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correct voigt amplitude

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This commit is contained in:
Benoît Sierro
2022-04-06 15:39:38 +02:00
parent 4dd36f8dc2
commit b7fb80473b
1 changed files with 76 additions and 37 deletions
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113
deconvolution.py
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@@ -1,13 +1,12 @@
from dataclasses import dataclass, field
from pathlib import Path
from typing import Callable
import click
import labmodule as lab
import numpy as np
from customfunc.app import PlotApp
from numpy.fft import fft, fftshift, ifft
from scipy.optimize import curve_fit
from scipy.interpolate import interp1d
LN2 = np.log(2)
@@ -32,6 +31,7 @@ class VoigtFitResult:
name: str
curve: np.ndarray = field(repr=False)
covariance: np.ndarray
amp: float
gaussian_width: float
lorentzian_width: float
@@ -114,21 +114,23 @@ def fft_gaussian(f: np.ndarray, dx: float, amp: float, width: float) -> np.ndarr
return gaussian(f, freq_amp, freq_width, 0)
def voigt(x: np.ndarray, gaussian_width: float, lorentzian_width: float) -> np.ndarray:
def voigt(x: np.ndarray, amp: float, gaussian_width: float, lorentzian_width: float) -> np.ndarray:
"""Voigt profile of max 1 with specified Gaussian and Lorentzian FWHM"""
conv = convolve(unit_gaussian(x, gaussian_width, 0), unit_lorentzian(x, lorentzian_width, 0))
if conv.max() > 0:
return conv / conv.max()
return conv * amp / conv.max()
return conv
def log_voigt(x: np.ndarray, gaussian_width: float, lorentzian_width: float) -> np.ndarray:
def log_voigt(
x: np.ndarray, amp: float, gaussian_width: float, lorentzian_width: float
) -> np.ndarray:
"""log of `voigt`"""
return np.log(voigt(x, gaussian_width, lorentzian_width))
return np.log(voigt(x, amp, gaussian_width, lorentzian_width))
def fft_voigt(
f: np.ndarray, dx: float, gaussian_width: float, lorentzian_width: float
f: np.ndarray, dx: float, amp: float, gaussian_width: float, lorentzian_width: float
) -> np.ndarray:
"""
returns the product of a `peak_func` and a `gaussian`
@@ -136,7 +138,8 @@ def fft_voigt(
given by `gaussian_width` and `lorentzian_width`
"""
profile = fft_gaussian(f, dx, 1, gaussian_width) * fft_lorentzian(f, dx, 1, lorentzian_width)
return profile / profile.max()
fac = amp / np.abs(np.fft.ifft(np.fft.fftshift(profile))).max()
return profile * fac
def single_func_fit(
@@ -192,13 +195,13 @@ def log_direct_voigt_fit(x: np.ndarray, y: np.ndarray) -> VoigtFitResult:
y_fit = np.log(y[ind])
x_fit = x[ind]
params, cov = curve_fit(log_voigt, x_fit, y_fit, bounds=[1e-12, np.inf])
curve = np.exp(log_voigt(x, *params))
curve = voigt(x, *params)
return VoigtFitResult("Log direct fit", curve, cov, *params)
def linear_fft_voigt_fit(f: np.ndarray, A: np.ndarray, x: np.ndarray, dx: float) -> VoigtFitResult:
params, cov = curve_fit(
lambda _f, _g, _l: fft_voigt(_f, dx, _g, _l), f, A, bounds=[1e-12, np.inf]
lambda _f, _a, _g, _l: fft_voigt(_f, dx, _a, _g, _l), f, A, bounds=[1e-12, np.inf]
)
curve = voigt(x, *params)
return VoigtFitResult("Linear FFT fit", curve, cov, *params)
@@ -335,25 +338,33 @@ def demo3():
@click.argument("file", type=click.Path(True, dir_okay=False))
@click.option("--all", "show_all", default=False, is_flag=True)
@main.command(name="fit")
def fit_measurement(file):
wl, intens = lab.osa.load_osa_spectrum(file)
wl *= 1e9
gauss_fit = single_func_fit(wl, intens, elevated_gaussian, extra_params=1)
