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@zonca
Created February 19, 2014 20:44
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{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 166
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 167
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import kepfit\n",
"import kepmsg\n",
"\n",
"length = 100000\n",
"time1 = np.arange(length, dtype=np.float) / 100.\n",
"true_p = [-1e-6, 1e-3, .2, 1., 3.]\n",
"data1 = np.polyval(true_p, time1)\n",
"#data1 += np.random.normal(length)\n",
"err1 = 1. * np.ones_like(data1)\n",
"nsig1 = 3.\n",
"niter1 = 3.\n",
"npoly1 = 4\n",
"pinit = np.zeros(npoly1 + 1, dtype=np.float)\n",
"pinit[0] = data1.mean()\n",
"logfile = \"kepler.log\"\n",
"verbose = True\n",
"kepmsg.log(logfile, \"logging\",verbose)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"logging\n"
]
}
],
"prompt_number": 168
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(time1, data1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 169,
"text": [
"[<matplotlib.lines.Line2D at 0xd864f90>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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HAlPdeSZQN+y1OlgNItOd5y7P+Uw9YLuLpzLWR5IJBMI+Uxf4NK9gBg4c+PN5IBAgEAjk\n9TaRhHPkCHToAE8+qc2l5MzS09NJT0+P+HULOka4AZYoLnXPa2I1EIA/AVcD92Ed6uOwjvDawByg\nEdYRvwR4DOsX+QQYDswAUtx1Hwa6Yn0lXbGO9eXAlS7OFe58f67YNE9EfCkYtFV5zzoL0tI0I10K\nJ5rLnowHbgLOw/ouBmA1hCuw5LAZeMi9dw0wwT1mYQki5zd8CjAaKA9MwxIIWJ/KWGyI7x4sgQDs\nBV4Alrnnz3N6AhHxrZdesqas+fOVQMQ7ifC/nmoi4jtTpkBKCixdCrVqeR2NxCNtjxuiJCK+sno1\nBAK2vW2LFl5HI/FKy56I+NDevbakyZAhSiASG1QTEYkTWVlw++1w2WWWRESKQzUREZ/p2xdKlYJB\ng/J/r0i0aD8RkTiQlgZTp1pHemn9q5UYouYskRi3ZAnccYcN5W3a1OtoJFGoOUvEB7Zvh3vusf3R\nlUAkFimJiMSoY8fg7ruhd29o187raETypuYskRgUDELPnnDoEHzwgWakS+RFc9kTEYmyV1+FFStg\n8WIlEIltifC/p2oiklDmzoX774fPP4eGDb2ORhKVaiIiCejbb21v9PHjlUAkPqhjXSRGHD5sS5o8\n8wzccovX0YgUjJqzRGJAdjZ07AhVq8LIkeoHkZKn5iyRBPLii7BjhzVjKYFIPFESEfHY5Mnw5pu2\npEm5cl5HI1I4ifA3j5qzJG598w3cfDN88glcc43X0YifaNkTkTi3dy906GDLuiuBSLxSTUTEA9ob\nRLymmohIHOvb1zrQtTeIxDt1rItEWVoaTJmivUEkMag5SySKvvgC7rwT0tPh17/2OhrxMzVnicSZ\nbdtsb5C331YCkcShJCISBUeO2JImjz9uuxSKJAo1Z4mUsOxs6NwZKlSA0