{
 "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": "2023-05-01T08:22:10.052701Z",
     "iopub.status.busy": "2023-05-01T08:22:10.052701Z",
     "iopub.status.idle": "2023-05-01T08:22:18.966552Z",
     "shell.execute_reply": "2023-05-01T08:22:18.965660Z"
    }
   },
   "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",
    "plt.rcParams['figure.figsize'] = [18, 12]\n",
    "plt.rcParams['figure.dpi'] = 200\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": "2023-05-01T08:22:18.969588Z",
     "iopub.status.busy": "2023-05-01T08:22:18.969588Z",
     "iopub.status.idle": "2023-05-01T08:22:18.982622Z",
     "shell.execute_reply": "2023-05-01T08:22:18.981611Z"
    }
   },
   "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": "2023-05-01T08:22:18.985552Z",
     "iopub.status.busy": "2023-05-01T08:22:18.984576Z",
     "iopub.status.idle": "2023-05-01T08:22:19.153769Z",
     "shell.execute_reply": "2023-05-01T08:22:19.153769Z"
    }
   },
   "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": "2023-05-01T08:22:19.157671Z",
     "iopub.status.busy": "2023-05-01T08:22:19.156666Z",
     "iopub.status.idle": "2023-05-01T08:22:25.361621Z",
     "shell.execute_reply": "2023-05-01T08:22:25.360841Z"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 3600x1147 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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(0,2e-5)\n",
    "ax.get_figure()"
   ]
  },
  {
   "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
}