{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# The Non Random Two Liquids (NRTL) model for *excess Gibbs energy* ($g^E$) and a case study of Liquid-Liquid equilibria of Ethyl acetate (1) + water (2) + ethanol (3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having previously discussed *Excess thermodynamic properties*, excess energy models, the NRTL model specifically, and VLE, we now proceed to study LLE." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Requirements" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The NumPy package\n", "for array structures and operations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The pyplot interface from the matplotlib package" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# physical constants in the international system of units" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8.3144598\n" ] } ], "source": [ "from scipy.constants import R\n", "print(R)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# some special purpose functions that will help us in our analyzes" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def stdv(v): #will standardize an array, transforming all values to between 0 and 1\n", " w=(v-np.min(v))/(np.max(v)-np.min(v))\n", " return w" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The NRTL model\n", "as presented in previous lecture\n", "* reproduced below in minimalist numpy/numba format" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numba import jit\n", "@jit\n", "def Gamma(T,c_x,q_alpha, q_A):\n", " q_tau = q_A/T\n", " q_G = np.exp(-(q_alpha*q_tau))\n", " l_D = ((1/((q_G.T) @ c_x)).T)\n", " q_E = (q_tau*q_G) * l_D \n", " gamma = np.exp(((q_E+(q_E.T))-(((q_G * l_D) * (c_x.T)) @ (q_E.T))) @ c_x)\n", " return gamma" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fitted parameters\n", "Parameters from Renon and Prausnitz, 1969." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Ethyl acetate (1) + water (2) + ethanol (3)\n", "\n", "alpha12 = 0.4\n", "\n", "alpha23 = 0.3\n", "\n", "alpha13 = 0.3\n", "\n", "# 6 binary Aij parameters\n", "Dg12 = 1335 * 4.184 #J/K\n", "Dg21 = 2510 * 4.184 #J/K\n", "\n", "Dg23 = 976 * 4.184 #J/K\n", "Dg32 = 88 * 4.184 #J/K\n", "\n", "Dg13 = 301 * 4.184 #J/K\n", "Dg31 = 322 * 4.184 #J/K" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Feeding the fitted parameters to the model in matrix structure:\n", "we will assemble the parameters in a matrix structure compatible with the current imlementation of the model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#assemble matrix with regressed parameters Dg_i,j, according to the model all diagonal terms are zero\n", "Dg = np.array([[0, Dg12, Dg13],\n", " [Dg21, 0, Dg23],\n", " [Dg31, Dg32, 0]])\n", "\n", "\n", "#assemble symmetric matrix alpha\n", "alpha = np.array([[0, alpha12, alpha13],\n", " [alpha12, 0, alpha23],\n", " [alpha13, alpha23, 0]])\n", "\n", "A= Dg/R" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* This notebook shall contain calculations at the temperature of 70 deg C" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "T = 273+70 #K" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Liq-Liq Equilibria Flash\n", "Now we start discussing the Liq-Liq equilibria flash calculation\n", "\n", "it works as follows:\n", "\n", "The degrees of Freedom for a flash calculation are temperature - T, pressure - P and global composition - z\n", "\n", "|\n", "\n", "**Equilibriuma criteria**\n", "\n", "$$\\mu_{i}^{\\rm {L1}}=\\mu_{i}^{\\rm {L2}}, \\forall i$$\n", "\n", "**Devised algorithm after analytical simplification of repeated contributions:**\n", "\n", "$$x_{i}^{\\rm {L1}} \\gamma_{i}^{\\rm {L1}}= x_{i}^{\\rm {L2}} \\gamma_{i}^{\\rm {L2}}, \\forall i$$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# algorithm\n", "based on Rachford and Rice (1952) approach to LV equilibrium\n", "\n", "\n", "Ref: Rachford HH, Rice JD, Petroleum Trans AIME 1952;195:327-328" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def ELLflash_explicit(Z,beta0,K0,MODEL,qolog):\n", " \n", " #copy