{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6ae409d4",
   "metadata": {},
   "source": [
    "# Masking of Data\n",
    "\n",
    "After an experiment has been performed, there are usually some parts of the data that contain noise or background. Whether these parts are spurious signals or well established background does not play an important role. In order to mask them out, a set of masks have been created, covering masks in 1D, 2D and 3D. Due to the ambiguity in defining e.g. a box in 3D, it is advantageous to generate a mask for then to plot it in an axes. This can be done either in 2D or 3D as needed, but notice that 1D masks are not possible to plot.\n",
    "\n",
    "The following masks are available:\n",
    "\n",
    "| Mask          | Dimensionality | Description                                 |\n",
    "|---------------|:--------------:|---------------------------------------------|\n",
    "| lineMask      |       1D       | Mask relative to two values                 |\n",
    "| indexMask     |       1D       | Mask relative to two indices                |\n",
    "| circleMask    |       2D       | Mask values inside or outside a circle      |\n",
    "| rectangleMask |       2D       | Mask values inside or outside a rectangle   |\n",
    "| boxMask       |       3D       | Mask values inside or outside a box         |\n",
    "| curratAxeMask |       N/A      | Mask out Currat-Axe for specified HKL point |\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e26599ff",
   "metadata": {},
   "source": [
    "When a mask is generated, one can specify the coordinates in which the masking is to take place. Usually, this is going to be the $H$, $K$, $L$, and $E$ axis of the data, but others are also supported. This also means that plotting the mask on to of the data provides some difficulty. This is the reason behind the “transformation” argument of the plot method. It takes a function as input which converts between the chosen coordinates and the plotted ones; an example will be shown below.\n",
    "\n",
    "Most often, masking is to be performed in many coordinates that might depend on each other. This could be masking Bragg peaks, where the position is known in ($H$, $K$, $L$) but only for a limited energy range. This means that masks are to be merged either using a boolean “and” or “or” command.\n",
    "\n",
    "Commands to combine masks:\n",
    "\n",
    "- <code> * </code> AND, requires both masks to be true for region to be masked\n",
    "- <code> + </code> OR, requires either masks to be true for region to be masked\n",
    "- <code> - </code> OR NOT, requires first mask to be true or second mask to be false to mask region\n",
    "\n",
    "In principle one can also utilize <code>/</code> AND NOT but experience has shown that it leads to more confusion. When wanting to negate a mask, insted reverse the parameter <code>maskInside</code>. These algebraic operators also have a given order of operation making it easier to construct more complicated mask combinations but the most efficient way is with the use of parentheses. \n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb701ec8",
   "metadata": {},
   "source": [
    "Combining any two masks generates a multiMask object that simply wraps the two masks together. It has both a plotting and a calling method taking care of the needed addition of multiple masks. In order to apply the mask to the data set one has to calculate the boolean matrix for that data set by calling the mask with the data set as argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5da2e52a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-01T08:22:54.957539Z",
