diff --git a/doc/source/usage/tutorial/AzimuthalFilter.ipynb b/doc/source/usage/tutorial/AzimuthalFilter.ipynb
new file mode 100644
index 000000000..03c567b21
--- /dev/null
+++ b/doc/source/usage/tutorial/AzimuthalFilter.ipynb
@@ -0,0 +1,717 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "abc04a99-5bd9-4c99-bcba-9a124bed6bb7",
+   "metadata": {},
+   "source": [
+    "# Filtering signal in azimuthal space\n",
+    "\n",
+    "Usually, diffraction signal presents a polar symmetry, this means all pixel with the same azimuthal angle (χ) have similar intensities. The best way to exploit this is to take the mean, what is called *azimuthal average*. But the average is very sensitive to outlier, like gaps, missing pixels, shadows, cosmic rays or reflection coming from larger crystallite. In this tutorial we will see two alternative ways to remove those unwanted signal and focus on the majority of pixels: **sigma clipping** and **median filtering**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7235a6ba-2b59-4156-84b9-86826c48e741",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.gui.matplotlib:matplotlib already loaded, setting its backend may not work\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.pyplot import subplots\n",
+    "%matplotlib inline\n",
+    "from pyFAI.gui import jupyter\n",
+    "import numpy, fabio, pyFAI\n",
+    "from pyFAI import benchmark\n",
+    "from pyFAI.test.utilstest import UtilsTest\n",
+    "\n",
+    "ai = pyFAI.load(UtilsTest.getimage(\"Pilatus6M.poni\"))\n",
+    "img = fabio.open(UtilsTest.getimage(\"Pilatus6M.cbf\")).data\n",
+    "fig, ax = subplots(1, 2)\n",
+    "jupyter.display(img, ax=ax[1])\n",
+    "jupyter.plot1d(ai.integrate1d(img, 1000), ax=ax[0])\n",
+    "ax[1].set_title(\"With a few Bragg peaks\")\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55fac493-e871-44b9-a8c7-b4014024182e",
+   "metadata": {},
+   "source": [
+    "## Azimuthal sigma-clipping\n",
+    "\n",
+    "The idea is to discard pixels which look like outliers in the distribution of all pixels contributing to a single azimuthal bin.\n",
+    "It requires an error model like *poisson* but it has been proven to be better to use the variance in the given azimuthal ring.\n",
+    "All details are available in this publication: https://doi.org/10.1107/S1600576724011038 also available at https://doi.org/10.48550/arXiv.2411.09515 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c2fe4732-a6d1-4919-97f3-7d27193e666e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = subplots(1, 2)\n",
+    "jupyter.display(img, ax=ax[1])\n",
+    "jupyter.plot1d(ai.sigma_clip(img, 1000, error_model=\"hybrid\", method=(\"no\", \"csr\", \"cython\")), ax=ax[0])\n",
+    "ax[1].set_title(\"With a few Bragg peaks\")\n",
+    "ax[0].set_title(\"Sigma_clipping\")\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a57a43a-85f0-4258-b4bc-76ad72d1b4e1",
+   "metadata": {},
+   "source": [
+    "Of course, *sigma-clip* takes several extra parameters like the number of iterations to perform, the cut-off, the error model, ... \n",
+    "There are alo a few limitation: \n",
+    "* The algorithm needs to be the CSR-sparse matrix multiplication: since several integration are needed, it makes no sense to use an histogram based algorithm.\n",
+    "* The algorithm is available with any implementation: Python (using scipy.saprse), Cython and OpenCL, and it runs just fine on GPU.\n",
+    "* Sigma-clipping is incompatible with any kind of pixel splitting: With pixel splitting, a single pixel can contribute to several azimuthal bins and discarding a pixel in one ring could disable it in the neighboring ring (or not, since bins are processed in parallel).\n",
+    "\n",
+    "### Sigma-clipping performances:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "480b7770-8292-4e39-ba35-6cfbf2150f38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "method = [\"no\", \"csr\", \"cython\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9da36255-dbc4-4206-b725-d233e4261265",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pilatus1M.poni\n",
+      "     Cython\n",
+      "6.77 ms ± 97.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "7.19 ms ± 53.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "     Python\n",
+      "10.2 ms ± 25.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "153 ms ± 243 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "     OpenCL\n",
+      "865 µs ± 40.9 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "2.6 ms ± 3.76 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "Pilatus2M.poni\n",
+      "     Cython\n",
+      "13.6 ms ± 124 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "15.3 ms ± 160 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "     Python\n",
+      "33.9 ms ± 60.5 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "520 ms ± 779 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "1.22 ms ± 69.7 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "6.01 ms ± 6.42 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "Eiger4M.poni\n",
+      "     Cython\n",
+      "21.2 ms ± 154 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "29.1 ms ± 381 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "     Python\n",
+      "61.3 ms ± 240 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.08 s ± 1.91 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "2.02 ms ± 34.7 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "10.5 ms ± 28 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "Pilatus6M.poni\n",
+      "     Cython\n",
+      "29.9 ms ± 542 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "33.3 ms ± 373 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "     Python\n",
+      "84.7 ms ± 436 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.51 s ± 1.11 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "2.66 ms ± 19.2 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "13.4 ms ± 36 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "Eiger9M.poni\n",
+      "     Cython\n",
+      "52.8 ms ± 560 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "67.2 ms ± 287 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "     Python\n",
+      "152 ms ± 386 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.82 s ± 3.49 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "3.97 ms ± 13.9 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "25 ms ± 21.4 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "Mar3450.poni\n",
+      "     Cython\n",
+      "58 ms ± 1.09 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "69.3 ms ± 205 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "     Python\n",
+      "168 ms ± 478 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.1 s ± 2.21 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "4.47 ms ± 30.8 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "27.5 ms ± 28.8 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "Fairchild.poni\n",
+      "     Cython\n",
+      "87.5 ms ± 793 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "113 ms ± 585 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "Compiler time: 0.25 s\n",
+      "     Python\n",
+      "385 ms ± 1.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8.89 s ± 17.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "4.36 ms ± 16.7 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "32.1 ms ± 69.3 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "CPU times: user 37min 21s, sys: 26.2 s, total: 37min 47s\n",
+      "Wall time: 5min 13s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time \n",
+    "perfs_integrate_python = {}\n",
+    "perfs_integrate_cython = {}\n",
+    "perfs_integrate_opencl = {}\n",
+    "perfs_sigma_clip_python = {}\n",
+    "perfs_sigma_clip_cython = {}\n",
+    "perfs_sigma_clip_opencl = {}\n",
+    "\n",
+    "for ds in pyFAI.benchmark.PONIS:\n",
+    "    ai = pyFAI.load(UtilsTest.getimage(ds))\n",
+    "    if ai.wavelength is None: ai.wavelength=1.54e-10\n",
+    "    img = fabio.open(UtilsTest.getimage(pyFAI.benchmark.datasets[ds])).data\n",
+    "    size = numpy.prod(ai.detector.shape)\n",
+    "    print(ds)\n",
+    "    print(\"     Cython\")\n",
+    "    meth = tuple(method)\n",
+    "    nbin = max(ai.detector.shape)\n",
+    "    perfs_integrate_cython[size] = %timeit -o ai.integrate1d(img, nbin, method=meth)\n",
+    "    perfs_sigma_clip_cython[size] = %timeit -o ai.sigma_clip(img, nbin, method=meth, error_model=\"azimuthal\")\n",
+    "    print(\"     Python\")\n",
+    "    meth = tuple(method[:2]+[\"python\"])\n",
+    "    perfs_integrate_python[size] = %timeit -o ai.integrate1d(img, nbin, method=meth)\n",
+    "    perfs_sigma_clip_python[size] = %timeit -o ai.sigma_clip(img, nbin, method=meth, error_model=\"azimuthal\")\n",
+    "\n",
+    "    print(\"     OpenCL\")\n",
+    "    meth = tuple(method[:2]+[\"opencl\"])\n",
+    "    perfs_integrate_opencl[size] = %timeit -o ai.integrate1d(img, nbin, method=meth)\n",
+    "    perfs_sigma_clip_opencl[size] = %timeit -o ai.sigma_clip(img, nbin, method=meth, error_model=\"azimuthal\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "48c02ebf-e7fa-4f49-a5db-2892f11e21e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Performance of Sigma-clipping vs integrate')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = subplots()\n",
+    "ax.set_xlabel(\"Image size (Mpix)\")\n",
+    "ax.set_ylabel(\"Frames per seconds\")\n",
+    "sizes = numpy.array(list(perfs_integrate_python.keys()))/1e6\n",
+    "ax.plot(sizes, [1/i.best for i in perfs_integrate_python.values()], label=\"Integrate/Python\", color='green', linestyle='dashed', marker='1')\n",
+    "ax.plot(sizes, [1/i.best for i in perfs_integrate_cython.values()], label=\"Integrate/Cython\", color='orange', linestyle='dashed', marker='1')\n",
+    "ax.plot(sizes, [1/i.best for i in perfs_integrate_opencl.values()], label=\"Integrate/OpenCL\", color='blue', linestyle='dashed', marker='1')\n",
+    "ax.plot(sizes, [1/i.best for i in perfs_sigma_clip_python.values()], label=\"Sigma-clip/Python\", color='green', linestyle='dotted', marker='2')\n",
+    "ax.plot(sizes, [1/i.best for i in perfs_sigma_clip_cython.values()], label=\"Sigma-clip/Cython\", color='orange', linestyle='dotted', marker='2')\n",
+    "ax.plot(sizes, [1/i.best for i in perfs_sigma_clip_opencl.values()], label=\"Sigma-clip/OpenCL\", color='blue', linestyle='dotted', marker='2')\n",
+    "ax.set_yscale(\"log\")\n",
+    "ax.legend()\n",
+    "\n",
