diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..c8cfe39
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.10
diff --git a/data/2022-04-20 Argon_696_15.CSV b/data/2022-04-20 Argon_696_15.CSV
new file mode 100644
index 0000000..403652a
--- /dev/null
+++ b/data/2022-04-20 Argon_696_15.CSV
@@ -0,0 +1,334 @@
+70CSV
+// AQ6370 OPTICAL SPECTRUM ANALYZER //
+25
+"CTRWL", 696.400000
+"SPAN", 0.600000
+"START WL", 696.100000
+"STOP WL", 696.700000
+"WLFREQ", 0
+"REFL", -60.000
+"LSCL",-2.0
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+"SMPLAUTO", 1
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+"HIGH3"
+"RAVG", 20
+"LSUNT",1
+"NMSKH","OFF"
+"RESCOR",0
+"RESPARM",10177
+"FREQPARM",10177
+
+# Complementary values
+"GAS FLOW",500
+"VOLTAGE",7900
+"FREQUENCY",38800
+
+
+
+
+"[TRACE DATA]"
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diff --git a/data/Argon_696_11.CSV b/data/Argon_696_11.CSV
new file mode 100644
index 0000000..1175747
--- /dev/null
+++ b/data/Argon_696_11.CSV
@@ -0,0 +1,280 @@
+70CSV
+// AQ6370 OPTICAL SPECTRUM ANALYZER //
+25
+"CTRWL", 696.408000
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+"STOP WL", 696.658000
+"WLFREQ", 0
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+
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+ 696.5920,1.932E-008
+ 696.5940,1.284E-008
+ 696.5960,3.616E-008
+ 696.5980,7.565E-009
+ 696.6000,1.777E-008
+ 696.6020,3.018E-008
+ 696.6040,2.756E-008
+ 696.6060,2.632E-008
+ 696.6080,2.508E-008
+ 696.6100,1.610E-008
+ 696.6120,2.048E-008
+ 696.6140,2.276E-008
+ 696.6160,3.312E-008
+ 696.6180,1.827E-008
+ 696.6200,1.413E-008
+ 696.6220,1.066E-008
+ 696.6240,2.336E-008
+ 696.6260,2.827E-008
+ 696.6280,2.613E-008
+ 696.6300,2.697E-008
+ 696.6320,2.651E-008
+ 696.6340,2.774E-008
+ 696.6360,2.892E-008
+ 696.6380,1.307E-008
+ 696.6400,6.363E-009
+ 696.6420,1.860E-008
+ 696.6440,8.710E-009
+ 696.6460,1.427E-008
+ 696.6480,2.932E-008
+ 696.6500,2.683E-008
+ 696.6520,1.777E-008
+ 696.6540,2.264E-008
+ 696.6560,-1.402E-009
+ 696.6580,5.626E-009
+ 696.6600,2.977E-008
+ 696.6620,3.118E-008
+ 696.6640,1.082E-008
+ 696.6660,1.869E-008
+ 696.6680,1.849E-008
+ 696.6700,2.608E-008
+ 696.6720,2.053E-008
+ 696.6740,2.180E-008
+ 696.6760,1.776E-008
+ 696.6780,2.488E-008
+ 696.6800,1.753E-008
+ 696.6820,1.883E-008
+ 696.6840,2.158E-008
+ 696.6860,2.333E-008
+ 696.6880,4.083E-009
+ 696.6900,1.714E-009
+ 696.6920,2.130E-008
+ 696.6940,2.228E-008
+ 696.6960,2.503E-008
+ 696.6980,2.694E-008
+ 696.7000,2.636E-008
+ 696.7020,5.236E-009
+ 696.7040,2.383E-008
+ 696.7060,2.497E-008
+ 696.7080,1.581E-008
+ 696.7100,2.930E-008
+ 696.7120,1.767E-008
+ 696.7140,1.976E-008
+ 696.7160,1.555E-008
+ 696.7180,1.269E-008
+ 696.7200,4.294E-008
+ 696.7220,2.694E-008
+ 696.7240,2.286E-008
+ 696.7260,1.887E-008
+ 696.7280,2.019E-008
+ 696.7300,1.563E-008
+ 696.7320,2.339E-008
+ 696.7340,2.159E-008
+ 696.7360,1.241E-008
+ 696.7380,1.705E-008
+ 696.7400,2.943E-008
+ 696.7420,3.443E-008
+ 696.7440,1.792E-008
+ 696.7460,1.642E-008
+ 696.7480,1.766E-008
+ 696.7500,1.664E-008
+ 696.7520,1.081E-008
+ 696.7540,2.831E-008
+ 696.7560,1.767E-008
+ 696.7580,2.383E-008
+ 696.7600,1.637E-008
+ 696.7620,2.354E-008
+ 696.7640,1.244E-008
+ 696.7660,1.427E-008
+ 696.7680,2.304E-008
+ 696.7700,2.030E-008
+ 696.7720,1.823E-008
+ 696.7740,2.385E-008
+ 696.7760,1.225E-008
+ 696.7780,2.137E-008
+ 696.7800,2.524E-008
+ 696.7820,2.150E-008
+ 696.7840,1.369E-008
+ 696.7860,1.434E-008
+ 696.7880,9.798E-009
+ 696.7900,2.189E-008
+ 696.7920,2.465E-008
+ 696.7940,3.869E-008
+ 696.7960,2.886E-008
+ 696.7980,2.891E-008
+ 696.8000,2.910E-008
diff --git a/deploy.nu b/deploy.nu
new file mode 100644
index 0000000..bd87f19
--- /dev/null
+++ b/deploy.nu
@@ -0,0 +1,18 @@
+#!/usr/bin/env nu
+
+let version = (open pyproject.toml).project.version
+let zipfile = $"linemeasurement-($version).zip"
+
+let filelist = (
+ git ls-files |
+ lines |
+ where {not ($in | str ends-with ".afphoto")} |
+ where {$in != "deploy.nu"}
+)
+
+^zip $zipfile ...$filelist
+
+scp -O $zipfile $"fibnas:/volume1/web/($zipfile)"
+scp -O $zipfile $"fibnas:/volume1/web/linemeasurement-latest.zip"
+
+rm $zipfile
diff --git a/extract_points_balcon.py b/extract_points_balcon.py
new file mode 100644
index 0000000..8074076
--- /dev/null
+++ b/extract_points_balcon.py
@@ -0,0 +1,138 @@
+from typing import Union
+from xml.dom import minidom
+import numpy as np
+import matplotlib.pyplot as plt
+
+MX = [485.4, 485.4, 486.8]
+MY = [1, 0, 0]
+
+
+def str_pair_to_float(s: str) -> np.ndarray:
+ return np.array([float(el) for el in s.split(",")])
+
+
+def parse_buffer(s: str) -> Union[float, complex]:
+ x, *y = s.split(",")
+ if y:
+ return float(x) + 1j * float(y[0])
+ else:
+ return float(x)
+
+
+def transform_matrix_and_offset(
+ x: tuple[float, float, float],
+ y: tuple[float, float, float],
+ rx: tuple[float, float, float],
+ ry: tuple[float, float, float],
+) -> tuple[np.ndarray, np.ndarray]:
+ a = -(
+ rx[0] * y[1] - rx[0] * y[2] - rx[1] * y[0] + rx[1] * y[2] + rx[2] * y[0] - rx[2] * y[1]
+ ) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
+ b = -(
+ rx[0] * x[1] - rx[0] * x[2] - rx[1] * x[0] + rx[1] * x[2] + rx[2] * x[0] - rx[2] * x[1]
