{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cbc59ec5-24a2-46e2-88fa-77347612c14a",
   "metadata": {},
   "source": [
    "## Simulation of measured TiO2 111 peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5185a8ec-cddb-467a-8888-1560900bd072",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Overwriting previous total file TiO2_111_total.csv\n",
      "#########################\n",
      "Old simulations found in /mnt/c/Users/tjh/OneDrive - NIST/pyMACS/Paper Calculations/TiO2 110 Bragg Peak/TiO2_111/Kidney_simulations/\n",
      " \n",
      "Successfully combined old simulations into /mnt/c/Users/tjh/OneDrive - NIST/pyMACS/Paper Calculations/TiO2 110 Bragg Peak/TiO2_111/Kidney_simulations/TiO2_111_total.csv\n",
      "\n",
      "Data matrix instantiated and ready to use.\n",
      "#########################\n",
      " \n",
      "Conversion of CIF to crystallographical LAU file successful. \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "#Add the directory of the module to the path.\n",
    "#sys.path.append('/media/sf_OneDrive_-_Johns_Hopkins/pyMACS/pyMACS')\n",
    "from pyMACS.virtualMACS import VirtualMACS\n",
    "import mcstasscript as ms\n",
    "\n",
    "\n",
    "macs = VirtualMACS('TiO2_111',cifName='TiO2.cif')\n",
    "macs.sample.formula_weight=79.87\n",
    "macs.sample.sample_widx=5e-3\n",
    "macs.sample.sample_widz=5e-3\n",
    "macs.sample.sample_widy=5e-3\n",
    "macs.sample.cif2lau()\n",
    "#Sample was oriented in the (HHL) plane, like so\n",
    "macs.sample.orient_u=[1,1,0]\n",
    "macs.sample.orient_v=[0,0,1]\n",
    "macs.sample.project_sample_realspace()\n",
    "\n",
    "#Assign simulation counts\n",
    "macs.n_mono=1e7\n",
    "macs.n_sample=1e6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a5b382c-1cd4-4737-8db8-b600d90c04ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/c/Users/tjh/OneDrive - NIST/GitHub/pyMACS/pyMACS\n",
      "/mnt/c/Users/tjh/OneDrive - NIST/pyMACS/Paper Calculations/TiO2 110 Bragg Peak\n",
      "#################\n",
      "\n",
      "Starting compilation of sample kidney geometry.\n",
      "\n",
      "Compilation of sample kidney geometry successful.\n",
      "\n",
      "#################\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scattering_def = ms.McStas_instr(\"scattering_definition\",checks=False)\n",
    "inc_scatter = scattering_def.add_component(\"inc_scatter\",\"Incoherent_process\")\n",
    "inc_scatter.sigma=macs.sample.sigma_inc\n",
    "inc_scatter.unit_cell_volume = macs.sample.cell_vol\n",
    "inc_scatter.packing_factor = 1\n",
    "inc_scatter.set_AT([0,0,0])\n",
    "\n",
    "#Single crystal process. \n",
    "crystal_scatter = scattering_def.add_component(\"crystal_scatter\",\"Single_crystal_process\")\n",
    "crystal_scatter.delta_d_d=0.005\n",
    "crystal_scatter.mosaic = 30.0\n",
    "#Projections of lattice vectors onto lab frame is handled by the previous helper process.\n",
    "labproj = macs.sample.labframe_mat\n",
    "crystal_scatter.ax = labproj[0,0]\n",
    "crystal_scatter.ay = labproj[0,1]\n",
