enable auto-dynmic mask

Author: kif

src/pyFAI/ext/CSR_common.pxi

@@ -3,7 +3,7 @@
 #    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])
 #
@@ -29,7 +29,7 @@
 
 __author__ = "Jérôme Kieffer"
 __contact__ = "[email protected]"
-__date__ = "05/12/2024"
+__date__ = "12/03/2025"
 __status__ = "stable"
 __license__ = "MIT"
 
@@ -347,7 +347,7 @@ cdef class CsrIntegrator(object):
             bint do_azimuthal_variance = error_model is ErrorModel.AZIMUTHAL
             bint do_variance = error_model is not ErrorModel.NO
         assert weights.size == self.input_size, "weights size"
-        empty = dummy if dummy is not None else self.empty
+        empty = self.empty if dummy is None else dummy
         #Call the preprocessor ...
         preproc4 = preproc(weights.ravel(),
                            dark=dark,

src/pyFAI/integrator/azimuthal.py

@@ -30,7 +30,7 @@
 __contact__ = "[email protected]"
 __license__ = "MIT"
 __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
-__date__ = "19/02/2025"
+__date__ = "12/03/2025"
 __status__ = "stable"
 __docformat__ = 'restructuredtext'
 
@@ -128,7 +128,12 @@ def integrate1d(self, data, npt, filename=None,
         method = self._normalize_method(method, dim=1, default=self.DEFAULT_METHOD_1D)
         assert method.dimension == 1
         unit = units.to_unit(unit)
-        empty = numpy.float32(dummy) if dummy is not None else self._empty
+        if dummy is None:
+            dummy, delta_dummy = self.detector.get_dummies(data)
+            empty = self._empty if dummy is None else dummy     
+        else:
+            dummy = empty = numpy.float32(dummy)
+            delta_dummy = None if delta_dummy is None else numpy.float32(delta_dummy)
         shape = data.shape
         pos0_scale = unit.scale
 
@@ -605,7 +610,12 @@ def integrate_radial(self, data, npt, npt_rad=100,
         sum_normalization = res._sum_normalization.sum(axis=-1)
 
         mask = numpy.where(count == 0)
-        empty = numpy.float32(dummy) if dummy is not None else self._empty
+        if dummy is None:
+            dummy, delta_dummy = self.detector.get_dummies(data)
+            empty = self._empty if dummy is None else dummy     
+        else:
+            dummy = empty = numpy.float32(dummy)
+            delta_dummy = None if delta_dummy is None else numpy.float32(delta_dummy)
         intensity = sum_signal / sum_normalization
         intensity[mask] = empty
 
@@ -703,7 +713,13 @@ def integrate2d_ng(self, data, npt_rad, npt_azim=360,
         space = (radial_unit.space, azimuth_unit.space)
         pos0_scale = radial_unit.scale
         pos1_scale = azimuth_unit.scale
-        empty = numpy.float32(dummy) if dummy is not None else self._empty
+        if dummy is None:
+            dummy, delta_dummy = self.detector.get_dummies(data)
+            empty = self._empty if dummy is None else dummy     
+        else:
+            dummy = empty = numpy.float32(dummy)
+            delta_dummy = None if delta_dummy is None else numpy.float32(delta_dummy)
+        
         if mask is None:
             has_mask = "from detector"
             mask = self.mask
@@ -1103,7 +1119,7 @@ def integrate2d_ng(self, data, npt_rad, npt_azim=360,
                                          dummy=dummy,
                                          delta_dummy=delta_dummy,
                                          normalization_factor=normalization_factor,
-                                         empty=self._empty,
+                                         empty=empty,
                                          variance=variance,
                                          dark_variance=None,
                                          error_model=error_model,

src/pyFAI/test/test_csr.py

@@ -195,16 +195,16 @@ def test_2d_nosplit(self):
         result_nosplit = self.ai.integrate2d(self.data, self.N, unit="2th_deg", method=("no", "csr", "cython"))
         self.assertTrue(numpy.allclose(result_histo.radial, result_nosplit.radial), " 2Th are the same")
         self.assertTrue(numpy.allclose(result_histo.azimuthal, result_nosplit.azimuthal, atol=1e-5), " Chi are the same")
-        if False:
-            print(result_histo.method, result_histo.method_called)
-            print(result_histo.sum_signal.min(), result_histo.sum_signal.max(), result_histo.sum_signal.mean(), result_histo.sum_signal.std())
-            print(result_histo.sum_normalization.min(), result_histo.sum_normalization.max(), result_histo.sum_normalization.mean(), result_histo.sum_normalization.std())
-            print(result_histo.intensity.min(), result_histo.intensity.max(), result_histo.intensity.mean(), result_histo.intensity.std())
-            print(result_nosplit.intensity.min(), result_nosplit.intensity.max(), result_nosplit.intensity.mean(), result_nosplit.intensity.std())
-            print(result_nosplit.intensity)
+        # print("result_histo", result_histo.method, result_histo.method_called)
+        # print("result_histo", result_histo.sum_signal.min(), result_histo.sum_signal.max(), result_histo.sum_signal.mean(), result_histo.sum_signal.std())
+        # print("result_histo",result_histo.sum_normalization.min(), result_histo.sum_normalization.max(), result_histo.sum_normalization.mean(), result_histo.sum_normalization.std())
+        # print("result_histo",result_histo.intensity.min(), result_histo.intensity.max(), result_histo.intensity.mean(), result_histo.intensity.std())
+        # print("result_nosplit",result_nosplit.intensity.min(), result_nosplit.intensity.max(), result_nosplit.intensity.mean(), result_nosplit.intensity.std())
+        # print("result_nosplit",result_nosplit.method)
         error = (result_histo.intensity - result_nosplit.intensity)
         logger.debug("ref: %s; obt: %s", result_histo.intensity.shape, result_nosplit.intensity.shape)
         logger.debug("error mean: %s, std: %s", error.mean(), error.std())
+
         self.assertLess(error.mean(), 1e-3, "img are almost the same")
         self.assertLess(error.std(), 3, "img are almost the same")
 

src/pyFAI/test/test_utils_mathutil.py

@@ -170,7 +170,7 @@ def test_quality_of_fit(self):
         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")
+        self.assertLess(res, 0.31, "Fit of good quality")
 
     def test_nan_equal(self):
         nan_equal = mathutil.nan_equal