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Jupyter Notebook: matplotlibでグラフ作成
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"# matplotlibでグラフ作成\n",
"\n",
"\n",
"## Python3のインストール\n",
"\n",
"Python3は,brew install python3 で普通にインストール.Puyhon3.6.0がインストールされます.\n",
"\n",
"あとは pip3 をアップグレードして,モジュール類を pip3 でインストールしています.\n",
"\n",
"```\n",
"brew install python3 # Python3.6.0\n",
"pip3 install --upgrade setuptools\n",
"pip3 install --upgrade pip\n",
"brew linkapps\n",
"pip3 install numpy\n",
"pip3 install scipy\n",
"pip3 install matplotlib\n",
"pip3 install Pillow\n",
"```\n",
"\n",
"## 通常のPythonでの使い方\n",
"\n",
"コマンドラインで入力データファイルと出力画像ファイルを指定\n",
"\n",
"```\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import sys\n",
"\n",
"# Parameter setting\n",
"param=sys.argv\n",
"fnameR=param[1] # input filename\n",
"fnameF=param[2] # output image file name\n",
"\n",
"......\n",
"\n",
"plt.savefig(fnameF,dpi=200, bbox_inches='tight', pad_inches=0.2)\n",
"plt.show()\n",
"plt.close()\n",
"```\n",
"\n",
"## Jupyter Notebookのインストール\n",
"\n",
"ここ( http://jupyter.org/install.html )に従ってインストール.\n",
"ただし,Python3, numpy, scipy, matplotlibは導入済みの環境です. \n",
"\n",
"```\n",
"pip3 install --upgrade pip\n",
"pip3 install jupyter\n",
"```\n",
"\n",
"これでインストールは完了.以下で起動できます.\n",
"\n",
"```\n",
"jupyter notebook\n",
"```\n",
"\n",
"起動すると,起動したディレクトリ内の,子ディレクトリとファイルが並んだ画面が現れます.\n",
"\n",
"\n",
"\n",
"## Jupyter Nootbookでグラグを表示\n",
"Jupyter notebookでグラフを表示するため,コードの先頭に以下を記載.\n",
"```\n",
"%matplotlib inline\n",
"``` \n",
"\n",
"でも毎回起動のたびに書き込むのはめんどう.そこで...\n",
"\n",
"ここ(http://keisanbutsuriya.hateblo.jp/entry/2015/11/28/220429) に従い,以下を実行.\n",
"\n",
"```\n",
"ipython profile create\n",
"```\n",
"\n",
"これで~/.ipython/profile_defaultにipython_config.pyというファイルができるのでこれを開いて編集.\n",
"\n",
"```\n",
"## lines of code to run at IPython startup.\n",
"#c.InteractiveShellApp.exec_lines = []\n",
"c.InteractiveShellApp.exec_lines = ['%matplotlib inline']\n",
"```\n",
"\n",
"上記の部分3行目 c.InteractiveShellApp.exec_lines = ['%matplotlib inline'] を書き込む.\n",
"\n",
"これで,毎回%matplotlib inlineを書き込まずにすむ.\n",
"\n",
"\n",
"## とりあえずプロット\n",
"ここではnumpyでファイル'out_weir.txt'からデータを読み込む\n",
"\n",
"まずは,x軸データをQQ,y軸をEL1およびEL2として,プロットしてみる\n",
"\n",
"入力データは以下のような形式のものである.\n",
"\n",
"```\n",
" elv1 elv2 Q h2/h1 x1 x2 x3\n",
" 63.000 60.290 0.000\n",
" 63.062 60.324 1.000 -43.461 0.062 -99.000 -99.000\n",
" 63.097 60.358 2.000 -27.262 0.097 -99.000 -99.000\n",
" 63.126 60.392 3.000 -20.684 0.126 -99.000 -99.000\n",
"\n",
" ...... ...... ..... ...... ...... ...... ......\n",
"```\n"
]
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"data": {
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"[<matplotlib.lines.Line2D at 0x105047588>]"
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95I+vuso3nzjggGjrEpH8lm4LfTgwPYRwDNAGWADcE0I4IYTQFngO+FWWavQt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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x104f93198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def DINP(fnameR):\n",
" data=np.loadtxt(fnameR,skiprows=1,usecols=(0,1,2))\n",
" EL1=data[:,0]\n",
" EL2=data[:,1]\n",
" QQ=data[:,2]\n",
" return EL1,EL2,QQ\n",
"\n",
"fnameR='out_weir.txt'\n",
