Make safe and other options of sigma_clip_ng available in legacy

Author: kif

pyFAI/azimuthalIntegrator.py

@@ -2899,21 +2899,21 @@ def medfilt1d(self, data, npt_rad=1024, npt_azim=512,
         return result
 
     def sigma_clip_legacy(self, data, npt_rad=1024, npt_azim=512,
-                           correctSolidAngle=True, polarization_factor=None,
-                           radial_range=None, azimuth_range=None,
-                           dark=None, flat=None,
-                           method="splitpixel", unit=units.Q,
-                           thres=3, max_iter=5, dummy=None, delta_dummy=None,
-                           mask=None, normalization_factor=1.0, metadata=None):
+                          correctSolidAngle=True, polarization_factor=None,
+                          radial_range=None, azimuth_range=None,
+                          dark=None, flat=None,
+                          method=("full", "histogram", "cython"), unit=units.Q,
+                          thres=3, max_iter=5, dummy=None, delta_dummy=None,
+                          mask=None, normalization_factor=1.0, metadata=None,
+                          safe=True, **kwargs):
         """Perform first a 2D integration and then an iterative sigma-clipping
         filter along each row. See the doc of scipy.stats.sigmaclip for the
         options `thres` and `max_iter`.
 
         :param data: input image as numpy array
-        :param npt_rad: number of radial points
+        :param npt_rad: number of radial points (alias: npt)
         :param npt_azim: number of azimuthal points
-        :param bool correctSolidAngle: correct for solid angle of each pixel
-                if True
+        :param bool correctSolidAngle: correct for solid angle of each pixel when set
         :param float polarization_factor: polarization factor between -1 (vertical)
                 and +1 (horizontal).
 
@@ -2928,17 +2928,22 @@ def sigma_clip_legacy(self, data, npt_rad=1024, npt_azim=512,
         :param ndarray flat: flat field image
         :param unit: unit to be used for integration
         :param method: pathway for integration and sort
-        :param thres: cut-off for n*sigma: discard any values with `|I-<I>|/sigma > thres`.
+        :param thres: cut-off for n*sigma: discard any values with `|I-<I>| > thres*σ`.
                 The threshold can be a 2-tuple with sigma_low and sigma_high.
         :param max_iter: maximum number of iterations
         :param mask: masked out pixels array
         :param float normalization_factor: Value of a normalization monitor
         :param metadata: any other metadata,
         :type metadata: JSON serializable dict
+        :param safe: unset to save some checks on sparse matrix shape/content.
         :return: Integrate1D-like result
 
         Nota: The initial 2D-integration requires pixel splitting
         """
+        #compatibility layer with sigma_clip_ng
+        if "npt" in kwargs:
+            npt_rad = kwargs["npt"]
+
         # We use NaN as dummies
         if dummy is None:
             dummy = numpy.NaN
@@ -2966,7 +2971,7 @@ def sigma_clip_legacy(self, data, npt_rad=1024, npt_azim=512,
                                  dummy=dummy, delta_dummy=delta_dummy,
                                  correctSolidAngle=correctSolidAngle,
                                  polarization_factor=polarization_factor,
-                                 normalization_factor=normalization_factor)
+                                 normalization_factor=normalization_factor,safe=safe)
         image = res2d.intensity
         if (method.impl_lower == "opencl"):
             if (method.algo_lower == "csr") and \
@@ -3079,7 +3084,7 @@ def sigma_clip_ng(self, data,
 
         Keep only pixels with intensty:
 
-            ``|I - <I>| < thres * std(I)``
+            ``|I - <I>| < thres * σ(I)``
 
         This enforces a symmetric, bell-shaped distibution (i.e. gaussian-like) and is very good at extracting
         background or amorphous isotropic scattering out of Bragg peaks.