+    "ax.set_title(\"Performance of Sigma-clipping vs integrate\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "147e1d95-d808-4735-a74e-a1b19b79f7ef",
+   "metadata": {},
+   "source": [
+    "The penalties is very limited in Cython, much more in Python.\n",
+    "\n",
+    "The biggest limitation of sigma-clipping is its incompatibility with pixel-splitting, feature needed when oversampling, i.e. taking many more points than the size of the diagonal of the image.\n",
+    "While oversampling is not recommended in general case (due to the cross-corelation between bins it creates), it can be a nessessary evil, especially when performing Rietveld refinement where 5 points per peaks are needed, resolution that cannot be obtained with the pixel-size/distance couple accessible by the experimental setup.\n",
+    "\n",
+    "## Median filter in Azimuthal space\n",
+    "\n",
+    "The idea is to sort all pixels contibuting to an azimuthal bin and to average out all pixel between the lower and upper quantile. \n",
+    "When those two thresholds are at one half, this filter provides actually the median.\n",
+    "In order to be compatible with pixel splitting, each pixel is duplicated as many times as it contributes to different bins.\n",
+    "After sorting fragments of pixels according to their normalization corrected signal, the cummulative sum of normalization is performed in order to detemine which fragments to average out.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b1a35b63-e77a-47c1-a5a1-09d06f44ec62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ai = pyFAI.load(UtilsTest.getimage(\"Pilatus6M.poni\"))\n",
+    "img = fabio.open(UtilsTest.getimage(\"Pilatus6M.cbf\")).data\n",
+    "\n",
+    "method = [\"full\", \"csr\", \"cython\"]\n",
+    "percentile=(40,60)\n",
+    "pol=0.99"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f160d7dd-c19d-4df1-890e-a7d94db55a16",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = subplots(1, 2)\n",
+    "jupyter.display(img, ax=ax[1])\n",
+    "jupyter.plot1d(ai.medfilt1d_ng(img, 1000, method=method, percentile=percentile, polarization_factor=pol), ax=ax[0])\n",
+    "ax[1].set_title(\"With a few Bragg peaks\")\n",
+    "ax[0].set_title(\"Median filtering\")\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7882217-35de-4f5e-9b87-efc5c87eb2cf",
+   "metadata": {},
+   "source": [
+    "Unlike the *sigma-clipping*, this median filter does not equires any error model; but the computationnal cost induced by the sort is huge. In addition, the median is very sensitive and requires a good geometry and modelisation of the polarization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "af56d591-e634-4bb8-a4a3-0828e745205a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pilatus1M.poni\n",
+      "     Cython\n",
+      "6.67 ms ± 114 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "24.2 ms ± 1.35 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "     Python\n",
+      "12.7 ms ± 36.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.18 s ± 4.07 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "864 µs ± 506 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
+      "9.94 ms ± 12.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "Pilatus2M.poni\n",
+      "     Cython\n",
+      "13.5 ms ± 748 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "67.9 ms ± 782 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "     Python\n",
+      "46 ms ± 81.9 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.05 s ± 23.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "1.31 ms ± 467 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
+      "32.9 ms ± 20.2 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "Eiger4M.poni\n",
+      "     Cython\n",
+      "22.9 ms ± 1.85 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "116 ms ± 1.13 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "     Python\n",
+      "82.9 ms ± 173 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7.04 s ± 33.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "2.15 ms ± 788 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "58.1 ms ± 20.8 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "Pilatus6M.poni\n",
+      "     Cython\n",
+      "31.4 ms ± 784 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "155 ms ± 1.45 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "     Python\n",
+      "113 ms ± 160 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10.3 s ± 69.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "2.89 ms ± 1.74 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "83 ms ± 76.4 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "Eiger9M.poni\n",
+      "     Cython\n",
+      "48.9 ms ± 491 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "243 ms ± 3.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     Python\n",
+      "182 ms ± 694 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "17.4 s ± 160 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "4.37 ms ± 1.58 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "148 ms ± 137 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "Mar3450.poni\n",
+      "     Cython\n",
+      "54.5 ms ± 771 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "257 ms ± 1.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     Python\n",
+      "227 ms ± 493 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19.8 s ± 33.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "5.12 ms ± 21.2 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "174 ms ± 394 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "Fairchild.poni\n",
+      "     Cython\n",
+      "86.3 ms ± 373 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "406 ms ± 6.62 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     Python\n",
+      "311 ms ± 830 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:pyFAI.opencl.azim_csr:Please upgrade to silx v2.2+\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24.4 s ± 94.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "     OpenCL\n",
+      "4.76 ms ± 12.9 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "185 ms ± 131 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "CPU times: user 22min 42s, sys: 26.1 s, total: 23min 8s\n",
+      "Wall time: 14min 10s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time \n",
+    "perf2_integrate_python = {}\n",
+    "perf2_integrate_cython = {}\n",
+    "perf2_integrate_opencl = {}\n",
+    "perf2_medfilt_python = {}\n",
+    "perf2_medfilt_cython = {}\n",
+    "perf2_medfilt_opencl = {}\n",
+    "\n",
+    "for ds in pyFAI.benchmark.PONIS:\n",
+    "    ai = pyFAI.load(UtilsTest.getimage(ds))\n",
+    "    if ai.wavelength is None: ai.wavelength=1.54e-10\n",
+    "    img = fabio.open(UtilsTest.getimage(pyFAI.benchmark.datasets[ds])).data\n",
+    "    size = numpy.prod(ai.detector.shape)\n",
+    "    print(ds)\n",
+    "    print(\"     Cython\")\n",
+    "    meth = tuple(method)\n",
+    "    nbin = max(ai.detector.shape)\n",
+    "    perf2_integrate_cython[size] = %timeit -o ai.integrate1d(img, nbin, method=meth)\n",
+    "    perf2_medfilt_cython[size] = %timeit -o ai.medfilt1d_ng(img, nbin, method=meth)\n",
+    "    print(\"     Python\")\n",
+    "    meth = tuple(method[:2]+[\"python\"])\n",
+    "    perf2_integrate_python[size] = %timeit -o ai.integrate1d(img, nbin, method=meth)\n",
+    "    perf2_medfilt_python[size] = %timeit -o ai.medfilt1d_ng(img, nbin, method=meth)\n",
+    "\n",
+    "    print(\"     OpenCL\")\n",
+    "    meth = tuple(method[:2]+[\"opencl\"])\n",
+    "    perf2_integrate_opencl[size] = %timeit -o ai.integrate1d(img, nbin, method=meth)\n",
+    "    perf2_medfilt_opencl[size] = %timeit -o ai.medfilt1d_ng(img, nbin, method=meth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a8caf1c6-630e-4056-a01a-1e1707437c2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = subplots()\n",
+    "ax.set_xlabel(\"Image size (Mpix)\")\n",
+    "ax.set_ylabel(\"Frames per seconds\")\n",
+    "sizes = numpy.array(list(perf2_integrate_python.keys()))/1e6\n",
+    "ax.plot(sizes, [1/i.best for i in perf2_integrate_python.values()], label=\"Integrate/Python\", color='green', linestyle='dashed', marker='1')\n",
+    "ax.plot(sizes, [1/i.best for i in perf2_integrate_cython.values()], label=\"Integrate/Cython\", color='orange', linestyle='dashed', marker='1')\n",
+    "ax.plot(sizes, [1/i.best for i in perf2_integrate_opencl.values()], label=\"Integrate/OpenCL\", color='blue', linestyle='dashed', marker='1')\n",
+    "ax.plot(sizes, [1/i.best for i in perf2_medfilt_python.values()], label=\"Medfilt/Python\", color='green', linestyle='dotted', marker='2')\n",
+    "ax.plot(sizes, [1/i.best for i in perf2_medfilt_cython.values()], label=\"Medfilt/Cython\", color='orange', linestyle='dotted', marker='2')\n",
+    "ax.plot(sizes, [1/i.best for i in perf2_medfilt_opencl.values()], label=\"Medfilt/OpenCL\", color='blue', linestyle='dotted', marker='2')\n",
+    "ax.set_yscale(\"log\")\n",
+    "ax.legend()\n",
+    "ax.set_title(\"Performance of Median filtering vs integrate\")\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00b7c06d-8f92-4cd4-b259-2ff6914eff4b",
+   "metadata": {},
+   "source": [
+    "As one can see, the penalities are much larger for OpenCL and Python than for Cython.\n",
+    "\n",
+    "## Conclusion\n",
+    "*Sigma-clipping* and *median-filtering* are alternatives to azimuthal integration and offer the ability to reject outlier. They are mot more difficult to use but slightly slower owing to their greater complexity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40609090-3f85-40e1-8d4e-30d42ecd6878",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/source/usage/tutorial/index.rst b/doc/source/usage/tutorial/index.rst
index 3b2a0bcb0..faff44b89 100644
--- a/doc/source/usage/tutorial/index.rst
+++ b/doc/source/usage/tutorial/index.rst
@@ -27,6 +27,7 @@ a good Python fluency and to a certain extent, of the pyFAI library.
    Flatfield
    Ellipse/ellipse
    PixelSplitting
+   AzimuthalFilter
    Variance/uncertainties
    LogScale/Guinier
    multi-geometry
diff --git a/src/pyFAI/detectors/_others.py b/src/pyFAI/detectors/_others.py
index 1506b8d93..f978f8785 100644
--- a/src/pyFAI/detectors/_others.py
+++ b/src/pyFAI/detectors/_others.py
@@ -58,7 +58,7 @@ def __init__(self, pixel1=15e-6, pixel2=15e-6, max_shape=None, orientation=0):
         Detector.__init__(self, pixel1=pixel1, pixel2=pixel2, max_shape=max_shape, orientation=orientation)
 