    "crystal_scatter.az = labproj[0,2]\n",
    "crystal_scatter.bx = labproj[1,0]\n",
    "crystal_scatter.by = labproj[1,1]\n",
    "crystal_scatter.bz = labproj[1,2]\n",
    "crystal_scatter.cx = labproj[2,0]\n",
    "crystal_scatter.cy = labproj[2,1]\n",
    "crystal_scatter.cz = labproj[2,2]\n",
    "crystal_scatter.reflections='\\\"'+\"TiO2.lau\"+'\\\"'\n",
    "crystal_scatter.barns=1\n",
    "crystal_scatter.packing_factor=1\n",
    "crystal_scatter.powder=0\n",
    "crystal_scatter.PG=0\n",
    "crystal_scatter.interact_fraction=0.8\n",
    "crystal_scatter.set_AT([0,0,0])\n",
    "crystal_scatter.set_ROTATED([0,0,0])\n",
    "\n",
    "scattering = scattering_def.add_component(\"TiO2\",\"Union_make_material\")\n",
    "scattering.process_string='\"crystal_scatter,inc_scatter\"'\n",
    "scattering.my_absorption=macs.sample.rho_abs\n",
    "scattering.set_AT([0,0,0])\n",
    "\n",
    "#Now, this pseudo-instrument will be saved as the scattering definition of the sample. \n",
    "macs.sample.scattering_def = scattering_def\n",
    "\n",
    "#Make a second object for the geometry. This particular case replicates the validation experiment for this package.\n",
    "geo_def = ms.McStas_instr(\"geometry_definition\",checks=False)\n",
    "\n",
    "sample_cube=geo_def.add_component(\"sample_cube\",\"Union_box\")\n",
    "sample_cube.xwidth=1.0*macs.sample.sample_widx\n",
    "sample_cube.yheight=1.0*macs.sample.sample_widy\n",
    "sample_cube.zdepth=1.0*macs.sample.sample_widz\n",
    "sample_cube.priority=100\n",
    "sample_cube.material_string='\\\"TiO2\\\"'\n",
    "sample_cube.number_of_activations=\"number_of_activations_sample\" #Do not change. \n",
    "sample_cube.set_AT([0,0,0],RELATIVE='crystal_assembly')\n",
    "sample_cube.set_ROTATED([0,0,0],RELATIVE='crystal_assembly')\n",
    "'''\n",
    "sample_cube_mask1 = geo_def.add_component(\"sample_cube_mask1\",\"Union_box\") #It's easier to rotate a mask rather than the sample itself.\n",
    "sample_cube_mask1.xwidth=macs.sample.sample_widx\n",
    "sample_cube_mask1.yheight=macs.sample.sample_widy\n",
    "sample_cube_mask1.zdepth=macs.sample.sample_widz\n",
    "sample_cube_mask1.priority=0\n",
    "sample_cube_mask1.material_string='\"Mask\"'\n",
    "sample_cube_mask1.number_of_activations=\"number_of_activations_sample\"\n",
    "sample_cube_mask1.mask_string='\"sample_cube\"'\n",
    "sample_cube_mask1.mask_setting='\"All\"'\n",
    "sample_cube_mask1.visualize=0\n",
    "sample_cube_mask1.set_AT([0,0,0],RELATIVE=\"crystal_assembly\")\n",
    "sample_cube_mask1.set_ROTATED([0,0,0], RELATIVE=\"crystal_assembly\")\n",
    "'''\n",
    "sample_plate = geo_def.add_component(\"sample_plate\",\"Union_cylinder\")\n",
    "sample_plate.radius=0.006\n",
    "sample_plate.yheight=0.002\n",
    "sample_plate.priority=40\n",
    "sample_plate.material_string='\"Al\"'\n",
    "plate_distance = macs.sample.sample_widy+0.002\n",
    "sample_plate.set_AT([0,plate_distance,0],RELATIVE=\"target\")\n",
    "sample_plate.set_ROTATED([0,0,0],RELATIVE=\"target\")\n",
    "\n",
    "sample_plate_rod = geo_def.add_component(\"sample_plate_rod\",\"Union_cylinder\")\n",
