Add MnF2 tutorial
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Jakob Lass committed
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"cells": [
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"cell_type": "markdown",
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"id": "8f9ac288-dfea-438b-a91c-971393102585",
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"metadata": {},
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"source": [
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"# Simulate Neutron Scattering Data"
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]
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},
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{
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"cell_type": "markdown",
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"id": "256c1739-3586-45cf-8fe3-cb89fdd1a935",
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"metadata": {},
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"source": [
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"> **_NOTE:_** This tutorial requires the installation of the [MJOLNIR package](https://mjolnir.readthedocs.io/). However, no data is required."
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]
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},
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{
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"cell_type": "markdown",
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"id": "be13ace3-32c1-4985-859f-8f85d48d2394",
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"metadata": {},
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"source": [
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"In this tutorial we will generate synthetic data in the exact same form as measured on the CAMEA instrumnet. We will utilize the case of measuring the magnetic dispersion of single crystal MnF$_2$ as described by [Yamany et al. 2010](https://www.doi.org/10.1139/P10-081). The synthetic data will consist of 12 sample rotation scans as measured on CAMEA. \n",
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"\n",
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"The dispersion relation of MnF$_2$ can be approximated analytically by:\n",
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"\n",
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"$$\\omega(H,K,L) = \\sqrt{\\left(2S z_2 J_2+D+2 z_1 S J_1 \\sin{L \\pi}^2\\right)^2-\\left(2 S z_2 J_2 \\cos(H \\pi)\\cos(K \\pi) \\cos(L \\pi)\\right)^2}$$\n",
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"\n",
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"In the above $J_1$, $J_2$, and $D$ are the magnetic coupling strengths and single-ion anisotropy which determine the amplitude of the dispersion. The parameters $S = 5/2$, $z_1 = 2$, and $z_2 = 8$ corresponds the the size of the magnetic spin, and the number of nearest and next-nearest neighbours in the MnF$_2$ lattice. \n",
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"\n",
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"From the above equation, the imporant fact is that it depends on the 3D vector $Q = (H,K,L)$. For the specfic crystal orientation, $(H,0,L)$ defines our scattering plane meaning $K$ is zero.\n",
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"\n",
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"The instrument resolution/response function of CAMEA is not perfect and thus data acquired get smeard. For simplicy, this smearing will be approximated by a 1D Gaussian smearing along the energy direction.\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d10ed368-5d6d-4507-bfee-602da4366810",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from MJOLNIR.Data import DataSet, Sample, Mask, DataFile, BackgroundModel\n",
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"from MJOLNIR import TasUBlibDEG\n",
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"from MJOLNIR.Geometry import Instrument\n",
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"import copy\n",
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"from scipy.optimize import minimize\n",
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"from scipy.interpolate import CubicSpline\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f22c9d8a-76af-48df-a0e9-732412b9650b",
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"metadata": {},
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"source": [
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"### Define needed functions for dispersion and SpinWave smearing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "d21fe066-3051-4872-a201-5f5f884a6390",
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"metadata": {},
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"outputs": [],
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"source": [
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"S = 5/2\n",
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"z1 = 2\n",
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"z2 = 8\n",
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"\n",
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"def MnF2(Q,J1,J2,D):\n",
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" return np.sqrt(np.power(2*S*z2*J2+D+2*z1*S*J1*np.sin(Q[2,:]*np.pi)**2,2.0)-\n",
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" np.power(2*S*z2*J2*np.cos(Q[0]*np.pi)*np.cos(Q[1]*np.pi)*np.cos(Q[2]*np.pi),2.0))\n",
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"\n",
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"def SpinWave(H,K,L,E):\n",
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" #ani = 1.2\n",
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" #amp = 0.5\n",
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" sigmaE = 0.25\n",
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" omega = MnF2(np.array([H,K,L]), J1=0.0354,J2=0.1499,D=0.131 )\n",
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" I = np.exp(-np.power(omega-E,2.0)/(2*sigmaE**2))\n",
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" \n",
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" return I"
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]
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},
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{
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"cell_type": "markdown",
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"id": "040d416e-5e5a-409e-8756-236c46dbd93e",
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"metadata": {},
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"source": [
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"## Generate CAMEA Data files\n",
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"\n",
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"For this tutorial we will generate 12 data files with the following settings\n",
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"\n",
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"- $E_i$ = 5, 5.13, 7, 7.13, 9, and 9.13 meV\n",
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"- $2\\theta$ = -40 and -44 deg\n",
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"- $A_3$ range are [ -45 , 45 ] deg at 5 and 5.13 meV\n",
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"- $A_3$ range are [ -37 , 37 ] deg at 7 and 7.13 meV\n",
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"- $A_3$ range are [ -29 , 29 ] deg at 9 and 9.13 meV\n",
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"\n",
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"The lattice parameters of MnF$_2$, needed to generate the correct scattering vectors are \n",
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"\n",
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"- $a$ = $b$ = 4.873 Å\n",
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"- $\\alpha$ = $\\beta$ = $\\gamma$ = 3.3107 deg\n",
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"\n",
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"For the alignment of the sample, two peaks in the scattering plane (which is $(H,0,L)$) are needed. These are\n",
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"\n",
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"- r1: [1,0,+1] at $A_3$ = 35.74 deg, $A_4$ = -95.21 deg, and incoming and outoging energy equal at $E_i$ = $E_f$ = 5.0 meV\n",
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"- r2: [1,0,-1] at $A_3$ =-32.35 deg, $A_4$ = -95.21 deg, and incoming and outoging energy equal at $E_i$ = $E_f$ = 5.0 meV\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "16297260-ace0-4bb7-bbf8-9e070ace8814",
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"metadata": {},
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"outputs": [],
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"source": [
