lectura_espectrales.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "rc('font',**{'family':'sans-serif','sans-serif':['Latin Modern']})\n",
    "rc('text', usetex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams[\"figure.figsize\"] = (5,4)\n",
    "\n",
    "M0= 8*pi * loadtxt(\"./solutions_varying_omega/g-10.00_h10.00/MKomar.dat\")\n",
    "omega0=loadtxt(\"./solutions_varying_omega/g-10.00_h10.00/omega.dat\")\n",
    "\n",
    "M1= 8*pi * loadtxt(\"./solutions_varying_omega/g-10.00_h0.50/MKomar.dat\")\n",
    "omega1=loadtxt(\"./solutions_varying_omega/g-10.00_h0.50/omega.dat\")\n",
    "\n",
    "M2= 8*pi * loadtxt(\"./solutions_varying_omega/g-10.00_h0.00/MKomar.dat\")\n",
    "omega2=loadtxt(\"./solutions_varying_omega/g-10.00_h0.00/omega.dat\")\n",
    "\n",
    "M3= 8*pi * loadtxt(\"./g0.00_h0.00/MKomar.dat\")\n",
    "omega3=loadtxt(\"./g0.00_h0.00/omega.dat\")\n",
    "\n",
    "\n",
    "xlabel(r\"$\\omega/\\mu$\", fontsize=14)\n",
    "ylabel(r\"$M/(M_{\\rm P}^2/\\mu)$\", fontsize=14)\n",
    "\n",
    "xlim(0,1)\n",
    "ylim(0,16)\n",
    "\n",
    "M0=append([0], M0)\n",
    "omega0=append([1],omega0)\n",
    "\n",
    "plot(omega3, M3, color=\"pink\", linestyle=\"-.\", label=r\"$g=0$,  $h=0.0$\")\n",
    "plot(omega0, M0, color=\"k\", linestyle=\"-\", label=r\"$g=-10$,  $h=10$\")\n",
    "#plot(omega1, M1, color=\"k\", linestyle=\"--\", label=r\"$g=-10$,  $h=0.5$\")\n",
    "plot(omega2, M2, color=\"k\", linestyle=\"--\", label=r\"$g=-10$,  $h=0.0$\")\n",
    "\n",
    "\n",
    "scatter(omega1[19],M1[19], color=\"orange\", marker=\"*\", label=\"stable conf.\")\n",
    "scatter(omega1[39],M1[39], color=\"red\", marker=\"o\", label=\"unstable conf.\")\n",
    "scatter(omega0[40],M0[40], color=\"red\", marker=\"o\")\n",
    "scatter(omega2[95],M1[95], color=\"red\", marker=\"o\")\n",
    "scatter(omega0[69],M0[69], color=\"red\", marker=\"o\")\n",
    "scatter(omega0[119],M0[119], color=\"orange\", marker=\"*\")\n",
    "scatter(omega0[159],M0[159], color=\"orange\", marker=\"*\")\n",
    "scatter(omega0[199],M0[199], color=\"orange\", marker=\"*\")\n",
    "scatter(omega0[239],M0[239], color=\"orange\", marker=\"*\")\n",
    "scatter(omega0[279],M0[279], color=\"orange\", marker=\"*\")\n",
    "scatter(omega0[319],M0[319], color=\"red\", marker=\"o\")\n",
    "\n",
    "legend(loc=\"best\", frameon=False, fontsize=10)\n",
    "\n",
    "savefig(\"M-omega.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4002005012531329"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "omega1[239]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.998     , 0.99549875, 0.99299749, 0.99049624,\n",
       "       0.98799499, 0.98549373, 0.98299248, 0.98049123, 0.97798997,\n",
       "       0.97548872, 0.97298747, 0.97048622, 0.96798496, 0.96548371,\n",
       "       0.96298246, 0.9604812 , 0.95797995, 0.9554787 , 0.95297744,\n",
       "       0.95047619, 0.94797494, 0.94547368, 0.94297243, 0.94047118,\n",
       "       0.93796992, 0.93546867, 0.93296742, 0.93046617, 0.92796491,\n",