+ ) / (-x[1] * y[0] + x[2] * y[0] + x[0] * y[1] - x[2] * y[1] - x[0] * y[2] + x[1] * y[2])
+ c = -(
+ ry[0] * y[1] - ry[0] * y[2] - ry[1] * y[0] + ry[1] * y[2] + ry[2] * y[0] - ry[2] * y[1]
+ ) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
+ d = -(
+ -ry[0] * x[1] + ry[0] * x[2] + ry[1] * x[0] - ry[1] * x[2] - ry[2] * x[0] + ry[2] * x[1]
+ ) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
+ e = -(
+ rx[0] * x[2] * y[1]
+ - rx[0] * x[1] * y[2]
+ - rx[1] * x[2] * y[0]
+ + rx[1] * x[0] * y[2]
+ + rx[2] * x[1] * y[0]
+ - rx[2] * x[0] * y[1]
+ ) / (-x[1] * y[0] + x[2] * y[0] + x[0] * y[1] - x[2] * y[1] - x[0] * y[2] + x[1] * y[2])
+ f = -(
+ -ry[0] * x[2] * y[1]
+ + ry[0] * x[1] * y[2]
+ + ry[1] * x[2] * y[0]
+ - ry[1] * x[0] * y[2]
+ - ry[2] * x[1] * y[0]
+ + ry[2] * x[0] * y[1]
+ ) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
+ return np.array([[a, b], [c, d]]), np.array([e, f])
+
+
+def parse_path(s: str) -> list[tuple[np.ndarray, np.ndarray]]:
+ INSTRUCTIONS = "mMlLhHvV"
+ lines: list[list[complex]] = []
+ current_line: list[complex] = []
+ pos = 0 + 0j
+
+ def add_line_point(new_pos):
+ nonlocal pos
+ if not current_line:
+ current_line.append(pos)
+ pos = new_pos
+ current_line.append(pos)
+
+ s = iter(s + "M")
+ instr = next(s)
+ buffer = ""
+ for char in s:
+ if char not in INSTRUCTIONS:
+ buffer += char
+ continue
+ arg = parse_buffer(buffer)
+ buffer = ""
+ if instr == "m":
+ if current_line:
+ lines.append(current_line)
+ current_line = []
+ pos += arg
+ elif instr == "M":
+ if current_line:
+ lines.append(current_line)
+ current_line = []
+ pos = arg
+ elif instr == "l":
+ add_line_point(pos + arg)
+ elif instr == "L":
+ add_line_point(arg)
+ elif instr == "v":
+ add_line_point(pos + 1j * arg)
+ elif instr == "V":
+ add_line_point(0 + 1j * arg)
+ elif instr == "h":
+ add_line_point(pos + arg)
+ elif instr == "H":
+ add_line_point(arg)
+ instr = char
+ if current_line:
+ lines.append(current_line)
+
+ lines = np.array(lines)
+ return [(line.real, line.imag) for line in lines]
+
+
+def main():
+ with minidom.parse("./data/Balcon2008.svg") as doc:
+ rects = [
+ (
+ float(rect.getAttribute("x")) + float(rect.getAttribute("width")) / 2,
+ float(rect.getAttribute("y")) + float(rect.getAttribute("height")) / 2,
+ )
+ for rect in doc.getElementsByTagName("rect")
+ ]
+ path_strings = [path.getAttribute("d") for path in doc.getElementsByTagName("path")]
+
+ px, py = parse_path(path_strings[0])[0]
+
+ M, off = transform_matrix_and_offset(px, py, MX, MY)
+
+ x, y = (np.array(rects).dot(M.T) + off).T
+ s = x.argsort()
+ x = x[s]
+ y = y[s]
+
+ np.savetxt("data/Balcon2008.csv", np.c_[x, y], fmt="%.5f")
+
+ plt.plot(x, y)
+ plt.show()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/extract_points_dong.py b/extract_points_dong.py
new file mode 100644
index 0000000..036c458
--- /dev/null
+++ b/extract_points_dong.py
@@ -0,0 +1,135 @@
+from typing import Union
+from xml.dom import minidom
+import numpy as np
+import matplotlib.pyplot as plt
+
+MX = [696, 696, 697.2]
+MY = [1.2, 0, 0]
+
+
+def str_pair_to_float(s: str) -> np.ndarray:
+ return np.array([float(el) for el in s.split(",")])
+
+
+def parse_buffer(s: str) -> Union[float, complex]:
+ x, *y = s.split(",")
+ if y:
+ return float(x) + 1j * float(y[0])
+ else:
+ return float(x)
+
+
+def transform_matrix_and_offset(
+ x: tuple[float, float, float],
+ y: tuple[float, float, float],
+ rx: tuple[float, float, float],
+ ry: tuple[float, float, float],
+) -> tuple[np.ndarray, np.ndarray]:
+ a = -(
+ rx[0] * y[1] - rx[0] * y[2] - rx[1] * y[0] + rx[1] * y[2] + rx[2] * y[0] - rx[2] * y[1]
+ ) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
+ b = -(
+ rx[0] * x[1] - rx[0] * x[2] - rx[1] * x[0] + rx[1] * x[2] + rx[2] * x[0] - rx[2] * x[1]
+ ) / (-x[1] * y[0] + x[2] * y[0] + x[0] * y[1] - x[2] * y[1] - x[0] * y[2] + x[1] * y[2])
+ c = -(
+ ry[0] * y[1] - ry[0] * y[2] - ry[1] * y[0] + ry[1] * y[2] + ry[2] * y[0] - ry[2] * y[1]
+ ) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
+ d = -(
+ -ry[0] * x[1] + ry[0] * x[2] + ry[1] * x[0] - ry[1] * x[2] - ry[2] * x[0] + ry[2] * x[1]
+ ) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
+ e = -(
+ rx[0] * x[2] * y[1]
+ - rx[0] * x[1] * y[2]
+ - rx[1] * x[2] * y[0]
+ + rx[1] * x[0] * y[2]
+ + rx[2] * x[1] * y[0]
+ - rx[2] * x[0] * y[1]
+ ) / (-x[1] * y[0] + x[2] * y[0] + x[0] * y[1] - x[2] * y[1] - x[0] * y[2] + x[1] * y[2])
+ f = -(
+ -ry[0] * x[2] * y[1]
+ + ry[0] * x[1] * y[2]
+ + ry[1] * x[2] * y[0]
+ - ry[1] * x[0] * y[2]
+ - ry[2] * x[1] * y[0]
+ + ry[2] * x[0] * y[1]
+ ) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
+ return np.array([[a, b], [c, d]]), np.array([e, f])
+
+
+def parse_path(s: str) -> list[tuple[np.ndarray, np.ndarray]]:
+ INSTRUCTIONS = "mMlLhHvV"
+ lines: list[list[complex]] = []
+ current_line: list[complex] = []
+ pos = 0 + 0j
+
+ def add_line_point(new_pos):
+ nonlocal pos
+ if not current_line:
+ current_line.append(pos)
+ pos = new_pos
+ current_line.append(pos)
+
+ s = iter(s + "M")
+ instr = next(s)
+ buffer = ""
+ for char in s:
+ if char not in INSTRUCTIONS:
+ buffer += char
+ continue
+ arg = parse_buffer(buffer)
+ buffer = ""
+ if instr == "m":