     def __repr__(self):
-        return f"Detector {self.name}%s\t PixelSize= {to_eng(self._pixel1)}m, {to_eng(self._pixel2)}m"
+        return f"Detector {self.name}\t PixelSize= {to_eng(self._pixel1)}m, {to_eng(self._pixel2)}m"
 
     def get_config(self):
         """Return the configuration with arguments to the constructor
diff --git a/src/pyFAI/ext/CSR_common.pxi b/src/pyFAI/ext/CSR_common.pxi
index 9a83af563..c8201f1f7 100644
--- a/src/pyFAI/ext/CSR_common.pxi
+++ b/src/pyFAI/ext/CSR_common.pxi
@@ -29,7 +29,7 @@
 
 __author__ = "Jérôme Kieffer"
 __contact__ = "Jerome.kieffer@esrf.fr"
-__date__ = "19/11/2024"
+__date__ = "05/12/2024"
 __status__ = "stable"
 __license__ = "MIT"
 
@@ -859,7 +859,9 @@ cdef class CsrIntegrator(object):
                 for i in range(start, stop):
                     former_element = element
                     element = work[i]
-                    if (qmin<=former_element.s0) and (element.s0 <= qmax):
+                    if ((element.s3!=0) and
+                        (((qmin<=former_element.s0) and (element.s0 <= qmax)) or
+                        ((qmin>=former_element.s0)  and (element.s0 >= qmax)))):   #specific case where qmin==qmax
                         acc_sig = acc_sig + element.s1
                         acc_var = acc_var + element.s2
                         acc_norm = acc_norm + element.s3
diff --git a/src/pyFAI/integrator/azimuthal.py b/src/pyFAI/integrator/azimuthal.py
index 0c209a2ba..2f47c1616 100644
--- a/src/pyFAI/integrator/azimuthal.py
+++ b/src/pyFAI/integrator/azimuthal.py
@@ -30,7 +30,7 @@
 __contact__ = "Jerome.Kieffer@ESRF.eu"
 __license__ = "MIT"
 __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
-__date__ = "10/10/2024"
+__date__ = "05/12/2024"
 __status__ = "stable"
 __docformat__ = 'restructuredtext'
 
@@ -49,6 +49,7 @@
 from ..io.ponifile import PoniFile
 error = None
 from ..method_registry import IntegrationMethod
+from ..utils.decorators import deprecated
 #
 from .load_engines import ocl_sort
 #ocl_azim_csr, ocl_azim_lut, , histogram, splitBBox, \
@@ -1147,8 +1148,8 @@ def integrate2d_ng(self, data, npt_rad, npt_azim=360,
 
     integrate2d = _integrate2d_ng = integrate2d_ng
 
-
-    def medfilt1d(self, data, npt_rad=1024, npt_azim=512,
+    @deprecated(since_version="2024.12.0", only_once=True, replacement="medfilt1d_ng", deprecated_since="2024.12.0")
+    def medfilt1d_legacy(self, data, npt_rad=1024, npt_azim=512,
                   correctSolidAngle=True,
                   radial_range=None, azimuth_range=None,
                   polarization_factor=None, dark=None, flat=None,
@@ -1285,6 +1286,305 @@ def medfilt1d(self, data, npt_rad=1024, npt_azim=512,
         result._set_normalization_factor(normalization_factor)
         return result
 
+    medfilt1d = medfilt1d_legacy
+
+    def medfilt1d_ng(self, data,
+                     npt=1024,
+                     correctSolidAngle=True,
+                     polarization_factor=None,
+                     variance=None,
+                     error_model=ErrorModel.NO,
+                     radial_range=None,
+                     azimuth_range=None,
+                     dark=None,
+                     flat=None,
+                     absorption=None,
+                     method=("full", "csr", "cython"),
+                     unit=units.Q,
+                     percentile=50,
+                     dummy=None,
+                     delta_dummy=None,
+                     mask=None,
+                     normalization_factor=1.0,
+                     metadata=None,
+                     safe=True,
+                     **kwargs):
+        """Performs iteratively the 1D integration with variance propagation
+        and performs a sigm-clipping at each iteration, i.e.
+        all pixel which intensity differs more than thres*std is
+        discarded for next iteration.
+
+        Keep only pixels with intensty:
+
+            ``|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.
+
+        :param data: input image as numpy array
+        :param npt_rad: number of radial points
+        :param bool correctSolidAngle: correct for solid angle of each pixel if True
+        :param float polarization_factor: polarization factor between:
+                -1 (vertical)
+                +1 (horizontal).
+                - 0 for circular polarization or random,
+                - None for no correction,
+                - True for using the former correction
+        :param radial_range: The lower and upper range of the radial unit. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored.
+        :type radial_range: (float, float), optional
+        :param azimuth_range: The lower and upper range of the azimuthal angle in degree. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored.
+        :type azimuth_range: (float, float), optional
+
+        :param ndarray dark: dark noise image
+        :param ndarray flat: flat field image
+        :param ndarray absorption: Detector absorption (image)
+        :param ndarray variance: the variance of the signal
+        :param str error_model: can be "poisson" to assume a poissonian detector (variance=I) or "azimuthal" to take the std² in each ring (better, more expenive)
+        :param unit: unit to be used for integration
+        :param IntegrationMethod method: IntegrationMethod instance or 3-tuple with (splitting, algorithm, implementation)
+        :param percentile: which percentile use for cutting out.
+                           percentil can be a 2-tuple to specify a region to average out,
+                           like: (25,75) to average the second and third quartile.
+        :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: set to False to skip some tests
+        :return: Integrate1D like result like
+
+        The difference with the previous `medfilt_legacy` implementation is that there is no 2D regrouping.
+        """
+        for k in kwargs:
+            if k == "npt_azim":
+                logger.warning("'npt_azim' argument is not used in sigma_clip_ng as not 2D intergration is performed anymore")
+            else:
+                logger.warning("Got unknown argument %s %s", k, kwargs[k])
+
+        error_model = ErrorModel.parse(error_model)
+        if variance is not None:
+            assert variance.size == data.size
+            error_model = ErrorModel.VARIANCE
+
+        unit = units.to_unit(unit)
+        if radial_range:
+            radial_range = tuple(radial_range[i] / unit.scale for i in (0, -1))
+        if azimuth_range is not None:
+            azimuth_range = self.normalize_azimuth_range(azimuth_range)
+        try:
+            quant_min = min(percentile)/100
+            quant_max = max(percentile)/100
+        except:
+            quant_min = quant_max = percentile/100.0
+
+        method = self._normalize_method(method, dim=1, default=self.DEFAULT_METHOD_1D)
+
+        if mask is None:
+            has_mask = "from detector"
+            mask = self.mask
+            mask_crc = self.detector.get_mask_crc()
+            if mask is None:
+                has_mask = False
+                mask_crc = None
+        else:
+            has_mask = "user provided"
+            mask = numpy.ascontiguousarray(mask)
+            mask_crc = crc32(mask)
+
+        if dark is None:
+            dark = self.detector.darkcurrent
+            if dark is None:
+                has_dark = False
+            else:
+                has_dark = "from detector"
+        else:
+            has_dark = "provided"
+
+        if flat is None:
+            flat = self.detector.flatfield
+            if dark is None:
+                has_flat = False
+            else:
+                has_flat = "from detector"
+        else:
+            has_flat = "provided"
+
+        if correctSolidAngle:
+            solidangle = self.solidAngleArray(data.shape, correctSolidAngle)
+        else:
+            solidangle = None
+
+        if polarization_factor is None:
+            polarization = polarization_crc = None
+        else:
+            polarization, polarization_crc = self.polarization(data.shape, polarization_factor, with_checksum=True)
+
+        if (method.algo_lower == "csr"):
+            "This is the only method implemented for now ..."
+            # Prepare LUT if needed!
+            # initialize the CSR integrator in Cython as it may be needed later on.
+            cython_method = IntegrationMethod.select_method(method.dimension, method.split_lower, method.algo_lower, "cython")[0]
+            if cython_method not in self.engines:
+                cython_engine = self.engines[cython_method] = Engine()
+            else:
+                cython_engine = self.engines[cython_method]
+            with cython_engine.lock:
+                # Validate that the engine used is the proper one
+                cython_integr = cython_engine.engine
+                cython_reset = None
+                if cython_integr is None:
+                    cython_reset = "of first initialization"
+                if (not cython_reset) and safe:
+                    if cython_integr.unit != unit:
+                        cython_reset = "unit was changed"
+                    if cython_integr.bins != npt:
+                        cython_reset = "number of points changed"
+                    if cython_integr.size != data.size:
+                        cython_reset = "input image size changed"
+                    if cython_integr.empty != self._empty:
+                        cython_reset = "empty value changed "
+                    if (mask is not None) and (not cython_integr.check_mask):
+                        cython_reset = "mask but CSR was without mask"
+                    elif (mask is None) and (cython_integr.check_mask):
+                        cython_reset = "no mask but CSR has mask"
+                    elif (mask is not None) and (cython_integr.mask_checksum != mask_crc):
+                        cython_reset = "mask changed"
+                    if (radial_range is None) and (cython_integr.pos0_range is not None):
+                        cython_reset = "radial_range was defined in CSR"
+                    elif (radial_range is not None) and cython_integr.pos0_range != (min(radial_range), max(radial_range)):
+                        cython_reset = "radial_range is defined but not the same as in CSR"
+                    if (azimuth_range is None) and (cython_integr.pos1_range is not None):
+                        cython_reset = "azimuth_range not defined and CSR had azimuth_range defined"
+                    elif (azimuth_range is not None) and cython_integr.pos1_range != (min(azimuth_range), max(azimuth_range)):
+                        cython_reset = "azimuth_range requested and CSR's azimuth_range don't match"
+                if cython_reset:
+                    logger.info("AI.sigma_clip_ng: Resetting Cython integrator because %s", cython_reset)
+                    split = method.split_lower
+                    if split == "pseudo":
+                        split = "full"
+                    try:
+                        cython_integr = self.setup_sparse_integrator(data.shape, npt, mask=mask,
+                                                       mask_checksum=mask_crc,
+                                                       unit=unit, split=split, algo="CSR",
+                                                       pos0_range=radial_range,
+                                                       pos1_range=azimuth_range,
+                                                       empty=self._empty,
+                                                       scale=False)
+                    except MemoryError:  # CSR method is hungry...
+                        logger.warning("MemoryError: falling back on forward implementation")
+                        cython_integr = None
+                        self.reset_engines()
+                        method = self.DEFAULT_METHOD_1D
+                    else:
+                        cython_engine.set_engine(cython_integr)
+            if method not in self.engines:
+                # instanciated the engine
+                engine = self.engines[method] = Engine()
+            else:
+                engine = self.engines[method]
+            with engine.lock:
+                # Validate that the engine used is the proper one
+                integr = engine.engine
+                reset = None
+                # This whole block uses CSR, Now we should treat all the various implementation: Cython, OpenCL and finally Python.
+
+                # Validate that the engine used is the proper one
+                if integr is None:
+                    reset = "of first initialization"
+                if (not reset) and safe:
+                    if integr.unit != unit:
+                        reset = "unit was changed"
+                    if integr.bins != npt:
+                        reset = "number of points changed"
+                    if integr.size != data.size:
+                        reset = "input image size changed"
+                    if integr.empty != self._empty:
+                        reset = "empty value changed "
+                    if (mask is not None) and (not integr.check_mask):
+                        reset = "mask but CSR was without mask"
+                    elif (mask is None) and (integr.check_mask):
+                        reset = "no mask but CSR has mask"
+                    elif (mask is not None) and (integr.mask_checksum != mask_crc):
+                        reset = "mask changed"
+                    # TODO
+                    if (radial_range is None) and (integr.pos0_range is not None):
+                        reset = "radial_range was defined in CSR"
+                    elif (radial_range is not None) and integr.pos0_range != (min(radial_range), max(radial_range)):
+                        reset = "radial_range is defined but not the same as in CSR"
+                    if (azimuth_range is None) and (integr.pos1_range is not None):
+                        reset = "azimuth_range not defined and CSR had azimuth_range defined"
+                    elif (azimuth_range is not None) and integr.pos1_range != (min(azimuth_range), max(azimuth_range)):
+                        reset = "azimuth_range requested and CSR's azimuth_range don't match"
+
+                if reset:
+                    logger.info("ai.sigma_clip_ng: Resetting ocl_csr integrator because %s", reset)
+                    csr_integr = self.engines[cython_method].engine
+                    if method.impl_lower == "opencl":
+                        try:
+                            integr = method.class_funct_ng.klass(csr_integr.lut,
+                                                                 image_size=data.size,
+                                                                 checksum=csr_integr.lut_checksum,
+                                                                 empty=self._empty,
+                                                                 unit=unit,
+                                                                 mask_checksum=csr_integr.mask_checksum,
+                                                                 bin_centers=csr_integr.bin_centers,
+                                                                 platformid=method.target[0],
+                                                                 deviceid=method.target[1])
+                        except MemoryError:
+                            logger.warning("MemoryError: falling back on default forward implementation")
+                            self.reset_engines()
+                            method = self.DEFAULT_METHOD_1D
+                        else:
+                            # Copy some properties from the cython integrator
+                            integr.pos0_range = csr_integr.pos0_range
+                            integr.pos1_range = csr_integr.pos1_range
+                            engine.set_engine(integr)
+                    elif method.impl_lower in ("python", "cython"):
+                        integr = method.class_funct_ng.klass(lut=csr_integr.lut,
+                                                             image_size=data.size,
+                                                             empty=self._empty,
+                                                             unit=unit,
+                                                             mask_checksum=csr_integr.mask_checksum,
+                                                             bin_centers=csr_integr.bin_centers)
+                        # Copy some properties from the cython integrator
+                        integr.pos0_range = csr_integr.pos0_range
+                        integr.pos1_range = csr_integr.pos1_range
+                        engine.set_engine(integr)
+                    else:
+                        logger.error(f"Implementation {method.impl_lower} not supported")
+                else:
+                    integr = self.engines[method].engine
+                kwargs = {"dark":dark, "dummy":dummy, "delta_dummy":delta_dummy,
+                          "variance":variance, "dark_variance":None,
+                          "flat":flat, "solidangle":solidangle, "polarization":polarization, "absorption":absorption,
+                          "error_model":error_model, "normalization_factor":normalization_factor,
+                          "quant_min":quant_min, "quant_max":quant_max}
+
+                intpl = integr.medfilt(data, **kwargs)
+        else:
+            raise RuntimeError("Not yet implemented. Sorry")
+        result = Integrate1dResult(intpl.position * unit.scale, intpl.intensity, intpl.sem)
+        result._set_method_called("sigma_clip_ng")
+        result._set_method(method)
+        result._set_compute_engine(str(method))
+        result._set_percentile(percentile)
+        result._set_unit(unit)
+        result._set_has_mask_applied(has_mask)
+        result._set_has_dark_correction(has_dark)
+        result._set_has_flat_correction(has_flat)
+        result._set_metadata(metadata)
+        result._set_sum_signal(intpl.signal)
+        result._set_sum_normalization(intpl.normalization)
+        result._set_sum_normalization2(intpl.norm_sq)
+        result._set_std(intpl.std)
+        result._set_sem(intpl.sem)
+        result._set_sum_variance(intpl.variance)
+        result._set_count(intpl.count)
+        result._set_polarization_factor(polarization_factor)
+        result._set_normalization_factor(normalization_factor)
+        result._set_error_model(error_model)
+        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,
diff --git a/src/pyFAI/opencl/azim_csr.py b/src/pyFAI/opencl/azim_csr.py
index 4c0db511e..c48de18fe 100644
--- a/src/pyFAI/opencl/azim_csr.py
+++ b/src/pyFAI/opencl/azim_csr.py
@@ -28,10 +28,11 @@
 