intial guesses to local variables\n", " beta=beta0.copy()\n", " K=K0.copy()\n", "\n", " converged = 0 # flag indicating not converged yet\n", " \n", " if qolog:\n", " itlogi=0\n", " itlogv=np.ndarray(2,dtype=object)\n", " itlogv[0]=np.zeros(100*100)\n", " itlogv[1]=np.zeros([100*100,3])\n", " \n", " #initialize local arrays\n", " a = np.zeros(3)\n", " XI = np.zeros(3)\n", " XII = np.zeros(3)\n", " \n", " looped2=0\n", " looped3=0\n", " \n", " #managing loop for fixed point point algorithm\n", " looping2 = 0\n", " while ( (looping2 == 0) # means do at least once\n", " or ((ResK)>0.0001 # convergence criteria\n", " and looping2 < 100) ): # max number of iterations\n", "\n", " prevK = K.copy()\n", " \n", " #print(np.may_share_memory(K,prevK))\n", " \n", " #Solve RachfordRice equation with a newton raphson loop\n", " looping3 = 0\n", " while ( (looping3 == 0) # means do at least once\n", " or ((ResBeta)>0.0001 # convergence criteria\n", " and looping3 < 100) ): #max number of iterations\n", " \n", " prevbeta = beta.copy()\n", "\n", " for i in range(3):\n", " a[i] = ( (K[i]-1) / (1+beta*(K[i]-1)) ) \n", " \n", " R=0 #residue funtion (F)\n", " for j in range(3):\n", " R+=Z[j]*a[j]\n", "\n", " J=0 #jacobian (Fprime)\n", " for j in range(3):\n", " J-=Z[j]*a[j]*a[j]\n", " \n", " beta -= R/J #NR step \n", " \n", " if beta[0,0]<0:\n", " beta[0,0]=0\n", " \n", " if 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(stdv(ans[6][0][:ans[5]]))\n", "plt.plot(stdv(ans[6][1][:ans[5],0]))\n", "plt.plot(stdv(ans[6][1][:ans[5],1]))\n", "plt.plot(stdv(ans[6][1][:ans[5],2]))\n", "\n", "plt.xlim(0,ans[5])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 8.45 ms per loop\n" ] } ], "source": [ "%timeit ans=ELLflash_explicit(Z,beta0,k0,MODEL,False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# using inear algebra" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def ELLflash(Z,beta0,K0,MODEL): #ELLflash_linalg\n", " \n", " beta=beta0.copy()\n", " K=K0.copy()\n", " \n", " converged = 0\n", " \n", " looped2 = 0\n", " looped3 = 0\n", " \n", " looping2 = 0\n", " while ( (looping2 == 0) # means do at least once\n", " or ((ResK)>0.0001 # convergence criteria\n", " and looping2 < 100) ): # max number of iterations\n", "\n", " prevK = K.copy() \n", " \n", " looping3 = 0\n", " while ( (looping3 == 0)\n", " or (ResBeta>0.0001 # convergence criteria\n", " and looping3 < 100) ): #max number of iterations\n", " \n", " prevbeta = beta.copy()\n", " \n", " a = (1/(1+beta*(K-1))) * (K-1)\n", " beta = beta+(1/((Z.T*a.T) @ a)) @ (Z.T @ a)\n", "\n", " if beta[0,0]<0:\n", " beta[0,0]=0\n", " \n", " if 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(stdv(ans[6][0][:ans[5]]))\n", "plt.plot(stdv(ans[6][1][:ans[5],0]))\n", "plt.plot(stdv(ans[6][1][:ans[5],1]))\n", "plt.plot(stdv(ans[6][1][:ans[5],2]))\n", "\n", "plt.xlim(0,ans[5])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# grid calc plot" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "210\n", "done 33\n" ] } ], "source": [ "nspace = 20\n", "\n", "Z2 = np.linspace(0,1,nspace)\n", "Z3 = np.linspace(0,1,nspace)\n", "\n", "Z2[0]=1e-9\n", "Z3[0]=1e-9\n", "\n", "\n", "Z2[-1]=1-1e-9\n", "Z3[-1]=1-1e-9\n", "\n", "\n", "rawZs = np.ndarray((nspace,nspace), dtype=object)\n", "\n", "flags = np.zeros((nspace,nspace))\n", "\n", "for i in range(nspace):\n", " for j in range(nspace):\n", " rawZs[i,j]=np.array([1-Z2[i]-Z3[j],Z2[i],Z3[j]])\n", " if ( Z2[i] + Z3[j] <= 1 ):\n", " flags[i,j] = 1\n", " \n", "\n", " \n", "Zs = rawZs[np.where(flags==1)]\n", "\n", "npts = Zs.shape[0]\n", "print(npts)\n", "\n", "Resultados_beta = np.zeros([npts])\n", "Resultados_conv = np.zeros([npts])\n", "Resultados_Z = np.zeros([npts,3])\n", "Resultados_XI = np.zeros([npts,3])\n", "Resultados_XII = np.zeros([npts,3])\n", "Resultados_K0 = np.ndarray(npts, dtype = object)\n", "Resultados_K = np.ndarray(npts, dtype = object)\n", "Resultados_beta0 = np.zeros([npts])\n", "\n", "\n", "sResultados_beta = np.zeros([npts])\n", "sResultados_conv = np.zeros([npts])\n", "sResultados_Z = np.zeros([npts,3])\n", "sResultados_XI = np.zeros([npts,3])\n", "sResultados_XII = np.zeros([npts,3])\n", "sResultados_K0 = np.ndarray(npts, dtype = object)\n", "sResultados_K = np.ndarray(npts, dtype = object)\n", "sResultados_beta0 = np.zeros([npts])\n", "\n", "MODEL = lambda x: Gamma(T,x,alpha,A)\n", "\n", "l=0\n", "for k in range(npts):\n", " beta0, K0 = iguess(np.array([Zs[k]]).T,MODEL)\n", " #print(beta0,K0)\n", " #print(Zs[k],beta0,K0)\n", " ans = ELLflash(np.array([Zs[k]]).T,beta0,K0,MODEL)\n", " \n", " if (0ans[2] and ans[3]!=0):\n", " #print(ans[0][0][0],ans[0][1][0],ans[0][2][0])\n", " #print(ans[0][0:3,0])\n", " Resultados_XI[l,:] = ans[0][0:3,0]#np.array([ans[0][0][0],ans[0][1][0],ans[0][2][0]])\n", " Resultados_XII[l,:] = ans[1][0:3,0] #np.array([ans[1][0][0],ans[1][1][0],ans[1][2][0]])\n", " Resultados_beta[l] = ans[2]\n", " Resultados_conv[l] = ans[3]\n", "\n", " Resultados_Z[l,:] = Zs[k]\n", " \n", " Resultados_beta0[l] = beta0[0,0]\n", " Resultados_K0[l] = K0\n", " \n", " Resultados_K[l] = Resultados_XII[l,:]/Resultados_XI[l,:]\n", " \n", " l+=1\n", " #print('PASS', Zs[k], ans[0][0:3,0], ans[1][0:3,0], ans[2])\n", " else:\n", " #print('FAIL', k, Zs[k])\n", " pass\n", "\n", "print('done',l)\n", " \n", "ndone=l-1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* of a grid with 210 points we foundo solutions for 33 points\n", "* lets soon see wether we tried calculations at conditions that do not have solution, or at conds that had solutions but our algorithms missed" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fResultados_XI = Resultados_XI[:][np.where(Resultados_conv!=0)]\n", "fResultados_XII = Resultados_XII[:][np.where(Resultados_conv!=0)]\n", "fResultados_beta = Resultados_beta[np.where(Resultados_conv!=0)]\n", "fResultados_Z = Resultados_Z[:][np.where(Resultados_conv!=0)]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# barycentric coords: (a,b,c)\n", "a=fResultados_XI[:,0]\n", "b=fResultados_XI[:,1]\n", "c=fResultados_XI[:,2]\n", "\n", "d=fResultados_XII[:,0]\n", "e=fResultados_XII[:,1]\n", "f=fResultados_XII[:,2]\n", "\n", "g=fResultados_Z[:,0]\n", "h=fResultados_Z[:,1]\n", "ii=fResultados_Z[:,2]\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# plot mole fractions results in axis of ethanol vc water" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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URRGRnFiiunJlhO7fz9Pxc+fyPIE/7NgBdOzIMw+33kr56Dx5uLw/dy6zJUaN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\njtZcEffdR8uTWSxbZk3x/O03Hr9ypfk+b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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.axis([0,1,0,1])\n", " \n", "plt.scatter(a,c) \n", "plt.scatter(d,f) \n", "\n", "ZS=np.zeros([npts,3])\n", "for i in range(npts):\n", " ZS[i,:]=Zs[i]\n", "\n", "plt.scatter(ZS[:,0],ZS[:,2],marker='o',s=80, facecolors='none', edgecolors='r') \n", " \n", "plt.scatter(g,ii,marker='*')\n", "\n", "#UNCOMMENT THIS LINE TO PLOT THE FAILED POINT\n", "#failedpoint= [0.42105263 , 0.52631579 , 0.05263158]\n", "#plt.scatter(failedpoint[0],failedpoint[2],marker='d')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# x crit = 0.2 aprox\n", "\n", "\n", "#refiltrar a e b LEFT and RIGHT of the crit point\n", "\n", "crit=.2\n", "\n", "al=a[np.where(a<=crit)]\n", "ar=a[np.where(a>crit)]\n", "dl=d[np.where(d<=crit)]\n", "dr=d[np.where(d>crit)]\n", "\n", "bl=b[np.where(a<=crit)]\n", "br=b[np.where(a>crit)]\n", "el=e[np.where(d<=crit)]\n", "er=e[np.where(d>crit)]\n", "\n", "cl=c[np.where(a<=crit)]\n", "cr=c[np.where(a>crit)]\n", "fl=f[np.where(d<=crit)]\n", "fr=f[np.where(d>crit)]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(33,) (33,) (33,) (33,)\n", "(22,) (22,) (11,) (11,)\n", "(11,) (11,) (22,) (22,)\n", "(33,) (33,) (33,) (33,)\n" ] } ], "source": [ "Ly = np.concatenate((al,dl),axis=0)\n", "Lx = np.concatenate((cl,fl),axis=0)\n", "\n", "Ry = np.concatenate((ar,dr),axis=0)\n", "Rx = np.concatenate((cr,fr),axis=0)\n", "\n", "sLy=np.sort(Ly)\n", "sLx=np.sort(Lx)\n", "sRy=np.sort(Ry)\n", "sRx=-np.sort(-Rx)\n", "\n", "print(a.shape,b.shape,d.shape,e.shape)\n", "print(al.shape,bl.shape,dl.shape,el.shape)\n", "print(ar.shape,br.shape,dr.shape,er.shape)\n", "print(Ly.shape,Ry.shape,Lx.shape,Rx.shape)\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Ac3MzV1xxFZs3Pxy118cFF1zIL3/5M3Jzc49Y3UREpiN1wYhM0fPPPz9m39VX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\n4jZ23MKJm7iqiasaeNLAlxqBUUuCRb2pxEET32/j+xH+UBJ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.axis([0,1,0,1])\n", " \n", "plt.scatter(Ly,Lx) \n", "plt.scatter(Ry,Rx) \n", "\n", "plt.plot(sLy,sLx)\n", "plt.plot(sRy,sRx)\n", "\n", "\n", "for i in range(0,ndone):\n", " plt.plot([a[i],d[i]],[c[i],f[i]])\n", "\n", "\n", "plt.scatter(g,ii, marker='*')\n", "\n", "plt.xlabel('mol. frac. water')\n", "plt.ylabel('mol. frac. ethanol')\n", "plt.title('LLE at '+str(T-273)+'degC') \n", " \n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# note that we tried flash at every open circle, but converged only at asteriscs\n", "# outside the visible phase diagram, we sholud not have convergence because there is indeed no soltuiotn foe LLE\n", "# however inside the phase envelope there is solution but our algorithm failed" ] }, { "cell_type": "code", "execution_count": 202, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1.]]\n", "0 0 means did not cnverge\n" ] }, { "data": { "image/png": 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4XgJAdE+vfJr3v32fdy95lx5Z8ocSQgijmboFoMNpgLm54f0A4lwNMJExANIC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\nNTdDY2P39wD4OnyUeEpiPgC4JCODg8bw1t69/fe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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "\n", "#what happended to some points:\n", "failedpoint= [0.42105263 , 0.52631579 , 0.05263158]\n", "\n", "ig = iguess(np.array([failedpoint]).T,MODEL)\n", "#print('ig')\n", "#print(ig)\n", "ans=ELLflash_explicit(np.array([failedpoint]).T,ig[0],(ig[1]),MODEL,True)\n", "#print(ans)\n", "\n", "#lets study those points in a different notebook!\n", "\n", "print(ans[2])\n", "print(ans[3],'0 means did not cnverge')\n", "\n", "plt.plot(stdv(ans[6][0][:ans[5]]))\n", "plt.plot(stdv(ans[6][1][:ans[5],0]))\n", "plt.plot(stdv(ans[6][1][:ans[5],1]))\n", "plt.plot(stdv(ans[6][1][:ans[5],2]))\n", "\n", "plt.xlim(0,ans[5])\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# divergence in BETA caused by not good enough initial guess fr K0\n", "# see below a trial with a different K0" ] }, { "cell_type": "code", "execution_count": 205, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.79986453]] vs 0.310193764201\n", "[[ 0.41875185 4.62153782 1.02404211]] vs [ 0.03473608 2.8461674 0.41370058]\n", "[[ 0.31019376]]\n", "1 1 means cnverge\n" ] }, { "data": { "image/png": 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IwSi4oKK0NLNlusEHFZsbNtMR7aC4qJd9JwYwq6qKWVVVPY5PKC9nvXIteveT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\nHN5R6nhhcSHf7viWuRvn8t7/3mPi1xMRhHaNe1MzqbTyJia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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#what happended to some points:\n", "failedpoint= [0.42105263 , 0.52631579 , 0.05263158]\n", "\n", "ig = iguess(np.array([failedpoint]).T,MODEL)\n", "#print('ig')\n", "#print(ig)\n", "ans=ELLflash_explicit(np.array([failedpoint]).T,ig[0],(ig[1])/2,MODEL,True)\n", "#print('ans')\n", "#print(ans)\n", "\n", "#print(ans[1])\n", "\n", "print(ig[0],'vs',ans[2][0,0])\n", "print(ig[1].T,'vs',(ans[1][:]/ans[0][:]))\n", "\n", "\n", "print(ans[2])\n", "print(ans[3],'1 means cnverge')\n", "\n", "#lets study those points in a different notebook!\n", "\n", "plt.plot(stdv(ans[6][0][:ans[5]]))\n", "plt.plot(stdv(ans[6][1][:ans[5],0]))\n", "plt.plot(stdv(ans[6][1][:ans[5],1]))\n", "plt.plot(stdv(ans[6][1][:ans[5],2]))\n", "\n", "plt.xlim(0,ans[5])\n", "\n", "plt.xlabel(r'iterations')\n", "plt.ylabel(r'variables')\n", "plt.title(r'iterations history')\n", "\n", "plt.savefig('9.png')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ig history" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "testZ2 = 0.52631579\n", "\n", "npts=100\n", "\n", "Zs = np.ndarray(npts, dtype=object)\n", "\n", "i=0\n", "for testZ1 in np.linspace(0,testZ2,npts):\n", " testZ3 = 1-testZ1-testZ2\n", " Zs[i] = np.asarray([testZ1,testZ2,testZ3])\n", " i+=1\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/segtovichisv/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:59: RuntimeWarning: invalid value encountered in true_divide\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "done 40\n" ] } ], "source": [ "Resultados_beta = np.zeros([npts])\n", "Resultados_conv = np.zeros([npts])\n", "Resultados_Z = np.zeros([npts,3])\n", "Resultados_XI = np.zeros([npts,3])\n", "Resultados_XII = np.zeros([npts,3])\n", "Resultados_K0 = np.ndarray(npts, dtype = object)\n", "Resultados_K = np.ndarray(npts, dtype = object)\n", "Resultados_beta0 = np.zeros([npts])\n", "\n", "sResultados_beta = np.zeros([npts])\n", "sResultados_conv = np.zeros([npts])\n", "sResultados_Z = np.zeros([npts,3])\n", "sResultados_XI = np.zeros([npts,3])\n", "sResultados_XII = np.zeros([npts,3])\n", "sResultados_K0 = np.ndarray(npts, dtype = object)\n", "sResultados_K = np.ndarray(npts, dtype = object)\n", "sResultados_beta0 = np.zeros([npts])\n", "\n", "MODEL = lambda x: Gamma(T,x,alpha,A)\n", "\n", "l=0\n", "ll=0\n", "for k in range(npts):\n", " beta0, K0 = iguess(np.array([Zs[k]]).T,MODEL)\n", " #print(beta0,K0)\n", " #print(Zs[k],beta0,K0)\n", " ans = ELLflash(np.array([Zs[k]]).T,beta0,K0,MODEL)\n", " \n", " if (0ans[2] and ans[3]!=0):\n", " #print(ans[0][0][0],ans[0][1][0],ans[0][2][0])\n", " #print(ans[0][0:3,0])\n", " Resultados_XI[l,:] = ans[0][0:3,0]#np.array([ans[0][0][0],ans[0][1][0],ans[0][2][0]])\n", " Resultados_XII[l,:] = ans[1][0:3,0] #np.array([ans[1][0][0],ans[1][1][0],ans[1][2][0]])\n", " Resultados_beta[l] = ans[2]\n", " Resultados_conv[l] = ans[3]\n", "\n", " Resultados_Z[l,:] = Zs[k]\n", " \n", " Resultados_beta0[l] = beta0[0,0]\n", " Resultados_K0[l] = K0\n", " \n", " Resultados_K[l] = Resultados_XII[l,:]/Resultados_XI[l,:]\n", " \n", " l+=1\n", " #print('PASS', Zs[k], ans[0][0:3,0], ans[1][0:3,0], ans[2])\n", " else:\n", " #print('FAIL', k, Zs[k])\n", " pass\n", " \n", "\n", " \n", " sResultados_conv[ll] = ans[3]\n", " sResultados_Z[ll,:] = Zs[k]\n", " sResultados_beta0[ll] = beta0[0,0]\n", " sResultados_K0[ll] = K0\n", " \n", " if ans[3]==1:\n", " sResultados_beta[ll] = ans[2]\n", " sResultados_K[ll] = ans[1][0:3,0]/ans[0][0:3,0]\n", " else:\n", " sResultados_beta[ll] = ans[2]*0\n", " sResultados_K[ll] = ans[1][0:3,0]/ans[0][0:3,0]*0\n", " \n", " ll+=1\n", " \n", "print('done',l)\n", " \n", "ndone=l-1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", "[ 1. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 1. 1.\n", " 1. 1. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0.9473989 0.92386607 0.90278913\n", " 0.88372061 0.86632151 0.85031202 0.83547414 0.82162759 0.80857401\n", " 0.79616533 0.78472324 0.77401123 0.76361938 0.75360881 0.74397174\n", " 0.73462545 0.72556555 0.71673969 0.70811277 0.69971917 0.\n", " 0.31691301 0.32501899 0.33303922 0.34100881 0.34891757 0.35678734\n", " 0.3645978 0.37235028 0.38001389 0.38755401 0.39492909 0.40207962\n", " 0.40894618 0.41546994 0.42160072 0.42729509 0.43252767 0.43729026\n", " 0.44158459 0.44542847]\n" ] }, { "data": { "image/png": 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Cw8N59913efjhhxkzZgyJiYn8+9//pkOHDqSn317NwM7Ojnbt2rF582batGmD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\ndF47d+6kYcOG7Nq1q9Q6CxYsIDk5mZ9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Biy/C5Mlw112wbJlvziOEaNIkqBDCy6xtrCT8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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iterable = (sResultados_conv[l] for l in range(66,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "print(axis)\n", "\n", "plt.plot((axis+1),label='conv')\n", "\n", "iterable = (sResultados_beta[l] for l in range(0,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "print(axis)\n", "\n", "FIRST_CONV = 66\n", "\n", "iterable = (sResultados_beta[l] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "plt.plot(axis,label='solution')\n", "\n", "iterable = (sResultados_beta0[l] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "plt.plot(axis,label='ig')\n", "\n", "'''[[ 0.79986453]] vs 0.310193764201\n", "[[ 0.41875185 4.62153782 1.02404211]] vs [ 0.03473608 2.8461674 0.41370058]'''\n", "\n", "plt.scatter([13],[0.79986453],marker='*',label='bad_ig')\n", "plt.scatter([13],[0.310193764201 ],label='failed_sol')\n", "plt.scatter([13],[1-0.310193764201 ],marker='d',label='comp_failed_sol')\n", "\n", "\n", "iterable = (sResultados_beta[l] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "plt.plot(1-axis,label='comp_solution')\n", "iterable = (sResultados_beta0[l] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "plt.plot(1-axis,label='comp_ig')\n", "\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# IG CLOSE TO SOLUTION '2'" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/segtovichisv/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:25: RuntimeWarning: divide by zero encountered in true_divide\n" ] }, { "data": { "image/png": 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qFYsWLUJrzd13381HH33ERx99xN13313tewQwduxYpk+fTu/evVmwYAEDBw7k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\njl4de9GrYy+/abTWWCutXrUYnoHGrpO7WP3tao7ajmLXtSu1mpSJzKR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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iterable = (sResultados_conv[l] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "print(axis)\n", "plt.semilogy(1000*(axis)+100,label='conv')\n", "\n", "\n", "iterable = (sResultados_K[l][0] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "plt.semilogy(axis,label='solution')\n", "\n", "iterable = (sResultados_K0[l][0] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "plt.semilogy(axis,label='ig')\n", "\n", "'''[[ 0.79986453]] vs 0.310193764201\n", "[[ 0.41875185 4.62153782 1.02404211]] vs [ 0.03473608 2.8461674 0.41370058]'''\n", "\n", "plt.scatter([13],[0.41875185],marker='*',label='bad_ig')\n", "plt.scatter([13],[0.03473608],label='failed_sol')\n", "plt.scatter([13],[1/0.03473608 ],marker='d',label='inv_failed_sol')\n", "\n", "\n", "iterable = (sResultados_K[l][0] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "plt.plot(1/axis,label='inv_solution')\n", "iterable = (sResultados_K0[l][0] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "plt.plot(1/axis,label='inv_ig')\n", "\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ig right in the middle, closer to solution 1" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/segtovichisv/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:24: RuntimeWarning: divide by zero encountered in true_divide\n" ] }, { "data": { "image/png": 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BvTglXT+jpJ+no8XHxxMfH2+Tlp6e7rDyy+2fkXnz5tG6dWtXN0OIckf+2ziX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\nAQgCO7qt3wHMyvQC59yfsyzHM8CFwAagNfsiioiIjFnFwHS879IhyXW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iterable = (sResultados_conv[l] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "print(axis)\n", "plt.semilogy(1000*(axis)+100,label='conv')\n", "\n", "\n", "iterable = (sResultados_K[l][1] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "plt.semilogy(axis,label='solution')\n", "\n", "iterable = (sResultados_K0[l][1] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "plt.semilogy(axis,label='ig')\n", "\n", "'''[[ 0.79986453]] vs 0.310193764201\n", "[[ 0.41875185 4.62153782 1.02404211]] vs [ 0.03473608 2.8461674 0.41370058]'''\n", "\n", "plt.scatter([13],[4.62153782],marker='*')\n", "plt.scatter([13],[2.8461674 ],label='failed_sol')\n", "plt.scatter([13],[1/2.8461674 ],marker='d',label='inv_failed_sol')\n", "\n", "iterable = (sResultados_K[l][1] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "plt.plot(1/axis,label='inv_solution')\n", "iterable = (sResultados_K0[l][1] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "plt.plot(1/axis,label='inv_ig')\n", "\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ig CLOSE TO FEASIBLE SOLUTION '1'" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/segtovichisv/anaconda3/lib/python3.5/site-packages/matplotlib/scale.py:93: RuntimeWarning: invalid value encountered in less_equal\n", " mask = a <= 0.0\n" ] }, { "data": { "image/png": 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gbZirXPIAzEtBdYmIiMiFtA08ABwG4HWYMTwUZswOAHgMwIWq+lR4zI6bYU6x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\nEwiYqZUCAey1LweDZjmcv7O5GVUNDS3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xiz = np.arange(FIRST_CONV,ll)\n", "\n", "iterable = (sResultados_conv[l] for l in range(FIRST_CONV,ll))\n", "caxis = np.fromiter(iterable, np.float)\n", "#print(axis)\n", "\n", "\n", "zaxis = np.ones(ll-FIRST_CONV)\n", "maxis = np.ma.masked_where(caxis == 0, np.ones(ll-FIRST_CONV))\n", "naxis = np.ma.masked_where(caxis == 1, np.ones(ll-FIRST_CONV))\n", "mxiz = np.ma.masked_where(caxis == 0, xiz)\n", "plt.semilogy()\n", "\n", "plt.scatter(xiz,50*(zaxis),marker='o', s=80, facecolors='none', edgecolors='r')\n", "plt.scatter(mxiz,50*(maxis),marker='x',label='converges')\n", "plt.scatter(mxiz,50*(naxis),marker='o', s=80, facecolors='none', edgecolors='r',label='diverges')\n", "\n", "\n", "\n", "iterable = (sResultados_K[l][2] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "maxis = np.ma.masked_where(caxis == 0, axis)\n", "plt.semilogy(mxiz,maxis,label='solution')\n", "\n", "\n", "\n", "\n", "iterable = (sResultados_K0[l][2] for l in range(FIRST_CONV,ll))\n", "axis = np.fromiter(iterable, np.float)\n", "maxis = np.ma.masked_where(caxis == 0, axis)\n", "plt.semilogy(mxiz,maxis,label='initial guess')\n", "\n", "'''[[ 0.79986453]] vs 0.310193764201\n", "[[ 0.41875185 4.62153782 1.02404211]] vs [ 0.03473608 2.8461674 0.41370058]'''\n", "\n", "plt.scatter([13+66],[1.02404211],marker='*')\n", "plt.scatter([13+66],[0.41370058 ],label='failed_sol')\n", "plt.scatter([13+66],[1/0.41370058 ],marker='d',label='inverse missed solution')\n", "\n", "iterable = (sResultados_K[l][2] for l in range(FIRST_CONV,ll))\n", "maxis = np.ma.masked_where(caxis == 0, axis)\n", "axis = np.fromiter(iterable, np.float)\n", "plt.semilogy(mxiz,1/maxis,label='inverse solution')\n", "iterable = (sResultados_K0[l][2] for l in range(FIRST_CONV,ll))\n", "maxis = np.ma.masked_where(caxis == 0, axis)\n", "axis = np.fromiter(iterable, np.float)\n", "plt.semilogy(mxiz,1/maxis,label='inverse initial guess')\n", "\n", "#for i in range(34):\n", "# print(mxiz[i],maxis[i])\n", "\n", "plt.xlim(FIRST_CONV,ll-1)\n", "\n", "plt.ylim(1e-1,1e2)\n", "\n", "#plt.legend(loc=0)\n", "\n", "plt.title(r'initial guess $\\times$ solution')\n", "plt.ylabel(r'$K_3$')\n", "plt.xlabel(r'input index')\n", "\n", "plt.savefig('fig7.png')\n", "\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# INITIAL GUESS RIGHT IN THE MIDDEL OF TWO FEASIBLE SOLUTIONS" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 9.845702 ]\n", " [ 0.14959554]\n", " [ 1.21599902]]\n" ] } ], "source": [ "print( sResultados_K0[l][:] )" ] }, { "cell_type": "code", "execution_count": 196, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/segtovichisv/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:52: RuntimeWarning: invalid value encountered in true_divide\n" ] } ], "source": [ "### given K3 = 1, beta = .5\n", "\n", "#grid K1 and K2 between .01 and 100\n", "\n", "nspace = 5\n", "\n", "K1s = np.logspace(-2,2,nspace)\n", "K2s = np.logspace(-2,2,nspace)\n", "\n", "Z = [0.42105263 , 0.52631579 , 0.05263158]\n", "K3=1\n", "beta0=np.array([[.5]])\n", "\n", "###\n", "\n", " \n", " \n", "npts = nspace*nspace\n", "\n", "histK1 = np.ndarray([nspace,nspace], dtype=object)\n", "histK2 = np.ndarray([nspace,nspace], dtype=object)\n", "\n", "for k in range(nspace):\n", " for l in range(nspace): # for each point in the grid,\n", " \n", " # calculate the initial guess\n", " K0 = np.asarray([[K1s[k], K2s[l], K3]]).T\n", "\n", " #then calculate the flash\n", " ans = ELLflash_explicit(np.array([Z]).T,beta0,K0,MODEL,True)\n", "\n", " \n", " histK1[k,l] = np.fromiter( (ans[6][1][i,0] for