{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building a LAS file from scratch" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import lasio\n", "\n", "import datetime\n", "import numpy\n", "import os\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1\n", "\n", "Create some fake data, and make some of the values at the bottom NULL (``numpy.nan``). Note that of course every curve in a LAS file is recorded against a reference/index, either depth or time, so we create that array too." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "depths = numpy.arange(10, 50, 0.5)\n", "fake_curve = numpy.random.random(len(depths))\n", "fake_curve[-10:] = numpy.nan # Add some null values at the bottom" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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tQP6AqhDcjjVr2Hc/6aTZx9hU7iMj3LasjoAqnCJ3Sa5lee4vhwoVm1Lue/dW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\nU+l3YnGeexxxuJDn7kLhMFd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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(depths, fake_curve)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2\n", "\n", "Create an empty LASFile object and review its header section" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "l = lasio.LASFile()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "{'Curves': [],\n", " 'Other': '',\n", " 'Parameter': [],\n", " 'Version': [HeaderItem(mnemonic=VERS, unit=, value=2.0, descr=CWLS log ASCII Standard -VERSION 2.0, original_mnemonic=VERS),\n", " HeaderItem(mnemonic=WRAP, unit=, value=NO, descr=One line per depth step, original_mnemonic=WRAP),\n", " HeaderItem(mnemonic=DLM, unit=, value=SPACE, descr=Column Data Section Delimiter, original_mnemonic=DLM)],\n", " 'Well': [HeaderItem(mnemonic=STRT, unit=m, value=nan, descr=START DEPTH, original_mnemonic=STRT),\n", " HeaderItem(mnemonic=STOP, unit=m, value=nan, descr=STOP DEPTH, original_mnemonic=STOP),\n", " HeaderItem(mnemonic=STEP, unit=m, value=nan, descr=STEP, original_mnemonic=STEP),\n", " HeaderItem(mnemonic=NULL, unit=, value=-9999.25, descr=NULL