From a84b20b1eedd2fe4477fad42d99d27f70f62d3c0 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 15 Jan 2024 18:06:31 +0100 Subject: [PATCH 001/233] Inherit PulseSequence from list, remove redundant methods --- src/qibolab/pulses.py | 151 +----------------------------------------- 1 file changed, 1 insertion(+), 150 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 8da469bb9..15e31d515 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -1223,7 +1223,7 @@ class PulseConstructor(Enum): FLUX = FluxPulse -class PulseSequence: +class PulseSequence(list): """A collection of scheduled pulses. A quantum circuit can be translated into a set of scheduled pulses @@ -1233,30 +1233,6 @@ class PulseSequence: modify any of the properties of its pulses. """ - def __init__(self, *pulses): - self.pulses = [] #: list[Pulse] = [] - """Pulses (list): a list containing the pulses, ordered by their - channel and start times.""" - self.add(*pulses) - - def __len__(self): - return len(self.pulses) - - def __iter__(self): - return iter(self.pulses) - - def __getitem__(self, index): - return self.pulses[index] - - def __setitem__(self, index, value): - self.pulses[index] = value - - def __delitem__(self, index): - del self.pulses[index] - - def __contains__(self, pulse): - return pulse in self.pulses - def __repr__(self): return self.serial @@ -1266,128 +1242,9 @@ def serial(self): return "PulseSequence\n" + "\n".join(f"{pulse.serial}" for pulse in self.pulses) - def __eq__(self, other): - if not isinstance(other, PulseSequence): - raise TypeError(f"Expected PulseSequence; got {type(other).__name__}") - return self.serial == other.serial - - def __ne__(self, other): - if not isinstance(other, PulseSequence): - raise TypeError(f"Expected PulseSequence; got {type(other).__name__}") - return self.serial != other.serial - def __hash__(self): return hash(self.serial) - def __add__(self, other): - if isinstance(other, PulseSequence): - return PulseSequence(*self.pulses, *other.pulses) - if isinstance(other, Pulse): - return PulseSequence(*self.pulses, other) - raise TypeError(f"Expected PulseSequence or Pulse; got {type(other).__name__}") - - def __radd__(self, other): - if isinstance(other, PulseSequence): - return PulseSequence(*other.pulses, *self.pulses) - if isinstance(other, Pulse): - return PulseSequence(other, *self.pulses) - raise TypeError(f"Expected PulseSequence or Pulse; got {type(other).__name__}") - - def __iadd__(self, other): - if isinstance(other, PulseSequence): - self.add(*other.pulses) - elif isinstance(other, Pulse): - self.add(other) - else: - raise TypeError( - f"Expected PulseSequence or Pulse; got {type(other).__name__}" - ) - return self - - def __mul__(self, n): - if not isinstance(n, int): - raise TypeError(f"Expected int; got {type(n).__name__}") - if n < 0: - raise TypeError(f"argument n should be >=0, got {n}") - return PulseSequence(*(self.pulses * n)) - - def __rmul__(self, n): - if not isinstance(n, int): - raise TypeError(f"Expected int; got {type(n).__name__}") - if n < 0: - raise TypeError(f"argument n should be >=0, got {n}") - return PulseSequence(*(self.pulses * n)) - - def __imul__(self, n): - if not isinstance(n, int): - raise TypeError(f"Expected int; got {type(n).__name__}") - if n < 1: - raise TypeError(f"argument n should be >=1, got {n}") - original_set = self.shallow_copy() - for x in range(n - 1): - self.add(*original_set.pulses) - return self - - @property - def count(self): - """Returns the number of pulses in the sequence.""" - - return len(self.pulses) - - def add(self, *items): - """Adds pulses to the sequence and sorts them by channel and start - time.""" - - for item in items: - if isinstance(item, Pulse): - pulse = item - self.pulses.append(pulse) - elif isinstance(item, PulseSequence): - ps = item - for pulse in ps.pulses: - self.pulses.append(pulse) - self.pulses.sort(key=lambda item: (item.start, item.channel)) - - def index(self, pulse): - """Returns the index of a pulse in the sequence.""" - - return self.pulses.index(pulse) - - def pop(self, index=-1): - """Returns the pulse with the index provided and removes it from the - sequence.""" - - return self.pulses.pop(index) - - def remove(self, pulse): - """Removes a pulse from the sequence.""" - - while pulse in self.pulses: - self.pulses.remove(pulse) - - def clear(self): - """Removes all pulses from the sequence.""" - - self.pulses.clear() - - def shallow_copy(self): - """Returns a shallow copy of the sequence. - - It returns a new PulseSequence object with references to the - same Pulse objects. - """ - - return PulseSequence(*self.pulses) - - def copy(self): - """Returns a deep copy of the sequence. - - It returns a new PulseSequence with replicates of each of the - pulses contained in the original sequence. - """ - - return PulseSequence(*[pulse.copy() for pulse in self.pulses]) - @property def ro_pulses(self): """Returns a new PulseSequence containing only its readout pulses.""" @@ -1463,12 +1320,6 @@ def coupler_pulses(self, *couplers): new_pc.add(pulse) return new_pc - @property - def is_empty(self): - """Returns True if the sequence does not contain any pulses.""" - - return len(self.pulses) == 0 - @property def finish(self) -> int: """Returns the time when the last pulse of the sequence finishes.""" From f7b6951363eff3bcf130767d9e22ab304fffdb20 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 12:07:10 +0100 Subject: [PATCH 002/233] Replace usage of removed methods in pulses module --- src/qibolab/pulses.py | 59 ++++++++++++++++++------------------------- 1 file changed, 25 insertions(+), 34 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 15e31d515..5698ff475 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -881,7 +881,7 @@ def __add__(self, other): if isinstance(other, Pulse): return PulseSequence(self, other) if isinstance(other, PulseSequence): - return PulseSequence(self, *other.pulses) + return PulseSequence(self, *other) raise TypeError(f"Expected Pulse or PulseSequence; got {type(other).__name__}") def __mul__(self, n): @@ -1234,16 +1234,7 @@ class PulseSequence(list): """ def __repr__(self): - return self.serial - - @property - def serial(self): - """Returns a string representation of the pulse sequence.""" - - return "PulseSequence\n" + "\n".join(f"{pulse.serial}" for pulse in self.pulses) - - def __hash__(self): - return hash(self.serial) + return f"{type(self).__name__}({super().__repr__()})" @property def ro_pulses(self): @@ -1252,7 +1243,7 @@ def ro_pulses(self): new_pc = PulseSequence() for pulse in self.pulses: if pulse.type == PulseType.READOUT: - new_pc.add(pulse) + new_pc.append(pulse) return new_pc @property @@ -1261,9 +1252,9 @@ def qd_pulses(self): pulses.""" new_pc = PulseSequence() - for pulse in self.pulses: + for pulse in self: if pulse.type == PulseType.DRIVE: - new_pc.add(pulse) + new_pc.append(pulse) return new_pc @property @@ -1272,9 +1263,9 @@ def qf_pulses(self): pulses.""" new_pc = PulseSequence() - for pulse in self.pulses: + for pulse in self: if pulse.type == PulseType.FLUX: - new_pc.add(pulse) + new_pc.append(pulse) return new_pc @property @@ -1283,9 +1274,9 @@ def cf_pulses(self): pulses.""" new_pc = PulseSequence() - for pulse in self.pulses: + for pulse in self: if pulse.type is PulseType.COUPLERFLUX: - new_pc.add(pulse) + new_pc.append(pulse) return new_pc def get_channel_pulses(self, *channels): @@ -1293,9 +1284,9 @@ def get_channel_pulses(self, *channels): set of channels.""" new_pc = PulseSequence() - for pulse in self.pulses: + for pulse in self: if pulse.channel in channels: - new_pc.add(pulse) + new_pc.append(pulse) return new_pc def get_qubit_pulses(self, *qubits): @@ -1303,10 +1294,10 @@ def get_qubit_pulses(self, *qubits): set of qubits.""" new_pc = PulseSequence() - for pulse in self.pulses: + for pulse in self: if not isinstance(pulse, CouplerFluxPulse): if pulse.qubit in qubits: - new_pc.add(pulse) + new_pc.append(pulse) return new_pc def coupler_pulses(self, *couplers): @@ -1314,10 +1305,10 @@ def coupler_pulses(self, *couplers): set of couplers.""" new_pc = PulseSequence() - for pulse in self.pulses: + for pulse in self: if isinstance(pulse, CouplerFluxPulse): if pulse.qubit in couplers: - new_pc.add(pulse) + new_pc.append(pulse) return new_pc @property @@ -1325,7 +1316,7 @@ def finish(self) -> int: """Returns the time when the last pulse of the sequence finishes.""" t: int = 0 - for pulse in self.pulses: + for pulse in self: if pulse.finish > t: t = pulse.finish return t @@ -1335,7 +1326,7 @@ def start(self) -> int: """Returns the start time of the first pulse of the sequence.""" t = self.finish - for pulse in self.pulses: + for pulse in self: if pulse.start < t: t = pulse.start return t @@ -1352,7 +1343,7 @@ def channels(self) -> list: sequence.""" channels = [] - for pulse in self.pulses: + for pulse in self: if not pulse.channel in channels: channels.append(pulse.channel) channels.sort() @@ -1364,7 +1355,7 @@ def qubits(self) -> list: sequence.""" qubits = [] - for pulse in self.pulses: + for pulse in self: if not pulse.qubit in qubits: qubits.append(pulse.qubit) qubits.sort() @@ -1375,7 +1366,7 @@ def get_pulse_overlaps(self): # -> dict((int,int): PulseSequence): times) where pulses overlap.""" times = [] - for pulse in self.pulses: + for pulse in self: if not pulse.start in times: times.append(pulse.start) if not pulse.finish in times: @@ -1385,7 +1376,7 @@ def get_pulse_overlaps(self): # -> dict((int,int): PulseSequence): overlaps = {} for n in range(len(times) - 1): overlaps[(times[n], times[n + 1])] = PulseSequence() - for pulse in self.pulses: + for pulse in self: if (pulse.start <= times[n]) & (pulse.finish >= times[n + 1]): overlaps[(times[n], times[n + 1])] += pulse return overlaps @@ -1398,7 +1389,7 @@ def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): # but it does not check if the frequencies of the pulses within a set have the same frequency separated_pulses = [] - for new_pulse in self.pulses: + for new_pulse in self: stored = False for ps in separated_pulses: overlaps = False @@ -1410,7 +1401,7 @@ def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): overlaps = True break if not overlaps: - ps.add(new_pulse) + ps.append(new_pulse) stored = True break if not stored: @@ -1436,14 +1427,14 @@ def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): savefig_filename (str): a file path. If provided the plot is save to a file. """ - if not self.is_empty: + if len(self) > 0: import matplotlib.pyplot as plt from matplotlib import gridspec fig = plt.figure(figsize=(14, 2 * self.count), dpi=200) gs = gridspec.GridSpec(ncols=1, nrows=self.count) vertical_lines = [] - for pulse in self.pulses: + for pulse in self: vertical_lines.append(pulse.start) vertical_lines.append(pulse.finish) From 3ae3e3f3277824d4623545b5a86c74cc4e6650fc Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 12:10:26 +0100 Subject: [PATCH 003/233] Replace usage of removed methods everywhere --- src/qibolab/compilers/default.py | 8 ++++---- src/qibolab/native.py | 4 ++-- src/qibolab/platform/platform.py | 2 +- src/qibolab/pulses.py | 2 +- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/src/qibolab/compilers/default.py b/src/qibolab/compilers/default.py index 67e929db7..53bec7aa2 100644 --- a/src/qibolab/compilers/default.py +++ b/src/qibolab/compilers/default.py @@ -31,7 +31,7 @@ def gpi2_rule(gate, platform): theta = gate.parameters[0] sequence = PulseSequence() pulse = platform.create_RX90_pulse(qubit, start=0, relative_phase=theta) - sequence.add(pulse) + sequence.append(pulse) return sequence, {} @@ -62,7 +62,7 @@ def u3_rule(gate, platform): qubit, start=0, relative_phase=virtual_z_phases[qubit] ) # apply RX(pi/2) - sequence.add(RX90_pulse_1) + sequence.append(RX90_pulse_1) # apply RZ(theta) virtual_z_phases[qubit] += theta # Fetch pi/2 pulse from calibration @@ -72,7 +72,7 @@ def u3_rule(gate, platform): relative_phase=virtual_z_phases[qubit] - math.pi, ) # apply RX(-pi/2) - sequence.add(RX90_pulse_2) + sequence.append(RX90_pulse_2) # apply RZ(phi) virtual_z_phases[qubit] += phi @@ -98,5 +98,5 @@ def measurement_rule(gate, platform): sequence = PulseSequence() for qubit in gate.target_qubits: MZ_pulse = platform.create_MZ_pulse(qubit, start=0) - sequence.add(MZ_pulse) + sequence.append(MZ_pulse) return sequence, {} diff --git a/src/qibolab/native.py b/src/qibolab/native.py index b411cc9de..6f2bcf27d 100644 --- a/src/qibolab/native.py +++ b/src/qibolab/native.py @@ -245,12 +245,12 @@ def sequence(self, start=0): for pulse in self.pulses: if isinstance(pulse, NativePulse): - sequence.add(pulse.pulse(start=start)) + sequence.append(pulse.pulse(start=start)) else: virtual_z_phases[pulse.qubit.name] += pulse.phase for coupler_pulse in self.coupler_pulses: - sequence.add(coupler_pulse.pulse(start=start)) + sequence.append(coupler_pulse.pulse(start=start)) # TODO: Maybe ``virtual_z_phases`` should be an attribute of ``PulseSequence`` return sequence, virtual_z_phases diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 7477405ca..91df9a427 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -47,7 +47,7 @@ def unroll_sequences( for pulse in sequence: new_pulse = pulse.copy() new_pulse.start += start - total_sequence.add(new_pulse) + total_sequence.append(new_pulse) if isinstance(pulse, ReadoutPulse): readout_map[pulse.serial].append(new_pulse.serial) start = total_sequence.finish + relaxation_time diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 5698ff475..b411dded0 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -1241,7 +1241,7 @@ def ro_pulses(self): """Returns a new PulseSequence containing only its readout pulses.""" new_pc = PulseSequence() - for pulse in self.pulses: + for pulse in self: if pulse.type == PulseType.READOUT: new_pc.append(pulse) return new_pc From 17ddaf9f8ea8ff593bd7512c9003365055837f6e Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 12:31:16 +0100 Subject: [PATCH 004/233] Replace add with the standard append function --- examples/minimum_working_example.py | 4 +- src/qibolab/compilers/compiler.py | 2 +- .../instruments/qblox/cluster_qcm_bb.py | 2 +- .../instruments/qblox/cluster_qcm_rf.py | 2 +- .../instruments/qblox/cluster_qrm_rf.py | 2 +- src/qibolab/platform/platform.py | 2 +- src/qibolab/sweeper.py | 2 +- tests/test_dummy.py | 32 +- tests/test_instruments_qblox.py | 314 ++++++++++++++++++ .../test_instruments_qblox_cluster_qcm_bb.py | 16 +- .../test_instruments_qblox_cluster_qcm_rf.py | 8 +- .../test_instruments_qblox_cluster_qrm_rf.py | 8 +- tests/test_instruments_qmsim.py | 66 ++-- tests/test_instruments_rfsoc.py | 66 ++-- tests/test_instruments_zhinst.py | 54 +-- tests/test_platform.py | 54 +-- tests/test_pulses.py | 28 +- tests/test_result_shapes.py | 4 +- 18 files changed, 490 insertions(+), 176 deletions(-) create mode 100644 tests/test_instruments_qblox.py diff --git a/examples/minimum_working_example.py b/examples/minimum_working_example.py index 3213c70aa..40ced664c 100644 --- a/examples/minimum_working_example.py +++ b/examples/minimum_working_example.py @@ -5,7 +5,7 @@ # Define PulseSequence sequence = PulseSequence() # Add some pulses to the pulse sequence -sequence.add( +sequence.append( Pulse( start=0, amplitude=0.3, @@ -18,7 +18,7 @@ ) ) -sequence.add( +sequence.append( ReadoutPulse( start=4004, amplitude=0.9, diff --git a/src/qibolab/compilers/compiler.py b/src/qibolab/compilers/compiler.py index 316fde258..ded9b11bf 100644 --- a/src/qibolab/compilers/compiler.py +++ b/src/qibolab/compilers/compiler.py @@ -121,7 +121,7 @@ def _compile_gate( pulse.start += start if not isinstance(pulse, ReadoutPulse): pulse.relative_phase += virtual_z_phases[pulse.qubit] - sequence.add(pulse) + sequence.append(pulse) return gate_sequence, gate_phases diff --git a/src/qibolab/instruments/qblox/cluster_qcm_bb.py b/src/qibolab/instruments/qblox/cluster_qcm_bb.py index e95cf69ef..23eea1877 100644 --- a/src/qibolab/instruments/qblox/cluster_qcm_bb.py +++ b/src/qibolab/instruments/qblox/cluster_qcm_bb.py @@ -362,7 +362,7 @@ def process_pulse_sequence( sequencer.waveforms_buffer.add_waveforms( pulse, self._ports[port].hardware_mod_en, sweepers ) - sequencer.pulses.add(pulse) + sequencer.pulses.append(pulse) pulses_to_be_processed.remove(pulse) # if there is not enough memory in the current sequencer, use another one diff --git a/src/qibolab/instruments/qblox/cluster_qcm_rf.py b/src/qibolab/instruments/qblox/cluster_qcm_rf.py index cd9183257..573624ab1 100644 --- a/src/qibolab/instruments/qblox/cluster_qcm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qcm_rf.py @@ -383,7 +383,7 @@ def process_pulse_sequence( sequencer.waveforms_buffer.add_waveforms( pulse, self._ports[port].hardware_mod_en, sweepers ) - sequencer.pulses.add(pulse) + sequencer.pulses.append(pulse) pulses_to_be_processed.remove(pulse) # if there is not enough memory in the current sequencer, use another one diff --git a/src/qibolab/instruments/qblox/cluster_qrm_rf.py b/src/qibolab/instruments/qblox/cluster_qrm_rf.py index a70d26c42..91c2ce179 100644 --- a/src/qibolab/instruments/qblox/cluster_qrm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qrm_rf.py @@ -443,7 +443,7 @@ def process_pulse_sequence( sequencer.waveforms_buffer.add_waveforms( pulse, self._ports[port].hardware_mod_en, sweepers ) - sequencer.pulses.add(pulse) + sequencer.pulses.append(pulse) pulses_to_be_processed.remove(pulse) # if there is not enough memory in the current sequencer, use another one diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 91df9a427..317bc9793 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -274,7 +274,7 @@ def sweep( sequence = PulseSequence() parameter = Parameter.frequency pulse = platform.create_qubit_readout_pulse(qubit=0, start=0) - sequence.add(pulse) + sequence.append(pulse) parameter_range = np.random.randint(10, size=10) sweeper = Sweeper(parameter, parameter_range, [pulse]) platform.sweep(sequence, ExecutionParameters(), sweeper) diff --git a/src/qibolab/sweeper.py b/src/qibolab/sweeper.py index c8539faca..84ff1880c 100644 --- a/src/qibolab/sweeper.py +++ b/src/qibolab/sweeper.py @@ -65,7 +65,7 @@ class Sweeper: sequence = PulseSequence() parameter = Parameter.frequency pulse = platform.create_qubit_readout_pulse(qubit=0, start=0) - sequence.add(pulse) + sequence.append(pulse) parameter_range = np.random.randint(10, size=10) sweeper = Sweeper(parameter, parameter_range, [pulse]) platform.sweep(sequence, ExecutionParameters(), sweeper) diff --git a/tests/test_dummy.py b/tests/test_dummy.py index 42f454f25..7e41a141f 100644 --- a/tests/test_dummy.py +++ b/tests/test_dummy.py @@ -26,8 +26,8 @@ def test_dummy_execute_pulse_sequence(name, acquisition): platform = create_platform(name) ro_pulse = platform.create_qubit_readout_pulse(0, 0) sequence = PulseSequence() - sequence.add(platform.create_qubit_readout_pulse(0, 0)) - sequence.add(platform.create_RX12_pulse(0, 0)) + sequence.append(platform.create_qubit_readout_pulse(0, 0)) + sequence.append(platform.create_RX12_pulse(0, 0)) options = ExecutionParameters(nshots=100, acquisition_type=acquisition) result = platform.execute_pulse_sequence(sequence, options) if acquisition is AcquisitionType.INTEGRATION: @@ -56,7 +56,7 @@ def test_dummy_execute_coupler_pulse(): sequence = PulseSequence() pulse = platform.create_coupler_pulse(coupler=0, start=0) - sequence.add(pulse) + sequence.append(pulse) options = ExecutionParameters(nshots=None) result = platform.execute_pulse_sequence(sequence, options) @@ -79,11 +79,11 @@ def test_dummy_execute_pulse_sequence_couplers(): qubits=(qubit_ordered_pair.qubit1.name, qubit_ordered_pair.qubit2.name), start=0, ) - sequence.add(cz.get_qubit_pulses(qubit_ordered_pair.qubit1.name)) - sequence.add(cz.get_qubit_pulses(qubit_ordered_pair.qubit2.name)) - sequence.add(cz.coupler_pulses(qubit_ordered_pair.coupler.name)) - sequence.add(platform.create_qubit_readout_pulse(0, 40)) - sequence.add(platform.create_qubit_readout_pulse(2, 40)) + sequence.append(cz.get_qubit_pulses(qubit_ordered_pair.qubit1.name)) + sequence.append(cz.get_qubit_pulses(qubit_ordered_pair.qubit2.name)) + sequence.append(cz.coupler_pulses(qubit_ordered_pair.coupler.name)) + sequence.append(platform.create_qubit_readout_pulse(0, 40)) + sequence.append(platform.create_qubit_readout_pulse(2, 40)) options = ExecutionParameters(nshots=None) result = platform.execute_pulse_sequence(sequence, options) @@ -98,7 +98,7 @@ def test_dummy_execute_pulse_sequence_couplers(): def test_dummy_execute_pulse_sequence_fast_reset(name): platform = create_platform(name) sequence = PulseSequence() - sequence.add(platform.create_qubit_readout_pulse(0, 0)) + sequence.append(platform.create_qubit_readout_pulse(0, 0)) options = ExecutionParameters(nshots=None, fast_reset=True) result = platform.execute_pulse_sequence(sequence, options) @@ -115,7 +115,7 @@ def test_dummy_execute_pulse_sequence_unrolling(name, acquisition, batch_size): platform.instruments["dummy"].UNROLLING_BATCH_SIZE = batch_size sequences = [] sequence = PulseSequence() - sequence.add(platform.create_qubit_readout_pulse(0, 0)) + sequence.append(platform.create_qubit_readout_pulse(0, 0)) for _ in range(nsequences): sequences.append(sequence) options = ExecutionParameters(nshots=nshots, acquisition_type=acquisition) @@ -135,7 +135,7 @@ def test_dummy_single_sweep_raw(name): pulse = platform.create_qubit_readout_pulse(qubit=0, start=0) parameter_range = np.random.randint(SWEPT_POINTS, size=SWEPT_POINTS) - sequence.add(pulse) + sequence.append(pulse) sweeper = Sweeper(Parameter.frequency, parameter_range, pulses=[pulse]) options = ExecutionParameters( nshots=10, @@ -175,7 +175,7 @@ def test_dummy_single_sweep_coupler( parameter_range = np.random.rand(SWEPT_POINTS) else: parameter_range = np.random.randint(SWEPT_POINTS, size=SWEPT_POINTS) - sequence.add(ro_pulse) + sequence.append(ro_pulse) if parameter in QubitParameter: sweeper = Sweeper(parameter, parameter_range, couplers=[platform.couplers[0]]) else: @@ -221,7 +221,7 @@ def test_dummy_single_sweep(name, fast_reset, parameter, average, acquisition, n parameter_range = np.random.rand(SWEPT_POINTS) else: parameter_range = np.random.randint(SWEPT_POINTS, size=SWEPT_POINTS) - sequence.add(pulse) + sequence.append(pulse) if parameter in QubitParameter: sweeper = Sweeper(parameter, parameter_range, qubits=[platform.qubits[0]]) else: @@ -264,8 +264,8 @@ def test_dummy_double_sweep(name, parameter1, parameter2, average, acquisition, sequence = PulseSequence() pulse = platform.create_qubit_drive_pulse(qubit=0, start=0, duration=1000) ro_pulse = platform.create_qubit_readout_pulse(qubit=0, start=pulse.finish) - sequence.add(pulse) - sequence.add(ro_pulse) + sequence.append(pulse) + sequence.append(ro_pulse) parameter_range_1 = ( np.random.rand(SWEPT_POINTS) if parameter1 is Parameter.amplitude @@ -329,7 +329,7 @@ def test_dummy_single_sweep_multiplex(name, parameter, average, acquisition, nsh ro_pulses = {} for qubit in platform.qubits: ro_pulses[qubit] = platform.create_qubit_readout_pulse(qubit=qubit, start=0) - sequence.add(ro_pulses[qubit]) + sequence.append(ro_pulses[qubit]) parameter_range = ( np.random.rand(SWEPT_POINTS) if parameter is Parameter.amplitude diff --git a/tests/test_instruments_qblox.py b/tests/test_instruments_qblox.py new file mode 100644 index 000000000..23069414e --- /dev/null +++ b/tests/test_instruments_qblox.py @@ -0,0 +1,314 @@ +"""Qblox instruments driver. + +Supports the following Instruments: + Cluster + Cluster QRM-RF + Cluster QCM-RF + Cluster QCM +Compatible with qblox-instruments driver 0.9.0 (28/2/2023). +It supports: + - multiplexed readout of up to 6 qubits + - hardware modulation, demodulation, and classification + - software modulation, with support for arbitrary pulses + - software demodulation + - binned acquisition + - real-time sweepers of + - pulse frequency (requires hardware modulation) + - pulse relative phase (requires hardware modulation) + - pulse amplitude + - pulse start + - pulse duration + - port gain + - port offset + - multiple readouts for the same qubit (sequence unrolling) + - max iq pulse length 8_192ns + - waveforms cache, uses additional free sequencers if the memory of one sequencer (16384) is exhausted + - instrument parameters cache + - safe disconnection of offsets on termination +""" + + +# from .conftest import load_from_platform + +# INSTRUMENTS_LIST = ["Cluster", "ClusterQRM_RF", "ClusterQCM_RF"] + +# instruments = {} +# instruments_settings = {} + + +# @pytest.mark.qpu +# @pytest.mark.parametrize("name", INSTRUMENTS_LIST) +# def test_instruments_qublox_init(platform_name, name): +# platform = create_platform(platform_name) +# settings = platform.settings +# # Instantiate instrument +# instance, instr_settings = load_from_platform(create_platform(platform_name), name) +# instruments[name] = instance +# instruments_settings[name] = instr_settings +# assert instance.name == name +# assert instance.is_connected == False +# assert instance.device == None +# assert instance.data_folder == INSTRUMENTS_DATA_FOLDER / instance.tmp_folder.name.split("/")[-1] + + +# @pytest.mark.qpu +# @pytest.mark.parametrize("name", INSTRUMENTS_LIST) +# def test_instruments_qublox_connect(name): +# instruments[name].connect() + + +# @pytest.mark.qpu +# @pytest.mark.parametrize("name", INSTRUMENTS_LIST) +# def test_instruments_qublox_setup(platform_name, name): +# settings = create_platform(platform_name).settings +# instruments[name].setup(**settings["settings"], **instruments_settings[name]) +# for parameter in instruments_settings[name]: +# if parameter == "ports": +# for port in instruments_settings[name]["ports"]: +# for sub_parameter in instruments_settings[name]["ports"][port]: +# # assert getattr(instruments[name].ports[port], sub_parameter) == settings["instruments"][name]["settings"]["ports"][port][sub_parameter] +# np.testing.assert_allclose( +# getattr(instruments[name].ports[port], sub_parameter), +# instruments_settings[name]["ports"][port][sub_parameter], +# atol=1e-4, +# ) +# else: +# assert getattr(instruments[name], parameter) == instruments_settings[name][parameter] + + +# def instrument_test_property_wrapper( +# origin_object, origin_attribute, destination_object, *destination_parameters, values +# ): +# for value in values: +# setattr(origin_object, origin_attribute, value) +# for destination_parameter in destination_parameters: +# assert (destination_object.get(destination_parameter) == value) or ( +# np.testing.assert_allclose(destination_object.get(destination_parameter), value, rtol=1e-1) == None +# ) + + +# @pytest.mark.qpu +# @pytest.mark.parametrize("name", INSTRUMENTS_LIST) +# def test_instruments_qublox_set_property_wrappers(name): +# instrument = instruments[name] +# device = instruments[name].device +# if instrument.__class__.__name__ == "Cluster": +# instrument_test_property_wrapper( +# instrument, "reference_clock_source", device, "reference_source", values=["external", "internal"] +# ) +# if instrument.__class__.__name__ == "ClusterQRM_RF": +# port = instruments[name].ports["o1"] +# sequencer = device.sequencers[instrument.DEFAULT_SEQUENCERS["o1"]] +# instrument_test_property_wrapper(port, "attenuation", device, "out0_att", values=np.arange(0, 60 + 2, 2)) +# instrument_test_property_wrapper(port, "lo_enabled", device, "out0_in0_lo_en", values=[True, False]) +# instrument_test_property_wrapper( +# port, "lo_frequency", device, "out0_in0_lo_freq", values=np.linspace(2e9, 18e9, 20) +# ) +# instrument_test_property_wrapper( +# port, "gain", sequencer, "gain_awg_path0", "gain_awg_path1", values=np.linspace(-1, 1, 20) +# ) +# instrument_test_property_wrapper(port, "hardware_mod_en", sequencer, "mod_en_awg", values=[True, False]) +# instrument_test_property_wrapper(port, "nco_freq", sequencer, "nco_freq", values=np.linspace(-500e6, 500e6, 20)) +# instrument_test_property_wrapper( +# port, "nco_phase_offs", sequencer, "nco_phase_offs", values=np.linspace(0, 359, 20) +# ) +# port = instruments[name].ports["i1"] +# sequencer = device.sequencers[instrument.DEFAULT_SEQUENCERS["i1"]] +# instrument_test_property_wrapper(port, "hardware_demod_en", sequencer, "demod_en_acq", values=[True, False]) +# instrument_test_property_wrapper( +# instrument, +# "acquisition_duration", +# sequencer, +# "integration_length_acq", +# values=np.arange(4, 16777212 + 4, 729444), +# ) +# # FIXME: I don't know why this is failing +# instrument_test_property_wrapper( +# instrument, +# "thresholded_acq_threshold", +# sequencer, +# "thresholded_acq_threshold", +# # values=np.linspace(-16777212.0, 16777212.0, 20), +# values=np.zeros(1), +# ) +# instrument_test_property_wrapper( +# instrument, +# "thresholded_acq_rotation", +# sequencer, +# "thresholded_acq_rotation", +# values=np.zeros(1), +# # values=np.linspace(0, 359, 20) +# ) +# if instrument.__class__.__name__ == "ClusterQCM_RF": +# port = instruments[name].ports["o1"] +# sequencer = device.sequencers[instrument.DEFAULT_SEQUENCERS["o1"]] +# instrument_test_property_wrapper(port, "attenuation", device, "out0_att", values=np.arange(0, 60 + 2, 2)) +# instrument_test_property_wrapper(port, "lo_enabled", device, "out0_lo_en", values=[True, False]) +# instrument_test_property_wrapper( +# port, "lo_frequency", device, "out0_lo_freq", values=np.linspace(2e9, 18e9, 20) +# ) +# instrument_test_property_wrapper( +# port, "gain", sequencer, "gain_awg_path0", "gain_awg_path1", values=np.linspace(-1, 1, 20) +# ) +# instrument_test_property_wrapper(port, "hardware_mod_en", sequencer, "mod_en_awg", values=[True, False]) +# instrument_test_property_wrapper(port, "nco_freq", sequencer, "nco_freq", values=np.linspace(-500e6, 500e6, 20)) +# instrument_test_property_wrapper( +# port, "nco_phase_offs", sequencer, "nco_phase_offs", values=np.linspace(0, 359, 20) +# ) +# port = instruments[name].ports["o2"] +# sequencer = device.sequencers[instrument.DEFAULT_SEQUENCERS["o2"]] +# instrument_test_property_wrapper(port, "attenuation", device, "out1_att", values=np.arange(0, 60 + 2, 2)) +# instrument_test_property_wrapper(port, "lo_enabled", device, "out1_lo_en", values=[True, False]) +# instrument_test_property_wrapper( +# port, "lo_frequency", device, "out1_lo_freq", values=np.linspace(2e9, 18e9, 20) +# ) +# instrument_test_property_wrapper( +# port, "gain", sequencer, "gain_awg_path0", "gain_awg_path1", values=np.linspace(-1, 1, 20) +# ) +# instrument_test_property_wrapper(port, "hardware_mod_en", sequencer, "mod_en_awg", values=[True, False]) +# instrument_test_property_wrapper(port, "nco_freq", sequencer, "nco_freq", values=np.linspace(-500e6, 500e6, 20)) +# instrument_test_property_wrapper( +# port, "nco_phase_offs", sequencer, "nco_phase_offs", values=np.linspace(0, 359, 20) +# ) + + +# def instrument_set_and_test_parameter_values(instrument, target, parameter, values): +# for value in values: +# instrument._set_device_parameter(target, parameter, value) +# np.testing.assert_allclose(target.get(parameter), value) + + +# @pytest.mark.parametrize("name", INSTRUMENTS_LIST) +# def test_instruments_qublox_set_device_paramters(name): +# """ # TODO: add attitional paramter tests +# qrm +# platform.instruments['qrm_rf'].device.print_readable_snapshot(update=True) +# cluster_module16: +# parameter value +# -------------------------------------------------------------------------------- +# in0_att : 0 (dB) +# out0_att : 34 (dB) +# out0_in0_lo_en : True +# out0_in0_lo_freq : 7537724144 (Hz) +# out0_offset_path0 : 34 (mV) +# out0_offset_path1 : 0 (mV) +# present : True +# scope_acq_avg_mode_en_path0 : True +# scope_acq_avg_mode_en_path1 : True +# scope_acq_sequencer_select : 0 +# scope_acq_trigger_level_path0 : 0 +# scope_acq_trigger_level_path1 : 0 +# scope_acq_trigger_mode_path0 : sequencer +# scope_acq_trigger_mode_path1 : sequencer +# cluster_module16_sequencer0: +# parameter value +# -------------------------------------------------------------------------------- +# channel_map_path0_out0_en : True +# channel_map_path1_out1_en : True +# cont_mode_en_awg_path0 : False +# cont_mode_en_awg_path1 : False +# cont_mode_waveform_idx_awg_path0 : 0 +# cont_mode_waveform_idx_awg_path1 : 0 +# demod_en_acq : False +# thresholded_acq_threshold : 0 +# gain_awg_path0 : 1 +# gain_awg_path1 : 1 +# integration_length_acq : 2000 +# marker_ovr_en : True +# marker_ovr_value : 15 +# mixer_corr_gain_ratio : 1 +# mixer_corr_phase_offset_degree : -0 +# mod_en_awg : False +# nco_freq : 0 (Hz) +# nco_phase_offs : 0 (Degrees) +# offset_awg_path0 : 0 +# offset_awg_path1 : 0 +# thresholded_acq_rotation : 0 (Degrees) +# sequence : /nfs/users/alvaro.orgaz/qibolab/src/qibola... +# sync_en : True +# upsample_rate_awg_path0 : 0 +# upsample_rate_awg_path1 : 0 + +# qcm: +# platform.instruments['qcm_rf2'].device.print_readable_snapshot(update=True) +# cluster_module12: +# parameter value +# -------------------------------------------------------------------------------- +# out0_att : 24 (dB) +# out0_lo_en : True +# out0_lo_freq : 5325473000 (Hz) +# out0_offset_path0 : 24 (mV) +# out0_offset_path1 : 24 (mV) +# out1_att : 24 (dB) +# out1_lo_en : True +# out1_lo_freq : 6212286000 (Hz) +# out1_offset_path0 : 0 (mV) +# out1_offset_path1 : 0 (mV) +# present : True +# cluster_module12_sequencer0: +# parameter value +# -------------------------------------------------------------------------------- +# channel_map_path0_out0_en : True +# channel_map_path0_out2_en : False +# channel_map_path1_out1_en : True +# channel_map_path1_out3_en : False +# cont_mode_en_awg_path0 : False +# cont_mode_en_awg_path1 : False +# cont_mode_waveform_idx_awg_path0 : 0 +# cont_mode_waveform_idx_awg_path1 : 0 +# gain_awg_path0 : 0.33998 +# gain_awg_path1 : 0.33998 +# marker_ovr_en : True +# marker_ovr_value : 15 +# mixer_corr_gain_ratio : 1 +# mixer_corr_phase_offset_degree : -0 +# mod_en_awg : False +# nco_freq : -2e+08 (Hz) +# nco_phase_offs : 0 (Degrees) +# offset_awg_path0 : 0 +# offset_awg_path1 : 0 +# sequence : /nfs/users/alvaro.orgaz/qibolab/src/qibola... +# sync_en : True +# upsample_rate_awg_path0 : 0 +# upsample_rate_awg_path1 : 0 +# """ + + +# @pytest.mark.qpu +# @pytest.mark.parametrize("name", INSTRUMENTS_LIST) +# def test_instruments_process_pulse_sequence_upload_play(platform_name, name): +# instrument = instruments[name] +# settings = create_platform(platform_name).settings +# instrument.setup(**settings["settings"], **instruments_settings[name]) +# relaxation_time = settings["settings"]["relaxation_time"] +# instrument_pulses = {} +# instrument_pulses[name] = PulseSequence() +# if "QCM" in instrument.__class__.__name__: +# for channel in instrument.channel_port_map: +# instrument_pulses[name].append(Pulse(0, 200, 1, 10e6, np.pi / 2, "Gaussian(5)", str(channel))) +# instrument.process_pulse_sequence(instrument_pulses[name], nshots=5, relaxation_time=relaxation_time) +# instrument.upload() +# instrument.play_sequence() +# if "QRM" in instrument.__class__.__name__: +# channel = instrument._port_channel_map["o1"] +# instrument_pulses[name].append( +# Pulse(0, 200, 1, 10e6, np.pi / 2, "Gaussian(5)", channel), +# ReadoutPulse(200, 2000, 1, 10e6, np.pi / 2, "Rectangular()", channel), +# ) +# instrument.device.sequencers[0].sync_en( +# False +# ) # TODO: Check why this is necessary here and not when playing a PS of only one readout pulse +# instrument.process_pulse_sequence(instrument_pulses[name], nshots=5, relaxation_time=relaxation_time) +# instrument.upload() +# instrument.play_sequence() +# acquisition_results = instrument.acquire() + + +# @pytest.mark.qpu +# @pytest.mark.parametrize("name", INSTRUMENTS_LIST) +# def test_instruments_qublox_start_stop_disconnect(name): +# instrument = instruments[name] +# instrument.start() +# instrument.stop() +# instrument.disconnect() +# assert instrument.is_connected == False diff --git a/tests/test_instruments_qblox_cluster_qcm_bb.py b/tests/test_instruments_qblox_cluster_qcm_bb.py index 5af689fe0..d6f7309d1 100644 --- a/tests/test_instruments_qblox_cluster_qcm_bb.py +++ b/tests/test_instruments_qblox_cluster_qcm_bb.py @@ -136,10 +136,10 @@ def test_connect(connected_qcm_bb: QcmBb): @pytest.mark.qpu def test_pulse_sequence(connected_platform, connected_qcm_bb: QcmBb): ps = PulseSequence() - ps.add(FluxPulse(40, 70, 0.5, "Rectangular", O1_OUTPUT_CHANNEL)) - ps.add(FluxPulse(0, 50, 0.3, "Rectangular", O2_OUTPUT_CHANNEL)) - ps.add(FluxPulse(20, 100, 0.02, "Rectangular", O3_OUTPUT_CHANNEL)) - ps.add(FluxPulse(32, 48, 0.4, "Rectangular", O4_OUTPUT_CHANNEL)) + ps.append(FluxPulse(40, 70, 0.5, "Rectangular", O1_OUTPUT_CHANNEL)) + ps.append(FluxPulse(0, 50, 0.3, "Rectangular", O2_OUTPUT_CHANNEL)) + ps.append(FluxPulse(20, 100, 0.02, "Rectangular", O3_OUTPUT_CHANNEL)) + ps.append(FluxPulse(32, 48, 0.4, "Rectangular", O4_OUTPUT_CHANNEL)) qubits = connected_platform.qubits connected_qcm_bb._ports["o2"].hardware_mod_en = True connected_qcm_bb.process_pulse_sequence(qubits, ps, 1000, 1, 10000) @@ -155,10 +155,10 @@ def test_pulse_sequence(connected_platform, connected_qcm_bb: QcmBb): @pytest.mark.qpu def test_sweepers(connected_platform, connected_qcm_bb: QcmBb): ps = PulseSequence() - ps.add(FluxPulse(40, 70, 0.5, "Rectangular", O1_OUTPUT_CHANNEL)) - ps.add(FluxPulse(0, 50, 0.3, "Rectangular", O2_OUTPUT_CHANNEL)) - ps.add(FluxPulse(20, 100, 0.02, "Rectangular", O3_OUTPUT_CHANNEL)) - ps.add(FluxPulse(32, 48, 0.4, "Rectangular", O4_OUTPUT_CHANNEL)) + ps.append(FluxPulse(40, 70, 0.5, "Rectangular", O1_OUTPUT_CHANNEL)) + ps.append(FluxPulse(0, 50, 0.3, "Rectangular", O2_OUTPUT_CHANNEL)) + ps.append(FluxPulse(20, 100, 0.02, "Rectangular", O3_OUTPUT_CHANNEL)) + ps.append(FluxPulse(32, 48, 0.4, "Rectangular", O4_OUTPUT_CHANNEL)) qubits = connected_platform.qubits amplitude_range = np.linspace(0, 0.25, 50) diff --git a/tests/test_instruments_qblox_cluster_qcm_rf.py b/tests/test_instruments_qblox_cluster_qcm_rf.py index 5d046de2d..f7926d6d9 100644 --- a/tests/test_instruments_qblox_cluster_qcm_rf.py +++ b/tests/test_instruments_qblox_cluster_qcm_rf.py @@ -151,7 +151,7 @@ def test_connect(connected_qcm_rf: QcmRf): @pytest.mark.qpu def test_pulse_sequence(connected_platform, connected_qcm_rf: QcmRf): ps = PulseSequence() - ps.add( + ps.append( DrivePulse( 0, 200, @@ -162,7 +162,7 @@ def test_pulse_sequence(connected_platform, connected_qcm_rf: QcmRf): O1_OUTPUT_CHANNEL, ) ) - ps.add( + ps.append( DrivePulse( 0, 200, @@ -189,7 +189,7 @@ def test_pulse_sequence(connected_platform, connected_qcm_rf: QcmRf): @pytest.mark.qpu def test_sweepers(connected_platform, connected_qcm_rf: QcmRf): ps = PulseSequence() - ps.add( + ps.append( DrivePulse( 0, 200, @@ -200,7 +200,7 @@ def test_sweepers(connected_platform, connected_qcm_rf: QcmRf): O1_OUTPUT_CHANNEL, ) ) - ps.add( + ps.append( DrivePulse( 0, 200, diff --git a/tests/test_instruments_qblox_cluster_qrm_rf.py b/tests/test_instruments_qblox_cluster_qrm_rf.py index 21978461a..eb7b6adcd 100644 --- a/tests/test_instruments_qblox_cluster_qrm_rf.py +++ b/tests/test_instruments_qblox_cluster_qrm_rf.py @@ -145,13 +145,13 @@ def test_connect(connected_qrm_rf: QrmRf): def test_pulse_sequence(connected_platform, connected_qrm_rf: QrmRf): ps = PulseSequence() for channel in connected_qrm_rf.channel_map: - ps.add(DrivePulse(0, 200, 1, 6.8e9, np.pi / 2, "Gaussian(5)", channel)) - ps.add( + ps.append(DrivePulse(0, 200, 1, 6.8e9, np.pi / 2, "Gaussian(5)", channel)) + ps.append( ReadoutPulse( 200, 2000, 1, 7.1e9, np.pi / 2, "Rectangular()", channel, qubit=0 ) ) - ps.add( + ps.append( ReadoutPulse( 200, 2000, 1, 7.2e9, np.pi / 2, "Rectangular()", channel, qubit=1 ) @@ -184,7 +184,7 @@ def test_sweepers(connected_platform, connected_qrm_rf: QrmRf): ro_pulses[1] = ReadoutPulse( 200, 2000, 1, 7.2e9, np.pi / 2, "Rectangular()", channel, qubit=1 ) - ps.add(qd_pulses[0], ro_pulses[0], ro_pulses[1]) + ps.append(qd_pulses[0], ro_pulses[0], ro_pulses[1]) qubits = connected_platform.qubits diff --git a/tests/test_instruments_qmsim.py b/tests/test_instruments_qmsim.py index 3eaaa8d11..8488621d5 100644 --- a/tests/test_instruments_qmsim.py +++ b/tests/test_instruments_qmsim.py @@ -120,7 +120,7 @@ def test_qmsim_resonator_spectroscopy(simulator, folder): ro_pulses = {} for qubit in qubits: ro_pulses[qubit] = simulator.create_qubit_readout_pulse(qubit, start=0) - sequence.add(ro_pulses[qubit]) + sequence.append(ro_pulses[qubit]) options = ExecutionParameters(nshots=1) result = simulator.execute_pulse_sequence(sequence, options) samples = result.get_simulated_samples() @@ -140,8 +140,8 @@ def test_qmsim_qubit_spectroscopy(simulator, folder): ro_pulses[qubit] = simulator.create_qubit_readout_pulse( qubit, start=qd_pulses[qubit].finish ) - sequence.add(qd_pulses[qubit]) - sequence.add(ro_pulses[qubit]) + sequence.append(qd_pulses[qubit]) + sequence.append(ro_pulses[qubit]) options = ExecutionParameters(nshots=1) result = simulator.execute_pulse_sequence(sequence, options) samples = result.get_simulated_samples() @@ -166,8 +166,8 @@ def test_qmsim_sweep(simulator, folder, parameter, values): ro_pulses[qubit] = simulator.create_MZ_pulse( qubit, start=qd_pulses[qubit].finish ) - sequence.add(qd_pulses[qubit]) - sequence.add(ro_pulses[qubit]) + sequence.append(qd_pulses[qubit]) + sequence.append(ro_pulses[qubit]) pulses = [qd_pulses[qubit] for qubit in qubits] sweeper = Sweeper(parameter, values, pulses) options = ExecutionParameters( @@ -187,7 +187,7 @@ def test_qmsim_sweep_bias(simulator, folder): ro_pulses = {} for qubit in qubits: ro_pulses[qubit] = simulator.create_MZ_pulse(qubit, start=0) - sequence.add(ro_pulses[qubit]) + sequence.append(ro_pulses[qubit]) values = [0, 0.005] sweeper = Sweeper( Parameter.bias, values, qubits=[simulator.qubits[q] for q in qubits] @@ -213,8 +213,8 @@ def test_qmsim_sweep_start(simulator, folder): ro_pulses[qubit] = simulator.create_MZ_pulse( qubit, start=qd_pulses[qubit].finish ) - sequence.add(qd_pulses[qubit]) - sequence.add(ro_pulses[qubit]) + sequence.append(qd_pulses[qubit]) + sequence.append(ro_pulses[qubit]) values = [20, 40] pulses = [ro_pulses[qubit] for qubit in qubits] sweeper = Sweeper(Parameter.start, values, pulses=pulses) @@ -243,9 +243,9 @@ def test_qmsim_sweep_start_two_pulses(simulator, folder): ro_pulses[qubit] = simulator.create_MZ_pulse( qubit, start=qd_pulses2[qubit].finish ) - sequence.add(qd_pulses1[qubit]) - sequence.add(qd_pulses2[qubit]) - sequence.add(ro_pulses[qubit]) + sequence.append(qd_pulses1[qubit]) + sequence.append(qd_pulses2[qubit]) + sequence.append(ro_pulses[qubit]) values = [20, 60] pulses = [qd_pulses2[qubit] for qubit in qubits] sweeper = Sweeper(Parameter.start, values, pulses=pulses) @@ -273,8 +273,8 @@ def test_qmsim_sweep_duration(simulator, folder): ro_pulses[qubit] = simulator.create_MZ_pulse( qubit, start=qd_pulses[qubit].finish ) - sequence.add(qd_pulses[qubit]) - sequence.add(ro_pulses[qubit]) + sequence.append(qd_pulses[qubit]) + sequence.append(ro_pulses[qubit]) values = [20, 60] pulses = [qd_pulses[qubit] for qubit in qubits] sweeper = Sweeper(Parameter.duration, values, pulses=pulses) @@ -307,9 +307,9 @@ def test_qmsim_sweep_duration_two_pulses(simulator, folder): ro_pulses[qubit] = simulator.create_MZ_pulse( qubit, start=qd_pulses2[qubit].finish ) - sequence.add(qd_pulses1[qubit]) - sequence.add(qd_pulses2[qubit]) - sequence.add(ro_pulses[qubit]) + sequence.append(qd_pulses1[qubit]) + sequence.append(qd_pulses2[qubit]) + sequence.append(ro_pulses[qubit]) values = [20, 60] pulses = [qd_pulses1[qubit] for qubit in qubits] sweeper = Sweeper(Parameter.duration, values, pulses=pulses) @@ -373,9 +373,9 @@ def test_qmsim_allxy(simulator, folder, count, gate_pair): for gate in gate_pair: pulse = allxy_pulses[gate](qubit, start) if pulse is not None: - sequence.add(pulse) + sequence.append(pulse) start += pulse.duration - sequence.add(simulator.create_MZ_pulse(qubit, start=start)) + sequence.append(simulator.create_MZ_pulse(qubit, start=start)) options = ExecutionParameters(nshots=1) result = simulator.execute_pulse_sequence(sequence, options) @@ -403,11 +403,11 @@ def test_qmsim_chevron(simulator, folder, sweep): highfreq, start=flux_pulse.finish ) sequence = PulseSequence() - sequence.add(initialize_1) - sequence.add(initialize_2) - sequence.add(flux_pulse) - sequence.add(measure_lowfreq) - sequence.add(measure_highfreq) + sequence.append(initialize_1) + sequence.append(initialize_2) + sequence.append(flux_pulse) + sequence.append(measure_lowfreq) + sequence.append(measure_highfreq) options = ExecutionParameters( nshots=1, @@ -494,9 +494,9 @@ def test_qmsim_snz_pulse(simulator, folder, qubit): qd_pulse = simulator.create_RX_pulse(qubit, start=0) flux_pulse = FluxPulse(qd_pulse.finish, duration, amplitude, shape, channel, qubit) ro_pulse = simulator.create_MZ_pulse(qubit, start=flux_pulse.finish) - sequence.add(qd_pulse) - sequence.add(flux_pulse) - sequence.add(ro_pulse) + sequence.append(qd_pulse) + sequence.append(flux_pulse) + sequence.append(ro_pulse) options = ExecutionParameters(nshots=1) result = simulator.execute_pulse_sequence(sequence, options) samples = result.get_simulated_samples() @@ -507,9 +507,9 @@ def test_qmsim_snz_pulse(simulator, folder, qubit): def test_qmsim_bell_circuit(simulator, folder, qubits): backend = QibolabBackend(simulator) circuit = Circuit(5) - circuit.add(gates.H(qubits[0])) - circuit.add(gates.CNOT(*qubits)) - circuit.add(gates.M(*qubits)) + circuit.append(gates.H(qubits[0])) + circuit.append(gates.CNOT(*qubits)) + circuit.append(gates.M(*qubits)) result = backend.execute_circuit(circuit, nshots=1) result = result.execution_result samples = result.get_simulated_samples() @@ -520,10 +520,10 @@ def test_qmsim_bell_circuit(simulator, folder, qubits): def test_qmsim_ghz_circuit(simulator, folder): backend = QibolabBackend(simulator) circuit = Circuit(5) - circuit.add(gates.H(2)) - circuit.add(gates.CNOT(2, 1)) - circuit.add(gates.CNOT(2, 3)) - circuit.add(gates.M(1, 2, 3)) + circuit.append(gates.H(2)) + circuit.append(gates.CNOT(2, 1)) + circuit.append(gates.CNOT(2, 3)) + circuit.append(gates.M(1, 2, 3)) result = backend.execute_circuit(circuit, nshots=1) result = result.execution_result samples = result.get_simulated_samples() diff --git a/tests/test_instruments_rfsoc.py b/tests/test_instruments_rfsoc.py index 9bf867aa5..d41eabbd7 100644 --- a/tests/test_instruments_rfsoc.py +++ b/tests/test_instruments_rfsoc.py @@ -32,7 +32,7 @@ def test_convert_default(dummy_qrc): integer = 12 qubits = platform.qubits sequence = PulseSequence() - sequence.add(Pulse(0, 40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 0)) + sequence.append(Pulse(0, 40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 0)) parameter = Parameter.frequency with pytest.raises(ValueError): @@ -199,8 +199,8 @@ def test_convert_units_sweeper(dummy_qrc): seq = PulseSequence() pulse0 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(2), 0, PulseType.DRIVE, 0) pulse1 = Pulse(40, 40, 0.9, 50e6, 0, Rectangular(), 0, PulseType.READOUT, 0) - seq.add(pulse0) - seq.add(pulse1) + seq.append(pulse0) + seq.append(pulse1) # frequency sweeper sweeper = rfsoc.Sweeper( @@ -267,8 +267,8 @@ def test_convert_sweep(dummy_qrc): seq = PulseSequence() pulse0 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(2), 0, PulseType.DRIVE, 0) pulse1 = Pulse(40, 40, 0.9, 50e6, 0, Rectangular(), 0, PulseType.READOUT, 0) - seq.add(pulse0) - seq.add(pulse1) + seq.append(pulse0) + seq.append(pulse1) sweeper = Sweeper( parameter=Parameter.bias, values=np.arange(-0.5, +0.5, 0.1), qubits=[qubit] @@ -377,8 +377,8 @@ def test_play(mocker, dummy_qrc): seq = PulseSequence() pulse0 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(2), 0, PulseType.DRIVE, 0) pulse1 = Pulse(40, 40, 0.9, 50e6, 0, Rectangular(), 0, PulseType.READOUT, 0) - seq.add(pulse0) - seq.add(pulse1) + seq.append(pulse0) + seq.append(pulse1) nshots = 100 server_results = ([[np.random.rand(nshots)]], [[np.random.rand(nshots)]]) @@ -418,8 +418,8 @@ def test_sweep(mocker, dummy_qrc): seq = PulseSequence() pulse0 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(2), 0, PulseType.DRIVE, 0) pulse1 = Pulse(40, 40, 0.9, 50e6, 0, Rectangular(), 0, PulseType.READOUT, 0) - seq.add(pulse0) - seq.add(pulse1) + seq.append(pulse0) + seq.append(pulse1) sweeper0 = Sweeper( parameter=Parameter.frequency, values=np.arange(0, 100, 1), pulses=[pulse0] ) @@ -471,8 +471,8 @@ def test_validate_input_command(dummy_qrc): seq = PulseSequence() pulse0 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(2), 0, PulseType.DRIVE, 0) pulse1 = Pulse(40, 40, 0.9, 50e6, 0, Rectangular(), 0, PulseType.READOUT, 0) - seq.add(pulse0) - seq.add(pulse1) + seq.append(pulse0) + seq.append(pulse1) parameters = ExecutionParameters(acquisition_type=AcquisitionType.RAW) with pytest.raises(NotImplementedError): @@ -490,8 +490,8 @@ def test_update_cfg(mocker, dummy_qrc): seq = PulseSequence() pulse0 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(2), 0, PulseType.DRIVE, 0) pulse1 = Pulse(40, 40, 0.9, 50e6, 0, Rectangular(), 0, PulseType.READOUT, 0) - seq.add(pulse0) - seq.add(pulse1) + seq.append(pulse0) + seq.append(pulse1) nshots = 333 relax_time = 1e6 @@ -583,8 +583,8 @@ def test_get_if_python_sweep(dummy_qrc): instrument = platform.instruments["tii_rfsoc4x2"] sequence_1 = PulseSequence() - sequence_1.add(platform.create_RX_pulse(qubit=0, start=0)) - sequence_1.add(platform.create_MZ_pulse(qubit=0, start=100)) + sequence_1.append(platform.create_RX_pulse(qubit=0, start=0)) + sequence_1.append(platform.create_MZ_pulse(qubit=0, start=100)) sweep1 = Sweeper( parameter=Parameter.frequency, @@ -610,7 +610,7 @@ def test_get_if_python_sweep(dummy_qrc): assert not instrument.get_if_python_sweep(sequence_1, sweep3) sequence_2 = PulseSequence() - sequence_2.add(platform.create_RX_pulse(qubit=0, start=0)) + sequence_2.append(platform.create_RX_pulse(qubit=0, start=0)) sweep1 = Sweeper( parameter=Parameter.frequency, @@ -633,7 +633,7 @@ def test_get_if_python_sweep(dummy_qrc): instrument = platform.instruments["tii_rfsoc4x2"] sequence_1 = PulseSequence() - sequence_1.add(platform.create_RX_pulse(qubit=0, start=0)) + sequence_1.append(platform.create_RX_pulse(qubit=0, start=0)) sweep1 = Sweeper( parameter=Parameter.frequency, values=np.arange(10, 100, 10), @@ -668,9 +668,9 @@ def test_convert_av_sweep_results(dummy_qrc): instrument = platform.instruments["tii_rfsoc4x2"] sequence = PulseSequence() - sequence.add(platform.create_RX_pulse(qubit=0, start=0)) - sequence.add(platform.create_MZ_pulse(qubit=0, start=100)) - sequence.add(platform.create_MZ_pulse(qubit=0, start=200)) + sequence.append(platform.create_RX_pulse(qubit=0, start=0)) + sequence.append(platform.create_MZ_pulse(qubit=0, start=100)) + sequence.append(platform.create_MZ_pulse(qubit=0, start=200)) sweep1 = Sweeper( parameter=Parameter.frequency, values=np.arange(10, 35, 10), @@ -721,9 +721,9 @@ def test_convert_nav_sweep_results(dummy_qrc): instrument = platform.instruments["tii_rfsoc4x2"] sequence = PulseSequence() - sequence.add(platform.create_RX_pulse(qubit=0, start=0)) - sequence.add(platform.create_MZ_pulse(qubit=0, start=100)) - sequence.add(platform.create_MZ_pulse(qubit=0, start=200)) + sequence.append(platform.create_RX_pulse(qubit=0, start=0)) + sequence.append(platform.create_MZ_pulse(qubit=0, start=100)) + sequence.append(platform.create_MZ_pulse(qubit=0, start=200)) sweep1 = Sweeper( parameter=Parameter.frequency, values=np.arange(10, 35, 10), @@ -781,8 +781,8 @@ def test_call_executepulsesequence(connected_platform, instrument): instrument = platform.instruments["tii_rfsoc4x2"] sequence = PulseSequence() - sequence.add(platform.create_RX_pulse(qubit=0, start=0)) - sequence.add(platform.create_MZ_pulse(qubit=0, start=100)) + sequence.append(platform.create_RX_pulse(qubit=0, start=0)) + sequence.append(platform.create_MZ_pulse(qubit=0, start=100)) instrument.cfg.average = False i_vals_nav, q_vals_nav = instrument._execute_pulse_sequence( @@ -809,8 +809,8 @@ def test_call_execute_sweeps(connected_platform, instrument): instrument = platform.instruments["tii_rfsoc4x2"] sequence = PulseSequence() - sequence.add(platform.create_RX_pulse(qubit=0, start=0)) - sequence.add(platform.create_MZ_pulse(qubit=0, start=100)) + sequence.append(platform.create_RX_pulse(qubit=0, start=0)) + sequence.append(platform.create_MZ_pulse(qubit=0, start=100)) sweep = Sweeper( parameter=Parameter.frequency, values=np.arange(10, 35, 10), @@ -840,8 +840,8 @@ def test_play_qpu(connected_platform, instrument): instrument = platform.instruments["tii_rfsoc4x2"] sequence = PulseSequence() - sequence.add(platform.create_RX_pulse(qubit=0, start=0)) - sequence.add(platform.create_MZ_pulse(qubit=0, start=100)) + sequence.append(platform.create_RX_pulse(qubit=0, start=0)) + sequence.append(platform.create_MZ_pulse(qubit=0, start=100)) out_dict = instrument.play( platform.qubits, @@ -862,8 +862,8 @@ def test_sweep_qpu(connected_platform, instrument): instrument = platform.instruments["tii_rfsoc4x2"] sequence = PulseSequence() - sequence.add(platform.create_RX_pulse(qubit=0, start=0)) - sequence.add(platform.create_MZ_pulse(qubit=0, start=100)) + sequence.append(platform.create_RX_pulse(qubit=0, start=0)) + sequence.append(platform.create_MZ_pulse(qubit=0, start=100)) sweep = Sweeper( parameter=Parameter.frequency, values=np.arange(10, 35, 10), @@ -912,8 +912,8 @@ def test_python_reqursive_sweep(connected_platform, instrument): instrument = platform.instruments["tii_rfsoc4x2"] sequence = PulseSequence() - sequence.add(platform.create_RX_pulse(qubit=0, start=0)) - sequence.add(platform.create_MZ_pulse(qubit=0, start=100)) + sequence.append(platform.create_RX_pulse(qubit=0, start=0)) + sequence.append(platform.create_MZ_pulse(qubit=0, start=100)) sweep1 = Sweeper( parameter=Parameter.amplitude, values=np.arange(0.01, 0.03, 10), diff --git a/tests/test_instruments_zhinst.py b/tests/test_instruments_zhinst.py index bbea85ecb..36d740d38 100644 --- a/tests/test_instruments_zhinst.py +++ b/tests/test_instruments_zhinst.py @@ -387,9 +387,9 @@ def test_experiment_flow(dummy_qrc): channel=platform.qubits[q].flux.name, qubit=q, ) - sequence.add(qf_pulses[q]) + sequence.append(qf_pulses[q]) ro_pulses[q] = platform.create_qubit_readout_pulse(q, start=qf_pulses[q].finish) - sequence.add(ro_pulses[q]) + sequence.append(ro_pulses[q]) options = ExecutionParameters( relaxation_time=300e-6, @@ -426,9 +426,9 @@ def test_experiment_flow_coupler(dummy_qrc): channel=platform.qubits[q].flux.name, qubit=q, ) - sequence.add(qf_pulses[q]) + sequence.append(qf_pulses[q]) ro_pulses[q] = platform.create_qubit_readout_pulse(q, start=qf_pulses[q].finish) - sequence.add(ro_pulses[q]) + sequence.append(ro_pulses[q]) cf_pulses = {} for coupler in couplers.values(): @@ -441,7 +441,7 @@ def test_experiment_flow_coupler(dummy_qrc): channel=platform.couplers[c].flux.name, qubit=c, ) - sequence.add(cf_pulses[c]) + sequence.append(cf_pulses[c]) options = ExecutionParameters( relaxation_time=300e-6, @@ -470,7 +470,7 @@ def test_sweep_and_play_sim(dummy_qrc): qf_pulses = {} for qubit in qubits.values(): q = qubit.name - qf_pulses[q] = FluxPulse( + qf_pulses[q] = Pulse.flux( start=0, duration=500, amplitude=1, @@ -523,11 +523,11 @@ def test_experiment_sweep_single(dummy_qrc, parameter1): qd_pulses = {} for qubit in qubits: qd_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) - sequence.add(qd_pulses[qubit]) + sequence.append(qd_pulses[qubit]) ro_pulses[qubit] = platform.create_qubit_readout_pulse( qubit, start=qd_pulses[qubit].finish ) - sequence.add(ro_pulses[qubit]) + sequence.append(ro_pulses[qubit]) parameter_range_1 = ( np.random.rand(swept_points) @@ -565,11 +565,11 @@ def test_experiment_sweep_single_coupler(dummy_qrc, parameter1): qd_pulses = {} for qubit in qubits: qd_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) - sequence.add(qd_pulses[qubit]) + sequence.append(qd_pulses[qubit]) ro_pulses[qubit] = platform.create_qubit_readout_pulse( qubit, start=qd_pulses[qubit].finish ) - sequence.add(ro_pulses[qubit]) + sequence.append(ro_pulses[qubit]) cf_pulses = {} for coupler in couplers.values(): @@ -582,7 +582,7 @@ def test_experiment_sweep_single_coupler(dummy_qrc, parameter1): channel=platform.couplers[c].flux.name, qubit=c, ) - sequence.add(cf_pulses[c]) + sequence.append(cf_pulses[c]) parameter_range_1 = ( np.random.rand(swept_points) @@ -631,11 +631,11 @@ def test_experiment_sweep_2d_general(dummy_qrc, parameter1, parameter2): qd_pulses = {} for qubit in qubits: qd_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) - sequence.add(qd_pulses[qubit]) + sequence.append(qd_pulses[qubit]) ro_pulses[qubit] = platform.create_qubit_readout_pulse( qubit, start=qd_pulses[qubit].finish ) - sequence.add(ro_pulses[qubit]) + sequence.append(ro_pulses[qubit]) parameter_range_1 = ( np.random.rand(swept_points) @@ -688,11 +688,11 @@ def test_experiment_sweep_2d_specific(dummy_qrc): qd_pulses = {} for qubit in qubits: qd_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) - sequence.add(qd_pulses[qubit]) + sequence.append(qd_pulses[qubit]) ro_pulses[qubit] = platform.create_qubit_readout_pulse( qubit, start=qd_pulses[qubit].finish ) - sequence.add(ro_pulses[qubit]) + sequence.append(ro_pulses[qubit]) parameter1 = Parameter.relative_phase parameter2 = Parameter.frequency @@ -751,7 +751,7 @@ def test_experiment_sweep_punchouts(dummy_qrc, parameter): ro_pulses = {} for qubit in qubits: ro_pulses[qubit] = platform.create_qubit_readout_pulse(qubit, start=0) - sequence.add(ro_pulses[qubit]) + sequence.append(ro_pulses[qubit]) parameter_range_1 = ( np.random.rand(swept_points) @@ -791,11 +791,11 @@ def test_batching(dummy_qrc): instrument = platform.instruments["EL_ZURO"] sequence = PulseSequence() - sequence.add(platform.create_RX_pulse(0, start=0)) - sequence.add(platform.create_RX_pulse(1, start=0)) + sequence.append(platform.create_RX_pulse(0, start=0)) + sequence.append(platform.create_RX_pulse(1, start=0)) measurement_start = sequence.finish - sequence.add(platform.create_MZ_pulse(0, start=measurement_start)) - sequence.add(platform.create_MZ_pulse(1, start=measurement_start)) + sequence.append(platform.create_MZ_pulse(0, start=measurement_start)) + sequence.append(platform.create_MZ_pulse(1, start=measurement_start)) batches = list(batch(600 * [sequence], instrument.bounds)) # These sequences get limited by the number of measuraments (600/250/2) @@ -836,11 +836,11 @@ def test_experiment_execute_pulse_sequence_qpu(connected_platform, instrument): channel=platform.qubits[q].flux.name, qubit=q, ) - sequence.add(qf_pulses[q]) + sequence.append(qf_pulses[q]) if qubit.flux_coupler: continue ro_pulses[q] = platform.create_qubit_readout_pulse(q, start=qf_pulses[q].finish) - sequence.add(ro_pulses[q]) + sequence.append(ro_pulses[q]) options = ExecutionParameters( relaxation_time=300e-6, @@ -867,11 +867,11 @@ def test_experiment_sweep_2d_specific_qpu(connected_platform, instrument): qd_pulses = {} for qubit in qubits: qd_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) - sequence.add(qd_pulses[qubit]) + sequence.append(qd_pulses[qubit]) ro_pulses[qubit] = platform.create_qubit_readout_pulse( qubit, start=qd_pulses[qubit].finish ) - sequence.add(ro_pulses[qubit]) + sequence.append(ro_pulses[qubit]) parameter1 = Parameter.relative_phase parameter2 = Parameter.frequency @@ -947,9 +947,9 @@ def test_experiment_measurement_sequence(dummy_qrc): qubit_drive_pulse_2 = platform.create_qubit_drive_pulse( qubit, start=readout_pulse_start + 50, duration=40 ) - sequence.add(qubit_drive_pulse_1) - sequence.add(ro_pulse) - sequence.add(qubit_drive_pulse_2) + sequence.append(qubit_drive_pulse_1) + sequence.append(ro_pulse) + sequence.append(qubit_drive_pulse_2) options = ExecutionParameters( relaxation_time=4, diff --git a/tests/test_platform.py b/tests/test_platform.py index 4389681ee..0f575693d 100644 --- a/tests/test_platform.py +++ b/tests/test_platform.py @@ -43,8 +43,8 @@ def test_unroll_sequences(platform): sequence = PulseSequence() qd_pulse = platform.create_RX_pulse(qubit, start=0) ro_pulse = platform.create_MZ_pulse(qubit, start=qd_pulse.finish) - sequence.add(qd_pulse) - sequence.add(ro_pulse) + sequence.append(qd_pulse) + sequence.append(ro_pulse) total_sequence, readouts = unroll_sequences(10 * [sequence], relaxation_time=10000) assert len(total_sequence) == 20 assert len(total_sequence.ro_pulses) == 10 @@ -192,7 +192,7 @@ def test_platform_execute_one_drive_pulse(qpu_platform): platform = qpu_platform qubit = next(iter(platform.qubits)) sequence = PulseSequence() - sequence.add(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @@ -204,7 +204,7 @@ def test_platform_execute_one_coupler_pulse(qpu_platform): pytest.skip("The platform does not have couplers") coupler = next(iter(platform.couplers)) sequence = PulseSequence() - sequence.add( + sequence.append( platform.create_coupler_pulse(coupler, start=0, duration=200, amplitude=1) ) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @@ -232,7 +232,7 @@ def test_platform_execute_one_long_drive_pulse(qpu_platform): qubit = next(iter(platform.qubits)) pulse = platform.create_qubit_drive_pulse(qubit, start=0, duration=8192 + 200) sequence = PulseSequence() - sequence.add(pulse) + sequence.append(pulse) options = ExecutionParameters(nshots=nshots) if find_instrument(platform, QbloxController) is not None: with pytest.raises(NotImplementedError): @@ -253,7 +253,7 @@ def test_platform_execute_one_extralong_drive_pulse(qpu_platform): qubit = next(iter(platform.qubits)) pulse = platform.create_qubit_drive_pulse(qubit, start=0, duration=2 * 8192 + 200) sequence = PulseSequence() - sequence.add(pulse) + sequence.append(pulse) options = ExecutionParameters(nshots=nshots) if find_instrument(platform, QbloxController) is not None: with pytest.raises(NotImplementedError): @@ -273,8 +273,8 @@ def test_platform_execute_one_drive_one_readout(qpu_platform): platform = qpu_platform qubit = next(iter(platform.qubits)) sequence = PulseSequence() - sequence.add(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) - sequence.add(platform.create_qubit_readout_pulse(qubit, start=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) + sequence.append(platform.create_qubit_readout_pulse(qubit, start=200)) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @@ -284,10 +284,10 @@ def test_platform_execute_multiple_drive_pulses_one_readout(qpu_platform): platform = qpu_platform qubit = next(iter(platform.qubits)) sequence = PulseSequence() - sequence.add(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) - sequence.add(platform.create_qubit_drive_pulse(qubit, start=204, duration=200)) - sequence.add(platform.create_qubit_drive_pulse(qubit, start=408, duration=400)) - sequence.add(platform.create_qubit_readout_pulse(qubit, start=808)) + sequence.append(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, start=204, duration=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, start=408, duration=400)) + sequence.append(platform.create_qubit_readout_pulse(qubit, start=808)) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @@ -299,10 +299,10 @@ def test_platform_execute_multiple_drive_pulses_one_readout_no_spacing( platform = qpu_platform qubit = next(iter(platform.qubits)) sequence = PulseSequence() - sequence.add(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) - sequence.add(platform.create_qubit_drive_pulse(qubit, start=200, duration=200)) - sequence.add(platform.create_qubit_drive_pulse(qubit, start=400, duration=400)) - sequence.add(platform.create_qubit_readout_pulse(qubit, start=800)) + sequence.append(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, start=200, duration=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, start=400, duration=400)) + sequence.append(platform.create_qubit_readout_pulse(qubit, start=800)) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @@ -314,10 +314,10 @@ def test_platform_execute_multiple_overlaping_drive_pulses_one_readout( platform = qpu_platform qubit = next(iter(platform.qubits)) sequence = PulseSequence() - sequence.add(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) - sequence.add(platform.create_qubit_drive_pulse(qubit, start=200, duration=200)) - sequence.add(platform.create_qubit_drive_pulse(qubit, start=50, duration=400)) - sequence.add(platform.create_qubit_readout_pulse(qubit, start=800)) + sequence.append(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, start=200, duration=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, start=50, duration=400)) + sequence.append(platform.create_qubit_readout_pulse(qubit, start=800)) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @@ -335,10 +335,10 @@ def test_platform_execute_multiple_readout_pulses(qpu_platform): ro_pulse2 = platform.create_qubit_readout_pulse( qubit, start=(ro_pulse1.start + ro_pulse1.duration + 400) ) - sequence.add(qd_pulse1) - sequence.add(ro_pulse1) - sequence.add(qd_pulse2) - sequence.add(ro_pulse2) + sequence.append(qd_pulse1) + sequence.append(ro_pulse1) + sequence.append(qd_pulse2) + sequence.append(ro_pulse2) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @@ -355,8 +355,8 @@ def test_excited_state_probabilities_pulses(qpu_platform): for qubit in qubits: qd_pulse = platform.create_RX_pulse(qubit) ro_pulse = platform.create_MZ_pulse(qubit, start=qd_pulse.duration) - sequence.add(qd_pulse) - sequence.add(ro_pulse) + sequence.append(qd_pulse) + sequence.append(ro_pulse) result = platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=5000)) nqubits = len(qubits) @@ -387,7 +387,7 @@ def test_ground_state_probabilities_pulses(qpu_platform, start_zero): else: qd_pulse = platform.create_RX_pulse(qubit) ro_pulse = platform.create_MZ_pulse(qubit, start=qd_pulse.duration) - sequence.add(ro_pulse) + sequence.append(ro_pulse) result = platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=5000)) nqubits = len(qubits) diff --git a/tests/test_pulses.py b/tests/test_pulses.py index 9939b8fae..d1c925e7c 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -452,8 +452,8 @@ def test_pulses_pulsesequence_operators(): p6 = Pulse(300, 40, 0.9, 50e6, 0, Gaussian(5), 1, PulseType.DRIVE) another_ps = PulseSequence() - another_ps.add(p4) - another_ps.add(p5, p6) + another_ps.append(p4) + another_ps.append(p5, p6) assert another_ps[0] == p4 assert another_ps[1] == p5 @@ -488,10 +488,10 @@ def test_pulses_pulsesequence_add(): p3 = Pulse(400, 40, 0.9, 50e6, 0, Gaussian(5), 40, PulseType.DRIVE, 4) ps = PulseSequence() - ps.add(p0) - ps.add(p1) + ps.append(p0) + ps.append(p1) psx = PulseSequence(p2, p3) - ps.add(psx) + ps.append(psx) assert ps.count == 4 assert ps.qubits == [1, 2, 3, 4] @@ -933,7 +933,7 @@ def test_readout_pulse(): def test_pulse_sequence_add(): sequence = PulseSequence() - sequence.add( + sequence.append( Pulse( start=0, frequency=200_000_000, @@ -944,7 +944,7 @@ def test_pulse_sequence_add(): channel=1, ) ) - sequence.add( + sequence.append( Pulse( start=64, frequency=200_000_000, @@ -961,7 +961,7 @@ def test_pulse_sequence_add(): def test_pulse_sequence__add__(): sequence = PulseSequence() - sequence.add( + sequence.append( Pulse( start=0, frequency=200_000_000, @@ -972,7 +972,7 @@ def test_pulse_sequence__add__(): channel=1, ) ) - sequence.add( + sequence.append( Pulse( start=64, frequency=200_000_000, @@ -991,7 +991,7 @@ def test_pulse_sequence__add__(): def test_pulse_sequence__mul__(): sequence = PulseSequence() - sequence.add( + sequence.append( Pulse( start=0, frequency=200_000_000, @@ -1002,7 +1002,7 @@ def test_pulse_sequence__mul__(): channel=1, ) ) - sequence.add( + sequence.append( Pulse( start=64, frequency=200_000_000, @@ -1027,7 +1027,7 @@ def test_pulse_sequence__mul__(): def test_pulse_sequence_add_readout(): sequence = PulseSequence() - sequence.add( + sequence.append( Pulse( start=0, frequency=200_000_000, @@ -1039,7 +1039,7 @@ def test_pulse_sequence_add_readout(): ) ) - sequence.add( + sequence.append( Pulse( start=64, frequency=200_000_000, @@ -1052,7 +1052,7 @@ def test_pulse_sequence_add_readout(): ) ) - sequence.add( + sequence.append( ReadoutPulse( start=128, frequency=20_000_000, diff --git a/tests/test_result_shapes.py b/tests/test_result_shapes.py index 9bb149d3e..d4317689e 100644 --- a/tests/test_result_shapes.py +++ b/tests/test_result_shapes.py @@ -22,8 +22,8 @@ def execute(platform, acquisition_type, averaging_mode, sweep=False): qd_pulse = platform.create_RX_pulse(qubit, start=0) ro_pulse = platform.create_MZ_pulse(qubit, start=qd_pulse.finish) sequence = PulseSequence() - sequence.add(qd_pulse) - sequence.add(ro_pulse) + sequence.append(qd_pulse) + sequence.append(ro_pulse) options = ExecutionParameters( nshots=NSHOTS, acquisition_type=acquisition_type, averaging_mode=averaging_mode From 4644fc47259cd22d276ef284214a2b39ce021bf0 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 15:55:55 +0100 Subject: [PATCH 005/233] Fix sequence iteration in backends --- src/qibolab/backends.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/qibolab/backends.py b/src/qibolab/backends.py index f17ce97f8..7df62649f 100644 --- a/src/qibolab/backends.py +++ b/src/qibolab/backends.py @@ -69,7 +69,7 @@ def assign_measurements(self, measurement_map, readout): containing the readout measurement shots. This is created in ``execute_circuit``. """ for gate, sequence in measurement_map.items(): - _samples = (readout[pulse.serial].samples for pulse in sequence.pulses) + _samples = (readout[pulse.serial].samples for pulse in sequence) samples = list(filter(lambda x: x is not None, _samples)) gate.result.backend = self gate.result.register_samples(np.array(samples).T) @@ -162,7 +162,7 @@ def execute_circuits(self, circuits, initial_states=None, nshots=1000): ) for gate, sequence in measurement_map.items(): samples = [ - readout[pulse.serial].popleft().samples for pulse in sequence.pulses + readout[pulse.serial].popleft().samples for pulse in sequence ] gate.result.backend = self gate.result.register_samples(np.array(samples).T) From fabb3939796aed472b0d94b270422cd04c6eba7b Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 15:56:24 +0100 Subject: [PATCH 006/233] Fix compiler tests related to sequences --- tests/test_compilers_default.py | 70 ++++++++++++++++----------------- 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index a1a85c366..eaa550b98 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -112,14 +112,14 @@ def test_gpi2_to_sequence(platform): circuit = Circuit(1) circuit.add(gates.GPI2(0, phi=0.2)) sequence = compile_circuit(circuit, platform) - assert len(sequence.pulses) == 1 + assert len(sequence) == 1 assert len(sequence.qd_pulses) == 1 - RX90_pulse = platform.create_RX90_pulse(0, start=0, relative_phase=0.2) - s = PulseSequence(RX90_pulse) + rx90_pulse = platform.create_RX90_pulse(0, start=0, relative_phase=0.2) + s = PulseSequence([rx90_pulse]) - np.testing.assert_allclose(sequence.duration, RX90_pulse.duration) - assert sequence.serial == s.serial + np.testing.assert_allclose(sequence.duration, rx90_pulse.duration) + assert sequence == s def test_u3_to_sequence(platform): @@ -127,19 +127,19 @@ def test_u3_to_sequence(platform): circuit.add(gates.U3(0, 0.1, 0.2, 0.3)) sequence = compile_circuit(circuit, platform) - assert len(sequence.pulses) == 2 + assert len(sequence) == 2 assert len(sequence.qd_pulses) == 2 - RX90_pulse1 = platform.create_RX90_pulse(0, start=0, relative_phase=0.3) - RX90_pulse2 = platform.create_RX90_pulse( - 0, start=RX90_pulse1.finish, relative_phase=0.4 - np.pi + rx90_pulse1 = platform.create_RX90_pulse(0, start=0, relative_phase=0.3) + rx90_pulse2 = platform.create_RX90_pulse( + 0, start=rx90_pulse1.finish, relative_phase=0.4 - np.pi ) - s = PulseSequence(RX90_pulse1, RX90_pulse2) + s = PulseSequence([rx90_pulse1, rx90_pulse2]) np.testing.assert_allclose( - sequence.duration, RX90_pulse1.duration + RX90_pulse2.duration + sequence.duration, rx90_pulse1.duration + rx90_pulse2.duration ) - assert sequence.serial == s.serial + assert sequence == s def test_two_u3_to_sequence(platform): @@ -148,25 +148,25 @@ def test_two_u3_to_sequence(platform): circuit.add(gates.U3(0, 0.4, 0.6, 0.5)) sequence = compile_circuit(circuit, platform) - assert len(sequence.pulses) == 4 + assert len(sequence) == 4 assert len(sequence.qd_pulses) == 4 - RX90_pulse = platform.create_RX90_pulse(0) + rx90_pulse = platform.create_RX90_pulse(0) - np.testing.assert_allclose(sequence.duration, 2 * 2 * RX90_pulse.duration) + np.testing.assert_allclose(sequence.duration, 2 * 2 * rx90_pulse.duration) - RX90_pulse1 = platform.create_RX90_pulse(0, start=0, relative_phase=0.3) - RX90_pulse2 = platform.create_RX90_pulse( - 0, start=RX90_pulse1.finish, relative_phase=0.4 - np.pi + rx90_pulse1 = platform.create_RX90_pulse(0, start=0, relative_phase=0.3) + rx90_pulse2 = platform.create_RX90_pulse( + 0, start=rx90_pulse1.finish, relative_phase=0.4 - np.pi ) - RX90_pulse3 = platform.create_RX90_pulse( - 0, start=RX90_pulse2.finish, relative_phase=1.1 + rx90_pulse3 = platform.create_RX90_pulse( + 0, start=rx90_pulse2.finish, relative_phase=1.1 ) - RX90_pulse4 = platform.create_RX90_pulse( - 0, start=RX90_pulse3.finish, relative_phase=1.5 - np.pi + rx90_pulse4 = platform.create_RX90_pulse( + 0, start=rx90_pulse3.finish, relative_phase=1.5 - np.pi ) - s = PulseSequence(RX90_pulse1, RX90_pulse2, RX90_pulse3, RX90_pulse4) - assert sequence.serial == s.serial + s = PulseSequence([rx90_pulse1, rx90_pulse2, rx90_pulse3, rx90_pulse4]) + assert sequence == s def test_cz_to_sequence(platform): @@ -200,17 +200,17 @@ def test_add_measurement_to_sequence(platform): circuit.add(gates.M(0)) sequence = compile_circuit(circuit, platform) - assert len(sequence.pulses) == 3 + assert len(sequence) == 3 assert len(sequence.qd_pulses) == 2 assert len(sequence.ro_pulses) == 1 - RX90_pulse1 = platform.create_RX90_pulse(0, start=0, relative_phase=0.3) - RX90_pulse2 = platform.create_RX90_pulse( - 0, start=RX90_pulse1.finish, relative_phase=0.4 - np.pi + rx90_pulse1 = platform.create_RX90_pulse(0, start=0, relative_phase=0.3) + rx90_pulse2 = platform.create_RX90_pulse( + 0, start=rx90_pulse1.finish, relative_phase=0.4 - np.pi ) - MZ_pulse = platform.create_MZ_pulse(0, start=RX90_pulse2.finish) - s = PulseSequence(RX90_pulse1, RX90_pulse2, MZ_pulse) - assert sequence.serial == s.serial + mz_pulse = platform.create_MZ_pulse(0, start=rx90_pulse2.finish) + s = PulseSequence([rx90_pulse1, rx90_pulse2, mz_pulse]) + assert sequence == s @pytest.mark.parametrize("delay", [0, 100]) @@ -220,9 +220,9 @@ def test_align_delay_measurement(platform, delay): circuit.add(gates.M(0)) sequence = compile_circuit(circuit, platform) - assert len(sequence.pulses) == 1 + assert len(sequence) == 1 assert len(sequence.ro_pulses) == 1 - MZ_pulse = platform.create_MZ_pulse(0, start=delay) - s = PulseSequence(MZ_pulse) - assert sequence.serial == s.serial + mz_pulse = platform.create_MZ_pulse(0, start=delay) + s = PulseSequence([mz_pulse]) + assert sequence == s From 62c25b044d6f1a6e40e81034225eb7e18d854d98 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 16:42:16 +0100 Subject: [PATCH 007/233] Fix tests directly targeted to pulses --- src/qibolab/pulses.py | 17 ++- tests/test_pulses.py | 249 +++++++++++------------------------------- 2 files changed, 76 insertions(+), 190 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index b411dded0..f6e27a8dc 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -1233,6 +1233,12 @@ class PulseSequence(list): modify any of the properties of its pulses. """ + def __add__(self, other): + return PulseSequence(super().__add__(other)) + + def __mul__(self, other): + return PulseSequence(super().__mul__(other)) + def __repr__(self): return f"{type(self).__name__}({super().__repr__()})" @@ -1378,7 +1384,7 @@ def get_pulse_overlaps(self): # -> dict((int,int): PulseSequence): overlaps[(times[n], times[n + 1])] = PulseSequence() for pulse in self: if (pulse.start <= times[n]) & (pulse.finish >= times[n + 1]): - overlaps[(times[n], times[n + 1])] += pulse + overlaps[(times[n], times[n + 1])] += [pulse] return overlaps def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): @@ -1405,7 +1411,7 @@ def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): stored = True break if not stored: - separated_pulses.append(PulseSequence(new_pulse)) + separated_pulses.append(PulseSequence([new_pulse])) return separated_pulses # TODO: Implement separate_different_frequency_pulses() @@ -1416,8 +1422,9 @@ def pulses_overlap(self) -> bool: overlap = False for pc in self.get_pulse_overlaps().values(): - if pc.count > 1: + if len(pc) > 1: overlap = True + break return overlap def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): @@ -1431,8 +1438,8 @@ def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): import matplotlib.pyplot as plt from matplotlib import gridspec - fig = plt.figure(figsize=(14, 2 * self.count), dpi=200) - gs = gridspec.GridSpec(ncols=1, nrows=self.count) + fig = plt.figure(figsize=(14, 2 * len(self)), dpi=200) + gs = gridspec.GridSpec(ncols=1, nrows=len(self)) vertical_lines = [] for pulse in self: vertical_lines.append(pulse.start) diff --git a/tests/test_pulses.py b/tests/test_pulses.py index d1c925e7c..2a33b1f4e 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -29,7 +29,7 @@ HERE = pathlib.Path(__file__).parent -def test_pulses_plot_functions(): +def test_plot_functions(): p0 = Pulse(0, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) p1 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) p2 = Pulse(0, 40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) @@ -37,7 +37,7 @@ def test_pulses_plot_functions(): p4 = FluxPulse(0, 40, 0.9, SNZ(t_idling=10), 0, 200) p5 = Pulse(0, 40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) p6 = Pulse(0, 40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.DRIVE, 2) - ps = p0 + p1 + p2 + p3 + p4 + p5 + p6 + ps = PulseSequence([p0, p1, p2, p3, p4, p5, p6]) wf = p0.modulated_waveform_i() plot_file = HERE / "test_plot.png" @@ -55,7 +55,7 @@ def test_pulses_plot_functions(): os.remove(plot_file) -def test_pulses_pulse_init(): +def test_pulse_init(): # standard initialisation p0 = Pulse( start=0, @@ -191,7 +191,7 @@ def test_pulses_pulse_init(): assert p12.finish == 5.5 + 34.33 -def test_pulses_pulse_attributes(): +def test_pulse_attributes(): channel = 0 qubit = 0 @@ -234,7 +234,7 @@ def test_pulses_pulse_attributes(): assert p0.finish == 100 -def test_pulses_is_equal_ignoring_start(): +def test_is_equal_ignoring_start(): """Checks if two pulses are equal, not looking at start time.""" p1 = Pulse(0, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) @@ -254,7 +254,7 @@ def test_pulses_is_equal_ignoring_start(): assert not p1.is_equal_ignoring_start(p4) -def test_pulses_pulse_serial(): +def test_pulse_serial(): p11 = Pulse(0, 40, 0.9, 50_000_000, 0, Gaussian(5), 0, PulseType.DRIVE) assert ( p11.serial @@ -266,13 +266,13 @@ def test_pulses_pulse_serial(): @pytest.mark.parametrize( "shape", [Rectangular(), Gaussian(5), GaussianSquare(5, 0.9), Drag(5, 1)] ) -def test_pulses_pulseshape_sampling_rate(shape): +def test_pulseshape_sampling_rate(shape): pulse = Pulse(0, 40, 0.9, 100e6, 0, shape, 0, PulseType.DRIVE) assert len(pulse.envelope_waveform_i(sampling_rate=1).data) == 40 assert len(pulse.envelope_waveform_i(sampling_rate=100).data) == 4000 -def test_pulseshape_eval(): +def testhape_eval(): shape = PulseShape.eval("Rectangular()") assert isinstance(shape, Rectangular) with pytest.raises(ValueError): @@ -331,7 +331,7 @@ def test_raise_shapeiniterror(): shape.envelope_waveform_q() -def test_pulses_pulseshape_drag_shape(): +def test_pulseshape_drag_shape(): pulse = Pulse(0, 2, 1, 4e9, 0, Drag(2, 1), 0, PulseType.DRIVE) # envelope i & envelope q should cross nearly at 0 and at 2 waveform = pulse.envelope_waveform_i(sampling_rate=10).data @@ -362,7 +362,7 @@ def test_pulses_pulseshape_drag_shape(): np.testing.assert_allclose(waveform, target_waveform) -def test_pulses_pulse_hash(): +def test_pulse_hash(): rp = Pulse(0, 40, 0.9, 100e6, 0, Rectangular(), 0, PulseType.DRIVE) dp = Pulse(0, 40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) hash(rp) @@ -383,7 +383,7 @@ def test_pulses_pulse_hash(): assert p1 == p3 -def test_pulses_pulse_aliases(): +def test_pulse_aliases(): rop = ReadoutPulse( start=0, duration=50, @@ -414,7 +414,7 @@ def test_pulses_pulse_aliases(): assert repr(fp) == "FluxPulse(0, 300, 0.9, Rectangular(), 0, 0)" -def test_pulses_pulsesequence_init(): +def test_pulsesequence_init(): p1 = Pulse(400, 40, 0.9, 100e6, 0, Drag(5, 1), 3, PulseType.DRIVE) p2 = Pulse(500, 40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) p3 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) @@ -422,14 +422,14 @@ def test_pulses_pulsesequence_init(): ps = PulseSequence() assert type(ps) == PulseSequence - ps = PulseSequence(p1, p2, p3) - assert ps.count == 3 and len(ps) == 3 + ps = PulseSequence([p1, p2, p3]) + assert len(ps) == 3 assert ps[0] == p1 assert ps[1] == p2 assert ps[2] == p3 - other_ps = p1 + p2 + p3 - assert other_ps.count == 3 and len(other_ps) == 3 + other_ps = PulseSequence([p1, p2, p3]) + assert len(other_ps) == 3 assert other_ps[0] == p1 assert other_ps[1] == p2 assert other_ps[2] == p3 @@ -441,11 +441,11 @@ def test_pulses_pulsesequence_init(): n += 1 -def test_pulses_pulsesequence_operators(): +def test_pulsesequence_operators(): ps = PulseSequence() - ps += ReadoutPulse(800, 200, 0.9, 20e6, 0, Rectangular(), 1) - ps = ps + ReadoutPulse(800, 200, 0.9, 20e6, 0, Rectangular(), 2) - ps = ReadoutPulse(800, 200, 0.9, 20e6, 0, Rectangular(), 3) + ps + ps += [ReadoutPulse(800, 200, 0.9, 20e6, 0, Rectangular(), 1)] + ps = ps + [ReadoutPulse(800, 200, 0.9, 20e6, 0, Rectangular(), 2)] + ps = [ReadoutPulse(800, 200, 0.9, 20e6, 0, Rectangular(), 3)] + ps p4 = Pulse(100, 40, 0.9, 50e6, 0, Gaussian(5), 3, PulseType.DRIVE) p5 = Pulse(200, 40, 0.9, 50e6, 0, Gaussian(5), 2, PulseType.DRIVE) @@ -453,7 +453,7 @@ def test_pulses_pulsesequence_operators(): another_ps = PulseSequence() another_ps.append(p4) - another_ps.append(p5, p6) + another_ps.extend([p5, p6]) assert another_ps[0] == p4 assert another_ps[1] == p5 @@ -461,59 +461,29 @@ def test_pulses_pulsesequence_operators(): ps += another_ps - assert ps.count == 6 + assert len(ps) == 6 assert p5 in ps # ps.plot() p7 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) - yet_another_ps = PulseSequence(p7) - assert yet_another_ps.count == 1 + yet_another_ps = PulseSequence([p7]) + assert len(yet_another_ps) == 1 yet_another_ps *= 3 - assert yet_another_ps.count == 3 + assert len(yet_another_ps) == 3 yet_another_ps *= 3 - assert yet_another_ps.count == 9 + assert len(yet_another_ps) == 9 p8 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) p9 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) - and_yet_another_ps = 2 * p9 + p8 * 3 - assert and_yet_another_ps.count == 5 + and_yet_another_ps = 2 * PulseSequence([p9]) + [p8] * 3 + assert len(and_yet_another_ps) == 5 -def test_pulses_pulsesequence_add(): - p0 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(5), 10, PulseType.DRIVE, 1) - p1 = Pulse(100, 40, 0.9, 50e6, 0, Gaussian(5), 20, PulseType.DRIVE, 2) - - p2 = Pulse(200, 40, 0.9, 50e6, 0, Gaussian(5), 30, PulseType.DRIVE, 3) - p3 = Pulse(400, 40, 0.9, 50e6, 0, Gaussian(5), 40, PulseType.DRIVE, 4) - - ps = PulseSequence() - ps.append(p0) - ps.append(p1) - psx = PulseSequence(p2, p3) - ps.append(psx) - - assert ps.count == 4 - assert ps.qubits == [1, 2, 3, 4] - assert ps.channels == [10, 20, 30, 40] - assert ps.start == 0 - assert ps.finish == 440 - - -def test_pulses_pulsesequence_clear(): - p1 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) - p2 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) - ps = 2 * p2 + p1 * 3 - assert ps.count == 5 - ps.clear() - assert ps.count == 0 - assert ps.is_empty - - -def test_pulses_pulsesequence_start_finish(): +def test_pulsesequence_start_finish(): p1 = Pulse(20, 40, 0.9, 200e6, 0, Drag(5, 1), 1, PulseType.DRIVE) p2 = Pulse(60, 1000, 0.9, 20e6, 0, Rectangular(), 2, PulseType.READOUT) - ps = p1 + p2 + ps = PulseSequence([p1]) + [p2] assert ps.start == p1.start assert ps.finish == p2.finish @@ -523,7 +493,7 @@ def test_pulses_pulsesequence_start_finish(): assert p2.finish is None -def test_pulses_pulsesequence_get_channel_pulses(): +def test_pulsesequence_get_channel_pulses(): p1 = DrivePulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) p2 = ReadoutPulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30) p3 = DrivePulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) @@ -531,15 +501,15 @@ def test_pulses_pulsesequence_get_channel_pulses(): p5 = ReadoutPulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20) p6 = DrivePulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) - ps = PulseSequence(p1, p2, p3, p4, p5, p6) + ps = PulseSequence([p1, p2, p3, p4, p5, p6]) assert ps.channels == [10, 20, 30] - assert ps.get_channel_pulses(10).count == 1 - assert ps.get_channel_pulses(20).count == 2 - assert ps.get_channel_pulses(30).count == 3 - assert ps.get_channel_pulses(20, 30).count == 5 + assert len(ps.get_channel_pulses(10)) == 1 + assert len(ps.get_channel_pulses(20)) == 2 + assert len(ps.get_channel_pulses(30)) == 3 + assert len(ps.get_channel_pulses(20, 30)) == 5 -def test_pulses_pulsesequence_get_qubit_pulses(): +def test_pulsesequence_get_qubit_pulses(): p1 = DrivePulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10, 0) p2 = ReadoutPulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30, 0) p3 = DrivePulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20, 1) @@ -548,15 +518,15 @@ def test_pulses_pulsesequence_get_qubit_pulses(): p6 = FluxPulse(600, 400, 0.9, Rectangular(), 40, 1) p7 = FluxPulse(900, 400, 0.9, Rectangular(), 40, 2) - ps = PulseSequence(p1, p2, p3, p4, p5, p6, p7) + ps = PulseSequence([p1, p2, p3, p4, p5, p6, p7]) assert ps.qubits == [0, 1, 2] - assert ps.get_qubit_pulses(0).count == 2 - assert ps.get_qubit_pulses(1).count == 4 - assert ps.get_qubit_pulses(2).count == 1 - assert ps.get_qubit_pulses(0, 1).count == 6 + assert len(ps.get_qubit_pulses(0)) == 2 + assert len(ps.get_qubit_pulses(1)) == 4 + assert len(ps.get_qubit_pulses(2)) == 1 + assert len(ps.get_qubit_pulses(0, 1)) == 6 -def test_pulses_pulsesequence_pulses_overlap(): +def test_pulsesequence_pulses_overlap(): p1 = DrivePulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) p2 = ReadoutPulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30) p3 = DrivePulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) @@ -564,14 +534,14 @@ def test_pulses_pulsesequence_pulses_overlap(): p5 = ReadoutPulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20) p6 = DrivePulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) - ps = PulseSequence(p1, p2, p3, p4, p5, p6) - assert ps.pulses_overlap == True - assert ps.get_channel_pulses(10).pulses_overlap == False - assert ps.get_channel_pulses(20).pulses_overlap == True - assert ps.get_channel_pulses(30).pulses_overlap == True + ps = PulseSequence([p1, p2, p3, p4, p5, p6]) + assert ps.pulses_overlap + assert not ps.get_channel_pulses(10).pulses_overlap + assert ps.get_channel_pulses(20).pulses_overlap + assert ps.get_channel_pulses(30).pulses_overlap -def test_pulses_pulsesequence_separate_overlapping_pulses(): +def test_pulsesequence_separate_overlapping_pulses(): p1 = DrivePulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) p2 = ReadoutPulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30) p3 = DrivePulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) @@ -579,7 +549,7 @@ def test_pulses_pulsesequence_separate_overlapping_pulses(): p5 = ReadoutPulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20) p6 = DrivePulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) - ps = PulseSequence(p1, p2, p3, p4, p5, p6) + ps = PulseSequence([p1, p2, p3, p4, p5, p6]) n = 70 for segregated_ps in ps.separate_overlapping_pulses(): n += 1 @@ -587,19 +557,22 @@ def test_pulses_pulsesequence_separate_overlapping_pulses(): pulse.channel = n -def test_pulses_pulse_pulse_order(): +def test_pulse_pulse_order(): t0 = 0 t = 0 p1 = DrivePulse(t0, 400, 0.9, 20e6, 0, Gaussian(5), 10) p2 = ReadoutPulse(p1.finish + t, 400, 0.9, 20e6, 0, Rectangular(), 30) p3 = DrivePulse(p2.finish, 400, 0.9, 20e6, 0, Drag(5, 50), 20) - ps1 = p1 + p2 + p3 - ps2 = p3 + p1 + p2 - assert ps1 == ps2 - assert hash(ps1) == hash(ps2) + ps1 = PulseSequence([p1, p2, p3]) + ps2 = PulseSequence([p3, p1, p2]) + + def sortseq(sequence): + return sorted(sequence, key=lambda item: (item.start, item.channel)) + assert sortseq(ps1) == sortseq(ps2) -def test_pulses_waveform(): + +def test_waveform(): wf1 = Waveform(np.ones(100)) wf2 = Waveform(np.zeros(100)) wf3 = Waveform(np.ones(100)) @@ -630,7 +603,7 @@ def modulate( return mod_signals[:, 0], mod_signals[:, 1] -def test_pulses_pulseshape_rectangular(): +def test_pulseshape_rectangular(): pulse = Pulse( start=0, duration=50, @@ -691,7 +664,7 @@ def test_pulses_pulseshape_rectangular(): ) -def test_pulses_pulseshape_gaussian(): +def test_pulseshape_gaussian(): pulse = Pulse( start=0, duration=50, @@ -758,7 +731,7 @@ def test_pulses_pulseshape_gaussian(): ) -def test_pulses_pulseshape_drag(): +def test_pulseshape_drag(): pulse = Pulse( start=0, duration=50, @@ -831,7 +804,7 @@ def test_pulses_pulseshape_drag(): ) -def test_pulses_pulseshape_eq(): +def test_pulseshape_eq(): """Checks == operator for pulse shapes.""" shape1 = Rectangular() @@ -931,100 +904,6 @@ def test_readout_pulse(): assert repr(pulse) == target -def test_pulse_sequence_add(): - sequence = PulseSequence() - sequence.append( - Pulse( - start=0, - frequency=200_000_000, - amplitude=0.3, - duration=60, - relative_phase=0, - shape="Gaussian(5)", - channel=1, - ) - ) - sequence.append( - Pulse( - start=64, - frequency=200_000_000, - amplitude=0.3, - duration=30, - relative_phase=0, - shape="Gaussian(5)", - channel=1, - ) - ) - assert len(sequence.pulses) == 2 - assert len(sequence.qd_pulses) == 2 - - -def test_pulse_sequence__add__(): - sequence = PulseSequence() - sequence.append( - Pulse( - start=0, - frequency=200_000_000, - amplitude=0.3, - duration=60, - relative_phase=0, - shape="Gaussian(5)", - channel=1, - ) - ) - sequence.append( - Pulse( - start=64, - frequency=200_000_000, - amplitude=0.3, - duration=30, - relative_phase=0, - shape="Gaussian(5)", - channel=1, - ) - ) - with pytest.raises(TypeError): - sequence + 2 - with pytest.raises(TypeError): - 2 + sequence - - -def test_pulse_sequence__mul__(): - sequence = PulseSequence() - sequence.append( - Pulse( - start=0, - frequency=200_000_000, - amplitude=0.3, - duration=60, - relative_phase=0, - shape="Gaussian(5)", - channel=1, - ) - ) - sequence.append( - Pulse( - start=64, - frequency=200_000_000, - amplitude=0.3, - duration=30, - relative_phase=0, - shape="Gaussian(5)", - channel=1, - ) - ) - with pytest.raises(TypeError): - sequence * 2.5 - with pytest.raises(TypeError): - sequence *= 2.5 - with pytest.raises(TypeError): - sequence *= -1 - with pytest.raises(TypeError): - sequence * -1 - with pytest.raises(TypeError): - 2.5 * sequence - - def test_pulse_sequence_add_readout(): sequence = PulseSequence() sequence.append( @@ -1063,7 +942,7 @@ def test_pulse_sequence_add_readout(): channel=11, ) ) - assert len(sequence.pulses) == 3 + assert len(sequence) == 3 assert len(sequence.ro_pulses) == 1 assert len(sequence.qd_pulses) == 1 assert len(sequence.qf_pulses) == 1 From 2864467cc429004497eb7edec727170745576799 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 16:47:46 +0100 Subject: [PATCH 008/233] Differentiate append from extend in dummies tests after add remotion --- tests/test_dummy.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/tests/test_dummy.py b/tests/test_dummy.py index 7e41a141f..8900c2fef 100644 --- a/tests/test_dummy.py +++ b/tests/test_dummy.py @@ -79,18 +79,16 @@ def test_dummy_execute_pulse_sequence_couplers(): qubits=(qubit_ordered_pair.qubit1.name, qubit_ordered_pair.qubit2.name), start=0, ) - sequence.append(cz.get_qubit_pulses(qubit_ordered_pair.qubit1.name)) - sequence.append(cz.get_qubit_pulses(qubit_ordered_pair.qubit2.name)) - sequence.append(cz.coupler_pulses(qubit_ordered_pair.coupler.name)) + sequence.extend(cz.get_qubit_pulses(qubit_ordered_pair.qubit1.name)) + sequence.extend(cz.get_qubit_pulses(qubit_ordered_pair.qubit2.name)) + sequence.extend(cz.coupler_pulses(qubit_ordered_pair.coupler.name)) sequence.append(platform.create_qubit_readout_pulse(0, 40)) sequence.append(platform.create_qubit_readout_pulse(2, 40)) options = ExecutionParameters(nshots=None) result = platform.execute_pulse_sequence(sequence, options) - test_pulses = "PulseSequence\nFluxPulse(0, 30, 0.05, GaussianSquare(5, 0.75), flux-2, 2)\nCouplerFluxPulse(0, 30, 0.05, GaussianSquare(5, 0.75), flux_coupler-1, 1)" test_phases = {1: 0.0, 2: 0.0} - assert test_pulses == cz.serial assert test_phases == cz_phases From b3b09934125908626f27adb997d567ff656f08f8 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 16:53:27 +0100 Subject: [PATCH 009/233] Wrap default copy, fix rfsoc tests --- src/qibolab/instruments/rfsoc/convert.py | 2 +- src/qibolab/pulses.py | 7 +++++-- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/src/qibolab/instruments/rfsoc/convert.py b/src/qibolab/instruments/rfsoc/convert.py index b15168206..34773489e 100644 --- a/src/qibolab/instruments/rfsoc/convert.py +++ b/src/qibolab/instruments/rfsoc/convert.py @@ -97,7 +97,7 @@ def _( """Convert PulseSequence to list of rfosc pulses with relative time.""" last_pulse_start = 0 list_sequence = [] - for pulse in sorted(sequence.pulses, key=lambda item: item.start): + for pulse in sorted(sequence, key=lambda item: item.start): start_delay = (pulse.start - last_pulse_start) * NS_TO_US pulse_dict = asdict(convert(pulse, qubits, start_delay, sampling_rate)) list_sequence.append(pulse_dict) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index f6e27a8dc..edd7b8351 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -1234,14 +1234,17 @@ class PulseSequence(list): """ def __add__(self, other): - return PulseSequence(super().__add__(other)) + return type(self)(super().__add__(other)) def __mul__(self, other): - return PulseSequence(super().__mul__(other)) + return type(self)(super().__mul__(other)) def __repr__(self): return f"{type(self).__name__}({super().__repr__()})" + def copy(self): + return type(self)(super().copy()) + @property def ro_pulses(self): """Returns a new PulseSequence containing only its readout pulses.""" From f6172786b277552e5525e4689b58c1fa556ab126 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 16:55:13 +0100 Subject: [PATCH 010/233] Add docstrings to wrapper methods --- src/qibolab/pulses.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index edd7b8351..7db74b38a 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -1234,15 +1234,19 @@ class PulseSequence(list): """ def __add__(self, other): + """Return self+value.""" return type(self)(super().__add__(other)) def __mul__(self, other): + """Return self*value.""" return type(self)(super().__mul__(other)) def __repr__(self): + """Return repr(self).""" return f"{type(self).__name__}({super().__repr__()})" def copy(self): + """Return a shallow copy of the sequence.""" return type(self)(super().copy()) @property From 9ad0c12838e442bcf9f377c9dbfea9bc35a98db0 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 16:57:47 +0100 Subject: [PATCH 011/233] Fix QM tests --- tests/test_instruments_qm.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index fee863370..33f61c23c 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -456,8 +456,8 @@ def test_qm_qubit_spectroscopy(mocker, qmplatform): ro_pulses[qubit] = platform.create_qubit_readout_pulse( qubit, start=qd_pulses[qubit].finish ) - sequence.add(qd_pulses[qubit]) - sequence.add(ro_pulses[qubit]) + sequence.append(qd_pulses[qubit]) + sequence.append(ro_pulses[qubit]) options = ExecutionParameters(nshots=1024, relaxation_time=100000) result = controller.play(platform.qubits, platform.couplers, sequence, options) @@ -471,8 +471,8 @@ def test_qm_duration_sweeper(mocker, qmplatform): qubit = 1 sequence = PulseSequence() qd_pulse = platform.create_RX_pulse(qubit, start=0) - sequence.add(qd_pulse) - sequence.add(platform.create_MZ_pulse(qubit, start=qd_pulse.finish)) + sequence.append(qd_pulse) + sequence.append(platform.create_MZ_pulse(qubit, start=qd_pulse.finish)) sweeper = Sweeper(Parameter.duration, np.arange(2, 12, 2), pulses=[qd_pulse]) options = ExecutionParameters(nshots=1024, relaxation_time=100000) if platform.name == "qm": From 4ebc32d921cfbec2f720b3f7229dd4c210002694 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 17:17:05 +0100 Subject: [PATCH 012/233] Fix doctests, first batch --- doc/source/getting-started/experiment.rst | 2 +- doc/source/tutorials/compiler.rst | 2 +- doc/source/tutorials/pulses.rst | 6 +++--- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/source/getting-started/experiment.rst b/doc/source/getting-started/experiment.rst index 8317fbcc5..af50ab09a 100644 --- a/doc/source/getting-started/experiment.rst +++ b/doc/source/getting-started/experiment.rst @@ -194,7 +194,7 @@ We leave to the dedicated tutorial a full explanation of the experiment, but her # define the pulse sequence sequence = PulseSequence() ro_pulse = platform.create_MZ_pulse(qubit=0, start=0) - sequence.add(ro_pulse) + sequence.append(ro_pulse) # define a sweeper for a frequency scan sweeper = Sweeper( diff --git a/doc/source/tutorials/compiler.rst b/doc/source/tutorials/compiler.rst index a445e8e7f..33d8edb67 100644 --- a/doc/source/tutorials/compiler.rst +++ b/doc/source/tutorials/compiler.rst @@ -84,7 +84,7 @@ The following example shows how to modify the compiler in order to execute a cir """X gate applied with a single pi-pulse.""" qubit = gate.target_qubits[0] sequence = PulseSequence() - sequence.add(platform.create_RX_pulse(qubit, start=0)) + sequence.append(platform.create_RX_pulse(qubit, start=0)) return sequence, {} diff --git a/doc/source/tutorials/pulses.rst b/doc/source/tutorials/pulses.rst index 51fa61d76..6fdab05e1 100644 --- a/doc/source/tutorials/pulses.rst +++ b/doc/source/tutorials/pulses.rst @@ -4,7 +4,7 @@ Pulses execution First, we create the pulse sequence that will be executed. We can do this by defining a :class:`qibolab.pulses.PulseSequence` object and adding different pulses (:class:`qibolab.pulses.Pulse`) through the -:func:`qibolab.pulses.PulseSequence.add()` method: +:func:`qibolab.pulses.PulseSequence.append()` method: .. testcode:: python @@ -20,7 +20,7 @@ pulses (:class:`qibolab.pulses.Pulse`) through the sequence = PulseSequence() # Add some pulses to the pulse sequence - sequence.add( + sequence.append( DrivePulse( start=0, frequency=200000000, @@ -31,7 +31,7 @@ pulses (:class:`qibolab.pulses.Pulse`) through the qubit=0, ) ) - sequence.add( + sequence.append( ReadoutPulse( start=70, frequency=20000000.0, From 9e741a584d7949daf520cb8baa25e364deff237e Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 17:27:36 +0100 Subject: [PATCH 013/233] Fix remaining doctests --- doc/source/main-documentation/qibolab.rst | 42 +++++++++++++---------- doc/source/tutorials/calibration.rst | 12 +++---- 2 files changed, 30 insertions(+), 24 deletions(-) diff --git a/doc/source/main-documentation/qibolab.rst b/doc/source/main-documentation/qibolab.rst index 13715609f..8635dfa68 100644 --- a/doc/source/main-documentation/qibolab.rst +++ b/doc/source/main-documentation/qibolab.rst @@ -64,9 +64,9 @@ Now we can create a simple sequence (again, without explicitly giving any qubit from qibolab.pulses import PulseSequence ps = PulseSequence() - ps.add(platform.create_RX_pulse(qubit=0, start=0)) # start time is in ns - ps.add(platform.create_RX_pulse(qubit=0, start=100)) - ps.add(platform.create_MZ_pulse(qubit=0, start=200)) + ps.append(platform.create_RX_pulse(qubit=0, start=0)) # start time is in ns + ps.append(platform.create_RX_pulse(qubit=0, start=100)) + ps.append(platform.create_MZ_pulse(qubit=0, start=200)) Now we can execute the sequence on hardware: @@ -380,15 +380,15 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P channel="channel", qubit=0, ) - sequence.add(pulse1) - sequence.add(pulse2) - sequence.add(pulse3) - sequence.add(pulse4) + sequence.append(pulse1) + sequence.append(pulse2) + sequence.append(pulse3) + sequence.append(pulse4) print(f"Total duration: {sequence.duration}") sequence_ch1 = sequence.get_channel_pulses("channel1") # Selecting pulses on channel 1 - print(f"We have {sequence_ch1.count} pulses on channel 1.") + print(f"We have {len(sequence_ch1)} pulses on channel 1.") .. testoutput:: python :hide: @@ -416,8 +416,8 @@ Typical experiments may include both pre-defined pulses and new ones: from qibolab.pulses import Rectangular sequence = PulseSequence() - sequence.add(platform.create_RX_pulse(0)) - sequence.add( + sequence.append(platform.create_RX_pulse(0)) + sequence.append( DrivePulse( start=0, duration=10, @@ -428,7 +428,7 @@ Typical experiments may include both pre-defined pulses and new ones: channel="0", ) ) - sequence.add(platform.create_MZ_pulse(0, start=0)) + sequence.append(platform.create_MZ_pulse(0, start=0)) results = platform.execute_pulse_sequence(sequence, options=options) @@ -500,9 +500,15 @@ A tipical resonator spectroscopy experiment could be defined with: from qibolab.sweeper import Parameter, Sweeper, SweeperType sequence = PulseSequence() - sequence.add(platform.create_MZ_pulse(0, start=0)) # readout pulse for qubit 0 at 4 GHz - sequence.add(platform.create_MZ_pulse(1, start=0)) # readout pulse for qubit 1 at 5 GHz - sequence.add(platform.create_MZ_pulse(2, start=0)) # readout pulse for qubit 2 at 6 GHz + sequence.append( + platform.create_MZ_pulse(0, start=0) + ) # readout pulse for qubit 0 at 4 GHz + sequence.append( + platform.create_MZ_pulse(1, start=0) + ) # readout pulse for qubit 1 at 5 GHz + sequence.append( + platform.create_MZ_pulse(2, start=0) + ) # readout pulse for qubit 2 at 6 GHz sweeper = Sweeper( parameter=Parameter.frequency, @@ -537,8 +543,8 @@ For example: sequence = PulseSequence() - sequence.add(platform.create_RX_pulse(0)) - sequence.add(platform.create_MZ_pulse(0, start=sequence[0].finish)) + sequence.append(platform.create_RX_pulse(0)) + sequence.append(platform.create_MZ_pulse(0, start=sequence[0].finish)) sweeper_freq = Sweeper( parameter=Parameter.frequency, @@ -635,8 +641,8 @@ Let's now delve into a typical use case for result objects within the qibolab fr measurement_pulse = platform.create_qubit_readout_pulse(0, start=0) sequence = PulseSequence() - sequence.add(drive_pulse_1) - sequence.add(measurement_pulse) + sequence.append(drive_pulse_1) + sequence.append(measurement_pulse) options = ExecutionParameters( nshots=1000, diff --git a/doc/source/tutorials/calibration.rst b/doc/source/tutorials/calibration.rst index 2da46cd39..206597074 100644 --- a/doc/source/tutorials/calibration.rst +++ b/doc/source/tutorials/calibration.rst @@ -44,7 +44,7 @@ around the pre-defined frequency. # create pulse sequence and add pulse sequence = PulseSequence() readout_pulse = platform.create_MZ_pulse(qubit=0, start=0) - sequence.add(readout_pulse) + sequence.append(readout_pulse) # allocate frequency sweeper sweeper = Sweeper( @@ -127,8 +127,8 @@ complex pulse sequence. Therefore with start with that: drive_pulse.duration = 2000 drive_pulse.amplitude = 0.01 readout_pulse = platform.create_MZ_pulse(qubit=0, start=drive_pulse.finish) - sequence.add(drive_pulse) - sequence.add(readout_pulse) + sequence.append(drive_pulse) + sequence.append(readout_pulse) # allocate frequency sweeper sweeper = Sweeper( @@ -220,13 +220,13 @@ and its impact on qubit states in the IQ plane. one_sequence = PulseSequence() drive_pulse = platform.create_RX_pulse(qubit=0, start=0) readout_pulse1 = platform.create_MZ_pulse(qubit=0, start=drive_pulse.finish) - one_sequence.add(drive_pulse) - one_sequence.add(readout_pulse1) + one_sequence.append(drive_pulse) + one_sequence.append(readout_pulse1) # create pulse sequence 2 and add pulses zero_sequence = PulseSequence() readout_pulse2 = platform.create_MZ_pulse(qubit=0, start=0) - zero_sequence.add(readout_pulse2) + zero_sequence.append(readout_pulse2) options = ExecutionParameters( nshots=1000, From ebe321dfcffe6b2e50cd3436e2997a5ac429d71c Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Jan 2024 15:56:24 +0100 Subject: [PATCH 014/233] Fix compiler tests related to sequences --- tests/test_compilers_default.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index eaa550b98..2e856f6e6 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -191,7 +191,7 @@ def test_cnot_to_sequence(): sequence = compile_circuit(circuit, platform) test_sequence, virtual_z_phases = platform.create_CNOT_pulse_sequence((2, 3)) assert len(sequence) == len(test_sequence) - assert sequence.pulses[0] == test_sequence.pulses[0] + assert sequence[0] == test_sequence[0] def test_add_measurement_to_sequence(platform): From 48f5aaec6e8e532263101d8678820e904a43c00c Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 12 Jan 2024 16:25:00 +0100 Subject: [PATCH 015/233] Drop pulse.serial --- examples/pulses_tutorial.ipynb | 4 ++-- src/qibolab/backends.py | 6 ++---- src/qibolab/instruments/dummy.py | 4 ++-- .../instruments/qblox/cluster_qrm_rf.py | 12 ++++++------ src/qibolab/instruments/qblox/controller.py | 18 +++++++++--------- src/qibolab/instruments/qm/sequence.py | 2 +- src/qibolab/instruments/qm/sweepers.py | 12 ++++++------ src/qibolab/instruments/rfsoc/convert.py | 2 +- src/qibolab/instruments/rfsoc/driver.py | 6 +++--- src/qibolab/platform/platform.py | 4 ++-- src/qibolab/pulses.py | 8 +------- tests/test_dummy.py | 12 ++++++------ tests/test_instruments_zhinst.py | 2 -- tests/test_platform.py | 2 +- tests/test_pulses.py | 4 ++-- 15 files changed, 44 insertions(+), 54 deletions(-) diff --git a/examples/pulses_tutorial.ipynb b/examples/pulses_tutorial.ipynb index 6076a7166..33c9fa88b 100644 --- a/examples/pulses_tutorial.ipynb +++ b/examples/pulses_tutorial.ipynb @@ -1129,7 +1129,7 @@ "outputs": [], "source": [ "for pulse in ps1.pulses:\n", - " print(pulse.serial)" + " print(pulse.id)" ] }, { @@ -1139,7 +1139,7 @@ "outputs": [], "source": [ "for pulse in ps2.pulses:\n", - " print(pulse.serial)" + " print(pulse.id)" ] }, { diff --git a/src/qibolab/backends.py b/src/qibolab/backends.py index 7df62649f..fc288fb6a 100644 --- a/src/qibolab/backends.py +++ b/src/qibolab/backends.py @@ -69,7 +69,7 @@ def assign_measurements(self, measurement_map, readout): containing the readout measurement shots. This is created in ``execute_circuit``. """ for gate, sequence in measurement_map.items(): - _samples = (readout[pulse.serial].samples for pulse in sequence) + _samples = (readout[pulse.id].samples for pulse in sequence) samples = list(filter(lambda x: x is not None, _samples)) gate.result.backend = self gate.result.register_samples(np.array(samples).T) @@ -161,9 +161,7 @@ def execute_circuits(self, circuits, initial_states=None, nshots=1000): MeasurementOutcomes(circuit.measurements, self, nshots=nshots) ) for gate, sequence in measurement_map.items(): - samples = [ - readout[pulse.serial].popleft().samples for pulse in sequence - ] + samples = [readout[pulse.id].popleft().samples for pulse in sequence] gate.result.backend = self gate.result.register_samples(np.array(samples).T) return results diff --git a/src/qibolab/instruments/dummy.py b/src/qibolab/instruments/dummy.py index ed78c2b76..bdbdda8e5 100644 --- a/src/qibolab/instruments/dummy.py +++ b/src/qibolab/instruments/dummy.py @@ -129,7 +129,7 @@ def play( for ro_pulse in sequence.ro_pulses: values = np.squeeze(self.get_values(options, ro_pulse, shape)) - results[ro_pulse.qubit] = results[ro_pulse.serial] = options.results_type( + results[ro_pulse.qubit] = results[ro_pulse.id] = options.results_type( values ) @@ -154,7 +154,7 @@ def sweep( for ro_pulse in sequence.ro_pulses: values = self.get_values(options, ro_pulse, shape) - results[ro_pulse.qubit] = results[ro_pulse.serial] = options.results_type( + results[ro_pulse.qubit] = results[ro_pulse.id] = options.results_type( values ) diff --git a/src/qibolab/instruments/qblox/cluster_qrm_rf.py b/src/qibolab/instruments/qblox/cluster_qrm_rf.py index 91c2ce179..63322f5d2 100644 --- a/src/qibolab/instruments/qblox/cluster_qrm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qrm_rf.py @@ -616,7 +616,7 @@ def process_pulse_sequence( # Acquisitions pulse = None for acquisition_index, pulse in enumerate(sequencer.pulses.ro_pulses): - sequencer.acquisitions[pulse.serial] = { + sequencer.acquisitions[pulse.id] = { "num_bins": num_bins, "index": acquisition_index, } @@ -993,9 +993,9 @@ def acquire(self): if len(sequencer.pulses.ro_pulses) == 1: pulse = sequencer.pulses.ro_pulses[0] frequency = self.get_if(pulse) - acquisitions[pulse.qubit] = acquisitions[pulse.serial] = ( - AveragedAcquisition(scope, duration, frequency) - ) + acquisitions[pulse.qubit] = acquisitions[ + pulse.id + ] = AveragedAcquisition(scope, duration, frequency) else: raise RuntimeError( "Software Demodulation only supports one acquisition per channel. " @@ -1004,8 +1004,8 @@ def acquire(self): else: # Hardware Demodulation results = self.device.get_acquisitions(sequencer.number) for pulse in sequencer.pulses.ro_pulses: - bins = results[pulse.serial]["acquisition"]["bins"] - acquisitions[pulse.qubit] = acquisitions[pulse.serial] = ( + bins = results[pulse.id]["acquisition"]["bins"] + acquisitions[pulse.qubit] = acquisitions[pulse.id] = ( DemodulatedAcquisition(scope, bins, duration) ) diff --git a/src/qibolab/instruments/qblox/controller.py b/src/qibolab/instruments/qblox/controller.py index f12567758..80acc322a 100644 --- a/src/qibolab/instruments/qblox/controller.py +++ b/src/qibolab/instruments/qblox/controller.py @@ -205,17 +205,17 @@ def _execute_pulse_sequence( shots_shape = (nshots,) + shape for ro_pulse in sequence.ro_pulses: if options.acquisition_type is AcquisitionType.DISCRIMINATION: - _res = acquisition_results[ro_pulse.serial].classified + _res = acquisition_results[ro_pulse.id].classified _res = np.reshape(_res, shots_shape) if options.averaging_mode is not AveragingMode.SINGLESHOT: _res = np.mean(_res, axis=0) elif options.acquisition_type is AcquisitionType.RAW: - i_raw = acquisition_results[ro_pulse.serial].raw_i - q_raw = acquisition_results[ro_pulse.serial].raw_q + i_raw = acquisition_results[ro_pulse.id].raw_i + q_raw = acquisition_results[ro_pulse.id].raw_q _res = i_raw + 1j * q_raw elif options.acquisition_type is AcquisitionType.INTEGRATION: - ires = acquisition_results[ro_pulse.serial].shots_i - qres = acquisition_results[ro_pulse.serial].shots_q + ires = acquisition_results[ro_pulse.id].shots_i + qres = acquisition_results[ro_pulse.id].shots_q _res = ires + 1j * qres if options.averaging_mode is AveragingMode.SINGLESHOT: _res = np.reshape(_res, shots_shape) @@ -223,7 +223,7 @@ def _execute_pulse_sequence( _res = np.reshape(_res, shape) acquisition = options.results_type(np.squeeze(_res)) - data[ro_pulse.serial] = data[ro_pulse.qubit] = acquisition + data[ro_pulse.id] = data[ro_pulse.qubit] = acquisition return data @@ -285,7 +285,7 @@ def sweep( # create a map between the pulse id, which never changes, and the original serial for pulse in sequence_copy.ro_pulses: - map_id_serial[pulse.id] = pulse.serial + map_id_serial[pulse.id] = pulse.id id_results[pulse.id] = None id_results[pulse.qubit] = None @@ -397,9 +397,9 @@ def _sweep_recursion( ) for pulse in sequence.ro_pulses: if results[pulse.id]: - results[pulse.id] += result[pulse.serial] + results[pulse.id] += result[pulse.id] else: - results[pulse.id] = result[pulse.serial] + results[pulse.id] = result[pulse.id] results[pulse.qubit] = results[pulse.id] else: # rt sweeps diff --git a/src/qibolab/instruments/qm/sequence.py b/src/qibolab/instruments/qm/sequence.py index 0e4fa58af..fdce211cc 100644 --- a/src/qibolab/instruments/qm/sequence.py +++ b/src/qibolab/instruments/qm/sequence.py @@ -221,7 +221,7 @@ def _find_previous(self, pulse): def add(self, qmpulse: QMPulse): pulse = qmpulse.pulse - self.pulse_to_qmpulse[pulse.serial] = qmpulse + self.pulse_to_qmpulse[pulse.id] = qmpulse previous = self._find_previous(pulse) if previous is not None: diff --git a/src/qibolab/instruments/qm/sweepers.py b/src/qibolab/instruments/qm/sweepers.py index 3110e1f3d..1e35e71fe 100644 --- a/src/qibolab/instruments/qm/sweepers.py +++ b/src/qibolab/instruments/qm/sweepers.py @@ -32,7 +32,7 @@ def _update_baked_pulses(sweeper, qmsequence, config): qmpulse = qmsequence.pulse_to_qmpulse[sweeper.pulses[0].serial] is_baked = isinstance(qmpulse, BakedPulse) for pulse in sweeper.pulses: - qmpulse = qmsequence.pulse_to_qmpulse[pulse.serial] + qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] if isinstance(qmpulse, BakedPulse): if not is_baked: raise_error( @@ -96,7 +96,7 @@ def _sweep_frequency(sweepers, qubits, qmsequence, relaxation_time): f = declare(int) with for_(*from_array(f, sweeper.values.astype(int))): for pulse, f0 in zip(sweeper.pulses, freqs0): - qmpulse = qmsequence.pulse_to_qmpulse[pulse.serial] + qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] qua.update_frequency(qmpulse.element, f + f0) _sweep_recursion(sweepers[1:], qubits, qmsequence, relaxation_time) @@ -115,7 +115,7 @@ def _sweep_amplitude(sweepers, qubits, qmsequence, relaxation_time): a = declare(fixed) with for_(*from_array(a, sweeper.values)): for pulse in sweeper.pulses: - qmpulse = qmsequence.pulse_to_qmpulse[pulse.serial] + qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] if isinstance(qmpulse, BakedPulse): qmpulse.amplitude = a else: @@ -129,7 +129,7 @@ def _sweep_relative_phase(sweepers, qubits, qmsequence, relaxation_time): relphase = declare(fixed) with for_(*from_array(relphase, sweeper.values / (2 * np.pi))): for pulse in sweeper.pulses: - qmpulse = qmsequence.pulse_to_qmpulse[pulse.serial] + qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] qmpulse.relative_phase = relphase _sweep_recursion(sweepers[1:], qubits, qmsequence, relaxation_time) @@ -169,7 +169,7 @@ def _sweep_start(sweepers, qubits, qmsequence, relaxation_time): with loop: for pulse in sweeper.pulses: - qmpulse = qmsequence.pulse_to_qmpulse[pulse.serial] + qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] # find all pulses that are connected to ``qmpulse`` and update their starts to_process = {qmpulse} while to_process: @@ -191,7 +191,7 @@ def _sweep_duration(sweepers, qubits, qmsequence, relaxation_time): dur = declare(int) with for_(*from_array(dur, values)): for pulse in sweeper.pulses: - qmpulse = qmsequence.pulse_to_qmpulse[pulse.serial] + qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] qmpulse.swept_duration = dur # find all pulses that are connected to ``qmpulse`` and align them if not isinstance(qmpulse, BakedPulse): diff --git a/src/qibolab/instruments/rfsoc/convert.py b/src/qibolab/instruments/rfsoc/convert.py index 34773489e..3162f34bb 100644 --- a/src/qibolab/instruments/rfsoc/convert.py +++ b/src/qibolab/instruments/rfsoc/convert.py @@ -124,7 +124,7 @@ def _( duration=pulse.duration * NS_TO_US, dac=dac, adc=adc, - name=pulse.serial, + name=pulse.id, type=pulse_type, ) return replace_pulse_shape(rfsoc_pulse, pulse.shape, sampling_rate) diff --git a/src/qibolab/instruments/rfsoc/driver.py b/src/qibolab/instruments/rfsoc/driver.py index bb8ae3442..3cad39f7c 100644 --- a/src/qibolab/instruments/rfsoc/driver.py +++ b/src/qibolab/instruments/rfsoc/driver.py @@ -279,7 +279,7 @@ def play( ) else: result = execution_parameters.results_type(i_pulse + 1j * q_pulse) - results[ro_pulse.qubit] = results[ro_pulse.serial] = result + results[ro_pulse.qubit] = results[ro_pulse.id] = result return results @@ -329,7 +329,7 @@ def play_sequence_in_sweep_recursion( """ res = self.play(qubits, couplers, sequence, execution_parameters) newres = {} - serials = [pulse.serial for pulse in or_sequence.ro_pulses] + serials = [pulse.id for pulse in or_sequence.ro_pulses] for idx, key in enumerate(res): if idx % 2 == 1: newres[serials[idx // 2]] = res[key] @@ -537,7 +537,7 @@ def convert_sweep_results( else: result = execution_parameters.results_type(i_vals + 1j * q_vals) - results[ro_pulse.qubit] = results[ro_pulse.serial] = result + results[ro_pulse.qubit] = results[ro_pulse.id] = result return results def sweep( diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 317bc9793..ef2e51af3 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -49,7 +49,7 @@ def unroll_sequences( new_pulse.start += start total_sequence.append(new_pulse) if isinstance(pulse, ReadoutPulse): - readout_map[pulse.serial].append(new_pulse.serial) + readout_map[pulse.id].append(new_pulse.id) start = total_sequence.finish + relaxation_time return total_sequence, readout_map @@ -234,7 +234,7 @@ def execute_pulse_sequences( # find readout pulses ro_pulses = { - pulse.serial: pulse.qubit + pulse.id: pulse.qubit for sequence in sequences for pulse in sequence.ro_pulses } diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 7db74b38a..b92585eec 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -168,7 +168,7 @@ def modulated_waveforms(self, sampling_rate=SAMPLING_RATE): pulse = self.pulse if abs(pulse._if) * 2 > sampling_rate: log.info( - f"WARNING: The frequency of pulse {pulse.serial} is higher than the nyqusit frequency ({int(sampling_rate // 2)}) for the device sampling rate: {int(sampling_rate)}" + f"WARNING: The frequency of pulse {pulse.id} is higher than the nyqusit frequency ({int(sampling_rate // 2)}) for the device sampling rate: {int(sampling_rate)}" ) num_samples = int(np.rint(pulse.duration * sampling_rate)) time = np.arange(num_samples) / sampling_rate @@ -818,12 +818,6 @@ def phase(self) -> float: """ return self.global_phase + self.relative_phase - @property - def serial(self) -> str: - """Returns a string representation of the pulse.""" - - return f"Pulse({self.start}, {self.duration}, {format(self.amplitude, '.6f').rstrip('0').rstrip('.')}, {format(self.frequency, '_')}, {format(self.relative_phase, '.6f').rstrip('0').rstrip('.')}, {self.shape}, {self.channel}, {self.type}, {self.qubit})" - @property def id(self) -> int: return id(self) diff --git a/tests/test_dummy.py b/tests/test_dummy.py index 8900c2fef..f0abb0559 100644 --- a/tests/test_dummy.py +++ b/tests/test_dummy.py @@ -65,7 +65,7 @@ def test_dummy_execute_coupler_pulse(): "CouplerFluxPulse(0, 30, 0.05, GaussianSquare(5, 0.75), flux_coupler-0, 0)" ) - assert test_pulse == pulse.serial + assert test_pulse == pulse.id def test_dummy_execute_pulse_sequence_couplers(): @@ -141,7 +141,7 @@ def test_dummy_single_sweep_raw(name): acquisition_type=AcquisitionType.RAW, ) results = platform.sweep(sequence, options, sweeper) - assert pulse.serial and pulse.qubit in results + assert pulse.id and pulse.qubit in results shape = results[pulse.qubit].magnitude.shape assert shape == (pulse.duration * SWEPT_POINTS,) @@ -187,7 +187,7 @@ def test_dummy_single_sweep_coupler( average = not options.averaging_mode is AveragingMode.SINGLESHOT results = platform.sweep(sequence, options, sweeper) - assert ro_pulse.serial and ro_pulse.qubit in results + assert ro_pulse.id and ro_pulse.qubit in results if average: results_shape = ( results[ro_pulse.qubit].magnitude.shape @@ -233,7 +233,7 @@ def test_dummy_single_sweep(name, fast_reset, parameter, average, acquisition, n average = not options.averaging_mode is AveragingMode.SINGLESHOT results = platform.sweep(sequence, options, sweeper) - assert pulse.serial and pulse.qubit in results + assert pulse.id and pulse.qubit in results if average: results_shape = ( results[pulse.qubit].magnitude.shape @@ -292,7 +292,7 @@ def test_dummy_double_sweep(name, parameter1, parameter2, average, acquisition, average = not options.averaging_mode is AveragingMode.SINGLESHOT results = platform.sweep(sequence, options, sweeper1, sweeper2) - assert ro_pulse.serial and ro_pulse.qubit in results + assert ro_pulse.id and ro_pulse.qubit in results if average: results_shape = ( @@ -356,7 +356,7 @@ def test_dummy_single_sweep_multiplex(name, parameter, average, acquisition, nsh results = platform.sweep(sequence, options, sweeper1) for ro_pulse in ro_pulses.values(): - assert ro_pulse.serial and ro_pulse.qubit in results + assert ro_pulse.id and ro_pulse.qubit in results if average: results_shape = ( results[ro_pulse.qubit].magnitude.shape diff --git a/tests/test_instruments_zhinst.py b/tests/test_instruments_zhinst.py index 36d740d38..41da1a18e 100644 --- a/tests/test_instruments_zhinst.py +++ b/tests/test_instruments_zhinst.py @@ -18,9 +18,7 @@ from qibolab.pulses import ( IIR, SNZ, - CouplerFluxPulse, Drag, - FluxPulse, Gaussian, Pulse, PulseSequence, diff --git a/tests/test_platform.py b/tests/test_platform.py index 0f575693d..f0f69753a 100644 --- a/tests/test_platform.py +++ b/tests/test_platform.py @@ -50,7 +50,7 @@ def test_unroll_sequences(platform): assert len(total_sequence.ro_pulses) == 10 assert total_sequence.finish == 10 * sequence.finish + 90000 assert len(readouts) == 1 - assert len(readouts[ro_pulse.serial]) == 10 + assert len(readouts[ro_pulse.id]) == 10 def test_create_platform(platform): diff --git a/tests/test_pulses.py b/tests/test_pulses.py index 2a33b1f4e..e7dc8419b 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -883,7 +883,7 @@ def test_pulse(): ) target = f"Pulse({pulse.start}, {pulse.duration}, {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, {format(pulse.frequency, '_')}, {format(pulse.relative_phase, '.6f').rstrip('0').rstrip('.')}, {pulse.shape}, {pulse.channel}, {pulse.type}, {pulse.qubit})" - assert pulse.serial == target + assert pulse.id == target assert repr(pulse) == target @@ -900,7 +900,7 @@ def test_readout_pulse(): ) target = f"ReadoutPulse({pulse.start}, {pulse.duration}, {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, {format(pulse.frequency, '_')}, {format(pulse.relative_phase, '.6f').rstrip('0').rstrip('.')}, {pulse.shape}, {pulse.channel}, {pulse.qubit})" - assert pulse.serial == target + assert pulse.id == target assert repr(pulse) == target From 3f23067524d1a427dc4c72f831981525388ae929 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 12 Jan 2024 17:36:41 +0100 Subject: [PATCH 016/233] Replace even more instances of direct serial access --- doc/source/getting-started/experiment.rst | 2 +- doc/source/tutorials/calibration.rst | 12 ++-- examples/pulses_tutorial.ipynb | 4 +- tests/test_instruments_rfsoc.py | 84 +++++++++-------------- tests/test_instruments_zhinst.py | 4 +- tests/test_pulses.py | 9 --- 6 files changed, 43 insertions(+), 72 deletions(-) diff --git a/doc/source/getting-started/experiment.rst b/doc/source/getting-started/experiment.rst index af50ab09a..283315ba3 100644 --- a/doc/source/getting-started/experiment.rst +++ b/doc/source/getting-started/experiment.rst @@ -215,7 +215,7 @@ We leave to the dedicated tutorial a full explanation of the experiment, but her results = platform.sweep(sequence, options, sweeper) # plot the results - amplitudes = results[ro_pulse.serial].magnitude + amplitudes = results[ro_pulse.id].magnitude frequencies = np.arange(-2e8, +2e8, 1e6) + ro_pulse.frequency plt.title("Resonator Spectroscopy") diff --git a/doc/source/tutorials/calibration.rst b/doc/source/tutorials/calibration.rst index 206597074..6f492bbc6 100644 --- a/doc/source/tutorials/calibration.rst +++ b/doc/source/tutorials/calibration.rst @@ -73,7 +73,7 @@ In few seconds, the experiment will be finished and we can proceed to plot it. import matplotlib.pyplot as plt - amplitudes = results[readout_pulse.serial].magnitude + amplitudes = results[readout_pulse.id].magnitude frequencies = np.arange(-2e8, +2e8, 1e6) + readout_pulse.frequency plt.title("Resonator Spectroscopy") @@ -154,7 +154,7 @@ We can now proceed to launch on hardware: results = platform.sweep(sequence, options, sweeper) - amplitudes = results[readout_pulse.serial].magnitude + amplitudes = results[readout_pulse.id].magnitude frequencies = np.arange(-2e8, +2e8, 1e6) + drive_pulse.frequency plt.title("Resonator Spectroscopy") @@ -242,13 +242,13 @@ and its impact on qubit states in the IQ plane. plt.xlabel("I [a.u.]") plt.ylabel("Q [a.u.]") plt.scatter( - results_one[readout_pulse1.serial].voltage_i, - results_one[readout_pulse1.serial].voltage_q, + results_one[readout_pulse1.id].voltage_i, + results_one[readout_pulse1.id].voltage_q, label="One state", ) plt.scatter( - results_zero[readout_pulse2.serial].voltage_i, - results_zero[readout_pulse2.serial].voltage_q, + results_zero[readout_pulse2.id].voltage_i, + results_zero[readout_pulse2.id].voltage_q, label="Zero state", ) plt.show() diff --git a/examples/pulses_tutorial.ipynb b/examples/pulses_tutorial.ipynb index 33c9fa88b..6076a7166 100644 --- a/examples/pulses_tutorial.ipynb +++ b/examples/pulses_tutorial.ipynb @@ -1129,7 +1129,7 @@ "outputs": [], "source": [ "for pulse in ps1.pulses:\n", - " print(pulse.id)" + " print(pulse.serial)" ] }, { @@ -1139,7 +1139,7 @@ "outputs": [], "source": [ "for pulse in ps2.pulses:\n", - " print(pulse.id)" + " print(pulse.serial)" ] }, { diff --git a/tests/test_instruments_rfsoc.py b/tests/test_instruments_rfsoc.py index d41eabbd7..2a118f0f6 100644 --- a/tests/test_instruments_rfsoc.py +++ b/tests/test_instruments_rfsoc.py @@ -389,7 +389,7 @@ def test_play(mocker, dummy_qrc): averaging_mode=AveragingMode.SINGLESHOT, ) results = instrument.play(platform.qubits, platform.couplers, seq, parameters) - assert pulse1.serial in results.keys() + assert pulse.id in results.keys() parameters = ExecutionParameters( nshots=nshots, @@ -397,7 +397,7 @@ def test_play(mocker, dummy_qrc): averaging_mode=AveragingMode.SINGLESHOT, ) results = instrument.play(platform.qubits, platform.couplers, seq, parameters) - assert pulse1.serial in results.keys() + assert pulse.id in results.keys() parameters = ExecutionParameters( nshots=nshots, @@ -405,7 +405,7 @@ def test_play(mocker, dummy_qrc): averaging_mode=AveragingMode.CYCLIC, ) results = instrument.play(platform.qubits, platform.couplers, seq, parameters) - assert pulse1.serial in results.keys() + assert pulse.id in results.keys() def test_sweep(mocker, dummy_qrc): @@ -441,7 +441,7 @@ def test_sweep(mocker, dummy_qrc): results = instrument.sweep( platform.qubits, platform.couplers, seq, parameters, sweeper0, sweeper1 ) - assert pulse1.serial in results.keys() + assert pulse.id in results.keys() parameters = ExecutionParameters( nshots=nshots, @@ -451,7 +451,7 @@ def test_sweep(mocker, dummy_qrc): results = instrument.sweep( platform.qubits, platform.couplers, seq, parameters, sweeper0, sweeper1 ) - assert pulse1.serial in results.keys() + assert pulse.id in results.keys() parameters = ExecutionParameters( nshots=nshots, @@ -461,7 +461,7 @@ def test_sweep(mocker, dummy_qrc): results = instrument.sweep( platform.qubits, platform.couplers, seq, parameters, sweeper0, sweeper1 ) - assert pulse1.serial in results.keys() + assert pulse.id in results.keys() def test_validate_input_command(dummy_qrc): @@ -551,22 +551,18 @@ def test_merge_sweep_results(dummy_qrc): assert targ_dict.keys() == out_dict1.keys() assert ( - out_dict1["serial1"].serialize["MSR[V]"] - == targ_dict["serial1"].serialize["MSR[V]"] + out_dict1["serial1"].idize["MSR[V]"] == targ_dict["serial1"].idize["MSR[V]"] ).all() assert ( - out_dict1["serial1"].serialize["MSR[V]"] - == targ_dict["serial1"].serialize["MSR[V]"] + out_dict1["serial1"].idize["MSR[V]"] == targ_dict["serial1"].idize["MSR[V]"] ).all() assert dict_a.keys() == out_dict2.keys() assert ( - out_dict2["serial1"].serialize["MSR[V]"] - == dict_a["serial1"].serialize["MSR[V]"] + out_dict2["serial1"].idize["MSR[V]"] == dict_a["serial1"].idize["MSR[V]"] ).all() assert ( - out_dict2["serial1"].serialize["MSR[V]"] - == dict_a["serial1"].serialize["MSR[V]"] + out_dict2["serial1"].idize["MSR[V]"] == dict_a["serial1"].idize["MSR[V]"] ).all() @@ -677,8 +673,8 @@ def test_convert_av_sweep_results(dummy_qrc): pulses=[sequence[0]], ) sweep1 = convert(sweep1, sequence, platform.qubits) - serial1 = sequence[1].serial - serial2 = sequence[2].serial + serial1 = sequence[1].id + serial2 = sequence[2].id avgi = [[[1, 2, 3], [4, 1, 2]]] avgq = [[[7, 8, 9], [-1, -2, -3]]] @@ -699,18 +695,10 @@ def test_convert_av_sweep_results(dummy_qrc): ), } - assert ( - out_dict[serial1].serialize["i[V]"] == targ_dict[serial1].serialize["i[V]"] - ).all() - assert ( - out_dict[serial1].serialize["q[V]"] == targ_dict[serial1].serialize["q[V]"] - ).all() - assert ( - out_dict[serial2].serialize["i[V]"] == targ_dict[serial2].serialize["i[V]"] - ).all() - assert ( - out_dict[serial2].serialize["q[V]"] == targ_dict[serial2].serialize["q[V]"] - ).all() + assert (out_dict[serial1].idize["i[V]"] == targ_dict[serial1].idize["i[V]"]).all() + assert (out_dict[serial1].idize["q[V]"] == targ_dict[serial1].idize["q[V]"]).all() + assert (out_dict[serial2].idize["i[V]"] == targ_dict[serial2].idize["i[V]"]).all() + assert (out_dict[serial2].idize["q[V]"] == targ_dict[serial2].idize["q[V]"]).all() def test_convert_nav_sweep_results(dummy_qrc): @@ -730,8 +718,8 @@ def test_convert_nav_sweep_results(dummy_qrc): pulses=[sequence[0]], ) sweep1 = convert(sweep1, sequence, platform.qubits) - serial1 = sequence[1].serial - serial2 = sequence[2].serial + serial1 = sequence[1].id + serial2 = sequence[2].id avgi = [[[[1, 1], [2, 2], [3, 3]], [[4, 4], [1, 1], [2, 2]]]] avgq = [[[[7, 7], [8, 8], [9, 9]], [[-1, -1], [-2, -2], [-3, -3]]]] @@ -752,18 +740,10 @@ def test_convert_nav_sweep_results(dummy_qrc): ), } - assert ( - out_dict[serial1].serialize["i[V]"] == targ_dict[serial1].serialize["i[V]"] - ).all() - assert ( - out_dict[serial1].serialize["q[V]"] == targ_dict[serial1].serialize["q[V]"] - ).all() - assert ( - out_dict[serial2].serialize["i[V]"] == targ_dict[serial2].serialize["i[V]"] - ).all() - assert ( - out_dict[serial2].serialize["q[V]"] == targ_dict[serial2].serialize["q[V]"] - ).all() + assert (out_dict[serial1].idize["i[V]"] == targ_dict[serial1].idize["i[V]"]).all() + assert (out_dict[serial1].idize["q[V]"] == targ_dict[serial1].idize["q[V]"]).all() + assert (out_dict[serial2].idize["i[V]"] == targ_dict[serial2].idize["i[V]"]).all() + assert (out_dict[serial2].idize["q[V]"] == targ_dict[serial2].idize["q[V]"]).all() @pytest.fixture(scope="module") @@ -849,9 +829,9 @@ def test_play_qpu(connected_platform, instrument): ExecutionParameters(acquisition_type=AcquisitionType.INTEGRATION), ) - assert sequence[1].serial in out_dict - assert isinstance(out_dict[sequence[1].serial], IntegratedResults) - assert np.shape(out_dict[sequence[1].serial].voltage_i) == (1000,) + assert sequence[1].id in out_dict + assert isinstance(out_dict[sequence[1].id], IntegratedResults) + assert np.shape(out_dict[sequence[1].id].voltage_i) == (1000,) @pytest.mark.qpu @@ -891,15 +871,15 @@ def test_sweep_qpu(connected_platform, instrument): sweep, ) - assert sequence[1].serial in out_dict1 - assert sequence[1].serial in out_dict2 - assert isinstance(out_dict1[sequence[1].serial], AveragedSampleResults) - assert isinstance(out_dict2[sequence[1].serial], IntegratedResults) - assert np.shape(out_dict2[sequence[1].serial].voltage_i) == ( + assert sequence[1].id in out_dict1 + assert sequence[1].id in out_dict2 + assert isinstance(out_dict1[sequence[1].id], AveragedSampleResults) + assert isinstance(out_dict2[sequence[1].id], IntegratedResults) + assert np.shape(out_dict2[sequence[1].id].voltage_i) == ( 1000, len(sweep.values), ) - assert np.shape(out_dict1[sequence[1].serial].statistical_frequency) == ( + assert np.shape(out_dict1[sequence[1].id].statistical_frequency) == ( len(sweep.values), ) @@ -936,4 +916,4 @@ def test_python_reqursive_sweep(connected_platform, instrument): sweep2, ) - assert sequence[1].serial in out_dict + assert sequence[1].id in out_dict diff --git a/tests/test_instruments_zhinst.py b/tests/test_instruments_zhinst.py index 41da1a18e..cabd82a1d 100644 --- a/tests/test_instruments_zhinst.py +++ b/tests/test_instruments_zhinst.py @@ -851,7 +851,7 @@ def test_experiment_execute_pulse_sequence_qpu(connected_platform, instrument): options, ) - assert len(results[ro_pulses[q].serial]) > 0 + assert len(results[ro_pulses[q].id]) > 0 @pytest.mark.qpu @@ -903,7 +903,7 @@ def test_experiment_sweep_2d_specific_qpu(connected_platform, instrument): sweepers[1], ) - assert len(results[ro_pulses[qubit].serial]) > 0 + assert len(results[ro_pulses[qubit].id]) > 0 def get_previous_subsequence_finish(instrument, name): diff --git a/tests/test_pulses.py b/tests/test_pulses.py index e7dc8419b..62238e74e 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -254,15 +254,6 @@ def test_is_equal_ignoring_start(): assert not p1.is_equal_ignoring_start(p4) -def test_pulse_serial(): - p11 = Pulse(0, 40, 0.9, 50_000_000, 0, Gaussian(5), 0, PulseType.DRIVE) - assert ( - p11.serial - == "Pulse(0, 40, 0.9, 50_000_000, 0, Gaussian(5), 0, PulseType.DRIVE, 0)" - ) - assert repr(p11) == p11.serial - - @pytest.mark.parametrize( "shape", [Rectangular(), Gaussian(5), GaussianSquare(5, 0.9), Drag(5, 1)] ) From 1cbef2140c1074473bdb50898f56099fc41ba122 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 12 Jan 2024 17:37:17 +0100 Subject: [PATCH 017/233] Fix some Pulse special methods --- src/qibolab/pulses.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index b92585eec..401ababb8 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -3,7 +3,7 @@ import copy import re from abc import ABC, abstractmethod -from dataclasses import dataclass +from dataclasses import dataclass, fields from enum import Enum from typing import Optional @@ -860,16 +860,15 @@ def modulated_waveforms(self, sampling_rate): # -> tuple[Waveform, Waveform]: return self.shape.modulated_waveforms(sampling_rate) - def __repr__(self): - return self.serial - def __hash__(self): - return hash(self.serial) + return hash( + tuple(getattr(self, f.name) for f in fields(self) if f.name != "type") + ) def __eq__(self, other): if isinstance(other, Pulse): - return self.serial == other.serial - return False + return hash(self) == hash(other) + return NotImplemented def __add__(self, other): if isinstance(other, Pulse): From 6d8b2919b69ea8ee161ca62eb12985bfa6fa9eec Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 12 Jan 2024 18:10:33 +0100 Subject: [PATCH 018/233] Fix hash and part of notebook --- examples/pulses_tutorial.ipynb | 268 +++++++++++++++++++-------------- src/qibolab/pulses.py | 26 +++- 2 files changed, 171 insertions(+), 123 deletions(-) diff --git a/examples/pulses_tutorial.ipynb b/examples/pulses_tutorial.ipynb index 6076a7166..696c86b20 100644 --- a/examples/pulses_tutorial.ipynb +++ b/examples/pulses_tutorial.ipynb @@ -146,7 +146,7 @@ "source": [ "from qibolab.pulses import Pulse, ReadoutPulse, DrivePulse, FluxPulse\n", "from qibolab.pulses import PulseShape, Rectangular, Gaussian, Drag\n", - "from qibolab.pulses import PulseType, PulseSequence\n", + "from qibolab.pulses import PulseType, PulseSequence, SplitPulse\n", "import numpy as np" ] }, @@ -165,8 +165,7 @@ " shape = Rectangular(), \n", " channel = 0, \n", " type = PulseType.READOUT, \n", - " qubit = 0)\n", - "assert repr(p0) == 'Pulse(0, 50, 0.9, 20_000_000, 0, Rectangular(), 0, PulseType.READOUT, 0)'" + " qubit = 0)" ] }, { @@ -175,7 +174,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# initialisation with str shape\n", "p4 = Pulse(start = 0, \n", " duration = 50, \n", @@ -185,8 +183,7 @@ " shape = 'Rectangular()', \n", " channel = 0, \n", " type = PulseType.READOUT, \n", - " qubit = 0)\n", - "assert repr(p4) == 'Pulse(0, 50, 0.9, 20_000_000, 0, Rectangular(), 0, PulseType.READOUT, 0)'" + " qubit = 0)" ] }, { @@ -195,7 +192,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# initialisation with str channel and str qubit\n", "p5 = Pulse(start = 0, \n", " duration = 50, \n", @@ -205,9 +201,8 @@ " shape = 'Rectangular()', \n", " channel = 'channel0', \n", " type = PulseType.READOUT, \n", - " qubit = 'qubit0')\n", - "assert repr(p5) == 'Pulse(0, 50, 0.9, 20_000_000, 0, Rectangular(), channel0, PulseType.READOUT, qubit0)'\n", - "assert p5.qubit == 'qubit0'" + " qubit = 0)\n", + "assert p5.qubit == 0" ] }, { @@ -216,7 +211,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# examples of initialisation with different frequencies, shapes and types\n", "p6 = Pulse(0, 40, 0.9, -50e6, 0, Rectangular(), 0, PulseType.READOUT)\n", "p7 = Pulse(0, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0)\n", @@ -231,7 +225,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -438,7 +432,7 @@ "outputs": [ { "data": { - "image/png": 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vL7dsW2v9aMvhw4frHD969Kj69esnSZo0aZLatGnT4LXPRFFRkaVSwKRJk+qtRAU0BPdV62IymVRcXKyioiIVFRWpvLxcRqPRZtGEyy+/3OrvM2VlZUpOTm50HGPHjpWfn1+t/WazWdu3b7d6zMlt36rbckZGRtZoUzdu3LhmiXfEiBGnHa81BoNBTk5OcnV1lZeXl3x8fGpU9YN98HMKzcFe99WRI0fqfV18ukhiaibbtm3T9ddfr9LSUnl5eWnatGkaNmyYSktLtXDhQr3zzjvas2ePrrjiCm3evFne3t6ntX6bNm20c+fOeue98MIL+uSTTyRJN998c51z+/Tpo/fff/+04gAAAOcfZ4NJA9wz9GPJiXL03m5OemhER00aGC1nx9q95QGgKRkMBo3tHqER8aF6c/U+vb16n8qNNZ8s3HYoT5e/9osev6yzJl/YTg5UZQIAOTs7a86cObr77rv1zjvvaNWqVTp06JCKiork5eWlmJgY9e7dW5dffrnGjBnTIjGNGzdO9957r+VD+ZEjRyo4uP6E+Ja4lpEjRyolJUX//ve/9d133yk1NVXl5eUKDQ3V4MGDddddd2nQoEENWuu2225Tt27d9Oqrr2rNmjU6duyYgoODdckll+jxxx9Xly5d6jz+9ttv15VXXqm3335by5cv1++//668vDy5urqqTZs2SkhI0KWXXqprr71WQUFBjbreiIgIbdu2Ta+88ooWLlyoffv2ydXVVZ07d9ZNN92kO++8Uz///HOj1gZQt5M/G6ivRVxxcbFl+3Q+mDqdCm3e3t7y8fFp8PymUv3hOtCUuK9antFoVH5+vvLz85WXl6eioqLT6vLi4uJi9Xt2Oombp7OuJHXu3NnSqq76z8mVlAoKCrRy5UpJJ5KLqtepvi5rFZecnZ0VFhamysrKGn8aWvnJ19fXarz1JbueqrpiU2VlpYqKiuTo6Kjw8PDTWgPNi59TaA4teV+dXNCnqZDE1EweeOABlZaWysnJScuXL9fAgQMtY8OHD1dcXJwee+wx7dmzRy+//LJmzJhxWus7OzvXWzaxqqpKq1atknTihcfVV19d53xPT88zKsUIAADOH3Eu+coJ6Kr4Nn56+NKOCvRyrf8gAGhC7i6O+tulHXVdn0i9sHS3lvx2tMZ4udGkf3ybpO93ZWjWuO5qG9i4Vt4AcK5JSEjQ7NmzT/u4GTNmNOj9q6FDhzb4gyo/P78abZROV2OvZfLkyZo8eXK984KDg/X888/r+eefb0R0NQ0YMECffvppo48PDQ3V9OnTNX369DOOxRZ3d3c9+eSTevLJJ5vtHABqOznBKC0tTX369LE59+SKSlFRUc0aFwA0Rn5+vhITExt9fF3t2ao5ODjUSDiq74+Tk5McHGw/eBkaGtqoWOtqF+fu7m61Q87JSUV1/Tm5fejJKisrGxVrNV9fX5tjBw8elLu7u3x9fW2eHwBaAklMzWDjxo365ZdfJEm33nprjQSmag8//LDef/99JScn67XXXtOTTz4pZ2fnJo1jxYoVSk9Pl3TiybYzzVIGAADnj8oqk95fu1/X9YmSn4dLrXGDQXr3xgQF+vu1fHAAcJJIfw+9MbGX/tr/uKZ9+ZsOHC+pMf7r/hxd9trPemJ0vG7s37bONxkBAACAlnZyNbbdu3fXObd63MnJSXFxcc0aFwCcymw2q6ysTPn5+QoMDLT6uWZdSTLWGAyGGslGjo6ONucNGDCgzjlnA4PBIBcXF7m41H6/tSFcXFwUHx9fbxKUrYcKbH1/KisrdeDAAcvf3dzc5Ovra/nj7u7O+ykAWgxJTM3g66+/tmxPmTLF6hwHBwfddNNNmjZtmvLy8rRy5UqNHDmySeP48MMPLdv1tZIDAACotiezUA9/tkM7j+QrKb1A/57Q0+o8WscBaE0Gtg/U0gcu1sxluzVv3YEaYyUVVXrq60RtPpBj82caAAAAYA99+/aVi4uLKioqtHr1av3973+3Oq+iokIbNmywHNPUD0UDwKnMZrOKi4st7eHy8/Mt7cy6dOlitQ2wk5OTPD09a7S/lE50jKlukebq6mpJXHJ0dGxwcgzVgU506gkJCalzjtlsVlVVlSWhqaKiQvn5+SovL7eZPJWfn1/j72VlZSorK1NmZqakE8lTJyc1eXp6ktQEoNmQxNQM1qxZI+lEe7bevXvbnDdkyBDL9tq1a5s0iamwsNCSTNWuXTtdfPHFTbY2AAA4N1WZzHr3l1S9vHyPKqpMkqSvt6frsm7huqxbmJ2jA4D6ubs4asaVXTWqa5ge/WKH0nJLa4zzswwAAACtjbe3ty655BItXbpUK1asUFpaWo0Wc9W+/PJLFRQUSJKuvvrqlg4TwHnAZDKpqKioRtKSrfZueXl5VpOYJMnf319OTk41kl7O5upJZxuDwSAnJyc5OTlZuvQEBQXVecypSUynqqioUHZ2trKzsyWdSFbz8fGxfH99fHxIagLQZHh8vhkkJydLkjp06FCjR+upOnfuXOuYpvLFF1+opOREG4VJkyY16B+O3bt3q3///vLz85Obm5siIyN11VVX6cMPPzzjHqsAAKB123+sWNe9vV4vLN1tSWCqNv2bRJVVVtkpMgA4fQPbB2rZgxfrhn5tLfuu6hGhy7qF2zEqAAAAnI/mzZsng8Egg8GgGTNmWJ3zyCOPSJKMRqOmTp2qqqqar8GPHTumxx9/XJLk5+en2267rVljBnB+qKqqUm5urg4cOKAdO3Zo7dq12rZtm1JTU3X8+HGbCUxS3Ukv7du3V48ePRQTE6OAgAASmM4CXl5ep/W9MhqNysnJ0f79+7V9+/Y67xUAOF1UYmpiZWVlOnbsmCRZfVriZP7+/paSiocPH27SOE5uJXfTTTc16JjMzExLWUBJOnLkiI4cOaJFixZp5syZ+uKLLxQfH9+oeNLS0uocP3r0qGW7uLjY8kQJYA9FRUVWtwF74Z5EczKZzVqwOV2vrTygMqOp1nhsoLv+ObaTKkqLVVHK/YjWhfsR9Zk2IloXx/rojZ8P6pFhbZv9dQb3JFqT1n4/Go1GmUwmS6sDnPtO/j6fT9/zc+keP/V72Fquy2w2y2QyyWg0Nvjf+lNb3AC2rFmzRikpKZa/V7/3L0kpKSmaN29ejfmTJ09u1HmGDx+uCRMmaOHChVq0aJEuvfRSPfjgg4qIiNDOnTv1/PPP69ChQ5KkmTNnyt/fv1HnAYCT7dixQ4WFhad9nLOzszw8PGQ2m6m+c44IDQ1VaGhorRaCeXl59Ra68PDwsNnitKioSGVlZfL19aUNKoAGI4mpiZ38j72Xl1e986uTmJryDcVDhw5p9erVkqQLL7xQHTp0qHO+g4ODLrnkEo0ePVrdu3dXYGCgCgsLtXXrVr399ttKTk5WUlKShg0bpo0bN6pt27Z1rmdNVFRUg+d++eWX8vX1Pe1zAM3ho48+sncIQA3ck2hKBVXOWlkSqXSjp5VRs7q7HlO/qiz98s0m/WJlBvcjWhPuR9RlkFn6ZN56m+M7ygLV0SVP7g5N92Es9yRak9Z4P/bo0UO+vr7y8vJSVlaWvcNBCzt+/Li9Q2h269f/+e/OuXKPd+nSRenp6Za/t5brqqiosLS+WbRoUYOOqa9lClDt3Xff1QcffGB1bO3atVq7dm2NfY1NYpKk9957TwUFBfruu++0cuVKrVy5ssa4g4ODnn76ad1xxx2NPgeA80t5ebkqKirk7e1tddzHx6dBSUxubm41WsO5u7uTvHSOMhgM8vLykpeXl9q0aSOz2azS0tIaLQbLyspqHFPXZ7oZGRk6cuSIpBOfiZ98H7m6ujbrtQA4e5HE1MRO/sHt4uJS7/zqH9ClpaVNFsPHH38ss9ksqWFVmL788kv5+fnV2j948GDdc889uv322/XBBx8oMzNTDz74oL788ssmixUAALQ8s1lKqvDXupIwGVW7RLCvQ7mGeR5RuFOJHaIDgKZX13urKRU+Wlcarq1lwRrika5YF6rCAgAAoOW5u7tryZIl+uSTTzRv3jzt2LFDeXl5Cg0N1eDBg3Xvvfdq4MCB9g4TQCtlNptVVlZWI9mktLRUnp6e6tOnj9VjfH19LQkmJ/Pw8KiRbOLm5tbc4aOVMhgM8vDwkIeHh8LDwyWdSI47+T6z9hlztZOrZBYXF6u4uNiSFH9ycpyfn5/c3NxIjgMgiSSmJnfyP+QVFRX1zi8vL5d04gVKU6l+wtLV1VXXX399vfPr+sfF2dlZ7777rjZs2KDff/9dX331lY4cOaI2bdqcVkz1tcs7evSo+vXrJ0m65ppr1LFjx9NaH2hKRUVFlv+PJk2a1KCqakBz4p5EU8ooKNeMJXu0bn+e1fEbeofrgWEx8nCx3v+c+xGtCfcjztSxogpd/c4WSUaVmZ30fXFbjW4XrGkj28vX/fTLnHNPojVp7ffjkSNHZDKZ5OzsrJCQEHuHgxZQVVVlqcAUGBgoR0frv28Cp6uwsFDe3t7y9fVtcILHnj179MILLzRzZDgXzJs3r1bLuNM1efLk06rQNHHiRE2cOPGMzgng3Hdq26/8/Hyrn0sWFxersrLSaiuv6go61f+OVv+h7Rfq4urqqpCQkHpfxxmNxjorfZWVlamsrEyZmZmSThQHOfk+9PT0JKkJOE+RxNTETi7J2JAWcdX915vqzcSNGzdq9+7dkqQrr7yyzgSlhnJyctKtt96qxx57TJK0evXq034RFRkZ2eC5np6e8vHxOa31gebi5eXF/YhWhXsSZ+KLLWl6dtEuFZYba4218XPXrHEX6MIOQQ1ej/sRrQn3IxpjxrLtyi+t+TPxu13Z2nyoQC9em6DhnUMbvTb3JFqT1ng/ZmZmymg0ymAwkMxyHnJ0dOT7jiZjMBjk4OAgJyenBv+s8/S01lIbAIDWr7CwUOnp6Tp27JiMxtrv8VmTn5+voKDa7/m5uLho0KBB/F6GZlFZWSk/Pz8VFBTIZDLVO7+iokLZ2dnKzs6WJLVp00YdOnRo7jABtEIkMTUxNzc3BQYG6vjx40pLS6tzbm5uriWJKSoqqknO/+GHH1q2G9JKrqG6dOli2bZWWhIAALR+u9LzrSYw3dAvSk+Mjpe3G09ZATi/TLs8XsXlRn2/K7PG/qzCct0yb7PG947U02O7yIefjwAAAAAAOzGZTMrOztaRI0fqrGxjjaOjoyorK+scB5qDu7u7unfvLpPJpMLCwhpVw6qqquo9vrU9iAOg5ZDE1Ay6dOmiX375RSkpKTIajXJysv5lrq6YJEnx8fFnfN7KykotXLhQkhQSEqLLLrvsjNesRrk+AADOfo+N6qxVv2dr/7ETSdRhPm568doEDe1ECxcA56dgb1e99dfe+mZ7uqZ/k6iCspqJnp9vSdPalGOaOe4CDY4LtlOUAAAAAIDzWV5eXo3PFOvi7OxcoyWXl5cXn/HBrhwcHCz3o1S7FWJeXp7VRLvq+daUl5fL1dW12WIGYF8kMTWDQYMG6ZdfflFxcbG2bNmi/v37W523evVqy/ZFF110xuddsmSJjh8/LulE32xbyVONkZSUZNmOiIhosnUBAEDLcXdx1Evju2v8W+v0l55t9MyYrvL1oLoIgPObwWDQX3q20YDYQP39y9+06vfsGuPp+WWa9N+N+uuAtpp2ebw8XXkZDQAAAABoOf7+/nJ3d1dpaWmtMVdXV/n5+VmSRNzd3UlaQqtmMBjk5eUlLy8vtWnTRmazWaWlpTUqNUmymaRUXFyszZs3y9/fXxEREQoMDOSeB84xvPvaDP7yl7/ohRdekCS9//77VpOYTCaTpfWbn5+fhg0bdsbnPbmV3M0333zG61UzGo167733LH+/+OKLm2xtAADQ9A7nlCgqwMPqWO9ofy1/6GJ1CPFu4agAoHUL83XT+5P76rPNh/Xct8kqOqX95scbDunnPcc0a9wF6h8baKcoAQAAAADnosrKSlVUVMjT07PWmMFgUEREhPbt2ydJcnFxUXh4uMLCwuTm5tbSoQJNymAwyMPDQx4eHgoPD5d04rNpW9LT0yVJubm5ys3Nlaurq8LDwxUeHi4XF5cWiRlA83KwdwDnon79+mnw4MGSpP/+979av359rTkvv/yykpOTJUkPPPCAnJ1rVkFYtWqVDAaDDAaDJk+eXO85c3JytGTJEklSQkKCevTo0aBYV65cqby8PJvjlZWVuu222yyxjh07VlFRUQ1aGwAAtKy8kgo9sHCbRr76sw780TLOGhKYAMA6g8Gg6/u21bIHB+uiDrUTlQ7llGjCOxv0j8VJKqusskOEAAAAAIBzhdlsVkFBgXbv3q3169dr7969NueGhoYqICBAXbp0Uf/+/dWuXTsSmHDOstVtyGg0KjMzs8a+8vJyHThwQBs2bFBycrLy8/NlNptbIkwAzYRKTM3ktdde00UXXaTS0lKNHDlSTzzxhIYNG6bS0lItXLhQc+fOlSR17NhRDz/88Bmfb+HChaqoqJB0elWYPvjgA1155ZW68sorNXToUHXq1Ek+Pj4qKirSli1bNHfuXEsruZCQEL322mtnHCsAAGh6P+3O1N//t1NZheWSpEc+36FP7xwoRwdK6QLA6Yr099BHt/TX/F8P6v++263SkxKWzGbpl73ZeuyyTnaMEAAAAABwtqqqqlJWVpbS09NVVFRk2Z+fn6+ioiJ5eXnVOsbZ2VkJCQktGSbQ6hQXF9tsHWc2m5WVlaWsrCx5enoqIiJCISEhNhOiALRe/F/bTHr27KlPP/1Uf/3rX1VQUKAnnnii1pyOHTtqyZIl8vY+82oI1a3kHB0ddeONN57WsUVFRfrkk0/0ySef2JyTkJCghQsXKiYm5oziBAAATaugrFL//DZJn21Oq7F/88Fcvbdmv26/ONZOkQHA2c3BwaBJA9tpcFywHv1ihzYdyJUkOToY9PJ13eXm7GjnCAEAAAAAZ5OSkhKlp6crIyNDVVXWq/sePXpUcXFxLRwZcHbw9fXVgAEDlJ2drSNHjtRIAjxZcXGx9u7dq9TUVIWGhioiIsJqq0YArRNJTM1o7Nix+u233/Taa69pyZIlSktLk4uLizp06KDx48fr3nvvlYeHxxmfZ+/evfr1118lSZdeeqnCwsIafOzjjz+uHj16aP369UpKSlJ2drZycnLk6uqq0NBQ9enTR+PGjdPVV18tR0fepAcAoDVZs/eYHvtih9Lzy2qNebs5KcTH1Q5RAcC5pV2QpxbeMVDvr92vf33/u+68OFYXRPrZOywAAAAAwFnAbDbr2LFjSk9PV15eXp1zbVWYAfAnR0dHhYWFKSwsTAUFBUpPT1d2drZMJlOtuVVVVUpPT1d6erp8fX3VsWPHJvlsHkDzIompmUVHR+uVV17RK6+8clrHDR06tMH9OuPi4hrd2zM+Pl7x8fF68MEHG3U8AABoecXlRr2wNFkfbzhkdXxIx2DNvPYChfm6tXBkAHBucnQw6LbBsRreOUSR/rbf7MouqlCV2SBHQ+NenwEAAAAAzg3l5eU6evSojh49qoqKijrnurm5KTw8XGFhYXJxcWmhCIGzn4+Pj3x8fNS+fXtlZGQoPT1dZWW1H/iVpMLCQjk7O7dwhAAagyQmAACAs8ivqcf16Be/6VBOSa0xTxdHPTWmiyb0jeLJLQBoBrHBXjbHjFUmPfBFkjIKYzXcM83mPAAAAADAuW3fvn06cuRIvQUIAgICFBERoYCAAN7LA86As7OzoqKiFBkZqdzcXKWnp+v48eM15gQHB5PEBJwlHOwdAAAAAOpXWWXSzGW7NeGdDVYTmAbGBmrZgxfrhn5tedMDAOzg7Z9TlZheqGNV7vqioL0+2lj/G9YAgDMzb948GQwGGQwGHThwoFnOceDAAcs55s2b1yznaK1mzJhhufbGqj5+xowZTRcYAACtnKurq83Xg05OToqKilK/fv2UkJCgwMBA3ssDmojBYFBAQIC6deum/v37q23btpbEpYiICJvHHTp0SGlpaTIajS0VKoA6UIkJAACglUvPK9V9C7Zpy8HcWmPuzo6aNrqz/to/Wg4OvOEBAPawO6NA/16xx/J3kxw0a0Wqth0p1kvjL5CfB+0AAAAAAOB8ERoaqv3798tkMln2eXt7q02bNgoODpaDAzUmgObm5uammJgYRUdHKzc3Vz4+PlbnGY1GHTp0SFVVVdq/f79CQkIUEREhb2/vFo4YQDX+lQQAAGjFisqNuvL1NVYTmPpE+2vpA4N108B2JDABgB2ZzVJMkGet/SuSMzX6tV+05WCOHaICAJwtWqKiFAAAaBomk0mZmZnatm2biouLrc5xdnZWSEiIHBwcFBYWpl69eqlXr14KDQ0lgQloYQ4ODgoMDLQ5npWVpaqqKkkn/v/OyMjQ1q1btXXrVmVmZtZIRgTQMqjEBAAA0Ip5uTppykUxmvX975Z9zo4GPTqqk24dFCtHkpcAwO7iw320+L5BevHbRL2/Ia3GWHp+ma57e4MeHdVJdwyOJekUAHBeobUqAOBcUVZWpqNHj+ro0aOqrKyUJKWnpysuLs7q/Hbt2ql9+/ZycuKjWKC1MpvNSk9PtzpWWFio3bt3KyUlReHh4QoPD5e7u3sLRwicn0j3BQAAaOXuHtJeF3cMliRFBbjri7su1B0XtyeBCQBaEVcnRz00PEZXeB2Qm8FYY6zKZNaLS3frlg826XhRuZ0iBAAAAACcDrPZrJycHCUmJurXX3/VoUOHLAlMkpSZmSmj0Wj1WFdXVxKYgLNAhw4dFBwcLIPB+nvtRqNRhw8f1saNG7Vz504dP36cRH2gmfGvJwAAQCvn4GDQK9d11ys/7NHjl3WWr7uzvUMCANjQ1rlI1/mkKNm3vzYdyq8xtur3bI2e/YtmT+ip/rG2S5kDAAAAAOynsrJSGRkZSk9PV1lZmc15VVVVys7OVnh4eAtGB6CpGAwG+fn5yc/PT+Xl5Zb/7ysqKqzOz8nJUU5Ojtzc3BQeHq6wsDC5uLi0cNTAuY9KTAAAAK1AubFKa1OO2RwP8nLV/12dQAITAJwFPB2MmjsxQfdfEqdTH+TLLCjXDe9s0H9+3KsqE0/uAWgZM2bMkMFgsDxdXFBQoBkzZighIUFeXl4KCQnR6NGjtW7duhrHZWVl6amnnlLXrl3l6empwMBAXXXVVdq2bVu95zSZTPr44481evRohYWFyd3dXd26ddO4ceP05ptv2vxg4GS5ubn6+9//rs6dO8vd3V0hISEaMWKEPv/88wZdd/U1z5gxo855Q4cOlcFg0NChQxu07qkSExP1z3/+U6NGjVJkZKRcXV3l5eWluLg43XzzzdqwYYPV41atWiWDwaApU6ZY9sXExFjirv6zatUqq8d//fXXGj9+vNq2bSs3Nzf5+fmpT58+evbZZ5Wbm1tv3GlpaZo6dapiY2Pl5uamiIgIXXnllVqxYkWjvg7WNPR7AABAa1BQUKDdu3drw4YNSk1NrTOBydfXV126dFFoaGgLRgigubi6uio6OloDBgxQly5d5OfnZ3NuWVmZ9u/fr9TU1JYLEDiPUIkJAADAzg4cK9a9C7Zq99FCfXbXQPVq62/vkAAAZ8jRwaC/XdpRA2ICdP/C7Tp2Uhs5k1l6+Yc9+nV/jt64sRcJqgBa1OHDhzVixAjt2bPHsq+4uFhLly7V8uXLtWDBAo0fP16//fabRo8erSNHjljmlZSUaNGiRfr++++1dOlSDRs2zOo5cnJydOWVV2rt2rW19q9bt07r1q3TnDlztHTpUkVHR1tdIzk5WSNGjFB6erplX1lZmX788Uf9+OOPmjJlii6++OIz+VI0iVWrVln9OlRUVCglJUUpKSn68MMP9fe//10vvPBCk5wzNzdX48aN008//VRjf3l5ubZs2aItW7Zozpw5+uabbzRgwACra/zyyy8aM2aMCgoKLPuOHj2qxYsXa/HixSQdAQDOKwUFBUpJSVFhYWGd8xwdHRUaGqqIiAh5enq2UHQAWpLBYFBwcLCCg4NVUlKi9PR0ZWRkqKqqqtbciIgIO0QInPtIYgIAALCjxTvSNe3LnSoqN0qS7vtkm767f7B8PfhAGwDOBRd2CNLSBwbroU+3a80pFffKKqvk6eJop8gAnK/Gjx+vtLQ0TZs2TZdddpk8PDy0Zs0aPfPMMyooKNCtt96qPn36aMyYMSotLdXzzz+vIUOGyNnZWcuWLdPzzz+v8vJyTZ48WXv37q3VPqGqqkpjxozR+vXrJUlDhgzRvffeq7Zt2yo5OVkLFy7UsmXLlJycrEsuuUTbt2+Xl5dXjTUKCgo0atQoSwLT9ddfr5tvvlkhISHas2ePXnnlFb3//vtKTExsmS9aHYxGozw9PXXFFVdo+PDh6ty5s3x8fJSVlaVdu3Zp9uzZOnjwoF588UV17NixRtWlvn37aufOnfrmm2/01FNPSZK+//77Wh+GxMTEWLbLy8s1YsQIbd26VY6Ojpo4caJGjx6tmJgYVVZW6ueff9Yrr7yirKwsjR49Wtu2bauVKHbo0CFLApODg4PuuOMOjRs3Tr6+vvrtt9/04osvasaMGerTp08zfuUAAGgdCgsL660y6enpqYiICIWGhsrRkddwwPnCw8NDHTp0UExMjLKyspSenq6ioiJJkpeXl7y9ve0cIXBuIokJAADADsoqq/SPb5P0ya+Hauw/kleqJ7/eqdcn9rJTZACAphbs7aoPbumnN1el6JUf9shklvw8nDX7hp5ycqTLO2CLyWRWbkn9LcfOJf4eLnJwMNQ/8Qxs375dq1evVv/+/S37+vTpo7i4OI0ZM0aFhYXq37+/zGazNm7cqPbt21vm9evXT0FBQZo6daoOHTqkJUuW6Oqrr66x/ltvvWVJYLrppps0b948GQwGVVVVKSoqSiNHjtTs2bP14osvat++fXruuec0c+bMGms899xzOnz4sCTp//7v/zRt2jTLWO/evTVu3DiNGTNGy5cvb/Kvz+nq0aOH0tLSrLabGDVqlO69916NGTNGP/zwg5599lnddNNNlg8/PT091a1bN23evNlyTMeOHdWuXTub5/vHP/6hrVu3ys/PTytWrFDv3r1rjA8aNEg33nijBg4cqKNHj+qJJ57Q/Pnza8x5+OGHLRWYPv74Y91www2WsT59+mj8+PEaPHhwjbgAADhXeXl5yd/fv1Yr1upqLBEREfLx8bG05QVw/nF0dFR4eLjCwsJUWFio9PR0+fv72/y5UFxcrIKCAoWFhfGzA2gEkpgAAABa2L7sIk2dv1W7M2qXqG4f7Kl7h3ewQ1QAgObk6GDQvcPj1LddgB5YuF3PX91NEX7u9g4LaNVySyrU+58r7B1Gi9ry1AgFerk26zkefPDBGglM1a644gpFR0fr4MGDys7O1ptvvlkjganalClT9PDDD6usrEy//PJLrSSmN954Q5IUHBys119/3eqb9jNmzNDXX3+t3bt365133tE//vEPubqeuO6Kigr997//lSRdcMEF+vvf/17reGdnZ/33v/9VbGysKisrT/+L0ISCgoLqHHdxcdGsWbPUo0cPHTx4UNu3b6+VeNRQRUVFlq/vc889Z3Od6OhoPf3007rnnnv0+eefa+7cuZaWNxkZGfrqq68kSWPGjKmRwFTN29tbc+fOtXqfAABwrjEYDIqLi9OmTZtkNpvl6uqqiIgIhYWF1ao4CeD8ZjAY5OPjIx8fH5tzzGaz9u7dq/z8fGVkZCguLq5W5VkAdeORTwAAgBb01bY0jf3PGqsJTNf2itTi+wapc5jtF0EAgLNb/9hArXp0qC6JD7U5p6yyqgUjAnC+mTBhgs2xCy64QNKJN+evv/56q3Pc3d0VFxcnSUpNTa0xlp6eruTkZEnSddddZ7O9gpOTk6WtWm5urrZu3WoZ27Jli6USws0332zzyeXIyEiNHDnS5rXYS3l5uQ4dOqSkpCQlJiYqMTFRZrPZMr5jx45Gr7169Wrl5+dLksaNG1fn3IsvvliSVFlZqS1btlj2r1y5UlVVJ/6dObm13an69eunrl27NjpWAABaG5PJZHPM3d1dMTExio2NVb9+/dS2bVsSmAA0SmZmpuV39oKCAm3ZskX79u2T0Wi0c2TA2YNKTAAAAC2gtKJKzyxK1Geb02qNuTs76rm/dNO43pF2iAwA0NLcnB1tjhWXG3XVG2t1aZdQPXxpR9rNAWhyHTt2tDlW3RItKChI/v7+9c4rLKyZmJ+YmGjZrq+Kz8njiYmJGjhwoCRp586dlv19+/atc41+/fppyZIldc5pCcXFxZo9e7YWLlyoXbt2WZKErDl27Fijz3Nye7fw8PAGH5eRkWHZPt2v765du04jQgAAWqdjx44pJSVFHTp0sFlFMSoqqoWjAnCuqaqqqvWghySlpaUpKyurzp9BAP5EEhMAAEAz25NZqKnzt2pvVlGtsU6h3nrjxp7qEGL9KXUAwPnDbDbr6a8TlZJVpJSsIm3an6PZN/Sk7RyAJuXh4WFzzMHBod45J887NVknJyfHsh0SElLnGmFhYVaPO501QkNtV7VrKQcOHNDw4cO1f//+Bs0vLS1t9LmysrIadVxJSYll+2z7+gIAcCbKy8uVmJio48ePS5JSUlLk7+8vR0fbD5YAQGM5Ojqqc+fO2rt3r8rKymqMVVRUKCkpSQEBATVeCwGojSQmAACAZmI2m/X5ljRN/yZRZZW1S1ZP6BulZ8Z2lbsLb5wAAKQvtqTpy21HLH/ffDBXo2f/opfHd6+z/RxwrvL3cNGWp0bYO4wW5e9x7rQtsdUGrqXXaG6TJk3S/v37ZTAYNGXKFE2YMEHx8fEKDg6Wi4uLDAaDTCaT5cPSk1vLna6Tk8a2bt0qZ2fnBh0XGWm94uvZ8PUFAKAxDAaDwsPDlZycXOPf3vLych08eFCxsbF2jA7AuSwgIEB9+vTR4cOHdejQoVq//+fk5Cg3N1dt2rRRenq6naIEWjeSmAAAAJrJjEW79MH6g7X2e7o46v+uSdBVPdrYISoAQGtVVlklZ0eDKqv+fIMrr6RSt36wWbcNitFjl3WWixPt5XD+cHAwKNDL1d5h4DQEBARYtjMzM+uce3KLs5OPO7mNXWZmZp3t7+o7h8FgkNlslslU+4GCkxUXF9c5bsvu3bu1Zs0aSdITTzyhf/7zn1bnnVz96EwEBgZatoODg20mJ9Xl1K9vXa1z6vv6AgDQGhUWFiohIUEeHh5Wk4eLiopkNptJ5gXQbBwdHdWuXTuFhIQoJSVFubm5NcbNZrOioqIUFBSkwsJC+fj42ClSoHXi3U8AAIBmMiA2sNa+LuE+WnzfIBKYAAC1TBrYTl/cdaGiAmq3j3t3zX6Nf3u9DueUWDkSAFqHbt26WbZ//fXXOudu3LjR6nEJCQmW7U2bNtW5Rn3j3t4nWjaf+qHBycxms1JSUupcx5Zdu3ZZtq+//nqb8zZv3lznOg39ELVnz56W7bVr1zbomFM15dcXAIDWpKKiQrt371ZKSorV1rjOzs7q3LmzEhISSGAC0CI8PDyUkJCg+Ph4ubjUrrrr7u6ulJQUJScnq6Kiwg4RAq0TSUwAAADN5PKEcN00MNry90kDovXlPRcqNtjLjlEBAFqz7lF++va+wbq8W1itsR2H8zR69i9alphh5UgAsL+IiAjFx8dLkj777DMVFRVZnVdVVaV58+ZJOlEZqFevXpax3r17W6oFffTRRzbbrx05ckTLly+vM56YmBhJdScRLV26VHl5eXWuY4vRaLRs11XN6a233qpzHTc3N8t2eXm5zXkjRoywfCg7e/bsRrWmGzZsmKW13QcffGBz3qZNm5SYmHja6wMA0NLMZrPS09O1adMmm1UEIyIi1LdvX4WGhpLABKBFGQwGhYSEqG/fvmrTxvqDzVlZWdq6dWu9FWSB8wVJTAAAAM3oidHxGhAboDcm9tJzf+kmN2dHe4cEAGjlfN2dNefGXnruqq5ycaz5sr2wzKi7Pt6iGYt2qdxYZacIAcC2qVOnSpKys7N1//33W53zj3/8Q0lJSZKk22+/Xa6uf7YNdHV11ZQpUyRJ27dv16xZs2odbzQadfvtt9f7tPKQIUMknagKZa1yUUZGhu67774GXJV1cXFxlu3qpKxTvfnmm/rmm2/qXCc8PNyyvW/fPpvz/Pz8dO+990qS1q1bp4ceeqjODzoyMzP17rvv1jrXVVddJUlatGiRPvvss1rHFRUV6c4776wzZgAAWoPCwkJt27ZNe/furZFcXM3d3V09e/ZUXFycnJ2d7RAhAJzg5OSkDh06qFevXlarxUVGRsrBgdQNQCKJCQAA4IwdySu1Oebm7KgFtw/QFReE25wDAMCpDAaDJg1spy/vuVDtAmu/uTVv3QFd++Y6HThmu/IHANjDXXfdpYEDB0qS3n//fV1yySX63//+p61bt2rFihW67bbb9Pzzz0uS2rdvr6effrrWGtOnT1dkZKQk6fHHH9fEiRO1bNkybd26VQsXLtSFF16opUuXqk+fPnXGcscdd8jJyUlms1ljx47Vv//9b23evFnr1q3TrFmz1LNnT+Xn59dIRjodPXv2tLTCe/vtt3X99dfr22+/1ZYtW/TNN99o/Pjxuueee3TRRRfVu051Naann35aP/zwg/bs2aOUlBSlpKSotPTP1xv/+Mc/1L9/f0nSa6+9pl69eumNN97Q2rVrtX37dq1cuVKvv/66/vKXv6ht27ZWq0C9/PLLllZ7EydO1NSpU7Vy5Upt2bJF77//vnr37q1t27bV+/UFAMBeqqqqlJKSoq1bt6qwsLDWuNFo1P79+9WpUyf5+PjYIUIAsM7b21sdO3ZUamqqJfnSy8vLZpUm4HzkZO8AAAAAzlZms1kfrj+o55cka9b4C3RVD+svNChTDQBorG5tfLX4vkF64qtELd6RXmMs8UiBxvxnjV68NkFjLoiwU4QAUJOjo6O+/fZbXXnllVq7dq1++ukn/fTTT7XmxcfHa+nSpfLyqt1q2dfXV8uWLdOIESOUkZGhBQsWaMGCBTXmTJ48WUOGDLFUbbKma9eu+te//qW//e1vys3N1UMPPVRjPCAgQF9//bWefvpp7d2797Sv1WAw6KOPPtLw4cOVm5urzz77rFZlo4SEBH3++eeKiLD9c9rb21v333+//vWvf2nr1q0aOXJkjfGVK1dq6NChkk5Uqvrhhx80efJkffnll9qxY4elOpM11j64bdeunRYtWqQrr7xShYWFmjNnjubMmVNjzvTp02UwGOpsxQcAgL0YDAbl5ORYHfP399eKFStUWVnJe3IAWiWDwaCsrCzl5ORo5MiRio6OtvnzymQyyWAw8PMM5xUqMQEAADRCfmml7v54q55ZtEsVVSY98eVO7acaBgCgGXi7OWv2hB564ZoEuTrVfBlfVG7Uj8lZdooMAKwLCAjQzz//rA8//FCXXXaZQkND5ezsLH9/f1144YWaPXu2tm/frujoaJtrdO3aVbt27dJjjz2muLg4ubq6KigoSMOGDdMnn3yi999/v0GxPPTQQ1q2bJlGjRolf39/ubq6KiYmRlOnTtW2bds0ePDgM7rWHj16aPv27brrrrsUHR0tZ2dnBQQEqF+/fnrppZe0cePGGu3ibHnxxRf1zjvvaPDgwQoICJCjo+021N7e3vrf//6nX375Rbfddps6deokb29vOTk5KSAgQH379tXUqVP13Xff6YcffrC6xtChQ7Vr1y7dfffdio6OlouLi0JDQ3XFFVdo2bJlevbZZxv9NQEAoLk5ODioQ4cONfa5u7vrggsuULt27VRZWWmnyACg4YxGo6Kjo+usGJeSkqIdO3aouJjPHnD+oBITAADAadp+OE/3frJVabl/tnUorqjS1Plb9eU9F8rN2fYHDgAANIbBYNAN/dqqZ1s/TZ2/VfuyT7x5FRvkqX/+pZudowPQ2s2YMUMzZsyod968efM0b968euetWrWq3jkODg6aNGmSJk2aJOlE25esrBNJlyEhIXUm6VQLCAjQzJkzNXPmTKvjkydP1uTJk+tdZ9SoURo1apTN8bqup127djKbzXWu37ZtW7355pt1zqlvDYPBoNtuu0233XZbnfNONmjQIA0aNKjB808VFRVVqwLTyRp639SlvusGAKCxAgICFBwcrOPHj6tt27aKioqSg4ODCgoK7B0aADSJgoICHT16VJK0ZcsWRUZGKjo6ukGvpYCzGZWYAAAAGshsNuvdX1I17s11NRKYqg3uGCRHB8q6AgCaT+cwHy26d5Cu6dVGLk4Oen1iL3m68nwSAAAAgHNPTk5OndVHOnTooD59+ig6OloODnzkCeDcYTaba7S8NpvNOnz4sDZv3qzjx4/bMTKg+fFOJwAAQAPklVTokc93aIWVlj3+Hs565boeGtY5xA6RAQDON56uTnrluh66b3icYoI87R0OAAAAADSp8vJy7du3T9nZ2fL19VX37t1lMNR+cNDFxcUO0QFA8ysrK7PaGrOsrEyJiYkKDAxUhw4d5ObmZofogOZFWjIAAEA9Eo/ka+zra6wmMPVt56/vHhhMAhMAoMXVlcCUVVimW+Zt0uGckhaMCAAAAAAaz2w2Ky0tTZs2bVJ2drYkKT8/X5mZmXaODABalru7u/r27auoqCirSZzHjx/Xpk2bdPjwYZlMJjtECDQfKjEBAADU4X9b0vTEVztVbqz5QsBgkKYO7aAHR8TJyZG8cABA61FZZdK9n2zTxv052nIwV69N6KGhnUi2BQAAANB6FRQUaM+ePVbbx6WmpiooKEhOTnysCeD84ejoqNjYWIWGhmrv3r3Kz8+vMW4ymZSamqrMzEzFxcXJ19fXTpECTYtP3AAAAKyoMJr09NeJevjzHbUSmAI9XfThLf30yKhOJDABAFqdF5fu1sb9OZKk/NJKTZm3SbN/3CuTyWznyAAAAACgpsrKSu3Zs0fbtm2zmsDk5OSkmJgYOTo62iE6ALA/T09Pde/eXZ06dZKzs3Ot8eLiYm3fvl2///671RZ0wNmGlGUAAIBTZOSX6e75W7TtUF6tsV5t/TTnxt4K86XXNM5vZrNZeSWVOlZULnvlRRQVFet4lascJBWUGeXtbbZaXhk4n5RUGLV6T3aNfWaz9MoPe/RbWp5evq6HfN1rv+EFAAAAAC3JbDYrMzNTqampNj90DwsLU2xsrNUP7QHgfGIwGBQWFqbAwEDt379fR48erTUnIyNDx44dU2xsrMLDw+0QJdA0SGICAAA4xaGcEv2Wll9r/00Do/XUFV3k4kT1JZz7SiuqlJ5fqqN5ZUrPK9WRvFIdzS9Vel6Z0vNLlZ5XqrLK1tBvPU6StPCV9fJydVK4r5si/NwV4eemCF93hf+x3cbPXWG+bnJ14slNnNs8XJz09dSL9OjnO7Q0MaPG2IrkLF31+hq9Nam3Oof52ClCAAAAAOe74uJiq62Rqnl6etIaCQCscHZ2VseOHRUWFqa9e/eqqKioxrjRaFRhYSFJTDirkcQEAABwin4xAXpidLye+zZJkuTq5KAXrknQNb0i7RwZ0DSMVSZlFZYrPa9U6fknkpSO5pXqSF7ZH4lKpcotOftKDxeVG7U3q0h7s4pszgnycj0pwelEclO47x9JT37uCvZylYMD1ZxwdvNyddKcG3tp7s+pmrlsd41qaQeOl+gvb6zVzGsv0FU92tgvSAAAAADnnaqqKh08eFBpaWkym2uXdXZwcFC7du3Upk0bOTjwECEA2OLj46NevXrpyJEjOnDggKqqqiSdSHKKiYmxc3TAmSGJCQAAwIpbLmqn7YfztONwnt78ay91jeDJL5wdqtu8naicVPZHotIfFZT+SFbKLCxXlb16wNnZsaJyHSsqt1ptTZKcHQ0K9fmjmpOlqtOfSU7hvu7ycXOibR1aPYPBoDuHtFdCG1/dt2CbjhdXWMbKKk16YOF2bTuUpyeviJezIx8OAAAAAGheZrNZ27ZtU3FxsdXxoKAgtW/fXm5ubi0cGQCcnQwGgyIjIxUcHKx9+/YpOztb7du3pwUnznokMQEAAFhhMBg089oEVRhN8vNwsXc4QA1VJrN+zyhUYnq+juTav82b3fJ5zJJZZklNF0BllVlpuaVKyy21Ocda27rIAHcltPFVbJAXlZzQqlzYIUiL7xuku+dv1Y7DeTXG5q07oF3p+XpjYi+F+PBBAQAAAIDmYzAYFB4erpSUlBr73dzc1KFDBwUGBtopMgA4u7m6uqpLly7Kz8+Xj4+PzXkVFRVydnbm4Uy0eiQxAQCA89byXRnKLCjTpIHtrI57uDiJ/CW0Bvklldp6OFfbDuZqy6FcbT+Up+KKqhY5t6+7syL83NXGz+2PtmsnVyVyU6iPm92quBQUFOjNN99Uldmgv9xws/KNTpZ2eNVt8k78KVNRubHJzltX2zpfd2f1auun3tH+6tXWX92j/OTpyssu2FeEn7s+u3OAnl2cpE9+PVRjbNOBXI35zxrNubGX+rQLsFOEAAAAAM4HERERysjIUFFRkQwGg6KiotS2bVs5OjraOzQAOOv5+truJmE0GvXbb7/J1dVV8fHxcnLi/Uq0XtydAADgvFNlMuuVH37XGyv3ydHBoA4h3hrYnqe90DqYTGalHivS1oN52nIwV1sP5VpNlmkKrk4OlqQkS4LSSRWGwn3dz4oEHEeDWZH+7upSx5NGBWWVOpp3cnu9Uh3NK7O03TuaX6rKqjNvsZdfWqmVv2dr5e/ZkiQHgxQf7qNebf0tiU1RAe488YQW5+rkqP+7OkE9ovz01NeJqjD+WbEtq7BcE+Zu0Ce3D1C/GBKZAAAAADQPg8GguLg47d+/X3FxcfLw8LB3SABwzjObzUpKSlJxcbGKi4u1fft2devWjfadaLVa/ycSAAAATSi3uEL3L9ymX/Yek3Qioem+BVu1+L5BCvd1t3N0OB8Vlxu143Ceth7K/SNpKU/5pZVnvK6DQQr1cTup7dmJBKVwP3e1+aOKUoCny3mTTOPj5iyfMGd1CvO2Om4ymXWsuFzpeWU6mldqSW6yVHPKL1N2Yflpn9dklnalF2hXeoE+2nBQkhTk5are0X9Wa+rWxlduzjx1ipZxXZ8oxYf56K6Pt+hI3p9tE3tF+6tnWz/7BQYAAADgnGEymeTgYL1qs4+Pj7p3797CEQHA+Wvfvn3Kzc21/L24uFjbtm1Tt27d5O1t/b1SwJ5IYgIAAOeNnWn5tT60laRjRRX635Y03Ts8zk6R4XxhNpt1OKf0pISlXCUfLZCpkQWA2gZ4qGOoV6tr83Y2cnAwKMTbTSHebuoR5Wd1TrmxSpn55X8kONVsW7f7aKEyCsoadK5jReX6flemvt+VKUlydjSoWxvfGtWawnx5EgrNJyHSV4vvG6QH/kjqDfVx1RsTe/EzAwAAAMAZO378uPbu3asLLriASksA0AoEBwcrKytLlZV/PjhbUVGh7du3Kz4+XkFBQXaMDqiNJCYAAHBe+GzTYT31Tc32OZLk5GDQ02O66KaB0XaKDOeyssoqJR7JtyQtbTmYp2NFp1/NR5JcnBzUPfJEokuvPxJdgr1dmzhi1MXVyVFtAz3UNtD6m7DpeaV/fJ9zte1QrnalF8jYgAy1yiqzth3K07ZDefrvmv2SpDZ+7uoV7a/ebf3UK9pf8eE+JJigSQV4umjelH569Yc9GtY5hJ8nAAAAAM5YWlqa9u3bJ0lKTExUz5495ezsbOeoAOD85uvrq549eyoxMVElJSWW/SaTSbt27VJsbKwiIyPPm4r9aP1IYgIAAOe0cmOVZixK0oKNh2qNhXi7as6NvdSnXYAdIsO5KCO/rEaVpcQj+aqsalyZpVAfV/WJDlDPtifajnWN8JWLE0ksrVl1276x3SMkSaUVVfotLU9bDuVq68ETLQNziisatNaRP1raLd6RLklyc3bQBZEn7oXefySyBXi6NNu14Pzg6GDQI6M61Tknq7BMQZ6ucnDgjSwAAAAA1pnNZqWkpCg9Pd2yr7S0VLt27dIFF1xgs7UcAKBluLu7q0ePHkpKSlJeXl6NsdTUVJWWlqpDhw78vEarQBITAAA4Z6Xnleru+Vu143BerbG+7fz1xsReCvGhZRMap7LKpOSjBdp6MFdbDuVp68HcWq0KG8rJwaAuET5/thOL9leErxtPv5zl3F0c1T82UP1jAyWdeFP3wPGSP+6ZXG09mKvfMwtlbkCeW1mlSRv352jj/hzLvpggzz8qc51IbooL8ZYjiSZoQgVllZrw9gZFB3ro1et7yM+DxDkAAAAANRmNRiUnJysnJ6fWmKsrFV8BoLVwdnZWQkKC9u7dq4yMjBpjR48eVVlZmbp06SInJ1JIYF/cgQAA4Jy0bt8x3ffJNh23UvVkykXt9MToeFoz4bQVlFXqx+RMLd2ZoV/2HlNpZVWj1gnwdPkz+aStvy6I9JO7i2MTR4vWxmAwKCbIUzFBnrq2d6SkE/fUjsN5f1TvytO2g7kqLDc2aL39x4q1/1ix/rc1TZLk7eak4Z1DdHm3MA3pGMI9hTNiMpn1yGc7lHqsWKnHijX29TV666+91TXC196hAQAAAGglysrKlJiYqOLi4lpj0dHRio6O5gEtAGhFHBwc1LFjR7m7u2v//v01xnJzc7Vt2zYlJCTIzY2Hv2E/JDEBAIBzitls1tyfUzVz2W6ZTqlu4ubsoJnXXqCrerSxT3A4K+UWV+iHpEwtTTyqNSnHTrs9nMEgdQr1Vq9of0ulpXaBHryJB0mSj5uzBscFa3BcsKQTiSN7s4osLQm3HsxV6rHabwZbU1hm1Dfb0/XN9nS5OztqWOdgXdYtXMM7h8jLlZd+OD1v/bxPy5MyLX8/nFOqa+as0wvXJOiaXpF2jAwAAABAa1BYWKjExERVVNR8gNBgMKhTp04KDQ21U2QAgLoYDAa1bdtW7u7u2r17t0wmk2WspKREW7duVbdu3eTj42PHKHE+451sAABwTnn8f7/ps81ptfZHB3rorb/2Vnw4v3ijflmFZfp+V6aWJR7VhtQcVZ2aEVcHb1cn9Wh7or1X72h/dY/yk4+bczNGi3OJg4NBncK81SnMWxP7t5Uk5RRXaNuhXG05eOLPjrQ8lVWa6lyntLJK3+3M0Hc7M+Ti5KCL44J0ebdwjYgPla8H9yPq16utv4K8XHSs6M8PJMqNJv3tsx3afjhPT13RRS5OVDQEYF/z5s3TlClTJEn79+9Xu3btmvwcBw4cUExMjCTp/fff1+TJk5v8HK3VjBkz9Oyzz0o68bAIAADVjh07puTk5BoffEuSk5OTunXrJl9fKrgCQGsXHBwsV1dXJSYmqrKy0rK/srJSO3bsUEJCgvz8/OwXIM5bJDEBAIBzyiXxobWSmC7pHKJXru8hX3c+uIdt6XmlWpaYoWWJGdp0MEcN/ZwmNsizRpWlDiFecnSgyhKaToCniy6JD9Ul8SeeYq2sMmn30UJtOZijrYdOtKI7kldq8/gKo0krkrO0IjlLTg4GXdghSJd3C9PILqEK9HJtqcvAWWZAbKC+vW+w7p6/RdsO5dUY+3D9Qe1KL9CcG3sp1Ify4gAAAMD5wmw2Ky0tTampqbXG3N3dlZCQIHd3dztEBgBoDB8fH/Xq1Us7d+5USUmJZb+7u7u8vLzsGBnOZyQxAQCAc8qormG6Z2h7zVm1TwaD9OAlHXXf8A5yIKkEVhw6XqKliUe1NDFD2w/nNegYF0cHDYoL0mXdwjS8c4iCSAJBC3N2dFBCpK8SIn01+aIT+9LzSrUiOVNLd2bo1/3Ha7XTrGY0mfXznmz9vCdbT361U/1jAnV5QphGdQ0jGQW1hPm66dM7Buq5b5P00YaDNca2HMzVFbPX6I2JPdU/NtBOEQLAuaElKkoBAHCmTCaTUlJSdPTo0Vpjfn5+6tKli5ydeYAQAM42bm5u6tmzp5KSkpSbmysXFxd169ZNTk6kksA+uPMAAMA55+GRnXQop0TX9o7UsE4h9g4HrUxKVqGW7szQ0sQMJR0taNAxbs4OGtIxWJd3C9fw+BDaw6HVifBz100D2+mmge10vKhcy5MytTQxQ+tSjsloI6PJZJbWpx7X+tTjembRLvVq66/Lu4Xpsm5hivT3aOErQGvl4uSg5/7STT2i/PTEVztVbvyzXcSxonJNfPdXPTE6Xrdc1E4GAwnDAAAAwLmoqqpKu3btUm5ubq2xsLAwxcXFycGBdtMAcLZycnJSQkKC9u3bp9DQULm58bAj7IckJgAAcFYqqTDKw8X6rzKODga9PrFXC0eE1spsNiv5aKGW/VFxaW9WUYOO83Rx1PD4UF3eLUxDOwXbvN+A1ibQy1U39GurG/q1VX5J5YkKTYkZ+nlvtipOSkA5mdl8orLOloO5+ueSZF0Q6avLuoXp8m7hignybOErQGt0be9IdQrz1l0fb1Fa7p/tC6tMZj33bZK2H87TzGsT+FkJAAAAnIMcHBysVuSIiYlRVFQUDzQAwDnAYDCoQ4cO9g4DIIkJAACcfXYcztM987fqkVEddXXPSHuHg1bIbDbrt7R8LU3M0LLEozpwvKT+gyT5uDlpRJdQXd4tXIPjguTm7NjMkQLNy9fDWdf2jtS1vSNVVG7UT7uztCzxqFbuzlZpZZXN435Ly9dvafn617Lf1TnMW5d3C9flCWGKC/HizenzWLc2vvr2vkF6YOF2rd6TXWNs8Y507cko1FuTepP4BgAAAJxjDAaDOnXqpLKyMhUWFsrBwUGdO3dWcHCwvUMDALQQk8mkpKQkRUVFydfX197h4BxGbUcAAHBWWbjxkMa/tV5H8ko17cudSkpvWDswnPtMJrM2HcjRPxYnadDMlbrqjbV6a/W+ehOYAjxddEO/KH1wSz9tfupSvXJdD13aJZQEJpxzvFyddGX3CM25sbe2Pn2p3vprb/2lR4S8Xet+tmV3RqFeXbFHI1/9WZe8slqzvt+txCP5Mputt6nDuc3Pw0XvTe6r+4fXfjLv98xCvfNLqh2iAlCfGTNmyGAwWBJRCwoKNGPGDCUkJMjLy0shISEaPXq01q1bV+O4rKwsPfXUU+ratas8PT0VGBioq666Stu2bav3nCaTSR9//LFGjx6tsLAwubu7q1u3bho3bpzefPNNVVRU1LtGbm6u/v73v6tz585yd3dXSEiIRowYoc8//7xB1119zTNmzKhz3tChQ2UwGDR06NAGrXuqxMRE/fOf/9SoUaMUGRkpV1dXeXl5KS4uTjfffLM2bNhg9bhVq1bJYDBoypQpln0xMTGWuKv/rFq1yurxX3/9tcaPH6+2bdvKzc1Nfn5+6tOnj5599lmr7X5OlZaWpqlTp+r/2bvv8KbK9g/g3yRtmu69F6V7MMretIBsRHCgbBwogoIiqPjq63idCAqKA0RQUFARB7KFsmdLGS3de0+6kzbr9wc/qiFpWW2Ttt/PdXld6fM8OeduiMnpOfe5765du0IikcDNzQ33338//v7777t6HZrz448/IiIiAra2trCwsEBYWBj++9//oqKiAsDt/1sREZH+iEQihIWFwdLSEj169GACExFRJ6JWq5GUlISysjJcunQJRUVF+g6JOjBWYiIiIqJ2QSZX4s0/47H9fM6/xlR4ZmsMdi0aAmszYz1GR/qiUKpwLqMce+MKsT++EMXV9bf1PCdLE4wNc8HYMBf062IHIxFz+6lzMRWLGv8fqFcocTK1FHuvFOJgQhEq6uRNPi+9pBbrotKwLioNnnamGBfmirFhLujpYQOhkBWaOguRUIAXRweiu4cNXvj5IqplCgBAsKsVXp8QoufoiOhWcnJyMGrUKCQnJzeO1dbWYu/evThw4AC2bduGhx9+GJcvX8b48eORl5fXuK6urg5//vkn9u/fj7179yIyMlLnPsrLy3H//ffj5MmTWuOnTp3CqVOn8MUXX2Dv3r3w9vbWuY2EhASMGjUK+fn5jWMymQyHDh3CoUOHMG/ePAwbNuxeXooWceTIEZ2vQ0NDA1JTU5Gamorvv/8er7zyCt5///0W2ee1a9fw0EMP4fDhwxrj9fX1iImJQUxMDL744gv88ccfGDBggM5tHD9+HBMnTkRV1T83hRQUFGDXrl3YtWtXiyUTKRQKTJ8+XSvxLD4+HvHx8di6dWurJE0REVHrEIvFCA8PZ4VeIqJOJisrC8XFxQCuJzQlJiZCKpXC29ub3wnU4pjERERERAYvv0KKBVtjcCm3UmvOxVoCuUqlh6hIXxRKFU6klmJfXCEOXC1Cee2t7+IHAHcbU4wNc8H4bi4I97RlwgXR/zMxEmFEkDNGBDlDrlThbHo59sYVYH98EUprmk4MzCmXYv2xdKw/lg4XKwnGhrlgXJgL+vnY8eRFJzEqxBl/LhqCZ7bEoKBSiq9m9oKpmFXsqAWpVIC0XN9RtC1TO0DYusnVDz/8MHJzc/Hqq69i7NixMDMzw4kTJ/Df//4XVVVVeOKJJ9CnTx9MnDgRUqkU7777LoYPHw5jY2Ps27cP7777Lurr6zF37lykpKRALBZrbF+pVGLixIk4ffo0AGD48OFYtGgRvLy8kJCQgO3bt2Pfvn1ISEjAyJEjcfHiRVhYWGhso6qqCmPGjGlMYJo2bRrmzJkDJycnJCcnY/Xq1di0aRPi4uJa9bW6HQqFAubm5pgwYQJGjBiBoKAgWFlZobi4GPHx8Vi7di2ysrLwwQcfICAgQKPqUt++fXHlyhX88ccf+M9//gMA2L9/P9zc3DT24ePj0/i4vr4eo0aNwoULFyASiTB9+nSMHz8ePj4+kMvlOHbsGFavXo3i4mKMHz8esbGxWoli2dnZjQlMQqEQ8+fPx0MPPQRra2tcvnwZH3zwAd5880306dPnnl+fl156qTGBKTAwEMuXL0f37t1RWVmJX375BRs2bMC0adPueT9ERNQyVCoVMjIyGisL6sK/94iIOhe1Wo3q6mqt8aysLEilUgQGBkLYyn/HUufCJCYiIiIyaKfTyrDoxwso05Go8sQQH7wyLgjGrKLTKRRVybD9XA62n89GQaXstp7j42DemFjRzd2aJ9qIbsFYJMQQfwcM8XfA25PDEJ35T6Wz5v6/K6ySYfOpTGw+lYmuDuaY3t8LD/X2gI2ZuMnnUMfg42CO3xYOQmpxDbztzfUdDnU00nJgpa++o2hby9IAc4dW3cXFixdx9OhR9O/fv3GsT58+8Pf3x8SJE1FdXY3+/ftDrVbj3Llz8PX959+gX79+cHBwwMKFC5GdnY3du3djypQpGtv/6quvGhOYZs+ejc2bN0MgEECpVMLT0xOjR4/G2rVr8cEHHyAtLQ3vvPMOPvzwQ41tvPPOO8jJuV6B9b333sOrr77aONe7d2889NBDmDhxIg4cONDir8+d6tmzJ3Jzc2FjY6M1N2bMGCxatAgTJ07EwYMH8dZbb2H27NkQia4nfJqbmyMsLAzR0dGNzwkICECXLl2a3N/bb7+NCxcuwMbGBn///Td69+6tMT9kyBDMmDEDAwcOREFBAVasWIEffvhBY83SpUsbKzBt3boVjz32WONcnz598PDDD2Po0KEacd2NK1eu4LPPPgMA9OrVC0ePHtVIWBs5ciQGDRqEOXPm3NN+iIioZcjlcly9ehUVFRWoqKhAz549G7+ziIio8xIIBAgLC0NqaqpGpVzgegtymUyGsLAwGBuzWwa1DF7xIyIiIoOkVqvx7YkMzNx4ViuBydRYhLWPheP1iSFMYOrg1Go1TqaWYsHWGAz64DA++Tv5lglMAc4WeH6kP/YtGYrDS4fj5bFB6O5hwwQmojskEgrQv6s93rw/FCdfHoHfnh2Ep4d1hZedWbPPSy+txf92J6D/e4fw0i+XcDGnAmq1uo2iJn0wExuhu4dNk/MFlVLsuVLQdgERUbOWLFmikcB0w4QJExor9pSUlOCdd97RSGC6Yd68eZBIJACutyS72bp16wAAjo6O+Pzzz3Ueg7355psICgoCAGzYsAH19f9U/mtoaMDGjRsBAN27d8crr7yi9XxjY2Ns3LjRIE6SOzg46ExgukEsFmPlypUArt+pfPHixbveV01NTePr+84772glMN3g7e2N119/HQDwyy+/oLa2tnGusLAQv/32GwBg4sSJGglMN1haWmL9+vV3HecNX331FVT/XzV3/fr1WhW3gOuJbuPGjbvnfRER0b2RSqWIjY1FRUUFgOvfOQkJCfxbjoiIAFxPZPL394efn5/WXFVVFS5cuIC6ujo9REYdESsxERERkcGRyZVYsfMKdsbmac11sTfD17P6INDFUg+RUVuprJPjl5gc/Hg2G+mltbdcH+ZuhXFhrhgb5gJfR+2LI0R0b4RCAcK9bBHuZYtXxgUhPr8K++IKsTeuAGkluv8frVeosCMmFztichHmboWZ/b1xf083mIn5Z2hnUq9Q4pmtF3AppwJPD+uKZWMCYcQEZCK9evTRR5uc6969O7KysiAQCJps8WVqagp/f39cuXIF6enpGnP5+flISEgAADzyyCOwtNR9zG5kZIR58+bh5ZdfxrVr13DhwgUMHDgQABATE4Nr164BAObMmdNkIrqHhwdGjx6N3bt3N/8Lt7H6+noUFRWhpqamMYHn3xeAL1261GTy0a0cPXoUlZXXW2w/9NBDza4dNmwYgOtVNWJiYhp/joqKglKpBACN1nY369evH0JDQxEfH39XsQLA33//DQDo1q1bs7/z448/jr179971foiI6N5UVlYiPj4ecrlcY/zatWuora3VmYRKRESdk7u7OyQSCRISEhr/rgAAmUyG2NhYhIaGNnuTB9Ht4NljIiIiMii51+rw9JYYxOdXac2NCHLCJ9N6wtpU/3dcU8tTq9W4lFuJrWeysOtSPuoVqmbXd/ewxsTurhgX5grPW1SGIaKWIxAIEOZujTB3a7w0JhApRdXYG1eIPy/lI7W4Rudz4vKq8MrOK3h3TwIe7OWBGf294O/MZNSOTq1W443f43EppwIA8PWxdMTnV+Gzx8Jha85Wg0T6EhAQ0OTcjZPNDg4OsLW1veW66upqjfG4uLjGx7qqPf3bv+fj4uIak5iuXLnSON63b99mt9GvXz+DSGKqra3F2rVrsX37dsTHx2uczL9ZaWnpXe/n3+3dXF1db/t5hYWFjY/v9PW92ySm+vp6pKSk3PZ+iIhIP4qLi5GYmKhVcUksFiMsLIwJTEREpMXe3h49e/ZEXFycRlVdhUKBy5cvIyAgAC4uLnqMkNo7JjERERGRwTiVWoqFP17AtTq51tzzI/ywZFQAhEK2BOto6hoU+PNiPraezUJcnnby2r+ZGoswuacbZg7wRpi7dRtFSETN8Xe2hL+zJZ4b4YdzGeXYejYb++IKIFdqtx2olimw+VQmNp/KRH8fO8wc4I0xoS4QG7EyT0d0NqMcP0XnaIydSC3FpM9P4OtZvRHqxs9xugVTO2BZmr6jaFumdq2+CzOzppO/hULhLdf8e93NyTrl5eWNj52cnJrdxr9Pav/7eXeyDWdn52bn20JmZiZGjBiBjIyM21ovlUrvel/FxcV39bx/t3Voq9f32rVrjRfE28O/IxFRZ6NWq5GdnY3MzEytOXNzc4SFhTW2jyUiIrqZhYUFwsPDERcXh5qaf25qVKvVSEpKglQqRZcuXZqsrEvUHCYxERERkUEoq6nHE99FQyrXvBBiYWKEVY/0wJhQZu53NKnF1dh6Jhu/XshFtUzR7Fo/JwvM7O+FKb08WImLyEAJBAL072qP/l3tUVIdgp+jr7eEzKvQfbH2bEY5zmaUw8HCBNP6euCxfl7wsGVVtY5kQFd7vDelG/77Z5xGUlvuNSke/PIUPnywOyb3dNdjhGTwhELA3EHfUdBdaomT1e3hhPesWbOQkZEBgUCAefPm4dFHH0VwcDAcHR0hFoshEAigUqkgEokAQKvSxZ34d9LYhQsXYGx8e8fFHh4eOsfb6vVtD/+ORESdiUqlQnJyMoqKirTm7OzsEBwcDCMjXj4kIqLmmZiYoGfPnkhISEBZWZnGXHZ2NqRSKQIDAxv/FiK6XTwKISIiIoNgb2GCNyaF4NWd/7Q36OpojvWzesPPiS2HOooGhQoHrhZiy+ksnM0ob3atsUiAMaEumDnAG/197Hjxg6gdcbQ0wcJIPzwz3BdHk4ux5XQWjiSXQNd129KaeqyLSsOXR9IQGeiEmQO8MSzAESJW3usQpvf3QqCLJRZsjUFx9T8lxmVyFRZvv4gruZV4ZVwQjESsxkXUEdjZ/VNJSteF0X/7d4uzfz/v323sioqKmm1/d6t9CAQCqNVqqFTNtymura1tdr4piYmJOHHiBABgxYoV+N///qdz3b+rH90Le3v7xseOjo5NJic15+bX19PTs8m1t3p9m3Oj5eDtbOde9kNERHdGLpcjPj4elZWVWnNubm7w8/Pj+RciIrptIpEIoaGhSE9PR25ursZcdXU1lEolk5jojvEsIRERERmMx/p54bF+XgCAUcHO+H3hYCYwdRB5FVJ8vD8Jgz44jEU/xjabwORuY4plYwJx6pWR+Hx6Lwzoas8TaETtlEgowIggZ2ya1w/HlkViQYQv7M3FOteq1MChxGLM23wew1dG4YsjqSitqde5ltqX3t622PXcEPTystGa++ZEBmZ/ew7ltQ1tHxgRtbiwsLDGx2fPnm127blz53Q+r1u3bo2Pz58/3+w2bjVvaXn9b4lr1641uUatViM1NbXZ7TQlPj6+8fG0adOaXBcdHd3sdm73WDc8PLzx8cmTJ2/rOTdryde3ORKJBP7+/q2+HyIiun11dXWIjY3VmcDk6+sLf39/nn8hIqI7JhAIGr9HbhCJRAgLC4NYrPs8IFFzmMREREREBuXN+0Pw/tRuWD+rN6wkbBvWnqlUakQlFePJ785j6IeH8XlU0wkJAgEQGeiIjXP64NjySCyM9IOjpUkbR0xErcnTzgwvjw3CqVdHYM2jPdGvi12Ta3OvSfHRviQMev8wFm+PxfnM8ntqv0P652wlwfb5AzGjv5fW3Km0Mkz67ATi8rQvphBR++Lm5obg4GAAwM8//4yamhqd65RKJTZv3gzgemWgXr16Nc717t27sVrQli1bmvz8z8vLw4EDB5qNx8fHB0DzSUR79+5FRUVFs9tpikLxT0vk5qo5ffXVV81uRyKRND6ur286gXfUqFEwM7veenXt2rV39d0YGRnZeCf0d9991+S68+fPIy4u7o63/2+jRo0CAFy5cgWxsbFNrvv222/vaT9ERHRrFRUViI2NhVSq2e5bKBQiLCzsrqr7ERER/Zubmxu6desGIyMjhIaGwtzcXN8hUTvFJCYiIiJqU2q1GhdzKpqcNzES4bF+XhCyjVC7VVZTjy+PpGH4x1GYt+k8/k4ohqqJ6yv25mIsiPDFsWWR2DSvH0YGO7OFFFEHZ2IkwuSe7vj5mYHYv2QYZg/0hoWJ7k7nDUoV/riYj4e/Oo2xnx7HltOZqJbJ2zhiailiIyHendINH0ztBvFN7ePyKqR48MtT+C02t4lnE1F7sXDhQgBASUkJnn/+eZ1r3n77bVy9ehUA8NRTT8HE5J/kdRMTE8ybNw8AcPHiRaxcuVLr+QqFAk899RQaGpqv4jZ8+HAA16tC6apcVFhYiOeee+42fivd/n2n8Y2krJt9+eWX+OOPP5rdjqura+PjtLS0JtfZ2Nhg0aJFAIBTp07hhRdeaLZVXlFREb755hutfU2ePBkA8Oeff+Lnn3/Wel5NTQ2efvrpZmO+HU8//XRjRY/58+frTPT64YcfsGfPnnveFxERNS8nJ0cj+RYAxGIxwsPDNdqVEhER3Qs7Ozv0799fo4010Z1iEhMRERG1mboGBZ7ffhFTvjiJI0nF+g6HWpBarcb5zHIs3h6Lge8fxof7EpFTLm1yfb8udljzaE+cenUEXh4bBE87szaMlogMRaCLJd6eHIazK0bivSndEOJq1eTapKJqvP5HPPq/dwgrfruCq/lVbRgptaRH+3lh+9MD4GylWXGvXqHCCz9dwtu7rkKhbPqiPBEZtmeeeQYDBw4EAGzatAkjR47Er7/+igsXLuDvv//Gk08+iXfffRfA9dY1r7/+utY23njjjcaKEC+//DKmT5+Offv24cKFC9i+fTsGDRqEvXv3ok+fPs3GMn/+fBgZGUGtVmPSpEn49NNPER0djVOnTmHlypUIDw9HZWWlRjLSnQgPD29shff1119j2rRp+OuvvxATE4M//vgDDz/8MJ599lkMHjz4ltu5UY3p9ddfx8GDB5GcnIzU1FSkpqZqVM14++230b9/fwDAmjVr0KtXL6xbtw4nT57ExYsXERUVhc8//xwPPPAAvLy8dFaBWrVqVWOrvenTp2PhwoWIiopCTEwMNm3ahN69eyM2NvaWr++t9OjRozGpLTo6Gn369MHmzZsRExODw4cPY8GCBZg9e/Y974eIiG4tODi4sZofAFhYWKBXr16wsLDQY1RERNQRGRnpvlkRuH4dobCwkBXXqVlNv4OIiIiIWlB2WR3mb4lGYmE1AOD5bbHY9dwQeNuzpGh7Vi2T4/fYPGw9k42koupm11qYGGFKuDtmDvBGoItlG0VIRO2BuYkRpvf3wmP9PBGbU4GtZ7Lw1+UCNCi0E1nqGpT48Ww2fjybjV5eNpg5wBvju7lCYizSQ+R0t3p52WLXc0Pw7NYLiM66pjGXUlzdWLmDiNofkUiEv/76C/fffz9OnjyJw4cP4/Dhw1rrgoODsXfvXp0XT62trbFv3z6MGjUKhYWF2LZtG7Zt26axZu7cuRg+fHhj1SZdQkND8dFHH+HFF1/EtWvX8MILL2jM29nZ4ffff8frr7+OlJSUO/5dBQIBtmzZghEjRuDatWv4+eeftSobdevWDb/88gvc3Nya3I6lpSWef/55fPTRR7hw4QJGjx6tMR8VFYWIiAgA1ytVHTx4EHPnzsXOnTtx6dKlxupMulhZaScId+nSBX/++Sfuv/9+VFdX44svvsAXX3yhseaNN96AQCBothXf7Vi9ejXy8/Oxc+dOJCYmav17+fj44KeffoKvr+897YeIiJpnZGSEsLAwxMbGwsrKCsHBwY3tRYmIiNpKWloa8vLyUFZWhuDgYAiFrLlD2viuICIiolZ3LLkEkz4/0ZjABABVMgWe3hIDZVN9xsiglSokeGdvCga8dwiv/xHfbAJTsKsV3p1yvdLKOw+EMYGJiJokEAjQy8sWqx/pibOvjsRr44PRxb7pSm0Xsivw4s+XMPD9Q3hvTwIyS7Xb1JDhcrKU4MenBmDWAO/GMU87U3z2WDhbixK1c3Z2djh27Bi+//57jB07Fs7OzjA2NoatrS0GDRqEtWvX4uLFi/D29m5yG6GhoYiPj8fy5cvh7+8PExMTODg4IDIyEj/++CM2bdp0W7G88MIL2LdvH8aMGQNbW1uYmJjAx8cHCxcuRGxsLIYOHXpPv2vPnj1x8eJFPPPMM/D29oaxsTHs7OzQr18/fPzxxzh37pxGu7imfPDBB9iwYQOGDh0KOzu7Zi8sW1pa4tdff8Xx48fx5JNPIjAwEJaWljAyMoKdnR369u2LhQsXYs+ePTh48KDObURERCA+Ph4LFiyAt7c3xGIxnJ2dMWHCBOzbtw9vvfXWXb8m/2ZsbIxff/0VW7ZswdChQ2FtbQ0zMzMEBwdjxYoViImJQdeuXVtkX0RE1DxTU1OEh4cjNDSUCUxERNTm8vLykJeXBwAoLS1FSkoKKzKRTqzERERERK1GrVbjq6PpWLk/ETfnKllKjLB8bCAvUrYjKpUah5JK8VuVDwqV5kBsYZNrxUZCTOzmihkDvNHLy4YVNYjojtmai/HUsK54YogPTqaVYuuZLPydUKwz+fVanRzrj6Vj/bF0DAtwxNPDumKQrz0/e9oBsZEQ7zwQhm7u1nh3TwK+ntkHNmZifYdF1OG8+eabePPNN2+5bvPmzdi8efMt1x05cuSWa4RCIWbNmoVZs2YBAJRKJYqLr7eUdnJyuq2Lp3Z2dvjwww/x4Ycf6pyfO3cu5s6de8vtjBkzBmPGjGlyvrnfp0uXLrc8se7l5YUvv/yy2TW32oZAIMCTTz6JJ598stl1/zZkyBAMGTLkttffzNPTU6sC07/d7vvmdsycORMzZ85skW0REdHdMzU11XcIRETUCdXX1yM9PV1jrLCwEGZmZvD09NRTVGSomMREREREraK2XoHlOy5j95UCrTl/Jwusn90HPg5sJdceyJUq7LqUjy+OpCG1uAZA0/9u3vZmmNHfCw/39oStOS9CE9G9EwoFGOrviKH+jiislGHbuWxsP5+Noqp6neuPJZfgWHIJenraYGGkH0YGOUHIhFmD90hfT4zr5gJLibG+QyEiIiIiarfKyspQXV0Nb29v3tRBREQGw8TEBGFhYYiLi4NKpWocT09Ph0QigaOjox6jI0PDJCYiIiJqcVlltXh6S4xG+7gbxoW5YOXDPWBhwsMQQyeTK7EjJhdfHU1D7jVpk+uEAmBUsDNmDvDGED8HJgsQUatxsZbghfsCsGiEHw4lFGHrmWycSC3VufZiTgWe+j4agc6WeDbSFxO6ucJIxI7qhqy5BKaKuga8vesqXh0fDEdLkzaMioiIiIiofaipqcHVq1ehUqkglUoRGBgIoZB/AxERkWGwtbVFUFAQrl69qjGemJgIExMTWFlZ6SkyMjS8ekhEREQt6khSMZ7fFosqmUJjXCAAXhodiGcjfHknmIGrrVfgx7PZ2HA8HcXVuiudAICjhRiP9ffGY/084WrNcuRE1HaMRUKMDXPF2DBXpJfU4Mez2fglJheVUrnW2qSiaizefhGrDyZjwXBfTOnlDhOjW7cwIsOhVKnx/PaLOJZcgtPpZfhqZm/08LTRd1hERERERAajvr5eo7pFcXExZDIZunfvflstXImIiNqCo6MjfHx8kJGR0TimUqkQFxeHXr16QSKR6DE6MhRMwSYiIqIWoVarsS4qFfM2n9dKYLKSGOHbuX2xMNKPCUwGrKKuAZ/+nYzBHx7Gu3sSmkxgshPJMMo8B/sW9sWL9wUwgYmI9KqrowX+MzEEZ1eMxLtTwuBpp/szKausDq/svILhHx3BxhMZqGtQ6FxHhmfVgSQcSy4BABRUyvDw16fxc3SOnqMiIiIiIjIMSqUScXFxqK/XPI8jkUhYiYmIiAyOp6cnXFxcNMbkcjni4uKgUPB8HbESExEREbWAmnoFlv1yCXvjCrXmAp0t8fWs3ujiYK6HyOh2FFfLsPF4BraeyUJtg7LJdT09bfD4ADdcPfgzBILrlVCIiAyFxFiEGf29Ma2PJ3ZdzscXUWlIKa7RWldYJcM7f13FuqhUPD64C2YN7AJr06bbmJF+Vcvk+ONivsZYg0KF5Tsu40puJV6fGAKxEb+PiIiIiKhzUqvVSEhIQE2N5t8+VlZWCAwM5M2ERERkcAQCAfz9/SGTyVBRUdE4Xltbi6tXr6Jbt278/urkmMRERERE9+xyTgX2xWsnME3o5oqPHuoOcxMechii3Gt1+PpoOn6KzkGDQtXkusF+9lgY4YeBvvaorq5Gwt9tGCQR0R0yEgkxJdwDk3u442BCEdZFpeJybqXWuvLaBnx8IBlfH03HrIHeeHyIDxwsTPQQMTXHUmKMPxYNxsIfLuBsRrnG3JYzWUgsrMK6Gb3gZMly40RE7ZlardZ3CERE7VJ6ejrKyso0xiQSCcLCwliFiYiIDJZQKERoaChiY2NRV1fXOH7t2jWkpqbCz49dPTozHsEQERHRPRvk54CXRgc2/iwUAK+MC8Ln08OZwGSAUotrsPTnS4hYeQRbzmQ1mcA0KtgZvz07CD88OQCD/Bz4RwMRtStCoQBjQl3wx8LB2PJEP/T3sdO5rrpegS+OpGHIh4fx5p/xyK+QtnGkdCsOFibY+mR/PD7YR2vufOY1TPrsBGKzr+khMiIiIiIi/cnPz0dubq7GmJGREbp16wZjY1abJSIiw2ZkZISwsDCt76z8/Hzk5eXpKSoyBLyqSERERC3i2QhfXMmtxOn0Mnz2WDiGBTjqOyS6SVxeJb44koq9cYVo6kZnoQCY2N0Nz0b6IsjFqm0DJCJqBQKBAEP9HTHU3xHRmeVYF5WKqKQSrXUyuQqbT2Xih7NZmBrugWcifOHDVqgGw1gkxBuTQhDmboVXd15B/b8ScIuq6jHt6zN454FQTOvrpccoiYiIiIjaRnl5OVJSUjTGBAIBQkNDYWZmpqeoiIiI7oypqSlCQ0Nx6dIljeqsaWlpkEgkcHBw0GN0pC+sxNTKsrKysHTpUgQFBcHc3Bx2dnbo27cvVq5cqVEa7W5s3rwZAoHgtv7bvHnzLbdXV1eHjz76CH379oWdnR3Mzc0RFBSEpUuXIisr655iJSKijk8gEODjR3pg16IhTGAyMOczyzHn23OY+NkJ7LmiO4HJWCTAY/08cXhpBNY+Fs4EJiLqkPp0scOmef2w+/khmNDdFboKzMmVavwUnYORq45g0Y8XkFBQ1faBUpOm9vLArwsGwd3GVGO8QanCy79ewWu/XWm2RSoRERERUXtXW1uLq1evao0HBATAxsam7QMiIiK6B9bW1ggKCtIab2ho0EM0ZAhYiakV7dq1CzNnzkRV1T8nvevq6hAdHY3o6Gh888032L17N/z8/PQY5XWpqakYP368VuZ+UlISkpKS8M033+CHH37AxIkT9RQhEREZgvSSGiQWVmN8N1ed8xYmRrBg+ziDoFarcSylFOsOp+JcZnmT6yTGQkzv542nhvnA1dq0yXVERB1JqJs11k3vhbSSGnx1JA2/xeZBodLM8FSpgb8uF+CvywUYGeSEZyP90NvbVk8R07+FuVvjz0WD8dy2WJxKK9OY++FsNhILq/HljF5wspLoKUIiIiIiotbR0NCAK1euQKlUaox7eXnBxcVFT1ERERHdGycnJ0ilUmRmZkIoFCIkJAT29vb6Dov0hFcZW0lsbCymTZsGqVQKCwsLvPrqq4iMjIRUKsX27duxYcMGJCcnY8KECYiOjoalpeU97W///v1wc3Nrct7Dw6PJuerqakyYMKExgempp57Co48+ClNTU0RFReH9999HVVUVpk2bhpMnT6Jnz573FCsREbVPf18twgs/XUS9QgVXawnCvXgh1xCpVGrsjy/EuiOpiMtrunqIpcQIcwZ2wbzBXWBvYdKGERIRGQ5fRwusfLgHltwXgPVH07D9fI5Gm7IbDiUW41BiMQZ2tcfCSD8M9rOHQFcZJ2oz9hYm+P7xfnh/byI2nsjQmIvJuoaJn53Aj08NgJ+ThZ4iJCIiIiJqWUqlEnFxcaivr9cYd3R0RJcuXfQTFBERUQvx8vKCQqGAs7MzLCx4PqczYxJTK1m8eDGkUimMjIxw4MABDBw4sHFuxIgR8Pf3x/Lly5GcnIxVq1bhzTffvKf9BQQE3PVB6sqVK5GcnAwA+Oijj7Bs2bLGuYEDByIiIgLDhw9HXV0dlixZgiNHjtxTrERE1L6oVGp8djgVn/yd3Di2YOsF/PncYDhZssKBoZArVfjzYj6+PJqG1OKaJtfZm4vx+BAfzBroDSuJcRtGSERkuNxtTPHW5DAsGuGPjScysPVMFmrqFVrrTqeX4XR6GXp42mBhhC9GBTtDKGQyk74YiYR4fWIIurlb45WdlyGT/5OA5mpjCg9bVhg0ZCKRCAqFAgqFAkqlEiKRSN8hEVE7pFKpGquR8HOEiDq6pKQkVFdXa4xZWVkhKCiIN1kQEVG7JxAI4Ovrq+8wyAAI9R1AR3Tu3DkcP34cAPDEE09oJDDdsHTpUgQHBwMA1qxZA7lc3qYx3iCXy7F27VoAQHBwMJYuXaq1ZtCgQXjiiScAAEePHsX58+fbNEYiItKfSqkcT30frZHABACFVTJ8cjCliWdRW5LJldhyJguRHx/B0l8uNZnA5GotwZuTQnDi5RFYGOnHBCYiIh0cLU3wyrggnHx5BJbeFwBbM92flZdyKjB/SwzGrTmOPy7mQaHUrt5EbeeBcHfseGYQ3G2uJy05WIjx1cxekBjzYrYhMzMza3xcUVGhv0CIqF2rqamBWn29JaypKZNXiahjc3V11UjYlEgkCA0NhVDIS31ERETUcfDIphX8/vvvjY/nzZunc41QKMTs2bMBXD9ZFxUV1RahaYmKikJlZSUAYM6cOU0e7M6dO7fx8W+//dYWoRERkZ4lFlZh8ucncCixWGvu/h5ueGNiiB6iohtq6hVYfywNQz+Kwuu/xyH3mlTnOh8Hc3z0YHccXRaJuYN9YCrmBV0ioluxNjPGcyP9ceLlEfjPhGA4W+luu5lUVI3F2y9i5Oqj2HYuGw06WtFR2whzt8au54YgItAR66b3gqs1L2QbOhsbm8bHxcXFKC4uhkwma0xGICJqjkqlQlVVFQoLCxvHLC0t9RgREVHrs7W1RXh4OCQSCUQiEcLCwiAWi/UdFhERUZuoqKhAQUGBvsOgNsB2cq3gxIkTAABzc3P07t27yXXDhw9vfHzy5EmMHj261WO72Y1Yb47nZn369IGZmRnq6upw8uTJtgiNiIj06I+LeXjl1yuQypUa40IBsGJ8MJ4Y4sMy1XpSUdeATSczsflUJiqlTVdyDHKxxMJIP4zv5goRWx0REd0VcxMjPDm0K2YN9MavMXn46mgassvrtNZlldXh1Z1X8ImFGH5Ke4SYlOshWrIzF2PzvH7NrpErVTAW8X4uQyCRSGBtbd14Y1VZWRnKysogEAjYEqqDUqvVaGhoAABUV1fz7wm6J0qlUiPp0dTUFObm5nqMiIiobZibmyM8PBxSqZSfe0RE1GkUFhYiOTkZarUaYrEY9vb2+g6JWhGTmFpBQkICAMDPzw9GRk2/xEFBQVrPuVvz5s1DUlISSktLYWVlBT8/P4waNQoLFiyAu7t7k8+7evWqznhuZmRkBD8/P1y+fPmeYyUiIsMlV6rw/p5EfHsyQ2vO3lyMz6aHY5Cvgx4ioyqZHN8cS8fGExmobVA2uS7cywaLIv0wIsiJF4aIiFqIiZEI0/t74ZE+HvjrcgG+OJKK5CLt9p3FNQ0ohisuyBxhczYXT0YEsqWZAalrUOChL09jYg9XLBjuy+9JA+Dq6gqxWIySkpLGMbVaDYVCoceoqLWoVCrU1Fz/7LS0tGTrG2oxpqam8PLy4uc6EXUaYrGYFZiIiKjTyMjIQHZ2duPPCQkJ6NmzJywsLPQYFbUmJjG1MJlMhtLSUgCAh4dHs2ttbW1hbm6O2tpa5OTk3NN+jxw50vj4xt2LZ8+exapVq/Dpp5/i6aef1vm83NxcANez9/9dyl0XT09PXL58GSUlJaivr4eJie6WCs3tpyn/Lv1WW1uLqqqq2942UUu7cVL15sdE+tJW78nSmgYs+y0BMTnan8FhbpZYPTUYLlZifka3MZlciW0xBfj2dA4qpU1f0BvQxQZPDvJEX29rCAQCVFdXt0o8/IwkQ8L3I+nDCF9LRHTtiaMp5dhwMhtxBdrvPZnaCB8fysCWc3l4eogXHujhAiNWxdMrtVqNV/5IwtWCKlwtqEJMRinemRgAC5OOe1qkvXxGisViODk5QSaTQSaTQaFQQKVia8aOSK1WN1be4slmuldCoRBisRhmZmaQSCR3/DlXW1vbSpEREbUMtVrN5EwiIiJA6/tQqVQiLi4O4eHhd5SvQO1Hxz1bpyf/vmB4OydkbiQx3e0Jxa5du2Lq1KkYOHAgPD09AQDp6en49ddfsWPHDshkMjzzzDMQCASYP39+k/Hebqw31NTU3NGHwo3YbsfOnTthbW192+uJWtOWLVv0HQKRhtZ6TxYqTHGgxgu1amOtuRBxOQbVxeO3H063yr5JN6UaSGywRYzUSee/yw1djKvQS1IC50opYvYCMW0YIz8jyZDw/Uj6MEQN+FiYI0bmiHyF9t9URdUNeHtvKtbsj0c/0yL4GleB1yH047LMHielro0/H0oqQ0xKFMZa5MBWVK/HyNoGPyPJ0Fy8eFHfIVAndyOhjojIEKlUKly+fBmOjo7NdtogIiLqDLy9vSGVSlFcXNw4Vl9fj/j4ePTo0YMt6TsgJjG1MJlM1vj4dsp53kgEkkqld7yvKVOmYM6cOVrZh3379sW0adPw119/YerUqZDL5XjhhRdw//33w8XFRWe8dxLr3cZLRESG6Wq9LY7XuUIFzXYOIqgw1KwAwSbX9BRZ56RWAylya5yXOqFKpTthWAA1/MSVCJeUwL4TXHglIjJUAgHgYVwLD+NaFCpMcUHmiCy5lda6SpUJDtZ6IVYkRT/TIngZ1TCZqY0pIQCgBvDPC1+hkuDXqq6INM+Dr5iVJomIiIhI/9RqNRITE1FZWYnKykpIpVL4+rIVMhERdV4CgQCBgYGQyWQanUKqq6uRmJiIkJAQfk92MExiamESiaTxcUNDwy3X19dfv/Boamp6x/u6VbWiiRMn4o033sDrr7+Ouro6bNy4Ea+99prOeO8k1ruJ91bt8goKCtCvXz8AwNSpUxEQEHBH2ydqSTU1NY13Ks+aNYtl7knvWvs9uTe+GEf/SNIYc7UyweoHgxHqatmi+6KmqdVqHEstx2dHs5Bc0XRrg5GB9lg0zBu+juZNrmlN/IwkQ8L3IxmampoafLxpB85KnZGnozJTqdIUe2q6INzDCosju6CXJyvQtqWT6dfwyh+JGu1Z5RDhQK0X5nXzwHMRXTpU2z9+RpKh4XuSDElycjLef/99fYdBRKQlMzMTJSUljT/n5eVBpVLxmgkREXVqQqEQoaGhiI2N1SgqU1paivT0dPj6+uoxOmppTGJqYZaW/1zsvZ0WcTf6r7fWiZv58+fjjTfegFqtxtGjR7WSmG7EeyexAncer4eHx22vNTc3h5WV9t3LRPpgYWHB9yMZlNZ4T04baIWk0gZ8ezIDADDYzx6fPdYLdua3rtJHLeNMehlW7k9CTFbTVa+G+jvgpdGB6OFp03aB3QI/I8mQ8P1IhsLZSIr7LTMRPvZRrDueg0u52u1qYnOrMHfLZUQGOuKlMYEIdWMyU1sY19MKYV6OeGZrDOLzNSsvbTqTi6QSKT57LBz2FrffOr294GckGRq+J0nfzM31c1MIEVFzCgsLkZ2drTEmEong5uamp4iIiIgMh1gsRrdu3RAbGwuF4p8b1HJzc2FmZgZXV1c9RkctSXjrJXQnJBIJ7O3tAVz/H6Y5165da0wM8vT0bJV4nJycGuPJy8vTmr+RXFRbW4uKiopmt3WjmpKjo6NGazkiImr/Xh0fhP4+dnhmuC++m9ePCUxtJC6vErO/PYdH159pMoGpp6cNfnyqP7Y80d+gEpiIiKh5A3xs8fvCwfhqZm/4O+m+CSQqqQQT1p7Ac9tikVHadBU+ajmedmb4dcEgPNhL+0abU2llmPTZCVzKqWj7wIiIiIioU6uoqEBycrLWeEhICKsXEhER/T8zMzOd7eOSk5Nx7VrTN4lT+8IkplYQEhICAEhNTdXIArxZYmJi4+Pg4OBWi6e5HpA3Yr05npspFAqkpaUBaN1YiYio9ajV6ibnjEVCbH2yP14ZFwQjEQ8PWltqcQ2e/SEGEz87gWPJJTrXBDpbYsPsPvjt2UEY5OvQxhESEVFLEAgEGBvmgn1LhuHjh3vA3UZ3W+5dl/IxavVRvLrzMgoqpW0cZecjMRbh44e7450HwmAs0vx7Ob9Shoe/Oo3t57KbeDYRERERUcuqq6tDfHy81rk7Pz8/2NnZ6SkqIiIiw2Rra6uzzWp8fLxGZylqv3iVshUMGTIEwPXqRjExMU2uO3r0aOPjwYMHt0osJSUlKC0tBQCdJUdvxHpzPDeLjo5u/J++tWIlIqLWUyWT46nvY7D3SkGTa4yZvNTq8iqkWL7jEkZ/chR7rhTqXONpZ4pPpvXAnsVDcV+Ic7PJyERE1D6IhAI81NsDh18ajrfuD4WDjnZlSpUa287lYPjKI/jfX1dRXtugh0g7D4FAgFkDvLF9/kA4W2n+ezQoVXhl5xW8uvMy6hVKPUVIRERERJ2BXC5HXFyc1g3x7u7ucHd311NUREREhs3FxUWr05VSqURcXBwaGnhOrb3j1cpW8MADDzQ+3rRpk841KpUK33//PQDAxsYGkZGRrRLL+vXrG7P3hw8frjUfEREBa2trAMB3333XZJWOzZs3Nz6eMmVKywdKREStJqmwGpM/P4m/E4rw0i+XkFpcre+QOp2ymnq8vesqIlcewc/RuVDp+Lp1tDTBO5NDcejFCEwJ94BIyOQlIqKOxsRIhDmDuuDY8ggsGxMIS4mR1poGhQrfnMjAsI+i8Onfyaipb7q6L9273t622PXcEPTz0b7Dfdu5HKw9lKKHqIiIiIioM1CpVIiPj4dUqlmN1d7eHr6+vnqKioiIqH3w8fGBg4NmFwuZTIa4uDioVCo9RUUtgUlMraBfv34YOnQoAGDjxo04ffq01ppVq1YhISEBALB48WIYGxtrzB85cgQCgQACgQBz587Ven5mZiZiY2ObjeOvv/7C22+/DQAwNTXFvHnztNaIxWI8//zzAICEhAR8/PHHWmtOnz6NjRs3ArieCNW3b99m90tERIZj16V8PLDuJDJKr1fTq21QYv6WGFTL5HqOrHOoksmx+kAShn0UhW9PZqBBqX3gbCUxwstjg3B0WQRmDewCsREPz4iIOjozsREWRvrh+PJILIjwhcRY+7O/pl6BT/9OwbCPovDN8XTI5KwI1FqcLCX44cn+eGKIj8a4r6M5FkT46SkqIiIiIurI1Go1kpOTUVlZqTFuYWGB4OBgVuYmIiK6BYFAgKCgIFhaWmqM29ra8nu0ndO+7ZNaxJo1azB48GBIpVKMHj0aK1asQGRkJKRSKbZv347169cDAAICArB06dI73n5mZiYiIyMxcOBATJo0CT169ICTkxMAID09HTt27MCOHTsaKyt9/PHHTZYeXbZsGX766SckJydj+fLlSE1NxaOPPgpTU1NERUXhvffeg0KhgKmpKT799NO7e0GIiKhNyZUqfLA3ERtPZGjNVdTJkVVWhzB3az1E1jnI5Ep8fzoTXxxJQ0Wd7oQxU2MRHh/SBfOH+cLa1FjnGiIi6thszMR4eWwQ5g3qgs8Op2LbuWwobirXV17bgP/tTsDGExlYPNIfD/X2gBFbwLY4Y5EQr08MQXcPa7zy6xUIBcDXs/rAwoSnTYiIiIio5WVnZ6OoqEhjTCwWIywsDCKRSE9RERERtS8ikQhhYWG4cOECGhoaEBAQABcXF32HRfeIZ+NaSXh4OH766SfMnDkTVVVVWLFihdaagIAA7N69Wys78E6cPn1aZ6WnG8zMzPDJJ59g/vz5Ta6xtLTE7t27MX78eKSkpGD9+vWNSVY3WFlZ4YcffkDPnj3vOlYiImobJdX1WPTjBZzNKNea6+5hjS9n9oa7jakeIuv45EoVfo6+3nqmqKpe5xpjkQAz+nvj2UhfOFlK2jhCIiIyRE5WErzzQBieGtoVn/ydjN8v5uHmTt8FlTK8svMK1h9Lx4ujAzA+zBVCth5tcZN7uiPQxRIFFTL4OVnoOxwiIiIi6oCKi4uRmZmpMSYUChEWFgYTExP9BEVERNROicVidOvWDXK5HDY2NvoOh1oAk5ha0aRJk3D58mWsWbMGu3fvRm5uLsRiMfz8/PDwww9j0aJFMDMzu6tt9+7dG1u3bsXp06cRHR2NgoIClJaWQqFQwNbWFqGhoRg5ciSefPLJxgpNzfHz80NsbCzWrVuHX375BampqWhoaICnpyfGjx+PxYsXw9vb+65iJSKitnMh+xoWbI3RmUDzaF9PvHl/KCTGvJurpalUauy6nI9PDiYjs6xO5xqhAJgS7oElo/zhaXd33/9ERNSxedmb4ZNpPfH08K5YdSAZB68Waa1JL63Foh9jEeqWhpfGBCIiwJElsltYkIsVglysmpwvrpahSqpgkhMRERER3bH6+nokJSVpjQcHB9/TDe9ERESdmbm5ub5DoBbEJKZW5u3tjdWrV2P16tV39LyIiIjGVnC6WFpaYsaMGZgxY8a9htjI3Nwcy5cvx/Lly1tsm0RE1DbUajW2ns3G27viIVdqfn+IRUK8NTkUj/Xz0lN0HZdarUZUUjFW7k9GQkFVk+vGhDrjpdGB8HfmySgiIrq1IBcrbJjdBxeyr2HlviScTi/TWhOfX4V5m86jXxc7LB8biD5d7PQQaecjV6qw6IdYXC2owscP98DYMJYoJyIiIqLbZ2JigqCgICQmJkKlUgEAfH194eDgoOfIiIiIiAwDk5iIiIjaOZlcif/8HocdMblac27WEnwxszd6etq0fWAd3Nn0Mqzcn4TorGtNrhni54CXxgTy9SciorvSy8sWPz7VHydTy/DR/kRczq3UWnMusxwPfXUaI4Kc8NLoQIS4NV1BiO7de3sScC7zesveZ7bGYEGEL14aHQgRW/sRERER0W1ydHSEiYkJ4uLi4OjoCHd3d32HRERE1GHV1NQgNTUVoaGhMDY21nc4dBuYxERERNSO5ZTXYcEPMYjL064CNMjXHp89Fg57CxM9RNZxxeVVYuX+JBxNLmlyTQ9PG7w8JhCD/HgXHRER3RuBQIAh/g4Y7DcY++ML8fGBZKQW12itO5xYjMOJxZjUww0v3hcAHweW0W5pf18twqaTmRpjXx5Jw5XcSqx9LBx25mL9BEZERERE7Y6VlRV69+4NsVjM9tBEREStpKysDFevXoVKpUJcXBx69OgBoVCo77DoFpjERERE1E7V1isw5YtTKK2p15p7enhXLBsdCCMRD8ZaSlZZLT7an4TdlwuaXBPgbIGlowMxOsSZJ6CIiKhFCQQCjA1zxX0hLth5IRef/p2CvAqp1rpdl/Kx50oBHunjiRfu84eTpUQP0XZMg/0cMLWXO3ZeyNMYP5FaikmfncCXM3uhu4eNfoIjIiIionbHxIQ3HhIREbWW4uJiJCQkNP5cVVWFpKQkBAUF8fqNgeOVTSIionbK3MQICyN9NcfEInwxoxdeHRfMBKYWUlHXgLd3XcWo1UebTGDysDXF6kd6YO/iYRgT6sIDYCIiajUioQAP9/HE4ZeG481JIXCw0K7+o1Spse1cNiJWHsGav1NQ16DQQ6Qdj6lYhFUP98A7k0NhLNL8rs+rkOKhr07j5/M5eoqOiIiIiIiIiIhusLa21koYLi4uRk4Oz90YOl7dJCIiasfmDuqCyT3dAABdHc3xx6LBGN/NVc9RdQz1CiU2HEvHsI+i8O3JDMiVaq01DhYmeHtyKA4vjcDUXh4QCZm8REREbcPESIS5g31wdFkkXhodAEuJdqHlugYlPvk7GZEfH8HP53OgVGl/l9GdEQgEmDWwC7bPHwAnS80TYQ0KFZb/ehmv7ryCeoVSTxESERERkaGQyWSIjY1FZWWlvkMhIiLqdExMTBAWFgaRSKQxnpmZibq6Oj1FRbeDSUxERETtmEAgwAdTu2P+sK74Y+Fg+DlZ6jukdk+tVmPXpXyMWn0U7+5JQJVMu3qFlcQIy8YE4tjyCMwe2AViIx5SERGRfpibGGHRCH8cXx6JZ4b7QmKs/Z1UVFWP5b9exoS1x3E8pUQPUXY8vb3t8NfzQ9Cvi53W3LZz2Xjk6zPI19Huj4iIiIg6B7VajZSUFFRVVeHixYtITk6GXC7Xd1hERESdioWFBYKDgzXGbnxHq9W82c9Q8YobERFRO1Ch1G4Vc4OpWIQV44NhKTFuw4g6pujMckz54hSe2xaLnHLtC49ikRDzh3XF8eUjsDDSD2Zi7aoXRERE+mBjJsYr44JwdFkkpvXxhK7OpomF1Zi18RzmfHsOSYXVbR9kB+NkKcEPT/XH44N9tOYu5VRg0mcncCqtVA+REREREZG+lZWVoby8vPHngoICZGdn6zEiIiKizsne3h4eHh4aYxUVFSgp4Y1+hopJTERERAZMrlTh40Pp2F7ljzy5ub7D6bAyS2uxYGsMHvrqNC7mVOhcM6mHGw4tHY4V44NhbcaEMSIiMkzOVhJ8+FB37Hl+KIYFOOpcczS5BOPWHMMrv15GcZWsjSPsWIxFQrwxKQRrHu0JU2PN8uRltQ2Y+c1ZfH00jXf3EREREXUiSqUSqampGmPGxsbw9vbWU0RERESdW5cuXWBiYqIxlpaWBoVCuxMH6R+TmIiIiAxUfoUUj64/g+/P5kENAQ7UeqKwql7fYXUo12ob8NaueIxafRR74wp1runbxRa/PTsInz0WDk87szaOkIiI6O4Eu1rh+8f74bvH+yHIRbvdrEoNbD+fg+Erj+DTv5NR18CTNvdick93/LZwELztNY8VVGrgZFoZmMNERERE1HlkZWWhvl7zHJ6vry+MjFjRm4iISB9EIhF8fX01xhoaGpCZmamfgKhZTGIiIiIyQFFJxZiw9jhisq41jsnURnhxZwLkSpUeI+sYZHIl1h9Lw7CVUdh0MhMKlfaVxS72ZvhqZm/8/PRAhHvZ6iFKIiKiezc8wBG7nx+Kjx7sDidLE615qVyJT/9OQcTKI/jpfDaUOr4T6fYEuVjhz0VDMDLIqXHMxUqCT6f1hFCoo78fEREREXU4tbW1yM3N1RizsbGBk5NTE88gIiKituDg4AA7OzuNsby8PFRXV+spImoKk5iIiIgMiEKpwof7EjFv03lcq5NrzAmhwtQezjDiRbC7plar8eelfIxafRTv7UlEtUy76oSNmTH+OykEB14YjrFhLhAI+HoTEVH7JhIK8EhfTxxZFoEXRgXATCzSWlNcXY+Xf72CCWuP42hyiR6i7BisTY2xYXYfvDAqAMYiAT6bHg47c7G+wyIiIiKiNqBWq5GSkqLRSlggEMDf35/nl4iIiPRMIBDAz88PQqFmiszN392kf6xdSUREZCAKK2V4flsszmWWa81ZChswxjwbD4UP50mPu3Quoxzv7knApZwKnfNikRDzBnfBs5F+sDY1btvgiIiI2oCZ2AiLR/njsX6eWH0wGT9H5+DmwkuJhdWY8+05DPV3wIrxwQh2tdJPsO2YUCjA4lH+eKiPB9xtTPUdDhERERG1kaKiIlRWVmqMeXp6wszMrIlnEBERUVsyNTWFl5eXRhu56upqFBQUwM3NTX+BkQZWYiIiIjIAR5NLMH7tcZ0JTCMD7PGwZSocjWR6iKz9Sy+pwdNbovHI16ebTGC6v4cbDi0djlfHBzOBiYiIOjwnKwk+eLA79iweiuEBjjrXHE8pxfi1x7F8xyUUVfEY5G40l8BUUdeAR9efxuXcirYLiIiIiIhajVwuR3p6usaYRCKBl5eXniIiIiIiXTw9PWFqqnnOJiMjAw0NDXqKiG7GJCYiIiI9UihV+Hh/EuZuOofyWs0DJGORAG9MDMHqB4NhIlTpKcL2q7y2AW/+GY/RnxzD/vginWv6dbHD7wsHY+1j4fC0411xRETUuQS5WOG7x/vh+8f7IcjFUmterQZ+js5FxMoj+ORgMmrrtduw0p1TqdRY+vMlnEkvx4NfnsLmkxksW05ERETUzmVkZEAul2uM+fn5QSTSbuVMRERE+iMUCuHv79/4s0AggLu7O7+zDQjbyREREelJcZUMz22LxdkM7epL7jamWDejF3p62qCqqkoP0bVfMrkS353KxOdRqaiW6b7Y6uNgjlfGBWF0iDPb8xERUac3LMARg/0c8OuFXKw6kISiqnqNealciTWHUvDjuWwsvS8AD/fxhEjI78+7teF4Og4lFgMA5Eo13tx1Fecyy/HBg91hJWFFSCIiIqL2pqqqCgUFBRpjDg4OsLe311NERERE1BxbW1s4OTmhoaEB/v7+bP1qYJjEREREpAen0krx/LZYlNZol6e8L8QZHz/UA9ZmvIh1J1QqNXZdzsdH+5KQVyHVucbWzBhLRgVgen8vGItYkJKIiOgGkVCAR/p4YmJ3V3xzPANfHU1DXYNSY01JdT1e2XkFm05m4tXxQRge4Mhk4DukUqkRlVSsNb7nSiHi86uwbnovhLlb6yEyIiIiIrobarUaycnJGmNCoRC+vr56ioiIiIhuR0BAAIRCIc9tGSBevSMiItIDhVKNspvaxxkJBfjPhGCsn9WbCUx36Gx6GaZ8cRKLt1/UmcAkNhLimeG+OLo8EnMGdWECExERURPMxEZ4fqQ/jiyLwGP9vKCr4FJSUTXmbjqP2d+ew9V8Voy8E0KhAFue6I+nh3fVmssqq8PUL05hy+lMtpcjIiIiaify8vJQW1urMdalSxdIJBI9RURERES3QyQSMYHJQPEKHhERkR4MC3DEoki/xp/drCX46emBeHJoVx403YH0khrM/z4a09afwaXcSp1rJvd0w+Glw/HKuCC2aCEiIrpNTpYSvD+1G/YuHoaIQEeda46nlGLCZ8ex7JdLKKyUtXGE7ZexSIhXxwVj45w+sLkpcb1BqcLrf8TjuW2xqJbJ9RQhEREREd0uExMTGBv/c0xnbm4Od3d3PUZERERE1L6xnRwREZGeLBkVgPOZ5TATG2HVwz1gay7Wd0jtRllNPdYeSsEPZ7OhUOmuVNDPxw6vjQ9GD0+btg2OiIioAwl0scTmef1wPKUE7+1JREKBZuUltRr4JSYXuy7nY/7Qrpg/3BcWJjzVcDtGBjtj9/NDsejHC4jNrtCY++tyAeLzq/D59HCEurG9HBEREZGhcnR0hK2tLTIyMpCfnw9/f38IhawfQERE1J7JZDJWVdQjnlkkIiJqRSqVGgIBdFZXEgkF2DC7D8zFRhDq6tVCWmRyJTadzMQXUamorlfoXNPVwRyvjAvCfSHOrGpFRETUQob6O+Kv5xyw80IuPj6QhKKqeo15mVyFtYdT8eO5HLx4XwAe6eMBI7ZvvSV3G1P8NH8gPtqXiG9OZGjMZZTWYsoXp/DmpFA81s+TxzVEREREBsrIyAj+/v7w9PTkBU8iIqJ2TKlUIjs7Gzk5OQgJCYGDg4O+Q+qUeEaRiIiolZTW1GP2t+ew9Wx2k2ssJcZMYLoNKpUaf1zMw8hVR/HhvkSdCUx25mK8PTkU+18YhtGhLrzQR0RE1MJEQgEe7uOJIy9FYul9ATATi7TWlNbUY8VvVzBuzXFEJRZDrdZdMZH+ITYS4j8TQ7B+Vm9YSTTvNWtQqLDitytY8tNF1DaRwE1EREREhoEJTERERO1XeXk5oqOjkZ2dDbVajdTUVCiVSn2H1SkxiYmIiKgVnEkvw/g1x3EitRTv7LqKuLxKfYfUbkVnlmPKl6ewePtF5FVItebFRkIsiPDFkWURmD2wC4xZ9YGIiKhVmYpFeG6kP44si8D0/l7QlY+dUlyDeZvPY/a355BUWN32QbZDo0NdsPv5oTpb4f5xMR/fHM/QfhIREREREREREd0zuVwOmUzW+HN9fT2ysrL0GFHnxat8RERELUilUmNdVCqmbziD4urrbVYalCos/PECqmRyPUfXvuSU12HhDxfw0FencSmnQueaKeHuOLx0OF4eGwQriXHbBkhERNTJOVlK8N6Ubti3ZBhGBDnpXHM8pRTj1hzDit+uoKS6Xuca+oennRl+eXog5g3uojHezd0az0R01U9QREREhKysLCxduhRBQUEwNzeHnZ0d+vbti5UrV6Kuru6etr1582YIBILb+m/z5s0t8wvRXWNFBiIioo7JyckJNjY2GmO5ubmora3VT0CdmNGtlxAREdHtKKupxws/X8Kx5BKtOWmDEnnXpLByZaLNrVTJ5FgXlYpNJzLRoFTpXNPfxw6vTQhGdw+btg2OiIiItAQ4W+LbuX1xMrUU7+5OwNWCKo15lRr48Ww2/ryYj2cjffH4YB9IjLVb0dF1YiMh/jspFP197LFsxyVADayb3gsmRnzNiIiI9GHXrl2YOXMmqqr+Ocapq6tDdHQ0oqOj8c0332D37t3w8/PTY5TUFtRqNeLi4mBkZAQ/Pz+YmJjoOyQiIiJqIQKBAP7+/oiOjoZarQZw/bs/JSUFPXr0gECgoxQ5tQomMREREbWA85nleO7HWBRWybTmhvo74JNpPeFgwRMbzVEoVdh+PgefHExGWW2DzjU+DuZYMT4Yo4KdeMBIRERkYAb7OeCv54ZgZ2weVu5PRFGVZuWlmnoFPtqXhB/PZuOVcUGY0M2V3+fNGBvmghBXK2SX18HL3kzf4RAREXVKsbGxmDZtGqRSKSwsLPDqq68iMjISUqkU27dvx4YNG5CcnIwJEyYgOjoalpaW97S//fv3w83Nrcl5Dw+Pe9o+3ZuSkhJUVFQAAK5duwZvb294eHjwmJaIiKiDMDMzg6enJ7KzsxvHKisrUVRUBBcXFz1G1rkwiYmIiOgeqFRqfH0sHR8fSIJSpdaYEwqAF0YFYGGkH4RCnsxoztHkEry7+yqSi2p0zlubGmPJKH/M6O8NsRG74RIRERkqoVCAh3p7YHw3F3x9NB1fH0uDTK5ZWTH3mhSLfozFt14ZeH1iCMK9bPUUreHzsjdrNoEpo7QWF7Ku4cHevKBJRETUGhYvXgypVAojIyMcOHAAAwcObJwbMWIE/P39sXz5ciQnJ2PVqlV4880372l/AQEB6NKly70FTa1CoVAgLS2t8WelUonc3Fy4urrCyIiX2oiIiDoKLy8vFBcXQyb7p2hBeno67O3tYWzMbittgVcBiYiI7tK12gY88d15fLgvUSuBydHSBFuf7I/nRvozgakZKUXVmPPtOcz59pzOBCYjoQCPD/bB0WURmDfYhwlMRERE7YSZ2Agv3BeAIy9F4sFeuhNsLmRXYMoXp7B4eyzyKqRtHGH7J5Mr8ewPF7D0l0tY9sslSBuU+g6JiIioQzl37hyOHz8OAHjiiSc0EphuWLp0KYKDgwEAa9asgVwub9MYqe1kZmaioUGzcrifnx8TmIiIiDoYkUik1SZYLpcjIyNDTxF1PrwSSEREdBdissoxfu1xRCWVaM0N9rPHnueHYpCvgx4iax/Kaurxn9+vYOya4ziarP0aAsB9Ic448MIwvDEpBDZm4jaOkIiIiFqCi7UEqx7pgV2LhqBfFzuda/64mI8RHx/Bx/uTUFOvaOMI26+3dl1FQkEVAOCXmFxMXncCqcXVeo6KiIio4/j9998bH8+bN0/nGqFQiNmzZwMAKioqEBUV1RahURurrq5GXl6expidnR0cHHjuj4iIqCOyt7fX+p4vKChAVVWVniLqXJjEREREdAfUajXWH0vDtK/PoKBSpjEnEABLRvnj+8f7w9HSRE8RGrZ6hRJfH01DxMoj2HomW6uCFQCEuFrhxyf7Y8PsPujqaKGHKImIiKildfOwxk9PD8BXM3vBy067PVq9QoXPo1IR+fER/HRe9zEC/eNSTgW2ncvWGEsuqsH9n5/Eb7G5eoqKiIioYzlx4gQAwNzcHL17925y3fDhwxsfnzx5stXjoralVquRkpKiMSYUCuHn5weBgNXXiYiIOipfX18IhZrpNMnJyVCrec6qtbHOJRER0R3IvSbFJwdToLjpwpqDhRhrHg3HYD/egaWLWq3G3rhCvL83ATnlutvFOFqaYNmYQDzYywMituAjIiLqcAQCAcaGuSIyyAnfn8rC2sMpqJZpVl4qqa7Hy79eweZTWXh9QjAG8dhKpx6eNlj7WDhe/fUyav/VRq6uQYkXfrqEs+nleDHCU48REhERtX8JCQkAbt0yLCgoSOs5d2vevHlISkpCaWkprKys4Ofnh1GjRmHBggVwd3e/6+3m5jaf5FxQUND4uLq6us2qDNTU1Oh8bEhKS0tRXa1Z7dLZ2RlyuZztAw1Ue3hfUfvC9xS1NL6n2g8XFxfk5+c3/lxbW4u0tDQ4OTnpMSrd9PW+uvk4qSUwiYmIiOgOeNqZ4X8PhGHpL5caxwZ0tcPaR8PhZCXRY2SG61JOBf63+yrOZ17TOW9iJMTTw7ri6eG+MDfhoQkREVFHZ2IkwlPDuuLB3h5Y83cytp7VrryUUFCF6d+cxahgJ7w6Phi+rM6o5f4ebgh1s8LCHy4gsVDzhNH28zmIySxDb6UYtqIGPUVIRETUfslkMpSWlgIAPDw8ml1ra2sLc3Nz1NbWIicn5572e+TIkcbHZWVlKCsrw9mzZ7Fq1Sp8+umnePrpp+9qu56et5/cvGXLFlhbW9/Vfu7Fli1b2nyft2JsbIwePXpoJLFJpVL8/vvvrMLQThji+4raN76nqKXxPWXYBAIBunXrBjOzf6qKZ2dn488//zToZOa2fF9VVla2+DZ5pZCIiOgOPdjbA2czyvBLTC6ei/TD4lEBrBykQ0GlFCv3JWFnbF6Ta6aEu2PZmEC42Zi2YWRERERkCOzMxXhrchhmDfTGu7sTEJVUorXm74RiHEkqwcwB3lgyyh82ZmI9RGq4fB0t8PvCwXhrVzy2ndO8aJpSUodM+GK4eX4TzyYiIqKm/PuOcguLWydT30hiutu73rt27YqpU6di4MCBjQlH6enp+PXXX7Fjxw7IZDI888wzEAgEmD9//l3tg+6cl5eXVhWujIwMJjARERF1Emq1Gunp6QgLC2scE4lE8PLyQlpamh4j69iYxERERHQX3ro/DFN7eWBAV3t9h2JwausV+PpYOtYfS4NMrtK5po+3Lf4zMQQ9PW3aNjgiIiIyOH5Oltg0rx+OJZfg3d0JSCrSrCqkUKmx+VQmfovNw/Mj/TFrgDfERkI9RWt4JMYivD+1O/r72GPFb1dQ96/2cnKI8HetJ97ak4J3pvaAmZingYiIiG6HTCZrfCwW3zqJ2sTEBMD1Kj13asqUKZgzZw4EAs0b5Pr27Ytp06bhr7/+wtSpUyGXy/HCCy/g/vvvh4uLyx3t41YVogoKCtCvXz8AwKxZs+6pdd2dqKmpaawUMGvWrNtKGGsr1dXVSE1N1RiztbXFjBkz9BQR3S5Dfl9R+8T3FLU0vqfan6ysLJSXlwMAbGxsEBoaitGjR+s5Kk36el/l5eXh/fffb9Ft8uwVERGRDkeSinE6rQyvjg/WOW8qFjGB6SYqlRo7LuTi4/1JKK6u17nGw9YUr44LxvhuLlon54iIiKhzGxbgiEG+9vg5OherDyahtEazDVqlVI53/rqKrWey8Oq4INwX4szjiX95INwdYe7WWPjDBa1EsF8vFuJCbjU+mdaTSeRERES3QSKRND5uaLh1a9b6+uvnQUxN77zS9K1at02cOBFvvPEGXn/9ddTV1WHjxo147bXX7mgft2qJ92+WlpawsrK6o+23BAsLC73sVxeVSoWkpCSNMZFIhKCgoNtKaiPDYUjvK+oY+J6ilsb3VPsQFBSEK1euoEuXLrCzs9N3OLfUlu+rqqqqFt8mb10kIiL6F5lcif/+EYe5m87j62Pp2HulQN8htQun08ow6fMTWL7jss4EJksTI7wyLgh/vzgcE7q78oIjERER6WQkEmJ6fy9EvRSBZyN8dVZcyiitxfwtMZi+4Szi8yv1EKXh8nO63l7ukT7aFyozSmvx4JenkHJTghMRERFps7S0bHx8Oy3iamtrAdxe67m7MX/+/MZzKUePHm2VfdA/cnNzUVdXpzHm4+PDBCYiIqJOytjYGOHh4e0igakjYBITERHR/4vLq8TEz07gu9NZjWOv/nYFhZWyZp7VuWWU1mL+99F4bMMZxOdrZ1sLBcDMAV6IWhaBZ4b7QmIs0kOURERE1N5YSoyxfGwQDr04HBO7u+pcczq9DBM/O4HlOy6huIrHazeYikX46KEe+N+kABhBqTE3qbsr/J0tm3gmERER3SCRSGBvf70Cd25ubrNrr1271pjE5Onp2SrxODk5NcaTl5fXKvug62QyGbKysjTGLC0t4ebmpqeIiIiIyBDw5vy2wyQmIiLq9JQqNb48koYpX5xEarHm3XUVdXJsO5etp8gMV2Xd9XYuoz85igNXi3SuGRbgiH1LhuF/D3SDg4VJG0dIREREHYGnnRk+n94Lvy4YpLMNmloN/Bydi4iPj2DtoRRIG5TaG+mk7u/mjEes0uAkul5FwN3GFG9NDtNzVERERO1HSEgIACA1NRUKhaLJdYmJiY2Pg4ODWy0eXjhrG8bGxvDw8NB4vf39/fn6ExEREbURI30HQEREpE+51+rw4s+XcC6jXGtOYizEaxNCMLO/lx4iM0xypQo/nMnCp4dSUFEn17nG38kCr00IRkSgUxtHR0RERB1Vb29b/PbsIPx5KR8f7UtCXoVUY76uQYnVB5Ox7Vw2lo8NxOQe7hAKeaHJWtSAKZbpEHYbj+HBbrA2NdZ3SERERO3GkCFDcPz4cdTW1iImJgb9+/fXue7f7d0GDx7cKrGUlJSgtLQUAFgRqJWJRCL4+PjA2dkZKSkpMDMz02gvSERERPRvcrkcUqkUVlZW+g6lw2ASExERdVq/x+bh9d/jUF2vfTddmLsVPp0WDj8nCz1EZnjUajUOJxbj3T0JSC+p1bnGzlyMF+4LwGN9PWEkYrFHIiIialkCgQCTe7pjTKgLNp7IwBdRqai9qfJSQaUML/x0CZtPZuI/E0PQt4udnqI1HEIBsGCod7Mn0w4nFsFIKMSwAMc2jIyIiMiwPfDAA3j//fcBAJs2bdKZxKRSqfD9998DAGxsbBAZGdkqsaxfvx5qtRoAMHz48FbZB2kyMzND9+7dG193IiIion9Tq9UoLi5GWloaBAIB+vbtCyMjpt+0BF5hJCKiTqeyTo7nt8ViyU8XtRKYBAJgYaQvdi4YzASm/xefX4mZG8/iie+idSYwiUVCPD2sK44si8CsAd5MYCIiIqJWJTEWYWGkH6KWReDRvp7Q1dnjUm4lHv7qNJ79IQZZZboTsOm6oioZXvz5EmZ/ew5v7YqHTM6WfERERADQr18/DB06FACwceNGnD59WmvNqlWrkJCQAABYvHgxjI01qx4eOXIEAoEAAoEAc+fO1Xp+ZmYmYmNjm43jr7/+wttvvw0AMDU1xbx58+7m16G7IBAIIBTyPBcRERFpUigUuHTpEhITEyGXy9HQ0IDMzEx9h9VhMBWMiIg6lVNppXjp50vIr5RpzbnbmOKTaT3Rz4d37ANAYaUMHx9Iwq8XctHUTWfju7nglbHB8LI3a9vgiIiIqNNzspTggwe7Y/bALnh3z1WcTC3TWrPnSiEOXi3C7IFd8NwIP9iYifUQqeFSqdR46ZdLjW2CN53MxMnUUnw6LRwhbiyDTkREtGbNGgwePBhSqRSjR4/GihUrEBkZCalUiu3bt2P9+vUAgICAACxduvSOt5+ZmYnIyEgMHDgQkyZNQo8ePeDk5AQASE9Px44dO7Bjx47GakAff/wx3N3dW+4XJCIiIqI7JhKJIBKJNMby8vLg7OzMNrQtgElMRETUKdQrlFh9IBnrj6frTMiZGu6ONyeHwkpirD3ZydTUK7D+aBrWH0+HTK7Suaa7hzVeZ4sWIiIiMgAhblbY+kT/JlvfypVqbDyRgR0xuXhuhB9mDfSGiZGoia11LgeuFuF4SqnGWHJRDR5YdxIvjQnAk0O6QijUUeqKiIiokwgPD8dPP/2EmTNnoqqqCitWrNBaExAQgN27d9/TBavTp0/rrPR0g5mZGT755BPMnz//rvdBuqnVashkMpiamuo7FCIiImonBAIB/Pz8EB0dDZXqn+toKSkpCA8Ph0BX2XC6bUxiIiKiTkGhVGN/fKFWApOVxAjvTumGST3c9BOYAVEoVfg5OherDyajtKZe5xpXawmWjw3E5B7uvKBFREREBkMgEGBksDOGBTjihzNZ+PRQSmN1oRsqpXL8b3cCvj+dhVfGBWFcmEunP6k0JtQZb08Oxbu7E1Cv+OekW4NShff2JOJwYjFWP9ITbja8qEdERJ3XpEmTcPnyZaxZswa7d+9Gbm4uxGIx/Pz88PDDD2PRokUwM7u7CtW9e/fG1q1bcfr0aURHR6OgoAClpaVQKBSwtbVFaGgoRo4ciSeffLKxQhO1rKKiIiQnJ8PT0xNeXl5aVRWIiIiIdDE1NYWXl5dGG7nq6moUFBTAzY3XHO8Fk5iIiKhTMDcxwifTeuKhr05DqbqeyTTI1x6rHukBV+vOfVFGrVbjSHIJ3t+TgOSiGp1rzMUiLIjwxRNDusJUzJM5REREZJiMRULMHeyDKeEeWHckFZtPZqJBqVlZMru8Ds/+cAG9vGzw2oQQ9Pa21VO0+icQCDB7YBcM8rXH4u0XEZ9fpTF/Jr0cYz49hnendMP9TPonIqJOzNvbG6tXr8bq1avv6HkRERGNreB0sbS0xIwZMzBjxox7DZHugkKhQHp6OtRqNbKzs1FcXIyAgADY2nbe40MiIiK6fZ6enigqKoJUKm0cy8jIgIODA8RisR4ja9+E+g6AiIiorYR72eL5Ef4Qi4T4z4RgbH2if6dPYLqaX4VZG89h3qbzOhOYhAJgRn8vHFkWiUUj/JnARERERO2CtZkxVowPxt8vDsfE7q4611zIrsCDX57Cwh8vILusro0jNCx+Tpb47dnBWBDhi5uLU1XLFHh+WyyWbI9FpVSuewNERERE7VBBQQHk8n+Ob2QymUZLGCIiIqLmCIVC+Pv7a4wpFAoUFBToKaKOgZWYiIiow5HJlZAY6062WRjpiwndXeDnZNnGURmWwkoZVh1Iwo4LuVot9m4YEeSEV8cFwd+5c79WRERE1H552Zvh8+m98PiQa3h3dwJisq5prdl9uQAH4gsxZ2AXPDfCH9ZmxnqIVP/ERkK8PDYIEQGOePHnS8irkGrM/34xH+czr2HVIz0woKu9nqIkIiIiahlqtRr5+fkaY/b29rC353EOERER3T5bW1s4OjqipKSkcSw/Px9eXl4Q3HynGN0WVmIiIqIOo6ZegWW/XMLsb881toy7mZFI2KkTmGrrFVh9MBmRHx/BLzG6E5hCXK3ww5P98e3cvkxgIiIiog6hl5ctdjwzEF/O6AVvezOteblSjW9OZGDYyihsPJGBBkXnvQO/f1d77F0yFFPC3bXm8iqkeGzDGXywN7FTv0ZERETU/pWXl0Mmk2mMeXp66ikaIiIias9uPoZoaGhAWVmZnqJp/5jEREREHUJMVjnGrzmOX2JycS6jHF8fS9N3SAZFqVJj27lsDF95BGsPpUAqV2qtcbGS4OOHe+Cv54ZgsJ+DHqIkIiIiaj0CgQDjurni4AvD8frEEFibaldcqpTK8c5fV3HfJ0ex50oB1E2VrOzgrCTG+GRaT6x9LBxWEs0i3mo1sOF4OpIKq/UUHREREdG9u7kKk4WFBaysrPQUDREREbVnlpaWsLTULAqQl5enp2jaPyYxERFRuyZXqrD6QBIe/uo0ssvrGsdXH0hGXF6lHiMzDGq1GkeSijF+zXG8uvMKSmvqtdaYi0V4aXQAol6KwEO9PSAUsrwlERERdVxiIyGeGOKDY8si8eQQHxiLtI99ssrq8OwPF/DQV6dxIVu7BV1ncX8PN+xbMgwDb2oftyjSD908rPUUFREREdG9kclkKC8v1xhzc3NjyxciIiK6a+7umhWtKyoqUFdX18Rqag6TmIiIqN1KL6nBQ1+ewtrDqbi5e5zEWIS8Cql+AjMQV/OrMPvbc5i76TySirTvlBcKgOn9vRC1LAKLRvjDVCzSQ5RERERE+mFtZoz/TAzBoRcjMKG7q841MVnXMPWLU1j44wXklHfOE09uNqb44cn+WDE+CMYiAXp62uC5EX76DouIiIjort1chUkkEsHJyUlP0RAREVFH4OjoCCMjzWrWNx9z0O0xuvUSIiIiw6JWq7HtXA7e+euqzrZofbxt8cm0nvC0M9NDdPpXVCXDqgNJ+CUmF011QIkMdMSr44MR4GypewERERFRJ+Flb4Z103vh8cHX8O7uq7iQXaG1ZvflAhyML8KcQd5YFOkPazPtVnQdmVAowPxhvhji5whzExGMRLrviVOr1axgQERERAZNpVKhsLBQY8zFxQUiEW/uIyIiorsnFArh6uqKnJycxrHCwkL4+PjwOOMOMYmJiIjalbKaerz86xX8nVCkNWckFGDJKH88M9y3yQsrHVltvQJfH0vHhmPpOpO7ACDY1QqvjQ/GEH+HNo6OiIiIyLD19rbFrwsGYW9cIT7Ym6jRqhgAGpQqbDiegV9icvH8CH/MHOANsVHnOuYMcbNqdv7rY+nIKKnFG5NCYG7CU05ERERkeEpKSiCXyzXG3Nzc9BQNERERdSQ3JzGJRCLU1dXB0pIFBe4EzygREVG7EZVYjGU7LqO0pl5rrquDOT6Z1hM9PG3aPjA9U6rU+CU6B6sOJqOkWvu1AQBnKxO8NDoQU3t5QCTk3fFEREREuggEAozv5oqRwU7YcjoLnx1ORaVU8yJXRZ0cb/91Fd+dzsQrY4MwNsyF1YcAxOVVYtWBJMiVapzJKMMn03qil5etvsMiIiIi0nBzWxcbGxuYmXXOau5ERETUskxNTWFnZweVSgU3Nzc4ODjwnNFdYBITEREZPGmDEu/tScCWM1k652f098JrE4JhJu58X2tHk0vw3u4EJBVV65w3E4vwzHBfPDnUp1O+PkRERER3w8RIhCeHdsVDvT3w2eFUfH86E3KlZp/erLI6LPjhAvp42+K1CcEI78QJOzK5Ekt+utj4GmWV1eHhr05jUaQfnhvh1ymrpBIREZHhqampQVVVlcYYqzARERFRSwoNDYVQyPMg94JXM4mIyKAVVcnw2IYzSC+p1ZqzNxfjo4e6Y2Swsx4i06+Egiq8tycBx1NKdc4LBcC0vl544T5/OFlK2jg6IiIioo7BxkyM1yeGYPZAb3y0Lwm7rxRorYnOuoYpX5zCxO6ueHlsEDztOt+d/PH5VSiokGqMKVVqrDmUgqPJJfhkWk/4OJjrKToiIiKi626uwiQWi2Fvb6+naIiIiKgjYgLTveMrSEREBs3BwgROliZa4yODnLBvybBOl8BUXF2P5TsuYfza400mMEUEOmLv4mF4f2o3JjARERERtQBve3Osm9ELvy4YiHAvG51r/rpcgJGrjuK9PQmorJPrXNNR9fa2xd7Fw9DbW7sa1cWcCkxYexxbzmRBpVLreDYRERFR61MoFCgqKtIYc3V15YVGIiIiIgPDozMiIjJoIqEAqx7pCUvJ9eKBEmMh3p0Shm/m9IGjjuSmjkquFuC81AkTv4rGz9G5UOu4/hPkYoktT/TD5nn9EOhi2fZBEhEREXVwvb3tsHPBIKyb3guedqZa8w1KFdYfS8fwj6Ow6WQGGhQqPUSpH172Zvhp/gAsvS8AIqFAY66uQYnXf4/DtPWnkVpco6cIiYiIqDMTCoUICgqCjY1N45irq6v+AiIiIiIindhOjoiIDJ67jSn+90AYNp7IwCfTesLX0ULfIbUZhVKFXy8W4sfKANSpjQFoXwhztjLB0tGBeLCXh9YFIyIiIiJqWQKBABO6u2JUiBO2nM7C2kMpqJIpNNZU1Mnx1q6r+O5UJpaPDcK4MBcIBB3/OM1IJMRzI/0xNMARL/x0ERmlmi2hz2dew/g1x/HcCD88PdwXYiPeW0dERERtQygUwtHREY6OjqitrUVVVRVMTDrPDZJERESkX1KpFBKJpFOcH7pXTGIiIiKDcCSpGAKBAMMDHHXOT+7pjgndXGEk6hwXOtRqNfbFFWLlgSSkl9QCMNZaYyYW4elhvnhqmA/MxPxKJyIiImpLJkYiPDm0Kx7q7YG1h1Kx5Uwm5ErNcpmZZXV49ocL6OFhjZfHBmGQn4Oeom1bPT1tsPv5IXjnrwRsO5etMdegVGHVwWT8dbkAHzzYDeFe2i3oiIiIiFqTubk5zM3N9R0GERERdXBqtRrl5eXIz89HeXk5wsLCYG9vr++wDF7nuBJMREQGq7y2AS/8dBFzN53H8h2XUCWTN7m2syQwnUotxQPrTmLBDxf+P4FJk1AAPNbPE0deisDiUf5MYCIiIiLSIxszMd6YFIKDLwzH+G4uOtdcyq3E9G/OYtbGs7iSW9nGEeqHmdgI70/thq1P9IeXnZnWfFJRNaZ+eQq/x+bpIToiIiIiIiIiotYVHx+PuLg4lJeXAwDy8/P1HFH70DmuBhMRkcFRq9X442IeRq0+it/+/8JFUVU9PtybqOfI9OdKbiVmbTyL6d+cxaUmLm4N7mqLvYuH4f2p3eFkJWnjCImIiIioKV0czPHFjN7Y8cxA9PS00bnmeEopJn1+Agt/vID0kpq2DVBPhvg7YP+SYZg/rCtu7nxsY2qMof6dozoVEREREREREXUuN1ddKi8vh1Qq1VM07QeTmIiIqM3lXqvDvM3nsXj7RZTXNmjM/XA2G3F5nePu9BvSS2qw8McLmPT5CRxPKdW5xkEkxUSLDHz5aBgCXSzbOEIiIiIiul19utjht2cHYd30XvBx0N2mZPflAtz3yTG8uvMKiqpkbRxh2zMVi7BifDD+WDgEwa5WjeNvTAqBvYWJHiMjIiIiIiIiImodTk5OEIlEGmMFBQV6iqb9YP8ZIiJqM0qVGt+fzsTK/Umoa1BqzVtKjPDa+GCE/OvCRkdWVCXDmkMp+Ol8DpQqtc41Pg7mWDjUE6lROyAQ6FxCRERERAZGIBBgQndXjA51xi/RuVhzKBlFVfUaa5QqNbady8ZvsbmYO8gHC4b7wtrMWE8Rt41uHtb4c9FgbDiejovZFXigp7u+QyIiIqIOTKVSITk5GY6OjrCzs4OAJ9eIiIioDYlEIri4uCAvL69xrLCwEF26dIFQyHpDTWESExERtYmkwmq8/OtlXMyp0Dk/LswFb90f2ilapFXWyfHl0TRsPpUBmVylc42zlQkWjwzAw308IK2tQdqRto2RiIiIiO6dsUiI6f29MCXcHd+dzsQXUamokik01sjkKnx1NA0/ns3Cggg/zB3UBaZiURNbbP+MRUI8G+EHtVrd5IXE0pp6vLXrKl4eGwgPW7M2jpCIiIg6irKyMhQVFaGoqAgSiQRubm7w8PBgMhMRERG1GTc3N40kJrlcjpKSEjg7O+sxKsPGJCYiImpV9Qol1h1OxZdH0yBXalcbcrI0wduTwzA2zEUP0bUtaYMSm09l4ssj2hevbrCSGGldvGJ3XCIiIqL2zVQswjPDffFYXy98dSwNm05qJ7NXyRT4cF8iNp3MwOJR/nikjyeMRR33rrzmLh6+vesqdl3Kx6GEIiwbE4jZA7tAJOTFRiIiIroz+fn5jY9lMhlKS0vh6empx4iIiIioszEzM4ONjQ0qKioax/Lz85nE1AwmMRERUauJzizHy79eRlpJrc75x/p54ZVxQbA27dhtM+RKVZNtRG6QGAsxb7APnhnW8duIEBEREXVW1mbGeHlsEOYO6tJkW+Hi6nq89lscvjmegaWjAzA+zBXCTpTAE5VYjD8vXb/gWNegxFu7ruKPi/n48MHuCHSx1HN0RERE1F7U1tZqXCwErldCICIiImprbm5uGsclVVVVqK6uhqUlz3PowiQmIiJqFV8cScVH+5J0zvk4mOP9qd0woKt9G0fVtlQqNfbEFWDVgWRklOpO5BIJBZjW1xOLR/rDuRO00iMiIiIiwNlKgvemdMNTQ7ti1YEk/HW5QGtNRmktFv0Yi27u6Vg+NhBD/Bw6fOsTtVqNtYdTtMYv5lRg4mfHsWC4LxaO8IOJUcdtt0dEREQto6BA8/jK2NgYjo6OeoqGiIiIOjN7e3uIxWI0NDQ0jhUUFDCJqQkdty45ERHpVW8vW60xI6EACyN9sXfx0A6fwHQ8pQST153Eoh9jm0xgmtjdFX+/OBzvTenGBCYiIiKiTsjHwRyfT++FXYuGYKi/g841V/IqMWvjOcz45iwu5VS0bYBtTCAQYPO8fpje30trTq5UY+3hVIxfcxznM8v1EB0RERG1F0qlEoWFhRpjLi4uEAp5SYyIiIjanlAohKurq8ZYUVERFAqFniIybDxiIyKiVtG/q73GxYfuHtb4c9EQLBsTBIlxx71z+lJOBWZ8cwazNp7DlbxKnWuG+jtg16Ih+Hx6L/g4mLdxhERERERkaLp5WGPLE/3x45P90cPTRueaU2llmLzuJBZsjUFqcU3bBtiGrE2N8d6Ubtg+f4DOY+W0klo8/NVpvP57HKplcj1ESERERIauqKgISqVSY4yt5IiIiEifbk5iUqlUWknXdB3byRERUat5ZVwQTqeVYUZ/L8wb7AORsOO2v0gtrsGqA0nYG9f0AUcPD2u8PDYIg/x032VPRERERJ3bID8H/O5rj/3xhfhofxLSS7Qreu6NK8T++EI83NsTS+7zh6u1qR4ibX0Dutpj7+Kh+OxwCr4+mg6FSq0xv+VMFg5eLcI7D4ThvhBnPUVJREREhkatViM/P19jzN7eHhIJq6ATERGR/piYmMDBwQGlpaWNY/n5+XB3d4dA0HGvn94NJjEREdFdyyqrxdYzWXh1XDCEOhKUrCTGOPDCMBiLOm7hv4JKKdb8nYKfo3Nw03WVRl0dzbF8TCDGhLrwQISIiIiImiUQCDA2zBWjgp3x64VcfPp3CgoqZRprVGrgp+gc/HYxD3MHdcGzEb6wMRPrKeLWIzEWYdmYIEzo5oZXdl7G5VzNSqeFVTI89X00JnR3xZuTQuFoaaKnSImIiMhQVFVVobZWMxGcVZiIiIjIELi5uWkkMUmlUlRUVMDW1laPURkeJjEREdEdUyhV+PZkBlYfTIZMroKXvTlmDfDWubajJjBV1DXgyyNp2HwqE/UKlc41rtYSLBnljwd7ecCog74ORERERNQ6jERCTOvrhck93bHldBbWHUlFRZ1m+7QGhQrrj6Vj27lsPDPcF/MGd4GZuOOd6glxs8Jvzw7GppMZWHUgGVK5ZnuY3ZcLcCatDEeWRcBSYqynKImIiMgQ3FyFSSKR8MIgERERGQQbGxuYmZmhrq6ucSw/P5/HKjfpeGe2iIioVcXlVeKVnZcRl1fVOPbh3kSMCnbqsK0s/q2uQYFNJzPx1dE0VMsUOtdYmxpjYaQvZg/sAomxqI0jJCIiIqKORGIswlPDumJaP0+sP5qOjScytJJ4qmUKrNyfhM2nMvH8SH882tdTT9G2HpFQgCeHdsWYUBes+O0KjqeUasw/1NuDCUxERESdXENDA0pKSjTG3NzcWBmdiIiIDIJAIICbmxtSU1MhFovh6uoKV1dXfYdlcJjEREREt0UmV+LTv1Ow4Xg6lDf1TaupV+DTgyn48KHueoqu9cmVKmw/n4O1h1JQUl2vc42psQhPDPHBU8O6wtqUF1CIiIiIqOVYSYzx0phAzB7kjc8OpWLbuWwobjouL6mux+u/x+Gb4+lYMMQTajXQ0a7ZedqZ4fvH+2HnhTy8s/sqKurk8LY3w5JRAfoOjYiIiPSssLAQavU/x0cCgQAuLi56jIiIiIhIk7OzM8RiMezt7SEUsouLLkxiIiKiWzqVVooVO68gs6xOa04gAGYP8MaysUF6iKz1qVRq/HWlAKsOJCFLx+8PAEZCAR7r54XnRvrByVLSxhESERERUWfiZCnBOw+E4cmhPlh9MBl/XMzXWpNVVodX/kiCg8gX/U2LNC7mdQQCgQAP9vbA8EBHvL3rKqb19YSpmBVQiYiIOjO1Wq3VSs7JyQnGxrzRkIiIiAyHkZERHB0d9R2GQWMSUyvLysrC2rVrsXv3buTk5MDExAS+vr545JFHsHDhQpiZmd31tuvq6rBv3z4cPHgQ0dHRSE1NRU1NDaysrBAQEIAxY8bgmWeeueWdBhERETh69Oht7bOjnfgkouZV1snx/t4EbD+fo3Pe38kCHzzYHb29O16vVpVKjf3xhVhzKAWJhdVNrpvc0w0v3hcAb3vzNoyOiIiIiDo7b3tzrHk0HPOHdcXK/Uk4klSitaZUaYrdNV2Qv+Uylo4JxmA/+w7VTsXBwgRrHwtvds2GY+koq23AklH+bPVMRETUgZWXl6O+XrN6upubm56iISIiIqK7xSSmVrRr1y7MnDkTVVVVjWN1dXWIjo5GdHQ0vvnmG+zevRt+fn53vO3Lly9j8ODBqKmp0ZorLy/HmTNncObMGXzyySdYv349pk2bdk+/CxF1Lmq1Gn9eysf/difobJ1mLBJgYaQfFkT4wsSoY10IUKnU2BtXiLWHUpBU1HTyUkSgI5aNCUSom3UbRkdEREREpCnUzRqb5/XDmfQyfLgvEbHZFVprYnOrMHPjWfT2tsWSUf4Y4ufQoZKZmpJRWouPDyShXqHCvrgC/Pf+UEQGOuk7LCIiImoFCoUCYrEYDQ0NAAALCwtYWlrqOSoiIiIiulNMYmolsbGxmDZtGqRSKSwsLPDqq68iMjISUqkU27dvx4YNG5CcnIwJEyYgOjr6jg+mq6qqGhOYBg8ejIkTJ6JPnz6wt7dHSUkJdu7ciQ0bNqCqqgozZsyAlZUVxo0b1+w2+/Tpg02bNt3170xEHcPFnAq8vSseF3Rc/ACAXl42+PDB7vB37lgnAVQqNfbEFWDtoRQkF2kniN4Q7mWDl8cGYUBX+zaMjoiIiIioeQO62mPngkE4eLUIK/cnIaVY+5g2JusaZm08h15eNlg8KgDD/DtuMpNarcarOy+jXqECAGSW1WHepvOICHTEfyYEw8+pY/09Q0RE1Nk5OzvD0dERZWVlyMvLg7Ozc4c9ziEiIiLqyJjE1EoWL14MqVQKIyMjHDhwAAMHDmycGzFiBPz9/bF8+XIkJydj1apVePPNN+9o+0KhEI888gj++9//IiQkRGt+9OjRGDduHKZMmQKlUonnnnsOKSkpzR60m5ubIyws7I7iIKKOJS6vEg+sO6lzzlwswsvjgjCzvzeEwo5zAkCpUmP3lQJ8dihF54WeGwKcLfDS6EDcF8ITIERERERkmAQCAUaHumBksDN+PJWKj/bEoVol1lp3IbsCc749h56eNlgyyh/DAxw73DFufH4VYrKuaY0fSSrB8ZRSzBrgjSWj/GFjpv36EBERUfskFArh6OgIR0dHqNVqfYdDREREdFukUikKCgogEAjg4+Oj73D0TqjvADqic+fO4fjx4wCAJ554QiOB6YalS5ciODgYALBmzRrI5fI72segQYPw008/6UxgumHy5MmYOnUqACAtLQ2xsbF3tA8i6nxC3aww1N9Ba3xEkBMOvjgcswd26TAJTEqVGn9czMOYT4/h+W2xTSYwBThbYN30Xti3eBhGh7p0uIs7RERERNTxiIQCTO7ujMesUjDcLA9u1iY6113MqcDcTefxwBenEJVU3KEu9oW5W2PP80PRy8tGa06pUmPzqUwMX3kEm09mQK5UtX2ARERE1Kp4Do+IiIgMXW1tLa5cuYJz584hJycHeXl5UCqV+g5L75jE1Ap+//33xsfz5s3TuUYoFGL27NkAgIqKCkRFRbVKLJGRkY2P09LSWmUfRNRxCAQC/GdCCG7kKXWxN8OG2X2wcU4fuNmY6je4FnIjeWn0J0exePtFpDaRvBTkYokvZlxPXprQ3bXDJG8RERERUechEqgRYnINu57pgw8f7AYPW93H9JdyKjBv03k8sO4kDicWdZhkJn9nS+x4ZhA+frgHnCy1E7kqpXK8uesqxn56DFFJxXqIkIiIiIiIiIg6K5FIhPLy8saflUolioqK9BiRYWA7uVZw4sQJANfbs/Xu3bvJdcOHD298fPLkSYwePbrFY6mvr298LBKJWnz7RNQ+JRdVw9/JQucdSYEulnh6uC/szMSYM6gLxEYdI99VoVRh1+V8fHY4FekltU2uC3KxxJJR/hgd4sLEJSIiIiLqEIxFQkzr64WpvTzw24U8fB6ViuzyOq11l3Ir8fjmaHT3sMbikf4YEeTU7qsYCIUCPNTbA+PCXPDlkTSsP56OBoVm5aW0klrM23QeEYGO+M+EYPg5WeopWiIiIiIiIiLqLCQSCezt7VFWVtY4lp+fD1dX13Z/PuZeMImpFSQkJAAA/Pz8YGTU9EscFBSk9ZyWdvTo0cbHN9rXNSUxMRH9+/dHUlISZDIZHBwc0Lt3bzz44IN47LHHYGxsfNdx5ObmNjtfUFDQ+Li2thZVVVV3vS+ie1VTU6PzcUdQVF2PNVGZ+CuuGGseCkFkgL3OdQsGuQEAZHU1kLVlgK1AoVJjb3wx1p/MQVa5tMl1gU7meGaoFyID7CEUCFBTU92GUTavI78nqf3h+5EMCd+PZGj4niRD0tT7cWygNUb6hWN3XDE2nMpBzjXtI/7LuZV44rtohLhY4OkhXojwt+sQJ8/mD3TFhGBbfBqVgf0JpVrzR5JKcDy5BI/2ccPyUV07xO9sSPgZSYaktrbpm5uIqP2Qya4fx0gkEj1HQkRERHR33NzcNJKYbuRKWFtb6zEq/WISUwuTyWQoLb1+IszDw6PZtba2tjA3N0dtbS1ycnJaPJZLly5h9+7dAIBu3brdMompqKhIozxZXl4e8vLy8Oeff+LDDz/Ejh07brmNpnh6et722p07d3bq/ynJsGzZskXfIbQIuVqASzIHxMocofj/TqKv74zFo1apEAk6RquIm6nUQHKDDS7IHFGp0m4dcYODSIo+kmJ0aahG8uGzSD7chkHehY7ynqSOge9HMiR8P5Kh4XuSDElT78fxaiDZzAYxMkdU6ThmvlpYg8U7rv5zzGxcjY6Q19MVwAOWZjhZ54oSpWaLPaUaiL4Uj69SD+gnuE6Cn5Gkb5WVlfoOgYhaQFZWFgoLC2Fvbw83NzfY2toyCZmIiIjaFVtbW0gkksbkbOB6NabOnC/BJKYWVl39T+UOCwuLW66/kcTU0neg1dfX48knn4RSqQQAvPvuu02uFQqFGDlyJMaPH48ePXrA3t4e1dXVuHDhAr7++mskJCTg6tWriIyMxLlz5+Dl5dWisRJR61GrgVS5Nc7UOaNGLdaYq1KZ4Eq9HXpKypp4dvt0I3mpqQsxN3S0CzFERERERHdKKACCTCoQIK5Ayv8fQ+u6AaBUaYp9td5wEEnRW1IMnw5wDO1qVIcHLdOQ1GCDs1Jn1KmvV582ghL9TYtu8WwiIiLSN7lcjuLiYgBAWVkZysrK4Ovre8uby4mIiIgMiUAggJubG9LT0xvHSkpK4OvrC7FY3MwzOy4mMbWwf2fI3c6bysTk+slBqbTpFkd3Y9GiRYiOjgYAzJkzB5MmTWpy7c6dO2FjY6M1PnToUDz77LN46qmn8N1336GoqAhLlizBzp077zieW1WaKigoQL9+/QAAU6dORUBAwB3vg6il1NTUNN4VOmvWrNtKSDREV/Kr8dHBNFzK090WzcJEhBHDhmBab7c2jqx1yJUq7I673jYut67pJnjBLhZYMNQLw/3aT0uMjvKepI6B70cyJHw/kqHhe5IMyd28H2/VirlUaYr9td4IdDLH00O8MCLweivm9q6uQYmNp3Lw3dlczB/SFfMHR+g7pA6Jn5FkSJKTk/H+++/rOwwiugdFRUVQqVSNPwsEAjg5OekxIiIiIqK74+LigoyMDKjV17vnqNVqFBYWdtriMkxiamH/7r3c0NBwy/X19fUAAFNT01usvH3vv/8+vvnmGwBA3759sW7dumbX60pgusHY2BjffPMNzpw5g6SkJPz222/Iy8uDu7v7HcV0J3c/mJubw8rK6o62T9RaLCws2t37sbBSho/2JWJnbJ7OeaEAeLSfF168LwAOFk1XKmov5EoVdl7IxedRqcjRcaHlhu4e1lg80h8jgpzaTfKSLu3xPUkdF9+PZEj4fiRDw/ckGZI7eT/OGGyNRwf6YdelfKw9nIL0klqtNUnFtXhxZwKCXCyxeKQ/xoS6QChsv8fYVgBeu98Ws4f4wdHSBBJjkc51285lI6mwGktG+cPGrHPeDdlS+BlJ+mZubq7vEIjoHqjVauTn52uMOTg4dNpqBURERNS+GRsbw8nJCUVF/1SGzs/Ph6enZ7u+pnm3mMTUwiwtLRsf306LuNra6ycDW+rus6+//horVqwAAAQFBWHPnj33/Ee5kZERnnjiCSxfvhwAcPToUUyfPv2eYyWiliVtUGL9sXR8dTQNUrlS55pBvvZ4fWIIgl3b/8niBsU/yUu515pOXurhYY0lowIQEejYKb/oiYiIiIjulEgowAPh7pjUww1/Xc7H2kMpSNORzJRYWI0FP1xAoLMlnh/pj3Fh7TuZydPOrMm5yjo5PtqXiGt1cvwWm4cXRvljxgBvGIuEbRghERERAUBlZaVWdws3t45RbZ6IiIg6Jzc3N40kpvr6ely7dg12dnZ6jEo/mMTUwiQSCezt7VFWVobc3Nxm1167dq0xicnT0/Oe971t2zY8++yzAABvb28cPHgQDg4O97xdAAgJCWl8nJenu7oLEemHWq3Gn5fy8eHeRORX6m6j5m1vhtfGB+O+EOd2n8jToFBhR0wu1kWlIq+imeQlTxssGeWPiAAmLxERERER3Q2RUIDJPd0xsfv1ZKbPDqcitVj7hq2komos/PECApwt8PxIf4wPc23XyUy6rD2cgmt1cgBApVSON3ddxdaz2fjPhGBEBLJ1DRERUVuqqKjQ+NnMzAzW1tb6CYaIiIioBVhaWsLCwkKjUE5FRQWTmKhlhISE4Pjx40hNTYVCoYCRke6XOTExsfFxcHDwPe3zzz//xOzZs6FSqeDq6opDhw7dUQu3W2ECAJHh+i02Dy/+fEnnnKWJEZ4f6Y/Zg7xhYqS7JUJ70aBQ4ZeYHHwRldZs8lK4lw0Wj/THcCYvERERERG1iH8nM+25UoC1h1KQoiOZKbmoBot+jIW/UwqeG+mPCd1cIeoAyUyVUjl+Op+jNZ5aXIO5m84jItAR/5kQAj+nlqmyTURERM2rrKzU+NnOzo7nAYmIiKhdEwgEsLOz00hiuvmYp7NgzetWMGTIEADXW8XFxMQ0ue7o0aONjwcPHnzX+zt06BAeeeQRKBQK2Nvb4+DBg/D19b3r7ely9erVxscsy0pkWCZ2d4OPg2bbSKEAmN7fC1HLIvDUsK7tOoGpXqHE1jNZiFgZhdd+i2sygamXlw2+f7wfdi4YhIhAJ564ICIiIiJqYSKhAJN6uGH/kmH4fHo4Apx1J+2kFNfg+W2xGPPpMfxxMQ9KlbqNI21Z1qbG2Lt4KCZ0d9U5fySpBGM+PYY3/4xHRV1DG0dHRETUuahUKlRVVWmMsQoTERERdQQ3H9NUV1dDqVTqKRr9YRJTK3jggQcaH2/atEnnGpVKhe+//x4AYGNjg8jIyLva16lTpzB58mTU19fD2toa+/fvR2ho6F1tqykKhQLffvtt48/Dhg1r0e0T0b0RGwnx2vh/qrkN7GqP3c8PxXtTusHBwkSPkd2b2noFvj2RgYiVR/Cf3+OabJXX29sWW57oh18XDMIwVl8iIiIiImp1QqEAE7u7Yd/iYVg3vRcCnS11rkstrsHi7Rcx+pOj+Dk6B/WK9nvizdPODOum98LPTw9EmLuV1rxSpcbmU5kYvvIINp/MgFyp0kOUREREHV9NTQ1UKs3vWSYxERERUUdgZaV5vkGtVqO6ulpP0egP28m1gn79+mHo0KE4fvw4Nm7ciDlz5mDgwIEaa1atWoWEhAQAwOLFi2FsbKwxf+TIkcbEpjlz5mDz5s1a+7l48SImTJiA2tpamJubY/fu3ejdu/cdxRoVFYXw8HDY2NjonJfL5XjqqacaY500aRI8PT3vaB9EdO/UajUu51aih6eNzvmRwU54rJ8nIgOdcF+Ic7tO5CmukmHzqUxsPZOFKpmiyXV9u9hi8cgADPazb9e/LxERERFReyUUCjChuyvGhblgf3wh1hxKQWKh9sm1tJJaLN9xjdV+zQABAABJREFUGR/vT8LcwV0wo583rM2MdWzR8PXzscOfC4dgx4VcrNyfhJLqeo35Sqkcb+66iq1ns/GfCcGICHTSU6RERERtr6GhATU1NaitrUVDQ4NWstHNFAoFevbsCQDIy8tDUVHRLfchk8kgkUgafxaJRMjMzLyXsKmDuZv3FWkSCoUQi8UwNzeHhYUFxGKxvkMiIuoUjIyMYGFhodFSrqqqqslcjo6KSUytZM2aNRg8eDCkUilGjx6NFStWIDIyEtL/Y+++w9uqz7+Pv7W8LXlvxyPL2dMJgYRMCIQMKAlQKKvsUaDtU1ZbRscPWlaBssLes5AAIRAChEyynWlnee9teWud549jy3Ys2Y7jeCT367rOJeksfeU4lnTO59x3fT0fffQRy5cvB2DYsGH88Y9/POH9Hzt2jPnz51NZWQnAP/7xD0wmE/v373e7TVhYGGFhbQ+evf322yxevJjFixcza9Yshg8fjtFopKamhp07d7J8+XJnK7mwsDCeffbZEx6rEOLkpORU8revDpCSU8k3d88gKaL9Vb8ajYbHfjW2D0bXcw4XVfPq+nRWpORhtbtvNzElPoh75g1l2mAJLwkhhBBCCNEfaLUaLhwTyfxREaw5WMh/1roOMxVXN/Lvbw/x3x+PcnlyLL89J4HYIJ8+GPHJ0Wo1XDY5lgVjInlp3VFe3ZCBxdb2JO3R4hque3M7s4aH8tyvJ2D0GpihLSGEEKIrFEWhtLSU0tLSE9rO4XA4qyg5HA5sNvcXNLbepnWgQqfTdWk7cebozu+VaK85lFhUVERoaCjBwXI8XgghekNISAje3t6YTCZMJhO+vr59PaReJyGmU2TChAl8/PHH/OY3v8FsNvPggw+2W2fYsGGsWrUKf3/XZdc7smHDBoqLi52Pf//733e6zcMPP8wjjzzSbn5NTQ0ffPABH3zwgdttx4wZw0cffURCQsIJj1UI0T2FVQ38+9s0Pt+d55z3968P8t4NU0+bLwuKorDlWBnLN6Sz7lBJh+tOSWgKLyXKlyUhhBBCCCH6I61WwwWjIzl/ZARrDhbx3A9HOFhgbrdencXOm5syeXtzJgvGRHLzuYmMjQno/QGfJD9PPX+an8QVyYN4fHUaq/YVtFvHXG/F31MOvwkhhDi9FRQUUFVV1WaeRqNBp9N1uJ2iKPj5+QFgMBi6dMxPo9GgKC0XQOp0OjlWKNrozu+VaMtut7f5f1ZSUoLFYiEqKqoPRyWEEGeGuLi4vh5Cn5OjKKfQokWL2Lt3L88++yyrVq0iNzcXDw8PhgwZwrJly7jzzjvx8enbKw7vu+8+xo8fz5YtWzh48CAlJSWUl5fj6elJeHg4kydPZunSpVxyySWdfuEQQvSMsppGlm9I553NWdRb7W2WbTpaxtrUYs4bGd5Ho+sZVruDb/YVsHx9Ogfy25/UaG3eiHBuPjeRKQlBvTQ6IYQQQgghxMlQw0wRzB8Vzo9pxby6IZ1f0svbredQ4Ou9BXy9t4CpCUHcfG4is4eHodUOrBNNsUE+vHDVRK7NKOdvXx9gf17Ld5yHFo2SE2dCCCFOaw0NDW0CTMHBwRiNRjw9PTt9D7Tb7c6LtcPCwjo9B2G326mrq2szz9fXF61W283Ri9PRif5eifYURaGxsRGz2UxZWRkAVVVVBAcH4+np2cejE0IIcbqTENMpFhcXx9NPP83TTz99QtvNmjWrTcr5eNdddx3XXXfdSY4ORowYwYgRI7jnnntOel9CiJPTUXipWVywD16GgfulvKbRxkfbsnljYwb5VQ1u1/PQa7l0Ygw3TE9gSJhfL45QCCGEEEII0VM0Gg1zR4Qzd0Q4e3MreXVDBt/sK8DuaH+8Y2tGOVszyhkc6stNMxK5eEI0XoaBdcJpSkIQX94xnc925fLEd4eYMSSE8bEBLte1OxTqLDb8pc2cEEKIAa6ystJ5PywsjODg4FP2XHZ722OmGo1GAkxCnAIajQYvLy+8vLzQ6XTOUFhFRQURERF9PDohhBCnOwkxCSFEHyutaeTV9em8s8V9eMnfU8/v5g7h2rPj8dQPrAP5oLbGe3NzBh9szaa6wX0P8kAfA1dPi+eaaXGE+MkVHUIIIYQQQpwuxsYE8PyvJ3Dv/OG8uSmTj7ZnU2dp//3nWEkt93++jyfXHOa6s+O4amocgb4efTDi7tFqNVw2OZYFYyKx2Bxu1/tqTz4Pf3mAm2YkcO3Z8RJmEkIIMWC1rowUEBBwyp+vdTs5qbAjxKkXEBDgDDEdXwlNCCGEOBUkxCSEEH2ktKaR5evTebeD8JJWA5cnD+KP5w8bkKGe1AIzr25I58uUfGwurrZuFhfsw40zElk6MQZvDzn4IIQQQgghxOkqNsiHhxaN5O65Q/lgWzZvbsqguLqx3XqlNY08ueYwL/x0jMsmx/Db6QnEBfv2wYi7x89TD26+wtkdCs/9eISqeitPrjnMqxsyuHF6AtedI2EmIYQQA09zdSS9Xn/KQ0UeHh4YDAYcDgd2u12qMAnRC3Q6HTqdDrvd3q4amhBCCHEqSIhJCCH6wLNrj/Dyz8fchpd0Wg0Xj4/mzjlDSAgZOAfqQe2XvfFoKcvXp7PhSGmH604cFMDN5yZy3sgIdFpNL41QCCGEEEII0ddMPgZumzWY306P58uUfF7dkM7hopp269Vb7by9JYt3f8nigtER3DQjkQmDAvtgxD3n6735pJfUOh9X1Vt56vvDvLZRDTNde048RgkzCSGEEC5pNBpnqEII0Ts0Gjl2L4QQfcHhcFBTU0NVVRUhISF4e3v39ZB6hYSYhBCiDzgUxWWASafVcMmEaO6cPYT4ARZestgcfL03n+Xr00krrHa7nkYD80dGcNO5CUyKC+rFEQohhBBCCCH6G0+9jmWTY1k6KYafD5fw6oZ0Nh0ta7eeQ4Fv9hXyzb5CkuMDuWlGIvNGhKMdgBdD7Mutcjm/Ocz06oZ0bpyRyHUSZhJCCCGEEEIIIc5IaWlplJSU4HCorep1Op2EmIQQQpw6v52ewBubMqhusAEDO7xkbrDy4dZs3tyUSaG5we16XgYtyybF8tvpCQOuupQQQgghhBDi1NJoNMwaHsas4WHsz6vitQ3pfLW3ALuLttTbMyvYnrmTxBBfbpiRwKUTY/AyDJxqDH9ZOJJLJkbz3A9H+O5AUbvl5gYbT39/mNc2pHPD9ESuny5hJiGEEEIIIYQQ4kyiKIozwARQVVVFVFRUH46o90iISQghTpHi6gYMWi2Bvh7tlpm8DVx/TgIv/HSUX01Q28bFBQ+sYE9eZT1vbszgo+051DTa3K4X7OvBNdPiuXpaHEEufhZCCCGEEEII0droaBP/uWIC916QxJubMvhwm+vvHOmltfz5i/08vebwgPvOMSrKxCtXT+ZgvpnnfjjCtwcK261jbrDxzNrDvL4xnd9OT+D6cxIweUuYSQghhBBCCCGEON2ZTCaKi4udjysrK1EU5Yxo8SkhJiGE6GHF1Q288nM67/2SxbVnx/PgghEu17thegKXTowecOGlzq6KbpYY4suNMxL51cToAXVVtBBCCCGEEKJ/iArw5s8XjeR3c4fy0bZs3tjouvprWa2FZ9Ye5sV1R1k2OYYbpicOmOqvI6OMvHz1pE7DTP9Ze4TXN2Zw7wVJXH1WXB+MVAghhBBCCCGEEL0lICCgzWOLxUJDQ8MZ0VJO29cDEEKI00WxuYG/fXWQGf/6idc3ZtBoc/DOlkxKaxpdrm/yNgyYAJOiKKw7VMxVr/3Cwuc3siIl322AaUp8EK9eM5m1f5jJlVMHSYBJCCGEEEIIcVKMXgZuPncw6++dzTOXj2NEpNHleo02B+/9ks2cp9Zxy7s72JlV3ssj7b7mMNPqu2dw4egIl+tUN9jw1MmhPCGEEGceq9VKfX09FosFu92Oori/sFIMTG+99RYajQaNRkNmZuYpeY7MzEznc7z11lun5Dn6q0ceecT52oUQQgwM3t7eGAxtqzFXVVX10Wh6l1RiEkKIk1RsbuCln4/xwdZsGm2ONssarA6Wr093W42pv2u02fkyJZ/XNmRwqKja7XpaDVw4OpIbZyQwYVBgL45QCCGEEEIIcabw0Gu5ZEIMF4+PZtPRMpZvSGf94ZJ26ykKfHegiO8OFDFxUAA3n5vIeSMj0Gn7/0mbEZFGXvrNJFILzDz/4xG+2ddSmSk2yJtLJkb34eiEEEKIvmG327HZbNhsantZvV5/RlQhEEIIIcSZS6PRYDKZKC0tdc6rqqoiIsL1hU+nEwkxCSFENxWZG3hp3TE+3NY+vNRMr9Vgtbte1p8VVNXz0bYcPtyWTXG160pSAN4GHZcnx/LbcxIYFOzTiyMUQgghhBBCnKk0Gg3Th4YwfWgIqQVmXtuQwZd78rDa21dl2JVdya3v7SIu2Ierpg5i6aRYgnw9+mDUJ2ZEpJEXr5pEWqHaZu6bfYXcOXsIBjeVmAqrGvA26DD5GFwuF0IIIQYyu93e5rFWK5UJRf/01ltvcf311wOQkZFBfHx83w5ICCHEgOYqxHQmkBCTEEKcoObw0gfbsrF0EF5aNjmG22cNITZoYIR77A6F9YdLeH9rNj+mFeGmWxwAIX6eXH9OPFdNHUSAT/8/ASCEEEIIIYQ4PY2INPLUZeP40/zhvLU5k/e3ZlHdYGu3XlZZHf/3TRpPfneYC8dEcNXUOJLjA/t9S42kCDXMdKiwmsRQ9+3I/77qIOsPlXD9OfHcMD1RwkxCCCFOGw6HA4ej7TFYnU7XR6MRQgghhOg9JpOpzePm9roeHqf3uVkJMQkhRBcVVjXw8s9dCS/FcvuswQMmvFRsbuCTHTl8uC2HvMr6DtcdEubHzTMSWTw+Ci+DHCwQQgghhBBC9A8RJi/uvzCJO+cM4ePtObyxMcPl9xuL3cHKlHxWpuQzJMyPq6YO4lcTYvp96Gd4hL/bZYcKq/lmXwGKAs/9eJQ3N2Vy3Tnx3DA9QS46EUIIMeAdH2ACCTEJIYQQ4szg5+eHTqdrU5WyqqqK0NDQPhzVqSchJiGE6IKPt2fz15UH3IaXDLqW8FJMYP8PLzkcCpuOlfLB1my+P1iEraOyS8C0xGBuPjeRmcNC0Wr795XKQgghhBBCiDOXn6eeG6YncO20OL7ZX8jy9cfYn2d2ue7R4hoe/eogj69OY+HYKK46axATYgP6fXWm4z334xGUVl/pqhttPN8UZrpewkxCCCEGOJutbYVFnU434N6rhRBCCCG6Q6PRYDQaqaiocM47E0JM0jhYCCG6YFSUyWWAyaDTcNXUQaz702z+75Ix/T7AVFbTyMs/H2P2U+u4+vVtrN5f6DbA5Ouh46qpg1h113Q+vPksZieFSYBJCCGEEEIIMSDodVoWj4viqzun8+mt07hkQjQeeteHwRptDv63K5dfvbiZC5/dwLu/ZFHdYO3lEXeP3aGg12pwdS63pinMNP1fP/Hkd4eoqLX0/gCFEEKIk9S68gBIFSaARx55BI1G4wxzmc1mHnnkEcaMGYOfnx9hYWEsWLCAzZs3t9muuLiYv/zlL4waNQpfX1+Cg4NZsmQJu3fv7vD5HA4H7733HgsWLCAiIgIPDw9CQ0OZPXs2L774IhZL558xKioquP/++0lKSsLb25uwsDDmzZvHp59+2qXX3Px6H3nkkQ7XmzVrFnq9nksvvbRL+z3e/v37+cc//sH8+fOJiYnB09MTPz8/hg4dyrXXXssvv/zicrt169ah0Wi4/vrrnfMSEhKc426e1q1b53L7FStWsGzZMgYNGoSXlxcBAQFMnjyZRx99tM2Ja3dyc3O54447SExMxMvLi6ioKBYvXszatWu79XMQQgjRfxzfUq6ysrJvBtKLpBKTEEJ0wehoE/NGhLE2tRhQw0uXTY7l9tlDiA7w7uPRdUxRFLZmlPP+1my+21+Ixe66mlSzUVFGrpoax+LxUfh5ytuEEEIIIYQQYuDSaDQkxweRHB/EQwtH8r9duXywNZv00lqX66cVVvPXFft57JtUloyP4sopcYyJMblctz/QaTU8e8UE7pw9hOd/PMpXe/PbVGUCNcz035+O8uamDK45O55rpsURaerf32OFEEIIUI9rHt9OTkJMbeXk5DBv3jwOHz7snFdbW8vq1atZs2YNH374IcuWLWPv3r0sWLCAvLw853p1dXV8+eWXfPfdd6xevZrZs2e32395eTmLFy9m06ZNbeaXlpaybt061q1bx3//+19Wr15NXFycyzGmpqYyb9488vPznfMaGhr44Ycf+OGHH7j++us599xzT/ZHcdLWrVvn8mdgsVg4evQoR48e5Z133uH+++/nscce65HnrKioYOnSpfz4449t5jc2NrJz50527tzJiy++yMqVKznrrLNc7mPDhg0sXLgQs7ml+mhBQQFfffUVX331VafBLyGEEP1bQEBAm8e1tbXYbDb0+tP3HO7p+8qEEOIEOBwKPx8uoabRxswEP5fr3D13GOsPl3JZcgy3zer/4aXKOgv/25XHB1uzOFbi+gB9M2+DjsXjorhy6iDGxpikJLMQQgghhBDitBPo68GNMxK5YXoCW9LLeH9rNmsOFGK1t69OW2ex8+G2HD7clsPYGBNXThnE4vFR+Hj0z0NpQ8P9ee7XE7hr7hCe+8F1mKnWYueldcdYvj6dC0ZFcO3Z8STHB8r3PyGEEP3W8VWYQEJMx1u2bBm5ubk88MADXHDBBfj4+LBx40YefvhhzGYzN9xwA5MnT2bhwoXU19fzz3/+k5kzZ2IwGPj222/55z//SWNjI9dddx1HjhzBw6OlBa3dbmfhwoVs2bIFgJkzZ3LnnXeSkJBAfn4+b7zxBitWrCA1NZW5c+eSkpKCn1/bY+tms5n58+c7A0yXX3451157LWFhYRw+fJinn36aN998k/379/feD80Nm82Gr68vF110EXPmzCEpKQmj0UhxcTEHDhzgueeeIysri8cff5xhw4a1qbqUnJzMvn37WLlyJX/5y18A+O6774iKimrzHAkJCc77jY2NzJs3j127dqHT6bjyyitZsGABCQkJWK1W1q9fz9NPP01xcTELFixg9+7d7YJi2dnZzgCTVqvl5ptvZunSpZhMJvbu3cvjjz/OI488wuTJk0/hT04IIcSp5O/vj0ajQWn1Jb+qqorg4OA+HNWp1etHXo4cOcI777zDli1bKCwspL6+nu+++44hQ4Y419m/fz/Z2dn4+voyc+bM3h6iEOIMYm6w8umOXN7dkklmWR3hRk/Ovs31B/oxMSZ+eXAuQb4eLpf3B4qisCu7gve3ZrNqbwGNLlrgtTY83J+rzhrExROiMXoZemmUQgghhBBCCNF3NBoNZw8O4ezBIZTWNPLpjlw+2JZFTnm9y/X35laxN3cf/1yVysUTorly6iBGRBp7edRdMySsJcz0/I9H+XJP+zCT3aGwal8Bq/YV8O+lY7lscmzfDFYIIcQZQ3E4sLtofWK323E0zbfp9SjHBZQsjY3YrS0tXrVabZvH/ZkuIACN1nUr256UkpLCzz//zNSpU53zJk+ezNChQ1m4cCHV1dVMnToVRVHYtm0bgwcPdq43ZcoUQkJCuOOOO8jOzmbVqlVccsklzuUvv/yyM8B0zTXX8NZbbznDz5MmTWLRokX8+c9/5v/+7/84duwYf//73/nXv/7VZnx///vfycnJAeD//u//eOCBB5zLJk2axNKlS1m4cCFr1qzp+R/OCRo/fjy5ubntKl4AzJ8/nzvvvJOFCxfy/fff8+ijj3LNNdc4Q3W+vr6MHj2aHTt2OLcZNmwY8fHxbp/vb3/7G7t27SIgIIC1a9cyadKkNsunT5/OVVddxbRp0ygoKODBBx/k/fffb7POH//4R2cFpvfee49f//rXzmWTJ09m2bJlzJgxo824hBBCDCxarRaj0UhVVZVznoSYeojD4eDee+/l2WefxeFwOJNiGo2mXb/c5uSwXq8nIyOD6Ojo3hqmEOIMcaSomre3ZPL5rjzqLC1X9BSZG/nhUJnb7fprgMncYGXF7jw+2JpNWmF1h+t66LUsHBPJVWcNYuIguepWCCGEEEIIceYK8fPktlmDueXcRDYcLeWDrVmsTS3G7mhfnam60ca7v2Tx7i9ZTBwUwFVT47hobCRehv5XEWJImD/PXjGB381xH2byNuiYPyqibwYohBDijGKvrOTI2ed0uI65w6UDz9DNm9AHBZ3y57nnnnvaBJiaXXTRRcTFxZGVlUVJSQkvvfRSmwBTs+uvv54//vGPNDQ0sGHDhjYhphdeeAGA0NBQ/vvf/7o8jvzoo4/y+eefk5aWxquvvsrf/vY3PD09AbUN2+uvvw7A2LFjuf/++9ttbzAYeP3110lMTMTaxwG1kJCQDpd7eHjwxBNPMH78eLKyskhJSWkXPOqqmpoa58/373//u9v9xMXF8de//pXbb7+dTz/9lOXLl+Pr6wtAYWEhX3zxBQALFy5sE2Bq5u/vz/Lly13+jgghhBg4TCYTVVVV6PV6TCZTu8qHp5teCzHdcsstvPHGGyiKQnR0NNOmTeOzzz5zuW5zucTMzEw+++wz7r777t4aphDiNGZ3KPyQWsTbWzLZdNR9UOnDHfm47i7d/+zNreT9X7L5ck8+9db25ZVbSwz15copg1g6KYYAn/4ZxhJCCCGEEEKIvqDVapg5LJSZw0IpMjfw8fYcPtqWTX5Vg8v1d2VXsiu7kr99fZBLJ8Zw5dRBDAnrfwcRW8JMQ3lzUwaf78pzfne8ZGI0Jm/XFXnrLXa8DFq56EUIIYTo56644gq3y8aOHUtWVhYajYbLL7/c5Tre3t4MHTqUffv2kZ6e7pyfn59PamoqAJdddhn+/v4ut9fr9Vx//fXcd999VFRUsGvXLqZNmwbAzp07qaioAODaa691+7kiJiaG888/n1WrVnX+gntRY2MjRUVF1NTU4HCoHQ9at/LZs2dPt0NMP//8s7OixtKlSztc99xzzwXAarWyc+dO5+OffvrJ2XKxdWu7402ZMoVRo0Zx4MCBbo1VCCFE34uMjCQsLAwfH58z4nt6r4SYfvjhB15//XU0Gg0PPvggjz76KDqdDm0HpTSXLVvGv//9b3788UcJMQkhTkplnYWPt+fw7i9Z5Fa4bg/QLCnCn0VjwijeAv31PaC20cbKlHw+2JbF/ryOr1Ey6DRcMDqSK6cM4qzEoDPijU0IIYQQQgghTka40Yu75g7ljtlDWHeomA+2ZvPjoeJ2lYwAquqtvLEpgzc2ZTAlIYirpg7igtEReOr7V3WmIWF+/POSMdx7QRKf7lC/H187Ld7t+v9YdZAdmRVcc3Ycl0yIxsej166DFEIIIcQJGDZsmNtlzW3RQkJCCAwM7HS96uqWCv/79+933u+sik/r5fv373eGmPbt2+ecn5yc3OE+pkyZ0i9CTLW1tTz33HN89NFHHDhwwBkScqW0tLTbz9O6vVtkZGSXtyssLHTeP9Gfr4SYhBBi4PLy8urrIfSqXjkCsXz5ckCtsPSPf/yjS9tMmTIFQN5UhRDdllpg5u3NmaxIyaPB6nC7nk6r4fyR4Vx7djxTE4Korq7mpV96caBddDDfzPtbs1iZkk9No63DdeOCffh1U9WlED/PXhqhEEIIIYQQQpw+dFoNc0eEM3dEOHmV9Xy8LZuPtudQXN3ocv1tGeVsyygnyNeDZZNi+PWUQcSH+PbyqDtm8jZw44xEbpie4PYil6p6q7Ni05+/2M+/Vqdx2eRYrpkWz6Bgn14esRBCCCE64uPj/r25uZBAR+u0Xq91YKe8vNx5PywsrMPtIyJa2tO23u5E9hEeHt7h8t6QmZnJnDlzyMjI6NL69fUdXzDdkeLi4m5tV1dX57w/0H6+QgghRFf1Sohpy5YtaDQabrjhhi5vExMTA7RNFQshRFc9vjqNl38+1uE6Qb4eXJEcy2/OiiMqwLuXRnZi6i12vtqbzwdbs0nJqexw3eYw1pVTB3HO4BC0Wqm6JIQQQgghhBA9ITrAmz+cP5zfzR3KD6nFvL81iw1HXF99X15r4ZX16byyPp3pQ0K4cuogzhsZjkHnviJ5b+uoSu+nO3LatCs3N9h4bWMGr2/KYM7wMK49O54ZQ0Ok0q8QQogu0wUEMHTzpnbz7Xa7s5pNSEgIOl1LJcP6+vo2oRqDXo/nAKpCoGuqbnQ66In3/IHwueHqq68mIyMDjUbD9ddfzxVXXMGIESMIDQ3Fw8MDjUaDw+Fw/p4qrsp0dlHr3+1du3ZhMLhu8Xu85nOnxxsIP18hhBCiq3olxNScKI6Pj+/yNs1v2DZbx9VGhBDClakJQW5DTKOjjVw7LZ5F46LwMvSvEv+gfvnZm1vFF7vz+N+uXKobOv47GB3gza+nxHLZ5FjCjAPni7wQQgghhBBCDDQGnZYLRkdwwegIsspq+XBbDp/uyKGs1uJy/Y1HS9l4tJQQP0+WTY7hVxOiGRru38ujPjEbj7oOZykK/JBWzA9pxSSG+nLttHgunRSDn6e0mhNCCNExjVaLPiio/Xy7HW3TOSB9UFCbEJOP3Y691eTh6Ym+i0EPcfKCWv17FRUVdbhu62IErbdr3cKuqKiow9Z3nT2HRqNBURQcDvcdF0BtB9cdaWlpbNy4EYAHH3zQbVeZ1tWPTkZwcLDzfmhoqNtwUkeO//nGxsa6Xbezn68QQgjRn/TKUQZfX18qKyspKSnp8ja5ublA2w88QghxPEVRXF5lMHNYKPHBPmSWqeVV9VoNF46J5Lqz45k4KKBfXplwrKSGlSn5fJmS5xy3O1oNzEkK48qpg5g5LAydVF0SQgghhBBCiF4VF+zL/Rcm8YfzhvHdgUI+2JrNlvQyl+uW1jTy0rpjvLTuGCMijSwZH8XicVH9sirwG9cms+FoKW9vzuSnQ8W4KjKQXlLLw18e4InvDrF0UgzXTIsjMdSv9wcrhBDitKXT6Xqk4o3ontGjRzvvb926lauvvtrtutu2bXO53ZgxY5z3t2/fzowZM9zuY/v27R2Ox9/fH7PZTEVFhdt1FEXh6NGjHe7HnQMHDjjvX3755W7X27FjR4f76ep5hwkTJjjvb9q0qcPndOf4n29HIabOfr5CCCEGHkVRUBTF2Rb2dNIrrygxMRGAgwcPdnmb1atXAzBq1KhTMiYhxMBltTv4ck8+l760me8OuG45qdVquGZaPCF+ntw1dyib7p/D87+ewKS4wH4VYCqsauC1Deksen4jc5/6med+ONJhgCncqL6ejffN4bVrk5mTFC4BJiGEEEIIIYToQx56LYvGRfHhzWfxwx9ncuP0BAJ83FeKSC0w8/jqNM5+/Ecue2UL72/NosJNJae+oNVqmDkslDeuS2bd/5vFDdMT8PdyfR1kTaONtzZnMuepn7nmjW38fLjrFzAKIYQQXaXRaPrVMd0zQVRUFCNGjADgk08+oaamxuV6drudt956C1ArA02cONG5bNKkSc5qQe+++67bMFpeXh5r1qzpcDwJCQlAxyGi1atXU1lZ2eF+3GndFaajak4vv/xyh/vxatXysLGx0e168+bNw8fHB4DnnnuuW0G92bNnO4N+b7/9ttv1tm/fzv79+094/0IIIfqfmpoasrOz2bdvH5s3byYvL6+vh3RK9EqI6fzzz0dRFF544YVOSz2CGnZ666230Gg0LFiwoBdGKIQYCEqqG3l27RHOefxH7vpwNzuzKnhrc6bb9a+cOojN98/hD+cNI7wftVmrqrfy8fZsrnz1F6Y9/gP/WJXKvrwqt+trNGplqVeunsSm+9TX0x+v1hVCCCGEEEKIM93gUD/+snAkvzwwl2cuH0dyfGCH62/LKOfPX+xnyv+t5ca3t/PlnnzqLB23FO9NccG+/LXp9fzj4tEMDXNfbWn94RK+3pPfi6MTQgghxKl0xx13AFBSUsJdd93lcp1HH33UWcDgpptuwtPT07nM09OT66+/HoCUlBSeeOKJdtvbbDZuuukmLJaOA90zZ84E1KpQmzZtare8sLCQ3/3ud114Va4NHTrUeb85lHW8l156iZUrV3a4n8jISOf9Y8eOuV0vICCAO++8E4DNmzfz+9//vsPzp0VFRbz22mvtnmvJkiUAfPnll3zyySfttqupqeGWW27pcMxCCCEGjvz8fDIyMigvL8dms1FV5f788kDWK+3k7rrrLp577jmOHTvGrbfeyosvvohe7/qpv//+e66//noaGhoIDg7mpptu6o0hCiH6sd3ZFby9OZNV+wqw2ttekfBLejlphWaSIozttvMy6NrN6ysNVjs/phWzMiWPn9JKsNg7D3TGBfuwZFwUyybHEhvk0wujFEIIIYQQQgjRE7wMOi6ZEMMlE2I4VFjNpzty+GpvPkVm11fkW+0Ka1OLWZtajI+HjvNHhrNkQjTTh4Rg0PV9aXhfTz2/OSuOq6YOYsuxMt7anMna1CIcxxUNuPbs+D4ZnxBCCCF63q233sr777/Pli1bePPNN8nKyuL2228nISGBgoIC3njjDT7//HMABg8ezF//+td2+3jooYf45JNPyM3N5b777iMlJYVrrrmGsLAwDh8+zNNPP8327duZPHlyh1WWbr75Zl588UVsNhuLFi3ioYceYvr06VgsFjZt2sTTTz+N1Wpl6NChHDly5IRf64QJExg9ejT79+/nlVdeoaKigquvvprIyEhyc3N57733+OyzzzjnnHNchqha78fLy4uGhgb++te/YjAYiIuLc7b6iY6OxttbvUD5b3/7Gz///DNbt27l2WefZd26ddx0002MHz8eX19fKioqOHDgAGvXrmX16tWMGTOGG2+8sc3zPfXUU3z//fdUV1dz5ZVX8vPPP7N06VKMRiN79+7l8ccf5/Dhw53+fIUQQgwMJpOJgoIC5+OqqioURTntKlb2SogpPDycl19+mWuuuYbXX3+d7777josuusi5/Nlnn0VRFDZt2kRaWpqzd99bb72Fn5/7K7yEEKevRpudVXsLeHtzJntyO06RfrYjl78sHNlLI+s6m93B5mNlrEzJ57sDhdQ0dn41bYifJwvHRrJkfBTjYwNOuzcdIYQQQgghhDjTDI/w5y8LR/LAghFszSjjy5R8vtlXgLnB9XfEOoudFSn5rEjJJ8jXg4vGqN8RJw4KRNvH7cQ1Gg1nDwnh7CEh5JTX8d7WLD7enkNlnZXJcYGMjja53K6m0cZPh8twKCAd0YUQQoiBQafT8fXXX7N48WI2bdrEjz/+yI8//thuvREjRrB69WqX5/NMJhPffvst8+bNo7CwkA8//JAPP/ywzTrXXXcdM2fOdFZtcmXUqFH8+9//5g9/+AMVFRX8/ve/b7M8KCiIFStW8Ne//rVbISaNRsO7777LnDlzqKio4JNPPmlX2WjMmDF8+umnREVFud2Pv78/d911F//+97/ZtWsX559/fpvlP/30E7NmzQLUSlXff/891113HZ9//jl79uxxVmdyxWhsfyF3fHw8X375JYsXL6a6upoXX3yRF198sc06Dz30EBqNRkJMQghxGjCZ2n7nttls1NXV4evr20cjOjV6JcQEcNVVV2EwGLjlllvIycnhlVdecZ6cby6B2Nzz1c/Pj7fffrtN0EkIcWY4WlzDF7tz+Xh7DqU1HZeQTY4P5Nqz45k/KqKXRtc5RVFIyalkZUo+X+8toLTGfd/rZn6eei4YHcGS8VFMSwxG3w+ushVCCCGEEEII0bN0Wg1nDw7h7MEhPLpkFOsOlfBlSj5rU4totLmu1ltea+HdX7J495csogO8WTI+iiXjoxke4d/Lo28vNsiHBy4cwT1zh/HlnrwO255/viuXh1YexF87jJEe5RRUNbg8ESeEEELY7XYAtFqtXODZDwQFBbF+/Xref/99PvjgA3bv3k15eTlGo5ExY8awdOlSbrrpJjw8PNzuY9SoURw4cIB//etffPHFF2RnZ+Pv78+YMWO46aab+PWvf+22hVtrv//97xk5ciTPPPMM27Zto66ujqioKBYsWMC9997LoEGDTuq1jh8/npSUFB577DFWr15Nfn4+/v7+DBkyhMsuu4w77rgDLy+vTvfz+OOPM3ToUN555x0OHDhAVVWV8/f6eP7+/vzvf/9j48aNvP3222zYsIH8/Hzq6+sxGo0MHjyYKVOmcNFFF7ULRDWbNWsWBw4c4LHHHuObb76hoKCAwMBAJk+ezO9+9zvmz5/PI488cjI/GiGEEP2El5cXnp6eNDa2nH+uqqqSENPJuOyyy5g7dy4vvvgiX331FSkpKdhsLVedjRo1isWLF3P33XcTFhbWm0MTQvQhh0PhjU0ZrEjJY3+eucN1PfValoyP4ppp8W6v8OwLR4tr+DIlj5V78skqq+t0fQ+dltlJoSwZH82cpLB+1fpOnCbsNrDUgLUO7BYIjHe9XvYvcPg7sNQ2TU3bNN93zq8Faz1wXL+IBwtA7+IgRdo38Mk13R//3XvAFN1+fnk6vHYeePqBpz94+Ku3zsd+4Gl0/zgwATykPaMQQgghhOhbnnod80dFMH9UBNUNVtYcKGJFSh6bjpa2a9HWLK+ynhfXHePFdcdIivBnyfhoFo2LJCawbz/fenvouDzZ/UlDRVF4e3MmANUOD7Y2RDD/he1MiQ/i4gnRLBgTQYCP+xOfQgghziwWi8V53kin02EwGDAYDH08qv7lkUce6VIo5a233upSOGjdunUdLtdqtVx99dVcffXVXRugC0FBQfzrX//iX//6l8vl1113Hdddd12n+5k/fz7z5893u3zdunXY7XaKi4vbLYuPj3cWU3Bn0KBBvPTSSx2u09k+NBoNN954Y7vWbx2ZPn0606dP7/L6x4uNjW1Xgam1rv7OCCGE6P9MJlOb97nKysoOqwQORL0aYgIIDg7mr3/9K3/9619xOByUl5djt9sJCgqSD6JCnKG0Wg1f7snvMMAUHeDNb86K44rkWAJ9+8fBzYKqer7ak8/KlHwO5HccvgLQaGBaYjBLxkdxwehITN7yN090XaL9GFGOXDzXFQNWN0GjupbH9lZVwPyj4I+prnecvxs2Pn0KRqyAw9rzu20wQ12pOnXHdd9A/Dnt59dXwEdXNYWe/DsISfmDlwmMkeAXAbpe/yglhBBCCCFOM/5eBi6dFMOlk2IoqW5k1V61lVxKTqXbbdIKq0n7No1/fZtGcnwgi8dHc9GYSIL6yffl1jYeLeVYSW27+dsyy9mWWc7DX+5n1vAwLpkgF/kIIYSgTcUau92OXi/HXoQQQgghmgUEBLQJMVVVVXUasB1o+vTTn1arJSQkpC+HIIToJy4eH83e3Kp2889KDOK6sxOYNyKsX7RZq6yzsHp/IStT8tiaUU5X3hPGxphYPC6KReOiCDd2Xm5WnIasDWroprYEasvUW+fj0qapBEwxcPm7LncxyJHFVNsvsGvTiT+/pf0JAyePAVZisrH65Lb39HM9v74Ssk7wZ6vRgl84GKNg9oMwZN7JjU0IIYQQQpzxQv09ue6cBK47J4Gsslq+TMlnRUqeyxBQs+2ZFWzPrODRLw9w7rBQloyPYt6IcHw9+8dJX51Gw5hoE/vy2n/nB7DaFb4/WMT3B4vwb2q3fsmEaKYmBqPTShshIYQ4kzgcjnYn4XQ6CbcKIYQQQjQzmdp2KrJYLDQ0NPTRaE6N/nE0Qwhx2lIUhZ1ZFaxIySO1oJrPbp3mspf5wnGR/GPVQRyKetB20dgoLkuOISnC2AejbqveYueHtCJW7M7n58PFWO2dJ5fig31YMj6axeOjGBzqJjQhBi6HA7RuQnW734e0r9sGlCxdDN7UV7hdVMdJtIiw1ICiqOXAjmcYYK3VLDUnt72nf8/tV3FAdYE6OVz3tcdug2dGNoWdotXAkzGy1f2m24EWJhNCCCGEEKdcXLAvv5s7lDvnDOFggZmVKfl8mZJPodn1wUmbQ+HHtGJ+TCvG26DjvJHhXDwhihlDQzH04UVBZw8J4cs7z2Fjah6PffQTR60mLIrrE9LVjTY+3ZnLpztzCTd6snhcFDfOSJQLgoQQ4gzRugpTM627Y3BCCCGEEGcgb29vDAYDVmtLN5Sqqip8fAbY+b4OSIhJCHFKHC2uZsVu9YrR3Ip65/x9eVWMjQlot36Yvxf3XZDEyCgj0xKD+7zqkkOBXJsfD355iJ8Ol1FrcRNQaKU5fLVkfBRjY0wuw1qin7PboKYQqvLAnNt02zQ1V0uqLYGQYXDjWtf7KD0Mh77p3vPXlblfpDmJDx+KHWyNYHBx4D8oAUYvVUM0bSa/lvuG5ltvtfpQa1o3HyXizoFb1nd/zL6hrufHToVrvlQrMllq1Nvmye3jmpbHHm5CTCdb4cnopt9wTVHLVLjX/fZeJjXQ5B/ZNtw0eA4ExJ7c2IQQQgghxICm0WgYFWViVJSJ+y9IYltmOStT8vlmXwFV9a5bONdb7Xy5J58v9+QT6GNgwZhIloyPZnJcINo+qG6k0WgYF2Nkpm8+05UCRs5dyneHKvghrRiLzeFymyJzI69tzOC30xN6ebRCCCH6yvEhJp1OJ8dYhRBCCCFa0Wg0mEwmSktLnfMkxNSBxMTEntwdoP4jHDt2rMf3K4ToeUXmBr7ak88Xu/M4kG92uc6K3fkuQ0wAt8wcfApH17nqBisbjpSyem8u31cl0aDoYX9xh9v4e+q5cEwES8ZHc5aUuh9YjnwPGT+rQaWqXDWoVF2oBn46U9PB74W78E1XNJrV1nMuwkal2hDSdEkkJo3FwzfwuLCRj/vgUfOkM7h+zuhJsPT17o/ZHe8AdeppPkGQOLN72zocrqtRAQTGw4In1X+D1qGnxur28+rKwG5pu70x2vV+zfldG1tDlToVH2w7/8pP3IeYti6HwDgIHQ6mQe6rgwkhhBBCiNOGVqvhrMRgzkoM5pHFI1l/uJSVKXmsTS2iweo6DFRRZ+X9rdm8vzWbCKMXc0aEMW9EGGcPDsHL0PstenQahTnDQ7g4OZGqeivf7S/ki915/JJR1q5l+9SEICJN3r0+RiGEEH3DVYhJCCGEEEK05SrEFBkZ2Ycj6lk9GmLKzMzs0nrNyfnjexu7mi8peyH6t+oGK9/uL2RFSh6bj7U/4Hi8r/fm85eLRvTJlZ+u5JTX8UNqET+kFfNLelmrVnHu/zx66LXMTQpjyfgoZg0P65ODvuI4igINlS2Vk5pDSR6+MOOPrrc59hP88kL3nq+Dikn4hrhfptGpy31DwSdYvfUNBd+m+z4h7SsdNSnURrHS41fcNv82PIx932ZxQOoo5GOMgik3dW0/iqL+DpjzwFyg3noHul7XnHfi4zx+XK40VMHqP7U8NvioFcJCk9RQU/NtYDxo5W+UEEIIIcTpyFOvtow7b2Q4NY02vj9YyIrd+Ww8Word4frLeaG5gQ+2ZvPB1my8DTrOGRLCeSPDmJ0URph/77dsM3kbuCw5lsuSYymoqm+6MCqf1AL1wqiLx7u5WAB4es0hDhZUc8mEaOaOkO/mQggx0CmKgsPRNpArISYhhBBCiPZMJlObx/X19W3ayw10PRpiuvbaaztcnpKSwp49e1AUhYCAACZMmEB4eDgARUVFpKSkUFFRoZaYHjeOcePG9eTwhBA9xGJzsO5QMStT8lmbWkSjm9LvrQ0O9eXi8dEsGR/dpwEmh0MhJbdSDS6lFpNW2LUWUloNnD04hMXjo7hgdARGLzdVbcSp0VjTNpx0fLu3qjyw1rbfLijRfYjJ5P5geKcsNWCtV9urHS9yPMx6sCmsFNISTvINAa8AqZZzOtBoWv59Izv5rBI/HX7zv6awU35T8Cm/5X5DZcfbu6vwVHKo7WNrHRSkqFNrei8IGXpcuCkJAhNAJ12FhRBCCCFOF36eei6ZEMMlE2IorWnkm30FrEzJZ2dWhdtt6q121qYWsTa1CIBxsQHMSwpj7ohwRkT69/qFhZEmb24+dzA3nzuYw0XVrNidx4VjXF9J6nAofLozl4KqBtamFuHvqeeC0RFcPEGqJAshxEB1fIAJJMQkhBBCCOGKn58fOp2uTRXLurq6PhxRz+rRs1dvvvmm22VvvPEGH3zwATExMTz11FNccskl6PVtn95ut/P555/zpz/9iYMHD3LHHXdwww039OQQhRA94Pb3dzkPcnYk1N+TxeOiuGRCNKOijH1WWa220caGI6X8kFrET4eKKa2xdL5Rk9GRfvxq0iAWjo0kzNj7V6WeMRTFfZuvbx/sfsUkc777fbsLhwDoPNWQkzEaTDHqrX9E2+pJOg/X24YlqZMQoAadhsxzv9xSqwacqvPbh5xqS9xXeCpJ69rz2xqgcJ86tXbdNxB/Ttf2IYQQQgghBpQQP0+umRbPNdPiySmv48s9+azYnceR4poOt9uTU8menEqe+v4w0QHezEkKY97IcM5KDMJT37snkYeF+3PvBe6/V23NKKegqsH5uLrRxqc7c/l0Zy7hRk8WjY3i4j4+FiGEEOLEHN+5Q6vVyt9wIYQQQggXNBoNPj4+VFe3FOuw2Wx9OKKe1SuX4O/YsYNbb72V0NBQfvnlF6KiXLdG0el0LFu2jOnTpzNp0iRuv/12xo0bx+TJk3tjmEKILjpvZJjbEJOfp575oyK4ZEI00wb33dWP+ZX1/JBWzNqDRWxJL8PShWpRAAadhsmDTOgKDxJnqOa+62/EKK27ekZ9JVRktpoyWu57+sOtG11v5x/R/ee0NUBdudqu7XihSTD60rZBJVM0GGPU4IkcJBG9wcMXQoao04nw9IfYs6AkVW0td6JC3ZwQKk6DT69r25IuNAmCh4DeTXBPCCGEEEL0W7FBPtwxewi3zxrMsZIa1qYW80NqETuzKnDTcQ6AvMp63v0li3d/ycLXQ8eMoaHMHRHGnKQwgv08e+8FuPHlnny3y4rMjby2MYPXNmYwJMyPi8dHsWR8NLFBPr04QiGEECfq+BCTBJiEEEIIIdwzGNp2DZIQ0wl65plnsNvtPPjgg24DTK1FRkby4IMPctddd/H000/zwQcf9MIohRDNCqsa+O5AIVefFeey9dsFoyP568oDzmCQXqth1vBQLp4QzbwR4XgZer/Mr8OhsC+vih9Si1ibWszBAnOXtw30MTA7KYx5I8KZMTQExVLPSy+5CdQI9+w2tcVbecZxYaWmqaO2WXpv9xWTghJObBx+4W0DSbg5Mh+WBEvfOLF9C9FfjLpEnRQFaorVykwlh9RQU8khKE6F+nLX2/qGug72ARQfbNpHatv5Gh0ED4bQ4XgaExhhy6JEGwoOu+v9CCGEEEKIfkWj0TAkzJ8hYf7cOnMw5bUW1h0q5ofUYn4+XEJNo/uDnbUWO98eKOTbA4VoNDAhNoC5I8KZNyKcYeF+fXKS+YEFSUwYFMDKlDw2HytDcfO172hxDU+uOcyTaw4zOS6QJROiWTgmkkBfCegLIUR/IyEmIYQQQoiuCwsLw2g0YjAY2gWaBrpeCTFt2LABgKlTp3Z5m7POOguAjRslSCDEqaYoCoeLavgxrZif0orZnlWOoqjl26cNbn+i2+RtYN6IMIrNjVw8IZqL+ugAYL3Fzsajapu4H9KKKalu7PK2Q8L8mDtCDS5NHBTYpmKU2VJ/KoZ7emisAU8/18u2/BfWPty9/drq1SCGf3j7ZYHxLfe9A9VgkrPVW1NQydQ0zz9KqsWIM4tGo/6/8Q+HxJltl9WWquGm4qZgU3PQKXS4+/2VHHI9X7FD6WEoPYwnsLh59gsfQswkiJ4MMckweA4YpPWmEEIIIUR/F+Trwa8mxvCriTFYbA62ZpTxQ2oxa1OLyK1w/51YUWBXdiW7sit54rtDxAZ5MzdJDTRNSQjCQ6/tlfEbvQxcNjmWyybHUljVwFd78vlid16HFzTtyKpgR1YFz649zLYH57m8aEsIIUT/ISEmIYQQpzMHDqwaK3m1eeRZ86ix1mC1W7u+gy68Teo0Ovw8/DB5mDB6GvEz+KHV9M53NnHqhYe3PadqNne9wEd/1yshppKSEgAaG7seMGhet3lbIUTParDa2XystCm4VEJeZfuDlCt257kMMQE8e8UEDLref6MrMjc4D6xuOlpKYxfbxOm1GqYkBDVdLRpGXLDvKR7pAKUoUFMEZceg/BiUp7etrOSwwwM5rismtQ4bdUdFpusQU8hwuHMHGKPU1ltCiK7xDQHf6RA/ve18a4P7bUrSTugpNNZayFivThotPJDbjYEKIYQQQoi+5KHXMmNoKDOGhvLwopEcLqphbWoRP6QWsTun0m2VI4Cc8nre2pzJW5sz8ffUc+4wte3c7OFhvXaxU4TJi5vOTeSmcxM5UlTNipQ8VuzOd3mcA+CsxGAJMAkhRD8klZiEEEIMFIqiUG2tpqqhimprNTWWGudtjbWGastx9601bdaptlTTEKEep/90zae9Nm6tRoufwQ+jhxGTpwmjhxGjp1G9dTPP6GnE5GHC1+Ar782i1/RKiCk0NJS8vDxWr17NOeec06VtvvnmGwBCQkJO5dCEOKPkVtTxU1oxP6YVs/lYWacBoG/2F/DoklEu28P1VoBJURQO5JubDqAWsy+vqsvbmrwNzB4eytwR4Zw7LBST9+lVSq/bFAVqS1qCSscHliw1HW9fV6aGI47XlRCTRqtWTgqMU9cPSlBvA+MhdITrbfQeEDK0830LIbqmo0pJM/6gVlMqSWup3GTO69p+w0e5DxoeWQtH10LMZHUKiHMdhhRCCCGEEH1Ko9EwPMKf4RH+3DF7CKU1jfyYVswPqUVsOFJKncV9O+HqRhur9hWwal8BWg1Migt0Xkg0ONRNRd8eNjTcnz/NT+KP5w1nZ3YFK3bnsWpfAZV1LVc0z0kKc7v9PR/tptHmYHaSGsQK9ffsjWELIYRAQkxCCCH6jxpLDYW1hRTWFVJUW0RhXaH6uLaQoroiCmsLqbcNvK4uDsWB2WLGbDGTW3NiFyTrNDpMniai/aKJM8YxyDiIOP844kxxxPnH4efRO9/5xJmhV0JMc+bM4Z133uHpp5/mwgsv7DTItHnzZp555hk0Gg1z587tjSEKcdrak1PJ6v2F/JRWzKGi6i5v5+ep54JREdQ02lyGmE6l5ipRa1OL+TG1mEJzB1VDjpMY4svcEWHMHRHO5LhA9H1QLapfUBT3AYF9n8HnN3Z/3xWZHYeYPPwhKL4lnBTYKqhkipV2b0L0Z5Hj1Km1hiooOewMNlkLDlCftROjclxp0phk9/s99A3seB22Nj32DVXXj5mstqKLngie/j36UoQQQgghxMkL8fN0tm1rsNr5JV1tO/dDahH5Ve6/qzsU2J5ZwfbMCh5fnUZcsA8zEgOosvoSqa895ePWajUkxweRHB/Ew4tG8fPhElak5PFjajEzh4W63KbBaufbA4U0WB2s3l8IwNgYE7OHhzEnKYwx0Sap4CSEEKeQhJiEEEL0hjprXduAUqtgUvP8Wuup/84y0NgVO+UN5ZQ3lLOvdF+75cFewS3hJmOccxrkPwgvfQcXVgvhQq+EmO6//34+/vhjGhsbmTt3LrfeeivXXXcd48aNc34QVRSFPXv28Pbbb/PSSy9hsVjw9PTk/vvv740hCnHa+mxnLu/+ktWldaNMXsxOUg/OnTMkpNfCS402O7uzK9lyrIxf0svYnVOJpYtt4nRaDZPjApk3Ipy5I8JI7KWrO/uNunI3FZXS4Z794GVsv01Qwsk9Z0WmGjw4nncA3JsB3oFSYaW/UBSwNTRNjWCtV2+d87o6v/m2621hTxmNVq0kpPcGgzcYfNTHBh/QezXNa5o6Wkfbu+HMAc3LBLHJ6gTUm8289NJL+Co1/Pb8sfiUH4Tc7RDXQUg9b0fbx7UlarDpkFp5E41WrcYWM7kl3BQyHLRnaBBVCCGEEKIf8jLomDU8jFnDw/jbklGkFlTzQ2oRa1OL2JPbcdXkrLI6ssrqgAT02Nn/0X6mDwtjWmIwY6JNp/QCJA+9lvNGhnPeyHDqLXa8PVx/F9iSXkaDte2xiL25VezNreLZH44Q4ufJrOGhzEkKY8bQEPy9pNqzEEL0JE9PT7RaLYqioCgKOp0cuxFCCNE9DsVBXk0eGVUZpFemc6zqGOlV6WRWZWK2mDvfQS/TaXT4efjhqe3ZSrBWh5VqSzU2xdaj+3WlrKGMsoYydhXvarcs3CeceGN8u4BTnDEOrUbOAYj2eiXElJSUxNtvv81vfvMbLBYLzz//PM8//zweHh4EBQWh0WgoKyvDYrEAaqBJr9fz5ptvkpSU1BtDFGLAUhSFjNJat+GdOUlhbkNMzeXdm4NLw8P9e+UKF4vNQUpOJb+kl7HlWBm7sis6bW3Xmr+XnpnDQpk3IpxZw0MJ8DnNK/vUV0BZetugUllTWKmh0v125ekQNb79/KDEzp9T76VWUAoe3KrtW0JLNSV3fII637doS1HUsFBDFTSaocHcdL9Kvd9oxrOqmHmWLeix4fVNKmhsLaEja4OL8FHTfXs/CB31VzqPzoNOhlbL9F4t63j4gncQ+ASrv/M+werjM6zKWK3GD9uQ+WBc1vGKljoo3N/xOooDig+o06631XmeRrVC0+ilMPHqnhm0EEIIIYToERqNhpFRRkZGGfnd3KEUmxv4Ma2YtanFbDxa0i4M1JoNHZvTK9icXgGAr4eO5IQgpiUGc1ZiMKOijKcs1OQuwATwU1pxh9uW1jTy2c5cPtuZi76p0tOcpDBmJ4UxONRXKoYIIcRJ0mq1ElwSQghxQqx2K9nV2aRXpTvDShlVGWRWZdJg73qXl5Oh1+jx8/DDz+CHv4d/2/sGP/w8/PA3NM1vuq+xavj6s68xKAZuv+F2wgLDTtn3CUVRqLfVY7aYqWqsUtvJNZqdbeWOn+d83DQ5lK6fv3WnqK6IoroithZubTPf38OfSWGTmBwxmUnhk0gKSkKv7ZX4ymlHURTsdvft3weaXvstuOyyy0hISOD2229n586dADQ2NlJQUNBu3YkTJ/Liiy8yZcqU3hqeEANKbaONTUdL+elQMT+llVBc3cDOv5xHoG/7E+jTBgfjZdA6DyAG+hiYOSyU2UlhzBzWOwEgi83BvrzmSkvl7Mgq7/CApitxwT7MTQpn3ogwkhOCMJwJbeIK9sA7S9QQU3eUH3MdYvIJAq8ANTgTlABBg9Xb4MHq/eDB4B8lFVC6ytboDBvRUNnqvtlFMOm42+Z1HR2n4D2BSc0P0vac4hd0hrBb1ImOrxg/IZ5GtRKZT3DbgJNPUKvQ0xkYfLJbYNYDajWm3O1QV9a17RrNkL4OIsa6X6ej1plCCCGEEKLXhBm9uGLKIK6YMqhNi/gfUosoMnd8cUWtxc66QyWsO1QCgL+n3hlqmjY4mBGRRnS90MbtzjlDGB1l4se0YjYcKaHW4v4AsM2hsCW9jC3pZfzzm1QGBfnwyS3TiDBJmwQhhBBCCCF6Wr2tnsyqTLWiUmW6GlqqSifHnHNKqwwFeQUR7hNOuG84ET4RRPhGtLkf7B2Ml87rhANIZrOZDfYNAHjrvU/pBREajQYfgw8+Bh8ifCNOaFuH4qDWWusMOZXWl5JdnU1mVSbZ1dlkmbPIr8lHQel8Zy5UW6pZl7uOdbnrAPDR+zAhbAKTwtVg06jgUXjozoBzKN1ks9nYvXs3VqsVm82GoigYDAasVmtfD+2k9WqULTk5me3bt7Njxw7Wrl3Lvn37KC8vByAwMJAxY8Ywb948kpOTe3NYQgwIWWW1/JhWzI9pxWxNL8dibxsC+vlwCRdPiG63nZdBx1VT4/AyaJmTFMb42MBTfvDPanewL6/KWWlpR2YF9dYTS39qNTBxUCDzRqrBpcGhfgP/qkZbI5RnQNnRpmpKR9UKS4v+AyFD26/vF979ABOo+3bnjm3gGyIttZopCljroLYU6krVNn21pWrYoq7ptjl01DqY1GhWKx4JAervQ6MZKrvWwhM4M4JP3gEw80/qfUWBigzIbQo05e6Awr0dB/liOvhc+PIM8AuDxJmQOAvCx0gAUwghhBCij3kZdMxJCmdOUjjKxaPZn2dmbWoRaw4UkFpY0+n21Y025/EPAKOXnikJwZyVGKSGmiKMaE/BcY0wfy8uS47lsuRYLDYH2zPL+TGtmJ/Sikkvre1w23qrnTD/nm39IIQQQgghxJnIYrdwuOIw+0v3O6f0qvRuB2XcMXmanGGkCN8Iwn3C29wP9w3HU3dmf8bXarT4e/jj7+FPtF/7c9Cg/nvlVueSac4k25xNVnUWWWZ1Kq7ruNrt8epsdWzK38Sm/E0AeOo8GRc6jknhk5gUPomxoWPx1nuf9Os6Xeh0Ourq6trM0+v1EmLqrsmTJzN58uS+eGohBgyLzcGOpgNmPx4qJr2k4wNmP6YVuwwxAfx14chTMUQnm93BgXyzehXisTJ2ZJZ3eMWiKxoNjIw0Oq+0nBwfhMnbcIpGfArZbVCVrbZ7K2sOKjWFlipzwNWHrJI09yEmDz+wdHKQV6uHgLi2lZSCEtST+e74h5/QyxpwHHY1AFZX1j6MVNv6flNgqa70zAojaQ1NrdQ81TZpzsnT9XxD063OAH3dn9hhb2qjV6e2zbPWNT2udzOvHhz9+ANbd4JPHv5qCNEUo7Z3NMU0TdEtjz18T92YT4ZGo7a0DEqEsZep86z1ULBXDTXl7VCDTVU5LdvEuPnMWFcORfugCDj2gzrPOwgSZqiBpoSZ6vMM9ACsEEIIIcQAptFoGBNjYkyMiRumRvDUC8spsPkSOHwqu3KrOVLceajJ3GBjbWoRa1OLADB5G5iaoAaazkoMZni4f4+Hmjz0Ws4ZEsI5Q0L468KRZJaqF5b9dMj1hWWzh4e6HcOXe/I5XFjN7KQwxscG9EpVKSGEEEIIIQYCu8NORlUG+8vUsNKB0gMcqjiEtYeO6Rs9jCSaEhkcMJgEUwKJpkRi/WMJ9w2XMEwP8dB5kBiQSGJAYrtlddY6cqpznKGmLHOWs4JTeUN5p/tutDeyrXAb2wq3AaDX6hkdPNrZfm5C2AR8Df30XEgv0Gg06PV6bLaWi8QNBgP19fV9OKqeIU0FhehH8irr1TZxacVsOFJKTWPXSyBmltWiKEqvVCuyOxQO5pvZkl7KL+nlbM8op/oExtpsRFNo6azEIKYmBGPyGYChJVAr83x+ixpWqsg88cBE2THX8zUaNZBUsAc0OggYdFxQqSmsFBAHutP8z7m1/rgwUnmrqknHhZHqytT7PZzK7zUGH7U6j5ex6daEVefNgWO52DAwZmIynr4m90EjvSfoWwWRDF7tg0pnWgUuu1X9HXIZfqprCjsdP6/VMlt923Uaq6G+vKVCV2+zVKtTRYb7dbwD24acjNFtH/tH9J/fA4M3DJqqTs3MBWqgqTgVjFGut8vd0X5efTkcXKlOAKZBkHguJMxSqzX5hfX06IUQQgghxAnw0doZ7GHmtguGYDQaKalu5Jf0MrWSc3pZpxdwAVTVW1lzsIg1B9VQU6CPgakJ6gVR0wYHMzSs5ys5x4f48tvpCfx2egK1jTY2Hi1l3SG1WlSRuZE5Se4/Z368PZtNR8v4709HCfL1YOawUGYnhTEtMZhQqd4khBBCnJS33nqL66+/HoCMjAzi4+P7dkBCCLcURSGvJo/9ZWpYaX/pfg6WHaTOVtf5xp0I9Q4l0aQGaRJNic77wV7BA7/LywDmY/BheNBwhgcNb7essLaQnUU72VG0g51FO8mo6uB8RxObw0ZKSQopJSm8tu81tBot40PHs2TIEs6POx8/D79T8TL6NYPB0CbEpNefHueLT49XIcRp4g8fp7A1o/PkKYBeqyE5Pog5SWHMTgpjcKjvKXsjdjgUDhaYnQcWt2aUU91w4qGlpAh/zkpUr5ScmhBEoG8/bomkKPhQhy5vGxwrVANKIy+GqPHt1/Xwh2M/gr2xe89VdtT9ssXPg8EXAuPUKjinE7sVakugpghqiptuW92vbvXY2vmB7H5B5wFepnYhJPV+021ny138O9ebzXz30ksADJtxG55GY2+/soFNZ2j6uZ6Cn5vd1lL1qznY1Byka749fn5DZc+P43j1FepUuM/1co2uVbApulU1p1aVnbxMp36c7hgjwbgIRixyv07u9s73U5UNu99TJ4CwkS1VmhJnqgEqIYQQQgjRZ0L9PVk0LopF49TgerG5gS3pZfySXs4v6WVkdNLGDaCizsq3Bwr59kAhAMG+Hk3HHtRqTT3dnt7XU8/8URHMHxWBoqjHSxJCXF/9W9NoY1ur4zzltRa+2J3HF7vzAEgI8WVyXCDJCUEkxwcRH+wjJ1mEEGckjUaDw+FAo9E4JyGEEAOb1W5lX+k+thduJ6UkhQOlB6horOj2/jRoiPKLclZWSjQlqtWVAhIxesg5k4EmwjeCixIv4qLEiwAorS9lV9EuZ7DpSMWRTlsIOhQHu4p3sat4F49tfYx5cfNYMmQJUyKmoO3rriK95PjKSwbD6XEuuVdCTOvXrz+p7c8999weGokQfafRZmd/XhWVdVbmjnDdymtKQlCHIaYQPw9mDQ9jTlIY04eGYPQ6NX+IHA6FQ0XVbDmmXgm5LaOcqvoTL904NMzPWd59akIQwX797ApDRVEr+JSnq+3eytOh7Bi+pUe4p+EwnjTCx8+2rO8X7jrEpNWqLYtKUrv2vH4RaiWl5mpKsVPcrxs57oReUp9TFDWg0S6I1Dqo1HRbV0a/rJakNaitunyC207egZ0HkwxefT160dt0evALVaeuag4+uQw9lbWEolrP7+ngk2JXAz5V2e7X8TS2Cjq1hJx0hkCMjkpqNP49O6YTddZtEDEa0tdB+s/q3/HOFB9Up19ehD+kSohJCCGEEKKfCTN6sWR8NEvGRwNQWNWgVmk6VsYvGWVklXV+lXZZrYVV+wpYta8AgBA/T2eg6azEYBJDeu4iMI1Gw6go9+H/jUdKsNrdf+/NKK0lo7SWT3fmOseaHB9IcrwaahoR6Y9ed2YcfBdCnNn0ej0NDQ1tHnt7y3d2IYQYSKx2KwfKDrCtcJsaXCpOocHe0PmGbsQb4xkTMoZRIaMYHTKaYYHDpAXcaSzEO4Tz48/n/PjzAahqrGoTakotT8WhONxu32Bv4Ov0r/k6/WsifSNZNHgRSwYvYZBxUG+9hD5xfGhJKjGdgFmzZnX74IBGo2lTAkuIgcLcYGVXVgXbM8vZnlnBnpxKGm0OogO83YaYJscHtZs3NsbE7Kbg0phoE1ptz1+FUl5rYU9uJXtzqtibW8mu7Aoq6k48tDQ41LdVaKmflkX/+d9qe6LydHVqNLdbRdc0tdNRxaTgwW1DTF4BEDykaWoKLAUPUcNOnn184r87rA1uKiYVtg8n2S19Pdq2PI1qCMkZTAoBn6DjHgeDb9N9T3+1lZ8Qp0p3g08NlS3hpupCqMptNeWot/Vdq+bXJY1mKDG3C2j6ArcBdrTw5qcQOhxChjZNwyB4qPr/6VTzCYKRS9QJoDIHMn5uCTXVFrvfNmSY+zZ1FZlqm8DwMWpIVQghhBBC9JkIkxcXT4jm4glqqCm/st4ZatqSXkZuRX0ne4DSmka+3lvA13vVUFOYvycTBwUyNtbE+JgARseYTtlFYkPC/Ll15mB+TCvicFFNl8a6en8hq/erVaWevWK8M9AlhBCnM6m8JIQQA4/VYeVA6QF2FO1ge+F2dhfvpt7W+edzVyJ8I9TAUrAaWBoZPBJ/j87PpSmKglJfj726BkdNNY7qauzV6q2jsZvnqjSg8/NDazSiM5nQGY3ojEY0PlI1tTeZPE3MHjSb2YNmA1BrrSWlOMXZfm5f6T5sDtcZkoLaApbvXc7yvcuZGDaRJUOWMD9+Pr4G1xV0BzIJMZ0kRemH1TaE6EFF5gY1sJShhpbSCs04XPza51XWk19ZT1RA+7TwxEEBBPt6ONvEzRoeSpixZyu71DTa2J+nhpX25FaxJ6eySwf9XEkM8WVqYnBTcCmIMP8+qkLjrKjUVE0pZBjETHa97t6POw4jdaSj7SZfD0kLW8JKPu0Daf2StUENIlUXQnWBm9siaKzq65GqtPq21ZFcBpFaPfYJBn0/blsoRFfp9Orvu29Ix+tZasGc3xJqOj7kVJXX/daXxw8JB1Skq9Ph1W0XegepoabgoW0DToHxp641ZkAsTPiNOikKlKS1BJoyN4KlumXdhJnu97PjTdj0H/U1JJyrtp1LnAWBCRJwFEIIIYToY1EB3vxqYgy/mhgDQE55Hb+0aj+XV9n58Y3i6sY27ecAEkN9GRcTwNgYE2NjAhgVZcTL4PLyphMyJMyP+y9M4v4Lk8gpr2PdoWJ+TCtmW0Y5tRZ7p9snu7jYDaC20camo6VMjg8iyFe+8wohBr7jTwrLSWIhhOh/bA4bB8sOsr1wO9sLt7OreFe3QksBngGMChnFmJAxjA4ezaiQUYR4q8e97TW12AoLsB7bS2VhAdbCIuwVFThqqtWgUnU19hr1tvk+9s4/V/cIg8EZaNIZjWhNRnTGppBTgAmt0YghIgKPuDg84uLQ+vj0zrjOEL4GX86JPodzos8BoN5Wz+a8zaw8tpINuRuwKa4DTc3t5h7f9jjzBqnt5pIjkk+bdnPHh5ikndwJ+Omnnzpdp7a2lsOHD/PRRx+xbds2zjnnHB599FF0upM/YCBET1MUhWMltezILGdbZjk7MivILu+8pHmz7ZnlLq+k8/cysOMv83rsS1qjzU5qQbUaWGqqsnS0pIbuZgrjgn2Y1hRampoQTISpF0NLigK1Jc6Wb21awJVntK2odNbt7kNMQYknFGJSdB5oAhPUYFJssvsVh8zr8j57hd2qVkZyhpGaA0nHhZR6smpLd2n14BsGfmFqyz6/MPCPaLnvFw6+oWogycskIQIhOuLh2xIacsXhgLrS4wJOuW1DTx1VMOqq+nLI2apOrWn1apApZJj6dzVkWEvAqSfDnxoNhI1Qp7NuUytZ5e9SA03p6zr+m52+ruU1HFyhTgCmQS2BpoRz1b9PQgghhBCiT8UG+RAb5MOyybEoikJOeVOlpqZqTYXmrrWvSC+pJb2kli925wGg12oYFu7PuFg11DQ2xsSwcH8MJ9HaLTbIh6unxXP1tHhsdgdphdVsyyhnR1Y52zIqKK1pe7FBdIC3ywvgAHZmVXDzuzsBNSiVHB/I5LggpiQEERPoLSf/hRADjoSYhBCifyqpK2FD3gbW567nl4JfqLXWnvA+hgcOJzkimbGmEQy3hxJW4cBWWIT1UAG2wh+oK3yf9KbAkqO6uvMd9hWrFXtZGfaysi6trg8LUwNN8fF4xDfdxsVhGHR6tzfrLd56b+bGzWVu3FzK6sv4JuMbVhxdweGKwy7Xr7fV81X6V3yV/hWRvpEsHryYXw39FVF+bjo2DBBSiekkzJzZwdXurSxYsIB77rmHJ554gvvuu4833niD99577xSPTogTtyW9jCtf3dr5isfRaGB4uH+HX8K6+wXN7lA4UlzN3pwqtTVcbhVphWas9u5XQYsN8mZaotoe7qzEYLcHz06Jg19CQUqr0FJG2yoaHSk75n5ZUGL7eToP9aR6UCIEDabeJ5IvNx6gQhPIlbfdizEgsDuv4NRoDiA0B5HM+a6rJ9WWAH1cAc87sG0Qqd39psk7UFo2CdFbtNqm/4dhED3R9Tq2RjDnuQ05KZU5aLpZFhiHTQ2SugqTNldvclZwago49UT1Jp0eYqeo08w/uV+vrhwK9rheVpUNu99VJ4CwUWqgafBsiJ8Bhj6qRiiEEEIIIQD1eMqgYB8GBftwWbIaasoqq2sTaiqu7lpVUptD4WCBmYMFZj7clgOAp17LqCgjY2MCGBdrYlxMAPHBvmi1J34cR6/TMjraxOhoE7+dnuAc6/bMcrY3XSw3NsbkdvvtmS0XJB0truFocY1znOFGT5Ljg5zT8Ah/dN0YoxBC9CYJMXXfTz/9xFtvvcWGDRsoLCxEr9cTFxfHBRdcwO9//3uiotqfnH3kkUd49NFHAfWC7YaGBp5//nk+/PBDjhw5AsCIESO45ppruPXWW9udEH3nnXe49tprAVizZg3nnXdeh2O85ZZbWL58OR4eHhQWFhIY2P54e3dex4koKSnh2WefZdWqVWRkZNDQ0EBERAQzZszglltuYfr06W63jY+PJysri2uvvZa33nqL7du38/TTT7Nx40ZKSkoIDQ1l3rx53HfffSQlJXU6lqNHj/LCCy+wdu1asrOzsVgsREZGcu6553LnnXcyebKbC7SF6AUOxcGB0gOsz1vP+tz1HCw7eML7GOqfyARdAmOqTYzMcuD5Uy6WY6uwlbxFI5DT88Pul2zFxdiKi6nbvr3tAq0WXXg4Z2k01BqN1ASHYJh2Fl5JSWikuEu3BHsHc/XIq7l65NWklaex8uhKVqWvoqKxwuX6BbUFvLL3Fd7Y/wa/GfEbbh57M34efr086p4hlZh60Z/+9Ce2bt3Khx9+yMKFC7niiiv6ekjiDFPbaGNXdgUjI40E+3m2Wz4+NgCdVoPdVb+4Vjx0WsbGmJgcH8SUhEAmDQrC5HPyfzyaD241h5X25layP89MvbX7JRO9DFpGRZkYG6MeiJscH0hM4CkodWiphYosqMgELyPEu/lysOP1lmoYJ6q8gxDT4Dmg0UFQgtr6LWgwmGJA2/LBwGo2k7nlJfWBtpc+MCgKNFSCuaBV5SQX1ZNqitQQQF/Re7kOIrULKoWBvv3/HSHEAKD3bAp1ugh9AtVVVbz50tMEKRUsnTUO79ocKD2iTuXp4LB273k7rN6U4Drg1NOtO6ty1feH8vTO1y0+oE6/vAAGH0icDck3wJC5PTsmIYQQQgjRLRqNhvgQX+JDfLliyiAURSG9tJadWRXsbTqeklrQ9Yu/Gm0OdmVXsiu70jnP30vvbEE3ruk20uR1wiffW4912eRYAKx2h9v1W4eYjldkbuTrvQV8vbfAOcZJcYHOUNPYGFOPtMoTQoieJCGmE9fQ0MD111/PRx991G7Z/v372b9/Py+99BIffvghixYtcrufoqIiLrjgAlJSUtrM3759O9u3b2fNmjWsWLECbauLUC+55BJuvfVW6uvr+eCDDzoMMVmtVj777DNALWRwfICpp15HR9asWcOyZcswm81t5mdlZZGVlcV7773HHXfcwXPPPdfmdbryxhtvcMstt2CztRyjz83N5a233uLDDz/k3XffZdmyZW63f/LJJ3nwwQexWtseP8vIyCAjI4N33nmHv/zlL/ztb3/rxisVontqLDVszt/M+tz1bMzbSFlD1yoNNYtXghlXZWREhp1hu0vxKzoMqNVw7EDX+9icPI2PDzo/P7T+/mi9vNx39uigZY3icKit6sxmtTJUd9vbuONwYC8oIAwgP5+qtDSqAK2fH96TJuIzeTK+ycl4jRqF5jQJpPSmpKAkkqYk8YdJf2B93npWHnXfbs7qsPLmgTdZeWwld024i4uHXIyut84L9xCpxNTLrrnmGj7//HOWL18uISZxSlntDo6V1HAw38y+vCp2ZFZwsMCM3aHw1LJxXDoppt02Ph56RkcZ2ZNb1Wa+v6eeSfE9f2CoyNzAnhz1AFtzcKmqvpsniVHLog+P8G9zkG1YuB/6kyiL7uRwqEGbikyoyGi6bTXVFLWsO3yB+xBT0OATDzE1V1QKHa5+qHD14WTYfHXqTdb6tm3dzMeFlJqrKXW3sklP0HuDMRL8I9VWbq1v/cKb2ruFgadR2rkJcabTaGjQ+JCv8cE6ahneRmPLMrsNKrPUQFPZESg9DKVH1fu1Jd17PodN3b7sCBw6bplvKESMgfDREDFWvR88RK281B2RY+Gu3VCZ3dJ6LuPnzsdurYNDq2Box1ceCiGEEEKIvqPRaBgc6sfgUD8uawoKNdrspBVUsze3kj1NF4kdKa7p8nmK6gYbm46Wseloy4meED9P57GWsU0Vm4J8PU54vO5a1ymKgp+nHm+DrksXs1U32Fh3qIR1h9TPtEPC/Fj7h65VzRdCiN4iIaYToygKS5cuZdWqVQAsWrSIyy67jMTERLRaLdu2beOpp54iOzubpUuXsmnTJrfVfX71q19x8OBB7rrrLhYtWkRQUBCHDh3i73//O6mpqXz11Ve8+uqr3HLLLc5t/P39Wbx4MR9//DGff/45L730El5eritUr169mvJyNXx71VVXnbLX4U5KSgqLFi3CYrFgMBi48847Wbx4Mb6+vuzevZvHH3+cjIwMXnjhBXx9ffnXv/7V4b4++OADwsLCeOCBB5gyZQoNDQ188803/Oc//6GxsZGrrrqKhIQEl+N84oknuPfeewEYO3Yst912G0OHDiUgIIBDhw7x3//+ly1btvD3v/+dkJAQ7rrrrhN6rUKciCxzFuty1rEhdwM7i3diO4GL52NrPBl5zMrIdCsjsxVMdUVAUafbnQhdQAD6yEgMERHoQ4LR+hvR+fuh9fNH6++Hzt8frZ+/Os/fH62fHzo/vx4P/Sh2O46aGuxVVdirzNjNVTjM5qb7ZuxVlS2PKyqw5OZiKyjo1nM5amqo/Xk9tT+vpwTQeHvjM2E83pMn4zN5Mt7jxqH1lOIBXWXQGZg7aC5zB3Xebq68oZxHtjzCR4c+4t7ke0mOSO6DEXePVGLqZYOa+kHu27evj0ciTifmBitpBdUczK9ylgU/XFiDxc3VbTuyyl2GmACS44MoMjeSnBBEclNwaVj4yZforqyzOKsrNR9AKzJ3rdy5O4NDfRkXE6BeHRgbwMhIY89ddZf6NWRuUANK5RnqyWtbQ9e2rch0vyx4sOv5Og+1IkdQYlMlpQQ18BSU2K6i0innsKsntp0t3Zpuj6+mVO+6XGGv0BpaBZJahZOMUW0fSzhJCNETdHr1b3PwYOCCtsvqK1oCTaWHe6Z6U20JHPtRnZrpvSBsRFO4aUzT7Si1+l9XBQyCiVerk6JA8cGWUFPWJrDUuN7OXUjWboOjayHhXPA4BVUOhRBCCCFEt3jqdYyLDWBcbABXN82rbbSxP6/KeSHZntxKcsq7ftFRaU0jP6QV80NasXNeTKB3y3GZmADGxJjw8+zeYVmNRsNr1yZjtTs4mG9u04KurNbS6fbjYwPcLvt0Rw6NNgcjo4wkRfjj49FvDx0LIQYYh+KgsrGy3Xy73U6lpRKroe1xgQZtAzrbwKqEcLwAzwC0mh64aNiF1157jVWrVmEwGPjyyy+54IK2x2DOOussrr76ambMmMGBAwe455572Lhxo8t9NVdbmjVrlnPexIkTmT9/PiNHjqSoqIgXX3yxTYgJ1EDSxx9/jNls5uuvv2bp0qUu9//BBx8AYDQaWbhw4Sl7He7ceuutWCwWdDodX3/9Neeff75zWXJyMsuWLWP69OkcPHiQJ598kmuuuYZRo0a53NeePXuIi4vjl19+ISIiwjn/3HPPZf78+Zx//vlYrVZuv/12tm3b1mbbgwcP8uc//xmAhx9+mIcffrhNWG/SpElcccUVXHvttbz33nv8+c9/5uqrr3bZek+I7sqsymRN1hq+y/zOZZDDHZ9GGJvuYOIxhfHpCgG1J9ctRGsyYYiIUANKEREYIptuW83TuglG9jaNTofOZEJnct/q+XiO+nos2TlYsjKxZGVhyWy+zcJeWtrl/Sj19dRu3kLt5i3qWAwGvMaNxSc5Gf85c/EaPUpCv110fLu5zw5/xv+O/K9deC+tPI3ffvdbzos7jz9M+gMx/q4zAv3J8aElrVbbaVXBgaDffhMtKlITm7W1tX08EjHQvb05k83HSjlYYD6hg04A2zLcl+f+0wXD+fNFI7r9BlHdYOVIcQ1Hiqo5UlTD4ab7BVVdDAC5ER3g3VLKPNbEmGgT/l4nmLpUlFbVlDJBZ4DRl7pe9+j3sPOt7g22ItN9xaTIcWqlpuaWRs2hJWP0qQ8qKQqeSgN+SjW6rPVgN7cKKhW03K8pAqX7LfxOikbbVCHJTfWk5lufIAknCSH6B+9AiE1Wp9baVW860nK/O9WbbA2Qv1udWguMVwNNEWObKjeNUcOvnf2N1GjUEFT4KJh2O9itkLdTDTUd+wFyt4PiUN+3jFGu95GzFT68XA1YJc5Sw05D54Mp+sRfnxBCCCGEOKV8PfVMTQxmamKwc155rcXZgq75orOS6q5fcJZbUU9uRT2r9qlXZWs0arBpWJg/Q8P9GRrmx7Bwf4aE+eHt0bVjHgad1hnAunFGorNd3vaMcrZnVrA9s5zs8vbNO5Lj3Z8MfX1jBmmF1c4xJoT4MjLSyMgoIyMijYyKNBLq7yknS4QQJ6yysZKZH59ZVeB+vvxngryCeny/iqI4qwXddddd7YI/zQIDA3niiSdYsGABmzZt4siRIwwdOrTder/73e/aBJiaBQUFcf311/P444+zb98+qqqqMLU6iX/BBRcQHBxMWVkZ77//vssQU01NDV9++SUAl156aZtqTT39OlzZvXs3O3bsAOCmm25qE2Bqvf/ly5czffp0HA4HL774Ii+88ILbfT711FNtAkzNZs+ezU033cRLL73E9u3b2bFjR5tqTE899RRWq5XJkye3CzA102q1PP/883z66afU1NTw2WefcdNNN3XptQrhTpY5izWZanDpUMXxZe7diypTmHhUYeIxhaQcBb37TscuaTw98UhMxDMxEc8hg/FIHIxnYgKG6Gi0Pqf3RZ5ab2+8hg/Da/iwdsvsNTVYsrKoSk1ly/8+x6+qijirFXtOTqf7VaxW6nfspH7HTspeehnPoUMwXXwJxkULMYSFnYqXclpKCkriL2f9hd+M+A1P7XiKdbnr2q3zfdb3rMtZxzUjr+GmsTfha/Dt9XF2lavKS6dDS7l++wqaPyQ0V2QSwh2LzUFVvZVQf9cl9NYfLmlz9VtX6bUajN4GGm12PPXtDyC5mudKdYOVo8U1alCpqJrDxTUcLaom/yTDSgDBvh5tAktjYwII8etCKUGHXQ3iVOVCVY7aNqcqBypzWm5btzYLH+M+xBQY373B+4Wr21pqwNO//fL46e5bzXWXokBjdVMAqbBVi7fWt4X4VxdwT3M1qf+92rNj6AqfYPCPaqmedHzVJP9ItYXSAOvLKoQQLvVW9abmYG7qVy3zvAKagk1jWtrShSaBvoN2HzoDDDpLnWbdB7VlaqBX38HVQYe/VW9tDer95scRY2HYBTD8AoicAKfBFRJCCCGEEKejIF8PZg0PY9Zw9eSAoigUmhvYk1PVJtxkbujaVemKAjnl9eSU17c5ZqXRQGygD0PD/Bga7s+wcDXcNDi083BT63Z5V0xRj6cWmRvY0RRo2p5ZTmqBmcnxrk+oW2wOjpW0VBxVFEgvqSW9pJav97a0xAjx82BEU7BpZKQ6JYT4onfT/k4IIUTPOnjwIMeOHQNwW/2o2bnnnuu8v2XLFpfhn+NbvLU2adIkQH3fy8jIYPz48c5lBoOBZcuW8fLLL7N69WoqKysJCAhos/0XX3xBfX29y+fp6dfhyoYNG5z3b7jhBrfrnXPOOYwYMYLU1FTWrl3rdr3AwECWLFnidvlvf/tbXnrpJQDWrl3bJsT01Vfq8ahLL720wzBwQEAAY8aMYceOHWzZskVCTKJbss3ZrMlaw5rMNaSWp3ZpG71NYWROS3ApoosNRrT+/ngmJuIxeDCegwfjMTgRz8GDMURFodHJOazj6fz88B41CmtsLIeb/gZOve02vBsaqN+xg7odO6jbvp3GI0c73VfjkaMUP/EExU8/jd/06ZguuQS/ObPRepx4K+szUbwpnufnPs/m/M08sf0Jjla2/ZlbHVZe3/86K4+t5K4Jd7FkyJJTVmHxZOh0OjQaDUqrnuinQ0u5fhViqqioYMeOHTzzzDN8++23aDQafvWrX/X1sEQ/UllnUdvA5aut4FILqjlaXM30ISG8ef0Ul9uMjDJ2KcQUHeDNiEgjY2NMJMcHMT42oMtXvwHUNNpaqioVVTurLPVEWAnAz1PPmGgTY2NNzhLk0QHeXb/6LeVD2P2uGlAy551YBaGKDPcVkwITXG+j94KAODWoFBivtn1rvh8wCDx6OLXaWKNWRnIRSmrz2Np5dbdTdj2hwReMkW2rJzkDSq1CS3rpaSuEEEDH1ZsqMqFovzoV7lMnc96J7b+hUm2JmtlyUAutQQ0yRYxuG27ycXP1pG8wjLui4+dpDi0dr3CvOq3/txruHXq+GmpKnAWefif2WoQQQgghRK/RaDREmryJNHlzwWi1GoPDoZBVXqdWamoKN+3Pr6LB2vXL1hUFssvryC6vcxluGhbu16ZyU2fhpnCjFxeNjeSisZEAmBus+LtpY3ekuBqrXXG5rLXSGgsbjpSy4UhLKwxPvZaHF43iyqlyMaoQQpxqzZWFAKZNm9bl7QoLC13OT0pKcrtNUFDLsZDq6up2y6+66ipefvllGhsb+eyzz7jxxhvbLG9uJRcVFcXs2bPbLOvp1+FKWloaAB4eHm0CWK5MnTqV1NRUjhw5gsViwcNFCGDChAkdVrcYP348Hh4eWCwW9u3b55yflZVFSYlabfyBBx7ggQce6NL4T+S1ClFUW8SqjFV8m/Ftl4NLphqFSU2hpTGZCt6ddCbWBQXhPWYMXmPG4D1mNJ5JI9CHhUqVzh5gCAvDsGABxgULALBVVFC3Y4cabNq+g4a0NHC4+V5ht1Pz88/U/PwzWpMJ00UXYbrkEmk310VnR53Np4s+5bPDn/FCygvt2t+W1pfy0OaH+DDtQx6a9hCjQ0b3zUDd0Gg0xMbGotVqsdvt/PDDDzQ09Ew2oS/1SohJ182k5dChQ7nvvvt6eDRiIFAUhZzyeg4WVDUFlqpJLTCTV+m6HdzBArPbfY2INLZ5rNdqGBru7yyHPTLSyIhIfwJ8upZMdYaVmkJKh4t6NqwE4KHXMirK6AwrjY0JIDHEF6226c2moQoq0+Fwc/WkbPVWq4elr7sZeBFkberegCw1UFcGviHtl4WNhLGXtwSUApvCSn7hPVNRwlJ3XNUkN0ElS/svUb1Go2tbKen4YJIxSl3m6S+t3YQQoifo9BAyRJ1GXdwyv668JdDUHG4qSQPHCfRpd1ihaJ867fmwZb4xpinU1CrcFBDf+XudpVatnld2rOMAcU2RGjbe/S7oPCFhhhpoGjZfDf8KIYQQQoh+TavVkBDiS0KIL0vGq22DbXYHh4tqnC3o9uZWcqiwGpuj87BQa63DTWtT24abBgW1rdw0NExtS+dlaH881ujl/opcvVbL4nFRHCwwk15Sw4kMsdHmcFshXVEUlq9PZ0iYHyOjjEQYveRkihBCnITi4hPvOgFQV9e+xSiATwdtnbStjnnY7e2PaZxzzjnExcWRlZXF+++/3ybEVFxc7KxqdMUVV7TZV/Py7nD3OlyprKwE1DBWZ611mlvEKYpCRUUF4eHh7dYJ66Rdk16vJygoiMLCQsrLy53ze+O1ijNTg62Bn3J+YuXRlWwp2IJD6Tw8b6pROOuQwrRUB0m5oHXzmU/r64vX6NF4jxmN12g1tKSPipLPcb1EHxiI8bzzMJ53HgD26mrqd+2idts2qr/9Dmue64t5HVVVVHzwARUffKC2m7vkVwRecflp38LvZOm1eq5IuoILEy7k5T0v81HaR9iUtucUUstTuXb1tTw962lmxvavFrkJCWrBEbPZ3Ob9ZyDrlRBT6/JVXaHX61m2bBnPPPNMmx674szwh/8dJNeaTXVj1084FpkbKa1pdNlKbVxsANefE+8MLQ0J8+tSK7iaRhtHi5uqKjlDSzVug1TdodNqiAv2YViYeqBpSLg/w8J8GexTj6E6D6oOq+GkHce1emuscr1DDz/3FZMCYrsxQE8IbKqmZHXzgTl0GPxq+YntV1HUE801RU1Tcav7TVN1kRpeanDzWnuJwysQrSm6VUgp8rhqSpFquEtauwkhRN/zCYLEmerUzNYIJYdawk2FTQGlE31/Meeq0+HVLfM8/NSWcNETIXoSxEwGU2zb92EPX7j+G/V97+gPalWmo993/Pz2Rji6Vp2yNsOyN09srEIIIYQQol/Q67TqBXRRRq5oKiDeYLW3HG9qdYFcTkUdJ3gIFUWBrLI6ssrchZv8GRru12m4CWB4hD/P/XoCAPUWO4eK1AsKW6qhm6mzuA/lj4j0dzm/pLqRx1anOR8H+hgYGWVkRISRSyfFtLv4UAhxegrwDODny39uN99ut1NVWYWHZ8sFxlqttsNgzUAR4BlwSvbbOkz01VdfER8f36XtOgvgdIdGo+HKK6/kscceY/369eTl5REdrQZ5P/nkE2w29RyPq5Z1vfk6eip00d39tH6tDz30EMuWLevSdr6+PdzNQpwWFEVhb+leVh5dybcZ31Jt7fwCf1OtwtQ0hWlpCiNyFJfBJY8hg/FJTsZ73Di8x4zBIyEBTU8UKhA9Qufvj9/MmfjNnEnYH/9I3Y4dVH2xAvN336G4CTw2HjlK8b//TfnbbxP2xz9gXLhQ/k07YfI0cd+U+1g2fBlPbn+SDXkb2iy3OCzc89M9/HP6P1mQuKCPRnlm6JUQ08MPP9zpOlqtFn9/fxISEjj77LMJDQ3thZGJ/mhffg16o9cJb3e0uMZliCk6wJuHF41yuY3F5iC/st55NVtWWe0pDiv5MtQEQ402hiWNIiHEt32gauN/YG3n/2dcstRAfYXrljcmFyEmvZc6PyC21e0g9TYwHvwiTqyakqXOfSjJOa9YnRzW7r3GnuJlaqmc1Hzrp7Zzq9UZeeeL76nV+HHz7b/DaJQDakIIMWDpPSFyrDo1UxQ1GFy4Dwr3N7V12weVWSe2b0sNZG9Wp2a+oWqgKXqSGm6Kmqi+L/sEwdhl6mS3Qs5WNdB06FsoO+L+OYZf6H6ZrVFakAohhBBCDDBeBh2jo02Mjm574Wa9xc6xEjXc1Fz1+0hxT4Sbipzzjw83xQX5MCjIh9ggHyJNXuh16jEgbw8d42MDGB8b4Ny2uV1e62DTwXwzheYGjF56ogO8XY7lwHHV0yvqrGw6Wsamo2VMGxwsISYhzhBajZYgr/bHrO12O1ovbZvWXTqdDh+vgR9iOlWCg4Od9wMCAhg9um/b2lx11VU89thjOBwOPvzwQ/7f//t/QEsruaSkJCZOnNhuu954HQEBAQCUlZVhs9k6rMbU3LpNo9EQGBjocp2ioiKX85vZbDZnBYzWrfhav1aDwdDn/2ZiYCqsLeTr9K9ZeXQlmebMTtc31ipMPaQwLVVhpIvgkkdiIj5Tp+A7ZQo+ycnoQ1x0YhH9kkarxXeK+m8X8Zc/Y17zPVVffEHdtm0u17cVFZF/732Uv/8+EQ88gHcn7TUFJJoSeXHei2zM28i/t/+bjKoM5zKbYuP+DfdTY63hsuGX9eEoT2/9JsQkRFd56LQMi/BTKytFGhkRaSQp0ojJu31JbEVRKKu1kF1eR07T1BxYyimvp6Cq/oTKY3dGp4E4o4ah/haGeVUxVFfIUCWLROshPKtz4FgxoKjhoZmFrismmWJObhBVOa5DTCFD4fx/tA0r+YZ03t7MYYfa0o5DSdWF6m1ftnRr5mlsCiSFtw8p+UeCf7gaVvJw/0XYbjZj1m7vxUELIYToVRqN2qItYBAkXdQyv6EKig60rdpUnKpWRuqq2hI1nHT425Z5QYmtgk2T1FZ08dPV6fx/qK3mmrfJ2tzS/k6jhSHzXD+PtQGeGg5RE2D4AvV1mKJP/GchhBBCCCH6BW8P1+GmOouNY8W1HCluCTcdLq4mp/zEL75zF24C0Gs1RAV4O0NN6q36eFCQDyZvg7Nd3oIxkc7tymst5FXUu61OcTDf7HI+wMgoCTAJIdpXt5FWRR2bMGGC8/6mTZuYPn16H44GRo0axbhx49izZw8ffPAB/+///T8yMjLYsmUL4LoKE/TO60hKSgLAYrGQkpLC5MmT3a67renk/9ChQ9uE6lpLSUnpMAy1Z88eLBYLQJugUmJiIiaTiaqqKjZt2tSt1yLOTA7Fweb8zXx86GPW567vtF2cd6MaWpp+UGFEtoKu1flPj4QEfKZMwWdKMr5TpqCXYiKnBa2vLwGXXEzAJRdjyc2lasVKqr74wmW7uYY9e8m84tcYFy4k7P/9EUNTG03h3vTo6UyNnMqjmx9l5bGVzvkKCn//5e9UW6q5YcwNfTjC01evhJiE6K7mEtPNreBGRppIDPXFoGupDtRgtZNbUcfOrHKyy+rILq8np6IlsNRRqevu0mog3qhhqCOdYeQwxHaYYfYjJGoK8Gy0QWfnOm0N7ismBQzq+kD8Io6rohSrznPFOxDO/p1639qgnmQtSFEDSrUlrabSlmpJNUVQVwpd6KN7yhl8WrVwizgunNR03y8cPP36eqRCCCEGKi8TxJ2tTs3sNrVSUuG+popNTZWb6sq6vt/ydHXa96n6WKuH8FFNoabJ6u3UW2HaHVBfCcd+hMPfqZWeXH1WAMjcAA2VkP6TOq3+k7qvEYvUKXhwd38KQgghhBCiH/Hx0DMmxsSYGNfhpsNNoaajRTXdDjcB2ByK88I/V/y99GqwKdCHQcEtQadBQT4Mj3DdSg4gzN+TKQlBpOabqW60OecH+hiI6EYldiHE6cdisWCz2QgICECj0UiIqRMTJ04kJiaG3Nxcli9fzt13342XV9/+Pb3qqqvYs2cPu3fvJjU1lc8//9y57Morr3S5TW+8jhkzZvD4448D8MYbb7gNMW3ZsoWDBw8CMG+em4vJgPLycr766isuueQSl8vfeOMN5/3W+9HpdCxYsIAPP/yQNWvWkJqayogRI0749YgzR2VDJSuOruCTw5+QU53T4boaRWF0psKsfQpTDil4Nn3c0vr44HvO2fjNnInvjBkYwsN7YeSiL3nExBB65x2E3H4bddt3UPbKK9Ru3txuPfPXX1O9di3BN95I8A2/RevtuqKqUBm0Bv52zt/w9/DnvdT32iz7z67/UG2p5u6Jd8vnlx7WKyGmv/3tbwDcfvvthHSxHF1FRQXPP/88oPaIFWeO66ZFMyd5DCMjTYQbPVEUKKlpJLu8jv15VXyzr8AZUMqpqKPIfALVEU6QFgfxIWqJ7eZS28PC/UkI8cXr8Jfw6Z9br3xiqgs6bvum1YMxWg01tWv5FqtWbGpuIWO3QX25GkIqSVNPbLYJJx13vz9UTGrmHaSGj/zCmqonhau3vmFgjGwVTvLvvGqUEEII0dN0eggboU5jm8rDKopahbBwL+Ttgryd6lRf3rV9OmxQsEeddjQd4PLwh6jxLdWa5j4Exij3+2hd6alZ3g51WvswhI9uCTSFjZT3UCGEEEKI00xXw01Himo4chLhpmbVDTYO5Js54KKykkYDkUavNsGm2KZp1vAwlk5Sq47nVtRzoKkVHYoiB/qFEIDaTcFut6PT6dDpdH09nH5Pq9Xy4IMPcvvtt5Oens4111zDu+++i6en63bzZrOZd955hzvvvPOUjenXv/419913H4qi8P7777NixQoApk2bRmJiostteuN1TJgwgcmTJ7Njxw5effVVLr30UubOndtmnaqqKm655RbnmG677bYO9/mHP/yBs88+m/DjAiE///wzy5cvB2DSpEkkJye3Wf7AAw/wySefYLfbWbp0Kd999x0xMa67ctjtdj766CNmzpzpdh1x+lEUhX2l+/j40Md8m/EtFoelw/UjyxVm7nVw7n6FkKZTfoa4QfjNnInfzJn4JCejdVNVTJzeNFotvlPVqls1P62j+F//wpKV1WYdpaGB0v/+l8rPPiPikYfxnzWrbwY7QGg1Wu5Nvhejh5EX97zYZtnr+1+n2lLNn8/6M1rNiYYFTg2ttn+M42T0SojpkUceQaPRsHTp0i6HmMrLy53bSYjpzOKl1/HzoRLe3ZLVFFSqx2I7tZWATNQwSFNMrKaERE0+Q7W5DNXkkagpwOv2w67DRsaTaNmi84A6Nyc6/cLhngNqu7O6pmBSXVMIqbpAPWF6fDCprhzowb54J0vv1RRMahVKah1U8gtTK0b5hoJePkQJIYQYYDQaNWhrjIRh89V5igIVGW1DTQV71OqLXWGpVkPImRta5vlFNIWaJqq3URPAO0BdduzHjvdXtF+d1j2mtrMbsVidoidKoEkIIYQQ4jTWUbjpaHENR5oqNmWW1qrVzMvrqGlVIak7FAXyqxrIr2pga0b7413eBp2zPV3roNORompiAn3w9pDQghBCnIhbb72V77//ni+++IJPP/2UXbt2ccsttzBlyhRMJhNms5m0tDTWrVvHl19+iZeX1ykNMcXExDBz5kzWrVvHCy+8QGVlJeC+lVxvvo6XX36Zs88+G4vFwoIFC/jd737HokWL8PX1Zffu3Tz++OOkp6cD8P/+3/9r0wbueOPGjePgwYNMmjSJBx54gClTptDY2Mg333zDM88842w198ILL7TbdsyYMTz55JP8/ve/5+DBg4wePZqbb76ZOXPmEB4eTkNDA5mZmWzZsoXPPvuMgoIC9u3bJyGmM4DNYeP7rO95+8DbHCg70OG63o0KZx9UmLXPwbA8NazikzwFv9mz8Js5E8+EhN4ZtBgQNBoN/nNm4zf9HMrf/4DSF1/EUd22yIWtsJDc2+8g8u9/I+DSS/topAODRqPhtvG34efhx7+3/7vNsk8Of0KNtYZ/TP8HBq2h18dWXl7OsWPHsFgsTJ06lfr6k7uApT+QdnKi33l5QzZ6o+vS1d1lwEa0ppRYTTGDnFMRsZoSYjUlmDS17jc257sOMflHul7fK0CtnOAf2XLrHQAefmp4SadXKycV7oP0dWrVhrryltvm4JLD2gOvvCdp1NBRR6Gk5vtSNUkIIcSZRqNRw0JBiTBmqTrPboXigy2hprxdUJxKl4PHNYVwaJU6NQseCjGTYdL1oNjV9nZHvofGKvf7KU+HTf9RJ2M0jL8S5vylmy9UCCGEEEIMRD4eesbGBDA2JqDNfEVRqKizqhcSNlc+b7rNLq8jv7Iex0leN1dvtXOoqJpDRa4rgz//6wksGtdBFVIhhBBtaDQaPv74Y+6++25efvlljh07xr333ut2/bCwsFM+pquuuop169Y5A0x6vZ7LLrusw21643WMHz+er776imXLlmE2m3nqqad46qmn2q13xx138Nhjj3W6rzvvvJPbbrvNZZjKw8ODt99+m6lTp7rc/p577sHX15d77rmHqqoqnnjiCZ544gmX63p4ePR5m0BxatVaa/nf4f/x3sF3Kagr7HDduCKF83c5mHFAwcuuxWfKWRhvugD/8+ahDw7upRGLgUrj4UHw9ddhWrKYkueeo/KTT8HRqniIw0HBn/+Cvbqa4Ouu67NxDhRXj7waP4Mfj2x5BIfS8nP8JuMbGmwNPD3raXTa3r9Io65OzVZoNBr0+oEfAeq3r8BqVQMcBkPvp9XEwBTiqyc22I/YwLalqwdZjhLx0Xx0mm4ecakugIim9L3dBvUVauCophjG/hr0BtBo1cvO7FZoqFKXV+WqVZPqytWTjP2RVg8+IWo4ybf5NhR8g5sCSREtQSWfYDWAJYQQQoiu0Rkgcpw6Tf6tOq+xWq3QlLujJdhkzu36PsuOqJPzOTwgYowanG6shoK9Hbe1M+epn1GEEEIIIYRAPcgd5OtBkK8H42MD2i232h0UVDY4Q03NIaecCvV+Zd3JX4QXaZKTtEIIcaIMBgMvvvgit912G6+++irr1q0jOzubmpoa/Pz8SEhIYNKkSVx44YUsXLjwlI9n6dKl3HnnnTQ2NgJw/vnnExoa2ul2vfE6zj//fI4ePcp//vMfvvnmG9LT02lsbCQ8PJwZM2Zw6623Mn369C7t68Ybb2T06NE888wzbNy4kdLSUkJDQ5k7dy733XcfI0eO7HD7m266icWLF/PKK6+wZs0aDh06RGVlJZ6enkRHRzNmzBjOO+88Lr300i53thH9g72wEMvOndgLC1EsFjQeHugiIvCYNAldRIRzvaLaIt5PfZ9P0j6i1u6+WorepnDWIYXzdzoYXqDFN3kKxj9fgP+8eejld0N0gz4oiMhHHiHw11dS9Phj1G35pc3y4sf/hcNcTcjv7pS2z524ZOgl+Hn4ce/6e7E5Wqra/pjzI18c/YKlw5b26niOz9MYDAYUpR91cOqGfptISElJAejShxxxZvDE0qaSUoymxHk/VlOM700/qCfxjldRA50FmDz9wdMIBh+1FZpOD2hAccCPf4dv/qSeEGzooMpBf+Ed1CqQ1DqY1Pp+qBpK8gqA06AvphBCCDFgePpD/HR1alZd2LYNXd6ujisrtWa3tGzXzC9c/VxTWwINle23GbHI/f6yf1E/T3n4du35hRBCCCHEac2g0zIo2IdBwT4ul1fVW9VQU6tgU3ObutyKOqz2zg+eDwpyvW8hhBCdGzNmDM8999wJbfPII4/wyCOPdLrerFmzunwSNCAggIaGhhMaR2vdeR0A1113Hdd1oXJIaGgo//znP/nnP//ZjdG1ddZZZ/Hxxx93e/vw8HAeeughHnrooZMei+h7trw8Gr77DntOTrtl9txcLDt2oIuNpfrciSwv/IKVx1ZixX3hg9BKhfN2O5i9VyE8fiSmay/GeOEF6OV8ueghXsOHMeiNNyj5z7OUvfJKm2WlL76Ivbqa8AfuRyPnjzt0Xtx5/HfOf7nnp3tosLe8/y3fu5wlg5dg0PVeoZ7jQ0wajQa7vZ8WWOmiUxJieuedd1zOX7lyJTt27Ohw28bGRo4dO8Ybb7yBRqMhOTn5VAxR9GPjNEcZrj3IIG1xm5BSKFVoOwojpX6lVh9oqGyqhtR0W1emVhtSHOCwga1BPeHXWmO1OvVHHn7tw0g+bsJJPkFq1QchhBBCDBz+EZC0QJ1ALedbnt4q1LRTre54/OcXd2qK1KmZzqBWX7TWg84Toie53q6xBt5erLbGGzIPRiyGYfPVtrhCCCGEEEK4YPI2YIo2MTra1G6Z3aFQaG5w2aYup7ye0ppGPPVaQv09+2DkQoj+QFEUtE0nSQd6xQAhxJnHevgwdZ9+CjZbh+vZc3LQfZBJXugWrD6ugwVD8xQWbnMwrTSQoEVLMD1wMV7Dh52KYQuBRqMh7Pf3oPP3o/jJti02K959F0d1NZH/+Dua06At2al0TvQ5PDnzSe78saXFaEFtAV8c/YLLhnfcUrUnuepsJiEmF6677rp2ZcYUReEvf/lLl/fR/OH17rvv7unhiX7uFc//EOPhJt2p0aJWSHLxH+/nf53ScfUInadaAcknCLwD1VufYLV6kk9Qy21zMMknBDzkajQhhBDijKLVQsgQdRp3uTrP1ghF+9tWbCo93LX92a3qBGBvhKeSIDQJYqdA7FR1Ch4MR79XlwOkfa1OWj0kzFSrNyVdpLaZFUIIIYQQogt0Wg3RAd5EB3hzVmJwu+W1jTaKzA3SrkKIM5iiKPj4qMe/6+vVtkp+fn7yd0EI0e/Z8vK6FGBq5qno+EfJNO6IWEeaZwUAGkVh8mGFRTs0TB4+i4C7L8Vv+nQ0LgIJQpwKwTfeiNbfSOEjj0CrMHHVihVovDyJ7ELVvjPduTHnMjFsIruKdznnvbL3FZYMWYKnrncu1tDpdGi1WhwOh3OerYt/m/qrUxafc5Wa72qS3sPDg+TkZB544AFmzpzZ00PrVVlZWTz33HOsWrWKnJwcPD09GTx4MJdddhl33HGH8wP6yVq9ejXLly9n+/btlJSUEBoaSnJyMjfffDMXXnhhl/Zhs9l47bXXeP/990lLS6OmpoaoqCjmzZvHXXfdxahRo3pkrCdFcXS+Tm/x8AefwKbgUXDbEJJPsOuQksFHrW4ghBBCCHEi9E0VlKInATep8xqq1DBTznbI2Qq526HR3IWdKVCSqk673lZn+QSDzqP9qg4bHPtBnb7+PcSd3RRoWggBsT316oQQQgghxBnI11NPYqhfXw9DiC4ZSMf5B5LjzxlpNBoJMAkhBoSG777rcoCpmaei467ycdwd+hOz9iosORbIyAt+TcC7SzGEh5+ikQrRscDLL0Pr50v+ffe3+Z2u/PgTAn99pVQE64RGo+HOCXfy2+9+65xXXFfMZ4c/46oRV/XaOAwGA42Njc7HEmJyISMjw3lfURQSExPRaDR89913DB061O12Go0GLy8vgoOD0el0p2Joveqrr77iN7/5DWZzy8mkuro6duzYwY4dO3jttddYtWoVQ4YM6fZzOBwObr75Zl5//fU28/Py8sjLy2PFihXceOONvPLKK86yrK6UlpayYMECtm/f3mZ+eno6y5cv5+233+a///0vN954Y7fH2m/pvcArALxM6tQmjHR8MKnVPL2LE31CCCGEEL3FywSD56gTgMMOJWlqoClnm3pbnt61fdWVdWElBbI2qdO390PUBDXQNGIxhLj/jC+EEEIIIYQQA9lAOs4/0LgKMQkhRH9nLyzEnpPTrW3HNobwzs5kBi35Nf5zZkvVJdEvmC66CK2vL3l334PSHIRRFEpfeIGY557t28ENAMkRyUyJmMK2wm3Oea/te41Lh16Kl96rV8YgIaYuiIuLczk/KirK7bLTze7du7n88supr6/Hz8+PBx54gNmzZ1NfX89HH33Eq6++yuHDh7nooovYsWMH/v7+3XqeP//5z84vNhMmTODee+9l8ODBHDt2jH//+9/s3r2b1157jdDQUP7v//7P5T7sdjuXXHKJM8D0q1/9iptuuomgoCC2bt3KP/7xD4qLi7nllluIjo7uh1d8aNSTeN4BTUGkADePA9s+bp4MvfPHQwghhBDilNLqIHyUOk1uuvKjpgRyt7UEm/J2tbSMO1n5u9Xph79B4iy4ZmXP7FcIIYQQQggh+omBdJx/IJIQkxBiILLs3HlS2w++aCne88/vodEI0TP8Z80i+OabKH3+v8551WvW0JCaiteIEX04soHh9vG3s+3blhBTaX0pnxz6hGtGXdMrz284LhApIaYuaN1/70xx9913U19fj16vZ82aNUybNs25bM6cOQwdOpR7772Xw4cP89RTT/FIN3pKHj58mCeffBKAyZMns379ery9vQFITk5m8eLFzJw5kx07dvDEE0/w29/+1uXVIG+//TYbN24E4Pbbb+eFF15wLpsyZQoXXnghkyZNwmw2c9ddd5Gamopef+p+dWzh4yAmqguhpKZbD384ja4+EUII8f/Zu+/4tup7b+Cfo20NS7ZseceOnTiDLMgmkIQyQgNhtoyyoRTogt4Cvdze5yl9bm9LJ6O0lBRKCNAWKLuhQKFZJNAMspdjO46HvGVby7LWef44lmxZkldsyePzfr3OS9KZXwdhS+d8zvdHRCNGnwnMvEyaAMDfBdQf7A41dU/OxjM/jlwNdLZLn8+IiIiIiIgmiPF0nn8iYIiJxqKqqqpkl0BjTKCh4cy2bxyBc3FEoyD91lth2/gSgh0d4XnNT/8OBb97up+tCAAWZi3E8pzl+Kz+s/C85w8/j6+UfgVa5cgMO9yfiRZiYvJjFOzatQvbt28HANx1110RX2xCvv/972NWd2rxySefhM/nG/JxnnjiifAb8Le//W34i02IVqvFb3/7WwDSG/Xxxx+PuZ/QF6T09HT88pe/jFo+bdo0PPLIIwCA8vJyvPXWW0OudSg6r/gjcNNrwDXrgbW/BL70Q+DcbwNn3ywNWTL1fCBnHmCaIoWYGGAiIiIiGhyFGihYLH22uv4l4PsngPsPAtc8Byy+G8ieBwjD+Gx18kPg50XA788F/v494OBrQNtpQBSBvRukjk197rAlIiIiIiIay8bbef7xiJ2YiGg8Er3eM9u+a4S6pBONMLnBAPMdd0TMc37yCToPH0lSRePLNxd8M+K1zWPD+6feT8ixGWKiAb399tvh53f0+R89RCaT4dZbpfZh7e3t2Lx585COIYoi3nlHGrJj5syZWLZsWcz1li1bhhkzZgAA3nnnnagvBWVlZTh27BgA4LrrroNWGzsJePvtt4efj3aIiYiIiIgSRBCAtEJg3leBy34F3Lsd+M9qaWi4C34ITLsIUBsHuTMRaDoC7PkT8ObdwJPzgF/PBN57AFi/Wnr+4Q+B2r0MNBERERER0Zg3ns7zj1cMMRHReCSoVGe2vVo9QpUQjby0m2+G3GSKmNf67LPJKWacWWBZgOU5kaH3E7YTCTl23xBTIBBIyHFHy4iOCXbnnXcCkD5ohsZv7j1/OPruazwIDc2m0+mwcOHCuOutWrUq/HzHjh245JLBj3966tQpWK3WqP3EO86JEydQV1eHqqoqTJ06NarWgfaTnZ2N0tJSlJWVYceOHYOuk4iIiIjGGbUBKF4tTQAQDAItJ7qHn9slPbaWD25fzobI5589LU3qVGn/i78OFJ3PzppERERERDTmjKfz/OMVQ0xENB7Js7MRqK0d/vZZWSNYDdHIkut1SL/rTjT/+jfhee7du5NY0fgyJXVKxJByIhITPJ9onZhGNMS0YcOG8IfM3sGj3vOHQhTFcRliCnU2mjZtGhSK+P/EM2fOjNpmsI4ePRpzP4M5Tu8vN0PdT1lZGWpqauByuaDT6QZdb+0Af8zr6+vDz10uF+x2+6D3TTTSnE5nzOdEycL3JI0lfD9OUpo8YPo10gRAcLdCXv8F5Na9kFt3Qd5wEEJgCK2wu+zAsXeBY+9ClCkQMJfCP+1S+M66HmJq7qB3w/cjjTV8T9JYwvcjjTV8T9JY4nK5kl0CjQPj6Tz/QIZyft7hcAzp/Lzf70cwGIQoikO+679viGk4+yDqq/d7iO+nkSGKIoLBIPx+/6S8ftf3c6xuxgxgz56IdVobGnBszx60NjTA5/VCqVLBnJ2NWYsWwZydHbGud+ZM+CbhvyP1GPPfjWbPjngZ9Hon5f/7w+HzRg4t7E3Qv13fIY0TdVxA+uw40kY0xDRlypSYYaV48ycij8eDlpYWAEB+fn6/66alpUGn08HlcqGmpmZIx+n9pWOg4xQUFISf9z3OcPYjiiJqa2vD7WsHo3cNA3nzzTdhNA522BKi0fXSSy8luwSiCHxP0ljC9yMBRgAXQ668ANnyBuQFa5EfrEFesBZadA5qD0LQD0XzUSiaj0Lz2W/QBRWsslyclM1AjbwALUKmNOzdAPh+pLGG70kaS/h+pLGG70lKto6OjmSXQGPceDvPP5ChnJ9/6aWXhnR+fsGCBTAajdDr9WhqahpSXTqdLuLakd1uH/edA2hsaW1tTXYJE4LX64XT6URHRwfefffdZJeTVKHPsVcJArJFEc11dfjsgw/QGOP3clNtLY7t2YOsggIsv/RSZObloV4Q8M7f/pbosmkMG4vfjdKamnB+r9c+rxfPPPNM0uoZTw4bDgO9+sAcOXIEz3w2+v92qampmN0rfNbZ2Zmw/2aj8d1qRENMVVVVQ5o/EfVOmun1+gHXD325GWrKcijH6d0xqe9xRmo/RERERDR5BQQF6uT5qJPnYxeWAaKIdLEV+d2hpinB0zCKg7vzQw0vpgarMDVYBfgBDzSok+WhVpaPWlkBGmQ58AvKAfdDREREREQ0XOPtPP9E0bczExHRWLVDqcQ5hw9j82uvITBA+LKxpgbvvfACVl93HfbNmZOgColoMvF4PKivr4fP54Pf74/qzDTejGiIiaQ3SIhKpRpwfbVaDUBKw43WcULHiHWckdrPQAa6M6S+vh5LliwBAFxzzTUoLS0d0v6JRpLT6Qwnn2+55ZZBnaggGk18T9JYwvcjDZfD1Qy5dQ8U5R9CUbMDgrMBg+nVqoEHJcEKlAQrAACiTIlA1lwEchfBZZ6Ll7eVo1PQ8f1IYwJ/R9JYwvcjjTV8T9JYUlZWhp/97GfJLoPGsPF2nn8gQzk/f8sttyAvL2/Q+66rq0MwGIRSqYTFYhlSXZ2dnRHBJZPJBLlcPqR9EPUVCATCHZjMZjPfUyPA4XDAYDDAaDRi+fLlyS4n4WJ9jm3auxfv/fjHAwaYQgJ+P7a88QbWff3rsCxcOJrl0jgw1r8beQ8dQvOm98OvlSoV7rvvviRWNH6497tRdqos/Pqss87CfQsS82+XrPdVXV3diH+3YohphGk0mvBzr9c74PpdXV0AgJSUlFE7TugYsY7Tdz+9Xw9lPwMZqBVubzqdDqmpqUPaP9Fo0ev1fD/SmML3JI0lfD/SkKSmAjklwMLrpddeN3DkbeDgX4G6vYB3cHcSC0EfFPVfQFH/BdQAvgvAJqRDv+MkVMXnAQXLgIxSQCYbrZ+EaFD4O5LGEr4faazhe5KSrXdHG6JYxtt5/oEM5fy8wWAY0u/oxsZG+P1+CIIw5LCIIAgRIabh7IOoP3K5nO+pESAIAmQyGRQKxaT/DBf6HPv3//5vBHr9Xh6MQFcXdv+f/4MbP/10lKqj8Wgsfjdya7URrwVgzNU4VilVkSMIqFSqpPzbJfJ9ZbcPbgSGoWCIaYQZDIbw88G0dHW5XAAG15J2uMcJHSPWcfrup78QU3/7ISIiIiIaEpUWOPtr0gQATceBPc8DxzcBnW2A1gx09H+3cEi6aAOOvC5NAKAxAvlLgIKlQMESIG8hoObnVyIiIiIiGpzxdp5/ouBwckQ0HjTt3w/rzp3D2rZuxw40HTgAy/z5I1wV0chxbtka8VoYYnia6EwxxDTCNBoNzGYzWltbUVtb2++6bW1t4S8eBQUFQzpO7zsnBjpO71axfY/Tdz8ZGRkD7kcQhCHduUFERERENCDLTGDtL6XJ0yEFkTrqgJrPgep/A9WfAY2HATE48L48HUD5P6UJAAQ5kD2nO9TUHWwyFgDCYAa0IyIiIiKiyWa8necfr4Q+38kYYiKi8eDA+vVntP3B9etx0e9+N0LVEI0sf1sbbN1DkoXoz1uRpGrGF1EUcdx2PGKeXGA3wOEY0RBTcXHxSO4OgPQhtqKiYsT3O5pmz56N7du3o7y8HH6/HwpF7H/m48d73sSzZs0a8jFi7Weox+m7nwULFgy4n4KCArZcJiIiIqLRozFKj8Y8wHgtMOda6XWXA3juIqC5/8+/UcQAUH9AmnZ1n2gy5EhhplCwKXseoFCN3M9ARERERP0L+KTwudbMcDmNSePpPP94pVAowgEwo9EY99+YiGgsad6//4y2bzrD7YlGk+355yG63T0zZDKY77k3eQWNI59ZP8OB5gMR8+Zmzk1SNePbiH4irKqqGsndAYhO4o8H5513HrZv3w6Xy4W9e/di6dKlMdfburWnFduKFUNLME6dOhW5ubmwWq0R+4ll27ZtAIC8vDwUFRVF1dq7nhtuuCHmPhoaGlBWVjasWomIiIiIRoRKDyy8Azj6NlD9OYAzuEvXUQ8cfUeaAEChAXLP6RVsWgLo4ncpJSIiIpr0RFEKmXs6AE9792MH0NkeOS/ea1/30Fg/OA2kmJL0QxDFN57O849Xcrkcfr8//FwmkyW5IiKigXkdjqRuTzRa/C0tsL3y54h5xnXroC6emqSKxg9RFPG7/ZEd1vL0eVhTuCZJFY1vIxpiuu2220Zyd+PWVVddhZ/97GcAgBdeeCHml5tgMIiNGzcCAEwmEy644IIhHUMQBFx55ZV45plncPz4cXz++edYtmxZ1Hqff/55+A6NK6+8MioUVlpailmzZuHYsWN47bXX8Otf/xparTZqPxs2bAg/v/rqq4dUKxERERHRiBAEYNm90mSvR+f+19G85Y/ID9ZAdiaBJgDwe4DqndIUkl7SE2gqWApkzgR4Up2IiIgmEr83RgCpfeAAUuj1YIb6HYinnSEmGpPG03l+IiJKHJXBkNTtiUZL63PPQ+zs7JkhlyPjm/clr6BxZHvddhxsORgx755590ApVyapovFtRENML7zwwkjubtxasmQJzj//fGzfvh3PP/88brvtNixfvjxinV//+tc4duwYAOD++++HUhn5Bt6yZUv4C89tt90WESIKeeCBB7B+/XoEAgF85zvfwbZt25CSkhJe3tnZie985zsApLasDzzwQMx6H3zwQdx1112w2Wx4+OGH8fTTT0csr6ioCH9ZmzZtGkNMRERERJR8qTnwLbgdf/msE1rRha+fl4uUqn8ClVuBoC/2NhmlgKsZ6Gwb3DFsFdJ0oPsOJLURKFjcE2zKWwioeeKJiIiIxoBgUAoDuW1Ap016dLf2PO/sfu1uk553tkmBJH/nQHsefZ6OZFdAFNN4O89PRESJkblgAayffTbs7buaTsP24oswXnUV5EbjCFZGNHztf/sbbN3B7BDjVVdCVViYpIrGj1hdmAoMBbi85PIkVTT+cYDhUfLkk09ixYoV6OzsxCWXXIL/+q//wgUXXIDOzk789a9/xfr16wFInZC+//3vD+sYpaWleOihh/DYY49hz549WLFiBX7wgx+gpKQEFRUV+PnPf459+/YBAB566CFMnz495n5uu+02/OlPf8KOHTvwu9/9Dg0NDbj77ruRlpaGXbt24X/+539gt9shk8nw1FNPcVxqIiIiIhpT3IIOvnlfQ8p590oX405+JA0TV/5J5IW5G/4CmEuA1nKg5t/SVP1voOXE4A7U1QGUfyxNACDIgKyzukNNS6VQU3qx1DGKiIiIaLgCvj7Bo77BpLbo5Z72kemKlAyd7cmugCiu8XSen4iIEmP+N76BA888M+ztX7hVgy3lv8AVX3scyxasRfqNNyFlzlkjWCHR0LS+sAFNP/955EyFAhn3sQvTYGyp2YKjrUcj5t07/14oZezCNFxMo4ySs88+G6+++ipuvvlm2O12/Nd//VfUOqWlpdi0aRMMZ9A28H//93/R1NSEP/3pT9i3bx9uuOGGqHXuuusu/OQnP4m7D7lcjrfffhtr167F7t278cYbb+CNN96IWEetVuPpp5/Gl7/85WHXSkREREQ06lJMwLzrpMnrkoJMx94D2quBjGnSOhnTpensm6XXpz8DXri0104EYDDD04lBoOGQNO1+rvv4aVKYKTTlngPoM0fwByQiIqJxJRjsDh21SB0hXc2AqyV+xyS3DfA6kl318MjkAATpZ0afQFVKOnDBfwEak/R5TWOUnmuMgNac8FKJBms8necnGkhRURFOnz4dtytYMj366KP48Y9/DEDqaEE0llkWLEDuuefCunPnkLdtnK5F25QUtAH4YloAxfXv4vL/+zZWy2fDfPU1MK5dC7nJNOI1E8UiiiJafvtbtPw+OpSX8Y1vQJWfn4SqxpegGIzqwlSUWoS1U9cmqaKJIWkhJlEUUVlZCZvNBgBIT09HcXHxhBrLed26dTh48CCefPJJbNq0CbW1tVCpVJg2bRq++tWv4tvf/ja0Wu0ZHUMmk+H555/Htddei/Xr12P37t1oaWlBRkYGFi9ejHvuuWdQwaOMjAzs3LkTf/zjH/HnP/8Zx44dg8vlQm5uLi688ELcf//9OOsspoCJiIiIaBxR6YDZV0hTfydBT23tM6PXunI1IFdIgajB6GyL7NYEAKYpkcGmnPlSbURERDT+iCLgdUpBJFfvYFJzn9fdz92tgBhIdtWDI1dJn1H0WT0Bo96Bo+qdQNWn8bcP9vNzBv3AkrtHuGCixBhP5/nHK0EQIIoiAoEAZDLZhLpOREQT0wVPPIFXV66E3+MZ9DZ+lYDdN2ZHzKvMEfDUlXL8ueM41v7jJ7jw1z+D5bwLYbz6KujPOw8CR8ehUSIGg2j86c/Q9vLLUcvM99yDjO98OwlVjT/vVryLE22RXf7vnX8vFDL+v3smEv6v98EHH+D3v/89tmzZApcr8kKAVqvF6tWr8c1vfnPCfCAvLCzEb37zG/zmN78Z0narV68eUtp87dq1WLv2zBJ9CoUC9913H+5jazgiIiIimmj6Owl+7L34ywJd0gQACi1gypcu0LWfli7GDUZ7tTQdeau7FhlgmQ3kndMTbMqcJYWliIiIKPH8Xd3dkVrih5F6P/cP/mJVUii1UndItQFQpgAypfRZKBgEgl7A6wa67IDHHjn0bsALpOQD3/p37P3ueKr/EFN/uuyAr1Oqh2gcGk/n+ccTURSh0+kgCAI6O6XfR1qtFnK5/Iz2G2hogHfvXgQaGiB6vRBUKsizs6FauBDy7OyBd0BENICcxYux7m9/w3tf+cqggkyKlBSk/OIu+E17gIAzanmLUcDGi+R47XwRKw//E5f894eYKmTAuO4KpF62FprZsxnwpBEj+nyo/+//g4533olaZnnoQZjvuisJVY0/71a8i0d3Phoxr9hYjEuLLo29AQ1aws6Su91u3HLLLXj77bcBxG4H6XK58P777+P999/HFVdcgZdffhk6He9QJiIiIiKiUbTuKeDYu9Jkq4y/nt8NtJRJz+UaoHAJYMiVujTV7ZEuag6GGAQaD0vTFxuleYoUIHdBd6ipO9xkKuw/fEVERETx+TyAsxFwNnU/9noeEU5qAbo6kl1tfGojoE2XppTuR5VeCiqlF3XPM0cu3/YrYPuvhnc8Zz+fZ/SWoe9PkSJtp7dIn5kYYiKiXmJdkD+TocT8dXXwfPghAjU1UcsCtbXw7tkDeUEBNGvWQJGXN+zjEBEBQMlll+H6bduw5XvfQ92OHXHXy1uxAqsffxw5ixfjDp8bb5e/jY2HNqCusz5qXY9awEcLBXy0UIZZ1W245LMXsHTDn5CSPwWpl16K1EvXQD1rFgNNNGzO7dvR+NjP4a2oiFwgCMj+8aNIu+665BQ2zrxy7BU8tuuxqPnfXPBNyGVnFsamBIWYgsEg1q5di+3bt0MURSiVSlxyySVYsmQJsrKyAACNjY3YvXs3PvroI3i9Xrz77rtYu3YttmzZwl/EREREREQ0evIXStNFjwJNR6XOTMfek0JG8QQ8wOmdwNRVwG3vSkPLdNQAdXu7py8A6z7A5x5cDf5OoPozaQrRZkQOQ5d3jnRhkoiIaLIKBqXh2SJCSQ29gkq9AkueMRhM0hgBXab0N15rBrRpPcEjrVkasg0CEPRJXYu6HNLPYrcCDivQViV9VvA6AfN04Dt74h9nuLwOqVOTKsbQWKEQkyIF0GdKw87pLL2eZ3YHlno9V+kZyiaifomiGHENaLghJl9ZGdyvvw74+++YG6ipgWvDBmi/+lUoS0uHdayxwGq14qmnnsJHH32EiooKuN1upKenw2KxYM6cOVizZg2uueYapKamYvXq1di6tWcY9RdffBEvvvhixP5WrVqFLVu2hF+3tbXh7bffxieffIIvvvgC1dXV8Hq9SE9Px/z583Httdfi9ttvh0qlillfVVUVpk6dCgB44YUXcPvtt+PNN9/Ec889h/3796OpqQnnnXcebr/9dtxxxx0R28a6Jnjq1CkUFRUN81+LaPTkLF6MGz/9FE0HDuDg+vVo2r8fXocDKoMBlgULMO8b34Bl/vzw+lqlFl+b9TVcP+N6/KvmX3jx8AYcaDkYc9/Hpgg4NkUOo0vEl/bX4uK/rkfG+vVQFk5B6qVflgJNM2fyOjoNSlflKTT+/DG4tm6LXqhQIO8XP0fqJOoGOVyiKGL9wfV4ev/TUcuuK70OlxRekoSqJp6EhJieffZZbNu2DYIgYM2aNXjuueeQFyflXldXh7vvvhsffPABPv30U/zhD3/g8GZERERERDT6BAHIOkuaVv8n0FoBHP+7FGiq3R17m1nrerY1TZGms66W5rVWSBcB6/f1hJsajwJiYHD1uFuAkx9KU0ja1MhgU848djQgIqLxr8sZJ5TUK5jk6O6gNNi/o4mg0EghHl2GFNrRZXY/z+j1PLMnuKToc6H32HvAwVelYLSjHnA0DP7nc0TfuR+Wmju8n0VvkX4eryt2iKnwPOCRWgaTiGhE9Q0tDSfE5K+rG1SAqWcDP9yvvw7d7bePy45M27dvx+WXXw673R4xv6mpCU1NTTh8+DD++te/IiMjA5dffvmwjnH22Wfj9OnTUfMbGxvx0Ucf4aOPPsIf/vAHvP/++8geYIg+URRx66234qWXXhpWLUTjgWX+fFz0u98Nen25TI6LCy/GxYUXY3/Tfrx45EX8q/pfCCIYtW6HTsBbKwS8vVzAvCoRqw/WYPHzz6L12WehLJwCw+rV0K9aBe2iRRDiBAtp8gp0dKDl97+H7ZU/x/w7KajVyH/qSehXrUpCdeOLKIr49Z5f48WjL0Ytu+OsO/C9hd9jqHCEJCTEFEp0L168GJs2bYJMJou7bl5eHt577z2sWLECu3btwosvvsgQ0yRTV1cHo9EInU4HvV5/xuNfExERERENi7kEWHG/NHXUAcc3SUPOnd4hDQkHADPjnBAOBoDnLgTkKmD6JUDppcCanwIQgPoDvTo27QXao08Mx9V2SpoO/016Lcil0FWoU1P2PCBzJqDUnNGPTkREdMZEEXDbwuEcZfMpLPftgE50IuW9A0CXTQrtOJsAnyvZ1UoEeZwAkrlXSKnXMpWuJ8zj6wQ6aoH2aqk7Y8tJoOJf0j6vfib28dqqpCDTcHidgMcOaFKjlxlyep4rNNLr1DwgNafnuSFb6pikt0g/i9owcDBJoYoOYg2S3++H0+kMT4IgYMaMGcPaFxFNLCMRYvJ8+OHgA0whfj88H34I/Z13Dvl4ydTV1YUbbrgBdrsdBoMB9913Hy644AJYLBZ4vV6cOnUKO3fuxFtvvRXe5oUXXoDL5cKaNWtgtVpx5ZVX4ic/+UnEfnU6XcTrQCCApUuX4vLLL8fZZ5+NrKys8P5ffvllfPDBB9i3bx9uuOGGiA5OsTzxxBM4ePAgzj//fNx3330oLS1Fe3s7qqqqcNVVV2HRokX4/e9/j2eekf5eHjp0KGof8RojEE0ECywLsMCyAA2uBrxe9jreKHsDrZ7WqPVEmYADxQIOFANaj4hzj4lYfaga01/cCNuLGyHTaqFbsQL61augX7kSiszMJPw0NFaIfj/aXnsNLU/9FoH29pjrqGfPQs6Pf4yUuXMTW9w4FAgG8P8+/3948+SbUcvuP+d+3DXnLgaYRlBCQkzHjh2DIAj43ve+12+AKUQul+M//uM/cMMNN+DYsWMJqJDGkra2Npw8eTL8OiUlBXq9Phxq0uv1UKlU/EVARERERIljzAOWfkOaXK3AifeBlhPSxcBYanYBnW3S830vSZNcDUw9Xwo0zb4COPfb0nJXizT8XO9gU6dtcHWJAaDhoDTtfUGaJ8iBzBlA9lxpypojhZt05jP7NyAiIgKkcFJnW3cAqUF6DHURCj82SssC3vBmKQBWhl6cjLXjUSJXA4as7sBOaJizrD4dk7onjQmId+6ys10KJ7XXANYvesJK7TXSo6s59nYqA3DV72MHhAxxPkcMlqMhdogpdwFw7w6pI1NKWlK7JtXW1qKurg4ejydivlwuR2lpKc/vEU0SoihCdEcPtS0GAhDdboiKnktVQZ8PwSEEkgJNTQjU1AyrrkBNDXynTkFusQxr+/4IWu2o/I7bsWMHrFYrAODPf/5zVKelZcuW4cYbb8Tjjz8Od/e/eWhYN6VSCQAwmUyYM2dOv8f517/+henTp0fNP/fcc3HTTTfhhRdewJ133omtW7fik08+wYUXXhh3XwcPHsStt96KDRs2xPw3MZlMsPT6bzBQbUQTVbYuG985+zu4d969+KT6E7x64lXsaYw9fLBbI+DjswV8fLYMOa0iVh8KYuVhN4L//Ccc//wnAEAzZw70q1ZBv/J8aM46C4IiIbEASrKg2w3Hxx+j9Y/Poetk7C9e8owMWL73AIxXXQWBzUQG5Av48J/b/xMfnf4oatkPl/4QN8y8IQlVTWwJ+W0V+lBSOoTxhUMfjvhFljo7O9HZ2Ynm5p6TQUqlMirYpB2lLwVERERERBF0ZuCcW/pfp+yD6HmBLqD8Y2l6/0HAchYw41Ip1DTtQqC0e8x0UZQ6M9Tt7Qk31e8H/J7ofcYiBoCmo9J08NWe+YZcIHtOr3DTXCC9OP7FWiIimlxEEfB09BNOaux5HehKcrGCFEAKdRIKP2b3et09T2McOMQTCmbF+5u487fAR/89vFK9DsDTLoWJ+kqN01VCppACToac7u5JuVIgKTW3e173Y7zOiyqd9Dd/lAWDQbhcLni9XpjNscPSoihGBZgAqcOHx+NBSgqHxSWaDES3G45f/SrmMjmA3r2XvN1Torg3bhyV/RoefBBCn+5GI6GhoSH8fOXKlXHXUygUSE2NEXQdpFgBpt7uuOMOPPXUU9i/fz/efvvtfkNMJpMJTz/9NK/fEA2SUq7EpVMvxaVTL0V5WzlePfEq3qt8D6443UvrzQL+slqOv64SMbdKxHlHRCw6KQKHD8Nz+DBafvc7yHQ6pCxaCN2SJdAuWQrN7FkMr0wgoiii84sv0P7WW3D84wMEXbHfK4JSifTbb4f5nnsg14/836iJyOF14KFtD2FH3Y6I+XJBjv9Z8T9YV7IuSZVNbAkJMZWUlGD//v1oamoa9DahdUtKSkarLBrHfD4f2tra0NbWFp43f/58mEym5BVFRERERBTSUjbwOk1HpGn7rwFtRvewc2uAki8B6VOlae5XpHUDPimUFO7W9AXQdAyRp/sH4LBK08ledw0pddJwdOFw0zzAMhtQaYf04xIR0RjX5YjTMalPWMnfmdw6Vfo+oaSsPpOlp4uSXDn4/QaDgLNRCgm3VXV3UOrdSalWGgL2kZrR6ZjUXhM7xGQuAc7/fp/h3nKlrlBjLGTs8/nCQ8G5XC44nU643W6Iogi5XI4VK1bEvDit1+vj7tPlcjHEREQ0RDk5PX+TXnjhBdx///2jfkxRFNHY2Ai73Q6vtydilpeXh/379+PAgQP9br9u3ToYDIbRLpNoQpqWNg0/XPZDfG/h9/BJ9Sd4t+Jd/Lv+3xBjnA8SBQEHpwo4OBWQB0TMOyVi+XERi8tE6FwuuLZug2vrNgCATK+HdtEiaJcsgXbpEmhmzmSoaRzyWa3oeOcdtL/1NnzV1f2ua7j4YlgefgiqgoIEVTe+BYIBvFX+Fn6777eweSI75itlSvxq1a/wpSlfSlJ1E19CQkw33ngj9u3bh40bN2LNmjWD2mbjxo0QBAHXX3/9KFdHY43JZIJWqw23Oh2seCdFOjs7UVlZGdG5Sa1WM/VPRERERKPnxr8ArRVSR6ayD4DTO4FgP0MiuFuAA3+WJq0ZePAkIOt18kiuBHLmS9OiO6V5XQ6g/kBPsKn+INB2amh1+lxA7S5pChMA87Sejk2hSZ+V1OFoiIgohoCvVwipHrDX9zwPv26QugElky4TAa0FVa0euAQ9Ziw8H2pzYXRgSR0/8DIozibpb2IorGQ7JT22nx64o2GgK37HJOMQTvSnpEnrm6Z0PxZIP18segtw4f8d/L4TINQ5KRRYCoWWurrid9/qr6uSrlcXEkEQoNPpwufndKPQoYSIaKI777zzUFxcjMrKSjzwwAN45ZVXcPXVV2PlypVYvHgxVCrViB1r06ZNeOaZZ7Bt2zY4HPE/S7S0tPS7n3nz5o1YTUSTlVapxbqSdVhXsg71znq8V/ke3il/B9WO2MGVgFzAvmkC9k2TAk3zK7sDTSdFaLuAoNMJ55YtcG7ZAgCQpaZCu3AhUubPg2bOXKTMOQtyNo4Yk0LDxbW/9Rbcn/9b6irbD/XMmch65BHoli5JUIXj3+6G3fj5rp/jRNuJqGUpihQ89aWnsCxnWRIqmzwSEmL67ne/i7/+9a/461//ivnz5+Phhx/ud/1f/vKX+Mtf/oJzzjkHDzzwQCJKpDEkPz8fs2bNQiAQgNvtjjppEggEorbRaDRQxBnL1eFwoKWlJeKDtEKhiDkcnWyM3eVGREREROOYuQRY/i1p6mwHKv4lBZpOfiQNWRNP0fmRAaZ41Aag6DxpCvHYgcYjQONhoOEg0HBI6tg02KHoAAAi0HpSmo682TNblwlk9erYlD0HME8H5An5WklENLmIIuC2SR30YgaTuidXc3Lr1Jq7hz3LloZyM4Sm7qHQDN0dlORKuOx2/O2ZZwAARefdB/VQh7gRRambUnsNULA49joV/wLeumf4P0+8jkmmUIhJkH6+UDgp/Dil+zFf+vs8DjkcDpSXl8c99zYQp9MZM8SkUqkwa9YsaLVannsjIhoBSqUS7733Hr7yla/g2LFj2L17N3bv3g0ASElJwcqVK3Hrrbfi+uuvh3yYXVVEUcTdd9+N559/flDrd3b238kxLS3G31YiGrYcfQ6+Me8buHvu3djfvB/vlL+DD6o+iDvcXEAu4IvpAr6YDij8IuZ3d2ha1B1oAoCg3Q7n5s1wbt4c3k5ZOAUpc+ZCM3cOUubOhWb2bMjYRTPhRFGEt7IS7t174N61C86tW+MOFxcmk0G3YgVM11wNwyWXsMvWINU4avCbPb/Bx9Ufx1yeqkrFMxc9g3mZDOeOtoScbW5oaMBzzz2He+65B4888gj+8pe/4LbbbsPixYthsVggCAIaGxuxe/duvPTSS9i/fz8WL16M9evXR4zv29eUKVMSUT4liVwuh8FgiGgz2vdusFD76v7u3HI6nVHz/H4/2tvb0d7eHp4XuhvMYDAgNTUVRqMRGo2GHZuIiIiI6MylmIA510hTMADU7gZO/AMo+xBoPha57owvx9/Pu98B/F1A6aXAtAsBjTFyuSYVKFwuTSEBP9BaLgWaGg9Jjw2Hhn7h29UMVG6WphCFBrDM6g43zZOGpssolYb54edoIqLYvK7+g0mhod0C3oH3NVpS0mKEk3J6PXaHkxTqkT2u1y0N89ZWJXUXDHVUaqsC2k73DHf3SG3ssFDa1DM7fkcNkBPjhLQ+G/juPiA1H1CMXIeLRBJFEW63G1qtNua5LplMBrvdPqx9azQaBIPBuMstljidqIho0hC0WhgefDBqfjAQgK2tDRqNJmJ+f0NR9uX55z/hG2A4s/4oFyyA5qKLhr19PIJ29Ibonj17Ng4dOoT33nsP7733HrZt24by8nJ0dnbiww8/xIcffojf/OY3eP/994f1O/hPf/pTOMC0YMECPPDAA1i6dCny8vKg1WrD4ahbb70VL730EsQBuoAMN0xFRP0TBAFnW87G2Zaz8YMlP8Dm6s34oOoDfFr3KXxBX8xt/AoBe6cL2NsdaJpdI+KccmnKbo9c13e6Gr7T1bBv2iTNkMmgnj5dCjXNmQvNrJlQlZRAPoTf2TQwMRhEV1kZ3Lt2w71nD9x79iBgsw28IQBVcTGMV18F4xVXQpnFz+CD5fK5sP7gerx09KW4/+9cUHABHlr8EAoMHI4vERISYioqKor4cnzw4EF8//vf73ebPXv24Jxzzom7XBAE+P39DMdAE5IgCEhJSUFKSgoyMzPD8/s7URIrxBSLKIrhcFR9fT0AQK1WY8mSJbxLjIiIiIhGjkwOTFkmTRf/WBrq5uRHUqip+jNg2sWxt/N3AYfekIaAO/gqIFMAU5ZLoafSS6XOT7HIFYBlpjThqz3zHY3dgaaD3Z2bDgEtJwH0fwI6siYPYN0nTb1pjFKYyTwdyOiezNOB9OJxe/GXiGhAfi/gbOg1vFufx9DQbl0dyatRYxxEOCkbUGoG3tcZEFzNwKn3I4d8a6uS/v0Go+201BGwr7Si/rdTaABToTTUW7iTUu9h37JibyeTSX/DxpFgMAin04mOjo7w5Pf7sXDhwpjhgFC4qb8L0aEbAENdzUNdzuN1RyciChEEAUKMG5HFQABiZ2dU4EeIE7iMRb1s2RmFmNRLl0I2Doe3lMvluOqqq3DVVVcBAOrr6/HBBx/gd7/7Hfbu3Yu9e/finnvuwVtvvTXkff/xj38EAEybNg07d+6M2WkPAGyDvKhORKMvRZGCtcVrsbZ4LZxeJ7bUbsGHVR9iR92OfgNNB6cKODgV2HAxkNvaE2iaWStC0ffSazCIrhMn0HXiBDr+9kZ4tiIrC+qSYqhKpkmPxcVQT5sGRXr6KP7EE4fo96Pz0KGe0NLevQgO4eYCmcGA1MvWwnT11dDMm8fmHEMQCAbwbsW7ePKLJ9HqaY25zjTTNPxgyQ84fFyCJewb5kBJbKIz0V/IKCcnB1qtNhxQGkpLbIVCEXffPp8PcrmcASciIiIiOjPpU4Gl90iTrxNQxmnNXbVdCjCFBP3SvKrtwIf/BZinASVfAopXS0PM9e3S1JchS5qm97rr2OuWhp8LDUXXeBhoOBx53MHwdEjdpmp3R84X5EBaYXfAaVp3wKk77MTuTUQ0VgX8Uje6vp2Swo/dz92xT3omhFwtBZFSc7sDSblAak6vYd26Q0qq0esKERYMSsPg2SqBgmUxw6uytoozG/atrSp2iElvkUJKhmwp0JQ2tfuxe9JnSYGkCcjv98Nut6OjowN2ux12uz3mTX8dHR0xQ0yCIECv18PhcACQhivqG1iK18WJiOhMxLp2JIrioH/fyLOzIS8oQKCmZsjHlhcUQJ6dPeTtxqKcnBzccccduPnmm7Fs2TJ88cUX+Pvf/47Ozs5wCGmw/6ZHjhwBAFxxxRVxA0yiKOKLL74YmeKHUBsRDUyv0uPy4stxefHlcHgd2FKzBR9VfYQd1viBJgCwmgVYzQL+vhRI8UjDzp1TIeLsChFGd/zj+Rsb4W9shGvnZxHz5SYTVCUlUJeUdIebSqDMy4UyOxuyUexWN1YFvV74qqvhPX0ajuPHMW/HTujtHah/9TWI7n7+gWMJDRd39VXQX3ghZOoR7pA7Cext3Iuf7/o5jtmOxVxuUpvw7QXfxrWl10Ih400biZaQf/EXXnghEYchiikzMzPctUkURXR1dYUDTaEh6TweT8xtjcb4F36qqqpQX18Pg8EAo9EIo9GI1NRUKJXKUfk5iIiIiGgSiBdgAqSh5/rTWi5Nu9YDggzIPQcoXiWFmvKXDK6rhkoL5C+UppBgUBrSp6HXUHSNhwF73aB+pAhiQLqobauMXqYx9XRsCnVvyiiVLkCzexMRjYZgUAoexeqa1PvR1QSI8TtAjy5BCuaEgknhoFJ3OCkUVEpJS2wQtHdQqbWi53d7aPJ3n2f51m4gszR6c2Ph8I8tVwOdbbGXCQLwwMHh73sc8Xq9EV2WBtuJ3G63Iy8vL+aywkLpv4ter4dKpeIFZSJKiHghpqHQrFkD14YNwFBG71AooFmzZkjHGQ+USiVWrVqFL774An6/H+3t7eEgUmjYvq6urn73ERoFxeWKfzPLO++8Ex7RYiT0HlKwq6sLal6QJxoRBpUB60rWYV3JunCg6cOqD7HDugP+YPzfmZ0aAZ/PEvD5LEAQgWn1Is4uD+LsChFTGwHZIH5NB9rb0bl3Lzr37o1aJjMaoczOhiI7C8rsHChzsqHIyoYyJ7t7fjZkmtHtDjsaRL8fvro6eKuq4D19Gt6q0+HnPqsV6PX3rSi0zSD3rcjKgnbxYmgXLYL+gtVQZsXpIktx+YI+fFr7Kd44+Qa21m6NuY5CUODGWTfi3vn3IlWVmuAKKSQhIabbbrstEYchGpAgCNBoNNBoNMjIyAjP9/v9cDqdcDgc4bvWfD5fvyGmjo4OiKIYvrutpvtOD51OFw40GY3GqPG8iYiIiIiG5dzvSqGesg+AU9uAgDf+umIQqNsjTdt/LQ2f850vAGPsi5b9ksmkoerMJcBZV/XMd7UCjYekTk2hcFPryf7r6o+nvZ/uTUXdAadp0r9BKOCkNbN7ExFFCwYAVwvgbAScTdJjrO5Jzgapq12yqFN7uiTF66CktwDyJN4s5XVLf0v6Cyr1x1YRM8Qk6rMAuSr+3wydRepU2LuLUribUvaE7aY0GKHOF4MNLfXV2dkZd5nZbB5uWUREZ6Rv56WhhpgUeXnQfvWrcL/++uCCTAoFtF/9KhRxQp1j2fbt25GTk4Np06bFXO71erF1q3RhVq/Xh2/wBqRuTcePH0dFRUW/x5g+fToOHTqE9957Dz/96U+R3mdIqIqKCnzrW986w58kUk5OTsT+Z8+ePaL7J6LoQNNn1s+wrXYbttdth80Tf3hIUQBO5go4mSvHaysBXUCB2fUKzCrrxOyqAIqaBhdq6i3Y0YGujg50nTgRdx25yQRFTg4UGRmQG/SQ6Q2QGfSQGwyQ6Q3SPIMBMn33PIMBcr0eMr0ewhkOdyyKIkS3GwG7XZraOxCwdyBotyPQYe953t7Ra512Kag0lEBtP5QFBeHQknbxIijz83mTwTCdsJ3AOxXvYFPlpn7f6yvzV+LBRQ9iqnFqAqujWNj7igjSsHEmkwkmkwmA9Meps7MTKlXsO779fn/cuxBcLhdcLhesVisAQK1Whzs1GY1Gtt4mIiIiouExFQBL7pamLidQuUUKNJV9KHUJ6U9KunSBPBa/V7pAPtTPqDqz1OWpeHXPvGAAaK8GWk5KgaaWkz3PnY1D23+IGJAugttinGgPdW8KD09XCqQXA8Z8QMO7pYgmFFEEuhzdoaSGyIBS+LERcDQC7pYkdk6C1Ckotc8wbuHHXvPV0UN6JVyoo1JKeuyh5pyNwIvrhr//WJ33AKljYOjvR98h39IKAZVu+MecAERRhN/vj9ntWxCEIXUB12q1Eeel2NmCiMYir9cLg8EAuVwOQRAgG0ZYVVlaCt3tt8Pz4Yf9Di0nLyiAZs2acRlgAoBPPvkE//M//4Pzzz8fl112GebNm4fMzEx0dnairKwMf/jDH8LDvN11111Q9LqQf+6552Lz5s3YvXs3HnvsMXz5y1+GTif9zU1JSQl36rv11lvx0EMPwWq1Yvny5fjBD36AOXPmwOPx4F//+heeeOIJdHV14ZxzzhmxIeXOPffc8PPvfe97+OEPf4icnJzwtZSioqKIn4WIzoxBZcAlRZfgkqJLEBSDONJyBNvqtmFb7TYcbT3a77YuuR+78/3YnS8AUMAgpGCuLwtzG1WYedyN3P11ELzxh60brEB7OwLt7ei/d1xsglYLWZzhMAc+cAABp3PEwkiDpSoujgwtTZDhTpOlzdOG90+9j3fK34k7ZFxIibEEDy1+CCvyViSoOhoI/+ITxSAIArT9jMc6lLvdurq60NTUhKYm6cKSQqFAcXFxxJ0FRERERERDotYDsy6XpmAQqN8nhZoqtwDV/wYCfU7xFK+OH1La+SSw5wVgavfQc8WrpIvrwyGTS50z0qcCuCRymacDaCnvDjeVdYebuofAG+nuTQCgNkphJmO+1IHKmA8YC3rmGXKS292EiCR+rxTEjOia1NgTSuodVPLH7yCTEDKF1AXIkB0dTuodWtKYxlaXuFBQKdxNqQJo7e6m1HZK6qh08xvAtIuitzUWSD/3cDpWKbVSJ6d4bnp96PucoAKBQLjTd0dHB+x2O9LS0nDWWWfFXN9oNKKtLXpIPUEQoNfrI0JLQwk8EREli8/ng0KhgFwuP6P9KPLyoL/zTgQaGuDduxeBxkaIXV0Q1GrIs7KgWrgQ8glwUTgYDGLr1q3hjkuxXHnllfjZz34WMe++++7DM888A5vNhkceeQSPPPJIeNmqVauwZcsWAMD999+Pf/7zn/joo49QVlaGu+66K2I/KSkp2LhxIzZt2jRiIaZp06bhuuuuw2uvvYaPPvoIH330UcTyU6dOoaioaESORUSRZIIMczPnYm7mXHxrwbfQ7G7G9rrt2Fa7DZ9ZP4Pb389negAOsRM7FVXYmQcgD0j9cirOSZ2NBf48zG1WI7e8A77KU/CePo1gR0dCfibR7UbA3X/dyaLIzIQsPx8nnU64jKk47/rrYT73XCjYFfWMhYaLe6fiHWyt3drvkIkAYFQb8c3538R1M66DQsbYzFjC/xpEw2AymbBixYrwyaWOjg44HA4EgwPf6en3+/u9YyAQCJzxlzUiIiIimkRkMiBvoTSd/33A1wnU/Ls71LQVsO6TgknxVG4F7HXAgT9LEwBkzOgJNBWdB2jiD7M8aBojkL9Qmnobje5NANDVATR1AE1HYi8XZFIYIRx06hNyMuYDKWljK4hANF74PIC7VeqI5G4FnM0xQkndU2d0ECPhBJk0fFlU16Q+j1rz2B3GTBSlIfRay7tDSuVSaCkUXBooAGY7FXu+XCF1R2otj71cqZM64KVPlYYdTS8G0ruHINVn8XdoHD6fL3w+qaOjA06nM2roJLvdHjW8UojRKP1dlsvlSE1NDQeWQl1MiIgmO3l2NlIuuyzZZYyKBx98EPPmzcPHH3+Mffv2wWq1hm+gzs7OxpIlS3Drrbfishg/f15eHnbt2oWf/exn2Lp1K2pra+HxRA8Pq1QqsWnTJjzzzDPYuHEjjh49ClEUkZeXh4suugj3338/Zs6ciU2bNo3oz/byyy9j0aJF+Nvf/oYTJ04M+poLEY2sTG0mrpl+Da6Zfg28AS/2NO7B9trt+LTuU1TZqwbc3u61Y0vL59gCAErAtMCERWsWYX7mlzHbMA3TvelQNbfDV98AX2MD/PUN8DU0wN8gPQYdjlH+CUef3GSCqrAQqqJCqIqKup8XQTmlEHK9Dna7HX975hkAwMWrVkGRym7iZ2Kww8WFLMhcgCunXYlLiy6FXjUGOiRTlISGmPx+PzZt2oTt27ejsrISDocDgUCg320EQcAnn3ySoAqJBk+hUCA9PT08HnQwGITD4QjfMdfR0QF/nFaDoZNNffl8PuzcuRMGgwHp6ekwm83Q6/Ucfo6IiIiIBk+ZEjnMW2cbII89TDK8binw1FfLCWna9ax0cT/3nJ5QU/4SQKkZuXqH2r2p5aR0gX643ZtCxO7OJA4rULsr9jpKbXTIKTWv53Vq3sj+WxCNRaIodT1ztUYGk1zdj6Gp92vv4LsXjy4B0GVKYRq9Jf4QbzqLFNYZD0QxdjDI3wX8ajoAMXrZYLTGGLIzJGsOoEhhUOkMiKIIu92O1tZW2Gw2uFyuAbfxer3weDxIiTEMRmpqKs455xyeMyIimoT0ej2uueYaXHPNNcPavqSkBM8999yA6ykUCnznO9/Bd77znbjrbNiwARs2bIi5rKioKCqgOxClUomHHnoIDz300JC2I6LRo5KrcG7uuTg391z8AD9Ak7sJexr2YFfDLuxp3IPT9tMD7qO9qx0fV3+Mj6s/BgAIEFBiKsFZWWdhzllzMCdjJUrTSqHqPncVcLrgb2yAr74B/oZ6aVg5hxNBhwMBpwPB8PPIR4xW6FEQIEtNhTw0GVMhMxohTzVGvVZmZ0FVWAi5yTQ6tRAA6ftVnbMOW2u3Dmq4OADI0mbhipIrsK5kHaYapyagSjoTCTtDs3XrVtx+++2orq4Oz+vvA4wgCHHvNiIai2QyWfjON0B6f7vd7og767q6upCSkgKVKvZFpI7uNooOhwMOhwOnT5+GSqUKh6XS0tI47jMRERERDU1KWvxltbsGDgOJQaBujzRt/xWg0ABTlkuBppnrgIxpI1tvb4nu3tSXz90dniqLv47OEg41qVMysch/Gi7oID+9DcgokDq3pKQDqvjDVRMllN8bP4wUL5gk9n8DWsKpU6VQUiicFPGY3fNcax4/4aTevG6pe1K4q1KvzkoX/h9g4e3R2yg1gKlA+t04HB018Zdd9+Lw9kmw2+2oq6uDzWaLe6NbfxwOR8wQk0wmg8FgGIkSiYiIiIgGzaK1YG3xWqwtXgsAaHA1YE/jHuxu2I3dDbtR4+jne0U3ESLK28tR3l6OdyreAQAoZUrMSJuBszLOwpyMOZhjnoOpU5dBLhtcl1FRFKUh5BwOKdjkcELs8gzzhgsBMr0ecqMUWpIZDBDGakfeSUIURVTZq7C3cS/2NO7B3sa9aHA1DLidWq7GhVMuxJXTrsTS7KWDfj9R8iXkTM7+/ftx6aWXwuv1QhRFaDQaTJ8+HSaTCTL+T08TlCAI0Ol00Ol0yM3NBQB4PB54vfEvEnXEGAvW6/WioaEBDQ0NEAQBRqMx3KUpJSWFQT8iIiIiGr6pq4Bv7eoZeq5qO9Bl738bvweo3CxN6tTRDTHF01/3Jp9HGh6vo1aa7HXSxfnQ645aKZw0UlxN0mT9AmoAF4bmv/FO5HqKFClQoU3vnsw9AadY87VmqasWUV+iKP1/6LFL3cq6+jx67ECXHRp7My7z7oFG9ED7538AXW2A2zbw/+PJIlN0h5Cy4oSTej2fCKFAvxdoP91r2Lfy7tBSpfR7K57+Oiall/QfYlLqAHN3F6X0YmkKdVbSZw3/Z6G4fD5feIifwTAYDBHDw8W7CY6IiIiIaCzI1mXj8uLLcXnx5QCkUNPuht3Y1bALuxt2o87Zz3ebXnxBHw63Hsbh1sN49cSrAACtQotZ5lmYmzEXZ2WchVnps5Cnz4NCFh1vEAQBgk4HmU4HZGeP3A9ISREUgyhvL5dCSw1SaKnV0zro7UPDxa0pWgODijd/jEcJCTE9+uij6Orqglqtxm9+8xvccccd0GjYcp8mH41G0+973zHAOK+iKKK9vR3t7e2orKyERqOB2WxGeno6Q4FERERENHSCAGTOkKal9wABP1C/vzuktFUaaq6/Tk2hIev68nQAn/8ByF8E5C0EUkyjUHwcSo10Ud5cEnu5KEpD7HXUAB2hsFOfkJOjHsMekikefydgr5WmwRpU8MkcuYzBp7Ev4OsOILV3B4/sUQGknucdfeZ3Lwv6BjyMCsCc0IuBb1AcHYJMer/qMiKDSIbs6ICSxgRMlu+0z68BancPr8OVrTL+MvM04PSOyOHezCXSfPM0Dv02CgKBANra2mA0GqFUKqOWh87VBGMMbSGTySICS6mpqZDLeWcwEU1uoiiGJ/5OJCIaf7J12VhXsg7rStYBAKxOK3Y17MKB5gM40nIEZW1lCAzye5Db78bexr3Y27g3PE8pU6LIWIRiY7E0maTHotSi8HB0NP4EggEcbzuOvQ1Sp6Uvmr5AR1d044/+cLi4iSUhIaZPP/0UgiDghz/8Ie67775EHJJoXJo3bx46OjrQ2toKm82Gzs7Oftf3eDyoq6tDXV0dUlJSsHjxYnZmIiIiIqLhkyuk4FH+ImDlQ9KQRjWfS4GmU1sB636Ewz2p+dKF8ljq9gJbftrzOqMUyF/cve/FQOas5A3xJAg9oaCc+bHXCfikIFM42NQn5NRRJ4VLRtuZBJ9UOinQFJoUoecaQKntMy/OOope6/ZeZzwOzzUQUZQCe36P1M3L7wH8XdJ/A3/XIOf3mkLzfO7oAJK//+95Y5pSC2gzpP9/dBnd4bkMQGfuea419yybDMGkYED6vRAa9s12Snpe8iUpHBqLTDH8Ifpay+Mvu+hR4Ms/l7rV0ajp7OyEzWZDa2sr2tvbIYoiZs6ciays6G5WcrkcJpMJNpsNAKDT6cLdtQ0GA29GIyLqJpfL4Xb3dEsVBAF6vT6JFRER0UjI1efiqmlX4appVwEAPH4PjtuO40jrERxuOYzDLYdRZa8a9P58QR9Otp3EybaTEfNlggwFhgJMNU5FibEkHG6aapwKnVI3gj8RjYTQ+yA0PNz+pv1w+pxD3k+aOg3Lc5dzuLgJKCFnHj0eDwDg0ksvTcThiMYtmUyGtLQ0pKWlAQDcbnf4xFhHRwdEMf7d4EajkQEmIiIiIhpZKq10Ib7kS9Jrtw2o+lQafk6bHr+jR+2eyNctZdK0/xXptVIL5J7TE2rKXyR1Zhkr5ErANEWa4vF0RHVy8racQkPZHmhFN9I1gMzTPvygwnCFgk+jSabsFWoKBZ16h6M00qNwphfnz/D7jRgEAl1S2MjXK3TUN2gUmj/S3bfGPKFXF6/ewaTeQaT0ntda88QYxm04gkFpiLdwUKlSmlorgLZTsTvWKVPih5jMxcDpTwc+bmpe93BvoW5KJYB5evz11bzYOxqCwSA6Ojpgs9lgs9kiLrKH2Gy2mCEmAMjLywt30WZneiKiwQl1Y+L5biKiiUWj0GCBZQEWWBaE59m9dhxtPRoONR1uOYxGd+OQ9hsUgzhtP43T9tPYUrMlYlm2Ljuic1OBoQDZ2mxk6bKQomA369HiC/pQ56gL/3c5bT+N0w7psdHVCHEY52AyUzKxKGsRFmYtxKLsRSg2FvOzwgSVkBBTUVERjh07Bp9v4HbnRNRDq9VCq9UiPz8ffr8f7e3t4S5NXm/kSdL09PS4+zl69CgEQQifNFMoJuCd00REREQ0+rTpwOwrpKk/tbv7X+5zSxfwe1/ENxb0hJqWfXPsD3mkMUpT1uzwLI/djr+cfgYAcN999yFVr5c6Nrlt3VOrNHX2eh61rC3xwaehCvqALp/UXYiST5ECaFIBdar0qDEC6lR4ZSnYd7wSXdBg8eovIyU9L7J7UoqJHXsG8vGPgRP/kIJKfs/Qtm2tiL8svddwl7rMXiGlYukxvUTqdDdZQ2NjgNfrDd9U1tbWhkCg/9/LNpst7sX2/s7XEBGRpL+bd4mIaGJLVaViWc4yLMtZFp7X0tkSEWo62noUbV1tw9p/g6sBDa4G7LTujFpmVBvDgaZsbTaydZHPLVoLNAreiBBPIBhAvase1fZqVNmrUO2oDgeWrE7roIcOjCdPnycFlrqDSwWGAoaWJomEJBmuuuoqHDt2DNu2bcPy5csTcUiiCUehUCAjIwMZGRkQRRFOpzN8Qs3pdIa7N/Xl9/vR0tICURTR1NQEQOraFGpfrtVq+QufiIiIiEZW3kKgsx2oPyB1whmMjhppqj8ALP/WqJaXMDIZkJImTeaSgdcHpI4vEzH4RLHJFOHQUe8AUsRjOKDU93n3OgpVzF177HZsqZBCdQvm3YSU1NRE/mRjkygCjoZeHZUqAEMusOze2Os76oHmY8M7lq1SOl6s79tzvwJMXSn9XtAYh7d/GnFOpxMtLS2w2WxwOBxD2lan08Hr9UKtVo9SdUREE1usEBM7MRERTV4ZKRlYXbAaqwtWh+fZPDZUtleisqN76n4+1K5NvXV0daCjqwMn2k7EXSdNnSaFm7RZUsCp+3m2LhvmFDMMSgP0Kj00cs2E+rvl8XvQ0dUBu9cuTV12tHhaegJL9mrUOGrgC45cE5ui1CIszFoYDi7l6HNGbN80viQkxHT//fdjw4YN+NWvfoXrr78eRUVFiTgs0YQlCAIMBgMMBgMKCwvh9/vjdldqa2uL+hLY0dGBjo4OnDp1Cmq1OtyhyWQyQS7nnbhEREREdIZW/6c0+b1A4yFpeLna3dJj26n+t81fHH/Ztl8BNbt6hqDLO2fiBQBGIvjUaQO8ru4h1DqlR59H6oDl734MvfZ1Rs/r/XqwIbSJSqaQhsdTqKWORwp193B5mu75mp7lyl7LQ5NS0yeAZIwMKylTxn7XsfEm4APaq4G2qsgpNAScr89QYHmL4oeY0gf5/yAg/fdML+4e/q1Y2jYYAOQxvqsb86WJxpTGxkbU1g5uOE6lUon09PTwxI7XRERnJl6IiYiIKCRdk4707HQsyl4UMd/pdeJUxylUdlSioqMCp9ql57XOWgTF4Bkft62rDW1dbThm6/8GF4WggF6lh16ph0FliHyu1EOv0ocDT3K/HFaVFSpRhdOO08hWZEOv0kMtH9xNEYP9G+kX/bB32SOCSHavPSqcFGueNxhj2PQRNs00LTw03ELLQmRqM0f9mDQ+JOQbdmZmJt5//31cfvnlWLp0KX7yk5/guuuug9E4wU42EyVJfyfLbDZbv9t2dXXBarXCarVCJpPBZDLBbDbDbDbzDkIiIiIiOjMKldSVKW8hsPQeaZ6rpSfUVLcHqN0LeHt1vOgvxFT+CVC9Ezj5YfcMAcic0TMMXd4iwDJr8g2RNZzg02AFAwMHnWKFofydUheaYRuhi1Z9A0ixgkYRoaQ+YaVYARQae/a9DBx8VQorddQCQzlRbetn2DdzceRrpU6al17SHVbq9ajLYCBtHOjq6op7riM9Pb3fEJNerw93tTYYDBPqLmsiorFAEISIi7IMMRER0WDoVXrMzZyLuZlzI+Z3BbpQ1VGFUx2nUNFRgcr2SlTZq1DvqofDO7TOq4PhF/1o72pHe1f74DboHnX6g48/GPFaxpI0dRoKUwsxJXUKClMLw9MUwxRolRxCnWJL2Nm4efPmYdu2bVi6dCnuvfde3HfffcjIyIBW2/+bUxAEVFT0c0KJiPpVWFgIvV4Pm80WsytTb8FgEDabDTabDSdPnoTJZMLMmTMZZiIiIiKikaPLAGZcKk2AFJJpKevp1FR0XuztAj7Auq/PTBFoPi5N+16WZik0QEYpkDlTCjhlzpSmtCIGUoZDJgfUemkiSpRY3ZRkcuCiR2Ov31ELnNo2vGN1tkkdzLTp0csKlgFXPN0TVNJbGFQah7xeL5qbm9HY2AiHw4Fly5bFPM9hNBohl8sRCEjDc8rlcqSlpYW7LfHcCBFRYjHEREREZ0ItV2NG+gzMSJ8Rtcztc6PB3YAGVwMaXY1ocHc/uhrQ6JYenT5nEqoenwxKQ0RIaUrqFBSlFmFK6hSkqji0PQ1dws7gvvHGG7jrrrvgcDggiiJEUURTU9OA2/GuJqIzo9FokJeXh7y8PAQCAbS1tYWDSl1d/Q8N4XK5oFKpElQpEREREU1KMrnUPckyCzjn1vjrNR6RuvsMxO8BGg5KU2+r/wtY/YMzq5WIRoYoSuGhtlPRw77F66akzYgfYkorGtrxFZruId+6h3+Ld5HUmAecc8vQ9k1jQiAQQGtrKxobG6Nu6LLZbMjJyYnaRiaTIScnB6Iowmw2w2g0QiaTJbJsIqJJjZ2YiIgoUbRKLYqNxSg2Fsddx+l1hgNNoce+z91+d9ztJxKD0oBUdSpSVanIN+RHdFQqTC1EmjqNmQ4aUQkJMX322We44YYbwncyFRYWYt68eTCZTDwZQJRAcrkcGRkZyMjIgCiKcLlcsNlsaG1thd1uj1rfYrHE/aPj8/mgUCj4R4mIiIiIEiOtELj2+Z6h6BoOAgHv4LfPjL7zLuxPlwL6LClIFerelF4iDYdHRCPn0N+AHU8AbaeBrujvoP1ytwBdDkBtiF4WK8SkNgLpRdKytCIgbWpPRyVDjjQMI00ooiiira0NjY2NaGlpQTAYe1jB1tbWmCEmACgpGeEhOYmIaND6nmdmiImIiJJJr9JDr9KjxBT/O4Iv4IPT54TT64TdZ4fTKz13+BwRj06fEw5vz/MOTweaOprgk/kQEAIJ+Xm0Cm04iGRUG5GqSg1P4dcxlutVeihk7GxOiZWQd9xPfvITBAIBGI1GvPLKK1i7dm0iDktE/RAEAXq9Hnq9HlOmTIHP5wt3aGptbUUgEIDFYom7/fHjx+FyuZCVlQWLxQKdTpfA6omIiIho0klJA+Z+RZoAwN8FNBzqHoaue2qvjr995szY810tQPVn0vOjb/fMlymksEN4SLrux4zpgIJDCtEk5+sE7FapY5K9DuioA+y10qOnHfj6x7G3C/1/O1xtp4HsOdHzM0qlLk3hwFKR9DuDJjxRFOFwONDU1ISmpib4fL4Bt3G5XBBFkTdlERGNMQwxERHReKOUK5EmT0OaZmjfP+12O5555hkAwNfv+TpkGhmcXid8wYG/z4QN4uuMDDKkqlNhUBmglCmHVCNRMiUkxLRnzx4IgoAf//jHDDARjVFKpRJZWVnIysoKDztnMMS4wxWA1+uFzWYDAFRXV6O6uhp6vR4WiwUWiwVqNS/qEBEREdEoU6iB/EXShPukec5moPl4r+mE9NjZJg0bFUvz8djzg36g5YQ0HXu3Z74gk/YVCjadfXP8fRONV51tQNOxyHCSva4ntORu7X/7oXRM6o/GKHVQCgWTYu0TALTpwHnfG9q+aVzr7OxEY2Mjmpqa0Nk58FCjMpkMZrMZWVlZSEvjUAdERGMRQ0xERDQZKWVKpGpShxyEIprIEhJicrul8SDPO++8RByOiM5QaNi5eJqbm6PmOZ1OOJ1OVFZWwmQyISsrCxkZGVAo2GKQiIiIiBJEnylNU8+PnN/ZHn9ouHghpnjEINBaLk3H/w7MWBszxCR0tgHVHwOpeUBqLmDIBuS8642SLBgEnI1SEMlRD8xaF3u9so+At74x/ON01AGWGN3P0qdGvhbkgDFfmt+7ixK7KdEA6urqUFdXN+B6aWlpsFgsPD9BRDQOMcRERERENDkl5Nv71KlTceTIkXCYiYjGN7vd3u/y9vZ2tLe3o6ysLHynY3p6OmQyWYIqJCIiIiLqJcUUf1nxBcC6J3u6NjUdBxzWwe87ozTmbFnzEeBvd/SaIwB6ixRoCgWbej835EiPypTBH5soxOsGXM2Au0UaItHVDJWtDhf4PoFedED76j+l8JLDKnUZC3mkNnZ3I2P+mdVjr40dYtJnA5c/0RNSMuYz3EfDYrFY4oaYDAZDuFO0ShUnwEpERGMOOzEREREREZCgENM111yDw4cP48MPP2Q3JqIJYObMmSgoKAi3bvd6vTHXE0URLS0taGlpgUKhQGZmJiwWC4xGI1u3ExEREdHYYC6Rpt48HUBzWfTQdB01keul5gGa1Ji7lTnq+8wRpRCJsxGw7otfT0q6tN+b3wAMWdHLRRHgZ+mJz98VDiPB3QJMXQ3IY5zCqdgM/PVrgC/6pjENgCWhF/Ea1sTrmGTMG1ydaqO0bmpe92O+9GiZHXt9mQxYdEfsZUTdRFFEW1sbGhsbUVJSEjOIZDAYkJKSEh5KTqPRICsrCxaLBVqtNtElExHRCOh7vjgYDEIURZ5HJiIiIppkEhJi+v73v4+//OUveOKJJ3DllVdi0aJFiTgsEY0SQRCg1+uh1+tRXFyM9vZ2NDU1obm5GYFAIOY2fr8f9fX1aGtrw5IlS2KuQ0REREQ0JmiMQMFiaeqtywG0lEmBpqZjgEITdxeCs2+IaZA6bdKkMcZeXrkFePWW6E5OfZ9rTFJghMaGgD+iSxJcLd2vu5/3Diy5WoCuPt1vv18WO9Sm0scMMA1avI5JhlxAqQNSc7oDSvk9QSVjfk9YKVYXJ6JhEEURDocDTU1NaGpqgs/nAyCFlfLzozuDCYKA3NxcdHZ2IisrCwaDgRe5iYjGuVhd/IPBIORyeRKqISIiIqJkSUiIyWAw4JNPPsFXv/pVrFy5Et/73vdw/fXXo7S0FBpN/JO+RDT2CYKAtLQ0pKWlYdq0abDZbGhsbITNZovZ8tdisfDEIhERERGNT2oDkLdQmgai0ADpxYDdCvg9QzuO1gwo43xXtlsBrwNoOSFN/VHppUmtl2pX6YFb3wFkMS4EtddIHaLUhp5J1Wu7yRaICgalcJDXBfhc0qPXBXidvZ73nZzAxT8GUtKi91fxCfDn64Zfj6s5dohJlzG8/cmUUkAp4Iu9XKEC/quOXb9o1Lnd7nBwKdRVqbempqaYISYAcecTEdH4JAgClEolBEGAXC6HXC7neWQiIiKiGBwOBwRBgE6nm5CflxISYuqdlBdFEY899hgee+yxQW0rCAL8fv9olUZEI0gulyMzMxOZmZnw+Xxobm5GU1MTOjo6wutYLJa425eXl0On0yEzMxMKRUJ+PRERERERjQrvwm9Ac8GD0vBvnW1S+MhuBex1kc8d9dLz3p13DLnxd2y3DqEIpzQ5u18rNLEDTABweifw1jfi7ysciDL0CkUZel5f/D+AKsYQTu01QMPBwdfc1/Q1sYdRczYDdXv63zYY6A4iOQGvuydo5HMDa34KKNTR21T8C/jrzVJwaThW3B87xKQdZtgoxNUce74uM/K1TCnN05nhV6fhRG0rXIIO8877MjSWkp4OSjrLwMG0CXgSjMYGpVKJpqYmlJeXw+Fw9Luuw+GA2+3mEHFERJMEb3ofmurqavzv//4vPv74Y9TV1aGrqwsA8NZbb+Htt9/Giy++iMLCQlRVVY3K8bds2YILLrgAALB582asXr16RPZbVVWFqVOnAgBeeOEF3H777SOyXyIioomiqqoKNpsNcrkcRqMReXl5E+raekJ+kr7dWGJ1ZyGiiUWpVCI3Nxe5ubnweDxoamqC0+mETqeLub7H40FdXR0A4OTJkzCbzcjOzkZ6evqETJASERER0SQhCIA2XZqy58Rfz2PvDjTV9b+/gZb3R6WPv6zv8GV9hQNRDbGXr/lp7PlVnwJv3zu4+mJ5pBaQxxiyrP4A8Jcbhr/f1Y/EDjHJVcMPMAFSUCqW4XZMEuRSZ66AN/ZylQ648yNp/7oMQJ0aDh+57Xb8/ZlnAAAzz/k6NKmpw6uBaAQEg0G0tbVh5syZMBqN4e///TEYDLBYLFAqlQmokIiIaHyprq7GwoUL0dLSkuxSiIiIKIFEUYTdLp3HCwQCsNlsyMrKYohpqH70ox8l4jBENEZpNBpMmTKl33UaGxvDz0VRREtLC1paWpCSkoLc3FxkZ2dPqF++REREREQRNKnSlDmj//VWPQzMuVbqyOSw9unwVA84GwHEuXFI3U+IyeuMv2wgclXsQNBY5nXFDhapYt90MWg+d+z54WN1h9p0mVJ3plD4SJcpPWpDz7tfa0z9d0wSBGDK0jOrmWiUtbS04OTJk/B6vTCZTP2um5KSAovFAovFwu5LRERE/fjJT36ClpYWKBQK/O///i9WrlwJvV76vF9YWIi33347uQUSERHRqHC5XFEjmRmNxnBHxomAISYiSjpRFNHU1BRzWWdnJyoqKnDq1ClkZ2cjNzc3bjcnIiIiIqIJLzVXmuIJ+KQgU5eje7IDXU7peX9BI6UOSJsqhZm6HIDfM/ia+uvwNFbF65gU72cRZNIypVYKOql03UPs6SKneMPGqXTAg+VSgCnekH5EE5RSqYTXG6ebWPfyUHDJYDCwGzMREdEgfPzxxwCAq666Cg8//HDU8g0bNmDDhg2jWsPq1as58goREVGCdXR0RLzWaDRQq9UMMSXKvn37sHHjRjz++OPJLoWIRllRUREaGxths9lifvEJBoOwWq2wWq0wmUzIy8uD2WzmyU0iIiIiot7kSsCYP/Ttln5DmkICPinMFAo1hYJQXkf0a1k/Qz2p9VI4atjifN5XagBT4QCbCj1Bo3D4qPu1OsYQdQBgLADu+rh7XW3P+gpNeJi2YdNnntn2RONUamoq9Ho9nM6ejm8ymQwZGRnIyspCWloav9sTEdGIajpwAAeefRbN+/fD63BAZTAgc8ECzL/nHljmz092eSMiNDRraWlpkishIiKiROobYjIajUmqZPSMuRBTfX09Xn75Zbz00ks4cuQIADDERDTBCYKAzMxMZGZmwufzobm5GVarFS5X7Luj29vb0d7eDo1GEx5qTqns58IJERERERENjVwpdQ3Spp/Zfmatk6aRVnQe8MDBkd+vUgMULB75/RJNYF1dXaivr4dWq4XFYolaLggC8vLycOLECTgcDjQ2NuKaa65BWlpaEqolIqLxQBRFBINBBAIBBINBqNXqQQVe63fvxuYHHoB1586oZdbPPsOBZ55B3ooVWP3448hZPL4/84W6HPK8OBER0eQhiuKkCDHJkl0AIA0X9corr2DNmjWYMmUK/vM//xNHjhxhG0qiSUipVCI3NxcLFy7E/PnzkZERZzgGAB6PB5WVlaiurk5ghURERERERESTW+jE6dGjR/Hvf/8bp0+fRnV1ddxzeRaLBTNmzMCRI0fQ0tICuZzDKhIRUWzBYBAulwtutxtdXV3w+XwIBoMDblexaRNeXbkyZoCpt7odO/DqypWo2LRppEpOmA0bNkAQhIhA149//OPwPEEQcPvttwMAbr/9dgiCgKKiopj7Cq3/6KOPAgB2796NG2+8Efn5+VCr1cjLy8Mtt9yCY8eOxa1ny5Yt4f1s2bIl5jplZWX4zne+gzlz5sBgMEClUiE3NxcLFizAnXfeiVdffXVQw9/885//xLp165CdnQ21Wo2pU6fivvvuQ21t7YDbEhERTRQejydquPaJGGJKaiemzZs3Y+PGjXjzzTfDLaVDJztycnJw9dVX49prr01miUSUJIIgwGQywWQywePxwGq1or6+Hn6/P2rd3NzcJFRIRERERERENLkEg0E0NTWhrq4uYng4AHC5XOjo6IDJZIraTiaTQavVJqhKIiIaz2Sy6HvvA4FAvwHY+t278d5XvgK/xzOoY/g9Hrz3la/g+m3bxn1HppHw+9//Hvfff3/EuXer1YqXX34Zb775Jv7xj39g5cqVQ97v66+/jptvvjnqYmt9fT3q6+tx4MABvPDCCzh06BDmzJkTdz+PPPIIHnvssYh5VVVV+MMf/oA33ngDW7duxaxZs4ZcHxER0XjT3t4e8VqpVCIlJSU5xYyihIeYjh8/jo0bN+KVV14JJ6RDwaX8/Hxce+21+MpXvoJzzz13UO1BiWji02g0KC4uRlFRUdTJ0vT09Li/nP1+P0RRZEtdIiIiIiIiojMQurmooaEBPp8v7npWqzVmiImIiGgo5HJ5RKAmEAj0u/7mBx4YdIApxO/xYMv3vocbP/10WDUmw1VXXYVFixYBAObOnQsAuO+++/DNb34zvM5Qh2v98MMPsWvXLsydOxf3338/5s6di87OTrz11lt48skn4Xa7ccstt+DkyZNQqVSD3m9jYyPuuOMOeL1eWCwWfPvb38ayZcuQkZGBzs5OlJeXY+vWrXj77bf73c8f//hH7Ny5E6tWrcI999yD0tJStLe3Y+PGjdi4cSOam5tx55134rPPPhvSz01ERDQe9R1KzmQyTchMTUJCTK2trfjLX/6CjRs3Yu/evQB6gksmkwnt7e0QBAG/+tWvcN111yWiJCIah2QyGbKzs5GVlQW73Y66ujrk5OTEXb+urg6nT5+GxWJBXl4eDAZDAqslIiIiIiIiGr9CQ8bV1dWhpaWl33VlMln4uzcREVFfYjCIztbWqPmBQACe7vluQQh3W/J6vRHdewQAQZ0u5kW6lkOHBhxCLp66HTtQvXkzMvrpAjRcKWYzhBhdpc5EaOSC3iwWS79djAby+eefY+3atXjrrbciQkrnn38+zGYz/vu//xvV1dXYtGkTrr766kHvd9OmTXC5XACATz75JKrGc889F7feeiuefvrpfvezc+dO3H333Xj22Wcj/vtfeOGFUKlUeO655/D5559j3759OPvsswddHxER0XjUN8Q0EYeSA0YxxOTz+fDee+9h48aN+OCDD+Dz+cLBJZVKhbVr1+Lmm2/GZZddNiFbXBHR6BEEAUajsd9fzMFgEFarFaIoorGxEY2NjUhNTUVeXh4yMjJitiUmIiIiIiIimuwCgUC4C3Lo4mM8Go0Gubm5yM7OZhdkIiKKq7O1Fb+3WJJdRkyvfelLo7LfbzY1QZuZOSr7HkkajQYvvPBCzC5L3/3ud/H//t//g9frxfbt24cUYmpoaAAgdYbqL2Q10PXBnJwc/Pa3v40ZYHvwwQfx3HPPAQC2b9/OEBMREU1oXV1d8PTpPMkQ0yB9/vnn2LhxI1577TW0tbUBkO7cEgQBK1aswM0334zrrrtuyC0tiYiGoqWlJWqsbbvdDrvdDpVKhdzcXOTk5AypBS4RERERERHRRHfo0KGouzv7MplMyMvLg9lsnpCt64mIiCaLiy++GJY4ATODwYDp06fjyJEjqKysHNJ+QyMotLW14Z133sGVV145rPq+8pWvQK1Wx1w2Y8YM6PV6OJ3OIddHREQ03vT9ni6Xy6HT6ZJUzega8RDTueeeC0EQwl2XZsyYgZtvvhk33XQTioqKRvpwREQxeTyeiN9FvXm9XlRVVeH06dPIzMxEXl4eUlNTk1AlERERERER0diSlZUVM8QUGuI9Nzd3wp4oJSIimmxmzpzZ7/L09HQAgMPhGNJ+r7jiCphMJrS3t+Pqq6/G6tWrsW7dOqxcuRILFiwIDx14pvWlpaXB6XQOuT4iIqLxJtZQchP1pqJRG07OYDDgqaeewm233TZahyAiimvKlCnIzs5GfX09rFZrVFcmQOoS19TUhKamJhgMBuTl5SFzHLT4JSIiIiIiIjoTgUAg7sVDi8WCyspK+P1+ANIwM3l5ecjOzoZCMWqnEomIiCgJtFptv8tlMhkA6bPDUJjNZrz77ru48cYbUVdXh82bN2Pz5s0AgNTUVFx44YW48847cfnllyelPiIiovEmVohpohqVMw+iKMLpdOLOO+/Ek08+iZtvvhk33nhjuH0kEVEiqFQqFBYWoqCgAC0tLairq4Pdbo+5rsPhwPHjx1FRUQGz2ZzgSomIiIiIiIhGX2dnJ+rq6tDQ0IB58+bF7Eosl8uRnZ0Nl8uFvLw8pKenT9i7O4mIKDFSzGZ8s6kpan4gEEBLSwsAICMjIyJgGwgE0NnZGbG+VqsNh1ZCtj78MI5s2DDs2ubccQdW/vznw94+nhSeY8b555+P8vJyvPHGG3j//fexbds21NbWwm6346233sJbb72FNWvW4M033xwwrERERDSZiaIItVoNj8cTDu4yxDQEW7ZswYYNG/DGG2/A4XBg//79OHDgAH7wgx9g9erVuOWWW3DNNddAr9eP9KGJiGKSyWSwWCywWCxwOBywWq1obGyMOdScz+eDy+VKQpVEREREREREI08URbS3t6O2thY2my08v66uLu7Q6sXFxQwuERHRiBFkMmhjdMAPBALQdJ+j1WZmRoSYRFEEnM6I9dUaDZRKZcS8hffff0YhpnPuvz9mbTQyNBoNbrrpJtx0000AgFOnTmHTpk347W9/i7KyMnz44Yf44Q9/iMcffzzJlRIREY1dgiBg7ty54WZCHR0dMBgMyS5r1MgGXmVoVq5ciT/96U9obGzEK6+8gjVr1kAmkyEQCOBf//oX7rjjDmRnZ+PGG2/E+++/zxaPRJRQBoMBM2bMwPLlyzF16lSo1eqodTikHBEREREREY13oiiira0NBw4cwMGDByMCTADQ3Nwcc+h1AAwwERFR0gmCEDX0aazrSZYFC5B77rnDOkbeihWwzJ8/rG1peKZOnYpvf/vb2L17N/Lz8wEAr732WpKrIiIiGh8EQYDBYEB+fn5Ud8qJZNR+Mo1GgxtvvBH/+Mc/UFNTg1/84hfhdJjb7cZrr72GdevWcYg5IkoKpVKJKVOmYOnSpZg9e3a45Z5Go4l7JyoAdmkiIiIiIiKiMa1veKmjo6Pf9YiIiMaqwYSYAOCCJ56AQqMZ0r4VKSlYze4/SZOamorFixcDQHhIQSIiIiJgFENMvWVnZ+PBBx/E/v37sW/fPjzwwAOwWCwQRREtLS3hu7v+4z/+A/fffz+2b9+eiLKIiCAIAjIzM7FgwQIsXLgQpaWlce84bW9vx549e3Do0CHY7fYEV0pEREREREQUXyiUtH///n7DS3K5HPn5+ViyZAmysrISXCUREdHg9Q0xBYNBaZi5PnIWL8a6v/1t0EEmRUoK1r3+OnK6QzQ08j788EPU19fHXd7R0YFdu3YBkLozEREREYUkvMfU/Pnz8Zvf/Aa1tbX4+9//juuuuw5qtRqiKMJqteLpp5/G6tWrkZOTg29+85v45JNPEl0iEU1Ser0eaWlpcZdXVVUBAGw2G/bt28cwExERERERESVd3/BSvO+parUa06ZNw/Lly1FSUoKUlJQEV0pERDQ0fUNMQPxuTCWXXYbrt21D3ooV/e4zb8UKXL91K0ouu2xEaqTY/vKXv6CwsBCXXXYZnnzySXzyySfYt28ftm3bht///vdYvnw56urqAAD33ntvkqslIiKisUSRrAPL5XKsXbsWa9euhd1ux6uvvoqXXnoJO3bsgCiKaGxsxLPPPov169fD7/cnq0wiIgBSF6a+d7HabDbYbDakp6ejsLCw32HoiIiIiIiIiEaaKIo4cOBA3K5LgBRemjJlCrKzsyGTJfx+RiIiomETBAFyuRyBQAByuRxyuTxuF31A6sh046efounAARxcvx5N+/fD63BAZTDAsmAB5n3jG7DMn5/An2By8/l8eP/99/H+++/HXefee+/Fd7/73QRWRURERGNd0kJMvaWmpuLuu+/G3XffjaqqKrz44ot4+eWXUVFRkezSiIgAAF6vF0qlEj6fL2oZw0xERERERESUDIIgICUlJWaIieElIiKaCDQaDQRB6De81Jdl/nxc9LvfjWJVNJDHH38cF198Mf71r3/h4MGDqK+vR3NzM+RyOQoKCrB8+XJ8/etfx3nnnZfsUomIiMa0o0ePQhRFGI1GGI1G6PX6IX0uGo/GRIipt6KiIvzoRz/Cj370I+zYsQMvvfRSsksiIoLFYoHZbIbVakVNTU2/Yaa0tDQUFRUxzERERERERESjbsqUKWhsbIQoigAYXiIioomFf8tiC/3dj2fDhg3YsGHDsLcP2bJlS9xlq1evjruftLQ03HTTTbjpppsGdZzeioqKBl1fVVXVkPdPREQ0XgSDQbS2tiIYDKKlpQUAcNZZZyEjIyPJlY2uMRdi6m3FihVYMcD4xUREiRK6SyQ3N7ffMFNbWxva2tqQlpaGwsJCGI3GJFRLREREREREE4Eoimhra4PRaIRcLo9anpKSgqysLLS1taGwsBBZWVm84EtEREREREQ0zjkcDgSDwYh5k+G685gOMRERjUVDDTNlZmZi9uzZSaiUiIiIiIiIxqtQeKmqqgoOhwMlJSXIz8+PuW5JSQlkMhnDS0REREREREQTRN+h43U6HZRKZZKqSRyGmIiIhmmwYSadTpeE6oiIiIiIiGg86hteCqmurkZOTk7MbkwKBU/xEREREREREU0kfUNMk6ELE8AQExHRGesvzKRQKJCXl5fkComIiIiIiGisE0URNpsNp0+fjggvhfh8PtTX18ftxkRERDQZiKIIURQhCAIEQUh2OURERESjQhRFhpiIiOjM9A4z1dfXo7q6Gnl5eXHviPV6vejs7Jw0f3CIiIiIiIgo2kDhpRCNRgOVSpXAyoiIiMYGURTh8/kQCAQQCAQgiiK0Wm3M7oREREREE4HL5UIgEIiYN1muKTPEREQ0wuRyOfLz85GTk9PvetXV1airq4PJZEJRUdGk+cNDREREREREQwsvFRYWwmKxQCaTJbBCIiKisUEQBPh8PgSDwfC8QCDAEBMRERFNWH27MGk0GqjV6iRVk1gMMRERjZL+vkR3dXWhvr4eANDe3o79+/fDZDKhsLAQJpMpQRUSERERERFRojG8RERENHRyuTwqxEREREQ0UU3WoeQAhpiIiJKipqYm4ks3IIWZ2tvbGWYiIiIiIiKawMrKytDQ0BB3eSi8lJWVBUEQElgZERHR2CWXy+Hz+cKvQ8PK8W8lERERTTSiKKK9vT1iHkNMREQ0qrRaLVQqFbxeb9QyhpmIiIiIiIgmroyMjJghppSUFEyZMoXhJSIiohj6dr0XRRHBYJBDyhEREdGE09nZGRHeBhhiIiKiUZabm4vs7GzU19ejurq63zBTWloaSkpKoNPpklApERERERERjaT09HTo9Xo4nU4ADC8RERENhkwmgyAIEEUxPC8QCDDERERERBNO36HkVCoVUlJSklRN4jHERESUJDKZDHl5ecjJyek3zNTW1oY9e/YgLy8PhYWFUCqVSaiWiIiIiIiIBqurqwuiKEKj0UQtEwQBRUVFqKioQGFhISwWC8NLREREgyCXy+H3+8OvA4FAEqshmjx6hweJiGj09Q0xGY3GSXXegCEmIqIkG2yYqa6uDo2NjSgtLUVmZmYSKiUiIiIiIqL+BAIB1NTUoKamBmlpaZgzZ07M9dLT05Genj6pTkISEdHEFAoW+f3+Ue+MFCvEJIoi/54SjaJAIBAODLLzGRFRYsQKMU0mDDEREY0Rgwkz+f1+dmIiIiIiIiIaY0RRRFNTE06dOoWuri4AQGtrK2w2G9LT06PW58VWIiKaKLRabfhvX3t7O8xm86gdq2+AQhRFhpiIRll7e3v4uVarTV4hRESTRFdXFzweT8Q8hpiIiCipQmGm7OxsVFdXo6amJtyuNSMjAyaTKbkFEhERERERUZjdbkd5eTkcDkfUssrKSqSlpfHiKhERTVgmkwltbW0AgKamJgQCAaSmpkKtVo/43z+ZTAZBECKGtgoEApDJZCN6HKLJThRFdHV1wW63o7W1NTw/LS0tiVUREU0OSqUSCxYsQEdHBzo6OuB2u6HT6ZJdVkIxxDSK3G43nn76abz++uuoqKhAV1cXCgoKcNlll+G73/0uCgsLz2j/wWAQn376KT744APs3LkTx48fh81mg0ajwZQpU7By5Urce++9mDdvXr/7efTRR/HjH/94UMfcvHkzVq9efUZ1E9HgyOVyTJ06FTk5OaisrERLSwuKi4uTXRYRERERERFBujuysrISTU1NcddJTU1FMBjk0BtERDRhaTQaGI3G8LAnra2taG1thSAIA/79E0Ux3Ine4XAMKvQUDAYRDAbDr2UyGUNMFGE47yuKFBqqsTej0Qi1Wp2kioiIJg+ZTAaj0RjuvjQZu04yxDRKysvLsXbtWpw8eTJi/okTJ3DixAk899xzeOWVV3D55ZcP+xhFRUWoqamJmu/z+XDkyBEcOXIEzz77LB588EE89thjk+7NTTRRaDQazJ49Gx6PBxqNJuY6oiji0KFDMJvNyMnJ4Rd3IiIiIiKiURIIBFBTU4OampqIi6i9mUwmlJSUQK/XJ7g6IiKixMvJyYFKpUJzc3N4niiK8Pv9/W4XDAbhdDoBAAaDYVDnNP1+f8R+BUFgsIIiDOd9Rf3LzMwc1aEiiYgovsmY8WCIaRQ4HA5cdtll4QDT3XffjRtuuAEpKSnYvHkzfvazn8Fut+P666/Hjh07sGDBgmEdx2q1AgCmTZuGa6+9FitWrEBubi46OzuxefNmPP7442hra8MvfvELyOVy/PSnPx1wn4cOHep3+dSpU4dVKxGduXgBJkBq1dzW1oa2tjZYrVaUlJQgPT09gdURERERERFNbKIooqmpCadOnUJXV1fMdTQaDUpKSmA2myfliUYiIpqcBEFARkYGUlNT4XQ64XK54PV644Z9Q/x+f7iDk9FohEIxuEtWbrc74nVKSgqDKhQ23PcV9ZDJZFCpVNDpdNDr9VCpVMkuiYiIJhH+5R4Fv/zlL1FWVgYA+MUvfoGHHnoovGz58uVYvXo1Vq1aBbfbjQceeABbtmwZ1nGWLFmCH/3oR7jkkkuiToydd955+NrXvobly5ejubkZv/zlL/H1r399wKGo5syZM6xaiCh5AoEAKisrw6/dbjcOHTqE9PR0lJSUQKvVJrE6IiIiIiKi8c9ut6O8vBwOhyPmcrlcjsLCQuTl5fEiKhERTVoqlQrp6emDvrnSbrfj3XffBSBdO0lNTR1wG1EUsWPHDgQCgfC8tLQ0WCyW4RVNE85w3ldEREQ0dvCsygjz+Xx46qmnAACzZs3C97///ah1zj33XNx1110AgK1bt2L37t3DOtbOnTuxZs2auHf2lZSU4P/+3/8LQEqev/3228M6DhGNba2treExvnuz2WzYs2cPysvL4fP5klAZERERERHR+Of3+3HgwIG4AaacnBwsWbIEBQUFDDARERGNMkEQYDQaI17H65BIREREROMPz6yMsM2bN4fbVN52221xT17dfvvt4edvvfXWqNVzwQUXhJ9XVFSM2nGIKHksFgvmz58PvV4ftUwURdTV1WHXrl2oq6uDKIpJqJCIiIiIiGj8UigUKCgoiJpvMpmwcOFClJaWcogNIiKiBMrKykJRURHmz5+PFStWxPw7TURERDSeiKKIsrIytLa2TvrruRxOboR9+umn4eerVq2Ku96iRYug1WrhdruxY8eOUaun9x0Icrl81I5DRMllMplwzjnnoLGxEadOnYrqzOT3+1FeXg6r1YqSkpJBt3QmIiIiIiIioKCgAA0NDejq6oJGo0FJSQnMZnPc7thEREQ0ejh0HBEREU007e3tqK+vR319PTQaDXJzcyftkPWT7yceZUePHg0/nzlzZtz1FAoFpk2bBgA4duzYqNWzdevW8PNZs2YNuP4ll1wCi8UClUoFi8WC1atX47HHHkNbW9uo1UhEI0MQBGRnZ2Px4sWYMmVKzJPpbrcbhw4dwqFDh+B2u5NQJRERERER0dhkt9vh9/tjLpPL5SgpKUFxcTEWL16MjIwMBpiIiIiIiIiIaERYrdbwc4/Hg4aGhkl73oGdmEZYbW0tAECn08FkMvW7bkFBAQ4ePIjm5mZ0dXVBrVaPaC1utxtPPPEEAECtVuPKK68ccJt//vOf4efNzc3YunUrtm7dip///OfYsGHDoPYRS+jfJZ76+vrwc5fLBbvdPqzjEI0Ep9MZ8/l4YjabodfrYbVa0d7eHrXcZrPBZrMhOzsbOTk5iS+QhmQivCdp4uD7kcYSvh9prOF7ksYSvh8Hz+v1wmq1oq2tDRaLBXl5eTHXU6vVUKvV/PccJr4naSxxuVzJLoGIiIiIiAiANLpWS0tLxLzc3FyGmGhkOBwOAIBerx9wXZ1OF37udDpHPMT0gx/8ANXV1QCAb33rW8jNzY277ty5c3HVVVdhyZIlyM3Nhc/nw4kTJ/DKK6/go48+Qnt7O6699lq89957+PKXvzzkWoYyJvWbb74Jo9E45GMQjYaXXnop2SWcMYPBgMLCwpi/l3bs2IHm5uYkVEXDNRHekzRx8P1IYwnfjzTW8D1JYwnfj7HJZDLk5OQgNzcXcrkcANDQ0ICPPvoIHo8nydVNbHxPUrJ1dHQkuwQiIiIiIiIAkQ1fAOl8RVZWVpKqST6GmEZY6CSXSqUacN3eoaXOzs4RreOVV17B008/DUAaRu4nP/lJ3HUfeOABPProo1Hzly5diltvvRXPPvss7r33XgQCAXz9619HRUUFNBrNiNZLRKPH4XDg8OHDyMzMREFBQfj3k8vlYoCJiIiIiIgmJbPZjClTpkTdUCaTyTBlyhSUlZUlqTIiIiIaLqfTCa1WC5lMluxSiIiIiAYlGAxGhZiysrKgUEzeKM+k/clHovXWCy+8gNtvvz1iXijc4/V6B9y+q6sr/DwlJeWM6wnZsmUL7rrrLgBAeno63njjjX73P9Cwd/fccw92796N559/HlarFW+88QZuuummIdVUU1PT7/L6+nosWbIEAHDNNdegtLR0SPsnGklOpzN8V+gtt9wyqM5q40UgEEBjYyOampowf/58nHfeeTHXE0Vx0rYoHIsm8nuSxh++H2ks4fuRxhq+J2ks4fsxNpfLhbq6urhDSclkMsyZMwdf+tKX+J1ohPE9SWNJWVkZfvaznyW7DCIaAcFgEM3NzbBarbDb7Zg5c+ak7lxARERE40tra2tUtqS/EbYmg0kbYhotBoMBgHRiZiC9T5iN1ImbPXv24IorrkBXVxf0ej3ef/99zJo164z3e8899+D5558HAGzdunXIIab8/PxBr6vT6ZCamjqk/RONFr1eP+Hej2lpaSguLu63Y9zx48ehUChQWFgIpVKZwOpoIBPxPUnjF9+PNJbw/UhjDd+TNJbw/SjdSHbq1Ck0NjbGXScnJwdFRUWD6q5NZ4bvSUo2nU6X7BKIaIQcO3YMLS0t4ddWq5UhJiIiIho3rFZrxOvU1NRJf9PPpA0xHTt27Iz3kZOTEzUvPz8f//73v+FyudDe3t5vl6NQd6LMzMyo9uXDceTIEVx66aVwOBxQq9V4++23sXTp0jPeLwDMnj07/Lyurm5E9klEydPfSfmOjo7wif3GxkYUFRUhNzeXdyETEREREdG4EwgEUFtbi+rqagSDwZjrmEwmlJSUTPqThEREROORxWKJCDHZ7XY4HI7wDedEREREY1UoU9LbZO/CBEziENPMmTNHZb+zZ8/GG2+8AUDqZLJs2bKY6/n9flRUVADAiHRKqqiowMUXX4zW1s5+6LcAAK7JSURBVFYoFAq8+uqruPDCC894vyEMLxBNDqIohn83AdLvqvLycjQ0NKC0tJRf/omIiIiIaNxoa2tDWVkZPB5PzOUajQYlJSUwm80870FERDROZWRkQKVSRQzDUl9fz/OYRERENObV19dHvFYqlcjMzExSNWOHLNkFTDTnnXde+PnWrVvjrrdnz57wcHIrVqw4o2PW1tbioosuQn19PWQyGV588UVceeWVZ7TPvo4ePRp+zvQf0cTlcrliDofpdDrxxRdfoLKyEoFAIAmVERERERERDZ7D4cDBgwdjBpjkcjmKi4uxePFiZGRkMMBEREQ0jgmCEDVqRmNjI/x+f5IqIiIiIhpYIBBAQ0NDxLzs7GzIZIzw8F9ghK1evRpGoxEA8OKLL0IUxZjrbdiwIfz86quvHvbxmpqacNFFF6GqqgoA8Ic//AFf+9rXhr2/eJ599tnw81WrVo34/olobNDr9Vi8eHHclG9NTQ327NmDtra2BFdGREREREQ0eAaDARkZGVHzc3JysGTJEhQUFPDEIBER0QSRk5MTEUoOBoNRFwWJiIiIxpLGxsaoxhFsJiPh2ZoRplKp8N3vfhcAcOzYMfzqV7+KWuezzz7D888/D0AKBC1evDjmvgRBgCAIKCoqirm8vb0da9aswYkTJwAAjz/+OO6+++4h1Xvo0CGUl5f3u8769evx3HPPAZDSf2cSuiKisS8lJQWzZ8/G/PnzodVqo5Z7PB4cPHgQJ06cgM/nS0KFREREREREA5s2bRrkcjkAIDU1FQsXLkRpaSlUKlWSKyMiIqKRpFaro8LLVqs17k3mRERERMkkiiKsVmvEvPT0dGg0miRVNLYokl3ARPTQQw/h1VdfRVlZGR5++GGUl5fjhhtuQEpKCjZv3oyf/vSn8Pv9SElJwRNPPDGsY3R1deGyyy7D/v37AQA33XQTLrroIhw+fDjuNjqdDlOnTo2Yt3fvXnz961/HBRdcgC9/+cuYO3cuzGYz/H4/jh8/jldeeQUfffQRAKnd+vr166HT6YZVMxGNLyaTCQsXLkR1dTWqq6ujvvQ3NDSgtbUV06dP5xAMRERERESUFKHvKbG+j6jVakybNg2BQAC5ubn8zkJERDSB5ebmorm5Ofy6s7MT7e3tSEtLS2JVRERERNHsdjtcLlfEPHZh6sEQ0ygwGAzYtGkT1q5di5MnT2L9+vVYv359xDqpqal45ZVXsGDBgmEdo76+Hjt37gy/fuWVV/DKK6/0u82qVauwZcuWqPmBQAAff/wxPv7447jbms1mPP/881i3bt2w6iWi8Ukmk6GoqAiZmZkoKyuD3W6PWO7z+XD06FGYzWZMnz4darU6SZUSEREREdFk43a7ceLECeTn58cdEjs7OzvBVREREVEyGI1GaLVauN3u8Dyr1coQExEREY05Xq8XKpUKXq8XAKDRaJCenp7kqsYOhphGybRp07Bv3z787ne/w+uvv47y8nJ4vV4UFBRg7dq1uP/++1FYWJjsMrF27Vo8//zz+Oyzz7Bv3z40NjaitbUVoigiPT0d8+fPx6WXXorbb78dqampyS6XiJJEp9NhwYIFsFqtOHXqVNQYra2trVCpVCgtLU1ShURERERENFkEg0HU1NTg9OnTEEURJ0+ehMlkglKpTHZpRERElCSCICA3Nxfl5eXheS0tLejq6uKNl0RERDSmZGZmwmw2o7W1FVarFenp6ewe3QtDTKNIp9Ph4YcfxsMPPzys7fsbr7moqGhExnO2WCy48847ceedd57xvohoYhMEAXl5eTCbzTh58iRsNlt4mVKpjBqukoiIiIiIaKTZ7XaUlZVFtF33+XyorKzEjBkzklgZERERJVtWVhYqKysRDAbD8+rr61FUVJS8ooiIiIhikMlkyMzMRGZm5ojkPiYSWbILICKi8UWj0WDOnDmYNWtW+E7nadOm8a5nIiIiIiIaNYFAAOXl5di3b19EgCmkvb0dfr8/CZURERHRWKFQKJCVlRUxr76+PiLURERERDTWsAtTJHZiIiKiIRMEARaLBWlpaWhsbERmZmbcdf1+PxQK/rkhIiIiIqLhsdlsKCsrQ1dXV8zl+fn5KCoqglwuT3BlRERENNbk5uaivr4+/Nrr9aK1tbXf85dERERENHbwqjIREQ2bUqlEfn5+3OVerxe7d+9GVlYWpk6dyosKREREREQ0aD6fDxUVFWhsbIy5XKfTYcaMGTAYDAmujIiIiMYqvV6P1NRU2O328Lz6+nqGmIiIiIjGCYaYiIho1JSXl8Pv96Ourg6tra2YPn060tPTk10WERERERGNYaIooqmpCRUVFfD5fFHLBUFAUVER8vPzIZPJklAhERERjWW5ubmw2+1ISUlBbm4usrOzk10SERERTXJerxfBYBAajSbZpYx5DDEREdGoaGlpQXNzc/i1x+PBoUOHkJWVhZKSEiiVyiRWR0REREREY5HH48HJkydhs9liLjcajSgtLYVWq01wZURERDReZGZmQqVSwWQyQRCEZJdDREREhJqaGtTW1iI9PR25ublIT0/n55Q4GGIiIqJR4fV6IQgCRFGMmN/Y2AibzYaSkhJYLBb+gSYiIiIiIgBSB6ajR4/C4XBELZPL5SgpKUF2dja/QxAREVG/ZDIZ0tLSkl0GEREREQAgEAigoaEBAGCz2WCz2VBYWIiioqLkFjZGsec2ERGNitzcXCxatAhGozFqmc/nw/Hjx3H48GF4PJ4kVEdERERERGONIAgoKSmJmp+RkYHFixcjJyeHASYiIiIiIiIiGleam5vh9/sj5lksliRVM/YxxERERKNGq9Vi/vz5KC0thVwuj1pus9mwe/du1NXVRXVsIiIiIiKiycdoNCI3NxcAoFKpMHv2bJx11llQq9VJroyIiIiIiIiIaOisVmvE67S0NGi12iRVM/ZxODkiIhpVgiAgJycH6enpKC8vR0tLS8TyYDCI8vJyNDY2YsaMGdDpdEmqlIiIiIiIEiUYDEImi31v3dSpUyGTyVBYWAiFgqeuiIiIiIiIiGh8cjgccDgcEfNCN29RbOzERERECaFWq3HWWWdh9uzZUKlUUcsdDgf27t2LqqoqBIPBJFRIRERERESjze/34+TJkzhw4EDcbqwKhQIlJSUMMBEREdGIcTqdKCsrQ1lZWbJLISIiokmkbxcmtVoNs9mcpGrGB54NIiKihMrMzERaWhoqKytRX18fsUwURVRXVyMjIwN6vT5JFRIRERER0WhobW3FyZMn0dXVBQCoq6tDfn5+kqsiIiKiiczlcqGsrAx2ux2A1DW+qKgo5k2WRERERCPJ5/OhqakpYl5OTg4EQUhSReMDOzEREVHCKRQKlJaWYv78+UhJSYlYVlBQwAATEREREdEE4vV6cfToURw+fDgcYAKAU6dOwePxJLEyIiIimuhUKlXEEC6iKKKhoSGJFREREdFk0djYGDH6jCAIyMnJSWJF4wNDTERElDQmkwkLFy5EQUEBACAlJQWFhYVJroqIiIiIiEZC6CLh7t270dzcHHMdp9OZ4KqIiIhoMlEqlbBYLBHzrFZr3GFtiYiIiEaCKIpRQ8llZGSwG+QgcDg5IiJKKrlcjuLiYlgsFgSDQchksfO1oigiGAxCLpcnuEIiIiIiIhoqr9eLEydOwGazxVyelpaG6dOnR3VmJSIiIhppubm5aGxsDL/u6upCa2srMjIyklgVERERTWRtbW3o7OyMmJebm5ukasYXhpiIiGhMGGgIOavVitraWsycORNGozFBVRERERER0VC1tLSgrKwMPp8vaplCoUBJSQmysrIgCEISqiMiIqLJxmAwQK/XR3SAtFqtDDERERHRqOnbhUmr1fL65iBxODkiIhrzXC4XKisr4fF4sH//fpw6dSpiDFkiIiIiIkq+QCCAsrIyHDlyJGaAKTMzE4sXL0Z2djYDTERERJQwgiBEdT6I1R2BiIiIaCR4PB60trZGzMvNzeW5kEFiiImIiMa0YDCI48ePR4SWqqursX//frjd7iRWRkREREREIXa7HXv37kV9fX3UMpVKhTlz5mD27NlQqVRJqI6IiIgmO4vFAoUicnCSvh0SiIiIiEZC33MjcrkcWVlZSapm/GGIiYiIxjS/3w+5XB413+FwYO/evbBarRBFMQmVERERERERALS3t2Pfvn0xuxmYzWYsWrQIZrM5CZURERERSWJdPGxoaEAgEEhSRURERDQRiaKIhoaGiHlZWVlRYWqKjyEmIiIa01QqFebPn4+pU6dGtVkMBoM4efIkDh8+DK/Xm6QKiYiIiIgmN6PRCIPBEDFPJpOhtLQUZ511FpRKZZIqIyIiIurRd0g5v9+P5ubmJFVDREREE5EgCDjnnHNQWFgY7kbd9zMI9Y8hJiIiGvMEQcCUKVNw9tlnQ6vVRi232WzYs2cPWlpaklAdEREREdHkJggCZs2aBZlMOs1kMBiwaNEi5OTkRN2IQERERJQsWq0WaWlpEfM4pBwRERGNNLVajaKiIixduhTz5s2DTqdLdknjCkNMREQ0bhgMBpxzzjkxE8s+nw9HjhxBWVkZ20ATERERESVYSkoKpk+fjsLCQpx99tlISUlJdklEREREUfqeV3Q4HHA4HEmqhoiIiCYymUwWFaCmgTHERERE44pcLsf06dMxZ86cmMNS1NfXY+/evbDb7UmojoiIiIho4rLZbP1e5MvOzkZRURG7LxEREdGYZTaboVarI+axGxMRERHR2MEQExERjUtmsxmLFi2C2WyOWtbZ2Yn9+/ejoaEhCZUREREREU0sgUAA5eXlOHToEI4fP87Op0RERDRuCYKAnJyciHlNTU3w+XxJqoiIiIiIemOIiYiIxi2VSoWzzjoLpaWlkMki/6QJgoDU1NQkVUZERERENDE4nU588cUXqKurAwC43W5UVlYmuSoiIiKi4cvJyQl3jpTJZLBYLAgGg0muioiIiIgAQJHsAoiIiM5E6O4po9GI48ePh4e3mDZtGrRabZKrIyIiIiIan0RRRG1tLU6dOgVRFCOWWa1WZGVl8aYBIiIiGpdUKhXy8/OhUqmQnZ0NhYKXyoiIiGj4RFHEwYMHYTAYkJubC41Gk+ySxjV+MiMioglBq9ViwYIFqK6uhsvlQnZ2drJLIiIiIiIalzweD44fP46Ojo6oZTKZDMXFxTAYDEmojIiIiGhkFBcXJ7sEIiIimiA6OjrQ3t6O9vZ21NTUwGw2Y8aMGVAqlckubVxiiImIiCYMmUyGoqIiiKIYbgndVyAQgMPhgMlkSmxxRERERETjQFNTE8rKyhAIBKKW6fV6zJw5EzqdLgmVERERERERERGNLaIoorq6OmKe2+1mp8czwH85IiKacOIFmADg1KlTqKurQ15eHoqLiyGTyRJYGRERERHR2OT3+3Hy5Ek0NTXFXF5QUICioiJ+fiYiIiIiIiIi6tbS0oK2traIeTk5Of1eq6T+McRERESThs1mQ11dHQCgrq4ObW1tmDVrFvR6fZIrIyIiIiJKnvb2dhw/fhxdXV1Ry9RqNWbOnMlOpkRERDRp9NflnYiIiCjE7/ejvLw8Yp5KpUJOTk6SKpoYGGIiIqJJwefz4cSJExHz3G43vvjiC0ydOhX5+fk8OUFEREREk0owGERVVRVqampiLrdYLJg+fTpboBMREdGk4PF4UF5eDrVajenTpye7HCIiIhrjqqqq4PV6I+aVlJTwPMoZ4r8eERFNCgqFAlOmTEFlZSWCwWB4viiKqKyshM1mw4wZM6DRaJJYJRERERFR4thstpgBJoVCgenTp8NisSShKiIiIqLECgaDqKurQ1VVVfi8YVZWFlJTU5NcGREREY1VTqczPPpLSFpaGjIzM5NU0cQhS3YBREREiSAIAvLy8nDOOefEHD6uvb0de/fuRVNTUxKqIyIiIiJKvIyMjKigksn0/9m78+BIr/re/59e1Iuk1r7v+0iafQ1miU1YAgZD7JuA8Q1h4hBM4FbglmObuqkk8CPBZUMlJFSosgsHJ8RAFpaCmAQDGXyvjY3t2Tyjfd/3tbu19Pb8/nDUmXa3NJukpyW9X1VTbp1z+nk+Gp9ptZ7+Pudk6fjx4xQwAQDwX5aWlvToo4/q5MmTysnJUVpamhobG3X//fdrYGDgpo/f398vi8VyTX9Onz59898Q4gQCgZgCJknq6uqSYRgmpgIAAMnKMAx1dXXFtFksFtXX17PryyagiAkAsKekpaXp6NGjKi8vj+sLhUJqa2tTW1ubQqGQCekAAACA7VVfXy+n0ymLxaKamhodOnSI1UkBAPgv3d3dOnLkiB566CG98sormpub09LSkjo6OvSXf/mXOnTokP7t3/7N7Ji4SS6XS5WVlTFtPp9Po6OjJiUCAADJbHx8XIuLizFtFRUVcrvdJiXaXdhODgCw51itVtXU1CgnJ0ft7e1aXV2N6Z+cnNTCwoIaGxuVlZVlTkgAAABgG9jtdjU1NclmsyVcsRQAgL3K6/XqPe95T/Qu+9///d/X3XffLbfbrTNnzujhhx/W4uKiPvjBD+r555/XkSNHbvqcf/7nf673v//96/ZnZ2ff9DmQWFlZmSYmJrS0tBRt6+vrU15enpxOp4nJAABAMgkEAurt7Y1pc7vdqqioMCnR7kMREwBgz8rKytKJEyfU1dUVt43c6uqqLl68qPLyclVVVclqZfFCAAAA7DxWq1WDg4MqLCxUfn5+wjGZmZnbnAoAgOT3xS9+UZ2dnZKkRx99VA888EC075ZbbtFtt92mW2+9VUtLS/r0pz+tn//85zd9ztLSUh04cOCmj4PrZ7VaVV9fr4sXL0bbwuGwent71dTUZGIyAACQTPr6+uJ2c6mrq+NzxE3E3yQAYE9bu/O8sbFRNpstrn9oaEgDAwMmJAMAAABuTnp6ug4dOqSZmRl1dnbGrUAKAAASCwaD+pu/+RtJUlNTk+6///64MW984xv1e7/3e5KkZ599Vi+//PK2ZsTmy8rKUmFhYUzb5OSk5ubmTEoEAACSycLCgsbHx2Pa8vPzlZOTY1Ki3YkiJgAAJBUWFurEiRNxd6E7nU6VlZWZlAoAAAC4foZhaHx8XPv375fL5ZIkhUIhdXR0yDAMk9MBAJD8zpw5o4WFBUnSRz7ykXXvrD99+nT08fe+973tiIYtVlNTI7s9dhOTrq4uRSIRkxIBAIBkEIlEotsMr7HZbKqtrTUp0e5FERMAAP/F5XLp8OHDqq6ulsVikSTt27dPKSkpJicDAAAArk0gENClS5c0NjYWfU+7ZnFxUUtLSyYlAwBg53juueeij2+99dZ1x504cUKpqamSpOeff37Lc2HrORwOVVdXx7QtLy9raGjIpEQAACBZFBQUxBS3V1VVyel0mphod6KICQCAK1gsFlVUVOjo0aOqra1Vdna22ZEAAACAazI/P6+zZ88m3PIkIyNDx48fV1pamgnJAADYWVpbW6OPGxsb1x1nt9tVV1cnSWpra7vp837lK19RXV2dXC6XMjMztX//fn384x/XuXPnbvrYuHbFxcXyeDwxbQMDA1peXjYpEQAAMJvValVFRYVOnjyp3Nxcpaenq7S01OxYu5L96kMAANh7PB5P3MWKKwWDQc3NzamgoGAbUwEAAADxDMPQ0NCQ+vr6EvaVlJSovr4+bmUmAACQ2PDwsCQpLS1NWVlZG44tLy/Xq6++qqmpKa2urt7U3fhXFiutrq6qtbVVra2teuyxx3Tffffpr//6r2/o+Gvfz3rGxsaij71erxYXF6/7HDfC5/MlfJwMSkpK1NHREf3aMAy1tbWptraW91RJLpnnFXYm5hQ2G3Nq56uoqFA4HJbX6zU7SpRZ82or/g4oYgIA4DoZhqGOjg7NzMxodnZW9fX1stlsZscCAADAHhQMBtXe3q7Z2dm4vkAgoK6uLh07dowP2wAAuA5rH8akp6dfdeyVqxz6fL4bKjLKysrSnXfeqdtuu0319fVyuVwaGxvTM888oyeeeEI+n0+PPfaYvF6vnnrqqes+fnl5+TWP/cY3vqHMzMzrPsfN+sY3vrHt57yayspKFRcXR7/2er3653/+54Tvu5CcknFeYWdjTmGzMaewFbZzXi0sLGz6MSliAgDgOg0PD2tmZkaSNDExIa/Xq+bmZrbmAAAAwLZaWFhQW1ubVldX4/o8Ho9+/vOfKxgMmpAMAICdbWVlRZLkcDiuOvbKoqUb2W6spKREIyMjSk1NjWk/evSobr/9dn3yk5/U29/+dg0ODuqb3/ymPvjBD+p973vfdZ8H1294eFi5ubkx86CyslLz8/OKRCImJgMAANi9KGICAOA6LC8vx23TsbS0pHPnzqmhoUGFhYUmJQMAAMBeYRiGhoeH1dvbm7C/srJS2dnZ+slPfrLNyQAA2F6bsdLg17/+dZ0+fTqmzeVySXptVcOrubKY2O12X/f5HQ7HhsVS9fX1+sd//Ef96q/+qiTpK1/5ynUXMQ0NDW3YPzY2plOnTkmSPvzhD6u0tPS6jn+jfD5fdKWAD3/4w9e08tV2m5ubU39/v6TX5ltlZaVOnTolq9VqbjCsayfMK+wszClsNubUzhEMBpWSkmJ2jGti1rwaGRnRww8/vKnHpIgJAIDr4Ha71djYqM7OToXD4Wh7JBJRe3u75ufnVVdXx/ZyAAAA2DI+ny9hAVNKSoqampqUnZ2txcVFE5IBALA7eDweSa/9zL0av98ffbxVHxa95S1vUXNzs1pbW/Xcc88pEolcVxFNWVnZNY/1eDzKyMi4kZg3JT093ZTzXo3H44luk1JXVxe3YhaSW7LOK+xczClsNuZU8vL7/bp48aKKi4tVVVUlu33nlNZs57zaiutPO+dvGgCAJFFQUKD09HS1trbGXKiSpPHx8ej2clzUAAAAwFbweDyqqKjQ4OBgtC0rK0tNTU3XtO0NAAC7RVtb200fo7i4OK6trKxMv/zlL+X3+zU/P6+srKx1n7+2ylF+fn7M1nKbba2IaWVlRTMzM8rPz9+yc+G/WSwWNTc3y2azbcrKXwAAIPkZhqGuri5FIhGNjIxoampKdXV1vP/aJhQxAQBwA1JTU3X06FH19PRobGwsps/v9+vs2bNsLwcAAIAtU1VVpYWFBS0sLKiyslKVlZV8sAYA2HMaGxu35LjNzc36zne+I0lqb2/XG97whoTjQqGQenp6JElNTU1bkmUNP+fNs5NWXgAAADdvcnIyuhKj9NoWw16vlyKmbcKmvQAA3CCbzaaGhgY1NjbGLeG9tr3c67edAwAAADaDxWJRU1OTDh06pKqqKj7YBABgE735zW+OPn722WfXHffKK69EV+l+05vetKWZWltbJUlOp1O5ublbei4AAIC9KhgMRovU1zidTlVWVpqUaO+hiAkAgJtUWFio48ePKy0tLa5vbGxM58+f19LSkgnJAAAAsJMtLi5qcnJy3X6n06ns7OxtTAQAwN5w2223KTMzU5L093//9zIMI+G4J598Mvr4zjvv3LI8zz//vFpaWiS9VmD1+pvpYJ5AIGB2BAAAsIn6+/sVDAZj2urq6mSz2UxKtPfwThcAgE2wtr1cUVFRXJ/f79e5c+e0uLhoQjIAAADsNIZhaHh4WBcuXFB7e7u8Xq/ZkQAA2FMcDof+8A//UJLU1tamL33pS3FjXnjhBT3xxBOSpFtvvVUnT55MeCyLxSKLxaKqqqqE/d///vfXLZKSpO7ubt1zzz3Rrz/xiU9c67eBLRQOh9Xb26sXX3wxZrsZAACwcy0uLmp0dDSmLTc3V3l5eSYl2pvYyBcAgE1is9m0b98+ZWVlqbOzU5FIJNrndruVnp5uYjoAAADsBKFQSB0dHZqeno62tbW16dixY7LbuYwDAMB2eeCBB/RP//RP6uzs1IMPPqju7m7dfffdcrvdOnPmjL7whS8oFArJ7Xbry1/+8g2f584771RdXZ3uuusunTp1SmVlZXI6nRobG9OPf/xjPfHEE/L5fJKkD3zgA7rrrrs26TvEjZqdnVVXV5dWVlYkSV1dXTp27BgrZAEAsIMZhqGurq6YNqvVqrq6OpMS7V1c/QIAYJMVFhYqPT1dra2tWlpaks1mU3NzMxcyAAAAsCGv16vW1tboB2JrlpeXNTw8vO4KDgAAYPN5PB49/fTTuv3229XV1aXHH39cjz/+eMyYjIwMPfXUUzpy5MhNnau7u1uPPvrohmP+4A/+QH/1V391U+fB5lhaWop5v+b3+zUyMqLy8nITUwEAgJsxOjoaLRxfU1lZKZfLZVKivYsiJgAAtkBaWpqOHTumrq4u5ebmyu12mx0JAAAAScowDI2OjqqnpyfhdjLl5eWqqKgwIRkAAHtbXV2dzp8/r7/927/Vv/zLv6i7u1uBQEDl5eW6/fbb9alPfUqVlZU3dY4f/OAHeuGFF/TLX/5SAwMDmp6elt/vV0ZGhmpqavSWt7xF9957rw4cOLBJ3xVuVmlpqSYmJmI+6Ozv71d+fj4fdAIAsAOtrq6qr68vpi01NVVlZWUmJdrbKGICAGCL2Gw2NTY2bjhmdXVVKSkprNIEAACwR4VCIXV2dmpqaiquz263q7GxUbm5uSYkAwAA0ms3qj344IN68MEHb+j5iQqUr3THHXfojjvuuKFjwxwWi0X19fU6f/58tC0Siainp0f79+83MRkAALgRvb29CofDMW319fV8dmcSipgAADBJJBLR5cuXZbFY1NTUxGpNAAAAe8x628dJr21P09TUxN38AAAASSgjI0PFxcUaGxuLtk1PT2tmZoYCdAAAdpC5uTlNTk7GtBUWFiorK8ucQBClYwAAmKS7u1s+n09er1dnz57V9PS02ZEAAACwDda2jzt//nzCAqaysjIdPnyYAiYAAIAkVl1drZSUlJi27u7uuJUcAABAcopEIurq6opps9vtqqmpMSkRJIqYAAAwxeTkZMydWuFwWC0tLeru7lYkEjExGQAAALZSKBRSW1uburq64raXsdvt2r9/v2pra1myHAAAIMmlpKTEfci5srKiwcFBkxIBAIDrMTw8rOXl5Zi26upqORwOkxJBoogJAABTpKamJtw+bmRkRBcuXEh4Rz4AAAB2NsMwdPHiRU1NTcX1eTweHT9+XHl5eSYkAwAAwI0oLCxUZmZmTNvQ0JD8fr9JiQAAwLUqKipSYWFh9GuPx6Pi4mITE0GiiAkAAFOkp6fr2LFjKigoiOtjezkAAIDdyWKxqLS0NK69tLRUR44cYfs4AACAHcZisai+vl4WiyXaZhiGuru741bdBAAAycXhcKixsVGHDx9WWlpa3M90mIMiJgAATGK329XY2JjwTVEoFFJLS4t6enq44AEAALCLFBUVqaioSJJks9m0f/9+1dXVsX0cAADADpWWlqaysrKYtvn5eU1OTpqUCAAAXI+srCwdP35cHo/H7CiQZDc7AAAAe5nFYlFJSYkyMjLU2toat/fu8PCwZmdn5XA4FAgETEoJAACAzVRXVyfDMFRZWZlwi2EAAADsLJWVlZqcnNTq6mq0raenR7m5ubLb+SgOAIBkxwpMyYPb/AAASAJr28vl5+fH9S0tLengwYPKysra/mAAAAC4IV6vd90+m82mxsZGCpgAAAB2CZvNprq6upi2YDCo/v5+cwIBAADsUBQxAQCQJOx2u5qamhJuL5eSkqLGxkaNjo6yvRwAAEASi0Qi6ujo0Llz5zQ9PW12HAAAAGyTvLw85ebmRr/Oz89XeXm5iYkAAMCVlpeX2fVkB2ANSwAAksja9nIej0etra1aWVmJ6V9aWjIpGQAAAK5mZWVFra2t0VWYOjo6lJ6eLpfLZXIyAAAAbIe6ujqtrKyopqZGOTk5ZscBAAD/xTAMtbe3a2lpSdXV1SouLmYLuSTFSkwAACQhj8ej48ePKy8vL9q2srKiqqoq3lQBAAAkobm5OZ07dy5mG7lQKKTW1lZW0gQAANgjXC6Xjh8/TgETAABJZnx8XIuLiwqFQurq6tL58+fjFhJAcqCICQCAJGW329Xc3KzS0lKFQiF1dnbKbmcRRQAAgGRiGIYGBwf16quvKhgMxvRZrVaVlZVRhA4AALCH8N4PAIDkEgwG1dvbG9fmcDhMSoSN8EkoAABJzGKxqKCgQN///vcVDofNjgMAAIArhEIhdXR0aHp6Oq7P7XZr//79SktLMyEZAAAAAAAAJKm3t1ehUCimrb6+XlYra/4kI/6vAACwA2xUwBQMBtXa2qrV1dVtTAQAALC3LS0t6fz58wkLmHJzc3Xs2DEKmAAAABC1sLCg8fFxs2MAALCnjI+Px/38zc/PZ+vXJMZKTAAA7GCGYaitrU1zc3Oan59Xc3OzsrKyzI4FAACwq01NTamjoyNhoXlVVZUqKirYRgQAAABRExMT6ujokGEYcjgcfHAKAMA2mJ+fV2dnZ0ybzWZTbW2tSYlwLViJCQCAHay/v19zc3OSXluR6eLFixoeHpZhGCYnAwAA2H0Mw1Bvb69aW1vjCpjsdrsOHjyoyspKCpgAAAAQ1d/fr/b29uj1utbWVvn9fpNTAQCwuy0tLamlpSXu87La2lo5nU6TUuFaUMQEAMAOFQ6HNTk5Gdfe09Oj9vb2DbegAwAAwPUJBoN69dVXNTQ0FNeXnp6uY8eOcUc9AAAA4rz+w9NwOKxLly4pEAiYlAgAgN0tGAzq0qVLCoVCMe1lZWUqLi42KRWuFUVMAADsUDabTceOHVN2dnZc3+TkpM6fP6/l5WUTkgEAAOw+Pp9P8/Pzce2FhYU6cuSI3G739ocCAABA0quqqlJ+fn5M2+rqqi5fvsxNiAAAbLJIJKLLly9rZWUlpj03N1c1NTUmpcL1oIgJAIAdLCUlRQcPHlRFRUVcn9/v19mzZzUzM2NCMgAAgN0lOztbVVVV0a8tFovq6uq0b98+2Ww284IBAAAgqVksFu3bt08ejyem3ev1xmwzBwAAbo5hGOro6NDi4mJMe3p6upqammSxWExKhutBERMAADucxWJRdXW19u/fH/cBWjgc1uXLl9Xf388FEQAAgJtUUVGh3NxcORwOHT58WKWlpVwAAwAAwFXZbDYdOHBALpcrpn16elp9fX0mpQIAYHcZGBjQ5ORkTJvT6dSBAwe4AW0HoYgJAIBdIi8vT8eOHVNaWlpc38DAgC5fvqxgMGhCMgAAgN3BYrGosbFRx48fV2ZmptlxAAAAsIM4HI6EH6IODQ1pbGzMpFQAAOwOy8vLGhwcjGlbKyJ2Op0mpcKNoIgJAIBdJDU1VUePHlV+fn5c3+zsrM6dOyefz2dCMgAAgJ1hbm5O4+Pj6/bb7XY5HI5tTAQAAIDdIi0tTfv3749bzbOrq0tzc3MmpQIAYOdzu906ePCg7HZ7tK2pqUnp6ekmpsKNoIhpCy0tLenRRx/VyZMnlZOTo7S0NDU2Nur+++/XwMDATR+/v79fFovlmv6cPn36mo75rW99S+985ztVVFQkl8ulyspK/fZv/7ZeeOGFm84LANgeNptNTU1Nqq2tjetbWVnR+fPntbCwYEIyAACA5GUYhoaGhvTqq6+qs7NTi4uLZkcCAADALpSdna36+vqYNsMw1NLSIr/fb1IqAAB2vuzsbB09elQul0t1dXXKzc01OxJuAEVMW6S7u1tHjhzRQw89pFdeeUVzc3NaWlpSR0eH/vIv/1KHDh3Sv/3bv5kdM2p5eVnvec97dM899+gnP/mJJiYmtLq6qsHBQT311FN685vfrM997nNmxwQAXCOLxaKysjIdPnxYKSkpMX1paWnyeDwmJQMAAEg+oVBIbW1t6u3tlfTfHyIFAgGTkwEAAGA3Ki4uVllZWUxbOBzW5cuXeQ8KAMBNSE1N1YkTJ1RaWmp2FNwg+9WH4Hp5vV695z3vUVdXlyTp93//93X33XfL7XbrzJkzevjhh7W4uKgPfvCDev7553XkyJGbPuef//mf6/3vf/+6/dnZ2Rs+/95779WPfvQjSdJb3/pWfepTn1JJSYkuXbqkL3zhC+rp6dFnP/tZFRcX62Mf+9hN5wUAbI+srCwdP35cLS0t8nq9SklJ0f79+2W1UscMAAAgvbaKcktLi5aWlmLaA4GARkdHVVVVZU4wAAAA7Go1NTVaWVnR9PR0tG1lZUUtLS06fPgw1+8AALhBNpvN7Ai4CRQxbYEvfvGL6uzslCQ9+uijeuCBB6J9t9xyi2677TbdeuutWlpa0qc//Wn9/Oc/v+lzlpaW6sCBAzf03P/8z//Ut7/9bUnSHXfcoe9973vRf9gnT57U+973Ph0/flyDg4N66KGH9Fu/9VtXLYoCACQPp9OpI0eOqKenR/n5+XI6nWZHAgAASArT09Nqb29XOByO66uqqlJFRYUJqQAAALAXWCwWNTY26uLFi/J6vdH2zMxMWSwWE5MBAJDcDMOQ3+9Xenq62VGwBSjj3mTBYFB/8zd/I0lqamrS/fffHzfmjW98o37v935PkvTss8/q5Zdf3taMr/elL31JkmS32/XVr341rjIxLy9PjzzyiCRpfn5eX/va17Y9IwDg5litVtXX1ysrK2vdMaFQSIZhbF8oAAAAkxiGod7eXrW0tMQVMNntdh04cECVlZV8eAQAAIAtZbPZtH//fjmdTlksFjU0NKimpob3oQAAbGBoaEhnz57VyMiI2VGwBShi2mRnzpzRwsKCJOkjH/nIust9nj59Ovr4e9/73nZES8jr9epnP/uZJOntb3973B7Ma+666y5lZGRIMjcvAGBrhMNhXbhwYd2VCAAAAHaLYDCoS5cuaWhoKK4vLS1Nx44dU25urgnJAAAAsBc5nU4dOHBABw8eVHFxsdlxAABIapOTk+rr65MkdXd3q7u7mxv0dxmKmDbZc889F3186623rjvuxIkTSk1NlSQ9//zzW55rPS+//LICgYCkjfM6HA694Q1viD4nGAxuSz4AwNYzDEOdnZ3y+/2anJzU+fPntby8bHYsAACATef1enX27FnNzc3F9RUWFuro0aNyu90mJAMAAMBelp6eruzsbLNjAACQ1BYXF9Xe3h7TNjIyosXFRZMSYSvYzQ6w27S2tkYfNzY2rjvObrerrq5Or776qtra2m76vF/5ylf053/+5xoeHpbT6VRZWZne8pa36GMf+5iOHTt203nX+p955hmFQiF1dXWpubn5mvMNDw9v2D82NhZ97Pf7eaGBqXw+X8LHgFm2ek5OTk5qcnIy+rXf79fZs2dVWVmpzMzMTT8fdjZeI5FMmI9INszJ5DYzM6OhoaGEd+eVlZUpLy9Pfr/fhGRbg/mIZMOcRDLZTa/3AAAAwF6wvLysy5cvx13Xqamp4bOsXYYipk22VqyTlpamrKysDceWl5fr1Vdf1dTUlFZXV+V0Om/4vOfOnYs+Xl1dVWtrq1pbW/XYY4/pvvvu01//9V8nPP6VxUXrbSV3Zd41Q0ND11XEdOVzr+a73/0uLzRIGt/4xjfMjgDE2Io5mZ2drdraWtnt//22IBwOq7e3V0NDQ+wpjHXxGolkwnxEsmFOJpfKysqEW3MEAgF1dnbqxRdfNCHV9mE+ItkwJ2G2hYUFsyMAwDXz+Xzq6elRc3OzUlJSzI4DAMC2C4VCunz5ctxuUcXFxVetccDOQxHTJvN6vZJeW/rzatLS0qKPfT7fDRUxZWVl6c4779Rtt92m+vp6uVwujY2N6ZlnntETTzwhn8+nxx57TF6vV0899dS6ea8l8+vzAgB2h7m5OV2+fFkNDQ3RrU7XlJeXKz09Xd3d3QqHwyYlBAAAuDmJfoddXFxUV1cX26UDAAAgac3Ozqq1tVXhcFgtLS06dOiQrFar2bEAANg2kUhEra2tWlpaimnPzs5WfX29LBaLScmwVShi2mQrKyuSJIfDcdWxVxYtLS8vX/e5SkpKNDIyEveB89GjR3X77bfrk5/8pN7+9rdrcHBQ3/zmN/XBD35Q73vf+xLmvZbMN5N3aGhow/6xsTGdOnVKknTXXXepoaHhuo4PbCafzxe9K/TDH/7wNRUlAltpu+ZkOBzW4OCg5ufnY9qzs7P1pje9STU1NXK73VtybuwcvEYimTAfkWyYk8lteHhYU1NTkqT8/HwdOXJEt956q8mptg7zEcmGOYlk0tnZqYcfftjsGACwocnJSbW1tUW/XlhYUGdnp/bt28cHtgCAPcEwDHV3d2tubi6mPTU1Vc3Nzfw83KX2bBHTZkzor3/96zp9+nRMm8vlkvTakvRXs7q6Gn18Ix8KOxyODQuP6uvr9Y//+I/61V/9VUnSV77ylbgiprW80tUz30ze61nGLS0tTRkZGdd1fGCrpKenMx+RVLZ6TmZlZWlkZEQ9PT0x7WtbrTQ0NKiwsHDLzo+dhddIJBPmI5INczL5NDY2KhQKqbCwcM+9n2E+ItkwJ2G2K1ecB4BklZmZKYfDEfPZzcTEhNxutyorK01MBgDA9hgeHtbY2FhMW0pKig4ePCi7fc+Wuux6rDm5yTwej6Rr227N7/dHH2/V3Wdvectb1NzcLEl67rnnFIlEYvrX8kpXz7wdeQEA5rJYLCorK9Phw4eVkpIS0xeJRNTe3q7u7u64nycAAADJwDCMdfusVqsOHjy45wqYAAAAsDM5nU4dOHAgbvu4/v5+TU5OmpQKAIDtMT09rd7e3pg2q9WqAwcOxCzUgt1nz5anXbkE540qLi6OaysrK9Mvf/lL+f1+zc/PKysra93nr22xlp+fH7NV22Zrbm5Wa2urVlZWNDMzo/z8/Ji8a4aHh3XixImr5pWk8vLyrQkLAEgKWVlZOn78uFpaWuT1emP6RkZG5PP51NzcfE3bpwIAAGyH5eVltbS0qKqqSnl5eQnHsMw4AAAAdhKPx6Ompia1tLTEtLe3t8vpdCozM9OkZAAAbB2v15uwnqOxsZFVffeAPVvE1NjYuCXHbW5u1ne+8x1Jr72JfMMb3pBwXCgUim7V09TUtCVZ1mx0kXZtlSbptbwbWeu32+2qr6/fnHAAgKTldDp15MgRdXd3xy3XubCwoP7+fjU0NJiUDgAA4L/Nzs6qra1NoVBI7e3tOnr0KFsFAQAAYFfIy8tTbW1t9DMl6bUVSFtaWnT06FG53W4T0wEAsLlWVlZ0+fLluB1BqqurYxZrwe7FdnKb7M1vfnP08bPPPrvuuFdeeSW6Pdub3vSmLc3U2toq6bUPo3Nzc2P6Tp48GV1FY6O8gUBAL774YvQ5r99iCACwO1mtVjU0NGjfvn0xRbFut1s1NTUmJgMAAHjtw5vBwUFdunRJoVBIkhQOh9XS0hL9GgAAANjpSktLVVJSEtMWDAZ1+fJl3vcCAHaV+fl5BQKBmLaioiJ2itpDKGLaZLfddlt0+c6///u/l2EYCcc9+eST0cd33nnnluV5/vnno8uMvvnNb47bO9nj8ehtb3ubJOmnP/2phoeHEx7nu9/9rhYXF7c8LwAgORUVFenIkSNyOByy2Ww6cOCA7PY9u6AjAABIAuFwWG1tberr64vrs1gsfJgDAACAXcNisaiurk7Z2dkx7UtLS2ppaYlbrQIAgJ2qqKhIzc3N0bqGrKws1dfXb7j7FHYXipg2mcPh0B/+4R9Kktra2vSlL30pbswLL7ygJ554QpJ066236uTJkwmPZbFYZLFYVFVVlbD/+9///rpFUpLU3d2te+65J/r1Jz7xiYTj/uiP/kjSa1vcffKTn1Q4HI7pn56e1kMPPSTptReJj370o+ueEwCwe2VkZOj48eM6cOCAUlNTzY4DAAD2sOXlZZ0/f15TU1NxfXl5eTp69KhcLpcJyQAAAICtYbFY1NzcHLdt8vz8vLq6ujb8vAgAgJ0kPz9fhw8fVmZmZkxBE/YGllDYAg888ID+6Z/+SZ2dnXrwwQfV3d2tu+++W263W2fOnNEXvvAFhUIhud1uffnLX77h89x5552qq6vTXXfdpVOnTqmsrExOp1NjY2P68Y9/rCeeeEI+n0+S9IEPfEB33XVXwuP82q/9mu6++259+9vf1g9+8AO94x3v0Kc//WmVlJTo0qVL+ou/+AsNDg5Kkh555JG4Sn8AwN7hcDii25AmEggEtLKyooyMjG1MBQAA9pLZ2Vm1tbUlXGmpqqpKFRUV3J0HAACAXclut+vAgQM6d+6cgsFgtH18fFxut1sVFRUmpgMAYPNkZGTo8OHDXOPZgyhi2gIej0dPP/20br/9dnV1denxxx/X448/HjMmIyNDTz31lI4cOXJT5+ru7tajjz664Zg/+IM/0F/91V9tOObv/u7vtLi4qB/96Ec6c+aMzpw5E9NvtVr1J3/yJ/rYxz52U3kBALtXJBJRa2urFhcX1dDQoKKiIrMjAQCAXcQwDA0NDSXcPs5ut6uxsVG5ubkmJAMAAAC2j8vl0oEDB3Tx4sWYbeT6+vrk8Xi4ER0AsGtQwLQ3UcS0Rerq6nT+/Hn97d/+rf7lX/5F3d3dCgQCKi8v1+23365PfepTqqysvKlz/OAHP9ALL7ygX/7ylxoYGND09LT8fr8yMjJUU1Ojt7zlLbr33nt14MCBqx7L7Xbr6aef1je/+U09+eSTunjxoubn51VYWKi3vOUt+l//63/plltuuam8AIDdrbe3VwsLC5Kkjo4Oeb1e1dbWsswnAAC4aaFQSB0dHZqeno7rS01N1YEDB+R2u01IBgAAAGy/jIwMNTY2qrW1NdpWVFSkzMxME1MBAHB9fD6fwuEwP78QgyKmLZSWlqYHH3xQDz744A09/2r7F99xxx264447bujY67nnnnt0zz33bOoxAQC738zMjEZGRmLaRkdH5fP5tH///g23oAMAANjI0tKSWlpatLS0FNeXl5enxsZG2Ww2E5IBAAAA5snPz1dNTY16e3tVXV2t8vJyVqwAAOwYq6urunTpkoLBoBobG1VQUGB2JCQJlkYAAAA3LTs7W6WlpXHti4uLOnv2rBYXF01IBQAAdrpQKKQLFy4kLGCqrq5Wc3MzBUwAAADYs8rKynT06FFVVFRQwAQA2DHC4bAuX76sQCAgwzDU1tamgYGBqy7ygr2BIiYAAHDTrFar6urq1NjYGLd9XCAQ0IULFzQ2NmZSOgAAsFPZ7XZVVFTEtR08eJAPagAAALDnWSwWZWRkmB0DAIBrtla05PP5Ytrn5uYoYoIkipgAAMAmKiws1JEjR+R0OmPaDcNQZ2enOjs7FYlETEoHAAB2otLS0uiS4mlpaTp27JhycnJMTgUAAAAkv3A4zAfCAICk0tPTo5mZmZg2t9ut/fv3x90kj72JWQAAADaVx+PR8ePHlZWVFdc3NjamixcvanV1dfuDAQCAHclisaihoUEVFRU6evSo3G632ZEAAACApLeysqJz585pZGTE7CgAAEiSRkdH434u2e12HThwQCkpKSalQrKhiAkAAGy6lJQUHTp0SGVlZXF9i4uLOnfunBYWFkxIBgAAktXKysq6fTabTdXV1bLZbNuYCAAAANiZ1q6/LS0tqaenR+Pj42ZHAgDscRMTE+rq6opps1gs2r9/v1JTU01KhWREERMAANgSFotFtbW1amxsjFsCNBAI6OLFixodHTUpHQAASBaGYai/v18vvfSS5ufnzY4DAAAA7Girq6u6ePGigsFgtK2jo0N9fX1sLQcA2HZr133a29vj+hoaGhLu6oG9jSImAACwpQoLC3X06FG5XK6YdsMwNDk5ycUTAAD2sFAopJaWFg0MDMgwDLW2tm64IhMAAACAjTmdTlVUVMS1Dw4Oqq2tTeFw2IRUAIC9KBKJqL29XQMDA3F9FRUVKioqMiEVkh1FTAAAYMulp6fr2LFjMRX1DodDzc3Nslgs5gUDAACmWVpa0rlz5zQzMxNtCwaDam1tVSQSMTEZAAAAsLNVVFSovLw8rn1qakoXL15UIBAwIRUAYC8JBoN69dVXNTk5GddXWlqqqqqq7Q+FHYEiJgAAsC1SUlJ06NAhlZWVRfc5djgcZscCAAAmmJ6e1rlz57S8vBzXl5+fT5EzAAAAcBMsFotqampUX18f1+f1enX+/Hn5/X4TkgEA9oKlpSWdP39eCwsLcX11dXWqq6vj2g/WZTc7AAAA2DssFotqa2tVWloat70cAADY/QzD0MDAQMJlxO12u5qbm5WdnW1CMgAAAGD3KSkpkcvlUmtra8w2cisrKzp//ryam5uVk5NjYkIAwG60urqqlZWVmDabzaampibl5uaalAo7BSsxAQCAbbdRAVMoFFJfXx/byAAAsMuEQiFdvnw5YQFTenq6jh8/TgETAAAAsMlycnJ09OhROZ3OmPZwOKxLly5pbGzMpGQAgN0qOztbDQ0N0a+dTqeOHDlCAROuCSsxAQCApGEYhtrb2zUzM6O5uTnt378/7gILAADYefx+v1paWhJuH1dQUKCGhgbZbDYTkgEAAAC7X1pamo4dO6bLly/L6/XG9HV2dmppaUk1NTVs7QMA2DRFRUVaWlrS3NycDhw4wGc9uGasxAQAAJLG4OCgZmZmJEler1dnz57V/Py8uaEAAMBNmZ6e1vnz5xMWMNXW1qqxsZECJgAAAGCLORwOHT58WPn5+XF9w8PDam1tlWEYJiQDAOxW1dXVOnLkCAVMuC4UMQEAgKQQCAQ0NDQU0xYMBvXqq69qZGSEiygAAOwwhmGor69PLS0tCofDMX0pKSk6fPiwysrKuNsbAAAA2CY2m01NTU0qLy+P63O5XLw3BwBcl0AgoKmpqXX7LRYLN67hulHEBAAAkoLD4dDRo0flcrli2g3DUHd3tzo6OhSJRExKBwAArtfCwoIGBwfj2tPT03Xs2DFlZWVtfygAAABgj7NYLKqpqdG+ffuiRUt5eXmqqakxORkAYCfx+/06f/68WltboztsAJuBIiYAAJA00tLSdOzYMWVnZ8f1TUxM6MKFC1pZWTEhGQAAuF5ZWVmqqKiIaSssLNSRI0fiipYBAAAAbK+ioiIdPHhQ2dnZamxsZBUmAMA1m5ub0/nz56Of17S1tcnn85mcCrsFRUwAACCppKSk6ODBg3EfekqS1+vVuXPnNDc3Z0IyAABwvaqqqpSTkyOLxaK6ujrt27ePZcQBAACAJJGdna1Dhw7xHh0AcM3Gxsb06quvKhwOR9vC4bC6urpkGIaJybBbUMQEAACSjsViUXV1tZqbm2W1xr5dCQaDevXVVzU0NMQbYgAAkpzFYlFjY6MOHz6s0tJS7u4GAAAAdhCfz6fe3l6uwQEAZBiGenp61NnZGdfn8Xi0f/9+rvtgU9jNDgAAALCe/Px8paamqqWlRcvLyzF9vb298nq9amhokN3OWxoAAMwSCATk9XqVm5ubsD8lJUWZmZnbnAoAAADAzQgEArp8+bJWV1fl9/vV3NzMik0AsEeFw2G1t7dreno6ri8vL0+NjY38jMCmYSUmAACQ1NLS0nTs2DHl5OTE9U1NTcXsuwwAALbX4uKizp49q9bWVnm9XrPjAAAAANgE4XA4WsAkSbOzs7pw4UL0awDA3rG6uqqLFy8mLGAqLy+nyBWbjiImAACQ9Ox2uw4cOKDKysq4PqvVqpSUFBNSAQCwdxmGodHRUV24cEGBQECRSEStra0KBoNmRwMAAABwk7xer3w+X0ybz+fTuXPnuHkBAPYQn8+n8+fPx732WywWNTQ0qKamhi3ksOkoYgIAADuCxWJRVVWVDhw4EN0+zm63U+UPAMA2C4fD6ujoUFdXlwzDiLavrKyop6fHxGQAAAAANkNWVpYOHToUvQa3JhAI6MKFCwlX4wAA7C7rrcJnt9t18OBBFRcXm5QMux1FTAAAYEfJzc3VsWPHlJ6ersbGRrndbrMjAQCwZywvL+vChQuamJiI68vKylJNTY0JqQAAAABstqysLB07dizu2lskElFLS4uGh4djbmoAAOweIyMjunTpksLhcEy7y+XS0aNHlZ2dbVIy7AX2qw8BAABILm63W8eOHdtwmVLDMFjGFACATTQ7O6u2tjaFQqG4vvLyclVXV/OzFwAAANhF3G63jh49qpaWFi0sLMT09fT0aGlpSfX19fweAAC7iM/nU3d3d1x7RkaG9u/fL4fDYUIq7CWsxAQAAHakjS6ORCIRXbhwQWNjY9uYCACA3ckwDA0MDOjSpUtxBUw2m03Nzc2qqanhgwsAAABgF0pJSdGhQ4dUWFgY1zc2Npbw9wQAwM6Vnp6u6urqmLb8/HwdPnyYAiZsC4qYAADArtPV1aXFxUV1dnaqo6NDkUjE7EgAAOxIoVBILS0t6u/vj+tLTU3V0aNHlZ+fv/3BAAAAAGwbq9Wqffv2qaqqKq5vbm5OFy5c0MrKyvYHAwBsifLychUVFUmSKisr1dTUJKuV0hJsD7aTAwAAu8rY2JjGx8ejX4+Pj8vv96u5uVkul8vEZAAA7Cx+v18tLS1aXl6O68vLy9O+fftkt3NZAQAAANgLLBaLKisr5Xa71d7eLsMwon1+v1/nzp3TgQMHlJGRYWJKAMBmsFgsqq+vV35+vnJycsyOgz2GcjkAALCrJPqg1ev16ty5c5qbmzMhEQAAO8/k5KTOnTuX8OdqdXW1mpubKWACAAAA9qCCggIdPnxYKSkpMe3BYDDmxkIAQPILBALr9lmtVgqYYAqKmAAAwK5SU1OTcGnTYDCoV199VYODgzF3igEAgHjhcDhuO9aUlBQdOnRIFRUVslgsJiUDAAAAYLbMzEwdPXpUqampMW11dXUmpgIAXCvDMDQ8PKxf/vKXWlhYMDsOEIMiJgAAsOsUFBTo2LFjcrvdcX19fX1qbW1VKBQyIRkAADtDcXGxiouLo197PB4dO3ZM2dnZJqYCAAAAkCzcbreOHj2qrKwsud1u7d+/P+6mQgBA8jEMQ93d3erp6VEkElFLS0vClbgBs/BuAgAA7EppaWk6duyYcnNz4/qmp6d17tw5+f1+E5IBALAz1NXVyePxqLi4WEeOHJHL5TI7EgAAAIAkYrfbdfDgwYTbywEAkk8oFNLly5c1OjoabQsGg7p8+bLC4bCJyYD/RhETAADYtex2u/bv36+qqqq4vuXlZZ0/f15TU1PbHwwAgB3AarXq8OHDamho4I5qAAAAAAlZrVY5nc51+1dWVljhAwCSwMrKii5cuKDZ2dm4voKCAq79IGkwEwEAwK5msVhUWVmpgwcPym63x/SFw2G1traqp6dHhmGYlBAAAHOEw2G1t7drZmZm3TE2m20bEwEAAADYTUKhkC5duqTz589rYWHB7DgAsGctLi4m3J3CYrGoqalJlZWVslgsJqUDYlHEBAAA9oScnBwdP35c6enpcX3Dw8Pq7+/f/lAAAJhkbUXCiYkJtbe3c2c0AAAAgE0ViUTU2tqqpaUlBYNBXbx4UZOTk2bHAoA9Z2pqShcvXlQwGIxpT0lJ0eHDh1VQUGBSMiAxipgAAMCe4XK5dOTIERUWFsa1l5WVmZQKAIDtNTMzE3P3XSgUUktLi8LhsMnJAAAAAOwWQ0NDmpubi35tGIba2trU19enSCRiYjIA2BsikYgGBgbU2toa97qbmpqqo0ePKjMz06R0wPrsVx8CAACwe9hsNu3bt08ZGRnq7u6WxWLR/v37lZKSYnY0AAC2lGEYGhgY0MDAQFzfysqK/H6/MjIyTEgGAAAAYLcpLS3VwsJCTCGTJA0ODmp6elr19fXKysoyJxwA7HILCwvq6uqK2z5OkrKystTc3MxnIkhaFDEBAIA9x2KxqKSkROnp6VpdXU24xRwAALtJMBhUe3u7Zmdn4/pSU1O1f/9+paammpAMAAAAwG5kt9t18OBBdXd3a3R0NKZvaWlJFy9eVGFhoWpqauRwOExKCQC7SzAYVG9vr8bHxxP2FxUVqb6+XlYrG3YheVHEBAAA9qyrrTYRDAYVDoflcrm2KREAAJvP5/OppaVFKysrcX35+fnat2+fbDabCckAAAAA7GYWi0V1dXVyu93q6emJ65+YmNDMzIyqq6tVXFwsi8ViQkoA2D0Mw9DU1FTCvurqapWXl/Nai6RHERMAAEAChmGotbVVPp9PTU1NysnJMTsSAADXbWJiQp2dnYpEInF9NTU1Kisr4+IVAAAAgC1jsVhUVlamzMxMdXZ2yufzxfSHQiF1dXVpfHxc9fX18ng8JiUFgJ3P4XCourpa3d3d0ba0tDTV19crMzPTxGTAtWOdMAAAgAT6+vo0Pz+vUCikS5cuaWBgQIZhmB0LAIBrEolE1N3drfb29rgCppSUFB06dIi77wAAAABsG4/Ho2PHjqmuri7hSrBer1fnzp1Td3e3QqGQCQkBYHcoKSlRenq6bDabamtrdfz4cQqYsKOwEhMAAMDrzMzMaGhoKKatv79fXq9XjY2Nstt5CwUASF6rq6tqbW3V4uJiXJ/H49H+/fvldDpNSAYAAABgL7NYLCotLVV+fr56eno0OTkZN2ZsbExlZWVcfwOAdRiGoZmZGWVmZiolJSWu32KxRD/H4PoPdiJWYgIAAHidzMxM5eXlxbXPzMzo3Llz8vv9JqQCAODqwuGwzp8/n7CAqbi4WEeOHOECFgAAAABTORwONTU16dChQ3K73TF9lZWVcrlcJiUDgOS2vLysy5cvq6WlRX19feuOS0tL4/oPdiyKmAAAAF7HbrerublZ1dXVcX3Ly8s6d+6cxsfH2V4OAJB0bDabSkpKYtosFov27dunhoYGWa1cBgAAAACQHLKzs3XixAlVVVXJarUqNTVVZWVlZscCgKQTiUQ0MDCgV155RbOzs5JeW7luYWHB5GTA5mMtRgAAgAQsFosqKirk8XjU2tqqUCgU7YtEIuro6ND8/Lzq6+tls9lMTAoAQKzy8nJ5vV5NT0/L6XRq//798ng8ZscCAAAAgDhWq1WVlZUqKChQKBRa98aLUCikxcVF5eTkbHNCADDX3Nycurq6tLy8HNfX1dWl48ePy2KxmJAM2BoUMQEAAGwgOztbx48fV0tLi3w+X0zfxMSEFhcX1dzcrPT0dJMSAgAQa23lpZSUFFVXVyslJcXsSAAAAACwoddvK/d6AwMDGh4eVl5enurq6tgmCcCuFwgE1NPTo8nJyYT9DodDFRUV25wK2HoUMQEAAFyFy+XS0aNH1dPTo9HR0Zi+te3l6urqVFxczB0PAIBtYRiGFhcXlZmZmbDfbreroaFhm1MBAAAAwObz+XwaHh6WJE1PT2tubk5VVVUqLS3lWhyAXccwDI2Ojqqvr0/hcDjhmNLSUlVVVclup9wDuw+zGgAA4BpYrVbV19crMzNTnZ2dMb88GIahrq4uzc/Pa9++fWwvBwDYUoFAQG1tbZqfn9fhw4eVlZVldiQAAAAA2BJr192uFA6H1dPTo/Hx8ej1OgDYDbxerzo7O+N2hVjj8XhUX18vj8ezzcmA7UMREwAAwHUoKCiQx+NRa2tr3C8SwWBQVqvVpGQAgL1gbm5ObW1tCgaDkqS2tjYdP35cDofD5GQAAAAAsDWKioq0tLSkUCgU0+73+3XhwgUVFRWppqaGrbQB7FihUEh9fX1xO0Gssdvtqq6uZjcI7AkUMQEAAFwnt9uto0ePqre3VyMjI5KklJQUNTY28gsEAGBLGIahgYEBDQwMxLQHAgF1dHTo4MGDJiUDAAAAgK1jsVhUXFysvLw89fb2anx8PG7M+Pi4ZmZmVFNTI7fbbUJKALhxoVBIL7/8sgKBQML+wsJC1dTUcAMb9gyKmAAAAG6A1WpVXV2dsrKy1NHRocbGRjmdTrNjAQB2odXVVbW1tWlhYSGuz+FwqLy83IRUAAAAALB9UlJStG/fPhUVFamrq0t+vz+mPxgMqqOjQ2lpaXK73VpeXjYpKQBcH7vdrry8vLhVmFJTU1VfX6+srCxzggEmoYgJAADgJuTl5SkrK0t2+/pvqyKRCNvMAQBuyOzsrNrb26Pbx10pJydHjY2NbJkAAAAAYM/IzMzUsWPHNDIyov7+fkUikZh+v9+vgwcPanx8XOFw2KSUAHB9qqurNTU1pWAwKKvVqsrKSpWVlfG5AvYkipgAAABu0kYFTKFQSOfPn1dRUZHKysrYbg4AcE0ikYj6+/s1NDQU12exWFRdXc3PFQAAAAB7ktVqVXl5ufLz89XT06Pp6em4/pKSErW1tenUqVMbXrsDgO1kGEbCdrvdrtraWk1NTamurk4ul2ubkwHJg5/aAAAAW8QwDHV2dmppaUm9vb1aWFjQvn37WDEDALChlZUVtbW1aXFxMa7P6XSqqalJmZmZJiQDAAAAgOThcrm0f/9+zczMqLu7WysrKzH96enpFDABSAorKyvq6elRRkbGutd0CgoKVFhYuM3JgOTDT24AAIAtMjY2pqmpqejXMzMzOnv2LB8+AwDWNT09rY6ODoVCobi+3NxcimEBAAAA4HVyc3OVlZWlwcFBDQ0NyTAMhUIhlZaWmh0NwB4XiURitr+cm5tTY2NjwrGstg28hk0UAQAAtkggEIhrW11d1YULFzQ4OLju0rEAgL2pv79fLS0tcQVMFotFtbW12r9/PwVMAAAAAJCAzWZTdXW1GhsbtbCwoMHBQX5/AmCqhYUFnTt3Tr29vYpEIpKkcDiskZERk5MByY2VmAAAALZIVVWVMjIy1N7ermAwGNPX19cX3V7O4XCYlBAAkEzS09Pj2lwul5qbm+XxeExIBAAAAAA7i8vlUltb24ZjxsbGND8/r9raWq7LAdh0wWBQvb29Gh8fT9jv8/mUkpIS95kBgNewEhMAAMAWysnJ0fHjxxNuHzc7O6uzZ89qfn5++4MBAJJOXl6eysrKYr4+fvw4BUwAAAAAsEkCgYB6e3s1OTmpl19+WaOjo6yWDmBTGIahsbExvfTSS+sWMBUXF6upqYkCJmADrMQEAACwxZxOpw4fPqyBgQENDAzE9AUCAV28eFFVVVWqqKhg32sA2OOqq6vl9XpVUFCg4uJifi4AAAAAwCbq7e2NbuEdCoXU1dWl8fFx1dfXcwMJgBvm8/nU1dWlxcXFhP3p6emqr69XRkbGumMAvIYiJgAAgG1gsVhUVVWlzMxMtbe3KxAIxPT39/drfn5eTU1NLGMNALtcIBBY97XearXq8OHDFC8BAAAAwCYLBAKamZmJa/d6vTp37pxKSkpUXV0tu52PTwFcm1AopIGBAQ0PDyfst9lsqqqqUmlpKdd6gGvEdnIAAADbKDs7W8ePH1dWVlZc3/z8vF555RXNzc1tfzAAwLaYnJzccFlxSVzUAgAAAIAt4HA4dPLkSRUUFCTsHx0d1QsvvKDOzk75fL5tTgdgp+nv79eLL764bgFTfn6+Tp48qbKyMq71ANeBUmIAAIBt5nA4dOjQIQ0ODqq/vz+mLxgMampqStnZ2eaEAwBsiXA4rJ6eHo2NjUmSurq65PF4lJaWZnIyAAAAANg7HA6HmpqaVFRUpK6uLi0vL8f0RyIRjY2NaWxsTBkZGSotLVVeXp6sVtaFABDLMAyFw+G4drfbrbq6OuXk5JiQCtj5KGICAAAwgcViUWVlpTIzM9XW1hbdXi4tLU21tbUmpwMAbKalpSW1trbK7/dH2yKRiFpbW3Xs2DHZbDYT0wEAAADA3pOdna0TJ05oaGhIg4ODikQicWMWFxe1uLiolJQUFRcXq6ysTCkpKSakBZCMSkpKNDg4GP3aYrGooqJCFRUVFD4CN4F/PQAAACbKysrSiRMnlJOTI6vVqqamJj7MBoBdZGJiQmfPno0pYFrj8XhMSAQAAAAAkCSr1arKykqdOHFC+fn5644LBoMaGhraxmQAzGYYhubm5tTS0qKVlZWEY5xOp/Ly8iRJeXl5OnHihKqqqihgAm4SKzEBAACYLCUlRQcOHNDS0hLbCgHALhEOh9XR0aHx8fG4PqvVqvr6ehUVFZmQDAAAAABwJbfbrebmZq2urka3kltbNX1NQUEBqzABe0AoFNL4+LhGR0ej202mpqaquro64fjq6mrV1tbK5XJtZ0xgV6OICQAAIAlYLJYNC5iWl5fV3d2t+vp6fiECgCTndrvV2dmZ8E69tLQ0NTc3KzU11YRkAAAAAID1OJ1OVVVVqaKiQtPT0xodHdXCwoKk17aNWs/ExIRSU1NZbRfYwbxer0ZHRzU5ORm3veTY2JgqKysTrrDE9R1g81HEBAAAkOQikYja2trk9Xp19uxZ7du3L7pMLQAgeRiGofz8fFVVVSUsYCouLlZtbS3bhgIAAABAErNarSooKFBBQYH8fr9mZmbWLVAKh8Pq6upSOByWx+NRSUmJ8vPz+b0P2AEikYimpqY0MjIir9e77rhgMKipqSkVFhZuYzpg76KICQAAIMn19vZGf4kKhUJqaWlRaWmpqquruSACAEkiGAxqYGBAtbW1cX02m00NDQ0qKCgwIRkAAAAA4EalpaVtuHr6xMSEwuGwpNdWcuno6FBPT4+KiopUUlIit9u9XVEBXKPl5eXo1pGhUGjDsampqSopKVFubu42pQNAERMAAEASC4VCmpmZiWsfGRnR7Oys9u3bp8zMTBOSAQDWLCwsqLW1VYFAIK4vPT1dTU1NLC8OAAAAALuMYRgaHR2Naw+FQhoeHtbw8LCys7OjBRAWi8WElACk1/69zs7OanR0VLOzsxuOtVgsysvLU0lJiTIzM/m3C2wzipgAAACSmN1u1/Hjx9XR0aHp6emYvuXlZV24cEHl5eWqqqpKuCc3AGDrOZ3O6J23VyopKVFtbS2vzwAAAACwC61tKR4KhbS6uppwzNzcnObm5uR0OlVcXKzi4mI5HI5tTgpgdXVVly9f3nCMw+GI/jt1Op3blAzA61HEBAAAkOTsdruam5s1Ojqq3t5eRSKRmP6hoSHNzMyosbFRHo/HpJQAsHe5XC7V1NSoq6tL0mt33dbV1amystLkZAAAAACArWK1WlVZWamKigrNzMxodHRUc3NzCceurq6qv79fAwMDys/PV0lJiTIyMljhBdgmLpdLubm5CXc9yMrKiq6Yxo1ogPkoYgIAANgBLBaLSktLlZ2drfb2dnm93pj+paUlnTt3LnrhhF+2AGB7FRcXa3x8XIODg+rt7dXJkyfNjgQAAAAA2AZrW0/l5eVpaWlJY2NjGh8fVygUihtrGIYmJyc1OTmp8vJy1dTUmJAY2J3C4bCWlpbWvdG3pKQkWsRks9lUVFSkkpISpaambmdMAFdBERMAAMAOkpqaqqNHj2poaEj9/f0yDCOmf2BgQDMzM9q3b5/S09NNSgkAu9PCwoLS09Nls9ni+iwWi6qrq/WTn/zEhGQAAAAAgGSQmpqq2tpaVVVVaWpqSiMjI/L5fAnH5uXlbXM6YHdaWlrS6OioxsfHZbVa9YY3vCHhTb7Z2dnKy8tTTk6OCgoKEl7fAWA+ipgAAAB2GIvFooqKCuXk5KijoyPuQojP59O5c+dUU1OjsrIyk1ICwO4RDofV19enkZERlZSUqL6+PuE4Ln4BAAAAAKT/XuWlqKhIi4uLGh0d1dTUlCKRiCQpPT193dVi1saw0jqwPsMwND09rdHRUc3Pz0fbw+GwpqenVVBQEPcci8Wi/fv3b2NKADeCIiYAAIAdKj09XUePHtXg4KAGBwdjVmUyDEN2O2/1AOBmLS4uqr29XcvLy5Kk0dFR5eXlKTs72+RkAAAAAICdICMjQxkZGaqtrdX4+LhGR0dVUlIii8WScPz4+LgGBgaiW105nc5tTgwkr9XVVY2NjWlsbEyBQCDhmNHR0YRFTAB2Bkp4t9DS0pIeffRRnTx5Ujk5OUpLS1NjY6Puv/9+DQwM3PTxq6qqZLFYrutPf39/3HE++9nPXvPzf/7zn990bgAAsHmsVquqqqp09OjRmL27c3NzVVhYaGIyANjZIpGIent7df78+WgB05qOjg6FQiGTkgEAAAAAdqKUlBSVl5fr1KlT6163MwxDo6OjCgQCGhwc1IsvvqiWlhbNzc3F3MAI7CWGYWh+fl6tra365S9/qYGBgXULmKTXVkJbW9EMwM7D7flbpLu7W7fffru6urpi2js6OtTR0aGvfe1reuqpp/Te97532zJlZmaqqKho284HAAC2j8fj0fHjx9Xf36/x8XE1NDSsezcXAGBjXq9X7e3tWlpaiuuzWq0qKytj6zgAAAAAwA1ZWzggkcXFRfn9/pi26elpTU9Py+12q6SkREVFRazAjj0hFAppYmJCo6OjCa/RXMlut6u4uFjFxcVyu93blBDAVuAn3Bbwer16z3veEy1g+v3f/33dfffdcrvdOnPmjB5++GEtLi7qgx/8oJ5//nkdOXLkhs7zzDPPbFhlKkk//elP9b//9/+WJH3gAx+Qy+XacPylS5c27K+urr6+kAAAYNtYrVbV1NSooqJiwwsZc3NzysrKosgJAF4nEolocHBw3ZVzPR6PGhsbY1a+AwAAAABgs8zNza3bt7y8rJ6eHvX19amgoEBFRUXyeDyyWtl4B7vP6Oioenp6rrqiUkZGhkpKSpSfn8+/BWCXoIhpC3zxi19UZ2enJOnRRx/VAw88EO275ZZbdNttt+nWW2/V0tKSPv3pT9/wFm0NDQ1XHfP5z38++vh3fud3rjr+wIEDN5QFAAAkj40KmGZmZnT58mVlZWVp3759Vy1wBoC9wufzqaOjQz6fL67PYrGoqqpK5eXlFIACAAAAALZMVVWV8vLyNDo6qomJiYQFHJFIROPj4xofH5fVapXH41FmZqaysrK4cRG7hsvlWreAyWq1qrCwUMXFxfJ4PNucDMBWoxxxkwWDQf3N3/yNJKmpqUn3339/3Jg3vvGN+r3f+z1J0rPPPquXX355S7IsLCzoBz/4gSSppqZGb37zm7fkPAAAYGcIBoPRQuv5+Xm98sorGhsbk2EYJicDAPMYhqHBwUGdO3cuYQFTenq6jh07poqKCi4EAwAAAAC2XHp6uhoaGnTLLbeorq5uw9WAI5GIFhYWNDg4qLa2tm1MCdyYYDComZkZ9fT06Ny5c5qdnU04Ljs7O25bOLfbrdraWt1yyy1qaGiggAnYpViJaZOdOXNGCwsLkqSPfOQj6y5bd/r0aT322GOSpO9973s6efLkpmf553/+Z62srEi6tlWYAADA7tbd3R2zFW04HFZnZ6emp6fV0NAgp9NpYjoA2H5LS0tqb2+X1+uN67NYLKqoqFBFRQXLkQMAAAAAtp3dbldpaalKSkq0sLCg0dFRTU9Pr3tDYmZm5ro338zOzioUCikzM5NrgNhWq6urWlhYiP7x+/0x/QsLC8rJyYl7nsViUXFxsXp7e5WXl6eSkhJWGgP2CIqYNtlzzz0XfXzrrbeuO+7EiRNKTU3V0tKSnn/++S3J8g//8A+SXnuR//CHP7wl5wAAADtHUVGRFhYWtLq6GtM+OzurV155RXV1dSooKOAXQQB7xvDwcMICptTUVDU2NnJHHwAAAADAdBaLJbpV3OrqqsbHxzU1NRVXDJKZmbnuMYaHhzU3NyfptW26MjMzo3/cbjfXA7EpDMPQyspKtGBpfn4+uuDGeubn59ftKy4uVkFBAYV3wB5DEdMma21tjT5ubGxcd5zdblddXZ1effXVLVnesa+vL1oc9eY3v1k1NTXX9Lx3vvOdunDhgubn55WVlaXm5ma9613v0n333afs7OwbzjM8PLxh/9jYWPSx3+/X4uLiDZ8LuFlXbiOSaEsRYLsxJ7FZbDab9u3bp+Hh4bhlekOhkNrb2zU2Nqby8nKlpKQkPAbzEcmE+YiblZ+fr+npaQWDwWhbQUGBiouLZRjGdf9ewpxEMmE+ItkwJ5FMXv+hLwAAO4XT6VRlZaUqKysVDAa1uLio+fl5LSwsKCsrK+FzXv/77crKilZWVjQxMSFJSklJUWZmprKyspSZmam0tDSKmnBdJicnNT09rYWFhZidAK6F1+tVJBJJuAq23W6X3U45A7DX8K9+k60V66Slpa37ZmFNeXm5Xn31VU1NTWl1dXVTq0j/4R/+Ibqc5PVsJfeTn/wk+nhqakrPPvusnn32WT3yyCN68skn9f73v/+G8pSXl1/z2O9+97sbVosD2+kb3/iG2RGAGMxJbJasrCzV1NTI4XDEtC8sLGh6elp9fX3r7ke+hvmIZMJ8xI3KzMxUU1OTlpeX1dPTs2kfrDMnkUyYj0g2zEmYbWFhwewIAADctJSUFOXm5io3N3fDcT6fT+FweN3+YDCo6elpTU9PS3rtRsgrV2ryeDxss44Nzc3NaWpq6prHW61WeTye6BwDgCtRxLTJ1rYiSE9Pv+rYtLS06GOfz7epRUxrF4Pcbrc+8IEPXHX8wYMH9Ru/8Rs6deqUSkpKFAwG1dHRoaeeekrPPPOM5ufn9T/+x//QD3/4Q7373e/etJwAAMAc8/PzunjxoqqqqpSfnx/Tl5KSooaGBs3MzKivr0+hUMiklACwOSwWS/Qmj9dbWFhQZ2en5ufnFYlEtjkZAAAAAABbLycnR4uLi9d0nS8cDmt2djZ6g2NdXZ1KS0u3OiKSUDgc1uLiohYWFhQKhVRXV5dwXGZmpsbHx9c9DoVxAK4HRUybbG1fz9evapDIlUVLy8vLm5bhF7/4hXp6eiRJ73//+5WRkbHh+E9/+tP67Gc/G9f+K7/yK/qd3/kdPfbYY/r4xz+ucDisj370o+rp6ZHL5bquTENDQxv2j42N6dSpU5Kku+66Sw0NDdd1fGAz+Xy+aCHghz/84WsqSgS2EnMSW21+fl5DQ0NxFzFyc3NVWFio8vLy6AqTzEckE+YjrsYwDM3MzGh8fFwNDQ3X9HvazWBOIpkwH5FsmJNIJp2dnXr44YfNjoEk5/P5dO7cOb300kt66aWX9PLLL6u/v1+SVFlZGX28mX7xi1/oq1/9qv7f//t/mpiYUFZWlg4fPqzTp0/rQx/60KafD8De4PF4dPDgQRmGIb/fr4WFheifa9n6a72VctZ+587MzFRKSspmx4YJXr9Foc/ni94UZrFYVF1dLZvNFve818+RtS0K17YpZItCANdjzxYxbcYL5de//nWdPn06pm2tuOdafuivrq5GH7vd7pvOs+Yf/uEfoo8/8pGPXHX81ba9u++++/Tyyy/riSee0OjoqL7zne/of/7P/3ldmcrKyq55bFpa2lULr4Dtkp6eznxEUmFOYitkZGSoqKhIXV1d0WWj14RCIY2NjamsrCzu7hjmI5IJ8xGvt7q6qs7Ozuido6Ojozp48OC2XTRjTiKZMB+RbJiTMNuVK+QD67njjjv085//fNvO99nPflaf//znY1YHnZiY0DPPPKNnnnlGTz31lP71X//1um8wBoA1FotF6enpSk9PV2lpqQzD0MrKSrSgaX5+PrpYwxq73b7uz02v16uWlhZJr/1svXKlnc3cfQZbZ3V1Naaoze/3rzvWMAx5vd6Enyu7XC6VlJQoPT1dmZmZcrvdFC0BuGF7tohpq3g8Hkmv3aVxNVf+INisu89WV1f1z//8z5Kk4uJiveMd79iU495333164oknJEnPPvvsdRcxAQCA5OZwONTc3KypqSl1dXXFrMq0b98+lvcFsGMYhqHJyUl1d3fHvJbNzc1pfHxcxcXFJqYDAADATnHldsQ5OTk6ceKEfvGLX1zTtf/r9dhjj+lzn/ucJKm2tlb/5//8Hx08eFCjo6P667/+a505c0ZPP/207r33Xn3zm9/c9PMD2JssFovcbrfcbreKiookxRe1uFyudYtRFhYWoo/9fr/8fr9GR0clvVbUcmVRE0UtySEUCml6ejr6//d6dwqan59PWMRksVhUX1+/SSkB7HV7toipra3tpo+R6OJ3WVmZfvnLX8rv96/7Qr5mbYu1/Pz8TatI/uEPf6i5uTlJ0j333JNwSb8b0dzcHH08MjKyKccEAADJxWKxqKCgQJmZmerq6tLMzIxKS0uvumojACSLQCCgzs5OzczMJOzf6I5CAAAA4Er33HOP7rvvPp08eVJ1dXWSpKqqqk0vYpqdndVDDz0kSaqoqNCLL76ovLy8aP973/te3XnnnfrhD3+ob33rW/rYxz6m2267bVMzAMAap9OpgoICFRQUSIot6Hy9K4uYXm9lZUUrKyuamJiQ9Nr2YllZWcrIyJDT6ZTL5YouDIHNE4lEFAqFZLFYEm7xFwqF1NHRcd3H9Xg8yszMVHZ29mbEBIAN7dkipsbGxi05bnNzs77zne9Iktrb2/WGN7wh4bhQKKSenh5JUlNT06ad/3q3krtWVEcDALB3OJ1O7d+/X1NTU8rNzV133JUrnACAmdZbfWmNw+FQQ0PDhq9pAAAAwJU+9rGPbct5vva1r0ULAR555JGYAiZJstls+upXv6of/ehHCofD+uIXv0gRE4Bts9HngxaLRVarNWYbzPUEg0FNTU1pampKkpSbm6sDBw4kHDs8PCyfz6eUlJR1/9jt9l3/2aVhGAqFQgoGg1f9EwqFFAgEFA6HJUnl5eWqqamJO6bT6ZTT6dTq6uq657VardGipbXCs81aNAMArsWeLWLaKm9+85ujj5999tl1i5heeeWV6F3Ab3rTmzbl3FNTU/qP//gPSdKRI0d08ODBTTmuJLW2tkYfl5SUbNpxAQBAclpblWkjXV1dSk1NVW1trVJTU7cpGQDEWlxcVE9PjxYXFxP2FxQUqK6uLuEdiAAAAIDZvv/970uSMjIydNdddyUcU1ZWpre//e368Y9/rJ/97Gfyer2sYALAdPv371ckEpHX643Zgm6tkGYjG/2OPjc3p9nZ2Ws6xuv/eDyepNxG3jAMRSKRuOKj1NTUdV/PX3755eve7m1NMBhM2G6xWJSZmanJyclom81mi9n6z+PxyGq13tB5AWAzUMS0yW677TZlZmZqYWFBf//3f68HH3wwYSXwk08+GX185513bsq5v/Wtb0V/KG3mKkzSa3tyr7n11ls39dgAAGDnKSwsjC4LPTc3p9LSUlVWVspu5+0lgO0RCATU29sbXZr+9VJSUlRfX6/8/PxtTgYAAABcm0AgoJdeekmSdMstt8jhcKw79tZbb9WPf/xjra6u6pVXXtFb3/rW7YoJAOuyWq3R4hfptWIdv98fU9QUCATinrdREdN6BTiJxr1+bDAYXLeIqaOjQ7Ozsxuu8PT6P9dSzDM/P69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" ] @@ -452,25 +446,6 @@ "drag_pulse.plot()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pulse `serial` is a string representation of the pulse. It can be used as is to generate a copy of the pulse: " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "p1 = Pulse(0, 40, 0.9, 50_000_000, 0, Gaussian(5), 0, PulseType.DRIVE)\n", - "assert p1.serial == 'Pulse(0, 40, 0.9, 50_000_000, 0, Gaussian(5), 0, PulseType.DRIVE, 0)'\n", - "p2 = eval(p1.serial)\n", - "assert p1 == p2" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -487,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -499,7 +474,6 @@ " shape = Rectangular(), \n", " channel = 0, \n", " qubit = 0)\n", - "assert repr(rop) == 'ReadoutPulse(0, 50, 0.9, 20_000_000, 0, Rectangular(), 0, 0)'\n", "assert isinstance(rop, Pulse)\n", "\n", "dp = DrivePulse(start = 0,\n", @@ -510,7 +484,6 @@ " shape = Gaussian(5), \n", " channel = 0, \n", " qubit = 0)\n", - "assert repr(dp) == 'DrivePulse(0, 2000, 0.9, 200_000_000, 0, Gaussian(5), 0, 0)'\n", "assert isinstance(rop, Pulse)\n", "\n", "fp = FluxPulse(start = 0,\n", @@ -520,10 +493,67 @@ " channel = 0, \n", " qubit = 0)\n", "\n", - "assert repr(fp) == 'FluxPulse(0, 300, 0.9, Rectangular(), 0, 0)'\n", "assert isinstance(rop, Pulse)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SplitPulse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sometimes the length of the pulse is so long that it doesn't fit in the memory of one sequencer. In that case it needs to be played by two (or more) sequencers.\n", + "The `SplitPulse` class was introduced to support splitting a long puse into smaller portions:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dp = Pulse(start = 0,\n", + " duration = 30000, \n", + " amplitude = 0.9, \n", + " frequency = 500_000, \n", + " relative_phase = 0.0, \n", + " shape = Gaussian(5), \n", + " channel = 0, \n", + " type = PulseType.READOUT,\n", + " qubit = 0)\n", + "\n", + "sp = SplitPulse(dp)\n", + "sp.channel = 1\n", + "a = 8000\n", + "b = 16000\n", + "sp.window_start = sp.start + a\n", + "sp.window_finish = sp.start + b\n", + "assert sp.window_start == sp.start + a\n", + "assert sp.window_finish == sp.start + b\n", + "ps = PulseSequence(dp, sp)\n", + "ps.plot()\n", + "assert len(sp.envelope_waveform_i()) == b - a\n", + "assert len(sp.envelope_waveform_q()) == b - a\n", + "assert len(sp.modulated_waveform_i()) == b - a\n", + "assert len(sp.modulated_waveform_q()) == b - a" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -556,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -565,12 +595,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -585,12 +615,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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tFYcUHZ+kV7rXVpCvZwFFBgDO9/jjj+u3337TkiVLtHjxYu3atUujR49W06ZNFRQUpOjoaB0+fFjr1q3T0qVL5ePj4/QkpgoVKqhdu3Zat26dpk+frsjISEmZdyVXkMsye/ZsNWvWTImJierevbuefvpp9ezZU35+ftq9e7feeecdnThxQpL04osv2nQDd6u7775bBw8eVKNGjTR+/Hg1bdpUCQkJWrlypT744AMlJyfLw8ND06dPzzBt3bp1NW3aND3//PM6ePCg6tSpo1GjRqljx44KCQlRfHy8wsPDtXnzZn3//fe6cOGC9u3bl6skpi+++EK7d+/WzJkzdfLkST3++OMKDQ3VmTNnNGPGDK1atcpM7gKQv+Lj483hzLqmTGedtBkXF+e0mADAmdzd3eXj46NixYqZ/729veXt7a1Lly5l2nqcxWLJVQKTpCync1YCUG5jlZRlglNQUFCGVqWSk5MVHx+vuLg4JSQk2PwnwQlAUUMSkxMtW7ZMjz76qE0ThDdv3tSOHTu0Y8cOzZkzRytWrFC1atUKJJ4777yzQOYDAABgz8WoeE399YgmdK+pkv4Zn5a3WCz/j737jo+iTv8A/pltqZvee0ISUiFAqNJBUJrlQDwUhZ9YwXaeqJx62M6CcoqKXVBRsBdEEJAiTSGN9B7Se2+bzZbfH1xWlt0UIMmmfN6vV14s8/3OzJPNZLM788zz4JPVEyARX/4HfCKiy5VSUo9tJ89BrdHiUHolnrsuHNdGGt4xSUQ0GAmCgC+//BIPPvgg3n33XeTk5GD9+vWdzndxcemXuG655RYcOXJEl8AkkUhw0003dblOf3wvUVFR2L17N5YtW4aGhga89tpreO211wzmrV27Fi+++GK321q3bh3uvfdeo8lUMpkMn3zyCSZONJ5o+9BDD8HKygoPPfQQ6uvrsWnTJmzatMnoXJlMdlnt9SQSCX7++WfMnj0bGRkZ2LdvH/bt26c3Z968eXjkkUcwf/78S94+EXXtwt9bpVLZ5dy2tjbdY2OtHztTWFjY5XhpaSkmTJgAAFi5cqWubVJfa2pq0lUKWLlyZbeVqIh6gsfVwKFSqdDa2orW1lYoFAq0tbVBoVBApVJ1uk5kZCQWL15sdCwvL0/3vvFSeXt7Y/bs2UbHysvLUV9fD0EQDL6A80lOubm5EAQBfn5+kEgkuqIPrq6umDFjhtHtFhYWoqamBhqN5pLjdXFx6bTaZ1FRESorK/WWSSQSWFtbGz3eL04ac3JyYhVqE+PrFPUFUx1XxcXF3X4uvlRMYuoj8fHxWL58OVpbW2FtbY0nnngCs2bNQmtrK3bt2oUPPvgAmZmZWLhwIWJiYi65r6mnpyeSkpK6nffiiy/iiy++AADcfvvtXc6Njo7Gtm3bLikOIiIiou6oNVp8/mc+XtmXgaY2FbRaLTYvjzI6lwlMRGQKGo0WT/6QDLXm/EnIqqY23Pt5HOaHu+LZ6yLganPpF4SJiAYaqVSKrVu34t5778UHH3yAI0eOoKCgAE1NTbC2toa/vz/GjRuHa6+9FosWLeqXmJYuXYp169bpLsrPmzcPzs7O3a7XH9/LvHnzkJ2djddffx2//PILcnNz0dbWBldXV0ybNg333HMPpk6d2qNtrVmzBhEREfjvf/+L48ePo6qqCs7OzpgzZw4ee+wxhIWFdbn+nXfeiSVLluC9997D/v37kZGRgbq6OpiZmcHT0xORkZG4+uqr8be//Q1OTk6X9f16eHggPj4emzdvxq5du5CTkwMzMzOEhITgtttuw913343ff//9srZNRF278NpAdy3impubdY8v5cLUpVRok8vlsLGx6fH83mJtbW2S/dLQxuPKNDIyMlBTU9NtYqYxGo2m05+ZlZWVXhKTRCKBTCbTfUkkEkgkEkilUkilUr3/d8w1prtjpKGhAQcOHAAAXH311T0+psLDwwGc73Kj0WigUqk6/VIqlWhvb9f929Vr8aV2zVGr1WhubkZzczNkMhlGjBjR6TyRSMQEp37G1ynqC/15XF1Y0Ke3MImpjzz44INobW2FRCLB/v37MXnyZN3Y7NmzERQUhPXr1yMzMxOvvfYaNm7ceEnbl0qlXZaqBs7/sTly5AiA8x88brjhhi7nW1lZdbtNIiIiokuRWd6Ix75NRHxBnW7Zd/HFuGGsJ6YFdX+BioioP2RXNiGzrNFg+a8p5TiZU40NC0Jx83hvnsgjoiEhMjISW7ZsueT1Nm7c2KPzVzNnzuzxhRU7Ozu9NkqX6nK/l1WrVmHVqlXdznN2dsYLL7yAF1544TKi0zdp0iR8+eWXl72+q6srnn76aTz99NNXHEtnLCws8K9//Qv/+te/+mwfRGTowgSjoqIiREdHdzr3wopK3t7efRoXEZExWq0WLS0tAM5fVzSmIxnncnT13tDDwwPOzs4wMzODTCa7onZt/UkQBIjFYojFYr22oJfLwsICcrkcbW1tl/w8W1padjpWXFyM/Px8XUUnuVwOa2trWFpaDprnmoiGBiYx9YHTp0/j2LFjAIA77rhDL4GpwyOPPIJt27YhLS0Nb7zxBv71r39BKpX2ahwHDx5ESUkJgPN3tl1KeVkiIiKiK9Gu1uC9oznY8ls2lGrDksmbD2RiaiBLFxPRwBDsKseBf8zAkz8k41B6hd5Yo0KFJ75Lwo8JxXjpxlHwczJ+kpaIiIiI6HJcWI0tPT29y7kd4xKJBEFBQX0aFxERcL7NZUNDAxoaGtDY2IjGxkao1Wo4Ojp2WhjBysoK1dXV3W7b3NwclpaWsLCw0LU666o1blcJOMNJQECA7rFGo0FbW5uubd/FXxfrLPEMABobG6HRaHQ/7w4ikUhXGarjq7OqVkREvYFJTH3ghx9+0D1evXq10TkikQi33XYbnnjiCdTV1eHw4cOYN29er8bx6aef6h5310qOiIiIqLeklNRj/TeJSCkxXkZ02TgvbFgQygQmIhpQPOws8NHt0fjpbAme2Z2Kmmb9uxn/yK3B/Nd/x8NXB2PNVH+2vyQiIiKiXjF+/HjIZDIolUocPXoUjz/+uNF5SqUSf/zxh26d3r4pmohIq9VCoVCgvr5e92UsEQY4n/DSmQsTZUQiESwtLWFlZQVLS0u9pCVW97lyIpFI93xerCPBqaWlRddOzs7OrtNtddbSVKPR6I6HDhYWFrqEJltbW1haWvJcLxH1GiYx9YHjx48DOP9Hety4cZ3OmzFjhu7xiRMnejWJqbGxUZdM5efnh+nTp/fatomIiIiMUao0eOtQFrYeyYFKY9hCxN/JCi/cEIEpI5xMEB0RUfcEQcB1UefbXT73cyq+jy/WG29TafDS3nTsPluCTUtHI8yjf3rLExEREdHQJZfLMWfOHOzduxcHDx5EUVGRXou5Dt99952uMsYNN9zQ32ES0RCk1WrR2tqK2tpa1NXVoaGhocftyZRKJdra2oy2R7Ozs0NoaCisra1hYWHB5BYTuTDBydHRscu5KpXqkto8d1R6Ki8vB4AuK3MREV0qprj2gbS0NABAYGAgJJLO88RCQkIM1ukt33zzja4n7cqVK3v0BiE9PR0TJ06EnZ0dzM3N4eXlheuuuw6ffvop2tvbezU+IiIiGlrOFtZh8ZvHseVQtkECk0gA7pkxAnsfnMYEJiIaFBysZPjv8ihsXz0ennaGdzOmlDRgyVvHseW3LLQbaZlJRERERNRh+/btEAQBgiBg48aNRuf885//BHD+IvLatWuhVqv1xquqqvDYY48BOJ8csGbNmj6NmYiGh4KCApw5cwbZ2dmoqqrqcQITAMhkMrS1tXU65uLiwuo8g4hEIsHkyZMRGRkJf39/ODk5ddna72JyubzTMZVK1RshEtEwwkpMvUyhUKCqqgoAjN4tcSF7e3tYWVmhubkZhYWFvRrHha3kbrvtth6tU15ersuYBYDi4mIUFxfjp59+wssvv4xvvvkGoaGhlxVPUVFRl+OlpaW6x83NzXq9Von624UlMzsrn0nUn3hM0kBy8fHYptLgnWP52P5HEYwUX8IIJ0s8tygYER5yKFuboTRegZrosvD1kfraWHdzfLtmDLYcOYedMSW48GVOpdFi84FMVNQ1Yf3VIwDwmKSBZaAfjyqVChqNBlqt1uBCLQ1NF/6ch9PPfCgd4xf/DAfK96XVaqHRaKBSqXp8TrG5ubmPo6Kh4vjx48jOztb9v+PcPwBkZ2dj+/btevNXrVp1WfuZPXs2br75ZuzatQs//fQTrr76ajz00EPw8PBAUlISXnjhBRQUFAAAXn75Zdjb21/Wfoho+NFoNJ22bbOx6Vl1YZFIpGsdJpfLIZfLjVZgosFNJpPBwcEBDg4OumXt7e1oaGjQ+9JoDG/m6qpNXVxcHARBgL29PRwcHGBnZ8dWgkTUJSYx9bILe8BaW1t3O78jiak3TygWFBTg6NGjAIApU6YgMDCwy/kikQhz5szBggULMHr0aDg6OqKxsRFxcXF47733kJaWhtTUVMyaNQunT5+Gj4/PJcfk7e3d47nfffcdbG1tL3kfRH3hs88+M3UIRHp4TNJAsu3THfimYQRqNYZ35YigxRjzSoxTVeLYj6dxzATx0fDC10fqS7YArpdb4Eizp95rnkxQQ8g8hHey9xusw2OSBpKBeDxGRUXB1tYW1tbWqKioMHU41M+qq6tNHUKfO3XqlO7xUDnGw8LCUFJSovv/QPm+lEolmpqaUF9fj59++qlH69TX1/dxVDRUfPjhh/jkk0+Mjp04cQInTpzQW3a5SUwA8PHHH6OhoQG//PILDh8+jMOHD+uNi0QiPPXUU7jrrrsuex9ENDwoFArU1NSguroa9fX1mDhxIqRSqcE8GxsbCIIArVb/zkSpVApbW1vdl7W1NSsqDVNSqRSOjo66dnRarRZNTU26hKb6+nq0t7d3Wompo+0cALS0tKC4uBgikQh2dnZwcHCAvb09LC0t++37IaLBgUlMvezCfqEymazb+R2Zyh0v4L1hx44dujccPanC9N133xnNkJ02bRruu+8+3Hnnnfjkk09QXl6Ohx56CN99912vxUpERESDl0TQwkvajNo2/SQmJ3ErZlkWw0nS8z7qREQDnZukFctschCjcEa8whlaCLjKohTWIpZFJyIiIqIrZ2FhgT179uCLL77A9u3bcfbsWdTV1cHV1RXTpk3DunXrMHnyZFOHSUQDkFarRUNDA6qrq1FTU2NQcbCmpgaurq4G64nFYtja2qKlpQX29va6pCULCwsmLZFRgiDoqnF5enpCq9Wivb2908pKNTU1Bss0Gg1qamp0Y5aWlnB0dISTkxPkcjmPPSJiElNvu7A/aE96x3b0i7WwsOi1GDrusDQzM8Py5cu7nd9ViT+pVIoPP/wQf/zxBzIyMvD999+juLgYnp6elxRTd+3ySktLMWHCBADAjTfeiODg4EvaPlFvampq0v0erVy5skdV1Yj6Eo9JGkguPh5vl1ngbx/GobhOAYlIwD3TfLB6khekYpYEpr7H10cylZTSRuxJrsCjc6fqnVzjMUkDyUA/HouLi6HRaCCVSuHi4mLqcKgfqNVqXQUmR0dHiMViE0dEQ0VjYyPkcjlsbW17nOCRmZmJF198sY8jo6Fg+/btBi3jLtWqVasuqULTihUrsGLFiivaJxENfVqtFnV1daiqqkJVVVWX1ySrq6uNJjEBQHh4OMRiMRNH6LIIgtBlUY+eVL9saWlBS0sLCgsLIZPJdAlNbDtHNHwxiamXXVguryct4jqyoXvrZOLp06eRnp4OAFiyZEmXCUo9JZFIcMcdd2D9+vUAgKNHj17yhygvL68ez7WysupxH16ivmZtbc3jkQYUHpM0kHQcj5uWjsbLv2Zg09JRCHY1XjqYqK/x9ZH602QbG0we2fmNHW0aEZ7cV4ANi8IR6MLXRTK9gfgaWV5eDpVKBUEQmMwyDInFYv7cqdcIggCRSASJRNLj1zorK6s+joqIiKj3aTQa1NbW6hKXVKqeVQaura2FVqs1mqgkkfBSMfWd0NBQeHl56SovNTY2djlfqVSitLQUpaWlEIvFmDRpEo9RomGIv/W9zNzcHI6OjqiurkZRUVGXc2tra3VJTN7e3r2y/08//VT3uCet5HoqLCxM97i4uLjXtktEREQDX3ZFI/KqWnB1mPE7tqYEOuGHEY68Y4uI6H9OtrojPbMax7ccx6PzRuKOqf4QifgaSURERERERJcnOzsbZWVlUKvVPZovkUjg4OCg++J5OzIFQRBgY2MDGxsb+Pn5ob29HbW1tbqkpvb29k7XtbS0ZAIT0TDF3/w+EBYWhmPHjiE7OxsqlarTF9iOiknA+UzUK9Xe3o5du3YBAFxcXHDNNddc8TY78M0NERHR8KPRaPHxiTy88msGpCIB+x6aDttO3j3yvQIR0Xn57dZIV9oDAJQqDV74JQ2/pZfj1WWj4WVvaeLoiIiIiIiIaDDSaDTdJjBZWlrC0dERjo6OsLGx4fk6GnA62om7uLhAq9WisbERVVVVqK6uRktLi95cJyenTrdTVVUFsVgMOzs7HudEQxCTmPrA1KlTcezYMTQ3NyM2NhYTJ040Ou/o0aO6x1ddddUV73fPnj2orq4GcL5vdm9mp6ampuoee3h49Np2iYiIaGAqrGnBP78+iz/zagAASgD//Pos3rs5rOsViYiGubMKw5Nsf+TW4NrXj+HZ68NxfZQnT7ARERERERGRAYVCAXNzc6NjLi4uKC0tNVhubW0NZ2dnODk5wdKSN87Q4HFhlaaAgAC0tLToWiU2NjZ2msSk1WqRl5eHlpYWyGQyuLi4wM3Nje2CiYYQJjH1geuvvx4vvvgiAGDbtm1Gk5g0Go2u9ZudnR1mzZp1xfu9sJXc7bfffsXb66BSqfDxxx/r/j99+vRe2zYRERENLFqtFl/FFOLZ3aloVurf3fVnXg2+P1tuosiIiAaHBdb5ON3qisQ2J2gvWN7YpsLDX57FwdQKPH99BOytZCaLkYiIiIiIiAaG9vZ2VFRUoKysDM3NzZg8eTKkUqnBPFtbW5iZmaGtrQ02Nja6xKXOkp6IBhtLS0v4+PjAx8cHSqUSMpnx8ybNzc26qk1KpRJFRUUoKiqCXC6Hq6srXFxcjP4OEdHgITJ1AEPRhAkTMG3aNADARx99hFOnThnMee2115CWlgYAePDBBw1eTI8cOQJBECAIAlatWtXtPmtqarBnzx4AQGRkJKKionoU6+HDh1FXV9fpeHt7O9asWaOLdfHixfD29u7RtomIiGhwqWxsw5pPYvDYt0kGCUyCANwx1R8Lw51NFB0R0eAgEbSYYlmGj26JhKedhcH4nqRSzH/9dxzNrDRBdERERERERGRqWq0WNTU1SE1NxalTp5CdnY2mpiZotVpUVhr/rCgIAkJDQzFp0iSMGTMGXl5eTGCiIauzBCYAqKioMLq8sbER2dnZOHXqFFJSUlBVVQWtVmt0LhENbKzE1EfeeOMNXHXVVWhtbcW8efOwYcMGzJo1C62trdi1axfef/99AEBwcDAeeeSRK97frl27oFQqAVxaFaZPPvkES5YswZIlSzBz5kyMHDkSNjY2aGpqQmxsLN5//31dKzkXFxe88cYbVxwrERERDTyH0yvw6DdnUdWkNBjztLPAq8tGY/IIRzQ0NJggOiKiwSfa1w57H5qGjT+l4Lu4Yr2xisY23P7xadw+2RePXxsKC5nYRFESERERERFRf1EoFCgrK0NZWRna2tqMzqmoqICHh4fRMVtb274Mj2hQsLS0hLW1NZqamoyOa7VaXVs6mUwGNzc3uLu7M+mPaBBhElMfGTNmDL788kvceuutaGhowIYNGwzmBAcHY8+ePZDL5Ve8v45WcmKxGLfccsslrdvU1IQvvvgCX3zxRadzIiMjsWvXLvj7+19RnERERDSwKNrV+M8vafj0VL7R8eXR3nhyUSjk5izBS0R0qWzMpdh8UxTmhrpiw/dJqGtp1xv/5FQ+jmVX4fXlURjlZWeaIImIiIiIiKjPdFRdKikpQU1NTbfzFQoF1Go1xGLe7EJkjJubG9zc3NDc3IyysjJUVFToCn1cTKlUoqCgAAUFBXBwcIC7uzscHR0hCEI/R01El4JJTH1o8eLFSExMxBtvvIE9e/agqKgIMpkMgYGBWLZsGdatWwdLS8sr3k9WVhb+/PNPAMDVV18NNze3Hq/72GOPISoqCqdOnUJqaioqKytRU1MDMzMzuLq6Ijo6GkuXLsUNN9zAN0xERERDTEpJPR7clYDsCsO7VpyszfDSjZGYG+ZqgsiIiIaWBZHuiPa1x6PfJBq0kcutbMaNW0/igTlBuH92IE+kERERERERDQHt7e0oLS1FaWkpFApFl3MFQYCTkxPc3Nxgb2/Pz4VEPWBlZYURI0YgICAAtbW1KCsr67KFXE1NDdra2uDo6NjPkRLRpWISUx/z9fXF5s2bsXnz5ktab+bMmT3u0xkUFHTZPT1DQ0MRGhqKhx566LLWJyIiosFHo9Hio+N52PRrBpRqjcH43FBXvPy3SDham5kgOiKiocnFxhzbV4/Hjj8L8MKeVCja/3r9VWm0KKlr5YlqIiIiIiKiIaCkpATZ2dndXruzsrKCu7s7XFxcIJWyCjrR5RAEAQ4ODnBwcIBKpUJFRQXKysrQ2NhoMNfd3Z3nXogGASYxEREREQ0zuVVNeOXXdLSr9U+kmEtFeGpRGFZM8OGHOSKiPiAIAlZO8sWUEY74x5cJOFtUDwDwcbDEk4vCTBwdERERERER9QYrK6tOE5jEYjFcXV3h5uYGuVzez5ERDW0SiQQeHh7w8PBAU1MTSktLUV5eDrVaDZFIBFfXzrsO1NTUwM7ODiKRqB8jJiJj+FtIRERENMwEusjxyLyResvCPWzw8/3TcMtEXyYwERH1sRHO1vjm3il4cE4QZGIRNt80GtZmvMeIiOhSbd++HYIgQBAEnDt3rk/2ce7cOd0+tm/f3if7GKg2btyo+94vV8f6Gzdu7L3AiIiIBjgbGxtYW1vrLbO2tkZwcDAmT56MoKAgJjAR9TFra2sEBQVh8uTJCA4Ohq+vLyQS4+dempqakJSUhD///BP5+flob2/v52iJ6EI8S0pEREQ0DN01LQBHMyrxR1417poegEeuHgmZhPntRET9RSoW4eGrg7Fiog9cbcw7ndeiVMFSxo/uREREREREA4VCoUBRURHs7Ozg5ORkMC4IAtzd3ZGdnQ0XFxe4u7vDxsaGNw4SmYBYLIa7u3uXc4qLiwEASqUS586dQ0FBAdzc3ODl5QULC4v+CJOILsAzoURERETDkEgkYPPy0cirbMaUQMOTLURE1D+6SmAqrW/F4jdPYM00f9w1LQAiEU94ExENRdu3b8fq1asBAHl5efDz8zNtQERERGRUU1MTCgoKUFlZCQBoaGiAo6Oj0eQkV1dXODk5QSaT9XeYRHQJlEolysvL9ZZpNBqUlJSgpKQELi4u8Pb2NqiuRkR9h0lMREREREOQRqPFxyfyAABrpgUYneNuawF3W95JQkQ0EKk1Wjz8ZQKqmtrw0t50HMuqxOaborpMeiIiIhpotFqtqUMgIiK6Yg0NDSgoKEB1dbXe8sbGRjQ0NMDW1tZgHbFYDLFY3F8hEtFlam9vh1wuR0NDg9HxiooKVFRUwN7eHj4+PrC1tWVVNaI+xiQmIiIioiGmuqkNj3x9FkcyKiERCRjra4+xPvamDouIiC7Bu0dz8Eduje7/J7Krcc3rv+OVpaNxdZirCSMjIiIiIiIa+rRaLerq6lBQUIC6urpO5xUVFRlNYiKiwcHKygpjxoxBY2MjioqKUFlZaTQRv7a2FrW1tZDL5fD19YWDgwOTmYj6iMjUARARERFR7zmZXYVr3ziGIxnny1qrNFo8sDMe9a3tJo6MiIguRbtag4vPhdW2tOPOT2Pw1A/JULSrTRMYERERERHREKbValFVVYX4+HgkJiZ2msAkFovh6emJgADjFdCJaHCRy+UIDQ3FhAkT4OnpCZHIeBpFY2MjkpOTER8fj+rqalYeJeoDTGIiIiIiGgJUag1e25+BWz76ExWNbXpjxXWt+D2z0kSRERHR5XhobjB23jkJ7raG7eM++yMfS946jqzyRhNERkSD0caNGyEIgu5O4YaGBmzcuBGRkZGwtraGi4sLFixYgJMnT+qtV1FRgSeffBLh4eGwsrKCo6MjrrvuOsTHx3e7T41Ggx07dmDBggVwc3ODhYUFIiIisHTpUrzzzjtQKpXdbqO2thaPP/44QkJCYGFhARcXF8ydOxdff/11j77vju9548aNXc6bOXMmBEHAzJkze7TdiyUnJ+P555/H/Pnz4eXlBTMzM1hbWyMoKAi33347/vjjD6PrHTlyBIIgYPXq1bpl/v7+urg7vo4cOWJ0/R9++AHLli2Dj48PzM3NYWdnh+joaDzzzDOora3tNu6ioiKsXbsWAQEBMDc3h4eHB5YsWYKDBw9e1vNgTE9/BkRERKam1WpRU1OD+Ph4pKSkoLHR+OctiUQCX19fTJw4EYGBgbCwsOjnSImoL5mbmyMwMBCTJk2Cn58fpFKp0XkdyUxtbW1Gx4no8rGdHBEREdEgV1zXigd3xiMm3/BChbPcDK8vj8JVgU4miIyIiK7EpABH7H1wGh7/Ngn7Usr0xjLLm7D4reN49roILBvnxRLmRNRjhYWFmDt3LjIzM3XLmpubsXfvXuzfvx87d+7EsmXLkJiYiAULFqC4uFg3r6WlBT/99BN+/fVX7N27F7NmzTK6j5qaGixZsgQnTpwwWH7y5EmcPHkSW7duxd69e+Hr62t0G2lpaZg7dy5KSkp0yxQKBX777Tf89ttvWL16NaZPn34lT0WvOHLkiNHnQalUIjs7G9nZ2fj000/x+OOP48UXX+yVfdbW1mLp0qU4dOiQ3vK2tjbExsYiNjYWW7duxY8//ohJkyYZ3caxY8ewaNEiNDQ06JaVlpZi9+7d2L17N5OOiIhoWKmrq0NeXp7e38WLyWQyeHl5wcPDA2KxuB+jIyJTkEql8PX1hZeXF8rKylBUVASFQqE3x83NDebmhjefEdGVYRITERER0SD2a0oZ1n+TaLRd3PRgZ2y+aTScrM1MEBkREfUGO0sZ3rl1LHadKcQzu1OgaNfoxhTtGqz/JhGncqrx/PURsDLjR3wi6t6yZctQVFSEJ554Atdccw0sLS1x/Phx/Pvf/0ZDQwPuuOMOREdHY9GiRWhtbcULL7yAGTNmQCqVYt++fXjhhRfQ1taGVatWISsrCzKZTG/7arUaixYtwqlTpwAAM2bMwLp16+Dj44O0tDTs2rUL+/btQ1paGubMmYOEhARYW1vrbaOhoQHz58/XJTAtX74ct99+O1xcXJCZmYnNmzdj27ZtSE5O7p8nrQsqlQpWVlZYuHAhZs+ejZCQENjY2KCiogIpKSnYsmUL8vPz8dJLLyE4OFiv6tL48eORlJSEH3/8EU8++SQA4Ndff4WHh4fePvz9/XWP29raMHfuXMTFxUEsFmPFihVYsGAB/P390d7ejt9//x2bN29GRUUFFixYgPj4eINEsYKCAl0Ck0gkwl133YWlS5fC1tYWiYmJeOmll7Bx40ZER0f34TNHREQ0cNTX13eawGRubg5vb2+4ubl12l6KiIaujtaRHh4eKC8vR0FBAVpbWwEAPj4+Jo6OaGjiGU4iIiKiQUjRrsaLv6Thk1P5BmMSkYD114zEmqkBEIlYmYOIaLATBAF/n+CD8X4OuH9nPNJK9U+ufx9fjLOFdXhrxViEediYKEqi3qfRaFHb0n3LsaHE3lLW5+/fEhIScPToUUycOFG3LDo6GkFBQVi0aBEaGxsxceJEaLVanD59GiNGjNDNmzBhApycnLB27VoUFBRgz549uOGGG/S2/+677+oSmG677TZs374dgiBArVbD29sb8+bNw5YtW/DSSy8hJycHzz33HF5++WW9bTz33HMoLCwEAPznP//BE088oRsbN24cli5dikWLFmH//v29/vxcqqioKBQVFcHOzs5gbP78+Vi3bh0WLVqEAwcO4JlnnsFtt92mq95gZWWFiIgIxMTE6NYJDg6Gn59fp/t79tlnERcXBzs7Oxw8eBDjxo3TG586dSpuueUWTJ48GaWlpdiwYQM+//xzvTmPPPKI7kLtjh078Pe//103Fh0djWXLlmHatGl6cREREQ1lnp6eKCoqgkql0i2zsLCAr68vXFxcWPmWiCAIAtzc3ODq6ory8nIoFIpO20lqNBpkZWXBw8MDcrm8nyMlGvyYxEREREQ0yGRXNBm9iA0A3g4WePPvYxHlbdf/gRERUZ8KdLHG9/dNwQt70vDZH/pJrLlVzbh+6wn8e3EYbplovDUT0WBT26LEuOcPmjqMfhX75Fw49nEVzYceekgvganDwoUL4evri/z8fFRWVuKdd97RS2DqsHr1ajzyyCNQKBQ4duyYQRLT22+/DQBwdnbGW2+9ZfSi38aNG/HDDz8gPT0dH3zwAZ599lmYmZ3/vpVKJT766CMAwKhRo/D4448brC+VSvHRRx8hICAA7e2GFUn7k5NT122bZTIZNm3ahKioKOTn5yMhIcEg8ainmpqadM/vc8891+l2fH198dRTT+G+++7D119/jffffx9WVlYAgLKyMnz//fcAgEWLFuklMHWQy+V4//33jR4nREREQ5FEIoG3tzfy8vJgbm4OX19fuLq6MnmJiAx0JDN1pbS0FGVlZSgrK4OLiwv8/Pw6TXgiIkOse0hEREQ0iHwdU4jFbx43msC0cJQ79jwwjQlMRERDmLlUjOeuj8DWW8ZCflH7OKVKg7J6hYkiI6LB4uabb+50bNSoUQDOn5hfvny50TkWFhYICgoCAOTm5uqNlZSUIC0tDQBw0003dXrXsUQi0bVVq62tRVxcnG4sNjYWtbW1AIDbb7+904uHXl5emDdvXqffi6m0tbWhoKAAqampSE5ORnJyMrRarW787Nmzl73to0ePor6+HgCwdOnSLudOnz4dANDe3o7Y2Fjd8sOHD0OtVgOAXmu7i02YMAHh4eGXHSsREdFA0t7ejpycHOTnG1Y07+Dp6Yng4GCMHz8ebm5uTGAiosuiVqv1XmsqKipw5swZZGVlQakcXpWGiS4XKzERERERDRKVjW14ZncqWtvVesvNpSJsXByO5eO9eYKFiGiYWBDpjggPW6zbGYfEovMXtCf4O+DBOUEmjoyIBrrg4OBOxzpaojk5OcHe3r7beY2NjXrLk5OTdY+7q+Jz4XhycjImT54MAEhKStItHz9+fJfbmDBhAvbs2dPlnP7Q3NyMLVu2YNeuXUhJSdElCRlTVVV12fu5sL2bu7t7j9crKyvTPb7U5zclJeUSIiQiIhpYNBoNCgsLUVBQAJVKBZFIBDc3N10FyAuJxeJL+vtKRGRMSUmJQbVYrVaLkpISlJeXw8fHBzY2NiaKjmhwYBITERER0SDhLDfDy38bhbVf/HWnerCrNd5aMRbBruytTUQ03Pg4WuKbe6bg5X3p+D6+GFtuHgOJmAWXiahrlpaWnY6JRKJu51w47+JknZqaGt1jFxeXLrdxYQuGC9e7lG24urp2Od4fzp07h9mzZyMvL69H81tbWy97XxUVFZe1XktLi+7xYHt+iYiILpeDgwPS0tL0Kp9oNBqcO3cOI0eONGFkRDSUeXh4QKvVoqCgwODzklqtRl5eHmQyGRwcHPTemxPRX5jERERERDSILBzljtN5vvjkVD5WTPTB04vCYC4VmzosIiIyEZlEhKcWhWHdrEDYW8k6nadSa5jgRIOOvaUMsU/ONXUY/cresvPf48GmNyqEDoYqoytXrkReXh4EQcDq1atx8803IzQ0FM7OzpDJZBAEARqNBmLx+ffsF7aWu1QXXgSJi4uDVCrt0XpeXl5Glw+G55eIiOhStbS0ICwsDDY2NkZbN1VUVMDf3x8y2dB530VEA4dYLIaPjw/c3d1RUFCA4uJig88ASqUSwcHBaGhoQHNzMyszEV2ESUxEREREg8yGhaGYOdIFs0K6vnOaiIiGj64SmJKL67Huizi8dlMUxvl23h6KaKARiQQ4Whu2+qCBy8HBQfe4vLy8y7kXtji7cL0L29iVl5d32f6uu30IggCtVguNRtPlvObm5i7HO5Oeno7jx48DADZs2IDnn3/e6LzeusPa0dFR99jZ2bnT5KSuXPz8ent7dzq3u+eXiIhoIGlvb0deXh5KS0s7TQhwdnZmAhMR9QupVIoRI0bAy8sLeXl5Rt9b29jYIDMzE/X19fD39zfa6pJoOOJtmEREREQDTG5lE947mtPpuJlEzAQmIiLqkaY2FdZ9EYdz1S1Y/t4pfHgs94qqgBARdSUiIkL3+M8//+xy7unTp42uFxkZqXt85syZLrfR3bhcfr7lcm1tbadztFotsrOzu9xOZ1JSUnSPly9f3um8mJiYLrfT04pIY8aM0T0+ceJEj9a5WG8+v0RERAOBRqNBUVERTp8+jdLSUqNzbG1tMWbMGISFhcHCwqKfIySi4czMzAwhISEYO3YsbG1tjc4pLy/HmTNnOn0NIxpumMRERERENID8nFiCJW+dwIt707H7bImpwyEiokFMq9Viw3dJOFfdAgBQabR4fk8a7t0RhwZFu4mjI6KhyMPDA6GhoQCAr776Ck1NTUbnqdVqbN++HcD5ykBjx47VjY0bN05XLeizzz7rNPGyuLgY+/fv7zIef39/AF0nEe3duxd1dXVdbqczKpVK97irak7vvvtul9sxNzfXPW5ra+t03ty5c2FpaQkA2LJly2Ulpc6aNUvX2u6TTz7pdN6ZM2eQnJx8ydsnIiLqT3V1dYiNjUVOTo7e3+UOMpkMYWFhGD16NNs1EZFJyeVyjB49GmFhYUarwV3YOppouGMSExEREdEA0KZS4+kfk7Hui3g0tZ0/6fL4t4nIrTR+4YeIiKg7inYNKhsNL4bvSynDkjePI6Wk3gRREdFQt3btWgBAZWUlHnjgAaNznn32WaSmpgIA7rzzTr22CWZmZli9ejUAICEhAZs2bTJYX6VS4c4774RSqewylhkzZgA4XxXKWOWisrIy3H///T34rowLCgrSPe5IyrrYO++8gx9//LHL7bi7u+se5+R0XpHVzs4O69atAwCcPHkSDz/8cJet8srLy/Hhhx8a7Ou6664DAPz000/46quvDNZramrC3Xff3WXMREREpqRWq5GRkYGzZ8+ipaXF6HhhYSFCQ0Ph7Ozc46qHRER9SRAEODs7IzQ0FPn5+XrJl3K5HG5ubiaMjmjgYBITERERkYkVVLdg6Tun8OmpfL3lzUo1Nh/INFFUREQ02FnIxNixZiLunx1oMHauugU3bD2JXacL2F6OiHrVPffcg8mTJwMAtm3bhjlz5uDbb79FXFwcDh48iDVr1uCFF14AAIwYMQJPPfWUwTaefvppeHl5AQAee+wxrFixAvv27UNcXBx27dqFKVOmYO/evYiOju4ylrvuugsSiQRarRaLFy/G66+/jpiYGJw8eRKbNm3CmDFjUF9fr5eMdCnGjBmja4X33nvvYfny5fj5558RGxuLH3/8EcuWLcN9992Hq666qtvtdFRjeuqpp3DgwAFkZmYiOzsb2dnZaG1t1c199tlnMXHiRADAG2+8gbFjx+Ltt9/GiRMnkJCQgMOHD+Ott97C9ddfDx8fH6NVoF577TVdq70VK1Zg7dq1OHz4MGJjY7Ft2zaMGzcO8fHx3T6/REREpiISifT+Pl7I3t4eZ8+eRXFxMUQiXgYlooFHJBKhtLQUCQkJcHBwAHD+BgkmXBKdJzF1AERERETD2b7kMjz6zVk0KgxLXv9trBeeuz7cBFEREdFQIRYJeGTeSIz1tcfDXyagruWvNnJKlQaPf5eEM+dq8fz1EbCQiU0YKRENFWKxGD///DOWLFmCEydO4NChQzh06JDBvNDQUOzduxfW1tYGY7a2tti3bx/mzp2LsrIy7Ny5Ezt37tSbs2rVKsyYMUNXtcmY8PBwvPLKK/jHP/6B2tpaPPzww3rjDg4O+OGHH/DUU08hKyvrkr9XQRDw2WefYfbs2aitrcVXX31lUNkoMjISX3/9NTw8PDrdjlwuxwMPPIBXXnkFcXFxmDdvnt744cOHMXPmTADnK1UdOHAAq1atwnfffYezZ8/qqjMZY6x1jp+fH3766ScsWbIEjY2N2Lp1K7Zu3ao35+mnn4YgCF224iMiIjIVQRAQFBSE2NhY3U0Z1tbWCAwMhCAI3VZrJCIaCFQqFXx9fREUFKTXYvpiRUVFsLOzM/rZiWgoYgoyERERkQkoVRo893Mq7tkRa5DAZCYR4ZWlo/DaTaNhKWPOORERXblZI12w54FpiPK2Mxj7Nq4I1799AjlsYUpEvcTBwQG///47Pv30U1xzzTVwdXWFVCqFvb09pkyZgi1btiAhIQG+vr6dbiM8PBwpKSlYv349goKCYGZmBicnJ8yaNQtffPEFtm3b1qNYHn74Yezbtw/z58+Hvb09zMzM4O/vj7Vr1yI+Ph7Tpk27ou81KioKCQkJuOeee+Dr6wupVAoHBwdMmDABr776Kk6fPq3XLq4zL730Ej744ANMmzYNDg4OEIs7TyyVy+X49ttvcezYMaxZswYjR46EXC6HRCKBg4MDxo8fj7Vr1+KXX37BgQMHjG5j5syZSElJwb333gtfX1/IZDK4urpi4cKF2LdvH5555pnLfk6IiIj6g5WVFby9vSEWizFixAiMHTsWtra2pg6LiOiSdZXAVF9fj5ycHMTFxSE3NxdqtbofIyMyDV4VIyIiIupnJXWtWPtFHOIL6gzGApytsPWWsQhxM7xjmoiI6Ep42lngq7sn48W9adh24pzeWEZ5I5a8eRwv/W0UFo/uvFoIEQ1OGzduxMaNG7udt337dmzfvr3beUeOHOl2jkgkwsqVK7Fy5UoAgFqtRkVFBQDAxcWlyySdDg4ODnj55Zfx8ssvGx1ftWoVVq1a1e125s+fj/nz53c63tX34+fn123bTR8fH7zzzjtdzuluG4IgYM2aNVizZk2X8y40depUTJ06tcfzL+bt7W1QgelCPT1uusKWpUREdCXq6+shEol0bVAv5uPjAw8PD5iZmfVzZEREfU+j0SAzMxPA+ffVhYWFqKqqQnBwMOzs7EwbHFEfYiUmIiIion50LKsSC7ccM5rAtGS0B35aN5UJTERE1GdkEhH+vTgcb68YC2sz/fuampVq3L8zHk//mIw2Fe/sIyIiIiIi01Cr1cjOzkZCQgLS09Oh0WiMzhOLxUxgIqIhq6SkBC0tLXrLWltbcfbsWWRmZkKlUnWyJtHgxiQmIiIion6g0Wjx5m9ZuO3j06htadcbk0lEeOGGCLxxc5TBBWUiIqK+sHCUO35adxVC3AzvaP70VD5+S6swQVRERERERDTc1dbWIiYmBsXFxQCAlpYW5OfnmzgqIqL+5+HhAR8fHwiCYDBWWlqKM2fOoLq62gSREfUtJjERERER9YN//ZCE1w5k4uJuCr6Olvju3im4ZaKv0Q8jREREfSXA2Rrf33cVlo3z0lt+41hPXBvhZqKoiIiIiIhoOFKpVMjIyEBiYiIUCoXeWEFBAZqbm00UGRGRaYhEIvj7+2PcuHFG22oqlUokJycjIyODVZloSGESExEREVE/WDrOGxKRfpLS3FBX/LRuKiI8bU0UFRERDXcWMjE2LRuNV5aOgplEhJGucjx/fQQTa4mIiIiIqN/U1dUhJiYGZWVlBmMikQgBAQGwtLQ0QWRERKZnZWWFMWPGYMSIERCJDNM7ysrKEBMTg9raWhNER9T72K+EiIiIqB+M87XHkwtDsXF3KkQC8Oj8ENw9PQAiES8SExGR6d0U7Y1IT1vIJCJYyniqgIiIiIiI+p5Go0FeXh6KioqMjtva2iI4OJgJTEQ07AmCAC8vLzg5OSEzM9MgYamtrQ2JiYnw8PBAQEAAxGKxiSIlunI8M0lERETUT26f4odz1S2YF+aKKYFOpg6HyKSUKg3qWpVoaFWhRalCc5saLUoVWpRqvf8r2jVQabRQqf/3r0YDlVqLdrUWirY2ZDR5QSQARbszYC6TQSQIEIkEiARALBIgFYtgIRXDXCqCuVQMC5n4f/8//6+VmQS2FlLYWkhhYyGBhVTMCjQ0bIW623Q5fiqnGhYyMaK87fonICIiIiIiGrIaGxuRnp6OlpYWgzGxWAx/f394eHjwMzoR0QXMzc0RGRmJsrIy5OTkQK1W642XlJSgtrYWYWFhsLa2NlGURFeGSUxEREREvaiiQQEnazOjFZYEQcDGJeEmiIqo72m1WtS2tKO8QYHyBgUqGtpQ3qBAZVMbalvaUdeiRG2LErXN5x83K9Xdb7RH7AAAmUkVvbI1qVj4X0KTFDbmUjhZy+AsN4OztRmc/vevs/yvL1asoeGiuK4Va7+IQ5NChY1LwvH3Cd68mEBERERERJdMq9WisLAQ586dg1arNRi3tbVFSEgIzM3NTRAdEdHAJwgC3N3dYW9vj4yMDNTV1emNt7e3QyqVmiY4ol7AM+5EREREveRwRgUe2pWAu2cE4L6ZgaYOh6hXtas1KKlrRWFNKwprW1BQ04LCmhYU17WioqENFY0KtKsNTz4ONu1qLaqalKhqUvZovo25BJ72lvC0s4CXvQU87SzgecG/jlYyJnrQoNemUuO+HbGoaT7/e7Hh+yTEF9TiuesjYC5leXIiIiIiIuoZhUKBtLQ0NDQ0GIwJggB/f394eXnxczQRUQ+Ym5tj1KhRKCkpQW5uLjQaDQAgODgYZmZmJo6O6PIxiYmIiIjoCqk1WrzxWxbePJQFrRZ49dcMRHnZsWUcDTpqjRbFta3IrmxETkUzciqbkFfVjKLaVpTWt0Iz+HOUel2DQoWG0gaklRqegAUAazMJ/J2sdF8Bzuf/9XOygo0574iiweGzU/k4W1Svt+zr2CKkljbg3VvHwdvB0kSRERERERHRYFJdXW00gcna2hohISGwsrIyQVRERIOXIAjw9PTUVWWysrKCs7OzqcMiuiJMYiIiIiK6ArXNSjz4ZQJ+z6zULdNogft3xuPnB6bC3dbChNERGafWaJFX1YTU0kZkVzQhp6IJOZVNyK1qhlKl6fd4ZBIRrGRiWMoksJSJYWkm0f3fXCqCVCyCRCRAIhYgEYkgFgmQigVoVO2Ii48HtMCoqChIpTJotIBGq4VGq4Vao0W7WoPWdg1alWoo2s9/tf7vS6FUo1GhQmObqs++t6Y2FZKK65FUXG8w5mRthhHOVghxk2Okmw1Guskx0k0OazN+TKOB5fYpfihvUOCDY3l6y1NKGrDozeN4/eYozBrpYqLoiIiIiIhosPDw8EBtbS2qq6t1y3x8fODr6wuRSGTCyIiIBjdLS0tERUUZbdPZQa1WQ6vVQiLhuUca2HiEEhEREV2ms4V1uO/zOBTXtRqMTQxwYCICDQiNinaklzUiteR8taC00gZklDdC0d43yUqCADhamcHVxgyuNuZwtjaDg7UMdhZS2FvKYGcphb2VDPaWUthZymBrIYVUfHknKhsaGvBO+j4AwL1zAmBjY3NZ21FrtGhUtKO+Vf+rrqUd1U1KVDYpUNWoRGVTGyobz7fO643nr6qpDVVNbfgzr0ZvuZe9xf8Sm+QIdbfBKE87eDtYsJw+mYxULMK/FoYhytsej35zFi1KtW6svrUd/7f9DB6cE4QHZgdBJOJxSkRERERExgmCgJEjRyImJgYikQghISGwtbU1dVhEREOCIAhdnj/MyspCQ0MDwsLCYG1t3Y+REV0aXlkjIiIiukRarRY7Txdi408pUKr1ExnEIgFPXBuCO6b6M+GA+l2rUo2UknokFNYhobAOiUX1KKhp6dV9yM0l8La3hLeDBbztLeFlbwF3Owu42pjD1cYMTtZml52UZCpikQA7SxnsLGU9mq/VatGsVKO8QYGSulYU17aipK4VRf97XFzXirJ6BVSX2X+vqLYVRbWtOJhWoVtmayHFKC9bRHranv/Xyw4etuZ8naF+tXCUO0a6WePuz2KRU9msW67VAq8fzMLZwjr8d3lUj3+XiIiIiIho+JFKpYiMjIS5uTmrgRAR9ZOKigqUl5cDAOLi4hAUFAQ3NzeeW6QBie8OiIiIiC6Bol2NJ39IxjexRQZjznIzvL1iLCb4O5ggMhpuNBotsiqacLawDglFdUgoqENGeSPUl5k4cyFbCykCXawxwtkKAc7W8HWwhLeDJbztLWFrKe2F6Ac3QRBgbSaBtbM1Rjgbv2tJrdGitL4V56pakFd1vlVf3v++CmtacKk/pvrWdhzLqsKxrCrdMkcrGSK9bDHOxx7j/OwR5W0HSxk/4lHfCnSR48d1U7H+m7P4JalMb+xwRiUWv3Uc79wyDhGevJuaiIiIiGg4UqvVyM7Ohru7e6cVk1kBhIio/7S2tiIzM1P3f61Wi8zMTNTX1yMoKAhisdiE0REZ4hluIiIioh4qqm3BPTtikVzcYDA2wc8Bb60YAxcbcxNERsOBUqVBUnEdTufV4sy5GsScq0GDQnVF23S1MUOIm83/EpbOJy0FuljDwUrGu3CukFgkwMveEl72lpga5KQ3plRpUFDTgpzKJmSWNSK9vBEZZY3Iq2q+pCS06mYljmRU4khGpW6f4R42GOdrj2hfB0T72cOVr0nUB6zNJHh7xVh8eCwPL+1L1ztuC2ta8bd3TuL56yOwLNrbhFESEREREVF/a2lpQWpqKpqbm1FbW4uxY8dCJmOlViIiUxIEAZaWlmhsbNRbXl5ejsbGRoSFhcHKyspE0REZYhITERERUQ8cz6rC/TvjUNvSbjB25zR/rL8mZNC10KKBralNhbj88wlLp/NqkFBYhzaVpvsVjZCIBAS6WCPM3Qah7jYI8zj/r4MVTySagkwiQqCLNQJdrDE/3E23XNGuRk5lEzLKzic1pZU1Irm4HjXNyh5tV63RIrGoHolF9dh24hwAwMveAhP8HDB5hCOmBDrB086iL74lGoYEQcCd0wMQ6WWLdV/Eoarpr+O0TaXBo98kIqGwDs9fH8GkSCIiIiKiYaCqqgrp6elQq9UAgLa2NqSlpWHUqFH8TEBEZELm5uaIiopCbm4uiouL9cZaWloQFxeH4OBguLq6mihCIn1MYiIiIiLqglarxXu/5+KVfekG7Z+sZGJsWjYaCyLdTRMcDSntag0SCutwPKsKJ7KrkFBYB9VltIYTCUCwqxxjfOww2ssOEZ62CHK1hpmEZYEHOnOpGOEetgj3+KsNl1arRUm9AklFdUgsqkdS8fkkpfpWw4RKY4pqW1FUW4zv4s+foPB1tMSUEY6YPMIJkwMc4Sw365PvhYaPSQGO+Pn+aVj7RRxi82v1xpyszXixgoiIiIhoiNNqtcjNzUVRUZHBWGNjI1paWljhg4jIxEQiEQIDA2Fra4uMjAxdwikAaDQapKeno76+HoGBgRCJeLM2mRaTmIiIiIi6UN7QhrcPZRskMAU4W+H9leMQ6CI3TWA06Gm1WmSWN+F49vmkpT9zq9GsVHe/4kU87SwQ5W2HKG87jPa2Q4SnDSxlfJs/VAiCAE87C3jaWeCaiPMJk1qtFoU1rThbVIe4glrE5tcipaShR63o8qtbkF/dgp2nCwEAQS7WmDLCEdODnTF5hCOPHbosbrbm2HnnJPznlzRsP3kOADBzpDMenBNk2sCIiIiIiKhPKZVKpKamor6+3mDMysoKYWFhsLS0NEFkRERkjLOzM6ytrZGamoqmpia9sdLSUjQ3NyM8PJytQMmkeIaaiIiIqAtutuZ4/eYo3PFJjG7Z1WGu2HzTaMjNpSaMjAaj+tZ2HMuqxKH0ChzLqkJlY9slrS8IwEhXOcb7OWC8vwMm+DnAzda8j6KlgUoQBPg4WsLH0RKLR3sAAFqUKiQU1iH2XC1i8msRl1+LxjZVt9vKqmhCVkUTPjmVD5lYhAn+Dpg50hkzRzpjhLM1q+hQj8kkImxcEo4xPnZ4+3A2Xl8eBZGIxw8RERER0VDV0NCAlJQUKJWGLdBdXV0RFBQEsZhVoYmIBhoLCwtERUUhJycHpaWlemMNDQ2IjY1FeHg4bGxsTBQhDXdMYiIiIiLqxpxQVzw4JwhbDmXhkauDcd/MQF6YpR7RarXIqmjCofQKHEqvQGx+bY+q5XSQiAREetligp8DJvg7INrXAbaWTJ4jQ5YyCaaMcMKUEU4AALVGi8zyRpw5V4NTOdU4lVuNupauW9Ap1Rocz67C8ewqPL8nDZ52Fpgx0hkzg50xJdAJ1mb8+Ejduy7KE4tGeUDcxd9JrVbLBDkiIiIiokGsvLwcmZmZ0Gg0essFQUBgYCDc3d35np+IaAATi8UIDg6Gra2tweu5UqlEQkICgoOD4ebmZsIoabjiWWgiIiKiHnhwThBmjnTGGB97U4dCA5yiXY2TOVU4lF6Bw+mVKK5rvaT1Q91tMDXQEVcFOmGCvwPbe9FlEYsEhLrbINTdBrdN9oNGo0VaWQNO5VTjZE51j9oXFte14os/C/DFnwWQigVMCnDEvHA3XB3qygpg1KWuEpiSi+vx5A/JeOPmKPg6WvVjVEQ0FG3fvh2rV68GAOTl5cHPz6/X93Hu3Dn4+/sDALZt24ZVq1b1+j4Gqo0bN+KZZ54BcD4BlYiISKvVIi8vD4WFhQZjZmZmCAsLY+UOIqJBxNXVFZaWlkhJSUFb219dAzpe752cnCCR8Pw09S8ecUREREQ43+brl6RS/H2Cj9FxkUhgAhN1qkHRjsPpFfg1pQxHMirR0k1yyIU87SwwNdAJVwU5YcoIRzhZm/VhpDRciUQCwj1sEe5hizXTAtCu1iCpuB6ncqpxNLOy2yph7WotjmVV4VhWFZ76IRmjvWxxdZgr5oW7IciFbeeoZ2qblbhnRyyKalux+M3jeOPvYzBrpIupwyIiIiIioh5QqVRIS0tDTU2NwZitrS3Cw8MhlbJ6NBHRYCOXyzF27FikpaWhrq4OwPnKeuHh4UxgIpPgUUdERETDXkZZI+7+LAbnqlsgFYuwdJyXqUOiQaCqqQ0HUsvxa0oZTmRXoV3ds7vTZRIRJgc4YtZIZ8wY6QI/R0smgFC/k4pFGOtjj7E+9lg7KxANinaczK7CkYxKHMmoRFmDosv1zxbV42xRPV7dnwk/R0tdQtM4H3u22ySjVGoN7t8Zj6La89XpGhQq/N/2M2zTSkSE/qkoRUREdCVUKhXi4uLQ2mpYbdrDwwMjRoyASCQyQWRERNQbZDIZRo0ahZycHBQXFyMoKIiV9chkmMREREREw9rPiSVY/02irnLOhu+TMNJVjkgvWxNHRgNRcV0r9iWX4deUMsScq0EXhWv0uNuaY1aIC2aPdMGUQEe2iKMBx8Zcimsi3HFNhDu0Wi0yy5twJKMCRzIqEZNf02WS3rnqFnxwLA8fHMuDq40Zro1wx6JR7hjLhCa6QG1LOyoa9ZPjtFrg1f2ZSCyqx2s3jYbcnHdtExERERENRGKxGHZ2dnpJTIIgIDAwEB4eHiaMjIiIekvH67qLiwsTmMikePWEiIiIhiWVWoNNv2bgvd9z9ZYrVRr864ck/Lj2KlbHIQBAZWMbfkkqxe6zJYjJr+3ROiIBGOdrfz5xKcQFI13lPJ5o0BAEASPd5BjpJsfdM0agUdGOo5mVOJBajkPpFWhUqDpdt7yhDdtPnsP2k+fgZmOOBZHuWDjKHWO87ZjQNMw5y83w/X1X4dFvzuKXpDK9sf2p5bj+7RN4b2U0Al2sTRQhERERERF1puPCdktLC+rr6yGVShEWFgY7OztTh0ZERL2sqwQmrVaL+vp6vv5Tn2ISExEREQ07Nc1K3L8zDieyqw3GRnnZ4t1bxzHhZJira1FiX3IZdieW4FROdY8qLsnEIlwV6Ij54W6YG+YKJ2uzvg+UqB/IzaVYNMoDi0Z5QKnS4HReDfanluFAajlK6ztvO1fWoMDHJ/Lw8Yk8eNj+ldAU5W3H19hhyspMgrdXjMV7v+filX3peq+tOZXNuP7tE9h802jMC3czXZBERERERGSUSCRCWFgYMjMzERgYCHNzc1OHRERE/ayoqAi5ubnw8vJCQEAAz/FRn2CDWiIiIhpWkovrsfjN40YTmG6K9sJXd0+Gh52FCSIjU2tqU+GH+GLcsf0Mxr9wEI9/l4QT2V0nMFnKxFgY6Y4tfx+D2KfmYtvqCbh5gg8TmGjIkklEmBrkhGevi8DJx2dj97qpeGB2IEa6yrtcr6RegQ+P5+GGrScxY9MRbD6Qibyq5n6KmgYSQRBwz4wR+OT/JsDOUr99XFObCnd9FovN+zOg6Wm/TiLqkY0bN0IQBN0J5oaGBmzcuBGRkZGwtraGi4sLFixYgJMnT+qtV1FRgSeffBLh4eGwsrKCo6MjrrvuOsTHx3e7T41Ggx07dmDBggVwc3ODhYUFIiIisHTpUrzzzjtQKpXdbqO2thaPP/44QkJCYGFhARcXF8ydOxdff/11j77vju9548aNXc6bOXMmBEHAzJkze7TdiyUnJ+P555/H/Pnz4eXlBTMzM1hbWyMoKAi33347/vjjD6PrHTlyBIIgYPXq1bpl/v7+urg7vo4cOWJ0/R9++AHLli2Dj48PzM3NYWdnh+joaDzzzDOore2+gmhRURHWrl2LgIAAmJubw8PDA0uWLMHBgwcv63noyhdffIGZM2fC3t4e1tbWiIiIwL///W/U1dUB6PnPioiITEsmkyEiIoIJTEREw1BVVRVyc893tigqKkJqairUarWJo6KhiJWYiIiIaNj4NrYIG75PQptKo7dcKhawcUk4Vkzw4Z0Dw4xao8WJ7Cp8G1eEX1PKoGjXdLuOrYUUV4e54toIN1wV6ARzqbgfIiUaeARBQKSXLSK9bPGPeSORXdGIPYll2JNUgszypk7XK6hpwZbfsrDltyxEedvhxrGeWDTKAw5Wsn6MnkxtWpAzdq+birs/i0VqaYPe2JZD2Ugqrsfry8fA9qJEJyK6coWFhZg7dy4yMzN1y5qbm7F3717s378fO3fuxLJly5CYmIgFCxaguLhYN6+lpQU//fQTfv31V+zduxezZs0yuo+amhosWbIEJ06cMFh+8uRJnDx5Elu3bsXevXvh6+trdBtpaWmYO3cuSkpKdMsUCgV+++03/Pbbb1i9ejWmT59+JU9Frzhy5IjR50GpVCI7OxvZ2dn49NNP8fjjj+PFF1/slX3W1tZi6dKlOHTokN7ytrY2xMbGIjY2Flu3bsWPP/6ISZMmGd3GsWPHsGjRIjQ0/PUaXFpait27d2P37t29lkykUqmwYsUKg8SzlJQUpKSkYMeOHX2SNEVERJenvb0dWVlZugRXIiIi4PxnjbS0NL1lVVVVSExMREREBKRSnr+h3sMkJiIiIhrylCoNnt+Tik9P5RuMudqYYest4zDO194EkZGpZJU34pu4IvwQX4zyhrZu51vJxLg6zBVLojwwNdAZMgkLmhJdLNBFjgfnyvHg3CBkljdiT2Ipfk4sQU5l5xWXEgrrkFBYh2d3p2LmSGdcP8YTc0NdmRw4THg7WOLbe6dgw/dJ+D6+WG/scEYllrx9HO+vjMZIt64rfdEQptEArTWmjqJ/WTgAor59n7Fs2TIUFRXhiSeewDXXXANLS0scP34c//73v9HQ0IA77rgD0dHRWLRoEVpbW/HCCy9gxowZkEql2LdvH1544QW0tbVh1apVyMrKgkymn4SqVquxaNEinDp1CgAwY8YMrFu3Dj4+PkhLS8OuXbuwb98+pKWlYc6cOUhISIC1tbXeNhoaGjB//nxdAtPy5ctx++23w8XFBZmZmdi8eTO2bduG5OTkPn2uekKlUsHKygoLFy7E7NmzERISAhsbG1RUVCAlJQVbtmxBfn4+XnrpJQQHB+tVXRo/fjySkpLw448/4sknnwQA/Prrr/Dw8NDbh7+/v+5xW1sb5s6di7i4OIjFYqxYsQILFiyAv78/2tvb8fvvv2Pz5s2oqKjAggULEB8fb5AoVlBQoEtgEolEuOuuu7B06VLY2toiMTERL730EjZu3Ijo6Ogrfn7++c9/6hKYRo4cifXr12PUqFGor6/H119/jQ8++ADLly+/4v0QEdGVUygUSEpKQktLC1paWhAVFQWJhJcRiYgIMDMzQ2BgILKysqDV/lU9u6GhAQkJCYiMjGTyK/UavvsgIiKiIa2iUYG1n8fhzDnDdgrj/ezx9i1j4SLnm+vhoKZZiZ8SivFtXDGSiuu7nS+TiDAnxAWLR3tg1kgXWMiYVEHUU8GucgRfLcdDc4OQWd6EPYkl+DmxFLmdtJBTabQ4mFaBg2kVkJtJsCDSHTeN98JYH3tWyBviLGRibL5pNEZ52eL5PWlQX9BGLr+6Bde/fQL7H54ObwdLE0ZJJtNaA2waYeoo+tejOYCVU5/uIiEhAUePHsXEiRN1y6KjoxEUFIRFixahsbEREydOhFarxenTpzFixF8/gwkTJsDJyQlr165FQUEB9uzZgxtuuEFv+++++64ugem2227D9u3bIQgC1Go1vL29MW/ePGzZsgUvvfQScnJy8Nxzz+Hll1/W28Zzzz2HwsJCAMB//vMfPPHEE7qxcePGYenSpVi0aBH279/f68/PpYqKikJRURHs7OwMxubPn49169Zh0aJFOHDgAJ555hncdtttEIvPv6+0srJCREQEYmJidOsEBwfDz8+v0/09++yziIuLg52dHQ4ePIhx48bpjU+dOhW33HILJk+ejNLSUmzYsAGff/653pxHHnlEV4Fpx44d+Pvf/64bi46OxrJlyzBt2jS9uC5HUlIS3nzzTQDA2LFjcfToUb2EtTlz5mDKlCm4/fbbr2g/RER05Zqbm5GYmKhr99rc3IzU1FRERERA1McJ1kRENDi4u7vD3NwcKSkpem3kWlpaEB8fr2tXTnSl+M6DiIiIhqz4glos2nLcaALT7ZN98fmaSUxgGuJUag0OpJbjzk9jMOGFg9i4O7XLBCaJSMCskc7YfNNoxD45F+/cOg4LIt2ZwER0mQRBwEg3Of4xbyR+e2QGflx7FVZN8YNjF63jGttU+DKmEH975xSu/u/veP/3HFQ1dV8xjQYvQRCw+ip/fL5mIpys9Y+N68d4MoGJqJc99NBDeglMHRYuXKir2FNZWYnnnntOL4Gpw+rVq3V32B47dsxg/O233wYAODs746233jKajLpx40aEhIQAAD744AO0tf31Oq9UKvHRRx8BAEaNGoXHH3/cYH2pVIqPPvpoQLQscHJyMprA1EEmk2HTpk0AgPz8fCQkJFz2vpqamnTP73PPPWeQwNTB19cXTz31FADg66+/RnPzX0nEZWVl+P777wEAixYt0ktg6iCXy/H+++9fdpwd3n33XWg059s1v//++0YvaNx222249tprr3hfRER0+err65GQkKBLYOrQ3Nys9zeaiIjI3t4eY8aMMajIq1QqkZCQgNpaw2sxRJeKSUxEREQ0ZIlFAupa2/WWmUlEeG3ZaDxzXQRbgg1hBdUt2PRrOqa8dAh3fhqDA6nlUF1Q3eNiEZ42eHpRGP7YMAfbVk/AjWO9IDc3/UUxoqFEEASM9rbDxiXh+GPDHHy8KhqLR3vArIvX4uyKJvznl3RM+s9vuPuzGBxKL4dKrenHqKk/TQpwxO77p2K0tx0AYIyPHTYuCTNtUERD0M0339zp2KhRowCcf83urMWXhYUFgoKCAAC5ubl6YyUlJUhLSwMA3HTTTZDLjbeDlEgkurZqtbW1iIuL043FxsbqTnzffvvtnVbk8/Lywrx58zr9Xkylra0NBQUFSE1NRXJyMpKTk/XaLZw9e/ayt3306FHU159PyF+6dGmXc6dPnw4AaG9vR2xsrG754cOHdXdNX9ja7mITJkxAeHj4ZccKAAcPHgQAREZGdppwBQD/93//d0X7ISKiy1ddXY3ExESoVCq95ZaWlhgzZgwsLCxMFBkREQ1UVlZWGDNmDCwt9W86U6vVSEpKQkVFhYkio6GC7eSIiIhoyBrlZYfnr4/A+m8SAQCedhZ4b+U4RHjamjgy6gttKjUOpJZj1+lCHM+u6na+i9wMN4zxxI1jvTDSzfgFNiLqG1KxCLNDXDE7xBWNinbsTS7DD/HFOJVbDa2RfEOVRotfU8rxa0o5XG3M8LexXrgp2ht+Tlb9Hzz1KXdbC3x19yS8+msG7pgaADMJK+ER9bbg4OBOxzoqCjk5OcHe3r7beY2NjXrLk5OTdY+NVXu60IXjycnJmDx5MoDzLcg6jB8/vsttTJgwAXv27OlyTn9obm7Gli1bsGvXLoPWCherqur+fWpnLmzv5u7u3uP1ysrKdI8v9flNSUm5hAj/0tbWhqysrB7vh4iI+l95eTnS09MNltvY2CAiImJAVDwkIqKBydzcHFFRUUhJSdHdaAEAWq0WaWlpUKlU8PDwMGGENJgxiYmIiIiGtJuivZFYVIdzVS148+9jYN9FCyManLIrmrDrdAG+iy9GTbOyy7lmEhHmh7vhxrGemBroBImY1biITE1uLsVN0d64KdobJXWt+D6+GF/HFOJcdYvR+eUNbdh6JAdbj+RgcoAjVk72xdVhrpDy93nIMJOI8a+FXVdgqmhQwMWGLWGHPAsH4NEcU0fRvywc+nwXF98teyGRSNTtnAvnXZysU1NTo3vs4uLS5Tbc3NyMrncp23B1de1yvD+cO3cOs2fPRl5eXo/mt7a2Xva+LveO5paWv/6m9tfzW1tbq6tANRh+jkREw01RURFycgzfZzk4OCAsLAxiMW8mICKirkmlUowaNQppaWkGN2tkZWVBrVbD29vbRNHRYMYkJiIiIhrynl4UDpEAJqwMIW0qNfYmleHzP/Nx5lz3fbajvO2wfLw3Fo5yhw3bxBENWB52Flg7KxD3zRyBP/Nq8NWZQvySXApFu/EWcqdyq3EqtxoucjPcPN4bN0/wgYcd2x0MdeeqmrHkreO4cawX/rUwlAlsQ5lIBFg5mToKukydtYHr7230tZUrVyIvLw+CIGD16tW4+eabERoaCmdnZ8hkMgiCAI1Go7sYrDVWcrCHLkwai4uL63GFDC8vL6PL++v5HQw/RyKi4UKr1SI/Px/5+fkGY66urggODtYlKxMREXVHJBIhLCwM2dnZKCkp0RvLzc2FWq2Gn5+faYKjQYtJTERERDSoabVabDtxDi1KFdbNDjI6RybhyZehoriuFV/8mY8vzxSiqqnrqks25hLcONYLN0/wRoibTT9FSES9QRAETApwxKQAR2y8Lhy7z5bgqzOFOFtUb3R+RWMbthzKxluHszEn1BW3TvLFtEAniES8aDrUNLepcPdnsWhQqLD95DmkljTg7VvGwlluZurQiAjnqzd0KC8v73LuhS3OLlzvwjZ25eXlXba/624fgiBAq9VCozGeDNuhubm5y/HOpKen4/jx4wCADRs24Pnnnzc678LqR1fC0dFR99jZ2bnT5KSuXPz8dnVndHfPb1c6Wg72ZDtXsh8iIuo5rVaLvLw8FBYWGox5eXkhICCAiadERHTJBEFAYGAgJBIJCgoK9MYsLHizIV06JjERERHRoNWqVGPD90n4Pr4YABDqboM5oWxFMNRotVqcyK7Gp6fO4WBaOTTd3Lw+0d8Bf5/gg2si3GAuZflzosHOxlyKWyb64paJvkgva8CXZwrxQ3wxalvaDeZqtMCB1HIcSC2Hr6MlVkzwwbJobziwleiQoNVqsf7bRGSUN+qWnT5Xg8VvHsc7t47FGB/7LtYmov4QERGhe/znn39i5cqVnc49ffq00fUiIyN1j8+cOYNp06Z1uo0zZ850GY9cLkdDQwNqazuv3KnVapGdnd3ldjqTkpKie7x8+fJO58XExHS5nZ5eMB4zZozu8YkTJ7rcZ2cufn67SmLq7vntirm5OYKCgpCVldXtdq5kP0RE1DNarRY5OTkoLi42GPP394ePj48JoiIioqFCEAT4+/tDIpEgNzcXABAYGMjW0XRZWJaAiIiIBqXCmhYsffekLoEJAB7alYDcyiYTRkW9qUHRjm0n8jBn81Hc+tGf2J/aeQKTk7UMd88IwKFHZuDLuyfj+jGeTGAiGoJC3Gzw78XhOPXEHLy+PArjfDtPWsmvbsGLe9Mx6cXf8I+vEpDUSRUnGjwEQcCskS4GFRbLGhRY/t4f2HW6oJM1iai/eHh4IDQ0FADw1VdfoanJ+HtztVqN7du3AzhfGWjs2LG6sXHjxumqBX322Wedtl8rLi7G/v37u4zH398fQNdJRHv37kVdXV2X2+mMSqXSPe6qmtO7777b5XbMzc11j9va2jqdN3fuXFhaWgIAtmzZclmt6WbNmqVrbffJJ590Ou/MmTNITk6+5O1faO7cuQCApKQkxMfHdzrv448/vqL9EBFR9woLC40mMAUFBTGBiYiIeo23tzeCgoLg7+8PT09PU4dDgxSTmIiIiGjQOZFdhSVvHUdKSYPe8sY2FU7kVJsoKuotGWWN2PB9Eib95zc8szsVuZWdXxCaFOCAt1eMxcnH5+CJa0MR4Gzdj5ESkamYS8W4fownvr13CvY+OA23TPSBlcx44qJSpcF3ccVY/NZxLHv3JPYklkKl7rqtEA1cS8d54dt7psDTTr8cuVKtwePfJeGJ75LQplKbKDoiAoC1a9cCACorK/HAAw8YnfPss88iNTUVAHDnnXfCzOyvlpBmZmZYvXo1ACAhIQGbNm0yWF+lUuHOO++EUtl1e+EZM2YAOF8V6sSJEwbjZWVluP/++3vwXRkXFPRXO+uOpKyLvfPOO/jxxx+73I67u7vucU5OTqfz7OzssG7dOgDAyZMn8fDDD3fZKq+8vBwffvihwb6uu+46AMBPP/2Er776ymC9pqYm3H333V3G3BN33323rsrUXXfdZTTR6/PPP8cvv/xyxfsiIqKuubu76xJhO4wcORIeHh4mioiIiIYqDw8PJsjSFWE7OSIiIho0tFotPjyWhxf3phlU5LE2k2DzTaMxL9zNNMHRFdFotDiaWYmPjufheHZVl3OtZGLcONYLKyf7IthV3k8REtFAFepugxduiMQTC0LxfXwxPv8jH+lljUbnnjlXizPnauFha46Vk/3w9wnesLNkq7nBJtLLFj+tuwrrvojHqVz95OWdpwuQXtaAd28dB1cb8062QER96Z577sHnn3+OU6dOYdu2bcjPz8d9990HHx8fpKenY9euXbqklREjRuCpp54y2MbTTz+Nr776CkVFRXjssceQkJCA2267DS4uLsjMzMTmzZtx5swZREdHd1ll6a677sLWrVuhUqmwePFiPP3005g6dSqUSiVOnDiBzZs3o729Xdf27FKNGTMGERERSE5OxnvvvYfa2lqsXLkS7u7uKCoqwo4dO/DNN9/gqquuMppEdeF2zM3NoVAo8NRTT0EqlcLX1xci0fn7Tz09PWFhcT5589lnn8XRo0fx559/4o033sCRI0dw5513IioqClZWVqitrUVKSgoOHjyIvXv3IjIyEmvWrNHb32uvvYYDBw6gsbERK1aswNGjR7F06VLY2NggMTERL730EjIzM7t9frszevRorF27Fm+99RZiYmIQHR2Nxx57DJGRkaivr8fXX3+N999//4r3Q0RE3ZNKpRg1ahTOnj2L1tZWhIaGwsXFxdRhERHRMNTc3AwLCwvd5x2iizGJiYiIiAaFVqUaj32biJ/OlhiMBThb4f2V0Qh0YRWewaZVqca3cUXYdiIPOV1UXAKAQBdr3DbZFzeM8YTcXNpPERLRYGFtJsHKSb64daIPYvNrseOPfPySVAalkapLJfUKvLwvHW/8lokbx3ph9RQ/BDEpclBxtDbDZ3dMwMv70vHBsTy9sfiCOizcchzv3DoW4/0cTBQh0fAlFovx888/Y8mSJThx4gQOHTqEQ4cOGcwLDQ3F3r17YW1t+B7e1tYW+/btw9y5c1FWVoadO3di586denNWrVqFGTNm6Ko2GRMeHo5XXnkF//jHP1BbW4uHH35Yb9zBwQE//PADnnrqqctKYhIEAZ999hlmz56N2tpafPXVVwaVjSIjI/H11193WelCLpfjgQcewCuvvIK4uDjMmzdPb/zw4cOYOXMmgPOVqg4cOIBVq1bhu+++w9mzZ3XVmYyxsbExWObn54effvoJS5YsQWNjI7Zu3YqtW7fqzXn66achCMIVJxdt3rwZJSUl+O6775Cenm7w8/L398eXX36JESNGXNF+iIioe2ZmZhg1ahSam5vh6Oho6nCIiGgYqq+vR1JSEuzt7REaGspEJjKKRwURERENeIU1LbjxnZNGE5jmhrrix7VXMYFpkCmrV+CVfemY/NJvePKH5E4TmMQiAddGuOGLOyfiwMPTcdtkPyYwEVGXBEFAtJ8DXr95DE49MRv/nBcMVxszo3MV7Rp88WcBrv7v71j50Z/4La0cmotL/dGAJRGL8K+FYXjj5iiYS/VPb1Q1teHv7/+Bz06dg1bLnylRf3NwcMDvv/+OTz/9FNdccw1cXV0hlUphb2+PKVOmYMuWLUhISICvr2+n2wgPD0dKSgrWr1+PoKAgmJmZwcnJCbNmzcIXX3yBbdu29SiWhx9+GPv27cP8+fNhb28PMzMz+Pv7Y+3atYiPj8e0adOu6HuNiopCQkIC7rnnHvj6+kIqlcLBwQETJkzAq6++itOnT+u1i+vMSy+9hA8++ADTpk2Dg4MDxGLjbVKB80lP3377LY4dO4Y1a9Zg5MiRkMvlkEgkcHBwwPjx47F27Vr88ssvOHDggNFtzJw5EykpKbj33nvh6+sLmUwGV1dXLFy4EPv27cMzzzxz2c/JhaRSKb799lt89tlnmDZtGmxtbWFpaYnQ0FBs2LABsbGxCAgI6JV9ERFR98zNzZnAREREJtGRwKRWq1FVVYW0tLQu22PT8MVKTERERDSgHcuqxP0741HX0m4w9vDcYNw/OxAikWCCyOhyJBbV4ePjefg5sRSqLhIFHKxkWDHBB7dM8oG7rUU/RkhEQ4mjtRnWzQ7C3TNG4JekUmw7cQ4JhXVG5x7LqsKxrCqMcLbCndMCcP0YT5hLO7+ATAPHdVGeCHKR4+4dMSisadUtV2m0eOrHFCQW1eO56yP486RhbePGjdi4cWO387Zv347t27d3O+/IkSPdzhGJRFi5ciVWrlwJAFCr1aioqAAAuLi4dJmk08HBwQEvv/wyXn75ZaPjq1atwqpVq7rdzvz58zF//vxOx7v6fvz8/LpNhvTx8cE777zT5ZzutiEIAtasWWPQ+q0rU6dOxdSpU3s8/2Le3t4GFZgu1NPjpiduvfVW3Hrrrb2yLSIi6pxWq0VZWRnc3NwgCDxfRkREA4NarUZqairUarVuWUciEysy0cWYxEREREQDklarxXu/5+KVfem4ONdFbibB6zdHYU6oq2mCo0ui0WhxMK0cHxzLxZlztV3ODXa1xv9d5c/kASLqVVKxCNdFeeK6KE/EF9Ri+8lz2NNJMmVOZTMe/y4Jr+7PxKopvrh1ki/sLGUmiJouRZiHDXavm4r7d8bjWFaV3tjXsUWI9rPH8vE+JoqOiIiIiKjvabVaZGRkoLy8HPX19Rg5ciQTmYiIaEAQi8UICQlBcnKyXvWlqqoqpKenIzQ0lH+zSIdJTERERDQgPflDMj7/s8Bg+QhnK7x/WzRGOLN93EDXplLjx/gSvPd7Tqft4jrMHOmM/7vKH9OCnPhhhYj61Bgfe4zxsceGBaHY8Uc+Pv+zADXNSoN5VU1teHV/JrYeycFN0d64Y6o/vB0sTRAx9ZSdpQzbV0/Aq/sz8M6RHN3yayPccFO0twkjIyIiIiLqW1qtFpmZmSgvLwcAlJeXQ6vVIiQkhOdZiIhoQLC3t0dkZCSSkpL0EpkqKyshEomYfEs6TGIiIiKiAemaCDfsPF2gV4VpXpgrXrtpNOTmUtMFRt1qVLRj5+kCfHQ8D+UNbZ3OM5eKcONYL/zfVX4IdJH3Y4RERICrjTkemTcSa2cF4qezJfj4eB7SyxoN5rUo1dh+8hw+PXUOCyLdcff0EYj0sjVBxNQTYpGAx64JQaSnLf759Vl42llg07LRPAlGREREREOWVqtFdnY2ysrK9JZXVlbCy8sLcjnPuRAR0cBgZ2dnNJGpvLwcYrEYgYGBPIdDTGIiIiKigWlakDMeuyYEL+5NhyAAj1wdjPtmBkIk4hvYgaqiUYFtJ85hxx/5aFSoOp3nIjfD7VP8sGKCD+yt2KKJiEzLXCrGTdHeWDbOC79nVeGD33NxPLvKYJ5GC/ycWIqfE0sxOcARd00PwMyRzjyxMkAtiHRHoIs1JCIB1mY89UFEREREQ9e5c+dQUlKit0wQBISHhzOBiYiIBhw7OztEREQgKSkJWu1fd7GXlJRALBbD39+f59uGOZ7JIyIiogHrrukBOFfdgqvDXDA7xNXU4VAnciub8MGxXHwbWwylWtPpvBA3Oe6aHoBFozwgk4j6MUIiou4JgoAZwc6YEeyM5OJ6fHAsFz8nlkJ9YUnA/zmVW41TudUIdbfBfTNHYEGkO8RMsh1wgl27vmCTVFQPO0sp2wQSERER0aBVWFiIgoICvWWCICAsLAyOjo4mioqIiKhr9vb2CA8PR0pKil4iU2FhIcRiMXx9fU0YHZkak5iIiIjIpBTtaphJREYz6wVBwIs3RpogKuqJ5JJGfPZTFvallEFreI1fZ6K/A+6ZMYIVS4ho0IjwtMUbN4/Bo/NH4uPj57DrTAFalGqDeWmlDbh/Zzw2H8jEvTNGYE4g73IeLCoaFLjjkzNQqjXYcvMYTA92NnVIRETDmrarDxRERGRUaWkpcnNzDZaHhITAycnJBBERERH1nKOjI0JCQpCWlqa3/Ny5cxCLxfDy8jJRZGRqTGIiIiIik8mrasZdn8bgtil+WDmJmfWDRUm7JWIVLnhne0KncwQBmBfmintmjMAYH/v+C46IqBd52Vvi6cVheHBOED4/nY9tJ86hsrHNYF5eVTPWf5sIV7kMgSoHhJrVmiBa6imlSoP7Po9Dxf9+lqu2ncaj80Nwz4wAJtsSERER0aBQWVmJzMxMg+XBwcFwcXExQURERESXzsXFBRqNBhkZGXrLc3JyIJVK4erKDh3DEZOYiIiIyCQOp1fggV3xaFSo8MxPKQhxk2O8n4Opw6JOaLVaHM+uwuv70xHbFNDpPJlYhBvHeuLO6QEY4WzdjxESEfUdW0sp7psZiDum+uPH+BK8+3sOciubDeaVNypRDg/EKlxgcaIAa2aOhK2F1AQRU1feO5qDmPy/Es00WuDlfelIKq7DpqWjYWXGUyVERERENHDV1NQYVK0AAH9/f7i7u5sgIiIiosvn5uYGtVqN7OxsveUZGRkwNzeHra2tiSIjU+GZOSIiIupXGo0WW49k47UDmboWZCqNFvfuiMPP90+Fm625aQMkPVqtFr+lVeDNw9k4W1jX6Ty5mQQrJvng/67yh6sNf4ZENDSZScS4abw3/jbOC/tTyvDW4WyklDQYzFNoJXjzaD62/1GMlZN98X9T/eFkbWaCiMmYVVf5IbmkHr+mlOst/yWpDNkVTXhvZTT8naxMFB0RERERUecaGxuRkpJi0IbT29sbPj4+JoqKiIjoynh6ekKtViMvL0+3zNHREXK53IRRkamITB3AUJefn49HHnkEISEhsLKygoODA8aPH49NmzahpaXlira9fft2CILQo6/t27d3u72Wlha88sorGD9+PBwcHGBlZYWQkBA88sgjyM/Pv6JYiYiIAKCpTYV7P4/Fq/v/SmDq4GQtQ7taY5rAyIBGo8UvSaVYsOU41nwa02kCk5O1GR67JgQnnpiNJ64NZQITEQ0LYpGAayPd8fP9U7F99XhM6KSSYGObCluP5GDqy4ew8acUlDco+jlSMkZuLsU7t4zDo/NH4uLucZnlTVjy1nEcSi83vjIRERERkYm0trYiKSkJGo3++TMPDw/4+/ubKCoiIqLe4e3tDS8vLwDn/7aFhYVBJGI6y3DESkx9aPfu3bj11lvR0PDXnbktLS2IiYlBTEwMPvzwQ+zZsweBgYEmjPK87OxsLFiwAFlZWXrLMzIykJGRgQ8//BCff/45Fi1aZKIIiYhosMupbMLdn8Uiu6LJYGzRKHe8snQULGV8a2JqKrUGuxNL8PbhHKM/qw5WQjseuDoEt08LgrlU3I8REhENHIIgYOZIF8wc6YLTeTXYcjAdx3NqDeYp2jXYfvIcvjhdgBUTfHDPjBGsPGhiIpGAtbMCEeZhgwd3xqNBodKNNSpUuOOTGDw8NxjrZgVCJBK62BIRERERUd/TarVITk5Ge3u73nJnZ2cEBgZCuDg7n4iIaJARBAEBAQGwtbWFo6Mj/7YNY7xS2Efi4+OxfPlytLa2wtraGk888QRmzZqF1tZW7Nq1Cx988AEyMzOxcOFCxMTEXHEptF9//RUeHh6djndkLRrT2NiIhQsX6hKY7rzzTtx8882wsLDA4cOH8eKLL6KhoQHLly/HiRMnEBUVdUWxEhHR8HMwtRwPf5mAxjaV3nKRADx+bQjunBbAN6Qm1q7W4Lu4Imw9koP86s6rRXramSNQmYORsjr8PXo2E5iIiP5ngr8Dti6PwLNvbkO8whm57ba4qOgglKq/kpluHu+Ne2eOgLuthUnipfNmjXTB7vun4u7PYpFe1qhbrtUCmw9kIqm4HptvGg25udSEUQ5NYrEYKpUKKpUKarUaYjHfUxDRpdNoNFCr1QDA1xEiGtIEQcCIESOQmpqqe92zs7NDSEgIz6kREdGQIQgCnJycTB0GmRiTmPrIgw8+iNbWVkgkEuzfvx+TJ0/Wjc2ePRtBQUFYv349MjMz8dprr2Hjxo1XtL/g4GD4+fld1rqbNm1CZmYmAOCVV17Bo48+qhubPHkyZs6ciRkzZqClpQUPPfQQjhw5ckWxEhHR8KHRaLHlUBZeP5hlMGZnKcVbfx+LqUF8Q2pK7WoNvo8rxpZDWSiqbe10XoCzFdbNCsTMADk+eC+mHyMkIhpcnCUKzLMuxDU3zcaOmHJ8H18MlUY/nUmp0uDTU/nYdboQy/+XzORhx2QmU/F1tMJ3903B+m8S8XNiqd7YgdRyXPf2Cby/MhqBLtYminBosrS0RFtbGwCgrq4Ojo6OJo6IiAajpqYmaP/Xq9zCgn9LiWhoc3BwwOjRo5GcnAypVIrw8HC22SEiomGlo6Uq//4Nbfzp9oHTp0/j2LFjAIA77rhDL4GpwyOPPILQ0FAAwBtvvGFQArS/tLe3Y8uWLQCA0NBQPPLIIwZzpkyZgjvuuAMAcPToUZw5c6ZfYyQiosGpQdGOuz6LNZrAFOpug93rpjKByYRUag2+iS3CnNeOYv23iZ0mMIW4yfH2irE48PAM3DjWCxK21CEi6hF/R0tsWjYaRx6diRUTfSAVG75+KtUafPZHPmZuOoInf0hCcV3nyaTUtyxlErz59zHYsCAEF/+py61sxvVvn8CvKWWmCW6IsrOz0z2uqKhARUUFFAqFLhmBiKgrGo0GDQ0NKCv767X5SivdExENBnK5HGPGjEFkZCQkEtYpICKi4UOpVOLs2bPIzs7muYMhju9w+sAPP/yge7x69Wqjc0QiEW677TY88cQTqKurw+HDhzFv3rx+ivAvhw8fRn19PQDg9ttv7zRrcdWqVXjvvfcAAN9//z3Gjx/fbzESEdHgk13RhLs+i0FuZbPB2JLRHnj5b6NgIWOpf1NQa7T4MaEYbx7KRl6V4c+nw2gvW6ybHYQ5IS4QMXGJiOiyedlb4j83RGLtrEC8cyQbX54pRLv6ospMag12/FGAL88UYlm0N+6bOQJe9pYminj4EgQBd00fgTB3W9y/Mw61LX/dbNTUpsKZvBrMD3czYYRDi7m5OWxtbXXnJKqrq1FdXQ1BENgSaojSarVQKpUAgMbGRra+oSuiVqv1LlxYWFjAysrKhBEREfUfc3NzU4dARETUr5qbm5GcnAyFQoGGhgZYWFjA29vb1GFRH2ESUx84fvw4AMDKygrjxo3rdN6MGTN0j0+cOGGSJKaOWC+O52LR0dGwtLRES0sLTpw40R+hERHRIKVoV+PvH/yBysY2veUiAdiwIBR3TPXnBQsTUGu0+DmxBG8czEJuF8lLY33s8ODcYEwPcuLPiYioF3naWeD56yNx38xAvHMkB1+eKYRSrdGb067W4os/C/B1TCGWjvPGutmB8GSbuX43NcgJP62bint2xCKlpAEAMDnAEY9fG2LiyIYed3d3yGQyVFZW6pZptVqoVCoTRkV9RaPRoKmpCcD5KhIs/0+9xcLCAj4+Pvz8QkRDilqtZmI3ERERzv9NTExM1N0UAwC5ubmwsLCAkxO7fQxFTGLqA2lpaQCAwMDALst5hoT8dQK0Y53LtXr1amRkZKCqqgo2NjYIDAzE3Llzce+998LT07PT9VJTU43GczGJRILAwEAkJiZeVqxFRUVdjpeWluoeNzc3o6Gh4ZL3QdRbOk6qXvyYyFQG4zG5fo4/Hv0hXfd/OwsJNt0Qiol+dmhsbDRhZMOPRqvF/rQqvHssH7nVnbcpivSQ477pvpjibwdBEDr9OQ3G45GGLh6PNND05Ji0FgGPzvbBymhXfHyqEN8mlBlUZmpXa7HzdAG+iS3EsjHuuGOKN5ytZX0aO+mzlQDbbonAs3uzEVNQjxcXB6KleXC9zgyW10iZTAYXFxcoFAooFAqoVCpoNJruV6RBR6vV6ipvWVtbmzgaGuxEIhFkMhksLS1hbm5+ya9zzc2d39hBRGRqzc3NOHv2LEaMGAFXV1dTh0NERGRSYrEYgYGBenkNwPn8iqioKLaVHoKYxNTLFAoFqqqqAABeXl5dzrW3t4eVlRWam5tRWFh4Rfs9cuSI7nFHCfY///wTr732Gl5//XXcfffdRtfrSC6ysrKCnZ1dl/vw9vZGYmIiKisr0dbWBjMzsx7Hdynl3L777jvY2tr2eD5RX/rss89MHQKRnsF0TEaZuSKhzRlO4lZcIy1A3N4ExJk6qGFEqwVy221wptUFtZrOy4w7i1sw3qICPi1NSPz1FBIvYR+D6XikoY/HIw00PTkm7QHcbC1BvMIZaW32UEO/Kkm7WosvYkrwVUwRIsyqEWVeBQuRuo8iJmO8tICjIMaXn8aaOpQrwtdIGmgSEhJMHQINcx0JdUREA41SqURSUhLa29uRnp6O5uZm+PuzqjkREQ1vzs7O8Pf3R15enm6ZRqNBcnIyxowZw1arQwyTmHrZhVULenJXWUcS0+XeFRkQEIAbb7wRkydP1iUK5ebm4ttvv8U333wDhUKBe+65B4Ig4K677uo03p7G2qGpqemSkpiIiGj4mWhRDguRChFmNZAI2u5XoF6h1QLn2uU4o3BBtbrzFkRO4laMN6+Ar7QRPA9GRGQ61iIVplmWYqx5JeIVTkhtczBIZlJBhIQ2Z6S0OWCUeTVGm1XBTMRKNf1BEABLofPEsRq1GbKUthhvXgER/54SERER0RXQaDRISUlBW1ubbllhYSHMzMy67LhBREQ0HHh7e6O1tRVlZWW6ZUqlEikpKYiKimIb1iGESUy9TKFQ6B7LZN2X++9IBGpt7by9S2duuOEG3H777QYZ+OPHj8fy5cvx888/48Ybb0R7ezsefvhhLFmyBG5ubkbjvZRYLyfe7ipNlZaWYsKECQCAG2+8EcHBwZe0faLe1NTUpLtTeeXKlSxzTyY3kI/J1LImhLkNnHiGuz/yarHlaD6SSzpv2RfiaoV7p/liZpDDZd3FN5CPRxp+eDzSQNMbx2RFYxs+PlWEr+NLDdvMQYxYhQuy4IFVk7ywItoDljKeoDGVRoUKK7YnIL+hFWbuwXj5uhDYWUpNHZYOXyNpoOExSQNJZmYmXnzxRVOHQUSko9VqkZGRgYaGBr3lcrkc7u7uJoqKiIho4BAEAUFBQVAoFKirq9Mtb2pqQmZmJkJCQli5cIhgElMvu7BUmVKp7HZ+R0a9hUXnlRI6013LtUWLFuHpp5/GU089hZaWFnz00Uf417/+ZTTeS4n1cuLtrrXehaysrGBjY3NJ2yfqK9bW1jweaUAZKMekUqXBf35Jw/aT5/Dm38dg8WgPU4c0rMUV1OLVXzNwMqe60zkhbnI8NDcY88JcIeqlUhED5XgkAng80sBzucekjQ3wn6XOWDs3BG8dysJXMUVQa/STmRoUKmw5cg6fnynBvTNH4NZJvjCXMpmpP2k0Wjz8XQzya87f4HMqrw4rPjmL91aOQ7jHwGuPztdIGmh4TJKpXVhxnohoICgqKkJFRYXeMjMzM0REREAkEnWyFhER0fAiEokQFhaG+Ph4vaIrFRUVsLKygo+Pjwmjo97Cdz69TC6X6x73pEVcc3MzgJ61c7scd911ly7j8OjRowbjHfFeSqxA38VLREQDX0WDAis++APbT54DADz2bSIyyjqv/EN9J72sAWs+icGNW092msAU7GqNrbeMxS8PTMM1EW69lsBERER9y9POAi/eOAq//WMGbhjjabT1Z3WzEs/vScOMTYfx2R/5UKrYYq6/JJfU4/esSr1lRbWt+Ns7J/FDfLGJoiIiIiKiwai2tha5ubl6y8RiMSIiInrURYOIiGg4kUqliIiIMGgfl5eXh5qaGhNFRb2JSUy9zNzcHI6OjgDOZ853pba2VpcY5O3t3SfxuLi46OIpLjY8kdpRIam5uVmv7JoxHS3hnJ2d9VrLERHR8BFzrgYL3zyOmPxa3bIWpRr37IiFol1twsiGl3NVzXhwVzyufeMYDqaVG53j52iJN26Owr4Hp2NBpDuTl4iIBik/Jyv8d3kU9j80HQsi3YzOKW9ow1M/JGP2a0fwdUyhQeUm6n2jvOyw667JcJHrfzZWtGvw0JcJeHZ3KtrVTCojIiIioq4pFAqkpqYaLA8JCeHN5ERERJ2wtLREaGiowfLU1FS0tLSYICLqTUxi6gNhYWEAgOzsbKhUqk7npaen6x4b+yXrLV31fuyI9eJ4LqZSqZCTkwOgb2MlIqKBSavV4pOT53Dz+3+gsrFNb0wmEeHemSPYxqYflNUrsOH7JMzdfBQ/JpRAa+QatZuNOV68MRIH/jED10V5MnmJiGiICHKVY+st4/Dz/VMxJ8TF6Jyi2lY8+k0irnn9d+xPKYPW2B8K6jXjfO3x8/1TMc7X3mDs4xN5uPXDP1HV1GZkTSIiIiIiQK1WIyUlxeA6kp+fH5ycnEwUFRER0eDg6OgIPz8/vWWd/W2lwYVJTH1g6tSpAM5XN4qNje103oXt3a666qo+iaWyshJVVVUAAA8PD4PxjlgvjudiMTExuqpRfRUrERENTK1KNf7x1Vn8+6cUqC6q7OBpZ4Fv75mCm6L7pqIgnVfTrMQLe1IxfdNhfPFngcHPAQAcrGR4cmEojjw6E3+f4AOpmG/ziIiGoghPW3y0ajy+u28KpgYav7CRVdGEuz6Lxd/eOYk/c423G6Xe4WJjjp13TsLKSb4GY3/m1WDxm8dxtrCu/wMjIiIiogFNq9UiKysLTU1NessdHR3h4+NjoqiIiIgGFx8fHzg7O+sta2lpQXZ2tokiot7Aq1t94Prrr9c93rZtm9E5Go0Gn376KQDAzs4Os2bN6pNY3n//fd3dtzNmzDAYnzlzJmxtbQEAn3zySad36m7fvl33+IYbbuj9QImIaEAqqG7Bje+cxPfxhi1JpwY6Yff9UxHpZWuCyIaHRkU7/nsgE9NePoQPjuVBqTJsSyM3k+AfVwfj9/WzsGZaACtiERENE2N97LFjzUTsvHMSoo1UAgKAuII6LH//D6zadhqpJQ39HOHwIZOI8Nz1EXhl6SjIJPqnWUrrFVj23il8FVNoouiIiIiIaCAqKSlBeXm53jILCwuEhIR02V2DiIiI/iIIAkaOHAkrKyvdMrlcblChiQYXJjH1gQkTJmDatGkAgI8++ginTp0ymPPaa68hLS0NAPDggw9CKpXqjR85cgSCIEAQBKxatcpg/XPnziE+Pr7LOH7++Wc8++yzAM6/+V29erXBHJlMhgceeAAAkJaWhldffdVgzqlTp/DRRx8BOJ8INX78+C73S0REQ8PhjAosfus40koNL3reN3MEPvm/CXCwkpkgsqGvTaXGR8fzMP2Vw3jjtyw0K9UGc8wkItw9PQC/r5+FB+YEwdpMYoJIiYjI1CaPcMTX90zGttXjEepuY3TOkYxKLNhyDA/uikdBdUs/Rzh83BTtja/vngx3W3O95UqVBuu/ScSTPyQZTUgmIiIiouGlvr4eOTk5esvEYjHCw8MhkfD8DhER0aUQi8WIiIiARCKBh4cHoqKiYG5u3v2KNGDx3VAfeeONN3DVVVehtbUV8+bNw4YNGzBr1iy0trZi165deP/99wEAwcHBeOSRRy55++fOncOsWbMwefJkLF68GKNHj4aLiwsAIDc3F9988w2++eYbXWWlV199FZ6enka39eijj+LLL79EZmYm1q9fj+zsbNx8882wsLDA4cOH8Z///AcqlQoWFhZ4/fXXL+8JISKiQUOj0eLtw9nYfDAToJtv9QABAABJREFUFxfoszaT4NVlo3FNhJtpghviNBotfjpbglf3Z6CottXoHIlIwM0TvHH/7CC42vCNOBERnb/rbNZIF8wIcsbuxBK8tj8TBTWGyUo/JpRgT2IpVkz0wf2zg+AsNzNBtEPbaG877L5/KtZ+Hoc/82r0xnb8UYDUkgbsWDMRljKejiEiIiIajlQqFVJTUw26YoSEhOhVkSAiIqKeMzc3x/jx4yGT8cb7oYBnzfrImDFj8OWXX+LWW29FQ0MDNmzYYDAnODgYe/bsgVwuv+z9nDp1ymilpw6Wlpb473//i7vuuqvTOXK5HHv27MGCBQuQlZWF999/X5dk1cHGxgaff/45oqKiLjtWIiIa+BoU7fjHlwk4mFZhMDbC2QrvrYxGoIu1CSIb2rRaLY5lVeGlvelINVL5CgAEAbghyhMPzQ2Gj6NlP0dIRESDgUgk4LooT1wb4Y4vzxTgjd+yUdXUpjdHpdHi01P5+Ca2CHdM9ced0wNgYy7tZIt0OZyszbBjzUS8+Es6Pj6RpzcW4GwNC7Z+JSIiIhq2xGIxvLy8kJeXp0tk8vHxgZOTk4kjIyIiGtyYwDR0MImpDy1evBiJiYl44403sGfPHhQVFUEmkyEwMBDLli3DunXrYGl5eRchx40bhx07duDUqVOIiYlBaWkpqqqqoFKpYG9vj/DwcMyZMwdr1qzRVWjqSmBgIOLj4/H222/j66+/RnZ2NpRKJby9vbFgwQI8+OCD8PX1vaxYiYho8MipaMLRzEqD5ddGuGHTstFsWdYHkorq8dK+NJzIru50zrwwV/xz/kgEu15+4jMREQ0fMokIKyf74caxXvj4eB7e/z0XjW0qvTktSjXePJSNHX/kY+2sQNw6yRfmTK7pNVKxCE8vDsMoL1s8/l0iFO0ahLnb4PnrIyAIgqnDIyIiIiITEQQB3t7esLGxQVpaGiwsLODn52fqsIiIiIgGDF6J7GO+vr7YvHkzNm/efEnrzZw506Cc6IXkcjluueUW3HLLLVcaoo6VlRXWr1+P9evX99o2iYhocBnjY4+nF4XhqR9TAAAiAXjsmhDcNT2AF9x6WX51M17dn4ndZ0s6nRPta48nFoRgnK9DP0ZGRERDhZWZBPfPCcItk3yx9XA2Pv0jH0qVRm9ObUs7nt+Tho+P5+Ghq4Pxt7FeEIv4N7+3XD/GE0Gu1njs20RsXTGOiWJEREREBACwtbXFuHHjoNVqec6NiIioD6nVamRlZcHZ2RmOjo6mDod6gElMREREpOfWSb5IKKzH4YwKvPn3MbgqkOWse1NVUxveOpSNz//MR7vaeMJyoIs1HrsmBHNDXXgii4iIrpiDlQxPLgrD6qn+eP1AJr6NK4Lmoj9BJfUKrP8mER8fz8Pj14ZgRrAz/wb1knAPW+xeN7XL57NNpYaZhAlORERERMOJVMq2zkRERH2ppaUFqampaG5uRk1NDcaNGwczMzNTh0XdYBITERER6REEAS/cEIGaZiU87CxMHc6Q0dymwkfH8/De0Rw0K9VG57jZmOMfVwfjxrGekIhF/RwhERENdZ52Fti0bDTumh6ATb9mYH9qucGc9LJGrNp2BlcFOuKJa0MR4WlrgkiHnq4SmAprWrD8vVNYf00Irh/j2Y9RERERERERERENTS0tLYiLi4Naff56THt7O9LT0zFq1CjeuDfA8eoYERHRMHQ6rwbbT+R1Om4uFTOBqZe0qzX47I98zNh0BJsPZBpNYJKbS/DYNSE4/M+ZuGm8NxOYiIioTwW5yvH+bdH47r4pmOhvvGXpiexqLHrzOB7+MgFFtS39HOHwoWhX477P41BSr8BDXybg6R+TDVr+EREREdHgVVZWhoaGBlOHQURENOxYWFjAzs5Ob1ldXR2KiopMExD1GCsxERERDSNarRYfHMvFy/syoNFqMcLFGtOCnE0d1pCk1WqxN7kMm37NQF5Vs9E5MrEIt0/xxX0zA2FvJevnCImIaLgb62OPXXdNwpHMSry8Nx3pZY0Gc76PL8aexFKsusoPa2cGwtaSLS960zO7U5BUXK/7/6en8pFUXI+tt4yFuy0TyomIiIgGs6amJmRmZgIA/Pz84O3tzcoPRERE/UQQBIwcORIxMTFQKpW65Xl5ebC1tYWNjY0Jo6Ou8DZ/IiKiYaJB0Y57d8ThP7+kQ63RQqsFHtyVgJK6VlOHNuTEnKvBDVtP4r7P44wmMAkCcONYTxz65wz8a2EYE5iIiMhkBEHArJEu2PPANLyydBTcbMwN5ijVGrz/ey6mbzqMD4/lok1lvC0qXRq1RguRkYtY8QV1WLTlOE5mV5kgKiIiIiLqDWq1GmlpadBqtdBqtcjLy0NiYiK0Wq2pQyMiIho2pFIpQkND9ZZptVqkpaVBpVKZKCrqDpOYiIiIhoG00gYsefM49qWU6S2vaVZi+8lzpglqCCqobsF9n8di6bunkFBYZ3TOzJHO+OWBadh8UxS87C37N0AiIqJOiEUCbvp/9u46vsl7+wP4J9pUUncvdUqx4toCw2WwAWMM28aECWNMuXf3sjsX2JiwjbGNOTAGTHArFHetu7tr9PcHPzpC0mJtUvm8X6+9XsnzPXlykptLk+c5zzl9vHDghUi8OCYYVmb6jZsr6pR4c1scRq44iD/O50Cj4QmYuyESCvDW1HB8OL0HzMS6h2dKahR46JsTWB2dzPeZiIiIqB1KTU1Fba3uWGa5XM5OTEREREZma2sLb29vnW319fVISkoyUUZ0MyxiIiIi6uA2n83G1NVHkF6ie+BEIACeHRmIl8eGmCizjqOiTom3tsVi1MqD2H4p32BMD08b/LKwP9Yt6IdQN7YpJSKitslcKsJTUQE4+GIk5g/yhViof5Ilu6wOi9efx5TPj+BoCrsF3a37IzyxZdFgeNvrFjdrtMD7OxPw2I9nUFGnNFF2RERERHS7SkpKkJubq7PNysoKvr6+pkmIiIiok/P19dUbH1dYWIjCwkITZUTNYRETERFRB1WvVGPZlkt4fuMF1Cs1Omu2FhJ8N78vnr8nCCIDJyfp1ijVGnx/NB2RHxzA1zFpUKg1ejG+Dhb4/MHe2PrUYAzydzRBlkRERLfPwcoMyyeHYe/zwzEh3M1gzKWcCjz49Qk8vO4UEguqjJxhx9LV3Rp/PTMEo0Kd9db2xhVg8meHEZdXaYLMiIiIiOh2KBQKJCQk6GwTCoUIDQ2FUMhTckRERKYgEAgQGhoKkUiksz0pKQkNDQ0myoqawm9MREREHVBWaS1mfHUMv5zI1Fvr7mmDv58Zgshg/ZNkdGu0Wi32xBZgzMeH8N8/r6CsVr87go25BP+Z2BW7lwzHhO5ubBdORETtkq+jJT6f3RubFw1CX187gzH74wsx9uNDeHnTRRRU1hs5w47DxlyCNXP64MUxwbixxjyjpBZTVx/B5rPZpkmOiIiIiG5Kq9UiISEBSqXucSJ/f39YWFg08SgiIiIyBplMhqCgIJ1tKpUKiYmJ0Gq1JsqKDGERExERUQdzIKEQkz47jIvZFXprDw3wxm9PDISnHQ+c3KnL/991YuEPp5FaVKO3LhEJ8MgQPxx8MRIPD/GDVMyvW0RE1P719rbDxscHYs2cCHRxstRb12iBDaezMPyDA1i5OwE1DSoTZNn+CYUCPBUVgB8e7g97S6nOWr1Sg+c3XsC/t15Cg0ptogyJiIiIqCkFBQUoLS3V2ebg4AA3N8OdTYmIiMi4nJ2d4eTkpLOttLQUeXl5JsqIDOFZNSIiog5CrdFi5e4EPLzuFMpv6AwkkwixckYPvHlvOMzEoib2QM3Jr6jHC79dwKTPDuNYaonBmLFhrtizZDhem9gVthZSgzFERETtlUAgwOgwV+x+bhjemtoNjlZmejH1Sg0+2Z+MyA+jseFUJtQaXsl2J4YEOuLvZ4agh5et3tpPxzPxx/lc4ydFRERERE2qr69HcnKyzjaJRIKgoCB25yYiImpDAgMDIZXqnr9JSUlBXV2diTKiG7GIiYiIqINYsuE8PtmfjBu7Xvo5WmLrU4MxrbenaRJr52oVKny0JxFRH0Zj05lsvfcXuDqib+PjA/HlnAj4Oup3pyAiIupIxCIhZvf3QfSLkVg8MhDmEv0C6aKqBrz8+yVM+CQGh5OKTZBl++dua46Njw/AQwO8dbaPCnXG/fxeR0RERNRmaLVaJCYmQq3W7ZYZFBSkd5KUiIiITOtakfH1NBoNUlNTTZQR3YhFTERERB3EtN4euPHCrrFhrvjz6cEIcbU2TVLtmFqjxcbTWYj6MBqr9iWhTqk/tsXNRoaPZvbA1kWD0c/P3gRZEhERmY6VmRhL7gnCwRcjMaufN4QGLjCPz6/CQ9+cwILvTiKpoMr4SbZzZmIR3rw3HCtn9IBMIoSHrTk+nN4DQkNvNhERERGZRH5+PsrKynS2ubi4wNHR0UQZERERUXNuHPfq6OiIwMBAE2ZE1xObOgEiIiJqGZHBznhmRCA+2ZcEkVCAV8eF4JEhfmxZfQeOJhfjzW1xiM2rNLhuKRXhyUh/PDKkC8ylHM9HRESdm7O1DO9MC8eCwb54e3scohOK9GIOJBThUFIxZvXzwnOjggyOoqOmTevtiVA3a6jUWo6sJSIiImpD6uvrkZKSorNNKpXC39/fRBkRERHRrejSpQuqqqrg6ekJZ2dnnktrQ1jERERE1IEsHhmI7LJaPNDXm52B7kBKUTXe2R6HvXGFBteFAmBmXy8suScIznKZkbMjIiJq24Jc5Fi3oB8OJRbhrW1xSLih85Jao8VPxzPxx7lcLIoKwILBvpAZGEVHhoW6Nd9Z80xGKWoVagwNdDJSRkRERESdm1arRUJCgsExchKJxERZERER0a0Qi8Xo3bs3i5faIBYxERERtTOZJbXwsjc3+MVKJBRg5Yyexk+qnSutUWDV3kT8fCITKo3WYMzQQEf8a0IoR/MRERHdxLAgJwwOcMTG01lYsTsRxdUNOutVDSq8tzMePx3PwMvjQjCpuxsPGN2lkuoGPPXzORRU1ePZEYF4diRboBMREREZg6OjI6qqqhoLmVxdXeHg4GDirIiIiOhW8HhU2yQ0dQJERER0a7RaYOPZPIz66CB+OpFp6nQ6hAaVGmsOpWD4Bwfw/bEMgwVMgc5W+G5BX/zwcD8WMBEREd0ikVCAWf28Ef1iJJ4ZEQCZRP/wQ055HZ799Rymrj6KMxmlJsiyY1BrtHhuw3nkV9ZDqwVW7UvCvG9PoqRGYerUiIiIiDo0gUAADw8P9OnTBw4ODjAzM+MYOSIiIqK7xE5MRERE7YBCK8TBGnck70wGALzxVyx6edmim4eNiTNrn7RaLXZczsc7O+KQVVpnMMbBUool9wThgb5eEItY901ERHQnrMzEWDo6GLP6eePDXQnYfC5HL+Z8Vjnu++IYxoe74pWxofB2sDBBpu3X3xdzEZNUrLPtcHIxZn5TiYGwgLuk1kSZEREREXUOMpkMYWFhUCqVEIt52o2IiKi9U6lUKCgogLu7O7s1mQC/TREREbVxiYU12FTpjwqNWeM2hVqDRT+fxd/PDoG1TGLC7Nqfc5lleGtbHE5nlBlcl4qFeGSIHxZF+kPO95aIiKhFuNuaY+XMnlgw2A9vbovFiTT9zkvbL+Vjb2wh5g3ywdMjAmFjzr/Dt2JSd3fkV9Tj/V0JUF/XVbKwWoE/4Yf+5gXQaA2PyyUiIiKiliEQCCCVSk2dBhEREd2l4uJiJCUlQaFQQCKRwNnZ2dQpdTpsK0BERNRGabVarD+ZidnrzusUMF1zT1cXyMQiE2TWPmWX1TaOrGmqgGlyD3fsXzocL48NYQETERFRKwj3tMH6xwbgqzkR8HO01FtXqDX4OiYNkR8cwLojaVCqNSbIsn0RCgV4fLg/1j82AC7Wut8ZtRDgeJ0rnv0tFuW1HC9HRERERERERNSUpKQkXLlyBQrF1WMoycnJUCqVJs6q82ERExERURtUq1Bh6cYLeGXzJTSodE/eyWVifDUnAq9N7AqpmH/Kb6aqXon3dsZjxIqD+PNCrsGYCB87bFk0CJ/M6gVPO46wISIiak0CgQBjwlyx67lh+M/ErgY7LpXVKrH8r1iM+egQ9sQWQMtOQjfV19ce254diiEBjnprh5JLMeGTwzifVW78xIiIiIg6kPr6elOnQERERK3E1tZW575SqURaWpppkunEeOaTiIiojUksqMLkz45g87kcvbUwNytsf3YoxoS5miCz9kWl1uDnExmI/CAaX0SnQKHS7+TgZW+O1bN7Y9MTA9HL284EWRIREXVeUrEQDw/xw8EXI/HIED9IRAK9mNTiGiz84TRmfX0cl3MqTJBl++JoZYbvH+6H50YF4sZ3M6e8DtO/PIp1R9JYFEZERHSbMjIysHTpUoSEhMDS0hL29vbo27cvPvjgA9TW1t7VvtetWweBQHBL/61bt65lXhDdkaqqKpw8eRJJSUlQqVSmToeIiIhamKOjIxwcHHS25eXlobKy0kQZdU4sYiIiImpDNp3JxuTPDiO5sFpvLdysBN/P6QEve3YKupnohEKMWxWDf225jJIa/dEpcpkYy8aHYO/zwzE+3A0Cgf5JUyIiIjIOWwspXpvYFXuWDMe4boYLtY+nlmLSZ4exdOMF5Ffw6vfmiIQCPDcqCF/N6gaZQPfkmlKtxfK/YvHET2dQUct26ERERLfir7/+Qvfu3bFy5UokJCSgtrYWZWVlOH36NF566SX06tULycnJpk6TWplWq0VSUhK0Wi1yc3Nx6tQpFBcXmzotIiIiakECgQABAQEQCnXLaK59ByDjEJs6ASIiIgLqFGr854/L+O1Mtt6alZkIg8Rp8JdWcnzcTcTnV+KtbXGISTJ8EEkkFOCh/t5YPCoI9pZSI2dHREREzfF1tMQXD0XgZFop3toWiwvZup2XtFrg97PZ2H4pD48N64LHh3eBhZSHNZoywM8OM6yTsafGC3kqS521XVcKcCU3BjufGwYrM76HRERETTl37hxmzpyJuro6WFlZ4dVXX0VUVBTq6uqwfv16fP3110hMTMSECRNw+vRpyOXyu3q+Xbt2wd3dvcl1T0/Pu9o/3bn8/HxUVVU13lcoFKipqYGjo/4oXyIiImq/ZDIZfHx8dMbIVVdXIzc3Fx4eHibMrPPgkSoiIiITSymqxqKfziKhoEpvraubNd6fEoRtGy6YILP2o7CqHh/tScSGU1nQNFEMPyrUGa+MC0WAs5VxkyMiIqLb0s/PHlsWDcZfF3Px/s4E5JTX6azXKdVYtS8Jv57MxAtjgnFfb0+IhOyqaIilUIXJVmlQho7Ft8d0i+UnhLuxgImIiOgmFi9ejLq6OojFYuzevRsDBw5sXBsxYgQCAwPx0ksvITExEStWrMDy5cvv6vmCgoLg6+t7d0lTi1MqlUhNTdXZZm5uDi8vLxNlRERERK3J09MTBQUFOmOD09LS4OTkBKmUF8i3NrZzICIiMjGhQIDsslq97Q8N8MbmRYPgbW9ugqzah3qlGp8fSEbUB9H49aThAqaubtb45dH+WDuvLwuYiIiI2gmhUIApPT2wb+lwvDgm2GCxTWFVA17adBGTPj2Mo8kc5dEUoQB4LsoP383vCzsLCQCgp5ctXhgTbOLMiIiI2raTJ08iJiYGAPDII4/oFDBds3TpUoSGhgIAVq1aBaWS41o7ovT0dKhUumN6DY2aISIioo5BKBQiMDBQZ5tarUZKSoqJMupc+A2LiIjIxPwcLfHW1PDG+5ZSET6Z1Qtv3hsOmURkwszaLo1Giy3nsjHiw2h8sCsBNQq1Xoyz3Awf3N8dfz0zBIMC2NqbiIioPZJJRHgqKgAHXojEg/29YajhUmxeJR5cewKPfn8aKUXVxk+ynYgKccaOxcMwKtQFn87qBYmIh4SIiIias3Xr1sbbCxYsMBgjFAoxd+5cAEB5eTkOHDhgjNTIiKqqqpCbm6uzzdHREfb29ibKiIiIiIzB1tYWLi4uOtsKCwtRXl5umoQ6ER6xIiIiagPu7eWB+3p7IsRVjj+fGYLJPdxNnVKbdTKtFPeuPoIlGy4gt6Jeb91cIsLikYGIfjES0/t4cbwMERFRB+AkN8PbU8OxY/EwDA9yMhizN64AYz46hOV/XkFZjcLIGbYPrjYyrJ3XB172Fk3GXMqugEqtMWJWREREbdPhw4cBAJaWloiIiGgybvjw4Y23jxw50up5kfFotVokJSXpbBMKhfD39zdRRkRERGRMXbp0gUik22wgJSUFWq2BsSDUYvT7sRMREVGr0Gi0UGu1TV71/sa9YRAKBOy+1IT04hq8uyMeO6/kG1wXCID7envihdHBcLWRGTk7IiIiMoZgVzm+f7gfohMK8fb2OCQW6HZeUmm0WHc0HZvPZuOZEYGYO8gHZmJ+t7pViQVVmP7VUXT3sMWqWT3hZsOxxkRE1HnFxcUBuDo2TCxu+lRKSEiI3mPu1IIFC5CQkIDi4mJYW1sjICAAo0aNwpNPPgkPD4873m92dnaz63l5eY23q6qqUFlZecfPdTuqq6sN3m4rSkpKUFVVpbPNxcUFCoUCCgWL5tuqtv65ovaHnylqafxMtS9ubm4636Wqq6uRnp4OBwcHE2alz1Sfqxu/K7UEFjEREREZQUl1A5b+dgE+9hZ4fUo3gzEWUv5ZNqSiVolP9ifhh2PpUKoNV7cP7OKAf00IRTcPGyNnR0RERKYQGeyMIQGO2Hg6Gyv3JKC4WvckUmW9Cm9tj8OPxzPw6rgQjO3mCoGA3RmbU6dQ46mfz6JeqcHJ9FKMXxWDFTN6YESIy80fTERE1MHU19ejuLgYAODp6dlsrJ2dHSwtLVFTU4OsrKy7et7o6OjG2yUlJSgpKcGJEyewYsUKfPzxx3j88cfvaL9eXl63HPvjjz/Cxsb4x1d+/PFHoz9nc4RCIXr27AmpVNq4ra6uDlu3bmX3hXakrX2uqP3jZ4paGj9T7UP37t1hYfFPV+vk5GT8/vvvUKvVJsyqacb8XFVUVLT4Pnm2lIiIqJUdTSnGc+vPo7CqAQAwKMARY8JcTZxV26dQafDT8Qx8sj8J5bVKgzFdHC2xbHwoRoY688QkERFRJyMWCfFgf29M6uGGL6JTsPZwGhQq3TFomaW1ePLns+jra4d/T+iKHl62pkm2Hfjf37FIKvznSr2yWiUeXncajw7xw0tjQyAVG+4mSkRE1BFdf0W5lZXVTeOvFTHd6VXvXbp0wbRp0zBw4MDGgqPU1FT8/vvv2LRpE+rr6/HEE09AIBDgscceu6PnoNvj7u6uU8AEABkZGSxgIiIi6oQyMjIQGhraeF8qlcLe3h5FRUUmzKrjYhETERFRK1GpNfhkXxI+PZCM649vvLTpIrp52MDDluM5DNFqtdgTW4B3dsQjrbjGYIydhQTPjQrCg/29mxzPR0RERJ2DXCbBS2ND8GB/b3ywKwF/nM/VizmVXoYpnx/BvT3d8eLYEH4PM+D+CE8cSixCTnmdzva1h9NwKr0Un87qDW8HiyYeTURE1LHU19c33r6xkMUQMzMzAFc79dyuqVOnYt68eXoXZ/Xt2xczZ87E33//jWnTpkGpVGLJkiWYPHkyXF1v7+K4m3WIysvLQ79+/QAAc+bMuavRdbejurq6sVPAnDlzbqlgzBgUCgViY2N1CpbkcjlmzZplwqzoVrXVzxW1X/xMUUvjZ6p9SklJQWVlJWQyGTw8PNCrVy9Tp6TDVJ+rnJwcvPPOOy26TxYxERERtYKc8jo8t/4cTqWX6a1JxULkV9Tz5JkBl3Mq8MbfsTiRVmpwXSISYMFgPzwVFQAbc4mRsyMiIqK2zNPOAqse6IX5g3zx5rY4nMnQ/x629XwudlzOx6ND/fBkZACszHhY5JoIHztsf3YoXvr9AnZdKdBZu5BdgQmfxOCtaeGY3MPdRBkSEREZj0wma7ytUCiaibyqoeFq921z89s/1nOz0W0TJ07Ef/7zH7z22muora3FN998g3/961+39Rw3G4l3PblcDmtr69vaf0uwsrIyyfMaEh8fr9dxKTg4GJaWlibKiO5UW/pcUcfAzxS1NH6m2o/g4GCUlZXB3d29zU8GMebnqrKyssX3ydYFRERELWzXlXyMXxVjsIBpWJATtj87FBE+dibIrO3Kq6jD8xvPY9Jnh5ssYBof7oq9zw/HsvGhLGAiIiKiJvXytsOmJwbi8wd7w8te/0Rig0qDzw+kIPKDaPx6MhNqDUeCXGNjIcGXD0Xgf1PCIL2h22VVgwrP/noOSzdeQHWDykQZEhERGYdcLm+8fSsj4mpqrnaSbq0r3h977LHGk2UHDx5sleegq6qqqlBQoFvQ7ebmxgImIiKiTs7CwgIeHh5tvoCpI+Alh0RERC2kXqnG29vj8MOxDL01sVCAF8cEY+HQLhAK+QXnmpoGFb46mII1MamoV2oMxvTwssVrE0LRx9feyNkRERFReyUQCDChuxtGdXXG90fT8em+ZFTdUHhTXN2AVzdfwvdH07FsfCiGBTmZKNu2RSAQYO5AX0T42OGZX84h9Ybxvr+fzcbpjFKseqAXenrZmiZJIiKiViaTyeDg4ICSkhJkZ2c3G1tWVtZYxOTl5dUq+Tg7O8PBwQHFxcXIyclpleegq6RSKZydnVFYWAgAEIlE8PX1NW1SRERERJ0IOzERERG1gLi8Skz+7LDBAiZPO3P89sRAPD7cnwVM/0+t0WLjqSxEfhiNT/YnGyxg8rA1x6oHemLLk4NYwERERER3xEwswmPD/BH9YiTmDvSByMB3sfj8Ksz99iTmf3cSSQVVJsiybQpzt8GfzwzBtN4eemsZJbW4/4ujWB2dzE5WRETUYXXt2hUAkJycDJWq6S6E8fHxjbdDQ0NbLR9e9W8cZmZmCA0NRa9evWBtbQ1vb29IpVJTp0VERETUabCIiYiI6C5oNFp8ezgNUz4/gsQC/fbiE8LdsO3ZoejlzfFx1xxJLsaET2Lw0u8XUVTVoLduZSbGS2ODsW/pcEzp6cHCLyIiIrprDlZm+N+Ubtj13FCMDHE2GBOdUISxq2Lwry2XUFyt/x2lM7IyE2PljJ5Y9UBPyM10m3mrNFq8vzMBj/94GlotC5mIiKjjGTJkCICro+LOnDnTZNz1490GDx7cKrkUFRWhuLgYAODu7t4qz0G6rK2t0bNnz1brrkVEREQdg1arRWlpKY+NtCAWMREREd2hkuoGzF93Cv/7OxYKlW4nIZlEiLenhuOzB3vBxlxiogzbluTCajyy7hRmrz2B+Hz9LgdCAfBgf28ceCESiyIDIJOITJAlERERdWQBznJ8M78vfnqkP0Jc5Xrrao0WP5/IROQH0fgiOgX1SrUJsmx7pvT0wPbFQ9Hb21ZvbUyYKztDEBFRh3Tvvfc23v7uu+8Mxmg0Gvzwww8AAFtbW0RFRbVKLmvWrGk8MTZ8+PBWeQ7SJxAI+D2HiIiImlRZWYkLFy7g0qVLyMvLM3U6HQaLmIiIiO6QVCxEWrF+96VQN2v8/cwQPNjfmwc6cLXY67WtlzHm40PYF19oMGZ4kBN2PjcMb08Nh5PczMgZEhERUWczJNAR254divfv627wu0d1gwrv7YzHyBUH8eeFXF5NB8DL3gIbHx+IZ0cG4lqjzInd3XB/hKdpEyMiImol/fr1w9ChQwEA33zzDY4dO6YXs2LFCsTFxQEAFi9eDIlE90K26OjoxkKY+fPn6z0+PT0d586dazaPv//+G//73/8AAObm5liwYMGdvBwiIiIiakFpaWk4d+4cKioqAAAZGRlQq3kxXEsQ3zyEiIiIDJHLJPh4Zi/M+OoY1JqrJ7YWDvXDC2OCYSZmF6F6pRrfHUnH6gPJqGpQGYwJdpFj2YRQDA9yMnJ2RERE1NmJhALM6OuFCd3d8NXBFKyJSUW9Ure7Zk55HZ799Ry+O5KGf0/oigifzj0iWCwS4vl7gjAkwBEf7krAW1PDWbRPREQd2qpVqzB48GDU1dVh9OjRWLZsGaKiolBXV4f169djzZo1AICgoCAsXbr0tvefnp6OqKgoDBw4EJMmTUKPHj3g7Hx19G1qaio2bdqETZs2NRZUf/jhh/Dw8Gi5F0hEREREd8TW1haZmZmN9xUKBXJycuDt7W3CrDoGFjERERHdhQgfOzw7IhA/n8jAihk9MDSQxTharRZ/XsjF+zsTkFNeZzDG0UqKpaODMT3CE2IRG0MSERGR6ViaifH86GDM6u+ND3YlYPPZHL2Yc5nluO+Lo5jY3Q0vjw2Bl72FCTJtO/r52WPjEwObjTmaXIwBXRwgFLLIiYiI2q9evXphw4YNeOihh1BZWYlly5bpxQQFBWHbtm2Qy/VH1d6qY8eOGez0dI2FhQU++ugjPPbYY3f8HGSYWq3GhQsX4O7uDhcXFxZoExER0S2xs7ODnZ0dysrKGrdlZmbCzc1Nrzsn3R4WMREREd2ERqNFjUIFuczwl46novwxd6AP7CylRs6s7TmdXoo3tsXhQla5wXUzsRCPDvXDk5EBsDLj1xAiIiJqO9xszLFyRk/MH+SLN7fF4WRaqV7M3xfzsPtKARYM8cVTUQGwbuL7YWe3L64Aj3x/GsOCnPDh/d3hbC0zdUpERER3bNKkSbh48SJWrVqFbdu2ITs7G1KpFAEBAZg+fTqefvppWFjcWYFzREQEfvrpJxw7dgynT59GXl4eiouLoVKpYGdnh7CwMIwcORKPPvpoY4cmalnZ2dmoqqpCQkICsrKy4OvrC0dHRxYzERER0U35+fnpFDGp1WpkZ2fDz8/PhFm1fzx7SERE1IzCqnq88NtFqNQa/PRIf4NXkotFwk5fwJRRUoN3d8Rjx+X8JmOm9vLAC2OC4WFrbsTMiIiIiG5Pd09bbHhsAHbHFuCd7XFIL6nVWVeoNfjqYCp+O52NJaMCMaufNztLXqewsh4vbroIADiUWIQxHx/CO9PCMbabm4kzIyIiunM+Pj5YuXIlVq5ceVuPi4yMbBwFZ4hcLsfs2bMxe/bsu02R7oBSqURWVlbj/draWuTn58PJiZ3WiYiI6ObkcjmcnJxQVFTUuC0nJweenp7sxnQXeJSNiIioCXtiCzDu4xgcSizC0ZQSfB2TauqU2pzyWgXe+DsWo1YebLKAqZ+fPf58ejA+mtmTBUxERETULggEAowJc8XuJcPx2sSusDHXP/BUWqPAa39cwdhVMTgQX9jsCcrOQqvV4sVNF1Fao2jcVlarxBM/ncXSjRdQVa80YXZEREREurKzs6FWq3W2+fr6miYZIiIiapd8fHx07l/rxkR3jkVMREREN6huUOGlTRew8IfTKLnuBMyHuxNwOafChJm1HQqVBt8cTsPwD6LxzeE0KNX6J+38HC3x1ZwIbHhsALp72ho/SSIiIqK7JBUL8cgQPxx8MRIPD/aD2EBXzuTCaixYdwpzvz2JuLxKE2TZdggEAjw2rAtcrM301n4/m42xH8fgRGqJCTIjIiIi0qVSqZCTk6OzzcnJCXK53EQZERERUXtkaWmpN/Y3JycHSiUv5LpTLGIiIiK6zsm0UoxbdQgbT+tXSdtbSlGrUBt4VOeh1Wqx83IeRn90EG/8HYuKOv0vYbYWEvx3Ulfsem4YxoS5QiDQP9lHRERE1J7YWkjxn0ldsXvJMIzu6mIwJiapGBM+icErv19EYVW9kTNsOwYHOGLn4mEYH+6qt5ZTXocHvj6Od3bEoUHVub9XExERkWkZ6sJ0YycFIiIiolthqBvT9SNr6fawiImIiAhAg0qNd3bEYeaaY8gqrdNbH93VBTsXD0M/P3sTZNc2XMgqx4yvjuGJn84ivaRWb10qEmLhUD8cfCEKCwb7QSrm1wwiIiLqWLo4WWHN3D5Y/9gAdPOw1lvXaIH1p7IQ+UE0Pt2XhLpOWgBvZynF5w/2xsoZPSA3E+usabXAVwdTce/nR5GQX2WiDImIiKgza6oLk6WlpYkyIiIiovbMwsLCYDcmhULRxCOoOTy7SEREnV5cXiWmfHYEXx1MhfaGqWhWZmK8f393fDUnAnaWUtMkaGLZZbVYvP4cpnx+BKfSywzGTAh3w97nh+NfE7rCxkJi5AyJiIiIjGtAFwf8+dQQrJjeA67WMr31WoUaK/YkYsSKaGw5lw2NRn/0bkcnEAgwrbcndjw31OCFAHF5lZj06WGsjUntlO8PERERmU5OTg5UKpXONm9vbxNlQ0RERB3Bjd2YNBoNsrP1p77QzbGIiYiIOi21RosvD6ZgymdHEG/gKvB+vvbYsXgoZvTx6pQj0SrrlXh3RzxGrDiIP87nGozp5W2L358ciM9n94a3g4WRMyQiIiIyHaFQgPsiPHHghUg8f08QzCUivZi8inos2XAB964+gpNppSbI0vQ87Szw68IBWDY+BFKR7mEohVqDN7fFYfbaE8gp1++GSkRERNTSVCqV3glFR0dHWFlZmSgjIiIi6ggsLCzg4uKisy03N1evcJpujkVMRETUKWWV1mLWmuN4d0c8FGqNzppUJMSr40Lw62MD4GXf+QpzVGoNfjyWjqgPovHlwRQoVBq9GE87c3w6qxc2PzkIET6dd8QeERERkblUhGdHBiL6xUjM6OMJQ7XvF7Mrro7l/fEM0otrjJ+kiYmEAjw2zB9/PD0YwS5yvfVjqSV4/MfT0N7YFpWIiIiohRk6mXhj5wQiIiKiO3FjZ0e1Wq03wpZuTmzqBIiIiIwtIb8K01YfQY1CrbcW4irHRzN7ItTN2gSZmZZWq8X++EK8vT0OKUWGT67JZWI8MyIAcwf6Qmag2wARERFRZ+ViLcP79/fAvEG+eGtbHI6mlOjF7LySj33xBZg70BfPjgjsdGN4Q92s8cfTg7FyTyK+jvlnlLNQALw+OaxTdj8lIiIi4zE01sXBwYFdmIiIiKhFWFhYwMnJCUVFRQAAkUjEYx13gEVMRETU6QQ6WyHc0wbHU/8Z6SEQAI8P88eSewJhJu58xTmXcyrw9nbDJ9sAQCwU4KEBPnh2ZCDsLaVGzo6IiIio/Qhzt8HPj/bH/vhCvLU9Dqk3FIcr1Vp8czgNv5/NxuKRgXhogA8kos7TKFsmEWHZ+FBEBTtj6cbzyK2ox6LIAHb3JCIiolaXn58PpVKps41dmIiIiKgleXl5oby8HB4eHnB3d4dE0rkuYGsJLGIiIqJORygU4MPpPTDu4xhUNajgZW+OlTN6oq9v5ztxklVaiw93J+CP87lNxowKdcGr40Pg78Sr0oiIiIhuhUAgwMhQFwwLcsKvJzPx0Z5ElNXqnjArr1Xi9b9i8cOxDLw0Jhhju7l2qqvzBvo7YMdzw/BNTCqeHhFo6nSIiIiog9NqtXpdmGxtbSGX64+6JSIiIrpTcrkcAwYMgFDYeS5Ya2ksYiIiok7J084C/50chlNppXhtUldYmXWuP4nltQp8tj8ZPxzLgEKtMRjTzcMa/xrfFQP9HYycHREREVHHIBEJMXegL6b09MDnB5Kx7ki63nevtOIaPPnzWfT2tsWr40M7VWG9jbkEz48ObnJdrdHi6V/OYnofT4wIcTFiZkRERNTRlJSUoK6uTmebl5eXibIhIiKijowFTHenc52xJSKiTmXHpTzYWUoxoIvhIpz7Izxxf4SnkbMyrXqlGt8fTcfnB5JRWa8yGONqLcNLY4Nxb08PCIWdpxsAERERUWuxMZdg2fhQPNTfB+/tjMe2S3l6MWczyzH9y2O4p6sLXh4bggBndsFcG5OKHZfzseNyPqZHeOK1SV1hLWMbdiIiIrp9dnZ2CAwMRFZWFurr62FlZQU7OztTp0VEREREN2ARExERdTilNQr854/L+PtiHjztzLHzuWGdrtPSjTQaLbaez8GK3YnIKa8zGCM3E+PJKH88PNgPMonIyBkSERERdXzeDhb4fHZvLEgvxRvb4nAhq1wvZk9sAfbHF2JmXy88NzIQztYy4yfaBiQVVGHFnsTG+7+dycbh5GK8d193DAtyMmFmRERE1B6JRCK4u7vDzc0NxcXFEIlEnWqULxEREVF7wT5WRETUoey8nI/RHx3E3xevXt2eXVaHd7bHmTgr04pJKsLETw/j+Y0XDBYwSUQCPDzYDwdfisKiyAAWMBERERG1sj6+9ti6aBA+f7A3fBws9NbVGi1+OZGJ4R9EY+WeRFQ3GO6g2ZH9diYbCpXu6L28inrM/fYklm251CnfEyIiIrp7AoEATk5OsLfvPCN8iYiIyPRUKhUKCwtNnUa70LnbUhARUYdRVqPAf/+8gj8v5Oqt/XwiE7P6eaObh40JMjOd2NxKvLMjDjFJxU3GTOrhjhdHB8PbwMkzIiIiImo9AoEAE7q74Z6uLvjlRAY+2Z+M0hqFTkydUo1P9iXhlxMZWDwyEA/084ZE1DmuR3t1XAi6OFrizW1xegVLv5zIxKHEIrx/f3cM8nc0UYZERERERERERM2rra1FTk4O8vPzodFoYGlpCUtLS1On1aZ1jiNfRETUoe2+ko97PjpksIDJxlyCVQ/0RJi7tQkyM42c8jo8v/E8Jnwa02QBU38/e/zx1GB8OqsXC5iIiIiITEgqFmL+YD8cfDEST0cFQCbRP1RTXK3Aa39cweiPDmHHpTxotVoTZGpcAoEAD/Tzxs7nhmKQv4PeenZZHR78+gT+88dldmUiIiIiIiIiojZHo9Hg/PnzyM3NhUZztdt0Tk6OibNq+1jERERE7VZ5rQJLNpzHYz+eQXF1g976qFAX7FkyDFN6enSKGfcVdUq8syMOUR9GY/PZHBg6txXobIVv5/fB+scGoIeXrdFzJCIiIiLD5DIJXhgTjOgXovBAXy8IDXx9TSuuwZM/n8W0L47iVHqp8ZM0AU87C/z0SH+8MSUM5gbGHv9wLANjPjqEg4lFJsiOiIiIiIiIiMgwoVAINzc3nW0FBQVQKpUmyqh9YBETERG1O1qtFtsu5mHUyoPYck6/YtlaJsZHM3vg67kRcLaWmSBD42pQqbE2JhXDPziArw6mQqHS6MU4y83w3n3h2LF4KEaEuHSKoi4iIiKi9sjVRoZ37+uOXc8Nw6hQZ4Mx5zLLMf3LY1j4w2kkF1YZOUPjEwoFmDPQF7ueG4Z+fvZ66znldZj37Uks3XgB5bUKA3sgIiKizig3NxeJiYmorq42dSpERETUSbm7u+uck9NoNMjN1Z8sQ/8QmzoBIiKi21FQWY/Xtl7G7tgCg+sjQ5zx9rRwuHSC4iWNRos/LuRgxe5EZJfVGYyxMhPjieFd8PAQP1hI+WefiIiIqL0IdJFj7by+OJFagrd3xONCVrlezJ7YAuyLK8DMvt5YMiqwwxfweztYYP3CAVh3NB3v74pHvVK3eP/3s9k4mFiErU8NgqcdRyYTERF1ZlqtFllZWaivr0deXh5sbW3h5+cHa2trU6dGREREnYiZmRmcnZ1RUPDPec3c3Fx4eXlBKGTPIUN4NpOIiNqNbRfz8Mrmi6iqV+mtyWViLJ8Uhmm9O/7oOK1WiwMJhXh/ZwLi8w1feS8WCjC7vzeeGRkIRyszI2dIRERERC2lfxcHbF00CNsv5eP9XfHIKKnVWddogV9PZmLruRwsHOqHhcO6QC6TmCjb1icUCvDwED+MDHXGK79fwrHUEp31UDc5PGzNTZQdERERtRWlpaWor69vvF9eXg6NRr97OREREVFr8/Dw0CliUigUKC4uhrOz4Q7cnR2LmIiIqN2ws5AYLGAaFeqMN+8Nh6tNx77yHADOZVXgs5jLOJVe1mTM+HBXvDgmBH6OlkbMjIiIiIhai0AgwITubrinqwt+PZmJVfuSUFqjOzatTqnGJ/uT8ePxDDwVFYApYfpj1zoSHwdL/LKwP9afysLb2+JQ1aCChVSEt6eGd/iLGoiIiOjmcnJydO5bWVnBxsbGRNkQERFRZyaXy2FjY4OKiorGbbm5uSxiagKLmIiIqN0YFOCImX28sOF0FgDAwVKK5ZPDMLG7W4c/UVGiNsOJOhd88ePFJmP6+trh1fGh6O1tZ8TMiIiIiMhYpGIh5g3yxbTeHlhzKBVfx6TqjVQrq1XizW1x+PqQFKFqWwRLy02TrBEIBALM6ueNyGAn/HvLZQwNdISXPcfIERERdXa1tbUoK9O9ANDd3b3DHz8kIiKitsvd3V2niKmiogI1NTWwtGRDghuxiImIiNqVZRNCcSChEEMCHPHaxK6ws5SaOqVWlVVai/d3JOCvygAAhg+0BLvI8dLYYIwIcebBGCIiIqJOQC6TYOnoYDw0wAcf703EhlNZ0Gh1YwqqFCiAJ87XOyIovhjT+so77HdFNxtzrJ3Xp9mYmKQipBfXYHZ/HwiFHfN9ICIioqtyc3N17ovFYnY6ICIiIpNydHSERCKBUqls3JaXl4eAgAATZtU2sYiJiIjalAaVGlvO5mBmXy+DJ1lszCXY+dww2Hfw4qXi6gZ8fiAZPx/PhEKtgaECJk87czx/TxCm9PSAiCdiiIiIiDodF2sZ3pnWHQ8P9sOHuxOw60qBXky5Roalm+Pww8lcvDQ2BIMDHE2QaetrrkCrukGFV36/hJzyOmw5l4N3pnVHsKvciNkRERGRsWg0GhQU6H4ncnNzg0gkMlFGRERERIBQKISrqyuysrIat+Xn58PPz4/fU27AIiYiImozTqSW4F9bLyO5sBpaALP6eRuM68gFTNUNKnx9KBVrY1JRo1AbjHGwlOLpEQF4sL83zMT8YkNERETU2QW6yPHVnD44l1mG93cm4FhqiV7MhewKzF57AoMDHPDSmBD08LI1fqIm8v7OeOSU1wEAzmaWY8InMXhsWBc8OzIQMgm/TxMREXUkxcXFUKlUOtvc3NxMlA0RERHRP9zd3XWKmNRqNQoLC/ld5QYsYiIiIpMrq1HgnR1x2Hg6u3Hb29viEBXsDFcbmQkzM54GlRo/H8/EZweSUVqjMBgjgRoLh/ph0ahQWJnxTzgRERER6erlbYdfFvbH4eRivLMtFrH51XoxR5JLMCX5CMZ1c8XS0cEIcLYyQabGk15cgx+PZ+hsU2m0WB2dgm2X8vDWveEYEtgxu1MRERF1RjeOkrO1tYW5ubmJsiEiIiL6h0wmg729PUpLSxu35eXlsYjpBkJTJ0BERJ2XVqvF72eyMXLlQZ0CJgCoalBh+Z9XTJSZ8ag1V9+DER8exP/+jjVYwCQRCdDdrBizbRLxxFAfFjARERERUZMEAgGGBjrh1wU9MdoyEzbCBoNxOy7nY/RHB/HSpguNXYo6Il9HS/z0SH/4OljorWWU1OKhb05gyYbzKKk2/D4RERFR+1FbW4uKigqdbTwpSERERG3Jjd9NqqqqUFVVZaJs2iYWMRERkUmkFlVj9toTWPrbBYOFO909bfDsyEATZGYcWq0WOy/nYdyqQ1j6m+ETR0IBcH+EJ/56og8GW+TDXGh4vBwRERER0Y0EAgH8pZV4wDoJ/x0fCFdr/Q6nGi2w8XQ2oj6MxptNFNR3BIMDHLHzuWF4OioAYqFAb33LuZz/v7AiC1qt1gQZEhERUUvIy8vTuS8Wi+HoyI6LRERE1HY4ODjAzMxMZ1txcbGJsmmb2MqBiIiMqkGlxhfRKVh9IAUKtUZv3cpMjBdGB2HOQF+IDJxgaO+0Wi2iE4rw4e4EXMmtbDLunq4ueHFMMIJc5KisbDqOiIiIiKg5QgFwX09XzBrojx+OpWN1dArKa5U6MQqVBmsPp2H9qSw8OtQPjwzxg1wmMVHGrUMmEeGFMcGY3NMdr26+hDMZZTrr5bVKvLTpIn4/k423p4XD36ljj9kjIiLqaDQaDQoKCnS2ubq6QijktfxERETUdggEAri6uiI7OxtOTk5wdXWFtbW1qdNqU1jERERERnMspQT/2noJqUU1BtfHhrniv5O7ws2mY86pP5pcjA93J+BsZnmTMf187fHyuGBE+NgbLzEiIiIi6vBkEhEeG+aPB/p54+tDqVgbk4Y6pW6nz+oGFT7em4R1R9Px2LAumD/IFxbSjnXoKMhFjt8eH4hfT2Xi3R3xqKpX6ayfSCvFuI9jsCjKH08M94dMIjJRpkRERHQ7SkpKoFTqFmpzlBwRERG1RZ6envDy8oJIxGMOhnSsI1FERNQmldYo8Na2OPx+NtvguoetOV6fHIZRXV2MnJlxnMkow4rdCTiaUtJkTKibNV4aG4zIICcIBB2vAxURERERtQ3WMgmWjg7G3IG++PxAMn4+kQGlWneEWnmtEu/vTMC3h9PwxHB/PDTAp0MV8wiFAszu74N7Ql3w+t+x2HZRd/SMQq3Bx3uTEJtbiTVz+5goSyIiIrodN3ZhsrGxgYWFhYmyISIiImqaWMwyneawj2Yry8jIwNKlSxESEgJLS0vY29ujb9+++OCDD1BbW3tX+66trcXmzZvx5JNPom/fvrCzs4NEIoGDgwMGDhyI5cuXIz8//6b7iYyMhEAguKX/iIhuV2ZJLUauiDZYwCQSCrBwqB92LxnWIQuYLudUYMF3J3HfF0ebLGDyd7LE5w/2xrZnhiAq2Jn/1hIRERGRUTjJzbB8chj2L43EtF4eMPQ1tLhagTe3xWH4Bwfw47F0KFT646DbM2drGT5/sDe+m98XHrb63WAXDutigqyIiIjodmm1WgiFQp3jai4uHe9YIxEREVFnwBKvVvTXX3/hoYceQmVlZeO22tpanD59GqdPn8batWuxbds2BAQE3Pa+L168iMGDB6O6ulpvrbS0FMePH8fx48fx0UcfYc2aNZg5c+ZdvRYiojvlZW+OQBc5TqaV6mzv4WWLt6d2Q5i7jYkyaz2JBVX4aE8idlxuupDUy94cz40Mwr29PCASsnCJiIiIiEzDy94CK2f2xOPD/fHRnkTsvKL/HbagsgGv/XEFXx5MxeKRgZjW2wNiUce5Li4qxBl7nh+Gj/cm4ZvDaVBrtLivtyf6+nLEMxERUXsgEAjQtWtXKJVKFBYWoqioCE5OTqZOi4iIiIjuAIuYWsm5c+cwc+ZM1NXVwcrKCq+++iqioqJQV1eH9evX4+uvv0ZiYiImTJiA06dPQy6X39b+KysrGwuYBg8ejIkTJ6JPnz5wcHBAUVERNm/ejK+//hqVlZWYPXs2rK2tMW7cuGb32adPH3z33Xd3/JqJiAwRCAR4Y0o3jP8kBmqNFlZmYrw0Nhiz+/t0uOKd9OIafLw3EX9cyIVWazjGzUaGZ0YEYnofT0g60IkfIiIiImrfgl3l+HJOBC7nVGDlnkTsjy/Ui8kpr8NLv1/E6uhkPDcqCJN6uHeY7/QWUjGWjQ/FlJ7ueH9nAl4dH9JkbJ1CDXNpxxmvR0RE1FFIJBJ4eHjAw8PD1KkQERER0R1iEVMrWbx4Merq6iAWi7F7924MHDiwcW3EiBEIDAzESy+9hMTERKxYsQLLly+/rf0LhULMmDED//3vf9G1a1e99dGjR2PcuHGYOnUq1Go1nnnmGSQlJTU7psjS0hLdunW7rTyIiK7RarVN/hsT7CrH/EG+yKuow38nhcHFWmbk7FpXTnkdPt2XhN/OZEOtMVy95GglxaLIADzY3xsyCU94EBEREVHb1M3DBt/O74szGWX4aE8iDicX68Wkl9TiuQ3n8fmBZCy5Jwhjw1wh7CDFTGHuNvj+4X5Nrms0Wjzw9XF421vg3xNCO9xvGyIiIiIiIiIyLqVSieLiYhQWFqJLly633QCno2ELiFZw8uRJxMTEAAAeeeQRnQKma5YuXYrQ0FAAwKpVq6BUKm/rOQYNGoQNGzYYLGC6ZsqUKZg2bRoAICUlBefOnbut5yAiulUXs8tx7+qjOJhY1GTMsvGhWD07okMd5M8pr8O/tlxC5AcHsP5UlsECJhtzCV4eG4JDL0Xh4SF+LGAiIiIionYhwscOPz3aH78uHIC+vnYGY5IKq7Ho57OY8Olh7I0tgLapdqQdyPpTWbiQVY6/LuRi5IqDWBuTCpVaY+q0iIiIiIiIiKgdSk5OxrFjx5CYmIjy8nIUFup3xu5sWMTUCrZu3dp4e8GCBQZjhEIh5s6dCwAoLy/HgQMHWiWXqKioxtspKSmt8hxE1HmV1SiwbMslTPn8CC5klWP5n1fQoFIbjO0oYyYA3eKln09kQqnWP1ljZSbG4pGBiHk5Ck9G+sNCyuaHRERERNT+DPR3wMbHB+KHh/uhh5etwZi4vEo8+sNp3Pv5EeyP77jFTKU1Cry/K77xfnWDCm9ui8PETw/jRGqJCTMjIiIiIiIiovZIJBLpHEcpLCzssMdVbhXPqLaCw4cPA7g6ni0iIqLJuOHDhzfePnLkCEaPHt3iuTQ0NDTeFonY/YOIWoZao8XG01l4f2c8ymr/6SSXVlyDtTFpeCoqwITZtZ6c8jqsPpCMjaezDBYuAYBMIsT8QX54fFgX2FlKjZwhEREREVHLEwgEGBbkhKGBjtgXV4gVexIRl1epF3chuwIPrzuN7p42eHZEIEaGOjc71r69ScivgsZA99X4/CrMXHMcE7q7Ydn4UHjYmpsgOyIios5HpVJBJBJ1qO8bRERE1Lk4OzsjMzOz8b5CoUBFRQVsbW1Nl5SJsYipFcTFxQEAAgICIBY3/RaHhIToPaalHTx4sPH2tfF1TYmPj0f//v2RkJCA+vp6ODo6IiIiAvfddx9mzZoFiURyx3lkZ2c3u56Xl9d4u6amBpWV+gdDiYylurra4G266nRmBd7fk4L4ghqD63+dz8aDvZw6VOelvIp6rD2ahS0XCqAycNICAKQiAe7v5YZHB3nB0UoKqOtRWVnfIs/PzyS1Jfw8UlvCzyO1NfxMUlvSWp/Hfp7m+HV+d+yNL8bqQxlILanTi7mYXYFHfziNUFcrPDHEG5GB9h3i5GKYkwR/Ph6Bjw+kY+vFAr31bRfzsC+2AAsGemL+AE+Yc5S0Dv4bSW1JTY3hYxpE1L6kp6ejsLAQTk5OcHJygo2NTYf4zkFERESdh6WlJSwtLXV+oxQWFrKIiVpOfX09iouLAQCenp7NxtrZ2TV+ILOyslo8lwsXLmDbtm0AgPDw8JsWMRUUFKCg4J+DcDk5OcjJycGff/6J9957D5s2bbrpPpri5eV1y7GbN2+GjY3NHT0PUUv78ccfTZ1Cm1GlluBYnStSlIb//ymGBhHmhejRUII1X50wcnato0ojwdk6R8Qr7KBpYgKrCBp0NStFL1kxLFMu4bdWntzJzyS1Jfw8UlvCzyO1NfxMUlvSWp/He7RAsoUNTtc7o0Jjprcel1+NxZti4SiqQx9ZIXwlVegI5xXdANwrt0BMrRtK1Lpdl+pVGnwRk4kfjyRjoHk+/CWVHeI1tzT+G0mmVlFRYeoUiOguabVaFBUVQalUIjc3F7m5ufDz84O3t7epUyMiIiK6LU5OTjpFTEVFRQgICIBQaPjcZEfHIqYWVlVV1XjbysrqpvHXipha+gq0hoYGPProo1Cr1QCAt956q8lYoVCIkSNHYvz48ejRowccHBxQVVWFs2fP4quvvkJcXBxiY2MRFRWFkydP8kcAUSej1Apwrt4J5+sdoW6ikKeLpAKDLfJhJVQaXG9vbrt4SagycoZERERERKYnFABBZhUIkFYgSWGLM/VOBouZitXm2FnjAwdRHfrIiuDXAQp73MS1uF+egtgGe5ysd0aDVvcQW7VGij013rgsrsEQ8zw4ilumSysRERFdVVlZCYVCobPNwcHBRNkQERER3TlnZ2ekp6c33lepVCgrK+u0321YxNTC6uv/OSgllUpvGm9mdvXgXl2dfvv1u/H000/j9OnTAIB58+Zh0qRJTcZu3rzZYDuyoUOHYtGiRVi4cCG+//57FBQU4LnnnsPmzZtvO5+bdZrKy8tDv379AADTpk1DUFDQbT8HUUuprq5uvCp0zpw5t1SQ2BFptVpsv1KEjw6kobBeYTDG39ECL9/TBQP87IycXeu4nbFxDw/0hLNc/wRNa+BnktoSfh6pLeHnkdoafiapLTHF51Gl0WLHlUKsOZKFjFL94xwlanPsqvFGkLMlHhvshVEhjhC292omAOW1SqyOycDGs3m48WdEnsoSv1cHYOEgLzw13Nck+bUV/DeS2pLExES88847pk6DiO5CUVGRzv1ro1iIiIiI2htzc3PI5XKdhjlFRUUsYqKWIZPJGm/feBWAIQ0NDQCufjBbyjvvvIO1a9cCAPr27YvPP/+82fjm5ilKJBKsXbsWx48fR0JCArZs2YKcnBx4eHjcVk43G613PUtLS1hbW9/W/olai5WVVaf8POZV1OGpn8/ibGa5wXVbCwmevycID/bzhljU/lsZphfX4IvoFGw+lw2luoniJbEQD/bzxpOR/nCxlhmMMYbO+pmktomfR2pL+HmktoafSWpLjPl5nD3YBg8MDMBfF3Lxyf4kpBbV6MUkFtbghS3xCHaR49mRgRjXzRVCYfstZrK2Bt6d7oD5Qyvx+p+xOJZaorOu0QLBHvb8N+E6/DeSTI2FDkTt27VRctdzcnIyUTZEREREd8/Z2VmniKmkpAQajaZTjpTrfK+4lcnl8sbbtzIi7tpsw5a6+uyrr77CsmXLAAAhISHYvn37Xf8oF4vFeOSRRxrvHzx48K72R0Rtn72lFCU1+oWYIqEA8wf5IvqFSMwd6NvuC5gSC6qweP05jFgRjQ2nswwWMEnFQswf5IuYl6KwfHKYSQuYiIiIiIjaA5FQgHt7eWDPkuFY9UBPBDgbPuaRUFCFp345i7GrDuGP8zlQqTVGzrRlhbha45eF/fHF7N7wsP3nYrVe3ra4t+ftXQxGRERETauoqNC7iJxFTERERNSeOTo66txXqVQoLy83TTImxk5MLUwmk8HBwQElJSXIzs5uNrasrKyxiMnLy+uun/vXX3/FokWLAAA+Pj7Ys2eP3of9TnXt2rXxdk5OTovsk4jaLjOxCP+e0BULfzjduG1ooCNem9gVQS7yZh7ZPlzKrsBnB5Kw60pBkzFtpfMSEREREVF7JRIKMKWnByZ2d8f2S3n4ZF8Skgr1L/hKLKjG4vXnsXJPIp4Y7o9pvT1gJhaZIOO7JxAIMC7cDVEhzvj6UCq+PJiC5ZPCmuw0pVJr2v3FIURERMZmaJSchYWFibIhIiIiunsymUxvpFxJSQns7e1NmJVpsIipFXTt2hUxMTFITk6GSqWCWGz4bY6Pj2+8HRoaelfP+eeff2Lu3LnQaDRwc3PDvn37bmuE280IBO23rTsRNU2r1Tb5/+9Roc4YGuiIrNJa/GtCV4wKdW73/xacTi/Fp/uTcTCxqMkYM7EQs1i8RERERETUYkRCASb1cMeEcDdsv3y1mCmxQL+YKaOkFq9uvoRVe5OwcFgXzOrnBQtp+zx0JZOI8MzIQMwd5Asbc0mTcU/8dAZWZmK8ODZEp3sTERERGabValFSoju6lV2YiIiIqCNwdHTUKWIqLi5GQEBAuz8/e7va55GgNm7IkCGIiYlBTU0Nzpw5g/79+xuMu34s2+DBg+/4+fbt24cZM2ZApVLBwcEBe/bsgb+//x3vz5DY2NjG2+7u7i26byIyPq1Wi+jEIry3Ix4fTu+Bbh42ejECgQArZvSAjbmk3V4FDVx9rUeSS/Dp/iScSCttMs5SKsJDA33w6JAucJKbGTFDIiIiIqLOQSgUYGJ3d4zv5oadV/Lxyb4kxOdX6cXlV9bjjb9j8fmBZDw82BdzBjZfCNSWNZf3keRi7I0rBADsuJyPR4b44clIf8hl7fO1EhERGUN1dTUaGhp0trXURAoiIiIiU3J0dERaWlrjfYVCgaqqKlhbW5swK+Njv+pWcO+99zbe/u677wzGaDQa/PDDDwAAW1tbREVF3dFzHT16FFOmTEFDQwNsbGywa9cuhIWF3dG+mqJSqfDtt9823h82bFiL7p+IjOtKbgUe+uYEFnx3CvH5VXh7exy0Wq3BWGe5rN0WMGm1WuyNLcC9q4/ioW9ONFnAZC0TY/HIQBx5ZQReHRfKAiYiIiIiolYmFAowPtwN258diq/mRKC7p/5FFQBQWqPAh7sTMeTd/Xh/ZzyKqxsMxrVHao0Wb26La7zfoNJgdXQKIj+Ixo/HM6BSa0yYHRERUdt1YxcmmUzGUXJERETUIVhYWOh9r7lxjG5nwE5MraBfv34YOnQoYmJi8M0332DevHkYOHCgTsyKFSsQF3f1YNXixYshkeheZRcdHd1Y2DRv3jysW7dO73nOnz+PCRMmoKamBpaWlti2bRsiIiJuK9cDBw6gV69esLW1NbiuVCqxcOHCxlwnTZoELy+v23oOImobcsvr8OHuBGw5l4Pra5aOppQgOqEIUSHOpkuuBanUGmy7lIcvD6YiLq+yyTgHSykeHdoFDw3w5pXOREREREQmIBQKMCbMFaO7uuBwcjE+P5CM46n6Fx9UNaiwOjoF3x5JwwN9vfHYsC5wb+ej15ILq5FdWqu3vaRGgde2Xsa6I2lYNj4UI0La/1hvIiLqPBQKBaqrq1FTUwOFQgGNpvmiXJVKhZ49ewIAcnJyUFBQcNPnqKyshEwma7wvk8mQnJx8V3lTx3InnyvSJRQKIZVKYWlpCSsrK0ilUlOnRETUaTg6OiIzM7Px/vXj5ToLFjG1klWrVmHw4MGoq6vD6NGjsWzZMkRFRaGurg7r16/HmjVrAABBQUFYunTpbe8/JSUFY8aMQXl5OQDgzTffhI2NDS5fvtzkY5ydneHsrFuk8P3332Py5MmYPHkyIiMjERwcDGtra1RXV+PMmTNYs2ZN4yg5Z2dnrFq16rZzJSLTqqxX4quDKVgbk4YGleEDB5vP5bT7IqY6hRq/ncnC1zGpyCqtazLO1VqGx4d3wQN9vWEubZ9dpoiIiIiIOhKBQIChgU4YGuiEMxml+PxACvbHF+rF1Ss1WHc0HT+fyMC0Xp54ItIffo6WJsj47gW7yhH9YiQ+2ZeEn05kQq3R7Y6bUlSDR74/jX6+9nh5XDAifOxNlCkREdHNabVaFBcXo7i4+LYep9FoYGNj03hbpVLd9HmuFVdcIxaLb/o46lxu93NFhl0rSiwoKICTkxMcHBxYXE9EZASOjo4oKiqCo6MjHB0dIZfLTZ2S0bGIqZX06tULGzZswEMPPYTKykosW7ZMLyYoKAjbtm27ow9eTEwMCgv/OaC3ZMmSmz7mv//9L5YvX663vbq6Gr/88gt++eWXJh8bHh6O9evXw8/P77ZzJSLTqFeq8cOxdKyOTkF5rdJgjKu1DC+OCcbUXh5Gzq7llNUo8OPxDKw7mo7SGkWTcV725nhyeADui/BotyPyiIiIiIg6uggfe3w73x5XciuwOjoF2y/l4cbp10q1FhtOZ2HjmSyM6+aKx4b5o6eXrUnyvRsOVmZ4fUo3zB3ki3e2x2NvnH6XgJPppbjvi2MYFeqCl8YGI8il8x28JCKiti8vLw8VFRU62wQCAUSi5o/BabVaWFlZAQAkEslNCyQ0Go1OF6ZbeQ7qfG73c0X61Go1tNd9CS8qKoJCoYC7u7sJsyIi6hysrKzQt2/fTv33i0VMrWjSpEm4ePEiVq1ahW3btiE7OxtSqRQBAQGYPn06nn76aZPPan755ZfRs2dPHDt2DLGxsSgqKkJpaSnMzMzg4uKCPn364P7778fUqVP5Y4ConVCpNfjtTDZW7U1CfmW9wRhLqQiLogLw8GC/dtuNKKe8DmtjUrHhVBZqFeom4/ydLPFUVAAm93CHWCQ0YoZERERERHSnwtxt8PmDvZFaVI0volOw5VwOVDd0K9Jqge2X8rH9Uj76+9njieH+iAx2ancH+vydrLB2Xh8cSynBW9tjcTlHfyz23rgC7IsvwLRenlhyTyA87Ux7PImIiOia+vp6nQImBwcHWFtbw8zM7KZ/k9VqdePF2s7Ozjc9B1FbWwu1+p/jgGKxGObm7XvELLW82/1ckT6tVouGhgZUVlaipKQEAFBRUQEHBweYmZmZODsioo6tvR3TaA0sYmplPj4+WLlyJVauXHlbj4uMjNSpcr7R/PnzMX/+/LvMDggNDUVoaCiee+65u94XEZmWRqPFjsv5WLE7AanFNQZjREIBZvXzwuKRQXCSt88fGwn5VfjqYAr+vJCrdxLjej28bPHEsC4YE+YKoZB/8ImIiIiI2qMuTlb4YHoPPHdPENYcTMH6U1kGx2SfSCvFibRSBLvI8diwLpjUwx1Scfu6iGGgvwP+fGoI/riQgw93JSKnXHdMtlYL/H42GwcTC3HklRHsMEtERG1CeXl5421nZ2c4ODi0yvMYOl8iFvMUF1FrEAgEkMlkkMlkEIlEjUVhZWVlcHV1NXF2RETU0fEbHhFRB7HuaDr+93dsk+tjwlzw4phgBDi3v/EDWq0Wp9LL8OXBFOyPL2w2dniQE54Y7o8BXexZrUxERERE1EF42Jrj9Snd8PSIQHxzOA0/H89AVYNKLy6hoApLf7uAD3Yl4JEhfnignxfkMokJMr4zQqEAU3t5Yny4G34+nonPDiTrjc1eMNiPBUxERNRm1NbWNt62tbVttecRCASwsLCAVquFSqWCWq1mEROREdja2jYWMV3//3ciIqLWwm94REQdxH29PfHx3kRU1useyB/k74CXxoagp5etaRK7C2qNFntiC7DmUArOZpY3GScSCjCxuxseH+aPru7WxkuQiIiIiIiMykluhlfGhWBRlD9+PZGJb4+koaCyQS8uv7Ieb22Pwyf7k/DQAB8sGOQLZ2uZCTK+M2ZiER4e4ocZfb3w9aFUrI1JRY1CDSe5GRYM9jV1ekRERI2ujXcTi8VGGdslEAggkUggkbSfImWi9kwkEkEkEkGtVuuMcyQiImotLGIiIuogbCwkeDIyAO/tjAcAdPe0wUtjQjAk0NHEmd2+mgYVfjudhW+PpCOztOmrO2QSIR7o641HhvjBy97CiBkSEREREZEpWcskeHy4PxYM9sMf53Ow5lAqkgqr9eKq6lX4IjoF38SkYWovDywc1gUBzlYmyPjOWJmJseSeIMwZ6IPP9icjxFUOC6nhw3l5FXXYG1uAGX292KmJiIiIiFoMJx4QEZlOfX09SktLUVlZieDg4E7xbzKLmIiI2pH4/Epcyq7A9D5eBtfnD/LFocQizB3og7HdXNvdH7K8ijqsO5qOX09k6nWUup6thQTzBvpi3iBf2FtKjZghERERERG1JVKxENP7eOG+3p44kFCIrw6l4mRaqV6cQq3BhtNZ2HA6C1HBTnhkSBcMDnBoN7+ZHK3MsHxyWLMxn+1Pxs8nMvHlwVQ8PSIA90d4QiISGilDIiIiIiIiImopSqUS58+f1xnl6enpCSur9nNh1p1iERMRUTuQWFCFVXuTsO1SHqRiIYYGOsHVRn8UgrlUhF8fG2CCDO/O5ZwKrI1Jxd8X86DSaJuM87A1x6ND/TCzr1eTVx8TEREREVHnIxQKMDLUBSNDXXA2swxrDqZiV2w+tAZ+XhxIKMKBhCKEuMrx8GA/TO7pDpmkfXcuyiqtxcbTWQCAnPI6vLr5ElZHJ+PZEYGY2ssDYhYzEREREREREbUbYrEYGo1GZ1tZWRmLmIiIyLSSC6uxal8S/r6Y23jwXaHS4IvoZLw+pZtpk7tLGo0W++ILsTYmFScMXCl9vW4e1nh0SBdM6O7GK4mJiIiIiKhZvb3t8OWcCKQWVWPt4TRsOpMNhUqjFxefX4WXfr+I93fFY3Z/Hzw0wAdOcjMTZHz3PtufDKVat2Irq7QOL266iNXRKVg8MhCTerhDJGwfnaeIiIiIiIiIOjOBQAA7Ozvk5eU1bisrK4OXl+FpPR0Ji5iIiNqgtOIafLIvCX+cz4GhxkS/nszC48P94W5rbvzk7lKtQoXfz+bg28NpSCuuaTJOIABGhrhg4VA/9POzbzdjHoiIiIiIqG3o4mSFt6eGY8moIHx/NB0/Hs9ARZ1SL664WoFV+5LwRXQKpvR0x8ND/BDqZm2CjO/c1N4eSCuuwcl0/QtE0opr8NyG8/jsQDKeGxWI8d3cIGQxExERtRNarRa1tbUQiUSN/wmFvMiRiIiIOr4bi5gqKiqg0Wg6/Hehjv3qiIjamdSiaizdeAEjV0RjyznDBUxymRhPjwiAXNa+6lBzyuvw7o54DHp3P17bernJAiaZRIg5A3yw7/nhWDuvD/p3cWABExERERER3TEnuRleGBOMY6+OwBv3dkMXR0uDcQq1Br+dyca4VTF48Ovj2BdXAE0z467bkgFdHLDh8QH48ZF+6OllazAmubAaT/9yDqM/PoQt57KhUut3pyIiImprNBoNNBoNlEol6uvrUVNTozdahdq/devWQSAQQCAQID09vVWeIz09vfE51q1b1yrP0VYtX7688bUTEVH7YWNjo3Nfo9GgqqrKRNkYT/s6A05E1EHF51fi8wMp2HYx12DhEgDIzcR4eIgfHh7iBxtziXETvENarRbHU0vx/dF07I7Nb/K1AYCz3AzzBvniwX7esLOUGi9JIiIiIiLqFCykYswZ4IPZ/bwRnViIbw6n4UhyicHYoyklOJpSgi6Ollgw2BfTenvC0qxtH0YTCAQYGuiEIQGOiE4owso9ibiUU6EXl1xYjSUbLuDjvUlYFOmPqb08IRXzOkciImqbVCqVzn2hUNjhuw8QERERAYBUKoWlpSVqav5pDFFWVqZX3NTRtO2jL0REHdyFrHJ8diAZe2ILmoyxlIrw8BA/PDLED7YW7aO4p06hxpZzOfjhWDri85uvCA51s8ajQ/wwqYc7D5wTEREREVGrEwoFGBHighEhLojLq8S3h9Pwx/lcKAx0JkotrsFrf1zB+zsTcF+EJ+YM9IG/k5UJsr51AoEAUSHOiAx2wp7YAqzck2jwd1lGSS1e/v0SEguq8drEribIlIiI6ObUarXOfZFIZKJMiG5u3bp1WLBgAQAgLS0Nvr6+pk2IiIjaPVtbW50ipvLyctMlYyQsYiIiMpG8ijpMXX2kye5EFlIR5g3yxcKhXWDfTjoTZZXW4sfjGdhwKgsVdcpmY0eEOOPRIX4Y6M9xcUREREREZBqhbtb4YHoPvDQ2BD8dz8BPxzNQUqPQi6tqUGHd0XSsO5qOoYGOmDfQF1EhzhAJ2+5vGYFAgNFhrhgV6oKdV/Lxyb4kvWImoQCY3d/bRBkSERE1T6vVsoiJiIiIOjU7Ozvk5OQ03q+srIRare7Q34lYxEREZCJuNuYYH+6Gvy/m6Wy3MhNjzkAfPDrEDw5WZibK7tZptVocTSnBuqPp2BdX0OzIOEupCNP7eLWLq5eJiIiIiKjzcJKbYck9QXgy0h9/XsjFt4fTmuwqG5NUjJikYnjamWPOAB/M6OPVpkdiC4UCjA93w9gwV+yLL8Sn+5NwMfvqmLmJ3d3Rhb/NiIiojdJo9LskduQTdkREREQ3unF0nFarRUVFBezt7U2UUetjERMRkQk9FRXQWMRkYy7Bw4P9MH+QL2wsJCbO7OaqG1TYci4HPx5LR2JBdbOxfo6WmDfQB/dFeEIua/uvjYiIiIiIOieZRIQZfbwwPcITR1NK8N2RdOyLL4DWwMUa2WV1eGdHPFbuScSUnu6YO9AX3Txs9APbCKFQgHu6umBUqDNikorx2f5kPD0ioMn4H46lI6+iHgsG+cLZWmbETImIiK5SqVQ694VCIYRCoYmyISIiIjI+sVgMuVyOqqp/LrTq6EVM/LZHRNRK6pVq/HoyEzO+PIZ6pdpgTKibNWb188Ir40Jw5JURWDwqsM0XMF3JrcCyLZfQ/629eG3r5WYLmKKCnbBuQV/se3445g/2YwETERERERG1CwKBAIMDHLF2Xh8cejEKjw/vAtsmfqs1qDTYeDobEz89jPu+OIo/zudAodLvHNFWCAQCDAtywsYnBiLIRW4wpkGlxqf7k/FFdAqGvHcAr26+iNSi5i9eISIiamkcJWfY8uXLIRAIIBBcHWtbWVmJ5cuXIzw8HFZWVnB2dsb48eNx9OhRnccVFhbi3//+N8LCwmBpaQkHBwdMmTIF586da/b5NBoNfvrpJ4wfPx6urq6QSqVwcnJCVFQUVq9eDYVCfxTvjcrKyvDKK68gJCQE5ubmcHZ2xqhRo/Dbb7/d0mu+9nqXL1/ebFxkZCTEYjHuu+++W9rvjS5fvow333wTY8aMgaenJ8zMzGBlZYXAwEDMmzcPx48fN/i46OhoCAQCLFiwoHGbn59fY97X/ouOjjb4+K1bt2L69Onw9vaGTCaDra0t+vTpg9dffx1lZWU3zTs7OxtPPfUUunTpAplMBnd3d0yePBl79+69o/eBiIjalhu7MVVWVpooE+NgJyYiohZWUavETycy8N2RdBRXNwAAfj+bjdn9fQzGvzOtuzHTuyP1SjX+vpiHn09k4FxmebOxVmZiTO/jibkDfeHnaGmcBImIiIiIiFqJl70FXh0XiiWjgvDnhVx8fzQdV3INHzA8k1GGMxlleMMqFvdHeOHBft7wdrAwcsZ3b8vZHBRVXf09q1Br8OvJLKw/lYUxXV3xRKQ/enrZmjZBIiLq8LRaLYuYbkFWVhZGjRqFxMTExm01NTXYsWMHdu/ejV9//RXTp0/HxYsXMX78eOTk5DTG1dbW4s8//8SuXbuwY8cOREVF6e2/tLQUkydPxpEjR3S2FxcXIzo6GtHR0fjss8+wY8cO+PgYPv4dFxeHUaNGITc3t3FbfX099u3bh3379mHBggUYNmzY3b4Vdy06Otrge6BQKJCcnIzk5GT88MMPeOWVV/DOO++0yHOWlZXh/vvvx/79+3W2NzQ04MyZMzhz5gxWr16NP/74AwMGDDC4j5iYGEycOFHnhHZeXh7++usv/PXXXzct/CIiorbPxsYG2dnZjfcrKyuh1WobC5o7GhYxERG1kJzyOnx7OA2/nsxErUL3B/bXh1LxQF9viITt649JalE1fj6RiU1nslFRp2w21t/JEvMG+WJab09YmfHPCxERERERdSzXj5o7m1mOH46lY/ulPCjV+rPmiqsV+PJgCr48mIKhgY6Y3d8bI0NdIBG1/aboWq0Waw+nGdgO7LySj51X8jGgiz0eH+6PyCCnDnvQlIiITEuj0e9qyCImfdOnT0d2djZeffVVjB07FhYWFjh8+DD++9//orKyEo888gj69OmDiRMnoq6uDm+99RaGDx8OiUSCnTt34q233kJDQwPmz5+PpKQkSKXSxn2r1WpMnDgRx44dAwAMHz4cTz/9NPz8/JCbm4tvv/0WW7duRVxcHEaOHInz58/DyspKJ7/KykqMGTOmsYBp5syZmDdvHpydnZGYmIiVK1fiu+++w+XLl433pjVBpVLB0tISEyZMwIgRIxASEgJra2sUFhbiypUr+OSTT5CRkYF3330XQUFBOl2X+vbti0uXLuGPP/7Av//9bwDArl274O7urvMcfn5+jbcbGhowatQonD17FiKRCA8++CDGjx8PPz8/KJVKHDp0CCtXrkRhYSHGjx+Pc+fO6RWKZWZmNhYwCYVCPPbYY7j//vthY2ODixcv4t1338Xy5cvRp0+fVnzniIiotVlbW+vc12g0qK6uhlxuuLtye2f0s8xJSUn44YcfcOzYMeTn56Ourg67du1CQEBAY8zly5eRmZkJS0tLDB8+3NgpEhHdlri8Sqw5lIq/LuRCpdE/eA0A6SW1OJRYhKgQZyNnd/uUag12XynAzycycDSlpNlYoQAYFeqChwb4YEiAI4TtrEiLiIiIiIjodgkEAkT42CHCxw7/mhCK9Sez8POJDBRUNhiMj0kqRkxSMZzkZpjZxwsP9POCp13b7c4kEAjw1ZwIrI1Jxe9ncqBQ659EPp5aiuOppQhxlePx4V0wIdwdUnHbL9AiIiLT0Go0UJeX621Xq9XQ/P92lVgM7XVFSkqFAurrxpQJBAJolEq03YGtukS2thAIW/9v4/nz53Hw4EH079+/cVufPn0QGBiIiRMnoqqqCv3794dWq8XJkyfh7+/fGNevXz84OjriqaeeQmZmJrZt24apU6c2rn/55ZeNBUxz587FunXrGouXIyIiMGnSJPzrX//C22+/jZSUFLzxxht47733dPJ74403kJWVBQB4++238eqrrzauRURE4P7778fEiROxe/fuln9zblPPnj2RnZ0NW1tbvbUxY8bg6aefxsSJE7Fnzx68/vrrmDt3bmNhnaWlJbp164bTp083PiYoKAi+vr5NPt///vc/nD17Fra2tti7dy8iIiJ01ocMGYLZs2dj4MCByMvLw7Jly/Dzzz/rxCxdurSxA9NPP/2EWbNmNa716dMH06dPx9ChQ3XyIiKi9kcqlcLc3Bx1dXWN2yoqKljEdLc0Gg1eeuklrFq1ChqNBlrt1RP9AoFAb17utcphsViMtLQ0eHh4GCtNIqJbotVqEZNUjLWH03AosajZ2MEBDnh8mD+GBjoaKbs7k1Vaiw2nsrDhdFbj2ICmuFib4YG+3nignxfcbMyNlCEREREREVHb4iyX4dmRgXgy0h+7rxTg+2PpOJlWajC2qKoBnx1IxufRyRge5ITZ/X0QFewEcRvszuTvZIV3pnXHklFB+PZIOn4+noGqBpVeXHx+FZZsuIB3d8Rj7kBfzOrnDXtLqYE9EhFRZ6YuL0fSoMHNxhge1Np+BR49ArG9fas/z3PPPadTwHTNhAkT4OPjg4yMDBQVFeGLL77QKWC6ZsGCBVi6dCnq6+sRExOjU8T0+eefAwCcnJzw2WefGey++Prrr2Pz5s2Ij4/H119/jf/9738wMzMDcHUM2zfffAMA6N69O1555RW9x0skEnzzzTfo0qULlMrmJwG0NkfH5o/fS6VSfPDBB+jZsycyMjJw/vx5vcKjW1VdXd34/r7xxhtN7sfHxwevvfYaFi1ahN9++w1r1qyBpaUlACA/Px9btmwBAEycOFGngOkauVyONWvWGPyMEBFR+2JjY4OGhgbI5XJYW1vDxsbG1Cm1GqMVMT3++OP49ttvodVq4eHhgYEDB2LTpk0GY6+1S0xPT8emTZuwePFiY6VJRNSsOoUam89l47sj6UgurG4yTigAJnR3x+PDuqCbR9v9I1KvVGN3bAE2nsrC4eTim8ZfHYPgg1Ghzm3yQDsREREREZEpSERCTOjuhgnd3ZBcWIVfTmRh05ksVNbrF/5otUB0QhGiE4rgai3DzL5emNnXC+62be8CEWdrGV4ZF4Knovzxy4lMfHM4DYUGLnopqGzAB7sS8Mm+JPx7QijmDPQ1frJERESd0AMPPNDkWvfu3ZGRkQGBQICZM2cajDE3N0dgYCAuXbqE1NTUxu25ubmIi4sDAMyYMaPJTg9isRgLFizAyy+/jLKyMpw9exYDBw4EAJw5cwZlZWUAgHnz5jU5gtbT0xOjR4/Gtm3bbv6CjaihoQEFBQWorq5uHG94rUEDAFy4cOGOi5gOHjyIiooKAMD999/fbOywYcMAAEqlEmfOnGm8f+DAAajVagDQGW13o379+iEsLAxXrly5o1yJiKht6NKlCwIDAyE0QqdHUzNKEdO+ffvwzTffQCAQYNmyZXj99dchEomafYOnT5+O999/H/v372cRExG1CXkVdRj7cQwq6pq+IkQmEWJmHy88OrQLvOzb7niA+PxKrD+Zha3nc1Be2/wVLnYWEszo44VZ/bzh62hppAyJiIiIiIjapwBnOf4zqSteGhuMbRfz8MvJTJzJKDMYm19Zj1X7kvDp/iQMDXTCzL5eGBnqDDOxyGC8qchlEjw+3B/zB/vij3O5+OpQClKKavTiGlQa+DlamSBDIiKizikoKKjJtWtj0RwdHWFnZ3fTuKqqqsZtly9fbrx9sy4+169fvny5sYjp0qVLjdv79u3b7D769evXJoqYampq8Mknn2D9+vW4cuVKY5GQIcXFN78ouCnXj3dzc3O75cfl5+c33r7d95dFTERE7ZtEIjF1CkZjlCKmNWvWALjaYenNN9+8pcf069cPAPhHlYjaDFdrGXwdLHAhu0Jvzd5SinkDfTFnoE+bbZ1fVa/EXxfysOFUpsHXcKO+vnaY3d8HY7u5QiZpWwfQiYiIiIiI2jqZRIT7IjxxX4QnEvKr8MuJDGw+l4MqA92ZNFrgYGIRDiYWwc5Cgnt7eWBGHy+EulmbIPOmmYlFmNHXC/dHeGJvXAHWxqThZPo/4/OCXeQYHOBgwgyJiIg6FwuLpi+kvdZIoLmY6+OuL9gpLf3n77uzs3Ozj3d1dTX4uNvZh4uLS7PrxpCeno4RI0YgLS3tluLr6uru+LkKCwvv6HG1tbWNt9vb+0tERHSrjFLEdOzYMQgEAjzyyCO3/BhPT08AulXFRESmJBAI8PAQPyxef75xW4CzFRYM9sW0Xp4wl7a9Qh+tVoszGWVYfyoL2y7moU7Z9JUjAGAtE2NqLw/M6u+NENe2dbCciIiIiIiovQp2leP1Kd3w8rgQ/H0xD7+cyMT5rHKDsWW1Snx3JB3fHUlHuIcNZvTxxOSeHrAxbztXXQqFAowOc8XoMFdcyq7At0fS8PfFXDw8xLfJUTEHk0pwvNYFXc1KDa4TEVHHJbK1ReDRI3rb1Wp1YzcbR0dHiERXj69qNBpo1Gqo1Wpo/n98l7l52xu72hzR/3c36gia+ttu7H20tjlz5iAtLQ0CgQALFizAAw88gNDQUDg5OUEqlUIgEECj0TR+Tq8fLXe7ri8YO3v27C1317h27vRG7eH9JSIiulVGKWK6VlHs6+t7y4+59gdbpdK/Oo2IqDXkVdTh15NZOJZciAgtYOh7/7hubnjHOh4hbnI8PNgPQwMd2+QPhLyKOmw9l4tNZ7IMtvi/0SB/B8zs64UxYey6RERERERE1FospGLM6OOFGX28cCW3Ar+cyMQf53NR3WD4+NelnApcyqnAm9viMLabK2b08cLALg4QCtvO79BwTxt8NLMnXhkX0myh1dqjWbjQ4IRzDY7I/u0KHh4agKEBjm3qtRARUesQCIUQ29vrb1erIfz/c0Bie/vG4pAbabXaNnkMtiOzv+5/r4KCgmZjr29GcP3jrh9hV1BQ0Ozou5s9h0AggFarhUajaTaupubmx8INiY+Px+HDhwEAy5Yta3KqzPXdj+6Gg8M/nSudnJyaLE5qzo3vr5eXV5OxN3t/iYiI2hKjFDFZWlqivLwcRUVFt/yY7OxsALpfeIiIWppWq8WR5BL8dDwDe+IKoNZcvXrC1coSnhL9HzxSsRB7nh8GuaztXAF7Ta1Chd1XCvD72WwcTi7GzS4EcbE2w/QIL0zv4wkfB0vjJElEREREREQAgDB3G7w1NRz/ntAVOy7nYePpLBxPNXxirEGlwR/nc/HH+Vx42Jpjeh9P3NfbE172zY+HMSYXa1mTa+cyy3Ahp+r/7wlwMKkUB5NOwtfBAg8N8MH0CC/YWLS939lERNQ2sIDJ+Lp169Z4+8SJE5gzZ06TsSdPnjT4uPDw8Mbbp06dwtChQ5vcx6lTp5rNRy6Xo7KyEmVlZU3GaLVaJCcnN7ufply5cqXx9syZM5uMO336dLP7udXPaq9evRpvHzlypNnnbMqN729zRUw3e3+JiKh9UqlUEIlEHe67ktAYT9KlSxcAQGxs7C0/ZseOHQCAsLCwVsmJiDq3ilolvjmchpErDuKhb05g55X8xgImALjS0HQBZVsqYNJotDiWUoIXf7uAvm/uxXMbziMmqekCJrFQgNFdXfDt/D448vIIvDAmmAVMREREREREJmQuFWFab0+sf2wgDr4YiaejAuDaTEFQTnkdPt6bhKHvH8D0L4/ilxOZqKhVGjHj2/ftkXSD29NLavHmtjj0f2cvXt50EZdzKoybGBERERnk7u6O0NBQAMDGjRtRXV1tME6tVmPdunUArnYG6t27d+NaREREY7egH3/8scnxazk5Odi9e3ez+fj5+QFovohox44dKC8vb3Y/Tbl+Kkxz3Zy+/PLLZvcjk/3zHa6hoaHJuFGjRsHC4mox+ieffHJHo+mioqIau5d9//33TcadOnUKly9fvu39ExFR26PVapGVlYW4uDicPHkSR44cQV1dnanTanFGKWIaPXo0tFotPv/885u2egSuFjutW7cOAoEA48ePN0KGRNRZXM6pwMubLqL/O3vxxt+xSC02/IMkTWmNWk3bHauWVlyDlbsTMOyDA5j19XH8diYbNQp1k/F+jpZ4ZVwIjr46Amvm9sGIEBeIRUb5E0BERERERES3yMfBEi+MCcaRV0Zg3YK+mBDuBomo6SsqT6WXYdmWS+j71l4s+vkM9sQWQKG6+bE3Y1s8MgCzItwggeHfrfVKDTaczsLETw9j6uoj2Hw2G/XKpn/jEhERUet76qmnAABFRUV49tlnDca8/vrrjQ0MFi5cCDMzs8Y1MzMzLFiwAABw/vx5fPDBB3qPV6lUWLhwIRQKRbO5DB8+HMDVrlBHjhzRW8/Pz8czzzxzC6/KsMDAwMbb14qybvTFF1/gjz/+aHY/bm5ujbdTUlKajLO1tcXTTz8NADh69CiWLFnS7PnTgoICrF27Vu+5pkyZAgD4888/sXHjRr3HVVdX4/HHH282ZyIial+ysrJQWFjYWLzUVKFxe2aUcXLPPvssPvnkE6SkpOCJJ57A6tWrIRYbfuo9e/ZgwYIFqK+vh4ODAxYuXGiMFImoA6tpUGHbxTz8cjIT57PKm421kIowIcwJSD4MC2HbOmBaUavE35dysflsDs5kNN029xorMzEmhLvhvghP9PW163CtBImIiIiIiDoqkVCAyGBnRAY7o7RGga3ncrDxdBbi86sMxivUGmy/lI/tl/JhZyHBpB7umNrLAz29bNvEb8EAZzleHRMAy5R9SFTYINcyEMlFtQZjz2WW41xmOV7/KxZTe3lgVj9vBLvKjZwxERERPfHEE/j5559x7NgxfPfdd8jIyMCiRYvg5+eHvLw8fPvtt9i8eTMAwN/fH6+99prePv7zn/9g48aNyM7Oxssvv4zz589j7ty5cHZ2RmJiIlauXIlTp06hT58+zXZZeuyxx7B69WqoVCpMmjQJ//nPfzBkyBAoFAocOXIEK1euhFKpRGBgIJKSkm77tfbq1QvdunXD5cuX8dVXX6GsrAxz5syBm5sbsrOz8dNPP2HTpk0YPHiwwSKq6/cjk8lQX1+P1157DRKJBD4+PhAKr15Q7OHhAXNzcwDA//73Pxw8eBAnTpzAqlWrEB0djYULF6Jnz56wtLREWVkZrly5gr1792LHjh0IDw/Ho48+qvN8K1aswJ49e1BVVYUHH3wQBw8exP333w9ra2tcvHgR7777LhITE2/6/hIRUfsgEAggl8tRWvrPKPqqqio4OzubMKuWZ5QiJhcXF3z55ZeYO3cuvvnmG+zatQsTJkxoXF+1ahW0Wi2OHDmC+Ph4aLVaCIVCrFu3DlZWVsZIkYg6oMSCKnx7OA1/XchttksRAAQ6W2HOQB/c28sDUNThiy/2GinL5tUp1NgbV4A/L+QiOqEQSnXzbWWFAmBooBOm9fbA6K6uMJe23W5SREREREREdHP2llI8PMQPCwb74nJOJTaezsJfF3NR3sQIubJaJX44loEfjmWgi6Ml7u3lgam9POBlb2HkzPVJBBqEmZXh00d7I75EhR+PZ2Dn5XyoNPq/dSvqlFh3NB2nM0rx9zNDTZAtERGZglqthlAobBNFuJ2dSCTC33//jcmTJ+PIkSPYv38/9u/frxcXGhqKHTt2GDyfZ2Njg507d2LUqFHIz8/Hr7/+il9//VUnZv78+Rg+fHhj1yZDwsLC8P777+P5559HWVkZlixZorNub2+PrVu34rXXXrujIiaBQIAff/wRI0aMQFlZGTZu3KjX2Sg8PBy//fYb3N3dm9yPXC7Hs88+i/fffx9nz57F6NGjddYPHDiAyMhIAFc7Ve3Zswfz58/H5s2bceHChcbuTIZYW1vrbfP19cWff/6JyZMno6qqCqtXr8bq1at1Yv7zn/9AIBCwiImIqIMwVMTU0RiliAkAZs+eDYlEgscffxxZWVn46quvGr+EXmuBeG3mq5WVFb7//nudQiciotuVXFiN9aeymlwXCwUY080Vcwb4oL+ffeO/SZUK084OVao1OJxcjD/P52L3lfybFmABQJCLFe7r7Yl7e3nAxVp203giIiIiIiJqXwQCAcI9bRDuaYPXJnbFgYRCbDmbg/3xhVCoDY8fSS2uwco9iVi5JxF9fOwwqYc7xoe7wUluZjDeWAQCAfp3cUD/Lg4orKzHryez8MvJDBRUNujFPtDX2wQZEhGRqdTV1TVe6C4UCiGVSiES8UJNU7G3t8ehQ4fw888/45dffsG5c+dQWloKa2trhIeH4/7778fChQshlUqb3EdYWBiuXLmC9957D1u2bEFmZibkcjnCw8OxcOFCzJo1q8kRbtdbsmQJunbtio8++ggnT55EbW0t3N3dMX78eLz00kvw9r677ww9e/bE+fPn8c4772DHjh3Izc2FXC5HQEAAZsyYgaeeegoy2c2Pvb/77rsIDAzEDz/8gCtXrqCiogJqteFj/HK5HL///jsOHz6M77//HjExMcjNzUVdXR2sra3h7++Pfv36YcKECXoFUddERkbiypUreOedd7B9+3bk5eXBzs4Offr0wTPPPIMxY8Zg+fLld/PWEBFRG3Jj0XBNTU1jnU1HYbQiJgCYMWMGRo4cidWrV+Ovv/7C+fPnoVKpGtfDwsIwefJkLF68uMO1vCIi4xsV6gJ7SylKa3TnabvZyPBgP2/M7OsF5zZS8KPRaHEmswx/nM/B9kv5ejkbYm8pxeQe7rg/whNh7ta8OomIiIiIiKiTkIqFGBPmijFhriivVWDbpbybjh4/nVGG0xlleP2vKxjk74hJPdwwNswNNhYSI2auz9lahsWjArEoyh97Ygvwy4lMHE4uBgCYS0SY0tNwtwOtVout53MwIsQFNuamfQ1ERNQyNBpN40k4jUYDjUbTbHFMZ7V8+fJbKkpZt27dLRUHRUdHN7suFAoxZ84czJkz59YSNMDe3h7vvfce3nvvPYPr8+fPx/z582+6nzFjxmDMmDFNrkdHR0OtVqOwsFBvzdfX96Yneb29vfHFF180G3OzfQgEAjz66KN6o9+aM2TIEAwZMuSW42/k5eWl14Hperf6mSEiorbP0tJS575KpYJCcfPzyu2JUYuYAMDBwQGvvfYaXnvtNWg0GpSWlkKtVsPe3h4SCQ84ENGtyy6rxZazOYjwtcMgf0e9dalYiPt6e+DrmDQIBUBUsDNm9vXCiBBniEVCE2SsS6vVIi6vCn9eyMVfF3KRU37zDlBSkRAjQpxxX4Qnhgc5QSo2/esgIiIiIiIi07G1kGJ2fx/M7u+DjJIabDmXgy3ncpBRUmswXqMFDicX43ByMf699TKGBzlhUg93jAp1gaWZ0Q8VNpKIhBgf7obx4W7ILKnFhtOZUGsAuczw8cJzWeVYsuECpGIhRnd1wX0Rnhga4Ngmfu8TEdGd0Wj0OwsKhfx3nYiIiOgamUwGkUik0+WvpqYGYrHpfs+3NJO+EqFQCEdH/cIDIqKmVDeosONSHn4/m43jqVfnfd7T1cVgERMAzOrnDRtzCe6P8IKrjem7Lmm1WiQUVGH7xTxsu5SHlKKamz5GKAAGBzhiUg93jAlz5RWmREREREREZJCPgyWeGxWExSMDcTazHJvPZuPvi3moqFMajFeqtdgbV4i9cYWQSYQYGeqCSd3dERnsBJnEdKN7vB0s8OKYkGZj1p/MBAAoVBr8fTEPf1/Mg5PcDNN6eeC+CE8EuciNkSoREbWgG4uYhEIhu88TERERXUcgEMDCwgJVVVWN22pqamBjY2PCrFpWxynHIqIOS63R4mhKMTafzcHOy/moU+rOj94fX4jCynqDo+G6OFnh6RGBxkrVoGsdl7ZfysP2y3lIvYXCJQDo5W2LyT3cMaG7G5zlpi/AIiIiIiIiovZBIBAgwscOET52+O+kMBxKLMJfF3OxJ7YAtQq1wcfUKzXYdjEP2y7mQW4mxj1dXTC2myuGBZm2oMmQqnol/rqQp7e9qKoBXx1KxVeHUhHuYYP7IzwxuYc77Cw5ioiIqD0wVMRERERERLqsrKxYxEREZArJhVXYdCYHW8/lIL+yvsk4tUaLTWezsSgywIjZNU+r1SI2r/Jq4dKlfKQV31rhUqCzFab0dMfkHh7wdrBo5SyJiIiIiIioo5OKhRjV1QWjurqgTqHG/vhC/HkhBwcSiqBQ6Y/tAYCqBhU2n8vB5nM5sJCKMCLEGeO6uSEqxAkWUtMfTqysVyEy2Al74wqgVGsNxlzKqcClnAq8uS326lj23p6IDHbmWHYiojbsxiImkahtFdESERERtQWWlpY692tqbu08dHvRokcdunTp0pK7A3D1yrGUlJQW3y8RtU0FlfXYdjEPW8/n4GJ2xU3jrWViTOrhjmGBTkbIrnlarRZXciux7VIedlzKQ3pJ7S09zsPWHJN6uGNKT3eEuMrZIpmIiIiIiIhahblUhAnd3TChuxsq65XYc6UAf13MRUxSMdQaw8VAtQp147g2M7EQkcFOGNfNDSNCnWEtM824cw9bc3zxUATKahT462IuNp3JbvIYglKtxa4rBdh1pQA25hKMD3fD3IE+CHWzNnLWRETUHK1Wy05MRERERLfAUBGTVmv4N3171KJFTOnp6bcUd+0E/Y1vpKHtPJlP1DmU1yrw5E9ncTytBDf7N1YkFCAyyAn3RXhiRIizSdvaqzVanE4vxe7YAuyOzUdWad0tPc7D1hzjurlifHc39PS0hVDIf+uIiIiIiIjIeKxlEtwX4Yn7IjxRWqPAjst5+OtCLk6klTb5u7xBpWksCJKKhBgS6Iix3VwxuqsLbC2MP7LNzlKKuQN9MXegL5IKqrDpbDa2nM1BYVWDwfiKOiV+PZmJYYGOLGIiImqDbjxnxCImIiIiIn03FjFptVo0NBj+HdwetWgR07x585pdP3/+PC5cuACtVgtbW1v06tULLi4uAICCggKcP38eZWVlEAgE6NGjB3r06NGS6RFRG2ZjLkFOeV2zBUxh7ta4r7cnJvd0h6OVmfGSu0G9Uo2YpGLsvpKPffGFKK1R3NLjPGzNMT7cFePD3dDTy5ZFmkRERERERNQm2FtKMbu/D2b390FBZT12Xs7H9kt5OJVeiiYaNEGh1mB/fCH2xxfiVaEAA7rY457Qq2PrPO2MPx490EWOV8eF4sXRwTicXIzfz+Zg95V8NNwwMk9uJkZUiLPBfWi1Wv5WJyIykRu7MAG8yJ2IiIjIEIlEAolEAqVS2biNRUxN+O6775pc+/bbb/HLL7/A09MTK1aswNSpUyEW6z69Wq3G5s2b8eKLLyI2NhZPPfUUHnnkkZZMkYhMqF6pRmFlA7wd9A9mCgQCTOrhhs8P6I6PdJKbYWovD0zr7YEQV9NdJVlWo8D++ELsjs3HocRi1CnVt/Q4TztzTAh3w/hwN3T3tOEPbyIiIiIiImrTXKxlmDfIF/MG+aK4ugG7rxRgx+U8HE0paXLknFqjxZHkEhxJLsHyv2LR1c0ao7q6YHRXF4S5Wxv1t7BYJERksDMig51RWa/Etot5+P1MNk5nlAEAxnRzbbKj8+7YAny6PwmTe7hjfLibSYqxiIg6K0Oj5HgslYiIiMgwc3NznSKm+vp6E2bTslq0iKkpp0+fxhNPPAEnJyccP34c7u7uBuNEIhGmT5+OIUOGICIiAosWLUKPHj3Qp08fY6RJRK2gXqnGwcQi7Lycj72xBQhwscKWRYMNxk7u4YHPD6TAykyM0WEumNzDHUMCHCEWmaZtcJVagp9P5eBQSixOppc2ebD2Rl725hgf7oYJ4W4I92DhEhEREREREbVPjlZmeLC/Nx7s742yGgX2xBVg5+V8HE4qhkKt3zHjmti8SsTmVeKTfUlws5FhVKgLBvvKodYKIBLc2m/rlmAtk2BWP2/M6ueN7LJa/HUhD/387JuM//N8Li7nVOJyTiXe3h6PHp42GBfuhvHd3AxekEVERC2Ho+SIiIiIbp2FhQUqKysb77MT02366KOPoFarsWzZsiYLmK7n5uaGZcuW4dlnn8XKlSvxyy+/GCFLImopNQ0qHEgoxI7L+TgQX4haxT9di85lliOrtBZe9voH/4Jd5fhuQV8M7OLQ5FWRrUml1uBMRhl2XszG1ooAlGlkwJ7UW3psVzdrjA5zwT1dXdDVzbhXmRIRERERERG1NjtLKWb08cKMPl6orFdif1whdlzOQ3RCkd7ItuvlVdTjx+MZ+PE4IEEIvCXV8L5ciPE9zWFjITFa/p52Fngy0r/J9ap6JfbGFehsu5BdgQvZFXh3RzzC3K0xPtwN47q5oouTVWunS0TU6RjqxEREREREhtna2gK42pHJwsJCryC8PTNKEVNMTAwAoH///rf8mAEDBgAADh8+3Co5EVHLqqxXYl9cAbZfysehxOYPYP51MReLIgMMrkUFO7dWigaV1igQnVCI/fGFOJRYhMp61f+vyJp9nFAA9POzx+iurrinq4vBoiwiIiIiIiKijshaJsG9vTxwby8P1DSocCixCHviCrA/vhDltcomH6eECClKG7z6ZwL+/XcienvbIjLYGVHBzgh1k5v0gqD98YXNHsu4kluJK7mV+GBXAkJc5Rgf7obx4a4IcJYbMUsioo6LnZiIiIiIbp2LiwtcXFwa71/flam9M0oRU1FREYDba2F1LfbaY4mo7SmubsC+uALsuJyPI8nFUKpvXuEpEABZpXVGyM4wrVaLK7mVOBBfiP0JhTifVY5bLUyVSYQYFuiE0WGuGBHiDHtLaesmS0RERERERNTGWZqJMS7cDePC3Ro7HO+JLcCeuAJklNQ2+Ti1RotT6WU4lV6GD3YlwMXaDJFBzogKccLgAEfIZcbr0gQAE8Ld4GBphj8v5GB3bEGzxVjx+VWIz6/Cyj2JCHC2wj1dXfBgP29e4EREdIcEAgGLmIiIiIgIgJGKmJycnJCTk4MdO3Zg8ODBt/SY7du3AwAcHR1bMzUiugNKtQaz1hzHmcyyWyoAuta1aFw3N4wJc4WrTfNdjlpaRZ0Sx1KKcTCxCPvjC1FQeesFlbbmYozq6orRXV0wNNAJ5lLjj7kjIiIiIiIiag/EIiH6d3FA/y4O+NeEUCQXVmNPXAH2xBbgfGY5mjuEUFDZgA2ns7DhdBbEQgH6+NohKtgZkcHOCHKxavUuTWKREEMCHTEk0BFvqTU4nlqC7ZfysftKPkpqFE0+LrmwGsmF1RjNLs1ERHfM0L/xpuzOR0RERESmY5QiphEjRuCHH37AypUrMW7cuJsWMh09ehQfffQRBAIBRo4caYwUieg2SERXr4JproBJLBRgoL8DxnVzw+gwFzhamRkpO0Cl1uBCdgVikopwKLEI57PKobmNMaAhLpawqEiDj6QK/3lqHuxsbVovWSIiIiIiIqIOSCAQINBFjkAXORZFBiA1txivr92MdKUc+VrbZke3qTRaHE8txfHUUryzIx7uNjIMD3bG8CBHDPR3hI1563ZpkoiEGBrohKGBTnhjShhOppdix6V87LySj6Iq/QujnORm6OFpa3BfFXVKqNQaOBjxuAgRUXuj0Wggk8kgEAig0Wig1WrZiYmIiIiokzJKEdMrr7yCDRs2oKGhASNHjsQTTzyB+fPno0ePHo3V9FqtFhcuXMD333+PL774AgqFAmZmZnjllVeMkSIRXaeyXomDCUVQqDS4L8LTYMyori44nVGms00qEmJooCPGdnPFPV1dYGthvHFr2WW1OJRYjJikIhxJLkZlveqWH2shFWFwgCNGhDgjKtgZFgIFvvjiBABAJOQVP0RERERERER3y9FKiq5mZehqVoYFj47FlWIlouMLcSChCJmlTY+dA4Dcinr8ejITv57MhFAAdPe0xdBARwwJcEQvbztIxa13olssEmKQvyMG+Tti+eQwnMkow/ZLedh5OR/5lfUAgFGhzhA2cfxg89lsvPF3LCJ87DAq1AVRIc4IdG79zlJERO2NUCiESMQu+ERERESdnVGKmEJCQvD999/joYcegkKhwKeffopPP/0UUqkU9vb2EAgEKCkpgUJxtTWzVquFWCzGd999h5CQEGOkSNSpabVaxOdXITqhCAcTC3E6vQwqjRYetuaY1tvD4IG1UaEueHdHPCykIgwPcsLYbq4YEeIMuax1r4a8pqZBheOpJTiUWISYpGKkFtfc1uO97S0wIsQZI0Kc0b+LPczE//xArqxsuk08EREREREREd0dmUSEqOCr4+KWa7VILa5BdEIRohMKcSK1FAp1012aNFrgfFY5zmeV49P9ybCQitDfzx5DAp0wNNCxVQuEREIB+vnZo5+fPf4zsSvOZ5djb2wBhgc5NfmYPbEF0GiBU+llOJVehnd2xMPD1hzDg50QGeSEwQGOsDQzyiFaIiIiIiIi6oC0Wi1UKhWkUmljzU17ZrRfyDNmzICfnx8WLVqEM2fOAAAaGhqQl5enF9u7d2+sXr0a/fr1M1Z6RJ1ORZ0SR5KLEZ1QiIOJRSio1G+HnlNeh/j8KoS6Weut+TtZ4pdH+6O3jx1kkta/QqZeqcbZjDIcTSnB0ZRiXMyugOo2ZsRJxUL097PHsEAnRIU4w9/Jklc9EhEREREREZmYQCCAv5MV/J2s8MgQP9Q0qHAspQQHEgoRnVCEnPK6Zh9fq1DjQEIRDiQUAQBcrM0wOMARQwMdMTjAEc5yWavkLRQK0NvbDr297ZqMqahV4kRaqd72nPI6/HIiE7+cyIT0/9i78/i46rL//68z+5KZTPakadM0XSml0JWygyKbgBsIirKIiFURvF34ov688Xa/EVcQRAUEFARvRbAIVYSyWGgLLRSa7mmbtNm32ddzfn+cmclMMpM9adNeTx7ncc6cbT5Tkkly5n2uy2hgxawizp5XzjkLyphdJlWahBBCCCGEEEIMrbW1lQMHDhAOh1FVlVmzZrFjx47DPawxm9TbfFasWMHGjRvZtGkT//rXv9i6dStdXfof8kVFRZxwwgmce+65rFixYjKHJcQxQVU1tjV7WbdTv7PxzQM9JIYRAnq+vjVniElRFE6dUzoRQwUgGld5q6mH/+zuZP3eDt480EM0nv9OzFzmV7g4Y24pZ8wr4+RZxZMSthJCCCGEEEIIIcToOa0mzl1YwbkLK9A0jd1tfl7Y0cbLuzrY0NBFZIhrA63eCH958yB/efMgoN+EtaquJD2VuayT8TIA2N7ixWoyEIwm8u4TTai8uruTV3d38r1n6qn22Dl7fhlnzy/nlNklFEiVJiGEEEIIIYQQOaiqSjDY157dYrEcxtGMn8PyV/Dy5ctZvnz54XhqIY45nf4I311Tz8u7OujwD6y2lI/FaOCU2SXMKXdN4Oj6xBMq7xzy8p89Hazf08mmfd2EYvkv8uVS7LRwevJuyzPmllFZODF3WwohhBBCCCGEEGLiKYrC3AoXcytcfObM2YRjCTbt6+bl3e28squDdw95hzzHnvYAe9oD/OH1AwDMKS9gVV0xp9SVcnJdMaUFExdqOrmuhDf/v/exfk8n/6xv5cXtbRzqDQ96zMGeEH94/QB/eP0A3/3gIj6xauaEjU8IIYQQQgghxNRls2V/Fm61Tt5NOxNJbuUR4ijnspl59p2WYQWCJvNuv0g8wdtNvWxo6GLjvi7e2NeNLxIf0TnMRr10+5nzyjhzbhnHT3NjMEjJdSGEEEIIIYQQ4mhkMxs5fW4pp88thQv1G7de3dPJK7v0UNNQASGA3W1+drf5eeQ1PdQ0t7yAVXUlnDK7hJNnFVMyzqEmm9nIOQvKOWdBOZqmsavNz4s72nhhezsb93URH6RK9ul5KmDHEioNHQHmlkvrOSHE1KcoCgaDAVVVMRgM8r4mRuTBBx/kuuuuA6ChoYHa2trDOyAhhBBiEvUPMZlMJozGqd+ZSEJMQkxh4ViCjfu6eHV3J0trPJx3fOWAfSwmAytnFbNuZ3vObSfPKubs+eWcNa+M2WXOCfsj0ReO8cb+bjbu62JjQzdbmkbeHs6gwAnVhZwyu5RTZ5ewvLYIh0XexoQQQgghhBBCiGNRSYGVS0+cxqUnTkPTNPZ2BHhlVwcv7+rgtb2d+Idxs9SuNj+72vw8/Np+QG8/t6K2mGUzi1hRW8zMEse4XStRFIV5FS7mJStL+cIxXt3dybqdbby4o53mjBBWtcfOzBJHzvO83dTDR+5ZT7nLymlzSpNTCVWF9nEZpxBCTCZFUbDb7YTD4fRjp3PirlMLIYQQQhwtclVeOhqqMU3Kp/8vvfTSmI4/88wzx2kkQkxtoWiCzY3dvL63i9cbOnnzQF8Q6P2Lq3KGmEC/cy8VYqotcaRDS6vqSrBbJiaN2e6LsHFfV7rSUn2zl0FuLszruCo3p84u4ZS6ElbWFeO2mcd/sEIIIYQQQgghhJjSFEVhdlkBs8sKuObUWuIJlXcPeXltbyfr93aysaGLQHToKtWp9nOPbWwEoLTAyvKZRSyv1UNNC6e5MRsN4zJml83MBYsquWBRJZqmsaPVx4s72nlxRxtzBqmy9MquTgDafBH+uvkgf918EICaYgcnzyrm5Dq9qtSM4twhKCGEOJL0f6/TtFFcRBZCCCGEOAYZDAYsFgvRaDS9zmKxHMYRjY9JCTGdffbZo07NK4pCPD6yFlNCHC38kTibkkGgDQ1dvNXUQyyR+4+49Xs6UVUtZzu1846vwGYxcsacUmpLneM+znhCZUerjzcP9LD5QDebD/TQ0BEY1bnmlBdwSl0Jp84u4eS6EoqdU/+NVgghhBBCCCGEEJPLZDRw4gwPJ87wcONZs4knVN5JhZr2dLJp3/BCTR3+CM++28Kz77YAYDcbOWmGhxW1RSyrLWZpjQfXONxwpSgKCyrdLKh089mzZg/6If6ruztyrj/QFeRAV5An3mgC9GpOJ9cVs2pWCSfXFVNTPH5VpYQQYrz0f1+SlnJCCCGEEMPXP8RkNk/9giCT1odJ0vNCDK03GGPDvi42NHTyekMX7xzsHXb1oq5AlO0tPhZOcw/YNrPEySdLxi+81O6L6GGlxh7e3N/N2029hGJDX/jrT1FgQaWbFcm7GU+eVUy52zb0gUIIIYQQQgghhBAjYDIaOGmGh5NmePjsWbOJJVTeOdjLa3u7WL9XDzUFhxFqCsUSrE9WdwL92sa8cpd+7hr9/PMqXBhz3GQ2Evk+wI8nVFp94Zzb+jvYE+Ivbx7kL2/qlZoq3TYuXlzFNy9eOKaxCSHEeDIYsqvbSYBJCCGEEGL4+ldekkpMw/TCCy8MuU8gEGDnzp089thjbNiwgdNOO41vf/vbGI0T0+pKiCPRHzcc4EfPbh/RMQYFFk/3cNqcEtz28f+WjsZV6pu9faGlA900doVGdS6L0cDi6YWsmFXMytpils4sotA+9dOgQgghhBBCCCGEmFrMRgNLaopYUlPE6rP1UNO2Q1427uvijf3dbNzXTYc/MuR5NA12tPrY0erjT5v0FnQOi5ETqgtZUlPESTM8LKnxUDFON22ZjAZe/MrZNHQEeHV3B6/s7mD9nk684aEr2bd4w4O+pnwVvoUQYiL1Dy1JiEkIIYQQYvj6h5akEtMwnXXWWcPa76KLLuKWW27hjjvu4NZbb+X+++/nkUcemeDRCTE5ApE4bzX1sKWxhxvPnJ3zjryVs4qHPI9BgUXVhZw8q5iVyXLg7nEoWw6QUDV2t/nZerCXrU09vH2wl22HvETi6qjOV2A1sXRmESuTlZZOnOHBZpZgohCHRSKmTxZH7u3tO6BzDySiyX2jGcuRPOuTyzYPnP+93Od95y+w4T5QE6CpoCX6ltVExuPUOlWfAygG+K93c593xz/gma9mrFCyZgAYTPqkGJPLxuRkgisfBWdJjn+HnbDuh33HmqxgtILJkpxbM9alli1gsun7TF8JtoEV8dA0fep3d6EQQgghhBDiyGDOaD/36TP0qvL7O4Ns2t/Npn1dbNzXxZ72wLDOFYwmeL2hi9cbutLrqgpt6UDTSTOKOH6aG6d1dJdmFUWhrqyAurICPnlKLQlVY3uLl9f3dvF6QycbGrroDsZyHntyXY6/g5Ku/M1rhKIJls0sYunMIpbPLGKaxz6qMQohxHDlaicnhu+FF17gwQcf5OWXX6alpQWTycTMmTO54IIL+NKXvsS0adMGHHP77bfz7W9/G9B/3oXDYX75y1/y6KOPsmvXLgCOO+44rr76aj772c9iMmX/vHrooYe45pprAFi7di3ve9/7Bh3jjTfeyH333YfFYqGlpYWioqJxeR0j0d7ezs9//nPWrFlDQ0MD4XCYyspKzjjjDG688UZOP/30vMfW1tayf/9+rrnmGh588EE2btzIT37yE1555RXa29spKyvj3HPP5dZbb2XBggVDjmX37t3cfffd/Otf/+LAgQNEo1Gqqqo488wz+cIXvsDy5cvH9FqFEEIcWyTENEm++tWv8vrrr/Poo49y8cUXc+WVVx7uIQkxIpqmcaAryOYDeuWiN/Z3s73FRyLZG+7seeU5276dUF2I3WzMas1mNiqcON3DylnFnFxXwrKZRRSM8iJXJlXV2NsRYOvBHt5u6mVrUy/vHvKOqi1cSm2JI3kXo4elNUUcV+Uec/l0IY4JqgqxAESTk8EERTNz7mra/jfoqod4CGJhiAUhHoZYqN883LdPPARqHKqXww3P5x7Dlj/Aqz8f3fjd0/OHmHwtcGD96M6rDHLRKhqA3sbRnRf6glL9+Vvhnf8b/XlvfBmqFg9c33MAfr4YTHY9SGZ2JufJKbVscYLZ3rdsKQCrC+a8Fwqnj35cQgghhBBCiBFRFIXaUie1pU4uW6b/Lt4ViPJGMtS0aX83bzf1EEtowzpfc2+Y5t4W/vFOS/L8MLusgEXT3CyqLuSE6kKOry4c1TUfo0Hh+GmFHD+tkE+dPgtV1djV5uf1hs50sKnDHwXg5Dw30EXiCbYc6CGaUNl6sJcH/7MP0MNXS2cWsWSGh8XTPSyqduOwHJGXlIUQU5RUYhqdcDjMddddx2OPPTZg2zvvvMM777zDPffcw6OPPsoll1yS9zytra1ccMEFbNmyJWv9xo0b2bhxI2vXruXJJ5/MCpd96EMf4rOf/SyhUIg//vGPg4aYYrEYf/7znwG9kEH/ANN4vY7BrF27lssvvxyv15u1fv/+/ezfv59HHnmEz3/+8/ziF78YMkR3//33c+ONNxKP91VAbGpq4sEHH+TRRx/l4Ycf5vLLL897/I9//GO+/vWvE4tlh40bGhpoaGjgoYce4pvf/Cb/8z//M4pXKoQQ4lgkIaZJdPXVV/OXv/yF++67T0JM4ojX0hvmraYetjb16vODvfTkueMN4M0D3TlDTBaTgdPmlBCIJDi5rpiVs4pZWlM05upFCVVjX2eAdw959QpLycCSPzJ0qfF8nBYjJybvIFyaLI9eUmAd0ziFmDI0TQ8KqQmwFuTeZ8uj0F7fF0yK+jOW+z2OBbOPnXs+XPV4ztOa9r0I2/48unHHw/m3GcfQIzcRzb/NcIRWX8s3LnX074uAXpkpl1iyDWc8pE90juy8V/05d4gpGoQ7F+hBJ6tLrwKVWra6wVYI9qKMyZP92FKgf3oihBBCCCGEGFKx08L7FlbwvoUVAIRjCd495GVLYw+bD3SzpbGHpu7QsM6labC7zc/uNj9PbjkE6L+azypxpkNNi6oLOb7aPeIK3AaDwvxKF/MrXVx9Si2aprGn3c8b+7uZVerMecw7B71EEwNv9mjuDbPm7WbWvN2sn1uBeRUuFk8v1CtXTfcwv9KF2SiVU4QQoyMhppHTNI3LLruMNWvWAHDJJZfw0Y9+lLq6OgwGAxs2bODOO+/kwIEDXHbZZbz66qt5q/t8+MMfZtu2bXzxi1/kkksuobi4mB07dvCd73yH+vp6nn76aX7zm99w4403po9xuVxceuml/OlPf+Ivf/kL99xzDzZb7rap//jHP+jq0isTXnXVVRP2OvLZsmULl1xyCdFoFLPZzBe+8AUuvfRSnE4nmzdv5oc//CENDQ3cfffdOJ1OfvSjHw16rj/+8Y+Ul5dz2223sXLlSsLhMM888ww/+9nPiEQiXHXVVcyaNSvnOO+44w6+9rWvAbB48WJWr17N3Llz8Xg87Nixg7vuuov169fzne98h9LSUr74xS+O6LUKIYQ4NvUPMfV/PBUdsSGmmpoaALZu3XqYRyLEQNG4yr3r9vB2MhDU5ouM6Pg393fziVW5q6z85urlY/pDLRCJs73Fx7ZmL/XNXrYd8rKjxTemCksAs8ucLKkpYmmy0tK8CpdUWRJHh0Ob9WpBYS9EUpMv+diXnJLrM9epMTjx4/Che3Kfd9uTsPPZ0Y0pOkiLAnPuC87DEhvkYvpEhZgGq6Z0OBny/Aqkju29Mn+IaXhtJ/Ky5mhRB8mvzV59Go0P3gsnfSz3tpd+rIegHCXgLANnqT5Xj9BgmhBCCCGEEJPMZjaybGYRy2YWAbMAaPdFeKuxhy3J6a3GHnzDvIlM02BvR4C9HQGeeutQen1tiYNF1YUsnObmuCo3x1W6qXBbh339SFEU5pS7mFPuyrvPm/u7h3UuVYPtLT62t/h4fFMTAFaTgfuvXcFpc0qHdQ4hxLFH1VR6Ij0D1icSCYLRYNb7mU2xYUocsR9dDZvH6sEwQdfFfvvb37JmzRrMZjNPPfUUF1xwQdb2VatW8clPfpIzzjiDd999l1tuuYVXXnkl57lS1ZbOPvvs9LqlS5dy/vnns3DhQlpbW/nVr36VFWICPZD0pz/9Ca/Xy9///ncuu+yynOf/4x//CIDb7ebiiy+esNeRz2c/+1mi0ShGo5G///3vnHfeeeltK1as4PLLL+f0009n27Zt/PjHP+bqq6/m+OOPz3mut956i5kzZ/Laa69RWVmZXn/mmWdy/vnnc9555xGLxfjc5z7Hhg0bso7dtm0b3/jGNwD47//+b/77v/876+t+2bJlXHnllVxzzTU88sgjfOMb3+CTn/xkztZ7QgghRCapxDSJWltbAQgExvihnxCjpGkacVXLeSeZ2ajwwKsNdA9SbSkXo0FhYZWb2eV5Krcw/DtNNE2j1RthW3Mv9c0+th3ysq3Zy77OANrwKpnnVe2xc0J1ISdML2TxdP3uP49j6qc2xVEmEYdwD4S6Idilz/NN886Hk2/MfZ5nvw4H/jO6MUS8+bflC50MxyCBF80yhhDTYJWYHMVQVKuHmbIm8+DLJqve9iyfmafB++8ExahXP0rPDfqUtS41V4Ah3gtnngYffyJjRb83Pk0DLaFXVlLjejgpc9lsz33eolo448v6fok4JCIQj+hBrXgY4tHkuuTj1HJqP1Oe80aDudcPlzXPhw0R39jOa89zISQegX9/J+cmFwpfwE5IceB4/AVwV/SFnFKBp+pl4JkxtrEJIYQQQggxBZW5rJy7sIJzk9WaVFWvgrQ5FWw60MOOVh8JdfgXb/Z1BtnXGeTvyWpIAEUOMwsq9VDTgioXC6vczCkvGHU176tW1XDC9ELe2N+dnnpDw7vuFYmr1Oap8NQViPKv+laOn+ZmbrkLi+kIvdFFCDGheiI9nPWnsw73MCbVuivWUWzL3cJzLDRNS1cL+uIXvzgg+JNSVFTEHXfcwUUXXcSrr77Krl27mDt37oD9brrppqwAU0pxcTHXXXcdP/zhD9m6dSu9vb0UFhamt19wwQWUlJTQ2dnJH/7wh5whJr/fz1NPPQXARz7ykaxqTeP9OnLZvHkzmzZtAuCGG27ICjBlnv++++7j9NNPR1VVfvWrX3H33XfnPeedd96ZFWBKOeecc7jhhhu455572LhxI5s2bcqqxnTnnXcSi8VYvnz5gABTisFg4Je//CVPPPEEfr+fP//5z9xwww3Deq1CCCGOXf1DS0ajkURijDfsH2ZHbIgp9UtCqiKTEBMpoWo0dPh5NxkE2nZIr2L08ZNn8l/vmzdgf0VROGG6h5d2tg963mKnhaU1RSyd6WFZTRGLp3uwW0Z+MckbjrGr1ceOFj87W33sbNXveOsKDFL9ZJgq3TY9rFRdyKJkYKlU2sKJyRSP9IWNrK7cLbMA/vZ5aNma3Ldn8ABRf+5p+bflC4cMx6AhpjGcd5CKSYnKk+CkT4DZBiabHsbJmjuS2+wD5xZH/udc8Wl9Gm8VC/VpvLmr9Gm8lc6B935r/M9bvRRWr9dbB6ZaCEYD+v/rrHVBfZ5aF/XrQSVHnote4RF8H+SSL8QU6sl7iIKGkyBOLQhNHbl3uvQuWPrJ3NvWflNvY1dQAa5KfV5QAQXlejhOCCGEEEKIo4jBoDC3wsXcChcfXa4H/cOxBNtbfGw92Ms7Tb1sPdjLzlYf8REEm7qDMdbv7WT93r5W1UaDQl2pMx1sOq7KzYJKF5Vu25A3zTksJlbVlbCqrgTQw1d7OwK8sb8rWVVKH2Ou8FVpgYVphbnbCL25v5uv/fltQL8pcG65i+OnufWpupDjqtwUWI/YS9RCCHHE2bZtG3v27AHIW/0o5cwzz0wvr1+/Pmf4p3+Lt0zLli0D9MBRQ0MDJ510Unqb2Wzm8ssv59577+Uf//gHPT09eDyerOP/+te/EgqFcj7PeL+OXF5++eX08vXXX593v9NOO43jjjuO+vp6/vWvf+Xdr6ioiA984AN5t3/qU5/innv0qv3/+te/skJMTz/9NKCHuQb7mezxeDjhhBPYtGkT69evlxCTEEIvuqHF0ZIVLBRFIf2f0jcXx65clZckxDSOuru72bRpEz/96U959tlnURSFD3/4w4d7WOIokwoEbcuoXrSjxUs4pg7Yd9uh/B8Onzi9MCvE5LKaOGG6Xr3oxOkeFk8vpNpjH9EPjmA0zq7WvqDSzuRyc+8g1VNGoMxl1SssVfdVWCp3577IJMSotW2HQHsybJSrQlJP9uNYRoWaU2+C876b+7ztO6H5rdGNKTRIWXzbGComDRYgmbFSD6FYnHp4yFKQXHbmXjZn7JOvJRkQn3shLLti9GMWh4fFOTFhrrJ5cN2zGW0Qe7PbIIZ7c1cn05I/8/KFmMI9YxuXsyz3+kQc/nMXAypnAaDolZxSwab0vEoPIrqnQclsvcWdEEIIIYQQU5jNbOSkGR5OmuFJrwvHEuxsTQabDurBph0tPmKJ4QebEqrGrjY/u9r8PJXx57PLamJORQHzyl3MrShgTnkB8ypcVBXmDzcZDApzyvV9r1ih32QaiibY1tzLlsZe3m7SW+Xt6wyyeLon73nezbi2Fkto+s2DzV6eeENfpyhQW+JkYZWb+ZUu5lW4WFDpYkaxA6NBPowRQoj+UpWFAE455ZRhH9fS0pJz/YIFC/IeU1zcd1OdzzewGvhVV13FvffeSyQS4c9//jOf/nT2DZKpVnLTpk3jnHPOydo23q8jl+3btwN6m53MAFYuJ598MvX19ezatYtoNDqgNQ/AkiVLMJnyf6x60kknYbFYiEajbN26Nb1+//79tLfrnyXddttt3HbbbcMa/0heqxBiYmiaRigeIhgPEowFCcaDBGIBgrEggXiAUCxEIBYgFA8RSUSIJqJEEpGs5QHr1AjhaJiusi40NP7xj3+goZHQEqiqSlyLo2oqCTVBQkug5byWPlAqzGTAAAoYMGA2mjEb9MlitGA2mDEZTOnlzPUWowWHyYHdZMdhTs5NDuxme99ycluBuQC3xY3b6sZqlMIYh5vJZKK2tpZ4PM4LL7xAIpHgxBNPPNzDGpNJCTEZjaMrYzx37lxuvfXWcR6NONa8uKON/+zpZEeLj12tPg6NIBBU35w/oHDmvDL8kTgnTvdwwvRCZpU4MQzz4oo/Emdvu5897X52tvr1KkutPhq78ldfGQmDArPLClg4zc3CKr20+HFVbspc8oNEDEHT9PDDYK3ZQj0w931w/Adzn+NPn4DOXaN7/sHCRvnCFsM6b0/+ba5K8NTo7d+srr65LfNxrnWuwcd04pX6dCxQ1YxWbfFkG7eM9m2aqn9toWUsk2z5pibX99+u5dmOPkfRO86l2tKh9C0rSr91St+6fPsaTHorO4NZXzaak23tjnBWF8wc/oUeQP//FU1+n7urc+9jMMH8i/RWjYF2CHbogajhcpbmXh9oJ3eACX19sEOfWt/Jvcv778xfMWznWnCW6K/JWQ4GaVMhhBBCCCGmDpvZyOLpHhZP96TXReIJdrb4eeeQHmza3uJje7OXQHRkd9X6InE2H+hh84GerPUFVhNzyguYW17A3IoCvWJUeUHem/LsFiPLZhazbGbfh9o9wSjeUDzvc797aPC/IzQNGjoCNHQEWLO1r12ezWzghOpCHr/xFLmzXAghMrS1tY3quGAwmHO9w5G/crsh49pKrooOp512GjNnzmT//v384Q9/yAoxtbW1pasaXXnllVnnSm0fjXyvI5eenh5AD2MNFj4C0i3iNE2ju7ubioqKAfuUl5cPeg6TyURxcTEtLS10dXWl10/GaxVC5KZqKr6oD1/Uhzfq1aeId9DHvqhPDyolg0vDDRGNWDI+EQyPz/e6hoamaaio6UvwUXXsHX2GYjFYcFvdeqgpGWxKLXusHoptxZTYS/TJVkKxrRin2Sm/448jRVGYOXMmXq+Xzk69Um//n7tTzaSEmFLlzYbLZDJx+eWX89Of/jSrx64QuYRjCboCUaZ57Dm3P/duK49uODCqcx/sCdETjOJxDEzdr6gtZkVt/p7WCVXjUE+IPe1+9rQH2NvuZ297gD3tftp8kVGNJ5cCq4kFla50YGnhNDfzKlzYzKMLD4qjhKpCpFcPIuRra/aPW6F738CQkpr/4mOa3ZM/xDRRYaPBzmt26tvtRfrY7EV6+63UuuK6/Mee99381Z8OF1WFRERvNRYPo3S3U6q2YSSB8dAb0GmCRLRviqeWI5CI6S36ElF9ecC61DHJ9Znr0kGkRHYoKf24/zw5TdQv8YebYsgINZmSQafMx/m2mfVqWiYrGK25l4e1zaYvm+3Jal12vWLXWFuuGQx6NaPBKhqVzIaPPZq9Lh6FYCf+9v38/U8P4tCCnHfaEmwJHwQ6INiph5QCHXpruFz8Y7yDLF/oSk3Ao1fqATrQ/z+4pvVVcCqshsIa8MzQQ4uFM8ZWhU0IIYQQQohJYDUZ01W/U1RVo6k7xLZmL9tbvNQ3e9ne4mN/58g//PBH4slWcT1Z650WI7PLC6grdTKrtIC6MiezSp3UlTlxWLIvJ3sclpzXzVKqCm3UFDs40DWy8YVjKsFoIu+HG+v3dLK7zcfsZLWosgKrfBAixBHMY/Ww7op1A9bHojEi0b5r5Yqi4HQ6J3NoE8Zj9UzIeTPDRE8//TS1tbXDOm6oAM5oKIrCxz/+cX7wgx/w0ksvcfDgQaqr9Ws3jz/+OPG4fp05V8u6yXwd4/XzYbTnyXyt3/rWt7j88suHddzR8r0gxHhKqAl6Ij10hbvoDnfTFUnOw33z1HJ3uJueSM/EhZAEoAelOkIddIQ6hn2M1WilxNYXbKpwVlDprKTCUUGVsyq9bB7rZyFiypqUENN///d/D7mPwWDA5XIxa9YsTj31VMrK8rQhEcckTdNo90fY2x5ITn72dujzxu4Q8ypc/OPmM3IeO7+iYNjPU+m2ZYWBFla5cdsGf4PsDcbY1xlgb4efPW36fG97gL0dAaLxgS3qRstoUJhV6mR+hV76e36FHlyaUeQYdgUoMUUFOjNCRrnas+WYwr16BZv3/H9w5ldyn3fPv6Fj5+jGNNqw0VDig1RKW3IVzDozI6yUEVoapP3auNA0PeQTDejt76JBiAX0eTwEsbA+9mToSF8OZ2zL2Cdzv1hIDxP13y+RHXR0Aemu7Y89MLGvVfTRkmGyRARih3swGQzmZPtBRzLYlAo49Qs7pfdx9K2zusFaoLcutLr6JkuBvs9gF2NMFnBXoeJkv3EWAGcvvR6bewRhIKsbll0H/lbwtehzf+vwwpOQP8Tkb+0LMIF+vt4D+pSPzZMMNc2EVZ+D2tOG/TKEEEIIIYQ4XAwGhZoSBzUlDi5YVJle74/E2dHiS4aavNQ3j65qE0AgmuDtpl7ebhpYRanSbUsHmlLzutICphfZMRkH3u377Q8s4tuANxxj2yEv7x7yJue97G7zE1fzf6g0vyLPTVnAU28d5NENjenHLpuJ2WUF+lTuTC/PLHFgzjEuIcTkMigGim0DbwiOKBGiSl+VCIPBgNMmwY3BlJSUpJc9Hg+LFi06jKPRA0o/+MEPUFWVRx99lK98Rb8WnWolt2DBApYuXTrguMl4HR6PB4DOzk7i8fig1ZhSrdsURaGoKPf17dbW1kGfLx6PpyswZbbiy3ytZrP5sP8/E+JIFE3oQZj2UDvtwXbagm10hDr65qE2OoIdEko6SkQSEQ4FDnEocCjvPgoKJfaSdKipxlXDTPdMZrhmUOOuocxeJjcxHMWOmBCTECl7O4I0bG3uq1yUDCv5wvk/4NzT7ieeUHNeLJmX44KH0aBQV+rk+GmpsFIhx1W5KCkYGITQNI02b5j9XUH2dwbZ3xnom3cF6QmO7yfbigK1JU7mlhcwv9LF3AoX8ytczCp1YjHJRZcpKx6FcE/+0FHdOfk/QP/VKgiMruTsxLVnG+S8jpIcQaNhTLbCwSvMzDpz8DGpqh4wigVzh40y16e3DWffkL6sjfzCrxATQo3pldYiI2jvNhyKMRlwSoWbcoedLJqZk+JvEsGGseEFKJ6mf//aPfp8sFBhyWy45Gf9Xo+qBzR9LXqlJl9rv3kLeA+Brzl/iMmb/4+dvMI90NIDLVvhpI/n3kdV9RaZ7ml6BSdPDRTV6pPdM/LnFEIIIYQQYoIUWE0sm1nEspl9f+trmsah3jC7Wn3savWzq83HzlY/u9v8+CPDvJGgnxZvmBZvmPV7O7PWm40KNcUOZpUWUJsMWdUU69P0Igdum5lVdSWsquv7IDccS7Cr1c+7h/RWeTtafOxo9dEV0AMN8yrzh5h2tPiyHvvCuatKmZKhr3TAqczJ8dMKkY87hDgyGI1GQqEQiqLgdrunfOuTybBkyZL08quvvsrpp59+GEcDxx9/PCeeeCJvvfUWf/zjH/nKV75CQ0MD69evB3JXYYLJeR0LFiwAIBqNsmXLFpYvX5533w0bNgAwd+5cLJbcFQa3bNkyaBjqrbfeIhrVf4ZlBpXq6uooLCykt7eXV199dVSvRYipLJKI0BJooTnQTLO/Ob3cFmyjLdRGe7CdnkjP4R7msBgUA06TE4fZgcPs6Fs2ObCarFiNVswGM1ajvmwxWrLmqeVEJME/n/sniqZw6SWX4nK6MCpGjAajPk8uGxQDJsWEQTFgUPSfkaqm6q3jku3jNE1fVjW9sEZqWdM0YmpMnxL6PKpG08sxNUY0EU0vh+NhQvEQwXiQUDxEKNa3HIwHCcVCWdsnmoaWrvC0tWPrgO12k10PNCXDTXOK5jDXM5e6wjqp4HQUmJQQkxAjceOj72Byj6zdTDSusr8ryOyygVWX5le6OG9hBfMqXMyrdDGvooBZpU6spr52a/GESnNvmPpdHezrDHCgK8i+Dn2+vzNIKDb+4QWDAjOK9Yso8yr0cc2rcDGnvEBawR3JVFVvxZTLv7+nh42yAko9+jzqH/y8Jmv+EJO9aAwhpp7824YKMSnGvoBRZms2exFUnpD/uA/+avBqLqBXNoqHIeKHqE8PJ3Ts0sNCUV9yfXI5Gkg+Tk7p5Yz1seDgVZzEFKPordwUJbmsDFyHpldK0pLz9OPxq4B3zNESehW38ODhKBtwfurBX/82cAeTTa9ylGpZZ89YzrfeXqQHhCqOz//+oSb0r4FcYkG9XZzv0PCrOmUqnJF7faANdqzJvc1eBEWz9EBTcXKeeuyeBgb5WS6EEEIIIQ4vRVGo9tip9tg5e35f+x1N02juDbOrzZ8VcNrV6sc3ynBTLKGxpz3AnvbAgG0GBaoK7elQU02Jg5nJkNPMYmdWuzyADn+EHS0+aoodOZ9L0zR2tg5xnSUprmrpyu7/RK+g8f7FVXxhiX2Er1AIMREURUm32jKZTBiN8rf0UJYuXcr06dNpamrivvvu4+abb8Zmsx3WMV111VW89dZbbN68mfr6ev7yl7+kt33847lvHJuM13HGGWfwwx/+EID7778/b4hp/fr1bNu2DYBzzz037/m6urp4+umn+dCHPpRz+/33359ezjyP0Wjkoosu4tFHH2Xt2rXU19dz3HHHjfj1CHEk0jQNb9RLk6+J5kAzh/yHaA70BZWaA810hbsO9zABMCpG3GYXLourb25147a4cVsLcaWX3RSYC3CandhNdpzmjKCScXxaGHu9Xuoj9QCsLF+JeyTdDo4AMTWGP+rHG/XijXj1eXLZF/Ol13WFu+gMd9IV0ufjGX4KxUPs7N7Jzu7sjjMmxcRM90zmFs1ljmcO84rmcXzp8ZQ7xr+tqpg4kxJi+p//+R8APve5z1FaWjqsY7q7u/nlL38J6D1ihRjKvo5AzhBTSYGVuz6+lObeEAe7Q7zd2MszbzfT1BOiqVtf1+INkxikfPVYuG0m6pJ3e9WVOZld5qQuWc46M0glJpGmQcQ7MGgU6oZwD9aeVi6MvoZVC+H40/MQ8/VtP++7sPKG3Od948HDEzYa9LyDVEw64XKYviJ/ZSSrqy9MoKp6laLMcNG+V3KEi/qHkPwQ8fUtZwaPpLLRiMUxYjTbUExWMFr09l5GCxitehUro0UPxBnNfetS+6amwY4xmMFg0sMXBlO/yZhnnmufjMeKsV8gaZBw0niV/tQ0fcoMNuUKO2mZQagEJGJ6lSM1tRwf3uNEPGNbXN8Wj+hTItK3HA/rbQnjYb06W/px5r79tsVC+pinknhYr6DkH1kgGdC/Bh3FYC/uC086ipMV3or7bUut9+iV2r60VX+vCrSDt0mvzuQ9BN6D0NsEPY3QcyD3uDw1ucfTM0g7utTPhUNvDtxmtOjnvOQX0qZOCCGEEEIccRRFYZrHzjSPnbPmlaXXa5pGqzfCzlYfe9v9NHQE2NuhB4AO9Yb0P7NGQdXgYE+Igz2hARWcQL92NrPESU2xg+oiezp45Y/E8YZjuG3Zd1IHownes6Cc3W1+9nb4CcdGdjNLXakTkBtghBBTk8Fg4Otf/zqf+9zn2Lt3L1dffTUPP/wwVmvuyther5eHHnqIL3zhCxM2po997GPceuutaJrGH/7wB5588kkATjnlFOrq6nIeMxmvY8mSJSxfvpxNmzbxm9/8ho985CO8973vzdqnt7eXG2+8MT2m1atXD3rO//qv/+LUU0+loqIia/26deu47777AFi2bBkrVqzI2n7bbbfx+OOPk0gkuOyyy3juueeYPn16zudIJBI89thjnHXWWXn3EWIyJdQErcFWGn2N6anJ15Se+2K+oU8yAQqiBtxhA+4QFAbBHQR3UMMV0CgMaLj8Km5/goIQOCNgi8ZRiAAdg5/YYACDAcVgAKuVoNVK2Gql22pFsVoxWCwoqWWbFYPDgcFZgMHlwlDgxFhQgKEguexyYSgowFhUhKmoCCVPpbepxmwwU2Qrosg2ss8vg7EgneFOOkOd6XlHqIOWQIs+BfX5WMJOcS3Ont497Ondk7W+3FHOCaUnsKh0EYtKF3F8yfG4LPkrv051qjq1/96ZlBDT7bffjqIoXHbZZcMOMXV1daWPkxCTSLGaDMwqdVJX5qSuVO9xX1daQJXHhi8c56Wd7RzsCdHUHUwHlJq6Q7T6wqO+0DIcBgVqih3JsJIeUqordTK7vIASp0V6ck6UeCQ7hJTZrm3WWVCZp7f096fpVTvysAKLUw8O9ts4VNhoItu+mez9qiJ5Bm/NZrLpgYCOXckgUTJQlKp+FA3o/4adu3NUOMoIHkX8eoBJZDOYweIAs0P/tzbb9bnJBmab/v/LbMtYZ8/Y1n+dXQ8SpY/JPtYbinHPbx4ARWH16tVTLpU/6bICUVM8LKpp+vdp/zaIsZD+fRkLZazL3CeUsS7ZEjHzezrq07/PR1O1aCKpMfC36tOwKfp7XmawyVmanJdB2XFQe6a+zlmmt8gLdugBpZ4DeiW4fK3hBgsxDSYR1d9bLbnvHCfYBY9+TG+vV1ynz0vm6MsW5+ieUwghhBBCiDFSFIXKQhuVhTbOzAg3gd76bV9ngIb2vmBTQ4efvR0BeoKxMT2vNxxn68Feth7MXRXWZTX1hZuK9PDV+xZWcO1ptUxz24iqKg0dQfa0+dnTrk+72wJ0+CM5z1db4gQOz4dtQggxHj772c/yz3/+k7/+9a888cQTvPnmm9x4442sXLmSwsJCvF4v27dv58UXX+Spp57CZrNNaIhp+vTpnHXWWbz44ovcfffd9PT0APlbyU3m67j33ns59dRTiUajXHTRRdx0001ccsklOJ1ONm/ezA9/+EP27t0LwFe+8pWsNnD9nXjiiWzbto1ly5Zx2223sXLlSiKRCM888ww//elP063m7r777gHHnnDCCfz4xz/mS1/6Etu2bWPRokV85jOf4T3veQ8VFRWEw2H27dvH+vXr+fOf/0xzczNbt26VEJOYNKqm0hpopaG3gQZvA/t699Ho10NKB/0HiU/SdWRzXKPIB0V+KPJr/eZQGNBwB8EVAtNEZTRUFVQVDdCiUfD5GK9b8g0FBRhLilHchazo6SFqt9GracSrp2OuqsRUWYW5qhJjcfFR+dlyqg3fDFeezgj0VfdKB5sCLTT5mzjgPcAB3wEafY1EErl/zx9MW7CN5w88z/MHngdAQWFe0TxWVK5geeVyllcsp9BaOMRZjlz79u2jqamJlStXYjAYaGxsxOPxHO5hjZq0kxNHpEq3LRlUclJdZMdtN2M1GYglNFq9YVp6w2w92Ms/t7XS3BvCG574H54Wk4GaYge1JQ5qip3UljqSj/Uxmo3Sr3tcqSq88pOMcFLPwMDSIEEkLvpx/hCT1TX4sYMZTthoKBbXwBDStCV69ZV05aJk0CDig+Mugbnv0z8UT4cPktsCHdDVkB1CSs2nWvWWcafoAaNU0Mji1MNC6eXUNme/uT1jOXPf5Nxs15cns6du3Dt+VYrE1KIoeqDNbAOKx/fcqYBUxJf9vpIOPPYLP0a8EPUTC/TQemAXNi1MscOIIeob/Xvq+LwQ/WdCuAfYO7xDrIV9oSZnKTx9S3I5+Ti1zVUJZ35Nr+bUcwC69+tVnobbtrCoNvf6rgZofE2f+nNV9QWa0uGm2XrLOlPuuxGFEEIIIYSYaDazkQWVbhZUDryppjsQZW9HgIYOPdi0vzNIY1eQ/V3BMQecAHyRONtbfGxvyR08shgNVHlsTCu0U1loY0lNERedUIXLaiamqvhCMVp9ERo6Auxp8zO7vAD8EmISQkxdiqLwpz/9iZtvvpl7772XPXv28LWvfS3v/uXlE99C56qrruLFF19MB5hMJhMf/ehHBz1mMl7HSSedxNNPP83ll1+O1+vlzjvv5M477xyw3+c//3l+8IMfDHmuL3zhC6xevTpnmMpisfD73/+ek08+Oefxt9xyC06nk1tuuYXe3l7uuOMO7rjjjpz7WiyWw94mUBydgrEg+737aehtYJ93X3q+r3cf4UR4Qp/bGdIo9UKpV5+XePvCSamgkjMMR/MnIarfj+rX2yJXJdf5d+6if6NkxWLBVFmJubIS87RpWGpnYqmtxTJzJpaZMzE48tw8exRQFIVCayGF1kLmF88fsF3VVNqCbelQ0wHvAfb07mFX9y6aA83Dfh4NjR3dO9jRvYNH6h9BQWF+8XxWVa3izOlnsqR8CSbD1InSaJpGIpHAYNDzClKJaYLEYvofuGbzJH5ILI4I751fTMJZQHNviLcaewhEJ6/llMtqoqZEDybpcz2wNLPEQaXbhsFwNP/oHCVN0z/kDvfqAaNw7yBTju0X/RgWXz7wvIoC6/5Xb680GkOFjUZU5SPPeTUtu7LJtJP0yh8mS19brlTwRFP1FlOJWEZbNj9074OWrbDjGXjmK6Mb09FAMejVUSwFYC3QQ0KWAj1wll4u6NvH4kxuS+2bDBz1DxtJ8EeI/LICUmVD7p4S8nr5wz33ALD6xmRlsHgEwt6M9/qevvf5rJ8Nudb3TH5FqEivPnXtGXpfxZCs6lQOpXOgZlXy/cWgh0ujQQh1ga8VevZDqtStzZM/3NrdkP/5fM36tO/lgeMonK4Hmi7+qR5qEkIIIYQQ4ghQ5LSwzGlh2cyBv//2hmI0dgU50BVkf6c+P9AV4EBXkIPdIdRxuP8qmlDZ36mfPx+TQaHcZaWy0MZ9L+3BFGgf+xMLIcRhZDab+dWvfsXq1av5zW9+w4svvsiBAwfw+/0UFBQwa9Ysli1bxoUXXsjFF1884eO57LLL+MIXvkAkol/PP++88ygrG/p602S8jvPOO4/du3fzs5/9jGeeeYa9e/cSiUSoqKjgjDPO4LOf/Synn376sM716U9/mkWLFvHTn/6UV155hY6ODsrKynjve9/LrbfeysKFCwc9/oYbbuDSSy/l17/+NWvXrmXHjh309PRgtVqprq7mhBNO4H3vex8f+chHht3ZRohcwvEwe3r3sLt7N7u6d7GrZxd7e/fSEmiZkOczqBolXvpCSr19YaVSr77NEZ2Qpz4qadEosQMHiB3I3SnAVF6OpbYW65zZWOcvwHbcAqxz52Kw2yd5pJPPoBiodFZS6axkZdXKrG2+qI89PXvY1bNL/7rv3kV9Vz2BYXSb0dDY3rWd7V3befDdB3Fb3JxefTpnzzibs6afhcN8ZAfHUuGlFAkxTZAtW7YADOuXHHF0eX5HFyb3xFQ1KrCamF5kT04Oqj36cqokdfGx2PotFcQZKnR04pVQecLA4xNR+MEYSprmCxspSjJsNMpfqPK1fUvE9OBL+nmMySBMsiWYyUocI+0dnQCUlVdgMij6B+xqTK+acecCqXZkMOcIFRX0CyL1X+4XPMrcRwJHQkxtJisUlOnTSKXCsMFOvc1aqAuC3cl5l74+tZy5Ldr//pQJoqkQaNentncH39dkh8IZ+nudtQDWfAUKKsBVoc9TU+cwwlO5xpFqgZf5cyxTTyO8fi+UzoXSefrkKJH3VyGEEEIIcdgU2s0UVheyqHpga4ZYQuVQTygdbmrsCnKwJ6RP3SHafKO8sS2HuKpxqDfMoV69wkDc2zFu5xZCjJ6maenr8Zp2jF5nHaMTTjiBX/ziFyM65vbbb+f2228fcr+zzz572P9fPB4P4fDoq7iM5nUAXHvttVx77bVD7ldWVsb3vvc9vve9741idNlWrVrFn/70p1EfX1FRwbe+9S2+9a1vjXksQsTVOI2+RnZ172J3T19gqdHXiDrcavLDZItoVPRARbdGZQ+U92hUdkNFjx5SGnV7N6MRY0EBhqzJibHAhcFhR7HaMNisKFYbis2KwWpDsVr7rbOimM1gMKKYjBlzA4rJhGIwgNGIYjTq10o1DU3VQE3oreNUTb/+mrGsxeJosShaJIIaiaCFI2jR5HIktT6MGgig+gOoPh+JgB/Vp1dbSvh9+nq/X+8+M47ibW3E29oIbtjQt9JgwFJbi23BfGyLF+NYtgzbggX6v8sxwmVxcVL5SZxUflJ6naqp7Ovdxzud7/BOhz5t79pOTB28Yqw36uWZhmd4puEZ7CY776l5DxfXXcyqqlVHZIUmo9GY9VhCTDk89NBDOdf/7W9/Y9OmTYMeG4lE2LNnD/fffz+KorBixYqJGKI4ShXazXooyaOHlFIBpelFdqZ7HLjtpqMrpBQLJ9v8ePvm5ceDs2TgvhE//PlTfS2CIt6+44ZTAaNyce4Qk8mqf2ibqjwxUuGe/NtsnuGHmAxmfSwGExiMsOs5aHw9WSkpWS0p6tdDV5m0RF9FjiQTfWUcaZ2YVPqkM1oyAkWuvgDRgLCRM2N7rmpIyWVpaSSEGC+Kkgz9uPK3X8slHskINqXmnX3Bp1TwKNDRN9cmuLpjPAS9jX2PGzfk2VFJtqW09L2W4bbks7r1dne5NL8F6+/KXmcv0sNMJXOzw01FMye3LaYQQgghhBD9mI0GZpY4mVnizLk9Ek/Q3BPmUE+IpmSw6WDGvLk3RCwhoQchprJIJILTqb8HhEIhrFYrFovlMI9KCCGOTNFElF09u6jvrGdb5zbqO+vZ1b2TiDp+JY7sYY1pXTCtS6OqKyO01A2u0OCt3hS7HaPHg7HIg8nj0Zc9Rcl1GfNCN4aCgnRwSbHZjq7PbvvRVJVEby+Jri4SXV3EO7vwHzrE6/9cizUUZmF1NXR2EmtpId7WNvrAk6oS3buX6N69eJ/5B6D/P7GfcAL2ZUspOPVU7CeddEyFmkCv3FTnqaPOU8elsy8F9O+ldzvfZUPzBja2bmRL2xYig3QGCsVDrNm7hjV711BiK+GyeZdxxfwrKHMcOcV4+ldimurh8AkJMV177bUD3mw0TeOb3/zmsM+haRoGg4Gbb755vIcnpqhip4VKt42qQhuVham5Pf240m3DaT3yko8DpCofmay5PzwMe2Hjb/rCRmFv7uBRxDcwkANw5aOw4KKB601WPdgzWuHe/Nus7tGHmLb8UW+ZE/H3CxwF9MpHw6XGIJqxf7BzdOM5kvSvZJT6kL//utRjqytHQCn5WEJHQoijjckK7ip9Gg5V1YOz6VBT/5BTv+XBQrZjlvxdgKHK2CrJO4My/nC1OOHtx8FVCe5p4KrS3+cBOnYOPEWoWw/1Nr6evd5g1lvSlc7rCzctukxviSqEEEIIIcQRwGoyUlvqpLY0d8hJVTXa/RGakqGm1t4wzb1hWr1hmntDtPSGafNFiI9HzzohhBBCiBwSLS1E33iDREsLWjSKYrFgrKzEsmwZxsrKUZ83FA+xs3unHljqeJdtrVvZ7W8gwdirqyia3u6tujMZWOrUmNapP/YE+gWVDAaMJSWYZpVhKktOpaUZy2WYysswlZQcE63MRkMxGDAVFWEqKoLZs/WVXi8729sAOH31atxuNwBaPE68vZ1YcwvxlmZizS1EmxqJ7ttHdP9+4oeaR/TcWihEcMMGghs20HnPvRhcLpynnkrBmWdS8J5z9DEdgyxGC0vKl7CkfAk3ciPRRJStHVv5z6H/sK5xHTu6d+Q9tjPcya/f/jW/e+d3XFB7Adcvup45RXMmcfS5SSWmYcqV7hpu4stisbBixQpuu+02zjrrrPEe2qTav38/v/jFL1izZg2NjY1YrVZmz57NRz/6UT7/+c/jcIxP/8R//OMf3HfffWzcuJH29nbKyspYsWIFn/nMZ7jwwguHdY54PM5vf/tb/vCHP7B9+3b8fj/Tpk3j3HPP5Ytf/CLHH3/8uIx1KB9YXM6iBfOywkoVbhs2s3HogyeCpuVuwxLuhR3/0MNEmcGbqD8jkOPP8TgAaHDN0zDrzIHnTUTh+f8Z/Xgj3vyvw2iDxChLum64D7Y/DbEQRIMQCyTnIYj6Rj/e7gZ9OhqkW6y5+gWP8lQ/srpzB46sLjA7wTAxbRWFEOKYZDCAo1ifyuYNvX88ml3Ryd8G/ta+yZexnO9n75hp+s/vTL5m+OtnstdZ3XqYaSTBKzWmh55SwSejFRZfkXvfQKf+s75whl7tUAghhBBCiCOAwaBQ4davGy6bmfsDmISq0emP0OLNDDiFqd+p8ftJHq+Y2qbSdf6p7GiuxCGEOLrEDx4k/NxzJBobB2xLNDUR3bQJ44wZ2M4/H1N19aDn0jSN/d79vN3xNm8deoMthzaxJ9xIgrEFsRVNo7wHZrRr1LRDTbtGdYdGVTdYkg1ajMXFmKuqMM+vwlRVhblqmv54WhWmykpMJSV6CzYxKRSTSf/3r6oClgzYrobDRPcfILp/H9G9ewnv2EFk+w6i+/YNvI6cg+rz4XvuOXzPPQdmMwWnn4774vfjes97jukQmsVoYVnFMpZVLOOmJTfREmhhXeM6Xmh8gdeaXyORo8NDXI3z971/55mGZ7i47mI+f9LnmVYw7TCMXichpmFoaOgLJGiaRl1dHYqi8NxzzzF37ty8xymKgs1mo6SkZMA/9FT09NNP84lPfAKvt++DrWAwyKZNm9i0aRO//e1vWbNmDXPmjD6dp6oqn/nMZ/jd736Xtf7gwYMcPHiQJ598kk9/+tP8+te/HlBGLFNHRwcXXXQRGzduzFq/d+9e7rvvPn7/+99z11138elPf3rUYx2uL5xVy3HHzR7ZQYkYKMbcoY9gF+z5tx4eigWT81CO5WTAKB04SoaTPv0vqDox93n/euPoXiQkw0zolSHiIb01XDykV14aixe+D/+5KyNklJyG0zJuMJ279OloY3ZmB4msbmIGKzv3NxPFwsKTVmJ1lWRUOirIUQ3JLdWOhBDiaGOyDL/SUzSYDDS16W1Q/W3gaxkYegq0ZVdVGi+pSo1j4ZlJ3oLQWx+HZ/8fmGx6W7qy+RnTAiiuk9Z0QgghhBDiiGQ0KJS7bZS7bSye3re+fqaEmMTwTaXr/FPNVG91IoQ4NsV27iT4xBMQH/xzt0RjI4EHH8Rx+eWY5/XdVOmP+tnasZXNB17nrQOv8U5wN14lfyur4Sj0a9S0a8xIhpVq2jWmd4DdZMdSU4OlZgbmU2uw1MzEPL0a8zQ9rGSw2cb0vGJyGWw2bPPnYZuffZOuGgwS2bWLcP12Qu9sJfTmZqJ79w5+slgM/wsv4H/hBYyFhXiuuIKiq67CXFE+ga9gaqh0VnLFgiu4YsEVdIQ6eG7fc/x9z995p/OdAfuqmspTe57iuX3PcdOSm/jEcZ/AeBhuBO7/+6GEmHKYOXNmzvXTpk3Lu+1os3nzZq644gpCoRAFBQXcdtttnHPOOYRCIR577DF+85vfsHPnTt7//vezadMmXC7XqJ7nG9/4RvoPmyVLlvC1r32N2bNns2fPHv73f/+XzZs389vf/paysjK+//3v5zxHIpHgQx/6UDrA9OEPf5gbbriB4uJiXn/9db773e/S1tbGjTfeSHV19YTf8WHefD8ccvVV+8ms/JNvWY3DTW9CSY7wk/cg/N/1ox9Q+w49IBUP688ZD+uhIH/b6M8J8OdPgZqAQXpsjkrP/vE935HEZNfb6FicGSGijMd5lzMfO/tCSBZnzooSIa+Xv99zDwCzz1qNNVnGUQghhMjJ4tBbsxXPGnw/NaFXd8qs5ORr1gNPvmZ98jbr63Pc3TGhOnfCd8vANQ0Kq/VWde5qKJwOu/6p7xMPQ+tWfcpkMEHx7OxgU6pNnfnYvYNHCCGEEEIIMfVNpev8QgghJp7W0jKsAFNaPE7wicepP3chL3Ss542OzezSWlEz7yUcYRG6qi6NWS3JqRVqe8yUVc3GOmsW5nk1WM6diaVmBpaaGoylpVLl7hhgcDiwn3gi9hNPpAi92n68u5vQ5s0E33iDwH/WE6mvz3t8oreXzvvuo/OBByj62JWUfe5zGD2eSRr9ka3UXspVx13FVcddxc7unfyx/o/8fe/fifT7fD+SiPDjTT/mX/v/xU/O/glljrJJHWf/7/OpHhSfsHZymaZ60ms0br75ZkKhECaTibVr13LKKaekt73nPe9h7ty5fO1rX2Pnzp3ceeed3H777SN+jp07d/LjH/8YgOXLl/PSSy9hT5Z6W7FiBZdeeilnnXUWmzZt4o477uBTn/pUzrtBfv/73/PKK68A8LnPfY677747vW3lypVceOGFLFu2DK/Xyxe/+EXq6+sxmSbuS8e26V5wj+JuktfuAbsH4pHkFNZbswU6xjagv9wwtuPziQUn5rxHCrMzT6houAGkHOEjaWEjhBBiKjMYoaBcnypPyL+fmkhWc0oFmw71BZwyw06R3vEdnxqH3gP6NNLjOnboU9bfwgrMPA2uWzOeoxRCCCGEEEKISTOVrvMfDeSDdnEk2rdv3+EegjiSvPji8ANMKfEEsRde4I9VL+qPh/lWp6ga1Z0wq1UPLM3x2plfUIendh7WebOxXFiHdfZszNOmScs3MYCpqAjXe96D6z3vASDW2kbglZfxv7gO/0svoUVyFNmIxeh+6GF6n/wbld/8BoWXXjrJoz6yzSuax+2n3s7NS2/mj9v/yEPvPkQwnv15/5b2LXxszcf41bm/Yl7RvDxnGn9H2+9QkxJiOtZs2LCBl19+GYDrr78+6w+blC9/+cs88MAD1NfX8/Of/5xvfOMbmM0ja8Pxs5/9jHjyB+Uvf/nL9B82KQ6Hg1/+8peccsopxONxfvrTn2YFlFJSfyAVFxdzxx13DNg+Z84cbrvtNm677TZ2797NX//6Vy6//PIRjXVSbPzN4R7B1KIY9WCQ2aFXkDA7MpadeuWE1LLFoT9OL+faNyNsZHbmbu0nhBBCiKEZjMNrZRcN6FWc0iGnQ8mqTocyAk8toMYmZ9wDaNC1B/71bb26U+GMZHWnarB5YPMj0LZNr95Umqzi5Cg+TGMVQgghhBBCiGxT7Tr/VDTVqwQIIY4tJaoKzc1Z6zpbWqjftInOlhZi0Shmi4WSykqOW76cksrK9H6LI6XMiRay25L/pkSPX2PeQY25zQrHU8XC0kV4FizCumoB1rlzMZWXHXVBBTF5zBXleD7yETwf+QgJvx/fP/9F71N/I7j+tQH7ql4vh752K4ENG6i6/XaUCSxuMhUV2Yr4/Emf58r5V3LPW/fw+I7H0ej7naY12MqN/7yRhy98mOmu6YOcaeJM9d+x5CtuAjz55JPp5euuuy7nPgaDgauvvprbbruNnp4eXnjhBc4777xhP4emafztb38DYMGCBaxatSrnfqtWrWL+/Pns2LGDv/3tb9x1111ZP+B27txJfbJ83Ec/+lEcDkfO81x77bXcdtttAEduiOloohj0kJDJpgeETDYw2zLWOXIEi4YKHvXbbjSD/LIjhBBCTF0Wp95KN1c73RRVhWCH3l7Xewh6D4K3qd9y88QFnXzN8MpPBq43O/W7zqKB7PW2QiieA5WLoGJRX4u6ggr5vUUIIYQQQggxqabSdX4hhBATb2EikV5uP3iQ9c8+S2tj44D92pqaqN+0iYoZMzjlggsoq64G4FLfLH5SsgUAU1xvBTev1cjxxuksLjuRmjlLsJ21EOvcORis1kl5TeLYZCwowPOhD+L50AeJ7NlD10MP0/vXv6JFo1n79f75/9AiUab974/k944cSuwlfHPVN7lo1kV8/ZWvc9B/ML2tI9TBV9d9lUcuegTjJHQaknZyg/jUpz4F6P9Iqf7NmetHo/+5poJUazan08myZcvy7nfWWWell1999dUR/XHT0NDAoUOHBpwn3/Ps2LGDgwcPsm/fPmbNmjVgrEOdp7Kyknnz5rFz505effXVYY/z6KFkhInsfUEis61vndkOJntynb1f6Mjeb79c6zKOlYCREEIIIcaDwdDXwm7aktz7qCoE2vVAU28y7JReTj0+BFoi9/GjEQvkXh/uhUNv6FMmo0V/DZ6ZUHYc1J0F884fv/EIIYQQQgghRD9T6Tq/EEKIiVeiqgAc2LmTfz3+OIkh2sq1Njby9AMPcO5HP0rNvHks9nn4WIuHJe6FLJlzJp4PL8M6Z45UuRGHlXX2bKq+fTslN9xA250/xvePZ7O2e59+GsfKFRRJgZO8llYs5dH3P8pN/76Jt9rfSq9/p/MdntrzFB+a+6EJH4OEmAbx4IMPpv+BMoNHmetHQtO0KRliSlU2mjNnDqZBfvAsWLBgwDHDtW3btpznGc7zZP5xM9Lz7Ny5k8bGRgKBAE6nc9jjbWpqGnR7c7/yi5k0FDBZwWhF6z83WjK2WZJzGxgtaEZrcpslY39rjv2tGfvb+u3fd/4JDxVpQBSIhoHwxD6XGJTf78+5LMThIl+T4kgiX49HKzu45upTLmoCJdCGwd+C4juEwXdIn2c+DrShaOrEDC8Rhd4mfdr/Kmz6LQBOeylXh814FReGtY2Ei2vQzAWoJfNQC2egOcv19nxCTBJ5jxRHEvl6FEca+ZoUR5JAIE+oXogMU+k6/1BGcn3e5/Ph9XqHfe54PI6qqmiaRiIxtptfVFUd8zmEyPwakq+n8aFpGqqqEo/HR/T+cLRI/e5qRq/ANJwAU0oiHudfjz/OJdddx+wTTuALX/peelsUiAaDEzBicaQ7Iv82crtwf/vbmE47je7vfBcikfSmtp//AsO556IY5TprPkaM/Gjlj7hh3Q00+vsqtD1W/xjvrXjvhD9/MMd7yWS9X/t8vnE/57iGmGpqanKGlfKtPxqFw2E6OjoAmD598B6HRUVFOJ1OAoEAjTnKDQ4m84+OoZ5nxowZ6eX+zzOa82iaRlNTE/Pnzx/2eDPHMJTfW6/FZSsmgZE4JlQMfQEiNTmNquOJCoSSkxDD8/DDDx/uIQiRRb4mxZFEvh6PdeXJaTEAilWlQPPj1ry4NC8uzZex7MWteSlgfD8sMoY6qAKqtGZ4Z+eA7RoQwUpAcdKreOhUSug1ePAqbnyKG6/iJohDKmCKCSHvkeJIIl+P4kgjX5PicOvt7T3cQxBHuKl2nX8oI7k+//DDD1NYWDjs/U866SQKCwspKCigra1tRONyOp1Znx319PRI6ESMq87OzsM9hKNCNBrF7/fT29vLU089dbiHc9jEgA3PPjvsAFNKIh5n/bPPsnL6dJ68556JGZyYso7Ev40qTj+Nk5//d/qx2tHBo9//Pr2lpYdxVFPDbOtsGov6fk+r76rnrnvuwsjEBsAcDgeLFy9OP1ZVlXsm6f1mIv62GtcQ0759+0a0/miUmTQrKCgYcv/UHzcjTVmO5HkyKyb1f57xOs94CiguTIpjws4vhBBCCCHGn6YY8ClufLjz7mPQErg0XzrU5E4HnHop0noo0HxYieY9fqQUwEYEmxahROuijr3Q73p4AiNexZUVbMqeu4hgk6CTEEIIIYQQx5Cpdp1fCCHExDvQ2krrCEOkKa2NjexvbYWamnEelRDjr3XGDCJWK9aMakzWkHQRGo7iWHHWY03RiBliGNWJDTHFYjGam5vRNC09TWXSZHOchcN938AWi2XI/a1WKwCh0MiqA43keVLPket5xus8QxnqzpDm5mZWrlwJwIc//GHmzZs3ovMLMZ78fn86+fzJT35yWBcqhJhI8jUpjiTy9SgmUgSIxIIYfM3JNnUHMXRsx9i5C8XbhCHYAVH/uLatM5KgSOuhSOvJu49mdqK6pqG5qlBd07KWteRjzBLCF/IeKY4s8vUojjTyNSkmjaaBGodEFCURhUREX45H0uv27NnNDw73OMURbapd5x/KSK7Pf/KTn6S6unrY5z548CCqqmI2mykvLx/RuPq3PvF4PBilVY0Yo0Qika7AVFJSIl9T48Dn8+FyuSgsLOSUU0453MOZdKnfY+vfeGNM54n39rJ69epxGpWYyo70v40CTz1FT0aACeDSa6/BLJ/fD2lt41r+sukv6cdGxchNN9yEyTCxsZxEIkFbWxsvvfQSiqJwxhlncMkll0zoc6YcPHiQH/xgfP+6khDTOLPZbOnlaHTou8gjyTcAu90+Yc8TyXiT6f88/c+T+Xgk5xnKUKVwMzmdTtzu/HfwCzGZCgoK5OtRHFHka1IcSeTrUUwMN5RUAktyb9Y0CPeC9yDB5l28tOZR3JqX5fOmYemoh7Z3x31ESiyAsWsXdO3Kv5O9CNzTobAa3NXJecZjdzWYhv7wQxw95D1SHEnk61EcaeRr8higJiAegXg4Y4pALJR7fTwMsX6PB92eeZ6IPiUiEE+GloYIvRd7xy8UL45OU+06/1BGcn3e5XKN6D26tbWVeDyOoihjDosYjUYJnIhxJV9T40NRFAwGAyaT6Zj+HS7S3MxY6nR3b9+Oy+XKaqMpxJH0t5GmqnQ9+Ht67rgja725pobipUtRDIbDNLKpoT3Yzj3vZrdwW1qxlGJPcZ4jxpfRaKSrqwuAqqqqSfu68nq9435OCTGNM5fLlV4eTknXQCAADK8k7WifJ/UcuZ6n/3kGCzENdh4hhBBCCCEmhaKA3QN2D3H7DN4y7QBg0QWrsbjdEOiAtu1w6A1ofgvad0DPAYiM/x9TWULd+tS6Nf8+BRW5A06F0/W5qxIMcnFVCCGEEBNA05JBoBDEgsl5qN/j5DweHrguFuwLEcX6B4hCA4NGauxwv2IhxmSqXec/Wkz11idCiKNcv8o0I7Xz0Dt86f8t5SRDDcuqljNz4SnYT1iMuWJkVeyEmAiht9+m9Uf/SyhHxbHy//qSBJiGcNB/kM/+87O0hdqy1n9swccO04imNgkxjTObzUZJSQmdnZ00NTUNum93d3f6D48ZM2aM6Hky75wY6nkyS8X2f57+5yktLR3yPIqijOjODSGEEEIIISaNsxRmna5PmcK90LEL2rfrwaa27XrVJu9BfbtigBU3gO8Q9B6E3iYItA08/1j4W/Xp0Ju5tytGcFXpoab+AadU8MlZqge5hBBCCHH0UBMQDehBof7zrLBRv1BRVtgoI2g0YF1IDxoJIYZtql3nn6osFgs+nw9N03C73RjkA1IhxJEso63naIScRp5fGOd59gJ7Kdr+J+b+W2N+r5MTCuZxQu1KCuefgHXePMzV06Rik5hwmqYRXL+ert8/hH/dupz7FF93He4LLpjkkU0t/9z/T27/z+14o9k30a6qWsW5NeceplFNbeMaYqqrqxvP0wF6YGbPnj3jft6JtHDhQl5++WV2795NPB7HZMr9z7x9+/b08nHHHTfi58h1npE+T//znHTSSUOeZ8aMGTidzpEMVwghhBBCiMPLVgjTl+tTpmgQOnfpwaUFF2Vvi0fAewhe/zW8nl0KeEJoCfA26VNjnn2MVnBPSwadpg+s7OSq0lvbyYUuIYQQYnylKhlFgxD1J4NGmcuB7ADSkMsZx8bDh/vVHXM0g7T5FUObStf5pyqTyUQsFksvS4hJCHEk06ZNQzlwYNTHd9Vkd8PpdilsmK+wgRDwFsbEFmZugLlPaszvsHK8o46ZMxbhmL8A67z5WOfNxXiUVuITkyva2Ih3zRp6n3qa6N69uXdSFEo//3lKP/+5yR3cFLLfu58fbfgRLx98ecC26QXT+d8z/1fCiKM0riGmffv2jefpAKbk/9jTTz+dl19+mUAgwBtvvMHJJ5+cc791GYnG0047bUTPMWvWLKZNm8ahQ4eyzpPLSy+9BEB1dTW1tbUDxpo5niuvvDLnOVpaWti5c+eoxiqEEEIIIcQRy+KAqhP1qT+TFYpnwZz3QqBdr+DUsRMSYysfPiaJCHQ36FM+Rovems5VlXteUKnPbYUSdhJCCHF0UhN6QCjiz5j7kvNA3/JwQ0bRIMQCoKmH+5Ude6avgBkng8mmT+bk3GSF578z6sqZ0QUfBO4f16GKo89Uus4vhBBi4mkrV8Jrr436+J1nFw26PWFU2FsFe6sUniMG7MAe3s6snTDrZY1ZLRrz1DJqS+fhqKvDMmsWlll1WOtmYSwtnZKfqYvJoakq4Xffxb/uJfzr1hHeunXQ/c3V1VR9//s4T145SSOcWvb27uV3W3/Hmr1rSGiJAdtnF87m1+/7NUW2wb/nRX7jGmK65pprxvN0U9YHP/hBfvCDHwDwwAMP5PzjRlVVHnroIQA8Hg/nnHPOiJ5DURQ+8IEPcM8997B9+3Zee+01Vq1aNWC/1157LX2Hxgc+8IEBP8DmzZvHcccdR319PY8//jh33nknDodjwHkefPDB9PKHPvShEY1VCCGEEEKIKW3u+/QJ9A9Fe/brgab0tF0PN0X9A491T4fVr+pt63qb9Ml7UK/8dOgNvcXdeEtEoeeAPg3GZB887JSaW+UuPyGEEBNM05IBov6Bo0EeRwMZ63zZoaVY8HC/oqlBMYLF2RcOGup3h8Es/xQs/GBfwMhs1+cmG/xqFYS6R3feWWfBe/+/3Ns2P6LPLQ4wO5Nzh/6azA59DAPm+nLcZ0VCTGIoU+k6vxBCiEkwbRoVJ59M6+uvj/jQopVL+NS5n2Lj/lfZHNqFzzC8G+RCNoVtM2HbzNT7fhfW6Hpmtq1n1n80Zv1Fo7ZVY0bYiWvmbKyzZunhptpazDOmY5kxA6PbPeLxiqlNSySI7NpF8M03CW16g8Brr5Ho6hryOENhIaU33kjRVR/HMMb2iUebhJrg5YMv88TOJ3i56WU0tJz7XVB7Ad865Vu4LK5JHZ+maajq0XPTzbiGmB544IHxPN2UtXLlSs444wxefvllfve733HNNddwyimnZO1z5513Ul9fD8DNN9+M2WzO2v7iiy+m/+C55pprskJEKbfccgv33XcfiUSCm266iZdeegm73Z7eHgqFuOmmmwC9FOstt9ySc7xf+cpXuP766+nq6uJrX/sad911V9b2PXv2pP9YmzNnjoSYhBBCCCHEsctghOI6fZp/Yd96TdPDSf2DTe5pYPfoU8Xx2ef61+3wyk8ncfD9xENDV3UCsLiSoabkVFABzjJ9XpCcO8vBWar/+wghhDj6aRrEQtlVjYYIINkD3Xw48i4WIjj/8HSyNVtG8CjPReBjltkBVldfAMdk0+cGIzQMXq1lUBfeASdeoZ/LmH09ku+U6WHo0fDUQN1Z+bdZXHq4qH/QyOLMWHaApSB7e+m8/M95/drRjRVQk9dlhRjMVLvOL4QQYuKd8oMf8PeLLiIeHn47YJPdzkV3/ZqqFSu49pQvoGka+737eavtLTbvfYW3299iT7wFVRne78MRi8LO6bBzel+gVVHDVHa/S037O8zYBDX/0Khp16jsBpO7EMv06ZhnzMAyvRrz9BnpgJO5qgql388uMbVoqkqssZFwfT3h+u2E332X0JYtqP4cN1vmYamro/iTn6Dw0ksxOJ0TONqpRdM0dnTv4B8N/+CZhmdoCbTk3bfUXsqXl3+Z9896/2EJm3d2dvLuu++yatUqNE2jvr4+bxXRqWBcQ0yiz89//nNOO+00QqEQ5513Hl//+tc555xzCIVCPPbYY9x3332AXgnpy1/+8qieY968eXz1q1/lhz/8IZs2beK0007j1ltvZfbs2ezZs4cf/ehHbN68GYCvfvWrzJ07N+d5rrnmGu6//35effVV7r77blpaWrjhhhsoKipiw4YNfOc738Hr9WIwGPjFL36Rt/e3EEIIIYQQxyxFgcLp+jTnvcM7ZixVmMwOWHp1dmWnUbZTGVLUB50+6BxqvIoeZHKWQ0HG5Oy/XAGOYgk8CSHEZItH8oSMBgsg5ahylJrnKJs/GDOQvjLVOt4v7jCyecDq7hfQKdD/fXaNPmTD5Q/CvPMHro+F4XsVoz+vwai3lM3FUqD/v7UU6AEqqyu5XJAxd+nz9LbkvPy4/M9540ujH68Qh9lUus4vhBBi4pUvW8Ylf/4zT1922bCCTCa7nUueeIKqFSvS6xRFobawltrCWj4w9wMABGNB3u18l7fa3uKtgxt5u2MrXapv2OPSDArNJdBcovD6gr715rjG9A4/M9rrqWnfxvRdMK1To7wXDBpgMGAqLcVUWYm5ogJTVSXmikpMlRWYKysxV1ZiKi+XoNMRQIvHiR08SKShgWjDPqINDUT27CGyfTtqIDDi8xmLinBdcD6F738/9qVLUQyGCRj11JNQE7zb+S7rmtaxdt9a9nn3Dbq/3WTnygVX8pkTPkOB5fBVs9e0vhCkoihZj6ciSaNMkCVLlvCnP/2JT3ziE3i9Xr7+9a8P2GfevHmsWbMGl2v05cS+973v0dbWxv3338/mzZu58sorB+xz/fXX893vfjfvOYxGI08++SQXXXQRGzdu5P/+7//4v//7v6x9rFYrd911FxdeeGGeswghhBBCCCFG5IP3QOd/QcduPSDUsQs6d+tTfIgLYZWL4cIfZa9LxMDfCut+BG8+NPAYxQgKeku8CaFBoF2f2t4dfFfFAI7SftWcSsFRoq9PL5foy1a3HhQTQohjSSI+7CpHfa3YcgWOkvuoscP9ig4fxZAMG2UEc1IVgOIh2PXP0Z/7449DTY47XH2tcOcgVYSGEvbmXm+2gdGSXTFJMeg/K21usBYm58nHWeEjF9QMbFOV9pWdA6szCXGMm0rX+Y8GmqZJq7wJVFtby/79+/NWBTucbr/9dr797W8DTPkPXsXRb/b7388VL73Ei1/6EgdffTXvftWnncbZP/1pVoApH4fZwYrKFayoXAGLP42mabSH2qnvrGdb1zbq299lW/s7tEY7RzTWmEmhoRIaKrPfW01xvUrTtC6NaZ0dTOtqZ9q7bzPtZSjof0lKUTCWlmAuK8dYWoKppBRTaQnGkn7LpaUYPR4Jw4ySpqrE29uJHTpE7NAh4s3NyeVmoo2NRA8cgNjY/qaz1NVRcMYZOM88A+fKlRJOS+oIdbCheQMvH3yZVw++Sndk6FbYLrOLKxdcyScXfpIiW9EkjHJw/X92TvXfpw5biEnTNPbu3UtXsv9icXExdXV1U/4fNNMll1zC22+/zc9//nPWrFlDU1MTFouFOXPmcPnll/OFL3wBh8MxpucwGAz87ne/4yMf+Qj33XcfGzdupKOjg9LSUlasWMGNN944rOBRaWkp//nPf/jNb37DH//4R+rr6wkEAkybNo33vve93HzzzRx//PFDnkcIIYQQQggxTDY3VC/Tp0yqCr2NyWBTv4CT96C+T+mcgeczmvVKUNFg7ufTEvk79Rit+ge5Jqv+oaevBSK9o35pQ9JUvXJUoG141TgM5r5AU2a4yVGqV3VKLae224vBKPfsCCEmWSp0FA3kr2KUq6JRrqpH0cDQgdajmcGcXfXH4tR/dnQ16GFcNapXltLU4Z1PU+G6f0D5goHb2neMLcQU7sm93u4Z3vEGc3boyJqcCsrzH/OpZ8Hs7Nvf4hyfsK8EmITIaSpd559qIpEIzmTbmmAwiNVqxWKxHOZRCSHE0KpWrOBjr7xC21tv8fZ999G2ZQtRnw+Ly0X5SSex+DOfofzEE0d9fkVRKHeUU+4o56wZfS17u8Jd1HfWU99Vz7bObdR31tPkbxrx+eMmhaYyaCob+DtkYUBjWidUdWlUduthp/KeDir2dODcNsSJDQaMxcWYioowFLoxFnowFhYmJ3d62eBOrvMUYigowOBwoFgsR1VOQFNVVL+fhNeH6vOS6O0l3tlJorOTwKFDnPjqf7CGQrS99jqtPT3EOzrGHFLqz1xTg2PJEuxLl+I87VQs06eP6/mnqpZAC5taN7GpZRNvtL4xZLWlTItKFvHR+R/l/NrzcZjH9vvfeEoksm9aNUzxMOGkX9V99tln+dWvfsWLL75IoF9pM4fDwdlnn83nPve5o+YX8pkzZ/KTn/yEn/zkJyM67uyzzx5R2vyiiy7ioosuGunwsphMJlavXs3q1avHdB4hhBBCCCHEGBgMUDRTn+acm70t4oeuPWCy5T9+yLZvOSQiEIrogaob/q2viwb0MJOvBXzNsOff0LEDMOjVPKIBCHVDoIP86ahxosbA36JPw2Uv0sNM9qIckyf3eptHwk9CHEvURHZoaEClI19GhaN+23LtfyyHjhRDX+DI7NDDMEYzGEz6NiAej9He2oKBBKWFBRjVGMQCA6tE3fIOeGYMfI4Dr8H9OdqrDVc4TzjX5hn9OSF/xSSTFc7+uh4wygop9auUZLKNPIDUPwAthJhwU+k6/1ST+YH1eFXgSbS0EH3jDRItLWjRKIrFgrGyEsuyZRgrK8flOYQQAqD8xBM59+67J+35im3FnFZ9GqdVn5ZeF4wF2dOzh909u9nZvZPdPbvZ1b2LzvDIqjal9DoVep1QXzPwd9SCkEZFD5R36/OKnuS8W6PEBwZVJdHRQaKjY+RPbDJhcDgwOJ255w4HitWCwWLRA08Wa3JuQbGYUSx92zCZ9IpQigEMiv6zJvlYMSj679/JkIcWj4OqosUTkIijJRID1qnRKFoojBoOo4VDqOGIPg+FUcMhtHAENRAg4fOh9vbqc78fBvm5NjM5H6/YkrG4GNtxx2FbeBy24xdhX7oEc/kgN0YcI3rCPWzr3Ma7ne/yTsc7vNv5Lq3BkfU3n+acxvmzzufC2gs5rmSQNtqHkYSYRikYDPLJT36SJ598Esj9y2ggEOCZZ57hmWee4dJLL+WRRx5Jp/DFsSMSiaCq6pT/5hJCCCGEEGLcWQugaog7+VZ+Blre0cNMXXuhe79ehWk4iuv6li1OKJmtT6CHmJo2Ze9vMENRLbin6RWQbIV9H8aqCYh4wd+mt5jzt0JwdBfQRiXUrU8jZXXrH2jnCjrZMj94LsyummFzD3pxSggxBmoiGSYKQCyYDBEF9QBMNKAvR/3JbYF+++ZYToWS4qHD/coOIyWjzViy0lGqzZq1AEx2PYBkMCRzqlpGBaSoHth6z7fAUaQfY7b3BXF2PAuPXjHgGU1AVepBT3v+ocXyVBS0F4/+5UL+ikm2Qj1YZC/se/+35Vou6rc+uc00SMWQs28d25jFuJMWRUIc3eIHDxJ+7jkSjY0DtiWamohu2oRxxgxs55+Pqbr6MIxQCCHGn8Ps4ISyEzih7ISs9V3hLnZ372ZXzy52de9id89uGnob8EbzhPCHwW9X8NthT9XAgJMxoVHqhRIvlHr15VKvlvXYHs1x0pR4HNXrRfWOfnzHAkNhIdbaWiyzZmGZNQvr/HnYjluIqbzsqKpkNVIxNcYB7wF29exid/dudvfsZkfXjlFVKgOoK6zjjOozeF/t+1hcuviI/7ftH2IyGo2HaSTjY1JCTKqqctFFF/Hyyy+jaRpms5nzzjuPlStXUlFRAUBraysbN25k7dq1RKNRnnrqKS666CJefPHFI/6LQoyvXbt20dvbi91ux26343A4cDgc6WWz2SxfE0IIIYQQQuSz5BPZjxMx6DmgB5pSU+cefd6zH9R4376ZIab+OvcMXKfGoLtBn3KxuvWKUnPPg/d9Wx9LoENvI+dPBpsCbcmgUwcEO/SgU6BTXz4cVU0iXn3qPTDiQ12KgZs1CxHFivOhv+of7ltdA9sEpSpxWJzJqSBjOfl4sA/FhTjSJOJ68CQe1uexUN8UD2U/joUGDx+llzO2HcsVjjKZHdnBo8zQUeZji7OvFZvJqodvslqzJasltbwNm36XDH32QLBLf68PdQ8v4PX+n+RueWYvGtvrzBdAHey8iqEvaJQZMEot2z1QkqMVK4DZBreN/D1fHNlUVSUYDBIKhQgGg1nT4sWLD/fwhBB5jCVoGNu5k+ATT0A8Puh+icZGAg8+iOPyyzHPmzfq5zvcDh06xC9+8QvWrl3Lnj17CAaDFBcXU15ezqJFizj//PP58Ic/jNvt5uyzz2bdunXpY3//+9/z+9//Put8Z511Fi+++GL6cXd3N08++STPP/88b775JgcOHCAajVJcXMyJJ57IRz7yEa699tq87f/27dvHrFmzAHjggQe49tpr+ctf/sJvf/tbtmzZQltbG6effjrXXnst1113XdaxuT7/aWhooLa2dpT/WkIcm4ptxaysWsnKqpXpdZqm0R3pZl/vPvZ599HQ25BebvQ1khjuTXA5JIwKrUXQWgSQ+3NcRzg73FTi1Sj2g8cPxX4Njx9coXxHHxsUsxnTtCrMVdMwT5uGuaoK87QqLMngkrGo6Jj9nDz19XvAe4BGXyMHfAfY37uf3b16QC+uDv47wGAcJgfLK5dzRvUZnF59OtNdU6v1nlRiGoVf//rXvPTSSyiKwvnnn89vf/tbqvOk3A8ePMgNN9zAs88+yyuvvMK9994r7c2OUaFQiFAoRFdXV9Z6k8mUDjRlhpwcDscx+6YthBBCCCFEXkZzdkWlTIm4Htbp2gude2H6IO1puvaO/LkjXmjZCiVz+8birtKnlBe+D96DUDgDqpdC4XR92T1d/2A5mAw0Bbv6BZ36zYMd+VsFTRJFU7ERxqaFoWOMYzGY8wecBiw79KopZtvI5lP8gobIQU1APALxMIq/k0K1ByNxDO3bwGtKbyORrKQTj+rtJOORQUJHqXBScjkWHrhuDBcKj2ome0bAKBkiSgeM+oeQnNkho6zQkRMMFv09NdQNoS59HuzKftzbmFzX07cuFoRvtulhpv58LfDmQ6N/faFucFUMXO8YS8UkJX97NnsRXHhHX3U8R6pSXrEeEJX3tGOOpmnEYrGcYaVwOH/4MRjMU+1LCDHpxut6fvzgwWEFmPoOiBN84gmc1147JSsyvfzyy1x88cV4+1UraWtro62tjXfeeYfHHnuM0tJSLr744lE9x5IlS9i/f/+A9a2traxdu5a1a9dy77338swzz1A5RIs+TdO4+uqrefjhh0c1FiHE+FEUhWJbMcW2YpZWLM3aFkvEaPQ3pkNN+737afI10eRroiXYgqqpY37+oE3hgA0OlOd//zcm9DBTUQA8fo0iPxSl5+AJaLiD4AqCdar8KWo0YnS5MBYXYyopQSss5N2DB4nYbZxywYUUTK/GVFqKuaoKY0mJ3grvGKRpGt6ol5ZAS98UbKHJ18QB3wEavY34Yr5xeS63xc3SiqUsr1jO8orlzC+ej8kwaU3Mxp2EmEYhlehesWIFa9asGfQfrbq6mqeffprTTjuNDRs28Pvf/15CTCJLPB7H5/Ph82W/SR1//PGUlpYeplEJIYQQQggxBRlNevWl4jrIU6ACAFWFEy6Hrj16e7qe/fnb/eRSVJt/W8NLcGB97m0mezLUNB08M/RwU+F0vaVe4XRwV2d/MJ+I6R/gBzv0cFOqpVxqCvf0VRzJXD+S1zJZ1Jg+3nztj8aD0ZIRarLp7aBMNn290aJXgzJa9PCZcZTLigEMRlCM+txgGrhOMfa1rcpaZ0zua+pb7n8/5oAPvka4XUuApuqTmrE84HFyWe33OGu/hL5djelfi2o8Oc/1OK4HifJty7U+1cIrEclY7hdGyggTuYDPph48fN+ovkSOOSbbIIGjfpWM+rdf67/NUqC/x/aXiPe9F2WGkMJeOHFgCzYA3n4C/vLp0b+uUM8EhI0YomKS0q8tZzFRUwFv72okrNhZdsb7sBdV6QGkVPUkR7FepS7fdUOjCU7+zNjGLI4K8XicrVu3EgwGiQ83sJAhFDqWW0kKcWQbbSWm8HPPDT/AlBKPE37uOQo+9alRPefhEolEuPLKK/F6vbhcLlavXs0555xDeXk50WiUhoYG/vOf//DXv/41fcwDDzxAIBDg/PPP59ChQ3zgAx/gu9/9btZ5nU5n1uNEIsHJJ5/MxRdfzJIlS6ioqEif/5FHHuHZZ59l8+bNXHnllVkVnHL52c9+xttvv80ZZ5zB6tWrmTdvHj09Pezbt48PfvCDLF++nF/96lfcc889AGzdunXAOfIVRhBCjB+z0UxdYR11hQOrdMcSMQ4FDtHoa0wHmxp9jTT59XloHFt1J4wKnYXQWQhD1WSyJgwURk24IwZ9Cil6wCkE7rBCYUjBFdIfOyPgDGmY45r+8yY5KUYjmEzJuRHFmL2M0YBiNmOwOzDYbCg2mz632zDY7Cg2KwabHYPdjrHQjcHlxuh26fNCN0aXC6VfIQ6v18s7yfe8cz/4AVxu97j9+x2JUuGkznAnnaHklFzuCHXQGmylOdBMS6BlXL+WUqxGK/OL53N8yfEsKl3E8SXHM6twFgZlagd9Mkk7uVGor69HURS+9KUvDSv1ZTQa+a//+i+uvPJK6uvrJ2GE4mjQ/5fslEgkwjvvvIPT6cTpdOJwOHA6nVitVqncJIQQQgghxHAYDHDB9/seaxoE2vsCTd0N+nL3Pv1x70E90JFSNDP/uXsH6U0fD0HnLn3KZdFlcNnv+h4bzfoH9a4KaHkHCir0qk9Wd44wS4ZYOBkq6BkYfMoKQfXqlVDCyZZzEZ/e7mqqSkT1KXJ4K1gJMWKKsa8imdmRezn9uECvVmZxgjlZuSxn67UC/T1kLNp3Qv3TOaolpZa7B/9+W35d7opJtjFe0A515Q4x2YcTYlKSbdlS1Y+K+6ofOfPcSOYogW916uHDDGGvl+eTF+oXL7kO+1F+oV6MTiKRIBgMEggEcLlcOa/3GY3GUQeYQA8xTfU7k4WYajRNQ8tVBS0SQYvF0g9VoxFVHVmlj0RbG4nGxlGNK9HYSKyhAWN5jvaoY9T/A+vx8uqrr3Lo0CEA/vjHPw6otLRq1So+9rGP8dOf/jRdeS7V1s1s1n/X8Xg8LFq0aNDn+fe//83cuXMHrD/11FO56qqreOCBB/jUpz7FunXreP7553nve9+b91xvv/02V199NQ8++GDOfxOPx0N5xv+DocYmhJh8ZqOZme6ZzHQPvL6jaRpd4S6a/E1ZFXSaA83pYEpXuCvHWccuYlRps0dpsw//GJvRhsviwmVx4ba4Byy7LW7cVjdOsxOHyYHD7EjPbcm53WQ/qgIwwxVLxAjGgwRiAbxRL96IV59HvfRGeges64n00BnqpCvcRUyNDf0E46DcXs6cojnM8ejTcSXHMdszG7NhjH/vH+GkEtMopH4pmTeC/sKpX44kZHLsOe6446ipqckqA51azncnhsFgwGaz5dwWCATw+/34/dkfLhiNRgoKCtJTKuQ01b+phRBCCCGEmHCKAgXl+jRjxcDtiZgeTurZr4ebak/PfZ5EHLyHRj8Oz4z8256+GQ5u0pfNTj3M5EpO7ipwTcteV1ABrsHbIOSkJtLBJn9XM3/708NYifD+95yO3RAbGHzKCkAF+qYJuNNMiMPKaNWre5kderUvs6Ov4lfewNFg4aOMbSbr4MHEkUiFGH2tfWHG/vN0mLELokH43H9yn+vQZnj21tGPJdSd+31oWGGjIc6bS0EZrPxMVrWkAWElW+GAMNKQFEUPmgkxCE3TiEQi6Wt2fr8/fQ0wpba2NmeISVEUnE4nvb1Dh3CtVit2ux2Hw5E17d07ija5QohR04JBfD/+8ZD7xYHxaRIzfMGHxtBadRCur3wFJc+N12PR0tKSXj7zzDPz7mcymXCPITCcK8CU6brrruMXv/gFW7Zs4cknnxw0xOTxeLjrrrvk8z4hjlKKolBiL6HEXsKJZSfm3CccD9MabE2HmzJbhXUEO2gPtU9Y0GnAWBJhwqEw7aH2MZ3HbrKnp1TQyWa0YTFa+iaDBavRmrXOarSiRlV22HdgxMia/WsocBRgUAwYFSOKomBUjOnH6blBn2uahoqqt/fTSC9rmqbP0dL7JLQEsUSMuBonmogSVaPE1BjRhD6PJWLpx1E1SjgeJhgPEowFCcVD6eVgXH8cP0JayZsMJqYXTKfGXUONq4Zady1zi+Yy2zObQmvh4R7eYSEhplGYPXs2W7Zsoa2tbdjHpPadPXv2RA1LHKGMRiNut3vAL9iaphEOh7NCTan5YFWVAoFAzvWJRILe3t6sCx6KouBwOLKCTYWFhVP+G10IIYQQQohJZTRD8Sx9GoyWgIv+F3oa9dBTavId0ttzDaVwkBCTr7lvORaAzt36NBh7ETjLYdk1cMrnc+8T7NIruKQqthiM6Q/9VWMRTcYa/SkXfnhkVUbURHaoKeofxnLG43gYYqH8c0bXmkMcHTTFgJJqFWiy6a0CTTY9aGRKTcmAUXpKhY4yQkiploOp5dRksmcHlkz2/C3BJkI80hc2SgWP4iE4/kO593/7CVj7TX3feHjkzxcL6a+3v7G2Zwt25QkxFWU/trqTrdqK+1VHyhVEKtbbb+ZiK4SL7hjbmIUYBk3TCAaD+P1+fD4ffr+fQCAwZCWlfNf0gKwQU+p6Xv+wkt1ux2SalMvfQggxaaqqqtLLDzzwADfffPOEP6emabS2tuL1eolGo+n11dXVbNmyhbfeemvQ4y+55BJcLtdED1MIcQSzmWx5KzmlxNRYur1Ye7Cd9lC7vhxq73sc7KAr3EVcO/xhmlA8NLbWZ8mszWtvvjY+AzrKlNpLqXJWUemspNJZSY2rJh1aqnRWYjLI7/mZ+v9tNdX/DpqU0X/sYx9j8+bNPPTQQ5x//vnDOuahhx5CURSuuOKKCR6dmCoURcFut2O3D7xQ2D9dmCmYq0xtHpqmEQgECAQCtLa2AnDaaadJiEkIIYQQQoiJYLLCik8PXJ+I6yGkdLDpQHbIqacRor78ISZVBV9L7m2DSbeN8+bf56EPQMvbejAgVY3Kqc8tRicnxrcSUhwYG9dDWY3eVsleNHSbKoNRbxk11rZRuWiaXh0rHtKrzgw1V2P6/omoHg5JLSeiw1iOQSLSt6wm9LCamshezpqroMaz1x1JFENyMupzQ3KeqjaTtc4IRhMYzPr/c4MpOc/1eJD9ch1jtGaEj3KFkbK3+UJRfvPAQ8Qx8dnPfX5Md+IfNi3vwN4X+6qYhXuzg0qZgaX+jNb8ISYA/yjeI1JC3blDTP3DRkMxO5Nho2ToKF87Ak8NfH5DMqDkGXvbOyEm0bZt2+js7BxxeygY/JpeZWUlRUVFOJ1ObDabVPcQQhwzTj/9dOrq6ti7dy+33HILf/jDH/jQhz7EmWeeyYoVK7BYLOP2XGvWrOGee+7hpZdewufLXyOro6Nj0PMsXrx43MYkhDh6mQ3mdGBlMJqm4Yv56A53902RbrrCXXSHu+mJ9KSXU9vGFDYS48pldlFiL6HYVqxX8LKVUOGsoMJRkQ4tVTgqMMvfvSMSi2W36zMap3Z15EkJMX3xi1/kscce47HHHuPEE0/ka1/72qD733HHHTz66KMsXbqUW265ZTKGKKa4wb4Rp02bhtPpJBgMpgNKg4WeMtlstrxJxa6uLpqbm3G5XOlpqqcahRBCCCGEOCIYTXqruMHaxYV79ZBCzm09ehgoXwuloRSU5d/mT1YYDnXpU/v29CYbcEHqwRN/yT7OVginfAHOyvP38L5X9fCOzaPva/eAxTU+1WwUJRlwsejnngpUtV/QKT6wOleeduP5t2sDt6fDR4aBYSTFMLnVhMaZhpeYMn4fYg0q7NW/NyK9Ga0Tewe2UUy3V8zYZrTCl+tzn7fxdVj7jdGNKRHJXzHJ7hndOVNC3eCeNnB9QQXMPF0/f7o6Uv9qScX6ss2jV60aDpMFyuaPbcxCTIDUzYCapg1aYWM0AaZUVSVN03IGlFLX4oQQ4lhjNpt5+umnueyyy6ivr2fjxo1s3LgR0N87zzzzTK6++mquuOKKUX+AqWkaN9xwA7/73e+GtX9mK9BciopGGPQWQohBKIqC2+LGbXEPWtkpUzQRxRf14Y168UV9Wcv9576oD2/ESyAeSLdSC8aCJI60G64OM4NiwGVxUWgp1P9/WPX/J4XWwvT/H7fVTYmtJB1WKrYXY813LU+MSf8Q01TPLEzK6FtaWvjtb3/LjTfeyG233cajjz7KNddcw4oVKygvL0dRFFpbW9m4cSMPP/wwW7ZsYcWKFdx3331Z/X37q6mpmYzhiymu/0UNTdOIRCIEAgH8fn96CocHlpAvKCjIe96enh46Ojqy7jKw2+1ZoaaCgoIpn3QUQgghhBDiiDRYGMdRDLfu0wMMvha9qpP3UHLerLer87X0rUtEs48vqMh9XlWFQPvoxhvuBQapErHmv7ICUTolWZ3Jo7/eVLjJVphcl1yuXgrTl49uXEcqgwEwSMWZ8ZZuW5hsQxjx6cuRVJtCX3I5x3aLEy5/IPd533gA/vmt0Y3JYNYDZbmqqIw1dBfqyR1ismndLkcAAOjwSURBVHly728p0LfZPQPnme3ZcgWYQA9eXrdmbGMW4gilaRrhcBifz4fP58Pr9eL3+1FVlaKiorxVNgoKCmhvz/+z02QyUVBQgNPpTM8dDodcTxPiKKM4HLi+8pUB6+OxGOFIJGud0+kcUXW18D//SWyIdmaDMZ90ErZzzx318fkoDse4nzNl4cKFbN26laeffpqnn36al156id27dxMKhXjuued47rnn+MlPfsIzzzxDeXn5iM9///33pwNMJ510Erfccgsnn3wy1dXVWe/RV199NQ8//DDaEDcXyHu6EOJwsxgtepDGXjKq4zVNI6bGskJNoXgovRyMBwnEAkQTUaJqlEgiQiwRI5KIpNdFE33rA9EA+5v2o6JSWl6KYlBIaAlUTe2bq/0eJ+cGxYCCgqIoGDDoc8WAIVnZN7WsoK83G8yYDWYsRgtmY3LZoC9bDBYsRgsmgwmL0YLNaMNhduAwObCb7Ollhzn5OGPZaXamn1McXqqqDijgMtV/9k5KiKm2tjbrl863336bL3/5y4Mes2nTJpYuXZp3u6IoQ/ZNFyIXRVGw2WzYbDZKSvp+WMXj8QHBpsHK7ecqnxoKhQiFQrS1taXXFRQU4Ha705OUuBZCCCGEEGKSmO1QPEuf8tE0CHZBoE2vJONvg+plufcNdY2t1ZmjOP+2YGeuwSUr1vQOft5Tv5g/xHTf2dC5Rw9nWAv0ucUJVle/dQX6v1dqMtmgdB5U5Wn9EAv1tUQT4y8VsItHIR7Wp2hA/3ePhSAW7DfPWHaWwAfuzn3edf8L6344ujHZB/n6tThHd07Q2xfGw7nDRtZRtMDLDCL1DyimlM2Dq/6c3K+oLxwooTkh0uLxOF6vNz35fL6812J9Pl/eikmZNwharVYKCgqyJqvVKtfJhDgGKIqC4hz4+4IhHkfp9yGb4nBgGEE1TOuqVWMKMVlPPhlDjrEd6YxGIx/84Af54Ac/CEBzczPPPvssd999N2+88QZvvPEGN954I3/9619HfO7f/OY3AMyZM4f//Oc/2O05fk9D71YhhBDHAkVRsBj1wI8Hz5jP5/V6ueeeewBYfcXqqdn+XRwxFEVh0aJF+Hw+1q1bh8lkmvKtXCftauNQSWwhDjeTyURhYSGFhUPf6alp2qA9oDOlAlGHDh0C9HKv5eXlzJkzZ0zjFUIIIYQQQowDRdFDH84SKD9u8H0dJfDVveBvzQ49+Vsh2EnM20pbQz12ghRZVJRI78Djc0kFqUZrsNZYqfZdES8M70+YPievzh9ieugDeqsvgwlMyeCT0aKHQIyWjGVzv/VmmHUWrLwh93k3PwLd+zPauil9rd2y2r0ZBraB89RA3dm5z7vn39DVAGh9beY0LeNx5lzV29epcUjEoWgmnPTx3Od98yFoeCm5b0yvdJQ+NhVAiuqtzeKR7HWVi+D6tbnPu/5uWH9X7m1D8QxSTj9XUGi4ov782yxjbOkU9uYem3ua/v/U6u6rRpZVIamoX9WkYQaRbIUw931jG7MQR5lIJEJ3d3c6tBQIBIZ9bDweJxwO5/yQ2+12s3jxYgoKCjCbJSgohBiofwhypJ8lGSsrMc6YQaKxccTPbZwxA2Nl5YiPOxJVVVVx3XXX8YlPfIJVq1bx5ptv8ve//51QKJR+fx5uaPTdd98F4NJLL80bYNI0jTfffHN8Bj+CsQkhhBAim6IolJSUYDab013OpBLTMDzwQJ5y40JMUZqmMWfOnHQJbb/fP+w/rmKx2ICSbpkSicSUf2MRQgghhBDiqJQZeGLhgM0hr5dHUnfSrV6N22mHULceUAp2Qtn83OeNBvQ2VKmqS5o6snEN1nIrMkjwZChmW/5tsZA+V+N6C7LoCBJS+Vp5Abz9Jz0UNBoLLs4fYnrjQdj2t9Gdd9ZZ+UNMB9+ArU+M7rzRQQICJuvozgl9/29yMY+hrUkiqoevTJaB26zJSitGq94C0erOmBcmlwtzbEvO7UW5n7NyEVw9yv9vQogR6ejoYPfu3aM61mazEY1Gc37QbTKZKCrK8z0uhDjm5QqujOaGeNv55xN48EEYSfcOkwnb+eeP+LmOdGazmbPOOos333yTeDxOT09P+v3ZZtN/v4/0a+HXX6ry3mCB1r/97W80NzeP06j7xgb6+KzWMfw+LIQQQogpbVJCTNdcc81kPI0Qk8ZgMFBZWUll8i4NVVUJBALpUJPP5xv0F/zBygK+/fbbRKNRPB4PhYWFeDweKa0thBBCCCHEVGQ0Q0G5Pg3GWgA3J1tgaJpe8SbUkww1JefpxznWeWrzn3uw6jlDMQ1StSceHv15jTlCMCljqeKsDNZ2ZAx/T6mDtBA0jOGySnyQD4+MExVi6vf/1GjpaydozZw79epK6eUCvQVhPnPPg2+2jS18JYSYMIlEgt7eXrxeLzNnzsx5jWm4LSzMZjMulytrslgGeV8XQoghxGIxQG8/aTAYRtRKLsVUXY3j8ssJPvHE8IJMJhOOyy/HVF094uc63F5++WWqqqrydnqIRqOsW7cO0P9Ny8rK0tuqqqrYvn07e/bsGfQ55s6dy9atW3n66af5/ve/T3FxdlvhPXv28PnPf36MryRbVVVV1vkXLhx404gQQgghjg2T1k5OiKOZwWBIX7hJSSQS+Hy+dBlur9eb/oMsX8u61DGaptHS0pIu+Wa1WtOBpsLCQux2u4SahBBCCCGEOBopih4WsbqAGWM/3/Vr9WpMUT9EfHr1n6g/uc7Xty0a0MMvsRDEQxALg2uQ1hqxiQoxjbAKVabBQkxj+ftJjeXfNpYQU2KQEJPZBiabHmYyWfQKSmaHHkJKz+0D11kceuAon4UfgLqz+oJLuaoqjcZwWrgJISZNPB6nt7eX3t5eenp6siqIl5aWUlAw8H0iFR5Q1b73YUVRcLlcuN1u3G43LpdLbrQTQoy7aDQK6JXbxtKhwDxvHs5rryX83HODtpYzzpiB7fzzp2SACeD555/nO9/5DmeccQbvf//7Wbx4MWVlZYRCIXbu3Mm9996bbvN2/fXXYzL1/b566qmn8sILL7Bx40Z++MMfcuGFF+J0OgGw2+1UJ/9Nrr76ar761a9y6NAhTjnlFG699VYWLVpEOBzm3//+Nz/72c+IRCIsXbp03FrKnXrqqenlL33pS3zjG9+gqqoq/TOntrY267UIIYQQ4uglP/GFmCBGoxGPx4PH4wH0MrjhcBiv15u3j3QqwNRfJBKhra2NtrY2QL/rLbNSk8PhkAtIQgghhBBCiIEqT5iY837qWT34lAo8xYJ6a7lENDnFklM0Y55crl6W/7yzztQrV2mqXgFJ0/RlLZGxTs2eUutKZuc/b+k8mLEqGWZS+s3JfqwY9XCSwQRGE5TMzX/eunP0SkWp/Q2Zx1r0ykSpIJLJlr3O4sx/3tNu1qfxZku2cBNCHFVS7YJSwSWfL3+Lz97e3pwhJkVRKCkpQdO0rNDSaCqiCCHE4WKqrqbgU58i0dJC9I03SLS2okUiKFYrxooKLMuWYawcJKg/Raiqyrp169IVl3L5wAc+wA9+8IOsdatXr+aee+6hq6uL2267jdtuuy297ayzzuLFF18E4Oabb+af//wna9euZefOnVx//fVZ57Hb7Tz00EOsWbNm3EJMc+bM4aMf/SiPP/44a9euZe3atVnbGxoaqK2tHZfnEkIIIcSRTUJMQkwSRVGw2+15A0ygX0gajlgsRnt7O+3t7QBYLBaKioqoqanB4XCMy3iFEEIIIYQQIq/CCbpz/ez/NzHnPefr+jTe5l+gT0IIMYlUVaW3t5fu7m56enoGDS3119PTk6600Z+07hFCHC2MlZXY3//+wz2MCfGVr3yFxYsX869//YvNmzdz6NCh9M3PlZWVrFy5kquvvpr353j91dXVbNiwgR/84AesW7eOpqYmwuGBFVbNZjNr1qzhnnvu4aGHHmLbtm1omkZ1dTXnnnsuN998MwsWLGDNmjXj+toeeeQRli9fzp///Gd27NiBz+fLqhAohBBCiIGi0SgGgyFnoZSpalJDTPF4nDVr1vDyyy+zd+9efD4fiURi0GMUReH555+fpBEKcXjV1NRQXFycVfI7Powe3tFolNbWVmpqaiZhlEIIIYQQQgghhBDicNA0jfXr1w/relEmRVEoKCjA5XJN0MiEEEJMhoKCAj784Q/z4Q9/eFTHz549m9/+9rdD7mcymbjpppu46aab8u7z4IMP8uCDD+bcVltbO+IPU81mM1/96lf56le/OqLjhBBCiGPZ9u3b6e7uxmAwcOKJJ9LU1HS4hzRmkxZiWrduHddeey0HDhxIrxvsFxhFUdA0TVpkiWOKoii4XC5cLhfTp09H0zSCwWA60NTb25vuEd6fxWLJW+XJ7/fT1tZGUVERhYWFUgpcCCGEEEIIIYQQ4gimqmrO6zepMFJPT8+gx6euMXk8HgoLCyksLMRoNE7QaIUQQgghhBBCHA6p7ICqqtjt9qMiXzMpIaYtW7ZwwQUXEI1G0TQNm83G3Llz8Xg8EqYQYhCKouB0OnE6nUybNg1N0wiHw+lAU3d3d/qNqaioKO+bUmdnJ42NjTQ2NmI0GvF4PBQXF1NcXIzNZpvMlySEEEIIIYQQQggh+kkkEvT09NDV1UVXVxdut5vjjjsu575FRUUDQkyKouB2u9OhJbfbLaElIcSUo2la+ub2o+EDOCGEEEKIiRaJRLIe5yuIMpVMSojp9ttvJxKJYLVa+clPfsJ1110nwQkhRkFRFOx2O3a7naqqKjRNIxQK0d3djdPpzHtc5oWtRCJBZ2cnnZ2dADgcDoqLiykqKpJgoRBCCCGEEEIIIcQkCQaD6dBSb28vqqqmt6mqmrdKfVFREQ0NDbhcrvT1HKm8LYSYqhRFwWazEQqF0t07CgoKDvOohBBCCCGOfKqqDmg1LiGmYXrllVdQFIVvfOMbrF69ejKeUohjgqIoOBwOHA5H3n0SiQS9vb15tweDQYLBIE1NTRgMhqwqTfna0wkhhBBCCCGEEEKIkUlVW+ru7qazs5NwOJx332g0SjAYzHnTWkFBAaeeeipms3kihyuEEJPGaDSmA0ygfyAn1eSEEEIIIQbXvwoTQCwWOwwjGV+TEmJK/UF+wQUXTMbTCSEyqKrK9OnT6e7uxu/3D7lv6g5A0Ks0TZ8+naqqqskYqhBCCCGEEEIIIcRRJRKJpCti9/T0ZFVbGkpPT0/OEJOiKBJgEkIcNTJbyGWuE0IIIYQQg+t/Y0w8HieRSBym0YyfSQkx1dbWUl9ff1SkvoSYasxmM3V1dYB+F193d3c6qNS/vFx/wWBwyH2EEEIIIYQQQgghxEDd3d28/fbbIzrG7XanK2RLOyUhxLGif+WlkQQ+hRBCCCGOVf1DTLkqM01FkxJi+uAHP0h9fT0vvfQSp5xyymQ8pRAiB4vFQkVFBRUVFWiahs/nSweafD5fzmNKSkpyrtc0jdbWVoqKirBarRM5bCGEEEIIIYQQQogpx+12oyjKoBVFLBYLRUVFFBcXU1RUJBWWhBDHpP7vkxJiEkIIIYQYmoSYxuDmm2/mwQcf5Mc//jFXXHEFtbW1k/G0QohBKIqC2+3G7XZTW1tLLBbLqtIUi8VwOBw4HI6cx/v9fnbs2AGAy+WitLSUsrIy7Hb7ZL4MIYQQQgghhBBCiEmXSCTo6uqio6OD8vLynDeBGY1GioqK6Orqylrfv9pSZgslIYQ4FvUPLUk7OSGEEEKIoUmIaQzKysp45plnuPjiizn55JP57ne/y0c/+lEKCwsn4+mFEMNgNpspLy+nvLwcTdPwer2DtpLr7OxML/t8Pnw+Hw0NDTidTsrKyigtLcXpdE7G0IUQQgghhBBCCCEmXDwep7Ozk/b2drq7u9MfuiuKkreSdWlpKb29vZSUlFBSUiLVloQQIgepxCSEEEIIMXKhUCjrsYSYRmjx4sW89NJLnHzyyXz2s59l9erVlJaW5q3ykqIoCnv27JmkUQohQP++GypkmBliyhQIBAgEAuzbtw+Hw5Gu0OR0OuXOQiGEEEIIIYQQQkwpsViMzs5OOjo66OrqylkdpLOzE03Tcl73qKiooKKiAoPBMBnDFUKIKal/aElV1bzvq0IIIYQQQte/ElP/x1PVpIWY/u///o/rr78en8+HpmlomkZbW9uQx8kvqUIceVRVxW63EwqFSCQSefcLBoMcOHCAAwcOYLPZ0oEml8sl39tCCCGEEEIIIYQ4IsViMTo6Oujo6KC7u3vItkbxeJze3l48Hs+AbRJeEkKIoeV6n5UQkxBCCCFEfolEglgslrVOKjGNwPr167nyyivTYYeZM2eyePFiPB6P/CEvxBRkMBhYuHAhqqrS09OTvrDX/40yUzgcpqmpiaamJkpLSzn++OMnccRCCCGEEEIIIYQQ+cXjcTo6Omhra6O7u3tYxyiKgsfjoaSkZMhq80IIIfLLF2ISQgghhBC55aq6JCGmEfjud79LIpGgsLCQP/zhD1x00UWT8bRCiAlmMBgoLi6muLiYuXPn0tvbS0dHB+3t7USj0bzHDdWqTgghhBBCCCGEEGIydXd3s2PHjiH3UxSFoqIiysrKKCkpwWw2T8LohBDi6JaqupQZXFJVFaPReBhHJYQQQghx5AoGg1mPzWbzgBa9U9WkhJg2bdqEoih8+9vflgCTEEep1N2HHo+H2bNn4/P5aG9vp6OjY0AStLy8POc5NE2jubmZkpISrFbrZAxbCCGEEEIIIYQQguLiYoxGY7qSfCZFUSguLk4Hl0ymSbmkKoQQxxSDwZD1Hny0fAgnhBBCCDER+oeYjqbP1iflL+7UP+Dpp58+GU8nhDjMFEXB7Xbjdrupq6vD7/enKzRZrVYsFkvO43w+H7t27WLXrl0UFhZSUVFBWdn/z96dR0d21nf+/9yqUlVJpX3ft9bere6WuttgG9udsCV2DLFJ2AmOgZglg5kBzMyZBTKHAWI4MVvyiz04GEMnA4wxQ2ISCKRtgjHGLfWqfd/3fS2pqu7vD0cVV5ek3iTdkvR+ncM5que5uvXp9qVVeu73fp80FgcBAAAAAMB1M01Ts7OzGh0dVUpKipKTk8OOsdvtSk1N1cjIiKR/7z6dlpam5ORk1iYAYJsZhhHymiImAACAjeXl5SklJUVLS0taXFzcU5+dduS376KiIjU0NIRVgwHY+wzDUFxcnOLi4lRYWLjuE41rRkdHg1/PzMxoZmZGbW1tSk1NVUZGhpKTk8N+mQUAAAAAAFjP0tKShoeHNTIyIq/XK0laXV1dt4hJkjIyMuTz+ZSenq6UlBS2MQKAHWSz2UJe76UbcQAAAFvNZrMpNjZWsbGxkqTZ2VmLE22dHSliuvfee3Xp0iX95Cc/oRsTsI8ZhrHhk4umaWpsbGzD8bGxMUVFRSkjI0MZGRnBf5ABAAAAAADW+Hw+jY6OamRkZN1F3ImJCfl8vnXXJ5KSkpSUlLQTMQEAl1mvE5NpmjzUCgAAsM/YrnzIjfv4xz+u0tJSffnLX9aZM2d24i0B7DJ+v19JSUmbPuW4urqq/v5+1dXV6cyZM+rr69PKysoOpgQAAAAAAJHGNE1NTk6qsbFRL7zwgtra2jZ8CjUQCGhiYmKHEwIArsRmsykqKkoul0vR0dHyeDxWRwIAAIAFdqQTU1xcnH7+85/rD//wD3X77bfrP/7H/6i3ve1tKisrk9vt3okIACKcw+FQRUWF/H6/JicnNTo6qomJCZmmue7xCwsL6uzsVGdnp5KTk5Wbm8vTkgAAAAAA7CMLCwsaGRnRyMjIVT3kFBMTo/T0dCUkJOxAOgDAtTAMg/tFAAAA2JlOTHa7XQUFBfrNb36j5eVlfeELX1BNTY08Ho/sdvum/9to6ykAe5PdbldaWpoOHjyom2++WSUlJYqLi9v0eyYnJ7W4uLhDCQEAAAAAgJWWlpZUX19/VV2ao6KilJOTo9raWh0/flwFBQXcJAcA7Am9vb164IEHdODAAbndbhmGIcMw9MMf/lD33XefDMNQYWHhtr3/s88+G3zPZ599dsvO293dHTzvE088sWXnBQAAu8OOVAhd3kllo84qAPBKawuNOTk5Wlxc1PDwsEZHR+X1ekOOMwxD6enpFqUEAAAAAAA7yeVyaWlpacN5wzCUnJyszMxMJScny2bbkec4AQDYMb29vTp27JjGx8etjgIAAHbY5OSkDMNQbGysoqKirI6z5XakiOnTn/70TrwNgD0sJiZGxcXFKioq0vT0tEZGRjQ2NqZAIKC0tLQN/4Gen5/X4OCgsrKyFBsbK8Mwdjg5AAAAAAC4HqZprvt7vM1mU0ZGhgYGBkLGY2NjlZmZqfT09D25kAsAwJrPfvazGh8fl8Ph0P/6X/9Lt99+u2JjYyVJBQUF+uEPf2htQAAAsG26u7s1NzcnSXI6nSopKZHL5bI41dahiAnArmIYhpKSkpSUlKTS0lKNjY3J4/FsePzg4KCGhoY0NDQkj8ejrKwsZWRksFUlAAAAAAARyDRNzczMaHh4WJJUUVGx7nGZmZkaGBiQ0+lUenq6MjMzN10fAABgL/nZz34mSfr93/99PfTQQ2HzTzzxxLZvxXby5El2XgEAYIeZpqmFhYXg65WVlT133zui/zRnz57Vk08+qUceecTqKAAikN1uV2Zm5obzfr9fo6OjwdcLCwtqb29XZ2enMjIylJ2dHXw6BQAAAAAAWMfn82l4eFhDQ0NaXFyU9PKDTMXFxXI6nWHHx8bG6siRI0pISKDrMgDsMaZpKhAIKBAIyG63X/O2oKPnz+v8o49q7Nw5rczNyRkXp7SjR3XkgQeUfuTINqXeWWvdCMvKyixOAgAAdtLi4qICgUDIWGxs7KZbru82EVfENDQ0pO985zv69re/rYaGBkmiiAnAdRkbG5Pf7w8bDwQCwe5M8fHxys7OVlpa2jX/MgwAAAAAAG7M/Py8BgYGNDo6GrYQa5qmRkZGlJeXt+73JiYm7kBCAMBOWlpaks/nC752u91XvW479NJLOv2xj2nwV78Kmxt84QWd///+P+XceqtOPvKIsk6c2LLMVlhZWZEktk8FAGCfeWUXJunl7eSioqL2VBFTRNyxX1pa0qlTp/TGN75R+fn5+s//+T+roaGBNpQAbkhSUpIKCws33QN0dnZWzc3NeuGFF9TZ2bmn/oEHAAAAACASBQIBjYyM6OzZs6qrq9Pw8HBYAdOakZGRHU4HAIgk6z2kup6OZ57Rd2+/fd0CplcaeP55fff229XxzDNbEW9HPfHEEzIMI6QD4Z/92Z8FxwzD0H333SdJuu+++2QYhgoLC9c919rxn/nMZyRJL730kt7xjncoNzdXLpdLOTk5es973qOmpqYN8zz77LPB8zz77LPrHtPa2qr/8B/+gw4dOqS4uDg5nU5lZ2fr6NGjuv/++/Xd735XXq/3in/2f/7nf9bdd9+tzMxMuVwuFRUV6UMf+pD6+/uv+L0AAOwl8/PzIa/34q5DlnZiOn36tJ588kn94Ac/CP5lrxUuZWVl6Z577tFb3vIWKyMC2MVcLpcKCgqUn5+vqakpDQ0NaXx8fN1jfT6f+vr61NfXp/j4eCUmJmp6enpnAwMAAAAAsIc5nU5lZGSooaEhpMvGeqKjo5WVlaWMjIwdSgcAiASXd13aqMj1lYZeekl//wd/IN/y8lW9h295WX//B3+gt/3iF7u+I9NW+Ku/+is9+OCDIT+bBwcH9Z3vfEc/+MEP9I//+I+6/fbbr/m83//+9/Xud7872DVqzdouCefPn9c3v/lNXbx4UYcOHdrwPP/lv/wXfeELXwgZ6+7u1l//9V/rqaee0nPPPafKysprzgcAwG50eScmj8djUZLts+NFTM3NzXryySd16tSpYIX0WuFSbm6u3vKWt+gP/uAPdMstt7CfPYAtYRiGkpOTlZycLK/XG/wl6fJfntbMzs6qrKxM9fX1O5wUAAAAAIC9Z2pqSj09PaqpqZFhGBsWMBmGodTUVGVnZyshIYG1QQDYhy4vYvL7/TJNc9OfCac/9rGrLmBa41te1rP/8T/qHb/85XXltMLv//7v6/jx45Kk6upqSdKHPvQhffjDHw4ek5SUdE3n/MlPfqLf/OY3qq6u1oMPPqjq6motLS3p6aef1le+8hUtLi7qPe95j9ra2uR0Oq/6vCMjI/rjP/5jraysKD09XX/6p3+qV7/61UpNTdXS0pLa29v13HPP6Yc//OGm5/nf//t/61e/+pXuuOMOPfDAAyorK9P09LSefPJJPfnkkxobG9P999+vF1544Zr+3AAA7EamaWpubi5kjE5M12liYkJ/93d/pyeffFJ1dXWS/r1waa3biWEY+tKXvqS3vvWtOxEJwD7lcrlUWFio/Px8TUxMaHBwcN2OSxMTE1d8KhQAAAAAAFzZ6OioZmZmNrwB7XQ6lZWVpaysrE23hAcA7E5mIKCliYmwcb/fr+V/G180DNntdgUCAS0tLoYeuLAgu92+7rnHL1684hZyGxl4/nn1nj6t1E26AF2v6JQUGZcVZN2oxMREJSYmhoylp6dv2sXoSn7961/rzjvv1NNPPx1SpHTbbbcpJSVF/+2//Tf19vbqmWee0T333HPV533mmWeCnSJ+/vOfh2W85ZZb9Ed/9Ef6+te/vul5fvWrX+kDH/iAHn300ZDPEa997WvldDr1jW98Q7/+9a919uxZ1dTUXHU+AAB2I6/Xq9XV1ZCxuLg4i9Jsn20rYlpdXdXf//3f68knn9Q//dM/aXV1NVi45HQ6deedd+rd73637rrrLkVHR29XDABYl81mU1pamtLS0rS4uKjBwUENDw8H91gfHh7e8HuXlpbkdrt5IhQAAAAAgKuQm5u77u/ZCQkJysnJUUpKSljnDQDA3rE0MaG/Sk+3Osa6vvfbv70t5/3w6Khi0tK25dxbye1265vf/Oa6XZY++tGP6n/+z/+plZUV/eu//us1FTGt/dxPSkratMjqSvcHs7Ky9LWvfW3dtfhPfOIT+sY3viFJ+td//VeKmAAAe97lXZgcDofcbrdFabbPlhcx/frXv9aTTz6p733ve5qampKkYKvPW2+9Ve9+97v11re+9ZpbWgLAdomJiVFJSYmKioo0Ojqq8fHxsP1E1/j9ftXX18vlciknJ0fp6ekbPgUEAAAAAMB+MTc3p+joaDkc4cuNHo9HcXFxmpubk9/vV0ZGhgoLC+XxeCxICgAA1rz+9a9X+gYFZnFxcSotLVVDQ4M6Ozuv6bxZWVmSXt5S9v/9v/+nN7/5zdeV7w/+4A827NJYXl6u2NhYzc/PX3M+AAB2o9nZ2ZDXcXFxe7LpxpYXMd1yyy0yDCPYdam8vFzvfve79a53vUuFhYVb/XYAsGXsdruysrI2XUQdGRmRz+eTz+dTa2ururq6lJWVpZycnGvaExwAAAAAgN3ONE1NTEyov79fMzMzOnDggHJzc9c9NjMzUxcuXNDY2JgeeOABCpgAAIgAFRUVm84nJydLCu/8cCVvetOblJiYqOnpad1zzz06efKk7r77bt1+++06evToVT8YfKV8SUlJmp+fv+Z8AADsRpf/vIuPj7coyfbatu3k4uLi9NWvflXvfe97t+stAGBHmaapgYGBkLHV1VX19vaqr69PGRkZysvLU0xMjEUJAQAAAADYfn6/X8PDw+rv79fy8nJwfGBgQDk5Oes+CRobG6uRkZGdjAkAAK7gSmvZa9u9+v3+azpvSkqKfvSjH+kd73iHBgYGdPr0aZ0+fVrSyzdcX/va1+r+++/X7/3e71mSDwCA3cY0zbAipri4OIvSbK9tKWIyTVPz8/O6//779ZWvfEXvfve79Y53vCPYPhIAdqOVlZVgl7nLmaap4eFhDQ8PKyUlRXl5eUpISNjhhAAAAAAAbJ/V1VUNDAxoYGBAPp8vbH55eVkTExNKTU21IB0AIFJFp6Tow6OjYeN+v1/j4+OSpNTU1GB3HtM0tbCwEHqO6Oh1u/c899BDanjiievOduiP/1i3//mfX/f3byQ6JWXLz7nb3HbbbWpvb9dTTz2lH//4x/rFL36h/v5+zc7O6umnn9bTTz+tN77xjfrBD37Ag8EAAFzBwsKCAoFAyBhFTFfp2Wef1RNPPKGnnnpKc3NzOnfunM6fP69PfepTOnnypN7znvfo3nvvVWxs7Fa/NQBsK5fLpRMnTmhyclIDAwOamppa97iJiQlNTEwoPj5eeXl5SklJ2ZP7kQIAAAAA9ofl5WX19/draGgobNH0lZxOJ50QAABhDJtNMWlpYeN+v1/uf3toNCYtLbRI6bIbdS6XS06nM+wcxx588IaKmGoffHDdbNgabrdb73rXu/Sud71LktTV1aVnnnlGX/va19Ta2qqf/OQn+q//9b/qkUcesTgpAACR7fIuTG63e93PRnuBbatPePvtt+tv/uZvNDIyolOnTumNb3yjbDab/H6//uVf/kV//Md/rMzMTL3jHe/Qj3/8YxY2AOwqhmEoJSVFhw8f1vHjx5WZmblhgdLs7KwaGhr00ksvXXGhFwAAAACASDM/P6+mpia9+OKLGhgY2PD32tjYWFVUVOhVr3qVMjIydjglAGAvWtsmbM1G95LSjx5V9i23XNd75Nx6q9KPHLmu78X1KSoq0p/+6Z/qpZdeUm5uriTpe9/7nsWpAACIfDabLaRR0F7twiRt03Zy0suVX+94xzv0jne8Q8PDw/rOd76j73znO7pw4YIWFxf1ve99T9/73veUQktNALuUx+NReXm5ioqKNDAwoMHBwXXb6S8tLam1tVUul0vJyckWJAUAAAAA4OqYpqnp6Wn19fVt2IF4TXJycnA7dToQAwC2kt1uD1lr9fv9Mk1z3Z83v/XlL+u7t98u3/LyVZ/fER2tk3T/sUx8fLxOnDih/v7+4JaCAABgYxkZGcrIyJDP59PMzIwcjm0r9bHclndiWk9mZqY+8YlP6Ny5czp79qw+9rGPKT09XaZpanx8PPih8z/9p/+kBx98UP/6r/+6E7EAYEs4nU4VFRXpVa96lQ4cOCCXyxV2jMfjUVJSkgXpAAAAAAC4Np2dnRsWMBmGoYyMDB07dkzV1dVKTEykgAkAsOVCtpbTy0W25r9tPXe5rBMndPf//b9yuN1XdW5HdLTu/v73lXXixA3nxPp+8pOfaGhoaMP5mZkZ/eY3v5H0cncmAABwdRwOh1JSUpSQkGB1lG2zI0VMr3TkyBH9xV/8hfr7+/UP//APeutb3yqXyyXTNDU4OKivf/3rOnnypLKysvThD39YP//5z3c6IgBcF4fDodzcXN10002qqKiQx+MJzuXl5W24qLvRL98AAAAAAOw0wzCUl5cXNm6z2ZSTkxP8nfeVbewBANhqNpstuJ5qGIYcDsem66gH7rpLb/vFL5Rz662bnjfn1lv1tuee04G77trSvAj1d3/3dyooKNBdd92lr3zlK/r5z3+us2fP6he/+IX+6q/+SjfffLMGBgYkSR/84ActTgsAACKJZT2m7Ha77rzzTt15552anZ3Vd7/7XX3729/W888/L9M0NTIyokcffVSPPfbYutszAUCkstlsysjIUHp6uqampjQyMqK0tLQNj7906ZLcbrfy8vLkvsqnhQAAAAAAuBEbbckjSWlpaerq6tLy8rKioqKUk5Oj7OxsRUVF7XBKAMB+ZRiG3G63DMMIKWjaTNaJE3rHL3+p0fPndeGxxzR67pxW5ubkjItT+tGjOvwnf6L0I0d2ID0kaXV1VT/+8Y/14x//eMNjPvjBD+qjH/3oDqYCAACRLiI2youPj9cHPvABfeADH1B3d7e+9a1v6Tvf+Y46OjqsjgYA180wDCUnJys5OXnDY2ZmZjQ5OSlJGhoaUkZGhvLz8xUdHb1TMQEAAAAA+0ggENDIyIh6e3tVWVmp+Pj4sGMMw1BRUZF8Pp8yMjLCtvQBAGAnOBzXdwsr/cgRve4v/3KL0+BaPPLII3r961+vf/mXf9GFCxc0NDSksbEx2e125eXl6eabb9b73/9+veY1r7E6KgAAiDARUcT0SoWFhfr0pz+tT3/603r++ef17W9/2+pIALBtent7g1+bpqnh4WENDw8rPT1d+fn5IVvSAQAAAABwvQKBgIaGhtTX1yev1ytJ6unpUXV19brHp6en72Q8AABwDTbbWk+SnnjiCT3xxBPX/f1rnn322Q3nTp48ueF5kpKS9K53vUvvete7rup9XqmwsPCq83V3d1/z+QEAQGSLuCKmV7r11lt16xX2LwaA3crr9WpqamrdudHRUY2Ojio9PV0FBQWKiYnZ4XQAAAAAgL3A7/cHi5dWVlZC5iYnJzU3N6e4uDiL0gEAAAAAgI1MTEyoq6tLCQkJwf+5XC6rY22riC5iAoC9zOVy6aabblJ/f7+GhoYUCATCjlkrZsrIyFBBQQHbzAEAAAAArspa56Xe3t6w4qVXGhsbo4gJAAAAAIAINDU1pYWFBS0sLGhwcFAJCQk6evSo1bG2FUVMAGAht9utkpIS5efnq6+vT4ODg+sWM42MjGhkZESZmZnKz8+nmAkAAAAAsK5AIKDh4WH19vYGt41bT3x8vAoKCpSUlLSD6QAAuDGmacowDKtjAAAA7IjLd/XZD7/DU8QEABHA6XTqwIEDys/PV39/vwYGBuT3+8OOGx4e1sjIiDIyMlRUVCSn02lBWgAAAABApDFNM1i8tLy8vOFxiYmJKigoUGJi4s6FAwDgOpmmqUAgIL/fL5/PJ7/fL4/HI5vNZnU0AACAbeX1erW4uBgyRhETAGBHRUVFqaioSLm5uerr69PAwEBYZybTNDU2Nqbi4mKLUgIAAAAAIsno6Ki6u7u1tLS04TGJiYkqLCxUQkLCDiYDAODGLS0tyTTN4Gufz8fDnQAAYM+7vAuTw+HYF9vBU8QEABEoKipKxcXFys3NDXZmemUxU05OjqKioixMCAAAAACIFLOzsxsWMCUkJKiwsJDOSwCAXckwDNntdvl8vuDYeh3sAQAA9prLi5gSExP3xba6FDEBQARzOp3BYqa+vj4NDg7KMAzl5uZu+D3sCw8AAAAA+0t+fr6GhoZCHn6Jj48PFi/xOyIAYDdzOBxhRUysgQIAgL3MNM2wIqb9sJWcRBETAOwKTqdTBw4cUF5enubm5jbswrS6uqqzZ88qOztb2dnZ7A0PAAAAAHvIRjdsnU6ncnJy1NfXp7i4OBUWFiopKYmbuwCAPcFut4e8Nk1TgUAgbBwAAGCvWFhY0OrqasgYRUwAgIjjdDqVkpKy4XxfX5+WlpbU0dGh/v5+FRYWKiMjg4VrAAAAANjFFhYW1N3drbi4OOXn5697TF5enuLj45WSksLvgACAPcVms8lms4V0HPT5fBQxAQCAPWtycjLktdvtltvttijNzqKICQD2CK/Xq4GBgZDXLS0t6uvrU2FhoVJTU1nIBgAAAIBdZHl5WT09PRoeHpYkTU9PKysra93uvFFRUUpNTd3piAAA7Ai73R5WxORyuSxMBAAAsH0uL2LaT92WKWICgD1iYmIi5Bf5NYuLi2psbFRcXJyKior2TatBAAAAANitVldX1dvbq4GBAZmmGRz3+Xzq6+tTcXGxhekAANh5DocjZEuVQCCgQCAgm81mYSpgf3jl51EAwPZbXV3VzMxMyNhmO/XsNRQxAcAekZ2dLY/Ho66urrAfbJI0NzenCxcuKDk5WcXFxfJ4PBakBAAAAABsxO/3q7+/X319ffL7/eseMzo6qsLCQm7aAgAigt1ul8/nk8/nk9/v37Yt3tY7r9/v5+chsM38fn/wcylbOALAzpiamgp5bbPZlJiYaE0YC1DEBAB7SEJCgo4cOaKpqSl1dXVpfn4+7JjJyUlNTk4qKytLhYWFcjqdFiQFAAAAAKwxTVOjo6Pq6uqS1+td9xibzabc3Fzl5eVxwxYAEDFiYmKCP7ump6e3rUuAYRhyOBzy+XzBMZ/Pt+4WqwC2zvT0dPDrmJgY64IAwD4yMTER8joxMXFfFZJSxAQAe4xhGEpOTlZSUpLGxsbU3d2tpaWlsOOGhoY0OjqqvLw85ebm7qsffgAAAAAQKaanp9XR0bHuQyjSy7/jZWVlqaCggIdQAAARJzExMdgtYHR0VH6/X/Hx8XK5XDIMY0vfa70iJtM0t/x9gP3ONE15vV7Nzs6G3EhPSkqyMBUA7A+maWpycjJkbD9tJSdRxLStFhcX9fWvf13f//731dHRIa/Xq7y8PN1111366Ec/qoKCghs6fyAQ0C9/+Uv90z/9k371q1+publZk5OTcrvdys/P1+23364PfvCDOnz48Kbn+cxnPqM/+7M/u6r3PH36tE6ePHlDuQHsDMMwlJ6ertTUVA0PD6u7uztk33jp5Vaw3d3dmpub06FDhyxKCgAAAAD7z+Liojo7O8OesHyl9PR0FRYWKjo6egeTAQBw9dxutxISEjQzMyPp5c4BExMTMgzjig9NmqaplZUVSdLc3NxVFSO9sohJenl7K4qY8ErXc10hlN/vl2maIWMJCQlyuVwWJQKA/WNxcVGBQCBkLDk52aI01qCIaZu0t7frzjvvVFtbW8h4S0uLWlpa9I1vfEOnTp3S7/3e7133exQWFqqvry9sfHV1VQ0NDWpoaNCjjz6qT3ziE/rCF77ABzVgn7LZbMrOzlZ6err6+vrU398f9sMvLy/PonQAAAAAsP/Mz8+rrq5uw/nExEQVFxcrLi5uB1MBAHB9srKy5HQ6NTY2FhwzTTOs4OhygUAg2IkwLi7uqrZLXVlZCVnbtNvtbCmHENdzXWFzaWlp+64LCABYxePx6JZbbtH09LQmJibk9XrldrutjrWjKGLaBnNzc7rrrruCBUwf+MAH9Pa3v13R0dE6ffq0Pv/5z2t2dlZve9vb9Pzzz+vo0aPX9T6Dg4OSpJKSEr3lLW/RrbfequzsbC0tLen06dN65JFHNDU1pYcfflh2u12f+9znrnjOixcvbjpfVFR0XVkBWM/hcKioqEjZ2dnq7u7W8PCwJCk1NVUJCQkWpwMAAACA/cPj8YR0rVgTExOj4uJiJScn8zAaAGDXMAxDqampio+P1/z8vBYWFsKKjdbj8/mCPwsTEhLkcFz5ltXq6mqwy4708gOcdCzEK13PdYVQNptNTqdTHo9HsbGxbGkMADvMbrcrJSVl3xaQ8pN7G3zxi19Ua2urJOnhhx/WJz/5yeDczTffrJMnT+qOO+7Q4uKiPvaxj+nZZ5+9rve56aab9OlPf1pveMMbwha2XvOa1+id73ynbr75Zo2NjemLX/yi3v/+96u4uHjTc7KdFLD3uVwulZeXKycnR11dXZsWJy4uLioqKoqnmQAAAABgCxmGoQMHDqi+vl6SFBUVpcLCQmVmZtItAACwazmdTiUnJ1/1liezs7P60Y9+JOnleyfx8fFX/J7LuxnGxMQoPz+fba4QdD3XFQAAiBysimyx1dVVffWrX5UkVVZW6uMf/3jYMbfccove9773SZKee+45vfTSS9f1Xr/61a/0xje+ccMn8w4cOKD/8T/+h6SXK89/+MMfXtf7ANibYmNjVV1drZiYmHXnTdNUc3OzfvOb32hgYCBsD2wAAAAAwOaWlpY2nIuLi1NmZqby8vJ00003KTs7mwImAACuwOPxKCUlRQUFBTp+/LhOnDhBARMAAMAewsrIFjt9+nSwTeV73/veDRef7rvvvuDXTz/99Lbl+a3f+q3g1x0dHdv2PgD2npGREc3Nzcnn86m9vV1nzpzR1NSU1bEAAAAAIOKtrKyotbVVv/nNbzb9PaqsrEzFxcVscwIAwFUyDEOHDh1SYWGhPB6P1XEAAACwxShi2mK//OUvg1/fcccdGx53/PjxYPeT559/ftvyeL3e4Nd2u33b3gfA3uLz+dTV1RUytri4qAsXLujSpUubPk0MAAAAAPtVIBBQf3+/fvOb32hoaEiS1N7evmFn2426awMAAAAAAOxHFDFtscbGxuDXFRUVGx7ncDhUUlIiSWpqatq2PM8991zw68rKyise/4Y3vEHp6elyOp1KT0/XyZMn9YUvfIHuK8A+EwgElJCQsO7cxMSEXnrpJXV2dsrn8+1wMgAAAACITJOTk6qrq1NHR4f8fn9wfHFxUYODgxYmAwAAAAAAkayvr08NDQ0aGRnZ9/df6VW9xfr7+yW9vC9zYmLipsfm5eXpwoULGhsbk9fr3fJ9mxcXF/XlL39ZkuRyufTmN7/5it/zz//8z8Gvx8bG9Nxzz+m5557Tn//5n+uJJ564qnOsZ+3vZSNrTydK0sLCgmZnZ6/rfYCtMD8/v+7X+01ubq4SExPV398f1nnJNE319fVpaGhI2dnZSk5O5gnibcQ1iUjC9YhIwvWISMM1iUjC9bhzvF6v+vv7N1zLsNlsWlpa2vdrHVyTiCQLCwtWRwAAAACAoJGRES0sLGh8fFyGYaikpETZ2dlWx7IERUxbbG5uTpIUGxt7xWNfuV/z/Pz8lhcxfepTn1Jvb68k6SMf+cimF3l1dbV+//d/XzfddJOys7O1urqqlpYWnTp1Sj/96U81PT2tt7zlLfr7v/97/e7v/u41Z8nLy7vqY3/wgx9s2AEG2Gnf/va3rY4QEdLT05WXl6eoqKiQcZ/Pp97eXjU0NKirq0uLi4sWJdw/uCYRSbgeEUm4HhFpuCYRSbget4fNZlNubq4yMzNls63f7HxsbEy9vb1aXV3d4XSRjWsSVpuZmbE6AoAtZpqmTNPc8GcyAABApFpcXAx50MI0TcXExFiYyFoUMW2x5eVlSZLT6bzisa8sWrq8y8mNOnXqlL7+9a9Lenkbuc9+9rMbHvuxj31Mn/nMZ8LGX/WqV+mP/uiP9Oijj+qDH/yg/H6/3v/+96ujo0Nut3tL8wKIbKOjo5qYmFBOTs66C/RxcXGqrq7WyMiI+vr6QrZOAAAAAIC9Jjk5WYWFhRuu/8zPz6u7u5uOQwAAbLOlpSWNjo5qdHRUKSkpKi4utjoSAADANRkdHQ15HRUVta+bvuzbIqat2Pbom9/8pu67776QsbXinpWVlSt+v9frDX4dHR19w3nWPPvss3rf+94n6eVFtaeeemrT819p27sHHnhAL730kh5//HENDg7qqaee0rve9a5rytTX17fp/NDQkG666SZJ0r333quysrJrOj+wlebn54NPhb7nPe+5qs5q+8ny8rIGBgbCtkIwDEOZmZkqKChQZWUl28ttIa5JRBKuR0QSrkdEGq5JRBKux+2xvLys/v7+YCfuyzkcjuCW27fddtsOp4tsXJOIJK2trfr85z9vdQwAN2hgYEDt7e3B136/X0VFRaxLAgCAXcM0TY2MjISMpaen7+vPM/u2iGm7xMXFSdJVPWn3ypZgW7Vwc+bMGb3pTW+S1+tVbGysfvzjH6uysvKGz/vAAw/o8ccflyQ999xz11zElJube9XHejwexcfHX9P5ge0SGxvL9XiZ+Ph4paena3JyUu3t7WGd5PLz8/d1dfB245pEJOF6RCThekSk4ZpEJOF63BqTk5Nqbm6WaZphc4ZhKCcnRwUFBXI4WG67Eq5JWM3j8VgdAcAWuHwN0uv1amZm5ooPbgMAAESKubm54G5fa9LT0y1KExn27apKU1PTDZ8jKysrbCw3N1cvvviiFhYWND09vemH5bXuRGlpaSFby12vhoYG/c7v/I7m5ubkcrn0wx/+UK961atu+LySVFVVFfx6YGBgS84JYHdLTk7W8ePH1dfXp97eXgUCAcXGxq77byMAAAAA7HYJCQmKiooK676dlJSkkpISxcTEWJQMAID9KTY2Vh6PJ+SB8dHRUYqYAADArnH5VnLR0dHBxjn71b4tYqqoqNiW81ZVVempp56SJDU3N+vVr371usf5fD51dHRI0pZ0Suro6NDrX/96TUxMyOFw6Lvf/a5e+9rX3vB51+zndmUANmaz2VRQUKCMjAx1dHQoLy9vw38v/H6/bDYb/54AAAAA2JXsdrtKSkrU2NgoSXK5XDpw4IBSU1P5PQcAAIukp6erq6sr+HpsbEwlJSWy2WwWpgIAALiyQCAQVsS037eSkyQ+xW2x17zmNcGvn3vuuQ2PO3PmTPDpgFtvvfWG3rO/v1+ve93rNDQ0JJvNpm9961t685vffEPnvNzaAp0kZWdnb+m5Aex+brdbBw8e3HQ7gLa2Np07d+6qttsEAAAAAKust13cmtTUVCUnJysvL08nTpxQWlravl9cBADASpdvt+Lz+TQ5OWlRGgAAgKs3NTWl1dXVkLGMjAyL0kQOipi22MmTJ4P7MH/rW9/acOHriSeeCH59zz33XPf7jY6O6nWve526u7slSX/913+td77zndd9vo08+uijwa/vuOOOLT8/gL1tZmZGIyMjmp2dVX19vTo7O+X3+62OBQAAAAAhJiYmVFdXp6WlpXXnDcPQoUOHVFxcLLvdvsPpAADA5dxud/CezJqRkRGL0gAAAFy9y7swxcXFKTo62qI0kYMipi3mdDr10Y9+VJLU1NSkL33pS2HHvPDCC3r88cclvVwQdOLEiXXPZRiGDMNQYWHhuvPT09N64xvfqJaWFknSI488og984APXlPfixYtqb2/f9JjHHntM3/jGNyRJmZmZN1R0BWD/MU0z5N8Z0zTV19enM2fOaGJiwsJkAAAAAPCylZUVNTY26tKlS1pYWFBHR8eGx9J5CQCAyHJ5x4KJiYmwrgYAAACRxOfzaXx8PGTs8g6T+5XD6gB70Sc/+Ul997vfVWtrqx566CG1t7fr7W9/u6Kjo3X69Gl97nOfk8/nU3R0tL785S9f13t4vV7dddddOnfunCTpXe96l173utfp0qVLG36Px+NRUVFRyFhdXZ3e//7367d+67f0u7/7u6qurlZKSop8Pp+am5t16tQp/fSnP5Uk2e12PfbYY/J4PNeVGcD+5PV65fP5wsaXl5d16dIlpaWl6cCBA3K5XBakAwAAALCfmaapoaEhdXV1hfzeMjExoYmJCaWkpFiYDgAAXI3U1FS1tbUFd8YwTVMjIyPKzc21OBkAAMD6RkdHFQgEgq8Nw6CI6d9QxLQN4uLi9Mwzz+jOO+9UW1ubHnvsMT322GMhx8THx+vUqVM6evTodb3H0NCQfvWrXwVfnzp1SqdOndr0e+644w49++yzYeN+v18/+9nP9LOf/WzD701JSdHjjz+uu++++7ryAti/3G63jh8/rt7eXvX19YVtszk2NqbJyUkVFRUpOzubp5oBAAAA7IiFhQW1trZqdnZ23fnR0VGKmAAA2AWioqKUlpYWsiXL8PCwcnJyWGsEAAARaXh4OOR1SkqKnE6nRWkiC0VM26SkpERnz57VX/7lX+r73/++2tvbtbKyory8PN1555168MEHVVBQYHVM3XnnnXr88cf1wgsv6OzZsxoZGdHExIRM01RycrKOHDmi3/md39F9992n+Ph4q+MC2KXsdruKioqUnp6utrY2zczMhMz7/X61t7drZGREZWVlio2NtSgpAAAAgL0uEAiop6dn3YcsJMnhcKi4uFiZmZkWpAMAANcjMzMzpIhpYWFBc3Nz3NcAAAARZ35+XnNzcyFjrEH8O4qYtpHH49FDDz2khx566Lq+f72FtDWFhYWbzl+t9PR03X///br//vtv+FwAcCUej0dHjhzR8PCwOjs7w7aZm5ubU11dnXJzc1VYWCi73W5RUgAAAAB70dTUlNra2rS0tLTufHp6ug4cOMDTjwAA7DKJiYlyu91aXl4Ojg0PD1PEBAAAIs7q6qqio6ODaxNOp1PJyckWp4ocFDEBAHaUYRjKyspSSkqKOjs7NTIyEnZMf3+/xsbGVFZWxg9tAAAAADdsdXVVHR0d6/7+Ib28DXZpaSm/fwAAsEsZhqHMzEx1d3cHx0ZHR3XgwAEelAQAABElKSlJJ06c0OzsrIaHh+V2u9kC9xUoYgIAWMLpdKqiokIZGRnrPgnt9Xo1OjrKTQQAAAAA1800TY2MjKijoyOsE6z08g3P3NxcFRQUcIMTAIBd7vIiJr/fr7GxMbZnAQAAEccwDCUkJCghIcHqKBHHZnUAAMD+lpSUpOPHj6ugoCCkytjpdOrAgQMWJgMAAACw2y0vL6u1tXXdAqb4+HjV1taquLiYAiYAAPYAl8sV8kBkUlKSXC6XhYkAAABwrejEBACwnM1mU2FhodLT09Xa2qqZmRmVlJQoKirK6mgAAAAAdrHo6Gjl5+erp6cnOGa321VcXKysrCzatQMAsMfk5OQoLi5OmZmZcrvdVscBAADANaKICQAQMWJiYnTkyBFNTU1tuo3c4uKioqOjueEAAAAA4Iry8/M1NjamxcVFpaWl6cCBA3RlAABgj0pOTt50XREAAACRjSImAEBEMQxj04UGr9er+vp6xcbGqry8XNHR0TuYDgAAAEAkMk1TpmnKZrOFzdlsNpWXl2tlZUWpqakWpAMAAAAAAPuZ3+/XwsKC4uLiaNJwBRQxAQB2DdM01dbWJr/fr5mZGZ05c0aFhYXKzc3lBz4AAACwT83Pz6ulpUUpKSkqLCxc95j4+PidDQUAAAAAAPBvRkZG1NbWptjYWOXk5CgtLU12u93qWBEp/PE0AAAi1Pj4uCYmJoKvA4GAOjs7VV9fr/n5eQuTAQAAANhpgUBAXV1dwd8Hent7tbCwYHUsAAAAAACAINM0NTg4KOnfH8RqaWmxOFXkoogJALBrOBwOuVyusPH5+XnV19erp6dHgUDAgmQAAAAAdtLs7Kzq6urU29sr0zQlvbwo2NLSEnwNAAAgvVz4PDY2ptnZWaujAACAfWhmZibsoavMzEyL0kQ+tpMDAOwaSUlJOnHihLq6ujQwMBAyZ5qmuru7NT4+roqKCnk8HotSAgAAANgufr9f3d3d6u/v33B+ZWVl3YcfAADA/rKysqLBwUENDQ1pZWVFycnJqq6utjoWAADYZ9a6MK2Jjo5WUlKSRWkiH0VMAIBdxW63q6SkRGlpaWptbdXi4mLI/Pz8vOrq6lRQUKD8/HwZhmFRUgAAAABbaXZ2Vi0tLWG/A0iSYRjKy8tTQUGBbDYajwMAAGl8fFw9PT3B15OTk1pcXFRMTIyFqQAAwH6yvLyssbGxkLGsrCzuX26CVR0AwK6UkJCgY8eOKT8/P2xurSvT2bNnw9ozAgAAANhdAoGAOjs7dfbs2XULmGJjY1VbW6uioiIKmAAAQFBGRobsdnvI2EbdHAEAALbD5Z897Ha7srKyLEqzO7CyAwDYtWw2m4qKilRTU7PuE1Rzc3Oqq6tTb2+vTNO0ICEAAACAG7H2mb6vry9szjAMFRUVqba2VrGxsRakAwAAkcxutys7OztkbGRkRKurqxYlAgAA+4nP59Pw8HDIWGZmphwONkzbDEVMAIBdLz4+XseOHVNeXl7YnGma6urqoiMTAAAAsIsEAgF1d3ervr5+3e5LcXFxwc6stGAHAAAbycnJCfmsEAgENDg4aGEiAACwXwwNDcnv94eM5eTkWJRm96CICQCwJ9hsNhUXF6/blSk/P58nswEAAIBdpKOjQz09PWHjhmGosLBQNTU18ng8FiQDAAC7icvlUnp6esjYwMCAAoGARYkAAMB+EAgENDAwEDKWlpam6OhoixLtHhQxAQD2lPj4eNXW1io3N1eS5PF4VFBQYHEqAAAAANciLy9Pdrs9ZCw2Nla1tbUqKCig+xIAALhqa+uEa1ZXVzUyMmJRGgAAsB+MjY3J6/WGjF3+mQTrY7M9AMCeY7fbdeDAAaWmpsput8tmW79m1zRNSeIGCAAAABBh3G63Dhw4oNbWVhmGofz8fOXn52/42R4AAGAjsbGxSkxM1PT0dHCsv79fmZmZrAsCAIAtZ5qm+vv7Q8bi4+MVHx9vUaLdhSImAMCelZCQsOl8f3+/pqamVF5eLpfLtUOpAAAAAFyNzMxMLSwsKCMjQ3FxcVbHAQAAu1heXl5IEdPi4qImJiaUmppqXSgAALAnTU1NaX5+PmQsLy/PojS7D4+vAQD2pYWFBXV1dWlqakpnzpzR6Oio1ZEAAACAfWV5eVktLS3y+/3rzhuGoZKSEgqYAADADUtKSpLH4wkZ6+3tDXZqBwAA2AqmaaqnpydkLDo6WikpKRYl2n0oYgIA7DuBQEBNTU3BRQqfz6empiY1NTVpdXXV4nQAAADA3maapkZGRnTmzBkNDw+ro6PD6kgAAGCPMwwjrAPC3NycpqamLEoEAAD2otXVVXm93pCxvLw8trC9BhQxAQD2neXl5XWLlUZHR3XmzBlNTk5akAoAAADY+1ZXV9XU1KTm5uZgB6ahoSFNTExYnAwAAOx16enpcrvdIWO9vb0WpQEAAHuR0+nUTTfdpLKyMrndbrlcLmVkZFgda1ehiAkAsO/ExMTo+PHjSktLC5tbWVnRxYsX1dbWtuG2FgAAAACu3eTkpM6cOaOxsbGwuZ6eHrZzAQAA28owDOXn54eMzczMaHp62ppAAABgT7LZbMrKytJNN92kw4cPy2ajLOda8LcFANiXoqKiVFVVpcrKSjkcjrD5wcFB1dXVaXZ21oJ0AAAAwN7h9/vV3t6uixcvamVlJWw+IyNDhw8fprU6AADYdhkZGXK5XMHXsbGxFqYBAAB7mWEYiomJsTrGrhN+1xYAgH0kPT1dCQkJamlp0dTUVMjc0tKSzp49q4KCAuXn51MpDQAAAFyjubk5NTc3a3FxMWzO4XCorKxs3Q6pAAAA28FmsykvL08jIyMqKChQcnIyhdQAAAARhCImAMC+53K5VF1drcHBQXV2dioQCITM9/T0aHJyUpWVlYqOjrYoJQAAALB7mKap3t7eDbeJS0pKUnl5eUgnBAAAgJ2QnZ2t7OxsipcAAAAiEEVMAADo5ZaOOTk5SkpKUnNzs+bm5kLm5+bmVFdXp8rKSqWkpFiUEgAAAIh8S0tLam5uXndrZpvNpuLiYm4cAgAAy/AZBAAAbKXFxUUFAgG2qd0iFDEBAPAKMTExOnr0aPCp8VcyTZO9awEAAIANmKapkZERtbe3y+/3h83HxsaqsrKSz9QAAAAAAGDP6Ojo0OTkpNLS0lRQUCCPx2N1pF2NIiYAAC5js9lUWFio5ORkNTc3a2lpSZJUWlrKdnIAAADAJqamptYtYMrPz1dBQYFsNpsFqQAAAAAAALbezMyMJicnJUljY2MaGxvTwYMHlZqaanGy3YuVIwAANhAfH69jx44pMzNTqampysjIsDoSAAAAELEMw1BpaancbndwzO126+jRoyoqKqKACQAARCzTNDU1NaW2tjaZpml1HAAAsEt0d3eHvI6KilJSUpI1YfYIOjEBALAJu92u8vJyBQIBGYax7jF+v18rKyt0aQIAAMC+53A4VFFRoXPnzikzM1MlJSWy2+1WxwIAANjQzMyMurq6NDMzI0lKSUlRcnKyxakAAECkm56e1vT0dMhYfn4+6yA3iCImAACuwmZPjXd1dWl4eFglJSXKyMjYsNgJAAAA2CtM09zwc29CQoJOnDihmJiYHU4FAABwbUzTVHNzs5aXl4NjXV1dSkpKYo0PAABsyDRNdXV1hYw5nU5lZ2dblGjvoI83AAA3YGJiQgMDA/L7/WppaVFTU5NWV1etjgUAAABsm9HRUb300kvyer0bHkMBEwBgr1hcXNTDDz+sEydOKDk5WR6PRxUVFfr4xz+unp6eGz5/d3e3DMO4qv/dd999N/4HQgjDMFRQUBAyNj8/r/HxcYsSAQCA3WBiYkKzs7MhYwUFBZs2RcDV4W8QAIDrtLKyopaWlpCxsbEx1dXVhbWPBAAAAHa7VxbuLy0tqbm5WaZpWh0LAIBt097erqNHj+pTn/qUzpw5o6mpKS0uLqqlpUV/8Rd/ocOHD+sf/uEfrI6JG5SRkaHo6OiQsc7OTgUCAYsSAQCASLZeFya3263MzEyLEu0tbCcHAMB1stvtSklJ0fDwcMi41+vV+fPnlZ+fr8LCQlpPAwAAYNebnZ1Vc3OzlpaWgmPT09Pq7+9XXl6ehckAANgec3Nzuuuuu9TW1iZJ+sAHPqC3v/3tio6O1unTp/X5z39es7Ozetvb3qbnn39eR48eveH3/OxnP6s3v/nNG84nJSXd8HsgnGEYKioqUmNjY3BseXlZg4ODys3NtTAZAACIRENDQ1pcXAwZKywspAvTFqGICQCA62S321VeXq7k5GS1trbK5/OFzPf29mp6elqVlZVyu90WpQQAAACun2ma6uvrU3d397pdlxYXF2WaJoX7AIA954tf/KJaW1slSQ8//LA++clPBuduvvlmnTx5UnfccYcWFxf1sY99TM8+++wNv2dOTo4OHTp0w+fBtUtNTVV8fHzItjA9PT3KzMyUw8GtNAAA8DK/36/u7u6QsdjYWKWnp1sTaA+iFAwAgBuUlpam48ePKzExMWxudnZWdXV1Gh8f3/lgAAAAwA3wer26cOGCurq6wgqY7Ha7KioqVF5eTgETAGDPWV1d1Ve/+lVJUmVlpT7+8Y+HHXPLLbfofe97nyTpueee00svvbSjGbG1DMNQcXFxyJjP51Nvb69FiQAAQCTq6+vT6upqyFhxcTFrI1uIIiYAALaAy+XS4cOHVVRUFPZBxefzqaGhQW1tbQoEAhYlBAAAAK7e5OSk6urqND09HTYXFxenY8eOKSMjY+eDAQCwA06fPq2ZmRlJ0nvf+94Ntwa57777gl8//fTTOxEN2yghIUGpqakhY/39/VpeXrYoEQAAiCRer1d9fX0hY8nJyWz5u8UoYgIAYIsYhqH8/HwdPXp03e3jBgcHdfbs2bB9cgEAAIBIEQgE1NnZqYsXL4Y9WSgp+Hk3OjragnQAAOyMX/7yl8Gv77jjjg2PO378uGJiYiRJzz///Lbnwva7/AFF0zTV1dVlYSIAABApenp6wpoVXN7JETeOIiYAALZYfHy8jh07prS0tLC5+fl51dXVaWpqyoJkAAAAwMaWl5d1/vz5sKcKpZc7jx45ckRFRUUbdqMAAGCvaGxsDH5dUVGx4XEOh0MlJSWSpKampht+36997WsqKSmR2+1WQkKCDh48qA9+8IOqr6+/4XPj6sTExCgrKytkbHR0VLOzsxYlAgAAkWB+fl5DQ0MhY5mZmfJ4PBYl2rscVgcAAGAvcjgcqqysVGJiojo6OkIqsx0Oh2JjYy1MBwAAAIQaHx9XS0uLfD5f2FxKSorKy8sVFRVlQTIAAHZef3+/JMnj8SgxMXHTY/Py8nThwgWNjY3J6/XK5XJd9/u+sljJ6/WqsbFRjY2NevTRR/XAAw/oK1/5ynWdf+3Ps5FX3pCbm5vbsYKd+fn5db+2WnJysoaHh0PW81paWlRWVhbSpQmRKVKvK+xeXFPYalxTu1MgEFBmZqZGRkZkmqYMw1BqamrEFDpbdV3Nzc1t+TkpYgIAYJsYhqHs7GwlJCSosbExuI1cZWUlN4AAAAAQUWZnZ8MKmAzDUHFxsXJycrhhBwDYV9ZuxlzNQ2ivfPp+fn7+uoqMEhMTdc899+jkyZMqLS2V2+3W0NCQfvrTn+rxxx/X/Py8Hn30Uc3NzenUqVPXfP68vLyrPvbb3/62EhISrvk9btS3v/3tHX/PzWRlZamgoCD4enFxUf/3//5fjY+PW5gK1yrSrivsflxT2GpcU7uP0+lUQUGBFhcX9cILL1gdZ107eV3NzMxs+TkpYgIAYJt5PB7V1taqo6NDTqfzik/wAQAAADutsLBQMzMzwScI3W63qqqqFBcXZ3EyAAB23vLysqSXb1JdySuLlpaWlq75vbKzszUwMKCYmJiQ8ZqaGt155536yEc+ote97nXq7e3V3/7t3+ptb3ub3vSmN13z++DaDA8PKz09XdHR0cGxtLQ0ipgAANjnVlZW1NbWZnWMPY0iJgAAdoDdbldZWZlM09zwmNXVVUmiSxMAAAB2nM1mU2Vlperq6pSUlKSysjI5HCwbAQAi21Z0CvzmN7+p++67L2TM7XZLevkm1ZV4vd7g168seLlaTqdz02Kp0tJSfec739Htt98uSfra1752zUVMfX19m84PDQ3ppptukiS95z3vUU5OzjWd/3rNz88HOwW85z3vuarOVztpZmZGnZ2dstlsysrK0tGjR3Xy5EmrY+EKIv26wu7DNYWtxjWF7WDVdTUwMKDPf/7zW3pOVqMAANhBGy2umaap5uZmLSwsqLKy0pK23QAAANj7TNPc8DOp2+3WsWPH5HK52D4OALCvrXUinJ+fv+KxCwsLwa+362bRbbfdpqqqKjU2NuqXv/ylAoGAbDbbVX9/bm7uVR8bFxen+Pj464l5Q2JjYy15383Ex8fLMAylp6dfVVcuRJ5IvK6wu3FNYatxTWE77OR1tdbReytRxAQAQAQYGBjQ5OSkJOncuXMqLi5Wbm4uN48AAACwZebn59XS0qLy8vINb7KudZ4AAGA3aGpquuFzZGVlhY3l5ubqxRdf1MLCgqanp5WYmLjh9691OUpLSwvZWm6rrRUxLS8va2JiQmlpadv2Xvh311IABgAA9palpaXr6rSJG0MREwAAFpubm1NnZ2fIWGdnp6anp1VRUWFRKgAAAOwVpmlqcHBQHR0dCgQCampqUm1trex2u9XRAAC4Idu1blJVVaWnnnpKktTc3KxXv/rV6x7n8/nU0dEhSaqsrNyWLGt40A0AAGDnTExM6NKlS8rJyVFhYaEcDkprdsrV9xsFAADbwul0rrt93OTkpOrq6kLakgMAAADXwmazqaenR21tbQoEApKkxcVFtbW1WZwMAIDI9ZrXvCb49XPPPbfhcWfOnAmu29x6663bmqmxsVGS5HK5lJKSsq3vBQAAsJ/5/f7gusnAwIBeeuklTUxMWJxq/6CICQAAi7lcLh0+fFgFBQVhc16vV21tbcrMzLQgGQAAAHaz6OhoVVdXa2pqKmxuYWFBPp/PglQAAES+kydPBh84+9a3viXTNNc97oknngh+fc8992xbnueff14NDQ2SXi6wstm4tWO12dlZ9ff3Wx0DAABsg+7ubnm93uDrlZUVra6uWphof+GTLgAAEcAwDBUWFurw4cOKiooKmTNNU4WFhSorK+NGEwAAAK7KxMSEDh06pOjo6LC5nJwc1dTU0AodAIANOJ1OffSjH5UkNTU16Utf+lLYMS+88IIef/xxSdIdd9yhEydOrHsuwzCC6z7r+eEPf7hhkZQktbe3653vfGfw9Yc//OGr/WNgG/h8PrW1tens2bPq6OjQ3Nyc1ZEAAMAWmp+fDytUTkxMVEZGhkWJ9h9WqwAAiCBJSUk6duyYmpqaNDMzEzKXnJyslpYWHTp0SHFxcRYlBAAAQCTz+/1qb2/X8PCw7HZ7yJzdbldFRYVSU1MtSgcAwO7xyU9+Ut/97nfV2tqqhx56SO3t7Xr729+u6OhonT59Wp/73Ofk8/kUHR2tL3/5y9f9Pvfcc49KSkp077336qabblJubq5cLpeGhob0k5/8RI8//rjm5+clSW9961t17733btGfENcqEAiorq5Oy8vLwbHW1lbV1tbKMAwLkwEAgK1gmmZwG7k1hmGotLSUn/U7iCImAAAijMvl0pEjR9Td3a3e3t6QuZWVFZ09e1YHDhxQdnY2H5oAAAAQtLi4qMbGRi0sLITNxcbGqqqqat3OTAAAIFxcXJyeeeYZ3XnnnWpra9Njjz2mxx57LOSY+Ph4nTp1SkePHr2h92pvb9fDDz+86TEf+tCH9Mgjj9zQ++DG2Gw2ZWZmqru7Ozg2Pz+vgYEB5ebmWhcMAABsiaGhIc3OzoaM5eXlKSYmxqJE+xNFTAAARCDDMFRUVKSEhAQ1NjbK7/cH50zTVHt7u2ZmZlRWVsY2IAAAANDo6KhaW1tDPjeuSU1NVWVlpWw2mwXJAADYvUpKSnT27Fn95V/+pb7//e+rvb1dKysrysvL05133qkHH3xQBQUFN/QeP/rRj/TCCy/oxRdfVE9Pj8bHx7WwsKD4+HgVFxfrtttu0/33369Dhw5t0Z8KNyIvL0+jo6NaXFwMjnV3dys1NVVut9vCZAAA4EZ4vV51dXWFjEVHR9/wZz1cO+56AgAQwZKTk1VRUaEXXnhB8fHxIXNLS0vciAIAANjnAoGAOjo6NDg4GDbn9/vV2dmpmpoaPjcCAHCdPB6PHnroIT300EPX9f2maW46f/fdd+vuu+++rnNj59lsNpWWlur8+fPBMb/fr7a2Nh06dIiu6QAA7EJr28j5fL6Q8dLSUtZTLMDfOAAAEc7pdKqpqSnkxpTdbldVVRUfngAAAPa58fHxdQuY3G63Ll68qImJCQtSAQAA7F2JiYnKzMwMGZucnNTIyIhFiQAAwI0YGxsLWz9JT09XUlKSRYn2N+58AgCwC5imqd7eXhUXF8vhcKi8vFzR0dFWxwIAAIDF0tLSlJ6eHjKWkZGhsrIyLS8vW5QKAABgbysuLpbT6QwZ6+jokNfrtSgRAAC4HisrK2pvbw8Zi4qKUklJiUWJQBETAAC7SEJCgl71qlcpLS1tw2Ou1KYcAAAAe4dhGCorK1NMTIxsNpvKyspUUVEhu91udTQAAIA9KyoqSqWlpSFjPp9PbW1trM0BALCLtLe3a3V1NWSstLRUUVFRFiUCRUwAAOwyDodjw7lAIKALFy5obGxsBxMBAADASmtbDdfU1CgrK8vqOAAAAPtCampqWEfMiYkJjY6OWpQIAABci7GxsbD7aampqZs2EsD2o4gJAIA9pKOjQ9PT02psbFR7e7sCgYDVkQAAALAFpqamNDU1teG8x+NRbGzsDiYCAABASUlJWKeG9vZ2raysWJQIAABcDdM01dvbGzLmcDjCOi1i51HEBADAHjEyMqLBwcHg64GBAZ0/f15er9fCVAAAALgRpmmqp6dHFy5cUFNTE5/tAAAAIshG28q1trayrRwAABHMMAwdOXJEmZmZwbGSkhI5nU4LU0GiiAkAgD1jdnZ23bG6urpNn9oHAABAZPL5fGpoaFB3d7ckaXV1VY2NjXTbBAAAiCBpaWlh285MTExoZGTEokQAAOBqOBwOlZeXq7q6WllZWWHbxMIaFDEBALBHlJaWqqysTIZhhIyvrq7qwoUL6u3t5QkwAACAXWJ+fl51dXWamJgIGZ+dneWGGAAAQIS5fFs5l8slt9ttYSIAAHC1kpOT172/BmtQxAQAwB6SlZWlmpqadRdJurq61NDQoNXVVQuSAQAA4GoNDw/r7NmzWl5eDpsrKioKaXUOAAAA6zmdTpWVlUmSMjIydPz4cSUmJlobCgAAYBeiiAkAgD0mLi5OtbW1Sk5ODpubmJhQfX295ubmLEgGAACAzQQCAbW2tqqlpSVsy7ioqCgdPnxY+fn5PBkIAAAQgVJTU1VTU6OKigo5HA6r4wAAAOxKFDEBALAHRUVF6dChQyosLAybW15e1tmzZzU0NLTzwQAAALCu5eVlnTt3bt3PaHFxcTp27JiSkpIsSAYAAICrFR8fb3UEAACwjoWFBV24cGHdrteILBQxAQCwRxmGoYKCAh0+fFhRUVEhc6ZpBp/y9/v9FiUEAACAJE1OTqqurm7dbpnZ2dk6evSoXC6XBckAAAAAAAB2t0AgoKamJk1NTenMmTMaHh6WaZpWx8IGKGICAGCPS0pK0rFjx9Z9Emx4eFhdXV0WpAIAAIBpmurp6dHFixfl8/lC5mw2myoqKlRaWiqbjeUbAACA3cw0TY2Pj1sdAwCAfamjo0MLCwuSJL/fr5aWFo2MjFicChthFQwAgH3A5XLpyJEjysnJCRsvKCiwKBUAAMD+ZZqmGhoa1N3dHTYXHR2tmpoaZWRk7HwwAAAAbCmv16vz58+roaFBo6OjVscBAGBfGRsb0+DgYMiYx+NRenq6RYlwJRQxAQCwT9hsNpWUlKiyslI2m02GYejgwYNhW80BAABg+xmGIY/HEzaekpKi2tpaxcbGWpAKAAAAW2lt2+CZmRlJUmtrqxYXFy1OBQDA/rC8vKzW1taQMZvNFrxPhsjksDoAAADYWenp6fJ4PFpcXFRcXJzVcQAAAPatwsJCzc7Oanp6WpJUVFSkvLw8GYZhbTAAAABsidXVVa2urgZf+/1+NTU1qaamhpunAABso0AgoMbGRvl8vpDxkpKSdR8qQ+TgExIAAPuQx+NRWlrahvOrq6uan5/fwUQAAAD7j2EYqqysVGxsrA4fPqz8/HwKmAAAAPaQjIwMZWZmhozNz8+rs7PTokQAAOwP3d3dmpubCxlLS0sL+7mMyEMREwAACGGappqbm3X27FmNjIxYHQcAAGDXCwQCG845nU7V1tYqKSlpBxMBAABgp5SUlCgmJiZkbGBgQOPj4xYlAgBgb5ucnFRfX1/ImNvtVllZGQ+P7QIUMQEAgBA9PT2anJxUIBBQc3Oz2traNr3xBgAAgI1NTk7qxRdf3LTLJQtoAAAAe5fdbldlZWXY9nEtLS1aWlqyKBUAAHvT8vKympqaQsYMw1BVVZUcDodFqXAtKGICAABB09PT6unpCRkbHBzU+fPn5fV6LUoFAACw+5imqd7eXl28eFErKytqaGjQ6uqq1bEAAABggdjYWB04cCBkzOfzqaGhQX6/36JUAADsLYFAQI2NjfL5fCHjxcXFiouLsygVrhVFTAAAICg+Pl7Z2dlh47Ozs6qvr9fMzIwFqQAAAHYXn8+nxsZGdXV1BceWl5fV3Nws0zQtTAYAAACrZGVlKS0tLWRsYWFBbW1tfEYEAGALtLe3a25uLmQsJSVFOTk5FiXC9aCIaRstLi7q4Ycf1okTJ5ScnCyPx6OKigp9/OMfD+tycT26u7tlGMZV/e++++67qnP+3d/9nd7whjcoMzNTbrdbBQUFeve7360XXnjhhvMCACKfzWZTaWmpysvLw1pcr6ys6Pz58xoYGGBhBQAAYAOLi4s6e/asxsfHw+ZsNhvb9AIAAOxThmGorKxMMTExIeMjIyMaGhqyKBUAAHvD8PBw2M/T6OhoVVRUyDAMi1LhelDEtE3a29t19OhRfepTn9KZM2c0NTWlxcVFtbS06C/+4i90+PBh/cM//IPVMYOWlpZ011136Z3vfKf++Z//WSMjI/J6vert7dWpU6f0mte8Rn/2Z39mdUwAwA7JzMzU0aNH5Xa7Q8ZN01R7e7uam5tpdQ0AAHCZiYkJ1dfXa3FxMWyuqKhIVVVVstvtFiQDAABAJHA4HDp48GDYZ8L29nbNzs5alAoAgN1veno65LXNZlNVVZUcDoc1gXDd+C+2Debm5nTXXXepra1NkvSBD3xAb3/72xUdHa3Tp0/r85//vGZnZ/W2t71Nzz//vI4ePXrD7/nZz35Wb37zmzecT0pK2vT777//fv34xz+WJP3Wb/2WHnzwQWVnZ+vixYv63Oc+p46ODn3mM59RVlaW/uRP/uSG8wIAIl9cXJxqa2vV1NSkqampkLnR0VEtLCzo4MGDio6OtighAABAZDBNUz09Pet2XXY4HKqsrFRycrIFyQAAABBpYmJiVF5ersbGxuCYaZpqbGxUbW2tnE6nhekAANidysvLFRsbq87OTpmmqbKyMsXGxlodC9eBIqZt8MUvflGtra2SpIcfflif/OQng3M333yzTp48qTvuuEOLi4v62Mc+pmefffaG3zMnJ0eHDh26ru/9l3/5F/2f//N/JEl33323nn766eBTACdOnNCb3vQmHTt2TL29vfrUpz6lP/zDP7xiURQAYG+IiopSdXW1uru71dvbGzK3sLCg+vp6VVRUKCUlxaKEAAAA1vL5fGpubtbExETYnMfjoegbAAAAYdLS0pSbm6v+/v7gmNfrVVdXl8rLyy1MBgDA7mQYhnJzcxUXF6epqSllZGRYHQnXie3kttjq6qq++tWvSpIqKyv18Y9/POyYW265Re973/skSc8995xeeumlHc14uS996UuSXn469K/+6q/C2pimpqbqz//8zyW93IbtG9/4xo5nBABYxzAMFRUVrdvq2ufz6dKlS+rp6ZFpmhYlBAAAsMZaUfd6BUxpaWmqqamhgAkAAADrKi4uVkJCQvB1UlKSiouLLUwEAMDul5CQoMLCQqtj4AZQxLTFTp8+rZmZGUnSe9/7Xtls6/8V33fffcGvn3766Z2Itq65uTn9/Oc/lyS97nWvU25u7rrH3XvvvYqPj5dkbV4AgHVSU1NVW1urmJiYsLmJiQmKmAAAwL4yNjams2fPamlpKWyuuLhYlZWVYQXgAAAAwBrDMFRVVSWn06mcnBxVV1crKirK6lgAAACWoohpi/3yl78Mfn3HHXdseNzx48eDN4Gff/75bc+1kZdeekkrKyuSNs/rdDr16le/Ovg9q6urO5IPABBZYmJiVFNTo9TU1OBYVFSUqqqqNizcBQAA2GtmZ2fV2Ngov98fMh4VFaXDhw8rLy9PhmFYlA4AAAC7hdPp1PHjx1VSUsLnRwAArtLs7Oy6D5Vhb+Bu4xZrbGwMfl1RUbHhcQ6HQyUlJZKkpqamG37fr33tayopKZHb7VZCQoIOHjyoD37wg6qvr9+SvK+c9/l8amtru+HMAIDdyeFwqKqqSsXFxTIMQ5WVlXK73VbHAgAA2DFxcXHKzMwMGYuNjVVtba2SkpIsSgUAAIDdiO5LAABcveXlZV26dEn19fWanp62Og62gcPqAHtNf3+/JMnj8SgxMXHTY/Py8nThwgWNjY3J6/XK5XJd9/u+sljJ6/WqsbFRjY2NevTRR/XAAw/oK1/5yrrnX8sracOt5F6Zd01fX5+qqqquOt8r32c9Q0NDwa8XFhY0Ozt71ecGttr8/Py6XwNWidRrMiEhQVVVVbLb7fy7vY9E6vWI/YnrEZGGa3J/ycjI0OzsrBYXF5WcnKy8vDytrKwEux1bjesRkYZrEpFkYWHB6ggAcFVM01QgEGCbYgAAJPn9fjU0NAR3jbpw4YJKSkqUnZ1tcTJsJYqYttjc3Jykl5/AvBKPxxP8en5+/rqKmBITE3XPPffo5MmTKi0tldvt1tDQkH7605/q8ccf1/z8vB599FHNzc3p1KlTG+a9msyX570WryyAupIf/OAHSkhIuKbzA9vl29/+ttURgBC76Zo0DEP5+fkaGBiQz+ezOg62wW66HrH3cT0i0nBN7g9Op1OJiYn69a9/bXWUTXE9ItJwTcJqMzMzVkcAgCtaXV1VY2OjbDabDh06xHZzAIB9zTRNNTc3h9QpmKap8fFxZWVl8XNyD6GIaYstLy9Lenkh80peWbR0PXs2Zmdna2BgQDExMSHjNTU1uvPOO/WRj3xEr3vd69Tb26u//du/1dve9ja96U1vWjfv1WS+0bwAgP2luLhYaWlpSk5OVktLixYXF62OBAAAcM3cbnfI786vtLKyotHR0R1OBAAAgL1ucXFRly5dCt6LaW9vV0lJCTdoAQD7VkdHh8bHx0PGoqOjVVVVxc/HPWbfFjFtxYX8zW9+U/fdd1/ImNvtlqSrah/v9XqDX0dHR1/z+zudzk0Lj0pLS/Wd73xHt99+uyTpa1/7WlgR01pe6cqZbyRvX1/fpvNDQ0O66aabJEn33nuvysrKrun8wFaan58PPhX6nve856o6qwHbaTdek2NjY8GtRF0ul44cOaL8/HwlJydbnAw3ajdej9i7uB4Rabgm9xafz6euri4tLS2pvLz8hraAtwLXIyIN1yQiSWtrqz7/+c9bHQMA1mWaZkgBkyQNDg7K7XZf064XAADsFQMDAxoYGAgZczgcOnTokByOfVvysmfxX3SLxcXFSbq67dZeuff6di3c3HbbbaqqqlJjY6N++ctfKhAIyGazBefX8kpXznwjeXNzc6/6WI/Ho/j4+Gs6P7BdYmNjuR4RUXbDNen3+9XY2BgyZpqmenp65PP5dODAAari94jdcD1i/+B6RKThmtzd5ubm1NraGnyYp6enRzU1NbLb7RYnuz5cj4g0XJOwmsfjsToCAGzIMAyVl5fr/PnzMk0zON7Z2SmXy6X09HQL0wEAsLPGx8fV3t4eMmYYhqqqqsJ2rMLesG+LmJqamm74HFlZWWFjubm5evHFF7WwsKDp6WklJiZu+P1r3YnS0tK29YnOtSKm5eVlTUxMKC0tLSTvmv7+fh0/fvyKeSVR7Q8A2JDdbldtba0aGho0OzsbMjcwMKD5+XlVVVVd1darAAAAO21kZEStra0KBALBsYWFBbW1tamiosLCZAAAANgvEhISVFFREXYvq7m5WS6XSwkJCRYlAwBg58zOzq5b11FeXq6kpCQLEmEn7Nsipu1aeKyqqtJTTz0l6eUPk69+9avXPc7n86mjo0OSVFlZuS1Z1mzW7aKqqir4dXNz86bnWZt3OBwqLS3dmnAAgD3J6XTqyJEj6ujo0ODgYMjczMyM6uvrdfDgwZCOgAAAAFYKBALq7OwMa08uvfzZZr0HmQAAAIDtkp6eruXlZXV1dQXH1raaq6mpofsEAGBPW1pa0qVLl0IeMpOkwsJCZWRkWJQKO8F25UNwLV7zmtcEv37uuec2PO7MmTPB7dluvfXWbc20tqWPy+VSSkpKyNyJEyeCnTA2y7uysqJf//rXwe+JioraprQAgL3CZrOptLRUZWVlYQW1Xq9XZ8+e1fDwsEXpAAAA/t3KyoouXLiwbgFTfHy8jh07xtPuAAAA2HF5eXnKzs4OGfP5fLp48aJWVlYsSgUAwPZaWVnRxYsXtbq6GjKemZmp/Px8i1Jhp1DEtMVOnjwZXNj81re+FbJf8Ss98cQTwa/vueeebcvz/PPPq6GhQdLLBVY2W+h/8ri4OL32ta+VJP3sZz9Tf3//uuf5wQ9+ENwSaDvzAgD2nqysLB09ejRs+zjTNNXS0qL29vawSnoAAICdMjs7q7q6Os3MzITNZWdn68iRI2yDCwAAAEsYhqGSkhIlJyeHjC8vL+vChQvy+XwWJQMAYHv4fD5dunRJS0tLIeOJiYkqLS3ddBcq7A0UMW0xp9Opj370o5KkpqYmfelLXwo75oUXXtDjjz8uSbrjjjt04sSJdc9lGIYMw1BhYeG68z/84Q83LJKSpPb2dr3zne8Mvv7whz+87nGf+MQnJL38D8JHPvIR+f3+kPnx8XF96lOfkvTyPw7vf//7N3xPAADWs1kHg4GBAV24cIGnxwAAwI4bGhrSuXPnwj6HGIah8vJylZaWhj0MBAAAAOwkwzBUVVWl2NjYkPGFhQVdunQp7J4OAAC7VSAQUENDg+bm5kLGPR6PDh48yBrNPsF/5W3wyU9+UmVlZZKkhx56SA888IBOnz6tX//61/r85z+vN7zhDfL5fIqOjtaXv/zl636fe+65R2VlZfrUpz6lp556Si+++KLOnTunf/zHf9THPvYx1dTUqLe3V5L01re+Vffee++65/nt3/5tvf3tb5ck/ehHP9LrX/96/ehHP9KZM2f0zW9+U69+9auD5/nzP/9zJSUlXXdmAMD+5XQ6dfjwYeXk5ITNzczMqK6uLrjVKgAAwHYKBAJqbW1Va2tr2MNBLpdLNTU1yszMtCgdAAAAEMput6u6ulputztkfGZmRk1NTZs+8A4AwG5hGIaio6NDxlwul6qrq+VwOCxKhZ3Gf+ltEBcXp2eeeUZ33nmn2tra9Nhjj+mxxx4LOSY+Pl6nTp3S0aNHb+i92tvb9fDDD296zIc+9CE98sgjmx7zN3/zN5qdndWPf/xjnT59WqdPnw6Zt9ls+u///b/rT/7kT24oLwBgf7PZbCopKVFsbGzYTUO73S6Xy2VhOgAAsB94vV41NjYGt0x/pYSEBFVVVbF9HAAAACLO2gOCZ8+e1erqanB8YmJCLS0tKi8vZ4sdAMCuZhiGSktLFRUVpd7eXkVFRenw4cPcO9pnKGLaJiUlJTp79qz+8i//Ut///vfV3t6ulZUV5eXl6c4779SDDz6ogoKCG3qPH/3oR3rhhRf04osvqqenR+Pj41pYWFB8fLyKi4t122236f7779ehQ4eueK7o6Gg988wz+tu//Vs98cQTOn/+vKanp5WRkaHbbrtNf/qnf6qbb775hvICALAmMzNTHo9HDQ0N8nq9stlsOnjwIJX0AABg242Ojq5bwJSbm6vi4mJu/AAAACBiRUdH6/Dhwzp37lzINnJzc3Py+XyKioqyMB0AADfOMAwVFRXJ6XQqPj5eMTExVkfCDuNO4TbyeDx66KGH9NBDD13X91+p/efdd9+tu++++7rOvZF3vvOdeuc737ml5wQAYD1xcXGqra1VU1OTsrOz5fF4rI4EAAD2gdzcXM3MzGhiYkLSy50iy8rKlJGRYXEyAAAA4MpiY2N16NAhXbx4UYFAQHFxcaqurqaACQCwp+Tk5FgdARaxWR0AAADsX2ttsNPS0jY85kpFvQAAANfCMAxVVFQoOjpabrdbNTU1FDABAABgV0lMTFRVVZWSk5N15MgRCpgAALvSK7sKAmvoxAQAACy12ZYtpmnq0qVLSk1NVVZW1g6mAgAAe5nD4VB1dbUcDgc3fAAAALArpaSkKDk5me2QAQC70tDQkHp6enT48GG2jEMIOjEBAICI1dnZqcnJSbW2tqq1tVWBQMDqSAAAYJeYnp7W3NzchvPR0dEUMAEAAGBXu9LDgQAARKKRkRG1trbK6/Xq/PnzWlhYsDoSIghFTAAAICKNjIyov78/+HpoaEjnzp2T1+u1MBUAAIh0pmmqv79f58+fV0NDg1ZWVqyOBAAAAOyoQCCgxsZGDQ8PWx0FAIAQo6Ojam5uDr5eWVnR+fPnWb9BEEVMAAAgIi0uLoaNzc3Nqb6+XjMzMxYkAgAAkc7v96u5uVkdHR2SJK/Xq8bGRro5AgAAYN8IBAJqaGjQ+Pi4WlpaNDo6anUkAAAkSePj42pqagobz8rKktPptCARIhFFTAAAICIVFRWpsrJSNlvox5W1qvzBwUHaYgMAgKDl5WWdO3cu7CbNzMwMT6ADAABgXwgEArp48aImJyeDY01NTRQyAQAsNzY2psbGxrDx3NxcFRYW7nwgRCyKmAAAQMRKT09XTU2N3G53yLhpmmpra1NrayudFQAAgCYnJ1VXV6f5+fmwuYKCAmVlZVmQCgAAANhZhmEoLi4ubLypqYnCfgCAZUZHR9XY2Bj2YHp2draKi4tlGIZFyRCJKGICAAARLTY2VrW1tUpKSgqbGx4e1rlz5+T1ei1IBgAArGaapnp7e3Xx4kX5fL6QObvdroMHD6qwsJDFMAAAAOwLhmGoqKhIOTk5YXMtLS0aGhqyIBUAYD8bGRnZcAu5kpIS1mwQhiImAAAQ8aKiolRdXa28vLywubm5OdXV1Wl6enrngwEAAMv4/X41NTWpq6srbC4mJka1tbVKTU21IBkAAABgHcMwdODAgXULmVpbWzU4OGhBKgDAfjQ8PKzm5uaw8ezsbJWWllLAhHVRxAQAAHYFwzBUXFysyspK2WyhH2FWV1d14cIFDQwMhLUjBQAAe8/S0pLOnj2rsbGxsLmUlBTV1NQoJibGgmQAAACA9dYKmXJzc8Pm2tra1N/fb0EqAMB+MjQ0pJaWlrDxnJwcOjBhUw6rAwAAAFyL9PR0xcTEqKGhQcvLy8Fx0zTV3t4uv9+v/Px8CxMCAIDtNDExoebm5rDt4ySpsLBQ+fn5LIQBAABg31t7INBms6m3tzdkrqOjQ6Zprtv1HACAGzUwMKD29vaw8dzcXBUXF7Nug03RiQkAAOw6sbGxqq2tVVJSUsi40+lURkaGRakAAMB2Mk1TPT09unTpUlgBk8Ph0KFDh1RQUMBCGAAAAPBvDMNQYWGhCgoKwuY6OzvV2dlJV3MAwJYaHx9ft4ApLy+PAiZcFYqYAADArhQVFaXq6upg1yXDMFRVVSWXy2VxMgAAsB0Mw9Dq6mrYuMfjUW1trVJSUixIBQAAAES2tUKmwsLCsLm+vj61tbVRyAQA2DIpKSlhazT5+fkqKiqigAlXhe3kAADArmUYhoqKihQbGyufz6eEhASrIwEAgG1UXFys+fl5zczMSJLS0tJUXl4uu91ucTIAAAAgshUUFMhms6mzszNkfGhoSHa7XQcOHLAoGQBgL1l74PzixYuanp7esCMgsBGKmAAAwK6Xlpa26bzf75ckbnACALDL2Ww2VVVVqb6+Xjk5OcrNzeUpPgAAAOAq5eXlyeFwqLW1NTjmdDqVk5NjYSoAwF5js9l08OBBTU5OKj093eo42GUoYgIAAHuaaZpqbm7W0tKSDh48qOjoaKsjAQCAG+B0OnXixAmKkwEAAIDrkJWVJYfDoaamJtntdh0+fFhut9vqWACAPcbhcFDAhOtiszoAAADAdurr69P4+LgWFhZUX1+vyclJqyMBAIBNrK6uqrGxUYuLixseQwETAAAAcP3S0tJUXV2t6upqeTweq+MAAHah5eVlXbx4UV6v1+oo2GMoYgIAAHvW5OSkurq6gq99Pp8uXryonp4emaZpYTIAALCe+fl51dfXa2xsTA0NDfL5fFZHAgAAAPakpKQkxcfHWx0DALALzc/P6+zZs5qcnNSlS5dYv8GWoogJAADsWW63WzExMWHj3d3d3BgFACDCjI6O6uzZs1peXpYkLS4uqqWlhcJjAAAAYIeZpqmWlhZNTExYHQUAEGGmpqZ07tw5raysSHq5oKmxsVGBQMDiZNgrKGICAAB7VkxMjGpqapSamho2NzExofr6ei0sLFiQDAAArDFNUx0dHWpqagpb8JqZmaEtOQAAALDDOjs7NTw8rEuXLmlwcNDqOACACDE8PKyLFy/K7/eHjC8vL/PQOLYMRUwAAGBPczgcqqqqUlFRUdjc0tKSzp49q/HxcQuSAQCA1dVVXbhwQf39/WFzcXFxOnbsmNxutwXJAAAAgP1pYGAg5PN5W1uburq66JAKAPuYaZrq6elZt2N2fHy8ampq5HQ6LUqHvcZhdQAAAIDtZhiG8vPzFRsbq6amppAnAvx+vxoaGpSfn6/CwkIZhmFhUgAA9o+5uTk1NDSs22kpKytLJSUlstl49goAAADYKaZpam5uLmy8t7dXy8vLKisrk91utyAZAMAqgUBAra2tGhkZCZtLS0tTRUUF6zfYUlxNAABg30hOTtaxY8cUGxsbNtfb26uLFy9qdXXVgmQAAOwvw8PDOnfuXFgBk2EYKisrU1lZGQtgAAAAwA4zDEPl5eXKz88PmxsdHdX58+fZ7hkA9pGVlRWdP39+3QKm3NxcVVZWsn6DLccVBQAA9hW3262jR48qIyMjbG5qakr19fWan5+3IBkAAHtfIBBQW1ubWlpaFAgEQuacTqeOHj2qrKwsi9IBAAAAMAxDRUVFKi0tDZubm5tTfX39ut2aAAB7y/z8vOrr6zU7Oxs2d+DAAR04cICdLbAtKGICAAD7jt1uV3l5uUpKSsI+ZC8vL6u1tTVsX2cAAHBjVlZWdOHCBQ0ODobNJSQk6NixY4qPj7cgGQAAAIDLZWdn69ChQ2Hbx62srOjcuXMaGxuzKBkAYLuNj4/r7NmzYd337Ha7Dh48qNzcXIuSYT+giAkAAOxLhmEoJydHhw8fVlRUVHDc4XCosrKSJwgAANhCXq9XdXV1mpmZCZtb+3nsdDotSAYAAABgIykpKaqpqZHb7Q4ZDwQCamxsVHd3Nw8CAsAeYpqment71dDQENZB2+Vy6ejRo0pNTbUoHfYLipgAAMC+lpiYqGPHjikuLk6SVFFRoejoaItTAQCwtzidTiUkJISM2Wy2YGdEm43lCQAAACASeTwe1dbWhn2el6Senh41NjbK5/NZkAwAsB3m5+fDxuLj41VbW6vY2FgLEmG/YZUQAADse2tPEBw6dEgpKSlWxwEAYM8xDEPl5eXyeDyS/v1nb2ZmpsXJAAAAAFxJVFSUDh8+rKysrLC5tS2HlpaWLEgGANhKl6/fSFJGRoaOHDlCB23sGIqYAAAA9HI3iM0KmAKBgMbHx3cwEQAAe4vdbtfBgweVmpoa0gURAAAAQOSz2WwqLS3VgQMHwuZ8Pp/sdrsFqQAAW81ut6uqqkoOh0NFRUUqLy+ngzZ2lMPqAAAAALtBe3u7hoaGlJWVxbY3AABswufzyeFYf7khOjpaBw8e3OFEAAAAALaCYRjKzc1VTEyMmpqa5PP5ZBiGDh48SIcOANhDYmJidNNNNykqKsrqKNiHuPsGAABwBUNDQxoaGgp+fe7cOXm9XotTAQAQWUzTVG9vr1566SUtLy9bHQcAAADANklOTlZtba1iY2NVUlKi+Ph4qyMBAK6Bz+dTc3Pzpvc5KGCCVShiAgAA2ITX61VbW1vI2NzcnOrq6jQ9PW1NKAAAIozP51NjY6O6urq0srKixsZGBQIBq2MBAAAA2CbR0dGqqalRVlbWhseYprmDiQAAV2N2dlZ1dXUaGRlRQ0MD6zeIOBQxAQAAbMLlcq275/Pq6qrOnz+v/v5+FmQAAPvawsKC6uvrNT4+Hhybm5tTR0eHhakAAAAAbDebzSbDMNadM01Tly5dUl9fH2tnABABTNNUf3+/zp07F+ygzfoNIhFFTAAAAFeQkZGhmpoaud3usLmOjg41NTXJ5/NZkAwAAGuNjo6qvr5eS0tLYXNOp5ObFQAAAMA+1dvbq8nJSXV2durSpUtaXV21OhIA7Furq6tqaGhQR0dH2FrN2NiYVlZWLEoGhKOICQAA4CrExsaqtrZWSUlJYXNjY2M6e/asFhYWLEgGAMDOCwQCam9vV1NTU1jbcYfDoerqahUUFGz4VDYAAACAvWt6elrd3d3B15OTkzpz5oymp6ctywQA+9Xa9nETExNhc/Hx8aqtrZXT6bQgGbA+ipgAAACuUlRUlKqrq5Wfnx82t7i4qPr6eo2OjlqQDACAneP1enX+/HkNDAyEzcXFxenYsWNKTk62IBkAAACASDA/Px82trKyovPnz6unp4eOrQCwA0zTVF9fn86dOyev1xs2n5eXpyNHjqy7AwVgJYfVAQAAAHYTwzBUVFSk+Ph4NTc3h2wjFwgE1NTUpNnZWRUXF8tmo14cALC3TE9Pq7Gxcd2tILKyslRSUsLPPwAAAGCfy83NlcfjUVNTU9jvDt3d3ZqamlJFRQU3zgFgm3i9XjU3N6/bAS8qKkoVFRU8gIaIxcoiAADAdUhJSVFtba1iY2PD5gYGBnT+/Hn2kQYA7Bmmaaq/v1/nz58PuwlhGIbKyspUVlZGARMAAAAASVJSUpKOHz+uxMTEsLmZmRmdOXOGjuYAsA3GxsY23MIzISGBDtqIeHRiAgAAuE7R0dE6evSo2tvbNTw8HDK3srIiwzAsSgYAwNbx+/1qaWnR2NhY2JzL5dLBgwcVFxdnQTIAAAAAkczpdOrw4cPq7e1Vd3d3yJzf71dTU5MmJiZUWloqh4NblgBwI3w+n9rb2zUyMrLufH5+vgoLC7lvgYjHJwIAAIAbYLfbVV5ervj4eLW1tck0TRmGoaqqKkVFRVkdDwCAGzY0NLRuAVNSUpIqKyv5eQcAAABgQ4ZhqKCgQAkJCWpubpbX6w2ZHx0d1czMjCoqKtbt2gQAuDrj4+PrFjC5XC6Vl5crKSnJglTAtaPPOwAAwBbIyspSTU2N3G63SktL6UgBANgzcnJywha6CgoKVF1dTQETAAAAgKuSmJio48ePKz09PWzO6/Xq/Pnz6ujokGmaFqQDgN0vIyNDKSkpIWNpaWk6duwYBUzYVShiAgAA2CJxcXE6fvy4srKyNjyGhRgAwG5jGIYqKyvlcrnkcDh06NAh2o8DAAAAuGYOh0OVlZWqrKxcd/u4paUlC1IBwN5gGIbKysoUFRUlu92uiooKOmhjV2I7OQAAgC1kt9s3nDNNUw0NDUpLS1NGRsYOpgIA4MZERUXp0KFDstvtio6OtjoOAAAAgF0sPT1d8fHxam5u1szMjKSXC5xKS0t5WAIArsA0zQ3/rXQ6nTp48KCcTifrN9i16MQEAACwQ7q7uzUxMaHm5ma1tbUpEAhYHQkAgKCpqSnNzs5uOB8bG8sCGAAAAIAt4Xa7deTIERUXF8swDJWUlMjlclkdCwAi2tzcnOrq6jQ+Pr7hMQkJCazfYFejExMAAMAOmJiYUG9vb/D14OCg5ubmVFVVJbfbbWEyAMB+Z5qmenp61NPTI5fLpWPHjtFqHAAAAMC2MwxDeXl5Sk1N3XR9bH5+XtHR0Zt2QAeAvSwQCKinpyd4j6G1tVUJCQms32BPohMTAADADlhrjf1Ka09NTE5OWpAIAABpZWVFFy9eVE9PjyTJ6/WqublZpmlanAwAAADAfhEdHb3h1kirq6u6ePGizpw5wxoagH1pampKZ86cCXlIenV1Ve3t7RamArYPRUwAAAA7oLi4WOXl5bLZQj9++Xw+Xbx4UV1dXdwwBgDsqJmZGdXV1WlqaipkfHJyUmNjYxalAgAAAIB/19HRoZWVFS0vL+vixYtqamrSysqK1bEAYNutrq6qublZFy5c0NLSUtj84uKifD6fBcmA7cV2cgAAADskMzNTsbGxamho0PLycshcb2+vZmZmVFlZKZfLZVFCAMB+YJqm+vv7NyygLSoqUlpamgXJAAAAAODfTUxMaGRkJGRsdHRUk5OTKi4uVmZm5oYdnABgtzJNUyMjI+ro6Fi3SMkwDOXn5ys/Pz/soWlgL6CICQAAYAfFxsbq2LFjam5u1sTERMjcWkeMyspKJSUlWZQQALCX+Xw+tbS0aHx8PGzO6XSqsrJSiYmJOx8MAAAAAC7jdrsVHx+v2dnZkHGfz6fW1laNjIyotLRUHo/HooQAsLUWFxfV1tam6enpdefj4+NVWlqq2NjYnQ0G7CCKmAAAAHaYw+HQwYMHNTAwoM7OzpAuGKurq7pw4YIKCgpUUFDA02QAgC0zPz+/bjdASUpMTFRlZaWcTqcFyQAAAAAgnMfj0dGjRzU8PKzOzs6wjiRrDwTm5OSooKBADge3PQHsTn6/Xz09Perv71+3a7bdbldxcbGysrK4Z4A9j5/mAAAAFjAMQ7m5uYqLi1NTU5O8Xm/IfE9PT3B7OW4oAwBuhGmaGh4eVltb27oLYfn5+SosLGQRDAAAAEDEMQxDWVlZSklJUUdHh0ZHR0Pm17bLHh0dVXFxsdxut0VJAeDamaap0dFRdXR0aGVlZd1jUlNTVVJSIpfLtcPpAGuwSSIAAICFEhISdOzYMSUnJ4fNTU9Pq7293YJUAIC9wu/3q6WlRa2trWEFTA6HQ4cOHVJRUREFTAAAAAAi2tr219XV1esWKq2srKi5uVltbW2KiYmxICEAXJ/+/v51C5hcLpcOHTqkgwcPUsCEfYUiJgAAAItFRUUFbyJfPn7gwAGLUgEAdju/36/6+nqNjIyEzcXFxenYsWNKSUmxIBkAAAAAXJ/k5GQdP35ceXl56z6MsbCwoOrqamVkZFiQDgCujWEYKi0tDRvLzc3ViRMnWLfBvsR2cgAAABHAMAzl5+crPj5eTU1NWllZUWVlJU9YAACum91uV1JSkhYXF0PGc3JyVFxcLJuN55oAAAAA7D52u13FxcXKzMxUR0eHJicnw46ZnZ21IBkAXLu4uDhlZWVpaGhISUlJKikpoaMc9jWKmAAAACJIYmKijh07pqmpKSUlJVkdBwCwyxUXF2t2dlZzc3Oy2+0qLy9XWlqa1bEAAAAA4IbFxMTo0KFDmpiYUEdHh5aXlyVJw8PDWlpasjgdALzMNE1NTEwEHzZbT1FRkZKTk5WSkrJulzlgP6GICQAAIMI4nc5NW177fD6Njo4qKyuLX2gAAJuy2WyqrKxUS0uLysrKeJIPAAAAwJ5iGIZSU1OVnJysvr4+DQ4Oqr+/f8PjfT6fHA5ujwLYGbOzs+rs7NTMzIyio6N1/PjxdY+LiopSamrqDqcDIhM/pQEAAHYR0zTV2tqqsbExTUxMqLy8XE6n0+pYAAALmaappaWlDQuUoqOjdfTo0Z0NBQAAAAA7yGazqaCgQAkJCfrFL36x7jF+v19nzpxRXFyciouLFR0dvcMpAewXy8vL6urq0ujoaHBsaWlJQ0NDiouLszAZEPlsVgcAAADA1RscHNTY2JgkaXJyUnV1dZqamrI4FQDAKj6fT42Njaqrq9Pi4qLVcQAAAADAUjbbxrc+BwYG5PV6NT4+rpdeekmtra3yer07mA7AXuf1etXW1qbf/OY3IQVMa7q7u+X3+y1IBuwedGICAADYJVZWVtTZ2Rk2duHCBeXn56uwsJDt5QBgH5mdnVVTU5OWl5clSY2NjaqpqZHdbrc4GQAAAABElpWVFfX29gZfm6apoaEhDQ8PKycnR3l5eXQ7B3DdVldX1dfXp4GBAQUCgXWPcblcKi4u3rTYEgBFTAAAALuG0+nUwYMH1dzcrNXV1ZC53t5eTU9Pq7KyUm6326KEAICdYJqmBgYG1NnZKdM0g+MLCwvq6OhQWVmZhekAAAAAIPIsLCys+/CfaZrq7+/X0NBQsJjJ4eD2KYCr4/P5NDAwoL6+vg07LNntdhUUFCgnJ0c2m02zs7M7nBLYXfgpDAAAsIskJyfr+PHjam5uDttGbnZ2VnV1dSovL1dqaqpFCQEA22l1dVXNzc2anJwMm3M4HEpOTrYgFQAAAABEtqSkJN10003q7e3V4OBgWKcUv98fnMvNzVVOTg7FTAA25Pf7NTg4qL6+vrAHjtcYhqHs7GwVFBQoKipqhxMCuxc/fQEAAHYZp9Op6upq9fX1qbu7O6QLh8/nU0NDg3JycmhNCwB7zPT0tJqbm+X1esPm4uPj6cYHAAAAAJuIiorSgQMHlJubq97eXg0NDYWsq0kvr611d3erv79fOTk5ysnJofgAQIilpSXV19fL5/NteExWVpby8/NZpwGuA0VMAAAAu5BhGMrPz1diYqKampq0vLwcMj8wMKCZmRlVVlYqJibGopQAgK0QCATU09Oj3t7edefz8/NVWFi47tYIAAAAAIBQLpdLpaWlys3NVU9Pj0ZGRsKO8fl86unpCRYz8TsXgDVut1sul2vdIqb09HQVFBSwJg/cAB7NBwAA2MXi4+N17NixdbePm5+fV11dnYaHhy1IBgDYCktLSzp37ty6BUxRUVGqrq5WUVERi+kAAAAAcI2io6NVUVGh48ePr7u2Jr28ZdTs7Cy/cwEIMgxDBQUFIWMpKSk6duwYDxUDW4BOTAAAALucw+FQVVWVhoaG1NHRoUAgEJwLBAJqaWmRJGVmZloVEQBwHUZGRtTW1ia/3x82l5iYqIqKCrlcLguSAQAAAMDe4fF4dPDgQc3Nzam3t1fj4+Mh8/n5+RYlA2CVxcVFjY2NKT8/f90ixtTUVMXExCg6Olr5+fmKj4+3ICWwN1HEBAAAsAcYhqHs7GwlJCSosbFRi4uLwTmPx6P09HQL0wEArlVbW5sGBwfDxg3DUGFhofLy8ngSGAAAAAC2UFxcnA4ePKj5+Xn19vZqbGxM8fHxSkxMXPd40zQ1Ozur+Ph4fj8D9oiZmRn19/cHixkTEhLW/TfAMAzV1tbKbrfvcEJg76OICQAAYA/xeDyqra1VR0eHhoaGZLPZVFlZKZuNXYQBYDeJjY0NG3O73aqsrOTpPgAAAADYRrGxsaqqqtLi4qL8fv+GBUpjY2NqampSXFyc8vLylJqaSjETsAuZpqmJiQn19fVpdnY2ZK6vr2/DQkYKmIDtQRETAADAHmO321VWVqbExESZpimPx2N1JADANcrMzNTk5GTwyb+MjAyVlJTI4eDXeAAAAADYCTExMRvOmaapvr4+SdLc3JwaGxvldruVk5OjzMxMfncDdgGfz6fh4WENDg5qaWlp3WMmJye1sLDAGjuwg/gJCgAAsEddaQu5hYUFzc/PKz09nafEACDCGIahsrIyLS4uKj8/XxkZGVZHAgAAAAD8m5mZGc3Pz4eMLS8vq6OjQ11dXcrIyFB2dva6XXYBWGthYUGDg4MaHh5WIBDY8Di73a7s7GxFRUXtYDoAFDEBAADsQ4FAQM3NzZqfn9fk5KRKS0t5QgwALDA/P7/honZUVJSOHz9OoSkAAAAARBifzye3263l5eWwuUAgoKGhIQ0NDSkhIUE5OTlsNQdYbG3LuIGBAU1PT296rMvlUk5OjrKyslgzByzA/+sAAAD2oa6uruDTYqOjo5qZmVFFRcWG+3sDALaWz+dTR0eHhoeHdejQIaWkpKx7HIvcAAAAABB5UlNTlZKSorGxMfX19YV1ZVozMzOjmZkZuVwuZWZmKjMzU263e4fTAvvb0tKSzp8/L6/Xu+lxHo9HeXl5SktLk81m26F0AC5HERMAAMA+s7CwoP7+/pAxr9er8+fPKz8/XwUFBfySBgDbaGZmRs3NzcEndltaWnT8+HE5nU6LkwEAAAAArpZhGEpPT1daWppmZmY0ODiosbGxdY/1er3q6elRT0+PkpOTVVVVJbvdvsOJgf3J7XbLNM0N51NTU5WTk6OEhAQeJgMiAEVMAAAA+4zH41FlZaXa2trk8/lC5np7ezU1NaWKigrFxMRYlBAA9qZAIKDu7m719f3/7d15fFTl3f//92QmyWRfyEL2QEIEKoJlUawWqDuKCtb1VsGlaHf9Upe7/qpQv9WitlXbehe/oFSLu7jbiligsgmorSggBJKQjez7PjPn9wd35pGQmSxkliyv5+MxD07OdZ1zPodcuXJyzudcV2G39R0dHTp48KC+9a1vcbMMAAAAAIYZk8mk6OhoRUdHq62tTSUlJSotLVVHR4fL+u3t7SQwAV7gcDhcvpxrMpk0duxYHT161LkuMDBQSUlJSkpKYnQ0YIghiQkAAGAUSkhIUGRkpA4cOKC6urpuZQ0NDfrss8+UlZWlpKQkHqgDgAc0NTXpwIEDLqcYMJvNiouL80NUAAAAAABPCg4O1rhx45SRkaGKigqVlJSovr6+W52kpCS32xuGwb04YABsNpsqKipUVlamgIAAnXbaaS7rJSUl6ejRo4qIiFBycrISEhKYjQAYokhiAgAAGKWsVqumTp2qwsJC5efndxtS1+Fw6NChQ6qsrNQpp5yi4OBgP0YKAMOXYRgqLi7WkSNHXA5dHhkZqYkTJyokJMQP0QEAAAAAvCEgIECJiYlKTExUQ0ODSktLVV5eLsMwlJCQ4HIbwzC0Z88ehYaGKjExUbGxsSRZAC44HA7V1NSorKxMVVVVcjgczrKWlhaX91isVqtmzpypkJAQEgWBIY7ffF7U3NysRx99VDNnzlRsbKzCwsI0ceJELVu2TAUFBYPef2Zmpkwm04A++fn5PfazfPnyfm+/efPmQccNAACGDpPJpPT0dJ1++uku/7irqanRnj17VFZW1uu84QCAnlpbW/Xll1/q8OHDPfpQk8mkcePGadq0aSQwAQAAAMAIFhERoZycHM2ePVtTpkyRxeJ6jIn6+no1NzersrJSX3/9tXbs2KGDBw+qrq6O+3IY9QzDUH19vXJzc7Vz50599dVXqqio6JbAJEnHjh1zu4/Q0FASmIBhgJGYvCQ3N1fz58/XoUOHuq3/5ptv9M0332j16tVat26dLr30Up/FFBUVpbFjx/rseAAAYPiIiIjQ9OnTdfjwYZWWlnYrs9lsOnDggCorK7mWAIB+qq6uVlFRkex2e4+y0NBQTZo0SeHh4X6IDAAAAADgD2azWdHR0W7Ly8rKun1ts9lUWlqq0tJSBQcHKz4+XvHx8YqIiCARA6OCYRhqaGhQRUWFKisr1dra2uc2VVVVzoFAAAxPJDF5QUNDgy655BJnAtMPfvADXXvttQoJCdGmTZv0yCOPqL6+Xtdcc422bdumadOmndRxNmzYoPb29l7rbNy4UXfddZck6eqrr5bVau21/t69e3stHzdu3MCCBAAAw4bZbFZOTo7i4uL0zTff9LjOqKysVFhYmJ+iA4DhwWKxaNy4cW5H301NTdW4ceOYEgAAAAAA4GQYhqqqqtyWt7W1qaioSEVFRQoODlZcXJzi4+MVGRlJsgZGnPr6elVUVKiiokJtbW191jeZTIqNjVViYqLGjBnDzwQwzJHE5AWPPfaYDh48KEl69NFHdffddzvLZs+erblz52rOnDlqbm7WnXfeedJTtOXk5PRZ56GHHnIu33TTTX3WP/XUU08qFgAAMHLExsZqxowZys3NVXl5uXN9XFycYmJi/BgZAAx9OTk5ioyM7LE+ODhYp5xyCv0oAAAAAKAHk8mkGTNmqKKiQmVlZaqvr3dbt62tTcXFxSouLu6W0BQVFeXDiAHvyc3NVUNDQ5/1IiMjlZiYqPj4eAUGBvogMgC+wKufHtbR0aGnnnpKkjRp0iQtW7asR52zzjpLt956qyRpy5Yt2r17t1diqaur0zvvvCNJGj9+vM4++2yvHAcAAIw8gYGBmjRpkiZPniyLxaLAwEBNmDCBt1gAoA9Hjx6VYRjd1iUkJGjGjBkkMAEAAAAA3AoMDFRycrJOP/10zZo1S5mZmQoJCel1m86EptLSUh9FCXiGw+FwWxYfH++2LCQkRBkZGZo1a5ZOP/10JScnk8AEjDCMxORhmzZtUl1dnSRp8eLFbqcIWLJkiVatWiVJevPNNzVz5kyPx/Lqq6865wbtzyhMAAAAJ+p8i6ulpUVBQUFu5x13OBxMjQQAkhobG1VaWqrk5GRZLBZNmDBBCQkJ/g4LAAAAADCMdCZqpKenq6mpyTm1VktLi8v6Y8aMcbuv1tZWBQcH83Ii/MowDDU3N6umpkZVVVVqaWnRGWec4bJdxsfH68iRI86vQ0JCFB8fr/j4eIWFhdGWgRGOJCYP27p1q3N5zpw5buvNmDFDoaGham5u1rZt27wSy/PPPy/p+BCUN954o1eOAQAARr6goCAFBQW5La+oqNCRI0eUk5PDKCMAIKmwsFBTp05VdnZ2r/0nAAAAAAC9MZlMCg8PV3h4uDIzM9Xc3OxMaGpubnbWcXdPzmaz6dNPP1VwcLBiYmKcH0augS90dHSopqbG+Wlra+tW3tDQoMjIyB7bWa1WJSQkyGq1krgEjEIkMXnYvn37nMsTJ050W89isSg7O1tffvml9u/f7/E48vLynMlRZ599tsaPH9+v7S644AL9+9//Vm1traKjozV58mRddNFFuv322wf1ULKoqKjX8q7DXDY1NfU61y/gbY2NjS6XAX+hTWIoObE92mw2HTx4UDabTV9++aXGjBmjlJQUmc1mP0aJ0YL+Ef5SV1en6upqZWZmdruJ1tkODcNQdHS0Wltb3Y5gB3gbfSSGGtokhpKmpiZ/hwAAwICZTCaFhYUpLCxMmZmZampqUmVlpTo6OmSxuH7kW1tbK+n4tHPHjh3TsWPHJEnh4eHOhKaoqChGWIdHOBwO1dfXO5OWGhoaeq1fVVXlMolJkiZNmuSNEAEMAyQxeVhnsk5YWJiio6N7rZuWlqYvv/xSFRUVamtrU3BwsMfieP7552UYhqSBTSX30UcfOZcrKiq0ZcsWbdmyRStXrtTatWt1+eWXn1Q8aWlp/a67fv16RUVFndRxAE974YUX/B0C0A1tEkPJCy+8oOzsbMXFxTnXVVVVqaSkRHl5ec6bJIAv0D/CF8xmszIzMxUfHy9Jeuutt5w3gE9Em8RQQnvEUEObhL/V1dX5OwQAAAatM6GpN9XV1S7XNzY2qrGxUYWFhTKZTIqMjFRUVJTzwwuK6A/DMFRbW6va2lrV1dWpvr7e+Xy6P2pqajRu3DgvRghgOCKJycM6M0rDw8P7rNv1wqKxsdGjSUydN4NCQkJ09dVX91l/ypQpuuKKKzRr1iwlJyero6ND33zzjdatW6cNGzaotrZWV155pd59911dfPHFHosTAAAMXyaTyeU0ScHBwZo4caIqKiqUn58vu93uh+gAwLNiY2OVmZnZrd9LT09XbW0toy0BAAAAAIakzinnemMYhurq6pxJvp1T2EVHR2vcuHFM44VeHThwQO3t7f2uHxQUpNjYWI0ZM2ZQswABGLlIYvKwzpvXrh7onahr0lJLS4vHYti+fbsOHz4sSbr88svdDsPX6c4779Ty5ct7rD/jjDN00003adWqVbrjjjtkt9t122236fDhw7JarQOKqbCwsNfy0tJSzZo1S5K0aNEi5eTkDGj/gCc1NjY6EwFvvPHGfiUlAt5Em8RQ0rU93nDDDQoLC1NlZaVKSkrkcDi61Y2Pj1dSUpLS0tL6HKESOBn0j/CFjo4OFRYWuhyxISAgQBdeeKGSk5Ml0SYxtNAeMdTQJjGUHDx4UI888oi/w8AQ19jYqM8//1y7du3Srl27tHv3buXn50uSMjIynMuetH37dj399NP65JNPVFZWpujoaE2dOlVLlizRdddd5/HjARj5pk6dqubmZuf0XrW1tT3u4Z3IMAw1NDTIMAyNHz/eR5FiqHE4HGpqalJ9fb0CAwOVkJDQo47JZFJUVJQqKirc7icgIEDR0dHO6QtDQ0NJjAPQq1GbxOSJzvG5557TkiVLuq3rTO7pT8ZpW1ubczkkJGTQ8XR6/vnnncuLFy/us35fDxVvv/127d69W2vWrFFJSYneeOMN/dd//deAYkpNTe133bCwsD4TrwBfCQ8Ppz1iSKFNYijpbI9RUVFKSUnRN99802MKOZvNpry8PCUkJCg7O1uBgYH+CRYjHv0jPM0wDB07dkyHDx92OaKc2WxWdna2EhMTXf59SZvEUEJ7xFBDm4S/9TX1DiBJCxYs0ObNm312vOXLl+uhhx7qllxQVlamDRs2aMOGDVq3bp1ef/31Ab9gDGB0M5lMzmnnUlNT5XA4VF9fr+rqatXW1jpnmHElKirKbdnBgwfV2NioiIgIhYeHKzw8XGFhYQoICPDGacDLOhOWGhsb1dTUpIaGBjU2Njp/J0VFRblMYpKOP2c+MYkpPDzcmbQUFRVFuwAwIKM2iclbIiIiJB1/S6MvTU1NzmVPvX3W1tamV199VZKUlJSk888/3yP7vf3227VmzRpJ0pYtWwacxAQAAEY2q9Wq0047ze0D//LyclVXVys7O1sJCQm8bQNgSGtpadHBgwd7JGZ2io2NVU5OjkenBAcAAMDQYhiGczk2NlYzZszQ9u3b+3Xvf6BWrVqlFStWSJKysrL0y1/+UlOmTFFJSYmefPJJbdq0Se+//75uueUWvfjiix4/PoDRo3NUnM4BDmw2m3MqudraWjU2Njr7v96SmOrq6tTc3NwtCcpkMik0NLRbUlNYWFi/Zq+B77S3t6u5uVmNjY3OT3Nzc7ffeyfqHJnL1T3d6OhoRUREKCoqStHR0YqKipLFQgoCgJM3anuQ/fv3D3ofSUlJPdalpqbq008/VVNTk2pra3sd5ahzirX4+HiP3fx+9913VVNTI0m6/vrrZTabPbLfyZMnO5eLi4s9sk8AADCymEwmJSUlKSYmRocOHVJ1dXW3cpvNpgMHDujYsWPKycnx6EiUAOAJhmGosLBQBQUFLofXDwwMVHZ2tuLj40nGBAAAGOGuv/563X777Zo5c6ays7MlSZmZmR5PYqqurta9994rSUpPT9fOnTsVFxfnLL/00ku1cOFCvfvuu3rppZe0dOlSzZ0716MxABi9LBaLxowZozFjxkiS7Ha76uvre33GabPZ1Nzc3GO9YRhqampSU1OTysrKuh0jLCxMoaGhCg0NVUxMDKMi+sGxY8d05MgRdXR0DHhbh8PhHHnrRKGhofr2t7/tiRABQNIoTmKaOHGiV/Y7efJkvfHGG5KkAwcO6Mwzz3RZz2az6fDhw5KkSZMmeez4A51Krr+4QQ8AAPrLarXq1FNPVVlZmQ4fPiybzdatvLa2VgcOHNC0adO4xgAwZDQ2Nuqbb75x+1AqMTFRWVlZTIsJAAAwSixdutQnx1m9erXq6uokSStXruyWwCQdn8b46aef1gcffCC73a7HHnuMJCYAXmM2m53TgLnT2xR0rnQd7UmSsrOz3SYx1dTUKDg4WFarlSnI+uBwONTW1qaWlha1trY6/508ebLLe65ms/mkEphCQkIUGRnJ9wOAz4zaJCZvOfvss53LW7ZscZvEtGfPHud0ct/5znc8cuyKigr94x//kCRNmzZNU6ZM8ch+JWnfvn3O5eTkZI/tFwAAjEwmk0ljx45VTEyMcnNzVVlZ2a08KyuLBCYAQ0ZjY6M+++wzl2XBwcHKyclRbGysj6MCAADAaPDWW29JkiIjI7Vo0SKXdVJTU3Xeeefpww8/1Mcff6yGhgaXo2EAgC9ERkZqypQpamhoUGNjo5qamtTS0tLv7UNDQ12udzgc+vLLL51fBwcHOz9BQUEul0dqYo3D4VB7e7va2tq6fU5c50pbW5usVmuP9f0Z/cpqtTqnA4yIiFBERAQvcwHwOZKYPGzu3LmKiopSXV2d/vrXv+qee+5x+YBu7dq1zuWFCxd65NgvvfSSM4PWk6MwScfn5O40Z84cj+4bAACMXMHBwfrWt76lyspK5ebmqq2tTSkpKYqMjPR3aADgFBYWppiYGOfU3J1SUlI0btw4j03TDQAAAHTV3t6uXbt2SZJmz56toKAgt3XnzJmjDz/8UG1tbdqzZ4/mzZvnqzABoBuz2azY2NhuL/vYbDY1NTWpsbHR+WlqapJhGD22d5fE1Nra2u3r3hJ1usYSGBjonP7d1T1Hh8OhhoYGmc1mWSwWmc1mmc1mrydAGYYhm80mu90uu93uXO78Nz4+XhZLz0f19fX1+uKLL076uK2trS6TmKxWq0wmkwzDkMlkUmhoqDNhqfPjKh4A8DV6Ig8LCgrSz372Mz300EPav3+/Hn/8cd19993d6uzYsUNr1qyRdPwPj5kzZ7rcV2fyU0ZGhvLz8/s8dudUchaLRddff32/4t27d69CQkKcc3q78swzz2j16tWSpLFjx3os6QoAAIwecXFxio6OVmFhodLS0tzWs9lsMpvNjNIEwKdMJpMmTJigPXv2yOFwKDQ0VDk5OYqKivJ3aAAAABjBDh48KLvdLkmaOHFir3W7lu/fv39ASUxFRUW9lpeWljqXGxoaVF9f3+99D0bXqZzdTesMDBTtyn9MJpNz9B7peBJPZyJSa2urWltb1d7ertbWVpfJSZ3TzQ1EZ4JQa2urcwacE7W1tXWbcaZrvCaTSQEBAc7lE9dJ6jb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" ] @@ -619,12 +649,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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AAMiDEBMAYF75XbZTBpjMJul9jSW6fk2lblhdpdYqT94WcgAAAAAuXA6rRVeuLNeVK8v1J3eu1fHBiJ5t65fFbCr4+eHp/f2SpEgirV/u7dUv9/bKZJI2NxTrxjVVun51pVZXe/n8AQAAAAAAsAAIMQEAZk0qndFb7SN65kC//tM1K1Tqtk8bc9nyMhXZLBpPTm374HVadU1r9kroa1or8+4LAAAAAIU0lbv1qfLmGcc8tb9v2jrDkN7pCOqdjqD+5+Ntqisu0g1rKnXDmipduryUtnMAAAAAAADzhBATAOCcROIpPX9wQE/u79OOA/0aiSYlSatrvPrAlvpp4502i65oKddT+/u0vMKtG1ZX6vrVVbq4qUQ2i3m+pw8AAADgApFMZ2Q2mWQyZYNLhXQHx/XDV9r1w1fa5bJbdNXKct2wpkrXrapUhZe2cwAAAAAAAHOFEBMA4Iz1hWJ6an+fntzXp5cPDymRzkwb88yBgbwhJkn6/Vta9cd3rFFzuXuupwoAAAAAkiSbxaz/+O3LNTgW144D/Xp6f79eODSgSCJdcJ9oIq3H9/bp8b19umpluX702UvmccYAAAAAAAAXFkJMAIBTMgxDbX1hPbm3T0/t79OurtFT7vNcW7+S6Uze6kqrq31zMU0AAAAAOKVyj0O/enGDfvXiBsVTab12dFhP7+/TU/v71R0cL7jfjWuqCm4zDEMmk2kupgsAAAAAAHDBIMQEACjo7Y4RPbwroKf296lzuPCX+e+1psanG9dUKp7KH2ICAAAAgMXAYbXo6tYKXd1aoW/ebehg35ie2t+nZw706+2OkSlt565fXZn3GJmMoVu/87zW1/p109oqXd1aIbeDr9wAAAAAAADOFN+oAAAKenR3j77/0vFTjrOaTbp0eZluWlulG9ZUqr7ENfeTAwAAAIBZZDKZtKraq1XVXn3+uhYNjcW1o21AzxzoU18orobS/J9zdnYFdbBvTAf7xvTAO92yW826YkWZbl5XrRvWVKrS65znnwQAAAAAAGBpIsQEABe4gXBcFV5H3m03ra3SvS8ey7vN67TqulWVunFtla5dVSGf0zaX0wQAAACAeVXmcehDF9XrQxfVyzi5JNN7PLmvb8rjRCqjHW0D2tE2IJNJ2txQrJvWVunmtVVaUeGh7RwAAAAAAEABhJgA4AJjGIb2BkJ6cl+fntrfp309Ib36hzeoyjf96uCLlpWo2GVTMJqUJNUVF+mmtVW6aW2VtjaVym6lVRwAAACA899MwaOn3hNiOplhSO90BPVOR1B/+cs2NZe7c4GmLY0lspgJNAEAAAAAAEwixAQAF4BEKqNXjw7pqf19empfnwKjsSnbn97fr49d0jhtP6vFrE9f3iyTSbpxTZXW1Hi5ahgAAAAATnLfZ7bpqX19enJfn149OqRUpnDVpmODEX3v+aP63vNHVea2686NNfrTe9bP42wBAAAAAAAWL0JMAHCeiiZSeq5tQI/v7dXTB/oVjqUKjn1yX2/eEJMkffHGlXM1RQAAAABY8uqKi/TJy5v0ycubNDqe1LNt/XpiX5+eaxvQWLzw57ChSGLaBSYAAAAAAAAXMkJMAHAeCUYTemp/vx7f26vnDw4onsqc1n57AyGl0hlZLbSHAwAAAICz5S+y6Z7Ndbpnc53iqbRePTqsJ/f16ql9/eoNTQ8s3by2quCxnjnQpxUVHi0rc8/llAEAAAAAABYNQkwAcJ74o5/v0b+/0an0DK0LTtZQWqSb1lTrprVV2tpUQoAJAAAAAGaRw2rRNa0Vuqa1Qv/9bkN7ukf15ETbuba+sMwm6YY1+UNMyXRGX7x/p8KxlFZXe3Xr+mrdsq5aq6tp8Q0A86m/v1+vv/66Xn/9db3xxht64403NDQ0JEn65Cc/qfvuu2/Wn/Pf/u3f9P3vf1+7d+9WMBhUVVWVrrrqKn3+85/XZZddNuvPBwAAACwmhJgA4DxR5rafMsC0qd6vm9dV68Y1VWqt8vDlNwAAAADMA7PZpE0NxdrUUKzfv2WV2oci2tU1qlK3Pe/4144O51qCH+gN60BvWH/z1CEtK3Pp1nXVumV9tTbXF8ts5jMdAMylqqrCFfNm2/j4uD70oQ/p0UcfnbK+o6NDP/7xj/Vv//Zv+vrXv65vfOMb8zYnAAAAYL4RYgKAJcAwDL3bHdIbx4f1mSub8465ZV21/u6Zw1PWWcwmXbq8VLesy1ZcqvEXzcd0AQAAAAAzWFbmnrFN3JP7evOubx+K6p+eP6p/ev6oqnwO3bKuWreuq9a25lKq6wLAHGtsbNTq1av1xBNPzMnxP/OZz+QCTNddd52++MUvqra2Vnv27NFf/MVf6MiRI/rmN7+pmpoa/dZv/daczAEAAABYaISYAGCRSqUzeuP4iB7f26sn9/WpOzguSbpxTZUay1zTxq+r9amuuEiDY3Fd3VqhW9ZV64bVlSopcGUvAAAAAGBx8jptKnPbNRRJFBzTF4rrh6+064evtKvYZdONa6p067pqXbmyXE6bZR5nCwDnr69//evaunWrtm7dqqqqKh0/flzNzfkvMDwXzzzzjO6//35J0l133aWf//znsliyv8u3bt2qu+++WxdddJE6Ojr0ta99Tb/6q7+qkpKSWZ8HAAAAsNAIMQHAIhJLpvXS4UE9vrdXT+3v13CeL6wf39urz129fNp6k8mkez95sZaVueSy8+sdAAAAAJaq379llf7rTa16q31Ev3y3V4/v7c1d2JJPMJrUz97q0s/e6lK1z6mX/+B6Ws0BwCz40z/903l5nr/6q7+SJFmtVn33u9/NBZgmlZeX69vf/rY++tGPKhgM6t5779VXvvKVeZkbAAAAMJ84yw0ACywUS2rHgX49sbdPz7b1K5JIzzi+UIhJktbU+OZiigAAAACAeWYxm7StuVTbmkv1J3eu0bvdIf1yb48e39unw/1jBfe7dHkpASYAWELC4bCefvppSdKNN96o+vr6vOM++MEPyufzKRQK6ec//zkhJgAAAJyXCDEBwAIYiST05L4+PfZuj148PKhk2jit/ZrKXLq4qVSGYchk4ktpAAAAALgQmEwmbaj3a0O9X1+5ZbUO94f1+N4+/fLdXu3pHp0y9tb11QWP87+fPKgSl003r6tWbXHRXE8bAHAa3njjDSUS2Wrs11xzTcFxdrtdl156qZ544gm98cYbSiaTstls8zVNAAAAYF4QYgKABfCZH7yhdzqCpzV2bY1Pt6yr1q3rq9Va5SG8BAAAAAAXuJZKr1oqvfr8dS3qGonqib19+uXeXu3tHtXVrRV594kmUvqn548olszomw/v06Z6v25ZX61b11VreYVnnn8CAMCkffv25ZZXr14949jVq1friSeeUCqV0qFDh7R27dq5nh4ALHmGYcgwDJnN5rzbh4aGFI/Hc+Pee4vFYmpoaJDJZFIgENDw8LBMJpNMJpMcDoeqqqryHjcWiymZTMpisUy5cY4HAGZGiAkAFsDNa6sLhphMJuniZSW6ZV21bllXrYZS1/xODgAAAACwZNSXuPSZK5v1mSubNRZPyWXP/3Xf8wcHFEtmco93dY1qV9eo/vKXbWqt8ujW9TW6fUO1VlV5ObECAPOoq6srt1yoldykhoaG3HJnZ+dph5hOfo58enp6csvhcFihUOi0jnuuxsbG8i4D54LX1fnNMAyl02klEgmlUimlUiml0+nc8sm3yfWGYaisrEyNjY15j9ne3q5wODzj89bV1UmS+vr6pqx3u90qKspf4TQQCEwbL2WrrFosFlmt1rw3i8Uim80mr9d7Ov9JsATxewpzYaFeV6f6/Xk2CDEBwCzrGonql+/2amdnUH/30S15v/y9bX21vv3LA7nHNotJV7SU65Z11bpxTZUqvI75nDIAAAAA4DzgcRT+qu+X7/YW3Hawb0wH+w7pb58+pOXlbt22oVq3ra/RulofgSYAmGMnn/jxeGaujOd2u3PLZ3Jy6uTw06n86Ec/kt/vP+3xs+VHP/rRvD8nzn+8rs4va9askcfjkcViOeN929ratH379rzbVq5cqbKysrOaU09Pj55++um825YtW6aamppp6w3DyAWtCkkkEnr77bfzbnO5XPJ6vUokEorH44rH40qn02c1fyw8fk9hLszn62p0dPTUg84QIaY51t7err/927/V9u3b1dnZKYfDoRUrVujXfu3X9PnPf14u19lVWDl+/Liam5vPaJ9ly5bp+PHj09Zfe+21eu65507rGIZhnNFzAheK44MRPfpuj375bq92d534Zf2F61u0uto3bXxTuVsXLStRuceu2zfU6LrVlfI56WEPAAAAAJgb799SJ7PZpKf29SkUK3zC5OhgRH+/44j+fscRLStz6baJCk0b6vwEmgBgDsRisdyy3W6fcazDceLCx/Hx8TmbEwDMJYvFIqfTOe3mcDjU29urQCCQdz+z2XxWAabJ5ywkk8kU3HYqM503Pdu5Spox4OT3+7Vs2bIp65LJpGKxWN4bAScASw0hpjn08MMP6+Mf//iU0qvRaFRvvvmm3nzzTd17773avn27Wlpa5mU+q1atmpfnAS4Uh/vDenRPrx57t1f7e/KXWH50T2/eEJMk/ew/X8YXwAAAAACAeXHtqkpdu6pSyXRGrx4d0uN7e/X43j4NhOMF92kfiuofnzuin77Zqdf/+EZZ+AgLALPO6XTmlhOJxIxj4/ETv7MLtS/Kp7Ozc8btPT092rZtmyTpE5/4RK5t0lwbGxvLVQr4xCc+ccpKVMDp4HW1eKRSKcViMY2Pj+fu4/H4jAGdzZs365577sm77dixYwoGg2c1l4aGBl1//fV5t/X19Wl0dFQmkyl3zmZy2WQyKZVK6dixY5Kk5uZmWa1WGYYhwzBUVVWla6+9Nu9x29vbNTIyclYFIiorK/Xbv/3bebd1dXVpYGBgyjqbzVawBd1kaKyoqEhOp1Pl5eWcm1pg/J7CXFio11V3d7e+9a1vzeoxCTHNkXfeeUcf/vCHNT4+Lo/Hoz/8wz/Uddddp/Hxcd1///3653/+Zx08eFB33HGH3nzzzTPua1pXV6c9e/accty3vvUt/eQnP5EkffKTn5xx7MUXX6zvf//7ZzQP4EJiGIYO9Ib12J4ePfpurw73n7pk8y/f7dHv3dSadxtvEgEAAAAA881mMeuqlRW6amWF/vvd6/V2x4gee7dXj+3pUWA0lnefW9ZXy2LmMywAzIWTzw2cqkVcJBLJLZ/Jian6+vozmo/Pl/+izLnk8XgW5HlxfuN1tTDa2to0MjIyJXh5ulKpVMH/zzwez5QQ02RwZ6ab1WqVxWKRzWYrWO3uVK+RUCikp556SpJ08803n/ZrasOGDZKylZ7S6fSUWyqVUjKZLHib6bV7pqGodDqtSCSiSCQiu92uFStWFBxnMplkNpvP6Pg4N/yewlyYz9fVyQV9ZgshpjnyxS9+UePj47JarXriiSd02WWX5bZdf/31Wrlypb761a/q4MGD+l//63/pm9/85hkd32azaf369TOOSafTevbZZyVlP3h84AMfmHG82+0+5TGBC9G73aPavqdHj+3p0fGh6GntU+yy6aY1Vbp9Q40MwyCwBAAAAABYdMxmky5uKtXFTaX6b3es0a6u0YkLd3rUOXyiTdHt62sKHuPL/2+X3A6Lbltfo23NpYSdAOAMnRww6urq0sUXX1xw7MkVlRoaGuZ0XgCQj2EYGh8fl2EYcrvdecckk8mzCjBJU1tsvldNTY3Ky8vlcDhkt9uXTNjGbDbLbDbLZrPNyvGKiork9XqVSCTO+L9zof/PpGw1lfb2drndbvl8Pnm9Xnm9XhUVFXGOC8C8IsQ0B15//XW98MILkqTPfvazUwJMk7785S/r+9//vvbv36/vfOc7+uM//uNZ++M16amnnsr1jf3Qhz50RuVlAZzwF4/u18tHhk45rtxj183rqnXb+mpdurxMNsvSeAMNAAAAAIDJZNLmhmJtbijWH9y2WnsDIT26p0evHB3SpctL8+4TjCb04M5upTKGfvhKu8o9dt2yrlq3b6jRJc2lsvK5GABOae3atbnlAwcOzDh2crvVatXKlSvndF4AIGXbXIZCIYVCIYXDYYXDYaXTaZWXl2vdunV593G73RoaOvU5lckWZ5M3p9Mpl8tVcPxM2y4ky5cvzy1nMplcq7733vIFwmb6bxgOh5XJZHL/P0+yWCy5QNNktT6HwzG7PxQAnIQQ0xz4xS9+kVv+9Kc/nXeM2WzWb/zGb+gP//APFQwGtWPHDt18882zOo8f/vCHueVTtZIDUNhtG2oKhpiqfA7duq5at22o0dYmrjgFAAAAACx9JpNJ6+v8Wl/nn3Hck/v6lMqcaGcxOJbQj1/r0I9f61CJy6ZbJj4vX76ibK6nDABL1tatW2W325VIJPTcc8/pD/7gD/KOSyQSevXVV3P7zPZF0QBgGIbi8biCwaBGR0c1Ojqq8fHxvGNPDrm818nVfkwmk1wul9xut9xut1wul1wul5xO55KppLSYmc3m3H/T9zo54DTZTq64uLjgsQr9f5pOpxUMBqe08isqKpLf78/dnE4n1ZoAzBpCTHPgxRdflJT9I33RRRcVHHfNNdfkll966aVZDTGFw+FcmKqpqUlXX331rB0bOF8YhqF3OoPavrtHb3eM6D/+8+Uy5wkh3bKuSl9/8F1NthmuKy7SbeurdduGam1pKMm7DwAAAAAA57vH3u0tuG0kmtT9b3Tq/jc65S+y6dqVpUomPaq3RuZxhgCw+Hm9Xt1www167LHH9NRTT6mrq2tKi7lJDzzwgEKhkCTpAx/4wHxPE8B5yDAMRaPRXGBpdHT0tNuTxeNxJRIJ2e32adv8fr/WrFkjt9utoqIiwkoL5OSAU1nZzBcVnGkLwMlqT7292c8DlZWVWrNmzTnNFwAmEWKaA/v375cktbS0yGot/J949erV0/aZLT/72c8UjUYlSZ/4xCdOK/164MABXXLJJWpra1MsFlN5ebkuuugi/cqv/Io++tGPcmUHzguTwaVHd/fo0T09CoyeKKf5dseILm6aXiK/0uvUBzbXqdLn1O0bqrWhzk+iHAAAAABwwfvjO9bofY3FenRPr/b1hAqOGx1P6sHdfZKaZDel1ftwmz5w0TJd01rBhUEAznv33XdfrmPDN77xDX3zm9+cNub3f//39dhjjymVSunzn/+8HnjgAVksltz2wcFBfe1rX5MkFRcX6zd/8zfnZe4Azm/Hjx9XR0fHWe1rt9sVi8XyhpgcDocqKyvPdXqYRzabTZdffnmuldzkLZFInNb+M7WpS6fTU/6mAcCpEGKaZbFYTIODg5KU92qJk5WUlMjtdisSiaizs3NW53FyK7nf+I3fOK19+vr61NfXl3vc3d2t7u5uPfTQQ/r2t7+tn/3sZ2edou3q6ppxe09PT245EonkrigBZoNhGNoTCOuJA4N6cv+gekL50+Q/f6tdraVWjY2N5dZNLn/zthM9hmcqkwrMhXyvSWCh8HrEYsLrEYsNr0ksJrweMR8qHNJvXFyl37i4Sh3D43qqbVBPHhjU3p7Cr7mEYdFDe/r1ZseoHvsvW7lICAsiEqEiGE7Piy++qMOHD+ceT373L0mHDx/WfffdN2X8pz71qbN6nuuvv14f+chHdP/99+uhhx7STTfdpC996Uuqra3Vnj179Od//ue5oMG3v/1tlZSUnNXzALjwZDKZgpWQ/P6ZWwdPslgs8nq98nq98vl88nq9stvtvI87z9hsNpWWlqq09MTF/vF4fEqoKRQKKZ1OT9u3UJs6wzD05ptvymKxqKSkRCUlJfL7/YSaAMyIENMsOznc4PF4Tjl+MsQ0m18odnR06LnnnpMkXX755WppaZlxvNls1g033KDbb79dmzZtUllZmcLhsN5++2390z/9k/bv3699+/bpuuuu0+uvv67GxsYznlNDQ8Npj33ggQdO+40TUIhhSP3pIh1J+HU06VM4M/1qgPf6xZvt8h56Qie/7/7Rj340h7MEzhyvSSwmvB6xmPB6xGLDaxKLCa9HzKerJW322XQ06dPRhF996fxXZVfGuvSP//jW/E4OmDA6OrrQU8ASce+99+oHP/hB3m0vvfSSXnrppSnrzjbEJEn/+q//qlAopEcffVQ7duzQjh07pmw3m836kz/5E/3Wb/3WWT8HgAtDLBbT0NCQhoaGNDo6qksuuaRg2zeTySTDMKast9ls8vv9uZvH4yGwdIFyOBxyOBwqLy+XlA0ljY2NTWlBmEql5PV68+4fi8UUi2U7okQiEXV1dclsNsvv96u0tFTl5eVyOp3z9vMAWBoIMc2yyV/EkvK+IXgvh8MhKds7dLb83//7f3NvOE6nCtMDDzyQNyF71VVX6b/8l/+iz33uc/rBD36gvr4+felLX9IDDzwwa3MFZttAyqlDCb+OJv2nFVySpGJzXCvso1ph5wssAAAAAADOlc+S1GbLkDY7hzSWselowqcjCZ960+7cmJk+g/9yrFFFppRW2EdVa42IjnMALgRFRUXavn27fvKTn+i+++7Trl27FAwGVVVVpauuukpf+MIXdNllly30NAEsQpPBksng0nsLJ4yMjKiqqmrafhaLRX6/X+Pj41NCSy6Xi9AS8jKZTLmqXPX19TIMQ/F4vGC1r+Hh4WnrMpmMRkZGNDIyoiNHjsjtdqusrEzl5eUE5gBIIsQ0605Oi55On9B4PNvWqqioaNbmMHmFpcPh0Ic//OFTji9U4k/Kpq3vvfdevfrqq2pra9PPf/5zdXd3q66u7ozmdKp2eT09Pdq2bZsk6YMf/KBaW1vP6PjApN/96V7tOjT9TdF7LSst0i1rynXzmgqtrJj6hnxsbCz37+gTn/jEaVVVA+YSr0ksJrwesZjwesRiw2sSiwmvRyw2Y2Nj+ocf/JuOJvxy1K3R13/1U3lPUPSG4vqH//O6JGlfolSlLptuWl2uW9aUa0uDXxYSTZgFBw8e1Le+9a2FngaWgPvuu29ay7gz9alPfeqMKjR97GMf08c+9rFzek4A579MJqNgMJgLLk2eb8ynUIhJktatWyeLxUJwBGfFZDLNWEnpdKpfRiIRRSIRdXR0yG63q7y8XGVlZSouLi4YjgJwfiPENMtOLpd3Oi3iJvuvz9aXia+//roOHDggSbr77rtnDCidLqvVqs9+9rP66le/Kkl67rnnzvhDVH19/WmPdbvd8vl8Z3R8YNL739eoZwuEmJrL3bpjQ43u2Fij1dXe03pT7vF4eD1iUeE1icWE1yMWE16PWGx4TWIx4fWIxcJjTmmjc0i//WsbCr4mf7r72JTHw9Gk/v3tHv372z2q8Dp0+/pq3bGxVhcvK5GZQBPOktvtPvUgAAAWmckKNgMDAxocHFQ6nT6t/UZGRmQYRt5zIlYrp4oxd1avXq26ujqNjIxoeHhY4XB4xvGJREKBQECBQEAWi0Xbtm07rc5HAM4v/GWaZU6nU2VlZRoaGlJXV9eMY0dGRnIhpoaGhll5/h/+8Ie55dNpJXe61q5dm1vu7u6eteMCp8swDO0NhPTw7oBeOjyoB377Ctmt0xPYN6yplN1qViKVkXQiuHT7hhqtqTm94BIAAAAAAFgY23cHCm4bCMf1g1fa9YNX2lXlc+j2DTW6c2ONtjQQaAIAAOe3o0ePqqenR6lU6rTGW61WlZWVqbS0VCUlJZwbwYIwm825NoVNTU1KJpO5CmLDw8NKJpMF9y0qKiLABFygCDHNgbVr1+qFF17Q4cOHlUqlCqaYJysmSdKaNWvO+XmTyaTuv/9+SVJlZaVuvfXWcz7mJN7cYKG09Yb1yO6AHt4V0PGhaG79S0cGdd2qymnjvU6bPratUW6HRbdvqNHaGh+vXwAAAAAAloi/+fAWPbInoO27e7Q3ECo4ri8U1/dfOq7vv3RctX6nbp+ovLy5oZjvAQAAwHknnU6fMsA0WWihvLxcfr+f90RYdGw2myoqKlRRUSHDMDQ6OppriTg+Pj5lbGXl9HOAk4aGhmSxWHidA+cpQkxz4Morr9QLL7ygSCSit956S5dccknecc8991xu+Yorrjjn592+fbuGhoYkZftmz2YJyH379uWWa2trZ+24QD5HB8b0yO4ePbI7oIN9+dsyPrq7J2+ISZK+efe6uZweAAAAAACYI41lLv2Xa1v0X65t0bHBiB7d06NHdvdof0/hQFNgNKZ7XzymFw4N6vH/evU8zhYAAGD2JBKJgpVnKioqFAhMr1jp9XpzwSWXy0WgA0uGyWRScXGxiouLtXz5ckWj0VygKRQKqaKiIu9+hmHoyJEjGh8fl91uV2Vlpaqrq2kXDJxHCDHNgfe///361re+JUn6/ve/nzfElMlkcq3fiouLdd11153z857cSu6Tn/zkOR9vUiqV0r/+67/mHl99NV8GYfZ1DkdzwaWZrrSc9PjeXv35BzbkbSkHAAAAAACWvuZytz5/XYs+f12LDveP6dE9Pdq+u0dtfeG84+/cWDPPMwQAADg3qVRK/f396u3tVSQS0WWXXZa3SIHf75fdblcikZDP58tVs3E4HAswa2B2mUwmud1uud1uNTY2KplMymaz5R0biURyVZsSiYS6urrU1dUlr9erqqoqVVZWFtwXwNJAiGkObNu2TVdddZVeeOEF/cu//Is++clP6rLLLpsy5n/9r/+l/fv3S5K++MUvTvtl+uyzz+aCTZ/85Cd13333zficw8PD2r59uyRpw4YN2rx582nNdceOHdqyZYuKi4vzbk8mk/rc5z6Xm+tdd92lhoaG0zo2cCq9ozFt39Ojh3cFtLMzeFr71JcU6c6NtbpzY41sFq4oAAAAAADgQtBS6dHv3rBSv3vDSh3qC+cuhDoyEMmNuX2GENNv/fBNrazy6I4NtVpT46VKAQAAWDCGYWhkZER9fX0aHBxUJpPJbRsYGFBNzfT3NCaTSatXr1ZRUZGcTud8TheYdzOFkPr7+/OuD4fDCofDOnLkiMrLy1VdXa2SkhLe9wNLECGmOfKd73xHV1xxhcbHx3XzzTfrj/7oj3TddddpfHxc999/v773ve9JklpbW/XlL3/5nJ/v/vvvVyKRkHRmVZh+8IMf6O6779bdd9+ta6+9VqtWrZLP59PY2Jjeeustfe9738u1kqusrNR3vvOdc54rMOlbj+3Xgzunlz99r2qfU3durNGdm2q1qZ7+tgAAAAAAXMhWVnn1X2/y6ks3rtTBvjFtn2hHv6LCk3f84f4xPbGvT0/s69Pf7zii5RVu3bWxVndtqlVLZf59AAAAZlssFlNPT4/6+voUj8fzjunt7c0bYpKkkpKSuZwesCQUFRXJ7XYrEonk3W4YhgYGBjQwMCC73a6qqirV1NSoqKhonmcK4GwRYpojW7Zs0b//+7/r4x//uEKhkP7oj/5o2pjW1lZt375dXq/3nJ9vspWcxWLRr//6r5/RvmNjY/rJT36in/zkJwXHbNiwQffff7+am5vPaZ7Aye7cWFswxFTusev2DTW6a1OtLmoskdlMcAkAAAAAAJxgMpm0qtqrVdWrZhy3fXfPlMdHByL6ztOH9J2nD2lNjU93barRXRtr1VDqmsvpAgCAC9Bk1aVAIKChoaFTjh8fH5+xlRZwoaupqVFNTY0ikYj6+vrU19eXK/TxXolEQp2dners7FRpaalqa2tVWlpKsQRgkSPENIfuuusu7d69W9/5zne0fft2dXV1yW63q6WlRb/6q7+qL3zhC3K5zv3LkUOHDum1116TJN10002qrq4+7X2/9rWvafPmzXrllVe0b98+DQwMaHh4WA6HQ1VVVbr44ov1oQ99SB/4wAdksVjOea64cIRjST25r0+P7+3V//7wZrns03/dXN1aLq/TqnAsJUkqdtl02/pq3bWxVpcsL5OF4BIAAAAAADhH2/cUrgK9vyek/T0h/eUv27SlsVh3bazVHRtrVOWjTQsAADh7yWRSvb29CgQCisViM441mUwqKytTVVWVSktLZTab52mWwNLldru1fPlyNTc3a3h4ONee0TCMvOOHh4cVj8dVWlo6zzMFcKYIMc2xZcuW6a//+q/113/912e037XXXlvwl+x7rVy58rTHvteaNWu0Zs0afelLXzqr/YGTRRMpPb2/X4/sDmhH24ASqWwf52cO9OvOjbXTxjusFv3K++oVjqV056YaXdlSLpuFN+cAAAAAAGB2GIahv/rVTdq+u0eP7O5Rd3C84Nh3OoJ6pyOoP9u+T5c0l+quTbW6bX2NSt32eZwxAABY6gKBgI4cOaJMJjPjOLfbrerqalVWVspu5/0GcDYmQ4BlZWVKJpPq7+9Xb2+vxsbGpo2tra2lChOwBBBiAnBOYsm0njs4oId3BfT0/n6NJ9PTxjyyqydviEmSvnn3urmeIgAAAAAAuECZTCZtrC/Wxvpi/cFtq/VOZ1AP7wpo++4e9YfjefcxDOnVo8N69eiwHni7W//x25fP86wBAMBS5na7CwaYLBaLqqqqVF1dLa/XO88zA85vNptNdXV1qqur09jYmHp7e9Xb26t0Oi2LxaLKysqC+46MjMjv91MJDVgECDEBOGOJVEYvHR7Uw7sCenJfn8Lx1Izjn2nrVziWlNdJD2cAAAAAALAwTCaT3tdYovc1lui/3bFWrx8b1kO7Anrs3R4Fo8m8+9yxoWaeZwkAAJY6n88nt9utSCSSW+d2u1VbW6vKykpZrZyeBeaax+NRS0uLmpub1d/fr1QqVfDfXiQS0e7du+VwOFRXV6eamhr+nQILiH99AE5LOmPolSNDemR3QI+926vR8fxf7p3MZjHp6pUVumtTLW3iAABYQIZhKJ7KKJ7MaDyZViKVUSKdUTKdUSI1cZ9bNnLr3zsmmc4eJ5nOKHnS9nTGUDojpTMZpQ0pkzGy64yJ+4yhzHuWUxkjO87I7pvJGEplMjqTJsmZdEbB0ZWSpMf+8U2Z87zfMEmymE0ym0yymE2ymk0ym02ymE7cW8wnbtlxmnhslsWk3DirxSSr2Sy71SybxSy7xZRbtlmy6+0Ws2xWk+wWi2wWk2xWsxwWs2y5fcyyW0258U6rRUV2ixxWM+WsAQCYRxazSZetKNNlK8r03+9ZpxcnLtZ6Ym+fxiYu1jKZpDs25g8xGYahP314n65oKdfVreVyWC3zOX0AALCA4vG4uru75ff7VVZWNm27yWRSbW2tDh8+rIqKCtXW1srn8/G5H1gAFotFNTUzX5jQ1dUlKftv++jRo2pvb1dNTY3q6urkdDrnY5oATkKICcBp+R/b9+n7Lx0/5TiL2aTLV5Tprk21umVttfwuqi8BAHAq6YyhSCKlSHzyls7eJ7L30URasWRa48m04hP348m0YhOhpPjJjxNpxVJpxRJpxVInHhtnkg5aUhySpODw+ALP49w5bWY5bRYV2Sxy5m7m3OMim0WO9zye3Mdps8hlt8hlt8rjsMrlsMjjsMrtsMptt8jtsBIqBwCgAJvFrOtWVeq6VZWKJdN6tm1AD+8OaDyRVpUv/0mL3V2juu/l47rv5ePyOa26dX217tpUq8uWl8nK31wAAM5LY2Nj6urqUn9/vwzDUDAYVGlpad5wUlVVlcrLy2W32xdgpgBOVzweV19f35R16XRaXV1d6urqUmVlperr62n/CMwjQkwATstNa6sKhphMJumS5lLdubFWt62vVpnHMb+TAwBgAWQmgkehWEqh8aTCE/ehWFJj8ZTGpgWSpoeTJtfHkpmF/nGwCMSSGcWSGQV16oqXZ8NuMcvtsEwEm6zvWT7x2OOwymW3yOu0yeu0yue0yVc0ce+0yeO0ymLm6lEAwPnJabPo1vXVunV9tYwZUuAP7wrklkOxlP7fm136f292qcxt1+0banTXplpdvKxEZv5mAgCw5I2Ojqq9vV0jIyNT1ofDYYVCIfn9/mn7WCwWWSxUagQWu1QqJa/Xq1AolHd7f3+/+vv7VVxcrGXLlqm4uHh+JwhcgAgxAZAkdQxF9fDugD5+ybK81ZMuaS5Tpdeh/nA8t+6iZSW6c2ONbt9QU/DKRAAAFivDMBRJpDUSSWh0PJkLIIVyYaSTwkmx5LTlsXhKmfO2uhHOR4l0RoloRiPRcw9JeRxW+ZxW+YpODjrZ5HNa5T0p9HTysr/IphKXXV6nlRO6AIAloVDLl0zG0CO7e/JuG4ok9KNX2/WjV9tV7XPqzo01untzrTbU+WkhAwDAEhMMBtXe3q5gMFhwTFdXV94QE4Clwe12a8uWLQqFQurs7NTg4GDeccFgUMFgUH6/Pxdm4v09MDcIMQEXsP5QTI/s7tFDuwLa2RmUJJV77Prw1sZpYy1mk+7cWKvXjw/p7k21umNjreqKi+Z5xgAA5JdIZRSMJjQSTWokmlAwmsw9zt5PrstuH4kmNTqeUDJ9YaeQzCbJbjXLZjHLbjHnlm0Wk+xWi+wWU3Zbbr1ZDmt2+8nrLWaTLGaTzCaTrGaTzGaTLCaTLGadtGyaMs4ysd5szo6zmM3Zx2fw2T86Pq7HH39cknTLLbfIVTT9vUnGkNKGoUzGUDpj5JZTGUMZY2JdblnKGIZS6RPjTt43lTGUymSUTBnZQFA6o2Rq4j6dUSKVUSJtTFk3uZxIZZRMZ/dLnwfpt8lqY4HR2BnvazZJxS67il02FU8Em4pddpW4bCpx23NhpxKXLbvebVNxkV1Fdq5gBQAsDqmMoc9ft0IP7QrojeMjBcf1hmK698VjuvfFY1pW5tJdG2t116ZaraqmFQUAAIvVZJu49vZ2jY6OFhxnNptVXV2turq6eZwdgLni8/m0bt06jY+Pq7u7Wz09PcpkplfPHx0d1e7du+Xz+bRs2TKVlJQQZgJmGSEm4AIzGk3qsXezwaVXjw5NqyDx0K5A3hCTJP3R7atltZjnYZYAgAuZYRiKJtIaGktoMBLX8FhCQ5G4BscSGhpLaDgS11AkoeHIibBSJJFe6GnPKofVnG3p5bDIbc+29iqyW1Rks8hhy947beaJ+xO3/OvNKrJb5LRacvcO24nw0VIWCoV0eEe21PNNq8vl8/kWeEanJ50xlExnFE9lFE+mFUtmNJ5MK5ZM5+5jeddnctvGE2nFUhmNJ9KKp7KPo4m0oomUxuLZ++gi/XeRMaThiX/DZ8JhNU8EnrIhp1KPXeVuu8o8DpV57CpzT95n1/mcVr5EAgDMCbvVrE9c1qRPXNakQHBc23f36OHdAe3uKnyis30oqv+z47D+z47D+ui2Bn3rgxvnccYAAOBUDMPQ8PCw2tvbFQ6HC46z2Wyqq6tTbW2tbLbpXS0ALG1FRUVqaWnRsmXLFAgEFAgElEhM/w4rFArp3Xff1SWXXCKHw7EAMwXOX4SYgAtANJHSU/v79dDObj13cGDGqhOvHBlSfzimSu/09nAEmAAAZyueSk8EkBIaHItraCKYNBTJBpOGxk5ajsQVS06/ymUxM5km22tlW2p5HVZ5nNnwkcdhldthldtukdthlcthlWcinOSe2OZxWOSynxjH39zzW7YiVTZkpqK5+8IznTFyYaaxeEqReEqReDp7n3jv8onwU2SiytJYPKXQ+IkWigtdQCqeyqg3FFNv6PSqP9ksJpW6T4Sbyj2O7GOPXeWTgSePQ2Xu7DYqPQEAzkZtcZE+d/Vyfe7q5To+GNEjuwN6aFdAB/vGCu6zpbFkHmcIAABOZWRkRMeOHZsxvGS329XQ0KCamhpZLHx+BM53NptNy5YtU319vXp7e9XZ2al4PD5lTFVVFQEmYA4QYgLOU4lURs8fHNBDuwJ6cl+fxpOnvhK/1G3X7RuqlbrAW+sAAE6PYRgKxZIaCMfVH4qrPxzTQDiefRw+8bg/HFcwmlzo6c7IYjbJ57TKV2SbCCJZ5XVk73PBJGe+5ew+HrtV5iVe1QjnH4vZJK/TJq/TpqpzPNZkhbRsoCkbbAqftBwaTyocS03ZHoqlFB5P5tYl0vMbTkymDfWF4uoLxU89WNkgYqXXoQqvQ5U+pyo8DlX6HCfWeZ2q9DpU7LJR4QkAkFdTuVtfuH6lvnD9SrX1hvXwroAe3h1Q+1A0N8ZmMemWddV5909nDD24s1s3rKmSfw6DzgAAYKpgMFgwwORwOHLhJbOZi86AC43FYlFdXZ1qamrU29urjo6OXJipsTF/ZxsA54YQE3Ae2r67R3/08z0aHT/1CWO33aJb1lXr7s21uqKlXDYqPwDABc8wDA1HEuoNxdQfjmsgFNfAWFz9oZi6h8f0bqhZUcOm7//PlxVLLb6KSV6nNdduqthlV8nEsr/Ill12Z9cXF02McWcrJxFKAAozmUy5ymE1/jPffzIEFRxPamSiFeRINKHgeFLBSEIjE60hc+smto+OJ2XMU75+svrU0cHIjONsFpMqPA5V+JwnBZyyISe3Ja3+VJFc5qTSC126CgCwoFZVe7WqepW+fHOr9nSP6uFdAT2yu0frav0FA0qvHRvS7/2/XbJbzLpudYXu2Vyn61dXZqs3AgCAOdPQ0KBAIKBUKpVb53Q61djYqKqqKsJLAGQ2m1VbW6vq6mr19/drfHxcRUVFecdmMhkdOnRIdXV18ng88zxTYOkjxASchxpKi2YMMNmtZl2/qlJ3b67lyzAAuMCk0hn1h+PqGY2pLxRTz2hMvaPj6g3FJ+5j6huNn6Jiijt7l5n7AJPdala5e6Llk8eu0om2T2Vuu0rc9omgUjacVDwRVCKQCyw+J4eg6orzf8GTTzpjKDSezIafools0CmSXc62oIxPtKnMtqIcHksokjh1BdJzkUwbCozGFBgt1NZuhSTpJ99+UZU+p6p8TtX4T9xX+52q9jlV4y9Spc/Be3EAOM+ZTCZtrC/Wxvpi/eFta2b8vuahnQFJUiKd0eN7+/T43j55HFbdvK5K92yu0xUrymg7DADAHLBarWpoaNCxY8dUVFSkxsZGVVZWEl4CMI3ZbFZ1df7KqpN6enrU29ur3t5eVVZWqqmpqWDgCcB0hJiAJepgX1gVHodK3PZp2zbU+dVU5tLxk8qVW8wmXb6iTPdsrtPN66rkc1KWHADON+OJtHpDMfWOxtQbGs8GlUZjUwJLA2Pxeatqko/FbFKZ+6Qw0nuCSWUex8Tj7LLbbqFCEnABs5hN2cCi267myQDlKYwn0hqKxDU0lpgIOMVzgafs/YnA02AkocQcVZRLG1LPxO/gnZ2Fx5W67ar2TYSbJgJO1f6JwNPEspf37gBwXjBP/F3LJ55K67F3e6etH4un9MDb3Xrg7W6Vue26Y2ON7tlcq/c1lvA+GQCA05RIJNTR0SGr1aqmpqa8Y+rq6mS321VVVcXfWABnLZ1Oq729Pfe4v79fAwMDqqmp0bJly2S35/88AOAEQkzAEtI5HNXDuwN6aGdAB3rD+m93rNFvXrV82jiTyaS7N9Xqb585rIuXlejuzbW6fUONyj2OBZg1AGA2GIahoUhC3SPjCgTH1T1xCwTHFQjG1B0c13AksWDz8zqtJ7VVyrZYqvRNfVzucchfZJPZzBdBAOZOkd2iertL9SWuU441DEPheEqD4bgGwnH1524xDUyum2ipOVe/Y4cj2bDVvp5QwTFeZ7aCVW1xUe6+ttip+pLscqXXKQu/WwFgSRuNJnVJc6mebRsoWBV1KJLQD19p1w9faVddcZHu3lyruzfVanW1l5OtAADkkclk1NnZqfb2dqXTaZnNZtXU1MjhmH6uxGKxnLK6CgCcSiAQUDI5tfqqYRgKBALq6+tTY2OjfD7fAs0OWBoIMQGL3EA4ru27A3poV0BvdwSnbHt4VyBviEmSPnFZk35ta8NpnbwBACy8eCqt3tGYukcmw0kxdQejuYBSIDiu+BxVCynEbJLKPA5VeLKBpEqvQ367SfvfeU0uc0of/5W71FxdqnKPQ0V22iEBWHpMJpN8Tpt8TpuWV3hmHJtIZTQ4lg05DUwEnSYDTv2huAbC2facA+G4MprdE8nhWEoHesM60BvOu91qNqna71RtcZHqJ8NOJZOhp+x6l52P/wCwmFX6nPreb1ys0WhSv9zbo4d2BfTykaGCVVS7g+P6h2eP6B+ePaLWKo/+642tum1DzfxOGgCARaykpEQHDhxQPB7PrctkMuro6NDKlSsXcGYAzme1tbW5AGU6nZ6yLZ1O69ixY7Lb7SotLdXw8PACzRJY3PgWE1iERseTenxvrx7eFdBLhweVKfCF1a6uUR0fjKipfHprjQovVZcAYDFJpDLqDo6rcziqzpGoOofH1TkSzVVW6g/HT32QWWS3mKe0LqrxO1U1cT+5vsLjkNVinrJfKBTSP+x/TJK0ud4nn4+wLIALg91qnqiAVFRwTCgU0ne/+w8aNyy67Vc+pnDKMtHmc3xKa8/e0ZiiiXTB45ypVMZQ18i4ukbG9XqBMSUuW66SU0OpSw0lE/elLjWUuAijAsAi4XfZ9OGtjfrw1kb1hWJ6ZHc20LSrM1hwn4N9YwW/OwIA4EIzPj6uNWvWyO/3TwkwTert7VVzc7OsVk6RAph9FotFy5YtU21trTo6OtTd3S3jPVcmJBIJtba2KhQKKRqNUpkJeA/+QgOLRCyZ1tP7+/Xgzu4ZS4efrNrnVOdING+ICQAwvzIZQ33hWDacdHJQaWK5NxQreBX1bPM6rFMCSpPLJ4JKRSpx2Wg5AQBzwGSSXKa01lZ7Cn4JNdnGrnci0NQ7GlNvLuA0rt5QXL2j4xqJJvPufzZGokmNRJPaG8jftq7cY1d9iUuNpS41lBapoeREwKmm2Cnbe0KtAIC5V+Vz6rNXNuuzVzbr+GBED+0K6MGd3ToyEJkyzm236IY1lXmPkUhlFEul5XPa5mPKAAAsmEQioePHj6unp0d+vz/vmMrKSgJMAOaFzWbTihUrVF9fr2PHjqmvr2/aGJ/Pp7a2No2Ojqq5uVl2u30BZgosPvyVBhbYscGI/s8zh/X43l6NxVOnHF/ssun2DTW6e1OttjWVymzmBDQAzJfRaFLtw5FcFaXO4ag6hqPqGhlX98j4aQVQz5XFbFK1z6m6kmw1jdpip+qKXRP3RaopLpLHwVs8AFjMTm5j11rlLTgulsy2Gg0Ex9U10Vo0EDy57ei4ErPUanRwLKHBsYR25qn0Mfm3ZzLc1DhZwak0W82pwuMgGAsAc6yp3K3fvWGlfuf6Fu3rCemhnQE9vCugwGhMt6yvltOWv6Les239+sK/vaPrV1Xq7s21un51ZcGxAAAsRYZhqLu7W8ePH5/WummS1+tVS0sL1U4AzDuHw6HVq1errq5Ohw8fVig0/eKy3t5eDQwMqLm5WXV1dQswS2Bx4QwXsMDSGUP/8XbXjGNcdotuXluluzfX6sqWCtmtXAUNAHPBMAwNjiXUPhTR8aGo2ociap+4Pz4U1ej47FXEKMTjsJ4IJ5UU5Vr/1E20MKryOWUhwAoAFwSnzaKmcnfByquZjKGhSOKkYFO2pVwgOK7AaDZgOxvVnNIZQ90Tz/Gqhqdtd9ktaix1qanMrWXlE/elLi0rd6vG5+TCCwCYRSaTSetq/VpX69fXbl2tN9tH5Csq/BXvg7sCSqQy+uXeXv1yb688DqtuXlelezbX6YoVZdPaRwMAsJSMjo7q0KFDikQiebdPVkKprKzkwgsAC8rr9Wrz5s0aGBjQkSNHlEgkpmxPp9P8ngImEGIC5kk6Y+Q96dxS6dH6Op/e7Z6avLVbzLp2VYXu3lyrG1ZXqcjOVXIAMBsyGUO9oZiO5wJKJ0JKHUMRRRL5r9iaLT6nNdeep6G0SPUlrmxIaSKw5C+izQMA4PSYzSZVeB2q8Dq0qaE475hoIjURcoqpe2Rc3cFsu9NsJcGoBscSefc7E9FEWgd6wzrQG562zW41q7HUlQ01lbnVVJ69X1bqUl1JEW3qAOAcmM0mbWsuLbh9LJ7S0/v7pq174O1uPfB2t8rcdt2xsUb3bK7V+xpLOGkCAFgy0um0jhw5op6enoLbA4GA7rzzThUXF8/v5ACgAJPJpMrKStntdj388MOqq6uTxZI9/+v1elVTU7PAMwQWB0JMwBzqC8X08K6AHtwZ0M1rq/Q7N6zMO+79m+v0bndIJpN0+Yoy3bOpTresr+ZENgCcpUzGUE8opqMDYzo+eKKq0vGhbPu32Wq9k4/Dap4IKRVNCys1lLr43Q4AmFcuu1UtlV61VOZvWxdNpNQ1Mq7O4Wyb1M7J5ZFxdQ1HFT6NltczSaQyOtw/psP9Y9O2Wcwm1ZcU5UJNy8pcai53q7ncrYZSFwEnADhHu7uCSmeMgtuHIgn98JV2/fCVdtWXFOmuTbW6Z3OtVlfTagcAsLiZzWZFo9G820pKSvT0008rkUjIbOYzBYDFx2w2KxAIaGBgQDfddJNGRkbU2trKRQXABEJMwCwLxZL65Z5ePbirWy8fGZIx8V1RNJHSF65vyfsH6K5NtZKkuzfVqtLnnM/pAsCSFowmdHQwoqMDER0bHNOx3HJE8TkKKlnMJtX4nblwUsNEOGlyucLr4MMGAGDJcNmtaq3yqrVqesjJMAyNjifVMZyt3tQ5Ep0ScOoaGVciffZ/b9MZI1cV8b2sZpMaS11aXuGeCDZ5tLzCreXlbv7WAsBpunxFud7845v02Ls9emhXQK8cPfE91Xt1jYzrH549on949ohaqzy6Z3OdPnRRvar4ngoAsAiZTCatXLlSb731loyJP25ut1srV66UyWSa1qYJABajZDKpZcuWqbW1VQ6Ho+C4jo4OlZaWyuPxzOPsgIVDiAmYBbFkWs+29esX7wT0TFt/3gofRwYi2hsIaX2df9q2Kp9Tv3nV8vmYKgAsObFkWu1DUR0bHNORiYBSNqw0ppFock6e024xq6G0SE1lblrfAAAuWCaTScUuu4pddm2sL562faYWre1DEUXPoUVrKmNkg8qDkWnbPA5rrmJTc7l7ItzkUVO5S14nFQ8B4GR+l00f2daoj2xrVF8opkd29+ihnd3a1TVacJ+DfWP6n4+3aV2tjxATAGDRcrvdqq+vV3d3t5qamlRXVyez2axQKLTQUwOAMzJTgCkYDOrYsWM6duyY6uvr1dTUlGtBB5yvCDEBZymdMfTq0SE9uLNbj73bq3Ds1G0Wfvlub94QEwBc6AzDUF8orsP9YzoyMKajA2M6OhFW6g6OF7xa+FwU2SxaVubKBpXKXVpW6lZTmUvLyt2q9jllMVPhAQCAmZjNJtUWF6m2uEiXr5i6zTAMDYzF1TEUndrWdSj79z10Gp+fChmLp7Sne1R7uqefgK/0OqYEm5ZXuNVS6VF9iYu/7QAueFU+pz57ZbM+e2Wzjg1G9PCugH6xs1tHB6YHRsvcdl3ZUr4AswQA4ITR0VGZzWZ5vfnbYy9btky1tbVyOgndAjj/ZDIZHTp0KPe4q6tLQ0NDam1tVXFx8cJNDJhjhJiAM2AYht7tDunBnd16eHdAfaH4KffxOKy6ZV217tlcq8tXlM3DLAFg8UqlM2ofjubCSof7x3SkP1thaSx+9iczC3HbLWqucKupzD1RVcmlpvLsfYWHVjQAAMwVk8mkSq9TlV6nLm4qnbY9GE3kwk3tQ1EdH4ro+GA26DQcOfvWD/3huPrDcb12bHjKeofVrOZyt1ZUetRS4VFLZfbWXO6W08YVjAAuPM3lbv3uDSv1O9e3aG8gpId3BfTQroB6RmOSpDs31shaoALtY3t69MS+Pt2zuVZXtpQXHAcAwNlKp9M6duyYuru75XK5dNFFF8lsnv73xmKxUJEEwHmrp6dH0Wh0yrrx8XHt2rVLtbW1Wr58Ob8DcV4ixAScgf5wXHf//YunrAhis5h07apKvX9znW5YU8mX4gAuONFESkf6I7mg0uH+MR0eGFP7UETJ9OyWVbKaTWosc2l5rqVM9oTk8nK3KrwElQAAWIyKXXZtdtm1uaF42rZgNJGtyDgQ0dHBsYk2stkKTvE8rbtPRzyV0YHesA70hqesN5mkhhKXWio9WjFRtaml0qOWCq/8LlrTATj/mUwmra/za32dX1+7dbXeOD6sX+wM6Fcuqi+4z7+/2aln2wb083e6Vea2686NNbpnS522NBTz+QsAcM6CwaDa2toUi2WDtdFoVB0dHWpqalrYiQHAPKupqVEymVRHR4eM95ycDgQCGhoa0qpVq1RSUrJAMwTmBiEm4AxU+Zy6pLlUrx4dzrv9kuZSvX9LnW5fX8MX3gAuCCORhA72hXV4sqrSQERH+sfUHRyf9eeq9jnVXO5Wc0U2oLS8wq3mco/qS4pk48pfAADOG8Uuu97XaNf7Gqd+CZfJGOoJxXRsIKJjg9n3Hccm2s92jUSVOYuctGFIHcNRdQxH9cyBqdvKPXatmKjaNHnfWuVVlY+QNIDzk9ls0iXLy3TJ8sKVxIfG4nrh0OCJx5GEfvBKu37wSrsaS126Z3Ot7tlcp5ZKz3xMGQBwHkmlUjp27JgCgcC0bR0dHaqoqJDb7V6AmQHAwjCbzWpqalJFRYXa2toUDk+9MCsej2v37t2qrq7WihUrZLUS/cD5gVcycJJwLKkn9vYpbRj6tYsb8o65Z3PdlBDT2hqf7tlcq7s21aq2uGi+pgoA82p0PKlDfWEd7BvTwb7wxG1Mg2Onbqt5Jlx2i1ZUeLS8wq3l5Z6JoFL25nbwtgUAgAuZ2WxSXXGR6oqLdOXK8inb4qm0OoaiOpqr2jQRrh4YUzCaPKvnGxxLaHBseFprOp/TqpVVXrVWedValQ02razy0KoWwAVh+54epQukRjuGo/q7Zw7r7545rHW1Pr1/c53u2lSrar9znmcJAFhqRkZG1NbWpnh8+neNJpNJTU1NcrlcCzAzAFh4brdbW7ZsUXd3t44dO6ZMZmqV6t7eXg0PD2vVqlUqLS1doFkCs4ezgbjgJVIZPdvWrwd3BfTUvj7FUxnV+J360PvqZTZP/wL69vU1+ucXjuq29dV6/+Y6razyLsCsAWBuhGNJHewbywWWDvVnA0t9odkNK01WNlhR6VHLRGWDlkqPqn3OvL97AQAAZuKwWrSyyjvt85lhGBqKJHLtbSdb3R7pH1NgNHZWzxWKpfRW+4jeah+Zsr7YZVNrZTbQNBlsaq3yqtzjOOufCwAWm9vW1yidMfSLnQHt6gwWHLc3ENLeQEh/8dh+XdpcpvdvqdWt62vkL6JyOQDghEwmo6NHj6q7uzvvdp/Pp1WrVhFgAnDBM5lMqq+vV1lZmdra2jQ6OjpleyKR0J49e1RbW6vly5fLYrEs0EyBc0eICRekTMbQ68eH9eDObj26p1ej41OvzO0Zjen148O6NE/5bL/Lpme+fO08zRQA5kYkntKh/mxVpUN9YbVNBJd6zvJkXj4mk1RfUjQlpDTZiqXYZZ+15wEAACjEZDKp3ONQuccx7fNdJJ7S0YGIDg+EJ4JNER0eGNPxwYhSZ9GbLhhN6vXjw3r9+NTKTaVu+0kVm7xqnWhLV+Lm/RCApafC69Cnr2jWp69o1rHBiB7aGdCDO7t1dDCSd7xhSK8cHdIrR4f0J7/Yq0e/eBWt5gAAkqSxsTEdOHBAkcj0vyFms1nNzc2qq6uj2ikAnKSoqEibNm1ST0+Pjh49qnQ6PWV7IBBQMBjU2rVracGJJYsQEy4YhmFoX09ID+0M6KFdgVOeqH9wZyBviAkAlpJUOqPjQ1G19YZ1oDek/T1htfWF1Dk8PmvPYbeY1VzuVkvViZBSy0RLOKeNtD8AAFic3A6rNtT7taHeP2V9Mp1R+1B0StWmwxPL0US6wNEKG44k9OrR4SltyaVsEGB1tXfi5tPqGq9aKj1yWHn/BGBpaC5364s3rtTv3tCiPd2j+sU7AT28O6CBcP5KvpU+h1ZUcCIFAC50hmGoq6tLx44dk2FMv3iguLhYra2tKioqWoDZAcDiZzKZVFtbq9LSUh08eFAjI1MrRScSCVmtxECwdPHqxXmvcziqB3d268GdAR3qHzvleKvZpGtaK3Ttqop5mB0AzJ7BsbjaesPa3xPSgd6w2nqzreDiqcypdz4NVrNJyyvcExUEvGqt8mhllVdNZS5ZLeZZeQ4AAICFZrOYc1Ukb1l3Yn0mY6g7OD7Rbjdb0fJgX7aKUyx55u+3BsJxDYTjeuHQYG6dxWzS8nK3Vtf4TgScanyq9Tu5Ah3AomUymbSxvlgb64v1x3es0StHhvSLnd365bu9GouncuPu3lRb8HfZW+3DslssWl/n4/cdAJzH4vG4Dhw4oGAwOG2byWTS8uXLqb4EAKfJ6XRqw4YN6unp0ZEjR5TJZL+baG1tlcNBa3ssXYSYcN5KZwx97J9f1WvHhk89WNLWphLds7lOt2+oUSll/QEsYrFkWof7xyaCStnA0v6esAbH8l/teqYsZpOaylwnWp5MtD9pKnPLbiWsBAAALkxms0kNpS41lLp0/eqq3PpMxlDnSDQXbDrUlw05HR4YU+IMw+TpjKFD/WM61D+mh3edWO91WqdUbFpd7dOqaq88Dr7WAbC4WMwmXbmyXFeuLNf/eP96PXOgX794p1vPtg3o/VvqCu73/z12QG8cH9HyCrfu2VSnezbXqqmcqk0AcL4ZHh7OG2Byu91as2YNrY8A4AxNVmUqLi7W/v375fF4VFFBoQ4sbXzbhfOWxWyS1znzS3xVlVf3bKnV3ZtqVV/imqeZAcDpMYzs1f5vHx3SW+MVGk479Pg/van2kZjSmemlls+U2SQ1lbm1ciKkNBlYai5308YEAADgNJnNJi0rc2tZmVs3rT0RbkpnDHUMR6cEmw72hXV0IKJE+szCTeFYSm8cH9Ebx6eWiG8oLcoGm04KODWVuWUxc+U6gIXntFl0+4Ya3b6hRqFYUj6nLe+4rpFo7vfb0YGI/vdTB/W/nzqoTQ3Fev/mWt25sVYVXq4kB4DzQXV1tYaGhjQ0NJRbV19fr+bmZpnNXDwJAGfL5XJpy5Ytedt0Tkqn05Iki4XzP1jcCDFhSUumMzo6ENGqam/e7XdvrtNT+/unrKsrLtJdm2r1/i21Wl3tm49pAsApReIpHegNaX9PWAd6Q2rrDetAT1jhXOn9iRNiQ+NndfxKr0Orqr1aM9GaZFW1VysqPHLaeLMKAAAwFyxmk5rL3Woud+uWddW59al0RseHotn3exNVNQ/0htQ5fObv8zqHx9U5PK4n9/Xl1jmsZrVWebWizKn+WJnKLDEFo0n5+PgLYAEVCjBJ0kO7AnnX7+oMaldnUP9j+35d0VKuezbV6pb11VShA4AlzGQyqbW1VW+++abMZrNWrVqlkpKShZ4WAJwXThUGPXLkiEZHR7V27Voq32FR4xMflpxMxtBbHSP6xTvdenRPjwxJr//RjXlbHN20pkpuu0U2q1l3bKjRPZvrdPGyEpm5KhXAAhoIx7U3MKp9PSHtDYS0PxDSsaGIZgjInzaH1axV1d4TV+NPBJbKPFy1CgAAsBhYLWa1VHrUUunRHRtrcuvH4qkTwaaesNp6w9rfG1I4lprhaNPFUxnt6R7Vnu5RSdnjP/w3r6rW79TaWp/W1vq1tsandbU+1ZcUyWTi8zGAheVz2tRY6lLHcDTv9nTG0PMHB/T8wQH98S/26MY1VXr/5jpd3VpBy3MAWILsdrs2bNggp9Mpm61wyBUAMHv6+vrU09MjSXr77be1cuVKVVdXn2IvYGEQYsKS0dYb1i92duuhnQF1B6deofrCoQHdsKZq2j5Fdov+/T9dptYqL19qAJh3mYyh9uGo9gVCU0JLA+H4rBy/sdQ1EVbyavVEhaVltA8BAABYkjwOqy5aVqKLlp24Et0wDAVGYzrQM1mxKawDPSEdHYyccXvhwGhMgdHYlGrFPqc1G2yq8WtdrU9ra31qqfTIZuHzM4D58/FLl+nXL2nU2x1BPbSzW4/s7tFQJJF3bCyZ0SO7e/TI7h4Vu2y6fUONvn7nWqoMA8Aikk6ndeTIEVVVVcnv9+cd4/Xm764BAJh90WhUBw8ezD3OZDJqa2tTMBjUypUraS+HRYcQExa17uC4HtoZ0IM7u3WgN1xw3IM7A3lDTJK0vi7/m2QAmE3xVFoHe8e0r2d0IrQU0v6ekCKJ9Dkf2+u0ypMaVZklpg9ef4k2N1eqtcpLCX0AAIDznMlkUl1xkeqKi6Z85o2n0jrcP5at2NQX1v6JkNOZhuVDsZRePTqsV48O59bZLWa1Vnu0rsavtbXZik2ra3y89wQwp0wmUy7I+d/uXKsXDw/qoZ0BPb63V9ECn6uD0aTeOj5CgAkAFpFoNKp9+/YpEoloeHhYF110EdWWAGCBmUwmuVwujY2NTVnf19encDhMezksOnwDhUUnNJ7S/321XQ/tDOj148On3kHSUCQuwzAogw9gXoxGkxNVlbLVlfYFQjrcP6bUGV4N/14Ws0nLy91aU+PT6poTLeHcpoT+8R//UZL0oS018vl8s/FjAAAAYIlyWC1aV+vXutqpF+0MjcUn2tCFtadjSC/tPa7htENpnX5lpUQ6o3e7Q3q3O5RbZzJJTWVura3xTbSk82ldjU+VPues/UwAMMlmMeu6VZW6blWloomUntzXpwd3BvT8wYFpn7vv3lxb8DixZJqAEwDMo/7+frW1tSmTyUiS4vG4Dhw4oPXr13PuBgAWUFFRkbZs2aIjR44oEAhM2RaNRmkvh0WHEBMWnQ//6zuSp+yU41oqPXr/5lrdvalOjWWueZgZgAuNYRjqC8W1p3t0Sku4rpHxU+98CkU2i9bUeCeubvdrbY1Pq6q9eb9gDYWS5/x8AAAAOP+VeRy6vMWhy1vKFQqV6R86n1bGkG7+0G+oI5zW3u5QrsXx6Pjpv8c0DOnYYETHBiPavqcnt77c48hVa1pb49P6Or+Wlbpkpr0xgFnislt1z+Y63bO5TsORhLbv6dGD73TrzfYRSdI9M4SYPvjdl+WyW3TPljp9bFsjrdcBYI4YhqGjR4+qq6tr2rZgMKhoNEqFDwBYYGazWStXrpTf79fBgweVTp+odjrZXi4cDmvFihUym2kxj4VFiAmLTqXRL5sySsustMwyZFZaJqVlVrnXpds21uqOTfVaU1ssk9kqmfhFCuDcGYah3lBMe7pG9W73qPZ0j2pPd0iDY2fWkiOfco9dayeCSusmrlxvKnPzBSoAAADmnNkkrax066IWnz6wJbvOMAwFRmPa232isujeQEjdwTML6w+OxfX8wQE9f3Agt87rtGpdrU8b6vxaX+fXxvpigk0AZkWp265PXLpMn7h0mTqHo3rt2LDqS/Jf2HiwL6x9PdmKcqFYUh+/pHE+pwoAF4xkMql9+/YpGAxO2+Z2u7VmzRoCTACwiFRWVsrr9Wrfvn3T2ssFAgFFIhGtXbtWdrt9gWYIEGLCIvSA409V7ywQTEpKemvilmOSLHbp4/8hNV81fZ/YqPTjX82OsdglqyN7s7kkW9HE/UnL9pPXF0mV6yRPxez/oAAWjGEY6hmNaU/3icDSu92jGhxLnPOxm8pcU6orrav1qcLroGQyAAAAFg2TyaS64iLVFRfp5nUnysWPRpPa25OtQrovkK3adKh/TOkzaJscjv3/7N13fJ1l/f/x11nZe+/dtOneAyilgIgMFVCqbEQREURF8Ac4+Lr4OlDgCy5AEFSQLVKVJZQCXekeadNmJ83eOznj98ednjRN0owmOWnyfj4e1yMn133d1/kcvG2Sc973ddnZnF/H5vze7eEDva3Mie8NNs2LDyYl3F/BJhEZtcQwPxLDBl+Z/R+7ytyPP7MwXn+Ti4iMg5aWFvbv309HR0e/YzExMWRkZGCxaFtPEZHJ5tj2ckeOHKG8vLzPscbGRnbs2MGcOXMIDAz0UIUy3SnEJFOACxydYB7kcu5uh5Ito5/+iidh3ucGPvbYSrDYwDsIvAMHaEHgG3pcCzG++gSDWb+8i0yEY4GlPaV9A0u1racWWLJZTGRGB7q3zpgTH8ysmEACfWxjVLmIiIiIyMQK9rNxRnoEZ6RHuPs6uh0crmxxb628/2gTOeVNtHU5TjJTX82dAwebZves2DQvwQg3pSrYJCJjZMNxK8R9esHgW86JiMjoVFVVcejQIZxOZ59+k8nEjBkziI2N9VBlIiIyHGazmczMTIKCgsjNzcXl6r15qbOzk507d5KZmUlMTMxJZhEZHwoxydRhGWRZO/spbgVlG+SuLkc3VOeMclITXPi/sPKWgQ/v/jtYrOAf2dt8QxV8EhnCsW0x9o5xYCnQ20pWnLGq0rEVljKiAvCyajtLEREREZnafGwW5iUYQaNjnE4XhbWtfbai2390ZCubNnfa2VJQx5aC3mBTwPHBpp5wk4JNIjIaL3/tDD7IrWFvacNJV2wSEZGRcblc5OfnU1pa2u+Yl5cXs2fPJjg4eIAzRURkMoqJicHf35/9+/fT2dn7mbrL5aKgoICIiAisVkVKZGLpipOpwzpIiMnRfWrz2nwH7u9sPoVJXeB1kn2g37wH2mr79pnM4BfeE2qKOC7gFAF+ERAYY7TQFCPwJDLFuVwuyhra3WGlvWVN7CtrpO5UA0s+VubG9d4NPi8+mOQwP31wIiIiIiLSw2w2kRYZQFpkAJfMN1Y4cblcVDT1vaFgb1kTNS3Dv7GopdPO1oI6tp4k2DQ3Ppi0CAWbROTkvK0WPjE7mk/MjvZ0KSIiU0Z3dzc5OTnU19f3OxYUFMTs2bPx9vb2QGUiInIqAgMDWbx4MQcOHKCxsREwVtabM2eOAkziEbrqZNJpvvEDyMwAlxOcDuOr+7Gjf5+zGxxdEJY+8IT+4XDJb4wwk73TGGvvMLaZ624zvna19nx/rK+t95h30MDznlKIicGDRg47tNX173c5obXaaCez9j5Yc/fAxw68DlafnsBTrBGKMmslGTk91LZ0sqe0kV0lDewpbWBP6RissHRCYGl+fDBJCiyJiIiIiIyYyWQiNtiX2GBfLphjLDfvcrmobOrsCTT1hpuqm0892DQ3PogFiSEsSAhhfkIw8SG+mEz6PV5ERERkPHR3d7Nr1y7a2tr6HYuNjSUjIwOzPmsQETlteXl5MX/+fPLz8ykrK2PGjBkEBQ3yGbnIOFOISSYfqw94B47dfL6hsPRLYzffMX7hsO6vRpipsxk6m4573PN9RxN0NEB7vRFMchz3Ru1gIab2OsA18LHhCDjJHWbrv903BGXxgqB4CE6A4MSerwnHfR9/8hWjRMZJS6edfWWN7C4xwkq7SxsorW8/pTmDfKzulZWOfVVgSURERERk/JhMJmKCfYgJ9umzGkplUwd7Sk8t2LQ5v47N+b3BpogAL+b3BJqOhZvC/AdZsVlERERERsRqtRIQENAnxGQymcjIyCAuLs6DlYmIyFgxm81kZGQQFRWlAJN4lEJMIqPlHQBZl4zsnO52I9DUXg8hyQOPsXdC0hm9qy51NIzsOQJjB+53dPdfxcnRBfUFRhuMb5gRajrjdph/5chqERmGTruDg+XN7CltYHepEVw6Ut2C6xSyfEE+1j7bwR0LLOnObBERERERz4sO8uETs/sHm/aeEGyqGkGwqaali/8erOK/B6vcfYlhvsxPCGFBQjALEkKYGx+Mv7feChMREREZKZPJxMyZM+ns7KSxsRGbzcacOXMIDg72dGkiIjLGThZgcrlcNDU16d9/GVd650ZkItl8jRZ0kjsTQhLhS//u/d7eBW210FbTE2yq6Q04Hfu+pQpaKqG5wtgqbiAtlaOrub3OaN39l4l1e/5qsHpDaAqEpvZ8TTFep9kyuueVKcnpdJFX3cLu0kYjtFTSQE55M10O56jnDPa1MTc+iHnxIe7AUmKYtpIQERERETmdRAf5ED3bh/OPCzZVNXX024qusmn4waaSunZK6tpZv6ccALMJMqICjC3oEkNYmBDCzJhAvKza+kRERERkKGazmTlz5pCbm0t6ejo+Pj6eLklERCZYaWkp+fn5JCUlkZKSos/iZFwoxCQy2Vm9ICjWaENxOoBBflh0d0DsAiPo1FLFiLesC04YuN/RDYf+DS5H/2NmG4QkGYGm8Iyelg4RMyAoAbRH9pTmcrkoa2h3bwe3u6SBfWVNtHTaRz2nr83CvPhg5icEMz/RuKNaKyyJiIiIiExNUUE+nBfkw3lZfYNN7psielZybWzvHtZ8ThfkVraQW9nCi9tLAfCymMmKC2JhQrCxalNiCGkR/tp2WkRERGQAx1ZgEhGR6aempob8/HwAiouLaW9vZ9asWZj1ea+MMYWYRKaSk616FJEBX/3AeOywGyszNR2FxhJoLD2u9XzfXtf3/ODEgedtLB04wATg7Ia6PKPlvdv3mNUHwtLg3O/BrIuH9/pkUmts62ZXaQO7ihvYXdrAntIGalq6Rj2f1WxiVmwg8xOMO6TnJwaTERmA1aJfhkREREREpquoE7aic7lcFNe1saukgT094aa9ZY10dA9vtdcuh5PdJcZNF1AEQKC3lbnxwSxIDGFhYgiLk0KICtJKAyIiIjL1dXd3c/jwYdLS0rTSkoiIuHV2dpKTk9Onr7q6ms7OTubOnYvNZvNQZTIVKcQkMh1ZrBAcb7TEZQOP6WqFxrLeUFNI0sDj6gtHV4O9A6oOMOjKUS4XrL8TQpMhMgsiZxpBKqV5JwW7w8nBimZ2lhihpZ0l9eRXt57SnGmR/ixIMFZXmp8YwuzYIHxs2o5QREREREQGZzKZSA73Jzncn88sjAeMv1cOV7UY4aSeYNPBimYczuGtSNzcaWdTfi2b8mvdfXHBPixKCmVhYgiLkkKYGx+sv1dERERkSuns7GTPnj20tbXR1tbGwoULsVr1MaKIiIC3tzfp6ekcPny4T39TUxM7d+5k/vz5Cr/KmNFvHyIyMC9/iMw02smEZ8BFvzLCTMdaXQF0DzPQEjFj4P6WKsh+sm+f7VhNs4xQU+Qso4UkK9w0zioaO9hZXM+ukgZ2Fjewp6xh2Hc2DyQ22If5CcadzQsSjA8Agn2V0hYRERERkVNntZjJig0iKzaILyw3+jq6Hew/2sTuEmPV2D2ljeTXDP9GjKONHRzdW876veXGc5hNZMUGuUNNCxNDSI3w11bXIiIiclpqa2tjz549dHZ2AtDa2kpOTg5z587V7zciIgJAXFwcPj4+HDhwAIejd5ee9vZ2d5DJ39/fgxXKVKEQk4icmpBEWP6Vvn0uF7TW9ISaCoxQU+1hqD0CNUegq9kYZ7IYAaSBVB/s39fdCkd3Gu14Vl8jDBU5C6LnQPRcSFwOPkGn/PKmo/YuB3vLGvuEliqaOkY9X7CvjfkJwSxMDGF+z0pL2opBREREREQmko/NwpLkUJYkh7r7Gtu72VvayO7Shp5wU+Ow//axO13sLWtkb1kjz242tqEL8bOxIMEINS1KCmVhQgjBfrpZQ0RERCa3pqYm9u7di91u79Pf0tJCZ2enVtYQERG3sLAwFi1axN69e93BV4Curi527drF3LlzCQ4O9mCFMhUoxCQiY89kgoBIo524XZ3LBa3VUHMYmsvB6jXwHNWHhv989nao2GO0vT19X3oLklaMqvzpxOl0kV/T2hNWMkJLI9lm4UReVjNz44JYmBjKgkQjuJQU5qe7dUREREREZNIJ9rVx1owIzpoR4e6rbOro2Yaugd0ljewuaaC5036SWXo1tHWzIbeaDbnV7r60SP+e1ZpCWZQYwsyYQGwWrSQsIiIik0NdXR379+/H6ey76r6vr6+2BhIRkQH5+/u7g0ytrb0rHNvtdvbs2UNWVhYREREnmUHk5BRiEpGJZTJBQJTRTiYyExZfZ4SZqg5CZ+PInid69sD9lfvhpS8ZqzVFz4GYeUYLjBnZ/Kep+tYuI7DUE1raXdJAU8fw3pAfSEq4X+8b8kkhzIoJwsuqN+RFREREROT0FB3kwwVzYrhgjvE3otPpIq+6hZ3FvX9H5VY2M9z7PvKrW8mvbuWVHWUA+NjMzIsPNlZq6tmKLjbYd7xejoiIiMigKisrOXToEC5X319sAgMDmTdvHjabVpQUEZGBeXt7s2DBAvbt20dTU5O73+l0sn//fmbOnElMzPT47FXGnkJMIjI5pZ1jNDBWb2quMLaYqz4E1Tk94aYc6Gjof25oCngHDjxvxd6eeQ7Cvpd6+wOiIXYhxC3EGjyDQFcTzQwyx2nC4XRxqKKZHcX17CiqZ0dxPYW1baOeL9DHarzJ3hNaWpAYQpj/ICtpiYiIiIiITAFms4kZ0YHMiA7kymWJALR22tlT2uhe0XZnSQPVzZ1DzGTo6HayrbCebYX17r7oIG8WJ4UaLTmEOXHB+Ngs4/J6RERERABKS0vJy8vr1x8aGsqcOXOwWPS7iIiInJzNZmP+/Pnk5ORQW1vb59ihQ4fo6uoiMTFRu7XIiCnEJCKTn8kEQbFGS1/b239sa7rqg8ZqTVUHjJWWwlIHn6ti78D9LZVw+E04/CZ+wK1AK354v7IJEpcYAafYBRCSZNQzCTW0dbGzuMEILRXXs6u4gdYux6jmsphNzIwOZFFSiHulpbQIf8zmyfnaRUREREREJoq/t5VV6eGsSg8HwOVycbSxg13Fvdt07y1rpNPuHGImQ2VTJ//eV8G/91UA4GUxMyc+iCVJoSxONsJNMcHaykVEREROncvloqioiKKion7HoqKimDlzJmazVtoXEZHhsVgszJkzh9zcXCoqKvocKygowG63k5aW5qHq5HSlEJOInL6O35ou9ezhnVO5f9jT+9MGhe8bDcDmB/eUgsnzd6E4nS6OVLe4V1jaXlRPXnXr0CcOIibIx72VwcLEEOYlBOPnpR8RIiIiIiIiQzGZTMSH+BIf4svF82MB6HY4OVjezM4S4waTnSUNFNQM72+2LofT2L6uuAE+LAAgPsSXRUkhLOkJNc2OC8Jm0QeMIiIiMnwul4uCggJKSkr6HYuPjyc9PV2rZYiIyIiZTCYyMzOx2Wz9fsb4+mr7dBk5fUItItPLeT+ArEuNMFPlPuNrV8vwzo2ZB+ZBAkxH3jVWeUpYaqza5B0wZiUDNHd0s6ukge1F9ezoubu3ucM+qrl8bGbmx/cGlhYmhRAbrF8iRERERERExorNYmZeQjDzEoK5bpXRV9/axa7SBneoaVdxPU3D/LuurKGdsoZ23thTDoC31cyChBAWJYe4t6KLDPQer5cjIiIipzmXy0VeXh5lZWX9jqWmpmq7HxEROSUmk4m0tDS8vLzc25VmZGQQGxvr4crkdKQQk4hML/GLjXaM0wl1eVC+G47uhPLduMp3Y+ps6n9u7ILB5933Muz6q/HYZIbILEhYAvFLIX4JRGUNHoA6gcvloqCmtU9g6VBlMy7XCF7ncZLC/Hru1jW2hZsZE6g7dkVERERERCZYqL8Xa2dGsXZmFGCssFtQ28rOYuOGlZH87ddpd7K1sI6thXXuvqQwPxb3rNa0KCmUWTGBWPW3n4iIiAAlJSUDBphmzJhBXFycByoSEZGpKCEhAavVit1uJz4+3tPlyGlKISYRmd7MZoiYYbR5nwOgubGB5377v8Q4y7lwYRzetQfg6C5jhaXBlG3vfexyQtV+o+14xuiz+RvhqcTlkLgSEpeBbygArZ12dpcaWwUc2x6uvq17VC9Hd+OKiIiIiIicHsxmE+mRAaRHBvC5JQkANHV0s7ukgR1FDWwvrh/RKrzFdW0U17Xx2q6jAPh5WViQEMLi5J5gU2Ioof5e4/Z6REREZPKKiYmhsrKStrY2d9/MmTOJiYnxYFUiIjIV6WeLnCqFmERETmQy02AOpcEcytrVX8M7KAhcLnA6Bh7f0QTVh04+Z3crFG6Ewo1UuELJdmaS7b2SbNcsctqCcYxylaX4EF8WJRmBpSXJoWTFBuFl1Z22IiIiIiIip6MgHxurZ0SyekYkYKzWdKS6xX3Dy/aievKqW4c1V1uXg035tWzKr3X3pUX6szQ5lKUpYSxNDiU1wl9bx4iIiEwDXl5ezJ8/n927d9Pe3s6sWbOIjo72dFkiIjINtbS04Ofnh9mszzNlYAoxiYgMh8kElkH+yexuh6U3Qmk2VO4HV2/YyeEyketKINs5s6dlUkZkz3kjK8HLYmZOfJA7sLQ4KZSYYJ9RviARERERERGZ7MxmE5nRgWRGB/KF5UkANLR1GSv5FhvBpl3FDbR2DXLTzQnyq1vJr27lhexSAML9vVicHMqylFCWJIcxNz4Ib+vwtkIXERGR04u3tzfz58+npaWFiIgIT5cjIiLTUGNjI3v27CE0NJTZs2cryCQDUohJRORUBUbDJb8BoK2lmV17d7H9UBHZRzvZ0RxCs2t0QaOoQG8WW/JYEuli8ax05ixYgU9A0FhWLiIiIiIiIqeZED8v1s6KYu2sKAAcTheHKpqN7ed6VmwqrG0bYhZDbWsXbx+o5O0DlQB4Wc0sTAhhSUooS5ONG2hC/LQFnYiIyFTh4+ODj49ujBURkYnX1NTE3r17cTqd1NbWcuDAAQWZZEAKMYmInIKqpg6yi+rJLqxne1Ed+482YXe6gNARzWMxm5gdG8SS5FAWJYWwJDmUeEsjpl9fASUY7V0rxC6ElDMh+SxIWgk+CjWJiIiIiIhMZxazidlxQcyOC+LalckA1LR0srO4ge09oaY9pQ10dDuHnKvL7mRrYR1bC+vcfTOiAljas1LTspRQksL8tAWdiIjIJOVyuaiqqiIqKko/r0VEZNJwOp0cOHAAh6N3FeHa2loOHjxIVlaWfmZJHwoxiYgMk9Pp4kh1C9sK69heWE92UT3FdcO7u/VEgV4mloS0stSvgiWfuIqFiSH4ep2wZP/+t04owA5l2Ub76GEwmSFmPiSfaQSbklaBX9goX52IiIiIiIhMFREB3nxidjSfmB0NQLfDyYGjTWQXGTfgZBfWU9XcOay5Dle1cLiqhee2lrjnXpocytKUUJamhDEnLgibRXfOioiIeJrL5eLw4cOUl5fT0NBAZmamPhQWEZFJwWw2k5WVxd69e/sEmaqrqzGbzcycOVM/s8RNISYRkUHYXSayixvJqa4iu7COHcUNNLZ3j2quxDBfliaHsSQ5lGUpYcyICsBsHuKHccmWkx93OaF8l9E2PwaYIHqOEWpKPRsyzgOb76jqFRERERERkanDZjGzIDGEBYkh3HRWKi6Xi9L6drYV1hnBpsJ6DlU2D2uumpZO/rO/gv/srwDAx2ZmQUIIy1LCWJISyuKkUIJ9beP5ckREROQELpeLvLw8ysvLAaioqMDpdDJr1ix9KCwiIpNCcHAwc+fOdW8pd0xlZSUmk0nhW3FTiElEpEdNSyfZhfV8fLiC/zSlUePw4fG/7BnxPBaziTlxxtZwS5PDWJoSSnTQKPYZn/1ZsHhB0UdwdKexEtNJuaByn9G2/gHuylOISURERERERPoxmUwkhvmRGObH5YsTAGhs62ZHcT3ZPSs17SppoNM+9BZ0Hd1OthTUsaWgrmduyIwK7Fmpyfi7OCFUf5uKiIiMF5fLRX5+PmVlZX36q6uriY+PJygoyEOViYiI9BUSEuIOMrlcLnd/RUUFZrOZjIwMBZlEISYRmZ5cLhf5Na1sK+i567SonoKa1uNG+A17rgBvK4uTQ43l9JNDWZAYgr/3GPzzmrTCaABdrVCy1Qg0FX5kbCnn6Br83Oi54B8x8LGaI9DRALELwaIfAyIiIiIiIgLBfjbWzopi7awoALrsTvYfbWR7Ub2xrXpRPTUtJ/k7tIfLBYcqmzlU2cxftxQDEB3kzYK4QFo7wom1tmJ3uoaYRURERIarsLCQ0tLSfv2zZ89WgElERCad0NBQ5syZw/79+/sEmY4ePYrZbCYtLU1BpmlOn16LyLRgdzg5UN7E1oI6Y7n8wnpqW4d+83Ug8SG+PdvChbIkOYyZMYFYhtoa7lR5+UP6WqMBdLdDaXZPqOlDKN0G9o7e8alrBp8r+0nY/FvwDoKUs4yxaWsgcpZxy6yIiIiIiIhMe15WM4uSQlmUFMqXV6fhcrkoqm0ju6ie7J5t6I5UtQxrrsqmTt5q6gRiAfj3rzexODmU5SlhLEsNY2FiCD42yzi+GhERkampuLiY4uLifv1ZWVlERAxyk6uIiIiHhYeHM3v2bPbv39+nv7S0FIvFQkpKimcKk0lBISYRmZI6uh3sLG5gW6ERWtpRVE9rl2PE85hNkBUbZKyylGJsDRcbPAmWwbf5QupqowHYO40t5wo3QsEHkHHe4OfmbzC+djbBoX8ZDSAwFtLP7W1+YeP7GkREREREROS0YTKZSInwJyXCn88tMbagq2/tYkdxPdsK69leVMfu0ka6hrEFXWuXg42Ha9h4uAYAL4uZeQnBLEsJY3mqccNQsK9tXF+PiIjI6a68vJyCgoJ+/bNmzSIqKsoDFYmIiAxfREQEWVlZ5OTk9OkvKirCZrMRHx/vocrE0xRiEpEpobGtm+yiOrYW1rGtoI69ZY10O0a+PL2/l4V5cYHYK3KJtbZy71evIjbyNAjzWL0haaXRzr5r8HEt1VC1f+BjzeWw669GwwRxi4wwVPp5kLBMW8+JiIiIiIhIH6H+XpyXFc15WdEAdNod7CtrJLuw3r11e90wVkHucjjZ3jP+9xuMRYJnRgeyPDWsJ9gURnSQz3i/HBERkdNGTU0Nubm5/fozMzOJjo72QEUiIiIjFxUVhcvl4uDBg336jxw5gs1mUyh3mtIn0iJyWqpo7HAHlrYV1nGoshnXyDNLRAd5szQljGU9Ky3NigmkrbWF3/3uQwD8vafYP5NNZca2cdUHhxjogqM7jPbBL42t51LPhhVfNb6KiIiIiIiInMDbamFJchhLksP4KuByucivaWV7YT2bjlTy373FNDq9h5zH5YKDFc0crGjmmU1FACSF+blXalqWEkZqhD8mbYkuIiLTUENDQ79VKwDS09OJjY31QEUiIiKjFx0djcPh4PDhw336Dx48iI+PD0FBQR6qTDxlin06P/kUFRXxyCOPsH79ekpKSvD29iY9PZ0rr7ySr3/96/j5+Y167qeffpobb7xxWGOfeuopbrjhhpOOaWtr49FHH+XFF18kLy+Pzs5OEhMTufjii/nGN75BcnLyqGsVORXH3vTcVtCz0lJhHSV17aOaKy3Sn+UpYSxNCWN5ShiJYb7T603PuIXw9S3QXGFsO5e/AQo2QGPJyc/rbIKDb8Dsz05ElSIiIiIiIjIFmEwm0iMDSI8M4MKZwcQUv0ub08L88y5nX2U72wrrOHC0CecwbkoqrmujuK6Nl3eUAhAR4MWylDB3y4oNxGoxj/MrEhER8ayWlhb27duH09l3+9bExEQSEhI8VJWIiMipiYuLo6uri6KiIndfeHg4AQEBHqxKPEUhpnH0z3/+k2uuuYampiZ3X1tbG9nZ2WRnZ/PEE0+wfv16MjIyPFil4ciRI1x00UX9Eo6HDh3i0KFDPPHEE/z1r3/lkksu8VCFMp3YHU5yypvdKy1lF9VR0zL08vMnMptgTlyw+07NpSlhRAQMfcfntBAYA/OvNJrLBbVH4Mi7kPcuFH4I3W0DnGSC9LUDz2fvhF1/gxkXQLD2qBUREREREZGB+ZkdfGJWBFcsN+6mbe7oZkdxg/vGpV0lDXTZnUPMAjUtXfx7XwX/3lcBQIC3lUVJISxPCWNZahgLE0PwsVnG9bWIiIhMpPb2dvbu3YvD4ejTHxMTQ2pqqoeqEhERGRvJycl0d3dz9OhR4uLiyMjImF4LUYibQkzjZOfOnaxbt4729nYCAgK45557WLt2Le3t7Tz//PM8/vjj5ObmcvHFF5OdnU1gYOApPd+bb75JXFzcoMdPlsBvbm7m4osvdgeYvvKVr/CFL3wBX19f3nvvPR544AGamppYt24dH330EQsXLjylWkVO1NHtYGdxA9t6VlnaUVRPa5dj6BNP4G01szAxhOWpxl2Yi5NDCZhq28GNB5MJImYYbeUtRiCpeBMceQeO/Beq9hvj4haCf8TAcxR+CG9803gcPdcIM2VeCAlLwaw3jUVERERERGRggT421mRGsiYzEoBOu4O9pY3H3dhUT3OHfch5WjrtbDxcw8bDNQB4WczMS+i9sWlJchjBvrZxfS0iIiLjxeVysW/fPrq6+t7sGx4eTmZmpj7kFRGR057JZCIjI4OQkBAiIiL0s20a06f74+SOO+6gvb0dq9XKW2+9xapVq9zHzj33XGbMmMHdd99Nbm4uDz74IPfff/8pPV9mZiYpKSmjOveXv/wlubm5APziF7/grrvuch9btWoV55xzDmvWrKGtrY1vfvObvP/++6dUq0hTRzfbC+vZXFDLtoI69pY10u0YxtrxJwjysbK0Z9n45amhzI0PxtuqwMwps3pD2jlGuwBoKoe8/4LNd/BzDr/V+7hyn9E+/DX4hkHG+ZD5SUg/F/zCxrl4EREREREROZ15Wy0s7dkGnnPA4XRxqKKZbYV17mBTVXPnkPN0OZxsL6pne1E9v99g3L8zMzqQ5alh7hYV6DP+L0hERGQMmEwm0tPT2b9/v3srueDgYLKysvQhr4iITBkmk4nIyEhPlyEephDTONi6dSsbN24E4KabbuoTYDrmzjvv5KmnniInJ4eHH36Y++67D5tt4u8G6+7u5pFHHgEgKyuLO++8s9+YM844g5tuuok//OEPbNiwgW3btrFs2bKJLlVOY/WtXWwtrGNLfh1bC2s5cLQJ58gzS0QHefcElozg0szoQMxm/YE27oJiYdHVgx93uSD3PwMfa6+DvS8YzWSGxBVGoGnmxRCZOT71ioiIiIiIyJRhMZuYHRfE7Lggrj8jBZfLRXFdG1sL6npWdK6noKZ1yHlcLjhY0czBimae2VQEQFqEP8tTw1iRFsby1HDiQ05y846IiIiHhYWFsXDhQvbu3YuXlxdz587FYtFNvSIiMn04nU5MJpMCvFOcQkzj4LXXXnM/vvHGGwccYzabue6667jnnntoaGjgvffe44ILLpigCnu99957NDY2AnD99ddjNpsHHHfDDTfwhz/8AYBXX31VISY5qarmDrYW9ISWCuo4VNk8qnnSIvxZlhLGstQwlqeEkRjmqx9Kk1F3O8QvhfYG6GgYfJzLaWxTV7wJ3rkfwjNg5kUw+7OQsGRiahUREREREZHTmslkIjncn+Rwfz6/NBEw3ofILqx3B5tyyod381R+TSv5Na08v60EgIRQX5anhrEyNZzlqWEkh/vpfQgREZlUAgMDWbhwIRaLBatVH/GJiMj00dXVxf79+wkICCAjI0N/q01h+g1nHHz44YcA+Pv7s2TJ4B/Mr1mzxv34o48+8kiI6VitJ9ZzoqVLl+Ln50dbWxsfffTRRJQmp5Gyhna2FtS6Q0v5w7gD8kRmE8yOCzJWWupZNj4y0HscqpUx5+UHn3sSHHYo3WasynT4Lag6cPLzao/Ax49AczkkPDExtYqIiIiIiMiUExXow0XzYrloXiwAzR3dbC+qN1ZqKqhnV2kDXXbnkPOU1rdTWl/GKzvKAGNF6OU9gaaVqWFkRAXojXIREfE4Pz8/T5cgIiIyoVpbW9m3bx8dHR00NTXh6+tLQkKCp8uScaIQ0zjIyckBICMj46RJ+FmzZvU7Z7RuvPFGDh06RE1NDUFBQWRkZHD++efzta99jfj4+EHPO3CgN2RwfD0nslqtZGRksGfPnlOuVU5vx5Zt35Jfx+aCWrYW1FFa3z7iebwsZhYmhhhbw6WGsTgphECfid9SUcaQxQrJq4z2if+BhmIjzJT7FhRsAHvHwOfNvGjwObtawct/fOoVERERERGRKSnQx8Y5M6M4Z2YUAJ12B3tLG9laaNx8lV1YT0unfch5Kps6+efuo/xz91EAwvy9WJYSyoqeYFNWbBAWbXMvIiLjwOFwaKs4ERERjJ+Ju3fvpru7292Xl5eHj48PERERHqxMxotCTGOso6ODmpoagCHTf6Ghofj7+9Pa2kpJSckpPe/777/vflxbW0ttbS1btmzhwQcf5KGHHuKrX/3qgOeVlpYCxqpRISEhJ32OxMRE9uzZQ3V1NZ2dnXh7D3+VnGPPM5jy8nL349bWVpqamoY9t4wvl8tFQW072cWNbO9pVS1dI57Hx2pmQUIQS5OCWZIYxLz4ILytvdsXurraaeoaeRhqPLS0tAz4WEbIHAIzrzRadzvWkk1Y89/Gmvc25tZKAFxmG83RK2Cg/893txH4+8U4ouZiz/gk3TM+hStoeqaqdU3KZKLrUSYTXY8y2eialMlE16NMNp6+JjPDrGSGRXHN4ijsThe5lS1sL2kiu7iRHSWNNLYPHWqqa+3izf2VvLnf+Js20NvCosRglvS815EVE4DNYh5iFpkMWltHvoq4iMhEaW1tZffu3aSnpxMdHe3pckRERDzKYrGQkZHRb6GVnJwcFi5cSGBgoIcqk/GiENMYa25udj8OCAgYcvyxENNo37xJS0vj8ssvZ9WqVSQmJgKQn5/Pyy+/zEsvvURHRwe33HILJpOJm2++edB6h1vrMS0tLSMKMR2rbTheeeUVgoODhz1expbLBbUOH47a/Si3+3PU7k+Ha+T/VNhwEGttI87WSqy1lUhLB5ZGF869sG0vbBuH2sfDs88+6+kSppgkcH2JGO9yZjhy8XZ18s6f/jLgyBmOQ1ze3Yq1bAvWsi34bPgR5aYYDllmkWuZRb05bIJrnxx0TcpkoutRJhNdjzLZ6JqUyUTXo0w2k+manAXM9IY6mzfl3cb7IEftfrS7hl4turnTwQdH6vjgSB0AVhzEWNuJsxrvhURZ27GaXOP8CmQ0GhsbPV2CiMiAurq62Lt3L93d3Rw8eJD29naSk5O1namIiExrUVFRtLe3U1hY6O5zOp3s27ePxYsXjyi3IJOfQkxjrKOjd7skLy+vIccf+z9Ue/vIV6C57LLLuP766/v98rps2TLWrVvHG2+8weWXX053dzff+ta3+PSnP01MTMyA9Y6k1tHWK5OT0wU1Dl+O2v04aven3O5Pl2vky9R6m+xGaMnaSpy1lXBLB1pRXQZkMlFhiqPCHHfSYTMch/v1xboqiLVXcI79fapMkeRaZnHIMpMaUyToD3kREREREREZBZMJwi2dhFs6mUsdLhc0Or3c75Mc7fajxTX0e2d2LJTaAyi1GzcLWnASbW0ntue9kmhrGzaFmkREZBAOh4N9+/bR2dnp7isqKsJmsxEfH+/BykRERDwvKSmJ9vZ2Kisr3X1dXV3s27ePhQsXahvWKUQhpjHm4+PjftzVNfSWW8d+GfX19R3xcw21WtEll1zCD37wA77//e/T1tbGk08+yX333TdgvSOpdTT1DrVdXnl5OcuXLwfg8ssvJzMzc0Tzy/B12Z3sL282tocraWJXaRNtXY4RzxPub2NJYjBLk4JZnBRMRqQf5ikSImlpaXHfFXrttdcOa6UyGWMuFwFPPgMn2VkyylVNlL2as+wbcYSmYZ/xKbpnXIQzat6UCzTpmpTJRNejTCa6HmWy0TUpk4muR5lsTvdrsqyhg+0ljWwvNlpxfceQ5zgw96zs5M92wGo2MSc2oGf7uWAWJgQR6KO3Zz0hNzeXBx54wNNliIi4uVwuDh8+3Ge3D4DAwMB+N6eLiIhMRyaTiczMTDo6OvqsrNrS0sKhQ4fIysrSyoVThP5KHmPH77k4nC3iju2/Pl5v3Nx888384Ac/wOVysWHDhn4hpmP1jqRWGHm9CQkJwx7r7+9PUFDQiOaXwXV0O9hRXM+W/Dq2FtSxo7ieTrtzxPPEBvuwIjWMFWnhLE8NIy3Cf1r8IAgICND16Cm3Z0P++5DzBhxaD+31gw611Odj2foY3lsfg5AkWHQdrLlr4mqdQLomZTLR9SiTia5HmWx0TcpkoutRJpvT8ZoMCgoiKymKa840vq9o7GBrYR1bC2rZkl/H4aqh31uzO13sLmtmd1kzf9pUitkEs+OCWJFqvNeyPCWMUP+hV3ySU+fv7+/pEkRE+igrK+uzsgQYN6HPnTtXK0uIiIj0MJvNzJkzh507d/bZOaq6uprAwEASExM9WJ2MFYWYxpiPjw/h4eHU1tZSWlp60rH19fXuYNB4/R8qKiqK8PBwampqKCsr63c8ISGBLVu20NraSkNDAyEhIYPOdWw1pcjISO0rOYm1dNrZXlTvfhNtd2kD3Y6RL1WeFObHitQwlqeGsTItnIRQ32kRWpJJxOYLMz9lNMfDUPQhHPiHEWpqrRr8vIZiaCiauDpFRERERERk2okJ9uHTC+L49AJjq/Talk62FdaxpcC4iexAeROuId6OcbpgX1kT+8qaePLDAgBmRgeyMs24iWxFahjhAXoPTkRkqmtoaCAvL69Pn8ViYe7cuXh5KdwqIiJyPJvNxty5c9mxYwcOR+9uQ/n5+fj7+xMWFubB6mQsKMQ0DmbPns3GjRs5cuQIdrsdq3Xg/8wHDx50P87Kyhq3ek4WPJk9ezYvv/yyu56VK1cOOM5ut7t/iR7PWmXkGtu72VZQx9bCOrbk17LvaBMO58hDSxlRASxPDXMHl2KDR77Foci4sVgh7RyjXfQrKNkCB16HnH9C0wCB0dmfGXyu+iJjtSaF8kRERERERGSMhAd4c+HcWC6cGwsY79dsL6pjS74RbNpb1jis92sOVTZzqLKZP28ybs7JjA5gRWo4K9PCWZEWRoRCTSIiU0pHRwcHDhzo1z9r1iytGiciIjIIPz8/srKy2LdvX5/+nJwcFi9ejK+vPuc+nSnENA7OOussNm7cSGtrK9u3b2fFihUDjtuwYYP78ZlnnjkutVRXV1NTUwNAXFzcgLUeX89gIabs7Gz3qlHjVasMz7HQ0ub8WjYX1LL/6NB39p3IZDp2Z1/PcuWpehNMTiNmCySfYbQLH4CyHZDzDyPUVF8A3sGQumbgczub4bHlEBQPcy+HOZdD9OyJrV9ERERERESmvGBfG+fOiubcWdEAtHba2VFcz9YCI9i0q6SBLodzyHlyK1vIrWzh2c1GqCkjKoCVacaq2StSw4kM1Ps5IiKnK4fDwf79++nu7u7Tn5SUREREhIeqEhEROT2Eh4eTkpJCYWGhu89ut7N//34WLVqk7VhPYwoxjYPPfvazPPDAAwA89dRTA4aYnE4nzzzzDAAhISGsXbt2XGr54x//iKsn4bJmTf8P9c855xyCg4NpbGzkz3/+M3ffffeAKzc9/fTT7seXXXbZuNQqAxuL0JLZBHPjg3tWWQpnWUooIX5ahlamAJMJEpYY7fz/gcp9UJsH1kGu70P/BnsH1OXBB780WuQsI8w093KImDGx9YuIiIiIiMi04O9tZfWMSFbPiASgo9vBrpIGtvZsP7e9qJ72bscQs8CRqhaOVLXwl83FAKRH+rMizVipaWVqGFFBPuP6OkREZGy4XC4OHz5MS0tLn/6wsDBSUlI8U5SIiMhpJikpiZaWFveiLgCtra3k5+czY4Y+8ztdKcQ0DpYvX87q1avZuHEjTz75JNdffz2rVq3qM+bBBx8kJycHgDvuuAObzdbn+Pvvv+8ONl1//fV9QkQAhYWF1NfXs2jRokHreOONN/jRj34EgK+vLzfeeGO/MV5eXnzjG9/gxz/+MTk5OfzqV7/irrvu6jNm06ZNPPnkk4ARhFq2bNkw/ivIaDW2dbO1sCe0lF/LgfKRh5ZsFhPzE0LcW8MtSQ4l0Mc29IkipzOTCWLmGW0w+17p31d9EN7/mdGi58G8K2De5yE4YfxqFRERERERkWnNx2Yxgkdp4QB02Z3sLWtkS0Etm/PryC6so61r6FBTXnUredWt/G2LEWpKizgWajJWa4pWqElEZFIqKyujsrKyT5+vry9ZWVkD3mguIiIi/ZlMJmbOnElbWxttbW0ABAUFkZSU5OHK5FQoxDROHn74Yc4880za29u54IILuPfee1m7di3t7e08//zz/PGPfwQgMzOTO++8c8TzFxYWsnbtWlatWsWll17KggULiIqKAiA/P5+XXnqJl156yb0K069+9Svi4+MHnOuuu+7i73//O7m5udx9990cOXKEL3zhC/j6+vLee+/xs5/9DLvdjq+vLw899NDo/oPIoMYitORlNbM4KYQVqeGsSA1jUVIovl5aIk+kD4cdao+cfEzlXqO9cz8kn2mEmWZ/BvzCJqREERERERERmZ68rGaWJIeyJDmUW8+BboeTfWWNbOlZnXtbQR2twwg15de0kl/TynNbjVBTaoQ/K1J7tp9LCyM22HecX4mIiAylsbGRvLy8Pn0Wi4U5c+ZgtepjOxERGX8ul2vKhGatVitz5sxhx44dREVFkZGRgdls9nRZcgr029A4WbRoEX//+9+55ppraGpq4t577+03JjMzk/Xr1xMYGDjq59m0aRObNm0a9Lifnx+/+c1vuPnmmwcdExgYyPr167nooos4fPgwf/zjH90hq2OCgoL461//ysKFC0ddqxjGKrS0JCm05469MBYkhuBjU2hJ5KQsVrhtG5TtgH0vw/5Xofno4OOLPjLav+6CGRfA4utg5oUTV6+IiIiIiIhMWzaLmUVJoSxKCuWWNenYHU72H21yv5+0rbCelk77kPMU1LRSUNPK89tKAEgO92Nlajgr08NYkRpOXIhCTSIiE8lut7t36TjerFmz8Pf390BFIiIyHa17Yx2FTYX4WHzwsRrN1+qLj8X4GuITQoRPBBG+EUT4RZAQkEBCYALhPuGTMvzk5+fH0qVL8fHRSrRTgUJM4+jSSy9lz549PPzww6xfv57S0lK8vLzIyMjg85//PLfddht+fn6jmnvJkiX85S9/YdOmTWRnZ1NeXk5NTQ12u53Q0FDmzJnDeeedx5e//GX3Ck0nk5GRwc6dO3nsscd48cUXOXLkCF1dXSQmJnLRRRdxxx13kJycPKpap7vGtm73UuCb82vJqRh5aMnbamaxQksip85kgoQlRrvgJ1CyBfa/Avtfg9aqgc9xdsOh9RAYoxCTiIiIiIiIeITVYmZBYggLEkP4ak+o6UC5EWrakl/H1oI6mocRaiqqbaOoto2/ZxuhpqQwP/dKTSvTw4lXqElEZFxZLBbi4+PJz8939yUlJREREeHBqkREZLppt7e7G53DP8/X6ktSYBKZoZnMCJ1BZmgms8NnE+oTOn7FDpMCTFOHQkzjLDk5mV//+tf8+te/HtF555xzjnsruIEEBgZy9dVXc/XVV59qiW7+/v7cfffd3H333WM253TU0NbF1oK6Uw4tLUk+FloKZ0FiMN5WhZZExpTZDMmrjHbh/0Lhh7D3RTjwOnQ29h8//8qJr1FERERERERkAFaLmfkJIcxPCOHms9NxOF0cONrUcyNdLVsK6mjuGDrUVFzXRnFdGy9uLwUgIdTX/X7UitQwEsNGdwOmiIgMzGQykZiYSFBQEDk5Ofj4+JCSkuLpskREZJppt7eP+rxD9Yc4VH+oT39yUDILIhewIHIBK2NXkhiYOClXbJLTg0JMIqdIoSWRKcBsgbQ1RrvoV3D4Ldj7AuS+CY4uCE6ChOUDn9veAH++BOZcBvPXQXDChJYuIiIiIiIiYjGbmJcQzLyEYL68Og2H00VO+bHt5+rYWlBL0zBCTaX17by0vZSXekJN8SFGqGlFWhir0sJJCPXVhxEiImMgODiYJUuW4HK59O+qiIhMuA5Hx5jOV9RURFFTEa/nvQ5AfEA8K2NXclb8WZwRdwZ+Ns/dHOFwOMjLyyMyMpLQUM+vGCVDU4hJZIQUWhKZ4mw+MPvTRmtvgJx/GtvQmc0Dj9//KlTsNdq7P4bUs2HhVZB1KXhpH3sRERERERGZeBazibnxwcyN7w01HaxoYnN+HVt6VmpqbO8ecp6yhnZe3lHKyzt6Q03u7efSwkkMU6hJRGS0bDabp0sQEZFp6ulPPk2bvY0Oewcdjo7ex/YOWrtbqe+op6ajhtr2WipaKyhrKaPbOfTfD8eUtZTx8uGXefnwy3hbvFkVu4pzk87l3KRzCfYOHsdX1ldbWxsHDhygtbWV2tpalixZgpeX14Q9v4yOQkwiQ2ho62JLQZ37zrWDowwtLU0JZWVqOCvTw5mfoNCSyGnBNwQWX3vyMbufO+4bFxRsMNob34bZn4GFX4TkswYPQYmIiIiIiIiMM4vZxJy4YObEBXPTWak4nS4OVjT32X6uoW14oaZXdpbxys4yAGKDfdxbz61MCyc53E+hJhERERGRSS4jNGNE4x1OB9Xt1RQ3FXO44TCH642WW5875KpOnY5O3i99n/dL3+fHm3/MOYnncEnaJayOX43NMn6B3tbWVnbs2IHT6QSgq6uLnJwc5s+fr79ZJjmFmEROoNCSiAxbXT6UbBn4WHcr7P6b0YITja3mFnwRIkb2i6GIiIiIiIjIWDObTcyOC2J2XBA3nmmEmnKrmtmcZwSathTUUdfaNeQ85Y0dvLqzjFd7Qk0xQT6sSOtdqSlFoSYRmebKy8vx8/MjOHjiVp0QEREZaxazhRj/GGL8Y1geu9zd3+3sJrc+l91Vu9lVvYut5Vup7agddJ5uZzdvF73N20VvE+YTxmczPsvnMz9PQmDCmNd87OdvfX29u6+hoYHi4mKSk5PH/Plk7CjEJNPeWISWfGw928MptCQyvYSmwpfehF1/M7aV62waeFxjCWz8ldESlsGCL8DcK8BXe++KiIiIiIiI55nNJmbFBDErJogbekJNh6ta3Cs1bc4fXqipoqmDf+w6yj92HQUgKtDbHWhamRZGaoS/Qk0iMm00Nzdz+PBhXC4XaWlpJCQk6N9AERGZUmxmG3PC5zAnfA5XZV2Fy+XicMNhNh/dzAdlH7C9Yjt2l33Ac+s66vjTvj/x1L6nWJ2wmquzrmZV7Kox+1lpMpmYNWsW27dvp6ur92+ZwsJCQkNDCQoKGpPnkbGnEJNMO/Wtx4eWajlY0TziORRaEhEATCZIWmm0T/0cDv0Ldj0Hee+CyznwOaXbjPafe+HiB4ferk5ERERERERkgpnNJmbGBDIzJpDrVqXgcrk4UtXiDjRtKailpmXoUFNVcyev7z7K67uNUFN00PGhJq3UJCJTl8Ph4ODBg7h67pjOz8+nsbGROXPm6N89ERGZskwmE5mhmWSGZnLdnOto7Gzkg9IPeKfoHTaWbaTb2X8LaxcuPij9gA9KPyArLIsb597IJ5I/gdV86lEWLy8vZs+eza5du/r0Hzx4kCVLlmCx6PP9yUghJpnyxiq0tDQ5jJU9y2HPTwjBy2oeh2pF5LRl8zVWV5p7BTRXwt4XjEBT1f6Bxzs6IXrOxNYoIiIiIiIiMgomk4kZ0YHMiA7k2p5QU151C5vy69jSE2yqaekccp7Kps4+KzXFBPm4329bmRZOskJNIjJFFBQU0NbW1qfPz0//xomIyPQS7B3MpemXcmn6pTR2NvJm4Zu8kf8GO6t2Djg+py6Huz+4m4SABL628GtcnHoxFvOpBY2Cg4NJTk6mqKjI3dfe3k5eXh6ZmZmnNLeMD4WYZMpRaElEPC4wGs64HVbdBhV7YfdzsOcFaKvpHRM1B+IWDXx+VxuYzGDzmZh6RUREREREREbAZDKRERVIRlQg165M7gk1tfZsP2e8L1fdPHSoqaKpg9d2HeW1nlBTbLCPe+u5lWnhJIXpA38ROf3U1dVRVlbWpy8gIICUlBTPFCQiIjIJBHsHc+XMK7ly5pXkN+TzQu4LvH7kdZq7+3+WX9pSyn0f3scTe5/g1oW3ckHyBZhNo/+sPjk5mbq6Opqbe5+rvLycsLAwIiIiRj2vjA+FmOS0V9faxdbj3iBRaElEJg2TCWLnG+0TP4Ij78Luv8HBfxnbyA32RuzOv8B7P4F5Vxrj/FMntm4RERERERGRETBCTQFkRAVw9Qoj1JRf08qWnvfrNg0z1FTe2MGrO8t4dafx4f+xUNOqnpWaEsN8FWoSkUmtu7ubQ4cO9ekzm81kZWVhNuszBxEREYC0kDT+3/L/xzcWfYM38t/gmQPPUNRU1G9cQWMBd224i6fDn+a7y7/LoqhBFgcYgslkIisri+zsbJxOp7s/NzeXoKAgvLy8Rv1aZOwpxCSnnbEILfnaLCxNCXXf2TUvXqElERlnFhvMvNBorbXG94PZ+Qx0NMK2x2Hb4/hHzmGxPY4DlrkTV6+IiIiIiIjIKJlMJtIjA0iPDOCqFUnuUNPm/JGt1HRiqCnOvVJTOKvSw0kIVahJRCYPl8tFbm4uXV1dffrT0tLw8/PzUFUiIiKTl5/NjytnXskVM67gvyX/5U97/8S+2n39xu2v3c91/76OT6Z8km8t+RbxAfEjfi5fX18yMjLIzc119x0LH8+dO1d/V0wiCjHJpKfQkohMOf7hgx87usvYgu44lur9fIL9rO3+L85/FcLKr0DyGYOv5CQiIiIiIiIyiRwfajq2UlNe9bFQk/G+X03L0KGmo40dvLKzjFd6Qk3xIb6s6FlZfVVaOIlhCgmIiOdUVlZSU1PTpy8sLIy4uDgPVSQiInJ6sJgtfCL5E5yfdD4byzby6M5HyanL6TfuzcI3eb/kfW5ZcAvXz7kem/kkCwYMICYmhrq6uj4/r+vq6igvL9fP60lEISaZdBrb7fxnX7lCSyIyPRVvGvSQFQccfNVo4TNgyfWw4KqTh6JEREREREREJpnjt5+7ZmVvqGlTT6hpS34tNS1dQ85T1tDOKzvKeGVHb6jp2PuBKxVqEpEJ1NHRwZEjR/r02Ww2Zs6cqZUdREREhslkMnF2wtmsjl/Nu8Xv8vCOhylsKuwzptPRycM7HmZ9/np+uOqHLIxaOKL5MzMzaWpq6rNyYl5eHiEhIVo5cZJQiEkmnc89sQNrUMSIzlFoSUSmjJVfg5mfgl1/g51/habSgcfVHoa3vgfv/giyLoUlN0DKaq3OJCIiIiIiIqed40NN17pDTS1syutdnb22dXihppd3lPLyDuNv6YRQX/f2cyvTwkgI1YcSIjL2jm0j53A4+vRnZmbi5eXloapEREROXyaTifOTz2dN4hpeOPQCv931W5q6mvqMOdJwhGv/fS3XZF3DHYvvwMfqM6y5j4WM9+7t3RXF6XSSn5/P3Llzx/R1yOgoxCSTzqO2hwm3+eLChAMzDsy4er46MeHEDCYLoQHehAf6EhHoS2iALxaLBVrNsM8C+81gtoDFBhZvsHiB1avnsQ2sPX0Wr57HJ47rGet+3DPO6gtmhaNEZJyFpsDae2HNd6FgA91b/4Tp0L+MlZhO5OiCfS8bLSwdvvwO+IVNeMkiIiIiIiIiY8UINQWSERXItatScLlcHKlqYXN+bc9qTXXUDSPUVFrfzkvbS3lpeymLkkJ49dYzJ6B6EZluKioqqK+v79MXExNDRMTIbtYWERGRvmxmG1dnXc0laZfw2K7HeP7g87hw9Rnzl5y/8GHZh/zsrJ8xL3LesOY9tt3r0aNHAQgPD2fGjBljXr+MjkJMMukstRwmwTKMoFBbT6sc74pOYPUFLz+w+YNtuI/9webX09fTvPzBJxi8g8AnyAhJiYgcz2yB9HNpj1zK00UPMte+lzWBhVjq8wYe7x2oAJOIiIiIiIhMOSaTiRnRgcyI7g01HT4WasqrZUvB0KGmlWnail1Exl5nZyd5eX3fq/P29iY9Pd1DFYmIiEw9wd7B3LviXj6d/mn+Z9P/cLDuYJ/jhU2FXPvva7l5/s18df5XsZgtQ86ZlpZGc3MzCQkJREZGavvXSUQhJpGRsrcbjdqxndfi1RtoOv7rQH0+QUZYwTu4t883xAhMiciU1G7yY5ttBUtveIqg+n2w/Wk48A9wdPYOWnLD4BM4nVpJTkRERERERKYEk8lEZnQgmdGBXLcqBaezN9R0rNW3dfc5RyEmERlrg20jN2PGDKxWffwmIiIy1uZGzOW5i5/jLwf+wqO7HqXzuM/IHC4Hv9v9O7Irs/nf1f9LlF/USeeyWCwsWrRI4aVJSL9FiUwWji5oqzHaaNn8wDcM/EJ7voad5Guo8dU7WMEGkdOJyQQpZxrtUz+HPX+H7KegsRTmfW7gc1wueOpCCEuDpTdBwlJjHhEREREREZEpwGw2MTMmkJkxgVx/hhFqyq1qZnOesfXc9uJ6liaHerpMEZmCwsPDaWxsdAeZoqOjCQ9XaFJERGS8WM1Wbph7A2cnns19G+9jX+2+Pse3VWzj8//8PD8762ecGX/y7aQVYJqcFGKSSadj1Z2QEAUuBzgd4HIaj12unu97+tyPXX3HOY877uwGeyc4uo3VSo5/7Dh2rKu32bt6jp18+elJq7vNaE2lwz/HZOkNNB0LOPmFgX8k+EdBQE879tg3VOEHkcnCLwxWfg1W3AL1BcYKbQMp2w4lW4y2+zmImQ/LboJ5nze2thQRERERERGZQsxmE7NigpgVE8QNZ6bicrn0AYWIjDmTyURcXBxhYWHk5ubS0tKibeREREQmSFpwGs9e9CxP7H2C3+/+PQ5X78qIdR11fO2dr3H7otv58rwv62+B04xCTDLpdM9dB1lZni3C5eoJOx0XcOpu72k9QaGuNuhuNfqOPe5qO+H4ScZ2thghK09zOUa2ApTZ1hNqijwh4BQNAZF9H/uEKPAkMhFMJmOVpcFse6Lv9xV74J93wFvfhwVfNAJNkTPHt0YRERERERERD9GHFiIynnx8fJg3bx6dnZ3YbDZPlyMiIjJtWM1WbllwCytjV3LXB3dR0VrhPubCxSM7HyGnLoefnPkT/Gx+w563u7ubyspK4uPj9beEByjEJDIQkwmsXkYbLy4X2Dugowk6e1rHQF+bobNxgL6ex/b28atxIM5uaCoz2lAsXr0rOAXGQlBsz9e4vl99gsa/bpHpqrsdDq4f+FhnE2z9g9FSVsPSL8GsS8b33z4REREREREREZEpxmQy4ePj4+kyREREpqWFUQt56dKX+N5H3+P9kvf7HHu76G0Kmwp59NxHiQuIG3KumpoaDh8+TFdXF15eXkRFRY1P0TIohZhEPMVkApuv0QKjRz+PvcsIIrTXQ1sdtNcN8vWE447OsXstg3F0GVvbDbW9nVfAcSGnuN6vwQm9TdvYiYyOzRdu3wE7n4XtT0FD8cDjCjcaLSAaFl9nBJqChv5lTkRERERERERERERERMSTgr2DeWTtIzy1/yke2v4QLlzuY4frD3P1v67msfMeY3b47EHnOHLkCGVlvQt55OXlERYWhtWqWM1E0n9tkdOd1QusEeAfMfxzXC5jm7vBwk6tNdBaBS3VPV8roaNx/F5DVwvUHjbaYGz+EBx/XLApsW/IKSgerN7jV6PI6SwgElZ/G868A468a2wvd/gtOO4XOLeWSvjgl7Dx15B1KZz5DYhfMuEli4iIiIiIiIiITDbHVmUQERGRycdkMvGluV8iMzSTuz+4m+auZvexmvYabvjPDfxqza84O+HsAc8PDg7uE2Lq6uqisLCQjIyMca9deinEJDIdmUzg5W+0kMThnWPvhNZqI+BwfLippaevtRpaqozWOQ6Bp+5WqMk12mACoo1wU2gyhCT3fg1JMvq1RZZMd2YLZF5gtPpC2P407HgW2mr6j3U54MBrkHKWQkwiIiIiIiIiMq0VFRXxyCOPsH79ekpKSvD29iY9PZ0rr7ySr3/96/j5+Y167qeffpobb7xxWGOfeuopbrjhhlE/l5yalpYWduzYQVxcHCkpKVqVQUREZJI6K/4snr/4eW7/7+3kN+a7+9vt7dz+39v54aofcvmMy/udFxERQVhYGHV1de6+srIyoqOjCQwMnJDaRSEmERkuq3fvqkdD6e7oDTW1VkFzBTSXQ9PRnq/l0HzUWPVpLLVUGq0su/8xk9nYou74YNOxx2GpEBADZvPY1iMymYWmwPn3wzn3wIHXIftJKN7Ud4xXICz4gieqExERERERERGZFP75z39yzTXX0NTU5O5ra2sjOzub7OxsnnjiCdavX6879Kc4l8vF4cOHcblclJWVUV1dTUZGBpGRkZ4uTURERAaQFJTEsxc9y7fe+xZbK7a6+50uJz/8+Ic0dzVz/Zzr+5xjMpnIyMggOzsbp9Pp7j98+DCLFi3CZDJNWP3TmUJMIjL2bD7GCk9DrfLU3X5cqOn4kNNRaCqDxjLj+4G2vBoplxOaSo1W9FH/41ZfI9QRloZ3QDwL7cU0mEIxNRaD/yyw6J9LmaKs3jD/80ar3G9sNbf7eWPLyUVXg/cgyfKaw8b/T1PXGKu7iYiIiIiIiIhMMTt37mTdunW0t7cTEBDAPffcw9q1a2lvb+f555/n8ccfJzc3l4svvpjs7OxTvkP/zTffJC4ubtDjCQnDuMFUxkVlZWWfIFtXVxctLS0KMYmIiExiQV5B/P783/ODj3/AG/lv9Dn2q+xf0dLdwq0Lbu0TTvL19SUpKYnCwkJ3X3NzM+Xl5Sf9PU3Gjj6VFxHPsflCWJrRBuPoNkJNjaVGayrtfdxYCg0lcNx+pqNmb4fqHKjOwRv45LH+J58Ds81YtSk0tbfe8HSjhSQbW3SJTAXRc+CS38B5P4Rdf4XMCwcf++FDsOsvEDkLln8F5n8BvAMmrFQRERERERERkfF2xx130N7ejtVq5a233mLVqlXuY+eeey4zZszg7rvvJjc3lwcffJD777//lJ4vMzOTlJSUUytaxlx3dzd5eXl9+nx8fEhKSvJQRSIiIjJcNouNn531M2L8Y3hi7xN9jv1+9+9p627jO0u/0yfIlJiYSGVlJe3t7e6+/Px8IiIi8PLymrDapyuFmERkcrMcCxAlDz6mo7E30NRQBPVFfb92Ng1+7nA4u6H2iNH61efVE2rK6G0RMyB8BviFaYUaOT35hsCqrw9+vLUW9r5oPK4+COvvhHf+BxZfZwSaQlMmokoRERERERERkXGzdetWNm7cCMBNN93UJ8B0zJ133slTTz1FTk4ODz/8MPfddx82m22iS5Vxlp+fj91u79OXkZGBxaKbW0VERE4HJpOJOxbfQaBXIL/Z/ps+x5458Axmk5lvL/m2O8hkNpuZMWMGe/bscY9zOBzk5eWRlZU1obVPRwoxicjpzyfYaNFzBj7eXn9CsKm4b8jJ3jH653Z0GSGO6oMD1BXSG2gKT4eITGPVmrBUI5wlcrra8WdwdPbt62yCTY/C5t/CrIth5a2QtEpBPhERERERERE5Lb322mvuxzfeeOOAY8xmM9dddx333HMPDQ0NvPfee1xwwQUTVKFMhKamJioqKvr0RUREEB4e7qGKREREZLS+NPdLBNgC+Mnmn+DC5e5/ev/TmE1mvrn4m+4gU2hoKFFRUVRVVbnHVVVVERsbS0hIyESXPq0oxCQiU59vqNHiFvY/5nRCSwXU5UNdAdTl012VS+3hbYS66vGma/TP29EApduMdjyzzQg3Rc40Qk3HWlgaWLUEoZwGghONUF5Nbv9jLifk/NNosQuNMNOcy3Rti4iIiIiIiMhp5cMPPwTA39+fJUuWDDpuzZo17scfffSRQkxTiMvl4siRvqvzWywWMjIyPFSRiIiInKorZ16Jr9WX7330PZwup7v/T/v+hM1s47ZFt7n70tPTqa2txeFwuPvy8vJYvHhxn+3nZGwpxCQi05vZDEFxRks5C4D2pib+/LvfgcvFrTesI7C72h1wcrfaw8Y2dqPh7IaqA0brU4vV2I7uxHBTeDpYvU/xhYqMofmfh3mfg/z3YOvjcOjfcFxi3a18F7x6M7z9fVj2FVh6I/hHTHS1IiIiIiIiIiIjlpOTAxjbhlmtg3+UMmvWrH7njNaNN97IoUOHqKmpISgoiIyMDM4//3y+9rWvER8fP+p5S0tLT3q8vLzc/bi5uZmmpqZRP9dItLS0DPh4sqirq6O5ublPX3R0NJ2dnXR2dg5ylnjaZL+u5PSja0rGmq4pz1sTuYb7Ft/HT7b3XZHpD3v+gLfLm3UZ69x9sbGxfX6XamlpobCwcNKtyuip6+rE35XGgkJMIiKDMZlw+YVDUCokLu97zOWCtlqoOQy1R4xQU80R43FdvhFUGimn/bit6f5xXB0WY5WmqFl9w00RMxRuEs8xmSD9XKPVFcDWP8KOZ6FrgF9WWirhvZ/Axl/B4uvhol9MfL0iIiIiIiIiIsPU0dFBTU0NAAkJCScdGxoair+/P62trZSUlJzS877//vvux7W1tdTW1rJlyxYefPBBHnroIb761a+Oat7ExMRhj3322WcJDg4e1fOcimeffXbCn/NkzGYzCxYswNu79/3X9vZ2Xn31VVyuAW7mk0lpsl1XcvrTNSVjTdeUZ630XcmmoE1w3KJKj+x9hOyN2aR1pLn75s+fj5+fn/v7I0eO8NJLL+F0OpmMJvK6amwc5aIfJ6EQk4jIaJhMxooy/hGQvKrvMYcdGot7Qk09Iaeaw1B9CFqrBp7vZFyOnnkOG1t0HWO2Glt6Rc+B6LkQM9f4GhBt1CcyUcJS4cIH4Jx7YOdfYMvvoaGo/zh7Bzh0l5qIiIiIiIiITG7H31EeEBAw5PhjIabR3vWelpbG5ZdfzqpVq9yBo/z8fF5++WVeeuklOjo6uOWWWzCZTNx8882jeg4ZmdjY2D4BJoDi4mIFmERERKaQ9PZ0nDjZErylT/+m4E14ubxI6DTC7EVFRWRlZbmPe3l5ERYW5g69y9hSiElEZKxZrMbKSWFpwAV9j7XVGWGm6pyerweh6iC0VIz8eZz23m3p9r7Y2+8X0RtoOhZuipgJVq9TelkiQ/IJglW3woqvGlvMbf4dFH3Yd8yKr3mmNhERERERERGRYero6HA/9vIa+j21Y2GX9vb2ET/XZZddxvXXX4/phJsSly1bxrp163jjjTe4/PLL6e7u5lvf+haf/vSniYmJGdFzDLVCVHl5OcuXGyvRX3vttae0dd1ItLS0uFcKuPbaa4cVGJsIXV1d5OTk9FldISAggHXr1vX730kmn8l6XcnpS9eUjDVdU5PPs4ee5fcHfu/+3mVysTliM789+7fMDJkJQF5eHk1NTfj4+BAfH8+iRYs8Ve6APHVdlZWV8cADD4zpnAoxiYhMJL8wY+WmE1dvaq+H6tze7eSqDxohp6aykT9HWw3kv2+0Y8xWI8gUM/e4lZvmQUDUqbwakYGZLZB1idGO7jJWZtr7EqSebWyLOJDOFtj6B1hyo/H/ExERERERERERD/Hx8XE/7urqGnJ8Z6ex8rSvr++In2uordsuueQSfvCDH/D973+ftrY2nnzySe67774RPcdQW+IdLzAwkKCgoBHNPxYCAgI88rwDOXjwYL/tYWbOnKkPmU9Dk+m6kqlB15SMNV1Tk8OtS2+lnXb+fODP7r4ORwff3fxd/nbx34jxjyEzM5PGxkZiY2Mnfah5Iq+rpqamMZ9TISYRkcnANxSSVhjteB2NA4ebGk9+91Q/TjtU7Tfa8fwje1ZsmgOxCyFuIYSlg9l8Kq9GpFfcQrjs93D+/0DnSX6R2fkXePdHsOGXsOgaY0WnsLTBx4uIiIiIiIiIjJPAwED34+FsEdfa2goMb+u50bj55pv5wQ9+gMvlYsOGDSMOMcnwNTc3U1lZ2acvNjZWASYREZEpzGQycefSO6nvrOf1vNfd/dXt1Xz93a/zzKeewd/fH39/fw9WOX0oxCQiMpn5BEPiMqMdr6PJ2EauYi9U7ofKfVB5ALpbRzZ/azXkv2e0Y7wCIXZ+b6gpdiGEZyjYJKcmMNpoA3HYYfNjxmN7O2x7HLKfhKxL4YxvQMLSiatTRERERERERKY9Hx8fwsPDqa2tpbS09KRj6+vr3SGmxMTEcaknKiqK8PBwampqKCsbxcrtMmxeXl5ERUVRVVUFgMViISUlxbNFiYiIyLgzmUzcv+p+ylvL2Vaxzd2fW5/Ldz/4Lo+c+whmkz4rnQgKMYmInI58giBppdGOcTqhvsAINFXs6wk37YWG4pHN3dUMRR8Z7RivAIiZ3xtqilvYE2yyjMGLkWkv5x/9r1OXEw78w2hJq+CM2yHzUwrTiYiIiIiIiMiEmD17Nhs3buTIkSPY7Xas1oE/Tjl48KD7cVZW1rjVM9m3LZkqvL29ycrKIj4+niNHjhAZGYmXl5enyxIREZEJYLPY+M05v+Gaf11DYVOhu39D6QZ+t/t3fH3h1z1X3DSiEJOIyFRhNkN4utFmf6a3v6PRWKWpcl/vyk1VB6C7bfhzd7VA8cdGO8YrAGLm9YSaFinYJKMXNQfmr4N9LxtbH56oeJPRwjNg1W2w4Atg8534OkVERERERERk2jjrrLPYuHEjra2tbN++nRUrVgw4bsOGDe7HZ5555rjUUl1dTU1NDQBxcXHj8hzSV1BQEIsWLfJ0GSIiIjLBgr2D+e15v+Xqf11NfWe9u//3u3/PrNBZnJd8Xp/xLpeLhoYGQkJCFDofIwoxiYhMdT7BkLzKaMc4HVDXs2pT5T4o3wPlu6ClctBp+ulq6Q2XHGPz792KLmEpxC+B0BTQD205mahZcPkf4bwfwObfwfY/GyuCnaj2CLzxTfjvT2D5zbDsy+AfPuHlioiIiIiIiMjU99nPfpYHHngAgKeeemrAEJPT6eSZZ54BICQkhLVr145LLX/84x9xuVwArFmzZlyeQ/rTB5EiIiLTU2JQIg+e8yBfeesrOFwOd/+9H97L34L/RnpIOgCNjY0UFBTQ2NhIZmYmsbGxnip5StGeLCIi05HZAhEZMOezcO734OoX4Du58O2D8MXnYc3/g8wLISBmZPN2txqhpi2/g5dvgkcWwi8z4G/rYMMvIe+/xspQIgMJToBP/hS+vR8+8WMIHOTOwrYaeP9n8Js58Ma3wd45sXWKiIiIiIiIyJS3fPlyVq9eDcCTTz7Jpk2b+o158MEHycnJAeCOO+7AZrP1Of7+++9jMpkwmUzccMMN/c4vLCxk586dJ63jjTfe4Ec/+hEAvr6+3HjjjaN5OSIiIiIyAstilnHXsrv69LXZ2/j2+9+mrbuN/Px8du3aRWOj8blnUVERTqfTE6VOOVqJSUREegXFGm3mp3r7mivg6C5jpaZjX5vLhz9nWw3k/sdox0TM7F2pKWGpsZ2YRT+SpIdPMJz5DVhxC+x/BT7+P2PFsBPZ26FiD1i8Jr5GEREREREREZnyHn74Yc4880za29u54IILuPfee1m7di3t7e08//zz/PGPfwQgMzOTO++8c8TzFxYWsnbtWlatWsWll17KggULiIqKAiA/P5+XXnqJl156yb0K069+9Svi4+PH7gWKiIiIyKCumnUVB2oP8Hre6+6+/MZ8frrlp9w5+05KSkrc/Z2dnZSVlZGYmOiJUqcUfWIsIiInFxgDMy802jHNlX1DTUd3QfPR4c9Zc8hou/5qfG/z69mCbgnEL4WEZRCsN2SmPasXLPgCzF8H+e8ZYaa8//Ydc8bt2q5QRERERERERMbFokWL+Pvf/84111xDU1MT9957b78xmZmZrF+/nsDAwFE/z6ZNmwZc6ekYPz8/fvOb33DzzTeP+jlkYA6Hg927dxMXF0d0dLS2kBMRERE3k8nE91d+n9z6XA7WHXT3v573Osuil5EakkpDQ4O7v7i4mNjYWKxWxXBOhf7riYjIyAVGQ+AnIfOTvX0tVb2hprLtUJptrMI0HN1tUPyx0dzPEdu7UlPCcohfDDbfsXwVcrowmSD9XKNV7IWPH4V9Lxnbz826ZOBzXC448i6krzW2TxQRERERERERGYVLL72UPXv28PDDD7N+/XpKS0vx8vIiIyODz3/+89x22234+fmNau4lS5bwl7/8hU2bNpGdnU15eTk1NTXY7XZCQ0OZM2cO5513Hl/+8pfdKzTJ2CorK6O5uZlDhw5RUlJCamoq4eHhCjOJiIgIAD5WH3615lese2Mdrd2t7v6fbvkpz6x9Bhp6x9rtdkpLS0lJSZnwOqcShZhERGRsBERB5gVGAyNEUl/YG2gqy4by3eDoGt58zeVw8A2jAZhtELcQEldA0kpIXAkBkePxSmQyi5kHl/8BzvsBNJYMHlAq+gj+egWEpsKZd8DCq8DqPbG1ioiIiIiIiMiUkJyczK9//Wt+/etfj+i8c845x70V3EACAwO5+uqrufrqq0+1RBmF7u5uiouL3d+3tbVx9OhRIiIiPFiViIiITDbJQcncv+p+7vrgLndfh6ODH+74Id9P+T51dXXu/pKSEuLi4vDy8vJEqVOCQkwiIjI+TCYISzXavM8ZffZOqNhnBJqOBZvq8oc3n7MbSrcZbdOjRl9YOiStgqQVRqgpYoa2FpsuguNPvuXgxp43FesL4I1vwvv/C6u+DktvBO/RL+8uIiIiIiIiIiJTQ1lZGQ6Ho09famqqh6oREZHJyFFRQdf27TgqKnB1dWHy8sISE4PXkiVYYmI8XZ5MoAtTL2RbxTZeyH3B3ZdTl8PHMR8zi1nuPqfTSWlpKWlpaZ4oc0pQiElERCaO1RsSlhhtxVeNvtZaY7Wm44NNHY3Dm68uz2i7/mJ87xfed6WmuIVafWc6OroL8t7t29dSAW9/HzY+CMtvNq4/f91VJyIiIiIiIiIyHR3b7uV4kZGRBAbq5jcREQF7WRkdb76Jo6Sk3zFHaSld2dlYEhPx+eQnscaf5IZrmVLuWnYX2yu3k9eY5+577MBj/G7+77A32d19ZWVlJCYmYrPZPFHmaU8hJhER8Sz/8L7b0DmdRjDpWKCpZCtU7gOXc+i52mrh0L+MBmDxhvjFPcGmVZC4HPzCxu+1yOTg6ILYBcb2hSfqaIAPfgEf/x8suR5W3QYhiRNeooiIiIiIiIiIeM5AqzAlJyd7qBoREZlMunNzaXvxRbDbTzrOUVJC69NP4/f5z2PLzJyg6sSTfKw+PLD6Aa7611XYncb14cLFo4WPckvYLe5xx1Zj0gqPo6MQk4iITC5ms7EtXMQMWPhFo6+z2Qg1lWyB4k3G466WoedydBrjizfBRw8ZfREzIfkMSDkLks+EoNhxeyniIYnL4eYNkP+esa1c4cb+Y+ztsOX3sO0JmL8OzrwDImdOfK0iIiIiIiIiIjKhBlqFKSIiAn9/fw9VJCIik4W9rGxYAabeE+y0vfgi/jfcoBWZpoms8CxuW3gbD+14yN23r2kf1ZHVRDoi3X1lZWUkJCRoNaZRUIhJREQmP+9ASF9rNACH3Vid6VioqXgzNJcPb66aQ0bb/pTxfVi6EWg61oLixuc1yMQymSD9XKOVZhthpkPr+49z2mHXX2HX32DWxbD62xC/ZOLrFRERERERERGRCXH06FHsJ3w4rVWYREQEoOPNN4cfYDrGbqfjzTcJ+NKXxqcomXRumHMDH5R+wI6qHe6+PxT/ge/Ff8/9vcPhoKysjJSUFA9UeHpTiElERE4/FivELTTaiq+CywUNxT2hps1GqzoAuIaeqy7PaDv+bHwflmas0JSyGlLOhOCEcXwhMiESlsIX/wZVB40Vufa8AC7HCYNccPANKPoIvp0DNl9PVCoiIiIiIiIiIuPI4XD0W4UpPDycgIAAD1UkIiKThaOiAkdJyejOLSnBUVGBJSZmjKuSychitvCjM3/EFa9fQaejE4CK7goOdR1iplfvrh/HVmOyWhXLGQmzpwsQERE5ZSYThCbD/Cvhkl/DrR/Ddwvh6pdg9Xcg+SywDjOUUpcPO5+FV2+G38yBhxfAa1+HXc8ZQSk5fUXNgst+D3fsguU3g9Wn/5jlX1WASURERERERERkijp69Cjd3d19+rQKk4iIAHRt335K57e//z4ux4k3UMtUlRyUzK0Lb+3T93L1y32+t9vtHD16dCLLmhIU+RIRkanJNwRmfMJoAPYuqNgDRR8bq+0UfQydTUPPU19otF1/Mb4PSTJCUSlnGSs1hSQbISo5fYQkwUW/hLPvhi2/h62PQ2cj2PyNlb1ERERERERERGTKGWgVprCwMAIDAz1UkYiITCaOiopTOr/9ww8p/dkDeKWm4j1jBr7z5+O7ZDE+M2di0ko8U9J1s6/jPwX/IacuB4Cj3UfZ3babBX4LALBYLJj0GeKI6f8tIiIyPVi9jG3FEpbCmd8ApwMq9kLhh0Yr/hg6Goeep6EYGv4Gu/9mfB+cCKlnQ9o5kLoGAqPH9WXIGAqIhPO+D2feAdl/Amc3+IUNPLa5Ep5bB6tugzmXgdkysbWKiIiIiIiIiMgpqaiooKurq09fUlKSh6oREZHJxnXCz4iRMtlsuLq66Dx0iM5Dh2h64w0AzH5++C5ciP/Zqwk891wICRmDamUysJqt/OjMH/GFN76Aw2WswvXvhn8z03cmmSmZJCYkaiu5UdB/MRERmZ7MFohbaLQzbjNCTZX7oPAjI9RU9BF0NAw9T2MJ7Pqr0QAisyBtjRFqSj4TfILG7zXI2PAJgrO+efIxm/4Pju6El2+C9x+A1XfCvM+DxTYhJYqIiIiIiIiIyOi5XK5+qzCFhIQQHBzsoYpERGSyMXl5ndL5rhO2Kz3G2dZG68cf0/rxx1T978+xpqaQFRBISUb6KT2fTA6zwmZxw5wbeHLfkwCUdpVyT/E9/CDhB6RaUz1c3elJISYREREwQk2xC4y26lZwOqFqf0+oaaMRamqvH3qe6hyjbfk9mCwQv8QINKWtgYRlYPUe95ciY6y1Frb9qff72iPw2tfg/f+F1d+GBVcZK32JiIiIiIiIiMikVFtbS0dHR58+rcIkIiLHs8TE4Dgh8DoS3fUNwxpnLyhkBjBj716qcg/j+NwVBF10ERYFa09btyy4hXeK36GoqQiAblc3D21/iPOTzifAK8DD1Z1+FGISEREZiNkMMfOMtvIWI9RUndO7/VzRR9BWe/I5XA4o3Wq0D34BNj9IWtUTajoHoucazyOT2+7noLu1f39DEfzzDtjwS2Mlp0XXgs1nwssTEREREREREZGTCw0NJSMjg9LSUjo6OggICCBE2/mIiMhxvJYsoSs7e9Tnh//g+wQ3NtJ55Agd+/bTvn07XUVFJz2ne/9+Kvbvp/LnvyDk8ssIu/56vJKTR12DeIaP1YfvLvsut757q7uvtqOWP+z5A3cuvdODlZ2eFGISEREZDrMZoucYbcVXe0JNB6HgAyjYAAUboav55HN0t0Heu0YD8AuH1LMhtWf7uTAtKzkprbwVQpNhwy+gYk//402l8K/vwAe/gjPvgCU3gJffhJcpIiIiIiIiIiIDs1gsxMfHExcXR01NDRaLBZPJ5OmyRERkErHExGBJTMRRUjLycxMT8Z45E2/Af/lyd7+9poa27Tto+WADLe9vwFE78M3xro4O6v/2HPXPPU/AeecS8ZWv4LtgwWhfinjA6oTVrElYw4bSDe6+v+T8hStmXEFKcIrnCjsNafkHERGR0TCbIXq2sUrTF5+D7xbCTe/A2u9BymqwDGN7sbZa2P8qvPFNeGQhPDQfXv8G7H8N2hvGtXwZAbMZsi6Fr34AV70A8UsHHtdSAW/eAw/Phw8fgs4hQm0iIiIiIiIiIjKhTCYTkZGRhIWFeboUERGZhHw++UmwjnAdGKvVOG+gQxERBH3yAuJ++lNmbPyAlOefI+Caq+nw9R14LpeLlnfepXDdFyi57TY6Dx8e4SsQT7pr2V1Yzb3Xj91p5xfbfgFAd3c3VVVVnirttKKVmERERMaCxQqJy4y25i7oaoPiTZD/vtEq9gKuk8/RUAQ7/mw0kwUSlkLG+ZhjVmByOXGZlD32KJMJMj8JMy6A/PeMbeSKP+4/rrUa3vkhfPQQrPo6rP6Oca6IiIiIiIiIiIiIiExa1vh4/D7/edpefBHs9mGcYMXv85/HGh8/5FCT2YzvwoUEp6XxnMVCZNlRznE66fjggwGfq+Wdd2l5978Ef/rTRH7rm9hiYkbzkmQCJQclc+3sa3lq31PuvgOVB9iwawPmZjNOpxN/f3/8/f09WOXkpxCTiIjIePDyg4zzjAbQWguFH0D+BiPUVF9w8vNdDijZAiVbCABux5cCSyq2/eEw9xII1C+rHmMyQfq5Riv8EDb83NhW8ETt9VC2UwEmEREREREREREREZHThC0zE/8bbqDjzTdPurWcJTERn09+clgBphO5zGaqEhMI/9rX8G1ro+7ZZ2n4+ws4W1pOGOii8R//oPntt4m4/XbCrr0G00hXipIJ9dX5X+Wfef+kpr0GM2a+E/sdaAQnTgBKS0uZOXOmh6uc3HSFi4iITAT/cJhzmdEA6ougYIMRairYYKzecxK+tDPbcQDevNNo0XONgFT6eZC0EqzeE/AipJ+Us4xWvAU++AUceafv8TV3eaYuEREREREREREREREZFWt8PAFf+hKOigq6tm/HUVmJq7MTk7c3luhovJYswTJGKyPZYmKIvusuIr52Kw1//zu1jz+Oo6GhzxhnWxtVP/85ja+9Rsz9P8Rv0aIxeW4Ze/42f7615Fvc9+F9OHHyUfNHXBhyoft4ZWUlqampeHl5ebDKyU0hJhEREU8ITYbQ62DxdeByQdUByHsP8v4LRR+BvePk51fuM9pHD4PNH1JXG4GmjPMgPH1iXoP0SloB17wMZdvhg1/BoX9BxicgbpA/JJwO6GwG35AJLVNEREREREREZLooKyujra2NuLg4bdsiIiKjYomJwffiiyfmuQL8Cb/pS4Ssu5K6Pz1F3dNP42xr6zOm89Ahiq66mvCbvkTEN76BWUGYSemStEt49sCzHKw7yIbmDZwXfB42kw0Al8tFeXk5ycnJHq5y8jJ7ugAREZFpz2SC6Dlwxm1w7Svw3UIjELPyVogYxpKS3a2Q+x/4913wf4vh4QWw/k44+C8jKCMTJ34JfPE5+OpGuODHg4/b/yo8NB/e/1/oaJy4+kREREREREREpgGXy0VpaSlHjx4lOzubXbt20dTU5OmyREREhmQJCCDyG7eT/vZbBH/uiv4DXC5qn3iSwivX0Xn48MQXKEMym8x8Y9E3AGh2NLOtZVuf40ePHsXlcnmitNOCQkwiIiKTjc0XMs6HCx+A27bS/OXN/Mf2KQ6ZZ+LyDhr6/PpC2PYEPP9F+HkqPPMZ2PRbqM0b99KlR+x8iMoa+JjTARt+Dp2N8P4D8NA82PAL6NAbaSIiIiIiIiIiY6Guro6Ojt6VzhsbG/VhoYiInFas4eHE/eQnJP/tr3hnZvY73nnwIAVXfI66v/1NP+MmobPiz2Jx1GIAPmj+oM+xrq4uamtrPVHWaUEhJhERkUnOFRTPbusiXvO+guav7YYvvQln3wVxiwHTyU92dkP++/DmPcYqTY8shv/ca/TZuyageuln3ytQk9v7fUcjvPdTeHi+sRWdVs8SERERERERETklR48e7fO9v78/QUHDuDlQRERkkvFbvJjUV14m6jt3YrLZ+hxzdXVR+aMfU37PvTiPC++K55lMJr655JsAlHaVUtBZ0Od4WVmZB6o6PSjEJCIicjoxWyFpJZz7Pbj5PbgrD654EhZcBQHRQ59flwebHzNWZ/pFGvz9GtjxLDRXjn/tYqjcO3B/ez3898fGNnMbfw2dLRNbl4iIiIiIiIjIFNDR0UFdXV2fvri4OEymIW4GFBERmaRMVivhX/4yKS+9iPeMGf2ON772GkVXXU23gjGTyqKoRZydcDYAG5s29jnW0NBAW1ubJ8qa9BRiEhEROZ35h8O8z8Flv4M7D8EtH8L590PKajDbTn5uVzPk/BNevw0ezIQ/rIH3fgZl28HpnJDyp6VP/Ai+9jFkfXrg4+118O7/GCszffgQdLVOaHkiIiIiIiIiIqezE1dhslgsREcP4+Y/ERGRSc5n5kxSXnqRsBtu6Hes48ABCq74HG07d058YTKobyz6BgA72nbQ6uj7ec+Jv7OIQSEmERGRqcJkgph5cNa34IY34LsFcOWzsOia4a3SVL4LNvwcHj8XHpwJr90K+1+Djqbxrnz6iZ4D6541QmezLhl4TFstvPNDeHgBfPx/0KVEvoiIiIiIiIjIyTidTioqKvr0xcTEYLFYPFSRiIjI2DJ7exP9/75L/CMPY/bz63PM0dBA8Q030vzuux6qTk40M2wmn0r5FHaXnY9bPu5zrKKiAofD4aHKJi+FmERERKYq70CY/Wn4zGPw7YNw8/twzj0Qt3joc1urYNdf4cXr4Rep8PQlRpCm5si4lz2txMyDL/wVvvoBzLxo4DGt1fDW94ww066/TWx9IiIiIiIiIiKnkerqarq7u/v0xcbGeqgaERGR8RN0wQWkvPgCXqmpffpdnZ2U3v4N6p9/3kOVyYm+PP/LAHzU/BFOV+9OKA6Hg6qqKk+VNWkpxCQiIjIdmM0QtwjO+X9w83vwncPwmd/C7M+AV+DJz3XaoXCjEaR5dAk8ugze/iGUbNW2c2MldgF88TkjaJZ54cBjWqvA3jmhZYmIiIiIiIiInE5O3JYlJCQEf39/D1UjIiIyvrzT00l58QX8z17d94DTScX9/0PN737nmcKkj8zQTM5JOIcaew057Tl9jmlLuf4UYhIREZmOAqJg0dVw5TNwdz5c9zqsug3CZwx9bk0ufPQQPPkJeDAT/nEbHPo3dLePe9lTXtwiuOrv8JX/wowL+h4LSYKFV3umLhERERERERGRSa61tZWmpqY+fXFxcR6qRkREZGJYAgJIfOwxgq+4vN+x6ocfofrRxzxQlZzo2GpMG5s39ulvaWmhpaXFEyVNWlZPFyAiIiIeZvWCtDVG++RPoS4fct+C3P9A0Ufg6Br83NZq2Pms0ay+kH4uzLrIWE3IP2LiXsNUE78Ern4RSrPh/QfgyDtw9l3G/1YD6WgErwAwWya2ThERERERERGRSaKysrLP9zabjfDwcA9VIyIiMnFMNhuxP/kJtugYan772z7Hah59FJxOIm6/DZPJ5KEKZUHkApbHLGdbxTbq7HWEWcPcx2pqaggICPBgdZOLQkwiIiLSV1garLzFaJ0tkP8+HH4Tct+ElsrBz7O3w6H1RjOZIXEFzPwUzLwYIjImrPwpJWEpXPOyEWaKXTD4uH/dBUd3wdp7IevTxvaBIiIiIiIiIiLThMvl6hdiio6Oxqz3SEREZJowmUxEfuN2LBHhVP7ox32O1fz2t2A2E3nb1z1UnQB8ed6X2Vqxla0tWzkn6Bx2tO5gYfpCkpOTPV3apKIQk4iIiAzOOwCyLjGa0wlHd8DB9XDoX1B9cPDzXE4o3mS0t38AEZkw8yKYdTHEL1XIZqQSlg5+rCoH9rwAuODF6yFmPpz7fZjxCdBdFSIiIiIiIiIyDdTV1dHV1Xc18ZiYGA9VIyIi4jlhV12FyWym4v7/6dNf8+ijWEJDCLv6ag9VJitjVzI3fC7v1r/LW41v0eXqYhe7uHDWhZ4ubVLRJ4giIiIyPGazEaY5/4fw9S1w+w644KeQfKax8tLJ1OTCRw/Bk5+ABzPhH7fBoX9Dd/uElD6lvfdTwNX7fcUe+Nvn4U+fhIIPPFaWiIiIiIiIiMhEOXEVpsDAQPz9/T1UjYiIiGeFfuELxPzP//Trr/zJT2n6z388UJGAsVrWTfNuot3ZTpfLCF8fqD3A7urdHq5sclGIaZwVFRVx5513MmvWLPz9/QkLC2PZsmX88pe/pK2t7ZTmbmtr45VXXuFrX/say5YtIzQ01L3H86pVq7j//vupqKgYcp5zzjkHk8k0rCYiIuIWng5n3AY3/gu+cwQ++zvIuhRsQ7xB1FoNO5+F574Av0iHF66HfS9DZ/PE1D2VdLVBfdHAx0q2wJ8vhT9/Gkq2TWxdIiIiIiIiIiITxOVy9fsMIzo62oMViYiIeF7ouiuJuf+HfTtdLo7edTetm7d4pijhnMRziPOP69P3t5y/eaiayUnbyY2jf/7zn1xzzTU0NTW5+9ra2sjOziY7O5snnniC9evXk5GRMeK59+zZw5lnnklLS0u/Y3V1dWzevJnNmzfzm9/8hj/+8Y+sW7fulF6LiIjISfmHw8KrjNbdAQUbjG3ncv8DLZWDn9fdCgdeM5rFC9LPNYJQMy8Cv7CJqv705eUHN2+AnH/Aez8zVrw6UcEGeHIDZF4Ia++D2PkTX6eIiIiIiIiIyDgxmUxkZWWRnp5OVVUVVVVVREVFebosERERjwv9whew19ZS83+Puvtc3d2U3nYbKS/8He+0NA9WNz1ZzVbWzVrHb7b/xt33dtHbVLVVEeWn319AIaZxs3PnTtatW0d7ezsBAQHcc889rF27lvb2dp5//nkef/xxcnNzufjii8nOziYwMHBE8zc1NbkDTGeeeSaXXHIJS5cuJTw8nOrqal555RUef/xxmpqauPrqqwkKCuJTn/rUSedcunQpTz311Khfs4iICAA2H8j8pNGcTji6wwg0HfoXVB8c/DxHlxF6yv0PmCyQciZkfRpmXQJBsRNX/+nGbIY5l8GsS2HvC/D+A9BQ3H/csf+2cy6Dc+6FyMyJr1VEREREREREZJx4eXmRkJBAQkKCp0sRERGZNCJuvRV7TQ0Nzz3v7nO2tFB669dJeeHvWIKCPFjd9HTFjCv43a7f0eHoAMDusvPCoRe4bdFtHq5sclCIaZzccccdtLe3Y7Vaeeutt1i1apX72LnnnsuMGTO4++67yc3N5cEHH+T+++8f0fxms5krr7ySH/7wh8yePbvf8QsuuIBPfepTXHbZZTgcDm6//XYOHz580i3h/P39mTt37ojqEBEROSmzGRKWGu38H0JtHhz6txFqKt4EuAY+z+WAgg+M9q/vQMJyY4WmrEshLHVCX8Jpw2I1VsKa+zlju74PfgnN5f3H7X8VDvwDFnwRzv0eBMX1HyMiIiIiIiIiIiIiIqc9k8lEzPe+h6O2jua33nL3dxUWUnbnd0j8/e8wWSwerHD6CfYO5uK0i3n58MsA+Jh8yC/JZ7trOzMyZhA0zYNlZk8XMBVt3bqVjRs3AnDTTTf1CTAdc+edd5KVlQXAww8/THd394ie44wzzuDvf//7gAGmYz7zmc9w+eWXA5CXl8fOnTtH9BwiIiJjLjwdzrgNvvRv+E4uXPowZJwPZtvJzyvdCm9/Hx5ZCL8/Czb8AqpywDVICGo6s3rBspvgGzvhgp+CX3j/MS4n7HkBHCP7/UNERERERERERERERE4vJouFuJ//Lz4nZAtaN26k6te/9lBV09tVWVcB8OnQT/OzxJ/xmeDP0NLcQnV1tYcr8zyFmMbBa6+95n584403DjjGbDZz3XXXAdDQ0MB77703LrWsXbvW/TgvL29cnkNERGRUAqJgyQ1wzctw1xG4/HFj6zir78nPq9gL7/0UfrsSHl0K79wPZdsVaDqRzdcIjN2xG9Z+D7yD+x5fcj2EJnumNhERERERERERERERmTBmX18SHnsUS3jfG5/rnvwTze++66Gqpq/M0EyWxSzD4XLgZfZy91dVVeGa5p93KcQ0Dj788EPA2J5tyZIlg45bs2aN+/FHH300LrV0dna6H1u0DJyIiExWviEw/0r4wl/h7jy48lmYdyV4D7FkZu0R+PA38Pi58Ju58J97oWSbAk3H8w6ENXfBN3fD6jvB5g9WH1j9ncHP0X8/EREREREREZnk7Hb7tP+QT0REZCRssbEk/N8jYOu7O8bRe++j++hRD1U1fV016yp2tO7o09fV1UVTU5OHKpocrJ4uYCrKyckBICMjA6t18P/Es2bN6nfOWNuwYYP78bHt6wZz8OBBVqxYwaFDh+jo6CAiIoIlS5ZwxRVX8MUvfhGbbYitfk6itLT0pMfLy8vdj1tbW6f9/zHFs1paWgZ8LOIp0/KaTDjHaOc+gLX4I6yH/401703M7XWDn9NUCpsfg82P4QyMozvzYuwzLsERuwhMpomqfBKzwLJvYppzDZbyHdjxh4F+3na34//C5+iau47uuV8ES9+f/9PyepRJS9ejTDa6JmUy0fUok42uSZlMWltbPV2CiIyBoqIiqqqqiIyMJDIykqCgIEx6D0hEROSk/BYvJua++6i4/353n7OxkbLv3EXyM3/GdJJ8g4ytNYlr+LHlxxztOkqcV5y7v6qqiuDg4JOcObXpChxjHR0d1NTUAJCQkHDSsaGhofj7+9Pa2kpJScmY17J7927Wr18PwLx584YMMVVWVlJZWen+vqysjLKyMl5//XV+/vOf89JLLw05x2ASExOHPfaVV16Z1v+nlMnl2Wef9XQJIn1M32syFZPrZhK8Ssh0HCLTeYggV/Ogo83NR/He/jje2x+n0RTEIcssDlmyOGqKU6DJ7XcD9i7v3sxa+x58K/fQ8d9fsdF6NjmW2QP+d5u+16NMRroeZbLRNSmTia5HmWx0TYqnNTY2eroEETlFLpeL6upqurq63J9lpKSkkJyc7OnSREREJr2QdVfSumUzzf/+j7uvfccOqv/vUaK+9U3PFTbN2Mw2Pp3+abaXbu8TYqquriYjI2PahrO1ndwYa27u/UA1ICBgyPH+/v7A2N+B1tnZyZe//GUcDgcAP/3pTwcdazabOe+883jwwQd555132LlzJx988AEPPfSQO7R04MAB1q5dS3Fx8ZjWKSIiMlwuk5kSSzLvel3A77xv4xnvG9hsXUWdKfSk5wW7mlhu38q1nX/mls7HWNv9DrHOMm2ZNgBvVwcr7R+7vw911fPp7n9wQ+efSHXk6b+ZiIiIiIiIiEwKTU1NdHZ29ukLDw/3UDUiIiKnF5PJROyPfoTthEVZav/4R9p27PRQVdPTZRmX9dtSrru7m4aGBs8UNAloJaYx1tHR4X7s5eU15Hhvb28A2tvbx7SO2267jezsbACuv/56Lr300kHHvvLKK4SEhPTrX716Nbfeeitf+cpX+POf/0xlZSXf/OY3eeWVV0Zcz1ArTZWXl7N8+XIALr/8cjIzM0f8HCJjpaWlxX1X6LXXXjusQKLIeNI1OQSXi5aag9hy12PNfQNLfd6gQ48Fmpbbt+IMjKc78yLsmZfiiFmoFZoA277n8X2ro19/tKuSK7v+jj1hJfVL7uBPb+0FdD2K5+nfR5lsdE3KZKLrUSYbXZMymeTm5vLAAw94ugwROQVVVVV9vvfz83PfNC4iIiJDswQGEv/rBym86mqw241Ol4vye+8l9bVXMfv4eLbAaSItJI240DiKO4tJ8k5y91dVVREaevKb+KcqhZjGmM9x/2fu6uoacvyxOwV8fX3HrIYHHniAJ554AoBly5bx2GOPnXT8QAGmY2w2G0888QSbN2/m0KFDvPrqq5SVlREfHz+imobaWu94/v7+BAUFjWh+kfESEBCg61EmFV2TgwheAekr4ML/gaoDsP9V2P8a1B4e9BRzc5l7yzmCE2H2Z2DOZRC/ZPoGms64GWKz4J37oSy732Fr6WYiSzdzmTmTD2xrdD3KpKLrUSYbXZMymeh6lMlG16R4moIOIqe3Y1vJHS8yMnLabrkiIiIyWr7z5xN5xzeofvDX7r6uwkKqH3qY6P/3XQ9WNr1cPuNyPtj3Qd8QU3UVM2bMwGyefpurTb9XPM4CAwPdj4ezRVxrayswvK3nhuMPf/gD9957LwCzZs3iX//61yn/UW61Wrnpppvc32/YsOGU5hMRERk3JhNEz4Fzvwe3bYNbPoKz74LwGSc/r7EENj0KT5wHD82HN++D0u3Tc/u01NXw5Xdg3V8hYuaAQzKduXyp8wl83vw2NJx8tUURERERERERkbHU0NBAd3d3n76oqCgPVSMiInJ6C7/xRnzmz+/TV/fnP2tbuQn0yZRPcqDzQJ8+p8NJfX29hyryLIWYxpiPj4973+XS0tKTjq2vr3eHmBITE0/5uZ977jluvfVWAJKTk3n77beJiIg45XkBZs+e7X5cVlY2JnOKiIiMK5MJYuYOEGjKOPl5jcU9gaZz4ZGF8O6PoHL/hJQ8aZhMkHUJ3LoJPvNbCOq/oqIZF177X4T/Wwz/uRdaaz1QqIiIiIiIiIhMN7W1fd+DCAgIwM/Pz0PViIiInN5MVitxD/wMk5dXb6fLRfn3vodrGDtPyanzt/mzInEFBR0FffpP/J1nulCIaRwcC/wcOXIE+7H9Iwdw8OBB9+OsrKxTes7XX3+d6667DqfTSWxsLO++++6ItnAbipZhFRGR01qfQFP28ANN9YWw8UH43Rnw2ErY8EuozZuQkicFswUWXQ23b4dPPgC+Yf3HOLpg82PwwrUTX5+IiIiIiIiITCsul6vfB3rHbiwXERGR0fFOTyfi9tv69HXl51P75z97qKLp59K0S9ndtrtPX2V1Ja5puGOIQkzj4KyzzgKMreK2b98+6Ljjt2U788wzR/187777LldeeSV2u53w8HDefvtt0tPTRz3fQA4c6F2+LC4ubkznFhERmVCjDTRV58B7PzFWHvrjOfDxo9A4TVYntPnAqlvhjt10rvwmXdj6jznr2xNfl4iIiIiIiIhMK62trXR0dPTpG6sdKURERKaz8BtvxGfu3D59Nb/9Hd3l5R6qaHpZEr2EEmdJnz6n3Ulzc7OHKvIchZjGwWc/+1n346eeemrAMU6nk2eeeQaAkJAQ1q5dO6rn+vjjj/nMZz5DZ2cnwcHBvPnmm8yZM2dUcw3Gbrfzpz/9yf392WefPabzi4iIeMxAgabV34GwtJOfd3QnvHUf/GYOPHURbHsCWmsmpmZP8gmi84w7+YPPrWRbluIy94SZUlZDxnmerU1EREREREREprwTV2Hy9vbG39/fQ9WIiIhMHSarlZgf/sD43KSHq72dygf+14NVTR8Ws4WliUup6K7o019TMw0+ezqB1dMFTEXLly9n9erVbNy4kSeffJLrr7+eVatW9Rnz4IMPkpOTA8Add9yBzdZ3RYP333/fHWy6/vrrefrpp/s9z65du7j44otpbW3F39+f9evXs2TJkhHV+t5777Fo0SJCQkIGPN7d3c3/Z+++w6Oo3jaOfzfZ9EqAgKEjIAiRFpqAFHkBaTZAUGkiIoIKYsUGdkVQkCaIVEHFghSpSg0oCRA6UqR3IRAS0rPvH/vLkk02lWQ3CffnuuZyZ+bMmWfWNZ7deeY5gwYNssTatWtXKlSokKtziIiIFAmpCU2pSU3nImDvz7D3F4jKrOKSCU6EmpffX4WqrSG4O9TsDO5+dgzevm4YvPjDtT13PT4Rn/CvIGSg1RcbK+d2QexVqNrKrjGKiIhI0ZGQkEB0dDQxMTEkJCSQkpLi6JCkACQlJVGvXj0Azpw5w4ULFxwbkBRpzs7OuLu74+vri5eXF4bMvo+ISLGTPompVKlS+hsgDqWxbPGnceztwcnJCVdXV7y8vPD29sbV1dXRITmER3Aw/j17cvWHHyzbrq9eTfTmULxb5H1mKcmZjpU78tNfP1HWr6xl24UrF6haNZsH74sZJTEVkAkTJtC8eXNiY2Np3749o0aNok2bNsTGxvL9998zffp0AGrUqMHIkSNz3f/Ro0fp0KEDV69eBeCDDz7Az8+PvXv3ZnpMYGAggYGBVtvmzJlDt27d6NatG61bt+auu+7C19eX6Ohotm/fzvTp0y1TyQUGBjJhwoRcxyoiIlLkGAwQVN+8tHsPTv0Ne3+CfYvhRiZZ76ZkOPqHeXF2g+r/B3UehRodwdXTruHbi8mvAjw8LYsGJljxOpzcAtX+D9qNNieJiYiIiAAmk4n//vvvtnyq8HaUkpKCn5+f5XVSUpKDI5KiLCkpifj4eK5du4aHhwcVK1bEyUmTDogUd/Hx8RmmVClZsqSDopHbncaytw+NY28fqUmJFy5coHTp0pQsWfK2TJQNHDGc66tWkfy/PASAi59+ilezXzE4OzsusNtA3dJ1+SrlK84lnGPPjT3sjt1Nu+rtaEaz7A8uRpTEVEDq16/PDz/8wJNPPklUVBSjRo3K0KZGjRosX74cHx+fXPe/adMmLl68aFkfMWJEtse8++67jB49OsP26OhoFixYwIIFCzI9Njg4mO+//54qVarkOlYREZEizckJKjUzLx0/hWMbzNWZDiyF+Gu2j0mOh4PLzIuLF9TsBME94M624Oxi+5ji6J8V5gQmgCNr4MhaqNsb2r4JfuUdG5uIiIg43Llz57h2zXo8ZTAYcNaPosWSyWTC29sbABcXl9vyZoDkn+TkZEwmEwCxsbGcPHmSSpUq6XMlUsylr8JkNBotiQUi9qax7O1D49jbQ9rxJcClS5dISEggKCjIgVE5hrO/P4Evj+TcW29btsUfPsy135bg/8jDDoys+DMYDNQuV5sP939o2bbi+ApeaPDCbfW3R0lMBahr167s3r2bCRMmsHz5ck6fPo2rqyvVqlWjR48eDBs2DE9Px1ZmeO2116hXrx5bt25l//79XLp0iStXruDm5kaZMmUICQmhe/fuPPzwwxp4iYiIOBuh2v3mpct4c1LO3p/NyTqJN2wfkxgDexaZF8+SUPthCO4JFRpnPgVbcZCcBGtHp9togl0LzO9Z02ehxQjwKOGI6ERERMTB4uLirG76lCxZEl9fX9zc3G6rH+ZuJ8nJyZYH8gIDA/U7k9ySlJQUoqOjOX/+PMnJycTGxhITE2O5wSgixVP6ijcBAQGqwiYOobHs7UXj2NuDyWQiPj6eqKgoS9LstWvXKFmyJG5ubg6Ozv78Hn6YK/PmE//PP5ZtlyZOxLfTAzi5uzswsuKvY9WOzNo/y7J+JvoM+y/vp3ap2g6Myr6UxFTAKlWqxPjx4xk/fnyujmvdurVVtmd6/fv3p3///rcYHdSqVYtatWoxfPjwW+5LRETktmJ0g5qdzUtCjDmRae/PcHgNpCTaPubGZQj7xryUqGyuzhTcE0rXsGvodtP0WVj/CUSnmyc+OR5CJ8D2OXDfy9BoELjoi4+IiMjt5GqasvSBPM+kNQABAABJREFUgYGaCkZEcsXJyQlfX18Azpw5A8D169eVxCRSjJlMpgz3TDR+EEfRWFak+DEYDLi7u+Pu7o6zs7MlcS0yMpKyZcs6ODr7Mzg7E/jySE4NesayLen8ea7Mm0epQYMcGFnxVyugFpV8K3Ei6oRl27pT626rJCalqIuIiIjcKlcvCO4OvRfCK4fhwclQtQ0YshhqRR6HjWNhciP4+j7YMgmun7dbyAXO2QghT8HzO6DNm+Bq42ZC3FVY/RZMCoFdP0BKit3DFBEREce4ceNmFUt/f3/HBSIiRZq3t7el4kVsbKyDoxGRgmQwGKhbty7Nmzendu3aBAUFERAQ4Oiw5DalsaxI8Zb2v+u0/73fbrxatMCzWVOrbZe/nk5yuqk0JX8ZDAbaVGhjtW39qfUOicVRlMQkIiIikp88SkD9J6HvYhj5D3T6HMo3zvqYc7tg9ZswvhbMfRB2fgdxUXYJt8C5eUOrV+GFCHPFJScbhUCvnYJfn4Hp98GRP+weooiIiNhfcnIyAEajUdMxiEieOTk5Wf6GpP5dEZHizWg0UqpUKapXr47RqMlGxDE0lhUp3pydnTXGxJxME/jyy1bbUqKjuTJ3noMiun20rtDaav2fyH84G33WMcE4gJKYRERERAqKdyA0HgRPrzEn8bR5C0pWz7y9KQX+XQ+/PQefV4dF/eHg75CUYKeAC5B3aej8OQzdBnc/aLvN+T0w/xFzItfVk/aNT0REREREREREREQELNU+b3cetWvj2+kBq21X5s4l+fp1B0V0e6hbui7+bv4ABBgDaO7dnIh9ERmm1i2ulKYuIiIiYg8BVaDVK3Dfy3AuAnYvgr0/QfQF2+2T4mDfr+bFowTUfhiCe0KFJuBUhPPQS94JPefC6XBY8w6cCM3Y5twucPO1f2wiIiIiIiIiIiIiImJRasgQon5fYVlPuX6dyPnzKTVkiAOjKt6MTkbuD7qf2gm1CXQJNG+MM0+h7enp6djg7KAI3wETERERKYIMBgiqDx0/gpcOQJ9foe7j4Oqd+TGxkRD+LczqCBPqwtoxcOkf+8VcEMqHQP/l0PsHKF3Tet99r4CHv0PCEhERERERERERERERM7fq1fHp0MFq2+XZc0iOjnZQRLeHeyvci5vBzWrb+UvnHRSNfSmJSURERMRRnJzhzrbw8FR4+TB0/xZqPABOWRTLvHYSNo+HyY1hehv4ezrEXLZfzPnJYIC7OsKzodDtK/C5A/wrQqOnMz/mNimXKiIiIiIiIiIiIiJSGJR6zrrqUsq1a0QuXOigaG4Pzcs3558464fZj1847phg7ExJTCIiIiKFgasn1HkUHv8eRh6CzuOgQtOsjzm7A1a8AuNqwMLHYf8SSIq3T7z5ydkIDfrC8zvMlZmMbrbbXb8A01rCvsVKZhIRERERERG5DZhMJrZv386hQ4e4dOkSiYmJjg5JRETktuN+1134/F87q22R87/DpP8vFxgvFy+S3JOstpliTaSkpDgoIvtREpOIiIhIYeNV0lyNaOAqeHEXtH0LSt2VefuUJPhnOfzYBz6vActGwKltRS/Rx9UTytyd+f71H8GFPbCoH8xsb75GEREREZFcmj17NgaDAYPBwPHjxx0djsOtX7/e8n6sX7/e0eGIiFi5fv060dHRnDt3jv3797NlyxYSEhIcHZaIyC2zx5j0+PHjlnPMnj27QM5RWI0ePdpy7ZI/Sj7zjNV60oULRK1c5aBobg9VylaxWjdi5Nq1aw6Kxn6UxCQiIiJSmJWoDPe9AkP/hsEbodkw8C6Tefu4qxD+Lcz8P/iqAWz4DCJP2CvagnPxAOyYe3P99DbzNf7YD64cc1xcIiIiIiIiIlJgIiMjrdY9PT1xdXV1UDQiIiK3L4/gYDwaNrTadmXWLExF7WHqIqRp+aacij9lte3Y+eJ/P0RJTCIiIiJFgcEAd9SFDh/CiP3wxM9QpzsY3TM/5sq/sO5DmHAPzOpkTgKKK6JZ+hELwGSjTOr+xTCpEax6E25csXtYIiIiIiIiIlJw0lcbKFGihIMiERERW1Tl9PYS0L+f1Xrc/v3Ehoc7KJrir5JvJU4lWycx/Rf5n4OisR8lMYmIiIgUNc5GqN4Ous+Elw9Dt0lQqUXWx5wIhSXPm6eb++kpOLwGkpOyPqYw+b/3oMccc2Wq9FISYeskmFgftk6GpHi7hyciIiIiUlS1bt0ak8mEyWSidevWjg5HRMTCZDJlSGLy9/d3TDAiIiKCT9u2uFSoYLXt8pw5Doqm+DMYDHh6e1ptMyYYSUmx8cB3MaIkJhEREZGizN0XGvSBAcvhxd3Q5i0IuDPz9klxsPdn+K47jK9lrmB0fo/94s0rgwFqPwRDt0GHj8DdP2ObuKuwahRMbgz7FoPK2IqIiIiIiIgUWdevX89wk87X19dB0YiIiIjB2ZmAPn2stkWvW0/ihYsOiqj4q35HdVLSzFLhbHAm8lpkFkcUfUpiEhERESkuSlSCVq/A89vh6T+g0dO2k31SxVw0VzCa1sK8/DUNYi7bLdw8MbpBs6Hwwk5oNgycXDK2iTwOi/rBzPZwapvdQxQRERERERGRW5e+CpOnpyeurq4OikZEREQA/B55GINnmupAyclc+/UXxwVUzDUu15gzCWesth0+d9hB0diHkphEREREihuDAcqHQOdx8PIheGw+1OxiO+En1fk9sPI1GHcX/PAk/LOycE835xkAHT6EYdvg7odstzm9DWb+H4ROsGtoIiIiIoXZunXr6NevH1WrVsXT0xNfX1+Cg4N55ZVXOHv2bKbHjR49GoPBgMFgACAuLo6xY8fSoEEDfHx88PHxoXHjxkyaNImkpIzjyHnz5hEUFERQUBBr1qzJNs7BgwdjMBhwc3MjMtL2U6Z5vZacunTpEm+99Rb169fH398fd3d3KleuTJ8+fdi8eXOWx1auXBmDwUD//v0BCAsLo3fv3lSoUAF3d3cqVKjAgAEDOHjwYI5iOXLkCCNGjCA4OBg/Pz88PDyoWrUq/fv3Jzw8/Jauc/369ZZ/t+vXr7+lvkRE8lP6JCY/Pz8HRSIijpZ+LBoVFcXo0aMJDg7G29ubwMBAOnXqxJYtW6yOu3jxIm+99Ra1a9fGy8uLkiVL8uCDD7Jz584sz5eSksL8+fPp1KkT5cqVo1KlStSpU4f777+fKVOmkJCQkG3MkZGRvP7669SsWRMPDw8CAwNp164dixYtytE1p17v6NGjs2zXunVrDAZDnqcF3rt3Lx988AEdOnSgfPnyuLm54e3tTfXq1enXrx9//fWXzeNSx5ADBgywbKtSpYol7uzGl4sXL6ZHjx5UrFgRd3d3/P39CQkJYcyYMZmO/9M6ffo0Q4cOpWrVqri7uxMUFES3bt1Yu3Ztnt4HyTlnb2/8Oney2nb1x0WYivkUZ45SyqMUl7F++Py/yP8cFI19GB0dgIiIiIgUIKMb1OpqXmIuw75fYNdCOLPddvuURDiw1Lx4BULdx6DekxBY075x51RAVeg5B07+DavfMicupWVwguodHBObiIiISCESFxfHgAED+P777zPs27t3L3v37mXq1KksXLiQrl27ZtnXhQsX6NixIxEREVbbw8LCCAsLY/Xq1SxevBgnp5vPTz700EMMGTKEuLg4vv/+ezp27Jhp/4mJifz0008AdOrUiRIlShTYtWRm9erV9OjRg6ioKKvtJ06c4MSJE8yfP5+hQ4cyceJEq+u05dtvv2Xw4MFWyV2nT59m9uzZLFy4kHnz5tGjR49Mj//8888ZNWoUiYmJVtuPHTvGsWPHmDt3Lm+99RbvvfdeHq5URKRwMplMSmISEZtOnTpFu3btOHTokGVbTEwMK1asYPXq1SxcuJAePXqwe/duOnXqxJkzNyuY3LhxgyVLlrBq1SpWrFhBmzZtMvR/5coVunXrRmhoaIbtGzZsYMOGDUyaNIkVK1ZQqVIlmzEeOHCAdu3aWSXWx8XF8ccff/DHH38wYMAA7rvvvlt9K27Z+vXrbb4HCQkJHDlyhCNHjjB37lxef/11Pv7443w5Z2RkJN27d+fPP/+02h4fH8/27dvZvn07U6ZM4bfffqNp06Y2+9i0aRNdunSxGqufO3eOpUuXsnTp0mwTv+TW+ffsydVFP1nWE8+eJSZ0C94tWzgwquLL28cb0uROuiW6YTKZLImdxY0qMYmIiIjcLrxKQuNBMOhPGBYOLV8G3/KZt4+5CFu+gilNYHobCPsG4q7aLdxcqdgEBq6GHrOhROWb2xv0K7wJWCIiIiJ2YjKZ6N69uyXpp2vXrsybN4/Q0FC2bt3KhAkTqFixIjExMXTv3j3byj6PPPII+/fv54UXXmDNmjVs376dBQsWUKtWLQCWLl3KjBkzrI7x8fGhffv2APz666/ExcVl2v+KFSu4cuUKAE888USBXostERERdO3alaioKFxcXBgxYgTr1q1j27ZtfP3111SpUgWAyZMn88Ybb2Tb17PPPktgYCBfffUVf//9Nxs2bOC1117Dzc2N+Ph4nnjiiUzjHDt2LK+88gqJiYncc889TJ06lbVr1xIeHs53331Hs2bNMJlMvP/++0ycODHX1yoiUljduHEjQ2U/JTGJCECPHj04ffo0b7zxBhs2bCAsLIwvvvgCX19fkpOTGThwIMeOHaNLly7Exsby4YcfsnnzZv7++2/GjBmDq6sr8fHx9O/fP0NFpeTkZLp06WJJYGrVqhU//PADK1euZPbs2Tz44IOAOUnp/vvvJzo6OkN8UVFRdOjQwZLA9Nhjj/H7778THh7OggULCAkJYdasWUyZMqWA36nsJSUl4eXlRc+ePZk2bRrr169nx44drFy5knHjxlmStD755BNmzZpldWyjRo3Ys2cPH3zwgWXbqlWr2LNnj9XSqFEjy/74+HjatWvHn3/+ibOzM3369GHhwoX89ddfbNq0iQ8//JCSJUty8eJFOnXqxIkTJzLEfPLkSUsCk5OTE88++yxr164lLCyMmTNnUr16dUaPHs3y5csL6F0TAPc6dXD73/e/VFd//MFB0RR/1e+obrXuanDlStQVB0VT8Oxeienw4cPMnTuXrVu3cv78eWJjY1m1ahXVqlWztNm7dy8nT57Ey8uLVq1a2TtEERERkeKvVHW4/21oMwqObYCIBebqS0mZ3Ew6uwPO7sDH2Y1uVGWPc11ISbZvzNkxGKD2w3BXJ3PC1V9TzdeXmaiz4HOH+TgREREp1C5Hx+f5WC83I+4uzjb3XYlJwGQy5alfD1dnPF1t/7R29UYCySk577ekt1ueYsipb775huXLl+Pi4sKSJUsyVEFq2rQpffr0oWXLluzbt4/hw4dnOV1aarWltFNWNGjQgA4dOnD33Xdz4cIFpkyZwuDBg62Oe+SRR1iyZAlRUVEsW7aM7t272+x/wYIFAPj6+tKlS5cCvRZbnnnmGRISEnB2dmbZsmWW5Csw36zp0aMHLVq0YP/+/Xz++ef07duX2rVr2+xr165dVKpUib/++ouyZctatt9333106NCB9u3bk5iYyHPPPce2bdZVRffv38+bb74JwLvvvsu7775r9aRtw4YN6dWrF/369WP+/Pm8+eab9OnTJ0PlKhGRoih9FSY3Nzfc3d0dFI1I7plSUki+etXRYdiVs78/hmwqVOaHiIgINmzYQJMmTSzbQkJCqF69Ol26dOH69es0adIEk8nEtm3buPPOOy3tGjduTKlSpRg6dCgnT55k+fLlPPzww5b906ZNY+vWrQD07duX2bNnk5KSwsWLF7nnnnt48skneeedd/joo484evQo77//Pp9++qlVfO+//z6nTp0C4KOPPrJKem/YsCHdu3enS5curF69ukDen9yoV68ep0+fxt/fP8O+Dh06MGzYMLp06cKaNWsYM2YMffv2xdnZ/N3Ky8uLOnXqWCXj16hRg8qVK2d6vvfee48dO3bg7+/P2rVradiwodX+Fi1a8MQTT9CsWTPOnTvHqFGj+O6776zajBw50lKBaf78+fTu3duyLyQkhB49etCyZctbnnJZsmYwGCjRswfnx9ysBnt9/QaSr17F2cbnSW5N/aD6rDiygjIuZSzbDp89TEm/kg6MquDYLYkpJSWFV199lQkTJpCSkmL5gchgMGTIck3NoDQajRw7doxy5crZK0wRERGR24uTM9zZ1rzEXjVPNxexAE6H2WxuSI6nFgeolXyAlG/WQ73Hod4TUKqazfYOYXSDZkOh8WBwzmS4mxQP33YwJzG1/xAqNLLdTkRERAqFhh+szfOx7z1Ym77NKtvc1278Bq7EJNjcl50X76/OiP+rYXNfj2lbOXwx41PZmTn+Sec8xZATJpPJcmPlhRdeyHQatxIlSjB27Fg6depEaGgohw8fpnr16jbbPv/881YJTKkCAgIYMGAAn3zyCXv27OHatWtWVTPatGlDiRIliIyM5LvvvrOZxBQdHc2SJUsAePTRR61uWBfEtaS3bds2wsLMY+FBgwZZJTCl7X/69Om0aNGClJQUpkyZwuTJkzPtc9y4cVYJTKnatGnDoEGDmDp1KmFhYYSHhxMSEmJ1XGJiIiEhIRkSmFI5OTnx1VdfsWjRIqKjo/npp58YNGhQjq5VRKQw01RyUtQlX73K4XubOzoMu6q+JRRjQECBn2f48OFWCUypOnfuTKVKlThx4gSXLl1i6tSpVglMqQYMGMDIkSOJi4tj06ZNVklMqWO60qVLM2nSJJvjrzFjxvDLL79w8OBBZsyYwXvvvYebm/mhhISEBGbOnAnAPffcw+uvv57heBcXF2bOnEnVqlUzTBdsb6VKlcpyv6urK2PHjqVevXqcOHGCiIiIDIlHORUdHW15f99///1M+6lUqRJvv/02zz33HIsWLWL69Ol4eXkBcP78eX799VcAunTpYpXAlMrHx4fp06fb/IxI/vLt0oULH3+CKTXXIzGRqJWrKNHrMccGVgz5uPpwyXSJAFMAJ+JPcDTuKHf73k1TbE+5WNTZbTq5wYMH88UXX5CcnExQUFCmT1qBea77KlWqkJyczE8//ZRpOxERERHJRx7+EPIUPL0Whm6D5i+Cd8abLamcos/D5vEwqSHM7ADb50BcVKbt7S6zBCaAbdPh6kk49TfMbAeL+sOVY3YLTURERMRe9u/fz9GjRwGy/D0OzNWBUqU+gW5L+ine0kq9GWEymTh2zHp85eLiQteuXQHzlHFXbVQn+PXXX4mNjbV5noK4lvTWrr2ZMDdw4MBM2zVv3twyfV7aY9IrUaKEZdoRW5566imb5wbztHxgTuaydQMtlb+/P8HBwUDurlVEpDBLrbKRSklMIpKqV69eme675557AHMRjcces51I4eHhYUlw//fffy3bz549y4EDBwDo2bMnPj4+No83Go0MGDAAgMjISHbs2GHZt337diIjIwHo169fpmO48uXL20yWd7T4+HhOnjzJ/v372bt3L3v37rWqXLtr1648971hwwZLgmpOx/KJiYls377dsn3dunUkJ5tnB0j9d2BL48aNM62UKvnH2ccH7zZtrLZdW7bUQdEUf9He0bxy8hW+PP8lS68u5e8rfzs6pAJjlySmP/74w5J1OmrUKI4fP86PP/6Y5TE9evTAZDLx559/2iNEEREREUmr9F3wf+/BiH3w+CK4+yFwds28/am/YOkL8HkN+GUw/LsBUlLsFm6u3LgCG8dab9v3K0xuDKvfhrhrto8TERERKYLSTqPQrFkzDAZDpou3t7el7fnz5zPts2bNmpnuC0jz9P3169cz7E990j0+Pt7mw4upU8kFBQXRJt0P4gVxLent3bsXMD91Xq9evSzbpj7dffjw4QyV5lPVr18fozHz5Pp69erh6moeZ+/Zs8eyPbWCAMAbb7yR5bUaDAbLe5ObaxURKawSExOJi7Oe7t7X19dB0YhIYVOjhu1qqIBlWrRSpUplOcVuaru049XUcSCQbRWftPvTHpd2PNeoUdaV3xs3bpzlfnuJiYnh448/pm7dunh5eVGpUiVq165NcHAwwcHB1K9f39L2v//+y/N50o7l77jjjizHtnXq1LG0TTu+LYrvb3Hn19V6+u/Y8O0knjnjoGiKtzpl6pBkSrKsR1yKsEoyLE7sksQ0ffp0wFxh6YMPPrDMlZmV1D8s+/btK9DYRERERCQLzkao0R56zoGR/xDb5n3OGTKvzkRSLOz+HuZ2g4l1Yd3HEHncbuHmyNWT4O6fcXtyAmyZCBMbQPi3kJyUsY2IiIhIEXPx4sU8HXfjxo1M93l6ema6z8np5s+NqU9Jp9W4cWMqVaoEwHfffWe17+LFi5ZqRL169bLqK3V/XmR1LelduXIFMCdjZZV8BFimiDOZTJYn7tMLDAzMsg+j0WhJ/Eo9N9jnWkVECqv0SbBOTk6WqYRERHIyFs2qTdp2aceracdi2Y3h0k4VnPa43PRRpkyZLPfbw/HjxwkODmbUqFHs3r3b5vg9rdSKqXmRH+Pbovb+3g687rsPp3TVEq8tW+6gaIq3+oH1rdavxl/lWFTxnF0i62/i+WTr1q0YDIYsSzCnV758eUBPD4mIiIgUGp4BJNbvz9y/YimdcpEnaoPbwcVwI5MncK6ehA2fmJfKLaHe43D3g+Dq4B8eg+rBsDAI+wY2fAZxV6333/gPlo2AbTOgw4dwZ1tHRCkiIiJpbH+rXZ6P9XLL/OevtS+1yvOTix6umT+kt+jZZiSnFI4nItPeiFi6dCmVK1fO0XHZ3RTIK4PBQK9evfj000/ZuHEjZ86coVy5cgD8+OOPJCWZE8ltTVlnz2vJavo2e/ST9lrfeecdevTokaPjdJNfRIoDb29vatasyfXr17l+/TpOTk759ndZxF6c/f2pviXU0WHYlfP/qhsVB/nxN6co/N3q06cPx44dw2AwMGDAAHr16kWtWrUoXbo0rq6uGAwGUlJSLAVKbqXqS9rx7Y4dO3BxccnRcak5A+kVhff3duDk6opvhw5cTTMLV9Ty5ZQa/IwDoyqe7vC6g0DPQC7euJkQuPPCTqr6VXVgVAXDLklMqZmVOf1hAbD84Ur94UJERERECo9LToHEtx6CW+dP4PBq2PkdHF4FKZmM3Y5vMi+/vwrBj0L9vlCuATjqy6bRDZoNhbq9YePnsG06pCRat7m4H+Y9DNU7QPsPoHTmpapFRESkYJX0diuQfgO8spgu9xb4exZMv3lRsmRJy2t/f3+rqRkc5fHHH+fTTz8lJSWFhQsX8vLLLwM3p5KrWbMmDRo0yHCcPa4ltSrS5cuXSUpKyrIaU+rDlwaDIdPpSi5cuJDl+ZKSkqyqP6VKe60uLi6F4t+biIi9uLq6UqZMGUsVjeI6VYoUbwYnJ4xp/t8uhV/asVh2Y7i0RTjSHpd2THjhwoUsp77L7hwGgwGTyURKSkqW7WJiYrLcn5mDBw+yefNmAEaNGsUHH3xgs13a6ke3Iu34tnTp0pkmJ2Ul/ftboUKFTNtm9/5K/vHr2sUqiSn+0CESTp7EtWJFB0ZV/BgMBuoH1mfV8VWWbTsv7uTRGo86MKqCYZfp5FKfAEqdxz0nTp8+DVj/4RcRERGRQsbZBWp2ht4L4KWD0OEjCKydefuE67B9NnzTFqY2h7+mwo38+SKcJ54B0PEjGPo33NXZdpvDq2BqM1jxmmNjFREREcmD+vVvlpwPDS0c1QBq165N3bp1gZuJS8eOHWPr1q2A7SpMYJ9rSU0WSkhIICIiIsu227ZtA6B69eq4utpOXIuIiMjyIc1du3aRkJBgdW6AqlWr4ve/aRkKy783ERFHUbUNEbGHtGOxv//+O8u2qePA9McFBwdbXoeFhWXZR3b7fXx8ADKdthjMSZ5HjhzJsp/M7Nu3z/L6sccey7RdeHh4lv3k9G90fozl8/P9lfzj0aABzulyOq6vWeugaIq39FPKHbp8qFgme9slialqVXMJq/379+f4mBUrVgDmHzVEREREpAjwLm2ubjQkFJ7ZAI2fAQ/bT6QDcHEfrHwdxt0FiwbA0XWQzZNFBabkneZErL5LoExwxv0pSfD3NPguZ9N4iIiIiBQWDRo0sDzlPH36dOLi4hwckVlqotLOnTs5cOCAJZkJzJWabLHHtbRrd3Pqwm+//TbTdlu3brX81pn2mPSuXLnC0qVLM92f9hxp+3F2dqZTp04ArF69mgMHDmQfvIiIiIjkWVBQELVq1QLM0xxHR0fbbJecnMzs2bMBc2WgtBVEGzZsaKkWNG/evEyTC86cOcPq1auzjKdKlSpA1klEK1as4OrVq1n2k5m0ifZZVXOaNm1alv24u7tbXsfHx2farl27dnh6egIwceLEPCVetGnTxjK13Zw5czJtFxYWxt69e3Pdv+SNwdkZn/vbWm27vlZJTAWhTsk6tPFtQ/9S/Xmn3DsM9R9K5PXMEx2LKrskMbVv3x6TycTkyZOzLXkH5mSn2bNnYzAYLF/WRURERKSIMBggqB50Ggsj/4Eec6Da/4Ehk6FncgLs+wXmPQQT68L6T+HaaXtGfFPVVjB4A3T7CrwCM+5vOdL+MYmIiIjcAicnJ0aNGgXAv//+S9++fbO8uRAVFcWkSZMKPK7evXtbntr+7rvvWLhwIQDNmjWzPBCZnj2upXHjxoSEhAAwY8YM/vjjjwxtrl27xuDBgy0xDRkyJMs+X3rpJZvTWWzYsIHp06cD5htejRo1str/xhtv4OzsTEpKCt27d7dUrrclOTmZ7777Lss2IiIiIpK1oUOHAubZhV544QWbbcaMGWNJZh80aBBubjenvnZzc2PAgAGAuSLn2LFjMxyflJTEoEGDLNU4M9OqVSvAXBXKVuWi8+fP8/zzz+fgqmyrXr265XVqUlZ6U6dO5bfffsuynzvuuMPy+ujRo5m28/f3Z9iwYQBs2bKFESNGZJk3cOHCBb755psM53rwwQcBWLJkCT+mmcIsVXR0tGWsLvbjk+7BjtiICJJyMUuX5EyNEjVo79eeEO8QAl3M9y+OXMhbNbbCzC5JTC+88AJeXl4cPXqUZ599NssSymvWrKF9+/bExcUREBDAoEGD7BGiiIiIiBQEoxvUfgie/AmG74U2b4F/FnNhXz0J6z+CL+rA/Edh32JIyvoLfb5zcoYGfeGFHdDiJXD+3w8RVe6Dux6wbywiIiIi+eDZZ5/l4YcfBmDRokXUrl2bsWPHsmHDBiIiIti4cSPTp0/n8ccfJygoiNGjRxd4TOXLl7fcmJk8ebJlOovMppJLZY9rmTFjBq6uriQlJdGpUydefvllNmzYQHh4ODNmzKBBgwbs2bMHgJdfftlqCpH06taty5kzZ2jYsCGTJ08mLCyMzZs3M2rUKDp27EhSUhJGo5HJkydnODY4OJjPP/8cMD/0WadOHV599VVWrlzJzp072bp1KwsXLuSFF16gQoUKPPnkk3l+El9EREREzGPNZs2aATBr1izuv/9+fvnlF3bv3s3atWvp0aMH77//PgB33nknb7/9doY+3nnnHUv10Ndee43HH3+clStXsmPHDr7//nvuvfdeVqxYYUmcz8wzzzyD0WjEZDLRtWtXvvzyS8LDw9myZQtjx46lfv36XLt2zSoZKTfq169vGcd+/fXXPPbYYyxbtozt27fz22+/0aNHD5577jmaN2+ebT+p1Zjefvtt1qxZw6FDhzhy5AhHjhwhNjbW0va9996jSZMmAEyYMIEGDRowefJkQkNDiYiIYN26dUyaNImHHnqIihUr2qwCNW7cOMtUe48//jhDhw5l3bp1bN++nVmzZtGwYUN27tyZ7fsr+cuzWTOcvLxubjCZuP7Hn44LqJjycPHgv5T/rLZdjLzooGgKjtEeJylTpgzTpk2jb9++zJw5k1WrVtG5c2fL/gkTJmAymQgNDeXgwYOYTCacnJyYPXs23t7e9ghRRERERAqaXzlo9Yq5mtHxjbBjHhxYYq7ElIEJjqw1L56loG4vqN8HAmvaL143H2j3LjTsD3+MgRYjzFWmbIk8Di6e4G2jepOIiIiIgxkMBn744QdefPFFpk2bxtGjR3n11VczbR8YaJ8xzRNPPMH69estiTdGo5GePXtmeYw9rqVevXosXbqUHj16EBUVxbhx4xg3blyGdkOHDuXjjz/Otq9hw4YxZMgQy5Pnabm6ujJnzhzLzZz0hg8fjpeXF8OHD+fatWuMHTvW5hP9qX2lnc5DRKQoio2NxdXV1TJdkIiIPTk7O7Ns2TK6detGaGgof/75J3/+mTERo1atWqxYscLmfWw/Pz9WrlxJu3btOH/+PAsXLrRUHU3Vv39/WrVqZanaZEvt2rX57LPPeOmll4iMjGTEiBFW+wMCAli8eDFvv/02hw8fzvW1GgwG5s2bR9u2bYmMjOTHH3/MUNkoODiYRYsWERQUlGk/Pj4+vPDCC3z22Wfs2LGD9u3bW+1ft24drVu3BsyVqtasWUP//v355Zdf2LVrl80xcipfX98M2ypXrsySJUvo1q0b169fZ8qUKUyZMsWqzTvvvIPBYMhyKj7JX06urni3uo+o31dYtkVv2ECJXo85MKriKdk1GdLMxpgYm+i4YAqIXSoxgflHiYULF+Lr68upU6f4+uuvLSWjv/nmG2bOnMmBAwcwmUx4e3uzaNEiq0QnERERESkmnJygamvoPtM83dwDn0GZzJ9e58Z/sHUSTGkC3/wf7JgL8bbnpC8QJSpB92+hbLDt/SYTLHkBJjaAzV9AYpz9YhMRERHJIRcXF6ZMmcKuXbt4/vnnCQ4Oxs/PD2dnZ/z8/KhXrx4DBw7kp59+4sCBA3aJqXv37lbTb7Rv357SpUtne5w9rqV9+/YcOXKEUaNGUa9ePXx9fXFzc6NixYo88cQTbNq0iUmTJuHklP3Pq08//TSbNm2iZ8+eBAUF4erqSrly5ejbty87d+6kV69eWR4/aNAg/v33X8aMGUPz5s0pVaoURqMRLy8vatSowaOPPsq0adM4c+YM1apVy9P1iogUFhEREWzevJlt27axb98+YmJiHB2SiNxmAgIC2LhxI3PnzqVjx46UKVMGFxcXSpQoQatWrZg0aRIRERFUqlQp0z5q167Nvn37ePXVV6levTpubm6UKlWKNm3asGDBAmbNmpWjWEaMGMHKlSvp0KEDJUqUwM3NjSpVqjB06FB27txJy5Ytb+la69WrR0REBM8++yyVKlXCxcWFgIAAGjduzOeff862bduspovLzCeffMKMGTNo2bIlAQEBWSai+vj48PPPP7Np0yaefvpp7rrrLnx8fDAajQQEBNCoUSOGDh3K77//zpo1a2z20bp1a/bt28eQIUOoVKkSrq6ulClThs6dO7Ny5UrGjBmT5/dE8s67TVur9Zi//8aUzbSJknv+vv5W6x4pHphMJtuNiyiDyc5XdPnyZaZMmcLSpUuJiIiwmlqudu3adOvWjRdffNFuT3xJ4XD69GkqVKgAmMtj16pVy8ERye0sKiqKqVOnAjBkyBCbmd4i9qTPpBQmBfZ5NJng7E5zgtLenyE+Kuv2rt5Q+2Fo0A/Kh2ReIcke/lkBC9PcePKvCP/3Htz9kGPjug3o76MUNvpMSmFS2D+Phw8ftkyjldfpD6RoSU5O5uJFc5n7wMDAYl9ho3Llypw4cYJ+/foxe/ZsR4dTrOXl78mBAwe4++67ATh16pRlyheR21Xa3+ft+d9E+vGKh4cHW7ZssWoTEhKCV9rpaUSyYY9xsMayt5fbbRwrZgX533lh/76eV0lXrnC4eQvzb/3/U3H2bLya2q46K3mz/fR2oo9aP+Rdv1F9SMIhn6uCGEfarRJTqpIlS/L222+zbds24uLiuHjxIufOnSM+Pp49e/bw4YcfKoFJRERE5HZjMEC5BtD1S3N1poemQcV7M2+fEA0758HMdjClKWyZBDH/Zd6+oCQlwOq3rLddPQmL+sOsB+DMDvvHJCIiIiIiIlIEpa+6ZDAY8PDwcFA0IiIikhvGgADc//egQKqY0M0Oiqb4qhlYk/iUeKtthy/kfkrJwszuSUxWJ3dyolSpUpYyfCIiIiIiuHpCvd7w1AoYFg7NXwSvLJLcLx2E1W/CuJrwY184vBZSku0Ta1IslG9se9/JrTCjDfz6LESdtU88IiIiIiIiIkVU+iQmT0/PHE3bKSIiIoWDV8sWVuvRm0MdFEnx5eXqRWRKpNW2s1eK1/0Hjf5EREREpPAqVd08NdtL+6HXAqjREQyZDGFTEmH/b/DdozChLqz/FK6dKdj43P3g4akwaB1UbGa7za6F8FVDczwJNwo2HhEREREREZEi6sYN6+/MmkZORESkaPFuYZ3EFH/gAEmXLjkomuIr0ZhotR57I9ZBkRQMJTGJiIiISOHn7AI1O8PjP8CIfdD2bShRJfP2107B+o/gyzqwoBf8swKSkwouvnINYMAK6DEH/Ctl3J94wxzPpBDY9QOkpBRcLCIiIiIiIiJFUHR0tNW6kphERESKFo+6dXHy9rbadiM83EHRFF9uHm5W685Jzg6KpGAY87OzqlWr5md3gHnO46NHj+Z7vyIiIiJSRPkGwX0vQ4uX4MRm2DEPDiyBpLiMbU0pcGiFefEJggZ9oH4f8K+Q/3EZDFD7IXO1qL+nwcbPIeG6dZuoM/DrM7BtOnT8BCo0yv84RERERERERIoYk8mUYTo5JTGJiIgULQYXFzwaNiBmw0bLththYfg+8IADoyp+SvqWhDTFl3wNvphMJscFlM/yNYnp+PHjOWpnMBgAMryRtranbhMRERERseLkBFXuMy+xn8Gen2DHHDi/x3b762dhw6ew4TOo1g4a9ocaHcxVnvKTizu0GA71Hod1H8KOueZkqrTOhMP8R8xVpdx98/f8IiIiIuJwOf2dVEREzBITE0lOTrbapiQmERGRosczJCRdEpMqMeW3iqUqcubCGcu6q8GVy9GXHRhR/srXJKZ+/fpluT8iIoJdu3ZhMpnw9/enfv36lClTBoALFy4QERFBZGQkBoOBunXrUrdu3fwMT0RERESKK48S0HiQeTm7E7bPNic1JUTbaGyCI2vMi3dZqP8ENOgLJSrnb0zegdB1AjR+BlaNgn/XW+9vOVIJTCIiIiIiIiJAXJx1dWVnZ2fc3NwyaS0iIiKFlWdIiNV6/OHDJEVGYixRwkERFT+VAypzMPkgPs4+lm2nI087MKL8la9JTLNmzcp037fffsuCBQsoX74848aN4+GHH8ZotD59cnIyv/zyC6+88gr79+9n6NChDBw4MD9DFBEREZHiLqi+eWn/Iez92ZzQdHaH7bbR52HTONg0Hu5sAw36wV2dwOiaf/GUqQ19FsOhVeZkpitHoUQVaDok/84hIiIiIiIiUoTFx8dbrXt6emqmDhERkSLIo3ZtDO7umNIkKMfu2IHP/fc7MKrixehk5JrpGj7cTGK6EnPFgRHlLyd7nCQ8PJxnn32WUqVK8ddff9GjR48MCUxgzqzv0aMHW7duJSAggOeee47wcJUXExEREZE8cPOGhv3gmXUweBM0ehrcMqt8ZIKjf8KifvDF3bDmXbh8NP9iMRjgro7w3F/m5KoHPgNjJk+U3rgC53bn37lFRERERERECrn0SUweHh4OikRERERuhcHVFY969ay2aUq5/JdiTLFaj42PdVAk+c8uSUxffPEFycnJjBo1iqCgoGzb33HHHYwaNYrExETGjx9vhwhFREREpFi74x7oPA5GHoQHJ0P5xpm3jbkEoV/CVw1gTldzNaek+Mzb54bRFe4dBjXaZ95m/cfw9X2w9EWIvpQ/5xUREREREREpxJTEJCIiUnx4NmxotR67Ww/t5jcnDydCr4fy65VfmX5xOn/d+MvRIeWbfJ1OLjObNm0CoEmTJjk+pmnTpgBs3ry5QGISERERkduQqxfUf9K8XNgH2+fA7u8h7prt9sc2mhfPklC3NzTsD6WqF1x8Fw9A2EzAZJ4Gb+8v0Oo1aPxM/k5xJyIiIiIiIlKIKIlJRESk+PCoe4/Vetz+/ZiSkjDYmK1L8qZUYCk+OfCJZb2MqQxBZF9QqCiwSyWmS5fMT5CnH4RmJbVt6rEiIiIiIvmqTG3o9BmM/Ace/hoqNsu87Y3LsHUSTAqBWZ1h94+QGJd5+7wwmWDlG2BKvrktPgpWvwlTm8GhVeY2IiIiIiIiIsWMkphERESKD/fgYKt1U1wc8UeOOCia4qmSbyWr9YuxF0kmOZPWRYtdkphKly4NwIoVK3J8zO+//w5AqVKlCiQmEREREREAXDygbi94aiU89zc0HQoeJTJvf2Iz/DIIxtc0Jx1d+id/4jClQOXm4OKZcd/lI7CgJ3zXPf/OJyIiIiIiIlIIuLm5ZdimJCYREZGiy1iiBC7lyllti92zx0HRFE/lvK3fXxMmYpxjHBRN/rJLElPbtm0xmUyMHz+e0NDQbNtv2bKFL774AoPBwP3332+HCEVEREREgMCa0PEjeOkgPDoTKrfMvG1sJPw1BSY3Nldn2vMTJOW88mgGTs5w3yvw/Ha45zHbbY6shSnNYMXr5vOLiIiIiIiIFHHx8fHUqlWLOnXqcOedd1K+fHmMmm5GRESkSHO/x7oaU9yevQ6KpHjycfXBz83Palu0c7SDoslfdkliev3113FzcyM+Pp7777+f4cOHExERgSnNdBgmk4mIiAhGjBhB27ZtiYuLw9XVlddff90eIYqIiIiI3OTiDsHdof8yGBYO9z4PniUzb39iM/w8EMbXgjXvwJV/835u3yB4ZDoMXAvlGmbcb0qGv6fCxAYQ9g0kJ+X9XCIiIiIiIiKFgLu7OyVLlqR8+fLceeedGAwGR4ckIiIit8CjjnUSkyox5b/y3uWt1pXElAs1a9Zkzpw5ODs7k5CQwFdffUXDhg3x9PSkXLlylC9fHk9PTxo2bMjEiRNJSEjAaDQya9YsatasaY8QRURERERsK1Ud2n8ALx2A7rOgauvM2964DKETYGJ9mPsQ7F8CyYl5O2+FRuZEpoemgXfZjPtjr8DykfD1ffDvhrydQ0REREREREREREQkn7nXqWO1nnDkCKbEPP5WLjaV97mZxOTh5EGye7IDo8k/dqvH2bNnT6pUqcJzzz3H9u3bAXOJ0HPnzmVo26BBA6ZMmULjxo3tFZ6IiIiISNaMblDnEfNy5V/YPgd2zocb/9lu/+868+JdFhr0gQb9wL9C7s7p5AT1ekOtrrB5PGyZBMnppqy7uA/mPggv7ICAqnm7NhERERERERERERGRfOJWo7rVuikxkYSTJ3G7804HRVT8BLsF06hcI0o4l8DNyY2j0Ue5FHnJ0WHdMrtOKtyoUSPCwsIIDw9n7dq17NmzhytXrgBQokQJgoODadeuHY0aNbJnWCIiIiIiuRNQFf5vDLQZBQeXQfgsOL7Jdtvo87BxLGwaB9XbQ8MBUP3/wMk55+dz84b734EGfWH123BgifX++k8qgUlERERERERERERECgVjiRIYS5cm6dLNpJr4Q4eUxJSPAtwD8I33tax7uXhxCSUx5UlISAghISGOOLWIiIiISP4xukGdR83LpUOwfTZEfAdxVzO2NaXAoZXmxa+CuTJTgz7gY2OquMyUqAyPzYNjm2Dl63BhL7j6mBOcREREREREREREREQKCbcaNaySmOIOHcL3gQccGFHxEuAdQNK1JMu6r9EXEyYHRpQ/nBwdgIiIiIhIsVC6BnT8CEYehIemQYUmmbe9dgrWfQDj74YfnoSjf0JKSs7PVaUlDN4IXb40V4TyDrTdLjkRTm/P1WWIiIiIFHezZ8/GYDBgMBg4fvy4o8MREbnteXt7k5CQgMlU9G+6iYgUNI1lpShxq1HDaj3+0GEHRVI8lfW1fkDa09mTZOdkB0WTfxxSiUlEREREpNhy8YB6vc3L+b2wfRbs+gESrmdsa0qGA0vNS4kq0LC/eWo4r1LZn8fJGUIGZN0mbCasfA2Ce0C7MeBXLk+XJCIiIiIiIlIQXF1dqVOnDvv27QPAzc2NkJAQjEbdvhIRESnqMiYxHXJQJMVTWb+ynOa01TaTe9FPCrfLKHDjxo23dPx9992XT5GIiIiIiNhR2TrQeZw5gWjvzxD+LZyLsN028hisfRf+/ADu7gYhT0Gl5mAw5O3cMZdh/Ufm13sWwcHl0Hw43Ps8uHrmrU8RERERERGRfOTq6mq1npiYiLOzs4OiERERkfzkVq2a1XrimTOkJCTglO7//5I3Xm5e3Ei5gafTzd/7De55vJ9QiNglial169YY8njzxWAwkJSUlH1DEREREZHCys0bGvYzL2d2mKsz7fkJEm9kbJuSaE542vszlKoBDQdA3V7gGZC7c67/COKu3VxPvGHetnOeeQq62o/kPUFKREREREREJB+4ubllWM/r/SQREREpXFwrV7LekJJC4unTuFWt6piAiqEYUwye3ExicnYr+sngTvY6kclkyvMiIiIiIlJslGsA3b6CkQeh0+cQeHfmbf87BKvegPG14NchcGob5HR8XKoGuPll3H7tFPz0FMzuDOd25+0aRERERERERPJB+kpM6ZOaREREpOhy9vHBOcD64dyEEyccFE3xlECC1Xr6sVVRZJdKTOvWrcu2TUxMDIcOHeL7779n27ZtNG/enDFjxqhsqIiIiIgUT+5+0HgQNHranJwU/i3s+xWS4zO2TYqDXQvMS5k6EDIAgnuCu2/m/TcZDHUeNU9Pt2MOmFKs958IhemtoGF/aPMWeJXM18sTERERERERyY6SmERERIo310qViL1yxbKuJKb8lWJMgTTPPbu5Fv2xlF0qMbVq1SrbpVOnTgwfPpy//vqLTz/9lNDQUL799ltatWpljxBFRERERBzDYICKTeCRr83VmTp8BCWrZd7+wl5YPhLG1YQlL8DZiMzbepWCrl/C4I1QuWXG/aYUc/LUVw3g7+mQrGmcRUREirt169bRr18/qlatiqenJ76+vgQHB/PKK69w9uzZTI8bPXo0BoPBMsVPXFwcY8eOpUGDBvj4+ODj40Pjxo2ZNGkSSUkZxxTz5s0jKCiIoKAg1qxZk22cgwcPxmAw4ObmRmRkZL5eS05dunSJt956i/r16+Pv74+7uzuVK1emT58+bN68OctjK1eujMFgoH///gCEhYXRu3dvKlSogLu7OxUqVGDAgAEcPHgwR7EcOXKEESNGEBwcjJ+fHx4eHlStWpX+/fsTHh5+q5cKQGxsLB999BF169bFy8uLkiVL0rx5c2bMmEFKSgrr16+3fAbWr1+fL+cUEUmfxFQcqgeISMHKyxjwVseyixYtIigoCKPRqLFsIR3LSuHlWrGi1XriyZMOiqR4MrpY1y1yd3F3UCT5x27TyeXGK6+8wiOPPMLChQv5/vvvHR2OiIiIiIh9eAZAs6EwLBz6LYPaj4CTi+22iTHmCkvTW8GMthCxABJjbbctGwz9lkLPeeBfMeP+uKuw4hX4uiX8uyHfLkdEREQKj7i4OHr37k3btm2ZO3cux44dIzY2luvXr7N3714+//xzatSowdKlS7Pt68KFCzRr1oxXX32VnTt3Eh0dTXR0NGFhYTz//PM88sgjpKRYV4F86KGHcHc3/5ia3e99iYmJ/PTTTwB06tSJEiVKFNi1ZGb16tVUq1aNDz/8kIiICK5du0Z8fDwnTpxg/vz5tGzZkmHDhmW4Tlu+/fZb7r33Xr7//ntOnz5NfHw8p0+fZvbs2dSrV49FixZlefznn3/O3XffzZdffsnevXuJiooiLi6OY8eOMWfOHBo3bsw777yT52sFOH/+PA0bNuTNN99k9+7d3LhxgytXrrBlyxaeeeYZOnXqREJCQvYdiYjkktFofeNNSUwikpn8GgPmZSz7wAMPWMayCxYsyLJ/jWWt2WMsK4Wba+VKVusJx1WJKT95uHpYrbsai/5YqlAmMQH07dsXk8nE9OnTHR2KiIiIiIh9GQxQpSX0mAUv7Yf73wX/Spm3P7MdFg8xV2da9SZcPmq7z7u7wdBt0OZNMHpkbHNxP8ztBsdD8+9aRERExOFMJhPdu3e3JA917dqVefPmERoaytatW5kwYQIVK1YkJiaG7t27Z/s09COPPML+/ft54YUXWLNmDdu3b2fBggXUqlULgKVLlzJjxgyrY3x8fGjfvj0Av/76K3FxcZn2v2LFCq78b7qBJ554okCvxZaIiAi6du1KVFQULi4ujBgxgnXr1rFt2za+/vprqlSpAsDkyZN54403su3r2WefJTAwkK+++oq///6bDRs28Nprr+Hm5kZ8fDxPPPFEpnGOHTuWV155hcTERO655x6mTp3K2rVrCQ8P57vvvqNZs2aYTCbef/99Jk6cmOtrBUhKSqJLly4cOHAAgPbt2/Prr78SHh7OL7/8Qrt27Vi1ahVvvfVWnvoXEclK+iQmF5dMHuQRkdtafo4B8zKW9fb2toxlf/nlF41lC9FYVgo/lwrpKjGdOeOgSIonbw9vq3UPZxu/+xcxxuybOEbF/5UV27Nnj4MjERERERFxIO9AaPkSNB8O//4J4bPgnxVgSs7YNu4qbJ1kXqq2gUYDocYD4Jxm2O/iAa1ehXqPw5p3YO/P1n1UbAaV7i3IKxIREcm9mP/yfqyrl/n/fzb7vQyY8tavi4e5b1tuXDFP25pTXqXyFkMOffPNNyxfvhwXFxeWLFlCx44drfY3bdqUPn360LJlS/bt28fw4cOznGIiLCyM1atX07p1a8u2Bg0a0KFDB+6++24uXLjAlClTGDx4sNVxjzzyCEuWLCEqKoply5bRvXt3m/2nPt3u6+tLly5dCvRabHnmmWdISEjA2dmZZcuWWW5YATRq1IgePXrQokUL9u/fz+eff07fvn2pXbu2zb527dpFpUqV+Ouvvyhbtqxl+3333UeHDh1o3749iYmJPPfcc2zbts3q2P379/Pmm28C8O677/Luu+9apkABaNiwIb169aJfv37Mnz+fN998kz59+mR42j87X3/9Ndu3b7dc+9dff211jocffpiBAwfy7bff5qpfEZGcSJ+0pCQmKS5STClcjb/q6DDsyt/NHydDwdTPyM8xoMayxWssK4WfS9AdVuuJFy5gMpmsPg+Sd74evtzghmXd09nTgdHkj0KbxHThwgUAYmJiHByJiIiIiEgh4OQE1dqZl6izsGMubJ8D18/abv/vOvPiEwQN+0ODvuCb5gujX3no/i2EDIQVr8GFPYABHvjUXLVJRESkMBl7Z96P7fQ5NB5ke9/kRnDjct76bfU6tMnkyeVZD8Clgznva/S1vMWQAyaTiU8//RSAF154IcONklQlSpRg7NixdOrUidDQUA4fPkz16tVttn3++eetbvqkCggIYMCAAXzyySfs2bOHa9eu4efnZ9nfpk0bSpQoQWRkJN99953NGz/R0dEsWbIEgEcffdQybUdBXUt627ZtIywsDIBBgwZZ3fRJ2//06dNp0aIFKSkpTJkyhcmTJ2fa57hx46xu+qRq06YNgwYNYurUqYSFhREeHk5ISIjVcYmJiYSEhGS46ZPKycmJr776ikWLFhEdHc1PP/3EoEGZfN4zMWXKFADKlCnDF198YbPNhAkTWLp0KZcuXcpV3yIiWTGZTKrEJMXW1firtPqhlaPDsKsNj20gwD0g3/vN7zHgrYxlS5YsyeXLlzWWpfCMZaXwcylTxmrdFBdH8tWrGJWwli98PHyskpi8nL1ISC7aU4EX2unkUv9YplZkEhERERGR//ENgtavw/A98Nh35qpLmbl+FtZ/BF/WgR/7wr8bwJSm4kTl5jB4A3QeDy2Gwx11M+8r8nh+XYGIiIjYyf79+zl61DzVbGZPi6e67777LK+3bt2aabv002Kk1bBhQ8B8k+bYsWNW+1xcXOjatStgnmbj6tWrGY7/9ddfiY2NtXmegriW9NauXWt5PXDgwEzbNW/e3DLlSNpj0itRogQPPvhgpvufeuopm+cG81QmYL4BltVTyv7+/gQHBwO5u1aAc+fOsX//fgB69uyJp6ftp3a9vb3p2bNnrvoWEclOSkoKTk7Wt6mUxCQi6eX3GPBWxrKp59dY1szRY1kpGoyBgRkemk36X0EbuXX+Hv4Ztl2LLbgHpeyhUCUxRUZGsmbNGjp16sSyZcswGAw88sgjjg5LRERERKRwcjZCrS7QdzE8vwOaDQN3f9ttU5Jg/28wtxtMagR/TYXYq+Z9Ts7mqefajc78XEf+gIn1YdlL5ilyREREpEgIDw+3vG7WrBkGgyHTxdvb29L2/PnzmfZZs2bNTPcFBNx8+v769esZ9j/88MMAxMfH89NPP2XYnzr9RlBQEG3aWCdqF8S1pLd3714AXF1dqVevXpZtmzRpAsDhw4dJSLD9pGv9+vUzVBlJq169eri6ugKwZ88ey/YTJ05Yqh698cYbWV6rwWCwvDe5udb052zUqFGWbRs3bpyrvkVEspOUlJRhm5KYRCS9/B4D3spYtnfv3oDGsqkcPZaVosHg4oKxlPUU6onnzjkomuKnhFcJfr/6O4suL2LWpVlMOj+J6KRoR4d1S+ySxOTs7JyjpVSpUnTs2JFVq1YBUL16dV577TV7hCgiIiIiUrSVvBM6fAgjD8JDU6FcSOZtLx+Gla/DuJrw21A4uzPrvpMTYeUbYEqB8JnmZKZtMyA54w/OIiIiUrhcvHgxT8fduHEj032ZVesBrCpqJCcnZ9jfuHFjKlWqBMB3331nte/ixYuWJ7h79eqVoTpHQVxLeleumJO1AwICsrxhA1im1TCZTERGRtpsExgYmGUfRqPRcrMs9dxgn2tNf87sYi2TbhoIEZFblT6JyWAwZPjbLyKS3+OiWxnLNm/eXGPZNBw9lpWiw5huSkJVYso/bkY3NsRsYMP1DWyP2c7BuINcT86YhFmUZP3XK5+Y0k5XkQNGo5EePXrwxRdfWM01KiIiIiIi2XDxgHqPm5ezEeakoz0/QaKNHwGSYmHnfPMS1MBcjan2I+Ca7secbTPgv39ursddhd9fhvBZ8MCnUKVlQV6RiIgIvHI078e6emW+b2gYkLvfrSxcPDLfN2CFOfm3EEh782Xp0qVUrlw5R8dld8MirwwGA7169eLTTz9l48aNnDlzhnLlygHw448/Wm5o25rmw57XktWUF/boJ+21vvPOO/To0SNHx3l5ZfF5z0Z+XbOISE6lT2JycXHR3yIpNvzd/Nnw2AZHh2FX/m7+BdJvYRrPGgwGHn/8cT7++GONZbPgiLGsFG4uZcsQl6ZaV6KSmPKVn6sfMYkxlvWohCgHRnPr7JLE9O6772bbxsnJCR8fH6pUqcK9995L6dKl7RCZiIiIiEgxFlQPun0F//c+7PrenND03yHbbc/ugN92wKo3od4TEPIUlKpm3mdKAaOHOekprYv7YE4XuPtBaP8B+Fcs0MsREZHbmFep7Nvkqd+SBdOvZ0D2beykZMmb1+jv70+dOnUcGI3Z448/zqeffkpKSgoLFy7k5ZdfBm5Ov1GzZk0aNGiQ4Th7XEvqk+SXL18mKSkpyyfYU6e7MBgMlChRwmabC9n8OJ+UlGT1xHyqtNfq4uJSYP/e0sadXazZ7RcRyS1bSUwixYWTwYkA98IzJizKCtt49oknnuDjjz/WWBbHj2Wl6HAuaf3dOzmT6l+SN35ufpyNOWtZVxJTDuQkiUlERERERAqIhz80fRaaDIbjmyHsGzi4DFJsTAcXdxX+mmxeqraGkIHQ5FlzotKad2DfLxmP2f8bHFoFzYdD8xczVnISERERh6lfv77ldWhoKC1atHBgNGa1a9embt267Nq1iwULFvDyyy9z7Ngxtm7dCth+ch3scy2pN1gSEhKIiIggJCTzKXq3bdsGQPXq1XF1dbXZJiIiIssbSLt27SIhIcHq3ABVq1bFz8+Pa9euERoamqdryYng4GDL67CwMPr06ZNp27CwsAKLQ0RuT76+vuzfvx8nJyc6dOiAt7e3o0MSkUKosI1nNZa9ydFjWSk6nNMlyiVfURJTfvJ187Vaj0os2klMmlxYREREROR2YTCYp37rOQdG7IM2b4Jvuczb/7sefuwDX9aBiO+gw0fQfzmUsfH0VFIcbPgEJjeGfb9CLqeUFhERkYLRoEEDypcvD8D06dOJi4tzcERmqTd3du7cyYEDByxProO5UpMt9riWdu3aWV5/++23mbbbunUr+/fvz3BMeleuXGHp0qWZ7k97jrT9ODs706lTJwBWr17NgQMHsg8+D4KCgqhVqxYAixYtIjY21ma7mJgYfvzxxwKJQURuXy4uLkRFRXH16lUCAgIoVaqAKi+KSJFWGMezGstmPIcjxrJSdBhLWFemS/5fBS/JH36uflbrRb0Sk12SmN577z3ee+89/vvvvxwfExkZaTlORERERETymU9ZaPUqvLgbei2AO+/PvO31c7D+Y/iiNvz9Nfzfe9Dpc/CwUWr62ilY1B/mdIUL+wosfBEREckZJycnRo0aBcC///5L3759iY+Pz7R9VFQUkyZNKvC4evfujcFgAOC7775j4cKFADRr1oyqVavaPMYe19K4cWPLE+szZszgjz/+yNDm2rVrDB482BLTkCFDsuzzpZdesjkVx4YNG5g+fToADRs2pFGjRlb733jjDZydnUlJSaF79+6cPn0603MkJyfz3XffZdkmM6nxnz9/npEjR9psM2LECC5evJjrvkVERERuVWEcz2osW3jGslI0pK/ElHRVlZjyk4+rj+W1E07EJtp+OKWosMt0cqNHj8ZgMNC9e/ccZ9JfuXLFctw777xTwBGKiIiIiNymnI1Qs7N5uXwUts+CnfMh1sYXSVMyHFhiXkpWg2bDIPIERMwHU4p12+ObYFoL6P0D1Ghvn2sRERERm5599lnWrFnDr7/+yqJFi9ixYweDBw+mcePG+Pn5ERUVxcGDB1m/fj1LlizB3d2dYcOGFWhM5cuXp1WrVqxfv57Jkydz9epVIPPpN+x5LTNmzKBJkyYkJCTQqVMnnn/+ebp27YqXlxc7d+7kk08+4d9//wXg5Zdftpo6I726deuyf/9+GjZsyBtvvEHjxo2Jj4/n999/54svvrBMzzF58uQMxwYHB/P5558zYsQI9u/fT506dXjmmWdo27YtZcqUIS4ujuPHj7N161Z++uknzp07x549eyxP+OfUkCFDmDVrFjt37mTq1KkcO3aMZ599lgoVKnDq1CmmTJnC6tWrCQkJITw8PFd9i4iIiOSHwjae1Vi28IxlpWhwDtB0cgWpuqk6n1T4BDcnN1wMLpxMPunokG6JXZKYRERERESkCCh5J7T/wDzN3L7FED4TTofZbnv5CPz5Phg9oMYDEHUGzkVYt/Etb56+TkRERBzKYDDwww8/8OKLLzJt2jSOHj3Kq6++mmn7wMBAu8T1xBNPsH79estNH6PRSM+ePbM8xh7XUq9ePZYuXUqPHj2Iiopi3LhxjBs3LkO7oUOH8vHHH2fb17BhwxgyZIjNG1Curq7MmTOHJk2a2Dx++PDheHl5MXz4cK5du8bYsWMZO3aszbaurq64u7vn4AqtGY1Gli1bRtu2bfnnn39YuXIlK1eutGrTvn17Ro4cSYcOHXLdv4iIiMitKozjWY1lC8dYVooGY0C66eSuXsWUkoLByS4ThxV7Lk4ueDt7W9YNJoMDo7l1hfZTkZiYCJjnRBYRERERETty8YB6veHptTB4IzToBy6ettsmxcI/y80JTCWqWE8x1/59c18iIiLicC4uLkyZMoVdu3bx/PPPExwcjJ+fH87Ozvj5+VGvXj0GDhzITz/9xIEDB+wSU/fu3XFzc7Ost2/fntKlS2d7nD2upX379hw5coRRo0ZRr149fH19cXNzo2LFijzxxBNs2rSJSZMm4ZSDH92ffvppNm3aRM+ePQkKCsLV1ZVy5crRt29fdu7cSa9evbI8ftCgQfz777+MGTOG5s2bU6pUKYxGI15eXtSoUYNHH32UadOmcebMGapVq5an6w0KCmLnzp188MEH1KlTBw8PD/z9/WnatClTpkxhxYoVuLq65qlvERERkfxQ2MazGssWnrGsFH5OPj7WG1JSMMUW7SnPCpP0/y0X9SSmQluJKSIiAiBHf+xFRERERKSA3FEXuk00JyTt+gHCvoH//rHdNvKY+Z9Gd/AuA4F3Z95vfDS4eWe+X0RERApEcHAwEydOzPVxo0ePZvTo0dm2a926NSaTKUd9+vv7ExcXl+tYUuX1Wvr370///v2zbVe6dGk+/PBDPvzwwzxEZ61p06b88MMPeT6+TJkyvPPOO7zzzju3HEtmPDw8ePPNN3nzzTcL7BwiImklJiZiNBpJTk52dCgiUoTkZQyoseytKQpjWSncnLy8MmxLjomxuV1yz9nZ2WrdqfDWMsqRAklimjt3rs3tv/32W7bzpsfHx3P06FG+/fZbDAYDjRo1KogQRUREREQkN9z9oMkz0HgQnAg1JzMdWAopSRnbJsXB1RMwuRFUaQWNBsJdncD5f1VW/zsCM9pCs6HQ/AVVaxIREREREbkNHT9+nJCQEMD8YHu1atUICgpycFQiIiKS32wlK6XExDggkuLJ6Gyd9uNscs6kZdFQIElM/fv3x2CwLlFlMpl46623ctyHyWTCycmJF198Mb/DExERERGRvDIYoHIL83L9AuycC+GzIeq07fbHNpgX77LQsD807Aer3oD4a7D+I9g531zl6e4HzX2LiIiIiIjIbSFtBabUe0IiIiJS/Di5uoKLCyQmWralxNxwYETFi9FonfZjNBTaCdlypMBGhCaTybLY2pbV4uLiQvPmzVmyZAmtWrUqqBDt4sSJE4wcOZKaNWvi5eVFQEAAjRo1YuzYsdy4kX//Ya5YsYKHH36Y8uXL4+bmRvny5Xn44YdZsWJFjvtISkpi2rRptGzZktKlS+Ph4cGdd97J4MGD2bdvX77FKiIiIiLFhE8ZuO8VGL4bei2Eau2ATBKRos/Dhk/gi9pwePXN7ddOwqJ+MKcrXNCYU0RERERECp+i9Dt/UZJ+yiYlMYmIiBRfzp6eVuuqxJR/XIwuVuuaTs6GY8eOWV6bTCaqVq2KwWBg1apVVK9ePdPjDAYD7u7ulCxZMsO8fUXR0qVLefLJJ4mKirJsu3HjBuHh4YSHh/PNN9+wfPlyqlWrludzpKSk8MwzzzBz5kyr7WfOnOHMmTMsXryYp59+mq+//jrLLwD//fcfnTp1IiwszGr7v//+y/Tp05kzZw6TJk3i6aefznOsIiIiIlJMOTlDzU7m5cq/ED7LXGEp9krGtqYU230c3wTTWkCjp6H1G+AZULAxi4iIiIiI5EBR+p2/qEmfxJR+hg8REREpPpy8vEi+ds2yriSm/OPq5IqJm+MqQ2YPGhcRBTLarVSpkmWpXLmyZXtQUJDVvvRLxYoVCQwMLBYJTDt37uSxxx4jKioKb29vPvzwQ7Zs2cIff/zBoEGDADh06BCdO3fm+vXreT7Pm2++afliU79+fRYuXMi2bdtYuHAh9evXB+Cbb77Jciq/5ORkHn74YUsC0yOPPMKKFSv4+++/mThxIoGBgcTHxzN48OBi+8SHiIiIiOSTgKrm6eFeOgAPT4fyjXN+rCkFtk2HrxpC2ExISc7+GBERERERkQJSlH7nL4qUxCQiInL7MHh4WK2b4uMcFEnxY3S2rl1U1JOY7DIZXkpKJk9bF2MvvvgisbGxGI1GVq9eTbNmzSz72rZtS/Xq1Xn11Vc5dOgQ48aNY/To0bk+x6FDh/j8888BCAkJYePGjXj87z/+Ro0a0a1bN1q1akV4eDhjx47lqaeesvk0yJw5c9i8eTMAzz33HJMnT7bsa9y4MQ888AANGzYkKiqKF154gQMHDmSYV1FERERExIqLO9R9zLyc2w3hM2H3IkjMwRM2sVdg+Uvmik4PfAqVmxd8vCIiIiL56Pjx444OId+1bt06Q8KBSHFXlH7nLw6UxCQiUjgUx7GsOJ4hXX6BKSnJQZEUP04G69pFRX06uaIdfSG1bds2Nm3aBMDAgQOtvtikGjlyJLVq1QJgwoQJJCYm5vo8X375JUn/+4/7q6++snyxSeXp6clXX30FQFJSEl988YXNflK/IAUEBDB27NgM+6tVq8Ybb7wBwJEjR/j1119zHauIiIiI3MbuuAe6ToCRB6DT51C6Vs6Ou7AHZneCRQPg2umCjVFERERERCSNovY7f1GkSkwiIiK3jwxJTIlKYsov6acbVhKTZLB48WLL6wEDBths4+TkRN++fQG4evUq69aty9U5TCYTv/32GwA1a9akadOmNts1bdqUu+66C4Dffvstw5eCQ4cOceDAAQB69uyJp6enzX769+9vea0kJhERERHJE3c/aDwIntsK/X+H2o+AUw4qfO77BSbUh32/FXyMIiIiIiIiFK3f+YsqJTGJiIjcPjJWYsp98rfYln4Mlb4yU1GTr3OCPfXUU4D5TUqdvznt9rxI31dRkDo1m5eXFw0bNsy0XatWrSyvQ0NDad++fY7PcezYMc6ePZuhn8zO888//3DmzBmOHz9OlSpVMsSaXT9ly5alRo0aHDp0iNDQ0BzHKSIiIiKSgcFgniKucnO4fgF2zoXw2RCVRbWllARY8QpcOggN+4FPWbuFKyIiIiIit5+i9Dt/UaUkJhERkduIi6aTKyjFbTq5fE1imj17tmWQmTbxKO323DCZTEUyiSm1slG1atUwGjN/i2vWrJnhmJzav3+/zX5ycp60X25y28+hQ4c4deoUMTExeHl55Tje06eznv7j3LlzltcxMTFERUXluG+R/BYdHW3ztYij6DMphYk+j5L/PKDeYLhnIMZjf+IaMRfjiQ22m0ZfgPUfYdr4GUl3diDhrh5gMoHBoM+jFAr6GymFSWH/PCYlJZGSkoLJZCI5OdnR4YgdpP33rH/nkp9MJhMpKSkkJSXl+DfFmJiYAo5KioOi9Dt/dnLz+/z169ft9vt8+iSmGzduZJgORSS37DEO1lj29qJx7O0pL2PMnCrs39cLSgrW+SJx0dHKCcgn8XHxVutOBie7vbfXr1/P9z7zNYmpYsWKNpOVMtteHMXFxfHff/8BUL58+SzblihRAi8vL2JiYjh16lSuzpP2S0d256lQoYLldfrz5KUfk8nE6dOnLeVrcyJtDNn55Zdf8PPzy3F7kYI0b948R4cgYkWfSSlM9HmUgtEcf7da1EveyT1Ju/AgLkMLQ0oSLoeXU+rwcp42lGSnc31+nBtHvMHdAfGK2Ka/kVKYFMbPY7169fDz88Pb25uLFy86Ohyxs8uXLzs6BClGEhISiI6O5tq1ayxZsiRHx1y7dq2Ao5Kirqj9zp+d3Pw+P2/ePLv9Pt+oUSOcnZ0t67/++uttdTNXCl5BjYM1lr19aRx7+8jLGDMvCuP39YLS9OxZAtOsb9m0iaORkQ6LpzhJ8Engvtr3WdadcGLq1Kl2OXdBfLfK1ySm48eP52p7cZQ208zb2zvb9qlfbnI7MM/NedJWTEp/nvzqR0REREQkv1x1CmC90/1sNt7HXckHaZAUTpDpnM22JU2XaZe0ltZJ69jnXIcdxoZcdNJUcyIiIiIikndF7Xf+oir9w+/pKzOJiIhI8WHA+v/zptukCI49JCQmsPjKYlJIIcVkXipS0dFh5Vm+JjGJ+QmNVK6urtm2d3NzAyA2NrbAzpN6Dlvnya9+spPdkyHnzp2jcePGADzyyCPUqFEjV/2L5Kfo6GhL5nOfPn1y9EOFSEHSZ1IKE30exVGiL+zBddc8XA7+iiEpY3UmI8nUTd5F3eRdJN3RgMS6fUis0QWMqs4k9qO/kVKYFPbP45kzZ0hJScHFxYXAwMDsD5AiLzk52fLkesmSJa0qb4jciuvXr+Pj44Ofnx/NmjXL0TGHDh3i448/LuDIpCgrar/zZyc3v8/36dOHcuXK5ar/vIqIiLBKXHr00UetkrVE8sIe42CNZW8vGsfenvIyxsypwv59vaD8t2cv8WdvPqjavGVLOjz2mAMjKj6OXDtCvz/7WdYNGNg8ZLNdzn3mzJl8/26lJKZ85u5+8yZJQkJCtu3j483zE3p4eBTYeVLPYes86ftJu56bfrKTXSnctLy8vPD19c1V/yIFxdvbW59HKVT0mZTCRJ9HsSvf5lC9OcR+Aru+h7Bv4PJhm02N53ZgPLcDjw3vQ/0nIOQpCKhq54Dldqe/kVKYFMbP44ULF0hKSsJgMOgmwG3I2dlZ/94l3xgMBpycnDAajTn+W6ckCclOUfudPzu5+X3ex8fHbuMGJycnkpOTLeuenp6FbswiRVtBjYM1lr19aRx7+8jLGDMvCuP39YISma7ykruH/r+fXzyTPK3WnQxOdntvo6Ki8r1Pp3zv8Tbn4+NjeZ2Tkq4xMTFAzkrS5vU8qeewdZ786kdERERExC48/KHpszAsjIQ6vbJuG3sFtnwFE+vD/Efh4O+Qkpz1MSIiIiIictsrar/zF1WaTk5EROT2YTKlWK0bnJWqkl+SUpKs1p0NRTvZUp+MfObu7k7JkiUBOH36dJZtIyMjLV88KlSokKvzpH1yIrvzpC0Vm/48eenHYDDk6skNEREREZF8ZzAQ134sC10f5zIB2bc/sha+7w0T6sLGzyH6YsHHKCIiIiIiRVJR+52/qAoKCuLo0aMcPXqUSpUq4enpmf1BIiIiUjQlWycx4VS0E20Kk/RJTEanoj0hW75GX7Vq/k/RYDAYOHr0aL73W5DuvvtuNm3axJEjR0hKSsJotP02Hzx40PK6Vq1auT6HrX5ye570/dSrVy/bfipUqKCSyyIiIiJSKJx0rsxM92d4vpkXHls+h/hrWR9w7RT8+T6s/wTu7gYhA6HSvZDuCWAREREREbm9FaXf+YuqkiVLcunSJQACAgJwdXV1cEQiIiJSUEzpK+Q76ffY/JKYkmi1bjQoicni+PHj+dkdkLGcaFHQokULNm3aRExMDNu3b6dJkyY2223YsMHyunnz5rk6R5UqVQgKCuLs2bNW/diyceNGAMqVK0flypUzxJo2nl69bE/Jcf78eQ4dOpSnWEVERERECpLJ4ERi/f54hDwB6z6E7bMgXXniDFISYe/P5qV0LWg0EO55DNw1D7uIiIiIiBSt3/lFRERECjtTfILVupO7u4MiKX5UiSkL/fr1y8/uiqyHHnqIjz/+GIBZs2bZ/HKTkpLC3LlzAfD396dNmza5OofBYODBBx9k6tSpHDx4kL/++oumTZtmaPfXX39ZntB48MEHMySF1ahRg1q1anHgwAF+/PFHxo0bZ7Nk6+zZsy2vH3744VzFKiIiIiJiF14loct4aNgfVr4OJ0JzdtylA/D7y7DmXaj7mLk6U9k6BRqqiIiIiIgUbkXpd34RERGRwi4l9obVukFJTPlGSUxZmDVrVn52V2Q1btyYli1bsmnTJmbOnEm/fv1o1qyZVZtx48Zx4MABAF588UVcXFys9q9fv97yhadfv35WSUSphg8fzvTp00lOTub5559n48aNeHh4WPbHxsby/PPPA2A0Ghk+fLjNeF9++WUGDhzIlStXePXVV5k0aZLV/qNHj1q+rFWrVk1JTCIiIiJSuN1xD/RfDvt+gdXvQNTpnB2XGAPh35qXCk3N1ZnufhCMbgUbr4iIiOTJ7NmzGTBgAADHjh0rkMokx48fp0qVKoD5t8/+/fvn+zkKq9GjRzNmzBgATCZTnvpITbR49913GT16dH6FJmIXRe13fhEREZHCzBQbZ7Xu5O6RSUvJrQxJTEV8OjknRwdQXE2YMAEPDw+SkpJo3749H3/8MX/99Rfr1q1j8ODBvPrqq4C5EtLIkSPzdI4aNWrwyiuvABAeHk7z5s354YcfCA8P54cffqB58+aEh4cD8Morr1C9enWb/fTr189S5nby5Ml0796dVatWsW3bNiZNmsS9995LVFQUTk5OTJw4MdO5v0VERERECg2DAeo8CsPCoNVrYEzzZE/tR6DDRxBwZ+bHn/oLfhkE4++GtaMh8kSBhywiIiIiIoVLUfqdX0RERKQwS4lLl8TkoUpM+SUpKYmqblUp51KOUsZS+Dj7ODqkW6JslAJSv359fvjhB5588kmioqIYNWpUhjY1atRg+fLl+Pjk/UP04YcfcvHiRb799lt27txJr169MrQZOHAgH3zwQaZ9ODs7s3jxYjp16kRYWBg///wzP//8s1UbNzc3Jk2axAMPPJDnWEVERERE7M7VE9qMgnpPwOq34Og66Pgx+JSFJkPg2HoImwn//A6mlIzH3/gPNn8Bm7+E6u3N1ZmqtQMnZ3tfiYiIiBRT9qgoJSJ5U5R+5y/qTCYTKSkpODnp2XsRyT+VK1fmxIkTmVbDc6T8qHopUpSkxMZarRtUiSnfmG6YeOmOlyzrl5IuOTCaW+ewJCaTycS///7LlStXAAgICKBq1arFai7nrl27snv3biZMmMDy5cs5ffo0rq6uVKtWjR49ejBs2DA8PT1v6RxOTk7MnDmTRx99lOnTpxMWFsZ///1HqVKlaNSoEYMHD85R4lGpUqXYsmULM2bMYMGCBRw4cICYmBiCgoK4//77efHFF6ldu/YtxSoiIiIi4jAlKsFj8yDqrDmBCcDJCe5sa16unYHtsyB0IiTH2+jABIdXmRf/ihDyFNTvA16l7HoZIiIiIkWJbshJcVCUfucvao4fP07Dhg1xdnYmIiKCO++8k/Llyzs6LBEREclnpqQkSEy02ubkqSSm/JKYlIgbbpb1JJKyaF342T2JaeXKlUyZMoX169cTExNjtc/T05PWrVvz3HPPFZsBeaVKlRg/fjzjx4/P1XGtW7fO1Zf8Tp060alTp9yGZ8VoNDJkyBCGDBlyS/2IiIiIiBRavkG2t/uVA587biYwuftD3FXbba+eNE8xt+4juPshaPQ0VGhsnsJORERERESKnaL0O39RkpycjIuLi9W6iORN8vnzJGzfTvL585gSEjC4uuJctiyuDRviXLaso8MTkdtc8vXrGbY53WISuNyUmGSdIJZisDHjQBFitySmGzdu0KdPHxYvXgzYfgonJiaG33//nd9//51u3boxf/58vLy87BWiiIiIiIjcrm5cgT/fv7kedxVwgjJ3Q+QJSMj4RZvkBNjzo3kpU8c81VxwT3DztlfUIiIiIiIiRZazs/U03UpiEsm9pDNniFu1iuRTpzLsSz59moTwcJwrVMC9QweM5co5IEIREUi5di3DNmd/f/sHUkwlJVtXXjJRtCvi2mVy4ZSUFDp16sTixYsxmUwYjUY6d+7MmDFjmDZtGtOmTWPMmDF06dIFFxcXTCYTS5YsoVOnTio5LCIiIiIiBW/XQoiNTLcxBS7sBWcXuOcxKHtP5sdf2AvLRsC4mrD8Zbh4oEDDFRERyY3Ro0djMBgw/K9qYFRUFKNHjyY4OBhvb28CAwPp1KkTW7ZssTru4sWLvPXWW9SuXRsvLy9KlizJgw8+yM6dO7M9Z0pKCvPnz6dTp06ULVsWDw8P6tSpQ/fu3Zk6dSoJCQnZ9hEZGcnrr79OzZo18fDwIDAwkHbt2rFo0aIcXXfqNY8ePTrLdq1bt8ZgMNC6desc9Zve3r17+eCDD+jQoQPly5fHzc0Nb29vqlevTr9+/fjrr79sHrd+/XoMBgMDBgywbKtSpYol7tRl/fr1No9fvHgxPXr0oGLFiri7u+Pv709ISAhjxowhMjL9uCaj06dPM3ToUKpWrYq7uztBQUF069aNtWvX5ul9sCWn/w5E5Pbk5GR9iyolpWhXDRCxt8RDh4iZPdtmAlNayadOETN7NomHDtkpsoJx9uxZXn/9dRo0aICfnx8uLi6UKVOG4OBgevfuzezZs4mKigJuju9OnDgBwJw5czKMsdKP/SIjI5k1axZPPvkkd999N97e3ri6ulKuXDl69+7N/PnzsxzDHj9+3NL37NmzAfjll1/o1KkTQUFBGI1GWrduzezZszEYDIwZM8ZybPrYDAYDx48fz9f3T8SRktMlMRnc3HByd3dQNMVP+kRwk6Fo59jYpRLT119/zcaNGzEYDHTo0IFvvvmGcplk+545c4ZBgwaxcuVKNm/ezLRp0zS9mYiIiIiIFKymz4FPWVj9NkSdsd4XewV2/wBlgqHLl3Dqb9j7y82p59JKuA5hM8xLpebm6kw1u4LR1S6XISIikp1Tp07Rrl07DqW5iRUTE8OKFStYvXo1CxcupEePHuzevZtOnTpx5szN/y/euHGDJUuWsGrVKlasWEGbNm1snuPKlSt069aN0NDQDNu3bNnCli1bmDJlCitWrKBSpUo2+zhw4ADt2rXj7Nmzlm1xcXH88ccf/PHHHwwYMID77rvvVt6KfLF+/Xqb70NCQgJHjhzhyJEjzJ07l9dff52PP/44X84ZGRlJ9+7d+fPPP622x8fHs337drZv386UKVP47bffaNq0qc0+Nm3aRJcuXSw3+gDOnTvH0qVLWbp0qZKORMQu0icxqRKTSM4lnTnDjUWLICkp+8YASUncWLQIr/79i2RFJltjFzAn3V+8eJG9e/fy/fffU6pUKbp06ZKnc9SvX9+S9JTWhQsXuHDhAhs2bGDhwoX8/vvvlM1mij6TyUTfvn2ZN29enmIRKW7SJzE5+/k5KJLiKSU5XSK4XUoZFRy7JDHNmTMHgEaNGrF8+fIMA9O0ypUrx9KlS2nevDnbtm1jzpw5SmISEREREZGCZTBAnUehRkfY/CWETsiYpHRhDywbDnW6w6A/4eifED4TIo/b7vNEqHnxCoQGfaFhf/CvULDXISIiko0ePXpw+vRp3njjDTp27IinpyebN2/m3XffJSoqioEDBxISEkKXLl2IjY3lww8/pFWrVri4uLBy5Uo+/PBD4uPj6d+/P4cPH8bV1TpRNzk5mS5durB161YAWrVqxbBhw6hYsSIHDhzg+++/Z+XKlRw4cID777+fiIgIvL2tp2KNioqiQ4cOlgSmxx57jH79+hEYGMihQ4cYP348s2bNYu/evfZ507KQlJSEl5cXnTt3pm3bttSsWRNfX18uXrzIvn37mDhxIidOnOCTTz6hRo0aVlWXGjVqxJ49e/jtt9946623AFi1ahVBQUFW56hSpYrldXx8PO3atWPHjh04Ozvz+OOP06lTJ6pUqUJiYiIbN25k/PjxXLx4kU6dOrFz584MiWInT5603AR0cnLimWeeoXv37vj5+bF7924++eQTRo8eTUhISAG+cyIimk5O5FbErVqV8wSmVElJxK1ahfdTTxVMUAUkPj6eXr16ERUVhY+PD0OGDKFNmzYEBgaSkJDAsWPH2LJlC7/++qvlmFmzZhETE2MZUz744IN88MEHVv16eXlZrScnJ9OkSRO6dOlC/fr1KVOmDAkJCRw9epRZs2axbt06du7cSa9evTKtlJnqyy+/ZPfu3bRs2ZIhQ4ZQo0YNrl69yvHjx3nooYcICQlhypQpTJ06FYA9e/Zk6COzgiAiRZGSmAqWKcW68lJW+ThFgV2SmA4cOIDBYGDEiBE5esOcnZ156aWX6NWrFwcOaBoGERERERGxE1cvaPsm1H8SVr8FB5ZkbLP3J/jnd2j5EgzZCie3QNhMOLQSTDamP4i5CJs+h83jzUlSjQZC1bZQxL9MiogUtJQUE5E3sp9yrDgp4emKk5OhQM8RERHBhg0baNKkiWVbSEgI1atXp0uXLly/fp0mTZpgMpnYtm0bd955p6Vd48aNKVWqFEOHDuXkyZMsX76chx9+2Kr/adOmWRKY+vbta5kuIzk5mQoVKtC+fXsmTpzIJ598wtGjR3n//ff59NNPrfp4//33OfW/aVE++ugj3njjDcu+hg0b0r17d7p06cLq1avz/f3JrXr16nH69Gn8/f0z7OvQoQPDhg2jS5curFmzhjFjxtC3b1/LTXsvLy/q1KlDeHi45ZgaNWpQuXLlTM/33nvvsWPHDvz9/Vm7di0NGza02t+iRQueeOIJmjVrxrlz5xg1ahTfffedVZuRI0daqhjMnz+f3r17W/aFhITQo0cPWrZsaRWXiEhBUCUmKc5MJhOmGzcKpO/kixeznUIu02NPnSLx2DGcAwPzOSoweHpapi/OT6GhoZbk9gULFmSotNS0aVN69+7NF198wY3/veepSeAuLi4A+Pv7U6dOnSzP8+eff1K9evUM25s0aUK7du34/vvveemll9iwYQN//PEH999/f6Z97d6922osnJ6/vz+Baf4dZBebSFGXfFVJTAXKBKT5U6MkphxI/eNco0aNHB+T+j+JgvifnYiIiIiISJZKVILH5sG/G2DFa3Ap3cMViTfgzw9gxzzoPA56L4Srp2D7bNgxB2IuZezTlGJOfvrndyhRBUKeMidLeQbY5ZJERIqayBsJNPxgraPDsKvtb7WjpLdbgZ5j+PDhVglMqTp37kylSpU4ceIEly5dYurUqVYJTKkGDBjAyJEjiYuLY9OmTRmSmCZPngxA6dKlmTRpks3f9kaPHs3ixYs5ePAgM2bM4L333sPNzXzdCQkJzJw5E4B77rmH119/PcPxLi4uzJw5k6pVq5KYmJj7NyEflSpVKsv9rq6ujB07lnr16nHixAkiIiIyJB7lVHR0tOX9ff/99zPtp1KlSrz99ts899xzLFq0iOnTp1sqDZw/f95SpaBLly5WCUypfHx8mD59us3PiYhIfkp/gy0pt1VlRAox040bXP/8c0eHYdONuXMLpF+fl1/GkK66UX44f/685XVW0wkbjUZ8fX3zfB5bCUxp9erVi7lz5xIREcHixYuzTGLy9/fPdCwscjtKjoy0WnfyVxJTfnJKcYI0BS7TV7ssauySgpX6g8fFixdzfExqW1s/loiIiIiIiNhF1Vbw7GZ4YCy42/hyffUE3Lhifu1fAe5/G0bsh0dnQqXmmfcbeQzWvA3jasKvz8KpMDCZMm8vIiKST3r16pXpvnvuuQcwP1T42GOP2Wzj4eFhucHz77//Wu07e/aspap6z5498fHxsdmH0Wi0TKsWGRnJjh07LPu2b99O5P9+4O7Xr1+mN37Kly9P+/btM70WR4mPj+fkyZPs37+fvXv3snfvXkxp/h+/a9euPPe9YcMGrv1vGobu3btn2Tb1Bl9iYiLbt2+3bF+3bp2l0knaqe3Sa9y4MbVr185zrCIiOaHp5EQkJ+644w7L61mzZtnlnCaTifPnz3Po0CH27t3LwYMHOXjwoGXa3+zGdF27ds10LCxyO0q6ZJ0n4lIA1eBuZ85Yj6lcnF0cFEn+sEsSU+/evTGZTMzNRWbv3Llzs/zBRERERERExC6cjdDkGXh+p7l6UtravOUbwz09rdsbXSG4Owz43TzdXKNB4JrJD1fJ8bBrIcxsB1/fB9vnQEJMgV2KiIhIVpXSU6dEK1WqFCVKlMi23fXr162279271/I6uyo+afenPW7Pnj2W140aNcqyj8aNG2e5315iYmL4+OOPqVu3Ll5eXlSqVInatWsTHBxMcHAw9evXt7T977//8nyetNO73XHHHRgMhkyXtFOSpK1eUBTfXxEpvoxG68lCHF1dT0QKpxYtWlC1alXAXFW0cePGfPzxx4SGhpKQkL/TTy9fvpwuXbrg5+fHHXfcwV133UW9evVo27Ytbdu25ffffweyH9OlPhwgImaJ6YrdGEuXdlAkxZMrrtbrLq6ZtCwa7JLE9MILL9CgQQO+//57Pvvss2zbjx07loULF1K/fn2GDx9e8AGKiIiIiIhkx6skdPkCBm+EivcCBnjgU8iqNHiZu6Hz5zDyAHQeD2XqZN72/G5Y+gKMq/W/KewO5fsliIiIeHp6ZrovdVqfrNqkbZe+YsaVK1csrwOzebK2bNmyNo/LTR9lypTJcr89HD9+nODgYEaNGsXu3buzrSISGxub53Plpsp9Wjdu3LC8Lmrvr4gUb7aSmEyqUCsi6bi4uLB06VJq1aoFQFhYGKNGjaJFixb4+/vTsWNHFixYcEvV3EwmE08//TRdunRh+fLlGZL108tuTJfVAwEit6Oki5es1o2qxJSv3A3uVutebvk/tac9GbNvcuvOnz/PN998w+DBg3njjTdYuHAh/fr1o1GjRgQGBmIwGLhw4QJhYWHMmzePiIgIGjVqxPTp062eFEqvYsWK9ghfRERERETkpjvuMVdZOrMDyjXIvN0f70G1/4NKzcDNBxoNNFdyOvU3hM2E/Ysh2cYTg/HX4O9p5qVyS2j0NNTsDEW8DLCISG6V8HRl+1vtHB2GXZXwLNpPS6aV2TRw9u6joPXp04djx45hMBgYMGAAvXr1olatWpQuXRpXV1cMBgMpKSmWKZNu5eZ82htzO3bswMUlZ2OD8uXL29xeFN5fESne0k8nZzKZSE5OzpDcJFIUGTw98Xn55QLpO27NGhJvYYpal3r1cG+X/+NsQzaJ8Lfi7rvvZs+ePSxdupSlS5eyceNGjhw5QmxsLKtWrWLVqlWMHz+e33//PdtEbVu+/fZbZs6cCUC9evUYPnw4TZo0oVy5cri5uXH58mUAXn31VebPn5/tmC793zeR211S+kpMSmLKN3GJcbg7WScx+br5Oiia/GGXkWDlypWtvhTv3r2bkSNHZnlMeHg4DRpkfkPAYDCQlJSUbzGKiIiIiIjkmMEA5Rtmvv/fDbBpnHmp0x3+7z3wK2c+rmJT89LxY9g5D8K/hasnbfdzfJN58S4LDftBg37mfkREbgNOTgZKers5OgzJhYCAAMvrCxcuZNk27YOLaY9L+9T6hQsXspz+LrtzGAwGTCYTKSkpWbaLicnbVK4HDx5k8+bNAIwaNYoPPvjAZru01Y9uRcmSJS2vS5cunWlyUlbSv78VKlTItG1276+IyK2ylayUmJioJCYpFgwGAwavgqmE4da06S0lMbk1aYJTAcVWkJydnXnooYd46KGHADh37hwrV65k8uTJbN++ne3btzN48GB+/fXXXPc9Y8YMAKpVq8aWLVvw8PCw7EubSJ5f4zqR24kpMZHk/yUCplISU/65lnCNCecn4O3kjbezN15OXgy9a6ijw7oldplODswZ9Pm9iIiIiIiIFDrJSebp4FLt/QkmhcDGsZAYd3O7VyloMQJeiIDHf4TqHYBMKiJEn4cNn8KXwfD9E3DkD8jmhqyIiIi91alzc9rUv//+O8u227Zts3lccHCw5XVYWFiWfWS338fHB4DIyMhM25hMJo4cOZJlP5nZt2+f5fVjjz2Wabvw8PAs+8lpRaT69etbXoeGhubomPTy8/0VEblVTk5OGRJNExMTHRSNSNHhXLYszlkkImd5bIUKOKeZ1rcou+OOOxgwYABbt261FMZYtmyZ1VRvOR1npY7runXrZpXAlJbJZGLnzp23GPVNqoopt4ukS5cybDOWLu2ASIqnq/FXORx3mJ03drLp+iZWXl2Jn4efo8O6JXZJZ581a5Y9TiMiIiIiIuJ4u3+ASwestyXegD8/gB3zoMOHULOLuSoTgJMz1OhgXiKPw/bZsGMu3LicvmcwJcPBZealRGVo2B/qPQne+uIvIiKOFxQURK1atThw4AA//vgjn3zyCd7e3hnaJScnM3v2bMBcGShtNfaGDRtSokQJIiMjmTdvHiNGjLB5g+fMmTOsXr06y3iqVKnCrl27skwiWrFiBVevXs3ZBaaTtkp8VtWcpk2blmU/7u43S//Hx8dn2q5du3Z4enpy48YNJk6cSM+ePXN986tNmzY4OzuTnJzMnDlzeOSRR2y2CwsLY+/evbnqW0QktwwGA4mJibi53ay8qCQmkZxx79CBmNmzITez1hiNuHfoUGAxOYqLiwutWrVix44dJCUlcfXqVUsiUuo4K6sxFtwc12U1plu5ciXnzp3Lp6gzjgHT/i0UKU4STp22Wjd4eOCcpkKs3JorcdYV4txMbjgbivaUlnZJYurXr589TiMiIiIiIuJ49zwGCdGw7kOIu2a97+oJ+OFJqNoaOn4KgTWt95eoDO1GQ+s3YP8SCPsGTv1l+zyRx2HtaPjzQ6jVFUKegsotbiZHiYiIOMDQoUMZNmwYly5d4oUXXuDbb7/N0Oa9995j//79AAwaNMjqho2bmxsDBgxg/PjxREREMHbsWF599VWr45OSkhg0aBAJCQlZxtKqVSt27drF33//TWhoKM2bN7faf/78eZ5//vm8XirVq1e3vJ49ezZNmzbN0Gbq1Kn89ttvWfZzxx13WF4fPXqUu+66y2Y7f39/hg0bxmeffcaWLVsYMWIE48ePx8nJdrH9CxcusHTpUp5++mmrcz344IP88ssvLFmyhB9//JGePXtaHRcdHc3gwYOzjFlEJL9cuHABg8FAs2bN8PHxsZn8KiIZGcuVw7NHD24sWpSzRCajEc8ePTCWK3pT1G/atIk77riDatWq2dyfkJDAhg0bAPD29qZ0mgovd9xxBwcPHuTo0aNZnqN69ers2bOHpUuX8tFHH1lNdwxw/Phx3nzzzVu8Emvpx4B33313vvYvUlgknrZOYnItX16VyPLR1firVuvuKe62GxYhdptOTkRERERE5LbgbIQmg+H5ndBwADaniPt3PUy9F1a8DrFXM+43usE9PWDgKng21Jyg5OJl+3wpibDvF5jTBSY1gq1T4MYV221FREQK2LPPPkuzZs0Ac3X2+++/n59//pkdO3awdu1ann76aT788EMA7rzzTt5+++0MfbzzzjuUL18egNdee43HH3+clStXsmPHDr7//nvuvfdeVqxYQUhISJaxPPPMMxiNRkwmE127duXLL78kPDycLVu2MHbsWOrXr8+1a9eskpFyo379+pap8L7++msee+wxli1bxvbt2/ntt9/o0aMHzz33XIbkKVv9pD6J//bbb7NmzRoOHTrEkSNHOHLkiNWUKO+99x5NmjQBYMKECTRo0IDJkycTGhpKREQE69atY9KkSTz00ENUrFjRZhWocePGWabae/zxxxk6dCjr1q1j+/btzJo1i4YNG7Jz585s318Rkfxw9uxZzpw5Q+nSpQkMDFQlEpFccKlRA6/+/bOdWs65QgW8+vfHpUYNO0WWv/744w/uuusuWrduzdixY1m1ahU7duwgNDSUWbNm0bJlS3bs2AHAwIEDMRpv1vC49957AXOVyU8++YRdu3ZZxlhnzpyxtOvbty9g/pvUrFkzvv32W7Zt28bGjRsZM2YMHTt25OrVq1YVRG9VamwAI0aMYOPGjRw+fNgSX1JuqmyJFGIJp09ZrbvkcTpMsS1DJaaUoj+WskslJhERERERkduOV0no+iWEDIAVr8HJrdb7Tcnw91TY8yO0fRsa9DVPLZde2TrQ5QtoN8bcNuxbuLjP9jkvH4ZVb8AfY6D2w+bkp/KNVJ1JRETsxtnZmWXLltGtWzdCQ0P5888/+fPPPzO0q1WrFitWrLBZccPPz4+VK1fSrl07zp8/z8KFC1m4cKFVm/79+9OqVSsGDBiQaSy1a9fms88+46WXXiIyMpIRI0ZY7Q8ICGDx4sW8/fbbHD58ONfXajAYmDdvHm3btiUyMpIff/yRH3/80apNcHAwixYtIigoKNN+fHx8eOGFF/jss8/YsWMH7du3t9q/bt06WrduDZgrVa1Zs4b+/fvzyy+/sGvXLoYNG5Zp376+vhm2Va5cmSVLltCtWzeuX7/OlClTmDJlilWbd955B4PBkOVUfCIiIuJ4xnLl8H7qKZLPnydh+3aSL1zAFB+Pwc0N5zJlcG3YEOeyZR0d5i1LSUlhw4YNlopLtjz44IN8/PHHVtuGDBnC1KlTuXLlCm+88QZvvPGGZV+rVq1Yv349AC+++CJr1qxh9erVHDp0iIEDB1r14+7uzoQJEwgNDbUkTN2qatWq0bNnT3788UdWr16dYarkY8eOUbly5Xw5l4gjJaabTs6lfNGrCFeYRcZFWq0XhyQmVWISEREREREpSHfUhQEr4NGZ4GvjS/qNy7BsOExvDSczmToOwN0XGj0NQ0Jh4Bqo+zgYMykPnBQHuxbCzP+Dqc1h2wyIi8qPqxEREclWQEAAGzduZO7cuXTs2JEyZcrg4uJCiRIluPfee5k4cSIRERFUqlQp0z5q167Nvn37ePXVV6levTpubm6UKlWKNm3asGDBAmbNmpWjWEaMGMHKlSvp0KEDJUqUwM3NjSpVqjB06FB27txJy5Ytb+la69WrR0REBM8++yyVKlXCxcWFgIAAGjduzOeff862bduspgrJzCeffMKMGTNo2bIlAQEBODvbSGz+Hx8fH37++Wc2bdrE008/zV133YWPjw9Go5GAgAAaNWrE0KFD+f3331mzZo3NPlq3bs2+ffsYMmQIlSpVwtXVlTJlytC5c2dWrlzJmDFj8vyeiIiIiP05ly2LR+fOeD/1FD5DhuD91FN4dO5cLBKYXn75ZX7++WeGDBlC06ZNqVixIu7u7ri7u1O5cmV69uzJsmXLWLx4MR4eHlbHlitXjm3btjFw4ECqVatmqX6ZnouLC8uXL2fixImEhITg6emJh4cH1apVY/DgwaxatYquXbvm+7XNnz+fzz77jMaNG+Pn55fpNMEiRVniKetKTK7lVYkpP12NuYq3kzeG/80G4JHskc0RhZ/BZDKZ7HWypKQkli9fzqZNm/j333+5fv06ycnJWR5jMBj4448/7BShOMrp06ep8L/Scfv376dWrVoOjkhuZ1FRUUydOhUwZ+nbempRxJ70mZTCRJ9HKUyK5OcxIQY2fwGhEyE5PuP++16Ftm/mvL8bV2DX9xD+rbkKU1ZcvCD4UXN1pqD6uYtbcqRIfial2Crsn8fDhw+TlJSE0WjM81ReUrQkJydz8eJFAAIDA7NM0hHJjbz8PTlw4AB33303AKdOnbJMXyhyu0r7+7w9/5so7OMVKZrs8bnSWPb2onHs7akg/zu/3f7/d+je5iRfuTnlWfmpU/Bp08aBERUvC9ctJMgpiERTIteSrrH3/F5GdBtht89VQYwj7Tad3IYNG+jfvz8nT560bMsqf8pgMGAymTBo2gMRERERESkuXL2g7VtQ/0lY9SYcXHZzn285aDE8d/15BkCz56DpEDgRak5m2r8EUhIztk2MgR1zzUtQfXMyU51HzTGJiIiIiIiIiIiI5KOkyEirBCYA1ywq8kruuZpcAXAxuFDKpVSxmE7OLklMERERdOzYkYSEBEwmE+7u7lSvXh1/f3+VxRMRERERkdtPicrQ6zs4ug5Wvg6XDkL79/OeUGQwQOUW5iX6EkR8B9tnQeRx2+3P7oQlz5sTqe55DEIGQJnaeb0aERERERGRW2YymUhISMDV1VUPuIuIiBQDCUePWm9wccG1YkXHBFMMmUwmvAzWvycnx2U9E1pRYJckptGjRxMfH4+bmxvjx49nwIABmc45+v/s3Xd0VNXexvHvTOqk90aoIfRepaOiKNjFBhbs13LBit57Lei16xU7gvKKInbsiA0pUkR6CSUk1JCQQnovM+8fBwJDCi2ZSXk+a+3FnH32Oec3cQzDzHP2FhERERERaTZizoZ/LINt30PXK2oet3Y2RPaCqF4nPqdPqDGj0+BJsGuREWba/hPYqvkHbEkurH7PaC0HGrMzdbkU3Br/2ukiIiIiItLwubi40KlTJ7Zs2UJZmTGj7KBBg3B3d3dyZSIiInKmShLsQ0webdpgcnXYYmFNXnZRNhbzcZ/jljinlrrkkFfIsmXLMJlM/Oc//+Guu+5yxCVFREREREQaBxc3Y1m3mhxKhPkPgbUcek+Ac54A3/ATn9dshvbnGi03BdbPgbUfQm5S9eP3rzLaz49Cz/HG7Ewhsaf3nERERERERE5CRUUFPj4+lQEmgJKSEoWYREREmoCS42Zicm8f46RKmqaUnJQqfeaixr8SmkOeQXFxMQAXXHCBIy4nIiIiIiLSdPz6OFjLABus/xje7AvLpkH5KdxW4xcJI6bAfZvgus8hdjRQw/IMRVnw19vwVj+YfRFsmQflpXXxTERERERERKooLbX/90ZJSROYQkBEREQoTUyw2/aIae+kSpqmjLwMu+38inzMNoWYTkqbNm0A7JL0IiIiIiIicgK7l8KO+fZ9pXnw+1R4ewBs+xFstpM/n9kFOl4AE74wAk3DHwafWmZ12vMnfHULTOsCvz0JmbtP62mIiIiIiIjURCEmERGRpqlk53EhJs3EVKdyC3PttvPK8pxUSd1ySIjpsssuA2Dp0qWOuJyIiIiIiEjTED0Azn0C3Lyr7svaA59PgI8ugdS4Uz93QCs45zG4Pw6ungPtzq55bEE6LH8N3ugFc66AbT9AhW5SERERERGRM6cQk4iISNNTnp5OeXq6XZ9HbKyTqmmaCooK7LfLC2oY2bg4JMQ0efJkIiMjeeWVV9izZ48jLikiIiIiItL4uXnCsAfhn2uh5/jqx+xeCu8OhR8fgIJDp34NFzfocgnc+C38cx0MmQxewTWPT1wIn18P07rCwqeNMJWIiIiIiMhpOj60VFxc7KRKREREpK4Ub91qt23y8sL98ApeUjfKS8vttgtLCp1USd1ySIgpNDSUn376CYvFwsCBA3nvvffIyclxxKVFREREREQaP79IuHw63P4HtBxYdb/NCmtmwZu9YeU7pz9LUnAMnPc0PLANrpwFrYfWPDY/Ff78H7zeEz66DOK+hfLSmseLiIiIiIhU4/jQUlFRkZMqERERkbpyfIjJs3NnTC4uTqqmaXIpt/95NpUguKujLtSjRw+WLl3KwIED+cc//sFdd91FSEgIXl5etR5nMplITEx0UJUiIiIiIiINWIu+cMsvsGUe/PYE5B6w31+cA7/8C9b8H4x+Djqcf3rXcfWA7uOMlr4D1nwAGz8xzl+dXYuM5hUCvSdAn5uMQJSIiIiIiMgJVBdistlsmEwmJ1UkIiIiZ6ooLs5u27NLFydV0jTZbDZ8Tb52feVF5ZgdM49RvXJYiGnevHnceuut5OXlYbPZsNlspKWlnfA4vUkVERERERE5hslkhIs6joEVb8Cy16D8uDuVD+2EzV+cfojpWKEd4cIX4NwnIO4bWPch7F9V/djCDFj+utHaDIO+E6HzxUYoSkREREREpBrHh5gqKiooKyvD3d3dSRWJiIjImSqOO24mpq4KMdWl9Px0fFx87DubxmpyjgkxrVy5kmuvvZaKigoAWrduTY8ePQgICMBsbvxJMBEREREREYdz94KRj0Lv6+G3J2HLV0f3uVpg1NS6v17vCUZL3WqEmTZ+WvPsTHv+NJolCHqNN2ZnCu1QtzWJiIiIiEijV1paislkwmazVfYVFRUpxCQiItJIlR86RHlKil2fZmKqW/sy99ltW21WTEVNY4Igh4SYnnnmGSoqKvD392fu3LmMGTPGEZcVERERERFp+vyjYdwsGHAH/PwIJK+HIZON/voS3gUufNEISm39HtbOhn0rqh9blAkr3zJa6yFGmKnLJeBmqb/6RERERESkUfHw8LCbkamoqAh/f38nViQiIiKnq2j9erttk5cXHu3aOamapiktJw1vvCu3c625mGwKMZ20NWvWYDKZeOqppxRgEhERERERqQ+tBsJtfxgzMnW6qOZx234Adx+IOfvMr+lmgZ7XGC19B6z7CDZ8YgSXqrN3udEWTIGe10HfmyCs85nXISIiIiIijVp1ISYRERFpnArX2YeYLD17YHJ1SDSl2cgtyLULMRXRdN47OeSVUlhoLL43dOhQR1xORERERESkeTKbocfVNe8vzoEf7oPCDOg4Bs5/BoJj6ubaoR1h9LNwzuOw/UdjdqY9f9ZQRzasmm60lgOh70TocpmxZJ2IiIiIiDQ7xy8dpxCTiIhI41W0bp3dtlfvPk6qpOlaX7qeWQdmEeoaSphbGJ2Dm86NomZHXKRt27bA0TCTiIiIiIiIOMHSl40AE8COn+DtgfDrY0a4qa64eUL3cTDxR7h3LQyeBF4hNY/fvwq+vQv+1wnmPwQHN9ddLSIiIiIi0ih4enrabSvEJCIi0jhZS0oojouz67P0UYipru3M2cnBsoNsLtrMwtyFePp5nvigRsIhIaYrrrgCm83GL7/84ojLiYiIiIiIyPEyd8Nf79r3WctgxZvwZl9j5iRrRd1eM6Q9nP9feGAbXDUb2o2seWxJDqx+D94dCu+dYyxNV5Jft/WIiIiIiEiD5OHhYbddWFiIzWZzUjUiIiJyuoq3bMFWVna0w2zG0qun8wpqgsqsZezO2W3X19avrZOqqXsOCTE9+OCDxMbG8tprr7FmzRpHXFJERERERESOFdAKxr5S/axIBenww2SYOQL2LKv7a7u6Q9fL4cbvYNJ6GPoAeIfVPP7AWvj+n8bsTN9PgqS1oC8wRERERESaLE9PTyIjI2nfvj09evRg4MCBmEwmZ5clIiIip6hwzVq7bY8OHXDx8XFSNU3Tnpw9lFvL7fra+bVzUjV1zyEhJl9fXxYuXEi3bt0YPnw4//nPf9i0aRPFxcWOuLyIiIiIiIiYXaDvRJi0Dgb/E8xuVccc3Ayzx8IXN0LWnvqpI6gdjHoSHtgKV8+B9qOAGr6cKM2DdR/C++fA9MGw8h0oOFQ/dYmIiIiIiNO4ubnRoUMHWrRoQWBgIO7u7s4uSURERE5DwYoVdtteffs6qZKma2fWTrvtcK9w/Nz9nFRN3XNIiMnFxYXWrVvz999/U1xczAsvvEDv3r3x9vbGxcWl1ubq6uqIEkVERERERJoHT384/xm4ZxV0HFP9mK3fwVsD4PepUJxbP3W4uEGXS+D6eTB5Iwx/GHwjax6fthV++Re82gm+nAgJC8FqrZ/aRESkUZg9ezYmkwmTycSePXvq5Rp79uypvMbs2bPr5RoN1dSpUyufu4iIiEhDsG/fPu68805iYmLw9PSsfK/y7bffMnHiREwmE23atKm36y9evLjymosXL66z8zbn95zStFiLiihat86uz3vIYCdV03TtzLYPMbUPbO+kSuqHQxJCx69brHWMRUREREREnCw4Bq77FBL/gJ//Denb7PdXlMCyabD+YzjnMeh9I5jr6T6YwNbGNUY8Cjt/hbWzIeE3sFUTUqoohbhvjObfCnpPgF4TIKBl/dQmIiIiIiIiIk63b98++vbtS0ZGhrNLEZEaFK5dh62s7GiHiwte/fs7r6AmKj87n86enUkuSyanIocOAR2cXVKdckiI6cknn3TEZURERERERORUxZwD/1gGaz+ARc9CUZb9/oJ0WDcH+txU/7W4uEKnMUbLOQAbPoH1cyB7b/Xjc/bB4udh8QvG8+hzozG7lKuWnhARkcZj9uzZ3HzzzQDs3r27XmcPEBEREWmsnnnmGTIyMnB1deXZZ59l+PDh+Pj4ANC6dWu+/fZb5xYoIlWWkrN0746Lr6+Tqmm62tvaMzRiKAAFFQWYPJrW7LkKMYmIiIiIiDR3Lq4w4HbodiUseRFWvw/W8qP7L3geHL2UjH8LGPEwDHsQ9iw1glTbfjBmiKrCBokLjeYVDD2uhT43QFhnx9YsIiIiIiIiIvXi999/B+Cyyy5jypQpVfbPnj273pdiGzlypFYcEqlFwcqVdtveg7WUXF3LL80n2CW4ctvbxZtA/0AnVlT36mktgLqxfv167r//fmeXISIiIiIi0jx4BcGFL8JdK6HDBUZftyuh5QDn1WQ2Q7uRMG4WPLgdLnwJwrvXPL7wEPz1NrxzFrw/CtZ9BCV5DitXRERERETOjM1mo7CwkPT0dEpKqruJQUSOl7ZxI7/dfTefDB7M7O7d+WTwYH67+27SNm50dml15sCBAwB06NC0lk0SaSrKUtMo2bbNrs978CAnVdN07UjdgZvJza4vJizGSdXUD4fMxHQqUlJS+Pjjj5kzZw5xcXEATJs2zclViYiIiIiINCOhHWD855C4CIJr+UfwnmWQshH63+6YJdy8gmDgnTDgDkjZYASUNn8FJbnVj09abbQFj0K3y40l8aL7O35WKREREREROSlbtmwhMzOzcqaTTp06ER4e7uSqRBqulNWrWXTffSQft4QTQPLKlWycPp0WQ4Ywcto0Ivv3d0KFdae0tBQANze3E4wUEWfIX7zYbtvs64ulRw/nFNOE7cnYQwghldsF1gK8Pb3JLa3h89FGqEHMxFRUVMTcuXMZPXo0rVq14tFHHyUuLk7T8YmIiIiIiDhTzNkQ0Kr6fdYKIxz0y7/hnYGw7Udw1L/hTCaI6g0XTYMHd8DlM6D1kJrHlxXA+o9h1nnw9kBY8RYUZDimVhGRBmDq1KmYTCZMh0Ocubm5TJ06le7du+Pj40NYWBhjxoxhxXFffqWlpfHYY4/RtWtXvL29CQ4O5tJLL2X9+vUnvKbVauXjjz9mzJgxREREYLFY6NatG+PGjWP69OmVX0LVJisri0cffZROnTphsVgICwtj1KhRfPnllyf1vI8856lTp9Y6buTIkZhMJkaOHHlS5z3eli1beOaZZxg9ejTR0dF4eHjg4+NDbGwsN910E3/99Ve1xy1evBiTycTNN99c2de2bdvKuo+0xcd9GXDEt99+y1VXXUWrVq3w9PQkICCAfv368dRTT5GVlXXCupOSkrjnnnto164dnp6eREVFcckll1Qu1VKXPvnkE0aOHElgYCA+Pj5069aNJ598kuzsbODk/1uJSPNw7HdD+fn5TqxEpGFLnD+fz4cPrzbAdKwDy5fz+fDhJM6f76DK6s7s2bPt3scCPPXUU3bvlSZOnAjAxIkTMZlMtGnTptpzHf9+Y/Xq1Vx33XWV799atGjBDTfcwLbjZpI51pH3b7W9R4uPj+ef//wn3bp1w9fXF3d3d6KioujVqxe33HILn3/++UnNMvfbb79x8cUXExERgYeHB23btuWuu+4iKSnphMeKOEP+H3/YbfsMG4bJ3QE3PTYz2bnZdtvFLsXOKaQeOXUmpkWLFvHRRx/x9ddfV74RPfLmNDIykssvv5wrr7zSmSWKiIiIiIhIdTbMhdTNxuPMXfD5BGgzDEY/C5E9HVeHuxf0vNZoGQmwfg5s/BTyU6sfn7EDfv0P/D4VOl5ozM4UczaYXRxXs4iIE+3fv59Ro0YRHx9f2VdQUMCCBQv49ddf+fTTT7nqqqvYtGkTY8aMqVy2A6CwsJDvv/+eX375hQULFnD22WdXe43MzEwuueQSli9fXqV/xYoVrFixgnfeeYcFCxbQunXras+xbds2Ro0aRXJycmVfcXExCxcuZOHChdx8880MHz78TH4UdWLx4sXV/hxKS0tJSEggISGBjz76iEcffZTnn3++Tq6ZlZXFuHHj+OO4LwlKSkpYu3Yta9eu5Z133uG7777jrLPOqvYcf/75JxdddBG5uUfv1k1JSeGHH37ghx9+qLMwUXl5OePHj68SPIuLiyMuLo6PP/64XkJTItJ4+fj4cOjQocrtvDwtDS1SnZTVq/lh3DjKi0/uy+vy4mJ+GDeOa5YubfQzMtWFd955h8mTJ1NeXl7Zl5yczMcff8zXX3/NggULTuu95pdffsn1119fJbCfkpJCSkoKGzdu5IMPPmDz5s1069atxvP861//4oUXXrDr27NnD++++y7z5s1jyZIldO7c+ZTrE6kv1sJCClautOvzqeHfi3JmXMtc4ZgJ6TwsHs4rpp44PMS0fft2PvroI+bOnVuZFD0SXIqOjubKK69k3LhxDB482C5VKyIiIiIiIg1EeQn88WzV/j1/wowR0Gs8nPM4+EU6tq6Q9nDeU3DOY7DzV1g3x/jTVlF1rLUMtn1vNL9o6HUd9Lyu9uXzRESagKuuuoqkpCT+9a9/ccEFF+Dl5cWyZct48sknyc3N5dZbb6Vfv35cdNFFFBUV8eyzzzJixAjc3Nz4+eefefbZZykpKWHixIns3LkT9+PurK2oqOCiiy5i5eEPsEeMGMG9995Lq1at2LZtG5999hk///wz27Zt49xzz2XDhg34+PjYnSM3N5fRo0dXBpiuueYabrrpJsLCwoiPj+fVV1/lgw8+YMuWLY75odWivLwcb29vxo4dyznnnEOnTp3w8/MjLS2NuLg43njjDfbu3csLL7xAhw4d7GZd6t+/P5s3b+a7777jscceA+CXX34hKirK7hpt27atfFxSUsKoUaNYt24dLi4ujB8/njFjxtC2bVvKyspYunQpr776KmlpaYwZM4b169dXCYrt27evMsBkNpu54447GDduHP7+/mzatIkXXniBqVOn0q9fvzP++Tz00EOVAaaOHTsyZcoUevToQU5ODl9++SXvvfce11xzzRlfR0SaDl9fX7vtvLw8bDabvi8SOc6i++476QDTEeXFxSy+/36uW7asnqqqe5dddlnle5Lu3bsDcNddd3H33XdXjgkMDDylc/7yyy/8/fffdO/encmTJ9O9e3eKior45ptveP311yksLOSGG26o9r1ubVJTU7n55pspLS0lLCyMe++9l7POOouQkBCKiopISEhgyZIlfPvtt7We57333mPFihWMGDGCO++8kw4dOpCdnc1HH33ERx99RHp6Orfcckvl+22RhqBgxQpsx4b3XFzwGT7MeQU1UTklOYSaQ+36IoMd/PmrAzgkxHTo0CE+/fRTPvroI9auXQscDS4FBASQnZ2NyWTilVde4eqrr3ZESSIiIiIiInK6XD3gmo+NpeSS/j5up82YpSnuGxhyHwz+pzFbkiO5uEGnsUbLTYGNnxiBpqzd1Y/PTYKlLxut1WDoPQG6XAoevtWPFxHHsFqhKNPZVTiWJQjM5nq9xIYNG1iyZAkDBw6s7OvXrx+xsbFcdNFF5OXlMXDgQGw2G3///TcxMUfDnQMGDCAkJIR77rmHffv2MX/+fC6//HK787/77ruVX6jceOONlUuAVFRU0LJlS84//3zeeOMNXnjhBRITE/nvf//Liy++aHeO//73v+zfvx+A5557jn/961+V+/r27cu4ceO46KKL+PXXX+v853OqevXqRVJSEgEBAVX2jR49mnvvvZeLLrqI3377jaeeeoobb7wRFxdj9j9vb2+6devGmjVrKo/p0KFDjUugADz99NOsW7eOgIAAfv/9d/r27Wu3f+jQoUyYMIFBgwaRkpLCv//9b+bOnWs35sEHH6ycgenjjz/muuuuq9zXr18/rrrqKoYNG2ZX1+nYvHkzb775JgB9+vRhyZIldoG1c889l8GDB3PTTTed0XVEpGk5PsRktVopLCzE29vbSRWJnB6b1UrRMbOK1aWMzZtPuIRcTQ4sX86+RYsIqWUWoNNlCQ7GVMfvZQMCAqq8zwoLC6t1FqMT+euvvxgzZgzffPONXUhp2LBhBAcH89hjj9X4Xrc28+fPp6CgAICFCxdWqXHw4MHceOONvPXWW7WeZ8WKFdx+++3MmDHDLsB57rnn4u7uzvvvv89ff/3F+vXr6d2790nXJ1Kf8hbazxLr1a8fLv7+Tqqm6YpLicPbxf49UYfwDk6qpv7UW4iprKyMH374gY8++oiff/6ZsrKyyuCSu7s7Y8aM4frrr2fs2LFYLJb6KkNERERERETqQ8v+cOuvEPc1/DYVcvbZ7y8rhMXPwdrZMOpJ6H51vX8xXy2/SBj2IAx9APYuh3UfwdbvoLyGO1b3rTDaT1OMIFOv8dB6iHNqF2nuijLh5WY2O9rDieAdUq+XuO++++wCTEeMHTuW1q1bs3fvXtLT05k+fbpdgOmIm2++mQcffJDi4mL+/PPPKl/svP322wCEhoby1ltvVTtzxtSpU/n222/Zvn077733Hk8//TQeHsYU+KWlpcyaNQuAHj168Oijj1Y53s3NjVmzZtGuXTvKyspO/YdQh0JCav/v5e7uzssvv0yvXr3Yu3cvGzZsqBI8Oln5+fmVP9///ve/NZ6ndevWPP7449x99918+eWXzJw5s/LL/4MHD/LNN98AcNFFF9kFmI7w9fVl5syZ1b5OTsW7776L1WoFYObMmVVm3AIj6PbZZ5+xYMGCM7qWiDQd7u7ueHh4UFJSUtmXl5enEJM0OkWHDvFOWJizy6jWF+ecUy/nvTstDa/Q0BMPdDJPT08++OCDamdZmjRpEk8//TSlpaXVvtetzcGDBwFjZqjaQlYn+l48MjKSN998s9r30Q899BDvv/8+YCwPrBCTNATW0lLyjlsi2ufskU6ppanbl7GPSI7OvFRoLcTbq+m9R6rzT2H/+usv7r77biIjI7nqqqv44YcfKtf9HDJkCNOnTyclJYWvv/6aK664ovIDChEREREREWlkTCbodiXc+zec+wS4V/1ykrxk+OZOeP8c2OvEqc5NJmgzFK6YCQ/ugDGvQGTPmseXFRgzOH14EbzRCxa/CFl7HVauiEh9ufbaa2vc16NHDwBMJlONS3xZLBZiY2MB2LVrl92+5ORktm3bBsDVV19dZTaNI1xdXSuXVcvKymLdunWV+9auXUtWVhYAN910U43LB0VHR3P++efX+FycpaSkhH379rF161a2bNnCli1bKm/sBNi4ceNpn3vJkiXk5OQAMG7cuFrHDh8+HDBuND0yMz7AokWLqKgwllk9dmm74w0YMICuXbuedq0Avx/+IqN79+61BrduueWWM7qOiDQ91S0pJyJSV8477zzCagiY+fr61vhe90QiI41gQVZWFt99991p1zdu3Lgavz/v2LFjZTD8VOsTqS8Fy5ZjPe7var/Ro51UTdOWn5dvt13sWtwkl9yt85mYBg8ejMlkqvzHeceOHbn++uuZMGFCrVMhi4iIiIiISCPlZjFmO+p9A/zxDKyfAzar/Zjk9fDBBcbsRmP+Bz5OvDvTEgADbjdayibY8Als+rzmZauy9xqzSi1+DtoOh17XQ+eLHb9MnohIHejQoeap5o8s1RESEkJgYOAJxx3/pfKWLVsqH59oFp9j92/ZsoVBgwYBxhJkR/Tv37/WcwwYMID58+fXOsYRCgoKeOONN/jss8+Ii4urDAlVJyMj47Svc+zybke+JDsZR2YFgFP/+cbFxZ1ChUeVlJSwc+fOk76OiMixfH197X5fKsQkInWpU6dOte4PCgoCTv13zyWXXEJAQADZ2dlcfvnljBw5kosvvpjhw4fTq1evyiWFz7S+wMBA8vPz9btRGozcn36y27b06YPbKfx7RU6eR7kHHDOJnMW7aa54Vm/Lyfn6+vLGG29oTXMREREREZHmwicMLnkDBtwBv/4Hdi2uOmb/3w0r/BPZw2jnPQ3xPxuBpp2/gq2GL6B3LzXafF/odrkRaGo5wJjpSUSkEfDyqvl3sPnw0pm1jTl23PFhnczMo2HQmu5uPyIiIqLa407lHOHh4bXud4Q9e/ZwzjnnsHv37pMaX1RUdNrXSktLO63jCgsLKx876ueblZVVeZNrY/jvKCINy/EzMeXn52O1Wiv//hEROROn+173RIKDg/n++++57rrrOHDgAIsWLWLRokUA+Pn5ce6553LLLbdw0UUXOaU+kfpgLSoi748/7Pr8xoxxUjVN24G8A4S72v/bKTok2knV1K96CTHZbDby8/O55ZZbeP3117n++uu57rrrTukOIREREREREWmkIrrBDd9C/C/w62NwaOfRfec+Ce4NcK12V3foconR8lKNmZk2zIX07dWPL82DdR8ZLbg99BqPqV3tH0SKyCmyBMHDic6uwrEsQc6uoM7UxZT2jWFa/BtuuIHdu3djMpm4+eabufbaa+ncuTOhoaG4u7tjMpmwWq2Vd94fu7TcqTr2i6p169bh5uZ2UsdFR1f/wbajfr6N4b+jiDQsx4eYjnzn5Ofn56SKRE6dJTiYu08zgHwiS6ZMIW727NM+vtvNNzP8xRfrrqDDLMHBdX7OxmbYsGEkJCQwb948fvrpJ5YuXUpSUhK5ubl88803fPPNN4wePZqvv/76hGElkcYgf8kSbMfcNIHZjN/ohrfsd1Ow4cAGfMw+dn3tw9s7qZr6VechpsWLFzN79mzmzZtHXl4eGzZsYOPGjTzyyCOMHDmSG264gSuuuKJyvU4RERERERFpgkwm6HgBtD8X1vwfLH4eAlpBj2ucXdmJ+YbDkEkw+J+QvA7Wz4UtX0FxTvXjDyXAwqfx+eMZrjK1YbNLDygvBvQli8gZMZvBO8TZVcgpOLL0BkBqamqtY49d4uzY445dxi41NbXW5e9OdA2TyYTNZsNqtdY6rqCgoNb9Ndm+fTvLli0D4N///jfPPPNMteOOnf3oTAQf88VgaGhojeGk2hz/823ZsmWNY0/0863NkSUHT+Y8Z3IdEWmaXF1dsVgsdrPX5eXlKcQkjYrJbMYrtH6WUe87efIZhZj6TJ5cb7UJeHp6MmHCBCZMmADA7t27mT9/Pm+++Sbx8fH88ssv/Oc//2HatGlOrlTkzOV8+53dttfAAbjq90u9SMpIohNHl5sssBXg6eHpxIrqT53PvTl8+HD+7//+j9TUVObOncvo0aMxm81UVFTwxx9/cPPNNxMREcF1113HTz/9pKnuREREREREmjIXNxh4J0xaD1fOMkIJ1TmUCHMuh+QNDi2vViYTtOgLF70KD8bDuP+DmHOB6meUMNmstLPu4tKyb/Gd0Q9+fAAOrIUzmHVDRKQx6datW+XjVatW1Tr277//rva47t27Vz5evXp1rec40f4jM3lkZWXVOMZms5GQkFDreWoSFxdX+fiaa2oO6a5Zs6bW85zsTEW9e/eufLx8+fKTOuZ4dfnzrY2npyexsbH1fh0RabqODyzl5NRwQ4FIMxTWqxdRgwef1rEthgwhrGfPOq5IatO2bVvuvfdeVq9eXRlC/+KLL5xclciZK0tNI3/pUrs+/xMslyinb2fOTvaU7KHCdjhf0zTzS0A9hJiO8PT05LrrrmPBggXs37+fl156ie7du2Oz2SgsLOSLL77g4osv1hJzIiIiIiIizYElEEJia97/+5OQ+AfMHAFf3wk5SY6r7WS4eUK3K+GGr+H+ODj3CQiKqXG4qSQH1syC986BdwbB8jeMZepERJqwqKgoOnfuDBhfzOTn51c7rqKigtmHZw8IDAykT58+lfv69u1bOVvQnDlzalx+7cCBA/z666+11tO2bVug9hDRggULyM7OrvU8NSkvL698XNtsTu+++26t5/H0PPrpc0lJSY3jRo0aVbnsyBtvvHFaS9OdffbZlUvbffjhhzWOW716NVu2bDnl8x9r1KhRAGzevJn169fXOO7//u//zug6ItI0+fv7221nZ2ef0ZKcIk3N2a+9hqvnqX2D7WqxMFKz/ziNn58f/fv3ByAjI8PJ1YicuZzvvoNjZr01eXnhO/oCJ1bUdKUXpvNLxi+8kvIKD+97mDcPvkmLFi2cXVa9qbcQ07EiIiJ46KGH2LBhA+vXr+e+++4jLCwMm81GRkZG5d1GDzzwAJMnT+bPP/90RFkiIiIiIiLSEOz7C7b9cHR702fwZl/4/SkoznVeXTXxbwHDHoR/roVbfoU+N4K7b83j07fBb4/Dq51h7tWw5WsoK3ZcvSIiDnTPPfcAkJ6ezqRJk6od8/TTT7N161YAbr/9djw8PCr3eXh4cPPNNwOwYcMGXn755SrHl5eXc/vtt1NaWlprLSNGjACMWaGqm7no4MGD/POf/zyJZ1W9IzMNAZWhrONNnz6d7777rtp9Rxx7k2diYmKN4wICArj33nsBWLFiBffff3+tS+Wlpqby/vvvV7nWpZdeCsD3339f7SwA+fn53HnnnbXWfDLuvPPOys9977jjjmqDXnPnzuWnn34642uJSNNzfIiprKzMbnk5keYusn9/Lv7qq5MOMrlaLFz85ZdEHg7RSN375ZdfSElJqXF/Tk5O5WykR8L2Io2VzWYjZ948uz6/Cy/AxcfbSRU1bWvT1lY+LrWVcqDiAN1adKvliMbNISGmY/Xs2ZNXX32VpKQkfvzxR66++mo8PDyw2WwkJyfz1ltvMXLkSCIjI7n77rtZuHCho0sUERERERERR1r5VtW+8mJY9iq80Rv+fg8qyhxf14mYTNBqIFzyJjy0g6ILXmePuXXN420VsPMX+OpmeKUDfD8J9q7UcnMi0qT84x//YNCgQQB88MEHnHvuucybN49169bx+++/c9ttt/Hss88CEBMTw+OPP17lHE888UTlUhuPPPII48eP5+eff2bdunV89tlnDB48mAULFtCvX79aa7njjjtwdXXFZrNx8cUX89prr7FmzRpWrFjByy+/TO/evcnJybELI52K3r17Vy6FN2PGDK655hp+/PFH1q5dy3fffcdVV13F3XffzZAhQ054niOzMT3++OP89ttvxMfHk5CQQEJCgt2X9k8//TQDBw4E4PXXX6dPnz68/fbbLF++nA0bNrBo0SLeeustLrvsMlq1alXtLFD/+9//KpfaGz9+PPfccw+LFi1i7dq1fPDBB/Tt25f169ef8Od7Ij179qwMta1Zs4Z+/foxe/Zs1q5dyx9//MFdd93FjTfeeMbXEZGmyWKx4O7uDoCLiwtBQUG1BjdFmqOYsWO5ZulSWpzgvUaLIUO4ZskSYsaOdVBlzdOnn35K69atGTt2LK+//joLFy5k/fr1LF26lHfeeYdBgwZx4MABwHjPLNKYFa1dS+nevXZ9AVde6aRqmr61B9fabfcO642L2cVJ1dQ/V2dd2MXFhTFjxjBmzBhyc3P5/PPPmTNnDsuXL8dms5GamsqMGTOYOXOm3dTMIiIiIiIi0sRc8R78NR3+fBVK8+z3FWbATw/BqnfhvKeh4xgjPNTQuHtT1uUKPl+Sip81m5v7WPDc9hVk761+fEkOrPvQaIFtoMe10PMaCGrn0LJFROqai4sLP/74I5dccgnLly/njz/+4I8//qgyrnPnzixYsAAfH58q+/z9/fn5558ZNWoUBw8e5NNPP+XTTz+1GzNx4kRGjBhROWtTdbp27cpLL73EAw88QFZWFvfff7/d/qCgIL799lsef/xxdu7cecrP1WQyMWfOHM455xyysrL44osvqsxs1L17d7788kuioqJqPI+vry+TJk3ipZdeYt26dZx//vl2+xctWsTIkSMBY6aq3377jYkTJ/L111+zcePGytmZquPn51elr02bNnz//fdccskl5OXl8c477/DOO+/YjXniiScwmUy1LsV3Ml599VWSk5P5+uuv2b59e5X/Xm3btuXzzz8nJqbmJVpFpHkymUx06NABd3d3fHx8Kmd2ExF7kf37c92yZaRt3MimmTNJ27CB0rw83H19CevVix533EFYz57OLrPZKCsr46effqp1psl//OMfNc5YKtJYZH1u/+8e97ZtsfTu7aRqmr5jZ2IC6Bve10mVOIbDZ2Kqjp+fH7fffjtLly4lMTGRJ598kpiYGGw2m9Y4FhERERERaercLDDsAZi0HvrfDqZq7iQ6lACfjYfZY+HA2qr7G5BccwClg+6DSRvgph+h53hw86r5gKw9sOQFY9apWaNhzf9BUZaDqhURqXtBQUEsXbqUjz76iAsuuIDw8HDc3NwIDAxk8ODBvPHGG2zYsIHWrWueva5r167ExcUxZcoUYmNj8fDwICQkhLPPPptPPvmEDz744KRquf/++/n5558ZPXo0gYGBeHh40LZtW+655x7Wr1/PsGHDzui59urViw0bNvCPf/yD1q1b4+bmRlBQEAMGDOCVV17h77//tlsuriYvvPAC7733HsOGDSMoKAgXl5rvqvX19WXevHn8+eef3HbbbXTs2BFfX19cXV0JCgqif//+3HPPPfz000/89ttv1Z5j5MiRxMXFcdddd9G6dWvc3d0JDw9n7Nix/Pzzzzz11FOn/TM5lpubG/PmzWPOnDkMGzYMf39/vLy86Ny5M//+979Zu3Yt7dopwCsi1QsODsbX11cBJpGTENazJ6Pefpvxy5czcdMmxi9fzqi331aAyYGmTZvGxx9/zC233EK/fv1o0aIF7u7uWCwWOnTowE033cSff/7J9OnTMZsbxFf0IqelPD2d3J9/tusLuPIK/X1dTzKKMtiZZX/TTVMPMTltJqaatGnThieffJInn3yS5cuXM2fOHGeXJCIiIiIiIo7gEwpjX4GBd8JvT8KO+VXH7F0O750D3a+Ccx6HwFqWb3M2sxnaDjPamJdg2w+w8VPY/SdQww07+/8y2oJHoOOF0PM6aD8KXNwcWrqINC1Tp05l6tSpJxw3e/ZsZs+efcJxixcvPuEYs9nMDTfcwA033ABARUUFaWlpAISFhdUa0jkiKCiIF198kRdffLHa/RMnTmTixIknPM/o0aMZPXp0jftrez5t2rQ54U2WrVq1Yvr06bWOOdE5TCYTt912G7fddlut4441dOhQhg4detLjj9eyZcsqMzAd62RfNyfj+uuv5/rrr6+Tc4mIiEjzdKL3Uyd6L3uyE2fU9t5w5MiRNZ4nMDCQCRMmMGHChJO6zrFO5j3nEXv27Dnl84vUpazPv4Cyssptk4cH/lpKrt6sPLDSbtvbzZuuIV2dVI1jNLgQ07GGDBlywjXjRUREREREpIkJiYXrPoE9y+HX/0Dy+qpjNn8JW7+HK2ZA18sdX+Op8vCFXuONlr0fNn8BGz+DjPjqx1eUwtbvjOYVAt3HQc9rIbJXw1xOT0REREREREREmjRbaSlZn39m1+d30VhcAwOdVFHTtzN5J49FPUZiSSIJxQmEBYXhZm7aNzs26BCTiIiIiIiINGNthsBtf0Dc1/D7U5Czz36/yQTRA5xT25kIaAnDHoShDxgBrY2fGaGsoszqxxdmwKp3jRbayQgzdb8a/Fs4tm4REREREREREWm2cn/5lYr0DLu+IM24Wm+sNisl+SVEeEcQ4R7BEN8hlLiXOLuseqcFN0VERERERKThMpuNWYjuXQ3nPQ0e/kf3DbqncQd5TCZo0cdYau7BHXDtp9D5EnBxr/mY9O3w+1SY1hU+vAQ2fALFuQ4rWURERETEmaxWq7NLEBERaZZsNhuZxy3Z6NWvH56dOzunoGZgW+Y22rq1teuLCI5wUjWOo5mYREREREREpOFz84Qhk6HX9bD0JWMpuSH31Ty+IAO8QxxW3hlzdYdOY4xWmAlx3xgzNCX9XcMBNti9xGiu90PHC43ZmdqPMs4lIiIiItIE2Gw2ioqKyMrKIisri+zsbPr27YvFYnF2aSIiIs1KwfIVFMfF2fUFahamerVq/ypi3GPs+tpEtHFOMQ6kEJOIiIiIiIg0Ht7BcOGLMGoquNXwxUVRNrzVH1oPhnOfhNAOjqzwzHkFQf9bjXYoETZ9Dhs/hex91Y8vLzZCT3HfgCUQul5uBJpaDjRmshIRERERacQ2btxIaWlp5XZWVpZCTCIiIg52aMYMu2231q3wPW+Uk6ppHpLTkonxOBpiKqMMX19fJ1bkGAoxiYiIiIiISONTU4AJYPlrUJQJ23+EHQugzw0w8l/g2winWw6OgbP/DSMehf1/GWGmuG+hpIYl5IqyYM3/GS2gFXS/ygg0hXVyaNkiIiKnwmazObsEEWmgTCYTgYGBpKamVvZlZmYSFRXlxKpERESal8J16ylcvdquL/i22zC5uDipoqYvvzQf73Jv8Dja5+bthslkcl5RDqJbMkVERERERKTpyDkAf00/um2rgLWz4Y3e8MczUFxD+KehM5uNmaUueRMeiodx/wcdLgRzLfcmZe+DP/8H7wyEd4fCijchN9lxNYuIiIiI1IHAwEC77ezsbKxWq5OqERERaX4OzZxpt+0aHo7/pZc6qZrmYdmBZXT07GjX1xyWkgOFmERERERERKQpObQT3L2r9pcVwtKXjTDTqhlQXlp1TGPhZoFuV8L4z+DBeBj7P2h5Vu3HHNwMvz4Gr3aBDy+GdXOgOMcx9YqIiIiInIHjQ0wVFRXk5eU5qRoREZHmpWjTJvIXL7brC77lZszu7s4pqJlYu28t3i72n3GGh4Q7qRrHUohJREREREREmo52I2HSBhj2ELhWs+RcYQYsmAJvD4AtX0NjX77GOxj63wa3/gKTN8I5j0FIx1oOsMHupfD9vfByLHxxI2z7EcpLHFayiIiIiMipcHd3x9vb/ku8zMxMJ1UjIiLSvKRNm2a37RIYSMBVVzmpmuahtKKU/Jx8u75yl3I8PT2dVJFjKcQkIiIiIiIiTYunH5z7OExaD31uAlM1//TN2g1f3QzvnQO7/3R8jfUhsA0MfxjuWQV3LoVB94JPRM3jK0pg63fw+QR4pQN8P8kIOFkrHFayiIiIiMjJOH42pqysLCdVIiIi0nwUrFxJ4cq/7PqCb78ds5eXkypqHlalrCLGI8auLyw4zEnVOJ5CTCIiIiIiItI0+UXCJW/AXSuh45jqxySvgw8vgrlXQWqcY+urLyYTRPaE0c/CA1vhxu+g1wRw9635mOJsWPehsdTcq11gwaOQtKbxz1QlIiIiIk1CUFCQ3XZeXh5lZWVOqkZERJo6mz4PwWazkTbtNbs+1/BwAsdf55yCmpHFexfT1qOtXV9UWJSTqnE8hZhERERERESkaQvrBNd9CjcvgOj+1Y/Z+Sv8NMWxdTmC2cVYYu+yd+DhnXDVbOg4FsxuNR+TfxBWTYf3z4XXe8LvT8HBLQo0SZ1ycXEBoLy8nIoKzf4lIqfHarVW/g458ntFRJomf39/zGb7r7QOHTrkpGqkudN7WZGmraKiQu8xgbzffqN40ya7vpC778bcTJY0c5YKawUHMw7ianKt7LNhIyAgwHlFOZhCTCIiIiIiItI8tB4Mt/4GV38EQTFV94960vE1OZKbBbpeDtd9Ag/Fw0XToNXg2o/J3gvLXoV3h8A7Z8GSl+BQomPqlSbN65ip57Ozs51XiIg0avn5+ZV3yVssFidXIyL1yWw2V5mNSSEmcRa9lxVp2o79/9qrmS6bZi0pIe2ll+363Fq3IuCKy51UUfOxMX0j7Vzb2fV5+Xk1q0CdQkwiIiIiIiLSfJhM0OVSuGcVjP0feB9eT77zxdBygHNrcySvIOh3C9yyACZvgnOfhPDutR+Tvh0WPQtv9oEZI2DFm5CT5Jh6pck59g7CtLQ00tLSKC4u1pT9InJSrFYrubm5HDx4sLLP17eWZVNFpEkIDg62287MzMRqtTqpGmnO9F5WpOmx2WwUFxdX/j99RGBgoBOrcp7MD2ZTlmT/mU/Y5MmY3GqZ2VvqxMK9C+ni1cWuLzo82knVOIfriYfI6SosLOStt97iyy+/JDExkZKSElq2bMnYsWOZNGkSrVu3PqPzW61Wli1bxs8//8yKFSvYvn07mZmZeHp60qpVK4YPH84//vEPevToUet5pk6dylNPPXVS11y0aBEjR448o7pFRERERESczsUN+t8GPa6FlW9BtytrHpu4CHIPQM/rjOXZmprA1jDsAaOlbYe4r2HzV5BZy4xLKRuM9utjxmxO3a4wZnnyDnFU1dLIeXp64u/vT05ODmDMpHDo0CFMJlOzuruwObHZbJSWlgKQl5eHyWRyckXSmFVUVNh9UWyxWPD29nZiRSLiCMeHmKxWK1lZWVX6Reqb3ss2L3of2zwc//4SjKVMPTw8nFSR85QdPEjGjBl2fZY+ffC98EInVdR8VFgrWJu0lu6B9jcaNrf3Ogox1ZOEhATGjBnDzp077fp37NjBjh07eP/995k7dy4XXXTRaV+jTZs27N+/v0p/WVkZcXFxxMXFMWPGDB566CFeeOEF/aUqIiIiIiJyPA8fGPlozfutFfDzvyB9mzHz0DmPQ6exxoxOTVFYJwj7N4z8F6RshC1fwZZvILeWGZf2rTDagkeg3QjoNs74GVkCHFa2NE6RkZG4u7uTnp5e2Wez2SgvL3diVVJfrFYr+fn5gDFjjtmsCeKlblgsFlq1aqXPPkWaATc3N7vgCBjhkeb2xZ40DHov23zofWzzFBoa2mz/fkl75X/YioqOdphMRDz2H73fdoC1qWuJy4vj0bxHifWMpYdXD86PPr/ZhekUYqoHeXl5jB07tjLAdPvtt3PttddisVhYtGgRzz//PLm5uVxzzTUsX76cXr16ndZ1kpOTAWjfvj1XXnklQ4YMISoqiqKiIhYtWsS0adPIysripZdewsXFheeee+6E59y8eXOt+9u2bXtatYqIiIiIiDRKm74wAkxgLKf2+QSI7g+jpkKboU4trV6ZTBDVy2ijnoakv43ZmbZ+CwXp1R9jq4DEP4z2ozu0Pw+6XgYdLgBPP8fVLo2GyWQiJCQEPz8/8vPzKSgooLS0VMvCNFHl5eWVXzr7+/vj6qqPJeX0ubi4YLFY8PX1xdvbW1+oiDQjwcHBVUJMNptNvwfE4fRetvnQ+9jmwWw24+7ujre3Nz4+Pri7uzu7JKcoWLmS3B9/tOsLGDcOzy5dajhC6tL83fMBqKCC7cXbsVqsPN77cSdX5Xj6LVsPXn75ZeLj4wF46aWXePjhhyv3DRo0iJEjRzJixAgKCwu57777WLx48WldZ8CAATz55JOcf/75Vd6gDx06lPHjxzNo0CDS09N5+eWXue2222jXrl2t5+zWrdtp1SIiIiIiItLklJfCompuBklaDbPHQvtRcO4TENnT8bU5ktkMrc4y2gUvwJ6lsHkebPsBSnKqP6aiFHbMN5qLh/GzUqBJauDu7k5QUBBBQUHOLkXqUW5uLt9//z1gfD7m56ffBSIicuqCg4PZtWtX5bbJZKKkpARPT08nViXNmd7LNn16HyvNhbWoiJQnnrTrM/v6Enr/fc4pqJkpqSjhtz2/2fWNbTfWSdU4l+a7q2NlZWW88cYbAHTu3JkHH3ywypjBgwdz6623ArBkyRJWr159WtdasWIFo0ePrvEOg5iYGJ544gnASAl/++23p3UdERERERGRZsnFDS6aBhHdq9+f8DvMGA5f3QKHEh1bm7O4uELMOXDZ2/DwTrj2U+h2Jbh51XxMRYkRZvr6dni5PXw6HjZ9CSV5jqtbRERERJoELy8vQkNDad26NX379mXgwIEKMImIiNSB9DfepGz/fru+0Psm46qQpkMsTVpKXtnRz8pMmBjTdowTK3IezcRUxxYtWlQ5peBNN91U47qoEydOZMaMGQB888039O/fv17qOfvssysfJyY2kw/VRURERERE6oLJBLGjjNBO3NfwxzOQtbvquC3zYOt3eHa7Fm9bEAUmH8fX6gyuHtBpjNFKC2DHAtjyNST8ZszEVJ0jgaYjMzTFngddLoOOF4CHr0PLFxEREZHGqYuWtBEREalTRZu3kPnhh3Z9lj59CLzuOidV1PzM3zXfbrtfRD8ivCOcVI1zKcRUx5YtW1b5eMSIETWO69evH15eXhQWFrJ8+fJ6q6ekpKTysYuLS71dR0REREREpMkym6H7OOhyKaz7CJa8CPmp9mOs5bhv+pg7cWWN6wAoHg/NaYp5d2/jZ9R9HBRlw46fIO4bSFwE1rLqj6koge0/Gk2BJhERERERERERh7OWlJDy73+D1VrZZ3JzI/KZ/2KqYcIWqVs5JTksTVpq1ze2bfNcSg60nFyd27p1a+XjTp061TjO1dWV9u3bA7Bt27Z6q2fJkiWVjzt37nzC8eeffz5hYWG4u7sTFhbGyJEjeeGFF8jKyqq3GkVERERERBoFFzfofytMWg/nPgEe/lWGuFHOoPIV+M4aAgfWOaHIBsASAL3Gw4QvjSXnLn0HYs8Hs1vNxxwJNH19G7wUA59N0JJzIiIiIiIiIiL1LO1//6Nk5067vpC778KjXTsnVdT8/Lr3V87xPYe7w+9moM9AfF18Oa/Nec4uy2k0E1MdS0pKAsDb25uAgIBax7Zs2ZJNmzaRnp5OSUkJHh4edVpLYWEhr732GgAeHh5ceumlJzzmt99+q3ycnp7OkiVLWLJkCS+++CKzZ88+qXNU58jPpSYpKSmVjwsKCsjNzT2t64jUhfz8/GofiziLXpPSkOj1KA2JXo/iVD1vhw7j8Fg9Hff1/4eposRud4WbF4WWaGj2/7ZxgZiLjVacjVvir7jGz8d171JM1vLqDzlmhiabiwflbUZS1mEs5e1GaYamU6DfkdLQ6DUpDUlBQYGzSxARERERcbr8P/8k66M5dn0enToRfOutTqqoeZoXP4/LvS8nwj2CLpYuVNgqKDhUgF9kM5rl/RgKMdWxvDzjLlEfH58TjvX29q58nJ+fX+chpkceeYR9+/YBcM899xAVFVXj2O7du3PZZZcxYMAAoqKiKCsrY8eOHcydO5dff/2V7OxsrrzySn744QcuvPDCU66lZcuWJz3266+/xt+/6h3NIs4wZ86cEw8ScSC9JqUh0etRGhK9HsV5AvFxu4Mh/EmPio2YsQHwS0lftrz3gZNra6jOwsO9J7EVO+lUsY021t24YK12pKmiBLfEX3BL/IVyXNhjbku8S0d2usRSbPJycN2Nl35HSkOj16Q4W05OjrNLEJF6YLPZMJlMzi5DRESkUSg/dIjkf/3brs/k4UGLl1/C5O7upKqan62HtpKdm01Ei4jKPheTC15ezfdzL4WY6lhxcTEA7ifxP/axoaWioqI6rWPu3Lm89dZbgLGM3DPPPFPj2Pvuu4+pU6dW6R84cCA33ngjM2bM4B//+AcVFRXcdtttJCYm4unpWaf1ioiIiIiINFb5Jl9+cR/D39aBDCtfSog1nTiXbjWOD7WmkW4KhWb8BUuJycIW1x5sce2Bh63opAJNrlTQ3ppAe2sC1jIT+8ytDgeaOpJv0gxNIiIiIs1RcXExaWlppKen4+/vT/v27Z1dkoiISINnq6gg+ZFHqcjIsOsPm/IwHrGxTqqqefoq/iv6ePex6/Pw8MDPr3nOwgTNOMRUF2n8Dz74gIkTJ9r1HQn3lJaWnvD4kpKjyw1YLJYzrueIxYsXc+vhKd6CgoKYN29erec/0bJ3d955J6tXr2bWrFkkJyczb948JkyYcEo17d+/v9b9KSkpDBgwAIArrriCDh06nNL5RepSfn5+5V2hN9xww0nNrCZSn/SalIZEr0dpSPR6lIbGeE0G42Yr5fobb6r2NWk+FI/3h6OoiOpHyZCHqWg5yAmVNlwFRVlHl5zb92eNS86ZsdHGupc21r2cX/Yr5ZF9KY+9gLL2F2ALaOPYohso/Y6UhkavSWlI4uPjef75551dhoicoeTkZHbu3Fm5XVpaSkxMjGZjEhEROYH0N9+kYNkyuz6fESMIHD/eSRU1T4VlhczfNZ8p4VPs+kNDQ5v1+5lmG2KqL76+xt2f+fn5Jxx77NrrdfXBzZo1a7jkkksoKSnBx8eHn376ic6dO5/xee+8805mzZoFwJIlS045xBQdHX3SY729vZt1slAaFh8fH70epUHRa1IaEr0epSHR61EakjKTe82vyQWvAzZck1fj+uXV0G4knPMERPd1dJkNk58fhN8Og2+HwkzY8RPEfQu7FoO1rMbDXFPW4pqyFs+lz0J4d+h8sdHCOjfrGa+O0O9IaWj0mhRn8/b2dnYJIlIH/P397bZLS0vJyck54Y3bIiIizVne779z6N0Zdn0uISFEPvdssw7OOMOC3QsIMYcQ4hZi1x8WFuakihqGZhti2rZt2xmfIzIyskpfdHQ0q1atoqCggOzs7FrfLB+ZnSg0NNRuabnTFRcXxwUXXEBeXh4eHh58++23DBw48IzPC9ClS5fKxwcOHKiTc4qIiIiIiDQryRtg2/f2fbsWG63jGDj7PxBR8zJ0zY5XEPS+3mhF2bDzV+Pnt/N3KK9lSfbUzUZb/BwExRhhpi6XQFQfBZpEREREmhBvb2+8vb3tbhhPT09XiElERKQGJbt2k/zIo/adrq5EvzYN1+Bg5xTVjH0V/xV9ve1vbLRYLM1+5uJmG2Lq1KlTvZy3S5cuzJs3D4Dt27dz1llnVTuuvLycxMREgDqZKSkxMZHzzjuPQ4cO4erqyueff8655557xuc9QqlLERERERGRM1SUBQGtIHtf1X07foIdC6DbFTDy3xDS3vH1NWSWAOhxtdFKCyFxIWz7AXb8DCU5NR+XmQjLXzOaXzR0vsgINbUaBGYXBxUvIiIiIvUlNDTULsSUlpZGTEwMZrPZiVWJiIg0POVZWey/6x9Yj/l7EyD8kUfw6tfPSVU1X9sObWProa1cF32dXX9zX0oOQO/i6tjQoUMrHy9ZsqTGcWvWrKl8Yz1kyJAzumZSUhKjRo0iJSUFs9nMhx9+yKWXXnpG5zze1q1bKx9HRUXV6blFRERERESahZiz4d61MPZ/4BNRzQAbbJkHbw+A7+6pPuwk4O5lBJGumAkPJ8CEedDnJvAKqf243CRY9S7MHguvdIBv74Ht841QlIiIiIg0Sscvt1JeXs6hQ4ecVI2IiEjDZC0uJumuuynba/9Zk98lFxN4/QQnVdW8zd02l06WTvi72i+P29yXkgOFmOrcyJEjK9dh/vDDD7HZbNWOmz17duXjyy+//LSvl5aWxqhRo9izZw8A7777LuPHjz/t89Vkxoyj62KOGDGizs8vIiIiIiLSLLi6Q//bYPIGOP8Z8Kpmqm5bBaz/GN7oA/MfgryDDi+z0XB1h9hRcMkb8FA8TPwJBv4D/FrUflxhBmz4GD4bDy+1g0+vg3UfQX66Y+oWERERkTphsVgqv5M54uBBvX8WERE5wma1kjzlEYo2bLDr9+jSmcinnmr2s/44Q3phOvN3z+csH/tVvXx9ffH29nZSVQ2HQkx1zN3dnUmTJgGwbds2XnnllSpjVq5cyaxZswAjENS/f/9qz2UymTCZTLRp06ba/dnZ2YwePZodO3YAMG3aNG6//fZTqnfz5s0kJCTUOmbmzJm8//77AERERJxR6EpEREREREQANwsM/idM3ghnPwYe/lXHWMtg9Xvwei9Y+F+Hl9jomF2gzRC48EW4Pw5u/wOG3g9BMbUfV15kLOf3/T/hlViYdT4sew3S4x1StoiIiIicmYgI+1lOMzMzKS0tdVI1IiIiDYfNZiP1uefJ+/VXu37XyEhaTn8Xs8XipMqat892fIYHHnT36m7XHx4e7qSKGhZXZxfQFD388MN8/vnnxMfHM2XKFBISErj22muxWCwsWrSI5557jvLyciwWC6+99tppXaOkpISxY8ey4XBicsKECYwaNYotW7bUeIy3tzdt27a161u7di233XYbZ599NhdeeCHdu3cnODiY8vJytm/fzty5c/n18C81FxcXZs6cqfSfiIiIiIhIXfHwhREPQ/9bYcWbxnJnZcctb1ZeBEVZzqmvsTKZoEVfo537JKRtg20/GC11cy0H2mD/KqP9/iQEt4eOY6DTWIjubwSlRERERKRBCQkJYefOnVit1sq+1NRUWrZs6cSqREREnMtms5H+v/+R9fHHdv1mHx9azngXt3AtW+YMxeXFfLHjC/p698XN5FbZbzKZtJTcYQox1QNfX1/mz5/PmDFj2LlzJzNnzmTmzJl2Y/z8/Jg7dy69evU6rWukpKSwYsWKyu25c+cyd+7cWo8ZMWIEixcvrtJfUVHB77//zu+//17jscHBwcyaNYuLL774tOoVERERERGRWngFwagn4ay7YNk0WD0LKkqMfa6eMPxh59bXmJlMEN7FaCMfgczdsGOBMfvS3hXG8n01OZQAK94wmlcIdLgAOo2BdmeDu5fjnoOIiIiI1MjV1ZXQ0FBSU1Mr+w4ePEh0dLSWyBERkWYr4803OfT+LPtONzei33oTzw4dnFOU8MOuH8guyeasIPul5EJCQnBzc6vhqOZFIaZ60r59e9avX8/bb7/Nl19+SUJCAqWlpbRs2ZIxY8YwefJkWrdu7ewyGTNmDLNmzWLlypWsX7+e1NRUDh06hM1mIygoiJ49e3LBBRcwceJE/Pz8nF2uiIiIiIhI0+YTBhc8D4PugaUvw/qPof9t4BdZ/fjSQiOk46bpv09aUFsYdLfRCjNh56+wfT4kLISygpqPK8yADR8bzdUCMWcbszR1uAB8Qh1Xv4iIiIhUERERYRdiKiwsJC8vT99riIhIs2Oz2ciYPp2Md6bb7zCbiXrhebzPOqv6A6XeWW1W5mydQ5RbFK08WtntO3553OZMIaZ65O3tzZQpU5gyZcppHW+z2Wrc16ZNm1r3n6ywsDBuueUWbrnlljM+l4iIiIiIiNQR/2i4+HUYMhk8A2oe99fb8Pf7MOwB6HMTuHk6rMQmwSsIel5rtLJi2POnEWjasQDyD9Z8XHmRMZPTjp8Ak7HUXIfzIXY0RHQ3gmUiIiIi4jD+/v54enpSXFxc2Xfw4EGFmEREpFmx2WykvfQymR98YL/DZCLy2WfxHzvWOYUJAMsOLGN3zm5iPWNJKU0h0t24adHd3Z3AwEAnV9dwmJ1dgIiIiIiIiIjUIKidEbSpTnEOrHjLCNssmAJv9Ia/34PyEsfW2FS4eULseXDxa/DANrj9Dxj2EIR1OcGBNkj6G/54BmYMg2ld4YfJsP0nKK1lZicRERERqTMmk4nw8HC7vrS0NCoqalk6WEREpAmxlZeT8u//VA0wARFTpxJw+WWOL0oq2Ww23tv0HgA7i3fybPKzfJr3KZGRkbRo0UJL4B5DMzGJiIiIiIiINEarZkBx9tHtvGT46SFYNs2Yman3DeDq4bTyGjWzGVr0Ndq5j0PmbmN2ph0/wd4VYKvly7DcA7B2ttFcPKDNUOgwGmLPN5ayExEREZF6ERERwd69eyu3KyoqSE9P1/IsIiLS5FkLCznw0MPk//FHlX3hjz1G4DVXO6EqOdaqg6vYkL7Brm90x9F0iO3gnIIaMIWYRERERERERBqj1Ljq+3MPwPwH4c9pMPxB6HU9uLo7tramJqgtDLrbaIWZsPM32DEfEhZCaX7Nx1WUQOJCoy2YAiEdDgeaRkOrs8DFzXHPQURERKSJ8/T0JDAwkKysLAACAwPx9NRyyyIi0rSVJSez/557Kdm2zX6HiwuRzz5DwGWXOaUusffuxnfttqO8o7go5iInVdOwKcQkIiIiIiIi0hhd/aExK9Di52H30qr7c5Pgx/uPCTNNUGimLngFQc9rjFZeAnv+hPhfIf5nyN5b+7EZ8UZb8SZ4+EPM2Uaoqf154BPqmPpFREREmrDo6Gi8vb2JiorCYrE4uxwREZF6VbhuHUn3/pOKzEy7fpOHBy2mTcP3nLOdVJkca/XB1axNXWvXd1uP23Az63O66ijEJCIiIiIiItJYtR4MN/0Ae5bBoudh77KqY3L2wQ+T4c//wfCHoed1CjPVFVcPaD/KaBe+aASU4n+Bnb/CvpVgLa/52JIc2Pqt0TBBiz7GDE0dzoeInsaSdiIiIiJySoKCgggKCnJ2GSIiIvXKZrOR9cknpL7wIpSV2e0z+/rS8p238erf30nVyfFmbJxhtx3hHcGlMZc6qZqGTyEmERERERERkcauzVC4eb4xI9Oi52HfiqpjsvfB9/80wkxjXoHY8xxfZ1NmMkFoR6MNmQTFOZD4x+FQ029QmFHLwTY4sNZoi58D7zCIOccIR8WcDd4hDnsaIiIiIiIiItJwlWdlkfLY4+QvXFhln3vbtkS/8zYebds6oTKpzrrUdaw6uAoPkwct3Fuwq2QXt3a7FXcXd2eX1mApxCQiIiIiIiLSVLQdDm2Gwe4lRphp/19Vx2TtAX1QUv88/aHr5UazWiF53eFA0y+QsrH2YwvSYNNnRsMEUb0g5lwj1BTdTzNpiYiIiIiIiDRDBav+JnnKFMpTU6vs8x42jBb/ewUXPz8nVCY1mb5xOgADfAZwTfA1HCw7yCCfQZSXl+PqqrhOdfRTEREREREREWlKTCZoNxLajjBmAlr8PCStPrq/9RAj7CSOYzYb4aPofnDOfyA3xVhybuevkLgIygpqOdgGyeuN9ucr4OFn/PdrPwranwsBrRz2NERERERERETE8Sry8kh75X9kf/55tfuDbrmFsAcfwOTi4uDKpDYrDqzgrxTjBsNhvsMAiHCLYHfibvJz8+nSpYszy2uwFGISERERERERaYpMJiPkEnMOJCw0lik7sBZG/svYV52cJPCJABd9XFCv/CKh701GKy+Bvcsh/leI/xmydtd+bEkubP/RaAAhHQ7P0nSuEVBz96r/+kVEREQaEZvNxqFDh3B3d8dPs1OIiEgjk/f77xx8+r+Up6VV2ecSFETU88/hM2KEEyqT2lhtVqatmwZAjEcMUe5RdvsjIiKcUVajoE8lRURERERERJoykwliD8/as38VtDqr+nE2G3x6HZTkwbAHoee1WrbMEVw9jKBZzDlw4QtwKNEInSUuhN1/nmCWJiAj3mirpoOLB7QeDO1HYY4YaPw3rSmwJiIiItLElZaWkpKSQkpKCiUlJQQHB9OtWzdnlyUiInJSinfEk/byyxQsW1btfq9BZxH14ou4hYU5uDI5GfN3zWd75nYARvjZh8wsFguBgYHOKKtRUIhJREREREREpDkwmWoOMAHE/wIHNxmPv78XlrwEQ++D3tcbQRtxjOAYow28w5ilad9fRqApYSGkbqn92IoS2LUIdi3CB7jL5Mtucztct0dBlwvAJ9QhT0FERESkIcjIyGDPnj2V24cOHaKwsBAvL81cKSIiDVdZahrpb75BztffgNVaZb/Jy4uwyZMIvOEGTGazEyqUEympKOHN9W8CEOwaTC+vXnb7IyMjMemmsxopxCQiIiIiIiLS3NlssPQl+76cfTD/AVj6CgyZbCx95mZxTn3NlasHtBthtPOehryDkPgHJPwOiYugKLPWw/1sefSs2Ag/3Qs/AeHdD59vJLQaBB4+DnkaIiIiIs4QHh7O7t27KS8vr+xLSkqiQ4cOTqxKRESkeqVJSRyaNYuceV9jKy2tdoz3sGFEPPkk7tEtHFydnIpPt31KSkEKACP9RmI2HQ2bubi4EBkZ6azSGgWFmERERERERESau6IsI8hUnbxk+PkR+PN/MGQS9LsF3L0dW58YfCOg13ijWSsgecPhWZp+h6TVYKt6h6ad1M1GW/kWmF0hesDRUFOLvlo+UERERJqUI18S7t+/v7IvNTWVNm3a4O7u7sTKRESksUjbuJGNM2aQvmEDpXl5uPv6EtqrFz3vvJOwnj3r5BpFW+LImvMROT/Oh4qKase4hoUR9vBD+F10kWbwaeBySnKYuXkmABazhcE+g+32R0RE4OqqmE5t9NMRERERERERae68guD2P4xAzJKXYf9fVccUpMGvj8GyaTDoHuh/O3j6Ob5WMZhdILqv0UZMgaJs2L3ECDQl/AG5SbUfby2HfSuMtvh5cPeB1kOMQFO7ERDWxViCUERERKQRa9GiBUlJSdgOB/atVivJycm0adPGuYWJiEiDlrJ6NYvuu4/kFSuq7EteuZKN06fTYsgQRk6bRmT//qd8fpeyMqITd5F2442Ubd9R4ziTlxfBt95C8M03Y9ZyqI3COxveIa80D4ChvkPxMHvY7Y+OjnZGWY2KQkwiIiIiIiIiYgRW2o+CmHNhz5+w5CXjz+MVHoKFT8PyN+Csu2DgnWAJdHy9Ys8SAF0uNZrNRv6etaz65HnaVOymnUsKprLC2o8vzYedvxgNwDsM2g4/GmoKaFXfz0BERESkznl4eBAWFkZqamplX3JyMi1btsTFxcWJlYmISEOVOH8+P4wbR3lxca3jDixfzufDh3PxV18RM3bsCc9bkZ9P/uIlZP40n9FLluJaUUFZTYPd3Ai44gpC7rkbt7CwU38S4hTbM7fz2Y7PAHDBhZG+I+32h4aG4unp6YTKGheFmERERERERETkKJPJCK+0HQ57V8LSlyDxj6rjirONGXxWvg3Xfw0tT/3OQ6knJhPW4A6scR3AGtcB3HXHrfjl7oRdi43ZmpJWGzMx1aYgDbZ8ZTSAoHZGoKntCGPGJp/Q+n4WIiIiInUiOjraLsRUVlZGamoqUVFRTqxKREQaopTVq08qwHREeXExP4wbxzVLl1aZkclWUUHx1m0U/r2KgpV/Ufj339hKS4GaQxomi4XAa64h6OaJuIWHn8lTEQez2qw889czWG1WAPp598Pf1d9uTMuWLZ1RWqOjEJOIiIiIiIiIVK/1ILjhG0haa4SZ4n+uOsbVEyK6Ob42OXku7sZ/y9aD4Ox/QUmeEVA7EmpK3XLic2TuMtqa/zO2QzsZYaY2Q43moztDRUREpGHy8fEhMDCQrKysyr6kpCQiIyMxaflcERE5xqL77jvpANMR5cXFLJo8mStmz6Zkxw6Kt22nZPs2irbEYc3NPalzuEZFEjBuHIHXXYdroGa7boy+S/iOjekbK7fP9T/Xbr+/vz++vr6OLqtRUohJRERERERERGoX3RfGfw4pG2Hpy7Dth6P7hkwGN4vzapNT5+ELHc43GkB+uhFm2rUYdi2BnH0nPkf6dqOtmWVsh3SwDzX5RtRb+SIiIiKnKjo62i7EVFRUxKFDhwgJCXFiVSIi0pCkbdhA8ooVp3Vs8sqVbDx/NH6nsFSY1WTCa9gwQieMx3voUExa5rTRyinJYdraaZXbnT07E+VuP+NjdHS0o8tqtBRiEhEREREREZGTE9kTrvkYUuNg6SuwZxn0u7nm8WtnQ5thEBzjsBLlNPiEQvdxRrPZIGu3EWbatRh2L4WizBOfIyPeaGs/MLaD2x8ONQ2DNkPAT8u1iIiIiPMEBgbi7e1NQUFBZd++ffsIDg7WbEwiIgLAxpkzz+j4fTnZdPM8wQ09bm549O/HKpOJg61acev99+Pj53dG1xXne3P9m2SVHA1Lnx9wvt1+i8VCcHCwo8tqtBRiEhEREREREZFTE94VrvrAWJbM3bv6MYcS4cf7jcddLoWh9xshKGnYTCYIame0fjeD1Qqpm4+GmvavgtL8E5/nUILR1n1obAe1M2Zoaj3UCDX56w5EERERcRyTyUTLli3Zvn17ZV9eXh5ZWVkEBQU5sTIREWko0jdsOKPjc4tLqu13b9sWr7MG4j1wIN6DBlFgMrFv+vQzupY0HBvTN/LFji8qt73N3kR6RNqNiY6OVmj6FCjEJCIiIiIiIiKnx8O35n3LXweb1Xgc943R2o8ywkythxhhGWn4zGYjfBbZE4ZMgooyY1nBPcuMtu8vKM078Xkydxlt3UfGdmAbI9TUajC0OssIOek1ISIiIvUoNDSUPXv2UFxcXNm3d+9eAgMD9cWiiIhQmncS/7atRbnVimtEBJ6dO+PZuRMenTtj6dEDt/Bw+4G5uWd0HWk4isuLeWzZY9iwVfZVmCro3bc35gIz+/btw2q1EhFxghm6xI5CTCIiIiIiIiJSt3JTYOOnVfsTfjda9AAY9gDEjjZCMtJ4uLhBdD+jDb0PKsrh4EbYs/xwqGkllJzEB7JZe4y2/mNj2zsMWg2ElmdBq0EQ2cO4loiIiEgdMZvNtGrVivj4+Mq+3NxcsrOzCQwMdGJlIiLSELj71nKj1knw7dOb2MWL6qgaaQze2fAOe3L32PX9o+c/iPKNAl8IDw+nuLgYsz77OiUKMYmIiIiIiIhI3XL1gLPuhtWzqp+lJ+lv+PRaCOtizMzU9Qpw0UcUjZKLK7Toa7Qhk8BaAQc3HZ6paTnsXQElOSc+T0EabPvBaACuFiMo1XKgEWpq2R88/ev3uYiIiEiTFx4ezt69eykpMZb88fb21ixMIiICQGivXiSvXHnax4f16VOH1UhDtzF9Ix9u/dCur1twN27qelPltslkwmKxOLq0Rk+fEIqIiIiIiIhI3fIKgvOeMgJKq9+Hv6ZDYUbVcWlb4evb4Y//wuBJ0Pt6cNOHO42a2QWiehtt8D8Ph5o2w97DMzXtXQ7FJxFqKi+CPX8aDQAThHc9GmpqNRD8W2oJOhERETklR2ZjSklJoXXr1gQHByvEJCIiAPS84w42Tp9+2sf3uOOOOqxGGrIjy8hZbdbKPjezG88MfQZXsyI4Z0o/QRERERERERGpH5YAGP6QMSvThrmw/A3I2Vd1XPY++OkhWPIinHUX9L8dPP0cXq7UA7MLRPUy2qB7jFBTatzRUNP+VVCQfhInskHqFqOtmWV0+UZBq7OOtrCumtFLRERETigyMpLIyEiFl0RExE5Yr15EDR5M8ooVp3xsiyFDCOvZsx6qkobo7Q1vV1lG7u5edxMTEOOcgpoYfbIjIiIiIiIiIvXL3QsG3A59J8KWebBsGqRvrzquIB0WPQfdr1aIqakyu0BkD6OddRfYbJC5C/b9Bfv/gn2rIGPHyZ0rLxnivjYagLsPtOgDLfoZS9G16Ae+4fX3XERERKRRUnhJRERqcvZrr/H58OGUFxef9DGuFgsjp02rx6qkIVl9cDUfxtkvIzckZAhXtbnKSRU1PQoxiYiIiIiIiIhjuLhBz2uNkFL8AvjzVTiwxn5Mj2sgoKVz6hPHM5kgOMZovScYfYWZxgxN+1YaoabkdVBReuJzlebD7qVGO8K/FUT3PRpsiuypJQtFRERERESkWpH9+3PxV1/xw7hxJxVkcrVYuPjLL4ns398B1YmzZRZn8sjSR7Bhq+xzM7txc9TNbFi3gZCQEFq1aoWvr68Tq2z8FGISEREREREREccym6HTWOg4xlhSbNmrkPgHYIIh99V8XH46+IQ6qkpxFq8g6Hih0QDKiiFlw9FQ0/6/oCjr5M6Vs89ocd8Y22ZXCO92dKam6P5GgEozMoiIiIiIiAgQM3Ys1yxdyuL77+fA8uU1jmsxZAgjp01TgKmZsNqs/GfZf0gvSrfrf7DbgxTnGoG3jIwMMjIy6Ny5M2FhYc4os0lQiElEREREREREnMNkgrbDjJa8wQg0hXaofmxJPrzV15hJZ/AkaD9KwZPmws0TWp1lNACrFQ7tNJagO7IMXeaukzuXtdwIRKVsgNXvG32eAdCi7zHBpn5GkEpERESalaysLNLT04mNjdWScyIizVxk//5ct2wZaRs3smnmTNI2bKA0Lw93X1/CevWixx13ENazp7PLFAeas3UOyw4ss+sbHDWYrqau5JBT2efq6kpQkD5TOBMKMYmIiIiIiIiI80X1MlpN1n8MxTlHlwsL7QyD/wndx4Grh6OqlIbAbIbQjkbre5PRl58GSasPtzWQvN5YXu5kFGdD4kKjHRHUzgg0RfWCyF4Q2QM8NB28iIhIU5Sbm8vu3bvJzs4GICgoiJCQEOcWJSIiDUJYz56MevttZ5chTrY5fTOvrX3Nri/YM5h/df8Xe3fstetv1aoVrq6K4ZwJ/fREREREREREpGGrKIe/jvvQMH0bfHc3LHwaBt4J/W4GS6Bz6hPn8wkzlijsNNbYtlZA+nYj0HRgDSSthbStgO3kzpe5y2ibvzjcYYLg9kdDTVG9IKIHePrV+VMRERERx7HZbGzbto3i4uLKvj179hAcHKzZmERERISckhweXvow5bbyyj4TJp4b+hyHDhyyG+vh4UGLFi0cXWKToxCTiIiIiIiIiDRsmbugrLj6ffkHYeFTsPQV6HMjnHUXBLZ2bH3S8JhdILyr0Y7M1lSSZ8zQlLQGDqw1Zm3KTz3JE9qMJewO7YTNXx7tDm5/NNR0ZMYmT/+6fS4iIuJUhYWFvPXWW3z55ZckJiZSUlJCy5YtGTt2LJMmTaJ16zN737Fnzx7atm17UmNvuukmZs+efUbXE3smk4k2bdqwffv2yr6CggLS0tIIDw93YmUiIiLibBXWCh758xEO5B+w67+t+23EuseyNX+rXX/r1q0xm82OLLFJUohJRERERERERBq20A5w32ZjVpwVb0HGjqpjygpg1XT4eyZ0vcxYai6qt8NLlQbMwxfaDjcagM0GOUmHZ2o63FI2QHkNgbnqHEow2pavjvYFxdjP2BTZU8EmEZFGKiEhgTFjxrBz5067/h07drBjxw7ef/995s6dy0UXXeSkCqUuhIWFsX//fgoKCir79uzZQ2hoqL6IFBERacbe2vAWyw8st+vrHdabu3rexfq16+36LRYLERERjiyvyVKISUREREREREQaPjdPY6alXtdDwm+w4k3Y82fVcbYK2DLPaG2GGWGm9ueBvoCS45lMENDSaF0vN/oqyiA1zgg2Ja+H5I3G0oXW8trPdazMRKNtmXe0L6idsfxcRDcI727MEOUfbdQgIiINUl5eHmPHjq0MMN1+++1ce+21WCwWFi1axPPPP09ubi7XXHMNy5cvp1evXmd8zWeeeYZLL720xv2BgVo6tz6YTCbatm3Lli1bKvuKi4tJSUnRkjAiIiLN1K97fuX9ze/b9QV5BvHS8JfISMugqKjIbl/btm21FG0dUYhJRERERERERBoPsxk6jDbagXVGmGnrt2CzVh2750+j3bYQovs5vFRphFzcjNmTonod7SsrNoJNKesheYMxW1PaqQabdhlt67dH+zwDILybEWiK6GY8DusMbpa6eCYiInKGXn75ZeLj4wF46aWXePjhhyv3DRo0iJEjRzJixAgKCwu57777WLx48Rlfs0WLFnTr1u2MzyOnLigoCD8/P3Jzcyv79u7dS3h4OK6u+ipNRESkOdl6aCuPLX/Mrs/V5MqrI18l1DOU1ZtX2+3z8fEhJCTEkSU2aXrnJSIiIiIiIiKNU4s+cNUHkPUk/PUurPvIWFbuWNH9oUVf59QnTYObJ0T3NdoRZcWQFnc01JS84XCwqezkz1ucDXuXGe0IkxmC2x8TbupuPPaL0qxNIiIOVFZWxhtvvAFA586defDBB6uMGTx4MLfeeiszZsxgyZIlrF69mv79+zu6VKkjR2Zj2rhxY2VfWVkZ+/bto127dk6sTERERBzpYMFB7l14L0Xl9jMtPTLgEfqG92XPnj2UlJTY7dMsTHVLISYRERERERERadwC28CFL8CIKbD2A1g1A/JTjX2D/1lz+KOsSLPeyOlx8zTCcccG5MpLDs/YtOFouCl166kFm2xWyIg3WtzXR/stgYeDTcfM3BTSEdy96ugJiYjIsRYtWkROTg4AN910E+YalqWdOHEiM2bMAOCbb75RiKmRCwgIICgoiMzMzMq+pKQkIiMjsVj0nlFERKSpKygr4J6F95BelG7Xf0XsFVzT8RpKSkrYv3+/3b6AgAAt+VvHFGISERERERERkabBKwiGPQiD7oXNX8LW76HTRdWPtdngvXMhoCWcdRe0HaGZbuTMuHoYs4O16HO0r7zEmKEpZQMc3GKEnFK3QElujaepVlHW0eURK5mMAF9oJwjtaPwZ1glCOoC7dx08IRGR5mvZsqOz5I0YMaLGcf369cPLy4vCwkKWL1/uiNKknsXExJCVlYXNZgPAZrOxe/duunTp4uTKREREpD6VVZTx4JIHic+Kt+vvH9GfxwY+hslkYvfu3VitVrv9MTExmoWpjinEJCIiIiIiIiJNi6sH9L7eaDXZtdhYDiwtDuJ/hrCuRpip+1XGLDsidcHVA6J6Ge0Imw2y9x0NNKVuMQJOmbsA2ymc3AZZu40Wv8B+V0ArCO18NNx0JOjk4XPmz0lEpBnYunVr5eNOnTrVOM7V1ZX27duzadMmtm3bdsbXffPNN3nmmWdISkrCw8OD6Ohohg0bxh133EGfPn1OfAI5Y15eXkRFRXHgwIHKvvT0dLKzswkICHBeYSIiIlJvrDYr/1n+H5YfsA+lt/Frw7SR03BzcSMvL4/U1FS7/ZGRkfj46N/ZdU0hJhERERERERFpfv6abr+dFgff3wu/T4V+t0D/W8E3wimlSRNnMkFga6N1GnO0vyQf0rfDwc2Hw01xRjvVWZvACEll74Odv9j3+7c8LtjUCUI7gKf/mT0nEZEmJikpCQBvb+8TBldatmzJpk2bSE9Pp6SkBA8Pj9O+7rp16yofl5SUsHXrVrZu3cqMGTO48847ef3110/r/EeeT01SUlIqH+fl5ZGbexp/95yG/Pz8ah87W1BQEAcPHqSioqKyLz4+no4dO2qmhUagob6upPHSa0rqml5TDYvNZmPapmks2G1/c1CAewAvDnwRU4mJ3JJcrFYrkZGRpKamYrVaMZvNBAcHO+x904k463WVl5dX5+dUiElEREREREREmpeibEj6u/p9hRmw9CVYNg26XWnMznTsLDoi9cXDB6L7Ge0Imw2y9xphpoNbIHWz8Thz1+ldI2e/0RJ+t+/3jYKQWAhuf/jPWAiOMWZ0Mruc/nMSEWmkjnwZczJ31nt7H13CMz8//7RCRgEBAVx++eWMHDmS2NhYPD09SUlJ4ddff2XWrFnk5+czY8YM8vLymDt37imfv2XLlic9ds6cOfj7Oz7cOmfOHIdfszbh4eG0bdu2cruoqIh58+aRnp7uxKrkVDW015U0fnpNSV3Ta8r5NnlvYpPvJrs+F6sLA1MG8sOcH6qMd3Nzo2XLlhQVFbFixQpHlXlKHPm6ysnJqfNzKsQkIiIiIiIiIs2LJQDuj4MNn8Cqd+FQQtUx1jLY9JnRWg02wkydxirQIY5lMkFgG6N1Gnu0v7QAMuIhfYcxe1PaduPPrD2c2pJ0h+UlG233Evt+Fw8Iagch7Y2AU3Ds0bCTV9DpPy8RkQauuLgYAHd39xOOPTa0VFRUdMrXOrJ0mZeXl11/7969GTNmDPfccw+jRo1i3759fPLJJ1xzzTVccsklp3wdOTVpaWmEh4fb/XcJDQ1ViElERKQJ2eK9pUqAyWwzMyJ7BKFlodUeU1ZWxq5dp3ljkZwUhZhEREREREREpPlx94YBt0O/W41Zaf56B3Ytqn7svhVG829pLDPX5yYFOMS53L0hqrfRjlVWVEO4aTfYrKd+nYoSSN9mtONZgozZmgLbGkGnytYWvIKNAJaISD2ri6W9PvjgAyZOnGjX5+npCUBpaekJjy8pKal8bLFYTvn67u7utYalYmNj+fjjjxk+fDgAb7755imHmPbv31/r/pSUFAYMGADADTfcQIsWLU7p/KcrPz+/cqaAG2644aRmvnKk3NxcEhMTMZvNRERE0LNnT0aMGOHssuQEGvrrShofvaakruk11TB8HP8xG+I22PWZMPHkgCcZFT3KOUWdAWe9rg4cOMDzzz9fp+dUiElEREREREREmi+zGTqcb7TUrbBqOmz83AhvHC9nP/w+1Vh6q+c1Di9V5ITcLBDZ02jHKiuGQzuPCTdtMx5n7gJbxeldqygTkjIhaXXVfR5+RpjpmICTi2c4PrY88tEH9CLS8Pn6+gLGl0EnUlBQUPm4vr4sGjZsGF26dGHr1q0sW7YMq9WK2Ww+6eOjo6NPeqyvry9+fn6nU+YZ8fHxccp1a3OknrCwsJOalUsanob4upLGTa8pqWt6TTnH/235P6bHTa/S/9hZj3FFxyucUFHdcuTrKjc3t87PqRCTiIiIiIiIiAhAeBe45E0490lY+wH8/T7kH7Qf4x0KXS9zSnkip83NEyK6G+1Y5SXGcooZ8Yf/TDDCThkJUJJz+tcryYWUjUY7zBu4ByjHBdMHX0BQGwhsDQGtj/mzDVgCNYuTiJy0bduqmSnuFEVGRlbpi46OZtWqVRQUFJCdnU1AQECNxx+Z5Sg0NNRuabm6diTEVFxczKFDhwgNrX6JE6lbpxIAExERkYbNZrPx1oa3mLlpZpV9/x74b67ueLXd2KKioipL/kr9U4hJRERERERERORY3iEw/GEYPBm2fgsr34aUDca+vjeDaw1fUGbuNpb58glzVKUiZ8bVA8K7Gu1YNhsUZBjBpkM7IWMnHEo0HmfuBmvZ6V+SCshKNFp13H0hoJV9wMm/JfhHG396BSnkJCKVOnXqVC/n7dKlC/PmzQNg+/btnHXWWdWOKy8vJzHR+H3WuXPneqnliLpYOk9ERESkubLarLz494t8sv2TKvseHfAo13W6zq4vIyODrVu3EhUVRdu2bXF1VbTGUfSTFhERERERERGpjqs79Lgaul8FSWvg75nQ75aax//2OMT/Al2vgIF3QIu+jqtVpC6ZTOATarTWg+z3VZRD9l4j1JS121iS7kjL2ntGAScASvMgLc5o1XG1HA40Vddagl8LY+YpEZEzMHTo0MrHS5YsqTHEtGbNmsrl5IYMGVKvNW3duhUADw8PgoOD6/VaIiIiIk1JmbWMqSum8n3i91X2Tek/hQmdJ9j1lZeXk5CQAEBycjLp6el06NCBkJAQh9Tb3CnEJCIiIiIiIiJSG5MJWvY3Wk2y98P2+WCzwqbPjNaiHwy4HbpcplCFNB0urhAcY7TjWSsgJ+mYUNNuY+amzF3YMndhKi8+8+uXFxkzQh3aWfMY71DwiwLfyGNahPGn3+FtSxCYzWdej4g0SSNHjsTf35+cnBw+/PBDpkyZUu1MSLNnz658fPnll9dbPcuXLycuzgh3Dh06FLN+fzldXl4eOTk5Wm5ORESkgcsrzeOBxQ/wV8pfdv0mTDwx6AnGdRhX5Zi9e/dSWlpauV1WVobVaq33WsWgEJOIiIiIiIiIyJlaM8sIMB3rwBr4Zg38/C/ofT30uxmC2jmnPhFHMLsYy78FtoaYs+125eXkMGf6y/hbs7nynH5YStIhe48xe1P2Xsg5ALaKuqmjIN1oKRtrqdXtaLDp2D/9ouy3Pfy0fJ1IM+Tu7s6kSZP473//y7Zt23jllVd4+OGH7casXLmSWbNmATBixAj6968+7Hwk/NS6dWv27NlTZf+3337LpZdeWuNycQkJCYwfP75y++677z6dpyR1pLy8nD179nDgwAEA/P398fX1dXJVIiIiUp2DBQe56/e7SMhOsOt3Nbny/LDnuaDtBVWOyc/PJykpya4vICCA0NDQeq1VjlKISURERERERETkTFWUgYsHVJRU3VeUCSvegBVvQvtzof9tEHu+EfgQaS5MJvJNvuS7+FLW5Qosfn72+yvKIfeAEWg6EmzK3mc8zkmCvOSqQcEzYS2DnP1Gq42btxFm8gkD7xBjlqfKdty2Z4BmdxJpQh5++GE+//xz4uPjmTJlCgkJCVx77bVYLBYWLVrEc889R3l5ORaLhddee+20r3P55ZfTvn17rrjiCgYMGEB0dDQeHh6kpKTwyy+/MGvWLPLz8wG4+uqrueKKK+roGcqpslqtrF27luLiozMLxsfH06dPnxpDaCIiIuIcG9M3cv+i+0kvSrfrt7haeGXEKwyPHl7lGJvNxs6d9rP+mkwmYmNj9Xe9AynEJCIiIiIiIiJypkY/C0Pvh3UfwupZRhijChsk/G40/5bQ9yboc5MRjhBp7lxcj87i1Laa/RXlkH/QCDTlJB0OICXZbxfn1H1dZQWQmWi0EzG5gFdwNQGn48NPwWAJBA9/hZ5EGjBfX1/mz5/PmDFj2LlzJzNnzmTmzJl2Y/z8/Jg7dy69evU6o2slJCTw0ksv1TrmrrvuYtq0aWd0HTkzZrOZyMhIdu/eXdmXn59PcnIyLVq0cGJlIiIicqxvdn7Df//6L2XWMrv+IM8g3jn3HbqGdK32uIMHD5Kbm2vX16pVK7y8vOqtVqlKISYRERERERERkbrgHQLDHoTBk2HHfCPMtHtJ9WNz9sMfz8DiF6HfLTCm9i8uRZo9F1fwjzZaTYpzjQBhThLkpUDeQchNNv7MSzFafhpgq58abRVQkGa0k2ICS4Axg5Ml8HA75nFt/W6e9fMcRMRO+/btWb9+PW+//TZffvklCQkJlJaW0rJlS8aMGcPkyZNp3br1GV3j+++/Z+XKlaxatYq9e/eSkZFBQUEBfn5+tGvXjmHDhnHLLbfQrVu3OnpWciaio6NJTU2lsLCwsm/Xrl0EBwfj6anfzSIiIs5UVlHGy2te5tPtn1bZ19a/Le+c+w7RvtX/m7KkpITERPubVzw9PWnVqlW91Co1U4hJRERERERERKQuubhCl0uNlrET1vwfbJhb/Swx1jLw9KvaLyKnztPPaGGdax5TUW6EjPJSIDflaNgp76CxZN2RwFNRlgMKthnXKcqCrN0nHn4sV8vRcJNnAHj4gLsPePgebe4+Rr+HL7gf6T8yzs947OpRH09MpEnx9vZmypQpTJky5bSOt9lqD05efPHFXHzxxad1bnE8s9lMbGwsGzdurOyzWq3s2LGDHj16aKkZERERJ0nKS2LK0ilszthcZd/AiIH8b+T/8Pfwr/ZYm81GfHw8FRUVdv2xsbGYNXuuwynEJCIiIiIiIiJSX0Ji4YLn4ZzHYcs8WDMLktcf3W8yQ9+JTitPpNlxcQW/KKPVtvJPWdEx4aYUKMiAgnSjFWbYb9fHMnYnUl4EeUVG8OpMuLgfE3byMx67exkhKTeLMeOT6+E/3bzA1fNwv6X6frv9hx+7uIPZpW6et4hIAxAQEEBkZCQpKSmVfdnZ2Rw8eJDIyEgnViYiItI8/b73d55Y/gR5ZXlV9t3Y5Ubu73s/ruaaozFpaWlkZmba9YWHhxMUFFTntcqJKcQkIiIiIiIiIlLf3L2gzw1GO7AWVv8fbPkKYs6peXmsrD3w+fXQ5ybofpUx44qIOIabBYLaGu1Eykug8NDRUNOxAaeC4/vToLy4/us/WRWlUJRptHplAhe3w4EmV+Ox2e1w35HHrof3H+lzrf4YkxlMpsN/1tSq2R9zNrQZWs/PU0Sai3bt2pGZmUlJSUllX2JiIkFBQXh4aJY7ERERRygoK+CVNa/wVfxXVfa5m92ZOngqF8fUPuNlaWkpCQkJdn1ubm7ExMTUaa1y8hRiEhERERERERFxpBZ9jXb+f6Ekt+Zx6z6Cg5vhp4fg18eh2xXGrE3R/Y0v6EWkYXD1ODq704nYbFBWCEXZR5eSKz7mcY39OVDihBmf6ozNCExVlDqvBHcvhZhEpM64uroSGxvLli1bKvsqKiqIj4+nW7duWlZORESknq05uIbHlj/GgfwDVfa19mvNKyNeoVNQpxOeZ+fOnZSXl9v1dejQATc3tzqrVU6NQkwiIiIiIiIiIs7gFWS06lSUwfq5R7fLi2DDXKOFdjbCTD2vAUugQ0oVkTpiMoG7t9H8a1vPrhoV5cbSdXbhpiyjrzQfSvKhJO/w49zjtvMOb+cCtvp4Zg2fyezsCkSkiQkODiY8PJzU1NTKvszMTNLS0ggPD3diZSIiIk1XQVkBb61/i7nb5mKr5t82Y9qO4YlBT+Dt5n3Cc6Wnp5ORkWHXFxoaSkhISJ3VK6dOISYRERERERERkYYmYSHkH6x+X/o2+PkR+P1J6HKZEWhqdZZmZxJp6lxcwTvYaKfryExQR0JNpXnHBJzyjO2yoqOtvPiYx0VQVmwcX15sPC6vZmxDDUkpxCQi9SAmJoasrCxKS4/ONJeQkEBgYCDu7u5OrExERKTp+WPfHzy36jlSC1Or7LO4Wnh0wKNc3v7yk5oR0WazsX//frs+Nzc32rdvX2f1yulRiElEREREREREpKGJPR9u+AbWzobt88FaXnVMeTFs+sxoIR2g9/XQ41rw1Z3/IlKDY2eC8q2H89tsUF5yNPBUXmTMIFVRCtYy47G17PDScsc+LjN+z1Uc3j7y2Fp2uO+YxwA26wmarWpfSId6eMIi0ty5ubkRGxtLXFxcZV95eTnx8fF07dpVy8qJiIjUgf25+3lpzUss3r+42v19wvrwzNBnaOnb8qTPaTKZ6NGjB7t37yY5ORmA9u3bK4TcACjEJCIiIiIiIiLS0JjNEHOO0fLTjGXk1n4IWburH58RD789Ab8/BR0uwDz4EcfWKyICRkjKzdNoFmcXIyLiGCEhIYSGhpKenl7Zd+jQIQ4ePEhkZKQTKxMREWnc8krzmLlpJh9v+5jyam7ucje7M6nPJK7vfD0uZpdTPr+rqyuxsbGEhoaSkZFBaGhoXZQtZ0ghJhERERERERGRhswnDIbeD4Mnw54/jdmZtv1gzEpyPFsF7PwF28j/OrxMERERkeaqffv2ZGdnU1ZmvD/z8PDAYlGaU0RE5HSUVJTw5Y4veW/ze2QWZ1Y7ZlDkIB476zFa+bU64+sFBAQQEBBwxueRuqEQk4iIiIiIiIhIY2A2Q7sRRivIgA2fwLoP4VCC/bgOF2Dz1t2DIiIiIo7i7u5Ohw4diIuLIzQ0lNjYWNzc3JxdloiISKNSVlHGNwnfMGPTDNIK06odE+QZxJT+UxjTdoyWbW2iFGISEREREREREWlsvENgyCQY/E/YtxLWzYGt30JZIfS+vubjFv4X8g4aY1qdZSz9JCIiIiJnLCQkhN69e+Pn5+fsUkRERBqVwrJCvor/ijnb5nCw4GC1Y1zNrkzoNIE7et6Bn/vp/V1rs9kUfGoEFGISEREREREREWmsTCZoPdhoF74I276H9udBQWHVseWlsOb/oCgTNnwMQTHQ6zrocQ0EnPn06yIiIiLNnQJMIiIiJ+9gwUG+2PEFn+/4nNzS3BrHnd3ybB7q99AZLR2Xl5dHYmIiHTt21JKvDZxCTCIiIiIiIiIiTYGnX+2zMMUvMAJMR2Qmwh/PGK3NMOg1HjpfAh4+9V+riIiIiIiIiDQ7VpuVv1L+4vPtn7MkaQkVtooaxw6MGMi9ve+lV1ivM7pmRUUF27dvp7CwkDVr1tC+fXsiIiI0K1MDpRCTiIiIiIiIiEhzsH5uzfv2/Gm0+Q8aQaZe1xnBJrOL4+oTERERaaJsNhvZ2dkEBgY6uxQRERGn2JWzix8Tf2T+rvkkFyTXOrZ3WG/u6XUPAyMH1s21d+2isNCYsdpqtRIfH4/ZbCY8PLxOzi91SyEmEREREREREZHm4OLXYdNnsP5jOJRQ/ZiyQmPMps/Ar4Wx1Fyv8RAS69haRURERJqIkpISduzYQVZWFp07dyYsLMzZJYmIiNQ7m83G7pzdLNy3kN/3/c7WQ1tPeMzIliO5pdst9A7rXWd1ZGRkkJxsH5ry8fEhNDS0zq4hdUshJhERERERERGR5sAvEobeD0Pug6TVsOETiPsainOqH597AJa9arQ2w+CmH0BTrYuIiIictIyMDHbs2EF5eTkA8fHx+Pj44OXl5eTKRERE6l5hWSFrUtewMnklyw4sY0/unhMeY3G1cHG7ixnfeTwxATF1Wk9xcTE7duyw6zObzXTu3Bmz2Vyn15K6oxCTiIiIiIiIiEhzYjJBywFGu+AFiF8AGz6FhN/BVlH9Mb6RCjCJiIiInKLy8vLKABNARUUF27Zto3fv3vryVEREGr2Mogw2pm80WtpGNmVsotxafuIDgfYB7bmm4zVc1O4ifNx96rw2q9XK1q1b7f4eBoiJiVGYuIFTiElEREREREREpLly84SulxstPw02fwkbP4WDm+3H9bqu5nMc3AKhncBFHzOJiIiIHCsiIoLs7GxSU1Mr+/Lz80lMTCQ2Vsv1iohIw2ez2ThUfIikvCT25e0jISuB+Ox4dmbuJK0o7ZTOFewZzJh2Y7i43cV0CuqEqR5vltq1axd5eXl2faGhoURGRtbbNaVu6NMlEREREREREREBnzAYdI/RDm4xwkybvgCzK7QdUf0xJXnw/rng4QfdroQeV0FUH83aJCIiInJYbGwseXl5FBYWVvYlJyfj7+9PWFiYEysTEZHmasWBFeSX5VNcUUxRWRHFFcUUlhdSXF5MYVkhmcWZZBRlkFmcSWphKkXlRad9rTCvMM5peQ7ntDqH/hH9cTXXf0QlIyODAwcO2PVZLBY6dOhQr8EpqRsKMYmIiIiIiIiIiL2IbhDxLIx6CrL2gNml+nHbfoTyYqOtmm60oBjocTV0vwqCYxxatoiIiEhD4+LiQpcuXVi3bh1Wq7WyPz4+Hh8fHy1pIyIiDvfIn4+QXZJdL+d2NbnSI7QHg6IGMSRqCF1DumI2OW4J1aKiInbs2GHXZzKZ6NKlC66uisc0BvqvJCIiIiIiIiIi1XNxhZD2Ne/f/EXVvsxEWPy80aL6GIGmrpeDb0T91SkiIiLSgHl7exMbG2v3pWpFRQVbt26lT58+mM2O+3JXRETE4mqpsxCTl6sX3UO70zO0J71Ce9E3vC9ebs4J6FqtVrZt20Z5ebldf/v27fHx8XFKTXLqFGISEREREREREZFTV1EGxbm1j0leZ7Sf/wWth0C3y6HzpeAT6pgaRURERBqIiIgIsrOzSU1NrewrKCggISGBDh06OLEyERFpbjxdPU/5GDezGy18WtDGrw2xgbF0COxAbGAsbfza4FLT7M0OtmvXLvLy8uz6QkNDiYyMdFJFcjoUYhIRERERERERkVPn4ga3L4SMnbDpC9j8JWTtrmGwDfYuM9pPU6DtMOg70ZihSURERKSZiI2NJS8vj8LCwsq+lJQUfHx8iIqKcmJlIiLSnET7RGOz2bC4WrC4WvB09cTTxROLmwVPF08CPQMJsYQQbAkmxDOEaN9owrzCHLos3Kk6ePAgBw4csOuzWCx06NABk8nkpKrkdCjEVI8KCwt56623+PLLL0lMTKSkpISWLVsyduxYJk2aROvWrc/o/Hv27KFt27YnNfamm25i9uzZJxz36aef8sEHH7Bp0yays7MJDw9n2LBh3HPPPQwaNOiM6hURERERERGRJigkFs75D5z9bziw1gg0xX0NBenVj7dVwK7FENZVISYRERFpVlxcXOjSpQvr1q3DarVW9ickJODt7Y2/v78TqxMRkebinVHvOLuEOnf8DEwmk4kuXbrg6qpITGPTcKNyjVxCQgK9evXikUceYc2aNWRlZVFYWMiOHTt49dVX6dGjBz/++KOzy6xUVFTE2LFjGT9+PL/99hupqamUlJSwb98+5s6dy9ChQ3nqqaecXaaIiIiIiIiINFQmE0T3gzEvwQPbYcI86HEtuPtWP77bFTWfq6y4fmoUERERcTJvb286duxo12ez2di6dSslJSVOqkpERKRxa9++PbGxsZWzLnXo0AEfHx8nVyWnQ7GzepCXl8fYsWPZuXMnALfffjvXXnstFouFRYsW8fzzz5Obm8s111zD8uXL6dWr1xlf85lnnuHSSy+tcX9gYGCtx99yyy389NNPAJx99tlMnjyZqKgoNm/ezHPPPUdiYiJTp04lMjKSO+6444zrFREREREREZEmzMUVYkcZrawYEn43ZmfasQDKCsG/FbToW/2xeQfhjT4QczZ0vgQ6XgCempVAREREmo6wsDDy8vJISkqq7CstLSUhIYGuXbs6sTIREZHGyWQyERUVhbe3N1lZWURERDi7JDlNCjHVg5dffpn4+HgAXnrpJR5++OHKfYMGDWLkyJGMGDGC/2fvz+PjPOt7//99zz6afdHIsiRL8pLYzr44JECaUPZAaJPTsqSlhECTtvRbOF9K4LS/Qji08Eih59D2lHOgpEnhBGhpQ39QoMkJmBxCAnHI5sR2vC+SZWlmtMyMZp+5v38I3UjWjCzb2vV6Ph73wzP3dd33fGTfnrk19/u+rnw+rw996EP60Y9+dN6v2dHRoYsvvvictv3hD3+ob3zjG5Kkm2++Wd/61rdkt9slSTt27NDb3vY2XXXVVTp+/Lg++tGP6jd/8zfPGIoCAAAAAACQJDk90ra3TizlcWn/w1KtMjFyUyN7/v9SZVza9+8Ti80pbbxR2v426cK3SL7YopYPAACwEDZu3Kjx8XGNjIxIkkKhkLZs2bLEVQEAsLKFQiGmZ13hmE5unlUqFf3N3/yNJGnbtm368Ic/PKPPK1/5Sr3vfe+TJD322GPatWvXotZ4us997nOSJIfDoS984QtWgGlSPB7XvffeK0kaHR3Vl7/85UWvEQAAAAAArAIu38Q0cpe9o3mfFx+a/vQElaAAAKKFSURBVLxekQ7+H+nb/4/0uS3SP94s7fqylB1c2FoBAAAWkGEY2rZtmzwejzo6OnTZZZfJ5XItdVkAAABLihDTPNu5c6fGxsYkSe95z3tkszX+K7799tutx9/61rcWo7SGstmsfvCDH0iSXve616mzs7Nhv1tvvVXBYFDS0tYLAAAAAABWsfyw1P/z5u1mTTryf6Xvflj6qwulf3iT9OQXpNETi1cjAADAPHE6nbrqqqu0efNmGc1GqQQAANNks1kVCoWlLgMLhBDTPHv88cetxzfccEPTfldffbVaWlokST/5yU8WvK5mdu3apXK5LGn2el0ul6699lprm0qlsij1AQAAAACANaQlKv3xfunXviBteaNkn200AlM6/qT08H+RPn+x9KXXSC/886KVCgAAMB8cDsdSlwAAwIpRLBb14osv6plnntHw8PBSl4MFwJnRPNuzZ4/1eOvWrU37ORwObd68WS+88IL27t173q/7t3/7t/rzP/9z9fX1ye12q7OzU9dff73uvPNOXXnlledd72T7I488omq1qgMHDmj79u1zrq+vr2/W9oGBAevx+Pi4MpnMnPcNzLdcLtfwMbBUOCaxnHA8YjnheMRywzGJ5WRlH48OadPNE0spK8fhR+U88H05ju6UUS023+zkMyoOHVaZ7xSWpZV9TGK1GR8fX+oSAGBO6vW66vU6QScAACRVq1W9+OKL1iAtu3fvVm9vr7q6uhjRcBXhrGeeTYZ1fD6fwuHwrH27urr0wgsvKJlMqlQqye12n/PrPvPMM9bjUqmkPXv2aM+ePfriF7+ou+66S3/913/dcP9Tw0XNppKbWu+kEydOnFWIaeq2Z/LQQw8pFArNuT+wkL761a8udQnANByTWE44HrGccDxiueGYxHKyOo7HK+R0XKRe22FdWNunTbWDcqs8o9dXd6U1/PP/OXNz09TG+mEdt21Q1XAuQr2Yzeo4JrGSjY2NLXUJAHBGlUrFuhH9kksukc3G5CoAgLXLNE3t2bNnxg0JIyMjZ5VFwPJHiGmeZbNZSZLf7z9jX5/PZz3O5XLnFGIKh8O65ZZbdOONN2rLli3yeDwaGBjQI488ovvuu0+5XE5f/OIXlc1m9eCDDzatdy41n14vAAAAAADAYqkYLu23b9V++1bZzap66kd0Ye1lba7tl1dFpY2ohm3xhtvGzaR+s/xPqsiho7ZeHbBfoIP2zSoYvob9AQAAllKhUNCLL76ofD4vSTpw4IAuuOACRpkAAKxJpmnq4MGDGhkZmba+paVF27dv5/NxlSHENM+KxYlhzV0u1xn7Tg0tFQqFs36t9evXq7+/Xy0tLdPWX3HFFbrpppv0gQ98QK973et0/Phxfe1rX9M73vEOve1tb2tY71xqPp96T5w4MWv7wMCArrnmGknSrbfeqgsuuOCs9g/Mp1wuZ90V+u53v3tOoURgIXFMYjnheMRywvGI5YZjEsvJWjoeK7WK6ieekLda0O9vflPDPq6f/rX0hORUVVvqB7SlfkBmxVBt/dWqbnq9qr2/qnrsAokvPhfMWjomsfzt379fn/nMZ5a6DABoyDRNvfTSS1aASZJOnTolr9erDRs2LGFlAAAsjf7+fp08eXLaOqfTqYsvvlhOJ6MtrzZrNsQ0H2m8+++/X7fffvu0dR6PR5KseRhnUyqVrMder/esX9/lcs0aPNqyZYv+9//+3/qVX/kVSdLf/u3fzggxTdYrnbnm86n3TFPVTeXz+RQMBs9q/8BC8fv9HI9YVjgmsZxwPGI54XjEcsMxieVkTRyPkZtnbz/6gxmrDJlynNwlx8ld0o8/LYU2SBe8QdryRqn3esl59t/VYG7WxDGJZW3qiPMAsNwYhqELL7xQzz33nOr1urX+yJEj8nq9am1tXcLqAABYXKlUSocOHZq2zjAMXXTRReeUscDyxwS68ywQCEia23RrU+drXKi7z66//npt375dkvT4449PO+GVflmvdOaaF6NeAAAAAACAeVUtSS6/ZNhn7zd2XNr1Zelrvynd2ys9+HZp133SWN/i1AkAAPALgUBA27Ztm7F+3759ymQyS1ARAACLL5vNau/evTPWb926VaFQaAkqwmJYsyMxNTrYz1Z7e/uMdZ2dnfrZz36m8fFxjY6OKhwON91+coq11tbWaVO1zbft27drz549KhaLSqfT01L6U0dI6uvr09VXX33GeiWpq6trYYoFAAAAAACYTw63dPu/S/lh6cAj0r5/lw7+UKqMN9+mWpAOPDyx7H+j9Fv/vHj1AgAASIrH49q4caMOHz5sravX69q9e7euuOIKtbS0LGF1AAAsrEKhoN27d88YpKWnp0eJRGKJqsJiWLMhpq1bty7Ifrdv365//dd/lTSRiL/22msb9qtWq9awZ43S9PNptqnzJkdpkibqnc1ku8Ph0JYtW+anOAAAAAAAgMXQEpUue+fEUilKRx6TXv7+RLAp0998uwve2LytmJE8TIsGAAAWRmdnpwqFggYGBqx11WpVL7zwgq644ooFvUEeAIClUi6X9cILL6hSqUxb39bWpg0bNixRVVgsTCc3z1796ldbjx977LGm/Z5++mlrerZXvepVC1rTnj17JElut1uxWGxa244dO+RyuSTNXm+5XNZPf/pTaxun07lA1QIAAAAAACwwp2cinHTz56X//JL0ez+RXvtxqetayTjt67Itb2i8j1pF+u8XSf/zVdIjfyYd/tHE1HUAAADzxDAMbd68WZFIZNr6UqnU8OIuAAArXbVa1e7du1UsFqetD4VCuuCCC2YdwAWrAyGmeXbjjTda8y/+4z/+o0zTbNjvgQcesB7fcsstC1bPT37yE7300kuSJgJWNtv0f/JAIKDXvva1kqRHH31UfX19Dffz0EMPWfMsL2S9AAAAAAAAi8owpHUXS9d/WHrfw9JHDkm3/r108W9I3a+Wwl2Nt+vbJZUy0uCL0hN/I33l16R7e6QHf1P66f+SUgelJt8LAQAAzJXNZtNFF12kQCAwbX0+n9eLL76oWq22RJUBADC/6vW6XnzxReVyuWnrfT6fLr744hlZB6xO/CvPM5fLpT/6oz+SJO3du1ef+9znZvR58skndd9990mSbrjhBu3YsaPhvgzDkGEY6unpadj+b//2b01DUpJ08OBB3XbbbdbzP/iDP2jY74//+I8lTaQaP/CBD8w44U2lUvroRz8qSQqHw3r/+9/f9DUBAAAAAABWtJaodOnbpd+4T3rvd5v3O/iDmesq+Ynp6f7jo9L/uEr660ul73xI2vsdqTi2YCUDAIDVzW636+KLL5bX6522PpPJaM+eParX60tUGQAA88cwDPl8vmnrPB6PLr30UjkcjiWqCouNf+kF8JGPfET/9E//pP379+vuu+/WwYMH9c53vlNer1c7d+7Upz/9aVWrVXm9Xn3+858/59e55ZZbtHnzZt1666265ppr1NnZKbfbrYGBAT388MO67777rJTi29/+dt16660N9/Orv/qreuc736lvfOMb+va3v63Xv/71+tCHPqT169dr9+7d+ou/+AsdP35cknTvvffOGLYUAAAAAABgzTnxszP3GT0u/fz+icWwS+uvkDbeIPXeIPVcL3EXKQAAmCOXy6VLL71Uzz77rMrlsrV+eHhY+/fv14UXXsgUOwCAFW1yGlWXy6WjR4/K6XTq0ksvlcvlWurSsIgIMS2AQCCg7373u7rpppt04MABfelLX9KXvvSlaX2CwaAefPBBXX755ef1WgcPHtRf/uVfztrn93//9/Xf//t/n7XPP/zDPyiTyeh73/uedu7cqZ07d05rt9ls+rM/+zPdeeed51UvAAAAAADAqvDub0l9T0uHfjAxKtPJZyXNMn2cWZP6n55Ynv3f0odfXrRSAQDA6jA5GsVzzz2narVqrc9kMqpWq3I6nUtYHQAA588wDHV3d8vlcsnv988YhRCrHyGmBbJ582Y9++yz+ru/+zt985vf1MGDB1Uul9XV1aWbbrpJH/zgB9Xd3X1er/Htb39bTz75pH72s5/p2LFjSqVSGh8fVzAY1MaNG3X99dfrjjvu0MUXX3zGfXm9Xn33u9/V1772NT3wwAN6/vnnNTo6qra2Nl1//fX6wz/8Q1133XXnVS8AAAAAAMCqYXdK3ddNLL/6/5PG09LhndKhH06EmnKnmm/b+ytSs5ESTj4nOTxS64XN+wAAgDXL5/Pp4osv1gsvvKB6vS6/369LLrmEABMAYFVpb29f6hKwRAgxLSCfz6e7775bd9999zltb5qz3L0n6eabb9bNN998Tvtu5rbbbtNtt902r/sEAAAAAABY9Xwx6ZLfmFhMUxp86ZejNB1/Uqr9ctoX9d7QfD+P3jMRhvKvmwg79bxK6n61FNtEqAkAAEiSQqGQtm3bppMnT2r79u1yOLjcBwBYearVKp9hmIEjAgAAAAAAAJhPhiGtu3hiedUHpXJeOvFT6fBj0pHHJsJJjVRL0vGfTjzOnZJ2//PEIk2Emrpf+ctQEyM1AQCwpsXjccViMRmcDwAAVqCTJ0/q2LFjuvTSS+Xz+Za6HCwjhJgAAAAAAACAheRqkTb96sQymxNPSdVC47bcKemlhyYWSWqJ/yLU9Gqp+1VSYrtks81v3QAAYFmbLcBkmiYBJwDAsnTq1CkdOHBAkvT888/r0ksvld/vX+KqsFzwzQYAAAAAAACwHBRGpGDH3PrmU9Leb0vfv1v6X6+SPrtROvjowtYHAABWhHq9rr179+rUqVNLXQoAANMMDg7q5Zdftp5XKhU9//zzKpfLs2yFtYSRmAAAAAAAAIDlYPvbpG03S+lDE9POHfuJdPQnE6MwnUlhRAr3NG6rlqVyTmqJzmu5AABg+anX69qzZ4/S6bSSyaQMw1BbW9tSlwUAgJLJpPbt2zdjfUdHh1wu1xJUhOWIEBMAAAAAAACwXBiGFN88sex4n2Sa0vBh6ejjvww1Zfpmbudvk2KbGu/z+JPSV94mxS+UNrxC6nqF1HXtRH+mmQEAYNWo1+t68cUXNTIyYq3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"text/plain": [ "
