{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Basic File I/O\n", "\n", "## Numpy\n", "* ```np.savetxt```\n", "* ```np.loadtxt```\n", "* ```np.column_stack```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Create some data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import numpy as np\n", "\n", "x = np.linspace(0,10,25)\n", "y_exp = x**0.6 + (np.random.rand(25) -0.5) # ploting data series below\n", "y_1 = x**0.6\n", "y_2 = np.exp(-0.2*x)*np.cos(2*x)*2" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Create a single 2-D array (matrix) for saving to a file" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. -0.32389329 0. 2. ]\n", " [ 0.41666667 0.74336245 0.59138902 1.23729826]\n", " [ 0.83333333 0.81505521 0.89637813 -0.16205647]\n", " [ 1.25 0.6690392 1.14326263 -1.24786255]\n", " [ 1.66666667 1.31372968 1.35865518 -1.40680032]\n", " [ 2.08333333 1.47338264 1.5532997 -0.68433875]\n", " [ 2.5 1.62951114 1.73286211 0.34409962]\n", " [ 2.91666667 2.12909154 1.90078099 1.00503411]\n", " [ 3.33333333 1.5916393 2.05933617 0.95225291]\n", " [ 3.75 2.4290252 2.21013479 0.32747786]\n", " [ 4.16666667 2.5185093 2.35436208 -0.4008769 ]\n", " [ 4.58333333 2.12316273 2.49292301 -0.77320831]\n", " [ 5. 3.00216109 2.6265278 -0.61735433]\n", " [ 5.41666667 3.12816232 2.75574651 -0.10934473]\n", " [ 5.83333333 2.86776065 2.88104523 0.38728711]\n", " [ 6.25 2.93408616 3.00281108 0.57174799]\n", " [ 6.66666667 3.06685286 3.12136995 0.37959133]\n", " [ 7.08333333 3.14361379 3.23699932 -0.01430654]\n", " [ 7.5 3.39452026 3.34993791 -0.33901857]\n", " [ 7.91666667 3.86639188 3.46039293 -0.40735687]\n", " [ 8.33333333 3.4006333 3.56854561 -0.21704892]\n", " [ 8.75 3.5441406 3.67455567 0.0762659 ]\n", " [ 9.16666667 4.20887605 3.7785647 0.2780916 ]\n", " [ 9.58333333 3.40124267 3.88069898 0.27952457]\n", " [10. 3.96130053 3.98107171 0.1104558 ]]\n" ] } ], "source": [ "data = np.column_stack([x,y_exp, y_1, y_2])\n", "print(data)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Save to file" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# x, y_exp, y_1, y_2\n", " 0.000, -0.324, 0.000, 2.000\n", " 0.417, 0.743, 0.591, 1.237\n", " 0.833, 0.815, 0.896, -0.162\n", " 1.250, 0.669, 1.143, -1.248\n", " 1.667, 1.314, 1.359, -1.407\n", " 2.083, 1.473, 1.553, -0.684\n", " 2.500, 1.630, 1.733, 0.344\n", " 2.917, 2.129, 1.901, 1.005\n", " 3.333, 1.592, 2.059, 0.952\n", " 3.750, 2.429, 2.210, 0.327\n", " 4.167, 2.519, 2.354, -0.401\n", " 4.583, 2.123, 2.493, -0.773\n", " 5.000, 3.002, 2.627, -0.617\n", " 5.417, 3.128, 2.756, -0.109\n", " 5.833, 2.868, 2.881, 0.387\n", " 6.250, 2.934, 3.003, 0.572\n", " 6.667, 3.067, 3.121, 0.380\n", " 7.083, 3.144, 3.237, -0.014\n", " 7.500, 3.395, 3.350, -0.339\n", " 7.917, 3.866, 3.460, -0.407\n", " 8.333, 3.401, 3.569, -0.217\n", " 8.750, 3.544, 3.675, 0.076\n", " 9.167, 4.209, 3.779, 0.278\n", " 9.583, 3.401, 3.881, 0.280\n", " 10.000, 3.961, 3.981, 0.110\n" ] } ], "source": [ "np.savetxt(\"data.dat\", data)\n", "\n", "#----- OR THIS\n", "\n", "np.savetxt(\"data.dat\", data, fmt='%10.3f', \n", " header=\"x, y_exp, y_1, y_2\",\n", " delimiter = ',')\n", "\n", "!cat data.dat" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Load from file" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. -0.324 0. 2. ]\n", " [ 0.417 0.743 0.591 1.237]\n", " [ 0.833 0.815 0.896 -0.162]\n", " [ 1.25 0.669 1.143 -1.248]\n", " [ 1.667 1.314 1.359 -1.407]\n", " [ 2.083 1.473 1.553 -0.684]\n", " [ 2.5 1.63 1.733 0.344]\n", " [ 2.917 2.129 1.901 1.005]\n", " [ 3.333 1.592 2.059 0.952]\n", " [ 3.75 2.429 2.21 0.327]\n", " [ 4.167 2.519 2.354 -0.401]\n", " [ 4.583 2.123 2.493 -0.773]\n", " [ 5. 