i in range(100) ), dtype=np.float)\n", " histK2[k,l] = np.fromiter( (ans[6][1][i,1] for i in range(100) ), dtype=np.float)\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 197, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.01 0.01 0.86792087 1.09588658 1.09336134 1.08841374\n", " 1.08369072 1.07926302 1.0751117 1.07121577 1.06755599 1.06411489\n", " 1.0608766 1.05782663 1.05495177 1.05223993 1.04968005 1.04726197\n", " 1.04497636 1.04281465 1.04076891 1.03883186 1.03699674 1.03525731\n", " 1.0336078 1.03204283 1.03055742 1.02914696 1.02780711 1.02653387\n", " 1.02532349 1.02417247 1.02307753 1.02203563 1.02104389 1.02009964\n", " 1.01920035 1.01834366 1.01752734 1.01674932 1.01600763 1.01530042\n", " 1.01462595 1.01398257 1.01336874 1.01278299 1.01222394 1.01169029\n", " 1.01118081 1.01069432 1.01022972 1.00978597 1.00936208 1.0089571\n", " 1.00857015 1.00820037 1.00784698 1.0075092 1.00718632 1.00687764\n", " 1.00658252 1.00630034 1.00603051 1.00577246 1.00552566 1.0052896\n", " 1.00506381 1.00484781 1.00464118 1.00444349 1.00425434 1.00407336\n", " 1.00390018 1.00373446 1.00357586 1.00342408 1.00327882 1.00313979\n", " 1.00300672 1.00287934 1.00275741 1.00264069 1.00252895 1.00242199\n", " 1.00231958 1.00222154 1.00212767 1.00203779 1.00195174 1.00186935\n", " 1.00179046 1.00171491 1.00164257 1.0015733 1.00150697 1.00144345\n", " 1.00138262 1.00132436 1.00126857 1.00121514]\n", "[ 0.01 0.01 0.72458545 0.91732793 0.92380401 0.92800565\n", " 0.93187024 0.93548513 0.93887286 0.94205114 0.94503589 0.94784151\n", " 0.95048113 0.95296665 0.95530897 0.95751803 0.95960292 0.96157198\n", " 0.96343286 0.96519262 0.96685774 0.96843419 0.9699275 0.97134279\n", " 0.97268478 0.97395785 0.9751661 0.97631329 0.97740295 0.97843836\n", " 0.97942259 0.98035848 0.98124871 0.98209578 0.98290201 0.98366961\n", " 0.98440061 0.98509696 0.98576045 0.98639279 0.98699558 0.98757033\n", " 0.98811845 0.98864129 0.9891401 0.98961607 0.99007033 0.99050394\n", " 0.99091791 0.99131318 0.99169066 0.99205119 0.99239558 0.9927246\n", " 0.99303896 0.99333937 0.99362646 0.99390086 0.99416316 0.99441391\n", " 0.99465364 0.99488286 0.99510205 0.99531167 0.99551214 0.99570388\n", " 0.99588729 0.99606274 0.99623058 0.99639115 0.99654479 0.99669179\n", " 0.99683245 0.99696705 0.99709587 0.99721914 0.99733712 0.99745005\n", " 0.99755813 0.99766159 0.99776062 0.99785541 0.99794616 0.99803304\n", " 0.99811621 0.99819584 0.99827207 0.99834506 0.99841495 0.99848187\n", " 0.99854594 0.99860729 0.99866604 0.9987223 0.99877617 0.99882776\n", " 0.99887716 0.99892448 0.99896979 0.99901318]\n", "5\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 197, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7Hfu8evZ59ezzWhwB3DSfH9hraJgAtgNrO+rXAg/Oss+Ds2z/6CyjDJCnL94K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\nYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(histK1[0,0])\n", "\n", "print(histK2[0,0])\n", "print(nspace)\n", "\n", "plt.plot(histK1[0,0])" ] }, { "cell_type": "code", "execution_count": 201, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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v7Nw5HReXCURHf4OdnaveIQ0YO+rrmZedzY6GBu4cOZLHg4NxsVMfiZZItahY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fUr8fEd8fMLJCZmvfEiBeDghIOkfp5Ks9hmhDR2/TvqMoeDSv7+3CazfdwrKYkQxykSG1fmytAr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\nLsdLlsAbb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FJ0qVbtknk8wdVtWpbj2f016o7ff1eT9izdfbGxsQQFBbFo0SJ27tzJokWL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\nQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for k in range(nspace):\n", " for l in range(nspace): # for each point in the grid,\n", " plt.loglog(histK1[k,l],histK2[k,l])\n", "\n", "plt.title(r'convergence path')\n", "plt.xlabel(r'$K_1$')\n", "plt.ylabel(r'$K_2$')\n", "\n", "plt.savefig('fig8.png') \n", " \n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## para cada k1 ca2, calc RES, plot contourf" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }