PCAEmiss.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Először is kell egy loop a fájlok beolvasására; ezeket mind el kell majd tárolni, szóval lehet először egy beolvasóciklus egy adott könyvtár összes fájljára, majd ezekre egyesével lefuttatom majd a normálást, a rebint, majd a főkompinens analízist. Tehát beolvasás:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from numpy import loadtxt\n",
    "from numpy import append\n",
    "import codecs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13787, 59)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=loadtxt('emisslines.txt')\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  8.66347088e+16   1.92386000e+01   1.80219000e+01 ...,   1.23107000e+00\n",
      "    1.07600000e+00   1.98866026e+00]\n",
      " [  8.66347089e+16   1.93329000e+01   1.85948000e+01 ...,   1.57473000e+00\n",
      "    2.04507000e+00   2.30221849e+00]\n",
      " [  8.66347089e+16   2.06914000e+01   1.90301000e+01 ...,  -1.50105000e-01\n",
      "    9.86770000e-01   2.33645120e-01]\n",
      " ..., \n",
      " [  8.31429941e+17   1.93667000e+01   1.74911000e+01 ...,   6.36030000e-01\n",
      "   -7.42585000e-02  -9.86889764e-01]\n",
      " [  8.31429941e+17   1.85669000e+01   1.75165000e+01 ...,   7.83868000e-01\n",
      "    8.87521000e-01  -4.27054880e-03]\n",
      " [  8.31429941e+17   1.91550000e+01   1.80001000e+01 ...,   1.07516000e+00\n",
      "    2.46128000e-01   7.60975260e-01]]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oszlopokat tartom meg, ezeket kiszedegetem az  adatsorból"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lineEWp=numpy.empty((10,13788))\n",
    "lineEWp[:,0]=[6565,4863,6718,6733,4342,3727,5008,4960,6585,6550]\n",
    "lineEWp[0,1:]=data[:,19]\n",
    "lineEWp[1,1:]=data[:,23]\n",
    "lineEWp[2,1:]=data[:,27]\n",
    "lineEWp[3,1:]=data[:,31]\n",
    "lineEWp[4,1:]=data[:,35]\n",
    "lineEWp[5,1:]=data[:,39]\n",
    "lineEWp[6,1:]=data[:,43]\n",
    "lineEWp[7,1:]=data[:,47]\n",
    "lineEWp[8,1:]=data[:,51]\n",
    "lineEWp[9,1:]=data[:,55]\n",
    "\n",
    "# sorbarendezem őket hullámhossz szerint\n",
    "\n",
    "lineEW=numpy.empty((10,13788))\n",
    "\n",
    "lineEW[0,:]=lineEWp[5,:]\n",
    "lineEW[1,:]=lineEWp[4,:]\n",
    "lineEW[2,:]=lineEWp[1,:]\n",
    "lineEW[3,:]=lineEWp[7,:]\n",
    "lineEW[4,:]=lineEWp[6,:]\n",
    "lineEW[5,:]=lineEWp[9,:]\n",
    "lineEW[6,:]=lineEWp[0,:]\n",
    "lineEW[7,:]=lineEWp[8,:]\n",
    "lineEW[8,:]=lineEWp[2,:]\n",
    "lineEW[9,:]=lineEWp[3,:]\n",
    "#print(lineEWp) # ez stimmel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.0115    1.14995   2.067    -0.744458  0.184125  1.05734   3.33148\n",
      "  2.17149   1.44231   1.15532 ]\n"
     ]
    }
   ],
   "source": [
    "print(lineEW[:,1]) # ez is jónak néz ki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save\n",
    "# numpy.save('./PCAfeldolgozas/lines.npy', lineEW) \n",
    "\n",
    "# Load\n",
    "# lineEW = numpy.load('./PCAfeldolgozas/lines.npy').item()\n",
    "# print(read_dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#lineEW=lineEWp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q8FTCGQBglVjuoiLLXYSEp/KvBACwSix1AbBn1fxFSDi5kYRzVb2nqh6oqk8v83hV1a9X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\nHYQzAAB0EM4AANBBOAMAQAfhDAAAHf4/KvZwObosIIkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c21ddf0e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(12,8)\n",
