Sinc+adjust+full+data+B940-2900.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab as pl\n",
    "import math\n",
    "import matplotlib as mpl\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cbook as cbook\n",
    "import serial\n",
    "from __future__ import unicode_literals\n",
    "from scipy import optimize\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def test_func(x,a,b,c):\n",
    "    return np.abs(c*np.sin(a*np.pi*(x-b))/(np.pi*(x-b)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xd=np.array([34.49052744,40.36125552,44.03046057,47.69966561,51.36887066,55.03807571,58.70728075,62.3764858,66.04569085,69.7148959,73.38410094,77.05330599,80.72251104,84.39171609,88.06092113,91.73012618,95.39933123,99.06853627,102.73774132,106.40694637])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yd=np.array([0.3231619245, 0.0997980899, 0.1217490035, 0.1289921024, 0.1250134947, 0.0811366555, 0.0958952668, 0.0876420892, 0.0868765745, 0.0995478131, 0.0717387375, 0.0729135744, 0.0615500348, 0.0482816085, 0.0647724509, 0.0487324745, 0.0462651211, 0.0452527926, 0.0731951987, 0.047376421])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe342939b50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a+LuDFi3p4hjlLSUG+d2D7lBHuUNuyz2d+gmDQ8CWJJuTXArsBqbmLDMF3NY8fz9wsKoq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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe342cd4f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(xd, yd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "params, params_covariance = optimize.curve_fit(test_func, xd, yd, p0=[18,165,75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  17.98936893,  165.05148107,  167.24009784])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "perr = np.sqrt(np.diag(params_covariance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.60600365e-04,   7.27306627e-04,   9.88414019e+00])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nstd = 1. # to draw 5-sigma intervals\n",
    "params_up = params + nstd * perr\n",
    "params_dw = params - nstd * perr\n",
    "\n",
    "fit = test_func(xd, *params)\n",
    "fit_up = test_func(xd, *params_up)\n",
    "fit_dw = test_func(xd, *params_dw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.09250652,  0.02575627,  0.01816283,  0.01821898,  0.02697091,\n",
       "        0.00536016,  0.01088111,  0.00612348,  0.00715608,  0.00968632,\n",
       "        0.01269503,  0.01615354,  0.01278017,  0.00243018,  0.00462681,\n",
       "        0.0079258 ,  0.00208376,  0.00487177,  0.02853224,  0.00442455,  0.        ])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Err=np.array([0.0925065222, 0.0257562677, 0.0181628275, 0.0182189791, 0.0269709143, 0.0053601573, 0.0108811132, 0.006123477, 0.0071560767, 0.0096863242, 0.0126950288, 0.0161535444, 0.0127801699, 0.0024301812, 0.0046268084, 0.0079257966, 0.0020837625, 0.0048717655, 0.0285322362, 0.0044245478, 0.0])\n",
    "Err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ruoyMDKa6apHro44dOzJ5cplnJ4qUkZWVRVZW1omGRYucr4r6Rbzwd409ANba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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe342f08850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe342b6be10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(12, 8))\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "plt.scatter(xd, yd, label='Data Visibilidad')\n",
    "plt.plot(xd, test_func(xd,params[0],params[1],params[2]),label='Sinc Fitted function', color='black')\n",
    "#plt.errorbar([xd], [yd], xerr=0, yerr=[Err], fmt='o', color='red', ecolor='navy', elinewidth=2, capsize=2, label=\"Error\")\n",
    "#plot(xd, fit, 'r', lw=2, label='best fit curve')\n",
    "ax.fill_between(xd, fit_up, fit_dw, alpha=.25, label='1-sigma interval')\n",
    "pl.axvline(96, linestyle='-.',color='grey')\n",
    "pl.axvline(95, linestyle='-.',color='grey')\n",
    "pl.axvline(95.5, linestyle='-.',color='red')\n",
    "plt.xlabel('B$_\\lambda$', size=20)\n",
    "plt.xticks(fontsize=14, rotation=0)\n",
    "plt.ylabel('Amplitude Visibility', size=20)\n",
    "plt.yticks(fontsize=14, rotation=0)\n",
    "plt.legend(loc='upper center',fontsize=15)\n",
    "plt.grid(True)\n",
    "plt.figure(figsize=(12, 8))\n",
    "filename = '/home/alfazoom/imfr-hal/python/Sesion24.08.2018/IMFR11GHz-full average-Visibilidad-fitted-02.jpg'\n",
    "plt.savefig(filename, dpi = 300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.989368925809583"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165.05148106986107"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167.24009783854805"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Calculo IMFR11GHz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pi=3.14159265359"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Null_IMFR11GHz_B_lambda = 95.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.010471204188481676"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_IMFR11GHz = 1/Null_IMFR11GHz_B_lambda\n",
    "null_IMFR11GHz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5999558064196707"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dsol_grad = (360*null_IMFR11GHz)/(2*pi)\n",
    "Dsol_grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Dsol_min = Dsol_grad*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Diametro Sol at 11GHz =', 35.997348385180246, 'minutos de arco')\n"
     ]
    }
   ],
   "source": [
    "print(\"Diametro Sol at 11GHz =\", Dsol_min, \"minutos de arco\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FIN\n"
     ]
    }
   ],
   "source": [
    "print(\"FIN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Dreal=725.27*Dsol_min*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Diametro Sol =', 1566467.8117991805, 'km')\n"
     ]
    }
   ],
   "source": [
    "print(\"Diametro Sol =\",Dreal,\"km\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Dsun_google=1391016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "error=1-(Dreal/Dsun_google)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.12613213061473094"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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