{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "97e0342c",
   "metadata": {},
   "source": [
    "# Fitting of sequential 1D cuts\n",
    "\n",
    "After a spectrum has been measured, it is sensible to extract some of the physical quantities of excitations. In this tutorial, one possible method of doing so by the use of QELine as well as LMFIT is presented. The data is the MnF2 data set where only as subset of the data is used. Specifically from 4.8 meV to 6.9 meV and the parameters wanted are the positions and widths of the magnon along two cuts in $Q$. More specifically, there will be performed a cut from (-1,0,-67) RLU to (-1,0,1) RLU to (0,0,1) RLU. For each of the two segments, the data is split into horizontal (energy) slices of same energy width and Gaussians are used to model the spin wave line shapes. Below is the data as seen before the fitting routine has been performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "acf18505",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-25T11:37:19.515358Z",
     "iopub.status.busy": "2023-07-25T11:37:19.515358Z",
     "iopub.status.idle": "2023-07-25T11:37:28.246301Z",
     "shell.execute_reply": "2023-07-25T11:37:28.245426Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from MJOLNIR.Data import DataSet\n",
    "from MJOLNIR import _tools # Usefull tools useful across MJOLNIR\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit.models import GaussianModel\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [18, 16]\n",
    "plt.rcParams['figure.dpi'] = 200\n",
    "\n",
    "\n",
    "numbers = '483-490,492-500'\n",
    "files = _tools.fileListGenerator(numbers,r'C:\\Users\\lass_j\\Documents\\CAMEA2018',year=2018)\n",
    "ds = DataSet.DataSet(files)\n",
    "ds.convertDataFile(binning=8)\n",
    "\n",
    "\n",
    "# utility function to return points in array before a given value\n",
    "def index_of(arrval, value):\n",
    "    \"\"\"Return index of array *at or below* value.\"\"\"\n",
    "    if value < min(arrval):\n",
    "        return 0\n",
    "    if np.sum(np.diff(arrval))>0: # Positive change\n",
    "        return max(np.where(arrval <= value)[0])\n",
    "    else:\n",
    "        return max(np.where(arrval >= value)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59981520",
   "metadata": {},
   "source": [
    "Define the two cuts to be performed; between (-1,0,-1.2) and (-1,0,1) further on to (0,0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c1a0766",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-25T11:37:28.248336Z",
     "iopub.status.busy": "2023-07-25T11:37:28.248336Z",
     "iopub.status.idle": "2023-07-25T11:37:31.114065Z",
     "shell.execute_reply": "2023-07-25T11:37:31.113129Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Define qut in Q space\n",
    "q1 = [-1,0,-1.2]\n",
    "q2 = [-1,0,1.0]\n",
    "q3 = [0,0,1.0]\n",
    "\n",
    "# Create two different energy ranges for the cut from q1 to q2 and q2 to q3\n",
    "EBins = np.array([np.linspace(ds.energy.min(),ds.energy.max(),31),np.linspace(ds.energy.min(),ds.energy.max(),25)],dtype=object)\n",
    "\n",
    "# Perform the cut and plot it. Returns a cutObject\n",
    "ax,Data,Bin = ds.plotCutQELine(QPoints=[q1,q2,q3],width=0.05,minPixel=0.01,EnergyBins=EBins,plotSeperator=True) #\n",
    "\n",
    "ax.set_clim(0,5e-6)\n",
    "ax.get_figure().set_size_inches(16,9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13010157",
   "metadata": {},
   "source": [
    "The following code is rahter complex but in a larger picture, first the cut between $q_1$ and $q_2$ is dealt with followed by $q_2$ and $q_3$. For each of the cuts, the returned data (in the form of Pandas Dataframes) are grouped together in energy slices, i.e. same energy transfers are grouped togehter. \n",
    "\n",
    "For the first cut, a fitting function is created consisting 3 Gaussians while for the second two Gaussians are used. For both cuts, energies below 1.6 meV are ignored while only for the second cut energies above 6.1 meV. \n",
    "\n",
    "For each energy cut the suitable model is fitted and the parameters are extracted and saved in the <code>center3D</code> and <code>center3DErr</code>. Lastly, the points are plotted on top of the figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e086995",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-25T11:37:31.117062Z",
     "iopub.status.busy": "2023-07-25T11:37:31.117062Z",
     "iopub.status.idle": "2023-07-25T11:37:32.709350Z",
     "shell.execute_reply": "2023-07-25T11:37:32.708601Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define plotting parameters for error bar plot to be created\n",
    "ErrorBarKwargs = {'markersize':4, 'capsize':2,'elinewidth':1,'markeredgewidth':2,'mfc':'white','fmt':'o'}\n",
    "\n",
    "out = [] # Holder for fitting results\n",
    "\n",
    "# Loop through the different cuts\n",
    "for ID,_cutdata in enumerate(Data):\n",
    "# ID: which segment, either 0: q1->q2, or 1: q2->q3\n",
    "# _data: pandas frame containing data for this cut\n",
    "\n",
    "    for _,_data in _cutdata.groupby('Energy'):\n",
    "        # Transpose as to easier perform fit\n",
    "        x = _data[['H','K','L','Energy']]\n",
    "\n",
    "        # Calculate intensity and remove points that has not been measured\n",
    "\n",
    "        y = _data['Int'].values\n",
    "        NoNNans = (_data['Monitor']>0).values\n",
    "\n",
    "        y = y[NoNNans]\n",
    "        x = x[NoNNans]\n",
    "\n",
    "\n",
    "        # Fit depends on which cut is in question\n",
    "        if ID==0:\n",
    "            # Fit consists of three 1D Gaussians\n",
    "            gauss1 = GaussianModel(prefix='g1_')\n",
    "            gauss2 = GaussianModel(prefix='g2_') # Give the Gaussians different prefixes\n",
    "            gauss3 = GaussianModel(prefix='g3_')\n",
    "\n",
    "            # Model is simply the sum of these\n",
    "            mod = gauss1 + gauss2 + gauss3\n",
    "\n",
    "            # Direction along which to fit\n",
    "            fittingX = x['L']\n",
    "\n",
    "            E = x['Energy'].values[0]\n",
    "            if E<1.6:\n",
    "                continue\n",
    "\n",
    "            # Cut out portions to make use of the automatic parameter estimation\n",
    "            ix1 = fittingX< -0.1 # All points before -0.1 is for first Gaussian\n",
    "            ix2 = np.logical_and(fittingX> -0.1,fittingX<0.5) # Between ix1 and ix2 is second Gaussian\n",
    "            ix3 = fittingX>0.5 # values bigger than 0.5\n",
    "            # Third Gaussian is from 0.5 and the rest.\n",
    "\n",
    "            # Use lmfit's parameter starting guess\n",
    "            pars = gauss1.guess(y[ix1],x=fittingX[ix1].values)\n",
    "            pars.update(gauss2.guess(y[ix2],x=fittingX[ix2].values))\n",
    "            pars.update(gauss3.guess(y[ix3],x=fittingX[ix3].values))\n",
    "\n",
    "        elif ID == 1:\n",
    "\n",
    "            # Repeat procedure from above for second segment with only 2 Gaussians or 1 depending on energy\n",
    "            fittingX = x['H'].values\n",
    "            gauss1 = GaussianModel(prefix='g1_')\n",
    "            E = x['Energy'].values[0]\n",
    "            if E<1.6:\n",
    "                continue\n",
    "            if E<6.1:\n",
    "\n",
    "                gauss2 = GaussianModel(prefix='g2_')\n",
    "\n",
    "                mod = gauss1 + gauss2\n",
    "\n",
    "                ix1 = fittingX<-0.5\n",
    "                ix2 = np.logical_not(ix1)\n",
    "\n",
    "                pars = gauss1.guess(y[ix1],x=fittingX[ix1])\n",
    "                pars.update(gauss2.guess(y[ix2],x=fittingX[ix2]))\n",
    "            elif E<6.45:\n",
    "                mod = gauss1\n",
    "                pars = gauss1.guess(y,x=fittingX)\n",
    "            else:\n",
    "                continue\n",
    "\n",
    "        # Perform fit and save results in the 'out' array\n",
    "        result = mod.fit(y, pars, x=fittingX)\n",
    "        out.append(result)\n",
    "\n",
    "        # For plotting Centres and widths are needed (Their errors are extracted as well, but unused)\n",
    "        centres = []\n",
    "        centres_err = []\n",
    "        widths = []\n",
    "        widths_err = []\n",
    "        for parname, par in result.params.items():\n",
    "            if 'center' in parname:\n",
    "                centres.append(par.value)\n",
    "                centres_err.append(par.stderr)\n",
    "            if 'sigma' in parname:\n",
    "                widths.append(par.value)\n",
    "                widths_err.append(par.stderr)\n",
    "\n",
    "        # Depending on segment, the centres of the Gaussians are either [-1,0,c] or [c,0,-1]\n",
    "        # 'Errors' refers to the errorbars and are in this case plotted as the widths\n",
    "        if ID == 0:\n",
    "            Center3D    = np.array([[-1.0,0,c] for c in centres])\n",
    "            Center3DErr = np.array([[-1.0,0,c+err] for c,err in zip(centres,widths)])\n",
    "\n",
    "        elif ID == 1:\n",
    "            Center3D    = np.array([[c,0,1] for c in centres])\n",
    "            Center3DErr = np.array([[c+err,0,1] for c,err in zip(centres,widths)])\n",
    "\n",
    "        # Calculate the position along the plot for the HKL points.\n",
    "        XPosition = np.concatenate([ax.calculatePositionInv(c.reshape(1,-1)) for c in Center3D])\n",
    "        XPositionErr = np.concatenate([ax.calculatePositionInv(CE.reshape(1,-1)) for CE in Center3DErr])-XPosition\n",
    "        # The above is needed as an axis only has one x-axis and plotting multiple segments require some trickery,\n",
    "        # resulting in rescaling and offsets. It is all taken care of in the converterFunction of the axis.\n",
    "\n",
    "\n",
    "        ax.errorbar(XPosition,[x.values[0][-1]]*len(XPosition),xerr=XPositionErr,c = 'b', **ErrorBarKwargs)\n",
    "        # plot the errorbar on top of intensity data\n",
    "        \n",
    "ax.get_figure()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860caa2b",
   "metadata": {},
   "source": [
    "If one does not like to work with pandas DataFrames, one can convert them into a set of numpy arrays. However, this then requires the user to deal with all of the indices themself. Further, if LMFIT is not your cup of tea, many other fitting routines exist for Python, e.g. the <code>scipy.optimize.curve_fit</code>."
   ]
  }
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