-4beb-a3cd-037e3f18c397  \n",
-       "6  2021-10-21 14:42:13.806000+0200  1507e843-ca7f-4670-a2a8-ad3bbc51fe0c  "
+       "0  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  \n",
+       "1  2021-11-03 10:28:02.751000+0100  84f3109a-924e-400d-853c-ab5f3a7a8fbf  \n",
+       "2  2021-11-03 10:25:55.416335+0100                                  None  \n",
+       "3  2021-11-03 10:23:20.785453+0100                                        \n",
+       "4  2021-11-03 10:25:55.416203+0100                                  None  \n",
+       "5  2021-11-03 10:23:20.785369+0100                                        \n",
+       "6  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  "
       ]
      },
-     "execution_count": 37,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1690,7 +1638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 45,
    "id": "84146c1a",
    "metadata": {},
    "outputs": [],
@@ -1700,18 +1648,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 46,
    "id": "5218f8e0",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1730,7 +1678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 47,
    "id": "25ddccd3",
    "metadata": {},
    "outputs": [
@@ -1740,9 +1688,9 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: d_theta\n",
-      "- value: -0.023041381974401358 ± 0.001382763290893336\n",
-      "- χ²: 1.8251091489996691\n",
-      "- quality: good\n",
+      "- value: -0.04621845427932837 ± 0.0009804174946234\n",
+      "- χ²: 7.510365714654594\n",
+      "- quality: bad\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
      ]
@@ -1764,7 +1712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 48,
    "id": "50423105",
    "metadata": {},
    "outputs": [],
@@ -1774,17 +1722,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 49,
    "id": "a075eacb",
    "metadata": {},
    "outputs": [],
    "source": [
-    "amp_sx_cal = FineSXAmplitudeCal(qubit, cals, schedule_name=\"sx\")"
+    "amp_sx_cal = FineSXAmplitudeCal(qubit, cals, backend=backend, schedule_name=\"sx\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 50,
    "id": "3d38a13f",
    "metadata": {},
    "outputs": [
@@ -1795,39 +1743,93 @@
        "<Figure size 779.079x144.48 with 1 Axes>"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "amp_sx_cal.circuits(backend)[5].draw(output=\"mpl\")"
+    "amp_sx_cal.circuits()[5].draw(output=\"mpl\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 51,
    "id": "6c00c3f5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Analysis callback <bound method FineAmplitudeCal.update_calibrations of <qiskit_experiments.library.calibration.fine_amplitude.FineSXAmplitudeCal object at 0x7f5b2f4e6e80>> failed:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/database_service/db_experiment_data.py\", line 298, in _wrapped_callback\n",
+      "    callback(self, **kwargs)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/library/calibration/fine_amplitude.py\", line 146, in update_calibrations\n",
+      "    BaseUpdater.add_parameter_value(\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/update_library.py\", line 94, in add_parameter_value\n",
+      "    cal.add_parameter_value(param_value, param, qubits, schedule)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/backend_calibrations.py\", line 395, in add_parameter_value\n",
+      "    self._parameter_inst_map_update(param_obj)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/backend_calibrations.py\", line 365, in _parameter_inst_map_update\n",
+      "    self.update_inst_map(schedules)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/backend_calibrations.py\", line 351, in update_inst_map\n",
+      "    schedule=self.get_schedule(\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py\", line 667, in get_schedule\n",
+      "    assigned_schedule = self._assign(schedule, qubits, assign_params, group, cutoff_date)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py\", line 864, in _assign\n",
+      "    return ret_schedule.assign_parameters(binding_dict, inplace=False)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/schedule.py\", line 1347, in assign_parameters\n",
+      "    return self._parameter_manager.assign_parameters(\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 408, in assign_parameters\n",
+      "    return visitor.visit(source)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 92, in visit\n",
+      "    return visitor(node)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 139, in visit_ScheduleBlock\n",
+      "    node._blocks = [self.visit(block) for block in node.blocks]\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 139, in <listcomp>\n",
+      "    node._blocks = [self.visit(block) for block in node.blocks]\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 92, in visit\n",
+      "    return visitor(node)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 189, in visit_Instruction\n",
+      "    node._operands = tuple(self.visit(op) for op in node.operands)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 189, in <genexpr>\n",
+      "    node._operands = tuple(self.visit(op) for op in node.operands)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 92, in visit\n",
+      "    return visitor(node)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 219, in visit_ParametricPulse\n",
+      "    return node.__class__(**new_parameters, name=node.name)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/library/parametric_pulses.py\", line 420, in __init__\n",
+      "    super().__init__(duration=duration, name=name, limit_amplitude=limit_amplitude)\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/library/parametric_pulses.py\", line 74, in __init__\n",
+      "    self.validate_parameters()\n",
+      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/library/parametric_pulses.py\", line 444, in validate_parameters\n",
+      "    raise PulseError(\n",
+      "qiskit.pulse.exceptions.PulseError: 'The amplitude norm must be <= 1, found: 37.1316214105175This can be overruled by setting Pulse.limit_amplitude.'\n",
+      "\n",
+      "Possibly incomplete analysis results: an analysis callback raised an error.\n"
+     ]
+    }
+   ],
    "source": [
-    "data_fine_sx = amp_sx_cal.run(backend).block_for_results()"
+    "data_fine_sx = amp_sx_cal.run().block_for_results()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 52,
    "id": "0117cb2d",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1838,7 +1840,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 57,
+   "id": "3d6416eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DbAnalysisResultV1\n",
+      "- name: @Parameters_FineAmplitudeAnalysis\n",
+      "- value: [-1.58899764  0.10409135] ± [0.00195221 0.00524669]\n",
+      "- χ²: 351.5342209701685\n",
+      "- quality: bad\n",
+      "- extra: <4 items>\n",
+      "- device_components: ['Q0']\n",
+      "- verified: False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data_fine_sx.analysis_results(0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
    "id": "5d5a1131",
    "metadata": {},
    "outputs": [
@@ -1848,9 +1875,9 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: d_theta\n",
-      "- value: 0.04145980422899995 ± 0.0015231144619942427\n",
-      "- χ²: 1.0258195129532202\n",
-      "- quality: good\n",
+      "- value: -1.5889976367837717 ± 0.0019522058755457033\n",
+      "- χ²: 351.5342209701685\n",
+      "- quality: bad\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
      ]
@@ -1870,7 +1897,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 54,
    "id": "ae984c47",
    "metadata": {},
    "outputs": [
@@ -1909,57 +1936,57 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.860512+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:40:51.746391+0200</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
+       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.883219+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496333+0200</td>\n",
-       "      <td></td>\n",
+       "      <td>2021-11-03 10:28:02.751000+0100</td>\n",
+       "      <td>84f3109a-924e-400d-853c-ab5f3a7a8fbf</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.433005+0.000000j</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.896407+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:39:49.487000+0200</td>\n",
-       "      <td>1b5c7f5c-2a93-4beb-a3cd-037e3f18c397</td>\n",
+       "      <td>2021-11-03 10:29:08.654000+0100</td>\n",
+       "      <td>e7e1f053-a5d2-4c94-9d65-da39a9274301</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.421870+0.000000j</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:46:58.782000+0200</td>\n",
-       "      <td>ce7fdf7f-1243-44f8-92b3-2861f75b955e</td>\n",
+       "      <td>2021-11-03 10:25:55.416335+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>amp</td>\n",
        "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.500000+0.000000j</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:40:51.746354+0200</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 10:23:20.785453+0100</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -1969,71 +1996,71 @@
        "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:21:13.496299+0200</td>\n",
-       "      <td></td>\n",
+       "      <td>2021-11-03 10:25:55.416203+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.866011+0.000000j</td>\n",
+       "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:39:49.487000+0200</td>\n",
-       "      <td>1b5c7f5c-2a93-4beb-a3cd-037e3f18c397</td>\n",
+       "      <td>2021-11-03 10:23:20.785369+0100</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.896207+0.000000j</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.430256+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:42:13.806000+0200</td>\n",
-       "      <td>1507e843-ca7f-4670-a2a8-ad3bbc51fe0c</td>\n",
+       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
+       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.902828+0.000000j</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>-37.131621-0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-10-21 14:44:44.903000+0200</td>\n",
-       "      <td>31ae56ba-bad3-4eed-9a80-7e54282318f7</td>\n",
+       "      <td>2021-11-03 10:29:39.109000+0100</td>\n",
+       "      <td>024bb1b5-d7f5-4245-8270-3b6e3720cdf3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "  parameter qubits schedule               value    group  valid  \\\n",
-       "0       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "1       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "2       amp   (0,)       sx  0.433005+0.000000j  default   True   \n",
-       "3       amp   (0,)       sx  0.421870+0.000000j  default   True   \n",
-       "4       amp     ()        x  0.500000+0.000000j  default   True   \n",
-       "5       amp     ()        x  0.500000+0.000000j  default   True   \n",
-       "6       amp   (0,)        x  0.866011+0.000000j  default   True   \n",
-       "7       amp   (0,)        x  0.896207+0.000000j  default   True   \n",
-       "8       amp   (0,)        x  0.902828+0.000000j  default   True   \n",
+       "  parameter qubits schedule                value    group  valid  \\\n",
+       "0       amp   (0,)        x   0.860512+0.000000j  default   True   \n",
+       "1       amp   (0,)        x   0.883219+0.000000j  default   True   \n",
+       "2       amp   (0,)        x   0.896407+0.000000j  default   True   \n",
+       "3       amp     ()       sx   0.250000+0.000000j  default   True   \n",
+       "4       amp     ()       sx   0.250000+0.000000j  default   True   \n",
+       "5       amp     ()        x   0.500000+0.000000j  default   True   \n",
+       "6       amp     ()        x   0.500000+0.000000j  default   True   \n",
+       "7       amp   (0,)       sx   0.430256+0.000000j  default   True   \n",
+       "8       amp   (0,)       sx -37.131621-0.000000j  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-10-21 14:40:51.746391+0200                                  None  \n",
-       "1  2021-10-21 14:21:13.496333+0200                                        \n",
-       "2  2021-10-21 14:39:49.487000+0200  1b5c7f5c-2a93-4beb-a3cd-037e3f18c397  \n",
-       "3  2021-10-21 14:46:58.782000+0200  ce7fdf7f-1243-44f8-92b3-2861f75b955e  \n",
-       "4  2021-10-21 14:40:51.746354+0200                                  None  \n",
-       "5  2021-10-21 14:21:13.496299+0200                                        \n",
-       "6  2021-10-21 14:39:49.487000+0200  1b5c7f5c-2a93-4beb-a3cd-037e3f18c397  \n",
-       "7  2021-10-21 14:42:13.806000+0200  1507e843-ca7f-4670-a2a8-ad3bbc51fe0c  \n",
-       "8  2021-10-21 14:44:44.903000+0200  31ae56ba-bad3-4eed-9a80-7e54282318f7  "
+       "0  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  \n",
+       "1  2021-11-03 10:28:02.751000+0100  84f3109a-924e-400d-853c-ab5f3a7a8fbf  \n",
+       "2  2021-11-03 10:29:08.654000+0100  e7e1f053-a5d2-4c94-9d65-da39a9274301  \n",
+       "3  2021-11-03 10:25:55.416335+0100                                  None  \n",
+       "4  2021-11-03 10:23:20.785453+0100                                        \n",
+       "5  2021-11-03 10:25:55.416203+0100                                  None  \n",
+       "6  2021-11-03 10:23:20.785369+0100                                        \n",
+       "7  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  \n",
+       "8  2021-11-03 10:29:39.109000+0100  024bb1b5-d7f5-4245-8270-3b6e3720cdf3  "
       ]
      },
-     "execution_count": 48,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2044,19 +2071,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 55,
    "id": "f7cb5878",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "ScheduleBlock(Play(Drag(duration=320, amp=(0.421870407153443+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "PulseError",
+     "evalue": "'The amplitude norm must be <= 1, found: 37.1316214105175This can be overruled by setting Pulse.limit_amplitude.'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mPulseError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_8466/2710879246.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sx\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqubit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py\u001b[0m in \u001b[0;36mget_schedule\u001b[0;34m(self, name, qubits, assign_params, group, cutoff_date)\u001b[0m\n\u001b[1;32m    665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m         \u001b[0;31m# Now assign the other parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 667\u001b[0;31m         \u001b[0massigned_schedule\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_assign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqubits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0massign_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcutoff_date\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[0mfree_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py\u001b[0m in \u001b[0;36m_assign\u001b[0;34m(self, schedule, qubits, assign_params, group, cutoff_date)\u001b[0m\n\u001b[1;32m    862\u001b[0m                 )\n\u001b[1;32m    863\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 864\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mret_schedule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbinding_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    866\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mschedules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/schedule.py\u001b[0m in \u001b[0;36massign_parameters\u001b[0;34m(self, value_dict, inplace)\u001b[0m\n\u001b[1;32m   1345\u001b[0m             \u001b[0mSchedule\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mupdated\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1346\u001b[0m         \"\"\"\n\u001b[0;32m-> 1347\u001b[0;31m         return self._parameter_manager.assign_parameters(\n\u001b[0m\u001b[1;32m   1348\u001b[0m             \u001b[0mpulse_program\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalue_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m         )\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36massign_parameters\u001b[0;34m(self, pulse_program, value_dict, inplace)\u001b[0m\n\u001b[1;32m    406\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalid_map\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m             \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParameterSetter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalid_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    409\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36mvisit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;34m\"\"\"Visit a node.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_visitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_visitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36mvisit_ScheduleBlock\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    137\u001b[0m         \"\"\"\n\u001b[1;32m    138\u001b[0m         \u001b[0;31m# accessing to protected member\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m         \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m         \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alignment_context\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit_AlignmentKind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malignment_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    137\u001b[0m         \"\"\"\n\u001b[1;32m    138\u001b[0m         \u001b[0;31m# accessing to protected member\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m         \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m         \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alignment_context\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit_AlignmentKind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malignment_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36mvisit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;34m\"\"\"Visit a node.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_visitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_visitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36mvisit_Instruction\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    187\u001b[0m         \"\"\"\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_parameterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m             \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_operands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mop\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    187\u001b[0m         \"\"\"\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_parameterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m             \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_operands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mop\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36mvisit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;34m\"\"\"Visit a node.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_visitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_visitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36mvisit_ParametricPulse\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    217\u001b[0m                 \u001b[0mnew_parameters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 219\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mnew_parameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/library/parametric_pulses.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, duration, amp, sigma, beta, name, limit_amplitude)\u001b[0m\n\u001b[1;32m    418\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msigma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    419\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_beta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbeta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit_amplitude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit_amplitude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/library/parametric_pulses.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, duration, name, limit_amplitude)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \"\"\"\n\u001b[1;32m     73\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit_amplitude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit_amplitude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mabstractmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/library/parametric_pulses.py\u001b[0m in \u001b[0;36mvalidate_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvalidate_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_is_parameterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mamp\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mamp\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlimit_amplitude\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m             raise PulseError(\n\u001b[0m\u001b[1;32m    445\u001b[0m                 \u001b[0;34mf\"The amplitude norm must be <= 1, found: {abs(self.amp)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m                 \u001b[0;34m+\u001b[0m \u001b[0;34m\"This can be overruled by setting Pulse.limit_amplitude.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mPulseError\u001b[0m: 'The amplitude norm must be <= 1, found: 37.1316214105175This can be overruled by setting Pulse.limit_amplitude.'"
+     ]
     }
    ],
    "source": [
@@ -2065,65 +2108,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "id": "f45f6482",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ScheduleBlock(Play(Drag(duration=320, amp=(0.902828399794558+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cals.get_schedule(\"x\", qubit)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "id": "68f6e469",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ScheduleBlock(Play(Drag(duration=320, amp=0.902828399794558j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cals.get_schedule(\"y\", qubit)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "id": "8c8369d4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2021.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import qiskit.tools.jupyter\n",
     "%qiskit_copyright"

From d862239c44fcdf7d584da24e29f98a4cf15fde6d Mon Sep 17 00:00:00 2001
From: Daniel Egger <deg@zurich.ibm.com>
Date: Wed, 3 Nov 2021 16:44:06 +0100
Subject: [PATCH 08/11] * Aligned tests to the gate naming.