VALUE, original_mnemonic=NULL),\n", " HeaderItem(mnemonic=COMP, unit=, value=, descr=COMPANY, original_mnemonic=COMP),\n", " HeaderItem(mnemonic=WELL, unit=, value=, descr=WELL, original_mnemonic=WELL),\n", " HeaderItem(mnemonic=FLD, unit=, value=, descr=FIELD, original_mnemonic=FLD),\n", " HeaderItem(mnemonic=LOC, unit=, value=, descr=LOCATION, original_mnemonic=LOC),\n", " HeaderItem(mnemonic=PROV, unit=, value=, descr=PROVINCE, original_mnemonic=PROV),\n", " HeaderItem(mnemonic=CNTY, unit=, value=, descr=COUNTY, original_mnemonic=CNTY),\n", " HeaderItem(mnemonic=STAT, unit=, value=, descr=STATE, original_mnemonic=STAT),\n", " HeaderItem(mnemonic=CTRY, unit=, value=, descr=COUNTRY, original_mnemonic=CTRY),\n", " HeaderItem(mnemonic=SRVC, unit=, value=, descr=SERVICE COMPANY, original_mnemonic=SRVC),\n", " HeaderItem(mnemonic=DATE, unit=, value=, descr=DATE, original_mnemonic=DATE),\n", " HeaderItem(mnemonic=UWI, unit=, value=, descr=UNIQUE WELL ID, original_mnemonic=UWI),\n", " HeaderItem(mnemonic=API, unit=, value=, descr=API NUMBER, original_mnemonic=API)]}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l.header" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's add some information to the header: \n", "\n", "- the date\n", "- the operator (in the Parameter section)\n", "- a description of the file in the Other section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's change the date." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "l.well.DATE = str(datetime.datetime.today())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's make a new item in the ~Parameters section for the operator. To do this we need to make a new ``HeaderItem``:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "l.params['ENGI'] = lasio.HeaderItem(\"ENGI\", \"\", \"kinverarity@hotmail.com\", \"Creator of this file...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally, add some free text to the ~Other section:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "l.other = \"Example of how to create a LAS file from scratch using lasio\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3\n", "\n", "Add the curves to the LAS file using the ``add_curve`` method:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "l.add_curve('DEPT', depths, unit='m')\n", "l.add_curve('FAKE_CURVE', fake_curve, descr='fake curve')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4\n", "\n", "Now let's write out two files: one according to the LAS file specification version 1.2, and one according to 2.0. Note that by default an empty ``LASFile`` object is version 2.0." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "fn = \"scratch_example_v2.las\"\n", "with open(fn, mode=\"w\") as f: # Write LAS file to disk\n", " l.write(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and let's see if that worked" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "~Version ---------------------------------------------------\n", "VERS. 2.0 : CWLS log ASCII Standard -VERSION 2.0\n", "WRAP. NO : One line per depth step\n", "DLM . SPACE : Column Data Section Delimiter\n", "~Well ------------------------------------------------------\n", "STRT.m 10.0 : START DEPTH\n", "STOP.m 49.5 : STOP DEPTH\n", "STEP.m 0.5 : STEP\n", "NULL. -9999.25 : NULL VALUE\n", "COMP. : COMPANY\n", "WELL. : WELL\n", "FLD . : FIELD\n", "LOC . : LOCATION\n", "PROV. : PROVINCE\n", "CNTY. : COUNTY\n", "STAT. : STATE\n", "CTRY. : COUNTRY\n", "SRVC. : SERVICE COMPANY\n", "DATE. 2016-02-13 16:52:39.445000 : DATE\n", "UWI . : UNIQUE WELL ID\n", "API . : API NUMBER\n", "~Curves ----------------------------------------------------\n", "DEPT .m : \n", "FAKE_CURVE. : fake curve\n", "~Params ----------------------------------------------------\n", "ENGI. kinverarity@hotmail.com : Creator of this file...\n", "~Other -----------------------------------------------------\n", "Example of how to create a LAS file from scratch using lasio\n", "~ASCII -----------------------------------------------------\n", " 10 0.026008\n", " 10.5 0.056402\n", " 11 0.58473\n", " 11.5 0.062889\n", " 12 0.063426\n", " 12.5 0.38872\n", " 13 0.75342\n", " 13.5 0.93299\n", " 14 0.70876\n", " 14.5 0.21405\n", " 15 0.948\n", " 15.5 0.96069\n", " 16 0.95769\n", " 16.5 0.47167\n", " 17 0.92044\n", " 17.5 0.45118\n", " 18 0.41889\n", " 18.5 0.42029\n", " 19 0.30528\n", " 19.5 0.15235\n", " 20 0.088225\n", " 20.5 0.38494\n", " 21 0.026402\n", " 21.5 0.55415\n", " 22 0.33504\n", " 22.5 0.27227\n", " 23 0.15694\n", " 23.5 0.67574\n", " 24 0.61331\n", " 24.5 0.39824\n", " 25 0.12945\n", " 25.5 0.14877\n", " 26 0.93639\n", " 26.5 0.2013\n", " 27 0.76118\n", " 27.5 0.44274\n", " 