       "       0.92546366, 0.92296241, 0.92046115, 0.9179599 , 0.91545865,\n",
       "       0.91295739, 0.91045614, 0.90795489, 0.90545363, 0.90295238,\n",
       "       0.90045113, 0.89794987, 0.89544862, 0.89294737, 0.89044612,\n",
       "       0.88794486, 0.88544361, 0.88294236, 0.8804411 , 0.87793985,\n",
       "       0.8754386 , 0.87293734, 0.87043609, 0.86793484, 0.86543358,\n",
       "       0.86293233, 0.86043108, 0.85792982, 0.85542857, 0.85292732,\n",
       "       0.85042607, 0.84792481, 0.84542356, 0.84292231, 0.84042105,\n",
       "       0.8379198 , 0.83541855, 0.83291729, 0.83041604, 0.82791479,\n",
       "       0.82541353, 0.82291228, 0.82041103, 0.81790977, 0.81540852,\n",
       "       0.81290727, 0.81040602, 0.80790476, 0.80540351, 0.80290226,\n",
       "       0.800401  , 0.79789975, 0.7953985 , 0.79289724, 0.79039599,\n",
       "       0.78789474, 0.78539348, 0.78289223, 0.78039098, 0.77788972,\n",
       "       0.77538847, 0.77288722, 0.77038596, 0.76788471, 0.76538346,\n",
       "       0.76288221, 0.76038095, 0.7578797 , 0.75537845, 0.75287719,\n",
       "       0.75037594, 0.74787469, 0.74537343, 0.74287218, 0.74037093,\n",
       "       0.73786967, 0.73536842, 0.73286717, 0.73036591, 0.72786466,\n",
       "       0.72536341, 0.72286216, 0.7203609 , 0.71785965, 0.7153584 ,\n",
       "       0.71285714, 0.71035589, 0.70785464, 0.70535338, 0.70285213,\n",
       "       0.70035088, 0.69784962, 0.69534837, 0.69284712, 0.69034586,\n",
       "       0.68784461, 0.68534336, 0.68284211, 0.68034085, 0.6778396 ,\n",
       "       0.67533835, 0.67283709, 0.67033584, 0.66783459, 0.66533333,\n",
       "       0.66283208, 0.66033083, 0.65782957, 0.65532832, 0.65282707,\n",
       "       0.65032581, 0.64782456, 0.64532331, 0.64282206, 0.6403208 ,\n",
       "       0.63781955, 0.6353183 , 0.63281704, 0.63031579, 0.62781454,\n",
       "       0.62531328, 0.62281203, 0.62031078, 0.61780952, 0.61530827,\n",
       "       0.61280702, 0.61030576, 0.60780451, 0.60530326, 0.60280201,\n",
       "       0.60030075, 0.5977995 , 0.59529825, 0.59279699, 0.59029574,\n",
       "       0.58779449, 0.58529323, 0.58279198, 0.58029073, 0.57778947,\n",
       "       0.57528822, 0.57278697, 0.57028571, 0.56778446, 0.56528321,\n",
       "       0.56278195, 0.5602807 , 0.55777945, 0.5552782 , 0.55277694,\n",
       "       0.55027569, 0.54777444, 0.54527318, 0.54277193, 0.54027068,\n",
       "       0.53776942, 0.53526817, 0.53276692, 0.53026566, 0.52776441,\n",
       "       0.52526316, 0.5227619 , 0.52026065, 0.5177594 , 0.51525815,\n",
       "       0.51275689, 0.51025564, 0.50775439, 0.50525313, 0.50275188,\n",
       "       0.50025063, 0.49774937, 0.49524812, 0.49274687, 0.49024561,\n",
       "       0.48774436, 0.48524311, 0.48274185, 0.4802406 , 0.47773935,\n",
       "       0.4752381 , 0.47273684, 0.47023559, 0.46773434, 0.46523308,\n",
       "       0.46273183, 0.46023058, 0.45772932, 0.45522807, 0.45272682,\n",
