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refactor: Clean up code
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@andrea-pasquale
andrea-pasquale committed Sep 2, 2024
1 parent eef60f3 commit 0f5688f
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Showing 2 changed files with 26 additions and 171 deletions.
2 changes: 0 additions & 2 deletions src/qibocal/protocols/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,6 @@
correct_virtual_z_phases,
correct_virtual_z_phases_signal,
cryoscope,
cryoscope_amplitude,
optimize_two_qubit_gate,
)
from .two_qubit_state_tomography import two_qubit_state_tomography
Expand Down Expand Up @@ -150,6 +149,5 @@
"standard_rb_2q",
"standard_rb_2q_inter",
"optimize_two_qubit_gate",
"cryoscope_amplitude",
"cryoscope",
]
195 changes: 26 additions & 169 deletions src/qibocal/protocols/two_qubit_interaction/cryoscope.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,6 @@
import numpy.typing as npt
import plotly.graph_objects as go
import scipy
from plotly.subplots import make_subplots
from qibolab.execution_parameters import (
AcquisitionType,
AveragingMode,
Expand All @@ -19,7 +18,7 @@

from qibocal.auto.operation import Data, Parameters, Results, Routine

from .filters import exponential_decay, single_exponential_correction
from ..utils import table_dict, table_html


def exponential_decay(x, a, t):
Expand Down Expand Up @@ -65,8 +64,6 @@ class CryoscopeParameters(Parameters):
"""Flux pulse duration step."""
flux_pulse_amplitude: float
"""Flux pulse amplitude."""
parabola_coefficients: list[float]
"""Coefficients computed using FluxAmplitudeDetuning."""
nshots: Optional[int] = None
"""Number of shots per point."""
padding: int = 0
Expand All @@ -85,17 +82,9 @@ class CryoscopeResults(Results):
"""Custom dtype for Cryoscope."""


def generate_waveform():
# zeros = np.zeros(20)
ones = np.ones(60)

return np.concatenate([ones])


def generate_sequences(
platform: Platform,
qubit: QubitId,
waveform: np.ndarray,
duration: int,
params: CryoscopeParameters,
):
Expand All @@ -111,7 +100,7 @@ def generate_sequences(
start=ry90.finish + params.padding,
duration=duration,
amplitude=params.flux_pulse_amplitude,
shape=Custom(waveform[:duration]),
shape=Custom(np.ones(duration)),
channel=platform.qubits[qubit].flux.name,
qubit=qubit,
)
Expand Down Expand Up @@ -154,8 +143,6 @@ class CryoscopeData(Data):

flux_pulse_amplitude: float
"""Flux pulse amplitude."""
waveform: list
"""Flux pulse waveform"""
data: dict[tuple[QubitId, str], npt.NDArray[CryoscopeType]] = field(
default_factory=dict
)
Expand All @@ -169,7 +156,6 @@ def _acquisition(
# define sequences of pulses to be executed
data = CryoscopeData(
flux_pulse_amplitude=params.flux_pulse_amplitude,
waveform=generate_waveform().tolist(),
)

sequences_x = []
Expand All @@ -187,7 +173,7 @@ def _acquisition(

for qubit in targets:
qubit_sequence_x, qubit_sequence_y = generate_sequences(
platform, qubit, np.array(data.waveform), duration, params
platform, qubit, duration, params
)
sequence_x += qubit_sequence_x
sequence_y += qubit_sequence_y
Expand Down Expand Up @@ -250,110 +236,42 @@ def _fit(data: CryoscopeData) -> CryoscopeResults:

def _plot(data: CryoscopeData, fit: CryoscopeResults, target: QubitId):
"""Cryoscope plots."""
figures = []
fitting_report = f"Cryoscope of qubit {target}"

fig = make_subplots(
rows=1,
cols=1,
subplot_titles=(),
)
fig = go.Figure()
qubit_X_data = data[(target, "MX")]
qubit_Y_data = data[(target, "MY")]
# fig.add_trace(
# go.Scatter(
# x=qubit_X_data.duration,
# y=qubit_X_data.prob_1,
# name="X",
# legendgroup="X",
# ),
# row=1,
# col=1,
# )

# fig.add_trace(
# go.Scatter(
# x=qubit_Y_data.duration,
# y=qubit_Y_data.prob_1,
# name="Y",
# legendgroup="Y",
# ),
# row=1,
# col=1,
# )

# minus sign for X_exp becuase I get -cos phase

X_exp = qubit_X_data.prob_1 - qubit_X_data.prob_0
Y_exp = qubit_Y_data.prob_0 - qubit_Y_data.prob_1
phase = np.unwrap(np.angle(X_exp + 1.0j * Y_exp))
# fig.add_trace(
# go.Scatter(
# x=qubit_X_data.duration,
# y=phase,
# name="phase",
# ),
# row=2,
# col=1,
# )

