{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f9ca45e",
   "metadata": {},
   "source": [
    "# Cutting through data in $Q$ and $E$\n",
    "\n",
    "After the initial viewing of the data acquired through the [Viewer3D](QuickPlottingofData.html) the next step is to perform cuts. Normally, what is wanted is the intensity measured as a function of $\\vec{Q}$ and energy in a 2D plot along a specified direction through space. Often these are specified using the RLU units and questions about the number neutrons hitting a point or if one actually has covered reciprocal space in such a fasion that an article picture can be generated. All of this can be check through the use of the <code>plotCutQELine</code> method as shown below. This method creates a dedicated axis element (and figure if needed) containing the intensity as function of position in $\\vec{Q}$ and energy with a given width, certain height in energy and a minimum pixel size along the $\\vec{Q}$ direction. As indicated by the name, one can provide a list of $\\vec{Q}$ points to be visited, with the only restriction that they should contain data and that they are connected. That is, providing three positions results in a plot with two segments connecting point 1 and 2 as well as 2 and 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0274db83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-25T11:37:00.254965Z",
     "iopub.status.busy": "2023-07-25T11:37:00.254965Z",
     "iopub.status.idle": "2023-07-25T11:37:08.429292Z",
     "shell.execute_reply": "2023-07-25T11:37:08.428435Z"
    }
   },
   "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",
    "\n",
    "\n",
    "numbers = '483-489,494-500' # String of data numbers\n",
    "fileList = _tools.fileListGenerator(numbers,r'C:\\Users\\lass_j\\Documents\\CAMEA2018',2018) # Create file list from 2018 in specified folder\n",
    "\n",
    "ds = DataSet.DataSet(fileList)\n",
    "ds.convertDataFile(saveFile=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d95e0cf",
   "metadata": {},
   "source": [
    "In this example a cut is made through reciprocal space between the following 5 points:\n",
    "\n",
    "- (0,0,0)\n",
    "- (0,0,1)\n",
    "- (-1,0,1)\n",
    "- (-1,0,0)\n",
    "- (0,0,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d9656e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-25T11:37:08.432292Z",
     "iopub.status.busy": "2023-07-25T11:37:08.431288Z",
     "iopub.status.idle": "2023-07-25T11:37:08.443351Z",
     "shell.execute_reply": "2023-07-25T11:37:08.443351Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define the positions to be cut through\n",
    "Q1 = np.array([0,0,0])\n",
    "Q2 = np.array([0,0,1])\n",
    "Q3 = np.array([-1,0,1])\n",
    "Q4 = np.array([-1,0,0])\n",
    "Q5 = np.array([0,0,1])\n",
    "# Collect them into one array\n",
    "QPoints = np.array([Q1,Q2,Q3,Q4,Q5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ef4af9",
   "metadata": {},
   "source": [
    "As for any cut in MJOLNIR, the width and the size of the bin along the cut direction is to be provided. For this cutting method these are called <code>width</code> and <code>minPixel</code>, respectively. Both of these values are to be provided in units of 1/AA.\n",
    "Additionally, the 'vertical' size of the bin, i.e. the energy direction, is also to be provided. In the this initial case the bins are to be of equal size and a total of 31 bins are wanted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7fdc908",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-25T11:37:08.446291Z",
     "iopub.status.busy": "2023-07-25T11:37:08.446291Z",
     "iopub.status.idle": "2023-07-25T11:37:08.594355Z",
     "shell.execute_reply": "2023-07-25T11:37:08.593427Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define orthogonal width and minimum pixel size along Q-cut\n",
    "width = 0.05 # 1/AA\n",
    "minPixel = 0.01 # 1/AA\n",
    "\n",
    "# Define energy bins\n",
    "EMin,EMax = ds.energy.min(), ds.energy.max()\n",
    "EnergyBins = np.linspace(EMin,EMax,31)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dc23f10",
   "metadata": {},
   "source": [
    "Now all that remains is to invoke the <code>plotCutQELine</code>, which internally calls <code>cutQELine</code> and generates a suitable axis into which the data are plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c8ce757",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-25T11:37:08.597294Z",
     "iopub.status.busy": "2023-07-25T11:37:08.596320Z",
     "iopub.status.idle": "2023-07-25T11:37:13.167034Z",
     "shell.execute_reply": "2023-07-25T11:37:13.166374Z"
    }
   },
   "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",
    "ax,DataLists,Bins = \\\n",
    "ds.plotCutQELine(QPoints=QPoints, width=width, minPixel=minPixel, \\\n",
    "                 EnergyBins=EnergyBins)\n",
    "\n",
    "# Change the colorbar of the plot\n",
    "ax.set_clim(0,1e-5)\n",
    "ax.get_figure().set_size_inches(16,9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7296fdc",
   "metadata": {},
   "source": [
    "With the above code the 2D cut through the MnF2 data set has been done. Is this performed in an interactive python version using the mouse allows for further study of the data. Hovering over a given pixel reveals the position as well as the intensity. Further, clicking (with the pointer) prints the position, intensity, and specific informations about how many neutron counts (cts), total normalization (Norm), total monitor (Mon), and pixels binned together (NormCount) in that bin. Many further tweaks are possible through the use of kwargs as explained in the Advanced plotCutQELine tutorial."
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}