28 0.0078593\n", " 28.5 0.86707\n", " 29 0.077415\n", " 29.5 0.61982\n", " 30 0.92987\n", " 30.5 0.4554\n", " 31 0.90304\n", " 31.5 0.23425\n", " 32 0.85763\n", " 32.5 0.03689\n", " 33 0.53212\n", " 33.5 0.48849\n", " 34 0.55165\n", " 34.5 0.55116\n", " 35 0.0071184\n", " 35.5 0.16983\n", " 36 0.82744\n", " 36.5 0.80642\n", " 37 0.46575\n", " 37.5 0.91243\n", " 38 0.72884\n", " 38.5 0.94173\n", " 39 0.16294\n", " 39.5 0.97191\n", " 40 0.25037\n", " 40.5 0.57772\n", " 41 0.40011\n", " 41.5 0.63539\n", " 42 0.70898\n", " 42.5 0.042878\n", " 43 0.20613\n", " 43.5 0.25082\n", " 44 0.75637\n", " 44.5 0.87049\n", " 45 -9999.25\n", " 45.5 -9999.25\n", " 46 -9999.25\n", " 46.5 -9999.25\n", " 47 -9999.25\n", " 47.5 -9999.25\n", " 48 -9999.25\n", " 48.5 -9999.25\n", " 49 -9999.25\n", " 49.5 -9999.25\n", "\n" ] } ], "source": [ "with open(fn, mode=\"r\") as f: # Show the result... \n", " print(f.read())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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tQP6AqhDcjjVr2Hc/6aTZx9hU7iMj3LasjoAqnCJ3Sa5lee4vhwoVm1Lue/dW\n15YxRe5pt6HSdzdF7nGpkOEJu2hRut+fdY/WOKR57kAxtswPfwhccAGwdu3M5xcs0BNYup57njRI\nIL8tMzjIx65aFR9UVSH3rPWQirBkAMdsmXpU7qrk7qotMzzMGUh51x6kDeg8vrvqIqailLvqIiZd\nzz1IoK2trLSzWlkA71971VWznw/u8aoC3UVMNmwZnYCqLF62YkU8uSfZMlEBbx00NLm7ki2Tp/SA\nhLRl0jx3l5U7kF+9pynaPBkzqso9KqC6eLF+QDWvcpfkeehQduUuF8XF+e69vcADCZtdHjgA/OY3\nwBvfGN8+VajaMl1dfN7+/mJsmTjlLrdBXLkymtxHRrhf58+Pb0Me372INEjAMXIfGuJB64pyz1N6\nIHjeNOXusucuv4u85N5Iyj2N3OfMYaLety97QBVItmYefBC4/PL4Uh4/+AHw+tdHv78t5d7UxIS5\na1e5AdW+Pv7e48g9qfRA2rlV0LDKffHi+lLuMlumqp67KXJ3Qbm7Qu5A+rhIC6jKdsQp96Ehngs3\nx5Twi7NkgGzkrjpPFi7kDJUiPPc4WyaN3JMsGYk8C5mKKBoGOEbuQ0PAUUfVn3JPUmiA25770BAT\nW70o9ywrVHVsGdWJm2bXpdkyae0YHARe+1rgS19i+yeIffuAzZuBSy6J/t+ODr3xqGrLAEzuO3YU\n47nH2TJBz33HjtrWfxJJwdTgub1y18DwMG/YUE/KvauLV+QleYyu2zInnljfyr2/f/YED8KWck+y\n69ICqkCyLTM0BPzxH7P18pWvzHztzjuZ2OPaacuWAWrkXpTnHqfcFy7k1xct4otsEJ7cLWB4uD6V\nO1BtW+akk6qn3KNuyaMCqs3N/NzAQPz5w6rQBLmnrX8I3/bHKfckW6azE/joR4Gbbpo5vpIsGaAa\ntkz4u40rHhcXUF20iP+Osmb27FEj96wB1YYkd2nL1JNyVyV3l22ZIsg9zyrVPIuYgHRrJkwcRXnu\naQHVNHuosxM4/XSgu5sXKwF8d/Too8C6dclts5EtA5i1ZYJjRVe5J5H73r12PfeGzJYZHuZOl7v2\nZIFryl0O4ipny6xaZd+WmTuXf1S3vQsiT/kBIJ3cbdgyXV28aE7Vc4+yPtJsGUkgH/sY8PnPc9u+\n/33gL