aM1I11ig5Y9EYkTzz9v\n29zOm6cEIolHSUSkBL3/vtU+tKSJJKpE+LtIzVkSk5YvtwmFs2fDFVd4HY3IqTQ6SySGbd8Od90F\nI0YogUhiUxIRibAff7Q1sXr3tkQiksjUnCUSQcGgbW+blATvvquOdIldGp0lEoP++lfYtAnmz1cC\nEX9QEhGJkA8/hL//3UZilS/vdTQi0ZEIfyupOUs8t3IltG4NM2ZA8+ZeRyOSP43OEokRmZnQrh2k\npiqBiP8oiYgUw+HDtirvI49Ax45eRyMSfQVJIm8Bu4BVYWXVgNnAemAWUCXstX7ABmAd0DqsvLm7\nxgZgWFh5OeB9V/4FUD/stR7uZ6wHuhcgVpGoOXnSRmJdeSU89ZTX0Yh4oyBJ5G2gTa6yp7EkcjEw\n1z0HaAp0cY9tgNcJtbmlAr2Ai9yRc81ewB5XNhTI2eutGvAccI07BnBqshLx1JNP2uq8qakaiSX+\nVZAksgDYl6usHZDmztOADu68PTAeOAFsATYCLYCaQCVgqXvfmLDPhF9rEtDSnd+G1XL2u2M2pycz\nEU+8/rp1ok+cCGXKeB2NiHeKOsS3OtbEhXus7s5rYU1SOTKA2lhSyQgrz3TluMdt7jwLOACc664V\n/pmMsM+IeGb6dHjhBVi0CKpW9ToaEW9FYp5I0B0iCe/rr6FHD5g8GX71K6+jEfFeUZPILqAGsBNr\nqtrtyjOBumHvq4PVIDLdee7ynM/UA7a7eCpjfSSZQCDsM3WBT/MKZuDAgT+fBwIBAoFAXm8TKZYd\nO2wk1vDhcP31XkcjUjjp6emkp6dH/LoF7Q5sAEwFLnXPB2O/6AdhnepV3GNTYBzWEV4bmAM0wmoq\nS4DHsH6RT4DhwAwgxV33YaAr1lfSFetYXw5c6eJc4c7354pNkw2lxB05AoGAbXH7zDNeRyNSfJGa\nbFiQC4wHbgLOw2ogzwEfAROwGsQWoDOhX+79gZ5Y/0YfYKYrbw6MBsoD07CEAjbEdyzQDEtMXd01\nAR5w1wN4kVAHfDglESlRJ09Cp05QqZK2t5XEEc0kEuuURKTEBIPQpw+sWmWjsbQ7oSQKreIrEgUv\nv2x7oy9YoAQikhclEZFf8N578MorsHgxVNE0V5E8qTlLJA/p6dC5M8ydC5demu/bReKOVvEVKSGr\nV0OXLlYTUQIROTMlEZEwmZnQti0MHQq33OJ1NCKxT0lExDlwwBJISoqtzisi+VOfiAjw00+WQC65\nBF59VXNBJPFpnkiIkogUS3Y2dO9us9InToTkZK8jEil5miciEgHBIDzxBHz3HcycqQQiUlhKIuJr\nL71kkwnnz4ezz/Y6GpH4oyQivvXGG/DWW7BwoSYTihSVkoj40gcf2MZSn30GNWt6HY1I/FLHuvjO\n7Nlw//32ePnlXkcj4g3NWBcpgqVLLYFMmqQEIhIJSiLiG2vXQrt21g9y441eRyOSGJRExBe2bIHb\nboPBg+GOO7yORiRxKIlIwsvMhJYtoW9fm1QoIpGjJCIJbfduuPVWeOghePRRr6MRSTxKIpKw9u6F\nVq1sWfe+fb2ORiQxaYivJKSDB60GctNN1g+iBRVFTqUFGEOUROQUR45AmzZw2WXw2mtKICJ5URIJ\nURKRnx07BnfeCXXqwKhRUEoNtiJ5UhIJURIRAI4fh3vugYoV4d13tSKvyJloxrpImGPH4O67oXx5\nGDtWCUQkWpREJO7lJJAKFWDcOChTxuuIRPxDSUTi2rFjcNddoSYsJRCR6FISkbiVk0DOOUc1EBGv\nKIlIXPrxR2jfHipXthpIae2MI+IJJRGJOz/+CB06QLVq8M47SiAiXlISkbhy6BDcfjucd56NwlIC\nEfGWkojEjb17bSmTxo1hzBglEJFYoCQicWHXLggEbDOpN97QPBCRWKEkIjFv61ZLHh07wv/8j9bC\nEoklahCQmLZxozVh9ekDf/qT19GISG6qiUjMWrXKmrD+/GclEJFYpZqIxKT586FzZxg+3DaVEpHY\nVNyayBbga2AlsNSVVQNmA+uBWUCVsPf3AzYA64DWYeXNgVXutWFh5eWA9135F0D9YsYrcWDSJOjU\nCcaPVwIRiXXFTSJBIAA0A65xZU9jSeRiYK57DtAU