"EL1,EL2,QQ=DINP(fnameR)\n",
"# Plot\n",
"fig=plt.figure()\n",
"plt.plot(QQ,EL1,color='red',lw=2.0,label='Upstream WL')\n",
"plt.plot(QQ,EL2,color='blue',lw=2.0,label='Downstream WL')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## グラフの見栄えを調整 (1)\n",
"\n",
"+ グラフの大きさを指定: fig=plt.figure(figsize=(8,6))\n",
"+ 軸ラベル表示(xlabel, ylabel)\n",
"+ 軸範囲設定 (xlim, ylim)\n",
"+ 目盛り設定(xticks, yticks)\n",
"+ グリッド表示(grid)\n",
"+ 凡例表示(legend)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x10426f780>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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dO3akTp06AUQlIhLbotkdbApsAp52zrUB5gKjvfc7Q+d7ARu9998c6wekpaVxwQUXsGnT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\nJPeIiIiWSXKPiIhomST3iIiIlvkvbm2nX8LDAj4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1040e57f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def DINP(fnameR):\n",
" data=np.loadtxt(fnameR,skiprows=1,usecols=(0,1,2))\n",
" EL1=data[:,0]\n",
" EL2=data[:,1]\n",
" QQ=data[:,2]\n",
" return EL1,EL2,QQ\n",
"\n",
"fnameR='out_weir.txt'\n",
"EL1,EL2,QQ=DINP(fnameR)\n",
"\n",
"qmin=0.0;qmax=700.0;dq=100\n",
"emin=60.0;emax=68.0;de=1.0\n",
"\n",
"fig=plt.figure(figsize=(8,6))\n",
"plt.plot(QQ,EL1,color='red',lw=2.0,label='Upstream WL')\n",
"plt.plot(QQ,EL2,color='blue',lw=2.0,label='Downstream WL')\n",
"plt.xlabel('Discharge (m$^3$/s)')\n",
"plt.ylabel('Elevation (EL.m)')\n",
"plt.xlim(qmin,qmax)\n",
"plt.ylim(emin,emax)\n",
"\n",
"plt.xticks(np.arange(qmin,qmax+dq,dq))\n",
"plt.yticks(np.arange(emin,emax+de,de))\n",
"\n",
"plt.grid()\n",
"plt.legend(shadow=True,loc='upper left',handlelength=3)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## グラフの見栄えを調整 (2)\n",
"\n",
"軸目盛りをtickerを用いて描画\n",
"\n",
"```\n",
"......\n",
"from matplotlib.ticker import *\n",
"......\n",
"ax = plt.gca()\n",
"ax.xaxis.set_major_locator(MultipleLocator(dq))\n",
"ax.xaxis.set_minor_locator(MultipleLocator(dq/10))\n",
"ax.yaxis.set_major_locator(MultipleLocator(de))\n",
"ax.yaxis.set_minor_locator(MultipleLocator(de/2))\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x103f856a0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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nngt16wYXp8QeJXeRRBLeRGXKFOuRT5liC5r3vlZ+/PGWwLt3t955q1aaLh0j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\n3HsBZ3vvtzjnvgc67O+au3N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mh+W9929774dnZGQEHUqlKyqCv/4VLrwQdu5M5fLL4Ysv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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x103842978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.ticker import *\n",
"\n",
"def DINP(fnameR):\n",
" data=np.loadtxt(fnameR,skiprows=1,usecols=(0,1,2))\n",
" EL1=data[:,0]\n",
" EL2=data[:,1]\n",
" QQ=data[:,2]\n",
" return EL1,EL2,QQ\n",
"\n",
"fnameR='out_weir.txt'\n",
"EL1,EL2,QQ=DINP(fnameR)\n",
"\n",
"qmin=0.0;qmax=700.0;dq=100\n",
"emin=60.0;emax=68.0;de=1.0\n",
"\n",
"fig=plt.figure(figsize=(8,6))\n",
"plt.plot(QQ,EL1,color='red',lw=2.0,label='Upstream WL')\n",
"plt.plot(QQ,EL2,color='blue',lw=2.0,label='Downstream WL')\n",
"plt.xlabel('Discharge (m$^3$/s)')\n",
"plt.ylabel('Elevation (EL.m)')\n",