     "iopub.status.busy": "2023-05-01T08:22:54.957539Z",
     "iopub.status.idle": "2023-05-01T08:22:59.438075Z",
     "shell.execute_reply": "2023-05-01T08:22:59.437270Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from MJOLNIR.Data import DataSet,Mask\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 = '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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec54065b",
   "metadata": {},
   "source": [
    "Below, 3 masks are generated, combined, and made to act on the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "39091455",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-01T08:22:59.441069Z",
     "iopub.status.busy": "2023-05-01T08:22:59.441069Z",
     "iopub.status.idle": "2023-05-01T08:22:59.691680Z",
     "shell.execute_reply": "2023-05-01T08:22:59.690805Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define circular mask at -1 0 0 in hkl with radius 0.25\n",
    "circle = Mask.circleMask(center=[-1,0],radiusPoint=[-1.25,0],coordinates=['h','l']) # only provide h,l\n",
    "\n",
    "\n",
    "# The circle mask is only to be on when energy is between 3.0 meV and 4.0 meV\n",
    "lowEnergy = Mask.lineMask(start=3.0,end=4.0,coordinates='energy')\n",
    "\n",
    "# Mask dispersion above -1 0 1 with a rectangle\n",
    "corner1 = np.array([-1.5,1.0]) # in h,l     (k=0.0)\n",
    "corner2 = np.array([-1.1,0.65]) # in h,l\n",
    "corner3 = np.array([-0.5,0.9]) # in h,l\n",
    "\n",
    "rectangle = Mask.rectangleMask(corner1,corner2,corner3,coordinates=['h','l'])\n",
    "# but only for energies not between 3.0 and 4.0, which is achieved by negating lowEnergy\n",
    "\n",
    "# the total mask then becomes\n",
    "mask = circle*lowEnergy+rectangle*lowEnergy.bar()\n",
    "\n",
    "# Calculate the mask for the current data set\n",
    "evaluatedMask = mask(ds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d15a693c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-01T08:22:59.693681Z",
     "iopub.status.busy": "2023-05-01T08:22:59.693681Z",
     "iopub.status.idle": "2023-05-01T08:22:59.708191Z",
     "shell.execute_reply": "2023-05-01T08:22:59.707133Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "circle: <class 'MJOLNIR.Data.Mask.circleMask'>\n",
      "lowEnergy: <class 'MJOLNIR.Data.Mask.circleMask'>\n",
      "rectangle: <class 'MJOLNIR.Data.Mask.rectangleMask'>\n",
      "mask: <class 'MJOLNIR.Data.Mask.MultiMask'>\n"
     ]
    }
   ],
   "source": [
    "print(f'circle: {type(circle)}')\n",
    "print(f'lowEnergy: {type(circle)}')\n",
    "print(f'rectangle: {type(rectangle)}')\n",
    "print(f'mask: {type(mask)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f42b74f2",
   "metadata": {},
   "source": [
    "As can be seen, the three masks have the expected type while the combined mask is of the type <code>MultiMask</code> signifying that it is a combination of masks. \n",
    "Calling any mask with a <code>DataSet</code> returns a list of masking arrays; one for each <code>DataFile</code> and having the same shape as the intensity of the <code>DataFile</code>. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "62d81ec1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-01T08:22:59.711178Z",
     "iopub.status.busy": "2023-05-01T08:22:59.711178Z",
     "iopub.status.idle": "2023-05-01T08:22:59.723713Z",
