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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
      ],
      "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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
      ],
      "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",
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   "display_name": "Python 3",
   "env": null,
   "interrupt_mode": "signal",
   "language": "python",
   "metadata": null,
   "name": "python3"
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 },
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