{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## crysolの実行\n", "\n", "crysolのヘルプを表示させてみると、" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-12-23T01:33:42.608856Z", "start_time": "2019-12-23T01:33:42.479191Z" }, "scrolled": true }, "outputs": [], "source": [ "!crysol -h" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", " Usage in the batch mode: \n", " crysol []...[] []\n", " [-param1 ]...[-paramN ] \n", "\n", " ：\n", " 途中略\n", " ：\n", " \n", " Examples: \n", " \n", " crysol 6lyz.pdb -lm 20 \n", " Calculate scattering intensity from the PDB file\n", " 6lyz.pdb with Lmax = 20 and without fitting \n", " \n", " crysol mod*.pdb exp.dat -un 2 \n", " Process PDB files with the names beginning with \n", " \"mod\" and fit experimental data exp.dat with \n", " scattering vector given in inverse nanometres. \n", " \n", " crysol *.sav lyzexp.dat \n", " Restore the scattering intensity from all \n", " sav files in the current directory and fit \n", " the experimental data in the file lyzexp.dat \n", "\n", "Report bugs to ." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ご覧のように、コマンド実行の結果がセルとして表示されているのがわかる。\n", "\n", "今、リゾチウムの結晶構造から `6lyz.pdb` を計算してみよう。\n", "`crysol` の引数はデータ点数を201とするために ``-ns 201`` としている。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-12-23T01:33:43.526806Z", "start_time": "2019-12-23T01:33:42.616727Z" }, "scrolled": true }, "outputs": [], "source": [ "!crysol 6lyz.pdb -lm 20 -ns 201" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "*** ------------------------------------------------ *** \n", " *** C R Y S O L Wintel/UNIX/Linux version 2.8.4 *** \n", " *** Please reference: D.Svergun, C.Barberato *** \n", " *** & M.H.J.Koch (1995) J. Appl.Cryst., 28, 768-773 *** \n", " *** Version (LMAX=99) for small and wide angles *** \n", " *** Last revised --- 10/12/18 10:00 *** \n", " *** Copyright (c) ATSAS Team *** \n", " *** EMBL, Hamburg Outstation, 1995 - 2018 *** \n", " *** ------------------------------------------------ *** \n", "\n", " ------------------------------------------------ \n", " Program options : \n", " 0 - evaluate scattering amplitudes and envelope \n", " 1 - evaluate only envelope and Flms \n", " 2 - read CRYSOL information from a .sav file \n", " Type crysol -help for batch mode use \n", " ------------------------------------------------ \n", "\n", " ------------------------------------------------ \n", " Following file names will be used: \n", " 6lyz01.log -- CRYSOL log-file (ASCII) \n", " 6lyz01.int -- scattering intensities (ASCII) \n", " 6lyz01.alm -- net partial amplitudes (binary)\n", " ------------------------------------------------ \n", " ---------- Reciprocal space grid -------------\n", " ( in s = 4*pi*sin(theta)/lambda [1/angstrom] ) \n", " Read atoms and evaluate geometrical center ... \n", " Number of atoms read .................................. : 1001\n", " Number of discarded waters ............................ : 101\n", " Percent processed 10 20 30 40 50 60 70 80 90 100\n", " Processing atoms :>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n", " Center of the excess electron density: 0.452 -0.004 0.276\n", " Processing envelope:>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n", " Processing envelope:>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n", " --- Structural parameters (sizes in angstroms) --- \n", " Electron rg : 13.99 Envelope Rg : 14.08 \n", " Shape Rg : 13.97 Envelope volume : 0.1806E+05\n", " Shell volume : 0.1129E+05 Envelope surface : 3150. \n", " Shell Rg : 18.98 Envelope radius : 25.49 \n", " Shell width : 3.000 Envelope diameter : 49.04 \n", " Molecular weight: 0.1432E+05 Dry volume : 0.1736E+05\n", " Displaced volume: 0.1741E+05 Average atomic rad.: 1.607 \n", " Number of residuals : 129\n", " Average atomic volume .................................. : 17.40\n", " Rg ( Atoms - Excluded volume + Shell ) ................. : 15.04\n", " Rg from the slope of net intensity ..................... : 15.41\n", " Average electron density ............................... : 0.4383\n", " Intensities saved to file 6lyz01.int\n", " I_abs(s)[cm^-1]/c[mg/ml] saved to file 6lyz01.abs\n", " Net amplitudes saved to file 6lyz01.alm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`abs` 、 `alm` 、 `int` それにプログラムのログである `log`　という拡張子のファイルが `crysol` 実行毎に生成される。\n", "\n", "散乱曲線のデータは `abs` と `int` であるが使うのは `int` の方である。