6OIe2wCvE1qGOBXoBVzkjjauvBewx5UNBQYV\nM16Jcf/3f/DYYzBrFtxyi9fRiEh+ItEnknusTDsgzZ2nAR3ceXtgPHACq8FsBFoANYFKhGoyY8I+\nE36tSUDLCMQrMSgYtL6PYcNg4UK44gqvIxKRgohETWQOsBx40JVVB3a5813uOUAtICPssxlA7TzK\nM1057nGbO88CDmDNZZJAsrKgVy+YPRsWLYKGDb2OSEQKqrgd6zcAO4DzsSasdbleD7qjRA0cOPDn\n80AgQCAQKOkfKRFy4IB1oCcnw7x5tieIiEReeno66enpEb9uJKdtDQAOYzWSALATa6qaB1xCqG/k\nb+5xhvvMd+49TVz5vcDvgIfdewZineqlCSWscNoeN05t3gx33GF9H0OHahkTkWiKhe1xz8b6MgAq\nYKOtVgFTgB6uvAcw2Z1PAboCZYGGWGf5UizZHMT6R5KAbsBHYZ/JuVZHrKNeEsDnn8MNN0Dv3vDq\nq0ogIvGqOP90qwP/CLvOu9iQ3uXABGxk1Rags3vPGle+BuvfSCHU1JUCjAbKA9OwGgjAKGAsNsR3\nD5aEJM69956NwBo9Gtq29ToaESmORFiFSM1ZcSIYhBdfhJEjYepUuOwyryMS8a9INWepEUGi4vBh\n6NkTvvsOliyBGjW8jkhEIkFrZ0mJ27QJrr8eKla05UyUQEQSh5KIlKiZMy2B9O4No0bBWWd5HZGI\nRJKas6REBIMweLDNQJ840fYDEZHEoyQiEXfwIPzHf8CWLdb/Ubeu1xGJSElRc5ZE1MqV0Lw5nHsu\nfPaZEohIolMSkYgIBiE1FVq3hhdesHP1f4gkPjVnSbEdPAgPPgj/+pctoHjxxV5HJCLRopqIFMuK\nFdZ8VbWqLWWiBCLiL6qJSJGcPAmDBsErr9gWtl21II2ILymJSKFt3gzdukHZsrB8OdSr53VEIuIV\nNWdJgQWDkJYG11wDd90Fc+YogYj4nWoiUiA7d8Ijj8D69TB3rhZPFBGjmoicUTAIY8bA5Zdbp/my\nZUogIhKimoj8oq1bbc2r7dth+nS48kqvIxKRWKOaiJwmO9smCzZvbosnLlumBCIieVNNRE7x5ZeQ\nkgKlStmy7U2beh2RiMQy1UQEgH37rOO8bVv4wx9g4UIlEBHJn5KIz2Vn217nTZrYBMI1a2wHwlL6\nP0NECkDNWT62cCE8+aQlj6lT4eqrvY5IROKN/t70oQ0b4J574L77rAlryRIlEBEpGiURH9mzBx5/\nHK67Dq66ylbd7dZNTVciUnT69eED+/fDc8/ZZMGffrJ+j379oHx5ryMTkXinJJLADh60DaIaNYKM\nDJvv8frrcMEFXkcmIolCSSQB7d8PL71kyWPDBtvn46234Fe/8joyEUk0SiIJJCPDRltdeCGsW2d7\nnI8ZAxdd5HVkIpKolEQSwDffQI8etjBiMAhffWXJ45JLvI5MRBKdkkicOnECJk2Cli2hVStLGJs2\nwZAhULeu19GJiF9osmGc2b4d3nwTRoywZquUFLj7bttlUEQk2pRE4sDx4/DJJ7ar4IIFtp/5jBlw\n6aVeRyYifpfkdQAREAwGg17HEHHBICxdaoljwgTr7+je3WaaV6rkdXQiEu+SkpIgAjlANZEYEgza\nXI5Jk+woVcoSx4oVUL++19GJiJxOScRjJ07YPI4PP7Tj7LOttjFhAjRrBkmJUFcUkYSlJOKBrVth\n5kzr1/j0U2jYEDp0sC1omzZV4hCR+JEIv65ivk8kM9M6xBcsgPR02L0bWreGNm3ssXp1ryMUEb+J\nVJ9IPCSRNsArQDIwEhiU6/WYSiLHj9vkvxUrYNEiSxwHD8Jvf2vHTTfZfuXJyV5HKiJ+5pckkgz8\nC7gVyASWAfcCa8Pe40kSCQbhhx9seZFVq2xv8i+/tOeNGlmiuO46uPFGmwgYjeXW09PTCQQCJf+D\n4oDuRYjuRYjuRYhfRmddA2wEtrjn7wHtOTWJlJhg0Jqetm6F776DzZttD45162DtWttatkkT68do\n3hwefNCG4nq1xLr+gYToXoToXoToXkRerCeR2sC2sOcZQIuiXiwrCw4ftuPQIdi7F77//vRj505L\nHNu2QYUKNry2Xj1o0MB2AOzWzWoXF1ygTnAR8bdYTyIFaqdq1cqGymZl2RF+fvRoKGmcOAEVK4aO\natXg/PNDR506Nqy2enVLHHXrWhIREZG8xfrf0dcCA7HOdYB+QDandq5vBC6MblgiInFvE9DI6yBK\nWmnsizYAygJfAU28DEhEROLL7dgIrY1YTURERERERMQ7bYB1wAbgKY9jiYa6wDxgNfAN8JgrrwbM\nBtYDs4AqYZ/ph92fdUDrqEUaPcnASmCqe+7Xe1EFmIgNfV+DjWD0673oh/0bWQWMA8rhn3vxFrAL\n++45ivLdm7trbACGlWC8nkrGmrcaAGXwR19JDeAKd14Ra+JrAgwG+rryp4C/ufOm2H0pg92njSTe\nTpZPAO8CU9xzv96LNKCnOy8NVMaf96IB8C2WOADeB3rgn3txI9CMU5NIYb57zkCrpdgcPYBphAY2\nJZTrgBlhz592h59MxmbyrwNyVt+q4Z6D/ZURXkObgY12SxR1gDnAzYRqIn68F5WxX5y5+fFeVMP+\nuKqKJdOpQCv8dS8acGoSKex3r8mpk7m7Am+c6QfGa9bNaxJibY9i8UID7C+OJdj/ILtc+S5C/8PU\nwu5LjkS7R0OB/8KGfOfw471oCHwPvA18CbwJVMCf92IvMATYCmwH9mNNOX68FzkK+91zl2eSzz2J\n1yQSOysuRl9FYBLQBziU67UgZ743iXLf7gB2Y/0hvzTXyS/3ojRwJfC6ezzC6bVyv9yLC4HHsT+y\namH/Vn6f6z1+uRd5ye+7F0m8JpFMrKM5R11OzZ6JqgyWQMZizVlgf13UcOc1sV+ucPo9quPKEsH1\nQDtgMzAeuAW7J368FxnuWOaeT8SSyU78dy+uAhYDe4As4EOs6duP9yJHYf5NZLjyOrnKE+2eAP6c\nhJgEjMGaccINJtS2+TSnd5yVxZo8NhH7KxQUxU2E+kT8ei8+Ay525wOx++DHe3E5NnKxPPad0oBH\n8Ne9aMCX/L7cAAAAiElEQVTpHeuF/e5LsBF+SSRwxzr4bxLib7H2/6+wZpyV2H/calgHc15D+Ppj\n92cdcFs0g42imwiNzvLrvbgcq4n8E/vruzL+vRd9CQ3xTcNq7365F+OxvqCfsD7jByjad88Z4rsR\nGF7iUYuIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiEh8+38akIJv0yejMAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xfe44e90>"
]
}
],
"prompt_number": 169
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"functype = 'poly' + str(npoly1)\n",
"coeffs, errors, covar, iiter, sigma, chi2, dof, fit, plotx1, ploty1, status = \\\n",
" kepfit.lsqclip(functype,pinit,time1,data1,err1,nsig1,nsig1,niter1,\n",
" logfile,verbose)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 170
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print true_p"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[-1e-06, 0.001, 0.2, 1.0, 3.0]\n"
]
}
],
"prompt_number": 172
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print coeffs[::-1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ -1.00000000e-06 1.00000000e-03 2.00000000e-01 1.00000000e+00\n",
" 3.00000000e+00]\n"
]
}
],
"prompt_number": 175
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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