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"dfs = [] # Container for generated DataFiles\n",
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"\n",
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"for i,ei in enumerate([5,7,9]):\n",
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" df = DataFile.DataFile()\n",
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"\n",
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" # Define sample and initialize sample - All of this is MJOLNIR specific\n",
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" s = Sample.Sample(a=4.873,b=4.873,c=3.3107,alpha=90,beta=90,gamma=90)\n",
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"\n",
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" r1 = np.array([1.0,0.0,1.0,35.74,-95.21,0.0,0,0.0,5.0,5.0])\n",
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" r2 = np.array([-1.0,0,1.0,-32.35,-94.64,0.0,0,0.0,5.0,5.0])\n",
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" s.plane_vector1 = r1\n",
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" s.plane_vector2 = r2\n",
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"\n",
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" # Vectors defining the main axes\n",
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" s.projectionVector1 = np.array([1.0,0.0,0.0])\n",
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" s.projectionVector2 = np.array([0.0,0.0,1.0])\n",
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" s.planeNormal = np.asarray([0.0,1.0,0.0])\n",
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"\n",
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" s.plane_vector1 = np.delete(s.plane_vector1,3)\n",
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" s.plane_vector1[5:7] = 0.0\n",
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" s.plane_vector2 = np.delete(s.plane_vector2,3)\n",
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" s.plane_vector2[5:7] = 0.0\n",
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" s.orientationMatrix = TasUBlibDEG.calcTasUBFromTwoReflections(s.cell, s.plane_vector1, s.plane_vector2)\n",
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"\n",
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" s.initialize()\n",
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" s.calculateProjections()\n",
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"\n",
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" s.UB = s.orientationMatrix\n",
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"\n",
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" # Start to populate the parameters for the data file\n",
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" df.A3 = np.linspace(-45-8*i,45-8*i,91)\n",
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" df.A4 = np.asarray([-40])\n",
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" df.Ei = np.asarray([ei])\n",
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" df.Monitor = np.asarray([125000]*len(df.A3))\n",
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" df.fileLocation = None\n",
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" df.name = 'Test'\n",
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"\n",
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" \n",
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" df.I = np.zeros([len(df.A3),104,1024])\n",
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" df.sample = s\n",
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"\n",
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" # For simplicity, df2 is a deep copy of df1 with only the A4 value changed\n",
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" df2 = copy.deepcopy(df)\n",
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" df2.A4 = np.asarray([-44])\n",
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"\n",
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" df3 = copy.deepcopy(df)\n",
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" df3.Ei = np.asarray([ei+0.13])\n",
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"\n",
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" df4 = copy.deepcopy(df2)\n",
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" df4.Ei = np.asarray([ei+0.13])\n",
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"\n",
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" dfs.append(df)\n",