---
 test/calibration/experiments/test_drag.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py
index 9b027f0b9d..19dc59ca66 100644
--- a/test/calibration/experiments/test_drag.py
+++ b/test/calibration/experiments/test_drag.py
@@ -55,7 +55,7 @@ def test_reps(self):
     def test_end_to_end(self):
         """Test the drag experiment end to end."""
 
-        backend = DragBackend(gate_name="xp")
+        backend = DragBackend(gate_name="Drag(xp)")
 
         drag = RoughDrag(1, self.x_plus)
 
@@ -67,7 +67,7 @@ def test_end_to_end(self):
 
         # Small leakage will make the curves very flat, in this case one should
         # rather increase beta.
-        backend = DragBackend(error=0.0051, gate_name="xp")
+        backend = DragBackend(error=0.0051, gate_name="Drag(xp)")
 
         drag = RoughDrag(0, self.x_plus)
         drag.set_analysis_options(p0={"beta": 1.2})
@@ -78,7 +78,7 @@ def test_end_to_end(self):
         self.assertEqual(result.quality, "good")
 
         # Large leakage will make the curves oscillate quickly.
-        backend = DragBackend(error=0.05, gate_name="xp")
+        backend = DragBackend(error=0.05, gate_name="Drag(xp)")
 
         drag = RoughDrag(1, self.x_plus, betas=np.linspace(-4, 4, 31))
         drag.set_run_options(shots=200)
@@ -110,16 +110,16 @@ def setUp(self):
     def test_default_circuits(self):
         """Test the default circuit."""
 
-        backend = DragBackend(error=0.005, gate_name="xp")
+        backend = DragBackend(error=0.005, gate_name="Drag(xp)")
 
         drag = RoughDrag(0, self.x_plus)
         drag.set_experiment_options(reps=[2, 4, 8])
-        drag.backend = DragBackend(gate_name="xp")
+        drag.backend = DragBackend(gate_name="Drag(xp)")
         circuits = drag.circuits()
 
         for idx, expected in enumerate([4, 8, 16]):
             ops = transpile(circuits[idx * 51], backend).count_ops()
-            self.assertEqual(ops["xp"], expected)
+            self.assertEqual(ops["Drag(xp)"], expected)
 
     def test_raise_multiple_parameter(self):
         """Check that the experiment raises with unassigned parameters."""
@@ -143,7 +143,7 @@ def setUp(self):
 
         library = FixedFrequencyTransmon()
 
-        self.backend = DragBackend(gate_name="x")
+        self.backend = DragBackend(gate_name="Drag(x)")
         self.cals = BackendCalibrations(self.backend, library)
         self.test_tol = 0.05
 

From 6c95c2e23f1faaf7e4cefec66ae4688a6a2c5311 Mon Sep 17 00:00:00 2001
From: Daniel Egger <deg@zurich.ibm.com>
Date: Wed, 3 Nov 2021 16:48:08 +0100
Subject: [PATCH 09/11] * Tutorial NB.

---
 docs/tutorials/calibrating_armonk.ipynb | 695 +++++++++++-------------
 1 file changed, 330 insertions(+), 365 deletions(-)

diff --git a/docs/tutorials/calibrating_armonk.ipynb b/docs/tutorials/calibrating_armonk.ipynb
index fbf59fad74..af9a71cea6 100644
--- a/docs/tutorials/calibrating_armonk.ipynb
+++ b/docs/tutorials/calibrating_armonk.ipynb
@@ -222,79 +222,79 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>σ</td>\n",
-       "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>8.000000e+01</td>\n",
+       "      <td>meas_lo_freq</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>6.993371e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785441+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882285+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>duration</td>\n",
-       "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>3.200000e+02</td>\n",
+       "      <td>qubit_lo_freq</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4.971589e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785387+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882258+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>duration</td>\n",
+       "      <td>σ</td>\n",
        "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>3.200000e+02</td>\n",
+       "      <td>x</td>\n",
+       "      <td>8.000000e+01</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785416+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882474+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>β</td>\n",
+       "      <td>duration</td>\n",
        "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.000000e+00</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>3.200000e+02</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785345+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882528+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>β</td>\n",
        "      <td>()</td>\n",
-       "      <td>sx</td>\n",
+       "      <td>x</td>\n",
        "      <td>0.000000e+00</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785429+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882483+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>qubit_lo_freq</td>\n",
-       "      <td>(0,)</td>\n",
-       "      <td>None</td>\n",
-       "      <td>4.971589e+09</td>\n",
+       "      <td>amp</td>\n",
+       "      <td>()</td>\n",
+       "      <td>x</td>\n",
+       "      <td>5.000000e-01</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.784983+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882460+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>amp</td>\n",
+       "      <td>σ</td>\n",
        "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>5.000000e-01</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>8.000000e+01</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785369+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882520+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -305,29 +305,29 @@
        "      <td>2.500000e-01</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785453+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882499+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>σ</td>\n",
+       "      <td>β</td>\n",
        "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>8.000000e+01</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.000000e+00</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785402+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882507+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>meas_lo_freq</td>\n",
-       "      <td>(0,)</td>\n",
-       "      <td>None</td>\n",
-       "      <td>6.993371e+09</td>\n",
+       "      <td>duration</td>\n",
+       "      <td>()</td>\n",
+       "      <td>x</td>\n",
+       "      <td>3.200000e+02</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785055+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882491+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -336,28 +336,28 @@
       ],
       "text/plain": [
        "       parameter qubits schedule         value    group  valid  \\\n",
-       "0              σ     ()       sx  8.000000e+01  default   True   \n",
-       "1       duration     ()        x  3.200000e+02  default   True   \n",
-       "2       duration     ()       sx  3.200000e+02  default   True   \n",
-       "3              β     ()        x  0.000000e+00  default   True   \n",
-       "4              β     ()       sx  0.000000e+00  default   True   \n",
-       "5  qubit_lo_freq   (0,)     None  4.971589e+09  default   True   \n",
-       "6            amp     ()        x  5.000000e-01  default   True   \n",
+       "0   meas_lo_freq   (0,)     None  6.993371e+09  default   True   \n",
+       "1  qubit_lo_freq   (0,)     None  4.971589e+09  default   True   \n",
+       "2              σ     ()        x  8.000000e+01  default   True   \n",
+       "3       duration     ()       sx  3.200000e+02  default   True   \n",
+       "4              β     ()        x  0.000000e+00  default   True   \n",
+       "5            amp     ()        x  5.000000e-01  default   True   \n",
+       "6              σ     ()       sx  8.000000e+01  default   True   \n",
        "7            amp     ()       sx  2.500000e-01  default   True   \n",
-       "8              σ     ()        x  8.000000e+01  default   True   \n",
-       "9   meas_lo_freq   (0,)     None  6.993371e+09  default   True   \n",
+       "8              β     ()       sx  0.000000e+00  default   True   \n",
+       "9       duration     ()        x  3.200000e+02  default   True   \n",
        "\n",
        "                         date_time exp_id  \n",
-       "0  2021-11-03 10:23:20.785441+0100   None  \n",
-       "1  2021-11-03 10:23:20.785387+0100   None  \n",
-       "2  2021-11-03 10:23:20.785416+0100   None  \n",
-       "3  2021-11-03 10:23:20.785345+0100   None  \n",
-       "4  2021-11-03 10:23:20.785429+0100   None  \n",
-       "5  2021-11-03 10:23:20.784983+0100   None  \n",
-       "6  2021-11-03 10:23:20.785369+0100   None  \n",
-       "7  2021-11-03 10:23:20.785453+0100   None  \n",
-       "8  2021-11-03 10:23:20.785402+0100   None  \n",
-       "9  2021-11-03 10:23:20.785055+0100   None  "
+       "0  2021-11-03 16:40:00.882285+0100   None  \n",
+       "1  2021-11-03 16:40:00.882258+0100   None  \n",
+       "2  2021-11-03 16:40:00.882474+0100   None  \n",
+       "3  2021-11-03 16:40:00.882528+0100   None  \n",
+       "4  2021-11-03 16:40:00.882483+0100   None  \n",
+       "5  2021-11-03 16:40:00.882460+0100   None  \n",
+       "6  2021-11-03 16:40:00.882520+0100   None  \n",
+       "7  2021-11-03 16:40:00.882499+0100   None  \n",
+       "8  2021-11-03 16:40:00.882507+0100   None  \n",
+       "9  2021-11-03 16:40:00.882491+0100   None  "
       ]
      },
      "execution_count": 9,
@@ -445,7 +445,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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rtiaw1A5UjL/fd999ad26NQDDhw/nkEMO4YILLmDGjBkAXHXVVRx22GEVM9B9+OGHnHvuuTzxxBP079+fb775hmeffZbhw4fTpk0b1q5dyx133EGjRo044YQTAHj//ff5+OOPGTx4MC1btuSzzz7jz3/+M126dGHw4MEx096tWzcWLlxIQUEBF198MQ8//HC1gv6UKVN4//33eeONN/jxx2KKilzAbNJkDzp0aEKMSp+0IOIKMBMmBDdhjh7tSu5LllQWeMLJzXWd95IiXMN+XdnSqdNeModgxMOrTnvVGaaSKqF5UV7uOso0bqyalVWZpsaN3f6A0V3VEk+nPb+ZM2dq586dNScnRw855JAqnfhuvvnmuIcrVVd1h+UFCtdpb+jQoTp06NCKx8uWLdODDjpImzRpoi1atNCTTz457DCwb7/9VkVE582bV600vPXWWzpy5Ejdc889tUGDBtq2bVs96aST9IUXXqg4J95heZHyOdqwvKFDh4a9JnCrjUhpX79+vY4aNUqbNWumbdq00bPOOks3bNhQ5Tr/533NmjV67LHHaps2bbRhw4basWNHPeuss3T58uUV1/zvf//TgoICbdWqlTZq1Eg7d+6s48aN0++//z5qGkOH5X3zzTfasWNHHTt2rJaXl4cdlldaqrp4sRuW9+9/L9TS0sh5efPNj+qSJapr11b+P6Zjp71YNm923yvRvg8bN1YtLq5dOonQac/zoJzMLdEfiKFD3VYdiQomtf3B4FXAnzpVVST6B1zEnZcqoXkxaZLrHZvoXrPp9IMzmtoE/LqoLuRHaanqZ5+pfvqp6g8/uMc1kYy8KC93gXvJEtWPPnLb0qVaEdDXrnWP/ccCt6VL3XHVzAz4qsn7vgkUKeBnQqe9tOCv7l292lX3FhfHd11tp1nUDO/w1r595A4sftWpwkr0zFTVnRnLmHSmCuvWwaefuu+YXbvg++/d43Xr0uP7Yv162LgxOC3l5e7xxo2u/bo8whys5eXu+O7dyU9ncbHrO9C1a/W+82OZOtVV9zduDP4uLE2buscTJlT2AUgGC/gx1CbgJiKYJGpe5rKy5Hx4Yxk1yr12rLQF9FeLqKY/uqKZP9/1io0mmb1mjUmkWMF0/fr4n2vFish9b2pq9+7YAT3WjxKR5H5/RfvOX78+OH01KYD42/jXr4fu3aFLF7jnHvfdduut7niyWMCPoToBN/SPX9tgkoh5mf0f3k8/jf2DJRnzOscappKb645H67CXyFqOggLw9XsD3JdPpPz1S2avWWMSJZ5gGm/pePduN168vBx+/DFxJeri4toHtPLy6GPZayvad/7GjZXf+bUtgOTluR9VK1fC2LGp6bRsAT+K6gTccH/82gaT6vxgiPTh8394VaP/YElG6dmvtlVY8f7oilUF53+PO3dWHk90k4MxXoknmMYqHYc2CagmtknA/yOiNrKyYo9lj1doISfWd355Odx9N1xzTWY2s1rAjyKegCsCZ54J+fnw9dfuj3/VVe7xu+9CkybRr2/SxP3SKympuq1ZE334BrjjTzwR/vWvvNJ9OEtKoLS06p+6pATuust9WMNdf/31Ljju2uX+UXfvdtXv1f1Ax1uFFS5gx/OjK9Y/YHl5cA3Brl2Vx1esSFyTgzFeiieYxiodJ6pJYPdu+Pxz+H//L7iGoGHDyh/9NaUKe+zhbn/9NXgL9z0aaVu3zm0rV8IDD7j7Tz8dPX27dmVRVgb33hu+APKXv7jvzeqkw30/1y5P4iWazj9Haqlfv366ZMmSGl9/661w883p/YvNSyJuy8pyW3Z2ZZtfkyauZNywodufkxO8NW7szvFvK1bAZ59VBt+cHJfv/frBJ5+4f+ZIGjZ06QgYCl0hNxcOOQQ+/rjyR8O0aYVMnFgQ8Xjo9RMm1GxioH79+lGbz1+qbN26Na7x+vVFpuRH6KQ0P/7oSuPRgn5WFuy9N4RbX6e01AXowO+7li138PPPjYPOa9myssbQ/2PA/1jVBfdIaRBJ3Pfpccf1Y9Om9P//iseMGa6AligislRV+4Xuz4BpDLzjr+6NVcqujQYNKqun/BNZBS7mFS3QVe91yti9u3qTkFReG/xP7Rdun5//l2tN+YP3++/HPjfW3NTRZgQtKYGPPoLLL3e/8kNnxkp2r1ljaiK0jT0vz20xZtVF1f3Y/ukn9+PcX4O3c2f4/6PQYO/21TzdiQj2/hpBf2EDXAEh9Hgk/trKmmjYsIzS0tjfow0bUq1JglI1oZBV6UcRTw/zWHJzYeTI8O3Xkya5fzZ/9VL37tCpk6suWrfO7Z80qXbzMvvdccfbNUp/06Ywc6arpsrJqXwPubnuh8kNN7hjkZouGjeGyy5zzQXLlrnS+n//C4sWwX/+45ojEtUeV1MNGkDPnt70mjU18/3331NQUMB+++3HgQceWDGzXl0Xbdjdxo2ukBKtSlq1sqNYURFs3uyWZo30o7lp06rVZnl57v+kZ0/Yd1/o3Rv228/dxtOHoE8f12QYeG5Wlnucnw99+7qavYMOct8fOTnQubN73K+fO963L3TsWPmded997v83VrX+unW1C65//nPs71ER14RYnSr9Sy+teZqqw0r4Ufh7mE+fHr602rBh7LaXX3918yc/+WTVaRb9VWM33RQ87/L48W7FpYkTK9dWrum8zLVVUgJ//7srBQcO0fHnxz33RP/FvGMHPPKIC5zheqHOmeP+oVPVhhWOv+Okv9esSX8NGjRgxowZHHTQQWzYsIG+ffty/PHH07RpU6+TllSR2tjB7W/b1v2fbd4c+TkaNapsSmvUyP3/NWoEv/wCa9cG19jl5e1k+/bK4nNWFrRo4V4jXJNCrCp7EVdDsNde0K4dfPmle738fPf/FxiMGzSAAw6o+hyqLh/WrnU1FaHfmVOnVv6YCF0bI55+WdGaCEVif++ncydfC/gxRFsI4cgjXUk1nnmRIwWTeBeWqem8zP4Pbzj+dvJoH94mTeC99yKfE0+Tg38kQbj1VeIZyQCR/8lyctzfpDbDhtL5H7QuGDNmDJs2beKll15K2HPm5+eTn58PuHnqW7duzebNm+t0wPcPu4sUUP3D7kL5Owa3aOHuRwp4rVq52oJoVN1zhWtSqG6nwUgBPZbAHz2BneYg+Dsz3NoY8XzflJbC0KGuJjL0O79tW5d/0b4z07mTr1XpxxDYw/yhh9wPAH9171NP1a6Hd3WG/YUbsxlPk0NWlvvlG9je5W9SuPzy2L92d+2qfZV7tKGH8Q6LGzEifLPI5ZdXv4rupZeCF2ZJ53/QTDBmzBhEpMr2ySefAHDvvffy1FNPAW6t+Msuuyyhr7906VLKysrYe++9E/q8sSxevJiTTjqJvfbaCxHhsccei+u6WbNm0bVrVxo3bkzfvn15++3gauJbbrmlSl62b9++yrC7TZuKuOWW8zj66DYMGtSY007bj6VLF9Gggfu/6t3bVX3vv78bkdKsWfT/d/91kZoEsrLc8Y0bwzcpbN8euwd+bYfUxZprINaonQ8+iP1907QpnH12+Ca+jh1rP6+Il6yEH6e8vPAl1GhV/v4e3pH++NUZZx/utWM1OQT2MH/zTffhDW1SaNIk+vWHHlr7ZoNoJehRo9wPkmjKy92PK9XwzSKx3kOfPrB0aWUVXWFhp4rjTZqkx0p9mW748OE8+eSTQfv8K7tFWmUuETZv3sy5557LnDlzkvYakWzbto0DDjiAc889t2KFwFjmzZvHlVdeyaxZsxg8eDCzZs3iuOOOY9myZXTqVPm57NWrF4UBazRnZ2cHlaC3bv2Z3/9+EAcdNJgZM16mZcs2rFv3Ha1ataVtWxfcasJ/XWBNQlaWu+9fSTlSk8LWrbE75flrCGrK/6Mn2uuUl7s2/cAqeX8NwMKFsZsP/QWAwGaLQNFqfdO9k6+V8GupNpPKJGKWt3hfPzs7/KxOsa4/66zYv4hjiVaCrs5MfJFmpor1Ht55B/74R9dOKQKHHLIxKG1NmiRuNER91ahRI9q3bx+0NfBVvYwZM4YTTjiBMWPGsGjRImbOnFlRcl21alXY5zv99NPZc889K5Z+BVi+fDm5ubnMnTsXcOuz//a3v+W6665j4MCByX6LVRx//PHcfvvtjBo1iqw4B5dPnz6dMWPGMHbsWHr37s39999Pfn4+Dz74YNB5DRo0CMrLNm3aBI1hf+KJu2jdOp8pU55g//37s9deXenf/yj22ad33CXoFSuqBjQR177ep4/7/xFxw/j69HEBP55pcWPVENSm01w8zQalpeHb38H9n6vWroTu5dS4tWUBv5Zq88dPxCxvtf3wxbp+9OjYzQbZ2bX7B6rtTHyx3kNWlrstKoKHH4aJE5czZQoce6z7YrjxRtfb+LXXor+OqZ17772Xww8/nPPPP5+ioiKKiooiVsPPmDGDs846iym+Xqs7d+7kzDPPZNSoUZxxxhmoKmPGjOHII4/knHPOifnat99+O82aNYu6hVatJ9quXbtYunQpI0aMCNo/YsQI3nvvvaB93333HR06dKBr166cccYZfP31d/z6a2WwW7ToOQ444DCuv/50Roxoy1lnHcQ///kA5eUaVwna3wa/c2f4qXP97eu5uW7MfoMG8c3kl5UFzZuH74Hfrl3Nax78EjFxT6NGMGxY7Rev8WJq3NqyKv0EqUkP73iqs+NtX65tD/NI18fTbDB+vPuHrmkVlz9gh+uYWJ1/olh54G+WKSx0syOCa+q4+mrXBnnssa4m4LbbIrczhvb6Nc5//vMfmjVrVvH4iCOO4NVXXw06Z4899iAnJ4fc3Fzax+glmZ+fz9VXX80DDzzA6tWrmTFjBlu2bGHmzJkAvPvuu8ybN48DDzyQ5557DoAnn3yS3/zmN2Gfb9y4cZx22mlRX3OvvfaK9TZrZdOmTZSVldHOXzfu065dO954442Kx4cddhiPPfYY++67Lz/88ANTptzG4YcPZO7cL2jZck9EYN2675g/fxZnnjmeMWOuY8WKT5g27XJatIB+/SL3kfD3cA+ssv/+ezd+v317F5AjBfV4O+U1bepmy4zWA7+m4plrIJbAkVPz57u8aN+++t83mcgCvofibYP3+kMYT5uVSPIDdjIcdZRr37/jDjdi4q67XJ+Ff/zD1RSY+AwZMoTZs2dXPG4Sa07pOHTp0oWWLVty1113MXv2bBYvXlwxA97gwYMpr8ak7K1ataJVq1a1TlMqHHfccRX3f/wRbr99ACef3I0FCx5n8uQJ/PILlJeX07t3Py677M9kZcG++x5McfHXPP30TCZNihzwYw3rA1elH46/dB1rJj//pDM16YEfi79j4caN4Y/HM/IocORUuL5RdZlV6XvMy7WR4xVvs0EmVnGBa5K48UY3xLJjR9eT96CD3MRAgZK5wFCmy83NpXv37hVbokrLffr0YdasWUyaNInDDz+8xs+TDlX6rVu3Jjs7m40h0Wrjxo1VajzKy93nbPVqaNKkGb167c+WLV/TrJkLyB065NOjx37k5FS2sffv35s1UYq/tV1NLy8v+Z3y4tGhg2seqOnIo/o8KsdK+B5LVHV2KtT1iWkGD3ZV+xdcAM8/Dyed5BbTGDXKlf4jTY4UONGHiS4nJ4eyakxfqarsv//+TJo0qVavmw5V+jk5OfTt25fXX3+d0QER5/XXX+fUU0+teFxaCt9+62bAc23fO1i16kuOO25YxTmDBg3i++9XcOCBlc//1Vdf0blz54ivH08Pd/9qeuHm2g8sXYf70ZCV5QJxsqeJ9Xcs7NjRfSdVd9ROOtSaesUCfpqo68E0U7RqBf/+t2vLnzYNzjjDTY385puxJ0fyC1xu0wTr0qULH374IatWraJZs2a0atUqYg/3mTNnsnjxYnr16kV2rGJbDMmo0t+2bRvffPMN4KrY16xZwyeffEKrVq0qhtg98MADPPDAA3z55ZcATJgwgXPOOYf+/fszaNAgHnroIdavX8+4ceMAV7oeO3Yihx9+Ivn5nSgp+YGbb76V7du3c95551W89vjx4xk4cCB/+tOfOP300/nf//7Hfffdx+233x4xvYlYTS/WsL3adsqrjqysujdsLulUtc5uffv2VeMsXLjQ6ySkjXjyorxcdcoU//JA0bfGjVWLi911mzer9uyp2qWLaq9efbW0NKlvJSG2bNlSq+vPO+88HTlyZFzHV6xYoQMGDNAmTZoooCtXrgx7zRdffKFNmjTRSy+9VLOysnT79u21SmN1xJsfCxcuVKDKdt5551Wcc/PNN6v7mq00c+ZM7dy5szZsmKP77XeILlq0SFVVS0tVv/hC9eijT9c2bfK1QYOG2qZNBx058hT99NMvqrz+Sy+9pAceeKA2atRIe/Tooffee6+Wl5dHTO8PP6guXar60UeRt6VL3Xmx8qK0VPWzz1Q//dSd78XnPNb3e+D/4uzZlf+jtZEp36PAEg0TE2153HqisLCQAit6AtXLi9NOcxMfRdO0qSvpf/99cKmiTZt+vPrqkpi9n72WbsvB7ty5k8MOO4z99tuPRx55hObNm/Puu+8yYMCAlLx+KvJj9+7gXuwtWsB337mao+zsyjHtUFmCru3naPdu12QVq0q/T5/Kavl0+2wE8mL56Uz5HrXlcY2pgd/8JnbAj7TAkL8OIFbvZxPsuuuu45dffuHBBx8kNzeXHj16cO+999KhQ4eg2egyUaRhcf6q9tBgD/H3oo8lXdrgjXesl74xUbRv70rw0fgXGIo0a2Ks3s+m0oIFC3jggQd46qmnKqbkvfHGG3nrrbeC2rAzVbRhceDamWMtjlObz1FgD3e/RE6MY9KbBXxjoohngaJ4Fhjy93420Y0YMYLS0lIGDRpUse+cc85h48aNLFy40MOU1V6sYXHxqO3nKHTq3MBhfXvtlb7NTiYxLOAbE0U8c/0PGhR7Lv5YvZ9N3RfP1LSxJOpz5J8Y58ADK6fONXWf/ZmNiSFwmM/OnZVVrtnZbpjP3nvDkiWVQ/XCqe2yoCbzxTMsLhb7HJnasBK+MTEEzjTYo4frVd2woavqP/TQ+BYYSsUMZCa9JWLhF/scmdqwgG9MnPyTI61fD3fe6fZdfLErtUWr9k/EsqAm88UzNS0kd3lZU79ZwDemBq64wk3Fu2GDux9uTQQR6/1sKjVoAHvuGfl4VparPbJe9CZZ7LeiMTWQnQ2PPup6N//973DqqVXXRGjUKHgSE1O/qYbv3Bk6Na0/uBcXu3b/hg0Tt7ysqd+shG9MDXXvXlm1P26cW8o0L8+V0jp3hmbN7EvaVPrxR9exs2FDN6FTtGFxDRq43vMdOlgvepM49jEyphYuuQSefRYKC93SnHPnuvsAJ56YT79+VWa3TDs7duygcePGXicjbSQjP8rKXN8PN+Vy5P4e6SadPxv5+fleJyHjWMA3phaysuBvf3NjmufNg8suc237AC+++KK3iYtTpswPnirJyI/Ro+GTT9ySy889lzkT3Nhno26xKn1jaqlrV7j6anf/mmvi64lt6o+XXoL5890UzfffnznB3tQ9ngR8EblERFaKyA4RWSoiR8Q4/ywR+URESkRkg4g8JSLtU5VeY2K55hrX0eqDD9yXuzHgOt1deaW7f+utkOFr/5gMl/KALyKnA/cCtwMHA+8Br4pI2H8FERkEPAk8DuwP/BbYD3g6Fek1Jh7Nm8OUKe7+dde5GfmMefRRt+xtr16uj4cxXvKihD8BeExV56jqclW9HCgC/hDh/MOBtap6j6quVNUPgPuBw1KUXmPi8vvfQ+/e7gv+wQe9To3xUkEBDBniSvXg5mmwnvbGaykN+CKSA/QFFoQcWgAMjHDZu0C+iJwoTmvgDOCV5KXUmOpr0ADuusvdnzrVVser79avh7Vr3QI1o0Z5nRpjUl/Cbw1kAxtD9m8EwrbJq+r7uAD/NLAL+BEQIPMXxzZ1zsiRrnRXXAy33+51aowXioth3TpYudI9vvba2s+hb0wiiKawS7GIdADWAUNVdXHA/snA2araK8w1+wGvAzOA14B84G7gE1U9N8z5FwEXAbRr167v3Llzk/BOMs+2bdto1qyZ18lIC8nOixUrmjFuXD8aNixn7twPaNVqV9JeKxHssxGsNvmxfr2bbvnNN/fmlVf2oVOnLVxxxcfk52fmtLj22QiWKfkxbNiwpapadRIQVU3ZBuQAu4HRIftnAosiXPMk8O+QfYMBBTpGe72+ffuqcRYuXOh1EtJGKvLit79VBdXrr0/6S9WafTaC1TQ/Jk1Szc11f/fQLTfXHc809tkIlin5ASzRMDExpRVNqroLWAocHXLoaFxv/XBygdDFR/2PraLMpKVrr3W3s2bB1q3epsUkX3ExTJsGJSXhj5eUuOM//5zSZBkTxIuAOR0YIyIXikhvEbkX6AA8BCAiT4jIEwHnvwicLCJ/EJFuvmF69wEfq+qalKfemDgMGABHHAG//AKzZ3udGpNs8+e7BZWiyc6GZ55JTXqMCSflAV9V5wFXAZOAT3DV88er6mrfKZ18m//8x3BD+S4DPgfmA18BJ6cqzcbUhL+Uf889bvU8U3dt2BC5dO9XUuLOM8YrEUeGiki3Gj7n96paGu0EVZ0FzIpwrCDMvvtxY++NyRjHHQf77w9ffOGW0B0zxusUmWRp3x6aNIke9HNz3XnGeCVaCf8b4OsabL9JYnqNyRhZWfDHP7r7d9/tVkozddOoUW4a3WjKytwiOsZ4JdbcT38Cvo3zubKBObVLjjF1yxlnwI03wrJl8PLLcOKJXqfIJENenlvPftWq8Mdzc2HCBGjZMpWpMiZYrID/kqp+GM8TiUg28Ejtk2RM3ZGT477oJ0xws/BZwK+bPv/cBfuGDV3NTmmpq9Fp2tSV7CdMcLMvGuOlaFX6RwBfxPtEqlrmu+bL2ibKmLrkwgtdye6dd+Djj71OjUmGe+91txddBEVF0L07dOniOmwWFbk59W1ZXOO1iAFfVd9V1e3VeTLfNTH6qhpTvzRvDuf65oScY41edc6mTfDUU+7+FVe46v0VK9zUumPHWjW+SR9xDcsTke9EpE+EYweIyHeJTZYxdcvYse726adhe7V+Rpt0N3s27Njh1lHo2dPr1BgTWbzj8LsAjSIcawx0TkhqjKmjDjgADj/czbr3z396nRqTKKWlMHOmu3/lld6mxZhYqjPxTqRVdvoBP9c+KcbUbf5S/lVXuRX1TOZ77TW3YE6vXjB8uNepMSa6iAFfRMaLyBoRWYML9i/6HwdsP+IWvvlPqhJsTKY67TRo0QK2bLFq/briscfc7fnnW6c8k/6iDcv7DnjTd/88YAluLfpAO4Fl2HA8Y2Jq2hROPRUefdR16pozx03YkpfndcpMTfz0E7zwghuG97vfeZ0aY2KLGPBV9XngeQBxP12nqurKFKXLmDpFFSZPdp32wLXlX3WV69U9caIbo20lxMzyj3+4Nvxjj3WT7hiT7mJNvAOAqp6f7IQYU5dNngzTpwcvouOfd336dHd7662pT5epOX91vq2RYDJFtMVzJgOPqOp63/1oVFXt68qYMPxrpe/YEf64f630q6+2MduZ4tBDYelS2GMPONnW7TQZIloJ/xZcZ7z1vvvRKGAB35gwqrNWur8nv0lv/mVuzzwTGjf2Ni3GxCtaG35WuPvGmOqxtdLrlh9/dNPlgquRKS62jpcmM1ggNybJ2rd3q6VFY2ulpz9VuOkm10GvrMztu+8+6NDB7ddIM5UYkyaqHfBFpK2IdArdkpE4Y+qCUaMqA0QktlZ6+vN3vAxc976kxPXNmD7dHTcmncU7l34LEXlUREqAImBlmM0YE0Zenht6F6mU36CBO24d9tKXv+NlpKYZf8fLn39OabKMqZa4huXhZtM7Ffgr8Bluwh1jTJz8a6FPm+aG5pWXu85eO3ZAs2YwZYq36TPRWcdLUxfEG/CPBa5R1ZnJTIwxdZWIG2c/YQIMGOCC/nXXwc03w8aNbojXoYd6nUoTiXW8NHVBddrwVyQtFcbUE3l5kJ8PnTvDxRfD6ae7/XPnepsuE1379tCkSfRzrOOlSXfxBvy5wInJTIgx9UVhodsAzjjD3c6b56r5TXoaNSq4s1441vHSpLt4q/QXADNEpDnwCrA59ARVfSuRCTOmPhgwwJX2V6+Gd9+FI47wOkUmnLw86NIFvv46/PHcXNdcYx0vTTqLN+A/77vtCowJ2K+A+G5jdGkxxoQScaX8O+90i7FYwE9Pv/zifpQBNGrkSvvl5W4FxLIyF+z9HTONSVfxBvxhSU2FMfWYP+A/84ybyKVBvP+VJmVeesl1tBwyBJ57rrLj5Q03uGp8K9mbTBDvanmLkp0QY+qrPn2gVy9YsQIWLoSjj/Y6RSbU/PnudvToyo6XYEPwTGaxqXWN8ZgInHqqu//CC96mxVS1dSu8+qq7f8op7jaw46UxmSKuEr6IxOqQp6p6VALSY0y9dPLJcPvt8PzzrlpfxOsUGb+XX4adO2HQIDdvvjGZKt4Sfhauc17g1hoYBPT0PTbG1FC/fq6a+Pvv4dNPvU6NCRRYnW9MJosr4KtqgaoOC9kOBPYDioHbk5pKY+q4rCw40TfTxfPPRz/XpM727fDKK+6+vzrfmExVqzZ8Vf0WuAO4OzHJMab+Oukkd2vt+OljwQL49Vc47DDYe2+vU2NM7SSi096PuGp9Y0wtHHmkm8Dl449h7VqvU2PADceDyh9jxmSyWgV8EdkTmAB8m5jkGFN/NWkCxxzj7lsp31sFBTB0aGV1/gkneJocYxIiroAvIitF5LuQbS2wATgKmJTUVBpTT1i1fvrYts2tftexI/zmN16nxpjai3dOr0W46XMD7QBWA8/42vKNMbU0cqTrwPfWW7BlC7Ro4XWK6p+yMigqcssWg5sIyYZJmrog3pn2xiQ5HcYYoE0bGDgQ3nkHXnvNhoKlkipMnuxm0vvmm8rVC596Cvbay82Vb4HfZDJPZtoTkUt8zQQ7RGSpiERdMkREckRkqu+anSKyRkSuSFV6jUmlk092t1atn1qTJ8P06S7wBy5VXFrq9k+e7F3ajEmElAd8ETkduBc3dv9g4D3gVRHpFOWyucCxwEVAL2A08P+SnFRjPOFvx3/55dhrsJvEKC6GadOgpCT88ZISd/znn1OaLGMSyosS/gTgMVWdo6rLVfVyoAj4Q7iTRWQErmPg8ar6uqquUtX/qmph6pJsTOpcdJEbnldc7Kr2TfLNnw/ZMRb4zs52Kxoak6lSGvBFJAfoCywIObQAGBjhst8CHwETRGStiHwtIveJSLPkpdQYb7Vq5W79i7aY5NqwobJ0v3t3+Ib6khJ3njGZSlRDO98n8cVEOgDrgKGqujhg/2TgbFXtFeaa/wAFwJvAVKAlcD/w/1R1VJjzL8JV/dOuXbu+c+fOTfwbyUDbtm2jWTP7jQTpnxdffQXLl7dkxoyD6NZtG3/965Kkvl6650cqbNrk1jEoL4fNmxtz++0DyM/fxtVXV+Z9Vpabba91aw8TmmL22QiWKfkxbNiwparaL3R/JgT8BcARQHtV/cW3bwTwmm/fxkiv169fP12yJLlflpmisLCQgoICr5ORFtI5L4qLYcAAtzpbURHs2gXr1iV3lbZ0zo9UKS52ebxjBwwevJZ33ulY5ZzGjd3fpGXL1KfPK/bZCJYp+SEiYQN+rav0RWTvGB3uAm0CyoB2Ifvb4SbxCacIWOcP9j7Lfbfxvq4xaU0VbrrJBZ1vvoHVq914cIBx49xxkzx5eTBxopvtcPnyPascz811x+tTsDd1TyLa8L/zbTGp6i5gKXB0yKGjcb31w3kX6BDSZu+fu391NdJpTNryDwnbsaNySJg/4L/yig0JS4WpU+G88+Cnn5pU7Gva1JXsJ0xwx43JZIkI+Lf6tnhNB8aIyIUi0ltE7gU6AA8BiMgTIvJEwPl/B34CHhWR/UVkEG5Y33xV/SEB6TfGU7GGhJWVwd1325CwZBOBnr6iRPPm0KUL3HOPq8a/9VabdMdkvnin1o1IVav1u1dV5/kW3ZkE5AOf44bc+UvrnULO3yYiw3Ed9T4CioHngOtqmXRj0kI8Q8JE3JCwsWNTk6b66vXX3W2HDtC+veW3qVtqHfBrQlVnAbMiHCsIs28FMCLJyTLGE4FDwiLZscOGhCXbzp2waJG7n5fnbVqMSYa4A76ItATGA4cDe+F6278HzFDVn5OROGPqg/btXaew7dsjn5OV5c4zyfPee+6HV9eu23j//fQfemVMdcW7PG4f4GvgeqAxsMx3ewPwlYjY4pHG1NCoUZUd9CIpL3ertpnk8Vfn9+tX7G1CjEmSeDvt3YfrONdDVYeo6mhVHYLrLb8Z175ujKkB/5Cw3Nzwx7N8/6X+KSUKCtxmEmuBb/5PC/imroo34B8K3BTQsQ4AVV0F3Az0T3C6jKlXpk51Q78aN64M8P4hYUOHusevveZd+uq6n36Cjz+GnBw48MCfvU6OMUkRb8D/CdgZ4dgO33FjTA2JuKFf69dD9+7BQ8L+8hd3zmuvwebNbt/q1TBnjhvSZ2rvzTfd5EaDB0PjxuWxLzAmA8Ub8B8ErhGRxoE7RaQJMBGYmeiEGVMf5eVBfj507uyGhLVsCX36QNu2bq73/Hw3E9+qVTB+vBs+dtNNNhNfbfmr862fhKnLIvbSF5HA8fUCdAbWiMgrwEbcdLjHA78CEVofjTHVVVgY/DgrywX8H35wc+v7+Xv1T5/ubm+tzvRXpoJqZYe9ESNgyxZv02NMskQbljcpwv5zw+y7EbDJP41JguJiWLEi8vGSEjdT39VX21zvNfHVV7BmjVsF76CDYPHimJcYk5EiVumralY1thjzhBljamr+fGgQY8aM7Gw3E5+pPn/p/qijKjtMGlMXxfx4i0iOiFwpIgekIkHGmGAbNriZ9qIpKbGZ+GrK334/wubyNHVczIDvW+HuDqBV8pNjjAnln4kvmtxcm4mvJkpLK/tMWIc9U9fFW4G1HOiWzIQYY8KLZya+sjIYPTo16alL/vtf2LoV9t0X9t7b69QYk1zxBvzJwE02ha4xqeefia9Jk/DHc3PdceuwV31vvOFuhw/3Nh3GpEK8i+dcCzQD/iciq4AiIHDkr6rq0ASnzRjjM9U3SPZPf6occ9+0qSvZT5hQedxUz1tvudujjvI2HcakQrwBvwy3YI4xxgP+mfiKi2HmTNhjD7j7bleNbyX7mikpgQ8+cHk71Iorph6IK+CHW6PeGJN6I0e6gF9e7mbiMzX33nuu097BB7tmE2PqOht1akwGOeIIVyLduhV++cXr1GS2hQvd7bBh3qbDmFSpVsAXkTwR6S8iQ0K3ZCXQGFOpWTMYONDdtxnhascf8I880tt0GJMqcVXp+xbN+RtwGm5e/XBstj1jUuDII+Hdd12HsxNP9Do1mWnrVvjwQzdD4RFHeJ0aY1Ij3hL+TUABcB4u4F8GXAi8A3wLnJCMxBljqvL3KH/zTW/TkcneeceNcOjbF1q08Do1xqRGvAH/VGAqMNf3+L+q+qhvKN6nwLHJSJwxpqoBA9yY/M8+cyvomeqz9ntTH8Ub8DsBX6hqGVAKNA049jfg9EQnzBgTXqNGMHiwu+8PXKZ6rP3e1EfxBvyfcBPvAHwP9Ak41hqIMAeYMSYZ/IHKP3GMid/PP8OSJW60w6BBXqfGmNSJd+KdD4CDgVeBZ4FbRaQ5sBu4GteWb4xJkYICd7tokafJyEj+0Q3Nm7vZCo2pL+IN+HfiqvUBbgO649r0s3E/Bv6Q+KQZYyLp29cFqxUroKgI8vO9TlHm8Ffn2wyFpr6Jq0pfVZeo6r9897eq6qm4Kv6WqjpQVdckM5HGmGANG1a249t4/OrxL5izcyfMmeOmKzamPqjxTHuqulNVtyQyMcaY+Pnnf/ev526iU4Wrr4bPP3ePN26E8eOhQwe46abKRYmMqasiBnwROVdE9qzOk/musVmpjUkBa8evnsmT4YEHgvdt3w47dsD06e64MXVZtBL+o0C3eJ9IRLJ913StbaKMMbH16we5ubB8uSutmsiKi2HaNNi1K/zxkhJ3/OefU5osY1IqWqc9AS4UkePifC5biMeYFGrY0A0re/11144/erTXKUpf8+e7aXSjyc6GZ56BHj1SkyZjUi1WL31bgNOYNDZ0qAv4hYUW8KPZsMFV30dTUuLOs4Bv6qqIAV9VrcRuTJrzt+Nbx73o2rd3MxTu3Bn5nNxcd54xdZUFdWMy2KGHunn1ly2zefWjGTUKdu+Ofk5ZmdWSmLrNAr4xGSwnBwYOdPdtPH5keXnQpk3k47m5MHGiTcZj6jYL+MZkOP94fBueF9kvv7iRDFlZrmo/y/fN17QpNG4MEybA1KneptGYZPMk4IvIJSKyUkR2iMhSETkizusGi8huEfk82Wk0JlNYO35s77zjJtYZMMBNRdy9O3TpAvfc4x7feqtbTMeYuizlAV9ETgfuBW7HLcjzHvCqiHSKcV0e8ATwZtITaUwG6d/flVI//xw2bfI6NenJX/sxdKir3s/Ph86dYexYq8Y39YcXJfwJwGOqOkdVl6vq5UARsRfg+SvwOPB+shNoTCZp1AgOP9zdt3b88AIDvjH1VUoDvojkAH2BBSGHFgADo1x3CdAOt1KfMSaEVetHtnUrLF3qJtbxd3AsLLS8MvVPxHH4IlIOxLuchKpqPEvttsYtqRs6EehGYHiEdPwGuBkYoKplEqOhTUQuAi4CaNeuHYX2Xw3Atm3bLC986mJetGixB3Awr7yyjVNOWVKta+tifgT68MM8ysr6sO++W1i69OOY59f1/KgOy4tgmZ4f0YL0VOIP+EkhIo2AecBEVV0ZzzWqOhuYDdCvXz8t8Bd96rnCwkIsL5y6mBeHHQbXXgvffdeMPn0KyKvGElZ1MT8CLfDVJ554You43mddz4/qsLwIlun5EW2mvVuS8HqbgDJc9XygdsCGMOfnA72BR0XkUd++LEBEZDdwvKqGNg8YU+80aeKC/ttvwxFHQOvWVmXtZ+33xjgpbcNX1V3AUuDokENH43rrh1oH/AY4KGB7CPjGdz/cNcbUS0OGuNtffvE2HemkpAQ++sgNuRs0yOvUGOOteNrdgYoOd8cBvYDGIYdVVW+N86mmA0+KyIfAu8A4oAMukCMiT/ie8FxVLQWCxtyLyA/ATlW1sfjGBBgyBP70Jwv4gT74AEpL4eCDbfidMXEFfBHpALwDdMG16/t7zgW28ccV8FV1nojsCUzCVdl/jquaX+07Jep4fGNMeAMHup7oW7fCypUwZ46bQ7467fl1jVXnG1Mp3ir9u4EfccFYgMOAbsCfcNXr3arzoqo6S1W7qGojVe2rqosDjhWoakGUa29R1QOq83rG1HWqcOedUF7uHq9ZA+PHQ4cOcNNN7nh9ZAHfmErxVukfAUwE1vsel6vqKmCyiGQD9wEnJz55xph4TJ4M06cHB3b/+u/Tp7vbW+NtdKsjdu50VfrgOjIaU9/FW8LfE1ivquXAdiCwkvAtoCDB6TLGxKm4GKZNcx3Uwikpccd//jmlyfJUQYEbtbBzJxxwAOy5p9cpMsZ78Qb8tbhJcwC+BUYEHOsP7Ehkoowx8Zs/37XdR5OdDc88k5r0pAt/50WrzjfGibdKfyEwFHgOeBiYKSIHAaXAMb59xhgPbNgQuXTvV1LizqtP/AHfP1zRmPou3oA/CWgFoKoPikgD4HQgF7gLNyufMcYD7dtDbm5lm304ubnuvPqivNwCvjGh4qrSV9VNqvpVwOP7VXWwqh6iqjeoqlXpG+ORUaOgrCz6OWVlMHp0atLjteJiWLXKBf127dxqgsYYb5bHNcYkUF4eTJzoSvHh5Oa643V94hlVNwSxQwdYu9bt++knG5pojF91ZtobCpyJG4sfbqa9oxKZMGNM/Kb6GtWmTYNduyrH4+fkwIQJlcfrMv/QxB0B9Y27d7utvg5NNCZQXCV8EbkY13FvFNASN/lO4GY1BcZ4SMQFs/XroXt3aNbM7b/2Wrc/xqrSGc+GJhoTW7wl/KuBvwMX+BbAMcakobw8yM93w/CWL4cPP/Q6RalRnaGJY8emJk3GpJt4S+Z7AY9asDcmM+yxh7t9911XpV3X2dBEY2KLN+AvpZrz5RtjvNOoEeyzD2zbBv/7n9epST7/0MRo6tvQRGNCxRvwrwCuEhEb0WpMmissdFtBQeXjus6GJhoTW7wB/0WgI7BQRLaKyJqQbXWsJzDGpJZ/Sln/inF1mX9oYuPQ8UM+9WVoojHRxNtp703ARrEak0H8Af/tt13pNlantkwSrvZi6lRYuhRefbVyX9Om7r3Xl6GJxkQTV8BX1TFJTocxJsE6dYIuXdysc59+Cocc4nWKkkuksrNi27auVH/DDa4a30r2xtj4eWPqtPpUra9a+T47dYLOnd0QPAv2xjhxlfBF5Nwoh8uBX4D/qerahKTKGJMQQ4fC44+7QDh+vNepSYziYigqcjMKzpnjOuzl5cHXX7v9/tK9MSZYvG34j1HZhh84Z1fgvnIRmQecb+P1jUkP/rbuxYvddLtZGVynp+qmzw2cPnj8eLjiCtchr2NHd15BAWzc6GlSjUlL8Qb8QcDTuN7684GNQDvgNOAE4BJgf9wyuauBGxKeUmNMtXXpAnvvDd9/D599Bn36eJ2imgs3V75/SeDp06FrV3e/oAD+8IeUJ8+YtBfv7/2JwFxVvVJV31bVr3y3lwP/AC5S1WnAX4AzkpVYY0z1iNSNdvx45sr/4gt331+rYYwJFm/AH4EbmhfOW4B/pbzFuGl4jTFpoi4E/Hjmygdo3hz23Tf56TEmE8Ub8HcCfSMc6wv42+yzgO21TZQxJnH8AX/x4sxdEz6eufLB9cyv6ysDGlNT8Qb8Z4ApInK1iHQWkSa+24nALcA833kHASsSn0xjTE117+5W0Nu0CZYt8zo1NRPPXPkABx6Y/LQYk6niDfgTgGeBu4DvgG2+2ztxnfiu9p33OXBtgtNojKmFutCOH89c+QCXX578tBiTqeIK+Kr6q6r+DugNjAGu993up6rnqOoO33kvq+riJKXVGFNDmb6Qjn+u/Gil/NxcOOyw1KXJmEwT77A8AFT1K+CrJKXFGJMkgSV81cxs5/bPhR84Dr9pU9i5E3bvhpEjM/N9GZMqEUv4ItJJRBoG3I+6pS7Jxpjq6tXLteP/8EPmtuOLwK23wvr1rl9Cly5wzz3w29+64zYcz5joolXprwQO9t1f5XscbTPGpCkROPJId/+tt7xNS23l5bkfL507w4UXwnvvuf0W8I2JLlqV/gXAtwH3M3RAjzEGXMB/+ml4882607ntm29cib9NG+jd2+vUGJPeIgZ8VX084P5jKUmNMSZp/CX8wkLX4z2eiWzSnb8TYkGBtd8bE0uNltIQkT1EpJ+IdEx0gowxydGli5tv/pdfoH9/r1NTO4WFlRtYdb4x8YjWae8YEbkjzP4bgB+A/wKrReTvIlKt3v7GGG8c5ZsEu7jY23QkgiosXOjuW8A3JrZoJfxxQM/AHSJyNHAb8CVwFfAwcDpwZZLSZ4xJIH+1/s8/e5qMhFi2DIqKXAc+a783JrZoJfODgVtD9p0P7ACOUdUNAOIazs7CrZRnjEljw4a5219+cWPZc3K8TU9tvPGGuz3qKGu/NyYe0Ur4banspe93NPCOP9j7vExITYAxJj01auSCfHk5TJqU2VX7/oA/fLi36TAmU0QL+FuBpv4HItID2BP4IOS8LUC1+vuKyCUislJEdojIUhE5Isq5p4jIAhH5UUS2ish/ReSk6ryeMfWdKtx0E3To4Er2ADNmuMfr12feKnqlpZXrAvj7JRhjoosW8L8ETg54fDJuLP6CkPO6AhvjfUEROR24F7gd12zwHvBqlNn6hgJvASN9578C/DvajwRjTLDJk2H6dNixo3Jfaal7vHGjO55JPvoItm6FffeFjjZWyJi4RAv49wAXish8EZkJTAE+A94NOe944NNqvOYE4DFVnaOqy1X1cqAI+EO4k1X1SlW9Q1U/VNVvVHUKsBT4bTVe05h6q7jYzT8faT358nJ3PJM68ll1vjHVFzHgq+pzuJ74hwLn4qryR6tWVv6JSHtgOK7UHZOI5AB9qVpLsAAYWI10NwcyuPXRmNSZPz/2JDvZ2fDMM6lJTyJYwDem+kRT2HgnIh2AdcDQwGV0RWQycLaq9orjOS4F7gAOUNXVYY5fBFwE0K5du75z585NVPIz2rZt22jWrJnXyUgL9S0viopcO32gl17qRmFhJ448cjUXXfQZa9c2o0MHN8Qt3f36azYnnjgIVeH559+hWbOyhD5/fft8RGN5ESxT8mPYsGFLVbVflQOqmrIN6IDrBzAkZP9kYEUc158KlAAnxvN6ffv2VeMsXLjQ6ySkjfqWF7NnqzZtquq65lXdpk1bqE2buvMywcsvu3QPGJCc569vn49oLC+CZUp+AEs0TEys0dS6tbAJKAPahexvB2yoenolERkFPAmcq6ovJid5xtQ9o0a5ufMjKSlpQFkZjB6dujTVhlXnG1MzKQ34qroL1+Hu6JBDR+N664clIqfhgv0YVZ2fvBQaU/fk5cHEiZCbG/7411/nMXEitGyZ0mTVmAV8Y2rGiznwpwNPisiHuB7/43BV/Q8BiMgTAKp6ru/xGbhgPxFY7OsoCLBLVTenOO3GZKSpU93ttGluHH55OTRs6IbmrV7discfj359utiwAT77zP14GTDA69QYk1lSXaWPqs7D9f6fBHwCDAaO18oOeJ18m9843A+TGbjhe/7tXylJsDF1gAjceqvrvNe9u1s57/rr3bHPP2/ladqq46233O2QIW7WQGNM/DxZ5U5VZwGzIhwriPbYGFNzeXmVPfFvuQX++ldYt64Rn34KBx3kZcriY9X5xtRcykv4xpj0IALHHuvu/+c/3qYlHqrw+uvuvgV8Y6rPAr4x9VgmBfzPPoO1a6F9e/jNb7xOjTGZx5MqfWOMdwoLK+8PHw5ZWcq77wpbtkCLFp4lK6aXXnK3I0dClhVVjKk2+7cxph5r2RL23/8Xdu+GN9/0OjXRvfyyux050tt0GJOpLOAbU8/17+9Gt6Zztf6mTfD++5CTY+33xtSUBXxj6rlDD3UB/9VXXce4dORPW0EBNG/udWqMyUwW8I2p53r02EbbtvD997B8udepCc9fnX/CCd6mw5hMZgHfmHouKwuOOcbdT8dq/dLSynRZ+70xNWcB3xhTMTzv1Ve9TUc4774Lv/wCvXtDt25ep8aYzGUB3xjDiBFuIp7Fi2HbNq9TE8yq841JDAv4xhhat4bDD3cL67zyitepCRY4/t4YU3MW8I0xAJx6qrt99llv0xHo22/hyy/dfAEDB3qdGmMymwV8YwwAp5zibl9+GX791du0+Pmr84891i3na4ypOZta1xgDuCVz+/WDJUvgtdfgt7/1Li0FBe42J8fdWvu9MbVnJXxjTIV0qtbfvdvN+5+VVTmKwBhTcxbwjTEV/AH/hRdg505v07JpkxuDX1AAe+7pbVqMqQss4BtjKvToAQceCFu2eL+Yzg8/uNszz/Q2HcbUFRbwjTFBRo1yt/Pne/P6xcVumt/iYsjOhqOO8iYdxtQ1FvCNMUH81frPP++q1FNFFW66CTp0gJUrK/fvt5/bn64L+xiTKSzgG2OC7Lcf7LsvbN4Mixal7nUnT4bp02HHjsrgXlbmHk+f7o4bY2rOAr4xpopUV+sXF8O0aVBSEv54SYk7/vPPqUmPMXWRBXxjTBX+av1//9uVspNt/nzXXh9NdjY880zy02JMXWUB3xhTRZ8+sM8+rqf8G28k//U2bIhcuvcrKXHnGWNqxgK+MaYKEbjgAnf/4YeT/3rt20NubvRzcnPdecaYmrGAb4wJ64ILoEEDNwnP+vWV+wsKKqe+TZRRo2I3HZSVwejRiX1dY+oTC/jGmLDat3fz6ZeVwV//mtzXysuDq692NQvh5ObCxIlu1TxjTM1YwDfGBAkswY8b525nz3Zz2yfT0KGVw/H8gb9pU2jcGCZMgKlTk/v6xtR1FvCNMRENGwbdu8PatfDqq4l73nDNAtOnu9sbbnBT/HbpAvfcA0VFcOutkUv/xpj4WMA3xkSUlQUXX+zuP/ywGy9fVASrV8OcOe5xInzxBfznP9CkiSvN5+dD584wdqxV4xuTKBbwjTFRjRnj1qV/+WUXiL/5BlatgvHj3TS4iZj21l+6HzPGVsYzJlks4BtjKoQrwbdu7arYwS2ZW17u7m/fHnna2+r05N+4EZ56ylXZjx+fqHdijAllAd8YE7RwTWgJfuJE+PrryNfWdtrbmTNh1y446aTKHxaFhW4zxiSOBXxjTNDCNaEl+Pvvr9wXSXWmvQ2sRXjgARfwwQ3LM8YkjwV8Y+q5srLoC9fs2hV7SF48096Gq0W4+mq3Kl+HDjBoUI2Sb4yJkwV8Y+q54uLYC9fEEjjtbaSe/OFqEXbtcrebNsHNN9cuDcaY6DwJ+CJyiYisFJEdIrJURI6Icf5Q33k7ROQ7ERmXqrQaU9eVlsZeuCaWsjI3PW60fgCxahFs+VtjkivlAV9ETgfuBW4HDgbeA14VkU4Rzu8KvOI772Dgz8D9InJqalJsTN3WsGHshWsaNnRD88LxT3s7fXr0fgCx5sq35W+NSS4vSvgTgMdUdY6qLlfVy4Ei4A8Rzh8HrFfVy33nzwEeByamKL3G1Gl5ebGDcVYWXHGFm+Y2cMa7nBw3Uc748bFL8KWl0V/Dlr81JrlSGvBFJAfoCywIObQAGBjhssPDnP8a0E9EGiY2hcbUP9nZroQeqZSfmwvXXAN33+1WzevRA/bYwx3r08fNcf/ss4ntB2CMSTzR2k6RVZ0XE+kArAOGqurigP2TgbNVtVeYa74CnlLVqQH7hgCLgA6qWhRy/kXARQDt2rXrO3fu3KS8l0yzbds2mjVr5nUy0oLlRTB/fqxf70rY/q+ErCx3v3171w7v99VXsGNHNlOm9Gfz5kaMGbOSESNWBy2hG8lPPzVm2rRDKS3N5txzP+fAAzdVHBNxPyBq+8OhtuzzUcnyIlim5MewYcOWqmq/KgdUNWUb0AFQYEjI/snAigjXfAVMDtk3xPc8+dFer2/fvmqchQsXep2EtGF5ESwwPzZvVu3ZU7VLF9XZs1WLi6ueP3So2+bPVxVRBdUTTlDNzXX3I20NGlSeH7rl5qpOmpSiNxyDfT4qWV4Ey5T8AJZomJiY6jb8TUAZ0C5kfzsgUuvdhgjn7/Y9nzEmQfLy4l+45tRT3ZS4WVnw0ktu2t1oysps+VtjvJTSgK+qu4ClwNEhh47G9cIP5/0I5y9R1RjdgIwxiRY47e1ZZ8E//uGq4cvKoEGDyNepQrt27seBLX9rTOp50Ut/OjBGRC4Ukd4ici+uqv8hABF5QkSeCDj/IWAvEZnhO/9CYAwwLdUJN8ZUddppMG+eC/ahM/JlBXzDjBoFn38OI0fa8rfGeCHK7/HkUNV5IrInMAnIBz4HjlfV1b5TOoWcv1JEjgfuwQ3dWw9coarPpjDZxtQbNVm05tRT4bnn4I9/hBUrKof5lZe7Hv2zZsGZZ1op3hgvpTzgA6jqLGBWhGMFYfYtAg5JcrKMMbUwcqTbdu2CwYPduPorroATTgju5W+M8YYnAd8YU3fl5Lgx9bm5cNFF4c+xpW+NST1bPMcYY4ypB6yEb4xJOCvBG5N+rIRvjDHG1AMW8I0xxph6wAK+McYYUw9YwDfGGGPqAQv4xhhjTD1gAd8YY4ypByzgG2OMMfWABXxjjDGmHrCAb4wxxtQDFvCNMcaYesACvjHGGFMPWMA3xhhj6gFRVa/TkDQi8iOw2ut0pInWwCavE5EmLC+CWX4Es/yoZHkRLFPyo7OqtgndWacDvqkkIktUtZ/X6UgHlhfBLD+CWX5UsrwIlun5YVX6xhhjTD1gAd8YY4ypByzg1x+zvU5AGrG8CGb5Eczyo5LlRbCMzg9rwzfGGGPqASvhG2OMMfWABXxjjDGmHrCAn+FEpLWIrBMRFZHWMc5tJyKPich6ESkRkf+ISI8w5/UXkddFZJuIbBWR92I9d7pIZH6ISBff84Tbrkn+u6kdy4tgif5fEZH2IvKkiGzwnfOpiJyd3HeRGJYXwZKQH/uIyL9F5EcR2SIi/xSRdsl9F7FZwM98jwKfxDpJRAR4DugB/BY4GDcp0Rsi0jTgvMOABUAhMADoC0wDShOa6uRJZH58D+SHbJcACsxPbLKTwvIiWEL/V4AngN7AycABvsdPisiQRCY6SSwvgiUsP3y3CwABjgQGATnAiyLibcxVVdsydAOuBN7EfagUaB3l3J6+c/oE7MsCfgAuDNj3HvAnr99buuRHmOteBxZ4/V4tL7zPD2AbcH7ItauBiV6/X8sL7/IDGAGUA3kB5+zh2zfcy/dqJfwMJSIHA9cC5+I+SLE08t3u8O9Q1XJgJzDY95xtgcOBIhF5R0R+EJG3ReSohCY+CZKRH2FeoxtwFGk+NMfyIlgS8+Md4DQR2VNEskTkZKAN8EZCEp4ElhfBkpQfjXA/CnYEXLfD9/xh/59SxQJ+BvJVGc0FLlfVdXFe9iWwBrhdRFqJSI6IXAt0xFXPAnTz3U4B/gYcA7wNvCYifRL2BhIsifkR6kLgR+D52qY5WSwvgiU5P07DfbFvwn3hPw2cqaqfJCr9iWR5ESyJ+fEBrsbjbhFp6nudaUA2kf+fUsICfma6D3hHVZ+N9wJVLQVOAfYBfgJKgGHAq1T+svV/Hh5W1b+p6v9U9QbgI2BcohKfBMnKjwoi0gA4H3jcd226srwIlsz8uA23mMpwoB9wN/BEGv84trwIlpT8UNUfgdHAccBW4BegJfAx8dUiJI/X7Se2VX8DVgFlwG7fVob7db2bONrfce1JbXz3/wvM9N3v6nue34Wc/1fgZa/fd6rzI+Sc//M9Z0+v36/lhff5gfvCD2rL9e1/A3jE6/dteeFdfoSc0xpo6bu/AbjGy/fcAJOJRuB6ffodiquCLwC+jnWxqv4C4BtK0g+4yXdoFbAe6BVySU/gs9okOMmSlR+BxgKLVPWr2iY2ySwvgiUrP3J9t2Uhl5SRvjWnlhfBkv6/oqqbfOccCbQFXqhtomvF619ZttV+w31Ag3qXAnvh2pv+L2DfaFz1Uzfc8JlVwLMhz3UVrgpqNNAduAE3JK+P1+/Ti/zwndcJ9+V1ttfvzfIiPfIDaIgLCouB/rhS7tW4KtsTvX6flhfe5YfvnPNxHaD3AX6Hq/7/i9fv0Ur4dVdDXEl9j4B9+cB0oB1QhBsre2vgRao6Q0QaAX8B9gS+AI5T1U9TkegkqlF++Pwe9yMo7ra+NGd5Eaza+aGqpSJyPHAH8CLQDPgGNzTtxRSlOxksL4LV9H+lF/BnoBXuB8GfgHuSnNaYbPEcY4wxph5I5/YVY4wxxiSIBXxjjDGmHrCAb4wxxtQDFvCNMcaYesACvjHGGFMPWMA3xhhj6gEL+MYkmIiMERGNsA33On2ZTkQKQvK0S8jxhiLyB99Kj8UiUioiRSLykoic41sLwH+u/2/VPczrNPAdu6Wa6dsdkLYLa/o+jUk0m3jHmOQZDawN2bfMi4TUUZfiFiQp8u8Qkea4hUz6AnNwi7j8jFvN7CTgUWAXMC+J6RoEdAD+lcTXMKbaLOAbkzyfqOo38ZwoIo1UdWeyE1THLFPVD0L23Y+b13yoqv435NjffeufN0lmolT1v6G1DsakA6vSNybFAqqRh4jIMyLyM261LX818vUi8qWI7BSR9SLyFxFpHPIc3UTkZREpEZEfReReEbk4tIo7XJW0iHTx7R8Tsn+oiLwpIltFZLuIvCYiB4ScUygi74jIcBH52Pf6n4vI/4V5n31E5N8i8pOI/CoiK0Tket+x+0Vko4g0DLmmue/176hBvu6Fm7f84TDBHgB1Sz6/V93n9j2/P9/CbYU1eU5jUslK+MYkT3ZgezGgqhq4otjTwD+AUVT+Lz4FnAjcCbwH9MbN090FOBVARHKA13El1UuBH4CLcet014iIjASeB17GBU2Aa4G3ReRAVf0+4PR9gHtxc4Vvwi2U8oyI7Ouv0RCR/kAhbk718bimjR7Agb7neBC4DLfU7j8DnvssoCnwcA3eRgGQDbxUg2tD/1b4nitQEW5BlED7A7OB5TV4TWNSygK+McnzZcjjd4HBAY/nq+of/Q9E5AjgdOA8VX3Ct/sNEdkMPCUiB6nqJ8B5uJW6DvdXaYvIq9RuCeN7cUvenhyQnoXAd7iAflXAua2BIar6te88fzv6acDtvnOm4VYIG6CqJb59b/mfQFWXicgi3A+VwIB/MbBAVVfW4D109N2uCdwpIkJw8C5X1fKQa0P/VlX4mlwqmhBEpA3wd1ztzPgapNeYlLIqfWOS5/9wa2z7t9+HHP93yONjcR3K5vuq9hv4Sp0LfMeH+G4PB74PbL/2BbB/UgO+9bz3AZ4Oed0S4P2A1/X72h/sfa/9A66WoZPv+XJxHdeeDgj24cwChvleHxE5FDiYmpXuo7kWt8Szf3sizDmhf6tDgQGRntBXy+L/+52sqjsSmWBjksFK+MYkz+cxOu0VhTxuC+QA2yOcv6fvNh/YGOZ4uH3xaOu7/atvC7Um5PHmMOfsBPz9DPJwhYnQEQqh/g1swJXqJwLjgPW4JVZrwv96nYAVAfsfA97w3X8hwrVV/lZhqvgDPQIcgKtl+bH6STUm9SzgG+Od0LWpfwJ2AEdEOH+977YI13Ycql2YfTtxPyIC7Rny+Cff7fVUBsZAuyKkJ5JioBzYK9pJvnXUHwEuEZG7gDOAv6jq7mq+nt8i3+uegOvj4H+dDbgfFohIdd9LFSJyA3AmcJyqWtu9yRhWpW9M+vgPrpS8h6ouCbP5A/77wN4iUlHlLCJZuDb0UKtxJdFAI0MerwBWAftHeN3/V5034avGfwf4nYjEGgL3MNASeAZohBs7XyOquhbXEfJiETmsps8TjYicCtwGXKqq4X4cGZO2rIRvTJpQ1UIR+QeuDX868CGuxNoFOB64VlW/Ah4HrgP+5Stt/oCrDm8R5mnnApNE5EZch7MjcKXTwNdVEbkUeN7XNv1PXO/7dsBAYI2qTq/m25mIK3G/LyJ/wVW3dwMOUtXLA157nYi8gGtDfzFkNEBNXIYbDbBQRObgaix+xjUzDAHaA1tr8sQi0g3X/r8A+H+BP7iALapqkyqZtGYB35j08jvgcuAC4EZclfwq4DV8bfSquktEjgYewHV8247rLf4y8FDI8/0ZV4K+DPcj4RXgHHzj/v1U9RURGeJ7zUdwQ/424H4kVHtWOlX9SEQGAVNxk+E0wtU2PBrm9GdwAb/WnfVUdYuIDAXG4ob4nYcb5rcJWIrrODm3hk/fCcgFjvFtgRbhhgUak7ZENbQZ0RiTiXwT6TwKdFXVVd6mJn4i8jSuV3+3MMPlwp1fACwEhuOGEta0zT8pRCQbVyvzDTBWVR/xNkXGOFbCN8Z4wlclfhBu7oEJ8QT7EG/4nifdfuDspOqkPcZ4zgK+McYr7wPbcH0SZlXjuqW4cfJ+6yOd6JHDAPHdX+VhOowJYlX6xhhjTD1gw/KMMcaYesACvjHGGFMPWMA3xhhj6gEL+MYYY0w9YAHfGGOMqQcs4BtjjDH1wP8H3hSs5wBJQ6UAAAAASUVORK5CYII=\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
@@ -471,8 +471,8 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: f01\n",
-      "- value: 4971644046.603253 ± 46329.06674455518 Hz\n",
-      "- χ²: 1.056178573009652\n",
+      "- value: 4971710631.072543 ± 46113.624847722356 Hz\n",
+      "- χ²: 1.1424156746459029\n",
       "- quality: good\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
@@ -531,13 +531,13 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>qubit_lo_freq</td>\n",
+       "      <td>meas_lo_freq</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>None</td>\n",
-       "      <td>4.971589e+09</td>\n",
+       "      <td>6.993371e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.784983+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882285+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -545,22 +545,22 @@
        "      <td>qubit_lo_freq</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>None</td>\n",
-       "      <td>4.971644e+09</td>\n",
+       "      <td>4.971589e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:24:42.182000+0100</td>\n",
-       "      <td>f808753b-c40e-4376-9ce8-548275a4b548</td>\n",
+       "      <td>2021-11-03 16:40:00.882258+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>meas_lo_freq</td>\n",
+       "      <td>qubit_lo_freq</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>None</td>\n",
-       "      <td>6.993371e+09</td>\n",
+       "      <td>4.971711e+09</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785055+0100</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 16:41:19.204000+0100</td>\n",
+       "      <td>426026dd-b836-4756-803f-ad9422826895</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -568,14 +568,14 @@
       ],
       "text/plain": [
        "       parameter qubits schedule         value    group  valid  \\\n",
-       "0  qubit_lo_freq   (0,)     None  4.971589e+09  default   True   \n",
-       "1  qubit_lo_freq   (0,)     None  4.971644e+09  default   True   \n",
-       "2   meas_lo_freq   (0,)     None  6.993371e+09  default   True   \n",
+       "0   meas_lo_freq   (0,)     None  6.993371e+09  default   True   \n",
+       "1  qubit_lo_freq   (0,)     None  4.971589e+09  default   True   \n",
+       "2  qubit_lo_freq   (0,)     None  4.971711e+09  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-11-03 10:23:20.784983+0100                                  None  \n",
-       "1  2021-11-03 10:24:42.182000+0100  f808753b-c40e-4376-9ce8-548275a4b548  \n",
-       "2  2021-11-03 10:23:20.785055+0100                                  None  "
+       "0  2021-11-03 16:40:00.882285+0100                                  None  \n",
+       "1  2021-11-03 16:40:00.882258+0100                                  None  \n",
+       "2  2021-11-03 16:41:19.204000+0100  426026dd-b836-4756-803f-ad9422826895  "
       ]
      },
      "execution_count": 16,
@@ -681,7 +681,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
@@ -707,8 +707,8 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: rabi_rate\n",
-      "- value: 0.5810497529891635 ± 0.0027460368083858143\n",
-      "- χ²: 1.2517635384635177\n",
+      "- value: 0.5835711622496985 ± 0.0028894642130689666\n",
+      "- χ²: 2.0874659580448207\n",
       "- quality: good\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
@@ -761,45 +761,45 @@
        "      <th>0</th>\n",
        "      <td>amp</td>\n",
        "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785453+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882460+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
+       "      <td>(0,)</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.500000+0.000000j</td>\n",
+       "      <td>0.856794+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785369+0100</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 16:42:27.534000+0100</td>\n",
+       "      <td>bc1dac04-bbfc-4282-97ae-90272616e093</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.430256+0.000000j</td>\n",
+       "      <td>0.428397+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
-       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
+       "      <td>2021-11-03 16:42:27.534000+0100</td>\n",
+       "      <td>bc1dac04-bbfc-4282-97ae-90272616e093</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.860512+0.000000j</td>\n",
+       "      <td>()</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
-       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
+       "      <td>2021-11-03 16:40:00.882499+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -807,16 +807,16 @@
       ],
       "text/plain": [
        "  parameter qubits schedule               value    group  valid  \\\n",
-       "0       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "1       amp     ()        x  0.500000+0.000000j  default   True   \n",
-       "2       amp   (0,)       sx  0.430256+0.000000j  default   True   \n",
-       "3       amp   (0,)        x  0.860512+0.000000j  default   True   \n",