     "shell.execute_reply": "2023-05-01T08:22:59.722893Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapes of the masks are:\t [(101, 104, 64), (101, 104, 64), (41, 104, 64), (41, 104, 64), (11, 104, 64), (141, 104, 64), (141, 104, 64)]\n",
      "Shapes of the I are:\t\t [(101, 104, 64), (101, 104, 64), (41, 104, 64), (41, 104, 64), (11, 104, 64), (141, 104, 64), (141, 104, 64)]\n"
     ]
    }
   ],
   "source": [
    "print('Shapes of the masks are:\\t',[m.shape for m in evaluatedMask])\n",
    "print('Shapes of the I are:\\t\\t',[df.I.shape for df in ds])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b7a37a",
   "metadata": {},
   "source": [
    "Despite having called the mask on the <code>DataSet</code> nothing has been masked yet. The mask is applied to the <code>DataSet</code> by assigning it to the <code>mask</code> property"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c5f2b145",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-01T08:22:59.726698Z",
     "iopub.status.busy": "2023-05-01T08:22:59.726698Z",
     "iopub.status.idle": "2023-05-01T08:22:59.739744Z",
     "shell.execute_reply": "2023-05-01T08:22:59.738853Z"
    }
   },
   "outputs": [],
   "source": [
    "# Apply the mask\n",
    "ds.mask = evaluatedMask\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5063b638",
   "metadata": {},
   "source": [
    "In order to plot the masks, a 3D view is created. This is not specifically needed but for the current example is a way to show all of the masks in action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d277d4f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-01T08:22:59.741767Z",
     "iopub.status.busy": "2023-05-01T08:22:59.741767Z",
     "iopub.status.idle": "2023-05-01T08:23:01.194397Z",
     "shell.execute_reply": "2023-05-01T08:23:01.193761Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\envs\\python39\\lib\\site-packages\\MJOLNIR\\Data\\Mask.py:342: UserWarning: It is not possible to plot a 1D masks.\n",
      "  warnings.warn('It is not possible to plot a 1D masks.')#raise NotImplementedError('It is not possible to plot a 1D masks.')\n"
     ]
    },
    {
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9T/Srow0FCAAAAAC9zYxygD5XSllSSvnpUsqnSil3JRlO8nCSezvlkVLKcCnlzlLKp0spv1BKWdRkzEDvOtyy60u/d1dKsex6rxnasTuL7n2wq23Hk05vKBoAAAAA6C8S5QB9rJTyfyS5M8mHk7wuyekZ+397OagMJTkjyWuTfCjJXaWUt81+xECv23vymuxf1r3s+tJb72omGCa09Ht3d9V3nnVqqi81AAAA/aa2vADQCEuvA/SpUsp/SfIrGUuEjya5qVPuTrItye5O10UZW4r99CQXJ7koydokHyylnF1r/bezGjjQ03aefWpXfd7mbVmweWszwTChJXdszMN5zuP10cULs2fdmiy6/+EGowIAAACA3idRDtCHSikvT/KrnepVSd5Za904yXvXJ3l/ktcneWcp5R9qrV+YmUiBfrPrrO5E+ZI7JvW/Fhoyb/vOzH94S4bXrnq8befZp0qUAwAAAMAELL0O0J9+uXP8TK31jZNNkidJrXVjrfUNST6bsdnov3z0O4C2GFm4IHvWrelqW3znfQ1Fw2Qt+UH3XwE7z1nfUCQAAAAA0D8kygH607MztoPRfzyOMd7TOV5y/OEAc8GuM09JBp74eFj27c+iex9sMCImY8kd3V9mGD7xhOxfsrChaAAAAI5B03uEN10AaIREOUB/Wt053nkcY9zTOS49zliAOWLXmad01RfdsykDIyMNRcNkLbz/4QzsHe5q273+pIaiAQAAAID+IFEO0J8e6hyfehxjXNw5mi4KJEl2rz+xq774rvsbioSpKLVm4X0PdbXtPk2iHAAAAACORqIcoD9dk7H9xd9bSlk81ZtLKcuS/EHGFnf6yvSGxqwZ3je5UgYmLsxdk3z/9y9emH0nrOi6ddFG36PpWXW0qyy654Guy7vXnzR2DQAAAAA4LL8ZB+hP/znJviSXJrmplPKWUsoJE91USllaSnljkuuTXJhkf5L3zmikQF/Yc2r3bPIyvC8LHtrSUDRM1cF7yQ+vXZWRRQsaigYAAGBqSm13AaAZQ00HAMDU1VpvKaVcmeTjSZ6U5H8mqaWUuzO2b/mWJLs73RcmWZHkjCRnJxnM2Gz00SS/UGv99iyHD/Sgg5ddX3TfQynVv9b7xcIHH0kZ3pc6f97jbbtPPSlLb7+nwagAAAAAoHdJlAP0qVrr/y6lvCDJ7yT5sYwlv8/qlIOzW+Wg+rVJ3lFrvXHGAwX6wu5Tu/e0PnjPa3pbGa1ZuOmR7D5j3eNte08+QaIcAAAAAI5Aohygj9Vav5nkVaWU85O8MslFSc5MsiZjM8lHkuxMsjXJbUluTnJ1rfXO2Y8W6FW1lAyf2L17g0R5/1nw4OauRPmek1Y3GA0AAAAA9DaJcoA5oNb6vSTfazoOoD8Nn7AidWiwq23hQ482FA3HauEDm7vqe09ak5pDlxQBAAAAAJKBpgMAAKBZe0/qnk0+tH1nBnfvbSgajtWCB7sT5SNLF2Vk6eKGogEAAACA3iZRDgDQcnsPWnZ9gdnkfWnelu0Z2Dvc1bbnoC9BAAAAAABjJMoBAFpueO2qrrpEeX8qSeYf9N4Nr17ZSCwAAAAA0OvsUQ4AfWr/o1sm1W9w1YoJ+4xMcqy+VCbxvcA6OvNxNGUSz7Z3zcqu+sHJVnrPhtGrDtv+W5/+Vj5+3b2P1/edMPGffwAAgMbVTmmjtj43QA8woxwAoMVG5w1lZNmSrrb5j25rKBqO19lrlnbVh09Y3lAkAAAAANDbJMoBAFps38plh7TN2/pYA5EwHc5e2/2lh2EzygEAAADgsCy9DgDQYgcnyoce25mB/SMNRcPxOmdt94zy0cULM7JoQQZ3720oIgAAgImVOlbaqK3PDdALzCgHAGix4VXdS3PP22I2eT9bv2pR5g2WrrbDrRoAAAAAAG0nUQ4A0GL7VnUnUedt2d5QJEyHocGBnLR8YVfb/qWLG4oGAAAAAHqXRDkAQIsdnEQd2r6joUiYLutWHJQoX7bkCD0BAAAAoL3sUQ4A0GIjSxZ11Yd27m4oEqbLITPKl5lRDgAA9AF7dQMwy8woBwBosUNmlO+QKO93h84olygHAAAAgINJlAPQN0opF5ZSPlpK2VhKGS6lbCqlfL6U8oomx57JuI5mYNHCSZW6Z++EZU6roxOXlqqlZGRxd1J1cOeuhqJhupy8onuVAEuvAwAAAMChJMoB6AullNckuTHJm5OcmmRekpOTvCrJX5dSPtDE2DMZF8y0kcULk4Huj4NmlPe/NUvnd9VHFi5oKBIAAAAA6F0S5QD0vFLKM5N8ImNJ6OuTvDjJmiSXJPlsp9uvlFJ+eTbHnsm4YDaMLDo0gTq4a08DkTCdli+a11UfXTj/CD0BAAB6RG15AaAREuUA9IP3JFmU5PYkP1Jr/fta6+Za6w1JrkhyVaffu0spy2Zx7JmMC2bc6PzuhGoZ3pdS/Qu93y1f2P2+jiyQKAcAAACAg0mUA9DTSikXJLm8U/3dWuuO8ddrrTXJO5OMJlmdsQT1jI89k3HBbDk4UT6wd7ihSJhOKw6aUV7nz0sd8LEfAAAAAMbzGzMAet0rO8ea5POH61BrvTfJTZ3q62Zp7JmMC2bF6IKDEuXD+xqKhOm0fNHQIW0jll8HAAAAgC4S5QD0uos6x7trrY8cpd+BhPSzZmnsmYwLZsXo/O7kqUT53HDw0uvJoasHAAAA9JJS210AaIZEOQC97szO8c4J+t3dOa4vpRw6nXL6x57JuGBWHLL0ukT5nDBv8DAf8S29DgAAAABd/MYMgF63pnPcMkG/bZ1jSbJyFsaeybhgdgyWrmrZP9pQIEynwYGSwYHu97YeLnkOAAAAAC1mZhsAvW5h57h7gn7jry88Yq/pG3sm45qcOrm1ucqQv+45vFq6k6dlkv9N0ftGRrvfy5FFCxqKZOo2jF7VdAgAAAAAtIDfnAPQ60Z6dOyZjCtJsmnTppl+CdqudM86nuyXL+g/u9eflMX3PNB0GADHZePGjUe97rMTAPSx2ilt1NbnBugBEuUA9LqdneNEs7EXjTufaJb3dIw9k3ElSS699NKjXn/Z4p+eynBwiDogUd4aB7/XAH3otNNOazoEAAAA5hCbFQLQ67Z2jism6LeycxzJxPuGT8fYMxkXzI6DZpSXUYnyuapGohwAAAAAxjOjHIBed1uSFyc5Y4J+p3eO99VaR2dh7JmMK0ly3XXXZd26dUe8/tbz/79TGQ4mJpc6Z9l/HpgL7r333qNe37Rp04Qr8gAAAMABEuUA9LqbO8ezSynLa63bj9Dv4s7xplkaeybjSpKsW7cu69evn+ptMGll/0hXvQ4ONhQJ023pgqHs2Lv/8friO+9rMBqA6eFzEQDMXaWOlTZq63MD9AJLrwPQ667uHAeTXH64DqWU05Jc1Kl+cZbGnsm4YFYcnCgfHZIonyv27Ot+bw9+rwEAAACg7STKAehptdY7klzbqb67lNK1J3gppSR5X8b+TnskyUdnY+yZjAtmSxk5aEa5RPmcMLx/NPsP2m9+YN/+I/QGAAAAgHaSKAegH7wjyWiSJyX5ainlZaWUNaWUi5N8KsmVnX7vqrXuHH9jKeXtpZRbO+Vwm1Ye89jHeS80buDgpdclyueE3cOHzh6XKAcAAHpabXkBoBH2KAeg59VabyilvDXJnyS5MMmXDtPt/bXWDx6mfU2S8zvni6dz7OOMCxp3yB7lQz4azgW7DpMULxLlAAAAANDFb0MB6Au11g+XUm5M8htJXpLkpCQ7ktyQ5IO11s81MfZMxjVd6t69TYdAjxrYO9xVH1k4v6FImE67DjujfF8DkRybywaunLDPhtGrZiESAAAAAOYyiXIA+kat9eYkb5niPe9K8q6ZGHs67oUmDe7e01UfXbggNUlpJhymyfbd3Unxsn8kZdRafgAAAG1XSnllkrcmeW6StUn2Jrk9yV8l+cNa68MNhgcw6yTKAQBaanB392oDdWgwdf5QyrBluvvZIzu6VwoY3Lm7oUgAAAAmqc17dc/Cc5dShpJ8OMmbDro0P8kzO+UXSimvq7X+08xHBNAbBpoOAACAZgzsPnRZ/pGFCxuIhOm0eUf3+zq4a88RegIAANASv5cnkuSfS/KCJGuSXJjkN5PsTHJiki+UUk5tJEKABkiUAwC01MDwvmRktKttZNGChqJhumzeedCM8l1mlAMAALRVKeWUJL/aqX6s1vq6WuvXaq2ba6231Fp/P8mLk+xPckKS32ooVIBZJ1EOANBSJYfuU75/6aJmgmHaPGJGOQAAAE94XZ7YhvffH65DrfWGJJ/pVC+fhZgAeoI9ygEAWmxo+86MLF38eH3/siUNRsN0ePix7kT5kEQ5AADQ40odK200C899SpLdSbbXWu8+Sr/bx/UHaAUzygEAWmze9h1d9f0rljYUCdPl3i3dS60Pbd/ZUCQAAAA0rdb627XWxUnOm6DruZ3jlhkOCaBnmFEOANBiBydR9y03o7zfbXx0V1d93rbHGooEAACAKTq5lHLUDrXWjccycK11+5GudfYxf3Wneu2xjA/QjyTKAaBPlaHJ/TU+Ojw8w5HQzw6ZUb7cjPJ+cNnAlYdtH503lM3veHNX27ytOw7bFwAAgJ5z/ST6HD2TPkVlLDP/x0kWdpo+OJ3jA/QyiXIAgBY7ZEa5pdf72uHev6HtEuUAAECPq53SRs0/9/uTXN45/4ta61eaDAZgNkmUAwC02Lwt3SuvjSxdnJEF8zO410oE/WjfymVd9cEduzKwf6ShaAAAAJiiS5I8MBsv1JlJ/r4kv9ZpujnJL87GawP0ColyAIAWm7/lsWRkNBkceLxtePWKLLr/4Qaj4lgNr17ZVZ+3xf7kAAAAfeSBY92DfCpKKfOT/I8kB/bu+m6Sl9VaLUkGtMrAxF0AAJiryuho5h80q/zgZCv9Y+/aVV31BY9saSgSAAAAelEp5YQkG/JEkvzGJC+qtc7KTHaAXmJGOQBAy83fvDXDa1Y+Xh9es6K5YDguwwclyuc/LFEOAAD0ieb36p7zSinnJLk6yXmdpi8mudJMcqCtzCgHAGi5+Zu3dtWHV686fEd6Wh0YyPAJ3V9yMKMcAACAJCmlPDXJP+WJJPmfJHm1JDnQZhLlAAAtN//hrV31vSev9kX+PjR8wvKuveaTQ99bAAAA2qeUcnaSv02yttP0H2qtv1Br3d9gWACNkygHAGi5hQ880lUfWbww+1csbSgajtXek9d01Ye278zg3uGGogEAAKAXlFLmJflkkpM7Te+otf5OgyEB9Ax7lANAn6r7J/el3zpqbjBHN7RtRwZ27cno4oWPt+1Ztybztll9rZ/sPmVtV33BpocbigQAAGBqSh0rbTQLz/2LSZ7dOf9fSf60lHLUb8dbjh1oCzPKAQBariRZuKl7Vvmeg2Yn0/v2HJQoX3S/RDkAAAD5tXHnb0jy2CQKQCtIlAMAcMjy63vWrT1CT3rR6PyhDK9Z2dW2UKIcAACg1Uopa5Kc03QcAL3K0usAAGThQct07123JqNDgxnYP9JQREzFnpPXJgPjvgM7MpoFD25uLiAAAICpqJ3SRjP43LXWRzK2kBwAh2FGOQAAWXjfw8no6OP1OjR4yFLe9K7dp53UVV/w0GZfcgAAAACAo5AoBwAgg3uHs+CB7hnIu05f11A0TNWuM0/pqi+698GGIgEAAACA/iBRDgBAkmTxPZu66rvPkCjvByPz52XPujVdbYvvur+haAAAAACgP0iUAwCQJFl0d3eifM+6NRmZP6+haJis3aev69qfvOwfyaKNZpQDAAD9o9R2FwCaIVEOAECSZNF9DyUj4/a1Hhg4ZElves+uM7tn/i/c+KD9yQEAAABgAhLlANCnyuDgtBVIkoH9I4fsbb3z3NMaiobJqEl2nr2+q82y6wAAAAAwMYlyAAAet/T793TVd56zPrWUhqJhIntPPCH7Vy7ralty530NRQMAAAAA/UOiHACAxy25/d6u+uiihdm9/qSGomEiO847o6s+b8v2zH94S0PRAAAAHKPa8gJAIyTKAQB43LzHdmbBg5u72nY+yfLrvWrnQYnypbfdHfP/AQAAAGBiEuUAAHQ5eFb5Y+efmZFRX3HvNcMnrMjwmpVdbUtuu+fwnQEAAACALhLlAAB0WXrrXV31kWVL8k8/2Hz4zjTmsSef1VUffGxnFm56uKFoAAAAAKC/SJQDANBlweathyy//pmb7msoGg6nJtn+tHO72pZ9z7LrAABAn2p6j/CmCwCNkCgHAOAQy779g676F2/ZlN3DIw1Fw8F2n35y9q9Y2tW27Nu3NxQNAAAAAPQfiXIAAA6x7Lt3JqOjj9d3Do/kS99+oMGIGO/g2eTzH3o0Cx58tKFoAAAAAKD/DDUdAAAws+r+fU2HQB8a2rk7i+/elF1nnfp4279735fywb/46yPes2H0qtkIbc57+aKfPur1kflD2fHW13a1Lb/l9lYtu/6y+f9qwj5/M/wXsxAJAAAAAP3KjHIAAA5r+be+31Xfs/6k7F27qqFoOGD7k89KnT/viYaR0Sz7zh3NBQQAAHCcSssLAM2QKAcA4LCWfv/uDO7Y1dW29eILGoqGJKlJtlx0Xlfbkh/cm6Fde5oJCAAAAAD6lEQ5AACHVUZrVvzLbV1tjz357IwsmN9QROw865TsW7msq23ljd9tKBoAAAAA6F8S5QAAHNGKf/leMjL6eL3On5dtzzjvKHcwkw6eTT7/oUez6N4HGooGAAAAAPqXRDkAAEc0tGN3ln7/7q62rc9+akaHBhuKqL32rFmZXaef3NW28sbv2M8OAADof7XlBYBGSJQDAHBUq667pas+snRRtl/4pIaiaa/Nlz61qz64a0+WfefOhqIBAAAAgP4mUQ4AwFEtfGBzFt95X1fbluc8LXXAR8nZsnf1iux40mldbStu/E4GRkYaiggAAAAA+pvfbgIAMKFV//ytrvr+5Uuz/WnnNBRN+zzynO7Z5AN79mbljd9tKBoAAIBpVpPS0mLpdYDmSJQDADChRfc+mIUbH+xqe/QFF9mrfBaMzSY/vatt5Q3fyeDwvoYiAgAAAID+N9R0AADAsfni9v85qX6XDVw5w5HQBiXJCV/7l9z/hpc93rZ/2ZJsfdZTcsLXb24usDlodO+ervpDz//hrvrAnuHWzyav+31JAAAAAIDjY0Y5AACTsviu+7Porvu72rY898LsX7SgoYjmvp1nnZpdZ5/a1bbyhm9ncO9wQxEBAAAAwNwgUQ4AwKSUJGuuuaGrbXTB/Dz6/Gc0E9AcVwdKHnnJJV1tg4/tzKrrv91QRAAAADOktrwA0AiJcgAAJm3hQ49m2bd/0NW27ZkXZO+JJzQU0dy17RnnZ3jNyq62Nf/wjQzs299MQAAAAAAwh0iUAwAwJau/+o2U/SNPNAwM5MGXPS8jo74GP132L12UzT98cVfbggceOeRLCgAAAADAsZEoBwBgSuZt35lV//ytrra9p6zNX3z97oYimltqkode+tyMLpzf1b7mK9enNBMSAAAAAMw5EuUAAEzZqq/fnHmPbutq+/0vfi8Pbt/TUERzx47zzsjO887oalt2y+1ZfO+DDUUEAAAwC5reJ9z+5ACtI1EOAMCUDYyM5sS/+aeutsf27s+vX/UvGbUE+zHbums4D7/0uV1tgzt3Z+2Xr28oIgAAAACYmyTKAQA4JovveSDLbr69q+2r338kH/7aXc0E1Odqrfm3f/mtjCxd1NW+9u+uy+CevQ1FBQAAAABz01DTAQAA0L/WfuW67DpzXUaWLXm87T2fvTkfvuJ3s+CRrYf03zB61SxG13suG3zjEa9tvei8PHxZ92zyxT+4N0tvvXOmw5qTLhu4clL92v7fJAAAAEBbmVEOAMAxG9wznJOvvrarrQ4N5oFXvyij83wnc7L2rlmZR15ySVfb4K49OelLX0tpKCYAAIDZUmq7CwDNkCgHAOC4LL57U1Zef0tX2/DaVXnwFc+Pf+9PbGT+vDzw6hemDg12tZ909bUZ2rG7oagAAAAAYG6TKAcA4Lit/odvZP5Dj3a17Xjy2dl66VMbiqg/1FLywKt+OMNrVna1r7zh21lyx8ZmggIAAACAFpAoBwDguA2MjGbd567JwJ7hrvZHXvis7DzzlIai6n2PvPDi7DpnfVfbggc3Z/Xf39hQRAAAAADQDhLlAABMi/lbtufkL/xDUsctuD4wkAde++LsPfGE5gLrUdueds4hM+4Hd+3Jus98JQMjow1FBQAA0IDa8gJAIyTKAQCYNkvu2JjVX72pq210wfzc9/rLMrxyWUNR9Z4d556Wh17+vO7GkZGs++w1mbd9RzNBAQAAAECLSJQDADCtVv3zt7L01ju72kaWLsr9V16Whx7b01BUvWPXGevywKtfmAx0fxQ/ccPXs+i+hxqKCgAAAADaRaIcAIBpVZKc9FfXZtE9D3S171u1PG/+06/noe3tTZbvPmVt7n/di1OHBrvaV17/7ay4+faGogIAAACA9hlqOgAAAOaegZGRrPv03+W+n3pF9p60+vH22x7ckRf8xmdy6ie+lHmP7TzsvRtGr5qtMKfVZYNvPOr1XaefnPuvfGnq/Hld7cu/dVvWfOX6mQwNAACgp5U6Vtqorc8N0AvMKAcAYEYMDu/LKVdtyLwt27va961ano3/6pWt2rP8sfNOz/0/8aOHJMmX3npnTvzSP6U0FBcAAAAAtJVEOQAAM2Zo156x2eMHJcv3r1iajW/6sew+9cSGIps9257xpDzw6hcestz64h/cm5O/8NWUavoAAAAAAMw2iXIAAGbUvMd2Zv1f/HXmP7Klq31kyaJs/MmXZ/vTzm0osplVBwby0EsvzUMve14y0P2xe+mtd2bdZ7+SMjraUHQAAAA9pLa8ANAIiXIA+kIp5cJSykdLKRtLKcOllE2llM+XUl4xDWO/spTyl52x95ZStpdSvlFKeU8pZe1R7vtAKaVOorz9eGOEfje0c3fWf/yLWfDg5u4Lg4N58Md+KA+/5NmpA3Pno+n+JQuz8Y2XZdszLzjk2vJvfi8nf/4fMjAiSQ4AAAAATZk7v40EYM4qpbwmyY1J3pzk1CTzkpyc5FVJ/rqU8oFjHHeolPLnSa5O8hOdsecnWZbkmUl+O8ktpZTnHWGIZx3L60JbDe7em1M//sUsvmPjIde2XvK03PumH8vwquUNRDa9dp55Su756Vdlz/qTDrm26mv/khP/5p8stw4AAAAADZMoB6CnlVKemeQTGUuOX5/kxUnWJLkkyWc73X6llPLLxzD87yV5U+f8c0le0Bn7wiS/mWRnkhOTfKGUcupBcQ0keUan+ksZS64fqfzRMcQGc9Lg8L6c8qm/y8rrv33Itb3r1uSen3l1PnHdPal9mEgenTeUhy57Tu6/8qUZWba461rZtz8n/dW1WXPtTSkNxQcAAAAAPGGo6QAAYALvSbIoye1JfqTWuqPTvrmUckWSTya5Msm7Syl/Vmt9bDKDllJOSfKrnerHaq1vHnd5c8Zmkn85yT8lOSHJbyUZv4T6+UmWds6vHRcXMIFSa9Z+5frMf2RrHnrZc5PBwcev1fnz8u8+fXM+983783+99ql50knLGox08v7+todzz8++OvtWHhrv0LYdWffZa7LwoUcbiAwAAKD3lTpW2qitzw3QC8woB6BnlVIuSHJ5p/q7Byej69iU03cmGU2yOskVUxj+dXniC2P//nAdaq03JPlMp3r5QZcv7hx3JvnOFF4X6Fhx8/dz2p9fnXmbtx1y7Z/u2JxXfuCr+d2rv5sde/c3EN3k/ODhHfnX//O6/Mz/c91hk+SL77w/p330ryTJAQAAAKDHmFEOQC97ZedYk3z+cB1qrfeWUm7K2H7hr0vykUmOfUqS3Um211rvPkq/28f1H+9AovwbtdaRSb4mULq/p7nwoS05/aN/lYdf/Kxsv+j8rmv7R2v++B/uyJ9+8TtZdcO3s+Ibt2ZweN+UXm7D6FUT9rls4MopjZkk+5YvzZZLn5p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      "text/plain": [
       "<Figure size 2200x1400 with 5 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a 3d viewer\n",
    "view = ds.View3D(0.05,0.05,0.05)\n",
    "# it is started in the hkl plane, where we can also plot our masks.\n",
    "# However, the transformation used by view3D is from qx,qy to hkl, so the inverse\n",
    "# is to be provided. This is stored in\n",
    "trans = view.ax.sample.inv_tr\n",
    "\n",
    "mask.plot(view.ax,transformation=trans,zorder=20)\n",
    "\n",
    "# tune the colorbar\n",
    "view.caxis=(1e-8,5e-6)\n",
    "\n",
    "view.Energy_slider.set_val(10)\n",
    "view.ax.get_figure()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7255e59f",
   "metadata": {},
   "source": [
    "As seen above, the data within the blue mask are masked out. This is because the energy transfer is within the <code>lowEnergy</code> mask but data within the orange rectangle are present. Changing the energy to be outside the <code>lowEnergy</code> mask enables the orange mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "363c1d4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-01T08:23:01.197396Z",
     "iopub.status.busy": "2023-05-01T08:23:01.196359Z",
     "iopub.status.idle": "2023-05-01T08:23:01.527395Z",
     "shell.execute_reply": "2023-05-01T08:23:01.526595Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x1400 with 5 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view.Energy_slider.set_val(40)\n",
    "view.ax.get_figure()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195e15d3",
   "metadata": {},
   "source": [
    "Lastly, the effect of the masks can also be seen in e.g. a QE cut:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "159d9302",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-01T08:23:01.529396Z",
     "iopub.status.busy": "2023-05-01T08:23:01.529396Z",
     "iopub.status.idle": "2023-05-01T08:23:02.302050Z",
     "shell.execute_reply": "2023-05-01T08:23:02.301400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x1147 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Along the Q-E direction the masking looks differently\n",
    "QPoints = np.array([[-1.0,0.0,-1.0],\n",
    "                [-1.0,0.0,1.25]])\n",
    "Energies = np.concatenate(ds.energy,axis=0)\n",
    "EnergyBins = np.linspace(np.min(Energies),np.max(Energies),31)\n",
    "ax,*_ = ds.plotCutQELine(QPoints=QPoints, width=0.05, minPixel=0.05, \\\n",
    "            EnergyBins=EnergyBins)\n",
    "\n",
    "# Change the colorbar of the plot\n",
    "ax.set_clim(0,5e-6)\n",
    "ax.get_figure()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "822c5936",
   "metadata": {},
   "source": [
    "## Combining Masks\n",
    "\n",
    "Masking has been implemented into MJOLNIR in such a way, that one can combine masks rather freely. With the above list of possible masks as building blocks, it is believed that almost all masks can be generated by combining the masks with the correct arithmetic operations. Masks are combined using the standard order of operators, meaning that AND (<code>*</code>) operations are performed before OR (<code>+</code>). In additionally, parentheses are allowed making it easier to perform more advanced mask combinations, where multiple masks are to be negated or AND’ed with another masks. This complexity also allows for situations where multiple combinations are equivalent. Using the masks defined in the example, then\n",
    "\n",
    "$$ (circle*lowEnergy).bar() = circle.bar()*lowEnergy.bar()$$\n",
    "\n",
    "Behind the scene, a pair-wise tree is created by multiMasks and all operations are propagated through this tree, remembering the mask relations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "133d88d8",
   "metadata": {},
   "source": [
    "## General Features\n",
    "\n",
    "All of the masks are build on a common base object with a set of methods. In detail, these methods are ensure by the use of a meta class, but the exact implementation is out of scope. What is ensured is that all masks have a <code>__call__</code> and a plot method. The <code>__call__</code> method is used to generate the mask needed in MJOLNIR, and depending on the attributes used to generate a mask it either takes coordinates with the correct dimensionality or an object with attributes matching the coordinates given to the mask. The plot method takes a matplotlib.axes object and, if applicable, a transformation function. This transform is needed when plotting masks on axis having other coordinates than the ones provided to the masks, or if the axis utilizes some sort of transformation under the hood. Unfortunately, this is often the case for MJOLNIR, where RLU, and other axes are used. In the above example, where the masks were to be plotted on an RLU object, the transformation from (h,l) to ($Q_x$,$Q_y$) was to be specified. This is found as an method on the <code>_ax.sample</code> object. Plotting of masks on axes is currently in a non-optimal state. Best way of achieving the goal is to find an example, as above, or simply write the maintainer.\n",
    "\n",
    "All masks can be combined with each others, independent of their attributes or coordinates as long as the datafile object to be masks has all of these attributes. As explained above, four different arithmetic operations exist, but on top of that, all masks has the bar method. This method simply negates the masks, in effect masking values outside of e.g. the circle, instead of inside.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b7ee41",
   "metadata": {},
   "source": [
    "## Masking Types\n",
    "\n",
    "Currently, there are 5 different masks, that are supported by MJOLNIR. These covers the main parts, where masks are needed, but might need extension down the line. In ascending order of dimensionality, these are presented below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a219af3",
   "metadata": {},
   "source": [
    "### lineMask\n",
    "The most simple for of masking is in 1D, where values are either inside our outside a given interval. The <code>lineMask</code> requires two attributes (start and end) to generate a mask. It is intended to mask 1D parameters as energy or to be combined with other masks to create more complex maskings. In the above example, the <code>lineMask</code> was used to give an energy dependency on the two masks of $H$ and $L$.\n",
    "\n",
    "### indexMask\n",
    "At times it be necessary to perform masking depending on the indices of the points. This could be when a full detector tube is to be masks, but the $A_4$ value is unknown. This can be performed by the indexMask, where a start and end index together with an axis defines the masks.\n",
    "\n",
    "### circleMask\n",
    "Increasing in complexity, the next step up is a circular mask where masking of two directions at the same time is possible. One can either proide the center and a point in which the circumference goes through or a center and a radius. If the center is given as a 3D position the masks turns into a spherical masks. Currently, there is no option for elongating the masks in any direction, i.e. to create an ellipse.\n",
    "\n",
    "### rectangleMask\n",
    "Instead of only being allowed to mask in circles, one can also mask a rectangle. This can be achieved with the rectangleMask. To create one, a starting corner is to be provided together with a second corner. From these two, the rectangle is created. Alternatively, one can provide 3 points. If this is done, the first two points are used to define an edge, and the last point gives the extend of the rectangle orthogonal to this edge. That is, the third point might not be located on the edge of the rectangle. This three-input option allows the creation of rotated rectangles. Points are then masked by rotating then such that the rectangle lines up with the coordinate axes and center at (0,0). This allows for two simple 1D checks.\n",
    "\n",
    "### boxMask\n",
    "Extending the rectangle into 3D creates the boxMask. This mask also supports two different inputs; either three corner points making the edges of the box parallel to the coordinate axes, or 4 point input. In this latter case, the two first points creates on edge, which is extended to form a rectangular base with width corresponding to the orthogonal distance to the third point. Lastly, this rectangle is extended by the orthogonal distance to the last point. Like the <code>rectangleMask</code>, points are masked by rotating the box such that it center is in (0,0,0) and edges are along the coordinate axes. Due to the complexity of 3D rotations, three rotations matrices are involved.\n",
    "\n",
    "### CurratAxeMask\n",
    "In all experiments having a strong Bragg peak, be it either magnetic or structural, there is a high chance that the accidental scattering of it polutes the inelastic signals. This mask traces out the paths of the spurions originating from the provided Bragg peaks across all scans."
   ]
  }
 ],
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