    "sample_plate_rod.radius=0.00125\n",
    "sample_plate_rod.yheight=0.0633\n",
    "sample_plate_rod.priority=41\n",
    "sample_plate_rod.material_string='\"Al\"'\n",
    "sample_plate_rod.set_AT([0,plate_distance+0.001+0.031,0], RELATIVE=\"target\")\n",
    "sample_plate_rod.set_ROTATED([0,0,0],RELATIVE=\"target\")\n",
    "\n",
    "sample_base = geo_def.add_component(\"sample_base\",\"Union_cylinder\")\n",
    "sample_base.radius=0.0065\n",
    "sample_base.yheight=0.013\n",
    "sample_base.priority=42\n",
    "sample_base.material_string='\\\"Al\\\"'\n",
    "sample_base.set_AT([0,0.0628,0],RELATIVE=\"target\")\n",
    "sample_base.set_ROTATED([0,0,0],RELATIVE=\"target\")\n",
    "\n",
    "sample_base_gap = geo_def.add_component(\"sample_base_gap\",\"Union_cylinder\")\n",
    "sample_base_gap.radius=0.004\n",
    "sample_base_gap.yheight=0.009\n",
    "sample_base_gap.priority=43\n",
    "sample_base_gap.material_string='\"Vacuum\"'\n",
    "sample_base_gap.set_AT([0,0.0668,0], RELATIVE=\"target\")\n",
    "sample_base_gap.set_ROTATED([0,0,0],RELATIVE=\"target\")\n",
    "\n",
    "macs.sample.geometry_def = geo_def\n",
    "\n",
    "macs.useOld=True\n",
    "\n",
    "useOld=True\n",
    "if useOld==True:\n",
    "    macs.useOld=True\n",
    "    #macs.prepare_old_expt_directory()\n",
    "    #macs.clean_expt_directory()\n",
    "else:\n",
    "    macs.data.data_matrix=False\n",
    "    #macs.clean_expt_directory()\n",
    "    macs.prepare_expt_directory()\n",
    "    macs.compileMonochromator()\n",
    "macs.prepare_expt_directory()\n",
    "\n",
    "macs.edit_instr_file()\n",
    "macs.compileInstr()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce0a8cca-5130-4294-888e-4ea8879153b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "scan_dir = 'TiO2_110 ng0 files/'\n",
    "#macs.simulate_ng0dir(scan_dir,n_threads=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b7a0128-ef3f-4ac9-8f94-31d2dfbad216",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Save the scan in one large csv file \n",
    "#macs.data.combine_csv_scans(preserve_old=True,flagstr='fpx')\n",
    "macs.data.load_data_matrix_from_csv('fpx_dataMatrix.csv')\n",
    "#macs.data.combine_all_csv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c21d7cce-e8b7-4dfb-ad70-ee7a4984f1d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "macs.data.project_data_QE(PTAI=True,which_data='mcstas')\n",
    "\n",
    "\n",
    "ng0_files = glob.glob(scan_dir+'*.ng0')\n",
    "for f in ng0_files:\n",
    "    macs.data.import_ng0_to_matrix(f)\n",
    "macs.data.project_data_QE(PTAI=True,which_data='macs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1dc5aa47-6c31-49ee-9c2a-409485273fc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qnet = [-1.93164  0.       0.     ]\n",
      "Qnet = [-1.93164  0.       0.     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f339e90e1a0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pyMACS\n",
    "cmap=\"viridis\"\n",
    "fig,ax = plt.subplots(2,3,figsize=(8,4))\n",
    "\n",