       "       0.45022556, 0.44772431, 0.44522306, 0.4427218 , 0.44022055,\n",
       "       0.4377193 , 0.43521805, 0.43271679, 0.43021554, 0.42771429,\n",
       "       0.42521303, 0.42271178, 0.42021053, 0.41770927, 0.41520802,\n",
       "       0.41270677, 0.41020551, 0.40770426, 0.40520301, 0.40270175,\n",
       "       0.4002005 , 0.39769925, 0.39519799, 0.39269674, 0.39019549,\n",
       "       0.38769424, 0.38519298, 0.38269173, 0.38019048, 0.37768922,\n",
       "       0.37518797, 0.37268672, 0.37018546, 0.36768421, 0.36518296,\n",
       "       0.3626817 , 0.36018045, 0.3576792 , 0.35517794, 0.35267669,\n",
       "       0.35017544, 0.34767419, 0.34517293, 0.34267168, 0.34017043,\n",
       "       0.33766917, 0.33516792, 0.33266667, 0.33016541, 0.32766416,\n",
       "       0.32516291, 0.32266165, 0.3201604 , 0.31765915, 0.31515789,\n",
       "       0.31265664, 0.31015539, 0.30765414, 0.30515288, 0.30265163,\n",
       "       0.30015038, 0.29764912, 0.29514787, 0.29264662, 0.29014536,\n",
       "       0.28764411, 0.28514286, 0.2826416 , 0.28014035, 0.2776391 ,\n",
       "       0.27513784, 0.27263659, 0.27013534, 0.26763409, 0.26513283,\n",
       "       0.26263158, 0.26013033, 0.25762907, 0.25512782, 0.25262657,\n",
       "       0.25012531, 0.24762406, 0.24512281, 0.24262155, 0.2401203 ,\n",
       "       0.23761905, 0.23511779, 0.23261654, 0.23011529, 0.22761404,\n",
       "       0.22511278, 0.22261153, 0.22011028, 0.21760902, 0.21510777,\n",
       "       0.21260652, 0.21010526, 0.20760401, 0.20510276, 0.2026015 ,\n",
       "       0.20010025, 0.197599  , 0.19509774, 0.19259649, 0.19009524,\n",
       "       0.18759398, 0.18509273, 0.18259148, 0.18009023, 0.17758897,\n",
       "       0.17508772, 0.17258647, 0.17008521, 0.16758396, 0.16508271,\n",
       "       0.16258145, 0.1600802 , 0.15757895, 0.15507769, 0.15257644,\n",
       "       0.15007519, 0.14757393, 0.14507268, 0.14257143, 0.14007018,\n",
       "       0.13756892, 0.13506767, 0.13256642, 0.13006516, 0.12756391,\n",
       "       0.12506266, 0.1225614 , 0.12006015, 0.1175589 , 0.11505764,\n",
       "       0.11255639, 0.11005514, 0.10755388, 0.10505263, 0.10255138,\n",
       "       0.10005013, 0.09754887, 0.09504762, 0.09254637, 0.09004511,\n",
       "       0.08754386, 0.08504261, 0.08254135, 0.0800401 , 0.07753885,\n",
       "       0.07503759, 0.07253634, 0.07003509, 0.06753383, 0.06503258,\n",
       "       0.06253133, 0.06003008, 0.05752882, 0.05502757, 0.05252632,\n",
       "       0.05002506, 0.04752381, 0.04502256, 0.0425213 , 0.04002005,\n",
       "       0.0375188 , 0.03501754, 0.03251629, 0.03001504, 0.02751378,\n",
       "       0.02501253, 0.02251128, 0.02001003, 0.01750877, 0.01500752,\n",
       "       0.01250627, 0.01000501, 0.00750376, 0.00500251, 0.00250125,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "N=17\n",
    "archivo=\"/home/landau/Codes/KADATH/Anhui/Kadath/SelfInteractingBS/bin/\"\n",
    "\n",
    "\n",
    "\n",
    "dominios=8\n",
    "r1_b=1.\n",
    "r2_b=2.\n",
    "r3_b=4.\n",
    "r4_b=8.\n",
    "r5_b=16.\n",
    "r6_b=32.\n",
    "r7_b=64.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cut_Kadath(func_dat):\n",
    "    YY=zeros((N*dominios,N))\n",
    "    for ll in range(dominios):\n",
    "        YY[N*ll:N*(ll+1),:]=loadtxt(archivo+func_dat, skiprows=4*(ll+1)+(N+1)*ll, max_rows=N, usecols=(arange(N)+2))\n",
    "    return YY\n",
    "\n",
    "def extract_coef(dom,FUNC):    #only for local use\n",
    "    return FUNC[N*dom:N*(dom+1),:]\n",
    "x=linspace(-1,1,N)            ##\n",
    "r0=r1_b*x\n",
    "r1=0.5*(r2_b-r1_b)*x+0.5*(r2_b+r1_b)\n",
    "r2=0.5*(r3_b-r2_b)*x+0.5*(r3_b+r2_b)\n",
    "r3=0.5*(r4_b-r3_b)*x+0.5*(r4_b+r3_b)\n",
    "r4=0.5*(r5_b-r4_b)*x+0.5*(r5_b+r4_b)\n",
    "r5=0.5*(r6_b-r5_b)*x+0.5*(r6_b+r5_b)\n",
    "r6=0.5*(r7_b-r6_b)*x+0.5*(r7_b+r6_b)\n",
    "r7=2*r7_b/(1.-x+1e-9)           ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.67662e-01, -2.27223e-01,  2.99855e-02, ..., -2.80078e-11,\n",
       "         4.24641e-12,  4.84612e-14],\n",
       "       [-1.76049e-17,  5.69998e-18,  7.82199e-18, ..., -8.35938e-18,\n",
       "        -4.97360e-18, -4.09401e-19],\n",
       "       [ 6.96672e-18,  6.70152e-18,  5.82624e-18, ...,  1.24831e-18,\n",
       "        -4.84398e-18,  1.45241e-18],\n",
       "       ...,\n",
       "       [-1.15256e-31,  2.26796e-31, -2.15996e-31, ..., -6.40512e-33,\n",
       "         9.36591e-33, -7.56809e-33],\n",
       "       [-8.57743e-32,  1.68809e-31, -1.60845e-31, ..., -4.86271e-33,\n",
       "         7.32123e-33, -5.98225e-33],\n",
       "       [-5.36521e-33,  1.05343e-32, -9.96690e-33, ..., -2.06087e-34,\n",
       "         2.10445e-34, -1.45157e-34]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PHI=cut_Kadath(\"phi.dat\")\n",
    "LAPSE=cut_Kadath(\"lapse.dat\")\n",
    "A=cut_Kadath(\"bigA.dat\")\n",
    "B=cut_Kadath(\"bigB.dat\")\n",
    "W=cut_Kadath(\"W.dat\")\n",
    "\n",
    "PHI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Cheb_serie(coef,xx,yy):  #dos dimensiones\n",
    "    sum_=0\n",
    "    for ii in range(len(coef[:,0])):\n",
    "        for jj in range(len(coef[0,:])):\n",
    "            sum_ += coef[ii,jj] * cos(2*ii * xx) * cos(jj * arccos(yy))\n",
    "    return sum_\n",
    "\n",
    "def Cheb_serie_even(coef,xx,yy):  #dos dimensiones\n",
    "    sum_=0\n",
    "    for ii in range(len(coef[:,0])):\n",
    "        for jj in range(len(coef[0,:])):\n",
    "            sum_ += coef[ii,jj] * cos(2*ii * xx) * cos(2 * jj * arccos(yy))\n",
    "    return sum_\n",
    "\n",
    "def Cheb_serie_odd(coef,xx,yy):  #dos dimensiones\n",
    "    sum_=0\n",
    "    for ii in range(len(coef[:,0])):\n",
    "        for jj in range(len(coef[0,:])):\n",
    "            sum_ += coef[ii,jj] * cos(2*ii * xx) * cos((2 * jj + 1) * arccos(yy))\n",
    "    return sum_\n",
    "\n",
    "\n",
    "def antiCheb_serie(coef,xx,yy):  #dos dimensiones\n",
    "    sum_=0\n",
    "    for ii in range(len(coef[:,0])):\n",
    "        for jj in range(len(coef[0,:])):\n",
    "            sum_ += coef[ii,jj] * cos((2*ii+1) * xx) * cos(jj * arccos(yy))\n",
    "    return sum_\n",
    "\n",
    "def antiCheb_serie_even(coef,xx,yy):  #dos dimensiones\n",
    "    sum_=0\n",
    "    for ii in range(len(coef[:,0])):\n",
    "        for jj in range(len(coef[0,:])):\n",
    "            sum_ += coef[ii,jj] * cos((2*ii+1) * xx) * cos(2 * jj * arccos(yy))\n",
    "    return sum_\n",
    "\n",
    "def antiCheb_serie_odd(coef,xx,yy):  #dos dimensiones\n",
    "    sum_=0\n",
    "    for ii in range(len(coef[:,0])):\n",
    "        for jj in range(len(coef[0,:])):\n",
    "            sum_ += coef[ii,jj] * cos((2*ii+1) * xx) * cos((2 * jj + 1) * arccos(yy))\n",
    "    return sum_\n",
    "\n",
    "\n",
    "def testCheb_serie(coef,xx,yy):  #dos dimensiones\n",
    "    sum_=0\n",
    "    for ii in range(len(coef[:,0])):\n",
    "        for jj in range(len(coef[0,:])):\n",
    "            sum_ += coef[ii,jj] * sin((2*ii+1) * xx) * cos(jj * arccos(yy))\n",
    "    return sum_\n",
    "\n",
    "def testCheb_serie_even(coef,xx,yy):  #dos dimensiones\n",
    "    sum_=0\n",
    "    for ii in range(len(coef[:,0])):\n",
    "        for jj in range(len(coef[0,:])):\n",
    "            sum_ += coef[ii,jj] * sin((2*ii+1) * xx) * cos(2 * jj * arccos(yy))\n",
    "    return sum_\n",
    "\n",
    "def testCheb_serie_odd(coef,xx,yy):  #dos dimensiones\n",
    "    sum_=0\n",
    "    for ii in range(len(coef[:,0])):\n",
    "        for jj in range(len(coef[0,:])):\n",
    "            sum_ += coef[ii,jj] * sin((2*ii+1) * xx) * cos((2 * jj + 1) * arccos(yy))\n",
    "    return sum_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 20.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta_fija = 90 * (pi/180.0) #grados\n",
    "y=theta_fija\n",
    "plot(r0,Cheb_serie_even(extract_coef(0,PHI),y,x))\n",
    "plot(r1,Cheb_serie(extract_coef(1,PHI),y,x))\n",
    "plot(r2,Cheb_serie(extract_coef(2,PHI),y,x))\n",
    "plot(r3,Cheb_serie(extract_coef(3,PHI),y,x))\n",
    "plot(r4,Cheb_serie(extract_coef(4,PHI),y,x))\n",
    "plot(r5,Cheb_serie(extract_coef(5,PHI),y,x))\n",
    "plot(r6,Cheb_serie(extract_coef(6,PHI),y,x))\n",
    "plot(r7,Cheb_serie(extract_coef(7,PHI),y,x))\n",
    "\n",
    "\n",
    "\n",
    "xlim(0,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.499452467043936e-19"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(antiCheb_serie(extract_coef(3,PHI),y,x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.49945247e-19, 4.21564183e-19, 3.24336308e-19, 2.50351042e-19,\n",
       "       1.93813350e-19, 1.50444644e-19, 1.17063964e-19, 9.12917444e-20,\n",
       "       7.13380624e-20, 5.58498465e-20, 4.37996884e-20, 3.44043101e-20,\n",
       "       2.70643672e-20, 2.13197268e-20, 1.68160571e-20, 1.32797377e-20,\n",
       "       1.04989326e-20])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "antiCheb_serie(extract_coef(3,PHI),y,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 20.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(r0,Cheb_serie_even(extract_coef(0,A),y,x))\n",