/4imYxt2jKLFvHxeWyZ5mbOZAlyRJI1FxYMKuTubRnDkFc03cEVRH8/qwNdSLWSpgaynBeo\nri1TlHIj8QIdAAAgAElEQVQHsvvueUr+AmrKXceWUZm4aeNCNaCaZssAwCtfCbz61cCttwLf/W6y\nJQPYt2WAfModmP0dRAVUm5r4QhCukxPkiDy2jCd3DcgPrXtbKJF1ow6Ac49bW2feCpsqPwAkK5W5\nc3mg5t0Y3AaGh3kCHDyYr1CViqJdtswcueso97SFTLaUe/B3GCoBVRVbRuLjHwc+9SlgyxbgoouS\n26ZD7pI4mxULmdgi9zghFkXCQeUetUpVxZbJ67k3XCqknBi6qVgSWTfqkAhbM0Up96amfHcrNjE8\nzH161FE86LNCVblnuUNQUe5TU0zSURfrsmyZ4O8wVBYx6ZD72WcD554LXHZZ/HaPEjpjUbcuu01y\nj/pcUX1kwpbJm+dehHJ3rnBYHuWe1W+XkOR+1FH82IRyl4NYpd5I3mp5NiBVhiTe447Ldh5byj1u\nQ5WwcpfkGLXq0LQtoxpQBfLluSeVH4gikH//9/R2AfrkrjNHJLnnHefB7yBpU5005S7JXRYBGx5O\nLz0Qd15VNKQtk1e5myJ3CVPKvbU1XS25mjEjv5OsqlrClnKfmIjeUCWs3OP8dkBfube0sIUWzo3X\nqdOta8vErVBVVe4AE1YaaQF65K6TKQPYUe6Tk3z3G1VCN8qeC3ruCxfy/8rvX1oyadUePblrQhJJ\n2cpdwpTnrqJSXAyqTk3VSCUPuU9O8kRMI70syj3uAhwmviRFreK5B98jrmiXzK5SsQXl5I6b5HlX\nqOYhkCrYMsGLd5IIC1/kDx/mcR1sc9CaUbFk5Hl94TANyAFZT8p9yRJOJVR5b9fIPWhl5FlBmmSJ\nBJHlAqJ6O55E7rp57kB0GqKqJQMwuSXd0ZlYoVoUuZdty8T57cDsfpSWTHAsrlxZ209VJVMGqIbn\n7hS516NyX7IEeOih9ONcVO5Bssqj3FUHs03lHrU6VULXlok6P6BH7ml3dKoB1TjPfXAwH7mriitd\nW2bRIs6syTuvwuQeJ8LC31PQb5cIK/e0TBnA2zLaqEfPXRUueu7BQZiH3FVJL8sq1aKUe9baNXHo\n6kq2JurVlunq4qJlWXYwCkKV3MP9GLUOJqst46tCasCVbBkJE+UHdN670ZV7Rwf3t84qVR3PPY6E\n0jbJ1lHuqoTa2alH7jorVFVjHHGwacsAvCF3XgT7P2oBU/C4KFsmiCC5q9oyvraMJlxT7ibKD6ii\nqyu5eFUZKFq5Z3mfJHLXUe46AVV5/jzK/YwzgL/5m/jX86xQle3Is97DVraMKegodxu2jPfcNRFU\n7i6Qe5HK/dxzgW98Y/ZS6TIRJKulS4H9+7OtotUZzLq+e5Ito5ots2ABX1jjyv6qVhzUIffFi4EP\nfCD+9SC5C6G3QjUvedi0ZUwhb0A1iOAq1SJsmYbLlpmaqpFp1tWaeck9HNQsUrm/4x3AEUcAn/tc\nMe+ngiBZtbayV7l/v/55dMjGlnJPCqjOmcPf/cGD0a/bCKimIRhQHRvjNoaX+MfZMnnJfe7c2XWW\n4uC6cg+PgyjPfenS2ibwtsldFkUsglecIXc5SJqaGlO5EwFf/zrX3n7yyWLeMw1hkshac12H9LIo\n9yjVpqPcAR43ceRuI6CahiB5xC2MirNl8pK7rLMUF6wNwkRGWRaoeu4qtkxTE6+83r5dz3PPQu7y\nu8kbUFaBM+QeHJCuBFSLVO4A3x5+8pPAe9+bveSxSYTJKqvvrmvLmFDuLS3ch9JqSQqoApyWGBfQ\nVlXuJrMgguQRZ33YsmUA9btnV2yZPAFVgH33LVt4zKis4s0aUC3KbwccIvcgkbgSUC1SuUtcfTUT\nzRe+UOz7RiE8ELOSu25A1YTnTjSTANLaMG9efEDbRkA1DSrK3ZYtA6iTu+u2jIpyB5jc/+u/WLWr\nqOqsAdWi0iABh8jdK3cGEXDbbby5wpYtxb53GFVW7sBM1ZZGvEnZSjYCqmkIk3sUgdqyZQA95e4C\nuasGVOP2ewiSuwry2jJFwJmqkK4q96LJHQCOPx74xCfYnvmnf+IB2dfHP3PmANddV4xnF0XuW7fm\nP08STCl3YKZqSwqoAmZsGZPk3tLCAc2JiWTPvWzlfvgwb2pfNHRsmWAsJUm5//d/85aEKqgCuSsp