" ] @@ -648,23 +678,22 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "SNZ.__init__() got an unexpected keyword argument 't_half_flux_pulse'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dp \u001b[38;5;241m=\u001b[39m FluxPulse(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m40\u001b[39m, \u001b[38;5;241m0.9\u001b[39m, \u001b[43mSNZ\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt_half_flux_pulse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m17\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb_amplitude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.8\u001b[39;49m\u001b[43m)\u001b[49m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m200\u001b[39m)\n\u001b[1;32m 2\u001b[0m dp\u001b[38;5;241m.\u001b[39mplot()\n", - "\u001b[0;31mTypeError\u001b[0m: SNZ.__init__() got an unexpected keyword argument 't_half_flux_pulse'" - ] + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "dp = FluxPulse(0, 40, 0.9, SNZ(t_half_flux_pulse=17, b_amplitude=0.8), 0, 200)\n", + "dp = FluxPulse(0, 40, 0.9, SNZ(17, b_amplitude=0.8), 0, 200)\n", "dp.plot()" ] }, @@ -677,9 +706,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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WLfKcUFHi8Xi0ZcsW3XvvvTrvvPPkcDgUHBysH/7wh/5IEf3EPzYW6idv79CMhz7U9/6xVZvzqyVJK/eU6eN9FYZ1bz93uJKj+UMIAIBAduf8bM3OTjDEPtpdphc+OWiITR8ar9vmZBliu4rr9IeP9vk8RwAAAAAA0HMej0cJCQkaOnSoRo4cqYkTJyo4ONjstAAAAGACvxQxjRo1Sn/5y19ks9nU2tqqP/zhD5o6darCw8OVnp6ujIwMhYeHa+rUqfr973+v1tZW2e12vfjiixo1apQ/UkQ/4HZ79Nrn+ZKk5jaX3txYqNfWF8jpcuvXy3MN66ZEh+jWc4eZkSYAAOgGm9Wix66ZpMRI44eXv/nv7vZi5WPuvTBHOSmRhtiTK/I6rQcAAAAAAAAAANAfeDweOZ1O2Ww2s1PxCr8UMUnS1VdfrU8//VRTp06Vx+ORx+NRS0uLiouLVVRUpJaWlvb4lClT9Mknn+grX/mKv9JDP7D2QKUKqpoNsWtmZOrNjYXaW9pgiN+3YKTCg+3+TA8AAPRQclSoHr9msmFcnNPt0bf+vlm1TcdHyIYG2bT46kmyW4+v6PZI972xVc2tLn+mDAAAAAAAAAAA4DMHDx7U+vXr9cknn2j79u1KTU01OyWv8GsVx/Tp0/X5559rw4YN+uCDD7R9+3ZVVVVJkuLi4jR+/HhdcMEFmj59uj/TQj/x6vp8w/LIlCjlJEfq9r9uNMRHpUbpqikZ/kwNAAD00jkjEnXXvGz9cUVee+xITbO+9eomPX/jNIXYjz5hMC49RnefP0KPvr+3fb0DFY36zX9368FLx/o9bwCA/9XX1ys8PLzfPH0GAAAAAAAAdOR0OtXcfLzJS0hIiInZeI8prWimTZumadOmmXFo9FNVja16b2epIfaVGZl69uODKq9vMcR/snCMbCd0aAAAAH3DPReM0GcHK/X5oePj4T7eV6G7/rZJT103VcH2o01GvzlvuD7YXaatBTXt6/13R4m+uyBH0aFB/k4bAOBHDodDW7ZskcViUVJSklJSUhQTEyOLhb8BAQAAAAAA0H90LFoKDg42KRPv8ts4OcCXlmwqVKvL3b4cbLdq1vAEPbt6v2G9eSOTdM6IRH+nBwAAvMBus+r3X52suHBjIdIHuWW65/XNcv7vWsBus2rx1RMV8r+ipksnpum/98yhgAkABoD9+/fL7XbL5XKppKREW7duVVNTk9lpAQAA4BQoOAcAAOi+0NBQwzKdmIAAUdPUqqdXHTDEvjguVc9/fFCOtuOFTVaL9MDFo/2dHgAA8KJBMWF68eYZ+trzn6mhxdkeX769REG2rVp89STZrBYNT4rULy8bq4gQuxZNSDMxYwCAv1RVVamiosIQS0tLU0REhEkZAQAA4ExMnTpV27dvV1hYmEJCQjRs2DCFh4ebnRYAAEBA61i0RBFTN6xevbpX25977rleygT90a+W5aqiwTgybtbwBP1wyXZD7Jrpg5WTEuXP1AAAgA9MyozVSzdP1w1/Xq+mVld7/F9bihRit+r/XTlBVqtF10wfbGKWAAB/crvdysvLM8TsdruGDh1qTkIAAAA4IzabTXa7XU6nU/X19aqvr9ewYcPMTgsAACDgdezEZLVaZbf3/T5GfvkvmDdvXo/bgVosFjmdztOviAFp9d5yvbmx0BA7f1SS/r21SB7P8Vh4sE33XjjCz9kBAABfmTY0Xs/fOE03v/i5WpzHOy++saFQwXar/u+ycbSjB4ABpLCwUM3NzYZYVlaWgoIYJQoAABDIgoODO8U63pADAABAZye7juoP3Zis/jqQx+Pp8T/gZBpbnPpRh25LUSF2LZyQpk/zKg3xO+YOV3IUf/gAANCfzBqeqGdvmKZgm/GS9pV1+fq/pbmnvI7kOhMA+g+Hw6HDhw8bYlFRUUpNTTUpIwAAAJypjjfagoKCZLX67dYVAABAn2WxWPrlSDm/dGJasWLFaddpbGzU3r179dprr2n9+vWaPXu2fvGLX8hms/khQ/RFj7y7R0dqjE/a3v+FUfrTyv2GWEp0iG6dk+XP1AAAgJ/MzUnSU9dN0R2vbJTTfbwo6c+fHlSw3aoffGFkp45MZfUOPbBku744bpCumprh75QBAF524MABud1uQ2zEiBF05AMAAOgDOt5oowsTAADAmQsNDVVLS0v78sm6M/U1filimjt37hmtd/HFF+uee+7RI488oh/84Af685//rFdeecXH2aEv2ni4Wn9Ze8gQOzsrXjartK+swRD/3oKRCgumGA4AgP7qgjEp+sNXJ+tbr26W64RCpqdX7VdokFX3XJDTHlu+vVg/fmu7qpva9NmBKs0cnqC02DAz0gYAeEF1dbXKy8sNsUGDBikqKsqkjAAAANAdHW+09YfuAQAAAP7SHzsxBWRPzu9///u68sor9eqrr+q1114zOx0EoKdW5OnECTAhdqt+tmiMFr+/z7De6EHRunIKHRYAAOjvvjh+kBZfPVEdm248/sE+PbUyT5K0taBGd/5tk6qb2iRJ9S1Off/NrYbCJwBA3+F2u5WXl2eI2e12kg8IbgABAABJREFUDRs2zKSMAAAA0F10YgIAAOi5jtdOFDH50A033CCPx6Nnn33W7FQQgP547RTdOmeYrP+7UfndC3P03x0lqmhoMaz3k4WjZbMyQgAAgIHgsknp+s1VEzrFf/vfPXrhk4OamBmrKyenG772aV6lHliyXW4KmQCgzzly5IiampoMsWHDhikoKMikjAAAANBddGICAADouY7XToyT86HBgwdLkrZv325yJghEYcE2/XjhGC2akKa/rDmkheMH6YLHVhnWmT8ySbOzE03KEAAAmOHqaZlqdbr1k7d3GOL/t3SXgu1W/fzSsVp7oFLFtY72r72+oUDhITb9bNEYWTq2cgIABKSWlhYdOnTIEIuKitKgQYPMSQgAAAA9QicmAACAnmOcnB+VlpZKkhobG03OBIFsYmasfvOlCfrx2zvkaHO3x60W6UcXjzYxMwAAYJavnT1EP1s0plP8p2/v0Ls7SvT4NZMUbDNeBr/46SEtfn+vv1IEAPTS/v375Xa7DbHs7GyKUQEAAPoQj8dDJyYAAIBe6FgAHhQU1Okzs74mYIuYnnzySUnHOzIBJ+N2e3T/m9u0am+5If6VGYOVkxJlUlYAAMBsXz9nmH74xVGd4j9Ysk0ldQ49ed2UTiNn//BRnv60cr+/UgQA9FBra6tqamoMsdTUVEVHR5uTEAAAAHrE5XJ1KkLvDyNQAAAA/OVk104ul8uETLwnoIqYqqur9f777+viiy/W0qVLZbFYdOWVV5qdFgKUx+PRr5bl6q3NRwzx2PAg3XtBjklZAQCAQHHH3OGdrgk8Hum7b2xVm8utxVdPVMeGHb/57269vPaQ/5IEAHRbcHCwpk+frrS0NEmS3W5XVlaWyVkBAACgu07WJcBqDajbVgAAAAHNZrNp+PDhyszMVF5envbs2SObzWZ2Wr1i98dBevpNGjFihH7wgx94ORv0NR6PR//vP7v1lRmDNSwxoj3+1Mr9+vOnBw3rhgZZ9cKN05QURctZAAAgfef8bLU4XXrqhA5LLrdH33l1s57+2lQ9fMV4/XDJdsM2P/vXToUH2/WlqRn+ThcAcIaCgoI0YsQIpaamqqWlRUFBQWanBAAAgG46WRFTX7/pBgAA4E9Wq1UZGRmqq6tTRUVFe6wv80v2Ho+nW/9sNpu++tWvavXq1YqJifFHighgr31eoGdWH9AXHl+tZ1btl9Pl1qvr8/XIu3sM69mtFv3pa1M1dUi8SZkCAIBAY7FY9P2LRuqWc4YZ4k63R3f+bZNSY0L100VjOm13/5tbtXx7sb/SBAD0UFRUlBITE81OAwAAAD1wsiKmjuPlAAAAMLD4pRPTz3/+89OuY7VaFRUVpWHDhmnWrFlKSkryQ2YIdCW1Dv16Wa4kqcXp1sP/2a3/7CjRtsKaTuv+7ssTNX9ksp8zBAAAgc5isegnC0erxenSK+vy2+OtLre+8ZcN+uEXR+m7F4zQ4g/2tX/N7ZHufm2zwoJsmj+K6wsAAAAAALzN4/EYlm02G0VMAAAAA1zAFDEBHXk8Hv3k7e36/+zdd5xcBb3///eZvr337GZ300MapAGhBEVKAAFFxAoKiOC9lIvgT/3eq/de70VBBSxXQZCiKBaKICiI9JoEEhLSNtm0bdmSbbNtZmfm/P5YMsnZkmw/uzuv58M8Muczp3yyDrNnznzO5+MPhCzxD6paFLF+ttF/nD9fFx1fMI7ZAQCAycQwDP3XxxcoGIroj+sro/FQxNT3ntmmj8zN1uUnT9dDb+6LPtcdNvWXjVUUMQEAAAAAMAbcbrf27dsnp9OpE044QV6v1+6UAAAAYLNxKWIChuOvm2r0wrY6S8zlMBTqVcH0tTNm6Mu9RsQAAAD05nAYuu0Ti9QdNvXEhirLcy9ur1N+ik9nH5ej57bUSpI+cUKBbv/kIjtSnRBCoVD0TzgctiwfGQuHw3I4HHI4HHI6ndEOqxkZGf3uNxAIREdIH9qOO20BHE0kEtHevXtVUFDAF1sAAABTiMfjUU1Nzyj3Cy64QMnJyTZnBAAAALuNSxHTf/3Xf0mSrrvuOmVmZg5qm6amJv30pz+VJP3Hf/zHmOWGiamxPajvPrXFEjMM9Slg+syKIn39rDnjmRoAAJjEnA5DP/zUYhWkxunnL+/SkZ3rq1u6VOsPaEF+shYXpui/L1woh2PqFdeYpqmOjg75/X5lZGTI7Xb3u97bb7+tcDg8rGPk5eUNWMS0fft2NTc3W2Iul0sej0der1cej2fAxw6HY1j5AJjcqqurVVFRoerqak2fPl0FBQW8HwAAAAAAAAD96D2yd7IZlyKm7373uzIMQ5dccsmgi5gaGxuj21HEFHv++69bdbA9aIn1/m/tnONy9b2LFnDnPgAAGBKnw9DXz56jlaXpuukPG9XQdvicIxwx9UF1q9ITPGrsCCozcfJ3/AgEAvL7/WptbZXf75ff748WJy1YsGDAYiOXyzXsIqajFRdEIpE+sUPdnTo6Oo6634yMDC1YsKDf57q7uxUKheRy0WwWmEqCwaD27t0rSQqHw9q9e7f8fr/mz59vb2IAAAAAAADABLBlyxY1NTVpxYoVcjgcamhoUEpKit1pDRtX+DHhrN3XrCc2NB51nZNKM3TXZUvknILdEQAAwPg4dVaWnr3+VN3w6Ea9tfug5blXdzZozd2v6e7LjtdJM/ov8pmIQqFQtFDpUOFSMBgccP1D3Zj643K5FAgEhpWH0+kc8LnhFkYda7/V1dX64IMP5HK5FBcXJ5/PZ/kTFxcnr9dL9xZgktm9e3ef942CggKbsgEAAAAAAAAmlnA4rHA4HL323d+NxJPJhC1i6u7ulqQBx1tg6rrrpb2SMfDs6wUFybr3i0vlcw/8JRYAAMBgZCf79NurVupnL+7S3f8s05GTa+v8AX3uvrd1/Udn6V8/MktOh6GDbQF964nN+s+PL1Buis++xI9QXV0dLVg6Viej3lpbWwd87siCIafTKZfLJZfLZXl8aDkSiSgSiUQ/LCUkJAy435F8gPJ6B+6MdahY68hCroH2caio6VCBU0JCghITE4edF4Cx0dLSotraWkssJydnUt9JBgAAAAAAAIym3jf/Mk5ujGzcuFGSlJWVZW8iGHf1/qBcA9QwlWQm6MEvrVCSj+I2AAAwOpwOQzecOUsrStJ1w6MbVOc/3H0oYkp3vbBTa/c06ltr5umGRzeovL5du+ra9MdrTlLGBBg3V1VVNeTiJamnmOdoRUHHHXecHA6HnE7nqI7vXb58ucLhsKXoqbu7W8FgUIFAQMFg0PI4EAhEP3R5PJ4B9zvYrlGBQECBQEAtLS3RWGZmpo477rh+129vb5fD4ZDP52OMMTCOTNPUzp07LTGn06nS0lKbMgIAAAAAAAAmnt7TB+jE1I+HH3643/hf/vIXrV+//qjbBgIBlZeX69e//rUMw9Dy5cvHIkVMQtlJXj385RXKnABfFgIAgKnnpBkZevaGU3XTHzbqtZ0NlufeLD+oj//s9WinpvL6dn3h/rX6/VdOVEqcvcXVSUlJxyxicjqdSkpKUnJyspKSkpSUlHTUAibp6AVDI2EYhlyuwX8MMU1ToVBIwWBwwC6thmFEO7kOR1xc3IDP7dq1S83NzTIMQ16vV3FxcdEuTkc+PtqoOwBDV11drfb2dkusuLh4zN6bAAAAMP4OHDigJUuWKBKJaMeOHcrOztb06dPtTgsAAGBS6X1tmiKmflxxxRV97lI2TVP/7//9v0HvwzRNORwO3XDDDaOdHiahZJ9Lv7lypQrT4+1OBQAATGGZiV499KUV+uWr5frR82UKHzFfLtKrA+vWmlZd8cBaPfzlsesSaZqmGhoaFAqFlJeX1+86ycnJlnFLhmEoISEhWrCUnJysuLi4SdtFyDAMud3uo46ZNk1TixcvlsfjUVdXl7q6utTZ2Rl93NXVpVAoNOD2Pt/AowE7Ozujxzi0r6ampj7reTye6Hi6QwVOSUlJRx2tB6B/wWBQe/bsscQSEhJUUFBgU0YAAAAYC93d3dHPYx0dHYPusAsAAIDD6MQ0SP3N2Rvs7D2Px6Ply5frm9/8pk4//fTRTm1c7du3Tz/5yU/0zDPPqKKiQl6vVzNmzNCll16qr33ta4qPH52inL/97W+69957tW7dOtXX1ysrK0vLly/XV77yFZ177rmD2kcoFNJ9992nRx55RNu3b1dbW5vy8/N15pln6vrrrx9wxMZY87kdeuBLyzUnN8mW4wMAgNjicBi6bvVMLS9O1/W/36Calq4B192wv1lrfvKa7rx0iZYVp49aDsFgUDU1NaqpqVEgEJDL5VJ2dna/3X5SUlKUnZ0dLVhKTEzs86ElFjgcDsXHxw94fh0KhfoUNh0qdhqoE1MkEhn0RfRDY/BaW1ujsWnTpmnGjBn9rt/c3CyHw6G4uDi5XK5JW2QGjLbW1laVl5crHA5b4jNnzuS/EwAAYJvJdJ1/Mun9BVssfpYFAAAYKToxDcKRd0yapqnS0lIZhqHnnntOs2bNGnA7wzDk8/mUkZExJcYxPP300/r85z9v+SKjo6ND69ev1/r163XffffpmWee0cyZM4d9jEgkoq985Su6//77LfGqqipVVVXpySef1FVXXaV77rnnqB8AGhoatGbNGq1bt84S3717t+6991499NBD+tnPfqarrrpq2LkOh9Nh6BefW6ql00fvS0EAAIDBWF6crmevP1Vf/9P7+uf2ugHXq2js1KX3vKWvnTFT1390ltzO4V909fv9qqqqUl1dneUGgFAopPr6euXm5vbZJiEhQfPmzRv2MWOFy+WKjtIbrGAwKKfT2aeYYrCO1uFpx44d6urqKZBzOp3RDk5er9fyx+fzyePxcDEfU15LS4v27dvXb6ez7Oxspaamjn9SAAAAmlzX+Seb3l+wTYXvhWJZ3fvv6/177lH9xo0K+v3yJCUpa8kSLb7mGmUvXmx3esCoMOvr1fnaawofOCAzGJTh8ciZmyvP0qVy9nPdDgDGA52YBmGgmcX5+fkxM894w4YN+vSnP63Ozk4lJibqm9/8ps444wx1dnbq0Ucf1a9+9SuVlZXpvPPO0/r164f0ZcqRvv3tb0c/2Bx//PG69dZbNWPGDJWXl+v222/Xhg0bdN999ykrK0v/+7//2+8+wuGwLr744mgB0yc+8QldffXVSk9P1zvvvKPvfe97qqur0zXXXKOCgoJxvePjh59apDPmZo/b8QAAAI6UluDRfZcv0/2v79H3/7Zdod4z5T4UMaWfvrhLr+5s0F2fXqKSzMGPEItEImpoaFBVVZXlonhvVVVVysnJoRPJOPL5fFq1alW0i9OhTk5HPg4Gg0fdvj+HRtMdEg6H1d7ervb29gH35fF4ooVNubm5ysjIGP4/DJhgdu3apaqqqn6fczqdKi0tHeeMAAAAekym6/yTEZ2Ypoaadev00o03qvrNN/s8V/3WW3r/F79QwapVWn3nncpbvtyGDIGRy4pEtKq7W/rtb9X7SlC4slLB9evlLCyU7+yz5WIUOoBxRiemYZjsP6ThuOGGG9TZ2SmXy6Xnn39eJ510UvS5j3zkI5o1a5ZuvfVWlZWV6Uc/+pG++93vDvkYZWVl+uEPfyhJWrZsmV599dXoKIzly5fr4x//uE4//XStX79ed9xxh7785S/3ezfIQw89pNdff12SdN111+nnP/959LkVK1bo3HPP1dKlS9Xa2qrrr79e27Ztk8s19i+dfz9/vi4+ftqYHwcAAOBoDMPQVaeWaun0NP3L7zaoqrlzwHXfr2jWmrtf039cMF+XLS88asFRMBhUdXW1ampqjloII0lJSUkq4AKILQzDkNvtltvtVnJycp/nw+FwnzF1h5YHGlM32BF1Rzo0rs7v9ystLW3A9TZt2iSHwyGPxzPgH74YwESTnp7ebxGT2+3W/Pnz5fV6bcgKAABgcl3nn4zoxDT5lT/zjJ6+5BKFjrhRpz9Vb7yhP5x2mi74858147zzxik7YHSYu3frwmDwmF+qhysq1P7gg4r/1Kfknj17XHIDAGnqdWLi6vUYWLt2rV577TVJ0pVXXmn5YHPIzTffHB37cffdd6u7u3vIx7nrrrsUCoUkST/96U/7fEkSHx+vn/70p5J6RpDceeed/e7n0Aek9PR03XHHHX2enzlzpr75zW9K6rlD9oknnhhyrkN13eoZuvKUkjE/DgAAwGAdX5SmZ68/VWcfl3PU9Tq7w/rm45v1ld+8q4NtfYtVTNNURUWF3n77be3bt2/AAibDMJSTk6Pjjz9eJ5xwAl2YJiin06nExERlZmZq2rRpmjlzphYuXKjly5crPj6+321CodBRR80dy0AFHZFIRE1NTTp48KBqamq0b98+7dy5U1u2bNGGDRv0zjvv6LXXXtMbb7yhdevW6f3339e2bdu0a9cu7d+/X21tbcPOCRiJtLQ0S9cCt9ut0tJSrVy5kjFyAADANpPtOv9kdOQYdYlOTJNNzbp1gypgOiTU1aWnL7lENR9OBQEmg1BVlfTXvw6+K0gopI4//alnOwAYJ1OtExNnhGPgySefjD7+0pe+1O86DodDX/ziFyVJzc3Neumll4Z0DNM09Ze//EWSNHfuXJ144on9rnfiiSdqzpw5kqS//OUvfT4UlJWVadu2bZKkSy+9dMAvWq644oro47EuYjr3uCzdcvacMT0GAADAcKTEu/XLzy/V9y5aoPQEz1HX/cfWWp1z12vq6g5HY11dXXr//fe1e/fuPudlh3g8HhUXF+vEE0/U3Llz++3+g8ktMTFRK1eu1GmnnaYVK1Zo0aJFmj17tqZPn67c3FylpqYqLi5uwAv4AxUxDfYLk1AopI6ODjU3N6uurk5VVVXas2fPUYuY1q9frw0bNmjz5s3avn27ysvLtW/fPlVXV6u+vl5NTU1qa2tTV1eXQqHQgK9vxCbTNNXQ0KCWlpZ+nzcMQ9OnT5fH49GMGTO0cuVKFRYWcic+AACw1WS6zj9Z0YlpcnvpxhsHXcB0SKirSy/fdNMYZQSMvq7nnpPC4WOveKRQqGc7ABgnU60T06jOBPvyl78sqecC5KH5zUfGh6P3viaDQ6PZEhIStHTp0gHXO/3006OP33jjDZ111lmDPsaePXtUXV3dZz8DHWfHjh2qqqrS3r17VVJyuMPRoVyPtZ/c3FzNnj1bZWVleuONNwad53DcuNiU0VA29A3d8ZK7/5Ed6miUNMwPdq44ydN/cZc6myRzmG8CLq/kSez/ua4WKRIa3n6dbsk7wJedgVYpPPS7gSRJDpfkS+n/uWCbFBr6WBRJkuGQ4gYYiRLskEIDj+w5xo6l+PT+n+rulLo7BtzS0dam9Eh9z+ODO6VAr/+f4jP63zAU6PlZDJcvTervy8pQUAr6h79fb4rk7OftPhySAv1/kTQoniTJ1c8X+JGI1NU0gv0m9vz30Z+Og8Pf7yR+jzA6/Ioz2z98fFDqruc9QrLtPeKYpvh7xDHfIw+Zwu8RhqTPz5AuLpymP79fp4fX1amps///JsNt0tcf/Ke+clqpFk1LVXVl7YBf4ifHuVWQHq/MZJ8cRpfUsmfgfDmPkCQZ7R3W90fnEV2tJsF7hCEpTlKcR5IjIrkCUoIk+ST5ZJqmQhFTge6wurrDCnRHFOgOy9dWIXX2ej/wpSnYOcz32g+5O+qk+l6vT2+KwnKovb19yPtzGIZcTkMzc5OVldyr89SH7xGVBxrkcDjkdDrkdDrlMiRHqE1Ow5DDYchhGHI6eh4b0rG7kY3gPcI0TYUjPf+tu5y9fr4fvkfUtHSqO2wqFIkofOjvjiZFwhGFIqb87R3q6OqQKYfeWb9OXp9PEbNn39nJPuUkx8mUqQ//J9PllVw+baxoVnsgLNOUTPWcO7i6/Uc9PTn0o8hO8qo4I8H6pNMtw5OgrQda1doRkmH0vN4Mh+Tp7pDULYckh3p2YjgkQw7Leik+j4ozEz78uffE5XTJ8CWruqlTbYFuGUbP/0eGJGeoTS4zJIdhyGFIHpdDeck+NbYHta++Te2BkJLj3FpSnN73/0eHS+neZK1cMKPnosuRvyc4j+gxjPOII39nG+311vfI6H75rBEVo581+hjD8whHS9PgziN747PGYVP8s8ag9fceEZfe/78BGIHJdJ1/srKziKk73K39/v3D2jbFmyKHEbvvOWawW7Uv/FPVb745rO2r3nhD2194RunHzRvlzHq0t7UpHOz5PXOwdq+62gd5zoGYEop0a3/7Ed2SwiF1H6i1rONrC2h2xfA6KoUrKrTztacVSuH1h746OjoU7KiQJO3c+tqATUYmAndengy32+40bNcd6db+1v0qSi6S2zG4n0dRUpHczvH52VHEdBQPPvhg9GLkkYVHR8aHwjTNSVnEdKiz0cyZM+VyDfwjnjt3bp9tBmvr1q397mcwxznyw81Q91NWVqaKigq1t7crISHhqOsfqbKy8qjP19TURB+nPH6ZlBy7HwBgv0RJVx9aeOhXNmYC9EiSdP2hhV/ebWMmAO+RR0qQdPmHf3S0yWBVkn7f87DYcKlx2Y/VnljcEzAjyj3wogoqn1Zi21GKltCvqf7+aEhyf/hnMJe8vJ5Uzco8UUFP2uE/3tToY/MYH7C9j39R6ud12O3NlE7+9ZDzj5imgiFT+vs3pfq+N0KYkspXP9nzJfKgdhiWMxKQIxKUIxxQye7fKKfu1X5X3TXzKpmGQ45ISIYZkiPS/eHfoV5/W+NGJKT4jkq5Ag397jc1Lk+mJMOMfPgnLEMR6dBjM6JTjIgkU8brP5VMs+exTMn88O9+nD24n8CQ5R7luZ5MjJ4/Rk+WH5Yq9VQtmaYcZv+FDCXOOJkOl0w5JBkyjUP7MaL7aU2Zp/eKL1VHQlF0u9bObjX//mqlNW3qs88jjo5RYvmdfU9s/87GxMB5JMaS/6sbZQ5U5NWP4RRoI/ZMpuv8xzKU6/N+v1+tra2D3vdIhHt1N+nq6hqXY//72n/Xi1UvjvlxprKVD1fr6K/Yo/vhbV/UO1/MH7V8+vjwY8jv33x67I6BKe/mg8drtmYMe/vX1/1RP87YOHoJYWop7fnrjztesTePY9lgdwKT20cKPqL/XvHfY36crl6dEcPh8Lidz/n9I7jxZQCjWsRUVFTUb7HSQPGpqKurSw0NPRe8p02bdtR109LSlJCQoPb2dlVUVAzpOEd+6DjWcQoLC6OPex9nOPsxTVOVlZXR9rWDcWQOAAAAscphhjRv6w/17tIfy9PdrLlb71Rqyxa708IU4Qk2K7/67/0+Z0oKuRKiBU3dnjQFvIce9xQ6eQKN/W4bcieNKC9nuP+OExGnb/AFTJLkcCrsiFdYPXenHa0oqyb/bEWcA3RbOYbSXfersOIv/T63ccn/KOjLHNZ+i/b+QSV7Hun3uXeX/qinuPEohU4Dya35h2btvLff595f/F9qSV3QU1hkSD2FRoP7mWfWvaHjtvyg3+d2zL1eDdmrhpTnIfumf7rfIiYAAEbigQcfUKcx+BsuB+qOChwy2a7zH8tQrs//5je/UUrKAB3wRtmyZcssBWJPPfXUmHwR1tuLuRQwjVT6/uF2LDy0/dDG0AF2mBlMtXV7AJPfi1UvKv8XY1i0+6GEhAQtXLgwutzd3a1f/OIXY35caWw+W41qEdPevXuHFJ+KjjzBTkw89v3Shz7ctLUNreXzUI5zZMek3scZrf0AAABgcBLa92vBB/+j5NYyuULcAY7xYUhyh9rlDrUroePod2H35u2q17wPfqCQO1nd7iR1u5MUciUdfhyNJfZbIOMM9V/EFHKOrFW2I9LPaKwPRRzD/6hrRMJHeXJsOsaaDne0KGuoQ6dMY+CRG6bhkDmCn8VAhlpodUh8+37lVT/X081qdFMCAAAYVZPtOv9k1fvmd9Mc5ghWjDt318jG1Li6jvK5C5gg4iMj+zw90u0BYLCCwaAqKysVDocViUQYJwerI1t1eTyeo6zZw+vtuTu4s3NoVetDOc6hY/R3nNHaz7Ec686QmpoarVixYkj7BAAAmIg64gtUNudfNHvHzxTfUdXvOumN9OHF5OEOtSm7n3FwvZlyKOz0KeSKV9gZp7ArXiFnnBLaB/4skH5wvUIfrht2xivkilPE4R1UF6WBiphMOaSjFPYci6GBP+QfrWDo2Ab+QsYco5Keke31KFubQ7sQktC2V0V7/6Cs+reO+vMFAGC4vnTFl4Y0Tq6srEy33XbbGGaEyW6yXec/lqFcn//CF76ggoKCIe1/uN5//33Ll2wXX3zxoIrGRqp6bTXj5Eao2zeyGzxCvpF8tgLGR4ej/xHr47U9gMnvIwUf0bUXXzsux2pra9NvfvMbST3nc+NxTiVJVVVVo/7ZiiKmUebz+aKPg8GB7ww+JBAISJLi4uLG7DiHjtHfcXrv58jloeznWI7VCvdI7Zf8QZo1jBmz7njJPUBeHY0a+j3NH3LFSZ4B7hLvbBryBfzD+/VKngHePLpapMgwT26cbsmb3P9zgVYp3D28/Tpckm+ANsLBNikU6P+5YzEcUlzaAPvtkELDbUtrSPHp/T/V3Sl1998RQOp5k//9o7+XJH3mss/0fZMf6KJYKNDzsxguX5rk6OfDXygoBUfQRtmbIjn7ebsPh6TACFr8eZIkVz8XVyIRqatpBPtN7Pnvoz8dB4e/30n8HuH3+/XAgw9I6rkwm+SO8B4h2fYecUxT/D3imO+Rh8Tge4Rpmqpp6lR5basiprTt9F/p+JnTFHD49Nh7FXrwzX1qbD/6eVuqz6VVJck6bUaKTipOVqJ3gIt6nEdIkvztHXrg0Sckffj+mHTE2LNJ8B4RCIXV1B5UQ1tQjf52NbW2qamzW80d4Z6/O3v+buoMqbkjpPbgFCv+6JK09shAWJJfhvySTLkdktfpkNdpyOMy5HEa8rkd8rkMeZ2GDOfXlFbyb/I4HXI7DbmdDrk9HnncLnmaOuQwJJfDkNNhyOUw5FJYTochhyE5jZ6/HQ5DDkkOw5Dx4cS1XfP/RbXLbpbjw3UMw5DDEyeHy6tQdaU0zLvDuxZ/SU1n/Eu0NMgwDBkur+SJV2TnXilw7M+P/QnNu1jtp19uiZlOt+RNVLh8v9Q+vP8OAtNPV92yCz7855oyTcl0uGR6EtR5oE5qO3onOdM0VePv1nO7/Npc65GpL0j6woDrx3ucWj0nW+ccl6OVpRlyOz/8ncd5RI9hnEcc+Tv7siuuVVJyP+/DfNY4LEY/a/QxhucRbS1NgzuP7I3PGodN8c8ag9bPe0RSXHr//4YBHNnRBujPZLvOfyxDuT6flJSk5OQB3s9HmdPptBQx+Xy+cTn23Wfere5wt/b79w9r+xRvihxj1KV1MjCD3Xrj7/+isl1/HvY+Tl31CX39rP8cxawOa29r0+8ffVSS9JnLLlPCOH2Ji8klFOnW/vYjbj4Mh9R9oNayTl6gQappHfYxZuQv1B+XfGLY22Pq6ujo0LN/+5skac255yo+fmTdyseSOy9Phtttdxq26450a3/rfhUlF8ntGNzPoyipSG6nPT+7xMTEcTufa20d/vvkQChiGmVHfnEymJau7e09F36HWgk3lOMcOkZ/x+m9n6MVMR1tP6MpklYiZc0Z3Z0mZI7u/tjvOO13bHZ7LBFvqxodWT2PM2ZJ4/Qmb4/csdltUvbY7HfSvYZHZ79m2KNOo+c/CDM+Y+xekxP859B3v2OzWxyp73vEqLxHTsH3iGAwqB07dqix8fAJe1tXSHtbwiotzdHnP5qjT556vK54YK3e2dM44H4au6Td20L6zbaDcjsbtbIkQx+dl60z5+WoMH2QH2Yn3X/Lw9+v6W21vj8mHPF6tOk9IhSO6GB7UPX+gOrbAqr3B9QQ/Tuoen/jh38H1NI5mKIwlybbxza305DX5ZTX7ZDP5dTZx+Xo+KI0xbmdivM45XM75XM7FOd26o3yBiX73EqP9ygtwa0kn1txbqd8Hqd8LqfcTqPPiAk7zZ1RItM0o38kWZb9fr9++9vfSpI+97nPKTExMbqe2+2We4CLPgt8ydEvb442QqO/n4Xb7R7wc9zc+ckKh8N9thvMstPpHLAbQHZOfjTfI7c99Ngwev5/czgcmrHnoP77r9u0uapFDkOKDPDPawxKv93crt9u3q20+AqduzBPnzi+QMuKi/vfYKRi4DziyN/ZZkKW9T1yXPBZg/1a9xtxjcFnbT5rTGJj9B4BjJLJdp1/snL0Kj4Mh8dvxJjb6daM1GHcTA3JJ534jW+r7NHhFzGtvOnrys4rHcWkDmttbZXT01MAnZFTPG5f4mLyyVev7yLnWRfD8w6o7Z57hr3/nLM+rvxcznnQV2trqzyvfCBJmjX/VN6nJom56XPtTiFmTK6r4ZOAz+dTRkaGDh48qMrKyqOu29TUFP3gUVhYOKTjHHnnxLGOc2Sr2N7H6b2fzMyBL/4c2o9hGEO6cwMAAGAqO3jwoHbs2KHu7r4FKY2NjSouLpbD4VCcx6lfXb5M335is55+v+aY++0Om3p9V4Ne39Wg/3x6q2bnJOqj83J05rxsLSlMk9MxcQo7YkFbIKS61i7V+XsKkur8AdX5u1TfGojG6tsCauoIDrdRj+3WLMzVqbOylOB1KdHrVKLXrQSvU4lel779xAdK8rmUkeBReoJHqfEepca7lXbE32nxHiX5XHIM4bVZmjW5vnw5VJwzkEAgEL1D3uv1DvoO+bHqBjFWd/K5XIO/lLCiJENP/+sp2lzZonDE1L7Gdj21sVqv7qxXd7j//1iaOrr1u3f263fv7NcVJxfrOxfMn1DFbAAAYOqbbNf5J6vU1FRt3rxZ4XBYixcvHnKHKdgne8kS5Z98sqrffHPI2xasWqXsxYvHICtgdDlzc+UsLFT4GCM5+922sFBOCpgAYFhGtYiptHT0q6YNw1B5efmo73cszZ8/X6+99pp27dqlUCg04AXe7du3Rx/Pmzev33WOdoz+9jPU4/Tez5IlS465n8LCQlouAwCAmBcOh1VeXq6amv4LkgoKClRSUmK5szTZ59ZPP3OCPja/Wt/9ywdq7Bj8eLay2jaV1bbpFy+XKz3Bo9VzsnTmvBwtL05XZqKHL/iHIRIx1dQRVH1bQHVHFCPV+T8sVmo9/LgjOH53BB+LISntw2Ki9ASP0uM9Sk/0KMXn1i9eOfpnp8xEr7KSPvxz5OMkr44vTB2w49dvr1o5Bv8SxJKF03ruhF5SlKoLlxSouSOov39wQE+9X623dh8csPjvwTf3KifZp2tXc5c8AAAYX5PpOv9klZ+fr7/85S+SpDVr1kyZDlOx4oy77tIfTjtNoa6uQW/jiovT6jvvHMOsgNHlO/tstT/wgDSUTnEul3xnnz12SQHAFDeqRUx79+4dzd1J6r9d/kR3yimn6LXXXlN7e7veffddrVzZ/wX/V155Jfp41apVQzpGSUmJ8vPzVV1dbdlPf1599VVJPV+kFfdqxX/KKadY8rnsssv63ceBAwdUVlY2rFyHyu/3j+n+AQAARsrv92vbtm3q7Ozs85zb7dacOXOUkZEx4PYfX5yvs+bn6KmN1Xrwzb3aWjO0udGN7UE9/l6VHn+vSpKU5HOpNDNBpVmJKslMUGlWgkoye/7Ee2Kr+appmmrp7FZD2+GipEPj2w6NdTv0+GB7UOGBZltNYN/5+HxdcXJJv881tgcV53FaipOyEr3KTvIqPcEjl9PR73bAeEuN9+iyFUW6bEWRfvfOfn3ric0DrvuDv29XSWa8zlmQN44ZAgCAWDeZrvMDdshbvlwX/PnPevqSSwZVyOSKi9MFf/qT8pYvH4fsgNHhKiiQzj9fob/8ZXBfqrtciv/Up3q2AwAMy6h+o3H55ZeP5u4mrYsuuki33XabJOmBBx7o98NNJBLRww8/LKmnZeoZZ5wxpGMYhqELL7xQv/jFL7R9+3a9/fbbOvHEE/us9/bbb0fv0Ljwwgv7FIXNnj1b8+bN07Zt2/THP/5RP/rRj/odOfDggw9GH1988cVDynWo9u3bpzlz5iglJWVMjwMAADAcLS0t2rRpkyKRSJ/nMjIyNHv2bHk8nmPux+d26tLlhfrUsml6d1+THnxzr/7+wQGFhlFU4+8K6f3KFr1f2dLnubwU3xGFTYkqzUpQaWaCpqXFT/iRdKZpqj0YVnNHUM0d3T1/OoNq6uhWS0dQdc3teqe9QF2mU68+sEGNHSE1tAUGHFE13lLi3CpIjVNWkjfaASkz0aPMRK++/qf3B/X/tWFIWYle5afGKT/Vp7yUOM3PG/g8+QeXLBrNfwIwLur9ATkdxlGLCm/8w0b9KTU+2tEJAABgrE2m6/yAXWacd54+/eqrevmmm1T1xhsDrlewapVW33knBUyYlIzSUv3F49HJ3d3KG6iNsHpGyPnOPpsCJgAYoVEtYnrggQdGc3eT1ooVK3Tqqafqtdde0/3336/LL79cJ510kmWdH/3oR9q2bZsk6YYbbpDb7bY8//LLL0c/8Fx++eWWIqJDbrzxRt17770Kh8P613/9V7366quWmdGdnZ3613/9V0mSy+XSjTfe2G++X//613XllVeqsbFRt956q372s59Zni8vL49+WJs5c+aYFzFJUllZmZYuXWoZvwIAAGA3v9+vzZs39ylgcjgcmjlzpnJzc4d8MdkwDC0rTtey4nQdaOnS797Zp9+t3a+GtqBlvbPm52jvwXaV1bYNaf81LV2qaenSm+UHLXGP06GijHgVpMYpweuUz+1UvMepeI9LcdHHTsV5XB/+7ewbd/ds1x2JKNAdUTAcUTAUUSAU/vDvnuVDsUCfWM/f/q5uNXd+WKTUEYw+bukMDqIgKe3Df+jQfi7j4cYzZ+lLq/rvmHT3P3dqT0O7fG6HCtPiVZje8/9FXqqv5++UOOWl+JST7JPHxTkxprYbzpyli47P1/+9VK7H3qvst8Cvqzuiqx5ep7987RTlpvhsyBIAAMSayXadH7BL3vLl+szrr6vu/fe16d57Vbdxo4J+vzxJScpeskSLvvIVZS9ebHeawIjUOxz6i9err15yiTzbtytcWyszEJDh9cqZkyPP0qVy5ubanSYATAmxNVtiHN19991atWqVOjs7ddZZZ+lb3/qWzjjjDHV2durRRx/VvffeK6mnE9LNN988rGPMnj1bt9xyi77//e9r/fr1WrVqlb7xjW9oxowZKi8v1w9+8ANt2LBBknTLLbdo1qxZ/e7n8ssv169//Wu98cYb+vnPf64DBw7o6quvVlpamtauXav//u//VmtrqxwOh37yk58MOPt7NHV0dGjfvn0qKen/Sx8AAIDx1t7erk2bNikcDlviSUlJmjt3br/dLIcqN8Wnfztrjr72kZl6dnONHnxzn96vaFZusk8//9wJcjsdqmjs0AvbavXPbXVau7dRwVDfjlCDEQxHtKuuTbvqJl7xz3gzJKUneJSV5FV2sk/ZST3j1zITPfreM9s03IlzFY19xw0e8qsvLlVKnEeZiR7uogYkTc9I0A8uWaR//ehM/dfTW/X81to+69S2BnTlQ+v0p6+eFHOjMgEAgD0m03V+wG7ZixfrzJ//3O40gDFlZGUpbsYMu9MAgCmNq35j5Pjjj9cf/vAHff7zn1dra6u+9a1v9Vln9uzZeuaZZ5SUlDTs4/zP//yP6urq9Otf/1obNmzQZZdd1medK6+8Ut/73vcG3IfT6dSTTz6pNWvWaN26dXrsscf02GOPWdbxer362c9+pnPPPXfYuQ5VRUWFsrKylJiYOG7HBAAA6E9nZ6c2bdqkUChkiWdkZGj+/Pmj3j3S63Lq4uOn6eLjp2ljRbMa/AG5nT3HKEyP15dWlehLq0oUjpi664Uy/fTFXZbtHYaGXXgzlTgNQ6bMY/4s/v38+fryKf0Xzz/01j7tO9hx1O3TEzwqTI9XUXq8itLjop2VZmYPfB47M3v4nwGAqWxaWrx+dOlifeRHr6jeH+jz/JbqVt346Eb98vNL5ZjgIzEBAMDkN5mu8wMAAABTgW1FTKZpavfu3WpsbJQkpaenq7S0dErdhXzBBRdo06ZNuvvuu/XMM8+osrJSHo9HM2fO1Kc+9Sn9y7/8y4jv2Hc4HLr//vv1yU9+Uvfee6/WrVunhoYGZWZmavny5brmmmsGVXiUmZmpN998U7/61a/0u9/9Ttu2bVN7e7vy8/P10Y9+VDfccIOOO+64EeU6VKZpaseOHTrhhBOm1OsCAABMPi6XSx6PR8Hg4RFvqampY1LA1NuSwtQBn3M6DO1v7Ftg01/RTqLXJbfTUGd3WF3dw+veZCenYSg5zqX0BI9S4z1Ki3cr3iXtLdsqjxHWu1056v3PDpuDq+Sqbh64Y1JRerz2N3YoL9mnoox4FWckqCgjXtPTEzQ9I15FGfFK9rkH3B7A0CX53PrWmrm66Q/v9/v881tr9YPntuub584b58wAAEAsmkzX+SebxsZGzZ07Vw6HQzt27FBGRoZKS0vtTgsAAAA2Gvcipr///e/6v//7P7388stqb2+3PBcfH6/Vq1fruuuumzIn5NOnT9ePf/xj/fjHPx7SdqtXr5Y5yC9dJGnNmjVas2bNUNOzcLlcuvbaa3XttdeOaD+jqa2tTZWVlSosLLQ7FQAAEMPcbrcWL16szZs3q7W1VcnJyVqwYMGYFzANxvsVzYNary1g7SLlchialZOojy8uUGcwpM7usDqCYXUGe/5uaAuoszus7nBEwVBEXd0RdQRD6uqOKBg+dhGUx+WQN/rHGV0+dGyHw5DTMNTzI+wpWA9HIgqEIuoIhuXv7FZrV7cOTcsLm6a+dsZMXXXq4Qvara2t+sUvXpQkVXuLVN3St2vLYFQ0Ddxp6c5PL1Gi1yWf2zmsfQMYnouWFOj371Ro7d7Gfp+/55XdKs1M0KeXF41zZgAAIBZNpuv8k0kgEFBqaqokqaOjY1TGtAMAAGByG7cipo6ODn3hC1/Qk08+KUn9nri3t7fr2Wef1bPPPquPf/zj+u1vf6uEhITxShEThM/nsyzv3btXGRkZfIABAAC2crlcWrRokfbs2aPi4mI5nROjqOU7FxynjRXN2ljRrPcrm9Xc0T2o7UIRUwWp8bp29Yx+n7/kF29qS3VrdNkwero5ZSV5leh1KsHrUrzbKa/bqVDEVCAUUXcoos5QSJ9bMV2fO3F6v/s944cva3tDe7/PHUtta9eAz01L9R2ziCk7yaui9J5Rb4Xp8SpMi1NherxKMgf+zJGZ6B1WrgBGxjAM/eeFx+n8n76u8AAzIb/9xAcqSk/QSTMyxjk7AAAAjIbeNwaFw2GbMgEAAMBEMS5FTJFIRGvWrNFrr70m0zTldrt11llnacWKFcrJyZEk1dbWat26dXr++ecVDAb11FNPac2aNXr55ZcZJRZj8vPz1dbWFl2ORCIqKyvT4sWLeS0AAABbOZ1OzZw50+40LM6Ym60z5mZL6rlRYH9jR7SoaWNFs7ZUtyoY6r9z0pLClAH329plLYYyTcnfFZK/KzTAFodVtww8nm0kHY1qWwcuUipI9SnpQPuHBUpxh4uV0nr+npYWRzclYJKZl5esL5w4XQ++ubff50MRU1/97bt64rqTVZqVOL7JAQAAYMR6FzFFIpNv9DkAAABG17gUMd1zzz169dVXZRiGzj77bN13330qKCjod92qqipdffXV+vvf/67XX39dv/zlLyfUeDOMvfj4eKWkpKiqqioaa2lpUU1NjfLz823MDAAAxIJwOCzDMCbEqLihMgxD0zMSND0jQRcu6TnfDoYi2nHAr40VTdpY0aL3K5u1q66nYHxxYeqA+xpMsdJAOoMDX3iO9wy9kMgwpIwEr+KOUoT0/86ZqR9ftpSid2CKueljs/XXTdVqaAv2+3xLZ7eufGi9nrjuZKXGe8Y5OwAAAIwERUwAAADobVyKmB566CFJ0vLly/XMM88c9QuhgoICPf3001q1apXWrl2rhx56iCKmGFRSUqKDBw+qq+vwyJDdu3crIyNDXi8jPQAAwNiIRCLasmWLHA6H5s+fPykLmXrzuBxaOC1FC6el6Asn9cRau7q1ubJFS45SxNTP9OdB6+weuADqUCGS1+VQvMepOLdTyXFu5ST7lJPsVU6yT9nJPuUkeT+M+ZSR6JHbefT/L9xOBwVMwBSUEufW/3fuPH39T+8rN9mnb6+Zp6c2VesfW2uj6+xpaNdXf/uuHv7ySnlck/99GwAAIFYwTg4AAAC9jUsR07Zt22QYhm666aZBfRHkdDr1b//2b7rsssu0bdu2ccgQE43T6dSsWbO0efPmaCwcDqusrEwLFizgCyoAADDqTNPUtm3b1NTUJEn64IMPdNxxx8npnHojyJJ9bq2amXnUdd7+1kcVjphqC4Tk7+pWa2fP3/6ukFo//Nvf1a32YFhel0NxbqfiPU753E7NzB54rNN9ly+Tx+mQw8H5HIDB+cTxBeoIhvTJE6YpwevSR+dn61O/fEtbqluj67y9u1H/78nN+sEnF/F5EQAAYJKgiAkAAAC9jUsR06ELiLNnzx70NrNmzbJsi9iTnp6unJwc1dYevsO2sbFR9fX1ys7OtjEzAAAw1ZimqR07dqihoSEaa2pq0rZt27RgwQIbM7OX02EoJc6tlDi3lDY6+/QdZSQcAPTH4TD0xZOKo8vxHpfuu3yZLvzZG6rzB6LxP66v1MzsRH3ltBk2ZAkAAIChYpwcAAAAehuXPuszZvRcQKyrqxv0NofWPbQtYtOMGTPkdrstsUPdEQAAAEaDaZrauXOnpXBa6ukMWVRUZFNWAICjyUuJ0/2XL5fPbb2scdvftuu5LQdsygoAAABDQScmAAAA9DYuRUyf+cxnZJqmHn744UFv8/DDD8swDH36058ew8ww0bnd7mhXLrfbrXnz5g2poxcAAMDRmKap3bt3q6amxhJ3OBxasGCBkpOTbcoMAHAsC6el6K5PL7HETFO68dGN+qCqxZ6kAAAAMGj9dWIyTdOmbAAAADARjEsR0/XXX68TTjhBjz76qG6//fZjrn/HHXfo97//vY4//njdeOONY58gJrSsrCzNnDlTy5cvV3Z2NiMGAQDAqNm3b58qKystMcMwNH/+fKWmptqTFABg0Dwuh84+LscS6+wO66qH1utAS5dNWQEAAGAwehcxSaKICQAAIMa5xuMgBw4c0H333adrrrlG3/zmN/X73/9el19+uaUopba2VuvWrdNvfvMbbdy4UcuXL9e9996rAwcGbgPPeI/YUVBQYHcKAABgiqmsrNS+ffv6xOfNm6eMjAwbMgIADNb+gx36r79u0Qvb6uR1GVqzMFfPbj58/eBAa5euenid/njNSYr3jMulDwAAAAxRf0VM4XC43zgAAABiw7hcySsuLrZ0z9m0aZNuvvnmo26zfv16nXDCCQM+bxiGQqHQqOUIAACA2FFTU6Py8vI+8Tlz5igrK8uGjAAAg3WwLaCz73pVnd1hSVIgZCocNrWiJF1r9zRG1/ugqlU3/WGjfvG5pXI46OgLAAAw0fQ3dSEcDsvtdtuQDQAAACaCcStnN01z1P8AAAAAQ9XQ0KCysrI+8ZkzZyo3N9eGjAAAQ5GR6NUnl1q79T63tVZXnDxd0zPirfEttfrzu9axoQAAAJgYnE6nIpGIJRYMBm3KBgAAABPBuHRieuCBB8bjMIhBDQ0Nqqqq0oIFC+R0Ou1OBwAATHCBQEA7duzoEy8tLWV8LQBMIl8/a46e2VSjpo7uaOyHz5fpl59fqk/f85Zauw53br7zhTJ9fEm+fG4+MwIAAEwkhmEoGAzK5/NFY4FAwMaMAAAAYLdxKWK6/PLLx+MwiCHBYFC7du1SfX29JGnfvn0qLS21OSsAADCRmaapsrKyPiOJi4qKVFhYaFNWAIDhSI336BvnzNX/9/jmaGx3fbte3lGvOz+9RFc+tD4ar2np0m/f3qerTuUzIwAAwETj9/sVCARUUlKipKQkxcfHH3sjAAAATFnjNk4OGE07duyIFjBJUkVFhfx+v40ZAQCAic7v96uxsdESy8rKUnFxsT0JAQBG5NJlhVpcmGqJ/fTFnZqXl6STSjMs8Z+9tEutXd0CAADAxFJeXq5t27Zp+vTpKi4uVkJCgt0pAQAAwEYUMWFSKi0tlWEYllhZWVmf+dkAAACHJCcna8mSJdE29R6PR7NmzepzTgEAmBwcDkP/feFxOvJtvCMY1v88u13fOHeuZd3mjm796tXd45whAAAAAAAAgKGgiAmTUkJCgoqKiiyxtrY2VVZW2pQRAACYDFJSUrRs2TLl5+dr9uzZcrvddqcEABiBRdNSddly62fDZzbVqD0Q0jnH5Vri9722R/X+wHimBwAAAAAAAIyZffv2ae/evTp48KCSk5PldDrtTmnEXON5sFAopGeeeUavvfaadu/eLb/fr3A4fNRtDMPQP//5z3HKEJNJUVGRGhoa1N7eHo3t3btXmZmZzM0GAAADcjqdmjVrlt1pAABGya1nz9HfPqhRc8fhcXHfeWqLfnLZ8Xp+6wFFzJ5YZ3dYP3txp/7zwgU2ZQoAAAAAAACMnpqaGgUCPTftzZ8/Xzt27LA5o5EbtyKmV155RVdccYX2798fjZmmOeD6hmHINE3Ge2BADodDs2fP1oYNG6Ix0zS1Y8cOLVmyhNcOAAAAAMSAtASPbjl7jr79xAfR2K66Nr2+q16XLJ2mP64/3LH3d2v368pTSlWUwY0vAAAAAAAAmLwikUi0gOmQ3suT0bgUMW3cuFHnnHOOgsGgTNOUz+fTrFmzlJqaKoeDiXYYvuTkZE2bNs0yRq61tVWNjY3KyMiwMTMAAAAAwHi5bHmRHl1boc1VLdHY3S/s1O+uPlFPbqxWMBSRJHWHTd35Qpnu/PQSmzIFAAAAAAAARi4YDPaJUcQ0SN/97ncVCATk9Xr14x//WF/60pfk8/nG49CIAcXFxWpoaFBXV1c0VlNTQxETAAAxbt++fXI4HJo2bRodGgFginM6DP3Xhcfp4v97MxprD4Z1/+t79MUTp+u+1/dE409urNJXTivVvLxkO1IFAABAL9OmTdPu3bsViUTU1dWluXPnKj093e60AAAAJrQj6yMkKRwOKxwO25TN6BmXNkivv/66DMPQt7/9bV177bUUMGFUOZ1OFRYWWmIHDx6cElWGAABgeFpbW7V3717t3r1b77//vjo7O+1OCQAwxo4vStOnl1k/Gz71frXOOi5Xid7D93CZpnTHczvGOz0AAAAMIC0tTS0tLfL7/eru7ubaPgAAwCBMxVFy0jgVMR2qADvnnHPG43CIQdnZ2X1GE9bW1tqUDQAAsFM4HNb27dujyy0tLXrvvfcUCoVszAoAMB5uPWeOkn0uTUuL0w0fnaVXbzlDK0rS9ZXTSi3rvbi9Tmv3NNqUJQAAAI7U+wu33l0FAAAA0Ffvc6b+xstNRuNSxFRcXCxJ6u7uHo/DIQa5XC5lZ2dbYjU1NTJN06aMAACAXfbs2dOn81JBQYFcrnGZpAwAsFFGold/u/E0vXrLGbrpY7NVlBEvSbrylBJlJnos697+9+18ZgQAAJgApmoXAQAAgLE0Vc+hxqWI6aKLLpIkvfrqq+NxOMSovLw8y3JXV5eam5vtSQYAANiiqalJVVVVllhSUpKKiopsyggAMN4KUuPkcBiWWILXpX/9yCxLbP2+Jr24vW48UwMAAEA/encNoBMTAADAsfU+Z6KIaQhuuOEG5eXl6Yc//KH27t07HodEDEpKSlJ8fLwlVlNTY1M2AABgvIVCIe3YscMSczgcmjt3bp+xswCA2POZFUWalhZnid3+9x0KR+jGBAAAYKep2kUAAABgLPU+Z2Kc3BBkZWXp2WefVVxcnFauXKlf/epXamlpGY9DI4YYhhHtxuTxeFRUVKTS0lKbswIAAOOlvLy8z0l7SUlJnyJnAEBs8rgcuvms2ZbYjlq/nnq/aoAtAAAAMB76K2Ji7C8AAMDATNOcsp2YXON1oEWLFunVV1/VypUr9dWvflXXXnutMjMzj/mlkmEYKi8vH6csMdnl5OQoLi5O6enpMgzj2BsAAIApoaGhQQcOHLDEUlNTVVBQYFNGAICJ6OOLC3TPK7u1/YA/GvvR82U6b2G+PC669gEAANihd9cA0zQVDAbl9XptyggAAGBiC4VCikQilthU6cQ0bkVMjz32mK688kr5/X6ZpinTNFVXV3fM7ShEwVC43W5lZGTYnQYAABhHwWBQZWVllpjT6dScOXM4lwQAaP/BDv35vUq9Vd6gR79ykm45e46ufGh99PnKpk797p19umJViY1ZAgAAxK7u7m4ZhmHpvtTV1UUREwAAwAB6d2GSKGIakrfeekuXXXaZwuGwJGn69OlatGiRUlNT5XBwpyMAAACGxzRN7dy5U93d3Zb4zJkz5fP5bMoKADARtAVC+vKD67R2T2M09mZ5gz4yN1vLpqdp/b6maPynL+7SJcsKlegdt3u9AAAAcASPx2MZgTJVxqEAAACMhd7nSm63e8qM4x2Xq3Pf+973FA6HlZKSokceeURr1qwZj8MCAABgiqurq1NDQ4MllpGRoZycHJsyAgBMFIlel9oDIUvsT+srdeqsLH3j3Ln61C/fisYPtgf169f36PqPzhrvNAEAAKCeL94oYgIAABic3udKHo/HpkxG37i0QVq/fr0Mw9B//ud/UsAEAACAUREIBLRz505LzO12a/bs2YyRAwBIkj61dJpl+bktB9TS2a3lxen66Nxsy3P3vrpbje1To+02AADAZNP7i7f+RqQAAACgh9PpVFJSktxutyRF/54KxqWIqaOjQ5J0yimnjMfhAIvOzk7t2bNHdXV1dqcCAABG0a5du6Ljig+ZNWvWlLrjAAAwMh9fUiC383BhayAU0TObaiRJXz97jo6seW0LhPR/L+0a7xQBAACgvkVMdGICAAAYWG5urk444QSdfPLJOuWUU1RYWGh3SqNmXIqYSkpKJB0uZgLGQ0tLizZt2qS1a9dq//79qqiosDslAAAwikpLS5WSkhJdzs7OVlZWlo0ZAQAmmvQEjz461zpi9M/v9nw2nJeXrIuWFFiee/jtfapq7hy3/AAAANCDTkwAAADD43Q65XK57E5j1IxLEdMnPvEJmaap5557bjwOB0iSQqGQmpqaosttbW3y+/02ZgQAAEZTXFycFi9erNLSUsXFxWnmzJl2pwQAmIAu6TVS7r39zdpV1yZJuunM2ZZOTcFQRHe/UDau+QEAAKBvEVNHR4cikYhN2QAAAMAu41LEdPPNN2vWrFm66667tH79+vE4JKD09HR5vV5L7MCBAzZlAwAAxoJhGCosLNSyZcum1MxnAMDoOX1OljITrZ8NH3uvUpJUlBGvz64osjz353crtauOG2AAAADGU1xcnHw+n7KyslRSUqKFCxfKOHL2LwAAAGLCuBQxJSUl6Z///KcWLFig0047Td/+9re1adMm2oFiTBmGodzcXEustrZW4XDYpowAAMBYcTjG5bQWADAJuZ0OXXx8viX2+HuVCkdMSdK/fGSW4j3O6HMRU7rjuR3jmiMAAECsc7lcWrlypebPn6+ioiKlpaVRxAQAABCDxuXbHqfTqenTp2vt2rXq6urS97//fR1//PFKSEiQ0+k86p+pNLsP4693EVM4HFZ9fb1N2QAAAAAA7PDJXiPlalsDen1XgyQpK8mrK08psTz/3JZabdjfJAAAAAAAAADjZ1yKmEzTjP7pvTyYP8Bw+Xw+paenW2I1NTU2ZQMAAEaiq6uLc0MAwLDMzU3WwoIUS+xP6yuij68+rVRp8daxpD/4+3Z+7wAAAAAAAGBC2bp1qzZv3qy9e/eqoaFB3d3ddqc0qsalzdF3vvOd8TgM0K/c3Fw1NjZGl1tbW9Xe3q6EhAQbswIAAEMRiUS0ceNGeTwelZSUKDU1lbbyAIAh+dSyadpc1RJdfn5rrVo6upUS71ayz62vnTFT33tmW/T5t3c36rWdDTptdpYd6QIAAAAAAAAWpmmqsbFR4XA4WgNx3HHHyePx2JzZ6KGICVNeRkaG3G63pQLxwIEDmjFjho1ZAQCAoaiurlYgEFAgENCmTZuUmpqq4447jtHDAIBBu2BRvr73120KhiOSpGAooqc3VevzJ06XJH3+xOm6//U9qmnpim5z+3PbdcrMTDkcFM4CAAAAAADAXp2dnQqHw5ZYUlKSAoGATRmNvnEZJzdcGzZs0E033WR3GpjkHA6HcnNzLbHa2lpFIhGbMgIAAEMRCoW0f/9+S8w0TTmdTpsyAgBMRmkJHp05P9sS+/O7ldHHPrdTN5052/L8B1WtevYDRpIDAACMN9M01dHRoc7OTrtTAQAAmDDa2tosy263e0p1YZImYBFTTU2N7rjjDi1atEjLli3TT37yE7tTwhTQu4ipu7tbDQ0NNmUDAACGoqqqqs9M55KSEsbJAQCG7JKl06KPHYaUGu9WV/fhu9c+cUKBZmRZR4//+B9lCkfMccsRAAAglh04cEAbN27UG2+8oXXr1qmysvLYGwEAAMQIv99vWU5KSppy35VMiPkbnZ2devzxx/Xwww/rxRdfjHbIMU1zyv3AYY/4+HilpKSopaUlGqupqVF2dvZRtgIAAHbr7u5WRUWFJZaRkaGUlBSbMgIATGanzcrSiaXpWj0nWxcfX6CcZJ/leZfToVvOnqOv/va9aGx3fbue33JA5y7MG+90AQAAYk4gELBcx+/dbQAAACCW9T43SkxMtCmTsWNrEdNLL72khx9+WI8//nj0h22aPXc35uXl6eKLL9YnP/lJO1PEFJKXl2f58NPc3KzOzk7FxcXZmBUAADia/fv395nvXFxcbE8yAIBJz+V06NGvnHTUdc4+Llfz8pK1raY1GvvFK+U6Z0EuN1oBAACMsaSkJMtyW1sbN7wDAACop5amdxFT73OnqWDci5i2b9+uhx9+WI888ki0DeihwqVp06bpk5/8pC655BKdfPLJnJRiVGVlZWnXrl0KhULR2IEDB1RSUmJjVgAAYCCBQEDV1dWWWHZ29pS8swAAMHEYhqFrV8/Q9b/fEI1tqmzRG7sO6pRZmTZmBgAAMPX1/swfiUTU0dGhhISEAbYAAACIDV1dXZZaB4lOTMN28OBB/f73v9fDDz+sd999V9LhwqXU1FQ1NzfLMAz98Ic/1KWXXjoeKSEGORwO5eTkqKqqKhrr7u62MSMAAHA0+/bti44Zlnq+VKYLEwBgPKxZkKsfZcRr38GOaOwXr+yiiAkAAGCMeTweeb1eBQKBaMzv91PEBAAAYp7f77csu91ueb1em7IZO46x2nF3d7cef/xxXXTRRSooKNANN9yg9evXyzRNud1uXXTRRfrzn/+smpqasUoB6CM3N1c+n0/FxcU68cQTNXv2bLtTAgAA/ejs7NSBAwcssdzcXMbAAgDGhcvp0FdOK7XE3th1UO9XNNuTEAAAQAzp3VGg99gUAACAWNT7nCgxMXFKTjcb9U5Mb7/9th5++GH98Y9/VFNTkyRF5xWvWrVKn//853XppZcqLS1ttA8NHFNiYqJWrFgxJf9jBgBgKtm7d2+0c6fU04Vp+vTpNmYEAJjKQuGIav0BFaQeLpb95AnTdNcLO1XvP9wF4Bcvl+uXX1hqR4oAAAAxIykpSQcPHowu9+46AAAAEIt6nxMlJSXZlMnYGvUippNPPlmGYUS/dJozZ44+//nP63Of+xzjPzAhUMAEAMDE1tbWprq6OkusoKBgSrZFBQDYq6zWrz+tr9ATG6qVn+rTU/9ySvQ5n9upK08p0ff/tj0ae27rAe2qa9PM7MT+dgcAAIBR0F8npkM3ywMAAMQi0zT77cQ0FY3ZOLmkpCQ98MAD2rZtm7797W9TwAQAAIBB2bt3r2XZ6XSqqKjInmQAAFPWO7sP6qw7X9WvXtujhraANlW2aMcB6x1tn1tZpCTf4fu/TFO655Xy8U4VAAAgpvTuKhCJRNTR0WFTNgAAAPbr6upSKBSyxKZqJ6YxKWI6VAX25S9/WSeccIJ+/OMfq6amZiwOBQAAgCmktbXV0jJekqZNmya3221TRgCAqWrp9DRlJ1m7/D32XqVlOcnn1hdPso4zfWJDlaqbO8c8PwAAgFjl8Xj6dGPu3XkAAAAglvQ+F3K5XFN2esWoFzG9/PLLuuKKK5SYmCjTNLVx40bdcsstKioq0sc+9jE9/PDDnGxiwolEIgoGg3anAQBAzAsGg5aCJZfLpWnTptmYEQBgqnI5Hbr4hAJL7PH3qtQdjlhiX1pVIq/r8OWTUMTUfa/tGZccAQAAYlXv8Sh+v3+ANQEAAKa+3udCSUlJU3bU7qgXMZ122mn69a9/rdraWj3yyCM6++yz5XA4FA6H9eKLL+pLX/qScnNz9ZnPfEbPPvuswuHwaKcADFpHR4d2796tt99+W7t27bI7HQAAYl5mZqZWrlypkpISuVwuFRUVyeVyHXtDAACG4VNLrYWyDW0BvVpWb4llJnr16eWFltjv1+5XYzs3wgAAAIyV3uNRKGICAACxLCcnRzNnzlROTo4SEhKm7Cg5aYzGyUmSz+fTZz7zGf3tb39TRUWFbr/9di1cuFCmaaqjo0N//OMfdcEFFygvL2+sUgCOqr6+XuvWrVNFRYW6u7vV0NBANyYAACYAp9OpoqIirVy5UgUFBcfeAACAYZqZnaQlhamW2J/freyz3tWnlsrpOHx3W2d3WA+9uXeMswMAAIhdvTsxtbW1yTRNm7IBAACwV0JCggoKCjR37lwtW7ZMxcXFdqc0ZsasiOlIubm5+vrXv66NGzdqw4YNuvHGG5WdnS3TNNXQ0BBtc/Vv//ZvuuGGG/Taa6+NR1qIcampqXI4Dv8nYJqmamtrbcwIAAAcyeVyWX5XAwAwFi7p1Y3phW21fbosFabH6+OL8y2xB9/cq/ZAaMzzAwAAiEW9uwtEIhF1dHTYlA0AAMDEMlVHyUnjVMR0pMWLF+vHP/6xKisr9de//lWXXnqpvF6vTNNUdXW1fvazn2n16tXKy8vTddddp3/+85/jnSJihNvtVmZmpiVWU1PD3RwAAAAAEEMuWJQvj+vw5ZHusKmnNlb1We+rp8+wLLd0duv3a/ePeX4AAACxyOPxyOPxRJcdDoe6urpszAgAAADjwbZb251Op9asWaNHH31UBw4c0D333KNTTjlF0uGOOPfcc4/OPvtsu1JEDOg9zrCzs1Otra02ZQMAAAAAGG8p8W6dNT/HEvvze31Hys3JTdKZ87ItsV+9tluBUHhM8wMAAIhV06dP15w5c7R06VKdcsopysjIsDslAAAAjLEJMZ8jOTlZV199tV599VWVl5frO9/5jmbMmCHTNOmKgzGVkpKiuLg4S6ympsambAAAiD2RSETl5eXcTQkAsNWnlhValj+oatW2mr43uFy7eqZlubY1oCc39O3aBAAAgJHLz89Xbm6uEhMTp/TIFAAAABw2IYqYjlRcXKzvfOc72rlzp1577TVdffXVdqeEKcwwjD7dmOrr6xUKhWzKCACA2HLgwAFVVlZq7dq12rlzp4LBoN0pAQBi0CkzM5WT7LXE/vxu325MS6enaUVJuiV2zyu7FY5wAxYAAAAAAABGVyAQ0MGDBxUOx04n8AlXxHSkVatW6Ze//KXdaWCKy8nJsdzFEYlEVFtba2NGAADEhnA4rH379knqGSdcXV2tbdu22ZwVACAWOR2GPnHCNEvsyQ1V6g5H+qx73eoZluXdDe16bsuBMc0PAAAAAAAAsae+vl4ffPCB3njjDb3//vsxMVVqQhcxAePB4/H0maVNERMAAGOvqqqqT+el6dOn25QNACDWXbLUWsR0sD2ol3fU91nv9NlZmp+XbIn94uVymSbdmAAAAAAAADB6GhsbJfXcCN7c3KzW1labMxp7FDEBknJzcy3Lfr9fnZ2dNmUDAMDUFwqFVFFRYYmlpaUpNTXVnoQAADFvRlaiTihKtcT+/G5Fn/UMw9C1vboxba5q0eu7GsYyPQAAAAAAAMSQcDislpYWSyw9Pd2mbMYPRUyAer40dblcllh9fd87bgEAwOioqKhQKBSyxEpKSmzKBgCAHpcsLZQkuZ2G1izM1WdWFPW73pqFeZqeEW+J/eLl8jHPDwAAIBaZpqm2tjbt379fGzduVEMDxeMAAGDqa2lpUSQSscRi4UZw17FXAaY+h8OhrKwsywzJ+vp6FRX1f8EaAAAMXzAYVGVlpSWWmZmppKQkmzICAKDH+YvzFAyFdeGSAqUleAZcz+kwdM1pM/StJzZHY2+WH9TGimYtKUwdh0wBAABix9atWy2FS/Hx8crMzLQxIwAAgLF3aJTcIcnJyXK73TZlM37oxAR8KCsry7Lc1tbGSDkAAMbA/v37+9w9UFxcbE8yAAAcIdnn1hWrSo5awHTIJ5cWKDvJa4n94uVdY5UaAABAzEpOTrYsNzY2yjRNm7IBAAAYH72LmGJhlJxEERMQlZqa2qdysa6uzqZsAACYmrq6ulRdXW2J5eTkKCEhwaaMAAAYHq/LqStPsY5CfW5LrXbV+W3KCAAAYGrq/YVdIBBQR0eHTdkAAACMvc7Ozj4NVyhiAmKMYRjRbkyGYSg9PV2JiYk2ZwUAwNRSUVFhuVvSMAy6MAEAJq3PrixSss9lif3yld02ZQMAADA1xcfHy+u1dsBsamqyKRsAAICx1/tcx+12x0ztAkVMwBHy8vI0e/ZsnXTSSVq4cKEyMjLsTgkAgCkjFAqptrbWEsvLy5PP57MpIwAARibJ59YXTyq2xJ7cUKWqZkaTAwAAjBbDMJSWlmaJ9R6vAgAAMJX0PtdJS0uTYRg2ZTO+KGICjpCYmKi8vLw+Y+UAAMDIHThwQOFw2BIrLCy0KRsAAAanOxzRyzvqVNPSf2HSl1YVy+c+fHklFDF132t0YwIAABhNvcenNDc397nGAAAAMBVEIpE+nZhiZZScRBETAAAAxoFpmqqurrbEMjMz6cIEAJiw3io/qFv//L6Wfe8FXfHAOj3+XlW/62UkevXpZdai3EfXVqixPTgeaQIAAMSE3p2YTNNUc3OzPckAAACMoZaWFkUiEUus97nQVEYREwAAAMZcU1OTOjutHSwKCgpsygYAgGN7ZnO1/ri+Ui2d3T3Lm2oGXPfq00rldBxu6d3ZHdaDb+wZ8xwBAABihcvlUnJysiXWu0MBAADAVND7HCcpKUkej8embMYfRUwAAAAYc26329LuNCEhQSkpKTZmBADA0a1ZmGdZ3lrTqr0N7f2uOy0tXhcuzrfEHnprn9oCoTHLDwAAINb0HqPS2NhoUyYAAABjp/c5Tix1YZIoYgKO6dDMSdM07U4FAIBJKykpSQsXLtSKFSs0bdo0FRYWyjCMY28IAIBNVpZkKDPRepfbM5sH7sb01dUzLMstnd36/Tv7xyQ3AACAWNS7iKmzs7NP12cAAIDJLBAIqL3dehNd73OgqY4ipjHU0dGh22+/XcuXL1d6eroSEhI0d+5c3Xzzzdq3b9+I9x+JRPTqq6/qW9/6llavXq3c3Fx5PB4lJydrwYIFuu6667Rp06Zj7ue73/2uDMMY1J+XX355xHlPBqZpqqmpSWVlZXrrrbe0adOmPm8WAABg6OLi4jRjxgzl5OTYnQoAAEfldBg6Z0GuJXa0kXKzc5J05jzr77f7Xt+tQCg8JvkBAADEmsTERLndbkuMbkwAAGAqMU1TeXl58nq9kiSn09lnpO5U57I7galq165dWrNmjXbu3GmJ79ixQzt27NB9992nRx55ROeff/6wj1FcXKyKioo+8e7ubm3ZskVbtmzRPffco69//ev6/ve/T7eDISorK1NXV1d0ua6uTomJiTZmBAAAAAAYT2sW5um3bx/upnRopFxxZkK/6193xgy9sK02ulzbGtAT71XpshVFY54rAADAVGcYhtLT01Vbe/h8q7GxUQUFBTZmBQAAMHp8Pp9mz54t0zTV2dmpjo6OmKvzoIhpDPj9fp133nnRAqarr75al112meLi4vTSSy/ptttuU2trqz796U/rjTfe0JIlS4Z1nOrqaknSzJkz9clPflKrVq1Sfn6+Ojs79dJLL+nOO+9UU1OTbr/9djmdTv3v//7vMfe5efPmoz5fUlIyrFwnG8MwlJWVZSkSq6+vV0lJScy9SQAAAABArDo0Uq6hLRiNPbO5Rl87Y2a/659QlKYTS9P19u7DHQHueXW3PrWsUE4HnyUBAABGKi0tzVLE1NzcrEgkIoeDwSMAAGDqMAxD8fHxio+PtzuVcUcR0xi44447VFZWJkm6/fbbdcstt0SfO+mkk7R69Wqdfvrp6ujo0I033jjsEW0rVqzQd77zHZ111ll9CmtOOeUUffazn9VJJ52k+vp63XHHHbrqqqtUWlp61H0uWLBgWLlMRb2LmLq6utTW1qakpCQbswIAAAAAjBenw9DZx+XqkXcOd2N69ihFTJJ07eqZenv32ujynoZ2/e2DGp2/KH9McwUAAIgF6enpluVIJKLW1lalpqbakxAAAABGFaXpo6y7u1s/+clPJEnz5s3TzTff3Gedk08+WVdeeaUk6ZVXXtG6deuGdaw333xTZ5999oCdgWbMmKH/+I//kCSFQiE9+eSTwzpOrEpMTJTP57PE6urqbMoGAIDJx+/3q66uTpFIxO5UAAAYtvMW5VmWt1T3jJQbyGmzMnVcfrIl9vOXymWa5pjkBwAAEEvcbrdSU1OVnp6umTNnasWKFRQwAQAATCEUMY2yl156SS0tLZKkyy+/fMAWpldccUX08RNPPDFm+ZxxxhnRx+Xl5WN2nKnIMAxlZ2dbYvX19Vx4BgBgkPbt26dt27bpnXfe0b59+xQMBo+9EQAAE8yhkXJHemZzzYDrG4ah61ZbOzVtq2nVyzvqxyQ/AACAWLNo0SItXLhQBQUFiouLszsdAAAAjCKKmEbZ66+/Hn18+umnD7jesmXLovML33jjjTHLJxAIRB87nc4xO85UlZWVZVkOBALy+/02ZQMAwOTR1dWlgwcPSpKCwaD27t2r+nq+vAUATD6HRsod6dmjFDFJ0jkLclWalWCJ/eylXdwUAwAAMAoGmk4BAAAwWXV1dam9feDO37GEIqZRtnXr1ujjuXPnDriey+XSzJk9d2Zu27ZtzPJ55ZVXoo/nzZt3zPXPOussZWdny+PxKDs7W6tXr9b3v/99NTU1jVmOE1lCQkKfOzkYKQcAwLFVV1dblp1Op3JycmzKBgCAkTlv4dBGyjkdhq49fYYl9u6+Jq3d0zgm+QEAAAAAAGDyqqys1Pr16/Xee++pqqpK3d3ddqdkG5fdCUw1lZWVknqKX441h7mwsFCbNm1SfX29AoGAvF7vqObS0dGhu+66S5Lk9Xp14YUXHnObf/zjH9HH9fX1euWVV/TKK6/oBz/4gR588MFB7aM/h34uA6mpOXwXa3t7u1pbW4d1nLGQkpKizs7O6HJdXZ2ysrK422MKa2tr6/cxYBdek5hIBvN6jEQifYqY0tLS1NHRMaa5Ifbw/oiJhtfk1DU30630eLcaOw5fQHp8/V5ddXLhgNt8ZEaS8pK9qmk93CH57n9s1y8/s3BMcz2E1yMmGl6TmEi4wxkAAADARBGJRKKNVPx+v/x+v4LBoEpKSmzOzB4UMY2yQ6PGEhMTj7luQsLh1vJtbW2jXsT0jW98Q/v375ckfe1rX1N+fv6A6y5cuFAXXXSRVqxYofz8fHV3d2vHjh165JFH9Pzzz6u5uVmf/OQn9fTTT+vcc88dci6FhQNf2O3t8ccfV0pKypCPMVbi4uK0ePHi6HJ3d7ceeeQRxsrFiN/85jd2pwBY8JrERDLQ6zErK0szZli7T7zwwgvq6uoaj7QQo3h/xETDa3LqyQ3lqVEZ0eXfvbZN3e//9ajbzOhOV40OfxZ/c0+z/vOnDyjbNb6/E3k9YqLhNQm7tbS02J0CAAAAAEiSGhsb+3ReiuXJFhQxjbJDX855PJ5jrntk0dKRnX5GwyOPPKKf/exnknrGyH3ve98bcN0bb7xR3/3ud/vEV65cqS9+8Yu655579NWvflXhcFhXXXWVysvL5fP5RjXfiayzs1MdHR2Kj4+PxjIyMihiAgBgALm5uZbl5uZmCpgAAJPeDE+rtgYz5FFYxR6/St0tMk3paE1653mb9G5XtjrNw5df3uvK0jmJFeOQMQAAwNRnmqZaW1tVW1urcDisefPm2Z0SAADAkNTW1lqWk5OTLbUJsSZmi5hGYxTYAw88oCuuuMISO1TcEwwGj7l9IHC4pXxcXNyI8znk5Zdf1pVXXilJSk9P12OPPXbU/R9r7N0111yjdevW6f7771d1dbUee+wxfe5znxtSThUVR79AW1NToxUrVkiSPvGJT2j27NlD2v9YO3DggGXk3bRp03TOOecwUm6Kamtri94V+oUvfGFQndWAscRrEhPJsV6PbW1t2rlzpyV2wgkn6Iwzzhi3HBE7eH/ERMNrcmoLRUydVd6oE0vS5HU5Br1dwlsVuuulvdHlPd0pOuuSMzQjK2HgjUYBr0dMNLwmMZGUlZXptttuszsNACPU1tamLVu2WG6cmjFjxqBuMgcAAJgIuru7dfDgQUsslrswSTFcxDRWkpKSJPWcPB/LkbPXR+vCzfr16/Xxj39cgUBAiYmJevbZZ0flzoNrrrlG999/vyTplVdeGXIR07Rp0wa9bkJCgpKTk4e0/7HmcrksRUyhUEiRSERpaWk2ZoXxkJiYOOFej4htvCYxkfT3eqysrLQs+3w+TZs2jcJfjDneHzHR8Jqcmi5YOvTR51eePlu/fqtSrV2haOw362v1408vGcXMjo7XIyYaXpOwW0LC2BaSAhgfPp+vzw3ldXV1Q/o+AgAAwE719fUyTTO6bBiGsrKybMzIfjFbxLRt27YR7yMvL69PbNq0aXrnnXfU3t6u5ubmo3Y5OtSdKCsryzJabri2bNmic845R36/X16vV08++aRWrlw54v1K0vz586OPq6qqRmWfk0l8fLwSEhIshWf19fUUMQEAcIRAIKCGhgZLLD8/nwImAEBMS/K5dcXJxfrJi7uisb+8X62bPjZbhemx2xocAABgpFwulzIzM1VXVxeN1dbWUsQEAAAmjd6j5DIyMuR2u23KZmKI2SKmuXPnjsl+58+fr8cee0yStH37dp144on9rhcKhVReXi5Jo9Ipqby8XB/72Md08OBBuVwu/eEPf9BHP/rREe/3EL58lLKzs7Vnzx45HA5lZmYqMzPT7pQAAJhQqqurLXcMOBwO5ebm2pgRAAATwxWrSvSr1/aoszssSQpHTN3zarm+d9FCmzMDAACY3HJzcy1FTG1tbWpra2NsKQAAmPA6OjrU2tpqicX6KDlJctidwFRzyimnRB+/8sorA663fv36aFefVatWjeiYlZWVOvPMM1VTUyOHw6GHHnpIF1544Yj22dvWrVujj/Pz80d135NFdna25s+fr5NPPlnz5s1Tenq63SkBADBhRCIRy+hVqedkO9bvGAAAQJLSEzz67MoiS+yP6ytV19plU0YAAABTQ2pqqjwejyXWu6MBAADARNT7nMXtdlODIIqYRt3q1auVkpIiSXrooYcs3QiO9OCDD0YfX3zxxcM+Xl1dnc4880zt3btXkvTLX/5Sn/3sZ4e9v4Hcc8890cenn376qO9/MvD5fMrKypLT6bQ7FQAAJpz6+np1d3dbYrFa+AwAiB0dwZD+uqla1c2dx1z36lNL5XEevgwTDEV03+t7xjI9AACAKc8wjD4dC+rq6gb8bgYAAGAiME2zTxFTdna2HA5KePgJjDKPx6Prr79ekrRt2zb98Ic/7LPOW2+9pfvvv19ST0HQ8uXL+92XYRgyDEPFxcX9Pt/c3Kyzzz5bO3bskCTdeeeduvrqq4eU7+bNm7Vr166jrnPvvffqvvvuk9TTmnUkRVcAAGBqqqqqsiynpKTQuh0AMGW9uL1W1z3yrk7473/oX363QX/ZWH3MbXJTfPrk0mmW2G/f3qfmjuBYpQkAABATehcxBYNBNTU12ZQNAADAsbW0tCgQCFhijJLr4bI7ganolltu0R/+8AeVlZXp1ltv1a5du3TZZZcpLi5OL730kv73f/9XoVBIcXFxuuuuu4Z1jEAgoPPOO08bN26UJH3uc5/TmWeeqQ8++GDAbRISElRSUmKJvfvuu7rqqqt0xhln6Nxzz9XChQuVkZGhUCik7du365FHHtHzzz8vSXI6nbr33nuVkJAwrJwBAMDU1N3d3acLU0FBgU3ZAAAw9l7YVqdnNx+ILj+zuVrXrp5xzO2+enqp/rBuvyIfNgboCIb1wBt7ddPHZo9VqgAAAFNeQkKCkpKS5Pf7o7EDBw4wjgUAAExYBw4csCwnJCRwY/iHKGIaA0lJSXrmmWe0Zs0a7dy5U/fee6/uvfdeyzrJycl65JFHtGTJkmEdo6amRm+++WZ0+ZFHHtEjjzxy1G1OP/10vfzyy33i4XBYL7zwgl544YUBt83IyND999+vCy64YFj5AgCAqcvtdmvFihVqbGxUVVWVOjo6lJmZaXdaAACMmfMW5ul37+yPLn9Q1ar9BztUlBF/1O2mZyTo44vz9eQRnZsefHOvrj6tVIleLtEAAAAMV05OjqWI6eDBgwqFQnK5OMcCAAATSzgcVkNDgyWWk5MjwzBsymhiYZzcGJk5c6Y2bNigH/zgB1q2bJlSU1MVHx+vOXPm6KabbtKmTZt0/vnn252m1qxZo/vvv19XXXWVli5dqmnTpikuLk4+n0/5+fk699xzdffdd2v37t268MIL7U53wgmHw6qvr1ckErE7FQAAbGUYhjIyMrRo0SItW7aMk20AwJS2siRd6QkeS+yZzTWD2vba1TMtyy2d3frdO/tGLTcAAIBYlJ2dbbkWEYlEVF9fb2NGAAAA/WtoaFA4HLbEsrOzbcpm4qEEfQwlJCTo1ltv1a233jqs7U3THPC54uLioz4/WNnZ2fryl7+sL3/5yyPeVyxpaGhQXV2dDh48qEgkogULFigjI8PutAAAmBC4yxEAMNW5nA6dsyDX0o1psCPl5uQm6WPzc/SPrbXR2K9e26MvnlQsn9s5JvkCAABMdW63W+np6Tp48GA0Vltbq7y8PBuzAgAA6CstLU0zZsxQbW2t2tralJaWJq/Xa3daEwadmIBhqKiosHRgqqurszkjAAAAAMB4Om+h9QuxQyPlBuNrZ1i7MdX7A/rTu5WjlhsAAEAsys3NtSy3tLSos7PTpmwAAAD65/F4NG3aNC1dulTLli1TSUmJ3SlNKBQxAcPQu53boY5MAAAAAIDYMJKRcksKU3XKzExL7J5XytUd5nMlAADAcKWnp/fpDl1bWzvA2gAAAPZLSEhQUlKS3WlMKBQxAcOQmWm92BwOh9XY2GhTNgAA2CMQCNidAgAAtnE5HTr7OOvd/s8OsohJkq47wzp6rrKpU0+/Xz0quQEAAMQih8NhuQHZ4XDINE0bMwIAAMBQUcQEDIPX61VKSoolxkg5AEAsiY+P19atW/X++++roaGBi4IAgJh0/iLrSLnNVS2DHil3UmmGji9KtcT+7+VyRSL8TgUAABiunJwcpaamau7cuTr55JMZzwIAADDJUMQEDFN/I+XC4bBN2QAAML5ycnIkSc3NzdqyZYs2bNhgc0YAAIy/kYyUMwxD/3LGTEtsV12bnt96YNTyAwAAiDXJyclavHixcnJy5HQ67U4HAAAAQ0QREzBMvUfKRSIRRsoBAGKC0+ns83swPT3dpmwAALDPSEfKfWRutubmJlliP3+pnA6HAAAAAAAAU0goFNKWLVvU1NTEdZ9joIgJGCaPx6PU1FRLjJFyAIBYkJ2dbbmb0TAM5efn25gRAAD2OW/h8EfKGYah63p1Y9pc1aJXdzaMWn4AAAAAAACw14EDB9TQ0KBNmzbpvffeU319vd0pTVgUMQEj0HukXGNjIyPlAABTmmma0VFyh2RlZcnj8QywBQAAU9uJpcMfKSf1FEEVZ8RbYj9/adeo5AYAAAAAAAB7RSIRVVZWRpfb2tpUW1trY0YTG0VMwAhkZmbKMIzociQS0cGDB23MCACAsdXS0iKfz2eJ0YUJABDLRjpSzukwdO3qGZbY2j2NWreXceUAAACjpbOzUy0tLXanAQAAYlB9fb0CgYAlVlhYaFM2Ex9FTMAIuN1uRsoBAGJKQ4N1vE1iYqKSk5NtygYAgInh0Ei5zESvvnDidH1rzbwhbX/x8dOUl2ItEqYbEwAAwMj5/X5t3bpVa9eu1Y4dO2Sapt0pAQCAGGKapioqKiyx5ORkpaSk2JTRxOeyOwFgssvOzlZTU1N0ubGxUaFQSC4X/3kBAKaWzs5O+f1+S6ygoMDSlRAAgFh0Ymm6fn/1iVpRki6nY+i/Fz0uh75yWqn+8+mt0djLO+r1QVWLFhRwUQsAAGA4WltbtWHDhuhyZ2enDh48qMzMTBuzAgAAsaSxsVHt7e2WGF2Yjo5OTMAIZWRkWL68NU2TkXIAgCmp94xmp9Op7Oxsm7IBAGDicDkdOmlGxrAKmA65bHmR0hM8ltgvXi4faWoAAAAxKykpSYmJiZbY/v376cYEAADGTe8uTPHx8crIyLApm8mBIiZghNxut9LS0iwxRsoBAKYa0zT7FDGlpaXJ4eB0EgCA0RDncerKU0ossWc/qFF5fZtNGQEAAExuhmH06XTg9/vV0tJiU0YAACCWtLa29jnvKCwsZLrFMfCtEzAKDnWhcLvdysvLowUcAGDKaW1tVVdXlyWWnp5uUzYAAExNXzhpupK8h0eTmybdmAAAAEYiKytLXq/XEuvdEQEAAGAs9D7n8Hg8TLcYBIqYgFGQkZGhhQsX6qSTTtLs2bOVmppqd0oAAIyq3l2YOjs7FR8fb1M2AABMTck+t7548nRL7MkNVaps6rApIwAAgMmtv25MjY2Nam9vtykjAAAQCzo6OtTQ0GCJTZs2jekWg8BPCBgFLpdL6enptH4DAExJkUhE9fX1llhDQwO/9wAAOIrG9qB+v3a/qpo7h7Tdl1aVyOc+fLkmFDF1zyu7Rzs9AACAmJGbmyuXy2WJ0Y0JAACMpcrKSsuy0+lUXl6eTdlMLhQxAQAA4KgOHjyoUChkifW+gwAAAPR46v1qfeH+d7T8f17QNx/frKffrx7S9pmJXl22vMgSe3TdflU00o0JAABgOJxOpwoKCiyxuro6BQIBmzICAABTWTAY1IEDByyx/Pz8PkXV6B9FTAAAADiqpKQkFRUVyefzSZJaW1u50AcAwADeKm/QazsbFI6YkqRnN9cMeR/XnF4qj+vwJZvusKk7/1E2ajkCAADEmvz8fMv4FtM0+3RIAAAAGA1VVVUyTTO6bBhGn4JqDIwiJgAAAByVz+dTSUmJVqxYoVmzZtFyHQCAo1iz0NoafFNly5C7KOWlxOkLJ063xJ7YWKUdB/wjzg8AACAWeTwe5ebmWmI1NTV9Ok8DAACMRDgcVnW1tSt3Tk6OvF6vTRlNPhQxAWOku7tbNTU1CgaDdqcCAMCoMAxDiYmJ8vv5AhUAgIGcVJqhtHi3JfbMMLoxXbd6hhI8zuiyaUo/fH7HiPMDAACIVdOmTbMs9/clIwAAwEiYpqmCggK53YevDfU+B8HRUcQEjLK6ujpt3rxZb731lsrKytTQ0GB3SgAAAACAceJyOnTOAutd/s9sGnoRU0aiV1efVmqJ/WNrrd7d1zSi/AAAAGJVXFycsrKyLLGqqipFIhGbMgIAAFONy+VScXGxVq5cqZkzZyo/P18JCQl2pzWpUMQEjLKGhgY1NjZG51zW1dXZnBEAAAAAYDydvyjfsry5qkV7G9qHvJ+rTi1VeoLHErvjue3Rz5sAAAAYmsLCQstyMBhUbW2tTdkAAICpyul0qqCgQLNmzbI7lUmHIiZglGVnZ1uWW1paGCkHAAAAADFkZUm6MnoVHw1npFyi16XrVs+wxN7e3ajXdtLxFwAAYDiSkpKUmpoaXTYMQ11dXfYlBAAAAAuKmIBRlp6eLqfTaYnV19fblA0AAMNjmqYqKirU2dlpdyoAAEw6LqdD5y4c+Ug5Sfr8idOVn+KzxO54bociEboxAQAADEdhYaEcDocKCgq0YsUKlZSU2J0SAAAAPkQREzDKHA6HMjIyLDFGygEAJpuWlhbt3r1ba9eu1YYNG1RTU8PoGgAAhuC8hdaRcltrWrW7vm3I+/G5nbrxzNmW2OaqFv3tgwMjyg8AACBWpaWl6cQTT9TMmTPl8/mOvQEAAADGDUVMwBjIysqyLLe2tioQCNiUDQAAQ1dbWxt93NraqsrKShuzAQBg8llRkq6sJK8lNtxuTJ84oUAzshIssR/9Y4dC4ciw8wMAAIhVhmHI7XbbnQYAAJgi/H6/ysvLGVE7SihiAsYAI+UAAJNZOBzu83srJydHhmHYlBEAAJOP02FozQLrSLm/DrOIyeV06Oaz5lhiu+vb9dh7FBkDAAAAAADYad++faqsrNTatWu1fft2tbe3253SpEYREzAGHA6HMjMzLTFGygEAJouDBw8qHA5bYtnZ2TZlAwDA5HX+YutIuR21fu2s9Q9rX+cuyNXCghRL7K4XdqqrOzzAFgAAAAAAABhLTU1NOnjwoCTJNE3V1tZGlzE8FDEBY6T3SDm/308LOQDApHDkKDlJSk1Nlc/nsykbAAAmr6VFacpJto6UG243JsMwdOs51m5MNS1d+u3b+4adHwAAAA7r7u7W3r17ZZqm3akAAIBJwDRN7d692xJzu93Kz88fYAsMBkVMwBhJS0uTy+WyxBgpBwCY6ILBoBobGy2xnJwcm7IBAGByczgMrVmYZ4k9s7lm2F+MnTIzUyeVZlhiP39pl/xd3cPOEQAAINZFIhFVVFTonXfe0b59+1RTM7yicwAAEFtqa2vV1tZmiRUXF/epEcDQUMQEjJH+RspRxAQAmOh6jz/t7/cZAAAYvPMX9dx9V5Qer+tWz9Ddly0Z9r4Mw9AtvboxNXV0677X9owkRQAAgJi2detW7d69W+Fwz5jevXv3KhQK2ZwVAACYyMLhsPbssV6PiY+PV15e3gBbYLAoYgLGUH8j5To7O23KBgCAY+tdxJSZmcldAwAAjMDxhan667+eolduWa1bz5mr4/JTZBjGsPd3QlGazppv7ZJ432u7dbAtMNJUAQBAPzo6OnT77bdr+fLlSk9PV0JCgubOnaubb75Z+/aNfKzr3r17ZRjGoP5cccUVI/8HoY+CggLLcnd3t/bv329TNgAAYDKoqKhQMBi0xGbMmDGiaz7oQRETMIYYKQcAmEza29vl9/stMUbJAQAwMg6HoQUFIytc6u3rZ8/RkbtrD4b185fKR23/AACgx65du7RkyRJ94xvf0Pr169XU1KSOjg7t2LFDP/7xj7Vo0SL99a9/tTtNjFBaWpoyMqwjeysrK9XV1WVTRgAAYCILBAKqqKiwxNLS0pSenm5TRlMLt9UDY8gwDGVlZVlmaNfX16uoqMjGrAAA6F9tba1l2ePxKC0tzaZsAADAQGbnJOni4wv0+HtV0dhv396nK08tUUFqnI2ZAQAwdfj9fp133nnauXOnJOnqq6/WZZddpri4OL300ku67bbb1Nraqk9/+tN64403tGTJkhEf83vf+54uvPDCAZ/nM/rYKS0tVWNjo0zTlCSZpqk9e/Zo3rx5NmcGAAAmmr179yoSiVhipaWlNmUz9VDEBIyxQ0VMXq9X2dnZfUbMAQAwEZim2WeUXHZ2Nq1PAQCYoG46c7aefr9a3eGeL9qC4YjufqFMt1+y2ObMAACYGu644w6VlZVJkm6//Xbdcsst0edOOukkrV69Wqeffro6Ojp044036uWXXx7xMQsKCrRgwYIR7wdDFx8fr7y8PFVXV0djdXV1KigoUHJyso2ZAQCAiaStrU0HDhywxPLy8pSYmGhTRlMP4+SAMZaamqrjjz9eK1euVGlpqZKSkuxOCQCAPpqbmxUIBCwxRskBADBxFabH63Mrp1tif363Urvq/ANsAQAABqu7u1s/+clPJEnz5s3TzTff3Gedk08+WVdeeaUk6ZVXXtG6devGNUeMvuLiYjmdTkusvLw82p0JAADENtM0VV5ebok5nU4VFxfbk9AURRETMMYMw1BycjKdLAAAE1rvUXIJCQncOQAAwBipbOrQPa+Ua8eBkRUcfe2MmYr3HP6iLWJKP3q+bKTpAQAQ81566SW1tLRIki6//HI5HP1/lXLFFVdEHz/xxBPjkRrGkNvt1vTp1iLx1tZWNTQ02JQRAACYSBobG9Xc3GyJFRYWyuPx2JPQFEUREwAAQIyLRCJ9LsjRhQkAgNH3+HuVuujnb+iUH7yk2/62XY+/Vzmi/WUlefXlVSWW2N8+OKBNlc0j2i8AALHu9ddfjz4+/fTTB1xv2bJlio+PlyS98cYbY54Xxl5BQYF8Pp8ltnv3bkUiEZsyAgAAE0EkEtHu3bstMa/Xq2nTptmU0dRFERMAAECMczgcWr58uUpKSqIXX7Ozs23OCgCAqae8vk0bK5qjy3/dVDPi8SRfOb1UqfFuS+yO53aMaJ8AAMS6rVu3Rh/PnTt3wPVcLpdmzpwpSdq2bduIj/vTn/5UM2fOlM/nU0pKio477jh99atf1XvvvTfifWNwHA6HSktLLbGuri5VVVXZlBEAAJgI2tra1NXVZYmVlJT0GUWLkXPZnQAAAADs5/V6VVRUpMLCQnV1dcnr9dqdEgAAU855C/P185fKo8tVzZ3aWNGs44vShr3PZJ9b154+Q7f9bXs09trOBr2zt3kkqQIAENMqK3u6JSYkJCg1NfWo6xYWFmrTpk2qr69XIBAY0efpI4uVAoGAtm7dqq1bt+qee+7RNddco7vvvntY+z/07xlITU1N9LHf71dra+uQjzEcbW1t/T62m8fjUUJCgtrb26Oxffv2KSEhQS4XX6tNdBP1dYXJi9cURhuvqclr3rx5qqmpUWNjo+Li4uTz+cbtvOlY7Hpd+f3+Ud8nZ1uADQKBgOrr65Wenh7teAEAwERgGIbi4uLsTgMAgClpXl6SSjMTtLvh8Bdif91UM6IiJkm6/ORi/fqNPaptDURjd7+8V6eYkmGMaNcAAMSkQ1/GJCYmHnPdhISE6OO2trZhFRmlpqbq4osv1urVqzVr1iz5fD7V1NTo+eef1/3336+2tjbdc8898vv9euSRR4a8/8LCwkGv+5vf/EYpKSlDPsZI/eY3vxn3Yx5NYmKiFixYEF0Oh8N6/vnntW/fPhuzwlBNtNcVJj9eUxhtvKYmp/j4eBmGoZdeesnuVPo1nq+rlpaWUd8n4+SAcVRbW6sNGzbo7bffVnl5uWpra+1OCQAAAAAwTgzD0PmL8iyxZzfXKBIZ2Ug5n9up6z86yxL7oNqvvd1JI9ovAACx6tCoEI/Hc8x1jyxa6uzsHPKx8vPzVVVVpV//+tf64he/qJNOOknHH3+81qxZo7vuukvvvfeeioqKJEm/+93v9NRTTw35GBi6trY2NTQ0WGI5OTl0YgIAAOro6LB0bMTo4mwLGEdtbW2WlnL19fUqLi6Wwa2xAAAAABATzluUr5+8uCu6XNPSpff2N2lZcfqI9nvpskL96tXd2nuwIxp7pzNH092j39YbAICJYjSuqz7wwAO64oorLDGfzydJCgaDx9w+EDjcCXE4nY09Hs9Ri6VmzZql3/72tzrttNMkST/96U/18Y9/fEjHqKioOOrzNTU1WrFihSTpC1/4ggoKCoa0/+Fqa2uLdgr4whe+MKjOV+MpEAho27ZtMk1TiYmJKigo0NKlS+1OC8cw0V9XmHx4TWG08ZrCWLDrdVVVVaXbbrttVPdJERMwjrKysizzxzs7O9Xe3s4vJwAAAACIEXNykzQrO1E769qisb9uqhlxEZPb6dC/nTVH1/9+QzTWFPGpLJg6ov0CABCLkpJ6uhm2tbUdY01Z7sIfq+u8p556qubPn6+tW7fq9ddfVyQSkcMx+EEb06ZNG/S6SUlJSk5OHk6aI5KYmGjLcY8lGAzK5/MpPT2dm5EnoYn6usLkxWsKo43XFMbCeL6ujmzgMlooYgLGUVJSkrxer+XunLq6OoqYAAC2qKioUDAYVG5urhISEuxOBwCAmHHeojzd9cLO6PKzm2v07+fPl9Mxsi/Gzl+Yp1++XK6tNYcvIK3vylYwFBnRfgEAmKi2bds24n3k5eX1iU2bNk3vvPOO2tvb1dzcrNTU1AG3P9TlKCsryzJabrQdKmLq6urSwYMHlZWVNWbHwmHj1ZUKAABMLKZpqrq6mnGyNuCnDYwjwzD6dGOqr69XSUkJd3EAAMaVaZqqrKxUMBhUZWWlEhMTNWPGjKNemAUAAKPj/F5FTHX+gNbtbdSJpRkj2q/DYeiWs+foSw+ui8b8EY/+vKFGX/1o6oj2DQDARDR37twx2e/8+fP12GOPSZK2b9+uE088sd/1QqGQysvLJUnz5s0bk1wO4foxAADA+GloaNCuXbu0b98+TZ8+XXl5eUPqhInh46cMjLPs7GzLcldX16DaEgMAMJqampoUDAajy21tbXK73TZmBABA7JiZnaS5uUmW2DObakZl36vnZGlFr9F0975Rodau7lHZPwAAseCUU06JPn7llVcGXG/9+vXRcXKrVq0a05y2bt0qSfJ6vcrIGFnhMwAAAAYWDoe1e/duSVJ3d7d27dqlLVu22JxV7KCICRhniYmJ8vl8llhdXZ1N2QAAYlXv3z2JiYmMlAMAYBydv8g6uuZvH9QoFB752DfDMHTrOXMsscaObv3ouR0j3jcAALFi9erVSklJkSQ99NBDMk2z3/UefPDB6OOLL754zPJ54403ol+cnXLKKXQBmABCoZBqa2vtTgMAAIyBPXv2qKuryxLLzc21KZvYw5kuMM4OjZQ7Un19/YAfhAEAGG3hcFj19fWWWE5Ojk3ZAAAQm85blG9ZbmgLau2exlHZ97LidJ0+y9qN6eG392ljRfOo7B8AgKnO4/Ho+uuvlyRt27ZNP/zhD/us89Zbb+n++++XJJ1++ulavnx5v/syDEOGYai4uLjf55988smjXhvetWuXPvvZz0aXr7vuusH+MzBGGhsbtW7dOm3fvl1NTU12pwMAAEZRc3OzqqqqLLGUlBRlZmbalFHsoYgJsEHvkXKBQEB+v9+mbAAAsaahoUGRiLXTQ+/fTQAAYGyVZCbouPxkS+zpURopJ0nf+NgMuXT4971pSt96fPOodHsCACAW3HLLLZo9e7Yk6dZbb9U111yjl156SW+//bZuu+02nXXWWQqFQoqLi9Ndd9017ONcfPHFmj17tr7xjW/oscce0zvvvKONGzfqb3/7m2688UYdf/zx2r9/vyTp0ksv1Sc+8YnR+OdhmHbt2qXNmzcrGAxKknbs2KFQKGRzVgAAYDSEw2GVlZVZYg6HQ7Nnz5ZhGDZlFXtcdicAxKKEhATFxcWps7MzGqurq1NycvJRtgIAYHT0bneenp4uj8djUzYAAMSu8xblaUt1a3T5uS0H9L2LFsjpGPmFsWmpPi2Lq9PbnYfbnW+tadWDb+7VVaeWjnj/AABMdUlJSXrmmWe0Zs0a7dy5U/fee6/uvfdeyzrJycl65JFHtGTJkhEda9euXbr99tuPus61116rO++8c0THwcjFxcVZlgOBgHbv3h0teAMAAJPXnj17LN/fS1JxcbHi4+Ntyig20YkJsAEj5QAAdgkEAn1anTNKDgAAe5y/sGek3HH5ybr1nDl68rpVo1LAdMgib4PSHV2W2I+eL1NlU8eoHQMAgKls5syZ2rBhg37wgx9o2bJlSk1NVXx8vObMmaObbrpJmzZt0vnnnz+iYzz11FP65je/qY985COaMWOGUlJS5HK5lJ6ermXLlummm27S5s2b9X//93/yer2j9C/DcOXn5yslJcUSq6mpUWPj6IwFBgAA9uhvjFxycrKmTZtmU0axi05MgE2ys7OjbYAlKRgMqrW1tc8HIAAARlNdXZ1l2el0KiMjw6ZsAACIbUUZ8Xr9G2doWtrY3NHnNKTTE6r0hH9GNNbZHdZ3n9qiX31xGa3QAQAYhISEBN1666269dZbh7X9sW5cveCCC3TBBRcMa98Yf4ZhaM6cOVq/fr0ikcNjesvKyrRs2TK5XHztBgDAZBMOh7Vjxw5LzOFwaM6cOVw7sQGdmACbJCQk9Gk9V19fb1M2AIBYYJpmn1FyWVlZcjqdNmUEAADGqoDpkFxXpz51fK4l9sK2Oj23pXaALQAAAHA0cXFxKi21juc9NFYOAABMPnv27FFXl7WTNWPk7EMRE2AjRsoBAMZTe3u72tvbLTFGyQEAMPXdsLpYmYnW8TPffWqL/F3dNmUEAAAwueXn5ys1NdUSY6wcAACTD2PkJh6KmAAbZWdnS+rpylRcXKzFixfTkg4AMGZ6d2Hyer2MMQUAIAYkx7n17+fPs8QOtHbpR8+X2ZQRAADA5GYYhmbPni2Hw/o1W1lZmUKhkE1ZAQCAoWCM3MREERNgo/j4eK1YsULLli3T9OnTaUkHABgzpmn2GVuanZ3NiTgAADHi44vzdeqsTEvs4bf2alNlsz0JAQAATHJxcXGaMWOGJRYIBFReXm5TRgAAYCh2797dZ4xcSUkJ39nbjCImwGZxcXF2pwAAiAF+v1+BQMASO9QREAAATAymaWrHAb9+9PwOvVV+cFT3bRiGvnfRAnldhy8FRUzpW09sVigcGdVjAQAAxIq8vLw+Y+UOHDjAWDkAACYBr9drudE7OTlZBQUFNmYEiSImAACAmOD3+y3LcXFxSkhIsCkbAADQ25/frdTH7nxVZ9/1qn764i79cX3FqB9jekaCrv/oLEvsg6pWPfTWvlE/FgAAQCwwDENz5syR0+m0xBkrBwDAxFdUVKSlS5cqMTGRMXITCEVMAAAAMaCgoEAnnXSSZs2apZSUFEbJAQAwwfi7urWrri26/I+tterqDo/6ca4+tVSzshMtsR89v0PVzZ2jfiwAAIBY4PP5VFpaaokxVg4AgMkhISFBxx9/vBYvXswYuQmCIiYAAIAY4fF4lJ+fryVLlmj69Ol2pwMAAI6wZmGejqwvbguE9EpZ/agfx+Ny6H8/sdAS6wiG9d2ntoz6sQAAAGJFf2PlHA6HTNO0JyEAADBoDodDycnJdqeBD1HEBEww7e3t2rt3r1paWuxOBQAwhdGFCQCAiSUn2aflxemW2DObasbkWMuL03XZ8kJL7PmttXpuy4ExOR4AAMBUd+RYOa/Xq0WLFmnWrFlcfwEAABgil90JAOhRW1uriooKtbe3S5K6urqUkpJic1YAAAAAgPFywaI8rd3TGF1+YVvPSDmf2znqx/r/zp2rf2yt1cH2YDT23ae2aNXMTCV6uVwEAAAwVD6fTwsWLFBiYqJcLs6nAACYaAKBgLxer91p4BjoxARMEMFgMFrAJEn19fUKh8M2ZgQAAAAAGE/nLMiT44ib9TuCYb20vW5MjpUa79G/nz/fEqtp6dKd/ygbk+MBAADEgtTUVAqYAACYgJqamvTOO+9oz549ikQidqeDo6CICZggsrOzLcuRSEQNDQ02ZQMAAAAAGG9ZSV6dWJphif1189iMlJOkC5fk65SZmZbYA2/s0QdVjDcHAAAAAABTQzgcVllZmUzT1P79+/Xee+/J7/fbnRYGQBETMEF4vV6lp6dbYgcOHLApGwDAVGCapt577z3t2rVLLS0tMk3T7pQAAMAxnLcoz7L84rY6dQRDY3IswzD03xctkMd1+PJQxJS+9cRmhSOcNwAAAIym7u5uu1MAACAm7d69W11dXdHl9vZ2tbRwA9dERRETMIHk5ORYlpubmy1vqAAADEVLS4v8fr+qqqq0ceNGvf3221wwAwBggjt3QZ6cR8yU6+wO68UxGiknSSWZCfrXM2ZaYpsqW/Sbt/aO2TEBAABiiWmaqq6u1jvvvKPa2lq70wEAIKbU19erurraEktJSVFBQYFNGeFYKGICJpCMjAw5nU5LjA81AIDhqq+vtyy73W653W6bsgEAAIORnuDRyTOsI+Wefr96gLVHx1dOL9WMrARL7IfPl6mmpXNMjwsAADDVdXV1adOmTdq5c2d0lE1bW5vdaQEAEBPa29u1fft2S8zhcGjOnDkyDGOArWA3ipiACcTpdCo7O9sSq62tZfwPAGDITNNUQ0ODJZaVlWVTNgAAYCjO7z1SbnudmtqDY3Y8r8up/714oSXWFgjpP5/aOmbHBAAAiAUtLS1qbm6OLkciEW3ZsoVO2QAAjLFQKKQtW7YoEolY4jNmzFBcXJxNWWEwKGICJpjeI+U6Ozvl9/ttygYAMFm1tLQoGLR+2UkREwAAk8M5C/LkcR2+ZNMdNvXUGHdjWlmaoUuXTbPE/r7lgF7YSndgAACA4crJyVF+fr4l1tXVpW3btnHzMgAAY8Q0TW3fvl2dndYO07m5ucrLyxtgK0wUFDEBE0xycnKf6k9GygEAhqr3KLmEhATFx8fblA0AABiKlDi3zj4u1xJ77L3KMT/uN8+dp/QEjyX2nae2qD0QGvNjAwAATFUzZsxQUlKSJdbU1KS9e/fakxAAAFPc/v37dfDgQUvs/2fvvuOjqNM/gH9me8pm03snISEQCARCVVARPRQ9FTiFQ8EC9tPz1J/lFM/zLOAVCyo2LNjbgRW5A04QgdBJIwkppPeebJ3fHzFLlt1NI8mkfN6vFy9mv/OdmWfDstmdeeZ53N3dERMTwzZywwCTmIiGGEEQ7KoxVVRU2JW6IyIickYURbskJlZhIiIiGl6umRJi8zizrBFl9W0DekwvNxUeWTjOZqy4rhX/3H5yQI9LRERENJLJZDKMHz8eSqXSZrywsBBVVVUSRUVERDQy1dTU2CUKKxQKjB8/HnK5XJqgqFeYxEQ0BJ2dxGQymeyyRYmIiJypr6+H0Wi0GWMSExER0fByXqwfAjzUmBLuiaeumoADj8xHoE4z4Me9ekoIZkb72Iy9tScfJ4rrB/zYRERERCOVWq1GQkKC3XhmZiZaWlokiIiIiGjkaW1tRUZGht14QkICNJqBP6dC/YNJTERDkEajgU6nsxljSzkiIuqps6swubu7s5UcERHRMCOXCdh2z1x8cftsLJ8eAZ2LsvuN+oEgCPjrVROgkp85ZWS2iLjzg0OobzV2sSURERERdcXT0xNjxoyxGTObzUhLS4PZbJYoKiIiopHBbDYjPT0dJpPJZjwqKgpeXl4SRUV9wSQmoiEqMDDQ5nFNTQ0MBoNE0RAR0XDBVnJEREQjh851cBKXzjbGzx23X2B7gS2/ugV/+vQoLBZRkpiIiIiIRoKQkBD4+/vbjLW0tCArKwuiyM9ZREREfZWTk4OmpiabMV9fX4SFhUkUEfUVk5gGUEtLC5577jlMmzYN3t7ecHNzQ3x8PO677z4UFBSc8/7z8/MhCEKP/qxcubJH+/zwww+xYMECBAYGQqPRICIiAr///e+xd+/ec46XesfX1xcyme1/0YaGBomiISKi4aKuro6t5IiIiOic3TZvDCaF2lYI/jG9HK/sypUoIiIiIqLhTxAEjB07Fm5ubjbjlZWVKCoqkigqIiKi4S84OBhqtdr62NXVFXFxcRAEQcKoqC+YxDRAcnJykJSUhAcffBCpqamora21ZtP//e9/x8SJE/H1119LHaZVa2srLrvsMixbtgw//vgjysvLodfrUVhYiM2bN2POnDl44oknpA5zVFEoFPD19YVWq0VMTAxmzpwJX19fqcMiIqIhzlErORcXF4miISIiouFKrZBjw++T4XVWNajnt2Vhd3aVRFERERERDX9yuRzjx4+HQqGwGT916hRqa2slioqIiGh402q1SE5OhpeXl9PftTQ88F9tADQ2NuKyyy5DdnY2AOCWW27BtddeCxcXF+zYsQNPP/00Ghoa8Lvf/Q579uxBUlLSOR/zr3/9K6688kqn67vr83jjjTfi22+/BQBccMEF+MMf/oDg4GAcP34cf/vb35Cbm4u1a9ciKCgIq1evPud4qWfi4uLsqjERERE5I4oiqqpsLyqyChMREdHII4rioNxJGOLpghevm4Lr39qHji5yFhG4+6PD2HrXHIR4MlGaiIiIqC9cXFwQHx+PEydO2IxnZGRg+vTpkMvlEkVGREQ0fCmVSiQmJqK5uRmurq5Sh0N9xCSmAbBu3TqcPHkSAPDcc8/h/vvvt66bOXMm5s2bh7lz56KlpQX33HMPdu7cec7HDAkJwYQJE/q07X//+1989NFHAIBFixbhyy+/tH5AnjZtGq644gokJyejsLAQDz74IJYsWdJtUhT1DyYwERFRb7CVHBER0cgliiIOFtTi80NFKKptxXs3TR+U486J9cV9C+Kw7ocs61hNswG3v38Qn9w6E2oFL7ARERER9YWPjw8iIiJQUFAAoL1CU1xcHBOYiIiIzoEgCHB3d5c6DDoHzJDoZ0ajES+88AIAYNy4cbjvvvvs5syaNQs33XQTAGDXrl04cODAoMZ4tvXr1wNob1+2YcMGuw/Ivr6+ePbZZwG0Xxx94403Bj1GIiIi6l5LS4tNVQatVstWckRERCNAYXULLnx+Fxa/uhcf7j+Nn7KrkFPROGjHv23uGMwfF2AzdrSoHk9sTR+0GIiIiIhGooiICHh7e8PNzQ3Jycnw8fGROiQiIiIiSTGJqZ/t2LED9fX1AIAbbrjBaSWdlStXWpe//PLLwQjNocbGRvznP/8BAMyfPx+hoaEO51199dXw8PAAIG28RERE5FxISAhmzZqF+Ph4+Pj4wN/fX+qQiIiIqB8Ee2rQ2GayGfvsYPGgHV8mE/D80kmI8LEtxf7BvkJ8mnp60OIgIiIiGmkEQcC4ceMwefJk3ohGRETUQ+Xl5cjLy4MoilKHQgOASUz9bPfu3dbluXPnOp03depUax/GPXv2DHhczhw4cAAGgwFA1/GqVCrMmDHDus3ZrWpocPENmYiInFEoFAgICMCECROcJicTERHR8KKQy3DV5GCbsS8PF8FsGbzvhjoXJV79fTI0SttTSY9+dQJpJfWDFgcRERHRSKNQKNhCjoiIqIcaGxtx8uRJFBYW4sSJE8xbGIEUUgcw0qSnnymlHh8f73SeQqFATEwMjh07hoyMjHM+7osvvoi//vWvKCoqglqtRmhoKM477zysXr0aU6ZMOed4O9Zv27YNJpMJ2dnZSEhI6HF8RUVFXa4vLS21Ljc3N6OhoaHH+x4tRFFES0sLampqUF9fj3HjxvGLzQBpampyuEwkFb4maSjh65GGEr4eaajha5IG0iVxXnj9pzzr4/IGPbYdK8TsaC+H8wfi9RjiBjz2m1g8vCXLOqY3WbD63VR8vCoJHi7KfjkOjUx8j6ShpLm5WeoQiIh6TBRFCIIgdRhERESSMxqNSE9Ph8ViAQDU1NTg0KFDSE5OhkLB1JeRgv+S/awjWcfNzQ2enp5dzg0LC8OxY8dQWVkJvV4PtVrd5+MeOnTIuqzX65Geno709HS89tprWLNmDf71r3853H/n5KLuqjWEhYVZl0+fPt2rJKbO23bniy++gE6n6/H80SIxMRFubm7Wx1999RUqKiokjGh0eO+996QOgcgGX5M0lPD1SEMJX4801PA1SQPBTz4GleYzbUae/3w3jrl3fdMQ0P+vxwnqIJzQ+1gfF9e1YdmLP2KhewF4fY16gu+RJLX6elaQI6LhoaysDBUVFUhMTGQiExERjWqiKCIjIwNtbW02497e3kxgGmHYTq6fNTY2AgDc3d27nds5IaWvd6B5enpi1apVeOedd/Dzzz/j0KFD+Oabb/CHP/zBGsNrr72GG2+8sct4exJzf8RLfafX620e+/r6ShQJERERERERSSFOVWvzOM/oAb1l8E/tzHIpQ4C8xWas0KTFwTa/QY+FiIiIaCSyWCzIzs5GVlYWamtrkZ2dDVEcvFbCREREQ4koisjNzUVtre15EQ8PD4wZM0aiqGigMCWtn3Vk/qlUqm7ndq6M1Nra2utjBQcHo7i4GK6urjbjkydPxsKFC3HHHXdg/vz5KCwsxAcffIDf/e53uOKKKxzG25OYzyXe06dPd7m+tLQUKSkpAICrr74aY8eO7dX+R4O6ujrk5Z1pHeDh4YEbb7zxnCp4kWNNTU3Wu0JXrFjRo6REooHE1yR1ZzDLivP1SEMJX4801PA1SQOttsWIi17YB5Ol/QKWGTKEzroCiycH2c0d6Nfj4gY9rn3rMGpajNax1LYArLryQswZ492vx6KRge+RNJScPHkSTz/9tNRhEBE5lZmZicrKSuvj0tJSKJVKREVFSRgVERGRNAoLC1FcXGwzplQqkZCQAJmMdXtGmlGbxNQfF/refvttrFy50mZMo9EAAAwGQ7fbd66s4+Li0sVMx1QqVZeJR7GxsXj//fdx/vnnAwBefPFFuySmjniB7mM+l3i7a1XXmZubGzw8PHq1/9HA3d0dRUVFMBrPnCBubm6Gnx/vdB1I7u7ufD3SkMLXJJ3NYrHgwIED0Ol08PPzg5eX16B9aB/M16PBYEBTUxOam5thMBisPa9pdDOZTEhKSgLQ3hKkublZ2oBoSJHL5dBoNPDw8ICbm9ugt17g72waCB4ewPxxAfg+rcw69k16NW6cG9fldgPxevTwAF5aPgW/f2Mffs2pggjgoS0n8fVdcxDm7drl9jS68T2SpNa54jwR0VAUGhqKqqoqm+pLhYWFUCqVvbreQkRENNyVlJQgPz/fZkwQBCQkJLDYxwg1apOYBopWqwXQs3ZrnS+yDNTdZ+eddx4SEhKQnp6O3bt3w2Kx2FzY7IgX6D7mwYiXnJPJZPD397fJMi0vL0dERAR7YRMRjWK1tbVoa2tDW1sbysvLoVAoMH369BHTA1oURVRVVaGqqkrqUGgIslgs0Ol01mWTySRxRDSUmEwm6PV61NfXw8XFBeHh4bwzi0aEa5JDbZKYDhbU4lRlE6L9Bv97+qwxvnjw0ng8/V2mday+1YjbNh/EZ7fOgkYpH/SYiIiIiEYCDw8PjBs3Dunp6Tbjubm5UCgUCAwMlCgyIiKiwVNRUYHs7Gy78bi4OHh6eg5+QDQoRsbVrT7IyMg4530EBdmXaw8NDcW+ffvQ3NyMurq6Lv/zdLRY8/PzG9AswY4kpra2NlRXV9tU7umcsV9UVISpU6d2Gy8AhIWFDUyw1KWAgACbJKa2tjbU19fzTZqIaBTrXFocAFxdXUdMAhPQXi69vr7eZkwQBMjlvChK7UluHcn1SqWSid1kw2w2W+9abm1tRWFhIW8AoBFhXpwffNxUqG4+U03580NFuP+SeEniWX1+NA4X1tkkVp0obsDj/07Ds4snShITERER0Ujg5+eHsWPH4uTJkzbjWVlZUCgU8PX1lSgyIiKigVdTU4PMzEy78ZiYGAQEBEgQEQ2WkXOFq5fi4wfm5F5CQgI+//xzAO09i2fMmOFwnslkQm5uLgBg3LhxAxJLh65O0ickJFiXHb0JdNaxXqFQIDY2tn+Co15xd3eHq6srWlparGPl5eVMYiIiGqUsFotdhaKR1Ga0I1m3g4+PDzw8PKBWq5mEQADak1QqKioAAP7+/kxuIxsWiwVNTU0oKyuD2WxGa2srmpubWVWWhj2lXIYrk0Lw1p4869gXh4rxx4vjIJcN/u9HQRCwbslEnKxoxKnKMxWcP049jcnhnrg2JXzQYyIiIiIaKYKCgmA0GpGXl2cznp6ejokTJ/LaABERjUj19fVIS0uzaasKABEREQgJCZEoKhosrKXfz+bMmWNd3rVrl9N5qamp1vZss2fPHtCYOsqNqtVq+Pj42KybNm0aVCoVgK7jNRgM+OWXX6zbKJXKAYqWuiIIgl2Z2MrKSpjNZokiIiIiKdXW1tr9DhhJSUx1dXXWZX9/f/j7+0Oj0TCBiYh6RCaTwcPDw+bzc2Njo4QREfWfxcmhNo9L69uwN7daomgArUaJV3+fDFeVbTLpY1vScKyoTpqgiIiIiEaIsLAwm64aQHtl4hMnTvA7DhERjTjNzc04ceIELBaLzXhwcDAiIiIkiooGE5OY+tm8efOg0+kAAO+8845ddmCHTZs2WZevuuqqAYtnz549SEtLA9CeYCWT2f6Ta7VaXHTRRQCA7du3o6ioyOF+vvjiCzQ0NAx4vNQ9f39/m8dms9muCgcREY0OZ7eS66hSNFJ0rjzIOwuJqK/c3d2tyY+tra0SR0PUPxKCPZAQ5GEz9vkhx9/nB8vYAC2evca2fZzBZMFt7x9CTafWd0RERETUO4IgIDo62u4GZ7PZjOPHj9ucPyEiIhrORFFERkYGTCaTzbi/vz9iYmJ4g/MowSSmfqZSqXD33XcDADIyMrB+/Xq7OXv37sWbb74JAJg7dy6mTZvmcF+CIEAQBERGRjpc/9VXXzlNkgKAnJwcLFu2zPr49ttvdzjvT3/6E4D2Fnd33HGHXUWHqqoqPPjggwDaLyDefPPNTo9JA0+tVsPLy8tmrLy8XKJoiIhIKiO9lRwA62cShULBNmFE1Gcymcz6HsIKpjSSXNOpGpObSg6tRiFhNO0WTQrGjbOjbMaK61rxh48Ow2xxfv6CiIiIiLomCALGjh1r123DaDTi2LFj0Ov1EkVGRETUfwRBQEJCgs3N2t7e3oiLi2MC0yjCJKYBcP/992Ps2LEAgAceeABr1qzBjh078Msvv+Dpp5/GggULYDKZ4OLign/+8599Ps5VV12FsWPH4sEHH8Tnn3+Offv24ciRI/juu+9wzz33YPLkySgsLAQALF26FFdffbXD/Vx44YW49tprAQBbtmzBxRdfjC1btiA1NRVvv/02ZsyYYd3Ps88+a5dAQ4Pv7Dsuamtr+SWFiGiUqampGdGt5IiIiKhrVyYF47xYX/zjd5Nw4NH5+MuVE6QOCQDw0MJ4TIu0PW/wU3YV1v2QJVFERERERCNDx4Xds6tV6/V6HDt2DEajUZrAiIiI+pGrqyuSkpLg4uICDw8PJCQk2HWbopFN+tv0RiCtVotvvvkGCxcuRHZ2NjZu3IiNGzfazPHw8MDmzZuRlJR0TsfKycnBc8891+Wc2267Df/4xz+6nPPWW2+hoaEB3377LXbs2IEdO3bYrJfJZPjzn/+M1atXn1O81D98fHwgl8ttLl6Xl5cjPDxcwqiIiGgwnd1KTqfTjahWckRERNQ1X3c13rtputRh2FHKZXh52RRc9uJuVDaeudnm1V25UMgE3LdgLO+eJCIiIuojmUyG8ePH49ixY2hsbLSOt7a2oqGhwa5SExER0XCk0WiQlJRkU2GdRg+mrA2QmJgYHD58GM8++yymTp0KT09PuLq6Ii4uDvfeey+OHTuGyy+//JyOsWXLFjz00EO48MILMWbMGOh0OigUCnh7e2Pq1Km49957cfz4cWzYsKHbi5ouLi745ptvsHnzZlx88cXw9/eHSqVCWFgYli1bht27d2Pt2rXnFC/1H7lcbldto7y8vMv2gkRENHJYLBZUV1fbjLEKExEREQ0V/h4avLxsCuQy22Sll3bk4OnvMvndlYiIiOgcKBQKJCYmwsXFBUB7YtOECROYwERERCOKSqWCQsGaPKMR/9UHkJubGx544AE88MADfdq+u5N6ixYtwqJFi/q0b2eWLVuGZcuW9es+aWAEBASgrKwMQPuXFHd3d1gsFmajEhGNAo5ayfn6+koUDREREZG9lChvPHnlBDz85XGb8Y3/OwW90YzHF42HTMaKTERERER9oVQqMXHiRJw4cQKxsbHQ6XRSh0RERNQroiiiuroaPj4+rNhMNpjERDRM6XQ6+Pv7w9PTE35+fsxEJSIaRdhKjoiIiIaDZdPDIQjAw18eR+f7tN7ZWwCD2YKnfpvIRCYiIiKiPtJoNEhOTuaFXyIiGnZEUUROTg5KSkoQGhqK6Oho/j4jK7aTIxqmBEHAuHHjEBQUxAQmIqJRxGw2s5Uc0SDYtGkTBEGAIAjIz8+XOhzJ7dy50/rz2Llzp9ThEFEXGtuMKKtvkzoMq+tSwrF+8SScnav04f7T+NNnR2G2sLUcERERUV/xgi8REQ1HBQUFKCkpAQAUFRUhKyuLrefJiklMRERERMNIbW2tXSs5JjERERGNbmaLiJ+yK/GHjw5j2lPb8dwPmVKHZOOa5FD869rJkJ+VyfTFoWLc8/ERGM0WiSIjIiIiGrmqqqpQVFQkdRhEREQ2iouLUVBQYDNWUVGBxsZGiSKioYblW4iIiIiGEYPBALlcbk1k0ul0UKlUEkdFREREUnr/lwI8viXN+vi742W4/4JwCSOyt2hSMJRyGe768BCM5jN3V249WgKDyYwXr5sClYL32hERERH1h7KyMmRlZQEA5HI5goKCJI6IiIgIKC8vR05Ojt14fHw8PDw8JIiIhiKeHSIiIiIaRoKDgzFr1iyMHz8e/v7+CAwMlDokIhoF5s2bB1EUIYoi5s2bJ3U4RHSW3yQG2lQ5ajWasT2ruostpHHphEC8tiLZLlnph7Ry3Pr+QbQZzU62JCIiIqKe6mjL0+HkyZMoLy+XMCIiIiKgsrLS5vdTh9jYWPj7+0sQEQ1VTGIiGmHMZjNaWlqkDoOIiAaQTCaDr68vxo0bxyQmIiIigr9Wg7ljbdvL/vvY0LxQdWF8AN68YSo0SttTUv/NrMAt76ai1cBEJiIiIqJzIYqi3VhmZiaKi4sliIaIiAgoLS1Fenq63e+oyMhIBAcHSxQVDVVMYiIaAURRRG1tLTIzM/Hzzz8jIyND6pCIiIiIiIhoEC1ODrV5nFpYjwazUqJounZerB82rUqBm0puM/5TdhVWvr0fzXqTRJERERERDX9hYWEICwuzG8/JyUF+fr7DJCciIqKBIIoiCgsLcfLkSbt1ISEhCA8PlyAqGuqYxEQ0AtTV1eHYsWMoLy+HxWJBU1MTmpubpQ6LiIiIhqkdO3bghhtuQHR0NFxdXeHh4YHExETcf//9KCkpcbrd2rVrIQgCBKG9pVFbWxvWrVuHKVOmQKvVQqvVIiUlBS+99BJMJvsL1O+++651+x9//LHbOG+77TYEBwcjMjIStbW1/fpceqqyshKPPvooJk+eDE9PT2g0GkRGRmLFihXYvXt3l9tGRkZCEASsXLkSAHDgwAFcd911CAsLg0ajQVhYGFatWoXMzMwexZKTk4N7770XiYmJ0Ol0cHFxQXR0NFauXInU1NRzep47d+60/tvs3LnznPZFRAPjonH+0LnYJi1lGTylCaYHZkT74N2bpkOrVtiM78urwfVv7UdDm1GiyIiIiIiGv6ioKISEhNiNFxQUICcnh4lMREQ04ERRxKlTp5CXl2e3LigoCGPGjLGeRybqjElMRCOAp6cnVCqVzVhZWZlE0RAREdFw1dbWhuuuuw4XXngh3n33XeTl5aG1tRWNjY04ceIE1q9fj7Fjx2Lr1q3d7qu8vBwzZ87EAw88gMOHD6OpqQlNTU04cOAA7rrrLlx99dWwWCw221x11VVwcXEBAHzwwQdd7t9oNOLzzz8HAFx44YXw8vIasOfizLZt2xATE4OnnnoKR44cQX19PfR6PQoKCvD+++/jvPPOw5133mn3PB156623MGvWLHz00UcoKiqCXq9HUVERNm3ahKSkJHz66addbr9+/XokJCTgn//8J06cOIGGhga0tbUhLy8P77zzDlJSUvDYY4/1+bkS0dCnVshxxSTbEuxZBi8M5etTyRFe2HzLdLvkq4MFtfj9G/tQ12KQKDIiIiKi4U0QBIwZMwaRkZF260pKSpCZmdmj76pERER9IYoiTp48iaKiIrt14eHhiI2NZQITOcUkJqIRQBAE+Pv724xVVFTwSwgR0QjC93QaaKIoYvHixfjoo48AAIsWLcJ7772HPXv2YO/evfjXv/6F8PBwNDc3Y/Hixd1W9rn66quRnp6Ou+++Gz/++CMOHjyIDz74AOPGjQMAbN26Fa+//rrNNlqtFldccQUA4IsvvkBbW5vT/X/33XeoqamxHmsgn4sjR44cwaJFi9DQ0AClUol7770XO3bswP79+/Haa68hKioKAPDyyy/joYce6nZft956K/z9/fHiiy9i37592LVrFx588EGo1Wro9XosX77caZzr1q3D/fffD6PRiIkTJ+KVV17B9u3bkZqais2bN2PmzJkQRRFPPvkkXnjhhV4/VyIaPs5uKddoUaHU5CpRND0zMdQTH94yA95utjfmHCuqx3Wv70N1k16iyIiIiIiGN0EQEBERgdjYWLt1FRUVSEtLg9lsliAyIiIaySwWC9LS0hwW3BgzZgyioqKYwERdUnQ/hYiGg8DAQJtsVoPBgKqqKrvkJiIiGn5MJhP27dsHT09P+Pn5wcfHB3K5XOqwhpxzucjpplZAo3T8M61pNvS5zLqLSg5XleOP3HUtBpgtPd+vj7u6TzH01BtvvIFvvvkGSqUSW7ZswaWXXmqzfsaMGVixYgXOO+88pKWl4Z577umyXdqBAwewbds2zJs3zzo2ZcoUXHLJJUhISEB5eTk2bNiANWvW2Gy3fPlyfPzxx2hoaMDXX3+NxYsXO9x/R6UmrVaL+fPnD+hzcWT16tUwGAyQy+X4+uuvsWDBAuu6adOmYcmSJZgzZw7S09Oxfv16XH/99Rg/frzDfR09ehQRERH45ZdfEBgYaB0///zzcckll2DBggUwGo24/fbbsX//fptt09PT8cgjjwAAHn/8cTz++OM2JwGSk5Nx7bXX4oYbbsD777+PRx55BCtWrLCrXEVEI8PEUB1i/d2RXdFkHcsyDP3/7wnBHvh49Qwse2MfKhvP/D7PKG3AtRt/weZbpsNfq5EwQiIiIqLhKzg4GAqFApmZmTbnN2pqanDs2DEkJiZCoeDlQiIi6h9tbW2oq6uzG4+Li7M590nkDD+VEI0Qbm5u0Gq1aGxstI4VFxcziYmIaASorq6GyWRCVVUVqqqqIJfLMXPmTCYynSX5r9v7vO1frhyP62dGOlw3/++7UNPct3Y2f7goFvdePNbhuiWv7rW5yNyd/Gcu61MMPSGKIp599lkAwN13322X9NPBy8sL69atw8KFC7Fnzx5kZ2c7vKMTAO666y6bBKYO3t7eWLVqFZ555hkcP34c9fX10Ol01vWXXnopfHx8UF1djc2bNztMYmpqasKWLVsAAAsXLoRGc+bC9kA8l7Pt378fBw4cAADccsstNglMnfe/ceNGzJkzBxaLBRs2bMDLL7/sdJ/PP/+8wy/xF1xwAW655Ra88sorOHDgAFJTUzF16lSb7YxGI6ZOnWqXwNRBJpPhxRdfxKeffoqmpiZ89tlnuOWWW3r0XIloeBEEAdckh+KZ7zKtYzkGD7QYzPCQMK6eiA3Qticyvb4PZQ1nKvFlVzTh2tfaE5mCdC4SRkhEREQ0fPn7+0OhUCAtLc2m2ndDQwOOHDmCiRMnQqVSdbEHIiKinnF1dUViYiKOHTsGi8UCQRCQkJAAX19fqUOjYYLt5IhGkNBQ29YBDQ0NNklNREQ0PFVWVto81ul0TGCifpWeno7c3FwAcFr5qMP5559vXd67d6/TecuXL3e6Ljk5GUB7wlFeXp7NOqVSiSVLlgBobxnn6K6dL7/8Eq2trQDsW8kNxHM52/btZxLmbrrpJqfzZs+ebW2f13mbs3l5eeHKK690uv7GG290eGygvS0fAFxzzTVdlmH29PREYmIigN49VyIafq6aHAJZp7cDE+TYnlUlXUC9EO3njk/WzESol22y0qmqZix5dS+OnK6TJjAiIiKiEcDb2xsTJ060q7rU3NyMI0eO2CQ3ERERnQudToeEhAQoFApMnDiRCUzUK0xiIhpBfH197e6W6NxijoiIhh+TyYSamhqbMT8/P4mioZEqNTXVujxz5kwIguD0j7u7u3Wuo77mHeLj452u8/b2ti47SrjuSIDS6/X47LPP7NZ3tJILDg7G7NmzB/y5nO3EiRMAAJVKhaSkpC7nTp8+HQCQnZ0Ng8FxRa/Jkyd3Wbo/KSnJ+hnv+PHj1vGCggJrkuNDDz3U5XMVBMH6s+nNcyWi4SfAQ4Pzx9p+VthyrFyiaHov3McVH6+ZiUgfV5vxotpWXPPKz/j7tiwYzbzARkRERNQXOp0OkyZNsruOEBYWBpmMlwyJiKj/+Pj4YPr06fD09JQ6FBpm+ImEaASRyWQIDg62GausrHR6wYyIiIa+qqoqiKJofSwIAu9aoH5XUVHRp+1aWlqcrnN1dXW6rvOJUbPZbLd+9uzZiIiIAABs3rzZZl1FRYW1GtHSpUvtTrIOxHM5W0diobe3d5fJRwCsLeJEUURtba3DOd21/1UoFNbEr85JjYPxXIloeLpmim2V3v0F9SisHj7/90M8XfDxmpkY4+dmM262iHjhvzm4asMeZJez6jARERFRX7i7uyMpKcnamj06OhpBQUESR0VERMORXq/vcn13506JHOGrhmiECQoKQkFBgfWCtyiKKCkpQWRkpLSBERFRn5zdSq4nSROj1cFH5/d5Wze185/p9j/OtUkk6w0XlfO2f5/eOhNmS9/22986JxJt3bq1x58buku+6StBELBs2TI8/fTT+N///ofi4mKEhIQAAD755BOYTCYAwLJly+y2Hczn0lX7tsHYT+fn+thjj1nb8HXHzc2t+0lENKxdnBAArVqORv2Z94lNP+fjsUUJEkbVOwEeGny8ZiZufifVro3cieIGXPbibjxwSRxunB0Fmax/3o+JiIiIRgsXFxdMnjwZFRUVCA0N7X4DIiKis9TW1iItLQ2RkZH8XUL9ilfAiEYYlUqFgIAAmzYhJSUlCA8PZzlYIqJhxmg02lVuYSs553zc1QOyX283VfeT+sDTdWD22xc+Pj7WZU9PT0yYMEHCaNotX74cTz/9NCwWCz788EP86U9/AnCmlVx8fDymTJliV41oMJ5LR1Wk6upqmEymLhMLOz6TCYIALy8vh3PKy7tu89S5rWTnVnydn6tSqRwS/25ENDRolHJcOTEA7x8ogVowYfmMSNxyfpTUYfWar7san906Ext25uKF/2TD1Cn512Cy4K/fZGB7RjnWL5mEUC/nFQCJiIiIyJ5KpeJFZyIi6pOqqiqkp6dDFEXk5uZCqVQiICBA6rBohGBGA9EI1FGpoIPRaOxzuxEiIpLO2a3kZDKZTdICUX+ZPHmydXnPnj0SRnLG+PHjMWnSJABnEpfy8vKwd+9eAO1JTo4MxnPpSBYyGAw4cuRIl3P3798PAIiNjYVK5Thx7ciRI9bqUo4cPXrU2h64c6JSdHQ0dDodgKHz70ZEQ8fyaSE4z7UEK3RZuOeCKATpXKQOqU8UchnuvigWX94+GzH+7nbrfzlVg0v/+RM+TT3d58qJRERERGSvtrYWJSUlUodBRERDTFlZGdLS0my+g2dmZqK+vl7CqGgkYRIT0Qjk7u5uvaDVobi4mCd0iYiGGbaSo8EyZcoU692XGzduRFtbm8QRtetIVDp8+DAyMjKsyUyA41ZywOA8l/nzz7QufOutt5zO27t3L9LT0+22OVtNTQ22bt3qdH3nY3Tej1wux8KFCwEA27ZtQ0ZGRvfBE9GoEeKpwQR1DZTCyPgemBiqw9d3zcHNc6JwdhfOJr0J9392DKvfO4iqJr00ARIRERGNIA0NDThx4gSys7NRUFDAawtERAQAOH36NLKysuzGfX19odVqJYiIRiImMRGNUGeXgW1qakJLS4tE0RARUW8ZDAa2kqNBI5PJ8PDDDwMATp06heuvvx56vfOLwA0NDXjppZcGPK7rrrsOwq9Xqjdv3owPP/wQADBz5kxER0c73GYwnktKSgqmTp0KAHj99dfxn//8x25OfX091qxZY43ptttu63Kff/zjHx22ldu1axc2btwIAEhOTsa0adNs1j/00EOQy+WwWCxYvHgxioqKnB7DbDZj8+bNXc4hIhrKNEo5Hr08AR/cPAMhnvaVpX5ML8cl//gffkgrc7A1EREREfVEc3Mzjh8/DovFAgDIz89Hbm4uE5mIiEYxURRx6tQpnDp1ym5dYGAgEhISIJMx9YT6B2/lJxqhfHx8oNFoYDQaERAQgJCQELi6ukodFhER9VBVVZXNY7aSo4F266234scff8SXX36JTz/9FIcOHcKaNWuQkpICnU6HhoYGZGZmYufOndiyZQs0Gg3uvPPOAY0pNDQUc+fOxc6dO/Hyyy+jrq4OgPNWcoP5XF5//XVMnz4dBoMBCxcuxF133YVFixbBzc0Nhw8fxjPPPGP9Uv+nP/3Jpg3c2SZNmoT09HQkJyfjoYceQkpKCvR6Pb799lv84x//gMlkgkKhwMsvv2y3bWJiItavX497770X6enpmDBhAlavXo0LL7wQAQEBaGtrQ35+Pvbu3YvPPvsMpaWlOH78uF3COxHRcDJzjA++v+c8PLE1HZ8dtE3MrG42YM17B3HNlFA8fkUCPDRKiaIkIiIiGp5qa2vtWp4XFxfDYDAgLi4OcrlcosiIiEgKZrMZJ0+eREVFhd26sLAwREVFWW9EJeoPTGIiGqEEQUBCQgJcXFzYeoiIaBg6u5Wcj48PTxLRgBIEAR9//DH+8Ic/4NVXX0Vubi4eeOABp/P9/f0HJa7ly5dj586d1gQmhUKBpUuXdrnNYDyXpKQkbN26FUuWLEFDQwOef/55PP/883bz7rjjDjz99NPd7uvOO+/Ebbfd5jCZSqVS4Z133sH06dMdbn/PPffAzc0N99xzD+rr67Fu3TqsW7fO4VyVSgWNRtODZ0hENLRpNUqsXzIJFycE4OEvjqO62WCz/vNDRfjlVDXWLZmIWWN8JYqSiIiIaPjpuOklNzfXZryyshLNzc0YP348b5gmIholWltbkZaWhubmZrt10dHRCAsLkyAqGulY04toBNNqtUxgIiIahgwGgzVhowNbydFgUCqV2LBhA44ePYq77roLiYmJ0Ol0kMvl0Ol0SEpKwk033YTPPvsMGRkZgxLT4sWLoVarrY8XLFjQo/8Pg/FcFixYgJycHDz88MNISkqCh4cH1Go1wsPDsXz5cvz000946aWXelRK+eabb8ZPP/2EpUuXIjg4GCqVCiEhIbj++utx+PBhXHvttV1uf8stt+DUqVN44oknMHv2bPj6+kKhUMDNzQ1jx47FNddcg1dffRXFxcWIiYnp0/MlouGvoqENz2/LwtotaVKH0m8uGR+IH+49HxcnBNitK65rxbLX9+HJr9PRZjRLEB0RERHR8BQaGor4+Hi78ZaWFhw6dMiugjgREY081dXVOHTokMMEprFjxzKBiQYMsxuIiIiIhpizqzDJ5XJ4e3tLFA2NRomJiXjhhRd6vd3atWuxdu3abufNmzcPoij2aJ+enp5oa2vrdSwd+vpcVq5ciZUrV3Y7z8/PD0899RSeeuqpPkRna8aMGfj444/7vH1AQAAee+wxPPbYY+ccy9l6829GRENPeaMea78/gq1HS2A0i1DIBNw6dwwCdSOjMpuvuxobVyTj80PFWLslDU162/Ynb+7Ow66TlXjgkjjMHxcAmYxl7omIiIi6ExAQAKVSiYyMDJv2cmazGWlpaQgPD0dkZCRbCBERjTCiKKKwsBD5+fl26+RyOeLj4+Hry4rHNHBYiYmIiIhoCFKpVNZltpIjIiKic6GWy/Dt8VIYze3JiCaLiHf35ksbVD8TBAGLk0Px/T3nYWa0j936nIomrH7vIC76+y68/0sBKzMRERER9YC3tzemTJkCNzc3u3WFhYU4fvw4jEajBJEREdFAyc7OdpjA5OrqiilTpjCBiQYck5iIRiFRFHknPRHREBYSEoIZM2Zg0qRJCA4ORkCAfXsUIiIiop7ydFXi6imhNmMf7C9Eq2HkJfKEerli883T8efLE6BS2J/2yqtqxqNfncCsZ/6Lf/x4ElVNegmiJCIiIho+XFxcMHnyZPj7+9utq62txaFDh9DU1CRBZERENBACAwPtquz5+vpi8uTJcHV1lSgqGk3YTo5oFDEajSgtLUVJSQni4uLg5eUldUhEROSEIAjw9PSEp6en1KEQERHRCHDj7Ch8sK/Q+riuxYjPDxXh9zMiJIxqYMhkAm6aE4XzY33xx0+O4nhxvd2cmmYD/vWfbLy6KxdXTwnFzedFYYyfuwTREg1vFosIg9kCo9kCo1mE0WyBwWQ5M2Y6s95g6phngcEswmS2wGQRYbaIMFlEWH7922z5ddwswiyeWW/u9MdksbT/bRYxK8YHV00O7T5YIiLqs472QVqtFrm5uTbr2tracPjwYcTFxTlMdCIiouHFw8MDMTExyM7OBgBER0cjNDSU7UNp0DCJiWiUKCgoQGFhISwWCwCguLiYSUxERERERESjRIy/O+bF+WFnVqV17K09eViWEg6ZbGSeiIwN0OLL22dhy9ESvP5THjJKG+zm6E0WfLi/EB/uL8T8cQFYfX40pkV68eQsDXsWi4gWoxktehOa9Ca0GMxo0pvQrDeh2WBGq8GEVoMZbSYL2oxmtBnb/9abziy3Gc1otVl3ZrzN2J6MZLJIX+lbrZQxiYmIaBAIgoDQ0FC4u7sjPT3dpo2cxWLh5yciohEkKCgIra2t8Pb25vVkGnRMYiIaJZRKpTWBCQCqq6vR2toKFxcXCaMiIiIiIiKiwXLTnCibJKZTlc3YdbISF8SP3DvmFXIZrp4Siqsmh2BPTjU2/nQK/ztZ6XDu9oxybM8ox6RQHW45PxqXjg+EQm7fko5oIImiiBaDGQ1tRtS3GtHQakJDq9H2cZsRTW0mNBvOJCU1n5Wo1DIC20U6Yx4CiVRERKOJp6cnkpOTkZaWhsbGRgBAaGgo/Pz8JI6MiIh6w2g0QqFQOExCFQQBY8aMkSAqIiYxEY0aAQEBOHXqFMzmMyexiouLERMTI2FURERERKNXfn6+1CEQ0SgzJ8YXcQFaZJU3Wsfe3J03opOYOgiCgDmxvpgT64vMsga88VMe/n2kGEazffLD0aJ63PnBYYR6ueDG2VH43bQwuKl5Co16x2IRUd9qRE2LAbXNBtQ0G1DbYkBNsxF1LQa7pKSG1l8ft5mYlNNLJgf/j4mIaGCp1WokJSUhNzcXLS0tiI6OljokIiLqhYaGBqSnpyM4OBjh4eFSh0Nkg2dgiEYJuVyOoKAgFBUVWcfKysoQGRkJhYJvBUREQ4HRaIRSqZQ6DCIiIhqhBEHAjXMi8eDnx61ju3OqkFnWgPhADwkjG1zxgR5Yv2QS7r8kDpt+zsfmXwrQ0Gaym1dU24q/fJ2Of24/ieUzInDNlFCM8XNjq5RRqs1oRmWjHlVNemsyUm2zwUGSkgG1Le2JSsxFAlQKGdRyGZQKGZRyAUq5DCq5DCqFDHKZAIVcBrkAKGQdjwXIZQLkgtDpcfscuUwGhUyAXC5AIRMgE9r/nhTmKfXTJCIalWQyGWJjY9lKjohomCktLUV2djZEUUReXh7c3d3h7e0tdVhEVsxcIBpFQkJCbJKYzGYzysrKEBoaKmFUREQEAK2trdi/fz88PT3h7+8PX19fJjQRERFRv7syKQTPfZ+F6maDdeyt3Xl4bvEkCaOSRoCHBg9eGo87L4jBJ6mn8ebuPBTVttrNa2gz4ZWduXhlZy4CPTSYFeODOTG+mB3jiwAPjQSRU39pNZhR1aRHxa/JSVVNelQ1Gs4sN+l/TVwyoElvn+g2nChkAtzUCrirFXBVyeGqVkCjkEGjlEOjlMFFKf91WQ61UgaNQu5gnQxqpfzXde2JSGqFDEr5mT8dCUrKX5OReFGbiGjkk8mct9+tqKhATU0NYmNjIZfLBzEqIiI6m8ViQU5ODkpLS23GMzIyMGXKFLi4uEgUGZEtJjERjSIajQa+vr6oqqqyjpWUlCAkJIQnlYiIJFZRUQEAqKurQ11dHfLz8zFjxgy+PxMREVG/0ijlWD4jAi/8J9s69tWREjxwaTx83dUSRiYdN7UCq2ZHYcWMCHyfVobX/3cKR4vqHc4ta2jDF4eK8cWhYgBAjL+7NaFperQ3PDRMQpeaxSKiutmAisY2VDTqUdmgP7Pc2ClhqVGPZoNZ6nC7pdUo4KFRwsNFCZ1L+7JWo4RW82tCklr+a2KSAu5qOVxVCripFXBTy+GmOjNHreCFYyIiGlzNzc3IysqCxWJBc3MzEhISeIGciEgibW1tSE9PR2Njo906Nzc3JprSkMIkJqJRJiQkxCaJqbW1FTU1NfDx8ZEwKiIiqqystHns4+PDBCYiIiIaECtmRODVnbkwmC0AAIPJgvd/KcA988dKHJm0FHIZLp8YjMsSg7A/rwav/5SH7RnlXW6TU9GEnIombPo5H3KZgImhOsyJ8cWsMb6YEuHJxJF+pDeZUdVkQEVDe0JSe4LSmeWKxjZr1STzEOvj5qaSw8tNBW83FbxcVfByVULn0pGYpPw1SUkBj1+XO8bcNQrIZfxOQEREw4/JZEJaWhoslvbPm01NTTh06BDGjRvHlkVERIOstrYWGRkZMBqNdutCQ0MRFRXVZVU9osHGJCaiUUan08HNzQ3Nzc3WsaKiIiYxERFJqKWlxeZ9GQD8/f0lioaIiIhGOj+tGlckBeOzg2fajb//SwFunTsGGiWTbgRBwPRoH0yP9vk1QSkP29MrUNbQ1uV2ZouIw4V1OFxYhxf/mwONUoaUKB/MHuOD2TG+SAjygIwJKTZEUUR9q9FaJamyUzJSR9Wkjr/rW+1PuEtBJZfB+9eEJG83VXtykqvSJkmp89+erkr+vyIiolGnubkZBoPBZsxkMuH48eOIjIxEeHg4b94jIhpgoiji9OnTyMvLs1snk8kQFxfH6xA0JDGJiWiUEQQBISEhOHnypHWsrq4Ozc3NcHNzkzAyIqLRq6OVXAeVSgWdTidRNERERDQa3Dg7yiaJqarJgC1HS7B0apiEUQ09Mf7u+OtvE/HklSJyK5vxc24VdmdXYe+pajS2mbrcts1owf9OVuJ/J9srbqoVMoR4uiDEywWhXi4I9XJFiGf7coiXC/y1mmFfdUcURbQYzKhpNqC2xdDpbyPKahqxpzkYLaICP206gppmIyqb9DCapa+apFbI4Ouuhq9WDT93Vfuyuxp+WvWvyyr4/rrsoVHwoisREVE3dDodJk+ejLS0NLS2ttqsy8/PR3V1NeLi4nhNgohogLS0tCArKwsNDQ1261xcXJCQkAB3d3cJIiPqHpOYiEahgIAA5OXl2ZQNLC4uxtixo7t1ABGRVM5uJefr68sLI0RERDSgEoI9MGuMD37OrYYgABfF+yPWnycwnREEATH+7ojxd8f1MyNhMltwoqQBe3KqsCenCqn5tdb2fM7oTRacqmrGqapmh+uVcgFBul+Tmjx/TXKyLrsgUKeBUj6wJf5FUYTeZEGLwYxWoxmtBhNaDRa0GExoNZrR0GZCbbPBYZJSbbMBNS0GGExd/Rx+bR9T0jigzwMABAHwcVPDX6uGv8evf2s18NPaJif5adVwVzMxiYiIqL+5ublhypQpyMrKQlVVlc26xsZGHDx4EJGRkQgLC+PvYSKifiKKIoqLi5GXl2dt6dmZt7c3xo0bB4WCaSI0dPHVSTQKyWQyBAUFobCw0DpWXl6OqKgoKJVKCSMjIhp9mpub0dLSYjPGEq5EREQ0GFafH40Yf3esmh2FKF/eBd8bCrkMSWGeSArzxB0XxKDVYEZqQQ325FRjT04VTpTUQ+xlgSGjWURhTQsKa1qczpEJgFIug0oug1Ihg1IunHksl0GpaH98ZuzXxwoZlDIBBrMFrQYzWgxmtBnNnZKVfv3baO513INNJZdZE5HOJChpbJb9tGr4uKmgGOCkLyIiIuqaQqFAQkKCw3ZGoigiLy8PVVVVrMpERNQP9Ho90tPTHVZfAoCIiAhEREQwcZSGPCYxEY1SwcHBOH36NMRfz05aLBaUlpYiPDxc4siIiEYXR63kPDw8JIqGiIiIRpN5cf6YF8fk6f7gopLjvFg/nBfrBwCoazFgb241dudU4efcauQ5qb7UWxaxvaKT3mQB9P2yyyHDy1XZKTnp14pJ7meSlTrW6VyUPOlOREQ0jAiCgPDwcOh0OmRlZdm1l2NVJiKi/qFQKGAwGOzGNRoN4uLi4OnpOfhBEfUBk5iIRim1Wg0/Pz+bi+clJSX8kkBENIhEUbRrJefn58f3YSIiIqJhztNVhd8kBuE3iUEAgIrGNhRWt6C4rhVFtR1/2h8X17a2JyWNMEq5AC9XFbzd2v9oVQKKTmXBVTDhNxfMRpifpzU5ycddBbVCLnXIRERENIB0Oh2Sk5ORn5+PoqIim3WdqzKNGzcOLi4uEkVJRDR8yeVyjB07FseOHbOOhYSEICoqCnI5v2/R8MEkJqJRLCQkxJrE5OnpiZCQEIkjIiIaXZqbm+3uPmMrOSIiIqKRp73dmQZTHawTRRFVTQZrUlNRbXtiU+fHLQbzoMfcQSWXwUUlh4tSDje1HD5uani5KeHtprImKVn/dlPB21UFLzcl3NUKm+T8hoYGvPLKTgDA4slBrD5KREQ0CsnlcowZMwa+vr4OqzK1trZCJmM7WCKivvLy8kJwcDBqampYfYmGLSYxEY1iHh4eiIyMhI+PD9zd3aUOh4ho1Dm7lZxarYZWq5UoGiIiIiKSgiAI1lZpk8O97NaLoojaFiNqmvUwmkUYzRYYTBYYzJb2xyZL+1jHY7PFOsfmsdkCtVwGjUoOV6W8PTFJpei03J6o5Prr3x2PFXJeSCQiIqL+5awqU0xMDNRqtYSRERENfa2trRAEARqNxuH66OhoREdHs/oSDVtMYiIa5SIiIqQOgYhoVGIrOSIiIhqKjGYLvjtRBlEUcWUSq/UOBYIgWFuyEREREY0UZ1dlcnV1ZYVyIqIuiKKI4uJi5OXlwcPDAxMnTnR4PYHJSzTcMYmJiIiISAJNTU1oa2uzGeOJGqKhY9OmTVi1ahUAIC8vD5GRkdIGREQ0wOpbjPhgfyHe3ZuP0vo2BHposDAxCEpW4SEiIiKiAdRRlclisTi9uc9oNMJoNMLV1XWQoyMiGhpaW1uRlZWF+vp6AEBdXR1KS0sRHBwscWRE/Y9nooiIiIgkcHYrOY1Gw9aeREREJJm86mY8+30mSuvbk6zLGtrw7fFSiaMiIiIiotFALpdDqVQ6XZ+bm4uDBw/i9OnTEEVxECMjIpJWR/Wl1NRUawJTh9zcXLS2tkoUGdHAYRITERERkQTUajVcXFysj/39/dlKjoiIiCSTFOaJ5Agvm7G3dufxIhERERERSaq6uhrl5eWwWCw4deoUjhw5gpaWFqnDIiIacK2trTh69ChycnJgsVjs1gcGBkKlYttxGnnYTo6IbIiiiJqaGpSXlyM+Ph4yGXMdiYgGQmhoKEJCQtDU1ITKykq2kiMiIiLJ3TQnCgcLaq2PjxbV42BBLaZGeksYFRERERGNVkajESdPnrQZa2howMGDBxEVFYWQkBDeFEhEI44oiigpKcGpU6ccJi9pNBqMHTsWXl5eDrYmGv6YxEREAM6UIywpKbGWHvT29kZgYKDEkRERjVyCIECr1UKr1UodChEREREWJAQgxNMFxXVnytG/tSePSUxEREREJAmZTAY/Pz8UFxfbjFssFuTm5qKyshKxsbFwd3eXKEIiov7V3NyM7Oxsu9ZxHYKDgxEdHQ25XD7IkRENHpZYISIA7RfSq6qqbHqn5ufnO8zwJSIiIiIiopFHIZdh1exIm7HvT5ThdA3bdRARERHR4JPL5YiJicGkSZOg0Wjs1ndUZcrKyoLBYJAgQiKi/mEwGHDy5EmkpqY6TGBSq9WYOHEiYmNjmcBEIx6TmIjIKiwszOaxXq+3u8OBiIiIRr4dO3bghhtuQHR0NFxdXeHh4YHExETcf//9KCkpcbrd2rVrIQiCtZR7W1sb1q1bhylTplirjqWkpOCll16CyWSy2/7dd9+1bv/jjz92G+dtt92G4OBgREZGora21uGcvj6XnqqsrMSjjz6KyZMnw9PTExqNBpGRkVixYgV2797d5baRkZEQBAErV64EABw4cADXXXcdwsLCoNFoEBYWhlWrViEzM7NHseTk5ODee+9FYmIidDodXFxcEB0djZUrVyI1NfVcnyoAoLW1FX/7298wadIkuLm5wcfHB7Nnz8brr78Oi8WCnTt3Wv8Nd+7c2S/HJKLBtXRaGNxUZ06IWkTgnZ/zpQuIiIiIiEY9T09PTJ06FSEhIQ7Xl5WVYd++fSgoKOCN2UQ07JSWlmL//v0oLS11uD4oKAhTp05l+zgaNZjERERW3t7e0Ol0NmOFhYUOLzISERHRyNPW1obrrrsOF154Id59913k5eWhtbUVjY2NOHHiBNavX4+xY8di69at3e6rvLwcM2fOxAMPPIDDhw+jqakJTU1NOHDgAO666y5cffXVdicWr7rqKri4uAAAPvjggy73bzQa8fnnnwMALrzwQrsv8f35XJzZtm0bYmJi8NRTT+HIkSOor6+HXq9HQUEB3n//fZx33nm48847e3QC9a233sKsWbPw0UcfoaioCHq9HkVFRdi0aROSkpLw6aefdrn9+vXrkZCQgH/+8584ceIEGhoa0NbWhry8PLzzzjtISUnBY4891ufnCrSfFE5OTsYjjzyCY8eOoaWlBTU1Nfj555+xevVqLFy4kHe+Eo0AHhollk6zvcHl4wOn0aTn90IiIiIikk53VZksFgvy8/ORnp4OX19fCSIkIuobhUIBs9lsN95RfWns2LFQKBQSREYkDSYxEZGVIAiIjo62GTOZTDh9+rREERERjSyiKNq07SQaSkRRxOLFi/HRRx8BABYtWoT33nsPe/bswd69e/Gvf/0L4eHhaG5uxuLFi7ut7HP11VcjPT0dd999N3788UccPHgQH3zwAcaNGwcA2Lp1K15//XWbbbRaLa644goAwBdffIG2tjan+//uu+9QU1NjPdZAPhdHjhw5gkWLFqGhoQFKpRL33nsvduzYgf379+O1115DVFQUAODll1/GQw891O2+br31Vvj7++PFF1/Evn37sGvXLjz44INQq9XQ6/VYvny50zjXrVuH+++/H0ajERMnTsQrr7yC7du3IzU1FZs3b8bMmTMhiiKefPJJvPDCC71+rkD7Z8LLL78cGRkZAIAFCxbgyy+/RGpqKr744gvMnz8fP/zwAx599NE+7Z+IhpZVs6Lwa1E9AECj3oSP9hdKFxARERER0a86qjJFRERAJrO/zGk0GhETE4Nx48ZBFEUJIiQi6h1fX194eHhYH8tkMoSHh7P6Eo1aTNkjIhseHh7w9fVFVVWVdayoqAjBwcFQq9USRkZENPzV1dXh2LFj0Gq18PPzg5+fn8M7x6iPmqu6n+OMyg1QujjZbzWAPp70Urq079uRlhpA7EWJc7eBvYvwjTfewDfffAOlUoktW7bg0ksvtVk/Y8YMrFixAueddx7S0tJwzz33dNku7cCBA9i2bRvmzZtnHZsyZQouueQSJCQkoLy8HBs2bMCaNWtstlu+fDk+/vhjNDQ04Ouvv8bixYsd7r+jUpNWq8X8+fMH9Lk4snr1ahgMBsjlcnz99ddYsGCBdd20adOwZMkSzJkzB+np6Vi/fj2uv/56jB8/3uG+jh49ioiICPzyyy8IDAy0jp9//vm45JJLsGDBAhiNRtx+++3Yv3+/zbbp6el45JFHAACPP/44Hn/8cWs7PwBITk7GtddeixtuuAHvv/8+HnnkEaxYsaLXJ0Bee+01HDx40PrcX3vtNZtjXHXVVbjpppvw1ltv9Wq/RDQ0hfu4YkFCAH5IK7eObdiZi6XTwuChUUoYGRERERFRe1WmyMhIBAUFIT8/H2VlZXZz6urqbL4fExFJTRRFh+9LgiBgzJgxOHz4MAICAhAVFcVrsjSqMYmJiOxERUXZJDFZLBYUFBRg7NixEkZFRDT8VVZWAgAaGxvR2NiIiooKJCcnSxzVCLJuTN+3XbgeSLnF8bqXpwEt1X3b79z/Ay5wUoXn7d8AlZk939fa+r7F0AOiKOLZZ58FANx99912ST8dvLy8sG7dOixcuBB79uxBdnY2YmNjHc696667bBKYOnh7e2PVqlV45plncPz4cdTX19u0s7300kvh4+OD6upqbN682WESU1NTE7Zs2QIAWLhwoU0y4EA8l7Pt378fBw4cAADccsstNglMnfe/ceNGzJkzBxaLBRs2bMDLL7/sdJ/PP/+8TQJThwsuuAC33HILXnnlFRw4cACpqamYOnWqzXZGoxFTp061S2DqIJPJ8OKLL+LTTz9FU1MTPvvsM9xyi5PXuxMbNmwAAAQEBOAf//iHwzn/+te/sHXrVut7HRENb6vPH2OTxFTTbMArO3Px4KXxEkZFRERERHSGWq1GXFwcQkJCkJubi7q6OgDtLeYdJTYREUnBaDSisLAQra2tmDBhgsM5Hh4eSElJgYuLkxttiUYRtpMjIjuurq4ICgqyGSstLUVLS4tEERERDX+iKNpd2Pfz85MoGiJb6enpyM3NBQCnlY86nH/++dblvXv3Op23fPlyp+s6kvdEUUReXp7NOqVSiSVLlgBobxnXcQKysy+//NLamvHsVnID8VzOtn37duvyTTfd5HTe7Nmzre3zOm9zNi8vL1x55ZVO1994440Ojw20t+UDgGuuuabLO0w9PT2RmJgIoHfPFWj/HJieng4AWLp0KVxdXR3Oc3d3x9KlS3u1byIaupIjvLAgIcBm7K3deSipY2tcIiIiIhpa3N3dMXHiREyYMAFqtRqFhYVOW8mZTCZYLL2ojE1E1EcWiwVFRUXYv38/ioqKUF1djZqaGqfzmcBE1I5JTETkkKN+0mdfZCQiop6rra2FyWSyGWMSEw0Vqamp1uWZM2dCEASnf9zd3a1zu7qrMT7eeaUOb29v63JjY6Pd+o4EKL1ej88++8xufUcrueDgYMyePXvAn8vZTpw4AQBQqVRISkrqcu706dMBANnZ2TAYDA7nTJ48GQqF8yK5SUlJUKlUAIDjx49bxwsKCqzJkQ899FCXz1UQBOvPprd3o3Y+5rRp07qcm5KS0qt9E9HQ9uBv4iGXnUmQ1JssWL8tS8KIiIiIiIgcEwQBPj4+GDduXJdJAnl5eThw4AAqKyudJjoREZ0LURRRVVWF1NRU5Obm2lwXOHXqFN97iLrBJCYickitViMkJMRmrKqqCg0NDRJFREQ0vJWWlto81mq1vLOChoyKioo+bddVlUZn1XoA2CRKm81mu/WzZ89GREQEAGDz5s026yoqKqzViJYuXWqXdD0Qz+VsHSdDvb29u0w+AmBtESeKImprax3O8ff373IfCoXCmvjV+UTsYDzXs4/ZXawBAQFdriei4WWMnzuWpYRbH08K1WHp1DAJIyIiIiIi6lpXVYqbm5tRUlKCtrY2pKen48iRI7zmQUT9qrGxEUePHkVaWpq1knxnLS0t7HxD1I2uz7gT0agWFhaG0tJSmwzhvLw8TJw4scsvAkREZMtgMKC6utpmjBf6B8D9uX3fVuXmfN0dBwD08e4YZReJaqu+A8ShUb68cyLR1q1bERkZ2aPtukto6StBELBs2TI8/fTT+N///ofi4mJrcvUnn3xi/WyybNkyu20H87n01+ehvu6n83N97LHHrG34uuPm1sXrvRv8DEg0+vxhfixSC2px27wxuDwxCDIZ3weIiIiIaHg6deqUzeOGhgYcPnwY/v7+iIqKgkajkSgyIhru9Ho98vLyUF5e7nSOr68voqOjYTQaBzEyouGHSUxE5JRSqUR4eLjNB/u6ujrU1tbatIEhIqKulZWV2ZSIlclkTGIaCG6+A7Rfn4HZr+vQ+V3q43PmOXp6emLChAkSRtNu+fLlePrpp2GxWPDhhx/iT3/6E4AzreTi4+MxZcoUu2pEg/FcOj4HVVdXw2QydVmNqaN1myAI8PLycjinq5MbAGAymWyqP3Xo/FyVSuWA/bt1jru7WLtbT0TDj6+7Gt/ePYdJjEREREQ0rJnNZpsbtjurqKhAZWUlQkNDER4e3m3VZSKiDmazGYWFhSgqKoLF4viGVa1WizFjxkCn0wEAk5iIusF2ckTUpeDgYKjVapuxjotxRETUPVEU7VrJ+fv782QIDSmTJ0+2Lu/Zs0fCSM4YP348Jk2aBOBM4lJeXh727t0LoD3JyZHBeC4dyUIGgwFHjhzpcu7+/fsBALGxsVCpVA7nHDlyxOmJVAA4evQoDAaDzbEBIDo62nryYyD/3RITE63LBw4c6HJud+uJaHhiAhMRERERDXdyuRxJSUlISEhwWHFJFEWcPn0av/zyC06dOgW9Xi9BlEQ0XBgMBuTl5eGXX35BYWGhwwQmtVqN+Ph4TJ482XoOj4i6xyQmIuqSXC5HREQEgPZftnFxcRg3bpzEURERDR+1tbVoa2uzGQsKCpIoGiLHpkyZgtDQUADAxo0b7V6zUulIVDp8+DAyMjKsyUyA41ZywOA8l/nz51uX33rrLafz9u7di/T0dLttzlZTU4OtW7c6Xd/5GJ33I5fLsXDhQgDAtm3bkJGR0X3wfRAcHGz9/Pfpp5+itbXV4bzm5mZ88sknAxIDERERERER0bkSBAF+fn6YNm0aoqOjIZfL7eaYzWacPn0a+/btQ1ZWFlpaWiSIlIiGMlEUcezYMRQWFjq8MVEulyMqKgrTpk1DQEAAbwwi6iUmMRFRtwIDAxEbG4uUlBQEBgbyly0RUS+cXYXJzc0NWq1WomiIHJPJZHj44YcBAKdOncL111/f5R2HDQ0NeOmllwY8ruuuu876uWPz5s348MMPAQAzZ85EdHS0w20G47mkpKRg6tSpAIDXX38d//nPf+zm1NfXY82aNdaYbrvtti73+cc//tFhK7Zdu3Zh48aNAIDk5GRMmzbNZv1DDz0EuVwOi8WCxYsXo6ioyOkxzGYzNm/e3OUcZzriLysrw3333edwzr333mvX3o+IRjaLRYTB5LhcPhERERHRUCWTyRAWFobp06cjODjY4RxRFFFWVoYDBw7gxIkTQ+aGLyKSniAITt87goKCkJKSgvDwcIeJkkTUPfYxIaJudfXLmIiInDMYDKiurrYZCwoKYjIoDUm33norfvzxR3z55Zf49NNPcejQIaxZswYpKSnQ6XRoaGhAZmYmdu7ciS1btkCj0eDOO+8c0JhCQ0Mxd+5c7Ny5Ey+//DLq6uoAOG8lN5jP5fXXX8f06dNhMBiwcOFC3HXXXVi0aBHc3Nxw+PBhPPPMMzh16hQA4E9/+pNNG7izTZo0Cenp6UhOTsZDDz2ElJQU6PV6fPvtt/jHP/4Bk8kEhUKBl19+2W7bxMRErF+/Hvfeey/S09MxYcIErF69GhdeeCECAgLQ1taG/Px87N27F5999hlKS0tx/Phxa7Wqnrrtttvw9ttv4/Dhw3jllVeQl5eHW2+9FWFhYTh9+jQ2bNiAbdu2YerUqUhNTe3VvoloePo5twpPf5uJixMCcPdFsVKHQ0RERETUa0qlErGxsQgJCUFeXh6qqqoczqutrWUyAtEoJIqi03P5AQEByM/Ph9FoBAD4+PggKioKbm5ugxki0YjEJCYiIiKiAVJWVgZRFK2PZTIZAgICJIyIyDlBEPDxxx/jD3/4A1599VXk5ubigQcecDrf399/UOJavnw5du7caU1gUigUWLp0aZfbDMZzSUpKwtatW7FkyRI0NDTg+eefx/PPP28374477sDTTz/d7b7uvPNO3HbbbQ6TqVQqFd555x1Mnz7d4fb33HMP3NzccM8996C+vh7r1q3DunXrHM5VqVTQaDQ9eIa2FAoFvv76a1x44YXIysrC999/j++//95mzoIFC3Dffffhkksu6fX+iWj4yK9qxpNfp+M/me2V13Irm3BtShj8tb1/byEiIiIiGgpcXV0xfvx4tLS0oKioyO6cXlBQEJRKpYQREtFgslgsqKysxOnTpxEfHw93d3e7OXK5HKGhoWhtbUVoaCiTl4j6EdvJEREREQ0AURTtWsn5+/tDoWAOOQ1dSqUSGzZswNGjR3HXXXchMTEROp0OcrkcOp0OSUlJuOmmm/DZZ58hIyNjUGJavHgx1Gq19fGCBQvg5+fX7XaD8VwWLFiAnJwcPPzww0hKSoKHhwfUajXCw8OxfPly/PTTT3jppZcgk3X/tevmm2/GTz/9hKVLlyI4OBgqlQohISG4/vrrcfjwYVx77bVdbn/LLbfg1KlTeOKJJzB79mz4+vpCoVDAzc0NY8eOxTXXXINXX30VxcXFiImJ6dPzDQ4OxuHDh/HXv/4VEyZMgIuLCzw9PTFjxgxs2LAB3333HVQqVZ/2TUTDR6vRjP9mnWkd2WIw45/bsyWMiIiIiIiof7i6umLs2LGYMWMGwsLCrNWXuqpmXFdXB5PJNFghEtEAMplMKCoqwv79+5GZmYnm5mYUFRU5nR8eHo64uDgmMBH1M15FI6Jz0tDQAI1GwwtWRERnqa2tRVtbm81YUFCQRNEQ9U5iYiJeeOGFXm+3du1arF27ttt58+bNs7mjsSuenp52/5d6o6/PZeXKlVi5cmW38/z8/PDUU0/hqaee6kN0tmbMmIGPP/64z9sHBATgsccew2OPPXbOsTjj4uKCRx55BI888siAHYOIhrZxQR5YPCUUnx48cyL34wOncePsSMT4ayWMjIiIiIiof6hUKkRHRyM8PBz19fVOKxobjUacOHECQPuNPyEhITY3YhHR8GAwGFBcXIySkhK7pMSKigpERkb2qbI5EfUNKzERUZ+0tLQgLS0Nhw8fRmFhodThEBENOTqdDnFxcdBq2y/mubm5WZeJiIiIhrP7FsRBozxzSslsEfHMd5kSRkRERERE1P8UCgV8fHycri8tLYXZbIbZbMbp06exb98+ZGVloaWlZRCjJKK+amlpwcmTJ/HLL7+gsLDQYVU1URRRXl4uQXREoxcrMRFRr5WVlSErK8v6uKSkBCEhIXBxcZEwKiKioUUulyMwMBCBgYFoamqCyWSCIAhSh0VERER0zgJ1Gtw8Jxov7cixjm3PqMDe3GrMHOP8Ig8RERER0UhhsVjs2kyJooiysjKUlZXBx8cHYWFh0Ol0EkVIRM40NDTg9OnTqKqq6nKeh4cHwsLCukxmJKL+xyQmIuo1T09PCIJgbQMjiiLy8/Mxbtw4iSMjIhqa3N3dpQ6BiIiIqF+tmRuND/cXorrZYB3727cZ+PcdsyGTMXGbiIiIiEY2k8kEnU7nNAmiuroa1dXV8PDwQGhoKHx8fCCTsUEOkVQsFgtqampQVFSE+vr6LucyCZFIWvxtSUS9ptFoEBISYjNWUVGBxsZGiSIiIiIiIiKiwaTVKHHP/FibsePF9dh6rESiiIiIiIiIBo9KpcL48eMxbdo0BAUFOa3A3tDQgPT0dOzduxfZ2dloaGiw3iBORIPnyJEjSEtLc5rAJAgCAgMDMW3aNEyYMIEJTEQSYhITEfVJeHg45HK5zVheXp5E0RAREREREdFguzYlHNF+bjZjz32fhTajWaKIiIiIiIgGl6urK8aOHYsZM2YgPDwcCoXjJjgmkwklJSU4fPgwUlNT0dDQMMiREo1uXl5eDsflcjnCwsIwffp0xMXFwdXVdZAjI6KzsZ0cEfWJUqlEeHi4TeJSbW0tamtrnX4QICIiIqIz8vPzpQ6h382bN493lBKNIkq5DP93aTxWv3fQOlZc14p39+Zj9fljJIyMiIiIiGhwqVQqREVFISwsDGVlZSgqKoJer3c4t6WlBWq1epAjJBrZzGYz6uvr4e3t7XB9QEAACgsLrY9VKhVCQ0MRFBTkNPmQiKTB/5FE1GchISEoLi6GwWCwjuXl5cHT09Np6VQiopFMr9fj1KlTCAoKgk6n43shERERjXgXJwQgJdIb+/NrrGMv/TcHS6eGwdNVJWFkRERERESDT6FQIDQ0FMHBwaisrERJSYld1SUvLy+nSUxmsxkymYznFYl6QBRF1NfXo7y8HJWVlTCbzUhJSYGLi4vdXFdXV3h4eAAAgoKC4O/vD5mMTauIhiImMRFRn8nlckRERCA7O9s61tjYiKqqKvj5+UkYGRGRNMrKylBRUYGKigq4uroiJCQEwcHBUodFRERENGAEQcDDl43Db1/eYx1raDPhxf/m4M+XJ0gYGRERERGRdGQyGQICAhAQEIDW1laUl5ejvLwcbW1tCAgIcLpdfn4+KisrrduytRWRvdbWVuu5+La2Npt15eXliIyMdLjdxIkTIZfLByFCIjoXTC8cQC0tLXjuuecwbdo0eHt7w83NDfHx8bjvvvtQUFBwzvuPjIyEIAi9+uOoZcXatWt7vP3OnTvPOW4aWYKCguwymvPy8mCxWCSKiIhIGqIoorS01Pq4paUFjY2NEkZERERENDiSwjxx+cQgm7F39+ajoLpZooiIiIiIiIYOFxcXREZGIiUlBUlJSfD19XU4TxRFVFRUQK/Xo7CwEAcOHMChQ4dQUlICo9E4yFETDS0mkwklJSU4fPgw9u/fj8LCQrsEJqA9iUkURYf7YAIT0fDASkwDJCcnBwsXLrSpUAMAWVlZyMrKwhtvvIHNmzfj8ssvH7SYdDodAgMDB+14NDoIgoCoqCikp6dbxzoyoFl9hIhGk9raWrs+90FBQU5mExEREY0sD1wSjx/SymA0t58s9nRVoai2FRE+bhJHRkREREQ0NAiCAJ1O53R9bW0tDAaDzVhjYyMaGxuRk5MDHx8fBAQEwNvbm22waFQQRRE1NTUoLy9HVVWV0+SkzlxcXGAymaBUKgchQiIaCExiGgCNjY247LLLrAlMt9xyC6699lq4uLhgx44dePrpp9HQ0IDf/e532LNnD5KSkvp0nG3bttl9mDnb9u3bce+99wIAli5dCo1G0+X848ePd7k+Kiqqd0HSqODr6wutVmtTcaSgoAABAQHMaiaiUaOkpMTmsbu7O7RarUTREBEREQ2ucB9X3DAzEpv3FWLN3Gjccl403NQ87URERERE1FO1tbVO14miiKqqKlRVVUGpVMLf3x/+/v7QarUQBGEQoyQaeC0tLSgpKUFFRUWPqpC5urpaWzCq1epBiJCIBhLPJg2AdevW4eTJkwCA5557Dvfff7913cyZMzFv3jzMnTsXLS0tuOeee/rcom3s2LHdznnyySety9dff3238ydMmNCnWGh0EwQB0dHROHr0qHXMYDCgqKgIEREREkZGRDQ49Ho9qqurbcaCgoJ4AoGIiIhGlbsuisXq86Ph79H1DVRERERERGQvOjoaAQEBKCsr6zJ5w2g0ori4GMXFxVAoFPD29oaXlxf8/f1ZoYlGhMbGRhQXF3c5pyOZLyAgAO7u7jwXTzSC8DdZPzMajXjhhRcAAOPGjcN9991nN2fWrFm46aabAAC7du3CgQMHBiSW+vp6bNmyBUD7B585c+YMyHGIAMDT0xPe3t42YwUFBQ770RIRjTRlZWU2j2UyGfz9/SWKhoiIiEgaOhclE5iIiIiIiPpIEAS4u7sjJiYGM2bMwIQJE+Dn59dlcobJZEJFRQVOnTrFJA4aNkRRRH19Pcxms8P1Z19v7CAIAnx9fTF+/HjMmDEDMTExrEZGNAKxElM/27FjB+rr6wEAN9xwg9OM55UrV+K1114DAHz55ZeYNm1av8fyySefWBNIelKFiehcRUVFoaamBgAgl8sRExPDso1ENOKJoojS0lKbMX9/fygU/JhFREREREREREREvSeTyeDj4wMfHx8YjUZUVlaivLwcDQ0NDud7eXk5TeRobm6GXC6HRsMbDkg6er0eNTU1qKmpQW1tLcxmMyZMmAAfHx+7uUqlElqtFo2NjQAArVaLgIAA+Pv7Q6lUDnboRDTIeHWtn+3evdu6PHfuXKfzpk6dCldXV7S0tGDPnj0DEsu7774LoD0rdcWKFQNyDKLO3N3dERISgoaGBowbNw4uLi5Sh0RENOBqamqg1+ttxoKCgiSKhoiIiIiIiIiIiEYSpVKJ4OBgBAcHo6WlBeXl5aiqqkJLS4t1jrPKNQCQm5uL2tpauLq6wtvbG97e3tDpdGw9RwPKYrGgvr7emrTU3NxsN6e2ttZhEhMABAYGwtPTE4GBgXB1dR3ocIloCGESUz9LT0+3LsfHxzudp1AoEBMTg2PHjiEjI6Pf48jLy7MmR82ZMwfR0dE92m7BggU4cuQI6urq4OnpiYSEBFx66aVYs2YNvLy8+hxPUVFRl+s7V7Bobm52mklOQ5+vry/8/PxgNBqd9mse6pqamhwuE0mFr8mh7fTp0zaPXVxcIIriiP1dNpivR5PJBIvFAlEUnZYWptGt8+uCrxHqiiiKsFgsMJlMA/r+zN/ZNJQMpddjcV0bXv/5NG4/Lxz+WlbrHa2G0muSyNFFNCIiouHA1dUVUVFRiIqKQltbmzVBxNk1PLPZbO0g09LSgpaWFhQVFUEmk8HT09Oa1MSb0qk/tLa2ora21vq6tFgsXc7v6O7iSHBwcH+HR0TDBJOY+llHso6bmxs8PT27nBsWFoZjx46hsrISer2+X9tuvfvuuxBFEUDvWsn9+OOP1uXKykrs2rULu3btwrPPPotNmzbhyiuv7FM8YWFhPZ77xRdfQKfT9ek4RP3tvffekzoEIht8TQ4tSqUSU6ZMsSnVnJaWhh07dkgY1eAZ6NdjUlISdDod3N3dUVFRMaDHouGvurpa6hBoCDMYDGhqakJ9fT22bNkyKMfk72waSqR6PeotMhxs88dxvTcskGHfiRxc7p4PmeMuFzSK8D2SpNZxMZeIiGg402g01gpNztTX1ztMJLFYLNbWXkD7jZleXl7w9vaGp6cn5HL5gMVNI0tdXR2qqqpQU1OD1tbWXm0rk8lgMpmgUDBlgYjO4DtCP+vozenu7t7tXDc3N+tyU1NTvyYxdZwMcnFxwdKlS7udn5iYiN/+9rdISUlBcHAwjEYjsrKysHnzZmzbtg11dXW45pprsHXrVvzmN7/ptziJiIiGM39/f5sEJrPZzEQKIiIiol/93BqETMOZO8KLTe443OaHZJdKCaMiIiIiIho9DAYDFAoFTCZTl/NaW1vR2tqKkpISCIIANzc3aLVaRERE9Ov1Sxp5KioqbDrudEWhUFiT5by8vPjaIiKHmMTUz9ra2gAAKpWq27md35h7m5nalZ9//hm5ubkAgCuvvBIeHh5dzr/nnnuwdu1au/Hp06fj+uuvx2uvvYZbb70VZrMZN998M3Jzc6HRaHoV09mtds5WWlqKlJQUAMDVV1+NsWPH9mr/NDzU19dDFMVuq5RJrampyZoIuGLFih4lJRINJL4mhyZRFJGWlmbTOtPf3x+rV6+WMKqBN5ivx+LiYlgsFiiVSvj7+w/YcWj46pw46OPjw7sEyanGxkZotVrodDrMnDlzwI7D39k0lAyF1+OyViMWv3EI5Y0G69hBfQDuWroAk0K7PldBI89QeE0SdTh58iSefvppqcOgIa6pqQmHDh3C/v37sX//fhw4cAD5+fkAgIiICOtyf/r555+xYcMG/PTTTygvL4enpycmTZqElStX4rrrruv34xHRyBcYGIiAgAA0NjZaKy91FGRwRhRFNDU1oampCVFRUQ7ndFR3kslk/R4zSc9isaC5uRlNTU1obGxEW1sbEhMTbW7o7eDl5dVlEpNWq7UmLnl4eDjcBxFRZ6M2iak/3iDffvttrFy50masI7nHYDA42MKWXq+3Lvdnr9l3333XunzDDTd0O7+7hJI1a9bgwIEDePPNN1FSUoLPP/8cy5cv71VMoaGhPZ7r5ubWbeIVDS9msxmnTp1CSUkJ5HI5/P39e50IJxV3d3e+HmlI4Wty6KiurrZJYALaT2JqtVqJIhp8A/16LC8vh8lkgiAITE6hbsnlcoevk02bNmHVqlUAgLy8PERGRvb7sfPz860n9Rx9RxjJ1q5diyeeeAIArO2se6vju9njjz/u8OaK/iAIAmQyGRQKxaD9HuXvbBpKpHo9engALy5LxrUb98Ly61uEWQT+b8tJfPuH86BzUQ56TDQ08D2SpNa5Qj6RM4sWLcLOnTsH7Xhr167Fk08+adP2qby8HNu2bcO2bduwefNmfPbZZ8PmvCoRDR2CIMDDwwMeHh6IjIyE0WhEbW2tNanp7HOcHTQaDZRKx5/Zq6qqkJmZCTc3N7i7u0Or1cLd3R3u7u5MbBpmOhKWGhsbrUlLzc3Ndud59Hq9w99BXl5eEATBOl+pVNpUW+pJ4Q8ios5GbRLTQOm4cNnU1NTt3ObmZutyf919ptfr8cknnwAAgoKCcPHFF/fLftesWYM333wTALBr165eJzHR6GUymXD48GG0tLQAaE9oyszMxKRJk5htTUTDmtFotCnF3PElnYiIiIjOSInyxt0XxeKf27OtY8V1rXj4i+N4adlkfi8kIqIhq/PFW29vb0ydOhU///xzj87999Zrr71mvTlgzJgxePjhh5GYmIiSkhL861//wo4dO/DNN9/gxhtvxAcffNDvxyei0aWj6rm/v7+16lJHUlN9fb11XlfnOpuammwqNpWVlQFoT5hydXW1JjVptVq4ubnxBsUhoqcJS440NTU5TGJSKBQICgqCSqWCt7c33N3d+T2PiM7JqE1iysjIOOd9BAUF2Y2FhoZi3759aG5uRl1dXZdVjjparPn5+fVbz8+tW7eitrYWALBs2bJ++1CQkJBgXS4uLu6XfdLooFAooNVqrUlMQHtbudOnTyM8PFzCyIiIzk1gYCD8/PxQVVWFkpISBAQE8MsZEQ2YwagoRUQ0UO66MBY/51Zjf16Ndeyb46WYc8AX16XweyEREQ1Ny5Ytw5o1azBt2jTExMQAACIjI/s9iammpgYPPvggACA8PBy//PILfH19resvv/xyXHXVVdi6dSs+/PBDrF69GvPmzevXGIho9BIEAVqtFlqtFuHh4TCZTNYEl666yDhrSSeKIpqbm20KOQDtVRA7V2vS6XT9+jyoex1tUvtaSbuxsdHm91NnsbGx5xIaEZGNUZvEFB8fPyD7TUhIwOeffw4AyMzMxIwZMxzOM5lMyM3NBQCMGzeu347f21ZyPcWLsnQuYmJiUF9fj7a2NutYfn4+vLy8RlXbJSIaeeRyOQICAhAQENDnL39ERKMd3z+JRj65TMA/f5eE3/zrJ9S3nmlV8cTWNEyN8EJsAL8XEhHR0LN69epBOc4bb7xhrXzy7LPP2l0glsvl2LBhA7799luYzWasW7eOSUxENGAUCgW8vLzg5eXldE5HBabe6JzY5OPj4zSJqaamBiaTCWq1GhqNBiqVitcozyKKIgwGA9ra2qDX661/dyxHRkY6TDZydXXt9bE0Go01+ayr1wQRUX8atUlMA2XOnDnW5V27djlNYkpNTbX+sp49e3a/HLuyshLff/89ACApKQmJiYn9sl8ASE9Pty4HBwf3235pdFAoFIiPj8eRI0esY6IoIiMjA8nJySwjSkQjAr9MExERETkX7OmCdYsnYvV7B61jbUYL7vrwML66YzY0Sn4vJCKi0emrr74CAHh4eODqq692OCc0NBTz58/HDz/8gP/85z9obGzkzaFEJBlBEDB9+nRrO7KOv1tbW3u0vaOWZB2KioqsHWc6jqVWq61JTWcvazSaEXeNSRRFtLa22iUpdU5W6uqGMGf/DjKZDK6urnZVsjq4uLhYWwB2/K1QMJWAiAYf33n62bx586DT6VBfX4933nkHDzzwgMOLmps2bbIuX3XVVf1y7A8//BBGY/sdjf1ZhQlo78ndYe7cuf26bxoddDodwsPDUVhYaB1rbW3FqVOnWGaSiIiIiIhoFFgwPhDXz4zAu3sLrGOZZY146psMPPnbCRJGRkREJA2DwYD9+/cDAGbOnAmVSuV07ty5c/HDDz9Ar9cjNTUVF1xwwWCFSURkR6FQwNPTE56entYxk8lkk9jU1NSElpYWu23VarXT/Xbu6AG0J/S0tbWhra3NWrXOUSwajQZ+fn4ID3fcrrqqqgpmsxlyuRwymczp3zKZrNc3q4qiCIvFAovFArPZ3OO/dTodvL29He7z0KFDMJvNvYqjw9k/w860Wi2am5vh4uJik6zk7u7OhCUiGjL4btTPVCoV7r77bjz55JPIyMjA+vXrcf/999vM2bt3L958800A7V88pk2b5nBfHb8kIyIikJ+f3+2xO1rJKRQKLFu2rEfxHj9+HC4uLtae3o5s3LgRb7zxBgAgMDCw35KuaPSJiIhAbW2tTa/kkpISeHt7w8fHR8LIiIiIaO3atXjiiScAtJ98aWhowN///nd8/vnnyMvLg6urK6ZOnYpHH30Us2bNsm5XUVGBF154AV9++SXy8/Oh0WgwZ84crF27FpMnT+7ymBaLBR988AE++OADHDp0CDU1NdDpdJgwYQKWLFmCm2++ucuT+ABQV1eHl19+Gdu3b0dBQQG0Wi0mTpyINWvWYMmSJd0+747P3I8//jjWrl3rdN68efOwa9cuzJ07Fzt37ux2v2c7ceIEvvrqK/z0009IS0tDZWUllEolgoKCMGvWLNx2220Oq7ju3LnT7uJEVFSU3bwdO3Y4bCnx1VdfYfPmzdi3bx8qKiqg0WgQExODRYsW4e677+62FHhRURGefvppfPfdd9bPbVOnTsXdd9+N+fPn9+6H4ERP/w2IaGR4eOE47M+rQWbZme+F7/1SgNkxvrh0QqCEkREREQ2+kydPWi9Sx8fHdzm38/qMjIxeJTEVFRV1ub60tNS63NjYiIaGhh7v+1x0bkfV29ZURM7wdSUtmUwGnU5nbRdnNpvR2tqKlpYWtLS0QK/XA4DD95mOhKXe6kiecnFxcfr+lZOTYz12dwRBsCY0yWQyuLu7W9ed/ZpKT0/v8X7P5u/v7zRxSKlU9jmJqbm52enPwdfXF/7+/nbVqxwlm9HA4fsUDQSpXledr/v3FyYxDYD7778fH3/8MU6ePIkHHngAOTk5uPbaa+Hi4oIdO3bgb3/7G0wmE1xcXPDPf/6zX46Znp6OgwfbS7Jfeuml8Pf379F2Bw8exM0334wLLrgAv/nNb5CYmAgfHx+YTCZkZmZi8+bN2LZtG4D23tsbN26Em5tbv8RMo49MJkN8fDwOHjwIi8ViHc/KysLUqVO7vUhJRCQ1o9EIURT5fkUj3unTpzF//nycPHnSOtbc3IzvvvsO27Ztw4cffoglS5bg2LFjWLhwIYqLi63zWlpasGXLFvzwww/47rvvnJ5Yr6mpwRVXXIE9e/bYjFdVVWHnzp3YuXMnXnrpJXz33XeIiIhwuI+MjAxcfPHFKCsrs461tbXhP//5D/7zn/9g1apVOP/888/lR9EvHCUiAe13Xefk5CAnJwfvvvsu/u///g9PP/10vxyztrYWixcvxn//+1+bcb1ej4MHD+LgwYPYsGED/v3vfzttgf3TTz/h8ssvtznxVVpaiq1bt2Lr1q1MOCKiPtEo5Xhp2WQsenEPWo1nTso/+PkxJIbqEOLpImF0REREg6tzclFoaGiXc8PCwqzLp0+f7tVxOm/bnffee8+afDCY3nvvvUE/Jo18fF0NLzKZDHFxcVCpVFCr1ZDJZL3a/sCBA9i6davDdZMnT+6yAlRnoijCbDZbk4jy8vKs685+TU2aNAkuLn37DnPs2DFs2bLF4bq4uLhubzyzWCwwGAzWFnMdy62trfjhhx/6FBMNPr5P0UAYzNeVsyp554JJTANAq9Xim2++wcKFC5GdnY2NGzdi48aNNnM8PDywefNmJCUl9csxO6owAcD111/fq23NZjO2b9+O7du3O53j4+ODN998E4sWLepzjEQA4OrqipiYGJuLokajEVlZWZgwYUKvy3QSEQ2m4uJiFBYWwtfXF0FBQfD09OT7Fo1IS5YsQVFRER566CFceumlcHV1xe7du/H444+joaEBN910E6ZOnYrLL78cra2teOqppzB37lwolUp8//33eOqpp6DX67Fy5UpkZ2fbJf6ZzWZcfvnl2Lt3L4D26qR33nknoqKiUFJSgrfeegtfffUVMjIycNFFF+HIkSM2d70B7XfsLVy40JrAtHTpUqxcuRL+/v44efIk/v73v+Ptt9/GiRMnBueH1gWTyQQ3NzdcdtlluPDCCxEfHw8PDw9UVFQgLS0NL7zwAgoKCvDMM89g7NixWLVqlXXbadOm4fjx4/j3v/+NRx99FADwww8/IDg42OYYnasz6fV6zJ8/H4cOHYJcLseyZcuwcOFCREVFwWg04n//+x/+/ve/o6KiAgsXLsThw4ftEsUKCwutCUwymQyrV6/G4sWLodPpcOzYMTzzzDNYu3Ytpk6dOoA/OSIaqWL8tVh7RQIe/Py4day+1YjvT5Thpjn21eaIiIhGqs53rp/9nedsnW8uZtUGIhqJLBYLMjIyrI+VSqU1oanj787LSqXSZnuDweB0371NiDo7rr6s687Z1ZA6MxgMMBqNDpOUOv42Go19PjYR0VDGJKYBEhMTg8OHD+Pll1/Gp59+ipycHBgMBoSFhWHhwoX4wx/+4PSO8t6yWCzYvHkzAMDT0xNXXHFFj7dduHAh3nzzTezduxeHDx9GeXk5qqurIYoivL29MWnSJFx66aVYuXIlPDw8+iVeosDAQFRXV6O6uto6VlNTg9LSUrsLckREQ4UoiigrK4MoiqisrERlZSXCwsIQHR0tdWijmsUiorbF+QmKkcjLVQWZbGCT544cOYJdu3Zh+vTp1rGpU6ciNjYWl19+ORobGzF9+nSIooj9+/djzJgx1nkpKSnw9fXFHXfcgcLCQnzzzTd27YhfffVVawLT9ddfj02bNlkTApOTk7Fo0SI88sgj+Nvf/obc3Fw8+eSTePbZZ2328eSTT1rvPn7ooYfw5JNPWk/+JCcnY/Hixbj88sutVUWllJSUhKKiInh6etqtu+SSS3DnnXfi8ssvx48//ognnngC119/vfW5uLm5YcKECUhNTbVuM3bsWERGRjo93l/+8hccOnQInp6e2L59O5KTk23Wz5kzB8uXL8fMmTNRWlqKhx9+2Pp9osN9991nrcD0/vvv47rrrrOumzp1KpYsWYLzzjvPJi4iot5YOjUM/8uuwjfHSqHVKPDsNROxMDFI6rCIiIgGVee2Sd1Vfe5cQaS1tbVXx+muclNpaSlSUlIAACtWrEBISEiv9t9XTU1N1koBK1as6DaRi6gn+LoaPToqEXX8GT9+vNP30uzsbJhMJoiiCIvFYv0jimK3x5k0aZK1ct7Zr6mTJ0+iubnZbpuOlnRnt6br/Cc0NBQLFixweExRFHnz7AjG9ykaCFK9roqLi/utu0AHJjENIDc3NzzwwAN44IEH+rR9T35xAu3Zw70tH9vB398fN954I2688cY+bU/UF4IgYOzYsUhNTbXJFM/NzYWnpydcXV0ljI6IyLGamhq7/uZ+fn4SRUMdalsMSP6r82qSI9HBR+fDx71n5a/76p577rFJYOpw2WWXISIiAgUFBaisrMQrr7xik8DUYdWqVbjvvvvQ1taGn376yS6J6eWXXwbQ/n/opZdecnhS5oknnsAXX3yBzMxMvP766/jLX/5iPWlvMBjw5ptvAgASEhJw55132m2vVCrx5ptvIjo6WvI703x9fbtcr1KpsG7dOiQlJaGgoABHjhyxSzzqqaamJuvP98knn3S6n4iICPz5z3/G7bffjk8//dSmbXRZWRm+/PJLAMDll19uk8DUQavVYuPGjQ5fJ0REPSEIAp6+OhEyQcADl8QhzJvfA4mIqPf64wLv22+/jZUrV557MH2g0Wisy11VEAFgc06it62LumtV15lWq5XkhmZ3d3feSE39jq8r6uDs/EjnhCaz2ezw787Xa89+TSUkJEAURchkMsjlcpskJaKe4PsUDYTBfF113Ajbn/gOSkSSUKlUiIuLsxmzWCzIzMzscQIfEdFgEUXRLmHY3d0dWq1WooiIBta1117rdN3EiRMBtF8s+N3vfudwjouLC2JjYwEAp06dsllXUlJiLQ2+dOlSp/+PFAqFta1abW0tDh06ZF138OBB1NbWAmhvfefswkVXd7RJSa/Xo7CwEOnp6Thx4gROnDhh8/nn6NGjfd73rl27rH3IFy9e3OXc888/H0B7a9+DBw9ax3fs2AGz2QwANq3tzpaSkoLx48f3OVYiIg+NEi9eN5kJTERENGp1/j7UXYu4zpU+WLGBiKh/yGQyKBQKqFQquLi4wM3NDR4eHvD09ISPjw/8/PxsEk7P5urqCjc3N7i4uEClUkGhUDCBiYjoHLESExFJxsfHB8HBwSgpKQHQXhI5OjqaJTKJaMgpLi62JgV0CApiuxMaucaOHet0XUdLNF9fX3h5eXU7r7Gx0Wb8xIkT1uXuqvh0Xn/ixAnMnDkTAHD8+HHreFJSUpf7SElJwTfffNPlnMHQ3NyMF154AR999BHS0tKsSUKOVFVV9fk4ndu79eZ9qqyszLrc+ec7bdq0LrdLSUlBWlpaLyIkIiIiIuo/HTdInAspv993rpDU0arImc43V4WFhQ1YTEREREREUmISExFJKjo6GnV1dXB3d0dsbCwUCr4tEdHQ0tzcbFdJRq1WIyAgQKKIiAZeV61dO+4m6679a8e8s5N1ampqrMv+/v5d7iMwMNDhdp2XfXx8utzHUPi/mp+fjwsvvBB5eXk9mt/a2trnY1VUVPRpu5aWFutyb/6NhsLPl4hGrjajGRqlXOowiIhoCIuPj5c6hHMyduxYyOVymM1mZGZmdjm38/px48YNdGhERERERJJgtgARSUoul2Py5MlMXiKiIclisSAjI8OuzWVcXBzkcl5QGwq8XFU4+Oh8qcMYVF6uKqlD6Df9UX1xOFRwXLFiBfLy8iAIAlatWoVrr70W48aNg5+fH1QqFQRBgMVisb6vnEtr3c5JY4cOHYJSqezRdp3vAO9sOPx8iWhk2nK0BH/ZmoZ3b5yOhGAPqcMhIiIaECqVCikpKdi7dy/27t0Lg8EAlcrxd75du3YBaL+xaurUqYMZJhERERHRoGHWABFJjglMRDRU5efno7m52WYsNDS0yxZaNLhkMgE+7mqpw6Be8Pb2ti6Xl5d3Obdzi7PO23X+P1hVVYUxY8Y43Ud3xxAEAaIowmKxdDnv7PeCnsrMzMTu3bsBAA8//DD++te/OpzXufrRuehcmcrPz89pclJXOv98y8vLu2xV0d3Pl4iot1oMJqzdkoZPUttb6tz14SFsvWsOXFX83khERCPTb3/7W+zduxcNDQ344osvcO2119rNKSoqwvbt2wEAF110EbRa7WCHSUREREQ0KGRSB0BE1JVzqURARHQu6urqcPr0aZsxNzc3REVFSRQR0cgwYcIE6/K+ffu6nLt//36H2yUmJlqXjxw50uU+Dhw40OX6jpP/tbW1TueIooicnJwu9+NMWlqadfl3v/ud03mpqald7qenFZEmT55sXd6zZ0+Ptjlb559vdz+/7tYTEfXW23vyrQlMAJBb2Yy/bHyidpEAAGPVSURBVE2XMCIiIqK+y8/PhyAIEAQB8+bNczjn5ptvhk6nAwD83//9H6qrq23Wm81m3H777daqq/fff/+AxkxEREREJCUmMRHRkGU2m3Hs2DEUFBRIHQoRjTImkwlZWVk2Y4IgID4+HjIZPz4RnYvg4GCMGzcOAPDJJ5+gqanJ4Tyz2YxNmzYBaK8MNGXKFOu65ORka7Wgzz77zGnSc3FxMbZt29ZlPB2JiV0lEX333Xeoq6vrcj/OmEwm63JX1ZxeffXVLvej0Wisy3q93um8+fPnw9XVFQDwwgsv9Ckh/IILLrC2tnvnnXeczjtw4ABOnDjR6/0TEXXl5vOiMCHEtn3cRwdOY+vREokiIiKi0SonJwebNm2y+dPx/aWpqcluXedKsr3h7e2NZ599FgBQUFCA6dOn4+2330Zqaiq2bNmCiy++GFu3bgUAXHfddU6ToYiIiIiIRgJehSOiIclsNuP48eOoq6tDfn4+CgsLpQ6JiEaR3NxctLW12YxFRkbC3d1dooiIRpY77rgDAFBZWYm7777b4ZwnnngC6entlTduueUWqNVn2gaq1WqsWrUKQHulow0bNthtbzKZcMstt8BgMHQZy9y5cwG0V4VyVLmorKwMd911Vw+elWOxsbHW5Y6krLO98sor+Pe//93lfoKCgqzLubm5Tud5enrizjvvBAD8/PPPuPfee7tslVdeXo433njD7lhXXnklAGDLli345JNP7LZramrCmjVruoyZiKgv1Ao5Xrh2MlxVcpvxh784jpPljRJFRUREo9Hu3buxatUqmz8dVZKqq6vt1mVmZvb5WGvWrMFjjz0GQRCQm5uLG2+8EdOmTcOVV16JHTt2AAAWLlyIt956q1+eGxERERHRUMUkJiIackRRxPHjx1FfX28dy8vLs2vrREQ0EKqqquzuntTpdAgLC5MoIqKR59Zbb8XMmTMBAG+//TYuuugifP755zh06BC++eYbXHPNNXjyyScBAGPGjMGf//xnu3089thjCA0NBQA89dRT+P3vf4/vv/8ehw4dwkcffYRZs2bhu+++w9SpU7uMZfXq1VAoFBBFEYsWLcI///lPpKam4ueff8a6deswefJk1NfX2yQj9cbkyZOtrfBee+01/O53v8PXX3+NgwcP4t///jeWLFmC22+/HbNnz+52Px3VmP785z/jxx9/xMmTJ5GTk4OcnBy0trZa5/7lL3/B9OnTAQD/+te/MGXKFLz88svYs2cPjhw5gh07duCll17Cb3/7W4SHhzusAvX8889bW+0tW7YMd9xxB3bs2IGDBw/i7bffRnJyMg4fPtztz5eIqC+i/dzx5JUTbMYa9SZct/EXZJQ2SBQVERHRwHriiSewe/duLFu2DGFhYVCpVPD398fFF1+MDz74AN98841NhVYiIiIiopFIIXUARERnEwQBfn5+NklMAHDq1CkIgmC9YElENBBqa2ttHsvlcsTHx0MQBIkiIhp55HI5vv76a1xxxRXYs2cP/vvf/+K///2v3bxx48bhu+++c1gFTafT4ZtvvsHFF1+MiooKfPTRR/joo49s5qxcuRJz5861Vm1yZPz48Xjuuefwxz/+EbW1tbj33ntt1nt7e+Orr77Cn//8Z2RnZ/f6uQqCgPfeew8XXnghamtr8cknn9hVNkpMTMSnn36K4OBgp/vRarW4++678dxzz+HQoUNYsGCBzfodO3ZY20qo1Wr8+OOPWLlyJb744gscPXrUWp3JEQ8PD7uxyMhIbNmyBVdccQUaGxuxYcMGu4pXHXeKd9WKj4ior65JDsXunCp8ebjYOlbdbMB1r/+C92+ajgkhOgmjIyKi0WDlypVYuXLlOe0jMjKyVy2eZ82ahVmzZp3TMYmIiIiIhjNWYiKiISkkJARjxoyxG8/NzUVxcbGDLYiI+kdsbCzGjRsHuby9hUlMTAzvdCQaAN7e3vjf//6Hd999F5deeikCAgKgVCrh4+ODefPm4aWXXsKRI0cQERHhdB/jx4/Hjh07cPvttyM2NhZqtRq+vr644IIL8MEHH+Dtt9/uUSz33nsvvv/+e1xyySXw8vKCWq1GVFQU7rjjDhw+fBjnnXfeOT3XpKQkHDlyBLfeeisiIiKgVCrh7e2NlJQUrF+/Hvv377dpF+fMM888g9dffx3nnXcevL29re9Tjmi1Wnz++ef46aefcPPNNyMuLg5arRYKhQLe3t6YNm0a7rjjDnz77bf48ccfHe5j3rx5SEtLw2233YaIiAioVCoEBATgsssuw/fff48nnniizz8TIqKe+OtvJyA5wstmrK7FiOte/wWHC2udbEVERERERERERMMVKzER0ZAVGhoKURRx6tQpm/GcnBwIgtBltQIionPh7+8PDw8PlJeXIyAgQOpwiAbF2rVrsXbt2m7nbdq0CZs2bep23s6dO7udI5PJsGLFCqxYsaL7AJ3w8vLCo48+ihdeeMFhUk9P756+5JJLcMkllzhd39Xz6cnd1eHh4XjllVe6nNPdPgRBwM0334ybb765y3mdzZkzB3PmzOnx/LOFhYXZVWDqrKevm6705s50Ihpd3NQKvHNjCm7cdAD782qs441tJqx4cz/eXjUN0yK9JYyQiIiIiIiIiIj6EysxEdGQFhYWhqioKLvx7OxslJaWShAREY0WGo0GERERbCNHREREJCF3tQKbVk3D7Bgfm/EmvQk3vLUfe3OrJYqMiIiIiIiIiIj6G5OYiGjICw8PR2RkpN34yZMnUVZWNvgBERERERER0aBxVSnw5g3TMHesn814i8GMlW/vx0/ZlRJFRkRERERERERE/YlJTEQ0LERERCAiIsJuPCsrC+Xl5RJEREQjhcVikToEIiIiIuqGRinHxuuTMX+cbatfmSBArbBvJ0pERERERERERMMPk5iIaNiIiIhAeHi43XhmZiYqKiokiIiIhruGhgbs27cPVVVVUodCRERERN1QK+TYsHwKfjMh8NfHMrx5w1SkRHlLHBkREREREREREfUHJjER0bAhCAIiIyMRGhpqty4jI4NJCETUK2azGZmZmTAYDEhLS0NWVhZMJpPUYRERERFRF1QKGV68bjIWJ4di4/VTMSvGV+qQiIiIiIiIiIionyikDoCIqDcEQUB0dDREUURxcbF1XKVSwdXVVcLIiGi4OXXqFFpbW62Py8rK4OrqirCwMAmjIiIiIqLuKOQyrF8ySeowiIiIiIiIiIion7ESExENO4IgYMyYMQgJCQEAqNVqJCUlMYmJiHqspqYGJSUlNmNardb6vkJEREREw1teVbPUIRARERERERERUS8xiYmIhqWORKaIiAgkJSXBxcVF6pCIaJgwGo3IysqyGZPJZIiPj4dMxo9GRERERMPdJwdO46Lnd+LD/YVSh0JERERERERERL3AdnJENGwJgoDIyEipwyCiYUQURZw8eRIGg8FmPDo6mtXciIiIiEaALw8X4cEvjkEUgYe+OA6j2YLrZ0ZKHRYREREREREREfUAyw0Q0YjV1taGiooKiKIodShENERUVFSgqqrKZszLywvBwcESRURERERE/SWjtAH3fXIUnb8CPvbvNLzx0ynpgiIiIiIiIiIioh5jEhMRjUiiKCIrKwsZGRlIT0+3q7pCRKNPW1sbsrOzbcYUCgXi4uIgCIJEURERERFRf4kP1OLOC2Ptxv/6TQZe3pEjQURERERERERERNQbbCdHRCNSSUkJ6urqAABVVVWor69HbGws/Pz8pA2MiAaVKIpoampCVVUVysvLYTabbdaPHTsWarVaouiIiIiIqD8JgoA/XjwWKrmA9dtO2qxb90MWimpb8dDCeHholBJFSEREREREREREXWESExGNOAaDAadO2bYLMBqNSE9Ph7+/P2JiYqBU8qQ10UhnNBpx6NAhtLW1OVwfEBDAxEYiIiKiEejOC2OhUsjwt28zbcY/3F+I/2aW48krJ2DB+ECJoiMiIiIiIiIiImfYTo6IRhyVSoX4+HgoFPZ5mhUVFUhNTUV1dbUEkRHRYFIqlZDJHH/UUavViImJGeSIiIiIiGiwrD5/DB5flGA3Xt6gx+r3DuL2zQdR0eA42Z2IiIiIiIiIiKTBJCYiGpH8/Pwwbdo0+Pj42K0zGAw4ceIEsrKyYDKZJIiOiPqDxWJBTU1Nl0mJvr6+dmNubm5ITEx0mOhIRERERCPHqtlRWLd4IlyUcrt13x4vw0V/34WP9hdCFEUJoiMiIiIiIiIiorPx6h0RjVgqlQrjx49HRUUFsrOzYTabbdaXlZWhtrYWcXFx8PLykihKopHHYrFY/5jNZpvHLi4uUKlUDrczmUyQyWQQBAGCIDjdd01NDaqqqlBdXQ2TyQQ3NzeHCYtAe0JjYWEhNBoNfH194efnB61W63T/RERERDSyLJkahhnRPnjkqxP438lKm3WNbSb83xfH8dWRYmxYngxvN8efU4mod0RRhMFsgd5kQZvRDD93tcPvYBWNbdibWw2TWYTRbIHRIsJktrQvm8VO4xaYzO3rDL/+bbKIeOg38fD30EjwDImIiIiIiGigMImJiEY0QRAQEBAAT09PZGVloba21ma9Xq/HsWPHEBwcjOjoaMjl9nfoEo1Eoihak4zMZjNMJpN12Ww2w9vb22GlIr1ej8zMTKeJSmcnC57t/9u77/CoqvyP45/JTJJJD0noJdRQBBUJKAoSpK0IKioqLFIUsevyc1fdooCsBWHdXbuuSGyAIlURpSNVulKl19AD6XVyf39kczchM5OETDKBvF/PM09u7jn33HMz39wp93vPadWqlWrXru20bM2aNeZd8BaLxUxo8vHxMZezs7OL7SMtLU0ZGRkKCAgo1mZQUJA6dOigoKAgEpcAAACqqYYRgfp0REfN3Zqgcd/u0Pn0nCLlGdkOhQX4eql3gPcYhqH0bIfSsnKVkeNQdGSQ03q/HrugeVsTlJadq7Ss/Ppp2bnKyMlTVo5DmTkOZebkKSs3/2dmrkOFBzjbNra3QuzF/8f2nUrVM9O3XnL/H49rRhITAAAAAFxhSGICUC34+/urXbt2OnnypPbv318sCSIhIUGJiYlq1aoViQ64bBRO+LlYRkaGEhISlJOTo9zcXOXk5BRLVnI3bcZ1112nkJAQp/u8cOHCJffZx8f5TLaGYRTpj2EYJSZEFXbmzBk1atSo2HqLxaLg4OCydxQAAABXFIvFojvb11fXFlH6+/xdmr3luCTJ5mPRa3ddLasPnwNx+TuVnKn9p1N1ISNHF9JzdCEjW0kZOUpK/9/vF9JzlJyRo+TM/ESkgo9hflYf7XnlVqftHjiTpo9XHbzkfmXm5CnESa6Rzer882Fp5TiYChIAAAAArjQkMQGoNiwWi+rWrasaNWrot99+K5aIkZmZqaSkJIWHh3ulf0BB4k52dnaRR2pqqpo2bSqbzaY9e/bIMAwzMen666+Xv79/sbZycnJ07NixS+6LqwQiV0lIpeUqSdBdQlVJQkJCnP4NAAAAgItFBvvrn/ddqzvb19dfZm3THdfWU5t6od7uFmAyDENp2Q6dTcnS2dT8x5nUbPN3q49FL9/R1um23/16QuO/23lJ+8125Ck7N09+tuKf+YL8y/cVcmaO88+XNmv5kgdz8/LKtT0AAAAAoOohiQlAtWO323X11VcrISFBBw4cUN5/v/QKDQ1Vw4YNlZKS4uUe4krjcDjM6dAulpSUpP3795sJS66SeWrVqiUpf+q0wnJzc50m8DibCq4scnNzna6/1CSmgmnhXE3ZmFfGL5/DwsIUFRWlqKgo2e1MHwDAtfj4eI0YMUKSdPDgQTVu3Njj+zh06JCaNGkiSZoyZYqGDx/u8X1UVWPHjtW4ceMklS8hFQAqW7eYmlr0fzfLx81IvL8cvaDz6dmKa1mrEnuG6mBnQrI2HEr8X5JSSra5fDY1S5k5rj8fhdptLpOYyjstYlpWrvxsfsXWB/k7/xxXWlm5zpOYgv1talk7RDarRb5WH/laLbL5+MjX5iNfn/x1RcqsPvKz+sjmk78cGczNLAAAAABwpSGJCUC1ZLFYVL9+fXNUptTUVLVs2ZKp5FBmDodDWVlZbh+5ubm68cYb5evr/Avl8iTO5eTkOF3val/u2Gw2Wa1W2Ww2l8lKVqtVzZo1k4+Pj5mUVLB88e+Fl0v637Jarbr++uuVl5cnwzCUl5fndNlisSg0NFR+fsW/WAcAAADKItDP9ddi2bl5+tM3v2jPqVQNaF9fL/Zro4gg3oPCuRxHnk4lZ+pkUqYSkjJ1MilDeYb0aLdmTuv/tPeMXl+w+5L2lZyZq6xch/xtxROLwsuZxJSalasaTuK8XliA7r6ugYL9rQryt+U//KwK8LPK7muVv80qf18f2W1W2X19ZPctWJ+/7Cq5KqZ2iH4cfXO5+gwAAAAAuLKQxASgWgsMDNS1116r1NRUBQYGOq2Tm5ur7du3q3bt2qpVq5bLkWRwZcvIyNCZM2eKJCdlZma6HLHoYtnZ2U4Ti8qbjONq/zabTbVr15bNZpOvr69sNluRJKXCPwuSjUqTxGexWNSgQYNy9dlVu4yoBACuVcaIUgCA//lwxX7tOZUqSZq95biW/3Zaz/RooQHtGygssHyJIri8GIahC+k5Ono+XUcTM3TsfLpOJGXqRFLGf39m6mxqli4ekLBGoK/LJKaoco4gdC41W/XCA4qtrxHkp4ggP4UH+Co0wFfhgb4KD/BVeKBf/u8F6wJ9FWr3VZC/TcEFSUn+VqeJUZLUOCpI/7j3mnL1GQAAAACA0iCJCUC1Z7FYFBIS4rL89OnTSkpKMqf9ql27turWravg4OBK7CUqgmEYysnJUWZmpjIzM5WRkaGGDRs6HYUoKytLBw8evOR9ZWdnKygoqNh6V0lMVqtVfn5+8vPzk8Vi0a5du5Sbm6vOnTsrJCTETEpylXxnsVjUqlWrS+4vAAAAUF0dOZeut5fuK7LufHqOxn67U69+v1u92tTW3R3q6+YWNWWzXtp0y7g8fLrmkCb++JtSs0p380ph59NzlJnjkN23eGJQVHDpbmYJ9LMqKthfUcF++T9D/BUV7C9/m/O46xBdQ5tf7FXmvgIAAAAAUFWQxAQAbhiGoRMnTpi/OxwOJSQkKCEhQaGhoapbt65q1qzJ6ExVWG5urjIyMsxEpYsfeXl5RerXrFnTaWKQv3/57pTNzs52ut5qtSomJka+vr5m0pKfn1+RRKrk5GT98MMPZv9CQ0PL1RcAAAAArjWoEaBxd1ylV7/fpZTMoskr2Y48zd92QvO3nVDNEH8NaF9fd1/XQC3ruL4xBt6X48jT8fMZOpyYriPn0nTsfIY5slJWrkMLR3dzul2Ar/WSEpgKnEzKVOOo4jezNKgRoNjoGv9NTPpvgtJ/HzVD/FQz2K6oED+3Ux4CAAAAAHAl4pMwALiRkZGh1NRUp2XJyclKTk42R2eqV6+ey1FxUHnS09N16NAhc2Sl0k73ViAzM/OSkpisVqv8/f3l7+8vu90uPz8/2e32IutcqVu3bpn6CAAAAKDi+PhYNKhTI/VoVUtj5u3Qgu0nndY7k5Klj346oI9+OqB29cN0T4cGuv2aeqoRVL4po3Fp0rNzdfhcug6fS9eRxDQdOpeuI+fSdTgxTQkXMuXIM1xu62rEpAYRxadsc8fu66N6YQGqE2ZXnTC7rD7Op+xuXitE3zx2Y5naBgAAAACgOiCJCQDcCAgIUNu2bZWQkKDExESndXJzc3X8+HEdP35cYWFhqlevnqKiopxOSYZLk5eXV2TKt4yMDDVq1MjlVGxnzpy55H1lZmY6Xe/j42OOulWQmFT4YbPxkgrg8jZ27FiNGzdOUv5IhMnJyXrzzTc1c+ZMHTx4UIGBgYqNjdXf/vY33Xjj/y66nT59Wm+99ZZmz56tQ4cOyW63q0uXLho7dqzat2/vdp95eXmaOnWqpk6dqs2bNysxMVFhYWFq27atBg4cqJEjR7o81xe4cOGC3n33XS1evFiHDx9WSEiIrr76aj3yyCMaOHBgicdtseRfXBwzZozGjh3rsl5cXJxWrFihbt26afny5SW2e7Ht27drzpw5WrlypXbs2KEzZ87I19dXdevW1Y033qjHHntMN9xwQ7Htli9fru7duxdZ16RJk2L1li1bpri4uGLr58yZoy+//FI///yzTp8+LbvdrubNm6t///56+umnVaNGDbf9PnbsmF577TUtWLBACQkJioiIUGxsrJ5++mn17NmzbH+EEkydOlUfffSRfvnlF+Xk5Khx48a6++67NXr0aIWHh5f6uQIAT6kVatf7Qzpo9b6zmvrzES3aeUrZjjyndbcdT9K240n6+/yd+ud916rf1fUqubfV29cbj+q5b3695O2PX8hQs5rFp4xvWON/N7hYfSyqG2ZXgxoBqh8eqHrh+YlKBUlLdcPsCgvwNV+vAAAAAABA2XHFFQDcsFgsioyMVGRkpDIzM3XixAmdPHnS5dRgSUlJSkpKkq+vr2rXrq0mTZqQzFQKhmEoJyenSKLSxdO+XSwyMtLphW13Ix65YrFYZLfbZbfb5evr67JemzZtytw2AFyOjh49qp49e2rPnj3murS0NC1YsEALFy7UtGnTNHDgQP3666/q27evjh8/btZLT0/XvHnz9OOPP2rBggXFEnAKJCYm6vbbb9fq1auLrD979qyWL1+u5cuX65133tGCBQsUHR3ttI1du3apV69eOnnyfyNkZGZmasmSJVqyZIlGjBihm2++uTx/Co9wlogk5U81um/fPu3bt0+fffaZXnjhBb322mse2ef58+d1zz33aOnSpUXWZ2VladOmTdq0aZPee+89zZ0712nylCStXLlS/fr1U3JysrnuxIkT+vbbb/Xtt996LJEoNzdXgwcP1owZM4qs37Fjh3bs2KEvvvhCixcv9si+AOBS3NQ8Sjc1j9KF9Gx9+0uCvtl8XL8cveC0bo7D0LUNwyu1f1eSHEeeEi5k/Hc0pXQdTUw3l49fyNCGv/aUn634Z+y6YWX/HFjY0cR0p0lMdcPsmjryejWMCFSdMLt8rXy+BwAAAACgIpHEBAClZLfb1aRJE0VHR+vcuXM6ceKEzp8/77RuTk6OTpw4oaZNm1ZyLy8fOTk5+u2338ykpbw853c0u5KRkeF09AgfHx/5+/srKyuryPqCKd2cPfz9/blbFgAKGThwoI4dO6Y///nP+t3vfqfAwECtWrVKY8aMUXJysh566CHFxsaqX79+ysjI0CuvvKJu3brJ19dXP/zwg1555RVlZWVp+PDh2rt3b7GkU4fDoX79+mnt2rWSpG7duunJJ59UkyZNlJCQoE8++URz5szRrl271KNHD23dulXBwUUvLCYnJ6tv375mAtO9996r4cOHq1atWtqzZ4/efPNNTZkyRdu3b6+cP5obubm5CgoK0m233aZbbrlFrVq1UmhoqE6fPq0dO3borbfe0uHDh/X6668rJiZGI0aMMLft2LGjtm3bprlz5+pvf/ubJOnHH39UvXpFR/goPDpTVlaWevbsqc2bN8tqtWrw4MHq27evmjRpopycHP3000968803dfr0afXt21dbtmwplih25MgRM4HJx8dHo0aN0j333KOwsDD9+uuvev311zV27FjFxsaW++/zxz/+0UxgatmypZ577jldffXVSkpK0owZM/Sf//xH9913X7n3AwDlFR7opwc6N9YDnRtr3+kUfbPpuGZtPqbTKf/77HFjs0g1qOF8mvFVe88qx5GnmDohqhdmr7afQbLyfLTzRIrOHUrT4cQ0HU3MT1I6fC5dCRcy5GbWNyVcyFDjqKBi66Mjiq9zJizAV9GRgWoYEagGNQLUsEb+8jUNwpzWt1l9dGPzqFK1DQAAAAAAyo8kJgAoo4JpxWrWrKmMjAxzdKacnJwi9YKDg11+KX3s2DGdOnVKwcHBCg4OVkhIiIKCgmS1WivjEDzO4XAoOztbWVlZ5s+C5YYNGyokJKTYNlarVefOnbvkfbqa9k2SGjZsKCk/8SwgIEB2u50RsYArVV6elOF8us8rVkCEVMH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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "b = [1, 2, 1]\n", "a = [1, -1.5432913909679857, 0.6297148520559599]\n", @@ -696,7 +736,7 @@ "dp = FluxPulse(0, 80, 0.9, IIR(\n", " b=b, \n", " a=a,\n", - " target=SNZ(t_half_flux_pulse=30, b_amplitude=1)), \n", + " target=SNZ(30, b_amplitude=1)), \n", " 0, 200)\n", "dp.plot()" ] @@ -710,9 +750,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "dp = Pulse(0, 40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE)\n", "dp.plot(sampling_rate=100)" @@ -742,9 +793,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'Envelope_Waveform_I(num_samples = 200, amplitude = 0.9, shape = Rectangular())'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "duration = 200 # ns\n", "amplitude = 0.9 \n", @@ -792,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -803,7 +865,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -814,34 +876,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# initialise a PulseSequence with multiple pulses at once\n", "ps = PulseSequence(p1, p2, p3)\n", "assert ps.count == 3 and len(ps) ==3\n", - "assert ps[0] == p1\n", + "assert ps[0] == p3\n", "assert ps[1] == p2\n", - "assert ps[2] == p3\n", + "assert ps[2] == p1\n", "# * please note that pulses are always sorted by channel first and then by their start time" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# initialise a PulseSequence with the sum of multiple pulses\n", "other_ps = p1 + p2 + p3\n", "assert other_ps.count == 3 and len(other_ps) ==3\n", - "assert other_ps[0] == p1\n", + "assert other_ps[0] == p3\n", "assert other_ps[1] == p2\n", - "assert other_ps[2] == p3\n", + "assert other_ps[2] == p1\n", "# * please note that pulses are always sorted by channel first and then by their start time\n", "\n", - "plist = [p1, p2, p3]\n", + "plist = [p3, p2, p1]\n", "n = 0\n", "for pulse in ps:\n", " assert plist[n] == pulse\n", @@ -850,7 +912,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -861,17 +923,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AssertionError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[29], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m yet_another_ps\u001b[38;5;241m.\u001b[39madd(p4)\n\u001b[1;32m 4\u001b[0m yet_another_ps\u001b[38;5;241m.\u001b[39madd(p5, p6)\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m yet_another_ps[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m p4\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m yet_another_ps[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m p5\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m yet_another_ps[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m==\u001b[39m p6\n", + "\u001b[0;31mAssertionError\u001b[0m: " + ] + } + ], "source": [ "# multiple pulses can be added at once\n", "yet_another_ps = PulseSequence()\n", "yet_another_ps.add(p4)\n", "yet_another_ps.add(p5, p6)\n", - "assert yet_another_ps[0] == p4\n", + "assert yet_another_ps[0] == p6\n", "assert yet_another_ps[1] == p5\n", - "assert yet_another_ps[2] == p6" + "assert yet_another_ps[2] == p4" ] }, { @@ -1104,44 +1178,6 @@ "assert ps1 == ps2" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hash(ps1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hash(ps2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for pulse in ps1.pulses:\n", - " print(pulse.serial)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for pulse in ps2.pulses:\n", - " print(pulse.serial)" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 401ababb8..068dd818e 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -861,15 +861,28 @@ def modulated_waveforms(self, sampling_rate): # -> tuple[Waveform, Waveform]: return self.shape.modulated_waveforms(sampling_rate) def __hash__(self): + """Hash the content. + + .. warning:: + + unhashable attributes are not taken into account, so there will be more + clashes than those usually expected with a regular hash + + .. todo:: + + This method should be eventually dropped, and be provided automatically by + freezing the dataclass (i.e. setting ``frozen=true`` in the decorator). + However, at the moment is not possible nor desired, because it contains + unhashable attributes and because some instances are mutated inside Qibolab. + """ return hash( - tuple(getattr(self, f.name) for f in fields(self) if f.name != "type") + tuple( + getattr(self, f.name) + for f in fields(self) + if f.name not in ("type", "shape") + ) ) - def __eq__(self, other): - if isinstance(other, Pulse): - return hash(self) == hash(other) - return NotImplemented - def __add__(self, other): if isinstance(other, Pulse): return PulseSequence(self, other) @@ -1061,7 +1074,6 @@ def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): ax2.legend() # ax2.axis([ -1, 1, -1, 1]) ax2.axis("equal") - plt.suptitle(self.serial) if savefig_filename: plt.savefig(savefig_filename) else: From 63c3b5a8307fdb2dc0a476b7aee3141609b91e66 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 17:51:43 +0100 Subject: [PATCH 019/233] Drop Waveform.serial, in base class and subclasses --- src/qibolab/pulses.py | 36 +----------------------------------- 1 file changed, 1 insertion(+), 35 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 068dd818e..a2a2eb89a 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -51,7 +51,6 @@ def __init__(self, data): """Initialises the waveform with a of samples.""" self.data: np.ndarray = np.array(data) - self.serial: str = "" def __len__(self): """Returns the length of the waveform, the number of samples.""" @@ -65,18 +64,7 @@ def __eq__(self, other): `Waveform.DECIMALS` decimal places, are all equal. """ - return self.__hash__() == other.__hash__() - - def __hash__(self): - """Returns a hash of the array of data, after rounding each sample to - `Waveform.DECIMALS` decimal places.""" - - return hash(str(np.around(self.data, Waveform.DECIMALS) + 0)) - - def __repr__(self): - """Returns the waveform serial as its string representation.""" - - return self.serial + return np.allclose(self.data, other.data) def plot(self, savefig_filename=None): """Plots the waveform. @@ -94,7 +82,6 @@ def plot(self, savefig_filename=None): plt.grid( visible=True, which="both", axis="both", color="#888888", linestyle="-" ) - plt.suptitle(self.serial) if savefig_filename: plt.savefig(savefig_filename) else: @@ -198,9 +185,7 @@ def modulated_waveforms(self, sampling_rate=SAMPLING_RATE): mod_signals = np.array(result) modulated_waveform_i = Waveform(mod_signals[:, 0]) - modulated_waveform_i.serial = f"Modulated_Waveform_I(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)}, frequency = {format(pulse._if, '_')}, phase = {format(global_phase + pulse.relative_phase, '.6f').rstrip('0').rstrip('.')})" modulated_waveform_q = Waveform(mod_signals[:, 1]) - modulated_waveform_q.serial = f"Modulated_Waveform_Q(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)}, frequency = {format(pulse._if, '_')}, phase = {format(global_phase + pulse.relative_phase, '.6f').rstrip('0').rstrip('.')})" return (modulated_waveform_i, modulated_waveform_q) def __eq__(self, item) -> bool: @@ -236,7 +221,6 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) waveform = Waveform(self.pulse.amplitude * np.ones(num_samples)) - waveform.serial = f"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -246,7 +230,6 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) waveform = Waveform(np.zeros(num_samples)) - waveform.serial = f"Envelope_Waveform_Q(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -290,7 +273,6 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: / (1 + self.g) ) - waveform.serial = f"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -300,7 +282,6 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) waveform = Waveform(np.zeros(num_samples)) - waveform.serial = f"Envelope_Waveform_Q(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -346,7 +327,6 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: ) ) ) - waveform.serial = f"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -356,7 +336,6 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) waveform = Waveform(np.zeros(num_samples)) - waveform.serial = f"Envelope_Waveform_Q(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -416,7 +395,6 @@ def fvec(t, gaussian_samples, rel_sigma, length=None): pulse = fvec(t, gaussian_samples, rel_sigma=self.rel_sigma) waveform = Waveform(self.pulse.amplitude * pulse) - waveform.serial = f"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -427,7 +405,6 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) waveform = Waveform(np.zeros(num_samples)) - waveform.serial = f"Envelope_Waveform_Q(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -470,7 +447,6 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: ) ) waveform = Waveform(i) - waveform.serial = f"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -493,7 +469,6 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: * i ) waveform = Waveform(q) - waveform.serial = f"Envelope_Waveform_Q(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -554,7 +529,6 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: data = data / np.max(np.abs(data)) data = np.abs(self.pulse.amplitude) * data waveform = Waveform(data) - waveform.serial = f"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -576,7 +550,6 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: data = data / np.max(np.abs(data)) data = np.abs(self.pulse.amplitude) * data waveform = Waveform(data) - waveform.serial = f"Envelope_Waveform_Q(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -634,7 +607,6 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: ) ) ) - waveform.serial = f"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -644,7 +616,6 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) waveform = Waveform(np.zeros(num_samples)) - waveform.serial = f"Envelope_Waveform_Q(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -685,7 +656,6 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: * (1 + np.tanh(self.alpha * (1 - x / num_samples))) / (1 + np.tanh(self.alpha / 2)) ** 2 ) - waveform.serial = f"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -693,7 +663,6 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(self.pulse.duration * sampling_rate) waveform = Waveform(np.zeros(num_samples)) - waveform.serial = f"Envelope_Waveform_Q(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -722,7 +691,6 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) waveform = Waveform(self.envelope_i * self.pulse.amplitude) - waveform.serial = f"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -735,7 +703,6 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) waveform = Waveform(self.envelope_q * self.pulse.amplitude) - waveform.serial = f"Envelope_Waveform_Q(num_samples = {num_samples}, amplitude = {format(self.pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {repr(self)})" return waveform raise ShapeInitError @@ -936,7 +903,6 @@ def copy(self): # -> Pulse|ReadoutPulse|DrivePulse|FluxPulse: self.qubit, ) else: - # return eval(self.serial) return Pulse( self.start, self.duration, From 8ac9c9ac42ed8187501488a0097ab82d58cac711 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 18:00:44 +0100 Subject: [PATCH 020/233] Mock frozen dataclass hash for Pulse --- src/qibolab/pulses.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index a2a2eb89a..0f5fa3038 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -41,8 +41,6 @@ class Waveform: Attributes: data (np.ndarray): a numpy array containing the samples. - serial (str): a string that can be used as a lable to identify the waveform. It is not automatically - generated, it must be set by the user. """ DECIMALS = 5 @@ -758,6 +756,17 @@ def __post_init__(self): # TODO: drop the cyclic reference self.shape.pulse = self + def __hash__(self): + """Return hash(self). + + .. todo:: + + this has to be replaced by turning :cls:`Pulse` into a _frozen_ dataclass + """ + return hash( + tuple(getattr(self, f.name) for f in fields(self) if f.name != "shape") + ) + @property def finish(self) -> Optional[int]: """Time when the pulse is scheduled to finish.""" From 2960d73ad6bd2fd0739bbca21a1a45f8dec15c4f Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 18:18:27 +0100 Subject: [PATCH 021/233] Fix some docstrings related warnings --- src/qibolab/pulses.py | 56 +++++++++++++------------------------------ 1 file changed, 16 insertions(+), 40 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 0f5fa3038..98bd59479 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -1231,8 +1231,7 @@ def copy(self): @property def ro_pulses(self): - """Returns a new PulseSequence containing only its readout pulses.""" - + """A new sequence containing only its readout pulses.""" new_pc = PulseSequence() for pulse in self: if pulse.type == PulseType.READOUT: @@ -1241,9 +1240,7 @@ def ro_pulses(self): @property def qd_pulses(self): - """Returns a new PulseSequence containing only its qubit drive - pulses.""" - + """A new sequence containing only its qubit drive pulses.""" new_pc = PulseSequence() for pulse in self: if pulse.type == PulseType.DRIVE: @@ -1252,9 +1249,7 @@ def qd_pulses(self): @property def qf_pulses(self): - """Returns a new PulseSequence containing only its qubit flux - pulses.""" - + """A new sequence containing only its qubit flux pulses.""" new_pc = PulseSequence() for pulse in self: if pulse.type == PulseType.FLUX: @@ -1263,9 +1258,7 @@ def qf_pulses(self): @property def cf_pulses(self): - """Returns a new PulseSequence containing only its coupler flux - pulses.""" - + """A new sequence containing only its coupler flux pulses.""" new_pc = PulseSequence() for pulse in self: if pulse.type is PulseType.COUPLERFLUX: @@ -1273,9 +1266,7 @@ def cf_pulses(self): return new_pc def get_channel_pulses(self, *channels): - """Returns a new PulseSequence containing only the pulses on a specific - set of channels.""" - + """Return a new sequence containing the pulses on some channels.""" new_pc = PulseSequence() for pulse in self: if pulse.channel in channels: @@ -1283,9 +1274,7 @@ def get_channel_pulses(self, *channels): return new_pc def get_qubit_pulses(self, *qubits): - """Returns a new PulseSequence containing only the pulses on a specific - set of qubits.""" - + """Return a new sequence containing the pulses on some qubits.""" new_pc = PulseSequence() for pulse in self: if not isinstance(pulse, CouplerFluxPulse): @@ -1294,9 +1283,7 @@ def get_qubit_pulses(self, *qubits): return new_pc def coupler_pulses(self, *couplers): - """Returns a new PulseSequence containing only the pulses on a specific - set of couplers.""" - + """Return a new sequence containing the pulses on some couplers.""" new_pc = PulseSequence() for pulse in self: if isinstance(pulse, CouplerFluxPulse): @@ -1306,8 +1293,7 @@ def coupler_pulses(self, *couplers): @property def finish(self) -> int: - """Returns the time when the last pulse of the sequence finishes.""" - + """The time when the last pulse of the sequence finishes.""" t: int = 0 for pulse in self: if pulse.finish > t: @@ -1316,8 +1302,7 @@ def finish(self) -> int: @property def start(self) -> int: - """Returns the start time of the first pulse of the sequence.""" - + """The start time of the first pulse of the sequence.""" t = self.finish for pulse in self: if pulse.start < t: @@ -1326,15 +1311,12 @@ def start(self) -> int: @property def duration(self) -> int: - """Returns duration of the sequence calculated as its finish - start times.""" - + """Duration of the sequence calculated as its finish - start times.""" return self.finish - self.start @property def channels(self) -> list: - """Returns list containing the channels used by the pulses in the - sequence.""" - + """List containing the channels used by the pulses in the sequence.""" channels = [] for pulse in self: if not pulse.channel in channels: @@ -1344,9 +1326,7 @@ def channels(self) -> list: @property def qubits(self) -> list: - """Returns list containing the qubits associated with the pulses in the - sequence.""" - + """The qubits associated with the pulses in the sequence.""" qubits = [] for pulse in self: if not pulse.qubit in qubits: @@ -1355,9 +1335,8 @@ def qubits(self) -> list: return qubits def get_pulse_overlaps(self): # -> dict((int,int): PulseSequence): - """Returns a dictionary of slices of time (tuples with start and finish + """Return a dictionary of slices of time (tuples with start and finish times) where pulses overlap.""" - times = [] for pulse in self: if not pulse.start in times: @@ -1375,9 +1354,8 @@ def get_pulse_overlaps(self): # -> dict((int,int): PulseSequence): return overlaps def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): - """Separates a sequence of overlapping pulses into a list of non- + """Separate a sequence of overlapping pulses into a list of non- overlapping sequences.""" - # This routine separates the pulses of a sequence into non-overlapping sets # but it does not check if the frequencies of the pulses within a set have the same frequency @@ -1405,8 +1383,7 @@ def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): @property def pulses_overlap(self) -> bool: - """Returns True if any of the pulses in the sequence overlap.""" - + """Whether any of the pulses in the sequence overlap.""" overlap = False for pc in self.get_pulse_overlaps().values(): if len(pc) > 1: @@ -1415,12 +1392,11 @@ def pulses_overlap(self) -> bool: return overlap def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): - """Plots the sequence of pulses. + """Plot the sequence of pulses. Args: savefig_filename (str): a file path. If provided the plot is save to a file. """ - if len(self) > 0: import matplotlib.pyplot as plt from matplotlib import gridspec From 6dff9a71ad72b5c8c7342ec56085668cd57f3d0b Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 18:23:57 +0100 Subject: [PATCH 022/233] Fix dummy tests, avoid serial check In general, the checks on the serial are quite trivial, just putting the exact same function generating the string that has been used on the other side The only non-trivial part of these tests is the check that the pulse has not been modified after the initialization However, if needed, we'll be able to restore these tests after freezing the dataclasses --- tests/test_dummy.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/tests/test_dummy.py b/tests/test_dummy.py index f0abb0559..c528304ee 100644 --- a/tests/test_dummy.py +++ b/tests/test_dummy.py @@ -61,12 +61,6 @@ def test_dummy_execute_coupler_pulse(): options = ExecutionParameters(nshots=None) result = platform.execute_pulse_sequence(sequence, options) - test_pulse = ( - "CouplerFluxPulse(0, 30, 0.05, GaussianSquare(5, 0.75), flux_coupler-0, 0)" - ) - - assert test_pulse == pulse.id - def test_dummy_execute_pulse_sequence_couplers(): platform = create_platform("dummy_couplers") From 10bc33f1bf86decba51bc59adc9a5bd6c32fc6af Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 18:29:47 +0100 Subject: [PATCH 023/233] Fix pulses tests, mainly dropping those based on the string representation --- tests/test_pulses.py | 101 +++++-------------------------------------- 1 file changed, 10 insertions(+), 91 deletions(-) diff --git a/tests/test_pulses.py b/tests/test_pulses.py index 62238e74e..c168f65d7 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -68,10 +68,7 @@ def test_pulse_init(): type=PulseType.READOUT, qubit=0, ) - assert ( - repr(p0) - == "Pulse(0, 50, 0.9, 20_000_000, 0, Rectangular(), 0, PulseType.READOUT, 0)" - ) + assert p0.relative_phase == 0.0 p1 = Pulse( start=100, @@ -84,10 +81,7 @@ def test_pulse_init(): type=PulseType.READOUT, qubit=0, ) - assert ( - repr(p1) - == "Pulse(100, 50, 0.9, 20_000_000, 0, Rectangular(), 0, PulseType.READOUT, 0)" - ) + assert p1.type is PulseType.READOUT # initialisation with non int (float) frequency p2 = Pulse( @@ -101,10 +95,6 @@ def test_pulse_init(): type=PulseType.READOUT, qubit=0, ) - assert ( - repr(p2) - == "Pulse(0, 50, 0.9, 20_000_000, 0, Rectangular(), 0, PulseType.READOUT, 0)" - ) assert isinstance(p2.frequency, int) and p2.frequency == 20_000_000 # initialisation with non float (int) relative_phase @@ -119,10 +109,6 @@ def test_pulse_init(): type=PulseType.READOUT, qubit=0, ) - assert ( - repr(p3) - == "Pulse(0, 50, 0.9, 20_000_000, 1, Rectangular(), 0, PulseType.READOUT, 0)" - ) assert isinstance(p3.relative_phase, float) and p3.relative_phase == 1.0 # initialisation with str shape @@ -137,10 +123,7 @@ def test_pulse_init(): type=PulseType.READOUT, qubit=0, ) - assert ( - repr(p4) - == "Pulse(0, 50, 0.9, 20_000_000, 0, Rectangular(), 0, PulseType.READOUT, 0)" - ) + assert isinstance(p4.shape, Rectangular) # initialisation with str channel and str qubit p5 = Pulse( @@ -154,10 +137,6 @@ def test_pulse_init(): type=PulseType.READOUT, qubit="qubit0", ) - assert ( - repr(p5) - == "Pulse(0, 50, 0.9, 20_000_000, 0, Rectangular(), channel0, PulseType.READOUT, qubit0)" - ) assert p5.qubit == "qubit0" # initialisation with different frequencies, shapes and types @@ -182,10 +161,6 @@ def test_pulse_init(): type=PulseType.READOUT, qubit=0, ) - assert ( - repr(p12) - == "Pulse(5.5, 34.33, 0.9, 20_000_000, 1, Rectangular(), 0, PulseType.READOUT, 0)" - ) assert isinstance(p12.start, float) assert isinstance(p12.duration, float) assert p12.finish == 5.5 + 34.33 @@ -385,7 +360,8 @@ def test_pulse_aliases(): channel=0, qubit=0, ) - assert repr(rop) == "ReadoutPulse(0, 50, 0.9, 20_000_000, 0, Rectangular(), 0, 0)" + assert rop.start == 0 + assert rop.qubit == 0 dp = DrivePulse( start=0, @@ -397,12 +373,13 @@ def test_pulse_aliases(): channel=0, qubit=0, ) - assert repr(dp) == "DrivePulse(0, 2000, 0.9, 200_000_000, 0, Gaussian(5), 0, 0)" + assert dp.amplitude == 0.9 + assert isinstance(dp.shape, Gaussian) fp = FluxPulse( start=0, duration=300, amplitude=0.9, shape=Rectangular(), channel=0, qubit=0 ) - assert repr(fp) == "FluxPulse(0, 300, 0.9, Rectangular(), 0, 0)" + assert fp.channel == 0 def test_pulsesequence_init(): @@ -570,9 +547,6 @@ def test_waveform(): assert wf1 != wf2 assert wf1 == wf3 np.testing.assert_allclose(wf1.data, wf3.data) - assert hash(wf1) == hash(str(np.around(np.ones(100), Waveform.DECIMALS) + 0)) - wf1.serial = "Serial works as a tag. The user can set is as desired" - assert repr(wf1) == wf1.serial def modulate( @@ -637,23 +611,6 @@ def test_pulseshape_rectangular(): pulse.shape.modulated_waveform_q(sampling_rate).data, mod_q ) - assert ( - pulse.shape.envelope_waveform_i().serial - == f"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)})" - ) - assert ( - pulse.shape.envelope_waveform_q().serial - == f"Envelope_Waveform_Q(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)})" - ) - assert ( - pulse.shape.modulated_waveform_i().serial - == f"Modulated_Waveform_I(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)}, frequency = {format(pulse._if, '_')}, phase = {format(global_phase + pulse.relative_phase, '.6f').rstrip('0').rstrip('.')})" - ) - assert ( - pulse.shape.modulated_waveform_q().serial - == f"Modulated_Waveform_Q(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)}, frequency = {format(pulse._if, '_')}, phase = {format(global_phase + pulse.relative_phase, '.6f').rstrip('0').rstrip('.')})" - ) - def test_pulseshape_gaussian(): pulse = Pulse( @@ -704,23 +661,6 @@ def test_pulseshape_gaussian(): pulse.shape.modulated_waveform_q(sampling_rate).data, mod_q ) - assert ( - pulse.shape.envelope_waveform_i().serial - == f"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)})" - ) - assert ( - pulse.shape.envelope_waveform_q().serial - == f"Envelope_Waveform_Q(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)})" - ) - assert ( - pulse.shape.modulated_waveform_i().serial - == f"Modulated_Waveform_I(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)}, frequency = {format(pulse._if, '_')}, phase = {format(global_phase + pulse.relative_phase, '.6f').rstrip('0').rstrip('.')})" - ) - assert ( - pulse.shape.modulated_waveform_q().serial - == f"Modulated_Waveform_Q(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)}, frequency = {format(pulse._if, '_')}, phase = {format(global_phase + pulse.relative_phase, '.6f').rstrip('0').rstrip('.')})" - ) - def test_pulseshape_drag(): pulse = Pulse( @@ -777,23 +717,6 @@ def test_pulseshape_drag(): pulse.shape.modulated_waveform_q(sampling_rate).data, mod_q ) - assert ( - pulse.shape.envelope_waveform_i().serial - == f"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)})" - ) - assert ( - pulse.shape.envelope_waveform_q().serial - == f"Envelope_Waveform_Q(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)})" - ) - assert ( - pulse.shape.modulated_waveform_i().serial - == f"Modulated_Waveform_I(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)}, frequency = {format(pulse._if, '_')}, phase = {format(global_phase + pulse.relative_phase, '.6f').rstrip('0').rstrip('.')})" - ) - assert ( - pulse.shape.modulated_waveform_q().serial - == f"Modulated_Waveform_Q(num_samples = {num_samples}, amplitude = {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, shape = {str(pulse.shape)}, frequency = {format(pulse._if, '_')}, phase = {format(global_phase + pulse.relative_phase, '.6f').rstrip('0').rstrip('.')})" - ) - def test_pulseshape_eq(): """Checks == operator for pulse shapes.""" @@ -873,9 +796,7 @@ def test_pulse(): channel=1, ) - target = f"Pulse({pulse.start}, {pulse.duration}, {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, {format(pulse.frequency, '_')}, {format(pulse.relative_phase, '.6f').rstrip('0').rstrip('.')}, {pulse.shape}, {pulse.channel}, {pulse.type}, {pulse.qubit})" - assert pulse.id == target - assert repr(pulse) == target + assert pulse.duration == duration def test_readout_pulse(): @@ -890,9 +811,7 @@ def test_readout_pulse(): channel=11, ) - target = f"ReadoutPulse({pulse.start}, {pulse.duration}, {format(pulse.amplitude, '.6f').rstrip('0').rstrip('.')}, {format(pulse.frequency, '_')}, {format(pulse.relative_phase, '.6f').rstrip('0').rstrip('.')}, {pulse.shape}, {pulse.channel}, {pulse.qubit})" - assert pulse.id == target - assert repr(pulse) == target + assert pulse.duration == duration def test_pulse_sequence_add_readout(): From fd0c78c86f8ce2198d354abc570c60df24dbbfd0 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 18:36:55 +0100 Subject: [PATCH 024/233] Fix rfsoc test, mostly broken by programmatic replacements --- tests/test_instruments_rfsoc.py | 56 ++++++++++++++++++++++----------- 1 file changed, 38 insertions(+), 18 deletions(-) diff --git a/tests/test_instruments_rfsoc.py b/tests/test_instruments_rfsoc.py index 2a118f0f6..f70a2f67c 100644 --- a/tests/test_instruments_rfsoc.py +++ b/tests/test_instruments_rfsoc.py @@ -389,7 +389,7 @@ def test_play(mocker, dummy_qrc): averaging_mode=AveragingMode.SINGLESHOT, ) results = instrument.play(platform.qubits, platform.couplers, seq, parameters) - assert pulse.id in results.keys() + assert pulse1.id in results.keys() parameters = ExecutionParameters( nshots=nshots, @@ -397,7 +397,7 @@ def test_play(mocker, dummy_qrc): averaging_mode=AveragingMode.SINGLESHOT, ) results = instrument.play(platform.qubits, platform.couplers, seq, parameters) - assert pulse.id in results.keys() + assert pulse1.id in results.keys() parameters = ExecutionParameters( nshots=nshots, @@ -405,7 +405,7 @@ def test_play(mocker, dummy_qrc): averaging_mode=AveragingMode.CYCLIC, ) results = instrument.play(platform.qubits, platform.couplers, seq, parameters) - assert pulse.id in results.keys() + assert pulse1.id in results.keys() def test_sweep(mocker, dummy_qrc): @@ -441,7 +441,7 @@ def test_sweep(mocker, dummy_qrc): results = instrument.sweep( platform.qubits, platform.couplers, seq, parameters, sweeper0, sweeper1 ) - assert pulse.id in results.keys() + assert pulse1.id in results.keys() parameters = ExecutionParameters( nshots=nshots, @@ -451,7 +451,7 @@ def test_sweep(mocker, dummy_qrc): results = instrument.sweep( platform.qubits, platform.couplers, seq, parameters, sweeper0, sweeper1 ) - assert pulse.id in results.keys() + assert pulse1.id in results.keys() parameters = ExecutionParameters( nshots=nshots, @@ -461,7 +461,7 @@ def test_sweep(mocker, dummy_qrc): results = instrument.sweep( platform.qubits, platform.couplers, seq, parameters, sweeper0, sweeper1 ) - assert pulse.id in results.keys() + assert pulse1.id in results.keys() def test_validate_input_command(dummy_qrc): @@ -551,18 +551,22 @@ def test_merge_sweep_results(dummy_qrc): assert targ_dict.keys() == out_dict1.keys() assert ( - out_dict1["serial1"].idize["MSR[V]"] == targ_dict["serial1"].idize["MSR[V]"] + out_dict1["serial1"].serialize["MSR[V]"] + == targ_dict["serial1"].serialize["MSR[V]"] ).all() assert ( - out_dict1["serial1"].idize["MSR[V]"] == targ_dict["serial1"].idize["MSR[V]"] + out_dict1["serial1"].serialize["MSR[V]"] + == targ_dict["serial1"].serialize["MSR[V]"] ).all() assert dict_a.keys() == out_dict2.keys() assert ( - out_dict2["serial1"].idize["MSR[V]"] == dict_a["serial1"].idize["MSR[V]"] + out_dict2["serial1"].serialize["MSR[V]"] + == dict_a["serial1"].serialize["MSR[V]"] ).all() assert ( - out_dict2["serial1"].idize["MSR[V]"] == dict_a["serial1"].idize["MSR[V]"] + out_dict2["serial1"].serialize["MSR[V]"] + == dict_a["serial1"].serialize["MSR[V]"] ).all() @@ -695,10 +699,18 @@ def test_convert_av_sweep_results(dummy_qrc): ), } - assert (out_dict[serial1].idize["i[V]"] == targ_dict[serial1].idize["i[V]"]).all() - assert (out_dict[serial1].idize["q[V]"] == targ_dict[serial1].idize["q[V]"]).all() - assert (out_dict[serial2].idize["i[V]"] == targ_dict[serial2].idize["i[V]"]).all() - assert (out_dict[serial2].idize["q[V]"] == targ_dict[serial2].idize["q[V]"]).all() + assert ( + out_dict[serial1].serialize["i[V]"] == targ_dict[serial1].serialize["i[V]"] + ).all() + assert ( + out_dict[serial1].serialize["q[V]"] == targ_dict[serial1].serialize["q[V]"] + ).all() + assert ( + out_dict[serial2].serialize["i[V]"] == targ_dict[serial2].serialize["i[V]"] + ).all() + assert ( + out_dict[serial2].serialize["q[V]"] == targ_dict[serial2].serialize["q[V]"] + ).all() def test_convert_nav_sweep_results(dummy_qrc): @@ -740,10 +752,18 @@ def test_convert_nav_sweep_results(dummy_qrc): ), } - assert (out_dict[serial1].idize["i[V]"] == targ_dict[serial1].idize["i[V]"]).all() - assert (out_dict[serial1].idize["q[V]"] == targ_dict[serial1].idize["q[V]"]).all() - assert (out_dict[serial2].idize["i[V]"] == targ_dict[serial2].idize["i[V]"]).all() - assert (out_dict[serial2].idize["q[V]"] == targ_dict[serial2].idize["q[V]"]).all() + assert ( + out_dict[serial1].serialize["i[V]"] == targ_dict[serial1].serialize["i[V]"] + ).all() + assert ( + out_dict[serial1].serialize["q[V]"] == targ_dict[serial1].serialize["q[V]"] + ).all() + assert ( + out_dict[serial2].serialize["i[V]"] == targ_dict[serial2].serialize["i[V]"] + ).all() + assert ( + out_dict[serial2].serialize["q[V]"] == targ_dict[serial2].serialize["q[V]"] + ).all() @pytest.fixture(scope="module") From 4cee4d575957f9c493aa198a1d41f0de76bc92b8 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 18:48:07 +0100 Subject: [PATCH 025/233] Replace waveform.serial with underlying data hash --- src/qibolab/instruments/qm/config.py | 2 +- src/qibolab/instruments/qm/sweepers.py | 2 +- src/qibolab/pulses.py | 16 ++++++++-------- tests/test_instruments_qm.py | 4 ++-- 4 files changed, 12 insertions(+), 12 deletions(-) diff --git a/src/qibolab/instruments/qm/config.py b/src/qibolab/instruments/qm/config.py index 41c1f3e4a..04beb8bc6 100644 --- a/src/qibolab/instruments/qm/config.py +++ b/src/qibolab/instruments/qm/config.py @@ -346,7 +346,7 @@ def register_waveform(self, pulse, mode="i"): self.waveforms[serial] = {"type": "constant", "sample": pulse.amplitude} else: waveform = getattr(pulse, f"envelope_waveform_{mode}")(SAMPLING_RATE) - serial = waveform.serial + serial = hash(waveform) if serial not in self.waveforms: self.waveforms[serial] = { "type": "arbitrary", diff --git a/src/qibolab/instruments/qm/sweepers.py b/src/qibolab/instruments/qm/sweepers.py index 1e35e71fe..2ccd91ff5 100644 --- a/src/qibolab/instruments/qm/sweepers.py +++ b/src/qibolab/instruments/qm/sweepers.py @@ -29,7 +29,7 @@ def maximum_sweep_value(values, value0): def _update_baked_pulses(sweeper, qmsequence, config): """Updates baked pulse if duration sweeper is used.""" - qmpulse = qmsequence.pulse_to_qmpulse[sweeper.pulses[0].serial] + qmpulse = qmsequence.pulse_to_qmpulse[sweeper.pulses[0].id] is_baked = isinstance(qmpulse, BakedPulse) for pulse in sweeper.pulses: qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 98bd59479..ff0ddb7e3 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -46,31 +46,31 @@ class Waveform: DECIMALS = 5 def __init__(self, data): - """Initialises the waveform with a of samples.""" - + """Initialise the waveform with a of samples.""" self.data: np.ndarray = np.array(data) def __len__(self): - """Returns the length of the waveform, the number of samples.""" - + """Return the length of the waveform, the number of samples.""" return len(self.data) + def __hash__(self): + """Hash the underlying data.""" + return hash(self.data.tobytes()) + def __eq__(self, other): - """Compares two waveforms. + """Compare two waveforms. Two waveforms are considered equal if their samples, rounded to `Waveform.DECIMALS` decimal places, are all equal. """ - return np.allclose(self.data, other.data) def plot(self, savefig_filename=None): - """Plots the waveform. + """Plot the waveform. Args: savefig_filename (str): a file path. If provided the plot is save to a file. """ - import matplotlib.pyplot as plt plt.figure(figsize=(14, 5), dpi=200) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 33f61c23c..1ce44d8e6 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -306,8 +306,8 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): "length": pulse.duration, "digital_marker": "ON", "waveforms": { - "I": pulse.envelope_waveform_i().serial, - "Q": pulse.envelope_waveform_q().serial, + "I": hash(pulse.envelope_waveform_i()), + "Q": hash(pulse.envelope_waveform_q()), }, } From 0dfcae30fab9cfad590ce2e043537b980b6929ad Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 18:56:42 +0100 Subject: [PATCH 026/233] Add note about waveform hash (lacking) reliability --- src/qibolab/pulses.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index ff0ddb7e3..fd3ddf986 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -54,7 +54,14 @@ def __len__(self): return len(self.data) def __hash__(self): - """Hash the underlying data.""" + """Hash the underlying data. + + .. todo:: + + In order to make this reliable, we should set the data as immutable. This we + could by making both the class frozen and the contained array readonly + https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags + """ return hash(self.data.tobytes()) def __eq__(self, other): From 1e130c208f9ed6ac2a301a1ddb89e639025c2edf Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 19:17:35 +0100 Subject: [PATCH 027/233] Fix QM issues by stringifying pulses ID QM requires some keys to be strings, because of the way they are later processed. And before they were (by accident, since we were using the serial as an identifier). --- src/qibolab/instruments/qm/sweepers.py | 2 +- tests/test_instruments_qm.py | 21 +++++++++++++++++++++ 2 files changed, 22 insertions(+), 1 deletion(-) diff --git a/src/qibolab/instruments/qm/sweepers.py b/src/qibolab/instruments/qm/sweepers.py index 2ccd91ff5..e745d6bde 100644 --- a/src/qibolab/instruments/qm/sweepers.py +++ b/src/qibolab/instruments/qm/sweepers.py @@ -182,7 +182,7 @@ def _sweep_start(sweepers, qubits, qmsequence, relaxation_time): def _sweep_duration(sweepers, qubits, qmsequence, relaxation_time): sweeper = sweepers[0] - qmpulse = qmsequence.pulse_to_qmpulse[sweeper.pulses[0].serial] + qmpulse = qmsequence.pulse_to_qmpulse[sweeper.pulses[0].id] if isinstance(qmpulse, BakedPulse): values = np.array(sweeper.values).astype(int) else: diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 1ce44d8e6..63f3db384 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -325,8 +325,21 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): }, } +<<<<<<< HEAD controller.config.register_element( platform.qubits[qubit], pulse, controller.time_of_flight, controller.smearing +======= + opx.config.register_element( + platform.qubits[qubit], pulse, opx.time_of_flight, opx.smearing + ) + opx.config.register_pulse(platform.qubits[qubit], pulse) + assert opx.config.pulses[str(pulse.id)] == target_pulse + assert target_pulse["waveforms"]["I"] in opx.config.waveforms + assert target_pulse["waveforms"]["Q"] in opx.config.waveforms + assert ( + opx.config.elements[f"{pulse_type}{qubit}"]["operations"][str(pulse.id)] + == pulse.id +>>>>>>> 5f1fb614 (Fix QM issues by stringifying pulses ID) ) qmpulse = QMPulse(pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) @@ -347,11 +360,19 @@ def test_qm_register_flux_pulse(qmplatform): "length": pulse.duration, "waveforms": {"single": "constant_wf0.005"}, } +<<<<<<< HEAD qmpulse = QMPulse(pulse) controller.config.register_element(platform.qubits[qubit], pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) assert controller.config.pulses[qmpulse.operation] == target_pulse assert target_pulse["waveforms"]["single"] in controller.config.waveforms +======= + opx.config.register_element(platform.qubits[qubit], pulse) + opx.config.register_pulse(platform.qubits[qubit], pulse) + assert opx.config.pulses[str(pulse.id)] == target_pulse + assert target_pulse["waveforms"]["single"] in opx.config.waveforms + assert opx.config.elements[f"flux{qubit}"]["operations"][str(pulse.id)] == pulse.id +>>>>>>> 5f1fb614 (Fix QM issues by stringifying pulses ID) def test_qm_register_pulses_with_different_frequencies(qmplatform): From 962245dec427cda9d059a3bf7c7c8d289c6db9f7 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 19:21:10 +0100 Subject: [PATCH 028/233] Drop serial from pulse subclasses --- src/qibolab/pulses.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index fd3ddf986..1c969cc5a 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -1093,10 +1093,6 @@ def __init__( qubit=qubit, ) - @property - def serial(self): - return f"ReadoutPulse({self.start}, {self.duration}, {format(self.amplitude, '.6f').rstrip('0').rstrip('.')}, {format(self.frequency, '_')}, {format(self.relative_phase, '.6f').rstrip('0').rstrip('.')}, {self.shape}, {self.channel}, {self.qubit})" - @property def global_phase(self): # readout pulses should have zero global phase so that we can @@ -1148,10 +1144,6 @@ def __init__( qubit=qubit, ) - @property - def serial(self): - return f"DrivePulse({self.start}, {self.duration}, {format(self.amplitude, '.6f').rstrip('0').rstrip('.')}, {format(self.frequency, '_')}, {format(self.relative_phase, '.6f').rstrip('0').rstrip('.')}, {self.shape}, {self.channel}, {self.qubit})" - class FluxPulse(Pulse): """Describes a qubit flux pulse. @@ -1186,10 +1178,6 @@ def modulated_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: def modulated_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: return self.shape.envelope_waveform_i(sampling_rate) - @property - def serial(self): - return f"{self.__class__.__name__}({self.start}, {self.duration}, {format(self.amplitude, '.6f').rstrip('0').rstrip('.')}, {self.shape}, {self.channel}, {self.qubit})" - class CouplerFluxPulse(FluxPulse): """Describes a coupler flux pulse. From 390e186efc96418eb31033ba4f12e336a12ecbb9 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 19:22:50 +0100 Subject: [PATCH 029/233] Remove duplicated hash method --- src/qibolab/pulses.py | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 1c969cc5a..8fa3bc7c7 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -763,17 +763,6 @@ def __post_init__(self): # TODO: drop the cyclic reference self.shape.pulse = self - def __hash__(self): - """Return hash(self). - - .. todo:: - - this has to be replaced by turning :cls:`Pulse` into a _frozen_ dataclass - """ - return hash( - tuple(getattr(self, f.name) for f in fields(self) if f.name != "shape") - ) - @property def finish(self) -> Optional[int]: """Time when the pulse is scheduled to finish.""" From eae6b9691a7c066cbdae6b7b16c22b431a7545b3 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 14:16:08 +0100 Subject: [PATCH 030/233] Drop Pulse subclasses --- src/qibolab/pulses.py | 141 ++---------------------------------------- 1 file changed, 4 insertions(+), 137 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 8fa3bc7c7..7c96c6f22 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -1,6 +1,4 @@ """Pulse and PulseSequence classes.""" - -import copy import re from abc import ABC, abstractmethod from dataclasses import dataclass, fields @@ -777,6 +775,10 @@ def global_phase(self): This phase is calculated from the pulse start time and frequency as `2 * pi * frequency * start`. """ + if self.type is PulseType.READOUT: + # readout pulses should have zero global phase so that we can + # calculate probabilities in the i-q plane + return 0 # pulse start, duration and finish are in ns return 2 * np.pi * self.frequency * self.start / 1e9 @@ -1052,141 +1054,6 @@ def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): plt.close() -class ReadoutPulse(Pulse): - """Describes a readout pulse. - - See - :class: `qibolab.pulses.Pulse` for argument desciption. - """ - - def __init__( - self, - start, - duration, - amplitude, - frequency, - relative_phase, - shape, - channel=0, - qubit=0, - ): - super().__init__( - start, - duration, - amplitude, - frequency, - relative_phase, - shape, - channel, - type=PulseType.READOUT, - qubit=qubit, - ) - - @property - def global_phase(self): - # readout pulses should have zero global phase so that we can - # calculate probabilities in the i-q plane - return 0 - - def copy(self): # -> Pulse|ReadoutPulse|DrivePulse|FluxPulse: - """Returns a new Pulse object with the same attributes.""" - - return ReadoutPulse( - self.start, - self.duration, - self.amplitude, - self.frequency, - self.relative_phase, - copy.deepcopy(self.shape), # self.shape, - self.channel, - self.qubit, - ) - - -class DrivePulse(Pulse): - """Describes a qubit drive pulse. - - See - :class: `qibolab.pulses.Pulse` for argument desciption. - """ - - def __init__( - self, - start, - duration, - amplitude, - frequency, - relative_phase, - shape, - channel=0, - qubit=0, - ): - super().__init__( - start, - duration, - amplitude, - frequency, - relative_phase, - shape, - channel, - type=PulseType.DRIVE, - qubit=qubit, - ) - - -class FluxPulse(Pulse): - """Describes a qubit flux pulse. - - Flux pulses have frequency and relative_phase equal to 0. Their i - and q components are equal. See - :class: `qibolab.pulses.Pulse` for argument desciption. - """ - - PULSE_TYPE = PulseType.FLUX - - def __init__(self, start, duration, amplitude, shape, channel=0, qubit=0): - super().__init__( - start, - duration, - amplitude, - 0, - 0, - shape, - channel, - type=self.PULSE_TYPE, - qubit=qubit, - ) - - def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """Flux pulses only have i component.""" - return self.shape.envelope_waveform_i(sampling_rate) - - def modulated_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - return self.shape.envelope_waveform_i(sampling_rate) - - def modulated_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - return self.shape.envelope_waveform_i(sampling_rate) - - -class CouplerFluxPulse(FluxPulse): - """Describes a coupler flux pulse. - - See - :class: `qibolab.pulses.FluxPulse` for argument desciption. - """ - - PULSE_TYPE = PulseType.COUPLERFLUX - - -class PulseConstructor(Enum): - """An enumeration to map each ``PulseType`` to the proper pulse - constructor.""" - - READOUT = ReadoutPulse - DRIVE = DrivePulse - FLUX = FluxPulse - - class PulseSequence(list): """A collection of scheduled pulses. From e3c13673599803dc8b59d788bb859d94dee65f61 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 14:16:40 +0100 Subject: [PATCH 031/233] Introduce alternative (simplified) constructor for flux pulses --- src/qibolab/pulses.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 7c96c6f22..6de1192b7 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -761,6 +761,10 @@ def __post_init__(self): # TODO: drop the cyclic reference self.shape.pulse = self + @classmethod + def flux(cls, start, duration, amplitude, shape, **kwargs): + return cls(start, duration, amplitude, 0, 0, shape, **kwargs) + @property def finish(self) -> Optional[int]: """Time when the pulse is scheduled to finish.""" From 4ba327521027f18c5a08a41388ff0d2a4dc92f5c Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 14:17:45 +0100 Subject: [PATCH 032/233] Replace usage of pulse subclasses (import related) Minimal replacement, such that pytest can at least report --- src/qibolab/compilers/compiler.py | 4 ++-- src/qibolab/native.py | 10 ++-------- src/qibolab/platform/platform.py | 15 +-------------- tests/test_pulses.py | 3 --- 4 files changed, 5 insertions(+), 27 deletions(-) diff --git a/src/qibolab/compilers/compiler.py b/src/qibolab/compilers/compiler.py index ded9b11bf..905d5e80c 100644 --- a/src/qibolab/compilers/compiler.py +++ b/src/qibolab/compilers/compiler.py @@ -15,7 +15,7 @@ u3_rule, z_rule, ) -from qibolab.pulses import PulseSequence, ReadoutPulse +from qibolab.pulses import PulseSequence, PulseType @dataclass @@ -119,7 +119,7 @@ def _compile_gate( # shift start time and phase according to the global sequence for pulse in gate_sequence: pulse.start += start - if not isinstance(pulse, ReadoutPulse): + if pulse is not PulseType.READOUT: pulse.relative_phase += virtual_z_phases[pulse.qubit] sequence.append(pulse) diff --git a/src/qibolab/native.py b/src/qibolab/native.py index 6f2bcf27d..ac5e52b5b 100644 --- a/src/qibolab/native.py +++ b/src/qibolab/native.py @@ -2,13 +2,7 @@ from dataclasses import dataclass, field, fields, replace from typing import List, Optional, Union -from qibolab.pulses import ( - CouplerFluxPulse, - FluxPulse, - PulseConstructor, - PulseSequence, - PulseType, -) +from qibolab.pulses import Pulse, PulseSequence, PulseType @dataclass @@ -79,7 +73,7 @@ def pulse(self, start, relative_phase=0.0): or :class:`qibolab.pulses.FluxPulse` with the pulse parameters of the gate. """ if self.pulse_type is PulseType.FLUX: - return FluxPulse( + return Pulse.flux( start + self.relative_start, self.duration, self.amplitude, diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index ef2e51af3..bf237668d 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -48,7 +48,7 @@ def unroll_sequences( new_pulse = pulse.copy() new_pulse.start += start total_sequence.append(new_pulse) - if isinstance(pulse, ReadoutPulse): + if pulse.type is PulseType.READOUT: readout_map[pulse.id].append(new_pulse.id) start = total_sequence.finish + relaxation_time return total_sequence, readout_map @@ -385,19 +385,6 @@ def create_qubit_readout_pulse(self, qubit, start): qubit = self.get_qubit(qubit) return self.create_MZ_pulse(qubit, start) - def create_qubit_flux_pulse(self, qubit, start, duration, amplitude=1): - qubit = self.get_qubit(qubit) - pulse = FluxPulse( - start=start, - duration=duration, - amplitude=amplitude, - shape="Rectangular", - channel=self.qubits[qubit].flux.name, - qubit=qubit, - ) - pulse.duration = duration - return pulse - def create_coupler_pulse(self, coupler, start, duration=None, amplitude=None): coupler = self.get_coupler(coupler) pulse = self.couplers[coupler].native_pulse.CP.pulse(start) diff --git a/tests/test_pulses.py b/tests/test_pulses.py index c168f65d7..e59e18c26 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -11,15 +11,12 @@ SNZ, Custom, Drag, - DrivePulse, - FluxPulse, Gaussian, GaussianSquare, Pulse, PulseSequence, PulseShape, PulseType, - ReadoutPulse, Rectangular, ShapeInitError, Waveform, From c6ee2305a3d7f51015013898fc309c459a341038 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 14:24:37 +0100 Subject: [PATCH 033/233] Strip all imports of removed objects --- src/qibolab/pulses.py | 4 +- tests/test_dummy.py | 2 +- .../test_instruments_qblox_cluster_qcm_bb.py | 2 +- .../test_instruments_qblox_cluster_qcm_rf.py | 2 +- .../test_instruments_qblox_cluster_qrm_rf.py | 2 +- tests/test_instruments_qm.py | 44 +++++-------------- tests/test_instruments_qmsim.py | 8 ++-- 7 files changed, 23 insertions(+), 41 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 6de1192b7..fdce0ce9a 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -763,7 +763,9 @@ def __post_init__(self): @classmethod def flux(cls, start, duration, amplitude, shape, **kwargs): - return cls(start, duration, amplitude, 0, 0, shape, **kwargs) + return cls( + start, duration, amplitude, 0, 0, shape, type=PulseType.FLUX, **kwargs + ) @property def finish(self) -> Optional[int]: diff --git a/tests/test_dummy.py b/tests/test_dummy.py index c528304ee..4aa828731 100644 --- a/tests/test_dummy.py +++ b/tests/test_dummy.py @@ -2,7 +2,7 @@ import pytest from qibolab import AcquisitionType, AveragingMode, ExecutionParameters, create_platform -from qibolab.pulses import CouplerFluxPulse, PulseSequence +from qibolab.pulses import PulseSequence from qibolab.qubits import QubitPair from qibolab.sweeper import Parameter, QubitParameter, Sweeper diff --git a/tests/test_instruments_qblox_cluster_qcm_bb.py b/tests/test_instruments_qblox_cluster_qcm_bb.py index d6f7309d1..cf1c8d643 100644 --- a/tests/test_instruments_qblox_cluster_qcm_bb.py +++ b/tests/test_instruments_qblox_cluster_qcm_bb.py @@ -6,7 +6,7 @@ from qibolab.instruments.abstract import Instrument from qibolab.instruments.qblox.cluster_qcm_bb import QcmBb from qibolab.instruments.qblox.port import QbloxOutputPort -from qibolab.pulses import FluxPulse, PulseSequence +from qibolab.pulses import PulseSequence from qibolab.sweeper import Parameter, Sweeper, SweeperType from .qblox_fixtures import connected_controller, controller diff --git a/tests/test_instruments_qblox_cluster_qcm_rf.py b/tests/test_instruments_qblox_cluster_qcm_rf.py index f7926d6d9..468eadd35 100644 --- a/tests/test_instruments_qblox_cluster_qcm_rf.py +++ b/tests/test_instruments_qblox_cluster_qcm_rf.py @@ -4,7 +4,7 @@ from qibolab.instruments.abstract import Instrument from qibolab.instruments.qblox.cluster_qcm_rf import QcmRf from qibolab.instruments.qblox.port import QbloxOutputPort -from qibolab.pulses import DrivePulse, PulseSequence +from qibolab.pulses import PulseSequence from qibolab.sweeper import Parameter, Sweeper, SweeperType from .qblox_fixtures import connected_controller, controller diff --git a/tests/test_instruments_qblox_cluster_qrm_rf.py b/tests/test_instruments_qblox_cluster_qrm_rf.py index eb7b6adcd..86199ab60 100644 --- a/tests/test_instruments_qblox_cluster_qrm_rf.py +++ b/tests/test_instruments_qblox_cluster_qrm_rf.py @@ -4,7 +4,7 @@ from qibolab.instruments.abstract import Instrument from qibolab.instruments.qblox.cluster_qrm_rf import QrmRf from qibolab.instruments.qblox.port import QbloxInputPort, QbloxOutputPort -from qibolab.pulses import DrivePulse, PulseSequence, ReadoutPulse +from qibolab.pulses import PulseSequence from qibolab.sweeper import Parameter, Sweeper, SweeperType from .qblox_fixtures import connected_controller, controller diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 63f3db384..154ca3ec9 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -9,7 +9,7 @@ from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions from qibolab.instruments.qm.controller import controllers_config from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence -from qibolab.pulses import FluxPulse, Pulse, PulseSequence, ReadoutPulse, Rectangular +from qibolab.pulses import Pulse, PulseType, PulseSequence, Rectangular from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, Sweeper @@ -54,8 +54,8 @@ def test_qmpulse_declare_output(acquisition_type): def test_qmsequence(): - qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) - ro_pulse = ReadoutPulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch1", qubit=0) + qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", PulseType.DRIVE, qubit=0) + ro_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch1", PulseType.READOUT, qubit=0) qmsequence = Sequence() with pytest.raises(AttributeError): qmsequence.add("test") @@ -80,7 +80,7 @@ def test_qmpulse_previous_and_next(): qmsequence.add(qd_pulse) for qubit in range(nqubits): ro_pulse = QMPulse( - ReadoutPulse( + Pulse( 40, 100, 0.05, @@ -88,6 +88,7 @@ def test_qmpulse_previous_and_next(): 0.0, Rectangular(), f"readout{qubit}", + PulseType.READOUT, qubit=qubit, ) ) @@ -102,7 +103,7 @@ def test_qmpulse_previous_and_next(): def test_qmpulse_previous_and_next_flux(): y90_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), f"drive1", qubit=1) x_pulse_start = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), f"drive2", qubit=2) - flux_pulse = FluxPulse( + flux_pulse = Pulse.flux( start=y90_pulse.finish, duration=30, amplitude=0.055, @@ -113,11 +114,11 @@ def test_qmpulse_previous_and_next_flux(): theta_pulse = Pulse(70, 40, 0.05, int(3e9), 0.0, Rectangular(), f"drive1", qubit=1) x_pulse_end = Pulse(70, 40, 0.05, int(3e9), 0.0, Rectangular(), f"drive2", qubit=2) - measure_lowfreq = ReadoutPulse( - 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout1", qubit=1 + measure_lowfreq = Pulse( + 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout1", PulseType.READOUT, qubit=1 ) - measure_highfreq = ReadoutPulse( - 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout2", qubit=2 + measure_highfreq = Pulse( + 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout2", PulseType.READOUT, qubit=2 ) drive11 = QMPulse(y90_pulse) @@ -325,21 +326,8 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): }, } -<<<<<<< HEAD controller.config.register_element( platform.qubits[qubit], pulse, controller.time_of_flight, controller.smearing -======= - opx.config.register_element( - platform.qubits[qubit], pulse, opx.time_of_flight, opx.smearing - ) - opx.config.register_pulse(platform.qubits[qubit], pulse) - assert opx.config.pulses[str(pulse.id)] == target_pulse - assert target_pulse["waveforms"]["I"] in opx.config.waveforms - assert target_pulse["waveforms"]["Q"] in opx.config.waveforms - assert ( - opx.config.elements[f"{pulse_type}{qubit}"]["operations"][str(pulse.id)] - == pulse.id ->>>>>>> 5f1fb614 (Fix QM issues by stringifying pulses ID) ) qmpulse = QMPulse(pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) @@ -352,7 +340,7 @@ def test_qm_register_flux_pulse(qmplatform): qubit = 2 platform = qmplatform controller = platform.instruments["qm"] - pulse = FluxPulse( + pulse = Pulse.flux( 0, 30, 0.005, Rectangular(), platform.qubits[qubit].flux.name, qubit ) target_pulse = { @@ -360,19 +348,11 @@ def test_qm_register_flux_pulse(qmplatform): "length": pulse.duration, "waveforms": {"single": "constant_wf0.005"}, } -<<<<<<< HEAD qmpulse = QMPulse(pulse) controller.config.register_element(platform.qubits[qubit], pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) assert controller.config.pulses[qmpulse.operation] == target_pulse assert target_pulse["waveforms"]["single"] in controller.config.waveforms -======= - opx.config.register_element(platform.qubits[qubit], pulse) - opx.config.register_pulse(platform.qubits[qubit], pulse) - assert opx.config.pulses[str(pulse.id)] == target_pulse - assert target_pulse["waveforms"]["single"] in opx.config.waveforms - assert opx.config.elements[f"flux{qubit}"]["operations"][str(pulse.id)] == pulse.id ->>>>>>> 5f1fb614 (Fix QM issues by stringifying pulses ID) def test_qm_register_pulses_with_different_frequencies(qmplatform): @@ -427,7 +407,7 @@ def test_qm_register_baked_pulse(qmplatform, duration): qubit = platform.qubits[3] controller = platform.instruments["qm"] controller.config.register_flux_element(qubit) - pulse = FluxPulse( + pulse = Pulse.flux( 3, duration, 0.05, Rectangular(), qubit.flux.name, qubit=qubit.name ) qmpulse = BakedPulse(pulse) diff --git a/tests/test_instruments_qmsim.py b/tests/test_instruments_qmsim.py index 8488621d5..9c20eaac9 100644 --- a/tests/test_instruments_qmsim.py +++ b/tests/test_instruments_qmsim.py @@ -23,7 +23,7 @@ from qibolab import AcquisitionType, AveragingMode, ExecutionParameters, create_platform from qibolab.backends import QibolabBackend -from qibolab.pulses import SNZ, FluxPulse, PulseSequence, Rectangular +from qibolab.pulses import Pulse, SNZ, PulseSequence, Rectangular from qibolab.sweeper import Parameter, Sweeper from .conftest import set_platform_profile @@ -388,7 +388,7 @@ def test_qmsim_chevron(simulator, folder, sweep): lowfreq, highfreq = 1, 2 initialize_1 = simulator.create_RX_pulse(lowfreq, start=0, relative_phase=0) initialize_2 = simulator.create_RX_pulse(highfreq, start=0, relative_phase=0) - flux_pulse = FluxPulse( + flux_pulse = Pulse.flux( start=initialize_2.finish, duration=31, amplitude=0.05, @@ -439,7 +439,7 @@ def test_qmsim_tune_landscape(simulator, folder, qubits, use_flux_pulse): y90_pulse = simulator.create_RX90_pulse(lowfreq, start=0, relative_phase=np.pi / 2) x_pulse_start = simulator.create_RX_pulse(highfreq, start=0, relative_phase=0) if use_flux_pulse: - flux_pulse = FluxPulse( + flux_pulse = Pulse.flux( start=y90_pulse.finish, duration=30, amplitude=0.055, @@ -492,7 +492,7 @@ def test_qmsim_snz_pulse(simulator, folder, qubit): shape = SNZ(t_half_flux_pulse=duration // 2, b_amplitude=2) channel = simulator.qubits[qubit].flux.name qd_pulse = simulator.create_RX_pulse(qubit, start=0) - flux_pulse = FluxPulse(qd_pulse.finish, duration, amplitude, shape, channel, qubit) + flux_pulse = Pulse.flux(qd_pulse.finish, duration, amplitude, shape, channel, qubit) ro_pulse = simulator.create_MZ_pulse(qubit, start=flux_pulse.finish) sequence.append(qd_pulse) sequence.append(flux_pulse) From 3af5f4a6059515a82fe5c57295aa13db91d06d48 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 14:29:54 +0100 Subject: [PATCH 034/233] Fix backends test, remove explicit copy methods To copy, both shallow and deep, just use the dedicated standard library module --- src/qibolab/native.py | 6 +-- src/qibolab/platform/platform.py | 4 +- src/qibolab/pulses.py | 65 +------------------------------- 3 files changed, 7 insertions(+), 68 deletions(-) diff --git a/src/qibolab/native.py b/src/qibolab/native.py index ac5e52b5b..2148a91e9 100644 --- a/src/qibolab/native.py +++ b/src/qibolab/native.py @@ -82,16 +82,16 @@ def pulse(self, start, relative_phase=0.0): qubit=self.qubit.name, ) - pulse_cls = PulseConstructor[self.pulse_type.name].value channel = getattr(self.qubit, self.pulse_type.name.lower()).name - return pulse_cls( + return Pulse( start + self.relative_start, self.duration, self.amplitude, self.frequency, relative_phase, self.shape, - channel, + type=self.pulse_type, + channel=channel, qubit=self.qubit.name, ) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index bf237668d..60af19d9a 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -1,5 +1,5 @@ """A platform for executing quantum algorithms.""" - +import copy from collections import defaultdict from dataclasses import dataclass, field, replace from typing import Dict, List, Optional, Tuple @@ -45,7 +45,7 @@ def unroll_sequences( start = 0 for sequence in sequences: for pulse in sequence: - new_pulse = pulse.copy() + new_pulse = copy.deepcopy(pulse) new_pulse.start += start total_sequence.append(new_pulse) if pulse.type is PulseType.READOUT: diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index fdce0ce9a..da7d98c3c 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -880,67 +880,6 @@ def __mul__(self, n): def __rmul__(self, n): return self.__mul__(n) - def copy(self): # -> Pulse|ReadoutPulse|DrivePulse|FluxPulse: - """Returns a new Pulse object with the same attributes.""" - - if type(self) == ReadoutPulse: - return ReadoutPulse( - self.start, - self.duration, - self.amplitude, - self.frequency, - self.relative_phase, - repr(self.shape), # self.shape, - self.channel, - self.qubit, - ) - elif type(self) == DrivePulse: - return DrivePulse( - self.start, - self.duration, - self.amplitude, - self.frequency, - self.relative_phase, - repr(self.shape), # self.shape, - self.channel, - self.qubit, - ) - - elif type(self) == FluxPulse: - return FluxPulse( - self.start, - self.duration, - self.amplitude, - self.shape, - self.channel, - self.qubit, - ) - else: - return Pulse( - self.start, - self.duration, - self.amplitude, - self.frequency, - self.relative_phase, - repr(self.shape), # self.shape, - self.channel, - self.type, - self.qubit, - ) - - def shallow_copy(self): # -> Pulse: - return Pulse( - self.start, - self.duration, - self.amplitude, - self.frequency, - self.relative_phase, - self.shape, - self.channel, - self.type, - self.qubit, - ) - def is_equal_ignoring_start(self, item) -> bool: """Check if two pulses are equal ignoring start time.""" return ( @@ -1134,7 +1073,7 @@ def get_qubit_pulses(self, *qubits): """Return a new sequence containing the pulses on some qubits.""" new_pc = PulseSequence() for pulse in self: - if not isinstance(pulse, CouplerFluxPulse): + if pulse.type is not PulseType.COUPLERFLUX: if pulse.qubit in qubits: new_pc.append(pulse) return new_pc @@ -1143,7 +1082,7 @@ def coupler_pulses(self, *couplers): """Return a new sequence containing the pulses on some couplers.""" new_pc = PulseSequence() for pulse in self: - if isinstance(pulse, CouplerFluxPulse): + if pulse.type is not PulseType.COUPLERFLUX: if pulse.qubit in couplers: new_pc.append(pulse) return new_pc From de22bec06ef92ac2453f414d4aa3acc13540bdef Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 14:51:34 +0100 Subject: [PATCH 035/233] Fix compilers tests --- src/qibolab/compilers/compiler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibolab/compilers/compiler.py b/src/qibolab/compilers/compiler.py index 905d5e80c..7bfa9f0e1 100644 --- a/src/qibolab/compilers/compiler.py +++ b/src/qibolab/compilers/compiler.py @@ -119,7 +119,7 @@ def _compile_gate( # shift start time and phase according to the global sequence for pulse in gate_sequence: pulse.start += start - if pulse is not PulseType.READOUT: + if pulse.type is not PulseType.READOUT: pulse.relative_phase += virtual_z_phases[pulse.qubit] sequence.append(pulse) From ca626cf0d4bebd744a5f3a1bfe35ac312694c1cc Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 15:50:56 +0100 Subject: [PATCH 036/233] Fix pulses tests --- tests/test_pulses.py | 125 ++++++++++++++++++++++++++++--------------- 1 file changed, 81 insertions(+), 44 deletions(-) diff --git a/tests/test_pulses.py b/tests/test_pulses.py index e59e18c26..aab708813 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -1,5 +1,5 @@ """Tests ``pulses.py``.""" - +import copy import os import pathlib @@ -30,8 +30,10 @@ def test_plot_functions(): p0 = Pulse(0, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) p1 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) p2 = Pulse(0, 40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) - p3 = FluxPulse(0, 40, 0.9, IIR([-0.5, 2], [1], Rectangular()), 0, 200) - p4 = FluxPulse(0, 40, 0.9, SNZ(t_idling=10), 0, 200) + p3 = Pulse.flux( + 0, 40, 0.9, IIR([-0.5, 2], [1], Rectangular()), channel=0, qubit=200 + ) + p4 = Pulse.flux(0, 40, 0.9, SNZ(t_idling=10), channel=0, qubit=200) p5 = Pulse(0, 40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) p6 = Pulse(0, 40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.DRIVE, 2) ps = PulseSequence([p0, p1, p2, p3, p4, p5, p6]) @@ -141,8 +143,12 @@ def test_pulse_init(): p7 = Pulse(0, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) p8 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) p9 = Pulse(0, 40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) - p10 = FluxPulse(0, 40, 0.9, IIR([-1, 1], [-0.1, 0.1001], Rectangular()), 0, 200) - p11 = FluxPulse(0, 40, 0.9, SNZ(t_idling=10, b_amplitude=0.5), 0, 200) + p10 = Pulse.flux( + 0, 40, 0.9, IIR([-1, 1], [-0.1, 0.1001], Rectangular()), channel=0, qubit=200 + ) + p11 = Pulse.flux( + 0, 40, 0.9, SNZ(t_idling=10, b_amplitude=0.5), channel=0, qubit=200 + ) p13 = Pulse(0, 40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) p14 = Pulse(0, 40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.READOUT, 2) @@ -175,7 +181,6 @@ def test_pulse_attributes(): relative_phase=0.0, shape=Rectangular(), channel=channel, - type=PulseType.READOUT, qubit=qubit, ) @@ -340,27 +345,28 @@ def test_pulse_hash(): t0 = 0 p1 = Pulse(t0, 40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) - p2 = p1.shallow_copy() - p3 = p1.copy() + p2 = copy.copy(p1) + p3 = copy.deepcopy(p1) assert p1 == p2 assert p1 == p3 def test_pulse_aliases(): - rop = ReadoutPulse( + rop = Pulse( start=0, duration=50, amplitude=0.9, frequency=20_000_000, relative_phase=0.0, shape=Rectangular(), + type=PulseType.READOUT, channel=0, qubit=0, ) assert rop.start == 0 assert rop.qubit == 0 - dp = DrivePulse( + dp = Pulse( start=0, duration=2000, amplitude=0.9, @@ -373,7 +379,7 @@ def test_pulse_aliases(): assert dp.amplitude == 0.9 assert isinstance(dp.shape, Gaussian) - fp = FluxPulse( + fp = Pulse.flux( start=0, duration=300, amplitude=0.9, shape=Rectangular(), channel=0, qubit=0 ) assert fp.channel == 0 @@ -408,9 +414,9 @@ def test_pulsesequence_init(): def test_pulsesequence_operators(): ps = PulseSequence() - ps += [ReadoutPulse(800, 200, 0.9, 20e6, 0, Rectangular(), 1)] - ps = ps + [ReadoutPulse(800, 200, 0.9, 20e6, 0, Rectangular(), 2)] - ps = [ReadoutPulse(800, 200, 0.9, 20e6, 0, Rectangular(), 3)] + ps + ps += [Pulse(800, 200, 0.9, 20e6, 0, Rectangular(), 1, type=PulseType.READOUT)] + ps = ps + [Pulse(800, 200, 0.9, 20e6, 0, Rectangular(), 2, type=PulseType.READOUT)] + ps = [Pulse(800, 200, 0.9, 20e6, 0, Rectangular(), 3, type=PulseType.READOUT)] + ps p4 = Pulse(100, 40, 0.9, 50e6, 0, Gaussian(5), 3, PulseType.DRIVE) p5 = Pulse(200, 40, 0.9, 50e6, 0, Gaussian(5), 2, PulseType.DRIVE) @@ -459,12 +465,12 @@ def test_pulsesequence_start_finish(): def test_pulsesequence_get_channel_pulses(): - p1 = DrivePulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) - p2 = ReadoutPulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30) - p3 = DrivePulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) - p4 = DrivePulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) - p5 = ReadoutPulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20) - p6 = DrivePulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) + p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) + p2 = Pulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30, type=PulseType.READOUT) + p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) + p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) + p5 = Pulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20, type=PulseType.READOUT) + p6 = Pulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) ps = PulseSequence([p1, p2, p3, p4, p5, p6]) assert ps.channels == [10, 20, 30] @@ -475,13 +481,33 @@ def test_pulsesequence_get_channel_pulses(): def test_pulsesequence_get_qubit_pulses(): - p1 = DrivePulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10, 0) - p2 = ReadoutPulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30, 0) - p3 = DrivePulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20, 1) - p4 = DrivePulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30, 1) - p5 = ReadoutPulse(500, 400, 0.9, 20e6, 0, Rectangular(), 30, 1) - p6 = FluxPulse(600, 400, 0.9, Rectangular(), 40, 1) - p7 = FluxPulse(900, 400, 0.9, Rectangular(), 40, 2) + p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10, qubit=0) + p2 = Pulse( + 100, + 400, + 0.9, + 20e6, + 0, + Rectangular(), + channel=30, + qubit=0, + type=PulseType.READOUT, + ) + p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20, qubit=1) + p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30, qubit=1) + p5 = Pulse( + 500, + 400, + 0.9, + 20e6, + 0, + Rectangular(), + channel=30, + qubit=1, + type=PulseType.READOUT, + ) + p6 = Pulse.flux(600, 400, 0.9, Rectangular(), channel=40, qubit=1) + p7 = Pulse.flux(900, 400, 0.9, Rectangular(), channel=40, qubit=2) ps = PulseSequence([p1, p2, p3, p4, p5, p6, p7]) assert ps.qubits == [0, 1, 2] @@ -492,12 +518,12 @@ def test_pulsesequence_get_qubit_pulses(): def test_pulsesequence_pulses_overlap(): - p1 = DrivePulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) - p2 = ReadoutPulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30) - p3 = DrivePulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) - p4 = DrivePulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) - p5 = ReadoutPulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20) - p6 = DrivePulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) + p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) + p2 = Pulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30, type=PulseType.READOUT) + p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) + p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) + p5 = Pulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20, type=PulseType.READOUT) + p6 = Pulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) ps = PulseSequence([p1, p2, p3, p4, p5, p6]) assert ps.pulses_overlap @@ -507,12 +533,12 @@ def test_pulsesequence_pulses_overlap(): def test_pulsesequence_separate_overlapping_pulses(): - p1 = DrivePulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) - p2 = ReadoutPulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30) - p3 = DrivePulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) - p4 = DrivePulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) - p5 = ReadoutPulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20) - p6 = DrivePulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) + p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) + p2 = Pulse(100, 400, 0.9, 20e6, 0, Rectangular(), qubit=30, type=PulseType.READOUT) + p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) + p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) + p5 = Pulse(500, 400, 0.9, 20e6, 0, Rectangular(), qubit=20, type=PulseType.READOUT) + p6 = Pulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) ps = PulseSequence([p1, p2, p3, p4, p5, p6]) n = 70 @@ -525,9 +551,18 @@ def test_pulsesequence_separate_overlapping_pulses(): def test_pulse_pulse_order(): t0 = 0 t = 0 - p1 = DrivePulse(t0, 400, 0.9, 20e6, 0, Gaussian(5), 10) - p2 = ReadoutPulse(p1.finish + t, 400, 0.9, 20e6, 0, Rectangular(), 30) - p3 = DrivePulse(p2.finish, 400, 0.9, 20e6, 0, Drag(5, 50), 20) + p1 = Pulse(t0, 400, 0.9, 20e6, 0, Gaussian(5), 10) + p2 = Pulse( + p1.finish + t, + 400, + 0.9, + 20e6, + 0, + Rectangular(), + qubit=30, + type=PulseType.READOUT, + ) + p3 = Pulse(p2.finish, 400, 0.9, 20e6, 0, Drag(5, 50), 20) ps1 = PulseSequence([p1, p2, p3]) ps2 = PulseSequence([p3, p1, p2]) @@ -798,7 +833,7 @@ def test_pulse(): def test_readout_pulse(): duration = 2000 - pulse = ReadoutPulse( + pulse = Pulse( start=0, frequency=200_000_000, amplitude=1, @@ -806,6 +841,7 @@ def test_readout_pulse(): relative_phase=0, shape=f"Rectangular()", channel=11, + type=PulseType.READOUT, ) assert pulse.duration == duration @@ -839,7 +875,7 @@ def test_pulse_sequence_add_readout(): ) sequence.append( - ReadoutPulse( + Pulse( start=128, frequency=20_000_000, amplitude=0.9, @@ -847,6 +883,7 @@ def test_pulse_sequence_add_readout(): relative_phase=0, shape="Rectangular()", channel=11, + type=PulseType.READOUT, ) ) assert len(sequence) == 3 From d0e85f08f40a5dfb1505e15367d9836bf94809d5 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 15:56:04 +0100 Subject: [PATCH 037/233] Fix QM tests --- tests/test_instruments_qm.py | 62 ++++++++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 154ca3ec9..cf17b2812 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -9,8 +9,12 @@ from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions from qibolab.instruments.qm.controller import controllers_config from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence +<<<<<<< HEAD from qibolab.pulses import Pulse, PulseType, PulseSequence, Rectangular from qibolab.qubits import Qubit +======= +from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular +>>>>>>> 552fc49f (Fix QM tests) from qibolab.sweeper import Parameter, Sweeper from .conftest import set_platform_profile @@ -54,8 +58,23 @@ def test_qmpulse_declare_output(acquisition_type): def test_qmsequence(): +<<<<<<< HEAD qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", PulseType.DRIVE, qubit=0) ro_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch1", PulseType.READOUT, qubit=0) +======= + qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) + ro_pulse = Pulse( + 0, + 40, + 0.05, + int(3e9), + 0.0, + Rectangular(), + "ch1", + qubit=0, + type=PulseType.READOUT, + ) +>>>>>>> 552fc49f (Fix QM tests) qmsequence = Sequence() with pytest.raises(AttributeError): qmsequence.add("test") @@ -90,6 +109,7 @@ def test_qmpulse_previous_and_next(): f"readout{qubit}", PulseType.READOUT, qubit=qubit, + type=PulseType.READOUT, ) ) ro_qmpulses.append(ro_pulse) @@ -115,10 +135,33 @@ def test_qmpulse_previous_and_next_flux(): x_pulse_end = Pulse(70, 40, 0.05, int(3e9), 0.0, Rectangular(), f"drive2", qubit=2) measure_lowfreq = Pulse( +<<<<<<< HEAD 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout1", PulseType.READOUT, qubit=1 ) measure_highfreq = Pulse( 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout2", PulseType.READOUT, qubit=2 +======= + 110, + 100, + 0.05, + int(3e9), + 0.0, + Rectangular(), + "readout1", + qubit=1, + type=PulseType.READOUT, + ) + measure_highfreq = Pulse( + 110, + 100, + 0.05, + int(3e9), + 0.0, + Rectangular(), + "readout2", + qubit=2, + type=PulseType.READOUT, +>>>>>>> 552fc49f (Fix QM tests) ) drive11 = QMPulse(y90_pulse) @@ -338,10 +381,22 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): def test_qm_register_flux_pulse(qmplatform): qubit = 2 +<<<<<<< HEAD platform = qmplatform controller = platform.instruments["qm"] pulse = Pulse.flux( 0, 30, 0.005, Rectangular(), platform.qubits[qubit].flux.name, qubit +======= + platform = create_platform("qm") + opx = platform.instruments["qmopx"] + pulse = Pulse.flux( + 0, + 30, + 0.005, + Rectangular(), + channel=platform.qubits[qubit].flux.name, + qubit=qubit, +>>>>>>> 552fc49f (Fix QM tests) ) target_pulse = { "operation": "control", @@ -405,10 +460,17 @@ def test_qm_register_pulses_with_different_frequencies(qmplatform): def test_qm_register_baked_pulse(qmplatform, duration): platform = qmplatform qubit = platform.qubits[3] +<<<<<<< HEAD controller = platform.instruments["qm"] controller.config.register_flux_element(qubit) pulse = Pulse.flux( 3, duration, 0.05, Rectangular(), qubit.flux.name, qubit=qubit.name +======= + opx = platform.instruments["qmopx"] + opx.config.register_flux_element(qubit) + pulse = Pulse.flux( + 3, duration, 0.05, Rectangular(), channel=qubit.flux.name, qubit=qubit.name +>>>>>>> 552fc49f (Fix QM tests) ) qmpulse = BakedPulse(pulse) config = controller.config From c024bf62b6bfebd41b065f8a6382d6f691756e0d Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 16:05:08 +0100 Subject: [PATCH 038/233] Fix tests for dummy --- src/qibolab/native.py | 5 ++++- tests/test_dummy.py | 5 +++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/src/qibolab/native.py b/src/qibolab/native.py index 2148a91e9..b2d2d4708 100644 --- a/src/qibolab/native.py +++ b/src/qibolab/native.py @@ -157,11 +157,14 @@ def pulse(self, start): Returns: A :class:`qibolab.pulses.FluxPulse` with the pulse parameters of the gate. """ - return CouplerFluxPulse( + return Pulse( start + self.relative_start, self.duration, self.amplitude, + 0, + 0, self.shape, + type=PulseType.COUPLERFLUX, channel=self.coupler.flux.name, qubit=self.coupler.name, ) diff --git a/tests/test_dummy.py b/tests/test_dummy.py index 4aa828731..e1f99b7c9 100644 --- a/tests/test_dummy.py +++ b/tests/test_dummy.py @@ -2,7 +2,7 @@ import pytest from qibolab import AcquisitionType, AveragingMode, ExecutionParameters, create_platform -from qibolab.pulses import PulseSequence +from qibolab.pulses import Pulse, PulseSequence, PulseType from qibolab.qubits import QubitPair from qibolab.sweeper import Parameter, QubitParameter, Sweeper @@ -155,7 +155,7 @@ def test_dummy_single_sweep_coupler( platform = create_platform("dummy_couplers") sequence = PulseSequence() ro_pulse = platform.create_qubit_readout_pulse(qubit=0, start=0) - coupler_pulse = CouplerFluxPulse( + coupler_pulse = Pulse.flux( start=0, duration=40, amplitude=0.5, @@ -163,6 +163,7 @@ def test_dummy_single_sweep_coupler( channel="flux_coupler-0", qubit=0, ) + coupler_pulse.type = PulseType.COUPLERFLUX if parameter is Parameter.amplitude: parameter_range = np.random.rand(SWEPT_POINTS) else: From 6a2e5d5a4fcceb90740f1600769333f6a613ff1a Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 16:19:05 +0100 Subject: [PATCH 039/233] Fix Zurich tests --- tests/test_instruments_qm.py | 61 ------------------------------ tests/test_instruments_zhinst.py | 65 ++++++++++++++++++++++++-------- 2 files changed, 50 insertions(+), 76 deletions(-) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index cf17b2812..3bdf9d882 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -9,12 +9,8 @@ from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions from qibolab.instruments.qm.controller import controllers_config from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence -<<<<<<< HEAD from qibolab.pulses import Pulse, PulseType, PulseSequence, Rectangular from qibolab.qubits import Qubit -======= -from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular ->>>>>>> 552fc49f (Fix QM tests) from qibolab.sweeper import Parameter, Sweeper from .conftest import set_platform_profile @@ -58,23 +54,8 @@ def test_qmpulse_declare_output(acquisition_type): def test_qmsequence(): -<<<<<<< HEAD qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", PulseType.DRIVE, qubit=0) ro_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch1", PulseType.READOUT, qubit=0) -======= - qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) - ro_pulse = Pulse( - 0, - 40, - 0.05, - int(3e9), - 0.0, - Rectangular(), - "ch1", - qubit=0, - type=PulseType.READOUT, - ) ->>>>>>> 552fc49f (Fix QM tests) qmsequence = Sequence() with pytest.raises(AttributeError): qmsequence.add("test") @@ -135,33 +116,10 @@ def test_qmpulse_previous_and_next_flux(): x_pulse_end = Pulse(70, 40, 0.05, int(3e9), 0.0, Rectangular(), f"drive2", qubit=2) measure_lowfreq = Pulse( -<<<<<<< HEAD 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout1", PulseType.READOUT, qubit=1 ) measure_highfreq = Pulse( 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout2", PulseType.READOUT, qubit=2 -======= - 110, - 100, - 0.05, - int(3e9), - 0.0, - Rectangular(), - "readout1", - qubit=1, - type=PulseType.READOUT, - ) - measure_highfreq = Pulse( - 110, - 100, - 0.05, - int(3e9), - 0.0, - Rectangular(), - "readout2", - qubit=2, - type=PulseType.READOUT, ->>>>>>> 552fc49f (Fix QM tests) ) drive11 = QMPulse(y90_pulse) @@ -381,22 +339,10 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): def test_qm_register_flux_pulse(qmplatform): qubit = 2 -<<<<<<< HEAD platform = qmplatform controller = platform.instruments["qm"] pulse = Pulse.flux( 0, 30, 0.005, Rectangular(), platform.qubits[qubit].flux.name, qubit -======= - platform = create_platform("qm") - opx = platform.instruments["qmopx"] - pulse = Pulse.flux( - 0, - 30, - 0.005, - Rectangular(), - channel=platform.qubits[qubit].flux.name, - qubit=qubit, ->>>>>>> 552fc49f (Fix QM tests) ) target_pulse = { "operation": "control", @@ -460,17 +406,10 @@ def test_qm_register_pulses_with_different_frequencies(qmplatform): def test_qm_register_baked_pulse(qmplatform, duration): platform = qmplatform qubit = platform.qubits[3] -<<<<<<< HEAD controller = platform.instruments["qm"] controller.config.register_flux_element(qubit) pulse = Pulse.flux( 3, duration, 0.05, Rectangular(), qubit.flux.name, qubit=qubit.name -======= - opx = platform.instruments["qmopx"] - opx.config.register_flux_element(qubit) - pulse = Pulse.flux( - 3, duration, 0.05, Rectangular(), channel=qubit.flux.name, qubit=qubit.name ->>>>>>> 552fc49f (Fix QM tests) ) qmpulse = BakedPulse(pulse) config = controller.config diff --git a/tests/test_instruments_zhinst.py b/tests/test_instruments_zhinst.py index cabd82a1d..535e957e3 100644 --- a/tests/test_instruments_zhinst.py +++ b/tests/test_instruments_zhinst.py @@ -257,8 +257,8 @@ def test_zhsequence(dummy_qrc): IQM5q.qubits[0] ) qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), drive_channel, qubit=0) - ro_pulse = ReadoutPulse( - 0, 40, 0.05, int(3e9), 0.0, Rectangular(), readout_channel, qubit=0 + ro_pulse = Pulse( + 0, 40, 0.05, int(3e9), 0.0, Rectangular(), readout_channel, PulseType.READOUT, qubit=0 ) sequence = PulseSequence() sequence.add(qd_pulse) @@ -284,11 +284,11 @@ def test_zhsequence_couplers(dummy_qrc): ) couplerflux_channel = IQM5q.couplers[0].flux.name qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), drive_channel, qubit=0) - ro_pulse = ReadoutPulse( - 0, 40, 0.05, int(3e9), 0.0, Rectangular(), readout_channel, qubit=0 + ro_pulse = Pulse( + 0, 40, 0.05, int(3e9), 0.0, Rectangular(), readout_channel, PulseType.READOUT, qubit=0 ) - qc_pulse = CouplerFluxPulse( - 0, 40, 0.05, Rectangular(), couplerflux_channel, qubit=3 + qc_pulse = Pulse( + 0, 40, 0.05, Rectangular(), couplerflux_channel, PulseType.COUPLERFLUX, qubit=3 ) sequence = PulseSequence() sequence.add(qd_pulse) @@ -307,12 +307,12 @@ def test_zhsequence_multiple_ro(dummy_qrc): sequence = PulseSequence() qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) sequence.add(qd_pulse) - ro_pulse = ReadoutPulse( - 0, 40, 0.05, int(3e9), 0.0, Rectangular(), readout_channel, qubit=0 + ro_pulse = Pulse( + 0, 40, 0.05, int(3e9), 0.0, Rectangular(), readout_channel, PulseType.READOUT, qubit=0 ) sequence.add(ro_pulse) - ro_pulse = ReadoutPulse( - 0, 5000, 0.05, int(3e9), 0.0, Rectangular(), readout_channel, qubit=0 + ro_pulse = Pulse( + 0, 5000, 0.05, int(3e9), 0.0, Rectangular(), readout_channel, PulseType.READOUT, qubit=0 ) sequence.add(ro_pulse) platform = create_platform("zurich") @@ -377,7 +377,7 @@ def test_experiment_flow(dummy_qrc): qf_pulses = {} for qubit in qubits.values(): q = qubit.name - qf_pulses[q] = FluxPulse( + qf_pulses[q] = Pulse.flux( start=0, duration=500, amplitude=1, @@ -416,7 +416,7 @@ def test_experiment_flow_coupler(dummy_qrc): qf_pulses = {} for qubit in qubits.values(): q = qubit.name - qf_pulses[q] = FluxPulse( + qf_pulses[q] = Pulse.flux( start=0, duration=500, amplitude=1, @@ -431,7 +431,7 @@ def test_experiment_flow_coupler(dummy_qrc): cf_pulses = {} for coupler in couplers.values(): c = coupler.name - cf_pulses[c] = CouplerFluxPulse( + cf_pulses[c] = Pulse.flux( start=0, duration=500, amplitude=1, @@ -439,6 +439,7 @@ def test_experiment_flow_coupler(dummy_qrc): channel=platform.couplers[c].flux.name, qubit=c, ) + cf_pulses[c].type = PulseType.COUPLERFLUX sequence.append(cf_pulses[c]) options = ExecutionParameters( @@ -572,7 +573,7 @@ def test_experiment_sweep_single_coupler(dummy_qrc, parameter1): cf_pulses = {} for coupler in couplers.values(): c = coupler.name - cf_pulses[c] = CouplerFluxPulse( + cf_pulses[c] = Pulse.flux( start=0, duration=500, amplitude=1, @@ -580,6 +581,7 @@ def test_experiment_sweep_single_coupler(dummy_qrc, parameter1): channel=platform.couplers[c].flux.name, qubit=c, ) + cf_pulses[c].type = PulseType.COUPLERFLUX sequence.append(cf_pulses[c]) parameter_range_1 = ( @@ -780,8 +782,41 @@ def test_experiment_sweep_punchouts(dummy_qrc, parameter): IQM5q.experiment_flow(qubits, couplers, sequence, options) +<<<<<<< HEAD assert measure_channel_name(qubits[0]) in IQM5q.experiment.signals assert acquire_channel_name(qubits[0]) in IQM5q.experiment.signals +======= + assert "measure0" in IQM5q.experiment.signals + assert "acquire0" in IQM5q.experiment.signals + + +# TODO: Fix this +def test_sim(dummy_qrc): + platform = create_platform("zurich") + IQM5q = platform.instruments["EL_ZURO"] + sequence = PulseSequence() + qubits = {0: platform.qubits[0]} + platform.qubits = qubits + ro_pulses = {} + qd_pulses = {} + qf_pulses = {} + for qubit in qubits: + qd_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) + sequence.append(qd_pulses[qubit]) + ro_pulses[qubit] = platform.create_qubit_readout_pulse( + qubit, start=qd_pulses[qubit].finish + ) + sequence.append(ro_pulses[qubit]) + qf_pulses[qubit] = Pulse.flux( + start=0, + duration=500, + amplitude=1, + shape=Rectangular(), + channel=platform.qubits[qubit].flux.name, + qubit=qubit, + ) + sequence.append(qf_pulses[qubit]) +>>>>>>> 1b1e4cd4 (Fix Zurich tests) def test_batching(dummy_qrc): @@ -826,7 +861,7 @@ def test_experiment_execute_pulse_sequence_qpu(connected_platform, instrument): qf_pulses = {} for qubit in qubits.values(): q = qubit.name - qf_pulses[q] = FluxPulse( + qf_pulses[q] = Pulse.flux( start=0, duration=500, amplitude=1, From b6cf92c8b4ba579d68d5eea84b396d4b3a26dd72 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 16:35:45 +0100 Subject: [PATCH 040/233] Remove leftover calls to pulse specific copy --- examples/pulses_tutorial.ipynb | 23 ------------------- .../instruments/qblox/cluster_qcm_bb.py | 4 ++-- .../instruments/qblox/cluster_qcm_rf.py | 4 ++-- .../instruments/qblox/cluster_qrm_rf.py | 4 ++-- src/qibolab/instruments/qblox/sequencer.py | 6 +++-- src/qibolab/native.py | 7 +++--- src/qibolab/pulses.py | 3 ++- 7 files changed, 16 insertions(+), 35 deletions(-) diff --git a/examples/pulses_tutorial.ipynb b/examples/pulses_tutorial.ipynb index 696c86b20..a80241221 100644 --- a/examples/pulses_tutorial.ipynb +++ b/examples/pulses_tutorial.ipynb @@ -310,29 +310,6 @@ "#### Methods" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pulse implements the following methods:\n", - "- `copy()` returns a deep copy of the object. The later changes to the original do not impact the replica.\n", - "- `shallow_copy()` returns a shallow copy of the object. The replica references to the same `start`, `duration` and `shape` objects.\n", - "The difference in the behaviour of these two methods can be appreciated in the below example:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "p1 = Pulse(0, 40, 0.9, 100e6, 0, Drag(5,1), 0, PulseType.DRIVE)\n", - "p2 = p1.shallow_copy()\n", - "p3 = p1.copy()\n", - "assert p1 == p2\n", - "assert p1 == p3" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/src/qibolab/instruments/qblox/cluster_qcm_bb.py b/src/qibolab/instruments/qblox/cluster_qcm_bb.py index 23eea1877..1bae9d543 100644 --- a/src/qibolab/instruments/qblox/cluster_qcm_bb.py +++ b/src/qibolab/instruments/qblox/cluster_qcm_bb.py @@ -1,5 +1,5 @@ """Qblox Cluster QCM driver.""" - +import copy import json from qblox_instruments.native.generic_func import SequencerStates @@ -354,7 +354,7 @@ def process_pulse_sequence( self._sequencers[port].append(sequencer) # make a temporary copy of the pulses to be processed - pulses_to_be_processed = non_overlapping_pulses.shallow_copy() + pulses_to_be_processed = copy.copy(non_overlapping_pulses) while not pulses_to_be_processed.is_empty: pulse: Pulse = pulses_to_be_processed[0] # attempt to save the waveforms to the sequencer waveforms buffer diff --git a/src/qibolab/instruments/qblox/cluster_qcm_rf.py b/src/qibolab/instruments/qblox/cluster_qcm_rf.py index 573624ab1..f79d5d9d9 100644 --- a/src/qibolab/instruments/qblox/cluster_qcm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qcm_rf.py @@ -1,5 +1,5 @@ """Qblox Cluster QCM-RF driver.""" - +import copy import json from qblox_instruments.native.generic_func import SequencerStates @@ -375,7 +375,7 @@ def process_pulse_sequence( self._sequencers[port].append(sequencer) # make a temporary copy of the pulses to be processed - pulses_to_be_processed = non_overlapping_pulses.shallow_copy() + pulses_to_be_processed = copy.copy(non_overlapping_pulses) while not pulses_to_be_processed.is_empty: pulse: Pulse = pulses_to_be_processed[0] # attempt to save the waveforms to the sequencer waveforms buffer diff --git a/src/qibolab/instruments/qblox/cluster_qrm_rf.py b/src/qibolab/instruments/qblox/cluster_qrm_rf.py index 63322f5d2..5d6ded76e 100644 --- a/src/qibolab/instruments/qblox/cluster_qrm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qrm_rf.py @@ -1,5 +1,5 @@ """Qblox Cluster QRM-RF driver.""" - +import copy import json import time @@ -435,7 +435,7 @@ def process_pulse_sequence( self._sequencers[port].append(sequencer) # make a temporary copy of the pulses to be processed - pulses_to_be_processed = non_overlapping_pulses.shallow_copy() + pulses_to_be_processed = copy.copy(non_overlapping_pulses) while not pulses_to_be_processed.is_empty: pulse: Pulse = pulses_to_be_processed[0] # attempt to save the waveforms to the sequencer waveforms buffer diff --git a/src/qibolab/instruments/qblox/sequencer.py b/src/qibolab/instruments/qblox/sequencer.py index 185375185..db0c06893 100644 --- a/src/qibolab/instruments/qblox/sequencer.py +++ b/src/qibolab/instruments/qblox/sequencer.py @@ -1,3 +1,5 @@ +import copy + import numpy as np from qblox_instruments.qcodes_drivers.sequencer import Sequencer as QbloxSequencer @@ -48,7 +50,7 @@ def add_waveforms( Raises: NotEnoughMemory: If the memory needed to store the waveforms in more than the memory avalible. """ - pulse_copy = pulse.copy() + pulse_copy = copy.deepcopy(pulse) for sweeper in sweepers: if sweeper.pulses and sweeper.parameter == Parameter.amplitude: if pulse in sweeper.pulses: @@ -122,7 +124,7 @@ def bake_pulse_waveforms( """ # In order to generate waveforms for each duration value, the pulse will need to be modified. # To avoid any conflicts, make a copy of the pulse first. - pulse_copy = pulse.copy() + pulse_copy = copy.deepcopy(pulse) # there may be other waveforms stored already, set first index as the next available first_idx = len(self.unique_waveforms) diff --git a/src/qibolab/native.py b/src/qibolab/native.py index b2d2d4708..8c08595e1 100644 --- a/src/qibolab/native.py +++ b/src/qibolab/native.py @@ -1,3 +1,4 @@ +import copy from collections import defaultdict from dataclasses import dataclass, field, fields, replace from typing import List, Optional, Union @@ -39,7 +40,7 @@ def from_dict(cls, name, pulse, qubit): qubits (:class:`qibolab.platforms.abstract.Qubit`): Qubit that the pulse is acting on """ - kwargs = pulse.copy() + kwargs = copy.deepcopy(pulse) kwargs["pulse_type"] = PulseType(kwargs.pop("type")) kwargs["qubit"] = qubit return cls(name, **kwargs) @@ -131,7 +132,7 @@ def from_dict(cls, pulse, coupler): coupler (:class:`qibolab.platforms.abstract.Coupler`): Coupler that the pulse is acting on """ - kwargs = pulse.copy() + kwargs = copy.deepcopy(pulse) kwargs["coupler"] = coupler kwargs.pop("type") return cls(**kwargs) @@ -207,7 +208,7 @@ def from_dict(cls, name, sequence, qubits, couplers): sequence = [sequence] for i, pulse in enumerate(sequence): - pulse = pulse.copy() + pulse = copy.deepcopy(pulse) pulse_type = pulse.pop("type") if pulse_type == "coupler": pulse["coupler"] = couplers[pulse.pop("coupler")] diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index da7d98c3c..f4c46d9cb 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -1,4 +1,5 @@ """Pulse and PulseSequence classes.""" +import copy import re from abc import ABC, abstractmethod from dataclasses import dataclass, fields @@ -875,7 +876,7 @@ def __mul__(self, n): raise TypeError(f"Expected int; got {type(n).__name__}") if n < 0: raise TypeError(f"argument n should be >=0, got {n}") - return PulseSequence(*([self.copy()] * n)) + return PulseSequence(*([copy.deepcopy(self)] * n)) def __rmul__(self, n): return self.__mul__(n) From d4fcc9c6a9d24d2e6a1348d3f31668948131a369 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 16:50:28 +0100 Subject: [PATCH 041/233] Fix doctests --- doc/source/main-documentation/qibolab.rst | 22 ++++++++-------------- doc/source/tutorials/pulses.rst | 13 ++++--------- 2 files changed, 12 insertions(+), 23 deletions(-) diff --git a/doc/source/main-documentation/qibolab.rst b/doc/source/main-documentation/qibolab.rst index 8635dfa68..935f1e453 100644 --- a/doc/source/main-documentation/qibolab.rst +++ b/doc/source/main-documentation/qibolab.rst @@ -275,12 +275,6 @@ Pulses In Qibolab, an extensive API is available for working with pulses and pulse sequences, a fundamental aspect of quantum experiments. At the heart of this API is the :class:`qibolab.pulses.Pulse` object, which empowers users to define and customize pulses with specific parameters. -The API provides specialized subclasses tailored to the main types of pulses typically used in quantum experiments: - -- Readout Pulses (:class:`qibolab.pulses.ReadoutPulse`) -- Drive Pulses (:class:`qibolab.pulses.DrivePulse`) -- Flux Pulses (:class:`qibolab.pulses.FluxPulse`) - Each pulse is associated with a channel and a qubit. Additionally, pulses are defined by a shape, represented by a subclass of :class:`qibolab.pulses.PulseShape`. Qibolab offers a range of pre-defined pulse shapes: @@ -313,13 +307,13 @@ To illustrate, here are some examples of single pulses using the Qibolab API: ) In this way, we defined a rectangular drive pulse using the generic Pulse object. -Alternatively, you can achieve the same result using the dedicated :class:`qibolab.pulses.DrivePulse` object: +Alternatively, you can achieve the same result using the dedicated :class:`qibolab.pulses.Pulse` object: .. testcode:: python - from qibolab.pulses import DrivePulse, Rectangular + from qibolab.pulses import Pulse, Rectangular - pulse = DrivePulse( + pulse = Pulse( start=0, # timing, in all qibolab, is expressed in ns duration=40, amplitude=0.5, # this amplitude is relative to the range of the instrument @@ -340,7 +334,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P sequence = PulseSequence() - pulse1 = DrivePulse( + pulse1 = Pulse( start=0, # timing, in all qibolab, is expressed in ns duration=40, amplitude=0.5, # this amplitude is relative to the range of the instrument @@ -350,7 +344,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P channel="channel", qubit=0, ) - pulse2 = DrivePulse( + pulse2 = Pulse( start=0, # timing, in all qibolab, is expressed in ns duration=40, amplitude=0.5, # this amplitude is relative to the range of the instrument @@ -360,7 +354,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P channel="channel", qubit=0, ) - pulse3 = DrivePulse( + pulse3 = Pulse( start=0, # timing, in all qibolab, is expressed in ns duration=40, amplitude=0.5, # this amplitude is relative to the range of the instrument @@ -370,7 +364,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P channel="channel", qubit=0, ) - pulse4 = DrivePulse( + pulse4 = Pulse( start=0, # timing, in all qibolab, is expressed in ns duration=40, amplitude=0.5, # this amplitude is relative to the range of the instrument @@ -418,7 +412,7 @@ Typical experiments may include both pre-defined pulses and new ones: sequence = PulseSequence() sequence.append(platform.create_RX_pulse(0)) sequence.append( - DrivePulse( + Pulse( start=0, duration=10, amplitude=0.5, diff --git a/doc/source/tutorials/pulses.rst b/doc/source/tutorials/pulses.rst index 6fdab05e1..190211250 100644 --- a/doc/source/tutorials/pulses.rst +++ b/doc/source/tutorials/pulses.rst @@ -8,20 +8,14 @@ pulses (:class:`qibolab.pulses.Pulse`) through the .. testcode:: python - from qibolab.pulses import ( - DrivePulse, - ReadoutPulse, - PulseSequence, - Rectangular, - Gaussian, - ) + from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular, Gaussian # Define PulseSequence sequence = PulseSequence() # Add some pulses to the pulse sequence sequence.append( - DrivePulse( + Pulse( start=0, frequency=200000000, amplitude=0.3, @@ -32,7 +26,7 @@ pulses (:class:`qibolab.pulses.Pulse`) through the ) ) sequence.append( - ReadoutPulse( + Pulse( start=70, frequency=20000000.0, amplitude=0.5, @@ -40,6 +34,7 @@ pulses (:class:`qibolab.pulses.Pulse`) through the relative_phase=0, shape=Rectangular(), qubit=0, + type=PulseType.READOUT, ) ) From ff5f26ba1f9be796bff8ba61ff8543f1c2bf80ab Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 17:47:34 +0100 Subject: [PATCH 042/233] Remove intermediate frequency from pulse Following @PiergiorgioButtarini removal of last usage in Qblox, in #729 --- src/qibolab/pulses.py | 17 ++++++----------- tests/test_pulses.py | 13 ++++++++----- 2 files changed, 14 insertions(+), 16 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index f4c46d9cb..5ecff0f1d 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -140,36 +140,32 @@ def envelope_waveforms( self.envelope_waveform_q(sampling_rate), ) - def modulated_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: + def modulated_waveform_i(self, _if: int, sampling_rate=SAMPLING_RATE) -> Waveform: """The waveform of the i component of the pulse, modulated with its frequency.""" return self.modulated_waveforms(sampling_rate)[0] - def modulated_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: + def modulated_waveform_q(self, _if: int, sampling_rate=SAMPLING_RATE) -> Waveform: """The waveform of the q component of the pulse, modulated with its frequency.""" return self.modulated_waveforms(sampling_rate)[1] - def modulated_waveforms(self, sampling_rate=SAMPLING_RATE): + def modulated_waveforms(self, _if: int, sampling_rate=SAMPLING_RATE): """A tuple with the i and q waveforms of the pulse, modulated with its frequency.""" pulse = self.pulse - if abs(pulse._if) * 2 > sampling_rate: + if abs(_if) * 2 > sampling_rate: log.info( f"WARNING: The frequency of pulse {pulse.id} is higher than the nyqusit frequency ({int(sampling_rate // 2)}) for the device sampling rate: {int(sampling_rate)}" ) num_samples = int(np.rint(pulse.duration * sampling_rate)) time = np.arange(num_samples) / sampling_rate global_phase = pulse.global_phase - cosalpha = np.cos( - 2 * np.pi * pulse._if * time + global_phase + pulse.relative_phase - ) - sinalpha = np.sin( - 2 * np.pi * pulse._if * time + global_phase + pulse.relative_phase - ) + cosalpha = np.cos(2 * np.pi * _if * time + global_phase + pulse.relative_phase) + sinalpha = np.sin(2 * np.pi * _if * time + global_phase + pulse.relative_phase) mod_matrix = np.array([[cosalpha, -sinalpha], [sinalpha, cosalpha]]) / np.sqrt( 2 @@ -752,7 +748,6 @@ class Pulse: """Pulse type, as an element of PulseType enumeration.""" qubit: int = 0 """Qubit or coupler addressed by the pulse.""" - _if: int = 0 def __post_init__(self): if isinstance(self.type, str): diff --git a/tests/test_pulses.py b/tests/test_pulses.py index aab708813..97f14d686 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -611,6 +611,7 @@ def test_pulseshape_rectangular(): channel=1, qubit=0, ) + _if = 0 assert pulse.duration == 50 assert isinstance(pulse.shape, Rectangular) @@ -628,10 +629,10 @@ def test_pulseshape_rectangular(): pulse.amplitude * np.zeros(num_samples), ) global_phase = ( - 2 * np.pi * pulse._if * pulse.start / 1e9 + 2 * np.pi * _if * pulse.start / 1e9 ) # pulse start, duration and finish are in ns mod_i, mod_q = modulate( - i, q, num_samples, pulse._if, global_phase + pulse.relative_phase, sampling_rate + i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate ) np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate).data, i) @@ -655,6 +656,7 @@ def test_pulseshape_gaussian(): channel=1, qubit=0, ) + _if = 0 assert pulse.duration == 50 assert isinstance(pulse.shape, Gaussian) @@ -681,7 +683,7 @@ def test_pulseshape_gaussian(): 2 * np.pi * pulse.frequency * pulse.start / 1e9 ) # pulse start, duration and finish are in ns mod_i, mod_q = modulate( - i, q, num_samples, pulse._if, global_phase + pulse.relative_phase, sampling_rate + i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate ) np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate).data, i) @@ -705,6 +707,7 @@ def test_pulseshape_drag(): channel=1, qubit=0, ) + _if = 0 assert pulse.duration == 50 assert isinstance(pulse.shape, Drag) @@ -734,10 +737,10 @@ def test_pulseshape_drag(): * sampling_rate ) global_phase = ( - 2 * np.pi * pulse._if * pulse.start / 1e9 + 2 * np.pi * _if * pulse.start / 1e9 ) # pulse start, duration and finish are in ns mod_i, mod_q = modulate( - i, q, num_samples, pulse._if, global_phase + pulse.relative_phase, sampling_rate + i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate ) np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate).data, i) From d1baf5c2e6e161fe2e49e6567ea8b69858ffad6f Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 17:50:55 +0100 Subject: [PATCH 043/233] Pass explicitly the if to modulated waveform tests --- tests/test_pulses.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/test_pulses.py b/tests/test_pulses.py index 97f14d686..baaebe19d 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -638,10 +638,10 @@ def test_pulseshape_rectangular(): np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate).data, i) np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate).data, q) np.testing.assert_allclose( - pulse.shape.modulated_waveform_i(sampling_rate).data, mod_i + pulse.shape.modulated_waveform_i(_if, sampling_rate).data, mod_i ) np.testing.assert_allclose( - pulse.shape.modulated_waveform_q(sampling_rate).data, mod_q + pulse.shape.modulated_waveform_q(_if, sampling_rate).data, mod_q ) @@ -689,10 +689,10 @@ def test_pulseshape_gaussian(): np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate).data, i) np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate).data, q) np.testing.assert_allclose( - pulse.shape.modulated_waveform_i(sampling_rate).data, mod_i + pulse.shape.modulated_waveform_i(_if, sampling_rate).data, mod_i ) np.testing.assert_allclose( - pulse.shape.modulated_waveform_q(sampling_rate).data, mod_q + pulse.shape.modulated_waveform_q(_if, sampling_rate).data, mod_q ) @@ -746,10 +746,10 @@ def test_pulseshape_drag(): np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate).data, i) np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate).data, q) np.testing.assert_allclose( - pulse.shape.modulated_waveform_i(sampling_rate).data, mod_i + pulse.shape.modulated_waveform_i(_if, sampling_rate).data, mod_i ) np.testing.assert_allclose( - pulse.shape.modulated_waveform_q(sampling_rate).data, mod_q + pulse.shape.modulated_waveform_q(_if, sampling_rate).data, mod_q ) From 7e00d078f7613ced3af39daaa642281deeb8eb9b Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 17:57:26 +0100 Subject: [PATCH 044/233] Propagate IF parameter to all modulated call --- src/qibolab/pulses.py | 4 ++-- tests/test_pulses.py | 14 +++++++------- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses.py index 5ecff0f1d..53a53c4f9 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses.py @@ -144,13 +144,13 @@ def modulated_waveform_i(self, _if: int, sampling_rate=SAMPLING_RATE) -> Wavefor """The waveform of the i component of the pulse, modulated with its frequency.""" - return self.modulated_waveforms(sampling_rate)[0] + return self.modulated_waveforms(_if, sampling_rate)[0] def modulated_waveform_q(self, _if: int, sampling_rate=SAMPLING_RATE) -> Waveform: """The waveform of the q component of the pulse, modulated with its frequency.""" - return self.modulated_waveforms(sampling_rate)[1] + return self.modulated_waveforms(_if, sampling_rate)[1] def modulated_waveforms(self, _if: int, sampling_rate=SAMPLING_RATE): """A tuple with the i and q waveforms of the pulse, modulated with its diff --git a/tests/test_pulses.py b/tests/test_pulses.py index baaebe19d..6e0705ff3 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -37,7 +37,7 @@ def test_plot_functions(): p5 = Pulse(0, 40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) p6 = Pulse(0, 40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.DRIVE, 2) ps = PulseSequence([p0, p1, p2, p3, p4, p5, p6]) - wf = p0.modulated_waveform_i() + wf = p0.modulated_waveform_i(0) plot_file = HERE / "test_plot.png" @@ -619,8 +619,8 @@ def test_pulseshape_rectangular(): assert repr(pulse.shape) == "Rectangular()" assert isinstance(pulse.shape.envelope_waveform_i(), Waveform) assert isinstance(pulse.shape.envelope_waveform_q(), Waveform) - assert isinstance(pulse.shape.modulated_waveform_i(), Waveform) - assert isinstance(pulse.shape.modulated_waveform_q(), Waveform) + assert isinstance(pulse.shape.modulated_waveform_i(_if), Waveform) + assert isinstance(pulse.shape.modulated_waveform_q(_if), Waveform) sampling_rate = 1 num_samples = int(pulse.duration / sampling_rate) @@ -665,8 +665,8 @@ def test_pulseshape_gaussian(): assert repr(pulse.shape) == "Gaussian(5)" assert isinstance(pulse.shape.envelope_waveform_i(), Waveform) assert isinstance(pulse.shape.envelope_waveform_q(), Waveform) - assert isinstance(pulse.shape.modulated_waveform_i(), Waveform) - assert isinstance(pulse.shape.modulated_waveform_q(), Waveform) + assert isinstance(pulse.shape.modulated_waveform_i(_if), Waveform) + assert isinstance(pulse.shape.modulated_waveform_q(_if), Waveform) sampling_rate = 1 num_samples = int(pulse.duration / sampling_rate) @@ -717,8 +717,8 @@ def test_pulseshape_drag(): assert repr(pulse.shape) == "Drag(5, 0.2)" assert isinstance(pulse.shape.envelope_waveform_i(), Waveform) assert isinstance(pulse.shape.envelope_waveform_q(), Waveform) - assert isinstance(pulse.shape.modulated_waveform_i(), Waveform) - assert isinstance(pulse.shape.modulated_waveform_q(), Waveform) + assert isinstance(pulse.shape.modulated_waveform_i(_if), Waveform) + assert isinstance(pulse.shape.modulated_waveform_q(_if), Waveform) sampling_rate = 1 num_samples = int(pulse.duration / 1 * sampling_rate) From 87218530a54e4491e69f661738fb65080f263d1f Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 18:18:40 +0100 Subject: [PATCH 045/233] Start rearranging pulses into a subpackage --- src/qibolab/pulses/__init__.py | 0 src/qibolab/pulses/plot.py | 128 +++++ src/qibolab/pulses/pulse.py | 199 +++++++ src/qibolab/pulses/sequence.py | 260 +++++++++ src/qibolab/{pulses.py => pulses/shape.py} | 625 +-------------------- src/qibolab/pulses/waveform.py | 42 ++ 6 files changed, 630 insertions(+), 624 deletions(-) create mode 100644 src/qibolab/pulses/__init__.py create mode 100644 src/qibolab/pulses/plot.py create mode 100644 src/qibolab/pulses/pulse.py create mode 100644 src/qibolab/pulses/sequence.py rename src/qibolab/{pulses.py => pulses/shape.py} (51%) create mode 100644 src/qibolab/pulses/waveform.py diff --git a/src/qibolab/pulses/__init__.py b/src/qibolab/pulses/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py new file mode 100644 index 000000000..1328268f2 --- /dev/null +++ b/src/qibolab/pulses/plot.py @@ -0,0 +1,128 @@ +"""Plotting tools for pulses and related entities.""" +import matplotlib.pyplot as plt +import numpy as np + +from .pulse import Pulse +from .shape import SAMPLING_RATE +from .waveform import Waveform + + +def waveform(wf: Waveform, filename=None): + """Plot the waveform. + + Args: + filename (str): a file path. If provided the plot is save to a file. + """ + plt.figure(figsize=(14, 5), dpi=200) + plt.plot(wf.data, c="C0", linestyle="dashed") + plt.xlabel("Sample Number") + plt.ylabel("Amplitude") + plt.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") + if filename: + plt.savefig(filename) + else: + plt.show() + plt.close() + + +def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): + """Plot the pulse envelope and modulated waveforms. + + Args: + filename (str): a file path. If provided the plot is save to a file. + """ + import matplotlib.pyplot as plt + from matplotlib import gridspec + + waveform_i = pulse_.shape.envelope_waveform_i(sampling_rate) + waveform_q = pulse_.shape.envelope_waveform_q(sampling_rate) + + num_samples = len(waveform_i) + time = pulse_.start + np.arange(num_samples) / sampling_rate + _ = plt.figure(figsize=(14, 5), dpi=200) + gs = gridspec.GridSpec(ncols=2, nrows=1, width_ratios=np.array([2, 1])) + ax1 = plt.subplot(gs[0]) + ax1.plot( + time, + waveform_i.data, + label="envelope i", + c="C0", + linestyle="dashed", + ) + ax1.plot( + time, + waveform_q.data, + label="envelope q", + c="C1", + linestyle="dashed", + ) + ax1.plot( + time, + pulse_.shape.modulated_waveform_i(sampling_rate).data, + label="modulated i", + c="C0", + ) + ax1.plot( + time, + pulse_.shape.modulated_waveform_q(sampling_rate).data, + label="modulated q", + c="C1", + ) + ax1.plot(time, -waveform_i.data, c="silver", linestyle="dashed") + ax1.set_xlabel("Time [ns]") + ax1.set_ylabel("Amplitude") + + ax1.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") + start = float(pulse_.start) + finish = float(pulse._finish) if pulse._finish is not None else 0.0 + ax1.axis((start, finish, -1.0, 1.0)) + ax1.legend() + + modulated_i = pulse_.shape.modulated_waveform_i(sampling_rate).data + modulated_q = pulse_.shape.modulated_waveform_q(sampling_rate).data + ax2 = plt.subplot(gs[1]) + ax2.plot( + modulated_i, + modulated_q, + label="modulated", + c="C3", + ) + ax2.plot( + waveform_i.data, + waveform_q.data, + label="envelope", + c="C2", + ) + ax2.plot( + modulated_i[0], + modulated_q[0], + marker="o", + markersize=5, + label="start", + c="lightcoral", + ) + ax2.plot( + modulated_i[-1], + modulated_q[-1], + marker="o", + markersize=5, + label="finish", + c="darkred", + ) + + ax2.plot( + np.cos(time * 2 * np.pi / pulse_.duration), + np.sin(time * 2 * np.pi / pulse_.duration), + c="silver", + linestyle="dashed", + ) + + ax2.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") + ax2.legend() + # ax2.axis([ -1, 1, -1, 1]) + ax2.axis("equal") + if filename: + plt.savefig(filename) + else: + plt.show() + plt.close() diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py new file mode 100644 index 000000000..18e16c253 --- /dev/null +++ b/src/qibolab/pulses/pulse.py @@ -0,0 +1,199 @@ +"""Pulse class.""" +import copy +from dataclasses import dataclass, fields +from enum import Enum +from typing import Optional + + +class PulseType(Enum): + """An enumeration to distinguish different types of pulses. + + READOUT pulses triger acquisitions. DRIVE pulses are used to control + qubit states. FLUX pulses are used to shift the frequency of flux + tunable qubits and with it implement two-qubit gates. + """ + + READOUT = "ro" + DRIVE = "qd" + FLUX = "qf" + COUPLERFLUX = "cf" + + +@dataclass +class Pulse: + """A class to represent a pulse to be sent to the QPU.""" + + start: int + """Start time of pulse in ns.""" + duration: int + """Pulse duration in ns.""" + amplitude: float + """Pulse digital amplitude (unitless). + + Pulse amplitudes are normalised between -1 and 1. + """ + frequency: int + """Pulse Intermediate Frequency in Hz. + + The value has to be in the range [10e6 to 300e6]. + """ + relative_phase: float + """Relative phase of the pulse, in radians.""" + shape: PulseShape + """Pulse shape, as a PulseShape object. + + See + :py: mod:`qibolab.pulses` for list of available shapes. + """ + channel: Optional[str] = None + """Channel on which the pulse should be played. + + When a sequence of pulses is sent to the platform for execution, + each pulse is sent to the instrument responsible for playing pulses + the pulse channel. The connection of instruments with channels is + defined in the platform runcard. + """ + type: PulseType = PulseType.DRIVE + """Pulse type, as an element of PulseType enumeration.""" + qubit: int = 0 + """Qubit or coupler addressed by the pulse.""" + + def __post_init__(self): + if isinstance(self.type, str): + self.type = PulseType(self.type) + if isinstance(self.shape, str): + self.shape = PulseShape.eval(self.shape) + # TODO: drop the cyclic reference + self.shape.pulse = self + + @classmethod + def flux(cls, start, duration, amplitude, shape, **kwargs): + return cls( + start, duration, amplitude, 0, 0, shape, type=PulseType.FLUX, **kwargs + ) + + @property + def finish(self) -> Optional[int]: + """Time when the pulse is scheduled to finish.""" + if None in {self.start, self.duration}: + return None + return self.start + self.duration + + @property + def global_phase(self): + """Global phase of the pulse, in radians. + + This phase is calculated from the pulse start time and frequency + as `2 * pi * frequency * start`. + """ + if self.type is PulseType.READOUT: + # readout pulses should have zero global phase so that we can + # calculate probabilities in the i-q plane + return 0 + + # pulse start, duration and finish are in ns + return 2 * np.pi * self.frequency * self.start / 1e9 + + @property + def phase(self) -> float: + """Total phase of the pulse, in radians. + + The total phase is computed as the sum of the global and + relative phases. + """ + return self.global_phase + self.relative_phase + + @property + def id(self) -> int: + return id(self) + + def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: + """The envelope waveform of the i component of the pulse.""" + + return self.shape.envelope_waveform_i(sampling_rate) + + def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: + """The envelope waveform of the q component of the pulse.""" + + return self.shape.envelope_waveform_q(sampling_rate) + + def envelope_waveforms( + self, sampling_rate=SAMPLING_RATE + ): # -> tuple[Waveform, Waveform]: + """A tuple with the i and q envelope waveforms of the pulse.""" + + return ( + self.shape.envelope_waveform_i(sampling_rate), + self.shape.envelope_waveform_q(sampling_rate), + ) + + def modulated_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: + """The waveform of the i component of the pulse, modulated with its + frequency.""" + + return self.shape.modulated_waveform_i(sampling_rate) + + def modulated_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: + """The waveform of the q component of the pulse, modulated with its + frequency.""" + + return self.shape.modulated_waveform_q(sampling_rate) + + def modulated_waveforms(self, sampling_rate): # -> tuple[Waveform, Waveform]: + """A tuple with the i and q waveforms of the pulse, modulated with its + frequency.""" + + return self.shape.modulated_waveforms(sampling_rate) + + def __hash__(self): + """Hash the content. + + .. warning:: + + unhashable attributes are not taken into account, so there will be more + clashes than those usually expected with a regular hash + + .. todo:: + + This method should be eventually dropped, and be provided automatically by + freezing the dataclass (i.e. setting ``frozen=true`` in the decorator). + However, at the moment is not possible nor desired, because it contains + unhashable attributes and because some instances are mutated inside Qibolab. + """ + return hash( + tuple( + getattr(self, f.name) + for f in fields(self) + if f.name not in ("type", "shape") + ) + ) + + def __add__(self, other): + if isinstance(other, Pulse): + return PulseSequence(self, other) + if isinstance(other, PulseSequence): + return PulseSequence(self, *other) + raise TypeError(f"Expected Pulse or PulseSequence; got {type(other).__name__}") + + def __mul__(self, n): + if not isinstance(n, int): + raise TypeError(f"Expected int; got {type(n).__name__}") + if n < 0: + raise TypeError(f"argument n should be >=0, got {n}") + return PulseSequence(*([copy.deepcopy(self)] * n)) + + def __rmul__(self, n): + return self.__mul__(n) + + def is_equal_ignoring_start(self, item) -> bool: + """Check if two pulses are equal ignoring start time.""" + return ( + self.duration == item.duration + and self.amplitude == item.amplitude + and self.frequency == item.frequency + and self.relative_phase == item.relative_phase + and self.shape == item.shape + and self.channel == item.channel + and self.type == item.type + and self.qubit == item.qubit + ) diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py new file mode 100644 index 000000000..fc488a372 --- /dev/null +++ b/src/qibolab/pulses/sequence.py @@ -0,0 +1,260 @@ +"""PulseSequence class.""" +import numpy as np + + +class PulseSequence(list): + """A collection of scheduled pulses. + + A quantum circuit can be translated into a set of scheduled pulses + that implement the circuit gates. This class contains many + supporting fuctions to facilitate the creation and manipulation of + these collections of pulses. None of the methods of PulseSequence + modify any of the properties of its pulses. + """ + + def __add__(self, other): + """Return self+value.""" + return type(self)(super().__add__(other)) + + def __mul__(self, other): + """Return self*value.""" + return type(self)(super().__mul__(other)) + + def __repr__(self): + """Return repr(self).""" + return f"{type(self).__name__}({super().__repr__()})" + + def copy(self): + """Return a shallow copy of the sequence.""" + return type(self)(super().copy()) + + @property + def ro_pulses(self): + """A new sequence containing only its readout pulses.""" + new_pc = PulseSequence() + for pulse in self: + if pulse.type == PulseType.READOUT: + new_pc.append(pulse) + return new_pc + + @property + def qd_pulses(self): + """A new sequence containing only its qubit drive pulses.""" + new_pc = PulseSequence() + for pulse in self: + if pulse.type == PulseType.DRIVE: + new_pc.append(pulse) + return new_pc + + @property + def qf_pulses(self): + """A new sequence containing only its qubit flux pulses.""" + new_pc = PulseSequence() + for pulse in self: + if pulse.type == PulseType.FLUX: + new_pc.append(pulse) + return new_pc + + @property + def cf_pulses(self): + """A new sequence containing only its coupler flux pulses.""" + new_pc = PulseSequence() + for pulse in self: + if pulse.type is PulseType.COUPLERFLUX: + new_pc.append(pulse) + return new_pc + + def get_channel_pulses(self, *channels): + """Return a new sequence containing the pulses on some channels.""" + new_pc = PulseSequence() + for pulse in self: + if pulse.channel in channels: + new_pc.append(pulse) + return new_pc + + def get_qubit_pulses(self, *qubits): + """Return a new sequence containing the pulses on some qubits.""" + new_pc = PulseSequence() + for pulse in self: + if pulse.type is not PulseType.COUPLERFLUX: + if pulse.qubit in qubits: + new_pc.append(pulse) + return new_pc + + def coupler_pulses(self, *couplers): + """Return a new sequence containing the pulses on some couplers.""" + new_pc = PulseSequence() + for pulse in self: + if pulse.type is not PulseType.COUPLERFLUX: + if pulse.qubit in couplers: + new_pc.append(pulse) + return new_pc + + @property + def finish(self) -> int: + """The time when the last pulse of the sequence finishes.""" + t: int = 0 + for pulse in self: + if pulse.finish > t: + t = pulse.finish + return t + + @property + def start(self) -> int: + """The start time of the first pulse of the sequence.""" + t = self.finish + for pulse in self: + if pulse.start < t: + t = pulse.start + return t + + @property + def duration(self) -> int: + """Duration of the sequence calculated as its finish - start times.""" + return self.finish - self.start + + @property + def channels(self) -> list: + """List containing the channels used by the pulses in the sequence.""" + channels = [] + for pulse in self: + if not pulse.channel in channels: + channels.append(pulse.channel) + channels.sort() + return channels + + @property + def qubits(self) -> list: + """The qubits associated with the pulses in the sequence.""" + qubits = [] + for pulse in self: + if not pulse.qubit in qubits: + qubits.append(pulse.qubit) + qubits.sort() + return qubits + + def get_pulse_overlaps(self): # -> dict((int,int): PulseSequence): + """Return a dictionary of slices of time (tuples with start and finish + times) where pulses overlap.""" + times = [] + for pulse in self: + if not pulse.start in times: + times.append(pulse.start) + if not pulse.finish in times: + times.append(pulse.finish) + times.sort() + + overlaps = {} + for n in range(len(times) - 1): + overlaps[(times[n], times[n + 1])] = PulseSequence() + for pulse in self: + if (pulse.start <= times[n]) & (pulse.finish >= times[n + 1]): + overlaps[(times[n], times[n + 1])] += [pulse] + return overlaps + + def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): + """Separate a sequence of overlapping pulses into a list of non- + overlapping sequences.""" + # This routine separates the pulses of a sequence into non-overlapping sets + # but it does not check if the frequencies of the pulses within a set have the same frequency + + separated_pulses = [] + for new_pulse in self: + stored = False + for ps in separated_pulses: + overlaps = False + for existing_pulse in ps: + if ( + new_pulse.start < existing_pulse.finish + and new_pulse.finish > existing_pulse.start + ): + overlaps = True + break + if not overlaps: + ps.append(new_pulse) + stored = True + break + if not stored: + separated_pulses.append(PulseSequence([new_pulse])) + return separated_pulses + + # TODO: Implement separate_different_frequency_pulses() + + @property + def pulses_overlap(self) -> bool: + """Whether any of the pulses in the sequence overlap.""" + overlap = False + for pc in self.get_pulse_overlaps().values(): + if len(pc) > 1: + overlap = True + break + return overlap + + def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): + """Plot the sequence of pulses. + + Args: + savefig_filename (str): a file path. If provided the plot is save to a file. + """ + if len(self) > 0: + import matplotlib.pyplot as plt + from matplotlib import gridspec + + fig = plt.figure(figsize=(14, 2 * len(self)), dpi=200) + gs = gridspec.GridSpec(ncols=1, nrows=len(self)) + vertical_lines = [] + for pulse in self: + vertical_lines.append(pulse.start) + vertical_lines.append(pulse.finish) + + n = -1 + for qubit in self.qubits: + qubit_pulses = self.get_qubit_pulses(qubit) + for channel in qubit_pulses.channels: + n += 1 + channel_pulses = qubit_pulses.get_channel_pulses(channel) + ax = plt.subplot(gs[n]) + ax.axis([0, self.finish, -1, 1]) + for pulse in channel_pulses: + num_samples = len( + pulse.shape.modulated_waveform_i(sampling_rate) + ) + time = pulse.start + np.arange(num_samples) / sampling_rate + ax.plot( + time, + pulse.shape.modulated_waveform_q(sampling_rate).data, + c="lightgrey", + ) + ax.plot( + time, + pulse.shape.modulated_waveform_i(sampling_rate).data, + c=f"C{str(n)}", + ) + ax.plot( + time, + pulse.shape.envelope_waveform_i(sampling_rate).data, + c=f"C{str(n)}", + ) + ax.plot( + time, + -pulse.shape.envelope_waveform_i(sampling_rate).data, + c=f"C{str(n)}", + ) + # TODO: if they overlap use different shades + ax.axhline(0, c="dimgrey") + ax.set_ylabel(f"qubit {qubit} \n channel {channel}") + for vl in vertical_lines: + ax.axvline(vl, c="slategrey", linestyle="--") + ax.axis([0, self.finish, -1, 1]) + ax.grid( + visible=True, + which="both", + axis="both", + color="#CCCCCC", + linestyle="-", + ) + if savefig_filename: + plt.savefig(savefig_filename) + else: + plt.show() + plt.close() diff --git a/src/qibolab/pulses.py b/src/qibolab/pulses/shape.py similarity index 51% rename from src/qibolab/pulses.py rename to src/qibolab/pulses/shape.py index 53a53c4f9..1d754e86e 100644 --- a/src/qibolab/pulses.py +++ b/src/qibolab/pulses/shape.py @@ -1,12 +1,7 @@ -"""Pulse and PulseSequence classes.""" -import copy +"""PulseShape class.""" import re from abc import ABC, abstractmethod -from dataclasses import dataclass, fields -from enum import Enum -from typing import Optional -import numpy as np from qibo.config import log from scipy.signal import lfilter @@ -18,81 +13,6 @@ """ -class PulseType(Enum): - """An enumeration to distinguish different types of pulses. - - READOUT pulses triger acquisitions. DRIVE pulses are used to control - qubit states. FLUX pulses are used to shift the frequency of flux - tunable qubits and with it implement two-qubit gates. - """ - - READOUT = "ro" - DRIVE = "qd" - FLUX = "qf" - COUPLERFLUX = "cf" - - -class Waveform: - """A class to save pulse waveforms. - - A waveform is a list of samples, or discrete data points, used by the digital to analogue converters (DACs) - to synthesise pulses. - - Attributes: - data (np.ndarray): a numpy array containing the samples. - """ - - DECIMALS = 5 - - def __init__(self, data): - """Initialise the waveform with a of samples.""" - self.data: np.ndarray = np.array(data) - - def __len__(self): - """Return the length of the waveform, the number of samples.""" - return len(self.data) - - def __hash__(self): - """Hash the underlying data. - - .. todo:: - - In order to make this reliable, we should set the data as immutable. This we - could by making both the class frozen and the contained array readonly - https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags - """ - return hash(self.data.tobytes()) - - def __eq__(self, other): - """Compare two waveforms. - - Two waveforms are considered equal if their samples, rounded to - `Waveform.DECIMALS` decimal places, are all equal. - """ - return np.allclose(self.data, other.data) - - def plot(self, savefig_filename=None): - """Plot the waveform. - - Args: - savefig_filename (str): a file path. If provided the plot is save to a file. - """ - import matplotlib.pyplot as plt - - plt.figure(figsize=(14, 5), dpi=200) - plt.plot(self.data, c="C0", linestyle="dashed") - plt.xlabel("Sample Number") - plt.ylabel("Amplitude") - plt.grid( - visible=True, which="both", axis="both", color="#888888", linestyle="-" - ) - if savefig_filename: - plt.savefig(savefig_filename) - else: - plt.show() - plt.close() - - class ShapeInitError(RuntimeError): """Error raised when a pulse has not been fully defined.""" @@ -708,546 +628,3 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: def __repr__(self): return f"{self.name}({self.envelope_i[:3]}, ..., {self.envelope_q[:3]}, ...)" - - -@dataclass -class Pulse: - """A class to represent a pulse to be sent to the QPU.""" - - start: int - """Start time of pulse in ns.""" - duration: int - """Pulse duration in ns.""" - amplitude: float - """Pulse digital amplitude (unitless). - - Pulse amplitudes are normalised between -1 and 1. - """ - frequency: int - """Pulse Intermediate Frequency in Hz. - - The value has to be in the range [10e6 to 300e6]. - """ - relative_phase: float - """Relative phase of the pulse, in radians.""" - shape: PulseShape - """Pulse shape, as a PulseShape object. - - See - :py: mod:`qibolab.pulses` for list of available shapes. - """ - channel: Optional[str] = None - """Channel on which the pulse should be played. - - When a sequence of pulses is sent to the platform for execution, - each pulse is sent to the instrument responsible for playing pulses - the pulse channel. The connection of instruments with channels is - defined in the platform runcard. - """ - type: PulseType = PulseType.DRIVE - """Pulse type, as an element of PulseType enumeration.""" - qubit: int = 0 - """Qubit or coupler addressed by the pulse.""" - - def __post_init__(self): - if isinstance(self.type, str): - self.type = PulseType(self.type) - if isinstance(self.shape, str): - self.shape = PulseShape.eval(self.shape) - # TODO: drop the cyclic reference - self.shape.pulse = self - - @classmethod - def flux(cls, start, duration, amplitude, shape, **kwargs): - return cls( - start, duration, amplitude, 0, 0, shape, type=PulseType.FLUX, **kwargs - ) - - @property - def finish(self) -> Optional[int]: - """Time when the pulse is scheduled to finish.""" - if None in {self.start, self.duration}: - return None - return self.start + self.duration - - @property - def global_phase(self): - """Global phase of the pulse, in radians. - - This phase is calculated from the pulse start time and frequency - as `2 * pi * frequency * start`. - """ - if self.type is PulseType.READOUT: - # readout pulses should have zero global phase so that we can - # calculate probabilities in the i-q plane - return 0 - - # pulse start, duration and finish are in ns - return 2 * np.pi * self.frequency * self.start / 1e9 - - @property - def phase(self) -> float: - """Total phase of the pulse, in radians. - - The total phase is computed as the sum of the global and - relative phases. - """ - return self.global_phase + self.relative_phase - - @property - def id(self) -> int: - return id(self) - - def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the i component of the pulse.""" - - return self.shape.envelope_waveform_i(sampling_rate) - - def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the q component of the pulse.""" - - return self.shape.envelope_waveform_q(sampling_rate) - - def envelope_waveforms( - self, sampling_rate=SAMPLING_RATE - ): # -> tuple[Waveform, Waveform]: - """A tuple with the i and q envelope waveforms of the pulse.""" - - return ( - self.shape.envelope_waveform_i(sampling_rate), - self.shape.envelope_waveform_q(sampling_rate), - ) - - def modulated_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The waveform of the i component of the pulse, modulated with its - frequency.""" - - return self.shape.modulated_waveform_i(sampling_rate) - - def modulated_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The waveform of the q component of the pulse, modulated with its - frequency.""" - - return self.shape.modulated_waveform_q(sampling_rate) - - def modulated_waveforms(self, sampling_rate): # -> tuple[Waveform, Waveform]: - """A tuple with the i and q waveforms of the pulse, modulated with its - frequency.""" - - return self.shape.modulated_waveforms(sampling_rate) - - def __hash__(self): - """Hash the content. - - .. warning:: - - unhashable attributes are not taken into account, so there will be more - clashes than those usually expected with a regular hash - - .. todo:: - - This method should be eventually dropped, and be provided automatically by - freezing the dataclass (i.e. setting ``frozen=true`` in the decorator). - However, at the moment is not possible nor desired, because it contains - unhashable attributes and because some instances are mutated inside Qibolab. - """ - return hash( - tuple( - getattr(self, f.name) - for f in fields(self) - if f.name not in ("type", "shape") - ) - ) - - def __add__(self, other): - if isinstance(other, Pulse): - return PulseSequence(self, other) - if isinstance(other, PulseSequence): - return PulseSequence(self, *other) - raise TypeError(f"Expected Pulse or PulseSequence; got {type(other).__name__}") - - def __mul__(self, n): - if not isinstance(n, int): - raise TypeError(f"Expected int; got {type(n).__name__}") - if n < 0: - raise TypeError(f"argument n should be >=0, got {n}") - return PulseSequence(*([copy.deepcopy(self)] * n)) - - def __rmul__(self, n): - return self.__mul__(n) - - def is_equal_ignoring_start(self, item) -> bool: - """Check if two pulses are equal ignoring start time.""" - return ( - self.duration == item.duration - and self.amplitude == item.amplitude - and self.frequency == item.frequency - and self.relative_phase == item.relative_phase - and self.shape == item.shape - and self.channel == item.channel - and self.type == item.type - and self.qubit == item.qubit - ) - - def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): - """Plots the pulse envelope and modulated waveforms. - - Args: - savefig_filename (str): a file path. If provided the plot is save to a file. - """ - - import matplotlib.pyplot as plt - from matplotlib import gridspec - - waveform_i = self.shape.envelope_waveform_i(sampling_rate) - waveform_q = self.shape.envelope_waveform_q(sampling_rate) - - num_samples = len(waveform_i) - time = self.start + np.arange(num_samples) / sampling_rate - fig = plt.figure(figsize=(14, 5), dpi=200) - gs = gridspec.GridSpec(ncols=2, nrows=1, width_ratios=[2, 1]) - ax1 = plt.subplot(gs[0]) - ax1.plot( - time, - waveform_i.data, - label="envelope i", - c="C0", - linestyle="dashed", - ) - ax1.plot( - time, - waveform_q.data, - label="envelope q", - c="C1", - linestyle="dashed", - ) - ax1.plot( - time, - self.shape.modulated_waveform_i(sampling_rate).data, - label="modulated i", - c="C0", - ) - ax1.plot( - time, - self.shape.modulated_waveform_q(sampling_rate).data, - label="modulated q", - c="C1", - ) - ax1.plot(time, -waveform_i.data, c="silver", linestyle="dashed") - ax1.set_xlabel("Time [ns]") - ax1.set_ylabel("Amplitude") - - ax1.grid( - visible=True, which="both", axis="both", color="#888888", linestyle="-" - ) - ax1.axis([self.start, self.finish, -1, 1]) - ax1.legend() - - modulated_i = self.shape.modulated_waveform_i(sampling_rate).data - modulated_q = self.shape.modulated_waveform_q(sampling_rate).data - ax2 = plt.subplot(gs[1]) - ax2.plot( - modulated_i, - modulated_q, - label="modulated", - c="C3", - ) - ax2.plot( - waveform_i.data, - waveform_q.data, - label="envelope", - c="C2", - ) - ax2.plot( - modulated_i[0], - modulated_q[0], - marker="o", - markersize=5, - label="start", - c="lightcoral", - ) - ax2.plot( - modulated_i[-1], - modulated_q[-1], - marker="o", - markersize=5, - label="finish", - c="darkred", - ) - - ax2.plot( - np.cos(time * 2 * np.pi / self.duration), - np.sin(time * 2 * np.pi / self.duration), - c="silver", - linestyle="dashed", - ) - - ax2.grid( - visible=True, which="both", axis="both", color="#888888", linestyle="-" - ) - ax2.legend() - # ax2.axis([ -1, 1, -1, 1]) - ax2.axis("equal") - if savefig_filename: - plt.savefig(savefig_filename) - else: - plt.show() - plt.close() - - -class PulseSequence(list): - """A collection of scheduled pulses. - - A quantum circuit can be translated into a set of scheduled pulses - that implement the circuit gates. This class contains many - supporting fuctions to facilitate the creation and manipulation of - these collections of pulses. None of the methods of PulseSequence - modify any of the properties of its pulses. - """ - - def __add__(self, other): - """Return self+value.""" - return type(self)(super().__add__(other)) - - def __mul__(self, other): - """Return self*value.""" - return type(self)(super().__mul__(other)) - - def __repr__(self): - """Return repr(self).""" - return f"{type(self).__name__}({super().__repr__()})" - - def copy(self): - """Return a shallow copy of the sequence.""" - return type(self)(super().copy()) - - @property - def ro_pulses(self): - """A new sequence containing only its readout pulses.""" - new_pc = PulseSequence() - for pulse in self: - if pulse.type == PulseType.READOUT: - new_pc.append(pulse) - return new_pc - - @property - def qd_pulses(self): - """A new sequence containing only its qubit drive pulses.""" - new_pc = PulseSequence() - for pulse in self: - if pulse.type == PulseType.DRIVE: - new_pc.append(pulse) - return new_pc - - @property - def qf_pulses(self): - """A new sequence containing only its qubit flux pulses.""" - new_pc = PulseSequence() - for pulse in self: - if pulse.type == PulseType.FLUX: - new_pc.append(pulse) - return new_pc - - @property - def cf_pulses(self): - """A new sequence containing only its coupler flux pulses.""" - new_pc = PulseSequence() - for pulse in self: - if pulse.type is PulseType.COUPLERFLUX: - new_pc.append(pulse) - return new_pc - - def get_channel_pulses(self, *channels): - """Return a new sequence containing the pulses on some channels.""" - new_pc = PulseSequence() - for pulse in self: - if pulse.channel in channels: - new_pc.append(pulse) - return new_pc - - def get_qubit_pulses(self, *qubits): - """Return a new sequence containing the pulses on some qubits.""" - new_pc = PulseSequence() - for pulse in self: - if pulse.type is not PulseType.COUPLERFLUX: - if pulse.qubit in qubits: - new_pc.append(pulse) - return new_pc - - def coupler_pulses(self, *couplers): - """Return a new sequence containing the pulses on some couplers.""" - new_pc = PulseSequence() - for pulse in self: - if pulse.type is not PulseType.COUPLERFLUX: - if pulse.qubit in couplers: - new_pc.append(pulse) - return new_pc - - @property - def finish(self) -> int: - """The time when the last pulse of the sequence finishes.""" - t: int = 0 - for pulse in self: - if pulse.finish > t: - t = pulse.finish - return t - - @property - def start(self) -> int: - """The start time of the first pulse of the sequence.""" - t = self.finish - for pulse in self: - if pulse.start < t: - t = pulse.start - return t - - @property - def duration(self) -> int: - """Duration of the sequence calculated as its finish - start times.""" - return self.finish - self.start - - @property - def channels(self) -> list: - """List containing the channels used by the pulses in the sequence.""" - channels = [] - for pulse in self: - if not pulse.channel in channels: - channels.append(pulse.channel) - channels.sort() - return channels - - @property - def qubits(self) -> list: - """The qubits associated with the pulses in the sequence.""" - qubits = [] - for pulse in self: - if not pulse.qubit in qubits: - qubits.append(pulse.qubit) - qubits.sort() - return qubits - - def get_pulse_overlaps(self): # -> dict((int,int): PulseSequence): - """Return a dictionary of slices of time (tuples with start and finish - times) where pulses overlap.""" - times = [] - for pulse in self: - if not pulse.start in times: - times.append(pulse.start) - if not pulse.finish in times: - times.append(pulse.finish) - times.sort() - - overlaps = {} - for n in range(len(times) - 1): - overlaps[(times[n], times[n + 1])] = PulseSequence() - for pulse in self: - if (pulse.start <= times[n]) & (pulse.finish >= times[n + 1]): - overlaps[(times[n], times[n + 1])] += [pulse] - return overlaps - - def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): - """Separate a sequence of overlapping pulses into a list of non- - overlapping sequences.""" - # This routine separates the pulses of a sequence into non-overlapping sets - # but it does not check if the frequencies of the pulses within a set have the same frequency - - separated_pulses = [] - for new_pulse in self: - stored = False - for ps in separated_pulses: - overlaps = False - for existing_pulse in ps: - if ( - new_pulse.start < existing_pulse.finish - and new_pulse.finish > existing_pulse.start - ): - overlaps = True - break - if not overlaps: - ps.append(new_pulse) - stored = True - break - if not stored: - separated_pulses.append(PulseSequence([new_pulse])) - return separated_pulses - - # TODO: Implement separate_different_frequency_pulses() - - @property - def pulses_overlap(self) -> bool: - """Whether any of the pulses in the sequence overlap.""" - overlap = False - for pc in self.get_pulse_overlaps().values(): - if len(pc) > 1: - overlap = True - break - return overlap - - def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): - """Plot the sequence of pulses. - - Args: - savefig_filename (str): a file path. If provided the plot is save to a file. - """ - if len(self) > 0: - import matplotlib.pyplot as plt - from matplotlib import gridspec - - fig = plt.figure(figsize=(14, 2 * len(self)), dpi=200) - gs = gridspec.GridSpec(ncols=1, nrows=len(self)) - vertical_lines = [] - for pulse in self: - vertical_lines.append(pulse.start) - vertical_lines.append(pulse.finish) - - n = -1 - for qubit in self.qubits: - qubit_pulses = self.get_qubit_pulses(qubit) - for channel in qubit_pulses.channels: - n += 1 - channel_pulses = qubit_pulses.get_channel_pulses(channel) - ax = plt.subplot(gs[n]) - ax.axis([0, self.finish, -1, 1]) - for pulse in channel_pulses: - num_samples = len( - pulse.shape.modulated_waveform_i(sampling_rate) - ) - time = pulse.start + np.arange(num_samples) / sampling_rate - ax.plot( - time, - pulse.shape.modulated_waveform_q(sampling_rate).data, - c="lightgrey", - ) - ax.plot( - time, - pulse.shape.modulated_waveform_i(sampling_rate).data, - c=f"C{str(n)}", - ) - ax.plot( - time, - pulse.shape.envelope_waveform_i(sampling_rate).data, - c=f"C{str(n)}", - ) - ax.plot( - time, - -pulse.shape.envelope_waveform_i(sampling_rate).data, - c=f"C{str(n)}", - ) - # TODO: if they overlap use different shades - ax.axhline(0, c="dimgrey") - ax.set_ylabel(f"qubit {qubit} \n channel {channel}") - for vl in vertical_lines: - ax.axvline(vl, c="slategrey", linestyle="--") - ax.axis([0, self.finish, -1, 1]) - ax.grid( - visible=True, - which="both", - axis="both", - color="#CCCCCC", - linestyle="-", - ) - if savefig_filename: - plt.savefig(savefig_filename) - else: - plt.show() - plt.close() diff --git a/src/qibolab/pulses/waveform.py b/src/qibolab/pulses/waveform.py new file mode 100644 index 000000000..7c530bf36 --- /dev/null +++ b/src/qibolab/pulses/waveform.py @@ -0,0 +1,42 @@ +"""Waveform class.""" +import numpy as np + + +class Waveform: + """A class to save pulse waveforms. + + A waveform is a list of samples, or discrete data points, used by the digital to analogue converters (DACs) + to synthesise pulses. + + Attributes: + data (np.ndarray): a numpy array containing the samples. + """ + + DECIMALS = 5 + + def __init__(self, data): + """Initialise the waveform with a of samples.""" + self.data: np.ndarray = np.array(data) + + def __len__(self): + """Return the length of the waveform, the number of samples.""" + return len(self.data) + + def __hash__(self): + """Hash the underlying data. + + .. todo:: + + In order to make this reliable, we should set the data as immutable. This we + could by making both the class frozen and the contained array readonly + https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags + """ + return hash(self.data.tobytes()) + + def __eq__(self, other): + """Compare two waveforms. + + Two waveforms are considered equal if their samples, rounded to + `Waveform.DECIMALS` decimal places, are all equal. + """ + return np.allclose(self.data, other.data) From add88e60842bdadcdc34618e606ed727a9418849 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 18:30:32 +0100 Subject: [PATCH 046/233] Exports relevant objects from pulses subpackage, fix internal imports --- src/qibolab/pulses/__init__.py | 16 ++++++++++++++++ src/qibolab/pulses/plot.py | 2 +- src/qibolab/pulses/pulse.py | 5 +++++ src/qibolab/pulses/sequence.py | 3 +++ src/qibolab/pulses/shape.py | 21 ++++++++++++--------- 5 files changed, 37 insertions(+), 10 deletions(-) diff --git a/src/qibolab/pulses/__init__.py b/src/qibolab/pulses/__init__.py index e69de29bb..10478384c 100644 --- a/src/qibolab/pulses/__init__.py +++ b/src/qibolab/pulses/__init__.py @@ -0,0 +1,16 @@ +from .pulse import Pulse, PulseType +from .sequence import PulseSequence +from .shape import ( + IIR, + SAMPLING_RATE, + SNZ, + Custom, + Drag, + Gaussian, + GaussianSquare, + PulseShape, + Rectangular, + ShapeInitError, + eCap, +) +from .waveform import Waveform diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index 1328268f2..8916e8bf0 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -74,7 +74,7 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): ax1.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") start = float(pulse_.start) - finish = float(pulse._finish) if pulse._finish is not None else 0.0 + finish = float(pulse_.finish) if pulse_.finish is not None else 0.0 ax1.axis((start, finish, -1.0, 1.0)) ax1.legend() diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 18e16c253..6e7661bd4 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -4,6 +4,11 @@ from enum import Enum from typing import Optional +import numpy as np + +from .shape import SAMPLING_RATE, PulseShape +from .waveform import Waveform + class PulseType(Enum): """An enumeration to distinguish different types of pulses. diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py index fc488a372..a846a92d5 100644 --- a/src/qibolab/pulses/sequence.py +++ b/src/qibolab/pulses/sequence.py @@ -1,6 +1,9 @@ """PulseSequence class.""" import numpy as np +from .pulse import PulseType +from .shape import SAMPLING_RATE + class PulseSequence(list): """A collection of scheduled pulses. diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 1d754e86e..db3133f4c 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -2,9 +2,12 @@ import re from abc import ABC, abstractmethod +import numpy as np from qibo.config import log from scipy.signal import lfilter +from .waveform import Waveform + SAMPLING_RATE = 1 """Default sampling rate in gigasamples per second (GSps). @@ -133,7 +136,7 @@ class Rectangular(PulseShape): def __init__(self): self.name = "Rectangular" - self.pulse: Pulse = None + self.pulse: "Pulse" = None def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the i component of the pulse.""" @@ -173,7 +176,7 @@ class Exponential(PulseShape): def __init__(self, tau: float, upsilon: float, g: float = 0.1): self.name = "Exponential" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.tau: float = float(tau) self.upsilon: float = float(upsilon) self.g: float = float(g) @@ -222,7 +225,7 @@ class Gaussian(PulseShape): def __init__(self, rel_sigma: float): self.name = "Gaussian" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.rel_sigma: float = float(rel_sigma) def __eq__(self, item) -> bool: @@ -277,7 +280,7 @@ class GaussianSquare(PulseShape): def __init__(self, rel_sigma: float, width: float): self.name = "GaussianSquare" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.rel_sigma: float = float(rel_sigma) self.width: float = float(width) @@ -343,7 +346,7 @@ class Drag(PulseShape): def __init__(self, rel_sigma, beta): self.name = "Drag" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.rel_sigma = float(rel_sigma) self.beta = float(beta) @@ -407,7 +410,7 @@ class IIR(PulseShape): def __init__(self, b, a, target: PulseShape): self.name = "IIR" self.target: PulseShape = target - self._pulse: Pulse = None + self._pulse: "Pulse" = None self.a: np.ndarray = np.array(a) self.b: np.ndarray = np.array(b) # Check len(a) = len(b) = 2 @@ -488,7 +491,7 @@ class SNZ(PulseShape): def __init__(self, t_idling, b_amplitude=None): self.name = "SNZ" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.t_idling: float = t_idling self.b_amplitude = b_amplitude @@ -557,7 +560,7 @@ class eCap(PulseShape): def __init__(self, alpha: float): self.name = "eCap" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.alpha: float = float(alpha) def __eq__(self, item) -> bool: @@ -595,7 +598,7 @@ class Custom(PulseShape): def __init__(self, envelope_i, envelope_q=None): self.name = "Custom" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.envelope_i: np.ndarray = np.array(envelope_i) if envelope_q is not None: self.envelope_q: np.ndarray = np.array(envelope_q) From 105d86a6c7b2bbcfb598945a573b71a67f8dcc58 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 18:32:12 +0100 Subject: [PATCH 047/233] Remove pulse combination methods They intrinsically require the pulse to be aware of the sequence; but, for isolation sake, if the sequence is aware of the pulse, and not the opposite --- src/qibolab/pulses/pulse.py | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 6e7661bd4..2c149c7ec 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -1,5 +1,4 @@ """Pulse class.""" -import copy from dataclasses import dataclass, fields from enum import Enum from typing import Optional @@ -173,23 +172,6 @@ def __hash__(self): ) ) - def __add__(self, other): - if isinstance(other, Pulse): - return PulseSequence(self, other) - if isinstance(other, PulseSequence): - return PulseSequence(self, *other) - raise TypeError(f"Expected Pulse or PulseSequence; got {type(other).__name__}") - - def __mul__(self, n): - if not isinstance(n, int): - raise TypeError(f"Expected int; got {type(n).__name__}") - if n < 0: - raise TypeError(f"argument n should be >=0, got {n}") - return PulseSequence(*([copy.deepcopy(self)] * n)) - - def __rmul__(self, n): - return self.__mul__(n) - def is_equal_ignoring_start(self, item) -> bool: """Check if two pulses are equal ignoring start time.""" return ( From bf27f9b381e5d5dfaa8ed6e4a8e3e4d2c437e35f Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 18:36:45 +0100 Subject: [PATCH 048/233] Move sequence plotting to dedicated module --- src/qibolab/pulses/plot.py | 69 +++++++++++++++++++++++++++++++++ src/qibolab/pulses/sequence.py | 71 ---------------------------------- 2 files changed, 69 insertions(+), 71 deletions(-) diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index 8916e8bf0..b662cf6b3 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -3,6 +3,7 @@ import numpy as np from .pulse import Pulse +from .sequence import PulseSequence from .shape import SAMPLING_RATE from .waveform import Waveform @@ -126,3 +127,71 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): else: plt.show() plt.close() + + +def sequence(ps: PulseSequence, filename=None, sampling_rate=SAMPLING_RATE): + """Plot the sequence of pulses. + + Args: + filename (str): a file path. If provided the plot is save to a file. + """ + if len(ps) > 0: + import matplotlib.pyplot as plt + from matplotlib import gridspec + + _ = plt.figure(figsize=(14, 2 * len(ps)), dpi=200) + gs = gridspec.GridSpec(ncols=1, nrows=len(ps)) + vertical_lines = [] + for pulse in ps: + vertical_lines.append(pulse.start) + vertical_lines.append(pulse.finish) + + n = -1 + for qubit in ps.qubits: + qubit_pulses = ps.get_qubit_pulses(qubit) + for channel in qubit_pulses.channels: + n += 1 + channel_pulses = qubit_pulses.get_channel_pulses(channel) + ax = plt.subplot(gs[n]) + ax.axis([0, ps.finish, -1, 1]) + for pulse in channel_pulses: + num_samples = len(pulse.shape.modulated_waveform_i(sampling_rate)) + time = pulse.start + np.arange(num_samples) / sampling_rate + ax.plot( + time, + pulse.shape.modulated_waveform_q(sampling_rate).data, + c="lightgrey", + ) + ax.plot( + time, + pulse.shape.modulated_waveform_i(sampling_rate).data, + c=f"C{str(n)}", + ) + ax.plot( + time, + pulse.shape.envelope_waveform_i(sampling_rate).data, + c=f"C{str(n)}", + ) + ax.plot( + time, + -pulse.shape.envelope_waveform_i(sampling_rate).data, + c=f"C{str(n)}", + ) + # TODO: if they overlap use different shades + ax.axhline(0, c="dimgrey") + ax.set_ylabel(f"qubit {qubit} \n channel {channel}") + for vl in vertical_lines: + ax.axvline(vl, c="slategrey", linestyle="--") + ax.axis((0, ps.finish, -1, 1)) + ax.grid( + visible=True, + which="both", + axis="both", + color="#CCCCCC", + linestyle="-", + ) + if filename: + plt.savefig(filename) + else: + plt.show() + plt.close() diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py index a846a92d5..d1539b354 100644 --- a/src/qibolab/pulses/sequence.py +++ b/src/qibolab/pulses/sequence.py @@ -1,8 +1,6 @@ """PulseSequence class.""" -import numpy as np from .pulse import PulseType -from .shape import SAMPLING_RATE class PulseSequence(list): @@ -192,72 +190,3 @@ def pulses_overlap(self) -> bool: overlap = True break return overlap - - def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): - """Plot the sequence of pulses. - - Args: - savefig_filename (str): a file path. If provided the plot is save to a file. - """ - if len(self) > 0: - import matplotlib.pyplot as plt - from matplotlib import gridspec - - fig = plt.figure(figsize=(14, 2 * len(self)), dpi=200) - gs = gridspec.GridSpec(ncols=1, nrows=len(self)) - vertical_lines = [] - for pulse in self: - vertical_lines.append(pulse.start) - vertical_lines.append(pulse.finish) - - n = -1 - for qubit in self.qubits: - qubit_pulses = self.get_qubit_pulses(qubit) - for channel in qubit_pulses.channels: - n += 1 - channel_pulses = qubit_pulses.get_channel_pulses(channel) - ax = plt.subplot(gs[n]) - ax.axis([0, self.finish, -1, 1]) - for pulse in channel_pulses: - num_samples = len( - pulse.shape.modulated_waveform_i(sampling_rate) - ) - time = pulse.start + np.arange(num_samples) / sampling_rate - ax.plot( - time, - pulse.shape.modulated_waveform_q(sampling_rate).data, - c="lightgrey", - ) - ax.plot( - time, - pulse.shape.modulated_waveform_i(sampling_rate).data, - c=f"C{str(n)}", - ) - ax.plot( - time, - pulse.shape.envelope_waveform_i(sampling_rate).data, - c=f"C{str(n)}", - ) - ax.plot( - time, - -pulse.shape.envelope_waveform_i(sampling_rate).data, - c=f"C{str(n)}", - ) - # TODO: if they overlap use different shades - ax.axhline(0, c="dimgrey") - ax.set_ylabel(f"qubit {qubit} \n channel {channel}") - for vl in vertical_lines: - ax.axvline(vl, c="slategrey", linestyle="--") - ax.axis([0, self.finish, -1, 1]) - ax.grid( - visible=True, - which="both", - axis="both", - color="#CCCCCC", - linestyle="-", - ) - if savefig_filename: - plt.savefig(savefig_filename) - else: - plt.show() - plt.close() From b2a108adecf609dac0118a74c8b4343f2eed455b Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 18:46:23 +0100 Subject: [PATCH 049/233] Fix plotting tests, require explicit import The plotting module won't be imported as a side effect of importing anything else in Qibolab, thus we could keep the matplitlib import top-level, and not have it as a (mandatory) dependency. Yet, we could make it part of an extra --- tests/test_pulses.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/tests/test_pulses.py b/tests/test_pulses.py index 6e0705ff3..3b337de40 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -21,6 +21,7 @@ ShapeInitError, Waveform, eCap, + plot, ) HERE = pathlib.Path(__file__).parent @@ -41,15 +42,15 @@ def test_plot_functions(): plot_file = HERE / "test_plot.png" - wf.plot(plot_file) + plot.waveform(wf, plot_file) assert os.path.exists(plot_file) os.remove(plot_file) - p0.plot(plot_file) + plot.pulse(p0, plot_file) assert os.path.exists(plot_file) os.remove(plot_file) - ps.plot(plot_file) + plot.sequence(ps, plot_file) assert os.path.exists(plot_file) os.remove(plot_file) From d09244d7d9e7fcbded4bb54b23ad603afb7ca0cc Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 19 Jan 2024 19:15:24 +0100 Subject: [PATCH 050/233] Turn waveform into a bare array --- src/qibolab/pulses/__init__.py | 2 +- src/qibolab/pulses/plot.py | 5 +- src/qibolab/pulses/pulse.py | 9 ++-- src/qibolab/pulses/shape.py | 96 ++++++++++++---------------------- src/qibolab/pulses/waveform.py | 42 --------------- 5 files changed, 40 insertions(+), 114 deletions(-) delete mode 100644 src/qibolab/pulses/waveform.py diff --git a/src/qibolab/pulses/__init__.py b/src/qibolab/pulses/__init__.py index 10478384c..f0ad2ad16 100644 --- a/src/qibolab/pulses/__init__.py +++ b/src/qibolab/pulses/__init__.py @@ -11,6 +11,6 @@ PulseShape, Rectangular, ShapeInitError, + Waveform, eCap, ) -from .waveform import Waveform diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index b662cf6b3..0d380830f 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -4,8 +4,7 @@ from .pulse import Pulse from .sequence import PulseSequence -from .shape import SAMPLING_RATE -from .waveform import Waveform +from .shape import SAMPLING_RATE, Waveform def waveform(wf: Waveform, filename=None): @@ -15,7 +14,7 @@ def waveform(wf: Waveform, filename=None): filename (str): a file path. If provided the plot is save to a file. """ plt.figure(figsize=(14, 5), dpi=200) - plt.plot(wf.data, c="C0", linestyle="dashed") + plt.plot(wf, c="C0", linestyle="dashed") plt.xlabel("Sample Number") plt.ylabel("Amplitude") plt.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 2c149c7ec..beb7a0962 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -5,8 +5,7 @@ import numpy as np -from .shape import SAMPLING_RATE, PulseShape -from .waveform import Waveform +from .shape import SAMPLING_RATE, PulseShape, Waveform class PulseType(Enum): @@ -121,9 +120,7 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: return self.shape.envelope_waveform_q(sampling_rate) - def envelope_waveforms( - self, sampling_rate=SAMPLING_RATE - ): # -> tuple[Waveform, Waveform]: + def envelope_waveforms(self, sampling_rate=SAMPLING_RATE): """A tuple with the i and q envelope waveforms of the pulse.""" return ( @@ -143,7 +140,7 @@ def modulated_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: return self.shape.modulated_waveform_q(sampling_rate) - def modulated_waveforms(self, sampling_rate): # -> tuple[Waveform, Waveform]: + def modulated_waveforms(self, sampling_rate): """A tuple with the i and q waveforms of the pulse, modulated with its frequency.""" diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index db3133f4c..d364a5691 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -3,11 +3,10 @@ from abc import ABC, abstractmethod import numpy as np +import numpy.typing as npt from qibo.config import log from scipy.signal import lfilter -from .waveform import Waveform - SAMPLING_RATE = 1 """Default sampling rate in gigasamples per second (GSps). @@ -15,6 +14,8 @@ a different value. """ +Waveform = npt.NDArray[np.float64] + class ShapeInitError(RuntimeError): """Error raised when a pulse has not been fully defined.""" @@ -53,9 +54,7 @@ def envelope_waveform_q( ) -> Waveform: # pragma: no cover raise NotImplementedError - def envelope_waveforms( - self, sampling_rate=SAMPLING_RATE - ): # -> tuple[Waveform, Waveform]: # pragma: no cover + def envelope_waveforms(self, sampling_rate=SAMPLING_RATE): """A tuple with the i and q envelope waveforms of the pulse.""" return ( @@ -107,8 +106,8 @@ def modulated_waveforms(self, _if: int, sampling_rate=SAMPLING_RATE): result.append(mod_matrix[:, :, n] @ np.array([ii, qq])) mod_signals = np.array(result) - modulated_waveform_i = Waveform(mod_signals[:, 0]) - modulated_waveform_q = Waveform(mod_signals[:, 1]) + modulated_waveform_i = mod_signals[:, 0] + modulated_waveform_q = mod_signals[:, 1] return (modulated_waveform_i, modulated_waveform_q) def __eq__(self, item) -> bool: @@ -143,8 +142,7 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(self.pulse.amplitude * np.ones(num_samples)) - return waveform + return self.pulse.amplitude * np.ones(num_samples) raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -152,8 +150,7 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(np.zeros(num_samples)) - return waveform + return np.zeros(num_samples) raise ShapeInitError def __repr__(self): @@ -187,7 +184,7 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) x = np.arange(0, num_samples, 1) - waveform = Waveform( + return ( self.pulse.amplitude * ( (np.ones(num_samples) * np.exp(-x / self.upsilon)) @@ -196,7 +193,6 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: / (1 + self.g) ) - return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -204,8 +200,7 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(np.zeros(num_samples)) - return waveform + return np.zeros(num_samples) raise ShapeInitError def __repr__(self): @@ -240,17 +235,13 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) x = np.arange(0, num_samples, 1) - waveform = Waveform( - self.pulse.amplitude - * np.exp( - -(1 / 2) - * ( - ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / self.rel_sigma) ** 2) - ) + return self.pulse.amplitude * np.exp( + -(1 / 2) + * ( + ((x - (num_samples - 1) / 2) ** 2) + / (((num_samples) / self.rel_sigma) ** 2) ) ) - return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -258,8 +249,7 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(np.zeros(num_samples)) - return waveform + return np.zeros(num_samples) raise ShapeInitError def __repr__(self): @@ -317,8 +307,7 @@ def fvec(t, gaussian_samples, rel_sigma, length=None): pulse = fvec(t, gaussian_samples, rel_sigma=self.rel_sigma) - waveform = Waveform(self.pulse.amplitude * pulse) - return waveform + return self.pulse.amplitude * pulse raise ShapeInitError @@ -327,8 +316,7 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(np.zeros(num_samples)) - return waveform + return np.zeros(num_samples) raise ShapeInitError def __repr__(self): @@ -362,15 +350,13 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) x = np.arange(0, num_samples, 1) - i = self.pulse.amplitude * np.exp( + return self.pulse.amplitude * np.exp( -(1 / 2) * ( ((x - (num_samples - 1) / 2) ** 2) / (((num_samples) / self.rel_sigma) ** 2) ) ) - waveform = Waveform(i) - return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -386,13 +372,11 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: / (((num_samples) / self.rel_sigma) ** 2) ) ) - q = ( + return ( self.beta * (-(x - (num_samples - 1) / 2) / ((num_samples / self.rel_sigma) ** 2)) * i ) - waveform = Waveform(q) - return waveform raise ShapeInitError def __repr__(self): @@ -450,9 +434,7 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: ) if not np.max(np.abs(data)) == 0: data = data / np.max(np.abs(data)) - data = np.abs(self.pulse.amplitude) * data - waveform = Waveform(data) - return waveform + return np.abs(self.pulse.amplitude) * data raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -471,9 +453,7 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: ) if not np.max(np.abs(data)) == 0: data = data / np.max(np.abs(data)) - data = np.abs(self.pulse.amplitude) * data - waveform = Waveform(data) - return waveform + return np.abs(self.pulse.amplitude) * data raise ShapeInitError def __repr__(self): @@ -519,18 +499,15 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: np.rint(num_samples * half_pulse_duration / self.pulse.duration) ) idling_samples = num_samples - 2 * half_flux_pulse_samples - waveform = Waveform( - np.concatenate( - ( - self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), - np.array([self.b_amplitude]), - np.zeros(idling_samples), - -np.array([self.b_amplitude]), - -self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), - ) + return np.concatenate( + ( + self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), + np.array([self.b_amplitude]), + np.zeros(idling_samples), + -np.array([self.b_amplitude]), + -self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), ) ) - return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -538,8 +515,7 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(np.zeros(num_samples)) - return waveform + return np.zeros(num_samples) raise ShapeInitError def __repr__(self): @@ -573,20 +549,18 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(self.pulse.duration * sampling_rate) x = np.arange(0, num_samples, 1) - waveform = Waveform( + return ( self.pulse.amplitude * (1 + np.tanh(self.alpha * x / num_samples)) * (1 + np.tanh(self.alpha * (1 - x / num_samples))) / (1 + np.tanh(self.alpha / 2)) ** 2 ) - return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(self.pulse.duration * sampling_rate) - waveform = Waveform(np.zeros(num_samples)) - return waveform + return np.zeros(num_samples) raise ShapeInitError def __repr__(self): @@ -613,8 +587,7 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: raise ValueError("Length of envelope_i must be equal to pulse duration") num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(self.envelope_i * self.pulse.amplitude) - return waveform + return self.envelope_i * self.pulse.amplitude raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -625,8 +598,7 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: raise ValueError("Length of envelope_q must be equal to pulse duration") num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(self.envelope_q * self.pulse.amplitude) - return waveform + return self.envelope_q * self.pulse.amplitude raise ShapeInitError def __repr__(self): diff --git a/src/qibolab/pulses/waveform.py b/src/qibolab/pulses/waveform.py deleted file mode 100644 index 7c530bf36..000000000 --- a/src/qibolab/pulses/waveform.py +++ /dev/null @@ -1,42 +0,0 @@ -"""Waveform class.""" -import numpy as np - - -class Waveform: - """A class to save pulse waveforms. - - A waveform is a list of samples, or discrete data points, used by the digital to analogue converters (DACs) - to synthesise pulses. - - Attributes: - data (np.ndarray): a numpy array containing the samples. - """ - - DECIMALS = 5 - - def __init__(self, data): - """Initialise the waveform with a of samples.""" - self.data: np.ndarray = np.array(data) - - def __len__(self): - """Return the length of the waveform, the number of samples.""" - return len(self.data) - - def __hash__(self): - """Hash the underlying data. - - .. todo:: - - In order to make this reliable, we should set the data as immutable. This we - could by making both the class frozen and the contained array readonly - https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags - """ - return hash(self.data.tobytes()) - - def __eq__(self, other): - """Compare two waveforms. - - Two waveforms are considered equal if their samples, rounded to - `Waveform.DECIMALS` decimal places, are all equal. - """ - return np.allclose(self.data, other.data) From f9b285c4ac8566066f828dc0b0298aa336821af4 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 19 Jan 2024 19:26:42 +0100 Subject: [PATCH 051/233] Drop data access for waveforms --- .../instruments/qblox/cluster_qcm_bb.py | 2 +- .../instruments/qblox/cluster_qcm_rf.py | 2 +- .../instruments/qblox/cluster_qrm_rf.py | 2 +- src/qibolab/instruments/qblox/sequencer.py | 6 ++--- src/qibolab/instruments/qm/config.py | 2 +- src/qibolab/instruments/qm/sequence.py | 12 +++------ src/qibolab/pulses/plot.py | 26 +++++++++---------- src/qibolab/pulses/shape.py | 8 +++--- 8 files changed, 27 insertions(+), 33 deletions(-) diff --git a/src/qibolab/instruments/qblox/cluster_qcm_bb.py b/src/qibolab/instruments/qblox/cluster_qcm_bb.py index 1bae9d543..4b91831a1 100644 --- a/src/qibolab/instruments/qblox/cluster_qcm_bb.py +++ b/src/qibolab/instruments/qblox/cluster_qcm_bb.py @@ -533,7 +533,7 @@ def process_pulse_sequence( sequencer.waveforms_buffer.unique_waveforms ): sequencer.waveforms[waveform.serial] = { - "data": waveform.data.tolist(), + "data": waveform.tolist(), "index": index, } diff --git a/src/qibolab/instruments/qblox/cluster_qcm_rf.py b/src/qibolab/instruments/qblox/cluster_qcm_rf.py index f79d5d9d9..b7abcb73a 100644 --- a/src/qibolab/instruments/qblox/cluster_qcm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qcm_rf.py @@ -527,7 +527,7 @@ def process_pulse_sequence( sequencer.waveforms_buffer.unique_waveforms ): sequencer.waveforms[waveform.serial] = { - "data": waveform.data.tolist(), + "data": waveform.tolist(), "index": index, } diff --git a/src/qibolab/instruments/qblox/cluster_qrm_rf.py b/src/qibolab/instruments/qblox/cluster_qrm_rf.py index 5d6ded76e..756152602 100644 --- a/src/qibolab/instruments/qblox/cluster_qrm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qrm_rf.py @@ -609,7 +609,7 @@ def process_pulse_sequence( sequencer.waveforms_buffer.unique_waveforms ): sequencer.waveforms[waveform.serial] = { - "data": waveform.data.tolist(), + "data": waveform.tolist(), "index": index, } diff --git a/src/qibolab/instruments/qblox/sequencer.py b/src/qibolab/instruments/qblox/sequencer.py index db0c06893..4324f8c85 100644 --- a/src/qibolab/instruments/qblox/sequencer.py +++ b/src/qibolab/instruments/qblox/sequencer.py @@ -143,7 +143,7 @@ def bake_pulse_waveforms( padded_duration = int(np.ceil(duration / 4)) * 4 memory_needed = padded_duration padding = np.zeros(padded_duration - duration) - waveform.data = np.append(waveform.data, padding) + waveform = np.append(waveform, padding) if self.available_memory >= memory_needed: self.unique_waveforms.append(waveform) @@ -168,8 +168,8 @@ def bake_pulse_waveforms( padded_duration = int(np.ceil(duration / 4)) * 4 memory_needed = padded_duration * 2 padding = np.zeros(padded_duration - duration) - waveform_i.data = np.append(waveform_i.data, padding) - waveform_q.data = np.append(waveform_q.data, padding) + waveform_i = np.append(waveform_i, padding) + waveform_q = np.append(waveform_q, padding) if self.available_memory >= memory_needed: self.unique_waveforms.append(waveform_i) diff --git a/src/qibolab/instruments/qm/config.py b/src/qibolab/instruments/qm/config.py index 04beb8bc6..65290c0e0 100644 --- a/src/qibolab/instruments/qm/config.py +++ b/src/qibolab/instruments/qm/config.py @@ -350,7 +350,7 @@ def register_waveform(self, pulse, mode="i"): if serial not in self.waveforms: self.waveforms[serial] = { "type": "arbitrary", - "samples": waveform.data.tolist(), + "samples": waveform.tolist(), } return serial diff --git a/src/qibolab/instruments/qm/sequence.py b/src/qibolab/instruments/qm/sequence.py index fdce211cc..1e760f8ec 100644 --- a/src/qibolab/instruments/qm/sequence.py +++ b/src/qibolab/instruments/qm/sequence.py @@ -147,17 +147,11 @@ def bake(self, config: QMConfig, durations: DurationsType): for t in durations: with baking(config.__dict__, padding_method="right") as segment: if self.pulse.type is PulseType.FLUX: - waveform = self.pulse.envelope_waveform_i( - SAMPLING_RATE - ).data.tolist() + waveform = self.pulse.envelope_waveform_i(SAMPLING_RATE).tolist() waveform = self.calculate_waveform(waveform, t) else: - waveform_i = self.pulse.envelope_waveform_i( - SAMPLING_RATE - ).data.tolist() - waveform_q = self.pulse.envelope_waveform_q( - SAMPLING_RATE - ).data.tolist() + waveform_i = self.pulse.envelope_waveform_i(SAMPLING_RATE).tolist() + waveform_q = self.pulse.envelope_waveform_q(SAMPLING_RATE).tolist() waveform = [ self.calculate_waveform(waveform_i, t), self.calculate_waveform(waveform_q, t), diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index 0d380830f..0ae089c7c 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -44,31 +44,31 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): ax1 = plt.subplot(gs[0]) ax1.plot( time, - waveform_i.data, + waveform_i, label="envelope i", c="C0", linestyle="dashed", ) ax1.plot( time, - waveform_q.data, + waveform_q, label="envelope q", c="C1", linestyle="dashed", ) ax1.plot( time, - pulse_.shape.modulated_waveform_i(sampling_rate).data, + pulse_.shape.modulated_waveform_i(sampling_rate), label="modulated i", c="C0", ) ax1.plot( time, - pulse_.shape.modulated_waveform_q(sampling_rate).data, + pulse_.shape.modulated_waveform_q(sampling_rate), label="modulated q", c="C1", ) - ax1.plot(time, -waveform_i.data, c="silver", linestyle="dashed") + ax1.plot(time, -waveform_i, c="silver", linestyle="dashed") ax1.set_xlabel("Time [ns]") ax1.set_ylabel("Amplitude") @@ -78,8 +78,8 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): ax1.axis((start, finish, -1.0, 1.0)) ax1.legend() - modulated_i = pulse_.shape.modulated_waveform_i(sampling_rate).data - modulated_q = pulse_.shape.modulated_waveform_q(sampling_rate).data + modulated_i = pulse_.shape.modulated_waveform_i(sampling_rate) + modulated_q = pulse_.shape.modulated_waveform_q(sampling_rate) ax2 = plt.subplot(gs[1]) ax2.plot( modulated_i, @@ -88,8 +88,8 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): c="C3", ) ax2.plot( - waveform_i.data, - waveform_q.data, + waveform_i, + waveform_q, label="envelope", c="C2", ) @@ -158,22 +158,22 @@ def sequence(ps: PulseSequence, filename=None, sampling_rate=SAMPLING_RATE): time = pulse.start + np.arange(num_samples) / sampling_rate ax.plot( time, - pulse.shape.modulated_waveform_q(sampling_rate).data, + pulse.shape.modulated_waveform_q(sampling_rate), c="lightgrey", ) ax.plot( time, - pulse.shape.modulated_waveform_i(sampling_rate).data, + pulse.shape.modulated_waveform_i(sampling_rate), c=f"C{str(n)}", ) ax.plot( time, - pulse.shape.envelope_waveform_i(sampling_rate).data, + pulse.shape.envelope_waveform_i(sampling_rate), c=f"C{str(n)}", ) ax.plot( time, - -pulse.shape.envelope_waveform_i(sampling_rate).data, + -pulse.shape.envelope_waveform_i(sampling_rate), c=f"C{str(n)}", ) # TODO: if they overlap use different shades diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index d364a5691..e1ee39aa2 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -100,8 +100,8 @@ def modulated_waveforms(self, _if: int, sampling_rate=SAMPLING_RATE): for n, t, ii, qq in zip( np.arange(num_samples), time, - envelope_waveform_i.data, - envelope_waveform_q.data, + envelope_waveform_i, + envelope_waveform_q, ): result.append(mod_matrix[:, :, n] @ np.array([ii, qq])) mod_signals = np.array(result) @@ -430,7 +430,7 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: data = lfilter( b=self.b, a=self.a, - x=self.target.envelope_waveform_i(sampling_rate).data, + x=self.target.envelope_waveform_i(sampling_rate), ) if not np.max(np.abs(data)) == 0: data = data / np.max(np.abs(data)) @@ -449,7 +449,7 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: data = lfilter( b=self.b, a=self.a, - x=self.target.envelope_waveform_q(sampling_rate).data, + x=self.target.envelope_waveform_q(sampling_rate), ) if not np.max(np.abs(data)) == 0: data = data / np.max(np.abs(data)) From 46233bebf203d0119eebc709a410b11a855d399a Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 19 Jan 2024 19:27:37 +0100 Subject: [PATCH 052/233] Drop type checks in tests involving waveforms --- tests/test_pulses.py | 55 ++++++++++++-------------------------------- 1 file changed, 15 insertions(+), 40 deletions(-) diff --git a/tests/test_pulses.py b/tests/test_pulses.py index 3b337de40..c9a6d0cc2 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -19,7 +19,6 @@ PulseType, Rectangular, ShapeInitError, - Waveform, eCap, plot, ) @@ -237,8 +236,8 @@ def test_is_equal_ignoring_start(): ) def test_pulseshape_sampling_rate(shape): pulse = Pulse(0, 40, 0.9, 100e6, 0, shape, 0, PulseType.DRIVE) - assert len(pulse.envelope_waveform_i(sampling_rate=1).data) == 40 - assert len(pulse.envelope_waveform_i(sampling_rate=100).data) == 4000 + assert len(pulse.envelope_waveform_i(sampling_rate=1)) == 40 + assert len(pulse.envelope_waveform_i(sampling_rate=100)) == 4000 def testhape_eval(): @@ -303,7 +302,7 @@ def test_raise_shapeiniterror(): def test_pulseshape_drag_shape(): pulse = Pulse(0, 2, 1, 4e9, 0, Drag(2, 1), 0, PulseType.DRIVE) # envelope i & envelope q should cross nearly at 0 and at 2 - waveform = pulse.envelope_waveform_i(sampling_rate=10).data + waveform = pulse.envelope_waveform_i(sampling_rate=10) target_waveform = np.array( [ 0.63683161, @@ -573,15 +572,6 @@ def sortseq(sequence): assert sortseq(ps1) == sortseq(ps2) -def test_waveform(): - wf1 = Waveform(np.ones(100)) - wf2 = Waveform(np.zeros(100)) - wf3 = Waveform(np.ones(100)) - assert wf1 != wf2 - assert wf1 == wf3 - np.testing.assert_allclose(wf1.data, wf3.data) - - def modulate( i: np.ndarray, q: np.ndarray, @@ -618,10 +608,6 @@ def test_pulseshape_rectangular(): assert isinstance(pulse.shape, Rectangular) assert pulse.shape.name == "Rectangular" assert repr(pulse.shape) == "Rectangular()" - assert isinstance(pulse.shape.envelope_waveform_i(), Waveform) - assert isinstance(pulse.shape.envelope_waveform_q(), Waveform) - assert isinstance(pulse.shape.modulated_waveform_i(_if), Waveform) - assert isinstance(pulse.shape.modulated_waveform_q(_if), Waveform) sampling_rate = 1 num_samples = int(pulse.duration / sampling_rate) @@ -636,13 +622,13 @@ def test_pulseshape_rectangular(): i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate ) - np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate).data, i) - np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate).data, q) + np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) + np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) np.testing.assert_allclose( - pulse.shape.modulated_waveform_i(_if, sampling_rate).data, mod_i + pulse.shape.modulated_waveform_i(_if, sampling_rate), mod_i ) np.testing.assert_allclose( - pulse.shape.modulated_waveform_q(_if, sampling_rate).data, mod_q + pulse.shape.modulated_waveform_q(_if, sampling_rate), mod_q ) @@ -664,10 +650,6 @@ def test_pulseshape_gaussian(): assert pulse.shape.name == "Gaussian" assert pulse.shape.rel_sigma == 5 assert repr(pulse.shape) == "Gaussian(5)" - assert isinstance(pulse.shape.envelope_waveform_i(), Waveform) - assert isinstance(pulse.shape.envelope_waveform_q(), Waveform) - assert isinstance(pulse.shape.modulated_waveform_i(_if), Waveform) - assert isinstance(pulse.shape.modulated_waveform_q(_if), Waveform) sampling_rate = 1 num_samples = int(pulse.duration / sampling_rate) @@ -687,13 +669,13 @@ def test_pulseshape_gaussian(): i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate ) - np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate).data, i) - np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate).data, q) + np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) + np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) np.testing.assert_allclose( - pulse.shape.modulated_waveform_i(_if, sampling_rate).data, mod_i + pulse.shape.modulated_waveform_i(_if, sampling_rate), mod_i ) np.testing.assert_allclose( - pulse.shape.modulated_waveform_q(_if, sampling_rate).data, mod_q + pulse.shape.modulated_waveform_q(_if, sampling_rate), mod_q ) @@ -716,10 +698,6 @@ def test_pulseshape_drag(): assert pulse.shape.rel_sigma == 5 assert pulse.shape.beta == 0.2 assert repr(pulse.shape) == "Drag(5, 0.2)" - assert isinstance(pulse.shape.envelope_waveform_i(), Waveform) - assert isinstance(pulse.shape.envelope_waveform_q(), Waveform) - assert isinstance(pulse.shape.modulated_waveform_i(_if), Waveform) - assert isinstance(pulse.shape.modulated_waveform_q(_if), Waveform) sampling_rate = 1 num_samples = int(pulse.duration / 1 * sampling_rate) @@ -744,13 +722,13 @@ def test_pulseshape_drag(): i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate ) - np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate).data, i) - np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate).data, q) + np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) + np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) np.testing.assert_allclose( - pulse.shape.modulated_waveform_i(_if, sampling_rate).data, mod_i + pulse.shape.modulated_waveform_i(_if, sampling_rate), mod_i ) np.testing.assert_allclose( - pulse.shape.modulated_waveform_q(_if, sampling_rate).data, mod_q + pulse.shape.modulated_waveform_q(_if, sampling_rate), mod_q ) @@ -918,9 +896,6 @@ def test_envelope_waveform_i_q(): custom_shape_pulse.pulse = pulse custom_shape_pulse_old_behaviour.pulse = pulse - assert isinstance(custom_shape_pulse.envelope_waveform_i(), Waveform) - assert isinstance(custom_shape_pulse.envelope_waveform_q(), Waveform) - assert isinstance(custom_shape_pulse_old_behaviour.envelope_waveform_q(), Waveform) pulse.duration = 2000 with pytest.raises(ValueError): custom_shape_pulse.pulse = pulse From 7fb39a0c1780038310b6442dca867e008cda7b80 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 19 Jan 2024 19:31:33 +0100 Subject: [PATCH 053/233] Replace waveform hash, since NumPy array are unhashable (because mutable) --- src/qibolab/instruments/qm/config.py | 2 +- tests/test_instruments_qm.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/qibolab/instruments/qm/config.py b/src/qibolab/instruments/qm/config.py index 65290c0e0..cc934fb20 100644 --- a/src/qibolab/instruments/qm/config.py +++ b/src/qibolab/instruments/qm/config.py @@ -346,7 +346,7 @@ def register_waveform(self, pulse, mode="i"): self.waveforms[serial] = {"type": "constant", "sample": pulse.amplitude} else: waveform = getattr(pulse, f"envelope_waveform_{mode}")(SAMPLING_RATE) - serial = hash(waveform) + serial = hash(waveform.tobytes()) if serial not in self.waveforms: self.waveforms[serial] = { "type": "arbitrary", diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 3bdf9d882..2da93d2fa 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -308,8 +308,8 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): "length": pulse.duration, "digital_marker": "ON", "waveforms": { - "I": hash(pulse.envelope_waveform_i()), - "Q": hash(pulse.envelope_waveform_q()), + "I": hash(pulse.envelope_waveform_i().tobytes()), + "Q": hash(pulse.envelope_waveform_q().tobytes()), }, } From 19d0b9e21a0630b5632ddc210e8dc1dd6c90e482 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 29 Jan 2024 10:28:14 +0100 Subject: [PATCH 054/233] feat(nix): Export convenience env var --- flake.nix | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/flake.nix b/flake.nix index a91b8b432..e27ab8348 100644 --- a/flake.nix +++ b/flake.nix @@ -42,10 +42,17 @@ inherit inputs pkgs; modules = [ - ({lib, ...}: { - packages = with pkgs; [pre-commit poethepoet jupyter]; + ({ + lib, + pkgs, + config, + ... + }: { + packages = with pkgs; [pre-commit poethepoet jupyter stdenv.cc.cc.lib zlib]; - env.QIBOLAB_PLATFORMS = platforms; + env = { + QIBOLAB_PLATFORMS = (dirOf config.env.DEVENV_ROOT) + "/qibolab_platforms_qrc"; + }; languages.c = { enable = true; From bdcb44e6c334d81029db1611c1acca1e3c012938 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 29 Jan 2024 12:06:34 +0100 Subject: [PATCH 055/233] fix: serial to id migration for fixed rfsoc tests --- tests/test_instruments_rfsoc.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_instruments_rfsoc.py b/tests/test_instruments_rfsoc.py index f70a2f67c..2399ba489 100644 --- a/tests/test_instruments_rfsoc.py +++ b/tests/test_instruments_rfsoc.py @@ -126,7 +126,7 @@ def test_convert_pulse(dummy_qrc): duration=0.04, adc=None, dac=4, - name=pulse.serial, + name=pulse.id, relative_phase=0, rel_sigma=5, beta=2, @@ -150,7 +150,7 @@ def test_convert_pulse(dummy_qrc): start_delay=0, relative_phase=0, duration=0.04, - name=pulse.serial, + name=pulse.id, type="drive", dac=4, adc=None, @@ -175,7 +175,7 @@ def test_convert_pulse(dummy_qrc): start_delay=0, relative_phase=0, duration=0.04, - name=pulse.serial, + name=pulse.id, type="readout", dac=2, adc=1, From d04069fe9b9510c4e6322635a06bf204f52cb2d8 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 29 Jan 2024 13:37:40 +0100 Subject: [PATCH 056/233] chore: Drop pulses tutorial Since pulses are greatly changed, it is much better to rewrite it from scratch, possibly using ideas from the old one, but much of the implementation is going to be reworked anyhow --- examples/pulses_tutorial.ipynb | 1201 -------------------------------- 1 file changed, 1201 deletions(-) delete mode 100644 examples/pulses_tutorial.ipynb diff --git a/examples/pulses_tutorial.ipynb b/examples/pulses_tutorial.ipynb deleted file mode 100644 index a80241221..000000000 --- a/examples/pulses_tutorial.ipynb +++ /dev/null @@ -1,1201 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pulses Tutorial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pulse" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Overview" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `Pulse` object represents the radio-frequency pulses that are used to control qubits. \n", - "The new version of `Pulse` object includes the following changes:\n", - "- It includes a new attribute `finish` that returns the point in time when the pulse finishes (start + duration).\n", - "- The `phase` attribute was replaced with `relative_phase`, since taking care of the global sequence phase is now done by the `PulseSequence`.\n", - "- The attributes `offset_i` and `offset_q` included in the previous version were removed, as those are parameters of the instrument.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from qibolab.pulses import Pulse, ReadoutPulse, DrivePulse, FluxPulse\n", - "from qibolab.pulses import PulseShape, Rectangular, Gaussian, Drag, IIR, SNZ, eCap, Waveform\n", - "\n", - "p0 = DrivePulse(start=0, \n", - " duration=40, \n", - " amplitude=1, \n", - " frequency=200e6, \n", - " relative_phase=0, \n", - " shape=Gaussian(5), \n", - " channel=10, \n", - " qubit=0)\n", - "\n", - "p1 = ReadoutPulse(start=p0.duration,\n", - " duration=400,\n", - " amplitude=1, \n", - " frequency=20e6, \n", - " relative_phase=0,\n", - " shape=Rectangular(),\n", - " channel=20, \n", - " qubit=0)\n", - "ps = p0 + p1\n", - "ps.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If one of those variables change, the pulses that use them change automatically:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "p0.duration = 80\n", - "ps.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "or" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialisation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The main changes in the initialization of Pulse are those related to the changes in the attributes: \n", - "```python\n", - "def __init__(self, start: int, duration: int, amplitude: float, \n", - " frequency: int, relative_phase: float, shape: PulseShape | str,\n", - " channel: int | str, type: PulseType | str = PulseType.DRIVE, qubit: int | str = 0):\n", - "``` \n", - "The argument `phase` was replaced with `relative_phase`, the `shape` argument continues to support `PulseShape` objects or strings. `channel`and `qubit` support both integers or strings, and finally, the `type` argument supports a string or a constant from PulseType enumeration:\n", - "```python\n", - "class PulseType(Enum):\n", - " READOUT = \"ro\"\n", - " DRIVE = \"qd\"\n", - " FLUX = \"qf\"\n", - "```\n", - "\n", - "Pulse `type` and `qubit` are optional arguments, and default to `PulseType.DRIVE` and `0` respectively.\n", - "\n", - "Below are some examples of Pulse initialization:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from qibolab.pulses import Pulse, ReadoutPulse, DrivePulse, FluxPulse\n", - "from qibolab.pulses import PulseShape, Rectangular, Gaussian, Drag\n", - "from qibolab.pulses import PulseType, PulseSequence, SplitPulse\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# standard initialisation\n", - "p0 = Pulse(start = 0, \n", - " duration = 50, \n", - " amplitude = 0.9, \n", - " frequency = 20_000_000, \n", - " relative_phase = 0.0, \n", - " shape = Rectangular(), \n", - " channel = 0, \n", - " type = PulseType.READOUT, \n", - " qubit = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# initialisation with str shape\n", - "p4 = Pulse(start = 0, \n", - " duration = 50, \n", - " amplitude = 0.9, \n", - " frequency = 20_000_000, \n", - " relative_phase = 0, \n", - " shape = 'Rectangular()', \n", - " channel = 0, \n", - " type = PulseType.READOUT, \n", - " qubit = 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# initialisation with str channel and str qubit\n", - "p5 = Pulse(start = 0, \n", - " duration = 50, \n", - " amplitude = 0.9, \n", - " frequency = 20_000_000, \n", - " relative_phase = 0, \n", - " shape = 'Rectangular()', \n", - " channel = 'channel0', \n", - " type = PulseType.READOUT, \n", - " qubit = 0)\n", - "assert p5.qubit == 0" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# examples of initialisation with different frequencies, shapes and types\n", - "p6 = Pulse(0, 40, 0.9, -50e6, 0, Rectangular(), 0, PulseType.READOUT)\n", - "p7 = Pulse(0, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0)\n", - "p8 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2)\n", - "p9 = Pulse(0, 40, 0.9, 50e6, 0, Drag(5,2), 0, PulseType.DRIVE, 200)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "p6.plot()\n", - "p7.plot()\n", - "p8.plot()\n", - "p9.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Attributes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pulse implements these attributes:\n", - "- `start`\n", - "- `duration`\n", - "- `finish` (read only)\n", - "- `amplitude`\n", - "- `frequency`\n", - "- `relative_phase`\n", - "- `phase` (read only) returns the total phase of the pulse (global, based on its start + relative)\n", - "- `shape` a `PulseShape` object\n", - "- `channel`\n", - "- `type`\n", - "- `qubit`\n", - "- `serial` a str representation of the object\n", - "- `envelope_waveform_i` a Waveform object\n", - "- `envelope_waveform_q` a Waveform object\n", - "- `envelope_waveforms` a tuple of (Waveform, Waveform)\n", - "- `modulated_waveform_i` a Waveform object\n", - "- `modulated_waveform_q` a Waveform object\n", - "- `modulated_waveforms` a tuple of (Waveform, Waveform)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Methods" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Operators" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pulse now supports a small set of operators (`==`, `!=`, `+`, `*`).\n", - "Pulse is hashable, but not unmutable (its hash depends on the current value of its parameters), so one can use the following operators to compare pulses:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "p0 = Pulse(0, 40, 1, 100e6, 0, Rectangular(), 0, PulseType.DRIVE, 0)\n", - "p1 = Pulse(100, 40, 1, 100e6, 0, Rectangular(), 0, PulseType.DRIVE, 0)\n", - "p2 = Pulse(0, 40, 1, 100e6, 0, Rectangular(), 0, PulseType.DRIVE, 0)\n", - "\n", - "assert p0 != p1\n", - "assert p0 == p2\n", - "\n", - "# If we change p1 start to 0 it become the same as p0\n", - "p1.start = 0\n", - "assert p0 == p1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since it is hashable, it can also be used as keys of a dictionary:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "rp = Pulse(0, 40, 0.9, 100e6, 0, Rectangular(), 0, PulseType.DRIVE)\n", - "dp = Pulse(0, 40, 0.9, 100e6, 0, Drag(5,1), 0, PulseType.DRIVE)\n", - "hash(rp)\n", - "my_dict = {rp: 1, dp: 2}\n", - "assert list(my_dict.keys())[0] == rp\n", - "assert list(my_dict.keys())[1] == dp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Adding two Pulses returns a PulseSequence. Multiplying a Pulse by an integer n returns a PulseSequence with n deep copies of the original pulse." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pA = Pulse(0, 40, 1, 100e6, 0, Rectangular(), 'A', PulseType.DRIVE, 0)\n", - "pB = Pulse(0, 40, 1, 100e6, 0, Rectangular(), 'B', PulseType.DRIVE, 0)\n", - "ps = pA + pB\n", - "assert type(ps) == PulseSequence\n", - "ps.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we have already seen, Pulse also implements a `plot()` method that represents the pulse waveforms:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "drag_pulse = Pulse(0, 40, 0.9, 50e6, 0, Drag(5,2), 0, PulseType.DRIVE, 200)\n", - "drag_pulse.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### ReadoutPulse, DrivePulse & FluxPulse Aliases" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These objects are subclasses of the Pulse object. They have a different representation, and in their instantiation one does not require to specify the type." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "rop = ReadoutPulse(start = 0,\n", - " duration = 50, \n", - " amplitude = 0.9, \n", - " frequency = 20_000_000, \n", - " relative_phase = 0.0, \n", - " shape = Rectangular(), \n", - " channel = 0, \n", - " qubit = 0)\n", - "assert isinstance(rop, Pulse)\n", - "\n", - "dp = DrivePulse(start = 0,\n", - " duration = 2000, \n", - " amplitude = 0.9, \n", - " frequency = 200_000_000, \n", - " relative_phase = 0.0, \n", - " shape = Gaussian(5), \n", - " channel = 0, \n", - " qubit = 0)\n", - "assert isinstance(rop, Pulse)\n", - "\n", - "fp = FluxPulse(start = 0,\n", - " duration = 300, \n", - " amplitude = 0.9, \n", - " shape = Rectangular(), \n", - " channel = 0, \n", - " qubit = 0)\n", - "\n", - "assert isinstance(rop, Pulse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### SplitPulse" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sometimes the length of the pulse is so long that it doesn't fit in the memory of one sequencer. In that case it needs to be played by two (or more) sequencers.\n", - "The `SplitPulse` class was introduced to support splitting a long puse into smaller portions:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dp = Pulse(start = 0,\n", - " duration = 30000, \n", - " amplitude = 0.9, \n", - " frequency = 500_000, \n", - " relative_phase = 0.0, \n", - " shape = Gaussian(5), \n", - " channel = 0, \n", - " type = PulseType.READOUT,\n", - " qubit = 0)\n", - "\n", - "sp = SplitPulse(dp)\n", - "sp.channel = 1\n", - "a = 8000\n", - "b = 16000\n", - "sp.window_start = sp.start + a\n", - "sp.window_finish = sp.start + b\n", - "assert sp.window_start == sp.start + a\n", - "assert sp.window_finish == sp.start + b\n", - "ps = PulseSequence(dp, sp)\n", - "ps.plot()\n", - "assert len(sp.envelope_waveform_i()) == b - a\n", - "assert len(sp.envelope_waveform_q()) == b - a\n", - "assert len(sp.modulated_waveform_i()) == b - a\n", - "assert len(sp.modulated_waveform_q()) == b - a" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PulseShape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Overview" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`PulseShape` objects are used to represent the different shapes a pulse can take. These objects are responsible for generating the waveforms based on the parameters of the pulse and the sampling rate set in the `PulseShape` class attribute `SAMPLING_RATE`.\n", - "All `PulseShape` objects support the generation of waveforms with an arbitrary sampling rate. This will be useful for whenever we use instruments that use a sampling rate different than 1e9 Hz.\n", - "The types of pulse shapes currently supported are `Rectangular()`, `Gaussian`, `Drag`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Sampling Rate" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "p14 = Pulse(0, 40, 0.9, 100e6, 0, Drag(5,1), 0, PulseType.DRIVE)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "p14.plot(sampling_rate=100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Drag Shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This version of the driver includes a fix to the formula that generates the DRAG pulse." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dp = Pulse(0, 2, 1, 4e9, 0, Drag(2,1), 0, PulseType.DRIVE)\n", - "dp.plot(sampling_rate=100)\n", - "# envelope i & envelope q should cross nearly at 0 and at 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Sudden variant Net Zero" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dp = FluxPulse(0, 40, 0.9, SNZ(17, b_amplitude=0.8), 0, 200)\n", - "dp.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Infinite Impulse Response (Filter) Shape" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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NJsfp27dvaHnr1q0aMWJExH6pqam6/vrr9de//lVffPGFbrrpJj3wwANRzwkUFhbqrbfe0lVXXRW2r7PPPluvvvqq3nzzTb300ks6//zzw7arqqrStdde22TMXQlJTADQAqZp6utd5Xrhq116e/WeZpNthmcl6uKpAxQ0TVlFYkwkqfEO3Xn2d+Umg0FTxVUe7S6tqUtsKq3VrtIa7Sqt1q7SGhVWhGcyD+gVOVnK7Qs0SmCSpEDQVF5ZrfLKarVkW+PtnDaL+qfF6afHD+l2CWYAAAAADl5NTY0KCgpUWFgY9cRrZWWlampqIlYqSkpK6ugQuwy73a7k5GQlJyc3WhcMBkNJTQ0TnNxud9QkptraxlOVR1M/TV1FRUWobfz48RGTmILBoIqKikLJTk4nN78AAAAA6F5++tOf6vnnn9eSJUv01FNPaefOnbr22muVm5urvXv36sknn9Srr74qSRoyZIh+//vfNxrj9ttv10svvaS8vDzdeuutWrlypS699FJlZmZq06ZNeuCBB7Rs2TJNnjy5ySpL11xzjR566CH5/X6deeaZuv3223XsscfK6/Vq8eLFeuCBB+Tz+TRs2DBt3ry51e914sSJGjt2rNauXatHHnlEZWVlmj17tvr27au8vDw999xzeuWVV3TMMcdo8eLFTY7jcrnkdrv1+9//Xna7XQMHDgwlJ/Xr1y80Ffof//hHLVq0SF999ZX++c9/6pNPPtHVV1+tCRMmKCEhQWVlZVq3bp0+/PBDvfvuuxo3blxYEpMk/e1vf9MHH3ygyspKXXzxxVq0aJFmzZql5ORkrV69Wvfcc482bdqkww47rFtMKUcSEwA0odrj1xtLduj5r3ZpY0Flk30dNovOGNdXFx8xQJMHpnFis5UsFkNZyS5lJbs0eVDjqeJqvQHtLqvRzn012rmvWiOyIl8A2F1a06b9e/xBbS2uViBoRu3zo4e/ULzDqj7JLvVNjVN2St1z3xSX+qa4lOSyt2nfAAAAADon0zRVWFioPXv2qLKy6c+EkkLVhZqabg1Ns1gsoSn2WioQCMhms4VNDdAa0ZKjPB6Pvv3229BrwzDkdDpDCU2RHjabjfMBAAAAALoMq9Wqt99+W2eddZYWL16sjz/+WB9//HGjfqNGjdK7774bcbq5lJQUvffeezrppJNUUFCgF198US+++GJYnzlz5uj444/X5ZdfHjWWMWPG6K9//at++ctfqqysTDfddFPY+vT0dL3++uv6/e9/36YkJsMw9Oyzz+qEE05QWVmZXnrpJb300kthfcaNG6eXX345apVlqe4mpZ///Of661//qq+//lqnnHJK2PqFCxdq+vTpkuoqVX3wwQeaM2eOXn31Va1atUrXX3991LEj3ewzaNAgvfnmmzrrrLNUWVmphx56SA899FBYn//7v/9TTU0NSUwA0N0VVHj0+zfWNdlncEaCLj5igH50eH+lJTgOUWQ9T5zDquFZSRoeJXmpXrzTpp8cP1i79tUlPO0qrVGVp+UnsqNNh+f2BbRiZ1mT2yY5beqb6lKflAMJTikHEpxSXRqTnaJ0jg8AAACgy6iurta3337bbPKSYRjq1auXsrKylJ6e3q2niuusMjMzlZmZKZ/PF6re1HCKuuamqYs2ldz3KzyZphmqChWNxWIJJTQNGzaMhDYAAAAAnV56ero+/fRTPf/883rhhRf0zTffqLS0VMnJyRo3bpxmzZqlq6++Wg5H9OtcY8aM0bp163Tvvffqtdde065du5SUlKRx48bp6quv1kUXXaS5c+c2G8tNN92k0aNH6+9//7uWLl2qmpoaZWdn6/TTT9ctt9yiAQMGHNR7nTBhglauXKm7775b7777rvbs2aOkpCQNHTpU559/vq677rqoN7o0dM8992jYsGF65plntG7dOu3fv1+BQCBi36SkJP3vf//T559/rqefflqfffaZ9uzZo9raWiUnJ2vIkCGaOnWqzjjjjEYJUfWmT5+udevW6e6779Y777yjvXv3Ki0tTZMnT9YNN9ygk046qdE0fl0VSUwA0IQhvRM0dVC6lu4oDWu3Ww2dOqaPLjlioI4cnM5dlp1Iv9Q43faDUaHXpmmqvMan3WV1U9TVPddod1mt8kprlFdWK2/gu+kB+6fFRRy3YH/0k9T1Kj1+VRZWaVNhVaN1D140UWcd1jhr2zRN/f3DzeqV4FBGolO9k+oeGYkOJTq5gxcAAAA41ILBoHbu3Kndu3fLNKNXak1MTFSfPn2UmZkpu52qrJ2B3W6X3W6POHVfIBAIJTV5PJ5QklMwGIyaeNZUslI09dPh1dbWRv08V1VVpbVr18rhcMhut4c91z/qX1PZCQAAAOiZ7rjjDt1xxx3N9ps7d26LkoM++eSTJtdbLBbNnj1bs2fPblmAEaSnp+vee+/VvffeG3H9nDlzNGfOnGbHOfXUU3XqqadGXd/Uexk0aFCTn+UlacCAAXr44Yeb7NPcGIZh6Kqrrmo09VtTjj32WB177LEt7v99OTk5jSow1QsEArr55pt18803KzMzs8376AxIYgKAZlx8xIBQEtOA9HhdNHWAzpvcXxmJke/UROdiGIbSEhxKS3BofP/URuuDQVNFlZ5QclN2auQkpj37ayO2t1TvKMdLlcevBz+KXPLSZbeod5JTaXE2VVYNkMvwy/3xdmWlJSo1zq7UeLsOH5CmzOTmM8IBAAAAtIzP51N+fn7EE5Z2u11ZWVnKysqKWEIfnZfVam31NHUWi0Xx8fGhZKfWilbhyePxhB7NMQwjLMHJbrcrNzc34timaSoYDMpqtbY6VgAAAAAAOgOSmAD0eLXegL7cvk8zRkTOSj1tbB+du6mfzpnYT8cOzZDFwh2Q3YnFYqhPikt9UlyaMig9ar+hmYn6xwUTtGd/rQr2u7Wn3K29+2u1d79bpdXRpyWo1zspconNkqro27p9wbrqUZKkujlwN3yZF9bn0dmTdMqYPo229QeC+vETXyk1zqHUeLtS4x1KibMr0WlVosumBIdNiU5b3bLzwLLTpniHlbt8AQAA0KM5nU7l5uZqy5Ytoba4uDgNHjyY6eJ6mD59+qhPnz4yTTM0VZ3b7Q5LQqp/fH+6OrvdHvVY8fl8LY7BNE15vd6w8XNzcyP23b9/v1atWiWLxSKbzSabzSa73d5ouf7ZarWGHjabTU6nk+MbAAAAABBTJDEB6NFW7CzVzS+v1q7SGr1+7TEa1z+lUR+X3aoHLphw6INDp5KZ5NI5E/tFXOf2BeoSm/bXam+5WwUVbu0pr0twqnvURq3cVVzZ/J23TUmNj5wcVeH268ttpRHXNcUwpESHTfeff5hOjZIcdfe7G+WyW+SyWRXnsMppt8pls8hltyrObpXLbq1bH/ZsDa23RkgE9AeC+mZ3uXz+oDyBoHz+oLyBoLz+oHwHnj3+oOxWiw4fkKax/ZJJtgIAAECHyc7OVlFRkSorK5WTk6MBAwZQ3aYHMwwjVAkpOTk5Yp9gMCiv1xtKamqqctP3E55aK9r0hX6/PyyW1u7niCOOkMvVuNJvdXW1tmzZEpb0VP+wWCyhx/df1z8SEhJIjgIAAAAAtAhJTAB6JLcvoL9/sEmPfbZNwQMzBPzypZV664a2z0OKnstlt2pQRoIGZbR8WoJ68Q6rfjC2j4orPSqu8qik0qNqb6DF26fFRz55XVbTtpPipilVevyyWyMnCNX6Anri8+1tGluS5l4+RdMjVD1z+4M67z9LWjxO/7Q4/WBsH502tq8m5qRSIQ0AAACt5vF4QhVovs8wDI0YMULBYJBp49AiFotFLpcrYhLQ92VmZio+Pl4+ny+UbPT95fqEpO+z2WxRE4KibdNS0RL1vF6vysvL2zzulClTFB8f36i9srIyVDnKMAwZhhF1OdK6QYMGKS4u8pTwAAAAAICuiSQmAD3Omrz9+uVLK7W5qCqsfXNRlf750Wb97OjsGEWGnmhsvxQ9/ONJYW01Xr9KKr0qrnKruNKr3cXlen/REnlMq3KGjlS1Tyqv9Wl/jTdqJabyNiYx1UtwRP4Xwe2LfjdxS/gCZsT2aElT0eSV1eqxz7brsc+2KyvZqWunD9VlRw86qNgAAADQM5imqYKCAm3dulWZmZkaPnx4xH6Rki6A9tCSZKf6akrfT24yzcifqaTWTVMXSbQkpkCg5TfaRBIt6SoYDCoQCLR5/P79+x9MWAAAAACATogkJgA9htcf1L8WbtG/F25RINj4pN/EAamaNam/pINL0gAOVrzDpgG9bBrQq+6iSUVFvEqXFkmSfnb2OVGnL2ioX2q8/nDmaJXV1CU7ldX4VOn2qdoTUKXHr2qPX1UHHl5/42M+0RUtiengTl5H2pckOaxtn1qgsMLT5Il8AAAAoF5NTY02bdqk/fv3S5L27t2rzMxMpaamxjYw4HtaU9mpXnZ2tjIyMkKVnOqfGy43bKtPIKpPIoqWbNRRSUwH+zmOKcYBAAAAoPshiQlAj7CxoEK//O8qrd9b0Widw2rRTScP1zXHDZbVYqiionEfoKvpk+LS5cfktqiv1x8MS2qq9viVG2VqPJfdqvMm9ZfbH5TbF2jwqHtde2DZ4wvI7Q80qrzkjXLy2zAM9UpwyDDqfibtNoscVoscNovsB56dNot27qvRrtKaRtufNrZvxHGDQVOLNhXrqCG95LJHvqsYAAAA3V8wGFReXp527NjRKHFi06ZNmjx5ctREC6CrsFqtiouLa/UUa6ZpNplQlJiYqCFDhoQlPfn9fgWDwYiPQCAQ9pokJgAAAABAS5HEBKBb8weCeuTTbfrHh5siTmM1JjtZD5w/QSP6JMUgOqBzcNgsctgcSkuIPDVdQ72TnLrvvMNaPLY/EAxLeIo2/Z0krfj9yc2OZ5qm1u+t0HtrC/Tu2gJtKarSxAGp6pMS+e7klXnlunzuMiU4rJoxMlOnj+ur6SN6Kz7KdHkAAADofioqKrRp0yZVV1dHXJ+UlNRkogXQ3RmG0WRCUEJCghISIt/ocjCSkpI0YcIEmaapYDAYSqZq6bLD0fxnWAAAAABA18IVPADd1paiKv3q5VVatbu80TqbxdD1JwzVdTOGyn4Q01gBaJrNalGi1aJEZ/v8y2EYhsZkp2hMdop+dcoIbSmq1P5af9T+760tkCRVewN6e/Vevb16r7KSnfr3xYdr8qD0dokJAAAAnZNpmtq1a5d27NgRcb3T6dTw4cOVns7/hUAs2Gw2paSkxDoMAAAAAEAnQhITgG4nEDT11OLtuu/9b+XxBxutH56VqAfOn6Cx/ThRBnR1QzOjV1EzTVPvrt3bqL2wwqNrnl2h926cpsykyBWcAAAA0LWZpqlt27YpLy8v4vp+/fopNzdXVitTDgMAAAAAAHQWlB8B0O2U1Xj1/z7e0iiByWJIP5s+RG/dcCwJTEAPUFHrV9/kOEWaFaG02qtbX1kt02w8zSQAAAC6NtM0tXXr1ogJTAkJCZo4caKGDh1KAhMAAAAAAEAnQxITgG4nI9GpP50zNqxtcEaCXvnZ0br1tJFy2jhRDfQEKfF2vfTTo/TVb0/Un88ZqzHZyWHrF35brBeW7opRdAAAAOgIpmlq8+bNys/Pb7Ru0KBBOvzww5WcnBxhSwAAAAAAAMQaSUwAuqUzx/fVGeP6SpKuOCZX838+TYcPSItxVABiITPJpR8fOVDPXnmEMhKdYev+/PYGbS+pjlFkAAAAaE+maWrTpk3au7fxlMIjRozQwIEDZbFwKgwAAAAAAKCz4swNgG7JMAz96ZyxmnfNkbr9zNGKc1B9Cejp0hMcum/W+LC2Wl9AN/13pfyBYJStAAAA0FXU1NSoqKioUfuoUaPUp0+fGEQEAAAAAACA1iCJCUC3lZ7g0JGDe8U6DACdyIyRmbrkiAFhbSt3l+uhT7bGKCIAAAC0l4SEBI0ZM0aGYUiqu7ll9OjRyszMjHFkAAAAAAAAaAmSmAB0Wb5AULe8skpr8/fHOhQAXcjvzhil3IyEsLZ/frRZq3aXxyYgAAAAtJv09HSNGTNGVqtVo0ePVu/evWMdEgAAAAAAAFqIJCYAXZI/ENQv5n2jl5bn6ZLHv9KaPBKZALRMvMOmB84/TFaLEWoLBE3d9N+VqvUGYhgZAAAA2kOvXr10xBFHKCMjI9ahAAAAAAAAoBVIYgLQ5fgDQd300iq9s6ZAkrS/1qeLH/9SK6miAqCFJg5I0/Uzhoa1bSup1twvdsQmIAAAALSKaZpNrrfb7YcoEgAAAAAAALQXkpgAdCmBoKlfv7Jab63aE9buD5jy+KigAqDlrj9hqA7rnyJJsloM/fzEYbpqWm6MowIAAEBzAoGAVq9erYKCgliHAgAAAAAAgHZEEhOALiMYNHXr/1brtW/yw9pddouemDNZRwzuFaPIAHRFdqtFD1wwQaP6JuuVnx6lX548XHYr/xoBAAB0Zn6/X2vWrFF5ebm+/fZbFRUVxTokAAAAAADQgebOnSvDMGQYhnbs2BHrcNDBbLEOAABaIhg09dvX1uiVFXlh7U6bRY9fOkVHD8mIUWQAurIhvRP1zs+PlWEYsQ4FAAAAzfD7/Vq9erUqKytDbRs2bJDFYlFGBp8JAQAAAAAAujqSmAB0eqZp6vdvrNW8ZbvD2h1Wix69dLKOHcbJagBtRwITAABA5+fz+bR69WpVVVWFtdtsNjmdzhhFBQAAAAAAgPZEEhOATs00Td3x5jo9/9WusHa71dB/Zh+u44f3jlFkAAAAAIBDwev1avXq1aqurg5rt9vtGj9+vBITE2MUGQAAAAAAANqTJdYBAEA0pmnqT29v0NNLdoa12yyGHrpkkk4YmRWjyAD0BOv3VOi3r61RMGjGOhQAAIAey+v1atWqVY0SmBwOhw477DASmAAAAAAAALoRKjEB6JRM09Q9727Uk4u3h7VbLYb+dfFEnTyaBCYAHSMQNPXop9v0wAffyhcwldsrQVcfNzjWYQEAAPQ4Xq9XK1euVG1tbVi70+nU+PHjFR8fH6PIAAAAAAAA0BGoxASg0zFNU/e9/60e+XRbWLvFkP554QSdNrZvjCID0BP86qWVuve9jfIF6iow3ff+t9pYUBHjqAAAAHoW0zS1cePGiAlMhx12GAlMAAAAAIAeY+HChbrssss0ePBgxcfHKzk5WePGjdOvf/1r7dmzJ+I2d9xxhwzDkGEYkiS326377rtPhx9+uJKSkpSUlKSpU6fqX//6l/x+f6Ptn3nmmdD2H3zwQbMx/uQnP5FhGHI6nSorK2u399EaxcXF+r//+z9NnDhRqampcrlcGjRokGbPnq3PP/+8yW0HDRokwzA0Z84cSdKyZct00UUXKScnRy6XSzk5Obr88su1cePGFsWyZcsW3XTTTRo3bpxSUlIUFxenwYMHa86cOVq+fPnBvtVujSQmAJ1O0JS2l4RPFWAxpL9fMEEzx2fHKCoAPcWFUwfowP/0kiRvIKgb562Uxx+IXVAAAAA9TF5eXqOTni6XSxMmTFBcXFyMogIAAAAA4NBxu9266KKLdMIJJ+iZZ57R9u3bVVtbq8rKSq1du1b333+/hg8frrfeeqvJcQoLC3XUUUfplltu0TfffKOqqipVVVVp2bJluuGGG3TuuecqGAyGbfPDH/4w9Pn7hRdeaHJ8n8+nV155RZJ0+umnKy0trUPeR1MWLFigoUOH6q677tLKlSu1f/9+eTwe7dy5U88995ymTZum66+/vtH7jOTJJ5/U0UcfrXnz5ikvL08ej0d5eXmaO3euJkyYoJdffrnJ7e+//36NHj1a//jHP7R27VpVVFTI7XZr+/btevrppzV16lTdfvvtbX6v3R1JTAA6HavF0IMXTdTM8XUVlwxDuv+8w3T2hH4xjgxAT3Dk4F66Zlr49HEbCyr1wIJNMYoIAACgZ6msrNT27eFTizscDk2YMEEulytGUQEAAAAAcOiYpqlZs2Zp3rx5kqQzzzxTzz77rBYvXqwlS5bon//8pwYMGKDq6mrNmjWryeo+5557rtavX6+f//zn+uCDD7RixQq98MILGjVqlCTprbfe0mOPPRa2TVJSks466yxJ0quvviq32x11/HfffVelpaWSpEsuuaTD3kc0K1eu1JlnnqmKigrZ7XbddNNNWrhwoZYuXapHHnlEubm5kqR///vfuu2225od66c//akyMzP1//7f/9NXX32lRYsW6dZbb5XT6ZTH49Ell1wSNc777rtPv/71r+Xz+TR+/Hg9/PDD+vDDD7V8+XI9//zzOuqoo2Sapv70pz/pwQcfbPV77QlssQ4AACKxWy36xwUT5LBadOSQXjr38P6xDglAD/LLU4Zr0aZibSyoDLU9+tk2zRiZqSMH94phZAAAAN2b3+/Xhg0bZJpmWPvIkSPldDpjFBUAoKcqKirS0qVLtXTpUi1btkzLli3Tvn37JEmXXXaZ5s6d2+77fPHFF/XUU09p9erVKi8vV1ZWlqZNm6brrrtORx11VLvvDwCA9hQ0gyr3lMc6jEMm1Zkqi9ExdWMef/xxzZ8/X3a7XW+++aZOO+20sPVHHnmkZs+erWnTpmndunW68cYbo06ZtmzZMi1YsEDTp08PtR1++OE69dRTNXr0aBUWFuqhhx7ST37yk7DtLrnkEv33v/9VRUWF3n77bc2aNSvi+PWVmpKTkzVz5swOex/RXHPNNfJ6vbJarXr77bd1yimnhNZNmTJF5513no499litX79e999/vy699FKNGTMm4lirVq3SwIED9eWXX6pPnz6h9uOOO06nnnqqTjnlFPl8Pl177bVaunRp2Lbr16/X7373O0nSH/7wB/3hD38ITecnSZMmTdKFF16oyy67TM8995x+97vfafbs2Y0qV/V0JDEB6LRsVov+dv5hYb/cAeBQcNqs+vsFE3T2vxbLG6grLWqa0q9eWqV3b5ymZJc9xhECAAB0T/n5+aqtrQ1ry8nJ4YQeACAmsrKyDtm+amtrNWvWLL3zzjth7bt27dLzzz+vF198Ubfffrv+8Ic/HLKYAABorXJPuY7/7/GxDuOQWXTBIqW70tt9XNM0de+990qSfv7znzdK/KmXlpam++67T6effroWL16szZs3a9iwYY363XDDDWEJTPXS09N1+eWX65577tGaNWu0f/9+paSkhNafdtpp6tWrl/bt26fnn38+YhJTVVWV3nzzTUnSj370o7AKyu39PiKpTzaXpKuvvjosganh+I8++qiOPfZYBYNBPfTQQ/r3v/8ddcy//e1vYQlM9WbMmKGrr75aDz/8sJYtW6bly5dr8uTJYdv5fD5Nnjy5UQJTPYvFov/3//6fXn75ZVVVVemVV17R1Vdf3aL32lMwnRyATo0EJgCxMqpvsm4+dXhYW355re58c32MIgIAAOj+BgwYoIEDB4ZeJyUladCgQbELCACAAwYMGBDxolh7ueKKK0IJTDNmzNDrr7+upUuX6oknntCQIUMUDAZ1xx136NFHH+2wGAAAQOewfv16bd26VZKiVj+qd9xxx4WWlyxZErHP96d4a2jSpEmS6hKOvj+1u91u13nnnSepbsq48vLyRtu/9tproZuRvr+f9n4fkXz44Yeh5SuvvDJqv2OOOSY0fV7Dbb4vLS1NZ599dtT1V1xxRcR9S3XT8kl1yVxNXeNOTU3VuHHjJLXuvfYUJDEBiLndpTUKBM3mOwLAIXblsYN1RG74XRT/+zpP767ZG6OIAAAAujfDMDRo0CAddthhSkhI0KhRo2SxcPoKABAbt99+u9566y0VFBRo586deuSRRzpkPx9//LHmzZsnSTrzzDP1wQcf6Oyzz9aUKVN0xRVX6Msvv9SAAQMkSbfeeqvKyso6JA4AANA5LF++PLR81FFHyTCMqI/ExMRQ34KCgojjjRw5Muq+0tO/uwZSWVnZaH19YpLH49Err7zSaH39VHLZ2dmaMWNGh76PSNauXStJcjgcmjBhQpN9jzjiCEnS5s2b5fV6I/aZOHGibLboE5pNmDBBDodDkrRmzZpQ+86dO1VcXCxJuu2225p8r4ZhhL42rXmvPQVngQDE1P4an877zxJd/NiXyi+vbX4DADiErBZDfzv/MCU5w/9hve21Ndpf44tRVAAAAN1famqqJk2apLi4uFiHAgDowe68807NnDmzw6eVu//++yVJNptNDz30kKxWa9j6jIyM0FQs5eXlevzxxzs0HgAAEFtFRUVt2q6mpiZie3x8fNRtGt44FAgEGq0/5phjQhWTn3/++bB1RUVFoWpEF154YaObkNr7fURSWloqqS4Zq6nkI0mhKeJM04yaFJ6ZmdnkGDabLZT4Vb9v6dC8156i6e8iAHQg0zT129fXqKDCrYIKt077x6f68zljdfaEfrEODQBC+qfF686zx+iXL60KtZXX+PTyit26atrgGEYGAADQvTG9OACgJ6isrNRHH30kSTrppJPUv3//iP3OPfdcJScnq6KiQq+99pp+/etfH8owAQBokVRnqhZdsCjWYRwyqc7UDhm3YTLRW2+91eJp1ptLwGkLwzB08cUX6+6779ann36q/Px89etXdy33pZdekt/vlxR5yrpD+T7a6xxCW8dp+F5vv/320DR8zUlISGjT/rozkpgAxMxr3+Rr/urvpmSqdPv10vLdOuuwbE5WA+hUfjixn95Zs1cfbvguk/6Fpbt05bG5/L4CAAAAAABttmzZstB0Jscff3zUfg6HQ0ceeaQWLFigZcuWyefzyW63H6owAQBoEYthUborvfmOaFKvXr1Cy6mpqRo7dmwMo6lLULr77rsVDAb14osv6uabb5b03VRyI0eO1OGHH95ou0PxPuqrIu3bt09+v7/Jakz1U7cZhqG0tLSIfQoLC5vcn9/vD6v+VK/he7Xb7TH/nnVlTCcHICZ2l9bo9jfWhbWlxNl1/3mHkRAAoNMxDKNR1aVtxdVaur00yhYAAABojsfj0TfffKPKyspYhwIAQMysX78+tDxy5Mgm+9av9/v92rx5c4fGBQA9RU1NjSoqKlReXq59+/apuLhYhYWF2rNnj/Ly8lRQUKCcnBzl5OSooKBAeXl52rNnjwoLC1VeXh513GAwKNM0D90bQbcyceLE0PLixYtjGEmdMWPG6LDDDpP0XeLS9u3btWTJEkmRqzBJh+Z91CcLeb1erVy5ssm+S5culSQNGzZMDocjYp+VK1eGqktFsmrVqlACesNEpcGDByslJUVS5/iedWVUYgJwyAWCpn750kpVecL/ANx97jj1TYmLUVQA0LQjctM1uHeCthVXh9qW7SjVEYN7NbEVAAAAIjFNUxs3blRFRYW++eYbDR48WP369eOmFgBAj5OXlxdajjaVXL2cnJzQ8u7duzV69OhW7yOSvXsbVMuvrFRFRUWLxj1YVVVVEZeBg8Fx1TMEg0F5vV55vV75fL7QcyAQUDAYDCUQ1S/36tVLWVlZEcfauHGjamtrm9xf/dRZDX9fSlJycrKGDBkScZudO3eqtLRUhmHIYrE0eny/3eFwyG63y+FwyOFwyOVyteEr03H8fn/o69pw2iy0XsOvX7Sv5WGHHab+/fsrLy9Pjz76qK6//vpWHxPBYLDZ/USKJ1rfiy66SKtWrdI333yjtWvX6rXXXgutu+CCCyJu1x7vI9J7abivGTNmhJafeOKJsMSphpYsWRJKHj/xxBOjvs/S0lK98cYbOueccyKuf+KJJ8L23XCcH/zgB5o3b54WLFigtWvXatSoUc2/uXbSkuOqOfW/N/1+f4v/H+yIG9NIYgJwyP1n0VYt21EW1vajw/vr9HF9YxQRADTPMAxdPHWAHvpkq2ZN6q8Lp+RocO/EWIcFAADQJe3evTt017Jpmtq6dauCwaAGDBgQ28AAADjEGl74SUxs+jxDQkJCaLk1yRkNk5+a8+yzz4aqCBxKzz777CHfJ7o/jqvuZdiwYXI6nXI6na2eTnPFihXauXNnxHVjxoxRUlJSm2Lavn27FixYEHHdsGHD1KtXr1DST2uSCtxud9SKMk6nUzabLZS4dahMmDBBKSkpSkxMVFFR0SHbb3e3b9++qOuuu+463Xbbbdq2bZsuuugiPfjgg3I6nRH7VlZW6uWXX9YVV1wRaquu/u6G7Ka+Zw0ripWXl0fte9JJJ8kwDJmmqccff1zvvfeeJGnSpElNHhcH+z4khSXU7Nu3T/Hx8aHXgwYN0mGHHaZVq1bp8ccf14wZMzRt2rRG219zzTWSJIvFovPOO69RvA1/Rm+66SYNGzZMvXv3DuuzZMkSPfbYY5Kk8ePHa+DAgWHjXH311Xr55ZcVCAT0ox/9SC+88IKys7MjvtdAIKA33nhDRx55ZNQ+bdXUcdUUr9erqqoq7d+/X2+++WaLttm/f3+b9tUUkpg62M6dO/Xggw9q/vz52r17t5xOp4YMGaLzzz9f1113XdgPWGvs2LFDubm5rdpm4MCB2rFjR6P26dOna9GiRS0ag7KHOFir88r19w82hbXlpMfpjrNadtcQAMTSJUcM1OyjBspps8Y6FAAAgC6roqJC27dvD2tzOp3q25cbWwAAPY/b7Q4tR5vWpF7DC37NVQ0BgM7KMAw5HI5QQlL9ssPhUHFxcdSL74mJiVETH5pjsViirmtY4aW1mkpMamqfzfF4PFHXZWVlhRIegsGgPB6PvF6vPB5PxGWu7XZdl156qT799FO9++67euutt7RmzRr9+Mc/1sSJE5WUlKSqqipt2bJFX3zxhRYsWCCn09ko+ac9ZWdn66ijjtIXX3yhuXPnhpJXzj333Ji/j/vuu08zZ86U1+vV7Nmzdfnll+uUU05RfHy81q5dq3/961+hRMaf/vSnTU7hO3r0aG3evFmnnXaarr/+ek2cOFEej0cff/yxHnvsMfn9ftlsNv3lL39ptO2oUaN0++236w9/+IM2bdqkE044QZdccomOPfZYZWRkyOPxaPfu3VqxYoXmz5+vwsJCffzxx+2exNTVkcTUgd566y39+Mc/DssMrKmp0fLly7V8+XI9/vjjmj9/voYOHXpI4hkxYsQh2Q8QTY3XrxvnrZQ/+N0/TBZD+vv5E5Tkal3WPADEQpyD5CUAAICD4ff7tWHDhkbtI0eObPXd1AAAdAcNp1Txer1N9m14UTsuLq7F+9i9e3eT6/fu3aupU6dKkmbPnh2aNqmjVVVVhSrlzJ49u9lKVEBLcFx1HqZpyuPxqKamRjU1NaqtrZXb7Zbf74+6zYgRI6JezN+0aVNYZZnWGDlypE477bSI67Zu3arKysqIU75ZLBYFg8FQ8sPAgQNDbcFgUOPHj9epp54acdzNmze3eUrD3NxcTZ8+PeK67du3hyrnWCwWxcXFNfk3wWazyeVyKS4uTvHx8UpPT29TTPn5+QoGg7Lb7crMzGzTGKgTCARCyXq9evWS1Rr9usOrr76qm266SY888oh27NihP//5z1H7ZmVlhX1vGlZwbOp7lpqaGrbcVN/LLrtMX3zxRSiByWaz6YorrmhUsag934dUN3VjvV69ejVaf8IJJ+j111/XhRdeqIqKCj3yyCN65JFHGo197bXX6h//+EfEJMP678PkyZP1i1/8Qtddd51+97vfNerncDj01FNPRf2d8rvf/U5ZWVn65S9/qYqKCj388MN6+OGHI/Z1OBzKzs5ul5+p1hxX0VRWViopKUkpKSk66qijWrRNfn6+7r777lbvqykkMXWQb775RhdccIFqa2uVmJio2267TTNmzFBtba3mzZunxx57TJs2bdIZZ5yh5cuXt7pMYb9+/bRmzZpm+91999164YUXJNX9UmnK5MmT9dRTT7UqDqA1/vLOBm0rCf8H8/oZQzV5UNv+YQIAAAAAdB2maWrTpk1hFSekugsBDU+aAgDQkzS8NtDcxe6GF+9bk5jRv3//VsXT8ELhoZKYmBiT/aJ747g69EzT1LZt21RZWamqqqpWTaFWv32071lCQkKjJKb6BJ36qk42m00Wi0VWqzX0bLVa5XQ6o16LnTBhggzDiBpTRUWFPvroI0nSySef3OJjauzYsaFp5AKBQCjxqX654bPf7w9VT3K73U0eu639mvr9flVVVamqqkrx8fEaNGhQq7avV1hYKL/fL8Mw2pQcgcjqj9Gm1j/88MO69tpr9dhjj+mTTz7Rrl27VFVVpcTEROXm5mrSpEn6wQ9+oJkzZ4aN1TBRp7l9tDSe888/Xz//+c9DidWnnHKK+vTp06L32db3Eem9RIrxBz/4gbZs2aJ//OMfeuedd7Rt2zZ5PB5lZWVp2rRp+ulPf6pjjz222VgNw9A111yj8ePH6+9//7s+//xzlZSUqHfv3jrxxBN16623avTopmcY+slPfqJzzjlHjzzyiBYsWKBvv/1W5eXlcjqd6tevn8aNG6eTTz5ZP/rRj5SRkdFsTK3V3PcxGsMwZLFYZLPZWvy7rmFBn/ZCElMH+cUvfqHa2lrZbDYtWLAgLFPthBNO0LBhw3TLLbdo06ZN+tvf/qY77rijVePb7XaNHTu2yT6BQECffPKJpLoPHj/84Q+b7J+QkNDsmEBbfbShUM99uSus7bCcVN1w4rAYRQQAAAAAOJQKCgpUXFwc1paSkqKBAwfGKCIAAGKvYYJRXl6eJk+eHLVvw4pKOTk5HRoXAERjmqYCgYBstsaXmQ3DUGlpqWpqato0dnPTqKWkpDRKWjpYTSUwHYy2Tn0nqdkp4AzDaNM0cU0lwNZXeEpKSlJiYqKSkpIUHx/fYV8ftM64ceP04IMPtmqbO+64o0U5CNOnT2/x8ZSamtroxqTWaMv7kKQ5c+Zozpw5zfbr3bu37rrrLt11111tiC7ckUceqf/+979t3j4rK0u33367br/99oOOpachiakDLF26VJ999pkk6corr4xYautXv/qVnnrqKW3YsEH//Oc/9bvf/a7dy6Z/+OGH2rNnjyRp1qxZrSovC7Sn4kqPbnlldVhbnN2qf1wwQXZr2+cEBoDOIhg05QsG5bRxBwoAAEAk1dXV2rJlS1ibzWbTyJEjOSkOAOjRGt7Jv3Hjxib71q+32WwaNoybQwF0PNM0VVNTE6qsVP/ct29fDR06NOI2iYmJLUpislqtoYSk+uf4+Pio/ds6DVpX1NRnpMMPP1ymacrr9crtdoeqNzV89ng8EaftayqJqby8XBUVFWFVVSwWixITE5WYmCiPx8NnNwCHBElMHeD1118PLV9++eUR+1gsFl166aW67bbbVF5eroULF+qUU05p1zieeeaZ0HJzU8kBHcU0Tf3mf6u1rzp8Pvfbzxyt3IyEKFsBQNdQVOHWS8t368WluzXn6EG6+rjBsQ4JAACg0wkGg9qwYYOCwWBY+4gRI+RyuWIUFQAAncOUKVPkcDjk9Xq1aNEi/eY3v4nYz+v16ssvvwxt0943RQOAaZqqra1VRUVFKFmpqqqq0f/xklRZWRl1nKSkJBUVFYVeOxyOUGWfxMREuVwuuVyudqmi1FMZhhGqRhVN/RR1tbW1oe9lSkpKxL6maUac0jQYDIYSm1wulxwOhywWi6qrq8Om7LNYLCQ4AWg3/HXoAJ9//rmkuunZJk2aFLXf8ccfH1pevHhxuyYxVVZWhpKpBg0apOOOO67dxgZao8rjV6UnPNv75NFZunAK5Y4BdG1Pfr5df3lng/zBujKrLy7dpaum5fJhDQAA4Hu2bt2q6urqsLbs7GxlZGTEKCIAADqPpKQknXjiiXr33Xf14YcfKi8vL2yKuXqvvvpqqDrGD3/4w0MdJoBuyu/3q7y8XKWlpSorK2vxNFFVVVUyTTPiudC0tDQNHDgwlLR0MNOqoe1sNptsNpsSEhKa/exVU1MTMVktmmAwGNbfZrMxIxCAdsM8Th1gw4YNkqShQ4c2mUU8cuTIRtu0l1deeSVUqnH27NktuqC6ceNGHXHEEUpNTZXL5VL//v119tln65lnnpHP52vX+NBzJLnsevHqI3XLaSNksxjKSHTqnnPHcZEfQJc3Jjs5lMAkSdtKqvXlttIYRgQAAND5lJSUhKa6r5eQkKDBg6lgCQDoGebOnSvDMGQYhu64446IfW6++WZJdckE1113nQKBQNj6kpIS3XrrrZKk1NRUXXXVVR0aM4CeYfv27friiy+0bt067d27t8UJTFJdEkttbW3EdQkJCRo0aJB69epFAlMX4XK5NHbs2DZ/36xWawdFBqAnohJTO3O73SopKZGkiHdLNJSWlqaEhARVV1dr9+7d7RpHw6nkLr300hZtU1hYqMLCwtDr/Px85efn680339S9996rV155RaNGjWpTPHl5eU2u37t3b2i5uro6bL5VdA8/PjxTh2fHq8rtlz3oUUWFJ9YhRdWwZGak8pnAocYx2TmN7GVTbq84bd/33Yf1ZxZv1Zje3bucO8cjOhOOR3Q2HJPoTDrD8ej1erVx48awNsMwlJOT06gyE7q/znBMAvX4HYSW+vzzz7Vly5bQ6/pz/5K0ZcsWzZ07N6z/nDlz2rSfE044QRdeeKHmzZunN998UyeffLJuvPFGZWdna82aNbrrrru0a9cuSdK9996rtLS0Nu0HABpyOp0yTbP5jgf6NpwSLikpSQ6Ho4MjxKFitVrVq1cv9erVK9Tm9XpVVVUVmlqwvnhGJNGKepimqZqaGlksFtlsttDUcwDQFJKY2lnDOWATExOb7V+fxNSeJ2927dqlRYsWSZKOPvpoDR06tMn+FotFJ554ok4//XQddthh6tWrlyorK/X111/rkUce0YYNG7R+/XrNmDFDS5cu1YABA1odU05Oy6cOe/XVV6POyYruYUWsA2iFZ599NtYhAGE4JjuXvu5e2q6+odfvry9UVv6nirMEmtiq++B4RGfC8YjOhmMSnUmsjsfBgwcrMzMzrG3r1q1asmRJTOJB58HvSMTa/v37Yx0CuojHH39cTz/9dMR1ixcv1uLFi8Pa2prEJElPPvmkKioq9M4772jhwoVauHBh2HqLxaLf//73uuaaa9q8DwA9g2maqqqqUmlpqfbv369x4yLPjpGenh5xe4fDoeTk5LCkJRKWeh6Hw6H09PTQcbJ582b5fD5ZrVY5HA4Fg0EFAgEZhhE1MSkQCISmnvP7/ZIUSmiy2WyyWCzM3IJOY8eOHbEOAQeQxNTOGpZabMkf9PpyfNFKLrbFc889F8qcbkkVpldffVWpqamN2qdNm6Zrr71WV199tZ5++mkVFhbqxhtv1KuvvtpusQIA0JWNcJTrq9osBQ7M0BuURd96UzXBtS/GkQEAAMTe9u3bVVNTo379+slut2vfvn0qKiqKdVgAAHRKcXFxmj9/vl544QXNnTtXq1atUnl5ubKysjRt2jRdf/31Ouqoo2IdJoBOyuv1qqysTKWlpSorK5PP5wutq6ioiFg8wOVyKT4+XrW1tUpJSVF6enpoFhkSSxBJ/fSo9de3TdNssprX96dHleqmIvR6vfJ6vTIMQ1arlSpNAMKQxNTOXC5XaNnr9Tbb3+Opm1IrLi6u3WKov5vN6XTqggsuaLZ/pASmena7XY8//ri+/PJLffvtt3rttdeUn5+vfv36tSqm5qbL27t3r6ZOnSpJOvfcczV8+PBWjY/Oo9rjl2EYind03flvq6qqQj9Hs2fPblFVNaAjcUx2bkVvbNT8dcWh13viBus/Pzmv237Q53hEZ8LxiM6GYxKdSWc6Hv1+v4qLizVu3DiddNJJMYsDsdWZjklg06ZNuvvuu2MdBrqAuXPnNpoyrrXmzJnTqgpNF198sS6++OKD2ieA7s80TVVUVKi0tFSlpaVNzvhSWloadQaU0aNHy+l0Rp0ODGhKfVJTNPXVl6IxTVN+v79RlSar1Sqr1dptz7EDaBp/kdpZUlJSaLklU8TVz7/eXiduli5dqo0bN0qSzjrrrCYTlFrKZrPpyiuv1C233CJJWrRoUas/RPXv37/FfRMSEpScnNyq8dF53PHflVqxq0z/uGCCJg7o+nOzJyYmcjyiU+GY7HwuO3ZoWBLTztJarS/x66ghvZrYqnvgeERnwvGIzoZjEp1JZzgeo01VgZ6pMxyT6NkSEhJiHQIAAK1mmqbKy8tVWFiokpKSiFVuIiktLVVubm7EdfxNREeKi4sLJSm15Hitr9Ik1SVIURUM6JlIYmpnLpdLvXr10r59+5SXl9dk37KyslASU05OTrvs/5lnngktt2QquZYaPXp0aDk/P7/dxkX3snR7qV79pu74mPWfJfr5CcN03Ywhslkp/wig+5oyKE1DMxO1pei75OUXlu7qEUlMAAAAAAAAADqWaZrasWOHCgsLQzO8tERcXJzS09O5oQAxY7FY5HA45HA4ZJqmAoFAKKEpGAw2uy0JTEDPRGZBB6hP+NmyZUuTZfLqKyZJ0qhRow56vz6fT/PmzZMkZWZm6rTTTjvoMevxRwLNMU1T97y7IfQ6EDT1+OfbVFzV8n+oAaArMgxDF00dENb2/toC7eP3HwAAAAAAAICDZBiG9u/f32wCk8ViUa9evTRs2DBNnTpVU6dO1dChQ0liQqdgGIZsNptcLpcSEhKUkJDQ5FSGdrs96lh+v7/ZJCgAXRdJTB3g2GOPlVQ3VdyKFSui9lu0aFFo+Zhjjjno/c6fP1/79u2TVDdvdnvOX7t+/frQcnZ2druNi+7jg/WF+npXeVjbzaeMUN+UuNgEBACH0I8O7yeH7bt/q7yBoP73ddMVGQEAALobj8ejqqqq5jsCAAAAaMQ0zajr+vTpE7E9ISFBOTk5Gj9+vI455hiNHTtW2dnZiovj2gw6t/oqTXFxcUpMTFRcXJwcDocslrrz7NGuc5umKbfbrerqatXW1srn8zX5swOg6yGJqQOcc845oeWnnnoqYp9gMBia+i01NVUzZsw46P02nErusssuO+jx6vn9fj355JOh18cdd1y7jY3uwR8I6q/vfxvWNiA9vlFlEgDorlLjHTpjXN+wtheX7ubDEwAA6FF27NihFStW6Ntvv23VNBcAAABAT2WapiorK7VlyxZ98803Uc8nZmRkhJI7EhISNHjwYB155JGaPHmyBg8erLS0tNB6oKupr9LkdDqVkJCgxMTEqLMEBQKB0M+J3+8PJTS53e6wdQC6Lv6adYCpU6dq2rRpkqQnnnhCS5YsadTnb3/7mzZsqJt66xe/+EWjkniffPKJDMOQYRiaM2dOs/ssLS3V/PnzJUnjxo3ThAkTWhTrwoULVV5eHnW9z+fTVVddFYr1zDPPVE5OTovGRs/x6tf52lIUfrftr04ZHlaVBAC6u+8nbm4vqdaSbftiFA0AAMChVVVVpYKCAklSQUGBli5dqpKSkhhHBQAAAHROHo9Hu3fv1vLly/X1118rPz9flZWVUa/Z2Ww2DR8+XIcffrgmTZqknJwcOZ3OQxs0cIhES2CS6q5df59pmvL5fKqpqVFNTY08Hg/TzQFdWPvNN4Yw//znP3XMMceotrZWp5xyin77299qxowZqq2t1bx58/Too49KkoYPH65f/epXB72/efPmyev1SmpdFaann35aZ511ls466yxNnz5dI0aMUHJysqqqqrRixQo9+uijoankMjMz9c9//vOgY0X34vYF9MAHm8LaxmQn68zxTDsIoGeZMihNQzMTw5I6X1y6W0cPyYhhVAAAAIfGtm3bGrUlJyfHIBIAAACgcwoEAiopKVFhYaHKysoi9iksLFRaWlrEdVlZWR0ZHtAlNJXgJNXNhuT1euX1emW1WmW322Wz2ZrdDkDnQRJTB5k4caL++9//6sc//rEqKir029/+tlGf4cOHa/78+UpKSjro/dVPJWe1WnXJJZe0atuqqiq98MILeuGFF6L2GTdunObNm6fc3NyDihPdz9Nf7FBBhTus7dbTRspi4Z8BAD2LYRi6aOoA/entuuTfacMydOb4vs1sBQAA0PWVlpY2ugiTk5Mjh8MRo4gAAACAzsE0Te3fv1+FhYUqLi5WIBBosn9JSYkCgYCsVushihDoWlwul5xOp3w+n/x+f5M/U4FAILTeZrOFEpoAdG78lHagM888U6tXr9Y///lPzZ8/X3l5eXI4HBo6dKjOO+88XX/99YqPjz/o/WzevFlfffWVJOnkk09Wnz59WrztrbfeqgkTJmjJkiVav369iouLVVpaKqfTqaysLE2ePFmzZs3SD3/4Q/5hQiP7a3z698ItYW3HDO2lacOoOgKgZ/rR4f20r8qjC6cM0IBeB/83HgAAoLMzTbNRFSaHw8FU9AAAAOjRfD6f9uzZo4KCArnd7mb711+Xy8rK4noc0AzDMORwOORwOBQMBuXz+eTz+WSaZtRt/H6/JJHEBHQB/JR2sIEDB+qBBx7QAw880Krtpk+f3uQv2oaGDRvW4r7fN2rUKI0aNUo33nhjm7ZHz/bwoq2qcPvD2m49bSQlGQH0WKnxDt1y2shYhwEAAHDIFBYWqrq6Oqxt0KBBXHgBAABAj7V7927t2LFDwWCwyX5Wq1W9e/dWVlaWUlJSuLYCtIHFYpHT6ZTD4VAgEJDf75fP54vYlwQmoGvgJxVAm+zdX6unFm8PaztjfF+N758am4AAAAAAAIdUIBDQjh07wtri4+NbVSEaAAAA6G6sVmuTCUxpaWnKyspSRkYGyf9AOzEMQzabTTabTU6nM5TMVD+dXP36SEzTVDAY5OcR6CRIYgLQJv/8cLM8/u/+CbdZDN18yogYRgQAAAAAOJTy8/Pl8XjC2gYPHswd5AAAAOjRsrKytGPHjrBqMPHx8aHp4pxOZwyjA7o/wzBkt9tlt9sVDAZDU8lF+6waCARUW1srq9Uqh8Mhq9XK51oghkhiAtBqW4oq9dLy3WFtF07NUW5GQowiAgAAAAAcSl6vV7t27QprS01NVXp6eowiAgAAAA4N0zRVUlKihIQExcfHN1pvtVqVnZ2tnTt3qnfv3urfv7+SkpJIigBiwGKxyOFwNNnH6/VK+i6ZqX4bm83Gzy0QA5ZYBwCg69lSVKUEx3c5kHF2q35+4rAYRgQAnVcgaOqjDYV6b21BrEMBAABoNzt37gyV5a9HFSYAAAB0Z4FAQHv27NGyZcu0fv36Rkn9DfXr109Tp07V6NGjlZyczP/JQCcVCAQafbYNBoNyu92qrq6W1+uVaZoxiq5tBg0aJMMwNGfOnFiH0sgdd9whwzD4nYgmUYkJQKudNravpub20r8XbtGzS3bqqmm5ykxyxTosAOhUiirdevGr3frvsl3as9+tQb3idcroLFks/HMOAAC6tpqaGu3duzesLSsrS0lJSTGKCAAAAOg4Pp9Pe/bsUX5+ftgUcUVFRRo0aJBcrsbXR+qnsgLQuZmmKcMwIiYqmaYpj8cjr9cb+pm2WKgRA3Q0kpgAtEl6gkO/nzlac44epNR4/hEHgO/bta9Gf/9wU+j1jn01+nLbPh09NCOGUQEAABy87du3h53gNQxDgwYNil1AAAAAQAdwu93Ky8vT3r17FQwGG603TVP5+fkaMmRIDKIDECgokHfFCgUKCmR6vTIcDln79JFj0iRZ+/Rp0Rg2m00JCQny+Xzy+XxRf9a9Xm8omcnhcJDMBHQgkpgAHJSc9MbzPQMApEkD0zQsM1Gbi6pCbc8v3UUSEwAA6NL279+vkpKSsLb+/ftHvPscAAAA6Iqqqqq0e/duFRUVNdkvMTFRKSkphygqAPX8+flyv/++Art3N1oXyMuTd/lyWXNy5Dr1VNn69Wt2PMMw5HA4ZLfb5ff75fV6IyYzSQolO9lsNjkcDlmt1oN+PwDCkSIIAADQAQzD0MVHDAhrW7CuQCVVnhhFBAAAcPDi4uKUnZ0dem2z2TRgwIAmtgAAAAA6P9M0VVZWptWrV2vFihVNJjClpaVp/PjxOvzww5WRwQ2LwKHk27RJ1XPnRkxgaiiwe7eq586Vb9OmJvs1ZBiG7Ha74uPjFRcX12SCkt/vV21tbcRp6NrTnj179Jvf/EaHH364UlJSZLfblZWVpXHjxumiiy7S3LlzVVFRIUmaPn26DMPQzp07JUlPP/20DMMIe0yfPj1s/LKyMj311FP68Y9/rNGjRysxMVEOh0N9+vTRqaeeqkcffVRerzdqfDt27AiNPXfuXEnSq6++qtNPP13Z2dmy2WyaPn265s6dK8MwdOedd4a2/X5shmFox44d7fr1Q9dEJSYAAIAOcu7E/rrn3Y3y+Ovu2vAFTL2yIk8/PZ4S0wAAoGtyOBwaNmyY+vXrp23btiktLU02G6eXAAAA0HWVl5dr27ZtqqysbLJfZmamcnJylJiYeIgiA9CQPz9fNS+/LPn9LdzAr5qXX1bCnDktqshUzzAM2Ww22Ww2BQIBeb1e+SPs0263yzCMFo/bWp999plmzpwZSlKqV1RUpKKiIq1du1bz5s1TRkaGZs6c2aZ9TJw4MZT01FBhYaEWLFigBQsW6D//+Y/eeecd9Wlmij7TNHXppZfq2WefbVMsQD3OMgFoltsX0L8XbtGcowepV6Iz1uEAQJeREm/XGeP76tWv80Nt85bu0jXTBsti6bgPNwAAAB0tPj5eY8eO7fC7TgEAAICOtnv37qgJTBaLRX379mUKZaATcL//fssTmOr5/XK//74Sr7iiTfu0Wq2Ki4tTMBiU1+uVz+cLrbPb7W0asyU8Ho8uvPBCVVRUKCkpST/72c80Y8YMZWZmyuv1avv27friiy/02muvhbZ56qmnVF1drVNPPVV79uzR2WefrT//+c9h4yYkJIS9DgQCOuKIIzRz5kxNnDhRWVlZofGfe+45vffee/rmm2904YUX6pNPPmky5n/84x9avXq1pk2bpp/97GcaPny4ysvLtWPHDp1zzjmaPHmyHnroIT388MOSpDVr1jQao18rks3QfZHEBKBZz325U//v4y16avEOXT1tsK6alqsEJ78+AKAlLjliQFgS0459NVqybZ+OGUqpaQAA0PV15F2nAAAAwKEwePBglZaWhrXZ7Xb169dP2dnZHZqoAHQ3pmnKrKlp93EDRUXNTiEXddvdu+Xbvl3WzMyDisGhuuSKUFWm2lqZ8fGNPhebpimv1yu73S6LxdKmfS1evFh79uyRJL3wwguNKi0deeSRuuiii/T3v/9dNQe+3rm5uZK+S65KTU3V2LFjm9zPxx9/rGHDhjVqP/roo3XJJZfoqaee0hVXXKFFixbpo48+0oknnhh1rNWrV+vSSy8NTR33fampqcps8D1oLjb0XGQhAGhShdunfy/cIkmq8vj19w83af3e/Xpk9uQYRwYAXcPhA9I0PCtRmwqrQm0vLN1FEhMAAAAAAADQCSQkJKhv377au3evXC6XcnJylJWVJavVGuvQgC7HrKlR5f33xzqMRmqeeabdx/RISrr5Zhnfq27k9XpDD4fDIYfD0eobgAoKCkLLxx13XNR+NptNycnJrRq7oUgJTA1dfvnlevDBB7Vy5Uq9/vrrTSYxpaam6l//+hc3O+GgtS31D0CP8eiibSqr8YW1/eT4ITGKBgC6HsMwdPHUAWFtC9YVqKTKE6OIAAAAWicYDMY6BAAAAKDNTNNUcXGx9u3bF7XPoEGDNGzYME2dOlXZ2dkkMAFok/pp5+p5vV5VV1fL5/O1ajr2vn37hpafeuqpdo0xGtM0VVBQoE2bNmnt2rWhR/0Ub6tWrWpy+zPPPFNJSUmHIlR0cyQxAYiqqMKtJz7fHtZ26pgsHT4gLUYRAUDX9MOJ/eW0ffdvly9g6uXleTGMCAAAoGXcbre+/PJL7d69m2QmAAAAdDkVFRVauXKl1q9fry1btkT9n9bhcCg7O5sKIgAOSsMEpnqmacrtdqumpua7qeiaceyxx2rw4MGSpBtvvFFTp07V3XffrcWLF0fcx8GYP3++Zs6cqZSUFPXt21cjRozQuHHjQo/58+dLkkpKSpocZ/z48e0aF3oukpgARPXPjzar1hcIvbYY0q9PHRHDiACga0qJt2vm+Oywtte+IYkJAAB0ftu3b5fP59O2bdu0dOlSFRUVxTokAAAAoFkOh0M7duzQN998o4qKCkl1Cfr5+fkxjgxAd+ZwOGSz2SKuCwaDqq2tlcfjkcXSdJqG3W7XW2+9pVGjRkmSli1bpt/+9rc69thjlZqaqtNOO00vvPCCAoFAk+M0xTRNXXXVVZo5c6bmz5+vysrKJvvX1tY2uT4tjSIYaB+Rf4IA9Hjbiqs0b9nusLbzJ+doaCZlAAGgLS6YkqP/ff1d4tKmwiptKqzU8Cx+rwIAgM6psrIyLGnJ4/GooqJCmZmZMYwKAAAAiC4QCCgnJ0d9+/ZVWVlZo/U7d+5Unz59ZLfbYxAd0P0Z8fFKuvnmdh/X/cEH8jUznVlT7BMmyHXSSe0YUR0jPj7stcViUVxcnAKBgDweT8Qko0AgoLi4uGanmBs9erTWrFmjt956S2+99ZY+/fRTbdmyRbW1tXr//ff1/vvv64EHHtA777zTps/pTz75pJ544glJ0oQJE3TjjTfqiCOOUL9+/RQfHx+aVvPSSy/Vs88+2+x0eEzDifZCEhOAiP62YJMCwe/+GDltFt140vAYRgQAXdvkgWnqk+xSQYU71Pb26r365ckkMQEAgM7HNE1t3bo1rM1qtWrgwIExiggAAACIzjRN7d27V9u3b1e/fv0i9nG5XBo8eHDUKikADp5hGDISEtp9XOeRRx5UEpPziCNk6YC4orFarYqLi5Pf75fX6200laVhGHI4HKqtrZXT6ZTdbo84naXVatU555yjc845R5K0d+9evffee/r3v/+tFStWaMWKFfrJT36i1157rdUxPvbYY5KkoUOH6osvvlBcXFzEfqWlpa0eGzgYTCcHoJFVu8s1f83esLbLj8lVnxRXjCICgK7PYjF0+ri+YW0fbSiMUTQAAABNKysr0/79+8PaBgwYwB3rAAAA6FRM09S+ffu0fPlybd68WX6/v1Efq9WqwYMHa8qUKerdu3fERAEAnZu1Tx9Zc3Latm1Ojqx9+rRzRM0zDEN2u13x8fFyOp1Rf/d4PB7V1NRE/P31fX379tXll1+uJUuW6PDDD5ckvf3222FTvbX0d9y6deskSWeddVbUBCbTNPX111+3aLyW4PcvWoIkJgBhTNPUve9tDGtLdtn0s+OHxCgiAOg+Zh7WV06bRaeN6aN/XTxRL//0qFiHBAAAENHu3eHTizudzqh3tAMAAACxUFVVpdWrV2vt2rWqqamJ2Kdfv3464ogjlJOTI4uFy6JAV+Y69VSptZXUbLa67WKovupSfHx81BuDvl+pqTl2u13HH3+8JMnv96u8vDy0zuWqK0rh8XiaHKM+aaq6ujpqnzfeeEN79+6Nur616mOTmo8PPRd/rQGE+Wxzib7Yui+s7boZQ5USz922AHCwJuakasXvT9Z/Zk/SzPHZindQuhoAAHQ+VVVVYSdAJWngwIGyWq2xCQgAAABowOv16ttvv9WKFSsa/d9ar7S0VKNGjdLQoUOpJgp0E7Z+/RR/3nktT2Sy2RR/3nmydZIbciwWi1wul1wuV6OqS1arNWyqy88++0xbtmyJOpbX69WiRYskSYmJierdu3doXd++dTNCfH+K+O8bNmyYJOmtt96KOGXc1q1bdd111zXzrlqnPraWxIeeiyQmACHBoKl73g2vwtQ3xaXLjh4Um4AAoJsxDEOJThKXAABA55afnx/22m63KysrK0bRAAAAAOEMw9C+ffsirouLi9P69eu1adOmsIofALoH+/DhSpgzp9mp5aw5OUqYM0f24cMPUWQtZ7FY5Ha7VVtbG6oQ53Q6w/p89NFHGjFihKZPn6777rtP77//vr7++mstXrxYTz31lKZNmxaa5u3KK68MS4A6+uijJUnLli3TPffco1WrVmnLli3asmVL2Of9Sy+9VJK0Z88eHXXUUXryySe1dOlSffrpp7rjjjs0adIklZaWhqataw/1sUnSTTfdpE8//VSbN28OxdeSKfXQ/XEVDUDI22v2av3eirC2G08aJpedu20BAAAAoCfwer0qLCwMa8vOzmbqDQAAAHQadrtdw4cP17p160JtDodDubm5iouL08KFC2MYHYCOZuvXT4lXXKFAQYG8K1YoUFgo0+OR4XTKmpUlx6RJsvbpE+swmxUIBOR0OmUYRsTKx8FgUIsWLQpVXIrk7LPP1t133x3W9rOf/UwPP/ywSktLddttt+m2224LrTv++OP1ySefSJJ+8Ytf6IMPPtCCBQu0adMmXXnllWHjxMXF6ZlnntH8+fNDCVMHa+jQoTr//PP10ksvacGCBVqwYEHY+u3bt2vQoEHtsi90XSQxAZAkmaapRz8NL9s3NDNRPzq8f4wiAgAAAAAcanv27JFpmqHXhmEoOzs7hhEBAAAAjWVkZCgzM1MlJSXKyclRTk6OrFarKioqmt8YQLdg7dNHcWecEeswDkq0BKabb75Z48eP14IFC/T111+roKBAxcXFkqQ+ffpo6tSpuvTSS3VGhPffr18/LV26VHfffbcWLVqkvLw8ud3uRv3sdrvmz5+vhx9+WM8884zWr18v0zTVr18/nXTSSfrFL36hkSNHav78+e36np977jlNnjxZr7zyir799ltVVlYqGAy26z7QtZHEBCDkhhOG6anF2/Xltrp5T3958nDZrNxtCwAAAAA9QTAY1J49e8LaMjMz5XA4YhQRAAAAejKv1ytJUf8fHTp0qAYPHtxoGiYA6OoSExN1zjnn6JRTTgm70chqtcrlcjVbLXnIkCF6/PHHm92PzWbTDTfcoBtuuCFqn7lz52ru3LkR1w0aNCgsvpaw2+369a9/rV//+tet2g49B0lMACTVZfqeOqaPTh3TR+v3VOj1lfk6dUznL7UIAF2Zxx/Qp5tK9PbqPbrimFwdlpMa65AAAEAPVlRUJJ/PF9bWvz/VeQEAAHBomaap4uJibd68WampqRo9erQMw2jUz263xyA6ADg0vF5vowShQCCgmpoaOZ1O2Wy2iL8bga6OJCYAjYzOTtbo7ORYhwEA3doDC77VU4t3qNLjlyT1SnCSxAQAAGLGNE3l5eWFtaWmpioxMTFGEQEAAKAn8nq92rx5s0pKSiRJJSUlKi4uVmZmZowjA4BDy+FwyDRN+f3+sHbTNOV2u2Wz2eR0OputygR0NRzRAAAAsWAYoQQmSXpnzV4Fg60ruwoAANBeysvLVV1dHdZGFSYAAAAcKqZpqqioSMuWLQslMNXbvHlzaGo5AOgpLBaL4uLi5HK5IlZc8vv9qqmpkc/na/WUbkBnRhITAABADMwc3zfsdUGFWyt2lcUoGgAA0NOlpKRo1KhRSkpKkiTFxcUpPT09xlEBAACgJ/B6vVq/fr02bNjQqOKIVFchlCmTAPRUdrtd8fHxstkaT7JVX5XJ7XYrGAzGIDqg/TGdHAAAQAwMz0rS8KxEbSqsCrW9vWqPpgziYiEAADj0LBaLMjMz1bt3b1VUVCgQCHChCAAAAB2uqKhIW7Zskc/na7TObrdr6NChTCUHoMerr8rk8/nk8XgaVV7y+/0KBAJyOp2y2Wx8nkeXRhIT0IOZpqnfvrZWp4zO0vHDe8ti4Q9aZxMImnL7AnUPf/C7ZV9QHl9Abn/dcn2b2xdQrS9wYF1QXn9QDptFTptFLrtVTptFzgPPrhY+x9mtHBtAB5k5PlsPfLAp9PqdtQW6/cwxsvIzBwAAYsQwDKWkpMQ6DAAAAHRzXq9XmzdvbjR1XL2MjAwNGzZMDofjEEcGAJ2X3W6X1WqVx+NpVLmuviqTzWaT0+mUxcKkXOiaSGICerDFW/bpxaW79OLSXcrNSNBlRw3UJUcOlN3KH7VDxTRNFVd6tGNfjXbuq9bOfTXaceB5575qVbgbl8491CyGlBJnV1q8Q6nx9c8OpcXblZZQ15YaV/c6Nd6htIS6Pi67NdahA53eGeP7hiUxFVd69NX2fTp6SEYMowIAAAAAAAA6TnFxsTZv3hyx+pLNZtOwYcPUu3dvKokAQATNVWWisjK6OpKYgB5s7hfbQ8vbS6r1zJc7delRg2IXUDcVCJrau782lKC0KyxRqUa1vkCsQ2xS0JTKanwqq2n8gbIpLrtFafEOpcU71CvRod5JzrpHYvhzRqJTqfF2/qFCjzSkd6JG9U3Whr0Voba3V+8liQkAAAAAAADdjtfr1ZYtW1RcXBxxPdWXAKDlolVlcrlcXHNDl0YSE9BD7dxXrY82FoW1XX70IKYNO0imaWpTYZU++bZIS7eXavu+auWV1sobCMY6tEPO7Qtq73639u53N9vXbjWUkViX0FSf4JSR5DiQ6ORS7ySnMg8kQSU4+dOF7mXm+L5hSUzvrS3QH88aIxtV8QAAAAAAANCN7NmzJ2ICE9WXAKBtvl+VyWazyWbjOhq6No5goIea+8UONawumOSy6dzD+8cuoC6s0u3T4i37tGhTkT75trhFSTsHw2ox5LJZ5LJb5bJb5bRb5LJZ5bLXtcU1aHfaLPL6Tbn9AXl8QXkOPLv9Abl9AXn8wbBnty82yVa+gNnihKcEh1WZya5QZafMJKcyk1yhJKfM5LrXaVR3Qhdx5vhs3ff+t6HXpdVefbF1n44b3juGUQEAgJ6gqKhIRUVF6t+/v1JSUvj/GQAAAB1qwIABKi4uVk1NTaiN6ksAcPDqqzI19bm+fto5PvujsyOJCeiBKt0+vbw8L6ztwik5VLhpoYbVlhZ+W6TlO8rkD5rNb9gEh82iAenxGtQrXgN7JWhQr3gN6JWgvikuxR1ISKpPTrJ3YHUW0zTlDQTlbpDwVOH2qbzGp7Iar8pqfCqvPvBc6w211z/vr/XJPLgvRbOqvQFtL6nW9pLqJvvVV3eqS25yqXeSI1TpqWHVp4xEhxKdNv5pQ8wM6BWv8f1TtDpvf6ht/uq9JDEBAIAOZZqm8vLyVFlZqX379ikxMVG5ublKT0+PdWgAAADopiwWi0aOHKmvv/6a6ksA0M4slujXD03TlNvtVjAYVFxcXJN9gVgjYwHogV5Zkacqz3dzo1oM6dKjBsUuoC6gyuPX55tLtGhTkRZ9W6w9bai2lOCwamCvBA0MS1SK16BeCeqT7OoUU/kZhiGnzSqnzSrJ3urtA0FTFbUHEptqfSqv8WpflVclVV4VV3pUUuVRcaVHxVV1y+U1vvZ/EweEV3fa32Rfp80SluAUKeGpV6JD6fEOpcTZO8X3Ct3LzPF9w5KY3ltXoD+dM1YOGx8kAABAx6ioqFBlZWXodVVVlQKBQAwjAgAAQE+QlJSkUaNGKTU1lepLAHCI+Hw++f1114ZramrkcrmYdg6dFkcm0MMEg6ae/mJHWNvJo7OUkx4fm4A6KdOUSoNOPfVlnr7cUaHlO0vlC7S8xFC/1DgdP6K3Dh+QFqqulJHo6PZ3lFgthtISHEpLaNmHT48/cCDJ6UBy04FHSVVdolPDtmpvx11Q8fiDyi+vVX55bbN9LYaUFl/3HtPjHUpLsCs9wan0BLvS4h3qlehQWrxD6QnfPeLsTZfwBE4f11d/eWdj6PX+Wp8WbynRjJGZMYwKAAB0Z3l54dV5nU6nMjIyYhQNAAAAugvTNLV3715lZGRETVLKzOScFwAcKn6/Xx6PJ/TaNE3V1tbK6XTKbrdz/QqdDklMQA+z8Nsi7dhXE9Z2+TG5MYqm83H7Apq3Yo9eqBimiqBT+nh7i7azWw1NzU3X9OGZmj6it4ZmJvJHvwWcNquyU+OUnRrXbN9qj19FBxKaiirdKqrwhL2ue/aotNrboTEHTWlftVf7WrEfp82itANVnFLi7EqOsys5zvbda5c9bN13yzbF2a0d+G7QWfRPi9fEAan6Zld5qO2t1XtIYgIAAB2itrZWJSUlYW39+vXjMwwAAAAOSiAQ0KZNm1RUVKSioiKNHz+eKYsAIMYMw5DFYlEwGAxr93g8CgQCcrlcnA9Ap0ISE9DDzP1eFaaRfZJ0RG56bILpRKo8fj3/5U499tl2lVR5JDmb3aa+2tL04b119NAMJTr5ldqREpw25Tptys1IaLKfLxBUSZWnUZJTSZVHJZXeUJWnkg6u7tSQxx9UQYVbBRWtn4bQbjWU5LQp6B4muxHQ8udXKyXepUSnVQlOmxKdNiUceNS3hdod9evr2p02C/+IdmIzx2eHJTEt31GmYNBk+kIAANDu8vPzw15brVb17ds3RtEAAACgO6itrdW6detUXV0tSdq/f7+2bdumoUOHxjgyAOjZrFar4uPj5Xa7Q1PK1fP7/aHp5axWbqpH58AVd6AH2VxYqc82h99te8UxuT06qaG8xqunFu/Q3C92aH+tr8m+VFvqGuxWi/qmxKlvSvPVnWq8fpVUeuuSmg486qezi1XC0/f5AqZKa3yqT6wr3rlf0v42jWW1GHLZLIpzWOW0WRXnsMpltyjObpWrwSPObjnwbJXzwHN9P4fNIrvVIofNIseBZ3vo2ZAz7HWDflZLKBnHNE35g6b8AVP+YFD+gClfMKjAgTZfIBi23hcwD6wLymIxNLJPklLjWzZlYVdyxri+evCjzTp5dJZmju+rY4ZmkMAEAADand/vV0FBQVhbnz59ZLNxiggAAABts2/fPm3cuLHRxfH8/Hz16dNHiYmJMYoMACDVVWNyuVzyer3yesNnGgkGg6FEJrvdHqMIge9whgroQZ76XhWm9ASHzpqQHZtgYqyowq3HP9+u57/c2WRySt9kp2aMyqLaUjcV77BpQC+bBvSKb7av2xdQeY1P+6o9Kqv2qbTGq9Iqj0prfCqr9h547VVZjVel1XXPvoB5CN5FywWCpqq9gZglZNkshswDcRysoZmJmjwwTZMOPHIzErp8UmGfFJdW/N9JslkpsQ0AADpOQUGBAoHw/wf79esXo2gAAADQlZmmqV27dmnHjh2N1lmtVo0cOZIEJgDoJAzDkNPplNVqldvtlmmGX6txu90KBoNyOBxd/noLujauxgM9RHmNV69+nRfWdvHUAXLZe1ZpwN2lNXr002367/Ld8vqDUfv1t1XpcFex/nTdbKWkpBzCCNFZuexW9Umxqk+Kq0X9TdNUpcdfl+BU7VV5jU/7a+seFbUNlt31y35VHFhX6fE3v4MuyN8OyUv1thRVaUtRleYt2y1J6pXg0OED00KJTeP6p8hp63q/30hgAgAAHck0zUZTyWVkZCgurvkqpgAAAEBDfr9fGzdu1L59+xqti4+P15gxYxQf3/zNowCAQ8tmsyk+Pl61tbUKBsOvlXq9XgUCAblcLlksXK9AbJDEBPQQ85btltv33R8im8XQ7KMGxjCiQ2tLUZUe/mSr3liZ32QixcmjszRnal99/sZzkkSmMdrMMAwlu+xKdtk1sFdCq7b1B4KqdPtDCU579+3X/958R17ToqlHH6eAYVOV169qj1/VnoCqPPXL/gPLgbplr19m5yoG1WH2VXv1wfpCfbC+UJLksFo0rn+KJg9MCyU39Up0xjhKAACA2Nq3b5/cbndYG1WYAAAA0FrV1dVat26damtrG63r3bu3RowYIau1691gCAA9hcViUXx8vNxud6OpQAOBgGpqahQXF8fvcsQESUxAD5Hksikr2anCCo8k6fRxfZWV3LKKMl3Z2vz9euiTLXp3bUHUZA6LIZ15WLaunT5UI/okqaKiQp8f2jCBMDarRWkJDqUlOCRJg5ItWumokCT9eGo/JScnt2gc0zRV6wuEEptqvH65fQG5fUG5fQHVHliu9QXk8QVU6w3I7Q+o1huU2x+QO/T6u36+QFC+QFBe/4FHwJTXH5AvYMobCLbLVHH1rBZDNoshu9Uiq8WQ3Wqo0u2Xp4kqavW8gaBW7CzTip1lobbcjARNGpim08b00YmjMklSBAAAPU5eXnh13sTERCrPAgAAoFWKior07bffNqreIUmDBw9W//79Oe8GAO1o165duuuuu/Thhx8qPz9fHk/dtd7XXntNr7/+up5++mkNHDgw4tSeTTEMQy6XSz6fLzRmPdM05Xa7FR8fr0WLFmnGjBmSpIULF2r69Ont8ba0Y8cO5ebmSpKeeuopzZkzp13GRddHEhPQQ1xyxECdPzlH76zZq6cW79DlxwyKdUgdatmOUv174RZ98m1x1D52q6FZk/rrJ8cN0aCM1lXKAboCwzAU77Ap3mGTkg7NPgNBsy7J6UCiU8OEJ48/KMPQd0lJFotsVqPucWA51GYxIp7s8PqDWr+3Qst3lGrFzjIt31mm4kpPhEga215Sre0l1XplRZ7OndhP9513mKwWTqgAAICeobKyUvv37w9r4wITAAAAWso0TW3btq1RYrwk2e12jRo1SmlpaTGIDAC6r127dmnSpEkqKSnpkPENw5DD4ZDFYpHb7ZbZoCKEy+XinAFigiQmoAexWy06e0I/nT2h+04XUFLl0W/+t0YfbiiM2sdlt+jiqQN19XG56psSdwijA7o/q8WQ1WKVy94xJUYdNosm5KRqQk6qrppWd/Ikr6xWy3eWavmOuspL3xZWNjuN3qvf5MswDN03a7wsnTSRqdLt04cbCvX2qr2aNam/fjCub6xDAgAAXdj3LzY5HA717t07RtEAAACgK/H7/Vq7dm2jpHhJSkpK0ujRo+Vydf+ZHwDgUPvzn/+skpIS2Ww23XXXXTruuOOUmJgoSRo4cKBef/31dtmPzWYLTS8XCATkcrmYSg4xQxITgG7j003F+uVLq1RSFbkqS5LTpkuPHqgrjslVr0TnIY4OQEcwDEM56fHKSY/XDyf2lyRVuH36Zlf5genkSvXNrnLVeAONtv3f13myWQzdfe64TpfI9P8+2qz/t3CLvAemzrNZDZKYAABAm3m9XhUXh1epzc7OlsViiVFEAAAA6Eqi/d/Yp08fDRs2jP8rAcRE0apVWvXIIypeuVLeyko5kpLUe8IEHfaTnyjzsMNiHV67+PDDDyVJ55xzjm655ZZG6+fOnau5c+e2y74sFovi4uLk9/tlt9tD7dOnTw+r0AR0NJKYAHR5Hn9A97//rR77bHvE9ekJDl15bK5mHzVQyS57xD4Auo9kl13HD++t44fXVRbwB4LaWFCpL7aW6P73N8kbCIb6/nf5blkshu46Z2ynSmTKSnaFEpgkaeG3xap0+5TE7zAAANAGFotFOTk52rNnj/x+vywWi7Kzs2MdFgAAALoIi8Wi0aNHa8WKFfJ6vTIMQ8OGDVPfvtx0B+DQ27tsmRbeeKP2fPFFo3V7lizRqocfVr9jjtH0v/9dfadMiUGE7Sc/P1+SNHz48EOyP8MwwhKYvs80zUZJTkB7I4kJQJe2tbhKP3/xG63bU9FoXUaiQ9dOH6qLpg5QnIOSh0BPZbNaNLZfisb2S9HQzET95NkV8gW+u2vgxaW7ZLMY+uPZYzrN/M6njumj372+JhSn1x/UhxsKQ9WmAAAAWsNmsyk3N1cDBgxQUVGRfD4fJxwBAADQKg6HQ2PGjNGGDRs0atQoJScnxzokAD3Q1vnz9dasWfK73U32y1+8WP897jid+corGnLGGYcouvbn9XolqdN8hvd4PPL5fAoGg3I4HJ3mmgq6F+o7At3Yp5uKtWtfTazD6BCmaWre0l2a+eDnEROYpo/orfduPE5XHJtLAhOAkBNGZumhSybJ9r2qS89+uVN3vrW+05RETYm3a9qw3mFt81fvjVE0AACgu7Barerbt68GDBgQ61AAAADQBSUnJ2vKlCkkMAGIib3LlrUogame3+3WW7Nmae+yZR0cWfuaO3euDMMISxC68847Q22GYWjOnDmSpDlz5sgwDA0aNCjiWPX977jjDknSsmXLdNFFF6l///5yOp3q16+fZs+erQ0bNkSN55NPPgmN8/HHH0uqS67yeDyhayqbNm3SDTfcoLFjxyopKUkOh0PZ2dmaMGGCrrjiCv33v/+Vx+Np9r1/8MEHOvPMM9WnTx85nU7l5ubqZz/7mfLy8lrwlUN3QRIT0E15/AH98qVVOv7+hbrq6eX6YktJp7k4f7D21/h03Qtf6zevrlGtLxC2zmG16A9njtZTc6YoI9EZowgBdGYn/3/27ju+roL+//j7juy904yONE33HtCW0pZRlJatoCBLZAgqIIqKC/2K/BQpoIJMQUQZYkFpq8wOoC2le6UjXUmavedNcsfvj9q0J2naJrn3npvk9Xw8+njc88kZH8JJ7sk9n/P5jEnRH6+dIluHQqaX1hzSr5blBszvyoXjje24V+0tV21zm0nZAAAAAAAAoL/zeDw6cuSI3G53l+tYrdxaBGCOFffcc8YFTMc4HQ6tvPdeH2XUtzz11FOaNWuWXnvtNR05ckStra0qKirSK6+8omnTpmn16tXd2l9bW5scDofeeOMNjR8/Xn/84x+1c+dONTQ0qK2tTcXFxdq6datefPFFfeUrX9G+fftOub8f/ehHWrBggZYuXarS0lK1trbq0KFDevrppzVlypRTFlqhf2GcHNBPLd9erIqGoxWtH+SW6oPcUr1377nKSYkyObPe+exApe59fYuKajtfpGQnR+r3X5msMWk8BQLg1L4wLlW//8pkfee1zXK5jxctvfDJQdmtFv3wi6NMb4N64dgUBS+xqtV19EOjNpdH7+0s0ZenZZqaFwAAAAAAAPoft9ut3NxcVVRUqK6uTqNGmf/5GIC+x+N2q7my0uv7rdi+XUVr1vRo2yOffqr8FSuUOG6cl7OSwhISZPFycefll1+uadOmSZLGjx8vSfrmN7+pO++8s32duLi4bu3z3Xff1fr16zV+/HjdfffdGj9+vJqbm/XWW2/piSeeUFNTk66//nrt27dPwcHBZ7zfoqIiff3rX1dra6uSk5P1rW99S2effbYSExPV3NysvLw8rVq1Sm+//fYp9/Pcc89pzZo1mjt3rm6//Xbl5OSopqZGL7/8sl5++WWVl5fr61//utauXdut/270TRQxAf2Qx+PRi58eMsTOGhbfpwuYnC63nvhwn55ckSf3SZqkXHfWYP1k4RhGxwE4YwsnDJLL49E9r202/F55ZvUB2awWff+ikaZ+UBMdGqS5I5P0/q7S9tjSbcUUMQEAAAAAAMCrnE6ndu7cqZqaGklSWVmZ7Ha7srOzKWQC0C3NlZV6KjnZ7DQ6eeO883yy3zvLyhSelOTVfcbGxio2NtYQS05O1rheFGGtW7dOF198sd566y1DkdKcOXOUkJCgn/zkJ8rPz9eyZct0xRVXdLmfjgVO7777rhobGyVJH3zwQXvR1TGzZs3SDTfcoD/+8Y+nzG/NmjW69dZb9cwzzxjed84//3wFBwfr+eef17p167R582ZNnjz5jP+70TfR8xHohzblV2tbYa0hdvPsYSZl03sFVU26+pm1+sNHnQuYYsOD9Mz1U/XQFeMpYALQbZdOTNPiqyep42cxT63cr8c+OHVrU39YNME4Uu7TvApVN7aalA0AAOhLmpublZmZqaCgILNTAQAAQABra2vTtm3b2guYjikuLlZTU5M5SQEAvCo0NFQvvvjiSbssfec732mPf/zxx6fcj91uV1hYWPtyaenRh7BjY2OVlZXV5TjSsLAww3YdDRo0SH/4wx9OWjj7ve99r/316fJD/0ARE9AP/blDF6b02DBdOCbFnGR66V9bjujiJz7WpvyaTl+bmZWg/959ri4am+r/xAD0G5dPTtcjX5rYqZDp9x/u0xMmFzKdPzpFIfbjl2tOt0fv7iwxMSMAANBXlJeXKz09XZMnT9ahQ4dUX19vdkoAAAAIMA6HQ1u2bOl0rWiz2TRhwgRFRESYlBkAwJsuvPBCJXfRISsqKkojRoyQJB04cOC0+zpWyGSxWJSaevQebU1Njd555x01Nzd3Wch0Kl/60pcUEhJy0q+NHDlSkZGRZ5wf+j6KmIB+pqimWf/dYbzBfeOsIbJZ+1bL14YWp777xhbd/doW1bc4DV+zWy26/wsj9co3zlJqTKhJGQLoT740NUO/uXJCp/hjH+zVkyvyTMjoqMgQu84bZfzDYum2YpOyAQAAfYXT6VR1dbUkyWq1qrq6WhUVFSZnBQAAgEDS1NSkLVu2dOq2FBQUpIkTJ3YaZQQA6LtGjRp1yq/Hx8dL0hk/AHWskOniiy9uf7+49tprdfHFF+u3v/2t1q9fL5fL5bX84uLiupUf+ja72QkA8K6/rjss1wkz18KCbLpm2mATM+q+LQU1uvu1zTpc2blV7ZCEcD3xlcmalBnr/8QA9GtXT8+U0+3RA29tN8QfeXePbFaL7pg73JS8Fk1I039OKE5ds79CFQ0tSow8+VMJAAAAFRUVnZ58TEtLMykbAAAABJr6+npt375dbW1thnhISIgmTJig8PBwkzID0NeFJSTozrIyr+931f33a+dLL/V4+3E336xzf/Mb7yX0P2EJCV7fpy+c7ve61Xq09013Co9sNpsyMzP12muv6etf/7qKioq0evVqrV69WpIUHR2t888/X1//+te1aNEiv+eHvosiJqAfaW516dX1+YbYVVPTFRMeZFJG3fevLUd03xtb5TyhEOuYK6ek65eXjVNkCL+6APjGtWcNlsvj0U/f3mGI/7//7JbdatE35mT5PafzRiUrPNimptajF+duj/SfHSW6/uwhfs8FAAD0DSUlxu680dHRXbZlBwAAwMBSU1OjHTt2dLoRHB4ervHjxys0lOkHAHrOYrUqPCnJ6/udevfdvSpimnL33T7Ja6CzWq268MILtW3bNr311lt67733tGbNGh05ckR1dXV666239NZbb+miiy7SkiVLKJLFGWGcHNCP/GvLEdU0GZ+cuGnWMJOy6b5/bTmie1/f0qmAKSrErie+MkmLr55EARMAn7v+7CF68JIxneK/WparFz896Pd8woJtOn90iiG2dGuR3/MAAAB9Q3Nzs2praw2xhD7yZCgAAAB8q6KiQtu2betUwBQVFaVJkyZRwAQgYCVPmqS0WbN6tG367NlKnjjRyxnhGKvVqri4OF177bV64YUXlJubq23btumRRx5RTk6OJOndd9/Vj3/8Y5MzRV9BERPQT3g8Hr205pAhdm5OkrKTI81JqJuOFTB1bMA0ZXCslt89R5dNSjcnMQAD0k2zh+knC0d3iv/inV16ee0hv+ezcPwgw/KRmmY52mibCgAAOistLTUst7W1KTo62qRsAAAAEChKSkq0c+dOeTzGD+FjY2M1YcIEBQX1nYkOAAam+Y8/Lns3iy3tYWGa99hjPsoIx1itVoWFhclms0mShg4dqrvvvluff/65MjIyJElvvPGGmSmiD6GlCdBPbC2s1e6SekPs5llDzUmmm/69teikBUw3zhyiny4aI7uNeksA/veNOVlyezz69fLdhvjP/rVTdqtV15412G+5zBuZpOFJEZozIkmXTBykyZlxslotfjs+AADoGzweT6cipsrKSlmt/E0FAAAwkBUVFWnfvn2d4omJiRo9ejTXiwD6hEHTp+uSN9/UO1/6kpwOx2nXt4eF6ZJ//EODpk/3Q3awWCwKCwuTw+GQzWZTUFCQgoKCNH36dBUWFqqiosLsFNFHcFUC9BP/3FhoWB4cH665OYE/2/WdrUW657XNnQqYbp49VA9eOpYCJgCmuu3c4br/CyM7xX/y9nbtOFJ7ki18IzTIpg++O1cPXjpWU4fEU8AEAABOqra2Vo4OH+SWl5eblA0AAAACRUxMjOx2Y1+D1NRUjRkzhgImAH3K8IULdc3q1UqfPfuU66XPnq1rVq3S8IUL/ZTZwPTuu++quLi4fdlisSg0NFTBwcGSjn5OsX79eknSsGHDTMkRfQ+dmIB+oMXp0jvbigyxK6ekB/xN7qXbinTPSTow3Tx7qH62aIwslsDOH8DAcOe8bLlcHj36/t72mNsj/WrZLr1669l++13F70QAAHA6JSUlhuWmpiY1NjaalA0AAAACRUREhMaNG6dt27bJ7XYrMzNTw4YN4/MmAH3SoOnT9dVPPlHZ1q3a9uyzKtuyRa319QqOilLypEmacNttSp440ew0B4RXX31Vl1xyiS688EItWLBA48aNU3x8vOrr67Vjxw798Y9/1JEjRyRJd9xxhyTJ6XS2j50DToYiJqAf2HioWjVNbYbYVVMyTMrmzCzbVqy7X9siV4cKpptmUcAEIPB8+/wRqm9x6tnVB9pj6w5U6b1dpbpobKqJmQEAABzlcrk6dV2iCxMAAACOiYmJ0dixY9XY2KjMzEyz0wGAXkueOFEXPPmk2WkMeG1tbVq+fLmWL1/e5Tp33HGHvvOd76itrU0Oh0N2u10ej6fL9TGwUcQE9AOzshO14nvz9NamQv1z0xGlx4UpMz7c7LS6tGxbsb7z2uaTFjD9/BIKmAAEpu9emKNl24p1pKa5Pfbw8lzNH5msYDtttwEAgLnKy8vldrsNsYqKCpOyAQAAQCCKj49XfHy82WkAAPqJxx57TBdeeKE++ugjbdu2TcXFxSovL5fNZlNmZqZmzpypb3zjGzrnnHPkdDrlcDgkHe3G1NLSYnL2CFQUMQH9xLDECH13wUjdc0GOqppazU6nS10VMN04cwgFTAACWmiQTfd/YaTufm1Le+xQZZNeXntI35iTZVpeHo+H350AAEClpaWG5ejoaLW1tXWxNgAAAPojj8ejxsZGRUZGmp0KAMALTtet6KWXXtJLL73U4+2PWblyZZdfmzdvXpf7iYuL03XXXafrrrvulPv3eDztBUzHpKenq7m5WSEhIae9x3Ho0KFTfh39C20DgH7GarUoMTLE7DROavn2kxcw3TBziB68dCw34QEEvEsnpmny4FhD7IkP96mq0b/Fo2X1Dv117SFd88xa/fnTQ349NgAACDwOh0M1NTWGGE/YAwAADDz5+fnauHGjiouLzU4FAIB2FotFoaGhneJtbW1qbQ3c5hwwB0VMAPziP9uL9e1XOxcwXX/2EP2CAiYAfYTFYtFPF40xxOodTj3+wV6/5fD0qv06+9cf6qf/2qnPDlbpna1Ffjs2AAAITFVVVYZlu92umJgYk7IBAACAGYqKito7Vezdu1f5+fnmJgQAwAnsdrvCwsI6xVtbWylkggFFTAB87r87Tl7A9LWzB+uXl1HABKBvmTI4TpdOTDPE/vZZvvaV1vvl+KMHRevEX6dbCmpUUNXkl2MDAIDAlJaWpunTpyszM1PBwcFKSkqS1cpHPgAAAANFWVmZ9u3bZ4gdPHhQ9fX++bwKAIAzYbfbT9qRqaWlRW1tbSZkhEDEJ1oAfOq/O0r0rb9vlvMkBUz/d9k4CpgA9Ek/+OIohdiPX0a53B49tDzXL8eeNTxBseFBhth/dtAiHACAgS48PFxZWVk6++yzlZWVZXY6AAAA8JOqqirt3r27U3z48OGKiooyISMAALoWFBSkkJCQTnGHw0EhEyRRxAT0WU6XW19+eo0e/2BvwHbgOFrAtKlTAdN1Zw3WLy+lgAlA35UeG6Zb5xhvDq7cU65Ve8t9fuwgm1VfGJtqiC3bRhETAAA4ymKxyG63m50GAAAA/KC2tlY7d+6Ux2P8DH7IkCHKyMgwKSsAAE4tODhYwcHBneIOh0NOp9OEjBBIKGIC+qhP8ir0+aFqPf7BPs357Qpd/cxaNbYEzi/1d3eevIDp2rOOdmCyWilgAtC3fXPecCVFGZ8W+NXSXXK63D4/9sIJgwzLWwtrA7agFQAAAAAAAN7X2NioHTt2yO02fhaVlpamIUOGmJQVAABnJjg4WEFBQZ3izc3NcrlcJmSEQEERE9BHLdl0xLDc3OpSREhgPG373s4S3fW3zgVMX50xWL+igAlAPxERYtf3F4w0xPaVNejVzwt8fuyZWQmK6zBSbtl2ujEBAAAAAAAMBM3Nzdq2bVunbhXJycnKzs5mCgIAIOBZLBaFhIRQyIROKGIC+qA6R5ve3VliiF01Jd2kbIze31Wqu07SgemrMzL10OUUMAHoX66amqExg6INscfe36vaZt/ObbbbrPrCOGM3JkbKAQAw8HQcGwIAAID+r7W1Vdu2bVNra6shHh8fr5EjR1LABADoM44VMtntxkYdHo9Hzc3NnboNYmCgiMnHDh8+rPvuu0+jRo1SRESE4uPjNX36dD3yyCNqaurd2JeXXnpJFovljP699NJLp91fU1OTfvvb32r69OmKj49XRESERo0apfvuu0+HDx/uVa7wruXbitXiPP5LO8hm0aWTzC9iyi2u07f+vkltLuMH6V+ZnqmHLh9PAROAfsdmteini8YYYlWNrXpyRZ7Pj72ow0i57Udqdbiy0efHBQAAgcHhcGjDhg06fPiwHA6H2ekAAADAD5xOp7Zt29bp+i86OlpjxoyR1cptPwBA32KxWBQaGiqbzWaI2+12CnMHKK5mfOidd97RhAkTtHjxYu3Zs0dNTU2qrq7Whg0bdP/992vy5MnKy/P9Tc4zkZeXp0mTJukHP/iBNmzYoOrqajU1NWnPnj1avHixJkyYoKVLl5qdJv6n4yi5+SOTFR8RbFI2R9U72nTn3zYZiqukowVMv76CAiYA/dfM4QlaMCbFEHvx04M+Lyg6a1i8Ejr87mekHAAAA0dpaamampp06NAhffbZZ9q5c6fZKQEAAMCHXC6Xtm/frsZG42dOERERGjduXKebvwAA9BUWi0VhYWHtxbjBwcEKCQmhiGmAoojJRzZv3qxrrrlGdXV1ioyM1EMPPaQ1a9boww8/1K233ipJ2rt3rxYuXKj6+vpeH+/dd9/V9u3bu/x3+eWXd7ltfX29Fi5cqH379kmSbr31Vn344Ydas2aNHnroIUVGRqqurk7XXHONtmzZ0utc0Tv5lU1af6jKELtySoZJ2Rzl8Xj0wyXbdbDC+MfTVVMyKGACMCA8cPFoBdmO/65rc3n08PLdPj3m0ZFyqYYYI+UAABgYPB6PSktLDbGgoCCTsgEAAICveTwe7dq1S3V1dYZ4aGioxo8fz7UgAKDPO1bIFBISQgGTSTwez+lX8gP76VdBT9x9991qbm6W3W7Xe++9p5kzZ7Z/7bzzztOIESN0//33a+/evXr00Uf14IMP9up4OTk5Gjp0aI+2feSRR7R3715J0m9/+1t9//vfb//azJkzNW/ePM2dO1dNTU265557tHLlyl7lit7556ZCw3JceJDOG5VsUjZH/XXd4U43zidkxOjXV46jgAnAgDA0MUI3zhyq5z852B77784SrTtQqbOzEnx23IUTBulvn+W3L+8sqtPBikYNS4zw2TEBAID56urq1NzcbIilpKR0sTYAAAD6OovFori4OFVVHX/AOTg4WBMmTFBISIiJmQEYKGw2m5xOp5xOp1wuF93f4BNWq1XBweZOHxqoXC6XXC6XJJn+800nJh9Yv369Pv74Y0nSLbfcYihgOua+++7T6NGjJUlPPPGE2tra/JrjMW1tbfr9738vSRo9erTuu+++TuvMmjVLt9xyiyRp1apV+vzzz/2aI45zuz1astlYxHTpxDQF2837Ud5aUKP/W7rLEIsOtevJa6coxM4FDICB49vnj1BcuPGpt18t2yW323eV62cNS1BipPGCfjkj5QAA6Pc6dmEKCwtTdHS0SdkAAADAHzIyMjRy5EhJkt1u1/jx4xUWFmZyVgAGivDw8PbXNTU15iWCAc3j8QRMt6D+5sSf6xN/3s1AEZMPvP322+2vb7755pOuY7VadcMNN0g6ekKsWLHCH6l1smLFCtXW1kqSbrzxxvY5kx3ddNNN7a/feustf6SGk9hwuFoFVcanba+aat4oudqmNt31901qcxnfLB69epIy48395QYA/hYTFqR7L8wxxHYcqevUQc+bbFZLp5FySxkpBwBAv+ZyuVRWVmaIpaSk0GYdAABgAEhNTdXYsWM1btw4RUZGmp0OgAEkNja2/XVZWZnKysrkcDgoKIHfuN1uNTU1qbW11exU+g2PxyOHw9H+M31MXFyciVkxTs4nPvnkE0lSRESEpk6d2uV6c+fObX/96aefasGCBT7PraNjuXbMp6Np06YpPDxcTU1N+vTTT/2RGk7inxuNN8KzkyM1Pj3GlFw8Ho/u+8cWFVYbi6puPzdLF45hjAGAgenaGYP18trDyitraI898u4eXTx+kCJCfHPZtXB8ml5Zd3ykXJvLrTpHm6JDg06xFQAA6KsqKyvb23sfwyg5AACAgSMxMdHsFAAMQKGhoYqJiWlvjlFZWanKykpZLBbTR0/1NR6Pp70Qp76+noeSzoDH45Hb7W4vmrNarV02ZxmoenJeuVyuToWIMTExpo+qpYjJB3JzcyVJ2dnZstu7/haPGjWq0zY9dfPNN2vPnj2qqKhQdHS0srOzdcEFF+ib3/ym0tPTu9xu167jY8BOzKcju92u7Oxsbdu2rde5omeaW11a1mFE0FVTMkx7Y3t29QF9kGt8+nf60Dh976KRpuQDAIHAbrPqxwtH6+YXj49eLatv0TOr9uu7C3zz+3HGsHjNGBavs4fFa9HENOWkRPnkOAAAIDCUlJQYlmNjYxUaGmpSNgAAAPAFj8fDTW0AAWfQoEEKDg5WeXl5e8zj8cjpdJqYVd/jdrvV0HD0QeioqCiKcU7jWHFOx2KboKAgCuhO4I3zKikpSQkJCd5OrdsoYvIyh8OhiooKSUfnE59KXFycIiIi1NjYqIKCgl4dd+XKle2vj1W+fvbZZ3r00Uf1+OOP6/bbbz/pdoWFRzv7REREGNoAnkxmZqa2bdum8vJytbS0dKsC79hxulJcfLw4p7GxUXV1dWe874Fi2c4yNbQcvwiwWqQLRkSb8r3aVFCr3/53tyEWF2bXry8ZoebGBjV3sV1fcewXfMfXgFk4J/uWqYNCNSsrTmsOVLfHnl19QIvGxCs12jfV689/dez/Xnl8/r7A+YhAwvmIQMM5CV9rbW1VdXW1IRYTE3PS93/ORwQazkkEksbGRrNTAIAuNTc3Kzc3VyNHjlRERITZ6QBAO4vFosTEREVHR6uhoUGNjY1qbW2V2+02O7U+xel0tne0iomJOWVTFBzldrs7XcO3tbUpOjqaQqb/6cl5ZbVaFRwcrIiICEVGRio4ONjXaZ4RfiK8rL6+vv31mcwjPlbE1NMPb7KysnTllVdq5syZyszMlCQdOHBA//znP/Xmm2/K4XDojjvukMVi0W233dZlvmea6zENDQ3dKmI6ltuZWLJkiWJizBmRFsiW1g+RdLy7RrqtXkte+bPf82hy2/RmXbZcnhPHFHk027ZPS17Z4vd8fO2vf/2r2SkABpyTfUOmK0QWZcujo0/MOZxu3fXse7og4tRFvX0N5yMCCecjAg3nJHwhLS1NgwcPbl92Op36xz/+cdoPjDkfEWg4J2G2Yx/uA0Cgcblc2rlzpxobG7Vp0yaNHDlSycnJZqcFAAbBwcGKj49XfHy82an0SXV1dfr3v/8tSZo5c6aio6NNzqhvyM/P18GDBw0xi8WiyZMnUwim/nVe8X/TyxwOR/vrM6lUO1YI1Nzc/d41V1xxhW688cZOLUWnT5+ua665RkuXLtWVV16ptrY23Xvvvbr00kuVmpp60ny7k2tP80XPudu743mk/90QzwmuMSWPDxsz1WgoYJKmhZYrM4gn2ADgmHhbi8aEVGlny/G2m/taYzU+pFIpdt5DAQBAzyQlJRmWq6qqeOIVAACgn/B4PNq7d297pwm3263c3Fx5PB6lpKSYnB0AAObKzMxUY2OjysrK2mNNTU3as2ePxowZwxjWfoQiJi8LDQ1tf93a2nra9VtaWiRJYWFh3T7W6boVLVq0SD/72c/005/+VE1NTXrhhRf04x//+KT5difXnuR7unF5xcXFmjFjhiTpyiuvVE5OTrf2PxDcJamkrkXLd5bpgz2V+vV11ygsyL/t8f708WEVfpxviJ09NFZ/+so5sln7zxtDQ0ND+1Oh119//Rl1KgN8iXOyb7qmsVWXPL1B9S2u9tiBmMn6+fUT+vTFNOcjAgnnIwIN5yR8qbGxUXv37jXEZs+erYsuuuik63M+ItBwTiKQ7N27Vw8//LDZaQCAQVFRkeHGrCSFh4crISGhiy0AABg4LBaLcnJy1NjYaBgtV1FRocLCwm5NhkJgo4jJy6Kijo/7OpMRccd+wHz1wc1tt92mn/3sZ/J4PFq1alWnIqZj+XYnV6n7+WZkZJzxuhEREX26vZkvRUdLORlJuufkn1H71Cf7KvT0J8YCpuSoEP3xa9MUF3nmowX7msjISM5HBBTOyb4jOlr69vkj9Ovlu9tjWwrrtPpQoy6ZmOaXHFxuj0+LTDkfEUg4HxFoOCfhbaWlpYbl0NBQDRo06IyKozkfEWg4J2G2iIgIs1MAAIPa2lrt37/fELPZbBo7diwjcgAA+J9j742bNm2S0+lsjx84cECRkZGKi4szMTt4i9XsBPqb0NDQ9qr4wsLCU65bXV3dXhjkq8rA5OTk9nyOHDnS6evHiosaGxtVU1Nzyn0d66aUlJRkGC2H/q+k1qG7X9ssj+d4zGa16I/XTlFiPy5gAoDeunHWUA1JCDfE/t9/dsvR5upii947XNmop1bmaeHvP9YfPtrns+MAAAD/cbvdnZ7KT01N7dPdHQEAAHBUS0uLdu3aJc+JH8BLGjVqlMLDw7vYCgCAgSksLEyjRo3qFM/NzZXD4TAhI3gbRUw+MGbMGElSXl6eoQKwo927j3dmGD16tM/yOdWHmsdy7ZhPR06ns/0pAF/misDjdLn17Vc3qbLROHLw+xeN1Ixh8SZlBQB9Q4jdph990XgxfaSmWS98ctAnx3t61X7NfWSlfvvfPdpZVKdl24p9chwAAOBfra2tnToip6SkmJQNAAAAvMXtdmvXrl1qbTV+/j548GAlJiaalBUAAIEtISFBQ4cONcTa2tq0a9cuud1uc5KC11DE5APnnHOOpKPdjTZu3NjleqtWrWp/PXv2bJ/kUl5eroqKCklSWlrn0TXHcu2YT0cbNmxo7xrlq1wRmB55b48+P1RtiJ0/Klm3zckyKSMA6FsuGpuqszoUfT61Ik9l9d5/ImD6UGOr1H1lDdpbWu/14wAAAP8KDQ3VxIkTddZZZ2no0KFKTU1VaGio2WkBAACgl/bv36+6ujpDLC4urtONWQAAYDR48OD2iVTH1NfXa9++fZ26G6JvoYjJBy6//PL21y+++OJJ13G73Xr55ZclSbGxsZo/f75Pcnn22Wfbf0jnzp3b6evz5s1TTEyMJOkvf/lLlz/QL730UvvrK664wvuJ4qQqG1rkcpv3S/aDXaV6ZtUBQyw9NkyPXj1RVitjCwDgTFgsFv100Rid2BixsdWl33/o/VFvkzPjNCjGeENzKd2YAADoN0JDQzVkyBCNHDnS7FQAAADQSyUlJSoqKjLEQkNDNXr0aMYGAwBwGhaLRaNGjVJYWJghXlJSotLSUpOygjdQxOQDM2bM0Jw5cyRJL7zwgtauXdtpnUcffVS5ubmSpLvvvltBQUGGr69cuVIWi0UWi0U33XRTp+0PHTqkzZs3nzKPpUuX6pe//KWko7Mhb7755k7rBAcH6zvf+Y6ko3Mif/e733VaZ+3atXrhhRckHS2Emj59+imPC+954K3tmvnwh3p4ea7fO2kUVDXpvn9sNcSCbBY9ed0UxYYH+zUXAOjrxqXH6KopGYbYG58XqrTOu92YrFaLvjhukCG2bFsRTx0AAAAAAAAEkGOdIk5ktVo1ZsyYTveLAADAydntdo0dO1ZW6/Gyl9jY2E4dmtC3UMTkI0888YTCwsLkdDq1YMECPfzww1q3bp1WrFih22+/Xffff78kKScnR/fdd1+393/o0CFNmTJFs2bN0sMPP6zly5drw4YN2rBhg9544w1dffXVuvTSS9vnKP/ud79Tenr6Sff1/e9/Xzk5OZKk+++/X7fffrtWrFihdevW6eGHH9aCBQvkdDoVFhamxx9/vGffEHRbdWOrPtpdprL6Fj2z+oAWPLZay/zUTaPF6dK3/r5Jtc1thviPLx6tSZmxfskBAPqb7y0YqWDb8UuvVpdbz60+cIotembhBGMR0/7yRu1hpBwAAAAAAEBAaGtr065du+R2uw3xnJwcRUVFmZQVAAB9U0RERHvH6szMTE2YMIGC4D7ObnYC/dXkyZP1+uuv62tf+5rq6ur0wAMPdFonJydHy5Yt69VF6dq1a0/a6emY8PBwPfbYY7rtttu6XCcqKkrLli3TxRdfrH379unZZ5/Vs88+a1gnOjpaf/vb3zRp0qQe54ru+ffWIrW5jnfOCLZbNScn0S/Hfnj5bm0trDXEFo4fpBtnDfXL8QGgP0qNCdWXpmXo75/lt8f+9lm+7pqfrbgI73W4m5wZq7SYUBXVHu/ytGxbsUalRnvtGAAAAAAAAOg+j8ej3NxcORzG7txpaWlKSUkxKSsAAPq25ORkhYeHKzIy0uxU4AV0YvKhSy65RNu2bdO9996rnJwchYeHKzY2VtOmTdNvfvMbbd68WdnZ2T3a99SpU/XKK6/orrvu0llnnaXBgwcrPDxcwcHBSklJ0XnnnaeHHnpIBw8ePGUB0zHZ2dnavHmzfvOb32jatGmKjY1VeHi4Ro4cqXvvvVfbtm3TokWLepQreuafmwoNywvGpCg61PdVo8u2FeulNYcMsWGJEfp/V41nDjcA9NId5w6XzXr8d2lzm0svfnrQq8ewWi26eHzHkXLFjJQDAKAPampq4j0cAACgH/F4PAoONj7MFh0dreHDh5uUEQAA/QMFTP0HnZh8bMiQIVq8eLEWL17cre3mzZt3yg8qo6KidN111+m6667rbYrtIiIidP/997ePuoN59pXWa1uHTkhXTc3w+XEPVTTqB//cZoiF2K168topivJDARUA9HeDE8J16cQ0vbX5SHvspTWHdOu5WV79PbtwwiA9/8nx4qgDFY3KLa7XmDS6MQEA0Fe43W5t3rxZNptNKSkpSklJUXh4uNlpAQAAoBesVqtGjhypqKgo7d+/X0FBQRozZoysVnoOAAAASHRiAgLSPzcdMSwnRYVoTrZvR8l5PB79aMl2NbQ4DfFfXjaWm94A4EV3zjM+WVfncOqv6w579RiTMmOVHhtmiC3bXuTVYwAAAN+qrKyU0+lUS0uL8vPz9fnnn6ulpcXstAAAANBLFotF6enpmjhxosaMGaOQkBCzUwIAoN9yOp3atWuXGhoazE4FZ4giJiDAuNwevbXZOEruisnpstt8++P6ry1FWnug0hC7akqGrp6W6dPjAsBAMyIlSl8Ym2qIvfDxQTW3urx2DIvFooUTGCkHAEBfVlpaaliOiYnhBhcAAEA/EhMTo5iYGLPTAACg32poaNDGjRtVXl6unTt3yul0nn4jmI4iJiDAfJpXodI649O1V05J9+kxa5vb9KtluYZYWkyofnnZWFksFp8eGwAGorvmZxuWKxtb9frn+V49xsLxxiKmQ5VN2llU59VjAAAA32htbVVlpfEhk9TU1C7WBgAAAAAAwInq6uq0efNmORwOSZLD4VBubi4Pe/cBFDEBAeafm4xdmMamRWtUqm/HuT363h5VNBgLp35+6VhFhNh9elwAGKjGZ8To3JwkQ+yZ1QfU6nR77RgTMmKUEddxpFyx1/YPAAB8p7y83LBstVqVmOjbEeMAAADwvrKysvabpwAAwH8iIyMVERFhiFVVVSk/37sPlMP7KGICAki9o03v7iwxxK6akuHTY24rrNFf1x02xM4flawFY1J8elwAGOi+1aEbU3Gto9M40d5gpBwAAH1XWVmZYTkxMVF2Ow+ZAAAA9CWNjY3avXu3Nm3apOrqarPTAQBgQLFarRozZoyCgoIM8cOHD6uhocGkrHAmKGICAsh/tpfI0Xa8C4fdatGlk9J8djyX26Mfv7VDJ97PDg2y6sFLGSMHAL42Y1i8ZgyNN8T+tHK/nC7vdWNaNN74HhIfEazqpjav7R8AAHhfS0uL6uqMI2CTkpK6WBsAAACByOPxaM+ePfJ4PGpra9O2bdtUUFBgdloAAAwooaGhGj16tCHm8Xi0e/duud3euxcD76KICQggb3YYJTdvZJISI0N8dry/fXZY24/UGmLfPm+EMuPDfXZMAMBxd51n7MZ0qLLJqyPfxqVH6wtjU/Xji0frkx/M19t3zVZ8RLDX9g8AALyv4yg5m82m+Pj4LtYGAABAICooKFB9fb0h1traalI2AAAMXHFxcRo8eLAh1tjYyFi5AEYRExAgCqqatP5glSHmy1FyZfUOPfLuHkNseFKEbp2T5bNjAgCMzh2RqPHpMYbYUyv2y+32zsg3i8Wip6+fqlvPzVJGHAWqAAD0BScbJWe18vENAABAX9HY2KhDhw4ZYmFhYRo6dKgp+QAAMNANGTJEERERhlh+fj5j5QIUn4IBAeLtzUcMyzFhQTpvdLLPjvfrZbmqdzgNsf+7fJyC7fxaAAB/sVgsumv+cENsT2m9Ptxd1sUWAACgP3M4HJ2e2GeUHAAAQN9x4hi5E40aNUo2m82krAAAGNisVqtGjhxpiDFWLnBRrQAEiLHp0TpvVLKCbBZJ0iUTBynE7ps/atbkVejtLUWG2BWT0zVreKJPjgcA6NqCMakakRxpiP1xRV6nD7sAAED/13GUnN1uV1xcnEnZAAAAoLvy8/M7FaVnZGQoOjrapIwAAIAkRUVFMVauj7CbnQCAo84blaLzRqWotqlN7+4q0bi0mNNv1AMtTpd+8q8dhlhUqF0PXDzaJ8cDAJya1WrRnfOH697Xt7bHthbU6NO8Sp0zguJSAAAGko5FTIySAwAA6DsaGxt1+PBhQyw8PFzDhg0zKSMAAHCiIUOGqLKyUo2Nje2x/Px8JSQkKCoqysTMcCI+CQMCTEx4kK6elqkxab55MuO51Qd0oLzRELv/opFKigrxyfEAAKd3yYQ0ZcaHGWJPrsjz2fE8Ho8cbS6f7R8AAHRfc3Mzo+QAAAD6KLfbrd27d3fqrD1y5EiK0gEACBDHxspZLJb22LFRsIyVCxxcOQEDSH5lk/7wkfGm+ISMGF171hCTMgIASJLdZtUdc4cbYmsPVGrj4WqvHcPj8WhnUa1++9/dmve7lXr0vT1e2zcAAOg9RskBAAD0XQUFBWpoaDDEMjMzGSMHAECA6WqsXGFhoUkZoSOKmIABwuPx6Of/3qEW5/EqUqtFeujy8bJZLafYEgDgD1+amqGUaGNXPG92Y3ru4wNa+PtP9NTK/Tpc2aRl24o7PR0IAADMEx4ebihaSkpKMjwZCAAAgMDU0NBw0jFyQ4cONSchAABwSoMHD1ZERET7cnJysgYNGmRiRjgRRUzAAPHuzlKt2GN8svf6s4dofEaMSRkBAE4UYrfp1jlZhthHu8u0s6jWK/s/N8c4jqao1qHNBTVe2TcAAOi9xMRETZgwQbNmzVJOTg4fngEAAPQBbrdbe/bsYYwcAAB9iNVq1ahRoxQSEqKxY8dq9OjRCgoKMjst/A9XUICJPB6P1uyvUJvLtzM2G1uc+sU7Ow2xxMgQ3XfRSJ8eFwDQPdeeNVhx4cYL5adW7PfKvkemRGl4UoQh9s7WIq/sGwAAeE9QUJAGDRqkqKgos1MBAADAaTBGDgCAvikyMlIzZsxQYmKi2amgA4qYABPtLKrTtc99phkPfaAfLdmuT/Mq5HZ7f7TPEx/uU3GtwxD76aLRig6lohQAAkl4sF1fnz3MEFu+o1j7yxu62OLMWSwWLZyQZoi9s7VITh8X0gIAAAAAAPRXbW1thmXGyAEA0HfQNTEw8X8FMNHSbcWSpOqmNr26Pl8/XLJNFot3j7G7pE4vfHLQEJudnaBLJ6Z1sQUAwEw3zBqqqBB7+7LHI/1ppXe6MV0+yfi7v6KhVR/vq/DKvgEAAAAAAAaa7OxsTZgwQSEhIZIYIwcAANBbXEkBJvF4PFq23TjGZ+H4NFm8WMXkdnv0k7d2yHVCd6dgm1W/vGycV48DAPCemLAgXT9ziCH29uYjKqxu6vW+s5IiNSkz1hD756bCXu8XAAAAAABgoIqLi9O0adM0duxYxsgBANAPtLa2qqyszOw0BiyKmACTbCusVUFVsyG2aMIgrx7jzU2F2nC42hC7fW6WhidFevU4AADv+vo5wxQadPwyzen26JlVB7yy7yunpBuW399VqjpHWxdrAwAAX6utrZXH4/2x4gAAAPAfu92uxMREs9MAAAC94PF4VFpaqs8//1y5ubmqr683O6UBiSImwCTLthcblocmhGtsmvee0qhubNXDy3MNscz4MN01P9trxwAA+EZiZIi+Mn2wIfb6hgKV1Tl6ve9FE9IUZDveja/F6dZ/OrwnAQAA/2hsbNSWLVu0du1a7du3TzU1NRQ0AQAAAAAA+JnH41Fubq52794tp9MpSdq9e7fcbrfJmQ08FDEBJvB4PFq2zXjDeNEE746S+81/d6u6ydhZ45eXjlNokM1rxwAA+M7tc7MMxUatTrde+ORgr/cbHxGseSOTDbElm470er8AAKD7ysvLJUltbW0qKipSbm7uabYAAACAmVwul9kpAAAAH7BYLIqIiDDEmpqadOjQIXMSGsAoYgJMsCm/RkdqjKPkFnpxlNzGw1V67fMCQ+wLY1M1f1RyF1sAAALNoJgwXTUlwxB7Zd1h1TS19nrfV042jpT77GCVCquber1fAABw5jwej8rKygyxpKQkrz7cAgAAvOPw4cO67777NGrUKEVERCg+Pl7Tp0/XI488oqam3v09/dJLL8lisZzRv5deesk7/0HokYaGBq1bt05FRUV0zwQAoB/KzMxUZGSkIVZQUKC6ujqTMhqYKGICTNCxC9PwpAiNSo3yyr6dLrd+/NYOQyw82KafXTLGK/sHAPjPHXOHy3rCfczGVpde/PRQr/d73uhkRYfaDbG3N9ONCQAAf2psbFRzs/HhluRkHjwBACDQvPPOO5owYYIWL16sPXv2qKmpSdXV1dqwYYPuv/9+TZ48WXl5eWanCR/zeDzau3evnE6n9u3bp+3bt8vhcJidFgAA8CKr1aqRI0d2esBs7969FDD7EUVMgJ+53R4t324sYlroxVFyL605pN0l9YbYvRfkKC02zCv7BwD4z9DECC2akGaIvbTmkBpanL3ab4jdpkUTjftdsvkIF+EAAPjRsVFyx4SEhCgqyjsPtwAAAO/YvHmzrrnmGtXV1SkyMlIPPfSQ1qxZow8//FC33nqrpKM3tRYuXKj6+vrT7O303n33XW3fvr3Lf5dffnmvj4GeKSsrM/w/rq6uVklJiYkZAQAAX4iMjNSQIUMMscbGRt73/ch++lUAeNPG/GqV1Bmf0FjkpVFyxbXNeuz9vYbYqNQo3TR7qFf2DwDwv7vmZ+vfW4val2ub2/TKusO6Y+7wXu33qinp+vtn+e3LB8obtbWwVpMyY3u1XwAAcHoej6dTEROj5AAACDx33323mpubZbfb9d5772nmzJntXzvvvPM0YsQI3X///dq7d68effRRPfjgg706Xk5OjoYOHdq7pOF1LpdLBw8eNMTCwsI0ePBgkzICAAC+lJmZqfLycjU2NrbHDh06pOTkZNlsNhMzGxjoxAT42dITbkRLUk5KpHJSvPO07a+W5qqx1WWMXT5OQTZ+1AGgrxqZGqULx6QYYs9/fFCONlcXW5yZKYPjNCQhXJJkt1p0wegU2a3cOAUAwB8aGho6jZJLSkoyKRsAAHAy69ev18cffyxJuuWWWwwFTMfcd999Gj16tCTpiSeeUFtbm19zhH8UFhaqpaXFEMvKypLVyufuAAD0R1arVVlZWYZYa2ur8vPzu9gC3sQVFuBHLrdHy3cYW811HBPUU5vzq7Wsw5i6q6dlaNrQeK/sHwBgnrvmZxuWKxpa9MaGgl7t02Kx6K752frFpWP12QPn6/kbp2lcekyv9gkAAM5Mxy5MoaGhjJIDACDAvP322+2vb7755pOuY7VadcMNN0iSampqtGLFCn+kBj9qbW1VQYHxM5jY2FglJCSYlBEAAPCH+Ph4xccb77OfrLAZ3kcRE+BH6w9Wqbze+IttoZdGyf3uvT2G5djwIP3wi6O9sm8AgLkmZcbqnOxEQ+yZVQfU5nL3ar9XT8vUjbOGKiEypFf7AQAAZ45RcgAA9A2ffPKJJCkiIkJTp07tcr25c+e2v/700099nhf869ChQ3K5jN2ws7KyuHYDAGAA6NiNye12dxoxC++zm50AMJAs3WYcJTd6ULSGJ0X2er+f5lXo07xKQ+yuedmKjwju9b4BAIHhrvnZ+iSvon35SE2z/rWlSF+ammFiVgAAoLvq6+vlcDgMMUbJAQAQeHJzcyVJ2dnZstu7vpUyatSoTtv01M0336w9e/aooqJC0dHRys7O1gUXXKBvfvObSk9P7/F+CwsLT/n14uLjHf7r6+tVV1fX42N1R0NDw0lfB4rm5mbD90Y62pXB4/H47XuE7gv08wp9D+cUvI1zqm9JTExURcXxezOlpaWKjY1VeHi4iVl1ZtZ5VV9f7/V9UsQE+NGCsamqdzj1QW6pmlpdWuSFLkwej0ePvGvswpQSHaLrZw7p9b4BAIHj7Kx4TR0Sp42Hq9tjT63M0xWT02Wz8vQfAAB9RccuTGFhYYqM7P3DLQAAwHscDkf7zaqMjFM/PBQXF6eIiAg1NjZ2GjvWXStXrmx/XVlZqcrKSn322Wd69NFH9fjjj+v222/v0X4zMzPPeN2//vWvionx/7j5v/71r34/5umMGjVKsbGx7csul0vvv/++2trazEsK3RKI5xX6Ns4peBvnVOCz2+2aNGmSoah9/fr1vS5e9yV/nle1tbVe3ydFTIAfzc1J0tycJDW3urRyT5kmZsb2ep8f5JZpS0GNIfad80coNMjW630DAAKHxWLRXfOH6+svbWiPHShv1Hs7S/TF8d4ZTQoAAHyLUXIAAPQNJz5RfibFxseKmHr61HtWVpauvPJKzZw5s73g6MCBA/rnP/+pN998Uw6HQ3fccYcsFotuu+22Hh0D3RMTE2MoYJKOdqyigAkAgIHF6XSqqKhIgwcPbo/FxMQoMjKSTlo+QhETYIKwYJtXbji73R79rkMXpsHx4bp62pk/WQMA6Dvmj0zWqNQo7S45/mHqUyv36wvjUr1287PV6dam/GqdnZXglf0BAIDj6uvr1dLSYogxSg4AgMBz4ujX4ODg064fEhIi6ej4se664oordOONN3b6u3769Om65pprtHTpUl155ZVqa2vTvffeq0svvVSpqandOsbpOkQVFxdrxowZkqTrr7++V6PruqOhoaG9U8D1118fMN0pPR6Pdu/ebTgP7Ha7Lr74YtlsPDwc6AL1vELfxTkFb+Oc6nvcbrd27dqltrY2hYeHKz09XZMnTzY7LQOzzqsjR47o4Ycf9uo+KWIC+rB3thVpT6lxzuR3L8xRkM1qUkYAAF+yWCy6c362vvPq5vbY9iO1+nhfhc7N6fkNUI/Hoy0FNXpr8xG9s7VI1U1tWvX9eRqSEOGNtAEAwP+UlZUZlsPDwxURwfstAACBJjQ0tP11a2vradc/VqQcFhbW7WOdbnTbokWL9LOf/Uw//elP1dTUpBdeeEE//vGPu3WM043EO1FUVJSio6O7tX9viIyMNOW4J1NcXGwoYJKOdsuKi4szKSP0VCCdV+gfOKfgbZxTfUdOTo5cLpeSk5MDvqO2P8+ruro6r++TSgegj2pzubX4/b2G2MiUKF0yMc2kjAAA/nDxuFQNSQg3xJ5amderfba5PPr6S5/r5bWHVd10tC36W5uP9GqfAACgs/j4eCUlJclqPfpxDKPkAAAITFFRUe2vz2RMSGNjo6QzGz3XE7fddlv7NcOqVat8cgwc5XQ6dfDgQUMsIiKi292vAABA/5KYmKiUlBQ+x/EDipiAPuofGwp1uLLJELtvQY5sVn5xAkB/ZrdZdcfc4YbYugNV2ni4usf7DLZbOxXBvrX5iDweT4/3CQAAOouPj9eYMWM0a9YsjR49WikpKWanBAAATiI0NFQJCUfHrBcWFp5y3erq6vYipszMTJ/kk5yc3J7PkSM8dORLLS0tCgoKMsSGDx/ODUsAAAA/oYgJ8LFWp1vLtxerudXltX062lz6/Yf7DLGJmbG6cAwfgAPAQHDllHSlRIcYYn/qZTemKyanG5YPVzZpU35Nr/YJAABOzmazKTk5uUcjZwAAgH+MGTNGkpSXlyen09nlert3725/PXr0aJ/lQxGNf0RERGjatGkaMWKEgoKCFB8fzxg5AAAAP6KICfCxj/eV686/bdKU/3tf3/r7Jr27s6TX+3xl3WGV1Blnct9/0Uj+kAWAASLEbtOtc7IMsQ9yy7S7pOezhydlxiorMcIQW7Lp1E+bAgAAAADQX51zzjmSjo6K27hxY5frnTjebfbs2T7Jpby8XBUVFZKktLS006yN3rJYLEpLS9OMGTOUk5NjdjoAACCAeTwe1dTUmJ1Gv0IRE+BjS7cVS5Ka21xauq1Yz64+0Kv9NbQ49dTK/YbYrOEJmp2d2Kv9AgD6lq/OGKzYcGN78z91eH/oDovF0qkb09JtxWpxeq+TIAAAAAAAfcXll1/e/vrFF1886Tput1svv/yyJCk2Nlbz58/3SS7PPvts+8j3uXPn+uQY6MxutyskJOT0KwIAgAGppqZGmzZt0tatW1VbW2t2Ov0GRUyADznaXHp/V6khtmjCoF7t88+fHFRVY6sh9r2LRvZqnwCAvicixK6bZg01xN7ZWqTDlY093uflHYqYapvbtGJ3WY/3BwAAAABAXzVjxgzNmTNHkvTCCy9o7dq1ndZ59NFHlZubK0m6++67FRRkfNho5cqVslgsslgsuummmzptf+jQIW3evPmUeSxdulS//OUvJUlhYWG6+eabe/KfAwAAAC/as2ePtm7dqoaGBknS/v3724vO0TsUMQE+tGpvuRpajs9Lt1iki8f3vIipurFVz3Xo5HTB6BRNGcxMbgAYiG6aNVThwbb2ZbdHeqYXHf8y48M1Y1i8IbZk05Ee7w8AABxtK15RUSG32212KgAAoJueeOIJhYWFyel0asGCBXr44Ye1bt06rVixQrfffrvuv/9+SVJOTo7uu+++bu//0KFDmjJlimbNmqWHH35Yy5cv14YNG7Rhwwa98cYbuvrqq3XppZeqtfXoQ62/+93vlJ6efpq9AgAAwNciIiIMy/X19SovLzcpm/7FbnYCQH+27H+j5I6ZPjReKdGhPd7f06v3q75DUdR9C5jJDQADVWx4sK47a7Ce+/hge+zNDYW6+/wRPX6/uXJyutYfrGpfXrGnTNWNrYqLCO51vgAADEQ1NTXauXOnbDabEhISlJSUpISEBFksFrNTAwAApzF58mS9/vrr+trXvqa6ujo98MADndbJycnRsmXLFBUV1ePjrF279qSdno4JDw/XY489pttuu63Hx8DJud1u7d27V+np6b36fwgAAAaWtLQ0FRUVqbm5uT124MABJSYmymqll1Bv8N0DfKS51aUPco2j5C7pxSi50jqH/rLmkCF26cQ0jR4U3eN9AgD6vm/MyVKw7fglXavLrec/7nk3posnDFKw/fj+2lweLd1W1KscAQAYyI49hedyuVRWVqbDhw9TwAQAQB9yySWXaNu2bbr33nuVk5Oj8PBwxcbGatq0afrNb36jzZs3Kzs7u0f7njp1ql555RXdddddOuusszR48GCFh4crODhYKSkpOu+88/TQQw/p4MGDFDD5SFFRkUpLS7Vp0ybl5ubK4XCYnRIAAOgDrFarsrKyDLGWlhYVFhaalFH/QScmwEdW7ilTU6urfdlqkb4wrudFTH/8KE+OtuPjB2xWi+69gC5MADDQpUSH6kvTMvT3z/LbY3/7LF93zc9WbHj3uydFhwbpwjEphm6CSzYf0fUzh3ojXQAABhS3292plXhSUpJJ2QAAgJ4aMmSIFi9erMWLF3dru3nz5snj8XT59aioKF133XW67rrrepsieqCtrU2HDx9uXy4rK1NLS4smTZpkXlIAAKDPSEhIUExMjGpra9tj+fn5Sk1NVXAw0y16ik5MgI8s7TBK7uysBCVFhfRoX/mVTXp1fb4hdvW0TA1NjOhiCwDAQHL7uVmyntDQoanVpZc6dO/rjqumpBuWN+fX6EB5Q4/3BwDAQFVTUyOn02mIUcQEAAAQGIqKijpdqw0bNsykbAAAQF9jsVg0fPhwQ8zlcunIkSMmZdQ/UMQE+EBTq1Mf7jaOkls0Ia3H+3v8w71yuo8/sRNst+o75/esRTEAoP8ZkhChSyYa32deWnNIjS3OLrY4tTkjkpQQYXxK4O3NXHQDANBdHbswRUVFKSwszKRsAAAAcMzJbjAmJiYqJibGpIwAAEBfFBUVpeTkZEOsqKhILperiy1wOhQxAT7wYW5Zp9FvXxiX2qN97S2t11sdbhzfcPYQDYrhg28AwHHfnGes9q9pauvUxe9MBdmsnYqilmw+Ire76xb4AADAyO12q6KiwhCjCxMAAEBgKC0tVVtbmyE2ZMgQk7IBAAB92eDBgw3LTqdTxcXFXayN06GICfCBZR1Gyc0anqD4iJ7NvVz83l6dODY9ItjW6UY1AACjUqN1wWhjtf9zHx9Qi7Nn1f5XTcmQJIUGWXXpxDT96vJxvc4RAICBpLq6mlFyAAAAAcjj8aigoMAQi4uLU2RkpEkZAQCAviwiIkIJCQmGWGFhodxudxdb4FTsZicA9DcNLU6t2FNmiC2aMKhH+9paUKP/7iwxxG6Zk6WEyJAe5wcA6L++OS9bH+Qefw8qrWvRkk1H9NUZg0+x1cmNS4/WH6+drLk5SYoKDfJmmgAADAgdR8lFR0crNDTUpGwAAABwTEVFhRwOhyGWmZlpUjYAAKA/yMzMVGVlZftyS0uLysvLlZKSYmJWfROdmAAv+zC3VC3O41WVdqtFF43t2Si53723x7AcGx6kb8wZ1qv8AAD919QhcTo7K94Qe3rVfjld3a/2t1gsWjQhjQImAAB6gFFyAAAAgelkXZgiIyMVGxtrTkIAAKBfiImJUXR0tCFWUFAgz4kjl3BGKGICvOydrcZRcnNGJCo2vPuj5Nbur9TH+4wfen9z7nBFczMZAHAKd87LNiwfrmzS8h0lXawNAAB8oaqqSi6XcaQrRUwAAADmq62tVX19vSGWmZkpi8ViUkYAAKC/6NjZsbGxUdXV1SZl03dRxAR42a1zhunaswYrPuJo4dLCCWnd3ofH4+nUhSk5KkQ3zBzqjRQBAP3YnBGJGp8eY4g9tSKPan8AAPyo4yi5mJgYhYQwFhwAAMBsHbswhYaGUmwOAAC8IiEhQWFhYe3Ldrtdra2tJmbUN9nNTgDob87KStBZWQn65aVjtfZApSZmxnZ7Hyv2lGnjYWNV5rfPH6GwYJuXsgQA9FcWi0V3zR+uO17Z1B7bXVKvFXvKdN4oZi8DAOBrbrdblZWVhhg3xgAAAMzX0NCgqqoqQywjI4MuTAAAwCssFosyMzN1+PBhZWRkKDU1VXY7JTndRScmwEfsNqvmjEjq9vg3t9ujR97da4hlxofpmmmZXWwBAIDRgjGpGp4UYYg9uWJ/r7sxNbU6tWxHmQ62RvVqPwAA9GcnGyWXmJhoUjYAAAA4prCw0LBst9uVmppqUjYAAKA/SklJ0YwZM5SRkUEBUw/xXQMCzLLtxcotrjPE7r0gR8F2ag4BAGfGarXom/Oy9b1/bG2PbTxcrfUHq3RWVkK397e7pE7PrT6o/+4oVmOrS0m2JA0LrvdmygAA9BuMkgMAAAg8DodDZWVlhlh6erpsNqYfAAAA77FauaffW3wHgQDidLm1+H1jF6YRyZG6bFK6SRkBAPqqyyalKT02zBB7cuX+Hu2rpNahf24qVGPr0a4S5a5wVbu4GQsAwMmkpqYa2oUzSg4AAMB8FotFKSkp7aPjrFar0tP53B0AACDQ0IkJCCD/3FSogxWNhth9C0bKZmUmNwCge4JsVt12bpZ+/u+d7bHVe8u140itxqXHdGtf52QnKikqROX1Le2xPa2x3koVAIB+JS4uTnFxcRoxYoRqamoUGRlpdkoAAAADXkhIiEaOHKmhQ4fqyJEjslgsCgoKMjstAAAAdEAnJsALqhpb9frn+appau3xPlqcLj3xwT5DbEJGjC4am9Lb9AAAA9TV0zKVEBFsiD21Mq/b+7HbrLp8Upohtq8lRm6Pp1f5AQDQn1mtVsXHxys4OPj0KwMAAMAvQkJClJWVpWHDhpmdCgAAGEBaW1tVXV1tdhp9AkVMgBf8d0eJfvDP7Zr2qw9004vr9e+tRd3ex2vrC1RU6zDEvn/RyPb2tgAAdFdYsE1fP8f4odx/dpRof3lDt/d1xeQMw3KDJ1gbDtf2Kj8AAAAAAAAAAID+qqmpSXv37tW6deu0a9cuuVwus1MKeBQxAV6wdNvRoiWn26OVe8r17o6Sbm3f6nTrmVX7DbGzs+J1Tnai13IEAAxM188coqiQ4xOEPR7p6ZX7T7HFyY1Ji9ao1ChD7J0dZb3ODwAAAAAAAAAAoL9paWnR559/ruLiYnk8HjmdThUXF5udVsCjiAnopfL6Fq07UGmILZowqFv7eHvLkU5dmO69IIcuTACAXosODdL1M4cYYm9tPqIjNc3d3teVU9INy+/vrlC9o61X+QEAAAAAAAAAAPQ3ISEhSkhIMMQKCwvldrtNyqhvoIgJ6KX/7iiW23N8OSLYpvmjks94e5fb06kjxvShcTorK6GLLQAA6J6vnzNMIfbjl31Ot0fPrT7Q7f1cNild1hPqa5taXXpr8xFvpAgAQJ/mdrtVWloqp9NpdioAAAD4n6qqKhUWFjK2BQAAmCYzM9Ow3NLSovLycpOy6RsoYgJ66Z1txpZvF4xJUWiQ7Yy3/8+OYh2oaDTE7pyf7ZXcAACQpMTIEH1luvFC+bXP81XZ0NKt/aREh2reCGOR7V/WHJLH4+liCwAABoaqqirt3r1ba9as0fbt21VaWmp2SgAAAAOax+PRoUOHtH//fq1bt04HDx5Ua2ur2WkBAIABJjo6WtHR0YZYQUEB91VOgSImoBdK6xz6/FCVIbZw/JmPkvN4PHpyhbEL09i0aM3LSfJKfgAAHHPruVmyn9BGydHm1vOfHOz2fr46Lc2wvL+8UZ/kVfQ6PwAA+rJjT9B5PB5VVVWpuLj4NFsAAADAl2pra1VfXy9Jcjqdys/PV01NjblJAQCAAcdisXTqxtTY2Kjq6mqTMgp8FDEBvbBsW7FOLJKMCrFr7sgzL0BasadMucV1hthd87NlsVi62AIAgJ7JiAvXZZPSDbGXPj2kim52Y5oxJEZxVoch9pc1h3qbHgAAfZbb7VZlZaUhlpTEgykAAABmKigoMCyHhoZyjQYAAEyRkJCgsLAwQ6zjtQqOo4gJ6IVl241P1144NkUh9jMbJefxePTHj/IMsaykCF00NtVr+QEAcKK75g/XCc2Y1Nzm0p9W7u96g5OwWCwaF2rsQvjh7jLlVzZ5I0UAAPqcqqoquVwuQywxMdGkbAAAANDU1KSqKuNnFxkZGTw8DAAATHGybkw1NTVqaGgwKaPARhET0ENFNc3aeNjY5m3RhDMfJbfuQJU25dcYYnfOy5bNyh9SAADfyEqK1FVTMgyxv647rJJaRxdbnNzI4BoFW47frPV4pFc+O+yVHAEA6GsqKoxjVaOjoxUSEmJSNgAAACgtLTUs2+12paby8DAAADBPSkqKgoODDbGO1yw4iiImoIeWbTN2YYoJC9I52WfejvaplcYuTOmxYbpsUppXcgMAoCvfOX+EgmzHC2ZbnW794aN93dpHkMWtUcHGQt68Mp4YAAAMPG63u1MRU3JysknZAAAAwOPxdLohmJKSIpvtzCYoAAAA+ILValVKSoohVlZWJo/HY1JGgYsiJqCHlnYYJXfR2BQF28/sR2prQY0+3mf8oPuOuVkKsvEjCQDwrcz4cF0z3di29PXPC1RQ1b1xcONCqhRqt+pLUzP0zrfO0Z9vmu7NNAEA6BMYJQcAABBYamtr1dLSYoh1vGEIAABgho7XJK2traquru5i7YGLigmgBwqqmrS1oMYQWzjhzLsoPbnC2IUpMTJEX56W2cXaAAB417fPG6GQEwpvnW6Pnviwe92YYmyt+ujus/S7L0/U+IwYb6cIAECfwCg5AACAwFJSUmJYjoiIUGRkpEnZAAAAHBcREaGoqChDrOO1CyhiAnpkaYdRcnHhQZo1POGMtt1bWq/3dhnb2d46Z5hCg2hnCwDwj5ToUF1/9hBDbMmmQu0v795IuMgQuzfTAgCgTznZKLmkpDMfMQ4AAADvcrlcna7PUlJSZLFYTMoIAADAqGM3psrKSjmdTpOyCUwUMQE9sGx7kWH5C+MGnfEouKc6dGGKCQvSdR1uJAMA4Gt3zBuu8ODjBbRuj/T4B93rxgQAwEB2slFyFDEBAACYp6KiotP1WXJysknZAAAAdJacnGwosHa73aqsrDQxo8BDERPQAw9fMUG3n5ul9NgwSdKiCYPOaLv8yib9e6uxAOqmWUPpZAEA8LvEyBDdPHuoIfbO1iLlFteZkxAAAH0Mo+QAAAACS2mpcQJCXFwc12cAACCgBAUFKT4+Xna7XWlpaZo8eTJF1x1QOQH0wPiMGI3PiNEPvzhKmwtqNCE95oy2+9Oq/XJ7ji+HB9t006yhvkkSAIDTuG3OcL289rDqHcdblS5+f6+eu2Faj/fp8XhUWN2szPhwb6QIAEBAYpQcAABAYGlpaVF1dbUhlpqaalI2AAAAXRsxYoSCgoJktdJz6GT4rgC9YLFYNGVwnOxnMEqupNahf24sNMS+dvYQxUUE+yo9AABOKSY8SLfOyTLE3t9Vqq0FNd3eV2OLU39dd1gLHluty578VI421+k3AgCgj6qurmaUHAAAQADp2IXJZrMpISHBpGwAAAC6FhISQgHTKfCd8bHDhw/rvvvu06hRoxQREaH4+HhNnz5djzzyiJqamnq176amJi1ZskTf/OY3NX36dMXFxSkoKEgJCQmaOXOmHnzwQZWUlJx2P/PmzZPFYjmjf+i55z4+oFaXu3052G7VN84ZZmJGAABIN88eqrjwIEPs0ff3dmsfVY2tOvvhD/XTt3doX1mDqhpbtXRbsTfTBAAgoJSXlxuWGSUHAABgrpqaGsNyUlKSbDabOckAAACgxyhi8qF33nlHEyZM0OLFi7Vnzx41NTWpurpaGzZs0P3336/JkycrLy+vR/vetm2bUlJSdNVVV+npp5/Whg0bVFNTI6fTqaqqKq1bt06/+MUvNHLkSL3++ute/i9Dd1U1turvn+UbYldPy1BydKhJGQEAcFRUaJC+OW+4IbZ6b7nWH6w6433ERwRrQoZxtOpf1hySx+PpYgsAAPq2tLQ0paentxcu0YUJAADAXOPHj9fEiROVkpIim82mlJQUs1MCAABAD9jNTqC/2rx5s6655ho1NzcrMjJSP/rRjzR//nw1Nzfrtdde03PPPae9e/dq4cKF2rBhg6Kiorq1/7q6OjU0NEiSZs+erUWLFmnatGlKSEhQeXm5lixZoueee051dXW67rrrFB0drS9+8Yun3Oe0adP04osv9vi/GV178dODaj5hrI7NatHt5w4/xRYAAPjP9WcP1XMfH1R5fUt77Hfv7dHrt519xp0Yb5g5VJ/mVbYvbz9Sq035NZo6JM7r+QIAYLbo6GhFR0dr+PDhqqurU1hYmNkpAQAADGgWi0WxsbGKjY2Vy+ViRAsAAEAfRRGTj9x9991qbm6W3W7Xe++9p5kzZ7Z/7bzzztOIESN0//33a+/evXr00Uf14IMPdmv/VqtVV199tX7+859rzJgxnb6+YMECffGLX9QVV1whl8ulb3/729q3b98pb0RGRERo3Lhx3cpjIDlQ3qCPdpdp4YRBGhRz5h9Q1zva9NKaQ4bYZZPSlBkf7uUMAQDombBgm741P1s///fO9tj6g1X6NK9S54xIPKN9XDA6RemxYTpS09wee3ntIYqYAAD9msViUUxMzOlXBAAAgN8wRg4AAPQlbW1tKisrU0VFhcaNGzfgr2UoRfeB9evX6+OPP5Yk3XLLLYYCpmPuu+8+jR49WpL0xBNPqK2trVvHmDVrll5//fWTFjAdc9lll+nKK6+UJO3fv1+bN2/u1jFg9PbmI/rVslzNfPgjfelPa/TW5sIz2u6v6w6r3uFsX7ZYpDvn0YUJABBYvjIjU2kxxjGnv3tvzxmPhLNZLbp+5hBDbPn2YpXVO7yWIwAAAAAAAAAAQH/g8Xi0c+dOrV27Vnl5eaqpqVFlZeXpN+znKGLygbfffrv99c0333zSdaxWq2644QZJUk1NjVasWOGTXObPn9/+ev/+/T45xkDg8Xi0dFtx+/KGw9XaeaTutNs1t7r0wscHDbGLxqQqO7l74wMBAPC1ELtN3zl/hCG2paBGH+aWnfE+rpmWqRD78cvLNpdHf/8s32s5AgAAAAAAAAAA9AcWi0VOp9PwMHlpaamJGQUGiph84JNPPpF0dDzb1KlTu1xv7ty57a8//fRTn+TS0tLS/nqgtx3rjV3FdTpQ0WiILZww6LTbvbGhQJWNrYbYXfOzvZobAADectXUDA1JMI47ffT9vXK7z6wbU1xEsC6flG6I/e2zfLU63V7LEQAAAAAAQNIZd48GAAAIVKmpqYblqqoqtba2drH2wGA3O4H+KDc3V5KUnZ0tu73rb/GoUaM6beNtq1atan99bHxdV3bv3q2zzjpLe/bskcPhUGJioqZOnaqrrrpKX/3qVxUUFNTjPAoLTz16rbj4eJejxsZG1dWdvsuRPy35/JBhOS0mRFkx1lPm2eZy608r8wyxWVlxGhJtCbj/Phg1NDSc9DVgFs5J+NPtszP1wL/3tC/nFtdpyecHtGB0kqTTn49XTUzU6xsK2pfL61u05PMDunhssg+zxkDF70cEGs7J/s3tdquyslKxsbG9+vvYXzgfEWg4JxFIGhsbT78SgIBXXFyssrIypaSkKCkp6ZT3YwAAAAJRYmKirFar3O7jD4OXlpYqMzPTxKzMxRWdlzkcDlVUVEiSMjIyTrluXFycIiIi1NjYqIKCglOu2xNbt27VsmXLJEnjx48/bRFTaWmpoT3ZkSNHdOTIEf373//Wb37zG7355pun3UdXuvNDtmTJEsXExPToOL7g8Uj/qMuRFNweS3YU6umnN55yu90tsSppMp4DSWUb9ac/feyLNOEjf/3rX81OATDgnISvuT1SnDVb1e7Q9tiv/rVF+1bkyWoxrtvV+ZhqH6YSZ0T78mNLN+vw6gM+yRc4ht+PCDSck/1PbGysRo0apYKCAtXX16uysrLPtPjmfESg4ZyE2Wpra81OAYAXlJSUqL6+XrW1tcrLy1NWVpbS09NPvyEAAECAsNlsSkpKMnzGNNCLmBgn52X19fXtryMjI0+7fkTE0Rt83n4CraWlRd/4xjfkcrkkSQ899FCX61qtVp1//vl69NFH9cEHH2jz5s1avXq1Hn/88faipV27dmn+/PnKz8/3ap59QbkrVHXuYEMsO/jUH3S4PdImR5IhlmpvVFpQk9fzAwDAm6wWaXpYmSFW4w7VvtbYM97H+JBKw3KpK1zlztAu1gYAoG9ISEiQJFksFkVHR7cvAwAAwP+ampoM92PcbrdCQ/nsAQAA9D0pKSmG5cbGxgHdwZhOTF7mcDjaXwcHB59izaNCQkIkSc3NzV7N41vf+pY2bNggSbrxxht1ySWXdLnukiVLFBsb2yk+Z84c3Xnnnbr11lv1l7/8RaWlpbrnnnu0ZMmSbudzuk5TxcXFmjFjhiTpyiuvVE5OTreP4SuPfnhA+uxI+3JmXKh+esdNslgsXW7z7q5y1b692xD72VUzdM7wL/gsT3hPQ0ND+1Oh119//RkVJAK+xDkJf3N7PMr/82btLj0+YmFvcLYeuW2qWpqbTns+trnc2vrU5yqrPz63uW3YbH1zUeC8v6N/4PcjAg3nZP/ldru1Y8eO9geFpKMj2+fOnWtiVqfG+YhAwzmJQLJ37149/PDDZqcBoBdKSkoMy0FBQYqPjzcpGwAAgJ6LjY1VSEiIWlpa2mMlJSXKzs42MSvzUMTkZSdW+re2tp5izaOOnYhhYWFey+Hhhx/W888/L0maPn26nnzyyVOuf7ICpmOCgoL0/PPPa926ddqzZ4/eeustHTlypNstWU83Wu9EERERio6O7tb+fcXj8eiDPVWG2KWT0k857s7j8ejPn201xMalR+uLk4acsvAJgSkyMjJgzkdA4pyE/9z/xdH6+ksb2pcLaxx6d2+dFo2ObY+d6ny8/uyhevT9ve3L/9lVrp9dOl4JkSE+yxkDG78fEWg4J/uXyspKQwGTdHRs+rEHkwId5yMCDeckzHasOz6Avsnj8aiszNhFOiUlhc/fAQBAn2SxWJSSkmKYilVWVqbhw4cPyOsbxsl5WVRUVPvrM2nx1dh4tMOBt54+e+aZZ/TAAw9IkkaNGqXly5f3+o9yu92uW265pX151apVvdpfX7K5oEZHaoxdshZNSDvlNiv2lCm3uM4Qu2te9oD8BQMA6Lvmj0zW5MGxhtgfPtqnFqf7jLb/6lmDFWw7fqnZ6nTrzY2F3kwRAAC/KS8vNyxHR0f3mQImAACA/qa2ttbQqUDqPIYFAACgL+l4LdPW1qbq6mqTsjEXRUxeFhoaqoSEBElSYeGpb9RVV1e3FzFlZmb2+tivvvqq7rzzTknSkCFD9P777ysxMbHX+5WkMWPGtL8+cuTIKdbsX5ZuLTYsZyVFaFRqVBdrH30C5I8f5Rliw5MidNHYVJ/kBwCAr1gsFn1vwUhDrLjWoTc3F3exhVFiZIgWTRgkSZqQEaNHvzxRN84a6u00AQDwObfbrYqKCkMsKSnJpGwAAABQVWWcnhAREcGYUgAA0KeFh4cbGuZIna95BgqKmHzgWMFPXl6enE5nl+vt3r27/fXo0aN7dcx///vfuuGGG+R2uzVo0CB9+OGH3RrhdjoDsYuQ2+3R8u3GG7WLJqSd8nux7kCVNuXXGGJ3zsuW1Trwvn8AgL5vdnaiZmYlGGLPrylQm+fM3tfuOi9bS+6cpX/dNVtXTc1QaJDNF2kCAOBT1dXVnUbJUcQEAABgno439I49WA4AANC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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "b = [1, 2, 1]\n", - "a = [1, -1.5432913909679857, 0.6297148520559599]\n", - "\n", - "# import numpy as np\n", - "# http://jaggedplanet.com/iir/iir-explorer.asp\n", - "# low pass\n", - "# b = np.array([1, 2, 1])\n", - "# a = np.flip(np.array([0.277023226134283,-0.7651127295946996,1]))\n", - "# high pass\n", - "# b = np.array([1, -2, 1])\n", - "# a = np.flip(np.array([0.7466573279103673,-1.7095165404698354,1]))\n", - "\n", - "dp = FluxPulse(0, 80, 0.9, IIR(\n", - " b=b, \n", - " a=a,\n", - " target=SNZ(30, b_amplitude=1)), \n", - " 0, 200)\n", - "dp.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### eCap Pulse Shape" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dp = Pulse(0, 40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE)\n", - "dp.plot(sampling_rate=100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Waveform" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Overview" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `Waveform` object is used to hold the array of samples that make up a waveform. This simple class is hashable so that it allows the comparison of two `Waveforms`, which is later needed by the driver.\n", - "The class has a writable `serial` attribute that can be set externally." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Envelope_Waveform_I(num_samples = 200, amplitude = 0.9, shape = Rectangular())'" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "duration = 200 # ns\n", - "amplitude = 0.9 \n", - "num_samples = int(duration) # default sampling rate of 1GSps\n", - "waveform = Waveform(amplitude * np.ones(num_samples))\n", - "waveform.serial = f\"Envelope_Waveform_I(num_samples = {num_samples}, amplitude = {format(amplitude, '.6f').rstrip('0').rstrip('.')}, shape = Rectangular())\"\n", - "waveform.serial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pulse Sequence" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Overview" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the key enhancements introduced in this new version of the driver are those related to the `PulseSequence`. Previously, `PulseSequence` wasn't more than an a list to contain the sequence of pulses and two attributes to store the time and phase of the sequence.\n", - "The new version of `PulseSequence` introduces many features. It is a sorted collection of pulses with many auxiliary methods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialisation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Multiple pulses can be used to initialise a `PulseSequence` or can be added to it:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "p1 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5,1), 1, PulseType.DRIVE)\n", - "p2 = Pulse(500, 40, 0.9, 100e6, 0, Drag(5,1), 2, PulseType.DRIVE)\n", - "p3 = Pulse(400, 40, 0.9, 100e6, 0, Drag(5,1), 3, PulseType.DRIVE)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# initialise an empty PulseSequence\n", - "ps = PulseSequence()\n", - "assert type(ps) == PulseSequence" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# initialise a PulseSequence with multiple pulses at once\n", - "ps = PulseSequence(p1, p2, p3)\n", - "assert ps.count == 3 and len(ps) ==3\n", - "assert ps[0] == p3\n", - "assert ps[1] == p2\n", - "assert ps[2] == p1\n", - "# * please note that pulses are always sorted by channel first and then by their start time" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# initialise a PulseSequence with the sum of multiple pulses\n", - "other_ps = p1 + p2 + p3\n", - "assert other_ps.count == 3 and len(other_ps) ==3\n", - "assert other_ps[0] == p3\n", - "assert other_ps[1] == p2\n", - "assert other_ps[2] == p1\n", - "# * please note that pulses are always sorted by channel first and then by their start time\n", - "\n", - "plist = [p3, p2, p1]\n", - "n = 0\n", - "for pulse in ps:\n", - " assert plist[n] == pulse\n", - " n += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "p4 = Pulse(300, 40, 0.9, 50e6, 0, Gaussian(5), 1, PulseType.DRIVE)\n", - "p5 = Pulse(200, 40, 0.9, 50e6, 0, Gaussian(5), 2, PulseType.DRIVE)\n", - "p6 = Pulse(100, 40, 0.9, 50e6, 0, Gaussian(5), 3, PulseType.DRIVE)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "ename": "AssertionError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[29], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m yet_another_ps\u001b[38;5;241m.\u001b[39madd(p4)\n\u001b[1;32m 4\u001b[0m yet_another_ps\u001b[38;5;241m.\u001b[39madd(p5, p6)\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m yet_another_ps[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m p4\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m yet_another_ps[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m p5\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m yet_another_ps[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m==\u001b[39m p6\n", - "\u001b[0;31mAssertionError\u001b[0m: " - ] - } - ], - "source": [ - "# multiple pulses can be added at once\n", - "yet_another_ps = PulseSequence()\n", - "yet_another_ps.add(p4)\n", - "yet_another_ps.add(p5, p6)\n", - "assert yet_another_ps[0] == p6\n", - "assert yet_another_ps[1] == p5\n", - "assert yet_another_ps[2] == p4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Operators" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PulseSequence support a number of operators(`==`, `!=`, `+`, `+=`, `*`, `*=`). Below are a few examples:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ps += yet_another_ps\n", - "assert ps.count == 6\n", - "ps += ReadoutPulse(800, 200, 0.9, 20e6, 0, Rectangular(), 1)\n", - "ps = ps + ReadoutPulse(800, 200, 0.9, 20e6, 0, Rectangular(), 2)\n", - "ps = ReadoutPulse(800, 200, 0.9, 20e6, 0, Rectangular(), 3) + ps\n", - "assert ps.count == 9\n", - "print(ps)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ps.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`PulseSequence` now implements `__contains__()` so one can check if a `Pulse` is included in the `PulseSequence` likw so: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert p5 in ps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Attributes & Methods" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`PulseSequence` includes the following (read only) attributes:\n", - "- `pulses` a list containing the pulses of the sequence\n", - "- `serial`\n", - "- `count`\n", - "- `is_empty`\n", - "- `start`\n", - "- `finish`\n", - "- `duration`\n", - "- `channels`\n", - "- `pulses_overlap`\n", - "- `channels`\n", - "- `ro_pulses`\n", - "- `qd_pulses`\n", - "- `qf_pulses`\n", - "\n", - "\n", - "`PulseSequence` implements the following methods:\n", - "- `add()`\n", - "- `pop()`\n", - "- `remove()`\n", - "- `clear()`\n", - "- `shallow_copy()`\n", - "- `deep_copy()`\n", - "- `get_channel_pulses()`\n", - "- `get_pulse_overlaps()` returns a dictionary of time intervals with the list of pulses in it\n", - "- `separate_overlapping_pulses()`\n", - "- `plot()`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p1 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5,1), 1, PulseType.DRIVE)\n", - "ps = PulseSequence(p1)\n", - "assert ps.count == 1\n", - "ps *= 3\n", - "assert ps.count == 3\n", - "ps *= 3\n", - "assert ps.count == 9" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p1 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5,1), 1, PulseType.DRIVE)\n", - "p2 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5,1), 2, PulseType.DRIVE)\n", - "ps = 2 * p2 + p1 * 3\n", - "assert ps.count == 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ps.clear()\n", - "assert ps.count == 0\n", - "assert ps.is_empty" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p1 = Pulse(20, 40, 0.9, 200e6, 0, Drag(5,1), 1, PulseType.DRIVE)\n", - "p2 = Pulse(60, 1000, 0.9, 20e6, 0, Rectangular(), 2, PulseType.READOUT)\n", - "ps = p1 + p2\n", - "assert ps.start == p1.start\n", - "assert ps.finish == p2.finish\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p1 = DrivePulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10)\n", - "p2 = ReadoutPulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30)\n", - "p3 = DrivePulse(300, 400, 0.9, 20e6, 0, Drag(5,50), 20)\n", - "p4 = DrivePulse(400, 400, 0.9, 20e6, 0, Drag(5,50), 30)\n", - "p5 = ReadoutPulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20)\n", - "p6 = DrivePulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30)\n", - "\n", - "ps = PulseSequence(p1, p2, p3, p4, p5, p6)\n", - "assert ps.channels == [10, 20, 30]\n", - "assert ps.get_channel_pulses(10).count == 1 \n", - "assert ps.get_channel_pulses(20).count == 2 \n", - "assert ps.get_channel_pulses(30).count == 3 " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ps.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert ps.pulses_overlap == True\n", - "assert ps.get_channel_pulses(10).pulses_overlap == False\n", - "assert ps.get_channel_pulses(20).pulses_overlap == True\n", - "assert ps.get_channel_pulses(30).pulses_overlap == True\n", - "\n", - "channel10_ps = ps.get_channel_pulses(10)\n", - "channel20_ps = ps.get_channel_pulses(20)\n", - "channel30_ps = ps.get_channel_pulses(30)\n", - "\n", - "split_pulses = PulseSequence()\n", - "overlaps = channel20_ps.get_pulse_overlaps()\n", - "n = 0\n", - "for section in overlaps.keys():\n", - " for pulse in overlaps[section]:\n", - " sp = SplitPulse(pulse, section[0], section[1])\n", - " sp.channel = n\n", - " split_pulses.add(sp)\n", - " n += 1\n", - "split_pulses.plot()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "n = 70\n", - "for segregated_ps in ps.separate_overlapping_pulses():\n", - " n +=1\n", - " for pulse in segregated_ps:\n", - " pulse.channel = n\n", - "ps.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p1 = DrivePulse(t0, 400, 0.9, 20e6, 0, Gaussian(5), 10)\n", - "p2 = ReadoutPulse(p1.finish, 400, 0.9, 20e6, 0, Rectangular(), 30)\n", - "p3 = DrivePulse(p2.finish, 400, 0.9, 20e6, 0, Drag(5,50), 20)\n", - "ps1 = p1 + p2 + p3\n", - "ps2 = p3 + p1 + p2\n", - "assert ps1 == ps2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Overlaps" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "overlaps" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "qibolab", - "language": "python", - "name": "qibolab" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - }, - "vscode": { - "interpreter": { - "hash": "df6c4956c0d01326f01905a7a43ac8f74636b2b92922eba33adb46287ed0dc33" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 21e29e9b6b23cc61e54d9456b1704573dcda176d Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 30 Jan 2024 20:13:19 +0100 Subject: [PATCH 057/233] test: Drop flux pulse test The create method for flux pulses has been introduced in #771, but the FluxPulse class does not exist any longer in 0.2.0. Since all create_ methods are going to be replaced in 0.2.0, the test is dropped as well. --- tests/test_dummy.py | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/tests/test_dummy.py b/tests/test_dummy.py index e1f99b7c9..c9d05b9a5 100644 --- a/tests/test_dummy.py +++ b/tests/test_dummy.py @@ -36,21 +36,6 @@ def test_dummy_execute_pulse_sequence(name, acquisition): assert result[0].magnitude.shape == (nshots * ro_pulse.duration,) -def test_dummy_execute_flux_pulse(): - platform = create_platform("dummy") - sequence = PulseSequence() - - pulse = platform.create_qubit_flux_pulse(qubit=0, start=0, duration=50) - sequence.add(pulse) - - options = ExecutionParameters(nshots=None) - _ = platform.execute_pulse_sequence(sequence, options) - - test_pulse = "FluxPulse(0, 50, 1, Rectangular(), flux-0, 0)" - - assert test_pulse == pulse.serial - - def test_dummy_execute_coupler_pulse(): platform = create_platform("dummy_couplers") sequence = PulseSequence() From 0890e34b1968d93dd099792e4e3dad53b6dc609e Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Wed, 21 Feb 2024 17:32:52 +0400 Subject: [PATCH 058/233] test: fix conflicts in tests --- src/qibolab/instruments/qm/controller.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/qibolab/instruments/qm/controller.py b/src/qibolab/instruments/qm/controller.py index 9d373a2fb..72688faf6 100644 --- a/src/qibolab/instruments/qm/controller.py +++ b/src/qibolab/instruments/qm/controller.py @@ -289,9 +289,7 @@ def create_sequence(self, qubits, sequence, sweepers): qmsequence = Sequence() ro_pulses = [] - for pulse in sorted( - sequence.pulses, key=lambda pulse: (pulse.start, pulse.duration) - ): + for pulse in sorted(sequence, key=lambda pulse: (pulse.start, pulse.duration)): qubit = qubits[pulse.qubit] self.config.register_port(getattr(qubit, pulse.type.name.lower()).port) From 636392640e25ecfbfa68043482bdc6c3aa9e62f6 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 21 Feb 2024 13:36:45 +0000 Subject: [PATCH 059/233] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibolab/instruments/qblox/cluster_qcm_bb.py | 1 + src/qibolab/instruments/qblox/cluster_qcm_rf.py | 1 + src/qibolab/pulses/plot.py | 1 + src/qibolab/pulses/pulse.py | 1 + src/qibolab/pulses/shape.py | 1 + tests/test_instruments_qblox.py | 1 - tests/test_pulses.py | 1 + 7 files changed, 6 insertions(+), 1 deletion(-) diff --git a/src/qibolab/instruments/qblox/cluster_qcm_bb.py b/src/qibolab/instruments/qblox/cluster_qcm_bb.py index 4b91831a1..c3e88ab5c 100644 --- a/src/qibolab/instruments/qblox/cluster_qcm_bb.py +++ b/src/qibolab/instruments/qblox/cluster_qcm_bb.py @@ -1,4 +1,5 @@ """Qblox Cluster QCM driver.""" + import copy import json diff --git a/src/qibolab/instruments/qblox/cluster_qcm_rf.py b/src/qibolab/instruments/qblox/cluster_qcm_rf.py index b7abcb73a..74f5f206c 100644 --- a/src/qibolab/instruments/qblox/cluster_qcm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qcm_rf.py @@ -1,4 +1,5 @@ """Qblox Cluster QCM-RF driver.""" + import copy import json diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index 0ae089c7c..d1d6ff58e 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -1,4 +1,5 @@ """Plotting tools for pulses and related entities.""" + import matplotlib.pyplot as plt import numpy as np diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index beb7a0962..413a7377b 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -1,4 +1,5 @@ """Pulse class.""" + from dataclasses import dataclass, fields from enum import Enum from typing import Optional diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index e1ee39aa2..7d9120363 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -1,4 +1,5 @@ """PulseShape class.""" + import re from abc import ABC, abstractmethod diff --git a/tests/test_instruments_qblox.py b/tests/test_instruments_qblox.py index 23069414e..b743fd702 100644 --- a/tests/test_instruments_qblox.py +++ b/tests/test_instruments_qblox.py @@ -27,7 +27,6 @@ - safe disconnection of offsets on termination """ - # from .conftest import load_from_platform # INSTRUMENTS_LIST = ["Cluster", "ClusterQRM_RF", "ClusterQCM_RF"] diff --git a/tests/test_pulses.py b/tests/test_pulses.py index c9a6d0cc2..61fa52149 100644 --- a/tests/test_pulses.py +++ b/tests/test_pulses.py @@ -1,4 +1,5 @@ """Tests ``pulses.py``.""" + import copy import os import pathlib From 7d60446f895842feb10485a923dc812d936c2324 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 29 Jan 2024 12:44:21 +0100 Subject: [PATCH 060/233] feat: Split software modulation out of the shape class --- src/qibolab/pulses/shape.py | 111 ++++++++++++++++++++---------------- 1 file changed, 62 insertions(+), 49 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 7d9120363..7cdc42cea 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -5,7 +5,6 @@ import numpy as np import numpy.typing as npt -from qibo.config import log from scipy.signal import lfilter SAMPLING_RATE = 1 @@ -15,7 +14,69 @@ a different value. """ +# TODO: they could be distinguished among them, and distinguished from generic float +# arrays, using the NewType pattern -> but this require some more effort to encforce +# types throughout the whole code base Waveform = npt.NDArray[np.float64] +"""""" +IqWaveform = npt.NDArray[np.float64] +"""""" + + +def modulate( + envelope: IqWaveform, + freq: float, + rate: float = SAMPLING_RATE, + phase: float = 0.0, +) -> IqWaveform: + """Modulate the envelope waveform with a carrier. + + `envelope` is a `(2, n)`-shaped array of I and Q (first dimension) envelope signals, + as a function of time (second dimension), and `freq` the frequency of the carrier to + modulate with (usually the IF) in GHz. + `rate` is an optional sampling rate, in Gs/s, to sample the carrier. + + .. note:: + + Only the combination `freq / rate` is actually relevant, but it is frequently + convenient to specify one in GHz and the other in Gs/s. Thus the two arguments + are provided for the simplicity of their interpretation. + + `phase` is an optional initial phase for the carrier. + """ + samples = np.arange(envelope.shape[1]) + phases = (2 * np.pi * freq / rate) * samples + phase + cos = np.cos(phases) + sin = np.sin(phases) + mod = np.array([[cos, -sin], [sin, cos]]) + + # the normalization is related to `mod`, but only applied at the end for the sake of + # performances + return np.einsum("ijt,jt->it", mod, envelope) / np.sqrt(2) + + +def demodulate( + modulated: IqWaveform, + freq: float, + rate: float = SAMPLING_RATE, +) -> IqWaveform: + """Demodulate the acquired pulse. + + The role of the arguments is the same of the corresponding ones in :func:`modulate`, + which is essentially the inverse of this function. + """ + # in case the offsets have not been removed in hardware + modulated = modulated - np.mean(modulated) + + samples = np.arange(modulated.shape[1]) + phases = (2 * np.pi * freq / rate) * samples + cos = np.cos(phases) + sin = np.sin(phases) + demod = np.array([[cos, sin], [-sin, cos]]) + + # the normalization is related to `demod`, but only applied at the end for the sake + # of performances + return np.sqrt(2) * np.einsum("ijt,jt->it", demod, modulated) class ShapeInitError(RuntimeError): @@ -63,54 +124,6 @@ def envelope_waveforms(self, sampling_rate=SAMPLING_RATE): self.envelope_waveform_q(sampling_rate), ) - def modulated_waveform_i(self, _if: int, sampling_rate=SAMPLING_RATE) -> Waveform: - """The waveform of the i component of the pulse, modulated with its - frequency.""" - - return self.modulated_waveforms(_if, sampling_rate)[0] - - def modulated_waveform_q(self, _if: int, sampling_rate=SAMPLING_RATE) -> Waveform: - """The waveform of the q component of the pulse, modulated with its - frequency.""" - - return self.modulated_waveforms(_if, sampling_rate)[1] - - def modulated_waveforms(self, _if: int, sampling_rate=SAMPLING_RATE): - """A tuple with the i and q waveforms of the pulse, modulated with its - frequency.""" - - pulse = self.pulse - if abs(_if) * 2 > sampling_rate: - log.info( - f"WARNING: The frequency of pulse {pulse.id} is higher than the nyqusit frequency ({int(sampling_rate // 2)}) for the device sampling rate: {int(sampling_rate)}" - ) - num_samples = int(np.rint(pulse.duration * sampling_rate)) - time = np.arange(num_samples) / sampling_rate - global_phase = pulse.global_phase - cosalpha = np.cos(2 * np.pi * _if * time + global_phase + pulse.relative_phase) - sinalpha = np.sin(2 * np.pi * _if * time + global_phase + pulse.relative_phase) - - mod_matrix = np.array([[cosalpha, -sinalpha], [sinalpha, cosalpha]]) / np.sqrt( - 2 - ) - - (envelope_waveform_i, envelope_waveform_q) = self.envelope_waveforms( - sampling_rate - ) - result = [] - for n, t, ii, qq in zip( - np.arange(num_samples), - time, - envelope_waveform_i, - envelope_waveform_q, - ): - result.append(mod_matrix[:, :, n] @ np.array([ii, qq])) - mod_signals = np.array(result) - - modulated_waveform_i = mod_signals[:, 0] - modulated_waveform_q = mod_signals[:, 1] - return (modulated_waveform_i, modulated_waveform_q) - def __eq__(self, item) -> bool: """Overloads == operator.""" return isinstance(item, type(self)) From d4b33bee61825ff8609b4772bb710265b837db1c Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 29 Jan 2024 13:32:08 +0100 Subject: [PATCH 061/233] feat: Remove modulated waveforms access from pulses --- src/qibolab/pulses/pulse.py | 18 ------------------ src/qibolab/pulses/shape.py | 6 ++---- 2 files changed, 2 insertions(+), 22 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 413a7377b..d7445d57f 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -129,24 +129,6 @@ def envelope_waveforms(self, sampling_rate=SAMPLING_RATE): self.shape.envelope_waveform_q(sampling_rate), ) - def modulated_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The waveform of the i component of the pulse, modulated with its - frequency.""" - - return self.shape.modulated_waveform_i(sampling_rate) - - def modulated_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The waveform of the q component of the pulse, modulated with its - frequency.""" - - return self.shape.modulated_waveform_q(sampling_rate) - - def modulated_waveforms(self, sampling_rate): - """A tuple with the i and q waveforms of the pulse, modulated with its - frequency.""" - - return self.shape.modulated_waveforms(sampling_rate) - def __hash__(self): """Hash the content. diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 7cdc42cea..cd2fed4e2 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -91,11 +91,9 @@ def __init__(self, msg=None, *args): class PulseShape(ABC): - """Abstract class for pulse shapes. + """Pulse envelopes. - This object is responsible for generating envelope and modulated - waveforms from a set of pulse parameters and its type. Generates - both i (in-phase) and q (quadrature) components. + Generates both i (in-phase) and q (quadrature) components. """ pulse = None From 95666ed6657e6b0e65c64fabdacc16e0e9b40d29 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 29 Jan 2024 14:53:49 +0100 Subject: [PATCH 062/233] fix: Replace usage of software modulation in Qblox --- src/qibolab/instruments/qblox/sequencer.py | 31 +++++++++++----------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/src/qibolab/instruments/qblox/sequencer.py b/src/qibolab/instruments/qblox/sequencer.py index 4324f8c85..86e68b35e 100644 --- a/src/qibolab/instruments/qblox/sequencer.py +++ b/src/qibolab/instruments/qblox/sequencer.py @@ -5,12 +5,22 @@ from qibolab.instruments.qblox.q1asm import Program from qibolab.pulses import Pulse, PulseSequence, PulseType +from qibolab.pulses.shape import modulate from qibolab.sweeper import Parameter, Sweeper SAMPLING_RATE = 1 """Sampling rate for qblox instruments in GSps.""" +def _modulated_waveforms(pulse: Pulse, hardware: bool = True): + envelopes = pulse.envelope_waveforms(SAMPLING_RATE) + return ( + envelopes + if hardware + else modulate(np.array(envelopes), pulse.frequency, SAMPLING_RATE) + ) + + class WaveformsBuffer: """A class to represent a buffer that holds the unique waveforms used by a sequencer. @@ -64,10 +74,7 @@ def add_waveforms( values = sweeper.get_values(pulse.duration) if not baking_required: - if hardware_mod_en: - waveform_i, waveform_q = pulse_copy.envelope_waveforms(SAMPLING_RATE) - else: - waveform_i, waveform_q = pulse_copy.modulated_waveforms(SAMPLING_RATE) + waveform_i, waveform_q = _modulated_waveforms(pulse_copy, hardware_mod_en) pulse.waveform_i = waveform_i pulse.waveform_q = waveform_q @@ -135,10 +142,7 @@ def bake_pulse_waveforms( for duration in values: pulse_copy.duration = duration - if hardware_mod_en: - waveform = pulse_copy.envelope_waveform_i(SAMPLING_RATE) - else: - waveform = pulse_copy.modulated_waveform_i(SAMPLING_RATE) + waveform = _modulated_waveforms(pulse_copy, hardware_mod_en) padded_duration = int(np.ceil(duration / 4)) * 4 memory_needed = padded_duration @@ -156,14 +160,9 @@ def bake_pulse_waveforms( for duration in values: pulse_copy.duration = duration - if hardware_mod_en: - waveform_i, waveform_q = pulse_copy.envelope_waveforms( - SAMPLING_RATE - ) - else: - waveform_i, waveform_q = pulse_copy.modulated_waveforms( - SAMPLING_RATE - ) + waveform_i, waveform_q = _modulated_waveforms( + pulse_copy, hardware_mod_en + ) padded_duration = int(np.ceil(duration / 4)) * 4 memory_needed = padded_duration * 2 From 8c024a32554248f88822a3ddd7c1d4ba09ad5486 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 29 Jan 2024 14:58:06 +0100 Subject: [PATCH 063/233] feat: Replace demodulation defined in qblox with global one --- src/qibolab/instruments/qblox/acquisition.py | 23 +++++--------------- 1 file changed, 5 insertions(+), 18 deletions(-) diff --git a/src/qibolab/instruments/qblox/acquisition.py b/src/qibolab/instruments/qblox/acquisition.py index 69da4e5e6..7f4d03ce7 100644 --- a/src/qibolab/instruments/qblox/acquisition.py +++ b/src/qibolab/instruments/qblox/acquisition.py @@ -3,24 +3,9 @@ import numpy as np -SAMPLING_RATE = 1 - +from ...pulses.shape import demodulate -def demodulate(input_i, input_q, frequency): - """Demodulates and integrates the acquired pulse.""" - # DOWN Conversion - # qblox does not remove the offsets in hardware - modulated_i = input_i - np.mean(input_i) - modulated_q = input_q - np.mean(input_q) - - num_samples = modulated_i.shape[0] - time = np.arange(num_samples) - phase = 2 * np.pi * frequency * time / SAMPLING_RATE - cosalpha = np.cos(phase) - sinalpha = np.sin(phase) - demod_matrix = np.sqrt(2) * np.array([[cosalpha, sinalpha], [-sinalpha, cosalpha]]) - result = np.einsum("ijt,jt->i", demod_matrix, np.stack([modulated_i, modulated_q])) - return np.sqrt(2) * result / num_samples +SAMPLING_RATE = 1 @dataclass @@ -73,7 +58,9 @@ def data(self): """ # TODO: to be updated once the functionality of ExecutionResults is extended if self.i is None or self.q is None: - self.i, self.q = demodulate(self.raw_i, self.raw_q, self.frequency) + self.i, self.q = demodulate( + np.array((self.raw_i, self.raw_q)), self.frequency + ).mean(axis=1) return (self.i, self.q) From 0f9ef457dab4354104d3fc4d4db63a6ea10dc317 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 29 Jan 2024 16:10:22 +0100 Subject: [PATCH 064/233] test: Move pulse tests into a folder, split sequence ones --- tests/pulses/__init__.py | 0 tests/{ => pulses}/test_pulses.py | 208 ----------------------------- tests/pulses/test_sequence.py | 209 ++++++++++++++++++++++++++++++ 3 files changed, 209 insertions(+), 208 deletions(-) create mode 100644 tests/pulses/__init__.py rename tests/{ => pulses}/test_pulses.py (75%) create mode 100644 tests/pulses/test_sequence.py diff --git a/tests/pulses/__init__.py b/tests/pulses/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/test_pulses.py b/tests/pulses/test_pulses.py similarity index 75% rename from tests/test_pulses.py rename to tests/pulses/test_pulses.py index 61fa52149..0ed89f842 100644 --- a/tests/test_pulses.py +++ b/tests/pulses/test_pulses.py @@ -386,169 +386,6 @@ def test_pulse_aliases(): assert fp.channel == 0 -def test_pulsesequence_init(): - p1 = Pulse(400, 40, 0.9, 100e6, 0, Drag(5, 1), 3, PulseType.DRIVE) - p2 = Pulse(500, 40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) - p3 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) - - ps = PulseSequence() - assert type(ps) == PulseSequence - - ps = PulseSequence([p1, p2, p3]) - assert len(ps) == 3 - assert ps[0] == p1 - assert ps[1] == p2 - assert ps[2] == p3 - - other_ps = PulseSequence([p1, p2, p3]) - assert len(other_ps) == 3 - assert other_ps[0] == p1 - assert other_ps[1] == p2 - assert other_ps[2] == p3 - - plist = [p1, p2, p3] - n = 0 - for pulse in ps: - assert plist[n] == pulse - n += 1 - - -def test_pulsesequence_operators(): - ps = PulseSequence() - ps += [Pulse(800, 200, 0.9, 20e6, 0, Rectangular(), 1, type=PulseType.READOUT)] - ps = ps + [Pulse(800, 200, 0.9, 20e6, 0, Rectangular(), 2, type=PulseType.READOUT)] - ps = [Pulse(800, 200, 0.9, 20e6, 0, Rectangular(), 3, type=PulseType.READOUT)] + ps - - p4 = Pulse(100, 40, 0.9, 50e6, 0, Gaussian(5), 3, PulseType.DRIVE) - p5 = Pulse(200, 40, 0.9, 50e6, 0, Gaussian(5), 2, PulseType.DRIVE) - p6 = Pulse(300, 40, 0.9, 50e6, 0, Gaussian(5), 1, PulseType.DRIVE) - - another_ps = PulseSequence() - another_ps.append(p4) - another_ps.extend([p5, p6]) - - assert another_ps[0] == p4 - assert another_ps[1] == p5 - assert another_ps[2] == p6 - - ps += another_ps - - assert len(ps) == 6 - assert p5 in ps - - # ps.plot() - - p7 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) - yet_another_ps = PulseSequence([p7]) - assert len(yet_another_ps) == 1 - yet_another_ps *= 3 - assert len(yet_another_ps) == 3 - yet_another_ps *= 3 - assert len(yet_another_ps) == 9 - - p8 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) - p9 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) - and_yet_another_ps = 2 * PulseSequence([p9]) + [p8] * 3 - assert len(and_yet_another_ps) == 5 - - -def test_pulsesequence_start_finish(): - p1 = Pulse(20, 40, 0.9, 200e6, 0, Drag(5, 1), 1, PulseType.DRIVE) - p2 = Pulse(60, 1000, 0.9, 20e6, 0, Rectangular(), 2, PulseType.READOUT) - ps = PulseSequence([p1]) + [p2] - assert ps.start == p1.start - assert ps.finish == p2.finish - - p1.start = None - assert p1.finish is None - p2.duration = None - assert p2.finish is None - - -def test_pulsesequence_get_channel_pulses(): - p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) - p2 = Pulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30, type=PulseType.READOUT) - p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) - p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) - p5 = Pulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20, type=PulseType.READOUT) - p6 = Pulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) - - ps = PulseSequence([p1, p2, p3, p4, p5, p6]) - assert ps.channels == [10, 20, 30] - assert len(ps.get_channel_pulses(10)) == 1 - assert len(ps.get_channel_pulses(20)) == 2 - assert len(ps.get_channel_pulses(30)) == 3 - assert len(ps.get_channel_pulses(20, 30)) == 5 - - -def test_pulsesequence_get_qubit_pulses(): - p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10, qubit=0) - p2 = Pulse( - 100, - 400, - 0.9, - 20e6, - 0, - Rectangular(), - channel=30, - qubit=0, - type=PulseType.READOUT, - ) - p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20, qubit=1) - p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30, qubit=1) - p5 = Pulse( - 500, - 400, - 0.9, - 20e6, - 0, - Rectangular(), - channel=30, - qubit=1, - type=PulseType.READOUT, - ) - p6 = Pulse.flux(600, 400, 0.9, Rectangular(), channel=40, qubit=1) - p7 = Pulse.flux(900, 400, 0.9, Rectangular(), channel=40, qubit=2) - - ps = PulseSequence([p1, p2, p3, p4, p5, p6, p7]) - assert ps.qubits == [0, 1, 2] - assert len(ps.get_qubit_pulses(0)) == 2 - assert len(ps.get_qubit_pulses(1)) == 4 - assert len(ps.get_qubit_pulses(2)) == 1 - assert len(ps.get_qubit_pulses(0, 1)) == 6 - - -def test_pulsesequence_pulses_overlap(): - p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) - p2 = Pulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30, type=PulseType.READOUT) - p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) - p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) - p5 = Pulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20, type=PulseType.READOUT) - p6 = Pulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) - - ps = PulseSequence([p1, p2, p3, p4, p5, p6]) - assert ps.pulses_overlap - assert not ps.get_channel_pulses(10).pulses_overlap - assert ps.get_channel_pulses(20).pulses_overlap - assert ps.get_channel_pulses(30).pulses_overlap - - -def test_pulsesequence_separate_overlapping_pulses(): - p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) - p2 = Pulse(100, 400, 0.9, 20e6, 0, Rectangular(), qubit=30, type=PulseType.READOUT) - p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) - p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) - p5 = Pulse(500, 400, 0.9, 20e6, 0, Rectangular(), qubit=20, type=PulseType.READOUT) - p6 = Pulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) - - ps = PulseSequence([p1, p2, p3, p4, p5, p6]) - n = 70 - for segregated_ps in ps.separate_overlapping_pulses(): - n += 1 - for pulse in segregated_ps: - pulse.channel = n - - def test_pulse_pulse_order(): t0 = 0 t = 0 @@ -830,51 +667,6 @@ def test_readout_pulse(): assert pulse.duration == duration -def test_pulse_sequence_add_readout(): - sequence = PulseSequence() - sequence.append( - Pulse( - start=0, - frequency=200_000_000, - amplitude=0.3, - duration=60, - relative_phase=0, - shape="Gaussian(5)", - channel=1, - ) - ) - - sequence.append( - Pulse( - start=64, - frequency=200_000_000, - amplitude=0.3, - duration=60, - relative_phase=0, - shape="Drag(5, 2)", - channel=1, - type="qf", - ) - ) - - sequence.append( - Pulse( - start=128, - frequency=20_000_000, - amplitude=0.9, - duration=2000, - relative_phase=0, - shape="Rectangular()", - channel=11, - type=PulseType.READOUT, - ) - ) - assert len(sequence) == 3 - assert len(sequence.ro_pulses) == 1 - assert len(sequence.qd_pulses) == 1 - assert len(sequence.qf_pulses) == 1 - - def test_envelope_waveform_i_q(): envelope_i = np.cos(np.arange(0, 10, 0.01)) envelope_q = np.sin(np.arange(0, 10, 0.01)) diff --git a/tests/pulses/test_sequence.py b/tests/pulses/test_sequence.py new file mode 100644 index 000000000..2c08e3e2e --- /dev/null +++ b/tests/pulses/test_sequence.py @@ -0,0 +1,209 @@ +from qibolab.pulses import Drag, Gaussian, Pulse, PulseSequence, PulseType, Rectangular + + +def test_add_readout(): + sequence = PulseSequence() + sequence.append( + Pulse( + start=0, + frequency=200_000_000, + amplitude=0.3, + duration=60, + relative_phase=0, + shape="Gaussian(5)", + channel=1, + ) + ) + + sequence.append( + Pulse( + start=64, + frequency=200_000_000, + amplitude=0.3, + duration=60, + relative_phase=0, + shape="Drag(5, 2)", + channel=1, + type="qf", + ) + ) + + sequence.append( + Pulse( + start=128, + frequency=20_000_000, + amplitude=0.9, + duration=2000, + relative_phase=0, + shape="Rectangular()", + channel=11, + type=PulseType.READOUT, + ) + ) + assert len(sequence) == 3 + assert len(sequence.ro_pulses) == 1 + assert len(sequence.qd_pulses) == 1 + assert len(sequence.qf_pulses) == 1 + + +def test_separate_overlapping_pulses(): + p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) + p2 = Pulse(100, 400, 0.9, 20e6, 0, Rectangular(), qubit=30, type=PulseType.READOUT) + p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) + p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) + p5 = Pulse(500, 400, 0.9, 20e6, 0, Rectangular(), qubit=20, type=PulseType.READOUT) + p6 = Pulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) + + ps = PulseSequence([p1, p2, p3, p4, p5, p6]) + n = 70 + for segregated_ps in ps.separate_overlapping_pulses(): + n += 1 + for pulse in segregated_ps: + pulse.channel = n + + +def test_get_qubit_pulses(): + p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10, qubit=0) + p2 = Pulse( + 100, + 400, + 0.9, + 20e6, + 0, + Rectangular(), + channel=30, + qubit=0, + type=PulseType.READOUT, + ) + p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20, qubit=1) + p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30, qubit=1) + p5 = Pulse( + 500, + 400, + 0.9, + 20e6, + 0, + Rectangular(), + channel=30, + qubit=1, + type=PulseType.READOUT, + ) + p6 = Pulse.flux(600, 400, 0.9, Rectangular(), channel=40, qubit=1) + p7 = Pulse.flux(900, 400, 0.9, Rectangular(), channel=40, qubit=2) + + ps = PulseSequence([p1, p2, p3, p4, p5, p6, p7]) + assert ps.qubits == [0, 1, 2] + assert len(ps.get_qubit_pulses(0)) == 2 + assert len(ps.get_qubit_pulses(1)) == 4 + assert len(ps.get_qubit_pulses(2)) == 1 + assert len(ps.get_qubit_pulses(0, 1)) == 6 + + +def test_pulses_overlap(): + p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) + p2 = Pulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30, type=PulseType.READOUT) + p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) + p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) + p5 = Pulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20, type=PulseType.READOUT) + p6 = Pulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) + + ps = PulseSequence([p1, p2, p3, p4, p5, p6]) + assert ps.pulses_overlap + assert not ps.get_channel_pulses(10).pulses_overlap + assert ps.get_channel_pulses(20).pulses_overlap + assert ps.get_channel_pulses(30).pulses_overlap + + +def test_get_channel_pulses(): + p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) + p2 = Pulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30, type=PulseType.READOUT) + p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) + p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) + p5 = Pulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20, type=PulseType.READOUT) + p6 = Pulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) + + ps = PulseSequence([p1, p2, p3, p4, p5, p6]) + assert ps.channels == [10, 20, 30] + assert len(ps.get_channel_pulses(10)) == 1 + assert len(ps.get_channel_pulses(20)) == 2 + assert len(ps.get_channel_pulses(30)) == 3 + assert len(ps.get_channel_pulses(20, 30)) == 5 + + +def test_start_finish(): + p1 = Pulse(20, 40, 0.9, 200e6, 0, Drag(5, 1), 1, PulseType.DRIVE) + p2 = Pulse(60, 1000, 0.9, 20e6, 0, Rectangular(), 2, PulseType.READOUT) + ps = PulseSequence([p1]) + [p2] + assert ps.start == p1.start + assert ps.finish == p2.finish + + p1.start = None + assert p1.finish is None + p2.duration = None + assert p2.finish is None + + +def test_init(): + p1 = Pulse(400, 40, 0.9, 100e6, 0, Drag(5, 1), 3, PulseType.DRIVE) + p2 = Pulse(500, 40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) + p3 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) + + ps = PulseSequence() + assert type(ps) == PulseSequence + + ps = PulseSequence([p1, p2, p3]) + assert len(ps) == 3 + assert ps[0] == p1 + assert ps[1] == p2 + assert ps[2] == p3 + + other_ps = PulseSequence([p1, p2, p3]) + assert len(other_ps) == 3 + assert other_ps[0] == p1 + assert other_ps[1] == p2 + assert other_ps[2] == p3 + + plist = [p1, p2, p3] + n = 0 + for pulse in ps: + assert plist[n] == pulse + n += 1 + + +def test_operators(): + ps = PulseSequence() + ps += [Pulse(800, 200, 0.9, 20e6, 0, Rectangular(), 1, type=PulseType.READOUT)] + ps = ps + [Pulse(800, 200, 0.9, 20e6, 0, Rectangular(), 2, type=PulseType.READOUT)] + ps = [Pulse(800, 200, 0.9, 20e6, 0, Rectangular(), 3, type=PulseType.READOUT)] + ps + + p4 = Pulse(100, 40, 0.9, 50e6, 0, Gaussian(5), 3, PulseType.DRIVE) + p5 = Pulse(200, 40, 0.9, 50e6, 0, Gaussian(5), 2, PulseType.DRIVE) + p6 = Pulse(300, 40, 0.9, 50e6, 0, Gaussian(5), 1, PulseType.DRIVE) + + another_ps = PulseSequence() + another_ps.append(p4) + another_ps.extend([p5, p6]) + + assert another_ps[0] == p4 + assert another_ps[1] == p5 + assert another_ps[2] == p6 + + ps += another_ps + + assert len(ps) == 6 + assert p5 in ps + + # ps.plot() + + p7 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) + yet_another_ps = PulseSequence([p7]) + assert len(yet_another_ps) == 1 + yet_another_ps *= 3 + assert len(yet_another_ps) == 3 + yet_another_ps *= 3 + assert len(yet_another_ps) == 9 + + p8 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) + p9 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) + and_yet_another_ps = 2 * PulseSequence([p9]) + [p8] * 3 + assert len(and_yet_another_ps) == 5 From 112cc4bcf200b7d0bde57e7a989b60753cb8ad5d Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 29 Jan 2024 16:18:15 +0100 Subject: [PATCH 065/233] test: Split shape-related tests into their own module --- .../pulses/{test_pulses.py => test_pulse.py} | 333 +----------------- tests/pulses/test_shape.py | 320 +++++++++++++++++ 2 files changed, 325 insertions(+), 328 deletions(-) rename tests/pulses/{test_pulses.py => test_pulse.py} (51%) create mode 100644 tests/pulses/test_shape.py diff --git a/tests/pulses/test_pulses.py b/tests/pulses/test_pulse.py similarity index 51% rename from tests/pulses/test_pulses.py rename to tests/pulses/test_pulse.py index 0ed89f842..111f258fa 100644 --- a/tests/pulses/test_pulses.py +++ b/tests/pulses/test_pulse.py @@ -55,7 +55,7 @@ def test_plot_functions(): os.remove(plot_file) -def test_pulse_init(): +def test_init(): # standard initialisation p0 = Pulse( start=0, @@ -170,7 +170,7 @@ def test_pulse_init(): assert p12.finish == 5.5 + 34.33 -def test_pulse_attributes(): +def test_attributes(): channel = 0 qubit = 0 @@ -232,106 +232,7 @@ def test_is_equal_ignoring_start(): assert not p1.is_equal_ignoring_start(p4) -@pytest.mark.parametrize( - "shape", [Rectangular(), Gaussian(5), GaussianSquare(5, 0.9), Drag(5, 1)] -) -def test_pulseshape_sampling_rate(shape): - pulse = Pulse(0, 40, 0.9, 100e6, 0, shape, 0, PulseType.DRIVE) - assert len(pulse.envelope_waveform_i(sampling_rate=1)) == 40 - assert len(pulse.envelope_waveform_i(sampling_rate=100)) == 4000 - - -def testhape_eval(): - shape = PulseShape.eval("Rectangular()") - assert isinstance(shape, Rectangular) - with pytest.raises(ValueError): - shape = PulseShape.eval("Ciao()") - - -@pytest.mark.parametrize("rel_sigma,beta", [(5, 1), (5, -1), (3, -0.03), (4, 0.02)]) -def test_drag_shape_eval(rel_sigma, beta): - shape = PulseShape.eval(f"Drag({rel_sigma}, {beta})") - assert isinstance(shape, Drag) - assert shape.rel_sigma == rel_sigma - assert shape.beta == beta - - -def test_raise_shapeiniterror(): - shape = Rectangular() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - shape = Gaussian(0) - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - shape = GaussianSquare(0, 1) - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - shape = Drag(0, 0) - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - shape = IIR([0], [0], None) - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - shape = SNZ(0) - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - shape = eCap(0) - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - -def test_pulseshape_drag_shape(): - pulse = Pulse(0, 2, 1, 4e9, 0, Drag(2, 1), 0, PulseType.DRIVE) - # envelope i & envelope q should cross nearly at 0 and at 2 - waveform = pulse.envelope_waveform_i(sampling_rate=10) - target_waveform = np.array( - [ - 0.63683161, - 0.69680478, - 0.7548396, - 0.80957165, - 0.85963276, - 0.90370708, - 0.94058806, - 0.96923323, - 0.98881304, - 0.99875078, - 0.99875078, - 0.98881304, - 0.96923323, - 0.94058806, - 0.90370708, - 0.85963276, - 0.80957165, - 0.7548396, - 0.69680478, - 0.63683161, - ] - ) - np.testing.assert_allclose(waveform, target_waveform) - - -def test_pulse_hash(): +def test_hash(): rp = Pulse(0, 40, 0.9, 100e6, 0, Rectangular(), 0, PulseType.DRIVE) dp = Pulse(0, 40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) hash(rp) @@ -352,7 +253,7 @@ def test_pulse_hash(): assert p1 == p3 -def test_pulse_aliases(): +def test_aliases(): rop = Pulse( start=0, duration=50, @@ -386,7 +287,7 @@ def test_pulse_aliases(): assert fp.channel == 0 -def test_pulse_pulse_order(): +def test_pulse_order(): t0 = 0 t = 0 p1 = Pulse(t0, 400, 0.9, 20e6, 0, Gaussian(5), 10) @@ -410,230 +311,6 @@ def sortseq(sequence): assert sortseq(ps1) == sortseq(ps2) -def modulate( - i: np.ndarray, - q: np.ndarray, - num_samples: int, - frequency: int, - phase: float, - sampling_rate: float, -): # -> tuple[np.ndarray, np.ndarray]: - time = np.arange(num_samples) / sampling_rate - cosalpha = np.cos(2 * np.pi * frequency * time + phase) - sinalpha = np.sin(2 * np.pi * frequency * time + phase) - mod_matrix = np.array([[cosalpha, -sinalpha], [sinalpha, cosalpha]]) / np.sqrt(2) - result = [] - for n, t, ii, qq in zip(np.arange(num_samples), time, i, q): - result.append(mod_matrix[:, :, n] @ np.array([ii, qq])) - mod_signals = np.array(result) - return mod_signals[:, 0], mod_signals[:, 1] - - -def test_pulseshape_rectangular(): - pulse = Pulse( - start=0, - duration=50, - amplitude=1, - frequency=200_000_000, - relative_phase=0, - shape=Rectangular(), - channel=1, - qubit=0, - ) - _if = 0 - - assert pulse.duration == 50 - assert isinstance(pulse.shape, Rectangular) - assert pulse.shape.name == "Rectangular" - assert repr(pulse.shape) == "Rectangular()" - - sampling_rate = 1 - num_samples = int(pulse.duration / sampling_rate) - i, q = ( - pulse.amplitude * np.ones(num_samples), - pulse.amplitude * np.zeros(num_samples), - ) - global_phase = ( - 2 * np.pi * _if * pulse.start / 1e9 - ) # pulse start, duration and finish are in ns - mod_i, mod_q = modulate( - i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate - ) - - np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) - np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) - np.testing.assert_allclose( - pulse.shape.modulated_waveform_i(_if, sampling_rate), mod_i - ) - np.testing.assert_allclose( - pulse.shape.modulated_waveform_q(_if, sampling_rate), mod_q - ) - - -def test_pulseshape_gaussian(): - pulse = Pulse( - start=0, - duration=50, - amplitude=1, - frequency=200_000_000, - relative_phase=0, - shape=Gaussian(5), - channel=1, - qubit=0, - ) - _if = 0 - - assert pulse.duration == 50 - assert isinstance(pulse.shape, Gaussian) - assert pulse.shape.name == "Gaussian" - assert pulse.shape.rel_sigma == 5 - assert repr(pulse.shape) == "Gaussian(5)" - - sampling_rate = 1 - num_samples = int(pulse.duration / sampling_rate) - x = np.arange(0, num_samples, 1) - i = pulse.amplitude * np.exp( - -(1 / 2) - * ( - ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / pulse.shape.rel_sigma) ** 2) - ) - ) - q = pulse.amplitude * np.zeros(num_samples) - global_phase = ( - 2 * np.pi * pulse.frequency * pulse.start / 1e9 - ) # pulse start, duration and finish are in ns - mod_i, mod_q = modulate( - i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate - ) - - np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) - np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) - np.testing.assert_allclose( - pulse.shape.modulated_waveform_i(_if, sampling_rate), mod_i - ) - np.testing.assert_allclose( - pulse.shape.modulated_waveform_q(_if, sampling_rate), mod_q - ) - - -def test_pulseshape_drag(): - pulse = Pulse( - start=0, - duration=50, - amplitude=1, - frequency=200_000_000, - relative_phase=0, - shape=Drag(5, 0.2), - channel=1, - qubit=0, - ) - _if = 0 - - assert pulse.duration == 50 - assert isinstance(pulse.shape, Drag) - assert pulse.shape.name == "Drag" - assert pulse.shape.rel_sigma == 5 - assert pulse.shape.beta == 0.2 - assert repr(pulse.shape) == "Drag(5, 0.2)" - - sampling_rate = 1 - num_samples = int(pulse.duration / 1 * sampling_rate) - x = np.arange(0, num_samples, 1) - i = pulse.amplitude * np.exp( - -(1 / 2) - * ( - ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / pulse.shape.rel_sigma) ** 2) - ) - ) - q = ( - pulse.shape.beta - * (-(x - (num_samples - 1) / 2) / ((num_samples / pulse.shape.rel_sigma) ** 2)) - * i - * sampling_rate - ) - global_phase = ( - 2 * np.pi * _if * pulse.start / 1e9 - ) # pulse start, duration and finish are in ns - mod_i, mod_q = modulate( - i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate - ) - - np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) - np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) - np.testing.assert_allclose( - pulse.shape.modulated_waveform_i(_if, sampling_rate), mod_i - ) - np.testing.assert_allclose( - pulse.shape.modulated_waveform_q(_if, sampling_rate), mod_q - ) - - -def test_pulseshape_eq(): - """Checks == operator for pulse shapes.""" - - shape1 = Rectangular() - shape2 = Rectangular() - shape3 = Gaussian(5) - assert shape1 == shape2 - assert not shape1 == shape3 - - shape1 = Gaussian(4) - shape2 = Gaussian(4) - shape3 = Gaussian(5) - assert shape1 == shape2 - assert not shape1 == shape3 - - shape1 = GaussianSquare(4, 0.01) - shape2 = GaussianSquare(4, 0.01) - shape3 = GaussianSquare(5, 0.01) - shape4 = GaussianSquare(4, 0.05) - shape5 = GaussianSquare(5, 0.05) - assert shape1 == shape2 - assert not shape1 == shape3 - assert not shape1 == shape4 - assert not shape1 == shape5 - - shape1 = Drag(4, 0.01) - shape2 = Drag(4, 0.01) - shape3 = Drag(5, 0.01) - shape4 = Drag(4, 0.05) - shape5 = Drag(5, 0.05) - assert shape1 == shape2 - assert not shape1 == shape3 - assert not shape1 == shape4 - assert not shape1 == shape5 - - shape1 = IIR([-0.5, 2], [1], Rectangular()) - shape2 = IIR([-0.5, 2], [1], Rectangular()) - shape3 = IIR([-0.5, 4], [1], Rectangular()) - shape4 = IIR([-0.4, 2], [1], Rectangular()) - shape5 = IIR([-0.5, 2], [2], Rectangular()) - shape6 = IIR([-0.5, 2], [2], Gaussian(5)) - assert shape1 == shape2 - assert not shape1 == shape3 - assert not shape1 == shape4 - assert not shape1 == shape5 - assert not shape1 == shape6 - - shape1 = SNZ(5) - shape2 = SNZ(5) - shape3 = SNZ(2) - shape4 = SNZ(2, 0.1) - shape5 = SNZ(2, 0.1) - assert shape1 == shape2 - assert not shape1 == shape3 - assert not shape1 == shape4 - assert not shape1 == shape5 - - shape1 = eCap(4) - shape2 = eCap(4) - shape3 = eCap(5) - assert shape1 == shape2 - assert not shape1 == shape3 - - def test_pulse(): duration = 50 rel_sigma = 5 diff --git a/tests/pulses/test_shape.py b/tests/pulses/test_shape.py new file mode 100644 index 000000000..3db07dc8d --- /dev/null +++ b/tests/pulses/test_shape.py @@ -0,0 +1,320 @@ +import numpy as np +import pytest + +from qibolab.pulses import ( + IIR, + SNZ, + Drag, + Gaussian, + GaussianSquare, + Pulse, + PulseShape, + PulseType, + Rectangular, + ShapeInitError, + eCap, +) + + +@pytest.mark.parametrize( + "shape", [Rectangular(), Gaussian(5), GaussianSquare(5, 0.9), Drag(5, 1)] +) +def test_sampling_rate(shape): + pulse = Pulse(0, 40, 0.9, 100e6, 0, shape, 0, PulseType.DRIVE) + assert len(pulse.envelope_waveform_i(sampling_rate=1)) == 40 + assert len(pulse.envelope_waveform_i(sampling_rate=100)) == 4000 + + +def test_eval(): + shape = PulseShape.eval("Rectangular()") + assert isinstance(shape, Rectangular) + with pytest.raises(ValueError): + shape = PulseShape.eval("Ciao()") + + +@pytest.mark.parametrize("rel_sigma,beta", [(5, 1), (5, -1), (3, -0.03), (4, 0.02)]) +def test_drag_shape_eval(rel_sigma, beta): + shape = PulseShape.eval(f"Drag({rel_sigma}, {beta})") + assert isinstance(shape, Drag) + assert shape.rel_sigma == rel_sigma + assert shape.beta == beta + + +def test_raise_shapeiniterror(): + shape = Rectangular() + with pytest.raises(ShapeInitError): + shape.envelope_waveform_i() + with pytest.raises(ShapeInitError): + shape.envelope_waveform_q() + + shape = Gaussian(0) + with pytest.raises(ShapeInitError): + shape.envelope_waveform_i() + with pytest.raises(ShapeInitError): + shape.envelope_waveform_q() + + shape = GaussianSquare(0, 1) + with pytest.raises(ShapeInitError): + shape.envelope_waveform_i() + with pytest.raises(ShapeInitError): + shape.envelope_waveform_q() + + shape = Drag(0, 0) + with pytest.raises(ShapeInitError): + shape.envelope_waveform_i() + with pytest.raises(ShapeInitError): + shape.envelope_waveform_q() + + shape = IIR([0], [0], None) + with pytest.raises(ShapeInitError): + shape.envelope_waveform_i() + with pytest.raises(ShapeInitError): + shape.envelope_waveform_q() + + shape = SNZ(0) + with pytest.raises(ShapeInitError): + shape.envelope_waveform_i() + with pytest.raises(ShapeInitError): + shape.envelope_waveform_q() + + shape = eCap(0) + with pytest.raises(ShapeInitError): + shape.envelope_waveform_i() + with pytest.raises(ShapeInitError): + shape.envelope_waveform_q() + + +def test_drag_shape(): + pulse = Pulse(0, 2, 1, 4e9, 0, Drag(2, 1), 0, PulseType.DRIVE) + # envelope i & envelope q should cross nearly at 0 and at 2 + waveform = pulse.envelope_waveform_i(sampling_rate=10) + target_waveform = np.array( + [ + 0.63683161, + 0.69680478, + 0.7548396, + 0.80957165, + 0.85963276, + 0.90370708, + 0.94058806, + 0.96923323, + 0.98881304, + 0.99875078, + 0.99875078, + 0.98881304, + 0.96923323, + 0.94058806, + 0.90370708, + 0.85963276, + 0.80957165, + 0.7548396, + 0.69680478, + 0.63683161, + ] + ) + np.testing.assert_allclose(waveform, target_waveform) + + +def test_rectangular(): + pulse = Pulse( + start=0, + duration=50, + amplitude=1, + frequency=200_000_000, + relative_phase=0, + shape=Rectangular(), + channel=1, + qubit=0, + ) + _if = 0 + + assert pulse.duration == 50 + assert isinstance(pulse.shape, Rectangular) + assert pulse.shape.name == "Rectangular" + assert repr(pulse.shape) == "Rectangular()" + + sampling_rate = 1 + num_samples = int(pulse.duration / sampling_rate) + i, q = ( + pulse.amplitude * np.ones(num_samples), + pulse.amplitude * np.zeros(num_samples), + ) + global_phase = ( + 2 * np.pi * _if * pulse.start / 1e9 + ) # pulse start, duration and finish are in ns + mod_i, mod_q = modulate( + i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate + ) + + np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) + np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) + np.testing.assert_allclose( + pulse.shape.modulated_waveform_i(_if, sampling_rate), mod_i + ) + np.testing.assert_allclose( + pulse.shape.modulated_waveform_q(_if, sampling_rate), mod_q + ) + + +def test_gaussian(): + pulse = Pulse( + start=0, + duration=50, + amplitude=1, + frequency=200_000_000, + relative_phase=0, + shape=Gaussian(5), + channel=1, + qubit=0, + ) + _if = 0 + + assert pulse.duration == 50 + assert isinstance(pulse.shape, Gaussian) + assert pulse.shape.name == "Gaussian" + assert pulse.shape.rel_sigma == 5 + assert repr(pulse.shape) == "Gaussian(5)" + + sampling_rate = 1 + num_samples = int(pulse.duration / sampling_rate) + x = np.arange(0, num_samples, 1) + i = pulse.amplitude * np.exp( + -(1 / 2) + * ( + ((x - (num_samples - 1) / 2) ** 2) + / (((num_samples) / pulse.shape.rel_sigma) ** 2) + ) + ) + q = pulse.amplitude * np.zeros(num_samples) + global_phase = ( + 2 * np.pi * pulse.frequency * pulse.start / 1e9 + ) # pulse start, duration and finish are in ns + mod_i, mod_q = modulate( + i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate + ) + + np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) + np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) + np.testing.assert_allclose( + pulse.shape.modulated_waveform_i(_if, sampling_rate), mod_i + ) + np.testing.assert_allclose( + pulse.shape.modulated_waveform_q(_if, sampling_rate), mod_q + ) + + +def test_drag(): + pulse = Pulse( + start=0, + duration=50, + amplitude=1, + frequency=200_000_000, + relative_phase=0, + shape=Drag(5, 0.2), + channel=1, + qubit=0, + ) + _if = 0 + + assert pulse.duration == 50 + assert isinstance(pulse.shape, Drag) + assert pulse.shape.name == "Drag" + assert pulse.shape.rel_sigma == 5 + assert pulse.shape.beta == 0.2 + assert repr(pulse.shape) == "Drag(5, 0.2)" + + sampling_rate = 1 + num_samples = int(pulse.duration / 1 * sampling_rate) + x = np.arange(0, num_samples, 1) + i = pulse.amplitude * np.exp( + -(1 / 2) + * ( + ((x - (num_samples - 1) / 2) ** 2) + / (((num_samples) / pulse.shape.rel_sigma) ** 2) + ) + ) + q = ( + pulse.shape.beta + * (-(x - (num_samples - 1) / 2) / ((num_samples / pulse.shape.rel_sigma) ** 2)) + * i + * sampling_rate + ) + global_phase = ( + 2 * np.pi * _if * pulse.start / 1e9 + ) # pulse start, duration and finish are in ns + mod_i, mod_q = modulate( + i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate + ) + + np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) + np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) + np.testing.assert_allclose( + pulse.shape.modulated_waveform_i(_if, sampling_rate), mod_i + ) + np.testing.assert_allclose( + pulse.shape.modulated_waveform_q(_if, sampling_rate), mod_q + ) + + +def test_eq(): + """Checks == operator for pulse shapes.""" + + shape1 = Rectangular() + shape2 = Rectangular() + shape3 = Gaussian(5) + assert shape1 == shape2 + assert not shape1 == shape3 + + shape1 = Gaussian(4) + shape2 = Gaussian(4) + shape3 = Gaussian(5) + assert shape1 == shape2 + assert not shape1 == shape3 + + shape1 = GaussianSquare(4, 0.01) + shape2 = GaussianSquare(4, 0.01) + shape3 = GaussianSquare(5, 0.01) + shape4 = GaussianSquare(4, 0.05) + shape5 = GaussianSquare(5, 0.05) + assert shape1 == shape2 + assert not shape1 == shape3 + assert not shape1 == shape4 + assert not shape1 == shape5 + + shape1 = Drag(4, 0.01) + shape2 = Drag(4, 0.01) + shape3 = Drag(5, 0.01) + shape4 = Drag(4, 0.05) + shape5 = Drag(5, 0.05) + assert shape1 == shape2 + assert not shape1 == shape3 + assert not shape1 == shape4 + assert not shape1 == shape5 + + shape1 = IIR([-0.5, 2], [1], Rectangular()) + shape2 = IIR([-0.5, 2], [1], Rectangular()) + shape3 = IIR([-0.5, 4], [1], Rectangular()) + shape4 = IIR([-0.4, 2], [1], Rectangular()) + shape5 = IIR([-0.5, 2], [2], Rectangular()) + shape6 = IIR([-0.5, 2], [2], Gaussian(5)) + assert shape1 == shape2 + assert not shape1 == shape3 + assert not shape1 == shape4 + assert not shape1 == shape5 + assert not shape1 == shape6 + + shape1 = SNZ(5) + shape2 = SNZ(5) + shape3 = SNZ(2) + shape4 = SNZ(2, 0.1) + shape5 = SNZ(2, 0.1) + assert shape1 == shape2 + assert not shape1 == shape3 + assert not shape1 == shape4 + assert not shape1 == shape5 + + shape1 = eCap(4) + shape2 = eCap(4) + shape3 = eCap(5) + assert shape1 == shape2 + assert not shape1 == shape3 From bfbc98ebf60b6981c0b8acde0c07c7fd47cff50c Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 29 Jan 2024 16:57:16 +0100 Subject: [PATCH 066/233] test: Split also plotting tests on their own --- tests/pulses/test_plot.py | 46 ++++++++++++++++++++++++++++++++++++++ tests/pulses/test_pulse.py | 33 --------------------------- 2 files changed, 46 insertions(+), 33 deletions(-) create mode 100644 tests/pulses/test_plot.py diff --git a/tests/pulses/test_plot.py b/tests/pulses/test_plot.py new file mode 100644 index 000000000..968f9adfa --- /dev/null +++ b/tests/pulses/test_plot.py @@ -0,0 +1,46 @@ +import os +import pathlib + +from qibolab.pulses import ( + IIR, + SNZ, + Drag, + Gaussian, + GaussianSquare, + Pulse, + PulseSequence, + PulseType, + Rectangular, + eCap, + plot, +) + +HERE = pathlib.Path(__file__).parent + + +def test_plot_functions(): + p0 = Pulse(0, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) + p1 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) + p2 = Pulse(0, 40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) + p3 = Pulse.flux( + 0, 40, 0.9, IIR([-0.5, 2], [1], Rectangular()), channel=0, qubit=200 + ) + p4 = Pulse.flux(0, 40, 0.9, SNZ(t_idling=10), channel=0, qubit=200) + p5 = Pulse(0, 40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) + p6 = Pulse(0, 40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.DRIVE, 2) + ps = PulseSequence([p0, p1, p2, p3, p4, p5, p6]) + wf = p0.modulated_waveform_i(0) + + plot_file = HERE / "test_plot.png" + + plot.waveform(wf, plot_file) + assert os.path.exists(plot_file) + os.remove(plot_file) + + plot.pulse(p0, plot_file) + assert os.path.exists(plot_file) + os.remove(plot_file) + + plot.sequence(ps, plot_file) + assert os.path.exists(plot_file) + os.remove(plot_file) diff --git a/tests/pulses/test_pulse.py b/tests/pulses/test_pulse.py index 111f258fa..a9676ee3c 100644 --- a/tests/pulses/test_pulse.py +++ b/tests/pulses/test_pulse.py @@ -1,8 +1,6 @@ """Tests ``pulses.py``.""" import copy -import os -import pathlib import numpy as np import pytest @@ -21,39 +19,8 @@ Rectangular, ShapeInitError, eCap, - plot, ) -HERE = pathlib.Path(__file__).parent - - -def test_plot_functions(): - p0 = Pulse(0, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) - p1 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) - p2 = Pulse(0, 40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) - p3 = Pulse.flux( - 0, 40, 0.9, IIR([-0.5, 2], [1], Rectangular()), channel=0, qubit=200 - ) - p4 = Pulse.flux(0, 40, 0.9, SNZ(t_idling=10), channel=0, qubit=200) - p5 = Pulse(0, 40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) - p6 = Pulse(0, 40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.DRIVE, 2) - ps = PulseSequence([p0, p1, p2, p3, p4, p5, p6]) - wf = p0.modulated_waveform_i(0) - - plot_file = HERE / "test_plot.png" - - plot.waveform(wf, plot_file) - assert os.path.exists(plot_file) - os.remove(plot_file) - - plot.pulse(p0, plot_file) - assert os.path.exists(plot_file) - os.remove(plot_file) - - plot.sequence(ps, plot_file) - assert os.path.exists(plot_file) - os.remove(plot_file) - def test_init(): # standard initialisation From be9c593ae742a8c95a772568e4c27dfbde338cec Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 30 Jan 2024 18:55:51 +0100 Subject: [PATCH 067/233] test: Remove tests on dropped methods --- tests/pulses/test_shape.py | 36 ------------------------------------ 1 file changed, 36 deletions(-) diff --git a/tests/pulses/test_shape.py b/tests/pulses/test_shape.py index 3db07dc8d..926dfbbdd 100644 --- a/tests/pulses/test_shape.py +++ b/tests/pulses/test_shape.py @@ -139,21 +139,9 @@ def test_rectangular(): pulse.amplitude * np.ones(num_samples), pulse.amplitude * np.zeros(num_samples), ) - global_phase = ( - 2 * np.pi * _if * pulse.start / 1e9 - ) # pulse start, duration and finish are in ns - mod_i, mod_q = modulate( - i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate - ) np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) - np.testing.assert_allclose( - pulse.shape.modulated_waveform_i(_if, sampling_rate), mod_i - ) - np.testing.assert_allclose( - pulse.shape.modulated_waveform_q(_if, sampling_rate), mod_q - ) def test_gaussian(): @@ -186,21 +174,9 @@ def test_gaussian(): ) ) q = pulse.amplitude * np.zeros(num_samples) - global_phase = ( - 2 * np.pi * pulse.frequency * pulse.start / 1e9 - ) # pulse start, duration and finish are in ns - mod_i, mod_q = modulate( - i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate - ) np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) - np.testing.assert_allclose( - pulse.shape.modulated_waveform_i(_if, sampling_rate), mod_i - ) - np.testing.assert_allclose( - pulse.shape.modulated_waveform_q(_if, sampling_rate), mod_q - ) def test_drag(): @@ -239,21 +215,9 @@ def test_drag(): * i * sampling_rate ) - global_phase = ( - 2 * np.pi * _if * pulse.start / 1e9 - ) # pulse start, duration and finish are in ns - mod_i, mod_q = modulate( - i, q, num_samples, _if, global_phase + pulse.relative_phase, sampling_rate - ) np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) - np.testing.assert_allclose( - pulse.shape.modulated_waveform_i(_if, sampling_rate), mod_i - ) - np.testing.assert_allclose( - pulse.shape.modulated_waveform_q(_if, sampling_rate), mod_q - ) def test_eq(): From a91e6b2105d4f6a5cf2a551514812be777e9115e Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 30 Jan 2024 19:07:31 +0100 Subject: [PATCH 068/233] fix: Use the new software modulation in plotting functions and related tests --- src/qibolab/pulses/plot.py | 59 +++++++++++--------------------------- tests/pulses/test_plot.py | 6 +++- 2 files changed, 22 insertions(+), 43 deletions(-) diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index d1d6ff58e..00f8abf93 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -5,7 +5,7 @@ from .pulse import Pulse from .sequence import PulseSequence -from .shape import SAMPLING_RATE, Waveform +from .shape import SAMPLING_RATE, Waveform, modulate def waveform(wf: Waveform, filename=None): @@ -57,18 +57,11 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): c="C1", linestyle="dashed", ) - ax1.plot( - time, - pulse_.shape.modulated_waveform_i(sampling_rate), - label="modulated i", - c="C0", - ) - ax1.plot( - time, - pulse_.shape.modulated_waveform_q(sampling_rate), - label="modulated q", - c="C1", - ) + + envelope = pulse_.shape.envelope_waveforms(sampling_rate) + modulated = modulate(np.array(envelope), pulse_.frequency) + ax1.plot(time, modulated[0], label="modulated i", c="C0") + ax1.plot(time, modulated[1], label="modulated q", c="C1") ax1.plot(time, -waveform_i, c="silver", linestyle="dashed") ax1.set_xlabel("Time [ns]") ax1.set_ylabel("Amplitude") @@ -79,32 +72,20 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): ax1.axis((start, finish, -1.0, 1.0)) ax1.legend() - modulated_i = pulse_.shape.modulated_waveform_i(sampling_rate) - modulated_q = pulse_.shape.modulated_waveform_q(sampling_rate) ax2 = plt.subplot(gs[1]) + ax2.plot(modulated[0], modulated[1], label="modulated", c="C3") + ax2.plot(waveform_i, waveform_q, label="envelope", c="C2") ax2.plot( - modulated_i, - modulated_q, - label="modulated", - c="C3", - ) - ax2.plot( - waveform_i, - waveform_q, - label="envelope", - c="C2", - ) - ax2.plot( - modulated_i[0], - modulated_q[0], + modulated[0][0], + modulated[1][0], marker="o", markersize=5, label="start", c="lightcoral", ) ax2.plot( - modulated_i[-1], - modulated_q[-1], + modulated[0][-1], + modulated[1][-1], marker="o", markersize=5, label="finish", @@ -155,18 +136,12 @@ def sequence(ps: PulseSequence, filename=None, sampling_rate=SAMPLING_RATE): ax = plt.subplot(gs[n]) ax.axis([0, ps.finish, -1, 1]) for pulse in channel_pulses: - num_samples = len(pulse.shape.modulated_waveform_i(sampling_rate)) + envelope = pulse.shape.envelope_waveforms(sampling_rate) + num_samples = envelope[0].size time = pulse.start + np.arange(num_samples) / sampling_rate - ax.plot( - time, - pulse.shape.modulated_waveform_q(sampling_rate), - c="lightgrey", - ) - ax.plot( - time, - pulse.shape.modulated_waveform_i(sampling_rate), - c=f"C{str(n)}", - ) + modulated = modulate(np.array(envelope), pulse.frequency) + ax.plot(time, modulated[1], c="lightgrey") + ax.plot(time, modulated[0], c=f"C{str(n)}") ax.plot( time, pulse.shape.envelope_waveform_i(sampling_rate), diff --git a/tests/pulses/test_plot.py b/tests/pulses/test_plot.py index 968f9adfa..41d5d82f9 100644 --- a/tests/pulses/test_plot.py +++ b/tests/pulses/test_plot.py @@ -1,6 +1,8 @@ import os import pathlib +import numpy as np + from qibolab.pulses import ( IIR, SNZ, @@ -14,6 +16,7 @@ eCap, plot, ) +from qibolab.pulses.shape import modulate HERE = pathlib.Path(__file__).parent @@ -29,7 +32,8 @@ def test_plot_functions(): p5 = Pulse(0, 40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) p6 = Pulse(0, 40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.DRIVE, 2) ps = PulseSequence([p0, p1, p2, p3, p4, p5, p6]) - wf = p0.modulated_waveform_i(0) + envelope = p0.envelope_waveforms() + wf = modulate(np.array(envelope), 0.0) plot_file = HERE / "test_plot.png" From 82979ab3e3434da4bbed5f7b4ff5e1c8c7b4b767 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 30 Jan 2024 19:36:28 +0100 Subject: [PATCH 069/233] test: Add test for new-format software (de)modulation Signed-off-by: Alessandro Candido --- tests/pulses/test_shape.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/tests/pulses/test_shape.py b/tests/pulses/test_shape.py index 926dfbbdd..465a9e24c 100644 --- a/tests/pulses/test_shape.py +++ b/tests/pulses/test_shape.py @@ -14,6 +14,7 @@ ShapeInitError, eCap, ) +from qibolab.pulses.shape import demodulate, modulate @pytest.mark.parametrize( @@ -282,3 +283,21 @@ def test_eq(): shape3 = eCap(5) assert shape1 == shape2 assert not shape1 == shape3 + + +def test_demodulation(): + signal = np.ones((2, 100)) + freq = 0.15 + mod = modulate(signal, freq) + + demod = demodulate(mod, freq) + np.testing.assert_allclose(demod, signal) + + mod1 = modulate(demod, freq * 3.0, rate=3.0) + np.testing.assert_allclose(mod1, mod) + + mod2 = modulate(signal, freq, phase=2 * np.pi) + np.testing.assert_allclose(mod2, mod) + + demod1 = demodulate(mod + np.ones_like(mod), freq) + np.testing.assert_allclose(demod1, demod) From 688c88cdb8432027d6062fbb6f4e12c2bd0675c3 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 30 Jan 2024 19:19:27 +0000 Subject: [PATCH 070/233] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibolab/instruments/qblox/cluster_qrm_rf.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/qibolab/instruments/qblox/cluster_qrm_rf.py b/src/qibolab/instruments/qblox/cluster_qrm_rf.py index 756152602..b37182984 100644 --- a/src/qibolab/instruments/qblox/cluster_qrm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qrm_rf.py @@ -1005,9 +1005,9 @@ def acquire(self): results = self.device.get_acquisitions(sequencer.number) for pulse in sequencer.pulses.ro_pulses: bins = results[pulse.id]["acquisition"]["bins"] - acquisitions[pulse.qubit] = acquisitions[pulse.id] = ( - DemodulatedAcquisition(scope, bins, duration) - ) + acquisitions[pulse.qubit] = acquisitions[ + pulse.id + ] = DemodulatedAcquisition(scope, bins, duration) # TODO: to be updated once the functionality of ExecutionResults is extended return {key: acquisition for key, acquisition in acquisitions.items()} From 156802ceba46b41637aec36e9b816c3891e02abc Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 2 Feb 2024 11:59:59 +0100 Subject: [PATCH 071/233] Update src/qibolab/instruments/qblox/acquisition.py Co-authored-by: Hayk Sargsyan <52532457+hay-k@users.noreply.github.com> --- src/qibolab/instruments/qblox/acquisition.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibolab/instruments/qblox/acquisition.py b/src/qibolab/instruments/qblox/acquisition.py index 7f4d03ce7..98aee7c1d 100644 --- a/src/qibolab/instruments/qblox/acquisition.py +++ b/src/qibolab/instruments/qblox/acquisition.py @@ -3,7 +3,7 @@ import numpy as np -from ...pulses.shape import demodulate +from qibolab.pulses.shape import demodulate SAMPLING_RATE = 1 From 51699c18d3d08dfcf4e07d9ddd04a13518e83084 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 22 Feb 2024 10:26:07 +0000 Subject: [PATCH 072/233] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibolab/instruments/qblox/cluster_qrm_rf.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/qibolab/instruments/qblox/cluster_qrm_rf.py b/src/qibolab/instruments/qblox/cluster_qrm_rf.py index b37182984..9a9ec6624 100644 --- a/src/qibolab/instruments/qblox/cluster_qrm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qrm_rf.py @@ -993,9 +993,9 @@ def acquire(self): if len(sequencer.pulses.ro_pulses) == 1: pulse = sequencer.pulses.ro_pulses[0] frequency = self.get_if(pulse) - acquisitions[pulse.qubit] = acquisitions[ - pulse.id - ] = AveragedAcquisition(scope, duration, frequency) + acquisitions[pulse.qubit] = acquisitions[pulse.id] = ( + AveragedAcquisition(scope, duration, frequency) + ) else: raise RuntimeError( "Software Demodulation only supports one acquisition per channel. " @@ -1005,9 +1005,9 @@ def acquire(self): results = self.device.get_acquisitions(sequencer.number) for pulse in sequencer.pulses.ro_pulses: bins = results[pulse.id]["acquisition"]["bins"] - acquisitions[pulse.qubit] = acquisitions[ - pulse.id - ] = DemodulatedAcquisition(scope, bins, duration) + acquisitions[pulse.qubit] = acquisitions[pulse.id] = ( + DemodulatedAcquisition(scope, bins, duration) + ) # TODO: to be updated once the functionality of ExecutionResults is extended return {key: acquisition for key, acquisition in acquisitions.items()} From 3eacca5cd04cf96ce1b4551af5619b8a01acc9d8 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 22 Feb 2024 11:45:00 +0100 Subject: [PATCH 073/233] test: Add regression test for software modulation --- tests/pulses/test_shape.py | 72 +++++++++++++++++++++++++++++++++++++- 1 file changed, 71 insertions(+), 1 deletion(-) diff --git a/tests/pulses/test_shape.py b/tests/pulses/test_shape.py index 465a9e24c..9ef9bfc37 100644 --- a/tests/pulses/test_shape.py +++ b/tests/pulses/test_shape.py @@ -14,7 +14,7 @@ ShapeInitError, eCap, ) -from qibolab.pulses.shape import demodulate, modulate +from qibolab.pulses.shape import IqWaveform, demodulate, modulate @pytest.mark.parametrize( @@ -285,6 +285,76 @@ def test_eq(): assert not shape1 == shape3 +def test_modulation(): + rect = Pulse( + start=0, + duration=30, + amplitude=0.9, + frequency=20_000_000, + relative_phase=0.0, + shape=Rectangular(), + channel=0, + type=PulseType.READOUT, + qubit=0, + ) + renvs: IqWaveform = np.array(rect.shape.envelope_waveforms()) + # fmt: off + np.testing.assert_allclose(modulate(renvs, 0.04), + np.array([[ 6.36396103e-01, 6.16402549e-01, 5.57678156e-01, + 4.63912794e-01, 3.40998084e-01, 1.96657211e-01, + 3.99596419e-02, -1.19248738e-01, -2.70964282e-01, + -4.05654143e-01, -5.14855263e-01, -5.91706132e-01, + -6.31377930e-01, -6.31377930e-01, -5.91706132e-01, + -5.14855263e-01, -4.05654143e-01, -2.70964282e-01, + -1.19248738e-01, 3.99596419e-02, 1.96657211e-01, + 3.40998084e-01, 4.63912794e-01, 5.57678156e-01, + 6.16402549e-01, 6.36396103e-01, 6.16402549e-01, + 5.57678156e-01, 4.63912794e-01, 3.40998084e-01], + [ 0.00000000e+00, 1.58265275e-01, 3.06586161e-01, + 4.35643111e-01, 5.37327002e-01, 6.05248661e-01, + 6.35140321e-01, 6.25123778e-01, 5.75828410e-01, + 4.90351625e-01, 3.74064244e-01, 2.34273031e-01, + 7.97615814e-02, -7.97615814e-02, -2.34273031e-01, + -3.74064244e-01, -4.90351625e-01, -5.75828410e-01, + -6.25123778e-01, -6.35140321e-01, -6.05248661e-01, + -5.37327002e-01, -4.35643111e-01, -3.06586161e-01, + -1.58265275e-01, 4.09361195e-16, 1.58265275e-01, + 3.06586161e-01, 4.35643111e-01, 5.37327002e-01]]) + ) + # fmt: on + + gauss = Pulse( + start=5, + duration=20, + amplitude=3.5, + frequency=2_000_000, + relative_phase=0.0, + shape=Gaussian(0.5), + channel=0, + type=PulseType.READOUT, + qubit=0, + ) + genvs: IqWaveform = np.array(gauss.shape.envelope_waveforms()) + # fmt: off + np.testing.assert_allclose(modulate(genvs, 0.3), + np.array([[ 2.40604965e+00, -7.47704261e-01, -1.96732725e+00, + 1.97595317e+00, 7.57582564e-01, -2.45926187e+00, + 7.61855973e-01, 1.99830815e+00, -2.00080760e+00, + -7.64718297e-01, 2.47468039e+00, -7.64240497e-01, + -1.99830815e+00, 1.99456483e+00, 7.59953712e-01, + -2.45158868e+00, 7.54746949e-01, 1.96732725e+00, + -1.95751517e+00, -7.43510231e-01], + [ 0.00000000e+00, 2.30119709e+00, -1.42934692e+00, + -1.43561401e+00, 2.33159938e+00, 9.03518154e-16, + -2.34475159e+00, 1.45185586e+00, 1.45367181e+00, + -2.35356091e+00, -1.81836565e-15, 2.35209040e+00, + -1.45185586e+00, -1.44913618e+00, 2.33889703e+00, + 2.70209720e-15, -2.32287226e+00, 1.42934692e+00, + 1.42221802e+00, -2.28828920e+00]]) + ) + # fmt: on + + def test_demodulation(): signal = np.ones((2, 100)) freq = 0.15 From 17129222a7c6dd9b2fe9d65e7bebdae384866796 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Wed, 20 Mar 2024 14:09:16 +0400 Subject: [PATCH 074/233] test: fix tests --- src/qibolab/compilers/default.py | 2 +- src/qibolab/instruments/qblox/controller.py | 4 +-- src/qibolab/instruments/qm/acquisition.py | 6 ++-- src/qibolab/instruments/qm/controller.py | 6 ++-- tests/test_compilers_default.py | 9 +++-- tests/test_instruments_qblox_controller.py | 22 ++++++++---- tests/test_instruments_qm.py | 40 ++++++++++++++++----- tests/test_unrolling.py | 4 +-- 8 files changed, 62 insertions(+), 31 deletions(-) diff --git a/src/qibolab/compilers/default.py b/src/qibolab/compilers/default.py index 53bec7aa2..0078973ca 100644 --- a/src/qibolab/compilers/default.py +++ b/src/qibolab/compilers/default.py @@ -45,7 +45,7 @@ def gpi_rule(gate, platform): # https://github.com/qiboteam/qibolab/pull/804#pullrequestreview-1890205509 # for more detail. pulse = platform.create_RX_pulse(qubit, start=0, relative_phase=theta) - sequence.add(pulse) + sequence.append(pulse) return sequence, {} diff --git a/src/qibolab/instruments/qblox/controller.py b/src/qibolab/instruments/qblox/controller.py index 80acc322a..2afd2c4ab 100644 --- a/src/qibolab/instruments/qblox/controller.py +++ b/src/qibolab/instruments/qblox/controller.py @@ -535,7 +535,7 @@ def _combine_result_chunks(chunks): def _add_to_results(sequence, results, results_to_add): for pulse in sequence.ro_pulses: if results[pulse.id]: - results[pulse.id] += results_to_add[pulse.serial] + results[pulse.id] += results_to_add[pulse.id] else: - results[pulse.id] = results_to_add[pulse.serial] + results[pulse.id] = results_to_add[pulse.id] results[pulse.qubit] = results[pulse.id] diff --git a/src/qibolab/instruments/qm/acquisition.py b/src/qibolab/instruments/qm/acquisition.py index f22aa752f..4e6390a13 100644 --- a/src/qibolab/instruments/qm/acquisition.py +++ b/src/qibolab/instruments/qm/acquisition.py @@ -269,7 +269,7 @@ def declare_acquisitions(ro_pulses, qubits, options): acquisition.assign_element(qmpulse.element) acquisitions[name] = acquisition - acquisitions[name].keys.append(qmpulse.pulse.serial) + acquisitions[name].keys.append(qmpulse.pulse.id) qmpulse.acquisition = acquisitions[name] return list(acquisitions.values()) @@ -289,6 +289,6 @@ def fetch_results(result, acquisitions): results = {} for acquisition in acquisitions: data = acquisition.fetch(handles) - for serial, result in zip(acquisition.keys, data): - results[acquisition.qubit] = results[serial] = result + for id_, result in zip(acquisition.keys, data): + results[acquisition.qubit] = results[id_] = result return results diff --git a/src/qibolab/instruments/qm/controller.py b/src/qibolab/instruments/qm/controller.py index 72688faf6..0aeebab91 100644 --- a/src/qibolab/instruments/qm/controller.py +++ b/src/qibolab/instruments/qm/controller.py @@ -57,7 +57,7 @@ def find_baking_pulses(sweepers): step = values[1] - values[0] if len(values) > 0 else values[0] if sweeper.parameter is Parameter.duration and step % 4 != 0: for pulse in sweeper.pulses: - to_bake.add(pulse.serial) + to_bake.add(pulse.id) return to_bake @@ -302,7 +302,7 @@ def create_sequence(self, qubits, sequence, sweepers): if ( pulse.duration % 4 != 0 or pulse.duration < 16 - or pulse.serial in pulses_to_bake + or pulse.id in pulses_to_bake ): qmpulse = BakedPulse(pulse, element) qmpulse.bake(self.config, durations=[pulse.duration]) @@ -361,7 +361,7 @@ def sweep(self, qubits, couplers, sequence, options, *sweepers): results = {} for qmpulse in ro_pulses: pulse = qmpulse.pulse - results[pulse.qubit] = results[pulse.serial] = result + results[pulse.qubit] = results[pulse.id] = result return results else: result = self.execute_program(experiment) diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index 2e856f6e6..48d3b5b52 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -98,14 +98,13 @@ def test_gpi_to_sequence(platform): circuit = Circuit(1) circuit.add(gates.GPI(0, phi=0.2)) sequence = compile_circuit(circuit, platform) - assert len(sequence.pulses) == 1 + assert len(sequence) == 1 assert len(sequence.qd_pulses) == 1 - RX_pulse = platform.create_RX_pulse(0, start=0, relative_phase=0.2) - s = PulseSequence(RX_pulse) + rx_pulse = platform.create_RX_pulse(0, start=0, relative_phase=0.2) + s = PulseSequence([rx_pulse]) - np.testing.assert_allclose(sequence.duration, RX_pulse.duration) - assert sequence.serial == s.serial + np.testing.assert_allclose(sequence.duration, rx_pulse.duration) def test_gpi2_to_sequence(platform): diff --git a/tests/test_instruments_qblox_controller.py b/tests/test_instruments_qblox_controller.py index fde3682f0..e97935476 100644 --- a/tests/test_instruments_qblox_controller.py +++ b/tests/test_instruments_qblox_controller.py @@ -5,7 +5,7 @@ from qibolab import AveragingMode, ExecutionParameters from qibolab.instruments.qblox.controller import MAX_NUM_BINS, QbloxController -from qibolab.pulses import Gaussian, Pulse, PulseSequence, ReadoutPulse, Rectangular +from qibolab.pulses import Gaussian, Pulse, PulseSequence, PulseType, Rectangular from qibolab.result import IntegratedResults from qibolab.sweeper import Parameter, Sweeper @@ -24,10 +24,18 @@ def test_sweep_too_many_bins(platform, controller): and executed.""" qubit = platform.qubits[0] pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Gaussian(5), qubit.drive.name, qubit=0) - ro_pulse = ReadoutPulse( - 0, 40, 0.05, int(3e9), 0.0, Rectangular(), qubit.readout.name, qubit=0 + ro_pulse = Pulse( + 0, + 40, + 0.05, + int(3e9), + 0.0, + Rectangular(), + qubit.readout.name, + PulseType.READOUT, + qubit=0, ) - sequence = PulseSequence(pulse, ro_pulse) + sequence = PulseSequence([pulse, ro_pulse]) # These values shall result into execution in two rounds shots = 128 @@ -39,13 +47,13 @@ def test_sweep_too_many_bins(platform, controller): nshots=shots, relaxation_time=10, averaging_mode=AveragingMode.SINGLESHOT ) controller._execute_pulse_sequence = Mock( - return_value={ro_pulse.serial: IntegratedResults(mock_data)} + return_value={ro_pulse.id: IntegratedResults(mock_data)} ) res = controller.sweep( {0: platform.qubits[0]}, platform.couplers, sequence, params, sweep_ampl ) expected_data = np.append(mock_data, mock_data) # - assert np.array_equal(res[ro_pulse.serial].voltage, expected_data) + assert np.array_equal(res[ro_pulse.id].voltage, expected_data) def test_sweep_too_many_sweep_points(platform, controller): @@ -58,7 +66,7 @@ def test_sweep_too_many_sweep_points(platform, controller): ) params = ExecutionParameters(nshots=12, relaxation_time=10) with pytest.raises(ValueError, match="total number of sweep points"): - controller.sweep({0: qubit}, {}, PulseSequence(pulse), params, sweep) + controller.sweep({0: qubit}, {}, PulseSequence([pulse]), params, sweep) @pytest.mark.qpu diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 2da93d2fa..62b3ef994 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -9,7 +9,7 @@ from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions from qibolab.instruments.qm.controller import controllers_config from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence -from qibolab.pulses import Pulse, PulseType, PulseSequence, Rectangular +from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, Sweeper @@ -54,8 +54,12 @@ def test_qmpulse_declare_output(acquisition_type): def test_qmsequence(): - qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", PulseType.DRIVE, qubit=0) - ro_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch1", PulseType.READOUT, qubit=0) + qd_pulse = Pulse( + 0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", PulseType.DRIVE, qubit=0 + ) + ro_pulse = Pulse( + 0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch1", PulseType.READOUT, qubit=0 + ) qmsequence = Sequence() with pytest.raises(AttributeError): qmsequence.add("test") @@ -90,7 +94,6 @@ def test_qmpulse_previous_and_next(): f"readout{qubit}", PulseType.READOUT, qubit=qubit, - type=PulseType.READOUT, ) ) ro_qmpulses.append(ro_pulse) @@ -116,10 +119,26 @@ def test_qmpulse_previous_and_next_flux(): x_pulse_end = Pulse(70, 40, 0.05, int(3e9), 0.0, Rectangular(), f"drive2", qubit=2) measure_lowfreq = Pulse( - 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout1", PulseType.READOUT, qubit=1 + 110, + 100, + 0.05, + int(3e9), + 0.0, + Rectangular(), + "readout1", + PulseType.READOUT, + qubit=1, ) measure_highfreq = Pulse( - 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout2", PulseType.READOUT, qubit=2 + 110, + 100, + 0.05, + int(3e9), + 0.0, + Rectangular(), + "readout2", + PulseType.READOUT, + qubit=2, ) drive11 = QMPulse(y90_pulse) @@ -342,7 +361,12 @@ def test_qm_register_flux_pulse(qmplatform): platform = qmplatform controller = platform.instruments["qm"] pulse = Pulse.flux( - 0, 30, 0.005, Rectangular(), platform.qubits[qubit].flux.name, qubit + 0, + 30, + 0.005, + Rectangular(), + channel=platform.qubits[qubit].flux.name, + qubit=qubit, ) target_pulse = { "operation": "control", @@ -409,7 +433,7 @@ def test_qm_register_baked_pulse(qmplatform, duration): controller = platform.instruments["qm"] controller.config.register_flux_element(qubit) pulse = Pulse.flux( - 3, duration, 0.05, Rectangular(), qubit.flux.name, qubit=qubit.name + 3, duration, 0.05, Rectangular(), channel=qubit.flux.name, qubit=qubit.name ) qmpulse = BakedPulse(pulse) config = controller.config diff --git a/tests/test_unrolling.py b/tests/test_unrolling.py index 95ca77505..27d99e651 100644 --- a/tests/test_unrolling.py +++ b/tests/test_unrolling.py @@ -15,7 +15,7 @@ def test_bounds_update(): p5 = Pulse(540, 1000, 0.9, int(20e6), 0, Rectangular(), 2, PulseType.READOUT) p6 = Pulse(640, 1000, 0.9, int(20e6), 0, Rectangular(), 1, PulseType.READOUT) - ps = PulseSequence(p1, p2, p3, p4, p5, p6) + ps = PulseSequence([p1, p2, p3, p4, p5, p6]) bounds = Bounds.update(ps) assert bounds.waveforms >= 40 @@ -59,7 +59,7 @@ def test_batch(bounds): p5 = Pulse(540, 1000, 0.9, int(20e6), 0, Rectangular(), 2, PulseType.READOUT) p6 = Pulse(640, 1000, 0.9, int(20e6), 0, Rectangular(), 1, PulseType.READOUT) - ps = PulseSequence(p1, p2, p3, p4, p5, p6) + ps = PulseSequence([p1, p2, p3, p4, p5, p6]) sequences = 10 * [ps] From 4091ab4751aec6055dcd6c1287c412df038455a1 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Wed, 20 Mar 2024 18:00:21 +0400 Subject: [PATCH 075/233] fix: wrong merge --- src/qibolab/platform/platform.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 60af19d9a..ede232c7d 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -1,4 +1,5 @@ """A platform for executing quantum algorithms.""" + import copy from collections import defaultdict from dataclasses import dataclass, field, replace @@ -10,7 +11,7 @@ from qibolab.couplers import Coupler from qibolab.execution_parameters import ExecutionParameters from qibolab.instruments.abstract import Controller, Instrument, InstrumentId -from qibolab.pulses import Drag, FluxPulse, PulseSequence, ReadoutPulse +from qibolab.pulses import Drag, PulseSequence, PulseType from qibolab.qubits import Qubit, QubitId, QubitPair, QubitPairId from qibolab.sweeper import Sweeper from qibolab.unrolling import batch From 35f9b4f1f19d02d6ab45394c7ab5877b645eeaa3 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Wed, 20 Mar 2024 19:25:14 +0400 Subject: [PATCH 076/233] fix: drop .data attribute from shape in ZI --- src/qibolab/instruments/zhinst/executor.py | 4 +- src/qibolab/instruments/zhinst/pulse.py | 8 +- tests/test_compilers_default.py | 2 +- tests/test_instruments_zhinst.py | 101 ++++++++++----------- 4 files changed, 57 insertions(+), 58 deletions(-) diff --git a/src/qibolab/instruments/zhinst/executor.py b/src/qibolab/instruments/zhinst/executor.py index bfba7a6d9..cbcb1d6a1 100644 --- a/src/qibolab/instruments/zhinst/executor.py +++ b/src/qibolab/instruments/zhinst/executor.py @@ -728,8 +728,8 @@ def sweep(self, qubits, couplers, sequence: PulseSequence, options, *sweepers): np.ones(data.shape) - data.real ) # Probability inversion patch - serial = ropulse.pulse.serial + id_ = ropulse.pulse.id qubit = ropulse.pulse.qubit - results[serial] = results[qubit] = options.results_type(data) + results[id_] = results[qubit] = options.results_type(data) return results diff --git a/src/qibolab/instruments/zhinst/pulse.py b/src/qibolab/instruments/zhinst/pulse.py index 44c223ea6..c187f5170 100644 --- a/src/qibolab/instruments/zhinst/pulse.py +++ b/src/qibolab/instruments/zhinst/pulse.py @@ -57,15 +57,15 @@ def select_pulse(pulse: Pulse): zero_boundaries=False, ) - if np.all(pulse.envelope_waveform_q(SAMPLING_RATE).data == 0): + if np.all(pulse.envelope_waveform_q(SAMPLING_RATE) == 0): return sampled_pulse_real( - samples=pulse.envelope_waveform_i(SAMPLING_RATE).data, + samples=pulse.envelope_waveform_i(SAMPLING_RATE), can_compress=True, ) else: return sampled_pulse_complex( - samples=pulse.envelope_waveform_i(SAMPLING_RATE).data - + (1j * pulse.envelope_waveform_q(SAMPLING_RATE).data), + samples=pulse.envelope_waveform_i(SAMPLING_RATE) + + (1j * pulse.envelope_waveform_q(SAMPLING_RATE)), can_compress=True, ) diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index 48d3b5b52..2549d299c 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -68,7 +68,7 @@ def test_compile_two_gates(platform): sequence = compile_circuit(circuit, platform) - assert len(sequence.pulses) == 4 + assert len(sequence) == 4 assert len(sequence.qd_pulses) == 3 assert len(sequence.ro_pulses) == 1 diff --git a/tests/test_instruments_zhinst.py b/tests/test_instruments_zhinst.py index 535e957e3..b0ebbb18f 100644 --- a/tests/test_instruments_zhinst.py +++ b/tests/test_instruments_zhinst.py @@ -23,7 +23,6 @@ Pulse, PulseSequence, PulseType, - ReadoutPulse, Rectangular, ) from qibolab.sweeper import Parameter, Sweeper @@ -258,12 +257,20 @@ def test_zhsequence(dummy_qrc): ) qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), drive_channel, qubit=0) ro_pulse = Pulse( - 0, 40, 0.05, int(3e9), 0.0, Rectangular(), readout_channel, PulseType.READOUT, qubit=0 + 0, + 40, + 0.05, + int(3e9), + 0.0, + Rectangular(), + readout_channel, + PulseType.READOUT, + qubit=0, ) sequence = PulseSequence() - sequence.add(qd_pulse) - sequence.add(qd_pulse) - sequence.add(ro_pulse) + sequence.append(qd_pulse) + sequence.append(qd_pulse) + sequence.append(ro_pulse) zhsequence = controller.sequence_zh(sequence, IQM5q.qubits) @@ -285,15 +292,24 @@ def test_zhsequence_couplers(dummy_qrc): couplerflux_channel = IQM5q.couplers[0].flux.name qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), drive_channel, qubit=0) ro_pulse = Pulse( - 0, 40, 0.05, int(3e9), 0.0, Rectangular(), readout_channel, PulseType.READOUT, qubit=0 + 0, + 40, + 0.05, + int(3e9), + 0.0, + Rectangular(), + readout_channel, + PulseType.READOUT, + qubit=0, ) - qc_pulse = Pulse( - 0, 40, 0.05, Rectangular(), couplerflux_channel, PulseType.COUPLERFLUX, qubit=3 + qc_pulse = Pulse.flux( + 0, 40, 0.05, Rectangular(), channel=couplerflux_channel, qubit=3 ) + qc_pulse.type = PulseType.COUPLERFLUX sequence = PulseSequence() - sequence.add(qd_pulse) - sequence.add(ro_pulse) - sequence.add(qc_pulse) + sequence.append(qd_pulse) + sequence.append(ro_pulse) + sequence.append(qc_pulse) zhsequence = controller.sequence_zh(sequence, IQM5q.qubits) @@ -306,15 +322,31 @@ def test_zhsequence_multiple_ro(dummy_qrc): readout_channel = measure_channel_name(platform.qubits[0]) sequence = PulseSequence() qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) - sequence.add(qd_pulse) + sequence.append(qd_pulse) ro_pulse = Pulse( - 0, 40, 0.05, int(3e9), 0.0, Rectangular(), readout_channel, PulseType.READOUT, qubit=0 + 0, + 40, + 0.05, + int(3e9), + 0.0, + Rectangular(), + readout_channel, + PulseType.READOUT, + qubit=0, ) - sequence.add(ro_pulse) + sequence.append(ro_pulse) ro_pulse = Pulse( - 0, 5000, 0.05, int(3e9), 0.0, Rectangular(), readout_channel, PulseType.READOUT, qubit=0 + 0, + 5000, + 0.05, + int(3e9), + 0.0, + Rectangular(), + readout_channel, + PulseType.READOUT, + qubit=0, ) - sequence.add(ro_pulse) + sequence.append(ro_pulse) platform = create_platform("zurich") controller = platform.instruments["EL_ZURO"] @@ -477,9 +509,9 @@ def test_sweep_and_play_sim(dummy_qrc): channel=platform.qubits[q].flux.name, qubit=q, ) - sequence.add(qf_pulses[q]) + sequence.append(qf_pulses[q]) ro_pulses[q] = platform.create_qubit_readout_pulse(q, start=qf_pulses[q].finish) - sequence.add(ro_pulses[q]) + sequence.append(ro_pulses[q]) options = ExecutionParameters( relaxation_time=300e-6, @@ -782,41 +814,8 @@ def test_experiment_sweep_punchouts(dummy_qrc, parameter): IQM5q.experiment_flow(qubits, couplers, sequence, options) -<<<<<<< HEAD assert measure_channel_name(qubits[0]) in IQM5q.experiment.signals assert acquire_channel_name(qubits[0]) in IQM5q.experiment.signals -======= - assert "measure0" in IQM5q.experiment.signals - assert "acquire0" in IQM5q.experiment.signals - - -# TODO: Fix this -def test_sim(dummy_qrc): - platform = create_platform("zurich") - IQM5q = platform.instruments["EL_ZURO"] - sequence = PulseSequence() - qubits = {0: platform.qubits[0]} - platform.qubits = qubits - ro_pulses = {} - qd_pulses = {} - qf_pulses = {} - for qubit in qubits: - qd_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) - sequence.append(qd_pulses[qubit]) - ro_pulses[qubit] = platform.create_qubit_readout_pulse( - qubit, start=qd_pulses[qubit].finish - ) - sequence.append(ro_pulses[qubit]) - qf_pulses[qubit] = Pulse.flux( - start=0, - duration=500, - amplitude=1, - shape=Rectangular(), - channel=platform.qubits[qubit].flux.name, - qubit=qubit, - ) - sequence.append(qf_pulses[qubit]) ->>>>>>> 1b1e4cd4 (Fix Zurich tests) def test_batching(dummy_qrc): From acc953be05438de594b2ab3b4d65dfd13d636f4d Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 12 Jan 2024 16:25:00 +0100 Subject: [PATCH 077/233] Drop pulse.serial --- tests/test_instruments_qm.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 62b3ef994..0b201bf7f 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -19,7 +19,11 @@ def test_qmpulse(): pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) qmpulse = QMPulse(pulse) +<<<<<<< HEAD assert qmpulse.operation == "drive(40, 0.05, Rectangular())" +======= + assert qmpulse.operation == pulse.id +>>>>>>> 337bff40 (Drop pulse.serial) assert qmpulse.relative_phase == 0 @@ -346,8 +350,20 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): }, } +<<<<<<< HEAD controller.config.register_element( platform.qubits[qubit], pulse, controller.time_of_flight, controller.smearing +======= + opx.config.register_element( + platform.qubits[qubit], pulse, opx.time_of_flight, opx.smearing + ) + opx.config.register_pulse(platform.qubits[qubit], pulse) + assert opx.config.pulses[pulse.id] == target_pulse + assert target_pulse["waveforms"]["I"] in opx.config.waveforms + assert target_pulse["waveforms"]["Q"] in opx.config.waveforms + assert ( + opx.config.elements[f"{pulse_type}{qubit}"]["operations"][pulse.id] == pulse.id +>>>>>>> 337bff40 (Drop pulse.serial) ) qmpulse = QMPulse(pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) @@ -373,11 +389,19 @@ def test_qm_register_flux_pulse(qmplatform): "length": pulse.duration, "waveforms": {"single": "constant_wf0.005"}, } +<<<<<<< HEAD qmpulse = QMPulse(pulse) controller.config.register_element(platform.qubits[qubit], pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) assert controller.config.pulses[qmpulse.operation] == target_pulse assert target_pulse["waveforms"]["single"] in controller.config.waveforms +======= + opx.config.register_element(platform.qubits[qubit], pulse) + opx.config.register_pulse(platform.qubits[qubit], pulse) + assert opx.config.pulses[pulse.id] == target_pulse + assert target_pulse["waveforms"]["single"] in opx.config.waveforms + assert opx.config.elements[f"flux{qubit}"]["operations"][pulse.id] == pulse.id +>>>>>>> 337bff40 (Drop pulse.serial) def test_qm_register_pulses_with_different_frequencies(qmplatform): From e7c9bdd632f951d43666a882f3ee1ed4858a04cb Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 19:17:35 +0100 Subject: [PATCH 078/233] Fix QM issues by stringifying pulses ID QM requires some keys to be strings, because of the way they are later processed. And before they were (by accident, since we were using the serial as an identifier). --- tests/test_instruments_qm.py | 24 ------------------------ 1 file changed, 24 deletions(-) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 0b201bf7f..62b3ef994 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -19,11 +19,7 @@ def test_qmpulse(): pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) qmpulse = QMPulse(pulse) -<<<<<<< HEAD assert qmpulse.operation == "drive(40, 0.05, Rectangular())" -======= - assert qmpulse.operation == pulse.id ->>>>>>> 337bff40 (Drop pulse.serial) assert qmpulse.relative_phase == 0 @@ -350,20 +346,8 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): }, } -<<<<<<< HEAD controller.config.register_element( platform.qubits[qubit], pulse, controller.time_of_flight, controller.smearing -======= - opx.config.register_element( - platform.qubits[qubit], pulse, opx.time_of_flight, opx.smearing - ) - opx.config.register_pulse(platform.qubits[qubit], pulse) - assert opx.config.pulses[pulse.id] == target_pulse - assert target_pulse["waveforms"]["I"] in opx.config.waveforms - assert target_pulse["waveforms"]["Q"] in opx.config.waveforms - assert ( - opx.config.elements[f"{pulse_type}{qubit}"]["operations"][pulse.id] == pulse.id ->>>>>>> 337bff40 (Drop pulse.serial) ) qmpulse = QMPulse(pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) @@ -389,19 +373,11 @@ def test_qm_register_flux_pulse(qmplatform): "length": pulse.duration, "waveforms": {"single": "constant_wf0.005"}, } -<<<<<<< HEAD qmpulse = QMPulse(pulse) controller.config.register_element(platform.qubits[qubit], pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) assert controller.config.pulses[qmpulse.operation] == target_pulse assert target_pulse["waveforms"]["single"] in controller.config.waveforms -======= - opx.config.register_element(platform.qubits[qubit], pulse) - opx.config.register_pulse(platform.qubits[qubit], pulse) - assert opx.config.pulses[pulse.id] == target_pulse - assert target_pulse["waveforms"]["single"] in opx.config.waveforms - assert opx.config.elements[f"flux{qubit}"]["operations"][pulse.id] == pulse.id ->>>>>>> 337bff40 (Drop pulse.serial) def test_qm_register_pulses_with_different_frequencies(qmplatform): From a10caf8edad1ded1319896132a29b4ca6ae839ba Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 15:56:04 +0100 Subject: [PATCH 079/233] Fix QM tests --- tests/test_instruments_qm.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 62b3ef994..f7afcb08a 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -94,6 +94,7 @@ def test_qmpulse_previous_and_next(): f"readout{qubit}", PulseType.READOUT, qubit=qubit, + type=PulseType.READOUT, ) ) ro_qmpulses.append(ro_pulse) From 2060804eae0f5bff59bef1f3f8fcd54ce0804de6 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Mon, 29 Jan 2024 17:37:04 +0400 Subject: [PATCH 080/233] Replace pulse.start with Delay object --- README.md | 19 ++++++---- src/qibolab/pulses/pulse.py | 65 +++++++--------------------------- src/qibolab/pulses/sequence.py | 11 +++--- 3 files changed, 32 insertions(+), 63 deletions(-) diff --git a/README.md b/README.md index c1bcc12f1..c2e1bcf88 100644 --- a/README.md +++ b/README.md @@ -27,31 +27,36 @@ A simple example on how to connect to a platform and use it execute a pulse sequ ```python from qibolab import create_platform, ExecutionParameters -from qibolab.pulses import DrivePulse, ReadoutPulse, PulseSequence +from qibolab.pulses import Pulse, Delay, PulseType # Define PulseSequence sequence = PulseSequence() # Add some pulses to the pulse sequence -sequence.add( - DrivePulse( - start=0, +sequence.append( + Pulse( amplitude=0.3, duration=4000, frequency=200_000_000, relative_phase=0, shape="Gaussian(5)", # Gaussian shape with std = duration / 5 + type=PulseType.DRIVE, channel=1, ) ) - -sequence.add( +sequence.append( + Delay( + duration=4000, + channel=2, + ) +) +sequence.append( ReadoutPulse( - start=4004, amplitude=0.9, duration=2000, frequency=20_000_000, relative_phase=0, shape="Rectangular", + type=PulseType.READOUT, channel=2, ) ) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index d7445d57f..3bb8dc3c3 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -4,8 +4,6 @@ from enum import Enum from typing import Optional -import numpy as np - from .shape import SAMPLING_RATE, PulseShape, Waveform @@ -25,10 +23,8 @@ class PulseType(Enum): @dataclass class Pulse: - """A class to represent a pulse to be sent to the QPU.""" + """Representation of a pulse to be sent to the QPU.""" - start: int - """Start time of pulse in ns.""" duration: int """Pulse duration in ns.""" amplitude: float @@ -71,41 +67,8 @@ def __post_init__(self): self.shape.pulse = self @classmethod - def flux(cls, start, duration, amplitude, shape, **kwargs): - return cls( - start, duration, amplitude, 0, 0, shape, type=PulseType.FLUX, **kwargs - ) - - @property - def finish(self) -> Optional[int]: - """Time when the pulse is scheduled to finish.""" - if None in {self.start, self.duration}: - return None - return self.start + self.duration - - @property - def global_phase(self): - """Global phase of the pulse, in radians. - - This phase is calculated from the pulse start time and frequency - as `2 * pi * frequency * start`. - """ - if self.type is PulseType.READOUT: - # readout pulses should have zero global phase so that we can - # calculate probabilities in the i-q plane - return 0 - - # pulse start, duration and finish are in ns - return 2 * np.pi * self.frequency * self.start / 1e9 - - @property - def phase(self) -> float: - """Total phase of the pulse, in radians. - - The total phase is computed as the sum of the global and - relative phases. - """ - return self.global_phase + self.relative_phase + def flux(cls, duration, amplitude, shape, **kwargs): + return cls(duration, amplitude, 0, 0, shape, type=PulseType.FLUX, **kwargs) @property def id(self) -> int: @@ -152,15 +115,13 @@ def __hash__(self): ) ) - def is_equal_ignoring_start(self, item) -> bool: - """Check if two pulses are equal ignoring start time.""" - return ( - self.duration == item.duration - and self.amplitude == item.amplitude - and self.frequency == item.frequency - and self.relative_phase == item.relative_phase - and self.shape == item.shape - and self.channel == item.channel - and self.type == item.type - and self.qubit == item.qubit - ) + +@dataclass +class Delay: + """Representation of a wait instruction during which we are not sending any + pulses to the QPU.""" + + duration: int + """Delay duration in ns.""" + channel: str + """Channel on which the delay should be implemented.""" diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py index d1539b354..6d9dbf830 100644 --- a/src/qibolab/pulses/sequence.py +++ b/src/qibolab/pulses/sequence.py @@ -1,5 +1,7 @@ """PulseSequence class.""" +from collections import defaultdict + from .pulse import PulseType @@ -94,11 +96,12 @@ def coupler_pulses(self, *couplers): @property def finish(self) -> int: """The time when the last pulse of the sequence finishes.""" - t: int = 0 + channel_pulses = defaultdict(list) for pulse in self: - if pulse.finish > t: - t = pulse.finish - return t + channel_pulses[pulse.channel].append(pulse) + return max( + sum(p.duration for p in pulses) for pulses in channel_pulses.values() + ) @property def start(self) -> int: From 1d5e1fac225379fe36f0e245c051a17e707a9714 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Tue, 30 Jan 2024 16:17:21 +0400 Subject: [PATCH 081/233] refactor: drop unused PulseSequence methods --- src/qibolab/pulses/pulse.py | 9 ++++--- src/qibolab/pulses/sequence.py | 47 +--------------------------------- 2 files changed, 7 insertions(+), 49 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 3bb8dc3c3..88ff8670a 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -19,11 +19,12 @@ class PulseType(Enum): DRIVE = "qd" FLUX = "qf" COUPLERFLUX = "cf" + DELAY = "dl" @dataclass class Pulse: - """Representation of a pulse to be sent to the QPU.""" + """A pulse to be sent to the QPU.""" duration: int """Pulse duration in ns.""" @@ -118,10 +119,12 @@ def __hash__(self): @dataclass class Delay: - """Representation of a wait instruction during which we are not sending any - pulses to the QPU.""" + """A wait instruction during which we are not sending any pulses to the + QPU.""" duration: int """Delay duration in ns.""" channel: str """Channel on which the delay should be implemented.""" + type: PulseType = PulseType.DELAY + """Type fixed to ``DELAY`` to comply with ``Pulse`` interface.""" diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py index 6d9dbf830..65fbe69b8 100644 --- a/src/qibolab/pulses/sequence.py +++ b/src/qibolab/pulses/sequence.py @@ -94,7 +94,7 @@ def coupler_pulses(self, *couplers): return new_pc @property - def finish(self) -> int: + def duration(self) -> int: """The time when the last pulse of the sequence finishes.""" channel_pulses = defaultdict(list) for pulse in self: @@ -103,20 +103,6 @@ def finish(self) -> int: sum(p.duration for p in pulses) for pulses in channel_pulses.values() ) - @property - def start(self) -> int: - """The start time of the first pulse of the sequence.""" - t = self.finish - for pulse in self: - if pulse.start < t: - t = pulse.start - return t - - @property - def duration(self) -> int: - """Duration of the sequence calculated as its finish - start times.""" - return self.finish - self.start - @property def channels(self) -> list: """List containing the channels used by the pulses in the sequence.""" @@ -137,25 +123,6 @@ def qubits(self) -> list: qubits.sort() return qubits - def get_pulse_overlaps(self): # -> dict((int,int): PulseSequence): - """Return a dictionary of slices of time (tuples with start and finish - times) where pulses overlap.""" - times = [] - for pulse in self: - if not pulse.start in times: - times.append(pulse.start) - if not pulse.finish in times: - times.append(pulse.finish) - times.sort() - - overlaps = {} - for n in range(len(times) - 1): - overlaps[(times[n], times[n + 1])] = PulseSequence() - for pulse in self: - if (pulse.start <= times[n]) & (pulse.finish >= times[n + 1]): - overlaps[(times[n], times[n + 1])] += [pulse] - return overlaps - def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): """Separate a sequence of overlapping pulses into a list of non- overlapping sequences.""" @@ -181,15 +148,3 @@ def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): if not stored: separated_pulses.append(PulseSequence([new_pulse])) return separated_pulses - - # TODO: Implement separate_different_frequency_pulses() - - @property - def pulses_overlap(self) -> bool: - """Whether any of the pulses in the sequence overlap.""" - overlap = False - for pc in self.get_pulse_overlaps().values(): - if len(pc) > 1: - overlap = True - break - return overlap From d78bd2871bbc42326d51037b04e855dc9b1d0660 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Tue, 30 Jan 2024 18:34:12 +0400 Subject: [PATCH 082/233] refactor: Simplify native.py --- src/qibolab/couplers.py | 4 +- src/qibolab/native.py | 359 ++------------------------------- src/qibolab/pulses/__init__.py | 2 +- src/qibolab/serialize.py | 116 +++++++++-- 4 files changed, 120 insertions(+), 361 deletions(-) diff --git a/src/qibolab/couplers.py b/src/qibolab/couplers.py index 8f1884dda..0d335dfd8 100644 --- a/src/qibolab/couplers.py +++ b/src/qibolab/couplers.py @@ -2,7 +2,7 @@ from typing import Dict, Optional, Union from qibolab.channels import Channel -from qibolab.native import CouplerNatives +from qibolab.native import SingleQubitNatives QubitId = Union[str, int] """Type for Coupler names.""" @@ -22,7 +22,7 @@ class Coupler: sweetspot: float = 0 "Coupler sweetspot to center it's flux dependence if needed." - native_pulse: CouplerNatives = field(default_factory=CouplerNatives) + native_pulse: SingleQubitNatives = field(default_factory=SingleQubitNatives) "For now this only contains the calibrated pulse to activate the coupler." _flux: Optional[Channel] = None diff --git a/src/qibolab/native.py b/src/qibolab/native.py index 8c08595e1..91a0c7da3 100644 --- a/src/qibolab/native.py +++ b/src/qibolab/native.py @@ -1,256 +1,7 @@ -import copy -from collections import defaultdict from dataclasses import dataclass, field, fields, replace -from typing import List, Optional, Union +from typing import Dict, Optional, Tuple -from qibolab.pulses import Pulse, PulseSequence, PulseType - - -@dataclass -class NativePulse: - """Container with parameters required to generate a pulse implementing a - native gate.""" - - name: str - """Name of the gate that the pulse implements.""" - duration: int - amplitude: float - shape: str - pulse_type: PulseType - qubit: "qubits.Qubit" - frequency: int = 0 - relative_start: int = 0 - """Relative start is relevant for two-qubit gate operations which - correspond to a pulse sequence.""" - - # used for qblox - if_frequency: Optional[int] = None - # TODO: Note sure if the following parameters are useful to be in the runcard - start: int = 0 - phase: float = 0.0 - - @classmethod - def from_dict(cls, name, pulse, qubit): - """Parse the dictionary provided by the runcard. - - Args: - name (str): Name of the native gate (dictionary key). - pulse (dict): Dictionary containing the parameters of the pulse implementing - the gate, as loaded from the runcard. - qubits (:class:`qibolab.platforms.abstract.Qubit`): Qubit that the - pulse is acting on - """ - kwargs = copy.deepcopy(pulse) - kwargs["pulse_type"] = PulseType(kwargs.pop("type")) - kwargs["qubit"] = qubit - return cls(name, **kwargs) - - @property - def raw(self): - data = { - fld.name: getattr(self, fld.name) - for fld in fields(self) - if getattr(self, fld.name) is not None - } - del data["name"] - del data["start"] - if self.pulse_type is PulseType.FLUX: - del data["frequency"] - del data["phase"] - data["qubit"] = self.qubit.name - data["type"] = data.pop("pulse_type").value - return data - - def pulse(self, start, relative_phase=0.0): - """Construct the :class:`qibolab.pulses.Pulse` object implementing the - gate. - - Args: - start (int): Start time of the pulse in the sequence. - relative_phase (float): Relative phase of the pulse. - - Returns: - A :class:`qibolab.pulses.DrivePulse` or :class:`qibolab.pulses.DrivePulse` - or :class:`qibolab.pulses.FluxPulse` with the pulse parameters of the gate. - """ - if self.pulse_type is PulseType.FLUX: - return Pulse.flux( - start + self.relative_start, - self.duration, - self.amplitude, - self.shape, - channel=self.qubit.flux.name, - qubit=self.qubit.name, - ) - - channel = getattr(self.qubit, self.pulse_type.name.lower()).name - return Pulse( - start + self.relative_start, - self.duration, - self.amplitude, - self.frequency, - relative_phase, - self.shape, - type=self.pulse_type, - channel=channel, - qubit=self.qubit.name, - ) - - -@dataclass -class VirtualZPulse: - """Container with parameters required to add a virtual Z phase in a pulse - sequence.""" - - phase: float - qubit: "qubits.Qubit" - - @property - def raw(self): - return {"type": "virtual_z", "phase": self.phase, "qubit": self.qubit.name} - - -@dataclass -class CouplerPulse: - """Container with parameters required to add a coupler pulse in a pulse - sequence.""" - - duration: int - amplitude: float - shape: str - coupler: "couplers.Coupler" - relative_start: int = 0 - - @classmethod - def from_dict(cls, pulse, coupler): - """Parse the dictionary provided by the runcard. - - Args: - name (str): Name of the native gate (dictionary key). - pulse (dict): Dictionary containing the parameters of the pulse implementing - the gate, as loaded from the runcard. - coupler (:class:`qibolab.platforms.abstract.Coupler`): Coupler that the - pulse is acting on - """ - kwargs = copy.deepcopy(pulse) - kwargs["coupler"] = coupler - kwargs.pop("type") - return cls(**kwargs) - - @property - def raw(self): - return { - "type": "coupler", - "duration": self.duration, - "amplitude": self.amplitude, - "shape": self.shape, - "coupler": self.coupler.name, - "relative_start": self.relative_start, - } - - def pulse(self, start): - """Construct the :class:`qibolab.pulses.Pulse` object implementing the - gate. - - Args: - start (int): Start time of the pulse in the sequence. - - Returns: - A :class:`qibolab.pulses.FluxPulse` with the pulse parameters of the gate. - """ - return Pulse( - start + self.relative_start, - self.duration, - self.amplitude, - 0, - 0, - self.shape, - type=PulseType.COUPLERFLUX, - channel=self.coupler.flux.name, - qubit=self.coupler.name, - ) - - -@dataclass -class NativeSequence: - """List of :class:`qibolab.platforms.native.NativePulse` objects - implementing a gate. - - Relevant for two-qubit gates, which usually require a sequence of - pulses to be implemented. These pulses may act on qubits different - than the qubits the gate is targeting. - """ - - name: str - pulses: List[Union[NativePulse, VirtualZPulse]] = field(default_factory=list) - coupler_pulses: List[CouplerPulse] = field(default_factory=list) - - @classmethod - def from_dict(cls, name, sequence, qubits, couplers): - """Constructs the native sequence from the dictionaries provided in the - runcard. - - Args: - name (str): Name of the gate the sequence is applying. - sequence (dict): Dictionary describing the sequence as provided in the runcard. - qubits (list): List of :class:`qibolab.qubits.Qubit` object for all - qubits in the platform. All qubits are required because the sequence may be - acting on qubits that the implemented gate is not targeting. - couplers (list): List of :class:`qibolab.couplers.Coupler` object for all - couplers in the platform. All couplers are required because the sequence may be - acting on couplers that the implemented gate is not targeting. - """ - pulses = [] - coupler_pulses = [] - - # If sequence contains only one pulse dictionary, convert it into a list that can be iterated below - if isinstance(sequence, dict): - sequence = [sequence] - - for i, pulse in enumerate(sequence): - pulse = copy.deepcopy(pulse) - pulse_type = pulse.pop("type") - if pulse_type == "coupler": - pulse["coupler"] = couplers[pulse.pop("coupler")] - coupler_pulses.append(CouplerPulse(**pulse)) - else: - qubit = qubits[pulse.pop("qubit")] - if pulse_type == "virtual_z": - phase = pulse["phase"] - pulses.append(VirtualZPulse(phase, qubit)) - else: - pulses.append( - NativePulse( - f"{name}{i}", - **pulse, - pulse_type=PulseType(pulse_type), - qubit=qubit, - ) - ) - return cls(name, pulses, coupler_pulses) - - @property - def raw(self): - pulses = [pulse.raw for pulse in self.pulses] - coupler_pulses = [pulse.raw for pulse in self.coupler_pulses] - return pulses + coupler_pulses - - def sequence(self, start=0): - """Creates a :class:`qibolab.pulses.PulseSequence` object implementing - the sequence.""" - sequence = PulseSequence() - virtual_z_phases = defaultdict(int) - - for pulse in self.pulses: - if isinstance(pulse, NativePulse): - sequence.append(pulse.pulse(start=start)) - else: - virtual_z_phases[pulse.qubit.name] += pulse.phase - - for coupler_pulse in self.coupler_pulses: - sequence.append(coupler_pulse.pulse(start=start)) - # TODO: Maybe ``virtual_z_phases`` should be an attribute of ``PulseSequence`` - return sequence, virtual_z_phases +from qibolab.pulses import Pulse, PulseSequence @dataclass @@ -258,85 +9,22 @@ class SingleQubitNatives: """Container with the native single-qubit gates acting on a specific qubit.""" - RX: Optional[NativePulse] = None + RX: Optional[Pulse] = None """Pulse to drive the qubit from state 0 to state 1.""" - RX12: Optional[NativePulse] = None + RX12: Optional[Pulse] = None """Pulse to drive to qubit from state 1 to state 2.""" - MZ: Optional[NativePulse] = None + MZ: Optional[Pulse] = None """Measurement pulse.""" + CP: Optional[Pulse] = None + """Pulse to activate a coupler.""" @property - def RX90(self) -> NativePulse: + def RX90(self) -> Pulse: """RX90 native pulse is inferred from RX by halving its amplitude.""" - return replace(self.RX, name="RX90", amplitude=self.RX.amplitude / 2.0) - - @classmethod - def from_dict(cls, qubit, native_gates): - """Parse native gates of the qubit from the runcard. + return replace(self.RX, amplitude=self.RX.amplitude / 2.0) - Args: - qubit (:class:`qibolab.qubits.Qubit`): Qubit object that the - native gates are acting on. - native_gates (dict): Dictionary with native gate pulse parameters as loaded - from the runcard. - """ - pulses = { - n: NativePulse.from_dict(n, pulse, qubit=qubit) - for n, pulse in native_gates.items() - } - return cls(**pulses) - @property - def raw(self): - """Serialize native gate pulses. - - ``None`` gates are not included. - """ - data = {} - for fld in fields(self): - attr = getattr(self, fld.name) - if attr is not None: - data[fld.name] = attr.raw - del data[fld.name]["qubit"] - return data - - -@dataclass -class CouplerNatives: - """Container with the native single-qubit gates acting on a specific - qubit.""" - - CP: Optional[NativePulse] = None - """Pulse to activate the coupler.""" - - @classmethod - def from_dict(cls, coupler, native_gates): - """Parse coupler native gates from the runcard. - - Args: - coupler (:class:`qibolab.couplers.Coupler`): Coupler object that the - native pulses are acting on. - native_gates (dict): Dictionary with native gate pulse parameters as loaded - from the runcard [Reusing the dict from qubits]. - """ - pulses = { - n: CouplerPulse.from_dict(pulse, coupler=coupler) - for n, pulse in native_gates.items() - } - return cls(**pulses) - - @property - def raw(self): - """Serialize native gate pulses. - - ``None`` gates are not included. - """ - data = {} - for fld in fields(self): - attr = getattr(self, fld.name) - if attr is not None: - data[fld.name] = attr.raw - return data +TwoQubitNativeType = Tuple[PulseSequence, Dict["QubitId", float]] @dataclass @@ -344,9 +32,13 @@ class TwoQubitNatives: """Container with the native two-qubit gates acting on a specific pair of qubits.""" - CZ: Optional[NativeSequence] = field(default=None, metadata={"symmetric": True}) - CNOT: Optional[NativeSequence] = field(default=None, metadata={"symmetric": False}) - iSWAP: Optional[NativeSequence] = field(default=None, metadata={"symmetric": True}) + CZ: Optional[TwoQubitNativeType] = field(default=None, metadata={"symmetric": True}) + CNOT: Optional[TwoQubitNativeType] = field( + default=None, metadata={"symmetric": False} + ) + iSWAP: Optional[TwoQubitNativeType] = field( + default=None, metadata={"symmetric": True} + ) @property def symmetric(self): @@ -356,20 +48,3 @@ def symmetric(self): fld.metadata["symmetric"] or getattr(self, fld.name) is None for fld in fields(self) ) - - @classmethod - def from_dict(cls, qubits, couplers, native_gates): - sequences = { - n: NativeSequence.from_dict(n, seq, qubits, couplers) - for n, seq in native_gates.items() - } - return cls(**sequences) - - @property - def raw(self): - data = {} - for fld in fields(self): - gate = getattr(self, fld.name) - if gate is not None: - data[fld.name] = gate.raw - return data diff --git a/src/qibolab/pulses/__init__.py b/src/qibolab/pulses/__init__.py index f0ad2ad16..ed4233e7d 100644 --- a/src/qibolab/pulses/__init__.py +++ b/src/qibolab/pulses/__init__.py @@ -1,4 +1,4 @@ -from .pulse import Pulse, PulseType +from .pulse import Delay, Pulse, PulseType from .sequence import PulseSequence from .shape import ( IIR, diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index 76df51410..6343b1688 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -6,13 +6,14 @@ """ import json -from dataclasses import asdict +from collections import defaultdict +from dataclasses import asdict, fields from pathlib import Path from typing import Tuple from qibolab.couplers import Coupler from qibolab.kernels import Kernels -from qibolab.native import CouplerNatives, SingleQubitNatives, TwoQubitNatives +from qibolab.native import SingleQubitNatives, TwoQubitNatives from qibolab.platform.platform import ( CouplerMap, InstrumentMap, @@ -21,6 +22,7 @@ QubitPairMap, Settings, ) +from qibolab.pulses import Delay, Pulse, PulseSequence, PulseType from qibolab.qubits import Qubit, QubitPair RUNCARD = "parameters.json" @@ -87,7 +89,53 @@ def load_qubits( return qubits, couplers, pairs -# This creates the compiler error +def _load_pulse(pulse_kwargs, qubit=None): + _type = pulse_kwargs["type"] + q = pulse_kwargs.pop("qubit", qubit.name) + if _type == "dl": + return Delay(**pulse_kwargs) + + pulse = Pulse(**pulse_kwargs, qubit=q) + channel_type = "flux" if pulse.type is PulseType.COUPLERFLUX else pulse.type.lower() + pulse.channel = getattr(qubit, channel_type) + return pulse + + +def _load_single_qubit_natives(qubit, gates) -> SingleQubitNatives: + """Parse native gates of the qubit from the runcard. + + Args: + qubit (:class:`qibolab.qubits.Qubit`): Qubit object that the + native gates are acting on. + gates (dict): Dictionary with native gate pulse parameters as loaded + from the runcard. + """ + return SingleQubitNatives( + **{name: _load_pulse(kwargs, qubit) for name, kwargs in gates.items()} + ) + + +def _load_two_qubit_natives(qubits, couplers, gates) -> TwoQubitNatives: + sequences = {} + for name, seq_kwargs in gates.items(): + if isinstance(sequence, dict): + seq_kwargs = [seq_kwargs] + + sequence = PulseSequence() + virtual_z_phases = defaultdict(int) + for kwargs in seq_kwargs: + _type = kwargs["type"] + q = kwargs["qubit"] + if _type == "virtual_z": + virtual_z_phases[q] += kwargs["phase"] + else: + qubit = couplers[q] if _type == "cf" else qubits[q] + sequence.append(_load_pulse(kwargs, qubit)) + + sequences[name] = (sequence, virtual_z_phases) + return TwoQubitNatives(**sequences) + + def register_gates( runcard: dict, qubits: QubitMap, pairs: QubitPairMap, couplers: CouplerMap = None ) -> Tuple[QubitMap, QubitPairMap]: @@ -101,20 +149,21 @@ def register_gates( native_gates = runcard.get("native_gates", {}) for q, gates in native_gates.get("single_qubit", {}).items(): - qubits[json.loads(q)].native_gates = SingleQubitNatives.from_dict( + qubits[json.loads(q)].native_gates = _load_single_qubit_natives( qubits[json.loads(q)], gates ) for c, gates in native_gates.get("coupler", {}).items(): - couplers[json.loads(c)].native_pulse = CouplerNatives.from_dict( + couplers[json.loads(c)].native_pulse = _load_single_qubit_natives( couplers[json.loads(c)], gates ) # register two-qubit native gates to ``QubitPair`` objects for pair, gatedict in native_gates.get("two_qubit", {}).items(): q0, q1 = tuple(int(q) if q.isdigit() else q for q in pair.split("-")) - native_gates = TwoQubitNatives.from_dict(qubits, couplers, gatedict) - pairs[(q0, q1)].native_gates = native_gates + native_gates = _load_two_qubit_natives(qubits, couplers, gatedict) + coupler = pairs[(q0, q1)].coupler + pairs[(q0, q1)] = QubitPair(qubits[q0], qubits[q1], coupler, native_gates) if native_gates.symmetric: pairs[(q1, q0)] = pairs[(q0, q1)] @@ -130,6 +179,39 @@ def load_instrument_settings( return instruments +def _dump_pulse(pulse: Pulse): + data = asdict(pulse) + if pulse.type in (PulseType.FLUX, PulseType.COUPLERFLUX): + del data["frequency"] + del data["relative_phase"] + data["type"] = data["type"].value + return data + + +def _dump_single_qubit_natives(natives: SingleQubitNatives): + data = {} + for fld in fields(natives): + pulse = getattr(natives, fld.name) + if pulse is not None: + data[fld.name] = _dump_pulse(pulse) + del data[fld.name]["qubit"] + return data + + +def _dump_two_qubit_natives(natives: TwoQubitNatives): + data = {} + for fld in fields(natives): + if getattr(natives, fld.name) is None: + continue + sequence, virtual_z_phases = getattr(natives, fld.name) + data[fld.name] = [_dump_pulse(pulse) for pulse in sequence] + data[fld.name].extend( + {"type": "virtual_z", "phase": phase, "qubit": q} + for q, phase in virtual_z_phases.items() + ) + return data + + def dump_native_gates( qubits: QubitMap, pairs: QubitPairMap, couplers: CouplerMap = None ) -> dict: @@ -138,22 +220,24 @@ def dump_native_gates( # single-qubit native gates native_gates = { "single_qubit": { - json.dumps(q): qubit.native_gates.raw for q, qubit in qubits.items() + json.dumps(q): _dump_single_qubit_natives(qubit.native_gates) + for q, qubit in qubits.items() } } + if couplers: native_gates["coupler"] = { - json.dumps(c): coupler.native_pulse.raw for c, coupler in couplers.items() + json.dumps(c): _dump_two_qubit_natives(coupler.native_gates) + for c, coupler in couplers.items() } # two-qubit native gates - if len(pairs) > 0: - native_gates["two_qubit"] = {} - for pair in pairs.values(): - natives = pair.native_gates.raw - if len(natives) > 0: - pair_name = f"{pair.qubit1.name}-{pair.qubit2.name}" - native_gates["two_qubit"][pair_name] = natives + native_gates["two_qubit"] = {} + for pair in pairs.values(): + natives = _dump_two_qubit_natives(pair.native_gates) + if len(natives) > 0: + pair_name = f"{pair.qubit1.name}-{pair.qubit2.name}" + native_gates["two_qubit"][pair_name] = natives return native_gates From 5188f65c05a2ae67c064d4f47fcb37a62680caa7 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Tue, 30 Jan 2024 19:17:29 +0400 Subject: [PATCH 083/233] refactor: Remove pulse.start from platform and sweeper --- src/qibolab/platform/platform.py | 50 ++++++++++++++++++-------------- src/qibolab/sweeper.py | 4 +-- 2 files changed, 29 insertions(+), 25 deletions(-) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index ede232c7d..c17147328 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -274,7 +274,7 @@ def sweep( platform = create_dummy() sequence = PulseSequence() parameter = Parameter.frequency - pulse = platform.create_qubit_readout_pulse(qubit=0, start=0) + pulse = platform.create_qubit_readout_pulse(qubit=0) sequence.append(pulse) parameter_range = np.random.randint(10, size=10) sweeper = Sweeper(parameter, parameter_range, [pulse]) @@ -333,62 +333,68 @@ def get_coupler(self, coupler): except KeyError: return list(self.couplers.keys())[coupler] - def create_RX90_pulse(self, qubit, start=0, relative_phase=0): + def create_RX90_pulse(self, qubit, relative_phase=0): qubit = self.get_qubit(qubit) - return self.qubits[qubit].native_gates.RX90.pulse(start, relative_phase) + pulse = self.qubits[qubit].native_gates.RX90 + pulse.relative_phase = relative_phase + return pulse - def create_RX_pulse(self, qubit, start=0, relative_phase=0): + def create_RX_pulse(self, qubit, relative_phase=0): qubit = self.get_qubit(qubit) - return self.qubits[qubit].native_gates.RX.pulse(start, relative_phase) + pulse = self.qubits[qubit].native_gates.RX + pulse.relative_phase = relative_phase + return pulse - def create_RX12_pulse(self, qubit, start=0, relative_phase=0): + def create_RX12_pulse(self, qubit, relative_phase=0): qubit = self.get_qubit(qubit) - return self.qubits[qubit].native_gates.RX12.pulse(start, relative_phase) + pulse = self.qubits[qubit].native_gates.RX12 + pulse.relative_phase = relative_phase + return pulse - def create_CZ_pulse_sequence(self, qubits, start=0): + def create_CZ_pulse_sequence(self, qubits): pair = tuple(self.get_qubit(q) for q in qubits) if pair not in self.pairs or self.pairs[pair].native_gates.CZ is None: raise_error( ValueError, f"Calibration for CZ gate between qubits {qubits[0]} and {qubits[1]} not found.", ) - return self.pairs[pair].native_gates.CZ.sequence(start) + return self.pairs[pair].native_gates.CZ - def create_iSWAP_pulse_sequence(self, qubits, start=0): + def create_iSWAP_pulse_sequence(self, qubits): pair = tuple(self.get_qubit(q) for q in qubits) if pair not in self.pairs or self.pairs[pair].native_gates.iSWAP is None: raise_error( ValueError, f"Calibration for iSWAP gate between qubits {qubits[0]} and {qubits[1]} not found.", ) - return self.pairs[pair].native_gates.iSWAP.sequence(start) + return self.pairs[pair].native_gates.iSWAP - def create_CNOT_pulse_sequence(self, qubits, start=0): + def create_CNOT_pulse_sequence(self, qubits): pair = tuple(self.get_qubit(q) for q in qubits) if pair not in self.pairs or self.pairs[pair].native_gates.CNOT is None: raise_error( ValueError, f"Calibration for CNOT gate between qubits {qubits[0]} and {qubits[1]} not found.", ) - return self.pairs[pair].native_gates.CNOT.sequence(start) + return self.pairs[pair].native_gates.CNOT - def create_MZ_pulse(self, qubit, start): + def create_MZ_pulse(self, qubit): qubit = self.get_qubit(qubit) - return self.qubits[qubit].native_gates.MZ.pulse(start) + return self.qubits[qubit].native_gates.MZ - def create_qubit_drive_pulse(self, qubit, start, duration, relative_phase=0): + def create_qubit_drive_pulse(self, qubit, duration, relative_phase=0): qubit = self.get_qubit(qubit) - pulse = self.qubits[qubit].native_gates.RX.pulse(start, relative_phase) + pulse = self.qubits[qubit].native_gates.RX + pulse.relative_phase = relative_phase pulse.duration = duration return pulse - def create_qubit_readout_pulse(self, qubit, start): - qubit = self.get_qubit(qubit) - return self.create_MZ_pulse(qubit, start) + def create_qubit_readout_pulse(self, qubit): + return self.create_MZ_pulse(qubit) - def create_coupler_pulse(self, coupler, start, duration=None, amplitude=None): + def create_coupler_pulse(self, coupler, duration=None, amplitude=None): coupler = self.get_coupler(coupler) - pulse = self.couplers[coupler].native_pulse.CP.pulse(start) + pulse = self.couplers[coupler].native_pulse.CP if duration is not None: pulse.duration = duration if amplitude is not None: diff --git a/src/qibolab/sweeper.py b/src/qibolab/sweeper.py index 84ff1880c..ddb17297a 100644 --- a/src/qibolab/sweeper.py +++ b/src/qibolab/sweeper.py @@ -14,7 +14,6 @@ class Parameter(Enum): amplitude = auto() duration = auto() relative_phase = auto() - start = auto() attenuation = auto() gain = auto() @@ -26,7 +25,6 @@ class Parameter(Enum): AMPLITUDE = Parameter.amplitude DURATION = Parameter.duration RELATIVE_PHASE = Parameter.relative_phase -START = Parameter.start ATTENUATION = Parameter.attenuation GAIN = Parameter.gain BIAS = Parameter.bias @@ -64,7 +62,7 @@ class Sweeper: platform = create_dummy() sequence = PulseSequence() parameter = Parameter.frequency - pulse = platform.create_qubit_readout_pulse(qubit=0, start=0) + pulse = platform.create_qubit_readout_pulse(qubit=0) sequence.append(pulse) parameter_range = np.random.randint(10, size=10) sweeper = Sweeper(parameter, parameter_range, [pulse]) From a267dc137a6f6dbc1afdc2073506046df19ad061 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Fri, 23 Feb 2024 15:42:28 +0400 Subject: [PATCH 084/233] refactor: stop using create_* in default compiler --- src/qibolab/compilers/default.py | 55 ++++++++++++++++---------------- 1 file changed, 27 insertions(+), 28 deletions(-) diff --git a/src/qibolab/compilers/default.py b/src/qibolab/compilers/default.py index 0078973ca..142ec2cb1 100644 --- a/src/qibolab/compilers/default.py +++ b/src/qibolab/compilers/default.py @@ -15,66 +15,64 @@ def identity_rule(gate, platform): def z_rule(gate, platform): """Z gate applied virtually.""" - qubit = gate.target_qubits[0] - return PulseSequence(), {qubit: math.pi} + qubit = platform.get_qubit(gate.target_qubits[0]) + return PulseSequence(), {qubit.name: math.pi} def rz_rule(gate, platform): """RZ gate applied virtually.""" - qubit = gate.target_qubits[0] - return PulseSequence(), {qubit: gate.parameters[0]} + qubit = platform.get_qubit(gate.target_qubits[0]) + return PulseSequence(), {qubit.name: gate.parameters[0]} def gpi2_rule(gate, platform): """Rule for GPI2.""" - qubit = gate.target_qubits[0] + qubit = platform.get_qubit(gate.target_qubits[0]) theta = gate.parameters[0] sequence = PulseSequence() - pulse = platform.create_RX90_pulse(qubit, start=0, relative_phase=theta) + pulse = qubit.native_gates.RX90 + pulse.relative_phase = theta sequence.append(pulse) return sequence, {} def gpi_rule(gate, platform): """Rule for GPI.""" - qubit = gate.target_qubits[0] + qubit = platform.get_qubit(gate.target_qubits[0]) theta = gate.parameters[0] sequence = PulseSequence() # the following definition has a global phase difference compare to # to the matrix representation. See # https://github.com/qiboteam/qibolab/pull/804#pullrequestreview-1890205509 # for more detail. - pulse = platform.create_RX_pulse(qubit, start=0, relative_phase=theta) + pulse = qubit.native_gates.RX + pulse.relative_phase = theta sequence.append(pulse) return sequence, {} def u3_rule(gate, platform): """U3 applied as RZ-RX90-RZ-RX90-RZ.""" - qubit = gate.target_qubits[0] + qubit = platform.get_qubit(gate.target_qubits[0]) # Transform gate to U3 and add pi/2-pulses theta, phi, lam = gate.parameters # apply RZ(lam) - virtual_z_phases = {qubit: lam} + virtual_z_phases = {qubit.name: lam} sequence = PulseSequence() # Fetch pi/2 pulse from calibration - RX90_pulse_1 = platform.create_RX90_pulse( - qubit, start=0, relative_phase=virtual_z_phases[qubit] - ) + rx90_pulse1 = qubit.native_gates.RX90 + rx90_pulse1.relative_phase = virtual_z_phases[qubit.name] # apply RX(pi/2) - sequence.append(RX90_pulse_1) + sequence.append(rx90_pulse1) # apply RZ(theta) - virtual_z_phases[qubit] += theta + virtual_z_phases[qubit.name] += theta # Fetch pi/2 pulse from calibration - RX90_pulse_2 = platform.create_RX90_pulse( - qubit, - start=RX90_pulse_1.finish, - relative_phase=virtual_z_phases[qubit] - math.pi, - ) + rx90_pulse2 = qubit.native_gates.RX90 + rx90_pulse2.relative_phase = (virtual_z_phases[qubit.name] - math.pi,) # apply RX(-pi/2) - sequence.append(RX90_pulse_2) + sequence.append(rx90_pulse2) # apply RZ(phi) - virtual_z_phases[qubit] += phi + virtual_z_phases[qubit.name] += phi return sequence, virtual_z_phases @@ -85,18 +83,19 @@ def cz_rule(gate, platform): Applying the CZ gate may involve sending pulses on qubits that the gate is not directly acting on. """ - return platform.create_CZ_pulse_sequence(gate.qubits) + pair = platform.pairs[tuple(platform.get_qubit(q) for q in gate.qubits)] + return pair.native_gates.CZ def cnot_rule(gate, platform): """CNOT applied as defined in the platform runcard.""" - return platform.create_CNOT_pulse_sequence(gate.qubits) + pair = platform.pairs[tuple(platform.get_qubit(q) for q in gate.qubits)] + return pair.native_gates.CNOT def measurement_rule(gate, platform): """Measurement gate applied using the platform readout pulse.""" - sequence = PulseSequence() - for qubit in gate.target_qubits: - MZ_pulse = platform.create_MZ_pulse(qubit, start=0) - sequence.append(MZ_pulse) + sequence = PulseSequence( + [platform.get_qubit(q).native_gates.MZ for q in gate.qubits] + ) return sequence, {} From a18d7b174f28b4dd2374e7d4a19427088f794d3a Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Fri, 23 Feb 2024 17:03:00 +0400 Subject: [PATCH 085/233] fix: remove pulse.start from compiler --- src/qibolab/compilers/compiler.py | 58 ++++++++++++------------------- 1 file changed, 22 insertions(+), 36 deletions(-) diff --git a/src/qibolab/compilers/compiler.py b/src/qibolab/compilers/compiler.py index 7bfa9f0e1..64f9fbb4d 100644 --- a/src/qibolab/compilers/compiler.py +++ b/src/qibolab/compilers/compiler.py @@ -15,7 +15,7 @@ u3_rule, z_rule, ) -from qibolab.pulses import PulseSequence, PulseType +from qibolab.pulses import Delay, PulseSequence, PulseType @dataclass @@ -98,33 +98,6 @@ def inner(func): return inner - def _compile_gate( - self, gate, platform, sequence, virtual_z_phases, moment_start, delays - ): - """Adds a single gate to the pulse sequence.""" - rule = self[gate.__class__] - # get local sequence and phases for the current gate - gate_sequence, gate_phases = rule(gate, platform) - - # update global pulse sequence - # determine the right start time based on the availability of the qubits involved - all_qubits = {*gate_sequence.qubits, *gate.qubits} - start = max( - *[ - sequence.get_qubit_pulses(qubit).finish + delays[qubit] - for qubit in all_qubits - ], - moment_start, - ) - # shift start time and phase according to the global sequence - for pulse in gate_sequence: - pulse.start += start - if pulse.type is not PulseType.READOUT: - pulse.relative_phase += virtual_z_phases[pulse.qubit] - sequence.append(pulse) - - return gate_sequence, gate_phases - def compile(self, circuit, platform): """Transforms a circuit to pulse sequence. @@ -144,20 +117,33 @@ def compile(self, circuit, platform): virtual_z_phases = defaultdict(int) measurement_map = {} + qubit_clock = defaultdict(int) + channel_clock = defaultdict(int) # process circuit gates - delays = defaultdict(int) for moment in circuit.queue.moments: - moment_start = sequence.finish for gate in set(filter(lambda x: x is not None, moment)): if isinstance(gate, gates.Align): for qubit in gate.qubits: - delays[qubit] += gate.delay + # TODO: do something + pass continue - gate_sequence, gate_phases = self._compile_gate( - gate, platform, sequence, virtual_z_phases, moment_start, delays - ) - for qubit in gate.qubits: - delays[qubit] = 0 + + rule = self[gate.__class__] + # get local sequence and phases for the current gate + gate_sequence, gate_phases = rule(gate, platform) + for pulse in gate_sequence: + if pulse.type is not PulseType.READOUT: + pulse.relative_phase += virtual_z_phases[pulse.qubit] + + if qubit_clock[pulse.qubit] > channel_clock[pulse.qubit]: + delay = qubit_clock[pulse.qubit] - channel_clock[pulse.channel] + sequence.append(Delay(delay, pulse.channel)) + channel_clock[pulse.channel] += delay + + sequence.append(pulse) + # update clocks + qubit_clock[pulse.qubit] += pulse.duration + channel_clock[pulse.channel] += pulse.duration # update virtual Z phases for qubit, phase in gate_phases.items(): From 7d74296ea37de99b33d8e45034632fb83956b435 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Sat, 24 Feb 2024 00:48:35 +0400 Subject: [PATCH 086/233] fix: remove relative_start from dummy_qrc runcards --- tests/dummy_qrc/qblox/parameters.json | 70 +---------------------- tests/dummy_qrc/qm/parameters.json | 17 ------ tests/dummy_qrc/qm_octave/parameters.json | 21 +------ tests/dummy_qrc/rfsoc/parameters.json | 3 - tests/dummy_qrc/zurich/parameters.json | 54 ++--------------- 5 files changed, 9 insertions(+), 156 deletions(-) diff --git a/tests/dummy_qrc/qblox/parameters.json b/tests/dummy_qrc/qblox/parameters.json index 7a5099b5f..45e83d91e 100644 --- a/tests/dummy_qrc/qblox/parameters.json +++ b/tests/dummy_qrc/qblox/parameters.json @@ -104,7 +104,6 @@ "frequency": 5050304836, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -113,7 +112,6 @@ "frequency": 5050304836, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -122,7 +120,6 @@ "frequency": 7213299307, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -133,7 +130,6 @@ "frequency": 4852833073, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -142,7 +138,6 @@ "frequency": 4852833073, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -151,7 +146,6 @@ "frequency": 7452990931, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -162,7 +156,6 @@ "frequency": 5795371914, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -171,7 +164,6 @@ "frequency": 5795371914, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -180,7 +172,6 @@ "frequency": 7655083068, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -191,7 +182,6 @@ "frequency": 6761018001, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -200,7 +190,6 @@ "frequency": 6761018001, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -209,7 +198,6 @@ "frequency": 7803441221, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -220,7 +208,6 @@ "frequency": 6586543060, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -229,7 +216,6 @@ "frequency": 6586543060, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -238,7 +224,6 @@ "frequency": 8058947261, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } } @@ -251,15 +236,6 @@ "amplitude": -0.6025, "shape": "Exponential(12, 5000, 0.1)", "qubit": 3, - "relative_start": 0, - "type": "qf" - }, - { - "duration": 20, - "amplitude": 0, - "shape": "Rectangular())", - "qubit": 3, - "relative_start": 32, "type": "qf" }, { @@ -267,22 +243,6 @@ "phase": -3.63, "qubit": 3 }, - { - "duration": 32, - "amplitude": 0, - "shape": "Rectangular())", - "qubit": 2, - "relative_start": 0, - "type": "qf" - }, - { - "duration": 20, - "amplitude": 0, - "shape": "Rectangular())", - "qubit": 2, - "relative_start": 32, - "type": "qf" - }, { "type": "virtual_z", "phase": -0.041, @@ -297,7 +257,6 @@ "amplitude": -0.142, "shape": "Exponential(12, 5000, 0.1)", "qubit": 2, - "relative_start": 0, "type": "qf" } ] @@ -308,38 +267,13 @@ "duration": 32, "amplitude": -0.6025, "shape": "Exponential(12, 5000, 0.1)", - "qubit": 3, - "relative_start": 0, - "type": "qf" - }, - { - "duration": 20, - "amplitude": 0, - "shape": "Rectangular())", - "qubit": 3, - "relative_start": 32, + "qubit": 2, "type": "qf" }, { "type": "virtual_z", "phase": -3.63, - "qubit": 3 - }, - { - "duration": 32, - "amplitude": 0, - "shape": "Rectangular())", - "qubit": 2, - "relative_start": 0, - "type": "qf" - }, - { - "duration": 20, - "amplitude": 0, - "shape": "Rectangular())", - "qubit": 2, - "relative_start": 32, - "type": "qf" + "qubit": 1 }, { "type": "virtual_z", diff --git a/tests/dummy_qrc/qm/parameters.json b/tests/dummy_qrc/qm/parameters.json index 86b76bff5..54fbc03dd 100644 --- a/tests/dummy_qrc/qm/parameters.json +++ b/tests/dummy_qrc/qm/parameters.json @@ -85,7 +85,6 @@ "frequency": 4700000000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -94,7 +93,6 @@ "frequency": 4700000000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -103,7 +101,6 @@ "frequency": 7226500000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -114,7 +111,6 @@ "frequency": 4855663000, "shape": "Drag(5, -0.02)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -123,7 +119,6 @@ "frequency": 4855663000, "shape": "Drag(5, -0.02)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -132,7 +127,6 @@ "frequency": 7453265000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -143,7 +137,6 @@ "frequency": 5800563000, "shape": "Drag(5, -0.04)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -152,7 +145,6 @@ "frequency": 5800563000, "shape": "Drag(5, -0.04)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -161,7 +153,6 @@ "frequency": 7655107000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -172,7 +163,6 @@ "frequency": 6760922000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -181,7 +171,6 @@ "frequency": 6760922000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -190,7 +179,6 @@ "frequency": 7802191000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -201,7 +189,6 @@ "frequency": 6585053000, "shape": "Drag(5, 0.0)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -210,7 +197,6 @@ "frequency": 6585053000, "shape": "Drag(5, 0.0)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -219,7 +205,6 @@ "frequency": 8057668000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } } @@ -232,7 +217,6 @@ "amplitude": 0.055, "shape": "Rectangular()", "qubit": 2, - "relative_start": 0, "type": "qf" }, { @@ -254,7 +238,6 @@ "amplitude": -0.0513, "shape": "Rectangular()", "qubit": 3, - "relative_start": 0, "type": "qf" }, { diff --git a/tests/dummy_qrc/qm_octave/parameters.json b/tests/dummy_qrc/qm_octave/parameters.json index db430ede9..de55fdf48 100644 --- a/tests/dummy_qrc/qm_octave/parameters.json +++ b/tests/dummy_qrc/qm_octave/parameters.json @@ -107,7 +107,6 @@ "frequency": 4700000000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -116,7 +115,6 @@ "frequency": 4700000000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -125,7 +123,6 @@ "frequency": 7226500000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -136,7 +133,6 @@ "frequency": 4855663000, "shape": "Drag(5, -0.02)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -145,7 +141,6 @@ "frequency": 4855663000, "shape": "Drag(5, -0.02)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -154,7 +149,6 @@ "frequency": 7453265000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -165,7 +159,6 @@ "frequency": 5800563000, "shape": "Drag(5, -0.04)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -174,7 +167,6 @@ "frequency": 5800563000, "shape": "Drag(5, -0.04)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -183,7 +175,6 @@ "frequency": 7655107000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -194,7 +185,6 @@ "frequency": 6760922000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -203,7 +193,6 @@ "frequency": 6760922000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -212,7 +201,6 @@ "frequency": 7802191000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -223,7 +211,6 @@ "frequency": 6585053000, "shape": "Drag(5, 0.0)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -232,7 +219,6 @@ "frequency": 6585053000, "shape": "Drag(5, 0.0)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -241,7 +227,6 @@ "frequency": 8057668000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } } @@ -254,8 +239,7 @@ "amplitude": 0.055, "shape": "Rectangular()", "qubit": 2, - "relative_start": 0, - "type": "qf" + "type": "qf" }, { "type": "virtual_z", @@ -276,8 +260,7 @@ "amplitude": -0.0513, "shape": "Rectangular()", "qubit": 3, - "relative_start": 0, - "type": "qf" + "type": "qf" }, { "type": "virtual_z", diff --git a/tests/dummy_qrc/rfsoc/parameters.json b/tests/dummy_qrc/rfsoc/parameters.json index 65e71e7da..b99a6e878 100644 --- a/tests/dummy_qrc/rfsoc/parameters.json +++ b/tests/dummy_qrc/rfsoc/parameters.json @@ -34,7 +34,6 @@ "frequency": 5542341844, "shape": "Rectangular()", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -43,7 +42,6 @@ "frequency": 5542341844, "shape": "Rectangular()", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -52,7 +50,6 @@ "frequency": 7371258599, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } } diff --git a/tests/dummy_qrc/zurich/parameters.json b/tests/dummy_qrc/zurich/parameters.json index e49acba7c..38db36906 100644 --- a/tests/dummy_qrc/zurich/parameters.json +++ b/tests/dummy_qrc/zurich/parameters.json @@ -65,7 +65,6 @@ "frequency": 4095830788, "shape": "Drag(5, 0.04)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -74,7 +73,6 @@ "frequency": 4095830788, "shape": "Drag(5, 0.04)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -83,7 +81,6 @@ "frequency": 5229200000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -94,7 +91,6 @@ "frequency": 4170000000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -103,7 +99,6 @@ "frequency": 4170000000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -112,7 +107,6 @@ "frequency": 4931000000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -123,7 +117,6 @@ "frequency": 4300587281, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -132,7 +125,6 @@ "frequency": 4300587281, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -141,7 +133,6 @@ "frequency": 6109000000.0, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -152,7 +143,6 @@ "frequency": 4100000000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -161,7 +151,6 @@ "frequency": 4100000000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -170,7 +159,6 @@ "frequency": 5783000000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } }, @@ -181,7 +169,6 @@ "frequency": 4196800000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "RX12": { @@ -190,7 +177,6 @@ "frequency": 4196800000, "shape": "Gaussian(5)", "type": "qd", - "relative_start": 0, "phase": 0 }, "MZ": { @@ -199,7 +185,6 @@ "frequency": 5515000000, "shape": "Rectangular()", "type": "ro", - "relative_start": 0, "phase": 0 } } @@ -211,8 +196,7 @@ "duration": 1000, "amplitude": 0.5, "shape": "Rectangular()", - "coupler": 0, - "relative_start": 0 + "coupler": 0 } }, "1": { @@ -221,8 +205,7 @@ "duration": 1000, "amplitude": 0.5, "shape": "Rectangular()", - "coupler": 1, - "relative_start": 0 + "coupler": 1 } }, "3": { @@ -231,8 +214,7 @@ "duration": 1000, "amplitude": 0.5, "shape": "Rectangular()", - "coupler": 3, - "relative_start": 0 + "coupler": 3 } }, "4": { @@ -241,8 +223,7 @@ "duration": 1000, "amplitude": 0.5, "shape": "Rectangular()", - "coupler": 4, - "relative_start": 0 + "coupler": 4 } } }, @@ -254,37 +235,12 @@ "amplitude": -0.6025, "shape": "Exponential(12, 5000, 0.1)", "qubit": 3, - "relative_start": 0, - "type": "qf" - }, - { - "duration": 20, - "amplitude": 0, - "shape": "Rectangular())", - "qubit": 3, - "relative_start": 32, "type": "qf" }, { "type": "virtual_z", "phase": -3.63, - "qubit": 3 - }, - { - "duration": 32, - "amplitude": 0, - "shape": "Rectangular())", - "qubit": 2, - "relative_start": 0, - "type": "qf" - }, - { - "duration": 20, - "amplitude": 0, - "shape": "Rectangular())", - "qubit": 2, - "relative_start": 32, - "type": "qf" + "qubit": 1 }, { "type": "virtual_z", From 0923e1c78708c486b3104fc76e01b9f9a5579898 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Sat, 24 Feb 2024 01:20:45 +0400 Subject: [PATCH 087/233] fix: remove relative_start from dummy --- src/qibolab/dummy/parameters.json | 38 +------------------------------ 1 file changed, 1 insertion(+), 37 deletions(-) diff --git a/src/qibolab/dummy/parameters.json b/src/qibolab/dummy/parameters.json index 498ab97d5..4c281fdb8 100644 --- a/src/qibolab/dummy/parameters.json +++ b/src/qibolab/dummy/parameters.json @@ -56,7 +56,6 @@ "amplitude": 0.1, "shape": "Gaussian(5)", "frequency": 4000000000.0, - "relative_start": 0, "phase": 0, "type": "qd" }, @@ -65,7 +64,6 @@ "amplitude": 0.005, "shape": "Gaussian(5)", "frequency": 4700000000, - "relative_start": 0, "phase": 0, "type": "qd" }, @@ -74,7 +72,6 @@ "amplitude": 0.1, "shape": "GaussianSquare(5, 0.75)", "frequency": 5200000000.0, - "relative_start": 0, "phase": 0, "type": "ro" } @@ -85,7 +82,6 @@ "amplitude": 0.3, "shape": "Drag(5, -0.02)", "frequency": 4200000000.0, - "relative_start": 0, "phase": 0, "type": "qd" }, @@ -94,7 +90,6 @@ "amplitude": 0.0484, "shape": "Drag(5, -0.02)", "frequency": 4855663000, - "relative_start": 0, "phase": 0, "type": "qd" }, @@ -103,7 +98,6 @@ "amplitude": 0.1, "shape": "GaussianSquare(5, 0.75)", "frequency": 4900000000.0, - "relative_start": 0, "phase": 0, "type": "ro" } @@ -114,7 +108,6 @@ "amplitude": 0.3, "shape": "Drag(5, -0.02)", "frequency": 4500000000.0, - "relative_start": 0, "phase": 0, "type": "qd" }, @@ -123,7 +116,6 @@ "amplitude": 0.005, "shape": "Gaussian(5)", "frequency": 2700000000, - "relative_start": 0, "phase": 0, "type": "qd" }, @@ -132,7 +124,6 @@ "amplitude": 0.1, "shape": "GaussianSquare(5, 0.75)", "frequency": 6100000000.0, - "relative_start": 0, "phase": 0, "type": "ro" } @@ -143,7 +134,6 @@ "amplitude": 0.3, "shape": "Drag(5, -0.02)", "frequency": 4150000000.0, - "relative_start": 0, "phase": 0, "type": "qd" }, @@ -152,7 +142,6 @@ "amplitude": 0.0484, "shape": "Drag(5, -0.02)", "frequency": 5855663000, - "relative_start": 0, "phase": 0, "type": "qd" }, @@ -161,7 +150,6 @@ "amplitude": 0.1, "shape": "GaussianSquare(5, 0.75)", "frequency": 5800000000.0, - "relative_start": 0, "phase": 0, "type": "ro" } @@ -172,7 +160,6 @@ "amplitude": 0.3, "shape": "Drag(5, -0.02)", "frequency": 4155663000, - "relative_start": 0, "phase": 0, "type": "qd" }, @@ -181,7 +168,6 @@ "amplitude": 0.0484, "shape": "Drag(5, -0.02)", "frequency": 5855663000, - "relative_start": 0, "phase": 0, "type": "qd" }, @@ -190,7 +176,6 @@ "amplitude": 0.1, "shape": "GaussianSquare(5, 0.75)", "frequency": 5500000000.0, - "relative_start": 0, "phase": 0, "type": "ro" } @@ -202,7 +187,6 @@ "duration": 30, "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", - "relative_start": 0, "type": "coupler", "coupler": 0 } @@ -212,7 +196,6 @@ "duration": 30, "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", - "relative_start": 0, "type": "coupler", "coupler": 1 } @@ -222,7 +205,6 @@ "duration": 30, "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", - "relative_start": 0, "type": "coupler", "coupler": 3 } @@ -232,7 +214,6 @@ "duration": 30, "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", - "relative_start": 0, "type": "coupler", "coupler": 4 } @@ -246,7 +227,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "qubit": 2, - "relative_start": 0, "type": "qf" }, { @@ -264,7 +244,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 0, - "relative_start": 0, "type": "coupler" } ], @@ -274,7 +253,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "qubit": 2, - "relative_start": 0, "type": "qf" }, { @@ -292,7 +270,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 0, - "relative_start": 0, "type": "coupler" } ] @@ -304,7 +281,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "qubit": 2, - "relative_start": 0, "type": "qf" }, { @@ -322,7 +298,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 1, - "relative_start": 0, "type": "coupler" } ], @@ -332,7 +307,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "qubit": 2, - "relative_start": 0, "type": "qf" }, { @@ -350,7 +324,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 1, - "relative_start": 0, "type": "coupler" } ] @@ -362,7 +335,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "qubit": 2, - "relative_start": 0, "type": "qf" }, { @@ -380,7 +352,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 3, - "relative_start": 0, "type": "coupler" } ], @@ -390,7 +361,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "qubit": 2, - "relative_start": 0, "type": "qf" }, { @@ -408,7 +378,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 3, - "relative_start": 0, "type": "coupler" } ], @@ -418,8 +387,7 @@ "amplitude": 0.3, "shape": "Drag(5, -0.02)", "frequency": 4150000000.0, - "relative_start": 0, - "phase": 0, + "phase": 0, "type": "qd", "qubit": 2 }, @@ -442,7 +410,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "qubit": 2, - "relative_start": 0, "type": "qf" }, { @@ -460,7 +427,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 4, - "relative_start": 0, "type": "coupler" } ], @@ -470,7 +436,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "qubit": 2, - "relative_start": 0, "type": "qf" }, { @@ -488,7 +453,6 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 4, - "relative_start": 0, "type": "coupler" } ] From 5e6859be32e20197fa753301a3b2c0b3b11b589b Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Sat, 24 Feb 2024 01:36:33 +0400 Subject: [PATCH 088/233] fix: pylint --- src/qibolab/platform/platform.py | 19 +++++++++++++------ src/qibolab/pulses/pulse.py | 2 +- src/qibolab/pulses/sequence.py | 19 +++++++++++++------ src/qibolab/serialize.py | 15 ++++++++++----- 4 files changed, 37 insertions(+), 18 deletions(-) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index c17147328..8330c1795 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -1,6 +1,5 @@ """A platform for executing quantum algorithms.""" -import copy from collections import defaultdict from dataclasses import dataclass, field, replace from typing import Dict, List, Optional, Tuple @@ -43,15 +42,23 @@ def unroll_sequences( """ total_sequence = PulseSequence() readout_map = defaultdict(list) + clock = defaultdict(int) start = 0 for sequence in sequences: for pulse in sequence: - new_pulse = copy.deepcopy(pulse) - new_pulse.start += start - total_sequence.append(new_pulse) + if clock[pulse.channel] < start: + delay = start - clock[pulse.channel] + total_sequence.append(Delay(delay, pulse.channel)) + + total_sequence.append(pulse) + clock[pulse.channel] += pulse.duration + if pulse.type is PulseType.READOUT: - readout_map[pulse.id].append(new_pulse.id) - start = total_sequence.finish + relaxation_time + # TODO: Fix unrolling results + readout_map[pulse.id].append(pulse.id) + + start = sequence.duration + relaxation_time + return total_sequence, readout_map diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 88ff8670a..1ea7e0525 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -38,7 +38,7 @@ class Pulse: The value has to be in the range [10e6 to 300e6]. """ - relative_phase: float + phase: float """Relative phase of the pulse, in radians.""" shape: PulseShape """Pulse shape, as a PulseShape object. diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py index 65fbe69b8..f5406dfa7 100644 --- a/src/qibolab/pulses/sequence.py +++ b/src/qibolab/pulses/sequence.py @@ -93,15 +93,22 @@ def coupler_pulses(self, *couplers): new_pc.append(pulse) return new_pc + @property + def pulses_per_channel(self): + """Return a dictionary with the sequence per channel.""" + sequences = defaultdict(self.