def fit_measurement(file, show_all):
raw_wl, intens = lab.osa.load_osa_spectrum(file)
raw_wl *= 1e9
gauss_fit = single_func_fit(raw_wl, intens, elevated_gaussian, extra_params=1)
wl0 = gauss_fit.x0
baseline = gauss_fit.params[-1]
intens -= baseline
gauss_fit.curve -= baseline
intens[intens < 0] = 0
wl -= wl0
with PlotApp(n=np.arange(len(wl) // 2)[1:]) as app:
lim = np.max(np.abs(raw_wl - wl0))
new_wl = np.linspace(-lim, lim, 2048)
intens = interp1d(raw_wl - wl0, intens, bounds_error=False, fill_value=0)(new_wl)
wl = new_wl
with PlotApp(n=np.arange(len(wl) // 2)) as app:
app.set_antialiasing(True)
ax1 = app["Measurement"]
ax2 = app["Fourier domain"]
ax1.set_line_data("Measured spectrum", wl, intens, width=2, color="red")
ax1.set_line_data("Initial Guassian fit", wl, gauss_fit.curve, width=2, color="blue")
ax1.plot_widget.plotItem.setLogMode(y=True)
ax1.set_line_data(
"Initial Guassian fit", raw_wl - wl0, gauss_fit.curve, width=2, color="blue"
)
# ax1.plot_widget.plotItem.setLogMode(y=True)
@app.update
def draw(n):
@@ -366,34 +377,62 @@ def fit_measurement(file):
ax1.set_line_data("Fitted spectrum", wl_fit, intens_fit)
trans = np.abs(np.fft.fft(intens_fit))[ind]
m = trans.max()
ax2.set_line_data("Transformed", f, trans)
ax2.lines["Transformed"].setZValue(10)
fft_fit = linear_fft_voigt_fit(f, trans / m, wl_fit, dwl)
fft_fit = linear_fft_voigt_fit(f, trans, wl_fit, dwl)
lin_fit = linear_direct_voigt_fit(wl_fit, intens_fit)
log_fit = log_direct_voigt_fit(wl_fit, intens_fit)
for fit in fft_fit, lin_fit, log_fit:
display_fit(wl_fit, dwl, f, fit, m)
display_fit(wl_fit, dwl, f, fit)
def display_fit(x: np.ndarray, dx: float, f: np.ndarray, fit: VoigtFitResult, amp: float):
def display_fit(x: np.ndarray, dx: float, f: np.ndarray, fit: VoigtFitResult):
ax2.set_line_data(
fit.name, f, amp * fft_voigt(f, dx, fit.gaussian_width, fit.lorentzian_width)
fit.name,
f,
fft_voigt(f, dx, fit.amp, fit.gaussian_width, fit.lorentzian_width),
)
ax1.set_line_data(fit.name, x, fit.curve)
ax1.set_line_data(
fit.name + "gauss",
x,
gaussian(x, 1, fit.gaussian_width, 0),
label=f"{fit.name} - Gaussian {fit.gaussian_width:.2f}",
width=1.5,
)
ax1.set_line_data(
fit.name + "lorentz",
x,
lorentzian(x, 1, fit.lorentzian_width, 0),
label=f"{fit.name} - Lorentzian {fit.lorentzian_width:.2f}",
width=1.5,
)
if show_all:
ax1.set_line_data(
fit.name + "gauss",
x,
gaussian(x, 1, fit.gaussian_width, 0),
label=f"{fit.name} - Gaussian {fit.gaussian_width:.3f}",
width=1.5,
)
ax1.set_line_data(
fit.name + "lorentz",
x,
lorentzian(x, 1, fit.lorentzian_width, 0),
label=f"{fit.name} - Lorentzian {fit.lorentzian_width:.3f}",
width=1.5,
)
@main.command(name="test", help="test scaling of voigt profile")
def test_amp():
x, dx, f, df = setup_axes(1001, (-20, 20))
with PlotApp(
lorentz_fwhm=np.geomspace(0.1, 1), gauss_fwhm=np.linspace(1, 10), amp=np.linspace(1, 7)
) as app:
app.set_antialiasing(True)
ax1 = app["Main"]
ax2 = app["Fourrier"]
ind = np.argsort(f)
@app.update
def draw(lorentz_fwhm, gauss_fwhm, amp):
vo = voigt(x, amp, gauss_fwhm, lorentz_fwhm)
trans = np.fft.fft(np.fft.fftshift(vo))
expected = fft_voigt(f, dx, amp, gauss_fwhm, lorentz_fwhm)
back_transformed = np.fft.fftshift(np.fft.ifft(expected).real)
ax1.set_line_data("original", x, vo)
ax1.set_line_data("back tranformed", x, back_transformed)
ax2.set_line_data("expected", f[ind], expected[ind])
ax2.set_line_data("original transformed", f[ind], trans[ind].real)
if __name__ == "__main__":