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" dfs.append(df2)\n",
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" \n",
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" dfs.append(df3)\n",
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" dfs.append(df4)\n",
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"\n",
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"# Collect all of the 12 data files into a DataSet\n",
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"ds = DataSet.DataSet(dfs)\n",
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"\n",
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"# Convert utilizing the standard binning 8\n",
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"ds.convertDataFile(binning=8)\n",
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"\n",
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"# To allow a comparison between treated and ground truth data, a compy of the initial DataSet is made\n",
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"ds_calculated = copy.deepcopy(ds)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "50a47452-5217-4aac-b071-2c57c7b83a5d",
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"metadata": {},
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"source": [
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"### Addition of noise and background\n",
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"As described in the documentation, AMBER expects the background signal to be smoothly varying in all 3 directions as well as to be independent of rotations around the origin, i.e. $(0,0,0)$. In the following, such a background, mimicing real world background, is generated and added to the intensity in the DataFiles. In addition, due to the Poisson nature of counting statistics, which neutron scattering falls under, the final neutron count will be found from the calcualted intensity subject to Poisson noice"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "be9bb08d-c82c-4718-92ed-6a5dc0260ff9",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"def A4BG(a4,A):\n",
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" gamma = 10\n",
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" return 2*A*((gamma/(a4**2+gamma**2))+0.02*np.exp(-np.power(a4+120,2.0)/(2*20**2)))\n",
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"\n",
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"# Containers to retain the different contributions\n",
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"trueForeground = []\n",
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"trueRandomBackground = []\n",
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"trueA3IndependentBackground = []\n",
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"\n",
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"for d,d_calcualted in zip(ds,ds_calculated):\n",
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" trueForeground.append(np.round(SpinWave(d.h,d.k,d.l,d.energy)*(d.Monitor*d.Norm)))\n",
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" trueRandomBackground.append(np.ones(d.I.shape)*0.01)\n",
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" trueA3IndependentBackground.append(A4BG(d.instrumentCalibrationA4+d.A4,A=50*10).reshape(1,104,64))\n",
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" d.I =np.random.poisson(trueForeground[-1])\n",
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" d.I+=np.random.poisson(trueRandomBackground[-1]) # constant Background\n",
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" d.I+=np.random.poisson(trueA3IndependentBackground[-1])\n",
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"\n",
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" d_calcualted.I =np.random.poisson(trueForeground[-1])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "012f2897-84af-45c1-af64-35755d735933",
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"metadata": {},
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"source": [
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"## Utilizing some MJOLNIR features to showcase the data\n",
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"Following are a couple of plots of the data using MJOLNIR functions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "73f245e4-a0e1-4db7-bb0a-ec28730c3e57",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 700x400 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"# Generate 2D cut in momentum transfer and energy\n",