+       "0       amp     ()        x  0.500000+0.000000j  default   True   \n",
+       "1       amp   (0,)        x  0.856794+0.000000j  default   True   \n",
+       "2       amp   (0,)       sx  0.428397+0.000000j  default   True   \n",
+       "3       amp     ()       sx  0.250000+0.000000j  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-11-03 10:23:20.785453+0100                                  None  \n",
-       "1  2021-11-03 10:23:20.785369+0100                                  None  \n",
-       "2  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  \n",
-       "3  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  "
+       "0  2021-11-03 16:40:00.882460+0100                                  None  \n",
+       "1  2021-11-03 16:42:27.534000+0100  bc1dac04-bbfc-4282-97ae-90272616e093  \n",
+       "2  2021-11-03 16:42:27.534000+0100  bc1dac04-bbfc-4282-97ae-90272616e093  \n",
+       "3  2021-11-03 16:40:00.882499+0100                                  None  "
       ]
      },
      "execution_count": 23,
@@ -845,7 +845,7 @@
     {
      "data": {
       "text/plain": [
-       "ScheduleBlock(Play(Drag(duration=320, amp=(0.43025575+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())"
+       "ScheduleBlock(Play(Drag(duration=320, amp=(0.42839677+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())"
       ]
      },
      "execution_count": 24,
@@ -866,7 +866,7 @@
     {
      "data": {
       "text/plain": [
-       "ScheduleBlock(Play(Drag(duration=320, amp=(0.86051151+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())"
+       "ScheduleBlock(Play(Drag(duration=320, amp=(0.85679354+0j), sigma=80, beta=0), DriveChannel(0)), name=\"x\", transform=AlignLeft())"
       ]
      },
      "execution_count": 25,
@@ -887,7 +887,7 @@
     {
      "data": {
       "text/plain": [
-       "ScheduleBlock(Play(Drag(duration=320, amp=0.86051151j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())"
+       "ScheduleBlock(Play(Drag(duration=320, amp=0.85679354j, sigma=80, beta=0), DriveChannel(0)), name=\"y\", transform=AlignLeft())"
       ]
      },
      "execution_count": 26,
@@ -988,68 +988,68 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.860512+0.000000j</td>\n",
+       "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
-       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
+       "      <td>2021-11-03 16:42:30.370765+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>amp</td>\n",
        "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:55.416335+0100</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 16:40:00.882460+0100</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
+       "      <td>(0,)</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>0.428397+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785453+0100</td>\n",
-       "      <td></td>\n",
+       "      <td>2021-11-03 16:42:27.534000+0100</td>\n",
+       "      <td>bc1dac04-bbfc-4282-97ae-90272616e093</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
+       "      <td>(0,)</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.500000+0.000000j</td>\n",
+       "      <td>0.856794+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:55.416203+0100</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 16:42:27.534000+0100</td>\n",
+       "      <td>bc1dac04-bbfc-4282-97ae-90272616e093</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>amp</td>\n",
        "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.500000+0.000000j</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785369+0100</td>\n",
-       "      <td></td>\n",
+       "      <td>2021-11-03 16:42:30.370841+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.430256+0.000000j</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
-       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
+       "      <td>2021-11-03 16:40:00.882499+0100</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1057,20 +1057,20 @@
       ],
       "text/plain": [
        "  parameter qubits schedule               value    group  valid  \\\n",
-       "0       amp   (0,)        x  0.860512+0.000000j  default   True   \n",
-       "1       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "2       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "3       amp     ()        x  0.500000+0.000000j  default   True   \n",
-       "4       amp     ()        x  0.500000+0.000000j  default   True   \n",
-       "5       amp   (0,)       sx  0.430256+0.000000j  default   True   \n",
+       "0       amp     ()        x  0.500000+0.000000j  default   True   \n",
+       "1       amp     ()        x  0.500000+0.000000j  default   True   \n",
+       "2       amp   (0,)       sx  0.428397+0.000000j  default   True   \n",
+       "3       amp   (0,)        x  0.856794+0.000000j  default   True   \n",
+       "4       amp     ()       sx  0.250000+0.000000j  default   True   \n",
+       "5       amp     ()       sx  0.250000+0.000000j  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  \n",
-       "1  2021-11-03 10:25:55.416335+0100                                  None  \n",
-       "2  2021-11-03 10:23:20.785453+0100                                        \n",
-       "3  2021-11-03 10:25:55.416203+0100                                  None  \n",
-       "4  2021-11-03 10:23:20.785369+0100                                        \n",
-       "5  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  "
+       "0  2021-11-03 16:42:30.370765+0100                                  None  \n",
+       "1  2021-11-03 16:40:00.882460+0100                                        \n",
+       "2  2021-11-03 16:42:27.534000+0100  bc1dac04-bbfc-4282-97ae-90272616e093  \n",
+       "3  2021-11-03 16:42:27.534000+0100  bc1dac04-bbfc-4282-97ae-90272616e093  \n",
+       "4  2021-11-03 16:42:30.370841+0100                                  None  \n",
+       "5  2021-11-03 16:40:00.882499+0100                                        "
       ]
      },
      "execution_count": 29,
@@ -1169,7 +1169,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
@@ -1195,8 +1195,8 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: beta\n",
-      "- value: -1.071187039504064 ± 0.017950959722479044\n",
-      "- χ²: 1.0808645373835433\n",
+      "- value: -1.1055031138199274 ± 0.018683794096014686\n",
+      "- χ²: 1.1196703236999246\n",
       "- quality: good\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
@@ -1250,11 +1250,11 @@
        "      <td>β</td>\n",
        "      <td>(0,)</td>\n",
        "      <td>x</td>\n",
-       "      <td>-1.071187</td>\n",
+       "      <td>-1.105503</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:27:29.247000+0100</td>\n",
-       "      <td>c395b263-bd79-47c1-8832-1353ce51ce58</td>\n",
+       "      <td>2021-11-03 16:43:59.621000+0100</td>\n",
+       "      <td>fa8c73a5-3a96-4581-b8e2-1e6384976dc2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1264,7 +1264,7 @@
        "      <td>0.000000</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:55.416292+0100</td>\n",
+       "      <td>2021-11-03 16:42:30.370857+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1275,7 +1275,7 @@
        "      <td>0.000000</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785429+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882507+0100</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1286,7 +1286,7 @@
        "      <td>0.000000</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:55.416175+0100</td>\n",
+       "      <td>2021-11-03 16:42:30.370808+0100</td>\n",
        "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1297,7 +1297,7 @@
        "      <td>0.000000</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785345+0100</td>\n",
+       "      <td>2021-11-03 16:40:00.882483+0100</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1306,18 +1306,18 @@
       ],
       "text/plain": [
        "  parameter qubits schedule     value    group  valid  \\\n",
-       "0         β   (0,)        x -1.071187  default   True   \n",
+       "0         β   (0,)        x -1.105503  default   True   \n",
        "1         β     ()       sx  0.000000  default   True   \n",
        "2         β     ()       sx  0.000000  default   True   \n",
        "3         β     ()        x  0.000000  default   True   \n",
        "4         β     ()        x  0.000000  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-11-03 10:27:29.247000+0100  c395b263-bd79-47c1-8832-1353ce51ce58  \n",
-       "1  2021-11-03 10:25:55.416292+0100                                  None  \n",
-       "2  2021-11-03 10:23:20.785429+0100                                        \n",
-       "3  2021-11-03 10:25:55.416175+0100                                  None  \n",
-       "4  2021-11-03 10:23:20.785345+0100                                        "
+       "0  2021-11-03 16:43:59.621000+0100  fa8c73a5-3a96-4581-b8e2-1e6384976dc2  \n",
+       "1  2021-11-03 16:42:30.370857+0100                                  None  \n",
+       "2  2021-11-03 16:40:00.882507+0100                                        \n",
+       "3  2021-11-03 16:42:30.370808+0100                                  None  \n",
+       "4  2021-11-03 16:40:00.882483+0100                                        "
       ]
      },
      "execution_count": 36,
@@ -1402,7 +1402,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
@@ -1428,8 +1428,8 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: d_theta\n",
-      "- value: -0.08077045198832339 ± 0.0010627568898399157\n",
-      "- χ²: 15.2839558816469\n",
+      "- value: -0.08009669103196095 ± 0.0010877479733272931\n",
+      "- χ²: 25.48854792104786\n",
       "- quality: bad\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
@@ -1458,8 +1458,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The ideal angle is 3.14 rad. We measured a deviation of -0.081 rad.\n",
-      "Thus, scale the 0.8832+0.0000j pulse amplitude by 1.026 to obtain 0.90653+0.00000j.\n"
+      "The ideal angle is 3.14 rad. We measured a deviation of -0.080 rad.\n",
+      "Thus, scale the 0.8792+0.0000j pulse amplitude by 1.026 to obtain 0.90221+0.00000j.\n"
      ]
     }
    ],
@@ -1521,79 +1521,79 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.860512+0.000000j</td>\n",
+       "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
-       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
+       "      <td>2021-11-03 16:42:30.370765+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.883219+0.000000j</td>\n",
+       "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:28:02.751000+0100</td>\n",
-       "      <td>84f3109a-924e-400d-853c-ab5f3a7a8fbf</td>\n",
+       "      <td>2021-11-03 16:40:00.882460+0100</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
+       "      <td>(0,)</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>0.428397+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:55.416335+0100</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 16:42:27.534000+0100</td>\n",
+       "      <td>bc1dac04-bbfc-4282-97ae-90272616e093</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.856794+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785453+0100</td>\n",
-       "      <td></td>\n",
+       "      <td>2021-11-03 16:42:27.534000+0100</td>\n",
+       "      <td>bc1dac04-bbfc-4282-97ae-90272616e093</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
+       "      <td>(0,)</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.500000+0.000000j</td>\n",
+       "      <td>0.879209+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:55.416203+0100</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 16:44:31.671000+0100</td>\n",
+       "      <td>b4966de8-a1ce-4822-b70d-775adffd4f45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>amp</td>\n",
        "      <td>()</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.500000+0.000000j</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785369+0100</td>\n",
-       "      <td></td>\n",
+       "      <td>2021-11-03 16:42:30.370841+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.430256+0.000000j</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
-       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
+       "      <td>2021-11-03 16:40:00.882499+0100</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1601,22 +1601,22 @@
       ],
       "text/plain": [
        "  parameter qubits schedule               value    group  valid  \\\n",
-       "0       amp   (0,)        x  0.860512+0.000000j  default   True   \n",
-       "1       amp   (0,)        x  0.883219+0.000000j  default   True   \n",
-       "2       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "3       amp     ()       sx  0.250000+0.000000j  default   True   \n",
-       "4       amp     ()        x  0.500000+0.000000j  default   True   \n",
-       "5       amp     ()        x  0.500000+0.000000j  default   True   \n",
-       "6       amp   (0,)       sx  0.430256+0.000000j  default   True   \n",
+       "0       amp     ()        x  0.500000+0.000000j  default   True   \n",
+       "1       amp     ()        x  0.500000+0.000000j  default   True   \n",
+       "2       amp   (0,)       sx  0.428397+0.000000j  default   True   \n",
+       "3       amp   (0,)        x  0.856794+0.000000j  default   True   \n",
+       "4       amp   (0,)        x  0.879209+0.000000j  default   True   \n",
+       "5       amp     ()       sx  0.250000+0.000000j  default   True   \n",
+       "6       amp     ()       sx  0.250000+0.000000j  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  \n",
-       "1  2021-11-03 10:28:02.751000+0100  84f3109a-924e-400d-853c-ab5f3a7a8fbf  \n",
-       "2  2021-11-03 10:25:55.416335+0100                                  None  \n",
-       "3  2021-11-03 10:23:20.785453+0100                                        \n",
-       "4  2021-11-03 10:25:55.416203+0100                                  None  \n",
-       "5  2021-11-03 10:23:20.785369+0100                                        \n",
-       "6  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  "
+       "0  2021-11-03 16:42:30.370765+0100                                  None  \n",
+       "1  2021-11-03 16:40:00.882460+0100                                        \n",
+       "2  2021-11-03 16:42:27.534000+0100  bc1dac04-bbfc-4282-97ae-90272616e093  \n",
+       "3  2021-11-03 16:42:27.534000+0100  bc1dac04-bbfc-4282-97ae-90272616e093  \n",
+       "4  2021-11-03 16:44:31.671000+0100  b4966de8-a1ce-4822-b70d-775adffd4f45  \n",
+       "5  2021-11-03 16:42:30.370841+0100                                  None  \n",
+       "6  2021-11-03 16:40:00.882499+0100                                        "
       ]
      },
      "execution_count": 44,
@@ -1643,7 +1643,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_fine2 = amp_x_cal.run(backend).block_for_results()"
+    "data_fine2 = amp_x_cal.run().block_for_results()"
    ]
   },
   {
@@ -1654,7 +1654,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
@@ -1688,8 +1688,8 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: d_theta\n",
-      "- value: -0.04621845427932837 ± 0.0009804174946234\n",
-      "- χ²: 7.510365714654594\n",
+      "- value: -0.05526699149655558 ± 0.0009785317206102975\n",
+      "- χ²: 5.662785981062492\n",
       "- quality: bad\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
@@ -1738,9 +1738,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 779.079x144.48 with 1 Axes>"
+       "<Figure size 658.679x144.48 with 1 Axes>"
       ]
      },
      "execution_count": 50,
@@ -1757,61 +1757,7 @@
    "execution_count": 51,
    "id": "6c00c3f5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Analysis callback <bound method FineAmplitudeCal.update_calibrations of <qiskit_experiments.library.calibration.fine_amplitude.FineSXAmplitudeCal object at 0x7f5b2f4e6e80>> failed:\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/database_service/db_experiment_data.py\", line 298, in _wrapped_callback\n",
-      "    callback(self, **kwargs)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/library/calibration/fine_amplitude.py\", line 146, in update_calibrations\n",
-      "    BaseUpdater.add_parameter_value(\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/update_library.py\", line 94, in add_parameter_value\n",
-      "    cal.add_parameter_value(param_value, param, qubits, schedule)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/backend_calibrations.py\", line 395, in add_parameter_value\n",
-      "    self._parameter_inst_map_update(param_obj)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/backend_calibrations.py\", line 365, in _parameter_inst_map_update\n",
-      "    self.update_inst_map(schedules)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/backend_calibrations.py\", line 351, in update_inst_map\n",
-      "    schedule=self.get_schedule(\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py\", line 667, in get_schedule\n",
-      "    assigned_schedule = self._assign(schedule, qubits, assign_params, group, cutoff_date)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py\", line 864, in _assign\n",
-      "    return ret_schedule.assign_parameters(binding_dict, inplace=False)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/schedule.py\", line 1347, in assign_parameters\n",
-      "    return self._parameter_manager.assign_parameters(\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 408, in assign_parameters\n",
-      "    return visitor.visit(source)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 92, in visit\n",
-      "    return visitor(node)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 139, in visit_ScheduleBlock\n",
-      "    node._blocks = [self.visit(block) for block in node.blocks]\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 139, in <listcomp>\n",
-      "    node._blocks = [self.visit(block) for block in node.blocks]\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 92, in visit\n",
-      "    return visitor(node)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 189, in visit_Instruction\n",
-      "    node._operands = tuple(self.visit(op) for op in node.operands)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 189, in <genexpr>\n",
-      "    node._operands = tuple(self.visit(op) for op in node.operands)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 92, in visit\n",
-      "    return visitor(node)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\", line 219, in visit_ParametricPulse\n",