+ if current_line:
+ lines.append(current_line)
+ current_line = []
+ pos += arg
+ elif instr == "M":
+ if current_line:
+ lines.append(current_line)
+ current_line = []
+ pos = arg
+ elif instr == "l":
+ add_line_point(pos + arg)
+ elif instr == "L":
+ add_line_point(arg)
+ elif instr == "v":
+ add_line_point(pos + 1j * arg)
+ elif instr == "V":
+ add_line_point(0 + 1j * arg)
+ elif instr == "h":
+ add_line_point(pos + arg)
+ elif instr == "H":
+ add_line_point(arg)
+ instr = char
+ if current_line:
+ lines.append(current_line)
+
+ lines = np.array(lines)
+ return [(line.real, line.imag) for line in lines]
+
+
+def main():
+ with minidom.parse("./data/Dong2005.svg") as doc:
+ circles = [
+ (float(circ.getAttribute("cx")), float(circ.getAttribute("cy")))
+ for circ in doc.getElementsByTagName("circle")
+ ]
+ path_strings = [path.getAttribute("d") for path in doc.getElementsByTagName("path")]
+
+ px, py = parse_path(path_strings[0])[0]
+
+ M, off = transform_matrix_and_offset(px, py, MX, MY)
+
+ x, y = (np.array(circles).dot(M.T) + off).T
+ s = x.argsort()
+ x = x[s]
+ y = y[s]
+
+ np.savetxt("data/Dong2005.csv", np.c_[x, y], fmt="%.5f")
+
+ plt.plot(x, y)
+ plt.show()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/playground.py b/playground.py
new file mode 100644
index 0000000..f1979cb
--- /dev/null
+++ b/playground.py
@@ -0,0 +1,50 @@
+from collections import namedtuple
+import itertools
+from scipy.optimize import root
+
+Args = namedtuple("Args", ["alpha", "w", "Te"])
+
+ARG1 = Args(0.04, 0.00409, 5_000)
+ARG2 = Args(0.032, 0.00537, 10_000)
+ARG3 = Args(0.023, 0.00873, 40_000)
+
+
+def L_stark(Ne: float, alpha: float, w: float, Te: float) -> float:
+ """computes stark broadening
+
+ Parameters
+ ----------
+ Ne : float
+ free electron density in 1e15 / cm^3
+ alpha : float
+ static ion-broadening parameter
+ w : float
+ electron impact parameter in nm/1e16/cm^3
+ Te : float
+ electron temperature in K
+
+ Returns
+ -------
+ float
+ Stark broadening in nm
+ """
+ Ne = 1e15 * Ne
+ return (
+ 2e-16
+ * (1 + 1.75e-4 * (Ne ** 0.25) * alpha * (1 - 0.068 * (Te ** -0.5) * (Ne ** (1 / 6))))
+ * w
+ * Ne
+ )
+
+
+def main():
+ for ls, args in itertools.product([0.001, 0.003, 0.009], [ARG1, ARG2, ARG3]):
+
+ def to_root(Ne):
+ return L_stark(Ne, *args) - ls
+
+ print(ls, args.Te, root(to_root, 1.0).x)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..dc6947e
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,18 @@
+[build-system]
+requires = ["setuptools >= 65.0.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "linemeasurement"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = ["click", "numpy", "plotapp", "scipy"]
+
+[project.scripts]
+linemeasurement = "linemeasurement.__main__:main"
+
+
+[tool.uv.sources]
+plotapp = { url = "http://130.92.113.172/plotapp-0.1.1.zip" }
diff --git a/src/linemeasurement/__main__.py b/src/linemeasurement/__main__.py
new file mode 100644
index 0000000..4486b18
--- /dev/null
+++ b/src/linemeasurement/__main__.py
@@ -0,0 +1,296 @@
+import click
+import numpy as np
+from linemeasurement.osa import load as osaload
+from linemeasurement.profiles import (
+ VoigtFitResult,
+ add_noise,
+ convolve,
+ elevated_gaussian,
+ fft_gaussian,
+ fft_lorentzian,
+ fft_voigt,
+ gaussian,
+ linear_direct_voigt_fit,
+ linear_fft_voigt_fit,
+ log_direct_voigt_fit,
+ lorentzian,
+ single_func_fit,
+ voigt,
+)
+from plotapp import PlotApp
+from scipy.interpolate import interp1d
+from scipy.signal import wiener
+
+LN2 = np.log(2)
+
+
+def setup_axes(n: int, lims=(-10, 10)) -> tuple[np.ndarray, float, np.ndarray, float]:
+ """Creates a time-like and a frequency-like array and returns their respective spacing"""
+ x, dx = np.linspace(*lims, n, retstep=True)
+ f = np.fft.fftfreq(n, dx)
+ return x, dx, f, f[1] - f[0]
+
+
+def freq_axis(x: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
+ """
+ given an array with equally spaced values, returns a corresponding frequency array,
+ an array of indices to automatically sort ffts and the x spacing
+ """
+ dx = np.diff(x).mean()
+ f = np.fft.fftfreq(len(x), dx)
+ ind = np.argsort(f)
+ return f[ind], ind, dx
+
+
+@click.group()
+def main():
+ pass
+
+
+@main.command(help="fit a lorentzian with a gaussian")
+def demo1():
+ x = np.linspace(-10, 10, 501)
+ with PlotApp(
+ FWHM=np.geomspace(0.1, 10),
+ relative_noise=np.linspace(0, 2),
+ absolute_noise=[0, *np.geomspace(0.001, 1)],
+ ) as app:
+ app.set_antialiasing(True)
+ app.params["FWHM"].value = 3.7
+
+ @app.update
+ def draw(FWHM, relative_noise, absolute_noise):
+ lo = lorentzian(x, 1, FWHM, 0)
+ lo = add_noise(lo, absolute_noise, relative_noise * 0.04)
+ fit = single_func_fit(x, lo, gaussian)
+ app[0].set_line_data("Lorentzian", x, lo, width=2)
+ app[0].set_line_data(
+ "gaus fit", x, fit.curve, width=2, label=f"Gaussian fit : FWHM = {fit.width}"
+ )
+
+
+@main.command(name="default", help="illustrate the influence of noises and fit a voigt profile")
+def demo2():
+ with PlotApp(
+ n=np.arange(256, 2046),
+ lorentz_fwhm=np.geomspace(0.1, 1),
+ gauss_fwhm=np.linspace(1, 10),
+ relative_noise=np.linspace(0, 2),
+ absolute_noise=[0, *np.geomspace(1e-6, 1, 201)],