 __authors__ = ["Jérôme Kieffer", "Giannis Ashiotis"]
 __license__ = "MIT"
-__date__ = "19/11/2024"
+__date__ = "06/12/2024"
 __copyright__ = "ESRF, Grenoble"
 __contact__ = "jerome.kieffer@esrf.fr"
 
+import math
 import logging
 from collections import OrderedDict
 import numpy
@@ -42,6 +43,7 @@
 else:
     raise ImportError("pyopencl is not installed")
 from ..containers import Integrate1dtpl, Integrate2dtpl, ErrorModel
+from ..utils import EPS32
 from . import processing, OpenclProcessing
 EventDescription = processing.EventDescription
 BufferDescription = processing.BufferDescription
@@ -70,11 +72,16 @@ class OCL_CSR_Integrator(OpenclProcessing):
                BufferDescription("solidangle", 1, numpy.float32, mf.READ_ONLY),
                BufferDescription("absorption", 1, numpy.float32, mf.READ_ONLY),
                BufferDescription("mask", 1, numpy.int8, mf.READ_ONLY),
+
                ]
     kernel_files = ["silx:opencl/doubleword.cl",
                     "pyfai:openCL/preprocess.cl",
                     "pyfai:openCL/memset.cl",
-                    "pyfai:openCL/ocl_azim_CSR.cl"
+                    "pyfai:openCL/ocl_azim_CSR.cl",
+                    "pyfai:openCL/collective/reduction.cl",
+                    "pyfai:openCL/collective/scan.cl",
+                    "pyfai:openCL/collective/comb_sort.cl",
+                    "pyfai:openCL/medfilt.cl"
                     ]
     mapping = {numpy.int8: "s8_to_float",
                numpy.uint8: "u8_to_float",
@@ -162,6 +169,7 @@ def __init__(self, lut, image_size, checksum=None,
                          BufferDescription("sem", self.bins, numpy.float32, mf.READ_WRITE),
                          BufferDescription("merged", self.bins, numpy.float32, mf.WRITE_ONLY),
                          BufferDescription("merged8", (self.bins, 8), numpy.float32, mf.WRITE_ONLY),
+                         BufferDescription("work4", (self.data_size, 4), numpy.float32, mf.READ_WRITE),
                          ]
         try:
             self.set_profiling(profile)
@@ -173,6 +181,7 @@ def __init__(self, lut, image_size, checksum=None,
         self.buffer_dtype = {i.name:numpy.dtype(i.dtype) for i in self.buffers}
         if numpy.allclose(self._data, numpy.ones(self._data.shape)):
             self.cl_mem["data"] = None
+            self.cl_kernel_args["csr_medfilt"]["data"] = None
             self.cl_kernel_args["csr_sigma_clip4"]["data"] = None
             self.cl_kernel_args["csr_integrate"]["data"] = None
             self.cl_kernel_args["csr_integrate4"]["data"] = None
@@ -180,6 +189,7 @@ def __init__(self, lut, image_size, checksum=None,
             self.send_buffer(self._data, "data")
         self.send_buffer(self._indices, "indices")
         self.send_buffer(self._indptr, "indptr")
+
         if "amd" in  self.ctx.devices[0].platform.name.lower():
             self.workgroup_size["csr_integrate4_single"] = (1, 1)  # Very bad performances on AMD GPU for diverging threads!
 
@@ -292,7 +302,16 @@ def compile_kernels(self, kernel_file=None):
             else:
                 wg_max = self.kernels.max_workgroup_size(kernel_name)
                 wg_min = self.kernels.min_workgroup_size(kernel_name)
-                self.workgroup_size[kernel_name] = (wg_min, wg_max)
+                if kernel_name=="csr_medfilt":
+                    # limit the wg size due to
+                    device = self.ctx.devices[0]
+                    maxthreads = device.local_mem_size/12/4
+                    self.workgroup_size[kernel_name] = (wg_min,
+                                                        min(wg_max, 2**(int(math.log2(maxthreads)))))
+                else:
+                    self.workgroup_size[kernel_name] = (wg_min, wg_max)
+
+
 
     def set_kernel_arguments(self):
         """Tie arguments of OpenCL kernel-functions to the actual kernels
@@ -317,7 +336,6 @@ def set_kernel_arguments(self):
                                                           ("normalization_factor", numpy.float32(1.0)),
                                                           ("apply_normalization", numpy.int8(0)),
                                                           ("output", self.cl_mem["output"])))
-
         self.cl_kernel_args["csr_integrate"] = OrderedDict((("output", self.cl_mem["output"]),
                                                             ("data", self.cl_mem["data"]),
                                                             ("indices", self.cl_mem["indices"]),
@@ -330,7 +348,6 @@ def set_kernel_arguments(self):
                                                             ("sum_count", self.cl_mem["sum_count"]),
                                                             ("merged", self.cl_mem["merged"]),
                                                             ("shared", pyopencl.LocalMemory(16))))
-
         self.cl_kernel_args["corrections4a"] = OrderedDict((("image", self.cl_mem["image_raw"]),
                                                             ("dtype", numpy.int8(0)),
                                                            ("error_model", numpy.int8(0)),
@@ -355,7 +372,6 @@ def set_kernel_arguments(self):
                                                            ("normalization_factor", numpy.float32(1.0)),
                                                            ("apply_normalization", numpy.int8(0)),
                                                            ("output4", self.cl_mem["output4"])))
-
         self.cl_kernel_args["csr_integrate4"] = OrderedDict((("output4", self.cl_mem["output4"]),
                                                             ("data", self.cl_mem["data"]),
                                                             ("indices", self.cl_mem["indices"]),
@@ -369,7 +385,6 @@ def set_kernel_arguments(self):
                                                             ("sem", self.cl_mem["sem"]),
                                                             ("shared", pyopencl.LocalMemory(32))
                                                              ))
-
         self.cl_kernel_args["csr_sigma_clip4"] = OrderedDict((("output4", self.cl_mem["output4"]),
                                                               ("data", self.cl_mem["data"]),
                                                               ("indices", self.cl_mem["indices"]),
@@ -384,9 +399,24 @@ def set_kernel_arguments(self):
                                                               ("sem", self.cl_mem["sem"]),
                                                               ("shared", pyopencl.LocalMemory(32))
                                                              ))
-
         self.cl_kernel_args["csr_integrate_single"] = self.cl_kernel_args["csr_integrate"]
         self.cl_kernel_args["csr_integrate4_single"] = self.cl_kernel_args["csr_integrate4"]
+        self.cl_kernel_args["csr_medfilt"] =     OrderedDict((("output4", self.cl_mem["output4"]),
+                                                              ("work4", self.cl_mem["work4"]),
+                                                              ("data", self.cl_mem["data"]),
+                                                              ("indices", self.cl_mem["indices"]),
+                                                              ("indptr", self.cl_mem["indptr"]),
+                                                              ("quant_min", numpy.float32(0.5)),
+                                                              ("quant_max", numpy.float32(0.5)),
+                                                              ("error_model", numpy.int8(1)),
+                                                              ("empty", numpy.float32(self.empty)),
+                                                              ("merged8", self.cl_mem["merged8"]),
+                                                              ("averint", self.cl_mem["averint"]),
+                                                              ("std", self.cl_mem["std"]),
+                                                              ("sem", self.cl_mem["sem"]),
+                                                              ("shared_int", pyopencl.LocalMemory(128)),
+                                                              ("shared_float", pyopencl.LocalMemory(128)),
+                                                             ))
         self.cl_kernel_args["memset_out"] = OrderedDict(((i, self.cl_mem[i]) for i in ("sum_data", "sum_count", "merged")))
         self.cl_kernel_args["memset_ng"] = OrderedDict(((i, self.cl_mem[i]) for i in ("averint", "std", "merged8")))
         self.cl_kernel_args["u8_to_float"] = OrderedDict(((i, self.cl_mem[i]) for i in ("tmp", "image")))
@@ -462,6 +492,27 @@ def send_buffer(self, data, dest, checksum=None, workgroup_size=None, convert=Tr
             self.on_device[dest] = checksum
         return dest_buffer
 
+    def get_buffer(self, name, out=None):
+        """retrive a Send a numpy array to the device, including the type conversion on the device if possible
+
+        :param name: name of the buffer
+        :param out: pre-allocated destination numpy array
+        :return: the numpy array
+        """
+        if out is None:
+            if name in self.cl_mem:
+                for buf in self.buffers:
+                    if buf.name == name:
+                        shape = buf.size
+                        dtype = buf.dtype
+                        out = numpy.empty(shape, dtype)
+            else:
+                logger.error("No such buffer declared")
+
+        ev = pyopencl.enqueue_copy(self.queue, out, self.cl_mem[name])
+        self.events.append(EventDescription(f"copy D->H {name}", ev))
+        return out
+
     def integrate_legacy(self, data, dummy=None, delta_dummy=None,
                          dark=None, flat=None, solidangle=None, polarization=None, absorption=None,
                          dark_checksum=None, flat_checksum=None, solidangle_checksum=None,
@@ -598,6 +649,7 @@ def integrate_legacy(self, data, dummy=None, delta_dummy=None,
                 integrate = self.kernels.csr_integrate_single(self.queue, wdim_bins, (wg_max,), *kw_int.values())
                 events.append(EventDescription("csr_integrate_single", integrate))
             else:
+                #TODO reshape this kernel with (wg, nbin)\(wg,1) rather than (self.bins * wg_min)\(wg_min) #2348
                 wdim_bins = (self.bins * wg_min),
                 kw_int["shared"] = pyopencl.LocalMemory(16 * wg_min)  # sizeof(float4) == 16
                 integrate = self.kernels.csr_integrate(self.queue, wdim_bins, (wg_min,), *kw_int.values())
@@ -1059,5 +1111,198 @@ def sigma_clip(self, data, dark=None, dummy=None, delta_dummy=None,
                              std, sem, merged[:, 7])
         return res
 
+    def medfilt(self, data, dark=None, dummy=None, delta_dummy=None,
+                   variance=None, dark_variance=None,
+                   flat=None, solidangle=None, polarization=None, absorption=None,
+                   dark_checksum=None, flat_checksum=None, solidangle_checksum=None,
+                   polarization_checksum=None, absorption_checksum=None, dark_variance_checksum=None,
+                   safe=True, error_model=ErrorModel.NO,
+                   normalization_factor=1.0,
+                   quant_min=0.5, quant_max=0.5,
+                   out_avgint=None, out_sem=None, out_std=None, out_merged=None):
+        """
+        Perform a median-filter/quantile mean in azimuthal space.
+
+
+        Averaging is performed using the CSR representation of the look-up table on all
+        arrays after sorting pixels by apparant intensity and taking only the selected ones
+        based on quantiles and the length of the ensemble.
+
+        :param dark: array of same shape as data for pre-processing
+        :param dummy: value for invalid data
+        :param delta_dummy: precesion for dummy assessement
+        :param variance: array of same shape as data for pre-processing
+        :param dark_variance: array of same shape as data for pre-processing
+        :param flat: array of same shape as data for pre-processing
+        :param solidangle: array of same shape as data for pre-processing
+        :param polarization: array of same shape as data for pre-processing
+        :param dark_checksum: CRC32 checksum of the given array
+        :param flat_checksum: CRC32 checksum of the given array
+        :param solidangle_checksum: CRC32 checksum of the given array
+        :param polarization_checksum: CRC32 checksum of the given array
+        :param safe: if True (default) compares arrays on GPU according to their checksum, unless, use the buffer location is used
+        :param preprocess_only: return the dark subtracted; flat field & solidangle & polarization corrected image, else
+        :param error_model: enum ErrorModel
+        :param normalization_factor: divide raw signal by this value
+        :param quant_min: start percentile/100 to use. Use 0.5 for the median (default). 0<=quant_min<=1
+        :param quant_max: stop percentile/100 to use. Use 0.5 for the median (default). 0<=quant_max<=1
+        :param out_avgint: destination array or pyopencl array for sum of all data
+        :param out_sem: destination array or pyopencl array for uncertainty on mean value
+        :param out_std: destination array or pyopencl array for uncertainty on pixel value
+        :param out_merged: destination array or pyopencl array for averaged data (float8!)
+        :return: namedtuple with "position intensity error signal variance normalization count"
+        """
+        error_model = ErrorModel.parse(error_model)
+        events = []
+        with self.sem:
+            kernel_correction_name = "corrections4a"
+            corrections4 = self.kernels.corrections4a
+            kw_corr = self.cl_kernel_args[kernel_correction_name]
+            kw_corr["image"] = self.send_buffer(data, "image", convert=False)
+            kw_corr["dtype"] = numpy.int8(32) if data.dtype.itemsize > 4 else dtype_converter(data.dtype)
+            wg = max(self.workgroup_size["memset_ng"])
+            wdim_bins = int(self.bins + wg - 1) // wg * wg,
+            memset = self.kernels.memset_out(self.queue, wdim_bins, (wg,), *list(self.cl_kernel_args["memset_ng"].values()))
+            events.append(EventDescription("memset_ng", memset))
+            kw_int = self.cl_kernel_args["csr_medfilt"]
+
+            if dummy is not None:
+                do_dummy = numpy.int8(1)
+                dummy = numpy.float32(dummy)
+                if delta_dummy is None:
+                    delta_dummy = numpy.float32(0.0)
+                else:
+                    delta_dummy = numpy.float32(abs(delta_dummy))
+            else:
+                do_dummy = numpy.int8(0)
+                dummy = numpy.float32(self.empty)
+                delta_dummy = numpy.float32(0.0)
+
+            kw_corr["do_dummy"] = do_dummy
+            kw_corr["dummy"] = dummy
+            kw_corr["delta_dummy"] = delta_dummy
+            kw_corr["normalization_factor"] = numpy.float32(normalization_factor)
+
+            if variance is not None:
+                error_model = ErrorModel.VARIANCE
+                self.send_buffer(variance, "variance")
+            kw_int["error_model"] = kw_corr["error_model"] = numpy.int8(error_model)
+            if dark_variance is not None:
+                if not dark_variance_checksum:
+                    dark_variance_checksum = calc_checksum(dark_variance, safe)
+                if dark_variance_checksum != self.on_device["dark_variance"]:
+                    self.send_buffer(dark_variance, "dark_variance", dark_variance_checksum)
+            else:
+                do_dark = numpy.int8(0)
+            kw_corr["do_dark"] = do_dark
+
+            if dark is not None:
+                do_dark = numpy.int8(1)
+                # TODO: what is do_checksum=False and image not on device ...
+                if not dark_checksum:
+                    dark_checksum = calc_checksum(dark, safe)
+                if dark_checksum != self.on_device["dark"]:
+                    self.send_buffer(dark, "dark", dark_checksum)
+            else:
+                do_dark = numpy.int8(0)
+            kw_corr["do_dark"] = do_dark
+
+            if flat is not None:
+                do_flat = numpy.int8(1)
+                if not flat_checksum:
+                    flat_checksum = calc_checksum(flat, safe)
+                if self.on_device["flat"] != flat_checksum:
+                    self.send_buffer(flat, "flat", flat_checksum)
+            else:
+                do_flat = numpy.int8(0)
+            kw_corr["do_flat"] = do_flat
+
+            if solidangle is not None:
+                do_solidangle = numpy.int8(1)
+                if not solidangle_checksum:
+                    solidangle_checksum = calc_checksum(solidangle, safe)
+                if solidangle_checksum != self.on_device["solidangle"]:
+                    self.send_buffer(solidangle, "solidangle", solidangle_checksum)
+            else:
+                do_solidangle = numpy.int8(0)
+            kw_corr["do_solidangle"] = do_solidangle
+
+            if polarization is not None:
+                do_polarization = numpy.int8(1)
+                if not polarization_checksum:
+                    polarization_checksum = calc_checksum(polarization, safe)
+                if polarization_checksum != self.on_device["polarization"]:
+                    self.send_buffer(polarization, "polarization", polarization_checksum)
+            else:
+                do_polarization = numpy.int8(0)
+            kw_corr["do_polarization"] = do_polarization
+
+            if absorption is not None:
+                do_absorption = numpy.int8(1)
+                if not absorption_checksum:
+                    absorption_checksum = calc_checksum(absorption, safe)
+                if absorption_checksum != self.on_device["absorption"]:
+                    self.send_buffer(absorption, "absorption", absorption_checksum)
+            else:
+                do_absorption = numpy.int8(0)
+            kw_corr["do_absorption"] = do_absorption
+
+            wg = max(self.workgroup_size[kernel_correction_name])
+            wdim_data = int(self.size + wg - 1) // wg * wg,
+            ev = corrections4(self.queue, wdim_data, (wg,), *list(kw_corr.values()))
+            events.append(EventDescription(kernel_correction_name, ev))
+
+            kw_int["quant_min"] = numpy.float32(quant_min)
+            kw_int["quant_max"] = numpy.float32(quant_max)
+            wg_min = max(self.workgroup_size["csr_medfilt"])
+            kw_int["shared_int"] = pyopencl.LocalMemory(4 * wg_min)
+            kw_int["shared_float"] = pyopencl.LocalMemory(8 * wg_min)
+            wdim_bins = (wg_min, self.bins)
+
+
+            integrate = self.kernels.csr_medfilt(self.queue, wdim_bins, (wg_min,1), *kw_int.values())
+            events.append(EventDescription("medfilt", integrate))
+
+            if out_merged is None:
+                merged = numpy.empty((self.bins, 8), dtype=numpy.float32)
+            else:
+                merged = out_merged.data
+            if out_avgint is None:
+                avgint = numpy.empty(self.bins, dtype=numpy.float32)
+            else:
+                avgint = out_avgint.data
+            if out_sem is None:
+                sem = numpy.empty(self.bins, dtype=numpy.float32)
+            elif out_sem is  False:
+                sem = None
+            else:
+                sem = out_sem.data
+
+            if out_std is None:
+                std = numpy.empty(self.bins, dtype=numpy.float32)
+            elif out_std is  False:
+                std = None
+            else:
+                std = out_std.data
+
+            if avgint is not None:
+                ev = pyopencl.enqueue_copy(self.queue, avgint, self.cl_mem["averint"])
+                events.append(EventDescription("copy D->H avgint", ev))
+            if std is not None:
+                ev = pyopencl.enqueue_copy(self.queue, std, self.cl_mem["std"])
+                events.append(EventDescription("copy D->H std", ev))
+            if sem is not None:
+                ev = pyopencl.enqueue_copy(self.queue, sem, self.cl_mem["sem"])
+                events.append(EventDescription("copy D->H sem", ev))
+
+            ev = pyopencl.enqueue_copy(self.queue, merged, self.cl_mem["merged8"])
+            events.append(EventDescription("copy D->H merged8", ev))
+        self.profile_multi(events)
+
+        res = Integrate1dtpl(self.bin_centers, avgint, sem, merged[:, 0], merged[:, 2], merged[:, 4], merged[:, 6],
+                             std, sem, merged[:, 7])
+        return res
+
+
     # Name of the default "process" method
     __call__ = integrate
diff --git a/src/pyFAI/opencl/test/test_collective.py b/src/pyFAI/opencl/test/test_collective.py
index abc90fe78..0f28d1236 100644
--- a/src/pyFAI/opencl/test/test_collective.py
+++ b/src/pyFAI/opencl/test/test_collective.py
@@ -33,7 +33,7 @@
 __contact__ = "jerome.kieffer@esrf.eu"
 __license__ = "MIT"
 __copyright__ = "2013 European Synchrotron Radiation Facility, Grenoble, France"
-__date__ = "12/11/2024"
+__date__ = "21/11/2024"
 
 import logging
 import numpy
@@ -250,7 +250,7 @@ def test_sort(self):
             data_d = pyopencl.array.to_device(self.queue, data)
             # print(ref.shape, (ref.shape[0],min(wg, self.max_valid_wg)), (1, min(wg, self.max_valid_wg)), positions)
             try:
-                evt = self.program.test_combsort_float(self.queue, (ref.shape[0],min(wg, self.max_valid_wg)), (1, min(wg, self.max_valid_wg)),
+                evt = self.program.test_combsort_float(self.queue, (min(wg, self.max_valid_wg), ref.shape[0]), (min(wg, self.max_valid_wg), 1),
                                                        data_d.data,
                                                        positions_d.data,
                                                        pyopencl.LocalMemory(4*min(wg, self.max_valid_wg)))
@@ -290,7 +290,7 @@ def test_sort4(self):
             data_d = pyopencl.array.to_device(self.queue, data)
             # print(ref.shape, (ref.shape[0],min(wg, self.max_valid_wg)), (1, min(wg, self.max_valid_wg)), positions)
             try:
-                evt = self.program.test_combsort_float4(self.queue, (ref.shape[0],min(wg, self.max_valid_wg)), (1, min(wg, self.max_valid_wg)),
+                evt = self.program.test_combsort_float4(self.queue, (min(wg, self.max_valid_wg), ref.shape[0]), (min(wg, self.max_valid_wg),1),
                                                        data_d.data,
                                                        positions_d.data,
                                                        pyopencl.LocalMemory(4*min(wg, self.max_valid_wg)))
diff --git a/src/pyFAI/opencl/test/test_ocl_azim_csr.py b/src/pyFAI/opencl/test/test_ocl_azim_csr.py
index cf2bce570..e8c31df26 100644
--- a/src/pyFAI/opencl/test/test_ocl_azim_csr.py
+++ b/src/pyFAI/opencl/test/test_ocl_azim_csr.py
@@ -33,7 +33,7 @@
 __contact__ = "jerome.kieffer@esrf.eu"
 __license__ = "MIT"
 __copyright__ = "2019-2021 European Synchrotron Radiation Facility, Grenoble, France"
-__date__ = "28/06/2022"
+__date__ = "06/12/2024"
 
 import logging
 import numpy
@@ -43,10 +43,9 @@
 if ocl:
     import pyopencl.array
 from ...test.utilstest import UtilsTest
-from silx.opencl.common import _measure_workgroup_size
 from ...integrator.azimuthal import AzimuthalIntegrator
 from ...method_registry import IntegrationMethod
-from scipy.ndimage import gaussian_filter1d
+from ...containers import ErrorModel
 logger = logging.getLogger(__name__)
 
 
@@ -81,10 +80,12 @@ def tearDownClass(cls):
         cls.ai = None
 
     @unittest.skipUnless(ocl, "pyopencl is missing")
-    def integrate_ng(self, block_size=None):
+    def integrate_ng(self, block_size=None, method_called="integrate_ng", extra=None):
         """
         tests the 1d histogram kernel, with variable workgroup size
         """
+        if extra is None:
+            extra={}
         from ..azim_csr import OCL_CSR_Integrator
         data = numpy.ones(self.ai.detector.shape)
         npt = 500
@@ -95,14 +96,14 @@ def integrate_ng(self, block_size=None):
                                                             dim=1, default=None, degradable=False)
 
         # Retrieve the CSR array
-        cpu_integrate = self.ai._integrate1d_legacy(data, npt, unit=unit, method=csr_method)
+        cpu_integrate = self.ai._integrate1d_ng(data, npt, unit=unit, method=csr_method)
         r_m = cpu_integrate[0]
         csr_engine = list(self.ai.engines.values())[0]
         csr = csr_engine.engine.lut
-        ref = self.ai._integrate1d_ng(data, npt, unit=unit, method=method)
-        integrator = OCL_CSR_Integrator(csr, data.size, block_size=block_size)
+        ref = self.ai._integrate1d_ng(data, npt, unit=unit, method=method, error_model="poisson")
+        integrator = OCL_CSR_Integrator(csr, data.size, block_size=block_size, empty=-1)
         solidangle = self.ai.solidAngleArray()
-        res = integrator.integrate_ng(data, solidangle=solidangle)
+        res = integrator.__getattribute__(method_called)(data, solidangle=solidangle, error_model=ErrorModel.POISSON, **extra)
         # for info, res contains: position intensity error signal variance normalization count
 
         # Start with smth easy: the position
@@ -112,23 +113,32 @@ def integrate_ng(self, block_size=None):
         if "AMD" in integrator.ctx.devices[0].platform.name:
             logger.warning("This test is known to be complicated for AMD-GPU, relax the constrains for them")
         else:
-            self.assertLessEqual(delta.max(), 1, "counts are almost the same")
-            self.assertEqual(delta.sum(), 0, "as much + and -")
+            if method_called=="integrate_ng":
+                self.assertLessEqual(delta.max(), 1, "counts are almost the same")
+                self.assertEqual(delta.sum(), 0, "as much + and -")
+            elif method_called=="medfilt":
+                pix = csr[2][1:]-csr[2][:-1]
+                self.assertTrue(numpy.allclose(res.count, pix), "all pixels have been counted")
 
-            # Intensities are not that different:
-            delta = ref.intensity - res.intensity
-            self.assertLessEqual(abs(delta.max()), 1e-5, "intensity is almost the same")
 
             # histogram of normalization
-            ref = self.ai._integrate1d_ng(solidangle, npt, unit=unit, method=method).sum_signal
-            sig = res.normalization
-            err = abs((sig - ref).max())
-            self.assertLess(err, 5e-4, "normalization content is the same: %s<5e-5" % (err))
+            # print(ref.sum_normalization)
+            # print(res.normalization)
+            err = abs((res.normalization - ref.sum_normalization))
+            # print(err)
+            self.assertLess(err.max(), 5e-4, "normalization content is the same: %s<5e-5" % (err.max))
 
             # histogram of signal
-            ref = self.ai._integrate1d_ng(data, npt, unit=unit, method=method).sum_signal
-            sig = res.signal
-            self.assertLess(abs((sig - ref).sum()), 5e-5, "signal content is the same")
+            self.assertLess(abs((res.signal - ref.sum_signal)).max(), 5e-5, "signal content is the same")
+
+            # histogram of variance
+            self.assertLess(abs((res.variance - ref.sum_variance)).max(), 5e-5, "signal content is the same")
+
+            # Intensities are not that different:
+            delta = ref.intensity - res.intensity
+            # print(delta)
+            self.assertLessEqual(abs(delta).max(), 1e-5, "intensity is almost the same")
+
 
     @unittest.skipUnless(ocl, "pyopencl is missing")
     def test_integrate_ng(self):
@@ -144,6 +154,20 @@ def test_integrate_ng_single(self):
         """
         self.integrate_ng(block_size=1)
 
+    @unittest.skipUnless(ocl, "pyopencl is missing")
+    def test_sigma_clip(self):
+        """
+        tests the sigma-clipping kernel, default block size
+        """
+        self.integrate_ng(None, "sigma_clip",{"cutoff":100.0, "cycle":0,})
+
+    @unittest.skipUnless(ocl, "pyopencl is missing")
+    def test_medfilt(self):
+        """
+        tests the median filtering kernel, default block size
+        """
+        self.integrate_ng(None, "medfilt", {"quant_min":0, "quant_max":1})
+
 
 def suite():
     loader = unittest.defaultTestLoader.loadTestsFromTestCase
diff --git a/src/pyFAI/resources/openCL/collective/comb_sort.cl b/src/pyFAI/resources/openCL/collective/comb_sort.cl
index a952c4f16..41c51c9bd 100644
--- a/src/pyFAI/resources/openCL/collective/comb_sort.cl
+++ b/src/pyFAI/resources/openCL/collective/comb_sort.cl
@@ -15,11 +15,9 @@ int inline first_step(int step, int size, float ratio)
 {
     while (step<size)
         step=next_step(step, ratio);
-    // if (get_local_id(0) == 0) printf("+ %d %d\n", step, size);
 
     while (step>=size)
         step=previous_step(step, ratio);
-    // if (get_local_id(0) == 0) printf("- %d %d\n", step, size);
     return step;
 }
 
@@ -61,8 +59,8 @@ int passe(global volatile float* elements,
           int step,
           local int* shared)
 {
-    int wg = get_local_size(1);
-    int tid = get_local_id(1);
+    int wg = get_local_size(0);
+    int tid = get_local_id(0);
     int cnt = 0;
     int i, j, k;
     barrier(CLK_GLOBAL_MEM_FENCE);
@@ -120,8 +118,8 @@ int passe_float4(global volatile float4* elements,
                  int step,
                  local int* shared)
 {
-    int wg = get_local_size(1);
-    int tid = get_local_id(1);
+    int wg = get_local_size(0);
+    int tid = get_local_id(0);
     int cnt = 0;
     int i, j, k;
 
@@ -171,14 +169,14 @@ int passe_float4(global volatile float4* elements,
         return 0;
 }
 
-// workgroup: (1, wg)
-// grid: (nb_lines, wg)
+// workgroup: (wg, 1)
+// grid:      (wg, nb_lines)
 // shared: wg*sizeof(int)
 kernel void test_combsort_float(global volatile float* elements,
                                 global int* positions,
                                 local  int* shared)
 {
-    int gid = get_group_id(0);
+    int gid = get_group_id(1);
     int step = 11;     // magic value
     float ratio=1.3f;  // magic value
     int cnt;
@@ -202,14 +200,14 @@ kernel void test_combsort_float(global volatile float* elements,
 
 }
 
-// workgroup: (1, wg)
-// grid: (nb_lines, wg)
+// workgroup: (wg, 1)
+// grid:      (wg, nb_lines)
 // shared: wg*sizeof(int)
 kernel void test_combsort_float4(global volatile float4* elements,
                                  global int* positions,
                                  local  int* shared)
 {
-    int gid = get_group_id(0);
+    int gid = get_group_id(1);
     int step = 11;     // magic value
     float ratio=1.3f;  // magic value
     int cnt;
diff --git a/src/pyFAI/resources/openCL/collective/reduction.cl b/src/pyFAI/resources/openCL/collective/reduction.cl
index e1c01d7df..a1222156b 100644
--- a/src/pyFAI/resources/openCL/collective/reduction.cl
+++ b/src/pyFAI/resources/openCL/collective/reduction.cl
@@ -17,7 +17,9 @@ int inline sum_int_reduction(local int* shared)
             shared[tid] += shared[tid+stride];
     }
     barrier(CLK_LOCAL_MEM_FENCE);
-    return shared[0];
+    int res = shared[0];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    return res;
 }
 
 /* sum all elements in a shared memory, same size as the workgroup size 0
@@ -35,7 +37,9 @@ int inline sum_int_atomic(local int* shared)
     if (tid)
         atomic_add(shared, value);
     barrier(CLK_LOCAL_MEM_FENCE);
-    return shared[0];
+    int res = shared[0];
+    barrier(CLK_LOCAL_MEM_FENCE);
+    return res;
 }
 
 /*
diff --git a/src/pyFAI/resources/openCL/collective/scan.cl b/src/pyFAI/resources/openCL/collective/scan.cl
index a4ff2d7bf..cda7a783a 100644
--- a/src/pyFAI/resources/openCL/collective/scan.cl
+++ b/src/pyFAI/resources/openCL/collective/scan.cl
@@ -1,6 +1,6 @@
 
 /*
- * Cumsum all elements in a shared memory
+ * Cumsum all elements in a shared memory along dim 0
  *
  * Nota: the first wg-size elements, are used.
  * The shared buffer needs to be twice this size of the workgroup
diff --git a/src/pyFAI/resources/openCL/medfilt.cl b/src/pyFAI/resources/openCL/medfilt.cl
new file mode 100644
index 000000000..ebf2d2ffb
--- /dev/null
+++ b/src/pyFAI/resources/openCL/medfilt.cl
@@ -0,0 +1,256 @@
+/*
+ *   Project: Azimuthal regroupping OpenCL kernel for PyFAI.
+ *            Preprocessing program
+ *
+ *
+ *   Copyright (C) 2024-2024 European Synchrotron Radiation Facility
+ *                           Grenoble, France
+ *
+ *   Principal authors: J. Kieffer (kieffer@esrf.fr)
+ *   Last revision: 06/12/2024
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+
+ */
+
+/**
+ * \file
+ *
+ * \brief OpenCL kernels performing median filtering | quantile averages
+ *
+ * Constant to be provided at build time:
+ *
+ * Files to be co-built:
+ *   collective/reduction.cl
+ *   collective/scan.cl
+ *   collective/comb_sort.cl
+ */
+
+#include "for_eclipse.h"
+
+float2 inline sum_float2_reduction(local float* shared)
+{
+    int wg = get_local_size(0) * get_local_size(1);
+    int tid = get_local_id(0) + get_local_size(0)*get_local_id(1);
+
+    // local reduction based implementation
+    for (int stride=wg>>1; stride>0; stride>>=1)
+    {
+        barrier(CLK_LOCAL_MEM_FENCE);
+        if ((tid<stride) && ((tid+stride)<wg))
+        {
+            int pos_here, pos_there;
+            float2 here, there;
+            pos_here = 2*tid;
+            pos_there = pos_here + 2*stride;
+            here.s0 = shared[pos_here];
+            here.s1 = shared[pos_here+1];
+            there.s0 = shared[pos_there];
+            there.s1 = shared[pos_there+1];
+            here = dw_plus_dw(here, there);
+            shared[2*tid] = here.s0;
+            shared[2*tid+1] = here.s1;
+        }
+
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+    float2 res = (float2)(shared[0], shared[1]);
+    barrier(CLK_LOCAL_MEM_FENCE);
+    return res;
+}
+
+
+/**
+ * \brief Performs sigma clipping in azimuthal rings based on a LUT in CSR form for background extraction
+ *
+ * Grid: 2D grid with one workgroup processes one bin in a collaborative manner,
+ *       dim 0: collaboarative working group size, probably optimal in the range 32-128
+ *       dim 1: index of bin, size=1
+ *
+ * @param weights      Float pointer to global memory storing the input image.
+ * @param coefs        Float pointer to global memory holding the coeficient part of the LUT
+ * @param indices      Integer pointer to global memory holding the corresponding index of the coeficient
+ * @param indptr       Integer pointer to global memory holding the pointers to the coefs and indices for the CSR matrix
+ * @param quant_min    start percentile/100 to use. Use 0.5 for the median
+ * @param quant_max    stop percentile/100 to use. Use 0.5 for the median
+ * @param error_model  0:disable, 1:variance, 2:poisson, 3:azimuthal, 4:hybrid
+ * @param summed       contains all the data
+ * @param averint      Average signal
+ * @param stdevpix     Float pointer to the output 1D array with the propagated error (std)
+ * @param stdevpix     Float pointer to the output 1D array with the propagated error (sem)
+ * @param shared_int   Buffer of shared memory of size WORKGROUP_SIZE * sizeof(int)
+ * @param shared_float Buffer of shared memory of size WORKGROUP_SIZE * 2 * sizeof(float)
+ * */
+
+
+
+kernel void
+csr_medfilt    (  const   global  float4  *data4,
+                          global  float4  *work4,
+                  const   global  float   *coefs,
+                  const   global  int     *indices,
+                  const   global  int     *indptr,
+                  const           float    quant_min,
+                                  float    quant_max,
+                  const           char     error_model,
+                  const           float    empty,
+                          global  float8  *summed,
+                          global  float   *averint,
+                          global  float   *stdevpix,
+                          global  float   *stderrmean,
+                          local   int*    shared_int,  // size of the workgroup size
+                          local   float*  shared_float // size of 2x the workgroup size
+                          )
+{
+    int bin_num = get_group_id(1);
+    int wg = get_local_size(0);
+    int tid = get_local_id(0);
+    int start = indptr[bin_num];
+    int stop = indptr[bin_num+1];
+    int size = stop-start;
+    int sum_cnt, cnt, step=11;
+    int idx;
+    char curr_error_model=error_model;
+    float8 result;
+    float sum=0.0f, ratio=1.3f;
+    float2 acc_sig, acc_nrm, acc_var, acc_nrm2;
+
+    // ensure the last element is always taken
+    if (quant_max == 1.0f)
+        quant_max = 1.000001f;
+
+    // first populate the work4 array from data4
+    for (int i=start+tid; i<stop; i+=wg)
+    {
+        float4 r4, w4;
+        int idx = indices[i];
+        float coef = (coefs == ZERO)?1.0f:coefs[i];
+        r4 = data4[idx];
+
+        w4.s0 = r4.s0 / r4.s2;
+        w4.s1 = r4.s0 * coef;
+        w4.s2 = r4.s1 * coef * coef;
+        w4.s3 = r4.s2 * coef;
+
+        work4[i] = w4;
+    }
+
+    barrier(CLK_GLOBAL_MEM_FENCE);
+
+    // then perform the sort in the work space along the s0 component
+
+    step = first_step(step, size, ratio);
+
+    for (step=step; step>0; step=previous_step(step, ratio))
+        cnt = passe_float4(&work4[start], size, step, shared_int);
+
+    while (cnt)
+        cnt = passe_float4(&work4[start], size, 1, shared_int);
+
+    // Then perform the cumsort of the weights to s0
+    // In blelloch scan, one workgroup can process 2wg in size.
+
+    barrier(CLK_GLOBAL_MEM_FENCE);
+
+    sum = 0.0f;
+    for (int i=0; i<(size + 2*wg-1)/(2*wg); i++)
+    {
+        idx = start + tid + 2*wg*i;
+
+        shared_float[tid] = (idx<stop)?work4[idx].s3:0.0f;
+        shared_float[tid+wg] = ((idx+wg)<stop)?work4[idx+wg].s3:0.0f;
+
+        blelloch_scan_float(shared_float);
+
+        if (idx<stop)
+            work4[idx].s0 = sum + shared_float[tid];
+        if ((idx+wg)<stop)
+            work4[idx+wg].s0 = sum + shared_float[tid+wg];
+        sum += shared_float[2*wg-1];
+		barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    barrier(CLK_GLOBAL_MEM_FENCE);
+
+    // Perform the sum for accumulator of signal, variance, normalization and count
+
+    cnt = 0;
+    acc_sig = (float2)(0.0f, 0.0f);
+    acc_var = (float2)(0.0f, 0.0f);
+    acc_nrm = (float2)(0.0f, 0.0f);
+    acc_nrm2 = (float2)(0.0f, 0.0f);
+
+    float qmin = quant_min * sum;
+    float qmax = quant_max * sum;
+
+    for (int i=start+tid; i<stop; i+=wg)
+    {
+        float q_last = (i>start)?work4[i-1].s0:0.0f;
+        float4 w = work4[i];
+        if((((q_last>=qmin) && (w.s0<=qmax))  // case several contribution
+         || ((q_last<=qmin) && (w.s0>=qmax))) // case unique contribution qmin==qmax
+         && (w.s3)) {                         // non empty
+            cnt ++;
+            acc_sig = dw_plus_fp(acc_sig, w.s1);
+            acc_var = dw_plus_fp(acc_var, w.s2);
+            acc_nrm = dw_plus_fp(acc_nrm, w.s3);
+            acc_nrm2 = dw_plus_dw(acc_nrm2, fp_times_fp(w.s3, w.s3));
+
+        }
+    }
+//  Now parallel reductions, one after the other :-/
+    shared_int[tid] = cnt;
+    cnt = sum_int_reduction(shared_int);
+
+    shared_float[2*tid] = acc_sig.s0;
+    shared_float[2*tid+1] = acc_sig.s1;
+    acc_sig = sum_float2_reduction(shared_float);
+
+    shared_float[2*tid] = acc_var.s0;
+    shared_float[2*tid+1] = acc_var.s1;
+    acc_var = sum_float2_reduction(shared_float);
+
+    shared_float[2*tid] = acc_nrm.s0;
+    shared_float[2*tid+1] = acc_nrm.s1;
+    acc_nrm = sum_float2_reduction(shared_float);
+
+    shared_float[2*tid] = acc_nrm2.s0;
+    shared_float[2*tid+1] = acc_nrm2.s1;
+    acc_nrm2 = sum_float2_reduction(shared_float);
+
+
+    // Finally store the accumulated value
+
+    if (tid == 0) {
+        summed[bin_num] = (float8)(acc_sig.s0, acc_sig.s1,
+                                   acc_var.s0, acc_var.s1,
+                                   acc_nrm.s0, acc_nrm.s1,
+                                   (float)cnt, acc_nrm2.s0);
+        if (acc_nrm2.s0 > 0.0f) {
+            averint[bin_num] = acc_sig.s0/acc_nrm.s0 ;
+            stdevpix[bin_num] = sqrt(acc_var.s0/acc_nrm2.s0) ;
+            stderrmean[bin_num] = sqrt(acc_var.s0) / acc_nrm.s0;
+        }
+        else {
+            averint[bin_num] = empty;
+            stderrmean[bin_num] = empty;
+            stdevpix[bin_num] = empty;
+        }
+    }
+} //end csr_medfilt kernel
diff --git a/src/pyFAI/resources/openCL/meson.build b/src/pyFAI/resources/openCL/meson.build
index 6aae56dc2..635d7bbe2 100644
--- a/src/pyFAI/resources/openCL/meson.build
+++ b/src/pyFAI/resources/openCL/meson.build
@@ -5,6 +5,7 @@ py.install_sources(
     'bitonic.cl',
     'deactivate_atomic64.cl',
     'kahan.cl',
+    'medfilt.cl',
     'memset.cl',
     'ocl_azim_CSR.cl',
     'ocl_azim_LUT.cl',
diff --git a/src/pyFAI/resources/openCL/ocl_azim_CSR.cl b/src/pyFAI/resources/openCL/ocl_azim_CSR.cl
index edb5e0b46..54076de2e 100644
--- a/src/pyFAI/resources/openCL/ocl_azim_CSR.cl
+++ b/src/pyFAI/resources/openCL/ocl_azim_CSR.cl
@@ -827,7 +827,7 @@ csr_integrate4_single(  const   global  float4  *weights,
  * @param indptr      Integer pointer to global memory holding the pointers to the coefs and indices for the CSR matrix
  * @param cutoff      Discard any value with |value - mean| > cutoff*sigma
  * @param cycle       number of cycle
- * @param error_model 0:diable, 1:variance, 2:poisson, 3:azimuthal, 4:hybrid
+ * @param error_model 0:disable, 1:variance, 2:poisson, 3:azimuthal, 4:hybrid
  * @param summed      contains all the data
  * @param averint     Average signal
  * @param stdevpix    Float pointer to the output 1D array with the propagated error (std)
diff --git a/src/pyFAI/test/test_medfilt_engine.py b/src/pyFAI/test/test_medfilt_engine.py
index d15a2a5ca..2bc1cd5ff 100644
--- a/src/pyFAI/test/test_medfilt_engine.py
+++ b/src/pyFAI/test/test_medfilt_engine.py
@@ -32,7 +32,7 @@
 __contact__ = "Jerome.Kieffer@ESRF.eu"
 __license__ = "MIT"
 __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
-__date__ = "19/11/2024"
+__date__ = "06/12/2024"
 
 import unittest
 import numpy
@@ -41,7 +41,7 @@
 from .utilstest import UtilsTest
 import fabio
 from .. import load
-
+from ..opencl import ocl
 
 class TestMedfilt(unittest.TestCase):
     """Test Azimuthal median filtering results
@@ -119,6 +119,39 @@ def test_cython(self):
         self.assertTrue(numpy.allclose(ref.std, obt.std), "std matches")
         self.assertTrue(numpy.allclose(ref.sem, obt.sem), "sem matches")
 
+    @unittest.skipUnless(ocl, "pyopencl is missing")
+    def test_opencl(self):
+        method = list(self.method)
+        method[-1] = "opencl"
+        method = tuple(method)
+        ref = self.ai.integrate1d(self.img, self.npt, unit="2th_rad", method=method, error_model="poisson")
+        engine = self.ai.engines[ref.method].engine
+        obt = engine.medfilt(self.img,
+                             solidangle=self.ai.solidAngleArray(),
+                             quant_min=0,quant_max=1,  # taking all like this: it works like a normal mean
+                             error_model="poisson")
+        self.assertTrue(numpy.allclose(ref.radial, obt.position), "radial matches")
+
+
+
+        thres = 1e-4
+        thres_cnt = 1
+
+
+        # self.assertLessEqual(numpy.sum(abs(ref.sum_signal-obt.signal)>thres), thres_cnt, "signal matches")
+        # self.assertLessEqual(numpy.sum(abs(ref.sum_variance-obt.variance)>thres), thres_cnt, "variance matches")
+        # self.assertLessEqual(numpy.sum(abs(ref.sum_normalization-obt.normalization)>thres), thres_cnt, "normalization matches")
+        # self.assertLessEqual(numpy.sum(abs(ref.sum_normalization2-obt.norm_sq)>thres), thres_cnt, "norm_sq matches")
+        self.assertTrue(numpy.allclose(engine._indptr[1:]-engine._indptr[:-1], obt.count), "count matches") # not valid with pixel splitting
+        self.assertTrue(numpy.allclose(ref.sum_signal, obt.signal, atol=1e-4, rtol=1e-6), "signal matches")
+        self.assertTrue(numpy.allclose(ref.sum_variance, obt.variance, atol=1e-4, rtol=1e-6), "variance matches")
+        self.assertTrue(numpy.allclose(ref.sum_normalization, obt.normalization, atol=1e-4, rtol=1e-6), "normalization matches")
+        self.assertTrue(numpy.allclose(ref.sum_normalization2, obt.norm_sq, atol=1e-2, rtol=1e-3), "norm_sq matches")
+
+        self.assertLessEqual(numpy.sum(abs(ref.intensity-obt.intensity)>thres), thres_cnt, "intensity matches")
+        self.assertLessEqual(numpy.sum(abs(ref.sigma-obt.sigma)>thres), thres_cnt, "sigma matches")
+        self.assertLessEqual(numpy.sum(abs(ref.std-obt.std>thres)), thres_cnt, "std matches")
+        self.assertLessEqual(numpy.sum(abs(ref.sem-obt.sem>thres)), thres_cnt, "sem matches")
 
 
 def suite():
diff --git a/version.py b/version.py
index 324cf9028..dbeb16fc1 100755
--- a/version.py
+++ b/version.py
@@ -47,7 +47,7 @@
 __authors__ = ["Jérôme Kieffer", "V. Valls"]
 __license__ = "MIT"
 __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
-__date__ = "10/10/2024"
+__date__ = "05/12/2024"
 __status__ = "production"
 __docformat__ = 'restructuredtext'
 __all__ = ["date", "version_info", "strictversion", "hexversion", "debianversion",
@@ -61,7 +61,7 @@
                        "final": 15}
 
 MAJOR = 2024
-MINOR = 11
+MINOR = 12
 MICRO = 0
 RELEV = "dev"  # <16
 SERIAL = 0  # <16