From c78d5beef85761f7cbe587d83d35f435690f202a Mon Sep 17 00:00:00 2001 From: GudniRos Date: Mon, 6 Jul 2015 09:54:21 -0700 Subject: [PATCH] Updated notebooks and added plotDataTypes used for plotting MTdata --- .../scipy2015/MT1DInversion_plotData.ipynb | 23 +- notebooks/scipy2015/MT1D_dobs.npy | Bin 576 -> 576 bytes notebooks/scipy2015/MT1D_dtrue.npy | Bin 576 -> 576 bytes .../MT1Dinversion_Scipy2015_NoStopping.ipynb | 205 ++++++--- ...version_Scipy2015_NoStopping_regMesh.ipynb | 294 ++++++++++--- .../MT1Dinversion_Scipy2015_targMisEqnD.ipynb | 232 +++++++--- ...ersion_Scipy2015_targMisEqnD_regMesh.ipynb | 241 ++++++++-- simpegMT/Utils/dataUtils.py | 1 - simpegMT/Utils/plotDataTypes.py | 416 ++++++++++++++++++ 9 files changed, 1182 insertions(+), 230 deletions(-) create mode 100644 simpegMT/Utils/plotDataTypes.py diff --git a/notebooks/scipy2015/MT1DInversion_plotData.ipynb b/notebooks/scipy2015/MT1DInversion_plotData.ipynb index db30158a..201aaa21 100644 --- a/notebooks/scipy2015/MT1DInversion_plotData.ipynb +++ b/notebooks/scipy2015/MT1DInversion_plotData.ipynb @@ -31,26 +31,30 @@ }, "outputs": [], "source": [ + "## Setup the modelling\n", + "# Setting up 1D mesh and conductivity models to forward model data.\n", + "\n", "# Frequency\n", "nFreq = 31\n", "freqs = np.logspace(3,-3,nFreq)\n", "# Set mesh parameters\n", - "ct = 10\n", - "air = simpeg.Utils.meshTensor([(ct,25,1.3)])\n", - "core = np.concatenate( ( np.kron(simpeg.Utils.meshTensor([(ct,5,-1.2)]),np.ones((3,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n", - "bot = simpeg.Utils.meshTensor([(core[0],25,-1.3)])\n", + "ct = 20\n", + "air = simpeg.Utils.meshTensor([(ct,16,1.4)])\n", + "core = np.concatenate( ( np.kron(simpeg.Utils.meshTensor([(ct,10,-1.3)]),np.ones((5,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n", + "bot = simpeg.Utils.meshTensor([(core[0],10,-1.4)])\n", "x0 = -np.array([np.sum(np.concatenate((core,bot)))])\n", "# Make the model\n", "m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)\n", + "\n", "# Setup model varibles\n", "active = m1d.vectorCCx<0.\n", - "layer1 = (m1d.vectorCCx<-200.) & (m1d.vectorCCx>=-600.)\n", - "layer2 = (m1d.vectorCCx<-2000.) & (m1d.vectorCCx>=-4000.)\n", + "layer1 = (m1d.vectorCCx<-500.) & (m1d.vectorCCx>=-800.)\n", + "layer2 = (m1d.vectorCCx<-3500.) & (m1d.vectorCCx>=-5000.)\n", "# Set the conductivity values\n", "sig_half = 2e-3\n", "sig_air = 1e-8\n", - "sig_layer1 = 1\n", - "sig_layer2 = .1\n", + "sig_layer1 = .2\n", + "sig_layer2 = .2\n", "# Make the true model\n", "sigma_true = np.ones(m1d.nCx)*sig_air\n", "sigma_true[active] = sig_half\n", @@ -63,8 +67,7 @@ "sigma_0[active] = sig_half\n", "m_0 = np.log(sigma_0[active])\n", "\n", - "\n", - "# Set the mapping# Set the mapping\n", + "# Set the mapping\n", "actMap = simpeg.Maps.ActiveCells(m1d, active, np.log(1e-8), nC=m1d.nCx)\n", "mappingExpAct = simpeg.Maps.ExpMap(m1d) * actMap" ] diff --git a/notebooks/scipy2015/MT1D_dobs.npy b/notebooks/scipy2015/MT1D_dobs.npy index 80d57253a4d3e4c18e69522d3fdd931b99bae181..8f2a35fb9dd6acba62aa22f9c986393ad5a58f88 100644 GIT binary patch delta 506 zcmV|`ael8BsuEg?>{$1#Y3VS_di*CP5ZJf zV+)IU4?&7O%<>OWuCiL^$a#y?5%6zpS6<3G<^DTlF&xIY?< z2m>-8+CKuSp}wJ?u0KM!-#}9!&_5)yr3Zy5qCXkCsq-dv#(zHvNtcMTzn(v~ zd~h?hy^uc*fOr}jL6tvM*E{Q)agINk`i}SPn2$f5&So2pnTp zf`~tFbwR-)RDXm&=Z(T~Usr!Wec}v{A_|2+6n@;FE0lOYq&za*?7@CNVq^LS87FZ+ zlWqVyFEw^QEQ((fC8cUVQiP)qRGn}?s_h`f;Q(eoy;8#auMBKIG!n2;m`7hfW}fqm zzc*z+VdrmL#c)?Y^geQC2<%@!r%jvU*k@BemOzGr_;Z0-KVmEs{YCUnKel*{^6ha_ zKcyiQ=fuiJKd1kWKPaM0KXQhycv2`pKPe+qx@(z3Kc&T)7|A9%Kf-0YbAR+eKiM2b w^|B~5KOj%26eHy~Kld4{TbRNyKMG15e^r|@KcUW9yBM4+KZTLjiDwNiKdtQSAOHXW delta 506 zcmVHs+c#b~{ z=Vo@gHf}#jv{vn{EQ3GuvGawAnrS~%aN?9T(0M(%8 z4P-x3m*#0mczd$GGCmb+K6G3^#f8cJxkW@jpChscB&S?I#Mud{ zs<1mht>?8$e4txDKt-p&l=nM7&o8>&=s#LN57Y|FpmSP7Kcw~q*%M~l=uEbKf`?lGamIqKYAi4%3Ov*KLduj wY3cbqKi|0rw_d_IKk2~C;|f!Rl)#@IMdff5x*T_djvGYQH;u z<3GfYMPFNq@;?u&1`ttm)j#&Dt+TmJ>p#Tcz7Mty$3Ju%XKJ1D;y(dpc{=c`xjzgq zBW5Xn**|u@fkF92u0JBWV68Lj&_6o*IDI(#qCa3g453Ch#(zIdIUK@TN18unyelOo z&b&XedjzHl!jwO#u&Hn7g|a`KIRe&}Ns>Qnbq9*A-KRfp{a*WI8j(M{Mu!XQLZ3f| zYQN+On-(ykEed=Fh7AmNT{6@0AYhaDD*g3E)IA1@pf;KkXU>z|a>!Kf5Xg wb(B%GI-|2YWxRl4_!jW@bP4zW*9<)Nw!L_^ELv z!(>16#^n}%5PxVtV+a~_<5*)q`X<&1G67*fXJ{OLWwK#E-s9k5WN1{qFwZ>XM#Iz|x z2k$&TohN5w`083e=2MSv4^BQmp(uJi@eNr&2(1XHHgkVNKVq$%w9W`sKSiv;+TNB% zKNr}>>u-fnKRFYEHt}ahKW-X5Tz6PWKO}QQ*lY_#KXijkD70=vKbZi_B^&WTKTnpZ wQON^7Kkycf!9UA8KbHZE)R0{_KW(Dbna6N8KN-?19Gy-wKd;>)j2ibbKeCMJ8~^|S diff --git a/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping.ipynb b/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping.ipynb index 6cbdb115..ea757722 100644 --- a/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping.ipynb +++ b/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping.ipynb @@ -38,23 +38,23 @@ "nFreq = 31\n", "freqs = np.logspace(3,-3,nFreq)\n", "# Set mesh parameters\n", - "ct = 10\n", - "air = simpeg.Utils.meshTensor([(ct,25,1.3)])\n", - "core = np.concatenate( ( np.kron(simpeg.Utils.meshTensor([(ct,5,-1.2)]),np.ones((3,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n", - "bot = simpeg.Utils.meshTensor([(core[0],25,-1.3)])\n", + "ct = 20\n", + "air = simpeg.Utils.meshTensor([(ct,16,1.4)])\n", + "core = np.concatenate( ( np.kron(simpeg.Utils.meshTensor([(ct,10,-1.3)]),np.ones((5,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n", + "bot = simpeg.Utils.meshTensor([(core[0],10,-1.4)])\n", "x0 = -np.array([np.sum(np.concatenate((core,bot)))])\n", "# Make the model\n", "m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)\n", "\n", "# Setup model varibles\n", "active = m1d.vectorCCx<0.\n", - "layer1 = (m1d.vectorCCx<-200.) & (m1d.vectorCCx>=-600.)\n", - "layer2 = (m1d.vectorCCx<-2000.) & (m1d.vectorCCx>=-4000.)\n", + "layer1 = (m1d.vectorCCx<-500.) & (m1d.vectorCCx>=-800.)\n", + "layer2 = (m1d.vectorCCx<-3500.) & (m1d.vectorCCx>=-5000.)\n", "# Set the conductivity values\n", "sig_half = 2e-3\n", "sig_air = 1e-8\n", - "sig_layer1 = 1\n", - "sig_layer2 = .1\n", + "sig_layer1 = .2\n", + "sig_layer2 = .2\n", "# Make the true model\n", "sigma_true = np.ones(m1d.nCx)*sig_air\n", "sigma_true[active] = sig_half\n", @@ -123,14 +123,14 @@ "# Assign the datas to the survey object\n", "survey.dtrue = d_true\n", "survey.dobs = d_obs\n", - "survey.std = survey.dobs*0 + std\n", + "survey.std = np.abs(survey.dobs*std) + 0.01*np.linalg.norm(survey.dobs) #survey.dobs*0 + std\n", "# Assign the data weight\n", - "survey.Wd = 1/(abs(survey.dobs)*survey.std)" + "survey.Wd = 1/survey.std #(abs(survey.dobs)*survey.std)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -149,13 +149,15 @@ "dmis = simpeg.DataMisfit.l2_DataMisfit(survey)\n", "# Regularization\n", "# Note: We want you use a mesh the corresponds to the domain we want to solve, the active cells.\n", - "if False:\n", + "if True:\n", " regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]],m1d.x0)\n", " reg = simpeg.Regularization.Tikhonov(regMesh)\n", "else:\n", " reg = simpeg.Regularization.Tikhonov(m1d,mapping=mappingExpAct)\n", - "reg.alpha_s = 1e-8\n", + "reg.smoothModel = True\n", + "reg.alpha_s = 1e-9\n", "reg.alpha_x = 1.\n", + "# reg.alpha_xx = 0.001\n", "# Inversion problem\n", "invProb = simpeg.InvProblem.BaseInvProblem(dmis, reg, opt)\n", "invProb.counter = C\n", @@ -163,17 +165,17 @@ "beta = simpeg.Directives.BetaSchedule()\n", "betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n", "saveModel = simpeg.Directives.SaveModelEveryIteration()\n", - "saveModel.fileName = 'Inversion_NoStopping'\n", + "saveModel.fileName = 'Inversion_NoStoppingregMesh_smoothTrue'\n", "# Create an inversion object\n", "inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,saveModel]) \n" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 6, "metadata": { "collapsed": false, - "scrolled": false + "scrolled": true }, "outputs": [ { @@ -184,46 +186,46 @@ "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", " ***Done using same solver as the problem***\n", "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n", - "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_NoStopping.npy'\n", + "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_NoStoppingregMesh_smoothTrue.npy'\n", "============================ Inexact Gauss Newton ============================\n", " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", "-----------------------------------------------------------------------------\n", - " 0 3.97e+05 1.32e+06 1.77e-07 1.32e+06 3.23e+05 0 \n", - " 1 3.97e+05 1.89e+05 3.57e-07 1.89e+05 4.74e+04 0 \n", - " 2 3.97e+05 3.88e+04 4.77e-06 3.88e+04 1.11e+04 0 Skip BFGS \n", - " 3 4.96e+04 3.69e+04 5.13e-06 3.69e+04 1.05e+04 0 Skip BFGS \n", - " 4 4.96e+04 2.71e+04 8.22e-06 2.71e+04 7.80e+03 0 Skip BFGS \n", - " 5 4.96e+04 2.34e+04 1.04e-05 2.34e+04 6.80e+03 0 Skip BFGS \n", - " 6 6.21e+03 2.11e+04 1.23e-05 2.11e+04 6.19e+03 0 Skip BFGS \n", - " 7 6.21e+03 1.27e+04 2.87e-05 1.27e+04 3.95e+03 0 Skip BFGS \n", - " 8 6.21e+03 1.07e+04 3.77e-05 1.07e+04 3.41e+03 0 Skip BFGS \n", - " 9 7.76e+02 9.53e+03 4.53e-05 9.53e+03 3.09e+03 0 Skip BFGS \n", - " 10 7.76e+02 5.51e+03 1.06e-04 5.51e+03 1.97e+03 0 Skip BFGS \n", - " 11 7.76e+02 4.57e+03 1.39e-04 4.57e+03 1.69e+03 0 Skip BFGS \n", - " 12 9.70e+01 4.02e+03 1.66e-04 4.02e+03 1.53e+03 0 Skip BFGS \n", - " 13 9.70e+01 2.27e+03 3.59e-04 2.27e+03 9.73e+02 0 Skip BFGS \n", - " 14 9.70e+01 1.83e+03 4.62e-04 1.83e+03 8.24e+02 0 Skip BFGS \n", - " 15 1.21e+01 1.57e+03 5.44e-04 1.57e+03 7.36e+02 0 Skip BFGS \n", - " 16 1.21e+01 8.91e+02 1.06e-03 8.91e+02 4.57e+02 0 Skip BFGS \n", - " 17 1.21e+01 6.95e+02 1.35e-03 6.95e+02 3.66e+02 0 Skip BFGS \n", - " 18 1.51e+00 5.92e+02 1.57e-03 5.92e+02 3.13e+02 0 Skip BFGS \n", - " 19 1.51e+00 3.56e+02 2.57e-03 3.56e+02 1.75e+02 0 Skip BFGS \n", - " 20 1.51e+00 3.22e+02 3.06e-03 3.22e+02 1.53e+02 0 Skip BFGS \n", - " 21 1.89e-01 2.75e+02 3.38e-03 2.75e+02 1.25e+02 0 \n", - " 22 1.89e-01 1.98e+02 4.78e-03 1.98e+02 8.83e+01 0 Skip BFGS \n", - " 23 1.89e-01 1.51e+02 5.79e-03 1.51e+02 7.53e+01 0 \n", - " 24 2.37e-02 1.19e+02 6.62e-03 1.19e+02 6.26e+01 0 Skip BFGS \n", - " 25 2.37e-02 8.19e+01 1.04e-02 8.19e+01 4.51e+01 0 Skip BFGS \n", - " 26 2.37e-02 7.08e+01 1.12e-02 7.08e+01 3.79e+01 1 \n", - " 27 2.96e-03 5.49e+01 1.16e-02 5.49e+01 3.51e+01 0 \n", - " 28 2.96e-03 4.67e+01 1.23e-02 4.67e+01 3.03e+01 1 \n", - " 29 2.96e-03 3.27e+01 1.32e-02 3.27e+01 3.13e+01 0 \n", - " 30 3.70e-04 2.51e+01 1.36e-02 2.51e+01 2.30e+01 0 \n", + " 0 2.08e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n", + " 1 2.08e+05 2.52e+04 2.37e-04 2.52e+04 6.20e+03 0 \n", + " 2 2.08e+05 3.46e+03 3.27e-04 3.53e+03 1.03e+03 0 Skip BFGS \n", + " 3 2.60e+04 1.72e+03 2.64e-04 1.73e+03 2.61e+02 0 Skip BFGS \n", + " 4 2.60e+04 1.03e+03 1.19e-02 1.34e+03 2.44e+02 0 Skip BFGS \n", + " 5 2.60e+04 6.85e+02 1.56e-02 1.09e+03 1.35e+02 0 \n", + " 6 3.26e+03 5.82e+02 1.78e-02 6.40e+02 1.19e+02 0 \n", + " 7 3.26e+03 3.67e+02 4.78e-02 5.22e+02 1.40e+02 0 \n", + " 8 3.26e+03 2.60e+02 5.72e-02 4.46e+02 2.06e+02 0 \n", + " 9 4.07e+02 2.10e+02 6.04e-02 2.34e+02 6.61e+01 0 \n", + " 10 4.07e+02 1.76e+02 9.60e-02 2.15e+02 1.65e+02 0 \n", + " 11 4.07e+02 1.56e+02 1.22e-01 2.05e+02 9.68e+01 0 \n", + " 12 5.09e+01 1.29e+02 1.28e-01 1.35e+02 5.89e+01 0 \n", + " 13 5.09e+01 1.24e+02 1.85e-01 1.34e+02 7.24e+01 0 \n", + " 14 5.09e+01 1.19e+02 2.56e-01 1.32e+02 7.95e+01 0 \n", + " 15 6.36e+00 1.10e+02 2.10e-01 1.12e+02 5.28e+01 0 \n", + " 16 6.36e+00 7.67e+01 3.60e-01 7.89e+01 5.36e+01 0 Skip BFGS \n", + " 17 6.36e+00 7.19e+01 3.11e-01 7.39e+01 3.66e+01 0 \n", + " 18 7.95e-01 6.99e+01 3.06e-01 7.02e+01 3.89e+01 0 Skip BFGS \n", + " 19 7.95e-01 6.92e+01 3.20e-01 6.94e+01 3.72e+01 0 \n", + " 20 7.95e-01 6.21e+01 3.14e-01 6.24e+01 8.88e+01 2 Skip BFGS \n", + " 21 9.94e-02 5.69e+01 2.69e-01 5.69e+01 3.19e+01 0 \n", + " 22 9.94e-02 5.62e+01 2.83e-01 5.63e+01 2.62e+01 0 \n", + " 23 9.94e-02 5.46e+01 2.44e-01 5.47e+01 4.84e+01 1 \n", + " 24 1.24e-02 5.41e+01 2.08e-01 5.41e+01 4.43e+01 1 Skip BFGS \n", + " 25 1.24e-02 5.36e+01 2.09e-01 5.36e+01 4.26e+01 1 \n", + " 26 1.24e-02 5.30e+01 1.99e-01 5.30e+01 2.34e+01 0 Skip BFGS \n", + " 27 1.55e-03 5.29e+01 1.76e-01 5.29e+01 3.42e+01 0 \n", + " 28 1.55e-03 5.28e+01 1.81e-01 5.28e+01 3.56e+01 0 \n", + " 29 1.55e-03 5.28e+01 1.75e-01 5.28e+01 3.75e+01 1 \n", + " 30 1.94e-04 4.75e+01 1.49e-01 4.75e+01 8.32e+01 1 \n", "------------------------- STOP! -------------------------\n", - "1 : |fc-fOld| = 7.6530e+00 <= tolF*(1+|f0|) = 1.3164e+05\n", - "1 : |xc-x_last| = 4.1960e+00 <= tolX*(1+|x0|) = 4.2689e+00\n", - "0 : |proj(x-g)-x| = 2.2988e+01 <= tolG = 1.0000e-01\n", - "0 : |proj(x-g)-x| = 2.2988e+01 <= 1e3*eps = 1.0000e-02\n", + "1 : |fc-fOld| = 5.2783e+00 <= tolF*(1+|f0|) = 2.1833e+04\n", + "0 : |xc-x_last| = 7.4766e+00 <= tolX*(1+|x0|) = 5.1104e+00\n", + "0 : |proj(x-g)-x| = 8.3205e+01 <= tolG = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 8.3205e+01 <= 1e3*eps = 1.0000e-02\n", "1 : maxIter = 30 <= iter = 30\n", "------------------------- DONE! -------------------------\n" ] @@ -236,14 +238,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "modList = []\n", - "modFiles = glob('*Inversion_NoStopping.npy')\n", + "modFiles = glob('*Inversion_NoStoppingregMesh_smoothTrue.npy')\n", "modFiles.sort()\n", "for f in modFiles:\n", " modList.append(np.load(f))" @@ -251,42 +253,119 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList)\n", - "plt.show()\n" + "# simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList)\n", + "# plt.show()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], + "source": [ + "# %matplotlib qt\n", + "# fig= simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList[-3:-1])\n", + "# fig.suptitle('No stopping-useMref ')\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/gudni/anaconda/lib/python2.7/site-packages/numpy/ma/core.py:2834: FutureWarning: Numpy has detected that you (may be) writing to an array returned\n", + "by numpy.diagonal or by selecting multiple fields in a record\n", + "array. This code will likely break in a future numpy release --\n", + "see numpy.diagonal or arrays.indexing reference docs for details.\n", + "The quick fix is to make an explicit copy (e.g., do\n", + "arr.diagonal().copy() or arr[['f0','f1']].copy()).\n", + " if (obj.__array_interface__[\"data\"][0]\n", + "/home/gudni/anaconda/lib/python2.7/site-packages/numpy/ma/core.py:2835: FutureWarning: Numpy has detected that you (may be) writing to an array returned\n", + "by numpy.diagonal or by selecting multiple fields in a record\n", + "array. This code will likely break in a future numpy release --\n", + "see numpy.diagonal or arrays.indexing reference docs for details.\n", + "The quick fix is to make an explicit copy (e.g., do\n", + "arr.diagonal().copy() or arr[['f0','f1']].copy()).\n", + " != self.