+ ) as app:
+ app.set_antialiasing(True)
+ v_ax = app["Voigt"]
+ l_ax = app["Lorentz"]
+ g_ax = app["Gauss"]
+ l_ax.link_x(v_ax)
+ g_ax.link_x(v_ax)
+
+ app.params["gauss_fwhm"].value = 5
+ app.params["n"].value = 1024
+ app.params_layout.setSpacing(0)
+
+ @app.update
+ def draw(n, lorentz_fwhm, gauss_fwhm, absolute_noise, relative_noise):
+ x, dx, f, df = setup_axes(n, (-20, 20))
+ gaus = gaussian(x, 1, gauss_fwhm, 0)
+ lore = lorentzian(x, 1, lorentz_fwhm, 0)
+ g_ax.set_line_data("Gaussian", x, gaus / gaus.max(), width=2)
+ l_ax.set_line_data("Lorentz", x, lore / lore.max(), width=2)
+
+ conv = convolve(gaus, lore)
+ conv /= conv.max()
+ conv_noise = add_noise(conv, absolute_noise, relative_noise * 0.1)
+ gaus_noise = add_noise(gaus, absolute_noise, relative_noise * 0.1)
+ v_ax.set_line_data("Voigt", x, conv, width=2)
+ v_ax.set_line_data("Noisy Voigt", x, conv_noise, width=2)
+ g_ax.set_line_data("Noisy Gaussian", x, gaus_noise, width=2)
+
+ lin_fit = linear_direct_voigt_fit(x, conv_noise)
+ log_fit = log_direct_voigt_fit(x, conv_noise)
+ transfo = np.fft.fft(np.fft.fftshift(conv_noise)).real
+ fft_fit = linear_fft_voigt_fit(f, transfo, x, dx)
+ for fit in lin_fit, log_fit, fft_fit:
+ display_fit(x, fit)
+
+ def display_fit(x: np.ndarray, fit: VoigtFitResult):
+ v_ax.set_line_data(fit.name, x, fit.curve)
+ g_ax.set_line_data(
+ fit.name + "gauss",
+ x,
+ gaussian(x, fit.amp, fit.gaussian_width, 0),
+ label=f"{fit.name} - Gaussian {fit.gaussian_width:.2f}",
+ width=1.5,
+ )
+ l_ax.set_line_data(
+ fit.name + "loren",
+ x,
+ lorentzian(x, fit.amp, fit.lorentzian_width, 0),
+ label=f"{fit.name} - Lorentzian {fit.lorentzian_width:.2f}",
+ width=1.5,
+ )
+
+
+@main.command(help="Explore fft fitting")
+def demo3():
+ x, dx, f, df = setup_axes(1001, (-20, 20))
+ with PlotApp(
+ lorentz_fwhm=np.geomspace(0.1, 1),
+ gauss_fwhm=np.linspace(1, 10),
+ relative_noise=np.linspace(0, 2),
+ absolute_noise=[0, *np.geomspace(1e-6, 1, 201)],
+ ) as app:
+ app.set_antialiasing(True)
+
+ ax = app[0]
+ ind = np.argsort(f)
+
+ @app.update
+ def draw(lorentz_fwhm, gauss_fwhm, absolute_noise, relative_noise):
+ gaus_clean = gaussian(x, 1, gauss_fwhm, 0)
+ lore_clean = lorentzian(x, 1, lorentz_fwhm, 0)
+ voig_clean = convolve(gaus_clean, lore_clean)
+
+ gaus = add_noise(gaus_clean, absolute_noise, relative_noise * 0.1)
+ lore = add_noise(lore_clean, absolute_noise, relative_noise * 0.1)
+ voig = add_noise(voig_clean, absolute_noise, relative_noise * 0.1)
+
+ gaus_f = np.fft.fft(np.fft.fftshift(gaus)).real
+ lore_f = np.fft.fft(np.fft.fftshift(lore)).real
+ voig_f = np.fft.fft(np.fft.fftshift(voig)).real
+
+ ax.set_line_data("Transformed Gaussian", f[ind], gaus_f[ind])
+ ax.set_line_data("Transformed Lorentz", f[ind], lore_f[ind])
+ ax.set_line_data("Transformed Voigt", f[ind], voig_f[ind])
+
+ gaus_ff = fft_gaussian(f, dx, 1, gauss_fwhm)
+ lore_ff = fft_lorentzian(f, dx, 1, lorentz_fwhm)
+ voig_ff = fft_voigt(f, dx, gauss_fwhm, lorentz_fwhm) * voig_clean.sum()
+
+ ax.set_line_data("Expected Gaussian", f[ind], gaus_ff[ind], width=1)
+ ax.set_line_data("Expected Lorentz", f[ind], lore_ff[ind], width=1)
+ ax.set_line_data("Expected Voigt", f[ind], voig_ff[ind], width=1)
+
+
+@click.option("--all", "show_all", default=False, is_flag=True)
+@click.option("--file", "-f", type=click.Path(True, dir_okay=False), required=True, prompt=True)
+@main.command(name="fit")
+def fit_measurement(file, show_all):
+ raw_wl, raw_intens = osaload(file, normalize=True)
+ _o = raw_wl.argsort()
+ raw_intens = raw_intens[_o]
+ raw_wl = raw_wl[_o]
+ del _o
+ if raw_wl[0] < 1:
+ raw_wl *= 1e9
+
+ with PlotApp(
+ center_wl=np.linspace(raw_wl.min(), raw_wl.max(), 8192),
+ span=np.geomspace(0.001, raw_wl[-1] - raw_wl[0], 1024),
+ filter=range(0, 30, 3),
+ ) as app:
+ app.set_antialiasing(True)
+
+ ax1 = app["Measurement"]
+ ax2 = app["Fourier domain"]
+ baseline = 0.0
+
+ app.params["center_wl"].value = raw_wl[raw_intens.argmax()]
+ app.params["span"].value = app.params["span"].values()[-1]
+
+ @app.update
+ def draw(center_wl, span, filter):
+ nonlocal baseline
+
+ # Crop spectrum around peak
+ left_index = np.argmin(np.abs(raw_wl - (center_wl - span / 2)))
+ right_index = np.argmin(np.abs(raw_wl - (center_wl + span / 2)))
+ intens = raw_intens[left_index : right_index + 1]
+ wl = raw_wl[left_index : right_index + 1]
+
+ print(len(wl), len(intens))
+
+ # Fit rough gaussian to find peak
+ gauss_fit = single_func_fit(wl, intens, elevated_gaussian, extra_params=1)
+ wl0 = gauss_fit.x0
+ baseline = intens[:10].mean()
+ # intens -= baseline
+ # gauss_fit.curve -= baseline
+ intens[intens < 0] = 0
+
+ # center spectrum around 0
+ ax1.set_line_data("Measured spectrum", raw_wl - wl0, raw_intens, width=2, color="red")
+ # ax1.set_line_data(
+ # "Initial Guassian fit", raw_wl - wl0, gauss_fit.curve, width=2, color="blue"
+ # )
+
+ new_wl = np.linspace(-span / 2, span / 2, 1024)
+ intens = interp1d(wl - wl0, intens, bounds_error=False, fill_value=0)(new_wl)
+ wl = new_wl
+ # wl -= wl0
+
+ intens_fit = intens - baseline
+ if filter:
+ intens_fit = wiener(intens_fit, filter)
+ f, ind, dwl = freq_axis(wl)
+
+ ax1.set_line_data("Spectrum to fit", wl, intens_fit + baseline)