# coeffs = [-9.10575082, -7.28208663e-3, -4.73157701e-5] # D2
coeffs = [[-9.09948820, 5.52686083e-3, -7.42079805e-5]] # D2
# coeffs = [-7.76584706, 2.25726809e-3, -3.76982885e-4] # D1
coeffs = [-7.76812980, 4.00605656e-02, -3.88473996e-4] # D1
coeffs = [-8.99178536, 5.19796241e-3, -1.61507231e-4] # D3
# coeffs = [-8.99334695, -6.70786688e-4, -2.15611619e-04] # D3
# coeffs = [-9.10575082e+00, -7.28208663e-03, -4.73157701e-05] # with filters

detuning = scipy.signal.savgol_filter(
phase / 2 / np.pi,
13,
3,
deriv=1,
delta=1, # step
delta=1,
)
# fig.add_trace(
# go.Scatter(
# x=qubit_X_data.duration,
# y=detuning,
# name="detuning",
# ),
# row=3,
# col=1,
# )

# fig.add_trace(
# go.Scatter(
# x=qubit_X_data.duration,
# y=np.polyval(
# coeffs,
# (data.flux_pulse_amplitude) * np.ones(len(qubit_X_data.duration)),
# ),
# name="fit",
# ),
# row=3,
# col=1,
# )

# TODO: understand why it works
step_response_freq = detuning / np.average(
detuning[-int(len(qubit_X_data.duration) / 2) :]
)
step_response_volt = np.sqrt(step_response_freq)

fig.add_trace(
go.Scatter(
x=qubit_X_data.duration,
y=step_response_volt,
name="Volt response",
),
row=1,
col=1,
)
)
print(step_response_volt)
fig.add_trace(
go.Scatter(
x=qubit_X_data.duration,
y=np.ones(len(qubit_X_data.duration)),
name="Volt response",
),
row=1,
col=1,
name="Ideal response",
)
)

from scipy import optimize
Expand All @@ -363,100 +281,39 @@ def _plot(data: CryoscopeData, fit: CryoscopeResults, target: QubitId):
qubit_X_data.duration,
step_response_volt,
)
# xplot = qubit_X_data.duration - 20
fig.add_trace(
go.Scatter(
x=qubit_X_data.duration,
y=exponential_decay(qubit_X_data.duration, A, tau),
name="Fit",
),
row=1,
col=1,
)
)

print(A, tau)

# fir, iir = filter_calc(exponential=[(A, tau)])

fir, iir = single_exponential_correction(A, tau)
print(f"FIR: {fir}\nIIR: {iir}")
from scipy import signal

no_filter = exponential_decay(qubit_X_data.duration, A, tau)
step_response_th = np.ones(len(qubit_X_data))
with_filter = no_filter * signal.lfilter(fir, [1, -iir[0]], step_response_th)

# fig.add_trace(
# go.Scatter(
# x=qubit_X_data.duration - 20,
# y=np.ones(len(qubit_X_data)),
# name="Theory",
# ),
# row=2,
# col=1,
# )

# fig.add_trace(
# go.Scatter(
# x = xplot,
# y=no_filter,
# name="No filter"
# ),
# row=3,
# col=1,
# )

fig.add_trace(
go.Scatter(x=qubit_X_data.duration, y=with_filter, name="With filter"),
row=1,
col=1,
)

# fig.add_trace(
# go.Scatter(
# x = xplot,
# y=np.ones(len(with_filter)),
# name="Theory"
# ),
# row=3,
# col=1,
# )

# detuning = np.abs(np.polyval(
# coeffs,
# (data.flux_pulse_amplitude) * np.array(data.waveform),
# ))
# fig.add_trace(
# go.Scatter(
# x=np.array(range(len(data.waveform))),
# y=(detuning - np.min(detuning) / (np.max(detuning) - np.min(detuning))),
# name="Waveform",
# ),
# row=4,
# col=1,
# )

# fig.add_trace(
# go.Scatter(
# x=qubit_X_data.duration,
# y=np.abs(scipy.signal.savgol_filter(
# (phase - phase[-1]) / 2 / np.pi,
# 13,
# 3,
# deriv=1,
# )),
# name="detuning",
# ),
# row=4,
# col=1,
# )

fig.update_layout(
# xaxis3_title="Flux pulse duration [ns]",
# yaxis1_title="Detuning [GHz]",
# yaxis2_title="Waveform",
# yaxis3_title="Detuning [GHz]",
xaxis_title="Flux pulse duration [ns]",
yaxis_title="Waveform [a.u.]",
)

fitting_report = table_html(
table_dict(
target,
["FIR", "IIR"],
[fir, iir],
display_error=False,
)
)

return [fig], fitting_report


Expand Down

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