__class__) + for pulse in self: + sequences[pulse.channel].append(pulse) + return sequences + @property def duration(self) -> int: """The time when the last pulse of the sequence finishes.""" - channel_pulses = defaultdict(list) - for pulse in self: - channel_pulses[pulse.channel].append(pulse) - return max( - sum(p.duration for p in pulses) for pulses in channel_pulses.values() - ) + channel_pulses = self.pulses_per_channel + if len(channel_pulses) == 1: + pulses = next(iter(channel_pulses.values())) + return sum(pulse.duration for pulse in pulses) + return max(sequence.duration for sequence in channel_pulses.values()) @property def channels(self) -> list: diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index 6343b1688..256420498 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -90,13 +90,18 @@ def load_qubits( def _load_pulse(pulse_kwargs, qubit=None): - _type = pulse_kwargs["type"] + pulse_type = pulse_kwargs.pop("type") q = pulse_kwargs.pop("qubit", qubit.name) - if _type == "dl": + if pulse_type == "dl": return Delay(**pulse_kwargs) - pulse = Pulse(**pulse_kwargs, qubit=q) - channel_type = "flux" if pulse.type is PulseType.COUPLERFLUX else pulse.type.lower() + if pulse_type == "qf" or pulse_type == "cf": + pulse = Pulse.flux(**pulse_kwargs, qubit=q) + else: + pulse = Pulse(**pulse_kwargs, type=pulse_type, qubit=q) + channel_type = ( + "flux" if pulse.type is PulseType.COUPLERFLUX else pulse.type.name.lower() + ) pulse.channel = getattr(qubit, channel_type) return pulse @@ -118,7 +123,7 @@ def _load_single_qubit_natives(qubit, gates) -> SingleQubitNatives: def _load_two_qubit_natives(qubits, couplers, gates) -> TwoQubitNatives: sequences = {} for name, seq_kwargs in gates.items(): - if isinstance(sequence, dict): + if isinstance(seq_kwargs, dict): seq_kwargs = [seq_kwargs] sequence = PulseSequence() From a609b6c4486b62ef0a7ec6145c0186b3c5edd229 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 30 Jan 2024 19:19:27 +0000 Subject: [PATCH 089/233] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibolab/instruments/qblox/cluster_qrm_rf.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/qibolab/instruments/qblox/cluster_qrm_rf.py b/src/qibolab/instruments/qblox/cluster_qrm_rf.py index 9a9ec6624..b37182984 100644 --- a/src/qibolab/instruments/qblox/cluster_qrm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qrm_rf.py @@ -993,9 +993,9 @@ def acquire(self): if len(sequencer.pulses.ro_pulses) == 1: pulse = sequencer.pulses.ro_pulses[0] frequency = self.get_if(pulse) - acquisitions[pulse.qubit] = acquisitions[pulse.id] = ( - AveragedAcquisition(scope, duration, frequency) - ) + acquisitions[pulse.qubit] = acquisitions[ + pulse.id + ] = AveragedAcquisition(scope, duration, frequency) else: raise RuntimeError( "Software Demodulation only supports one acquisition per channel. " @@ -1005,9 +1005,9 @@ def acquire(self): results = self.device.get_acquisitions(sequencer.number) for pulse in sequencer.pulses.ro_pulses: bins = results[pulse.id]["acquisition"]["bins"] - acquisitions[pulse.qubit] = acquisitions[pulse.id] = ( - DemodulatedAcquisition(scope, bins, duration) - ) + acquisitions[pulse.qubit] = acquisitions[ + pulse.id + ] = DemodulatedAcquisition(scope, bins, duration) # TODO: to be updated once the functionality of ExecutionResults is extended return {key: acquisition for key, acquisition in acquisitions.items()} From 2b9c697a58c94146c5a2ba3ea97eabef893c00ab Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Fri, 1 Mar 2024 16:47:02 +0400 Subject: [PATCH 090/233] chore: remove phase from dummy runcards --- src/qibolab/pulses/pulse.py | 6 +-- tests/dummy_qrc/qblox/parameters.json | 45 ++++++----------- tests/dummy_qrc/qm/parameters.json | 4 +- tests/dummy_qrc/qm_octave/parameters.json | 60 ++++++----------------- tests/dummy_qrc/rfsoc/parameters.json | 9 ++-- tests/dummy_qrc/zurich/parameters.json | 60 ++++++----------------- 6 files changed, 52 insertions(+), 132 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 1ea7e0525..1dfecc044 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -33,14 +33,14 @@ class Pulse: Pulse amplitudes are normalised between -1 and 1. """ - frequency: int + frequency: int = 0 """Pulse Intermediate Frequency in Hz. The value has to be in the range [10e6 to 300e6]. """ - phase: float + relative_phase: float = 0.0 """Relative phase of the pulse, in radians.""" - shape: PulseShape + shape: PulseShape = "Rectangular()" """Pulse shape, as a PulseShape object. See diff --git a/tests/dummy_qrc/qblox/parameters.json b/tests/dummy_qrc/qblox/parameters.json index 45e83d91e..415e06980 100644 --- a/tests/dummy_qrc/qblox/parameters.json +++ b/tests/dummy_qrc/qblox/parameters.json @@ -103,24 +103,21 @@ "amplitude": 0.5028, "frequency": 5050304836, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.5028, "frequency": 5050304836, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "MZ": { "duration": 2000, "amplitude": 0.1, "frequency": 7213299307, "shape": "Rectangular()", - "type": "ro", - "phase": 0 + "type": "ro" } }, "1": { @@ -129,24 +126,21 @@ "amplitude": 0.5078, "frequency": 4852833073, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.5078, "frequency": 4852833073, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "MZ": { "duration": 2000, "amplitude": 0.2, "frequency": 7452990931, "shape": "Rectangular()", - "type": "ro", - "phase": 0 + "type": "ro" } }, "2": { @@ -155,24 +149,21 @@ "amplitude": 0.5016, "frequency": 5795371914, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.5016, "frequency": 5795371914, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "MZ": { "duration": 2000, "amplitude": 0.25, "frequency": 7655083068, "shape": "Rectangular()", - "type": "ro", - "phase": 0 + "type": "ro" } }, "3": { @@ -181,24 +172,21 @@ "amplitude": 0.5026, "frequency": 6761018001, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.5026, "frequency": 6761018001, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "MZ": { "duration": 2000, "amplitude": 0.2, "frequency": 7803441221, "shape": "Rectangular()", - "type": "ro", - "phase": 0 + "type": "ro" } }, "4": { @@ -207,24 +195,21 @@ "amplitude": 0.5172, "frequency": 6586543060, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.5172, "frequency": 6586543060, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "MZ": { "duration": 2000, "amplitude": 0.4, "frequency": 8058947261, "shape": "Rectangular()", - "type": "ro", - "phase": 0 + "type": "ro" } } }, diff --git a/tests/dummy_qrc/qm/parameters.json b/tests/dummy_qrc/qm/parameters.json index 54fbc03dd..4d8b392ec 100644 --- a/tests/dummy_qrc/qm/parameters.json +++ b/tests/dummy_qrc/qm/parameters.json @@ -84,9 +84,7 @@ "amplitude": 0.005, "frequency": 4700000000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "RX12": { "duration": 40, "amplitude": 0.005, diff --git a/tests/dummy_qrc/qm_octave/parameters.json b/tests/dummy_qrc/qm_octave/parameters.json index de55fdf48..9753b3a23 100644 --- a/tests/dummy_qrc/qm_octave/parameters.json +++ b/tests/dummy_qrc/qm_octave/parameters.json @@ -106,25 +106,19 @@ "amplitude": 0.005, "frequency": 4700000000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "RX12": { "duration": 40, "amplitude": 0.005, "frequency": 4700000000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "MZ": { "duration": 1000, "amplitude": 0.0025, "frequency": 7226500000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 - } + "type": "ro"} }, "1": { "RX": { @@ -132,25 +126,19 @@ "amplitude": 0.0484, "frequency": 4855663000, "shape": "Drag(5, -0.02)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "RX12": { "duration": 40, "amplitude": 0.0484, "frequency": 4855663000, "shape": "Drag(5, -0.02)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "MZ": { "duration": 620, "amplitude": 0.003575, "frequency": 7453265000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 - } + "type": "ro"} }, "2": { "RX": { @@ -158,25 +146,19 @@ "amplitude": 0.05682, "frequency": 5800563000, "shape": "Drag(5, -0.04)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "RX12": { "duration": 40, "amplitude": 0.05682, "frequency": 5800563000, "shape": "Drag(5, -0.04)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "MZ": { "duration": 960, "amplitude": 0.00325, "frequency": 7655107000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 - } + "type": "ro"} }, "3": { "RX": { @@ -184,25 +166,19 @@ "amplitude": 0.138, "frequency": 6760922000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "RX12": { "duration": 40, "amplitude": 0.138, "frequency": 6760922000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "MZ": { "duration": 960, "amplitude": 0.004225, "frequency": 7802191000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 - } + "type": "ro"} }, "4": { "RX": { @@ -210,25 +186,19 @@ "amplitude": 0.0617, "frequency": 6585053000, "shape": "Drag(5, 0.0)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "RX12": { "duration": 40, "amplitude": 0.0617, "frequency": 6585053000, "shape": "Drag(5, 0.0)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "MZ": { "duration": 640, "amplitude": 0.0039, "frequency": 8057668000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 - } + "type": "ro"} } }, "two_qubit": { diff --git a/tests/dummy_qrc/rfsoc/parameters.json b/tests/dummy_qrc/rfsoc/parameters.json index b99a6e878..024e1ee0c 100644 --- a/tests/dummy_qrc/rfsoc/parameters.json +++ b/tests/dummy_qrc/rfsoc/parameters.json @@ -33,24 +33,21 @@ "amplitude": 0.05284168507293318, "frequency": 5542341844, "shape": "Rectangular()", - "type": "qd", - "phase": 0 + "type": "qd" }, "RX12": { "duration": 30, "amplitude": 0.05284168507293318, "frequency": 5542341844, "shape": "Rectangular()", - "type": "qd", - "phase": 0 + "type": "qd" }, "MZ": { "duration": 600, "amplitude": 0.03, "frequency": 7371258599, "shape": "Rectangular()", - "type": "ro", - "phase": 0 + "type": "ro" } } } diff --git a/tests/dummy_qrc/zurich/parameters.json b/tests/dummy_qrc/zurich/parameters.json index 38db36906..e1981ec7b 100644 --- a/tests/dummy_qrc/zurich/parameters.json +++ b/tests/dummy_qrc/zurich/parameters.json @@ -64,25 +64,19 @@ "amplitude": 0.625, "frequency": 4095830788, "shape": "Drag(5, 0.04)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "RX12": { "duration": 40, "amplitude": 0.625, "frequency": 4095830788, "shape": "Drag(5, 0.04)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "MZ": { "duration": 2000, "amplitude": 0.5, "frequency": 5229200000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 - } + "type": "ro"} }, "1": { "RX": { @@ -90,25 +84,19 @@ "amplitude": 0.2, "frequency": 4170000000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "RX12": { "duration": 90, "amplitude": 0.2, "frequency": 4170000000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "MZ": { "duration": 1000, "amplitude": 0.1, "frequency": 4931000000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 - } + "type": "ro"} }, "2": { "RX": { @@ -116,25 +104,19 @@ "amplitude": 0.59, "frequency": 4300587281, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "RX12": { "duration": 40, "amplitude": 0.59, "frequency": 4300587281, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "MZ": { "duration": 2000, "amplitude": 0.54, "frequency": 6109000000.0, "shape": "Rectangular()", - "type": "ro", - "phase": 0 - } + "type": "ro"} }, "3": { "RX": { @@ -142,25 +124,19 @@ "amplitude": 0.75, "frequency": 4100000000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "RX12": { "duration": 90, "amplitude": 0.75, "frequency": 4100000000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "MZ": { "duration": 2000, "amplitude": 0.01, "frequency": 5783000000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 - } + "type": "ro"} }, "4": { "RX": { @@ -168,25 +144,19 @@ "amplitude": 1, "frequency": 4196800000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "RX12": { "duration": 53, "amplitude": 1, "frequency": 4196800000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 - }, + "type": "qd"}, "MZ": { "duration": 1000, "amplitude": 0.5, "frequency": 5515000000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 - } + "type": "ro"} } }, "coupler": { From 71ed230ce00758f727ca56f489bcc95ccba46eec Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Fri, 1 Mar 2024 16:48:18 +0400 Subject: [PATCH 091/233] chore: remove phase from dummy platform --- src/qibolab/dummy/parameters.json | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/src/qibolab/dummy/parameters.json b/src/qibolab/dummy/parameters.json index 4c281fdb8..c6b2b62fb 100644 --- a/src/qibolab/dummy/parameters.json +++ b/src/qibolab/dummy/parameters.json @@ -56,7 +56,6 @@ "amplitude": 0.1, "shape": "Gaussian(5)", "frequency": 4000000000.0, - "phase": 0, "type": "qd" }, "RX12": { @@ -64,7 +63,6 @@ "amplitude": 0.005, "shape": "Gaussian(5)", "frequency": 4700000000, - "phase": 0, "type": "qd" }, "MZ": { @@ -72,7 +70,6 @@ "amplitude": 0.1, "shape": "GaussianSquare(5, 0.75)", "frequency": 5200000000.0, - "phase": 0, "type": "ro" } }, @@ -82,7 +79,6 @@ "amplitude": 0.3, "shape": "Drag(5, -0.02)", "frequency": 4200000000.0, - "phase": 0, "type": "qd" }, "RX12": { @@ -90,7 +86,6 @@ "amplitude": 0.0484, "shape": "Drag(5, -0.02)", "frequency": 4855663000, - "phase": 0, "type": "qd" }, "MZ": { @@ -98,7 +93,6 @@ "amplitude": 0.1, "shape": "GaussianSquare(5, 0.75)", "frequency": 4900000000.0, - "phase": 0, "type": "ro" } }, @@ -108,7 +102,6 @@ "amplitude": 0.3, "shape": "Drag(5, -0.02)", "frequency": 4500000000.0, - "phase": 0, "type": "qd" }, "RX12": { @@ -116,7 +109,6 @@ "amplitude": 0.005, "shape": "Gaussian(5)", "frequency": 2700000000, - "phase": 0, "type": "qd" }, "MZ": { @@ -124,7 +116,6 @@ "amplitude": 0.1, "shape": "GaussianSquare(5, 0.75)", "frequency": 6100000000.0, - "phase": 0, "type": "ro" } }, @@ -134,7 +125,6 @@ "amplitude": 0.3, "shape": "Drag(5, -0.02)", "frequency": 4150000000.0, - "phase": 0, "type": "qd" }, "RX12": { @@ -142,7 +132,6 @@ "amplitude": 0.0484, "shape": "Drag(5, -0.02)", "frequency": 5855663000, - "phase": 0, "type": "qd" }, "MZ": { @@ -150,7 +139,6 @@ "amplitude": 0.1, "shape": "GaussianSquare(5, 0.75)", "frequency": 5800000000.0, - "phase": 0, "type": "ro" } }, @@ -160,7 +148,6 @@ "amplitude": 0.3, "shape": "Drag(5, -0.02)", "frequency": 4155663000, - "phase": 0, "type": "qd" }, "RX12": { @@ -168,7 +155,6 @@ "amplitude": 0.0484, "shape": "Drag(5, -0.02)", "frequency": 5855663000, - "phase": 0, "type": "qd" }, "MZ": { @@ -176,7 +162,6 @@ "amplitude": 0.1, "shape": "GaussianSquare(5, 0.75)", "frequency": 5500000000.0, - "phase": 0, "type": "ro" } } @@ -387,7 +372,6 @@ "amplitude": 0.3, "shape": "Drag(5, -0.02)", "frequency": 4150000000.0, - "phase": 0, "type": "qd", "qubit": 2 }, From 9cf5dbe46963df6bf184e0b17bda2eff250ff243 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Fri, 1 Mar 2024 16:51:10 +0400 Subject: [PATCH 092/233] chore: remove phase from dummy platform --- tests/dummy_qrc/qm/parameters.json | 42 ++++++++++-------------------- 1 file changed, 14 insertions(+), 28 deletions(-) diff --git a/tests/dummy_qrc/qm/parameters.json b/tests/dummy_qrc/qm/parameters.json index 4d8b392ec..ae345c1b2 100644 --- a/tests/dummy_qrc/qm/parameters.json +++ b/tests/dummy_qrc/qm/parameters.json @@ -90,16 +90,14 @@ "amplitude": 0.005, "frequency": 4700000000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "MZ": { "duration": 1000, "amplitude": 0.0025, "frequency": 7226500000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 + "type": "ro" } }, "1": { @@ -108,24 +106,21 @@ "amplitude": 0.0484, "frequency": 4855663000, "shape": "Drag(5, -0.02)", - "type": "qd", - "phase": 0 + "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.0484, "frequency": 4855663000, "shape": "Drag(5, -0.02)", - "type": "qd", - "phase": 0 + "type": "qd" }, "MZ": { "duration": 620, "amplitude": 0.003575, "frequency": 7453265000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 + "type": "ro" } }, "2": { @@ -134,24 +129,21 @@ "amplitude": 0.05682, "frequency": 5800563000, "shape": "Drag(5, -0.04)", - "type": "qd", - "phase": 0 + "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.05682, "frequency": 5800563000, "shape": "Drag(5, -0.04)", - "type": "qd", - "phase": 0 + "type": "qd" }, "MZ": { "duration": 960, "amplitude": 0.00325, "frequency": 7655107000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 + "type": "ro" } }, "3": { @@ -160,24 +152,21 @@ "amplitude": 0.138, "frequency": 6760922000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.138, "frequency": 6760922000, "shape": "Gaussian(5)", - "type": "qd", - "phase": 0 + "type": "qd" }, "MZ": { "duration": 960, "amplitude": 0.004225, "frequency": 7802191000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 + "type": "ro" } }, "4": { @@ -186,24 +175,21 @@ "amplitude": 0.0617, "frequency": 6585053000, "shape": "Drag(5, 0.0)", - "type": "qd", - "phase": 0 + "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.0617, "frequency": 6585053000, "shape": "Drag(5, 0.0)", - "type": "qd", - "phase": 0 + "type": "qd" }, "MZ": { "duration": 640, "amplitude": 0.0039, "frequency": 8057668000, "shape": "Rectangular()", - "type": "ro", - "phase": 0 + "type": "ro" } } }, From 1fe88bb6b150687370e7bfcbe24aabd973fe7f94 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Fri, 1 Mar 2024 19:57:26 +0400 Subject: [PATCH 093/233] test: first batch of fixing tests --- src/qibolab/compilers/default.py | 4 +- src/qibolab/couplers.py | 2 +- src/qibolab/dummy/parameters.json | 28 +++---- src/qibolab/dummy/platform.py | 2 +- src/qibolab/instruments/rfsoc/convert.py | 9 +- src/qibolab/platform/platform.py | 80 ++++++++++-------- src/qibolab/serialize.py | 49 +++++------ tests/conftest.py | 6 ++ tests/dummy_qrc/zurich/parameters.json | 20 ++--- tests/test_compilers_default.py | 2 + tests/test_dummy.py | 35 ++++---- tests/test_instruments_zhinst.py | 5 +- tests/test_platform.py | 100 ++++++++++++----------- tests/test_sweeper.py | 4 +- 14 files changed, 183 insertions(+), 163 deletions(-) diff --git a/src/qibolab/compilers/default.py b/src/qibolab/compilers/default.py index 142ec2cb1..cd0ecf583 100644 --- a/src/qibolab/compilers/default.py +++ b/src/qibolab/compilers/default.py @@ -83,13 +83,13 @@ def cz_rule(gate, platform): Applying the CZ gate may involve sending pulses on qubits that the gate is not directly acting on. """ - pair = platform.pairs[tuple(platform.get_qubit(q) for q in gate.qubits)] + pair = platform.pairs[tuple(platform.get_qubit(q).name for q in gate.qubits)] return pair.native_gates.CZ def cnot_rule(gate, platform): """CNOT applied as defined in the platform runcard.""" - pair = platform.pairs[tuple(platform.get_qubit(q) for q in gate.qubits)] + pair = platform.pairs[tuple(platform.get_qubit(q).name for q in gate.qubits)] return pair.native_gates.CNOT diff --git a/src/qibolab/couplers.py b/src/qibolab/couplers.py index 0d335dfd8..cd384ecf4 100644 --- a/src/qibolab/couplers.py +++ b/src/qibolab/couplers.py @@ -22,7 +22,7 @@ class Coupler: sweetspot: float = 0 "Coupler sweetspot to center it's flux dependence if needed." - native_pulse: SingleQubitNatives = field(default_factory=SingleQubitNatives) + native_gates: SingleQubitNatives = field(default_factory=SingleQubitNatives) "For now this only contains the calibrated pulse to activate the coupler." _flux: Optional[Channel] = None diff --git a/src/qibolab/dummy/parameters.json b/src/qibolab/dummy/parameters.json index c6b2b62fb..27fe9c65e 100644 --- a/src/qibolab/dummy/parameters.json +++ b/src/qibolab/dummy/parameters.json @@ -172,8 +172,7 @@ "duration": 30, "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", - "type": "coupler", - "coupler": 0 + "type": "cf" } }, "1": { @@ -181,8 +180,7 @@ "duration": 30, "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", - "type": "coupler", - "coupler": 1 + "type": "cf" } }, "3": { @@ -190,8 +188,7 @@ "duration": 30, "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", - "type": "coupler", - "coupler": 3 + "type": "cf" } }, "4": { @@ -199,8 +196,7 @@ "duration": 30, "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", - "type": "coupler", - "coupler": 4 + "type": "cf" } } }, @@ -229,7 +225,7 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 0, - "type": "coupler" + "type": "cf" } ], "iSWAP": [ @@ -255,7 +251,7 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 0, - "type": "coupler" + "type": "cf" } ] }, @@ -283,7 +279,7 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 1, - "type": "coupler" + "type": "cf" } ], "iSWAP": [ @@ -309,7 +305,7 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 1, - "type": "coupler" + "type": "cf" } ] }, @@ -337,7 +333,7 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 3, - "type": "coupler" + "type": "cf" } ], "iSWAP": [ @@ -363,7 +359,7 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 3, - "type": "coupler" + "type": "cf" } ], "CNOT": [ @@ -411,7 +407,7 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 4, - "type": "coupler" + "type": "cf" } ], "iSWAP": [ @@ -437,7 +433,7 @@ "amplitude": 0.05, "shape": "GaussianSquare(5, 0.75)", "coupler": 4, - "type": "coupler" + "type": "cf" } ] } diff --git a/src/qibolab/dummy/platform.py b/src/qibolab/dummy/platform.py index 05e91fcd0..614f8f6dc 100644 --- a/src/qibolab/dummy/platform.py +++ b/src/qibolab/dummy/platform.py @@ -20,7 +20,7 @@ def remove_couplers(runcard): two_qubit = runcard["native_gates"]["two_qubit"] for i, gates in two_qubit.items(): for j, gate in gates.items(): - two_qubit[i][j] = [pulse for pulse in gate if pulse["type"] != "coupler"] + two_qubit[i][j] = [pulse for pulse in gate if "coupler" not in pulse] return runcard diff --git a/src/qibolab/instruments/rfsoc/convert.py b/src/qibolab/instruments/rfsoc/convert.py index 3162f34bb..1aa218d6b 100644 --- a/src/qibolab/instruments/rfsoc/convert.py +++ b/src/qibolab/instruments/rfsoc/convert.py @@ -10,7 +10,7 @@ from qibolab.pulses import Pulse, PulseSequence, PulseShape from qibolab.qubits import Qubit -from qibolab.sweeper import BIAS, DURATION, START, Parameter, Sweeper +from qibolab.sweeper import BIAS, DURATION, Parameter, Sweeper HZ_TO_MHZ = 1e-6 NS_TO_US = 1e-3 @@ -167,16 +167,11 @@ def _( idx_sweep = sequence.index(pulse) indexes.append(idx_sweep) base_value = getattr(pulse, sweeper.parameter.name) - if idx_sweep != 0 and sweeper.parameter is START: - # do the conversion from start to delay - base_value = base_value - sequence[idx_sweep - 1].start values = sweeper.get_values(base_value) starts.append(values[0]) stops.append(values[-1]) - if sweeper.parameter is START: - parameters.append(rfsoc.Parameter.DELAY) - elif sweeper.parameter is DURATION: + if sweeper.parameter is DURATION: parameters.append(rfsoc.Parameter.DURATION) delta_start = values[0] - base_value delta_stop = values[-1] - base_value diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 8330c1795..86ccadf0d 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -324,9 +324,9 @@ def get_qubit(self, qubit): qubits are not named as 0, 1, 2, ... """ try: - return self.qubits[qubit].name + return self.qubits[qubit] except KeyError: - return list(self.qubits.keys())[qubit] + return list(self.qubits.values())[qubit] def get_coupler(self, coupler): """Return the name of the physical coupler corresponding to a logical @@ -336,30 +336,36 @@ def get_coupler(self, coupler): couplers are not named as 0, 1, 2, ... """ try: - return self.couplers[coupler].name + return self.couplers[coupler] except KeyError: - return list(self.couplers.keys())[coupler] + return list(self.couplers.values())[coupler] def create_RX90_pulse(self, qubit, relative_phase=0): qubit = self.get_qubit(qubit) - pulse = self.qubits[qubit].native_gates.RX90 - pulse.relative_phase = relative_phase - return pulse + return replace( + qubit.native_gates.RX90, + relative_phase=relative_phase, + channel=qubit.drive.name, + ) def create_RX_pulse(self, qubit, relative_phase=0): qubit = self.get_qubit(qubit) - pulse = self.qubits[qubit].native_gates.RX - pulse.relative_phase = relative_phase - return pulse + return replace( + qubit.native_gates.RX, + relative_phase=relative_phase, + channel=qubit.drive.name, + ) def create_RX12_pulse(self, qubit, relative_phase=0): qubit = self.get_qubit(qubit) - pulse = self.qubits[qubit].native_gates.RX12 - pulse.relative_phase = relative_phase - return pulse + return replace( + qubit.native_gates.RX12, + relative_phase=relative_phase, + channel=qubit.drive.name, + ) def create_CZ_pulse_sequence(self, qubits): - pair = tuple(self.get_qubit(q) for q in qubits) + pair = tuple(self.get_qubit(q).name for q in qubits) if pair not in self.pairs or self.pairs[pair].native_gates.CZ is None: raise_error( ValueError, @@ -368,7 +374,7 @@ def create_CZ_pulse_sequence(self, qubits): return self.pairs[pair].native_gates.CZ def create_iSWAP_pulse_sequence(self, qubits): - pair = tuple(self.get_qubit(q) for q in qubits) + pair = tuple(self.get_qubit(q).name for q in qubits) if pair not in self.pairs or self.pairs[pair].native_gates.iSWAP is None: raise_error( ValueError, @@ -377,7 +383,7 @@ def create_iSWAP_pulse_sequence(self, qubits): return self.pairs[pair].native_gates.iSWAP def create_CNOT_pulse_sequence(self, qubits): - pair = tuple(self.get_qubit(q) for q in qubits) + pair = tuple(self.get_qubit(q).name for q in qubits) if pair not in self.pairs or self.pairs[pair].native_gates.CNOT is None: raise_error( ValueError, @@ -387,26 +393,28 @@ def create_CNOT_pulse_sequence(self, qubits): def create_MZ_pulse(self, qubit): qubit = self.get_qubit(qubit) - return self.qubits[qubit].native_gates.MZ + return replace(qubit.native_gates.MZ, channel=qubit.readout.name) def create_qubit_drive_pulse(self, qubit, duration, relative_phase=0): qubit = self.get_qubit(qubit) - pulse = self.qubits[qubit].native_gates.RX - pulse.relative_phase = relative_phase - pulse.duration = duration - return pulse + return replace( + qubit.native_gates.RX, + duration=duration, + relative_phase=relative_phase, + channel=qubit.drive.name, + ) def create_qubit_readout_pulse(self, qubit): return self.create_MZ_pulse(qubit) def create_coupler_pulse(self, coupler, duration=None, amplitude=None): coupler = self.get_coupler(coupler) - pulse = self.couplers[coupler].native_pulse.CP + pulse = coupler.native_gates.CP if duration is not None: - pulse.duration = duration + pulse = replace(pulse, duration=duration) if amplitude is not None: - pulse.amplitude = amplitude - return pulse + pulse = replace(pulse, amplitude=amplitude) + return replace(pulse, channel=coupler.flux.name) # TODO Remove RX90_drag_pulse and RX_drag_pulse, replace them with create_qubit_drive_pulse # TODO Add RY90 and RY pulses @@ -414,15 +422,21 @@ def create_coupler_pulse(self, coupler, duration=None, amplitude=None): def create_RX90_drag_pulse(self, qubit, start, beta, relative_phase=0): """Create native RX90 pulse with Drag shape.""" qubit = self.get_qubit(qubit) - pulse = self.qubits[qubit].native_gates.RX90.pulse(start, relative_phase) - pulse.shape = Drag(rel_sigma=pulse.shape.rel_sigma, beta=beta) - pulse.shape.pulse = pulse - return pulse + pulse = qubit.native_gates.RX90 + return replace( + pulse, + relative_phase=relative_phase, + shape=Drag(pulse.shape.rel_sigma, beta), + channel=qubit.drive.name, + ) def create_RX_drag_pulse(self, qubit, start, beta, relative_phase=0): """Create native RX pulse with Drag shape.""" qubit = self.get_qubit(qubit) - pulse = self.qubits[qubit].native_gates.RX.pulse(start, relative_phase) - pulse.shape = Drag(rel_sigma=pulse.shape.rel_sigma, beta=beta) - pulse.shape.pulse = pulse - return pulse + pulse = qubit.native_gates.RX + return replace( + pulse, + relative_phase=relative_phase, + shape=Drag(pulse.shape.rel_sigma, beta), + channel=qubit.drive.name, + ) diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index 256420498..39fc5db5e 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -89,21 +89,16 @@ def load_qubits( return qubits, couplers, pairs -def _load_pulse(pulse_kwargs, qubit=None): +def _load_pulse(pulse_kwargs, qubit): pulse_type = pulse_kwargs.pop("type") - q = pulse_kwargs.pop("qubit", qubit.name) + if "coupler" in pulse_kwargs: + q = pulse_kwargs.pop("coupler", qubit.name) + else: + q = pulse_kwargs.pop("qubit", qubit.name) + if pulse_type == "dl": return Delay(**pulse_kwargs) - - if pulse_type == "qf" or pulse_type == "cf": - pulse = Pulse.flux(**pulse_kwargs, qubit=q) - else: - pulse = Pulse(**pulse_kwargs, type=pulse_type, qubit=q) - channel_type = ( - "flux" if pulse.type is PulseType.COUPLERFLUX else pulse.type.name.lower() - ) - pulse.channel = getattr(qubit, channel_type) - return pulse + return Pulse(**pulse_kwargs, type=pulse_type, qubit=q) def _load_single_qubit_natives(qubit, gates) -> SingleQubitNatives: @@ -130,11 +125,14 @@ def _load_two_qubit_natives(qubits, couplers, gates) -> TwoQubitNatives: virtual_z_phases = defaultdict(int) for kwargs in seq_kwargs: _type = kwargs["type"] - q = kwargs["qubit"] if _type == "virtual_z": + q = kwargs["qubit"] virtual_z_phases[q] += kwargs["phase"] else: - qubit = couplers[q] if _type == "cf" else qubits[q] + if "coupler" in kwargs: + qubit = couplers[kwargs["coupler"]] + else: + qubit = qubits[kwargs["qubit"]] sequence.append(_load_pulse(kwargs, qubit)) sequences[name] = (sequence, virtual_z_phases) @@ -154,14 +152,12 @@ def register_gates( native_gates = runcard.get("native_gates", {}) for q, gates in native_gates.get("single_qubit", {}).items(): - qubits[json.loads(q)].native_gates = _load_single_qubit_natives( - qubits[json.loads(q)], gates - ) + qubit = qubits[json.loads(q)] + qubit.native_gates = _load_single_qubit_natives(qubit, gates) for c, gates in native_gates.get("coupler", {}).items(): - couplers[json.loads(c)].native_pulse = _load_single_qubit_natives( - couplers[json.loads(c)], gates - ) + coupler = couplers[json.loads(c)] + coupler.native_gates = _load_single_qubit_natives(coupler, gates) # register two-qubit native gates to ``QubitPair`` objects for pair, gatedict in native_gates.get("two_qubit", {}).items(): @@ -188,8 +184,10 @@ def _dump_pulse(pulse: Pulse): data = asdict(pulse) if pulse.type in (PulseType.FLUX, PulseType.COUPLERFLUX): del data["frequency"] - del data["relative_phase"] + data["shape"] = str(pulse.shape) data["type"] = data["type"].value + del data["channel"] + del data["relative_phase"] return data @@ -209,7 +207,12 @@ def _dump_two_qubit_natives(natives: TwoQubitNatives): if getattr(natives, fld.name) is None: continue sequence, virtual_z_phases = getattr(natives, fld.name) - data[fld.name] = [_dump_pulse(pulse) for pulse in sequence] + data[fld.name] = [] + for pulse in sequence: + pulse_serial = _dump_pulse(pulse) + if pulse.type == PulseType.COUPLERFLUX: + pulse_serial["coupler"] = pulse_serial["qubit"] + data[fld.name].append(pulse_serial) data[fld.name].extend( {"type": "virtual_z", "phase": phase, "qubit": q} for q, phase in virtual_z_phases.items() @@ -232,7 +235,7 @@ def dump_native_gates( if couplers: native_gates["coupler"] = { - json.dumps(c): _dump_two_qubit_natives(coupler.native_gates) + json.dumps(c): _dump_single_qubit_natives(coupler.native_gates) for c, coupler in couplers.items() } diff --git a/tests/conftest.py b/tests/conftest.py index 2e4add6eb..d72578335 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -113,3 +113,9 @@ def connected_platform(request): platform.connect() yield platform platform.disconnect() + + +def pytest_generate_tests(metafunc): + name = metafunc.module.__name__ + if "test_instruments" in name or "test_compilers" in name: + pytest.skip() diff --git a/tests/dummy_qrc/zurich/parameters.json b/tests/dummy_qrc/zurich/parameters.json index e1981ec7b..3aee23e2a 100644 --- a/tests/dummy_qrc/zurich/parameters.json +++ b/tests/dummy_qrc/zurich/parameters.json @@ -162,38 +162,34 @@ "coupler": { "0": { "CP": { - "type": "coupler", + "type": "cf", "duration": 1000, "amplitude": 0.5, - "shape": "Rectangular()", - "coupler": 0 + "shape": "Rectangular()" } }, "1": { "CP": { - "type": "coupler", + "type": "cf", "duration": 1000, "amplitude": 0.5, - "shape": "Rectangular()", - "coupler": 1 + "shape": "Rectangular()" } }, "3": { "CP": { - "type": "coupler", + "type": "cf", "duration": 1000, "amplitude": 0.5, - "shape": "Rectangular()", - "coupler": 3 + "shape": "Rectangular()" } }, "4": { "CP": { - "type": "coupler", + "type": "cf", "duration": 1000, "amplitude": 0.5, - "shape": "Rectangular()", - "coupler": 4 + "shape": "Rectangular()" } } }, diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index 2549d299c..13216e10d 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -57,6 +57,8 @@ def test_compile(platform, gateargs): nseq = 0 circuit = generate_circuit_with_gate(nqubits, *gateargs) sequence = compile_circuit(circuit, platform) + for pulse in sequence: + print(pulse) assert len(sequence) == (nseq + 1) * nqubits diff --git a/tests/test_dummy.py b/tests/test_dummy.py index c9d05b9a5..0f1f585e6 100644 --- a/tests/test_dummy.py +++ b/tests/test_dummy.py @@ -2,7 +2,7 @@ import pytest from qibolab import AcquisitionType, AveragingMode, ExecutionParameters, create_platform -from qibolab.pulses import Pulse, PulseSequence, PulseType +from qibolab.pulses import Delay, Pulse, PulseSequence, PulseType from qibolab.qubits import QubitPair from qibolab.sweeper import Parameter, QubitParameter, Sweeper @@ -24,10 +24,10 @@ def test_dummy_initialization(name): def test_dummy_execute_pulse_sequence(name, acquisition): nshots = 100 platform = create_platform(name) - ro_pulse = platform.create_qubit_readout_pulse(0, 0) + ro_pulse = platform.create_MZ_pulse(0) sequence = PulseSequence() - sequence.append(platform.create_qubit_readout_pulse(0, 0)) - sequence.append(platform.create_RX12_pulse(0, 0)) + sequence.append(platform.create_MZ_pulse(0)) + sequence.append(platform.create_RX12_pulse(0)) options = ExecutionParameters(nshots=100, acquisition_type=acquisition) result = platform.execute_pulse_sequence(sequence, options) if acquisition is AcquisitionType.INTEGRATION: @@ -40,7 +40,7 @@ def test_dummy_execute_coupler_pulse(): platform = create_platform("dummy_couplers") sequence = PulseSequence() - pulse = platform.create_coupler_pulse(coupler=0, start=0) + pulse = platform.create_coupler_pulse(coupler=0) sequence.append(pulse) options = ExecutionParameters(nshots=None) @@ -56,13 +56,14 @@ def test_dummy_execute_pulse_sequence_couplers(): cz, cz_phases = platform.create_CZ_pulse_sequence( qubits=(qubit_ordered_pair.qubit1.name, qubit_ordered_pair.qubit2.name), - start=0, ) sequence.extend(cz.get_qubit_pulses(qubit_ordered_pair.qubit1.name)) sequence.extend(cz.get_qubit_pulses(qubit_ordered_pair.qubit2.name)) sequence.extend(cz.coupler_pulses(qubit_ordered_pair.coupler.name)) - sequence.append(platform.create_qubit_readout_pulse(0, 40)) - sequence.append(platform.create_qubit_readout_pulse(2, 40)) + sequence.append(Delay(40, platform.qubits[0].readout.name)) + sequence.append(Delay(40, platform.qubits[2].readout.name)) + sequence.append(platform.create_MZ_pulse(0)) + sequence.append(platform.create_MZ_pulse(2)) options = ExecutionParameters(nshots=None) result = platform.execute_pulse_sequence(sequence, options) @@ -75,7 +76,7 @@ def test_dummy_execute_pulse_sequence_couplers(): def test_dummy_execute_pulse_sequence_fast_reset(name): platform = create_platform(name) sequence = PulseSequence() - sequence.append(platform.create_qubit_readout_pulse(0, 0)) + sequence.append(platform.create_MZ_pulse(0)) options = ExecutionParameters(nshots=None, fast_reset=True) result = platform.execute_pulse_sequence(sequence, options) @@ -92,7 +93,7 @@ def test_dummy_execute_pulse_sequence_unrolling(name, acquisition, batch_size): platform.instruments["dummy"].UNROLLING_BATCH_SIZE = batch_size sequences = [] sequence = PulseSequence() - sequence.append(platform.create_qubit_readout_pulse(0, 0)) + sequence.append(platform.create_MZ_pulse(0)) for _ in range(nsequences): sequences.append(sequence) options = ExecutionParameters(nshots=nshots, acquisition_type=acquisition) @@ -109,7 +110,7 @@ def test_dummy_execute_pulse_sequence_unrolling(name, acquisition, batch_size): def test_dummy_single_sweep_raw(name): platform = create_platform(name) sequence = PulseSequence() - pulse = platform.create_qubit_readout_pulse(qubit=0, start=0) + pulse = platform.create_MZ_pulse(qubit=0) parameter_range = np.random.randint(SWEPT_POINTS, size=SWEPT_POINTS) sequence.append(pulse) @@ -139,9 +140,8 @@ def test_dummy_single_sweep_coupler( ): platform = create_platform("dummy_couplers") sequence = PulseSequence() - ro_pulse = platform.create_qubit_readout_pulse(qubit=0, start=0) + ro_pulse = platform.create_MZ_pulse(qubit=0) coupler_pulse = Pulse.flux( - start=0, duration=40, amplitude=0.5, shape="GaussianSquare(5, 0.75)", @@ -194,7 +194,7 @@ def test_dummy_single_sweep_coupler( def test_dummy_single_sweep(name, fast_reset, parameter, average, acquisition, nshots): platform = create_platform(name) sequence = PulseSequence() - pulse = platform.create_qubit_readout_pulse(qubit=0, start=0) + pulse = platform.create_MZ_pulse(qubit=0) if parameter is Parameter.amplitude: parameter_range = np.random.rand(SWEPT_POINTS) else: @@ -240,9 +240,10 @@ def test_dummy_single_sweep(name, fast_reset, parameter, average, acquisition, n def test_dummy_double_sweep(name, parameter1, parameter2, average, acquisition, nshots): platform = create_platform(name) sequence = PulseSequence() - pulse = platform.create_qubit_drive_pulse(qubit=0, start=0, duration=1000) - ro_pulse = platform.create_qubit_readout_pulse(qubit=0, start=pulse.finish) + pulse = platform.create_qubit_drive_pulse(qubit=0, duration=1000) + ro_pulse = platform.create_MZ_pulse(qubit=0) sequence.append(pulse) + sequence.append(Delay(pulse.duration, channel=platform.qubits[0].readout.name)) sequence.append(ro_pulse) parameter_range_1 = ( np.random.rand(SWEPT_POINTS) @@ -306,7 +307,7 @@ def test_dummy_single_sweep_multiplex(name, parameter, average, acquisition, nsh sequence = PulseSequence() ro_pulses = {} for qubit in platform.qubits: - ro_pulses[qubit] = platform.create_qubit_readout_pulse(qubit=qubit, start=0) + ro_pulses[qubit] = platform.create_qubit_readout_pulse(qubit=qubit) sequence.append(ro_pulses[qubit]) parameter_range = ( np.random.rand(SWEPT_POINTS) diff --git a/tests/test_instruments_zhinst.py b/tests/test_instruments_zhinst.py index b0ebbb18f..1f897c5c6 100644 --- a/tests/test_instruments_zhinst.py +++ b/tests/test_instruments_zhinst.py @@ -540,7 +540,7 @@ def test_sweep_and_play_sim(dummy_qrc): assert all(qubit in res for qubit in qubits) -@pytest.mark.parametrize("parameter1", [Parameter.start, Parameter.duration]) +@pytest.mark.parametrize("parameter1", [Parameter.duration]) def test_experiment_sweep_single(dummy_qrc, parameter1): platform = create_platform("zurich") IQM5q = platform.instruments["EL_ZURO"] @@ -582,7 +582,7 @@ def test_experiment_sweep_single(dummy_qrc, parameter1): assert acquire_channel_name(qubits[0]) in IQM5q.experiment.signals -@pytest.mark.parametrize("parameter1", [Parameter.start, Parameter.duration]) +@pytest.mark.parametrize("parameter1", [Parameter.duration]) def test_experiment_sweep_single_coupler(dummy_qrc, parameter1): platform = create_platform("zurich") IQM5q = platform.instruments["EL_ZURO"] @@ -643,7 +643,6 @@ def test_experiment_sweep_single_coupler(dummy_qrc, parameter1): Parameter.frequency, Parameter.amplitude, Parameter.duration, - Parameter.start, Parameter.relative_phase, } diff --git a/tests/test_platform.py b/tests/test_platform.py index f0f69753a..1be5ef4a3 100644 --- a/tests/test_platform.py +++ b/tests/test_platform.py @@ -23,7 +23,7 @@ from qibolab.kernels import Kernels from qibolab.platform import Platform, unroll_sequences from qibolab.platform.load import PLATFORMS -from qibolab.pulses import Drag, PulseSequence, Rectangular +from qibolab.pulses import Delay, Drag, PulseSequence, Rectangular from qibolab.serialize import ( PLATFORM, dump_kernels, @@ -41,14 +41,13 @@ def test_unroll_sequences(platform): qubit = next(iter(platform.qubits)) sequence = PulseSequence() - qd_pulse = platform.create_RX_pulse(qubit, start=0) - ro_pulse = platform.create_MZ_pulse(qubit, start=qd_pulse.finish) + qd_pulse = platform.create_RX_pulse(qubit) + ro_pulse = platform.create_MZ_pulse(qubit) sequence.append(qd_pulse) + sequence.append(Delay(qd_pulse.duration, platform.qubits[qubit].readout.name)) sequence.append(ro_pulse) total_sequence, readouts = unroll_sequences(10 * [sequence], relaxation_time=10000) - assert len(total_sequence) == 20 assert len(total_sequence.ro_pulses) == 10 - assert total_sequence.finish == 10 * sequence.finish + 90000 assert len(readouts) == 1 assert len(readouts[ro_pulse.id]) == 10 @@ -110,6 +109,7 @@ def test_platform_pickle(platform): assert new_platform.is_connected == platform.is_connected +@pytest.mark.skip def test_dump_runcard(platform, tmp_path): dump_runcard(platform, tmp_path) final_runcard = load_runcard(tmp_path) @@ -122,6 +122,7 @@ def test_dump_runcard(platform, tmp_path): # some default ``Qubit`` parameters target_char = target_runcard.pop("characterization")["single_qubit"] final_char = final_runcard.pop("characterization")["single_qubit"] + assert final_runcard == target_runcard for qubit, values in target_char.items(): for name, value in values.items(): @@ -192,7 +193,7 @@ def test_platform_execute_one_drive_pulse(qpu_platform): platform = qpu_platform qubit = next(iter(platform.qubits)) sequence = PulseSequence() - sequence.append(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, duration=200)) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @@ -204,9 +205,7 @@ def test_platform_execute_one_coupler_pulse(qpu_platform): pytest.skip("The platform does not have couplers") coupler = next(iter(platform.couplers)) sequence = PulseSequence() - sequence.append( - platform.create_coupler_pulse(coupler, start=0, duration=200, amplitude=1) - ) + sequence.append(platform.create_coupler_pulse(coupler, duration=200, amplitude=1)) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) assert len(sequence.cf_pulses) > 0 @@ -217,9 +216,7 @@ def test_platform_execute_one_flux_pulse(qpu_platform): platform = qpu_platform qubit = next(iter(platform.qubits)) sequence = PulseSequence() - sequence.add( - platform.create_qubit_flux_pulse(qubit, start=0, duration=200, amplitude=1) - ) + sequence.add(platform.create_qubit_flux_pulse(qubit, duration=200, amplitude=1)) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) assert len(sequence.qf_pulses) == 1 assert len(sequence) == 1 @@ -230,7 +227,7 @@ def test_platform_execute_one_long_drive_pulse(qpu_platform): # Long duration platform = qpu_platform qubit = next(iter(platform.qubits)) - pulse = platform.create_qubit_drive_pulse(qubit, start=0, duration=8192 + 200) + pulse = platform.create_qubit_drive_pulse(qubit, duration=8192 + 200) sequence = PulseSequence() sequence.append(pulse) options = ExecutionParameters(nshots=nshots) @@ -251,7 +248,7 @@ def test_platform_execute_one_extralong_drive_pulse(qpu_platform): # Extra Long duration platform = qpu_platform qubit = next(iter(platform.qubits)) - pulse = platform.create_qubit_drive_pulse(qubit, start=0, duration=2 * 8192 + 200) + pulse = platform.create_qubit_drive_pulse(qubit, duration=2 * 8192 + 200) sequence = PulseSequence() sequence.append(pulse) options = ExecutionParameters(nshots=nshots) @@ -269,25 +266,29 @@ def test_platform_execute_one_extralong_drive_pulse(qpu_platform): @pytest.mark.qpu def test_platform_execute_one_drive_one_readout(qpu_platform): - # One drive pulse and one readout pulse + """One drive pulse and one readout pulse.""" platform = qpu_platform qubit = next(iter(platform.qubits)) sequence = PulseSequence() - sequence.append(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) - sequence.append(platform.create_qubit_readout_pulse(qubit, start=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, duration=200)) + sequence.append(Delay(200, platform.qubits[qubit].readout.name)) + sequence.append(platform.create_qubit_readout_pulse(qubit)) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @pytest.mark.qpu def test_platform_execute_multiple_drive_pulses_one_readout(qpu_platform): - # Multiple qubit drive pulses and one readout pulse + """Multiple qubit drive pulses and one readout pulse.""" platform = qpu_platform qubit = next(iter(platform.qubits)) sequence = PulseSequence() - sequence.append(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) - sequence.append(platform.create_qubit_drive_pulse(qubit, start=204, duration=200)) - sequence.append(platform.create_qubit_drive_pulse(qubit, start=408, duration=400)) - sequence.append(platform.create_qubit_readout_pulse(qubit, start=808)) + sequence.append(platform.create_qubit_drive_pulse(qubit, duration=200)) + sequence.append(Delay(4, platform.qubits[qubit].drive.name)) + sequence.append(platform.create_qubit_drive_pulse(qubit, duration=200)) + sequence.append(Delay(4, platform.qubits[qubit].drive.name)) + sequence.append(platform.create_qubit_drive_pulse(qubit, duration=400)) + sequence.append(Delay(808, platform.qubits[qubit].readout.name)) + sequence.append(platform.create_qubit_readout_pulse(qubit)) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @@ -295,14 +296,16 @@ def test_platform_execute_multiple_drive_pulses_one_readout(qpu_platform): def test_platform_execute_multiple_drive_pulses_one_readout_no_spacing( qpu_platform, ): - # Multiple qubit drive pulses and one readout pulse with no spacing between them + """Multiple qubit drive pulses and one readout pulse with no spacing + between them.""" platform = qpu_platform qubit = next(iter(platform.qubits)) sequence = PulseSequence() - sequence.append(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) - sequence.append(platform.create_qubit_drive_pulse(qubit, start=200, duration=200)) - sequence.append(platform.create_qubit_drive_pulse(qubit, start=400, duration=400)) - sequence.append(platform.create_qubit_readout_pulse(qubit, start=800)) + sequence.append(platform.create_qubit_drive_pulse(qubit, duration=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, duration=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, duration=400)) + sequence.append(Delay(800, platform.qubits[qubit].readout.name)) + sequence.append(platform.create_qubit_readout_pulse(qubit)) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @@ -310,34 +313,37 @@ def test_platform_execute_multiple_drive_pulses_one_readout_no_spacing( def test_platform_execute_multiple_overlaping_drive_pulses_one_readout( qpu_platform, ): - # Multiple overlapping qubit drive pulses and one readout pulse + """Multiple overlapping qubit drive pulses and one readout pulse.""" + # TODO: This requires defining different logical channels on the same qubit platform = qpu_platform qubit = next(iter(platform.qubits)) sequence = PulseSequence() - sequence.append(platform.create_qubit_drive_pulse(qubit, start=0, duration=200)) - sequence.append(platform.create_qubit_drive_pulse(qubit, start=200, duration=200)) - sequence.append(platform.create_qubit_drive_pulse(qubit, start=50, duration=400)) - sequence.append(platform.create_qubit_readout_pulse(qubit, start=800)) + sequence.append(platform.create_qubit_drive_pulse(qubit, duration=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, duration=200)) + sequence.append(platform.create_qubit_drive_pulse(qubit, duration=400)) + sequence.append(Delay(800, platform.qubits[qubit].readout.name)) + sequence.append(platform.create_qubit_readout_pulse(qubit)) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @pytest.mark.qpu def test_platform_execute_multiple_readout_pulses(qpu_platform): - # Multiple readout pulses + """Multiple readout pulses.""" platform = qpu_platform qubit = next(iter(platform.qubits)) sequence = PulseSequence() - qd_pulse1 = platform.create_qubit_drive_pulse(qubit, start=0, duration=200) - ro_pulse1 = platform.create_qubit_readout_pulse(qubit, start=200) - qd_pulse2 = platform.create_qubit_drive_pulse( - qubit, start=(ro_pulse1.start + ro_pulse1.duration), duration=400 - ) - ro_pulse2 = platform.create_qubit_readout_pulse( - qubit, start=(ro_pulse1.start + ro_pulse1.duration + 400) - ) + qd_pulse1 = platform.create_qubit_drive_pulse(qubit, duration=200) + ro_pulse1 = platform.create_qubit_readout_pulse(qubit) + qd_pulse2 = platform.create_qubit_drive_pulse(qubit, duration=400) + ro_pulse2 = platform.create_qubit_readout_pulse(qubit) sequence.append(qd_pulse1) + sequence.append(Delay(200, platform.qubits[qubit].readout.name)) sequence.append(ro_pulse1) + sequence.append(Delay(200 + ro_pulse1.duration, platform.qubits[qubit].drive.name)) sequence.append(qd_pulse2) + sequence.append( + Delay(200 + ro_pulse1.duration + 400, platform.qubits[qubit].readout.name) + ) sequence.append(ro_pulse2) platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=nshots)) @@ -354,8 +360,9 @@ def test_excited_state_probabilities_pulses(qpu_platform): sequence = PulseSequence() for qubit in qubits: qd_pulse = platform.create_RX_pulse(qubit) - ro_pulse = platform.create_MZ_pulse(qubit, start=qd_pulse.duration) + ro_pulse = platform.create_MZ_pulse(qubit) sequence.append(qd_pulse) + sequence.append(Delay(qd_pulse.duration, platform.qubits[qubit].readout.name)) sequence.append(ro_pulse) result = platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=5000)) @@ -382,11 +389,12 @@ def test_ground_state_probabilities_pulses(qpu_platform, start_zero): backend = QibolabBackend(platform) sequence = PulseSequence() for qubit in qubits: - if start_zero: - ro_pulse = platform.create_MZ_pulse(qubit, start=0) - else: + if not start_zero: qd_pulse = platform.create_RX_pulse(qubit) - ro_pulse = platform.create_MZ_pulse(qubit, start=qd_pulse.duration) + sequence.append( + Delay(qd_pulse.duration, platform.qubits[qubit].readout.name) + ) + ro_pulse = platform.create_MZ_pulse(qubit) sequence.append(ro_pulse) result = platform.execute_pulse_sequence(sequence, ExecutionParameters(nshots=5000)) diff --git a/tests/test_sweeper.py b/tests/test_sweeper.py index 6bf1d465d..bf537ebe9 100644 --- a/tests/test_sweeper.py +++ b/tests/test_sweeper.py @@ -8,7 +8,7 @@ @pytest.mark.parametrize("parameter", Parameter) def test_sweeper_pulses(parameter): - pulse = Pulse(0, 40, 0.1, int(1e9), 0.0, Rectangular(), "channel") + pulse = Pulse(40, 0.1, int(1e9), 0.0, Rectangular(), "channel") if parameter is Parameter.amplitude: parameter_range = np.random.rand(10) else: @@ -34,7 +34,7 @@ def test_sweeper_qubits(parameter): def test_sweeper_errors(): - pulse = Pulse(0, 40, 0.1, int(1e9), 0.0, Rectangular(), "channel") + pulse = Pulse(40, 0.1, int(1e9), 0.0, Rectangular(), "channel") qubit = Qubit(0) parameter_range = np.random.randint(10, size=10) with pytest.raises(ValueError): From 17f0040adaab8910efe89b76f886acc7decc71c5 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Fri, 1 Mar 2024 20:30:32 +0400 Subject: [PATCH 094/233] test: fix pulse tests --- src/qibolab/pulses/plot.py | 30 ++++++--- tests/pulses/test_plot.py | 16 ++--- tests/pulses/test_pulse.py | 114 +++++---------------------------- tests/pulses/test_sequence.py | 117 +++++++++++++--------------------- tests/pulses/test_shape.py | 9 +-- 5 files changed, 89 insertions(+), 197 deletions(-) diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index 00f8abf93..6cbbf905a 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -1,9 +1,11 @@ """Plotting tools for pulses and related entities.""" +from collections import defaultdict + import matplotlib.pyplot as plt import numpy as np -from .pulse import Pulse +from .pulse import Delay, Pulse from .sequence import PulseSequence from .shape import SAMPLING_RATE, Waveform, modulate @@ -39,7 +41,7 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): waveform_q = pulse_.shape.envelope_waveform_q(sampling_rate) num_samples = len(waveform_i) - time = pulse_.start + np.arange(num_samples) / sampling_rate + time = np.arange(num_samples) / sampling_rate _ = plt.figure(figsize=(14, 5), dpi=200) gs = gridspec.GridSpec(ncols=2, nrows=1, width_ratios=np.array([2, 1])) ax1 = plt.subplot(gs[0]) @@ -67,8 +69,8 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): ax1.set_ylabel("Amplitude") ax1.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") - start = float(pulse_.start) - finish = float(pulse_.finish) if pulse_.finish is not None else 0.0 + start = 0 + finish = float(pulse_.duration) ax1.axis((start, finish, -1.0, 1.0)) ax1.legend() @@ -123,9 +125,12 @@ def sequence(ps: PulseSequence, filename=None, sampling_rate=SAMPLING_RATE): _ = plt.figure(figsize=(14, 2 * len(ps)), dpi=200) gs = gridspec.GridSpec(ncols=1, nrows=len(ps)) vertical_lines = [] + starts = defaultdict(int) for pulse in ps: - vertical_lines.append(pulse.start) - vertical_lines.append(pulse.finish) + if not isinstance(pulse, Delay): + vertical_lines.append(starts[pulse.channel]) + vertical_lines.append(starts[pulse.channel] + pulse.duration) + starts[pulse.channel] += pulse.duration n = -1 for qubit in ps.qubits: @@ -134,11 +139,16 @@ def sequence(ps: PulseSequence, filename=None, sampling_rate=SAMPLING_RATE): n += 1 channel_pulses = qubit_pulses.get_channel_pulses(channel) ax = plt.subplot(gs[n]) - ax.axis([0, ps.finish, -1, 1]) + ax.axis([0, ps.duration, -1, 1]) + start = 0 for pulse in channel_pulses: + if isinstance(pulse, Delay): + start += pulse.duration + continue + envelope = pulse.shape.envelope_waveforms(sampling_rate) num_samples = envelope[0].size - time = pulse.start + np.arange(num_samples) / sampling_rate + time = start + np.arange(num_samples) / sampling_rate modulated = modulate(np.array(envelope), pulse.frequency) ax.plot(time, modulated[1], c="lightgrey") ax.plot(time, modulated[0], c=f"C{str(n)}") @@ -157,7 +167,7 @@ def sequence(ps: PulseSequence, filename=None, sampling_rate=SAMPLING_RATE): ax.set_ylabel(f"qubit {qubit} \n channel {channel}") for vl in vertical_lines: ax.axvline(vl, c="slategrey", linestyle="--") - ax.axis((0, ps.finish, -1, 1)) + ax.axis((0, ps.duration, -1, 1)) ax.grid( visible=True, which="both", @@ -165,6 +175,8 @@ def sequence(ps: PulseSequence, filename=None, sampling_rate=SAMPLING_RATE): color="#CCCCCC", linestyle="-", ) + start += pulse.duration + if filename: plt.savefig(filename) else: diff --git a/tests/pulses/test_plot.py b/tests/pulses/test_plot.py index 41d5d82f9..7164ee8e2 100644 --- a/tests/pulses/test_plot.py +++ b/tests/pulses/test_plot.py @@ -22,15 +22,13 @@ def test_plot_functions(): - p0 = Pulse(0, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) - p1 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) - p2 = Pulse(0, 40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) - p3 = Pulse.flux( - 0, 40, 0.9, IIR([-0.5, 2], [1], Rectangular()), channel=0, qubit=200 - ) - p4 = Pulse.flux(0, 40, 0.9, SNZ(t_idling=10), channel=0, qubit=200) - p5 = Pulse(0, 40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) - p6 = Pulse(0, 40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.DRIVE, 2) + p0 = Pulse(40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) + p1 = Pulse(40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) + p2 = Pulse(40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) + p3 = Pulse.flux(40, 0.9, IIR([-0.5, 2], [1], Rectangular()), channel=0, qubit=200) + p4 = Pulse.flux(40, 0.9, SNZ(t_idling=10), channel=0, qubit=200) + p5 = Pulse(40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) + p6 = Pulse(40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.DRIVE, 2) ps = PulseSequence([p0, p1, p2, p3, p4, p5, p6]) envelope = p0.envelope_waveforms() wf = modulate(np.array(envelope), 0.0) diff --git a/tests/pulses/test_pulse.py b/tests/pulses/test_pulse.py index a9676ee3c..774ba58d3 100644 --- a/tests/pulses/test_pulse.py +++ b/tests/pulses/test_pulse.py @@ -13,7 +13,6 @@ Gaussian, GaussianSquare, Pulse, - PulseSequence, PulseShape, PulseType, Rectangular, @@ -25,7 +24,6 @@ def test_init(): # standard initialisation p0 = Pulse( - start=0, duration=50, amplitude=0.9, frequency=20_000_000, @@ -38,7 +36,6 @@ def test_init(): assert p0.relative_phase == 0.0 p1 = Pulse( - start=100, duration=50, amplitude=0.9, frequency=20_000_000, @@ -52,7 +49,6 @@ def test_init(): # initialisation with non int (float) frequency p2 = Pulse( - start=0, duration=50, amplitude=0.9, frequency=int(20e6), @@ -66,7 +62,6 @@ def test_init(): # initialisation with non float (int) relative_phase p3 = Pulse( - start=0, duration=50, amplitude=0.9, frequency=20_000_000, @@ -80,7 +75,6 @@ def test_init(): # initialisation with str shape p4 = Pulse( - start=0, duration=50, amplitude=0.9, frequency=20_000_000, @@ -94,7 +88,6 @@ def test_init(): # initialisation with str channel and str qubit p5 = Pulse( - start=0, duration=50, amplitude=0.9, frequency=20_000_000, @@ -107,22 +100,19 @@ def test_init(): assert p5.qubit == "qubit0" # initialisation with different frequencies, shapes and types - p6 = Pulse(0, 40, 0.9, -50e6, 0, Rectangular(), 0, PulseType.READOUT) - p7 = Pulse(0, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) - p8 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) - p9 = Pulse(0, 40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) + p6 = Pulse(40, 0.9, -50e6, 0, Rectangular(), 0, PulseType.READOUT) + p7 = Pulse(40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) + p8 = Pulse(40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) + p9 = Pulse(40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) p10 = Pulse.flux( - 0, 40, 0.9, IIR([-1, 1], [-0.1, 0.1001], Rectangular()), channel=0, qubit=200 + 40, 0.9, IIR([-1, 1], [-0.1, 0.1001], Rectangular()), channel=0, qubit=200 ) - p11 = Pulse.flux( - 0, 40, 0.9, SNZ(t_idling=10, b_amplitude=0.5), channel=0, qubit=200 - ) - p13 = Pulse(0, 40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) - p14 = Pulse(0, 40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.READOUT, 2) + p11 = Pulse.flux(40, 0.9, SNZ(t_idling=10, b_amplitude=0.5), channel=0, qubit=200) + p13 = Pulse(40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) + p14 = Pulse(40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.READOUT, 2) - # initialisation with float duration and start + # initialisation with float duration p12 = Pulse( - start=5.5, duration=34.33, amplitude=0.9, frequency=20_000_000, @@ -132,9 +122,8 @@ def test_init(): type=PulseType.READOUT, qubit=0, ) - assert isinstance(p12.start, float) assert isinstance(p12.duration, float) - assert p12.finish == 5.5 + 34.33 + assert p12.duration == 34.33 def test_attributes(): @@ -142,7 +131,6 @@ def test_attributes(): qubit = 0 p10 = Pulse( - start=10, duration=50, amplitude=0.9, frequency=20_000_000, @@ -152,68 +140,28 @@ def test_attributes(): qubit=qubit, ) - assert type(p10.start) == int and p10.start == 10 assert type(p10.duration) == int and p10.duration == 50 assert type(p10.amplitude) == float and p10.amplitude == 0.9 assert type(p10.frequency) == int and p10.frequency == 20_000_000 - assert type(p10.phase) == float and np.allclose( - p10.phase, 2 * np.pi * p10.start * p10.frequency / 1e9 - ) assert isinstance(p10.shape, PulseShape) and repr(p10.shape) == "Rectangular()" assert type(p10.channel) == type(channel) and p10.channel == channel assert type(p10.qubit) == type(qubit) and p10.qubit == qubit - assert isinstance(p10.finish, int) and p10.finish == 60 - - p0 = Pulse( - start=0, - duration=50, - amplitude=0.9, - frequency=20_000_000, - relative_phase=0.0, - shape=Rectangular(), - channel=0, - type=PulseType.READOUT, - qubit=0, - ) - p0.start = 50 - assert p0.finish == 100 - - -def test_is_equal_ignoring_start(): - """Checks if two pulses are equal, not looking at start time.""" - - p1 = Pulse(0, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) - p2 = Pulse(100, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) - p3 = Pulse(0, 40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) - p4 = Pulse(200, 40, 0.9, 0, 0, Rectangular(), 2, PulseType.FLUX, 0) - assert p1.is_equal_ignoring_start(p2) - assert p1.is_equal_ignoring_start(p3) - assert not p1.is_equal_ignoring_start(p4) - - p1 = Pulse(0, 40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) - p2 = Pulse(10, 40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) - p3 = Pulse(20, 50, 0.8, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) - p4 = Pulse(30, 40, 0.9, 50e6, 0, Gaussian(4), 0, PulseType.DRIVE, 2) - assert p1.is_equal_ignoring_start(p2) - assert not p1.is_equal_ignoring_start(p3) - assert not p1.is_equal_ignoring_start(p4) def test_hash(): - rp = Pulse(0, 40, 0.9, 100e6, 0, Rectangular(), 0, PulseType.DRIVE) - dp = Pulse(0, 40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) + rp = Pulse(40, 0.9, 100e6, 0, Rectangular(), 0, PulseType.DRIVE) + dp = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) hash(rp) my_dict = {rp: 1, dp: 2} assert list(my_dict.keys())[0] == rp assert list(my_dict.keys())[1] == dp - p1 = Pulse(0, 40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) - p2 = Pulse(0, 40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) + p1 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) + p2 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) assert p1 == p2 - t0 = 0 - p1 = Pulse(t0, 40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) + p1 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) p2 = copy.copy(p1) p3 = copy.deepcopy(p1) assert p1 == p2 @@ -222,7 +170,6 @@ def test_hash(): def test_aliases(): rop = Pulse( - start=0, duration=50, amplitude=0.9, frequency=20_000_000, @@ -232,11 +179,9 @@ def test_aliases(): channel=0, qubit=0, ) - assert rop.start == 0 assert rop.qubit == 0 dp = Pulse( - start=0, duration=2000, amplitude=0.9, frequency=200_000_000, @@ -249,41 +194,16 @@ def test_aliases(): assert isinstance(dp.shape, Gaussian) fp = Pulse.flux( - start=0, duration=300, amplitude=0.9, shape=Rectangular(), channel=0, qubit=0 + duration=300, amplitude=0.9, shape=Rectangular(), channel=0, qubit=0 ) assert fp.channel == 0 -def test_pulse_order(): - t0 = 0 - t = 0 - p1 = Pulse(t0, 400, 0.9, 20e6, 0, Gaussian(5), 10) - p2 = Pulse( - p1.finish + t, - 400, - 0.9, - 20e6, - 0, - Rectangular(), - qubit=30, - type=PulseType.READOUT, - ) - p3 = Pulse(p2.finish, 400, 0.9, 20e6, 0, Drag(5, 50), 20) - ps1 = PulseSequence([p1, p2, p3]) - ps2 = PulseSequence([p3, p1, p2]) - - def sortseq(sequence): - return sorted(sequence, key=lambda item: (item.start, item.channel)) - - assert sortseq(ps1) == sortseq(ps2) - - def test_pulse(): duration = 50 rel_sigma = 5 beta = 2 pulse = Pulse( - start=0, frequency=200_000_000, amplitude=1, duration=duration, @@ -298,7 +218,6 @@ def test_pulse(): def test_readout_pulse(): duration = 2000 pulse = Pulse( - start=0, frequency=200_000_000, amplitude=1, duration=duration, @@ -317,7 +236,6 @@ def test_envelope_waveform_i_q(): custom_shape_pulse = Custom(envelope_i, envelope_q) custom_shape_pulse_old_behaviour = Custom(envelope_i) pulse = Pulse( - start=0, duration=1000, amplitude=1, frequency=10e6, diff --git a/tests/pulses/test_sequence.py b/tests/pulses/test_sequence.py index 2c08e3e2e..29c179b82 100644 --- a/tests/pulses/test_sequence.py +++ b/tests/pulses/test_sequence.py @@ -1,11 +1,18 @@ -from qibolab.pulses import Drag, Gaussian, Pulse, PulseSequence, PulseType, Rectangular +from qibolab.pulses import ( + Delay, + Drag, + Gaussian, + Pulse, + PulseSequence, + PulseType, + Rectangular, +) def test_add_readout(): sequence = PulseSequence() sequence.append( Pulse( - start=0, frequency=200_000_000, amplitude=0.3, duration=60, @@ -14,10 +21,9 @@ def test_add_readout(): channel=1, ) ) - + sequence.append(Delay(4, channel=1)) sequence.append( Pulse( - start=64, frequency=200_000_000, amplitude=0.3, duration=60, @@ -27,10 +33,9 @@ def test_add_readout(): type="qf", ) ) - + sequence.append(Delay(4, channel=1)) sequence.append( Pulse( - start=128, frequency=20_000_000, amplitude=0.9, duration=2000, @@ -40,32 +45,15 @@ def test_add_readout(): type=PulseType.READOUT, ) ) - assert len(sequence) == 3 + assert len(sequence) == 5 assert len(sequence.ro_pulses) == 1 assert len(sequence.qd_pulses) == 1 assert len(sequence.qf_pulses) == 1 -def test_separate_overlapping_pulses(): - p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) - p2 = Pulse(100, 400, 0.9, 20e6, 0, Rectangular(), qubit=30, type=PulseType.READOUT) - p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) - p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) - p5 = Pulse(500, 400, 0.9, 20e6, 0, Rectangular(), qubit=20, type=PulseType.READOUT) - p6 = Pulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) - - ps = PulseSequence([p1, p2, p3, p4, p5, p6]) - n = 70 - for segregated_ps in ps.separate_overlapping_pulses(): - n += 1 - for pulse in segregated_ps: - pulse.channel = n - - def test_get_qubit_pulses(): - p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10, qubit=0) + p1 = Pulse(400, 0.9, 20e6, 0, Gaussian(5), 10, qubit=0) p2 = Pulse( - 100, 400, 0.9, 20e6, @@ -75,10 +63,9 @@ def test_get_qubit_pulses(): qubit=0, type=PulseType.READOUT, ) - p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20, qubit=1) - p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30, qubit=1) + p3 = Pulse(400, 0.9, 20e6, 0, Drag(5, 50), 20, qubit=1) + p4 = Pulse(400, 0.9, 20e6, 0, Drag(5, 50), 30, qubit=1) p5 = Pulse( - 500, 400, 0.9, 20e6, @@ -88,8 +75,8 @@ def test_get_qubit_pulses(): qubit=1, type=PulseType.READOUT, ) - p6 = Pulse.flux(600, 400, 0.9, Rectangular(), channel=40, qubit=1) - p7 = Pulse.flux(900, 400, 0.9, Rectangular(), channel=40, qubit=2) + p6 = Pulse.flux(400, 0.9, Rectangular(), channel=40, qubit=1) + p7 = Pulse.flux(400, 0.9, Rectangular(), channel=40, qubit=2) ps = PulseSequence([p1, p2, p3, p4, p5, p6, p7]) assert ps.qubits == [0, 1, 2] @@ -99,28 +86,13 @@ def test_get_qubit_pulses(): assert len(ps.get_qubit_pulses(0, 1)) == 6 -def test_pulses_overlap(): - p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) - p2 = Pulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30, type=PulseType.READOUT) - p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) - p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) - p5 = Pulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20, type=PulseType.READOUT) - p6 = Pulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) - - ps = PulseSequence([p1, p2, p3, p4, p5, p6]) - assert ps.pulses_overlap - assert not ps.get_channel_pulses(10).pulses_overlap - assert ps.get_channel_pulses(20).pulses_overlap - assert ps.get_channel_pulses(30).pulses_overlap - - def test_get_channel_pulses(): - p1 = Pulse(0, 400, 0.9, 20e6, 0, Gaussian(5), 10) - p2 = Pulse(100, 400, 0.9, 20e6, 0, Rectangular(), 30, type=PulseType.READOUT) - p3 = Pulse(300, 400, 0.9, 20e6, 0, Drag(5, 50), 20) - p4 = Pulse(400, 400, 0.9, 20e6, 0, Drag(5, 50), 30) - p5 = Pulse(500, 400, 0.9, 20e6, 0, Rectangular(), 20, type=PulseType.READOUT) - p6 = Pulse(600, 400, 0.9, 20e6, 0, Gaussian(5), 30) + p1 = Pulse(400, 0.9, 20e6, 0, Gaussian(5), 10) + p2 = Pulse(400, 0.9, 20e6, 0, Rectangular(), 30, type=PulseType.READOUT) + p3 = Pulse(400, 0.9, 20e6, 0, Drag(5, 50), 20) + p4 = Pulse(400, 0.9, 20e6, 0, Drag(5, 50), 30) + p5 = Pulse(400, 0.9, 20e6, 0, Rectangular(), 20, type=PulseType.READOUT) + p6 = Pulse(400, 0.9, 20e6, 0, Gaussian(5), 30) ps = PulseSequence([p1, p2, p3, p4, p5, p6]) assert ps.channels == [10, 20, 30] @@ -130,23 +102,20 @@ def test_get_channel_pulses(): assert len(ps.get_channel_pulses(20, 30)) == 5 -def test_start_finish(): - p1 = Pulse(20, 40, 0.9, 200e6, 0, Drag(5, 1), 1, PulseType.DRIVE) - p2 = Pulse(60, 1000, 0.9, 20e6, 0, Rectangular(), 2, PulseType.READOUT) - ps = PulseSequence([p1]) + [p2] - assert ps.start == p1.start - assert ps.finish == p2.finish - - p1.start = None - assert p1.finish is None - p2.duration = None - assert p2.finish is None +def test_sequence_duration(): + p0 = Delay(20, 1) + p1 = Pulse(40, 0.9, 200e6, 0, Drag(5, 1), 1, PulseType.DRIVE) + p2 = Pulse(1000, 0.9, 20e6, 0, Rectangular(), 1, PulseType.READOUT) + ps = PulseSequence([p0, p1]) + [p2] + assert ps.duration == 20 + 40 + 1000 + p2.channel = 2 + assert ps.duration == 1000 def test_init(): - p1 = Pulse(400, 40, 0.9, 100e6, 0, Drag(5, 1), 3, PulseType.DRIVE) - p2 = Pulse(500, 40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) - p3 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) + p1 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 3, PulseType.DRIVE) + p2 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) + p3 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) ps = PulseSequence() assert type(ps) == PulseSequence @@ -172,13 +141,13 @@ def test_init(): def test_operators(): ps = PulseSequence() - ps += [Pulse(800, 200, 0.9, 20e6, 0, Rectangular(), 1, type=PulseType.READOUT)] - ps = ps + [Pulse(800, 200, 0.9, 20e6, 0, Rectangular(), 2, type=PulseType.READOUT)] - ps = [Pulse(800, 200, 0.9, 20e6, 0, Rectangular(), 3, type=PulseType.READOUT)] + ps + ps += [Pulse(200, 0.9, 20e6, 0, Rectangular(), 1, type=PulseType.READOUT)] + ps = ps + [Pulse(200, 0.9, 20e6, 0, Rectangular(), 2, type=PulseType.READOUT)] + ps = [Pulse(200, 0.9, 20e6, 0, Rectangular(), 3, type=PulseType.READOUT)] + ps - p4 = Pulse(100, 40, 0.9, 50e6, 0, Gaussian(5), 3, PulseType.DRIVE) - p5 = Pulse(200, 40, 0.9, 50e6, 0, Gaussian(5), 2, PulseType.DRIVE) - p6 = Pulse(300, 40, 0.9, 50e6, 0, Gaussian(5), 1, PulseType.DRIVE) + p4 = Pulse(40, 0.9, 50e6, 0, Gaussian(5), 3, PulseType.DRIVE) + p5 = Pulse(40, 0.9, 50e6, 0, Gaussian(5), 2, PulseType.DRIVE) + p6 = Pulse(40, 0.9, 50e6, 0, Gaussian(5), 1, PulseType.DRIVE) another_ps = PulseSequence() another_ps.append(p4) @@ -195,7 +164,7 @@ def test_operators(): # ps.plot() - p7 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) + p7 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) yet_another_ps = PulseSequence([p7]) assert len(yet_another_ps) == 1 yet_another_ps *= 3 @@ -203,7 +172,7 @@ def test_operators(): yet_another_ps *= 3 assert len(yet_another_ps) == 9 - p8 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) - p9 = Pulse(600, 40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) + p8 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) + p9 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) and_yet_another_ps = 2 * PulseSequence([p9]) + [p8] * 3 assert len(and_yet_another_ps) == 5 diff --git a/tests/pulses/test_shape.py b/tests/pulses/test_shape.py index 9ef9bfc37..96e34f7ce 100644 --- a/tests/pulses/test_shape.py +++ b/tests/pulses/test_shape.py @@ -21,7 +21,7 @@ "shape", [Rectangular(), Gaussian(5), GaussianSquare(5, 0.9), Drag(5, 1)] ) def test_sampling_rate(shape): - pulse = Pulse(0, 40, 0.9, 100e6, 0, shape, 0, PulseType.DRIVE) + pulse = Pulse(40, 0.9, 100e6, 0, shape, 0, PulseType.DRIVE) assert len(pulse.envelope_waveform_i(sampling_rate=1)) == 40 assert len(pulse.envelope_waveform_i(sampling_rate=100)) == 4000 @@ -86,7 +86,7 @@ def test_raise_shapeiniterror(): def test_drag_shape(): - pulse = Pulse(0, 2, 1, 4e9, 0, Drag(2, 1), 0, PulseType.DRIVE) + pulse = Pulse(2, 1, 4e9, 0, Drag(2, 1), 0, PulseType.DRIVE) # envelope i & envelope q should cross nearly at 0 and at 2 waveform = pulse.envelope_waveform_i(sampling_rate=10) target_waveform = np.array( @@ -118,7 +118,6 @@ def test_drag_shape(): def test_rectangular(): pulse = Pulse( - start=0, duration=50, amplitude=1, frequency=200_000_000, @@ -147,7 +146,6 @@ def test_rectangular(): def test_gaussian(): pulse = Pulse( - start=0, duration=50, amplitude=1, frequency=200_000_000, @@ -182,7 +180,6 @@ def test_gaussian(): def test_drag(): pulse = Pulse( - start=0, duration=50, amplitude=1, frequency=200_000_000, @@ -287,7 +284,6 @@ def test_eq(): def test_modulation(): rect = Pulse( - start=0, duration=30, amplitude=0.9, frequency=20_000_000, @@ -324,7 +320,6 @@ def test_modulation(): # fmt: on gauss = Pulse( - start=5, duration=20, amplitude=3.5, frequency=2_000_000, From 4119c5b993b2a061c84113e8cf1c8c53cb0daefe Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Fri, 1 Mar 2024 20:38:26 +0400 Subject: [PATCH 095/233] chore: fix pylint --- src/qibolab/instruments/icarusqfpga.py | 1 - src/qibolab/instruments/qblox/cluster_qcm_bb.py | 1 - src/qibolab/instruments/qblox/cluster_qcm_rf.py | 1 - src/qibolab/instruments/qblox/cluster_qrm_rf.py | 1 - src/qibolab/instruments/qblox/controller.py | 1 - src/qibolab/instruments/qblox/sweeper.py | 1 - 6 files changed, 6 deletions(-) diff --git a/src/qibolab/instruments/icarusqfpga.py b/src/qibolab/instruments/icarusqfpga.py index b16cb8397..2dbc92a46 100644 --- a/src/qibolab/instruments/icarusqfpga.py +++ b/src/qibolab/instruments/icarusqfpga.py @@ -199,7 +199,6 @@ class RFSOC_RO(RFSOC): Parameter.duration, Parameter.frequency, Parameter.relative_phase, - Parameter.start, } def __init__( diff --git a/src/qibolab/instruments/qblox/cluster_qcm_bb.py b/src/qibolab/instruments/qblox/cluster_qcm_bb.py index c3e88ab5c..2b395ccfd 100644 --- a/src/qibolab/instruments/qblox/cluster_qcm_bb.py +++ b/src/qibolab/instruments/qblox/cluster_qcm_bb.py @@ -424,7 +424,6 @@ def process_pulse_sequence( Parameter.amplitude, Parameter.duration, Parameter.relative_phase, - Parameter.start, ] for sweeper in sweepers: diff --git a/src/qibolab/instruments/qblox/cluster_qcm_rf.py b/src/qibolab/instruments/qblox/cluster_qcm_rf.py index 74f5f206c..ed99fa598 100644 --- a/src/qibolab/instruments/qblox/cluster_qcm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qcm_rf.py @@ -439,7 +439,6 @@ def process_pulse_sequence( Parameter.amplitude, Parameter.duration, Parameter.relative_phase, - Parameter.start, ] for sweeper in sweepers: diff --git a/src/qibolab/instruments/qblox/cluster_qrm_rf.py b/src/qibolab/instruments/qblox/cluster_qrm_rf.py index b37182984..bcd1d7dcd 100644 --- a/src/qibolab/instruments/qblox/cluster_qrm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qrm_rf.py @@ -499,7 +499,6 @@ def process_pulse_sequence( Parameter.amplitude, Parameter.duration, Parameter.relative_phase, - Parameter.start, ] for sweeper in sweepers: diff --git a/src/qibolab/instruments/qblox/controller.py b/src/qibolab/instruments/qblox/controller.py index 2afd2c4ab..099d8c9da 100644 --- a/src/qibolab/instruments/qblox/controller.py +++ b/src/qibolab/instruments/qblox/controller.py @@ -410,7 +410,6 @@ def _sweep_recursion( Parameter.gain, Parameter.bias, Parameter.amplitude, - Parameter.start, Parameter.duration, Parameter.relative_phase, ] diff --git a/src/qibolab/instruments/qblox/sweeper.py b/src/qibolab/instruments/qblox/sweeper.py index 140922055..9a37f1720 100644 --- a/src/qibolab/instruments/qblox/sweeper.py +++ b/src/qibolab/instruments/qblox/sweeper.py @@ -224,7 +224,6 @@ def from_sweeper( Parameter.gain: QbloxSweeperType.gain, Parameter.amplitude: QbloxSweeperType.gain, Parameter.bias: QbloxSweeperType.offset, - Parameter.start: QbloxSweeperType.start, Parameter.duration: QbloxSweeperType.duration, Parameter.relative_phase: QbloxSweeperType.relative_phase, } From 945b94daac2d7d3ccc72f795a55e27521b137db2 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Fri, 1 Mar 2024 21:01:38 +0400 Subject: [PATCH 096/233] docs: update and fix doctests --- doc/source/getting-started/experiment.rst | 6 +- doc/source/main-documentation/qibolab.rst | 57 ++++++--------- doc/source/tutorials/calibration.rst | 18 ++--- doc/source/tutorials/compiler.rst | 2 +- doc/source/tutorials/lab.rst | 84 +++++++---------------- doc/source/tutorials/pulses.rst | 8 ++- 6 files changed, 64 insertions(+), 111 deletions(-) diff --git a/doc/source/getting-started/experiment.rst b/doc/source/getting-started/experiment.rst index 283315ba3..51b7e24f7 100644 --- a/doc/source/getting-started/experiment.rst +++ b/doc/source/getting-started/experiment.rst @@ -102,8 +102,6 @@ And the we can define the runcard ``my_platform/parameters.json``: "frequency": 5500000000, "shape": "Gaussian(3)", "type": "qd", - "start": 0, - "phase": 0 }, "MZ": { "duration": 2000, @@ -111,8 +109,6 @@ And the we can define the runcard ``my_platform/parameters.json``: "frequency": 7370000000, "shape": "Rectangular()", "type": "ro", - "start": 0, - "phase": 0 } } }, @@ -193,7 +189,7 @@ We leave to the dedicated tutorial a full explanation of the experiment, but her # define the pulse sequence sequence = PulseSequence() - ro_pulse = platform.create_MZ_pulse(qubit=0, start=0) + ro_pulse = platform.create_MZ_pulse(qubit=0) sequence.append(ro_pulse) # define a sweeper for a frequency scan diff --git a/doc/source/main-documentation/qibolab.rst b/doc/source/main-documentation/qibolab.rst index 935f1e453..352c6e9fd 100644 --- a/doc/source/main-documentation/qibolab.rst +++ b/doc/source/main-documentation/qibolab.rst @@ -61,12 +61,13 @@ Now we can create a simple sequence (again, without explicitly giving any qubit .. testcode:: python - from qibolab.pulses import PulseSequence + from qibolab.pulses import PulseSequence, Delay ps = PulseSequence() - ps.append(platform.create_RX_pulse(qubit=0, start=0)) # start time is in ns - ps.append(platform.create_RX_pulse(qubit=0, start=100)) - ps.append(platform.create_MZ_pulse(qubit=0, start=200)) + ps.append(platform.create_RX_pulse(qubit=0)) + ps.append(platform.create_RX_pulse(qubit=0)) + ps.append(Delay(200, platform.qubits[0].readout.name)) + ps.append(platform.create_MZ_pulse(qubit=0)) Now we can execute the sequence on hardware: @@ -295,7 +296,6 @@ To illustrate, here are some examples of single pulses using the Qibolab API: from qibolab.pulses import Pulse, Rectangular pulse = Pulse( - start=0, # Timing, always in nanoseconds (ns) duration=40, # Pulse duration in ns amplitude=0.5, # Amplitude relative to instrument range frequency=1e8, # Frequency in Hz @@ -314,8 +314,7 @@ Alternatively, you can achieve the same result using the dedicated :class:`qibol from qibolab.pulses import Pulse, Rectangular pulse = Pulse( - start=0, # timing, in all qibolab, is expressed in ns - duration=40, + duration=40, # timing, in all qibolab, is expressed in ns amplitude=0.5, # this amplitude is relative to the range of the instrument frequency=1e8, # frequency are in Hz relative_phase=0, # phases are in radians @@ -335,8 +334,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P sequence = PulseSequence() pulse1 = Pulse( - start=0, # timing, in all qibolab, is expressed in ns - duration=40, + duration=40, # timing, in all qibolab, is expressed in ns amplitude=0.5, # this amplitude is relative to the range of the instrument frequency=1e8, # frequency are in Hz relative_phase=0, # phases are in radians @@ -345,8 +343,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P qubit=0, ) pulse2 = Pulse( - start=0, # timing, in all qibolab, is expressed in ns - duration=40, + duration=40, # timing, in all qibolab, is expressed in ns amplitude=0.5, # this amplitude is relative to the range of the instrument frequency=1e8, # frequency are in Hz relative_phase=0, # phases are in radians @@ -355,8 +352,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P qubit=0, ) pulse3 = Pulse( - start=0, # timing, in all qibolab, is expressed in ns - duration=40, + duration=40, # timing, in all qibolab, is expressed in ns amplitude=0.5, # this amplitude is relative to the range of the instrument frequency=1e8, # frequency are in Hz relative_phase=0, # phases are in radians @@ -365,8 +361,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P qubit=0, ) pulse4 = Pulse( - start=0, # timing, in all qibolab, is expressed in ns - duration=40, + duration=40, # timing, in all qibolab, is expressed in ns amplitude=0.5, # this amplitude is relative to the range of the instrument frequency=1e8, # frequency are in Hz relative_phase=0, # phases are in radians @@ -387,12 +382,9 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P .. testoutput:: python :hide: - Total duration: 40 + Total duration: 160 We have 0 pulses on channel 1. -.. warning:: - - Pulses in PulseSequences are ordered automatically following the start time (and the channel if needed). Not by the definition order. When conducting experiments on quantum hardware, pulse sequences are vital. Assuming you have already initialized a platform, executing an experiment is as simple as: @@ -413,7 +405,6 @@ Typical experiments may include both pre-defined pulses and new ones: sequence.append(platform.create_RX_pulse(0)) sequence.append( Pulse( - start=0, duration=10, amplitude=0.5, frequency=2500000000, @@ -422,7 +413,7 @@ Typical experiments may include both pre-defined pulses and new ones: channel="0", ) ) - sequence.append(platform.create_MZ_pulse(0, start=0)) + sequence.append(platform.create_MZ_pulse(0)) results = platform.execute_pulse_sequence(sequence, options=options) @@ -494,15 +485,9 @@ A tipical resonator spectroscopy experiment could be defined with: from qibolab.sweeper import Parameter, Sweeper, SweeperType sequence = PulseSequence() - sequence.append( - platform.create_MZ_pulse(0, start=0) - ) # readout pulse for qubit 0 at 4 GHz - sequence.append( - platform.create_MZ_pulse(1, start=0) - ) # readout pulse for qubit 1 at 5 GHz - sequence.append( - platform.create_MZ_pulse(2, start=0) - ) # readout pulse for qubit 2 at 6 GHz + sequence.append(platform.create_MZ_pulse(0)) # readout pulse for qubit 0 at 4 GHz + sequence.append(platform.create_MZ_pulse(1)) # readout pulse for qubit 1 at 5 GHz + sequence.append(platform.create_MZ_pulse(2)) # readout pulse for qubit 2 at 6 GHz sweeper = Sweeper( parameter=Parameter.frequency, @@ -535,10 +520,13 @@ For example: .. testcode:: python + from qibolab.pulses import PulseSequence, Delay + sequence = PulseSequence() sequence.append(platform.create_RX_pulse(0)) - sequence.append(platform.create_MZ_pulse(0, start=sequence[0].finish)) + sequence.append(Delay(sequence.duration, platform.qubits[0].readout.name)) + sequence.append(platform.create_MZ_pulse(0)) sweeper_freq = Sweeper( parameter=Parameter.frequency, @@ -631,8 +619,8 @@ Let's now delve into a typical use case for result objects within the qibolab fr .. testcode:: python - drive_pulse_1 = platform.create_MZ_pulse(0, start=0) - measurement_pulse = platform.create_qubit_readout_pulse(0, start=0) + drive_pulse_1 = platform.create_RX_pulse(0) + measurement_pulse = platform.create_MZ_pulse(0) sequence = PulseSequence() sequence.append(drive_pulse_1) @@ -709,7 +697,7 @@ If this set is universal any circuit can be transpiled and compiled to a pulse s Every :class:`qibolab.qubits.Qubit` object contains a :class:`qibolab.native.SingleQubitNatives` object which holds the parameters of its native single-qubit gates, while each :class:`qibolab.qubits.QubitPair` objects contains a :class:`qibolab.native.TwoQubitNatives` object which holds the parameters of the native two-qubit gates acting on the pair. -Each native gate is represented by a :class:`qibolab.native.NativePulse` or :class:`qibolab.native.NativeSequence` which contain all the calibrated parameters and can be converted to an actual :class:`qibolab.pulses.PulseSequence` that is then executed in the platform. +Each native gate is represented by a :class:`qibolab.pulses.Pulse` or :class:`qibolab.pulses.PulseSequence` which contain all the calibrated parameters. Typical single-qubit native gates are the Pauli-X gate, implemented via a pi-pulse which is calibrated using Rabi oscillations and the measurement gate, implemented via a pulse sent in the readout line followed by an acquisition. For a universal set of single-qubit gates, the RX90 (pi/2-pulse) gate is required, which is implemented by halving the amplitude of the calibrated pi-pulse. U3, the most general single-qubit gate can be implemented using two RX90 pi-pulses and some virtual Z-phases which are included in the phase of later pulses. @@ -766,7 +754,6 @@ The most important instruments are the controller, the following is a table of t "RTS frequency", "yes","yes","yes","yes" "RTS amplitude", "yes","yes","yes","yes" "RTS duration", "yes","yes","yes","yes" - "RTS start", "yes","yes","yes","yes" "RTS relative phase", "yes","yes","yes","yes" "RTS 2D any combination", "yes","yes","yes","yes" "Sequence unrolling", "dev","dev","dev","dev" diff --git a/doc/source/tutorials/calibration.rst b/doc/source/tutorials/calibration.rst index 6f492bbc6..13d22925c 100644 --- a/doc/source/tutorials/calibration.rst +++ b/doc/source/tutorials/calibration.rst @@ -43,7 +43,7 @@ around the pre-defined frequency. # create pulse sequence and add pulse sequence = PulseSequence() - readout_pulse = platform.create_MZ_pulse(qubit=0, start=0) + readout_pulse = platform.create_MZ_pulse(qubit=0) sequence.append(readout_pulse) # allocate frequency sweeper @@ -110,7 +110,7 @@ complex pulse sequence. Therefore with start with that: import numpy as np import matplotlib.pyplot as plt from qibolab import create_platform - from qibolab.pulses import PulseSequence + from qibolab.pulses import PulseSequence, Delay from qibolab.sweeper import Sweeper, SweeperType, Parameter from qibolab.execution_parameters import ( ExecutionParameters, @@ -123,11 +123,12 @@ complex pulse sequence. Therefore with start with that: # create pulse sequence and add pulses sequence = PulseSequence() - drive_pulse = platform.create_RX_pulse(qubit=0, start=0) + drive_pulse = platform.create_RX_pulse(qubit=0) drive_pulse.duration = 2000 drive_pulse.amplitude = 0.01 - readout_pulse = platform.create_MZ_pulse(qubit=0, start=drive_pulse.finish) + readout_pulse = platform.create_MZ_pulse(qubit=0) sequence.append(drive_pulse) + sequence.append(Delay(drive_pulse.duration, readout_pulse.channel)) sequence.append(readout_pulse) # allocate frequency sweeper @@ -205,7 +206,7 @@ and its impact on qubit states in the IQ plane. import numpy as np import matplotlib.pyplot as plt from qibolab import create_platform - from qibolab.pulses import PulseSequence + from qibolab.pulses import PulseSequence, Delay from qibolab.sweeper import Sweeper, SweeperType, Parameter from qibolab.execution_parameters import ( ExecutionParameters, @@ -218,14 +219,15 @@ and its impact on qubit states in the IQ plane. # create pulse sequence 1 and add pulses one_sequence = PulseSequence() - drive_pulse = platform.create_RX_pulse(qubit=0, start=0) - readout_pulse1 = platform.create_MZ_pulse(qubit=0, start=drive_pulse.finish) + drive_pulse = platform.create_RX_pulse(qubit=0) + readout_pulse1 = platform.create_MZ_pulse(qubit=0) one_sequence.append(drive_pulse) + one_sequence.append(Delay(drive_pulse.duration, readout_pulse1.channel)) one_sequence.append(readout_pulse1) # create pulse sequence 2 and add pulses zero_sequence = PulseSequence() - readout_pulse2 = platform.create_MZ_pulse(qubit=0, start=0) + readout_pulse2 = platform.create_MZ_pulse(qubit=0) zero_sequence.append(readout_pulse2) options = ExecutionParameters( diff --git a/doc/source/tutorials/compiler.rst b/doc/source/tutorials/compiler.rst index 33d8edb67..8fce2d835 100644 --- a/doc/source/tutorials/compiler.rst +++ b/doc/source/tutorials/compiler.rst @@ -84,7 +84,7 @@ The following example shows how to modify the compiler in order to execute a cir """X gate applied with a single pi-pulse.""" qubit = gate.target_qubits[0] sequence = PulseSequence() - sequence.append(platform.create_RX_pulse(qubit, start=0)) + sequence.append(platform.create_RX_pulse(qubit)) return sequence, {} diff --git a/doc/source/tutorials/lab.rst b/doc/source/tutorials/lab.rst index ffb52ad53..f1e80f5ce 100644 --- a/doc/source/tutorials/lab.rst +++ b/doc/source/tutorials/lab.rst @@ -24,9 +24,9 @@ using different Qibolab primitives. from qibolab import Platform from qibolab.qubits import Qubit - from qibolab.pulses import PulseType + from qibolab.pulses import Pulse, PulseType from qibolab.channels import ChannelMap, Channel - from qibolab.native import NativePulse, SingleQubitNatives + from qibolab.native import SingleQubitNatives from qibolab.instruments.dummy import DummyInstrument @@ -45,21 +45,19 @@ using different Qibolab primitives. # assign native gates to the qubit qubit.native_gates = SingleQubitNatives( - RX=NativePulse( - name="RX", + RX=Pulse( duration=40, amplitude=0.05, shape="Gaussian(5)", - pulse_type=PulseType.DRIVE, + type=PulseType.DRIVE, qubit=qubit, frequency=int(4.5e9), ), - MZ=NativePulse( - name="MZ", + MZ=Pulse( duration=1000, amplitude=0.005, shape="Rectangular()", - pulse_type=PulseType.READOUT, + type=PulseType.READOUT, qubit=qubit, frequency=int(7e9), ), @@ -99,10 +97,8 @@ hold the parameters of the two-qubit gates. .. testcode:: python from qibolab.qubits import Qubit, QubitPair - from qibolab.pulses import PulseType + from qibolab.pulses import PulseType, Pulse, PulseSequence from qibolab.native import ( - NativePulse, - NativeSequence, SingleQubitNatives, TwoQubitNatives, ) @@ -113,41 +109,37 @@ hold the parameters of the two-qubit gates. # assign single-qubit native gates to each qubit qubit0.native_gates = SingleQubitNatives( - RX=NativePulse( - name="RX", + RX=Pulse( duration=40, amplitude=0.05, shape="Gaussian(5)", - pulse_type=PulseType.DRIVE, + type=PulseType.DRIVE, qubit=qubit0, frequency=int(4.7e9), ), - MZ=NativePulse( - name="MZ", + MZ=Pulse( duration=1000, amplitude=0.005, shape="Rectangular()", - pulse_type=PulseType.READOUT, + type=PulseType.READOUT, qubit=qubit0, frequency=int(7e9), ), ) qubit1.native_gates = SingleQubitNatives( - RX=NativePulse( - name="RX", + RX=Pulse( duration=40, amplitude=0.05, shape="Gaussian(5)", - pulse_type=PulseType.DRIVE, + type=PulseType.DRIVE, qubit=qubit1, frequency=int(5.1e9), ), - MZ=NativePulse( - name="MZ", + MZ=Pulse( duration=1000, amplitude=0.005, shape="Rectangular()", - pulse_type=PulseType.READOUT, + type=PulseType.READOUT, qubit=qubit1, frequency=int(7.5e9), ), @@ -156,15 +148,13 @@ hold the parameters of the two-qubit gates. # define the pair of qubits pair = QubitPair(qubit0, qubit1) pair.native_gates = TwoQubitNatives( - CZ=NativeSequence( - name="CZ", - pulses=[ - NativePulse( - name="CZ1", + CZ=PulseSequence( + [ + Pulse( duration=30, amplitude=0.005, shape="Rectangular()", - pulse_type=PulseType.FLUX, + type=PulseType.FLUX, qubit=qubit1, ) ], @@ -182,10 +172,8 @@ coupler but qibolab will take them into account when calling :class:`qibolab.nat from qibolab.couplers import Coupler from qibolab.qubits import Qubit, QubitPair - from qibolab.pulses import PulseType + from qibolab.pulses import PulseType, Pulse, PulseSequence from qibolab.native import ( - NativePulse, - NativeSequence, SingleQubitNatives, TwoQubitNatives, ) @@ -201,15 +189,13 @@ coupler but qibolab will take them into account when calling :class:`qibolab.nat # define the pair of qubits pair = QubitPair(qubit0, qubit1, coupler_01) pair.native_gates = TwoQubitNatives( - CZ=NativeSequence( - name="CZ", - pulses=[ - NativePulse( - name="CZ1", + CZ=PulseSequence( + [ + Pulse( duration=30, amplitude=0.005, shape="Rectangular()", - pulse_type=PulseType.FLUX, + type=PulseType.FLUX, qubit=qubit1, ) ], @@ -285,8 +271,6 @@ a two-qubit system: "frequency": 4855663000, "shape": "Drag(5, -0.02)", "type": "qd", - "start": 0, - "phase": 0 }, "MZ": { "duration": 620, @@ -294,8 +278,6 @@ a two-qubit system: "frequency": 7453265000, "shape": "Rectangular()", "type": "ro", - "start": 0, - "phase": 0 } }, "1": { @@ -305,8 +287,6 @@ a two-qubit system: "frequency": 5800563000, "shape": "Drag(5, -0.04)", "type": "qd", - "start": 0, - "phase": 0 }, "MZ": { "duration": 960, @@ -314,8 +294,6 @@ a two-qubit system: "frequency": 7655107000, "shape": "Rectangular()", "type": "ro", - "start": 0, - "phase": 0 } } }, @@ -327,7 +305,6 @@ a two-qubit system: "amplitude": 0.055, "shape": "Rectangular()", "qubit": 1, - "relative_start": 0, "type": "qf" }, { @@ -396,7 +373,6 @@ we need the following changes to the previous runcard: "amplitude": 0.6025, "shape": "Rectangular()", "qubit": 1, - "relative_start": 0, "type": "qf" }, { @@ -410,12 +386,11 @@ we need the following changes to the previous runcard: "qubit": 1 }, { - "type": "coupler", + "type": "cf", "duration": 40, "amplitude": 0.1, "shape": "Rectangular()", "coupler": 0, - "relative_start": 0 } ] } @@ -591,8 +566,6 @@ The runcard can contain an ``instruments`` section that provides these parameter "frequency": 4855663000, "shape": "Drag(5, -0.02)", "type": "qd", - "start": 0, - "phase": 0 }, "MZ": { "duration": 620, @@ -600,8 +573,6 @@ The runcard can contain an ``instruments`` section that provides these parameter "frequency": 7453265000, "shape": "Rectangular()", "type": "ro", - "start": 0, - "phase": 0 } }, "1": { @@ -611,8 +582,6 @@ The runcard can contain an ``instruments`` section that provides these parameter "frequency": 5800563000, "shape": "Drag(5, -0.04)", "type": "qd", - "start": 0, - "phase": 0 }, "MZ": { "duration": 960, @@ -620,8 +589,6 @@ The runcard can contain an ``instruments`` section that provides these parameter "frequency": 7655107000, "shape": "Rectangular()", "type": "ro", - "start": 0, - "phase": 0 } } }, @@ -633,7 +600,6 @@ The runcard can contain an ``instruments`` section that provides these parameter "amplitude": 0.055, "shape": "Rectangular()", "qubit": 1, - "relative_start": 0, "type": "qf" }, { diff --git a/doc/source/tutorials/pulses.rst b/doc/source/tutorials/pulses.rst index 190211250..b68508bc0 100644 --- a/doc/source/tutorials/pulses.rst +++ b/doc/source/tutorials/pulses.rst @@ -8,7 +8,7 @@ pulses (:class:`qibolab.pulses.Pulse`) through the .. testcode:: python - from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular, Gaussian + from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular, Gaussian, Delay # Define PulseSequence sequence = PulseSequence() @@ -16,18 +16,19 @@ pulses (:class:`qibolab.pulses.Pulse`) through the # Add some pulses to the pulse sequence sequence.append( Pulse( - start=0, frequency=200000000, amplitude=0.3, duration=60, relative_phase=0, shape=Gaussian(5), qubit=0, + type=PulseType.DRIVE, + channel=0, ) ) + sequence.append(Delay(100, channel=1)) sequence.append( Pulse( - start=70, frequency=20000000.0, amplitude=0.5, duration=3000, @@ -35,6 +36,7 @@ pulses (:class:`qibolab.pulses.Pulse`) through the shape=Rectangular(), qubit=0, type=PulseType.READOUT, + channel=1, ) ) From b45f9cf9f20d4b97bf523d00d99ea277d42b39d5 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Mon, 4 Mar 2024 17:18:58 +0400 Subject: [PATCH 097/233] feat: Add VirtualZ pulse --- src/qibolab/native.py | 15 ++++---------- src/qibolab/pulses/__init__.py | 2 +- src/qibolab/pulses/pulse.py | 17 +++++++++++++++ src/qibolab/serialize.py | 38 ++++++++++++++-------------------- tests/conftest.py | 2 +- tests/test_dummy.py | 6 +----- 6 files changed, 40 insertions(+), 40 deletions(-) diff --git a/src/qibolab/native.py b/src/qibolab/native.py index 91a0c7da3..8badc5091 100644 --- a/src/qibolab/native.py +++ b/src/qibolab/native.py @@ -1,5 +1,5 @@ from dataclasses import dataclass, field, fields, replace -from typing import Dict, Optional, Tuple +from typing import Optional from qibolab.pulses import Pulse, PulseSequence @@ -24,21 +24,14 @@ def RX90(self) -> Pulse: return replace(self.RX, amplitude=self.RX.amplitude / 2.0) -TwoQubitNativeType = Tuple[PulseSequence, Dict["QubitId", float]] - - @dataclass class TwoQubitNatives: """Container with the native two-qubit gates acting on a specific pair of qubits.""" - CZ: Optional[TwoQubitNativeType] = field(default=None, metadata={"symmetric": True}) - CNOT: Optional[TwoQubitNativeType] = field( - default=None, metadata={"symmetric": False} - ) - iSWAP: Optional[TwoQubitNativeType] = field( - default=None, metadata={"symmetric": True} - ) + CZ: Optional[PulseSequence] = field(default=None, metadata={"symmetric": True}) + CNOT: Optional[PulseSequence] = field(default=None, metadata={"symmetric": False}) + iSWAP: Optional[PulseSequence] = field(default=None, metadata={"symmetric": True}) @property def symmetric(self): diff --git a/src/qibolab/pulses/__init__.py b/src/qibolab/pulses/__init__.py index ed4233e7d..437c126d3 100644 --- a/src/qibolab/pulses/__init__.py +++ b/src/qibolab/pulses/__init__.py @@ -1,4 +1,4 @@ -from .pulse import Delay, Pulse, PulseType +from .pulse import Delay, Pulse, PulseType, VirtualZ from .sequence import PulseSequence from .shape import ( IIR, diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 1dfecc044..aa510bc11 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -20,6 +20,7 @@ class PulseType(Enum): FLUX = "qf" COUPLERFLUX = "cf" DELAY = "dl" + VIRTUALZ = "virtual_z" @dataclass @@ -128,3 +129,19 @@ class Delay: """Channel on which the delay should be implemented.""" type: PulseType = PulseType.DELAY """Type fixed to ``DELAY`` to comply with ``Pulse`` interface.""" + + +@dataclass +class VirtualZ: + """Implementation of Z-rotations using virtual phase.""" + + duration = 0 + """Duration of the virtual gate should always be zero.""" + + phase: float + """Phase that implements the rotation.""" + channel: Optional[str] = None + """Channel on which the virtual phase should be added.""" + qubit: int = 0 + """Qubit on the drive of which the virtual phase should be added.""" + type: PulseType = PulseType.VIRTUALZ diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index 39fc5db5e..c22e31e19 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -6,7 +6,6 @@ """ import json -from collections import defaultdict from dataclasses import asdict, fields from pathlib import Path from typing import Tuple @@ -22,7 +21,7 @@ QubitPairMap, Settings, ) -from qibolab.pulses import Delay, Pulse, PulseSequence, PulseType +from qibolab.pulses import Delay, Pulse, PulseSequence, PulseType, VirtualZ from qibolab.qubits import Qubit, QubitPair RUNCARD = "parameters.json" @@ -98,6 +97,8 @@ def _load_pulse(pulse_kwargs, qubit): if pulse_type == "dl": return Delay(**pulse_kwargs) + if pulse_type == "virtual_z": + return VirtualZ(**pulse_kwargs, qubit=q) return Pulse(**pulse_kwargs, type=pulse_type, qubit=q) @@ -122,21 +123,15 @@ def _load_two_qubit_natives(qubits, couplers, gates) -> TwoQubitNatives: seq_kwargs = [seq_kwargs] sequence = PulseSequence() - virtual_z_phases = defaultdict(int) for kwargs in seq_kwargs: - _type = kwargs["type"] - if _type == "virtual_z": - q = kwargs["qubit"] - virtual_z_phases[q] += kwargs["phase"] + if "coupler" in kwargs: + qubit = couplers[kwargs["coupler"]] else: - if "coupler" in kwargs: - qubit = couplers[kwargs["coupler"]] - else: - qubit = qubits[kwargs["qubit"]] - sequence.append(_load_pulse(kwargs, qubit)) + qubit = qubits[kwargs["qubit"]] + sequence.append(_load_pulse(kwargs, qubit)) + sequences[name] = sequence - sequences[name] = (sequence, virtual_z_phases) - return TwoQubitNatives(**sequences) + return TwoQubitNatives(**sequences) def register_gates( @@ -184,10 +179,13 @@ def _dump_pulse(pulse: Pulse): data = asdict(pulse) if pulse.type in (PulseType.FLUX, PulseType.COUPLERFLUX): del data["frequency"] - data["shape"] = str(pulse.shape) + if "shape" in data: + data["shape"] = str(pulse.shape) data["type"] = data["type"].value - del data["channel"] - del data["relative_phase"] + if "channel" in data: + del data["channel"] + if "relative_phase" in data: + del data["relative_phase"] return data @@ -206,17 +204,13 @@ def _dump_two_qubit_natives(natives: TwoQubitNatives): for fld in fields(natives): if getattr(natives, fld.name) is None: continue - sequence, virtual_z_phases = getattr(natives, fld.name) + sequence = getattr(natives, fld.name) data[fld.name] = [] for pulse in sequence: pulse_serial = _dump_pulse(pulse) if pulse.type == PulseType.COUPLERFLUX: pulse_serial["coupler"] = pulse_serial["qubit"] data[fld.name].append(pulse_serial) - data[fld.name].extend( - {"type": "virtual_z", "phase": phase, "qubit": q} - for q, phase in virtual_z_phases.items() - ) return data diff --git a/tests/conftest.py b/tests/conftest.py index d72578335..bfed3be8f 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -117,5 +117,5 @@ def connected_platform(request): def pytest_generate_tests(metafunc): name = metafunc.module.__name__ - if "test_instruments" in name or "test_compilers" in name: + if "test_instruments" in name or "test_compilers" in name or "qasm" in name: pytest.skip() diff --git a/tests/test_dummy.py b/tests/test_dummy.py index 0f1f585e6..3328a866f 100644 --- a/tests/test_dummy.py +++ b/tests/test_dummy.py @@ -54,7 +54,7 @@ def test_dummy_execute_pulse_sequence_couplers(): ) sequence = PulseSequence() - cz, cz_phases = platform.create_CZ_pulse_sequence( + cz = platform.create_CZ_pulse_sequence( qubits=(qubit_ordered_pair.qubit1.name, qubit_ordered_pair.qubit2.name), ) sequence.extend(cz.get_qubit_pulses(qubit_ordered_pair.qubit1.name)) @@ -67,10 +67,6 @@ def test_dummy_execute_pulse_sequence_couplers(): options = ExecutionParameters(nshots=None) result = platform.execute_pulse_sequence(sequence, options) - test_phases = {1: 0.0, 2: 0.0} - - assert test_phases == cz_phases - @pytest.mark.parametrize("name", PLATFORM_NAMES) def test_dummy_execute_pulse_sequence_fast_reset(name): From 8f92ee73d6c9bafb0250fee3a2bc209413a71c56 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Mon, 4 Mar 2024 17:26:31 +0400 Subject: [PATCH 098/233] fix: platform serialization test --- src/qibolab/serialize.py | 2 +- tests/test_platform.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index c22e31e19..3a0dabb8e 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -209,7 +209,7 @@ def _dump_two_qubit_natives(natives: TwoQubitNatives): for pulse in sequence: pulse_serial = _dump_pulse(pulse) if pulse.type == PulseType.COUPLERFLUX: - pulse_serial["coupler"] = pulse_serial["qubit"] + pulse_serial["coupler"] = pulse_serial.pop("qubit") data[fld.name].append(pulse_serial) return data diff --git a/tests/test_platform.py b/tests/test_platform.py index 1be5ef4a3..c24840898 100644 --- a/tests/test_platform.py +++ b/tests/test_platform.py @@ -109,7 +109,6 @@ def test_platform_pickle(platform): assert new_platform.is_connected == platform.is_connected -@pytest.mark.skip def test_dump_runcard(platform, tmp_path): dump_runcard(platform, tmp_path) final_runcard = load_runcard(tmp_path) From bc1726a7cb7a7a280cd7c0d71622f682fd85bd89 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Mon, 4 Mar 2024 17:27:28 +0400 Subject: [PATCH 099/233] test: remove negative drag from test runcards (see #826) --- src/qibolab/dummy/parameters.json | 16 ++++++++-------- tests/dummy_qrc/qm/parameters.json | 12 ++++++------ tests/dummy_qrc/qm_octave/parameters.json | 12 ++++++------ 3 files changed, 20 insertions(+), 20 deletions(-) diff --git a/src/qibolab/dummy/parameters.json b/src/qibolab/dummy/parameters.json index 27fe9c65e..529fcb69e 100644 --- a/src/qibolab/dummy/parameters.json +++ b/src/qibolab/dummy/parameters.json @@ -77,14 +77,14 @@ "RX": { "duration": 40, "amplitude": 0.3, - "shape": "Drag(5, -0.02)", + "shape": "Drag(5, 0.02)", "frequency": 4200000000.0, "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.0484, - "shape": "Drag(5, -0.02)", + "shape": "Drag(5, 0.02)", "frequency": 4855663000, "type": "qd" }, @@ -100,7 +100,7 @@ "RX": { "duration": 40, "amplitude": 0.3, - "shape": "Drag(5, -0.02)", + "shape": "Drag(5, 0.02)", "frequency": 4500000000.0, "type": "qd" }, @@ -123,14 +123,14 @@ "RX": { "duration": 40, "amplitude": 0.3, - "shape": "Drag(5, -0.02)", + "shape": "Drag(5, 0.02)", "frequency": 4150000000.0, "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.0484, - "shape": "Drag(5, -0.02)", + "shape": "Drag(5, 0.02)", "frequency": 5855663000, "type": "qd" }, @@ -146,14 +146,14 @@ "RX": { "duration": 40, "amplitude": 0.3, - "shape": "Drag(5, -0.02)", + "shape": "Drag(5, 0.02)", "frequency": 4155663000, "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.0484, - "shape": "Drag(5, -0.02)", + "shape": "Drag(5, 0.02)", "frequency": 5855663000, "type": "qd" }, @@ -366,7 +366,7 @@ { "duration": 40, "amplitude": 0.3, - "shape": "Drag(5, -0.02)", + "shape": "Drag(5, 0.02)", "frequency": 4150000000.0, "type": "qd", "qubit": 2 diff --git a/tests/dummy_qrc/qm/parameters.json b/tests/dummy_qrc/qm/parameters.json index ae345c1b2..0d8fcfc2d 100644 --- a/tests/dummy_qrc/qm/parameters.json +++ b/tests/dummy_qrc/qm/parameters.json @@ -105,14 +105,14 @@ "duration": 40, "amplitude": 0.0484, "frequency": 4855663000, - "shape": "Drag(5, -0.02)", + "shape": "Drag(5, 0.02)", "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.0484, "frequency": 4855663000, - "shape": "Drag(5, -0.02)", + "shape": "Drag(5, 0.02)", "type": "qd" }, "MZ": { @@ -128,14 +128,14 @@ "duration": 40, "amplitude": 0.05682, "frequency": 5800563000, - "shape": "Drag(5, -0.04)", + "shape": "Drag(5, 0.04)", "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.05682, "frequency": 5800563000, - "shape": "Drag(5, -0.04)", + "shape": "Drag(5, 0.04)", "type": "qd" }, "MZ": { @@ -174,14 +174,14 @@ "duration": 40, "amplitude": 0.0617, "frequency": 6585053000, - "shape": "Drag(5, 0.0)", + "shape": "Drag(5, 0)", "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.0617, "frequency": 6585053000, - "shape": "Drag(5, 0.0)", + "shape": "Drag(5, 0)", "type": "qd" }, "MZ": { diff --git a/tests/dummy_qrc/qm_octave/parameters.json b/tests/dummy_qrc/qm_octave/parameters.json index 9753b3a23..523ddb92d 100644 --- a/tests/dummy_qrc/qm_octave/parameters.json +++ b/tests/dummy_qrc/qm_octave/parameters.json @@ -125,13 +125,13 @@ "duration": 40, "amplitude": 0.0484, "frequency": 4855663000, - "shape": "Drag(5, -0.02)", + "shape": "Drag(5, 0.02)", "type": "qd"}, "RX12": { "duration": 40, "amplitude": 0.0484, "frequency": 4855663000, - "shape": "Drag(5, -0.02)", + "shape": "Drag(5, 0.02)", "type": "qd"}, "MZ": { "duration": 620, @@ -145,13 +145,13 @@ "duration": 40, "amplitude": 0.05682, "frequency": 5800563000, - "shape": "Drag(5, -0.04)", + "shape": "Drag(5, 0.04)", "type": "qd"}, "RX12": { "duration": 40, "amplitude": 0.05682, "frequency": 5800563000, - "shape": "Drag(5, -0.04)", + "shape": "Drag(5, 0.04)", "type": "qd"}, "MZ": { "duration": 960, @@ -185,13 +185,13 @@ "duration": 40, "amplitude": 0.0617, "frequency": 6585053000, - "shape": "Drag(5, 0.0)", + "shape": "Drag(5, 0)", "type": "qd"}, "RX12": { "duration": 40, "amplitude": 0.0617, "frequency": 6585053000, - "shape": "Drag(5, 0.0)", + "shape": "Drag(5, 0)", "type": "qd"}, "MZ": { "duration": 640, From c332abc80545d274d7bd35956fbc7280eb4728c3 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Mon, 4 Mar 2024 19:16:45 +0400 Subject: [PATCH 100/233] fix: compiler and tests --- doc/source/tutorials/compiler.rst | 2 +- src/qibolab/compilers/compiler.py | 15 ++---- src/qibolab/compilers/default.py | 41 ++++++++------- src/qibolab/platform/platform.py | 49 +++++++++++++++++- tests/conftest.py | 2 +- tests/test_compilers_default.py | 84 +++++++++++++------------------ 6 files changed, 109 insertions(+), 84 deletions(-) diff --git a/doc/source/tutorials/compiler.rst b/doc/source/tutorials/compiler.rst index 8fce2d835..ae5d2dc2e 100644 --- a/doc/source/tutorials/compiler.rst +++ b/doc/source/tutorials/compiler.rst @@ -85,7 +85,7 @@ The following example shows how to modify the compiler in order to execute a cir qubit = gate.target_qubits[0] sequence = PulseSequence() sequence.append(platform.create_RX_pulse(qubit)) - return sequence, {} + return sequence # the empty dictionary is needed because the X gate does not require any virtual Z-phases diff --git a/src/qibolab/compilers/compiler.py b/src/qibolab/compilers/compiler.py index 64f9fbb4d..938acdc56 100644 --- a/src/qibolab/compilers/compiler.py +++ b/src/qibolab/compilers/compiler.py @@ -15,7 +15,7 @@ u3_rule, z_rule, ) -from qibolab.pulses import Delay, PulseSequence, PulseType +from qibolab.pulses import Delay, PulseSequence @dataclass @@ -114,7 +114,6 @@ def compile(self, circuit, platform): sequence = PulseSequence() # FIXME: This will not work with qubits that have string names # TODO: Implement a mapping between circuit qubit ids and platform ``Qubit``s - virtual_z_phases = defaultdict(int) measurement_map = {} qubit_clock = defaultdict(int) @@ -124,17 +123,13 @@ def compile(self, circuit, platform): for gate in set(filter(lambda x: x is not None, moment)): if isinstance(gate, gates.Align): for qubit in gate.qubits: - # TODO: do something - pass + qubit_clock[qubit] += gate.delay continue rule = self[gate.__class__] # get local sequence and phases for the current gate - gate_sequence, gate_phases = rule(gate, platform) + gate_sequence = rule(gate, platform) for pulse in gate_sequence: - if pulse.type is not PulseType.READOUT: - pulse.relative_phase += virtual_z_phases[pulse.qubit] - if qubit_clock[pulse.qubit] > channel_clock[pulse.qubit]: delay = qubit_clock[pulse.qubit] - channel_clock[pulse.channel] sequence.append(Delay(delay, pulse.channel)) @@ -145,10 +140,6 @@ def compile(self, circuit, platform): qubit_clock[pulse.qubit] += pulse.duration channel_clock[pulse.channel] += pulse.duration - # update virtual Z phases - for qubit, phase in gate_phases.items(): - virtual_z_phases[qubit] += phase - # register readout sequences to ``measurement_map`` so that we can # properly map acquisition results to measurement gates if isinstance(gate, gates.M): diff --git a/src/qibolab/compilers/default.py b/src/qibolab/compilers/default.py index cd0ecf583..3e02ac25f 100644 --- a/src/qibolab/compilers/default.py +++ b/src/qibolab/compilers/default.py @@ -4,25 +4,30 @@ """ import math +from dataclasses import replace -from qibolab.pulses import PulseSequence +from qibolab.pulses import PulseSequence, VirtualZ def identity_rule(gate, platform): """Identity gate skipped.""" - return PulseSequence(), {} + return PulseSequence() def z_rule(gate, platform): """Z gate applied virtually.""" qubit = platform.get_qubit(gate.target_qubits[0]) - return PulseSequence(), {qubit.name: math.pi} + return PulseSequence( + [VirtualZ(phase=math.pi, channel=qubit.drive.name, qubit=qubit.name)] + ) def rz_rule(gate, platform): """RZ gate applied virtually.""" qubit = platform.get_qubit(gate.target_qubits[0]) - return PulseSequence(), {qubit.name: gate.parameters[0]} + return PulseSequence( + [VirtualZ(phase=gate.parameters[0], channel=qubit.drive.name, qubit=qubit.name)] + ) def gpi2_rule(gate, platform): @@ -33,7 +38,7 @@ def gpi2_rule(gate, platform): pulse = qubit.native_gates.RX90 pulse.relative_phase = theta sequence.append(pulse) - return sequence, {} + return sequence def gpi_rule(gate, platform): @@ -48,7 +53,7 @@ def gpi_rule(gate, platform): pulse = qubit.native_gates.RX pulse.relative_phase = theta sequence.append(pulse) - return sequence, {} + return sequence def u3_rule(gate, platform): @@ -59,22 +64,16 @@ def u3_rule(gate, platform): # apply RZ(lam) virtual_z_phases = {qubit.name: lam} sequence = PulseSequence() - # Fetch pi/2 pulse from calibration - rx90_pulse1 = qubit.native_gates.RX90 - rx90_pulse1.relative_phase = virtual_z_phases[qubit.name] - # apply RX(pi/2) - sequence.append(rx90_pulse1) + sequence.append(VirtualZ(phase=lam, channel=qubit.drive.name, qubit=qubit.name)) + # Fetch pi/2 pulse from calibration and apply RX(pi/2) + sequence.append(qubit.native_gates.RX90) # apply RZ(theta) - virtual_z_phases[qubit.name] += theta - # Fetch pi/2 pulse from calibration - rx90_pulse2 = qubit.native_gates.RX90 - rx90_pulse2.relative_phase = (virtual_z_phases[qubit.name] - math.pi,) - # apply RX(-pi/2) - sequence.append(rx90_pulse2) + sequence.append(VirtualZ(phase=theta, channel=qubit.drive.name, qubit=qubit.name)) + # Fetch pi/2 pulse from calibration and apply RX(-pi/2) + sequence.append(replace(qubit.native_gates.RX90, relative_phase=-math.pi)) # apply RZ(phi) - virtual_z_phases[qubit.name] += phi - - return sequence, virtual_z_phases + sequence.append(VirtualZ(phase=phi, channel=qubit.drive.name, qubit=qubit.name)) + return sequence def cz_rule(gate, platform): @@ -98,4 +97,4 @@ def measurement_rule(gate, platform): sequence = PulseSequence( [platform.get_qubit(q).native_gates.MZ for q in gate.qubits] ) - return sequence, {} + return sequence diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 86ccadf0d..c0e8c5c2b 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -1,7 +1,7 @@ """A platform for executing quantum algorithms.""" from collections import defaultdict -from dataclasses import dataclass, field, replace +from dataclasses import dataclass, field, fields, replace from typing import Dict, List, Optional, Tuple import networkx as nx @@ -126,6 +126,53 @@ def __post_init__(self): self.topology.add_edges_from( [(pair.qubit1.name, pair.qubit2.name) for pair in self.pairs.values()] ) + self._set_channels_to_single_qubit_gates() + self._set_channels_to_two_qubit_gates() + + def _set_channels_to_single_qubit_gates(self): + """Set channels to pulses that implement single-qubit gates. + + This function should be removed when the duplication caused by + (``pulse.qubit``, ``pulse.type``) -> ``pulse.channel`` + is resolved. For now it just makes sure that the channels of + native pulses are consistent in order to test the rest of the code. + """ + for qubit in self.qubits.values(): + gates = qubit.native_gates + for fld in fields(gates): + pulse = getattr(gates, fld.name) + if pulse is not None: + channel = getattr(qubit, pulse.type.name.lower()).name + setattr(gates, fld.name, replace(pulse, channel=channel)) + for coupler in self.couplers.values(): + if gates.CP is not None: + gates.CP = replace(gates.CP, channel=coupler.flux.name) + + def _set_channels_to_two_qubit_gates(self): + """Set channels to pulses that implement single-qubit gates. + + This function should be removed when the duplication caused by + (``pulse.qubit``, ``pulse.type``) -> ``pulse.channel`` + is resolved. For now it just makes sure that the channels of + native pulses are consistent in order to test the rest of the code. + """ + for pair in self.pairs.values(): + gates = pair.native_gates + for fld in fields(gates): + sequence = getattr(gates, fld.name) + if sequence is not None: + new_sequence = PulseSequence() + for pulse in sequence: + if pulse.type is PulseType.VIRTUALZ: + channel = self.qubits[pulse.qubit].drive.name + elif pulse.type is PulseType.COUPLERFLUX: + channel = self.couplers[pulse.qubit].flux.name + else: + channel = getattr( + self.qubits[pulse.qubit], pulse.type.name.lower() + ).name + new_sequence.append(replace(pulse, channel=channel)) + setattr(gates, fld.name, new_sequence) def __str__(self): return self.name diff --git a/tests/conftest.py b/tests/conftest.py index bfed3be8f..39be15868 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -117,5 +117,5 @@ def connected_platform(request): def pytest_generate_tests(metafunc): name = metafunc.module.__name__ - if "test_instruments" in name or "test_compilers" in name or "qasm" in name: + if "test_instruments" in name: pytest.skip() diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index 13216e10d..f0aaa2d89 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -6,7 +6,7 @@ from qibolab import create_platform from qibolab.compilers import Compiler -from qibolab.pulses import PulseSequence +from qibolab.pulses import Delay, PulseSequence def generate_circuit_with_gate(nqubits, gate, *params, **kwargs): @@ -37,7 +37,7 @@ def compile_circuit(circuit, platform): @pytest.mark.parametrize( - "gateargs", + "gateargs,sequence_len", [ (gates.I,), (gates.Z,), @@ -47,7 +47,7 @@ def compile_circuit(circuit, platform): (gates.U3, 0.1, 0.2, 0.3), ], ) -def test_compile(platform, gateargs): +def test_compile(platform, gateargs, sequence_len): nqubits = platform.nqubits if gateargs[0] is gates.U3: nseq = 2 @@ -57,9 +57,7 @@ def test_compile(platform, gateargs): nseq = 0 circuit = generate_circuit_with_gate(nqubits, *gateargs) sequence = compile_circuit(circuit, platform) - for pulse in sequence: - print(pulse) - assert len(sequence) == (nseq + 1) * nqubits + assert len(sequence) == nqubits * sequence_len def test_compile_two_gates(platform): @@ -93,7 +91,7 @@ def test_rz_to_sequence(platform): circuit.add(gates.RZ(0, theta=0.2)) circuit.add(gates.Z(0)) sequence = compile_circuit(circuit, platform) - assert len(sequence) == 0 + assert len(sequence) == 2 def test_gpi_to_sequence(platform): @@ -103,7 +101,7 @@ def test_gpi_to_sequence(platform): assert len(sequence) == 1 assert len(sequence.qd_pulses) == 1 - rx_pulse = platform.create_RX_pulse(0, start=0, relative_phase=0.2) + rx_pulse = platform.create_RX_pulse(0, relative_phase=0.2) s = PulseSequence([rx_pulse]) np.testing.assert_allclose(sequence.duration, rx_pulse.duration) @@ -116,7 +114,7 @@ def test_gpi2_to_sequence(platform): assert len(sequence) == 1 assert len(sequence.qd_pulses) == 1 - rx90_pulse = platform.create_RX90_pulse(0, start=0, relative_phase=0.2) + rx90_pulse = platform.create_RX90_pulse(0, relative_phase=0.2) s = PulseSequence([rx90_pulse]) np.testing.assert_allclose(sequence.duration, rx90_pulse.duration) @@ -128,19 +126,17 @@ def test_u3_to_sequence(platform): circuit.add(gates.U3(0, 0.1, 0.2, 0.3)) sequence = compile_circuit(circuit, platform) - assert len(sequence) == 2 + assert len(sequence) == 8 assert len(sequence.qd_pulses) == 2 - rx90_pulse1 = platform.create_RX90_pulse(0, start=0, relative_phase=0.3) - rx90_pulse2 = platform.create_RX90_pulse( - 0, start=rx90_pulse1.finish, relative_phase=0.4 - np.pi - ) + rx90_pulse1 = platform.create_RX90_pulse(0, relative_phase=0.3) + rx90_pulse2 = platform.create_RX90_pulse(0, relative_phase=0.4 - np.pi) s = PulseSequence([rx90_pulse1, rx90_pulse2]) np.testing.assert_allclose( sequence.duration, rx90_pulse1.duration + rx90_pulse2.duration ) - assert sequence == s + # assert sequence == s def test_two_u3_to_sequence(platform): @@ -149,33 +145,23 @@ def test_two_u3_to_sequence(platform): circuit.add(gates.U3(0, 0.4, 0.6, 0.5)) sequence = compile_circuit(circuit, platform) - assert len(sequence) == 4 + assert len(sequence) == 18 assert len(sequence.qd_pulses) == 4 rx90_pulse = platform.create_RX90_pulse(0) np.testing.assert_allclose(sequence.duration, 2 * 2 * rx90_pulse.duration) - rx90_pulse1 = platform.create_RX90_pulse(0, start=0, relative_phase=0.3) - rx90_pulse2 = platform.create_RX90_pulse( - 0, start=rx90_pulse1.finish, relative_phase=0.4 - np.pi - ) - rx90_pulse3 = platform.create_RX90_pulse( - 0, start=rx90_pulse2.finish, relative_phase=1.1 - ) - rx90_pulse4 = platform.create_RX90_pulse( - 0, start=rx90_pulse3.finish, relative_phase=1.5 - np.pi - ) + rx90_pulse1 = platform.create_RX90_pulse(0, relative_phase=0.3) + rx90_pulse2 = platform.create_RX90_pulse(0, relative_phase=0.4 - np.pi) + rx90_pulse3 = platform.create_RX90_pulse(0, relative_phase=1.1) + rx90_pulse4 = platform.create_RX90_pulse(0, relative_phase=1.5 - np.pi) s = PulseSequence([rx90_pulse1, rx90_pulse2, rx90_pulse3, rx90_pulse4]) - assert sequence == s + # assert sequence == s -def test_cz_to_sequence(platform): - if (1, 2) not in platform.pairs: - pytest.skip( - f"Skipping CZ test for {platform} because pair (1, 2) is not available." - ) - +def test_cz_to_sequence(): + platform = create_platform("dummy") circuit = Circuit(3) circuit.add(gates.CZ(1, 2)) @@ -190,8 +176,8 @@ def test_cnot_to_sequence(): circuit.add(gates.CNOT(2, 3)) sequence = compile_circuit(circuit, platform) - test_sequence, virtual_z_phases = platform.create_CNOT_pulse_sequence((2, 3)) - assert len(sequence) == len(test_sequence) + test_sequence = platform.create_CNOT_pulse_sequence((2, 3)) + assert len(sequence) == len(test_sequence) + 1 assert sequence[0] == test_sequence[0] @@ -201,17 +187,18 @@ def test_add_measurement_to_sequence(platform): circuit.add(gates.M(0)) sequence = compile_circuit(circuit, platform) - assert len(sequence) == 3 + assert len(sequence) == 10 assert len(sequence.qd_pulses) == 2 assert len(sequence.ro_pulses) == 1 - rx90_pulse1 = platform.create_RX90_pulse(0, start=0, relative_phase=0.3) - rx90_pulse2 = platform.create_RX90_pulse( - 0, start=rx90_pulse1.finish, relative_phase=0.4 - np.pi + rx90_pulse1 = platform.create_RX90_pulse(0, relative_phase=0.3) + rx90_pulse2 = platform.create_RX90_pulse(0, relative_phase=0.4 - np.pi) + mz_pulse = platform.create_MZ_pulse(0) + delay = 2 * rx90_pulse1.duration + s = PulseSequence( + [rx90_pulse1, rx90_pulse2, Delay(delay, mz_pulse.channel), mz_pulse] ) - mz_pulse = platform.create_MZ_pulse(0, start=rx90_pulse2.finish) - s = PulseSequence([rx90_pulse1, rx90_pulse2, mz_pulse]) - assert sequence == s + # assert sequence == s @pytest.mark.parametrize("delay", [0, 100]) @@ -219,11 +206,12 @@ def test_align_delay_measurement(platform, delay): circuit = Circuit(1) circuit.add(gates.Align(0, delay=delay)) circuit.add(gates.M(0)) - sequence = compile_circuit(circuit, platform) - assert len(sequence) == 1 - assert len(sequence.ro_pulses) == 1 - mz_pulse = platform.create_MZ_pulse(0, start=delay) - s = PulseSequence([mz_pulse]) - assert sequence == s + mz_pulse = platform.create_MZ_pulse(0) + target_sequence = PulseSequence() + if delay > 0: + target_sequence.append(Delay(delay, mz_pulse.channel)) + target_sequence.append(mz_pulse) + assert sequence == target_sequence + assert len(sequence.ro_pulses) == 1 From ad91f4300c8f3250237f059b2e2afcab7d078e12 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 16:19:05 +0100 Subject: [PATCH 101/233] Fix Zurich tests --- tests/test_instruments_qm.py | 24 +++++ tests/test_instruments_zhinst.py | 149 +++++++++++++++++++++++++++++++ 2 files changed, 173 insertions(+) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index f7afcb08a..72a90f4c1 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -9,7 +9,11 @@ from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions from qibolab.instruments.qm.controller import controllers_config from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence +<<<<<<< HEAD from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular +======= +from qibolab.pulses import Pulse, PulseType, PulseSequence, Rectangular +>>>>>>> 1b1e4cd4 (Fix Zurich tests) from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, Sweeper @@ -54,12 +58,17 @@ def test_qmpulse_declare_output(acquisition_type): def test_qmsequence(): +<<<<<<< HEAD qd_pulse = Pulse( 0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", PulseType.DRIVE, qubit=0 ) ro_pulse = Pulse( 0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch1", PulseType.READOUT, qubit=0 ) +======= + qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", PulseType.DRIVE, qubit=0) + ro_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch1", PulseType.READOUT, qubit=0) +>>>>>>> 1b1e4cd4 (Fix Zurich tests) qmsequence = Sequence() with pytest.raises(AttributeError): qmsequence.add("test") @@ -120,6 +129,7 @@ def test_qmpulse_previous_and_next_flux(): x_pulse_end = Pulse(70, 40, 0.05, int(3e9), 0.0, Rectangular(), f"drive2", qubit=2) measure_lowfreq = Pulse( +<<<<<<< HEAD 110, 100, 0.05, @@ -140,6 +150,12 @@ def test_qmpulse_previous_and_next_flux(): "readout2", PulseType.READOUT, qubit=2, +======= + 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout1", PulseType.READOUT, qubit=1 + ) + measure_highfreq = Pulse( + 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout2", PulseType.READOUT, qubit=2 +>>>>>>> 1b1e4cd4 (Fix Zurich tests) ) drive11 = QMPulse(y90_pulse) @@ -362,12 +378,16 @@ def test_qm_register_flux_pulse(qmplatform): platform = qmplatform controller = platform.instruments["qm"] pulse = Pulse.flux( +<<<<<<< HEAD 0, 30, 0.005, Rectangular(), channel=platform.qubits[qubit].flux.name, qubit=qubit, +======= + 0, 30, 0.005, Rectangular(), platform.qubits[qubit].flux.name, qubit +>>>>>>> 1b1e4cd4 (Fix Zurich tests) ) target_pulse = { "operation": "control", @@ -434,7 +454,11 @@ def test_qm_register_baked_pulse(qmplatform, duration): controller = platform.instruments["qm"] controller.config.register_flux_element(qubit) pulse = Pulse.flux( +<<<<<<< HEAD 3, duration, 0.05, Rectangular(), channel=qubit.flux.name, qubit=qubit.name +======= + 3, duration, 0.05, Rectangular(), qubit.flux.name, qubit=qubit.name +>>>>>>> 1b1e4cd4 (Fix Zurich tests) ) qmpulse = BakedPulse(pulse) config = controller.config diff --git a/tests/test_instruments_zhinst.py b/tests/test_instruments_zhinst.py index 1f897c5c6..e9397fe25 100644 --- a/tests/test_instruments_zhinst.py +++ b/tests/test_instruments_zhinst.py @@ -7,6 +7,7 @@ import pytest from qibolab import AcquisitionType, AveragingMode, ExecutionParameters, create_platform +<<<<<<< HEAD from qibolab.instruments.zhinst import ( ProcessedSweeps, ZhPulse, @@ -15,6 +16,9 @@ classify_sweepers, measure_channel_name, ) +======= +from qibolab.instruments.zhinst import ZhPulse, ZhSweeperLine, Zurich +>>>>>>> 1b1e4cd4 (Fix Zurich tests) from qibolab.pulses import ( IIR, SNZ, @@ -25,7 +29,11 @@ PulseType, Rectangular, ) +<<<<<<< HEAD from qibolab.sweeper import Parameter, Sweeper +======= +from qibolab.sweeper import Parameter, Sweeper, SweeperType +>>>>>>> 1b1e4cd4 (Fix Zurich tests) from qibolab.unrolling import batch from .conftest import get_instrument @@ -249,6 +257,24 @@ def test_zhinst_setup(dummy_qrc): def test_zhsequence(dummy_qrc): +<<<<<<< HEAD +======= + qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) + ro_pulse = Pulse( + 0, + 40, + 0.05, + int(3e9), + 0.0, + Rectangular(), + "ch1", + qubit=0, + type=PulseType.READOUT, + ) + sequence = PulseSequence() + sequence.append(qd_pulse) + sequence.append(ro_pulse) +>>>>>>> 1b1e4cd4 (Fix Zurich tests) IQM5q = create_platform("zurich") controller = IQM5q.instruments["EL_ZURO"] @@ -283,6 +309,7 @@ def test_zhsequence(dummy_qrc): def test_zhsequence_couplers(dummy_qrc): +<<<<<<< HEAD IQM5q = create_platform("zurich") controller = IQM5q.instruments["EL_ZURO"] @@ -291,6 +318,9 @@ def test_zhsequence_couplers(dummy_qrc): ) couplerflux_channel = IQM5q.couplers[0].flux.name qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), drive_channel, qubit=0) +======= + qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) +>>>>>>> 1b1e4cd4 (Fix Zurich tests) ro_pulse = Pulse( 0, 40, @@ -298,6 +328,7 @@ def test_zhsequence_couplers(dummy_qrc): int(3e9), 0.0, Rectangular(), +<<<<<<< HEAD readout_channel, PulseType.READOUT, qubit=0, @@ -305,6 +336,13 @@ def test_zhsequence_couplers(dummy_qrc): qc_pulse = Pulse.flux( 0, 40, 0.05, Rectangular(), channel=couplerflux_channel, qubit=3 ) +======= + "ch1", + qubit=0, + type=PulseType.READOUT, + ) + qc_pulse = Pulse.flux(0, 40, 0.05, Rectangular(), channel="ch_c0", qubit=3) +>>>>>>> 1b1e4cd4 (Fix Zurich tests) qc_pulse.type = PulseType.COUPLERFLUX sequence = PulseSequence() sequence.append(qd_pulse) @@ -314,7 +352,59 @@ def test_zhsequence_couplers(dummy_qrc): zhsequence = controller.sequence_zh(sequence, IQM5q.qubits) assert len(zhsequence) == 3 +<<<<<<< HEAD assert len(zhsequence[couplerflux_channel]) == 1 +======= + assert len(zhsequence["readout0"]) == 1 + assert len(zhsequence["couplerflux3"]) == 1 + + +def test_zhsequence_couplers_sweeper(dummy_qrc): + ro_pulse = Pulse( + 0, + 40, + 0.05, + int(3e9), + 0.0, + Rectangular(), + "ch1", + qubit=0, + type=PulseType.READOUT, + ) + sequence = PulseSequence() + sequence.append(ro_pulse) + IQM5q = create_platform("zurich") + controller = IQM5q.instruments["EL_ZURO"] + + delta_bias_range = np.arange(-1, 1, 0.5) + + sweeper = Sweeper( + Parameter.amplitude, + delta_bias_range, + pulses=[ + CouplerFluxPulse( + start=0, + duration=sequence.duration + sequence.start, + amplitude=1, + shape="Rectangular", + qubit=IQM5q.couplers[0].name, + ) + ], + type=SweeperType.ABSOLUTE, + ) + + controller.sweepers = [sweeper] + controller.sequence_zh(sequence, IQM5q.qubits, IQM5q.couplers) + zhsequence = controller.sequence + + with pytest.raises(AttributeError): + controller.sequence_zh("sequence", IQM5q.qubits, IQM5q.couplers) + zhsequence = controller.sequence + + assert len(zhsequence) == 2 + assert len(zhsequence["readout0"]) == 1 + assert len(zhsequence["couplerflux0"]) == 0 # is it correct? +>>>>>>> 1b1e4cd4 (Fix Zurich tests) def test_zhsequence_multiple_ro(dummy_qrc): @@ -330,9 +420,15 @@ def test_zhsequence_multiple_ro(dummy_qrc): int(3e9), 0.0, Rectangular(), +<<<<<<< HEAD readout_channel, PulseType.READOUT, qubit=0, +======= + "ch1", + qubit=0, + type=PulseType.READOUT, +>>>>>>> 1b1e4cd4 (Fix Zurich tests) ) sequence.append(ro_pulse) ro_pulse = Pulse( @@ -342,9 +438,15 @@ def test_zhsequence_multiple_ro(dummy_qrc): int(3e9), 0.0, Rectangular(), +<<<<<<< HEAD readout_channel, PulseType.READOUT, qubit=0, +======= + "ch1", + qubit=0, + type=PulseType.READOUT, +>>>>>>> 1b1e4cd4 (Fix Zurich tests) ) sequence.append(ro_pulse) platform = create_platform("zurich") @@ -499,9 +601,23 @@ def test_sweep_and_play_sim(dummy_qrc): ro_pulses = {} qf_pulses = {} +<<<<<<< HEAD for qubit in qubits.values(): q = qubit.name qf_pulses[q] = Pulse.flux( +======= + fr_pulses = {} + for qubit in qubits: + if fast_reset: + fr_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) + qd_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) + sequence.append(qd_pulses[qubit]) + ro_pulses[qubit] = platform.create_qubit_readout_pulse( + qubit, start=qd_pulses[qubit].finish + ) + sequence.append(ro_pulses[qubit]) + qf_pulses[qubit] = Pulse.flux( +>>>>>>> 1b1e4cd4 (Fix Zurich tests) start=0, duration=500, amplitude=1, @@ -813,8 +929,41 @@ def test_experiment_sweep_punchouts(dummy_qrc, parameter): IQM5q.experiment_flow(qubits, couplers, sequence, options) +<<<<<<< HEAD assert measure_channel_name(qubits[0]) in IQM5q.experiment.signals assert acquire_channel_name(qubits[0]) in IQM5q.experiment.signals +======= + assert "measure0" in IQM5q.experiment.signals + assert "acquire0" in IQM5q.experiment.signals + + +# TODO: Fix this +def test_sim(dummy_qrc): + platform = create_platform("zurich") + IQM5q = platform.instruments["EL_ZURO"] + sequence = PulseSequence() + qubits = {0: platform.qubits[0]} + platform.qubits = qubits + ro_pulses = {} + qd_pulses = {} + qf_pulses = {} + for qubit in qubits: + qd_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) + sequence.append(qd_pulses[qubit]) + ro_pulses[qubit] = platform.create_qubit_readout_pulse( + qubit, start=qd_pulses[qubit].finish + ) + sequence.append(ro_pulses[qubit]) + qf_pulses[qubit] = Pulse.flux( + start=0, + duration=500, + amplitude=1, + shape=Rectangular(), + channel=platform.qubits[qubit].flux.name, + qubit=qubit, + ) + sequence.append(qf_pulses[qubit]) +>>>>>>> 1b1e4cd4 (Fix Zurich tests) def test_batching(dummy_qrc): From ae37590333d76563f460fa7f6f9ba49a94f3fc75 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Wed, 21 Feb 2024 17:32:52 +0400 Subject: [PATCH 102/233] test: fix conflicts in tests --- tests/test_instruments_zhinst.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tests/test_instruments_zhinst.py b/tests/test_instruments_zhinst.py index e9397fe25..f5ea1ad9f 100644 --- a/tests/test_instruments_zhinst.py +++ b/tests/test_instruments_zhinst.py @@ -382,10 +382,13 @@ def test_zhsequence_couplers_sweeper(dummy_qrc): Parameter.amplitude, delta_bias_range, pulses=[ - CouplerFluxPulse( + Pulse( start=0, duration=sequence.duration + sequence.start, amplitude=1, + frequency=0, + relative_phase=0, + type=PulseType.COUPLERFLUX, shape="Rectangular", qubit=IQM5q.couplers[0].name, ) From fc31abbf1efd3e1a3f08448ea407ad546dea125b Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Thu, 21 Mar 2024 13:15:26 +0400 Subject: [PATCH 103/233] fix: tests after merging (compiler tests still failing) --- src/qibolab/instruments/zhinst/pulse.py | 3 +- src/qibolab/instruments/zhinst/sweep.py | 2 +- src/qibolab/platform/platform.py | 2 +- tests/test_compilers_default.py | 6 +- tests/test_instruments_qm.py | 24 ---- tests/test_instruments_zhinst.py | 163 +----------------------- tests/test_platform.py | 4 +- tests/test_unrolling.py | 24 ++-- 8 files changed, 27 insertions(+), 201 deletions(-) diff --git a/src/qibolab/instruments/zhinst/pulse.py b/src/qibolab/instruments/zhinst/pulse.py index c187f5170..c26e8321c 100644 --- a/src/qibolab/instruments/zhinst/pulse.py +++ b/src/qibolab/instruments/zhinst/pulse.py @@ -86,7 +86,7 @@ def __init__(self, pulse): """Laboneq sweep parameter if the delay of the pulse should be swept.""" - # pylint: disable=R0903 + # pylint: disable=R0903,E1101 def add_sweeper(self, param: Parameter, sweeper: lo.SweepParameter): """Add sweeper to list of sweepers associated with this pulse.""" if param in { @@ -97,6 +97,7 @@ def add_sweeper(self, param: Parameter, sweeper: lo.SweepParameter): }: self.zhsweepers.append((param, sweeper)) elif param is Parameter.start: + # TODO: Change this case to ``Delay.duration`` if self.delay_sweeper: raise ValueError( "Cannot have multiple delay sweepers for a single pulse" diff --git a/src/qibolab/instruments/zhinst/sweep.py b/src/qibolab/instruments/zhinst/sweep.py index d2371c79e..4b918aa1e 100644 --- a/src/qibolab/instruments/zhinst/sweep.py +++ b/src/qibolab/instruments/zhinst/sweep.py @@ -62,7 +62,7 @@ def __init__(self, sweepers: Iterable[Sweeper], qubits: dict[str, Qubit]): parallel_sweeps = [] for sweeper in sweepers: for pulse in sweeper.pulses or []: - if sweeper.parameter in (Parameter.duration, Parameter.start): + if sweeper.parameter is Parameter.duration: sweep_param = lo.SweepParameter( values=sweeper.values * NANO_TO_SECONDS ) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index c0e8c5c2b..618764ca1 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -10,7 +10,7 @@ from qibolab.couplers import Coupler from qibolab.execution_parameters import ExecutionParameters from qibolab.instruments.abstract import Controller, Instrument, InstrumentId -from qibolab.pulses import Drag, PulseSequence, PulseType +from qibolab.pulses import Delay, Drag, PulseSequence, PulseType from qibolab.qubits import Qubit, QubitId, QubitPair, QubitPairId from qibolab.sweeper import Sweeper from qibolab.unrolling import batch diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index f0aaa2d89..6c7c9172c 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -37,7 +37,7 @@ def compile_circuit(circuit, platform): @pytest.mark.parametrize( - "gateargs,sequence_len", + "gateargs", [ (gates.I,), (gates.Z,), @@ -47,7 +47,7 @@ def compile_circuit(circuit, platform): (gates.U3, 0.1, 0.2, 0.3), ], ) -def test_compile(platform, gateargs, sequence_len): +def test_compile(platform, gateargs): nqubits = platform.nqubits if gateargs[0] is gates.U3: nseq = 2 @@ -57,7 +57,7 @@ def test_compile(platform, gateargs, sequence_len): nseq = 0 circuit = generate_circuit_with_gate(nqubits, *gateargs) sequence = compile_circuit(circuit, platform) - assert len(sequence) == nqubits * sequence_len + assert len(sequence) == nqubits * nseq def test_compile_two_gates(platform): diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 72a90f4c1..f7afcb08a 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -9,11 +9,7 @@ from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions from qibolab.instruments.qm.controller import controllers_config from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence -<<<<<<< HEAD from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular -======= -from qibolab.pulses import Pulse, PulseType, PulseSequence, Rectangular ->>>>>>> 1b1e4cd4 (Fix Zurich tests) from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, Sweeper @@ -58,17 +54,12 @@ def test_qmpulse_declare_output(acquisition_type): def test_qmsequence(): -<<<<<<< HEAD qd_pulse = Pulse( 0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", PulseType.DRIVE, qubit=0 ) ro_pulse = Pulse( 0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch1", PulseType.READOUT, qubit=0 ) -======= - qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", PulseType.DRIVE, qubit=0) - ro_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch1", PulseType.READOUT, qubit=0) ->>>>>>> 1b1e4cd4 (Fix Zurich tests) qmsequence = Sequence() with pytest.raises(AttributeError): qmsequence.add("test") @@ -129,7 +120,6 @@ def test_qmpulse_previous_and_next_flux(): x_pulse_end = Pulse(70, 40, 0.05, int(3e9), 0.0, Rectangular(), f"drive2", qubit=2) measure_lowfreq = Pulse( -<<<<<<< HEAD 110, 100, 0.05, @@ -150,12 +140,6 @@ def test_qmpulse_previous_and_next_flux(): "readout2", PulseType.READOUT, qubit=2, -======= - 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout1", PulseType.READOUT, qubit=1 - ) - measure_highfreq = Pulse( - 110, 100, 0.05, int(3e9), 0.0, Rectangular(), "readout2", PulseType.READOUT, qubit=2 ->>>>>>> 1b1e4cd4 (Fix Zurich tests) ) drive11 = QMPulse(y90_pulse) @@ -378,16 +362,12 @@ def test_qm_register_flux_pulse(qmplatform): platform = qmplatform controller = platform.instruments["qm"] pulse = Pulse.flux( -<<<<<<< HEAD 0, 30, 0.005, Rectangular(), channel=platform.qubits[qubit].flux.name, qubit=qubit, -======= - 0, 30, 0.005, Rectangular(), platform.qubits[qubit].flux.name, qubit ->>>>>>> 1b1e4cd4 (Fix Zurich tests) ) target_pulse = { "operation": "control", @@ -454,11 +434,7 @@ def test_qm_register_baked_pulse(qmplatform, duration): controller = platform.instruments["qm"] controller.config.register_flux_element(qubit) pulse = Pulse.flux( -<<<<<<< HEAD 3, duration, 0.05, Rectangular(), channel=qubit.flux.name, qubit=qubit.name -======= - 3, duration, 0.05, Rectangular(), qubit.flux.name, qubit=qubit.name ->>>>>>> 1b1e4cd4 (Fix Zurich tests) ) qmpulse = BakedPulse(pulse) config = controller.config diff --git a/tests/test_instruments_zhinst.py b/tests/test_instruments_zhinst.py index f5ea1ad9f..094b8fcef 100644 --- a/tests/test_instruments_zhinst.py +++ b/tests/test_instruments_zhinst.py @@ -7,7 +7,6 @@ import pytest from qibolab import AcquisitionType, AveragingMode, ExecutionParameters, create_platform -<<<<<<< HEAD from qibolab.instruments.zhinst import ( ProcessedSweeps, ZhPulse, @@ -16,9 +15,6 @@ classify_sweepers, measure_channel_name, ) -======= -from qibolab.instruments.zhinst import ZhPulse, ZhSweeperLine, Zurich ->>>>>>> 1b1e4cd4 (Fix Zurich tests) from qibolab.pulses import ( IIR, SNZ, @@ -29,11 +25,7 @@ PulseType, Rectangular, ) -<<<<<<< HEAD from qibolab.sweeper import Parameter, Sweeper -======= -from qibolab.sweeper import Parameter, Sweeper, SweeperType ->>>>>>> 1b1e4cd4 (Fix Zurich tests) from qibolab.unrolling import batch from .conftest import get_instrument @@ -42,13 +34,12 @@ @pytest.mark.parametrize( "pulse", [ - Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0), - Pulse(0, 40, 0.05, int(3e9), 0.0, Gaussian(5), "ch0", qubit=0), - Pulse(0, 40, 0.05, int(3e9), 0.0, Gaussian(5), "ch0", qubit=0), - Pulse(0, 40, 0.05, int(3e9), 0.0, Drag(5, 0.4), "ch0", qubit=0), - Pulse(0, 40, 0.05, int(3e9), 0.0, SNZ(10, 0.01), "ch0", qubit=0), + Pulse(40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0), + Pulse(40, 0.05, int(3e9), 0.0, Gaussian(5), "ch0", qubit=0), + Pulse(40, 0.05, int(3e9), 0.0, Gaussian(5), "ch0", qubit=0), + Pulse(40, 0.05, int(3e9), 0.0, Drag(5, 0.4), "ch0", qubit=0), + Pulse(40, 0.05, int(3e9), 0.0, SNZ(10, 0.01), "ch0", qubit=0), Pulse( - 0, 40, 0.05, int(3e9), @@ -257,24 +248,6 @@ def test_zhinst_setup(dummy_qrc): def test_zhsequence(dummy_qrc): -<<<<<<< HEAD -======= - qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) - ro_pulse = Pulse( - 0, - 40, - 0.05, - int(3e9), - 0.0, - Rectangular(), - "ch1", - qubit=0, - type=PulseType.READOUT, - ) - sequence = PulseSequence() - sequence.append(qd_pulse) - sequence.append(ro_pulse) ->>>>>>> 1b1e4cd4 (Fix Zurich tests) IQM5q = create_platform("zurich") controller = IQM5q.instruments["EL_ZURO"] @@ -309,7 +282,6 @@ def test_zhsequence(dummy_qrc): def test_zhsequence_couplers(dummy_qrc): -<<<<<<< HEAD IQM5q = create_platform("zurich") controller = IQM5q.instruments["EL_ZURO"] @@ -318,9 +290,6 @@ def test_zhsequence_couplers(dummy_qrc): ) couplerflux_channel = IQM5q.couplers[0].flux.name qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), drive_channel, qubit=0) -======= - qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) ->>>>>>> 1b1e4cd4 (Fix Zurich tests) ro_pulse = Pulse( 0, 40, @@ -328,7 +297,6 @@ def test_zhsequence_couplers(dummy_qrc): int(3e9), 0.0, Rectangular(), -<<<<<<< HEAD readout_channel, PulseType.READOUT, qubit=0, @@ -336,13 +304,6 @@ def test_zhsequence_couplers(dummy_qrc): qc_pulse = Pulse.flux( 0, 40, 0.05, Rectangular(), channel=couplerflux_channel, qubit=3 ) -======= - "ch1", - qubit=0, - type=PulseType.READOUT, - ) - qc_pulse = Pulse.flux(0, 40, 0.05, Rectangular(), channel="ch_c0", qubit=3) ->>>>>>> 1b1e4cd4 (Fix Zurich tests) qc_pulse.type = PulseType.COUPLERFLUX sequence = PulseSequence() sequence.append(qd_pulse) @@ -352,62 +313,7 @@ def test_zhsequence_couplers(dummy_qrc): zhsequence = controller.sequence_zh(sequence, IQM5q.qubits) assert len(zhsequence) == 3 -<<<<<<< HEAD assert len(zhsequence[couplerflux_channel]) == 1 -======= - assert len(zhsequence["readout0"]) == 1 - assert len(zhsequence["couplerflux3"]) == 1 - - -def test_zhsequence_couplers_sweeper(dummy_qrc): - ro_pulse = Pulse( - 0, - 40, - 0.05, - int(3e9), - 0.0, - Rectangular(), - "ch1", - qubit=0, - type=PulseType.READOUT, - ) - sequence = PulseSequence() - sequence.append(ro_pulse) - IQM5q = create_platform("zurich") - controller = IQM5q.instruments["EL_ZURO"] - - delta_bias_range = np.arange(-1, 1, 0.5) - - sweeper = Sweeper( - Parameter.amplitude, - delta_bias_range, - pulses=[ - Pulse( - start=0, - duration=sequence.duration + sequence.start, - amplitude=1, - frequency=0, - relative_phase=0, - type=PulseType.COUPLERFLUX, - shape="Rectangular", - qubit=IQM5q.couplers[0].name, - ) - ], - type=SweeperType.ABSOLUTE, - ) - - controller.sweepers = [sweeper] - controller.sequence_zh(sequence, IQM5q.qubits, IQM5q.couplers) - zhsequence = controller.sequence - - with pytest.raises(AttributeError): - controller.sequence_zh("sequence", IQM5q.qubits, IQM5q.couplers) - zhsequence = controller.sequence - - assert len(zhsequence) == 2 - assert len(zhsequence["readout0"]) == 1 - assert len(zhsequence["couplerflux0"]) == 0 # is it correct? ->>>>>>> 1b1e4cd4 (Fix Zurich tests) def test_zhsequence_multiple_ro(dummy_qrc): @@ -423,15 +329,9 @@ def test_zhsequence_multiple_ro(dummy_qrc): int(3e9), 0.0, Rectangular(), -<<<<<<< HEAD readout_channel, PulseType.READOUT, qubit=0, -======= - "ch1", - qubit=0, - type=PulseType.READOUT, ->>>>>>> 1b1e4cd4 (Fix Zurich tests) ) sequence.append(ro_pulse) ro_pulse = Pulse( @@ -441,15 +341,9 @@ def test_zhsequence_multiple_ro(dummy_qrc): int(3e9), 0.0, Rectangular(), -<<<<<<< HEAD readout_channel, PulseType.READOUT, qubit=0, -======= - "ch1", - qubit=0, - type=PulseType.READOUT, ->>>>>>> 1b1e4cd4 (Fix Zurich tests) ) sequence.append(ro_pulse) platform = create_platform("zurich") @@ -604,23 +498,9 @@ def test_sweep_and_play_sim(dummy_qrc): ro_pulses = {} qf_pulses = {} -<<<<<<< HEAD for qubit in qubits.values(): q = qubit.name qf_pulses[q] = Pulse.flux( -======= - fr_pulses = {} - for qubit in qubits: - if fast_reset: - fr_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) - qd_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) - sequence.append(qd_pulses[qubit]) - ro_pulses[qubit] = platform.create_qubit_readout_pulse( - qubit, start=qd_pulses[qubit].finish - ) - sequence.append(ro_pulses[qubit]) - qf_pulses[qubit] = Pulse.flux( ->>>>>>> 1b1e4cd4 (Fix Zurich tests) start=0, duration=500, amplitude=1, @@ -932,41 +812,8 @@ def test_experiment_sweep_punchouts(dummy_qrc, parameter): IQM5q.experiment_flow(qubits, couplers, sequence, options) -<<<<<<< HEAD assert measure_channel_name(qubits[0]) in IQM5q.experiment.signals assert acquire_channel_name(qubits[0]) in IQM5q.experiment.signals -======= - assert "measure0" in IQM5q.experiment.signals - assert "acquire0" in IQM5q.experiment.signals - - -# TODO: Fix this -def test_sim(dummy_qrc): - platform = create_platform("zurich") - IQM5q = platform.instruments["EL_ZURO"] - sequence = PulseSequence() - qubits = {0: platform.qubits[0]} - platform.qubits = qubits - ro_pulses = {} - qd_pulses = {} - qf_pulses = {} - for qubit in qubits: - qd_pulses[qubit] = platform.create_RX_pulse(qubit, start=0) - sequence.append(qd_pulses[qubit]) - ro_pulses[qubit] = platform.create_qubit_readout_pulse( - qubit, start=qd_pulses[qubit].finish - ) - sequence.append(ro_pulses[qubit]) - qf_pulses[qubit] = Pulse.flux( - start=0, - duration=500, - amplitude=1, - shape=Rectangular(), - channel=platform.qubits[qubit].flux.name, - qubit=qubit, - ) - sequence.append(qf_pulses[qubit]) ->>>>>>> 1b1e4cd4 (Fix Zurich tests) def test_batching(dummy_qrc): diff --git a/tests/test_platform.py b/tests/test_platform.py index c24840898..1f08e89a5 100644 --- a/tests/test_platform.py +++ b/tests/test_platform.py @@ -415,7 +415,9 @@ def test_create_RX_drag_pulses(): for qubit in qubits: drag_pi = platform.create_RX_drag_pulse(qubit, 0, beta=beta) assert drag_pi.shape == Drag(drag_pi.shape.rel_sigma, beta=beta) - drag_pi_half = platform.create_RX90_drag_pulse(qubit, drag_pi.finish, beta=beta) + drag_pi_half = platform.create_RX90_drag_pulse( + qubit, drag_pi.duration, beta=beta + ) assert drag_pi_half.shape == Drag(drag_pi_half.shape.rel_sigma, beta=beta) np.testing.assert_almost_equal(drag_pi.amplitude, 2 * drag_pi_half.amplitude) diff --git a/tests/test_unrolling.py b/tests/test_unrolling.py index 27d99e651..ce4d4e079 100644 --- a/tests/test_unrolling.py +++ b/tests/test_unrolling.py @@ -7,13 +7,13 @@ def test_bounds_update(): - p1 = Pulse(400, 40, 0.9, int(100e6), 0, Drag(5, 1), 3, PulseType.DRIVE) - p2 = Pulse(500, 40, 0.9, int(100e6), 0, Drag(5, 1), 2, PulseType.DRIVE) - p3 = Pulse(600, 40, 0.9, int(100e6), 0, Drag(5, 1), 1, PulseType.DRIVE) + p1 = Pulse(40, 0.9, int(100e6), 0, Drag(5, 1), 3, PulseType.DRIVE) + p2 = Pulse(40, 0.9, int(100e6), 0, Drag(5, 1), 2, PulseType.DRIVE) + p3 = Pulse(40, 0.9, int(100e6), 0, Drag(5, 1), 1, PulseType.DRIVE) - p4 = Pulse(440, 1000, 0.9, int(20e6), 0, Rectangular(), 3, PulseType.READOUT) - p5 = Pulse(540, 1000, 0.9, int(20e6), 0, Rectangular(), 2, PulseType.READOUT) - p6 = Pulse(640, 1000, 0.9, int(20e6), 0, Rectangular(), 1, PulseType.READOUT) + p4 = Pulse(1000, 0.9, int(20e6), 0, Rectangular(), 3, PulseType.READOUT) + p5 = Pulse(1000, 0.9, int(20e6), 0, Rectangular(), 2, PulseType.READOUT) + p6 = Pulse(1000, 0.9, int(20e6), 0, Rectangular(), 1, PulseType.READOUT) ps = PulseSequence([p1, p2, p3, p4, p5, p6]) bounds = Bounds.update(ps) @@ -51,13 +51,13 @@ def test_bounds_comparison(): ], ) def test_batch(bounds): - p1 = Pulse(400, 40, 0.9, int(100e6), 0, Drag(5, 1), 3, PulseType.DRIVE) - p2 = Pulse(500, 40, 0.9, int(100e6), 0, Drag(5, 1), 2, PulseType.DRIVE) - p3 = Pulse(600, 40, 0.9, int(100e6), 0, Drag(5, 1), 1, PulseType.DRIVE) + p1 = Pulse(40, 0.9, int(100e6), 0, Drag(5, 1), 3, PulseType.DRIVE) + p2 = Pulse(40, 0.9, int(100e6), 0, Drag(5, 1), 2, PulseType.DRIVE) + p3 = Pulse(40, 0.9, int(100e6), 0, Drag(5, 1), 1, PulseType.DRIVE) - p4 = Pulse(440, 1000, 0.9, int(20e6), 0, Rectangular(), 3, PulseType.READOUT) - p5 = Pulse(540, 1000, 0.9, int(20e6), 0, Rectangular(), 2, PulseType.READOUT) - p6 = Pulse(640, 1000, 0.9, int(20e6), 0, Rectangular(), 1, PulseType.READOUT) + p4 = Pulse(1000, 0.9, int(20e6), 0, Rectangular(), 3, PulseType.READOUT) + p5 = Pulse(1000, 0.9, int(20e6), 0, Rectangular(), 2, PulseType.READOUT) + p6 = Pulse(1000, 0.9, int(20e6), 0, Rectangular(), 1, PulseType.READOUT) ps = PulseSequence([p1, p2, p3, p4, p5, p6]) From eff36f45c90c39350275e8efeaca84630bdf47a6 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 21 Mar 2024 09:15:52 +0000 Subject: [PATCH 104/233] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibolab/instruments/qblox/cluster_qrm_rf.py | 13 +++++++------ tests/test_instruments_qmsim.py | 2 +- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/src/qibolab/instruments/qblox/cluster_qrm_rf.py b/src/qibolab/instruments/qblox/cluster_qrm_rf.py index bcd1d7dcd..9d582e859 100644 --- a/src/qibolab/instruments/qblox/cluster_qrm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qrm_rf.py @@ -1,4 +1,5 @@ """Qblox Cluster QRM-RF driver.""" + import copy import json import time @@ -992,9 +993,9 @@ def acquire(self): if len(sequencer.pulses.ro_pulses) == 1: pulse = sequencer.pulses.ro_pulses[0] frequency = self.get_if(pulse) - acquisitions[pulse.qubit] = acquisitions[ - pulse.id - ] = AveragedAcquisition(scope, duration, frequency) + acquisitions[pulse.qubit] = acquisitions[pulse.id] = ( + AveragedAcquisition(scope, duration, frequency) + ) else: raise RuntimeError( "Software Demodulation only supports one acquisition per channel. " @@ -1004,9 +1005,9 @@ def acquire(self): results = self.device.get_acquisitions(sequencer.number) for pulse in sequencer.pulses.ro_pulses: bins = results[pulse.id]["acquisition"]["bins"] - acquisitions[pulse.qubit] = acquisitions[ - pulse.id - ] = DemodulatedAcquisition(scope, bins, duration) + acquisitions[pulse.qubit] = acquisitions[pulse.id] = ( + DemodulatedAcquisition(scope, bins, duration) + ) # TODO: to be updated once the functionality of ExecutionResults is extended return {key: acquisition for key, acquisition in acquisitions.items()} diff --git a/tests/test_instruments_qmsim.py b/tests/test_instruments_qmsim.py index 9c20eaac9..6b7cf83df 100644 --- a/tests/test_instruments_qmsim.py +++ b/tests/test_instruments_qmsim.py @@ -23,7 +23,7 @@ from qibolab import AcquisitionType, AveragingMode, ExecutionParameters, create_platform from qibolab.backends import QibolabBackend -from qibolab.pulses import Pulse, SNZ, PulseSequence, Rectangular +from qibolab.pulses import SNZ, Pulse, PulseSequence, Rectangular from qibolab.sweeper import Parameter, Sweeper from .conftest import set_platform_profile From df94773990253dfdd9dbf994b50f482e86e4255d Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Thu, 21 Mar 2024 13:33:04 +0400 Subject: [PATCH 105/233] fix: compiler tests --- tests/test_compilers_default.py | 30 ++++++++++++------------------ 1 file changed, 12 insertions(+), 18 deletions(-) diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index 6c7c9172c..e226137cf 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -37,27 +37,21 @@ def compile_circuit(circuit, platform): @pytest.mark.parametrize( - "gateargs", + "gateargs,sequence_len", [ - (gates.I,), - (gates.Z,), - (gates.GPI, np.pi / 8), - (gates.GPI2, -np.pi / 8), - (gates.RZ, np.pi / 4), - (gates.U3, 0.1, 0.2, 0.3), + ((gates.I,), 1), + ((gates.Z,), 2), + ((gates.GPI, np.pi / 8), 3), + ((gates.GPI2, -np.pi / 8), 3), + ((gates.RZ, np.pi / 4), 2), + ((gates.U3, 0.1, 0.2, 0.3), 10), ], ) -def test_compile(platform, gateargs): +def test_compile(platform, gateargs, sequence_len): nqubits = platform.nqubits - if gateargs[0] is gates.U3: - nseq = 2 - elif gateargs[0] in (gates.GPI, gates.GPI2): - nseq = 1 - else: - nseq = 0 circuit = generate_circuit_with_gate(nqubits, *gateargs) sequence = compile_circuit(circuit, platform) - assert len(sequence) == nqubits * nseq + assert len(sequence) == nqubits * sequence_len def test_compile_two_gates(platform): @@ -68,7 +62,7 @@ def test_compile_two_gates(platform): sequence = compile_circuit(circuit, platform) - assert len(sequence) == 4 + assert len(sequence) == 13 assert len(sequence.qd_pulses) == 3 assert len(sequence.ro_pulses) == 1 @@ -166,8 +160,8 @@ def test_cz_to_sequence(): circuit.add(gates.CZ(1, 2)) sequence = compile_circuit(circuit, platform) - test_sequence, virtual_z_phases = platform.create_CZ_pulse_sequence((2, 1)) - assert sequence == test_sequence + test_sequence = platform.create_CZ_pulse_sequence((2, 1)) + assert sequence[0] == test_sequence[0] def test_cnot_to_sequence(): From d6b4273502982abdd015be71f18c0efb886d0cfd Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Thu, 21 Mar 2024 17:17:47 +0400 Subject: [PATCH 106/233] refactor: simplify compiler rules --- src/qibolab/compilers/compiler.py | 29 ++++++++++++++++++++++++++--- src/qibolab/compilers/default.py | 30 ++++++++++-------------------- 2 files changed, 36 insertions(+), 23 deletions(-) diff --git a/src/qibolab/compilers/compiler.py b/src/qibolab/compilers/compiler.py index 938acdc56..191971e80 100644 --- a/src/qibolab/compilers/compiler.py +++ b/src/qibolab/compilers/compiler.py @@ -98,6 +98,31 @@ def inner(func): return inner + def get_sequence(self, gate, platform): + """Get pulse sequence implementing the given gate using the registered + rules. + + Args: + gate (:class:`qibo.gates.Gate`): Qibo gate to convert to pulses. + platform (:class:`qibolab.platform.Platform`): Qibolab platform to read the native gates from. + """ + # get local sequence for the current gate + rule = self[type(gate)] + if isinstance(gate, gates.M): + qubits = [platform.get_qubit(q) for q in gate.qubits] + gate_sequence = rule(gate, qubits) + elif len(gate.qubits) == 1: + qubit = platform.get_qubit(gate.target_qubits[0]) + gate_sequence = rule(gate, qubit) + elif len(gate.qubits) == 2: + pair = platform.pairs[ + tuple(platform.get_qubit(q).name for q in gate.qubits) + ] + gate_sequence = rule(gate, pair) + else: + raise NotImplementedError(f"{type(gate)} is not a native gate.") + return gate_sequence + def compile(self, circuit, platform): """Transforms a circuit to pulse sequence. @@ -126,9 +151,7 @@ def compile(self, circuit, platform): qubit_clock[qubit] += gate.delay continue - rule = self[gate.__class__] - # get local sequence and phases for the current gate - gate_sequence = rule(gate, platform) + gate_sequence = self.get_sequence(gate, platform) for pulse in gate_sequence: if qubit_clock[pulse.qubit] > channel_clock[pulse.qubit]: delay = qubit_clock[pulse.qubit] - channel_clock[pulse.channel] diff --git a/src/qibolab/compilers/default.py b/src/qibolab/compilers/default.py index 3e02ac25f..bad8eb4ea 100644 --- a/src/qibolab/compilers/default.py +++ b/src/qibolab/compilers/default.py @@ -9,30 +9,27 @@ from qibolab.pulses import PulseSequence, VirtualZ -def identity_rule(gate, platform): +def identity_rule(gate, qubit): """Identity gate skipped.""" return PulseSequence() -def z_rule(gate, platform): +def z_rule(gate, qubit): """Z gate applied virtually.""" - qubit = platform.get_qubit(gate.target_qubits[0]) return PulseSequence( [VirtualZ(phase=math.pi, channel=qubit.drive.name, qubit=qubit.name)] ) -def rz_rule(gate, platform): +def rz_rule(gate, qubit): """RZ gate applied virtually.""" - qubit = platform.get_qubit(gate.target_qubits[0]) return PulseSequence( [VirtualZ(phase=gate.parameters[0], channel=qubit.drive.name, qubit=qubit.name)] ) -def gpi2_rule(gate, platform): +def gpi2_rule(gate, qubit): """Rule for GPI2.""" - qubit = platform.get_qubit(gate.target_qubits[0]) theta = gate.parameters[0] sequence = PulseSequence() pulse = qubit.native_gates.RX90 @@ -41,9 +38,8 @@ def gpi2_rule(gate, platform): return sequence -def gpi_rule(gate, platform): +def gpi_rule(gate, qubit): """Rule for GPI.""" - qubit = platform.get_qubit(gate.target_qubits[0]) theta = gate.parameters[0] sequence = PulseSequence() # the following definition has a global phase difference compare to @@ -56,9 +52,8 @@ def gpi_rule(gate, platform): return sequence -def u3_rule(gate, platform): +def u3_rule(gate, qubit): """U3 applied as RZ-RX90-RZ-RX90-RZ.""" - qubit = platform.get_qubit(gate.target_qubits[0]) # Transform gate to U3 and add pi/2-pulses theta, phi, lam = gate.parameters # apply RZ(lam) @@ -76,25 +71,20 @@ def u3_rule(gate, platform): return sequence -def cz_rule(gate, platform): +def cz_rule(gate, pair): """CZ applied as defined in the platform runcard. Applying the CZ gate may involve sending pulses on qubits that the gate is not directly acting on. """ - pair = platform.pairs[tuple(platform.get_qubit(q).name for q in gate.qubits)] return pair.native_gates.CZ -def cnot_rule(gate, platform): +def cnot_rule(gate, pair): """CNOT applied as defined in the platform runcard.""" - pair = platform.pairs[tuple(platform.get_qubit(q).name for q in gate.qubits)] return pair.native_gates.CNOT -def measurement_rule(gate, platform): +def measurement_rule(gate, qubits): """Measurement gate applied using the platform readout pulse.""" - sequence = PulseSequence( - [platform.get_qubit(q).native_gates.MZ for q in gate.qubits] - ) - return sequence + return PulseSequence([qubit.native_gates.MZ for qubit in qubits]) From a933d3ce019ba47f552fb123266f5c6688c22fbd Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Thu, 21 Mar 2024 17:36:46 +0400 Subject: [PATCH 107/233] refactor: native two qubit to empty PulseSequence --- src/qibolab/native.py | 14 ++++++++++---- src/qibolab/platform/platform.py | 8 ++++---- src/qibolab/serialize.py | 15 +++++++-------- 3 files changed, 21 insertions(+), 16 deletions(-) diff --git a/src/qibolab/native.py b/src/qibolab/native.py index 8badc5091..bbf55bb35 100644 --- a/src/qibolab/native.py +++ b/src/qibolab/native.py @@ -29,15 +29,21 @@ class TwoQubitNatives: """Container with the native two-qubit gates acting on a specific pair of qubits.""" - CZ: Optional[PulseSequence] = field(default=None, metadata={"symmetric": True}) - CNOT: Optional[PulseSequence] = field(default=None, metadata={"symmetric": False}) - iSWAP: Optional[PulseSequence] = field(default=None, metadata={"symmetric": True}) + CZ: PulseSequence = field( + default_factory=lambda: PulseSequence(), metadata={"symmetric": True} + ) + CNOT: PulseSequence = field( + default_factory=lambda: PulseSequence(), metadata={"symmetric": False} + ) + iSWAP: PulseSequence = field( + default_factory=lambda: PulseSequence(), metadata={"symmetric": True} + ) @property def symmetric(self): """Check if the defined two-qubit gates are symmetric between target and control qubits.""" return all( - fld.metadata["symmetric"] or getattr(self, fld.name) is None + fld.metadata["symmetric"] or len(getattr(self, fld.name)) == 0 for fld in fields(self) ) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 618764ca1..50d90101f 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -160,7 +160,7 @@ def _set_channels_to_two_qubit_gates(self): gates = pair.native_gates for fld in fields(gates): sequence = getattr(gates, fld.name) - if sequence is not None: + if len(sequence) > 0: new_sequence = PulseSequence() for pulse in sequence: if pulse.type is PulseType.VIRTUALZ: @@ -413,7 +413,7 @@ def create_RX12_pulse(self, qubit, relative_phase=0): def create_CZ_pulse_sequence(self, qubits): pair = tuple(self.get_qubit(q).name for q in qubits) - if pair not in self.pairs or self.pairs[pair].native_gates.CZ is None: + if pair not in self.pairs or len(self.pairs[pair].native_gates.CZ) == 0: raise_error( ValueError, f"Calibration for CZ gate between qubits {qubits[0]} and {qubits[1]} not found.", @@ -422,7 +422,7 @@ def create_CZ_pulse_sequence(self, qubits): def create_iSWAP_pulse_sequence(self, qubits): pair = tuple(self.get_qubit(q).name for q in qubits) - if pair not in self.pairs or self.pairs[pair].native_gates.iSWAP is None: + if pair not in self.pairs or len(self.pairs[pair].native_gates.iSWAP) == 0: raise_error( ValueError, f"Calibration for iSWAP gate between qubits {qubits[0]} and {qubits[1]} not found.", @@ -431,7 +431,7 @@ def create_iSWAP_pulse_sequence(self, qubits): def create_CNOT_pulse_sequence(self, qubits): pair = tuple(self.get_qubit(q).name for q in qubits) - if pair not in self.pairs or self.pairs[pair].native_gates.CNOT is None: + if pair not in self.pairs or len(self.pairs[pair].native_gates.CNOT) == 0: raise_error( ValueError, f"Calibration for CNOT gate between qubits {qubits[0]} and {qubits[1]} not found.", diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index 3a0dabb8e..413d14679 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -202,15 +202,14 @@ def _dump_single_qubit_natives(natives: SingleQubitNatives): def _dump_two_qubit_natives(natives: TwoQubitNatives): data = {} for fld in fields(natives): - if getattr(natives, fld.name) is None: - continue sequence = getattr(natives, fld.name) - data[fld.name] = [] - for pulse in sequence: - pulse_serial = _dump_pulse(pulse) - if pulse.type == PulseType.COUPLERFLUX: - pulse_serial["coupler"] = pulse_serial.pop("qubit") - data[fld.name].append(pulse_serial) + if len(sequence) > 0: + data[fld.name] = [] + for pulse in sequence: + pulse_serial = _dump_pulse(pulse) + if pulse.type == PulseType.COUPLERFLUX: + pulse_serial["coupler"] = pulse_serial.pop("qubit") + data[fld.name].append(pulse_serial) return data From d7a52f850f90c551dbed455a3c8bf8b610f34db8 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Thu, 21 Mar 2024 17:38:59 +0400 Subject: [PATCH 108/233] fix: doctest --- doc/source/tutorials/compiler.rst | 7 ++----- src/qibolab/compilers/default.py | 12 ++++-------- 2 files changed, 6 insertions(+), 13 deletions(-) diff --git a/doc/source/tutorials/compiler.rst b/doc/source/tutorials/compiler.rst index ae5d2dc2e..68d4dbef7 100644 --- a/doc/source/tutorials/compiler.rst +++ b/doc/source/tutorials/compiler.rst @@ -80,12 +80,9 @@ The following example shows how to modify the compiler in order to execute a cir # define a compiler rule that translates X to the pi-pulse - def x_rule(gate, platform): + def x_rule(gate, qubit): """X gate applied with a single pi-pulse.""" - qubit = gate.target_qubits[0] - sequence = PulseSequence() - sequence.append(platform.create_RX_pulse(qubit)) - return sequence + return PulseSequence([qubit.native_gates.RX]) # the empty dictionary is needed because the X gate does not require any virtual Z-phases diff --git a/src/qibolab/compilers/default.py b/src/qibolab/compilers/default.py index bad8eb4ea..c59360c88 100644 --- a/src/qibolab/compilers/default.py +++ b/src/qibolab/compilers/default.py @@ -31,24 +31,20 @@ def rz_rule(gate, qubit): def gpi2_rule(gate, qubit): """Rule for GPI2.""" theta = gate.parameters[0] - sequence = PulseSequence() - pulse = qubit.native_gates.RX90 - pulse.relative_phase = theta - sequence.append(pulse) + pulse = replace(qubit.native_gates.RX90, relative_phase=theta) + sequence = PulseSequence([pulse]) return sequence def gpi_rule(gate, qubit): """Rule for GPI.""" theta = gate.parameters[0] - sequence = PulseSequence() # the following definition has a global phase difference compare to # to the matrix representation. See # https://github.com/qiboteam/qibolab/pull/804#pullrequestreview-1890205509 # for more detail. - pulse = qubit.native_gates.RX - pulse.relative_phase = theta - sequence.append(pulse) + pulse = replace(qubit.native_gates.RX, relative_phase=theta) + sequence = PulseSequence([pulse]) return sequence From 8dd0752a3974cd66f0e8f599514f61a1d153e361 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Thu, 21 Mar 2024 18:46:40 +0400 Subject: [PATCH 109/233] refactor: remove clock from unrolling --- src/qibolab/platform/platform.py | 19 ++++++++----------- src/qibolab/pulses/sequence.py | 2 +- 2 files changed, 9 insertions(+), 12 deletions(-) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 50d90101f..744c00f41 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -42,22 +42,19 @@ def unroll_sequences( """ total_sequence = PulseSequence() readout_map = defaultdict(list) - clock = defaultdict(int) - start = 0 + channels = {pulse.channel for sequence in sequences for pulse in sequence} for sequence in sequences: + total_sequence.extend(sequence) + # TODO: Fix unrolling results for pulse in sequence: - if clock[pulse.channel] < start: - delay = start - clock[pulse.channel] - total_sequence.append(Delay(delay, pulse.channel)) - - total_sequence.append(pulse) - clock[pulse.channel] += pulse.duration - if pulse.type is PulseType.READOUT: - # TODO: Fix unrolling results readout_map[pulse.id].append(pulse.id) - start = sequence.duration + relaxation_time + length = sequence.duration + relaxation_time + pulses_per_channel = sequence.pulses_per_channel + for channel in channels: + delay = length - pulses_per_channel[channel].duration + total_sequence.append(Delay(delay, channel)) return total_sequence, readout_map diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py index f5406dfa7..8a8675053 100644 --- a/src/qibolab/pulses/sequence.py +++ b/src/qibolab/pulses/sequence.py @@ -96,7 +96,7 @@ def coupler_pulses(self, *couplers): @property def pulses_per_channel(self): """Return a dictionary with the sequence per channel.""" - sequences = defaultdict(self.__class__) + sequences = defaultdict(type(self)) for pulse in self: sequences[pulse.channel].append(pulse) return sequences From 5f2afad49bb1d0ad5b198b6d837542c6fe0ce17f Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Mon, 15 Apr 2024 14:35:48 +0300 Subject: [PATCH 110/233] fix: remove FluxPulse --- src/qibolab/instruments/zhinst/executor.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/qibolab/instruments/zhinst/executor.py b/src/qibolab/instruments/zhinst/executor.py index cbcb1d6a1..ad7693124 100644 --- a/src/qibolab/instruments/zhinst/executor.py +++ b/src/qibolab/instruments/zhinst/executor.py @@ -13,7 +13,7 @@ from qibolab.couplers import Coupler from qibolab.instruments.abstract import Controller from qibolab.instruments.port import Port -from qibolab.pulses import FluxPulse, PulseSequence, PulseType +from qibolab.pulses import PulseSequence, PulseType from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, Sweeper from qibolab.unrolling import Bounds @@ -341,7 +341,7 @@ def create_sub_sequences( if len(measurement_groups) == 1: for ch in other_channels: for pulse in self.sequence[ch]: - if not isinstance(pulse.pulse, FluxPulse): + if not pulse.pulse.type in (PulseType.FLUX, PulseType.COUPLERFLUX): break start, end = measurement_start_end[0] if pulse.pulse.start < end and pulse.pulse.finish > start: From d77372df6eabc99bc959ff3b993e109ee7b8e2ff Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 16:35:45 +0100 Subject: [PATCH 111/233] Remove leftover calls to pulse specific copy --- src/qibolab/native.py | 364 +++++++++++++++++++++++++++++++++++++++--- 1 file changed, 345 insertions(+), 19 deletions(-) diff --git a/src/qibolab/native.py b/src/qibolab/native.py index bbf55bb35..8c08595e1 100644 --- a/src/qibolab/native.py +++ b/src/qibolab/native.py @@ -1,7 +1,256 @@ +import copy +from collections import defaultdict from dataclasses import dataclass, field, fields, replace -from typing import Optional +from typing import List, Optional, Union -from qibolab.pulses import Pulse, PulseSequence +from qibolab.pulses import Pulse, PulseSequence, PulseType + + +@dataclass +class NativePulse: + """Container with parameters required to generate a pulse implementing a + native gate.""" + + name: str + """Name of the gate that the pulse implements.""" + duration: int + amplitude: float + shape: str + pulse_type: PulseType + qubit: "qubits.Qubit" + frequency: int = 0 + relative_start: int = 0 + """Relative start is relevant for two-qubit gate operations which + correspond to a pulse sequence.""" + + # used for qblox + if_frequency: Optional[int] = None + # TODO: Note sure if the following parameters are useful to be in the runcard + start: int = 0 + phase: float = 0.0 + + @classmethod + def from_dict(cls, name, pulse, qubit): + """Parse the dictionary provided by the runcard. + + Args: + name (str): Name of the native gate (dictionary key). + pulse (dict): Dictionary containing the parameters of the pulse implementing + the gate, as loaded from the runcard. + qubits (:class:`qibolab.platforms.abstract.Qubit`): Qubit that the + pulse is acting on + """ + kwargs = copy.deepcopy(pulse) + kwargs["pulse_type"] = PulseType(kwargs.pop("type")) + kwargs["qubit"] = qubit + return cls(name, **kwargs) + + @property + def raw(self): + data = { + fld.name: getattr(self, fld.name) + for fld in fields(self) + if getattr(self, fld.name) is not None + } + del data["name"] + del data["start"] + if self.pulse_type is PulseType.FLUX: + del data["frequency"] + del data["phase"] + data["qubit"] = self.qubit.name + data["type"] = data.pop("pulse_type").value + return data + + def pulse(self, start, relative_phase=0.0): + """Construct the :class:`qibolab.pulses.Pulse` object implementing the + gate. + + Args: + start (int): Start time of the pulse in the sequence. + relative_phase (float): Relative phase of the pulse. + + Returns: + A :class:`qibolab.pulses.DrivePulse` or :class:`qibolab.pulses.DrivePulse` + or :class:`qibolab.pulses.FluxPulse` with the pulse parameters of the gate. + """ + if self.pulse_type is PulseType.FLUX: + return Pulse.flux( + start + self.relative_start, + self.duration, + self.amplitude, + self.shape, + channel=self.qubit.flux.name, + qubit=self.qubit.name, + ) + + channel = getattr(self.qubit, self.pulse_type.name.lower()).name + return Pulse( + start + self.relative_start, + self.duration, + self.amplitude, + self.frequency, + relative_phase, + self.shape, + type=self.pulse_type, + channel=channel, + qubit=self.qubit.name, + ) + + +@dataclass +class VirtualZPulse: + """Container with parameters required to add a virtual Z phase in a pulse + sequence.""" + + phase: float + qubit: "qubits.Qubit" + + @property + def raw(self): + return {"type": "virtual_z", "phase": self.phase, "qubit": self.qubit.name} + + +@dataclass +class CouplerPulse: + """Container with parameters required to add a coupler pulse in a pulse + sequence.""" + + duration: int + amplitude: float + shape: str + coupler: "couplers.Coupler" + relative_start: int = 0 + + @classmethod + def from_dict(cls, pulse, coupler): + """Parse the dictionary provided by the runcard. + + Args: + name (str): Name of the native gate (dictionary key). + pulse (dict): Dictionary containing the parameters of the pulse implementing + the gate, as loaded from the runcard. + coupler (:class:`qibolab.platforms.abstract.Coupler`): Coupler that the + pulse is acting on + """ + kwargs = copy.deepcopy(pulse) + kwargs["coupler"] = coupler + kwargs.pop("type") + return cls(**kwargs) + + @property + def raw(self): + return { + "type": "coupler", + "duration": self.duration, + "amplitude": self.amplitude, + "shape": self.shape, + "coupler": self.coupler.name, + "relative_start": self.relative_start, + } + + def pulse(self, start): + """Construct the :class:`qibolab.pulses.Pulse` object implementing the + gate. + + Args: + start (int): Start time of the pulse in the sequence. + + Returns: + A :class:`qibolab.pulses.FluxPulse` with the pulse parameters of the gate. + """ + return Pulse( + start + self.relative_start, + self.duration, + self.amplitude, + 0, + 0, + self.shape, + type=PulseType.COUPLERFLUX, + channel=self.coupler.flux.name, + qubit=self.coupler.name, + ) + + +@dataclass +class NativeSequence: + """List of :class:`qibolab.platforms.native.NativePulse` objects + implementing a gate. + + Relevant for two-qubit gates, which usually require a sequence of + pulses to be implemented. These pulses may act on qubits different + than the qubits the gate is targeting. + """ + + name: str + pulses: List[Union[NativePulse, VirtualZPulse]] = field(default_factory=list) + coupler_pulses: List[CouplerPulse] = field(default_factory=list) + + @classmethod + def from_dict(cls, name, sequence, qubits, couplers): + """Constructs the native sequence from the dictionaries provided in the + runcard. + + Args: + name (str): Name of the gate the sequence is applying. + sequence (dict): Dictionary describing the sequence as provided in the runcard. + qubits (list): List of :class:`qibolab.qubits.Qubit` object for all + qubits in the platform. All qubits are required because the sequence may be + acting on qubits that the implemented gate is not targeting. + couplers (list): List of :class:`qibolab.couplers.Coupler` object for all + couplers in the platform. All couplers are required because the sequence may be + acting on couplers that the implemented gate is not targeting. + """ + pulses = [] + coupler_pulses = [] + + # If sequence contains only one pulse dictionary, convert it into a list that can be iterated below + if isinstance(sequence, dict): + sequence = [sequence] + + for i, pulse in enumerate(sequence): + pulse = copy.deepcopy(pulse) + pulse_type = pulse.pop("type") + if pulse_type == "coupler": + pulse["coupler"] = couplers[pulse.pop("coupler")] + coupler_pulses.append(CouplerPulse(**pulse)) + else: + qubit = qubits[pulse.pop("qubit")] + if pulse_type == "virtual_z": + phase = pulse["phase"] + pulses.append(VirtualZPulse(phase, qubit)) + else: + pulses.append( + NativePulse( + f"{name}{i}", + **pulse, + pulse_type=PulseType(pulse_type), + qubit=qubit, + ) + ) + return cls(name, pulses, coupler_pulses) + + @property + def raw(self): + pulses = [pulse.raw for pulse in self.pulses] + coupler_pulses = [pulse.raw for pulse in self.coupler_pulses] + return pulses + coupler_pulses + + def sequence(self, start=0): + """Creates a :class:`qibolab.pulses.PulseSequence` object implementing + the sequence.""" + sequence = PulseSequence() + virtual_z_phases = defaultdict(int) + + for pulse in self.pulses: + if isinstance(pulse, NativePulse): + sequence.append(pulse.pulse(start=start)) + else: + virtual_z_phases[pulse.qubit.name] += pulse.phase + + for coupler_pulse in self.coupler_pulses: + sequence.append(coupler_pulse.pulse(start=start)) + # TODO: Maybe ``virtual_z_phases`` should be an attribute of ``PulseSequence`` + return sequence, virtual_z_phases @dataclass @@ -9,19 +258,85 @@ class SingleQubitNatives: """Container with the native single-qubit gates acting on a specific qubit.""" - RX: Optional[Pulse] = None + RX: Optional[NativePulse] = None """Pulse to drive the qubit from state 0 to state 1.""" - RX12: Optional[Pulse] = None + RX12: Optional[NativePulse] = None """Pulse to drive to qubit from state 1 to state 2.""" - MZ: Optional[Pulse] = None + MZ: Optional[NativePulse] = None """Measurement pulse.""" - CP: Optional[Pulse] = None - """Pulse to activate a coupler.""" @property - def RX90(self) -> Pulse: + def RX90(self) -> NativePulse: """RX90 native pulse is inferred from RX by halving its amplitude.""" - return replace(self.RX, amplitude=self.RX.amplitude / 2.0) + return replace(self.RX, name="RX90", amplitude=self.RX.amplitude / 2.0) + + @classmethod + def from_dict(cls, qubit, native_gates): + """Parse native gates of the qubit from the runcard. + + Args: + qubit (:class:`qibolab.qubits.Qubit`): Qubit object that the + native gates are acting on. + native_gates (dict): Dictionary with native gate pulse parameters as loaded + from the runcard. + """ + pulses = { + n: NativePulse.from_dict(n, pulse, qubit=qubit) + for n, pulse in native_gates.items() + } + return cls(**pulses) + + @property + def raw(self): + """Serialize native gate pulses. + + ``None`` gates are not included. + """ + data = {} + for fld in fields(self): + attr = getattr(self, fld.name) + if attr is not None: + data[fld.name] = attr.raw + del data[fld.name]["qubit"] + return data + + +@dataclass +class CouplerNatives: + """Container with the native single-qubit gates acting on a specific + qubit.""" + + CP: Optional[NativePulse] = None + """Pulse to activate the coupler.""" + + @classmethod + def from_dict(cls, coupler, native_gates): + """Parse coupler native gates from the runcard. + + Args: + coupler (:class:`qibolab.couplers.Coupler`): Coupler object that the + native pulses are acting on. + native_gates (dict): Dictionary with native gate pulse parameters as loaded + from the runcard [Reusing the dict from qubits]. + """ + pulses = { + n: CouplerPulse.from_dict(pulse, coupler=coupler) + for n, pulse in native_gates.items() + } + return cls(**pulses) + + @property + def raw(self): + """Serialize native gate pulses. + + ``None`` gates are not included. + """ + data = {} + for fld in fields(self): + attr = getattr(self, fld.name) + if attr is not None: + data[fld.name] = attr.raw + return data @dataclass @@ -29,21 +344,32 @@ class TwoQubitNatives: """Container with the native two-qubit gates acting on a specific pair of qubits.""" - CZ: PulseSequence = field( - default_factory=lambda: PulseSequence(), metadata={"symmetric": True} - ) - CNOT: PulseSequence = field( - default_factory=lambda: PulseSequence(), metadata={"symmetric": False} - ) - iSWAP: PulseSequence = field( - default_factory=lambda: PulseSequence(), metadata={"symmetric": True} - ) + CZ: Optional[NativeSequence] = field(default=None, metadata={"symmetric": True}) + CNOT: Optional[NativeSequence] = field(default=None, metadata={"symmetric": False}) + iSWAP: Optional[NativeSequence] = field(default=None, metadata={"symmetric": True}) @property def symmetric(self): """Check if the defined two-qubit gates are symmetric between target and control qubits.""" return all( - fld.metadata["symmetric"] or len(getattr(self, fld.name)) == 0 + fld.metadata["symmetric"] or getattr(self, fld.name) is None for fld in fields(self) ) + + @classmethod + def from_dict(cls, qubits, couplers, native_gates): + sequences = { + n: NativeSequence.from_dict(n, seq, qubits, couplers) + for n, seq in native_gates.items() + } + return cls(**sequences) + + @property + def raw(self): + data = {} + for fld in fields(self): + gate = getattr(self, fld.name) + if gate is not None: + data[fld.name] = gate.raw + return data From f738c7ec77002fd83e8bbfea06a5663fb49373d5 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 18:18:40 +0100 Subject: [PATCH 112/233] Start rearranging pulses into a subpackage --- src/qibolab/pulses/plot.py | 136 ++++++------------- src/qibolab/pulses/pulse.py | 132 +++++++++++++------ src/qibolab/pulses/sequence.py | 133 ++++++++++++++++--- src/qibolab/pulses/shape.py | 233 +++++++++++++++++---------------- src/qibolab/pulses/waveform.py | 42 ++++++ 5 files changed, 415 insertions(+), 261 deletions(-) create mode 100644 src/qibolab/pulses/waveform.py diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index 6cbbf905a..1328268f2 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -1,13 +1,10 @@ """Plotting tools for pulses and related entities.""" - -from collections import defaultdict - import matplotlib.pyplot as plt import numpy as np -from .pulse import Delay, Pulse -from .sequence import PulseSequence -from .shape import SAMPLING_RATE, Waveform, modulate +from .pulse import Pulse +from .shape import SAMPLING_RATE +from .waveform import Waveform def waveform(wf: Waveform, filename=None): @@ -17,7 +14,7 @@ def waveform(wf: Waveform, filename=None): filename (str): a file path. If provided the plot is save to a file. """ plt.figure(figsize=(14, 5), dpi=200) - plt.plot(wf, c="C0", linestyle="dashed") + plt.plot(wf.data, c="C0", linestyle="dashed") plt.xlabel("Sample Number") plt.ylabel("Amplitude") plt.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") @@ -41,53 +38,72 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): waveform_q = pulse_.shape.envelope_waveform_q(sampling_rate) num_samples = len(waveform_i) - time = np.arange(num_samples) / sampling_rate + time = pulse_.start + np.arange(num_samples) / sampling_rate _ = plt.figure(figsize=(14, 5), dpi=200) gs = gridspec.GridSpec(ncols=2, nrows=1, width_ratios=np.array([2, 1])) ax1 = plt.subplot(gs[0]) ax1.plot( time, - waveform_i, + waveform_i.data, label="envelope i", c="C0", linestyle="dashed", ) ax1.plot( time, - waveform_q, + waveform_q.data, label="envelope q", c="C1", linestyle="dashed", ) - - envelope = pulse_.shape.envelope_waveforms(sampling_rate) - modulated = modulate(np.array(envelope), pulse_.frequency) - ax1.plot(time, modulated[0], label="modulated i", c="C0") - ax1.plot(time, modulated[1], label="modulated q", c="C1") - ax1.plot(time, -waveform_i, c="silver", linestyle="dashed") + ax1.plot( + time, + pulse_.shape.modulated_waveform_i(sampling_rate).data, + label="modulated i", + c="C0", + ) + ax1.plot( + time, + pulse_.shape.modulated_waveform_q(sampling_rate).data, + label="modulated q", + c="C1", + ) + ax1.plot(time, -waveform_i.data, c="silver", linestyle="dashed") ax1.set_xlabel("Time [ns]") ax1.set_ylabel("Amplitude") ax1.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") - start = 0 - finish = float(pulse_.duration) + start = float(pulse_.start) + finish = float(pulse._finish) if pulse._finish is not None else 0.0 ax1.axis((start, finish, -1.0, 1.0)) ax1.legend() + modulated_i = pulse_.shape.modulated_waveform_i(sampling_rate).data + modulated_q = pulse_.shape.modulated_waveform_q(sampling_rate).data ax2 = plt.subplot(gs[1]) - ax2.plot(modulated[0], modulated[1], label="modulated", c="C3") - ax2.plot(waveform_i, waveform_q, label="envelope", c="C2") ax2.plot( - modulated[0][0], - modulated[1][0], + modulated_i, + modulated_q, + label="modulated", + c="C3", + ) + ax2.plot( + waveform_i.data, + waveform_q.data, + label="envelope", + c="C2", + ) + ax2.plot( + modulated_i[0], + modulated_q[0], marker="o", markersize=5, label="start", c="lightcoral", ) ax2.plot( - modulated[0][-1], - modulated[1][-1], + modulated_i[-1], + modulated_q[-1], marker="o", markersize=5, label="finish", @@ -110,75 +126,3 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): else: plt.show() plt.close() - - -def sequence(ps: PulseSequence, filename=None, sampling_rate=SAMPLING_RATE): - """Plot the sequence of pulses. - - Args: - filename (str): a file path. If provided the plot is save to a file. - """ - if len(ps) > 0: - import matplotlib.pyplot as plt - from matplotlib import gridspec - - _ = plt.figure(figsize=(14, 2 * len(ps)), dpi=200) - gs = gridspec.GridSpec(ncols=1, nrows=len(ps)) - vertical_lines = [] - starts = defaultdict(int) - for pulse in ps: - if not isinstance(pulse, Delay): - vertical_lines.append(starts[pulse.channel]) - vertical_lines.append(starts[pulse.channel] + pulse.duration) - starts[pulse.channel] += pulse.duration - - n = -1 - for qubit in ps.qubits: - qubit_pulses = ps.get_qubit_pulses(qubit) - for channel in qubit_pulses.channels: - n += 1 - channel_pulses = qubit_pulses.get_channel_pulses(channel) - ax = plt.subplot(gs[n]) - ax.axis([0, ps.duration, -1, 1]) - start = 0 - for pulse in channel_pulses: - if isinstance(pulse, Delay): - start += pulse.duration - continue - - envelope = pulse.shape.envelope_waveforms(sampling_rate) - num_samples = envelope[0].size - time = start + np.arange(num_samples) / sampling_rate - modulated = modulate(np.array(envelope), pulse.frequency) - ax.plot(time, modulated[1], c="lightgrey") - ax.plot(time, modulated[0], c=f"C{str(n)}") - ax.plot( - time, - pulse.shape.envelope_waveform_i(sampling_rate), - c=f"C{str(n)}", - ) - ax.plot( - time, - -pulse.shape.envelope_waveform_i(sampling_rate), - c=f"C{str(n)}", - ) - # TODO: if they overlap use different shades - ax.axhline(0, c="dimgrey") - ax.set_ylabel(f"qubit {qubit} \n channel {channel}") - for vl in vertical_lines: - ax.axvline(vl, c="slategrey", linestyle="--") - ax.axis((0, ps.duration, -1, 1)) - ax.grid( - visible=True, - which="both", - axis="both", - color="#CCCCCC", - linestyle="-", - ) - start += pulse.duration - - if filename: - plt.savefig(filename) - else: - plt.show() - plt.close() diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index aa510bc11..18e16c253 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -1,11 +1,9 @@ """Pulse class.""" - +import copy from dataclasses import dataclass, fields from enum import Enum from typing import Optional -from .shape import SAMPLING_RATE, PulseShape, Waveform - class PulseType(Enum): """An enumeration to distinguish different types of pulses. @@ -19,14 +17,14 @@ class PulseType(Enum): DRIVE = "qd" FLUX = "qf" COUPLERFLUX = "cf" - DELAY = "dl" - VIRTUALZ = "virtual_z" @dataclass class Pulse: - """A pulse to be sent to the QPU.""" + """A class to represent a pulse to be sent to the QPU.""" + start: int + """Start time of pulse in ns.""" duration: int """Pulse duration in ns.""" amplitude: float @@ -34,14 +32,14 @@ class Pulse: Pulse amplitudes are normalised between -1 and 1. """ - frequency: int = 0 + frequency: int """Pulse Intermediate Frequency in Hz. The value has to be in the range [10e6 to 300e6]. """ - relative_phase: float = 0.0 + relative_phase: float """Relative phase of the pulse, in radians.""" - shape: PulseShape = "Rectangular()" + shape: PulseShape """Pulse shape, as a PulseShape object. See @@ -69,8 +67,41 @@ def __post_init__(self): self.shape.pulse = self @classmethod - def flux(cls, duration, amplitude, shape, **kwargs): - return cls(duration, amplitude, 0, 0, shape, type=PulseType.FLUX, **kwargs) + def flux(cls, start, duration, amplitude, shape, **kwargs): + return cls( + start, duration, amplitude, 0, 0, shape, type=PulseType.FLUX, **kwargs + ) + + @property + def finish(self) -> Optional[int]: + """Time when the pulse is scheduled to finish.""" + if None in {self.start, self.duration}: + return None + return self.start + self.duration + + @property + def global_phase(self): + """Global phase of the pulse, in radians. + + This phase is calculated from the pulse start time and frequency + as `2 * pi * frequency * start`. + """ + if self.type is PulseType.READOUT: + # readout pulses should have zero global phase so that we can + # calculate probabilities in the i-q plane + return 0 + + # pulse start, duration and finish are in ns + return 2 * np.pi * self.frequency * self.start / 1e9 + + @property + def phase(self) -> float: + """Total phase of the pulse, in radians. + + The total phase is computed as the sum of the global and + relative phases. + """ + return self.global_phase + self.relative_phase @property def id(self) -> int: @@ -86,7 +117,9 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: return self.shape.envelope_waveform_q(sampling_rate) - def envelope_waveforms(self, sampling_rate=SAMPLING_RATE): + def envelope_waveforms( + self, sampling_rate=SAMPLING_RATE + ): # -> tuple[Waveform, Waveform]: """A tuple with the i and q envelope waveforms of the pulse.""" return ( @@ -94,6 +127,24 @@ def envelope_waveforms(self, sampling_rate=SAMPLING_RATE): self.shape.envelope_waveform_q(sampling_rate), ) + def modulated_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: + """The waveform of the i component of the pulse, modulated with its + frequency.""" + + return self.shape.modulated_waveform_i(sampling_rate) + + def modulated_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: + """The waveform of the q component of the pulse, modulated with its + frequency.""" + + return self.shape.modulated_waveform_q(sampling_rate) + + def modulated_waveforms(self, sampling_rate): # -> tuple[Waveform, Waveform]: + """A tuple with the i and q waveforms of the pulse, modulated with its + frequency.""" + + return self.shape.modulated_waveforms(sampling_rate) + def __hash__(self): """Hash the content. @@ -117,31 +168,32 @@ def __hash__(self): ) ) - -@dataclass -class Delay: - """A wait instruction during which we are not sending any pulses to the - QPU.""" - - duration: int - """Delay duration in ns.""" - channel: str - """Channel on which the delay should be implemented.""" - type: PulseType = PulseType.DELAY - """Type fixed to ``DELAY`` to comply with ``Pulse`` interface.""" - - -@dataclass -class VirtualZ: - """Implementation of Z-rotations using virtual phase.""" - - duration = 0 - """Duration of the virtual gate should always be zero.""" - - phase: float - """Phase that implements the rotation.""" - channel: Optional[str] = None - """Channel on which the virtual phase should be added.""" - qubit: int = 0 - """Qubit on the drive of which the virtual phase should be added.""" - type: PulseType = PulseType.VIRTUALZ + def __add__(self, other): + if isinstance(other, Pulse): + return PulseSequence(self, other) + if isinstance(other, PulseSequence): + return PulseSequence(self, *other) + raise TypeError(f"Expected Pulse or PulseSequence; got {type(other).__name__}") + + def __mul__(self, n): + if not isinstance(n, int): + raise TypeError(f"Expected int; got {type(n).__name__}") + if n < 0: + raise TypeError(f"argument n should be >=0, got {n}") + return PulseSequence(*([copy.deepcopy(self)] * n)) + + def __rmul__(self, n): + return self.__mul__(n) + + def is_equal_ignoring_start(self, item) -> bool: + """Check if two pulses are equal ignoring start time.""" + return ( + self.duration == item.duration + and self.amplitude == item.amplitude + and self.frequency == item.frequency + and self.relative_phase == item.relative_phase + and self.shape == item.shape + and self.channel == item.channel + and self.type == item.type + and self.qubit == item.qubit + ) diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py index 8a8675053..fc488a372 100644 --- a/src/qibolab/pulses/sequence.py +++ b/src/qibolab/pulses/sequence.py @@ -1,8 +1,5 @@ """PulseSequence class.""" - -from collections import defaultdict - -from .pulse import PulseType +import numpy as np class PulseSequence(list): @@ -94,21 +91,27 @@ def coupler_pulses(self, *couplers): return new_pc @property - def pulses_per_channel(self): - """Return a dictionary with the sequence per channel.""" - sequences = defaultdict(type(self)) + def finish(self) -> int: + """The time when the last pulse of the sequence finishes.""" + t: int = 0 for pulse in self: - sequences[pulse.channel].append(pulse) - return sequences + if pulse.finish > t: + t = pulse.finish + return t + + @property + def start(self) -> int: + """The start time of the first pulse of the sequence.""" + t = self.finish + for pulse in self: + if pulse.start < t: + t = pulse.start + return t @property def duration(self) -> int: - """The time when the last pulse of the sequence finishes.""" - channel_pulses = self.pulses_per_channel - if len(channel_pulses) == 1: - pulses = next(iter(channel_pulses.values())) - return sum(pulse.duration for pulse in pulses) - return max(sequence.duration for sequence in channel_pulses.values()) + """Duration of the sequence calculated as its finish - start times.""" + return self.finish - self.start @property def channels(self) -> list: @@ -130,6 +133,25 @@ def qubits(self) -> list: qubits.sort() return qubits + def get_pulse_overlaps(self): # -> dict((int,int): PulseSequence): + """Return a dictionary of slices of time (tuples with start and finish + times) where pulses overlap.""" + times = [] + for pulse in self: + if not pulse.start in times: + times.append(pulse.start) + if not pulse.finish in times: + times.append(pulse.finish) + times.sort() + + overlaps = {} + for n in range(len(times) - 1): + overlaps[(times[n], times[n + 1])] = PulseSequence() + for pulse in self: + if (pulse.start <= times[n]) & (pulse.finish >= times[n + 1]): + overlaps[(times[n], times[n + 1])] += [pulse] + return overlaps + def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): """Separate a sequence of overlapping pulses into a list of non- overlapping sequences.""" @@ -155,3 +177,84 @@ def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): if not stored: separated_pulses.append(PulseSequence([new_pulse])) return separated_pulses + + # TODO: Implement separate_different_frequency_pulses() + + @property + def pulses_overlap(self) -> bool: + """Whether any of the pulses in the sequence overlap.""" + overlap = False + for pc in self.get_pulse_overlaps().values(): + if len(pc) > 1: + overlap = True + break + return overlap + + def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): + """Plot the sequence of pulses. + + Args: + savefig_filename (str): a file path. If provided the plot is save to a file. + """ + if len(self) > 0: + import matplotlib.pyplot as plt + from matplotlib import gridspec + + fig = plt.figure(figsize=(14, 2 * len(self)), dpi=200) + gs = gridspec.GridSpec(ncols=1, nrows=len(self)) + vertical_lines = [] + for pulse in self: + vertical_lines.append(pulse.start) + vertical_lines.append(pulse.finish) + + n = -1 + for qubit in self.qubits: + qubit_pulses = self.get_qubit_pulses(qubit) + for channel in qubit_pulses.channels: + n += 1 + channel_pulses = qubit_pulses.get_channel_pulses(channel) + ax = plt.subplot(gs[n]) + ax.axis([0, self.finish, -1, 1]) + for pulse in channel_pulses: + num_samples = len( + pulse.shape.modulated_waveform_i(sampling_rate) + ) + time = pulse.start + np.arange(num_samples) / sampling_rate + ax.plot( + time, + pulse.shape.modulated_waveform_q(sampling_rate).data, + c="lightgrey", + ) + ax.plot( + time, + pulse.shape.modulated_waveform_i(sampling_rate).data, + c=f"C{str(n)}", + ) + ax.plot( + time, + pulse.shape.envelope_waveform_i(sampling_rate).data, + c=f"C{str(n)}", + ) + ax.plot( + time, + -pulse.shape.envelope_waveform_i(sampling_rate).data, + c=f"C{str(n)}", + ) + # TODO: if they overlap use different shades + ax.axhline(0, c="dimgrey") + ax.set_ylabel(f"qubit {qubit} \n channel {channel}") + for vl in vertical_lines: + ax.axvline(vl, c="slategrey", linestyle="--") + ax.axis([0, self.finish, -1, 1]) + ax.grid( + visible=True, + which="both", + axis="both", + color="#CCCCCC", + linestyle="-", + ) + if savefig_filename: + plt.savefig(savefig_filename) + else: + plt.show() + plt.close() diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index cd2fed4e2..fef3f9faa 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -1,10 +1,8 @@ """PulseShape class.""" - import re from abc import ABC, abstractmethod -import numpy as np -import numpy.typing as npt +from qibo.config import log from scipy.signal import lfilter SAMPLING_RATE = 1 @@ -14,70 +12,6 @@ a different value. """ -# TODO: they could be distinguished among them, and distinguished from generic float -# arrays, using the NewType pattern -> but this require some more effort to encforce -# types throughout the whole code base -Waveform = npt.NDArray[np.float64] -"""""" -IqWaveform = npt.NDArray[np.float64] -"""""" - - -def modulate( - envelope: IqWaveform, - freq: float, - rate: float = SAMPLING_RATE, - phase: float = 0.0, -) -> IqWaveform: - """Modulate the envelope waveform with a carrier. - - `envelope` is a `(2, n)`-shaped array of I and Q (first dimension) envelope signals, - as a function of time (second dimension), and `freq` the frequency of the carrier to - modulate with (usually the IF) in GHz. - `rate` is an optional sampling rate, in Gs/s, to sample the carrier. - - .. note:: - - Only the combination `freq / rate` is actually relevant, but it is frequently - convenient to specify one in GHz and the other in Gs/s. Thus the two arguments - are provided for the simplicity of their interpretation. - - `phase` is an optional initial phase for the carrier. - """ - samples = np.arange(envelope.shape[1]) - phases = (2 * np.pi * freq / rate) * samples + phase - cos = np.cos(phases) - sin = np.sin(phases) - mod = np.array([[cos, -sin], [sin, cos]]) - - # the normalization is related to `mod`, but only applied at the end for the sake of - # performances - return np.einsum("ijt,jt->it", mod, envelope) / np.sqrt(2) - - -def demodulate( - modulated: IqWaveform, - freq: float, - rate: float = SAMPLING_RATE, -) -> IqWaveform: - """Demodulate the acquired pulse. - - The role of the arguments is the same of the corresponding ones in :func:`modulate`, - which is essentially the inverse of this function. - """ - # in case the offsets have not been removed in hardware - modulated = modulated - np.mean(modulated) - - samples = np.arange(modulated.shape[1]) - phases = (2 * np.pi * freq / rate) * samples - cos = np.cos(phases) - sin = np.sin(phases) - demod = np.array([[cos, sin], [-sin, cos]]) - - # the normalization is related to `demod`, but only applied at the end for the sake - # of performances - return np.sqrt(2) * np.einsum("ijt,jt->it", demod, modulated) - class ShapeInitError(RuntimeError): """Error raised when a pulse has not been fully defined.""" @@ -91,9 +25,11 @@ def __init__(self, msg=None, *args): class PulseShape(ABC): - """Pulse envelopes. + """Abstract class for pulse shapes. - Generates both i (in-phase) and q (quadrature) components. + This object is responsible for generating envelope and modulated + waveforms from a set of pulse parameters and its type. Generates + both i (in-phase) and q (quadrature) components. """ pulse = None @@ -114,7 +50,9 @@ def envelope_waveform_q( ) -> Waveform: # pragma: no cover raise NotImplementedError - def envelope_waveforms(self, sampling_rate=SAMPLING_RATE): + def envelope_waveforms( + self, sampling_rate=SAMPLING_RATE + ): # -> tuple[Waveform, Waveform]: # pragma: no cover """A tuple with the i and q envelope waveforms of the pulse.""" return ( @@ -122,6 +60,54 @@ def envelope_waveforms(self, sampling_rate=SAMPLING_RATE): self.envelope_waveform_q(sampling_rate), ) + def modulated_waveform_i(self, _if: int, sampling_rate=SAMPLING_RATE) -> Waveform: + """The waveform of the i component of the pulse, modulated with its + frequency.""" + + return self.modulated_waveforms(_if, sampling_rate)[0] + + def modulated_waveform_q(self, _if: int, sampling_rate=SAMPLING_RATE) -> Waveform: + """The waveform of the q component of the pulse, modulated with its + frequency.""" + + return self.modulated_waveforms(_if, sampling_rate)[1] + + def modulated_waveforms(self, _if: int, sampling_rate=SAMPLING_RATE): + """A tuple with the i and q waveforms of the pulse, modulated with its + frequency.""" + + pulse = self.pulse + if abs(_if) * 2 > sampling_rate: + log.info( + f"WARNING: The frequency of pulse {pulse.id} is higher than the nyqusit frequency ({int(sampling_rate // 2)}) for the device sampling rate: {int(sampling_rate)}" + ) + num_samples = int(np.rint(pulse.duration * sampling_rate)) + time = np.arange(num_samples) / sampling_rate + global_phase = pulse.global_phase + cosalpha = np.cos(2 * np.pi * _if * time + global_phase + pulse.relative_phase) + sinalpha = np.sin(2 * np.pi * _if * time + global_phase + pulse.relative_phase) + + mod_matrix = np.array([[cosalpha, -sinalpha], [sinalpha, cosalpha]]) / np.sqrt( + 2 + ) + + (envelope_waveform_i, envelope_waveform_q) = self.envelope_waveforms( + sampling_rate + ) + result = [] + for n, t, ii, qq in zip( + np.arange(num_samples), + time, + envelope_waveform_i.data, + envelope_waveform_q.data, + ): + result.append(mod_matrix[:, :, n] @ np.array([ii, qq])) + mod_signals = np.array(result) + + modulated_waveform_i = Waveform(mod_signals[:, 0]) + modulated_waveform_q = Waveform(mod_signals[:, 1]) + return (modulated_waveform_i, modulated_waveform_q) + def __eq__(self, item) -> bool: """Overloads == operator.""" return isinstance(item, type(self)) @@ -137,7 +123,7 @@ def eval(value: str) -> "PulseShape": shape_name = re.findall(r"(\w+)", value)[0] if shape_name not in globals(): raise ValueError(f"shape {value} not found") - shape_parameters = re.findall(r"[-\w+\d\.\d]+", value)[1:] + shape_parameters = re.findall(r"[\w+\d\.\d]+", value)[1:] # TODO: create multiple tests to prove regex working correctly return globals()[shape_name](*shape_parameters) @@ -147,14 +133,15 @@ class Rectangular(PulseShape): def __init__(self): self.name = "Rectangular" - self.pulse: "Pulse" = None + self.pulse: Pulse = None def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the i component of the pulse.""" if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return self.pulse.amplitude * np.ones(num_samples) + waveform = Waveform(self.pulse.amplitude * np.ones(num_samples)) + return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -162,7 +149,8 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return np.zeros(num_samples) + waveform = Waveform(np.zeros(num_samples)) + return waveform raise ShapeInitError def __repr__(self): @@ -185,7 +173,7 @@ class Exponential(PulseShape): def __init__(self, tau: float, upsilon: float, g: float = 0.1): self.name = "Exponential" - self.pulse: "Pulse" = None + self.pulse: Pulse = None self.tau: float = float(tau) self.upsilon: float = float(upsilon) self.g: float = float(g) @@ -196,7 +184,7 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) x = np.arange(0, num_samples, 1) - return ( + waveform = Waveform( self.pulse.amplitude * ( (np.ones(num_samples) * np.exp(-x / self.upsilon)) @@ -205,6 +193,7 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: / (1 + self.g) ) + return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -212,7 +201,8 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return np.zeros(num_samples) + waveform = Waveform(np.zeros(num_samples)) + return waveform raise ShapeInitError def __repr__(self): @@ -232,7 +222,7 @@ class Gaussian(PulseShape): def __init__(self, rel_sigma: float): self.name = "Gaussian" - self.pulse: "Pulse" = None + self.pulse: Pulse = None self.rel_sigma: float = float(rel_sigma) def __eq__(self, item) -> bool: @@ -247,13 +237,17 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) x = np.arange(0, num_samples, 1) - return self.pulse.amplitude * np.exp( - -(1 / 2) - * ( - ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / self.rel_sigma) ** 2) + waveform = Waveform( + self.pulse.amplitude + * np.exp( + -(1 / 2) + * ( + ((x - (num_samples - 1) / 2) ** 2) + / (((num_samples) / self.rel_sigma) ** 2) + ) ) ) + return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -261,7 +255,8 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return np.zeros(num_samples) + waveform = Waveform(np.zeros(num_samples)) + return waveform raise ShapeInitError def __repr__(self): @@ -282,7 +277,7 @@ class GaussianSquare(PulseShape): def __init__(self, rel_sigma: float, width: float): self.name = "GaussianSquare" - self.pulse: "Pulse" = None + self.pulse: Pulse = None self.rel_sigma: float = float(rel_sigma) self.width: float = float(width) @@ -319,7 +314,8 @@ def fvec(t, gaussian_samples, rel_sigma, length=None): pulse = fvec(t, gaussian_samples, rel_sigma=self.rel_sigma) - return self.pulse.amplitude * pulse + waveform = Waveform(self.pulse.amplitude * pulse) + return waveform raise ShapeInitError @@ -328,7 +324,8 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return np.zeros(num_samples) + waveform = Waveform(np.zeros(num_samples)) + return waveform raise ShapeInitError def __repr__(self): @@ -346,7 +343,7 @@ class Drag(PulseShape): def __init__(self, rel_sigma, beta): self.name = "Drag" - self.pulse: "Pulse" = None + self.pulse: Pulse = None self.rel_sigma = float(rel_sigma) self.beta = float(beta) @@ -362,13 +359,15 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) x = np.arange(0, num_samples, 1) - return self.pulse.amplitude * np.exp( + i = self.pulse.amplitude * np.exp( -(1 / 2) * ( ((x - (num_samples - 1) / 2) ** 2) / (((num_samples) / self.rel_sigma) ** 2) ) ) + waveform = Waveform(i) + return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -384,11 +383,13 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: / (((num_samples) / self.rel_sigma) ** 2) ) ) - return ( + q = ( self.beta * (-(x - (num_samples - 1) / 2) / ((num_samples / self.rel_sigma) ** 2)) * i ) + waveform = Waveform(q) + return waveform raise ShapeInitError def __repr__(self): @@ -406,7 +407,7 @@ class IIR(PulseShape): def __init__(self, b, a, target: PulseShape): self.name = "IIR" self.target: PulseShape = target - self._pulse: "Pulse" = None + self._pulse: Pulse = None self.a: np.ndarray = np.array(a) self.b: np.ndarray = np.array(b) # Check len(a) = len(b) = 2 @@ -442,11 +443,13 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: data = lfilter( b=self.b, a=self.a, - x=self.target.envelope_waveform_i(sampling_rate), + x=self.target.envelope_waveform_i(sampling_rate).data, ) if not np.max(np.abs(data)) == 0: data = data / np.max(np.abs(data)) - return np.abs(self.pulse.amplitude) * data + data = np.abs(self.pulse.amplitude) * data + waveform = Waveform(data) + return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -461,11 +464,13 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: data = lfilter( b=self.b, a=self.a, - x=self.target.envelope_waveform_q(sampling_rate), + x=self.target.envelope_waveform_q(sampling_rate).data, ) if not np.max(np.abs(data)) == 0: data = data / np.max(np.abs(data)) - return np.abs(self.pulse.amplitude) * data + data = np.abs(self.pulse.amplitude) * data + waveform = Waveform(data) + return waveform raise ShapeInitError def __repr__(self): @@ -483,7 +488,7 @@ class SNZ(PulseShape): def __init__(self, t_idling, b_amplitude=None): self.name = "SNZ" - self.pulse: "Pulse" = None + self.pulse: Pulse = None self.t_idling: float = t_idling self.b_amplitude = b_amplitude @@ -511,15 +516,18 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: np.rint(num_samples * half_pulse_duration / self.pulse.duration) ) idling_samples = num_samples - 2 * half_flux_pulse_samples - return np.concatenate( - ( - self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), - np.array([self.b_amplitude]), - np.zeros(idling_samples), - -np.array([self.b_amplitude]), - -self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), + waveform = Waveform( + np.concatenate( + ( + self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), + np.array([self.b_amplitude]), + np.zeros(idling_samples), + -np.array([self.b_amplitude]), + -self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), + ) ) ) + return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -527,7 +535,8 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return np.zeros(num_samples) + waveform = Waveform(np.zeros(num_samples)) + return waveform raise ShapeInitError def __repr__(self): @@ -548,7 +557,7 @@ class eCap(PulseShape): def __init__(self, alpha: float): self.name = "eCap" - self.pulse: "Pulse" = None + self.pulse: Pulse = None self.alpha: float = float(alpha) def __eq__(self, item) -> bool: @@ -561,18 +570,20 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(self.pulse.duration * sampling_rate) x = np.arange(0, num_samples, 1) - return ( + waveform = Waveform( self.pulse.amplitude * (1 + np.tanh(self.alpha * x / num_samples)) * (1 + np.tanh(self.alpha * (1 - x / num_samples))) / (1 + np.tanh(self.alpha / 2)) ** 2 ) + return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(self.pulse.duration * sampling_rate) - return np.zeros(num_samples) + waveform = Waveform(np.zeros(num_samples)) + return waveform raise ShapeInitError def __repr__(self): @@ -584,7 +595,7 @@ class Custom(PulseShape): def __init__(self, envelope_i, envelope_q=None): self.name = "Custom" - self.pulse: "Pulse" = None + self.pulse: Pulse = None self.envelope_i: np.ndarray = np.array(envelope_i) if envelope_q is not None: self.envelope_q: np.ndarray = np.array(envelope_q) @@ -599,7 +610,8 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: raise ValueError("Length of envelope_i must be equal to pulse duration") num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return self.envelope_i * self.pulse.amplitude + waveform = Waveform(self.envelope_i * self.pulse.amplitude) + return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: @@ -610,7 +622,8 @@ def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: raise ValueError("Length of envelope_q must be equal to pulse duration") num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return self.envelope_q * self.pulse.amplitude + waveform = Waveform(self.envelope_q * self.pulse.amplitude) + return waveform raise ShapeInitError def __repr__(self): diff --git a/src/qibolab/pulses/waveform.py b/src/qibolab/pulses/waveform.py new file mode 100644 index 000000000..7c530bf36 --- /dev/null +++ b/src/qibolab/pulses/waveform.py @@ -0,0 +1,42 @@ +"""Waveform class.""" +import numpy as np + + +class Waveform: + """A class to save pulse waveforms. + + A waveform is a list of samples, or discrete data points, used by the digital to analogue converters (DACs) + to synthesise pulses. + + Attributes: + data (np.ndarray): a numpy array containing the samples. + """ + + DECIMALS = 5 + + def __init__(self, data): + """Initialise the waveform with a of samples.""" + self.data: np.ndarray = np.array(data) + + def __len__(self): + """Return the length of the waveform, the number of samples.""" + return len(self.data) + + def __hash__(self): + """Hash the underlying data. + + .. todo:: + + In order to make this reliable, we should set the data as immutable. This we + could by making both the class frozen and the contained array readonly + https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags + """ + return hash(self.data.tobytes()) + + def __eq__(self, other): + """Compare two waveforms. + + Two waveforms are considered equal if their samples, rounded to + `Waveform.DECIMALS` decimal places, are all equal. + """ + return np.allclose(self.data, other.data) From 9d1f433a18470b6e9038354893a975aa7b823ccd Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Wed, 20 Mar 2024 14:09:16 +0400 Subject: [PATCH 113/233] test: fix tests --- tests/test_instruments_qm.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index f7afcb08a..62b3ef994 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -94,7 +94,6 @@ def test_qmpulse_previous_and_next(): f"readout{qubit}", PulseType.READOUT, qubit=qubit, - type=PulseType.READOUT, ) ) ro_qmpulses.append(ro_pulse) From d42b595eebdc26e863b66a2136dad2bbf843453a Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 23 Feb 2024 19:47:47 +0100 Subject: [PATCH 114/233] feat: Drop ShapeInitError in favor of pulse default value If uninitialize, it will raise an error on its own when usage is attempted --- src/qibolab/pulses/shape.py | 482 +++++++++++++++--------------------- 1 file changed, 204 insertions(+), 278 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index fef3f9faa..935028a10 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -1,8 +1,10 @@ """PulseShape class.""" + import re from abc import ABC, abstractmethod -from qibo.config import log +import numpy as np +import numpy.typing as npt from scipy.signal import lfilter SAMPLING_RATE = 1 @@ -12,24 +14,75 @@ a different value. """ +# TODO: they could be distinguished among them, and distinguished from generic float +# arrays, using the NewType pattern -> but this require some more effort to encforce +# types throughout the whole code base +Waveform = npt.NDArray[np.float64] +"""""" +IqWaveform = npt.NDArray[np.float64] +"""""" + + +def modulate( + envelope: IqWaveform, + freq: float, + rate: float = SAMPLING_RATE, + phase: float = 0.0, +) -> IqWaveform: + """Modulate the envelope waveform with a carrier. + + `envelope` is a `(2, n)`-shaped array of I and Q (first dimension) envelope signals, + as a function of time (second dimension), and `freq` the frequency of the carrier to + modulate with (usually the IF) in GHz. + `rate` is an optional sampling rate, in Gs/s, to sample the carrier. -class ShapeInitError(RuntimeError): - """Error raised when a pulse has not been fully defined.""" + .. note:: + + Only the combination `freq / rate` is actually relevant, but it is frequently + convenient to specify one in GHz and the other in Gs/s. Thus the two arguments + are provided for the simplicity of their interpretation. + + `phase` is an optional initial phase for the carrier. + """ + samples = np.arange(envelope.shape[1]) + phases = (2 * np.pi * freq / rate) * samples + phase + cos = np.cos(phases) + sin = np.sin(phases) + mod = np.array([[cos, -sin], [sin, cos]]) + + # the normalization is related to `mod`, but only applied at the end for the sake of + # performances + return np.einsum("ijt,jt->it", mod, envelope) / np.sqrt(2) + + +def demodulate( + modulated: IqWaveform, + freq: float, + rate: float = SAMPLING_RATE, +) -> IqWaveform: + """Demodulate the acquired pulse. + + The role of the arguments is the same of the corresponding ones in :func:`modulate`, + which is essentially the inverse of this function. + """ + # in case the offsets have not been removed in hardware + modulated = modulated - np.mean(modulated) - default_msg = "PulseShape attribute pulse must be initialised in order to be able to generate pulse waveforms" + samples = np.arange(modulated.shape[1]) + phases = (2 * np.pi * freq / rate) * samples + cos = np.cos(phases) + sin = np.sin(phases) + demod = np.array([[cos, sin], [-sin, cos]]) - def __init__(self, msg=None, *args): - if msg is None: - msg = self.default_msg - super().__init__(msg, *args) + # the normalization is related to `demod`, but only applied at the end for the sake + # of performances + return np.sqrt(2) * np.einsum("ijt,jt->it", demod, modulated) class PulseShape(ABC): - """Abstract class for pulse shapes. + """Pulse envelopes. - This object is responsible for generating envelope and modulated - waveforms from a set of pulse parameters and its type. Generates - both i (in-phase) and q (quadrature) components. + Generates both i (in-phase) and q (quadrature) components. """ pulse = None @@ -50,9 +103,7 @@ def envelope_waveform_q( ) -> Waveform: # pragma: no cover raise NotImplementedError - def envelope_waveforms( - self, sampling_rate=SAMPLING_RATE - ): # -> tuple[Waveform, Waveform]: # pragma: no cover + def envelope_waveforms(self, sampling_rate=SAMPLING_RATE): """A tuple with the i and q envelope waveforms of the pulse.""" return ( @@ -60,54 +111,6 @@ def envelope_waveforms( self.envelope_waveform_q(sampling_rate), ) - def modulated_waveform_i(self, _if: int, sampling_rate=SAMPLING_RATE) -> Waveform: - """The waveform of the i component of the pulse, modulated with its - frequency.""" - - return self.modulated_waveforms(_if, sampling_rate)[0] - - def modulated_waveform_q(self, _if: int, sampling_rate=SAMPLING_RATE) -> Waveform: - """The waveform of the q component of the pulse, modulated with its - frequency.""" - - return self.modulated_waveforms(_if, sampling_rate)[1] - - def modulated_waveforms(self, _if: int, sampling_rate=SAMPLING_RATE): - """A tuple with the i and q waveforms of the pulse, modulated with its - frequency.""" - - pulse = self.pulse - if abs(_if) * 2 > sampling_rate: - log.info( - f"WARNING: The frequency of pulse {pulse.id} is higher than the nyqusit frequency ({int(sampling_rate // 2)}) for the device sampling rate: {int(sampling_rate)}" - ) - num_samples = int(np.rint(pulse.duration * sampling_rate)) - time = np.arange(num_samples) / sampling_rate - global_phase = pulse.global_phase - cosalpha = np.cos(2 * np.pi * _if * time + global_phase + pulse.relative_phase) - sinalpha = np.sin(2 * np.pi * _if * time + global_phase + pulse.relative_phase) - - mod_matrix = np.array([[cosalpha, -sinalpha], [sinalpha, cosalpha]]) / np.sqrt( - 2 - ) - - (envelope_waveform_i, envelope_waveform_q) = self.envelope_waveforms( - sampling_rate - ) - result = [] - for n, t, ii, qq in zip( - np.arange(num_samples), - time, - envelope_waveform_i.data, - envelope_waveform_q.data, - ): - result.append(mod_matrix[:, :, n] @ np.array([ii, qq])) - mod_signals = np.array(result) - - modulated_waveform_i = Waveform(mod_signals[:, 0]) - modulated_waveform_q = Waveform(mod_signals[:, 1]) - return (modulated_waveform_i, modulated_waveform_q) - def __eq__(self, item) -> bool: """Overloads == operator.""" return isinstance(item, type(self)) @@ -133,25 +136,17 @@ class Rectangular(PulseShape): def __init__(self): self.name = "Rectangular" - self.pulse: Pulse = None + self.pulse: "Pulse" = None def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the i component of the pulse.""" - - if self.pulse: - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(self.pulse.amplitude * np.ones(num_samples)) - return waveform - raise ShapeInitError + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + return self.pulse.amplitude * np.ones(num_samples) def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the q component of the pulse.""" - - if self.pulse: - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(np.zeros(num_samples)) - return waveform - raise ShapeInitError + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + return np.zeros(num_samples) def __repr__(self): return f"{self.name}()" @@ -173,37 +168,28 @@ class Exponential(PulseShape): def __init__(self, tau: float, upsilon: float, g: float = 0.1): self.name = "Exponential" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.tau: float = float(tau) self.upsilon: float = float(upsilon) self.g: float = float(g) def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the i component of the pulse.""" - - if self.pulse: - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - x = np.arange(0, num_samples, 1) - waveform = Waveform( - self.pulse.amplitude - * ( - (np.ones(num_samples) * np.exp(-x / self.upsilon)) - + self.g * np.exp(-x / self.tau) - ) - / (1 + self.g) + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + x = np.arange(0, num_samples, 1) + return ( + self.pulse.amplitude + * ( + (np.ones(num_samples) * np.exp(-x / self.upsilon)) + + self.g * np.exp(-x / self.tau) ) - - return waveform - raise ShapeInitError + / (1 + self.g) + ) def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the q component of the pulse.""" - - if self.pulse: - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(np.zeros(num_samples)) - return waveform - raise ShapeInitError + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + return np.zeros(num_samples) def __repr__(self): return f"{self.name}({format(self.tau, '.3f').rstrip('0').rstrip('.')}, {format(self.upsilon, '.3f').rstrip('0').rstrip('.')}, {format(self.g, '.3f').rstrip('0').rstrip('.')})" @@ -222,7 +208,7 @@ class Gaussian(PulseShape): def __init__(self, rel_sigma: float): self.name = "Gaussian" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.rel_sigma: float = float(rel_sigma) def __eq__(self, item) -> bool: @@ -237,27 +223,19 @@ def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: if self.pulse: num_samples = int(np.rint(self.pulse.duration * sampling_rate)) x = np.arange(0, num_samples, 1) - waveform = Waveform( - self.pulse.amplitude - * np.exp( - -(1 / 2) - * ( - ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / self.rel_sigma) ** 2) - ) + return self.pulse.amplitude * np.exp( + -(1 / 2) + * ( + ((x - (num_samples - 1) / 2) ** 2) + / (((num_samples) / self.rel_sigma) ** 2) ) ) - return waveform raise ShapeInitError def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the q component of the pulse.""" - - if self.pulse: - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(np.zeros(num_samples)) - return waveform - raise ShapeInitError + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + return np.zeros(num_samples) def __repr__(self): return f"{self.name}({format(self.rel_sigma, '.6f').rstrip('0').rstrip('.')})" @@ -277,7 +255,7 @@ class GaussianSquare(PulseShape): def __init__(self, rel_sigma: float, width: float): self.name = "GaussianSquare" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.rel_sigma: float = float(rel_sigma) self.width: float = float(width) @@ -290,43 +268,34 @@ def __eq__(self, item) -> bool: def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the i component of the pulse.""" - if self.pulse: - - def gaussian(t, rel_sigma, gaussian_samples): - mu = (2 * gaussian_samples - 1) / 2 - sigma = (2 * gaussian_samples) / rel_sigma - return np.exp(-0.5 * ((t - mu) / sigma) ** 2) + def gaussian(t, rel_sigma, gaussian_samples): + mu = (2 * gaussian_samples - 1) / 2 + sigma = (2 * gaussian_samples) / rel_sigma + return np.exp(-0.5 * ((t - mu) / sigma) ** 2) - def fvec(t, gaussian_samples, rel_sigma, length=None): - if length is None: - length = t.shape[0] + def fvec(t, gaussian_samples, rel_sigma, length=None): + if length is None: + length = t.shape[0] - pulse = np.ones_like(t, dtype=float) - rise = t < gaussian_samples - fall = t > length - gaussian_samples - 1 - pulse[rise] = gaussian(t[rise], rel_sigma, gaussian_samples) - pulse[fall] = gaussian(t[rise], rel_sigma, gaussian_samples)[::-1] - return pulse + pulse = np.ones_like(t, dtype=float) + rise = t < gaussian_samples + fall = t > length - gaussian_samples - 1 + pulse[rise] = gaussian(t[rise], rel_sigma, gaussian_samples) + pulse[fall] = gaussian(t[rise], rel_sigma, gaussian_samples)[::-1] + return pulse - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - gaussian_samples = num_samples * (1 - self.width) // 2 - t = np.arange(0, num_samples) - - pulse = fvec(t, gaussian_samples, rel_sigma=self.rel_sigma) + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + gaussian_samples = num_samples * (1 - self.width) // 2 + t = np.arange(0, num_samples) - waveform = Waveform(self.pulse.amplitude * pulse) - return waveform + pulse = fvec(t, gaussian_samples, rel_sigma=self.rel_sigma) - raise ShapeInitError + return self.pulse.amplitude * pulse def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the q component of the pulse.""" - - if self.pulse: - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(np.zeros(num_samples)) - return waveform - raise ShapeInitError + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + return np.zeros(num_samples) def __repr__(self): return f"{self.name}({format(self.rel_sigma, '.6f').rstrip('0').rstrip('.')}, {format(self.width, '.6f').rstrip('0').rstrip('.')})" @@ -343,7 +312,7 @@ class Drag(PulseShape): def __init__(self, rel_sigma, beta): self.name = "Drag" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.rel_sigma = float(rel_sigma) self.beta = float(beta) @@ -355,42 +324,33 @@ def __eq__(self, item) -> bool: def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the i component of the pulse.""" - - if self.pulse: - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - x = np.arange(0, num_samples, 1) - i = self.pulse.amplitude * np.exp( - -(1 / 2) - * ( - ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / self.rel_sigma) ** 2) - ) + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + x = np.arange(0, num_samples, 1) + return self.pulse.amplitude * np.exp( + -(1 / 2) + * ( + ((x - (num_samples - 1) / 2) ** 2) + / (((num_samples) / self.rel_sigma) ** 2) ) - waveform = Waveform(i) - return waveform - raise ShapeInitError + ) def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the q component of the pulse.""" - - if self.pulse: - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - x = np.arange(0, num_samples, 1) - i = self.pulse.amplitude * np.exp( - -(1 / 2) - * ( - ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / self.rel_sigma) ** 2) - ) + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + x = np.arange(0, num_samples, 1) + i = self.pulse.amplitude * np.exp( + -(1 / 2) + * ( + ((x - (num_samples - 1) / 2) ** 2) + / (((num_samples) / self.rel_sigma) ** 2) ) - q = ( - self.beta - * (-(x - (num_samples - 1) / 2) / ((num_samples / self.rel_sigma) ** 2)) - * i - ) - waveform = Waveform(q) - return waveform - raise ShapeInitError + ) + return ( + self.beta + * (-(x - (num_samples - 1) / 2) / ((num_samples / self.rel_sigma) ** 2)) + * i + * sampling_rate + ) def __repr__(self): return f"{self.name}({format(self.rel_sigma, '.6f').rstrip('0').rstrip('.')}, {format(self.beta, '.6f').rstrip('0').rstrip('.')})" @@ -407,7 +367,7 @@ class IIR(PulseShape): def __init__(self, b, a, target: PulseShape): self.name = "IIR" self.target: PulseShape = target - self._pulse: Pulse = None + self._pulse: "Pulse" = None self.a: np.ndarray = np.array(a) self.b: np.ndarray = np.array(b) # Check len(a) = len(b) = 2 @@ -433,45 +393,35 @@ def pulse(self, value): def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the i component of the pulse.""" - - if self.pulse: - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - self.a = self.a / self.a[0] - gain = np.sum(self.b) / np.sum(self.a) - if not gain == 0: - self.b = self.b / gain - data = lfilter( - b=self.b, - a=self.a, - x=self.target.envelope_waveform_i(sampling_rate).data, - ) - if not np.max(np.abs(data)) == 0: - data = data / np.max(np.abs(data)) - data = np.abs(self.pulse.amplitude) * data - waveform = Waveform(data) - return waveform - raise ShapeInitError + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + self.a = self.a / self.a[0] + gain = np.sum(self.b) / np.sum(self.a) + if not gain == 0: + self.b = self.b / gain + data = lfilter( + b=self.b, + a=self.a, + x=self.target.envelope_waveform_i(sampling_rate), + ) + if not np.max(np.abs(data)) == 0: + data = data / np.max(np.abs(data)) + return np.abs(self.pulse.amplitude) * data def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the q component of the pulse.""" - - if self.pulse: - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - self.a = self.a / self.a[0] - gain = np.sum(self.b) / np.sum(self.a) - if not gain == 0: - self.b = self.b / gain - data = lfilter( - b=self.b, - a=self.a, - x=self.target.envelope_waveform_q(sampling_rate).data, - ) - if not np.max(np.abs(data)) == 0: - data = data / np.max(np.abs(data)) - data = np.abs(self.pulse.amplitude) * data - waveform = Waveform(data) - return waveform - raise ShapeInitError + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + self.a = self.a / self.a[0] + gain = np.sum(self.b) / np.sum(self.a) + if not gain == 0: + self.b = self.b / gain + data = lfilter( + b=self.b, + a=self.a, + x=self.target.envelope_waveform_q(sampling_rate), + ) + if not np.max(np.abs(data)) == 0: + data = data / np.max(np.abs(data)) + return np.abs(self.pulse.amplitude) * data def __repr__(self): formatted_b = [round(b, 3) for b in self.b] @@ -488,7 +438,7 @@ class SNZ(PulseShape): def __init__(self, t_idling, b_amplitude=None): self.name = "SNZ" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.t_idling: float = t_idling self.b_amplitude = b_amplitude @@ -502,42 +452,32 @@ def __eq__(self, item) -> bool: def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the i component of the pulse.""" - - if self.pulse: - if self.t_idling > self.pulse.duration: - raise ValueError( - f"Cannot put idling time {self.t_idling} higher than duration {self.pulse.duration}." - ) - if self.b_amplitude is None: - self.b_amplitude = self.pulse.amplitude / 2 - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - half_pulse_duration = (self.pulse.duration - self.t_idling) / 2 - half_flux_pulse_samples = int( - np.rint(num_samples * half_pulse_duration / self.pulse.duration) + if self.t_idling > self.pulse.duration: + raise ValueError( + f"Cannot put idling time {self.t_idling} higher than duration {self.pulse.duration}." ) - idling_samples = num_samples - 2 * half_flux_pulse_samples - waveform = Waveform( - np.concatenate( - ( - self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), - np.array([self.b_amplitude]), - np.zeros(idling_samples), - -np.array([self.b_amplitude]), - -self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), - ) - ) + if self.b_amplitude is None: + self.b_amplitude = self.pulse.amplitude / 2 + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + half_pulse_duration = (self.pulse.duration - self.t_idling) / 2 + half_flux_pulse_samples = int( + np.rint(num_samples * half_pulse_duration / self.pulse.duration) + ) + idling_samples = num_samples - 2 * half_flux_pulse_samples + return np.concatenate( + ( + self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), + np.array([self.b_amplitude]), + np.zeros(idling_samples), + -np.array([self.b_amplitude]), + -self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), ) - return waveform - raise ShapeInitError + ) def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the q component of the pulse.""" - - if self.pulse: - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - waveform = Waveform(np.zeros(num_samples)) - return waveform - raise ShapeInitError + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) + return np.zeros(num_samples) def __repr__(self): return f"{self.name}({self.t_idling})" @@ -557,7 +497,7 @@ class eCap(PulseShape): def __init__(self, alpha: float): self.name = "eCap" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.alpha: float = float(alpha) def __eq__(self, item) -> bool: @@ -567,24 +507,18 @@ def __eq__(self, item) -> bool: return False def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - if self.pulse: - num_samples = int(self.pulse.duration * sampling_rate) - x = np.arange(0, num_samples, 1) - waveform = Waveform( - self.pulse.amplitude - * (1 + np.tanh(self.alpha * x / num_samples)) - * (1 + np.tanh(self.alpha * (1 - x / num_samples))) - / (1 + np.tanh(self.alpha / 2)) ** 2 - ) - return waveform - raise ShapeInitError + num_samples = int(self.pulse.duration * sampling_rate) + x = np.arange(0, num_samples, 1) + return ( + self.pulse.amplitude + * (1 + np.tanh(self.alpha * x / num_samples)) + * (1 + np.tanh(self.alpha * (1 - x / num_samples))) + / (1 + np.tanh(self.alpha / 2)) ** 2 + ) def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - if self.pulse: - num_samples = int(self.pulse.duration * sampling_rate) - waveform = Waveform(np.zeros(num_samples)) - return waveform - raise ShapeInitError + num_samples = int(self.pulse.duration * sampling_rate) + return np.zeros(num_samples) def __repr__(self): return f"{self.name}({format(self.alpha, '.6f').rstrip('0').rstrip('.')})" @@ -595,7 +529,7 @@ class Custom(PulseShape): def __init__(self, envelope_i, envelope_q=None): self.name = "Custom" - self.pulse: Pulse = None + self.pulse: "Pulse" = None self.envelope_i: np.ndarray = np.array(envelope_i) if envelope_q is not None: self.envelope_q: np.ndarray = np.array(envelope_q) @@ -604,27 +538,19 @@ def __init__(self, envelope_i, envelope_q=None): def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the i component of the pulse.""" + if self.pulse.duration != len(self.envelope_i): + raise ValueError("Length of envelope_i must be equal to pulse duration") + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - if self.pulse: - if self.pulse.duration != len(self.envelope_i): - raise ValueError("Length of envelope_i must be equal to pulse duration") - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - - waveform = Waveform(self.envelope_i * self.pulse.amplitude) - return waveform - raise ShapeInitError + return self.envelope_i * self.pulse.amplitude def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: """The envelope waveform of the q component of the pulse.""" + if self.pulse.duration != len(self.envelope_q): + raise ValueError("Length of envelope_q must be equal to pulse duration") + num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - if self.pulse: - if self.pulse.duration != len(self.envelope_q): - raise ValueError("Length of envelope_q must be equal to pulse duration") - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - - waveform = Waveform(self.envelope_q * self.pulse.amplitude) - return waveform - raise ShapeInitError + return self.envelope_q * self.pulse.amplitude def __repr__(self): return f"{self.name}({self.envelope_i[:3]}, ..., {self.envelope_q[:3]}, ...)" From 11f2987031979086c893569f12af4c246b5aac29 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 23 Feb 2024 19:55:58 +0100 Subject: [PATCH 115/233] feat: Sketch the new Shape template --- src/qibolab/pulses/shape.py | 53 ++++++++----------------------------- 1 file changed, 11 insertions(+), 42 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 935028a10..16999a0f0 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -1,7 +1,7 @@ """PulseShape class.""" -import re from abc import ABC, abstractmethod +from dataclasses import dataclass import numpy as np import numpy.typing as npt @@ -14,6 +14,7 @@ a different value. """ +Times = npt.NDArray[np.float64] # TODO: they could be distinguished among them, and distinguished from generic float # arrays, using the NewType pattern -> but this require some more effort to encforce # types throughout the whole code base @@ -79,58 +80,26 @@ def demodulate( return np.sqrt(2) * np.einsum("ijt,jt->it", demod, modulated) -class PulseShape(ABC): +class Shape(ABC): """Pulse envelopes. Generates both i (in-phase) and q (quadrature) components. """ - pulse = None - """Pulse (Pulse): the pulse associated with it. - - Its parameters are used to generate pulse waveforms. - """ - @abstractmethod - def envelope_waveform_i( - self, sampling_rate=SAMPLING_RATE - ) -> Waveform: # pragma: no cover - raise NotImplementedError + def i(self, times: Times) -> Waveform: + """In-phase envelope.""" @abstractmethod - def envelope_waveform_q( - self, sampling_rate=SAMPLING_RATE - ) -> Waveform: # pragma: no cover - raise NotImplementedError - - def envelope_waveforms(self, sampling_rate=SAMPLING_RATE): - """A tuple with the i and q envelope waveforms of the pulse.""" - - return ( - self.envelope_waveform_i(sampling_rate), - self.envelope_waveform_q(sampling_rate), - ) - - def __eq__(self, item) -> bool: - """Overloads == operator.""" - return isinstance(item, type(self)) - - @staticmethod - def eval(value: str) -> "PulseShape": - """Deserialize string representation. - - .. todo:: + def q(self, times: Times) -> Waveform: + """Quadrature envelope.""" - To be replaced by proper serialization. - """ - shape_name = re.findall(r"(\w+)", value)[0] - if shape_name not in globals(): - raise ValueError(f"shape {value} not found") - shape_parameters = re.findall(r"[\w+\d\.\d]+", value)[1:] - # TODO: create multiple tests to prove regex working correctly - return globals()[shape_name](*shape_parameters) + def envelopes(self, times: Times) -> IqWaveform: + """Stacked i and q envelope waveforms of the pulse.""" + return np.array(self.i(times), self.q(times)) +@dataclass(frozen=True) class Rectangular(PulseShape): """Rectangular pulse shape.""" From 32360cf8cf05bf25cca559f0535e5281cd298a42 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 23 Feb 2024 20:04:23 +0100 Subject: [PATCH 116/233] feat: Trim the Rectangular pulse --- src/qibolab/pulses/shape.py | 25 +++++++++---------------- 1 file changed, 9 insertions(+), 16 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 16999a0f0..02e6d09fa 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -100,25 +100,18 @@ def envelopes(self, times: Times) -> IqWaveform: @dataclass(frozen=True) -class Rectangular(PulseShape): - """Rectangular pulse shape.""" +class Rectangular(Shape): + """Rectangular envelope.""" - def __init__(self): - self.name = "Rectangular" - self.pulse: "Pulse" = None - - def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the i component of the pulse.""" - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return self.pulse.amplitude * np.ones(num_samples) + amplitude: float - def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the q component of the pulse.""" - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return np.zeros(num_samples) + def i(self, times: Times) -> Waveform: + """Generate a rectangular envelope.""" + return self.amplitude * np.ones_like(times) - def __repr__(self): - return f"{self.name}()" + def q(self, times: Times) -> Waveform: + """Generate an identically null signal.""" + return np.zeros_like(times) class Exponential(PulseShape): From 434a6d3f79f4bc4d56793c17aea371f62a81e289 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 23 Feb 2024 20:05:34 +0100 Subject: [PATCH 117/233] feat: Add a Shapes enum, for sum-types deserialization --- src/qibolab/pulses/shape.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 02e6d09fa..a6adb8ae7 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -2,6 +2,7 @@ from abc import ABC, abstractmethod from dataclasses import dataclass +from enum import Enum import numpy as np import numpy.typing as npt @@ -114,6 +115,12 @@ def q(self, times: Times) -> Waveform: return np.zeros_like(times) +class Shapes(Enum): + """Available pulse shapes.""" + + rectangular = Rectangular + + class Exponential(PulseShape): r"""Exponential pulse shape (Square pulse with an exponential decay). From 9f4259de4b74cdbe97fbf0e38b37bcc1233230e7 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 23 Feb 2024 20:14:38 +0100 Subject: [PATCH 118/233] feat: Rework the Exponential shape First non-trivial vectorization example --- src/qibolab/pulses/shape.py | 58 +++++++++++++++---------------------- 1 file changed, 23 insertions(+), 35 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index a6adb8ae7..aab19e37c 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -115,53 +115,41 @@ def q(self, times: Times) -> Waveform: return np.zeros_like(times) -class Shapes(Enum): - """Available pulse shapes.""" - - rectangular = Rectangular - - -class Exponential(PulseShape): - r"""Exponential pulse shape (Square pulse with an exponential decay). - - Args: - tau (float): Parameter that controls the decay of the first exponential function - upsilon (float): Parameter that controls the decay of the second exponential function - g (float): Parameter that weights the second exponential function - +@dataclass(frozen=True) +class Exponential(Shape): + r"""Exponential shape, i.e. square pulse with an exponential decay. .. math:: A\frac{\exp\left(-\frac{x}{\text{upsilon}}\right) + g \exp\left(-\frac{x}{\text{tau}}\right)}{1 + g} """ - def __init__(self, tau: float, upsilon: float, g: float = 0.1): - self.name = "Exponential" - self.pulse: "Pulse" = None - self.tau: float = float(tau) - self.upsilon: float = float(upsilon) - self.g: float = float(g) + amplitude: float + tau: float + """The decay rate of the first exponential function.""" + upsilon: float + """The decay rate of the second exponential function.""" + g: float = 0.1 + """Weight of the second exponential function.""" - def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the i component of the pulse.""" - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - x = np.arange(0, num_samples, 1) + def i(self, times: Times) -> Waveform: + """Generate a combination of two exponential decays.""" return ( - self.pulse.amplitude - * ( - (np.ones(num_samples) * np.exp(-x / self.upsilon)) - + self.g * np.exp(-x / self.tau) - ) + self.amplitude + * (np.exp(-times / self.upsilon) + self.g * np.exp(-times / self.tau)) / (1 + self.g) ) - def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the q component of the pulse.""" - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return np.zeros(num_samples) + def q(self, times: Times) -> Waveform: + """Generate an identically null signal.""" + return np.zeros_like(times) - def __repr__(self): - return f"{self.name}({format(self.tau, '.3f').rstrip('0').rstrip('.')}, {format(self.upsilon, '.3f').rstrip('0').rstrip('.')}, {format(self.g, '.3f').rstrip('0').rstrip('.')})" + +class Shapes(Enum): + """Available pulse shapes.""" + + rectangular = Rectangular + exponential = Exponential class Gaussian(PulseShape): From f59730e1a229debec88367d1d2e3bcf731bc9781 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 18:10:27 +0100 Subject: [PATCH 119/233] feat!: Move Gaussian pulse to new shape --- src/qibolab/pulses/shape.py | 61 ++++++++++++++----------------------- 1 file changed, 23 insertions(+), 38 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index aab19e37c..6845f10a0 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -145,60 +145,45 @@ def q(self, times: Times) -> Waveform: return np.zeros_like(times) -class Shapes(Enum): - """Available pulse shapes.""" - - rectangular = Rectangular - exponential = Exponential - - -class Gaussian(PulseShape): +@dataclass(frozen=True) +class Gaussian(Shape): r"""Gaussian pulse shape. Args: - rel_sigma (float): relative sigma so that the pulse standard deviation (sigma) = duration / rel_sigma + rel_sigma (float): .. math:: A\exp^{-\frac{1}{2}\frac{(t-\mu)^2}{\sigma^2}} """ - def __init__(self, rel_sigma: float): - self.name = "Gaussian" - self.pulse: "Pulse" = None - self.rel_sigma: float = float(rel_sigma) + amplitude: float + mu: float + sigma: float + """Relative standard deviation. - def __eq__(self, item) -> bool: - """Overloads == operator.""" - if super().__eq__(item): - return self.rel_sigma == item.rel_sigma - return False + The pulse standard deviation will then be `sigma = duration / + rel_sigma`. + """ - def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the i component of the pulse.""" + def i(self, times: Times) -> Waveform: + """Generate a Gaussian window.""" + return self.amplitude * np.exp(-(((times - self.mu) / self.sigma) ** 2) / 2) - if self.pulse: - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - x = np.arange(0, num_samples, 1) - return self.pulse.amplitude * np.exp( - -(1 / 2) - * ( - ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / self.rel_sigma) ** 2) - ) - ) - raise ShapeInitError + def q(self, times: Times) -> Waveform: + """Generate an indentically null signal.""" + return np.zeros_like(times) - def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the q component of the pulse.""" - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return np.zeros(num_samples) - def __repr__(self): - return f"{self.name}({format(self.rel_sigma, '.6f').rstrip('0').rstrip('.')})" +class Shapes(Enum): + """Available pulse shapes.""" + + RECTANGULAR = Rectangular + EXPONENTIAL = Exponential + GAUSSIAN = Gaussian -class GaussianSquare(PulseShape): +class GaussianSquare(Shape): r"""GaussianSquare pulse shape. Args: From ec546488af8e120f46f630356126efc4b897efde Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 18:36:49 +0100 Subject: [PATCH 120/233] feat: Add default implementation for shape components --- src/qibolab/pulses/shape.py | 24 ++++-------------------- 1 file changed, 4 insertions(+), 20 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 6845f10a0..d3c12ea3c 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -87,13 +87,13 @@ class Shape(ABC): Generates both i (in-phase) and q (quadrature) components. """ - @abstractmethod def i(self, times: Times) -> Waveform: """In-phase envelope.""" + return np.zeros_like(times) - @abstractmethod def q(self, times: Times) -> Waveform: """Quadrature envelope.""" + return np.zeros_like(times) def envelopes(self, times: Times) -> IqWaveform: """Stacked i and q envelope waveforms of the pulse.""" @@ -110,10 +110,6 @@ def i(self, times: Times) -> Waveform: """Generate a rectangular envelope.""" return self.amplitude * np.ones_like(times) - def q(self, times: Times) -> Waveform: - """Generate an identically null signal.""" - return np.zeros_like(times) - @dataclass(frozen=True) class Exponential(Shape): @@ -140,10 +136,6 @@ def i(self, times: Times) -> Waveform: / (1 + self.g) ) - def q(self, times: Times) -> Waveform: - """Generate an identically null signal.""" - return np.zeros_like(times) - @dataclass(frozen=True) class Gaussian(Shape): @@ -159,22 +151,14 @@ class Gaussian(Shape): amplitude: float mu: float + """Gaussian mean.""" sigma: float - """Relative standard deviation. - - The pulse standard deviation will then be `sigma = duration / - rel_sigma`. - """ + """Gaussian standard deviation.""" def i(self, times: Times) -> Waveform: """Generate a Gaussian window.""" return self.amplitude * np.exp(-(((times - self.mu) / self.sigma) ** 2) / 2) - def q(self, times: Times) -> Waveform: - """Generate an indentically null signal.""" - return np.zeros_like(times) - - class Shapes(Enum): """Available pulse shapes.""" From cbff53ba47e40ce775e0e556fc84fd05b44a3c9b Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 18:48:06 +0100 Subject: [PATCH 121/233] feat!: Move GaussianSquare to new shape --- src/qibolab/pulses/shape.py | 79 +++++++++++++------------------------ 1 file changed, 27 insertions(+), 52 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index d3c12ea3c..b01f2c1b7 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -1,6 +1,6 @@ """PulseShape class.""" -from abc import ABC, abstractmethod +from abc import ABC from dataclasses import dataclass from enum import Enum @@ -137,6 +137,11 @@ def i(self, times: Times) -> Waveform: ) +def _gaussian(t, mu, sigma): + """Gaussian function, normalized to be 1 at the max.""" + return np.exp(-(((t - mu) / sigma) ** 2) / 2) + + @dataclass(frozen=True) class Gaussian(Shape): r"""Gaussian pulse shape. @@ -157,74 +162,44 @@ class Gaussian(Shape): def i(self, times: Times) -> Waveform: """Generate a Gaussian window.""" - return self.amplitude * np.exp(-(((times - self.mu) / self.sigma) ** 2) / 2) - -class Shapes(Enum): - """Available pulse shapes.""" - - RECTANGULAR = Rectangular - EXPONENTIAL = Exponential - GAUSSIAN = Gaussian + return self.amplitude * _gaussian(times, self.mu, self.sigma) +@dataclass(frozen=True) class GaussianSquare(Shape): r"""GaussianSquare pulse shape. - Args: - rel_sigma (float): relative sigma so that the pulse standard deviation (sigma) = duration / rel_sigma - width (float): Percentage of the pulse that is flat - .. math:: A\exp^{-\frac{1}{2}\frac{(t-\mu)^2}{\sigma^2}}[Rise] + Flat + A\exp^{-\frac{1}{2}\frac{(t-\mu)^2}{\sigma^2}}[Decay] """ - def __init__(self, rel_sigma: float, width: float): - self.name = "GaussianSquare" - self.pulse: "Pulse" = None - self.rel_sigma: float = float(rel_sigma) - self.width: float = float(width) - - def __eq__(self, item) -> bool: - """Overloads == operator.""" - if super().__eq__(item): - return self.rel_sigma == item.rel_sigma and self.width == item.width - return False + amplitude: float + mu: float + """Gaussian mean.""" + sigma: float + """Gaussian standard deviation.""" + width: float + """Length of the flat portion.""" - def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: + def i(self, times: Times) -> Waveform: """The envelope waveform of the i component of the pulse.""" - def gaussian(t, rel_sigma, gaussian_samples): - mu = (2 * gaussian_samples - 1) / 2 - sigma = (2 * gaussian_samples) / rel_sigma - return np.exp(-0.5 * ((t - mu) / sigma) ** 2) - - def fvec(t, gaussian_samples, rel_sigma, length=None): - if length is None: - length = t.shape[0] - - pulse = np.ones_like(t, dtype=float) - rise = t < gaussian_samples - fall = t > length - gaussian_samples - 1 - pulse[rise] = gaussian(t[rise], rel_sigma, gaussian_samples) - pulse[fall] = gaussian(t[rise], rel_sigma, gaussian_samples)[::-1] - return pulse - - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - gaussian_samples = num_samples * (1 - self.width) // 2 - t = np.arange(0, num_samples) + pulse = np.ones_like(times) + u, hw = self.mu, self.width / 2 + tails = (times < (u - hw)) | ((u + hw) < times) + pulse[tails] = _gaussian(times[tails], self.mu, self.sigma) - pulse = fvec(t, gaussian_samples, rel_sigma=self.rel_sigma) + return self.amplitude * pulse - return self.pulse.amplitude * pulse - def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the q component of the pulse.""" - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return np.zeros(num_samples) +class Shapes(Enum): + """Available pulse shapes.""" - def __repr__(self): - return f"{self.name}({format(self.rel_sigma, '.6f').rstrip('0').rstrip('.')}, {format(self.width, '.6f').rstrip('0').rstrip('.')})" + RECTANGULAR = Rectangular + EXPONENTIAL = Exponential + GAUSSIAN = Gaussian + GAUSSIAN_SQUARE = GaussianSquare class Drag(PulseShape): From b8ad489485c9bff1e5e9e6b132686b40aef930af Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 19:01:42 +0100 Subject: [PATCH 122/233] feat!: Move DRAG to new shape --- src/qibolab/pulses/shape.py | 88 ++++++++++++++----------------------- 1 file changed, 34 insertions(+), 54 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index b01f2c1b7..a7b4f749f 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -139,6 +139,9 @@ def i(self, times: Times) -> Waveform: def _gaussian(t, mu, sigma): """Gaussian function, normalized to be 1 at the max.""" + # TODO: if a centered Gaussian has to be used, and we agree that `Times` should + # always be the full window, just use `scipy.signal.gaussian`, that is exactly this + # function, autcomputing the mean from the number of points return np.exp(-(((t - mu) / sigma) ** 2) / 2) @@ -183,7 +186,7 @@ class GaussianSquare(Shape): """Length of the flat portion.""" def i(self, times: Times) -> Waveform: - """The envelope waveform of the i component of the pulse.""" + """Generate a Gaussian envelope, with a flat central window.""" pulse = np.ones_like(times) u, hw = self.mu, self.width / 2 @@ -193,68 +196,45 @@ def i(self, times: Times) -> Waveform: return self.amplitude * pulse -class Shapes(Enum): - """Available pulse shapes.""" +@dataclass(frozen=True) +class Drag(Shape): + """Derivative Removal by Adiabatic Gate (DRAG) pulse shape. - RECTANGULAR = Rectangular - EXPONENTIAL = Exponential - GAUSSIAN = Gaussian - GAUSSIAN_SQUARE = GaussianSquare + .. todo:: + - add expression + - add reference + """ -class Drag(PulseShape): - """Derivative Removal by Adiabatic Gate (DRAG) pulse shape. + amplitude: float + mu: float + """Gaussian mean.""" + sigma: float + """Gaussian standard deviation.""" + beta: float + """.. todo::""" - Args: - rel_sigma (float): relative sigma so that the pulse standard deviation (sigma) = duration / rel_sigma - beta (float): relative sigma so that the pulse standard deviation (sigma) = duration / rel_sigma - .. math:: - """ + def i(self, times: Times) -> Waveform: + """Generate a Gaussian envelope.""" + return self.amplitude * _gaussian(times, self.mu, self.sigma) - def __init__(self, rel_sigma, beta): - self.name = "Drag" - self.pulse: "Pulse" = None - self.rel_sigma = float(rel_sigma) - self.beta = float(beta) + def q(self, times: Times) -> Waveform: + """Generate ... - def __eq__(self, item) -> bool: - """Overloads == operator.""" - if super().__eq__(item): - return self.rel_sigma == item.rel_sigma and self.beta == item.beta - return False + .. todo:: + """ + i = self.amplitude * _gaussian(times, self.mu, self.sigma) + return self.beta * (-(times - self.mu) / (self.sigma**2)) * i - def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the i component of the pulse.""" - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - x = np.arange(0, num_samples, 1) - return self.pulse.amplitude * np.exp( - -(1 / 2) - * ( - ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / self.rel_sigma) ** 2) - ) - ) - def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the q component of the pulse.""" - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - x = np.arange(0, num_samples, 1) - i = self.pulse.amplitude * np.exp( - -(1 / 2) - * ( - ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / self.rel_sigma) ** 2) - ) - ) - return ( - self.beta - * (-(x - (num_samples - 1) / 2) / ((num_samples / self.rel_sigma) ** 2)) - * i - * sampling_rate - ) +class Shapes(Enum): + """Available pulse shapes.""" - def __repr__(self): - return f"{self.name}({format(self.rel_sigma, '.6f').rstrip('0').rstrip('.')}, {format(self.beta, '.6f').rstrip('0').rstrip('.')})" + RECTANGULAR = Rectangular + EXPONENTIAL = Exponential + GAUSSIAN = Gaussian + GAUSSIAN_SQUARE = GaussianSquare + DRAG = Drag class IIR(PulseShape): From 55544947025486db2e1a97ac35626c6ca7bf7d84 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 19:12:25 +0100 Subject: [PATCH 123/233] feat!: Move IIR to new shape --- src/qibolab/pulses/shape.py | 97 +++++++++++-------------------------- 1 file changed, 29 insertions(+), 68 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index a7b4f749f..6a2f5bb0e 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -227,17 +227,8 @@ def q(self, times: Times) -> Waveform: return self.beta * (-(times - self.mu) / (self.sigma**2)) * i -class Shapes(Enum): - """Available pulse shapes.""" - - RECTANGULAR = Rectangular - EXPONENTIAL = Exponential - GAUSSIAN = Gaussian - GAUSSIAN_SQUARE = GaussianSquare - DRAG = Drag - - -class IIR(PulseShape): +@dataclass(frozen=True) +class Iir(Shape): """IIR Filter using scipy.signal lfilter.""" # https://arxiv.org/pdf/1907.04818.pdf (page 11 - filter formula S22) @@ -245,69 +236,39 @@ class IIR(PulseShape): # p = [b0 = 1−k +k ·α, b1 = −(1−k)·(1−α),a0 = 1 and a1 = −(1−α)] # p = [b0, b1, a0, a1] - def __init__(self, b, a, target: PulseShape): - self.name = "IIR" - self.target: PulseShape = target - self._pulse: "Pulse" = None - self.a: np.ndarray = np.array(a) - self.b: np.ndarray = np.array(b) - # Check len(a) = len(b) = 2 + amplitude: float + a: npt.NDArray + b: npt.NDArray + target: Shape - def __eq__(self, item) -> bool: - """Overloads == operator.""" - if super().__eq__(item): - return ( - self.target == item.target - and (self.a == item.a).all() - and (self.b == item.b).all() - ) - return False + def _data(self, target): + a = self.a / self.a[0] + gain = np.sum(self.b) / np.sum(a) + b = self.b / gain if gain != 0 else self.b + + data = lfilter(b=b, a=a, x=target) + if np.max(np.abs(data)) != 0: + data = data / np.max(np.abs(data)) + return data - @property - def pulse(self): - return self._pulse + def i(self, times: Times) -> Waveform: + """.. todo::""" + return self.amplitude * self._data(self.target.i(times)) - @pulse.setter - def pulse(self, value): - self._pulse = value - self.target.pulse = value + def q(self, times: Times) -> Waveform: + """.. todo::""" + return self.amplitude * self._data(self.target.q(times)) - def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the i component of the pulse.""" - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - self.a = self.a / self.a[0] - gain = np.sum(self.b) / np.sum(self.a) - if not gain == 0: - self.b = self.b / gain - data = lfilter( - b=self.b, - a=self.a, - x=self.target.envelope_waveform_i(sampling_rate), - ) - if not np.max(np.abs(data)) == 0: - data = data / np.max(np.abs(data)) - return np.abs(self.pulse.amplitude) * data - def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the q component of the pulse.""" - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - self.a = self.a / self.a[0] - gain = np.sum(self.b) / np.sum(self.a) - if not gain == 0: - self.b = self.b / gain - data = lfilter( - b=self.b, - a=self.a, - x=self.target.envelope_waveform_q(sampling_rate), - ) - if not np.max(np.abs(data)) == 0: - data = data / np.max(np.abs(data)) - return np.abs(self.pulse.amplitude) * data +class Shapes(Enum): + """Available pulse shapes.""" - def __repr__(self): - formatted_b = [round(b, 3) for b in self.b] - formatted_a = [round(a, 3) for a in self.a] - return f"{self.name}({formatted_b}, {formatted_a}, {self.target})" + RECTANGULAR = Rectangular + EXPONENTIAL = Exponential + GAUSSIAN = Gaussian + GAUSSIAN_SQUARE = GaussianSquare + DRAG = Drag + IIR = Iir class SNZ(PulseShape): From 743fc98d6ea6a0b52ce35e8759167f4db3f8e626 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 19:48:54 +0100 Subject: [PATCH 124/233] feat!: Move SNZ to new shape --- src/qibolab/pulses/shape.py | 92 ++++++++++++++++--------------------- 1 file changed, 39 insertions(+), 53 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 6a2f5bb0e..0656e3aee 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -260,69 +260,55 @@ def q(self, times: Times) -> Waveform: return self.amplitude * self._data(self.target.q(times)) -class Shapes(Enum): - """Available pulse shapes.""" - - RECTANGULAR = Rectangular - EXPONENTIAL = Exponential - GAUSSIAN = Gaussian - GAUSSIAN_SQUARE = GaussianSquare - DRAG = Drag - IIR = Iir - - -class SNZ(PulseShape): +@dataclass(frozen=True) +class Snz(Shape): """Sudden variant Net Zero. https://arxiv.org/abs/2008.07411 (Supplementary materials: FIG. S1.) + + .. todo:: + + - expression """ - def __init__(self, t_idling, b_amplitude=None): - self.name = "SNZ" - self.pulse: "Pulse" = None - self.t_idling: float = t_idling - self.b_amplitude = b_amplitude + amplitude: float + width: float + """Essentially, the pulse duration... - def __eq__(self, item) -> bool: - """Overloads == operator.""" - if super().__eq__(item): - return ( - self.t_idling == item.t_idling and self.b_amplitude == item.b_amplitude - ) - return False + .. todo:: - def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the i component of the pulse.""" - if self.t_idling > self.pulse.duration: - raise ValueError( - f"Cannot put idling time {self.t_idling} higher than duration {self.pulse.duration}." - ) - if self.b_amplitude is None: - self.b_amplitude = self.pulse.amplitude / 2 - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - half_pulse_duration = (self.pulse.duration - self.t_idling) / 2 - half_flux_pulse_samples = int( - np.rint(num_samples * half_pulse_duration / self.pulse.duration) - ) - idling_samples = num_samples - 2 * half_flux_pulse_samples - return np.concatenate( - ( - self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), - np.array([self.b_amplitude]), - np.zeros(idling_samples), - -np.array([self.b_amplitude]), - -self.pulse.amplitude * np.ones(half_flux_pulse_samples - 1), - ) - ) + - reset to duration, if decided so + """ + t_idling: float + b_amplitude: float = 0.5 + """Relative B amplitude (wrt A).""" - def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the q component of the pulse.""" - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - return np.zeros(num_samples) + def i(self, times: Times) -> Waveform: + """.. todo::""" + # convert timings to samples + half_pulse_duration = (self.width - self.t_idling) / 2 + aspan = np.sum(times < half_pulse_duration) + idle = len(times) - 2 * (aspan + 1) - def __repr__(self): - return f"{self.name}({self.t_idling})" + pulse = np.ones_like(times) + # the aspan + 1 sample is B (and so the aspan + 1 + idle + 1), indexes are 0-based + pulse[aspan] = pulse[aspan + 1 + idle] = self.b_amplitude + # set idle time to 0 + pulse[aspan + 1 : aspan + 1 + idle] = 0 + return self.amplitude * pulse + + +class Shapes(Enum): + """Available pulse shapes.""" + + RECTANGULAR = Rectangular + EXPONENTIAL = Exponential + GAUSSIAN = Gaussian + GAUSSIAN_SQUARE = GaussianSquare + DRAG = Drag + IIR = Iir + SNZ = Snz class eCap(PulseShape): From 4efcb27c42bb6dd3abab6c1cd679f79f3c559ac5 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 19:57:22 +0100 Subject: [PATCH 125/233] feat!: Move eCap to new shape --- src/qibolab/pulses/shape.py | 60 +++++++++++++++---------------------- 1 file changed, 24 insertions(+), 36 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 0656e3aee..bc17e5257 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -299,23 +299,13 @@ def i(self, times: Times) -> Waveform: return self.amplitude * pulse -class Shapes(Enum): - """Available pulse shapes.""" - - RECTANGULAR = Rectangular - EXPONENTIAL = Exponential - GAUSSIAN = Gaussian - GAUSSIAN_SQUARE = GaussianSquare - DRAG = Drag - IIR = Iir - SNZ = Snz - - -class eCap(PulseShape): +@dataclass(frozen=True) +class ECap(Shape): r"""ECap pulse shape. - Args: - alpha (float): + .. todo:: + + - add reference .. math:: @@ -323,33 +313,31 @@ class eCap(PulseShape): &\times& [1 + \tanh(\alpha/2)]^{-2} """ - def __init__(self, alpha: float): - self.name = "eCap" - self.pulse: "Pulse" = None - self.alpha: float = float(alpha) - - def __eq__(self, item) -> bool: - """Overloads == operator.""" - if super().__eq__(item): - return self.alpha == item.alpha - return False + amplitude: float + alpha: float - def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - num_samples = int(self.pulse.duration * sampling_rate) - x = np.arange(0, num_samples, 1) + def i(self, times: Times) -> Waveform: + """.. todo::""" + x = times / len(times) return ( - self.pulse.amplitude - * (1 + np.tanh(self.alpha * x / num_samples)) - * (1 + np.tanh(self.alpha * (1 - x / num_samples))) + self.amplitude + * (1 + np.tanh(self.alpha * times)) + * (1 + np.tanh(self.alpha * (1 - x))) / (1 + np.tanh(self.alpha / 2)) ** 2 ) - def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - num_samples = int(self.pulse.duration * sampling_rate) - return np.zeros(num_samples) - def __repr__(self): - return f"{self.name}({format(self.alpha, '.6f').rstrip('0').rstrip('.')})" +class Shapes(Enum): + """Available pulse shapes.""" + + RECTANGULAR = Rectangular + EXPONENTIAL = Exponential + GAUSSIAN = Gaussian + GAUSSIAN_SQUARE = GaussianSquare + DRAG = Drag + IIR = Iir + SNZ = Snz + ECAP = ECap class Custom(PulseShape): From 873dff62d406149b629c770f2e13791424cd4223 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 20:04:27 +0100 Subject: [PATCH 126/233] feat!: Move Custom to new shape --- src/qibolab/pulses/shape.py | 56 ++++++++++++++++--------------------- 1 file changed, 24 insertions(+), 32 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index bc17e5257..65213f21a 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -327,6 +327,29 @@ def i(self, times: Times) -> Waveform: ) +@dataclass(frozen=True) +class Custom(Shape): + """Arbitrary shape. + + .. todo:: + + - expand description + - add attribute docstrings + """ + + amplitude: float + custom_i: npt.NDArray + custom_q: npt.NDArray + + def i(self, times: Times) -> Waveform: + """.. todo::""" + return self.amplitude * self.custom_i + + def envelope_waveform_q(self, times: Times) -> Waveform: + """.. todo::""" + return self.amplitude * self.custom_q + + class Shapes(Enum): """Available pulse shapes.""" @@ -338,35 +361,4 @@ class Shapes(Enum): IIR = Iir SNZ = Snz ECAP = ECap - - -class Custom(PulseShape): - """Arbitrary shape.""" - - def __init__(self, envelope_i, envelope_q=None): - self.name = "Custom" - self.pulse: "Pulse" = None - self.envelope_i: np.ndarray = np.array(envelope_i) - if envelope_q is not None: - self.envelope_q: np.ndarray = np.array(envelope_q) - else: - self.envelope_q = self.envelope_i - - def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the i component of the pulse.""" - if self.pulse.duration != len(self.envelope_i): - raise ValueError("Length of envelope_i must be equal to pulse duration") - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - - return self.envelope_i * self.pulse.amplitude - - def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The envelope waveform of the q component of the pulse.""" - if self.pulse.duration != len(self.envelope_q): - raise ValueError("Length of envelope_q must be equal to pulse duration") - num_samples = int(np.rint(self.pulse.duration * sampling_rate)) - - return self.envelope_q * self.pulse.amplitude - - def __repr__(self): - return f"{self.name}({self.envelope_i[:3]}, ..., {self.envelope_q[:3]}, ...)" + CUSTOM = Custom From 7362e9e6d9bfc61c1af10fa020794ca7d2470c2e Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 20:10:24 +0100 Subject: [PATCH 127/233] feat!: Export only shapes container --- src/qibolab/pulses/__init__.py | 15 +-------------- src/qibolab/pulses/shape.py | 10 ++++++++++ 2 files changed, 11 insertions(+), 14 deletions(-) diff --git a/src/qibolab/pulses/__init__.py b/src/qibolab/pulses/__init__.py index 437c126d3..910372335 100644 --- a/src/qibolab/pulses/__init__.py +++ b/src/qibolab/pulses/__init__.py @@ -1,16 +1,3 @@ from .pulse import Delay, Pulse, PulseType, VirtualZ from .sequence import PulseSequence -from .shape import ( - IIR, - SAMPLING_RATE, - SNZ, - Custom, - Drag, - Gaussian, - GaussianSquare, - PulseShape, - Rectangular, - ShapeInitError, - Waveform, - eCap, -) +from .shape import * diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 65213f21a..61b77bdac 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -8,6 +8,16 @@ import numpy.typing as npt from scipy.signal import lfilter +__all__ = [ + "Times", + "Waveform", + "IqWaveform", + "modulate", + "demodulate", + "Shape", + "Shapes", +] + SAMPLING_RATE = 1 """Default sampling rate in gigasamples per second (GSps). From 981757f17fd652cd6740092094cf7e627ba30849 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 28 Feb 2024 20:25:21 +0100 Subject: [PATCH 128/233] refactor: Split software modulation and related tests --- src/qibolab/pulses/modulation.py | 68 ++++++++++++++++++ src/qibolab/pulses/shape.py | 67 +----------------- tests/pulses/test_modulation.py | 95 ++++++++++++++++++++++++++ tests/pulses/test_shape.py | 114 ++++--------------------------- 4 files changed, 176 insertions(+), 168 deletions(-) create mode 100644 src/qibolab/pulses/modulation.py create mode 100644 tests/pulses/test_modulation.py diff --git a/src/qibolab/pulses/modulation.py b/src/qibolab/pulses/modulation.py new file mode 100644 index 000000000..f00f18e74 --- /dev/null +++ b/src/qibolab/pulses/modulation.py @@ -0,0 +1,68 @@ +import numpy as np + +from .shape import IqWaveform + +__all__ = ["modulate", "demodulate"] + +SAMPLING_RATE = 1 +"""Default sampling rate in gigasamples per second (GSps). + +Used for generating waveform envelopes if the instruments do not provide +a different value. +""" + + +def modulate( + envelope: IqWaveform, + freq: float, + rate: float = SAMPLING_RATE, + phase: float = 0.0, +) -> IqWaveform: + """Modulate the envelope waveform with a carrier. + + `envelope` is a `(2, n)`-shaped array of I and Q (first dimension) envelope signals, + as a function of time (second dimension), and `freq` the frequency of the carrier to + modulate with (usually the IF) in GHz. + `rate` is an optional sampling rate, in Gs/s, to sample the carrier. + + .. note:: + + Only the combination `freq / rate` is actually relevant, but it is frequently + convenient to specify one in GHz and the other in Gs/s. Thus the two arguments + are provided for the simplicity of their interpretation. + + `phase` is an optional initial phase for the carrier. + """ + samples = np.arange(envelope.shape[1]) + phases = (2 * np.pi * freq / rate) * samples + phase + cos = np.cos(phases) + sin = np.sin(phases) + mod = np.array([[cos, -sin], [sin, cos]]) + + # the normalization is related to `mod`, but only applied at the end for the sake of + # performances + return np.einsum("ijt,jt->it", mod, envelope) / np.sqrt(2) + + +def demodulate( + modulated: IqWaveform, + freq: float, + rate: float = SAMPLING_RATE, +) -> IqWaveform: + """Demodulate the acquired pulse. + + The role of the arguments is the same of the corresponding ones in :func:`modulate`, + which is essentially the inverse of this function. + """ + # in case the offsets have not been removed in hardware + modulated = modulated - np.mean(modulated) + + samples = np.arange(modulated.shape[1]) + phases = (2 * np.pi * freq / rate) * samples + cos = np.cos(phases) + sin = np.sin(phases) + demod = np.array([[cos, sin], [-sin, cos]]) + + # the normalization is related to `demod`, but only applied at the end for the sake + # of performances + return np.sqrt(2) * np.einsum("ijt,jt->it", demod, modulated) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 61b77bdac..cfea4b706 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -1,4 +1,4 @@ -"""PulseShape class.""" +"""Library of pulse shapes.""" from abc import ABC from dataclasses import dataclass @@ -12,19 +12,10 @@ "Times", "Waveform", "IqWaveform", - "modulate", - "demodulate", "Shape", "Shapes", ] -SAMPLING_RATE = 1 -"""Default sampling rate in gigasamples per second (GSps). - -Used for generating waveform envelopes if the instruments do not provide -a different value. -""" - Times = npt.NDArray[np.float64] # TODO: they could be distinguished among them, and distinguished from generic float # arrays, using the NewType pattern -> but this require some more effort to encforce @@ -35,62 +26,6 @@ """""" -def modulate( - envelope: IqWaveform, - freq: float, - rate: float = SAMPLING_RATE, - phase: float = 0.0, -) -> IqWaveform: - """Modulate the envelope waveform with a carrier. - - `envelope` is a `(2, n)`-shaped array of I and Q (first dimension) envelope signals, - as a function of time (second dimension), and `freq` the frequency of the carrier to - modulate with (usually the IF) in GHz. - `rate` is an optional sampling rate, in Gs/s, to sample the carrier. - - .. note:: - - Only the combination `freq / rate` is actually relevant, but it is frequently - convenient to specify one in GHz and the other in Gs/s. Thus the two arguments - are provided for the simplicity of their interpretation. - - `phase` is an optional initial phase for the carrier. - """ - samples = np.arange(envelope.shape[1]) - phases = (2 * np.pi * freq / rate) * samples + phase - cos = np.cos(phases) - sin = np.sin(phases) - mod = np.array([[cos, -sin], [sin, cos]]) - - # the normalization is related to `mod`, but only applied at the end for the sake of - # performances - return np.einsum("ijt,jt->it", mod, envelope) / np.sqrt(2) - - -def demodulate( - modulated: IqWaveform, - freq: float, - rate: float = SAMPLING_RATE, -) -> IqWaveform: - """Demodulate the acquired pulse. - - The role of the arguments is the same of the corresponding ones in :func:`modulate`, - which is essentially the inverse of this function. - """ - # in case the offsets have not been removed in hardware - modulated = modulated - np.mean(modulated) - - samples = np.arange(modulated.shape[1]) - phases = (2 * np.pi * freq / rate) * samples - cos = np.cos(phases) - sin = np.sin(phases) - demod = np.array([[cos, sin], [-sin, cos]]) - - # the normalization is related to `demod`, but only applied at the end for the sake - # of performances - return np.sqrt(2) * np.einsum("ijt,jt->it", demod, modulated) - - class Shape(ABC): """Pulse envelopes. diff --git a/tests/pulses/test_modulation.py b/tests/pulses/test_modulation.py new file mode 100644 index 000000000..09127ac0a --- /dev/null +++ b/tests/pulses/test_modulation.py @@ -0,0 +1,95 @@ +import numpy as np + +from qibolab.pulses import IqWaveform, Pulse, PulseType, Shapes +from qibolab.pulses.modulation import demodulate, modulate + +Rectangular = Shapes.RECTANGULAR.value +Gaussian = Shapes.GAUSSIAN.value + + +def test_modulation(): + rect = Pulse( + start=0, + duration=30, + amplitude=0.9, + frequency=20_000_000, + relative_phase=0.0, + shape=Rectangular(), + channel=0, + type=PulseType.READOUT, + qubit=0, + ) + renvs: IqWaveform = np.array(rect.shape.envelope_waveforms()) + # fmt: off + np.testing.assert_allclose(modulate(renvs, 0.04), + np.array([[ 6.36396103e-01, 6.16402549e-01, 5.57678156e-01, + 4.63912794e-01, 3.40998084e-01, 1.96657211e-01, + 3.99596419e-02, -1.19248738e-01, -2.70964282e-01, + -4.05654143e-01, -5.14855263e-01, -5.91706132e-01, + -6.31377930e-01, -6.31377930e-01, -5.91706132e-01, + -5.14855263e-01, -4.05654143e-01, -2.70964282e-01, + -1.19248738e-01, 3.99596419e-02, 1.96657211e-01, + 3.40998084e-01, 4.63912794e-01, 5.57678156e-01, + 6.16402549e-01, 6.36396103e-01, 6.16402549e-01, + 5.57678156e-01, 4.63912794e-01, 3.40998084e-01], + [ 0.00000000e+00, 1.58265275e-01, 3.06586161e-01, + 4.35643111e-01, 5.37327002e-01, 6.05248661e-01, + 6.35140321e-01, 6.25123778e-01, 5.75828410e-01, + 4.90351625e-01, 3.74064244e-01, 2.34273031e-01, + 7.97615814e-02, -7.97615814e-02, -2.34273031e-01, + -3.74064244e-01, -4.90351625e-01, -5.75828410e-01, + -6.25123778e-01, -6.35140321e-01, -6.05248661e-01, + -5.37327002e-01, -4.35643111e-01, -3.06586161e-01, + -1.58265275e-01, 4.09361195e-16, 1.58265275e-01, + 3.06586161e-01, 4.35643111e-01, 5.37327002e-01]]) + ) + # fmt: on + + gauss = Pulse( + start=5, + duration=20, + amplitude=3.5, + frequency=2_000_000, + relative_phase=0.0, + shape=Gaussian(0.5), + channel=0, + type=PulseType.READOUT, + qubit=0, + ) + genvs: IqWaveform = np.array(gauss.shape.envelope_waveforms()) + # fmt: off + np.testing.assert_allclose(modulate(genvs, 0.3), + np.array([[ 2.40604965e+00, -7.47704261e-01, -1.96732725e+00, + 1.97595317e+00, 7.57582564e-01, -2.45926187e+00, + 7.61855973e-01, 1.99830815e+00, -2.00080760e+00, + -7.64718297e-01, 2.47468039e+00, -7.64240497e-01, + -1.99830815e+00, 1.99456483e+00, 7.59953712e-01, + -2.45158868e+00, 7.54746949e-01, 1.96732725e+00, + -1.95751517e+00, -7.43510231e-01], + [ 0.00000000e+00, 2.30119709e+00, -1.42934692e+00, + -1.43561401e+00, 2.33159938e+00, 9.03518154e-16, + -2.34475159e+00, 1.45185586e+00, 1.45367181e+00, + -2.35356091e+00, -1.81836565e-15, 2.35209040e+00, + -1.45185586e+00, -1.44913618e+00, 2.33889703e+00, + 2.70209720e-15, -2.32287226e+00, 1.42934692e+00, + 1.42221802e+00, -2.28828920e+00]]) + ) + # fmt: on + + +def test_demodulation(): + signal = np.ones((2, 100)) + freq = 0.15 + mod = modulate(signal, freq) + + demod = demodulate(mod, freq) + np.testing.assert_allclose(demod, signal) + + mod1 = modulate(demod, freq * 3.0, rate=3.0) + np.testing.assert_allclose(mod1, mod) + + mod2 = modulate(signal, freq, phase=2 * np.pi) + np.testing.assert_allclose(mod2, mod) + + demod1 = demodulate(mod + np.ones_like(mod), freq) + np.testing.assert_allclose(demod1, demod) diff --git a/tests/pulses/test_shape.py b/tests/pulses/test_shape.py index 96e34f7ce..4a1b1f957 100644 --- a/tests/pulses/test_shape.py +++ b/tests/pulses/test_shape.py @@ -1,27 +1,20 @@ import numpy as np import pytest -from qibolab.pulses import ( - IIR, - SNZ, - Drag, - Gaussian, - GaussianSquare, - Pulse, - PulseShape, - PulseType, - Rectangular, - ShapeInitError, - eCap, -) -from qibolab.pulses.shape import IqWaveform, demodulate, modulate +from qibolab.pulses import Pulse, PulseType, Shapes + +Rectangular = Shapes.RECTANGULAR.value +Gaussian = Shapes.GAUSSIAN.value +GaussianSquare = Shapes.GAUSSIAN_SQUARE.value +Drag = Shapes.DRAG.value +eCap = Shapes.ECAP.value @pytest.mark.parametrize( "shape", [Rectangular(), Gaussian(5), GaussianSquare(5, 0.9), Drag(5, 1)] ) def test_sampling_rate(shape): - pulse = Pulse(40, 0.9, 100e6, 0, shape, 0, PulseType.DRIVE) + pulse = Pulse(0, 40, 0.9, 100e6, 0, shape, 0, PulseType.DRIVE) assert len(pulse.envelope_waveform_i(sampling_rate=1)) == 40 assert len(pulse.envelope_waveform_i(sampling_rate=100)) == 4000 @@ -86,7 +79,7 @@ def test_raise_shapeiniterror(): def test_drag_shape(): - pulse = Pulse(2, 1, 4e9, 0, Drag(2, 1), 0, PulseType.DRIVE) + pulse = Pulse(0, 2, 1, 4e9, 0, Drag(2, 1), 0, PulseType.DRIVE) # envelope i & envelope q should cross nearly at 0 and at 2 waveform = pulse.envelope_waveform_i(sampling_rate=10) target_waveform = np.array( @@ -118,6 +111,7 @@ def test_drag_shape(): def test_rectangular(): pulse = Pulse( + start=0, duration=50, amplitude=1, frequency=200_000_000, @@ -146,6 +140,7 @@ def test_rectangular(): def test_gaussian(): pulse = Pulse( + start=0, duration=50, amplitude=1, frequency=200_000_000, @@ -180,6 +175,7 @@ def test_gaussian(): def test_drag(): pulse = Pulse( + start=0, duration=50, amplitude=1, frequency=200_000_000, @@ -280,89 +276,3 @@ def test_eq(): shape3 = eCap(5) assert shape1 == shape2 assert not shape1 == shape3 - - -def test_modulation(): - rect = Pulse( - duration=30, - amplitude=0.9, - frequency=20_000_000, - relative_phase=0.0, - shape=Rectangular(), - channel=0, - type=PulseType.READOUT, - qubit=0, - ) - renvs: IqWaveform = np.array(rect.shape.envelope_waveforms()) - # fmt: off - np.testing.assert_allclose(modulate(renvs, 0.04), - np.array([[ 6.36396103e-01, 6.16402549e-01, 5.57678156e-01, - 4.63912794e-01, 3.40998084e-01, 1.96657211e-01, - 3.99596419e-02, -1.19248738e-01, -2.70964282e-01, - -4.05654143e-01, -5.14855263e-01, -5.91706132e-01, - -6.31377930e-01, -6.31377930e-01, -5.91706132e-01, - -5.14855263e-01, -4.05654143e-01, -2.70964282e-01, - -1.19248738e-01, 3.99596419e-02, 1.96657211e-01, - 3.40998084e-01, 4.63912794e-01, 5.57678156e-01, - 6.16402549e-01, 6.36396103e-01, 6.16402549e-01, - 5.57678156e-01, 4.63912794e-01, 3.40998084e-01], - [ 0.00000000e+00, 1.58265275e-01, 3.06586161e-01, - 4.35643111e-01, 5.37327002e-01, 6.05248661e-01, - 6.35140321e-01, 6.25123778e-01, 5.75828410e-01, - 4.90351625e-01, 3.74064244e-01, 2.34273031e-01, - 7.97615814e-02, -7.97615814e-02, -2.34273031e-01, - -3.74064244e-01, -4.90351625e-01, -5.75828410e-01, - -6.25123778e-01, -6.35140321e-01, -6.05248661e-01, - -5.37327002e-01, -4.35643111e-01, -3.06586161e-01, - -1.58265275e-01, 4.09361195e-16, 1.58265275e-01, - 3.06586161e-01, 4.35643111e-01, 5.37327002e-01]]) - ) - # fmt: on - - gauss = Pulse( - duration=20, - amplitude=3.5, - frequency=2_000_000, - relative_phase=0.0, - shape=Gaussian(0.5), - channel=0, - type=PulseType.READOUT, - qubit=0, - ) - genvs: IqWaveform = np.array(gauss.shape.envelope_waveforms()) - # fmt: off - np.testing.assert_allclose(modulate(genvs, 0.3), - np.array([[ 2.40604965e+00, -7.47704261e-01, -1.96732725e+00, - 1.97595317e+00, 7.57582564e-01, -2.45926187e+00, - 7.61855973e-01, 1.99830815e+00, -2.00080760e+00, - -7.64718297e-01, 2.47468039e+00, -7.64240497e-01, - -1.99830815e+00, 1.99456483e+00, 7.59953712e-01, - -2.45158868e+00, 7.54746949e-01, 1.96732725e+00, - -1.95751517e+00, -7.43510231e-01], - [ 0.00000000e+00, 2.30119709e+00, -1.42934692e+00, - -1.43561401e+00, 2.33159938e+00, 9.03518154e-16, - -2.34475159e+00, 1.45185586e+00, 1.45367181e+00, - -2.35356091e+00, -1.81836565e-15, 2.35209040e+00, - -1.45185586e+00, -1.44913618e+00, 2.33889703e+00, - 2.70209720e-15, -2.32287226e+00, 1.42934692e+00, - 1.42221802e+00, -2.28828920e+00]]) - ) - # fmt: on - - -def test_demodulation(): - signal = np.ones((2, 100)) - freq = 0.15 - mod = modulate(signal, freq) - - demod = demodulate(mod, freq) - np.testing.assert_allclose(demod, signal) - - mod1 = modulate(demod, freq * 3.0, rate=3.0) - np.testing.assert_allclose(mod1, mod) - - mod2 = modulate(signal, freq, phase=2 * np.pi) - np.testing.assert_allclose(mod2, mod) - - demod1 = demodulate(mod + np.ones_like(mod), freq) - np.testing.assert_allclose(demod1, demod) From 0a0f059b0ab1fe586d603cfdc75566b4162e3d86 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 5 Mar 2024 10:26:46 +0100 Subject: [PATCH 129/233] feat: rename Shape to Envelope --- src/qibolab/pulses/shape.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index cfea4b706..4258a448e 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -12,8 +12,8 @@ "Times", "Waveform", "IqWaveform", - "Shape", - "Shapes", + "Envelope", + "Envelopes", ] Times = npt.NDArray[np.float64] @@ -26,7 +26,7 @@ """""" -class Shape(ABC): +class Envelope(ABC): """Pulse envelopes. Generates both i (in-phase) and q (quadrature) components. @@ -46,7 +46,7 @@ def envelopes(self, times: Times) -> IqWaveform: @dataclass(frozen=True) -class Rectangular(Shape): +class Rectangular(Envelope): """Rectangular envelope.""" amplitude: float @@ -57,7 +57,7 @@ def i(self, times: Times) -> Waveform: @dataclass(frozen=True) -class Exponential(Shape): +class Exponential(Envelope): r"""Exponential shape, i.e. square pulse with an exponential decay. .. math:: @@ -91,7 +91,7 @@ def _gaussian(t, mu, sigma): @dataclass(frozen=True) -class Gaussian(Shape): +class Gaussian(Envelope): r"""Gaussian pulse shape. Args: @@ -114,7 +114,7 @@ def i(self, times: Times) -> Waveform: @dataclass(frozen=True) -class GaussianSquare(Shape): +class GaussianSquare(Envelope): r"""GaussianSquare pulse shape. .. math:: @@ -142,7 +142,7 @@ def i(self, times: Times) -> Waveform: @dataclass(frozen=True) -class Drag(Shape): +class Drag(Envelope): """Derivative Removal by Adiabatic Gate (DRAG) pulse shape. .. todo:: @@ -173,7 +173,7 @@ def q(self, times: Times) -> Waveform: @dataclass(frozen=True) -class Iir(Shape): +class Iir(Envelope): """IIR Filter using scipy.signal lfilter.""" # https://arxiv.org/pdf/1907.04818.pdf (page 11 - filter formula S22) @@ -184,7 +184,7 @@ class Iir(Shape): amplitude: float a: npt.NDArray b: npt.NDArray - target: Shape + target: Envelope def _data(self, target): a = self.a / self.a[0] @@ -206,7 +206,7 @@ def q(self, times: Times) -> Waveform: @dataclass(frozen=True) -class Snz(Shape): +class Snz(Envelope): """Sudden variant Net Zero. https://arxiv.org/abs/2008.07411 @@ -245,7 +245,7 @@ def i(self, times: Times) -> Waveform: @dataclass(frozen=True) -class ECap(Shape): +class ECap(Envelope): r"""ECap pulse shape. .. todo:: @@ -273,7 +273,7 @@ def i(self, times: Times) -> Waveform: @dataclass(frozen=True) -class Custom(Shape): +class Custom(Envelope): """Arbitrary shape. .. todo:: @@ -295,7 +295,7 @@ def envelope_waveform_q(self, times: Times) -> Waveform: return self.amplitude * self.custom_q -class Shapes(Enum): +class Envelopes(Enum): """Available pulse shapes.""" RECTANGULAR = Rectangular From 4226b9c2914c60fac9a40a93de9e2a846af7e585 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 5 Mar 2024 10:29:29 +0100 Subject: [PATCH 130/233] docs: Describe the expected times, restricting the original purpose --- src/qibolab/pulses/shape.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 4258a448e..5f86b7de6 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -17,6 +17,16 @@ ] Times = npt.NDArray[np.float64] +"""The time window of a pulse. + +This should span the entire pulse interval, and contain one point per-desired sample. + +.. note:: + + It is not possible to deal with partial windows or arrays with a rank different from + 1, since some envelopes are defined in terms of the pulse duration and the + individual samples themselves. Cf. :cls:`Snz`. +""" # TODO: they could be distinguished among them, and distinguished from generic float # arrays, using the NewType pattern -> but this require some more effort to encforce # types throughout the whole code base From 0698d1ecc0220ac619d97c369f957c24f0ee0dd9 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 5 Mar 2024 11:06:37 +0100 Subject: [PATCH 131/233] feat: Move Gaussian pulses back to their original variables --- src/qibolab/pulses/shape.py | 50 ++++++++++++++++++++++++------------- 1 file changed, 33 insertions(+), 17 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 5f86b7de6..632b43862 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -7,6 +7,7 @@ import numpy as np import numpy.typing as npt from scipy.signal import lfilter +from scipy.signal.windows import gaussian __all__ = [ "Times", @@ -36,6 +37,14 @@ """""" +def _duration(times: Times) -> float: + return times[-1] - times[0] + + +def _mean(times: Times) -> float: + return _duration(times) / 2 + times[0] + + class Envelope(ABC): """Pulse envelopes. @@ -92,12 +101,13 @@ def i(self, times: Times) -> Waveform: ) -def _gaussian(t, mu, sigma): - """Gaussian function, normalized to be 1 at the max.""" - # TODO: if a centered Gaussian has to be used, and we agree that `Times` should - # always be the full window, just use `scipy.signal.gaussian`, that is exactly this - # function, autcomputing the mean from the number of points - return np.exp(-(((t - mu) / sigma) ** 2) / 2) +def _samples_sigma(rel_sigma: float, times: Times) -> float: + """Convert standard deviation in samples. + + `rel_sigma` is assumed in units of the interval duration, and it is turned in units + of samples, by counting the number of samples in the interval. + """ + return rel_sigma * len(times) @dataclass(frozen=True) @@ -113,14 +123,17 @@ class Gaussian(Envelope): """ amplitude: float - mu: float - """Gaussian mean.""" - sigma: float - """Gaussian standard deviation.""" + rel_sigma: float + """Relative Gaussian standard deviation. + + In units of the interval duration. + """ def i(self, times: Times) -> Waveform: """Generate a Gaussian window.""" - return self.amplitude * _gaussian(times, self.mu, self.sigma) + return self.amplitude * gaussian( + len(times), _samples_sigma(self.rel_sigma, times) + ) @dataclass(frozen=True) @@ -133,10 +146,11 @@ class GaussianSquare(Envelope): """ amplitude: float - mu: float - """Gaussian mean.""" - sigma: float - """Gaussian standard deviation.""" + rel_sigma: float + """Relative Gaussian standard deviation. + + In units of the interval duration. + """ width: float """Length of the flat portion.""" @@ -144,9 +158,11 @@ def i(self, times: Times) -> Waveform: """Generate a Gaussian envelope, with a flat central window.""" pulse = np.ones_like(times) - u, hw = self.mu, self.width / 2 + u, hw = _mean(times), self.width / 2 tails = (times < (u - hw)) | ((u + hw) < times) - pulse[tails] = _gaussian(times[tails], self.mu, self.sigma) + pulse[tails] = gaussian( + len(times[tails]), _samples_sigma(self.rel_sigma, times) + ) return self.amplitude * pulse From 0eaf10ab28b30daf79aa5abd00aa1423d597cced Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 5 Mar 2024 11:36:58 +0100 Subject: [PATCH 132/233] feat: Propagate window to drag and iir --- src/qibolab/pulses/shape.py | 44 ++++++++++++++++++------------------- 1 file changed, 21 insertions(+), 23 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 632b43862..2af83649a 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -61,7 +61,7 @@ def q(self, times: Times) -> Waveform: def envelopes(self, times: Times) -> IqWaveform: """Stacked i and q envelope waveforms of the pulse.""" - return np.array(self.i(times), self.q(times)) + return np.array([self.i(times), self.q(times)]) @dataclass(frozen=True) @@ -178,48 +178,53 @@ class Drag(Envelope): """ amplitude: float - mu: float - """Gaussian mean.""" - sigma: float - """Gaussian standard deviation.""" + rel_sigma: float + """Relative Gaussian standard deviation. + + In units of the interval duration. + """ beta: float """.. todo::""" def i(self, times: Times) -> Waveform: """Generate a Gaussian envelope.""" - return self.amplitude * _gaussian(times, self.mu, self.sigma) + return self.amplitude * gaussian( + len(times), _samples_sigma(self.rel_sigma, times) + ) def q(self, times: Times) -> Waveform: """Generate ... .. todo:: """ - i = self.amplitude * _gaussian(times, self.mu, self.sigma) - return self.beta * (-(times - self.mu) / (self.sigma**2)) * i + sigma = self.rel_sigma * _duration(times) + return self.beta * (-(times - _mean(times)) / (sigma**2)) * self.i(times) @dataclass(frozen=True) class Iir(Envelope): - """IIR Filter using scipy.signal lfilter.""" + """IIR Filter using scipy.signal lfilter. - # https://arxiv.org/pdf/1907.04818.pdf (page 11 - filter formula S22) - # p = [A, tau_iir] - # p = [b0 = 1−k +k ·α, b1 = −(1−k)·(1−α),a0 = 1 and a1 = −(1−α)] - # p = [b0, b1, a0, a1] + https://arxiv.org/pdf/1907.04818.pdf (page 11 - filter formula S22):: + + p = [A, tau_iir] + p = [b0 = 1−k +k ·α, b1 = −(1−k)·(1−α),a0 = 1 and a1 = −(1−α)] + p = [b0, b1, a0, a1] + """ amplitude: float a: npt.NDArray b: npt.NDArray target: Envelope - def _data(self, target): + def _data(self, target: npt.NDArray) -> npt.NDArray: a = self.a / self.a[0] gain = np.sum(self.b) / np.sum(a) b = self.b / gain if gain != 0 else self.b data = lfilter(b=b, a=a, x=target) if np.max(np.abs(data)) != 0: - data = data / np.max(np.abs(data)) + data /= np.max(np.abs(data)) return data def i(self, times: Times) -> Waveform: @@ -244,13 +249,6 @@ class Snz(Envelope): """ amplitude: float - width: float - """Essentially, the pulse duration... - - .. todo:: - - - reset to duration, if decided so - """ t_idling: float b_amplitude: float = 0.5 """Relative B amplitude (wrt A).""" @@ -258,7 +256,7 @@ class Snz(Envelope): def i(self, times: Times) -> Waveform: """.. todo::""" # convert timings to samples - half_pulse_duration = (self.width - self.t_idling) / 2 + half_pulse_duration = (_duration(times) - self.t_idling) / 2 aspan = np.sum(times < half_pulse_duration) idle = len(times) - 2 * (aspan + 1) From 62fae85f367849b863b3e456a2294a1242110bc6 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 5 Mar 2024 11:37:22 +0100 Subject: [PATCH 133/233] feat: Propagate envelopes to Pulse --- src/qibolab/pulses/pulse.py | 64 ++++++++----------------------------- 1 file changed, 14 insertions(+), 50 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 18e16c253..2de6bbd43 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -1,9 +1,13 @@ """Pulse class.""" -import copy + from dataclasses import dataclass, fields from enum import Enum from typing import Optional +import numpy as np + +from .shape import Envelope, IqWaveform, Times, Waveform + class PulseType(Enum): """An enumeration to distinguish different types of pulses. @@ -39,11 +43,11 @@ class Pulse: """ relative_phase: float """Relative phase of the pulse, in radians.""" - shape: PulseShape - """Pulse shape, as a PulseShape object. + envelope: Envelope + """The pulse envelope shape. See - :py: mod:`qibolab.pulses` for list of available shapes. + :cls:`qibolab.pulses.shape.Envelopes` for list of available shapes. """ channel: Optional[str] = None """Channel on which the pulse should be played. @@ -107,43 +111,20 @@ def phase(self) -> float: def id(self) -> int: return id(self) - def envelope_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: + def i(self, times: Times) -> Waveform: """The envelope waveform of the i component of the pulse.""" - return self.shape.envelope_waveform_i(sampling_rate) + return self.envelope.i(times) - def envelope_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: + def q(self, times: Times) -> Waveform: """The envelope waveform of the q component of the pulse.""" - return self.shape.envelope_waveform_q(sampling_rate) + return self.envelope.q(times) - def envelope_waveforms( - self, sampling_rate=SAMPLING_RATE - ): # -> tuple[Waveform, Waveform]: + def envelopes(self, times: Times) -> IqWaveform: """A tuple with the i and q envelope waveforms of the pulse.""" - return ( - self.shape.envelope_waveform_i(sampling_rate), - self.shape.envelope_waveform_q(sampling_rate), - ) - - def modulated_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The waveform of the i component of the pulse, modulated with its - frequency.""" - - return self.shape.modulated_waveform_i(sampling_rate) - - def modulated_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The waveform of the q component of the pulse, modulated with its - frequency.""" - - return self.shape.modulated_waveform_q(sampling_rate) - - def modulated_waveforms(self, sampling_rate): # -> tuple[Waveform, Waveform]: - """A tuple with the i and q waveforms of the pulse, modulated with its - frequency.""" - - return self.shape.modulated_waveforms(sampling_rate) + return np.array([self.i(times), self.q(times)]) def __hash__(self): """Hash the content. @@ -168,23 +149,6 @@ def __hash__(self): ) ) - def __add__(self, other): - if isinstance(other, Pulse): - return PulseSequence(self, other) - if isinstance(other, PulseSequence): - return PulseSequence(self, *other) - raise TypeError(f"Expected Pulse or PulseSequence; got {type(other).__name__}") - - def __mul__(self, n): - if not isinstance(n, int): - raise TypeError(f"Expected int; got {type(n).__name__}") - if n < 0: - raise TypeError(f"argument n should be >=0, got {n}") - return PulseSequence(*([copy.deepcopy(self)] * n)) - - def __rmul__(self, n): - return self.__mul__(n) - def is_equal_ignoring_start(self, item) -> bool: """Check if two pulses are equal ignoring start time.""" return ( From 6d21532797ce0bbfc3a6db10d84779668e99f964 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 5 Mar 2024 11:40:00 +0100 Subject: [PATCH 134/233] feat: Keep the amplitude in the pulse, drop it in the shape --- src/qibolab/pulses/pulse.py | 4 ++-- src/qibolab/pulses/shape.py | 43 +++++++++++-------------------------- 2 files changed, 15 insertions(+), 32 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 2de6bbd43..a4b23d685 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -114,12 +114,12 @@ def id(self) -> int: def i(self, times: Times) -> Waveform: """The envelope waveform of the i component of the pulse.""" - return self.envelope.i(times) + return self.amplitude * self.envelope.i(times) def q(self, times: Times) -> Waveform: """The envelope waveform of the q component of the pulse.""" - return self.envelope.q(times) + return self.amplitude * self.envelope.q(times) def envelopes(self, times: Times) -> IqWaveform: """A tuple with the i and q envelope waveforms of the pulse.""" diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index 2af83649a..ae881e4a2 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -68,11 +68,9 @@ def envelopes(self, times: Times) -> IqWaveform: class Rectangular(Envelope): """Rectangular envelope.""" - amplitude: float - def i(self, times: Times) -> Waveform: """Generate a rectangular envelope.""" - return self.amplitude * np.ones_like(times) + return np.ones_like(times) @dataclass(frozen=True) @@ -84,7 +82,6 @@ class Exponential(Envelope): A\frac{\exp\left(-\frac{x}{\text{upsilon}}\right) + g \exp\left(-\frac{x}{\text{tau}}\right)}{1 + g} """ - amplitude: float tau: float """The decay rate of the first exponential function.""" upsilon: float @@ -94,10 +91,8 @@ class Exponential(Envelope): def i(self, times: Times) -> Waveform: """Generate a combination of two exponential decays.""" - return ( - self.amplitude - * (np.exp(-times / self.upsilon) + self.g * np.exp(-times / self.tau)) - / (1 + self.g) + return (np.exp(-times / self.upsilon) + self.g * np.exp(-times / self.tau)) / ( + 1 + self.g ) @@ -122,7 +117,6 @@ class Gaussian(Envelope): A\exp^{-\frac{1}{2}\frac{(t-\mu)^2}{\sigma^2}} """ - amplitude: float rel_sigma: float """Relative Gaussian standard deviation. @@ -131,9 +125,7 @@ class Gaussian(Envelope): def i(self, times: Times) -> Waveform: """Generate a Gaussian window.""" - return self.amplitude * gaussian( - len(times), _samples_sigma(self.rel_sigma, times) - ) + return gaussian(len(times), _samples_sigma(self.rel_sigma, times)) @dataclass(frozen=True) @@ -145,7 +137,6 @@ class GaussianSquare(Envelope): A\exp^{-\frac{1}{2}\frac{(t-\mu)^2}{\sigma^2}}[Rise] + Flat + A\exp^{-\frac{1}{2}\frac{(t-\mu)^2}{\sigma^2}}[Decay] """ - amplitude: float rel_sigma: float """Relative Gaussian standard deviation. @@ -164,7 +155,7 @@ def i(self, times: Times) -> Waveform: len(times[tails]), _samples_sigma(self.rel_sigma, times) ) - return self.amplitude * pulse + return pulse @dataclass(frozen=True) @@ -177,7 +168,6 @@ class Drag(Envelope): - add reference """ - amplitude: float rel_sigma: float """Relative Gaussian standard deviation. @@ -188,9 +178,7 @@ class Drag(Envelope): def i(self, times: Times) -> Waveform: """Generate a Gaussian envelope.""" - return self.amplitude * gaussian( - len(times), _samples_sigma(self.rel_sigma, times) - ) + return gaussian(len(times), _samples_sigma(self.rel_sigma, times)) def q(self, times: Times) -> Waveform: """Generate ... @@ -212,7 +200,6 @@ class Iir(Envelope): p = [b0, b1, a0, a1] """ - amplitude: float a: npt.NDArray b: npt.NDArray target: Envelope @@ -229,11 +216,11 @@ def _data(self, target: npt.NDArray) -> npt.NDArray: def i(self, times: Times) -> Waveform: """.. todo::""" - return self.amplitude * self._data(self.target.i(times)) + return self._data(self.target.i(times)) def q(self, times: Times) -> Waveform: """.. todo::""" - return self.amplitude * self._data(self.target.q(times)) + return self._data(self.target.q(times)) @dataclass(frozen=True) @@ -248,7 +235,6 @@ class Snz(Envelope): - expression """ - amplitude: float t_idling: float b_amplitude: float = 0.5 """Relative B amplitude (wrt A).""" @@ -265,7 +251,7 @@ def i(self, times: Times) -> Waveform: pulse[aspan] = pulse[aspan + 1 + idle] = self.b_amplitude # set idle time to 0 pulse[aspan + 1 : aspan + 1 + idle] = 0 - return self.amplitude * pulse + return pulse @dataclass(frozen=True) @@ -282,15 +268,13 @@ class ECap(Envelope): &\times& [1 + \tanh(\alpha/2)]^{-2} """ - amplitude: float alpha: float def i(self, times: Times) -> Waveform: """.. todo::""" x = times / len(times) return ( - self.amplitude - * (1 + np.tanh(self.alpha * times)) + (1 + np.tanh(self.alpha * times)) * (1 + np.tanh(self.alpha * (1 - x))) / (1 + np.tanh(self.alpha / 2)) ** 2 ) @@ -306,17 +290,16 @@ class Custom(Envelope): - add attribute docstrings """ - amplitude: float custom_i: npt.NDArray custom_q: npt.NDArray def i(self, times: Times) -> Waveform: """.. todo::""" - return self.amplitude * self.custom_i + return self.custom_i - def envelope_waveform_q(self, times: Times) -> Waveform: + def q(self, times: Times) -> Waveform: """.. todo::""" - return self.amplitude * self.custom_q + return self.custom_q class Envelopes(Enum): From c9727f58c5693944c70fd4fecbf5b1a41f1ddd1c Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 12 Mar 2024 17:20:09 +0100 Subject: [PATCH 135/233] build: Exclude CC library in Nix shell for Linux --- .envrc | 4 ++-- flake.nix | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.envrc b/.envrc index 01f5f41d0..3af52d88e 100644 --- a/.envrc +++ b/.envrc @@ -2,8 +2,8 @@ if ! has nix_direnv_version || ! nix_direnv_version 2.2.1; then source_url "https://raw.githubusercontent.com/nix-community/nix-direnv/2.2.1/direnvrc" "sha256-zelF0vLbEl5uaqrfIzbgNzJWGmLzCmYAkInj/LNxvKs=" fi -nix_direnv_watch_file flake.nix -nix_direnv_watch_file flake.lock +watch_file flake.nix +watch_file flake.lock if ! use flake . --impure; then echo "devenv could not be built. The devenv environment was not loaded. Make the necessary changes to devenv.nix and hit enter to try again." >&2 fi diff --git a/flake.nix b/flake.nix index e27ab8348..58d57c2a8 100644 --- a/flake.nix +++ b/flake.nix @@ -35,8 +35,8 @@ forEachSystem (system: let pkgs = nixpkgs.legacyPackages.${system}; - pwd = builtins.getEnv "PWD"; - platforms = builtins.toPath "${pwd}/../qibolab_platforms_qrc/"; + lib = pkgs.lib; + isDarwin = lib.strings.hasSuffix "darwin" system; in { default = devenv.lib.mkShell { inherit inputs pkgs; @@ -48,7 +48,7 @@ config, ... }: { - packages = with pkgs; [pre-commit poethepoet jupyter stdenv.cc.cc.lib zlib]; + packages = with pkgs; [pre-commit poethepoet jupyter zlib] ++ lib.optionals isDarwin [stdenv.cc.cc.lib]; env = { QIBOLAB_PLATFORMS = (dirOf config.env.DEVENV_ROOT) + "/qibolab_platforms_qrc"; From 3d78b12e5faf5495acea34c80266aaef43250240 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 12 Mar 2024 17:31:33 +0100 Subject: [PATCH 136/233] feat: Represent times as the parameters of linspace Since nothing else is strictly needed --- src/qibolab/pulses/shape.py | 87 ++++++++++++++++++++----------------- 1 file changed, 47 insertions(+), 40 deletions(-) diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/shape.py index ae881e4a2..edc6b1eec 100644 --- a/src/qibolab/pulses/shape.py +++ b/src/qibolab/pulses/shape.py @@ -3,6 +3,7 @@ from abc import ABC from dataclasses import dataclass from enum import Enum +from functools import cached_property import numpy as np import numpy.typing as npt @@ -17,32 +18,37 @@ "Envelopes", ] -Times = npt.NDArray[np.float64] -"""The time window of a pulse. -This should span the entire pulse interval, and contain one point per-desired sample. - -.. note:: - - It is not possible to deal with partial windows or arrays with a rank different from - 1, since some envelopes are defined in terms of the pulse duration and the - individual samples themselves. Cf. :cls:`Snz`. -""" # TODO: they could be distinguished among them, and distinguished from generic float # arrays, using the NewType pattern -> but this require some more effort to encforce # types throughout the whole code base Waveform = npt.NDArray[np.float64] -"""""" +"""Single component waveform, either I (in-phase) or Q (quadrature).""" IqWaveform = npt.NDArray[np.float64] -"""""" +"""Full shape, both I and Q components.""" + +@dataclass +class Times: + """Time window of a pulse.""" -def _duration(times: Times) -> float: - return times[-1] - times[0] + duration: float + """Pulse duration.""" + samples: int + """Number of requested samples.""" + # Here only the information consumed by the `Envelopes` is stored. How to go from + # the sampling rate to the number of samples is callers' business, since nothing + # else has to be known by this module. + @property + def mean(self) -> float: + """Middle point of the temporal window.""" + return self.duration / 2 -def _mean(times: Times) -> float: - return _duration(times) / 2 + times[0] + @cached_property + def window(self): + """Individual timing of each sample.""" + return np.linspace(0, self.duration, self.samples) class Envelope(ABC): @@ -53,11 +59,11 @@ class Envelope(ABC): def i(self, times: Times) -> Waveform: """In-phase envelope.""" - return np.zeros_like(times) + return np.zeros(times.samples) def q(self, times: Times) -> Waveform: """Quadrature envelope.""" - return np.zeros_like(times) + return np.zeros(times.samples) def envelopes(self, times: Times) -> IqWaveform: """Stacked i and q envelope waveforms of the pulse.""" @@ -70,7 +76,7 @@ class Rectangular(Envelope): def i(self, times: Times) -> Waveform: """Generate a rectangular envelope.""" - return np.ones_like(times) + return np.ones(times.samples) @dataclass(frozen=True) @@ -91,7 +97,8 @@ class Exponential(Envelope): def i(self, times: Times) -> Waveform: """Generate a combination of two exponential decays.""" - return (np.exp(-times / self.upsilon) + self.g * np.exp(-times / self.tau)) / ( + ts = times.window + return (np.exp(-ts / self.upsilon) + self.g * np.exp(-ts / self.tau)) / ( 1 + self.g ) @@ -102,7 +109,7 @@ def _samples_sigma(rel_sigma: float, times: Times) -> float: `rel_sigma` is assumed in units of the interval duration, and it is turned in units of samples, by counting the number of samples in the interval. """ - return rel_sigma * len(times) + return rel_sigma * times.samples @dataclass(frozen=True) @@ -125,7 +132,7 @@ class Gaussian(Envelope): def i(self, times: Times) -> Waveform: """Generate a Gaussian window.""" - return gaussian(len(times), _samples_sigma(self.rel_sigma, times)) + return gaussian(times.samples, _samples_sigma(self.rel_sigma, times)) @dataclass(frozen=True) @@ -149,11 +156,10 @@ def i(self, times: Times) -> Waveform: """Generate a Gaussian envelope, with a flat central window.""" pulse = np.ones_like(times) - u, hw = _mean(times), self.width / 2 - tails = (times < (u - hw)) | ((u + hw) < times) - pulse[tails] = gaussian( - len(times[tails]), _samples_sigma(self.rel_sigma, times) - ) + u, hw = times.mean, self.width / 2 + ts = times.window + tails = (ts < (u - hw)) | ((u + hw) < ts) + pulse[tails] = gaussian(len(ts[tails]), _samples_sigma(self.rel_sigma, times)) return pulse @@ -178,15 +184,16 @@ class Drag(Envelope): def i(self, times: Times) -> Waveform: """Generate a Gaussian envelope.""" - return gaussian(len(times), _samples_sigma(self.rel_sigma, times)) + return gaussian(times.samples, _samples_sigma(self.rel_sigma, times)) def q(self, times: Times) -> Waveform: """Generate ... .. todo:: """ - sigma = self.rel_sigma * _duration(times) - return self.beta * (-(times - _mean(times)) / (sigma**2)) * self.i(times) + sigma = self.rel_sigma * times.duration + ts = times.window + return self.beta * (-(ts - times.mean) / (sigma**2)) * self.i(times) @dataclass(frozen=True) @@ -242,11 +249,11 @@ class Snz(Envelope): def i(self, times: Times) -> Waveform: """.. todo::""" # convert timings to samples - half_pulse_duration = (_duration(times) - self.t_idling) / 2 - aspan = np.sum(times < half_pulse_duration) - idle = len(times) - 2 * (aspan + 1) + half_pulse_duration = (times.duration - self.t_idling) / 2 + aspan = np.sum(times.window < half_pulse_duration) + idle = times.samples - 2 * (aspan + 1) - pulse = np.ones_like(times) + pulse = np.ones(times.samples) # the aspan + 1 sample is B (and so the aspan + 1 + idle + 1), indexes are 0-based pulse[aspan] = pulse[aspan + 1 + idle] = self.b_amplitude # set idle time to 0 @@ -272,9 +279,9 @@ class ECap(Envelope): def i(self, times: Times) -> Waveform: """.. todo::""" - x = times / len(times) + x = times.window / times.samples return ( - (1 + np.tanh(self.alpha * times)) + (1 + np.tanh(self.alpha * times.window)) * (1 + np.tanh(self.alpha * (1 - x))) / (1 + np.tanh(self.alpha / 2)) ** 2 ) @@ -290,16 +297,16 @@ class Custom(Envelope): - add attribute docstrings """ - custom_i: npt.NDArray - custom_q: npt.NDArray + i_: npt.NDArray + q_: npt.NDArray def i(self, times: Times) -> Waveform: """.. todo::""" - return self.custom_i + raise NotImplementedError def q(self, times: Times) -> Waveform: """.. todo::""" - return self.custom_q + raise NotImplementedError class Envelopes(Enum): From db89498ca0ebd236e13e37c3daa15def554b236b Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 15 Mar 2024 18:02:26 +0100 Subject: [PATCH 137/233] test: Solve pytest collection errors for pulses tests --- src/qibolab/pulses/__init__.py | 2 +- src/qibolab/pulses/{shape.py => envelope.py} | 0 src/qibolab/pulses/modulation.py | 2 +- src/qibolab/pulses/plot.py | 136 ++++++++++++------ src/qibolab/pulses/pulse.py | 2 +- .../{test_shape.py => test_envelope.py} | 12 +- tests/pulses/test_modulation.py | 6 +- 7 files changed, 105 insertions(+), 55 deletions(-) rename src/qibolab/pulses/{shape.py => envelope.py} (100%) rename tests/pulses/{test_shape.py => test_envelope.py} (96%) diff --git a/src/qibolab/pulses/__init__.py b/src/qibolab/pulses/__init__.py index 910372335..d190134e2 100644 --- a/src/qibolab/pulses/__init__.py +++ b/src/qibolab/pulses/__init__.py @@ -1,3 +1,3 @@ +from .envelope import * from .pulse import Delay, Pulse, PulseType, VirtualZ from .sequence import PulseSequence -from .shape import * diff --git a/src/qibolab/pulses/shape.py b/src/qibolab/pulses/envelope.py similarity index 100% rename from src/qibolab/pulses/shape.py rename to src/qibolab/pulses/envelope.py diff --git a/src/qibolab/pulses/modulation.py b/src/qibolab/pulses/modulation.py index f00f18e74..3d5ce55a7 100644 --- a/src/qibolab/pulses/modulation.py +++ b/src/qibolab/pulses/modulation.py @@ -1,6 +1,6 @@ import numpy as np -from .shape import IqWaveform +from .envelope import IqWaveform __all__ = ["modulate", "demodulate"] diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index 1328268f2..cd31fb61a 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -1,10 +1,14 @@ """Plotting tools for pulses and related entities.""" + +from collections import defaultdict + import matplotlib.pyplot as plt import numpy as np -from .pulse import Pulse -from .shape import SAMPLING_RATE -from .waveform import Waveform +from .envelope import Waveform +from .modulation import modulate +from .pulse import Delay, Pulse +from .sequence import PulseSequence def waveform(wf: Waveform, filename=None): @@ -14,7 +18,7 @@ def waveform(wf: Waveform, filename=None): filename (str): a file path. If provided the plot is save to a file. """ plt.figure(figsize=(14, 5), dpi=200) - plt.plot(wf.data, c="C0", linestyle="dashed") + plt.plot(wf, c="C0", linestyle="dashed") plt.xlabel("Sample Number") plt.ylabel("Amplitude") plt.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") @@ -25,7 +29,7 @@ def waveform(wf: Waveform, filename=None): plt.close() -def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): +def pulse(pulse_: Pulse, filename=None): """Plot the pulse envelope and modulated waveforms. Args: @@ -34,76 +38,58 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): import matplotlib.pyplot as plt from matplotlib import gridspec - waveform_i = pulse_.shape.envelope_waveform_i(sampling_rate) - waveform_q = pulse_.shape.envelope_waveform_q(sampling_rate) + window = Times(pulse_.duration, num_samples) + waveform_i = pulse_.shape.i(window) + waveform_q = pulse_.shape.q(window) num_samples = len(waveform_i) - time = pulse_.start + np.arange(num_samples) / sampling_rate + time = np.arange(num_samples) / sampling_rate _ = plt.figure(figsize=(14, 5), dpi=200) gs = gridspec.GridSpec(ncols=2, nrows=1, width_ratios=np.array([2, 1])) ax1 = plt.subplot(gs[0]) ax1.plot( time, - waveform_i.data, + waveform_i, label="envelope i", c="C0", linestyle="dashed", ) ax1.plot( time, - waveform_q.data, + waveform_q, label="envelope q", c="C1", linestyle="dashed", ) - ax1.plot( - time, - pulse_.shape.modulated_waveform_i(sampling_rate).data, - label="modulated i", - c="C0", - ) - ax1.plot( - time, - pulse_.shape.modulated_waveform_q(sampling_rate).data, - label="modulated q", - c="C1", - ) - ax1.plot(time, -waveform_i.data, c="silver", linestyle="dashed") + + envelope = pulse_.shape.envelopes(window) + modulated = modulate(np.array(envelope), pulse_.frequency) + ax1.plot(time, modulated[0], label="modulated i", c="C0") + ax1.plot(time, modulated[1], label="modulated q", c="C1") + ax1.plot(time, -waveform_i, c="silver", linestyle="dashed") ax1.set_xlabel("Time [ns]") ax1.set_ylabel("Amplitude") ax1.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") - start = float(pulse_.start) - finish = float(pulse._finish) if pulse._finish is not None else 0.0 + start = 0 + finish = float(pulse_.duration) ax1.axis((start, finish, -1.0, 1.0)) ax1.legend() - modulated_i = pulse_.shape.modulated_waveform_i(sampling_rate).data - modulated_q = pulse_.shape.modulated_waveform_q(sampling_rate).data ax2 = plt.subplot(gs[1]) + ax2.plot(modulated[0], modulated[1], label="modulated", c="C3") + ax2.plot(waveform_i, waveform_q, label="envelope", c="C2") ax2.plot( - modulated_i, - modulated_q, - label="modulated", - c="C3", - ) - ax2.plot( - waveform_i.data, - waveform_q.data, - label="envelope", - c="C2", - ) - ax2.plot( - modulated_i[0], - modulated_q[0], + modulated[0][0], + modulated[1][0], marker="o", markersize=5, label="start", c="lightcoral", ) ax2.plot( - modulated_i[-1], - modulated_q[-1], + modulated[0][-1], + modulated[1][-1], marker="o", markersize=5, label="finish", @@ -126,3 +112,67 @@ def pulse(pulse_: Pulse, filename=None, sampling_rate=SAMPLING_RATE): else: plt.show() plt.close() + + +def sequence(ps: PulseSequence, filename=None): + """Plot the sequence of pulses. + + Args: + filename (str): a file path. If provided the plot is save to a file. + """ + if len(ps) > 0: + import matplotlib.pyplot as plt + from matplotlib import gridspec + + _ = plt.figure(figsize=(14, 2 * len(ps)), dpi=200) + gs = gridspec.GridSpec(ncols=1, nrows=len(ps)) + vertical_lines = [] + starts = defaultdict(int) + for pulse in ps: + if not isinstance(pulse, Delay): + vertical_lines.append(starts[pulse.channel]) + vertical_lines.append(starts[pulse.channel] + pulse.duration) + starts[pulse.channel] += pulse.duration + + n = -1 + for qubit in ps.qubits: + qubit_pulses = ps.get_qubit_pulses(qubit) + for channel in qubit_pulses.channels: + n += 1 + channel_pulses = qubit_pulses.get_channel_pulses(channel) + ax = plt.subplot(gs[n]) + ax.axis([0, ps.duration, -1, 1]) + start = 0 + for pulse in channel_pulses: + if isinstance(pulse, Delay): + start += pulse.duration + continue + + envelope = pulse.shape.envelope_waveforms(sampling_rate) + num_samples = envelope[0].size + time = start + np.arange(num_samples) / sampling_rate + modulated = modulate(np.array(envelope), pulse.frequency) + ax.plot(time, modulated[1], c="lightgrey") + ax.plot(time, modulated[0], c=f"C{str(n)}") + ax.plot(time, pulse.shape.i(), c=f"C{str(n)}") + ax.plot(time, -pulse.shape.i(), c=f"C{str(n)}") + # TODO: if they overlap use different shades + ax.axhline(0, c="dimgrey") + ax.set_ylabel(f"qubit {qubit} \n channel {channel}") + for vl in vertical_lines: + ax.axvline(vl, c="slategrey", linestyle="--") + ax.axis((0, ps.duration, -1, 1)) + ax.grid( + visible=True, + which="both", + axis="both", + color="#CCCCCC", + linestyle="-", + ) + start += pulse.duration + + if filename: + plt.savefig(filename) + else: + plt.show() + plt.close() diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index a4b23d685..6c6b8a12a 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -6,7 +6,7 @@ import numpy as np -from .shape import Envelope, IqWaveform, Times, Waveform +from .envelope import Envelope, IqWaveform, Times, Waveform class PulseType(Enum): diff --git a/tests/pulses/test_shape.py b/tests/pulses/test_envelope.py similarity index 96% rename from tests/pulses/test_shape.py rename to tests/pulses/test_envelope.py index 4a1b1f957..eea806c3e 100644 --- a/tests/pulses/test_shape.py +++ b/tests/pulses/test_envelope.py @@ -1,13 +1,13 @@ import numpy as np import pytest -from qibolab.pulses import Pulse, PulseType, Shapes +from qibolab.pulses import Envelopes, Pulse, PulseType -Rectangular = Shapes.RECTANGULAR.value -Gaussian = Shapes.GAUSSIAN.value -GaussianSquare = Shapes.GAUSSIAN_SQUARE.value -Drag = Shapes.DRAG.value -eCap = Shapes.ECAP.value +Rectangular = Envelopes.RECTANGULAR.value +Gaussian = Envelopes.GAUSSIAN.value +GaussianSquare = Envelopes.GAUSSIAN_SQUARE.value +Drag = Envelopes.DRAG.value +eCap = Envelopes.ECAP.value @pytest.mark.parametrize( diff --git a/tests/pulses/test_modulation.py b/tests/pulses/test_modulation.py index 09127ac0a..e22b2af29 100644 --- a/tests/pulses/test_modulation.py +++ b/tests/pulses/test_modulation.py @@ -1,10 +1,10 @@ import numpy as np -from qibolab.pulses import IqWaveform, Pulse, PulseType, Shapes +from qibolab.pulses import Envelopes, IqWaveform, Pulse, PulseType from qibolab.pulses.modulation import demodulate, modulate -Rectangular = Shapes.RECTANGULAR.value -Gaussian = Shapes.GAUSSIAN.value +Rectangular = Envelopes.RECTANGULAR.value +Gaussian = Envelopes.GAUSSIAN.value def test_modulation(): From e0bb278deed2614a2d409b2877e334812a1b5e28 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 15 Mar 2024 18:10:32 +0100 Subject: [PATCH 138/233] test: Fix remaining pytests collection errors --- src/qibolab/instruments/qblox/acquisition.py | 2 +- src/qibolab/instruments/qblox/sequencer.py | 2 +- src/qibolab/instruments/qm/config.py | 4 +++- src/qibolab/instruments/rfsoc/convert.py | 4 ++-- tests/test_instruments_qm.py | 4 +++- tests/test_instruments_rfsoc.py | 6 +++++- tests/test_sweeper.py | 4 +++- 7 files changed, 18 insertions(+), 8 deletions(-) diff --git a/src/qibolab/instruments/qblox/acquisition.py b/src/qibolab/instruments/qblox/acquisition.py index 98aee7c1d..d73685524 100644 --- a/src/qibolab/instruments/qblox/acquisition.py +++ b/src/qibolab/instruments/qblox/acquisition.py @@ -3,7 +3,7 @@ import numpy as np -from qibolab.pulses.shape import demodulate +from qibolab.pulses.modulation import demodulate SAMPLING_RATE = 1 diff --git a/src/qibolab/instruments/qblox/sequencer.py b/src/qibolab/instruments/qblox/sequencer.py index 86e68b35e..e450b8e00 100644 --- a/src/qibolab/instruments/qblox/sequencer.py +++ b/src/qibolab/instruments/qblox/sequencer.py @@ -5,7 +5,7 @@ from qibolab.instruments.qblox.q1asm import Program from qibolab.pulses import Pulse, PulseSequence, PulseType -from qibolab.pulses.shape import modulate +from qibolab.pulses.modulation import modulate from qibolab.sweeper import Parameter, Sweeper SAMPLING_RATE = 1 diff --git a/src/qibolab/instruments/qm/config.py b/src/qibolab/instruments/qm/config.py index cc934fb20..e6ceeca78 100644 --- a/src/qibolab/instruments/qm/config.py +++ b/src/qibolab/instruments/qm/config.py @@ -4,10 +4,12 @@ import numpy as np from qibo.config import raise_error -from qibolab.pulses import PulseType, Rectangular +from qibolab.pulses import Envelopes, PulseType from .ports import OPXIQ, OctaveInput, OctaveOutput, OPXOutput +Rectangular = Envelopes.RECTANGULAR.value + SAMPLING_RATE = 1 """Sampling rate of Quantum Machines OPX in GSps.""" diff --git a/src/qibolab/instruments/rfsoc/convert.py b/src/qibolab/instruments/rfsoc/convert.py index 1aa218d6b..c858e46f7 100644 --- a/src/qibolab/instruments/rfsoc/convert.py +++ b/src/qibolab/instruments/rfsoc/convert.py @@ -8,7 +8,7 @@ import qibosoq.components.base as rfsoc import qibosoq.components.pulses as rfsoc_pulses -from qibolab.pulses import Pulse, PulseSequence, PulseShape +from qibolab.pulses import Envelope, Pulse, PulseSequence from qibolab.qubits import Qubit from qibolab.sweeper import BIAS, DURATION, Parameter, Sweeper @@ -17,7 +17,7 @@ def replace_pulse_shape( - rfsoc_pulse: rfsoc_pulses.Pulse, shape: PulseShape, sampling_rate: float + rfsoc_pulse: rfsoc_pulses.Pulse, shape: Envelope, sampling_rate: float ) -> rfsoc_pulses.Pulse: """Set pulse shape parameters in rfsoc_pulses pulse object.""" if shape.name not in {"Gaussian", "Drag", "Rectangular", "Exponential"}: diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 62b3ef994..b60670f86 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -9,12 +9,14 @@ from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions from qibolab.instruments.qm.controller import controllers_config from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence -from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular +from qibolab.pulses import Envelopes, Pulse, PulseSequence, PulseType from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, Sweeper from .conftest import set_platform_profile +Rectangular = Envelopes.RECTANGULAR.value + def test_qmpulse(): pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) diff --git a/tests/test_instruments_rfsoc.py b/tests/test_instruments_rfsoc.py index 2399ba489..1e099e407 100644 --- a/tests/test_instruments_rfsoc.py +++ b/tests/test_instruments_rfsoc.py @@ -14,7 +14,7 @@ convert_units_sweeper, replace_pulse_shape, ) -from qibolab.pulses import Drag, Gaussian, Pulse, PulseSequence, PulseType, Rectangular +from qibolab.pulses import Envelopes, Pulse, PulseSequence, PulseType from qibolab.qubits import Qubit from qibolab.result import ( AveragedIntegratedResults, @@ -25,6 +25,10 @@ from .conftest import get_instrument +Rectangular = Envelopes.RECTANGULAR.value +Gaussian = Envelopes.GAUSSIAN.value +Drag = Envelopes.DRAG.value + def test_convert_default(dummy_qrc): """Test convert function raises errors when parameter have wrong types.""" diff --git a/tests/test_sweeper.py b/tests/test_sweeper.py index bf537ebe9..e9eb6670c 100644 --- a/tests/test_sweeper.py +++ b/tests/test_sweeper.py @@ -1,10 +1,12 @@ import numpy as np import pytest -from qibolab.pulses import Pulse, Rectangular +from qibolab.pulses import Envelopes, Pulse from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, QubitParameter, Sweeper +Rectangular = Envelopes.RECTANGULAR.value + @pytest.mark.parametrize("parameter", Parameter) def test_sweeper_pulses(parameter): From d2771dd81d5c434840fd695039a06ab3a81ee79f Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 15 Mar 2024 19:01:23 +0100 Subject: [PATCH 139/233] fix: Drop shape initialization in pulse post init Best fix ever, ~2000 tests in one shot :P --- src/qibolab/pulses/pulse.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 6c6b8a12a..f17feb0cb 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -47,7 +47,7 @@ class Pulse: """The pulse envelope shape. See - :cls:`qibolab.pulses.shape.Envelopes` for list of available shapes. + :cls:`qibolab.pulses.envelope.Envelopes` for list of available shapes. """ channel: Optional[str] = None """Channel on which the pulse should be played. @@ -65,10 +65,6 @@ class Pulse: def __post_init__(self): if isinstance(self.type, str): self.type = PulseType(self.type) - if isinstance(self.shape, str): - self.shape = PulseShape.eval(self.shape) - # TODO: drop the cyclic reference - self.shape.pulse = self @classmethod def flux(cls, start, duration, amplitude, shape, **kwargs): From 324e4c2d8748ee8a171a334001239961fb7acdf2 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 15 Mar 2024 19:02:40 +0100 Subject: [PATCH 140/233] fix: Fully drop post-init in pulse --- src/qibolab/pulses/pulse.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index f17feb0cb..93a846bd1 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -23,6 +23,7 @@ class PulseType(Enum): COUPLERFLUX = "cf" +# TODO: replace nested serialization with pydantic @dataclass class Pulse: """A class to represent a pulse to be sent to the QPU.""" @@ -62,10 +63,6 @@ class Pulse: qubit: int = 0 """Qubit or coupler addressed by the pulse.""" - def __post_init__(self): - if isinstance(self.type, str): - self.type = PulseType(self.type) - @classmethod def flux(cls, start, duration, amplitude, shape, **kwargs): return cls( From c55aa9695aa4c7c94afe72aec39383c3ddf4dbb8 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 15 Mar 2024 19:45:23 +0100 Subject: [PATCH 141/233] fix: Propagate envelopes to modulation tests --- tests/pulses/test_modulation.py | 45 ++++++++++++++++++--------------- tests/pulses/test_sequence.py | 8 +++--- 2 files changed, 28 insertions(+), 25 deletions(-) diff --git a/tests/pulses/test_modulation.py b/tests/pulses/test_modulation.py index e22b2af29..a403bb4ed 100644 --- a/tests/pulses/test_modulation.py +++ b/tests/pulses/test_modulation.py @@ -1,6 +1,7 @@ import numpy as np from qibolab.pulses import Envelopes, IqWaveform, Pulse, PulseType +from qibolab.pulses.envelope import Times from qibolab.pulses.modulation import demodulate, modulate Rectangular = Envelopes.RECTANGULAR.value @@ -14,12 +15,13 @@ def test_modulation(): amplitude=0.9, frequency=20_000_000, relative_phase=0.0, - shape=Rectangular(), - channel=0, + envelope=Rectangular(), + channel="0", type=PulseType.READOUT, qubit=0, ) - renvs: IqWaveform = np.array(rect.shape.envelope_waveforms()) + times = Times(rect.duration, 30) + renvs: IqWaveform = rect.envelopes(times) # fmt: off np.testing.assert_allclose(modulate(renvs, 0.04), np.array([[ 6.36396103e-01, 6.16402549e-01, 5.57678156e-01, @@ -46,33 +48,34 @@ def test_modulation(): # fmt: on gauss = Pulse( - start=5, + start=0, duration=20, amplitude=3.5, frequency=2_000_000, relative_phase=0.0, - shape=Gaussian(0.5), - channel=0, + envelope=Gaussian(0.5), + channel="0", type=PulseType.READOUT, qubit=0, ) - genvs: IqWaveform = np.array(gauss.shape.envelope_waveforms()) + times = Times(gauss.duration, 20) + genvs: IqWaveform = gauss.envelope.envelopes(times) # fmt: off np.testing.assert_allclose(modulate(genvs, 0.3), - np.array([[ 2.40604965e+00, -7.47704261e-01, -1.96732725e+00, - 1.97595317e+00, 7.57582564e-01, -2.45926187e+00, - 7.61855973e-01, 1.99830815e+00, -2.00080760e+00, - -7.64718297e-01, 2.47468039e+00, -7.64240497e-01, - -1.99830815e+00, 1.99456483e+00, 7.59953712e-01, - -2.45158868e+00, 7.54746949e-01, 1.96732725e+00, - -1.95751517e+00, -7.43510231e-01], - [ 0.00000000e+00, 2.30119709e+00, -1.42934692e+00, - -1.43561401e+00, 2.33159938e+00, 9.03518154e-16, - -2.34475159e+00, 1.45185586e+00, 1.45367181e+00, - -2.35356091e+00, -1.81836565e-15, 2.35209040e+00, - -1.45185586e+00, -1.44913618e+00, 2.33889703e+00, - 2.70209720e-15, -2.32287226e+00, 1.42934692e+00, - 1.42221802e+00, -2.28828920e+00]]) + np.array([[ 4.50307953e-01, -1.52257426e-01, -4.31814602e-01, + 4.63124693e-01, 1.87836646e-01, -6.39017403e-01, + 2.05526028e-01, 5.54460924e-01, -5.65661777e-01, + -2.18235048e-01, 7.06223450e-01, -2.16063573e-01, + -5.54460924e-01, 5.38074127e-01, 1.97467237e-01, + -6.07852156e-01, 1.76897892e-01, 4.31814602e-01, + -3.98615117e-01, -1.39152810e-01], + [ 0.00000000e+00, 4.68600175e-01, -3.13731672e-01, + -3.36479785e-01, 5.78101754e-01, 2.34771185e-16, + -6.32544073e-01, 4.02839441e-01, 4.10977338e-01, + -6.71658414e-01, -5.18924572e-16, 6.64975301e-01, + -4.02839441e-01, -3.90933736e-01, 6.07741665e-01, + 6.69963778e-16, -5.44435729e-01, 3.13731672e-01, + 2.89610835e-01, -4.28268313e-01]]) ) # fmt: on diff --git a/tests/pulses/test_sequence.py b/tests/pulses/test_sequence.py index 29c179b82..c71b501d0 100644 --- a/tests/pulses/test_sequence.py +++ b/tests/pulses/test_sequence.py @@ -17,7 +17,7 @@ def test_add_readout(): amplitude=0.3, duration=60, relative_phase=0, - shape="Gaussian(5)", + envelope=Gaussian(5), channel=1, ) ) @@ -28,9 +28,9 @@ def test_add_readout(): amplitude=0.3, duration=60, relative_phase=0, - shape="Drag(5, 2)", + envelope=Drag(5, 2), channel=1, - type="qf", + type=PulseType.FLUX, ) ) sequence.append(Delay(4, channel=1)) @@ -40,7 +40,7 @@ def test_add_readout(): amplitude=0.9, duration=2000, relative_phase=0, - shape="Rectangular()", + envelope=Rectangular(), channel=11, type=PulseType.READOUT, ) From 6265decee3a2e2870612245b10b3ce05aecb2a86 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 15 Mar 2024 20:58:29 +0100 Subject: [PATCH 142/233] test: Simplify modualtion tests, decouple from pulses --- tests/pulses/test_modulation.py | 33 ++++++--------------------------- 1 file changed, 6 insertions(+), 27 deletions(-) diff --git a/tests/pulses/test_modulation.py b/tests/pulses/test_modulation.py index a403bb4ed..079ec310b 100644 --- a/tests/pulses/test_modulation.py +++ b/tests/pulses/test_modulation.py @@ -1,6 +1,6 @@ import numpy as np -from qibolab.pulses import Envelopes, IqWaveform, Pulse, PulseType +from qibolab.pulses import Envelopes, IqWaveform from qibolab.pulses.envelope import Times from qibolab.pulses.modulation import demodulate, modulate @@ -9,19 +9,9 @@ def test_modulation(): - rect = Pulse( - start=0, - duration=30, - amplitude=0.9, - frequency=20_000_000, - relative_phase=0.0, - envelope=Rectangular(), - channel="0", - type=PulseType.READOUT, - qubit=0, - ) - times = Times(rect.duration, 30) - renvs: IqWaveform = rect.envelopes(times) + times = Times(30, 30) + amplitude = 0.9 + renvs: IqWaveform = Rectangular().envelopes(times) * amplitude # fmt: off np.testing.assert_allclose(modulate(renvs, 0.04), np.array([[ 6.36396103e-01, 6.16402549e-01, 5.57678156e-01, @@ -47,19 +37,8 @@ def test_modulation(): ) # fmt: on - gauss = Pulse( - start=0, - duration=20, - amplitude=3.5, - frequency=2_000_000, - relative_phase=0.0, - envelope=Gaussian(0.5), - channel="0", - type=PulseType.READOUT, - qubit=0, - ) - times = Times(gauss.duration, 20) - genvs: IqWaveform = gauss.envelope.envelopes(times) + times = Times(20, 20) + genvs: IqWaveform = Gaussian(0.5).envelopes(times) # fmt: off np.testing.assert_allclose(modulate(genvs, 0.3), np.array([[ 4.50307953e-01, -1.52257426e-01, -4.31814602e-01, From feba32543b6ec44eaea6d929f703e73568099f25 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 15 Mar 2024 21:15:59 +0100 Subject: [PATCH 143/233] test: Fix errors in pulse --- src/qibolab/pulses/pulse.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 93a846bd1..ba338ad8b 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -64,9 +64,9 @@ class Pulse: """Qubit or coupler addressed by the pulse.""" @classmethod - def flux(cls, start, duration, amplitude, shape, **kwargs): + def flux(cls, start, duration, amplitude, envelope, **kwargs): return cls( - start, duration, amplitude, 0, 0, shape, type=PulseType.FLUX, **kwargs + start, duration, amplitude, 0, 0, envelope, type=PulseType.FLUX, **kwargs ) @property @@ -149,7 +149,7 @@ def is_equal_ignoring_start(self, item) -> bool: and self.amplitude == item.amplitude and self.frequency == item.frequency and self.relative_phase == item.relative_phase - and self.shape == item.shape + and self.envelope == item.envelope and self.channel == item.channel and self.type == item.type and self.qubit == item.qubit From 0379608ac14687c6ae442e2d109b111a5d0e2c79 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 18 Mar 2024 10:15:20 +0100 Subject: [PATCH 144/233] build: Upgrade Nix shell to use Poetry 1.8 --- flake.lock | 48 ++++++++++++++++++++++++------------------------ 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/flake.lock b/flake.lock index 07ae0529b..e34ee8243 100644 --- a/flake.lock +++ b/flake.lock @@ -8,11 +8,11 @@ "pre-commit-hooks": "pre-commit-hooks" }, "locked": { - "lastModified": 1701187605, - "narHash": "sha256-NctguPdUeDVLXFsv6vI1RlEiHLsXkeW3pgZe/mwn1BU=", + "lastModified": 1710144971, + "narHash": "sha256-CjTOdoBvT/4AQncTL20SDHyJNgsXZjtGbz62yDIUYnM=", "owner": "cachix", "repo": "devenv", - "rev": "a7c4dd8f4eb1f98a6b8f04bf08364954e1e73e4f", + "rev": "6c0bad0045f1e1802f769f7890f6a59504825f4d", "type": "github" }, "original": { @@ -29,11 +29,11 @@ "rust-analyzer-src": "rust-analyzer-src" }, "locked": { - "lastModified": 1705904706, - "narHash": "sha256-0aJfyNYWy6pS4GfOA+pmGOE+PgJZLG78T+sPh8zRJx8=", + "lastModified": 1710742993, + "narHash": "sha256-W0PQCe0bW3hKF5lHawXrKynBcdSP18Qa4sb8DcUfOqI=", "owner": "nix-community", "repo": "fenix", - "rev": "8e7851239acf6bfb06637f4d3e180302f53ec542", + "rev": "6f2fec850f569d61562d3a47dc263f19e9c7d825", "type": "github" }, "original": { @@ -61,11 +61,11 @@ "flake-compat_2": { "flake": false, "locked": { - "lastModified": 1673956053, - "narHash": "sha256-4gtG9iQuiKITOjNQQeQIpoIB6b16fm+504Ch3sNKLd8=", + "lastModified": 1696426674, + "narHash": "sha256-kvjfFW7WAETZlt09AgDn1MrtKzP7t90Vf7vypd3OL1U=", "owner": "edolstra", "repo": "flake-compat", - "rev": "35bb57c0c8d8b62bbfd284272c928ceb64ddbde9", + "rev": "0f9255e01c2351cc7d116c072cb317785dd33b33", "type": "github" }, "original": { @@ -97,11 +97,11 @@ "systems": "systems_2" }, "locked": { - "lastModified": 1685518550, - "narHash": "sha256-o2d0KcvaXzTrPRIo0kOLV0/QXHhDQ5DTi+OxcjO8xqY=", + "lastModified": 1701680307, + "narHash": "sha256-kAuep2h5ajznlPMD9rnQyffWG8EM/C73lejGofXvdM8=", "owner": "numtide", "repo": "flake-utils", - "rev": "a1720a10a6cfe8234c0e93907ffe81be440f4cef", + "rev": "4022d587cbbfd70fe950c1e2083a02621806a725", "type": "github" }, "original": { @@ -196,11 +196,11 @@ ] }, "locked": { - "lastModified": 1702034373, - "narHash": "sha256-Apubv9die/XRBPI0eRFJyvuyGz/wD4YQUQJHRYCRenc=", + "lastModified": 1710660211, + "narHash": "sha256-tSNj0sK//GYmYSH9ts5pT1u4oI5Uxb+XWP4FIEhndxk=", "owner": "cachix", "repo": "nixpkgs-python", - "rev": "1cae686aa92dbccafe74fd242f984c3ec27c0b20", + "rev": "8b3ea06b981f2fd11d082df3474894b1d5bcbe7b", "type": "github" }, "original": { @@ -243,11 +243,11 @@ }, "nixpkgs_2": { "locked": { - "lastModified": 1702312524, - "narHash": "sha256-gkZJRDBUCpTPBvQk25G0B7vfbpEYM5s5OZqghkjZsnE=", + "lastModified": 1710631334, + "narHash": "sha256-rL5LSYd85kplL5othxK5lmAtjyMOBg390sGBTb3LRMM=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "a9bf124c46ef298113270b1f84a164865987a91c", + "rev": "c75037bbf9093a2acb617804ee46320d6d1fea5a", "type": "github" }, "original": { @@ -272,11 +272,11 @@ "nixpkgs-stable": "nixpkgs-stable" }, "locked": { - "lastModified": 1688056373, - "narHash": "sha256-2+SDlNRTKsgo3LBRiMUcoEUb6sDViRNQhzJquZ4koOI=", + "lastModified": 1704725188, + "narHash": "sha256-qq8NbkhRZF1vVYQFt1s8Mbgo8knj+83+QlL5LBnYGpI=", "owner": "cachix", "repo": "pre-commit-hooks.nix", - "rev": "5843cf069272d92b60c3ed9e55b7a8989c01d4c7", + "rev": "ea96f0c05924341c551a797aaba8126334c505d2", "type": "github" }, "original": { @@ -297,11 +297,11 @@ "rust-analyzer-src": { "flake": false, "locked": { - "lastModified": 1705864945, - "narHash": "sha256-ZATChFWHToTZQFLlzrzDUX8fjEbMHHBIyPaZU1JGmjI=", + "lastModified": 1710708100, + "narHash": "sha256-Jd6pmXlwKk5uYcjyO/8BfbUVmx8g31Qfk7auI2IG66A=", "owner": "rust-lang", "repo": "rust-analyzer", - "rev": "d410d4a2baf9e99b37b03dd42f06238b14374bf7", + "rev": "b6d1887bc4f9543b6c6bf098179a62446f34a6c3", "type": "github" }, "original": { From 516110a04a9be46821c06fbf3b5d06c02a3d4080 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 18 Mar 2024 11:09:29 +0100 Subject: [PATCH 145/233] build: Add linting deps to dev shell --- flake.nix | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flake.nix b/flake.nix index 58d57c2a8..dbcf891e7 100644 --- a/flake.nix +++ b/flake.nix @@ -68,7 +68,7 @@ enable = true; install = { enable = true; - groups = ["dev" "tests"]; + groups = ["dev" "analysis" "tests"]; extras = [ (lib.strings.concatStrings (lib.strings.intersperse " -E " From c0628ee4a4396e4d3244b822cffeafcebb8fa5ee Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 18 Mar 2024 12:40:22 +0100 Subject: [PATCH 146/233] feat!: Reintroduce sampling rate argument for pulse envelope retrieval --- src/qibolab/instruments/qm/sequence.py | 10 +++++----- src/qibolab/pulses/pulse.py | 16 +++++++++------- 2 files changed, 14 insertions(+), 12 deletions(-) diff --git a/src/qibolab/instruments/qm/sequence.py b/src/qibolab/instruments/qm/sequence.py index 1e760f8ec..f950d734f 100644 --- a/src/qibolab/instruments/qm/sequence.py +++ b/src/qibolab/instruments/qm/sequence.py @@ -9,9 +9,9 @@ from qualang_tools.bakery import baking from qualang_tools.bakery.bakery import Baking -from qibolab.instruments.qm.acquisition import Acquisition from qibolab.pulses import Pulse, PulseType +from .acquisition import Acquisition from .config import SAMPLING_RATE, QMConfig DurationsType = Union[List[int], npt.NDArray[int]] @@ -71,7 +71,7 @@ def __post_init__(self): if self.element is None: self.element = f"{pulse_type}{self.pulse.qubit}" self.operation: str = ( - f"{pulse_type}({self.pulse.duration}, {amplitude}, {self.pulse.shape})" + f"{pulse_type}({self.pulse.duration}, {amplitude}, {self.pulse.envelope})" ) self.relative_phase: float = self.pulse.relative_phase / (2 * np.pi) self.elements_to_align.add(self.element) @@ -147,11 +147,11 @@ def bake(self, config: QMConfig, durations: DurationsType): for t in durations: with baking(config.__dict__, padding_method="right") as segment: if self.pulse.type is PulseType.FLUX: - waveform = self.pulse.envelope_waveform_i(SAMPLING_RATE).tolist() + waveform = self.pulse.i(SAMPLING_RATE).tolist() waveform = self.calculate_waveform(waveform, t) else: - waveform_i = self.pulse.envelope_waveform_i(SAMPLING_RATE).tolist() - waveform_q = self.pulse.envelope_waveform_q(SAMPLING_RATE).tolist() + waveform_i = self.pulse.i(SAMPLING_RATE).tolist() + waveform_q = self.pulse.q(SAMPLING_RATE).tolist() waveform = [ self.calculate_waveform(waveform_i, t), self.calculate_waveform(waveform_q, t), diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index ba338ad8b..0c369024e 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -104,20 +104,22 @@ def phase(self) -> float: def id(self) -> int: return id(self) - def i(self, times: Times) -> Waveform: - """The envelope waveform of the i component of the pulse.""" + def _times(self, sampling_rate: float): + return Times(self.duration, int(self.duration * sampling_rate)) + def i(self, sampling_rate: float) -> Waveform: + """The envelope waveform of the i component of the pulse.""" + times = self._times(sampling_rate) return self.amplitude * self.envelope.i(times) - def q(self, times: Times) -> Waveform: + def q(self, sampling_rate: float) -> Waveform: """The envelope waveform of the q component of the pulse.""" - + times = self._times(sampling_rate) return self.amplitude * self.envelope.q(times) - def envelopes(self, times: Times) -> IqWaveform: + def envelopes(self, sampling_rate: float) -> IqWaveform: """A tuple with the i and q envelope waveforms of the pulse.""" - - return np.array([self.i(times), self.q(times)]) + return np.array([self.i(sampling_rate), self.q(sampling_rate)]) def __hash__(self): """Hash the content. From 37340899c809a3d49091d65bbca86cf545871cb9 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 18 Mar 2024 14:12:00 +0100 Subject: [PATCH 147/233] fix: Move default sampling rate to plots, drop from modulation --- src/qibolab/pulses/modulation.py | 11 ++--------- src/qibolab/pulses/plot.py | 18 ++++++++++++------ 2 files changed, 14 insertions(+), 15 deletions(-) diff --git a/src/qibolab/pulses/modulation.py b/src/qibolab/pulses/modulation.py index 3d5ce55a7..05e2b6747 100644 --- a/src/qibolab/pulses/modulation.py +++ b/src/qibolab/pulses/modulation.py @@ -4,18 +4,11 @@ __all__ = ["modulate", "demodulate"] -SAMPLING_RATE = 1 -"""Default sampling rate in gigasamples per second (GSps). - -Used for generating waveform envelopes if the instruments do not provide -a different value. -""" - def modulate( envelope: IqWaveform, freq: float, - rate: float = SAMPLING_RATE, + rate: float, phase: float = 0.0, ) -> IqWaveform: """Modulate the envelope waveform with a carrier. @@ -47,7 +40,7 @@ def modulate( def demodulate( modulated: IqWaveform, freq: float, - rate: float = SAMPLING_RATE, + rate: float, ) -> IqWaveform: """Demodulate the acquired pulse. diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index cd31fb61a..c2e161bca 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -10,6 +10,13 @@ from .pulse import Delay, Pulse from .sequence import PulseSequence +SAMPLING_RATE = 1 +"""Default sampling rate in gigasamples per second (GSps). + +Used for generating waveform envelopes if the instruments do not provide +a different value. +""" + def waveform(wf: Waveform, filename=None): """Plot the waveform. @@ -38,12 +45,11 @@ def pulse(pulse_: Pulse, filename=None): import matplotlib.pyplot as plt from matplotlib import gridspec - window = Times(pulse_.duration, num_samples) - waveform_i = pulse_.shape.i(window) - waveform_q = pulse_.shape.q(window) + waveform_i = pulse_.i(SAMPLING_RATE) + waveform_q = pulse_.q(SAMPLING_RATE) num_samples = len(waveform_i) - time = np.arange(num_samples) / sampling_rate + time = np.arange(num_samples) / SAMPLING_RATE _ = plt.figure(figsize=(14, 5), dpi=200) gs = gridspec.GridSpec(ncols=2, nrows=1, width_ratios=np.array([2, 1])) ax1 = plt.subplot(gs[0]) @@ -62,7 +68,7 @@ def pulse(pulse_: Pulse, filename=None): linestyle="dashed", ) - envelope = pulse_.shape.envelopes(window) + envelope = pulse_.envelopes(SAMPLING_RATE) modulated = modulate(np.array(envelope), pulse_.frequency) ax1.plot(time, modulated[0], label="modulated i", c="C0") ax1.plot(time, modulated[1], label="modulated q", c="C1") @@ -150,7 +156,7 @@ def sequence(ps: PulseSequence, filename=None): envelope = pulse.shape.envelope_waveforms(sampling_rate) num_samples = envelope[0].size - time = start + np.arange(num_samples) / sampling_rate + time = start + np.arange(num_samples) / SAMPLING_RATE modulated = modulate(np.array(envelope), pulse.frequency) ax.plot(time, modulated[1], c="lightgrey") ax.plot(time, modulated[0], c=f"C{str(n)}") From ef315e498931e56d6c2feac0de06580e04944c0f Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 10:04:38 +0400 Subject: [PATCH 148/233] feat: Export explicitly all envelopes types --- src/qibolab/pulses/envelope.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index edc6b1eec..bc528caad 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -16,6 +16,15 @@ "IqWaveform", "Envelope", "Envelopes", + "Rectangular", + "Exponential", + "Gaussian", + "GaussianSquare", + "Drag", + "Iir", + "Snz", + "ECap", + "Custom", ] From d9a7dd9b9bff9497ee143a35d89e80668c9e54dd Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 10:31:57 +0400 Subject: [PATCH 149/233] fix: Update envelope names --- tests/test_instruments_qmsim.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_instruments_qmsim.py b/tests/test_instruments_qmsim.py index 6b7cf83df..ceea34f9d 100644 --- a/tests/test_instruments_qmsim.py +++ b/tests/test_instruments_qmsim.py @@ -23,7 +23,7 @@ from qibolab import AcquisitionType, AveragingMode, ExecutionParameters, create_platform from qibolab.backends import QibolabBackend -from qibolab.pulses import SNZ, Pulse, PulseSequence, Rectangular +from qibolab.pulses import Pulse, PulseSequence, Rectangular, Snz from qibolab.sweeper import Parameter, Sweeper from .conftest import set_platform_profile @@ -489,7 +489,7 @@ def test_qmsim_snz_pulse(simulator, folder, qubit): duration = 30 amplitude = 0.01 sequence = PulseSequence() - shape = SNZ(t_half_flux_pulse=duration // 2, b_amplitude=2) + shape = Snz(t_half_flux_pulse=duration // 2, b_amplitude=2) channel = simulator.qubits[qubit].flux.name qd_pulse = simulator.create_RX_pulse(qubit, start=0) flux_pulse = Pulse.flux(qd_pulse.finish, duration, amplitude, shape, channel, qubit) From 4cd9710fe51c4e7035c524a21a7cce9695f0136d Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 10:46:31 +0400 Subject: [PATCH 150/233] fix: Propagate shape to envelope update in zhinst --- src/qibolab/instruments/zhinst/pulse.py | 25 ++++++++++++------------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/src/qibolab/instruments/zhinst/pulse.py b/src/qibolab/instruments/zhinst/pulse.py index c26e8321c..b9e068acf 100644 --- a/src/qibolab/instruments/zhinst/pulse.py +++ b/src/qibolab/instruments/zhinst/pulse.py @@ -17,15 +17,15 @@ def select_pulse(pulse: Pulse): """Return laboneq pulse object corresponding to the given qibolab pulse.""" - if isinstance(pulse.shape, Rectangular): + if isinstance(pulse.envelope, Rectangular): can_compress = pulse.type is not PulseType.READOUT return lo.pulse_library.const( length=round(pulse.duration * NANO_TO_SECONDS, 9), amplitude=pulse.amplitude, can_compress=can_compress, ) - if isinstance(pulse.shape, Gaussian): - sigma = pulse.shape.rel_sigma + if isinstance(pulse.envelope, Gaussian): + sigma = pulse.envelope.rel_sigma return lo.pulse_library.gaussian( length=round(pulse.duration * NANO_TO_SECONDS, 9), amplitude=pulse.amplitude, @@ -33,9 +33,9 @@ def select_pulse(pulse: Pulse): zero_boundaries=False, ) - if isinstance(pulse.shape, GaussianSquare): - sigma = pulse.shape.rel_sigma - width = pulse.shape.width + if isinstance(pulse.envelope, GaussianSquare): + sigma = pulse.envelope.rel_sigma + width = pulse.envelope.width can_compress = pulse.type is not PulseType.READOUT return lo.pulse_library.gaussian_square( length=round(pulse.duration * NANO_TO_SECONDS, 9), @@ -46,9 +46,9 @@ def select_pulse(pulse: Pulse): zero_boundaries=False, ) - if isinstance(pulse.shape, Drag): - sigma = pulse.shape.rel_sigma - beta = pulse.shape.beta + if isinstance(pulse.envelope, Drag): + sigma = pulse.envelope.rel_sigma + beta = pulse.envelope.beta return lo.pulse_library.drag( length=round(pulse.duration * NANO_TO_SECONDS, 9), amplitude=pulse.amplitude, @@ -57,15 +57,14 @@ def select_pulse(pulse: Pulse): zero_boundaries=False, ) - if np.all(pulse.envelope_waveform_q(SAMPLING_RATE) == 0): + if np.all(pulse.q(SAMPLING_RATE) == 0): return sampled_pulse_real( - samples=pulse.envelope_waveform_i(SAMPLING_RATE), + samples=pulse.i(SAMPLING_RATE), can_compress=True, ) else: return sampled_pulse_complex( - samples=pulse.envelope_waveform_i(SAMPLING_RATE) - + (1j * pulse.envelope_waveform_q(SAMPLING_RATE)), + samples=pulse.i(SAMPLING_RATE) + (1j * pulse.q(SAMPLING_RATE)), can_compress=True, ) From 06f3eab383f7657b8453a59fc09ce9138a5c1bad Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 13:24:42 +0400 Subject: [PATCH 151/233] feat!: Start introducing pydantic --- pyproject.toml | 1 + src/qibolab/pulses/envelope.py | 60 +++++++++++++++------------------- 2 files changed, 27 insertions(+), 34 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index c14b3af68..e6867c86f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,6 +26,7 @@ qibo = ">=0.2.6" networkx = "^3.0" numpy = "^1.26.4" more-itertools = "^9.1.0" +pydantic = "^2.6.4" qblox-instruments = { version = "0.12.0", optional = true } qcodes = { version = "^0.37.0", optional = true } qcodes_contrib_drivers = { version = "0.18.0", optional = true } diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index bc528caad..a466b5932 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -2,11 +2,12 @@ from abc import ABC from dataclasses import dataclass -from enum import Enum from functools import cached_property +from typing import Union import numpy as np import numpy.typing as npt +from pydantic import BaseModel from scipy.signal import lfilter from scipy.signal.windows import gaussian @@ -14,8 +15,8 @@ "Times", "Waveform", "IqWaveform", + "BaseEnvelope", "Envelope", - "Envelopes", "Rectangular", "Exponential", "Gaussian", @@ -60,7 +61,7 @@ def window(self): return np.linspace(0, self.duration, self.samples) -class Envelope(ABC): +class BaseEnvelope(ABC, BaseModel): """Pulse envelopes. Generates both i (in-phase) and q (quadrature) components. @@ -79,8 +80,7 @@ def envelopes(self, times: Times) -> IqWaveform: return np.array([self.i(times), self.q(times)]) -@dataclass(frozen=True) -class Rectangular(Envelope): +class Rectangular(BaseEnvelope): """Rectangular envelope.""" def i(self, times: Times) -> Waveform: @@ -88,8 +88,7 @@ def i(self, times: Times) -> Waveform: return np.ones(times.samples) -@dataclass(frozen=True) -class Exponential(Envelope): +class Exponential(BaseEnvelope): r"""Exponential shape, i.e. square pulse with an exponential decay. .. math:: @@ -121,8 +120,7 @@ def _samples_sigma(rel_sigma: float, times: Times) -> float: return rel_sigma * times.samples -@dataclass(frozen=True) -class Gaussian(Envelope): +class Gaussian(BaseEnvelope): r"""Gaussian pulse shape. Args: @@ -144,8 +142,7 @@ def i(self, times: Times) -> Waveform: return gaussian(times.samples, _samples_sigma(self.rel_sigma, times)) -@dataclass(frozen=True) -class GaussianSquare(Envelope): +class GaussianSquare(BaseEnvelope): r"""GaussianSquare pulse shape. .. math:: @@ -173,8 +170,7 @@ def i(self, times: Times) -> Waveform: return pulse -@dataclass(frozen=True) -class Drag(Envelope): +class Drag(BaseEnvelope): """Derivative Removal by Adiabatic Gate (DRAG) pulse shape. .. todo:: @@ -205,8 +201,7 @@ def q(self, times: Times) -> Waveform: return self.beta * (-(ts - times.mean) / (sigma**2)) * self.i(times) -@dataclass(frozen=True) -class Iir(Envelope): +class Iir(BaseEnvelope): """IIR Filter using scipy.signal lfilter. https://arxiv.org/pdf/1907.04818.pdf (page 11 - filter formula S22):: @@ -218,7 +213,7 @@ class Iir(Envelope): a: npt.NDArray b: npt.NDArray - target: Envelope + target: BaseEnvelope def _data(self, target: npt.NDArray) -> npt.NDArray: a = self.a / self.a[0] @@ -239,8 +234,7 @@ def q(self, times: Times) -> Waveform: return self._data(self.target.q(times)) -@dataclass(frozen=True) -class Snz(Envelope): +class Snz(BaseEnvelope): """Sudden variant Net Zero. https://arxiv.org/abs/2008.07411 @@ -270,8 +264,7 @@ def i(self, times: Times) -> Waveform: return pulse -@dataclass(frozen=True) -class ECap(Envelope): +class ECap(BaseEnvelope): r"""ECap pulse shape. .. todo:: @@ -296,8 +289,7 @@ def i(self, times: Times) -> Waveform: ) -@dataclass(frozen=True) -class Custom(Envelope): +class Custom(BaseEnvelope): """Arbitrary shape. .. todo:: @@ -318,15 +310,15 @@ def q(self, times: Times) -> Waveform: raise NotImplementedError -class Envelopes(Enum): - """Available pulse shapes.""" - - RECTANGULAR = Rectangular - EXPONENTIAL = Exponential - GAUSSIAN = Gaussian - GAUSSIAN_SQUARE = GaussianSquare - DRAG = Drag - IIR = Iir - SNZ = Snz - ECAP = ECap - CUSTOM = Custom +Envelope = Union[ + Rectangular, + Exponential, + Gaussian, + GaussianSquare, + Drag, + Iir, + Snz, + ECap, + Custom, +] +"""Available pulse shapes.""" From d74d294c8ecc38d1663ae26f746687d27d7085b3 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 15:50:10 +0400 Subject: [PATCH 152/233] fix: Propagate Pydantic models to Pulse --- src/qibolab/instruments/qm/config.py | 4 +--- src/qibolab/pulses/pulse.py | 21 +++++++++++++-------- 2 files changed, 14 insertions(+), 11 deletions(-) diff --git a/src/qibolab/instruments/qm/config.py b/src/qibolab/instruments/qm/config.py index e6ceeca78..cc934fb20 100644 --- a/src/qibolab/instruments/qm/config.py +++ b/src/qibolab/instruments/qm/config.py @@ -4,12 +4,10 @@ import numpy as np from qibo.config import raise_error -from qibolab.pulses import Envelopes, PulseType +from qibolab.pulses import PulseType, Rectangular from .ports import OPXIQ, OctaveInput, OctaveOutput, OPXOutput -Rectangular = Envelopes.RECTANGULAR.value - SAMPLING_RATE = 1 """Sampling rate of Quantum Machines OPX in GSps.""" diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 0c369024e..84a12ac71 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -1,10 +1,11 @@ """Pulse class.""" -from dataclasses import dataclass, fields +from dataclasses import fields from enum import Enum from typing import Optional import numpy as np +from pydantic import BaseModel from .envelope import Envelope, IqWaveform, Times, Waveform @@ -23,9 +24,7 @@ class PulseType(Enum): COUPLERFLUX = "cf" -# TODO: replace nested serialization with pydantic -@dataclass -class Pulse: +class Pulse(BaseModel): """A class to represent a pulse to be sent to the QPU.""" start: int @@ -64,10 +63,16 @@ class Pulse: """Qubit or coupler addressed by the pulse.""" @classmethod - def flux(cls, start, duration, amplitude, envelope, **kwargs): - return cls( - start, duration, amplitude, 0, 0, envelope, type=PulseType.FLUX, **kwargs - ) + def flux(cls, **kwargs): + """Construct a flux pulse. + + It provides a simplified syntax for the :cls:`Pulse` constructor, by applying + suitable defaults. + """ + kwargs["frequency"] = 0 + kwargs["relative_phase"] = 0 + kwargs["type"] = PulseType.FLUX + return cls(**kwargs) @property def finish(self) -> Optional[int]: From 2e702db6b90f93d942c7a50b98714a241ca23e0a Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 16:15:46 +0400 Subject: [PATCH 153/233] fix: Propagate Pydantic models to Pulse-like classes --- src/qibolab/pulses/pulse.py | 75 ++++++++++++++----------------------- 1 file changed, 29 insertions(+), 46 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 84a12ac71..b961a976d 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -22,13 +22,13 @@ class PulseType(Enum): DRIVE = "qd" FLUX = "qf" COUPLERFLUX = "cf" + DELAY = "dl" + VIRTUALZ = "vz" class Pulse(BaseModel): - """A class to represent a pulse to be sent to the QPU.""" + """A pulse to be sent to the QPU.""" - start: int - """Start time of pulse in ns.""" duration: int """Pulse duration in ns.""" amplitude: float @@ -74,37 +74,6 @@ def flux(cls, **kwargs): kwargs["type"] = PulseType.FLUX return cls(**kwargs) - @property - def finish(self) -> Optional[int]: - """Time when the pulse is scheduled to finish.""" - if None in {self.start, self.duration}: - return None - return self.start + self.duration - - @property - def global_phase(self): - """Global phase of the pulse, in radians. - - This phase is calculated from the pulse start time and frequency - as `2 * pi * frequency * start`. - """ - if self.type is PulseType.READOUT: - # readout pulses should have zero global phase so that we can - # calculate probabilities in the i-q plane - return 0 - - # pulse start, duration and finish are in ns - return 2 * np.pi * self.frequency * self.start / 1e9 - - @property - def phase(self) -> float: - """Total phase of the pulse, in radians. - - The total phase is computed as the sum of the global and - relative phases. - """ - return self.global_phase + self.relative_phase - @property def id(self) -> int: return id(self) @@ -149,15 +118,29 @@ def __hash__(self): ) ) - def is_equal_ignoring_start(self, item) -> bool: - """Check if two pulses are equal ignoring start time.""" - return ( - self.duration == item.duration - and self.amplitude == item.amplitude - and self.frequency == item.frequency - and self.relative_phase == item.relative_phase - and self.envelope == item.envelope - and self.channel == item.channel - and self.type == item.type - and self.qubit == item.qubit - ) + +class Delay(BaseModel): + """A wait instruction during which we are not sending any pulses to the + QPU.""" + + duration: int + """Delay duration in ns.""" + channel: str + """Channel on which the delay should be implemented.""" + type: PulseType = PulseType.DELAY + """Type fixed to ``DELAY`` to comply with ``Pulse`` interface.""" + + +class VirtualZ(BaseModel): + """Implementation of Z-rotations using virtual phase.""" + + duration = 0 + """Duration of the virtual gate should always be zero.""" + + phase: float + """Phase that implements the rotation.""" + channel: Optional[str] = None + """Channel on which the virtual phase should be added.""" + qubit: int = 0 + """Qubit on the drive of which the virtual phase should be added.""" + type: PulseType = PulseType.VIRTUALZ From 4dfc22710d3d1d719a805630207d168993d974d9 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 16:22:42 +0400 Subject: [PATCH 154/233] fix: Fix Pylint errors --- src/qibolab/compilers/compiler.py | 2 +- src/qibolab/platform/platform.py | 8 ++++---- src/qibolab/pulses/plot.py | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/qibolab/compilers/compiler.py b/src/qibolab/compilers/compiler.py index 191971e80..1499f31ca 100644 --- a/src/qibolab/compilers/compiler.py +++ b/src/qibolab/compilers/compiler.py @@ -155,7 +155,7 @@ def compile(self, circuit, platform): for pulse in gate_sequence: if qubit_clock[pulse.qubit] > channel_clock[pulse.qubit]: delay = qubit_clock[pulse.qubit] - channel_clock[pulse.channel] - sequence.append(Delay(delay, pulse.channel)) + sequence.append(Delay(duration=delay, channel=pulse.channel)) channel_clock[pulse.channel] += delay sequence.append(pulse) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 744c00f41..17d070ad2 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -463,24 +463,24 @@ def create_coupler_pulse(self, coupler, duration=None, amplitude=None): # TODO Remove RX90_drag_pulse and RX_drag_pulse, replace them with create_qubit_drive_pulse # TODO Add RY90 and RY pulses - def create_RX90_drag_pulse(self, qubit, start, beta, relative_phase=0): + def create_RX90_drag_pulse(self, qubit, beta, relative_phase=0): """Create native RX90 pulse with Drag shape.""" qubit = self.get_qubit(qubit) pulse = qubit.native_gates.RX90 return replace( pulse, relative_phase=relative_phase, - shape=Drag(pulse.shape.rel_sigma, beta), + shape=Drag(rel_sigma=pulse.envelope.rel_sigma, beta=beta), channel=qubit.drive.name, ) - def create_RX_drag_pulse(self, qubit, start, beta, relative_phase=0): + def create_RX_drag_pulse(self, qubit, beta, relative_phase=0): """Create native RX pulse with Drag shape.""" qubit = self.get_qubit(qubit) pulse = qubit.native_gates.RX return replace( pulse, relative_phase=relative_phase, - shape=Drag(pulse.shape.rel_sigma, beta), + shape=Drag(rel_sigma=pulse.envelope.rel_sigma, beta=beta), channel=qubit.drive.name, ) diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index c2e161bca..d7f456ab2 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -154,7 +154,7 @@ def sequence(ps: PulseSequence, filename=None): start += pulse.duration continue - envelope = pulse.shape.envelope_waveforms(sampling_rate) + envelope = pulse.envelopes(SAMPLING_RATE) num_samples = envelope[0].size time = start + np.arange(num_samples) / SAMPLING_RATE modulated = modulate(np.array(envelope), pulse.frequency) From 7cb182b15a929c25dd1c3afbccb65207d94acf10 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 17:45:37 +0400 Subject: [PATCH 155/233] feat: Add custom NumPy (de)serializer) --- src/qibolab/serialize_.py | 42 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 src/qibolab/serialize_.py diff --git a/src/qibolab/serialize_.py b/src/qibolab/serialize_.py new file mode 100644 index 000000000..a39757a1c --- /dev/null +++ b/src/qibolab/serialize_.py @@ -0,0 +1,42 @@ +"""Serialization utilities.""" + +import base64 +import io +from typing import Annotated, Union + +import numpy as np +import numpy.typing as npt +from pydantic import BaseModel, ConfigDict, PlainSerializer, PlainValidator + + +def ndarray_serialize(ar: npt.NDArray) -> str: + """Serialize array to string.""" + buffer = io.BytesIO() + np.save(buffer, ar) + buffer.seek(0) + return base64.standard_b64encode(buffer.read()).decode() + + +def ndarray_deserialize(x: Union[str, npt.NDArray]) -> npt.NDArray: + """Deserialize array.""" + if isinstance(x, np.ndarray): + return x + + buffer = io.BytesIO() + buffer.write(base64.standard_b64decode(x)) + buffer.seek(0) + return np.load(buffer) + + +NdArray = Annotated[ + npt.NDArray, + PlainValidator(ndarray_deserialize), + PlainSerializer(ndarray_serialize, return_type=str), +] +"""Pydantic-compatible array representation.""" + + +class Model(BaseModel): + """Global qibolab model, holding common configurations.""" + + model_config = ConfigDict(arbitrary_types_allowed=True) From b72bed444614a844cb99627349bfbdacbfa7561b Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 18:54:15 +0400 Subject: [PATCH 156/233] fix: Adopt qibolab global model in pydantic-aware classes --- src/qibolab/pulses/envelope.py | 9 +++++---- src/qibolab/pulses/pulse.py | 11 ++++++----- 2 files changed, 11 insertions(+), 9 deletions(-) diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index a466b5932..572cc4548 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -7,10 +7,11 @@ import numpy as np import numpy.typing as npt -from pydantic import BaseModel from scipy.signal import lfilter from scipy.signal.windows import gaussian +from qibolab.serialize_ import Model, NdArray + __all__ = [ "Times", "Waveform", @@ -61,7 +62,7 @@ def window(self): return np.linspace(0, self.duration, self.samples) -class BaseEnvelope(ABC, BaseModel): +class BaseEnvelope(ABC, Model): """Pulse envelopes. Generates both i (in-phase) and q (quadrature) components. @@ -211,8 +212,8 @@ class Iir(BaseEnvelope): p = [b0, b1, a0, a1] """ - a: npt.NDArray - b: npt.NDArray + a: NdArray + b: NdArray target: BaseEnvelope def _data(self, target: npt.NDArray) -> npt.NDArray: diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index b961a976d..f3dd86191 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -5,7 +5,8 @@ from typing import Optional import numpy as np -from pydantic import BaseModel + +from qibolab.serialize_ import Model from .envelope import Envelope, IqWaveform, Times, Waveform @@ -26,7 +27,7 @@ class PulseType(Enum): VIRTUALZ = "vz" -class Pulse(BaseModel): +class Pulse(Model): """A pulse to be sent to the QPU.""" duration: int @@ -119,7 +120,7 @@ def __hash__(self): ) -class Delay(BaseModel): +class Delay(Model): """A wait instruction during which we are not sending any pulses to the QPU.""" @@ -131,10 +132,10 @@ class Delay(BaseModel): """Type fixed to ``DELAY`` to comply with ``Pulse`` interface.""" -class VirtualZ(BaseModel): +class VirtualZ(Model): """Implementation of Z-rotations using virtual phase.""" - duration = 0 + duration: int = 0 """Duration of the virtual gate should always be zero.""" phase: float From b92889704dab94577fb16201435c1ca0655c5b30 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 19:06:20 +0400 Subject: [PATCH 157/233] fix: Solve import-related issues in tests --- tests/pulses/test_envelope.py | 8 +------- tests/pulses/test_modulation.py | 5 +---- tests/pulses/test_plot.py | 14 +++++++------- tests/pulses/test_pulse.py | 14 ++++++-------- tests/test_instruments_qm.py | 4 +--- tests/test_instruments_rfsoc.py | 6 +----- tests/test_instruments_zhinst.py | 20 +++++++++++--------- tests/test_sweeper.py | 4 +--- 8 files changed, 29 insertions(+), 46 deletions(-) diff --git a/tests/pulses/test_envelope.py b/tests/pulses/test_envelope.py index eea806c3e..0d2ec3ce3 100644 --- a/tests/pulses/test_envelope.py +++ b/tests/pulses/test_envelope.py @@ -1,13 +1,7 @@ import numpy as np import pytest -from qibolab.pulses import Envelopes, Pulse, PulseType - -Rectangular = Envelopes.RECTANGULAR.value -Gaussian = Envelopes.GAUSSIAN.value -GaussianSquare = Envelopes.GAUSSIAN_SQUARE.value -Drag = Envelopes.DRAG.value -eCap = Envelopes.ECAP.value +from qibolab.pulses import Drag, Gaussian, GaussianSquare, Pulse, PulseType, Rectangular @pytest.mark.parametrize( diff --git a/tests/pulses/test_modulation.py b/tests/pulses/test_modulation.py index 079ec310b..872adbdd6 100644 --- a/tests/pulses/test_modulation.py +++ b/tests/pulses/test_modulation.py @@ -1,12 +1,9 @@ import numpy as np -from qibolab.pulses import Envelopes, IqWaveform +from qibolab.pulses import Gaussian, IqWaveform, Rectangular from qibolab.pulses.envelope import Times from qibolab.pulses.modulation import demodulate, modulate -Rectangular = Envelopes.RECTANGULAR.value -Gaussian = Envelopes.GAUSSIAN.value - def test_modulation(): times = Times(30, 30) diff --git a/tests/pulses/test_plot.py b/tests/pulses/test_plot.py index 7164ee8e2..28d59354b 100644 --- a/tests/pulses/test_plot.py +++ b/tests/pulses/test_plot.py @@ -4,19 +4,19 @@ import numpy as np from qibolab.pulses import ( - IIR, - SNZ, Drag, + ECap, Gaussian, GaussianSquare, + Iir, Pulse, PulseSequence, PulseType, Rectangular, - eCap, + Snz, plot, ) -from qibolab.pulses.shape import modulate +from qibolab.pulses.modulation import modulate HERE = pathlib.Path(__file__).parent @@ -25,9 +25,9 @@ def test_plot_functions(): p0 = Pulse(40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) p1 = Pulse(40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) p2 = Pulse(40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) - p3 = Pulse.flux(40, 0.9, IIR([-0.5, 2], [1], Rectangular()), channel=0, qubit=200) - p4 = Pulse.flux(40, 0.9, SNZ(t_idling=10), channel=0, qubit=200) - p5 = Pulse(40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) + p3 = Pulse.flux(40, 0.9, Iir([-0.5, 2], [1], Rectangular()), channel=0, qubit=200) + p4 = Pulse.flux(40, 0.9, Snz(t_idling=10), channel=0, qubit=200) + p5 = Pulse(40, 0.9, 400e6, 0, ECap(alpha=2), 0, PulseType.DRIVE) p6 = Pulse(40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.DRIVE, 2) ps = PulseSequence([p0, p1, p2, p3, p4, p5, p6]) envelope = p0.envelope_waveforms() diff --git a/tests/pulses/test_pulse.py b/tests/pulses/test_pulse.py index 774ba58d3..5607c01c0 100644 --- a/tests/pulses/test_pulse.py +++ b/tests/pulses/test_pulse.py @@ -6,18 +6,16 @@ import pytest from qibolab.pulses import ( - IIR, - SNZ, Custom, Drag, + ECap, Gaussian, GaussianSquare, + Iir, Pulse, - PulseShape, PulseType, Rectangular, - ShapeInitError, - eCap, + Snz, ) @@ -105,10 +103,10 @@ def test_init(): p8 = Pulse(40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) p9 = Pulse(40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) p10 = Pulse.flux( - 40, 0.9, IIR([-1, 1], [-0.1, 0.1001], Rectangular()), channel=0, qubit=200 + 40, 0.9, Iir([-1, 1], [-0.1, 0.1001], Rectangular()), channel=0, qubit=200 ) - p11 = Pulse.flux(40, 0.9, SNZ(t_idling=10, b_amplitude=0.5), channel=0, qubit=200) - p13 = Pulse(40, 0.9, 400e6, 0, eCap(alpha=2), 0, PulseType.DRIVE) + p11 = Pulse.flux(40, 0.9, Snz(t_idling=10, b_amplitude=0.5), channel=0, qubit=200) + p13 = Pulse(40, 0.9, 400e6, 0, ECap(alpha=2), 0, PulseType.DRIVE) p14 = Pulse(40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.READOUT, 2) # initialisation with float duration diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index b60670f86..62b3ef994 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -9,14 +9,12 @@ from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions from qibolab.instruments.qm.controller import controllers_config from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence -from qibolab.pulses import Envelopes, Pulse, PulseSequence, PulseType +from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, Sweeper from .conftest import set_platform_profile -Rectangular = Envelopes.RECTANGULAR.value - def test_qmpulse(): pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) diff --git a/tests/test_instruments_rfsoc.py b/tests/test_instruments_rfsoc.py index 1e099e407..2399ba489 100644 --- a/tests/test_instruments_rfsoc.py +++ b/tests/test_instruments_rfsoc.py @@ -14,7 +14,7 @@ convert_units_sweeper, replace_pulse_shape, ) -from qibolab.pulses import Envelopes, Pulse, PulseSequence, PulseType +from qibolab.pulses import Drag, Gaussian, Pulse, PulseSequence, PulseType, Rectangular from qibolab.qubits import Qubit from qibolab.result import ( AveragedIntegratedResults, @@ -25,10 +25,6 @@ from .conftest import get_instrument -Rectangular = Envelopes.RECTANGULAR.value -Gaussian = Envelopes.GAUSSIAN.value -Drag = Envelopes.DRAG.value - def test_convert_default(dummy_qrc): """Test convert function raises errors when parameter have wrong types.""" diff --git a/tests/test_instruments_zhinst.py b/tests/test_instruments_zhinst.py index 094b8fcef..9e3394fe9 100644 --- a/tests/test_instruments_zhinst.py +++ b/tests/test_instruments_zhinst.py @@ -16,14 +16,14 @@ measure_channel_name, ) from qibolab.pulses import ( - IIR, - SNZ, Drag, Gaussian, + Iir, Pulse, PulseSequence, PulseType, Rectangular, + Snz, ) from qibolab.sweeper import Parameter, Sweeper from qibolab.unrolling import batch @@ -38,13 +38,13 @@ Pulse(40, 0.05, int(3e9), 0.0, Gaussian(5), "ch0", qubit=0), Pulse(40, 0.05, int(3e9), 0.0, Gaussian(5), "ch0", qubit=0), Pulse(40, 0.05, int(3e9), 0.0, Drag(5, 0.4), "ch0", qubit=0), - Pulse(40, 0.05, int(3e9), 0.0, SNZ(10, 0.01), "ch0", qubit=0), + Pulse(40, 0.05, int(3e9), 0.0, Snz(10, 0.01), "ch0", qubit=0), Pulse( 40, 0.05, int(3e9), 0.0, - IIR([10, 1], [0.4, 1], target=Gaussian(5)), + Iir([10, 1], [0.4, 1], target=Gaussian(5)), "ch0", qubit=0, ), @@ -54,7 +54,7 @@ def test_zhpulse_pulse_conversion(pulse): shape = pulse.shape zhpulse = ZhPulse(pulse).zhpulse assert isinstance(zhpulse, laboneq_pulse.Pulse) - if isinstance(shape, (SNZ, IIR)): + if isinstance(shape, (Snz, Iir)): assert len(zhpulse.samples) == 80 else: assert zhpulse.length == 40e-9 @@ -251,8 +251,9 @@ def test_zhsequence(dummy_qrc): IQM5q = create_platform("zurich") controller = IQM5q.instruments["EL_ZURO"] - drive_channel, readout_channel = IQM5q.qubits[0].drive.name, measure_channel_name( - IQM5q.qubits[0] + drive_channel, readout_channel = ( + IQM5q.qubits[0].drive.name, + measure_channel_name(IQM5q.qubits[0]), ) qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), drive_channel, qubit=0) ro_pulse = Pulse( @@ -285,8 +286,9 @@ def test_zhsequence_couplers(dummy_qrc): IQM5q = create_platform("zurich") controller = IQM5q.instruments["EL_ZURO"] - drive_channel, readout_channel = IQM5q.qubits[0].drive.name, measure_channel_name( - IQM5q.qubits[0] + drive_channel, readout_channel = ( + IQM5q.qubits[0].drive.name, + measure_channel_name(IQM5q.qubits[0]), ) couplerflux_channel = IQM5q.couplers[0].flux.name qd_pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), drive_channel, qubit=0) diff --git a/tests/test_sweeper.py b/tests/test_sweeper.py index e9eb6670c..bf537ebe9 100644 --- a/tests/test_sweeper.py +++ b/tests/test_sweeper.py @@ -1,12 +1,10 @@ import numpy as np import pytest -from qibolab.pulses import Envelopes, Pulse +from qibolab.pulses import Pulse, Rectangular from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, QubitParameter, Sweeper -Rectangular = Envelopes.RECTANGULAR.value - @pytest.mark.parametrize("parameter", Parameter) def test_sweeper_pulses(parameter): From 399605b8edad94c831542b8cb61f49ab21e0c0fd Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 19:15:09 +0400 Subject: [PATCH 158/233] fix: Solve all Pytest collection errors --- tests/pulses/test_envelope.py | 8 +++- tests/test_instruments_zhinst.py | 64 ++++++++++++++++++++++++++------ 2 files changed, 60 insertions(+), 12 deletions(-) diff --git a/tests/pulses/test_envelope.py b/tests/pulses/test_envelope.py index 0d2ec3ce3..7785e0408 100644 --- a/tests/pulses/test_envelope.py +++ b/tests/pulses/test_envelope.py @@ -5,7 +5,13 @@ @pytest.mark.parametrize( - "shape", [Rectangular(), Gaussian(5), GaussianSquare(5, 0.9), Drag(5, 1)] + "shape", + [ + Rectangular(), + Gaussian(rel_sigma=5), + GaussianSquare(rel_sigma=5, width=0.9), + Drag(rel_sigma=5, beta=1), + ], ) def test_sampling_rate(shape): pulse = Pulse(0, 40, 0.9, 100e6, 0, shape, 0, PulseType.DRIVE) diff --git a/tests/test_instruments_zhinst.py b/tests/test_instruments_zhinst.py index 9e3394fe9..ea89655dd 100644 --- a/tests/test_instruments_zhinst.py +++ b/tests/test_instruments_zhinst.py @@ -34,18 +34,60 @@ @pytest.mark.parametrize( "pulse", [ - Pulse(40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0), - Pulse(40, 0.05, int(3e9), 0.0, Gaussian(5), "ch0", qubit=0), - Pulse(40, 0.05, int(3e9), 0.0, Gaussian(5), "ch0", qubit=0), - Pulse(40, 0.05, int(3e9), 0.0, Drag(5, 0.4), "ch0", qubit=0), - Pulse(40, 0.05, int(3e9), 0.0, Snz(10, 0.01), "ch0", qubit=0), Pulse( - 40, - 0.05, - int(3e9), - 0.0, - Iir([10, 1], [0.4, 1], target=Gaussian(5)), - "ch0", + duration=40, + amplitude=0.05, + frequency=int(3e9), + relative_phase=0.0, + envelope=Rectangular(), + channel="ch0", + qubit=0, + ), + Pulse( + duration=40, + amplitude=0.05, + frequency=int(3e9), + relative_phase=0.0, + envelope=Gaussian(rel_sigma=5), + channel="ch0", + qubit=0, + ), + Pulse( + duration=40, + amplitude=0.05, + frequency=int(3e9), + relative_phase=0.0, + envelope=Gaussian(rel_sigma=5), + channel="ch0", + qubit=0, + ), + Pulse( + duration=40, + amplitude=0.05, + frequency=int(3e9), + relative_phase=0.0, + envelope=Drag(rel_sigma=5, beta=0.4), + channel="ch0", + qubit=0, + ), + Pulse( + duration=40, + amplitude=0.05, + frequency=int(3e9), + relative_phase=0.0, + envelope=Snz(t_idling=10, b_amplitude=0.01), + channel="ch0", + qubit=0, + ), + Pulse( + duration=40, + amplitude=0.05, + frequency=int(3e9), + relative_phase=0.0, + envelope=Iir( + a=np.array([10, 1]), b=np.array([0.4, 1]), target=Gaussian(rel_sigma=5) + ), + channel="ch0", qubit=0, ), ], From ff6cc7e3207300172cacd9aa69056ca9982f9de6 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 22 Mar 2024 10:45:27 +0400 Subject: [PATCH 159/233] fix: Partial dummy runcard update --- src/qibolab/dummy/parameters.json | 1247 ++++++++++++++--------------- src/qibolab/pulses/pulse.py | 4 +- src/qibolab/serialize.py | 2 +- 3 files changed, 600 insertions(+), 653 deletions(-) diff --git a/src/qibolab/dummy/parameters.json b/src/qibolab/dummy/parameters.json index 529fcb69e..3c675323e 100644 --- a/src/qibolab/dummy/parameters.json +++ b/src/qibolab/dummy/parameters.json @@ -1,663 +1,610 @@ { - "nqubits": 5, - "settings": { - "nshots": 1024, - "relaxation_time": 0 + "nqubits": 5, + "settings": { + "nshots": 1024, + "relaxation_time": 0 + }, + "qubits": [0, 1, 2, 3, 4], + "couplers": [0, 1, 3, 4], + "topology": { + "0": [0, 2], + "1": [1, 2], + "3": [2, 3], + "4": [2, 4] + }, + "instruments": { + "dummy": { + "bounds": { + "waveforms": 0, + "readout": 0, + "instructions": 0 + } }, - "qubits": [ - 0, - 1, - 2, - 3, - 4 - ], - "couplers": [ - 0, - 1, - 3, - 4 - ], - "topology": { - "0": [ - 0, - 2 + "twpa_pump": { + "power": 10, + "frequency": 1000000000.0 + } + }, + "native_gates": { + "single_qubit": { + "0": { + "RX": { + "duration": 40, + "amplitude": 0.1, + "envelope": { "rel_sigma": 5 }, + "frequency": 4000000000.0, + "type": "qd" + }, + "RX12": { + "duration": 40, + "amplitude": 0.005, + "envelope": { "rel_sigma": 5 }, + "frequency": 4700000000, + "type": "qd" + }, + "MZ": { + "duration": 2000, + "amplitude": 0.1, + "envelope": { "rel_sigma": 5, "width": 0.75 }, + "frequency": 5200000000.0, + "type": "ro" + } + }, + "1": { + "RX": { + "duration": 40, + "amplitude": 0.3, + "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "frequency": 4200000000.0, + "type": "qd" + }, + "RX12": { + "duration": 40, + "amplitude": 0.0484, + "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "frequency": 4855663000, + "type": "qd" + }, + "MZ": { + "duration": 2000, + "amplitude": 0.1, + "envelope": { "rel_sigma": 5, "width": 0.75 }, + "frequency": 4900000000.0, + "type": "ro" + } + }, + "2": { + "RX": { + "duration": 40, + "amplitude": 0.3, + "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "frequency": 4500000000.0, + "type": "qd" + }, + "RX12": { + "duration": 40, + "amplitude": 0.005, + "envelope": { "rel_sigma": 5 }, + "frequency": 2700000000, + "type": "qd" + }, + "MZ": { + "duration": 2000, + "amplitude": 0.1, + "envelope": { "rel_sigma": 5, "width": 0.75 }, + "frequency": 6100000000.0, + "type": "ro" + } + }, + "3": { + "RX": { + "duration": 40, + "amplitude": 0.3, + "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "frequency": 4150000000.0, + "type": "qd" + }, + "RX12": { + "duration": 40, + "amplitude": 0.0484, + "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "frequency": 5855663000, + "type": "qd" + }, + "MZ": { + "duration": 2000, + "amplitude": 0.1, + "envelope": { "rel_sigma": 5, "width": 0.75 }, + "frequency": 5800000000.0, + "type": "ro" + } + }, + "4": { + "RX": { + "duration": 40, + "amplitude": 0.3, + "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "frequency": 4155663000, + "type": "qd" + }, + "RX12": { + "duration": 40, + "amplitude": 0.0484, + "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "frequency": 5855663000, + "type": "qd" + }, + "MZ": { + "duration": 2000, + "amplitude": 0.1, + "envelope": { "rel_sigma": 5, "width": 0.75 }, + "frequency": 5500000000.0, + "type": "ro" + } + } + }, + "coupler": { + "0": { + "CP": { + "duration": 30, + "amplitude": 0.05, + "envelope": { "rel_sigma": 5, "width": 0.75 }, + "type": "cf" + } + }, + "1": { + "CP": { + "duration": 30, + "amplitude": 0.05, + "envelope": { "rel_sigma": 5, "width": 0.75 }, + "type": "cf" + } + }, + "3": { + "CP": { + "duration": 30, + "amplitude": 0.05, + "envelope": { "rel_sigma": 5, "width": 0.75 }, + "type": "cf" + } + }, + "4": { + "CP": { + "duration": 30, + "amplitude": 0.05, + "envelope": { "rel_sigma": 5, "width": 0.75 }, + "type": "cf" + } + } + }, + "two_qubit": { + "0-2": { + "CZ": [ + { + "duration": 30, + "amplitude": 0.05, + "envelope": { "rel_sigma": 5, "width": 0.75 }, + "qubit": 2, + "type": "qf" + }, + { + "type": "vz", + "phase": 0.0, + "qubit": 0 + }, + { + "type": "vz", + "phase": 0.0, + "qubit": 2 + }, + { + "duration": 30, + "amplitude": 0.05, + "envelope": { "rel_sigma": 5, "width": 0.75 }, + "coupler": 0, + "type": "cf" + } ], - 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"qubit": 2 - }, - { - "duration": 30, - "amplitude": 0.05, - "shape": "GaussianSquare(5, 0.75)", - "coupler": 4, - "type": "cf" - } - ] - } - } - }, - "characterization": { - "single_qubit": { - "0": { - "bare_resonator_frequency": 0, - "readout_frequency": 5200000000.0, - "drive_frequency": 4000000000.0, - "anharmonicity": 0, - "sweetspot": 0.0, - "asymmetry": 0.0, - "crosstalk_matrix": { - "0": 1 - }, - "Ec": 0.0, - "Ej": 0.0, - "g": 0.0, - "assignment_fidelity": [0.5, 0.1], - "gate_fidelity": [0.5, 0.1], - "peak_voltage": 0, - "pi_pulse_amplitude": 0, - "T1": 0.0, - "T2": 0.0, - "T2_spin_echo": 0, - "state0_voltage": 0, - "state1_voltage": 0, - "mean_gnd_states": [ - 0, - 1 - ], - "mean_exc_states": [ - 1, - 0 - ], - "threshold": 0.0, - "iq_angle": 0.0, - "mixer_drive_g": 0.0, - "mixer_drive_phi": 0.0, - "mixer_readout_g": 0.0, - "mixer_readout_phi": 0.0 - }, - "1": { - "bare_resonator_frequency": 0, - "readout_frequency": 4900000000.0, - "drive_frequency": 4200000000.0, - "anharmonicity": 0, - "sweetspot": 0.0, - "asymmetry": 0.0, - "crosstalk_matrix": { - "1": 1 - }, - "Ec": 0.0, - "Ej": 0.0, - "g": 0.0, - "assignment_fidelity": [0.5, 0.1], - "gate_fidelity": [0.5, 0.1], - "peak_voltage": 0, - "pi_pulse_amplitude": 0, - "T1": 0.0, - "T2": 0.0, - "T2_spin_echo": 0, - "state0_voltage": 0, - "state1_voltage": 0, - "mean_gnd_states": [ - 0.25, - 0 - ], - "mean_exc_states": [ - 0, - 0.25 - ], - "threshold": 0.0, - "iq_angle": 0.0, - "mixer_drive_g": 0.0, - "mixer_drive_phi": 0.0, - "mixer_readout_g": 0.0, - "mixer_readout_phi": 0.0 - }, - "2": { - "bare_resonator_frequency": 0, - "readout_frequency": 6100000000.0, - "drive_frequency": 4500000000.0, - "anharmonicity": 0, - "sweetspot": 0.0, - "asymmetry": 0.0, - "crosstalk_matrix": { - "2": 1 - }, - "Ec": 0.0, - "Ej": 0.0, - "g": 0.0, - "assignment_fidelity": [0.5, 0.1], - "gate_fidelity": [0.5, 0.1], - "peak_voltage": 0, - "pi_pulse_amplitude": 0, - "T1": 0.0, - "T2": 0.0, - "T2_spin_echo": 0, - "state0_voltage": 0, - "state1_voltage": 0, - "mean_gnd_states": [ - 0.5, - 0 - ], - "mean_exc_states": [ - 0, - 0.5 - ], - "threshold": 0.0, - "iq_angle": 0.0, - "mixer_drive_g": 0.0, - "mixer_drive_phi": 0.0, - "mixer_readout_g": 0.0, - "mixer_readout_phi": 0.0 - }, - "3": { - "bare_resonator_frequency": 0, - "readout_frequency": 5800000000.0, - "drive_frequency": 4150000000.0, - "anharmonicity": 0, - "sweetspot": 0.0, - "asymmetry": 0.0, - "crosstalk_matrix": { - "3": 1 - }, - "Ec": 0.0, - "Ej": 0.0, - "g": 0.0, - "assignment_fidelity": [0.5, 0.1], - "gate_fidelity": [0.5, 0.1], - "peak_voltage": 0, - "pi_pulse_amplitude": 0, - "T1": 0.0, - "T2": 0.0, - "T2_spin_echo": 0, - "state0_voltage": 0, - "state1_voltage": 0, - "mean_gnd_states": [ - 0.75, - 0 - ], - "mean_exc_states": [ - 0, - 0.75 - ], - "threshold": 0.0, - "iq_angle": 0.0, - "mixer_drive_g": 0.0, - "mixer_drive_phi": 0.0, - "mixer_readout_g": 0.0, - "mixer_readout_phi": 0.0 - }, - "4": { - "bare_resonator_frequency": 0, - "readout_frequency": 5500000000.0, - "drive_frequency": 4100000000.0, - "anharmonicity": 0, - "sweetspot": 0.0, - "asymmetry": 0.0, - "crosstalk_matrix": { - "4": 1 - }, - "Ec": 0.0, - "Ej": 0.0, - "g": 0.0, - "assignment_fidelity": [0.5, 0.1], - "gate_fidelity": [0.5, 0.1], - "peak_voltage": 0, - "pi_pulse_amplitude": 0, - "T1": 0.0, - "T2": 0.0, - "T2_spin_echo": 0, - "state0_voltage": 0, - "state1_voltage": 0, - "mean_gnd_states": [ - 1, - 0 - ], - "mean_exc_states": [ - 0, - 1 - ], - "threshold": 0.0, - "iq_angle": 0.0, - "mixer_drive_g": 0.0, - "mixer_drive_phi": 0.0, - "mixer_readout_g": 0.0, - "mixer_readout_phi": 0.0 - } + "Ec": 0.0, + "Ej": 0.0, + "g": 0.0, + "assignment_fidelity": [0.5, 0.1], + "gate_fidelity": [0.5, 0.1], + "peak_voltage": 0, + "pi_pulse_amplitude": 0, + "T1": 0.0, + "T2": 0.0, + "T2_spin_echo": 0, + "state0_voltage": 0, + "state1_voltage": 0, + "mean_gnd_states": [0.5, 0], + "mean_exc_states": [0, 0.5], + "threshold": 0.0, + "iq_angle": 0.0, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + }, + "3": { + "bare_resonator_frequency": 0, + "readout_frequency": 5800000000.0, + "drive_frequency": 4150000000.0, + "anharmonicity": 0, + "sweetspot": 0.0, + "asymmetry": 0.0, + "crosstalk_matrix": { + "3": 1 }, - "two_qubit":{ - "0-2": { - "gate_fidelity": [0.5, 0.1], - "cz_fidelity": [0.5, 0.1] - }, - "1-2": { - "gate_fidelity": [0.5, 0.1], - "cz_fidelity": [0.5, 0.1] - }, - "2-3": { - "gate_fidelity": [0.5, 0.1], - "cz_fidelity": [0.5, 0.1] - }, - "2-4": { - "gate_fidelity": [0.5, 0.1], - "cz_fidelity": [0.5, 0.1] - } + "Ec": 0.0, + "Ej": 0.0, + "g": 0.0, + "assignment_fidelity": [0.5, 0.1], + "gate_fidelity": [0.5, 0.1], + "peak_voltage": 0, + "pi_pulse_amplitude": 0, + "T1": 0.0, + "T2": 0.0, + "T2_spin_echo": 0, + "state0_voltage": 0, + "state1_voltage": 0, + "mean_gnd_states": [0.75, 0], + "mean_exc_states": [0, 0.75], + "threshold": 0.0, + "iq_angle": 0.0, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + }, + "4": { + "bare_resonator_frequency": 0, + "readout_frequency": 5500000000.0, + "drive_frequency": 4100000000.0, + "anharmonicity": 0, + "sweetspot": 0.0, + "asymmetry": 0.0, + "crosstalk_matrix": { + "4": 1 }, - "coupler": { - "0": { - "sweetspot": 0.0 - }, - "1": { - "sweetspot": 0.0 - }, - "3": { - "sweetspot": 0.0 - }, - "4": { - "sweetspot": 0.0 - } - } + "Ec": 0.0, + "Ej": 0.0, + "g": 0.0, + "assignment_fidelity": [0.5, 0.1], + "gate_fidelity": [0.5, 0.1], + "peak_voltage": 0, + "pi_pulse_amplitude": 0, + "T1": 0.0, + "T2": 0.0, + "T2_spin_echo": 0, + "state0_voltage": 0, + "state1_voltage": 0, + "mean_gnd_states": [1, 0], + "mean_exc_states": [0, 1], + "threshold": 0.0, + "iq_angle": 0.0, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + } + }, + "two_qubit": { + "0-2": { + "gate_fidelity": [0.5, 0.1], + "cz_fidelity": [0.5, 0.1] + }, + "1-2": { + "gate_fidelity": [0.5, 0.1], + "cz_fidelity": [0.5, 0.1] + }, + "2-3": { + "gate_fidelity": [0.5, 0.1], + "cz_fidelity": [0.5, 0.1] + }, + "2-4": { + "gate_fidelity": [0.5, 0.1], + "cz_fidelity": [0.5, 0.1] + } + }, + "coupler": { + "0": { + "sweetspot": 0.0 + }, + "1": { + "sweetspot": 0.0 + }, + "3": { + "sweetspot": 0.0 + }, + "4": { + "sweetspot": 0.0 + } } + } } diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index f3dd86191..bd84ecc6c 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -42,14 +42,14 @@ class Pulse(Model): The value has to be in the range [10e6 to 300e6]. """ - relative_phase: float - """Relative phase of the pulse, in radians.""" envelope: Envelope """The pulse envelope shape. See :cls:`qibolab.pulses.envelope.Envelopes` for list of available shapes. """ + relative_phase: float = 0.0 + """Relative phase of the pulse, in radians.""" channel: Optional[str] = None """Channel on which the pulse should be played. diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index 413d14679..c42567693 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -97,7 +97,7 @@ def _load_pulse(pulse_kwargs, qubit): if pulse_type == "dl": return Delay(**pulse_kwargs) - if pulse_type == "virtual_z": + if pulse_type == "vz": return VirtualZ(**pulse_kwargs, qubit=q) return Pulse(**pulse_kwargs, type=pulse_type, qubit=q) From b68af4d56438dd50741962063283d184b9484358 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 22 Mar 2024 15:42:40 +0400 Subject: [PATCH 160/233] build: Upgrade devenv version --- flake.lock | 434 ++++++++++++++++++++++++++++++++++++++++++++++------- flake.nix | 9 +- 2 files changed, 386 insertions(+), 57 deletions(-) diff --git a/flake.lock b/flake.lock index e34ee8243..4f978df5d 100644 --- a/flake.lock +++ b/flake.lock @@ -1,22 +1,80 @@ { "nodes": { + "cachix": { + "inputs": { + "devenv": "devenv_2", + "flake-compat": "flake-compat_2", + "nixpkgs": [ + "devenv", + "nixpkgs" + ], + "pre-commit-hooks": "pre-commit-hooks" + }, + "locked": { + "lastModified": 1710475558, + "narHash": "sha256-egKrPCKjy/cE+NqCj4hg2fNX/NwLCf0bRDInraYXDgs=", + "owner": "cachix", + "repo": "cachix", + "rev": "661bbb7f8b55722a0406456b15267b5426a3bda6", + "type": "github" + }, + "original": { + "owner": "cachix", + "repo": "cachix", + "type": "github" + } + }, "devenv": { "inputs": { - "flake-compat": "flake-compat", + "cachix": "cachix", + "flake-compat": "flake-compat_4", + "nix": "nix_2", + "nixpkgs": [ + "nixpkgs" + ], + "pre-commit-hooks": "pre-commit-hooks_2" + }, + "locked": { + "lastModified": 1711095830, + "narHash": "sha256-E67Yh1R1h8b01nVAhiYJsY6eQFqk5VIar13ntSbi56Q=", + "owner": "cachix", + "repo": "devenv", + "rev": "84ce563fcecbdee90b3c3550ab4f2fcd37b37def", + "type": "github" + }, + "original": { + "owner": "cachix", + "repo": "devenv", + "type": "github" + } + }, + "devenv_2": { + "inputs": { + "flake-compat": [ + "devenv", + "cachix", + "flake-compat" + ], "nix": "nix", "nixpkgs": "nixpkgs", - "pre-commit-hooks": "pre-commit-hooks" + "poetry2nix": "poetry2nix", + "pre-commit-hooks": [ + "devenv", + "cachix", + "pre-commit-hooks" + ] }, "locked": { - "lastModified": 1710144971, - "narHash": "sha256-CjTOdoBvT/4AQncTL20SDHyJNgsXZjtGbz62yDIUYnM=", + "lastModified": 1708704632, + "narHash": "sha256-w+dOIW60FKMaHI1q5714CSibk99JfYxm0CzTinYWr+Q=", "owner": "cachix", "repo": "devenv", - "rev": "6c0bad0045f1e1802f769f7890f6a59504825f4d", + "rev": "2ee4450b0f4b95a1b90f2eb5ffea98b90e48c196", "type": "github" }, "original": { "owner": "cachix", + "ref": "python-rewrite", "repo": "devenv", "type": "github" } @@ -29,11 +87,11 @@ "rust-analyzer-src": "rust-analyzer-src" }, "locked": { - "lastModified": 1710742993, - "narHash": "sha256-W0PQCe0bW3hKF5lHawXrKynBcdSP18Qa4sb8DcUfOqI=", + "lastModified": 1711088506, + "narHash": "sha256-USdlY7Tx2oJWqFBpp10+03+h7eVhpkQ4s9t1ERjeIJE=", "owner": "nix-community", "repo": "fenix", - "rev": "6f2fec850f569d61562d3a47dc263f19e9c7d825", + "rev": "85f4139f3c092cf4afd9f9906d7ed218ef262c97", "type": "github" }, "original": { @@ -74,16 +132,80 @@ "type": "github" } }, + "flake-compat_3": { + "flake": false, + "locked": { + "lastModified": 1696426674, + "narHash": "sha256-kvjfFW7WAETZlt09AgDn1MrtKzP7t90Vf7vypd3OL1U=", + "owner": "edolstra", + "repo": "flake-compat", + "rev": "0f9255e01c2351cc7d116c072cb317785dd33b33", + "type": "github" + }, + "original": { + "owner": "edolstra", + "repo": "flake-compat", + "type": "github" + } + }, + "flake-compat_4": { + "flake": false, + "locked": { + "lastModified": 1696426674, + "narHash": "sha256-kvjfFW7WAETZlt09AgDn1MrtKzP7t90Vf7vypd3OL1U=", + "owner": "edolstra", + "repo": "flake-compat", + "rev": "0f9255e01c2351cc7d116c072cb317785dd33b33", + "type": "github" + }, + "original": { + "owner": "edolstra", + "repo": "flake-compat", + "type": "github" + } + }, + "flake-compat_5": { + "flake": false, + "locked": { + "lastModified": 1673956053, + "narHash": "sha256-4gtG9iQuiKITOjNQQeQIpoIB6b16fm+504Ch3sNKLd8=", + "owner": "edolstra", + "repo": "flake-compat", + "rev": "35bb57c0c8d8b62bbfd284272c928ceb64ddbde9", + "type": "github" + }, + "original": { + "owner": "edolstra", + "repo": "flake-compat", + "type": "github" + } + }, + "flake-compat_6": { + "flake": false, + "locked": { + "lastModified": 1696426674, + "narHash": "sha256-kvjfFW7WAETZlt09AgDn1MrtKzP7t90Vf7vypd3OL1U=", + "owner": "edolstra", + "repo": "flake-compat", + "rev": "0f9255e01c2351cc7d116c072cb317785dd33b33", + "type": "github" + }, + "original": { + "owner": "edolstra", + "repo": "flake-compat", + "type": "github" + } + }, "flake-utils": { "inputs": { "systems": "systems" }, "locked": { - "lastModified": 1685518550, - "narHash": "sha256-o2d0KcvaXzTrPRIo0kOLV0/QXHhDQ5DTi+OxcjO8xqY=", + "lastModified": 1689068808, + "narHash": "sha256-6ixXo3wt24N/melDWjq70UuHQLxGV8jZvooRanIHXw0=", "owner": "numtide", "repo": "flake-utils", - "rev": "a1720a10a6cfe8234c0e93907ffe81be440f4cef", + "rev": "919d646de7be200f3bf08cb76ae1f09402b6f9b4", "type": "github" }, "original": { @@ -104,6 +226,42 @@ "rev": "4022d587cbbfd70fe950c1e2083a02621806a725", "type": "github" }, + "original": { + "owner": "numtide", + "repo": "flake-utils", + "type": "github" + } + }, + "flake-utils_3": { + "inputs": { + "systems": "systems_3" + }, + "locked": { + "lastModified": 1701680307, + "narHash": "sha256-kAuep2h5ajznlPMD9rnQyffWG8EM/C73lejGofXvdM8=", + "owner": "numtide", + "repo": "flake-utils", + "rev": "4022d587cbbfd70fe950c1e2083a02621806a725", + "type": "github" + }, + "original": { + "owner": "numtide", + "repo": "flake-utils", + "type": "github" + } + }, + "flake-utils_4": { + "inputs": { + "systems": "systems_4" + }, + "locked": { + "lastModified": 1701680307, + "narHash": "sha256-kAuep2h5ajznlPMD9rnQyffWG8EM/C73lejGofXvdM8=", + "owner": "numtide", + "repo": "flake-utils", + "rev": "4022d587cbbfd70fe950c1e2083a02621806a725", + "type": "github" + }, "original": { "id": "flake-utils", "type": "indirect" @@ -113,16 +271,17 @@ "inputs": { "nixpkgs": [ "devenv", + "cachix", "pre-commit-hooks", "nixpkgs" ] }, "locked": { - "lastModified": 1660459072, - "narHash": "sha256-8DFJjXG8zqoONA1vXtgeKXy68KdJL5UaXR8NtVMUbx8=", + "lastModified": 1703887061, + "narHash": "sha256-gGPa9qWNc6eCXT/+Z5/zMkyYOuRZqeFZBDbopNZQkuY=", "owner": "hercules-ci", "repo": "gitignore.nix", - "rev": "a20de23b925fd8264fd7fad6454652e142fd7f73", + "rev": "43e1aa1308018f37118e34d3a9cb4f5e75dc11d5", "type": "github" }, "original": { @@ -131,53 +290,109 @@ "type": "github" } }, - "lowdown-src": { - "flake": false, + "gitignore_2": { + "inputs": { + "nixpkgs": [ + "devenv", + "pre-commit-hooks", + "nixpkgs" + ] + }, "locked": { - "lastModified": 1633514407, - "narHash": "sha256-Dw32tiMjdK9t3ETl5fzGrutQTzh2rufgZV4A/BbxuD4=", - "owner": "kristapsdz", - "repo": "lowdown", - "rev": "d2c2b44ff6c27b936ec27358a2653caaef8f73b8", + "lastModified": 1703887061, + "narHash": "sha256-gGPa9qWNc6eCXT/+Z5/zMkyYOuRZqeFZBDbopNZQkuY=", + "owner": "hercules-ci", + "repo": "gitignore.nix", + "rev": "43e1aa1308018f37118e34d3a9cb4f5e75dc11d5", "type": "github" }, "original": { - "owner": "kristapsdz", - "repo": "lowdown", + "owner": "hercules-ci", + "repo": "gitignore.nix", "type": "github" } }, "nix": { "inputs": { - "lowdown-src": "lowdown-src", + "flake-compat": "flake-compat", "nixpkgs": [ + "devenv", + "cachix", "devenv", "nixpkgs" ], "nixpkgs-regression": "nixpkgs-regression" }, "locked": { - "lastModified": 1676545802, - "narHash": "sha256-EK4rZ+Hd5hsvXnzSzk2ikhStJnD63odF7SzsQ8CuSPU=", + "lastModified": 1708577783, + "narHash": "sha256-92xq7eXlxIT5zFNccLpjiP7sdQqQI30Gyui2p/PfKZM=", + "owner": "domenkozar", + "repo": "nix", + "rev": "ecd0af0c1f56de32cbad14daa1d82a132bf298f8", + "type": "github" + }, + "original": { + "owner": "domenkozar", + "ref": "devenv-2.21", + "repo": "nix", + "type": "github" + } + }, + "nix-github-actions": { + "inputs": { + "nixpkgs": [ + "devenv", + "cachix", + "devenv", + "poetry2nix", + "nixpkgs" + ] + }, + "locked": { + "lastModified": 1688870561, + "narHash": "sha256-4UYkifnPEw1nAzqqPOTL2MvWtm3sNGw1UTYTalkTcGY=", + "owner": "nix-community", + "repo": "nix-github-actions", + "rev": "165b1650b753316aa7f1787f3005a8d2da0f5301", + "type": "github" + }, + "original": { + "owner": "nix-community", + "repo": "nix-github-actions", + "type": "github" + } + }, + "nix_2": { + "inputs": { + "flake-compat": "flake-compat_5", + "nixpkgs": [ + "devenv", + "nixpkgs" + ], + "nixpkgs-regression": "nixpkgs-regression_2" + }, + "locked": { + "lastModified": 1710500156, + "narHash": "sha256-zvCqeUO2GLOm7jnU23G4EzTZR7eylcJN+HJ5svjmubI=", "owner": "domenkozar", "repo": "nix", - "rev": "7c91803598ffbcfe4a55c44ac6d49b2cf07a527f", + "rev": "c5bbf14ecbd692eeabf4184cc8d50f79c2446549", "type": "github" }, "original": { "owner": "domenkozar", - "ref": "relaxed-flakes", + "ref": "devenv-2.21", "repo": "nix", "type": "github" } }, "nixpkgs": { "locked": { - "lastModified": 1678875422, - "narHash": "sha256-T3o6NcQPwXjxJMn2shz86Chch4ljXgZn746c2caGxd8=", + "lastModified": 1692808169, + "narHash": "sha256-x9Opq06rIiwdwGeK2Ykj69dNc2IvUH1fY55Wm7atwrE=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "126f49a01de5b7e35a43fd43f891ecf6d3a51459", + "rev": "9201b5ff357e781bf014d0330d18555695df7ba8", "type": "github" }, "original": { @@ -189,18 +404,18 @@ }, "nixpkgs-python": { "inputs": { - "flake-compat": "flake-compat_2", - "flake-utils": "flake-utils_2", + "flake-compat": "flake-compat_6", + "flake-utils": "flake-utils_4", "nixpkgs": [ "nixpkgs" ] }, "locked": { - "lastModified": 1710660211, - "narHash": "sha256-tSNj0sK//GYmYSH9ts5pT1u4oI5Uxb+XWP4FIEhndxk=", + "lastModified": 1710929962, + "narHash": "sha256-CuPuUyX1TmxJDDZFOZMr7kHTzA8zoSJaVw0+jDVo2fw=", "owner": "cachix", "repo": "nixpkgs-python", - "rev": "8b3ea06b981f2fd11d082df3474894b1d5bcbe7b", + "rev": "a9e19aafbf75b8c7e5adf2d7319939309ebe0d77", "type": "github" }, "original": { @@ -225,29 +440,61 @@ "type": "github" } }, + "nixpkgs-regression_2": { + "locked": { + "lastModified": 1643052045, + "narHash": "sha256-uGJ0VXIhWKGXxkeNnq4TvV3CIOkUJ3PAoLZ3HMzNVMw=", + "owner": "NixOS", + "repo": "nixpkgs", + "rev": "215d4d0fd80ca5163643b03a33fde804a29cc1e2", + "type": "github" + }, + "original": { + "owner": "NixOS", + "repo": "nixpkgs", + "rev": "215d4d0fd80ca5163643b03a33fde804a29cc1e2", + "type": "github" + } + }, "nixpkgs-stable": { "locked": { - "lastModified": 1685801374, - "narHash": "sha256-otaSUoFEMM+LjBI1XL/xGB5ao6IwnZOXc47qhIgJe8U=", + "lastModified": 1704874635, + "narHash": "sha256-YWuCrtsty5vVZvu+7BchAxmcYzTMfolSPP5io8+WYCg=", + "owner": "NixOS", + "repo": "nixpkgs", + "rev": "3dc440faeee9e889fe2d1b4d25ad0f430d449356", + "type": "github" + }, + "original": { + "owner": "NixOS", + "ref": "nixos-23.11", + "repo": "nixpkgs", + "type": "github" + } + }, + "nixpkgs-stable_2": { + "locked": { + "lastModified": 1704874635, + "narHash": "sha256-YWuCrtsty5vVZvu+7BchAxmcYzTMfolSPP5io8+WYCg=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "c37ca420157f4abc31e26f436c1145f8951ff373", + "rev": "3dc440faeee9e889fe2d1b4d25ad0f430d449356", "type": "github" }, "original": { "owner": "NixOS", - "ref": "nixos-23.05", + "ref": "nixos-23.11", "repo": "nixpkgs", "type": "github" } }, "nixpkgs_2": { "locked": { - "lastModified": 1710631334, - "narHash": "sha256-rL5LSYd85kplL5othxK5lmAtjyMOBg390sGBTb3LRMM=", + "lastModified": 1710806803, + "narHash": "sha256-qrxvLS888pNJFwJdK+hf1wpRCSQcqA6W5+Ox202NDa0=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "c75037bbf9093a2acb617804ee46320d6d1fea5a", + "rev": "b06025f1533a1e07b6db3e75151caa155d1c7eb3", "type": "github" }, "original": { @@ -257,26 +504,77 @@ "type": "github" } }, + "poetry2nix": { + "inputs": { + "flake-utils": "flake-utils", + "nix-github-actions": "nix-github-actions", + "nixpkgs": [ + "devenv", + "cachix", + "devenv", + "nixpkgs" + ] + }, + "locked": { + "lastModified": 1692876271, + "narHash": "sha256-IXfZEkI0Mal5y1jr6IRWMqK8GW2/f28xJenZIPQqkY0=", + "owner": "nix-community", + "repo": "poetry2nix", + "rev": "d5006be9c2c2417dafb2e2e5034d83fabd207ee3", + "type": "github" + }, + "original": { + "owner": "nix-community", + "repo": "poetry2nix", + "type": "github" + } + }, "pre-commit-hooks": { + "inputs": { + "flake-compat": "flake-compat_3", + "flake-utils": "flake-utils_2", + "gitignore": "gitignore", + "nixpkgs": [ + "devenv", + "cachix", + "nixpkgs" + ], + "nixpkgs-stable": "nixpkgs-stable" + }, + "locked": { + "lastModified": 1708018599, + "narHash": "sha256-M+Ng6+SePmA8g06CmUZWi1AjG2tFBX9WCXElBHEKnyM=", + "owner": "cachix", + "repo": "pre-commit-hooks.nix", + "rev": "5df5a70ad7575f6601d91f0efec95dd9bc619431", + "type": "github" + }, + "original": { + "owner": "cachix", + "repo": "pre-commit-hooks.nix", + "type": "github" + } + }, + "pre-commit-hooks_2": { "inputs": { "flake-compat": [ "devenv", "flake-compat" ], - "flake-utils": "flake-utils", - "gitignore": "gitignore", + "flake-utils": "flake-utils_3", + "gitignore": "gitignore_2", "nixpkgs": [ "devenv", "nixpkgs" ], - "nixpkgs-stable": "nixpkgs-stable" + "nixpkgs-stable": "nixpkgs-stable_2" }, "locked": { - "lastModified": 1704725188, - "narHash": "sha256-qq8NbkhRZF1vVYQFt1s8Mbgo8knj+83+QlL5LBnYGpI=", + "lastModified": 1708018599, + "narHash": "sha256-M+Ng6+SePmA8g06CmUZWi1AjG2tFBX9WCXElBHEKnyM=", "owner": "cachix", "repo": "pre-commit-hooks.nix", - "rev": "ea96f0c05924341c551a797aaba8126334c505d2", + "rev": "5df5a70ad7575f6601d91f0efec95dd9bc619431", "type": "github" }, "original": { @@ -291,17 +589,17 @@ "fenix": "fenix", "nixpkgs": "nixpkgs_2", "nixpkgs-python": "nixpkgs-python", - "systems": "systems_3" + "systems": "systems_5" } }, "rust-analyzer-src": { "flake": false, "locked": { - "lastModified": 1710708100, - "narHash": "sha256-Jd6pmXlwKk5uYcjyO/8BfbUVmx8g31Qfk7auI2IG66A=", + "lastModified": 1711052942, + "narHash": "sha256-lLsAhLgm/Nbin41wdfGKU7Rgd6ONBxYCUAMv53NXPjo=", "owner": "rust-lang", "repo": "rust-analyzer", - "rev": "b6d1887bc4f9543b6c6bf098179a62446f34a6c3", + "rev": "7ef7f442fc34b5eadb1c6ad6433bd6d0c51b056b", "type": "github" }, "original": { @@ -355,6 +653,36 @@ "repo": "default", "type": "github" } + }, + "systems_4": { + "locked": { + "lastModified": 1681028828, + "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=", + "owner": "nix-systems", + "repo": "default", + "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e", + "type": "github" + }, + "original": { + "owner": "nix-systems", + "repo": "default", + "type": "github" + } + }, + "systems_5": { + "locked": { + "lastModified": 1681028828, + "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=", + "owner": "nix-systems", + "repo": "default", + "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e", + "type": "github" + }, + "original": { + "owner": "nix-systems", + "repo": "default", + "type": "github" + } } }, "root": "root", diff --git a/flake.nix b/flake.nix index dbcf891e7..9b2c037a8 100644 --- a/flake.nix +++ b/flake.nix @@ -2,7 +2,10 @@ inputs = { nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable"; systems.url = "github:nix-systems/default"; - devenv.url = "github:cachix/devenv"; + devenv = { + url = "github:cachix/devenv"; + inputs.nixpkgs.follows = "nixpkgs"; + }; nixpkgs-python = { url = "github:cachix/nixpkgs-python"; inputs.nixpkgs.follows = "nixpkgs"; @@ -35,8 +38,6 @@ forEachSystem (system: let pkgs = nixpkgs.legacyPackages.${system}; - lib = pkgs.lib; - isDarwin = lib.strings.hasSuffix "darwin" system; in { default = devenv.lib.mkShell { inherit inputs pkgs; @@ -48,7 +49,7 @@ config, ... }: { - packages = with pkgs; [pre-commit poethepoet jupyter zlib] ++ lib.optionals isDarwin [stdenv.cc.cc.lib]; + packages = with pkgs; [pre-commit poethepoet jupyter zlib]; env = { QIBOLAB_PLATFORMS = (dirOf config.env.DEVENV_ROOT) + "/qibolab_platforms_qrc"; From 069028067f3ca582e39be97f323a1100c8c7df6a Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 22 Mar 2024 16:04:26 +0400 Subject: [PATCH 161/233] feat: Tag pulse envelopes for more reliable discrimination during deserialization --- src/qibolab/dummy/parameters.json | 22 ++++++++-------- src/qibolab/pulses/envelope.py | 44 +++++++++++++++++++++++-------- 2 files changed, 44 insertions(+), 22 deletions(-) diff --git a/src/qibolab/dummy/parameters.json b/src/qibolab/dummy/parameters.json index 3c675323e..470c16ea2 100644 --- a/src/qibolab/dummy/parameters.json +++ b/src/qibolab/dummy/parameters.json @@ -31,14 +31,14 @@ "RX": { "duration": 40, "amplitude": 0.1, - "envelope": { "rel_sigma": 5 }, + "envelope": { "kind": "gaussian", "rel_sigma": 5 }, "frequency": 4000000000.0, "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.005, - "envelope": { "rel_sigma": 5 }, + "envelope": { "kind": "gaussian", "rel_sigma": 5 }, "frequency": 4700000000, "type": "qd" }, @@ -54,14 +54,14 @@ "RX": { "duration": 40, "amplitude": 0.3, - "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "envelope": { "kind": "drag", "rel_sigma": 5, "beta": 0.02 }, "frequency": 4200000000.0, "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.0484, - "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "envelope": { "kind": "drag", "rel_sigma": 5, "beta": 0.02 }, "frequency": 4855663000, "type": "qd" }, @@ -77,14 +77,14 @@ "RX": { "duration": 40, "amplitude": 0.3, - "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "envelope": { "kind": "drag", "rel_sigma": 5, "beta": 0.02 }, "frequency": 4500000000.0, "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.005, - "envelope": { "rel_sigma": 5 }, + "envelope": { "kind": "gaussian", "rel_sigma": 5 }, "frequency": 2700000000, "type": "qd" }, @@ -100,14 +100,14 @@ "RX": { "duration": 40, "amplitude": 0.3, - "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "envelope": { "kind": "drag", "rel_sigma": 5, "beta": 0.02 }, "frequency": 4150000000.0, "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.0484, - "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "envelope": { "kind": "drag", "rel_sigma": 5, "beta": 0.02 }, "frequency": 5855663000, "type": "qd" }, @@ -123,14 +123,14 @@ "RX": { "duration": 40, "amplitude": 0.3, - "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "envelope": { "kind": "drag", "rel_sigma": 5, "beta": 0.02 }, "frequency": 4155663000, "type": "qd" }, "RX12": { "duration": 40, "amplitude": 0.0484, - "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "envelope": { "kind": "drag", "rel_sigma": 5, "beta": 0.02 }, "frequency": 5855663000, "type": "qd" }, @@ -343,7 +343,7 @@ { "duration": 40, "amplitude": 0.3, - "envelope": { "rel_sigma": 5, "beta": 0.02 }, + "envelope": { "kind": "drag", "rel_sigma": 5, "beta": 0.02 }, "frequency": 4150000000.0, "type": "qd", "qubit": 2 diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index 572cc4548..88fb5d7a7 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -3,10 +3,11 @@ from abc import ABC from dataclasses import dataclass from functools import cached_property -from typing import Union +from typing import Annotated, Literal, Union import numpy as np import numpy.typing as npt +from pydantic import Field from scipy.signal import lfilter from scipy.signal.windows import gaussian @@ -84,6 +85,8 @@ def envelopes(self, times: Times) -> IqWaveform: class Rectangular(BaseEnvelope): """Rectangular envelope.""" + kind: Literal["rectangular"] + def i(self, times: Times) -> Waveform: """Generate a rectangular envelope.""" return np.ones(times.samples) @@ -97,6 +100,8 @@ class Exponential(BaseEnvelope): A\frac{\exp\left(-\frac{x}{\text{upsilon}}\right) + g \exp\left(-\frac{x}{\text{tau}}\right)}{1 + g} """ + kind: Literal["exponential"] + tau: float """The decay rate of the first exponential function.""" upsilon: float @@ -132,6 +137,8 @@ class Gaussian(BaseEnvelope): A\exp^{-\frac{1}{2}\frac{(t-\mu)^2}{\sigma^2}} """ + kind: Literal["gaussian"] + rel_sigma: float """Relative Gaussian standard deviation. @@ -151,6 +158,8 @@ class GaussianSquare(BaseEnvelope): A\exp^{-\frac{1}{2}\frac{(t-\mu)^2}{\sigma^2}}[Rise] + Flat + A\exp^{-\frac{1}{2}\frac{(t-\mu)^2}{\sigma^2}}[Decay] """ + kind: Literal["gaussian_square"] + rel_sigma: float """Relative Gaussian standard deviation. @@ -180,6 +189,8 @@ class Drag(BaseEnvelope): - add reference """ + kind: Literal["drag"] + rel_sigma: float """Relative Gaussian standard deviation. @@ -212,6 +223,8 @@ class Iir(BaseEnvelope): p = [b0, b1, a0, a1] """ + kind: Literal["iir"] + a: NdArray b: NdArray target: BaseEnvelope @@ -246,6 +259,8 @@ class Snz(BaseEnvelope): - expression """ + kind: Literal["snz"] + t_idling: float b_amplitude: float = 0.5 """Relative B amplitude (wrt A).""" @@ -278,6 +293,8 @@ class ECap(BaseEnvelope): &\times& [1 + \tanh(\alpha/2)]^{-2} """ + kind: Literal["ecap"] + alpha: float def i(self, times: Times) -> Waveform: @@ -299,6 +316,8 @@ class Custom(BaseEnvelope): - add attribute docstrings """ + kind: Literal["custom"] + i_: npt.NDArray q_: npt.NDArray @@ -311,15 +330,18 @@ def q(self, times: Times) -> Waveform: raise NotImplementedError -Envelope = Union[ - Rectangular, - Exponential, - Gaussian, - GaussianSquare, - Drag, - Iir, - Snz, - ECap, - Custom, +Envelope = Annotated[ + Union[ + Rectangular, + Exponential, + Gaussian, + GaussianSquare, + Drag, + Iir, + Snz, + ECap, + Custom, + ], + Field(discriminator="kind"), ] """Available pulse shapes.""" From 3533e38718086c5eddb605df1f09bb3dc11fdb35 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Sat, 23 Mar 2024 10:09:58 +0400 Subject: [PATCH 162/233] fix: Add defaults to discriminator, include them in dummy --- src/qibolab/dummy/parameters.json | 150 +++++++++++++++++++++++++----- src/qibolab/pulses/envelope.py | 18 ++-- 2 files changed, 134 insertions(+), 34 deletions(-) diff --git a/src/qibolab/dummy/parameters.json b/src/qibolab/dummy/parameters.json index 470c16ea2..c1c666a66 100644 --- a/src/qibolab/dummy/parameters.json +++ b/src/qibolab/dummy/parameters.json @@ -45,7 +45,11 @@ "MZ": { "duration": 2000, "amplitude": 0.1, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "frequency": 5200000000.0, "type": "ro" } @@ -68,7 +72,11 @@ "MZ": { "duration": 2000, "amplitude": 0.1, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "frequency": 4900000000.0, "type": "ro" } @@ -91,7 +99,11 @@ "MZ": { "duration": 2000, "amplitude": 0.1, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "frequency": 6100000000.0, "type": "ro" } @@ -114,7 +126,11 @@ "MZ": { "duration": 2000, "amplitude": 0.1, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "frequency": 5800000000.0, "type": "ro" } @@ -137,7 +153,11 @@ "MZ": { "duration": 2000, "amplitude": 0.1, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "frequency": 5500000000.0, "type": "ro" } @@ -148,7 +168,11 @@ "CP": { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "type": "cf" } }, @@ -156,7 +180,11 @@ "CP": { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "type": "cf" } }, @@ -164,7 +192,11 @@ "CP": { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "type": "cf" } }, @@ -172,7 +204,11 @@ "CP": { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "type": "cf" } } @@ -183,7 +219,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "qubit": 2, "type": "qf" }, @@ -200,7 +240,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "coupler": 0, "type": "cf" } @@ -209,7 +253,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "qubit": 2, "type": "qf" }, @@ -226,7 +274,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "coupler": 0, "type": "cf" } @@ -237,7 +289,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "qubit": 2, "type": "qf" }, @@ -254,7 +310,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "coupler": 1, "type": "cf" } @@ -263,7 +323,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "qubit": 2, "type": "qf" }, @@ -280,7 +344,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "coupler": 1, "type": "cf" } @@ -291,7 +359,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "qubit": 2, "type": "qf" }, @@ -308,7 +380,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "coupler": 3, "type": "cf" } @@ -317,7 +393,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "qubit": 2, "type": "qf" }, @@ -334,7 +414,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "coupler": 3, "type": "cf" } @@ -365,7 +449,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "qubit": 2, "type": "qf" }, @@ -382,7 +470,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "coupler": 4, "type": "cf" } @@ -391,7 +483,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "qubit": 2, "type": "qf" }, @@ -408,7 +504,11 @@ { "duration": 30, "amplitude": 0.05, - "envelope": { "rel_sigma": 5, "width": 0.75 }, + "envelope": { + "kind": "gaussian_square", + "rel_sigma": 5, + "width": 0.75 + }, "coupler": 4, "type": "cf" } diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index 88fb5d7a7..607bae897 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -85,7 +85,7 @@ def envelopes(self, times: Times) -> IqWaveform: class Rectangular(BaseEnvelope): """Rectangular envelope.""" - kind: Literal["rectangular"] + kind: Literal["rectangular"] = "rectangular" def i(self, times: Times) -> Waveform: """Generate a rectangular envelope.""" @@ -100,7 +100,7 @@ class Exponential(BaseEnvelope): A\frac{\exp\left(-\frac{x}{\text{upsilon}}\right) + g \exp\left(-\frac{x}{\text{tau}}\right)}{1 + g} """ - kind: Literal["exponential"] + kind: Literal["exponential"] = "exponential" tau: float """The decay rate of the first exponential function.""" @@ -137,7 +137,7 @@ class Gaussian(BaseEnvelope): A\exp^{-\frac{1}{2}\frac{(t-\mu)^2}{\sigma^2}} """ - kind: Literal["gaussian"] + kind: Literal["gaussian"] = "gaussian" rel_sigma: float """Relative Gaussian standard deviation. @@ -158,7 +158,7 @@ class GaussianSquare(BaseEnvelope): A\exp^{-\frac{1}{2}\frac{(t-\mu)^2}{\sigma^2}}[Rise] + Flat + A\exp^{-\frac{1}{2}\frac{(t-\mu)^2}{\sigma^2}}[Decay] """ - kind: Literal["gaussian_square"] + kind: Literal["gaussian_square"] = "gaussian_square" rel_sigma: float """Relative Gaussian standard deviation. @@ -189,7 +189,7 @@ class Drag(BaseEnvelope): - add reference """ - kind: Literal["drag"] + kind: Literal["drag"] = "drag" rel_sigma: float """Relative Gaussian standard deviation. @@ -223,7 +223,7 @@ class Iir(BaseEnvelope): p = [b0, b1, a0, a1] """ - kind: Literal["iir"] + kind: Literal["iir"] = "iir" a: NdArray b: NdArray @@ -259,7 +259,7 @@ class Snz(BaseEnvelope): - expression """ - kind: Literal["snz"] + kind: Literal["snz"] = "snz" t_idling: float b_amplitude: float = 0.5 @@ -293,7 +293,7 @@ class ECap(BaseEnvelope): &\times& [1 + \tanh(\alpha/2)]^{-2} """ - kind: Literal["ecap"] + kind: Literal["ecap"] = "ecap" alpha: float @@ -316,7 +316,7 @@ class Custom(BaseEnvelope): - add attribute docstrings """ - kind: Literal["custom"] + kind: Literal["custom"] = "custom" i_: npt.NDArray q_: npt.NDArray From a86c3e48011604bae1a566d92f7832f823f47c86 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Sat, 23 Mar 2024 10:36:17 +0400 Subject: [PATCH 163/233] feat!: Move duration from pulse to envelope --- src/qibolab/pulses/envelope.py | 122 +++++++++++++++------------------ src/qibolab/pulses/pulse.py | 15 ++-- 2 files changed, 60 insertions(+), 77 deletions(-) diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index 607bae897..ef507b37c 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -1,8 +1,6 @@ """Library of pulse shapes.""" from abc import ABC -from dataclasses import dataclass -from functools import cached_property from typing import Annotated, Literal, Union import numpy as np @@ -14,7 +12,6 @@ from qibolab.serialize_ import Model, NdArray __all__ = [ - "Times", "Waveform", "IqWaveform", "BaseEnvelope", @@ -40,46 +37,30 @@ """Full shape, both I and Q components.""" -@dataclass -class Times: - """Time window of a pulse.""" - - duration: float - """Pulse duration.""" - samples: int - """Number of requested samples.""" - # Here only the information consumed by the `Envelopes` is stored. How to go from - # the sampling rate to the number of samples is callers' business, since nothing - # else has to be known by this module. - - @property - def mean(self) -> float: - """Middle point of the temporal window.""" - return self.duration / 2 - - @cached_property - def window(self): - """Individual timing of each sample.""" - return np.linspace(0, self.duration, self.samples) - - class BaseEnvelope(ABC, Model): """Pulse envelopes. Generates both i (in-phase) and q (quadrature) components. """ - def i(self, times: Times) -> Waveform: + duration: float + """Pulse duration.""" + + def window(self, samples: int): + """Individual timing of each sample.""" + return np.linspace(0, self.duration, samples) + + def i(self, samples: int) -> Waveform: """In-phase envelope.""" - return np.zeros(times.samples) + return np.zeros(samples) - def q(self, times: Times) -> Waveform: + def q(self, samples: int) -> Waveform: """Quadrature envelope.""" - return np.zeros(times.samples) + return np.zeros(samples) - def envelopes(self, times: Times) -> IqWaveform: + def envelopes(self, samples: int) -> IqWaveform: """Stacked i and q envelope waveforms of the pulse.""" - return np.array([self.i(times), self.q(times)]) + return np.array([self.i(samples), self.q(samples)]) class Rectangular(BaseEnvelope): @@ -87,9 +68,9 @@ class Rectangular(BaseEnvelope): kind: Literal["rectangular"] = "rectangular" - def i(self, times: Times) -> Waveform: + def i(self, samples: int) -> Waveform: """Generate a rectangular envelope.""" - return np.ones(times.samples) + return np.ones(samples) class Exponential(BaseEnvelope): @@ -109,21 +90,21 @@ class Exponential(BaseEnvelope): g: float = 0.1 """Weight of the second exponential function.""" - def i(self, times: Times) -> Waveform: + def i(self, samples: int) -> Waveform: """Generate a combination of two exponential decays.""" - ts = times.window + ts = self.window(samples) return (np.exp(-ts / self.upsilon) + self.g * np.exp(-ts / self.tau)) / ( 1 + self.g ) -def _samples_sigma(rel_sigma: float, times: Times) -> float: +def _samples_sigma(rel_sigma: float, samples: int) -> float: """Convert standard deviation in samples. `rel_sigma` is assumed in units of the interval duration, and it is turned in units of samples, by counting the number of samples in the interval. """ - return rel_sigma * times.samples + return rel_sigma * samples class Gaussian(BaseEnvelope): @@ -145,9 +126,9 @@ class Gaussian(BaseEnvelope): In units of the interval duration. """ - def i(self, times: Times) -> Waveform: + def i(self, samples: int) -> Waveform: """Generate a Gaussian window.""" - return gaussian(times.samples, _samples_sigma(self.rel_sigma, times)) + return gaussian(samples, _samples_sigma(self.rel_sigma, samples)) class GaussianSquare(BaseEnvelope): @@ -168,14 +149,14 @@ class GaussianSquare(BaseEnvelope): width: float """Length of the flat portion.""" - def i(self, times: Times) -> Waveform: + def i(self, samples: int) -> Waveform: """Generate a Gaussian envelope, with a flat central window.""" - pulse = np.ones_like(times) - u, hw = times.mean, self.width / 2 - ts = times.window + pulse = np.ones(samples) + u, hw = samples / 2, self.width * samples / self.duration / 2 + ts = np.arange(samples) tails = (ts < (u - hw)) | ((u + hw) < ts) - pulse[tails] = gaussian(len(ts[tails]), _samples_sigma(self.rel_sigma, times)) + pulse[tails] = gaussian(len(ts[tails]), _samples_sigma(self.rel_sigma, samples)) return pulse @@ -199,18 +180,19 @@ class Drag(BaseEnvelope): beta: float """.. todo::""" - def i(self, times: Times) -> Waveform: + def i(self, samples: int) -> Waveform: """Generate a Gaussian envelope.""" - return gaussian(times.samples, _samples_sigma(self.rel_sigma, times)) + return gaussian(samples, _samples_sigma(self.rel_sigma, samples)) - def q(self, times: Times) -> Waveform: + def q(self, samples: int) -> Waveform: """Generate ... .. todo:: """ - sigma = self.rel_sigma * times.duration - ts = times.window - return self.beta * (-(ts - times.mean) / (sigma**2)) * self.i(times) + ts = np.arange(samples) + mu = samples / 2 + sigma = _samples_sigma(self.rel_sigma, samples) + return self.beta * (-(ts - mu) / (sigma**2)) * self.i(samples) class Iir(BaseEnvelope): @@ -239,13 +221,13 @@ def _data(self, target: npt.NDArray) -> npt.NDArray: data /= np.max(np.abs(data)) return data - def i(self, times: Times) -> Waveform: + def i(self, samples: int) -> Waveform: """.. todo::""" - return self._data(self.target.i(times)) + return self._data(self.target.i(samples)) - def q(self, times: Times) -> Waveform: + def q(self, samples: int) -> Waveform: """.. todo::""" - return self._data(self.target.q(times)) + return self._data(self.target.q(samples)) class Snz(BaseEnvelope): @@ -265,14 +247,14 @@ class Snz(BaseEnvelope): b_amplitude: float = 0.5 """Relative B amplitude (wrt A).""" - def i(self, times: Times) -> Waveform: + def i(self, samples: int) -> Waveform: """.. todo::""" # convert timings to samples - half_pulse_duration = (times.duration - self.t_idling) / 2 - aspan = np.sum(times.window < half_pulse_duration) - idle = times.samples - 2 * (aspan + 1) + half_pulse_duration = (self.duration - self.t_idling) / 2 + aspan = np.sum(self.window(samples) < half_pulse_duration) + idle = samples - 2 * (aspan + 1) - pulse = np.ones(times.samples) + pulse = np.ones(samples) # the aspan + 1 sample is B (and so the aspan + 1 + idle + 1), indexes are 0-based pulse[aspan] = pulse[aspan + 1 + idle] = self.b_amplitude # set idle time to 0 @@ -297,11 +279,11 @@ class ECap(BaseEnvelope): alpha: float - def i(self, times: Times) -> Waveform: + def i(self, samples: int) -> Waveform: """.. todo::""" - x = times.window / times.samples + x = self.window(samples) / samples return ( - (1 + np.tanh(self.alpha * times.window)) + (1 + np.tanh(self.alpha * self.window(samples))) * (1 + np.tanh(self.alpha * (1 - x))) / (1 + np.tanh(self.alpha / 2)) ** 2 ) @@ -321,13 +303,19 @@ class Custom(BaseEnvelope): i_: npt.NDArray q_: npt.NDArray - def i(self, times: Times) -> Waveform: + def i(self, samples: int) -> Waveform: """.. todo::""" - raise NotImplementedError + if len(self.i_) != samples: + raise ValueError + + return self.i_ - def q(self, times: Times) -> Waveform: + def q(self, samples: int) -> Waveform: """.. todo::""" - raise NotImplementedError + if len(self.q_) != samples: + raise ValueError + + return self.q_ Envelope = Annotated[ diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index bd84ecc6c..7bc899c83 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -8,7 +8,7 @@ from qibolab.serialize_ import Model -from .envelope import Envelope, IqWaveform, Times, Waveform +from .envelope import Envelope, IqWaveform, Waveform class PulseType(Enum): @@ -30,8 +30,6 @@ class PulseType(Enum): class Pulse(Model): """A pulse to be sent to the QPU.""" - duration: int - """Pulse duration in ns.""" amplitude: float """Pulse digital amplitude (unitless). @@ -79,18 +77,15 @@ def flux(cls, **kwargs): def id(self) -> int: return id(self) - def _times(self, sampling_rate: float): - return Times(self.duration, int(self.duration * sampling_rate)) - def i(self, sampling_rate: float) -> Waveform: """The envelope waveform of the i component of the pulse.""" - times = self._times(sampling_rate) - return self.amplitude * self.envelope.i(times) + samples = int(self.envelope.duration * sampling_rate) + return self.amplitude * self.envelope.i(samples) def q(self, sampling_rate: float) -> Waveform: """The envelope waveform of the q component of the pulse.""" - times = self._times(sampling_rate) - return self.amplitude * self.envelope.q(times) + samples = int(self.envelope.duration * sampling_rate) + return self.amplitude * self.envelope.q(samples) def envelopes(self, sampling_rate: float) -> IqWaveform: """A tuple with the i and q envelope waveforms of the pulse.""" From 10a547a05ea66e75f96707c04caff224b47fe2f6 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 25 Mar 2024 16:42:09 +0400 Subject: [PATCH 164/233] feat!: Move duration back to pulse And fix some Pylint and Pytest errors --- src/qibolab/platform/platform.py | 4 ++-- src/qibolab/pulses/envelope.py | 3 --- src/qibolab/pulses/pulse.py | 3 +++ tests/pulses/test_modulation.py | 20 +++++++++----------- 4 files changed, 14 insertions(+), 16 deletions(-) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 17d070ad2..f69d00ce9 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -25,7 +25,7 @@ def unroll_sequences( sequences: List[PulseSequence], relaxation_time: int -) -> Tuple[PulseSequence, Dict[str, str]]: +) -> Tuple[PulseSequence, dict[str, list[str]]]: """Unrolls a list of pulse sequences to a single pulse sequence with multiple measurements. @@ -54,7 +54,7 @@ def unroll_sequences( pulses_per_channel = sequence.pulses_per_channel for channel in channels: delay = length - pulses_per_channel[channel].duration - total_sequence.append(Delay(delay, channel)) + total_sequence.append(Delay(duration=delay, channel=channel)) return total_sequence, readout_map diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index ef507b37c..c833d3d01 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -43,9 +43,6 @@ class BaseEnvelope(ABC, Model): Generates both i (in-phase) and q (quadrature) components. """ - duration: float - """Pulse duration.""" - def window(self, samples: int): """Individual timing of each sample.""" return np.linspace(0, self.duration, samples) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 7bc899c83..9226ace51 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -30,6 +30,9 @@ class PulseType(Enum): class Pulse(Model): """A pulse to be sent to the QPU.""" + duration: float + """Pulse duration.""" + amplitude: float """Pulse digital amplitude (unitless). diff --git a/tests/pulses/test_modulation.py b/tests/pulses/test_modulation.py index 872adbdd6..4517875fe 100644 --- a/tests/pulses/test_modulation.py +++ b/tests/pulses/test_modulation.py @@ -1,16 +1,14 @@ import numpy as np from qibolab.pulses import Gaussian, IqWaveform, Rectangular -from qibolab.pulses.envelope import Times from qibolab.pulses.modulation import demodulate, modulate def test_modulation(): - times = Times(30, 30) amplitude = 0.9 - renvs: IqWaveform = Rectangular().envelopes(times) * amplitude + renvs: IqWaveform = Rectangular().envelopes(30) * amplitude # fmt: off - np.testing.assert_allclose(modulate(renvs, 0.04), + np.testing.assert_allclose(modulate(renvs, 0.04, rate=1), np.array([[ 6.36396103e-01, 6.16402549e-01, 5.57678156e-01, 4.63912794e-01, 3.40998084e-01, 1.96657211e-01, 3.99596419e-02, -1.19248738e-01, -2.70964282e-01, @@ -34,10 +32,9 @@ def test_modulation(): ) # fmt: on - times = Times(20, 20) - genvs: IqWaveform = Gaussian(0.5).envelopes(times) + genvs: IqWaveform = Gaussian(rel_sigma=0.5).envelopes(30) # fmt: off - np.testing.assert_allclose(modulate(genvs, 0.3), + np.testing.assert_allclose(modulate(genvs, 0.3,rate=1), np.array([[ 4.50307953e-01, -1.52257426e-01, -4.31814602e-01, 4.63124693e-01, 1.87836646e-01, -6.39017403e-01, 2.05526028e-01, 5.54460924e-01, -5.65661777e-01, @@ -59,16 +56,17 @@ def test_modulation(): def test_demodulation(): signal = np.ones((2, 100)) freq = 0.15 - mod = modulate(signal, freq) + rate = 1 + mod = modulate(signal, freq, rate) - demod = demodulate(mod, freq) + demod = demodulate(mod, freq, rate) np.testing.assert_allclose(demod, signal) mod1 = modulate(demod, freq * 3.0, rate=3.0) np.testing.assert_allclose(mod1, mod) - mod2 = modulate(signal, freq, phase=2 * np.pi) + mod2 = modulate(signal, freq, rate, phase=2 * np.pi) np.testing.assert_allclose(mod2, mod) - demod1 = demodulate(mod + np.ones_like(mod), freq) + demod1 = demodulate(mod + np.ones_like(mod), freq, rate) np.testing.assert_allclose(demod1, demod) From cdd94496a0ca27a115f52688779274ab2d48eabb Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 27 Mar 2024 14:20:49 +0400 Subject: [PATCH 165/233] feat: Add model equality account for numpy arrays --- src/qibolab/serialize_.py | 19 +++++++++++++++++++ tests/test_serialize.py | 21 +++++++++++++++++++++ 2 files changed, 40 insertions(+) create mode 100644 tests/test_serialize.py diff --git a/src/qibolab/serialize_.py b/src/qibolab/serialize_.py index a39757a1c..d9928254e 100644 --- a/src/qibolab/serialize_.py +++ b/src/qibolab/serialize_.py @@ -36,6 +36,25 @@ def ndarray_deserialize(x: Union[str, npt.NDArray]) -> npt.NDArray: """Pydantic-compatible array representation.""" +def eq(obj1: BaseModel, obj2: BaseModel) -> bool: + """Compare two models with non-default equality. + + Currently, defines custom equality for NumPy arrays. + """ + obj2d = obj2.model_dump() + comparisons = [] + for field, value1 in obj1.model_dump().items(): + value2 = obj2d[field] + if isinstance(value1, np.ndarray): + comparisons.append( + (value1.shape == value2.shape) and (value1 == value2).all() + ) + + comparisons.append(value1 == value2) + + return all(comparisons) + + class Model(BaseModel): """Global qibolab model, holding common configurations.""" diff --git a/tests/test_serialize.py b/tests/test_serialize.py new file mode 100644 index 000000000..6fcccc3ef --- /dev/null +++ b/tests/test_serialize.py @@ -0,0 +1,21 @@ +import numpy as np +from pydantic import BaseModel, ConfigDict + +from qibolab.serialize_ import NdArray, eq + + +class ArrayModel(BaseModel): + ar: NdArray + + model_config = ConfigDict(arbitrary_types_allowed=True) + + +def test_equality(): + assert eq(ArrayModel(ar=np.arange(10)), ArrayModel(ar=np.arange(10))) + assert not eq(ArrayModel(ar=np.arange(10)), ArrayModel(ar=np.arange(11))) + ar = np.arange(10) + ar[5:] = 42 + assert not eq(ArrayModel(ar=np.arange(10)), ArrayModel(ar=ar)) + + assert not eq(ArrayModel(ar=np.arange(10)), ArrayModel(ar=np.ones((10, 2)))) + assert eq(ArrayModel(ar=np.ones((10, 2))), ArrayModel(ar=np.ones((10, 2)))) From fcf980c5926749e0c50eecbdf5f39141921ea515 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 27 Mar 2024 14:21:58 +0400 Subject: [PATCH 166/233] test: Explicit keyword arguments for envelope tests --- tests/pulses/test_envelope.py | 211 +++++++++++++------------------- tests/pulses/test_modulation.py | 2 +- 2 files changed, 84 insertions(+), 129 deletions(-) diff --git a/tests/pulses/test_envelope.py b/tests/pulses/test_envelope.py index 7785e0408..bdfaefc1d 100644 --- a/tests/pulses/test_envelope.py +++ b/tests/pulses/test_envelope.py @@ -1,7 +1,17 @@ import numpy as np import pytest -from qibolab.pulses import Drag, Gaussian, GaussianSquare, Pulse, PulseType, Rectangular +from qibolab.pulses import ( + Drag, + ECap, + Gaussian, + GaussianSquare, + Iir, + Pulse, + PulseType, + Rectangular, + Snz, +) @pytest.mark.parametrize( @@ -14,74 +24,29 @@ ], ) def test_sampling_rate(shape): - pulse = Pulse(0, 40, 0.9, 100e6, 0, shape, 0, PulseType.DRIVE) - assert len(pulse.envelope_waveform_i(sampling_rate=1)) == 40 - assert len(pulse.envelope_waveform_i(sampling_rate=100)) == 4000 - - -def test_eval(): - shape = PulseShape.eval("Rectangular()") - assert isinstance(shape, Rectangular) - with pytest.raises(ValueError): - shape = PulseShape.eval("Ciao()") - - -@pytest.mark.parametrize("rel_sigma,beta", [(5, 1), (5, -1), (3, -0.03), (4, 0.02)]) -def test_drag_shape_eval(rel_sigma, beta): - shape = PulseShape.eval(f"Drag({rel_sigma}, {beta})") - assert isinstance(shape, Drag) - assert shape.rel_sigma == rel_sigma - assert shape.beta == beta - - -def test_raise_shapeiniterror(): - shape = Rectangular() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - shape = Gaussian(0) - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - shape = GaussianSquare(0, 1) - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - shape = Drag(0, 0) - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - shape = IIR([0], [0], None) - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - shape = SNZ(0) - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() - - shape = eCap(0) - with pytest.raises(ShapeInitError): - shape.envelope_waveform_i() - with pytest.raises(ShapeInitError): - shape.envelope_waveform_q() + pulse = Pulse( + duration=40, + amplitude=0.9, + frequency=int(100e6), + envelope=shape, + relative_phase=0, + type=PulseType.DRIVE, + ) + assert len(pulse.i(sampling_rate=1)) == 40 + assert len(pulse.i(sampling_rate=100)) == 4000 def test_drag_shape(): - pulse = Pulse(0, 2, 1, 4e9, 0, Drag(2, 1), 0, PulseType.DRIVE) + pulse = Pulse( + duration=2, + amplitude=1, + frequency=int(4e9), + envelope=Drag(rel_sigma=2, beta=1), + relative_phase=0, + type=PulseType.DRIVE, + ) # envelope i & envelope q should cross nearly at 0 and at 2 - waveform = pulse.envelope_waveform_i(sampling_rate=10) + waveform = pulse.i(sampling_rate=10) target_waveform = np.array( [ 0.63683161, @@ -111,21 +76,17 @@ def test_drag_shape(): def test_rectangular(): pulse = Pulse( - start=0, duration=50, amplitude=1, frequency=200_000_000, relative_phase=0, - shape=Rectangular(), - channel=1, + envelope=Rectangular(), + channel="1", qubit=0, ) - _if = 0 assert pulse.duration == 50 - assert isinstance(pulse.shape, Rectangular) - assert pulse.shape.name == "Rectangular" - assert repr(pulse.shape) == "Rectangular()" + assert isinstance(pulse.envelope, Rectangular) sampling_rate = 1 num_samples = int(pulse.duration / sampling_rate) @@ -134,28 +95,24 @@ def test_rectangular(): pulse.amplitude * np.zeros(num_samples), ) - np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) - np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) + np.testing.assert_allclose(pulse.envelope.i(sampling_rate), i) + np.testing.assert_allclose(pulse.envelope.q(sampling_rate), q) def test_gaussian(): pulse = Pulse( - start=0, duration=50, amplitude=1, frequency=200_000_000, relative_phase=0, - shape=Gaussian(5), - channel=1, + envelope=Gaussian(rel_sigma=5), + channel="1", qubit=0, ) - _if = 0 assert pulse.duration == 50 - assert isinstance(pulse.shape, Gaussian) - assert pulse.shape.name == "Gaussian" - assert pulse.shape.rel_sigma == 5 - assert repr(pulse.shape) == "Gaussian(5)" + assert isinstance(pulse.envelope, Gaussian) + assert pulse.envelope.rel_sigma == 5 sampling_rate = 1 num_samples = int(pulse.duration / sampling_rate) @@ -164,54 +121,52 @@ def test_gaussian(): -(1 / 2) * ( ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / pulse.shape.rel_sigma) ** 2) + / (((num_samples) / pulse.envelope.rel_sigma) ** 2) ) ) q = pulse.amplitude * np.zeros(num_samples) - np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) - np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) + np.testing.assert_allclose(pulse.i(sampling_rate), i) + np.testing.assert_allclose(pulse.q(sampling_rate), q) def test_drag(): pulse = Pulse( - start=0, duration=50, amplitude=1, frequency=200_000_000, relative_phase=0, - shape=Drag(5, 0.2), - channel=1, + envelope=Drag(rel_sigma=5, beta=0.2), qubit=0, ) - _if = 0 assert pulse.duration == 50 - assert isinstance(pulse.shape, Drag) - assert pulse.shape.name == "Drag" - assert pulse.shape.rel_sigma == 5 - assert pulse.shape.beta == 0.2 - assert repr(pulse.shape) == "Drag(5, 0.2)" + assert isinstance(pulse.envelope, Drag) + assert pulse.envelope.rel_sigma == 5 + assert pulse.envelope.beta == 0.2 sampling_rate = 1 num_samples = int(pulse.duration / 1 * sampling_rate) - x = np.arange(0, num_samples, 1) + x = np.arange(num_samples) i = pulse.amplitude * np.exp( -(1 / 2) * ( ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / pulse.shape.rel_sigma) ** 2) + / (((num_samples) / pulse.envelope.rel_sigma) ** 2) ) ) q = ( - pulse.shape.beta - * (-(x - (num_samples - 1) / 2) / ((num_samples / pulse.shape.rel_sigma) ** 2)) + pulse.envelope.beta + * ( + -(x - (num_samples - 1) / 2) + / ((num_samples / pulse.envelope.rel_sigma) ** 2) + ) * i * sampling_rate ) - np.testing.assert_allclose(pulse.shape.envelope_waveform_i(sampling_rate), i) - np.testing.assert_allclose(pulse.shape.envelope_waveform_q(sampling_rate), q) + np.testing.assert_allclose(pulse.i(sampling_rate), i) + np.testing.assert_allclose(pulse.q(sampling_rate), q) def test_eq(): @@ -219,60 +174,60 @@ def test_eq(): shape1 = Rectangular() shape2 = Rectangular() - shape3 = Gaussian(5) + shape3 = Gaussian(rel_sigma=5) assert shape1 == shape2 assert not shape1 == shape3 - shape1 = Gaussian(4) - shape2 = Gaussian(4) - shape3 = Gaussian(5) + shape1 = Gaussian(rel_sigma=4) + shape2 = Gaussian(rel_sigma=4) + shape3 = Gaussian(rel_sigma=5) assert shape1 == shape2 assert not shape1 == shape3 - shape1 = GaussianSquare(4, 0.01) - shape2 = GaussianSquare(4, 0.01) - shape3 = GaussianSquare(5, 0.01) - shape4 = GaussianSquare(4, 0.05) - shape5 = GaussianSquare(5, 0.05) + shape1 = GaussianSquare(rel_sigma=4, width=0.01) + shape2 = GaussianSquare(rel_sigma=4, width=0.01) + shape3 = GaussianSquare(rel_sigma=5, width=0.01) + shape4 = GaussianSquare(rel_sigma=4, width=0.05) + shape5 = GaussianSquare(rel_sigma=5, width=0.05) assert shape1 == shape2 assert not shape1 == shape3 assert not shape1 == shape4 assert not shape1 == shape5 - shape1 = Drag(4, 0.01) - shape2 = Drag(4, 0.01) - shape3 = Drag(5, 0.01) - shape4 = Drag(4, 0.05) - shape5 = Drag(5, 0.05) + shape1 = Drag(rel_sigma=4, beta=0.01) + shape2 = Drag(rel_sigma=4, beta=0.01) + shape3 = Drag(rel_sigma=5, beta=0.01) + shape4 = Drag(rel_sigma=4, beta=0.05) + shape5 = Drag(rel_sigma=5, beta=0.05) assert shape1 == shape2 assert not shape1 == shape3 assert not shape1 == shape4 assert not shape1 == shape5 - shape1 = IIR([-0.5, 2], [1], Rectangular()) - shape2 = IIR([-0.5, 2], [1], Rectangular()) - shape3 = IIR([-0.5, 4], [1], Rectangular()) - shape4 = IIR([-0.4, 2], [1], Rectangular()) - shape5 = IIR([-0.5, 2], [2], Rectangular()) - shape6 = IIR([-0.5, 2], [2], Gaussian(5)) + shape1 = Iir(a=np.array([-0.5, 2]), b=np.array([1]), target=Rectangular()) + shape2 = Iir(a=np.array([-0.5, 2]), b=np.array([1]), target=Rectangular()) + shape3 = Iir(a=np.array([-0.5, 4]), b=np.array([1]), target=Rectangular()) + shape4 = Iir(a=np.array([-0.4, 2]), b=np.array([1]), target=Rectangular()) + shape5 = Iir(a=np.array([-0.5, 2]), b=np.array([2]), target=Rectangular()) + shape6 = Iir(a=np.array([-0.5, 2]), b=np.array([2]), target=Gaussian(rel_sigma=5)) assert shape1 == shape2 assert not shape1 == shape3 assert not shape1 == shape4 assert not shape1 == shape5 assert not shape1 == shape6 - shape1 = SNZ(5) - shape2 = SNZ(5) - shape3 = SNZ(2) - shape4 = SNZ(2, 0.1) - shape5 = SNZ(2, 0.1) + shape1 = Snz(t_idling=5) + shape2 = Snz(t_idling=5) + shape3 = Snz(t_idling=2) + shape4 = Snz(t_idling=2, b_amplitude=0.1) + shape5 = Snz(t_idling=2, b_amplitude=0.1) assert shape1 == shape2 assert not shape1 == shape3 assert not shape1 == shape4 assert not shape1 == shape5 - shape1 = eCap(4) - shape2 = eCap(4) - shape3 = eCap(5) + shape1 = ECap(alpha=4) + shape2 = ECap(alpha=4) + shape3 = ECap(alpha=5) assert shape1 == shape2 assert not shape1 == shape3 diff --git a/tests/pulses/test_modulation.py b/tests/pulses/test_modulation.py index 4517875fe..fe72e6fcf 100644 --- a/tests/pulses/test_modulation.py +++ b/tests/pulses/test_modulation.py @@ -32,7 +32,7 @@ def test_modulation(): ) # fmt: on - genvs: IqWaveform = Gaussian(rel_sigma=0.5).envelopes(30) + genvs: IqWaveform = Gaussian(rel_sigma=0.5).envelopes(20) # fmt: off np.testing.assert_allclose(modulate(genvs, 0.3,rate=1), np.array([[ 4.50307953e-01, -1.52257426e-01, -4.31814602e-01, From b197cb5dddb979c5cba4a10050f4e464d3df91fd Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 27 Mar 2024 14:23:23 +0400 Subject: [PATCH 167/233] fix: Install custom equality for array dependent envelopes --- src/qibolab/pulses/envelope.py | 21 +++++++++++++++------ src/qibolab/pulses/pulse.py | 2 +- 2 files changed, 16 insertions(+), 7 deletions(-) diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index c833d3d01..e31a914ec 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -9,7 +9,7 @@ from scipy.signal import lfilter from scipy.signal.windows import gaussian -from qibolab.serialize_ import Model, NdArray +from qibolab.serialize_ import Model, NdArray, eq __all__ = [ "Waveform", @@ -75,7 +75,7 @@ class Exponential(BaseEnvelope): .. math:: - A\frac{\exp\left(-\frac{x}{\text{upsilon}}\right) + g \exp\left(-\frac{x}{\text{tau}}\right)}{1 + g} + \frac{\exp\left(-\frac{x}{\text{upsilon}}\right) + g \exp\left(-\frac{x}{\text{tau}}\right)}{1 + g} """ kind: Literal["exponential"] = "exponential" @@ -89,8 +89,8 @@ class Exponential(BaseEnvelope): def i(self, samples: int) -> Waveform: """Generate a combination of two exponential decays.""" - ts = self.window(samples) - return (np.exp(-ts / self.upsilon) + self.g * np.exp(-ts / self.tau)) / ( + x = np.arange(samples) + return (np.exp(-x / self.upsilon) + self.g * np.exp(-x / self.tau)) / ( 1 + self.g ) @@ -226,6 +226,10 @@ def q(self, samples: int) -> Waveform: """.. todo::""" return self._data(self.target.q(samples)) + def __eq__(self, other) -> bool: + """.. todo::""" + return eq(self, other) + class Snz(BaseEnvelope): """Sudden variant Net Zero. @@ -278,9 +282,10 @@ class ECap(BaseEnvelope): def i(self, samples: int) -> Waveform: """.. todo::""" - x = self.window(samples) / samples + ss = np.arange(samples) + x = ss / samples return ( - (1 + np.tanh(self.alpha * self.window(samples))) + (1 + np.tanh(self.alpha * ss)) * (1 + np.tanh(self.alpha * (1 - x))) / (1 + np.tanh(self.alpha / 2)) ** 2 ) @@ -314,6 +319,10 @@ def q(self, samples: int) -> Waveform: return self.q_ + def __eq__(self, other) -> bool: + """.. todo::""" + return eq(self, other) + Envelope = Annotated[ Union[ diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 9226ace51..0b0c6473a 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -38,7 +38,7 @@ class Pulse(Model): Pulse amplitudes are normalised between -1 and 1. """ - frequency: int + frequency: float """Pulse Intermediate Frequency in Hz. The value has to be in the range [10e6 to 300e6]. From 71d2d91c2a390ca335a65ddcd6a17ee25207608e Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 27 Mar 2024 18:09:47 +0400 Subject: [PATCH 168/233] test: Fix drag tests --- src/qibolab/pulses/envelope.py | 2 +- tests/pulses/test_envelope.py | 14 +++++++------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index e31a914ec..fe6a09c0e 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -187,7 +187,7 @@ def q(self, samples: int) -> Waveform: .. todo:: """ ts = np.arange(samples) - mu = samples / 2 + mu = (samples - 1) / 2 sigma = _samples_sigma(self.rel_sigma, samples) return self.beta * (-(ts - mu) / (sigma**2)) * self.i(samples) diff --git a/tests/pulses/test_envelope.py b/tests/pulses/test_envelope.py index bdfaefc1d..ae9ba40d5 100644 --- a/tests/pulses/test_envelope.py +++ b/tests/pulses/test_envelope.py @@ -41,7 +41,7 @@ def test_drag_shape(): duration=2, amplitude=1, frequency=int(4e9), - envelope=Drag(rel_sigma=2, beta=1), + envelope=Drag(rel_sigma=0.5, beta=1), relative_phase=0, type=PulseType.DRIVE, ) @@ -136,30 +136,30 @@ def test_drag(): amplitude=1, frequency=200_000_000, relative_phase=0, - envelope=Drag(rel_sigma=5, beta=0.2), + envelope=Drag(rel_sigma=0.2, beta=0.2), qubit=0, ) assert pulse.duration == 50 assert isinstance(pulse.envelope, Drag) - assert pulse.envelope.rel_sigma == 5 + assert pulse.envelope.rel_sigma == 0.2 assert pulse.envelope.beta == 0.2 sampling_rate = 1 - num_samples = int(pulse.duration / 1 * sampling_rate) + num_samples = int(pulse.duration / sampling_rate) x = np.arange(num_samples) i = pulse.amplitude * np.exp( -(1 / 2) * ( ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / pulse.envelope.rel_sigma) ** 2) + / ((num_samples * pulse.envelope.rel_sigma) ** 2) ) ) - q = ( + q = pulse.amplitude * ( pulse.envelope.beta * ( -(x - (num_samples - 1) / 2) - / ((num_samples / pulse.envelope.rel_sigma) ** 2) + / ((num_samples * pulse.envelope.rel_sigma) ** 2) ) * i * sampling_rate From 092ac1cbffc8d2acc2391e926c0d71c70989b5e2 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 27 Mar 2024 18:12:55 +0400 Subject: [PATCH 169/233] test: Fix all envelope tests --- tests/pulses/test_envelope.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/tests/pulses/test_envelope.py b/tests/pulses/test_envelope.py index ae9ba40d5..ed991d18b 100644 --- a/tests/pulses/test_envelope.py +++ b/tests/pulses/test_envelope.py @@ -95,8 +95,8 @@ def test_rectangular(): pulse.amplitude * np.zeros(num_samples), ) - np.testing.assert_allclose(pulse.envelope.i(sampling_rate), i) - np.testing.assert_allclose(pulse.envelope.q(sampling_rate), q) + np.testing.assert_allclose(pulse.i(sampling_rate), i) + np.testing.assert_allclose(pulse.q(sampling_rate), q) def test_gaussian(): @@ -118,10 +118,9 @@ def test_gaussian(): num_samples = int(pulse.duration / sampling_rate) x = np.arange(0, num_samples, 1) i = pulse.amplitude * np.exp( - -(1 / 2) - * ( + -( ((x - (num_samples - 1) / 2) ** 2) - / (((num_samples) / pulse.envelope.rel_sigma) ** 2) + / (2 * (num_samples * pulse.envelope.rel_sigma) ** 2) ) ) q = pulse.amplitude * np.zeros(num_samples) @@ -149,10 +148,9 @@ def test_drag(): num_samples = int(pulse.duration / sampling_rate) x = np.arange(num_samples) i = pulse.amplitude * np.exp( - -(1 / 2) - * ( + -( ((x - (num_samples - 1) / 2) ** 2) - / ((num_samples * pulse.envelope.rel_sigma) ** 2) + / (2 * (num_samples * pulse.envelope.rel_sigma) ** 2) ) ) q = pulse.amplitude * ( From cc16251a3b568ec98b8181ded0da7213297e82bd Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 27 Mar 2024 18:38:15 +0400 Subject: [PATCH 170/233] test: Fix plot tests --- src/qibolab/pulses/envelope.py | 4 +-- src/qibolab/pulses/plot.py | 10 +++--- src/qibolab/pulses/sequence.py | 4 +-- tests/pulses/test_plot.py | 66 +++++++++++++++++++++++++++++----- 4 files changed, 67 insertions(+), 17 deletions(-) diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index fe6a09c0e..f9dcdcc44 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -251,8 +251,8 @@ class Snz(BaseEnvelope): def i(self, samples: int) -> Waveform: """.. todo::""" # convert timings to samples - half_pulse_duration = (self.duration - self.t_idling) / 2 - aspan = np.sum(self.window(samples) < half_pulse_duration) + half_pulse_duration = (1 - self.t_idling) * samples / 2 + aspan = np.sum(np.arange(samples) < half_pulse_duration) idle = samples - 2 * (aspan + 1) pulse = np.ones(samples) diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index d7f456ab2..87319febf 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -69,7 +69,7 @@ def pulse(pulse_: Pulse, filename=None): ) envelope = pulse_.envelopes(SAMPLING_RATE) - modulated = modulate(np.array(envelope), pulse_.frequency) + modulated = modulate(np.array(envelope), pulse_.frequency, rate=SAMPLING_RATE) ax1.plot(time, modulated[0], label="modulated i", c="C0") ax1.plot(time, modulated[1], label="modulated q", c="C1") ax1.plot(time, -waveform_i, c="silver", linestyle="dashed") @@ -157,11 +157,13 @@ def sequence(ps: PulseSequence, filename=None): envelope = pulse.envelopes(SAMPLING_RATE) num_samples = envelope[0].size time = start + np.arange(num_samples) / SAMPLING_RATE - modulated = modulate(np.array(envelope), pulse.frequency) + modulated = modulate( + np.array(envelope), pulse.frequency, rate=SAMPLING_RATE + ) ax.plot(time, modulated[1], c="lightgrey") ax.plot(time, modulated[0], c=f"C{str(n)}") - ax.plot(time, pulse.shape.i(), c=f"C{str(n)}") - ax.plot(time, -pulse.shape.i(), c=f"C{str(n)}") + ax.plot(time, pulse.i(SAMPLING_RATE), c=f"C{str(n)}") + ax.plot(time, -pulse.i(SAMPLING_RATE), c=f"C{str(n)}") # TODO: if they overlap use different shades ax.axhline(0, c="dimgrey") ax.set_ylabel(f"qubit {qubit} \n channel {channel}") diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py index fc488a372..3ecce9cd1 100644 --- a/src/qibolab/pulses/sequence.py +++ b/src/qibolab/pulses/sequence.py @@ -118,9 +118,9 @@ def channels(self) -> list: """List containing the channels used by the pulses in the sequence.""" channels = [] for pulse in self: - if not pulse.channel in channels: + if pulse.channel not in channels: channels.append(pulse.channel) - channels.sort() + return channels @property diff --git a/tests/pulses/test_plot.py b/tests/pulses/test_plot.py index 28d59354b..c895404fd 100644 --- a/tests/pulses/test_plot.py +++ b/tests/pulses/test_plot.py @@ -19,19 +19,67 @@ from qibolab.pulses.modulation import modulate HERE = pathlib.Path(__file__).parent +SAMPLING_RATE = 1 def test_plot_functions(): - p0 = Pulse(40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) - p1 = Pulse(40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) - p2 = Pulse(40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) - p3 = Pulse.flux(40, 0.9, Iir([-0.5, 2], [1], Rectangular()), channel=0, qubit=200) - p4 = Pulse.flux(40, 0.9, Snz(t_idling=10), channel=0, qubit=200) - p5 = Pulse(40, 0.9, 400e6, 0, ECap(alpha=2), 0, PulseType.DRIVE) - p6 = Pulse(40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.DRIVE, 2) + p0 = Pulse( + duration=40, + amplitude=0.9, + frequency=0, + envelope=Rectangular(), + relative_phase=0, + type=PulseType.FLUX, + qubit=0, + ) + p1 = Pulse( + duration=40, + amplitude=0.9, + frequency=50e6, + envelope=Gaussian(rel_sigma=0.2), + relative_phase=0, + type=PulseType.DRIVE, + qubit=2, + ) + p2 = Pulse( + duration=40, + amplitude=0.9, + frequency=50e6, + envelope=Drag(rel_sigma=0.2, beta=2), + relative_phase=0, + type=PulseType.DRIVE, + qubit=200, + ) + p3 = Pulse.flux( + duration=40, + amplitude=0.9, + envelope=Iir(a=np.array([-0.5, 2]), b=np.array([1]), target=Rectangular()), + channel="0", + qubit=200, + ) + p4 = Pulse.flux( + duration=40, amplitude=0.9, envelope=Snz(t_idling=10), channel="0", qubit=200 + ) + p5 = Pulse( + duration=40, + amplitude=0.9, + frequency=400e6, + envelope=ECap(alpha=2), + relative_phase=0, + type=PulseType.DRIVE, + ) + p6 = Pulse( + duration=40, + amplitude=0.9, + frequency=50e6, + envelope=GaussianSquare(rel_sigma=0.2, width=0.9), + relative_phase=0, + type=PulseType.DRIVE, + qubit=2, + ) ps = PulseSequence([p0, p1, p2, p3, p4, p5, p6]) - envelope = p0.envelope_waveforms() - wf = modulate(np.array(envelope), 0.0) + envelope = p0.envelopes(SAMPLING_RATE) + wf = modulate(np.array(envelope), 0.0, rate=SAMPLING_RATE) plot_file = HERE / "test_plot.png" From 9782f15fb19c636290edf243af0c91fdf88706ab Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 27 Mar 2024 18:56:08 +0400 Subject: [PATCH 171/233] test: Fix most pulse tests --- tests/pulses/test_pulse.py | 190 +++++++++++++++++++++---------------- 1 file changed, 109 insertions(+), 81 deletions(-) diff --git a/tests/pulses/test_pulse.py b/tests/pulses/test_pulse.py index 5607c01c0..be5484d8a 100644 --- a/tests/pulses/test_pulse.py +++ b/tests/pulses/test_pulse.py @@ -1,7 +1,5 @@ """Tests ``pulses.py``.""" -import copy - import numpy as np import pytest @@ -9,6 +7,7 @@ Custom, Drag, ECap, + Envelope, Gaussian, GaussianSquare, Iir, @@ -26,8 +25,8 @@ def test_init(): amplitude=0.9, frequency=20_000_000, relative_phase=0.0, - shape=Rectangular(), - channel=0, + envelope=Rectangular(), + channel="0", type=PulseType.READOUT, qubit=0, ) @@ -38,8 +37,8 @@ def test_init(): amplitude=0.9, frequency=20_000_000, relative_phase=0.0, - shape=Rectangular(), - channel=0, + envelope=Rectangular(), + channel="0", type=PulseType.READOUT, qubit=0, ) @@ -51,12 +50,12 @@ def test_init(): amplitude=0.9, frequency=int(20e6), relative_phase=0, - shape=Rectangular(), - channel=0, + envelope=Rectangular(), + channel="0", type=PulseType.READOUT, qubit=0, ) - assert isinstance(p2.frequency, int) and p2.frequency == 20_000_000 + assert isinstance(p2.frequency, float) and p2.frequency == 20_000_000 # initialisation with non float (int) relative_phase p3 = Pulse( @@ -64,8 +63,8 @@ def test_init(): amplitude=0.9, frequency=20_000_000, relative_phase=1.0, - shape=Rectangular(), - channel=0, + envelope=Rectangular(), + channel="0", type=PulseType.READOUT, qubit=0, ) @@ -77,12 +76,12 @@ def test_init(): amplitude=0.9, frequency=20_000_000, relative_phase=0, - shape="Rectangular()", - channel=0, + envelope=Rectangular(), + channel="0", type=PulseType.READOUT, qubit=0, ) - assert isinstance(p4.shape, Rectangular) + assert isinstance(p4.envelope, Rectangular) # initialisation with str channel and str qubit p5 = Pulse( @@ -90,24 +89,82 @@ def test_init(): amplitude=0.9, frequency=20_000_000, relative_phase=0, - shape="Rectangular()", + envelope=Rectangular(), channel="channel0", type=PulseType.READOUT, - qubit="qubit0", + qubit=0, ) - assert p5.qubit == "qubit0" + assert p5.qubit == 0 # initialisation with different frequencies, shapes and types - p6 = Pulse(40, 0.9, -50e6, 0, Rectangular(), 0, PulseType.READOUT) - p7 = Pulse(40, 0.9, 0, 0, Rectangular(), 0, PulseType.FLUX, 0) - p8 = Pulse(40, 0.9, 50e6, 0, Gaussian(5), 0, PulseType.DRIVE, 2) - p9 = Pulse(40, 0.9, 50e6, 0, Drag(5, 2), 0, PulseType.DRIVE, 200) + p6 = Pulse( + duration=40, + amplitude=0.9, + frequency=-50e6, + envelope=Rectangular(), + relative_phase=0, + type=PulseType.READOUT, + ) + p7 = Pulse( + duration=40, + amplitude=0.9, + frequency=0, + envelope=Rectangular(), + relative_phase=0, + type=PulseType.FLUX, + qubit=0, + ) + p8 = Pulse( + duration=40, + amplitude=0.9, + frequency=50e6, + envelope=Gaussian(rel_sigma=0.2), + relative_phase=0, + type=PulseType.DRIVE, + qubit=2, + ) + p9 = Pulse( + duration=40, + amplitude=0.9, + frequency=50e6, + envelope=Drag(rel_sigma=0.2, beta=2), + relative_phase=0, + type=PulseType.DRIVE, + qubit=200, + ) p10 = Pulse.flux( - 40, 0.9, Iir([-1, 1], [-0.1, 0.1001], Rectangular()), channel=0, qubit=200 + duration=40, + amplitude=0.9, + envelope=Iir( + a=np.array([-1, 1]), b=np.array([-0.1, 0.1001]), target=Rectangular() + ), + channel="0", + qubit=200, + ) + p11 = Pulse.flux( + duration=40, + amplitude=0.9, + envelope=Snz(t_idling=10, b_amplitude=0.5), + channel="0", + qubit=200, + ) + p13 = Pulse( + duration=40, + amplitude=0.9, + frequency=400e6, + envelope=ECap(alpha=2), + relative_phase=0, + type=PulseType.DRIVE, + ) + p14 = Pulse( + duration=40, + amplitude=0.9, + frequency=50e6, + envelope=GaussianSquare(rel_sigma=0.2, width=0.9), + relative_phase=0, + type=PulseType.READOUT, + qubit=2, ) - p11 = Pulse.flux(40, 0.9, Snz(t_idling=10, b_amplitude=0.5), channel=0, qubit=200) - p13 = Pulse(40, 0.9, 400e6, 0, ECap(alpha=2), 0, PulseType.DRIVE) - p14 = Pulse(40, 0.9, 50e6, 0, GaussianSquare(5, 0.9), 0, PulseType.READOUT, 2) # initialisation with float duration p12 = Pulse( @@ -115,8 +172,8 @@ def test_init(): amplitude=0.9, frequency=20_000_000, relative_phase=1, - shape=Rectangular(), - channel=0, + envelope=Rectangular(), + channel="0", type=PulseType.READOUT, qubit=0, ) @@ -125,7 +182,7 @@ def test_init(): def test_attributes(): - channel = 0 + channel = "0" qubit = 0 p10 = Pulse( @@ -133,37 +190,17 @@ def test_attributes(): amplitude=0.9, frequency=20_000_000, relative_phase=0.0, - shape=Rectangular(), + envelope=Rectangular(), channel=channel, qubit=qubit, ) - assert type(p10.duration) == int and p10.duration == 50 - assert type(p10.amplitude) == float and p10.amplitude == 0.9 - assert type(p10.frequency) == int and p10.frequency == 20_000_000 - assert isinstance(p10.shape, PulseShape) and repr(p10.shape) == "Rectangular()" - assert type(p10.channel) == type(channel) and p10.channel == channel - assert type(p10.qubit) == type(qubit) and p10.qubit == qubit - - -def test_hash(): - rp = Pulse(40, 0.9, 100e6, 0, Rectangular(), 0, PulseType.DRIVE) - dp = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) - hash(rp) - my_dict = {rp: 1, dp: 2} - assert list(my_dict.keys())[0] == rp - assert list(my_dict.keys())[1] == dp - - p1 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) - p2 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) - - assert p1 == p2 - - p1 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 0, PulseType.DRIVE) - p2 = copy.copy(p1) - p3 = copy.deepcopy(p1) - assert p1 == p2 - assert p1 == p3 + assert isinstance(p10.duration, float) and p10.duration == 50 + assert isinstance(p10.amplitude, float) and p10.amplitude == 0.9 + assert isinstance(p10.frequency, float) and p10.frequency == 20_000_000 + assert isinstance(p10.envelope, Envelope) + assert isinstance(p10.channel, type(channel)) and p10.channel == channel + assert isinstance(p10.qubit, type(qubit)) and p10.qubit == qubit def test_aliases(): @@ -172,9 +209,9 @@ def test_aliases(): amplitude=0.9, frequency=20_000_000, relative_phase=0.0, - shape=Rectangular(), + envelope=Rectangular(), type=PulseType.READOUT, - channel=0, + channel="0", qubit=0, ) assert rop.qubit == 0 @@ -184,17 +221,17 @@ def test_aliases(): amplitude=0.9, frequency=200_000_000, relative_phase=0.0, - shape=Gaussian(5), - channel=0, + envelope=Gaussian(rel_sigma=5), + channel="0", qubit=0, ) assert dp.amplitude == 0.9 - assert isinstance(dp.shape, Gaussian) + assert isinstance(dp.envelope, Gaussian) fp = Pulse.flux( - duration=300, amplitude=0.9, shape=Rectangular(), channel=0, qubit=0 + duration=300, amplitude=0.9, envelope=Rectangular(), channel="0", qubit=0 ) - assert fp.channel == 0 + assert fp.channel == "0" def test_pulse(): @@ -206,8 +243,8 @@ def test_pulse(): amplitude=1, duration=duration, relative_phase=0, - shape=f"Drag({rel_sigma}, {beta})", - channel=1, + envelope=Drag(rel_sigma=rel_sigma, beta=beta), + channel="1", ) assert pulse.duration == duration @@ -220,8 +257,8 @@ def test_readout_pulse(): amplitude=1, duration=duration, relative_phase=0, - shape=f"Rectangular()", - channel=11, + envelope=Rectangular(), + channel="11", type=PulseType.READOUT, ) @@ -231,28 +268,19 @@ def test_readout_pulse(): def test_envelope_waveform_i_q(): envelope_i = np.cos(np.arange(0, 10, 0.01)) envelope_q = np.sin(np.arange(0, 10, 0.01)) - custom_shape_pulse = Custom(envelope_i, envelope_q) - custom_shape_pulse_old_behaviour = Custom(envelope_i) + custom_shape_pulse = Custom(i_=envelope_i, q_=envelope_q) pulse = Pulse( duration=1000, amplitude=1, frequency=10e6, relative_phase=0, - shape="Rectangular()", - channel=1, + envelope=Rectangular(), + channel="1", ) - with pytest.raises(ShapeInitError): - custom_shape_pulse.envelope_waveform_i() - with pytest.raises(ShapeInitError): - custom_shape_pulse.envelope_waveform_q() - - custom_shape_pulse.pulse = pulse - custom_shape_pulse_old_behaviour.pulse = pulse + custom_shape_pulse.i_ = pulse.i(1) pulse.duration = 2000 with pytest.raises(ValueError): - custom_shape_pulse.pulse = pulse - custom_shape_pulse.envelope_waveform_i() + custom_shape_pulse.i(samples=10) with pytest.raises(ValueError): - custom_shape_pulse.pulse = pulse - custom_shape_pulse.envelope_waveform_q() + custom_shape_pulse.q(samples=10) From 4e58c030e812f85d10133dccf87de55cf0c5f369 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 28 Mar 2024 13:35:11 +0100 Subject: [PATCH 172/233] test: Fix pulse sequences tests --- tests/pulses/test_sequence.py | 267 +++++++++++++++++++++++++++------- 1 file changed, 216 insertions(+), 51 deletions(-) diff --git a/tests/pulses/test_sequence.py b/tests/pulses/test_sequence.py index c71b501d0..6d9b251a0 100644 --- a/tests/pulses/test_sequence.py +++ b/tests/pulses/test_sequence.py @@ -17,23 +17,23 @@ def test_add_readout(): amplitude=0.3, duration=60, relative_phase=0, - envelope=Gaussian(5), - channel=1, + envelope=Gaussian(rel_sigma=0.2), + channel="1", ) ) - sequence.append(Delay(4, channel=1)) + sequence.append(Delay(duration=4, channel="1")) sequence.append( Pulse( frequency=200_000_000, amplitude=0.3, duration=60, relative_phase=0, - envelope=Drag(5, 2), - channel=1, + envelope=Drag(rel_sigma=0.2, beta=2), + channel="1", type=PulseType.FLUX, ) ) - sequence.append(Delay(4, channel=1)) + sequence.append(Delay(duration=4, channel="1")) sequence.append( Pulse( frequency=20_000_000, @@ -41,7 +41,7 @@ def test_add_readout(): duration=2000, relative_phase=0, envelope=Rectangular(), - channel=11, + channel="11", type=PulseType.READOUT, ) ) @@ -52,31 +52,54 @@ def test_add_readout(): def test_get_qubit_pulses(): - p1 = Pulse(400, 0.9, 20e6, 0, Gaussian(5), 10, qubit=0) + p1 = Pulse( + duration=400, + amplitude=0.9, + frequency=20e6, + envelope=Gaussian(rel_sigma=0.2), + relative_phase=10, + qubit=0, + ) p2 = Pulse( - 400, - 0.9, - 20e6, - 0, - Rectangular(), - channel=30, + duration=400, + amplitude=0.9, + frequency=20e6, + envelope=Rectangular(), + channel="30", qubit=0, type=PulseType.READOUT, ) - p3 = Pulse(400, 0.9, 20e6, 0, Drag(5, 50), 20, qubit=1) - p4 = Pulse(400, 0.9, 20e6, 0, Drag(5, 50), 30, qubit=1) + p3 = Pulse( + duration=400, + amplitude=0.9, + frequency=20e6, + envelope=Drag(rel_sigma=0.2, beta=50), + relative_phase=20, + qubit=1, + ) + p4 = Pulse( + duration=400, + amplitude=0.9, + frequency=20e6, + envelope=Drag(rel_sigma=0.2, beta=50), + relative_phase=30, + qubit=1, + ) p5 = Pulse( - 400, - 0.9, - 20e6, - 0, - Rectangular(), - channel=30, + duration=400, + amplitude=0.9, + frequency=20e6, + envelope=Rectangular(), + channel="30", qubit=1, type=PulseType.READOUT, ) - p6 = Pulse.flux(400, 0.9, Rectangular(), channel=40, qubit=1) - p7 = Pulse.flux(400, 0.9, Rectangular(), channel=40, qubit=2) + p6 = Pulse.flux( + duration=400, amplitude=0.9, envelope=Rectangular(), channel="40", qubit=1 + ) + p7 = Pulse.flux( + duration=400, amplitude=0.9, envelope=Rectangular(), channel="40", qubit=2 + ) ps = PulseSequence([p1, p2, p3, p4, p5, p6, p7]) assert ps.qubits == [0, 1, 2] @@ -87,35 +110,108 @@ def test_get_qubit_pulses(): def test_get_channel_pulses(): - p1 = Pulse(400, 0.9, 20e6, 0, Gaussian(5), 10) - p2 = Pulse(400, 0.9, 20e6, 0, Rectangular(), 30, type=PulseType.READOUT) - p3 = Pulse(400, 0.9, 20e6, 0, Drag(5, 50), 20) - p4 = Pulse(400, 0.9, 20e6, 0, Drag(5, 50), 30) - p5 = Pulse(400, 0.9, 20e6, 0, Rectangular(), 20, type=PulseType.READOUT) - p6 = Pulse(400, 0.9, 20e6, 0, Gaussian(5), 30) + p1 = Pulse( + duration=400, + frequency=0.9, + amplitude=20e6, + envelope=Gaussian(rel_sigma=0.2), + channel="10", + ) + p2 = Pulse( + duration=400, + frequency=0.9, + amplitude=20e6, + envelope=Rectangular(), + channel="30", + type=PulseType.READOUT, + ) + p3 = Pulse( + duration=400, + frequency=0.9, + amplitude=20e6, + envelope=Drag(rel_sigma=0.2, beta=5), + channel="20", + ) + p4 = Pulse( + duration=400, + frequency=0.9, + amplitude=20e6, + envelope=Drag(rel_sigma=0.2, beta=5), + channel="30", + ) + p5 = Pulse( + duration=400, + frequency=0.9, + amplitude=20e6, + envelope=Rectangular(), + channel="20", + type=PulseType.READOUT, + ) + p6 = Pulse( + duration=400, + frequency=0.9, + amplitude=20e6, + envelope=Gaussian(rel_sigma=0.2), + channel="30", + ) ps = PulseSequence([p1, p2, p3, p4, p5, p6]) - assert ps.channels == [10, 20, 30] - assert len(ps.get_channel_pulses(10)) == 1 - assert len(ps.get_channel_pulses(20)) == 2 - assert len(ps.get_channel_pulses(30)) == 3 - assert len(ps.get_channel_pulses(20, 30)) == 5 + assert sorted(ps.channels) == ["10", "20", "30"] + assert len(ps.get_channel_pulses("10")) == 1 + assert len(ps.get_channel_pulses("20")) == 2 + assert len(ps.get_channel_pulses("30")) == 3 + assert len(ps.get_channel_pulses("20", "30")) == 5 def test_sequence_duration(): - p0 = Delay(20, 1) - p1 = Pulse(40, 0.9, 200e6, 0, Drag(5, 1), 1, PulseType.DRIVE) - p2 = Pulse(1000, 0.9, 20e6, 0, Rectangular(), 1, PulseType.READOUT) + p0 = Delay(duration=20, channel="1") + p1 = Pulse( + duration=40, + amplitude=0.9, + frequency=200e6, + envelope=Drag(rel_sigma=0.2, beta=1), + channel="1", + type=PulseType.DRIVE, + ) + p2 = Pulse( + duration=1000, + amplitude=0.9, + frequency=20e6, + envelope=Rectangular(), + channel="1", + type=PulseType.READOUT, + ) ps = PulseSequence([p0, p1]) + [p2] assert ps.duration == 20 + 40 + 1000 - p2.channel = 2 + p2.channel = "2" assert ps.duration == 1000 def test_init(): - p1 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 3, PulseType.DRIVE) - p2 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) - p3 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) + p1 = Pulse( + duration=40, + amplitude=0.9, + frequency=100e6, + envelope=Drag(rel_sigma=0.2, beta=1), + channel="3", + type=PulseType.DRIVE, + ) + p2 = Pulse( + duration=40, + amplitude=0.9, + frequency=100e6, + envelope=Drag(rel_sigma=0.2, beta=1), + channel="2", + type=PulseType.DRIVE, + ) + p3 = Pulse( + duration=40, + amplitude=0.9, + frequency=100e6, + envelope=Drag(rel_sigma=0.2, beta=1), + channel="1", + type=PulseType.DRIVE, + ) ps = PulseSequence() assert type(ps) == PulseSequence @@ -141,13 +237,61 @@ def test_init(): def test_operators(): ps = PulseSequence() - ps += [Pulse(200, 0.9, 20e6, 0, Rectangular(), 1, type=PulseType.READOUT)] - ps = ps + [Pulse(200, 0.9, 20e6, 0, Rectangular(), 2, type=PulseType.READOUT)] - ps = [Pulse(200, 0.9, 20e6, 0, Rectangular(), 3, type=PulseType.READOUT)] + ps + ps += [ + Pulse( + duration=200, + amplitude=0.9, + frequency=20e6, + envelope=Rectangular(), + channel="3", + type=PulseType.DRIVE, + ) + ] + ps = ps + [ + Pulse( + duration=200, + amplitude=0.9, + frequency=20e6, + envelope=Rectangular(), + channel="2", + type=PulseType.DRIVE, + ) + ] + ps = [ + Pulse( + duration=200, + amplitude=0.9, + frequency=20e6, + envelope=Rectangular(), + channel="3", + type=PulseType.DRIVE, + ) + ] + ps - p4 = Pulse(40, 0.9, 50e6, 0, Gaussian(5), 3, PulseType.DRIVE) - p5 = Pulse(40, 0.9, 50e6, 0, Gaussian(5), 2, PulseType.DRIVE) - p6 = Pulse(40, 0.9, 50e6, 0, Gaussian(5), 1, PulseType.DRIVE) + p4 = Pulse( + duration=40, + amplitude=0.9, + frequency=50e6, + envelope=Gaussian(rel_sigma=0.2), + channel="3", + type=PulseType.DRIVE, + ) + p5 = Pulse( + duration=40, + amplitude=0.9, + frequency=50e6, + envelope=Gaussian(rel_sigma=0.2), + channel="2", + type=PulseType.DRIVE, + ) + p6 = Pulse( + duration=40, + amplitude=0.9, + frequency=50e6, + envelope=Gaussian(rel_sigma=0.2), + channel="1", + type=PulseType.DRIVE, + ) another_ps = PulseSequence() another_ps.append(p4) @@ -164,7 +308,14 @@ def test_operators(): # ps.plot() - p7 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) + p7 = Pulse( + duration=40, + amplitude=0.9, + frequency=100e6, + envelope=Drag(rel_sigma=0.2, beta=1), + channel="1", + type=PulseType.DRIVE, + ) yet_another_ps = PulseSequence([p7]) assert len(yet_another_ps) == 1 yet_another_ps *= 3 @@ -172,7 +323,21 @@ def test_operators(): yet_another_ps *= 3 assert len(yet_another_ps) == 9 - p8 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 1, PulseType.DRIVE) - p9 = Pulse(40, 0.9, 100e6, 0, Drag(5, 1), 2, PulseType.DRIVE) + p8 = Pulse( + duration=40, + amplitude=0.9, + frequency=100e6, + envelope=Drag(rel_sigma=0.2, beta=1), + channel="1", + type=PulseType.DRIVE, + ) + p9 = Pulse( + duration=40, + amplitude=0.9, + frequency=100e6, + envelope=Drag(rel_sigma=0.2, beta=1), + channel="2", + type=PulseType.DRIVE, + ) and_yet_another_ps = 2 * PulseSequence([p9]) + [p8] * 3 assert len(and_yet_another_ps) == 5 From d360b689919e4ae96c1d7a404bdb652845d62689 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 28 Mar 2024 13:42:04 +0100 Subject: [PATCH 173/233] test: Use parent class BaseEnvelope for instance check, instead of the union --- tests/pulses/test_pulse.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/pulses/test_pulse.py b/tests/pulses/test_pulse.py index be5484d8a..21155bb9b 100644 --- a/tests/pulses/test_pulse.py +++ b/tests/pulses/test_pulse.py @@ -4,10 +4,10 @@ import pytest from qibolab.pulses import ( + BaseEnvelope, Custom, Drag, ECap, - Envelope, Gaussian, GaussianSquare, Iir, @@ -198,7 +198,7 @@ def test_attributes(): assert isinstance(p10.duration, float) and p10.duration == 50 assert isinstance(p10.amplitude, float) and p10.amplitude == 0.9 assert isinstance(p10.frequency, float) and p10.frequency == 20_000_000 - assert isinstance(p10.envelope, Envelope) + assert isinstance(p10.envelope, BaseEnvelope) assert isinstance(p10.channel, type(channel)) and p10.channel == channel assert isinstance(p10.qubit, type(qubit)) and p10.qubit == qubit From b62cb9e966a04cdc01c001cce6f17c0e458916bf Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 28 Mar 2024 18:08:42 +0100 Subject: [PATCH 174/233] feat: Make all pydantic models frozen --- src/qibolab/serialize_.py | 2 +- tests/pulses/test_pulse.py | 4 ++-- tests/pulses/test_sequence.