+
+ trans = np.abs(np.fft.fft(intens_fit))[ind]
+ ax2.set_line_data("Transformed", f, trans)
+ ax2.lines["Transformed"].setZValue(10)
+
+ display_fit(wl, dwl, f, linear_direct_voigt_fit(wl, intens_fit))
+ # display_fit(wl, dwl, f, linear_fft_voigt_fit(f, trans, wl, dwl))
+ # display_fit(wl, dwl, f, log_direct_voigt_fit(wl, intens_fit))
+
+ def display_fit(x: np.ndarray, dx: float, f: np.ndarray, fit: VoigtFitResult):
+ ax2.set_line_data(
+ fit.name,
+ f,
+ fft_voigt(f, dx, fit.amp, fit.gaussian_width, fit.lorentzian_width),
+ )
+ ax1.set_line_data(fit.name, x, fit.curve + baseline)
+ if show_all:
+ ax1.set_line_data(
+ fit.name + "gauss",
+ x,
+ gaussian(x, 1, fit.gaussian_width, 0) + baseline,
+ label=f"{fit.name} - Gaussian {fit.gaussian_width:.4g}",
+ width=1.5,
+ )
+ ax1.set_line_data(
+ fit.name + "lorentz",
+ x,
+ lorentzian(x, 1, fit.lorentzian_width, 0) + baseline,
+ label=f"{fit.name} - Lorentzian {fit.lorentzian_width:.4g}",
+ 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__":
+ main()
diff --git a/deconvolution.py b/src/linemeasurement/deconvolution.py
similarity index 77%
rename from deconvolution.py
rename to src/linemeasurement/deconvolution.py
index def8257..7ff5fc7 100644
--- a/deconvolution.py
+++ b/src/linemeasurement/deconvolution.py
@@ -2,11 +2,13 @@ from dataclasses import dataclass, field
from typing import Callable
import click
-import labmodule as lab
import numpy as np
-from customfunc.app import PlotApp
-from scipy.optimize import curve_fit
+from plotapp import PlotApp
from scipy.interpolate import interp1d
+from scipy.optimize import curve_fit
+from scipy.signal import wiener
+
+from .osa import load as osaload
LN2 = np.log(2)
@@ -43,6 +45,56 @@ class VoigtFitResult:
)
+def load_osa_spectrum(
+ data: Union[os.PathLike, str], lvl_col: int = 1, normalize: bool = True, ret_props=False
+) -> tuple[np.ndarray, np.ndarray]:
+ """Read files from Yokogawa optical spectrum analyzers
+
+ Parameters
+ ----------
+ data : os.PathLike | str
+ path to the file or raw data
+ lvl_col : int, optional
+ index of the intensity column, by default 1
+ normalize : bool, optional
+ whether to shift the maximum to 0, by default True
+
+ Returns
+ -------
+ wavelength : np.ndarray
+ wavelength in m
+ intensity : np.ndarray
+ recorded intensity in dB
+ """
+ props = {}
+ if isinstance(data, str) and not os.path.exists(data):
+ s = data.splitlines()
+ else:
+ with open(data) as f:
+ s = f.readlines()
+ s = iter(s)
+ for line in s:
+ if "[TRACE DATA]" in line:
+ break
+ if ret_props and line.startswith('"'):
+ k, v = parse_osa_prop_line(line)
+ props[k] = v
+ to_load = "\n".join([el.strip() for el in s])
+ wl, intens = np.loadtxt(
+ StringIO(to_load), delimiter=",", usecols=(0, lvl_col), unpack=True, comments='"'
+ )
+ if normalize:
+ if (intens < 0).sum() < len(intens) / 10:
+ intens -= intens.min()
+ intens /= intens.max()
+ else:
+ intens -= intens.max()
+ if ret_props:
+ return 1e-9 * wl, intens, props
+ else:
+ return 1e-9 * wl, intens
+
+
def setup_axes(n: int, lims=(-10, 10)) -> tuple[np.ndarray, float, np.ndarray, float]:
"""Creates a time-like and a frequency-like array and returns their respective spacing"""
x, dx = np.linspace(*lims, n, retstep=True)
@@ -61,6 +113,15 @@ def freq_axis(x: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
return f[ind], ind, dx
+def rough_fwhm(x: np.ndarray, y: np.ndarray) -> float:
+ m2 = y.max() / 2
+ ind = np.where(y >= m2)[0]
+ return (
+ x[np.minimum(ind + 1, len(x) - 1)][x[ind].argmax()]
+ - x[np.maximum(0, ind - 1)][x[ind].argmin()]
+ )
+
+
def lorentzian(x: np.ndarray, amp: float, width: float, x0: float) -> np.ndarray:
"""Lorentzian function of max `amp`, FWHM `width` and position `x0`"""
gamma = (width * 0.5) ** 2
@@ -89,13 +150,13 @@ def fft_lorentzian(f: np.ndarray, dx: float, amp: float, width: float) -> np.nda
def gaussian(x: np.ndarray, amp: float, width: float, x0: float) -> np.ndarray:
"""Gaussian function of max `amp`, FWHM `width` and position `x0`"""
- sig2 = width ** 2 / (8 * LN2)
+ sig2 = width**2 / (8 * LN2)
return amp * np.exp(-((x - x0) ** 2) / (2 * sig2))
def elevated_gaussian(x: np.ndarray, amp: float, width: float, x0: float, y0: float) -> np.ndarray:
"""Gaussian function of max `amp`, FWHM `width`, position `x0` and baseline `y0`"""
- sig2 = width ** 2 / (8 * LN2)
+ sig2 = width**2 / (8 * LN2)
return amp * np.exp(-((x - x0) ** 2) / (2 * sig2)) + y0
@@ -177,15 +238,25 @@ def single_func_fit(
fct = _fct
am = y.argmax()
+ xm = x.mean()
+ fwhm = rough_fwhm(x, y)
params, cov = curve_fit(
- fct, x, y, sigma=1 / y if use_weights else None, p0=[y[am], 1, x[am]] + [0] * extra_params
+ fct,
+ x - xm,
+ y,
+ sigma=1 / y if use_weights else None,
+ p0=[y[am], fwhm, x[am] - xm] + [0] * extra_params,
)
+ params[2] += xm
curve = fct(x, *params)
return FitResult(curve, cov, params[0], params[1], params[2], tuple(params))
def linear_direct_voigt_fit(x: np.ndarray, y: np.ndarray, use_weights=False) -> VoigtFitResult:
- params, cov = curve_fit(voigt, x, y, sigma=1 / y if use_weights else None)
+ fwhm = rough_fwhm(x, y)
+ params, cov = curve_fit(
+ voigt, x, y, sigma=1 / y if use_weights else None, p0=[y.max(), fwhm / 2, fwhm / 2]
+ )
curve = voigt(x, *params)
return VoigtFitResult("Linear direct fit", curve, cov, *params)
@@ -200,8 +271,13 @@ def log_direct_voigt_fit(x: np.ndarray, y: np.ndarray) -> VoigtFitResult:
def linear_fft_voigt_fit(f: np.ndarray, A: np.ndarray, x: np.ndarray, dx: float) -> VoigtFitResult:
+ fwhm = 4 * LN2 / (np.pi * rough_fwhm(f, A))
params, cov = curve_fit(
- lambda _f, _a, _g, _l: fft_voigt(_f, dx, _a, _g, _l), f, A, bounds=[1e-12, np.inf]
+ lambda _f, _amp, _gau, _lor: fft_voigt(_f, dx, _amp, _gau, _lor),
+ f,
+ A,
+ bounds=[1e-12, np.inf],
+ p0=[A.max(), fwhm, fwhm / 10],
)
curve = voigt(x, *params)
return VoigtFitResult("Linear FFT fit", curve, cov, *params)
@@ -249,7 +325,11 @@ def demo2():
absolute_noise=[0, *np.geomspace(1e-6, 1, 201)],
) as app:
app.set_antialiasing(True)
- ax = app["Main Plot"]
+ v_ax = app["Voigt"]
+ l_ax = app["Lorentz"]
+ g_ax = app["Gauss"]
+ l_ax.link_x(v_ax)
+ g_ax.link_x(v_ax)
app.params["gauss_fwhm"].value = 5
app.params["n"].value = 1024
@@ -260,37 +340,37 @@ def demo2():
x, dx, f, df = setup_axes(n, (-20, 20))
gaus = gaussian(x, 1, gauss_fwhm, 0)
lore = lorentzian(x, 1, lorentz_fwhm, 0)
- ax.set_line_data("Gaussian", x, gaus / gaus.max(), width=2)
- ax.set_line_data("Lorentz", x, lore / lore.max(), width=2)
+ g_ax.set_line_data("Gaussian", x, gaus / gaus.max(), width=2)
+ l_ax.set_line_data("Lorentz", x, lore / lore.max(), width=2)
conv = convolve(gaus, lore)
conv /= conv.max()
conv_noise = add_noise(conv, absolute_noise, relative_noise * 0.1)
gaus_noise = add_noise(gaus, absolute_noise, relative_noise * 0.1)
- ax.set_line_data("Voigt", x, conv, width=2)
- ax.set_line_data("Noisy Voigt", x, conv_noise, width=2)
- ax.set_line_data("Noisy Gaussian", x, gaus_noise, width=2)
+ v_ax.set_line_data("Voigt", x, conv, width=2)
+ v_ax.set_line_data("Noisy Voigt", x, conv_noise, width=2)
+ g_ax.set_line_data("Noisy Gaussian", x, gaus_noise, width=2)
lin_fit = linear_direct_voigt_fit(x, conv_noise)
log_fit = log_direct_voigt_fit(x, conv_noise)
transfo = np.fft.fft(np.fft.fftshift(conv_noise)).real
- fft_fit = linear_fft_voigt_fit(f, transfo / transfo.max(), x, dx)
+ fft_fit = linear_fft_voigt_fit(f, transfo, x, dx)
for fit in lin_fit, log_fit, fft_fit:
display_fit(x, fit)
def display_fit(x: np.ndarray, fit: VoigtFitResult):
- ax.set_line_data(fit.name, x, fit.curve)
- ax.set_line_data(
+ v_ax.set_line_data(fit.name, x, fit.curve)
+ g_ax.set_line_data(
fit.name + "gauss",
x,
- gaussian(x, 1, fit.gaussian_width, 0),
+ gaussian(x, fit.amp, fit.gaussian_width, 0),
label=f"{fit.name} - Gaussian {fit.gaussian_width:.2f}",
width=1.5,
)
- ax.set_line_data(
+ l_ax.set_line_data(
fit.name + "loren",
x,
- lorentzian(x, 1, fit.lorentzian_width, 0),
+ lorentzian(x, fit.amp, fit.lorentzian_width, 0),
label=f"{fit.name} - Lorentzian {fit.lorentzian_width:.2f}",
width=1.5,
)
@@ -337,25 +417,26 @@ def demo3():
ax.set_line_data("Expected Voigt", f[ind], voig_ff[ind], width=1)
-@click.argument("file", type=click.Path(True, dir_okay=False))
@click.option("--all", "show_all", default=False, is_flag=True)
+@click.option("--file", "-f", type=click.Path(True, dir_okay=False), required=True, prompt=True)
@main.command(name="fit")
def fit_measurement(file, show_all):
- raw_wl, intens = lab.osa.load_osa_spectrum(file)
- raw_wl *= 1e9
+ raw_wl, intens = osaload(file, normalize=True)
+ if raw_wl[0] < 1:
+ 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
+ baseline = intens[:10].mean()
+ # intens -= baseline
+ # gauss_fit.curve -= baseline
intens[intens < 0] = 0
lim = np.max(np.abs(raw_wl - wl0))
- new_wl = np.linspace(-lim, lim, 2048)
+ new_wl = np.linspace(-lim, lim, 1024)
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:
+ with PlotApp(num_skipped=np.arange(len(wl) // 2), filter=range(0, 30, 3)) as app:
app.set_antialiasing(True)
ax1 = app["Measurement"]
@@ -367,14 +448,17 @@ def fit_measurement(file, show_all):
# ax1.plot_widget.plotItem.setLogMode(y=True)
@app.update
- def draw(n):
- s = slice(n, -n) if n else slice(None)
+ def draw(num_skipped, filter):
+ n, r = divmod(num_skipped, 2)
+ s = slice(n + r, -n) if n else slice(None)
wl_fit = wl[s]
- intens_fit = intens[s] - intens[s].min()
+ intens_fit = intens[s] - baseline
+ if filter:
+ intens_fit = wiener(intens_fit, filter)
f, ind, dwl = freq_axis(wl_fit)
- ax1.set_line_data("Fitted spectrum", wl_fit, intens_fit)
+ ax1.set_line_data("Fitted spectrum", wl_fit, intens_fit + baseline)
trans = np.abs(np.fft.fft(intens_fit))[ind]
ax2.set_line_data("Transformed", f, trans)
@@ -392,20 +476,20 @@ def fit_measurement(file, show_all):
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, x, fit.curve + baseline)
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}",
+ gaussian(x, 1, fit.gaussian_width, 0) + baseline,
+ label=f"{fit.name} - Gaussian {fit.gaussian_width:.4g}",
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}",
+ lorentzian(x, 1, fit.lorentzian_width, 0) + baseline,
+ label=f"{fit.name} - Lorentzian {fit.lorentzian_width:.4g}",
width=1.5,
)
diff --git a/src/linemeasurement/osa.py b/src/linemeasurement/osa.py
new file mode 100644
index 0000000..e5448d9
--- /dev/null
+++ b/src/linemeasurement/osa.py
@@ -0,0 +1,112 @@