|
||||
"q1 = np.array([-1.5, 0.0, 0]) # r.l.u.\n",
|
||||
"q2 = np.array([0, 0.0, 0.0]) # r.l.u.\n",
|
||||
"EMin = 0.0 # meV\n",
|
||||
"EMax = 6.0 # meV\n",
|
||||
"dE = 0.05 # meV\n",
|
||||
"width = 0.03 # 1/Å orthogonal to cutting direction\n",
|
||||
"minPixel = 0.03 # 1/Å bin size along cutting direction\n",
|
||||
"\n",
|
||||
"ax, *d = ds.plotCutQE(q1=q1, q2=q2, EMin=EMin, EMax=EMax, dE = dE, \n",
|
||||
" width=width, minPixel=minPixel, vmin=0.0, vmax=5,\n",
|
||||
" colorbar = True)\n",
|
||||
"\n",
|
||||
"# Generate a constant energy cut\n",
|
||||
"EMin = 4.5 # meV\n",
|
||||
"EMax = 4.55 # meV\n",
|
||||
"dqx = 0.03 # 1/Å bin size\n",
|
||||
"dqy = 0.03 # 1/Å bin size\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"ax, *d = ds.plotQPlane(EMin=EMin,EMax=EMax, xBinTolerance=dqx, yBinTolerance=dqy, \n",
|
||||
" vmin=0.0, vmax=5,colorbar = True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "6bd0bbba-3255-45ac-861a-236085bc28b3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initialize AMBER background"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "f7ea4236-8f1e-4edd-90d0-8b90ad7f1469",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Anaconda\\envs\\python311\\Lib\\site-packages\\MJOLNIR\\Data\\BackgroundModel.py:309: RuntimeWarning: invalid value encountered in divide\n",
|
||||
" self.I = np.divide(self.data[0] * self.data[-1], self.data[1] * self.data[2])\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"AB = BackgroundModel.AMBERBackground(ds,dQx = 0.03, dQy = 0.03, dE = 0.05, beta=100)\n",
|
||||
"AB.set_radial_bins()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "bf3bb3ac-b1c3-4e9f-9926-87d43d6ac4a4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Minimize RMSQ for beta\n",
|
||||
"\n",
|
||||
"> **_NOTE:_** Running the cross-normalization will take quite some time. > 5-10 minutes!\n",
|
||||
"\n",
|
||||
"To perform a cross validation of the $\\beta$ value, on needs to set the qantile level where only background is left. That is, byt masking out higher intensity regions one can assume that the original minimization problem only contain the data and the background, i.e.\n",
|
||||
"\n",
|
||||
"$\n",
|
||||
"\\min_{X,b} \\frac{1}{2}\\lVert Y-X-\\mathcal{R}b\\rVert_{2}^2+\\lambda\\vert| X |\\vert_{1} +\\frac{\\beta}{2} \\mathrm{Tr} \\left( b^T L_{b} b \\right) +\\frac{\\mu}{2} \\boldsymbol{1}_{n_x}^TX^T L_{\\omega} X\\boldsymbol{1}_{n_y}\n",
|
||||
"$\n",
|
||||
"\n",
|
||||
"becomes\n",
|
||||
"\n",
|
||||
"$\n",
|
||||
"\\min_{b} \\frac{1}{2}\\lVert Y-\\mathcal{R}b\\rVert_{2}^2+\\frac{\\beta}{2} \\mathrm{Tr} \\left( b^T L_{b} b \\right) \n",
|
||||
"$\n",
|
||||
"\n",
|
||||
"For the above data set, a value of 0.75 (the default setting) is suitable. \n",
|
||||
"\n",
|
||||
"### Minimization methods\n",
|
||||
"\n",
|
||||
"One can do two things to find the optimal $\\beta$ value\n",
|
||||
"\n",
|
||||
"1. Calculate the RMSE across multiple decades and interpolate\n",
|
||||
"2. Perform a minimization\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"It is adviced to first perform a coarse calculation for then to perform the minimization as the latter can be quite time-consuming."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "af82019c-84fd-47ff-844c-81c2becb4a7f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"q=0.75, beta=array([ 1, 3, 7, 10, 30, 70, 100, 300, 700, 1000]), l=0.2665642114325497, mu=0.2427590851339083, n_epochs=20, verbose=False\n",
|
||||
"Test - ( 1 )\n",
|
||||
"RMSE - ( 0.2665642114325497 1 0.2427590851339083 ) : 0.10329160145964443\n",
|
||||
"Test - ( 3 )\n",
|
||||
"RMSE - ( 0.2665642114325497 3 0.2427590851339083 ) : 0.1028468402733826\n",
|
||||
"Test - ( 7 )\n",
|
||||
"RMSE - ( 0.2665642114325497 7 0.2427590851339083 ) : 0.10225770010882033\n",
|
||||
"Test - ( 10 )\n",
|
||||
"RMSE - ( 0.2665642114325497 10 0.2427590851339083 ) : 0.1019720100920882\n",
|
||||
"Test - ( 30 )\n",
|
||||
"RMSE - ( 0.2665642114325497 30 0.2427590851339083 ) : 0.10156595368428951\n",
|
||||
"Test - ( 70 )\n",
|
||||
"RMSE - ( 0.2665642114325497 70 0.2427590851339083 ) : 0.10227250242411776\n",
|
||||
"Test - ( 100 )\n",
|
||||
"RMSE - ( 0.2665642114325497 100 0.2427590851339083 ) : 0.10292055384172495\n",
|
||||
"Test - ( 300 )\n",
|
||||
"RMSE - ( 0.2665642114325497 300 0.2427590851339083 ) : 0.10781518872596527\n",
|
||||
"Test - ( 700 )\n",
|
||||