    "plot(r1,Cheb_serie(extract_coef(1,A),y,x))\n",
    "plot(r2,Cheb_serie(extract_coef(2,A),y,x))\n",
    "plot(r3,Cheb_serie(extract_coef(3,A),y,x))\n",
    "plot(r4,Cheb_serie(extract_coef(4,A),y,x))\n",
    "plot(r5,Cheb_serie(extract_coef(5,A),y,x))\n",
    "plot(r6,Cheb_serie(extract_coef(6,A),y,x))\n",
    "plot(r7,Cheb_serie(extract_coef(7,A),y,x))\n",
    "\n",
    "plot(r0,Cheb_serie_even(extract_coef(0,LAPSE),y,x))\n",
    "plot(r1,Cheb_serie(extract_coef(1,B),y,x))\n",
    "plot(r2,Cheb_serie(extract_coef(2,B),y,x))\n",
    "plot(r3,Cheb_serie(extract_coef(3,B),y,x))\n",
    "plot(r4,Cheb_serie(extract_coef(4,B),y,x))\n",
    "plot(r5,Cheb_serie(extract_coef(5,LAPSE),y,x))\n",
    "plot(r6,Cheb_serie(extract_coef(6,LAPSE),y,x))\n",
    "plot(r7,Cheb_serie(extract_coef(7,LAPSE),y,x))\n",
    "\n",
    "xlim(0,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.44249065e-15, -3.41948692e-14,  4.30766534e-14, -1.31006317e-14,\n",
       "       -4.90718577e-14,  6.59472477e-14,  7.77156117e-15, -7.12763182e-14,\n",
       "        2.15383267e-14,  5.92859095e-14, -4.15223411e-14, -3.26405569e-14,\n",
       "        5.08482145e-14, -1.35447209e-14, -1.59872116e-14,  1.50990331e-14,\n",
       "        1.11022302e-15])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cheb_serie(extract_coef(3,B),y,x)-Cheb_serie(extract_coef(3,A),y,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "MATRIX=zeros((N*dominios,N*4))\n",
    "MATRIX[:,0:N]=PHI\n",
    "MATRIX[:,N:2*N]=LAPSE\n",
    "MATRIX[:,2*N:3*N]=A\n",
    "MATRIX[:,3*N:4*N]=B\n",
    "\n",
    "# FOR ROTATING\n",
    "#MATRIX=zeros((N*dominios,N*5))\n",
    "#MATRIX[:,0:N]=PHI\n",
    "#MATRIX[:,N:2*N]=LAPSE\n",
    "#MATRIX[:,2*N:3*N]=A\n",
    "#MATRIX[:,3*N:4*N]=B\n",
    "#MATRIX[:,4*N:5*N]=W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "savetxt(\"omega0p6_g-10_h10.dat\",MATRIX)\n",
    "#savetxt(\"omega0p9_g-10_h0p5.dat\",MATRIX)\n",
    "#savetxt(\"omega0p95_g-10_h0p5.dat\",MATRIX)\n",
    "#savetxt(\"omega0p7584_g-10_h0p5.dat\",MATRIX)\n",
    "#savetxt(\"omega0p9_g0_h0.dat\",MATRIX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.67662e-01, -2.27223e-01,  2.99855e-02, ...,  6.76599e-11,\n",
       "        -1.55541e-11,  1.04450e-12],\n",
       "       [-1.76049e-17,  5.69998e-18,  7.82199e-18, ..., -3.88569e-15,\n",
       "         3.15477e-15, -2.97668e-16],\n",
       "       [ 6.96672e-18,  6.70152e-18,  5.82624e-18, ..., -3.73815e-15,\n",
       "         3.16490e-15, -3.27818e-16],\n",
       "       ...,\n",
       "       [-1.15256e-31,  2.26796e-31, -2.15996e-31, ...,  0.00000e+00,\n",
       "         0.00000e+00,  0.00000e+00],\n",
       "       [-8.57743e-32,  1.68809e-31, -1.60845e-31, ...,  0.00000e+00,\n",
       "         0.00000e+00,  0.00000e+00],\n",
       "       [-5.36521e-33,  1.05343e-32, -9.96690e-33, ...,  0.00000e+00,\n",
       "         0.00000e+00,  0.00000e+00]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}