\ndyJaT0RbiehpIro+4bhziGiciK7QbUhwYmRR7pOT3HF5albYKD+QFddcw7nQGzYA3/wm8OCDvPfk\nxo3Azp3FtMGULZMlW0Z1larq0vM8yl3HljE5cSWBlGXLqAgsF2yZvAFVgMn90CE95Z7Fcy8qDRJQ\nUO5E1ATgywD+HMAuAJuJ6G4hxNaI424E8JMsDQkOyCzKfWCAJ2jWhRuAO8od4M9x222zn3/qKeCR\nR4Dly+23YXiYbRKJPOR+/PFqx8oSyf390ZMwDNWl53kCqmXYMkCt/UkBVW/LmAuoAurknsdzd0m5\nrwHwjBBimxBiHMAmAJdGHPdBAN8HsC9LQ/Iq97yWjHxfV5R7HM46i8m9CJQRUAX0fHdTyj0toFom\nuevaMnmKhklUIVtGkmua5y77aHyc+zKq7MPy5Wx1qqxOleetB3I/FsBLgcc7pp/7A4hoGYDLhBD/\nCiCTG5zXczdN7jIVUXVvyKJw1lnAo48W815RnvuePXqFvQD9Aa3ju+so9zwB1aKzZYCZ5B4XULVl\ny6gKLBdsGdUxIDki6u6+pYVFRREB1aply/wzgKAXr03webNlTJO7i6odAFavLk65h8lq7lz9wl5R\n50mDzkImVeVetYAqUFulmuS5e1tG3ZaJs2Qk1qwBTj5Z7f2l6NMtF+JatsxOACsCj5dPPxfEnwLY\nREQE4EgAFxPRuBDinvDJNmzY8Ie/u7u70T0dng5ODDmop6bUPXST5J60J2PZOOUUJr6BAfv7rUYN\nRGnNLF6c7zxJWLIE2Kdo7o2Oxp/bVCpkXEA1fHdpWpVltWUabRHT2Fh8vwdtmTRyv+suvTZI372l\nRf1/VL+bnp4e9PT06DUoBBVy3wxgFRGtBLAbwFsBvC14gBDiRPk3EX0DwI+iiB2YSe5BBD90U1Nt\nYKtOFhPk3tzMX5QMYrmo3JubOYvmsceA886z+15RhCjJ/Ywz8p0nCUcdBbz8stqxqqmQKgFV3Tz3\ncCXJMgKqZZO7K7ZMHGmHlXtUjntWyO9HR2QNDaltMRgUvgCwceNG7fal6mIhxCSAawHcD+BJAJuE\nEFuI6BoiujrqX7RbgdkTQ9d3j1ucoAup3l1V7gBbMyZ893/5l+QJnKTcdaBLNkceaYbcdZR7nC0z\nOcl3cuHNl/Nus6cClVTIOFsm7x6lVbNlVAKqqhlYqsjiuzuVCgkAQogfAzg19NxXY459X5aGDA8z\ncUjo+u5pt1yqkOTuqnIHzGXMfOITwJ/9GXDmmdGvJyl3HegO6KOOUq8+aWoRU5wtI88fXjQWlYZY\nTwHVjg41a8wFW0Y1oGqKI4Ln1s11dy1bphCEP7SucjdN7i4rdxPkLgTbCnGVEAFz5K7rRbuk3KOC\nqfLcthcxpQVUvS3Df6vuxGSa3LPkujckuYcnX9nKvYyiYao480wuC5yncuTAAP9/ErmXacuYVO5T\nU+l3YnGeexxxuJDn7kLhMFdsGZW7N1ueuw6qmAqZG+EPXbZyL6NomCrmzWOSfeaZ7OeQwcD+/vhj\nTCh3uYuQzq27qYCqVG3yu0zKvEqyZVSUuxDpQVtdqK5QDa87KLL8gCu2TJznHlTuLnjuDavcgx/a\nK/dk5LVmDhzg33HKfXycFW84zUu3auPICE+wcEAyCYsW1e4s0qCi2lQUdV5bZnycLx46aXFpSFPu\nTU3cr+F+agRbJqjIdZS7J/cS4Fq2jMvKHcifMSOVexy5y4ttOJCoq9yzWBVNTTwJ5QUoCSrKPS2Y\nCjB5jo7OJkpVW8a0JQOkB1SBaGumKHKX60Fct2Vseu66AdUis2WcIffwgPTKPRmmlHucLRNHVvPm\nsaKPywkPIyvRqAZVVSoCqhAvEVszYUGhasvY8FLTAqqyHcGsnakptYtZGlTIfWyM71TyFOvLiqwB\nVRc894Yj97zKvZE8dyA/uff28i19nHKPG4REeuo9q1JRDaqqlB9Q9cKjgqqqtoxt5Z5E7uF2yL2I\n80BFXJVlyQDqyr21tbbdovfcS0KebJmJCT7WxHL8zk72Xl1X7suXs8+7Z0+2/z9wgMucJtkycWSl\nQ+5ZFa1qUFXlllyVeKOCqnHnL5Lck4KWYVvGFHmoKPeygqmAekCVqNZH3nMvCXny3Pv746u96ULe\nmruu3Iny+e69vcAJJyQrd1Pk7oJyVyHeqKBqFZR70JYpktzLVO4tLbx6eGoqfU2KXGx06FD+EiVR\n51WFzKhquFTIPMrd5BW5CouYJPJYMwcOACeemOy5x5GEri1TtnJX9aCjlLtOQNW0IlMJqEZ5/0Uq\n97LIPajIkzx3gNu4bx9/JpMlvHUXMR0+zO9fVBlxJ8hdpt0FFZKOcrdB7i6XH5DIQ+69vUzuWWyZ\ns88GfvlLtfexHVA1rdyjbJmylLtKQLVRbRmg9h2kCbG2NrYvTVoygL4tU6QlAzhC7nJiBNPudJW7\nqSh4lZR7HltGKnfdgCoAXHYZ8LOf8W1uGrKSnilbRnruqgFVb8swWlpYcCXVKy/TlgHUyb293Q1y\nLzINEnCM3IPQ9dwbUbm/4hXACy/ob2wC1Dx33VRIgC+kr3sdcPfd6e+TlWxM2TI6yj2vLVNGQDXK\nlslbERJgodXRkewplz1HguQeF1CVx9kidx3PvSGVe9SH9p57OlpbgdNOA554Qv9/DxwAVqzgzxml\nztLI6qqrgDvuSH8fm6mQQsQra2Cmcs8aUC3TlpHkoZMKaZJA0gRWVWyZ9naOEZnMcQf0PfeGJPe8\nyr1RPXcgmzUjc36POAKYPz/aXkkbiG96E/vu4Q0ros5jK6CatohGKncbAdXmZu7HyUl+bIvcBwZY\nRccF4cI13U2Te5LAcsWWSbrAAzVyL9uWKbJoGOAIuUd9aK/c1ZAlqHrwIH/O5mZODYuyZlRK5F50\nEfCDHyS/V96AatJm3Cq347rKXTWgSjRTNdtaodrXl6yObSv3pDlYtgBqa+PvK22VrA+oloioW3ev\n3NWQRbn39rJqB5jco4KqKgPxqquATZuSj8mqaGWAPWkMqNyO665QVQ2oAjOJ1ZZy7+11m9zLtmUO\nHUq+wAN2A6rec09B1MTwyl0Np52mX/r3wIHaBtcLF0aTuwpZveENwObNyTv25BnQadaMSgpc3hWq\nSTnUwTREW+R+6FAygdq0ZdLmoAu2zKFD6fNUKveyPfeGzJaJGpC6yt1kKmQVyg9IHH00t1clLVFC\nRbmrkFVHB3DJJcCdd8Yfk4f00oKqOsrddEAVmK3cbSxiAtxW7lUg9/Z2HkfelikBeZW7rVTIKih3\nIuCkk4DnnlP/n97emnKP89xVB2KaNVO2ctcJqOoUDgueH7Cn3IO/09oA8MWp0WwZFXIHPLmXgqRs\nmaSAmoRJW6alhQONBw9WQ7kDwKpVwLPPqh9/4EBNueexZQBg/Xrg8ceBnTujX8+jaNNWqaood1uF\nw4CZC4hsrVAN/o6CrRWqQDWyZQYG1GwZwJN7KYj60DICnrRCTsJ0tbfOTla3VVDugD65h5V71oAq\nwH30pjcB3/te9Ot5skjy2jLSjx4aqmZAtaWF78zSbJmyUiFdsWVUAqqAHc9dN6DacKmQcRNDWiRJ\nkBtHmKz21tnJ6rYRlHvWVMggkhY0lWnLyG3v+vvtBFRtkzsRj0GXPfcq2DJeuZeIuCuaSvGigwd5\nUurs0ZkGGVRtZOWuQ1YXXsgZOy++mO88YeRV7gC/3tdnPs9dntsmuQPp5N7otoz33OPhBLnH+bIq\nyt20JSPfF2gM5R7nuesMxJYWJvioSpFlKndAj9xlEH9qqvacqi1j65Z77tz0gGoj2zIqnru8QJoW\na75wmALiVI+Kcje9LyJQ+wKqotyXL2c1rppdZFq5A/ErZctW7nICqrRhzhw+PtiPZdoyQPm2jOu1\nZVQ897Y28xwBZPPcG47c4z502cq9KuTe1AQcfzzw/PNqx6t47rpKNIrchcg3oPNmywC111VJKBxU\nraItY6IqJFBftoxpjgBq9X4mJtSOb0hyz6PcTW96C1TPlgH0rJmgco+yZbJsBybLIARTV8fG8u08\nY8KWUVkIFEQ4qKqj3G1MXBXl3qi2jFzBq3KBt0Husg2q1kxDknucSixbube0mD2vTaiS++QkTwh5\nmxply0i1qhOkPvpoPj6Y757Xh168mC/esvJiGKrKva1N/bOEg6oqyn1ign/S7IEs0LFlTO/RWS/Z\nMraUuzy3Krn39xdL7gXt5peMONWj6rnbIPf29pk7Q7mOVat4MVEa+vu5zK8kuyhbJovCIGJr5tFH\nOQaQ9TxBzJnDF6HeXlbxYagqdx2yC9syKgFVuQLWxnhJC6gGbZmREb0LWRrqpbbMunVcg8kGVIuH\n/epXfPE95RQ77YiCE8o9T567LXKvit8uoarcg347wINTiJnqI6v6W716pu9uQkUmBVVVlbuOusxi\ny9jc0V7HljF92++6LdPWpnbHtGgRCw8bUC0e9ulPA9dfX9zm2IAj5B43KMtS7l1d1fLbAXVyD/rt\nAKvNsO+e1U4JB1VNkE2S725LuevaMmWTezAds2hyL9uWCf4uAyq2zO9+Bzz2GPDudxfTJgknyD2v\ncreRClk15b5yJe82E8yciEJYuQOzrZmswcFwbXlTyj0Pube16bWhq0vflrFJ7scdByxbFv96kNxN\nFg0DqpEtE/xdBlTI/dOfBv7+74tvpzOee1y2TFm2TNWUe3Mz74n64ovAqafGHxdW7sDsoGpWsjr1\nVGDHDiaZri4zSjLNlkkrO2FCuSfZMgMDdsn9ppuSXw/Wc29EWyb4uwykee5btvDivttvL65NEqUr\n96QIv0rZX1upkFVT7gCX/k2zZqKUe5Qtk4UkmpuB00+vBXZNpAcm2TJJfriErnLPElAtuiBUVBuA\n4sk9aePuIuACuad57jfeCHzoQ8VmyUiUTu6jo/G50F6560HFd7ep3IGZvrsJ0ssbUE3zrMPQCajK\nTBWbyj0NZZH75CRXbLWR/qkK+b2U2YYkW+aFF4D//E/gAx8otk0SSuROROuJaCsRPU1E10e8/nYi\nenT651dE9ErVBiQRgIpy99kyNaiQu4rnnoeUg+RuW7nb8NyzBlTLUGaAXVtGElew1o6E3KmszHRh\nF5R7Erl/9rPANdfYKX2gglRyJ6ImAF8GsA7AGQDeRkThrNHnAbxWCLEawCcBfE21AUkTI025C8Gk\nZLrz1q4FPvxhs+csAlmVe9iWyUNWwaCqKeVeNLm7FFBNg03l3tQU7ymXbckA7pB7VP/s2sVlsP/2\nb4tvk4SKcl8D4BkhxDYhxDiATQAuDR4ghHhICCHp4SEAx6o2IGlipCn3gQEeYKZXki5axHuDVg2q\nyt2mLXPmmcATT/BtexEBVdOpkEFbZnKSVWtcbnK9kzsQb82UHUwF3CD3OM/95puBd74TWLKk+DZJ\nqJD7sQBeCjzegWTy/p8A/q9qA5IGZJpyt2HJVBknnABs355cyCi4ObZElC2TlSQWLOAB/eyzZkiv\njDx3qdylao+zHlwg9+AK1aLJ3RXl7qLnvnkzcPHFxbcnCKOpkER0PoD3AnhN3DEbNmz4w9/d3d1o\nbu7OrNxt5LhXGW1twDHHMMGfeGL0MbaVO1Dz3YeG8iuXvMr9fe9T26pRIqjc07Jx5OrQspV70HM3\nVRFSIo7cy85xB9xQ7nHk/sIL8XNQBT09Pejp6cl+AqiR+04AKwKPl08/NwNEdCaAWwGsF0L0xZ0s\nSO4A8JOfxE8Mr9z1IdMh4wZWlHKPSoU88sjsbZBlCEwEGjs749NlVcj96KP13i8YUE0KpgIzlbtp\nUlVFczP3j7TBwhfuvIgTWN6WYUSR+8QEr/dYuTL7ebu7u9Hd3f2Hxxs3btQ+h4otsxnAKiJaSUSt\nAN4K4J7gAUS0AsCdAN4phHhOpwFJBJCm3G3kuFcdSb77+DgTwPz5M583tUJVQhYQMxFQJYpX7yrk\nrosoWyYOLtgyQM2aaVRbpmzPPRxQ3bGD71jLzrhLJXchxCSAawHcD+BJAJuEEFuI6Boiunr6sI8D\nWAzgK0T0MBH9l2oDkiaGV+76SCJ32V9NoW/dli1jKkUwLmPGBrnr2jJlL2KS7RgbK5bcvS3DiFLu\nzz+fz5IxBSXPXQjxYwCnhp77auDvvwLwV1kakCfP3ZP7bKxaFb2XKRDttwPmVqhKrFjBk//FF82Q\nXlxQNc02yQJJ7kLo2TJlk3sZyt0VcnctoPr885zcUDZKX6GapO7a2thKiNuswZP7bCQp9yi/HTCv\n3InYd3/8cXPKvShbRm5SMjqqZ8uUtYgJqNkypguHAW7bMk1NHHNwTbnnDaaaghPkHkckRMlLoD25\nz8aJJ/Lgirogxil3Se5yizwTNsPq1Zw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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(l['DEPT'], l['FAKE_CURVE'])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "os.remove(fn)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }