{ "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": [ "/nobackup/scratch/kieffer/py310/bin/python3 3.10.12 (main, Nov 6 2024, 20:22:13) [GCC 11.4.0]\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": "stdout", "output_type": "stream", "text": [ "CPU times: user 42.2 s, sys: 1.17 s, total: 43.3 s\n", "Wall time: 42.4 s\n" ] }, { "data": { "text/plain": [ "np.int64(111)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pyFAI\n", "from pyFAI.detectors import Detector\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 = pyFAI.load({\"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: 72.724 fps\n", "Integration speed: 58.679 fps\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3d3378cc9b0649209ed2e76505eaf040", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "