{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-01-07T09:00:37.432325Z", "start_time": "2020-01-07T09:00:37.219009Z" } }, "outputs": [], "source": [ "!gnom -h" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Usage: gnom [OPTIONS] \n", "\n", "Indirect transform for SAS data processing -- evaluates the P(r)\n", "\n", "Known Arguments:\n", " FILE Experimental data file\n", "\n", "Known Options:\n", " -h, --help Print usage information and exit\n", " -v, --version Print version information and exit\n", " --seed= Set the seed for the random number generator\n", " --first= first point of the data file to use (default: 1)\n", " --last= last point of the data file to use (default: all)\n", " --system= system type, one of 0...6 (default: 0)\n", " --rmin= minimum characteristic size of SYSTEM (default: 0.0)\n", " --rmax= maximum characteristic size of SYSTEM (required)\n", " --rad56= (no description)\n", " --force-zero-rmin=Zero condition at r=rmin (default: YES)\n", " --force-zero-rmax=Zero condition at r=rmax (default: YES)\n", " --nr= number of points in real space (default: automatic)\n", " --alpha= alpha value (default: automatic)\n", " -o, --output= output file name (default: stdout)\n", "\n", "Mandatory arguments to long options are mandatory for short options too.\n", "\n", "Report bugs to ." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`gnom`に入力ファイルと最大長$Dmax$の予想値を入れてやると$P_{fit}(r)$関数が出力される。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-01-07T09:00:37.587484Z", "start_time": "2020-01-07T09:00:37.436362Z" }, "scrolled": false }, "outputs": [], "source": [ "!gnom 6lyz.dat -rmax 50 -o 6lyz.out" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!head -n +60 \"6lyz.out\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", " #### G N O M Version 5.0 (r12314) ####\n", " Fri Apr 10 11:30:41 2020\n", "\n", " #### Configuration ####\n", "\n", " :\n", " 　　略\n", " :\n", "\n", " #### Results ####\n", "\n", " Parameter DISCRP OSCILL STABIL SYSDEV POSITV VALCEN SMOOTH\n", " Weight 1.000 3.000 3.000 3.000 1.000 1.000 1.000\n", " Sigma 0.300 0.600 0.120 0.120 0.120 0.120 0.600\n", " Ideal 0.700 1.100 0.000 1.000 1.000 0.950 0.000\n", " Current 0.003 1.349 0.002 0.100 1.000 0.947 0.103\n", " --------------------------------------------------------\n", " Estimate 0.005 0.842 1.000 0.000 1.000 0.999 0.971\n", "\n", " Angular range: 0.0000 to 0.5000\n", " Reciprocal space Rg: 0.1536E+02\n", " Reciprocal space I(0): 0.2682E+03\n", "\n", " Real space range: 0.0000 to 50.0000\n", " Real space Rg: 0.1535E+02 +- 0.1278E+00\n", " Real space I(0): 0.2682E+03 +- 0.1894E+01\n", "\n", " Highest ALPHA (theor): 0.4031E+06\n", " Current ALPHA: 0.5615E-02\n", "\n", " Total Estimate: 0.6538 (a REASONABLE solution)\n", "\n", "\n", "\n", " #### Experimental Data and Fit ####\n", "\n", " S J EXP ERROR J REG I REG\n", "\n", " 0.000000E+00 0.268216E+03 0.804647E+01 0.268197E+03 0.268197E+03\n", " 0.250000E-02 0.268083E+03 0.804250E+01 0.268066E+03 0.268066E+03\n", " 0.500000E-02 0.267688E+03 0.803065E+01 0.267671E+03 0.267671E+03\n", " :\n", " 略\n", " :\n", " 0.492500E+00 0.138261E+01 0.414783E-01 0.138267E+01 0.138267E+01\n", " 0.495000E+00 0.138138E+01 0.414414E-01 0.138123E+01 0.138123E+01\n", " 0.497500E+00 0.138070E+01 0.414210E-01 0.138026E+01 0.138026E+01\n", " 0.500000E+00 0.138052E+01 0.414157E-01 0.137969E+01 0.137969E+01\n", "\n", " #### Real Space Data ####\n", "\n", " Distance distribution function of particle \n", "\n", "\n", " R P(R) ERROR\n", "\n", " 0.0000E+00 0.0000E+00 0.0000E+00\n", " 0.4464E+00 0.1602E-01 0.1713E-01\n", " :\n", " 略\n", " :\n", " 0.1607E+02 0.9318E+00 0.4651E-01\n", " 0.1652E+02 0.9464E+00 0.5132E-01\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "上に表示されているのは、出力ファイル`6lyz.out`の中身の一部である。\n", "\n", "これからもわかるように、ファイルは単純な構造をしておらず、最初にフィットパラメータ、次に散乱曲線とのフィット、最後にお目当ての$P(r)$関数が入っている。\n", "\n", "必要な部分を`gnom`のoutputから抽出するpythonルーチンは、先程紹介した `shapes_module` の `read_pr` というメソッドを使用してもよいが、ここでは以下の筆者のコード `out_read` を紹介する。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "def out_read(fn):\n", "# input: file name (str)\n", "# output: q,I_{exp}(q),sI_{exp}(q),I_{fit}(q),r,P(r),sP(r),Rg from P(r),I(0) from P(r)\n", "# written by T.Fujisawa\n", "#\n", " fin=open(fn,'r')\n", " lines = []\n", " for line in fin:\n", " #print line\n", " lines.append(line)\n", " npts=len(lines)\n", " j=0\n", " while j < npts:\n", " linet=lines[j]\n", " if 'Real space Rg:' in linet:\n", " a=linet.split(':')[-1]\n", " Rg_r=float(a.split('+-')[0])\n", " j=j+1\n", " elif 'Real space I(0):' in linet:\n", " a=linet.split(':')[-1]\n", " I0_r=float(a.split('+-')[0])\n", " j=j+1\n", " elif ' S J EXP ERROR J REG I REG' in linet:\n", " iqcash=[]\n", " j=j+2\n", " linet=lines[j]\n", " while 'Distance' not in linet:\n", " iqcash.append(linet)\n", " j=j+1\n", " linet=lines[j] \n", " j=j+1\n", " linet=lines[j]\n", " elif ' R P(R) ERROR' in linet:\n", " prcash=[]\n", " j=j+1\n", " while j < npts-1:\n", " j=j+1\n", " linet=lines[j]\n", " prcash.append(linet)\n", " else:\n", " j=j+1\n", " \n", " ra=[]\n", " Pra=[]\n", " sPra=[]\n", " \n", " for line in prcash:\n", " rt=line[:14]\n", " Prt=line[15:26]\n", " sPrt=line[27:38]\n", " #if (' ' not in iqt)or(qt!='n'):\n", " if rt.isspace():\n", " break\n", " if '#' in rt:\n", " continue\n", " elif ' ' not in Prt:\n", " ra.append(float(rt))\n", " Pra.append(float(Prt))\n", " sPra.append(float(sPrt))\n", " \n", " \n", " qe=[]\n", " iqe=[]\n", " siqe=[]\n", " iqm=[]\n", " for line in iqcash:\n", " qt=line[:17]\n", " iqt=line[18:32]\n", " siqt=line[33:47]\n", " iqmt=line[48:62]\n", " #if (' ' not in iqt)or(qt!='n'):\n", " if qt.isspace():\n", " break\n", " if '#' in qt:\n", " continue\n", " elif ' ' not in iqt:\n", " #print iqt\n", " qe.append(float(qt))\n", " iqe.append(float(iqt))\n", " siqe.append(float(siqt))\n", " iqm.append(float(iqmt))\n", " \n", " \n", " return (qe,iqe,siqe,iqm,ra,Pra,sPra,Rg_r,I0_r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "上の関数`out_read`に`out`ファイルを入れてやると目的の情報を抽出してくれる。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "q,I,sI,Im,r,Pr,sPr,Rg,I0=out_read('6lyz.out')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "基本的にjupyter-notebookにおいては入力ファイルをnotebookファイルと同じディレクトリに入れておくものだが、ファイル名の打ち間違いなども\n", "あるので筆者はGUIで処理するようにしている。pythonにおいてはデフォルトのGUI、`thkinter`を使用する。入力したファイル名はセルに出力するようにしておく。\n", "\n", "上の実行例は以下のように変更すると使いやすくなる。