Calibration_MJW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from util_mjw.MicFileTool import MicFile\n",
    "import util_mjw.Simulation as Gsim\n",
    "import util_mjw.RotRep as Rot\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from util_mjw.config import Config\n",
    "from scipy import ndimage\n",
    "from scipy.ndimage.measurements import label,find_objects,center_of_mass\n",
    "from scipy import optimize\n",
    "import json\n",
    "import os\n",
    "from util_mjw.calibration_misc import getCenter2,fetch_images,process_images,GetVertex,optimize_detector\n",
    "import util.Simulation as sim\n",
    "import h5py\n",
    "from scipy.interpolate import griddata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Extract the windows around the Bragg Peaks from a small grain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mic_in_file = \"AuxData/Ti7_WithHRM_2ndLoad_z1_.mic.LBFS\"\n",
    "mic_out_file = \"Ti7HRM2nd.hdf5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Cfg' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/user/3320/ipykernel_1890281/592754086.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0mGrainIDMap\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mgrainID\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmicFile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m     \u001b[0mds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"origin\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mu'units'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mu'mm'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Cfg' is not defined"
     ]
    }
   ],
   "source": [
    "a=MicFile()\n",
    "grid_x,grid_y=np.meshgrid(np.arange(-0.5,0.2,0.002),np.arange(-0.4,0.4,0.002))\n",
    "grid_c = griddata(a.snp[:,0:2],a.snp[:,9],(grid_x,grid_y),method='nearest')\n",
    "grid_e1 = griddata(a.snp[:,0:2],a.snp[:,6],(grid_x,grid_y),method='nearest')\n",
    "grid_e2 = griddata(a.snp[:,0:2],a.snp[:,7],(grid_x,grid_y),method='nearest')\n",
    "grid_e3 = griddata(a.snp[:,0:2],a.snp[:,8],(grid_x,grid_y),method='nearest')\n",
    "\n",
    "g = np.where(grid_c>0.8,1,0)\n",
    "\n",
    "labels,num_features = label(g)\n",
    "\n",
    "ll = np.float32(labels.copy())\n",
    "ll[ll==0] = np.nan\n",
    "\n",
    "\n",
    "GrainDict={}\n",
    "for l in np.sort(np.unique(labels))[1:]:\n",
    "    \n",
    "    com =center_of_mass(g,labels,l)\n",
    "    com = (int(com[0]),int(com[1]))\n",
    "    GrainDict[l] = (grid_e1[com],grid_e2[com],grid_e3[com])\n",
    "GrainIDMap=np.zeros(grid_c.shape,dtype=int)\n",
    "for grainID in GrainDict:\n",
    "    \n",
    "    (e1,e2,e3)=GrainDict[grainID]\n",
    "    tmp = grid_c > 0.3\n",
    "    tmp*=np.absolute(grid_e1 - e1)<1\n",
    "    tmp*=np.absolute(grid_e2 - e2)<1\n",
    "    tmp*=np.absolute(grid_e3 - e3)<1\n",
    "    GrainIDMap[tmp]= grainID \n",
    "\n",
    "with h5py.File(Cfg.micFile,'w') as f:\n",
    "    ds=f.create_dataset(\"origin\", data = np.array([-0.5,-0.4]))\n",
    "    ds.attrs[u'units'] = u'mm'\n",
    "    ds=f.create_dataset(\"stepSize\", data = np.array([0.002,0.002]))\n",
    "    ds.attrs[u'units'] = u'mm'\n",
    "    f.create_dataset(\"Xcoordinate\", data = grid_x)\n",
    "    f.create_dataset(\"Ycoordinate\", data = grid_y)\n",
    "    f.create_dataset(\"Confidence\", data = grid_c)\n",
    "    f.create_dataset(\"Ph1\", data = grid_e1)\n",
    "    f.create_dataset(\"Psi\", data = grid_e2)\n",
    "    f.create_dataset(\"Ph2\", data = grid_e3)\n",
    "    f.create_dataset(\"GrainID\", data = GrainIDMap)\n",
    "    \n",
    "gg = np.float32(GrainIDMap.copy())\n",
    "gg[gg==0] = np.nan\n",
    "fig,ax = plt.subplots(ncols=4,figsize=(20,7))\n",
    "ax[0].imshow(grid_e3,origin='lower')\n",
    "ax[1].imshow(g,origin='lower')\n",
    "ax[2].imshow(ll,origin='lower')\n",
    "ax[3].imshow(gg,origin='lower')\n",
    "plt.show()\n",
    "print(GrainIDMap.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "############ Experimental Parameters ##########\n",
    "grain_ID = 24\n",
    "Cfg = Config(f'ConfigFiles/test_all/Grain_{grain_ID}.yml')\n",
    "path = f'Calibration_Files/grain_{grain_ID}/'\n",
    "\n",
    "\n",
    "################### raw data #######################\n",
    "raw_data='/mnt/data/sparrow_data/NF-DataSets/2013-07_NF-Strain1/V/Suter_Jul13/NF/Ti7_WithHRM_Under2ndLoad/Ti7_WithHRM_Under2ndLoad__'\n",
    "\n",
    "if f'grain_{grain_ID}' not in os.listdir('Calibration_Files/'):\n",
    "    os.mkdir(path)\n",
    "    os.mkdir(path+'Ps_bf/')\n",
    "    os.mkdir(path+'Ps_filtered/')\n",
    "    \n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "Det1=Gsim.Detector(config=Cfg)\n",
    "crystal_str=Gsim.CrystalStr(config=Cfg)\n",
    "crystal_str.getRecipVec()\n",
    "crystal_str.getGs(Cfg.maxQ)\n",
    "o_mat=Rot.EulerZXZ2Mat(np.array(Cfg.euler)/180.0*np.pi)\n",
    "pks,Gs,Info=Gsim.GetProjectedVertex(Det1,crystal_str,o_mat,Cfg.etalimit/180*np.pi,\n",
    "                                    Cfg.grainPos,getPeaksInfo=True,\n",
    "                                    omegaL=Cfg.omgRange[0],omegaU=Cfg.omgRange[1],energy=Cfg.energy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# allpks,alllims = fetch_images(raw_data,pks,window,dx,dy,path)\n",
    "# flucThresh=4\n",
    "# Nfile = len(allpks)\n",
    "# Im = process_images(path,Nfile,window,flucThresh)\n",
    "# goodidx = np.arange(Nfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the Center of Mass of each Bragg Peak"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write down all the center of mass for the good Peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "center_of_mass = []\n",
    "goodidx = np.arange(45)\n",
    "for idx in goodidx:\n",
    "    tmp=np.load(path+f'Ps_filtered/Im{idx}.npy')\n",
    "    Omeg = np.argmax(tmp[18:30,50:100,100:200].sum(axis=(1,2)))+18\n",
    "    Py,Px,cy,cx,co = getCenter2(tmp,Omeg,dx=50,dy=50,do=5)\n",
    "    center = np.array([co,cx,cy])\n",
    "    center_of_mass.append(center)\n",
    "center_of_mass = np.stack(center_of_mass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start Calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,oldPs,newPs,absCOM,goodidx = optimize_detector(path,center_of_mass,goodidx,Cfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check the position of simulated Bragg peaks after calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6812524785223308 0.6012475879505154\n",
      "22\n"
     ]
    }
   ],
   "source": [
    "dist0 = np.absolute(np.linalg.norm(oldPs-absCOM,axis=1)).mean()\n",
    "dist1 = np.absolute(np.linalg.norm(newPs-absCOM,axis=1)).mean()\n",
    "print(dist0,dist1)\n",
    "print(len(oldPs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,2,figsize=(17,4))\n",
    "ax[0].hist(oldPs[:,2]-absCOM[:,2],label='before calibration',bins=np.arange(-0.2,0.35,0.05),alpha=0.5)\n",
    "ax[0].hist(newPs[:,2]-absCOM[:,2],label='after calibration',bins=np.arange(-0.2,0.35,0.05),alpha=0.5)\n",
    "ax[0].legend(loc='upper right', fontsize=15)\n",
    "ax[0].set_xlabel(r'$\\Omega$ difference $(^\\circ)$', fontsize=20)\n",
    "ax[1].scatter(oldPs[:,0]-absCOM[:,0],oldPs[:,1]-absCOM[:,1],label='before calibration')\n",
    "ax[1].scatter(newPs[:,0]-absCOM[:,0],newPs[:,1]-absCOM[:,1],label='after calibration',alpha=0.5)\n",
    "ax[1].set_xlabel('horizontal difference (pixels)', fontsize=20)\n",
    "ax[1].set_ylabel('vertical difference (pixels)', fontsize=20)\n",
    "ax[1].legend(loc='upper right', fontsize=15)\n",
    "ax[0].tick_params(axis='both', which='major', labelsize=20)\n",
    "ax[1].tick_params(axis='both', which='major', labelsize=20)\n",
    "plt.savefig('calibration.png',dpi=100,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "\n",
    "x = list([float(xx) for xx in x])\n",
    "\n",
    "for i in range(40):\n",
    "    if f'Grain_{i}.yml' in os.listdir('ConfigFiles/test_all/'):\n",
    "        with open(f'ConfigFiles/test_all/Grain_{i}.yml') as f:\n",
    "            data = yaml.safe_load(f)\n",
    "    \n",
    "        \n",
    "        with open(f'ConfigFiles/test_all/Grain_{i}.yml', 'w') as file:\n",
    "            data['JCenter'] += float(x[0])\n",
    "            data['KCenter'] += float(x[1])\n",
    "            data['Ldistance'] += float(x[2]*1e-3)\n",
    "            data['tilt'] = [float(a) for a in list(np.array(data['tilt'])+x[3:6])]\n",
    "            data['grainPos'] = [float(a) for a in list(np.array(data['grainPos'])+np.array([x[6],x[7],0])*1e-3)]\n",
    "            documents = yaml.dump(data, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}