View_XRD_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is part of the various tests for scimap. It's intended to allow for visual evaluation of fits, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "import math\n",
    "from matplotlib import pyplot as plt\n",
    "# plt.xkcd()\n",
    "import pandas as pd\n",
    "import scimap\n",
    "import numpy as np\n",
    "from scipy import fftpack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare HDF File for Later Unit Tests\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scimap.import_aps_34IDE_map(\n",
    "    directory=\"test-data-xrd/xrd-map-34-ID-E/\",\n",
    "    wavelength=0.4959,\n",
    "    shape=scimap.shape(rows=13, columns=11),\n",
    "    step_size=scimap.default_units.um(1),                      \n",
    "    beamstop=0.49,                            \n",
    "    hdf_filename=\"test-data-xrd/xrd-map-34-ID-E.hdf\"\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Corundum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25b0965278>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c5Kr5VAkAAAB2x3G7N1fVI5M8uLX2jqr6R0n+XkZNxnck+fXW2kf2ppo7p3sz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\nF232OBlvl1e8oh/vb6EXgJO2VWv/ddc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FiH5fvKj7ZWLCX7vtfzMZ/e/JSR92W+fEhP+Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25b08abf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corundum_scan = scimap.XRDScan(filename=\"test-data-xrd/corundum.brml\")\n",
    "corundum_scan.plot_diffractogram()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove a Peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5404 5404\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6f5efa0a58>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3r+pov1DekP+DzqDbYCCmX8V/H1gHXJ7+vCGX/lHgOkmrgYNpwNgK/FnaabwAeBuwPiIO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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6f5f0cd630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q, y = scimap.remove_peak_from_df(x=corundum_scan.scattering_lengths,\n",
    "                          y=corundum_scan.intensities,\n",
    "                          xrange=(2, 3))\n",
    "print(len(q), len(y))\n",
    "plt.plot(q, y, marker='+', linestyle=\"None\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Peak Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.80822894,  2.46585258,  2.64543732,  2.90382698,  3.0176798 ,\n",
       "        3.61645787,  3.92817384])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converted old unit-test angles to q\n",
    "celref_peaks = [\n",
    "            ('012', 3.4746228816945104, 25.637288649553085),\n",
    "            ('104', 2.5479680737754244, 35.22223164557721),\n",
    "            ('110', 2.375, 37.88141047624646),\n",
    "            ('006', 2.1636666666666664, 41.74546075011751),\n",
    "            ('113', 2.0820345582756135, 43.46365474219995),\n",
    "            ('024', 1.7373114408472552, 52.68443192186963),\n",
    "            ('116', 1.5994489779586798, 57.62940019834231),\n",
    "]\n",
    "two_theta = np.array([p[2] for p in celref_peaks])\n",
    "scimap.twotheta_to_q(two_theta, scimap.tubes['Cu'].kalpha.num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbcb5953978>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z2UB3rzKzQcCWuPrD4tYvATaG5bEm5S8ke9DS0tKDt2OxGLFYLFlVERHJgJQDJAyI9WY2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\nSbcj8p4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbcb8b299e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot fitting for a peak\n",
    "scan = scimap.XRDScan(filename=\"test-data-xrd/corundum.brml\",\n",
    "                     phase=scimap.standards.Corundum())\n",
    "df = scan.diffractogram\n",
    "scan.refinement.fit_peaks(scattering_lengths=df.index, \n",
    "                          intensities=df['subtracted'])\n",
    "scan.refinement.plot(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbcb579b048>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A8g//kA6ClT9gNmVK6pf++tc3lr3+Ogwblg40lacl3nFHGjS+887qr7NyZQqN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\nMytzKJiZWZlDwczMyv4/t48BosvsytIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbcb57c6a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scan = scimap.XRDScan(filename=\"test-data-xrd/corundum.brml\",\n",
    "                     phase=scimap.standards.Corundum())\n",
    "plt.plot(scan.scattering_lengths, scan.background())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Guassian Curve for Testing Fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.5/site-packages/numpy/core/numeric.py:482: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f25af21e0b8>]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TSKX7+99h7bVhn31yJ5Eqq1AoUCgUSr5fMY3gQ4FHSY3gc4E7gcNijL0ugxZC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\n/okVQI/MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25af06dc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5, 5, num=51)\n",
    "a = 1\n",
    "b = 0\n",
    "c = 2 / 2*math.sqrt(2*math.log(2))\n",
    "y = a * np.exp(-(x-b)**2/(2*c**2))\n",
    "rands = (np.random.rand(*x.shape)-0.5) / 5\n",
    "plt.figure(figsize=(0.9*11, 0.9*8.5))\n",
    "plt.plot(x, y + rands, marker=\"+\", linestyle=\":\")\n",
    "# plt.plot(x, rands)\n",
    "plt.xlim(-5, 5)\n",
    "plt.xticks(np.arange(-5, 6, step=1))\n",
    "plt.yticks(np.arange(0, 1.1, step=0.1));\n",
    "plt.savefig('/home/mwolf/gaussian_curve.pdf')\n",
    "\n",
    "# FFT Transform\n",
    "ffty = fftpack.fft(x=y+rands)\n",
    "# fftx = fftpack.fftfreq(n=x)\n",
    "ffty[0:52] = 0\n",
    "newy = fftpack.ifft(ffty)\n",
    "# plt.figure()\n",
    "plt.plot(x, newy)\n",
    "# plt.plot(range(0, len(ffty)), ffty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Kα1/Kα1 Splitting\n",
    "The graph below is based on X-ray energies taken from NIST: http://physics.nist.gov/cgi-bin/XrayTrans/search.pl?download=tab&element=All&trans=KL2&trans=KL3&lower=&upper=&units=eV\n",
    "\n",
    "It shows that the splitting between $k_{α1}$ and $k_{α2}$ is propritional to the square of the $k_α^2$ value itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f25af876e48>]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Yzewz4ff/RTO7P9sxdiSDn53RZvakmT0X/vxMjiLO9pjZPWa21cxe6OCe28Pf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25afb09b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('test-data-xrd/xray-energies.csv', skiprows=4, sep=\"\\t\")\n",
    "kalpha1 = data[data['Trans.'] == \"KL2\"]\n",
    "kalpha2 = data[data['Trans.'] == \"KL3\"]\n",
    "\n",
    "# convert to separate numpy arrays\n",
    "Z = kalpha1['Num'].values\n",
    "assert np.all(np.equal(Z, kalpha2['Num'].values))\n",
    "kalpha1 = kalpha1['Direct (eV)'].values\n",
    "kalpha2 = kalpha2['Direct (eV)'].values\n",
    "\n",
    "kalpha = (kalpha1 + 0.5 * kalpha2)/1.5\n",
    "split = (kalpha2-kalpha1)\n",
    "\n",
    "plt.plot(kalpha**2, split, marker=\"o\", linestyle=\":\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
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   "outputs": [],
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   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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