{ "cells": [ { "cell_type": "markdown", "id": "intensive-failure", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "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, Nov 30 2024, 21:22:50) [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": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min, sys: 48.3 s, total: 1min 49s\n", "Wall time: 1min 50s\n" ] }, { "data": { "text/plain": [ "np.int64(110)" ] }, "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: 36.362 fps\n", "Integration speed: 29.962 fps\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "125244450bbb41cc8b9cee66ff1c2f89", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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