    "fig.subplots_adjust(hspace=0.5,wspace=0.5)\n",
    "U,V,I,Err = macs.data.take_slice([-1.05,-0.95,24],[-0.1,0.1,24],[-0.07,0.07],which_data='macs')\n",
    "ax[0,0].pcolormesh(U,V,I.T,cmap=cmap,vmin=0,vmax=3e-4)\n",
    "ax[0,0].set_xlabel('[HH0]')\n",
    "ax[0,0].set_ylabel('[00L]')\n",
    "#ax[0,0].set_title(\"TiO2, MACS\")\n",
    "\n",
    "U,V,I,Err = macs.data.take_slice([-1.05,-0.95,22],[-0.03,0.03],[-0.6,0.6,11],which_data='macs')\n",
    "ax[0,1].pcolormesh(U,V,I.T,cmap=cmap,vmin=0,vmax=1e-4)\n",
    "ax[0,1].set_xlabel('[HH0]')\n",
    "ax[0,1].set_ylabel('E (meV)')\n",
    "#ax[0,1].set_title(\"TiO2, MACS\")\n",
    "\n",
    "U,V,I,Err = macs.data.take_slice([-1.05,-0.95],[-0.1,0.1,22],[-0.6,0.6,11],which_data='macs')\n",
    "\n",
    "ax[0,2].pcolormesh(U,V,I.T,cmap=cmap,vmin=0,vmax=2e-5)\n",
    "ax[0,2].set_xlabel('[00L]')\n",
    "ax[0,2].set_ylabel('E (meV)')\n",
    "#ax[0,2].set_title(\"TiO2 , MACS\")\n",
    "\n",
    "\n",
    "U,V,I,Err = macs.data.take_slice([-1.05,-0.95,24],[-0.1,0.1,24],[-0.07,0.07],which_data='mcstas')\n",
    "ax[1,0].pcolormesh(U,V,I.T,cmap=cmap,vmin=0,vmax=7e1)\n",
    "ax[1,0].set_xlabel('[HH0]')\n",
    "ax[1,0].set_ylabel('[00L]')\n",
    "#ax[1,0].set_title(\"TiO2, McStas\")\n",
    "\n",
    "U,V,I,Err = macs.data.take_slice([-1.05,-0.95,22],[-0.03,0.03],[-0.6,0.6,11],which_data='mcstas')\n",
    "#fig,ax = plt.subplots(1,2,figsize=(5,3),sharex=True,sharey=True)\n",
    "ax[1,1].pcolormesh(U,V,I.T,cmap=cmap,vmin=0,vmax=4e1)\n",
    "ax[1,1].set_xlabel('[HH0]')\n",
    "ax[1,1].set_ylabel('E (meV)')\n",
    "#ax[1,1].set_title(\"TiO2, MACS\")\n",
    "\n",
    "U,V,I,Err = macs.data.take_slice([-1.05,-0.95],[-0.1,0.1,22],[-0.6,0.6,11],which_data='mcstas')\n",
    "\n",
    "ax[1,2].pcolormesh(U,V,I.T,cmap=cmap)\n",
    "ax[1,2].set_xlabel('[00L]')\n",
    "ax[1,2].set_ylabel('E (meV)')\n",
    "#ax[1,2].set_title(\"TiO2 , MACS\")\n",
    "#fig.savefig(\"TiO2_A3scan_test.pdf\",bbox_inches=\"tight\")\n",
    "\n",
    "#Overplot the tabulated resolution ellipsoid\n",
    "qvec = np.array([-1.0,-1.0,0.0])\n",
    "\n",
    "qxpt,qzpt = pyMACS.scripting.hkl_to_labframe(qvec[0],qvec[1],0,macs)\n",
    "M_load,M_diag_load,Q_hkw_load = pyMACS.scripting.macs_resfunc(qvec[0],qvec[1],0.0,0.0,5.0,macsobj=macs,\n",
    "                                             gen_plot=False,verbose=False,calc_mode=\"load_cov\")\n",
    "\n",
    "ellips, proj_ellips = pyMACS.scripting.res_ellipses(M_load,Qmean=np.array([-1.0,0,0]),macsobj=macs)\n",
    "\n",
    "#Small deviations in Q to account for misalignment in experiment\n",
    "ax[1,0].plot(ellips[0][0,:],ellips[0][1,:],'r-')\n",
    "ax[1,1].plot(ellips[1][0,:],ellips[1][1,:],'r-')\n",
    "ax[1,2].plot(ellips[2][0,:],ellips[2][1,:],'r-')\n",
    "ax[1,0].plot(proj_ellips[0][0,:],proj_ellips[0][1,:],'r--')\n",
    "ax[1,1].plot(proj_ellips[1][0,:],proj_ellips[1][1,:],'r--')\n",
    "ax[1,2].plot(proj_ellips[2][0,:],proj_ellips[2][1,:],'r--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e186929b-e966-45d1-ad08-aa1dfd9ff77b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}