+import os
+from functools import lru_cache
+from io import StringIO
+from pathlib import Path
+from typing import Any, Union
+
+import numpy as np
+
+
+def parse_osa_prop_line(s: str) -> tuple[str, Any]:
+ vals = s.split('"')
+ if vals[0]:
+ raise ValueError()
+ key = vals[1].lower().replace(" ", "_")
+ if len(vals) <= 2:
+ return key, True
+ val_str = vals[2].replace(",", "").strip()
+ if val_str == '"OFF"':
+ return key, False
+ for fct in int, float:
+ try:
+ val = fct(val_str)
+ return key, val
+ except ValueError:
+ continue
+
+ return key, val_str
+
+
+@lru_cache
+def load_osa_spectrum(
+ data: Union[os.PathLike, str], lvl_col: int = 1, normalize: bool = True, ret_props=False
+) -> tuple[np.ndarray, np.ndarray]:
+ """Read files from Yokogawa optical spectrum analyzers
+
+ Parameters
+ ----------
+ data : os.PathLike | str
+ path to the file or raw data
+ lvl_col : int, optional
+ index of the intensity column, by default 1
+ normalize : bool, optional
+ whether to shift the maximum to 0, by default True
+
+ Returns
+ -------
+ wavelength : np.ndarray
+ wavelength in m
+ intensity : np.ndarray
+ recorded intensity in dB
+ """
+ props = {}
+ if isinstance(data, str) and not os.path.exists(data):
+ s = data.splitlines()
+ else:
+ with open(data) as f:
+ s = f.readlines()
+ s = iter(s)
+ for line in s:
+ if "[TRACE DATA]" in line:
+ break
+ if ret_props and line.startswith('"'):
+ k, v = parse_osa_prop_line(line)
+ props[k] = v
+ to_load = "\n".join([el.strip() for el in s])
+ wl, intens = np.loadtxt(
+ StringIO(to_load), delimiter=",", usecols=(0, lvl_col), unpack=True, comments='"'
+ )
+ if normalize:
+ if (intens < 0).sum() < len(intens) / 10:
+ intens -= intens.min()
+ intens /= intens.max()
+ else:
+ intens -= intens.max()
+ if ret_props:
+ return 1e-9 * wl, intens, props
+ else:
+ return 1e-9 * wl, intens
+
+
+def load_avantes(
+ data: Union[os.PathLike, str], usecols=(0, 1), skiprows=8
+) -> tuple[np.ndarray, np.ndarray]:
+ if isinstance(data, str):
+ s = data
+ else:
+ s = Path(data).read_text()
+ s = s.replace(",", ".")
+ return np.loadtxt(StringIO(s), delimiter=";", usecols=usecols, skiprows=skiprows, unpack=True)
+
+
+def load(path: os.PathLike, normalize=False) -> tuple[np.ndarray, np.ndarray]:
+ s = Path(path).read_text()
+ wl = None
+ if "[TRACE DATA]" in s:
+ wl, i = load_osa_spectrum(s, normalize=False)
+ elif "Integration time [ms]" in s:
+ wl, i = load_avantes(s)
+ else:
+ for lim in [None, ",", ";"]:
+ try:
+ wl, i = np.loadtxt(StringIO(s), delimiter=lim, unpack=True)
+ break
+ except ValueError:
+ continue
+ if wl is None:
+ raise ValueError(f"Could not load a spectrum from {path}")
+
+ if normalize:
+ i -= i.min()
+ i /= i.max()
+ return wl, i
diff --git a/src/linemeasurement/profiles.py b/src/linemeasurement/profiles.py
new file mode 100644
index 0000000..ff8e1a6
--- /dev/null
+++ b/src/linemeasurement/profiles.py
@@ -0,0 +1,214 @@
+from dataclasses import dataclass, field
+from typing import Callable
+
+import numpy as np
+from scipy.optimize import curve_fit
+
+LN2 = np.log(2)
+
+
+@dataclass
+class FitResult:
+ curve: np.ndarray = field(repr=False)
+ covariance: np.ndarray
+ amp: float
+ width: float
+ x0: float = 0
+ params: tuple[float, ...] = field(default_factory=tuple)
+
+
+@dataclass
+class VoigtFitResult:
+ name: str
+ curve: np.ndarray = field(repr=False)
+ covariance: np.ndarray
+ amp: float
+ gaussian_width: float
+ lorentzian_width: float
+
+ @property
+ def descr(self) -> str:
+ return (
+ f"{self.name} - Gaussian width : {self.gaussian_width:.2f}, "
+ f"Lorentzian width : {self.lorentzian_width:.2f}"
+ )
+
+
+def rough_fwhm(x: np.ndarray, y: np.ndarray) -> float:
+ m2 = y.max() / 2
+ ind = np.where(y >= m2)[0]
+ return (
+ x[np.minimum(ind + 1, len(x) - 1)][x[ind].argmax()]
+ - x[np.maximum(0, ind - 1)][x[ind].argmin()]
+ )
+
+
+def lorentzian(x: np.ndarray, amp: float, width: float, x0: float) -> np.ndarray:
+ """Lorentzian function of max `amp`, FWHM `width` and position `x0`"""
+ gamma = (width * 0.5) ** 2
+ return amp * gamma / ((x - x0) ** 2 + gamma)
+
+
+def unit_lorentzian(x: np.ndarray, width: float, x0: float) -> np.ndarray:
+ """Normalized Lorentzian function of FWHM `width` and position `x0`"""
+ return lorentzian(x, 1 / (np.pi * width * 0.5), width, x0)
+
+
+def peak_func(x: np.ndarray, amp: float, width: float, x0: float) -> np.ndarray:
+ """2-sided decaying exp of max `amp`, FWHM `width` and position `x0`"""
+ a = 2 * LN2 / width
+ return amp * np.exp(-np.abs(x - x0) * a)
+
+
+def fft_lorentzian(f: np.ndarray, dx: float, amp: float, width: float) -> np.ndarray:
+ """
+ Same as `peak_func` but parametrized as the fourier transform
+ of a Lorentzian function of max `amp` and FWHM `width`
+ """
+ gamma_sqrt = width / 2
+ return amp * np.pi * gamma_sqrt * np.exp(-2 * np.pi * gamma_sqrt * np.abs(f)) / dx
+
+
+def gaussian(x: np.ndarray, amp: float, width: float, x0: float) -> np.ndarray:
+ """Gaussian function of max `amp`, FWHM `width` and position `x0`"""
+ sig2 = width**2 / (8 * LN2)
+ return amp * np.exp(-((x - x0) ** 2) / (2 * sig2))
+
+
+def elevated_gaussian(x: np.ndarray, amp: float, width: float, x0: float, y0: float) -> np.ndarray:
+ """Gaussian function of max `amp`, FWHM `width`, position `x0` and baseline `y0`"""
+ sig2 = width**2 / (8 * LN2)
+ return amp * np.exp(-((x - x0) ** 2) / (2 * sig2)) + y0
+
+
+def unit_gaussian(x: np.ndarray, width: float, x0: float) -> np.ndarray:
+ """Normalized Gaussian function of FWHM `width` and position `x0`"""
+ return gaussian(x, np.sqrt(4 * LN2 / np.pi) / width, width, x0)
+
+
+def fft_gaussian(f: np.ndarray, dx: float, amp: float, width: float) -> np.ndarray:
+ """
+ Same as `gaussian` but parametrized as the fourier transform
+ of another Gaussian function of max `amp` and FWHM `width`
+ """ # exp_param =
+ freq_amp = amp * width / 2 * np.sqrt(np.pi / LN2) / dx
+ freq_width = 4 * LN2 / (width * np.pi)
+ return gaussian(f, freq_amp, freq_width, 0)
+
+
+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 * amp / conv.max()
+ return conv
+
+
+def log_voigt(
+ x: np.ndarray, amp: float, gaussian_width: float, lorentzian_width: float
+) -> np.ndarray:
+ """log of `voigt`"""
+ return np.log(voigt(x, amp, gaussian_width, lorentzian_width))
+
+
+def fft_voigt(
+ 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`
+ parameterized as the Fourier transform of a Voigt profile
+ given by `gaussian_width` and `lorentzian_width`
+ """
+ profile = fft_gaussian(f, dx, 1, gaussian_width) * fft_lorentzian(f, dx, 1, lorentzian_width)
+ fac = amp / np.abs(np.fft.ifft(np.fft.fftshift(profile))).max()
+ return profile * fac
+
+
+def single_func_fit(
+ x: np.ndarray,
+ y: np.ndarray,
+ fct: Callable[[np.ndarray, float, float, float], np.ndarray],
+ force_center=False,
+ use_weights=False,
+ extra_params: int = 0,
+) -> FitResult:
+ """fits a single bell-like function (Gaussian or Lorentian, not Voigt)
+
+ Parameters
+ ----------
+ x : np.ndarray
+ x input
+ y : np.ndarray
+ corresponding y data
+ fct : Callable[[np.ndarray, float, float, float], np.ndarray]
+ function to fit
+ force_center : bool, optional
+ assume the bell is centered at x=0, by default False
+ use_weights : bool, optional
+ give more importance to higher y values, by default False
+
+ Returns
+ -------
+ FitResult
+ resuts of the fit
+ """
+ if force_center:
+
+ def _fct(_x: np.ndarray, amp: float, width: float):
+ return fct(_x, amp, width, 0)
+
+ fct = _fct
+ am = y.argmax()
+ xm = x.mean()
+ fwhm = rough_fwhm(x, y)
+ params, cov = curve_fit(
+ fct,
+ x - xm,
+ y,
+ sigma=1 / y if use_weights else None,
+ p0=[y[am], fwhm, x[am] - xm] + [0] * extra_params,
+ )
+ params[2] += xm
+ curve = fct(x, *params)
+ return FitResult(curve, cov, params[0], params[1], params[2], tuple(params))
+
+
+def linear_direct_voigt_fit(x: np.ndarray, y: np.ndarray, use_weights=False) -> VoigtFitResult:
+ fwhm = rough_fwhm(x, y)
+ params, cov = curve_fit(
+ voigt, x, y, sigma=1 / y if use_weights else None, p0=[y.max(), fwhm / 2, fwhm / 2]
+ )
+ curve = voigt(x, *params)
+ return VoigtFitResult("Linear direct fit", curve, cov, *params)
+
+
+def log_direct_voigt_fit(x: np.ndarray, y: np.ndarray) -> VoigtFitResult:
+ ind = y > 0
+ 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 = 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:
+ fwhm = 4 * LN2 / (np.pi * rough_fwhm(f, A))
+ params, cov = curve_fit(
+ lambda _f, _amp, _gau, _lor: fft_voigt(_f, dx, _amp, _gau, _lor),
+ f,
+ A,
+ bounds=[1e-12, np.inf],
+ p0=[A.max(), fwhm, fwhm / 10],
+ )
+ curve = voigt(x, *params)
+ return VoigtFitResult("Linear FFT fit", curve, cov, *params)
+
+
+def convolve(gau: np.ndarray, lor: np.ndarray) -> np.ndarray:
+ return np.convolve(gau, lor, mode="same")
+
+
+def add_noise(arr: np.ndarray, abs_fac: float, rel_fac: float):
+ return (
+ arr + abs_fac * np.random.rand(len(arr)) + rel_fac * arr * (np.random.rand(len(arr)) - 0.5)
+ )
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..8a95415
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,231 @@
+version = 1
+requires-python = ">=3.10"
+
+[[package]]
+name = "click"
+version = "8.1.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "colorama", marker = "platform_system == 'Windows'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/00/2e/d53fa4befbf2cfa713304affc7ca780ce4fc1fd8710527771b58311a3229/click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28", size = 97941 },
+]
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
+]
+
+[[package]]
+name = "linemeasurement"
+version = "0.1.0"
+source = { editable = "." }
+dependencies = [
+ { name = "click" },
+ { name = "numpy" },
+ { name = "plotapp" },
+ { name = "scipy" },
+]
+
+[package.metadata]
+requires-dist = [
+ { name = "click" },
+ { name = "numpy" },
+ { name = "plotapp", url = "http://130.92.113.172/plotapp-0.1.1.zip" },
+ { name = "scipy" },
+]
+
+[[package]]
+name = "numpy"
+version = "2.1.1"
+source = { registry = "https://pypi.org/simple" }
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diff --git a/wolfram.txt b/wolfram.txt
new file mode 100644
index 0000000..b4ec4e0
--- /dev/null
+++ b/wolfram.txt
@@ -0,0 +1 @@
+{A = a * x_1 + b * y_1 + e; B = c * x_1 + d * y_1 + f; C = a * x_2 + b * y_2 + e; D = c * x_2 + d * y_2 + f; E = a * x_3 + b * y_3 + e; F = c * x_3 + d * y_3 + f} solve for a, b, c, d, e, f
\ No newline at end of file