{ "cells": [ { "cell_type": "markdown", "id": "intensive-failure", "metadata": {}, "source": [ "# Error model validator\n", "\n", "Build a set of data with a known statistical distribution and validate the error propagation by ensuring the integrated data follow the 𝜒² distribution.\n", "\n", "This requires plenty of memory and is pretty compute intensive." ] }, { "cell_type": "code", "execution_count": 1, "id": "broadband-priority", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/jerome/.venv/py311/bin/python3 3.11.2 (main, Mar 13 2023, 12:18:29) [GCC 12.2.0]\n" ] } ], "source": [ "%matplotlib widget\n", "import time\n", "start_time = time.perf_counter()\n", "import sys\n", "print(sys.executable, sys.version)\n", "import numpy\n", "from scipy.stats import chi2 as chi2_dist\n", "from matplotlib.pyplot import subplots\n", "from pyFAI.method_registry import IntegrationMethod" ] }, { "cell_type": "code", "execution_count": 2, "id": "broken-archive", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jerome/.venv/py311/lib/python3.11/site-packages/pyopencl/cache.py:495: CompilerWarning: Non-empty compiler output encountered. Set the environment variable PYOPENCL_COMPILER_OUTPUT=1 to see more.\n", " _create_built_program_from_source_cached(\n", "/home/jerome/.venv/py311/lib/python3.11/site-packages/pyopencl/cache.py:499: CompilerWarning: Non-empty compiler output encountered. Set the environment variable PYOPENCL_COMPILER_OUTPUT=1 to see more.\n", " prg.build(options_bytes, devices)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 57.4 s, sys: 6.76 s, total: 1min 4s\n", "Wall time: 1min 3s\n" ] }, { "data": { "text/plain": [ "113" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyFAI.detectors import Detector\n", "from pyFAI.azimuthalIntegrator import AzimuthalIntegrator\n", "from pyFAI.units import to_unit\n", "\n", "class Validator:\n", " def __init__(self, nimg = 100, npt=700, shape = (1024, 1024), pix = 100e-6, I0=1e4):\n", " self.pix = pix\n", " self.shape = shape\n", " self.npt = npt\n", " self.nimg = nimg\n", " self.I0 = I0\n", " self.unit = to_unit(\"r_mm\")\n", " self._ai = None\n", " self._img = None\n", " self._dataset = None\n", " \n", " @property\n", " def ai(self):\n", " if self._ai is None:\n", " detector = Detector(self.pix, self.pix)\n", " detector.shape=detector.max_shape=self.shape\n", " self._ai = AzimuthalIntegrator(dist=1.0, detector=detector)\n", " return self._ai\n", " \n", " def build_image(self):\n", " \"Reconstruction of diffusion image\"\n", " r_max = self.ai.detector.get_pixel_corners().max(axis=(0,1,2))\n", " r = numpy.linspace(0, 50*numpy.dot(r_max,r_max)**0.5, self.npt)\n", " I = self.I0/(1.0+r*r) #Lorentzian shape\n", " \n", " img = self.ai.calcfrom1d(r, I, dim1_unit=self.unit, \n", " correctSolidAngle=False, \n", " polarization_factor=None)\n", " return img\n", " \n", " @property\n", " def img(self):\n", " if self._img is None:\n", " self._img = self.build_image()\n", " return self._img\n", " \n", " def build_dataset(self):\n", " return numpy.random.poisson(self.img, (self.nimg,) + self.shape)\n", " \n", " @property\n", " def dataset(self):\n", " if self._dataset is None:\n", " self._dataset = self.build_dataset()\n", " return self._dataset\n", " \n", " @staticmethod\n", " def chi2(res1, res2):\n", " \"\"\"Calculate the 𝜒² value for a pair of 1d integrated data\"\"\"\n", " I = res1.intensity\n", " J = res2.intensity\n", " l = len(I)\n", " assert len(J) == l\n", " sigma_I = res1.sigma\n", " sigma_J = res2.sigma\n", " return ((I-J)**2/(sigma_I**2+sigma_J**2)).sum()/(l-1)\n", " \n", " \n", " def plot_distribution(self, kwargs, nbins=100, integrate=None, ax=None, label=\"Integrated curves\" ):\n", " ai = self.ai\n", " dataset = self.dataset\n", " ai.reset()\n", " results = []\n", " c2 = []\n", " kwargs = kwargs.copy()\n", " if integrate is None:\n", " integrate = ai.integrate1d_ng\n", " t0 = time.perf_counter()\n", " if \"npt\" in kwargs:\n", " npt = kwargs[\"npt\"]\n", " else:\n", " npt = kwargs[\"npt\"] = self.npt\n", " \n", " if \"unit\" not in kwargs:\n", " kwargs[\"unit\"] = self.unit\n", " for i in range(self.nimg):\n", " data = dataset[i, :, :]\n", " r = integrate(data, **kwargs)\n", " results.append(r) \n", " for j in results[:i]:\n", " c2.append(self.chi2(r, j))\n", " print(f\"Integration speed: {self.nimg/(time.perf_counter()-t0):6.3f} fps\")\n", " c2 = numpy.array(c2)\n", " if ax is None:\n", " fig, ax = subplots()\n", " h,b,_ = ax.hist(c2, nbins, label=\"Measured distibution\")\n", " y_sim = chi2_dist.pdf(b*(npt-1), npt)\n", " y_sim *= h.sum()/y_sim.sum()\n", " ax.plot(b, y_sim, label=r\"Chi^2 distribution\")\n", " ax.set_title(label)\n", " ax.legend()\n", " return ax\n", "\n", "# kwarg = {\"npt\":npt, \n", "# \"correctSolidAngle\":False, \n", "# \"polarization_factor\":None,\n", "# \"safe\":False}\n", "validator = Validator(nimg = 1000)\n", "%time validator.dataset.min()" ] }, { "cell_type": "code", "execution_count": 3, "id": "tight-maintenance", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IntegrationMethod(1d int, no split, CSR, python)\n", "Integration speed: 42.444 fps\n", "Integration speed: 33.344 fps\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": 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", 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