Use science_signal
This commit is contained in:
parent
002c7ecd9d
commit
363971e2c1
2 changed files with 65 additions and 120 deletions
|
|
@ -1,34 +1,7 @@
|
|||
from __future__ import annotations
|
||||
from science_signal.signal import Signal
|
||||
from science_signal import interpolate, interpolate_abciss
|
||||
from numpy.typing import NDArray
|
||||
from numpy import arange, float64, zeros, pi, sqrt
|
||||
|
||||
|
||||
def interpolate_abciss(signals: tuple[Signal, ...]) -> NDArray[float64]:
|
||||
"""
|
||||
return the axis that would be used by the interpolate function
|
||||
"""
|
||||
rates: list[float] = [signal.rate for signal in signals]
|
||||
|
||||
start: float = max([signal.x[0] for signal in signals])
|
||||
end: float = min([signal.x[-1] for signal in signals])
|
||||
|
||||
return arange(start, end, 1 / max(rates))
|
||||
|
||||
|
||||
def interpolate(signals: tuple[Signal, ...]) -> tuple[Signal, ...]:
|
||||
"""
|
||||
Interpolate multiple signals with a single abciss list, which has
|
||||
the smallest interval and the bigget rate
|
||||
"""
|
||||
splines = [signal.spline() for signal in signals]
|
||||
|
||||
x = interpolate_abciss(signals)
|
||||
|
||||
new_signals = [
|
||||
Signal(x, spline(x), signals[0].settings) for spline in splines
|
||||
]
|
||||
|
||||
return tuple(new_signals)
|
||||
from numpy import float64, zeros, pi, sqrt
|
||||
|
||||
|
||||
def compute_light(
|
||||
|
|
@ -43,7 +16,7 @@ def compute_light(
|
|||
Compute the projection from a given coupling and movement
|
||||
"""
|
||||
frequencies = interpolate_abciss(
|
||||
(movement.sin().psd().sqrt(), coupling[0].abs())
|
||||
movement.sin().psd().sqrt(), abs(coupling[0])
|
||||
)
|
||||
parts = zeros(
|
||||
(
|
||||
|
|
@ -56,25 +29,28 @@ def compute_light(
|
|||
phase = (index + 1) * 4 * pi / wavelength
|
||||
|
||||
factor_n = (movement * phase).sin().psd().sqrt()
|
||||
coupling_n = coupling[0].abs()
|
||||
coupling_n = abs(coupling[0])
|
||||
factor_d = (movement * phase).cos().psd().sqrt()
|
||||
coupling_d = coupling[1].abs()
|
||||
coupling_d = abs(coupling[1])
|
||||
|
||||
factor_n, coupling_n, factor_d, coupling_d = interpolate(
|
||||
(factor_n, coupling_n, factor_d, coupling_d)
|
||||
factor_n, coupling_n, factor_d, coupling_d
|
||||
)
|
||||
|
||||
parts[index] = (
|
||||
sqrt(scatter_factor[index])
|
||||
* power_in
|
||||
/ power_out
|
||||
* (coupling_n * factor_n + coupling_d * factor_d).y
|
||||
parts[index] = opt_compute_light(
|
||||
scatter_factor[index],
|
||||
factor_n,
|
||||
coupling_n,
|
||||
factor_d,
|
||||
coupling_d,
|
||||
power_in,
|
||||
power_out,
|
||||
phase,
|
||||
)
|
||||
|
||||
return Signal(
|
||||
frequencies,
|
||||
sum(parts),
|
||||
movement.settings,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -86,12 +62,14 @@ def opt_compute_light(
|
|||
coupling_d: Signal,
|
||||
power_in: float,
|
||||
power_out: float,
|
||||
phase: float,
|
||||
) -> NDArray[float64]:
|
||||
"""
|
||||
Optimized computing of light with pre-computed factor
|
||||
"""
|
||||
return (
|
||||
sqrt(scatter_factor)
|
||||
/ phase
|
||||
* power_in
|
||||
/ power_out
|
||||
* (coupling_n * factor_n + coupling_d * factor_d).y
|
||||
|
|
@ -108,6 +86,7 @@ def fit_compute_light(
|
|||
power_out: float,
|
||||
data: Signal,
|
||||
reference: Signal,
|
||||
phase: float,
|
||||
) -> float:
|
||||
"""
|
||||
Scalar function used to find the right scattering factor
|
||||
|
|
@ -124,13 +103,10 @@ def fit_compute_light(
|
|||
coupling_d=coupling_d,
|
||||
power_in=power_in,
|
||||
power_out=power_out,
|
||||
phase=phase,
|
||||
),
|
||||
factor_n.settings,
|
||||
)
|
||||
+ reference
|
||||
- data
|
||||
).y
|
||||
)
|
||||
|
||||
|
||||
from backscattering_analyzer.signal import Signal # no circular import
|
||||
|
|
|
|||
|
|
@ -1,15 +1,16 @@
|
|||
from sys import argv
|
||||
|
||||
from scipy.interpolate import CubicSpline
|
||||
from backscattering_analyzer.settings import Settings
|
||||
from backscattering_analyzer.signal import Signal
|
||||
from science_signal.signal import Signal
|
||||
from science_signal import interpolate
|
||||
from backscattering_analyzer import (
|
||||
compute_light,
|
||||
fit_compute_light,
|
||||
opt_compute_light,
|
||||
interpolate,
|
||||
)
|
||||
from numpy import loadtxt, logspace, pi
|
||||
from numpy import argmin, loadtxt, logspace, pi, array
|
||||
from scipy.io.matlab import loadmat
|
||||
from scipy.optimize import Bounds, minimize
|
||||
|
||||
|
||||
class Analyzer:
|
||||
|
|
@ -53,9 +54,7 @@ class Analyzer:
|
|||
self.settings.log("loading bench movement")
|
||||
try:
|
||||
data = loadtxt(file).T
|
||||
self.bench_movement = (
|
||||
Signal(data[0], data[1], self.settings) * 1e-6
|
||||
) # um
|
||||
self.bench_movement = Signal(data[0], data[1]) * 1e-6 # um
|
||||
except OSError:
|
||||
raise Exception("{file} does not exist".format(file=file))
|
||||
|
||||
|
|
@ -67,9 +66,7 @@ class Analyzer:
|
|||
self.settings.log("loading mirror movement")
|
||||
try:
|
||||
data = loadtxt(file).T
|
||||
self.mirror_movement = (
|
||||
Signal(data[0], data[1], self.settings) * 1e-6
|
||||
)
|
||||
self.mirror_movement = Signal(data[0], data[1]) * 1e-6
|
||||
except OSError:
|
||||
raise Exception("{file} does not exist".format(file=file))
|
||||
|
||||
|
|
@ -81,7 +78,7 @@ class Analyzer:
|
|||
self.settings.log("loading excited h(t)")
|
||||
try:
|
||||
data = loadtxt(file).T
|
||||
self.data_signal = Signal(data[0], data[1], self.settings)
|
||||
self.data_signal = Signal(data[0], data[1])
|
||||
except OSError:
|
||||
raise Exception("{file} does not exist".format(file=file))
|
||||
|
||||
|
|
@ -93,9 +90,7 @@ class Analyzer:
|
|||
self.settings.log("loading reference h(t)")
|
||||
try:
|
||||
data = loadtxt(file).T
|
||||
self.reference_signal = Signal(
|
||||
data[0], data[1], self.settings
|
||||
)
|
||||
self.reference_signal = Signal(data[0], data[1])
|
||||
except OSError:
|
||||
raise Exception("{file} does not exist".format(file=file))
|
||||
|
||||
|
|
@ -114,7 +109,6 @@ class Analyzer:
|
|||
Signal(
|
||||
self.modelisation["freq"][0],
|
||||
coupling,
|
||||
self.settings,
|
||||
)
|
||||
for coupling in coupling_values
|
||||
]
|
||||
|
|
@ -134,13 +128,14 @@ class Analyzer:
|
|||
- self.mirror_movement * self.settings.calib_mirror
|
||||
)
|
||||
.detrend("linear")
|
||||
.low_pass_filter(5 * self.settings.vibration_frequency)
|
||||
.filter(end=5 * self.settings.vibration_frequency)
|
||||
)
|
||||
|
||||
def compute_light(self) -> None:
|
||||
"""
|
||||
Compute projection with current bench excitation
|
||||
"""
|
||||
self.settings.log("computing backscatterd light projection")
|
||||
self.projection = compute_light(
|
||||
scatter_factor=self.settings.scattering_factor,
|
||||
coupling=self.coupling,
|
||||
|
|
@ -159,17 +154,17 @@ class Analyzer:
|
|||
guess = self.settings.scattering_factor[0]
|
||||
phase = 4 * pi / self.settings.wavelength
|
||||
factor_n = (self.movement * phase).sin().psd().sqrt()
|
||||
coupling_n = self.coupling[0].abs()
|
||||
coupling_n = abs(self.coupling[0])
|
||||
factor_d = (self.movement * phase).cos().psd().sqrt()
|
||||
coupling_d = self.coupling[1].abs()
|
||||
coupling_d = abs(self.coupling[1])
|
||||
|
||||
coupling_d = coupling_d.cut_x(
|
||||
10, 200
|
||||
) # cut signal between 10 and 200 Hz
|
||||
coupling_d = coupling_d.cut(
|
||||
11,
|
||||
25,
|
||||
) # cut signal between 10 and 200 Hz (updated to 11-15 to test)
|
||||
|
||||
factor_n, coupling_n, factor_d, coupling_d, data, reference = (
|
||||
interpolate(
|
||||
(
|
||||
factor_n,
|
||||
coupling_n,
|
||||
factor_d,
|
||||
|
|
@ -178,65 +173,10 @@ class Analyzer:
|
|||
self.reference_signal.psd().sqrt(),
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
bounds = Bounds(1e-30, 1e-3)
|
||||
min_result = minimize(
|
||||
fit_compute_light,
|
||||
guess,
|
||||
(
|
||||
factor_n,
|
||||
coupling_n,
|
||||
factor_d,
|
||||
coupling_d,
|
||||
self.settings.power_in,
|
||||
self.settings.power_out,
|
||||
data,
|
||||
reference,
|
||||
),
|
||||
method="Powell",
|
||||
bounds=bounds,
|
||||
)
|
||||
|
||||
if not min_result.success:
|
||||
raise Exception(min_result.message)
|
||||
|
||||
self.settings.log(
|
||||
"found the best scattering factor in {} iterations".format(
|
||||
min_result.nit
|
||||
)
|
||||
)
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
projection = Signal(
|
||||
factor_n.x,
|
||||
opt_compute_light(
|
||||
scatter_factor=min_result.x,
|
||||
factor_n=factor_n,
|
||||
coupling_n=coupling_n,
|
||||
factor_d=factor_d,
|
||||
coupling_d=coupling_d,
|
||||
power_in=self.settings.power_in,
|
||||
power_out=self.settings.power_out,
|
||||
),
|
||||
self.settings,
|
||||
)
|
||||
|
||||
_ = plt.loglog(projection.x, projection.y, label="projection")
|
||||
_ = plt.loglog(reference.x, reference.y, label="référence")
|
||||
_ = plt.loglog(data.x, data.y, label="data")
|
||||
_ = plt.loglog(
|
||||
reference.x, projection.y + reference.y, label="somme"
|
||||
)
|
||||
_ = plt.legend()
|
||||
_ = plt.show()
|
||||
|
||||
"""
|
||||
as the minimization does not work (why ?), visual help
|
||||
"""
|
||||
x = logspace(-50, 0, 1000)
|
||||
y = [
|
||||
x = logspace(-15, -3, 1000)
|
||||
y = array(
|
||||
[
|
||||
fit_compute_light(
|
||||
x[i],
|
||||
factor_n,
|
||||
|
|
@ -247,10 +187,39 @@ class Analyzer:
|
|||
self.settings.power_out,
|
||||
data,
|
||||
reference,
|
||||
phase,
|
||||
)
|
||||
for i in range(len(x))
|
||||
]
|
||||
)
|
||||
|
||||
factor: float = x[argmin(y)]
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
projection = Signal(
|
||||
factor_n.x,
|
||||
opt_compute_light(
|
||||
scatter_factor=factor,
|
||||
factor_n=factor_n,
|
||||
coupling_n=coupling_n,
|
||||
factor_d=factor_d,
|
||||
coupling_d=coupling_d,
|
||||
power_in=self.settings.power_in,
|
||||
power_out=self.settings.power_out,
|
||||
phase=phase,
|
||||
),
|
||||
)
|
||||
_ = plt.loglog(x, y)
|
||||
_ = plt.show()
|
||||
|
||||
return min_result.x
|
||||
_ = plt.loglog(projection.x, projection.y, label="projection")
|
||||
_ = plt.loglog(reference.x, reference.y, label="référence")
|
||||
_ = plt.loglog(data.x, data.y, label="data")
|
||||
_ = plt.loglog(
|
||||
reference.x, projection.y + reference.y, label="somme"
|
||||
)
|
||||
_ = plt.legend()
|
||||
_ = plt.show()
|
||||
|
||||
return factor
|
||||
|
|
|
|||
Loading…
Reference in a new issue