3.002 2.627 -0.617]\n", " [ 5.417 3.128 2.756 -0.109]\n", " [ 5.833 2.868 2.881 0.387]\n", " [ 6.25 2.934 3.003 0.572]\n", " [ 6.667 3.067 3.121 0.38 ]\n", " [ 7.083 3.144 3.237 -0.014]\n", " [ 7.5 3.395 3.35 -0.339]\n", " [ 7.917 3.866 3.46 -0.407]\n", " [ 8.333 3.401 3.569 -0.217]\n", " [ 8.75 3.544 3.675 0.076]\n", " [ 9.167 4.209 3.779 0.278]\n", " [ 9.583 3.401 3.881 0.28 ]\n", " [10. 3.961 3.981 0.11 ]]\n" ] } ], "source": [ "loaded_data = np.loadtxt(\"data.dat\", delimiter = ',') # delimiter needed if used above\n", "print(loaded_data)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## General File IO\n", "### Input\n", "#### ```readline``` function" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# x, y_exp, y_1, y_2\n", "\n", " 0.000, -0.324, 0.000, 2.000\n", "\n" ] } ], "source": [ "ifile = open(\"data.dat\", \"r\") # open a file for reading\n", "s = ifile.readline()\n", "print(s)\n", "s = ifile.readline() # readline function will read the next line in the file\n", "print(s)\n", "ifile.close() # close the file when you're done\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### ```readlines``` function" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# x, y_exp, y_1, y_2\n", " 0.000, -0.324, 0.000, 2.000\n", " 0.417, 0.743, 0.591, 1.237\n", " 0.833, 0.815, 0.896, -0.162\n", " 1.250, 0.669, 1.143, -1.248\n", " 1.667, 1.314, 1.359, -1.407\n", " 2.083, 1.473, 1.553, -0.684\n", " 2.500, 1.630, 1.733, 0.344\n", " 2.917, 2.129, 1.901, 1.005\n", " 3.333, 1.592, 2.059, 0.952\n", " 3.750, 2.429, 2.210, 0.327\n", " 4.167, 2.519, 2.354, -0.401\n", " 4.583, 2.123, 2.493, -0.773\n", " 5.000, 3.002, 2.627, -0.617\n", " 5.417, 3.128, 2.756, -0.109\n", " 5.833, 2.868, 2.881, 0.387\n", " 6.250, 2.934, 3.003, 0.572\n", " 6.667, 3.067, 3.121, 0.380\n", " 7.083, 3.144, 3.237, -0.014\n", " 7.500, 3.395, 3.350, -0.339\n", " 7.917, 3.866, 3.460, -0.407\n", " 8.333, 3.401, 3.569, -0.217\n", " 8.750, 3.544, 3.675, 0.076\n", " 9.167, 4.209, 3.779, 0.278\n", " 9.583, 3.401, 3.881, 0.280\n", " 10.000, 3.961, 3.981, 0.110\n" ] } ], "source": [ "ifile = open(\"data.dat\", \"r\")\n", "lines = ifile.readlines() # readlines function gets a list of all lines\n", "for line in lines :\n", " print(line, end='')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### ```split``` function" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 10.000, 3.961, 3.981, 0.110\n", "\n", "['10.000,', '3.961,', '3.981,', '0.110']\n", "10.000,\n", "3.961,\n", "3.981,\n", "0.110\n" ] } ], "source": [ "words = line.split()\n", "print(line)\n", "print(words)\n", "for w in words:\n", " print(w)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Output\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Some header\n", "some new line. This isnt a new line\n", "The third line" ] } ], "source": [ "ofile = open(\"output.dat\", \"w\") # Open a file for writing: ofile can be any name\n", "ofile.write(\"Some header\\n\") # write some text: note the \"\\n\" makes a new line\n", "ofile.write(\"some new line.\") # write another line of text (note there is no \\n)\n", "ofile.write(\" This isnt a new line\\n\") # and another\n", "ofile.write(\"The third line\") # and other\n", "ofile.close() # close the file\n", "\n", "!cat output.dat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Triple strings preserve white space" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hi there,\n", "How is your day going so far?\n", "\n", " I hope you are good." ] } ], "source": [ "ofile = open(\"triplestring.dat\", \"w\")\n", "s = \"\"\"Hi there,\n", "How is your day going so far?\n", "\n", " I hope you are good.\"\"\"\n", "\n", "ofile.write(s)\n", "ofile.close()\n", "!cat triplestring.dat" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "base", "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": "3.14.6" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }