fpraktikum/tem/auswertung/gold_diffr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from utility import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "profile = load_profiles('../messungen/gold_diffr/peaks.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 1 Axes>,\n <matplotlib.axes._subplots.AxesSubplot at 0x7fe8d121e0a0>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": [
       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\n"
      ],
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plot_diffr_profile(profile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": [
       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\n"
      ],
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib inline\n",
    "candidates, d_candidates, sigma_candidates \\\n",
    "    = analyze_diffr_profile(profile, limits=[100, -10],\n",
    "                            distance=15, save=('profile', 'gold_diffr'),\n",
    "                            chosen_indices=[0,1,3,5,6,7,8,9,10,11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0004262332293208009"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_candidates[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 & 0.2183 & 0.0016 & 0.0072 \\\\\n",
      "2 & 0.1933 & 0.0012 & 0.0028 \\\\\n",
      "3 & 0.1331 & 0.0006 & 0.0027 \\\\\n",
      "4 & 0.1136 & 0.0004 & 0.0024 \\\\\n",
      "5 & 0.09664 & 0.00031 & 7e-05 \\\\\n",
      "6 & 0.085 & 0.00024 & 0.00177 \\\\\n",
      "7 & 0.07721 & 0.0002 & 0.00087 \\\\\n",
      "8 & 0.07329 & 0.00018 & 0.0006 \\\\\n",
      "9 & 0.0664 & 0.00015 & 0.00013 \\\\\n",
      "10 & 0.06415 & 0.00014 & 0.00077 \\\\\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(generate_analysis_table(np.array([candidates, d_candidates, sigma_candidates]).T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 3,  4,  8, 11, 16, 19, 24, 27, 32, 35]), array([[3.78094479e-01, 2.72366684e-03, 1.24513781e-02],\n",
      "       [3.86548125e-01, 2.46542098e-03, 5.56833397e-03],\n",
      "       [3.76420964e-01, 1.65316633e-03, 7.74768705e-03],\n",
      "       [3.76932014e-01, 1.41365569e-03, 8.05399689e-03],\n",
      "       [3.86548125e-01, 1.23271049e-03, 2.82504740e-04],\n",
      "       [3.70497148e-01, 1.03921852e-03, 7.71979144e-03],\n",
      "       [3.78241159e-01, 9.63708943e-04, 4.25625720e-03],\n",
      "       [3.80837908e-01, 9.21111916e-04, 3.11474336e-03],\n",
      "       [3.75596192e-01, 8.22964899e-04, 7.33410901e-04],\n",
      "       [3.79527828e-01, 8.03465379e-04, 4.55889192e-03]]), array([0.        , 0.00845365, 0.00167351, 0.00116246, 0.00845365,\n",
      "       0.00759733, 0.00014668, 0.00274343, 0.00249829, 0.00143335]))\n"
     ]
    }
   ],
   "source": [
    "hypothesis = evaluate_hypothesis(np.array([candidates, d_candidates, sigma_candidates]).T, maximum=80, hints=[(5,19)])\n",
    "print(hypothesis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 & \\(\\sqrt{3}\\) & 0.3781 & 0.0027 & 0.0125 & 0.000 \\\\\n",
      "2 & \\(\\sqrt{4}\\) & 0.3865 & 0.0025 & 0.0056 & 0.008 \\\\\n",
      "3 & \\(\\sqrt{8}\\) & 0.3764 & 0.0017 & 0.0077 & 0.002 \\\\\n",
      "4 & \\(\\sqrt{11}\\) & 0.3769 & 0.0014 & 0.0081 & 0.001 \\\\\n",
      "5 & \\(\\sqrt{16}\\) & 0.38655 & 0.00123 & 0.00028 & 0.008 \\\\\n",
      "6 & \\(\\sqrt{19}\\) & 0.3705 & 0.001 & 0.0077 & 0.008 \\\\\n",
      "7 & \\(\\sqrt{24}\\) & 0.3782 & 0.001 & 0.0043 & 0.000 \\\\\n",
      "8 & \\(\\sqrt{27}\\) & 0.3808 & 0.0009 & 0.0031 & 0.003 \\\\\n",
      "9 & \\(\\sqrt{32}\\) & 0.3756 & 0.0008 & 0.0007 & 0.002 \\\\\n",
      "10 & \\(\\sqrt{35}\\) & 0.3795 & 0.0008 & 0.0046 & 0.001 \\\\\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(generate_hypethsesis_table(*hypothesis))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "a, d_a, sigma_a = determine_lattice_constant(hypothesis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3809, 0.001, 0.0052)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scientific_round(a, d_a, sigma_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\(\\sqrt{3}\\) & \\(\\mqty(1 & 1 & 1)\\) \\\\\n",
      "\\(\\sqrt{4}\\) & \\(\\mqty(2 & 0 & 0)\\) \\\\\n",
      "\\(\\sqrt{8}\\) & \\(\\mqty(2 & 2 & 0)\\) \\\\\n",
      "\\(\\sqrt{11}\\) & \\(\\mqty(3 & 1 & 1)\\) \\\\\n",
      "\\(\\sqrt{16}\\) & \\(\\mqty(4 & 0 & 0)\\) \\\\\n",
      "\\(\\sqrt{19}\\) & \\(\\mqty(3 & 3 & 1)\\) \\\\\n",
      "\\(\\sqrt{24}\\) & \\(\\mqty(4 & 2 & 2)\\) \\\\\n",
      "\\(\\sqrt{27}\\) & \\(\\mqty(3 & 3 & 3)\\), \\(\\mqty(5 & 1 & 1)\\) \\\\\n",
      "\\(\\sqrt{32}\\) & \\(\\mqty(4 & 4 & 0)\\) \\\\\n",
      "\\(\\sqrt{35}\\) & \\(\\mqty(5 & 3 & 1)\\) \\\\\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(generate_miller_table(hypothesis[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4033916337519268"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hypothesis[1][:,0].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.027602905569007182"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(.4016-.413)/.413"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "argv": [
    "/usr/bin/python3",
    "-m",
    "ipykernel_launcher",
    "-f",
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   ],
   "display_name": "Python 3",
   "env": null,
   "interrupt_mode": "signal",
   "language": "python",
   "metadata": null,
   "name": "python3"
  },
  "name": "gold_diffr.ipynb"
 },
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}