-      "    return node.__class__(**new_parameters, name=node.name)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/library/parametric_pulses.py\", line 420, in __init__\n",
-      "    super().__init__(duration=duration, name=name, limit_amplitude=limit_amplitude)\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/library/parametric_pulses.py\", line 74, in __init__\n",
-      "    self.validate_parameters()\n",
-      "  File \"/home/daniel/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/library/parametric_pulses.py\", line 444, in validate_parameters\n",
-      "    raise PulseError(\n",
-      "qiskit.pulse.exceptions.PulseError: 'The amplitude norm must be <= 1, found: 37.1316214105175This can be overruled by setting Pulse.limit_amplitude.'\n",
-      "\n",
-      "Possibly incomplete analysis results: an analysis callback raised an error.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data_fine_sx = amp_sx_cal.run().block_for_results()"
    ]
@@ -1824,7 +1770,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x360 with 1 Axes>"
       ]
@@ -1840,7 +1786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 53,
    "id": "3d6416eb",
    "metadata": {},
    "outputs": [
@@ -1850,9 +1796,9 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: @Parameters_FineAmplitudeAnalysis\n",
-      "- value: [-1.58899764  0.10409135] ± [0.00195221 0.00524669]\n",
-      "- χ²: 351.5342209701685\n",
-      "- quality: bad\n",
+      "- value: [0.79618143 0.02732817 0.47355097] ± [0.01300009 0.00098153 0.00362947]\n",
+      "- χ²: 1.144205682189689\n",
+      "- quality: good\n",
       "- extra: <4 items>\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
@@ -1865,7 +1811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "id": "5d5a1131",
    "metadata": {},
    "outputs": [
@@ -1875,9 +1821,9 @@
      "text": [
       "DbAnalysisResultV1\n",
       "- name: d_theta\n",
-      "- value: -1.5889976367837717 ± 0.0019522058755457033\n",
-      "- χ²: 351.5342209701685\n",
-      "- quality: bad\n",
+      "- value: 0.027328167719946905 ± 0.0009815320929519311\n",
+      "- χ²: 1.144205682189689\n",
+      "- quality: good\n",
       "- device_components: ['Q0']\n",
       "- verified: False\n"
      ]
@@ -1897,7 +1843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "id": "ae984c47",
    "metadata": {},
    "outputs": [
@@ -1936,131 +1882,131 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.860512+0.000000j</td>\n",
+       "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
-       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
+       "      <td>2021-11-03 16:42:30.370765+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.883219+0.000000j</td>\n",
+       "      <td>0.500000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:28:02.751000+0100</td>\n",
-       "      <td>84f3109a-924e-400d-853c-ab5f3a7a8fbf</td>\n",
+       "      <td>2021-11-03 16:40:00.882460+0100</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>amp</td>\n",
        "      <td>(0,)</td>\n",
-       "      <td>x</td>\n",
-       "      <td>0.896407+0.000000j</td>\n",
+       "      <td>sx</td>\n",
+       "      <td>0.428397+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:29:08.654000+0100</td>\n",
-       "      <td>e7e1f053-a5d2-4c94-9d65-da39a9274301</td>\n",
+       "      <td>2021-11-03 16:42:27.534000+0100</td>\n",
+       "      <td>bc1dac04-bbfc-4282-97ae-90272616e093</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
+       "      <td>(0,)</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>0.421071+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:55.416335+0100</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 16:45:38.341000+0100</td>\n",
+       "      <td>aee3d73b-423a-45c9-918a-8fff8c537b19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
-       "      <td>sx</td>\n",
-       "      <td>0.250000+0.000000j</td>\n",
+       "      <td>(0,)</td>\n",
+       "      <td>x</td>\n",
+       "      <td>0.856794+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785453+0100</td>\n",
-       "      <td></td>\n",
+       "      <td>2021-11-03 16:42:27.534000+0100</td>\n",
+       "      <td>bc1dac04-bbfc-4282-97ae-90272616e093</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
+       "      <td>(0,)</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.500000+0.000000j</td>\n",
+       "      <td>0.879209+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:55.416203+0100</td>\n",
-       "      <td>None</td>\n",
+       "      <td>2021-11-03 16:44:31.671000+0100</td>\n",
+       "      <td>b4966de8-a1ce-4822-b70d-775adffd4f45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>amp</td>\n",
-       "      <td>()</td>\n",
+       "      <td>(0,)</td>\n",
        "      <td>x</td>\n",
-       "      <td>0.500000+0.000000j</td>\n",
+       "      <td>0.894954+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:23:20.785369+0100</td>\n",
-       "      <td></td>\n",
+       "      <td>2021-11-03 16:45:02.477000+0100</td>\n",
+       "      <td>cb306dbb-ca97-42ce-9b7e-b5bcf4fe65dd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>0.430256+0.000000j</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:25:52.837000+0100</td>\n",
-       "      <td>d4d482d0-34c7-476a-870e-6984aa387f94</td>\n",
+       "      <td>2021-11-03 16:42:30.370841+0100</td>\n",
+       "      <td>None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>amp</td>\n",
-       "      <td>(0,)</td>\n",
+       "      <td>()</td>\n",
        "      <td>sx</td>\n",
-       "      <td>-37.131621-0.000000j</td>\n",
+       "      <td>0.250000+0.000000j</td>\n",
        "      <td>default</td>\n",
        "      <td>True</td>\n",
-       "      <td>2021-11-03 10:29:39.109000+0100</td>\n",
-       "      <td>024bb1b5-d7f5-4245-8270-3b6e3720cdf3</td>\n",
+       "      <td>2021-11-03 16:40:00.882499+0100</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "  parameter qubits schedule                value    group  valid  \\\n",
-       "0       amp   (0,)        x   0.860512+0.000000j  default   True   \n",
-       "1       amp   (0,)        x   0.883219+0.000000j  default   True   \n",
-       "2       amp   (0,)        x   0.896407+0.000000j  default   True   \n",
-       "3       amp     ()       sx   0.250000+0.000000j  default   True   \n",
-       "4       amp     ()       sx   0.250000+0.000000j  default   True   \n",
-       "5       amp     ()        x   0.500000+0.000000j  default   True   \n",
-       "6       amp     ()        x   0.500000+0.000000j  default   True   \n",
-       "7       amp   (0,)       sx   0.430256+0.000000j  default   True   \n",
-       "8       amp   (0,)       sx -37.131621-0.000000j  default   True   \n",
+       "  parameter qubits schedule               value    group  valid  \\\n",
+       "0       amp     ()        x  0.500000+0.000000j  default   True   \n",
+       "1       amp     ()        x  0.500000+0.000000j  default   True   \n",
+       "2       amp   (0,)       sx  0.428397+0.000000j  default   True   \n",
+       "3       amp   (0,)       sx  0.421071+0.000000j  default   True   \n",
+       "4       amp   (0,)        x  0.856794+0.000000j  default   True   \n",
+       "5       amp   (0,)        x  0.879209+0.000000j  default   True   \n",
+       "6       amp   (0,)        x  0.894954+0.000000j  default   True   \n",
+       "7       amp     ()       sx  0.250000+0.000000j  default   True   \n",
+       "8       amp     ()       sx  0.250000+0.000000j  default   True   \n",
        "\n",
        "                         date_time                                exp_id  \n",
-       "0  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  \n",
-       "1  2021-11-03 10:28:02.751000+0100  84f3109a-924e-400d-853c-ab5f3a7a8fbf  \n",
-       "2  2021-11-03 10:29:08.654000+0100  e7e1f053-a5d2-4c94-9d65-da39a9274301  \n",
-       "3  2021-11-03 10:25:55.416335+0100                                  None  \n",
-       "4  2021-11-03 10:23:20.785453+0100                                        \n",
-       "5  2021-11-03 10:25:55.416203+0100                                  None  \n",
-       "6  2021-11-03 10:23:20.785369+0100                                        \n",
-       "7  2021-11-03 10:25:52.837000+0100  d4d482d0-34c7-476a-870e-6984aa387f94  \n",
-       "8  2021-11-03 10:29:39.109000+0100  024bb1b5-d7f5-4245-8270-3b6e3720cdf3  "
+       "0  2021-11-03 16:42:30.370765+0100                                  None  \n",
+       "1  2021-11-03 16:40:00.882460+0100                                        \n",
+       "2  2021-11-03 16:42:27.534000+0100  bc1dac04-bbfc-4282-97ae-90272616e093  \n",
+       "3  2021-11-03 16:45:38.341000+0100  aee3d73b-423a-45c9-918a-8fff8c537b19  \n",
+       "4  2021-11-03 16:42:27.534000+0100  bc1dac04-bbfc-4282-97ae-90272616e093  \n",
+       "5  2021-11-03 16:44:31.671000+0100  b4966de8-a1ce-4822-b70d-775adffd4f45  \n",
+       "6  2021-11-03 16:45:02.477000+0100  cb306dbb-ca97-42ce-9b7e-b5bcf4fe65dd  \n",
+       "7  2021-11-03 16:42:30.370841+0100                                  None  \n",
+       "8  2021-11-03 16:40:00.882499+0100                                        "
       ]
      },
-     "execution_count": 54,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2071,35 +2017,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "id": "f7cb5878",
    "metadata": {},
    "outputs": [
     {
-     "ename": "PulseError",
-     "evalue": "'The amplitude norm must be <= 1, found: 37.1316214105175This can be overruled by setting Pulse.limit_amplitude.'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mPulseError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_8466/2710879246.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sx\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqubit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py\u001b[0m in \u001b[0;36mget_schedule\u001b[0;34m(self, name, qubits, assign_params, group, cutoff_date)\u001b[0m\n\u001b[1;32m    665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m         \u001b[0;31m# Now assign the other parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 667\u001b[0;31m         \u001b[0massigned_schedule\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_assign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqubits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0massign_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcutoff_date\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[0mfree_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-experiments/qiskit_experiments/calibration_management/calibrations.py\u001b[0m in \u001b[0;36m_assign\u001b[0;34m(self, schedule, qubits, assign_params, group, cutoff_date)\u001b[0m\n\u001b[1;32m    862\u001b[0m                 )\n\u001b[1;32m    863\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 864\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mret_schedule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbinding_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    866\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mschedules\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/schedule.py\u001b[0m in \u001b[0;36massign_parameters\u001b[0;34m(self, value_dict, inplace)\u001b[0m\n\u001b[1;32m   1345\u001b[0m             \u001b[0mSchedule\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mupdated\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1346\u001b[0m         \"\"\"\n\u001b[0;32m-> 1347\u001b[0;31m         return self._parameter_manager.assign_parameters(\n\u001b[0m\u001b[1;32m   1348\u001b[0m             \u001b[0mpulse_program\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalue_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m         )\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36massign_parameters\u001b[0;34m(self, pulse_program, value_dict, inplace)\u001b[0m\n\u001b[1;32m    406\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalid_map\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m             \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParameterSetter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalid_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    409\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36mvisit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;34m\"\"\"Visit a node.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_visitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_visitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36mvisit_ScheduleBlock\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    137\u001b[0m         \"\"\"\n\u001b[1;32m    138\u001b[0m         \u001b[0;31m# accessing to protected member\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m         \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m         \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alignment_context\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit_AlignmentKind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malignment_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    137\u001b[0m         \"\"\"\n\u001b[1;32m    138\u001b[0m         \u001b[0;31m# accessing to protected member\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m         \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m         \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alignment_context\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit_AlignmentKind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malignment_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36mvisit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;34m\"\"\"Visit a node.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_visitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_visitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36mvisit_Instruction\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    187\u001b[0m         \"\"\"\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_parameterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m             \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_operands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mop\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    187\u001b[0m         \"\"\"\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_parameterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m             \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_operands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mop\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36mvisit\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;34m\"\"\"Visit a node.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0mvisitor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_visitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvisitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_visitor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/parameter_manager.py\u001b[0m in \u001b[0;36mvisit_ParametricPulse\u001b[0;34m(self, node)\u001b[0m\n\u001b[1;32m    217\u001b[0m                 \u001b[0mnew_parameters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 219\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mnew_parameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/library/parametric_pulses.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, duration, amp, sigma, beta, name, limit_amplitude)\u001b[0m\n\u001b[1;32m    418\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sigma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msigma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    419\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_beta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbeta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 420\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit_amplitude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit_amplitude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/library/parametric_pulses.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, duration, name, limit_amplitude)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \"\"\"\n\u001b[1;32m     73\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mduration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit_amplitude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit_amplitude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mabstractmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Documents/IBM/qiskit/qiskit-terra/qiskit/pulse/library/parametric_pulses.py\u001b[0m in \u001b[0;36mvalidate_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvalidate_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_is_parameterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mamp\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mamp\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlimit_amplitude\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m             raise PulseError(\n\u001b[0m\u001b[1;32m    445\u001b[0m                 \u001b[0;34mf\"The amplitude norm must be <= 1, found: {abs(self.amp)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m                 \u001b[0;34m+\u001b[0m \u001b[0;34m\"This can be overruled by setting Pulse.limit_amplitude.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mPulseError\u001b[0m: 'The amplitude norm must be <= 1, found: 37.1316214105175This can be overruled by setting Pulse.limit_amplitude.'"
-     ]
+     "data": {
+      "text/plain": [
+       "ScheduleBlock(Play(Drag(duration=320, amp=(0.421071121202659+0j), sigma=80, beta=0), DriveChannel(0)), name=\"sx\", transform=AlignLeft())"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -2108,30 +2038,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "id": "f45f6482",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ScheduleBlock(Play(Drag(duration=320, amp=(0.894953534996499+0j), sigma=80, beta=-1.10550311381993), DriveChannel(0)), name=\"x\", transform=AlignLeft())"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cals.get_schedule(\"x\", qubit)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "id": "68f6e469",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ScheduleBlock(Play(Drag(duration=320, amp=0.894953534996499j, sigma=80, beta=-1.10550311381993), DriveChannel(0)), name=\"y\", transform=AlignLeft())"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cals.get_schedule(\"y\", qubit)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "id": "8c8369d4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2021.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import qiskit.tools.jupyter\n",
     "%qiskit_copyright"