"RMSE - ( 0.2665642114325497 700 0.2427590851339083 ) : 0.11480259052941344\n",
|
||||
"Test - ( 1000 )\n",
|
||||
"RMSE - ( 0.2665642114325497 1000 0.2427590851339083 ) : 0.11836036118710512\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# We define a list of reasonable beta values distributed over 3 orders of magnitude\n",
|
||||
"\n",
|
||||
"beta = [1,3,7,10,30,70,100,300,700,1000]\n",
|
||||
"RMSE = AB.cross_validation(q=0.75, beta = beta,verbose=False,n_epochs=20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"id": "ef25cca1-bc0b-4842-a7a3-3dbfd173d052",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.legend.Legend at 0x211ecd07490>"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# As a guide to the eye, a Cubic Spline will be added\n",
|
||||
"\n",
|
||||
"interpolation = CubicSpline(np.asarray(beta),RMSE)\n",
|
||||
"x = np.linspace(np.min(beta),np.max(beta),201)\n",
|
||||
"y = interpolation(x)\n",
|
||||
"\n",
|
||||
"fig,ax = plt.subplots()\n",
|
||||
"\n",
|
||||
"ax.scatter(beta,RMSE,label='Calculated')\n",
|
||||
"ax.plot(x,y,'--',label='Interpolation')\n",
|
||||
"\n",
|
||||
"ax.set_xlabel('Beta')\n",
|
||||
"ax.set_ylabel('RMSE')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"x2 = np.linspace(0,100,501)\n",
|
||||
"y2 = interpolation(x2)\n",
|
||||
"\n",
|
||||
"xmin,ymin = x2[np.argmin(y2)],y2[np.argmin(y2)]\n",
|
||||
"\n",
|
||||
"fig,ax = plt.subplots()\n",
|
||||
"ax.scatter(beta,RMSE,label='Calculated')\n",
|
||||
"ax.plot(x2,y2,'--',label='Interpolation')\n",
|
||||
"ax.scatter(xmin,ymin,label='minimum',color='r')\n",
|
||||
"\n",
|
||||
"ax.set_xlabel('Beta')\n",
|
||||
"ax.set_ylabel('RMSE')\n",
|
||||
"ax.set_xlim(0,100)\n",
|
||||
"ax.set_ylim(0.1010,0.1040)\n",
|
||||
"ax.legend()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "de8fef6b-5e63-43b7-bbf7-928d2993adda",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# If the above has been run, utilize that value otherwise set ymin to 23.4\n",
|
||||
"\n",
|
||||
"xmin = 23.4\n",
|
||||
"\n",
|
||||
"func = lambda beta: AB.cross_validation(q=0.75, beta = beta,verbose=False,n_epochs=20)\n",
|
||||
"\n",
|
||||
"result = minimize(func,x0 = [xmin])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4425261a-3419-499d-ab84-1ac281750b8c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Generation of AMBER background"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"id": "804af4a9-47e5-49cf-bb7b-7d5aa51022d8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"AB.beta = xmin"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"id": "9bd8bd87-6230-4d1d-a980-3c83c5fc1573",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" Iteration 1\n",
|
||||
" Loss function: 89216.17289327279\n",
|
||||
" Iteration 2\n",
|
||||
" Loss function: 68093.90388265462\n",
|
||||
" Iteration 3\n",
|
||||
" Loss function: 57929.508628268115\n",
|
||||
" Iteration 4\n",
|
||||
" Loss function: 51147.228477046934\n",
|
||||
" Iteration 5\n",
|
||||
" Loss function: 46560.03500671341\n",
|
||||
" Iteration 6\n",
|
||||
" Loss function: 43283.23323431091\n",
|
||||
" Iteration 7\n",
|
||||
" Loss function: 40937.53702364261\n",
|
||||
" Iteration 8\n",
|
||||
" Loss function: 39308.08183709762\n",
|
||||
" Iteration 9\n",
|
||||
" Loss function: 38236.35126931614\n",
|
||||
" Iteration 10\n",
|
||||
" Loss function: 37568.299703508696\n",
|
||||
" Iteration 11\n",
|
||||
" Loss function: 37178.28929735461\n",
|
||||
" Iteration 12\n",
|
||||
" Loss function: 36963.03718997033\n",
|
||||
" Iteration 13\n",
|
||||
" Loss function: 36850.84062922201\n",
|
||||
" Iteration 14\n",
|
||||
" Loss function: 36796.66347072205\n",
|
||||
" Iteration 15\n",
|
||||
" Loss function: 36772.34667854443\n",
|
||||
" Iteration 16\n",
|
||||
" Loss function: 36761.725670802814\n",
|
||||
" Iteration 17\n",
|
||||
" Loss function: 36757.366819518116\n",
|
||||
" Iteration 18\n",
|
||||
" Loss function: 36755.782935800766\n",
|
||||
" Iteration 19\n",
|
||||
" Loss function: 36755.3751834626\n",
|
||||
" Iteration 20\n",
|
||||
" Loss function: 36755.43029325381\n",
|
||||
" Iteration 21\n",
|
||||
" Loss function: 36755.63232500783\n",
|
||||
" Iteration 22\n",
|
||||
" Loss function: 36755.84605170578\n",
|
||||
" Iteration 23\n",
|
||||
" Loss function: 36756.02667786971\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"AB.generateAMBER(n_epochs=25)\n",
|
||||