\n", "\n", "実際にファイルの中身を一部表示させると、" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-12-23T01:33:43.652205Z", "start_time": "2019-12-23T01:33:43.531530Z" }, "scrolled": true }, "outputs": [], "source": [ "!head \"6lyz00.int\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ " Dif/Atom/Shape/Bord 6lyz.pdb Dro: 0.030 Ra: 1.6074 Rg: 15.41 Vtot: 18057. \n", " 0.000000E+00 0.484317E+07 0.582674E+08 0.338289E+08 0.147208E+06\n", " 0.250000E-02 0.484078E+07 0.582433E+08 0.338151E+08 0.147088E+06\n", " 0.500000E-02 0.483365E+07 0.581719E+08 0.337741E+08 0.146731E+06\n", " 0.750000E-02 0.482179E+07 0.580530E+08 0.337059E+08 0.146137E+06\n", " 0.100000E-01 0.480523E+07 0.578869E+08 0.336106E+08 0.145308E+06\n", " 0.125000E-01 0.478402E+07 0.576741E+08 0.334884E+08 0.144250E+06\n", " 0.150000E-01 0.475822E+07 0.574149E+08 0.333397E+08 0.142965E+06\n", " 0.175000E-01 0.472792E+07 0.571101E+08 0.331648E+08 0.141462E+06\n", " 0.200000E-01 0.469315E+07 0.567603E+08 0.329640E+08 0.139740E+06" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ここで最初の行は理論散乱曲線を計算するためのパラメータで、使用するのは\n", "1列目の $q$ と2列目の $I(q)$ のみである。残念ながら`crysol`の出力はすぐ使えるようになっていないので加工して保存してやる必要がある。\n", "\n", "## 理論データの保存\n", "\n", "さっそくこのファイルを読み込んでみよう。普通にpythonでファイルを読むこともできるが、せっかくなので、 \n", "`pandas` (https://pandas.pydata.org/) と呼ばれるpythonで\n", "データ解析を行うためのパッケージを使ってみる。\n", "\n", "`pandas`には1次元の`Series`と2次元の`DataFrame`という２つのデータ構造が用意されているが、小角では普通`DataFrame`を使う。\n", "\n", "`pandas`だけでなく描画や数値計算など必要なモジュールをインポートしておく" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-12-23T01:34:12.743072Z", "start_time": "2019-12-23T01:34:12.436291Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "タンパク質溶液散乱の場合、散乱強度 $I(q)$ は重量濃度 $c$ で割った形で実験値として使用するので、２列目の $I(q)$\n", "を分子体積 `Vtot` で割っておく。\n", "\n", "`Vtot` は ``header`` に書きこまれているので、最初に `Vtot` だけ取り出しておく。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vtot= 18057.0\n" ] } ], "source": [ "f=open(\"6lyz00.int\")\n", "header=f.readline()\n", "Vtot=float(header.split(\":\")[-1])\n", "print('Vtot=',Vtot)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "df=pd.read_csv(\"6lyz00.int\",delim_whitespace=True,header=0,names=[\"q\",\"I\"],usecols=[0,1])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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qI
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" ], "text/plain": [ " q I\n", "0 0.0000 4843170.0\n", "1 0.0025 4840780.0\n", "2 0.0050 4833650.0\n", "3 0.0075 4821790.0\n", "4 0.0100 4805230.0\n", ".. ... ...\n", "196 0.4900 24998.3\n", "197 0.4925 24965.8\n", "198 0.4950 24943.6\n", "199 0.4975 24931.3\n", "200 0.5000 24928.1\n", "\n", "[201 rows x 2 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "先程書いたように $I(q)$ を $I(q)/c$ に対応させるために２列めに$I(q)/Vtot$、３列目には各点の誤差を入れるのが小角散乱における標準フォーマット\n", "なので仮想的に$I(q)$の3%の値を入れておく。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " q I sI\n", "0 0.0000 268.215650 8.046470\n", "1 0.0025 268.083292 8.042499\n", "2 0.0050 267.688431 8.030653\n", "3 0.0075 267.031622 8.010949\n", "4 0.0100 266.114526 7.983436\n", ".. ... ... ...\n", "196 0.4900 1.384410 0.041532\n", "197 0.4925 1.382611 0.041478\n", "198 0.4950 1.381381 0.041441\n", "199 0.4975 1.380700 0.041421\n", "200 0.5000 1.380523 0.041416\n", "\n", "[201 rows x 3 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"I\"]=df[\"I\"]/Vtot\n", "df[\"sI\"]=df[\"I\"]*3./100\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "このようにして作った`df`をファイル`6lyz.dat`に出力する。\n", "header行だけ先に書いておき" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "f=open(\"6lyz.dat\",\"w\")\n", "headerline=\"# q I(q) sI(q)\\n\"\n", "f.write(headerline)\n", "f.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "次にdataを追記する。フォーマットは指定できる。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "df.to_csv(\"6lyz.dat\",sep=' ',mode=\"a\",float_format='%.6e', header=False, index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ファイルの中身は" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!head \"6lyz.dat\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# q I(q) sI(q)\n", "0.000000e+00 2.682157e+02 8.046470e+00\n", "2.500000e-03 2.680833e+02 8.042499e+00\n", "5.000000e-03 2.676884e+02 8.030653e+00\n", "7.500000e-03 2.670316e+02 8.010949e+00\n", "1.000000e-02 2.661145e+02 7.983436e+00\n", "1.250000e-02 2.649399e+02 7.948197e+00\n", "1.500000e-02 2.635111e+02 7.905333e+00\n", "1.750000e-02 2.618331e+02 7.854993e+00\n", "2.000000e-02 2.599075e+02 7.797225e+00" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## データのプロット\n", "データのプロットはpythonのmatplotlib (https://matplotlib.org/) を使うのが一般的である。\n", "縦軸に$I(q)$の対数を取る。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2019-12-23T01:34:13.724965Z", "start_time": "2019-12-23T01:34:12.769373Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax=df.plot(x='q',y='I',logy=True,fontsize=20,label='6lyz.pdb')\n", "ax.set_xlabel('$q$',fontsize=25)\n", "ax.set_ylabel('$log(I(q))$',fontsize=25)\n", "#plt.savefig('Iq.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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 }