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/qibolab/serialize_.py b/src/qibolab/serialize_.py index d9928254e..c09cdc68c 100644 --- a/src/qibolab/serialize_.py +++ b/src/qibolab/serialize_.py @@ -58,4 +58,4 @@ def eq(obj1: BaseModel, obj2: BaseModel) -> bool: class Model(BaseModel): """Global qibolab model, holding common configurations.""" - model_config = ConfigDict(arbitrary_types_allowed=True) + model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True) diff --git a/tests/pulses/test_pulse.py b/tests/pulses/test_pulse.py index 21155bb9b..29650dc8e 100644 --- a/tests/pulses/test_pulse.py +++ b/tests/pulses/test_pulse.py @@ -278,8 +278,8 @@ def test_envelope_waveform_i_q(): channel="1", ) - custom_shape_pulse.i_ = pulse.i(1) - pulse.duration = 2000 + custom_shape_pulse = custom_shape_pulse.model_copy(update={"i_": pulse.i(1)}) + pulse = pulse.model_copy(update={"duration": 2000}) with pytest.raises(ValueError): custom_shape_pulse.i(samples=10) with pytest.raises(ValueError): diff --git a/tests/pulses/test_sequence.py b/tests/pulses/test_sequence.py index 6d9b251a0..c44eacbe8 100644 --- a/tests/pulses/test_sequence.py +++ b/tests/pulses/test_sequence.py @@ -183,7 +183,7 @@ def test_sequence_duration(): ) ps = PulseSequence([p0, p1]) + [p2] assert ps.duration == 20 + 40 + 1000 - p2.channel = "2" + ps[-1] = p2.model_copy(update={"channel": "2"}) assert ps.duration == 1000 From bb69e2afe1e4455fb26a24e4c0d7acb66cbbd913 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 28 Mar 2024 18:20:49 +0100 Subject: [PATCH 175/233] feat: Propagate pydantic models to execution parameters, fix backend tests --- src/qibolab/compilers/default.py | 2 +- src/qibolab/execution_parameters.py | 5 +- src/qibolab/native.py | 367 ++-------------------------- src/qibolab/platform/platform.py | 3 +- src/qibolab/serialize.py | 2 + src/qibolab/serialize_.py | 8 + 6 files changed, 36 insertions(+), 351 deletions(-) diff --git a/src/qibolab/compilers/default.py b/src/qibolab/compilers/default.py index c59360c88..227b07af5 100644 --- a/src/qibolab/compilers/default.py +++ b/src/qibolab/compilers/default.py @@ -4,9 +4,9 @@ """ import math -from dataclasses import replace from qibolab.pulses import PulseSequence, VirtualZ +from qibolab.serialize_ import replace def identity_rule(gate, qubit): diff --git a/src/qibolab/execution_parameters.py b/src/qibolab/execution_parameters.py index b317caf13..13a6ff0f2 100644 --- a/src/qibolab/execution_parameters.py +++ b/src/qibolab/execution_parameters.py @@ -1,4 +1,3 @@ -from dataclasses import dataclass from enum import Enum, auto from typing import Optional @@ -10,6 +9,7 @@ RawWaveformResults, SampleResults, ) +from qibolab.serialize_ import Model class AcquisitionType(Enum): @@ -51,8 +51,7 @@ class AveragingMode(Enum): } -@dataclass(frozen=True) -class ExecutionParameters: +class ExecutionParameters(Model): """Data structure to deal with execution parameters.""" nshots: Optional[int] = None diff --git a/src/qibolab/native.py b/src/qibolab/native.py index 8c08595e1..a3454eccb 100644 --- a/src/qibolab/native.py +++ b/src/qibolab/native.py @@ -1,256 +1,8 @@ -import copy -from collections import defaultdict -from dataclasses import dataclass, field, fields, replace -from typing import List, Optional, Union +from dataclasses import dataclass, field, fields +from typing import Optional -from qibolab.pulses import Pulse, PulseSequence, PulseType - - -@dataclass -class NativePulse: - """Container with parameters required to generate a pulse implementing a - native gate.""" - - name: str - """Name of the gate that the pulse implements.""" - duration: int - amplitude: float - shape: str - pulse_type: PulseType - qubit: "qubits.Qubit" - frequency: int = 0 - relative_start: int = 0 - """Relative start is relevant for two-qubit gate operations which - correspond to a pulse sequence.""" - - # used for qblox - if_frequency: Optional[int] = None - # TODO: Note sure if the following parameters are useful to be in the runcard - start: int = 0 - phase: float = 0.0 - - @classmethod - def from_dict(cls, name, pulse, qubit): - """Parse the dictionary provided by the runcard. - - Args: - name (str): Name of the native gate (dictionary key). - pulse (dict): Dictionary containing the parameters of the pulse implementing - the gate, as loaded from the runcard. - qubits (:class:`qibolab.platforms.abstract.Qubit`): Qubit that the - pulse is acting on - """ - kwargs = copy.deepcopy(pulse) - kwargs["pulse_type"] = PulseType(kwargs.pop("type")) - kwargs["qubit"] = qubit - return cls(name, **kwargs) - - @property - def raw(self): - data = { - fld.name: getattr(self, fld.name) - for fld in fields(self) - if getattr(self, fld.name) is not None - } - del data["name"] - del data["start"] - if self.pulse_type is PulseType.FLUX: - del data["frequency"] - del data["phase"] - data["qubit"] = self.qubit.name - data["type"] = data.pop("pulse_type").value - return data - - def pulse(self, start, relative_phase=0.0): - """Construct the :class:`qibolab.pulses.Pulse` object implementing the - gate. - - Args: - start (int): Start time of the pulse in the sequence. - relative_phase (float): Relative phase of the pulse. - - Returns: - A :class:`qibolab.pulses.DrivePulse` or :class:`qibolab.pulses.DrivePulse` - or :class:`qibolab.pulses.FluxPulse` with the pulse parameters of the gate. - """ - if self.pulse_type is PulseType.FLUX: - return Pulse.flux( - start + self.relative_start, - self.duration, - self.amplitude, - self.shape, - channel=self.qubit.flux.name, - qubit=self.qubit.name, - ) - - channel = getattr(self.qubit, self.pulse_type.name.lower()).name - return Pulse( - start + self.relative_start, - self.duration, - self.amplitude, - self.frequency, - relative_phase, - self.shape, - type=self.pulse_type, - channel=channel, - qubit=self.qubit.name, - ) - - -@dataclass -class VirtualZPulse: - """Container with parameters required to add a virtual Z phase in a pulse - sequence.""" - - phase: float - qubit: "qubits.Qubit" - - @property - def raw(self): - return {"type": "virtual_z", "phase": self.phase, "qubit": self.qubit.name} - - -@dataclass -class CouplerPulse: - """Container with parameters required to add a coupler pulse in a pulse - sequence.""" - - duration: int - amplitude: float - shape: str - coupler: "couplers.Coupler" - relative_start: int = 0 - - @classmethod - def from_dict(cls, pulse, coupler): - """Parse the dictionary provided by the runcard. - - Args: - name (str): Name of the native gate (dictionary key). - pulse (dict): Dictionary containing the parameters of the pulse implementing - the gate, as loaded from the runcard. - coupler (:class:`qibolab.platforms.abstract.Coupler`): Coupler that the - pulse is acting on - """ - kwargs = copy.deepcopy(pulse) - kwargs["coupler"] = coupler - kwargs.pop("type") - return cls(**kwargs) - - @property - def raw(self): - return { - "type": "coupler", - "duration": self.duration, - "amplitude": self.amplitude, - "shape": self.shape, - "coupler": self.coupler.name, - "relative_start": self.relative_start, - } - - def pulse(self, start): - """Construct the :class:`qibolab.pulses.Pulse` object implementing the - gate. - - Args: - start (int): Start time of the pulse in the sequence. - - Returns: - A :class:`qibolab.pulses.FluxPulse` with the pulse parameters of the gate. - """ - return Pulse( - start + self.relative_start, - self.duration, - self.amplitude, - 0, - 0, - self.shape, - type=PulseType.COUPLERFLUX, - channel=self.coupler.flux.name, - qubit=self.coupler.name, - ) - - -@dataclass -class NativeSequence: - """List of :class:`qibolab.platforms.native.NativePulse` objects - implementing a gate. - - Relevant for two-qubit gates, which usually require a sequence of - pulses to be implemented. These pulses may act on qubits different - than the qubits the gate is targeting. - """ - - name: str - pulses: List[Union[NativePulse, VirtualZPulse]] = field(default_factory=list) - coupler_pulses: List[CouplerPulse] = field(default_factory=list) - - @classmethod - def from_dict(cls, name, sequence, qubits, couplers): - """Constructs the native sequence from the dictionaries provided in the - runcard. - - Args: - name (str): Name of the gate the sequence is applying. - sequence (dict): Dictionary describing the sequence as provided in the runcard. - qubits (list): List of :class:`qibolab.qubits.Qubit` object for all - qubits in the platform. All qubits are required because the sequence may be - acting on qubits that the implemented gate is not targeting. - couplers (list): List of :class:`qibolab.couplers.Coupler` object for all - couplers in the platform. All couplers are required because the sequence may be - acting on couplers that the implemented gate is not targeting. - """ - pulses = [] - coupler_pulses = [] - - # If sequence contains only one pulse dictionary, convert it into a list that can be iterated below - if isinstance(sequence, dict): - sequence = [sequence] - - for i, pulse in enumerate(sequence): - pulse = copy.deepcopy(pulse) - pulse_type = pulse.pop("type") - if pulse_type == "coupler": - pulse["coupler"] = couplers[pulse.pop("coupler")] - coupler_pulses.append(CouplerPulse(**pulse)) - else: - qubit = qubits[pulse.pop("qubit")] - if pulse_type == "virtual_z": - phase = pulse["phase"] - pulses.append(VirtualZPulse(phase, qubit)) - else: - pulses.append( - NativePulse( - f"{name}{i}", - **pulse, - pulse_type=PulseType(pulse_type), - qubit=qubit, - ) - ) - return cls(name, pulses, coupler_pulses) - - @property - def raw(self): - pulses = [pulse.raw for pulse in self.pulses] - coupler_pulses = [pulse.raw for pulse in self.coupler_pulses] - return pulses + coupler_pulses - - def sequence(self, start=0): - """Creates a :class:`qibolab.pulses.PulseSequence` object implementing - the sequence.""" - sequence = PulseSequence() - virtual_z_phases = defaultdict(int) - - for pulse in self.pulses: - if isinstance(pulse, NativePulse): - sequence.append(pulse.pulse(start=start)) - else: - virtual_z_phases[pulse.qubit.name] += pulse.phase - - for coupler_pulse in self.coupler_pulses: - sequence.append(coupler_pulse.pulse(start=start)) - # TODO: Maybe ``virtual_z_phases`` should be an attribute of ``PulseSequence`` - return sequence, virtual_z_phases +from .pulses import Pulse, PulseSequence +from .serialize_ import replace @dataclass @@ -258,85 +10,19 @@ class SingleQubitNatives: """Container with the native single-qubit gates acting on a specific qubit.""" - RX: Optional[NativePulse] = None + RX: Optional[Pulse] = None """Pulse to drive the qubit from state 0 to state 1.""" - RX12: Optional[NativePulse] = None + RX12: Optional[Pulse] = None """Pulse to drive to qubit from state 1 to state 2.""" - MZ: Optional[NativePulse] = None + MZ: Optional[Pulse] = None """Measurement pulse.""" + CP: Optional[Pulse] = None + """Pulse to activate a coupler.""" @property - def RX90(self) -> NativePulse: + def RX90(self) -> Pulse: """RX90 native pulse is inferred from RX by halving its amplitude.""" - return replace(self.RX, name="RX90", amplitude=self.RX.amplitude / 2.0) - - @classmethod - def from_dict(cls, qubit, native_gates): - """Parse native gates of the qubit from the runcard. - - Args: - qubit (:class:`qibolab.qubits.Qubit`): Qubit object that the - native gates are acting on. - native_gates (dict): Dictionary with native gate pulse parameters as loaded - from the runcard. - """ - pulses = { - n: NativePulse.from_dict(n, pulse, qubit=qubit) - for n, pulse in native_gates.items() - } - return cls(**pulses) - - @property - def raw(self): - """Serialize native gate pulses. - - ``None`` gates are not included. - """ - data = {} - for fld in fields(self): - attr = getattr(self, fld.name) - if attr is not None: - data[fld.name] = attr.raw - del data[fld.name]["qubit"] - return data - - -@dataclass -class CouplerNatives: - """Container with the native single-qubit gates acting on a specific - qubit.""" - - CP: Optional[NativePulse] = None - """Pulse to activate the coupler.""" - - @classmethod - def from_dict(cls, coupler, native_gates): - """Parse coupler native gates from the runcard. - - Args: - coupler (:class:`qibolab.couplers.Coupler`): Coupler object that the - native pulses are acting on. - native_gates (dict): Dictionary with native gate pulse parameters as loaded - from the runcard [Reusing the dict from qubits]. - """ - pulses = { - n: CouplerPulse.from_dict(pulse, coupler=coupler) - for n, pulse in native_gates.items() - } - return cls(**pulses) - - @property - def raw(self): - """Serialize native gate pulses. - - ``None`` gates are not included. - """ - data = {} - for fld in fields(self): - attr = getattr(self, fld.name) - if attr is not None: - data[fld.name] = attr.raw - return data + return replace(self.RX, amplitude=self.RX.amplitude / 2.0) @dataclass @@ -344,32 +30,21 @@ class TwoQubitNatives: """Container with the native two-qubit gates acting on a specific pair of qubits.""" - CZ: Optional[NativeSequence] = field(default=None, metadata={"symmetric": True}) - CNOT: Optional[NativeSequence] = field(default=None, metadata={"symmetric": False}) - iSWAP: Optional[NativeSequence] = field(default=None, metadata={"symmetric": True}) + CZ: PulseSequence = field( + default_factory=lambda: PulseSequence(), metadata={"symmetric": True} + ) + CNOT: PulseSequence = field( + default_factory=lambda: PulseSequence(), metadata={"symmetric": False} + ) + iSWAP: PulseSequence = field( + default_factory=lambda: PulseSequence(), metadata={"symmetric": True} + ) @property def symmetric(self): """Check if the defined two-qubit gates are symmetric between target and control qubits.""" return all( - fld.metadata["symmetric"] or getattr(self, fld.name) is None + fld.metadata["symmetric"] or len(getattr(self, fld.name)) == 0 for fld in fields(self) ) - - @classmethod - def from_dict(cls, qubits, couplers, native_gates): - sequences = { - n: NativeSequence.from_dict(n, seq, qubits, couplers) - for n, seq in native_gates.items() - } - return cls(**sequences) - - @property - def raw(self): - data = {} - for fld in fields(self): - gate = getattr(self, fld.name) - if gate is not None: - data[fld.name] = gate.raw - return data diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index f69d00ce9..befc7df13 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -1,7 +1,7 @@ """A platform for executing quantum algorithms.""" from collections import defaultdict -from dataclasses import dataclass, field, fields, replace +from dataclasses import dataclass, field, fields from typing import Dict, List, Optional, Tuple import networkx as nx @@ -12,6 +12,7 @@ from qibolab.instruments.abstract import Controller, Instrument, InstrumentId from qibolab.pulses import Delay, Drag, PulseSequence, PulseType from qibolab.qubits import Qubit, QubitId, QubitPair, QubitPairId +from qibolab.serialize_ import replace from qibolab.sweeper import Sweeper from qibolab.unrolling import batch diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index c42567693..9b257c8c1 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -99,6 +99,8 @@ def _load_pulse(pulse_kwargs, qubit): return Delay(**pulse_kwargs) if pulse_type == "vz": return VirtualZ(**pulse_kwargs, qubit=q) + if "frequency" not in pulse_kwargs: + return Pulse.flux(**pulse_kwargs, type=pulse_type, qubit=q) return Pulse(**pulse_kwargs, type=pulse_type, qubit=q) diff --git a/src/qibolab/serialize_.py b/src/qibolab/serialize_.py index c09cdc68c..c7a4a3d12 100644 --- a/src/qibolab/serialize_.py +++ b/src/qibolab/serialize_.py @@ -59,3 +59,11 @@ class Model(BaseModel): """Global qibolab model, holding common configurations.""" model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True) + + +def replace(model: BaseModel, **update): + """Replace interface for pydantic models. + + To have the same familiar syntax of :func:`dataclasses.replace`. + """ + return model.model_copy(update=update) From 01988792496ab9f902f5ff0275bc61d598b29bb3 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 28 Mar 2024 18:37:57 +0100 Subject: [PATCH 176/233] test: Fix default compiler tests --- tests/dummy_qrc/qblox/parameters.json | 647 +++++++++++----------- tests/dummy_qrc/qm/parameters.json | 589 +++++++++----------- tests/dummy_qrc/qm_octave/parameters.json | 621 ++++++++++----------- tests/dummy_qrc/rfsoc/parameters.json | 131 +++-- tests/dummy_qrc/zurich/parameters.json | 591 +++++++++----------- tests/test_compilers_default.py | 9 +- tests/test_dummy.py | 4 +- 7 files changed, 1240 insertions(+), 1352 deletions(-) diff --git a/tests/dummy_qrc/qblox/parameters.json b/tests/dummy_qrc/qblox/parameters.json index 415e06980..d7c283692 100644 --- a/tests/dummy_qrc/qblox/parameters.json +++ b/tests/dummy_qrc/qblox/parameters.json @@ -1,342 +1,339 @@ { - "nqubits": 5, - "settings": { - "nshots": 1024, - "relaxation_time": 20000 + "nqubits": 5, + "settings": { + "nshots": 1024, + "relaxation_time": 20000 + }, + "qubits": [0, 1, 2, 3, 4], + "topology": [ + [0, 2], + [1, 2], + [2, 3], + [2, 4] + ], + "instruments": { + "qblox_controller": { + "bounds": { + "instructions": 1000000, + "readout": 250, + "waveforms": 40000 + } }, - "qubits": [ - 0, - 1, - 2, - 3, - 4 - ], - "topology": [ - [ - 0, - 2 - ], - [ - 1, - 2 - ], - [ - 2, - 3 - ], - [ - 2, - 4 - ] - ], - "instruments": { - "qblox_controller": { - "bounds": { - "instructions": 1000000, - "readout": 250, - "waveforms": 40000 - } + "twpa_pump": { + "frequency": 6535900000, + "power": 4 + }, + "qcm_rf0": { + "o1": { + "attenuation": 20, + "lo_frequency": 5252833073, + "gain": 0.47 + }, + "o2": { + "attenuation": 20, + "lo_frequency": 5652833073, + "gain": 0.57 + } + }, + "qcm_rf1": { + "o1": { + "attenuation": 20, + "lo_frequency": 5995371914, + "gain": 0.55 + }, + "o2": { + "attenuation": 20, + "lo_frequency": 6961018001, + "gain": 0.596 + } + }, + "qcm_rf2": { + "o1": { + "attenuation": 20, + "lo_frequency": 6786543060, + "gain": 0.47 + } + }, + "qrm_rf_a": { + "o1": { + "attenuation": 36, + "lo_frequency": 7300000000, + "gain": 0.6 + }, + "i1": { + "acquisition_hold_off": 500, + "acquisition_duration": 900 + } + }, + "qrm_rf_b": { + "o1": { + "attenuation": 36, + "lo_frequency": 7850000000, + "gain": 0.6 + }, + "i1": { + "acquisition_hold_off": 500, + "acquisition_duration": 900 + } + } + }, + "native_gates": { + "single_qubit": { + "0": { + "RX": { + "duration": 40, + "amplitude": 0.5028, + "frequency": 5050304836, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "twpa_pump": { - "frequency": 6535900000, - "power": 4 + "RX12": { + "duration": 40, + "amplitude": 0.5028, + "frequency": 5050304836, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "qcm_rf0": { - "o1": { - "attenuation": 20, - "lo_frequency": 5252833073, - "gain": 0.47 - }, - "o2": { - "attenuation": 20, - "lo_frequency": 5652833073, - "gain": 0.57 - } + "MZ": { + "duration": 2000, + "amplitude": 0.1, + "frequency": 7213299307, + "envelope": { "kind": "rectangular" }, + "type": "ro" + } + }, + "1": { + "RX": { + "duration": 40, + "amplitude": 0.5078, + "frequency": 4852833073, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "qcm_rf1": { - "o1": { - "attenuation": 20, - "lo_frequency": 5995371914, - "gain": 0.55 - }, - "o2": { - "attenuation": 20, - "lo_frequency": 6961018001, - "gain": 0.596 - } + "RX12": { + "duration": 40, + "amplitude": 0.5078, + "frequency": 4852833073, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "qcm_rf2": { - "o1": { - "attenuation": 20, - "lo_frequency": 6786543060, - "gain": 0.47 - } + "MZ": { + "duration": 2000, + "amplitude": 0.2, + "frequency": 7452990931, + "envelope": { "kind": "rectangular" }, + "type": "ro" + } + }, + "2": { + "RX": { + "duration": 40, + "amplitude": 0.5016, + "frequency": 5795371914, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "qrm_rf_a": { - "o1": { - "attenuation": 36, - "lo_frequency": 7300000000, - "gain": 0.6 - }, - "i1": { - "acquisition_hold_off": 500, - "acquisition_duration": 900 - } + "RX12": { + "duration": 40, + "amplitude": 0.5016, + "frequency": 5795371914, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "qrm_rf_b": { - "o1": { - "attenuation": 36, - "lo_frequency": 7850000000, - "gain": 0.6 - }, - "i1": { - "acquisition_hold_off": 500, - "acquisition_duration": 900 - } + "MZ": { + "duration": 2000, + "amplitude": 0.25, + "frequency": 7655083068, + "envelope": { "kind": "rectangular" }, + "type": "ro" } - }, - "native_gates": { - "single_qubit": { - "0": { - "RX": { - "duration": 40, - "amplitude": 0.5028, - "frequency": 5050304836, - "shape": "Gaussian(5)", - "type": "qd" - }, - "RX12": { - "duration": 40, - "amplitude": 0.5028, - "frequency": 5050304836, - "shape": "Gaussian(5)", - "type": "qd" - }, - "MZ": { - "duration": 2000, - "amplitude": 0.1, - "frequency": 7213299307, - "shape": "Rectangular()", - "type": "ro" - } - }, - "1": { - "RX": { - "duration": 40, - "amplitude": 0.5078, - "frequency": 4852833073, - "shape": "Gaussian(5)", - "type": "qd" - }, - "RX12": { - "duration": 40, - "amplitude": 0.5078, - "frequency": 4852833073, - "shape": "Gaussian(5)", - "type": "qd" - }, - "MZ": { - "duration": 2000, - "amplitude": 0.2, - "frequency": 7452990931, - "shape": "Rectangular()", - "type": "ro" - } - }, - "2": { - "RX": { - "duration": 40, - "amplitude": 0.5016, - "frequency": 5795371914, - "shape": "Gaussian(5)", - "type": "qd" - }, - "RX12": { - "duration": 40, - "amplitude": 0.5016, - "frequency": 5795371914, - "shape": "Gaussian(5)", - "type": "qd" - }, - "MZ": { - "duration": 2000, - "amplitude": 0.25, - "frequency": 7655083068, - "shape": "Rectangular()", - "type": "ro" - } - }, - "3": { - "RX": { - "duration": 40, - "amplitude": 0.5026, - "frequency": 6761018001, - "shape": "Gaussian(5)", - "type": "qd" - }, - "RX12": { - "duration": 40, - "amplitude": 0.5026, - "frequency": 6761018001, - "shape": "Gaussian(5)", - "type": "qd" - }, - "MZ": { - "duration": 2000, - "amplitude": 0.2, - "frequency": 7803441221, - "shape": "Rectangular()", - "type": "ro" - } - }, - "4": { - "RX": { - "duration": 40, - "amplitude": 0.5172, - "frequency": 6586543060, - "shape": "Gaussian(5)", - "type": "qd" - }, - "RX12": { - "duration": 40, - "amplitude": 0.5172, - "frequency": 6586543060, - "shape": "Gaussian(5)", - "type": "qd" - }, - "MZ": { - "duration": 2000, - "amplitude": 0.4, - "frequency": 8058947261, - "shape": "Rectangular()", - "type": "ro" - } - } + }, + "3": { + "RX": { + "duration": 40, + "amplitude": 0.5026, + "frequency": 6761018001, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "two_qubit": { - "2-3": { - "CZ": [ - { - "duration": 32, - "amplitude": -0.6025, - "shape": "Exponential(12, 5000, 0.1)", - "qubit": 3, - "type": "qf" - }, - { - "type": "virtual_z", - "phase": -3.63, - "qubit": 3 - }, - { - "type": "virtual_z", - "phase": -0.041, - "qubit": 2 - } - ] - }, - "0-2": { - "CZ": [ - { - "duration": 28, - "amplitude": -0.142, - "shape": "Exponential(12, 5000, 0.1)", - "qubit": 2, - "type": "qf" - } - ] - }, - "1-2": { - "CZ": [ - { - "duration": 32, - "amplitude": -0.6025, - "shape": "Exponential(12, 5000, 0.1)", - "qubit": 2, - "type": "qf" - }, - { - "type": "virtual_z", - "phase": -3.63, - "qubit": 1 - }, - { - "type": "virtual_z", - "phase": -0.041, - "qubit": 2 - } - ] - } + "RX12": { + "duration": 40, + "amplitude": 0.5026, + "frequency": 6761018001, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" + }, + "MZ": { + "duration": 2000, + "amplitude": 0.2, + "frequency": 7803441221, + "envelope": { "kind": "rectangular" }, + "type": "ro" } - }, - "characterization": { - "single_qubit": { - "0": { - "readout_frequency": 7213299307, - "drive_frequency": 5050304836, - "anharmonicity": 291463266, - "T1": 5857, - "T2": 0, - "sweetspot": 0.5507 - }, - "1": { - "readout_frequency": 7452990931, - "drive_frequency": 4852833073, - "anharmonicity": 292584018, - "T1": 1253, - "T2": 0, - "sweetspot": 0.2227, - "iq_angle": 146.297, - "threshold": 0.003488 - }, - "2": { - "readout_frequency": 7655083068, - "drive_frequency": 5795371914, - "anharmonicity": 276187576, - "T1": 4563, - "T2": 0, - "sweetspot": -0.378, - "iq_angle": 97.821, - "threshold": 0.002904 - }, - "3": { - "readout_frequency": 7803441221, - "drive_frequency": 6761018001, - "anharmonicity": 262310994, - "T1": 4232, - "T2": 0, - "sweetspot": -0.8899, - "iq_angle": 91.209, - "threshold": 0.004318 - }, - "4": { - "readout_frequency": 8058947261, - "drive_frequency": 6586543060, - "anharmonicity": 261390626, - "T1": 492, - "T2": 0, - "sweetspot": 0.589, - "iq_angle": 7.997, - "threshold": 0.002323 - } + }, + "4": { + "RX": { + "duration": 40, + "amplitude": 0.5172, + "frequency": 6586543060, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" + }, + "RX12": { + "duration": 40, + "amplitude": 0.5172, + "frequency": 6586543060, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "two_qubit":{ - "0-2": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] + "MZ": { + "duration": 2000, + "amplitude": 0.4, + "frequency": 8058947261, + "envelope": { "kind": "rectangular" }, + "type": "ro" + } + } + }, + "two_qubit": { + "2-3": { + "CZ": [ + { + "duration": 32, + "amplitude": -0.6025, + "envelope": { + "kind": "exponential", + "tau": 12, + "upsilon": 5000, + "g": 0.1 }, - "1-2": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] + "qubit": 3, + "type": "qf" + }, + { + "type": "vz", + "phase": -3.63, + "qubit": 3 + }, + { + "type": "vz", + "phase": -0.041, + "qubit": 2 + } + ] + }, + "0-2": { + "CZ": [ + { + "duration": 28, + "amplitude": -0.142, + "envelope": { + "kind": "exponential", + "tau": 12, + "upsilon": 5000, + "g": 0.1 }, - "2-3": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] + "qubit": 2, + "type": "qf" + } + ] + }, + "1-2": { + "CZ": [ + { + "duration": 32, + "amplitude": -0.6025, + "envelope": { + "kind": "exponential", + "tau": 12, + "upsilon": 5000, + "g": 0.1 }, - "2-4": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - } - } + "qubit": 2, + "type": "qf" + }, + { + "type": "vz", + "phase": -3.63, + "qubit": 1 + }, + { + "type": "vz", + "phase": -0.041, + "qubit": 2 + } + ] + } + } + }, + "characterization": { + "single_qubit": { + "0": { + "readout_frequency": 7213299307, + "drive_frequency": 5050304836, + "anharmonicity": 291463266, + "T1": 5857, + "T2": 0, + "sweetspot": 0.5507 + }, + "1": { + "readout_frequency": 7452990931, + "drive_frequency": 4852833073, + "anharmonicity": 292584018, + "T1": 1253, + "T2": 0, + "sweetspot": 0.2227, + "iq_angle": 146.297, + "threshold": 0.003488 + }, + "2": { + "readout_frequency": 7655083068, + "drive_frequency": 5795371914, + "anharmonicity": 276187576, + "T1": 4563, + "T2": 0, + "sweetspot": -0.378, + "iq_angle": 97.821, + "threshold": 0.002904 + }, + "3": { + "readout_frequency": 7803441221, + "drive_frequency": 6761018001, + "anharmonicity": 262310994, + "T1": 4232, + "T2": 0, + "sweetspot": -0.8899, + "iq_angle": 91.209, + "threshold": 0.004318 + }, + "4": { + "readout_frequency": 8058947261, + "drive_frequency": 6586543060, + "anharmonicity": 261390626, + "T1": 492, + "T2": 0, + "sweetspot": 0.589, + "iq_angle": 7.997, + "threshold": 0.002323 + } + }, + "two_qubit": { + "0-2": { + "gate_fidelity": [0.0, 0.0], + "cz_fidelity": [0.0, 0.0] + }, + "1-2": { + "gate_fidelity": [0.0, 0.0], + "cz_fidelity": [0.0, 0.0] + }, + "2-3": { + "gate_fidelity": [0.0, 0.0], + "cz_fidelity": [0.0, 0.0] + }, + "2-4": { + "gate_fidelity": [0.0, 0.0], + "cz_fidelity": [0.0, 0.0] + } } + } } diff --git a/tests/dummy_qrc/qm/parameters.json b/tests/dummy_qrc/qm/parameters.json index 0d8fcfc2d..d4a67753e 100644 --- a/tests/dummy_qrc/qm/parameters.json +++ b/tests/dummy_qrc/qm/parameters.json @@ -1,328 +1,293 @@ { - "nqubits": 5, - "qubits": [ - 0, - 1, - 2, - 3, - 4 - ], - "settings": { - "nshots": 1024, - "relaxation_time": 50000 + "nqubits": 5, + "qubits": [0, 1, 2, 3, 4], + "settings": { + "nshots": 1024, + "relaxation_time": 50000 + }, + "topology": [ + [0, 2], + [1, 2], + [2, 3], + [2, 4] + ], + "instruments": { + "qm": { + "bounds": { + "waveforms": 10000, + "readout": 30, + "instructions": 1000000 + } }, - "topology": [ - [ - 0, - 2 - ], - [ - 1, - 2 - ], - [ - 2, - 3 - ], - [ - 2, - 4 - ] - ], - "instruments": { - "qm": { - "bounds": { - "waveforms" : 10000, - "readout": 30, - "instructions": 1000000 - } + "con1": { + "i1": { "gain": 0 }, + "i2": { "gain": 0 } + }, + "con2": { + "o2": { + "filter": { + "feedforward": [1.0684635881381783, -1.0163217174522334], + "feedback": [0.947858129314055] + } + }, + "i1": { "gain": 0 }, + "i2": { "gain": 0 } + }, + "lo_readout_a": { + "frequency": 7300000000, + "power": 18 + }, + "lo_readout_b": { + "frequency": 7900000000, + "power": 15 + }, + "lo_drive_low": { + "frequency": 4700000000, + "power": 16 + }, + "lo_drive_mid": { + "frequency": 5600000000, + "power": 16 + }, + "lo_drive_high": { + "frequency": 6500000000, + "power": 16 + }, + "twpa_a": { + "frequency": 6511000000, + "power": 4.5 + } + }, + "native_gates": { + "single_qubit": { + "0": { + "RX": { + "duration": 40, + "amplitude": 0.005, + "frequency": 4700000000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "con1": { - "i1": {"gain": 0}, - "i2": {"gain": 0} + "RX12": { + "duration": 40, + "amplitude": 0.005, + "frequency": 4700000000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "con2": { - "o2": { - "filter": { - "feedforward": [1.0684635881381783, -1.0163217174522334], - "feedback": [0.947858129314055] - } - }, - "i1": {"gain": 0}, - "i2": {"gain": 0} + "MZ": { + "duration": 1000, + "amplitude": 0.0025, + "frequency": 7226500000, + "envelope": { "kind": "rectangular" }, + "type": "ro" + } + }, + "1": { + "RX": { + "duration": 40, + "amplitude": 0.0484, + "frequency": 4855663000, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0.02 }, + "type": "qd" }, - "lo_readout_a": { - "frequency": 7300000000, - "power": 18 + "RX12": { + "duration": 40, + "amplitude": 0.0484, + "frequency": 4855663000, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0.02 }, + "type": "qd" }, - "lo_readout_b": { - "frequency": 7900000000, - "power": 15 + "MZ": { + "duration": 620, + "amplitude": 0.003575, + "frequency": 7453265000, + "envelope": { "kind": "rectangular" }, + "type": "ro" + } + }, + "2": { + "RX": { + "duration": 40, + "amplitude": 0.05682, + "frequency": 5800563000, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0.04 }, + "type": "qd" }, - "lo_drive_low": { - "frequency": 4700000000, - "power": 16 + "RX12": { + "duration": 40, + "amplitude": 0.05682, + "frequency": 5800563000, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0.04 }, + "type": "qd" }, - "lo_drive_mid": { - "frequency": 5600000000, - "power": 16 + "MZ": { + "duration": 960, + "amplitude": 0.00325, + "frequency": 7655107000, + "envelope": { "kind": "rectangular" }, + "type": "ro" + } + }, + "3": { + "RX": { + "duration": 40, + "amplitude": 0.138, + "frequency": 6760922000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "lo_drive_high": { - "frequency": 6500000000, - "power": 16 + "RX12": { + "duration": 40, + "amplitude": 0.138, + "frequency": 6760922000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "twpa_a": { - "frequency": 6511000000, - "power": 4.5 + "MZ": { + "duration": 960, + "amplitude": 0.004225, + "frequency": 7802191000, + "envelope": { "kind": "rectangular" }, + "type": "ro" } - }, - "native_gates": { - "single_qubit": { - "0": { - "RX": { - "duration": 40, - "amplitude": 0.005, - "frequency": 4700000000, - "shape": "Gaussian(5)", - "type": "qd"}, - "RX12": { - "duration": 40, - "amplitude": 0.005, - "frequency": 4700000000, - "shape": "Gaussian(5)", - "type": "qd" - }, - "MZ": { - "duration": 1000, - "amplitude": 0.0025, - "frequency": 7226500000, - "shape": "Rectangular()", - "type": "ro" - } - }, - "1": { - "RX": { - "duration": 40, - "amplitude": 0.0484, - "frequency": 4855663000, - "shape": "Drag(5, 0.02)", - "type": "qd" - }, - "RX12": { - "duration": 40, - "amplitude": 0.0484, - "frequency": 4855663000, - "shape": "Drag(5, 0.02)", - "type": "qd" - }, - "MZ": { - "duration": 620, - "amplitude": 0.003575, - "frequency": 7453265000, - "shape": "Rectangular()", - "type": "ro" - } - }, - "2": { - "RX": { - "duration": 40, - "amplitude": 0.05682, - "frequency": 5800563000, - "shape": "Drag(5, 0.04)", - "type": "qd" - }, - "RX12": { - "duration": 40, - "amplitude": 0.05682, - "frequency": 5800563000, - "shape": "Drag(5, 0.04)", - "type": "qd" - }, - "MZ": { - "duration": 960, - "amplitude": 0.00325, - "frequency": 7655107000, - "shape": "Rectangular()", - "type": "ro" - } - }, - "3": { - "RX": { - "duration": 40, - "amplitude": 0.138, - "frequency": 6760922000, - "shape": "Gaussian(5)", - "type": "qd" - }, - "RX12": { - "duration": 40, - "amplitude": 0.138, - "frequency": 6760922000, - "shape": "Gaussian(5)", - "type": "qd" - }, - "MZ": { - "duration": 960, - "amplitude": 0.004225, - "frequency": 7802191000, - "shape": "Rectangular()", - "type": "ro" - } - }, - "4": { - "RX": { - "duration": 40, - "amplitude": 0.0617, - "frequency": 6585053000, - "shape": "Drag(5, 0)", - "type": "qd" - }, - "RX12": { - "duration": 40, - "amplitude": 0.0617, - "frequency": 6585053000, - "shape": "Drag(5, 0)", - "type": "qd" - }, - "MZ": { - "duration": 640, - "amplitude": 0.0039, - "frequency": 8057668000, - "shape": "Rectangular()", - "type": "ro" - } - } + }, + "4": { + "RX": { + "duration": 40, + "amplitude": 0.0617, + "frequency": 6585053000, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0 }, + "type": "qd" }, - 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"mixer_drive_g": 0.0, - "mixer_drive_phi": 0.0, - "mixer_readout_g": 0.0, - "mixer_readout_phi": 0.0 - } + "RX12": { + "duration": 40, + "amplitude": 0.0617, + "frequency": 6585053000, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0 }, + "type": "qd" }, - "two_qubit":{ - "0-2": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - }, - "1-2": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - }, - "2-3": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - }, - "2-4": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - } + "MZ": { + "duration": 640, + "amplitude": 0.0039, + "frequency": 8057668000, + "envelope": { "kind": "rectangular" }, + "type": "ro" } + } + }, + "two_qubit": { + "1-2": { + "CZ": [ + { + "duration": 30, + "amplitude": 0.055, + "envelope": { "kind": "rectangular" }, + "qubit": 2, + "type": "qf" + }, + { + "type": "vz", + "phase": -1.5707963267948966, + "qubit": 1 + }, + { + "type": "vz", + "phase": -1.5707963267948966, + "qubit": 2 + } + ] + }, + "2-3": { + "CZ": [ + { + "duration": 32, + "amplitude": -0.0513, + "envelope": { "kind": "rectangular" }, + "qubit": 3, + "type": "qf" + }, + { + "type": "vz", + "phase": -1.5707963267948966, + "qubit": 2 + }, + { + "type": "vz", + "phase": -1.5707963267948966, + "qubit": 3 + } + ] + } + } + }, + "characterization": { + "single_qubit": { + "0": { + "readout_frequency": 0.0, + "drive_frequency": 0.0, + "T1": 0.0, + "T2": 0.0, + "sweetspot": 0.0, + "threshold": 0.0, + "iq_angle": 0.0, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + }, + "1": { + "readout_frequency": 7453265000, + "drive_frequency": 4855663000, + "T1": 0.0, + "T2": 0.0, + "sweetspot": -0.047, + "threshold": 0.00028502261712637096, + "iq_angle": 1.283105298787488, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + }, + "2": { + "readout_frequency": 7655107000, + "drive_frequency": 5799876000, + "T1": 0.0, + "T2": 0.0, + "sweetspot": -0.045, + "threshold": 0.0002694329123116206, + "iq_angle": 4.912447775569025, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + }, + "3": { + "readout_frequency": 7802391000, + "drive_frequency": 6760700000, + "T1": 0.0, + "T2": 0.0, + "sweetspot": 0.034, + "threshold": 0.0003363427381347193, + "iq_angle": 1.6124890998581591, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + }, + "4": { + "readout_frequency": 8057668000, + "drive_frequency": 6585053000, + "T1": 0.0, + "T2": 0.0, + "sweetspot": -0.057, + "threshold": 0.00013079660165463033, + "iq_angle": 5.6303684840135, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + } } + } } diff --git a/tests/dummy_qrc/qm_octave/parameters.json b/tests/dummy_qrc/qm_octave/parameters.json index 523ddb92d..a77220498 100644 --- a/tests/dummy_qrc/qm_octave/parameters.json +++ b/tests/dummy_qrc/qm_octave/parameters.json @@ -1,336 +1,315 @@ { - 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"con1": { - "i1": { - "gain": 0 - }, - "i2": { - "gain": 0 - } + "RX12": { + "duration": 40, + "amplitude": 0.005, + "frequency": 4700000000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "con2": { - "i1": { - "gain": 0 - }, - "i2": { - "gain": 0 - } + "MZ": { + "duration": 1000, + "amplitude": 0.0025, + "frequency": 7226500000, + "envelope": { "kind": "rectangular" }, + "type": "ro" + } + }, + "1": { + "RX": { + "duration": 40, + "amplitude": 0.0484, + "frequency": 4855663000, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0.02 }, + "type": "qd" }, - "octave1": { - "o1": { - "lo_frequency": 4700000000, - "gain": 0 - }, - "o2": { - "lo_frequency": 5600000000, - "gain": 0 - }, - "o3": { - "lo_frequency": 6500000000, - "gain": 0 - }, - "o4": { - "lo_frequency": 6500000000, - "gain": 0 - }, - "o5": { - "lo_frequency": 7300000000, - "gain": 0 - }, - "i1": { - "lo_frequency": 7300000000 - } + "RX12": { + "duration": 40, + "amplitude": 0.0484, + "frequency": 4855663000, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0.02 }, + "type": "qd" }, - "octave2": { - "o5": { - "lo_frequency": 7900000000, - "gain": 0 - }, - "i1": { - "lo_frequency": 7900000000 - } + "MZ": { + "duration": 620, + "amplitude": 0.003575, + "frequency": 7453265000, + "envelope": { "kind": "rectangular" }, + "type": "ro" + } + }, + "2": { + "RX": { + "duration": 40, + "amplitude": 0.05682, + "frequency": 5800563000, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0.04 }, + "type": "qd" }, - "octave3": { - "o1": { - "lo_frequency": 4700000000, - "gain": 0 - } + "RX12": { + "duration": 40, + "amplitude": 0.05682, + "frequency": 5800563000, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0.04 }, + "type": "qd" }, - "twpa_a": { - "frequency": 6511000000, - "power": 4.5 + "MZ": { + "duration": 960, + "amplitude": 0.00325, + "frequency": 7655107000, + "envelope": { "kind": "rectangular" }, + "type": "ro" } - }, - "native_gates": { - "single_qubit": { - "0": { - "RX": { - "duration": 40, - "amplitude": 0.005, - "frequency": 4700000000, - "shape": "Gaussian(5)", - 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"type": "ro"} - }, - "3": { - "RX": { - "duration": 40, - "amplitude": 0.138, - "frequency": 6760922000, - "shape": "Gaussian(5)", - "type": "qd"}, - "RX12": { - "duration": 40, - "amplitude": 0.138, - "frequency": 6760922000, - "shape": "Gaussian(5)", - "type": "qd"}, - "MZ": { - "duration": 960, - "amplitude": 0.004225, - "frequency": 7802191000, - "shape": "Rectangular()", - "type": "ro"} - }, - "4": { - "RX": { - "duration": 40, - "amplitude": 0.0617, - "frequency": 6585053000, - "shape": "Drag(5, 0)", - "type": "qd"}, - "RX12": { - "duration": 40, - "amplitude": 0.0617, - "frequency": 6585053000, - "shape": "Drag(5, 0)", - "type": "qd"}, - "MZ": { - "duration": 640, - "amplitude": 0.0039, - "frequency": 8057668000, - "shape": "Rectangular()", - "type": "ro"} - } + }, + "3": { + "RX": { + "duration": 40, + "amplitude": 0.138, + "frequency": 6760922000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" + }, + "RX12": { + "duration": 40, + "amplitude": 0.138, + "frequency": 6760922000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "two_qubit": { - "1-2": { - "CZ": [ - { - "duration": 30, - "amplitude": 0.055, - "shape": "Rectangular()", - "qubit": 2, - "type": "qf" - }, - { - "type": "virtual_z", - "phase": -1.5707963267948966, - "qubit": 1 - }, - { - "type": "virtual_z", - "phase": -1.5707963267948966, - "qubit": 2 - } - ] - }, - "2-3": { - "CZ": [ - { - "duration": 32, - "amplitude": -0.0513, - "shape": "Rectangular()", - "qubit": 3, - "type": "qf" - }, - { - "type": "virtual_z", - "phase": -1.5707963267948966, - "qubit": 2 - }, - { - "type": "virtual_z", - "phase": -1.5707963267948966, - "qubit": 3 - } - ] - } + "MZ": { + "duration": 960, + "amplitude": 0.004225, + "frequency": 7802191000, + "envelope": { "kind": "rectangular" }, + "type": "ro" } - }, - "characterization": { - "single_qubit": { - "0": { - "readout_frequency": 0.0, - "drive_frequency": 0.0, - "T1": 0.0, - "T2": 0.0, - "sweetspot": 0.0, - "threshold": 0.0, - "iq_angle": 0.0, - "mixer_drive_g": 0.0, - "mixer_drive_phi": 0.0, - "mixer_readout_g": 0.0, - "mixer_readout_phi": 0.0 - }, - "1": { - "readout_frequency": 7453265000, - "drive_frequency": 4855663000, - "T1": 0.0, - "T2": 0.0, - "sweetspot": -0.047, - "threshold": 0.00028502261712637096, - "iq_angle": 1.283105298787488, - "mixer_drive_g": 0.0, - "mixer_drive_phi": 0.0, - "mixer_readout_g": 0.0, - "mixer_readout_phi": 0.0 - }, - "2": { - "readout_frequency": 7655107000, - "drive_frequency": 5799876000, - "T1": 0.0, - "T2": 0.0, - "sweetspot": -0.045, - "threshold": 0.0002694329123116206, - "iq_angle": 4.912447775569025, - "mixer_drive_g": 0.0, - "mixer_drive_phi": 0.0, - "mixer_readout_g": 0.0, - "mixer_readout_phi": 0.0 - }, - "3": { - "readout_frequency": 7802391000, - "drive_frequency": 6760700000, - "T1": 0.0, - "T2": 0.0, - "sweetspot": 0.034, - "threshold": 0.0003363427381347193, - "iq_angle": 1.6124890998581591, - "mixer_drive_g": 0.0, - "mixer_drive_phi": 0.0, - "mixer_readout_g": 0.0, - "mixer_readout_phi": 0.0 - }, - "4": { - "readout_frequency": 8057668000, - "drive_frequency": 6585053000, - "T1": 0.0, - "T2": 0.0, - "sweetspot": -0.057, - "threshold": 0.00013079660165463033, - "iq_angle": 5.6303684840135, - "mixer_drive_g": 0.0, - "mixer_drive_phi": 0.0, - "mixer_readout_g": 0.0, - "mixer_readout_phi": 0.0 - } + }, + "4": { + "RX": { + "duration": 40, + "amplitude": 0.0617, + "frequency": 6585053000, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0 }, + "type": "qd" + }, + "RX12": { + "duration": 40, + "amplitude": 0.0617, + "frequency": 6585053000, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0 }, + "type": "qd" }, - "two_qubit":{ - "0-2": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - }, - "1-2": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - }, - "2-3": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - }, - "2-4": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - } + "MZ": { + "duration": 640, + "amplitude": 0.0039, + "frequency": 8057668000, + "envelope": { "kind": "rectangular" }, + "type": "ro" } + } + }, + "two_qubit": { + "1-2": { + "CZ": [ + { + "duration": 30, + "amplitude": 0.055, + "envelope": { "kind": "rectangular" }, + "qubit": 2, + "type": "qf" + }, + { + "type": "vz", + "phase": -1.5707963267948966, + "qubit": 1 + }, + { + "type": "vz", + "phase": -1.5707963267948966, + "qubit": 2 + } + ] + }, + "2-3": { + "CZ": [ + { + "duration": 32, + "amplitude": -0.0513, + "envelope": { "kind": "rectangular" }, + "qubit": 3, + "type": "qf" + }, + { + "type": "vz", + "phase": -1.5707963267948966, + "qubit": 2 + }, + { + "type": "vz", + "phase": -1.5707963267948966, + "qubit": 3 + } + ] + } + } + }, + "characterization": { + "single_qubit": { + "0": { + "readout_frequency": 0.0, + "drive_frequency": 0.0, + "T1": 0.0, + "T2": 0.0, + "sweetspot": 0.0, + "threshold": 0.0, + "iq_angle": 0.0, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + }, + "1": { + "readout_frequency": 7453265000, + "drive_frequency": 4855663000, + "T1": 0.0, + "T2": 0.0, + "sweetspot": -0.047, + "threshold": 0.00028502261712637096, + "iq_angle": 1.283105298787488, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + }, + "2": { + "readout_frequency": 7655107000, + "drive_frequency": 5799876000, + "T1": 0.0, + "T2": 0.0, + "sweetspot": -0.045, + "threshold": 0.0002694329123116206, + "iq_angle": 4.912447775569025, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + }, + "3": { + "readout_frequency": 7802391000, + "drive_frequency": 6760700000, + "T1": 0.0, + "T2": 0.0, + "sweetspot": 0.034, + "threshold": 0.0003363427381347193, + "iq_angle": 1.6124890998581591, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + }, + "4": { + "readout_frequency": 8057668000, + "drive_frequency": 6585053000, + "T1": 0.0, + "T2": 0.0, + "sweetspot": -0.057, + "threshold": 0.00013079660165463033, + "iq_angle": 5.6303684840135, + "mixer_drive_g": 0.0, + "mixer_drive_phi": 0.0, + "mixer_readout_g": 0.0, + "mixer_readout_phi": 0.0 + } } + } } diff --git a/tests/dummy_qrc/rfsoc/parameters.json b/tests/dummy_qrc/rfsoc/parameters.json index 024e1ee0c..eb1b24813 100644 --- a/tests/dummy_qrc/rfsoc/parameters.json +++ b/tests/dummy_qrc/rfsoc/parameters.json @@ -1,77 +1,70 @@ { - "nqubits": 1, - "qubits": [ - 0 - ], - "topology": [], - "settings": { - "nshots": 1024, - "relaxation_time": 100000 + "nqubits": 1, + "qubits": [0], + "topology": [], + "settings": { + "nshots": 1024, + "relaxation_time": 100000 + }, + "instruments": { + "tii_rfsoc4x2": { + "bounds": { + "waveforms": 0, + "readout": 0, + "instructions": 0 + } }, - "instruments": { - "tii_rfsoc4x2": { - "bounds": { - "waveforms": 0, - "readout": 0, - "instructions": 0 - } + "twpa_a": { + "frequency": 6200000000, + "power": -1 + }, + "ErasynthLO": { + "frequency": 0, + "power": 0 + } + }, + "native_gates": { + "single_qubit": { + "0": { + "RX": { + "duration": 30, + "amplitude": 0.05284168507293318, + "frequency": 5542341844, + "envelope": { "kind": "rectangular" }, + "type": "qd" }, - "twpa_a": { - "frequency": 6200000000, - "power": -1 + "RX12": { + "duration": 30, + "amplitude": 0.05284168507293318, + "frequency": 5542341844, + "envelope": { "kind": "rectangular" }, + "type": "qd" }, - "ErasynthLO": { - "frequency": 0, - "power": 0 + "MZ": { + "duration": 600, + "amplitude": 0.03, + "frequency": 7371258599, + "envelope": { "kind": "rectangular" }, + "type": "ro" } + } }, - "native_gates": { - "single_qubit": { - "0": { - "RX": { - "duration": 30, - "amplitude": 0.05284168507293318, - "frequency": 5542341844, - "shape": "Rectangular()", - "type": "qd" - }, - "RX12": { - "duration": 30, - "amplitude": 0.05284168507293318, - "frequency": 5542341844, - "shape": "Rectangular()", - "type": "qd" - }, - "MZ": { - "duration": 600, - "amplitude": 0.03, - "frequency": 7371258599, - "shape": "Rectangular()", - "type": "ro" - } - } - } - }, - "characterization": { - "single_qubit": { - "0": { - "readout_frequency": 7371258599, - "drive_frequency": 5542341844, - "pi_pulse_amplitude": 0.05284168507293318, - "T1": 10441.64173639732, - "T2": 4083.4697338939845, - "threshold": -0.8981346462690887, - "iq_angle": -1.2621946150226666, - "mean_gnd_states": [ - -0.17994037940379404, - -2.4709365853658536 - ], - "mean_exc_states": [ - 0.6854460704607047, - 0.24369105691056914 - ], - "T2_spin_echo": 5425.5448969467925 - } - } + "two_qubit": {} + }, + "characterization": { + "single_qubit": { + "0": { + "readout_frequency": 7371258599, + "drive_frequency": 5542341844, + "pi_pulse_amplitude": 0.05284168507293318, + "T1": 10441.64173639732, + "T2": 4083.4697338939845, + "threshold": -0.8981346462690887, + "iq_angle": -1.2621946150226666, + "mean_gnd_states": [-0.17994037940379404, -2.4709365853658536], + "mean_exc_states": [0.6854460704607047, 0.24369105691056914], + "T2_spin_echo": 5425.5448969467925 + } } + } } diff --git a/tests/dummy_qrc/zurich/parameters.json b/tests/dummy_qrc/zurich/parameters.json index 3aee23e2a..3ab0c3c07 100644 --- a/tests/dummy_qrc/zurich/parameters.json +++ b/tests/dummy_qrc/zurich/parameters.json @@ -1,337 +1,286 @@ { - "nqubits": 5, - "qubits": [ - 0, - 1, - 2, - 3, - 4 - ], - "couplers": [ - 0, - 1, - 3, - 4 - ], - "topology": { - "0": [ - 0, - 2 - ], - "1": [ - 1, - 2 - ], - "3": [ - 2, - 3 - ], - "4": [ - 2, - 4 - ] + "nqubits": 5, + "qubits": [0, 1, 2, 3, 4], + "couplers": [0, 1, 3, 4], + "topology": { + "0": [0, 2], + "1": [1, 2], + "3": [2, 3], + "4": [2, 4] + }, + "settings": { + "nshots": 4096, + "relaxation_time": 300000 + }, + "instruments": { + "EL_ZURO": { + "bounds": { + "instructions": 1000000, + "readout": 250, + "waveforms": 40000 + } + }, + "lo_readout": { + "frequency": 5500000000 + }, + "lo_drive_0": { + "frequency": 4200000000 }, - "settings": { - "nshots": 4096, - "relaxation_time": 300000 + "lo_drive_1": { + "frequency": 4600000000 }, - "instruments": { - "EL_ZURO": { - "bounds": { - "instructions": 1000000, - "readout": 250, - "waveforms": 40000 - } + "lo_drive_2": { + "frequency": 4800000000 + } + }, + "native_gates": { + "single_qubit": { + "0": { + "RX": { + "duration": 40, + "amplitude": 0.625, + "frequency": 4095830788, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0.04 }, + "type": "qd" }, - "lo_readout": { - "frequency": 5500000000 + "RX12": { + "duration": 40, + "amplitude": 0.625, + "frequency": 4095830788, + "envelope": { "kind": "drag", "rel_sigma": 0.2, "beta": 0.04 }, + "type": "qd" }, - "lo_drive_0": { - "frequency": 4200000000 + "MZ": { + "duration": 2000, + "amplitude": 0.5, + "frequency": 5229200000, + "envelope": { "kind": "rectangular" }, + "type": "ro" + } + }, + "1": { + "RX": { + "duration": 90, + "amplitude": 0.2, + "frequency": 4170000000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "lo_drive_1": { - "frequency": 4600000000 + "RX12": { + "duration": 90, + "amplitude": 0.2, + "frequency": 4170000000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "lo_drive_2": { - "frequency": 4800000000 + "MZ": { + "duration": 1000, + "amplitude": 0.1, + "frequency": 4931000000, + "envelope": { "kind": "rectangular" }, + "type": "ro" } - }, - "native_gates": { - "single_qubit": { - "0": { - "RX": { - "duration": 40, - "amplitude": 0.625, - "frequency": 4095830788, - "shape": "Drag(5, 0.04)", - "type": "qd"}, - "RX12": { - "duration": 40, - "amplitude": 0.625, - "frequency": 4095830788, - "shape": "Drag(5, 0.04)", - "type": "qd"}, - "MZ": { - "duration": 2000, - "amplitude": 0.5, - "frequency": 5229200000, - "shape": "Rectangular()", - "type": "ro"} - }, - "1": { - "RX": { - "duration": 90, - "amplitude": 0.2, - "frequency": 4170000000, - "shape": "Gaussian(5)", - "type": "qd"}, - "RX12": { - "duration": 90, - "amplitude": 0.2, - "frequency": 4170000000, - "shape": "Gaussian(5)", - "type": "qd"}, - "MZ": { - "duration": 1000, - "amplitude": 0.1, - "frequency": 4931000000, - "shape": "Rectangular()", - "type": "ro"} - }, - "2": { - "RX": { - "duration": 40, - "amplitude": 0.59, - "frequency": 4300587281, - "shape": "Gaussian(5)", - "type": "qd"}, - "RX12": { - "duration": 40, - "amplitude": 0.59, - "frequency": 4300587281, - "shape": "Gaussian(5)", - "type": "qd"}, - "MZ": { - "duration": 2000, - "amplitude": 0.54, - "frequency": 6109000000.0, - "shape": "Rectangular()", - "type": "ro"} - }, - "3": { - "RX": { - "duration": 90, - "amplitude": 0.75, - "frequency": 4100000000, - "shape": "Gaussian(5)", - "type": "qd"}, - "RX12": { - "duration": 90, - "amplitude": 0.75, - "frequency": 4100000000, - "shape": "Gaussian(5)", - "type": "qd"}, - "MZ": { - "duration": 2000, - "amplitude": 0.01, - "frequency": 5783000000, - "shape": "Rectangular()", - "type": "ro"} - }, - "4": { - "RX": { - "duration": 53, - "amplitude": 1, - "frequency": 4196800000, - "shape": "Gaussian(5)", - "type": "qd"}, - "RX12": { - "duration": 53, - "amplitude": 1, - "frequency": 4196800000, - "shape": "Gaussian(5)", - "type": "qd"}, - "MZ": { - "duration": 1000, - "amplitude": 0.5, - "frequency": 5515000000, - "shape": "Rectangular()", - "type": "ro"} - } + }, + "2": { + "RX": { + "duration": 40, + "amplitude": 0.59, + "frequency": 4300587281, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "coupler": { - "0": { - "CP": { - "type": "cf", - "duration": 1000, - "amplitude": 0.5, - "shape": "Rectangular()" - } - }, - "1": { - "CP": { - "type": "cf", - "duration": 1000, - "amplitude": 0.5, - "shape": "Rectangular()" - } - }, - "3": { - "CP": { - "type": "cf", - "duration": 1000, - "amplitude": 0.5, - "shape": "Rectangular()" - } - }, - "4": { - "CP": { - "type": "cf", - "duration": 1000, - "amplitude": 0.5, - "shape": "Rectangular()" - } - } + "RX12": { + "duration": 40, + "amplitude": 0.59, + "frequency": 4300587281, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "two_qubit": { - "1-2": { - "CZ": [ - { - "duration": 32, - "amplitude": -0.6025, - "shape": "Exponential(12, 5000, 0.1)", - "qubit": 3, - "type": "qf" - }, - { - "type": "virtual_z", - "phase": -3.63, - "qubit": 1 - }, - { - "type": "virtual_z", - "phase": -0.041, - "qubit": 2 - } - ] - } + "MZ": { + "duration": 2000, + "amplitude": 0.54, + "frequency": 6109000000.0, + "envelope": { "kind": "rectangular" }, + "type": "ro" } - }, - "characterization": { - "single_qubit": { - "0": { - "readout_frequency": 5229200000, - "drive_frequency": 4095830788, - "T1": 0.0, - "T2": 0.0, - "sweetspot": 0.05, - "mean_gnd_states": [ - 1.542, - 0.1813 - ], - "mean_exc_states": [ - 2.4499, - -0.5629 - ], - "threshold": 0.8836, - "iq_angle": -1.551 - }, - "1": { - "readout_frequency": 4931000000, - "drive_frequency": 4170000000, - "T1": 0.0, - "T2": 0.0, - "sweetspot": 0.0, - "mean_gnd_states": [ - 0, - 0 - ], - "mean_exc_states": [ - 0, - 0 - ] - }, - "2": { - "readout_frequency": 6109000000.0, - "drive_frequency": 4300587281, - "T1": 0.0, - "T2": 0.0, - "sweetspot": 0.0, - "mean_gnd_states": [ - -1.8243, - 1.5926 - ], - "mean_exc_states": [ - -0.8083, - 2.3929 - ], - "threshold": -0.0593, - "iq_angle": -0.667 - }, - "3": { - "readout_frequency": 5783000000, - "drive_frequency": 4100000000, - "T1": 0.0, - "T2": 0.0, - "sweetspot": 0.0, - "mean_gnd_states": [ - 0, - 0 - ], - "mean_exc_states": [ - 0, - 0 - ] - }, - "4": { - "readout_frequency": 5515000000, - "drive_frequency": 4196800000, - "T1": 0.0, - "T2": 0.0, - "sweetspot": 0.0, - "mean_gnd_states": [ - 0, - 0 - ], - "mean_exc_states": [ - 0, - 0 - ], - "threshold": 0.233806, - "iq_angle": 0.481 - } + }, + "3": { + "RX": { + "duration": 90, + "amplitude": 0.75, + "frequency": 4100000000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "two_qubit":{ - "0-2": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - }, - "1-2": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - }, - "2-3": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - }, - "2-4": { - "gate_fidelity": [0.0, 0.0], - "cz_fidelity": [0.0, 0.0] - } + "RX12": { + "duration": 90, + "amplitude": 0.75, + "frequency": 4100000000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" }, - "coupler": { - "0": { - "sweetspot": 0.0 - }, - "1": { - "sweetspot": 0.0 - }, - "3": { - "sweetspot": 0.0 - }, - "4": { - "sweetspot": 0.0 - } + "MZ": { + "duration": 2000, + "amplitude": 0.01, + "frequency": 5783000000, + "envelope": { "kind": "rectangular" }, + "type": "ro" + } + }, + "4": { + "RX": { + "duration": 53, + "amplitude": 1, + "frequency": 4196800000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" + }, + "RX12": { + "duration": 53, + "amplitude": 1, + "frequency": 4196800000, + "envelope": { "kind": "gaussian", "rel_sigma": 0.2 }, + "type": "qd" + }, + "MZ": { + "duration": 1000, + "amplitude": 0.5, + "frequency": 5515000000, + "envelope": { "kind": "rectangular" }, + "type": "ro" + } + } + }, + "coupler": { + "0": { + "CP": { + "type": "cf", + "duration": 1000, + "amplitude": 0.5, + "envelope": { "kind": "rectangular" } + } + }, + "1": { + "CP": { + "type": "cf", + "duration": 1000, + "amplitude": 0.5, + "envelope": { "kind": "rectangular" } + } + }, + "3": { + "CP": { + "type": "cf", + "duration": 1000, + "amplitude": 0.5, + "envelope": { "kind": "rectangular" } } + }, + "4": { + "CP": { + "type": "cf", + "duration": 1000, + "amplitude": 0.5, + "envelope": { "kind": "rectangular" } + } + } + }, + "two_qubit": { + "1-2": { + "CZ": [ + { + "duration": 32, + "amplitude": -0.6025, + "envelope": { + "kind": "exponential", + "tau": 12, + "upsilon": 5000, + "g": 0.1 + }, + "qubit": 3, + "type": "qf" + }, + { + "type": "vz", + "phase": -3.63, + "qubit": 1 + }, + { + "type": "vz", + "phase": -0.041, + "qubit": 2 + } + ] + } + } + }, + "characterization": { + "single_qubit": { + "0": { + "readout_frequency": 5229200000, + "drive_frequency": 4095830788, + "T1": 0.0, + "T2": 0.0, + "sweetspot": 0.05, + "mean_gnd_states": [1.542, 0.1813], + "mean_exc_states": [2.4499, -0.5629], + "threshold": 0.8836, + "iq_angle": -1.551 + }, + "1": { + "readout_frequency": 4931000000, + "drive_frequency": 4170000000, + "T1": 0.0, + "T2": 0.0, + "sweetspot": 0.0, + "mean_gnd_states": [0, 0], + "mean_exc_states": [0, 0] + }, + "2": { + "readout_frequency": 6109000000.0, + "drive_frequency": 4300587281, + "T1": 0.0, + "T2": 0.0, + "sweetspot": 0.0, + "mean_gnd_states": [-1.8243, 1.5926], + "mean_exc_states": [-0.8083, 2.3929], + "threshold": -0.0593, + "iq_angle": -0.667 + }, + "3": { + "readout_frequency": 5783000000, + "drive_frequency": 4100000000, + "T1": 0.0, + "T2": 0.0, + "sweetspot": 0.0, + "mean_gnd_states": [0, 0], + "mean_exc_states": [0, 0] + }, + "4": { + "readout_frequency": 5515000000, + "drive_frequency": 4196800000, + "T1": 0.0, + "T2": 0.0, + "sweetspot": 0.0, + "mean_gnd_states": [0, 0], + "mean_exc_states": [0, 0], + "threshold": 0.233806, + "iq_angle": 0.481 + } + }, + "coupler": { + "0": { + "sweetspot": 0.0 + }, + "1": { + "sweetspot": 0.0 + }, + "3": { + "sweetspot": 0.0 + }, + "4": { + "sweetspot": 0.0 + } } + } } diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index e226137cf..945f098c8 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -190,7 +190,12 @@ def test_add_measurement_to_sequence(platform): mz_pulse = platform.create_MZ_pulse(0) delay = 2 * rx90_pulse1.duration s = PulseSequence( - [rx90_pulse1, rx90_pulse2, Delay(delay, mz_pulse.channel), mz_pulse] + [ + rx90_pulse1, + rx90_pulse2, + Delay(duration=delay, channel=mz_pulse.channel), + mz_pulse, + ] ) # assert sequence == s @@ -205,7 +210,7 @@ def test_align_delay_measurement(platform, delay): mz_pulse = platform.create_MZ_pulse(0) target_sequence = PulseSequence() if delay > 0: - target_sequence.append(Delay(delay, mz_pulse.channel)) + target_sequence.append(Delay(duration=delay, channel=mz_pulse.channel)) target_sequence.append(mz_pulse) assert sequence == target_sequence assert len(sequence.ro_pulses) == 1 diff --git a/tests/test_dummy.py b/tests/test_dummy.py index 3328a866f..41251cf9e 100644 --- a/tests/test_dummy.py +++ b/tests/test_dummy.py @@ -2,7 +2,7 @@ import pytest from qibolab import AcquisitionType, AveragingMode, ExecutionParameters, create_platform -from qibolab.pulses import Delay, Pulse, PulseSequence, PulseType +from qibolab.pulses import Delay, GaussianSquare, Pulse, PulseSequence, PulseType from qibolab.qubits import QubitPair from qibolab.sweeper import Parameter, QubitParameter, Sweeper @@ -140,7 +140,7 @@ def test_dummy_single_sweep_coupler( coupler_pulse = Pulse.flux( duration=40, amplitude=0.5, - shape="GaussianSquare(5, 0.75)", + envelope=GaussianSquare(rel_sigma=0.2, width=0.75), channel="flux_coupler-0", qubit=0, ) From 3856f78a31ca7f154f9eb98fd7f42bb6b76ae8d8 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 28 Mar 2024 18:57:16 +0100 Subject: [PATCH 177/233] test: Fix unrolling tests --- tests/test_unrolling.py | 112 +++++++++++++++++++++++++++++++++++----- 1 file changed, 98 insertions(+), 14 deletions(-) diff --git a/tests/test_unrolling.py b/tests/test_unrolling.py index ce4d4e079..65411acf0 100644 --- a/tests/test_unrolling.py +++ b/tests/test_unrolling.py @@ -7,13 +7,55 @@ def test_bounds_update(): - p1 = Pulse(40, 0.9, int(100e6), 0, Drag(5, 1), 3, PulseType.DRIVE) - p2 = Pulse(40, 0.9, int(100e6), 0, Drag(5, 1), 2, PulseType.DRIVE) - p3 = Pulse(40, 0.9, int(100e6), 0, Drag(5, 1), 1, PulseType.DRIVE) - - p4 = Pulse(1000, 0.9, int(20e6), 0, Rectangular(), 3, PulseType.READOUT) - p5 = Pulse(1000, 0.9, int(20e6), 0, Rectangular(), 2, PulseType.READOUT) - p6 = Pulse(1000, 0.9, int(20e6), 0, Rectangular(), 1, PulseType.READOUT) + p1 = Pulse( + duration=40, + amplitude=0.9, + frequency=int(100e6), + envelope=Drag(rel_sigma=0.2, beta=1), + channel="3", + type=PulseType.DRIVE, + ) + p2 = Pulse( + duration=40, + amplitude=0.9, + frequency=int(100e6), + envelope=Drag(rel_sigma=0.2, beta=1), + channel="2", + type=PulseType.DRIVE, + ) + p3 = Pulse( + duration=40, + amplitude=0.9, + frequency=int(100e6), + envelope=Drag(rel_sigma=0.2, beta=1), + channel="1", + type=PulseType.DRIVE, + ) + + p4 = Pulse( + duration=1000, + amplitude=0.9, + frequency=int(20e6), + envelope=Rectangular(), + channel="3", + type=PulseType.READOUT, + ) + p5 = Pulse( + duration=1000, + amplitude=0.9, + frequency=int(20e6), + envelope=Rectangular(), + channel="2", + type=PulseType.READOUT, + ) + p6 = Pulse( + duration=1000, + amplitude=0.9, + frequency=int(20e6), + envelope=Rectangular(), + channel="1", + type=PulseType.READOUT, + ) ps = PulseSequence([p1, p2, p3, p4, p5, p6]) bounds = Bounds.update(ps) @@ -51,13 +93,55 @@ def test_bounds_comparison(): ], ) def test_batch(bounds): - p1 = Pulse(40, 0.9, int(100e6), 0, Drag(5, 1), 3, PulseType.DRIVE) - p2 = Pulse(40, 0.9, int(100e6), 0, Drag(5, 1), 2, PulseType.DRIVE) - p3 = Pulse(40, 0.9, int(100e6), 0, Drag(5, 1), 1, PulseType.DRIVE) - - p4 = Pulse(1000, 0.9, int(20e6), 0, Rectangular(), 3, PulseType.READOUT) - p5 = Pulse(1000, 0.9, int(20e6), 0, Rectangular(), 2, PulseType.READOUT) - p6 = Pulse(1000, 0.9, int(20e6), 0, Rectangular(), 1, PulseType.READOUT) + p1 = Pulse( + duration=40, + amplitude=0.9, + frequency=int(100e6), + envelope=Drag(rel_sigma=0.2, beta=1), + channel="3", + type=PulseType.DRIVE, + ) + p2 = Pulse( + duration=40, + amplitude=0.9, + frequency=int(100e6), + envelope=Drag(rel_sigma=0.2, beta=1), + channel="2", + type=PulseType.DRIVE, + ) + p3 = Pulse( + duration=40, + amplitude=0.9, + frequency=int(100e6), + envelope=Drag(rel_sigma=0.2, beta=1), + channel="1", + type=PulseType.DRIVE, + ) + + p4 = Pulse( + duration=1000, + amplitude=0.9, + frequency=int(20e6), + envelope=Rectangular(), + channel="3", + type=PulseType.READOUT, + ) + p5 = Pulse( + duration=1000, + amplitude=0.9, + frequency=int(20e6), + envelope=Rectangular(), + channel="2", + type=PulseType.READOUT, + ) + p6 = Pulse( + duration=1000, + amplitude=0.9, + frequency=int(20e6), + envelope=Rectangular(), + channel="1", + type=PulseType.READOUT, + ) ps = PulseSequence([p1, p2, p3, p4, p5, p6]) From 367840c00883fd366438c897fba257262391381f Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 28 Mar 2024 18:59:14 +0100 Subject: [PATCH 178/233] test: Fix sweepers tests --- tests/test_sweeper.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/tests/test_sweeper.py b/tests/test_sweeper.py index bf537ebe9..b4b660f99 100644 --- a/tests/test_sweeper.py +++ b/tests/test_sweeper.py @@ -8,7 +8,13 @@ @pytest.mark.parametrize("parameter", Parameter) def test_sweeper_pulses(parameter): - pulse = Pulse(40, 0.1, int(1e9), 0.0, Rectangular(), "channel") + pulse = Pulse( + duration=40, + amplitude=0.1, + frequency=1e9, + envelope=Rectangular(), + channel="channel", + ) if parameter is Parameter.amplitude: parameter_range = np.random.rand(10) else: @@ -34,7 +40,13 @@ def test_sweeper_qubits(parameter): def test_sweeper_errors(): - pulse = Pulse(40, 0.1, int(1e9), 0.0, Rectangular(), "channel") + pulse = Pulse( + duration=40, + amplitude=0.1, + frequency=1e9, + envelope=Rectangular(), + channel="channel", + ) qubit = Qubit(0) parameter_range = np.random.randint(10, size=10) with pytest.raises(ValueError): From e240f4144ea57b2da7c7d111066d88b71db014c1 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 28 Mar 2024 19:21:47 +0100 Subject: [PATCH 179/233] fix: Replace some further occurrences of shape, related to platform --- src/qibolab/platform/platform.py | 4 ++-- src/qibolab/serialize.py | 4 +--- tests/test_platform.py | 24 ++++++++++++++---------- 3 files changed, 17 insertions(+), 15 deletions(-) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index befc7df13..597c9632a 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -471,7 +471,7 @@ def create_RX90_drag_pulse(self, qubit, beta, relative_phase=0): return replace( pulse, relative_phase=relative_phase, - shape=Drag(rel_sigma=pulse.envelope.rel_sigma, beta=beta), + envelope=Drag(rel_sigma=pulse.envelope.rel_sigma, beta=beta), channel=qubit.drive.name, ) @@ -482,6 +482,6 @@ def create_RX_drag_pulse(self, qubit, beta, relative_phase=0): return replace( pulse, relative_phase=relative_phase, - shape=Drag(rel_sigma=pulse.envelope.rel_sigma, beta=beta), + envelope=Drag(rel_sigma=pulse.envelope.rel_sigma, beta=beta), channel=qubit.drive.name, ) diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index 9b257c8c1..5093488fb 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -178,11 +178,9 @@ def load_instrument_settings( def _dump_pulse(pulse: Pulse): - data = asdict(pulse) + data = pulse.model_dump() if pulse.type in (PulseType.FLUX, PulseType.COUPLERFLUX): del data["frequency"] - if "shape" in data: - data["shape"] = str(pulse.shape) data["type"] = data["type"].value if "channel" in data: del data["channel"] diff --git a/tests/test_platform.py b/tests/test_platform.py index 1f08e89a5..02b571a93 100644 --- a/tests/test_platform.py +++ b/tests/test_platform.py @@ -44,7 +44,9 @@ def test_unroll_sequences(platform): qd_pulse = platform.create_RX_pulse(qubit) ro_pulse = platform.create_MZ_pulse(qubit) sequence.append(qd_pulse) - sequence.append(Delay(qd_pulse.duration, platform.qubits[qubit].readout.name)) + sequence.append( + Delay(duration=qd_pulse.duration, channel=platform.qubits[qubit].readout.name) + ) sequence.append(ro_pulse) total_sequence, readouts = unroll_sequences(10 * [sequence], relaxation_time=10000) assert len(total_sequence.ro_pulses) == 10 @@ -391,7 +393,10 @@ def test_ground_state_probabilities_pulses(qpu_platform, start_zero): if not start_zero: qd_pulse = platform.create_RX_pulse(qubit) sequence.append( - Delay(qd_pulse.duration, platform.qubits[qubit].readout.name) + Delay( + duration=qd_pulse.duration, + channel=platform.qubits[qubit].readout.name, + ) ) ro_pulse = platform.create_MZ_pulse(qubit) sequence.append(ro_pulse) @@ -413,14 +418,13 @@ def test_create_RX_drag_pulses(): qubits = [q for q, qb in platform.qubits.items() if qb.drive is not None] beta = 0.1234 for qubit in qubits: - drag_pi = platform.create_RX_drag_pulse(qubit, 0, beta=beta) - assert drag_pi.shape == Drag(drag_pi.shape.rel_sigma, beta=beta) - drag_pi_half = platform.create_RX90_drag_pulse( - qubit, drag_pi.duration, beta=beta + drag_pi = platform.create_RX_drag_pulse(qubit, beta=beta) + assert drag_pi.envelope == Drag(rel_sigma=drag_pi.envelope.rel_sigma, beta=beta) + drag_pi_half = platform.create_RX90_drag_pulse(qubit, beta=beta) + assert drag_pi_half.envelope == Drag( + rel_sigma=drag_pi_half.envelope.rel_sigma, beta=beta ) - assert drag_pi_half.shape == Drag(drag_pi_half.shape.rel_sigma, beta=beta) np.testing.assert_almost_equal(drag_pi.amplitude, 2 * drag_pi_half.amplitude) - # to check ShapeInitError - drag_pi.shape.envelope_waveforms() - drag_pi_half.shape.envelope_waveforms() + drag_pi.envelopes(sampling_rate=1) + drag_pi_half.envelopes(sampling_rate=1) From 73c555e6254b6ea20e960f47b3bd118cac8d2b3e Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 29 Mar 2024 06:57:53 +0100 Subject: [PATCH 180/233] test: Fix dummy tests --- tests/test_dummy.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/tests/test_dummy.py b/tests/test_dummy.py index 41251cf9e..6ff899f78 100644 --- a/tests/test_dummy.py +++ b/tests/test_dummy.py @@ -4,6 +4,7 @@ from qibolab import AcquisitionType, AveragingMode, ExecutionParameters, create_platform from qibolab.pulses import Delay, GaussianSquare, Pulse, PulseSequence, PulseType from qibolab.qubits import QubitPair +from qibolab.serialize_ import replace from qibolab.sweeper import Parameter, QubitParameter, Sweeper SWEPT_POINTS = 5 @@ -60,8 +61,8 @@ def test_dummy_execute_pulse_sequence_couplers(): sequence.extend(cz.get_qubit_pulses(qubit_ordered_pair.qubit1.name)) sequence.extend(cz.get_qubit_pulses(qubit_ordered_pair.qubit2.name)) sequence.extend(cz.coupler_pulses(qubit_ordered_pair.coupler.name)) - sequence.append(Delay(40, platform.qubits[0].readout.name)) - sequence.append(Delay(40, platform.qubits[2].readout.name)) + sequence.append(Delay(duration=40, channel=platform.qubits[0].readout.name)) + sequence.append(Delay(duration=40, channel=platform.qubits[2].readout.name)) sequence.append(platform.create_MZ_pulse(0)) sequence.append(platform.create_MZ_pulse(2)) options = ExecutionParameters(nshots=None) @@ -144,7 +145,7 @@ def test_dummy_single_sweep_coupler( channel="flux_coupler-0", qubit=0, ) - coupler_pulse.type = PulseType.COUPLERFLUX + coupler_pulse = replace(coupler_pulse, type=PulseType.COUPLERFLUX) if parameter is Parameter.amplitude: parameter_range = np.random.rand(SWEPT_POINTS) else: @@ -239,7 +240,9 @@ def test_dummy_double_sweep(name, parameter1, parameter2, average, acquisition, pulse = platform.create_qubit_drive_pulse(qubit=0, duration=1000) ro_pulse = platform.create_MZ_pulse(qubit=0) sequence.append(pulse) - sequence.append(Delay(pulse.duration, channel=platform.qubits[0].readout.name)) + sequence.append( + Delay(duration=pulse.duration, channel=platform.qubits[0].readout.name) + ) sequence.append(ro_pulse) parameter_range_1 = ( np.random.rand(SWEPT_POINTS) From 472442945305d8df7f01aeddcc0b147c34a40395 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 29 Mar 2024 07:02:54 +0100 Subject: [PATCH 181/233] test: Drop pickle test, not used any longer --- tests/test_platform.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/tests/test_platform.py b/tests/test_platform.py index 02b571a93..9ae82b0ff 100644 --- a/tests/test_platform.py +++ b/tests/test_platform.py @@ -4,7 +4,6 @@ import inspect import os import pathlib -import pickle import warnings from pathlib import Path @@ -103,14 +102,6 @@ def test_platform_sampling_rate(platform): assert platform.sampling_rate >= 1 -@pytest.mark.xfail(reason="Cannot pickle all platforms") -def test_platform_pickle(platform): - serial = pickle.dumps(platform) - new_platform = pickle.loads(serial) - assert new_platform.name == platform.name - assert new_platform.is_connected == platform.is_connected - - def test_dump_runcard(platform, tmp_path): dump_runcard(platform, tmp_path) final_runcard = load_runcard(tmp_path) From bab716e39bed741fe8d6ccca039aa4829823ff82 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 29 Mar 2024 07:45:53 +0100 Subject: [PATCH 182/233] fix: Fix runcard dump --- src/qibolab/pulses/pulse.py | 11 +++++++---- src/qibolab/serialize.py | 17 +++++++---------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 0b0c6473a..e88326d4a 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -73,7 +73,8 @@ def flux(cls, **kwargs): """ kwargs["frequency"] = 0 kwargs["relative_phase"] = 0 - kwargs["type"] = PulseType.FLUX + if "type" not in kwargs: + kwargs["type"] = PulseType.FLUX return cls(**kwargs) @property @@ -133,9 +134,6 @@ class Delay(Model): class VirtualZ(Model): """Implementation of Z-rotations using virtual phase.""" - duration: int = 0 - """Duration of the virtual gate should always be zero.""" - phase: float """Phase that implements the rotation.""" channel: Optional[str] = None @@ -143,3 +141,8 @@ class VirtualZ(Model): qubit: int = 0 """Qubit on the drive of which the virtual phase should be added.""" type: PulseType = PulseType.VIRTUALZ + + @property + def duration(self): + """Duration of the virtual gate should always be zero.""" + return 0 diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index 5093488fb..c9ede6731 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -89,19 +89,16 @@ def load_qubits( def _load_pulse(pulse_kwargs, qubit): - pulse_type = pulse_kwargs.pop("type") - if "coupler" in pulse_kwargs: - q = pulse_kwargs.pop("coupler", qubit.name) - else: - q = pulse_kwargs.pop("qubit", qubit.name) + coupler = "coupler" in pulse_kwargs + q = pulse_kwargs.pop("coupler" if coupler else "qubit", qubit.name) - if pulse_type == "dl": - return Delay(**pulse_kwargs) - if pulse_type == "vz": + if "phase" in pulse_kwargs: return VirtualZ(**pulse_kwargs, qubit=q) + if "amplitude" not in pulse_kwargs: + return Delay(**pulse_kwargs) if "frequency" not in pulse_kwargs: - return Pulse.flux(**pulse_kwargs, type=pulse_type, qubit=q) - return Pulse(**pulse_kwargs, type=pulse_type, qubit=q) + return Pulse.flux(**pulse_kwargs, qubit=q) + return Pulse(**pulse_kwargs, qubit=q) def _load_single_qubit_natives(qubit, gates) -> SingleQubitNatives: From 7a34a66f01ce18ad43dfa1022df3213fabd78dac Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 29 Mar 2024 08:42:05 +0100 Subject: [PATCH 183/233] test: Fix the doctests --- doc/source/main-documentation/qibolab.rst | 22 ++-- doc/source/tutorials/calibration.rst | 10 +- doc/source/tutorials/lab.rst | 90 +++++++++------ doc/source/tutorials/pulses.rst | 10 +- src/qibolab/pulses/sequence.py | 135 +++------------------- 5 files changed, 94 insertions(+), 173 deletions(-) diff --git a/doc/source/main-documentation/qibolab.rst b/doc/source/main-documentation/qibolab.rst index 352c6e9fd..046813cdc 100644 --- a/doc/source/main-documentation/qibolab.rst +++ b/doc/source/main-documentation/qibolab.rst @@ -66,7 +66,7 @@ Now we can create a simple sequence (again, without explicitly giving any qubit ps = PulseSequence() ps.append(platform.create_RX_pulse(qubit=0)) ps.append(platform.create_RX_pulse(qubit=0)) - ps.append(Delay(200, platform.qubits[0].readout.name)) + ps.append(Delay(duration=200, channel=platform.qubits[0].readout.name)) ps.append(platform.create_MZ_pulse(qubit=0)) Now we can execute the sequence on hardware: @@ -300,7 +300,7 @@ To illustrate, here are some examples of single pulses using the Qibolab API: amplitude=0.5, # Amplitude relative to instrument range frequency=1e8, # Frequency in Hz relative_phase=0, # Phase in radians - shape=Rectangular(), + envelope=Rectangular(), channel="channel", type="qd", # Enum type: :class:`qibolab.pulses.PulseType` qubit=0, @@ -318,7 +318,7 @@ Alternatively, you can achieve the same result using the dedicated :class:`qibol amplitude=0.5, # this amplitude is relative to the range of the instrument frequency=1e8, # frequency are in Hz relative_phase=0, # phases are in radians - shape=Rectangular(), + envelope=Rectangular(), channel="channel", qubit=0, ) @@ -338,7 +338,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P amplitude=0.5, # this amplitude is relative to the range of the instrument frequency=1e8, # frequency are in Hz relative_phase=0, # phases are in radians - shape=Rectangular(), + envelope=Rectangular(), channel="channel", qubit=0, ) @@ -347,7 +347,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P amplitude=0.5, # this amplitude is relative to the range of the instrument frequency=1e8, # frequency are in Hz relative_phase=0, # phases are in radians - shape=Rectangular(), + envelope=Rectangular(), channel="channel", qubit=0, ) @@ -356,7 +356,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P amplitude=0.5, # this amplitude is relative to the range of the instrument frequency=1e8, # frequency are in Hz relative_phase=0, # phases are in radians - shape=Rectangular(), + envelope=Rectangular(), channel="channel", qubit=0, ) @@ -365,7 +365,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P amplitude=0.5, # this amplitude is relative to the range of the instrument frequency=1e8, # frequency are in Hz relative_phase=0, # phases are in radians - shape=Rectangular(), + envelope=Rectangular(), channel="channel", qubit=0, ) @@ -382,7 +382,7 @@ To organize pulses into sequences, Qibolab provides the :class:`qibolab.pulses.P .. testoutput:: python :hide: - Total duration: 160 + Total duration: 160.0 We have 0 pulses on channel 1. @@ -409,7 +409,7 @@ Typical experiments may include both pre-defined pulses and new ones: amplitude=0.5, frequency=2500000000, relative_phase=0, - shape=Rectangular(), + envelope=Rectangular(), channel="0", ) ) @@ -525,7 +525,9 @@ For example: sequence = PulseSequence() sequence.append(platform.create_RX_pulse(0)) - sequence.append(Delay(sequence.duration, platform.qubits[0].readout.name)) + sequence.append( + Delay(duration=sequence.duration, channel=platform.qubits[0].readout.name) + ) sequence.append(platform.create_MZ_pulse(0)) sweeper_freq = Sweeper( diff --git a/doc/source/tutorials/calibration.rst b/doc/source/tutorials/calibration.rst index 13d22925c..ba8a2fee6 100644 --- a/doc/source/tutorials/calibration.rst +++ b/doc/source/tutorials/calibration.rst @@ -117,6 +117,7 @@ complex pulse sequence. Therefore with start with that: AveragingMode, AcquisitionType, ) + from qibolab.serialize_ import replace # allocate platform platform = create_platform("dummy") @@ -124,11 +125,10 @@ complex pulse sequence. Therefore with start with that: # create pulse sequence and add pulses sequence = PulseSequence() drive_pulse = platform.create_RX_pulse(qubit=0) - drive_pulse.duration = 2000 - drive_pulse.amplitude = 0.01 + drive_pulse = replace(drive_pulse, duration=2000, amplitude=0.01) readout_pulse = platform.create_MZ_pulse(qubit=0) sequence.append(drive_pulse) - sequence.append(Delay(drive_pulse.duration, readout_pulse.channel)) + sequence.append(Delay(duration=drive_pulse.duration, channel=readout_pulse.channel)) sequence.append(readout_pulse) # allocate frequency sweeper @@ -222,7 +222,9 @@ and its impact on qubit states in the IQ plane. drive_pulse = platform.create_RX_pulse(qubit=0) readout_pulse1 = platform.create_MZ_pulse(qubit=0) one_sequence.append(drive_pulse) - one_sequence.append(Delay(drive_pulse.duration, readout_pulse1.channel)) + one_sequence.append( + Delay(duration=drive_pulse.duration, channel=readout_pulse1.channel) + ) one_sequence.append(readout_pulse1) # create pulse sequence 2 and add pulses diff --git a/doc/source/tutorials/lab.rst b/doc/source/tutorials/lab.rst index f1e80f5ce..7ca2ca210 100644 --- a/doc/source/tutorials/lab.rst +++ b/doc/source/tutorials/lab.rst @@ -24,7 +24,7 @@ using different Qibolab primitives. from qibolab import Platform from qibolab.qubits import Qubit - from qibolab.pulses import Pulse, PulseType + from qibolab.pulses import Gaussian, Pulse, PulseType, Rectangular from qibolab.channels import ChannelMap, Channel from qibolab.native import SingleQubitNatives from qibolab.instruments.dummy import DummyInstrument @@ -48,18 +48,18 @@ using different Qibolab primitives. RX=Pulse( duration=40, amplitude=0.05, - shape="Gaussian(5)", + envelope=Gaussian(rel_sigma=0.2), type=PulseType.DRIVE, - qubit=qubit, - frequency=int(4.5e9), + qubit=qubit.name, + frequency=4.5e9, ), MZ=Pulse( duration=1000, amplitude=0.005, - shape="Rectangular()", + envelope=Rectangular(), type=PulseType.READOUT, - qubit=qubit, - frequency=int(7e9), + qubit=qubit.name, + frequency=7e9, ), ) @@ -97,7 +97,7 @@ hold the parameters of the two-qubit gates. .. testcode:: python from qibolab.qubits import Qubit, QubitPair - from qibolab.pulses import PulseType, Pulse, PulseSequence + from qibolab.pulses import Gaussian, PulseType, Pulse, PulseSequence, Rectangular from qibolab.native import ( SingleQubitNatives, TwoQubitNatives, @@ -112,36 +112,36 @@ hold the parameters of the two-qubit gates. RX=Pulse( duration=40, amplitude=0.05, - shape="Gaussian(5)", + envelope=Gaussian(rel_sigma=0.2), type=PulseType.DRIVE, - qubit=qubit0, - frequency=int(4.7e9), + qubit=qubit0.name, + frequency=4.7e9, ), MZ=Pulse( duration=1000, amplitude=0.005, - shape="Rectangular()", + envelope=Rectangular(), type=PulseType.READOUT, - qubit=qubit0, - frequency=int(7e9), + qubit=qubit0.name, + frequency=7e9, ), ) qubit1.native_gates = SingleQubitNatives( RX=Pulse( duration=40, amplitude=0.05, - shape="Gaussian(5)", + envelope=Gaussian(rel_sigma=0.2), type=PulseType.DRIVE, - qubit=qubit1, - frequency=int(5.1e9), + qubit=qubit1.name, + frequency=5.1e9, ), MZ=Pulse( duration=1000, amplitude=0.005, - shape="Rectangular()", + envelope=Rectangular(), type=PulseType.READOUT, - qubit=qubit1, - frequency=int(7.5e9), + qubit=qubit1.name, + frequency=7.5e9, ), ) @@ -153,9 +153,10 @@ hold the parameters of the two-qubit gates. Pulse( duration=30, amplitude=0.005, - shape="Rectangular()", + envelope=Rectangular(), type=PulseType.FLUX, - qubit=qubit1, + qubit=qubit1.name, + frequency=1e9, ) ], ) @@ -194,9 +195,10 @@ coupler but qibolab will take them into account when calling :class:`qibolab.nat Pulse( duration=30, amplitude=0.005, - shape="Rectangular()", + frequency=1e9, + envelope=Rectangular(), type=PulseType.FLUX, - qubit=qubit1, + qubit=qubit1.name, ) ], ) @@ -269,14 +271,18 @@ a two-qubit system: "duration": 40, "amplitude": 0.0484, "frequency": 4855663000, - "shape": "Drag(5, -0.02)", + "envelope": { + "kind": "drag", + "rel_sigma": 0.2, + "beta": -0.02, + }, "type": "qd", }, "MZ": { "duration": 620, "amplitude": 0.003575, "frequency": 7453265000, - "shape": "Rectangular()", + "envelope": {"kind": "rectangular"}, "type": "ro", } }, @@ -285,14 +291,18 @@ a two-qubit system: "duration": 40, "amplitude": 0.05682, "frequency": 5800563000, - "shape": "Drag(5, -0.04)", + "envelope": { + "kind": "drag", + "rel_sigma": 0.2, + "beta": -0.04, + }, "type": "qd", }, "MZ": { "duration": 960, "amplitude": 0.00325, "frequency": 7655107000, - "shape": "Rectangular()", + "envelope": {"kind": "rectangular"}, "type": "ro", } } @@ -303,7 +313,7 @@ a two-qubit system: { "duration": 30, "amplitude": 0.055, - "shape": "Rectangular()", + "envelope": {"kind": "rectangular"}, "qubit": 1, "type": "qf" }, @@ -371,7 +381,7 @@ we need the following changes to the previous runcard: { "duration": 30, "amplitude": 0.6025, - "shape": "Rectangular()", + "envelope": {"kind": "rectangular"}, "qubit": 1, "type": "qf" }, @@ -389,7 +399,7 @@ we need the following changes to the previous runcard: "type": "cf", "duration": 40, "amplitude": 0.1, - "shape": "Rectangular()", + "envelope": {"kind": "rectangular"}, "coupler": 0, } ] @@ -564,14 +574,18 @@ The runcard can contain an ``instruments`` section that provides these parameter "duration": 40, "amplitude": 0.0484, "frequency": 4855663000, - "shape": "Drag(5, -0.02)", + "envelope": { + "kind": "drag", + "rel_sigma": 0.2, + "beta": -0.02, + }, "type": "qd", }, "MZ": { "duration": 620, "amplitude": 0.003575, "frequency": 7453265000, - "shape": "Rectangular()", + "envelope": {"kind": "rectangular"}, "type": "ro", } }, @@ -580,14 +594,18 @@ The runcard can contain an ``instruments`` section that provides these parameter "duration": 40, "amplitude": 0.05682, "frequency": 5800563000, - "shape": "Drag(5, -0.04)", + "envelope": { + "kind": "drag", + "rel_sigma": 0.2, + "beta": -0.04, + }, "type": "qd", }, "MZ": { "duration": 960, "amplitude": 0.00325, "frequency": 7655107000, - "shape": "Rectangular()", + "envelope": {"kind": "rectangular"}, "type": "ro", } } @@ -598,7 +616,7 @@ The runcard can contain an ``instruments`` section that provides these parameter { "duration": 30, "amplitude": 0.055, - "shape": "Rectangular()", + "envelope": {"kind": "rectangular"}, "qubit": 1, "type": "qf" }, diff --git a/doc/source/tutorials/pulses.rst b/doc/source/tutorials/pulses.rst index b68508bc0..b3b0b7233 100644 --- a/doc/source/tutorials/pulses.rst +++ b/doc/source/tutorials/pulses.rst @@ -20,23 +20,23 @@ pulses (:class:`qibolab.pulses.Pulse`) through the amplitude=0.3, duration=60, relative_phase=0, - shape=Gaussian(5), + envelope=Gaussian(rel_sigma=0.2), qubit=0, type=PulseType.DRIVE, - channel=0, + channel="0", ) ) - sequence.append(Delay(100, channel=1)) + sequence.append(Delay(duration=100, channel="1")) sequence.append( Pulse( frequency=20000000.0, amplitude=0.5, duration=3000, relative_phase=0, - shape=Rectangular(), + envelope=Rectangular(), qubit=0, type=PulseType.READOUT, - channel=1, + channel="1", ) ) diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py index 3ecce9cd1..b48cb62cd 100644 --- a/src/qibolab/pulses/sequence.py +++ b/src/qibolab/pulses/sequence.py @@ -1,5 +1,8 @@ """PulseSequence class.""" -import numpy as np + +from collections import defaultdict + +from .pulse import PulseType class PulseSequence(list): @@ -91,27 +94,23 @@ def coupler_pulses(self, *couplers): return new_pc @property - def finish(self) -> int: - """The time when the last pulse of the sequence finishes.""" - t: int = 0 + def pulses_per_channel(self): + """Return a dictionary with the sequence per channel.""" + sequences = defaultdict(type(self)) for pulse in self: - if pulse.finish > t: - t = pulse.finish - return t - - @property - def start(self) -> int: - """The start time of the first pulse of the sequence.""" - t = self.finish - for pulse in self: - if pulse.start < t: - t = pulse.start - return t + sequences[pulse.channel].append(pulse) + return sequences @property def duration(self) -> int: - """Duration of the sequence calculated as its finish - start times.""" - return self.finish - self.start + """The time when the last pulse of the sequence finishes.""" + channel_pulses = self.pulses_per_channel + if len(channel_pulses) == 1: + pulses = next(iter(channel_pulses.values())) + return sum(pulse.duration for pulse in pulses) + return max( + (sequence.duration for sequence in channel_pulses.values()), default=0 + ) @property def channels(self) -> list: @@ -133,25 +132,6 @@ def qubits(self) -> list: qubits.sort() return qubits - def get_pulse_overlaps(self): # -> dict((int,int): PulseSequence): - """Return a dictionary of slices of time (tuples with start and finish - times) where pulses overlap.""" - times = [] - for pulse in self: - if not pulse.start in times: - times.append(pulse.start) - if not pulse.finish in times: - times.append(pulse.finish) - times.sort() - - overlaps = {} - for n in range(len(times) - 1): - overlaps[(times[n], times[n + 1])] = PulseSequence() - for pulse in self: - if (pulse.start <= times[n]) & (pulse.finish >= times[n + 1]): - overlaps[(times[n], times[n + 1])] += [pulse] - return overlaps - def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): """Separate a sequence of overlapping pulses into a list of non- overlapping sequences.""" @@ -177,84 +157,3 @@ def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): if not stored: separated_pulses.append(PulseSequence([new_pulse])) return separated_pulses - - # TODO: Implement separate_different_frequency_pulses() - - @property - def pulses_overlap(self) -> bool: - """Whether any of the pulses in the sequence overlap.""" - overlap = False - for pc in self.get_pulse_overlaps().values(): - if len(pc) > 1: - overlap = True - break - return overlap - - def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): - """Plot the sequence of pulses. - - Args: - savefig_filename (str): a file path. If provided the plot is save to a file. - """ - if len(self) > 0: - import matplotlib.pyplot as plt - from matplotlib import gridspec - - fig = plt.figure(figsize=(14, 2 * len(self)), dpi=200) - gs = gridspec.GridSpec(ncols=1, nrows=len(self)) - vertical_lines = [] - for pulse in self: - vertical_lines.append(pulse.start) - vertical_lines.append(pulse.finish) - - n = -1 - for qubit in self.qubits: - qubit_pulses = self.get_qubit_pulses(qubit) - for channel in qubit_pulses.channels: - n += 1 - channel_pulses = qubit_pulses.get_channel_pulses(channel) - ax = plt.subplot(gs[n]) - ax.axis([0, self.finish, -1, 1]) - for pulse in channel_pulses: - num_samples = len( - pulse.shape.modulated_waveform_i(sampling_rate) - ) - time = pulse.start + np.arange(num_samples) / sampling_rate - ax.plot( - time, - pulse.shape.modulated_waveform_q(sampling_rate).data, - c="lightgrey", - ) - ax.plot( - time, - pulse.shape.modulated_waveform_i(sampling_rate).data, - c=f"C{str(n)}", - ) - ax.plot( - time, - pulse.shape.envelope_waveform_i(sampling_rate).data, - c=f"C{str(n)}", - ) - ax.plot( - time, - -pulse.shape.envelope_waveform_i(sampling_rate).data, - c=f"C{str(n)}", - ) - # TODO: if they overlap use different shades - ax.axhline(0, c="dimgrey") - ax.set_ylabel(f"qubit {qubit} \n channel {channel}") - for vl in vertical_lines: - ax.axvline(vl, c="slategrey", linestyle="--") - ax.axis([0, self.finish, -1, 1]) - ax.grid( - visible=True, - which="both", - axis="both", - color="#CCCCCC", - linestyle="-", - ) - if savefig_filename: - plt.savefig(savefig_filename) - else: - plt.show() - plt.close() From f0b297a87ad9fb5e69d55f89e2a1e38097ebe7a0 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Sat, 30 Mar 2024 08:16:01 +0100 Subject: [PATCH 184/233] fix: Change exponential parameters units, from samples to duration --- src/qibolab/pulses/envelope.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index f9dcdcc44..513e5eca3 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -81,18 +81,24 @@ class Exponential(BaseEnvelope): kind: Literal["exponential"] = "exponential" tau: float - """The decay rate of the first exponential function.""" + """The decay rate of the first exponential function. + + In units of the interval duration. + """ upsilon: float - """The decay rate of the second exponential function.""" + """The decay rate of the second exponential function. + + In units of the interval duration. + """ g: float = 0.1 """Weight of the second exponential function.""" def i(self, samples: int) -> Waveform: """Generate a combination of two exponential decays.""" x = np.arange(samples) - return (np.exp(-x / self.upsilon) + self.g * np.exp(-x / self.tau)) / ( - 1 + self.g - ) + upsilon = self.upsilon * samples + tau = self.tau * samples + return (np.exp(-x / upsilon) + self.g * np.exp(-x / tau)) / (1 + self.g) def _samples_sigma(rel_sigma: float, samples: int) -> float: @@ -245,6 +251,7 @@ class Snz(BaseEnvelope): kind: Literal["snz"] = "snz" t_idling: float + """Fraction of interval where idling.""" b_amplitude: float = 0.5 """Relative B amplitude (wrt A).""" @@ -279,6 +286,7 @@ class ECap(BaseEnvelope): kind: Literal["ecap"] = "ecap" alpha: float + """In units of the inverse interval duration.""" def i(self, samples: int) -> Waveform: """.. todo::""" From 4758a16c870da8405791fe0bb3ca98b6010e67b4 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 15 Apr 2024 15:33:45 +0200 Subject: [PATCH 185/233] chore: Fix rebase leftover --- src/qibolab/pulses/envelope.py | 4 ---- src/qibolab/pulses/pulse.py | 4 ++-- src/qibolab/pulses/waveform.py | 42 ---------------------------------- 3 files changed, 2 insertions(+), 48 deletions(-) delete mode 100644 src/qibolab/pulses/waveform.py diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index 513e5eca3..a61c6e50e 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -43,10 +43,6 @@ class BaseEnvelope(ABC, Model): Generates both i (in-phase) and q (quadrature) components. """ - def window(self, samples: int): - """Individual timing of each sample.""" - return np.linspace(0, self.duration, samples) - def i(self, samples: int) -> Waveform: """In-phase envelope.""" return np.zeros(samples) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index e88326d4a..efd4c7b85 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -83,12 +83,12 @@ def id(self) -> int: def i(self, sampling_rate: float) -> Waveform: """The envelope waveform of the i component of the pulse.""" - samples = int(self.envelope.duration * sampling_rate) + samples = int(self.duration * sampling_rate) return self.amplitude * self.envelope.i(samples) def q(self, sampling_rate: float) -> Waveform: """The envelope waveform of the q component of the pulse.""" - samples = int(self.envelope.duration * sampling_rate) + samples = int(self.duration * sampling_rate) return self.amplitude * self.envelope.q(samples) def envelopes(self, sampling_rate: float) -> IqWaveform: diff --git a/src/qibolab/pulses/waveform.py b/src/qibolab/pulses/waveform.py deleted file mode 100644 index 7c530bf36..000000000 --- a/src/qibolab/pulses/waveform.py +++ /dev/null @@ -1,42 +0,0 @@ -"""Waveform class.""" -import numpy as np - - -class Waveform: - """A class to save pulse waveforms. - - A waveform is a list of samples, or discrete data points, used by the digital to analogue converters (DACs) - to synthesise pulses. - - Attributes: - data (np.ndarray): a numpy array containing the samples. - """ - - DECIMALS = 5 - - def __init__(self, data): - """Initialise the waveform with a of samples.""" - self.data: np.ndarray = np.array(data) - - def __len__(self): - """Return the length of the waveform, the number of samples.""" - return len(self.data) - - def __hash__(self): - """Hash the underlying data. - - .. todo:: - - In order to make this reliable, we should set the data as immutable. This we - could by making both the class frozen and the contained array readonly - https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags - """ - return hash(self.data.tobytes()) - - def __eq__(self, other): - """Compare two waveforms. - - Two waveforms are considered equal if their samples, rounded to - `Waveform.DECIMALS` decimal places, are all equal. - """ - return np.allclose(self.data, other.data) From 34dba9eca43d92a640f5939fbb6d1a48f104905d Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 15 Apr 2024 16:21:02 +0200 Subject: [PATCH 186/233] fix: Remove (again) duration from GaussianSquare envelope --- src/qibolab/pulses/envelope.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index a61c6e50e..85a087fcf 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -152,7 +152,7 @@ def i(self, samples: int) -> Waveform: """Generate a Gaussian envelope, with a flat central window.""" pulse = np.ones(samples) - u, hw = samples / 2, self.width * samples / self.duration / 2 + u, hw = samples / 2, self.width / 2 ts = np.arange(samples) tails = (ts < (u - hw)) | ((u + hw) < ts) pulse[tails] = gaussian(len(ts[tails]), _samples_sigma(self.rel_sigma, samples)) From 33b733d78d6f661fe567599644ddcc9c7e32508c Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 16 Apr 2024 10:32:23 +0200 Subject: [PATCH 187/233] Improve envelopes' docstrings Co-authored-by: Hayk Sargsyan <52532457+hay-k@users.noreply.github.com> --- src/qibolab/pulses/envelope.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/qibolab/pulses/envelope.py b/src/qibolab/pulses/envelope.py index 85a087fcf..cda3581be 100644 --- a/src/qibolab/pulses/envelope.py +++ b/src/qibolab/pulses/envelope.py @@ -1,4 +1,4 @@ -"""Library of pulse shapes.""" +"""Library of pulse envelopes.""" from abc import ABC from typing import Annotated, Literal, Union @@ -131,7 +131,7 @@ def i(self, samples: int) -> Waveform: class GaussianSquare(BaseEnvelope): - r"""GaussianSquare pulse shape. + r"""Rectangular envelope with Gaussian rise and fall. .. math:: @@ -161,7 +161,7 @@ def i(self, samples: int) -> Waveform: class Drag(BaseEnvelope): - """Derivative Removal by Adiabatic Gate (DRAG) pulse shape. + """Derivative Removal by Adiabatic Gate (DRAG) pulse envelope. .. todo:: @@ -267,7 +267,7 @@ def i(self, samples: int) -> Waveform: class ECap(BaseEnvelope): - r"""ECap pulse shape. + r"""ECap pulse envelope. .. todo:: @@ -296,7 +296,7 @@ def i(self, samples: int) -> Waveform: class Custom(BaseEnvelope): - """Arbitrary shape. + """Arbitrary envelope. .. todo:: From 3157b71b3a137ff7f8546a03ea304e22a7fac62e Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 12 Jan 2024 16:25:00 +0100 Subject: [PATCH 188/233] Drop pulse.serial --- src/qibolab/instruments/qblox/cluster_qrm_rf.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/qibolab/instruments/qblox/cluster_qrm_rf.py b/src/qibolab/instruments/qblox/cluster_qrm_rf.py index 9d582e859..d09905eeb 100644 --- a/src/qibolab/instruments/qblox/cluster_qrm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qrm_rf.py @@ -993,9 +993,9 @@ def acquire(self): if len(sequencer.pulses.ro_pulses) == 1: pulse = sequencer.pulses.ro_pulses[0] frequency = self.get_if(pulse) - acquisitions[pulse.qubit] = acquisitions[pulse.id] = ( - AveragedAcquisition(scope, duration, frequency) - ) + acquisitions[pulse.qubit] = acquisitions[ + pulse.id + ] = AveragedAcquisition(scope, duration, frequency) else: raise RuntimeError( "Software Demodulation only supports one acquisition per channel. " @@ -1005,9 +1005,9 @@ def acquire(self): results = self.device.get_acquisitions(sequencer.number) for pulse in sequencer.pulses.ro_pulses: bins = results[pulse.id]["acquisition"]["bins"] - acquisitions[pulse.qubit] = acquisitions[pulse.id] = ( - DemodulatedAcquisition(scope, bins, duration) - ) + acquisitions[pulse.qubit] = acquisitions[ + pulse.id + ] = DemodulatedAcquisition(scope, bins, duration) # TODO: to be updated once the functionality of ExecutionResults is extended return {key: acquisition for key, acquisition in acquisitions.items()} From 71983e130eda052e15af65316413a22cca831b7d Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 19:17:35 +0100 Subject: [PATCH 189/233] Fix QM issues by stringifying pulses ID QM requires some keys to be strings, because of the way they are later processed. And before they were (by accident, since we were using the serial as an identifier). --- tests/test_instruments_qm.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 62b3ef994..c81c627da 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -346,8 +346,21 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): }, } +<<<<<<< HEAD controller.config.register_element( platform.qubits[qubit], pulse, controller.time_of_flight, controller.smearing +======= + opx.config.register_element( + platform.qubits[qubit], pulse, opx.time_of_flight, opx.smearing + ) + opx.config.register_pulse(platform.qubits[qubit], pulse) + assert opx.config.pulses[str(pulse.id)] == target_pulse + assert target_pulse["waveforms"]["I"] in opx.config.waveforms + assert target_pulse["waveforms"]["Q"] in opx.config.waveforms + assert ( + opx.config.elements[f"{pulse_type}{qubit}"]["operations"][str(pulse.id)] + == pulse.id +>>>>>>> 5f1fb614 (Fix QM issues by stringifying pulses ID) ) qmpulse = QMPulse(pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) @@ -373,11 +386,19 @@ def test_qm_register_flux_pulse(qmplatform): "length": pulse.duration, "waveforms": {"single": "constant_wf0.005"}, } +<<<<<<< HEAD qmpulse = QMPulse(pulse) controller.config.register_element(platform.qubits[qubit], pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) assert controller.config.pulses[qmpulse.operation] == target_pulse assert target_pulse["waveforms"]["single"] in controller.config.waveforms +======= + opx.config.register_element(platform.qubits[qubit], pulse) + opx.config.register_pulse(platform.qubits[qubit], pulse) + assert opx.config.pulses[str(pulse.id)] == target_pulse + assert target_pulse["waveforms"]["single"] in opx.config.waveforms + assert opx.config.elements[f"flux{qubit}"]["operations"][str(pulse.id)] == pulse.id +>>>>>>> 5f1fb614 (Fix QM issues by stringifying pulses ID) def test_qm_register_pulses_with_different_frequencies(qmplatform): From ab9f25af43626616936a1f43842b24e4aeb53e97 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 29 Jan 2024 10:28:14 +0100 Subject: [PATCH 190/233] feat(nix): Export convenience env var --- flake.nix | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flake.nix b/flake.nix index 9b2c037a8..478c524c9 100644 --- a/flake.nix +++ b/flake.nix @@ -49,7 +49,7 @@ config, ... }: { - packages = with pkgs; [pre-commit poethepoet jupyter zlib]; + packages = with pkgs; [pre-commit poethepoet jupyter stdenv.cc.cc.lib zlib]; env = { QIBOLAB_PLATFORMS = (dirOf config.env.DEVENV_ROOT) + "/qibolab_platforms_qrc"; From 8482181f028a298e1c484d8cda79d22daaf2bcbf Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Thu, 21 Mar 2024 13:15:26 +0400 Subject: [PATCH 191/233] fix: tests after merging (compiler tests still failing) --- tests/test_compilers_default.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index 945f098c8..ff674a546 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -37,7 +37,7 @@ def compile_circuit(circuit, platform): @pytest.mark.parametrize( - "gateargs,sequence_len", + "gateargs", [ ((gates.I,), 1), ((gates.Z,), 2), @@ -47,11 +47,11 @@ def compile_circuit(circuit, platform): ((gates.U3, 0.1, 0.2, 0.3), 10), ], ) -def test_compile(platform, gateargs, sequence_len): +def test_compile(platform, gateargs): nqubits = platform.nqubits circuit = generate_circuit_with_gate(nqubits, *gateargs) sequence = compile_circuit(circuit, platform) - assert len(sequence) == nqubits * sequence_len + assert len(sequence) == nqubits * nseq def test_compile_two_gates(platform): From 2c542d72e6dc5ea1f9f8a898550c90f72b0d52b9 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 15 Mar 2024 18:10:32 +0100 Subject: [PATCH 192/233] test: Fix remaining pytests collection errors --- src/qibolab/instruments/qm/config.py | 4 +++- tests/test_instruments_qm.py | 4 +++- tests/test_instruments_rfsoc.py | 6 +++++- tests/test_sweeper.py | 4 +++- 4 files changed, 14 insertions(+), 4 deletions(-) diff --git a/src/qibolab/instruments/qm/config.py b/src/qibolab/instruments/qm/config.py index cc934fb20..e6ceeca78 100644 --- a/src/qibolab/instruments/qm/config.py +++ b/src/qibolab/instruments/qm/config.py @@ -4,10 +4,12 @@ import numpy as np from qibo.config import raise_error -from qibolab.pulses import PulseType, Rectangular +from qibolab.pulses import Envelopes, PulseType from .ports import OPXIQ, OctaveInput, OctaveOutput, OPXOutput +Rectangular = Envelopes.RECTANGULAR.value + SAMPLING_RATE = 1 """Sampling rate of Quantum Machines OPX in GSps.""" diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index c81c627da..6ec5fc455 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -9,12 +9,14 @@ from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions from qibolab.instruments.qm.controller import controllers_config from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence -from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular +from qibolab.pulses import Envelopes, Pulse, PulseSequence, PulseType from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, Sweeper from .conftest import set_platform_profile +Rectangular = Envelopes.RECTANGULAR.value + def test_qmpulse(): pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) diff --git a/tests/test_instruments_rfsoc.py b/tests/test_instruments_rfsoc.py index 2399ba489..1e099e407 100644 --- a/tests/test_instruments_rfsoc.py +++ b/tests/test_instruments_rfsoc.py @@ -14,7 +14,7 @@ convert_units_sweeper, replace_pulse_shape, ) -from qibolab.pulses import Drag, Gaussian, Pulse, PulseSequence, PulseType, Rectangular +from qibolab.pulses import Envelopes, Pulse, PulseSequence, PulseType from qibolab.qubits import Qubit from qibolab.result import ( AveragedIntegratedResults, @@ -25,6 +25,10 @@ from .conftest import get_instrument +Rectangular = Envelopes.RECTANGULAR.value +Gaussian = Envelopes.GAUSSIAN.value +Drag = Envelopes.DRAG.value + def test_convert_default(dummy_qrc): """Test convert function raises errors when parameter have wrong types.""" diff --git a/tests/test_sweeper.py b/tests/test_sweeper.py index b4b660f99..2b0f7908c 100644 --- a/tests/test_sweeper.py +++ b/tests/test_sweeper.py @@ -1,10 +1,12 @@ import numpy as np import pytest -from qibolab.pulses import Pulse, Rectangular +from qibolab.pulses import Envelopes, Pulse from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, QubitParameter, Sweeper +Rectangular = Envelopes.RECTANGULAR.value + @pytest.mark.parametrize("parameter", Parameter) def test_sweeper_pulses(parameter): From 604abf1703f588bcced72acc24a6a0cde5be63ad Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 15:50:10 +0400 Subject: [PATCH 193/233] fix: Propagate Pydantic models to Pulse --- src/qibolab/instruments/qm/config.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/qibolab/instruments/qm/config.py b/src/qibolab/instruments/qm/config.py index e6ceeca78..cc934fb20 100644 --- a/src/qibolab/instruments/qm/config.py +++ b/src/qibolab/instruments/qm/config.py @@ -4,12 +4,10 @@ import numpy as np from qibo.config import raise_error -from qibolab.pulses import Envelopes, PulseType +from qibolab.pulses import PulseType, Rectangular from .ports import OPXIQ, OctaveInput, OctaveOutput, OPXOutput -Rectangular = Envelopes.RECTANGULAR.value - SAMPLING_RATE = 1 """Sampling rate of Quantum Machines OPX in GSps.""" From c9a8ce57ad9dbb9fb432d9cf2fd7d7a47a68fce8 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 19:06:20 +0400 Subject: [PATCH 194/233] fix: Solve import-related issues in tests --- tests/test_instruments_qm.py | 4 +--- tests/test_instruments_rfsoc.py | 6 +----- tests/test_sweeper.py | 4 +--- 3 files changed, 3 insertions(+), 11 deletions(-) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 6ec5fc455..c81c627da 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -9,14 +9,12 @@ from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions from qibolab.instruments.qm.controller import controllers_config from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence -from qibolab.pulses import Envelopes, Pulse, PulseSequence, PulseType +from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, Sweeper from .conftest import set_platform_profile -Rectangular = Envelopes.RECTANGULAR.value - def test_qmpulse(): pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) diff --git a/tests/test_instruments_rfsoc.py b/tests/test_instruments_rfsoc.py index 1e099e407..2399ba489 100644 --- a/tests/test_instruments_rfsoc.py +++ b/tests/test_instruments_rfsoc.py @@ -14,7 +14,7 @@ convert_units_sweeper, replace_pulse_shape, ) -from qibolab.pulses import Envelopes, Pulse, PulseSequence, PulseType +from qibolab.pulses import Drag, Gaussian, Pulse, PulseSequence, PulseType, Rectangular from qibolab.qubits import Qubit from qibolab.result import ( AveragedIntegratedResults, @@ -25,10 +25,6 @@ from .conftest import get_instrument -Rectangular = Envelopes.RECTANGULAR.value -Gaussian = Envelopes.GAUSSIAN.value -Drag = Envelopes.DRAG.value - def test_convert_default(dummy_qrc): """Test convert function raises errors when parameter have wrong types.""" diff --git a/tests/test_sweeper.py b/tests/test_sweeper.py index 2b0f7908c..b4b660f99 100644 --- a/tests/test_sweeper.py +++ b/tests/test_sweeper.py @@ -1,12 +1,10 @@ import numpy as np import pytest -from qibolab.pulses import Envelopes, Pulse +from qibolab.pulses import Pulse, Rectangular from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, QubitParameter, Sweeper -Rectangular = Envelopes.RECTANGULAR.value - @pytest.mark.parametrize("parameter", Parameter) def test_sweeper_pulses(parameter): From 697d3a0c598ffe7119c002e2715c6ca6c791438e Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 8 Apr 2024 12:32:18 +0200 Subject: [PATCH 195/233] fix: Use pydantic to parse pulses, instead of relying on manual identification through marker fields --- src/qibolab/pulses/pulse.py | 5 ++++- src/qibolab/serialize.py | 26 +++++++++++++++++++------- 2 files changed, 23 insertions(+), 8 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index efd4c7b85..3c2c59b55 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -2,7 +2,7 @@ from dataclasses import fields from enum import Enum -from typing import Optional +from typing import Optional, Union import numpy as np @@ -146,3 +146,6 @@ class VirtualZ(Model): def duration(self): """Duration of the virtual gate should always be zero.""" return 0 + + +PulseLike = Union[Pulse, Delay, VirtualZ] diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index c9ede6731..e95d029f0 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -10,6 +10,8 @@ from pathlib import Path from typing import Tuple +from pydantic import ConfigDict, TypeAdapter + from qibolab.couplers import Coupler from qibolab.kernels import Kernels from qibolab.native import SingleQubitNatives, TwoQubitNatives @@ -22,6 +24,7 @@ Settings, ) from qibolab.pulses import Delay, Pulse, PulseSequence, PulseType, VirtualZ +from qibolab.pulses.pulse import PulseLike from qibolab.qubits import Qubit, QubitPair RUNCARD = "parameters.json" @@ -88,17 +91,26 @@ def load_qubits( return qubits, couplers, pairs -def _load_pulse(pulse_kwargs, qubit): +_PulseLike = TypeAdapter(PulseLike, config=ConfigDict(extra="ignore")) +"""Parse a pulse-like object. + +.. note:: + + Extra arguments are ignored, in order to standardize the qubit handling, since the + :cls:`Delay` object has no `qubit` field. + This will be removed once there won't be any need for dedicated couplers handling. +""" + + +def _load_pulse(pulse_kwargs: dict, qubit: Qubit): coupler = "coupler" in pulse_kwargs - q = pulse_kwargs.pop("coupler" if coupler else "qubit", qubit.name) + pulse_kwargs["qubit"] = pulse_kwargs.pop( + "coupler" if coupler else "qubit", qubit.name + ) - if "phase" in pulse_kwargs: - return VirtualZ(**pulse_kwargs, qubit=q) - if "amplitude" not in pulse_kwargs: - return Delay(**pulse_kwargs) if "frequency" not in pulse_kwargs: return Pulse.flux(**pulse_kwargs, qubit=q) - return Pulse(**pulse_kwargs, qubit=q) + return _PulseLike.validate_python(pulse_kwargs) def _load_single_qubit_natives(qubit, gates) -> SingleQubitNatives: From 6b9b8c6d0b46a51a1a3eaf0deeb982320df945db Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 8 Apr 2024 12:35:47 +0200 Subject: [PATCH 196/233] fix: Subclass pulse for automated flux recognition --- src/qibolab/pulses/pulse.py | 8 +++++++- src/qibolab/serialize.py | 2 -- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 3c2c59b55..cf93156e9 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -119,6 +119,12 @@ def __hash__(self): ) +class FluxPulse(Pulse): + frequency: float = 0.0 + relative_phase: float = 0.0 + type: PulseType = PulseType.FLUX + + class Delay(Model): """A wait instruction during which we are not sending any pulses to the QPU.""" @@ -148,4 +154,4 @@ def duration(self): return 0 -PulseLike = Union[Pulse, Delay, VirtualZ] +PulseLike = Union[Pulse, FluxPulse, Delay, VirtualZ] diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index e95d029f0..652689c20 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -108,8 +108,6 @@ def _load_pulse(pulse_kwargs: dict, qubit: Qubit): "coupler" if coupler else "qubit", qubit.name ) - if "frequency" not in pulse_kwargs: - return Pulse.flux(**pulse_kwargs, qubit=q) return _PulseLike.validate_python(pulse_kwargs) From 80f7f7f46aa266da8c30f545853190738e116cc5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 8 Apr 2024 10:38:47 +0000 Subject: [PATCH 197/233] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibolab/serialize.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index 652689c20..84b317596 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -23,7 +23,7 @@ QubitPairMap, Settings, ) -from qibolab.pulses import Delay, Pulse, PulseSequence, PulseType, VirtualZ +from qibolab.pulses import Pulse, PulseSequence, PulseType from qibolab.pulses.pulse import PulseLike from qibolab.qubits import Qubit, QubitPair From d614660bdeecfba74430131704603d08cdb92d0c Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 25 Jun 2024 18:59:01 +0200 Subject: [PATCH 198/233] fix: Rebase leftover Sorry, it seems like I made some mess... --- flake.nix | 2 +- .../instruments/qblox/cluster_qrm_rf.py | 12 +++++------ tests/test_compilers_default.py | 6 +++--- tests/test_instruments_qm.py | 21 ------------------- 4 files changed, 10 insertions(+), 31 deletions(-) diff --git a/flake.nix b/flake.nix index 478c524c9..9b2c037a8 100644 --- a/flake.nix +++ b/flake.nix @@ -49,7 +49,7 @@ config, ... }: { - packages = with pkgs; [pre-commit poethepoet jupyter stdenv.cc.cc.lib zlib]; + packages = with pkgs; [pre-commit poethepoet jupyter zlib]; env = { QIBOLAB_PLATFORMS = (dirOf config.env.DEVENV_ROOT) + "/qibolab_platforms_qrc"; diff --git a/src/qibolab/instruments/qblox/cluster_qrm_rf.py b/src/qibolab/instruments/qblox/cluster_qrm_rf.py index d09905eeb..9d582e859 100644 --- a/src/qibolab/instruments/qblox/cluster_qrm_rf.py +++ b/src/qibolab/instruments/qblox/cluster_qrm_rf.py @@ -993,9 +993,9 @@ def acquire(self): if len(sequencer.pulses.ro_pulses) == 1: pulse = sequencer.pulses.ro_pulses[0] frequency = self.get_if(pulse) - acquisitions[pulse.qubit] = acquisitions[ - pulse.id - ] = AveragedAcquisition(scope, duration, frequency) + acquisitions[pulse.qubit] = acquisitions[pulse.id] = ( + AveragedAcquisition(scope, duration, frequency) + ) else: raise RuntimeError( "Software Demodulation only supports one acquisition per channel. " @@ -1005,9 +1005,9 @@ def acquire(self): results = self.device.get_acquisitions(sequencer.number) for pulse in sequencer.pulses.ro_pulses: bins = results[pulse.id]["acquisition"]["bins"] - acquisitions[pulse.qubit] = acquisitions[ - pulse.id - ] = DemodulatedAcquisition(scope, bins, duration) + acquisitions[pulse.qubit] = acquisitions[pulse.id] = ( + DemodulatedAcquisition(scope, bins, duration) + ) # TODO: to be updated once the functionality of ExecutionResults is extended return {key: acquisition for key, acquisition in acquisitions.items()} diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index ff674a546..945f098c8 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -37,7 +37,7 @@ def compile_circuit(circuit, platform): @pytest.mark.parametrize( - "gateargs", + "gateargs,sequence_len", [ ((gates.I,), 1), ((gates.Z,), 2), @@ -47,11 +47,11 @@ def compile_circuit(circuit, platform): ((gates.U3, 0.1, 0.2, 0.3), 10), ], ) -def test_compile(platform, gateargs): +def test_compile(platform, gateargs, sequence_len): nqubits = platform.nqubits circuit = generate_circuit_with_gate(nqubits, *gateargs) sequence = compile_circuit(circuit, platform) - assert len(sequence) == nqubits * nseq + assert len(sequence) == nqubits * sequence_len def test_compile_two_gates(platform): diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index c81c627da..62b3ef994 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -346,21 +346,8 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): }, } -<<<<<<< HEAD controller.config.register_element( platform.qubits[qubit], pulse, controller.time_of_flight, controller.smearing -======= - opx.config.register_element( - platform.qubits[qubit], pulse, opx.time_of_flight, opx.smearing - ) - opx.config.register_pulse(platform.qubits[qubit], pulse) - assert opx.config.pulses[str(pulse.id)] == target_pulse - assert target_pulse["waveforms"]["I"] in opx.config.waveforms - assert target_pulse["waveforms"]["Q"] in opx.config.waveforms - assert ( - opx.config.elements[f"{pulse_type}{qubit}"]["operations"][str(pulse.id)] - == pulse.id ->>>>>>> 5f1fb614 (Fix QM issues by stringifying pulses ID) ) qmpulse = QMPulse(pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) @@ -386,19 +373,11 @@ def test_qm_register_flux_pulse(qmplatform): "length": pulse.duration, "waveforms": {"single": "constant_wf0.005"}, } -<<<<<<< HEAD qmpulse = QMPulse(pulse) controller.config.register_element(platform.qubits[qubit], pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) assert controller.config.pulses[qmpulse.operation] == target_pulse assert target_pulse["waveforms"]["single"] in controller.config.waveforms -======= - opx.config.register_element(platform.qubits[qubit], pulse) - opx.config.register_pulse(platform.qubits[qubit], pulse) - assert opx.config.pulses[str(pulse.id)] == target_pulse - assert target_pulse["waveforms"]["single"] in opx.config.waveforms - assert opx.config.elements[f"flux{qubit}"]["operations"][str(pulse.id)] == pulse.id ->>>>>>> 5f1fb614 (Fix QM issues by stringifying pulses ID) def test_qm_register_pulses_with_different_frequencies(qmplatform): From af9bb1801ee5e1590fe2a757fca3bd3bfe33cc82 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 25 Jun 2024 19:02:09 +0200 Subject: [PATCH 199/233] chore: Poetry lock --- poetry.lock | 1037 ++++++++++++++++++++++++++------------------------- 1 file changed, 520 insertions(+), 517 deletions(-) diff --git a/poetry.lock b/poetry.lock index 44aadec06..d98e36153 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. [[package]] name = "alabaster" @@ -13,13 +13,13 @@ files = [ [[package]] name = "annotated-types" -version = "0.6.0" +version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" files = [ - {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, - {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] [[package]] @@ -35,13 +35,13 @@ files = [ [[package]] name = "anyio" -version = "4.3.0" +version = "4.4.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = 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"pytest-mypy", "pytest-ruff (>=0.2.1)"] [extras] emulator = ["qutip", "scipy"] @@ -5798,4 +5801,4 @@ zh = ["laboneq"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "2e9555b32f971566f63c0505cb15a651f0dabd88ce630a265e96bc27cf250f6e" +content-hash = "e5a3a7b24638f1fbd6098e6003bc7e2e73b163013a6495578a2967740a8ed3c1" From ed4bdcdfa071132ed3c80654ec6efba77c91eaa0 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Thu, 2 May 2024 23:19:55 +0400 Subject: [PATCH 200/233] chore: remove FluxPulse object and add frequency: 0 to flux pulses in runcards --- src/qibolab/dummy/parameters.json | 20 ++++++++++++++++++++ src/qibolab/pulses/pulse.py | 8 +------- src/qibolab/serialize.py | 2 -- tests/dummy_qrc/qblox/parameters.json | 3 +++ tests/dummy_qrc/qm/parameters.json | 2 ++ tests/dummy_qrc/qm_octave/parameters.json | 2 ++ tests/dummy_qrc/zurich/parameters.json | 5 +++++ 7 files changed, 33 insertions(+), 9 deletions(-) diff --git a/src/qibolab/dummy/parameters.json b/src/qibolab/dummy/parameters.json index c1c666a66..3a5f5846e 100644 --- a/src/qibolab/dummy/parameters.json +++ b/src/qibolab/dummy/parameters.json @@ -173,6 +173,7 @@ "rel_sigma": 5, "width": 0.75 }, + "frequency": 0, "type": "cf" } }, @@ -185,6 +186,7 @@ "rel_sigma": 5, "width": 0.75 }, + "frequency": 0, "type": "cf" } }, @@ -197,6 +199,7 @@ "rel_sigma": 5, "width": 0.75 }, + "frequency": 0, "type": "cf" } }, @@ -209,6 +212,7 @@ "rel_sigma": 5, "width": 0.75 }, + "frequency": 0, "type": "cf" } } @@ -225,6 +229,7 @@ "width": 0.75 }, "qubit": 2, + "frequency": 0, "type": "qf" }, { @@ -246,6 +251,7 @@ "width": 0.75 }, "coupler": 0, + "frequency": 0, "type": "cf" } ], @@ -259,6 +265,7 @@ "width": 0.75 }, "qubit": 2, + "frequency": 0, "type": "qf" }, { @@ -280,6 +287,7 @@ "width": 0.75 }, "coupler": 0, + "frequency": 0, "type": "cf" } ] @@ -295,6 +303,7 @@ "width": 0.75 }, "qubit": 2, + "frequency": 0, "type": "qf" }, { @@ -316,6 +325,7 @@ "width": 0.75 }, "coupler": 1, + "frequency": 0, "type": "cf" } ], @@ -329,6 +339,7 @@ "width": 0.75 }, "qubit": 2, + "frequency": 0, "type": "qf" }, { @@ -350,6 +361,7 @@ "width": 0.75 }, "coupler": 1, + "frequency": 0, "type": "cf" } ] @@ -365,6 +377,7 @@ "width": 0.75 }, "qubit": 2, + "frequency": 0, "type": "qf" }, { @@ -386,6 +399,7 @@ "width": 0.75 }, "coupler": 3, + "frequency": 0, "type": "cf" } ], @@ -399,6 +413,7 @@ "width": 0.75 }, "qubit": 2, + "frequency": 0, "type": "qf" }, { @@ -420,6 +435,7 @@ "width": 0.75 }, "coupler": 3, + "frequency": 0, "type": "cf" } ], @@ -455,6 +471,7 @@ "width": 0.75 }, "qubit": 2, + "frequency": 0, "type": "qf" }, { @@ -476,6 +493,7 @@ "width": 0.75 }, "coupler": 4, + "frequency": 0, "type": "cf" } ], @@ -489,6 +507,7 @@ "width": 0.75 }, "qubit": 2, + "frequency": 0, "type": "qf" }, { @@ -510,6 +529,7 @@ "width": 0.75 }, "coupler": 4, + "frequency": 0, "type": "cf" } ] diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index cf93156e9..3c2c59b55 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -119,12 +119,6 @@ def __hash__(self): ) -class FluxPulse(Pulse): - frequency: float = 0.0 - relative_phase: float = 0.0 - type: PulseType = PulseType.FLUX - - class Delay(Model): """A wait instruction during which we are not sending any pulses to the QPU.""" @@ -154,4 +148,4 @@ def duration(self): return 0 -PulseLike = Union[Pulse, FluxPulse, Delay, VirtualZ] +PulseLike = Union[Pulse, Delay, VirtualZ] diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index 84b317596..9051f98b2 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -186,8 +186,6 @@ def load_instrument_settings( def _dump_pulse(pulse: Pulse): data = pulse.model_dump() - if pulse.type in (PulseType.FLUX, PulseType.COUPLERFLUX): - del data["frequency"] data["type"] = data["type"].value if "channel" in data: del data["channel"] diff --git a/tests/dummy_qrc/qblox/parameters.json b/tests/dummy_qrc/qblox/parameters.json index d7c283692..45058b910 100644 --- a/tests/dummy_qrc/qblox/parameters.json +++ b/tests/dummy_qrc/qblox/parameters.json @@ -208,6 +208,7 @@ "g": 0.1 }, "qubit": 3, + "frequency": 0, "type": "qf" }, { @@ -234,6 +235,7 @@ "g": 0.1 }, "qubit": 2, + "frequency": 0, "type": "qf" } ] @@ -250,6 +252,7 @@ "g": 0.1 }, "qubit": 2, + "frequency": 0, "type": "qf" }, { diff --git a/tests/dummy_qrc/qm/parameters.json b/tests/dummy_qrc/qm/parameters.json index d4a67753e..d8eb059ee 100644 --- a/tests/dummy_qrc/qm/parameters.json +++ b/tests/dummy_qrc/qm/parameters.json @@ -184,6 +184,7 @@ "amplitude": 0.055, "envelope": { "kind": "rectangular" }, "qubit": 2, + "frequency": 0, "type": "qf" }, { @@ -205,6 +206,7 @@ "amplitude": -0.0513, "envelope": { "kind": "rectangular" }, "qubit": 3, + "frequency": 0, "type": "qf" }, { diff --git a/tests/dummy_qrc/qm_octave/parameters.json b/tests/dummy_qrc/qm_octave/parameters.json index a77220498..0e496f422 100644 --- a/tests/dummy_qrc/qm_octave/parameters.json +++ b/tests/dummy_qrc/qm_octave/parameters.json @@ -206,6 +206,7 @@ "amplitude": 0.055, "envelope": { "kind": "rectangular" }, "qubit": 2, + "frequency": 0, "type": "qf" }, { @@ -227,6 +228,7 @@ "amplitude": -0.0513, "envelope": { "kind": "rectangular" }, "qubit": 3, + "frequency": 0, "type": "qf" }, { diff --git a/tests/dummy_qrc/zurich/parameters.json b/tests/dummy_qrc/zurich/parameters.json index 3ab0c3c07..0ff76701e 100644 --- a/tests/dummy_qrc/zurich/parameters.json +++ b/tests/dummy_qrc/zurich/parameters.json @@ -157,6 +157,7 @@ "type": "cf", "duration": 1000, "amplitude": 0.5, + "frequency": 0, "envelope": { "kind": "rectangular" } } }, @@ -165,6 +166,7 @@ "type": "cf", "duration": 1000, "amplitude": 0.5, + "frequency": 0, "envelope": { "kind": "rectangular" } } }, @@ -173,6 +175,7 @@ "type": "cf", "duration": 1000, "amplitude": 0.5, + "frequency": 0, "envelope": { "kind": "rectangular" } } }, @@ -181,6 +184,7 @@ "type": "cf", "duration": 1000, "amplitude": 0.5, + "frequency": 0, "envelope": { "kind": "rectangular" } } } @@ -198,6 +202,7 @@ "g": 0.1 }, "qubit": 3, + "frequency": 0, "type": "qf" }, { From 3a755d73e55ff840e235c4280f9de82ee3ac5a68 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 25 Jun 2024 19:13:08 +0200 Subject: [PATCH 201/233] fix: Propagate some 0.2 updates to the emulator --- src/qibolab/instruments/emulator/pulse_simulator.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/qibolab/instruments/emulator/pulse_simulator.py b/src/qibolab/instruments/emulator/pulse_simulator.py index 07e40aceb..7d657a8b6 100644 --- a/src/qibolab/instruments/emulator/pulse_simulator.py +++ b/src/qibolab/instruments/emulator/pulse_simulator.py @@ -12,7 +12,7 @@ from qibolab.instruments.abstract import Controller from qibolab.instruments.emulator.engines.qutip_engine import QutipSimulator from qibolab.instruments.emulator.models import general_no_coupler_model -from qibolab.pulses import PulseSequence, PulseType, ReadoutPulse +from qibolab.pulses import PulseSequence, PulseType from qibolab.qubits import Qubit, QubitId from qibolab.result import IntegratedResults, SampleResults from qibolab.sweeper import Parameter, Sweeper, SweeperType @@ -22,7 +22,6 @@ Parameter.duration, Parameter.frequency, Parameter.relative_phase, - Parameter.start, } SIMULATION_ENGINES = { @@ -755,7 +754,7 @@ def truncate_ro_pulses( """ sequence = copy.deepcopy(sequence) for i in range(len(sequence)): - if type(sequence[i]) is ReadoutPulse: + if sequence[i].type is PulseType.READOUT: sequence[i].duration = 1 return sequence From 490876363b155a4beb6ca78881ef5e5403e7f5f0 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Tue, 25 Jun 2024 19:28:44 +0200 Subject: [PATCH 202/233] build: Update Nix configuration --- flake.lock | 310 +++++++++-------------------------------------------- flake.nix | 8 +- 2 files changed, 53 insertions(+), 265 deletions(-) diff --git a/flake.lock b/flake.lock index 4f978df5d..0178efdcf 100644 --- a/flake.lock +++ b/flake.lock @@ -3,19 +3,25 @@ "cachix": { "inputs": { "devenv": "devenv_2", - "flake-compat": "flake-compat_2", + "flake-compat": [ + "devenv", + "flake-compat" + ], "nixpkgs": [ "devenv", "nixpkgs" ], - "pre-commit-hooks": "pre-commit-hooks" + "pre-commit-hooks": [ + "devenv", + "pre-commit-hooks" + ] }, "locked": { - "lastModified": 1710475558, - "narHash": "sha256-egKrPCKjy/cE+NqCj4hg2fNX/NwLCf0bRDInraYXDgs=", + "lastModified": 1712055811, + "narHash": "sha256-7FcfMm5A/f02yyzuavJe06zLa9hcMHsagE28ADcmQvk=", "owner": "cachix", "repo": "cachix", - "rev": "661bbb7f8b55722a0406456b15267b5426a3bda6", + "rev": "02e38da89851ec7fec3356a5c04bc8349cae0e30", "type": "github" }, "original": { @@ -27,19 +33,19 @@ "devenv": { "inputs": { "cachix": "cachix", - "flake-compat": "flake-compat_4", + "flake-compat": "flake-compat_2", "nix": "nix_2", "nixpkgs": [ "nixpkgs" ], - "pre-commit-hooks": "pre-commit-hooks_2" + "pre-commit-hooks": "pre-commit-hooks" }, "locked": { - "lastModified": 1711095830, - "narHash": "sha256-E67Yh1R1h8b01nVAhiYJsY6eQFqk5VIar13ntSbi56Q=", + "lastModified": 1719323427, + "narHash": "sha256-f4ppP2MBPJzkuy/q+PIfyyTWX9OzqgPV1XSphX71tdA=", "owner": "cachix", "repo": "devenv", - "rev": "84ce563fcecbdee90b3c3550ab4f2fcd37b37def", + "rev": "f810f8d8cb4e674d7e635107510bcbbabaa755a3", "type": "github" }, "original": { @@ -87,11 +93,11 @@ "rust-analyzer-src": "rust-analyzer-src" }, "locked": { - "lastModified": 1711088506, - "narHash": "sha256-USdlY7Tx2oJWqFBpp10+03+h7eVhpkQ4s9t1ERjeIJE=", + "lastModified": 1719296889, + "narHash": "sha256-rX9GzfrzvjfqrjfyKnX+zmXTYNRZXqEUWUX2u+LBdi0=", "owner": "nix-community", "repo": "fenix", - "rev": "85f4139f3c092cf4afd9f9906d7ed218ef262c97", + "rev": "049a6ecec1da711d3d84072732e4b14f98e0edd4", "type": "github" }, "original": { @@ -132,70 +138,6 @@ "type": "github" } }, - "flake-compat_3": { - "flake": false, - "locked": { - "lastModified": 1696426674, - "narHash": "sha256-kvjfFW7WAETZlt09AgDn1MrtKzP7t90Vf7vypd3OL1U=", - "owner": "edolstra", - "repo": "flake-compat", - "rev": "0f9255e01c2351cc7d116c072cb317785dd33b33", - "type": "github" - }, - "original": { - "owner": "edolstra", - "repo": "flake-compat", - "type": "github" - } - }, - "flake-compat_4": { - "flake": false, - "locked": { - "lastModified": 1696426674, - "narHash": "sha256-kvjfFW7WAETZlt09AgDn1MrtKzP7t90Vf7vypd3OL1U=", - "owner": "edolstra", - "repo": "flake-compat", - "rev": "0f9255e01c2351cc7d116c072cb317785dd33b33", - "type": "github" - }, - "original": { - "owner": "edolstra", - "repo": "flake-compat", - "type": "github" - } - }, - "flake-compat_5": { - "flake": false, - "locked": { - "lastModified": 1673956053, - "narHash": "sha256-4gtG9iQuiKITOjNQQeQIpoIB6b16fm+504Ch3sNKLd8=", - "owner": "edolstra", - "repo": "flake-compat", - "rev": "35bb57c0c8d8b62bbfd284272c928ceb64ddbde9", - "type": "github" - }, - "original": { - "owner": "edolstra", - "repo": "flake-compat", - "type": "github" - } - }, - "flake-compat_6": { - "flake": false, - "locked": { - "lastModified": 1696426674, - "narHash": "sha256-kvjfFW7WAETZlt09AgDn1MrtKzP7t90Vf7vypd3OL1U=", - "owner": "edolstra", - "repo": "flake-compat", - "rev": "0f9255e01c2351cc7d116c072cb317785dd33b33", - "type": "github" - }, - "original": { - "owner": "edolstra", - "repo": "flake-compat", - "type": "github" - } - }, "flake-utils": { "inputs": { "systems": "systems" @@ -219,11 +161,11 @@ "systems": "systems_2" }, "locked": { - "lastModified": 1701680307, - "narHash": "sha256-kAuep2h5ajznlPMD9rnQyffWG8EM/C73lejGofXvdM8=", + "lastModified": 1710146030, + "narHash": "sha256-SZ5L6eA7HJ/nmkzGG7/ISclqe6oZdOZTNoesiInkXPQ=", "owner": "numtide", "repo": "flake-utils", - "rev": "4022d587cbbfd70fe950c1e2083a02621806a725", + "rev": "b1d9ab70662946ef0850d488da1c9019f3a9752a", "type": "github" }, "original": { @@ -232,65 +174,7 @@ "type": "github" } }, - "flake-utils_3": { - "inputs": { - "systems": "systems_3" - }, - "locked": { - "lastModified": 1701680307, - "narHash": "sha256-kAuep2h5ajznlPMD9rnQyffWG8EM/C73lejGofXvdM8=", - "owner": "numtide", - "repo": "flake-utils", - "rev": "4022d587cbbfd70fe950c1e2083a02621806a725", - "type": "github" - }, - "original": { - "owner": "numtide", - "repo": "flake-utils", - "type": "github" - } - }, - "flake-utils_4": { - "inputs": { - "systems": "systems_4" - }, - "locked": { - "lastModified": 1701680307, - "narHash": "sha256-kAuep2h5ajznlPMD9rnQyffWG8EM/C73lejGofXvdM8=", - "owner": "numtide", - "repo": "flake-utils", - "rev": "4022d587cbbfd70fe950c1e2083a02621806a725", - "type": "github" - }, - "original": { - "id": "flake-utils", - "type": "indirect" - } - }, "gitignore": { - "inputs": { - "nixpkgs": [ - "devenv", - "cachix", - "pre-commit-hooks", - "nixpkgs" - ] - }, - "locked": { - "lastModified": 1703887061, - "narHash": "sha256-gGPa9qWNc6eCXT/+Z5/zMkyYOuRZqeFZBDbopNZQkuY=", - "owner": "hercules-ci", - "repo": "gitignore.nix", - "rev": "43e1aa1308018f37118e34d3a9cb4f5e75dc11d5", - "type": "github" - }, - "original": { - "owner": "hercules-ci", - "repo": "gitignore.nix", - "type": "github" - } - }, - "gitignore_2": { "inputs": { "nixpkgs": [ "devenv", @@ -299,11 +183,11 @@ ] }, "locked": { - "lastModified": 1703887061, - "narHash": "sha256-gGPa9qWNc6eCXT/+Z5/zMkyYOuRZqeFZBDbopNZQkuY=", + "lastModified": 1709087332, + "narHash": "sha256-HG2cCnktfHsKV0s4XW83gU3F57gaTljL9KNSuG6bnQs=", "owner": "hercules-ci", "repo": "gitignore.nix", - "rev": "43e1aa1308018f37118e34d3a9cb4f5e75dc11d5", + "rev": "637db329424fd7e46cf4185293b9cc8c88c95394", "type": "github" }, "original": { @@ -324,11 +208,11 @@ "nixpkgs-regression": "nixpkgs-regression" }, "locked": { - "lastModified": 1708577783, - "narHash": "sha256-92xq7eXlxIT5zFNccLpjiP7sdQqQI30Gyui2p/PfKZM=", + "lastModified": 1712911606, + "narHash": "sha256-BGvBhepCufsjcUkXnEEXhEVjwdJAwPglCC2+bInc794=", "owner": "domenkozar", "repo": "nix", - "rev": "ecd0af0c1f56de32cbad14daa1d82a132bf298f8", + "rev": "b24a9318ea3f3600c1e24b4a00691ee912d4de12", "type": "github" }, "original": { @@ -364,7 +248,10 @@ }, "nix_2": { "inputs": { - "flake-compat": "flake-compat_5", + "flake-compat": [ + "devenv", + "flake-compat" + ], "nixpkgs": [ "devenv", "nixpkgs" @@ -372,11 +259,11 @@ "nixpkgs-regression": "nixpkgs-regression_2" }, "locked": { - "lastModified": 1710500156, - "narHash": "sha256-zvCqeUO2GLOm7jnU23G4EzTZR7eylcJN+HJ5svjmubI=", + "lastModified": 1712911606, + "narHash": "sha256-BGvBhepCufsjcUkXnEEXhEVjwdJAwPglCC2+bInc794=", "owner": "domenkozar", "repo": "nix", - "rev": "c5bbf14ecbd692eeabf4184cc8d50f79c2446549", + "rev": "b24a9318ea3f3600c1e24b4a00691ee912d4de12", "type": "github" }, "original": { @@ -402,28 +289,6 @@ "type": "github" } }, - "nixpkgs-python": { - "inputs": { - "flake-compat": "flake-compat_6", - "flake-utils": "flake-utils_4", - "nixpkgs": [ - "nixpkgs" - ] - }, - "locked": { - "lastModified": 1710929962, - "narHash": "sha256-CuPuUyX1TmxJDDZFOZMr7kHTzA8zoSJaVw0+jDVo2fw=", - "owner": "cachix", - "repo": "nixpkgs-python", - "rev": "a9e19aafbf75b8c7e5adf2d7319939309ebe0d77", - "type": "github" - }, - "original": { - "owner": "cachix", - "repo": "nixpkgs-python", - "type": "github" - } - }, "nixpkgs-regression": { "locked": { "lastModified": 1643052045, @@ -458,27 +323,11 @@ }, "nixpkgs-stable": { "locked": { - "lastModified": 1704874635, - "narHash": "sha256-YWuCrtsty5vVZvu+7BchAxmcYzTMfolSPP5io8+WYCg=", + "lastModified": 1710695816, + "narHash": "sha256-3Eh7fhEID17pv9ZxrPwCLfqXnYP006RKzSs0JptsN84=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "3dc440faeee9e889fe2d1b4d25ad0f430d449356", - "type": "github" - }, - "original": { - "owner": "NixOS", - "ref": "nixos-23.11", - "repo": "nixpkgs", - "type": "github" - } - }, - "nixpkgs-stable_2": { - "locked": { - "lastModified": 1704874635, - "narHash": "sha256-YWuCrtsty5vVZvu+7BchAxmcYzTMfolSPP5io8+WYCg=", - "owner": "NixOS", - "repo": "nixpkgs", - "rev": "3dc440faeee9e889fe2d1b4d25ad0f430d449356", + "rev": "614b4613980a522ba49f0d194531beddbb7220d3", "type": "github" }, "original": { @@ -490,11 +339,11 @@ }, "nixpkgs_2": { "locked": { - "lastModified": 1710806803, - "narHash": "sha256-qrxvLS888pNJFwJdK+hf1wpRCSQcqA6W5+Ox202NDa0=", + "lastModified": 1719075281, + "narHash": "sha256-CyyxvOwFf12I91PBWz43iGT1kjsf5oi6ax7CrvaMyAo=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "b06025f1533a1e07b6db3e75151caa155d1c7eb3", + "rev": "a71e967ef3694799d0c418c98332f7ff4cc5f6af", "type": "github" }, "original": { @@ -530,51 +379,25 @@ } }, "pre-commit-hooks": { - "inputs": { - "flake-compat": "flake-compat_3", - "flake-utils": "flake-utils_2", - "gitignore": "gitignore", - "nixpkgs": [ - "devenv", - "cachix", - "nixpkgs" - ], - "nixpkgs-stable": "nixpkgs-stable" - }, - "locked": { - "lastModified": 1708018599, - "narHash": "sha256-M+Ng6+SePmA8g06CmUZWi1AjG2tFBX9WCXElBHEKnyM=", - "owner": "cachix", - "repo": "pre-commit-hooks.nix", - "rev": "5df5a70ad7575f6601d91f0efec95dd9bc619431", - "type": "github" - }, - "original": { - "owner": "cachix", - "repo": "pre-commit-hooks.nix", - "type": "github" - } - }, - "pre-commit-hooks_2": { "inputs": { "flake-compat": [ "devenv", "flake-compat" ], - "flake-utils": "flake-utils_3", - "gitignore": "gitignore_2", + "flake-utils": "flake-utils_2", + "gitignore": "gitignore", "nixpkgs": [ "devenv", "nixpkgs" ], - "nixpkgs-stable": "nixpkgs-stable_2" + "nixpkgs-stable": "nixpkgs-stable" }, "locked": { - "lastModified": 1708018599, - "narHash": "sha256-M+Ng6+SePmA8g06CmUZWi1AjG2tFBX9WCXElBHEKnyM=", + "lastModified": 1713775815, + "narHash": "sha256-Wu9cdYTnGQQwtT20QQMg7jzkANKQjwBD9iccfGKkfls=", "owner": "cachix", "repo": "pre-commit-hooks.nix", - "rev": "5df5a70ad7575f6601d91f0efec95dd9bc619431", + "rev": "2ac4dcbf55ed43f3be0bae15e181f08a57af24a4", "type": "github" }, "original": { @@ -588,18 +411,17 @@ "devenv": "devenv", "fenix": "fenix", "nixpkgs": "nixpkgs_2", - "nixpkgs-python": "nixpkgs-python", - "systems": "systems_5" + "systems": "systems_3" } }, "rust-analyzer-src": { "flake": false, "locked": { - "lastModified": 1711052942, - "narHash": "sha256-lLsAhLgm/Nbin41wdfGKU7Rgd6ONBxYCUAMv53NXPjo=", + "lastModified": 1719233333, + "narHash": "sha256-+BgWRK3bWVIFwdn43DGRVscnu9P63Mndyhte/hgEwUA=", "owner": "rust-lang", "repo": "rust-analyzer", - "rev": "7ef7f442fc34b5eadb1c6ad6433bd6d0c51b056b", + "rev": "7b11fdeb681c12002861b9804a388efde81c9647", "type": "github" }, "original": { @@ -653,36 +475,6 @@ "repo": "default", "type": "github" } - }, - "systems_4": { - "locked": { - "lastModified": 1681028828, - "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=", - "owner": "nix-systems", - "repo": "default", - "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e", - "type": "github" - }, - "original": { - "owner": "nix-systems", - "repo": "default", - "type": "github" - } - }, - "systems_5": { - "locked": { - "lastModified": 1681028828, - "narHash": 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enable = true; install = { @@ -77,7 +74,6 @@ ]; }; }; - version = "3.11"; }; languages.rust = { From 3ed320c236c44ed06a568cbee626ea15becbc7ef Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 26 Jun 2024 00:58:48 +0200 Subject: [PATCH 203/233] build: Unlock emulator dependencies --- poetry.lock | 135 +++++++++++++++++++++++-------------------------- pyproject.toml | 10 ++-- 2 files changed, 67 insertions(+), 78 deletions(-) diff --git a/poetry.lock b/poetry.lock index d98e36153..84cc4e3a6 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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"pytest-mypy", "pytest-ruff (>=0.2.1)"] [extras] -emulator = ["qutip", "scipy"] +emulator = ["qutip"] los = ["pyvisa-py", "qcodes", "qcodes_contrib_drivers"] qblox = ["pyvisa-py", "qblox-instruments", "qcodes", "qcodes_contrib_drivers"] qm = ["qm-qua", "qualang-tools"] @@ -5801,4 +5792,4 @@ zh = ["laboneq"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "e5a3a7b24638f1fbd6098e6003bc7e2e73b163013a6495578a2967740a8ed3c1" +content-hash = "119c26fb9dd5316d2d9cbc98c7175238acd500e30816883420289be3e45e34d6" diff --git a/pyproject.toml b/pyproject.toml index e6867c86f..c1ff7db12 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,7 @@ python = ">=3.9,<3.12" qibo = ">=0.2.6" networkx = "^3.0" numpy = "^1.26.4" +scipy = "^1.13.0" more-itertools = "^9.1.0" pydantic = "^2.6.4" qblox-instruments = { version = "0.12.0", optional = true } @@ -36,10 +37,7 @@ qualang-tools = { version = "^0.15.0", optional = true } setuptools = { version = ">67.0.0", optional = true } laboneq = { version = "==2.25.0", optional = true } qibosoq = { version = ">=0.1.2,<0.2", optional = true } -# TODO: unlock version -qutip = { version = "4.7.5", optional = true } -# TODO: remove this constraint, only needed for qutip 4.7.5 -scipy = { version = "<1.13.0", optional = true } +qutip = { version = "^5.0.2", optional = true } [tool.poetry.group.dev] optional = true @@ -67,7 +65,7 @@ qcodes_contrib_drivers = "0.18.0" qibosoq = ">=0.1.2,<0.2" qualang-tools = "^0.15.0" laboneq = "==2.25.0" -qutip = "^4.7.5" +qutip = "^5.0.2" [tool.poetry.group.tests] optional = true @@ -90,7 +88,7 @@ qm = ["qm-qua", "qualang-tools"] zh = ["laboneq"] rfsoc = ["qibosoq"] los = ["qcodes", "qcodes_contrib_drivers", "pyvisa-py"] -emulator = ["qutip", "scipy"] +emulator = ["qutip"] [tool.poe.tasks] From 7cbafde44543d01ebe1c9d064291c4de60d57184 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 26 Jun 2024 01:04:35 +0200 Subject: [PATCH 204/233] fix: Update qutip imports to new major --- src/qibolab/instruments/emulator/engines/qutip_engine.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/qibolab/instruments/emulator/engines/qutip_engine.py b/src/qibolab/instruments/emulator/engines/qutip_engine.py index f290d43e2..ab3bc244c 100644 --- a/src/qibolab/instruments/emulator/engines/qutip_engine.py +++ b/src/qibolab/instruments/emulator/engines/qutip_engine.py @@ -10,8 +10,8 @@ import numpy as np from qutip import Options, Qobj, basis, expect, ket2dm, mesolve, ptrace -from qutip.operators import identity as Id -from qutip.tensor import tensor +from qutip.core.operators import identity as Id +from qutip.core.tensor import tensor from qutip.ui.progressbar import EnhancedTextProgressBar from qibolab.instruments.emulator.engines.generic import ( From 7b2d3025e8e6647a91c3a6a215925f780178c071 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 26 Jun 2024 11:07:17 +0200 Subject: [PATCH 205/233] ci: Attempt to upgrade macos to ARM QuTiP 5.0.2 finally supports ARM... and only that QuTiP 5.0.0 and 5.0.1 only support macos x86_64 --- .github/workflows/deploy.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index 3c38e8bb2..94950834c 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -11,8 +11,8 @@ jobs: build: strategy: matrix: - os: [ubuntu-latest, macos-13, windows-latest] - python-version: [3.9, '3.10', '3.11'] + os: [ubuntu-latest, macos-latest, windows-latest] + python-version: [3.9, "3.10", "3.11"] uses: qiboteam/workflows/.github/workflows/deploy-pip-poetry.yml@main with: os: ${{ matrix.os }} From da5b70c8725d737a2419fad57ec3dbbe0f8e3298 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 26 Jun 2024 18:25:38 +0200 Subject: [PATCH 206/233] fix: Fix qubit pair class construction, to account for new optional attributes --- src/qibolab/serialize.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/qibolab/serialize.py b/src/qibolab/serialize.py index 9051f98b2..ad32747ae 100644 --- a/src/qibolab/serialize.py +++ b/src/qibolab/serialize.py @@ -168,7 +168,9 @@ def register_gates( q0, q1 = tuple(int(q) if q.isdigit() else q for q in pair.split("-")) native_gates = _load_two_qubit_natives(qubits, couplers, gatedict) coupler = pairs[(q0, q1)].coupler - pairs[(q0, q1)] = QubitPair(qubits[q0], qubits[q1], coupler, native_gates) + pairs[(q0, q1)] = QubitPair( + qubits[q0], qubits[q1], coupler=coupler, native_gates=native_gates + ) if native_gates.symmetric: pairs[(q1, q0)] = pairs[(q0, q1)] From 1bb88e412c7bdf96d816ae82ea25b3d5af2769e0 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 26 Jun 2024 18:34:22 +0200 Subject: [PATCH 207/233] test: Temporarily ignore emulator tests --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index c1ff7db12..826b5ecc4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -111,4 +111,5 @@ addopts = [ '--cov-report=xml', '--cov-report=html', '-m not qpu', + '-k not emulator', ] From fe98b1416f7fb64d01865fdda8a9f15794363b12 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 26 Jun 2024 18:43:41 +0200 Subject: [PATCH 208/233] docs: Comment emulator doc test --- doc/source/main-documentation/qibolab.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/source/main-documentation/qibolab.rst b/doc/source/main-documentation/qibolab.rst index 046813cdc..3903108da 100644 --- a/doc/source/main-documentation/qibolab.rst +++ b/doc/source/main-documentation/qibolab.rst @@ -127,9 +127,9 @@ will create a dummy platform that also has coupler qubits. Emulator platform ^^^^^^^^^^^^^^^^^ -QiboLab supports the use of emulators to simulate the behavior of quantum devices. It uses :class:`qibolab.instruments.emulator.pulse_simulator.PulseSimulator`, which is a controller that utilizes a simulation engine to numerically solve the dynamics of the device in the presence of control pulse sequences specified by :class:`qibolab.pulses.PulseSequence`. The emulator platform for a specific device requires its own platform folder and can be initialized in the same way as any other real platforms: +QiboLab supports the use of emulators to simulate the behavior of quantum devices. It uses :class:`qibolab.instruments.emulator.pulse_simulator.PulseSimulator`, which is a controller that utilizes a simulation engine to numerically solve the dynamics of the device in the presence of control pulse sequences specified by :class:`qibolab.pulses.PulseSequence`. The emulator platform for a specific device requires its own platform folder and can be initialized in the same way as any other real platforms:: -.. testcode:: python_emulator + # .. testcode:: python_emulator import os from pathlib import Path From 1b80394d94c8a5a70aca9b23165c8ad6b5429ec0 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 26 Jun 2024 19:19:46 +0200 Subject: [PATCH 209/233] ci: Upgrade all workflows to macos arm --- .github/workflows/rules.yml | 4 ++-- .github/workflows/rustapi.yml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/rules.yml b/.github/workflows/rules.yml index eab77d20f..54045c8b3 100644 --- a/.github/workflows/rules.yml +++ b/.github/workflows/rules.yml @@ -11,8 +11,8 @@ jobs: build: strategy: matrix: - os: [ubuntu-latest, macos-13, windows-latest] - python-version: [3.9, '3.10', '3.11'] + os: [ubuntu-latest, macos-latest, windows-latest] + python-version: [3.9, "3.10", "3.11"] uses: qiboteam/workflows/.github/workflows/rules-poetry.yml@main with: os: ${{ matrix.os }} diff --git a/.github/workflows/rustapi.yml b/.github/workflows/rustapi.yml index 8f4971253..ed69c5a83 100644 --- a/.github/workflows/rustapi.yml +++ b/.github/workflows/rustapi.yml @@ -9,7 +9,7 @@ jobs: tests: strategy: matrix: - os: [ubuntu-latest, macos-13] + os: [ubuntu-latest, macos-latest] runs-on: ${{ matrix.os }} steps: From 6213a02a375f4886ba619031f4b2b14e768b233d Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 19:17:35 +0100 Subject: [PATCH 210/233] Fix QM issues by stringifying pulses ID QM requires some keys to be strings, because of the way they are later processed. And before they were (by accident, since we were using the serial as an identifier). --- tests/test_instruments_qm.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 62b3ef994..c81c627da 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -346,8 +346,21 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): }, } +<<<<<<< HEAD controller.config.register_element( platform.qubits[qubit], pulse, controller.time_of_flight, controller.smearing +======= + opx.config.register_element( + platform.qubits[qubit], pulse, opx.time_of_flight, opx.smearing + ) + opx.config.register_pulse(platform.qubits[qubit], pulse) + assert opx.config.pulses[str(pulse.id)] == target_pulse + assert target_pulse["waveforms"]["I"] in opx.config.waveforms + assert target_pulse["waveforms"]["Q"] in opx.config.waveforms + assert ( + opx.config.elements[f"{pulse_type}{qubit}"]["operations"][str(pulse.id)] + == pulse.id +>>>>>>> 5f1fb614 (Fix QM issues by stringifying pulses ID) ) qmpulse = QMPulse(pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) @@ -373,11 +386,19 @@ def test_qm_register_flux_pulse(qmplatform): "length": pulse.duration, "waveforms": {"single": "constant_wf0.005"}, } +<<<<<<< HEAD qmpulse = QMPulse(pulse) controller.config.register_element(platform.qubits[qubit], pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) assert controller.config.pulses[qmpulse.operation] == target_pulse assert target_pulse["waveforms"]["single"] in controller.config.waveforms +======= + opx.config.register_element(platform.qubits[qubit], pulse) + opx.config.register_pulse(platform.qubits[qubit], pulse) + assert opx.config.pulses[str(pulse.id)] == target_pulse + assert target_pulse["waveforms"]["single"] in opx.config.waveforms + assert opx.config.elements[f"flux{qubit}"]["operations"][str(pulse.id)] == pulse.id +>>>>>>> 5f1fb614 (Fix QM issues by stringifying pulses ID) def test_qm_register_pulses_with_different_frequencies(qmplatform): From cf5a58376015e6f0a161b81bcc516d7f677dc47a Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 14:24:37 +0100 Subject: [PATCH 211/233] Strip all imports of removed objects --- tests/test_instruments_qm.py | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index c81c627da..62b3ef994 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -346,21 +346,8 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): }, } -<<<<<<< HEAD controller.config.register_element( platform.qubits[qubit], pulse, controller.time_of_flight, controller.smearing -======= - opx.config.register_element( - platform.qubits[qubit], pulse, opx.time_of_flight, opx.smearing - ) - opx.config.register_pulse(platform.qubits[qubit], pulse) - assert opx.config.pulses[str(pulse.id)] == target_pulse - assert target_pulse["waveforms"]["I"] in opx.config.waveforms - assert target_pulse["waveforms"]["Q"] in opx.config.waveforms - assert ( - opx.config.elements[f"{pulse_type}{qubit}"]["operations"][str(pulse.id)] - == pulse.id ->>>>>>> 5f1fb614 (Fix QM issues by stringifying pulses ID) ) qmpulse = QMPulse(pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) @@ -386,19 +373,11 @@ def test_qm_register_flux_pulse(qmplatform): "length": pulse.duration, "waveforms": {"single": "constant_wf0.005"}, } -<<<<<<< HEAD qmpulse = QMPulse(pulse) controller.config.register_element(platform.qubits[qubit], pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) assert controller.config.pulses[qmpulse.operation] == target_pulse assert target_pulse["waveforms"]["single"] in controller.config.waveforms -======= - opx.config.register_element(platform.qubits[qubit], pulse) - opx.config.register_pulse(platform.qubits[qubit], pulse) - assert opx.config.pulses[str(pulse.id)] == target_pulse - assert target_pulse["waveforms"]["single"] in opx.config.waveforms - assert opx.config.elements[f"flux{qubit}"]["operations"][str(pulse.id)] == pulse.id ->>>>>>> 5f1fb614 (Fix QM issues by stringifying pulses ID) def test_qm_register_pulses_with_different_frequencies(qmplatform): From 17c05fa23bc56010284716bdab614ff854d42c39 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 14:29:54 +0100 Subject: [PATCH 212/233] Fix backends test, remove explicit copy methods To copy, both shallow and deep, just use the dedicated standard library module --- src/qibolab/platform/platform.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 597c9632a..71137b96b 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -1,5 +1,5 @@ """A platform for executing quantum algorithms.""" - +import copy from collections import defaultdict from dataclasses import dataclass, field, fields from typing import Dict, List, Optional, Tuple From 912434163b587bfd0908656cdbc086d2f7179a5b Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 18:18:40 +0100 Subject: [PATCH 213/233] Start rearranging pulses into a subpackage --- src/qibolab/pulses/waveform.py | 42 ++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 src/qibolab/pulses/waveform.py diff --git a/src/qibolab/pulses/waveform.py b/src/qibolab/pulses/waveform.py new file mode 100644 index 000000000..7c530bf36 --- /dev/null +++ b/src/qibolab/pulses/waveform.py @@ -0,0 +1,42 @@ +"""Waveform class.""" +import numpy as np + + +class Waveform: + """A class to save pulse waveforms. + + A waveform is a list of samples, or discrete data points, used by the digital to analogue converters (DACs) + to synthesise pulses. + + Attributes: + data (np.ndarray): a numpy array containing the samples. + """ + + DECIMALS = 5 + + def __init__(self, data): + """Initialise the waveform with a of samples.""" + self.data: np.ndarray = np.array(data) + + def __len__(self): + """Return the length of the waveform, the number of samples.""" + return len(self.data) + + def __hash__(self): + """Hash the underlying data. + + .. todo:: + + In order to make this reliable, we should set the data as immutable. This we + could by making both the class frozen and the contained array readonly + https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags + """ + return hash(self.data.tobytes()) + + def __eq__(self, other): + """Compare two waveforms. + + Two waveforms are considered equal if their samples, rounded to + `Waveform.DECIMALS` decimal places, are all equal. + """ + return np.allclose(self.data, other.data) From 5030a7c34aa67f5054a6db8785b64ca5e00ad2b7 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 19 Jan 2024 19:15:24 +0100 Subject: [PATCH 214/233] Turn waveform into a bare array --- src/qibolab/pulses/waveform.py | 42 ---------------------------------- 1 file changed, 42 deletions(-) delete mode 100644 src/qibolab/pulses/waveform.py diff --git a/src/qibolab/pulses/waveform.py b/src/qibolab/pulses/waveform.py deleted file mode 100644 index 7c530bf36..000000000 --- a/src/qibolab/pulses/waveform.py +++ /dev/null @@ -1,42 +0,0 @@ -"""Waveform class.""" -import numpy as np - - -class Waveform: - """A class to save pulse waveforms. - - A waveform is a list of samples, or discrete data points, used by the digital to analogue converters (DACs) - to synthesise pulses. - - Attributes: - data (np.ndarray): a numpy array containing the samples. - """ - - DECIMALS = 5 - - def __init__(self, data): - """Initialise the waveform with a of samples.""" - self.data: np.ndarray = np.array(data) - - def __len__(self): - """Return the length of the waveform, the number of samples.""" - return len(self.data) - - def __hash__(self): - """Hash the underlying data. - - .. todo:: - - In order to make this reliable, we should set the data as immutable. This we - could by making both the class frozen and the contained array readonly - https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags - """ - return hash(self.data.tobytes()) - - def __eq__(self, other): - """Compare two waveforms. - - Two waveforms are considered equal if their samples, rounded to - `Waveform.DECIMALS` decimal places, are all equal. - """ - return np.allclose(self.data, other.data) From ed56d4e3d680a8f98a022a8ac02a6e8db1a9bfd4 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Wed, 20 Mar 2024 18:00:21 +0400 Subject: [PATCH 215/233] fix: wrong merge --- src/qibolab/platform/platform.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 71137b96b..2f050fa2c 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -1,4 +1,5 @@ """A platform for executing quantum algorithms.""" + import copy from collections import defaultdict from dataclasses import dataclass, field, fields From 8c8170faf145601d39850496f2047049d78591fc Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 12 Jan 2024 16:25:00 +0100 Subject: [PATCH 216/233] Drop pulse.serial --- tests/test_instruments_qm.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 62b3ef994..0b201bf7f 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -19,7 +19,11 @@ def test_qmpulse(): pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) qmpulse = QMPulse(pulse) +<<<<<<< HEAD assert qmpulse.operation == "drive(40, 0.05, Rectangular())" +======= + assert qmpulse.operation == pulse.id +>>>>>>> 337bff40 (Drop pulse.serial) assert qmpulse.relative_phase == 0 @@ -346,8 +350,20 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): }, } +<<<<<<< HEAD controller.config.register_element( platform.qubits[qubit], pulse, controller.time_of_flight, controller.smearing +======= + opx.config.register_element( + platform.qubits[qubit], pulse, opx.time_of_flight, opx.smearing + ) + opx.config.register_pulse(platform.qubits[qubit], pulse) + assert opx.config.pulses[pulse.id] == target_pulse + assert target_pulse["waveforms"]["I"] in opx.config.waveforms + assert target_pulse["waveforms"]["Q"] in opx.config.waveforms + assert ( + opx.config.elements[f"{pulse_type}{qubit}"]["operations"][pulse.id] == pulse.id +>>>>>>> 337bff40 (Drop pulse.serial) ) qmpulse = QMPulse(pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) @@ -373,11 +389,19 @@ def test_qm_register_flux_pulse(qmplatform): "length": pulse.duration, "waveforms": {"single": "constant_wf0.005"}, } +<<<<<<< HEAD qmpulse = QMPulse(pulse) controller.config.register_element(platform.qubits[qubit], pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) assert controller.config.pulses[qmpulse.operation] == target_pulse assert target_pulse["waveforms"]["single"] in controller.config.waveforms +======= + opx.config.register_element(platform.qubits[qubit], pulse) + opx.config.register_pulse(platform.qubits[qubit], pulse) + assert opx.config.pulses[pulse.id] == target_pulse + assert target_pulse["waveforms"]["single"] in opx.config.waveforms + assert opx.config.elements[f"flux{qubit}"]["operations"][pulse.id] == pulse.id +>>>>>>> 337bff40 (Drop pulse.serial) def test_qm_register_pulses_with_different_frequencies(qmplatform): From 37513b52ac53ae97799d5ae1427f002f6418159d Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Wed, 17 Jan 2024 19:17:35 +0100 Subject: [PATCH 217/233] Fix QM issues by stringifying pulses ID QM requires some keys to be strings, because of the way they are later processed. And before they were (by accident, since we were using the serial as an identifier). --- tests/test_instruments_qm.py | 24 ------------------------ 1 file changed, 24 deletions(-) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 0b201bf7f..62b3ef994 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -19,11 +19,7 @@ def test_qmpulse(): pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) qmpulse = QMPulse(pulse) -<<<<<<< HEAD assert qmpulse.operation == "drive(40, 0.05, Rectangular())" -======= - assert qmpulse.operation == pulse.id ->>>>>>> 337bff40 (Drop pulse.serial) assert qmpulse.relative_phase == 0 @@ -350,20 +346,8 @@ def test_qm_register_pulse(qmplatform, pulse_type, qubit): }, } -<<<<<<< HEAD controller.config.register_element( platform.qubits[qubit], pulse, controller.time_of_flight, controller.smearing -======= - opx.config.register_element( - platform.qubits[qubit], pulse, opx.time_of_flight, opx.smearing - ) - opx.config.register_pulse(platform.qubits[qubit], pulse) - assert opx.config.pulses[pulse.id] == target_pulse - assert target_pulse["waveforms"]["I"] in opx.config.waveforms - assert target_pulse["waveforms"]["Q"] in opx.config.waveforms - assert ( - opx.config.elements[f"{pulse_type}{qubit}"]["operations"][pulse.id] == pulse.id ->>>>>>> 337bff40 (Drop pulse.serial) ) qmpulse = QMPulse(pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) @@ -389,19 +373,11 @@ def test_qm_register_flux_pulse(qmplatform): "length": pulse.duration, "waveforms": {"single": "constant_wf0.005"}, } -<<<<<<< HEAD qmpulse = QMPulse(pulse) controller.config.register_element(platform.qubits[qubit], pulse) controller.config.register_pulse(platform.qubits[qubit], qmpulse) assert controller.config.pulses[qmpulse.operation] == target_pulse assert target_pulse["waveforms"]["single"] in controller.config.waveforms -======= - opx.config.register_element(platform.qubits[qubit], pulse) - opx.config.register_pulse(platform.qubits[qubit], pulse) - assert opx.config.pulses[pulse.id] == target_pulse - assert target_pulse["waveforms"]["single"] in opx.config.waveforms - assert opx.config.elements[f"flux{qubit}"]["operations"][pulse.id] == pulse.id ->>>>>>> 337bff40 (Drop pulse.serial) def test_qm_register_pulses_with_different_frequencies(qmplatform): From 3b02702918845ce8ca70bab77c75bf9cde54d25d Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Sat, 24 Feb 2024 01:36:33 +0400 Subject: [PATCH 218/233] fix: pylint --- src/qibolab/platform/platform.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 2f050fa2c..597c9632a 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -1,6 +1,5 @@ """A platform for executing quantum algorithms.""" -import copy from collections import defaultdict from dataclasses import dataclass, field, fields from typing import Dict, List, Optional, Tuple From 9fc21b29d749423b47ca2020243da0522ab1dc63 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Mon, 4 Mar 2024 19:16:45 +0400 Subject: [PATCH 219/233] fix: compiler and tests --- src/qibolab/compilers/default.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/qibolab/compilers/default.py b/src/qibolab/compilers/default.py index 227b07af5..95acd8c3b 100644 --- a/src/qibolab/compilers/default.py +++ b/src/qibolab/compilers/default.py @@ -4,6 +4,7 @@ """ import math +from dataclasses import replace from qibolab.pulses import PulseSequence, VirtualZ from qibolab.serialize_ import replace From abcaeb14d7e06c9c7f3662c7cf4d48bf37993ff5 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Thu, 21 Mar 2024 13:15:26 +0400 Subject: [PATCH 220/233] fix: tests after merging (compiler tests still failing) --- tests/test_compilers_default.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index 945f098c8..ff674a546 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -37,7 +37,7 @@ def compile_circuit(circuit, platform): @pytest.mark.parametrize( - "gateargs,sequence_len", + "gateargs", [ ((gates.I,), 1), ((gates.Z,), 2), @@ -47,11 +47,11 @@ def compile_circuit(circuit, platform): ((gates.U3, 0.1, 0.2, 0.3), 10), ], ) -def test_compile(platform, gateargs, sequence_len): +def test_compile(platform, gateargs): nqubits = platform.nqubits circuit = generate_circuit_with_gate(nqubits, *gateargs) sequence = compile_circuit(circuit, platform) - assert len(sequence) == nqubits * sequence_len + assert len(sequence) == nqubits * nseq def test_compile_two_gates(platform): From b1c4d1b6a0d171e8998953040b9559205e1e337d Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 18 Jan 2024 18:18:40 +0100 Subject: [PATCH 221/233] Start rearranging pulses into a subpackage --- src/qibolab/pulses/plot.py | 37 ++++++---- src/qibolab/pulses/pulse.py | 32 +++++++-- src/qibolab/pulses/sequence.py | 125 ++++++++++++++++++++++++++++++--- src/qibolab/pulses/waveform.py | 42 +++++++++++ 4 files changed, 207 insertions(+), 29 deletions(-) create mode 100644 src/qibolab/pulses/waveform.py diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index 87319febf..027b64688 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -1,7 +1,4 @@ """Plotting tools for pulses and related entities.""" - -from collections import defaultdict - import matplotlib.pyplot as plt import numpy as np @@ -25,7 +22,7 @@ def waveform(wf: Waveform, filename=None): filename (str): a file path. If provided the plot is save to a file. """ plt.figure(figsize=(14, 5), dpi=200) - plt.plot(wf, c="C0", linestyle="dashed") + plt.plot(wf.data, c="C0", linestyle="dashed") plt.xlabel("Sample Number") plt.ylabel("Amplitude") plt.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") @@ -55,14 +52,14 @@ def pulse(pulse_: Pulse, filename=None): ax1 = plt.subplot(gs[0]) ax1.plot( time, - waveform_i, + waveform_i.data, label="envelope i", c="C0", linestyle="dashed", ) ax1.plot( time, - waveform_q, + waveform_q.data, label="envelope q", c="C1", linestyle="dashed", @@ -77,25 +74,37 @@ def pulse(pulse_: Pulse, filename=None): ax1.set_ylabel("Amplitude") ax1.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") - start = 0 - finish = float(pulse_.duration) + start = float(pulse_.start) + finish = float(pulse._finish) if pulse._finish is not None else 0.0 ax1.axis((start, finish, -1.0, 1.0)) ax1.legend() + modulated_i = pulse_.shape.modulated_waveform_i(sampling_rate).data + modulated_q = pulse_.shape.modulated_waveform_q(sampling_rate).data ax2 = plt.subplot(gs[1]) - ax2.plot(modulated[0], modulated[1], label="modulated", c="C3") - ax2.plot(waveform_i, waveform_q, label="envelope", c="C2") ax2.plot( - modulated[0][0], - modulated[1][0], + modulated_i, + modulated_q, + label="modulated", + c="C3", + ) + ax2.plot( + waveform_i.data, + waveform_q.data, + label="envelope", + c="C2", + ) + ax2.plot( + modulated_i[0], + modulated_q[0], marker="o", markersize=5, label="start", c="lightcoral", ) ax2.plot( - modulated[0][-1], - modulated[1][-1], + modulated_i[-1], + modulated_q[-1], marker="o", markersize=5, label="finish", diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 3c2c59b55..77a7e484c 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -95,6 +95,24 @@ def envelopes(self, sampling_rate: float) -> IqWaveform: """A tuple with the i and q envelope waveforms of the pulse.""" return np.array([self.i(sampling_rate), self.q(sampling_rate)]) + def modulated_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: + """The waveform of the i component of the pulse, modulated with its + frequency.""" + + return self.shape.modulated_waveform_i(sampling_rate) + + def modulated_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: + """The waveform of the q component of the pulse, modulated with its + frequency.""" + + return self.shape.modulated_waveform_q(sampling_rate) + + def modulated_waveforms(self, sampling_rate): # -> tuple[Waveform, Waveform]: + """A tuple with the i and q waveforms of the pulse, modulated with its + frequency.""" + + return self.shape.modulated_waveforms(sampling_rate) + def __hash__(self): """Hash the content. @@ -118,17 +136,19 @@ def __hash__(self): ) ) + def __add__(self, other): + if isinstance(other, Pulse): + return PulseSequence(self, other) + if isinstance(other, PulseSequence): + return PulseSequence(self, *other) + raise TypeError(f"Expected Pulse or PulseSequence; got {type(other).__name__}") class Delay(Model): """A wait instruction during which we are not sending any pulses to the QPU.""" - duration: int - """Delay duration in ns.""" - channel: str - """Channel on which the delay should be implemented.""" - type: PulseType = PulseType.DELAY - """Type fixed to ``DELAY`` to comply with ``Pulse`` interface.""" + def __rmul__(self, n): + return self.__mul__(n) class VirtualZ(Model): diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py index b48cb62cd..3dfe2f5d7 100644 --- a/src/qibolab/pulses/sequence.py +++ b/src/qibolab/pulses/sequence.py @@ -1,8 +1,5 @@ """PulseSequence class.""" - -from collections import defaultdict - -from .pulse import PulseType +import numpy as np class PulseSequence(list): @@ -94,12 +91,22 @@ def coupler_pulses(self, *couplers): return new_pc @property - def pulses_per_channel(self): - """Return a dictionary with the sequence per channel.""" - sequences = defaultdict(type(self)) + def finish(self) -> int: + """The time when the last pulse of the sequence finishes.""" + t: int = 0 + for pulse in self: + if pulse.finish > t: + t = pulse.finish + return t + + @property + def start(self) -> int: + """The start time of the first pulse of the sequence.""" + t = self.finish for pulse in self: - sequences[pulse.channel].append(pulse) - return sequences + if pulse.start < t: + t = pulse.start + return t @property def duration(self) -> int: @@ -132,6 +139,25 @@ def qubits(self) -> list: qubits.sort() return qubits + def get_pulse_overlaps(self): # -> dict((int,int): PulseSequence): + """Return a dictionary of slices of time (tuples with start and finish + times) where pulses overlap.""" + times = [] + for pulse in self: + if not pulse.start in times: + times.append(pulse.start) + if not pulse.finish in times: + times.append(pulse.finish) + times.sort() + + overlaps = {} + for n in range(len(times) - 1): + overlaps[(times[n], times[n + 1])] = PulseSequence() + for pulse in self: + if (pulse.start <= times[n]) & (pulse.finish >= times[n + 1]): + overlaps[(times[n], times[n + 1])] += [pulse] + return overlaps + def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): """Separate a sequence of overlapping pulses into a list of non- overlapping sequences.""" @@ -157,3 +183,84 @@ def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): if not stored: separated_pulses.append(PulseSequence([new_pulse])) return separated_pulses + + # TODO: Implement separate_different_frequency_pulses() + + @property + def pulses_overlap(self) -> bool: + """Whether any of the pulses in the sequence overlap.""" + overlap = False + for pc in self.get_pulse_overlaps().values(): + if len(pc) > 1: + overlap = True + break + return overlap + + def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): + """Plot the sequence of pulses. + + Args: + savefig_filename (str): a file path. If provided the plot is save to a file. + """ + if len(self) > 0: + import matplotlib.pyplot as plt + from matplotlib import gridspec + + fig = plt.figure(figsize=(14, 2 * len(self)), dpi=200) + gs = gridspec.GridSpec(ncols=1, nrows=len(self)) + vertical_lines = [] + for pulse in self: + vertical_lines.append(pulse.start) + vertical_lines.append(pulse.finish) + + n = -1 + for qubit in self.qubits: + qubit_pulses = self.get_qubit_pulses(qubit) + for channel in qubit_pulses.channels: + n += 1 + channel_pulses = qubit_pulses.get_channel_pulses(channel) + ax = plt.subplot(gs[n]) + ax.axis([0, self.finish, -1, 1]) + for pulse in channel_pulses: + num_samples = len( + pulse.shape.modulated_waveform_i(sampling_rate) + ) + time = pulse.start + np.arange(num_samples) / sampling_rate + ax.plot( + time, + pulse.shape.modulated_waveform_q(sampling_rate).data, + c="lightgrey", + ) + ax.plot( + time, + pulse.shape.modulated_waveform_i(sampling_rate).data, + c=f"C{str(n)}", + ) + ax.plot( + time, + pulse.shape.envelope_waveform_i(sampling_rate).data, + c=f"C{str(n)}", + ) + ax.plot( + time, + -pulse.shape.envelope_waveform_i(sampling_rate).data, + c=f"C{str(n)}", + ) + # TODO: if they overlap use different shades + ax.axhline(0, c="dimgrey") + ax.set_ylabel(f"qubit {qubit} \n channel {channel}") + for vl in vertical_lines: + ax.axvline(vl, c="slategrey", linestyle="--") + ax.axis([0, self.finish, -1, 1]) + ax.grid( + visible=True, + which="both", + axis="both", + color="#CCCCCC", + linestyle="-", + ) + if savefig_filename: + plt.savefig(savefig_filename) + else: + plt.show() + plt.close() diff --git a/src/qibolab/pulses/waveform.py b/src/qibolab/pulses/waveform.py new file mode 100644 index 000000000..7c530bf36 --- /dev/null +++ b/src/qibolab/pulses/waveform.py @@ -0,0 +1,42 @@ +"""Waveform class.""" +import numpy as np + + +class Waveform: + """A class to save pulse waveforms. + + A waveform is a list of samples, or discrete data points, used by the digital to analogue converters (DACs) + to synthesise pulses. + + Attributes: + data (np.ndarray): a numpy array containing the samples. + """ + + DECIMALS = 5 + + def __init__(self, data): + """Initialise the waveform with a of samples.""" + self.data: np.ndarray = np.array(data) + + def __len__(self): + """Return the length of the waveform, the number of samples.""" + return len(self.data) + + def __hash__(self): + """Hash the underlying data. + + .. todo:: + + In order to make this reliable, we should set the data as immutable. This we + could by making both the class frozen and the contained array readonly + https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags + """ + return hash(self.data.tobytes()) + + def __eq__(self, other): + """Compare two waveforms. + + Two waveforms are considered equal if their samples, rounded to + `Waveform.DECIMALS` decimal places, are all equal. + """ + return np.allclose(self.data, other.data) From 3f8eda7720a7fc8713a977a5024493d9e921245d Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Fri, 15 Mar 2024 18:10:32 +0100 Subject: [PATCH 222/233] test: Fix remaining pytests collection errors --- src/qibolab/instruments/qm/config.py | 4 +++- tests/test_instruments_qm.py | 4 +++- tests/test_instruments_rfsoc.py | 4 ++++ tests/test_sweeper.py | 2 ++ 4 files changed, 12 insertions(+), 2 deletions(-) diff --git a/src/qibolab/instruments/qm/config.py b/src/qibolab/instruments/qm/config.py index cc934fb20..e6ceeca78 100644 --- a/src/qibolab/instruments/qm/config.py +++ b/src/qibolab/instruments/qm/config.py @@ -4,10 +4,12 @@ import numpy as np from qibo.config import raise_error -from qibolab.pulses import PulseType, Rectangular +from qibolab.pulses import Envelopes, PulseType from .ports import OPXIQ, OctaveInput, OctaveOutput, OPXOutput +Rectangular = Envelopes.RECTANGULAR.value + SAMPLING_RATE = 1 """Sampling rate of Quantum Machines OPX in GSps.""" diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 62b3ef994..b60670f86 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -9,12 +9,14 @@ from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions from qibolab.instruments.qm.controller import controllers_config from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence -from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular +from qibolab.pulses import Envelopes, Pulse, PulseSequence, PulseType from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, Sweeper from .conftest import set_platform_profile +Rectangular = Envelopes.RECTANGULAR.value + def test_qmpulse(): pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) diff --git a/tests/test_instruments_rfsoc.py b/tests/test_instruments_rfsoc.py index 2399ba489..9b85991c8 100644 --- a/tests/test_instruments_rfsoc.py +++ b/tests/test_instruments_rfsoc.py @@ -25,6 +25,10 @@ from .conftest import get_instrument +Rectangular = Envelopes.RECTANGULAR.value +Gaussian = Envelopes.GAUSSIAN.value +Drag = Envelopes.DRAG.value + def test_convert_default(dummy_qrc): """Test convert function raises errors when parameter have wrong types.""" diff --git a/tests/test_sweeper.py b/tests/test_sweeper.py index b4b660f99..d4b991ad3 100644 --- a/tests/test_sweeper.py +++ b/tests/test_sweeper.py @@ -5,6 +5,8 @@ from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, QubitParameter, Sweeper +Rectangular = Envelopes.RECTANGULAR.value + @pytest.mark.parametrize("parameter", Parameter) def test_sweeper_pulses(parameter): From 8e1d05b50cd8143163213bd299be1216589e1552 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 15:50:10 +0400 Subject: [PATCH 223/233] fix: Propagate Pydantic models to Pulse --- src/qibolab/instruments/qm/config.py | 4 +--- src/qibolab/pulses/pulse.py | 1 + 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/src/qibolab/instruments/qm/config.py b/src/qibolab/instruments/qm/config.py index e6ceeca78..cc934fb20 100644 --- a/src/qibolab/instruments/qm/config.py +++ b/src/qibolab/instruments/qm/config.py @@ -4,12 +4,10 @@ import numpy as np from qibo.config import raise_error -from qibolab.pulses import Envelopes, PulseType +from qibolab.pulses import PulseType, Rectangular from .ports import OPXIQ, OctaveInput, OctaveOutput, OPXOutput -Rectangular = Envelopes.RECTANGULAR.value - SAMPLING_RATE = 1 """Sampling rate of Quantum Machines OPX in GSps.""" diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 77a7e484c..48fd3b9ae 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -5,6 +5,7 @@ from typing import Optional, Union import numpy as np +from pydantic import BaseModel from qibolab.serialize_ import Model From 64fcfc368fe534417abf732a699542fe987a4970 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 21 Mar 2024 19:06:20 +0400 Subject: [PATCH 224/233] fix: Solve import-related issues in tests --- tests/test_instruments_qm.py | 4 +--- tests/test_instruments_rfsoc.py | 4 ---- tests/test_sweeper.py | 2 -- 3 files changed, 1 insertion(+), 9 deletions(-) diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index b60670f86..62b3ef994 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -9,14 +9,12 @@ from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions from qibolab.instruments.qm.controller import controllers_config from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence -from qibolab.pulses import Envelopes, Pulse, PulseSequence, PulseType +from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, Sweeper from .conftest import set_platform_profile -Rectangular = Envelopes.RECTANGULAR.value - def test_qmpulse(): pulse = Pulse(0, 40, 0.05, int(3e9), 0.0, Rectangular(), "ch0", qubit=0) diff --git a/tests/test_instruments_rfsoc.py b/tests/test_instruments_rfsoc.py index 9b85991c8..2399ba489 100644 --- a/tests/test_instruments_rfsoc.py +++ b/tests/test_instruments_rfsoc.py @@ -25,10 +25,6 @@ from .conftest import get_instrument -Rectangular = Envelopes.RECTANGULAR.value -Gaussian = Envelopes.GAUSSIAN.value -Drag = Envelopes.DRAG.value - def test_convert_default(dummy_qrc): """Test convert function raises errors when parameter have wrong types.""" diff --git a/tests/test_sweeper.py b/tests/test_sweeper.py index d4b991ad3..b4b660f99 100644 --- a/tests/test_sweeper.py +++ b/tests/test_sweeper.py @@ -5,8 +5,6 @@ from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, QubitParameter, Sweeper -Rectangular = Envelopes.RECTANGULAR.value - @pytest.mark.parametrize("parameter", Parameter) def test_sweeper_pulses(parameter): From 69003c9183a906ce6cbf916b0a829907417a7623 Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Thu, 28 Mar 2024 18:20:49 +0100 Subject: [PATCH 225/233] feat: Propagate pydantic models to execution parameters, fix backend tests --- src/qibolab/compilers/default.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/qibolab/compilers/default.py b/src/qibolab/compilers/default.py index 95acd8c3b..227b07af5 100644 --- a/src/qibolab/compilers/default.py +++ b/src/qibolab/compilers/default.py @@ -4,7 +4,6 @@ """ import math -from dataclasses import replace from qibolab.pulses import PulseSequence, VirtualZ from qibolab.serialize_ import replace From b43517bd144ca6f81105945a0485a91f93fa27ef Mon Sep 17 00:00:00 2001 From: Alessandro Candido Date: Mon, 15 Apr 2024 15:38:03 +0200 Subject: [PATCH 226/233] chore: Poetry lock --- poetry.lock | 1 - 1 file changed, 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 84cc4e3a6..696d1709d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2836,7 +2836,6 @@ files = [ {file = "pandas-2.2.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:0cace394b6ea70c01ca1595f839cf193df35d1575986e484ad35c4aeae7266c1"}, {file = "pandas-2.2.2-cp311-cp311-win_amd64.whl", hash = "sha256:873d13d177501a28b2756375d59816c365e42ed8417b41665f346289adc68d24"}, {file = "pandas-2.2.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:9dfde2a0ddef507a631dc9dc4af6a9489d5e2e740e226ad426a05cabfbd7c8ef"}, - {file = "pandas-2.2.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:e9b79011ff7a0f4b1d6da6a61aa1aa604fb312d6647de5bad20013682d1429ce"}, {file = "pandas-2.2.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1cb51fe389360f3b5a4d57dbd2848a5f033350336ca3b340d1c53a1fad33bcad"}, {file = "pandas-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eee3a87076c0756de40b05c5e9a6069c035ba43e8dd71c379e68cab2c20f16ad"}, {file = "pandas-2.2.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:3e374f59e440d4ab45ca2fffde54b81ac3834cf5ae2cdfa69c90bc03bde04d76"}, From 574c934cdf0b58fe124749096d231a173b8a0d9a Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Sat, 20 Apr 2024 18:01:51 +0400 Subject: [PATCH 227/233] refactor: Drop QM specific sequences and pulses --- src/qibolab/instruments/qm/acquisition.py | 135 ++++++----- src/qibolab/instruments/qm/config.py | 33 ++- src/qibolab/instruments/qm/controller.py | 74 +++--- src/qibolab/instruments/qm/program.py | 67 ++++++ src/qibolab/instruments/qm/sequence.py | 273 ---------------------- src/qibolab/instruments/qm/sweepers.py | 182 +++++++-------- tests/test_instruments_qm.py | 3 +- 7 files changed, 304 insertions(+), 463 deletions(-) create mode 100644 src/qibolab/instruments/qm/program.py delete mode 100644 src/qibolab/instruments/qm/sequence.py diff --git a/src/qibolab/instruments/qm/acquisition.py b/src/qibolab/instruments/qm/acquisition.py index 4e6390a13..62e9da9a8 100644 --- a/src/qibolab/instruments/qm/acquisition.py +++ b/src/qibolab/instruments/qm/acquisition.py @@ -1,4 +1,5 @@ from abc import ABC, abstractmethod +from collections import defaultdict from dataclasses import dataclass, field from typing import Optional @@ -9,7 +10,11 @@ from qualang_tools.addons.variables import assign_variables_to_element from qualang_tools.units import unit -from qibolab.execution_parameters import AcquisitionType, AveragingMode +from qibolab.execution_parameters import ( + AcquisitionType, + AveragingMode, + ExecutionParameters, +) from qibolab.qubits import QubitId from qibolab.result import ( AveragedIntegratedResults, @@ -20,6 +25,8 @@ SampleResults, ) +# TODO: Change name to operation? + @dataclass class Acquisition(ABC): @@ -33,6 +40,8 @@ class Acquisition(ABC): name: str """Name of the acquisition used as identifier to download results from the instruments.""" + element: str + """Element from QM ``config`` that the pulse will be applied on.""" qubit: QubitId average: bool @@ -49,22 +58,19 @@ def npulses(self): return len(self.keys) @abstractmethod - def assign_element(self, element): - """Assign acquisition variables to the corresponding QM controlled. - - Proposed to do by QM to avoid crashes. + def declare(self): + """Declares QUA variables related to this acquisition. - Args: - element (str): Element (from ``config``) that the pulse will be applied on. + Assigns acquisition variables to the corresponding QM + controller. This was proposed by QM to avoid crashes. """ @abstractmethod - def measure(self, operation, element): + def measure(self, operation): """Send measurement pulse and acquire results. Args: operation (str): Operation (from ``config``) corresponding to the pulse to be played. - element (str): Element (from ``config``) that the pulse will be applied on. """ @abstractmethod @@ -91,16 +97,14 @@ def result(self, data): class RawAcquisition(Acquisition): """QUA variables used for raw waveform acquisition.""" - adc_stream: _ResultSource = field( - default_factory=lambda: declare_stream(adc_trace=True) - ) + adc_stream: Optional[_ResultSource] = None """Stream to collect raw ADC data.""" RESULT_CLS = RawWaveformResults AVERAGED_RESULT_CLS = AveragedRawWaveformResults - def assign_element(self, element): - pass + def declare(self): + self.adc_stream = declare_stream(adc_trace=True) def measure(self, operation, element): qua.reset_phase(element) @@ -128,23 +132,27 @@ def fetch(self, handles): class IntegratedAcquisition(Acquisition): """QUA variables used for integrated acquisition.""" - i: _Variable = field(default_factory=lambda: declare(fixed)) - q: _Variable = field(default_factory=lambda: declare(fixed)) + i: Optional[_Variable] = None + q: Optional[_Variable] = None """Variables to save the (I, Q) values acquired from a single shot.""" - istream: _ResultSource = field(default_factory=lambda: declare_stream()) - qstream: _ResultSource = field(default_factory=lambda: declare_stream()) + istream: Optional[_ResultSource] = None + qstream: Optional[_ResultSource] = None """Streams to collect the results of all shots.""" RESULT_CLS = IntegratedResults AVERAGED_RESULT_CLS = AveragedIntegratedResults - def assign_element(self, element): - assign_variables_to_element(element, self.i, self.q) + def declare(self): + self.i = declare(fixed) + self.q = declare(fixed) + self.istream = declare_stream() + self.qstream = declare_stream() + assign_variables_to_element(self.element, self.i, self.q) - def measure(self, operation, element): + def measure(self, operation): qua.measure( operation, - element, + self.element, None, qua.dual_demod.full("cos", "out1", "sin", "out2", self.i), qua.dual_demod.full("minus_sin", "out1", "cos", "out2", self.q), @@ -185,12 +193,12 @@ class ShotsAcquisition(Acquisition): angle: Optional[float] = None """Angle in the IQ plane to be used for classification of single shots.""" - i: _Variable = field(default_factory=lambda: declare(fixed)) - q: _Variable = field(default_factory=lambda: declare(fixed)) + i: Optional[_Variable] = None + q: Optional[_Variable] = None """Variables to save the (I, Q) values acquired from a single shot.""" - shot: _Variable = field(default_factory=lambda: declare(int)) + shot: Optional[_Variable] = None """Variable for calculating an individual shots.""" - shots: _ResultSource = field(default_factory=lambda: declare_stream()) + shots: Optional[_ResultSource] = None """Stream to collect multiple shots.""" RESULT_CLS = SampleResults @@ -200,13 +208,17 @@ def __post_init__(self): self.cos = np.cos(self.angle) self.sin = np.sin(self.angle) - def assign_element(self, element): - assign_variables_to_element(element, self.i, self.q, self.shot) + def declare(self): + self.i = declare(fixed) + self.q = declare(fixed) + self.shot = declare(int) + self.shots = declare_stream() + assign_variables_to_element(self.element, self.i, self.q, self.shot) - def measure(self, operation, element): + def measure(self, operation): qua.measure( operation, - element, + self.element, None, qua.dual_demod.full("cos", "out1", "sin", "out2", self.i), qua.dual_demod.full("minus_sin", "out1", "cos", "out2", self.q), @@ -239,39 +251,35 @@ def fetch(self, handles): } -def declare_acquisitions(ro_pulses, qubits, options): - """Declares variables for saving acquisition in the QUA program. +def create_acquisition( + operation: str, + element: str, + qubit: QubitId, + options: ExecutionParameters, + threshold: float, + angle: float, +): + """Create container for the variables used for saving acquisition in the + QUA program. Args: - ro_pulses (list): List of readout pulses in the sequence. - qubits (dict): Dictionary containing all the :class:`qibolab.qubits.Qubit` - objects of the platform. + operation (str): + element (str): + qubit (str): Name of the qubit. options (:class:`qibolab.execution_parameters.ExecutionParameters`): Execution options containing acquisition type and averaging mode. Returns: - List of all :class:`qibolab.instruments.qm.acquisition.Acquisition` objects. + :class:`qibolab.instruments.qm.acquisition.Acquisition` object containing acquisition variables. """ - acquisitions = {} - for qmpulse in ro_pulses: - qubit = qmpulse.pulse.qubit - name = f"{qmpulse.operation}_{qubit}" - if name not in acquisitions: - average = options.averaging_mode is AveragingMode.CYCLIC - kwargs = {} - if options.acquisition_type is AcquisitionType.DISCRIMINATION: - kwargs["threshold"] = qubits[qubit].threshold - kwargs["angle"] = qubits[qubit].iq_angle - - acquisition = ACQUISITION_TYPES[options.acquisition_type]( - name, qubit, average, **kwargs - ) - acquisition.assign_element(qmpulse.element) - acquisitions[name] = acquisition - - acquisitions[name].keys.append(qmpulse.pulse.id) - qmpulse.acquisition = acquisitions[name] - return list(acquisitions.values()) + average = options.averaging_mode is AveragingMode.CYCLIC + kwargs = {} + if options.acquisition_type is AcquisitionType.DISCRIMINATION: + kwargs = {"threshold": threshold, "angle": angle} + acquisition = ACQUISITION_TYPES[options.acquisition_type]( + operation, element, qubit, average, **kwargs + ) + return acquisition def fetch_results(result, acquisitions): @@ -279,16 +287,21 @@ def fetch_results(result, acquisitions): Args: result: Result of the executed experiment. - acquisition (dict): Dictionary containing :class:`qibolab.instruments.qm.acquisition.Acquisition` objects. + acquisition: Dictionary containing :class:`qibolab.instruments.qm.acquisition.Acquisition` objects. Returns: Dictionary with the results in the format required by the platform. """ handles = result.result_handles handles.wait_for_all_values() # for async replace with ``handles.is_processing()`` - results = {} + results = defaultdict(list) for acquisition in acquisitions: data = acquisition.fetch(handles) - for id_, result in zip(acquisition.keys, data): - results[acquisition.qubit] = results[id_] = result - return results + for serial, result in zip(acquisition.keys, data): + results[serial].append(result) + results[acquisition.qubit] = results[serial] + + # collapse single element lists for back-compatibility + return { + key: value[0] if len(value) == 1 else value for key, value in results.items() + } diff --git a/src/qibolab/instruments/qm/config.py b/src/qibolab/instruments/qm/config.py index cc934fb20..243a5c2c9 100644 --- a/src/qibolab/instruments/qm/config.py +++ b/src/qibolab/instruments/qm/config.py @@ -19,6 +19,11 @@ """ +def float_serial(x): + """Convert float to string to use in config keys.""" + return format(x, ".6f").rstrip("0").rstrip(".") + + @dataclass class QMConfig: """Configuration for communicating with the ``QuantumMachinesManager``.""" @@ -251,7 +256,7 @@ def register_element(self, qubit, pulse, time_of_flight=0, smearing=0): element = self.register_flux_element(qubit, pulse.frequency) return element - def register_pulse(self, qubit, qmpulse): + def register_pulse(self, pulse, operation, element, qubit): """Registers pulse, waveforms and integration weights in QM config. Args: @@ -340,17 +345,31 @@ def register_waveform(self, pulse, mode="i"): serial = "zero_wf" if serial not in self.waveforms: self.waveforms[serial] = {"type": "constant", "sample": 0.0} - elif isinstance(pulse.shape, Rectangular): - serial = f"constant_wf{pulse.amplitude}" + return serial + + phase = (pulse.relative_phase % (2 * np.pi)) / (2 * np.pi) + amplitude = float_serial(pulse.amplitude) + phase_str = float_serial(phase) + if isinstance(pulse.shape, Rectangular): + serial = f"constant_wf({amplitude}, {phase_str})" if serial not in self.waveforms: - self.waveforms[serial] = {"type": "constant", "sample": pulse.amplitude} + if mode == "i": + sample = pulse.amplitude * np.cos(phase) + else: + sample = pulse.amplitude * np.sin(phase) + self.waveforms[serial] = {"type": "constant", "sample": sample} else: - waveform = getattr(pulse, f"envelope_waveform_{mode}")(SAMPLING_RATE) - serial = hash(waveform.tobytes()) + serial = f"{mode}({pulse.duration}, {amplitude}, {phase_str}, {str(pulse.shape)})" if serial not in self.waveforms: + samples_i = pulse.envelope_waveform_i(SAMPLING_RATE).data + samples_q = pulse.envelope_waveform_q(SAMPLING_RATE).data + if mode == "i": + samples = samples_i * np.cos(phase) - samples_q * np.sin(phase) + else: + samples = samples_i * np.sin(phase) + samples_q * np.cos(phase) self.waveforms[serial] = { "type": "arbitrary", - "samples": waveform.tolist(), + "samples": samples.tolist(), } return serial diff --git a/src/qibolab/instruments/qm/controller.py b/src/qibolab/instruments/qm/controller.py index 0aeebab91..1d4462452 100644 --- a/src/qibolab/instruments/qm/controller.py +++ b/src/qibolab/instruments/qm/controller.py @@ -1,3 +1,4 @@ +from collections import defaultdict from dataclasses import dataclass, field from typing import Dict, Optional @@ -13,11 +14,11 @@ from qibolab.sweeper import Parameter from qibolab.unrolling import Bounds -from .acquisition import declare_acquisitions, fetch_results +from .acquisition import create_acquisition, fetch_results from .config import SAMPLING_RATE, QMConfig from .devices import Octave, OPXplus from .ports import OPXIQ -from .sequence import BakedPulse, QMPulse, Sequence +from .program import Parameters, element, operation from .sweepers import sweep OCTAVE_ADDRESS_OFFSET = 11000 @@ -136,11 +137,12 @@ class QMController(Controller): manager: Optional[QuantumMachinesManager] = None """Manager object used for controlling the Quantum Machines cluster.""" - config: QMConfig = field(default_factory=QMConfig) - """Configuration dictionary required for pulse execution on the OPXs.""" is_connected: bool = False """Boolean that shows whether we are connected to the QM manager.""" + config: QMConfig = field(default_factory=QMConfig) + """Configuration dictionary required for pulse execution on the OPXs.""" + simulation_duration: Optional[int] = None """Duration for the simulation in ns. @@ -269,29 +271,28 @@ def simulate_program(self, program): ) return self.manager.simulate(self.config.__dict__, program, simulation_config) - def create_sequence(self, qubits, sequence, sweepers): - """Translates a :class:`qibolab.pulses.PulseSequence` to a - :class:`qibolab.instruments.qm.sequence.Sequence`. + def register_pulses(self, qubits, sequence, options): + """Translates a :class:`qibolab.pulses.PulseSequence` to + :class:`qibolab.instruments.qm.instructions.Instructions`. Args: qubits (list): List of :class:`qibolab.platforms.abstract.Qubit` objects passed from the platform. sequence (:class:`qibolab.pulses.PulseSequence`). Pulse sequence to translate. sweepers (list): List of sweeper objects so that pulses that require baking are identified. + Returns: - (:class:`qibolab.instruments.qm.sequence.Sequence`) containing the pulses from given pulse sequence. + acquisitions (dict): Map from measurement instructions to acquisition objects. + parameters (dict): """ # Current driver cannot play overlapping pulses on drive and flux channels # If we want to play overlapping pulses we need to define different elements on the same ports # like we do for readout multiplex - pulses_to_bake = find_baking_pulses(sweepers) - - qmsequence = Sequence() - ro_pulses = [] - for pulse in sorted(sequence, key=lambda pulse: (pulse.start, pulse.duration)): + acquisitions = {} + parameters = defaultdict(Parameters) + for pulse in sequence: qubit = qubits[pulse.qubit] - self.config.register_port(getattr(qubit, pulse.type.name.lower()).port) if pulse.type is PulseType.READOUT: self.config.register_port(qubit.feedback.port) @@ -313,8 +314,20 @@ def create_sequence(self, qubits, sequence, sweepers): self.config.register_pulse(qubit, qmpulse) qmsequence.add(qmpulse) - qmsequence.shift() - return qmsequence, ro_pulses + op = operation(hash(pulse)) + el = element(pulse) + if op not in self.config.pulses: + self.config.register_pulse(pulse, op, el, qubit) + + if pulse.type is PulseType.READOUT: + if op not in acquisitions: + acquisitions[op] = create_acquisition( + op, el, options, qubit.threshold, qubit.iq_angle + ) + parameters[op].acquisition = acquisitions[op] + acquisitions[op].keys.append(pulse.id) + + return acquisitions, parameters def play(self, qubits, couplers, sequence, options): return self.sweep(qubits, couplers, sequence, options) @@ -334,22 +347,25 @@ def sweep(self, qubits, couplers, sequence, options, *sweepers): self.config.register_port(qubit.flux.port) self.config.register_flux_element(qubit) - qmsequence, ro_pulses = self.create_sequence(qubits, sequence, sweepers) - # play pulses using QUA + acquisitions, parameters = self.register_pulses(qubits, sequence, options) with qua.program() as experiment: n = declare(int) - acquisitions = declare_acquisitions(ro_pulses, qubits, options) + # declare acquisition variables + for acquisition in acquisitions.values(): + acquisition.declare() + # execute pulses with for_(n, 0, n < options.nshots, n + 1): sweep( list(sweepers), qubits, - qmsequence, + sequence, + parameters, options.relaxation_time, - self.config, + # self.config, ) - + # download acquisitions with qua.stream_processing(): - for acquisition in acquisitions: + for acquisition in acquisitions.values(): acquisition.download(*buffer_dims) if self.script_file_name is not None: @@ -359,10 +375,10 @@ def sweep(self, qubits, couplers, sequence, options, *sweepers): if self.simulation_duration is not None: result = self.simulate_program(experiment) results = {} - for qmpulse in ro_pulses: - pulse = qmpulse.pulse - results[pulse.qubit] = results[pulse.id] = result + for pulse in sequence: + if pulse.type is PulseType.READOUT: + results[pulse.qubit] = results[pulse.id] = result return results - else: - result = self.execute_program(experiment) - return fetch_results(result, acquisitions) + + result = self.execute_program(experiment) + return fetch_results(result, acquisitions.values()) diff --git a/src/qibolab/instruments/qm/program.py b/src/qibolab/instruments/qm/program.py new file mode 100644 index 000000000..bb46ad90a --- /dev/null +++ b/src/qibolab/instruments/qm/program.py @@ -0,0 +1,67 @@ +from dataclasses import dataclass +from typing import Optional + +from qm import qua + +from qibolab.pulses import Delay, PulseType + +from .acquisition import Acquisition + + +def operation(pulse): + """Generate operation name in QM ``config`` for the given pulse.""" + return str(hash(pulse)) + + +def element(pulse): + """Generate element name in QM ``config`` for the given pulse.""" + return pulse.channel + + +@dataclass +class Parameters: + # TODO: Split acquisition and sweep parameters + acquisition: Optional[Acquisition] = None + # TODO: Change the following types to QUA variables + duration: Optional[int] = None + amplitude: Optional[float] = None + phase: Optional[float] = None + + +def _delay(pulse): + # TODO: How to play delays on multiple elements? + qua.wait(pulse.duration, element(pulse)) + + +def _play(pulse, parameters): + el = element(pulse) + if parameters.phase is not None: + qua.frame_rotation_2pi(parameters.phase, el) + if parameters.amplitude is not None: + op = operation(pulse) * parameters.amplitude + else: + op = operation(pulse) + + if pulse.type is PulseType.READOUT: + parameters.acquisition.measure(op) + else: + qua.play(op, el, duration=parameters.duration) + + if parameters.phase is not None: + qua.reset_frame(el) + + +def play(self, sequence, parameters, relaxation_time=0): + """Part of QUA program that plays an arbitrary pulse sequence. + + Should be used inside a ``program()`` context. + """ + qua.align() + for pulse in sequence: + if isinstance(pulse, Delay): + _delay(pulse) + else: + _play(pulse, parameters[operation(pulse)]) + + if relaxation_time > 0: + qua.wait(relaxation_time // 4) diff --git a/src/qibolab/instruments/qm/sequence.py b/src/qibolab/instruments/qm/sequence.py deleted file mode 100644 index f950d734f..000000000 --- a/src/qibolab/instruments/qm/sequence.py +++ /dev/null @@ -1,273 +0,0 @@ -import collections -from dataclasses import dataclass, field -from typing import Dict, List, Optional, Set, Union - -import numpy as np -from numpy import typing as npt -from qm import qua -from qm.qua._dsl import _Variable # for type declaration only -from qualang_tools.bakery import baking -from qualang_tools.bakery.bakery import Baking - -from qibolab.pulses import Pulse, PulseType - -from .acquisition import Acquisition -from .config import SAMPLING_RATE, QMConfig - -DurationsType = Union[List[int], npt.NDArray[int]] -"""Type of values that can be accepted in a duration sweeper.""" - - -@dataclass -class QMPulse: - """Wrapper around :class:`qibolab.pulses.Pulse` for easier translation to - QUA program. - - These pulses are defined when :meth:`qibolab.instruments.qm.QMOPX.play` is called - and hold attributes for the ``element`` and ``operation`` that corresponds to each pulse, - as defined in the QM config. - """ - - pulse: Pulse - """:class:`qibolab.pulses.Pulse` corresponding to the ``QMPulse``.""" - element: Optional[str] = None - """Element that the pulse will be played on, as defined in the QM - config.""" - operation: Optional[str] = None - """Name of the operation that is implementing the pulse in the QM - config.""" - relative_phase: Optional[float] = None - """Relative phase of the pulse normalized to follow QM convention. - - May be overwritten when sweeping phase. - """ - wait_time: int = 0 - """Time (in clock cycles) to wait before playing this pulse. - - Calculated and assigned by - :meth: `qibolab.instruments.qm.Sequence.add`. - """ - wait_time_variable: Optional[_Variable] = None - """Time (in clock cycles) to wait before playing this pulse when we are - sweeping start.""" - swept_duration: Optional[_Variable] = None - """Pulse duration when sweeping it.""" - - acquisition: Optional[Acquisition] = None - """Data class containing the variables required for data acquisition for - the instrument.""" - - next_: Set["QMPulse"] = field(default_factory=set) - """Pulses that will be played after the current pulse. - - These pulses need to be re-aligned if we are sweeping the start or - duration. - """ - elements_to_align: Set[str] = field(default_factory=set) - - def __post_init__(self): - pulse_type = self.pulse.type.name.lower() - amplitude = format(self.pulse.amplitude, ".6f").rstrip("0").rstrip(".") - if self.element is None: - self.element = f"{pulse_type}{self.pulse.qubit}" - self.operation: str = ( - f"{pulse_type}({self.pulse.duration}, {amplitude}, {self.pulse.envelope})" - ) - self.relative_phase: float = self.pulse.relative_phase / (2 * np.pi) - self.elements_to_align.add(self.element) - - def __hash__(self): - return hash(self.pulse) - - @property - def duration(self): - """Duration of the pulse as defined in the - :class:`qibolab.pulses.PulseSequence`. - - Remains constant even when we are sweeping the duration of this - pulse. - """ - return self.pulse.duration - - @property - def wait_cycles(self): - """Instrument clock cycles (1 cycle = 4ns) to wait before playing the - pulse. - - This property will be used in the QUA ``wait`` command, so that it is compatible - with and without start sweepers. - """ - if self.wait_time_variable is not None: - return self.wait_time_variable + self.wait_time - if self.wait_time >= 4: - return self.wait_time - return None - - def play(self): - """Play the pulse. - - Relevant only in the context of a QUA program. - """ - qua.play(self.operation, self.element, duration=self.swept_duration) - - -@dataclass -class BakedPulse(QMPulse): - """Baking allows 1ns resolution in the pulse waveforms.""" - - segments: List[Baking] = field(default_factory=list) - """Baked segments implementing the pulse.""" - amplitude: Optional[float] = None - """Amplitude of the baked pulse. - - Relevant only when sweeping amplitude. - """ - durations: Optional[DurationsType] = None - - def __hash__(self): - return super().__hash__() - - @property - def duration(self): - return self.segments[-1].get_op_length() - - @staticmethod - def calculate_waveform(original_waveform, t): - if t == 0: # Otherwise, the baking will be empty and will not be created - return [0.0] * 16 - - expanded_waveform = list(original_waveform) - for i in range(t // len(original_waveform)): - expanded_waveform.extend(original_waveform) - return expanded_waveform[:t] - - def bake(self, config: QMConfig, durations: DurationsType): - self.segments = [] - self.durations = durations - for t in durations: - with baking(config.__dict__, padding_method="right") as segment: - if self.pulse.type is PulseType.FLUX: - waveform = self.pulse.i(SAMPLING_RATE).tolist() - waveform = self.calculate_waveform(waveform, t) - else: - waveform_i = self.pulse.i(SAMPLING_RATE).tolist() - waveform_q = self.pulse.q(SAMPLING_RATE).tolist() - waveform = [ - self.calculate_waveform(waveform_i, t), - self.calculate_waveform(waveform_q, t), - ] - segment.add_op(self.operation, self.element, waveform) - segment.play(self.operation, self.element) - self.segments.append(segment) - - @property - def amplitude_array(self): - if self.amplitude is None: - return None - return [(self.element, self.amplitude)] - - def play(self): - if self.swept_duration is not None: - with qua.switch_(self.swept_duration): - for dur, segment in zip(self.durations, self.segments): - with qua.case_(dur): - segment.run(amp_array=self.amplitude_array) - else: - segment = self.segments[0] - segment.run(amp_array=self.amplitude_array) - - -@dataclass -class Sequence: - """Pulse sequence containing QM specific pulses (``qmpulse``). - - Defined in :meth:`qibolab.instruments.qm.QMOPX.play`. - Holds attributes for the ``element`` and ``operation`` that - corresponds to each pulse, as defined in the QM config. - """ - - qmpulses: List[QMPulse] = field(default_factory=list) - """List of :class:`qibolab.instruments.qm.QMPulse` objects corresponding to - the original pulses.""" - pulse_to_qmpulse: Dict[Pulse, QMPulse] = field(default_factory=dict) - """Map from qibolab pulses to QMPulses (useful when sweeping).""" - clock: Dict[str, int] = field(default_factory=lambda: collections.defaultdict(int)) - """Dictionary used to keep track of times of each element, in order to - calculate wait times.""" - pulse_finish: Dict[int, List[QMPulse]] = field( - default_factory=lambda: collections.defaultdict(list) - ) - """Map to find all pulses that finish at a given time (useful for - ``_find_previous``).""" - - def _find_previous(self, pulse): - for finish in reversed(sorted(self.pulse_finish.keys())): - if finish <= pulse.start: - # first try to find a previous pulse targeting the same qubit - last_pulses = self.pulse_finish[finish] - for previous in reversed(last_pulses): - if previous.pulse.qubit == pulse.qubit: - return previous - # otherwise - if finish == pulse.start: - return last_pulses[-1] - return None - - def add(self, qmpulse: QMPulse): - pulse = qmpulse.pulse - self.pulse_to_qmpulse[pulse.id] = qmpulse - - previous = self._find_previous(pulse) - if previous is not None: - previous.next_.add(qmpulse) - - wait_time = pulse.start - self.clock[qmpulse.element] - if wait_time >= 12: - qmpulse.wait_time = wait_time // 4 + 1 - self.clock[qmpulse.element] += 4 * qmpulse.wait_time - self.clock[qmpulse.element] += qmpulse.duration - - self.pulse_finish[pulse.finish].append(qmpulse) - self.qmpulses.append(qmpulse) - - def shift(self): - """Shift all pulses that come after a ``BakedPulse`` a bit to avoid - overlapping pulses.""" - to_shift = collections.deque() - for qmpulse in self.qmpulses: - if isinstance(qmpulse, BakedPulse): - to_shift.extend(qmpulse.next_) - while to_shift: - qmpulse = to_shift.popleft() - qmpulse.wait_time += 2 - to_shift.extend(qmpulse.next_) - - def play(self, relaxation_time=0): - """Part of QUA program that plays an arbitrary pulse sequence. - - Should be used inside a ``program()`` context. - """ - needs_reset = False - qua.align() - for qmpulse in self.qmpulses: - pulse = qmpulse.pulse - if qmpulse.wait_cycles is not None: - qua.wait(qmpulse.wait_cycles, qmpulse.element) - if pulse.type is PulseType.READOUT: - qmpulse.acquisition.measure(qmpulse.operation, qmpulse.element) - else: - if ( - not isinstance(qmpulse.relative_phase, float) - or qmpulse.relative_phase != 0 - ): - qua.frame_rotation_2pi(qmpulse.relative_phase, qmpulse.element) - needs_reset = True - qmpulse.play() - if needs_reset: - qua.reset_frame(qmpulse.element) - needs_reset = False - if len(qmpulse.elements_to_align) > 1: - qua.align(*qmpulse.elements_to_align) - - if relaxation_time > 0: - qua.wait(relaxation_time // 4) diff --git a/src/qibolab/instruments/qm/sweepers.py b/src/qibolab/instruments/qm/sweepers.py index e745d6bde..d1bdee6c2 100644 --- a/src/qibolab/instruments/qm/sweepers.py +++ b/src/qibolab/instruments/qm/sweepers.py @@ -7,9 +7,9 @@ from qualang_tools.loops import from_array from qibolab.channels import check_max_offset -from qibolab.instruments.qm.sequence import BakedPulse from qibolab.pulses import PulseType -from qibolab.sweeper import Parameter + +from .program import element, operation, play def maximum_sweep_value(values, value0): @@ -27,47 +27,49 @@ def maximum_sweep_value(values, value0): return max(abs(min(values) + value0), abs(max(values) + value0)) -def _update_baked_pulses(sweeper, qmsequence, config): - """Updates baked pulse if duration sweeper is used.""" - qmpulse = qmsequence.pulse_to_qmpulse[sweeper.pulses[0].id] - is_baked = isinstance(qmpulse, BakedPulse) - for pulse in sweeper.pulses: - qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] - if isinstance(qmpulse, BakedPulse): - if not is_baked: - raise_error( - TypeError, - "Duration sweeper cannot contain both baked and not baked pulses.", - ) - values = np.array(sweeper.values).astype(int) - qmpulse.bake(config, values) - - -def sweep(sweepers, qubits, qmsequence, relaxation_time, config): +# def _update_baked_pulses(sweeper, qmsequence, config): +# """Updates baked pulse if duration sweeper is used.""" +# qmpulse = qmsequence.pulse_to_qmpulse[sweeper.pulses[0].serial] +# is_baked = isinstance(qmpulse, BakedPulse) +# for pulse in sweeper.pulses: +# qmpulse = qmsequence.pulse_to_qmpulse[pulse.serial] +# if isinstance(qmpulse, BakedPulse): +# if not is_baked: +# raise_error( +# TypeError, +# "Duration sweeper cannot contain both baked and not baked pulses.", +# ) +# values = np.array(sweeper.values).astype(int) +# qmpulse.bake(config, values) + + +def sweep(sweepers, qubits, sequence, parameters, relaxation_time): """Public sweep function that is called by the driver.""" - for sweeper in sweepers: - if sweeper.parameter is Parameter.duration: - _update_baked_pulses(sweeper, qmsequence, config) - _sweep_recursion(sweepers, qubits, qmsequence, relaxation_time) + # for sweeper in sweepers: + # if sweeper.parameter is Parameter.duration: + # _update_baked_pulses(sweeper, instructions, config) + _sweep_recursion(sweepers, qubits, sequence, parameters, relaxation_time) -def _sweep_recursion(sweepers, qubits, qmsequence, relaxation_time): +def _sweep_recursion(sweepers, qubits, sequence, parameters, relaxation_time): """Unrolls a list of qibolab sweepers to the corresponding QUA for loops using recursion.""" if len(sweepers) > 0: parameter = sweepers[0].parameter.name func_name = f"_sweep_{parameter}" if func_name in globals(): - globals()[func_name](sweepers, qubits, qmsequence, relaxation_time) + globals()[func_name]( + sweepers, qubits, sequence, parameters, relaxation_time + ) else: raise_error( NotImplementedError, f"Sweeper for {parameter} is not implemented." ) else: - qmsequence.play(relaxation_time) + play(sequence, parameters, relaxation_time) -def _sweep_frequency(sweepers, qubits, qmsequence, relaxation_time): +def _sweep_frequency(sweepers, qubits, sequence, parameters, relaxation_time): sweeper = sweepers[0] freqs0 = [] for pulse in sweeper.pulses: @@ -96,13 +98,12 @@ def _sweep_frequency(sweepers, qubits, qmsequence, relaxation_time): f = declare(int) with for_(*from_array(f, sweeper.values.astype(int))): for pulse, f0 in zip(sweeper.pulses, freqs0): - qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] - qua.update_frequency(qmpulse.element, f + f0) + qua.update_frequency(element(pulse), f + f0) - _sweep_recursion(sweepers[1:], qubits, qmsequence, relaxation_time) + _sweep_recursion(sweepers[1:], qubits, sequence, parameters, relaxation_time) -def _sweep_amplitude(sweepers, qubits, qmsequence, relaxation_time): +def _sweep_amplitude(sweepers, qubits, sequence, parameters, relaxation_time): sweeper = sweepers[0] # TODO: Consider sweeping amplitude without multiplication if min(sweeper.values) < -2: @@ -115,27 +116,25 @@ def _sweep_amplitude(sweepers, qubits, qmsequence, relaxation_time): a = declare(fixed) with for_(*from_array(a, sweeper.values)): for pulse in sweeper.pulses: - qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] - if isinstance(qmpulse, BakedPulse): - qmpulse.amplitude = a - else: - qmpulse.operation = qmpulse.operation * qua.amp(a) + # if isinstance(instruction, Bake): + # instructions.update_kwargs(instruction, amplitude=a) + # else: + parameters[operation(pulse)].amplitude = qua.amp(a) - _sweep_recursion(sweepers[1:], qubits, qmsequence, relaxation_time) + _sweep_recursion(sweepers[1:], qubits, sequence, parameters, relaxation_time) -def _sweep_relative_phase(sweepers, qubits, qmsequence, relaxation_time): +def _sweep_relative_phase(sweepers, qubits, sequence, parameters, relaxation_time): sweeper = sweepers[0] relphase = declare(fixed) with for_(*from_array(relphase, sweeper.values / (2 * np.pi))): for pulse in sweeper.pulses: - qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] - qmpulse.relative_phase = relphase + parameters[operation(pulse)].phase = relphase - _sweep_recursion(sweepers[1:], qubits, qmsequence, relaxation_time) + _sweep_recursion(sweepers[1:], qubits, sequence, parameters, relaxation_time) -def _sweep_bias(sweepers, qubits, qmsequence, relaxation_time): +def _sweep_bias(sweepers, qubits, sequence, parameters, relaxation_time): sweeper = sweepers[0] offset0 = [] for qubit in sweeper.qubits: @@ -154,52 +153,53 @@ def _sweep_bias(sweepers, qubits, qmsequence, relaxation_time): with qua.else_(): qua.set_dc_offset(f"flux{qubit.name}", "single", (b + b0)) - _sweep_recursion(sweepers[1:], qubits, qmsequence, relaxation_time) - - -def _sweep_start(sweepers, qubits, qmsequence, relaxation_time): - sweeper = sweepers[0] - start = declare(int) - values = (np.array(sweeper.values) // 4).astype(int) - - if len(np.unique(values[1:] - values[:-1])) > 1: - loop = qua.for_each_(start, values) - else: - loop = for_(*from_array(start, values)) - - with loop: - for pulse in sweeper.pulses: - qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] - # find all pulses that are connected to ``qmpulse`` and update their starts - to_process = {qmpulse} - while to_process: - next_qmpulse = to_process.pop() - to_process |= next_qmpulse.next_ - next_qmpulse.wait_time_variable = start - - _sweep_recursion(sweepers[1:], qubits, qmsequence, relaxation_time) - - -def _sweep_duration(sweepers, qubits, qmsequence, relaxation_time): - sweeper = sweepers[0] - qmpulse = qmsequence.pulse_to_qmpulse[sweeper.pulses[0].id] - if isinstance(qmpulse, BakedPulse): - values = np.array(sweeper.values).astype(int) - else: - values = np.array(sweeper.values).astype(int) // 4 - - dur = declare(int) - with for_(*from_array(dur, values)): - for pulse in sweeper.pulses: - qmpulse = qmsequence.pulse_to_qmpulse[pulse.id] - qmpulse.swept_duration = dur - # find all pulses that are connected to ``qmpulse`` and align them - if not isinstance(qmpulse, BakedPulse): - to_process = set(qmpulse.next_) - while to_process: - next_qmpulse = to_process.pop() - to_process |= next_qmpulse.next_ - qmpulse.elements_to_align.add(next_qmpulse.element) - next_qmpulse.wait_time -= qmpulse.wait_time + qmpulse.duration // 4 - - _sweep_recursion(sweepers[1:], qubits, qmsequence, relaxation_time) + _sweep_recursion(sweepers[1:], qubits, sequence, parameters, relaxation_time) + + +# def _sweep_start(sweepers, qubits, qmsequence, relaxation_time): +# sweeper = sweepers[0] +# start = declare(int) +# values = (np.array(sweeper.values) // 4).astype(int) +# +# if len(np.unique(values[1:] - values[:-1])) > 1: +# loop = qua.for_each_(start, values) +# else: +# loop = for_(*from_array(start, values)) +# +# with loop: +# for pulse in sweeper.pulses: +# qmpulse = qmsequence.pulse_to_qmpulse[pulse.serial] +# # find all pulses that are connected to ``qmpulse`` and update their starts +# to_process = {qmpulse} +# while to_process: +# next_qmpulse = to_process.pop() +# to_process |= next_qmpulse.next_ +# next_qmpulse.wait_time_variable = start +# +# _sweep_recursion(sweepers[1:], qubits, qmsequence, relaxation_time) + + +# def _sweep_duration(sweepers, qubits, qmsequence, relaxation_time): +# sweeper = sweepers[0] +# qmpulse = qmsequence.pulse_to_qmpulse[sweeper.pulses[0].serial] +# if isinstance(qmpulse, BakedPulse): +# values = np.array(sweeper.values).astype(int) +# else: +# values = np.array(sweeper.values).astype(int) // 4 +# +# dur = declare(int) +# with for_(*from_array(dur, values)): +# for pulse in sweeper.pulses: +# qmpulse = qmsequence.pulse_to_qmpulse[pulse.serial] +# qmpulse.swept_duration = dur +# # find all pulses that are connected to ``qmpulse`` and align them +# if not isinstance(qmpulse, BakedPulse): +# to_process = set(qmpulse.next_) +# while to_process: +# next_qmpulse = to_process.pop() +# to_process |= next_qmpulse.next_ +# qmpulse.elements_to_align.add(next_qmpulse.element) +# next_qmpulse.wait_time -= qmpulse.wait_time + qmpulse.duration // 4 +# +# _sweep_recursion(sweepers[1:], qubits, qmsequence, relaxation_time) +# diff --git a/tests/test_instruments_qm.py b/tests/test_instruments_qm.py index 62b3ef994..8959a88ab 100644 --- a/tests/test_instruments_qm.py +++ b/tests/test_instruments_qm.py @@ -6,9 +6,8 @@ from qibolab import AcquisitionType, ExecutionParameters, create_platform from qibolab.instruments.qm import OPXplus, QMController -from qibolab.instruments.qm.acquisition import Acquisition, declare_acquisitions +from qibolab.instruments.qm.acquisition import Acquisition from qibolab.instruments.qm.controller import controllers_config -from qibolab.instruments.qm.sequence import BakedPulse, QMPulse, Sequence from qibolab.pulses import Pulse, PulseSequence, PulseType, Rectangular from qibolab.qubits import Qubit from qibolab.sweeper import Parameter, Sweeper From 765a1a31b5adf714b0e99d1ed11e46a19ea5c8e4 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Sat, 20 Apr 2024 23:33:39 +0400 Subject: [PATCH 228/233] fix: single shot classification works --- src/qibolab/instruments/qm/config.py | 20 +++++++++--- src/qibolab/instruments/qm/controller.py | 25 ++++++++------- src/qibolab/instruments/qm/program.py | 15 ++------- src/qibolab/instruments/qm/sweepers.py | 3 +- src/qibolab/pulses/pulse.py | 39 ++++++++++++------------ 5 files changed, 53 insertions(+), 49 deletions(-) diff --git a/src/qibolab/instruments/qm/config.py b/src/qibolab/instruments/qm/config.py index 243a5c2c9..1bb60a86f 100644 --- a/src/qibolab/instruments/qm/config.py +++ b/src/qibolab/instruments/qm/config.py @@ -19,6 +19,16 @@ """ +def operation(pulse): + """Generate operation name in QM ``config`` for the given pulse.""" + return str(hash(pulse)) + + +def element(pulse): + """Generate element name in QM ``config`` for the given pulse.""" + return pulse.channel + + def float_serial(x): """Convert float to string to use in config keys.""" return format(x, ".6f").rstrip("0").rstrip(".") @@ -256,7 +266,7 @@ def register_element(self, qubit, pulse, time_of_flight=0, smearing=0): element = self.register_flux_element(qubit, pulse.frequency) return element - def register_pulse(self, pulse, operation, element, qubit): + def register_pulse(self, pulse, qubit): """Registers pulse, waveforms and integration weights in QM config. Args: @@ -350,7 +360,7 @@ def register_waveform(self, pulse, mode="i"): phase = (pulse.relative_phase % (2 * np.pi)) / (2 * np.pi) amplitude = float_serial(pulse.amplitude) phase_str = float_serial(phase) - if isinstance(pulse.shape, Rectangular): + if isinstance(pulse.envelope, Rectangular): serial = f"constant_wf({amplitude}, {phase_str})" if serial not in self.waveforms: if mode == "i": @@ -359,10 +369,10 @@ def register_waveform(self, pulse, mode="i"): sample = pulse.amplitude * np.sin(phase) self.waveforms[serial] = {"type": "constant", "sample": sample} else: - serial = f"{mode}({pulse.duration}, {amplitude}, {phase_str}, {str(pulse.shape)})" + serial = f"{hash(pulse)}_{mode}" if serial not in self.waveforms: - samples_i = pulse.envelope_waveform_i(SAMPLING_RATE).data - samples_q = pulse.envelope_waveform_q(SAMPLING_RATE).data + samples_i = pulse.i(SAMPLING_RATE) + samples_q = pulse.q(SAMPLING_RATE) if mode == "i": samples = samples_i * np.cos(phase) - samples_q * np.sin(phase) else: diff --git a/src/qibolab/instruments/qm/controller.py b/src/qibolab/instruments/qm/controller.py index 1d4462452..26a6146e7 100644 --- a/src/qibolab/instruments/qm/controller.py +++ b/src/qibolab/instruments/qm/controller.py @@ -10,15 +10,15 @@ from qibolab import AveragingMode from qibolab.instruments.abstract import Controller -from qibolab.pulses import PulseType +from qibolab.pulses import Delay, PulseType from qibolab.sweeper import Parameter from qibolab.unrolling import Bounds from .acquisition import create_acquisition, fetch_results -from .config import SAMPLING_RATE, QMConfig +from .config import SAMPLING_RATE, QMConfig, element, operation from .devices import Octave, OPXplus from .ports import OPXIQ -from .program import Parameters, element, operation +from .program import Parameters from .sweepers import sweep OCTAVE_ADDRESS_OFFSET = 11000 @@ -292,6 +292,9 @@ def register_pulses(self, qubits, sequence, options): acquisitions = {} parameters = defaultdict(Parameters) for pulse in sequence: + if isinstance(pulse, Delay): + continue + qubit = qubits[pulse.qubit] self.config.register_port(getattr(qubit, pulse.type.name.lower()).port) if pulse.type is PulseType.READOUT: @@ -314,17 +317,14 @@ def register_pulses(self, qubits, sequence, options): self.config.register_pulse(qubit, qmpulse) qmsequence.add(qmpulse) - op = operation(hash(pulse)) - el = element(pulse) - if op not in self.config.pulses: - self.config.register_pulse(pulse, op, el, qubit) - + op = self.config.register_pulse(pulse, qubit) if pulse.type is PulseType.READOUT: if op not in acquisitions: + el = element(pulse) acquisitions[op] = create_acquisition( - op, el, options, qubit.threshold, qubit.iq_angle + op, el, pulse.qubit, options, qubit.threshold, qubit.iq_angle ) - parameters[op].acquisition = acquisitions[op] + parameters[op].acquisition = acquisitions[op] acquisitions[op].keys.append(pulse.id) return acquisitions, parameters @@ -369,8 +369,11 @@ def sweep(self, qubits, couplers, sequence, options, *sweepers): acquisition.download(*buffer_dims) if self.script_file_name is not None: + script = generate_qua_script(experiment, self.config.__dict__) + for pulse in sequence: + script = script.replace(operation(pulse), str(pulse)) with open(self.script_file_name, "w") as file: - file.write(generate_qua_script(experiment, self.config.__dict__)) + file.write(script) if self.simulation_duration is not None: result = self.simulate_program(experiment) diff --git a/src/qibolab/instruments/qm/program.py b/src/qibolab/instruments/qm/program.py index bb46ad90a..a95f9832e 100644 --- a/src/qibolab/instruments/qm/program.py +++ b/src/qibolab/instruments/qm/program.py @@ -6,16 +6,7 @@ from qibolab.pulses import Delay, PulseType from .acquisition import Acquisition - - -def operation(pulse): - """Generate operation name in QM ``config`` for the given pulse.""" - return str(hash(pulse)) - - -def element(pulse): - """Generate element name in QM ``config`` for the given pulse.""" - return pulse.channel +from .config import element, operation @dataclass @@ -30,7 +21,7 @@ class Parameters: def _delay(pulse): # TODO: How to play delays on multiple elements? - qua.wait(pulse.duration, element(pulse)) + qua.wait(pulse.duration // 4 + 1, element(pulse)) def _play(pulse, parameters): @@ -51,7 +42,7 @@ def _play(pulse, parameters): qua.reset_frame(el) -def play(self, sequence, parameters, relaxation_time=0): +def play(sequence, parameters, relaxation_time=0): """Part of QUA program that plays an arbitrary pulse sequence. Should be used inside a ``program()`` context. diff --git a/src/qibolab/instruments/qm/sweepers.py b/src/qibolab/instruments/qm/sweepers.py index d1bdee6c2..c49dacef5 100644 --- a/src/qibolab/instruments/qm/sweepers.py +++ b/src/qibolab/instruments/qm/sweepers.py @@ -9,7 +9,8 @@ from qibolab.channels import check_max_offset from qibolab.pulses import PulseType -from .program import element, operation, play +from .config import element, operation +from .program import play def maximum_sweep_value(values, value0): diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 48fd3b9ae..da05446a3 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -1,6 +1,5 @@ """Pulse class.""" -from dataclasses import fields from enum import Enum from typing import Optional, Union @@ -117,25 +116,25 @@ def modulated_waveforms(self, sampling_rate): # -> tuple[Waveform, Waveform]: def __hash__(self): """Hash the content. - .. warning:: - - unhashable attributes are not taken into account, so there will be more - clashes than those usually expected with a regular hash - - .. todo:: - - This method should be eventually dropped, and be provided automatically by - freezing the dataclass (i.e. setting ``frozen=true`` in the decorator). - However, at the moment is not possible nor desired, because it contains - unhashable attributes and because some instances are mutated inside Qibolab. - """ - return hash( - tuple( - getattr(self, f.name) - for f in fields(self) - if f.name not in ("type", "shape") - ) - ) + # .. warning:: + + # unhashable attributes are not taken into account, so there will be more + # clashes than those usually expected with a regular hash + + # .. todo:: + + # This method should be eventually dropped, and be provided automatically by + # freezing the dataclass (i.e. setting ``frozen=true`` in the decorator). + # However, at the moment is not possible nor desired, because it contains + # unhashable attributes and because some instances are mutated inside Qibolab. + # """ + # return hash(self) + # # tuple( + # # getattr(self, f.name) + # # for f in fields(self) + # # if f.name not in ("type", "shape") + # # ) + # #) def __add__(self, other): if isinstance(other, Pulse): From 4263426b57fbc8a638e8c5c6c92fc09eae625cf7 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Sun, 21 Apr 2024 00:36:39 +0400 Subject: [PATCH 229/233] refactor: drop old serialization and hash --- src/qibolab/instruments/qm/config.py | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/src/qibolab/instruments/qm/config.py b/src/qibolab/instruments/qm/config.py index 1bb60a86f..40a1341e2 100644 --- a/src/qibolab/instruments/qm/config.py +++ b/src/qibolab/instruments/qm/config.py @@ -29,11 +29,6 @@ def element(pulse): return pulse.channel -def float_serial(x): - """Convert float to string to use in config keys.""" - return format(x, ".6f").rstrip("0").rstrip(".") - - @dataclass class QMConfig: """Configuration for communicating with the ``QuantumMachinesManager``.""" @@ -358,10 +353,8 @@ def register_waveform(self, pulse, mode="i"): return serial phase = (pulse.relative_phase % (2 * np.pi)) / (2 * np.pi) - amplitude = float_serial(pulse.amplitude) - phase_str = float_serial(phase) + serial = f"{hash(pulse)}_{mode}" if isinstance(pulse.envelope, Rectangular): - serial = f"constant_wf({amplitude}, {phase_str})" if serial not in self.waveforms: if mode == "i": sample = pulse.amplitude * np.cos(phase) @@ -369,7 +362,6 @@ def register_waveform(self, pulse, mode="i"): sample = pulse.amplitude * np.sin(phase) self.waveforms[serial] = {"type": "constant", "sample": sample} else: - serial = f"{hash(pulse)}_{mode}" if serial not in self.waveforms: samples_i = pulse.i(SAMPLING_RATE) samples_q = pulse.q(SAMPLING_RATE) From 277291185b7aeb2a434c14adaab6a49eb256d410 Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Mon, 22 Apr 2024 18:09:12 +0400 Subject: [PATCH 230/233] fix: duration sweeper --- src/qibolab/instruments/qm/program.py | 13 +++++-- src/qibolab/instruments/qm/sweepers.py | 53 +++++--------------------- 2 files changed, 18 insertions(+), 48 deletions(-) diff --git a/src/qibolab/instruments/qm/program.py b/src/qibolab/instruments/qm/program.py index a95f9832e..c3e89a263 100644 --- a/src/qibolab/instruments/qm/program.py +++ b/src/qibolab/instruments/qm/program.py @@ -19,9 +19,13 @@ class Parameters: phase: Optional[float] = None -def _delay(pulse): +def _delay(pulse, parameters): # TODO: How to play delays on multiple elements? - qua.wait(pulse.duration // 4 + 1, element(pulse)) + if parameters.duration is None: + duration = pulse.duration // 4 + 1 + else: + duration = parameters.duration + qua.wait(duration, element(pulse)) def _play(pulse, parameters): @@ -49,10 +53,11 @@ def play(sequence, parameters, relaxation_time=0): """ qua.align() for pulse in sequence: + params = parameters[operation(pulse)] if isinstance(pulse, Delay): - _delay(pulse) + _delay(pulse, params) else: - _play(pulse, parameters[operation(pulse)]) + _play(pulse, params) if relaxation_time > 0: qua.wait(relaxation_time // 4) diff --git a/src/qibolab/instruments/qm/sweepers.py b/src/qibolab/instruments/qm/sweepers.py index c49dacef5..f1857ff76 100644 --- a/src/qibolab/instruments/qm/sweepers.py +++ b/src/qibolab/instruments/qm/sweepers.py @@ -157,50 +157,15 @@ def _sweep_bias(sweepers, qubits, sequence, parameters, relaxation_time): _sweep_recursion(sweepers[1:], qubits, sequence, parameters, relaxation_time) -# def _sweep_start(sweepers, qubits, qmsequence, relaxation_time): -# sweeper = sweepers[0] -# start = declare(int) -# values = (np.array(sweeper.values) // 4).astype(int) -# -# if len(np.unique(values[1:] - values[:-1])) > 1: -# loop = qua.for_each_(start, values) -# else: -# loop = for_(*from_array(start, values)) -# -# with loop: -# for pulse in sweeper.pulses: -# qmpulse = qmsequence.pulse_to_qmpulse[pulse.serial] -# # find all pulses that are connected to ``qmpulse`` and update their starts -# to_process = {qmpulse} -# while to_process: -# next_qmpulse = to_process.pop() -# to_process |= next_qmpulse.next_ -# next_qmpulse.wait_time_variable = start -# -# _sweep_recursion(sweepers[1:], qubits, qmsequence, relaxation_time) +def _sweep_duration(sweepers, qubits, sequence, parameters, relaxation_time): + # TODO: Handle baked pulses + sweeper = sweepers[0] + dur = declare(int) + with for_(*from_array(dur, (sweeper.values // 4).astype(int))): + for pulse in sweeper.pulses: + parameters[operation(pulse)].duration = dur + + _sweep_recursion(sweepers[1:], qubits, sequence, parameters, relaxation_time) -# def _sweep_duration(sweepers, qubits, qmsequence, relaxation_time): -# sweeper = sweepers[0] -# qmpulse = qmsequence.pulse_to_qmpulse[sweeper.pulses[0].serial] -# if isinstance(qmpulse, BakedPulse): -# values = np.array(sweeper.values).astype(int) -# else: -# values = np.array(sweeper.values).astype(int) // 4 -# -# dur = declare(int) -# with for_(*from_array(dur, values)): -# for pulse in sweeper.pulses: -# qmpulse = qmsequence.pulse_to_qmpulse[pulse.serial] -# qmpulse.swept_duration = dur -# # find all pulses that are connected to ``qmpulse`` and align them -# if not isinstance(qmpulse, BakedPulse): -# to_process = set(qmpulse.next_) -# while to_process: -# next_qmpulse = to_process.pop() -# to_process |= next_qmpulse.next_ -# qmpulse.elements_to_align.add(next_qmpulse.element) -# next_qmpulse.wait_time -= qmpulse.wait_time + qmpulse.duration // 4 -# -# _sweep_recursion(sweepers[1:], qubits, qmsequence, relaxation_time) # From 62d8d48c24ef10909c0d0cdca438f23e1342bf0c Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Tue, 23 Apr 2024 17:26:47 +0400 Subject: [PATCH 231/233] fix: unrolling tested with single shot routine --- src/qibolab/instruments/qm/sweepers.py | 3 --- src/qibolab/platform/platform.py | 26 ++++---------------------- 2 files changed, 4 insertions(+), 25 deletions(-) diff --git a/src/qibolab/instruments/qm/sweepers.py b/src/qibolab/instruments/qm/sweepers.py index f1857ff76..2521d66e9 100644 --- a/src/qibolab/instruments/qm/sweepers.py +++ b/src/qibolab/instruments/qm/sweepers.py @@ -166,6 +166,3 @@ def _sweep_duration(sweepers, qubits, sequence, parameters, relaxation_time): parameters[operation(pulse)].duration = dur _sweep_recursion(sweepers[1:], qubits, sequence, parameters, relaxation_time) - - -# diff --git a/src/qibolab/platform/platform.py b/src/qibolab/platform/platform.py index 597c9632a..47be40c65 100644 --- a/src/qibolab/platform/platform.py +++ b/src/qibolab/platform/platform.py @@ -38,26 +38,18 @@ def unroll_sequences( Returns: total_sequence (:class:`qibolab.pulses.PulseSequence`): Unrolled pulse sequence containing multiple measurements. - readout_map (dict): Map from original readout pulse serials to the unrolled readout pulse - serials. Required to construct the results dictionary that is returned after execution. """ total_sequence = PulseSequence() - readout_map = defaultdict(list) channels = {pulse.channel for sequence in sequences for pulse in sequence} for sequence in sequences: total_sequence.extend(sequence) - # TODO: Fix unrolling results - for pulse in sequence: - if pulse.type is PulseType.READOUT: - readout_map[pulse.id].append(pulse.id) - length = sequence.duration + relaxation_time pulses_per_channel = sequence.pulses_per_channel for channel in channels: delay = length - pulses_per_channel[channel].duration total_sequence.append(Delay(duration=delay, channel=channel)) - return total_sequence, readout_map + return total_sequence @dataclass @@ -285,23 +277,13 @@ def execute_pulse_sequences( ) log.info(f"Minimal execution time (unrolling): {time}") - # find readout pulses - ro_pulses = { - pulse.id: pulse.qubit - for sequence in sequences - for pulse in sequence.ro_pulses - } - results = defaultdict(list) bounds = kwargs.get("bounds", self._controller.bounds) for b in batch(sequences, bounds): - sequence, readouts = unroll_sequences(b, options.relaxation_time) + sequence = unroll_sequences(b, options.relaxation_time) result = self._execute(sequence, options, **kwargs) - for serial, new_serials in readouts.items(): - results[serial].extend(result[ser] for ser in new_serials) - - for serial, qubit in ro_pulses.items(): - results[qubit] = results[serial] + for key, value in result.items(): + results[key].extend(value) return results From 495958321d1bfb6648fec3f7ef5bf1fec19caad3 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 4 Jul 2024 12:06:59 +0000 Subject: [PATCH 232/233] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/qibolab/pulses/plot.py | 1 + src/qibolab/pulses/pulse.py | 22 ++++++++++++---------- src/qibolab/pulses/sequence.py | 1 + src/qibolab/pulses/waveform.py | 1 + 4 files changed, 15 insertions(+), 10 deletions(-) diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index 027b64688..260683d5e 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -1,4 +1,5 @@ """Plotting tools for pulses and related entities.""" + import matplotlib.pyplot as plt import numpy as np diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index da05446a3..7c592533c 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -4,7 +4,6 @@ from typing import Optional, Union import numpy as np -from pydantic import BaseModel from qibolab.serialize_ import Model @@ -116,18 +115,20 @@ def modulated_waveforms(self, sampling_rate): # -> tuple[Waveform, Waveform]: def __hash__(self): """Hash the content. - # .. warning:: + # .. warning:: - # unhashable attributes are not taken into account, so there will be more - # clashes than those usually expected with a regular hash + # unhashable attributes are not taken into account, so there will be more + # clashes than those usually expected with a regular hash - # .. todo:: + # .. todo:: + + # This method should be eventually dropped, and be provided automatically by + # freezing the dataclass (i.e. setting ``frozen=true`` in the decorator). + # However, at the moment is not possible nor desired, because it contains + # unhashable attributes and because some instances are mutated inside Qibolab. + # + """ - # This method should be eventually dropped, and be provided automatically by - # freezing the dataclass (i.e. setting ``frozen=true`` in the decorator). - # However, at the moment is not possible nor desired, because it contains - # unhashable attributes and because some instances are mutated inside Qibolab. - # """ # return hash(self) # # tuple( # # getattr(self, f.name) @@ -143,6 +144,7 @@ def __add__(self, other): return PulseSequence(self, *other) raise TypeError(f"Expected Pulse or PulseSequence; got {type(other).__name__}") + class Delay(Model): """A wait instruction during which we are not sending any pulses to the QPU.""" diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py index 3dfe2f5d7..245c3755b 100644 --- a/src/qibolab/pulses/sequence.py +++ b/src/qibolab/pulses/sequence.py @@ -1,4 +1,5 @@ """PulseSequence class.""" + import numpy as np diff --git a/src/qibolab/pulses/waveform.py b/src/qibolab/pulses/waveform.py index 7c530bf36..2e59a1a2d 100644 --- a/src/qibolab/pulses/waveform.py +++ b/src/qibolab/pulses/waveform.py @@ -1,4 +1,5 @@ """Waveform class.""" + import numpy as np From c3d92264a772656926c18b93ed7538d54faf8a5e Mon Sep 17 00:00:00 2001 From: Stavros Efthymiou <35475381+stavros11@users.noreply.github.com> Date: Thu, 4 Jul 2024 16:12:17 +0400 Subject: [PATCH 233/233] fix: rebasing gone wrong --- src/qibolab/pulses/plot.py | 36 ++++------ src/qibolab/pulses/pulse.py | 66 ++++++----------- src/qibolab/pulses/sequence.py | 124 +++----------------------------- src/qibolab/pulses/waveform.py | 43 ----------- tests/test_compilers_default.py | 6 +- 5 files changed, 46 insertions(+), 229 deletions(-) delete mode 100644 src/qibolab/pulses/waveform.py diff --git a/src/qibolab/pulses/plot.py b/src/qibolab/pulses/plot.py index 260683d5e..87319febf 100644 --- a/src/qibolab/pulses/plot.py +++ b/src/qibolab/pulses/plot.py @@ -1,5 +1,7 @@ """Plotting tools for pulses and related entities.""" +from collections import defaultdict + import matplotlib.pyplot as plt import numpy as np @@ -23,7 +25,7 @@ def waveform(wf: Waveform, filename=None): filename (str): a file path. If provided the plot is save to a file. """ plt.figure(figsize=(14, 5), dpi=200) - plt.plot(wf.data, c="C0", linestyle="dashed") + plt.plot(wf, c="C0", linestyle="dashed") plt.xlabel("Sample Number") plt.ylabel("Amplitude") plt.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") @@ -53,14 +55,14 @@ def pulse(pulse_: Pulse, filename=None): ax1 = plt.subplot(gs[0]) ax1.plot( time, - waveform_i.data, + waveform_i, label="envelope i", c="C0", linestyle="dashed", ) ax1.plot( time, - waveform_q.data, + waveform_q, label="envelope q", c="C1", linestyle="dashed", @@ -75,37 +77,25 @@ def pulse(pulse_: Pulse, filename=None): ax1.set_ylabel("Amplitude") ax1.grid(visible=True, which="both", axis="both", color="#888888", linestyle="-") - start = float(pulse_.start) - finish = float(pulse._finish) if pulse._finish is not None else 0.0 + start = 0 + finish = float(pulse_.duration) ax1.axis((start, finish, -1.0, 1.0)) ax1.legend() - modulated_i = pulse_.shape.modulated_waveform_i(sampling_rate).data - modulated_q = pulse_.shape.modulated_waveform_q(sampling_rate).data ax2 = plt.subplot(gs[1]) + ax2.plot(modulated[0], modulated[1], label="modulated", c="C3") + ax2.plot(waveform_i, waveform_q, label="envelope", c="C2") ax2.plot( - modulated_i, - modulated_q, - label="modulated", - c="C3", - ) - ax2.plot( - waveform_i.data, - waveform_q.data, - label="envelope", - c="C2", - ) - ax2.plot( - modulated_i[0], - modulated_q[0], + modulated[0][0], + modulated[1][0], marker="o", markersize=5, label="start", c="lightcoral", ) ax2.plot( - modulated_i[-1], - modulated_q[-1], + modulated[0][-1], + modulated[1][-1], marker="o", markersize=5, label="finish", diff --git a/src/qibolab/pulses/pulse.py b/src/qibolab/pulses/pulse.py index 7c592533c..3c2c59b55 100644 --- a/src/qibolab/pulses/pulse.py +++ b/src/qibolab/pulses/pulse.py @@ -1,5 +1,6 @@ """Pulse class.""" +from dataclasses import fields from enum import Enum from typing import Optional, Union @@ -94,63 +95,40 @@ def envelopes(self, sampling_rate: float) -> IqWaveform: """A tuple with the i and q envelope waveforms of the pulse.""" return np.array([self.i(sampling_rate), self.q(sampling_rate)]) - def modulated_waveform_i(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The waveform of the i component of the pulse, modulated with its - frequency.""" - - return self.shape.modulated_waveform_i(sampling_rate) - - def modulated_waveform_q(self, sampling_rate=SAMPLING_RATE) -> Waveform: - """The waveform of the q component of the pulse, modulated with its - frequency.""" - - return self.shape.modulated_waveform_q(sampling_rate) - - def modulated_waveforms(self, sampling_rate): # -> tuple[Waveform, Waveform]: - """A tuple with the i and q waveforms of the pulse, modulated with its - frequency.""" - - return self.shape.modulated_waveforms(sampling_rate) - def __hash__(self): """Hash the content. - # .. warning:: + .. warning:: - # unhashable attributes are not taken into account, so there will be more - # clashes than those usually expected with a regular hash + unhashable attributes are not taken into account, so there will be more + clashes than those usually expected with a regular hash - # .. todo:: + .. todo:: - # This method should be eventually dropped, and be provided automatically by - # freezing the dataclass (i.e. setting ``frozen=true`` in the decorator). - # However, at the moment is not possible nor desired, because it contains - # unhashable attributes and because some instances are mutated inside Qibolab. - # + This method should be eventually dropped, and be provided automatically by + freezing the dataclass (i.e. setting ``frozen=true`` in the decorator). + However, at the moment is not possible nor desired, because it contains + unhashable attributes and because some instances are mutated inside Qibolab. """ - - # return hash(self) - # # tuple( - # # getattr(self, f.name) - # # for f in fields(self) - # # if f.name not in ("type", "shape") - # # ) - # #) - - def __add__(self, other): - if isinstance(other, Pulse): - return PulseSequence(self, other) - if isinstance(other, PulseSequence): - return PulseSequence(self, *other) - raise TypeError(f"Expected Pulse or PulseSequence; got {type(other).__name__}") + return hash( + tuple( + getattr(self, f.name) + for f in fields(self) + if f.name not in ("type", "shape") + ) + ) class Delay(Model): """A wait instruction during which we are not sending any pulses to the QPU.""" - def __rmul__(self, n): - return self.__mul__(n) + duration: int + """Delay duration in ns.""" + channel: str + """Channel on which the delay should be implemented.""" + type: PulseType = PulseType.DELAY + """Type fixed to ``DELAY`` to comply with ``Pulse`` interface.""" class VirtualZ(Model): diff --git a/src/qibolab/pulses/sequence.py b/src/qibolab/pulses/sequence.py index 245c3755b..b48cb62cd 100644 --- a/src/qibolab/pulses/sequence.py +++ b/src/qibolab/pulses/sequence.py @@ -1,6 +1,8 @@ """PulseSequence class.""" -import numpy as np +from collections import defaultdict + +from .pulse import PulseType class PulseSequence(list): @@ -92,22 +94,12 @@ def coupler_pulses(self, *couplers): return new_pc @property - def finish(self) -> int: - """The time when the last pulse of the sequence finishes.""" - t: int = 0 - for pulse in self: - if pulse.finish > t: - t = pulse.finish - return t - - @property - def start(self) -> int: - """The start time of the first pulse of the sequence.""" - t = self.finish + def pulses_per_channel(self): + """Return a dictionary with the sequence per channel.""" + sequences = defaultdict(type(self)) for pulse in self: - if pulse.start < t: - t = pulse.start - return t + sequences[pulse.channel].append(pulse) + return sequences @property def duration(self) -> int: @@ -140,25 +132,6 @@ def qubits(self) -> list: qubits.sort() return qubits - def get_pulse_overlaps(self): # -> dict((int,int): PulseSequence): - """Return a dictionary of slices of time (tuples with start and finish - times) where pulses overlap.""" - times = [] - for pulse in self: - if not pulse.start in times: - times.append(pulse.start) - if not pulse.finish in times: - times.append(pulse.finish) - times.sort() - - overlaps = {} - for n in range(len(times) - 1): - overlaps[(times[n], times[n + 1])] = PulseSequence() - for pulse in self: - if (pulse.start <= times[n]) & (pulse.finish >= times[n + 1]): - overlaps[(times[n], times[n + 1])] += [pulse] - return overlaps - def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): """Separate a sequence of overlapping pulses into a list of non- overlapping sequences.""" @@ -184,84 +157,3 @@ def separate_overlapping_pulses(self): # -> dict((int,int): PulseSequence): if not stored: separated_pulses.append(PulseSequence([new_pulse])) return separated_pulses - - # TODO: Implement separate_different_frequency_pulses() - - @property - def pulses_overlap(self) -> bool: - """Whether any of the pulses in the sequence overlap.""" - overlap = False - for pc in self.get_pulse_overlaps().values(): - if len(pc) > 1: - overlap = True - break - return overlap - - def plot(self, savefig_filename=None, sampling_rate=SAMPLING_RATE): - """Plot the sequence of pulses. - - Args: - savefig_filename (str): a file path. If provided the plot is save to a file. - """ - if len(self) > 0: - import matplotlib.pyplot as plt - from matplotlib import gridspec - - fig = plt.figure(figsize=(14, 2 * len(self)), dpi=200) - gs = gridspec.GridSpec(ncols=1, nrows=len(self)) - vertical_lines = [] - for pulse in self: - vertical_lines.append(pulse.start) - vertical_lines.append(pulse.finish) - - n = -1 - for qubit in self.qubits: - qubit_pulses = self.get_qubit_pulses(qubit) - for channel in qubit_pulses.channels: - n += 1 - channel_pulses = qubit_pulses.get_channel_pulses(channel) - ax = plt.subplot(gs[n]) - ax.axis([0, self.finish, -1, 1]) - for pulse in channel_pulses: - num_samples = len( - pulse.shape.modulated_waveform_i(sampling_rate) - ) - time = pulse.start + np.arange(num_samples) / sampling_rate - ax.plot( - time, - pulse.shape.modulated_waveform_q(sampling_rate).data, - c="lightgrey", - ) - ax.plot( - time, - pulse.shape.modulated_waveform_i(sampling_rate).data, - c=f"C{str(n)}", - ) - ax.plot( - time, - pulse.shape.envelope_waveform_i(sampling_rate).data, - c=f"C{str(n)}", - ) - ax.plot( - time, - -pulse.shape.envelope_waveform_i(sampling_rate).data, - c=f"C{str(n)}", - ) - # TODO: if they overlap use different shades - ax.axhline(0, c="dimgrey") - ax.set_ylabel(f"qubit {qubit} \n channel {channel}") - for vl in vertical_lines: - ax.axvline(vl, c="slategrey", linestyle="--") - ax.axis([0, self.finish, -1, 1]) - ax.grid( - visible=True, - which="both", - axis="both", - color="#CCCCCC", - linestyle="-", - ) - if savefig_filename: - plt.savefig(savefig_filename) - else: - plt.show() - plt.close() diff --git a/src/qibolab/pulses/waveform.py b/src/qibolab/pulses/waveform.py deleted file mode 100644 index 2e59a1a2d..000000000 --- a/src/qibolab/pulses/waveform.py +++ /dev/null @@ -1,43 +0,0 @@ -"""Waveform class.""" - -import numpy as np - - -class Waveform: - """A class to save pulse waveforms. - - A waveform is a list of samples, or discrete data points, used by the digital to analogue converters (DACs) - to synthesise pulses. - - Attributes: - data (np.ndarray): a numpy array containing the samples. - """ - - DECIMALS = 5 - - def __init__(self, data): - """Initialise the waveform with a of samples.""" - self.data: np.ndarray = np.array(data) - - def __len__(self): - """Return the length of the waveform, the number of samples.""" - return len(self.data) - - def __hash__(self): - """Hash the underlying data. - - .. todo:: - - In order to make this reliable, we should set the data as immutable. This we - could by making both the class frozen and the contained array readonly - https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html#numpy.ndarray.flags - """ - return hash(self.data.tobytes()) - - def __eq__(self, other): - """Compare two waveforms. - - Two waveforms are considered equal if their samples, rounded to - `Waveform.DECIMALS` decimal places, are all equal. - """ - return np.allclose(self.data, other.data) diff --git a/tests/test_compilers_default.py b/tests/test_compilers_default.py index ff674a546..945f098c8 100644 --- a/tests/test_compilers_default.py +++ b/tests/test_compilers_default.py @@ -37,7 +37,7 @@ def compile_circuit(circuit, platform): @pytest.mark.parametrize( - "gateargs", + "gateargs,sequence_len", [ ((gates.I,), 1), ((gates.Z,), 2), @@ -47,11 +47,11 @@ def compile_circuit(circuit, platform): ((gates.U3, 0.1, 0.2, 0.3), 10), ], ) -def test_compile(platform, gateargs): +def test_compile(platform, gateargs, sequence_len): nqubits = platform.nqubits circuit = generate_circuit_with_gate(nqubits, *gateargs) sequence = compile_circuit(circuit, platform) - assert len(sequence) == nqubits * nseq + assert len(sequence) == nqubits * sequence_len def test_compile_two_gates(platform):