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GNOM file: /media/fujisawa/2124879b-e9d4-4135-8fd5-9b114fd41989/home/backup/gifu_19/grad/SAXS_WG/report/source/M_M/6lyz.out\n" ] } ], "source": [ "import tkinter\n", "from tkinter import messagebox as tkMessageBox\n", "from tkinter import filedialog as tkFileDialog\n", "from tkinter import simpledialog as tkSimpleDialog\n", "\n", "root=tkinter.Tk()\n", "root.withdraw()\n", "\n", "inFile=tkFileDialog.askopenfilename(title='Open GNOM file',filetypes=[(\"I(q) file\",\"*.out\")])\n", "print(\"GNOM file:\",inFile)\n", "q,I,sI,Im,r,Pr,sPr,Rg,I0=out_read(inFile)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "これで、`q`,`I`,`sI`,`Im`,`r`,`Pr`,`sPr`,`Rg`,`I0`　の情報が格納された。\n", "\n", "`crysol` の時と同様、`pandas`にデータを格納してプロットをする。\n", "\n", "必要なモジュールをインポートしておき、" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-01-07T09:00:38.263715Z", "start_time": "2020-01-07T09:00:37.716363Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import shapes_module as sm\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.optimize" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "まずは散乱曲線のフィットを`df`というデータフレームに変換する" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-01-07T09:00:40.714079Z", "start_time": "2020-01-07T09:00:40.682524Z" }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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qI(q)errorI(q)_fit
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" ], "text/plain": [ " q I(q) error I(q)_fit\n", "0 0.0000 268.216 8.04647 268.197\n", "1 0.0025 268.083 8.04250 268.066\n", "2 0.0050 267.688 8.03065 267.671\n", "3 0.0075 267.032 8.01095 267.014\n", "4 0.0100 266.115 7.98344 266.098" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({'q': q, 'I(q)': I,'error':sI,'I(q)_fit':Im})\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "同様に$P(r)$関数の方も`df2`というデータフレームに変換する。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-01-07T09:00:44.490868Z", "start_time": "2020-01-07T09:00:44.473054Z" }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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rP(r)Error
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\n", "
" ], "text/plain": [ " r P(r) Error\n", "0 0.0000 0.00000 0.00000\n", "1 0.4464 0.01602 0.01713\n", "2 0.8929 0.03216 0.02797\n", "3 1.3390 0.04850 0.03289\n", "4 1.7860 0.06513 0.03254" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = pd.DataFrame({'r': r, 'P(r)': Pr,'Error':sPr})\n", "df2.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "間接フーリエ変換の結果のチェックは$I(q)$のフィットの具合と$P(r)$の形などで判断するのでプロットをすると、" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-01-07T09:00:45.695736Z", "start_time": "2020-01-07T09:00:44.495878Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '$P(r)$')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(15, 8))\n", "\n", "ax1 = fig.add_subplot(121)\n", "df.plot(ax=ax1,x='q',y='I(q)',color='red',label='I(q)',fontsize=25,logy=True)\n", "df.plot(ax=ax1,x='q',y='I(q)_fit',color='blue',alpha=0.5,label='I(q)_fit',fontsize=25,logy=True)\n", "ax1.set_xlabel('$q$',fontsize=25)\n", "ax1.set_ylabel('$log(I(q))$',fontsize=25)\n", "\n", "ax2 = fig.add_subplot(122)\n", "df2.plot(ax=ax2, legend=False,x='r',y='P(r)',fontsize=25)\n", "ax2.set_xlabel('$r$',fontsize=25)\n", "ax2.set_ylabel('$P(r)$',fontsize=25)\n", "\n", "#fig.savefig(\"gnom_plot.png\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 2 }