Using an EDS map of a standard, calculate k-factors from it by fitting the slopes of element ratios.

Fit Bruker kfactors from map.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating k-factors for Bruker detector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note this stack was mislabled when I took the data.  It is not Fo$_{87}$.  It is Fo$_{82}$.  The quant should be like so:\n",
    "    \n",
    "```\n",
    "Input data:  \n",
    "Element             At %  \n",
    "O                  4.000  \n",
    "Mg                 1.650  \n",
    "Si                 1.000  \n",
    "Fe                 0.350  \n",
    "Total:             7.000  \n",
    "\n",
    "\n",
    "Quantification results:  \n",
    "Element       At%      Wt%  \n",
    "O          57.143   42.178  \n",
    "Mg         23.571   26.430  \n",
    "Si         14.286   18.510  \n",
    "Fe          5.000   12.882  \n",
    "\n",
    "--- Olivine Analysis ---  \n",
    "  \n",
    "Mg/(Mg+Fe) = 0.825  \n",
    "Mg/(M1+M2) = 0.825  \n",
    "Fe/(M1+M2) = 0.175  \n",
    "  \n",
    "Cations per 4 oxygens:  \n",
    "    Element:    #         \n",
    "         Mg:    1.650  \n",
    "         Si:    1.000  \n",
    "         Fe:    0.350  \n",
    " Total Cats:    3.000  \n",
    "```\n",
    "\n",
    "The k-factors reported by the Bruker software are:\n",
    "\n",
    "```\n",
    "Element k-factor\n",
    "O       1.130\n",
    "Mg      0.919\n",
    "Si      1.000\n",
    "Fe      1.618\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial configuration: we need to choose a Bruker stack to load.\n",
    "StackName = 'Fo87 after some processing.bcf'\n",
    "# How much to bin the pixels in order to beat down noise.\n",
    "BinningFactor = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# These are includes and busywork functions.\n",
    "\n",
    "%pylab inline\n",
    "import hyperspy.api as hs\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "#from ipywidgets.widgets import interactive, fixed, interact\n",
    "from skimage.exposure import equalize_hist, equalize_adapthist, rescale_intensity\n",
    "from skimage import filters\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import os\n",
    "\n",
    "def MakeRGBFromMaps(R,G,B, bits=16, equalize=False, cutnoise=False):\n",
    "    MaxPixelVal = (2**bits)-1\n",
    "    \n",
    "    # Make a Signal2D from the three images and copy over the metadata.\n",
    "    x = hs.stack([R,G,B])\n",
    "    x = hs.signals.Signal2D(x).T\n",
    "    x.metadata = R.metadata\n",
    "    x.original_metadata = R.original_metadata\n",
    "    x.metadata.General.title = R.metadata.Sample.xray_lines[0] + '(R) ' + G.metadata.Sample.xray_lines[0] + '(G) ' + B.metadata.Sample.xray_lines[0] + '(B)'\n",
    "    \n",
    "    rdata = x.data[:,:,0]\n",
    "    gdata = x.data[:,:,1]\n",
    "    bdata = x.data[:,:,2]\n",
    "            \n",
    "    if cutnoise==True:\n",
    "        NoiseCutAggressiveness = 1 # 0 means no cut, 1 means full cut.\n",
    "        rdata[rdata < filters.threshold_triangle(rdata)*NoiseCutAggressiveness] = 0\n",
    "        gdata[gdata < filters.threshold_triangle(gdata)*NoiseCutAggressiveness] = 0\n",
    "        bdata[bdata < filters.threshold_triangle(bdata)*NoiseCutAggressiveness] = 0\n",
    " \n",
    "    if equalize==True:\n",
    "        rdata = equalize_hist(rdata)\n",
    "        gdata = equalize_hist(gdata)\n",
    "        bdata = equalize_hist(bdata)\n",
    "        rdata = rescale_intensity(rdata)*MaxPixelVal\n",
    "        gdata = rescale_intensity(gdata)*MaxPixelVal\n",
    "        bdata = rescale_intensity(bdata)*MaxPixelVal\n",
    "    else:\n",
    "        rdata = rescale_intensity(rdata)\n",
    "        gdata = rescale_intensity(gdata)\n",
    "        bdata = rescale_intensity(bdata)\n",
    "\n",
    "\n",
    "    x.data[:,:,0] = rdata\n",
    "    x.data[:,:,1] = gdata\n",
    "    x.data[:,:,2] = bdata\n",
    "\n",
    "    # Convert to RGB.\n",
    "    if bits==16:\n",
    "        x.change_dtype('uint16')\n",
    "        x.change_dtype('rgb16')\n",
    "    else:\n",
    "        x.change_dtype('uint8')\n",
    "        x.change_dtype('rgb8')    \n",
    "    \n",
    "    # Copy the axes info over from the R map.\n",
    "    x.axes_manager = R.axes_manager.copy()\n",
    "\n",
    "    return(x)\n",
    "\n",
    "def EqualizeAxis(fignum=None):\n",
    "    # Because of a bug in Hyperspi, I sometimes have to set the axis to equal after we rebin.\n",
    "    if fignum == None:\n",
    "        fig=plt.gcf()\n",
    "    else:\n",
    "        fig = plt.figure(fignum)\n",
    "    ax = fig.axes\n",
    "    if type(ax) != list:\n",
    "        ax.axis('equal')\n",
    "    else:\n",
    "        for a in ax:\n",
    "            a.axis('equal')\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stack dimensions are: (596, 616, 1024).\n",
      "New stack dimensions are: (37, 38, 1024) with binning: 16\n",
      "├── Acquisition_instrument\n",
      "│   └── TEM\n",
      "│       ├── Detector\n",
      "│       │   └── EDS\n",
      "│       │       ├── azimuth_angle = 45.0\n",
      "│       │       ├── detector_type = SuperX\n",
      "│       │       ├── elevation_angle = 18.0\n",
      "│       │       ├── energy_resolution_MnKa = 130.0\n",
      "│       │       └── real_time = 235045.88799999998\n",
      "│       ├── Stage\n",
      "│       │   └── tilt_alpha = 0.0\n",
      "│       ├── beam_energy = 80\n",
      "│       └── magnification = 10000\n",
      "├── General\n",
      "│   ├── date = 2013-07-15\n",
      "│   ├── original_filename = Fo87 after some processing.bcf\n",
      "│   ├── time = 14:28:28\n",
      "│   └── title = EDX\n",
      "├── Sample\n",
      "│   ├── elements = ['Cl', 'Fe', 'Mg', 'O', 'Si']\n",
      "│   ├── name = Fo82 3\n",
      "│   └── xray_lines = ['Cl_Ka', 'Fe_Ka', 'Mg_Ka', 'O_Ka', 'Si_Ka']\n",
      "└── Signal\n",
      "    ├── binned = True\n",
      "    ├── quantity = X-rays (Counts)\n",
      "    └── signal_type = EDS_TEM\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Load the actual stack, bin it, and display the metadata\n",
    "\n",
    "HAADF, EDS = hs.load(StackName)\n",
    "print(f'Stack dimensions are: {EDS.data.shape}.')\n",
    "if BinningFactor > 1:\n",
    "    EDS = EDS.rebin(scale=(BinningFactor, BinningFactor, 1))\n",
    "    print(f'New stack dimensions are: {EDS.data.shape} with binning: {BinningFactor}')\n",
    "#EDS.plot()\n",
    "\n",
    "# Also save the HAADF file to disk.\n",
    "HAADF.save(os.path.join('Maps', 'HAADF.tif'), overwrite=True)\n",
    "HAADF.save(os.path.join('MapsNoPtGa', 'HAADF.tif'), overwrite=True)\n",
    "print(EDS.metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 403,
       "width": 777
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do the quantifications.  First let's make a sum spectrum for the whole cube.  We will use this to\n",
    "# set thresholds and the like.\n",
    "sumspec = EDS.sum()\n",
    "\n",
    "# Auto find lines that are in the spectrum.\n",
    "sumspec.add_lines()\n",
    "\n",
    "# But we are going to choose to specifically quantify just certain lines.\n",
    "ElLines = ['O_Ka', 'Mg_Ka', 'Si_Ka', 'Ca_Ka', 'Fe_Ka', 'Cu_Ka']\n",
    "\n",
    "# Pixels outside the sample will produce noisy results that mess up the fit.  Mask them out.\n",
    "mask = EDS.vacuum_mask(threshold=BinningFactor**2)\n",
    "EDS.data[mask] = 0\n",
    "\n",
    "# Estimate background positions around each peak so we can subtract the Brehmsstrahlung.\n",
    "Backgrounds = EDS.estimate_background_windows(xray_lines=ElLines)\n",
    "\n",
    "# Estimate FWHM values for the peaks.\n",
    "Windows = EDS.estimate_integration_windows(xray_lines=ElLines)\n",
    "\n",
    "# Show our fitting parameters.\n",
    "EDS.sum().plot(integration_windows=Windows, background_windows=Backgrounds)\n",
    "plt.gcf().set_size_inches(12,6)\n",
    "\n",
    "# Extract the actual peak intensities, background subtracted.\n",
    "Elements = EDS.get_lines_intensity(ElLines, background_windows=Backgrounds)\n",
    "\n",
    "# For each line, package it into a dictionary and save the images to tif files on the disk.\n",
    "# We can use the dictionary to access individual maps later.\n",
    "Elements = [El.T for El in Elements]\n",
    "ElementDict = dict()\n",
    "for El in Elements:\n",
    "    El.change_dtype('uint16')\n",
    "    El.save(os.path.join('Maps', El.metadata.Sample.xray_lines[0]+'.tif'), overwrite=True)\n",
    "    ElementDict[El.metadata.Sample.elements[0]] = El\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1168,
       "width": 1130
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot all the maps side by side, and save that to the disk too.\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "hs.plot.plot_images(Elements, per_row=ceil(sqrt(len(Elements))), colorbar=None, axes_decor='off', saturated_pixels=1, fig=fig)\n",
    "plt.show()\n",
    "fig.savefig(os.path.join('Maps', 'Mosaic.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Zack/anaconda3/envs/conda37/lib/python3.7/site-packages/hyperspy/drawing/utils.py:838: UserWarning: Sorry, colorbar is not implemented for RGB images.\n",
      "  warnings.warn(\"Sorry, colorbar is not implemented for RGB images.\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 345,
       "width": 608
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make an RGB.  We can see from the fact that it is not perfectly white that there are thickness effects.\n",
    "# Notice the Fe peak is strongest in the thickest part of the sample.\n",
    "ImgLin = MakeRGBFromMaps(ElementDict['Fe'],ElementDict['Mg'],ElementDict['Si'])\n",
    "ImgEnh = MakeRGBFromMaps(ElementDict['Fe'],ElementDict['Mg'],ElementDict['Si'], equalize=True, cutnoise=True)\n",
    "#Img.plot()\n",
    "hs.plot.plot_images((ImgLin, ImgEnh))\n",
    "plt.gcf().set_size_inches(10,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O/Si polyfit = [  1.58927038 556.1199356 ]\n",
      "Mg/Si polyfit = [  1.56167441 -51.05355081]\n",
      "Fe/Si polyfit = [   0.43993208 -108.13623591]\n",
      "K_O/Si = 1.434\n",
      "K_Mg/Si = 0.914\n",
      "K_Fe/Si = 1.582\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Fe counts')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 207,
       "width": 562
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Actual quantification ratios in Wt %\n",
    "actualFeoverSi = 12.882/18.510\n",
    "actualMgoverSi = 26.430/18.510\n",
    "actualOoverSi = 42.178/18.510\n",
    "\n",
    "# Turn the images which contain counts into just a list of numbers.\n",
    "# Notice for Fe we measure the Fe_Ka line only, but Ka/(Ka+Kb) = 0.87\n",
    "Fe_counts = ElementDict['Fe'].data.ravel() * 1/0.87\n",
    "O_counts = ElementDict['O'].data.ravel()\n",
    "Mg_counts = ElementDict['Mg'].data.ravel()\n",
    "Si_counts = ElementDict['Si'].data.ravel()\n",
    "\n",
    "# Because we masked pixels, we will have a bunch that have a value of zero.\n",
    "# This will mess up our fits, so we need to remove all the zero pixels from the fit.\n",
    "O_counts = O_counts[Fe_counts>0]\n",