From 639addbeb00281a0c0f8a76408898c7aea1de226 Mon Sep 17 00:00:00 2001
From: Daniel Egger <deg@zurich.ibm.com>
Date: Wed, 3 Nov 2021 16:49:44 +0100
Subject: [PATCH 10/11] * Config test.

---
 test/calibration/experiments/test_drag.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/test/calibration/experiments/test_drag.py b/test/calibration/experiments/test_drag.py
index 19dc59ca66..6b0bafad2e 100644
--- a/test/calibration/experiments/test_drag.py
+++ b/test/calibration/experiments/test_drag.py
@@ -167,3 +167,12 @@ def test_experiment_config(self):
         loaded_exp = RoughDragCal.from_config(config)
         self.assertNotEqual(exp, loaded_exp)
         self.assertEqual(config, loaded_exp.config)
+
+        with pulse.build(name="xp") as sched:
+            pulse.play(pulse.Drag(160, 0.5, 40, Parameter("β")), pulse.DriveChannel(0))
+
+        exp = RoughDrag(0, backend=self.backend, schedule=sched)
+        config = exp.config
+        loaded_exp = RoughDrag.from_config(config)
+        self.assertNotEqual(exp, loaded_exp)
+        self.assertEqual(config, loaded_exp.config)

From d69e23e0daab791f398c85b405003becace30394 Mon Sep 17 00:00:00 2001
From: Daniel Egger <deg@zurich.ibm.com>
Date: Thu, 4 Nov 2021 09:05:39 +0100
Subject: [PATCH 11/11] * Args order and set options.

---
 qiskit_experiments/library/calibration/rough_drag_cal.py | 4 ++--
 qiskit_experiments/library/characterization/drag.py      | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/qiskit_experiments/library/calibration/rough_drag_cal.py b/qiskit_experiments/library/calibration/rough_drag_cal.py
index 22272817b3..4710400563 100644
--- a/qiskit_experiments/library/calibration/rough_drag_cal.py
+++ b/qiskit_experiments/library/calibration/rough_drag_cal.py
@@ -39,18 +39,19 @@ def __init__(
         self,
         qubit: int,
         calibrations: BackendCalibrations,
+        backend: Optional[Backend] = None,
         schedule_name: str = "x",
         betas: Iterable[float] = None,
         cal_parameter_name: Optional[str] = "β",
         auto_update: bool = True,
         group: str = "default",
-        backend: Optional[Backend] = None,
     ):
         r"""see class :class:`RoughDrag` for details.
 
         Args:
             qubit: The qubit for which to run the rough drag calibration.
             calibrations: The calibrations instance with the schedules.
+            backend: Optional, the backend to run the experiment on.
             schedule_name: The name of the schedule to calibrate. Defaults to "x".
             betas: A list of drag parameter values to scan. If None is given 51 betas ranging
                 from -5 to 5 will be scanned.
@@ -59,7 +60,6 @@ def __init__(
             auto_update: Whether or not to automatically update the calibrations. By
                 default this variable is set to True.
             group: The group of calibration parameters to use. The default value is "default".
-            backend: Optional, the backend to run the experiment on.
         """
         schedule = calibrations.get_schedule(
             schedule_name, qubit, assign_params={cal_parameter_name: Parameter("β")}, group=group
diff --git a/qiskit_experiments/library/characterization/drag.py b/qiskit_experiments/library/characterization/drag.py
index cda21afb9d..fded2ed3ba 100644
--- a/qiskit_experiments/library/characterization/drag.py
+++ b/qiskit_experiments/library/characterization/drag.py
@@ -145,7 +145,7 @@ def __init__(
         super().__init__([qubit], backend=backend)
 
         if betas is not None:
-            self.experiment_options.betas = betas
+            self.set_experiment_options(betas=betas)
 
         if len(schedule.parameters) != 1:
             raise QiskitError(
@@ -153,7 +153,7 @@ def __init__(
                 f"exactly one free parameter, found {schedule.parameters} parameters."
             )
 
-        self.experiment_options.schedule = schedule
+        self.set_experiment_options(schedule=schedule)
 
     def _pre_circuit(self) -> QuantumCircuit:
         """A circuit with operations to perform before the Drag."""