diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..286dcb3 --- /dev/null +++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/.ipynb_checkpoints/tutorial1-checkpoint.ipynb b/.ipynb_checkpoints/tutorial1-checkpoint.ipynb index 0bdf0e8..9d204eb 100755 --- a/.ipynb_checkpoints/tutorial1-checkpoint.ipynb +++ b/.ipynb_checkpoints/tutorial1-checkpoint.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "kanatani" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -685,13 +692,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2016-04-13T22:41:18.151992", "start_time": "2016-04-13T22:41:18.145822" }, - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -799,12 +806,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0 0.0\n", + "2.0 0.0\n", + "2.0 1.0\n" + ] + }, + { + "data": { + "image/png": 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MkpsHN34webLbj6ewFynlysHw4fD660G9fPD69HFVYtO9bYt5Qb0L2HZ4G9sO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\nWwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(-3.0*np.pi, 3.0*np.pi, 256)\n", + "sigmax0c=[[1.0,0.0,\"blue\"],[2.0,0.0,\"red\"],[2.0,1.0,\"green\"]]\n", + "for sigma,x0,c in sigmax0c:\n", + " print sigma,x0 \n", + " y = sigma/np.sqrt(2.0*np.pi)*np.exp(-(x-x0)**2.0/2.0/sigma**2.0)\n", + " plt.plot(x, y,color=c)\n", + " plt.xlabel(\"x\",fontsize=14)\n", + " plt.ylabel('f(x)',fontsize=14)\n", + " plt.xlim(x.min() * 1.1, x.max() * 1.1)\n", + " plt.ylim(y.min() * 1.1, y.max() * 1.1)" + ] }, { "cell_type": "markdown", @@ -824,12 +862,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#一つのファイルやファイルの集合に対して時間とともに加えられていく変更を記録するシステムで、後で特定バージョンを呼び出すことができるようにするためのもの" + ] }, { "cell_type": "markdown", @@ -845,7 +885,9 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#並行して行われる複数の機能追加やバージョン管理を支援するための機能,履歴の流れを分岐して記録していく" + ] }, { "cell_type": "markdown", @@ -861,7 +903,9 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#ファイルやディレクトリの追加・変更をリポジトリに記録する操作" + ] }, { "cell_type": "markdown", @@ -877,7 +921,9 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#複数の履歴の流れを合流させること,ブランチを統合ブランチに統合させる" + ] }, { "cell_type": "markdown", @@ -893,7 +939,9 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#リモートリポジトリで自分の手元のローカルリポジトリの変更履歴を共有するために、ローカルリポジトリ内の変更履歴をアップロードする操作" + ] }, { "cell_type": "markdown", @@ -909,7 +957,9 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#リモートリポジトリからローカルリポジトリに、他の人がプッシュした変更内容を取り込む操作" + ] }, { "cell_type": "markdown", diff --git a/.ipynb_checkpoints/tutorial2-checkpoint.ipynb b/.ipynb_checkpoints/tutorial2-checkpoint.ipynb index 1daf2a3..53c8cb4 100644 --- a/.ipynb_checkpoints/tutorial2-checkpoint.ipynb +++ b/.ipynb_checkpoints/tutorial2-checkpoint.ipynb @@ -11,9 +11,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Numpy.array, 線形代数\n", + "1. Numpy.array\n", "\n", - "関数・クラス" + "2. 線形代数" ] }, { @@ -324,17 +324,43 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2016-04-14T17:44:10.734183", "start_time": "2016-04-14T17:44:10.732362" }, - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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OLS+7n0VY84YNzlDuRN7RHuM89OZTmx8++PXXWleqpPXt21Z/fWdx44bWX3xh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\nKZPJlyEh5mzltdegaVNTjNKeZKW8EMJ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# データの読み込み、描画" + "%matplotlib inline \n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "x = np.linspace(0, 1.0, 20)\n", + "y = np.sin(x*5.0) + np.random.randn(len(x))*0.1\n", + "plt.plot(x, y, 'ro')\n", + "\n", + "Nname=[[1,'N=1'],[3,'N=3'],[10,'N=10']]\n", + "for N,name in Nname:\n", + " z = np.poly1d(np.polyfit(x, y, N))\n", + " t = np.linspace(0, 1.0, 200)\n", + " plt.plot(t,z(t),'-',label=name)\n", + " plt.ylim(y.min() * 1.1, y.max() * 1.1)\n", + " plt.legend(loc='upper right')\n", + " plt.xlabel(\"x\",fontsize=14)\n", + " plt.ylabel('y',fontsize=14)" ] }, { diff --git a/.ipynb_checkpoints/tutorial3-checkpoint.ipynb b/.ipynb_checkpoints/tutorial3-checkpoint.ipynb index aa30e00..b27229d 100644 --- a/.ipynb_checkpoints/tutorial3-checkpoint.ipynb +++ b/.ipynb_checkpoints/tutorial3-checkpoint.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2016-04-14T18:06:11.631499", @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2016-04-14T18:07:35.308000", @@ -63,10 +63,10 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, @@ -74,7 +74,7 @@ "data": { "image/png": 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LiqBrV51laJKhoEBHfiR9F6N9++Cdd2wzliSpVw969cru8MVEJ/QpU2wxrqQ5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\nEJbQjTEmISyhG2NMQvx/u7BLb8aDDPkAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -113,7 +113,18 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "A=1.0\n", + "ganma=\n", + "x0=\n", + "x=np.arange(0, 6, 0.1)\n", + "y=A*ganma/(1+((x-x0)/ganma)**2.0)\n", + "plt.plot(x, y)\n", + "plt.xlabel(\"x\",fontsize=14)\n", + "plt.ylabel('L',fontsize=14)\n", + "plt.xlim(x.min() * 1.1, x.max() * 1.1)\n", + "plt.ylim(y.min() * 1.1, y.max() * 1.1)" + ] }, { "cell_type": "markdown", diff --git a/tutorial1.ipynb b/tutorial1.ipynb index 0bdf0e8..9d204eb 100755 --- a/tutorial1.ipynb +++ b/tutorial1.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "kanatani" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -685,13 +692,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2016-04-13T22:41:18.151992", "start_time": "2016-04-13T22:41:18.145822" }, - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -799,12 +806,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0 0.0\n", + "2.0 0.0\n", + "2.0 1.0\n" + ] + }, + { + "data": { + "image/png": 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MkpsHN34webLbj6ewFynlysHw4fD660G9fPD69HFVYtO9bYt5Qb0L2HZ4G9sO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\nWwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(-3.0*np.pi, 3.0*np.pi, 256)\n", + "sigmax0c=[[1.0,0.0,\"blue\"],[2.0,0.0,\"red\"],[2.0,1.0,\"green\"]]\n", + "for sigma,x0,c in sigmax0c:\n", + " print sigma,x0 \n", + " y = sigma/np.sqrt(2.0*np.pi)*np.exp(-(x-x0)**2.0/2.0/sigma**2.0)\n", + " plt.plot(x, y,color=c)\n", + " plt.xlabel(\"x\",fontsize=14)\n", + " plt.ylabel('f(x)',fontsize=14)\n", + " plt.xlim(x.min() * 1.1, x.max() * 1.1)\n", + " plt.ylim(y.min() * 1.1, y.max() * 1.1)" + ] }, { "cell_type": "markdown", @@ -824,12 +862,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#一つのファイルやファイルの集合に対して時間とともに加えられていく変更を記録するシステムで、後で特定バージョンを呼び出すことができるようにするためのもの" + ] }, { "cell_type": "markdown", @@ -845,7 +885,9 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#並行して行われる複数の機能追加やバージョン管理を支援するための機能,履歴の流れを分岐して記録していく" + ] }, { "cell_type": "markdown", @@ -861,7 +903,9 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#ファイルやディレクトリの追加・変更をリポジトリに記録する操作" + ] }, { "cell_type": "markdown", @@ -877,7 +921,9 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#複数の履歴の流れを合流させること,ブランチを統合ブランチに統合させる" + ] }, { "cell_type": "markdown", @@ -893,7 +939,9 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#リモートリポジトリで自分の手元のローカルリポジトリの変更履歴を共有するために、ローカルリポジトリ内の変更履歴をアップロードする操作" + ] }, { "cell_type": "markdown", @@ -909,7 +957,9 @@ "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "#リモートリポジトリからローカルリポジトリに、他の人がプッシュした変更内容を取り込む操作" + ] }, { "cell_type": "markdown", diff --git a/tutorial2.ipynb b/tutorial2.ipynb index e6bc8ae..53c8cb4 100644 --- a/tutorial2.ipynb +++ b/tutorial2.ipynb @@ -324,17 +324,43 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2016-04-14T17:44:10.734183", "start_time": "2016-04-14T17:44:10.732362" }, - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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OLS+7n0VY84YNzlDuRN7RHuM89OZTmx8++PXXWleqpPXt21Z/fWdx44bWX3xh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\nKZPJlyEh5mzltdegaVNTjNKeZKW8EMJ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# データの読み込み、描画" + "%matplotlib inline \n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "x = np.linspace(0, 1.0, 20)\n", + "y = np.sin(x*5.0) + np.random.randn(len(x))*0.1\n", + "plt.plot(x, y, 'ro')\n", + "\n", + "Nname=[[1,'N=1'],[3,'N=3'],[10,'N=10']]\n", + "for N,name in Nname:\n", + " z = np.poly1d(np.polyfit(x, y, N))\n", + " t = np.linspace(0, 1.0, 200)\n", + " plt.plot(t,z(t),'-',label=name)\n", + " plt.ylim(y.min() * 1.1, y.max() * 1.1)\n", + " plt.legend(loc='upper right')\n", + " plt.xlabel(\"x\",fontsize=14)\n", + " plt.ylabel('y',fontsize=14)" ] }, {