    "Mg_counts = Mg_counts[Fe_counts>0]\n",
    "Si_counts = Si_counts[Fe_counts>0]\n",
    "Fe_counts = Fe_counts[Fe_counts>0]\n",
    "\n",
    "# Now fit lines to each element.\n",
    "Ofit = np.polyfit(Si_counts, O_counts, deg=1)\n",
    "print(f'O/Si polyfit = {Ofit}')\n",
    "Mgfit = np.polyfit(Si_counts, Mg_counts, deg=1)\n",
    "print(f'Mg/Si polyfit = {Mgfit}')\n",
    "Fefit = np.polyfit(Si_counts, Fe_counts, deg=1)\n",
    "print(f'Fe/Si polyfit = {Fefit}')\n",
    "\n",
    "# The k-factors are the actual quantification ratios / the slopes of the fit lines.\n",
    "print(f'K_O/Si = {actualOoverSi/Ofit[0]:0.3f}')\n",
    "print(f'K_Mg/Si = {actualMgoverSi/Mgfit[0]:0.3f}')\n",
    "print(f'K_Fe/Si = {actualFeoverSi/Fefit[0]:0.3f}')\n",
    "\n",
    "# We are going to plot our fits on top of the scatterplots.\n",
    "# Make an artificial x-axis using the range of silicon counts.\n",
    "Si_xaxis = np.linspace(np.min(Si_counts), np.max(Si_counts), 1000)\n",
    "\n",
    "# Print scatterplots showing the data and the fits.\n",
    "fig,ax = plt.subplots(1,3, figsize=(9,3))\n",
    "\n",
    "ax[0].scatter(Si_counts, O_counts)\n",
    "ax[0].plot(Si_xaxis, Ofit[0]*Si_xaxis + Ofit[1], 'r')\n",
    "ax[0].set_xlabel('Si counts')\n",
    "ax[0].set_ylabel('O counts')\n",
    "\n",
    "ax[1].scatter(Si_counts, Mg_counts)\n",
    "ax[1].plot(Si_xaxis, Mgfit[0]*Si_xaxis + Mgfit[1], 'r')\n",
    "ax[1].set_xlabel('Si counts')\n",
    "ax[1].set_ylabel('Mg counts')\n",
    "\n",
    "ax[2].scatter(Si_counts, Fe_counts)\n",
    "ax[2].plot(Si_xaxis, Fefit[0]*Si_xaxis + Fefit[1], 'r')\n",
    "ax[2].set_xlabel('Si counts')\n",
    "ax[2].set_ylabel('Fe counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the scatter plots are pretty straight indicating that thickness effectds didn't totally screw things up.  However, if there were no thickness effects, the plots should all go through the origin exactly, and since they don't this is an indication that our k-factors aren't perfect.\n",
    "\n",
    "In principle, you could do a better job by fitting a thinner sample if you have one.\n",
    "\n",
    "K-factors will be influenced by the method that you use to fit them.  In this case, we are integrating ROI's and subtracting off a linear background.  If you were to use another method such as fitting gaussians, then your k-factors would also differ.\n",
    "\n",
    "The factors we found can be compared to the k-factors that the Bruker software recommends:\n",
    "```\n",
    "K-factor    Ours    Bruker\n",
    "K_O/Si      1.434   1.130\n",
    "K_Mg/Si     0.914   0.919\n",
    "K_Fe/Si     1.582   1.618\n",
    "```\n",
    "\n",
    "Notice they are in pretty good agreemenet except that the oxygen is pretty far off.  There are two reasons for this:\n",
    "\n",
    "1) Oxygen has an asymmetric peak and is sitting on a string Brehmsstrahlung background that is not linear.  Therefore, our peak integration method differs from Bruker's substatially they use fancier peak fitting.  \n",
    "2) The sample is thick enough that absorption corrections are not negligible (notice the large y-intercept.\n",
    "\n",
    "Notice the Fe k-factors is pretty close but not perfect.  The most likely reason is that there is a little uncertainty in the quantification of the sample itself.  Is it homogenous?  Is it exactly Fo$_{82}$?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook Fit Bruker kfactors from map.ipynb to html\n",
      "[NbConvertApp] Writing 626657 bytes to Fit Bruker kfactors from map.html\n"
     ]
    }
   ],
   "source": [
    "!jupyter-nbconvert --to html \"Fit Bruker kfactors from map.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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