"plt.xlim(qmin,qmax)\n",
"plt.ylim(emin,emax)\n",
"\n",
"ax = plt.gca()\n",
"ax.xaxis.set_major_locator(MultipleLocator(dq))\n",
"ax.xaxis.set_minor_locator(MultipleLocator(dq/10))\n",
"ax.yaxis.set_major_locator(MultipleLocator(de))\n",
"ax.yaxis.set_minor_locator(MultipleLocator(de/2))\n",
"\n",
"plt.grid()\n",
"plt.legend(shadow=True,loc='upper left',handlelength=3)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"## 情報の追加\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10d9cff98>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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UxSPBXQjhWNeumXFxyzj5pk0mmuRWpYpJS2Z5BAWxITGRsPDw0i+zi7NslWoJ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d8e7b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def DINP(fnameR):\n",
" data=np.loadtxt(fnameR,skiprows=1,usecols=(0,1,2))\n",
" EL1=data[:,0]\n",
" EL2=data[:,1]\n",
" QQ=data[:,2]\n",
" return EL1,EL2,QQ\n",
"\n",
"fnameR='out_weir.txt'\n",
"EL1,EL2,QQ=DINP(fnameR)\n",
"\n",
"qmin=0.0;qmax=700.0;dq=100\n",
"emin=60.0;emax=68.0;de=1.0\n",
"\n",
"fig=plt.figure(figsize=(8,6))\n",
"plt.plot(QQ,EL1,color='red',lw=2.0,label='Upstream WL')\n",
"plt.plot(QQ,EL2,color='blue',lw=2.0,label='Downstream WL')\n",
"plt.xlabel('Discharge (m$^3$/s)')\n",
"plt.ylabel('Elevation (EL.m)')\n",
"plt.xlim(qmin,qmax)\n",
"plt.ylim(emin,emax)\n",
"\n",
"plt.xticks(np.arange(qmin,qmax+dq,dq))\n",
"plt.yticks(np.arange(emin,emax+de,de))\n",
"\n",
"plt.grid()\n",
"plt.legend(shadow=True,loc='upper left',handlelength=3)\n",
"\n",
"QQ1=87\n",
"for i in range(0,len(EL1)):\n",
" if QQ[i]==QQ1: break\n",
"elv1=EL1[i]; s1='EL'+'{0:.1f}'.format(elv1); s2='Q='+'{0:.0f}m$^3$/s'.format(QQ1)\n",
"plt.plot([0,QQ1],[elv1,elv1],color='black',lw=1)\n",
"plt.plot([QQ1,QQ1],[emin,elv1],color='black',lw=1)\n",
"plt.text(QQ1,elv1,s1, ha='right', va='bottom', fontsize=9)\n",
"plt.text(QQ1+5,61,s2, ha='left', va='bottom', rotation=90, fontsize=9)\n",
"\n",
"QQ1=290\n",
"for i in range(0,len(EL1)):\n",
" if QQ[i]==QQ1: break\n",
"elv1=EL1[i]; s1='EL'+'{0:.1f}'.format(elv1); s2='Q='+'{0:.0f}m$^3$/s'.format(QQ1)\n",
"plt.plot([0,QQ1],[elv1,elv1],color='black',lw=1)\n",
"plt.plot([QQ1,QQ1],[emin,elv1],color='black',lw=1)\n",
"plt.text(QQ1,elv1,s1, ha='right', va='bottom', fontsize=9)\n",
"plt.text(QQ1+5,61,s2, ha='left', va='bottom', rotation=90, fontsize=9)\n",
"\n",
"QQ1=580\n",
"for i in range(0,len(EL1)):\n",
" if QQ[i]==QQ1: break\n",
"elv1=EL1[i]; s1='EL'+'{0:.1f}'.format(elv1); s2='Q='+'{0:.0f}m$^3$/s'.format(QQ1)\n",
"plt.plot([0,QQ1],[elv1,elv1],color='black',lw=1)\n",
"plt.plot([QQ1,QQ1],[emin,elv1],color='black',lw=1)\n",
"plt.text(QQ1,elv1,s1, ha='right', va='bottom', fontsize=9)\n",
"plt.text(QQ1+5,61,s2, ha='left', va='bottom', rotation=90, fontsize=9)\n",
"\n",
"plt.hlines([60.3],qmin,qmax,linestyle='solid')\n",
"plt.hlines([63.0],qmin,qmax,linestyle='dashed')\n",
"plt.text(0.5*(qmin+qmax),63.0,'EL.63.0 (Top of weir, B=50m)', ha='center', va='bottom')\n",
"plt.text(0.5*(qmin+qmax),60.3,'EL.60.3 (Bottom of river)', ha='center', va='bottom')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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