"AB.applyAMBER()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"id": "7f104c8d-50a2-4ae7-80ef-75f31a0083c2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 700x400 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 700x400 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"# Generate 2D cut in momentum transfer and energy\n",
|
||||
"q1 = np.array([-1.5, 0.0, 0]) # r.l.u.\n",
|
||||
"q2 = np.array([0, 0.0, 0.0]) # r.l.u.\n",
|
||||
"EMin = 0.0 # meV\n",
|
||||
"EMax = 6.0 # meV\n",
|
||||
"dE = 0.05 # meV\n",
|
||||
"width = 0.03 # 1/Å orthogonal to cutting direction\n",
|
||||
"minPixel = 0.03 # 1/Å bin size along cutting direction\n",
|
||||
"\n",
|
||||
"ax, *d = ds.plotCutQE(q1=q1, q2=q2, EMin=EMin, EMax=EMax, dE = dE, \n",
|
||||
" width=width, minPixel=minPixel, vmin=0.0, vmax=3,\n",
|
||||
" colorbar = True, backgroundSubtraction=False)\n",
|
||||
"\n",
|
||||
"ax, *d = ds.plotCutQE(q1=q1, q2=q2, EMin=EMin, EMax=EMax, dE = dE, \n",
|
||||
" width=width, minPixel=minPixel, vmin=0.0, vmax=3,\n",
|
||||
" colorbar = True, backgroundSubtraction=True)\n",
|
||||
"\n",
|
||||
"# Generate a constant energy cut\n",
|
||||
"EMin = 4.5 # meV\n",
|
||||
"EMax = 4.55 # meV\n",
|
||||
"dqx = 0.03 # 1/Å bin size\n",
|
||||
"dqy = 0.03 # 1/Å bin size\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"ax, *d = ds.plotQPlane(EMin=EMin,EMax=EMax, xBinTolerance=dqx, yBinTolerance=dqy, \n",
|
||||
" vmin=0.0, vmax=3,colorbar = True, backgroundSubtraction=False)\n",
|
||||
"ax, *d = ds.plotQPlane(EMin=EMin,EMax=EMax, xBinTolerance=dqx, yBinTolerance=dqy, \n",
|
||||
" vmin=0.0, vmax=3,colorbar = True, backgroundSubtraction=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2e14f805-37bf-434c-ad98-90a701e1b2fd",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create 1D cuts with overplotting"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"id": "40c4ef59-1e92-46c9-b27b-bdd8f50cc972",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"Q1 = np.array([-1.0, 0, 0])\n",
|
||||
"\n",
|
||||
"ax1D001 = DataSet.generate1DAxisE(Q1,rlu=True,showQ = False)\n",
|
||||
"ax1D001SUB = DataSet.generate1DAxisE(Q1,rlu=True,showQ = False)\n",
|
||||
"\n",
|
||||
"titles = [\"AMBER\",\"True\"]\n",
|
||||
"fgDone = False\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"for dds,title in zip([ds,ds_calculated],titles): \n",
|
||||
" \n",
|
||||
" \n",
|
||||
" #title = bg*'Subtracted'+(1-bg)*'Foreground'#'['+', '.join(['{:.0f}'.format(x) for x in q])+'] '+bg*'Subtracted'\n",
|
||||
" if dds == ds_calculated:\n",
|
||||
" ax,data,bins = dds.plotCut1DE(q=Q1, E1 = -0.2, E2 =7.0, width = 0.1, minPixel=0.05,label='Subtracted',ax=None)#,backgroundSubtraction=Fal,plotForeground=True)\n",
|
||||
" else:\n",
|
||||
" ax,data,bins = dds.plotCut1DE(q=Q1, E1 = -0.2, E2 =7.0, width = 0.1, minPixel=0.05,label='Subtracted',ax=None,backgroundSubtraction=True,plotForeground=True)\n",
|
||||
"\n",
|
||||
" plt.close(ax.get_figure())\n",
|
||||
" \n",
|
||||
" pos = data['BinDistance'].to_numpy()\n",
|
||||
"\n",
|
||||
" if dds != ds_calculated:\n",
|
||||
" bgvalue = data['Int_Bg'].to_numpy()\n",
|
||||
" bgerr = data['Int_Bg_err'].to_numpy()\n",
|
||||
" fgvalue = data['Int_Fg'].to_numpy()\n",
|
||||
" fgerr = data['Int_Fg_err'].to_numpy()\n",
|
||||
" subvalue = data['Int'].to_numpy()\n",
|
||||
" suberr = data['Int_err'].to_numpy()\n",
|
||||
" else:\n",
|
||||
" fgvalue = data['Int']\n",
|
||||
" fgerr = data['Int_err']\n",
|
||||
" subvalue = fgvalue\n",
|
||||
" suberr = fgerr\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" #if not fgDone:\n",
|
||||
" ax1D001.errorbar(pos,fgvalue,yerr=fgerr,label='Foreground',fmt='.')\n",
|
||||
" # fgDone = True\n",
|
||||
" \n",
|
||||
" c = ax1D001SUB.errorbar(pos,subvalue,yerr=suberr,label=title,fmt='.')[0]\n",
|
||||
" color = c.get_color()\n",
|
||||
" \n",
|
||||
" ax1D001.plot(pos,bgvalue,color='k',zorder=-10)\n",
|
||||
" ax1D001.fill_between(pos,bgvalue-bgerr,bgvalue+bgerr,color=color,label=title+' Background',alpha=0.5,zorder=-11)\n",
|
||||
"\n",
|
||||
"for ax in [ax1D001]:\n",
|
||||
" ax.set_ylim(-0.3,2)\n",
|
||||
" ax.legend()\n",
|
||||
" \n",
|
||||
" ax.set_ylabel('Int [arb. unit]')\n",
|
||||
" ax.get_figure().tight_layout()\n",
|
||||
" \n",
|
||||
"for ax in [ax1D001SUB]:\n",
|
||||
" ax.set_ylim(-0.15,1.5)\n",
|
||||
" ax.legend()\n",
|
||||
" ax.set_ylabel('Int [arb. unit]')\n",
|
||||
" ax.get_figure().tight_layout()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.12.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in new issue
Block a user