    "plt.stem(lineEW[:,0],lineEW[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A vonal EW-k be vannak töltve a **lineEW**-be, az első oszlop a laboratóriumi hullámhosszak, a többi a line EW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  6.56500000e+03  -3.53204270e-02]\n",
      " [  4.86300000e+03  -2.64172435e-02]\n",
      " [  6.71800000e+03  -2.20115147e-02]\n",
      " [  6.73300000e+03  -1.99779625e-02]\n",
      " [  4.34200000e+03  -1.99111704e-02]\n",
      " [  3.72700000e+03  -2.58998561e-02]\n",
      " [  5.00800000e+03  -1.29712879e-02]\n",
      " [  4.96000000e+03  -6.29341211e-03]\n",
      " [  6.58500000e+03  -2.65902329e-02]\n",
      " [  6.55000000e+03  -1.82441867e-02]]\n"
     ]
    }
   ],
   "source": [
    "atlagemiss=numpy.empty((10,2))\n",
    "atlagemiss[:,0]=lineEW[:,0]\n",
    "for k in range (0,10):\n",
    "    for j in range (0,13787):\n",
    "        atlagemiss[k,1]+=lineEW[k,j+1]\n",
    "    atlagemiss[k,1]=atlagemiss[k,1]/13787\n",
    "print(atlagemiss)\n",
    "#numpy.save('./PCAfeldolgozas/emissatlagnosorb.npy',atlagemiss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2c21dca40f0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YlJkbgd+PiLOBfcAuYGWz65IkSZLK1PRgDZCZNwE3TWr70ITlNcCaKmqRJEmSmqFdTl6U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\nSZJUAoO1JEmSVAKDtSRJklSC/x+dbgql/KTH9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c21dbcf208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.stem(lineEW[:,0],lineEW[:,1])\n",
    "plt.subplot(212)\n",
    "plt.stem(atlagemiss[:,0],atlagemiss[:,1],'g')\n",
    "plt.ylabel('Átlagok')\n",
    "plt.xlabel('Hullámhossz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for j in range (0,13787):\n",
    "    lineEW[:,j+1]=lineEW[:,j+1]-atlagemiss[:,1]\n",
    "#numpy.save('./PCAfeldolgozas/emiss_levontatlag.npy',lineEW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oIJwBAKCDcAYAgA7CGQAAOghnAADoIJwBAKCDcAYAgA7CGQAAOghnAADoIJwBAKCDcAYA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c21dbcf9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.stem(lineEW[:,0],lineEW[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Először létrehozom a korrelációmátrixot, ebben oszloponként vannak bent az egyes log(**EW**)-k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2.0115    3.23478   2.9072   ...,  2.2671    2.15383   3.24671 ]\n",
      " [ 1.14995   1.66416   0.34619  ...,  0.535753  0.880342  1.58702 ]\n",
      " [ 2.067     2.55159   1.15192  ...,  0.571888  1.57319   1.84907 ]\n",
      " ..., \n",
      " [ 2.17149   3.03584   2.11629  ...,  2.16274   1.46358   1.06472 ]\n",
      " [ 1.44231   2.33382   1.17874  ...,  1.16978   1.17064   1.47272 ]\n",
      " [ 1.15532   2.05502   1.08947  ...,  0.407996  0.48248   0.988038]]\n"
     ]
    }
   ],
   "source": [
    "korrelaciomatrix=numpy.empty((10,13787))\n",
    "korrelaciomatrix[:,:]=lineEW[:,1:]\n",
    "print(korrelaciomatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xxT=numpy.dot(korrelaciomatrix, korrelaciomatrix.T)\n",
    "xxT.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majd erre futtatom le az SVD-t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10, 10), (10, 10), (10,))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U, s, V = numpy.linalg.svd(xxT)\n",
    "U.shape, V.shape, s.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.45037438 -0.20541958 -0.33964113 -0.07610391 -0.2525721  -0.16850415\n",
      "  -0.54528539 -0.3497343  -0.27234763 -0.21865046]\n",
      " [ 0.07824255 -0.08939555  0.04183146 -0.73156991 -0.55397801 -0.03194613\n",
      "   0.26347654  0.21628943 -0.06692734 -0.14265227]\n",
      " [ 0.59205818 -0.21219375 -0.1126488  -0.05776307  0.2268854  -0.62955559\n",
      "   0.07654112 -0.26531368 -0.11385753 -0.22668027]\n",
      " [ 0.11419759 -0.53515509 -0.32832443 -0.03987526  0.34134983  0.36450691\n",
      "  -0.08434321  0.49777188 -0.21581341 -0.20082314]\n",
      " [ 0.3496525  -0.07586332 -0.23427959 -0.30013279 -0.02011282  0.12777797\n",
      "  -0.47402383 -0.09534958  0.43746869  0.5339668 ]\n",
      " [-0.20869359  0.37434471  0.08718783 -0.58996058  0.64610862  0.04060586\n",
      "  -0.08659692 -0.11318437 -0.10926333 -0.09645952]\n",
      " [ 0.43713964  0.62538283 -0.19812266  0.1207318  -0.20191217  0.21265845\n",
      "  -0.30637343  0.14689425 -0.24177991 -0.32262086]\n",
      " [ 0.19008445 -0.24168901  0.37477676 -0.02749006 -0.03054256  0.51210348\n",
      "  -0.04666223 -0.50496252  0.21755426 -0.44335518]\n",
      " [ 0.18408766 -0.10303541  0.259148   -0.0230163  -0.04152816  0.20411757\n",
      "   0.00657137 -0.22873912 -0.74343541  0.48924273]\n",
      " [ 0.02336077 -0.1112264   0.67309049  0.00960498 -0.00567225 -0.27251908\n",
      "  -0.54035988  0.40242727  0.00275794 -0.07547824]]\n"
     ]
    }
   ],
   "source": [
    "print(V)\n",
    "numpy.save('./PCAfeldolgozas/EmissVnoatl.npy',V)\n",
    "V4=V[0:4,:]\n",
    "numpy.save('./PCAfeldolgozas/EmissV4noatl.npy',V4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Végül kiszámoljuk a főkomponenseket: lényegében csak a $V$-vel szorozzuk az összes oszlopvektort. Tehát ha vissza akarom majd kapni az **EW**-ket az együtthatókból, akkor csak a ezen mátrix inverzével kell szorozni az együtthatók vektorát."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PCsemiss=numpy.dot(V, korrelaciomatrix)\n",
    "# numpy.save('./PCAfeldolgozas/PCsemiss.npy',PCsemiss)\n",
    "PCsemiss4en=numpy.dot(V4, korrelaciomatrix)\n",
    "#numpy.save('./PCAfeldolgozas/PCsemiss4ennosorb.npy',PCsemiss4en)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -5.23367625e+00   1.63599220e+00  -6.14046022e-01  -3.29761371e-01\n",
      "  -5.17147593e-02  -1.13002443e-02  -1.36408174e-01  -1.50145768e-02\n",
      "   3.09309084e-02   4.46816218e-03]\n"
     ]
    }
   ],
   "source": [
    "print(PCsemiss[:,0])\n",
    "#numpy.save('./PCAfeldolgozas/PCAemissGX1.npy',PCsemiss[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'atlagemiss' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-9a0d63e1c438>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mtemp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mPCsemiss\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mlogEWred\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mV\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mlogEWk\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlogEWred\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0matlagemiss\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmintapontok\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlogEWk\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlineEW\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlineEW\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0matlagemiss\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'atlagemiss' is not defined"
     ]
    }
   ],
   "source": [
    "figsize(12,8)\n",
    "mintapontok=[6565,4863,6718,6733,4342,3727,5008,4960,6585,6550]\n",
    "temp=PCsemiss[:,0]\n",
    "logEWred=numpy.dot(V.T,temp)\n",
    "logEWk=logEWred+atlagemiss[:,1]\n",
    "plt.stem(mintapontok[:],logEWk[:],'r')\n",
