Quality of geometry fit

src/pyFAI/test/test_utils_mathutil.py

@@ -4,7 +4,7 @@
 #    Project: Azimuthal integration
 #             https://github.com/silx-kit/pyFAI
 #
-#    Copyright (C) 2015-2024 European Synchrotron Radiation Facility, Grenoble, France
+#    Copyright (C) 2015-2025 European Synchrotron Radiation Facility, Grenoble, France
 #
 #    Principal author:       Jérôme Kieffer ([email protected])
 #
@@ -32,15 +32,20 @@
 __contact__ = "[email protected]"
 __license__ = "MIT"
 __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
-__date__ = "16/05/2024"
+__date__ = "21/01/2025"
 
 import unittest
 import numpy
 import os
 import logging
 from . import utilstest
 logger = logging.getLogger(__name__)
+import fabio
 from .. import utils
+from .. import load
+from .. import calibrant
+from ..utils import mathutil
+
 
 import scipy.ndimage
 
@@ -148,19 +153,24 @@ def test_interp_filter(self):
         self.assertLess(abs(y - utils.mathutil.interp_filter(z)).max(), 0.01, "error is small")
 
     def test_is_far_from_group_cython(self):
-        from ..utils.mathutil import is_far_from_group_python, is_far_from_group_cython
         rng = utilstest.UtilsTest.get_rng()
         pt = rng.uniform(size=2)
         pts = list(rng.uniform(size=(10,2)))
         dst2 = rng.uniform()**2
-        ref = is_far_from_group_python(pt, pts, dst2)
-        res = is_far_from_group_cython(pt, pts, dst2)
+        ref = mathutil.is_far_from_group_python(pt, pts, dst2)
+        res = mathutil.is_far_from_group_cython(pt, pts, dst2)
         self.assertEqual(ref, res, "cython implementation matches *is_far_from_group*")
 
     def test_allclose_mod(self):
-        from ..utils.mathutil import allclose_mod
-        self.assertTrue(allclose_mod(numpy.arctan2(+1e-10, -1), numpy.arctan2(-1e-10, -1)),"angles matches modulo 2pi")
-
+        self.assertTrue(mathutil.allclose_mod(numpy.arctan2(+1e-10, -1), numpy.arctan2(-1e-10, -1)),"angles matches modulo 2pi")
+
+    def test_quality_of_fit(self):
+        img = fabio.open(utilstest.UtilsTest.getimage("Pilatus1M.edf")).data
+        ai = load(utilstest.UtilsTest.getimage("Pilatus1M.poni"))
+        cal = calibrant.get_calibrant("AgBh")
+        cal.wavelength = ai.wavelength
+        res = mathutil.quality_of_fit(img, ai, cal, rings=[0,1], npt_azim=36, npt_rad=100)
+        self.assertLess(res, 0.3, "Fit of good quality")
 
 def suite():
     loader = unittest.defaultTestLoader.loadTestsFromTestCase

src/pyFAI/utils/mathutil.py

@@ -34,7 +34,7 @@
 __contact__ = "[email protected]"
 __license__ = "MIT"
 __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
-__date__ = "13/01/2025"
+__date__ = "21/01/2025"
 __status__ = "production"
 
 import logging
@@ -44,6 +44,7 @@
 import numpy
 import time
 import scipy.ndimage
+from scipy.signal import peak_widths
 from .decorators import deprecated
 
 try:
@@ -939,3 +940,65 @@ def allclose_mod(a, b, modulo=2*numpy.pi, **kwargs):
     """
     di = numpy.minimum((a-b)%modulo, (b-a)%modulo)
     return numpy.allclose(modulo*0.5, (di+modulo*0.5), **kwargs)
+
+
+def quality_of_fit(img, ai, calibrant,
+                   npt_rad=1000, npt_azim=360,
+                   unit="q_nm^-1",
+                   method=("full", "csr", "cython"),
+                   empty = numpy.nan, rings=None):
+    """Provide an indicator for the quality of fit of a given geometry for an image
+
+    :param img: 2D image with a calibration image (containing rings)
+    :param ai: azimuthal integrator object (instance of pyFAI.integrator.azimuthal.AzimuthalIntegrator)
+    :param calibrant: calibration object, instance of pyFAI.calibrant.Calibrant
+    :param npt_rad: int with the number of radial bins
+    :param npt_azim: int with the number of azimuthal bins
+    :param unit: typically "2th_deg" or "q_nm^-1", the quality of fit should be largely independant from the space.
+    :param method: integration method
+    :param empty: value of the empy bins, discarded values
+    :param rings: list of rings to evaluate (0-based)
+    :return: QoF indicator, similar to reduced χ²,  the smaller, the better
+    """
+
+    ai.empty = empty
+    q_theo = calibrant.get_peaks(unit=unit)
+    res = ai.integrate2d(img, npt_rad, npt_azim, method=method, unit=unit)
+    if rings is None:
+        rings = list(range(len(calibrant.get_2th())))
+    q_theo = q_theo[rings]
+    idx_theo = abs(numpy.add.outer(res.radial,-q_theo)).argmin(axis=0)
+    idx_maxi = numpy.empty((res.azimuthal.size, q_theo.size))+numpy.nan
+    idx_fwhm = numpy.empty((res.azimuthal.size, q_theo.size))+numpy.nan
+    signal = res.intensity
+    gradient = numpy.gradient(signal, axis=-1)
+    minima = numpy.where(numpy.logical_and(gradient[:,:-1]<0, gradient[:,1:]>=0))
+    maxima = numpy.where(numpy.logical_and(gradient[:,:-1]>0, gradient[:,1:]<0))
+    for idx in range(res.azimuthal.size):
+        for ring in rings:
+            q_th = q_theo[ring]
+            idx_th = idx_theo[ring]
+            if (q_th<=res.radial[0]) or (q_th>=res.radial[-1]):
+                continue
+            maxi = maxima[1][maxima[0]==idx]
+            mini = minima[1][minima[0]==idx]
+            idx_max = maxi[abs(maxi-idx_th).argmin()]
+            idx_inf = mini[mini<idx_max]
+            if idx_inf.size:
+                idx_inf = idx_inf[-1]
+                idx_sup = mini[mini>idx_max]
+                if idx_sup.size:
+                    idx_sup = idx_sup[0]
+                    if idx_inf< idx_th< idx_sup:
+                        sub = signal[idx, idx_inf:idx_sup+1] - numpy.linspace(signal[idx, idx_inf],signal[idx, idx_sup], 1+idx_sup-idx_inf)
+                        com = (sub*numpy.linspace(idx_inf, idx_sup, 1+idx_sup-idx_inf)).sum()/sub.sum()
+                        if numpy.isfinite(com):
+                            width = peak_widths(sub, [numpy.argmax(sub)])[0][0]
+                            if width==0:
+                                print(f" #{idx},{ring}: {idx_inf} < th:{idx_th} max:{idx_max} com:{com:.3f} < {idx_sup}; fwhm={width}")
+                                print(signal[idx, idx_inf:idx_sup+1])
+                                print(sub)
+                            else:
+                                idx_fwhm[idx, ring] = width
+                                idx_maxi[idx, ring] = idx_max
+    return numpy.nanmean((2.355*(idx_maxi-idx_theo)/idx_fwhm)**2)