__array_interface__[\"data\"][0]):\n" + ] + } + ], "source": [ "%matplotlib qt\n", - "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList)\n", + "fig = simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n", + "fig.suptitle('No stopping-useMref')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "%matplotlib qt\n", - "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,modList[-3:-1])\n", "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4.26403069e+01, 3.00486580e+02, 1.66374083e+02,\n", + " 7.56749193e+00, 1.95232762e+00, 1.28591697e+01,\n", + " 9.25428204e+01, 1.58941963e+02, 1.23328303e+02,\n", + " 7.51647545e+01, 4.59620120e+01, 2.84873346e+01,\n", + " 1.67854087e+01, 9.65288171e+00, 5.69417961e+00,\n", + " 4.11415753e+00, 4.04091270e+00, 5.89702878e+00,\n", + " 1.03685013e+01, 1.49615226e+01, 1.78450685e+01,\n", + " 2.21298842e+01, 3.14894964e+01, 5.03867813e+01,\n", + " 8.57783978e+01, 1.39225191e+02, 2.10683526e+02,\n", + " 3.13430053e+02, 4.54348237e+02, 6.35619644e+02,\n", + " 8.20077602e+02, 9.86597201e+02, 1.13311026e+03,\n", + " 1.22681391e+03, 1.23177188e+03, 1.13462480e+03,\n", + " 9.82900015e+02, 7.79370193e+02, 5.53800277e+02,\n", + " 3.42690620e+02, 1.91257904e+02, 1.00287868e+02,\n", + " 4.40936077e+01, 1.57081219e+01, 4.84819824e+00,\n", + " 2.37649055e+00, 2.81483584e+00, 7.91239926e+00,\n", + " 3.26193232e+01, 1.10105145e+02, 2.29971251e+02,\n", + " 3.48665470e+02, 4.30985817e+02, 4.28578461e+02,\n", + " 3.43909988e+02, 2.60243364e+02, 2.15181922e+02,\n", + " 2.03527866e+02, 2.38158572e+02, 3.70346010e+02,\n", + " 6.61570166e+02, 1.12877904e+03, 1.82064196e+03,\n", + " 2.57897162e+03, 3.02323079e+03])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1/np.exp(mopt)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping_regMesh.ipynb b/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping_regMesh.ipynb index dc52b296..7eea1ba1 100644 --- a/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping_regMesh.ipynb +++ b/notebooks/scipy2015/MT1Dinversion_Scipy2015_NoStopping_regMesh.ipynb @@ -53,8 +53,8 @@ "# Set the conductivity values\n", "sig_half = 2e-3\n", "sig_air = 1e-8\n", - "sig_layer1 = 1\n", - "sig_layer2 = .1\n", + "sig_layer1 = .2\n", + "sig_layer2 = .2\n", "# Make the true model\n", "sigma_true = np.ones(m1d.nCx)*sig_air\n", "sigma_true[active] = sig_half\n", @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -154,6 +154,7 @@ " reg = simpeg.Regularization.Tikhonov(regMesh)\n", "# else:\n", "# reg = simpeg.Regularization.Tikhonov(m1d,mapping=mapAct)\n", + "reg.smoothModel = False\n", "reg.alpha_s = 1e-8\n", "reg.alpha_x = 1.\n", "# Inversion problem\n", @@ -163,17 +164,17 @@ "beta = simpeg.Directives.BetaSchedule()\n", "betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n", "saveModel = simpeg.Directives.SaveModelEveryIteration()\n", - "saveModel.fileName = 'Inversion_NoStoppingregMesh'\n", + "saveModel.fileName = 'Inversion_NoStoppingregMesh_smoothFalse'\n", "# Create an inversion object\n", "inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,saveModel]) \n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "collapsed": false, - "scrolled": false + "scrolled": true }, "outputs": [ { @@ -184,21 +185,48 @@ "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", " ***Done using same solver as the problem***\n", "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n", - "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_NoStoppingregMesh.npy'\n", + "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_NoStoppingregMesh_smoothFalse.npy'\n", "============================ Inexact Gauss Newton ============================\n", " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", "-----------------------------------------------------------------------------\n", - " 0 3.07e+06 5.84e+06 2.95e-02 5.93e+06 1.45e+06 0 \n", - " 1 3.07e+06 6.43e+05 2.81e-02 7.29e+05 1.64e+05 0 \n", - " 2 3.07e+06 6.97e+04 2.75e-02 1.54e+05 1.99e+04 0 Skip BFGS \n", - " 3 3.84e+05 1.01e+04 2.79e-02 2.08e+04 2.95e+03 0 Skip BFGS \n", - " 4 3.84e+05 5.52e+03 2.97e-02 1.69e+04 3.93e+02 0 Skip BFGS \n", - " 5 3.84e+05 5.44e+03 2.93e-02 1.67e+04 7.12e+01 0 Skip BFGS \n", - "------------------------------------------------------------------\n", - "0 : ft = 1.6712e+04 <= alp*descent = 1.6712e+04\n", - "1 : maxIterLS = 10 <= iterLS = 10\n", - "------------------------- End Linesearch -------------------------\n", - "The linesearch got broken. Boo.\n" + " 0 7.58e+05 5.84e+06 0.00e+00 5.84e+06 1.45e+06 0 \n", + " 1 7.58e+05 6.42e+05 2.33e-04 6.43e+05 1.64e+05 0 \n", + " 2 7.58e+05 6.95e+04 7.43e-04 7.00e+04 2.00e+04 0 Skip BFGS \n", + " 3 9.47e+04 9.80e+03 1.31e-03 9.92e+03 2.87e+03 0 Skip BFGS \n", + " 4 9.47e+04 4.94e+03 5.58e-03 5.47e+03 3.72e+02 0 Skip BFGS \n", + " 5 9.47e+04 4.62e+03 7.58e-03 5.33e+03 3.58e+01 0 Skip BFGS \n", + " 6 1.18e+04 4.61e+03 7.61e-03 4.70e+03 5.85e+02 0 Skip BFGS \n", + " 7 1.18e+04 3.57e+03 3.44e-02 3.97e+03 3.49e+02 0 \n", + " 8 1.18e+04 3.27e+03 3.71e-02 3.71e+03 3.65e+02 0 \n", + " 9 1.48e+03 3.19e+03 3.67e-02 3.24e+03 5.87e+02 0 \n", + " 10 1.48e+03 3.11e+03 5.52e-02 3.19e+03 5.02e+02 0 \n", + " 11 1.48e+03 3.10e+03 5.75e-02 3.18e+03 5.14e+02 2 \n", + " 12 1.85e+02 3.09e+03 5.75e-02 3.10e+03 5.39e+02 3 \n", + " 13 1.85e+02 3.06e+03 7.96e-02 3.07e+03 4.75e+02 0 \n", + " 14 1.85e+02 3.06e+03 8.25e-02 3.07e+03 4.80e+02 3 \n", + " 15 2.31e+01 3.05e+03 8.50e-02 3.05e+03 4.82e+02 4 \n", + " 16 2.31e+01 3.04e+03 1.39e-01 3.04e+03 4.80e+02 2 \n", + " 17 2.31e+01 3.01e+03 1.59e-01 3.01e+03 4.72e+02 2 \n", + " 18 2.89e+00 3.01e+03 1.26e-01 3.01e+03 4.87e+02 1 \n", + " 19 2.89e+00 2.45e+03 1.96e-01 2.45e+03 5.42e+02 1 \n", + " 20 2.89e+00 2.11e+03 4.55e-01 2.12e+03 8.59e+02 0 Skip BFGS \n", + " 21 3.61e-01 1.95e+03 5.59e-01 1.95e+03 4.88e+02 0 \n", + " 22 3.61e-01 1.72e+03 5.09e-01 1.72e+03 3.32e+02 0 \n", + " 23 3.61e-01 1.56e+03 6.15e-01 1.56e+03 2.82e+02 0 Skip BFGS \n", + " 24 4.52e-02 1.49e+03 7.06e-01 1.49e+03 2.61e+02 1 \n", + " 25 4.52e-02 1.45e+03 8.86e-01 1.45e+03 2.72e+02 3 Skip BFGS \n", + " 26 4.52e-02 1.41e+03 7.88e-01 1.41e+03 3.08e+02 0 \n", + " 27 5.64e-03 1.41e+03 7.02e-01 1.41e+03 2.64e+02 0 \n", + " 28 5.64e-03 1.27e+03 7.59e-01 1.27e+03 3.48e+02 0 \n", + " 29 5.64e-03 9.93e+02 1.47e+00 9.93e+02 4.30e+02 1 \n", + " 30 7.05e-04 8.33e+02 3.08e+00 8.33e+02 5.44e+02 1 Skip BFGS \n", + "------------------------- STOP! -------------------------\n", + "1 : |fc-fOld| = 1.5990e+02 <= tolF*(1+|f0|) = 5.8433e+05\n", + "0 : |xc-x_last| = 3.4554e+01 <= tolX*(1+|x0|) = 4.2689e+00\n", + "0 : |proj(x-g)-x| = 5.4392e+02 <= tolG = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 5.4392e+02 <= 1e3*eps = 1.0000e-02\n", + "1 : maxIter = 30 <= iter = 30\n", + "------------------------- DONE! -------------------------\n" ] } ], @@ -209,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -224,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -251,13 +279,15 @@ } ], "source": [ - "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n", - "plt.show()\n" + "%matplotlib inline\n", + "fig = simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n", + "fig.supplot\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -270,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -283,7 +313,29 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "78.694562868192662" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.norm(mopt - reg.mref)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": { "collapsed": false }, @@ -302,7 +354,7 @@ " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081])" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -313,37 +365,7 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", - " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", - " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", - " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", - " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", - " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", - " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", - " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", - " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m_0" - ] - }, - { - "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -369,7 +391,7 @@ " -5.82076609e-11])" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -414,6 +436,160 @@ "m1d.gridN[active]" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "self = reg\n", + "r1 = self.W * ( self.mapping * (m_0) )\n", + "r2 = self.Ws * ( self.mapping * (self.mref) )" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", + " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", + " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", + " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", + " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", + " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", + " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", + " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081,\n", + " -6.2146081, -6.2146081, -6.2146081, -6.2146081, -6.2146081])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "( self.mapping * (m_0) )" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<45x45 sparse matrix of type ''\n", + "\twith 45 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "self.Ws" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "self.mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-1.7347234759768071e-18" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "0.5*(r1.dot(r1)-r2.dot(r2))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.029467074824486239" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r1.dot(r1)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.029467074824486243" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r2.dot(r2)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD.ipynb b/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD.ipynb index 5f82a465..b1195a93 100644 --- a/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD.ipynb +++ b/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD.ipynb @@ -30,23 +30,23 @@ "nFreq = 31\n", "freqs = np.logspace(3,-3,nFreq)\n", "# Set mesh parameters\n", - "ct = 10\n", - "air = simpeg.Utils.meshTensor([(ct,25,1.3)])\n", - "core = np.concatenate( ( np.kron(simpeg.Utils.meshTensor([(ct,5,-1.2)]),np.ones((3,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n", - "bot = simpeg.Utils.meshTensor([(core[0],25,-1.3)])\n", + "ct = 20\n", + "air = simpeg.Utils.meshTensor([(ct,16,1.4)])\n", + "core = np.concatenate( ( np.kron(simpeg.Utils.meshTensor([(ct,10,-1.3)]),np.ones((5,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n", + "bot = simpeg.Utils.meshTensor([(core[0],10,-1.4)])\n", "x0 = -np.array([np.sum(np.concatenate((core,bot)))])\n", "# Make the model\n", "m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)\n", "\n", "# Setup model varibles\n", "active = m1d.vectorCCx<0.\n", - "layer1 = (m1d.vectorCCx<-200.) & (m1d.vectorCCx>=-600.)\n", - "layer2 = (m1d.vectorCCx<-2000.) & (m1d.vectorCCx>=-4000.)\n", + "layer1 = (m1d.vectorCCx<-500.) & (m1d.vectorCCx>=-800.)\n", + "layer2 = (m1d.vectorCCx<-3500.) & (m1d.vectorCCx>=-5000.)\n", "# Set the conductivity values\n", "sig_half = 2e-3\n", "sig_air = 1e-8\n", - "sig_layer1 = 1\n", - "sig_layer2 = .1\n", + "sig_layer1 = .2\n", + "sig_layer2 = .2\n", "# Make the true model\n", "sigma_true = np.ones(m1d.nCx)*sig_air\n", "sigma_true[active] = sig_half\n", @@ -109,19 +109,19 @@ "else:\n", " d_true = survey.dpred(m_true)\n", " np.save('MT1D_dtrue.npy',d_true)\n", - " d_obs = std*abs(d_true)*np.random.randn(*d_true.shape)\n", + " d_obs = d_true + std*abs(d_true)*np.random.randn(*d_true.shape)\n", " np.save('MT1D_dobs.npy',d_obs)\n", "# Assign the dobs\n", "survey.dtrue = d_true\n", "survey.dobs = d_obs\n", - "survey.std = survey.dobs*0 + std\n", + "survey.std = np.abs(survey.dobs*std) + 0.01*np.linalg.norm(survey.dobs) #survey.dobs*0 + std\n", "# Assign the data weight\n", - "survey.Wd = 1/(abs(survey.dobs)*survey.std)" + "survey.Wd = 1/survey.std #(abs(survey.dobs)*survey.std)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -140,12 +140,13 @@ "dmis = simpeg.DataMisfit.l2_DataMisfit(survey)\n", "# Regularization\n", "# Either have to use \n", - "if False:\n", + "if True:\n", " regMesh = simpeg.Mesh.TensorMesh([m1d.hx[problem.mapping.sigmaMap.maps[-1].indActive]],m1d.x0)\n", " reg = simpeg.Regularization.Tikhonov(regMesh)\n", "else:\n", " reg = simpeg.Regularization.Tikhonov(m1d,mapping=mappingExpAct)\n", - "reg.alpha_s = 1e-6\n", + "reg.smoothModel = True\n", + "reg.alpha_s = 1e-7\n", "reg.alpha_x = 1.\n", "\n", "# Inversion problem\n", @@ -156,14 +157,14 @@ "betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n", "targmis = simpeg.Directives.TargetMisfit()\n", "saveModel = simpeg.Directives.SaveModelEveryIteration()\n", - "saveModel.fileName = 'Inversion_TargMisEqnD'\n", + "saveModel.fileName = 'Inversion_TargMisEqnD_smoothTrue'\n", "# Create an inversion object\n", "inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,targmis,saveModel]) \n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false @@ -177,53 +178,166 @@ "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", " ***Done using same solver as the problem***\n", "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n", - "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnD.npy'\n", + "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnD_smoothTrue.npy'\n", "============================ Inexact Gauss Newton ============================\n", " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", "-----------------------------------------------------------------------------\n", - " 0 7.52e+05 1.32e+06 4.79e-07 1.32e+06 3.23e+05 0 \n", - " 1 7.52e+05 1.91e+05 1.61e-06 1.91e+05 4.88e+04 0 \n", - " 2 7.52e+05 8.47e+04 3.06e-06 8.47e+04 2.41e+04 0 Skip BFGS \n", - " 3 9.41e+04 7.39e+04 3.58e-06 7.39e+04 2.12e+04 0 Skip BFGS \n", - " 4 9.41e+04 4.00e+04 7.90e-06 4.00e+04 1.19e+04 0 Skip BFGS \n", - " 5 9.41e+04 3.37e+04 1.00e-05 3.37e+04 1.02e+04 0 Skip BFGS \n", - " 6 1.18e+04 3.00e+04 1.18e-05 3.00e+04 9.10e+03 0 Skip BFGS \n", - " 7 1.18e+04 1.71e+04 2.61e-05 1.71e+04 5.44e+03 0 Skip BFGS \n", - " 8 1.18e+04 1.41e+04 3.38e-05 1.41e+04 4.61e+03 0 Skip BFGS \n", - " 9 1.47e+03 1.25e+04 4.02e-05 1.25e+04 4.12e+03 0 Skip BFGS \n", - " 10 1.47e+03 6.91e+03 8.84e-05 6.91e+03 2.48e+03 0 Skip BFGS \n", - " 11 1.47e+03 5.65e+03 1.15e-04 5.65e+03 2.09e+03 0 Skip BFGS \n", - " 12 1.84e+02 4.93e+03 1.36e-04 4.93e+03 1.87e+03 0 Skip BFGS \n", - " 13 1.84e+02 2.73e+03 2.85e-04 2.73e+03 1.14e+03 0 Skip BFGS \n", - " 14 1.84e+02 2.22e+03 3.66e-04 2.22e+03 9.55e+02 0 Skip BFGS \n", - " 15 2.30e+01 1.95e+03 4.32e-04 1.95e+03 8.48e+02 0 Skip BFGS \n", - " 16 2.30e+01 1.16e+03 8.20e-04 1.16e+03 5.20e+02 0 Skip BFGS \n", - " 17 2.30e+01 9.61e+02 1.03e-03 9.61e+02 4.28e+02 0 Skip BFGS \n", - " 18 2.87e+00 8.51e+02 1.19e-03 8.51e+02 3.75e+02 0 Skip BFGS \n", - " 19 2.87e+00 5.60e+02 2.03e-03 5.60e+02 2.24e+02 0 Skip BFGS \n", - " 20 2.87e+00 4.64e+02 2.44e-03 4.64e+02 1.82e+02 0 Skip BFGS \n", - " 21 3.59e-01 4.09e+02 2.74e-03 4.09e+02 1.59e+02 0 Skip BFGS \n", - " 22 3.59e-01 2.75e+02 3.99e-03 2.75e+02 1.10e+02 0 Skip BFGS \n", - " 23 3.59e-01 2.28e+02 4.59e-03 2.28e+02 8.98e+01 0 \n", - " 24 4.48e-02 1.85e+02 4.88e-03 1.85e+02 8.62e+01 0 Skip BFGS \n", - " 25 4.48e-02 1.26e+02 6.70e-03 1.26e+02 7.39e+01 0 Skip BFGS \n", - " 26 4.48e-02 8.27e+01 7.76e-03 8.27e+01 5.01e+01 0 \n", - " 27 5.61e-03 6.54e+01 8.37e-03 6.54e+01 5.22e+01 1 \n", + " 0 2.78e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n", + " 1 2.78e+05 2.52e+04 3.34e-05 2.52e+04 6.20e+03 0 \n", + " 2 2.78e+05 3.46e+03 1.32e-04 3.50e+03 1.03e+03 0 Skip BFGS \n", + " 3 3.48e+04 1.76e+03 6.52e-04 1.78e+03 2.62e+02 0 Skip BFGS \n", + " 4 3.48e+04 1.12e+03 8.86e-03 1.43e+03 1.98e+02 0 Skip BFGS \n", + " 5 3.48e+04 8.39e+02 1.18e-02 1.25e+03 1.18e+02 0 \n", + " 6 4.35e+03 6.98e+02 1.46e-02 7.61e+02 1.46e+02 0 \n", + " 7 4.35e+03 4.05e+02 4.96e-02 6.20e+02 1.60e+02 0 \n", + " 8 4.35e+03 2.67e+02 5.64e-02 5.12e+02 1.81e+02 0 \n", + " 9 5.43e+02 2.25e+02 5.88e-02 2.56e+02 7.07e+01 0 \n", + " 10 5.43e+02 1.96e+02 7.46e-02 2.37e+02 1.23e+02 0 \n", + " 11 5.43e+02 1.63e+02 7.82e-02 2.05e+02 5.88e+01 0 \n", + " 12 6.79e+01 1.21e+02 1.32e-01 1.30e+02 1.24e+02 0 Skip BFGS \n", + " 13 6.79e+01 1.16e+02 1.57e-01 1.27e+02 1.15e+02 0 \n", + " 14 6.79e+01 1.04e+02 1.99e-01 1.17e+02 8.66e+01 1 \n", + " 15 8.49e+00 9.98e+01 1.83e-01 1.01e+02 8.00e+01 1 \n", + " 16 8.49e+00 9.88e+01 2.31e-01 1.01e+02 8.13e+01 0 \n", + " 17 8.49e+00 9.59e+01 2.05e-01 9.76e+01 9.01e+01 0 \n", + " 18 1.06e+00 6.64e+01 2.03e-01 6.66e+01 5.84e+01 0 Skip BFGS \n", "------------------------- STOP! -------------------------\n", - "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.3164e+05\n", - "0 : |xc-x_last| = 5.4748e+00 <= tolX*(1+|x0|) = 4.2689e+00\n", - "0 : |proj(x-g)-x| = 5.2197e+01 <= tolG = 1.0000e-01\n", - "0 : |proj(x-g)-x| = 5.2197e+01 <= 1e3*eps = 1.0000e-02\n", - "0 : maxIter = 50 <= iter = 28\n", + "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.1833e+04\n", + "1 : |xc-x_last| = 2.2460e+00 <= tolX*(1+|x0|) = 5.1104e+00\n", + "0 : |proj(x-g)-x| = 5.8352e+01 <= tolG = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 5.8352e+01 <= 1e3*eps = 1.0000e-02\n", + "0 : maxIter = 50 <= iter = 19\n", "------------------------- DONE! -------------------------\n" ] } ], "source": [ + "%timeit\n", "# Run the inversion, given the background model as a start.\n", "mopt = inv.run(m_0)" ] }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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k5eWxYcMG3nrrLU466SSvbFYW7YOMCF6Vmcm42lsPuV7bu3cvs2fPplevXiHd\nzt2zZw/Lli1jyZIlrF27lg4dOtC9e3d69OihLYB4idyOHTvYuXNnmX+vvfZa4uPLzlJWXFxMfn6+\njsStA+rSdUCTPqVqIFwnuzGmNXAdcCreYp0C/IzX9WGsiATOsBpFkTj/PvjgA6699loeeughhg8f\nXmHSt7BzZ97Kza3VWNTBy8/PZ9myZaxbt45zzz032uFEhIiQm5vL3r17ycjIiHY4KkIO5jrgnEsC\nb/3d2o0qOE36lKqBcCR9xpjLgWeABsB3wP6J69oBPYC9wG9E5PWaHCecInX+LV68mMGDB3PxxRdT\nsHEjO8vN6bdvzx42L1nC3/7v/xjw179qa1I1LV++nKKiIrp37x7tUOokEWHZsmVMnToVYwynn346\nnTp1inZYKkJCuQ445xoAA4DbgJ3ABGvtW5GIrzRN+pSqgZomfcaY/kAO8CbwVxFZWW5/e7zBTpcA\nmSISfFX0CIvk+bd161YuueQSli9fTnp6OgkJZbsit2nRglOXL+eok0/m12PGEBcfMyvVxTyfz8fU\nqVP59ttvGTp0qPahPEgiwtKlS5k2bRpxcXFkZmbSuXNn/fJRz1R1HXDOpQFX4s1TPAlYAYwFBltr\nI3qbQpM+pWogDEnfh0CxiAyuoty7QIKIxMR9skiff/v27aNdu3asXx94lzszM5P/vfceEy66iAZp\naVz8yiskVDDQQB2Qn5/PpEmT2LdvH0OHDtW+Y9U0efJkOnToQKdOnTTZq6cquw74b+deD2QAL1lr\np/u3fw78zVo7M3KRgs7wqFR09cNba7cqY/1l66XExEQ6d+5c4f7kww7jio8+Ii4+nlfOPpu92wOm\nOFSlbNy4kWeffZYjjjiCq6++OqIJX35+Pl988QU+X0zPPx6ys88+W1v3VGX6A+cDL1trpzvn4p1z\nQ4F1wJxIBxPVKVuUUjQAdoRQbpe/rKpAQnIyQ157jcm33sqF7dtzRNeuJCQnlymjq3d4RITTTz+d\nnj17RvzYiYmJrF27lg8++IDzzz9fkyV1yHLOJQA3AJOstdP8r/sDffESvoh/89GkT6noWgGcDgQO\nSy0r019WVcLExXH2Y4/x9Pvv0+nrrwP26+odnpYtW0Ztkt+EhASGDRvGyy+/zKeffsoZZ5xRJxI/\nEWHv3r2kpKREOxRVdwjeQLz9I3WH4d3mLQTGWWsjPgu9Jn1KRdcLwL3GmC9F5JNgBYwxZwC3A3+L\naGR1REGTVmS5AAAgAElEQVRBQZnXxhiatmsH5eb/U7EjOTmZK6+8knHjxpGSksKAAQOiHVKliouL\nef/99wG48MILoxyNqiustcXOuTHAy865EXi3dKcDr1lrS+7wOOea4a277gM2W2tn1FZMmvQpFV3/\nBQYBk40xXwDvAGv8+9oBFwADgQ+B/0QlwhiRnp4esG3NmjXk5uayc+dODjvssMgHVQds27YtJldz\nSElJ4aqrruKFF16gTZs2dOjQIdohBVVQUMDEiROJj49nyJAh0Q5HxYCcnBxyQpwQ3lo7zzk3EGgC\nrLbWlvmW6py7ATgGb3quz4AHnXM3W2s/Cm/UHh29q1QNhGmevnjg98AteIleaauBx4D/iEjM9HyP\nlfNPRBg9ejQrVqzgww8/JNnfh09X7/Ds27ePMWPGMGrUqJBW1IiG3bt3k5qaGpO3ePPy8nj11Vdp\n3bo15557bsBSaUpB6NcB59wwYI219mv/6xF4U7l8Czxnrc11zp0NZAPnWWu3hDtW/Q1WKspEpFhE\nHhWR9nhJ38n+RzsR6SAij8VSwrdfdnZ2yN92a4sxhv/+9780btyYESNGHDIjQsNl7ty5HHXUUTGb\n8AE0bNgwJhO+PXv2MHbsWLp27cp5552nCZ8Kh2nA4QDOuQ7A8cAvwFbgFedcG2vtZODi2kj4QFv6\nlKqRurTmYjjF2vmXn5/PmWeeyQknnMAjjzzCrSNGsL1Un76CnTvZ+N13dL3wQp6YODF6gUZQUVER\nY8aM4fLLL6dVq1bRDqfOERHWrl2rE1arKlXnOuCcuwI4E7jdWrvFOfdvYKq19p1aCdJP+/QpFUXG\nmJuBCSKy8SDf85qIbK69yOqWlJQU3nvvPQYMGMDDDz8cdFqWaf/4B6unTEF8Pkw9aLWZN28erVq1\nqpMJ348//siHH35Ip06d6NSpE23bto14S5sxRhM+VSucc3HAUcAif8LXEW+Ghs9q+9ja0qdUDYRh\nRQ4fcJKIfBNi+XhgH3C8iMyr7nFrKlbPv59++on+/fvzz3/+kyuvvLLMPl9xMS9mZdHlggs4+fbb\noxRhZBQVFfGf//yHYcOG0bp162iHc9B8Ph/r1q1j+fLlfP/992zbto2OHTvSp0+fWhnwISIxeYtZ\n1Q3VbOnrCvwPbwaHLLxpux621u4Mf4QHaNKnVA2EKen7Aq9fRyjigIvRpK9Cixcv5le/+hWdO3em\nWbNmZfa1PuIIjps2jas/+YSWGRlRirD2FRUVkZubS48ePaIdSljs2rWLFStW0LhxYzp16hS2eouK\nili8eDFff/01F1xwQdTmLlR1W3WvA865TsAZeH36cqy1Id/xqS5N+pSqgTAkfTl4E3geTB0C3CAi\ny6t73JqK9fMvIyODBQsWBGzPzMxkzKhRTL/vPn4zZw6JqalRiE6F27x580hKSqJjx44hTZ6cl5fH\nnDlzmDt3Li1atKBv374cc8wx2tqnqqUu9e3WPn1KRZGIZEU7hkNR06ZNK9x37JVXsuLDD/n0T3/i\nnP/+N4JRqdqSkJDAd999x/vvv0/z5s3p1KkTxxxzDK1atQpI5HJzc3nnnXfo0aMHw4cP58gjj4xS\n1EpFnrb0KVUDdekbXjjF+vmXlZXF1CDz9GVmZpKTk8Pe7dt5qndvzn3ySTqdc04UIlS1oaioiDVr\n1rBixQpWrlzJyJEjA1r+CgoK8Pl8upyaCpu6dB3Qlj6lVL3ToGlTLnzpJd66/HJu/PZbGjZvHu2Q\nVBgkJCTQsWNHOnbsWGGZ/RN4K1UfHfrzFiilVBDpmZn0vuYa3rvuOmK51TJUPp+PZcuWHRKfRSlV\nO7SlTyl1yCm/Tm9eXh4LFiwIWIP2NOc4r1Ur3uzShcblpjZpmp4edL6/WLVo0SLmzp1Lly5doh2K\nUipGadKnlDrkjAuSrD377LOMGTOGPXv2kOoftRuflESzjh3pPHs2rFhRpvyqSAQaJj6fj2nTpnHO\nOefoCFSlVIX09q5SMcIYM8kYc64xRs/LWjBq1CgyMjK46aabymw/FKZtWbJkCampqbRv3z7aoSil\nYpheXJSKHc2A94C1xpgHjDF6ny6MjDE8+eSTzJw5M2hLYF0lIkybNo1TTz1VW/mUUpXSpE+pGOGf\ns68T8BwwDFhqjPnKGHO9MaZxVIM7RDRq1IiJEydyxx13sGjRomiHExa5ubklExMrpVRlNOlTKoaI\nyEoRuQtoj7c8zw/Av4H1xpiXjDGnRTXAQ0DPnj158MEHueSSS8jLy4t2ODXWuXNnLr30Um3lU6oO\ncM4lOeeSonV8nZxZqRqozUk5jTENgUuBm4DjgJ+BNsB3wAgRmV8bxw0xtjp//o0cOZKioiKaxcWx\nY80aAPbu2MGWpUtpc+KJpHXoUKdG7yqloiOU64BzrgEwALgN2AlMsNa+FYn4StOkT6kaqI2kzxiT\nBYwAhgBFwGvAWBGZa4zpAYwBWohIz3Ae9yBjrPPn3549e+jbty+33HILo0aNKtn+8hln0OOyy+hz\n3XVRjE4pVVdUdR1wzqUBVwJnAZOAFcBYYLC1NjcyUXp0yhalYoQxxgLD8W7tTgNGA2+KSP7+MiKy\n2Bjzd2B6dKI8IDs7m6ysLLKysqIdSrWkpqYyceJEevfuzVNPPUWjRo0A2Lt9O1t+9zv6T53Kiy+9\nFOUolVJ1mf9W7hVAb+Bf1trp/u1r8QbvRZQmfUrFjhuAccDzIvJ9JeWWAVFvhsrOzo52CDXWtWtX\nOnTowNy5cwP2Nf3mmyhEpJQ6xPQHzgfus9ZOd87FAxcB64A5B1ORc64bcAFeNx+AtcB71tqlodah\nAzmUih1HicidVSR8iMgvIjIuQjEd8lq0aBF0+441a/AVF0c4mtCsWrWKxYsXRzsMpVQlnHMJeF/m\nJ1lrp/lfnwL0xUv4fM65kLoHOef+jNfVB2CW/xEHvOac+0uoMWlLn1KxY58xpp+IBDQxGWOOB2aJ\nSHwU4qqX4hITWfzGGxx7+eXRDqWM4uJiPv74Y047TQdyKxXjBNgLFPpfDwMy/K/HWWtLvlU659oA\nja21yyqoaxTQ3Vq7r/RG59zDwBLgn6EEpC19SsWOyr7xJeIN6lAR0jQ9nen/+Afi80U7lDK++eYb\nGjduTNeuXaMdilKqEv6kbgxwh3MuBzgXWAk8aK3dsb+cc+5o4I/At865X1dQXTEHbuuW1tq/LyTa\n0qdUFBlj2gHtOJDw9THGNChXrAHeaN7VkYtMNUhLIzE/n6WTJtF96NBohwPArl27mDFjBiNHjtR5\n+ZSKkpycHHJyckIqa62d55wbCDQBVltrCwCcc3HWWp9z7ijgRrwv9tcB9zrn9llrPytX1a3AZ865\n74Gf/Nva4k3ofxMh0ilblKqBmk7ZYozJBu4KoWg+cL2IjK/uscLpUDr/RowYwerVqwFvSbN58+bR\nqlUrTj75ZP4yZAhT/vY3bpg/HxMX/Rsjb7/9No0bN2bQoEHRDkUp5RfqdcA5Nwz40Vo70/86ARgE\nfABkWmu/dM5l4U3Mf5+1dne598cDJ+K1+Ane3K1zrLUh3wXSpE+pGghD0tccaO5/uRBvLqfvyhUr\nBH4Ukb3VPU64Hcrn35IlS8jMzGT27Nm0a9eOZ/r0ITM7m64XXBDVuPbt28ekSZO46KKLSEqK2oT+\nSqlyDiLpaw30sNZ+6pxLLtXqdzPeqNzLrbWbnHOp1to9oR7fOdfIWhvS8kLR/+qqVD0mIptEZJGI\nLAI6AG/tf13qsTyWEr5DXffu3bnjjju47rrrEBFO/fvfmXbPPUQ7yU1MTGTYsGGa8ClVR1lr1/kT\nvpOBwVBym3cMsApI9ZcLOeHzWxJqwYj36TPGDAR+DXQF0vCaKLfhzT32sYh8EemYlIoWY0wqkO9v\nNtsEJBhjKjwvReRg/xioarjtttt4++23eeqpp/jtjTeSYy3ff/wxnc45J9qhKaXqvnXAM865RGvt\neOfcCXhJ4GMVvcE5d1sl9TUO9cARu71rjGkGvIM3R80qYCmw3b87DS8JbI+30sBFIvJLFfUdsreX\nVN0Rhtu7PuAkEfnG/7wyEitTttSH8y83N5f+/fsza9Ys8ufM4et//5vrZs7UARRKqTKqcx1wzvUE\nxuPlPJcBf7fWPlFJ+b3AQ8C+crsM8AdrbZNQjhvJlr4xQAugr4jMDlbAPxfZq/6yV0UwNqWi5Vq8\nIfz7n6sY0aVLF+68805GjhzJF59/ztTsbFZ+9hkdzzgj2qEppeo4a+0i59z5eH26X7bWfl3FW+YD\n71hrA1bxcM6FvEJTJJO+84ARFSV8ACIyxxjzZ+DFyIWlVPSUXllDV9mIPbfccguTJk3i8See4LS/\n/pVpd99Nh0GDItbat3nzZlJTU2nYsGFEjqeUihxr7RpgTYjFRwJbK9h3QqjHjOTt3V+A60Tk7SrK\nXYS39mhaFeUO+dtLKvbV9PZuubpexltm538iEpvrf/nVp/NvxYoV9OvXjxnTpvHJ4MEMfu450rOy\nav24Pp+PZ555hgEDBtCjR49aP55SqnrCeR2obZFM+l4ATgWuEZEZFZTpD7wETBWRSm911aeLjopd\nYU76ZgO/An4B3gZeB76IxV/0+nb+jRkzhgkTJpAuwtYlS2iZkVFmf9P0dB4dNy6sx5w1axa5ublc\nffXV2o9QqRgWyaTPOfc+3gDY/ccTYCcwG3jaWlvpTA+RvL17K/AGMM0YswFvtO7+gRxN8QZytAQ+\nAf4QwbiUigkicoIxpgPe+ozD8GZn32SMeROYICLToxpgPXbTTTcxadIkcn/8kfN37ICpU8vsXxXm\n4+Xl5TFt2jRGjBihCZ9SqrRVwBF4d4UM3rViF9AZeBa4urI3RyzpE5EdwFnGmH6UnbIFYDPeCJaP\nRaSqzoxKHbJEZCXewtn/NMZ0wTuhLwVGG2N+FpG2UQ2wnoqLi+P555+ne9eunAQcWcvH+/zzz+nd\nuzdHHlnbR1JK1TEnW2uPL/X6PefcHGvt8c65xVW9OeLz9InITGBmpI+rVF0jIrn+bhG7gdsIvti2\nipAOHTrQuEEDnt23j5YcuLcCkLBsWdiOs23bNlatWsVvf/vbsNWplDpkNHTOtfMPAsE51w7YP9Kr\nsKo3RzzpC6fs7OyS51lZWWRFoHO1qt8OZqHt6jLGtAIuwWvlOwmvG8QkvD5+KorijPHWxCu3vcXe\n8C2YkpaWxujRo3XlDaVUMLcB051z+6f66gCMds41JISZT2Ju7V1jzHNAnA7kUHVBmAdyjMa7lXsK\nkAe8C0wAPhWR8hNyVlXXxcBReCOBc0ttv0lE/huGWOvl+deyaVM27tgRsL1FkyZs2L49yDuUUoe6\nSI/edc41ALr4X+ZWNXijtFhcezcLOC3aQSgVBQ8CG4ChQEsRuUZEPqpGwvcAcDNwDPCpMab0wKiQ\nJ/FUgRIaNAi6PS6hTt80UUrVEc65JOAG4C7/43rnXGKo74+5v1Qicky0Y1AqSpqLyO4w1HMucJyI\n7DPGOOBNY0wbEbk9DHXXa8d07crPGzcGbE/dvZvdmzbRsHnzKESllKpHnsTL3R7H61p8tX/bqFDe\nHHNJnzEmCa+Vo3y3GaUOaWFK+MDrHrHPX+dWY8zZwKvGmOeJzdb9Oq9h8+aMP+88rpkyhaRqrJ4h\nIjo1i1L1gL+lDmttlYMuKnCCtbZXqdefO+cWhvrmiF4AjDE3GWNWGmP2GmMWGGOGBynWh/BPe6VU\nTDLGbDbGHFfqeWWPTSFWu94Y02f/CxEpwBsU4gOODf+nqD/S09PJzMwsebRu3ZqWLVuSkZVF8549\nefPSS/EVFR10vdOnT2f27ApXqFRK1XHOuQbOuTOA94BXnHNDqllVkXOu5I6oc64jEPIfnYi19Blj\nLgPG4E0o+C3QD3jBGHMBcKWIlO6IqF95VX3xOLCp1PNwGAGU6QfoX9ZtlH8KGFVN48qturFjxw66\nd+/ODTfcQN8TTuD1wYP54Le/5fxnngm55a6wsJBZs2YxcuTIWohYKRVtzrk04ErgLLzBeSuAsc65\nRdba3ErfHOgO4Avn3P7GsXS8dXlDEsll2OYAU0TkjlLbBgLj8Vr2zhORLcaYk4CvRKTSVsj6OnpQ\nxZa6tOZiOOn5d8CECRO49957mTt3LlJQwLisLLoMHkzmXXeF9P6ZM2eydu1aLrnkklqOVClVGyq7\nDvhv514PZAAvWWun+7d/DvzNWnvQ8xaXGr0reKN3C0KONYJJ3y7gfBHJKbc9HfgYr9XxbLzJ7jXp\nU3VCmKds+QIYLSIBM/0aYzoDT4nI6TWovzHe+telV8PZhrck4lQRyTuIuvT88xMRzj77bAYNGsQd\nd9xB3saNXHzMMTRu3ZrGrVqVKVt+jd6ioiLGjBnD5ZdfTqtyZZVSdUMVSd9pwJ+B+6y105xz8cBF\nwAXAtdbakGZn8N8O3r/mbvm1d7HWTgqlnkgO5NiFt15cGSKy2hjTH/gA+Ar4RwRjUiqWZAGHVbCv\nCZBZnUqNMXGAA/4IpAB78JI98JK/VGCPMeYRwGo2d3CMMTzxxBP07duXSy+9lHbt2nFkt250nj0b\nli8vU7Z8Z+WFCxfSvHlzTfiUOgQ55xLwpleZ5E/4EoD+QF9gDl4/61Cdjz/Bq0DMJX3zgQuBN8vv\nEJFfjDGDgInAY1T+wZSqV4wxyXhzV26oZhUW+AOQDUwoPzLeGNMWb6CHxTv3bLWDrac6duzIrbfe\nys0338y7775LYmpqSO/bu3cvAwYMqOXolFJRIsBeDiyPNgzvNm8hMM5aW+ycM9baKnMea+2IcAQU\nydu7lwK34vXd+6WCMgnAE8AZItK+ivq0QUJFXU1v7xpjLKEnWQ+KyJ+rcYyfgbtF5Okqyv0Gr6Wv\nyjV+9fwLVFBQQO/evbn//vt559FHaT91akCZVZmZjKvlZfyUUpFVxe3dPsDLwGZgHTAdeM1au905\nF2+tLY5gqLG3DFuo9KKjYkEYkr4TgRP9L8cADwNryhUrBJaKyPRqHmM3MFhEPq+i3EDgfRGpspnK\nGCPWHshVde1rT05ODsOHD2dAu3Z0njEjYL8mfUrVfeXXYHfOVXodcM61xOuiszrYoAvnXF+gGK+v\nX2/gv9bayeGOGzTpU6pGwjyQYwTwgYhsCUd9per9HO8PysUVDdYwxjTC6xMSLyIDQ6hTz78KXHPN\nNcz54gsuXbs2YJ8mfUodekK9DjjnhuElfrP8r18CluN13xmHt7pGCvA88KK11lfqvZdYayc65zpY\na1dWN9aYW5FDqXrsVSC+9AZjzFlAN2CaiMyrZr2/Bz4D1hhj/oc3Wne7f18Tf/1nAQVAlQmfqtyD\nDz5Iert2zP3Vr2jWqBEAmxYvJvmww2iXnh7d4JRS0TQdr08fzrkMvD5+Q621/3DOnQv8CHwKfFo6\n4fO7E2/cw1vAcdUNQFv6lKqBMLf0TQK2i8i1/tc3A4/iJWPxwBAReb+adacBNwK/xpvfqfyULR/j\nTQmzPXgNAfXp+VeJZ599lrFjx/LVV18RFxfHpkWLeGnQIG7+/ntMcjKJiSGvj66UinHVvQ44584D\n/o23aEVrYCow2Vq7OUjZz/AGhpyAlzyWJtbawaEcU9fhVCp29MVLvjDecg53AI/gTanyHN43vWoR\nkW0i8k8ROVVEWohIkv/RQkQyReT+UBM+VbUZM2awZMkSunbtSlZWFpfedBMv+HxckJnJm2++yZIl\nS6IdolIqSpxzcQDW2g+Ad/Dm8duIN8AjIOHzOwf4O7AFeAiv/3fpR0j09q5SseNwYL3/+bFAG7zW\nNzHGvAlcVZsHN8akAEeWn9KlItnZ2TqAowJr1qxh165d7Nq1ixUrVpRsT05I4GddfUOpem3/rVvn\n3CV4LXxP4OVjFbYWWmsLga+dc/2stZudc43820OeVB806VMqlmwE2gMz8PrYrRGR7/37Uji4iTyr\n41y8dSHjqyoIXtKnDk7GaafRIj+fhAT906uU4iu8RO9tID7E1TlaOuc+wWskwDm3GbjGWrsolAPq\n7V2lYsdE4AFjzEN4zf0vldqXgbdId22rd+sIR0paWhqt0tNZ/9RT7N2ud9KVqu+stT8Db1lr91lr\n94b4tmeAP1prj7bWHg3c5t8WEv26qVTs+AuwE6+j7pPAfaX2HY/XCnfQjDFTCG2Vm+YhllPV0L9/\nf9atW8eJZ5/N148+Spa2lCpV71VjcuZUa+2UUu/Pcc41DPXNmvQpFSNEZB9wdwX7LqpB1acCuUBV\nowdSanAMVYUtW7awd+9eTn36aZ498UT63nwzKc2aRTsspVTdsso593e8VT4McCUQ8rx9mvQpdehb\njLeix7DKChljhgJvhFqpDuSoWHq5+fj27NnDnDlzuOiii0jr0IFuF1/MVw8/zMB7741OgEqpuupa\nwOFNpg/e9C3XhvpmnadPqRoIwzJsm4EzRWS+/3llRESaV+MYTwO/FpGjqyg3FHhDRKrs66vn38H7\ny1/+wk8//cQrr7zC9jVreKZPH27KzSX1iCOiHZpSqgbCOV9rbdOkT6kaCEPSlw08KyI/+59XRkTE\nVeMYxwDd8dbVrfCk8U/Z0kJEVodQp55/B2n37t1069aNV155hVNPPZUPR48mqXFjznjggWiHppSq\nAU36IkAvOioW1KWTPZz0/KueiRMncvfddzNv3jzyN27kqd69Gb1kCY1atIh2aEqpaqpL1wGdskWp\nGGaM6WaMudAY0zrasaiDt379ekonx0OHDqVly5Y8/vjjHHbUURx71VV8qS19SqkI0ZY+pWogzGvv\nPgP4RORG/+thwKt4X87y8PrlfRmOY9WUnn9V27x5M+PGjWP06NE0bHhgRoVly5YxYMAAFi5cSCPg\niR49GL1oEY1ba16vVF0UiZY+59x/Sr0Uys6pKtbam0OpR1v6lIodZ1F2Ie178BbibgP8jwqmc4mW\n7OxscnJyoh1GTBIRPvzwQzIzM8skfABdu3bl2muv5U9/+hONW7UiY+RIZtx/f5QiVUrVEXP9j2Sg\nD7Acb8L+DCAp1Eq0pU+pGghzS18+3kje6caYzsAyoLeIfGeMOROYICJp4ThWTen5V7kFCxYwa9Ys\nRo0aRVxc4HfrvLw8unXrxvjx4zmuc2ee6N6dGxcs4LCjjopCtEqpmohknz7n3CzglP1LtjnnEoEZ\n1tq+obxf5+lTKnb8ArT0Px8IbBSR7/yvDSGuiauiKz8/n88++4zLLrssaMIH0KhRIx5++GF+97vf\nkZWRwerUVKafeCKHd+5cUqZpejqPjhsXoaiVUpHgnEsCsNYWVrOKpsBhwFb/68b+bSHRpE+p2PEx\n4IwxzYE/UXai5B7A6mgEpQ7ON998Q9euXWnTpk2l5S655BKefvppZn31FeesXettXL++ZP+q2gxS\nKRVRzrkGwAC8tXJ3OucmWGvfqkZV9wPznHNT8BoDMoHsUN+st3eVqoEw395tCjyCt/but8BNIrLD\nv28G8JWI/Ckcx6opPf8q5vP5KCoqIimp6m42S5cu5bjevblp3z4al9u3KjOTcdpnUqmYV9V1wDmX\nhrdc2ll4K2msAMYCg621uQd7POdcK6Av3oCOb6y166t4Swlt6VMqRojIdipYTkdETolwOKqa4uLi\nQkr4ALp168YxLVrw2dq11GRxZaVUbPLfzr0C6A38y1o73b99LXDQi2875z631g4E3gmyrUqa9CkV\nY4wx3YFfAW2BF0RkvX9VjY0isiu60alw21NQwBpgA9Cg1PaEZcuiFJFSKoz6A+cD91lrpzvn4oGL\ngHXAnFArcc6lAKnAkc650sniYXgzPIREp2xRKkYYYxoZYyYCi4Dn8KZsaeXffR9goxVbMDplS3js\nKSzEB2wE1pR67Ny9O6pxKaVqxjmXANwATLLWTvO/PgXv1uwcwOecC7V70A3+93ThwPQtc4H3gP+G\nGpO29CkVOx4B+uGN3P0S2Ftq30fAHcDtUYgrqOzs7GiHEDP27dtHYmJitd6b0KAB7NgRWOeePezZ\nupXUww+vaXhKqegQvL/j+0fqDsObV68QGGetLd5f0DnXA4i31i4MVpG19lHgUefczdbaMdUNSAdy\nKFUDYR7IsQW4VUReMcYk4P1hOF5E5hljTgfeE5FG4ThWTen5d8CqVav47LPPGDVqFMYc/K9CVlYW\nU6dODdh+7FFHcVunTlz1v/8RX82EUilV+yq7Djjn+gAvA5vxbulOB16z1m53zsVba4v9rX0ZwHjg\nj9baj4PUcwKwdv+gDefcNcAQvFkdsq21v4QSq97eVSp2pABbKtjXGCiuYJ+KkuLiYj766CMGDBhQ\nrYSvMmkdOpCYmsrkW24Ja71KqZrJyckhOzu75FEZa+08vLs3NwAjrbVPlk74/MXirbXzgdHAv51z\nJwWp6hmgAMA5dyre1C0vAjv9+0KiSZ9SsWMOcE0F+4YAX0UwFhWCr776imbNmtGlS5ew122MYcj4\n8ayZOpXZTzwR9vqVUtWTlZUVctIHYK3d4J+a5ULnXD//tmIA51xDYJhzrrW1dgre0ptHBqkmrlRr\n3jDgaWvtW9bavwGdQo1dkz6lYsffgIuNMZ8Do/zbzjHGvAJcSowN5Kjvtm/fzsyZMzn77LNr1MqX\nnp5OZmZmyaNXr14kJyfTpk0bkg87jMvff5+pd9/Nys8/D2P0SqkomA40BHDOHQZgrd2NN3D/e+fc\nbXiTN+8J8t54/5JrAIOAKaX2hTw+QwdyKBUj/Gvuno7XbP8f/2YHfA0MFJFvohacCjB58mROOukk\n0tJqthzyuCBLrV199dW0bOmtyJfWoQNDX3+dN4cNY+SMGRzeKeQv9UqpGGKtXQesc86djjcl14vO\nuThr7Vjn3CBgId6EzTlB3v4aMNU5twUvKdw/318nYHuoMWhLn1IxwBiTbIy5EtgsIgOAJnh/FA4T\nkf4i8mV0I1TlnXLKKZx88sm1UvfDDz/Miy++yLfffgtAelYWp91zD68PHsze7SH/fVdKxaZVwO3O\nuZLXKOkAACAASURBVCustT7n3PF4/f5+rCDhw1p7L14r4AvAKdZan3+XAX4f6oF19K5SNRCu0bvG\nuz+YD5wlIoFDOWOMMUastWRlZZGVlRXtcA5JY8eO5ZlnnuGrr74iPj4egKHdurF740aaH3tsmVvK\nTdPTeTRIi6FSqvZV5zrgn6LlVSAHb4k2a62t9c67mvQpVQNhnrJlNvCMiDwbjvpqk55/tc/n85GV\nlcWwYcP43e9+B8A1mZl0mDYtoKyu06tU9FT3OuCcOxo4Aki01s4Kf2SBtE+fUrHjVuBFY8wG4GMR\nKYp2QCp64uLieOqpp8jMzOSiiy6idevWYZ8WRikVPdbaH4EfI3lM7dOnVOx4B2/ZtXeBAmPMFmPM\n5lKPTVGOT0VY9+7dufHGG7lF5+pTSoWBtvQpFTser2K/3k+NoqKiInJycjj99NOJi4vc9+U777yT\nXr168cEHH0TsmEqpQ5MmfUrFCBHJjnYMqmLffvstGzdujGjCB5CSksKTTz7JqFGj6N+2bUSPrZQ6\ntEQ86TPGDAR+DXQF0vBaL7YBy/D6MX0R6ZiUUqoyxcXFzJgxg6FDh0bl+IMGDWLAgAEsmT+fxMxM\nAESEdbNn06xTJ1qlp0clLqVU3RKx0bvGmGZ4fZZOwZujZikHJhRMw0sC2+NNOHiRiFS6eLCOHlSx\nIJyjd+uS+nb+zZkzh2XLlnHVVVdFLYZNmzbRs2dPPvnkk/9n787joyqvBo7/TggQICwJCIigAVFB\nUARBRVRS0IIgWNwQtYqI+qpV61JRq95c+2pV1FZ9qyJK0VpBpeIuCGiQRZGtIrKJguyyE0LClpz3\njzuBIZlJJsmsyfl+PvMhc+eZO+cCM3PyLOfhtNNOA2DhmDH88PbbXDN5csziMqa6S6TvgWj29D0P\nNAPOVNW5gRqISFe8ujXPA7H7dDXGGJ+iXr5LL700pnE0bdqUtm3bcu6559KlSxfvi6awkHVz5vDh\ngAG889FHMY3PGBP/opn0XQQMDZbwAajqPBEZAbwevbCMMRWRlZVVLYozr1mzhqZNm9IqDubT1axZ\nk9zcXL4qVqsvZU5USnwZYxJcNJO+QrztQsoivrbGmDiWlZUV6xCionXr1hx33HGxDgMgaJ2+/B07\n2L5yJelt20Y5ImNMIonmMrQPgKdF5JxgDUSkB/A0MDFqURkTJ0SkUETOCPJYVxEpiHZMxhPtFbvl\nVb9FC2Y//XSswzDGxLlofpL9EVgJfCUiG0TkCxF5z3f7QkQ24C3i+BG4K4pxGZMIagK2Q4cJqMEx\nx/DDO++Qu2lTrEMxxpTCdd1aruvWitXrR214V1V3AX1EpDtHlmwB2IKX8H2mqt9EKyZjYk1EjgOO\n4/DUhy4iklKsWQowFFgdvchMIqlRqxanXHUV3zz3HOf/9a+xDscYU4zruinAucA9QI7rum87jvOf\naMcRtZIt4VbdSkaY+FTZpfoikgU8EkLTfOBGVX2roq8VTvb+i42hQ4eyevXqQ/e3bt3KypUrueSS\nS/jHY48xumtX7vj5Z1IaNoxdkMZUM2V9D7iumwZcDfQB3sMb0XwNGOg4zvLoROmxHTmMia0XgQm+\nnxfhfTB8X6zNfmCNqu6NZmDV2YIFC9izZw/nnnturEM5wtixY0scu/3229m4cSONMjJoe+GFzB81\nih733Rf94OLQH4cOZadfklykUUYGfw/wd2lMuPmGcq8COgFPOY4zw3d8HZAe7XjiLukTkVeBJFUd\nVlZb/9WD1aF0RDyrm3Qh+VozhhFMAg7E8PUrRlU3A5sBRKQNsEFV98c2quqtoKCAr776iksuuSTW\noYRk5MiRdO/enVGjRjHovvt4s29fzrzjDpJTis8SqH52rl5N6+nTSxxfFYNYTLXVAxgAPO44zgzX\ndWsAg4ANwLxoBxN3w7sishKooaqty2hnw0txRGQgqh9W6hzp6ens2LGjQs9NS0tj+/ZSN3GJiEhU\nYheR2sAxeHP5jqCqS8L5WhVVld9/CxYs4IcffuD3v/99rEMJ2YoVK+jRowfTpk1j8QMPcNLFF3P6\nTTfFOqyYO75ZMwo2by5xPLlZM1baohcTJsG+B1zXTQbeBL5wHOcV3/0eeHWL1wH/B6jjOFErUxd3\nPX2qaoWmEkx6ejqwI2gNsVClpaVRVROJUIjIMcAreAudAlGgRvQiqn4KCgqYMWMGgwYNinUo5XLi\niSfy7LPPMnjwYN577jmm3nYbnW+4gaQaifHfpTzDsCe3bcv2rVtLtE1v0oQlK1eSu2kTP376KSs+\n/phtmzezK8DrNc3PD0/gxpROgb14U3QABgOn+e6PdRznUBku13XPAPY6jrMokgHFXU9fqKpyT0O8\nKasHLi0tjR07zql0T18iCmdPn4h8CnQB/oq3N3WJYV5VzQ7Ha1VWVX3/JWIvn7+hQ4ciIpy7YgVn\n/vGPdLj88liHFJKhmZmBh2F79mRsdvYRx5o3asSvu0qmco1r1+bRjh1ZuWIFSaecwt7mzXn1ww/Z\nd7BkpaNGSUnMHD+e9pdckjCJsYlfpX0PuK7bBfgXXpWSotJ04xzH2em6bpLjOIWu6zYHzgOygHsd\nx/k0YrFG+4NbROoDPYGTOFyyZQewDJiuqrkhnqdKfunEg+JJXihDp+EY3k1EYU76dgE3qerb4Thf\nJFXV99/s2bNp1apVXGy5VhG5ubl07dqVG/r1o+H06dw4b16le+CjIVjSN7tBA6475xxqN2hArQYN\nqN2gAVe98ALb9u0r0TYJSK5Zk9atW9OufXvatWvHS88/T06AXr1kEQYdeywnqTLw/vu5duRIdgT4\njCvqPTTGX3Z2Ntl+v4y4rlvW6t3mQENgteM4+3zHavj39PmO9cBb1XuV4zgLIhF71JI+EUkCXOBu\noA6Qh5fsgZf81fUdexZwyvpGqapfOvHAl8iU8zmW9IXhXCuBu1T1o3CcL5Ls/Re/Fi1aRO/evbmt\nYUOuffll2px/fqxDKlOwpG9pp048+thj7MvJOXTr/+c/s+tAyUVbjVNT2bBtG7VqHa5727J5c9b/\n+mvJto0acekVVzBxwgRq7d/P1txcSqaR0KxhQzbt3FmpazNVX6jfA67rDgbWOI7zte++ADiOo0VJ\noOu6zwLvFrUJt2juyOHg7bSRBWSoaqqqtvLdUvEK1Gb5tTExkpaWhogccfPm7ZkIewQYISIJUWQt\nKyvriN92TXw49dRTefTRR3n74EG+fPzxWIcTkj0BFlsApDRqxIn9+3PKkCHUz8zk/xYsICdAwgeQ\nXKPGEQkfQNt27QK27dipE6NGjWLTli38Z+pUkmrGsvKAqUZm4LdAz3EcBYr+07Z2XfdCYAgQsYUd\n0ezpWw88qqqjymh3E15P3zFltLOehijyH/INNNxrPX1hOde7wJlAfWAu4N/FIICq6hXheK3Ksvdf\nfFNVGqSmsj8vj4b16pGUfHjNXrwNWa6bM4drzzmHngHm3q3q2ZNHxozh0Ucf5ZNPPuGuu+7i708+\nyZacnBJtA/XKFS9mXSQjI+OImofB5gk2bdAg4HFj/JX3e8B13euAy/FGN08C1gOpeKOf4xzHGR+R\nQInu6t1GeHvvluUnDs/1M3HCP8lLhDlCCeoovP//gvfbX1PfcfUdsyzLhEREqJOcTC6wZc+eWIcT\n1K41a3jnkktY06QJLxWbe1dQWMiBBQv4qFs3br/9dlauXEnDhg15c8wYkgJ8BqU3aVLiWKBi1uWx\nIzeXZcuW0S5Ij6ExFTQbb/RzAnArXpHZQqDQcZyIvmGjmfR9gzd0NSfYYg0RSQVGABEZyzYVV7yn\nz4SfqmbGOobqZv/+/axbt442bdrEOpSwC5QYxZN9u3czbsAAzrr7bj786COmB5jT16pVKxYuXEjj\nxo0PHYtmL2WNwkJ6dO/ObbffzoMPPkiKFbw2YeA4zo+u6/YHRgMXOY4zFsB13YhPuYvm8O7JwFSg\nNjAZb7VuUV98Q6A93r50+4Deqrq0jPPZ8FIEBCvPUtYKXhveDft5BTga2KKqcbfVSFV5/02ePJk9\ne/YkzO4b5RHPQ5aFBQW8PWgQ9Zo2ZcDo0fzmN78JmPT17NkzKvNGg9X+q1erFlcWFDC/Qwd+2riR\nl156ifMTYGGMia6Kfg+4rnsK8AYwEFgfjSLNUevpU9UlItIB+B+84rO9KVmyZSTwsqracqkwKs9O\nF9W9QHKsiUh/vG7/0/AKMXcDFojIaLySRm/GMr6qZN26dSxevJhbbrkl1qFE1b6cHD67807OuvNO\n0mLUwzn1/vvZv3s3V0yYgIhUeCeecCmt93DVF1/Q6Moruejaaxk+fDhJSUk0b968xKKR4vMEjSmL\n4zjfu657nuM4u6P1mlHdkUNVd+AVnv1rNF+3utuxY4clcglARK4FxgD/Bv4B/NPv4R+BG/C29DGV\ndPDgQT788EP69OlD3bp1Yx1OVBXUrk3NOnUYfcYZZGRm0v3uu3l61Ch2/fJLibaBdsSorAWvvcby\n999n+Jw57MrN5d5772XZsmVhfY1wat2rF9d98QX/7teP0cOHM2z0aL7+2mYgmbAJqTZxuMTdNmzm\nSJXZj7aIzcFLGH8GnlbV+0UkmSOTvh+Ae2MTVtUzY8YM0tPT6dChQ6xDiZhACxsOFhSQs3cvkwsK\n+MvPP7PojTeYeO21LNu8mbN3l+xsWBXmmFZnZ/PFgw9y3fTpfPD559x1111cfvnldOvWjVmzZoX5\n1cKnaceO3PD117zVrx/5AYaBAVZWMnEtz1Z0purwlW2JGkv6oqSiyVsKYShauGMHbsQndQ+I8Pmr\nheOAz4M8thdoEMVYqqyDBw+ycuVKrrzyyiq9Ej3YkOX27du5+OKLue7GG3n99dfpesstzOzUCX74\nIayvXzyJOZCXx6aFC2ly1ll8ec89/PLLL0ycOJGzzjqLoUOHkpxc8usoIyMjrDFVRoNjjuH6GTN4\nMEjN0l07d5KTk0ODBhV7m+5cvTrwVnQVOpsxgVnSFwGBEryiuXKuCE4VHGrNkoGxDqEqWIe39+4X\nAR47ndBKHpkyJCcnM3z48Cqd8JUmPT2dKVOmcN111/Hb3/6W999/n7oBegXBm/9XUR9PmsRBv90w\nFNgN7Jo5E/cvf2HixImH5sUlyly42g0aULNePQjw93KgoIDWrVtz/fXXc8cdd3DssceGXCfQmGix\npC+CQtmz1hg/rwKOiGwCPvAdSxKR84H7gL/ELLIqpromfEVSUlIYN24cI0aMoEePHuRt3Up2gHb7\nvvuO0d260fXWW+l45ZX86ZZbQh6CzN27l5IboHnbpT300ENhuIrYCPZ/p2Hduny7YAHPP/88nTt3\npk+fPixfvpwFC0rfQrXgwAGWTZzIxoULaR2JgI3xY0lfBBQletX9i8WU21NAK+B1Dm/DMxtvFe/L\nqvpcrAIzVU9SUhIjR47kuOOO447bbw9Y+btpaiqZrsvcf/yDqffdx88pKZy+bl2JdquAg/v2sW3F\nCrb88AObf/iB/bmB56cn16gR3guJstSUFFIClLzZt3s3P40ezYM338wjjzzC6NGjefvttwOeY+Wy\nZeT++ivzX3mF+S+/TPoJJ9CgZUtYsqRE220rVpC3bRt1/WoVGlNRUavTF26JUCestHl8Va0X0Or0\nhfWcbfFKGjUBtgNfqOrycL5GZSXC+8+ErnGjRmwPkMgc06wZ6zZtAmD7Tz8xNDMzYNI3s04dMlVp\nlJEBGRnMy8/n9enTAyaSgbZLSyRDMzMDzr1bfvrpXHfeeXz/1luktWlDp2uvpd9997ElwAKZuklJ\n3FivHj0HD+bs22+n2amn0rZ58yOGw4scrFOHO1NT6ek4dL355iO21DPxIVL1WiPB/vdEUKCkrmhO\nX3p6erl6AqtakmiOJCJ1gF3AFar6PjZ/L6zWrVtH8+bNAy4WMHDKaacFLI581NFHs3fvXlJSUkg/\n/niWHDjAokAnSEnh2r/9jdf/9S8WfvstQ4YMIW3ePLbH8RZwFdUoIyPg4oqmGRn0efZZLnjqKVZO\nnsyiN95gf4CED+AgMKl5c1759785eeFCunTpwta8PAKVzD6mQQOunTKFSXfeyfyXX6bvc8/x3Btv\n2EpfUyH2CRgj5U3gbKi4alPVfBHZjPd9YMJo165djBs3juuvv54mQRYsmMB+/PFH0tPTOfnkkznz\nzDPZkpNDwKUdO3bw5ltvceONN3LxxReTkpLC+xMmQICkLznBtzIrK6lKSk7mxP79ObF/f2777DMI\nkPil1a/PshUryM3NZdGiRSxcuJAPP/yQXQHaNm3RgkYnnsi106axbOJEPrzhBt7dsIGa+/eXaJu8\nbBl/r/CVmerAkr4EkZaWVu7Ez3oHE84o4A4R+VxVS36im3JTVT7++GPOPPNMS/gqoGvXrnz22Wcs\nWLCAOXPmsO9A4B0Bm9Svz+TJk484dn7fvkFXrlYX9evWpU6ARK4o8U1NTeXss8/m7LPP5t133+XX\nAMO7y5cvJy0tjQ4dOtCtWzc6jxjBnjvuCNgr2Gzv3nBfggkz13VrATiOE5PPeEv6EkRFkjfrHUw4\nDYGOwCoRmQb8CkdOi1LV+2IRWKL67rvv2L17Nz169Ih1KHEtWCKWkZFBnTp16NGjBz169ODZp55i\nfYDEpHaAXU2sJAmc064drQP8fa1q1y7kc3Tr1o1PP/2UhQsXMnfuXKbPnMnug+EfELDi0JHlum4K\ncC5wD5Djuu7bjuP8J9pxWNIXZSlpaVEolOx7Lcqf+KUA91fo1aw4cxhcBuwDBO/DwZ/gJYCW9IVo\n7dq1TJkyhWuvvZYaCb5iNNJCTdDatmsXMOlrW44kpjoJNv+vUTl7O+vWrXso8QaY+vHH/Bpg4c3O\nnBzeffFFBt10E8nJyeWqE2jFoSPHdd004GqgD/A23raar7muu9hxnKgu0rOkL8pGRHG41anAc9LT\n08kqx84hRUPIVpy58lQ1I9YxlEdWVhaZmZlkZmbGOpSAvv/+ey6++GKaNWsW61BMNVWeHrLSeltD\nlZSUxD133cX1d95Jrx49mPnf/7IjQHJY2S3jTOh8w7lXAZ2ApxzHmeE7vg4IvL1LBFnSZ45gC0xM\nqLKysmIdQqn69esX6xCqnHAkJiaw8gyHJ6ekQIBkLr1JE1avW8eXo0cz5sknyQnQBuBAfj7bVqxg\n6/LlbFu+nG0rVrDJikNHSg+8obDHHceZ4bpuDWAQsAGYF+1grE6fqZTy7ilc1RaXhLs+k3hZ9DnA\nCXij7UdQ1RfD9VqVYe8/Y2InlGFbVaVZ/fpsCbCCug7gHnccLdq3p/FJJ9H4xBN5etQoTl5UsiDP\nnMaNefn992nVo4f9kh9EsO8B13WTgTeBLxzHecV3vwdwEd62m/8HFDqOE7UPU+vpM5VyePeR0Ioz\n24dGcCLSDG/f3falNIuLpM8YEzuh9AqKSNBCzgdr1OCvOTkMatGCay6+mNN79mT+o48yJ0Dbwn37\n+OD666mTnk73e++l/aBB3D18uC36CI0Ce4GilbqDgdN898c6jlNQ1NB13fpAuuM4v0QyIOvpM2ER\natJX1XoGw9nTJyJvAm2Ay4G1wFl4K3ivBq4FLlLVuCjaHI/vv8LCQpKSkmIdhjFxo3mjRgEXfDRr\n2JD5P/zA+PHjefPNN9m6dSs7t28nNy+vRNtjmjVjzfr1LP/wQ75++ml2b9zI9KQkOv30U4m2q3r2\nZGx2diQuJa6V9j3gum4X4F/AFrwh3RnAOMdxdrqum+Q4TqFvZW8n4J/Ag47jvB+xWOPtgztU8fil\nU51Fahs235sp7OcNlzAnfWuBO4EPgAPAWar6re+xh4FzVfW34Xityoq399+KFSuYO3cuV199daxD\nMSZuBNvaLblZM1b6ttcDWLx4Mf369WPt2rUl2vbs2ZNsv0Ru7ddfc/PAgZy5dWuJttUl6cvOzj7i\n78R13VK/B1zXbY5Xkmu14zj7fMdqOI5T4LquOI6jruumAy8BzYHLHMfZEonYbXjXxLVQilLHe29g\nOTQCtqpqgYjkAE39HpsNjIhNWPFt06ZNfPDBBwwZMiTWoRgTVy7q2zfoMKy/jh070qZNm4BJ38aN\nG8nJyaFBgwYAtOrenaYdOkCA8i7VRfGqBa7rltrecZxNwCbXdQe7rvuL4zjfFEv46uCt8P0J+Ahv\nz/WICJr0ichICLhfdlmeU9X1FQ/JmMNCSeaq0DzBVUBL389LgGuAj333LyKCHwSJavfu3YwbN45+\n/frRsmXLsp9gTDUSjvl127Zt49hjj6Vv375cc8019OnTh5nLlpEdoG3h/Pkc3Ls34bfai6AZQGc4\noqcvFbgOb/HeN8C7/glhuAMorafvHmATXrHYUAjQChgPWNJnTPl9ClwAvAX8BfhQRNbh7cd7LNbT\nd4QDBw4wfvx4Tj/9dDp06BDrcIypkjp27Mh7773Hu+++y5NPPsmwYcPI3bmT/ABt0/bu5YUTTqBn\nVhanXXdd0IUk1ZXjOBvw5vUBtAWWA0OBE4Gv8RK+g5FK+KCUOX0iUgh0V9VAC3oCtU/GW5HSVVUX\nhC/EoK8XV3OKqrtIzekLRWmLQyI99Bvuki3Fzt0Nr55THeBzVf0sEq9TEfHw/ps3bx5r167ld7/7\nXVXq7TUmJkLdvWPVqlV0OuUUdgcoBXNMs2Z8PXEi0+6/nz2bN9Prscd4+YMP2PVLyQWpVWmlb3m/\nB1zXbQosxkv0vgOWAv9xHGd/JBM+KD3pGws8qqo/h3Qi71P3n4CjqhFdcux7vZh/6ZjDYpn0lSbS\nC0EimfTFs3h4/6kqhYWFtsWaMVGWmZnJ9ABz+lq1asXEiRPp3LkzP3/+OdMeeAD3+++pVVBQom3x\nxSSJrCLfA67rnoq3aO9rx3Gu8h2LaMIHtnrXhEm8Jn3BegHD1QMYiaRPRPoA3YCjgY3At6r6eThf\no7Ii9f5TVfLz89mzZw+5ubnk5uayZ88eGjduzAknnBD21zPGlF9pSV/t2rU5ePAgl156KZdecgkD\nzj+fbfklB4ObNWzIpp07oxFuxFX0e8B13U7AZ0B3YJ1/3b5IsQF3U6UFS+zicThQRFoA7wNdgc2+\nWzPgKBGZD/yuqi+SWrBgAVOnTiU1NZV69eod+jMtLS3WoRljytCmTRu+/PJLvv/+eyZMmMCwG25g\n+969sQ4rbjmO853ruh0cxwm9eG0lhdzTJyLH4O0f14LA20PdF97QyozHevriSLz29AXj3wNYmV6/\nMNfp+xg4FbhSVWf7He+Bt0Bqkar2D8drVVYke/riMSE3xhwW6vw/gCb167MtN7dE2yb167MlJ6fC\n540niTTNJ6SePhG5EnjDd3cLh7cUAW/VrgJRTfqMqQz/JC+OkoxewA3+CR+Aqs4SkRHAq7EJK7zW\nr1/PnDlz6NevHynFSjvE0b+FMSaI8iRgyUHm3G7dvZtu3bpx4YUXcuGFF3LGGWewevXqgMPGJnxC\nHd59DJgA/I+q5pTV2BhTIZshYCUEfMcjUqE9GlSVn376iVmzZrF9+3a6d+9uCzCMqQaSU1IgwFZw\nDYFbL7yQZfv2cfPNN7N+/fqQPxP+OHSo7f1bQaEmfU2A1yzhM1VR0a4fcbCzx+OAKyLzVHVd0UER\naQW4vscTzurVq5k0aRKqytlnn03Hjh0t4TOmmji/b9+AQ7bNGjZkx5gxXPrAAzy5aBHr1q2jd+/e\nbNlS8nfbvcXmBX48aVLg7eWWLePvYYu8ago16XsfyASmRS4UY2KjKNGLg6HFC4DGwE8isoDDCzm6\n4PXy9RaR3vimVKjqFTGLFA6trC261apVi5NOOqlEu5SUFHr16sUJJ5wQD3/HxpgoKm0oeMeqVbz5\n29+St2ULPR2Ho48+mhUrVpRoN3/+fI477jjOO+88zj33XHbu2cO2AOdrZotGyhRq0vcH4F8i8irw\nBVBinbWqfhrOwIypho4CfgRW+u43BPbi7btb9DgcnkcbUy+//DL16tWjXr161K1bl2OOOSZgu+bN\nm9O8efMoR2eMiXdprVtz/cyZvNmnD3lbtwatqXr22WczatQoZsyYwecffcT2AAtDgIDPj9TikGBD\nzPEu1KTvBLxVhRnAsACPK2DjNcZUgqpmRvo1ROQVVb0pHOe69957w3EaY0w1ltqsGUOnT2f8wIGs\nmj+fpg0alBgR+HXNGnZNmQJvvMFZ69fzRa1abN+/v8S5tuTkMKBXL3r260eXLl3o0qVLxBaH7Fy9\nmtYJuOgk1KTvNSAH6A/8xJGrd40xiePCWAdgjDH+Uho25OpJk3inZUvODDCvevru3ayfM4dejz1G\n6969Gdm4MQRI+hrWrk2D779n6urVTEhLY/Hy5Rw8eDDkOE5u25btW7eWOJ7epAlLVq484liilowL\nNek7CbhEVSeF40VFpD7eBsNFFVd3ACtUdXc4zm9MohKRU4EHgDPwduTYAHwLPKmq34V4jsJSHk7M\nTypjTJVWs04djurYEb76qsRjLbt355I33zx0P9iK4HqNGvH66tV889xzzB45kkduvJGhr7/Oln37\nSrSdNWsWv//972nfvj0nn3wy7du3Z9uWLWzOCbxedc+WLWyYN48Nc+eyYe5cPp8xg9qVuN5YCTXp\n+xZoVdkXE5ELgEfwthxJKvZwoYjMxtvvd2plX8uYUBUVao71rg8i8jvgXbw5fe/iLd5oClwMzBWR\nwao6MYRTbQC6qOrmYucXYE14ozbGmPAIttArKfnIVCXYiuCMjAySU1I4Z8QIThs6lC8ffpj9Abbh\nBKhfuza9evVi6dKlvPrqqyxdujRowpe3axd/b9uWVl270qJbNzoNHUrh9On8srv8/VSu69YCcBwn\nJiOmIe3IISKdgdeBkXgreAMt5Mgr4xxXAOOAScDbwFK8Hj7wevzaAYPxhp+GqOo7ZZzPduSII4m2\nI4c/XzX1yjw3XDtyLAe+By73/88tIknAO8ApqlpyeWzJ87wE/FtVZwZ47FVVHR6GWO39Z4wJ+zAT\nZgAAIABJREFUq6GZmQHnya3q2ZOx2dkVOmfT+vXZEmDhR3rNmvzjwgvJ376d/B07yN++nUc3biRQ\n2pcEJNeqxdFHH03r1q1p3bo177z1Fnv8ehDL+h5wXTcFOBe4B2+63NuO4/ynQhdVCaH29M33/fl6\nkMdDWcjhAM+Usl3bXLwVwk8BWXhfcsaEnf8WbEDMe/j8tALuKJ5NqWqhb+V8KL18qOotpTxW6YTP\nGGMSRYN69agbIOnTlBROu/566qSnk5KWRp20NJ7q0IGcAL19RzVsyNotW1i7di0///wzq1at4t23\n3w45Btd104CrgT54nV4/Aq+5rrvYcZzlFb22igg16Qu0Yre82gCfhNDuU+COMLyeMQHt2LEjXifh\nzgc6AJMDPNaBw798GWNMldMoI4NVQY5X1Dnt2tE6QCHnVV260O53vzviWGl1RGvWrEmbNm1o06YN\nAP/+979DWhXsG869CugEPOU4zgzf8XVAesgXEiYhJX2qOra0x0WkZginWQkMAsr6W7oYLws2prq5\nC3hbRGrh9eptxpvTdwlwA3CliNQtalzWlIrifAuoeuItzPJfRLUMmK6qgYtfVSPZ2dlkZmbGOoyw\ns+tKLNX1umK9hVp6kyblOh6iHsAA4HHHcWa4rlsDLxfaAMyrzIkrIqSkT0T+V1UfCvJYHeA/QL8y\nTvMQMEFEOuIN3S7j8NzAhkB74HK8nT8uCyUuY8pSfCgX4mo4t7hvfX8+TuAt1771+znk2pi+OYEu\ncDdQB8jjyPm0dYE8EXkWcKrzZL3q+mWbqOy6Ekssrqs8vYfFy7KUJsPv+cF6/FzXTQZuBt5zHOcr\n3/0ewJl4CV+h67oC4DhOVD53i6+gDeZOEflz8YO+noNJeENPpVLVD4DfAAXAC0A28F/fbbrvWAGQ\n6WtrTKUVDeX632K8v25phpXjdkM5zuvg9SJmARmqmqqqrXy3VOA432NFbUKW7Te5OtjPodwPdiyU\nxyrSrjznseuy6wrlsYq0K8957Loqdl1/HzuWsdnZJW7+vYoVua6xY8eSnZ1d1nMVb1elopW6g4GL\nfPfHOo5T4DiOFiV8vsUeERVq0jcQeFBE7i46ICLpeFuytcBbkVImVZ2pqn2ABkBH3/PO9f3cQFX7\nquqscsRvTJWhqmNLu+GtyPW/H6rhwD2qOlJVS5RsUdW1qvo03qqyci30sC+l0u+XFZNdV2jtynMe\nuy67rlAeq0i78nIcpwB4HviT67rZeBtc/AyMdBznUKFB13X7ua47Ahjlum6fiATjE1LJFgAR6QO8\njzdE9D7wue+hC1R1U2TCKzWe6jwKFXfitWRLZcqxlOP8YSnZEuT8SUAvYAgwSFXLPfFXRPYAA1V1\nWhntegMfqWrd0tr52tqbzxhjfEr7HnBdtzneNLbVjuPsK/bYSCAV2AYswhv1HOA4zrclThQGISd9\nACIyEG8+3ja8SYh9VDWsY2Ui0soXV6lFZC3piy+xTPoCzdsrkpaWFtHh3EglfSLSHS/Ruxxohvee\ne0dVb6vAuabhTZ24JNhiDRFJBd4Daqhq7woHbowxJiDXdQcDvziO843v/lNAE+A54GfHcXa7rvtX\n4BPHcUrUWQ2HoAs5RCTQwoyDwFt4w73PAGcVLXFW1U/DFNMqQAhxkroxcVyCpVx8W7ANAa7Em2e3\nD6iN17v+f6oa+iaSR7odmAr8IiKTCbyIqo/v9SzhM8aYyPgK6ALgum4voD7e8O8PjuMcdF23M97n\nf8TWNZS2evfjMp77lt/PIa8kDMEwvKTPmCpPRI7HS/SG4CVfu/DqWd4DfAOsAxZUIuFDVZeISAfg\nf/B2vOlNyZItI4GXVbXEbjvGGGMqz3GcjRyuV3wq3iKPlb6ErwPe5/CzRT2BkVBa0tcmUi9aGlV9\nI9S2WVlZh37OzMyskkvcTXwJYbVWef0I5OP9EnUvMFVVDwCISKNwvYiq7gD+6rsZY4yJAV+JlmTg\nRLyEL9d13dPxEr7PgLGRfP1yzemLJzanL75Eck5faXP2IPLz9kpT2Tl9IrIKbyh3Jd6cuvdU9Vvf\nY42A7XhljL4KR7xlxFIHOKqs+bQhnGc63rBxEt5Ktet9SWfC8s01HgscDRQCn6jqiJgGFSa+vZoH\nAC1UNdSKDnHPVxP2DbxJ8kuBq6tKAfIq/G9WJd9ngT4Ts7KyWgBT8Arx9wWexSvjsieisQRLnESk\nAZCrqoUhn6yM54hIPeBSvH/QFcCHqlpQrE0b4CFVLXXrN0v64kskk75Ir8CtjHAs5PBbtHEF3g4c\n6/FWyE/DSwSjlfRdBrytqpWaqiEi9VV1t+/nZ4D9qvpAOGKMFRFpjvcFu8C3A9EU4HlVfS/GoVWa\niJyD93m8qYolEDOB/1XVSSLyJLBPVR+JdVzhUIX/zark+yzYZ6Lruhl4U23UcZz/RiWWUpK+QuCs\nol6HMk8kkoxXcLCrqi4I8PjRwGy8Xo08vF0AVgC/V9W5fu3OAmaX9R/Zkr74YklfWM5VA6+A+RC8\nrdca+h56C3jO/30SCb6k751wfYn4ys28BCxX1WfDcc54ISLPAytV9flYxxIuIlJYVRIIEWkGzFfV\nlr77JwITVbXMjQQSSVX6Nwukqr3P4uEzsaxt2HqISKibzpXVO/BXvEmLJ6nqj76Vis8B00XkOlV9\nN8TXMVVMKMO31YGv13sqMFVEbsFbdDEEb5/Gq0Rkhaq2K+95ReRLvMVWZWkaYrtQXvNToCvenMU7\nwnHOeCEijYHfARfEOhYTVEu8RVBF1gKtYhSLqYCq9j6Ll8/Esn5DeAZvFW8ot7KWGPcCslT1RwBV\nXYS3ivAFYLz/bh+megm0VVqCbJsWMaq6X1U/UNUr8ZKxa/B6xiviPKA53vzAYLd9wFFAkogU+BLF\nEkTkZBGZJiJ7RGS9iLi+316Lx9/P95oz8X65iwkRaSsio0RkUTiuS0RqAxOAv6nq8kjHH0y4ryte\nhPG64qICRFX9d4LIXlus3meRvKZ4+UyMxOrd9UGOpwNH7Nzhm/s3QkR+AZ4XkZZ4xZ+NMT6qugdv\niPetstoG8QOwVFUHB2sgXuH113x3lxOgx09E0vB6Ihfj1epsi/eLYRLwcIC4C0XkDWB8BeMOh5Px\neky/xvu8q/B1+Ybf/403bPi3iEdeurBdV5wJ13Wtw+vtK3IsR/b8RUtYrkdEbgD+4HvKrar6dcQj\nL1skru0WYC6xe59F9N8rLj4TS+thCecN7y/ovlIevxSvdMVCoCCE86mJHzCgEs9N3H9LX+xRex9V\n5AaMAtaU0UaAy/BWzE0AvgjQ5gG8nUFS/Y79CdgD1PfdbwQ083v8EeCfMbx28fu5wtflO/YqMCbW\n/57hvi6/f//CqnRdeD0qF/p+fgr4SyJfT6Bzx/LfLFLXFsv3WSSuKd4+E6PZfTwZuDFYF6iq/gcv\nw25NnHTNm/BIT09HRILeqsucvRgaCfxBRIK+r9T7NPqE0nv4LwQm65FlL94G6gA9fffTgI9E5DsR\n+Q6vFtU9lQm+MnzXVZbSrus8ABHpgVc4/nQRWei7/aHkqaIjDNdV9O+FiLwKrAFURNaKyCthDbYc\nwnldeL1Gj4nICqAdXuIXVWG+nkPi4d8sEtcW6/dZhP694uozsayFHOH0DPAl3rYjuwI1UNVs8cpX\nnBHFuEyEVZVt0hKVqq7EqwNYVrt8YHUpueFJeMMa/s9ZIyJ5vsc+VtVVJN77t7TraodXK2wWZc+B\njjdl/nv5jg2PQWyVEep1fY9vy6s4F9L1FHs8Uf7NynVtCfI+K+81xdVnYtSSPlXdAGwoftw3T2YK\ncLOq/qiqS/EKaRpj4ksah/fs9beDw9u6JSK7rsRS1a6rql2Pv6p4bQl9TfGQUQuQidcDaIwxxhhj\nIiCaw7umiileXy/YsKDN2asydnC4YLS/NN9jicquK7FUteuqatfjrypeW0JfU8hJn4gcA1wEHAOk\nFH9cVe8LY1wmAfjP1YvkjhwmbiwD2vsfEG+vzLq+xxKVXVdiqWrXVdWux19VvLaEvqaQhndFZBDe\nJsH/B9wAXO53u8L3Z4Wo6kG8ws0VLTxrjImOz4A+IpLqd2ww3raK02MTUljYdSWWqnZdVe16/FXF\na0voawq1p+9xvJIrQ1U17NsjqGp2uM9pjAmdiNQB+vvuHgPUF28vXvBWr+YDL+NtH/SeeBvYHw84\nwLPFyhfEDbsuu65YqmrX468qXltVvKYSQixYmAucH6tigkFiUhNeaWlpileBPKRbWlraoedWpjhz\nIiMBijOHcgMy8AozFwIFvlvRz8f6tWsPTMP7rXY94OJX0DTebnZddl12PXZt1fmait/EdwGlEpEp\nwPuq+o8yG0eJiGgosZvQiQgV/TutrnP6fH9nVkzcGGNM3As6vCsidf3u3gW8JSJ7gM8JUKNGVfPC\nH54xxhhjjAmH0ub0BRqbHhOkrQI1Kh+OMcYYY4yJhNKSvmFRi8IYY4wxxkRU0KRPVcdGMQ4TQcWL\nKAdjRZSNMcaYqivUOn0/i0inII+dIiI/hzcsE05FRZTLum3fHvZqPMYYY4yJE6HuvZsB1A7yWF2g\nVViiMcYYY4wxEVHa6t2GePvLFZWjOFpEji3WLAWvEvX6yIRnjDHGGGPCobSFHHcBj/jdn1hK23vD\nE44xxhhjjImE0pK+t4B5vp8/xEvsiu+Pux9Yrqq/RCA2E4JQFmnYAg1jjDHGlLZ6dwW+JE9EegHz\nVXV3tAIzoSlapGFMaUTkd8CjwInABuAFVf1bgHYPArcAjYG5wB2q+l00YzXGGBMZpfX0HaKq2QAi\nchLQDTga2AjMU9VlEYvOGFNpItIDeA94FbgbOAt4UkQKVfU5v3YPAA/h9eovA+4BpopIR1X9NfqR\nG2OMCadQ995tgPeFcSnewo5cIBVvJ473gBtUNSeCcQaKyfbepXL75YY3Dtt7N16JyGQgRVV7+h17\nGrgeaK6qB0QkBfgVGKmq/+trUxdYDYxS1YejH7kxxiQe13XHAP2BzY7jnOJ3/HbgVqAA+MRxnBHR\nji3Uki0vAhcAvwdSVbUBXtJ3re/4S5EJzxgTBp2AKcWOTQHS8Hr9AM4G6gPvFDXw7af9EXBhFGI0\nxpiq4p9AX/8Druv+BhgInOo4Tkfg6VgEFmrSdzFwn6q+5fsiQFXzVPXfwJ98j5sISU9PR0QC3myR\nhglBCt6iK39F99v7/myH99vnj8XaLfM9ZowxJgSO48wAiq+wvAX4q+M4B3xttkQ9MEKc0wfswZv8\nHcgGvOFeEyG2WMNU0kq8ubj+zvD9me77Mw3IDTBnYgdQV0SSVfVgBGM0xpiq7ATgPNd1Hwf2Avc6\njjOvjOeEXag9ff8A7vXN8TlEROrh9fTZ8K4x8etlYJCIDBeRNBHpg1eHE6AwhnEZY0x1kQykOY5z\nFl7e9E4Z7SMWRCga4GWpa0RkCrAZaIY3ny8fmCsiTxU1VtX7wh2oMabCxuDN63sJeAWv5/5+4AVg\nk6/NDiBVSq6QSgPyivfyiYh1PRtjjE8IC/rW4S18xXGcua7rFrqu29hxnG2Rj+6wUHv6LgcO4A3j\ndsebjHgWsBs4CFzma3OF709jTJxQ1UJVvR1oApyC9wvbHN/D3/j+XAbUANoWe3o7YGmQ8+I4Dqpa\n6s+h3A92LJTHKtKurOfbddl12XXZdYV6C9H7QC8A13VPBGpFO+GD0Ov0ZUQ4DkPw3TVssYYJB1Xd\nBewCEJFbgVnqFWEHmA3k4P3i9pivTV1gAN7wcECZmZll/hzK/WDHQnmsIu3Kcx67LruuUB6rSLvy\nnMeuK/6vq4jruuOAnkBj13XX4m1pOwYY47ru93gL6a4N64uGKKQ6ffGoKtbpi5eaexVhdfril4ic\nCZwL/BdvqsYQvKkZ56jqYr929wMP4803WY5XyLkb0EFVtxQ7Z5V7/wFkZWWRlZUV6zDCzq4rsdh1\nJZZE+B4oEurwLiLSSUTeEZGfRWS/iHTxHX9cRKyOlzHx6wBeD95EvPpRKUAP/4QPQFWfwOvlewCv\nPl8qcEHxhK8qC/dv/Pn5+SxevDjmv8yF+7rihV1XYqmq15VIQt2R40LgQ7whoC8AB+iqqgtExAHO\nVNV+EY20ZExVrqfBevoSTyL9hhdOVfH9F25r1qzhvffeQ0Ro2bIlAwcOpGbNmrEOyxgTZon0PRBq\nT99fgbHqbeP0WLHH/gt0DmtUxhiTwFavXs0777xDv379uPXWWwGYPHlyjKMyxlR3ofb07QUuUtWp\nIpKMNwmxqKfvN8AkVa0d4ViLx1Tlehqspy/xJNJveOFUFd9/4VRYWEheXh6pqamAt9J5//791K4d\n1Y9JY0wUJNL3QKh1+rYAxwNTAzx2MrAmbBFVcU+mp7PXt0L3Cbyy3EVSAFcS4v9NAANiHYAxcSMp\nKelQwgfel4IlfMaYWAs16RsHPCoiPwBfFx0UkZOAEXhLkU0I9u7YgePrIclK4J694rJkYKxDMMYY\nY0wpQp3T9wgwF/gKWOs79gGwGFgEPB7+0IwxJr5t376dt956i/z8/HI/t7CwkDlz5lBQUBCByIwx\npqRQizPvBS4Skd7A+XiV/bcDU1V1SgTjM8aYuLRo0SImT55Mz549SUlJKffzCwoKWL16NUuWLGHw\n4MHUrVu37CcZY0wlxKQ4s4jUB07E29cTvH0/V6jq7nKcIyEnkrsih4Z3E3nhRnG2kKN6SdT3Xzjk\n5uYydepU1q1bx2WXXUbz5s0rfC5VZdq0aSxZsoQhQ4Zw1FFHhTFSY0w0JNL3QJk9fSKShFe9/0y8\nPTsBfsWb2ze1PJ/8InIB3lBxd0oOLReKyGzgUVUNtGDEGGNiKi8vj5deeolTTjmFm266iVq1alXq\nfCLC+eefT5MmTRg7diyXXnopbdq0CVO0xhhzpFKTPt+uG+PxNmE/CGzFS9bSfc/9UUSuVNWFZb2Q\niFyBtyBkEjAMbxP3oo1m0/A2dh8MTBaRIar6ToWuyBhjIqRu3brcdtttYR+KPe2002jYsCELFiyw\npM8YEzFBF3KISDO8BC0fuBBooKotVLU53v6d/YF9wCQRaRrCaznAM6raX1XfUNW5qrrSd5urqv9S\n1YuAZ4CsSl6XMcaPiFwtIgtFZLeIrBOR10Xk6ADtHhSRtSKSJyLTRaRTLOKNZ5Gae9e6dWsuvfTS\niJzbGGOg9NW7t+MlfOep6mTfYg7AW9ihqp8B5+GVmrs9hNdqA3wSQrtPfW2NMWEgIpcA/wJmAAPx\nyiydB3wicrgwpIg8ADyEtwPPRUAuMNX3C2C18vPPPzNjxoxYh2GMMWFV2vDub4GXVHVXsAaqulNE\nXgIuAR4u47VWAoOA6WW0uxj4sYw2xpjQXQnMV9U7ig6ISA5e2aUTgeUikgLcDzyuqi/62nwDrAb+\nQNnv7yph48aNTJ06lZ07d9K7d+9Yh2OMMWFVWtLXFpgfwjnm4/UclOUhYIKIdATeAZYBO32PNQTa\nA5cDmcBlIZzPGBO6nGL3i36ZK+rpOxuoj/feBEBV80TkI7zpHVU+6Zs8eTKLFy/mvPPOo0uXLtSo\nUSPWIRljTFiVlvQ15PAXQ2l2483xK5WqfuDbp/dh4AWgZrEmB4AvgUxVnRXC6xpjQvMK8LGI/B6v\nd6858L/ANFVd5mvTDiigZC/7MrwFVlXa+vXrWbJkCX/4wx/iZru0Xbt2sXfvXpo1q3aj68aYCCkt\n6Qu15oyG2lZVZwJ9RKQ23l6+/nX6flLVfSG+ZpWQlpZGeno627dvj3UoJg6IyEi891N5Paeq64M9\nqKpTRWQ48Brwuu/wbI7sUU8DcgOUYNoB1BWRZFU9WIHYEkKLFi0YOnRo3CR84CWiX375JTfffDPJ\nyaHumGmMiTXXdcfgLXbd7DjOKcUeuwcYCTRxHCfqX/5lfZJMFpGyPujL/WnkS+6WlPd5Vc327dvx\nm0dvzD3AJrxV8aEQoBVeWaWgSZ+I9AdGA88Cn+H19GUBE0XkfFUtrETMVYKIkJaWVnbDKDr55JP5\n/vvv+eqrr+jVq1eswzHGhO6feCOab/gfdF23FV7d419iERSUnrA9Wo7zhK00v4i0wtspZE24zmlM\nAhmkqnNCaSgiycD+EJo+AUxQ1Qf8nvtfvKHbi4GJeD16qVJyq400IC9QL19WVtahnzMzM8nMzAwl\nbFMO/fr14+WXX6ZDhw42zGtMgnAcZ4bruhkBHnoWuA9vmk1MBE36VDUrinH4W4XXg2GzqE118waw\npRztC3zP2VZGuzYcHtYFQFVXiEg+h8sjLcN7z7XlyHl97fAKqZfgn/SZI/1x6FB2rl5d4nijjAz+\nPnZsyOepX78+vXr14qOPPmLYsGEkJZVWZcsYE69c170YWOc4ziLXdWMWRzxOFBlG6PMJjakyVHVo\nOdsrEMpzVgNd/A+ISHugju8x8Ob45QBXAI/52tQFBgAvlycuAx9PmsTBX38tcTx52TL+Xs5zdenS\nhSVLlrB+/XpatWoVngCNMVHjum5d4EG8od0iMclz4i7pU9U3ym7lseElE23Z2dlkZ2fHOozy+gfw\ngohswNtlpxneHtir8Iqho6p7ReQJ4GER2QEsB+72Pf+F6IccWarKe++9R48ePWjevHnIzxs6dCir\nA/TgZWRkMNavB293fj6bAzy/2d69AY6WTkS45pprbP6vMXGiAt8DxwMZwHe+Xr6WwHzXdc9wHCfQ\nR0XExE3SJyItgK2qGsocJcCGl0z0Ff/lIlLd9CLiEHyubCFer9x3qlpWsXNU9UXfgqxbgZvxSjHN\nAB5Q1Xy/dk+ISBLwANAYmAtcoKrlGXJOCCtWrGDTpk0cddRR5Xre6tWrmT49+F/57o0bmfXSS+Tl\nFC+L6Nm3ezdzXniBU666irqNG4f8upbwGRM/yvs94DjO93i/bBe1XwWcHo+rd6NCRBoC6/AKM38V\n22iiKy0t7dDKQSvdYvzcDqQARRu95gKpvp/z8Obf1RaR74C+qlpyLNGPqr6CV6+vVKr6OPB4RYNO\nBAcPHmTy5Mn079+fGjVqlGv+3cply0q0A/hm9mzapqezcedOCmvU4IAIlKh+4/0j/m30aNIeeICe\n/frRbfhwLrrlFnZsKzktM71JE5asXFmRSzTGxJDruuOAnkBj13XXAo84jvNPvyZhW/xaXlFL+sqo\nQZbi+/MWEbkIQFXvi0pgMVaU6Nlv8qaYfsCbwJ+Bj3zDryl4e+f+L97cV/DKtTwLXB2TKBPQN998\nQ9OmTTn++OMB2Ll6Na0D9N6tKna/oKCA3N27A56zxsGD/PnGGzn/5ptp2bo1R6el8euukrXtU1NS\naNWnD59PnsybH39Mm88/Z/WuXZR/0NcYE68cxxlSxuNtSns8kqLZ03cP3pDUDrwJjP4JYNGStEy8\nGmWKt6zZmOrq/4AnVfXdogOquhd4R0TqA8+rahcR+Qu+hRembLt372b27NkMHz68zLbrvvmGf7Rv\nT36NGnyTk8P0zZvJ3Re4hGL9Bg24/sknD91PTkmBAElfvfr1GTlyJCNHjmTLli188cUXDL3mGjhY\nvrrXGzdu5Oijjy7Xc4wxJppJ33N4vRNv4H2Z5RU9ICKNgO3AlaHMUTKmGjgF2BjksU3Ayb6fl+Pt\nmWtCsH//fnr37k16ejoAe3fuZOqcOQE/CA+kpNC0fXs+nTqVC845h9EDBnDjvfeyNS8vQOsjnd+3\nb9AFH0WOOuooBg8ezJ0338zeAAniwX37OJCXR826dY84XlBQwIQJE+jbty8nnHBCmbEYY0yRqCV9\nqnqXiIzGWwk4TETuV9V/F28WrXiMiXM/An8UkWn+2xP6hnj/iJfsgbe7Rqnz+cxhjRs3prFvAcXS\niRP57A9/IL+ggECzaZNycjj1jDN4btSoQws+7nVd6gVI+pJTUo64P7YctfiC2bt/P3/PyOD0m26i\n2223Uf/oow/NP0xt3py1y5ezefFioPz1/4wx1VNUF3Ko6hKgt4hcBjwjIrcBdwIrohmHMQngDrxy\nKmtFZApe0eameHWe6uLt6wjQGfhPTCJMULmbNvHZ7bfz66JFXDp+PE8NGBBwKLZJ/frcf//9Rxw7\np107Wgeov7eqXbuwx5mvyk/9+nHC1q28ePLJnDRwIJt/+IGT5s+HOnXgzjtpPXs2HDhQYv6hMcYE\nEpPVu6o6QUQ+wSsNkY23H2i1Zqt4jT9VzRaRE/B69brhFVfehLen499VdYOv3YjYRZlYVJXvXn+d\nKffdR5fhwxn0r3+RnJLCwSCLqGrWqVPiWKOMjIAJViO/YdvySm/SJODxhmlpbNi+nSc2b+b17Gy2\nfPYZv44fz0kA+fmwfj20bQtLA26YYowxJcSsZIuvPtgjIvJPvL1BvwP2xCqeWLNVvKY4VV0P/CnW\ncSSi4mVYDuTns23FCkSEMdOmcXTnzmzatIm77rqL3Pz8gOdoG6D3LhJDqKWVZVFVnnnmGc777W95\n7bXXaHnmmTBjhvfg0qXQvr0lfcaYkMVDnb5f8IatBquqDfMa40dETgZOB1oBY1R1k68H8FdVDVwB\n2BxZhqV9e/jlF8jL4+fzzqPpqafy4osv4jgOw4cPp1u3bsycOTO2AQchItx77710796dIUOGkLN1\nKw3xyh+kLF1Ki/37+RlvezdjjClLPCR9SXhFDFPLamhMdSEiqXhDuZcCB/Deq5PwhngfA9YA98Ys\nwESRng4DBsCLLwKwY88ezj77bGrWrMmXX35Jx44dGTp0KDVq1Cjx1IxKDNmGW48ePViwYAHHNG/O\nodmHe/awfNEioGLbuxljqp94SPqMMSU9C3QHegOz4Ij6vZ/iDfuGlPSJSDZwXpCHu6vqHF+7B4Fb\nOLwF2x2q+l1Fgo+1GUuXkg2ce9557Pr6a/6bm8tOYM+CBYx65RWGDRtGUpJXHjQcK22joUmTJjRp\n3JgNm0tu1VlYUBCDiIwxiSap7CbGmBi4BLhfVb/E22vX3xrguHKc6xbgLL9bd6BoRfAV8Lz7AAAg\nAElEQVRcABF5AHgI+CtwEd6OYVNFpFmgE8YzVWXnjh38mpJCy3btmDZ/PmuB3UDj1FSGDx9+KOFL\nNCe0bx/weP39+1n89ttRjsYYk2hi3tOnqgdFpBdWtsUYf3WArUEeqw+E3LWjqkfM9BeRWngrgsep\naqGv9t/9wOOq+qKvzTfAauAPwMPljj6Gvnz4YQoLCji1c2d+/PFH8vzq6iVqsleWZqecwqQ77qBO\nejrHX3BBrMMxxsSpuPgEVNVsVc2NdRzGxJF5wHVBHrsUmF2Jc/cFGgHjfPfPxksk3ylq4Nsx5yPg\nwkq8TtTNfuYZlkyYQM2GDenSpQvz588/4vHiRZSrilqpqVw+YQLvXX016+fOjXU4xpg4FfOePnOk\ntLQ00tPTrVafeQhveHUaULT/bj8RuRu4jOBz9EJxJbBWVYuWrLbD6zn8sVi7ZcDgSrxOVC147TW+\nfeEF0kaMYMcddzBu3Dh2FSu6HKgMSyIpvrjkwIEDLFu2jK5du3LsOecw8NVXGTdgAEOzs2mS4Ndq\njAk/S/rizPbt261Wn0FVZ/imPTyBt3UhgAt8A/RW1W8rcl4RqQsMBF7yO5wG5Kpq8W0QdwB1RSRZ\nVQ9W5PWiZcmECUx96CFW9e3L5JEjOfXUU1mwYEGswwq7QItOFi5cyOuvv86sWbM4Z+BA8rZt482+\nfRk2cyYNWraMfpDGmLhlSZ8xcUpVZwHn+hK1NGCnqla2gPkAvG3cxpXVMFH89PnnjP+f/+HzY48l\n/ddfmTdvHnfffTf169cv0TaeyrCES+fOncnOzuaFF16gU6dOdL7+evK2bOGKjh05qkMHatSseUR7\n26fXmOrLkj5j4pxvfl1emQ1DcyXwo6r6d4PtAFJFRIr19qUBefHcy7d29myeueIK3qtZk1suvpiH\nH36YpKSkhCnDEi5XXHEF27ZtY/jw4YwfP56z//Qnaj3/PG1nl5z6afv0GlN9WdJnTJzwbUlYfIg1\nYFNAVXVYOc/fEG9hxhPFHloG1ADacuS8vnZA0D2+srKyDv2cmZlJZmZmecIpt5PbtmX71sMLmgsP\nHmT3nj3sF+GTTz+lb9++EX39eNaiRQvS09PZtGkTzz//PHfeeSdpxx/v7c9rjIkq13XHAP2BzY7j\nnOI7NhKvHNZ+4CfgesdxdgU/S2RY0mdM/DiFI5O+Y4GjgM2+WzPf/a142xeW1yCgFiWHdmcDOcAV\neLt9FM39GwC8HOxk/klfNGzfupVfd5X8jGySmnoo4du2bRv79u2jRYsWUY0t1kSEjh070qlTJ666\n6iq6du1qc4ONiZ1/4s3FfsPv2OfACMdxCl3XfQJ4AK9UVlRZ0mdMnFDVrkU/i8hA4G/AIFWd7Xe8\nB/A68JcKvMSVwH9VdXmx190rIk8AD4vIDmA5cLfv4ReIczX8au/NmjWLtLS0apf0AVxwwQWICGPG\njGHw4MH0OPbYWIdkTLXkOM4M13Uzih2b4nd3Dl7praiLizp9xpgSngAe9k/44NDijkeAJ8tzMhFp\nAvQCxgd6XFWfwOvlewCvPl8qcIGqbil/6LGxb98+li5dymmnnRbrUGKiqGevf//+DBs2jK+WLAm9\ngrcxJpqG4W2nGXXW0xeHrFafAVoTfPFGnu/xkKnqVryh3dLaPA48Xp7zRouqkrt3b6ltFi9eTEZG\nRsBVu9WN4zg8M3Ikz9SqRf06dSg4cICD+fnUql+fxuvWxTo8Y6ot13X/DOx3HOetWLy+JX1xyGr1\nGWAB4IjIt6q6oeigiBwDZAHzgz2xqsnLy2P48OHs3bev1Hbz58+nV69eUYoqvtWoUYPTunRh9uzZ\n5O3ff/iBnBzad+4cu8CMqQKys7PJzs4u9/Nc1x0K9AN6hzmkkFnSZ0x8uhmYDKwWkXkcXshxOt5C\njj4xjC1q1qxZw6BBg2iiStOaNSmoU6fEL0TpTZqwceNG8vLyaNOmTYwijT81i9XnM8aER/FqBa7r\nlvkc13X7An8CejqOU/qwRQRZ0mdMHFLVxSLSFrgeOANojlda5V/AP1U1P5bxRcPMmTO54ooruKp3\nb1pkZ/Puzz8H3WEiPz+fyy67jKQkm6a8YcMG9pXSK7o3wApoY0z4uK47DugJNHFddy3g4M2XrgVM\n8SWJXzuOc2u0Y7Okz5g45UvsXvTdqpXRo0fz0EMP8fSIEWx64gmumjKl1C3F6tSpQ0vbcgyAXbt2\nMW/evOCPr1kTxWjM/7N33+FRldkDx78njRBKEkpIKCH00EE6iETFVcTeVkVXbKhr2+Kq2Ib5ueuq\nq64utrWsWLELYgFECSCgdAhCQHonARIghNR5f3/cSUwyN6RNZibJ+TzPPGTufee9Z4CbOfNW1fA4\nHI5rbA7/z+eB2NCkTynlNxMnTmTHjh3Fz10uF1u3biUrK4vvPv2UhddfzyVvv01s//7+C7KO6dKl\nCzNnziQkxP7Xe97x46StX09Mnz4+jkwp5W+a9CkVIETkCDC2zBZppyofDKQDScaYdbUaXC3ZsWMH\nCxYs8Dg+fOhQfr7zTs78v/+j27hxfois7goLC6NTp04kJiZSUGDtoGeMYe3atbRu3ZquzZqx+Omn\nufSddyqoSSlV32jSp1TgiAK6i0hlB/mGuF9T7+7jI6mp9LzjDgZNmuTvUOqknj17YozhxRdfLD6W\nkpLC2WefzRtz5vD+kCFk7txJVMeOfoxSKeVr9e7DItCFR0fjrORyLJUtFxgu9HcA9YVf1m7yl/Im\nHISEhXH2ExUvGZiWlkarVq10AkcZ3bt355tvviEvL4+wMGt5xr59+3LllVfy5L//zUW33MLSZ59l\n3H/+4+dIlVK+JMZUZn/3wCMipq7GXhkiQl16fyIXYcyX/g7D59z/Tl7JzkUkqZovXWGMyfJGDJVV\n0/svPz+fF154gfvvv9/2/3nbmBj2HjxYYR3//ve/ue2224iMjKx2LPXVtm3b6NChQ6mlWw4fPkzP\nnj2Z9dFHJF9+OXdt2kST1q39GKVSdZ83PwdqmyZ9AUqTvrqhLt3s3lST+y85OZk777yT9u3bs3LJ\nEg5neearbSIjOZCZecp61qxZw4YNG7j22murFUdDNXXqVGbOnMm9XbrQJCaGsx6vzjbOSqkidelz\nQLt3lWoARCQEuA+4GeiANQHkE2PMX8qUewi4A2gJLAfuMcasrer1ys7KBasr9+DBgxQUFPD8889z\n6aWX0i0ujqY2SV9IeHiF11i1ahUjR46samgN3h133MF///tfMi+/nI2PPsqo+++nkW5dp1SDoEmf\nUg3DNOBMrC3cUoF4oGfJAiIyGXgEKzlMBf4KzBORPsaYU/e1llHerNwOHTqwceNGmjRpAsDpiYl0\nsunG3Z6YeMr609LSyMjIoHv37lUJSwEhISE8//zz3HbbbTx+5pmsfO01Rv71r/4OSynlA5r0KVXP\nich5wFVAP2NMajllwoEHgSeMMS+7j/0E7ADuAh4t+5qibYgSEhKYNm1apWLp3LlzccJXE6tWrWLg\nwIE6gaOaxo4dS9++fVkfF8eB555j6F13EdKokb/DUkrVMk36lKr/bgK+Ly/hcxsJNAM+LjpgjMkW\nkVnAOGySvqKWvBMnTvDFF1/w66+/Fj+WLFlSqcDys7Mr/y5KiI+Pp127dtV6bUOTn59PcHCwR4L8\nzDPPMHz4cBx9+rDuvfc47eab/RShUspX9GuyUvXfUOBXEXlRRI6KyAkR+UxE4kqUSQQKgV/LvDbV\nfa5cv/zyC//73/84cOAAp512Go888ginnXZahUEZl4vMnTtZ26UL28eMKfWISkg45Wt79eqlM3Yr\n6d1332XPnj0ex7t27cott9xCcqNGLHn6aVyFhX6ITinlS9rSp1SAEpH+wMPAYKA9MNwYs0pEngAW\nGWO+rWRVccBEYA3we6A58DTwBTDcXSYayLKZkpsBRIhIiDGmwK7yoUOHMmvWrFLH/v73v1cY1MrX\nX+eyzp258ccfCQoOruRbUVUVGxvL3r17iY+P9zj38MMP06NHD3q0aEHqjBn0uvxyP0SolPIVTfqU\nCkAiMg74ElgCvA04SpzOBe4GKpv0FS0lcLExJsNd/35ggYgkGWOSvRJ0CQnltNQVHT+2dy/zH3mE\nG+bP14SvlsXFxXnMpC7SrFkznnjiCf52770sv+EG2v7nP0iJReGjEhJ4vpLjNZVSgU+TPqUC0z+B\nacaYW93LrZRM+tYAt1ehriPA1qKEz20xkAf0BpKxWvSaiucCfNFAdnmtfGDN1E1OTi6e2AFUOLHj\n27vvZvAddxDTp08V3oaqjtjYWJYuXVru+T/84Q/cd889NDtxgs4LF5Y6t722g1NK+ZQmfUoFpkSs\npVPsHANaVKGujYDdwncCFCV4qUAw0JXS4/oS3a/3MGbMGMBqvSuZ8FUYzOefc2jjRi6fPr3SrwEw\nxlBQUFBqhwlVsZiYGDIyMsjPz7f9uwsKCqJRSAgzgRWUHugdknqquT9KqbpGJ3IoFZjSgS7lnOsF\n7KpCXV8BfUWkZYljZwChWK2GYHUjH8Na2gUAEYnA2lTZths5OTmZ5OTkSi/XApCTmcm3d9/Nha+/\nXuUlQnbu3Mm7775bpdcoCA4OJj4+nsxT7HBS6HLhAnYDO0s8snJyfBOkUsontKVPqcA0Hfg/EfkF\nKO6bE5EewAPA/6pQ12vAPcAs9ySQ5sBTwHfGmCUAxpgcEXkSeFREMoBNQNFuHVNr+maKfHf//fS4\n+GLiTz+9yq9ds2YNPXv2rLig8nD99df7OwSlVADwS9InIs2A7ljjhcAaT7TZGHPcH/EoFYAew2rR\nWwgccB+bCcQCc4AnKluRMea4iJwF/Af4EGss3wzgz2XKPSkiQcBkftuG7RxjTHrN3oplx4IFbPn2\nW+5Yv77Kr83NzSU1NZWxY8d6IxSllKo1Tqfzf8B4IM3hcPR1H2sBfAR0xFr0/iqHw3HqDcZrgU+7\nd0XkHBFZhJXkLQfmuh/LgQwRWSgi+ltdNXjGmBxjzAXAOVizd98EPgDGG2MuMMbkVbG+rcaY8caY\npsaYFsaYm4wxR23KPWGM6WCMiTDGjKnOvrt2CnJymHXrrZz/0kuEV2N9vQ0bNpCQkEDTpk29EY4q\no7y9jiuzB7JSysNbwHlljj0IfOdwOLoD37uf+5zPkj4RuQqYjTVu6CZgGFZrX3f3zze6z81xl1Wq\nwTPGfG+MmWyMudUY84AxZq6/Y6qOBY8/TuyAAfS46KJqvX7NmjUMGDDAy1GpIl3L2eu4c9euPo5E\nqbrP4XAUNW6VdBHWF3jcf17i06DcfNm96wCeNcbcX8755cC7IvI01qbwH5dTTql6T0R6AZHGmKXu\n5xFYW6H1BH4wxvzHn/FV5E8TJ5LpXhsuLyuLA2vX0m7IEBZPnFjldd8KCwuJioqiW7du3g9UAaXX\nVTTGsGrVKqJCQgg5eNB/QSlVv7RxOBxFN9RBoI0/gvBl0tcZ+LoS5b7BGnSuVEP2MtZaekWTOJ7G\nag3/EXhKRMKNMU/7K7iKZO7YQSf33rwAPQCWLmV7WFiV6woODubSSy/1XnAN1NGjR3G5XERHR3uc\nKzsDe9GiRUyYMIFxeXlsmT2brueV7alSSlWXw+EwTqez7O5HPuHLpG8LcCmwoIJyF+O5/6dSDU1v\n4FkAEQkDrgf+bIx5TUT+BNyGlQgqVSm//PILR48eZdy4cRWWHT16NEOHDuVgZCSzJk3ijpSUao3F\nVKo+KlquqooOOp3OWIfDccDpdMYBad6PrGK+TPoeAT4VkT5YXbepQNHMlUisbqsrgSTgCh/GpVQg\nagIUTbQYDjQFPnM/Xw0k+CEmVYfFxcWxadOmSpd/6qmnGDZsGP8+/3zm3ncfF73+ei1Gp1TdkZSU\nVGpBeqfTWZmXfQncgLVc1g1YKyj4nM+SPmPMTBE5E2tc0lSshWFLygfmA0nGmMW+ikupALUDGIG1\nZMslwGpjzGH3uVaALm+kqiQ2NpYDBw5gjCm1v255unTpwsSJE5mfnk6/775jy5w5dD33XB9EqipS\ncsxsSWX3Sq5sOeVdTqdzOjAGaOV0OndjLcH1JPCx0+m8GfeSLf6Izafr9BljfgTOFZFGWLsNlFyn\nb6sxJteX8SgVwJ4FXhGRK4GBWOP5iowB1vklqkoq0J0cAk7jxo2JiIjgyJEjtGzZsuIXAI888gg9\nevTgMqeTWbfeWue6eetr0lN2zGyRsnslV7ZcXRPo/64Oh+Oack75fUk6vyzO7E7uNvjj2krVBcaY\nN0XkV2Ao8IAx5vsSpzOAf/snsso5eeQIqzp0ILpz51LHo0rMEq3I6tWryc3NZfjw4V6OruGKi4tj\n//79lU76oqKieOyxx3j+00+557zz+O5vf+PC116r5Si9pzaSnokTJ7LDJuFISEio0paEtSH/5EnS\nN26kMC8PV34+OUc9luIsV6AnUiXV12TWFwJuGzYR6QCIMaYqe4sqVe8YYxZide+WPe7wQziVdmTL\nFoZnZHDXpk00btGi2vWsXLmSMWPGeDEylZiYSGho2ZE1pzZp0iRefPFFXLfdxo777mPr3Ll0+d3v\nainCwDdv9mz22ixlsyU11SfXzztxgqxyltI5uG4dH192GcFhYQSFhrJw/Xp+simXv3QpPz71FG0H\nDybutNNoHB3NV7NnU2BTb0hqKs+XORbIia86tYBL+rCSdQGC/R2IUv4mIu2xFjD32BrBGPNNJeuY\niP1evbcbY14rUe4h4A5+24LtnursyJHscDDs3ntrlPClp6dz7NgxunTpUu06lKd+/fpV+TWhoaE8\n88wz3HfffXzxyivF3byNmjevhQh9w5jqr5ZR3tAFu+NVSY5O1dL23BtvsPW770h5/302f/UVJ4Ls\n91VoP2wYd5aYVfpYVBQ7bVr7WgUHk3XgAAumTOHAmjU0adOGI4cPe6wmDNDG5n3t2LGDBTYtbb5S\nmFelDYlUCYGY9N2ElfQp1WC596f+BDhVk0pVd9Q5EzhZ4nlxb4iITMaaYX8f1sz6vwLzRKSPMabS\nK/SmrV/Ptu+/Z/yrr1YxtNJWr15Nv379CCrnw0351vnnn88LL7zAHU4nTfLymNejB6169Cg+H4hd\ngKeyd9kyFv/rXwyYOJEmrVvTq2tXjhw65FGuRatWbNiyBYBt27Yxc+ZMjmRl2dZ56PhxJk2axIgR\nIxgxYgTdu3evUnJUXpflil9/5bl27Yjq1Im+EyZw7nPP8Ua/fmy0qSOkkq2NhUFBhIwfT/SYMQQf\nO0batm3k/uMftmWzc3N5++23adq0afEjq5y/AzvebBV0FRay6vXX2bt8OXZ7xRzdtQvjciH6e6Nc\nAZf0GWPeqWzZKVOmFP9cdgq1UrWhmuszVcc/gXhgNLAIa43LTGACcBZwbTXqXG6MyS57UETCsfaB\nfMIY87L72E9YM8zuwppxXynzH32UUfffT6NmzaoRnqWwsJCUlBRuuOGGatehvEtEePbZZxk6aBD3\n5ufTGODAgeLzdW0sVeuePUn/5RemdutGt/PP59CBA6SfOOFRLr+ggEceeYQvPv+ctAMHGN61K42M\nId+mzqbBwbRr3Ji5s2fz+OOPk5mZSbZNnWDfFfxjairJNmVdR48ybfVqWpbYkSYH2GtTtlVeHtOn\nT2fHjh3s2LGDjGyP2x2ArNxcnnzySZo2bUqTJk1o2rQpppxEKT8/n++//56srCxOnDhBVlYWGzfa\npZywfv167r33Xjp06FD8SE1N5eeff7YtX9apEsQn7r6br++4g+CwMGL794eVKz3KZR86xPQLL+SS\nt98molWrSl2zoQm4pK8qSiZ99U10dDQtWrTgyJEj/g5FlVDN9Zmq43ysZKvot+U+Y8xyYIGIPAf8\nDWtdy6oorwV9JNCMElsfGmOyRWQWMI5KJn17ly9n7/LlXPbBB1UMq7T09HRiYmJopb+0A0rfvn3p\n0KoVC/fvp64s3NIoMpJFYWG0Gz681DI1bRISuGTaNE5mZLD2nXfI//BD29dnnjjB0qlTOaOggMGn\nnUa7wYP5af16sk6e9CgbDLT7+WdCU1IY060bTU4/nTs//BC7JSnS0tOZcPbZRIvQPCeHxpmZHDp4\nELtpF62Dg0kvLCQlOZkDBw5w4MABgsrZ2SYnP58ZM2aQkJDAgAED6N6jB+vXr/coN3LUKObNm1fq\n2MyPPuKkTbdpuMvF79LS+N2zzxLTuzdg/R60a8GMiYmhY8eO7Nmzh59//pndu3ezfNky21hT1qwh\nNTWVzp07E+Z+P+WNl/xl2TL6zJ7N2CefpP8f/sC6m25ie9OmHuV6xMfTOi6O/w4cyGUffEDH0aNt\nr92Q+SzpE5HTgMYl1+ATkXFYLQy9AYO16KxT1+mDI0eOVGotLVVvtQF2GWMKROQEUHKA3Df8tlBz\nVWwVkZbAVuC5EuP5EoFCPHfCSQV+X9nK5z/yCGc88gihjRtXI7TfxMbGct1119WoDlU7BiYkMGv/\nfoZQ+j9koLqyZ0/o1Yux//yn7fnG0dEMv/dewh57DI4d8zjfonFjPl6+nBZduxZ3GUZOn04Tm6Qv\npEULbvnpJwpyczmwZg17f/6ZkOnTba8bZgyNTpxgD5CWnc2+o0dtEz6A9GPHuOiii4iNjSUuLo7Y\n2FiCg+2HvA8aNIiPPvqo+HnJnysSEh4ONuP/Ilq3puu4cbx95pn0uuIKkk7xRTcmJoa//OUvpY7F\nRkVx0KberKwsLrzwQnbv3k379u3p0aMHRzLsRhWCq6CAOzdsKB4nnInVDVFWQlAQ5zz1FAlJSXxy\n5ZUMvftuRk+erN29Jfiype8VrBWpFwOIyE3AG1gLMj+P1QpxNlZLxhXGGL+sVq1UgNgNxLp/3gJc\nCMxxPx+K1cNTWfuwxustw2qQuAZ4VUQijDHPY62XmWU8R7dnABEiEmKMKTjVBXYuXMjhX39l4E03\nVSGs8ukXntpz7Ngxtm/fTv/+/av82pXbttEIeB2IKXE8uJzuPn8yLhfrp0/nmq++qrCsq5yJHcFh\nYbTs3r3UsQvOO6/cCRcAIY0a0X7YMNoPG2YlkzYJT9PmzfnfT6Xn1baJjCTNJvFsExnJ5s2bSx1b\nu3atbTdoWQnlLJFkd3zseeeV27U6/N576X/99SQ7nbzcqxc7XS5imjf3uE/T9uypMKYiURERrP3p\nJ/Jyc9m2Ywdbtmxhwfff25bNBl5+6y169epFz5492b59OwsXeixsUKzbuHFMWrmSz665hn+99BJR\nCQkEl2kdrWvjUL3Fl0lfT6xVqYs8BLxsjLmrxLHHReRVwImftihRKkDMw/oS9AnwHPC2u7U8DzgD\n9768lWGMmQvMLXFojnsc38Mi8kJNAzXG8MPDD5M0ZYrHL1YVeAoLC/n++++rlfRl5eQU7525s8Tx\nFpmZdsX9atfixTSKjKRN376nLLd27dpyJ2fYqUqi0DQ8nHCbpC8k3GMyvtVCbpP02ZWtrKpMlKio\nbOMWLRj3wgsMueMOfhg1iuE2sa5v25afnn+e4/v2kbV/P8f37SPXphxA7vHjvFiUUIsgIoTk2u/P\nEBoczO7du5kzZw4bNmxg//79tuVKfm9t3q4dN/zwA49FRhK8dKlHWbulaBoCXyZ9Lqwu3CIdsT7Q\nyvqM0rsPKNUQ3Q9EABhj3hWRLKwxfOHAncB/a1j/Z1jbAHXEatFrKiJSprUvGsiuqJVv65w5ZB8+\nTN8JE2oYkvKFqKgo8vLyOHHiBE2aNKnSa8vrAnQVFLDu/ffpF0D/B1I++IC+1556vtPMmTO55ZZb\niG3dmkKbhKNFDceVnp6YSCebMWrbExM9jnVNTLQdz9bVpmxVWvC8rVViopVI24zpO5GeTsa2bTRr\n25bWvXvTrG1botasIdJmbHpImzbcX2IyEMCzUVEctetibtSI55//LUU7/fTTWbzYcxTYokWL6N27\nNz179ix+ZItgNzLebimahrD+oC+Tvh+B6/itxWEDMAQo+z9nMPYTk5RqMNyzbLNLPP8C+MKblyjx\nZypWt29XSo/rSwTblSEAayKVMYZVr73G5bffTlA544xUYBGR4p05una1W/iifOUlJj1PO405f/4z\n0Z060WHkSG+FWm2FeXls/PRTbl2xwva8MYann36aqVOn8s033zBkyJBaiSMqIcF2ZrPdzjRVSeQC\nNQGJ6d2bcf/5T6ljY/r2td89wyaZrWzLaEiIfeoyatQoXnzxRTZu3MjGjRuZMWMGR23GXwKcPHmS\nLRs30rlHj+Klofy9/qAv+DLpmwwsEZH3gKlYEzjeEZEWWOP6isb0/cl9TikFiEgw0KjscbvlV6rg\nCuCQMWaniBwEjmG1/P3Dfc0IrHGE5S64N2XKFDZ89hnt2rblhsceK69Ypa1YsYIOHTrQpk2bGtel\nTi02NpYDBw5UOekrT1iTJlwybRofX345Ny1ZQnSnTl6pt7q2zp1Lq8REojp29DiXm5vLbbfdxrp1\n6/jpp59o3759rcVRla7gQE3kaqoqiW9F4yUrEhwcTP/+/UsNXShvIsmJvDwG9e5NTlAQnTp2pPeA\nAaxcvty2XrsldsprFQx0Pkv6jDEpIjIa60OkZAf7g/yW5GUA9xtjajzOSKm6TEQigSeAy7DGzJed\n2WCo5K41IvIp1j33C9Y9/3usBO9uAGNMjog8CTwqIhnAJqBoCt7U8up1FRYy/9FH+d2zz9Z44kVB\nQQHz58/nlltuqVE9qnLi4uLYtGmT1+o7duwY3c4/n9MnT2b6BRdw05IlhEdGeq3+qkp5/336XHut\nxwdzXl4ev/zyC1FRUWzYsKHK3duq6qqS+Fa2rDe6t1tFRrIlNZWlr7/OvDfeIP2nn2y7+AHSDx/m\nz3/+M507dy5+fPftt+xLS6v09QKFT9fpM8asAYaLSC9gGNbsRAGOYHUjLTXG6P4qSllfji7AmuG+\nEWsCR3VtAm4FOmDdb78A1xtj3i8qYIx5UkSCsFrki7ZhO8cYk15epSkffEDjFocBY64AACAASURB\nVC3oet55NQjNsnHjRtq0aUN0dHSN61IVS0hIKHfZj4peV9ahQ4fYtGkTBw8eZOjdd3MoNZXPrr6a\na2bNIqicbrjalJeVxa/ffsu4qVPZ8dFHtt11AwYM0ISvBqrSelcbqtIqWt441JDwcJrGxnLOo48y\n9pFH2L1kCTPPPJOThYUeZRuHhtK2bVs2btzIN998w7Zt2+pkwgcgNdmD0CsBWF1X84BJxpiy64Sd\n6nU2K0zULyJSoz0ifUnkIoz50t9h+Jz738jr64uIyBHgAWPM696u2xtExLzQuTMX/e9/JIwZU+P6\n3nzzTUaNGkWizTgfFfgeffRRFi1axHfffUewCB+MH0/LHj08xnf5wrr332f99Olc+9VX5S4iPGbM\nGF/trKP8rCqTM8rrCo4EHmjWjMj4+OLHDdOmcaREy6C3PgecTmfJ3hVD6V4e43A47qlJ/YGwI4cA\nY7B2BFBKWbKx1uoLWN8cOcIqh6PG613t27eP48eP073Memiq7pgyZQoXXHABDzzwAM899xxXfPQR\nF3fsyOvffkvzdu1Kla3t9dFS3n+ffu7FvevKl2ZVe6rSKljeRJLgmBj+vGkTR3ftKn5Qe/+3ivaX\nGwn0Aj7CypOuxOqlqZFASPqUUp6eBf4oInONMS5/B2MnNTOI1AUphKTu9FjvauLEO9ixw7P7IyEh\nhmnTXilVLjQ0hNzcfM4668pyy6nAFhwczPvvv8+QIUMYPHgw1157La0SE+m+bBls2VKqbG3u03si\nPZ3dS5Zw5SefcOLECTZs2FCLV1P1TblL7PTsSXhUFOFRUbTp1w+A0IceAptt64o4nc7JWCuWuIAU\n4EaHw2E/aLAEh8Mxzf36O4DTHQ5Hvvv5K1iroNSIJn1KBQgR+Re/LaUiQH9gk4jMBzxWvzXG3O/D\n8DzsZBQAbXI8v3zu2JHGggV229KneZT78ccCgoIgP9+UWw4qn0gGQll/X7+qvPW+Pv/8c8aOHUuf\nPn34eWcai202awtJ3elxzFt/B38c0ofu48ezLz2dSy65hKxyFl1OTd1se1w1bFUZq1iyVbDs/2in\n05mANY66p8PhyHU6nR8BVwNvVyUcoDlw2P28mftYjfg96XPvLXoWoHehauiupPQC5gYIBc4pU07c\n5/ya9BXJPBHEzTf/h8aNw4iIaERERCN27UrH7vdTRkYWP/64gUaNQgkPDyU7O5fCwiBsxk57qGwi\nGQhl/X19qFoi5a331b9/f1544QUuvfRSjmcb0t1fDEqqyZeEisqu//UDQi64gOHDhzN58mSeeOJZ\ncnJO2JQNLfUsEJL0+vrloy69r0was4OWnmXx3E88rGlrsnLck4GObit7+hiQD0Q4nc5CrIX2q7r+\n8JPAKqfTWbSk3RhgShXr8OD3pA/AGJPs7xiU8jdjTIK/Y6iORqGGESMSOXkyj+zsXLKzc8nPt8/i\ndu1K58EH3yY3N5+cnHy2bNkJeK7ptmDBepo0uZKwsBD3I5T09E1AZ4+ya9Zs57zzHISGhhAaGkxo\naAgbN5bcuvg3W7ce4OGH3yUkJJiQkCCCg4PKTVD378/gnXd+IDg4qPiRnn4MbD4AMjKyWLhwPUFB\nVrljx7Ipm1gAnDiRy+bNewkKEoKCgggKEnJy7BIYyM8vJDMzCxEpLl9YaN/Tb4yh0J05Fy2fs317\nGgsXetZtzEFcLhcul4vPP/+CCy+8EJfLfnxSYaGLEydyMMZgjKGgwP7fNT+/gEOHjmGMYezY81mw\nYBFvvfk2nuPQoaDAsGfPoeK4gXL/DrKzc9m0aQ/G4I7ZkJWVg91qRUfSM5i2bSO/pG7m8cefYejQ\nkbRvP4e0NM+PuYQE2L//CGFhIYSGhrBt2wEWLbJ7b4H3haIqZf19/doq6+/rA8S0787GrUVlSyd9\nDofjiNPpfBbYBZwE5jgcjnk2FZfL4XC85XQ6Z2OtdGKABx0Oh/3+c1Xg99m71aWzdwOLzt5tWETE\nWGs3Q5vIXziQubXU+aSky21/eY4ZE0py8mcVljvjjBC+/XY6ubn55OXlk5dXwBVX3MSyZZ5/1f36\n5fLkk0+Qn19Afn4h+fkFTJniZNMmzyU5OnXK5Oab76KgoLD48d57r7Fnj+e3+zZtDnLOOb+nsNBV\n/EhO/pTDh9t6lI2M3E2/fudSWGglJuvXf0dWVrxHucaNt9O+/ajiBMblMuzfv5S8vC4eZYODf6Vp\n0wG4XK7ipOfkyRSMsZvwsomgoJ7Fvy+sPzcBPWzLilizpG+7LZGvv97N7t0rbcuKbCY8vA/i3hv1\n5MkUXK5uNrFuISpqoPs1VkP0oUMfAmG4dxMsIY+Y6PMJjYgoLp+W9rPt30F4+HY6dBhVKknevn0h\n2dkJZUoWEiQ/EhIkdEu8jEaNIhERNm36nqwszwWaw8K2Eh09iPz8QvLyCsjKWmP7/hs12kanTmcQ\nHh5KeHgY4eFhrF07m4yMdh5l4+LSufLKmwgNDXZ/qQjmvfdeZedOzy7uTp0yueOOP5U69sorz7N9\nu+eXj4SEDG688U6MMe7/My7eeedVdu3yrLddu0NcccWNxeU+//xt9u9v7VGuTZuDnHvu1RhDcb3f\nffcR6emeX5RattzPGWdcWlzWGMOPP87gyBHP+yA6ei9Dh14AUFx++fKvyMz0XAA7MnIPgwadX+L/\nLKxe/S1Hj3qWbd58NwMGnOcuZ1i7dg7HjnWwLdev37mlPjdTUubalm3WbBd9+/6u1LGUlLkcP+55\n3zZrtos+fUqXXb++ZNlZpT4HnE5nF2AWMBo4irXl7KcOh+N9KsnpdH7vcDjOruhYVQVES5+yFx0d\nXfytPTo6miM2+xeq+ktE2mDtUDMUiAP2AcuAF4wxnqON/aQmG8KXR0SKu4qLNG4chtVjUlp0dFPG\njRtU6tirr0axaZNn2fj41jz88FWlji1d+iV79niWTUxsz7vv/qXUsaSk5bZJ6oABnUlOfrJEOftk\ndujQ7iQnl97kpLyyp5/ei+Tk6ZUqO2ZMn1LJdGXLzpw5k5tvbsd99/2znOS7N8nJn1Yi1p4kJ5f+\nPAsL/Zz8ghyg9B6nocHhOOLW0yoxkfGvvEKTmJhy6x02zPPvKza2I9nZJfdddQHHEWNYO3sOiWPH\nVhjviBGJJCe/U2G5/v07MW3aZHJy8sjJyScnJ4977llORoZHUZo0aUSnTjEUFLiKv1CUJz+/kLS0\nox7H7Lhchvz8guKkNzg4hKAg+++ZjRqFkpAQU1y2SROPjXwAiIxswpln9kMEgoKCEIFVq74h3WZV\nzjZtIpkwIQkRipP/rVsXYPdx1K5dS+699yKK1moXEf72t5/J9BiRDB07tuahh650l7OO/fnPK1i3\nzrNsp05t+L//+21f53vvXcPatfblnnji+uJrA9x991rWrPEs26VLLE8/PbHUsbvuWldO2TieeebG\n4uerVi3j73//nOPHy13gfDCwxOFwHAZwOp2fY83GrTDpczqdjbG+KbV2Op0lM/vmgOe3jSrSpC+A\nlUzyarrjgapbRGQU8C1WlvMd1l7VMcDtwF0icr4xpsYzuWpizBir+zIh4QyPcwkJMdh1iVjH4fDh\nw+zdu7fCcqp2xcbGsn9/jXuMbEU0acLRo56b2kc0bcKklStJdjp5pV8/xk0td9MXW9YYPc+MI4RQ\nepx1VnXDtdW4cRg9e5ZuJWrZshl2Xz7atWvJn/50calj8+Z9zM6dnmW7dInlX/+6sdSx5cu/sv3y\n0alTGx5//LpSx3744RN27PAs26FDq1IxfPrpW2zZ4lkuLi6aiRNLNxi98caLpKZ6lm3dOpLLLy+9\nn/LzzzfH7u+gZctmHl/A/vnPprZlo6ObcvbZ/T2O2ZWNimrCmDF9Sj0vr9zo0b1LHYuMtC8bGdmE\nUaN6VbJsBCNH9ix+PnJkTz799EsOHiwq6zElIRV41J3A5QBjsb6wV8ZtwL1AW35bvgXgOPBiJeso\nlyZ9SgWmF7Fu+AuMMcUj0UWkKfAV1vZoA/0UG4BHy1JJFc0kXbZsGY0aNarSjNOqJIj+Luvv61dW\nXFwc69atq5X3FR4eZrcRAuHhYYSEhzP2n/8k8ZJLmHHDDWzbc5iYZs2QMi1YaXvKdg2XX2/jiEZI\nUFC141XKWxwOx1qn0/kOsAKrKXoV8FolX/s88LzT6bzH4XB4fXVzTfqUCkyJwJUlEz4AY0yWiDwD\nfGr/ssCXm5vLunXruP3226v0uqokiP4u6+/rQ+USntjYWNLT03nzzRcrtS1bVa6fmNidgwc9WxFP\nnjzB9OnTueyyy2g/bBi3rV7N3O7dGbTHc7GM7QN+2+0lOzubWbNmkZ9vv9RZz16eu7lUNt5ASNLr\n65ePhvC+bDZ9weFwPA087Xnm1JxO5xBgT1HC53Q6bwAuB3YAUxwOR43GeelEjjoi0Cd16EQOr9e7\nCnjZGPOGzblbgT8aY6rc0ici7bBG+EcATY0x2SXOPQTcwW97795jjLEZOVOz+2/58uVs376dq666\nquLCqtbt3buXuLg4gsq0ktVUeVug9erVi7i4ONavX8/NN9/MbbfdxllDh1JgsyhucEwMr7z7Lu+/\n/z5ffvklQ4YMYdeuXWza5DmWSrdWU/7izc8Bp9O5GjjbPQP4DKwdOe7C6tlJdDgcV9Skfm3pUyow\n3QW8JyJZwBfGmFwRaQRcBkwGrq9mvf/CGhtSat0REZkMPALchzUe5a/APBHp481JI8YYli1bxvjx\n471Vpaqhdu1qPDbcVoLNgrZFx6dNm0ZqaiqvvPIKAwcOJOvYMez2NpC0NB5++GEmTJjAU089RWxs\nLElJSbZJn1L1RFCJ1rzfA/91OByfAZ85nU7bL+FVoUmfUoFpJlZr3AcA7uSvqfvcSWBGick9xhhT\n4SAlETkDOBd4Aiv5KzoeDjwIPGGMedl97Ces7oS7gEdr/nYsu3btQkTo2NFzGQ1Vv1S052liYiIv\nvPAC//jHP2jbujV5BQUeZZoFB/PDzJk0a/vb8iAlk0njcrF76VLaDhpUbpKpVB0T7HQ6Q93br40F\nJpU4V+OcTZM+pQLTS1UoW2E/q4gEY03+cGKtFl/SSKwtfj4urtCYbBGZBYzDi0lffHw8119/vc5G\nV8WaNm1KRKNGHM/xnOkbEhzMqwMGMPappxgwcSIiUiqZ3PTllyx55hluXLjQhxErVaumAwucTuch\nIBtYBOB0Orthsx1nVWnSp1QAMsZM8XKVt2NtEfESnl3DiUAh8GuZ46lY3QteIyI0a9bMm1WqeiAk\nPBy7KbmNo6O5fvZsZt50E798+CEXvPYaUSVaiVM++IC+117ry1CVqlUOh+MfTqfzB6wtheY6HI6i\nbXgEuLum9WvSp1Q9JyItgf8DJhhjCm1a2aKBLJuZGRlAhIiEGGM8+96U8pKuiYnstZnI0TUxkdgB\nA7jl559Z8swzvDZoEGu7dCEkPBxTWMiepUtpt3s3wR9+SFRCAs9X0KWsVF3gcDiW2hzzWAywOjTp\nU6r++wew1Bgz29+BqMD01Vdf0alTJ3r37l1x4VpwqkkfAMGhoYyePJnESy7humHDGHn8OABdAJYs\nAcBzwRelVFma9ClVj4lIb+BG4AwRKdrYs2jF2yhrD10ygKbiuQ5LNJBdXivflClTin9OSkoiKSnJ\ny9ErX2nWrBn79+/3W9JX0aSPIq179iR24EDQMXxKVYsmfUrVb92wxvJ5dBcAe4A3sAYOBwNdKT2u\nLxHYWF7FERERDBo0iNGjRxMWFnbKIFasWEH//v0JDQ2tYvjKF2JjY1m+fLm/w6gUnQSkVPV5dzVO\npVSgWQQklXk85T43DmvpliVYM3qLV0sWkQjgQqz9f23dfvvtHDt2jBdffJG1a9eWu3j43r17WbJk\nSaV2fFD+ERcXx/79+wN6AXilVM1pS59SAUhEXMBwY4zHJt0iMhj42RhTYRZljDkMlOoLE5HO7h8X\nFe3IISJPAo+KSAbWjh1/cZeZWl7dzZs359JLL2XPnj0sWrSInj172rb4LVu2jMGDB3t9xwflPUUz\nqo8fP07z5s39HI1SqrZo0qdU3RMK1HQ2bakmHWPMkyIShLXbR9E2bOcYY9Irqqh9+/Zcc801tudO\nnDjB5s2bOe+882oYrqpNIkJcXBxpaWkBn/RFJSTYTtqI0sWZlaqQ7r1bR+jeu4HJm3suikhHoCPW\nekzzgT8CG8oUCwcmAoOMMT28cd3qqOz9t3DhQjIzM7nooot8EJWqicLCQu2CV6oaamsP9tqgLX1K\nBY4bgcdKPH+5nHIngVtrP5yacblczJ8/n9tuu83foahK0IRPqfpPW/rqCG3pC0xebumLAYr20F0H\nTABSyhTLA3YZYzz3rPKhyt5/x48f1x04lFL1mrb0KaWqzBiTBqRB8WSLfcaYPP9GVTOa8CmlVODQ\npE+pAGSM2QEgIo2Adlhj+cqWKTveTymlVABwOp1RWOug9saaOHeTw+H4yb9R6Tp9SgUkEWknIl9j\njd/bAqwv8yjb7atUjRUWFnL06FF/h6FUffAC8I3D4egJ9OMUC937ko7pqyNatGhBRkbGKctER0dz\n5MgRH0VUmo7p83q93wCnAf/E+mXh0c1rjEn29nUrq6Hdfw3Fzp07mTt3LrfeGvDzhJQKGGU/B5xO\nZySw2uFwdD7Fy/xCu3friMokc7o9Ub0yCphkjPnI34GohqNt27akp6eTl5dX4dZ6SqlydQLSnU7n\nW0B/YCVwr8PhyPZvWNq9q1SgSgf8/gtCNSyhoaHExsayd+9ef4eiVF0WgtVT87LD4TgNOAE86N+Q\nLNrSp1Rgegx4QEQWGmN0kJXymQ4dOrBz5046derk71CUCkjJyckkJyefqsgeYI/D4Vjufv4pAZL0\naUufUoHpUiAe2CEic0Xk4xKPT0Tk48pWJCJXiMgSETkkIidFJFVEHhaR0DLlHhKR3SKSLSILRKS/\nt9+UCnzx8fHs3r3b32EoFbCSkpKYMmVK8aMsh8NxANjtdDq7uw+NBX7xYYjl0pY+pQJTa2Ar1pZs\nYfy2aLNxH6vKLIoWwDzgKSATGAZMAWKBuwFEZDLwCHAfkAr8FZgnIn2MMQdr+F5UHRIfH09Kik4O\nV6qG7gbedzqdYVi/y2/0czyAzt6tV/y5a4fO3q1bROTvwJ3GmGgRCQcOAv8yxvzdfT4C2AH81xjz\nqM3r9f5TSinq1ueAX7p3RaSZiAwSkbHuxyAR0aX7lbIhlrZlu2Nr6AhQVN9IoBlQ3GVsjMkGZgHj\nvHhNpZRSfuTTpE9EzhGRRUAGsByY634sBzJEZKGIjPVlTEoFKhEZLyLLgFxgN9DXffx1EbmuGvUF\ni0iEiJyO1fXwqvtUIlAI/FrmJanuc0oppeoBnyV9InIVMBs4BtyENa6ou/sxDKu/+xgwx11WqQZL\nRP4AzMRamPlWrHF8RX4Fbq5GtSeALGAhsBi43308Gsiy6a/NACJERMf+KqVUPeDLX+YO4FljzP3l\nnF8OvCsiT2MNMq/07ESl6qGHgWeMMQ+6k663Spz7BWvCRVUNByKwvmQ9BrwC3FbTQJVSStUNvkz6\nOgNfV6LcN8A9tRyLUoGuI9bQBzs5QPOqVmiMWeP+cYmIHALedn/JygCaiufsjGgg2xhTYFdfyaUK\nkpKSSEpKqmpIKoDt3LkTESE+Pt7foSilvMSXSd8WrLXHFlRQ7mI8xxYp1dDswVrR/Qebc4Ow7qea\nWO3+syNWF3Iw0JXS914ip9gk3G59KlV/pKWlsW/fPk36lKpHfJn0PQJ8KiJ9sLpuU7HWDAOIBHoC\nVwJJwBU+jEupQPQG4BCRA1hj+wCC3BOd7gcer2H9o9x/bgf2Y42nvQr4BxQv2XIhv032UA1MfHw8\nP/30k7/DUEp5kc+SPmPMTBE5E3gUmMpvy0UUyQfmA0nGmMW+ikupAPU00AF4G3C5jy3BapF71Rjz\nQmUrEpHZwHfABqxZuqOAvwAfGmO2u8s8CTwqIhnAJvd5sO5V1QDFxMSQnZ1NVlYWTZs29Xc4Sikv\n8OmsPGPMj8C5ItII6II1ZgisMUVbjTG5voxHqUBljHEBd4rIv4GzgVZYa+v9YIzZVMXqlgETgQSg\nAGt1+Acp0YpnjHlSRIKAyUBLrIlV5xhj0mv2TlRdJSJ06NCBXbt20atXL3+Ho5TyAt2Rox7RHTl8\nrzZWYheRxsBR4CpjzAxv1u0tev81DD/++CNZWVmcd955/g5FqYBVl3bkCLj1t0SkA1YyusvfsSjl\nD8aYkyKShtUqp5Tf9OrVi6NHj/o7DKWUlwRc0oc1sFywxi4p1VD9F7hHROYaY/L8HYxqmFq0aEGL\nFi38HYZSyksCMem7idK7DyjVEEUCfYDtIvI9cBAo1Z96ioXOlVJKKQ8Bl/QZY96pbFldHFb5WnJy\nMsnJyb641BVYe+4KMLrMOcFKADXpU0opVWk6kaMe0YkcvleXBvB6k95/SillqUufA0G+vJiIXCoi\nH7ofSe5j54rIWhHJEpEUEbndlzEppZRSSjUEPuveFZFrgfewtn86CswWkRuB/wFfAO9jbS/1sogU\nGmNe91VsSgUiERHgdKAbEF72vDHmZZ8HpRqkWbNmMWjQINq2bevvUJSqM5xOZzCwAtjjcDgu9Hc8\n4Nsxffdh7STwRwARmQhMA543xjxQVEhE9gF/BDTpUw2WiLTB2ne35ymKadKnfCIoKIidO3dq0qdU\n1dyLtRNSM38HUsSX3bvdgE9KPP8cayu2r8uU+xpr43elGrJnsVrEO7ifDwc6Ye1hvRno7qe4VAPU\nsWNHdu3SpVOVqiyn09keOB9rH/WAGe/ny6TvKBBb4nlMmT+LtHKXVaohGwM8AxwoOmCM2WmMeQJr\nKESlW/lE5CoR+VpE9onIcRFZISJX25R7SER2i0i2iCwQkf7eeCOq7ouPj2fXrl1+myimVB30b+Bv\n/LZ3ekDwZdL3PfC4iIwXkdFY3bdLAYeIdAEQke7AY8CPPoxLqUAUBRwyxhQCxyj95WgJMLIKdf0J\na3/re4ALgfnAByJyV1EBEZmM1Yr4T+ACIAuY5+5mVg1c8+bNCQ0N5fDhw/4ORamA53Q6LwDSHA7H\nagKolQ98O6ZvMlbX7Sz384VYTZ9fAr+KyEmgMbDDXVaphmw70N798wbgOuAr9/MLgCNVqOsCY0zJ\n8ski0hb4C/CiiIQDDwJPFE0OEZGfsO7Fu4BHq/smVP1R1NrXqlUrf4eilF9VYr3WkcBFTqfzfKxJ\neM2dTuc7DofjD76I71R8uk6fiAQBie7r/uI+FgJcDHTB+qD72hiTXYm6dJ2wMnSdPt+rrfWZRORJ\noI0x5kYRGYf15egg1n688cADxph/1aD+vwGPG2PCReQsYB6QaIzZXKLMm0B/Y8xgm9fr/dfA5OTk\nEBYWRlCQT1f6UtXkclm9ivrvVftO9TngdDrHAPc1xNm7GGNcWK0WJbmwWhMmGWN+9WU8SgUqY8yD\nJX7+VkRGApditYbPNcZ8W8NLjAA2uX9OBAqBsvdfKvD7Gl5H1RPh4R6rBqkAsWPHDvbu3UtGRgaZ\nmZlkZGRw9OhRrr76arp29ZwXuWDBAqKioujcuTPNmgXMxNL6LGC+IQfCNmxBWIPW9X9eDUVHR2Mt\n7Va91x45UpUeQ+VLxpjlwHJv1CUiZ2O1rt/oPhQNZNk03WUAESISYowp8Ma1lVLed/DgQbKysoiJ\niaFHjx5ERUURFRVFaGiobfnmzZuzefNmZs+eTfPmzencuTNdunShc+fO2jLoZQ6HYwGwwN9xFAmE\npE95SU2Stuomi6p2ici5wBAgDtgPLDPGzK1BfQnAB8CMquxzrZQKXMOGDatS+YEDBzJw4EBcLhf7\n9u1j69atLF++nC5dutRShCpQaNKnVAByT7SYAQwG0tyPNkBrEVkJXGKM2VvFOlsA32KNnZ1Q4lQG\n0FQ8B+pFA9nayqdU+Q4cOMDq1as544wzaNKkSa1dx+VysWLFCgYOHFhuC15VBQUF0b59e9q3b19x\nYVUv+D3pM8YUuAeSb66wsFINx2tY61qeboxZUnRQREYBH7rPj69sZSISgTX7NwRrNm9OidOpQDDW\nouglx/UlAhvLq3PKlCnFPyclJZGUlFTZcFQdZYwhKyurwY4Dc7lcHt2fRX8XL730EsOGDWPEiBGE\nhYV59bppaWnMmDGDxo0b07t3b68lfZVx8OBBWrdurd2+9YRPZ+96k84e9K6azvzV2bterzcbuNkY\nM93m3LXAG8aYiErWFQLMxGo1HGmM2VrmfDjWItD/Msb8w30sAmvJlleNMY/Z1Kn3XwNUWFjIU089\nxV//+lcaNWrk73B8av369SxatIhJkyYRHBzscT4jI4P58+ezfft2Ro8ezaBBg2zLVUVhYSGLFy/m\n559/5uyzz2bgwIE+HYpjjOHDDz8kOzubiy++WJfrKUdtfQ7UBr+39CmlbKUBJ8s5dxJIr0JdLwPj\nsPaBbC0irUucW2WMyXEvEfOoiGRgzer9i/v81KqFreqz4OBg2rZty+7du21nhQYau5a57OxsMjIy\naNeuXaXqKCgoYM6cOWzdupUrr7yy3EQuOjqayy67jAMHDrBw4UJ69+5do+7e3Nxc3n77bSIiIpg0\naRKRkZHVrqu6RISrr76aFStW8NZbbzFixAhGjhzp11Y/YwzHjx+nefPmxcf27t1LcHAwsbGxp3il\nAm3pU27a0lc9tdjSNwm4ExhvjNlT4ngHrEXOXzLG/LeSdW3HWtuvbJwG6GSM2eUu9xBwB9ASa6bw\nPcaYteXUqfdfA/XDDz8AcNZZZ/k5klNzuVy89957nHHGGSQkJBQf3717N5988gnt27fnzDPPpHXr\n1uXWkZmZySeffELz5s25+OKLfb5szbZt2+jUqVNATLTLzMxk1qxZnDx5kksvvfSUf2+1xeVy8fXX\nX3P06FGuu+664uMpKSnFrbAhIb5vy6pLLX2a9ClAk77qqsWk7xOstfRaPbzC4wAAH1tJREFUA6v4\nbSLHaVitfIuLigLGGHOVt2OoID69/xqoLVu28OOPPzJx4kR/h3JK33//Pfv27WPChAkeLVP5+fks\nX76cxYsX061bN5KSkoiKiipVJicnh5deeomRI0cyfPhwryReS5YsYcGCBYhIqcewYcM444wzalx/\nbTPGsGbNGtq1a0dMTEzFL/CivLw8PvnkEwCuuOKKUsMLjDF8/PHHtGrVirPPPtuncYEmfT6hHzre\npUlf9dRi0peM1RJXXt1F/1hFSd+Z3o7hVPT+a7hycnJ47rnneOCBB2o8Zq22bN68ma+//ppJkyad\nsos1NzeXJUuWsGLFCm6//XaPCSrHjx/36qSVgoICCgsLMcaUeoSGhnp98oevuVyu4iTW27Kysvjg\ngw+IjY1l/Pjxtv/vsrKyePXVV7nmmmsq3XXvLZr0+YB+6HiXJn3VU5dudm/S+69h++yzzzj77LM9\nWscCQWZmJm+88QZXXXUV8fHxlXpNXl5enU+6/G3r1q189NFHtGjRghYtWhAdHU2LFi2Ii4ujbdu2\n1a43Pz+fV155hQEDBjB69OhTJpXr169n4cKFPu/mrUufA5r0KUCTvuqqSze7N+n9pwLVBx98QKdO\nnRgxYoS/Q2lwcnJyyMjI4MiRI8WPFi1aMHr0aI+yxphKtwoePnyYli1bVljOGMNXX33F4MGDiYuL\nq3L81VWXPgc06VOAJn3VVZs3u4j0AyYDQ7F25NgHLAOeKm+Cha/o/acCVXZ2No0bNw6IyQ+qfIsX\nLyYlJYX4+Hg6duxIx44dadq0qb/DqhZN+nxAP3S8S5O+6qnFMX2XAJ8AW7DW2EsHYrD2zO0M/N4Y\n84W3r1uF+PT+U0pVW2FhIfv372fnzp3s2rWLXbt2ERERwXnnnUe3bt38HV6VaNLnA/qh412a9FVP\nLSZ9m4AU4MqS/9FFJAj4GOhrjOnh7etWIT69/5RSXmOMIS0tjYiIiDq340tdSvp0XxWlAlMH4PWy\nmZUxxgW8gbXunlJK1QsiQps2bepcwlfXaNKnVGBaCfQu51xv93ml/GrdunWkpKT47frGGDZu3IjL\n5fJbDCpwGWNYtWoV+fn5/g4lYGjSp1Rg+jNwp4g8KCI9RCTa/edkrF0z/iQiEUUPP8eqGqhmzZqx\nYMGCGg0NqYmVK1eSnJxMYWGhX66vApuIsH379uJdZJTuvatUoFrm/vMJ96O882At1ByYq+Sqei0h\nIYGwsDA2b95Mjx6+HWK6b98+5s+fz0033URoaKhPr63qjnHjxvHKK6+QmJhIx44dfXJNp9PZAXgH\na/KdAV5zOBz/8cnFK6AtfUoFppuq8Lj5VBWJSFcR+a+IrBORQhGZX065h0Rkt4hki8gCEenvxfej\n6iERYdSoUSxZssSn183MzOTjjz/m/PPPr9T6barhioiIYPz48Xz55Zfk5eX56rL5wJ8dDkdvYDhw\np9Pp7Omri5+Kzt5VgM7erS5/zdoSkVBjTKUGqojIRcCLwFKgL3DAGHNWmTKTgUeB+4BU4K9Y6wP2\nMcYctKlT7z8FWNtvTZ06lcsuu4wOHTrU+vWysrJ46623GDJkCMOHD6/166n64fPPP8flcnHRRRd5\nffeVij4HnE7nDGCqw+H43qsXrgZt6VOqjhCRIBEZKyJvAh6J2CnMMsbEG2N+D2ywqTcceBB4whjz\nsjHmB+BKrG6Ju7wRu6q/goKCGDFiBNu3b/fZ9UaNGqUJn6qS8ePH07x5c59f1+l0JgADgZ99fnEb\n2tKnAG3pqy5ftPSJyAjgGqxErA1wGPjYGHNnNer6FGhRsqVPRM4C5gGJxpjNJY6/CfQ3xgy2qUfv\nP1WsKltqKVXflPc54HQ6mwLJwN8dDscMnwdmQydyKBWA3FuwXQNcDXQEcoFGwF+AF40xBV68XCJQ\nCPxa5ngq8HsvXkfVU5rwqYYkOTmZ5OTkU5ZxOp2hwGfAe4GS8IEmfUoFDBHpgpXoXQP0BI4CX2ON\nr/sJ2AOs8nLCBxANZNk03WUAESISUgvXVEopv8vLy2PVqlUMGTKE4ODKLYKQlJREUlJS8XOn01nq\nvNPpFOBNYIPD4Xjee9HWnCZ9SgWOX4GTwAdYEyrmFU3WEJEofwamlD+4XC5Wr17NwIEDCQrSIejK\n+/Lz89myZQspKSlccskltG7d2hvVjgKuA9Y5nc7V7mOTHQ7HbG9UXhOa9CkVOHZideWOwRq3d5jS\n6/HVlgygqXgO1IsGsstr5ZsyZUrxz2W/+SpVU8YYvv76aw4fPky/fv006VO1okmTJkyYMIGVK1cy\nbdo0Tj/9dIYPH16jIQsOh+NHAnSirE7kUIBO5Kgub0/kKDFp4yqshT33AjOA74HPgSRjzMIa1H+q\niRw9jDG/ljj+JtDPGDPEph69/5StlStXYoxh8GCP+T+VZozhu+++Y9euXVx//fU0atTIixEqZS8j\nI4MZM2YgIkyYMKHSi377a+mu6gjITFSphsoYs9QYcw/QDvgdMBerm+Bzd5FJIuKRhNXQEuAYVqIJ\ngHtrtwuBb718LVXPtWnThsWLF9doP9xFixaxdetWJkyYoAmf8pno6GhuuOEGRo4cWW93edGkT6kA\nZIwpNMbMM8bcjLVMy6XAx+4/fxaR1MrWJSKNReQKEbkCK5mMKXouIo2NMTnAk8BDIvJHETkb+MT9\n8qlefWOq3mvfvj3NmzdnwwaPJSErJSUlhbVr13LdddfRuHFjL0en1KkFBQXRvXt323NHjhxh3759\nfttr2ht0TJ9SAc4YkwfMBGaKSBPgYqylXCqrDVbCCNaCy7ifG6ATsMsY86SIBAGTgZbAcuAcY0y6\nF96CamBGjhzJggUL6N27d5XHRnXv3p34+HiaNWtWS9EpVT2HDh1izpw5uFwuevbsSe/evWnbtq2/\nw6oSn4/pc7cijMNaGywa64MnA2tNsG/duwFUph4dU+RFOqaveurSWA5v0vtPnYoxhpdffpnzzz+f\nTp06+TscpbzGGMPBgwfZsGEDGzZsIDIykj/84Q915nPAZ0mfiLTAGpB+OrAd2Ahkuk9HYyWBnYBF\nwKXGmCMV1KcfOl6kSV/1aNKnlL21a9dy8uTJU26XlpeX5/V9UJXyFWMMJ0+epEmTJnXmc8CX3bv/\nwepmGmaMWW5XQEQGA++7y17nw9iUUkp5Uf/+/cs9d+TIEebNm0d+fj4TJkzwYVRKeY+IEBER4e8w\nqsSXSd8FwMTyEj4AY8wKEXkAeNt3YSmllPKF7OxsFixYQEpKCiNGjDhlK6BSyvt8mfS5gMo0f4q7\nrFJKqXpi1apVzJs3jz59+nDnnXfSpEkTf4ekVIPjy6RvJvCMiKQbY360KyAio4BngC98GJf6//bO\nPV6Oosrj3x9BEQiPC1kBAQmCYIKyqBBxWUhEDUQEXZ4LuisgImTZj+wHEcGFmwuLGkCB3QUEQ8hG\nQBLxQRABIRAeShQEREJEIYmEJGgIQUN4BMjZP6omt2/fmTsz9073zPSc7+dTn5murq5Tp3rqzOl6\nteM4TsZ0dXVx/PHHM2LEiGYXxXE6ljwXcmxG2Cbi48BzhNW6pYUcmxMWcmxN2Iz2KDP7a5X8fCJ5\nA/GFHIPDF3I4juN0Nu30P5BbT1904g6Ir5lKbtkCsJywavdWM5ubV5kcx3Ecx3E6hdw3ZzazB4AH\n8pbrOI7jOI7Tyfhr2BzHcRzHcTqAlnP6JE2RNLXZ5XCcTkPSaEmzJa2WtERST3w1m+M4jlMAWvHd\nu+OAYc0uhON0EpK6gDuBx4FDgJ2BbxEeDM9uYtEcx3GcBtFyTp+Z7dzsMjhOB3ISsAFwqJm9BMyW\ntCkwSdIFZraqucVzHMdxhkrLOX2O4zSFCcDt0eErMQOYDIwFftqUUjmO47QhPT09BwKXEEYup3R3\nd09ucpGAJszpk7SJpE9KOk3Sf8VwmqSDJA3Puzy1MGfOnMLL6urqQtKgA9xcU7otttgiV72cmtmV\nsHfmOszsGeDleK4jKOpvx/VqL1yv9qanp2cY8L/AgcBo4Oienp5RzS1VIDenT9J6ks4jbMw8C+gB\nPhdDD3Az8JykcxW8iJahE5y+F154ATMbdICDBzzf3d2NmbFy5cpc9XJqpovezdKTrKR3P83CU9Tf\njuvVXrhebc8Y4Knu7u5F3d3drwM3AJ9qcpmAfHv6uoH/ACYBI81suJltH8NwYId4rpRmSJT7cSXj\nyn0v91nLj9RlATyfm6x2qsOiU63eaj2uFFfLucGkqycf18v1quXcYNLVk4/r1fp6JdgWWJw4fjbG\nNZ08nb4TgNPM7MI4bNQHM1tsZhcBp8W0Q6KoTkSryoIVuclqpzpsI1YCm5WJ74rnylJU4+16DXxc\nrUyuV23p6snH9Wp9vRK07Dsq83z37mrgEDObXSXdR4GbzWyjKulatlKdzqJd3rk4EJLuAZaY2TGJ\nuO2BPwEHm9ktqfTe/hzHcSLJ/4Genp69gUnd3d0HxuMzgbWtsJgjz9W7c4EzJP0qtUJwHXEhxxnU\n8Jq2IvzROk4LcStwuqThifZ5FGEhxz3pxN7+HMdxKvIQ8O6enp6RwFKCLT26mQUqkWdP32jC5q8b\nALcTVgqWJo5vBowCDgBeAz5qZvNzKZjjOEjaHHiCsDnzZGAnwubMF5vZOc0sm+M4TrvR09Mzgd4t\nW67u7u7+RpOLBOTo9MG6Xf9PIuwJtiu9qwJXEpzAW4HvmFm5VYSO42SIpFGEbQY+TGiTU4BJlqeR\ncBzHcTIjV6cvbyRdARwMvMPMMlu0Ium9wHRgODAf+EylIewGyMpLp+2BacA2wFrgFjM7I0N59xB6\nfNcDFgDHmVmm+7tIugw4OeN6XASsBtbEqKPN7PeVr2h/mnEvsybv9pAnedmUvMnTLudNge9ZIdtZ\nK9nEwvxYKnAd8IEc5HwHOMvMdiH0WH4lQ1l56fQ6cLqZjQbeD3xI0qEZyvukme1hZrsDT5NtHSJp\nX2Bjsl9lZcAEM3t/DIV2+CK53sucyLs95EleNiVv8rTLeVPUe1bUdtYyNrHlnD5JH5Q0tRF5mdn9\nZvaXRuRVCUlbEfYdvC1GXQ0clpW8PHSKcp4zs4fj99eBx4DtMpS3CsIm3oQn8+VZyZK0AfAN4MtA\nHgsSOmrRQ573Mi/ybg95kpdNyZO87XLeFPGeQXHbWSvZxJZz+oAdgWObXYg62I6w8WKJxcD2TSpL\nJkjaEvg0YQFOlnJ+Rnhjy3uByzIUdQ4wxcyez1BGkpskPRpfOdgR77vO8V7mTl7twRkShbfLRado\n7axVbGKer2EbK2m/CuFoSTdJehqYSYWeEUmjJc2WtFrSEkk90XMeTHl2lnSlpMckvSnp7kHKrNqL\n00BZeepVSrcBcCNhFeeTWcoys08AWwP3A5dmIUvS7sAYM5smlX/dX4P12sfM9gD2IbyD8cvl8mo2\ned7LPMmzPeRJnjYlT/K0y3lQ1PsE2erWrHaWpU6tYhPz7HUoW3kJBmykCit/7yRsKXEIsDNhS4n1\ngLNjms8Dp8RLJprZQPv9jSasIn6AUA/95nbVIpPwNJnsfn4nfZ8wGymrFhomS9IwwtyR35jZxVnK\nKmFmayVNJ7yrMAtZ/wCMlrQwcd0CYC8zW9FovcxsafxcLelq4IvpvFqEPO9lnuTZHvIkT5uSJ3na\n5TxoiD51/rflRRa6nQw8SPPaWab3qyVsopnlEgjv6boO2I3QvVkp/DYUq9/1Z8Y8hifiTiesjNxk\nALkC1paLT3y/EbhrsDIJnvuE+P0C4LysZA2kUwZ6TQGmDlS3jZAFbA5slTh/DnBNlnWYOJ/ZbwPY\nCNg0fl8fuCb922iVkOe9bEe9YtyA7aFd9SrlV8mmtKteVLHL7aZPubybec8ytMlNa2dZ6NRqNjHP\n7uO5hIm188zs8UqB8AaAckwAbre+S+5nABsCY8tdIGkK8AxgkhZLuqp0zmLtV6FWmScD50v6A/Ae\ngoFZRyNlDaRTg2TtF+XsAxwPfFDSIzGcksykgXp1ATdL+q2k3wK7EN7BnIWsNP3ybaCsrYF7ok6P\nElamnV9D3rmT573MkzzbQ57kaVPyJE+7nAdZ2a1WuGdZ6NbsdpbR/Wopm5jn8O4twL/UkO5lYFmZ\n+F0JXarrMLNnJL0cz/00fYGZnTCIctYt08x+x9CXz9cqa6g6VZP1HsLeSL+gMXM+q+plZguBMXnI\nSl9gZsOykmVmCwjbDhSFPO9lnuTZHvIkT5uSJ3na5Txoxn9bXtSlW5u0s3p1aimbmFvlmtnlZvbh\nGpKW3s6Rpove17al03eViW8Eecp0WS6r1Smqzq5Xe1E0vYqmT5Ii6tbWOjXdo5Y0TNJdkt7d7LI4\njuM4juMUlaY7fYTJqOOATaqkW0l4jUmarnguC/KU6bJcVqtTVJ1dr/aiaHoVTZ8kRdStrXVqBaev\nVn4PjEpGKLynbyPKDwe3m0yX5bJanaLq7Hq1F0XTq2j6JCmibm2tUzs5fbcCB0ganog7irDw454C\nyHRZLqvVKarOrld7UTS9iqZPkiLq1t46NWuvmGQAxgOfBQ4nbIr4ePx+OLCh9e51sxT4OfBR4ERg\nFXDuIGVumJCRqUyX5bJaPRRVZ9fL9XJ9XLdO1qmfjs0uQKzEkcDaGN6MofT9nYl0o4DZBI96CdBD\nYjPFVpXpslxWq4ei6ux6uV6uj+vWyTqlg6ICjuM4juM4ToFppzl9juM4juM4ziBxp89xHMdxHKcD\ncKfPcRzHcRynA3Cnz3Ecx3EcpwNwp89xHMdxHKcDcKfPcRzHcRynA3Cnz3Ecx3EcpwNwp89xHMdx\nHKcDKKTTJ2mSpLWJsFTSjyXtkoGsOZJ+UEf6IyV9bqj5xGumSXowcTxGUnc9eVTJP1mHu6fObSnp\nYkmLJL0qaYmkqyW9M5VuZLz+E40q1wDlXdTg/JK/o7rujeO0CmXsYSn8vNllayckjUvU3cpEfEUb\nl7hmdB1ykveo5uscpxbWb3YBMuSvwAHx+47AucCdkkaZ2eoGyjkJeL2O9EcCWwL/N8R8IOj0tsTx\nGKCb8EqYRnERcCPwx1KEpHcA9xF+P18HniC8vuYrwEOSxpnZEw0sQ0UkHQn80cweASzG7QTsb2bf\nHWL23yW8XPvyUt6O06Yk7WEyzqmfY4A/ZJj/3sAHgcsylOF0KEV2+t4ws1/H77+OvUAPABMITkxD\nMLPfNysfM1vQCNlVWJSoxxKXA5sCu5vZshh3n6SfAA8B1wIfyKFsEJzRyZIeB94q6SzgE8B/DjVj\nM1sCLJG0aqh5OU6TeaNMOy6LpA3N7JWsC9TGPJblQ62Z/VrSRlnl73Q2hRzercBj8XNkMlLSCZLm\nxSHKRZJOT53fTdJtklZIeknSE5ImJs73GZaVtJ2km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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "fig = simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n", + "fig.suptitle('Target - smooth true')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "reg.alpha_xx = 0.001\n", + "saveModel.fileName = 'Inversion_TargMisEqnD_smoothTrue_Wxx'" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", + " ***Done using same solver as the problem***\n", + "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnD_smoothTrue_Wxx.npy'\n", + "============================ Inexact Gauss Newton ============================\n", + " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", + "-----------------------------------------------------------------------------\n", + " 0 2.00e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n", + " 1 2.00e+05 2.52e+04 3.34e-05 2.52e+04 6.20e+03 0 \n", + " 2 2.00e+05 3.46e+03 1.33e-04 3.49e+03 1.03e+03 0 Skip BFGS \n", + " 3 2.49e+04 1.72e+03 8.63e-04 1.74e+03 2.61e+02 0 Skip BFGS \n", + " 4 2.49e+04 1.02e+03 1.30e-02 1.35e+03 2.51e+02 0 Skip BFGS \n", + " 5 2.49e+04 6.65e+02 1.68e-02 1.08e+03 1.37e+02 0 \n", + " 6 3.12e+03 5.70e+02 1.88e-02 6.28e+02 1.17e+02 0 \n", + " 7 3.12e+03 3.62e+02 4.82e-02 5.13e+02 1.38e+02 0 \n", + " 8 3.12e+03 2.61e+02 5.68e-02 4.38e+02 1.98e+02 0 \n", + " 9 3.90e+02 2.09e+02 6.09e-02 2.33e+02 6.57e+01 0 \n", + " 10 3.90e+02 1.77e+02 9.89e-02 2.15e+02 1.73e+02 0 \n", + " 11 3.90e+02 1.50e+02 1.15e-01 1.95e+02 1.29e+02 1 \n", + " 12 4.87e+01 1.34e+02 1.32e-01 1.41e+02 7.79e+01 0 \n", + " 13 4.87e+01 1.16e+02 1.58e-01 1.24e+02 7.38e+01 1 \n", + " 14 4.87e+01 9.60e+01 2.56e-01 1.08e+02 1.22e+02 0 \n", + " 15 6.09e+00 9.18e+01 2.69e-01 9.34e+01 8.58e+01 0 \n", + " 16 6.09e+00 6.21e+01 3.72e-01 6.44e+01 6.72e+01 0 \n", + "------------------------- STOP! -------------------------\n", + "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.1833e+04\n", + "1 : |xc-x_last| = 5.7707e-01 <= tolX*(1+|x0|) = 5.1104e+00\n", + "0 : |proj(x-g)-x| = 6.7181e+01 <= tolG = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 6.7181e+01 <= 1e3*eps = 1.0000e-02\n", + "0 : maxIter = 50 <= iter = 17\n", + "------------------------- DONE! -------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ -4.14711455, -8.48909373, -7.52827336, -3.49932909,\n", + " -1.00331819, -2.70007482, -3.1711545 , -3.00405765,\n", + " -2.7650382 , -2.66048875, -2.66349897, -2.71130228,\n", + " -2.77717345, -2.7947959 , -2.66720877, -2.36129372,\n", + " -1.99640404, -1.65920128, -1.57027047, -1.94765493,\n", + " -2.75336364, -3.7261836 , -4.91018688, -6.25134639,\n", + " -7.7042116 , -9.04768822, -10.22631443, -11.38316489,\n", + " -12.47719857, -13.45601604, -14.16107916, -14.62352295,\n", + " -14.8899775 , -14.90719522, -14.61381454, -14.01919889,\n", + " -13.24084833, -12.17168104, -10.79110293, -9.09917198,\n", + " -7.36420672, -5.75429753, -4.14109747, -2.68084574,\n", + " -1.58271625, -1.1145424 , -1.14047311, -1.70470304,\n", + " -2.80682095, -4.11322574, -5.17550422, -5.9471688 ,\n", + " -6.43756507, -6.50919184, -6.12669826, -5.55246011,\n", + " -5.10992856, -4.95756043, -5.27203799, -6.08576941,\n", + " -7.12571201, -8.10996496, -9.02794122, -9.72407268, -10.05187723])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "moptWxx = inv.run(m_0)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "moptWxx = Out[11]" + ] + }, { "cell_type": "code", "execution_count": 14, @@ -233,9 +347,9 @@ "outputs": [ 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+m4YNadChA/3+9KcKH6cqy8jIICYmhmbNmpVeWAgRcP5cB4wxNYArgUk4d11m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\nc4JxWEdY97UPcJCZZdMMHyWMLN2Y65ndBWwFbAf8H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sdPoQ9cHp06c5dOgQZ53lPfdxWloaGzZsICsrq+iVk5PDkCFDuPjii73KFxQU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lxjycjI8HkiG7Dfbrcn2Wy2fXa7vQtwwP+R1S2QSd89wFsiMhir6zYTqJy5EofVbXUpkA6Y3dSN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\nwzAC1tInIvOA04DfqOoKD2VOBV4BVqjqb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OHe/YAzJC0K3A3sAhxPaDfvEqbIrJjIN5Fg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\n4zgdgDt9juM4juM4HcD/A+ruGYdtU+yYAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -244,19 +358,11 @@ ], "source": [ "%matplotlib inline\n", - "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n", - "plt.show()\n" + "fig = simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,moptWxx])\n", + "fig.suptitle('Target - smooth true-Wxx as 0.001')\n", + "plt.show()" ] }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, diff --git a/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD_regMesh.ipynb b/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD_regMesh.ipynb index 3f3ba509..df338faa 100644 --- a/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD_regMesh.ipynb +++ b/notebooks/scipy2015/MT1Dinversion_Scipy2015_targMisEqnD_regMesh.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -30,23 +30,23 @@ "nFreq = 31\n", "freqs = np.logspace(3,-3,nFreq)\n", "# Set mesh parameters\n", - "ct = 10\n", - "air = simpeg.Utils.meshTensor([(ct,25,1.3)])\n", - "core = np.concatenate( ( np.kron(simpeg.Utils.meshTensor([(ct,5,-1.2)]),np.ones((3,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n", - "bot = simpeg.Utils.meshTensor([(core[0],25,-1.3)])\n", + "ct = 20\n", + "air = simpeg.Utils.meshTensor([(ct,16,1.4)])\n", + "core = np.concatenate( ( np.kron(simpeg.Utils.meshTensor([(ct,10,-1.3)]),np.ones((5,))) , simpeg.Utils.meshTensor([(ct,5)]) ) )\n", + "bot = simpeg.Utils.meshTensor([(core[0],10,-1.4)])\n", "x0 = -np.array([np.sum(np.concatenate((core,bot)))])\n", "# Make the model\n", "m1d = simpeg.Mesh.TensorMesh([np.concatenate((bot,core,air))], x0=x0)\n", "\n", "# Setup model varibles\n", "active = m1d.vectorCCx<0.\n", - "layer1 = (m1d.vectorCCx<-200.) & (m1d.vectorCCx>=-600.)\n", - "layer2 = (m1d.vectorCCx<-2000.) & (m1d.vectorCCx>=-4000.)\n", + "layer1 = (m1d.vectorCCx<-500.) & (m1d.vectorCCx>=-800.)\n", + "layer2 = (m1d.vectorCCx<-3500.) & (m1d.vectorCCx>=-5000.)\n", "# Set the conductivity values\n", "sig_half = 2e-3\n", "sig_air = 1e-8\n", - "sig_layer1 = 1\n", - "sig_layer2 = .1\n", + "sig_layer1 = .2\n", + "sig_layer2 = .2\n", "# Make the true model\n", "sigma_true = np.ones(m1d.nCx)*sig_air\n", "sigma_true[active] = sig_half\n", @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -109,19 +109,19 @@ "else:\n", " d_true = survey.dpred(m_true)\n", " np.save('MT1D_dtrue.npy',d_true)\n", - " d_obs = std*abs(d_true)*np.random.randn(*d_true.shape)\n", + " d_obs = d_true + std*abs(d_true)*np.random.randn(*d_true.shape)\n", " np.save('MT1D_dobs.npy',d_obs)\n", "# Assign the dobs\n", "survey.dtrue = d_true\n", "survey.dobs = d_obs\n", - "survey.std = survey.dobs*0 + std\n", + "survey.std = np.abs(survey.dobs*std) + 0.01*np.linalg.norm(survey.dobs) #survey.dobs*0 + std\n", "# Assign the data weight\n", - "survey.Wd = 1/(abs(survey.dobs)*survey.std)" + "survey.Wd = 1/survey.std #(abs(survey.dobs)*survey.std)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -132,7 +132,7 @@ "# Define a counter\n", "C = simpeg.Utils.Counter()\n", "# Set the optimization\n", - "opt = simpeg.Optimization.InexactGaussNewton(maxIter = 50)\n", + "opt = simpeg.Optimization.InexactGaussNewton(maxIter = 30)\n", "opt.counter = C\n", "opt.LSshorten = 0.5\n", "opt.remember('xc')\n", @@ -145,9 +145,10 @@ " reg = simpeg.Regularization.Tikhonov(regMesh)\n", "else:\n", " reg = simpeg.Regularization.Tikhonov(m1d,mapping=mappingExpAct)\n", - "reg.alpha_s = 1e-6\n", + "reg.smoothModel = False\n", + "reg.alpha_s = 1e-7\n", "reg.alpha_x = 1.\n", - "\n", + "# reg.alpha_xx = .001\n", "# Inversion problem\n", "invProb = simpeg.InvProblem.BaseInvProblem(dmis, reg, opt)\n", "invProb.counter = C\n", @@ -156,14 +157,14 @@ "betaest = simpeg.Directives.BetaEstimate_ByEig(beta0_ratio=0.75)\n", "targmis = simpeg.Directives.TargetMisfit()\n", "saveModel = simpeg.Directives.SaveModelEveryIteration()\n", - "saveModel.fileName = 'Inversion_TargMisEqnDregMesh'\n", + "saveModel.fileName = 'Inversion_TargMisEqnDregMesh_smoothFalse'\n", "# Create an inversion object\n", "inv = simpeg.Inversion.BaseInversion(invProb, directiveList=[beta,betaest,targmis,saveModel]) \n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false @@ -177,18 +178,35 @@ "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", " ***Done using same solver as the problem***\n", "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n", - "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnDregMesh.npy'\n", + "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnDregMesh_smoothFalse.npy'\n", "============================ Inexact Gauss Newton ============================\n", " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", "-----------------------------------------------------------------------------\n", - " 0 5.15e+05 1.32e+06 2.95e+00 2.83e+06 3.23e+05 0 \n", - " 1 5.15e+05 1.66e+05 2.77e+00 1.59e+06 3.93e+04 0 \n", - " 2 5.15e+05 2.22e+04 2.75e+00 1.44e+06 7.05e+03 0 Skip BFGS \n", - "------------------------------------------------------------------\n", - "0 : ft = 1.4405e+06 <= alp*descent = 1.4405e+06\n", - "1 : maxIterLS = 10 <= iterLS = 10\n", - "------------------------- End Linesearch -------------------------\n", - "The linesearch got broken. Boo.\n" + " 0 1.40e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n", + " 1 1.40e+05 2.50e+04 2.22e-03 2.53e+04 5.66e+03 0 \n", + " 2 1.40e+05 3.35e+03 4.89e-03 4.04e+03 9.84e+02 0 Skip BFGS \n", + " 3 1.75e+04 1.58e+03 6.55e-03 1.69e+03 2.60e+02 0 Skip BFGS \n", + " 4 1.75e+04 7.68e+02 2.89e-02 1.28e+03 2.85e+02 0 \n", + " 5 1.75e+04 7.29e+02 2.02e-02 1.08e+03 1.38e+02 0 \n", + " 6 2.19e+03 6.63e+02 2.30e-02 7.13e+02 1.47e+02 0 \n", + " 7 2.19e+03 4.87e+02 7.57e-02 6.52e+02 2.24e+02 0 \n", + " 8 2.19e+03 4.93e+02 7.29e-02 6.52e+02 2.84e+02 0 \n", + " 9 2.74e+02 4.39e+02 6.85e-02 4.57e+02 2.08e+02 1 \n", + " 10 2.74e+02 2.65e+02 3.60e-01 3.63e+02 3.83e+02 0 \n", + " 11 2.74e+02 1.77e+02 4.10e-01 2.89e+02 2.70e+02 1 \n", + " 12 3.42e+01 1.33e+02 4.04e-01 1.47e+02 8.75e+01 0 \n", + " 13 3.42e+01 1.13e+02 4.75e-01 1.30e+02 1.15e+02 2 \n", + " 14 3.42e+01 1.00e+02 5.68e-01 1.20e+02 4.64e+01 0 \n", + " 15 4.27e+00 9.00e+01 6.16e-01 9.27e+01 1.41e+02 1 \n", + " 16 4.27e+00 8.35e+01 8.42e-01 8.71e+01 1.65e+02 1 Skip BFGS \n", + " 17 4.27e+00 7.62e+01 7.55e-01 7.94e+01 1.48e+02 2 \n", + "------------------------- STOP! -------------------------\n", + "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.1833e+04\n", + "1 : |xc-x_last| = 4.2045e+00 <= tolX*(1+|x0|) = 5.1104e+00\n", + "0 : |proj(x-g)-x| = 1.4824e+02 <= tolG = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 1.4824e+02 <= 1e3*eps = 1.0000e-02\n", + "0 : maxIter = 30 <= iter = 18\n", + "------------------------- DONE! -------------------------\n" ] } ], @@ -199,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -226,9 +244,9 @@ }, { "data": { - "image/png": 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9McDj9AJmA42Az3FG6xZ/y2wK9ARGAseA4dbaVQEcM+yfvzvGj2fNjBnEt2pF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\n6UoC9PiJSDpeT+QyvJlmrfC+GJYBHgoQd66IvAK8WcK4I+EUvB7TeXh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xJvk5jjPLdd3m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\n63OViJS3vJRGo9FoNGWilGL79u3k5+d7W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kLS1MT1PsOykraVNEfSnyW9IukZSWfGa93A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\nAAAAAElFTkSuQmCC\n", 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WLVrEeeedx2sPPMCyhx7iusWLMRWM8g0nEeHpp5+mf//+9OvXL9Lh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oxApKoVqnoB1pZp84B/AvOBn6jqBapa5WF/21X1J6oaparxqjpDVYtdtJulqimqGqmqk1V1bWvi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\ncVjD8JbMzExfFaydhrXnrgATm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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -238,16 +256,171 @@ "source": [ "%matplotlib inline\n", "simpegmt.Utils.dataUtils.plotMT1DModelData(problem,[m_0,mopt])\n", + "plt.suptitle('Target misfit-smooth False')\n", "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], + "source": [ + "reg.alpha_xx = 0.001\n", + "saveModel.fileName = 'Inversion_TargMisEqnDregMesh_smoothFalseWxx'" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", + " ***Done using same solver as the problem***\n", + "SimPEG.SaveModelEveryIteration will save your models as: '###-Inversion_TargMisEqnDregMesh_smoothFalseWxx.npy'\n", + "============================ Inexact Gauss Newton ============================\n", + " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", + "-----------------------------------------------------------------------------\n", + " 0 2.39e+05 2.18e+05 0.00e+00 2.18e+05 4.01e+04 0 \n", + " 1 2.39e+05 2.50e+04 2.21e-03 2.55e+04 5.64e+03 0 \n", + " 2 2.39e+05 3.36e+03 4.76e-03 4.50e+03 9.90e+02 0 Skip BFGS \n", + " 3 2.99e+04 1.75e+03 5.04e-03 1.91e+03 2.80e+02 0 Skip BFGS \n", + " 4 2.99e+04 9.62e+02 1.78e-02 1.49e+03 2.24e+02 0 Skip BFGS \n", + " 5 2.99e+04 7.14e+02 1.93e-02 1.29e+03 1.09e+02 0 \n", + " 6 3.74e+03 5.87e+02 2.24e-02 6.71e+02 1.34e+02 0 Skip BFGS \n", + " 7 3.74e+03 3.16e+02 5.95e-02 5.38e+02 2.60e+02 0 \n", + " 8 3.74e+03 3.28e+02 4.17e-02 4.84e+02 1.11e+02 0 \n", + " 9 4.67e+02 2.99e+02 4.41e-02 3.19e+02 1.06e+02 1 \n", + " 10 4.67e+02 2.23e+02 1.46e-01 2.91e+02 3.71e+02 0 \n", + " 11 4.67e+02 2.34e+02 1.10e-01 2.85e+02 1.88e+02 0 \n", + " 12 5.84e+01 1.71e+02 1.24e-01 1.78e+02 1.36e+02 0 \n", + " 13 5.84e+01 7.84e+01 1.68e-01 8.82e+01 8.21e+01 0 \n", + " 14 5.84e+01 6.37e+01 2.01e-01 7.54e+01 6.83e+01 1 \n", + "------------------------- STOP! -------------------------\n", + "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.1833e+04\n", + "1 : |xc-x_last| = 3.0163e+00 <= tolX*(1+|x0|) = 5.1104e+00\n", + "0 : |proj(x-g)-x| = 6.8281e+01 <= tolG = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 6.8281e+01 <= 1e3*eps = 1.0000e-02\n", + "0 : maxIter = 30 <= iter = 15\n", + "------------------------- DONE! -------------------------\n" + ] + } + ], + "source": [ + "moptWxx = inv.run(m_0)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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buGTJEiIjI+ss+HWlhInAng63zRUAngY8AXxeHAC6nGxZVgzQQmv9U120x/QE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SnFXudDXREUl1buiUtJSqIk23v6ZWiHDmRmZdUrjuqGjgF6h7Vi8k038Jt77qF1x461nr+olPIf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\nsjolXe0iSr2cbvZEyjuZzyyBdXLSylkibEq1OjF8C7K5eSFuo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structArray.view((np.float,len(structArray.dtype.names))) + +def plotIsoFreqNSimpedance(ax,freq,array,flag,par='abs',colorbar=True,colorNorm='SymLog',cLevel=True,contour=True): + + indUniFreq = np.where(freq==array['freq']) + + + x, y = array['x'][indUniFreq],array['y'][indUniFreq] + if par == 'abs': + zPlot = np.abs(array[flag][indUniFreq]) + cmap = plt.get_cmap('OrRd_r')#seismic') + level = np.logspace(0,-5,31) + clevel = np.logspace(0,-4,5) + plotNorm = colors.LogNorm() + elif par == 'real': + zPlot = np.real(array[flag][indUniFreq]) + cmap = plt.get_cmap('RdYlBu') + if cLevel: + level = np.concatenate((-np.logspace(0,-10,31),np.logspace(-10,0,31))) + clevel = np.concatenate((-np.logspace(0,-8,5),np.logspace(-8,0,5))) + else: + level = np.linspace(zPlot.min(),zPlot.max(),100) + clevel = np.linspace(zPlot.min(),zPlot.max(),10) + if colorNorm=='SymLog': + plotNorm = colors.SymLogNorm(1e-10,linscale=2) + else: + plotNorm = colors.Normalize() + elif par == 'imag': + zPlot = np.imag(array[flag][indUniFreq]) + cmap = plt.get_cmap('RdYlBu') + level = np.concatenate((-np.logspace(0,-10,31),np.logspace(-10,0,31))) + clevel = np.concatenate((-np.logspace(0,-8,5),np.logspace(-8,0,5))) + plotNorm = colors.SymLogNorm(1e-10,linscale=2) + if cLevel: + level = np.concatenate((-np.logspace(0,-10,31),np.logspace(-10,0,31))) + clevel = np.concatenate((-np.logspace(0,-8,5),np.logspace(-8,0,5))) + else: + level = np.linspace(zPlot.min(),zPlot.max(),100) + clevel = np.linspace(zPlot.min(),zPlot.max(),10) + if colorNorm=='SymLog': + plotNorm = colors.SymLogNorm(1e-10,linscale=2) + elif colorNorm=='Lin': + plotNorm = colors.Normalize() + if contour: + cs = ax.tricontourf(x,y,zPlot,levels=level,cmap=cmap,norm=plotNorm)#,extend='both') + else: + uniX,uniY = np.unique(x),np.unique(y) + X,Y = np.meshgrid(np.append(uniX-25,uniX[-1]+25),np.append(uniY-25,uniY[-1]+25)) + cs = ax.pcolor(X,Y,np.reshape(zPlot,(len(uniY),len(uniX))),cmap=cmap,norm=plotNorm) + if colorbar: + plt.colorbar(cs,cax=ax.cax,ticks=clevel,format='%1.2e') + ax.set_title(flag+' '+par,fontsize=8) + return cs + +def plotIsoFreqNSDiff(ax,freq,arrayList,flag,par='abs',colorbar=True,cLevel=True,mask=None,contourLine=True,useLog=False): + + indUniFreq0 = np.where(freq==arrayList[0]['freq']) + indUniFreq1 = np.where(freq==arrayList[1]['freq']) + seicmap = plt.get_cmap('RdYlBu')#seismic') + x, y = arrayList[0]['x'][indUniFreq0],arrayList[0]['y'][indUniFreq0] + if par == 'abs': + if useLog: + zPlot = (np.log10(np.abs(arrayList[0][flag][indUniFreq0])) - np.log10(np.abs(arrayList[1][flag][indUniFreq1])))/np.log10(np.abs(arrayList[1][flag][indUniFreq1])) + else: + zPlot = (np.abs(arrayList[0][flag][indUniFreq0]) - np.abs(arrayList[1][flag][indUniFreq1]))/np.abs(arrayList[1][flag][indUniFreq1]) + if mask: + maskInd = np.logical_or(np.abs(arrayList[0][flag][indUniFreq0])< 1e-3,np.abs(arrayList[1][flag][indUniFreq1]) < 1e-3) + zPlot = np.ma.array(zPlot) + zPlot[maskInd] = mask + if cLevel: + level = np.arange(-200,201,10) + clevel = np.arange(-200,201,25) + else: + level = np.linspace(zPlot.min(),zPlot.max(),100) + clevel = np.linspace(zPlot.min(),zPlot.max(),10) + elif par == 'real': + if useLog: + zPlot = (np.log10(np.real(arrayList[0][flag][indUniFreq0])) -np.log10(np.real(arrayList[1][flag][indUniFreq1])))/np.log10(np.abs((np.real(arrayList[1][flag][indUniFreq1])))) + else: + zPlot = (np.real(arrayList[0][flag][indUniFreq0]) -np.real(arrayList[1][flag][indUniFreq1]))/np.abs((np.real(arrayList[1][flag][indUniFreq1]))) + if mask: + maskInd = np.logical_or(np.abs(np.real(arrayList[0][flag][indUniFreq0])) < 1e-3,np.abs(np.real(arrayList[1][flag][indUniFreq1])) < 1e-3) + zPlot = np.ma.array(zPlot) + zPlot[maskInd] = mask + if cLevel: + level = np.arange(-200,201,10) + clevel = np.arange(-200,201,25) + else: + level = np.linspace(zPlot.min(),zPlot.max(),100) + clevel = np.linspace(zPlot.min(),zPlot.max(),10) + elif par == 'imag': + if useLog: + zPlot = (np.log10(np.imag(arrayList[0][flag][indUniFreq0])) -np.log10(np.imag(arrayList[1][flag][indUniFreq1])))/np.log10(np.abs((np.imag(arrayList[1][flag][indUniFreq1])))) + else: + zPlot = (np.imag(arrayList[0][flag][indUniFreq0]) -np.imag(arrayList[1][flag][indUniFreq1]))/np.abs((np.imag(arrayList[1][flag][indUniFreq1]))) + if mask: + maskInd = np.logical_or(np.abs(np.imag(arrayList[0][flag][indUniFreq0])) < 1e-3,np.abs(np.imag(arrayList[1][flag][indUniFreq1])) < 1e-3) + zPlot = np.ma.array(zPlot) + zPlot[maskInd] = mask + if cLevel: + level = np.arange(-200,201,10) + clevel = np.arange(-200,201,25) + else: + level = np.linspace(zPlot.min(),zPlot.max(),100) + clevel = np.linspace(zPlot.min(),zPlot.max(),10) + cs = ax.tricontourf(x,y,zPlot*100,levels=level*100,cmap=seicmap,extend='both') #,norm=colors.SymLogNorm(1e-2,linscale=2)) + if contourLine: + csl = ax.tricontour(x,y,zPlot*100,levels=clevel*100,colors='k') + plt.clabel(csl, fontsize=7, inline=1,fmt='%1.1e',inline_spacing=10) + if colorbar: + cb = plt.colorbar(cs,cax=ax.cax,ticks=clevel*100,format='%1.1e') + for t in cb.ax.get_yticklabels(): + t.set_rotation(60) + t.set_fontsize(8) + + ax.set_title(flag+' '+par,fontsize=8) + +def plotIsoFreqNStipper(ax,freq,array,flag,par='abs',colorbar=True,colorNorm='SymLog',cLevel=True,contour=True): + + indUniFreq = np.where(freq==array['freq']) + + x, y = array['x'][indUniFreq],array['y'][indUniFreq] + if par == 'abs': + cmap = plt.get_cmap('OrRd_r')#seismic') + zPlot = np.abs(array[flag][indUniFreq]) + if cLevel: + level = np.logspace(-4,0,33) + clevel = np.logspace(-4,0,5) + else: + level = np.linspace(zPlot.min(),zPlot.max(),100) + clevel = np.linspace(zPlot.min(),zPlot.max(),10) + if colorNorm=='SymLog': + plotNorm = colors.LogNorm() + else: + plotNorm = colors.Normalize() + elif par == 'real': + cmap = plt.get_cmap('RdYlBu') + zPlot = np.real(array[flag][indUniFreq]) + if cLevel: + level = np.concatenate((-np.logspace(0,-4,33),np.logspace(-4,0,33))) + clevel = np.concatenate((-np.logspace(0,-4,5),np.logspace(-4,0,5))) + else: + level = np.linspace(zPlot.min(),zPlot.max(),100) + clevel = np.linspace(zPlot.min(),zPlot.max(),10) + if colorNorm=='SymLog': + plotNorm = colors.SymLogNorm(1e-4,linscale=2) + else: + plotNorm = colors.Normalize() + elif par == 'imag': + cmap = plt.get_cmap('RdYlBu') + zPlot = np.imag(array[flag][indUniFreq]) + if cLevel: + level = np.concatenate((-np.logspace(0,-4,33),np.logspace(-4,0,33))) + clevel = np.concatenate((-np.logspace(0,-4,5),np.logspace(-4,0,5))) + else: + level = np.linspace(zPlot.min(),zPlot.max(),100) + clevel = np.linspace(zPlot.min(),zPlot.max(),10) + if colorNorm=='SymLog': + plotNorm = colors.SymLogNorm(1e-4,linscale=2) + else: + plotNorm = colors.Normalize() + if contour: + cs = ax.tricontourf(x,y,zPlot,levels=level,cmap=cmap,norm=plotNorm)#,extend='both') + else: + uniX,uniY = np.unique(x),np.unique(y) + X,Y = np.meshgrid(np.append(uniX-25,uniX[-1]+25),np.append(uniY-25,uniY[-1]+25)) + cs = ax.pcolor(X,Y,np.reshape(zPlot,(len(uniY),len(uniX))),levels=level,cmap=cmap,norm=plotNorm,edgecolors='k', linewidths=0.5) + if colorbar: + plt.colorbar(cs,cax=ax.cax,ticks=clevel,format='%1.2e') + ax.set_title(flag+' '+par,fontsize=8) + +def plotIsoStaImpedance(ax,loc,array,flag,par='abs',pSym='s',pColor=None): + + appResFact = 1/(8*np.pi**2*10**(-7)) + treshold = 1.0 # 1 meter + indUniSta = np.sqrt(np.sum((rec2nd(array[['x','y']])-loc)**2,axis=1)) < treshold + freq = array['freq'][indUniSta] + + if par == 'abs': + zPlot = np.abs(array[flag][indUniSta]) + elif par == 'real': + zPlot = np.real(array[flag][indUniSta]) + elif par == 'imag': + zPlot = np.imag(array[flag][indUniSta]) + elif par == 'res': + zPlot = (appResFact/freq)*np.abs(array[flag][indUniSta])**2 + elif par == 'phs': + zPlot = np.arctan2(array[flag][indUniSta].imag,array[flag][indUniSta].real)*(180/np.pi) + + if not pColor: + if 'xx' in flag: + lab = 'XX' + pColor = 'g' + elif 'xy' in flag: + lab = 'XY' + pColor = 'r' + elif 'yx' in flag: + lab = 'YX' + pColor = 'b' + elif 'yy' in flag: + lab = 'YY' + pColor = 'y' + + ax.plot(freq,zPlot,color=pColor,marker=pSym,label=flag) + + +def plotPsudoSectNSimpedance(ax,sectDict,array,flag,par='abs',colorbar=True,colorNorm='None',cLevel=None,contour=True): + + indSect = np.where(sectDict.values()[0]==array[sectDict.keys()[0]]) + + # Define the plot axes + if 'x' in sectDict.keys()[0]: + x = array['y'][indSect] + else: + x = array['x'][indSect] + y = array['freq'][indSect] + + if par == 'abs': + zPlot = np.abs(array[flag][indSect]) + cmap = plt.get_cmap('OrRd_r')#seismic') + if cLevel: + level = np.logspace(0,-5,31,endpoint=True) + clevel = np.logspace(0,-4,5,endpoint=True) + else: + level = np.linspace(zPlot.min(),zPlot.max(),100,endpoint=True) + clevel = np.linspace(zPlot.min(),zPlot.max(),10,endpoint=True) + + elif par == 'ares': + zPlot = np.abs(array[flag][indSect])**2/(8*np.pi**2*10**(-7)*array['freq'][indSect]) + cmap = plt.get_cmap('RdYlBu')#seismic) + if cLevel: + zMax = np.log10(cLevel[1]) + zMin = np.log10(cLevel[0]) + else: + zMax = (np.ceil(np.log10(np.abs(zPlot).max()))) + zMin = (np.floor(np.log10(np.abs(zPlot).min()))) + level = np.logspace(zMin,zMax,(zMax-zMin)*8+1,endpoint=True) + clevel = np.logspace(zMin,zMax,(zMax-zMin)*2+1,endpoint=True) + plotNorm = colors.LogNorm() + + elif par == 'aphs': + zPlot = np.arctan2(array[flag][indSect].imag,array[flag][indSect].real)*(180/np.pi) + cmap = plt.get_cmap('RdYlBu')#seismic) + if cLevel: + zMax = cLevel[1] + zMin = cLevel[0] + else: + zMax = (np.ceil(zPlot).max()) + zMin = (np.floor(zPlot).min()) + level = np.arange(zMin,zMax+.1,1) + clevel = np.arange(zMin,zMax+.1,10) + plotNorm = colors.Normalize() + + elif par == 'real': + zPlot = np.real(array[flag][indSect]) + cmap = plt.get_cmap('Spectral') #('RdYlBu') + if cLevel: + zMax = np.log10(cLevel[1]) + zMin = np.log10(cLevel[0]) + else: + zMax = (np.ceil(np.log10(np.abs(zPlot).max()))) + zMin = (np.floor(np.log10(np.abs(zPlot).min()))) + level = np.concatenate((-np.logspace(zMax,zMin-.125,(zMax-zMin)*8+1,endpoint=True),np.logspace(zMin-.125,zMax,(zMax-zMin)*8+1,endpoint=True))) + clevel = np.concatenate((-np.logspace(zMax,zMin,(zMax-zMin)*1+1,endpoint=True),np.logspace(zMin,zMax,(zMax-zMin)*1+1,endpoint=True))) + plotNorm = colors.SymLogNorm(np.abs(level).min(),linscale=0.1) + elif par == 'imag': + zPlot = np.imag(array[flag][indSect]) + cmap = plt.get_cmap('Spectral') #('RdYlBu') + + if cLevel: + zMax = np.log10(cLevel[1]) + zMin = np.log10(cLevel[0]) + else: + zMax = (np.ceil(np.log10(np.abs(zPlot).max()))) + zMin = (np.floor(np.log10(np.abs(zPlot).min()))) + level = np.concatenate((-np.logspace(zMax,zMin-.125,(zMax-zMin)*8+1,endpoint=True),np.logspace(zMin-.125,zMax,(zMax-zMin)*8+1,endpoint=True))) + clevel = np.concatenate((-np.logspace(zMax,zMin,(zMax-zMin)*1+1,endpoint=True),np.logspace(zMin,zMax,(zMax-zMin)*1+1,endpoint=True))) + plotNorm = colors.SymLogNorm(np.abs(level).min(),linscale=0.1) + + if colorNorm=='SymLog': + plotNorm = colors.SymLogNorm(np.abs(level).min(),linscale=0.1) + elif colorNorm=='Lin': + plotNorm = colors.Normalize() + elif colorNorm=='Log': + plotNorm = colors.LogNorm() + if contour: + cs = ax.tricontourf(x,y,zPlot,levels=level,cmap=cmap,norm=plotNorm)#,extend='both') + else: + uniX,uniY = np.unique(x),np.unique(y) + X,Y = np.meshgrid(np.append(uniX-25,uniX[-1]+25),np.append(uniY-25,uniY[-1]+25)) + cs = ax.pcolor(X,Y,np.reshape(zPlot,(len(uniY),len(uniX))),cmap=cmap,norm=plotNorm) + if colorbar: + csB = plt.colorbar(cs,cax=ax.cax,ticks=clevel,format='%1.2e') + # csB.on_mappable_changed(cs) + ax.set_title(flag+' '+par,fontsize=8) + return cs, csB + return cs,None + + +def plotPsudoSectNSDiff(ax,sectDict,arrayList,flag,par='abs',colorbar=True,colorNorm='SymLog',cLevel=None,contour=True,mask=None,useLog=False): + + def sortInArr(arr): + return np.sort(arr,order=['freq','x','y','z']) + # Find the index for the slice + indSect0 = np.where(sectDict.values()[0]==arrayList[0][sectDict.keys()[0]]) + indSect1 = np.where(sectDict.values()[0]==arrayList[1][sectDict.keys()[0]]) + # Extract and sort the mats + arr0 = sortInArr(arrayList[0][indSect0]) + arr1 = sortInArr(arrayList[1][indSect1]) + + # Define the plot axes + if 'x' in sectDict.keys()[0]: + x0 = arr0['y'] + x1 = arr1['y'] + else: + x0 = arr0['x'] + x1 = arr1['x'] + y0 = arr0['freq'] + y1 = arr1['freq'] + + + if par == 'abs': + if useLog: + zPlot = (np.log10(np.abs(arr0[flag])) - np.log10(np.abs(arr1[flag])))/np.log10(np.abs(arr1[flag])) + else: + zPlot = (np.abs(arr0[flag]) - np.abs(arr1[flag]))/np.abs(arr1[flag]) + if mask: + maskInd = np.logical_or(np.abs(arr0[flag])< 1e-3,np.abs(arr1[flag]) < 1e-3) + zPlot = np.ma.array(zPlot) + zPlot[maskInd] = mask + cmap = plt.get_cmap('RdYlBu')#seismic) + elif par == 'ares': + arF = 1/(8*np.pi**2*10**(-7)) + if useLog: + zPlot = (np.log10((arF/arr0['freq'])*np.abs(arr0[flag])**2) - np.log10((arF/arr1['freq'])*np.abs(arr1[flag])**2))/np.log10((arF/arr1['freq'])*np.abs(arr1[flag])**2) + else: + zPlot = ((arF/arr0['freq'])*np.abs(arr0[flag])**2 - (arF/arr1['freq'])*np.abs(arr1[flag])**2)/((arF/arr1['freq'])*np.abs(arr1[flag])**2) + if mask: + maskInd = np.logical_or(np.abs(arr0[flag])< 1e-3,np.abs(arr1[flag]) < 1e-3) + zPlot = np.ma.array(zPlot) + zPlot[maskInd] = mask + cmap = plt.get_cmap('Spectral')#seismic) + + elif par == 'aphs': + if useLog: + zPlot = (np.log10(np.arctan2(arr0[flag].imag,arr0[flag].real)*(180/np.pi)) - np.log10(np.arctan2(arr1[flag].imag,arr1[flag].real)*(180/np.pi)) )/np.log10(np.arctan2(arr1[flag].imag,arr1[flag].real)*(180/np.pi)) + else: + zPlot = ( np.arctan2(arr0[flag].imag,arr0[flag].real)*(180/np.pi) - np.arctan2(arr1[flag].imag,arr1[flag].real)*(180/np.pi) )/(np.arctan2(arr1[flag].imag,arr1[flag].real)*(180/np.pi)) + if mask: + maskInd = np.logical_or(np.abs(arr0[flag])< 1e-3,np.abs(arr1[flag]) < 1e-3) + zPlot = np.ma.array(zPlot) + zPlot[maskInd] = mask + cmap = plt.get_cmap('Spectral')#seismic) + elif par == 'real': + if useLog: + zPlot = (np.log10(arr0[flag].real) - np.log10(arr1[flag].real))/np.log10(arr1[flag].real) + else: + zPlot = (arr0[flag].real - arr1[flag].real)/arr1[flag].real + if mask: + maskInd = np.logical_or(arr0[flag].real< 1e-3,arr1[flag].real < 1e-3) + zPlot = np.ma.array(zPlot) + zPlot[maskInd] = mask + cmap = plt.get_cmap('Spectral') #('Spectral') + + elif par == 'imag': + if useLog: + zPlot = (np.log10(arr0[flag].imag) - np.log10(arr1[flag].imag))/np.log10(arr1[flag].imag) + else: + zPlot = (arr0[flag].imag - arr1[flag].imag)/arr1[flag].imag + if mask: + maskInd = np.logical_or(arr0[flag].imag< 1e-3,arr1[flag].imag < 1e-3) + zPlot = np.ma.array(zPlot) + zPlot[maskInd] = mask + cmap = plt.get_cmap('Spectral') #('RdYlBu') + + if cLevel: + zMax = np.log10(cLevel[1]) + zMin = np.log10(cLevel[0]) + else: + zMax = (np.ceil(np.log10(np.abs(zPlot).max()))) + zMin = (np.floor(np.log10(np.abs(zPlot).min()))) + + + if colorNorm=='SymLog': + level = np.concatenate((-np.logspace(zMax,zMin-.125,(zMax-zMin)*8+1,endpoint=True),np.logspace(zMin-.125,zMax,(zMax-zMin)*8+1,endpoint=True))) + clevel = np.concatenate((-np.logspace(zMax,zMin,(zMax-zMin)*1+1,endpoint=True),np.logspace(zMin,zMax,(zMax-zMin)*1+1,endpoint=True))) + plotNorm = colors.SymLogNorm(np.abs(level).min(),linscale=0.1) + elif colorNorm=='Lin': + if cLevel: + level = np.arange(cLevel[0],cLevel[1]+.1,(cLevel[1] - cLevel[0])/50.) + clevel = np.arange(cLevel[0],cLevel[1]+.1,(cLevel[1] - cLevel[0])/10.) + else: + level = np.arange(zPlot.min(),zPlot.max(),(zPlot.max() - zPlot.min())/50.) + clevel = np.arange(zPlot.min(),zPlot.max(),(zPlot.max() - zPlot.min())/10.) + plotNorm = colors.Normalize() + elif colorNorm=='Log': + level = np.logspace(zMin-.125,zMax,(zMax-zMin)*8+1,endpoint=True) + clevel = np.logspace(zMin,zMax,(zMax-zMin)*2+1,endpoint=True) + plotNorm = colors.LogNorm() + if contour: + cs = ax.tricontourf(x0,y0,zPlot*100,levels=level*100,cmap=cmap,norm=plotNorm,extend='both')#,extend='both') + else: + uniX,uniY = np.unique(x0),np.unique(y0) + X,Y = np.meshgrid(np.append(uniX-25,uniX[-1]+25),np.append(uniY-25,uniY[-1]+25)) + cs = ax.pcolor(X,Y,np.reshape(zPlot,(len(uniY),len(uniX))),cmap=cmap,norm=plotNorm) + if colorbar: + csB = plt.colorbar(cs,cax=ax.cax,ticks=clevel*100,format='%1.2e') + # csB.on_mappable_changed(cs) + ax.set_title(flag+' '+par + ' diff',fontsize=8) + return cs, csB + return cs,None