{ "cells": [ { "cell_type": "markdown", "id": "20ec9038-c184-4d5d-8c15-bcf7edba9c77", "metadata": {}, "source": [ "# Optimizer - simple" ] }, { "cell_type": "code", "execution_count": 1, "id": "fa52e5ca", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "0.4495182509432407\n" ] } ], "source": [ "import nexus as nx\n", "import math\n", "import numpy as np\n", "from scipy.signal import argrelextrema\n", "import matplotlib.pyplot as plt\n", "\n", "# ------------------------- Fe layer --------------------------\n", "# the thickness is made to be fitted\n", "# set with boundaries between 0 and 10.000\n", "lay_Fe = nx.Layer(id = \"Fe\",\n", " material = nx.Material.Template(nx.lib.material.Fe_enriched),\n", " thickness = nx.Var(2000, 0, 1e5, True))\n", "\n", "site = nx.Hyperfine(magnetic_field = 33)\n", "\n", "lay_Fe.material.hyperfine_sites = [site]\n", "\n", "sample = nx.Sample(id = \"simple layer\",\n", " layers = [lay_Fe],\n", " geometry = \"f\")\n", "\n", "beam = nx.Beam()\n", "beam.LinearSigma()\n", "\n", "exp = nx.Experiment(beam = beam,\n", " objects = sample,\n", " isotope = nx.lib.moessbauer.Fe57,\n", " id = \"my exp\")\n", "\n", "detuning = np.arange(-200, 200.1, 0.2)\n", "\n", "energy_spec = nx.EnergySpectrum(exp,\n", " detuning,\n", " electronic = True)\n", "\n", "intensity = energy_spec()\n", "\n", "plt.plot(detuning, intensity)\n", "plt.xlabel('detuning (Gamma)')\n", "plt.ylabel('intensity')\n", "plt.show()\n", "\n", "print(min(intensity))" ] }, { "cell_type": "code", "execution_count": 2, "id": "4dd6f594", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.4495182509432407\n", "\n", "Run Optimizer instance with id: opt id \n", "\n", "Starting optimizer with 1 provided measurement dependencies and 1 fit parameter(s):\n", "\n", " no. | id | initial value | min | max\n", " 0 | | 2000 | 0 | 100000\n", "\n", "Using 0 equality constraint(s) on parameter(s):\n", "\n", "Using 0 inequality constraint(s).\n", "\n", "Residual start value: 0.449518\n", "\n", "\n", "Calling ceres solver with fit method LevMar\n", "\n", "Ceres Solver Report: Iterations: 16, Initial cost: 1.010333e-01, Final cost: 1.624058e-06, Termination: CONVERGENCE\n", "\n", "Optimizer finished with 1 fit parameter(s):\n", "\n", " no. | id | fit value | initial value | min | max\n", " 0 | | 100000 | 2000 | 0 | 100000\n", "\n", "and 0 equality constraint(s) on parameter(s):\n", "\n", "and 0 inequality constraint(s).\n", "\n", "Optimized residual from 0.449518 to 0.00180225\n", "\n", "Optimizer instance finished. id:opt id\n", "\n", "\n" ] } ], "source": [ "# define your own Optimizer derived from nx.Optimizer\n", "class NexusOptimizer(nx.Optimizer):\n", " # this is just the standard initialization of the class\n", " # keep it like this\n", " def __init__(self, measurements, id):\n", " super().__init__(measurements, id)\n", "\n", " # the implementation of the residual, the value to be minimized\n", " def Residual(self):\n", " # here we need to calculate the energy spectrum and get its minimum\n", " intensity = energy_spec()\n", " # the residual is 'min(intensity) - 0' because we target the intensity minimum to be zero (minimized)\n", " residual = min(intensity)\n", " return residual\n", "\n", "# initialize the optimizer with the method(s) to be used in the optimizer implementation\n", "optimizer = NexusOptimizer(measurements = [energy_spec],\n", " id = \"opt id\")\n", "\n", "# let's see what the residual returns\n", "print(optimizer.Residual())\n", "\n", "optimizer.Evaluate()" ] }, { "cell_type": "code", "execution_count": 3, "id": "f4bdb78c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(detuning, energy_spec.result)\n", "plt.xlabel('detuning (Gamma)')\n", "plt.ylabel('intensity')\n", "plt.show()" ] } ], "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": "3.12.0" } }, "nbformat": 4, "nbformat_minor": 5 }