    "plt.stem(lineEW[:,0],lineEW[:,1]+atlagemiss[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.9, 0.9)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MRSsiEpiKVkQkMBWtiEhgKloRkcBUtCJS1pqaMo9SUtGKiAQ2ZNGa2WIzm2Fmifq6rojI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fe6791b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(18,8)\n",
    "#V=numpy.load('./PCAfeldolgozas/EmissV.npy')\n",
    "eig5=V[0:5,:]\n",
    "mintapontok=[3727,4342,4863,4960,5008,6550,6565,6585,6718,6733]\n",
    "figsize(5,9)\n",
    "f, (ax1, ax2, ax3, ax4) = plt.subplots(4,1, sharex=True, sharey=True)\n",
    "ax1.vlines(mintapontok,0,eig5[0,:],label='eig1',color='blue')\n",
    "ax1.grid(True)\n",
    "ax1.legend(loc=4)\n",
    "ax1.set_ylabel('$\\log{\\mathrm{EW}}$')\n",
    "ax2.vlines(mintapontok,0,eig5[1,:],label='eig2',color='blue')\n",
    "ax2.grid(True)\n",
    "ax2.legend(loc=4)\n",
    "ax2.set_ylabel('$\\log{\\mathrm{EW}}$')\n",
    "ax3.vlines(mintapontok,0,eig5[2,:],label='eig3',color='blue')\n",
    "ax3.grid(True)\n",
    "ax3.legend(loc=4)\n",
    "ax3.set_ylabel('$\\log{\\mathrm{EW}}$')\n",
    "ax4.vlines(mintapontok,0,eig5[3,:],label='eig4',color='blue')\n",
    "ax4.grid(True)\n",
    "ax4.set_ylabel('$\\log{\\mathrm{EW}}$')\n",
    "ax4.legend(loc=4)\n",
    "xlabel('$\\lambda$ [$\\AA$]')\n",
    "xlim(3700,6800)\n",
    "ylim(-0.9,0.9)\n",
    "# savefig('emisspca.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beck féle sajátvektorok behozatala"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eig4=numpy.empty((4,10))\n",
    "eig4[0]=[-0.342929,-0.204615,-0.336994,-0.0787687,-0.252934,-0.167194,-0.539688,-0.345591,-0.271008,-0.218075]\n",
    "eig4[1]=[0.100571,0.0604791,-0.0792591,0.715315,0.53597,-0.0131608,-0.310997,-0.260573,0.039487,0.114431]\n",
    "eig4[2]=[0.520538,-0.16036,-0.0854877,-0.192218,0.0709786,-0.633041,0.0753818,-0.292177,-0.073244,-0.188573]\n",
    "eig4[3]=[0.574171,0.251172,0.1207,-0.00264742,-0.224108,0.00223063,-0.0562945,-0.17211,0.126566,0.160372]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.342929   -0.204615   -0.336994   -0.0787687  -0.252934   -0.167194\n",
      "  -0.539688   -0.345591   -0.271008   -0.218075  ]\n",
      " [ 0.100571    0.0604791  -0.0792591   0.715315    0.53597    -0.0131608\n",
      "  -0.310997   -0.260573    0.039487    0.114431  ]\n",
      " [ 0.520538   -0.16036    -0.0854877  -0.192218    0.0709786  -0.633041\n",
      "   0.0753818  -0.292177   -0.073244   -0.188573  ]\n",
      " [ 0.574171    0.251172    0.1207     -0.00264742 -0.224108    0.00223063\n",
      "  -0.0562945  -0.17211     0.126566    0.160372  ]]\n"
     ]
    }
   ],
   "source": [
    "print(eig4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PCEm4=numpy.dot(eig4, korrelaciomatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.26098737 -2.27057574  0.56246088 ...,  0.98861481  1.33506512\n",
      "   0.05242739]\n",
      " [-1.3947852   0.63487149  1.35401799 ...,  1.19813882 -0.79536532\n",
      "   1.05906224]\n",
      " [-0.42066657 -1.14496818  0.08228931 ..., -0.35408247  0.44369296\n",
      "   0.98906058]\n",
      " [-0.08967943  0.45253986 -0.31496764 ..., -0.79676863 -0.14148378\n",
      "   0.52532918]]\n"
     ]
    }
   ],
   "source": [
    "print(PCEm4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numpy.save('./PCAfeldolgozas/PCsemiss4.npy',PCEm4)\n",
    "numpy.save('./PCAfeldolgozas/emieig4.npy',eig4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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