notebook_adv_diff_SimpleAdj.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simplified Adjoint Notebook\n",
    "\n",
    "In this notebook, we implement the adjoint idea in a very basic, unelegant manner. But this is the only way to go before we find out more about nutils' inner mechanisms.\n",
    "Here, we one extra domain for every local adjoint problem to be solved. A union of these domains is supposed to form the total domain. Using a separate domain for each problem allows us to utilize nutils's boundary and interface topology functionality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nutils import *\n",
    "import numpy, unittest\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import collections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting problem specific parameters\n",
    "\n",
    "We start with a domain of only two elements for our problem. This leaves us with one internal and two external nodes.\n",
    "Hence, we will have to solve two external Adjoint problems (restricted to one element only) and one internal Adjoint problem (restricted to two elements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "nelems = 2         # number of elements of complete domain\n",
    "nelemsExt = 1       # number of elements of external Adjoint problems\n",
    "nelemsInt = 2       # number of elements of internal Adjoint problems\n",
    "degree = 1\n",
    "tol  = 1e-5\n",
    "ndims = 1\n",
    "extent = 1\n",
    "v = 200           # convective velocity\n",
    "k = 1             # diffusivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare the different domains\n",
    "\n",
    "As we solely concentrate on the 2-element case here, we assign and create every domain individually. In a next step, this shall be generalized to an arbitrary number of elements using iterable structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "domainTot, geomTot   = mesh.rectilinear([numpy.linspace(0,extent,nelems+1)]*ndims)          # [0, 1]\n",
    "domainExt1, geomExt1 = mesh.rectilinear([numpy.linspace(0,extent/2,nelemsExt+1)]*ndims)        # [0, 0.5]\n",
    "domainExt2, geomExt2 = mesh.rectilinear([numpy.linspace(extent/2,extent,nelemsExt+1)]*ndims)   # [0.5, 1]\n",
    "domainInt, geomInt   = mesh.rectilinear([numpy.linspace(0,extent,nelemsInt+1)]*ndims)          # [0, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create namespace\n",
    "\n",
    "Put the necessary variables and parameters in a nutils Namespace for easier access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare residual\n",
    "ns = function.Namespace()\n",
    "ns.pi = numpy.pi\n",
    "ns.k = k\n",
    "ns.v = v\n",
    "h = extent / nelems\n",
    "ns.h = h\n",
    "ns.ext = extent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assign geometries and create bases\n",
    "\n",
    "In this section, we create geometry variables corresponding with every domain. Furthermore, we create bases for every problem to be solved **from scratch** (this is different from the basis slicing approach previously attempted). Every basis is then associated with a discrete solution vector. It is these discrete solution vectors that will eventually build the perturbations for our primal problem.\n",
    "\n",
    "Note that the **primal** base is **CG** whereas our **perturbation** bases are **DG**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign names to geometries\n",
    "ns.x = geomTot\n",
    "\n",
    "# Create individual bases\n",
    "ns.basisTot  = domainTot.basis('lagrange', degree=degree)\n",
    "ns.basisTot0 = domainExt1.basis('lagrange', degree=degree)        # corresponds to phi_1\n",
    "ns.basisTot1 = domainInt.basis('lagrange', degree=degree)         # corresponds to phi_2\n",
    "ns.basisTot2 = domainExt2.basis('lagrange', degree=degree)        # corresponds to phi_3\n",
    "ns.basisExt1 = domainExt1.basis('discont', degree=degree)         # corresponds to ψ1\n",
    "ns.basisExt2 = domainExt2.basis('discont', degree=degree)         # corresponds to ψ3\n",
    "ns.basisInt  = domainInt.basis('discont', degree=degree)          # corresponds to ψ2\n",
    "\n",
    "# Build solution functions\n",
    "ns.u  = 'basisTot_n ?lhs_n'\n",
    "ns.uExt1 = 'basisExt1_n ?lhsExt1_n'\n",
    "ns.uExt2 = 'basisExt2_n ?lhsExt2_n'\n",
    "ns.uInt  = 'basisInt_n ?lhsInt_n'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stabilization  and method choice variables\n",
    "\n",
    "In this section, the user can adjust some stabilization parameters\n",
    "\n",
    "1. `beta` and `gamma`: Setting  `beta` and `gamma` in different manners corresponds to different methods. `beta = 1` and `gamma = 0` corresponds to the D*G method of Oden, Babuska and Baumann*, which is stable for polynomials of degree two and higher. Setting `gamma > 0` stabilizes the formulation for linear polynomials and corresponds to the *interior penalty collocation method*. If you encounter instabilities, study the behaviour for high and zero `gamma`s.\n",
    "2. `C` determines the strength of the upwinding of the advective DG fluxes. For conservative upwinding, pick `C = v` (according to *Brezzi* it is `C = v/2`, but the 0.5 factor is already included in our implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tuning parameters\n",
    "ns.beta = 1             # beta = 1 creates skew-symmetric bilinear form (more stable in this case)\n",
    "ns.gamma = 100           # DG stabilization (especially for linear degree)\n",
    "ns.C = ' v '            # Advection Upwinding coefficient \n",
    "\n",
    "# DG Stabilization parameters (all the same for an equidistant mesh)\n",
    "ns.tauZero = ' gamma k / h'\n",
    "ns.tauN    = ' gamma k / h'\n",
    "ns.tauA    = ' gamma k / h'\n",
    "\n",
    "# SUPG stabilization parameter\n",
    "if(numpy.exp(v/k) == float('inf')):\n",
    "    ns.tau = ' h / (2 v)'\n",
    "else:\n",
    "    ns.peclet = ' 0.5 v h / k'\n",
    "    ns.tau = ' ((cosh(peclet) / sinh(peclet)) - (1 / peclet)) h / (2 v) ' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boundary conditions and analytical solution\n",
    "\n",
    "In this section, boundary conditions for the various problems are set.\n",
    "Furthermore, the analytical solution to the primal problem is defined here, in order to enable an evaluation of the success of the method later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Source term and BCs for primal problem\n",
    "ns.f = 0\n",
    "ns.gl = 1\n",
    "ns.gr = 0\n",
    "\n",
    "# BCs for adjoint problems (all = 0 as we would like to maintain the interpolation property)\n",
    "ns.glInt = 0\n",
    "ns.grInt = 0\n",
    "ns.glExt1 = 0\n",
    "ns.grExt1 = 0\n",
    "ns.glExt2 = 0\n",
    "ns.grExt2 = 0\n",
    "\n",
    "\n",
    "# Analytical solution of primal problem\n",
    "# Catch potential overflow exceptions\n",
    "if(numpy.exp(v/k) == float('inf')):\n",
    "    # exp(v/k) -> infinity (triggers overflow exception)\n",
    "    # Find expression for ns.g\n",
    "    pass\n",
    "else:\n",
    "    ns.analytical = '( (gr - gl) / (exp(v / k) - 1) ) (exp( (v / k) x_0) - 1) + gl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BCs and analytical solution\n",
    "#ns.z = 2\n",
    "#ns.gl  = 'cos( z pi x_0 )'\n",
    "#ns.gr  = 'cos( z pi x_0 )'\n",
    "#ns.dgl = '-(z pi) sin( z pi x_0 )'\n",
    "#ns.dgr = '-(z pi) sin( z pi x_0 )'\n",
    "#ns.ddg = '-(z pi)^2 cos( z pi x_0 )'\n",
    "\n",
    "#ns.f   = ' - ddg'\n",
    "\n",
    "# u(x) = [(gr-gl)/(exp(v/k) - 1)] [exp((v/k) x) - 1] + gl\n",
    "# u'(x)= (v/k)[(gr-gl)/(exp(v/k) - 1] [exp((v/k) x)]\n",
    "#   with u(0) = gl, u(extent) = gr "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solution of the adjoint problems\n",
    "\n",
    "In this section, we actually solve the local adjoint problems. For each node of our primal domain - there are three of these in total - we solve an affiliated adjoint problem.\n",
    "\n",
    "At first, we take a look at the **primal problem** to be solved and the adjoint problems connected with that. We would like to solve the following primal BVP (an advection-diffusion-equation with a zero RHS):\n",
    ">> a u,x - ĸ u,xx = 0 on \\[ 0, 1\\]<br>\n",
    ">> u(0) = 1 <br>\n",
    ">> u(1) = 0 <br>\n",
    "\n",
    "The **associated weak form BVP** for the polynomial trial space V and the polynomial test space W is (V and W are both subspaces of the infinite-dimensional Hilbert Space U). \n",
    "> Find u in V such that: \n",
    ">> a(u, w) + b(u, w) = l(w) for all w in W\n",
    "\n",
    "Here, a is a positive definite and symmetric, b is a skew symmetric bilinear form, and l is a linear form on U.\n",
    "Given a basis {φ} ... (will complete this part later) **Adjoint problems**\n",
    "> Find φi, i = 1,2, ..., n such that\n",
    ">> a(u', ψi) + b(u', ψi) + b(u', φi) for all u' in V' \n",
    "\n",
    "**IMPORTANT NOTE: We assume the support of the perturbations to match with the support of the corresponding primal basis functions.**\n",
    "\n",
    "### Leftmost node adjoint problem\n",
    "We start with the adjoint problem corresponding to the leftmost node. Here we solve the system \n",
    "> a(ψ1, uExt1) + b(ψ1, uExt1) + b(ψ1, φ1).\n",
    "\n",
    "Here, pert1 is the actual stabilizing perturbation to be solved for. It is defined as the inner product of ψ1 and the corresponding coefficient vector lhsExt1, i.e.\n",
    "> uExt1 = <ψ1, lhsExt1>. There are some subtleties in here, though.\n",
    "\n",
    "All we have to do is insert the correct variables into the advective and diffusive parts of our well-known formulation (check, e.g. the notebook_adv_diff_Hughes).\n",
    "\n",
    "**NOTE: We will have to figure out how to add SUPG stabilization. In order to do this, we have to find the strong form corresponding to the weak adjoint problem**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ns.x = geomExt1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# construct residual\n",
    "# Element integrals\n",
    "# Diffusive part and RHS\n",
    "#resExt1 = domainExt1.integral('k uExt1_,0 basisExt1_n,0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part perturbation\n",
    "#resExt1 += domainExt1.integral('-v uExt1_,0 basisExt1_n' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part perturbation\n",
    "    #res += domain.integral('-v basis_n,0 u' @ ns, geometry=ns.x, degree=5)\n",
    "# Advective part primal basis\n",
    "#resExt1 += domainExt1.integral('-v basisTot_0,0 basisExt1_n' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part primal basis\n",
    "    #res += domain.integral('-v basis_n,0 u' @ ns, geometry=ns.x, degree=5)\n",
    "# SUPG stabilized part (will be added later)\n",
    "#res += domain.integral(' tau v basis_n,0 (v u_,0 - k (u_,0)_,0 - f )' @ ns, geometry=ns.x, degree=5)\n",
    "\n",
    "# Interface integrals\n",
    "# Diffusive part\n",
    "#    part one                      |\n",
    "#                                  v  minus might be necessary here because of jump definitions (likewise on second +)\n",
    "#resExt1 += domainExt1.interfaces.integral(' [uExt1] n_0 k {basisExt1_n,0} + beta k {uExt1_,0} [basisExt1_n] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #                                  v  minus might be necessary here because of jump definitions (likewise on second +)\n",
    "    #res += domain.interfaces.integral(' [basis_n] n_0 k {u_,0} + beta k {basis_n,0} [u] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#    part two (stabilized)\n",
    "#resExt1 += domainExt1.interfaces.integral(' tauA [uExt1] n_0 [basisExt1_n] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #    part two (stabilized)\n",
    "    #res += domain.interfaces.integral(' tauA [basis_n] n_0 [u] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part perturbation\n",
    "#resExt1 += domainExt1.interfaces.integral('- v [uExt1] n_0 ({basisExt1_n} - .5 C [basisExt1_n] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part perturbation\n",
    "    #res += domain.interfaces.integral('- v [basis_n] n_0 ({u} - .5 C [u] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "# Advective part primal basis\n",
    "#resExt1 += domainExt1.interfaces.integral('- v [basisTot_0] n_0 ({basisExt1_n} - .5 C [basisExt1_n] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part primal basis\n",
    "    #res += domain.interfaces.integral('- v [basis_n] n_0 ({u} - .5 C [u] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "\n",
    "# Boundary integrals LHS\n",
    "#    part one\n",
    "#resExt1 += domainExt1.boundary.integral('- k uExt1 basisExt1_n,0 + beta k uExt1_,0 basisExt1_n ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #    part one\n",
    "    #res += domain.boundary.integral('- k basis_n u_,0 + beta k basis_n,0 u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#    part two (stabilized)\n",
    "#resExt1 += domainExt1.boundary['left'].integral(' tauZero uExt1 basisExt1_n ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#resExt1 += domainExt1.boundary['right'].integral(' tauN uExt1 basisExt1_n ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #    part two (stabilized)\n",
    "    #res += domain.boundary['left'].integral(' tauZero basis_n u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res += domain.boundary['right'].integral(' tauN basis_n u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "\n",
    "# Boundary integrals RHS (weakly imposed boundary conditions) (including stabilization)\n",
    "# Diffusive part (keep an eye on the signs)\n",
    "#resExt1 += domainExt1.boundary['left'].integral(' ( k basisExt1_n,0 - tauZero basisExt1_n) glExt1 ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#resExt1 += domainExt1.boundary['right'].integral(' ( k basisExt1_n,0 - tauN basisExt1_n) glExt1 ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    # Diffusive part\n",
    "    #res -= domain.boundary['left'].integral(' ( beta k basis_n,0 + tauZero basis_n) gl ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res -= domain.boundary['right'].integral(' ( beta k basis_n,0 + tauN basis_n) gr ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part perturbation\n",
    "#resExt1 += domainExt1.boundary['left'].integral('v uExt1 n_0 (0.5 (glExt1 + basisExt1_n) - .5 C (glExt1 - basisExt1_n) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#resExt1 += domainExt1.boundary['right'].integral('v uExt1 n_0 (0.5 (grExt1 + basisExt1_n) - .5 C (grExt1 - basisExt1_n) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    # Advective part perturbation\n",
    "    #res += domain.boundary['left'].integral('v basis_n n_0 (0.5 (gl + u) - .5 C (gl - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res += domain.boundary['right'].integral('v basis_n n_0 (0.5 (gr + u) - .5 C (gr - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part primal basis\n",
    "#resExt1 += domainExt1.boundary['left'].integral('v basisTot_0 n_0 (0.5 (glExt1 + basisExt1_n) - .5 C (glExt1 - basisExt1_n) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#resExt1 += domainExt1.boundary['right'].integral('v basisTot_0 n_0 (0.5 (grExt1 + basisExt1_n) - .5 C (grExt1 - basisExt1_n) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    # Advective part primal basis\n",
    "    #res += domain.boundary['left'].integral('v basis_n n_0 (0.5 (gl + u) - .5 C (gl - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res += domain.boundary['right'].integral('v basis_n n_0 (0.5 (gr + u) - .5 C (gr - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: the cell above and below this text differ only in one respect. In the cell above, we explicitly upwind the new test functions, whereas we explicitly downwind the new unknown. This is of interest when it comes to the boundary integral expressions. Depending on which part we up- or downwind, we impose the weak BCs to different constituents of the system. The cell above applies the BCs weakly to the new test functions (should be pretty useless).\n",
    "\n",
    "The cell below, however, applies the BCs weakly to the new unknown, which is what we want. The difference between upwinding and downwinding is simply a matter of switching the sign before the upwinding factor C. Note, however, that although the approach in the cell below is intuitively nicer, we might actually cause problems by potentially breaking such things as elliptic regularity (which I do not know too much about).\n",
    "\n",
    "**Residual for SUPG**: ĸ psi,xx + v(psi,x + phi,x) = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct residual\n",
    "# Element integrals\n",
    "# Diffusive part and RHS\n",
    "resExt1 = domainExt1.integral('k uExt1_,0 basisExt1_n,0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part perturbation\n",
    "resExt1 += domainExt1.integral('-v uExt1_,0 basisExt1_n' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part perturbation\n",
    "    #res += domain.integral('-v basis_n,0 u' @ ns, geometry=ns.x, degree=5)\n",
    "# Advective part primal basis\n",
    "resExt1 += domainExt1.integral('-v basisTot0_0,0 basisExt1_n' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part primal basis\n",
    "    #res += domain.integral('-v basis_n,0 u' @ ns, geometry=ns.x, degree=5)\n",
    "# SUPG stabilized part (will be added later)\n",
    "resExt1 -= domainExt1.integral(' tau v basisExt1_n,0 (k (uExt1_,0)_,0 + v (uExt1_,0 + basisTot0_0,0) )' @ ns, geometry=ns.x, degree=5)\n",
    "\n",
    "# Interface integrals\n",
    "# Diffusive part\n",
    "#    part one                      |\n",
    "#                                  v  minus might be necessary here because of jump definitions (likewise on second +)\n",
    "#resExt1 += domainExt1.interfaces.integral(' [uExt1] n_0 k {basisExt1_n,0} + beta k {uExt1_,0} [basisExt1_n] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #                                  v  minus might be necessary here because of jump definitions (likewise on second +)\n",
    "    #res += domain.interfaces.integral(' [basis_n] n_0 k {u_,0} + beta k {basis_n,0} [u] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#    part two (stabilized)\n",
    "#resExt1 += domainExt1.interfaces.integral(' tauA [uExt1] n_0 [basisExt1_n] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #    part two (stabilized)\n",
    "    #res += domain.interfaces.integral(' tauA [basis_n] n_0 [u] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part perturbation\n",
    "#resExt1 += domainExt1.interfaces.integral('- v [uExt1] n_0 ({basisExt1_n} - .5 C [basisExt1_n] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part perturbation\n",
    "    #res += domain.interfaces.integral('- v [basis_n] n_0 ({u} - .5 C [u] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "# Advective part primal basis\n",
    "#resExt1 += domainExt1.interfaces.integral('- v [basisTot_0] n_0 ({basisExt1_n} - .5 C [basisExt1_n] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part primal basis\n",
    "    #res += domain.interfaces.integral('- v [basis_n] n_0 ({u} - .5 C [u] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "\n",
    "# Boundary integrals LHS\n",
    "#    part one\n",
    "resExt1 += domainExt1.boundary.integral('- k uExt1 basisExt1_n,0 + beta k uExt1_,0 basisExt1_n ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #    part one\n",
    "    #res += domain.boundary.integral('- k basis_n u_,0 + beta k basis_n,0 u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#    part two (stabilized)\n",
    "resExt1 += domainExt1.boundary['left'].integral(' tauZero uExt1 basisExt1_n ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "resExt1 += domainExt1.boundary['right'].integral(' tauN uExt1 basisExt1_n ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #    part two (stabilized)\n",
    "    #res += domain.boundary['left'].integral(' tauZero basis_n u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res += domain.boundary['right'].integral(' tauN basis_n u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "\n",
    "# Boundary integrals RHS (weakly imposed boundary conditions) (including stabilization)\n",
    "# Diffusive part (keep an eye on the signs)\n",
    "resExt1 += domainExt1.boundary['left'].integral(' ( k basisExt1_n,0 - tauZero basisExt1_n) glExt1 ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "resExt1 += domainExt1.boundary['right'].integral(' ( k basisExt1_n,0 - tauN basisExt1_n) grExt1 ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    # Diffusive part\n",
    "    #res -= domain.boundary['left'].integral(' ( beta k basis_n,0 + tauZero basis_n) gl ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res -= domain.boundary['right'].integral(' ( beta k basis_n,0 + tauN basis_n) gr ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part perturbation\n",
    "resExt1 += domainExt1.boundary['left'].integral('v basisExt1_n n_0 (0.5 (glExt1 + uExt1) + .5 C (glExt1 - uExt1) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "resExt1 += domainExt1.boundary['right'].integral('v basisExt1_n n_0 (0.5 (grExt1 + uExt1) + .5 C (grExt1 - uExt1) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    # Advective part perturbation\n",
    "    #res += domain.boundary['left'].integral('v basis_n n_0 (0.5 (gl + u) - .5 C (gl - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res += domain.boundary['right'].integral('v basis_n n_0 (0.5 (gr + u) - .5 C (gr - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part primal basis\n",
    "resExt1 += domainExt1.boundary['left'].integral('v basisExt1_n n_0 (0.5 (glExt1 + basisTot0_0) + .5 C (glExt1 - basisTot0_0) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "resExt1 += domainExt1.boundary['right'].integral('v basisExt1_n n_0 (0.5 (grExt1 + basisTot0_0) + .5 C (grExt1 - basisTot0_0) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    # Advective part primal basis\n",
    "    #res += domain.boundary['left'].integral('v basis_n n_0 (0.5 (gl + u) - .5 C (gl - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res += domain.boundary['right'].integral('v basis_n n_0 (0.5 (gr + u) - .5 C (gr - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solving for the unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here comes the Jacobian\n",
      "(2, 2)\n",
      "[[ -1.98960000e+04   1.11022302e-14]\n",
      " [  1.92000000e+02  -1.98960000e+04]]\n",
      "[-20098.    198.]\n",
      "solve > solver returned with residual 4e-12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/slukic/Coding/Python/nutils/nutils/warnings.py:30: NutilsWarning: using explicit inflation; this is usually a bug.\n",
      "  warnings.warn(message, category, stacklevel=stacklevel)\n"
     ]
    }
   ],
   "source": [
    "# find lhs such that res == 0 and substitute this lhs in the namespace\n",
    "# lhs = solver.solve_linear('lhs', res, constrain=cons)\n",
    "lhsExt1 = solver.solve_linear('lhsExt1', resExt1)\n",
    "ns = ns(lhsExt1=lhsExt1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f24ea40f9b0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAEAb9pfF6/PeG2Zl557GfPWMY/POymFFT6IdE5IAAACgjfpR/Yp88o756d6lIjOmTsrJYw4sehLtnJAEAAAAbUypVMo3frsw3/794hzSr3tqqqsydnDvomfRAQhJAAAA0IbsbmjMJ26fnx/PeTLHDu2baZMrM6h3t6Jn0UEISQAAANBGbN6xNxddX5/7l27Ma48cnG+ffVx6dPHQnv3HRxsAAAC0Acs37MiU2geyZN32TDlpZD795qNSUe5kNvYvIQkAAABauTnLn84FM+qzcceefObNR2Xqyw8tehIdlJAEAAAArdivHlqVD/5wbsrKkmvOnZh/HTek6El0YEISAAAAtEKlUinX3bs0X/rFIxnQs0uum1yV8cP6FT2LDk5IAgAAgFamobEpX/jZw5l53xMZM6hXaqZUZVj/HkXPAiEJAAAAWpPtuxty2c1z8rtH1+bEUQPy/fMmpm/3zkXPgiRCEgAAALQaa7bsytTauix4akvecfwhueIdx6ZLp/KiZ8HfCUkAAADQCjy6ekum1tTlqc278uHXjs1lrxmTsrKyomfBPxCSAAAAoGB/XrQul9wwO7saGvP1d47P6ROHFj0JnpGQBAAAAAW6tW5FPvXj+enRpSIzp74sJ44eUPQkeFZCEgAAABSgVCrl679ZmO/8YXGGHtA9tdVVGTOod9Gz4DkJSQAAALCf7W5ozEd/NC93PfhUxg/tm2mTqzKwd9eiZ8HzEpIAAABgP3p6+55cfP2sPLBsY143bnC+eeaEdO9SUfQsaBYhCQAAAPaTJzZsT3VNXZas3573vPzQfOqNR6ai3MlstB1CEgAAAOwHs554OhfOrM+mHXvy+beOy+STRhY9CV4wIQkAAAD2sV/MX5UP3TI3FWVl+cF5lXntUYOLngQvipAEAAAA+0ipVMoP7lmS//rloxnYu2umT67KMUP7Fj0LXjQhCQAAAPaBhsamfPauBbnx/uUZO7hXpk+pytADehQ9C14SIQkAAABa2LbdDfnATbPzx8fW5eQxA/K9cyemT7fORc+Cl0xIAgAAgBa0evOuTK2ty8OrtuSdE4fmS28/Jl06lRc9C1qEkAQAAAAt5JFVW1JdU5fVW3blI6eOzQdePSZlZWVFz4IWIyQBAABAC/jTwnV5/42zs6ehKd8887i8bcIhRU+CFickAQAAwEt00/3L8+k7H0qvrp0y8z2TcsKoAUVPgn1CSAIAAIAXqamplK/+5rF874+PZ1j/7qmtnpTRA3sVPQv2GSEJAAAAXoRdexvzbz96MD+btyrHDeuXaZMrc2CvrkXPgn1KSAIAAIAXaOP2PbloZn3qn3g6bzh6SL5x5nHp1rmi6FmwzwlJAAAA8AIsXb891TUPZNmGHbnwFYfmk284MuXlTmajYxCSAAAAoJnql23MhTPrs3nn3nzxtHE578SRRU+C/UpIAgAAgGb46YNP5SM/ejCdyssybXJlXn3E4KInwX4nJAEAAMBzKJVK+f6fluS/f/VoBvXumulTqnL0IX2LngWFEJIAAADgWextbMpn7nwoNz+wIkcM6Z3pU6pycL/uRc+CwghJAAAA8Ay27tqb9980J/csXJdXHHZgrj7n+PTu1rnoWVAoIQkAAAD+j1Wbd6a6pi6Prt6as6qG5YtvOzqdK8qLngWFE5IAAADgf1nw1OZMra3Lmi2789HXHZ5L/mV0ysrKip4FrYKQBAAAAP/jD4+tzQdunJ29jaV8++wJeev4g4ueBK2KkAQAAABJbvjbE/nsXQvSu1un1FRXZtKh/YueBK2OkAQAAECH1tRUyn//6tFcc8+SjBjQIzVTqjJqYK+iZ0GrJCQBAADQYe3a25jLb52bX8xfneOH98u151dmQK+uRc+CVktIAgAAoEPasG13LpxZn9nLN+VNxxyUr79rfLp1rih6FrRqQhIAAAAdzpJ12zKlpi7LN+7Ixa8alY+/7oiUlzuZDZ6PkAQAAECH8sDSjbno+vps3dWQL7396JzzshFFT4I2Q0gCAACgw7hz7pP56I/mpXNFWaZNrswphw8qehK0KUISAAAA7V6pVMrVf3w8X/31YxnSp1umT6nKUQf3KXoWtDlCEgAAAO3a3sam/MePH8ot9StyxJDeqamuykF9uxc9C9okIQkAAIB2a8uuvXn/jbPz50Xr86qxA/Odd09I726di54FbZaQBAAAQLv05KadmVpTl8fWbM3Zk4bni6eNS6eK8qJnQZsmJAEAANDuPPTk5kytrcvarbvziTcckYtfOSplZWVFz4I2T0gCAACgXfndI2ty6c1z0tBUynfePSFvPvbgoidBuyEkAQAA0G5cf9+yfPauBenbvXNmnl+ZypH9i54E7YqQBAAAQJvX1FTKl3/xSKbduzSHHtgzNVOqMvLAnkXPgnZHSAIAAKBN27mnMR++ZW5+tWB1KkcckGvPr8wBPbsUPQvaJSEJAACANmv9tt25YEZ95q7YlLeMPzhfPePYdOtcUfQsaLeEJAAAANqkxWu3pbr2gazYuDOX/Mvo/Nu/Hp7yciezwb4kJAEAANDm/G3Jhlw0sz7b9zTmincck7MmDS96EnQIQhIAAABtyo/nrMzHbpuXrp0qMn1KVV41dmDRk6DDEJIAAABoE0qlUq76/eJc+duFOahvt0yfUpUjD+pT9CzoUIQkAAAAWr09DU351I/n57ZZK3PUQX0yfUpVhvTtVvQs6HCEJAAAAFq1zTv35n03zMpfH9+QUw4fmKvefXx6dfVwForgMw8AAIBWa+XTO1JdU5dFa7flnJcNz+ffOi6dKsqLngUdlpAEAABAqzRv5aa8Z0Z91m3dnU+98Yhc+IpRKSsrK3oWdGhCEgAAAK3Obx9ek8tunpOmUilXn3N83njMQUVPAiIkAQAA0MrU/mVpPv+zh3NAjy6ZNrkyxw8/oOhJwP8QkgAAAGgVGptK+dLPH8n0vyzNqAN7pqa6KiMG9Cx6FvC/CEkAAAAUbueexnzwh3Pym4fXZNLI/vnB+RPTr0eXomcB/4eQBAAAQKHWbd2dC2bU5cGVm3PacQfnK2ccm66dKoqeBTwDIQkAAIDCLFqzNdW1dVn59M5c+uoxufzUsU5mg1ZMSAIAAKAQf318fS6+flZ27mnMV04/Nu+qGlb0JOB5CEkAAADsd7fPWplP3DEv3TpVpLZ6Ul5+2IFFTwKaQUgCAABgvymVSvnm3Yvyrd8tysF9u6WmelIOH9K76FlAMwlJAAAA7Bd7Gpryidvn5Y45T+boQ/pk+uSqDOrTrehZwAsgJAEAALDPbd6xN++9YVbuW7IhrzliUL599oT07OohKbQ1PmsBAADYp1Zs3JEpNQ/k8XXbM/nEEfnMW8alotzJbNAWCUkAAADsM3NXbMoFM+qyYfuefPrNR2XqySNTViYiQVslJAEAALBP/HrB6nzwh3OSJN87Z2Jef/SQghcBL5WQBAAAQIu77t6l+c+fP5z+Pbpk2uTKTBh+QNGTgBYgJAEAANBiGptK+eLPHk7tX5dl9MCeqZkyKcMH9Ch6FtBChCQAAABaxI49Dbns5jm5+5G1OWFU/1xzbmX69uhc9CygBQlJAAAAvGRrt+zKe2bUZ/6Tm/P2CYfkitOPSddOFUXPAlqYkAQAAMBLsnDN1lTX1OXJTTtz2WsOy4dfe5iT2aCdEpIAAAB40e5dtD7vu2FWdu5tzNfeOT5nTBxa9CRgHxKSAAAAeFFurV+RT90xP927VGTm1Ek5acyBRU8C9jEhCQAAgBekVCrlyt8uzFW/X5xD+nVPbXVVDhvcu+hZwH4gJAEAANBsuxsa8/Hb5uUnc5/KsUP7Ztrkygzq3a3oWcB+IiQBAADQLJt27MlF18/KA0s35tSjBudbZx2XHl08rISOxGc8AAAAz2v5hh2ZUvtAlqzbnuqTR+Y/3nRUKsqdzAYdjZAEAADAc5q9/OlcOKM+G3fsyWfefFSmvvzQoicBBRGSAAAAeFa/nL8qH7plbsrKkmvOnZh/HTek6ElAgcpb4i9pamrKpZdemtGjR2fMmDH5zne+84zX7dq1K29729syduzYjB8/PqeeemoWL17cEhMAAABoQaVSKdfesySX3DQ7vbt1zi0XnSgiAS0Tkm644YY8/PDDWbhwYR544IF89atfzYIFC57x2osuuiiPPfZYHnzwwZx22mm54IILWmICAAAALaShsSmfuXNBvvSLRzJ6YK/8+JKTMn5Yv6JnAa1Ai4SkW265JRdeeGEqKirSv3//nHnmmbn55pv/6bpu3brljW98Y8rK/t8Lsp1wwglZtmxZS0wAAACgBWzf3ZCLrp+V6//2RE4aPSC3v++kDOvfo+hZQCvRIiFp+fLlGTFixN9vjxw5MsuXL3/eP/etb30rp5122rO+/8orr8zQoUP//rZt27aWmAsAAMAzWLNlV951zX35/aNrc/rxQ1NbPSl9u3cuehbQijTrxbZPPPHELFq06BnfN2fOnBd1x1/+8pezePHi/O53v3vWay6//PJcfvnlf789dOjQF3VfAAAAPLdHV2/J1Jq6PLV5Vy4/dWwuffWYv/82CcD/r1kh6b777nvO9w8fPjxPPPFETjzxxCTJsmXLMnz48Ge9/mtf+1ruuOOO3H333enRw1MkAQAAinTPwnW55MbZ2d3QmCvfNT7vON4P8YFn1iK/2vbOd74z1157bRobG7Nx48bccsstOfPMM5/x2iuvvDI333xzfvvb36ZfPy/WBgAAUKRb6panurYu5WXJzKkvE5GA59SsZyQ9n/POOy91dXU57LDDUlZWlssvvzzHHHNMkuSuu+7KXXfdlWnTpmXlypX5yEc+klGjRuWUU05JknTt2jX3339/S8wAAACgmZqaSvn6bx/Ld//weIYe0D211VUZM6h30bOAVq6sVCqVih7RXEOHDs3KlSuLngEAANCm7drbmI/eNi8/ffCpjB/WL9POr8zA3l2LngUU5IX0lhZ5RhIAAABtw9Pb9+Si6+tTt+zpvG7c4HzzzAnp3qWi6FlAGyEkAQAAdBDL1m9PdW1dlq7fnve8/NB86o1HpqLcyWxA8wlJAAAAHcCsJ57OhTPrs2nHnnzhtHE5/8SRRU8C2iAhCQAAoJ37+bxV+fCtc1NRVpZrz6/Ma44cXPQkoI0SkgAAANqpUqmUa+5Zkit++WgG9u6a6ZOrcszQvkXPAtowIQkAAKAdamhsymfvWpAb71+esYN7paZ6Ug7p173oWUAbJyQBAAC0M9t2N+QDN83OHx9bl5ePOTBXn3t8+nTrXPQsoB0QkgAAANqR1Zt3pbq2Lo+s2pJ3VQ7Nl95+TDpXlBc9C2gnhCQAAIB24uGntmRqbV1Wb9mVf/vXsXn/KWNSVlZW9CygHRGSAAAA2oE/PrY2779xdvY2lvKts47LaccdUvQkoB0SkgAAANq4m+5fnk/f+VB6de2U6VMm5mWjBhQ9CWinhCQAAIA2qqmplK/8+rF8/0+PZ3j/Hqmprsrogb2KngW0Y0ISAABAG7Rrb2M+8qMH8/N5qzJheL9MO78yA3p1LXoW0M4JSQAAAG3Mxu17cuHM+sx64um84egh+caZx6Vb54qiZwEdgJAEAADQhixdvz3VNQ9k2YYdufiVo/Lx1x+R8nInswH7h5AEAADQRtQt25iLZtZn8869+eLbjs55J4woehLQwQhJAAAAbcBPH3wqH7n1wXSqKMt1k6tyyhGDip4EdEBCEgAAQCtWKpXyvT89nq/86rEM6t0106dU5ehD+hY9C+ighCQAAIBWam9jUz79k4fyw7oVOWJI70yfUpWD+3UvehbQgQlJAAAArdDWXXtzyY2z8+dF6/OKww7M1eccn97dOhc9C+jghCQAAIBW5qlNOzO1ti6Prt6as6qG5YtvOzqdK8qLngUgJAEAALQmC57anKm1dVmzZXc+9vrD875XjU5ZWVnRswCSCEkAAACtxh8eXZsP3DQ7extLuersCXnL+IOLngTwD4QkAACAVuD6vz2Rz975UPp075zaqZWpGtm/6EkA/0RIAgAAKFBTUylX/OrR/OCeJRkxoEdqqyfl0AN7Fj0L4BkJSQAAAAXZtbcxl986N7+YvzoTRxyQH5w3MQN6dS16FsCzEpIAAAAKsGHzJ5aMAAAgAElEQVTb7lwwsz5zlm/Km449KF9/5/h061xR9CyA5yQkAQAA7GePr9uW6pq6LN+4I+991eh87HWHp7zcyWxA6yckAQAA7Ef3L9mQi66flW27G/Lltx+Td79seNGTAJpNSAIAANhP7pz7ZD76o3npXFGW6yZX5l8OH1T0JIAXREgCAADYx0qlUr77h8X52m8WZkifbpk+pSpHHdyn6FkAL5iQBAAAsA/tbWzKv/94fm6tX5kjD+qT6VMqc1Df7kXPAnhRhCQAAIB9ZMuuvbnkhtm5d/H6vGrswHz3nOPTq6uHYUDb5SsYAADAPvDkpp2ZWlOXx9ZszbtfNjxfeOu4dKooL3oWwEsiJAEAALSw+Ss3Z+qMuqzbujuffMMRueiVo1JWVlb0LICXTEgCAABoQb97ZE0+cNOcNJZK+e67j8+bjj2o6EkALUZIAgAAaCEz71uWz921IH27d860yZWZOKJ/0ZMAWpSQBAAA8BI1NZXy5V88kmn3Ls2hB/ZMzZSqjDywZ9GzAFqckAQAAPAS7NzTmA/fMje/WrA6VSMPyA/Oq8wBPbsUPQtgnxCSAAAAXqR1W3fngpn1eXDFprxl/MH56hnHplvniqJnAewzQhIAAMCLsHjttlTXPpAVG3fm/aeMzkdOPTzl5U5mA9o3IQkAAOAF+tuSDbloZn2272nMFe84JmdNGl70JID9QkgCAAB4AX48Z2U+dtu8dO1UkZopVXnl2IFFTwLYb4QkAACAZiiVSvn27xbnG3cvzEF9u6WmuipHDOlT9CyA/UpIAgAAeB57GpryqR/Pz22zVmbcwX0yfUpVBvfpVvQsgP1OSAIAAHgOm3fuzftumJW/Pr4hrz5iUK46e0J6dvVQCuiYfPUDAAB4Fis27sjU2rosWrst554wPJ97y7h0qigvehZAYYQkAACAZzBv5aZMra3Phu278+9vPDIXvOLQlJWVFT0LoFBCEgAAwP/x24fX5LKb56SpVMrV7z4+bzjmoKInAbQKQhIAAMD/UvOXpfnCzx5O/x5dcu3kyhw//ICiJwG0GkISAABAksamUv7z5w+n5i/LMmpgz9ROmZThA3oUPQugVRGSAACADm/HnoZ88Idz89uH12TSof3zg/Mmpl+PLkXPAmh1hCQAAKBDW7t1Vy6YUZ95KzfntOMOzlfOODZdO1UUPQugVRKSAACADmvRmq2ZUlOXJzftzGWvHpMPnzrWyWwAz0FIAgAAOqS/Ll6fi2+YlZ17GvOVM47NuyqHFT0JoNUTkgAAgA7ntlkr84nb56V754rUVk/Kyw87sOhJAG2CkAQAAHQYpVIp37x7Ub71u0U5pF/3TJ9SlcOH9C56FkCbISQBAAAdwp6Gpnzi9nm5Y86TOfqQPpk+uSqD+nQrehZAmyIkAQAA7d7mHXtz8Q31+duSjXntkYPyrbMmpGdXD4cAXihfOQEAgHZtxcYdmVLzQB5ftz2TTxyRz7xlXCrKncwG8GIISQAAQLs1d8WmXDCjLhu278mn33xUpp48MmVlIhLAiyUkAQAA7dKvHlqdD90yJ0nyvXMm5vVHDyl4EUDbJyQBAADtSqlUynX3Ls2XfvFIBvTskmmTq3LcsH5FzwJoF4QkAACg3WhsKuULP12QGfc9kdEDe6a2elKG9e9R9CyAdkNIAgAA2oUdexpy2c1zcvcja3PCqP655tzK9O3RuehZAO2KkAQAALR5a7fsyntm1Gf+k5vzjgmH5IrTj02XTuVFzwJod4QkAACgTXts9dZMra3Lk5t25oOvOSwfeu1hTmYD2EeEJAAAoM26d9H6vO+GWdm5tzFfe+f4nDFxaNGTANo1IQkAAGiTbq1fkU/dMT/du1Rk5tRJOWnMgUVPAmj3hCQAAKBNKZVKufK3C3PV7xfnkH7dU1tdlcMG9y56FkCHICQBAABtxu6Gxnzstnm5c+5TOXZo30ybXJlBvbsVPQugwxCSAACANmHTjj256PpZeWDpxpx61OB866zj0qOLhzQA+5OvugAAQKv3xIbtqa6ty5J12zP15EPz7286MhXlTmYD2N+EJAAAoFWbvfzpXDCjPk/v2JPPvuWoVJ98aNGTADosIQkAAGi1fjl/VT50y9yUl5XlB+dV5tSjBhc9CaBDE5IAAIBWp1QqZdqfl+bLv3wkA3p2zfQplTl2aL+iZwF0eEISAADQqjQ0NuVzP12QG/62PIcN6pWa6qoMPaBH0bMAiJAEAAC0Itt3N+QDN83OHx5bl5NGD8j3zp2Yvt07Fz0LgP8hJAEAAK3Cmi27MrW2Lgue2pIzJg7Nl99+TLp0Ki96FgD/i5AEAAAU7tHVW1JdU5dVm3fl8lPH5tJXj0lZWVnRswD4P4QkAACgUPcsXJdLbpyd3Q2N+caZ4/P2CUOLngTAsxCSAACAwvzwgeX59588lJ5dKnL9e16WE0YNKHoSAM9BSAIAAPa7pqZSvvabx3L1Hx/PsP7dUzOlKmMG9S56FgDPQ0gCAAD2q117G/PR2+blpw8+leOG9cu0yZU5sFfXomcB0AxCEgAAsN88vX1PLrq+PnXLns7rxg3ON8+ckO5dKoqeBUAzCUkAAMB+sWz99lTX1mXp+u254OWH5pNvPDIV5U5mA2hLhCQAAGCfm/XExlwwoz6bd+7NF04bl/NPHFn0JABeBCEJAADYp34+b1U+fOvcVJSV5drzK/OaIwcXPQmAF0lIAgAA9olSqZRr7lmSK375aAb17prpU6py9CF9i54FwEsgJAEAAC2uobEpn7lrQW66f3kOH9w706urcki/7kXPAuAlEpIAAIAWtW13Q95/4+z8aeG6vHzMgbn63OPTp1vnomcB0AKEJAAAoMWs2rwzU2vr88iqLTmzclj+8+1Hp3NFedGzAGghQhIAANAiHn5qS6bW1mX1ll356OsOzyX/MjplZWVFzwKgBQlJAADAS/aHx9bmAzfOzt7GUr511nE57bhDip4EwD4gJAEAAC/JTfcvz6fvfCi9unZKTXVlJh3av+hJAOwjQhIAAPCiNDWV8t+/fjTX/GlJhvfvkZrqqowe2KvoWQDsQ0ISAADwgu3a25iP3Ppgfj5/VSYM75dp51dmQK+uRc8CYB8TkgAAgBdk4/Y9uXBmfWY98XTeeMyQXPmu49Ktc0XRswDYD4QkAACg2Zau357qmgeybMOOXPzKUfn4649IebmT2QA6CiEJAABolrplG3PhzPps2bk3//m2o3PuCSOKngTAfiYkAQAAz+uuB5/Kv936YDpVlOW6yVU55YhBRU8CoABCEgAA8KxKpVKu/uPj+eqvH8vgPl1z3eSqHH1I36JnAVAQIQkAAHhGexub8umfPJQf1q3IEUN6p6a6Kgf17V70LAAKJCQBAAD/ZOuuvbnkxtn586L1eeXYgfnuuyekd7fORc8CoGBCEgAA8A+e2rQzU2vr8ujqrTl70rB84bSj07mivOhZALQCQhIAAPB3Dz25OVNr67J26+58/PVH5L2vGpWysrKiZwHQSghJAABAkuQPj67N+2+anYamUq46e0LeMv7goicB0MoISQAAQK6/b1k+e9eC9O3eOTPPr0zlyP5FTwKgFRKSAACgA2tqKuWKXz2aH9yzJCMH9EhN9aQcemDPomcB0EoJSQAA0EHt2tuYD98yN798aHUmjjgg155fmf49uxQ9C4BWTEgCAIAOaP223blwZn3mLN+UNx17UL7+zvHp1rmi6FkAtHJCEgAAdDCPr9uW6pq6LN+4I+991eh87HWHp7zcyWwAPD8hCQAAOpD7l2zIRdfPyrbdDfmvdxyTsycNL3oSAG2IkAQAAB3EnXOfzEd/NC+dK8oyfUpVXjV2YNGTAGhjhCQAAGjnSqVSvvP7xfn6bxdmSJ9umT6lKkcd3KfoWQC0QUISAAC0Y3sbm/KpO+bnR7NW5qiD+mT6lKoM6dut6FkAtFFCEgAAtFNbdu3NJTfMzr2L1+dfDh+Y77z7+PTq6iEAAC+e7yIAANAOrXx6R6bW1mXhmm0552XD8/m3jkunivKiZwHQxglJAADQzsxfuTlTZ9Rl3dbd+eQbjshFrxyVsrKyomcB0A4ISQAA0I7c/fCaXHrznDSWSvnuu4/Pm449qOhJALQjQhIAALQTM/66LJ//6YL069El154/MRNH9C96EgDtjJAEAABtXGNTKV/+xSO57t6lOfTAnqmtrsqIAT2LngVAOyQkAQBAG7ZzT2M+dMuc/HrBmlSNPCA/OK8yB/TsUvQsANopIQkAANqodVt354KZ9Xlwxaa8dfzB+coZx6Zb54qiZwHQjglJAADQBi1euzVTauqy8umd+cApY3L5qWNTXu5kNgD2LSEJAADamPse35CLr6/P9j2N+e/Tj8mZVcOLngRAByEkAQBAG3LH7JX5+O3z0q1TRWqrq/KKwwYWPQmADkRIAgCANqBUKuXbv1ucb9y9MAf37Zbp1VU5YkifomcB0MEISQAA0MrtaWjKJ++Yn9tnr8y4g/tk+pSqDO7TrehZAHRAQhIAALRim3fuzXuvn5X7lmzIq48YlKvOnpCeXf03HoBi+A4EAACt1IqNOzK1ti6L1m7LeSeMyGffclQ6VZQXPQuADkxIAgCAVujBFZvynhn12bB9d/7jTUfmPS8/NGVlZUXPAqCDE5IAAKCV+c2C1bnsh3NSKiXfO+f4vP7og4qeBABJhCQAAGhVpt+7NF/8+cPp36NLrp1cmeOHH1D0JAD4OyEJAABagcamUr74s4dT+9dlGTWwZ2qnTMrwAT2KngUA/0BIAgCAgu3Y05DLbp6bux9Zk5cd2j/XnDcx/Xp0KXoWAPwTIQkAAAq0duuuXDCjPvNWbs7bjjs4/33GsenaqaLoWQDwjIQkAAAoyMI1W1NdU5cnN+3MZa8ekw+fOtbJbAC0akISAAAU4K+L1+fiG2Zl557GfOWMY/OuymFFTwKA5yUkAQDAfnbbrJX5xO3z0r1zRWZMnZSTxxxY9CQAaBYhCQAA9pNSqZRv3L0o3/7dohzSr3tqqqsydnDvomcBQLMJSQAAsB/sbmjMJ2+fnzvmPJljDumb6yZXZlCfbkXPAoAXREgCAIB9bPOOvbno+vrcv3RjXnvkoHz77Anp0cV/xQFoe3z3AgCAfWjFxh2ZUvNAHl+3PVNOGplPv/moVJQ7mQ2AtklIAgCAfWTO8qdz4cz6bNi+J59581GZ+vJDi54EAC+JkAQAAPvArx5anQ/+cE7KypLvnzsxrxs3pOhJAPCSCUkAANCCSqVSrrt3ab70i0cyoGeXTJtcleOG9St6FgC0CCEJAABaSENjU77ws4cz874nMmZQr9RMqcqw/j2KngUALUZIAgCAFrB9d0Muu3lOfvfo2pwwqn+uObcyfXt0LnoWALQoIQkAAF6itVt2ZeqMujz05Ja8Y8IhueL0Y9OlU3nRswCgxQlJAADwEjy6ekum1tTlqc278qHXHpYPvuawlJWVFT0LAPYJIQkAAF6kexetz/tumJVdDY35+jvH5/SJQ4ueBAD7lJAEAAAvwq11K/KpH89P9y4VmTF1Uk4afWDRkwBgnxOSAADgBSiVSvn6bxbmO39YnKEHdE9tdVXGDOpd9CwA2C+EJAAAaKbdDY352G3zcufcpzJ+aN9Mm1yVgb27Fj0LAPYbIQkAAJrh6e17cvH1s/LAso3516MG51tnTUj3LhVFzwKA/UpIAgCA5/HEhu2prqnLkvXbM/XkQ/PvbzoyFeVOZgOg4xGSAADgOcx64ulcOLM+m3bsyefeclSmnHxo0ZMAoDBCEgAAPItfzF+VD98yN+VlZbnmvMqcetTgoicBQKGEJAAA+D9KpVKu/fOSfPkXj2Zg766ZPrkqxwztW/QsACickAQAAP9LQ2NTPvfTBbnhb8szdnCvTJ9SlaEH9Ch6FgC0CkISAAD8j227G3LpTbPzh8fW5eQxA3L1ORPTt3vnomcBQKshJAEAQJLVm3dlam1dHl61JWdMHJovv/2YdOlUXvQsAGhVhCQAADq8R1ZtydTauqzavCsfOXVsPvDqMSkrKyt6FgC0Oi3yI5ampqZceumlGT16dMaMGZPvfOc7z/tnampqUlZWlp/85CctMQEAAF6UPy1cl3d+/76s37Y73zhzfC59zWEiEgA8ixZ5RtINN9yQhx9+OAsXLszmzZszYcKEnHLKKRk3btwzXr9s2bJce+21OeGEE1ri7gEA4EW5+YHl+Y+fPJReXTvl+ve8LCeMGlD0JABo1VrkGUm33HJLLrzwwlRUVKR///4588wzc/PNNz/jtU1NTbngggty1VVXpWvXri1x9wAA8II0NZXy3796NJ+8Y34O7tctt7/vJBEJAJqhRZ6RtHz58owYMeLvt0eOHJm//e1vz3jtlVdemZNPPjkTJ05sibsGAIAXZNfexvzbjx7Mz+atynHD+mXa5Moc2MsPOAGgOZoVkk488cQsWrToGd83Z86cZt/ZQw89lNtvvz333HNPs66/8sorc+WVV/799rZt25p9XwAA8H9t3L4nF82sT/0TT+f144bkG2cel+5dKoqeBQBtRrNC0n333fec7x8+fHieeOKJnHjiiUn+32sgDR8+/P9j777D864L/f+/knRvOoHu0kKhLdA2qaKCoOJeoIjIaFLWEcXRc1TU4x4HFy5EWU3KEMtBVMS9J0q6aGkppXRR6C7dM8n9++N7fl6eA2qBtJ+Mx+O6+sd95dPcr/6TfPLsnfv9lOv+8Ic/ZOXKlRkzZkySZN26dbnsssuydu3avP3tb3/K9dOnT8/06dP/9njIkCEHMxcAAJ5ixaZdqam9Pys3786lp47MB191fMrLvak2ADwTZaVSqfRcP0ldXV1uvfXW/PznP//bm23fe++9mTBhwj/9e6effnre85735I1vfONBPc+QIUOyZs2a5zoXAIB2ZvbKLbn0ltnZtudAPvH6cbnwlBFFTwKAFuOZ9JZmebPtCy+8MGPHjs2YMWNSVVWV6dOn/y0i3XPPPbnkkkua42kAAOAZu3fBE3nbTX/Nvoam3HhRpYgEAM9Bs7wi6XDxiiQAAA5WqVTKt363PJ/76ZIM7Nk5M6qrMn5w76JnAUCL80x6S7Oc2gYAAC1JQ2NTPvKDRbnj/tU5blDPzKipyuA+XYueBQCtnpAEAECbsmPvgbzj2/Py+6Ubc+qY/vnG+ZPSq0vHomcBQJsgJAEA0Gas3bYnNbX1WbJuR86tHJpPnzU+HSua5W1BAYAISQAAtBGLntiWaXX1Wb99X973iuNyxenHpKysrOhZANCmCEkAALR6v3l4Q955+9wcaCzlq289OW84eXDRkwCgTRKSAABo1W77y6p87J5F6dmlQ2prKjNlZN+iJwFAmyUkAQDQKjU1lfK5ny7J9b9fnuH9uqW2uiqjBvQoehYAtGlCEgAArc7eA4359zsfyI8Wrs2kYX1y40WV6dejc9GzAKDNE5IAAGhVNu/cl0tvmZ25q7fmNROOypfeclK6dKwoehYAtAtCEgAArcbyjTtTU1efVZt35/IXj8oHXjE25eVOZgOAw0VIAgCgVbh/xZZcduvsbN9zIJ9+4/hc8PzhRU8CgHZHSAIAoMX7wfzH877/XpCOFWW5uboqZxw3sOhJANAuCUkAALRYpVIp1/320XzhZw9nUK/OmVFdlXFH9y56FgC0W0ISAAAt0oHGpvzn9x7MrNmPZeyRPVNbU5WjenctehYAtGtCEgAALc72vQfyjtvn5g+PbMppxw7IN942MT27dCx6FgC0e0ISAAAtyuNb92RabX0eXr8j500Zlk++YVw6VpQXPQsAiJAEAEAL8uDj2zKtrj4bduzLVa8am8tPG5WysrKiZwEA/0NIAgCgRfjVQ+tz5R3z0tBUytfPm5jXnXR00ZMAgP9DSAIAoHC33rcyH7tnUXp37ZhbLqpM5Yi+RU8CAJ6GkAQAQGGamkr5r588lBv/sCIj+nVLbc2UjOzfvehZAMA/ICQBAFCIPfsb895Z8/PTRetSOfyI3HBRZfp271T0LADgnxCSAAA47Dbt3JdLZs7O/Me25rUnHpUvnnNSunSsKHoWAPAvCEkAABxWyzbsTE3d/Xlsy568/fRj8r6XH5fyciezAUBrICQBAHDY/GX55lx+65zs3NeQ/zp7Qs6bMqzoSQDAMyAkAQBwWHxv3pq8/64F6dyhIjOqq/LiYwcUPQkAeIaEJAAADqlSqZSv/3pZrvnF0hzVu0tmVFfl+KN6FT0LAHgWhCQAAA6Z/Q1N+dD3FuauOWtywlG9MqO6Kkf27lL0LADgWRKSAAA4JLbtOZC33zYnf350c04/bkCufduk9Ojs9hMAWjPfyQEAaHZrntydmtr6PLJhZ85/3rB84vXj0qGivOhZAMBzJCQBANCsFqzZmotnzs7GHfvyoVePzaWnjkpZWVnRswCAZiAkAQDQbH6xeH3edce8NJVKue78SXn1hKOKngQANCMhCQCAZlH3pxX5xL2Lc0S3TrnxospMHn5E0ZMAgGYmJAEA8Jw0NpXymR89lBl/WpFR/buntqYqw/t1L3oWAHAICEkAADxre/Y35t3fmZefL16fKSP65voLJ+eI7p2KngUAHCJCEgAAz8rGHftyycz6PLBmW95w8tH5/JtPTOcOFUXPAgAOISEJAIBn7JH1O1JTV581T+7JlS8ZnelnHutkNgBoB4QkAACekT8/uimX3zone/Y35vNvOjFvqRpa9CQA4DARkgAAOGjfnbMmV929IF06VKS2piqnjhlQ9CQA4DASkgAA+JdKpVK+8stH8tVfPZKje3fJjJqqjD2yV9GzAIDDTEgCAOCf2t/QlKu+uyB3z3s84wf3ys1TqzKoV5eiZwEABRCSAAD4h7btPpB/u21O7lu+OS8ZOzBfP29iund2CwkA7ZW7AAAAntZjW3anuvb+PLpxVy46ZXg++toT0qGivOhZAECBhCQAAJ5i/mNbc8nM+mzetT//+Zrjc/GLRqasrKzoWQBAwYQkAAD+l58tWpd3f2deSqXkm+dPyivHH1X0JACghRCSAAD4m5v/uCKf/tHi9O3WKTdNrczEYUcUPQkAaEGEJAAA0thUyqfuXZy6P6/MqAHdU1c9JcP6dSt6FgDQwghJAADt3O79DXnXHfPyy4c25Hkj++b6CyenT7dORc8CAFogIQkAoB3bsH1vLp45Owsf35azJg7O1W+akM4dKoqeBQC0UEISAEA7tXT9jtTU1ufxrXvyrpeOyXtfNsbJbADAPyUkAQC0Q398ZFPeftuc7DnQmC+8+cScUzm06EkAQCsgJAEAtDN3zn4sH7p7Ybp2qsjMaVPywtH9i54EALQSQhIAQDtRKpVyzS+W5uu/XpbBfbqmtqYqxw7qWfQsAKAVEZIAANqBfQ2N+cBdC/L9+U/kxCG9c9PUygzs2aXoWQBAKyMkAQC0cVt3789lt87J/Su25GXHD8rXzjs53Tq5DQQAnjl3EAAAbdjqzbtTXXd/lm/cleoXjMhHXntCKsqdzAYAPDtCEgBAGzV39ZO5dObsbNm9Px997QmZ9qKRRU8CAFo5IQkAoA36ycK1ec+s+SkrS66/YHJePu7IoicBAG2AkAQA0IaUSqXc9IcV+exPHkq/7p1y89SqnDS0T9GzAIA2QkgCAGgjGhqb8okfLs6tf1mV0QN7pLa6KkP7dit6FgDQhghJAABtwK59Dbnyjnn59ZINOWVUv3zrwsnp3bVj0bMAgDZGSAIAaOXWb9+baXX1WfTE9pw9aXCuPvvEdOpQXvQsAKANEpIAAFqxJeu2p6a2Pmu37c17X3Zs3vXS0SkrKyt6FgDQRglJAACt1O+XbswVt8/NvobGXPOWk3L2pCFFTwIA2jghCQCgFfrO/avz4e8/mO6dKnLLtOfllGP6FT0JAGgHhCQAgFakqamUL/3i4XzjN49myBFdU1dTldEDexY9CwBoJ4QkAIBWYu+BxrzvrgX54QNP5KQhvXPT1KoM6Nm56FkAQDsiJAEAtAJP7tqfy26dnfqVT+YV4wblK+dOTNdOFUXPAgDaGSEJAKCFW7lpV2rq6rNi065c/KKR+dCrj09FuZPZAIDDT0gCAGjB5qzakktvmZOtu/fnE68fl6kvGFH0JACgHROSAABaqB8tWJv33jk/FWVlueHCyrzshEFFTwIA2jkhCQCghSmVSrn+98tz9U+WZEDPzpkxtSoThvQuehYAgJAEANCSNDQ25aP3LMq3/7o6xw7qkRnVVRlyRLeiZwEAJBGSAABajJ37GvKO2+fmd0s35oWj++WbF0xOry4di54FAPA3QhIAQAuwbtve1NTV56G123PO5CH5zFkT0qlDedGzAAD+FyEJAKBgi5/Ynml19Vm3fW/+4+XH5h1njE5ZWVnRswAAnkJIAgAo0G8f3pB33D43BxpL+cq5J+eNEwcXPQkA4B8SkgAACvLtv67OR37wYHp07pCbqyfn+aP6FT0JAOCfEpIAAA6zpqZSPvezJbn+d8szrG+31NZU5ZgBPYqeBQDwLwlJAACH0d4Djfn3Ox/IjxauzclD++SmqZXp36Nz0bMAAA6KkAQAcJhs2bU/l94yO3NWPZlXjT8yXz735HTpWFH0LACAgyYkAQAcBis27UpN7f1ZuXl3LjttVK565diUlzuZDQBoXYQkAIBDrH7lllx2y+xs23Mgn3rDuFx4yoiiJwEAPCtCEgDAIXTPA0/kP+58IB0qynLT1Mq8ZOygoicBADxrQhIAwCFQKpXyzd89ms//9OEM7Nk5M6qrMn5w76JnAQA8J0ISAEAzO9DYlI98/8F8p/6xjD2yZ2ZUV+XoPl2LngUA8JwJSQAAzWjH3gO54va5+cMjm3LqmP657vxJ6dmlY9GzAACahZAEANBMnti6J9Pq6rNk3Y6cWzk0nz5rfDpWlBc9CwCg2QhJAADN4MHHt+XimfVZv31f3veK43LF6Q++M7wAACAASURBVMekrKys6FkAAM1KSAIAeI5+s2RD3vHtuWloLOVr503M6086uuhJAACHhJAEAPAc3PqXVfnYDx5Mr64dM3NaZapG9C16EgDAISMkAQA8C01NpVz90yW54ffLM7xft9RWV2XUgB5FzwIAOKSEJACAZ2jvgca8d9b8/OTBdZk0rE9uvKgy/Xp0LnoWAMAhJyQBADwDm3fuyyW3zM681VvzmglH5UtvOSldOlYUPQsA4LAQkgAADtKjG3emprY+q7fszr+9+Ji8/xXHpbzcyWwAQPshJAEAHIS/Lt+cy26dk537GvKZs8bn/OcNL3oSAMBhJyQBAPwLP5j/eN733wvSsaIsN02tzBnHDSx6EgBAIYQkAIB/oFQq5Ru/WZYv/nxpjuzVJTOqq3LC0b2KngUAUBghCQDgaRxobMqHv7cwd85ek7FH9kxtTVWO6t216FkAAIUSkgAA/o/tew/kitvm5o/LNuXFxw7ItW+bmJ5dOhY9CwCgcEISAMDfeXzrntTU3p+l63fmbc8blk++flw6VJQXPQsAoEUQkgAA/sfCNdsybWZ9Nu7Yl6teNTaXnzYqZWVlRc8CAGgxhCQAgCS/XLw+V94xL42lUq5928S89sSji54EANDiCEkAQLt3y30r8/F7FqV31465aWplJg/vW/QkAIAWSUgCANqtxqZSPvvjh3LzH1dkZP/uqa2uyoj+3YueBQDQYglJAEC7tGd/Y94za15+tmh9qkYckRsurMwR3TsVPQsAoEUTkgCAdmfjjn255JbZeeCxrXndSUfnC28+MV06VhQ9CwCgxROSAIB2ZdmGHamurc+aJ/fkitOPyX+8/LiUlzuZDQDgYAhJAEC7cd+jm3P5rbOza39jrj57Qt46ZVjRkwAAWhUhCQBoF743b03ef9eCdO5Qkdrqqpx27ICiJwEAtDpCEgDQppVKpXztV8vy5V8uzVG9u2RGdVWOP6pX0bMAAFolIQkAaLP2NzTlg3cvzHfnrsm4o3tlRnVVBvXqUvQsAIBWS0gCANqkbXsO5O23zcmfH92cM44bkGvfNindO7v1AQB4LtxNAQBtzmNbdmdaXX0e2bAzFzx/WD7+unHpUFFe9CwAgFZPSAIA2pQFa7ZmWt3sbN61Lx9+9fG55NSRKSsrK3oWAECbICQBAG3Gzxety7u/Mz9NpVKue9ukvGrCUUVPAgBoU4QkAKBNqP3Tinzy3sXp261TbpxamUnDjih6EgBAmyMkAQCtWmNTKZ/+0eLU/mllRvXvntqaqgzv173oWQAAbZKQBAC0Wrv3N+Td35mfXyxenykj+uaGiyanT7dORc8CAGizhCQAoFXasGNvLpk5OwvWbMsbTj46n3/ziencoaLoWQAAbZqQBAC0Oo+s35Hq2vo8vnVPrnzJ6Ew/81gnswEAHAZCEgDQqvx52aZcftuc7NnfmM+/6cS8pWpo0ZMAANoNIQkAaDXumrMmV313Qbp2rEhdzZS8aEz/oicBALQrQhIA0OKVSqV8+ZeP5Gu/eiSD+3TNjOqqHHdkz6JnAQC0O0ISANCi7W9oylXfXZC75z2e8YN7ZcbUqgzs1aXoWQAA7ZKQBAC0WNt2H8jlt83OX5ZvycuOH5ivvnViund2+wIAUBR3YgBAi/TYlt2prr0/j27clamnDM9HXzcuFeVOZgMAKJKQBAC0OPMf25pLZtZn8679+chrT8i0F45IWZmIBABQNCEJAGhRfvrgurxn1rwkyTfPn5xXjj+y4EUAAPz/hCQAoEUolUq5+Y8r8pkfP5R+3TvlpqlVOXlon6JnAQDwd4QkAKBwjU2lfPKHizLzvlU5ZkD31NVMydC+3YqeBQDA/yEkAQCF2rWvIe+6Y15+tWRDnj+qb66/oDK9u3UsehYAAE9DSAIACrNh+95Mm1mfBx/fnrMnDs7VbzoxnTqUFz0LAIB/QEgCAArx8LodmVZXn8e37sm7Xzom73nZGCezAQC0cEISAHDY/fGRTXn7bXOy50BjvnjOSXnz5CFFTwIA4CAISQDAYXXn7MfyobsXpmunitwybUpeMLp/0ZMAADhIQhIAcFiUSqVc84ul+fqvl2Vwn66pq6nKmEE9i54FAMAzICQBAIfcvobGvP+uBfnB/Cdy4pDeuWlqZQb27FL0LAAAniEhCQA4pLbu3p/Lbp2T+1dsyZknDMpX33pyunVyCwIA0Bq5iwMADpnVm3enuu7+LN+4K9NeODIffs3xqSh3MhsAQGslJAEAh8Tc1U/m0pmzs2X3/nzsdSek5oUji54EAMBzJCQBAM3uJwvX5j2z5qe8rCw3XFiZM08YVPQkAACagZAEADSbUqmUm/6wIp/9yUPp171zZlRX5sQhfYqeBQBAMxGSAIBm0dDYlI//cFFu+8vqjBnYIzOqqzK0b7eiZwEA0IyEJADgOdu1ryHv/Pbc/ObhjXnBMf3yzQsmp3fXjkXPAgCgmQlJAMBzsn773kyrq8+iJ7bnzZOH5LNnTUinDuVFzwIA4BAQkgCAZ23Juu2pqa3P2m17M/3MY3PlS0anrKys6FkAABwiQhIA8Kz8funGXHH73OxraMyXzz0pZ00cUvQkAAAOMSEJAHjGvnP/6nz4+w+me6eK3Hrx8/L8Uf2KngQAwGEgJAEAB62pqZQv/vzhXPfbRzO0b9fUVldl9MCeRc8CAOAwEZIAgIOy90Bj3nfXgvzwgSdy8tA+uWlqZfr36Fz0LAAADiMhCQD4l57ctT+X3To79SufzCvGDcpXzp2Yrp0qip4FAMBhJiQBAP/Uyk27UlNXnxWbduWSF43MB199fCrKncwGANAeCUkAwD80Z9WWXDJzdrbtOZBPvmFcLjplRNGTAAAokJAEADytHy1Ym/feOT8VZWW58aLKvPT4QUVPAgCgYEISAPC/lEqlXP/75bn6J0sysGfnzKiuyvjBvYueBQBACyAkAQB/09DYlI/esyjf/uvqHDeoZ2bUVGVwn65FzwIAoIUQkgCAJMnOfQ15x+1z87ulG/Oi0f1z3QWT0qtLx6JnAQDQgghJAEDWbtuTaXWz89Da7Tm3cmg+fdb4dKwoL3oWAAAtjJAEAO3c4ie2Z1pdfdZt35v3veK4XHH6MSkrKyt6FgAALZCQBADt2G8f3pB33D43BxpL+epbT84bTh5c9CQAAFowIQkA2qlv/3V1PvKDB9OzS4fU1lRmysi+RU8CAKCFE5IAoJ1pairlcz9bkut/tzzD+nZLbU1VjhnQo+hZAAC0AkISALQjew805t/vfCA/Wrg2E4f1yU0XVaZfj85FzwIAoJUQkgCgndiya38uvWV25qx6Mq+ecGSuecvJ6dKxouhZAAC0IkISALQDKzbtSk3t/Vm5eXcuP21UPvDKsSkvdzIbAADPTHlzfJKmpqZceeWVOeaYYzJ69Ohce+21//Daffv25Z3vfGfGjBmTCRMm5IILLmiOCQDAP1C/ckvOuu5PWb1ldz79xvH54KuPF5EAAHhWmuUVSbfddlsWL16cpUuXZtu2bZk4cWLOOOOMjBs37inXXnXVVSkrK8vSpUtTVlaWdevWNccEAOBp3PPAE/mPOx9Ih4qy3Dy1KmeMHVj0JAAAWrFmeUXSrFmzcumll6aioiJ9+/bNueeemzvuuOMp1+3atSs333xzPvOZz6Ss7P/9T+iRRx7ZHBMAgL9TKpVy3W+X5V13zMsR3TvmzstPEZEAAHjOmiUkrV69OsOHD//b4xEjRmT16tVPue7RRx9N375989nPfjaVlZU59dRT86tf/ao5JgAA/+NAY1M+ePfCfP6nD2fskT3z/Xe8MOMH9y56FgAAbcBB/WrbKaeckkceeeRpPzZv3ryDfrKGhoasWrUqJ5xwQq6++urMmzcvZ555ZhYtWpRBgwY95fprrrkm11xzzd8e79y586CfCwDaox17D+SK2+fmD49symnHDsg33jYxPbt0LHoWAABtxEGFpPvuu++ffnzYsGFZtWpVTjnllCTJypUrM2zYsKe9rry8POeff36SZOLEiRk5cmQWLlz4tCFp+vTpmT59+t8eDxky5GDmAkC79MTWPZlWV58l63bkvClD88k3jE/HimZ58TEAACRppl9tO+ecc3LjjTemsbExW7ZsyaxZs3Luuec+5br+/fvnpS99aX72s58lSVasWJEVK1bk+OOPb44ZANBuPfj4tpx13Z+yZN2OfOCVY/PZsyaISAAANLtmObXtwgsvTH19fcaMGZOysrJMnz49EyZMSJLcc889ueeee3LTTTclSb71rW/l4osvzgc+8IGUl5fn+uuvz+DBg5tjBgC0S79ZsiHv+PbcNDSV8vXzJuZ1Jx1d9CQAANqoslKpVCp6xMEaMmRI1qxZU/QMAGgxbv3LqnzsBw+md9eOufGiylSO6Fv0JAAAWpln0lua5RVJAMDh1dRUytU/XZIbfr88I/p1S23NlIzs373oWQAAtHFCEgC0MnsPNOa9s+bnJw+uS+XwI3LDRZXp271T0bMAAGgHhCQAaEU279yXS26ZnXmrt+a1Jx6VL55zUrp0rCh6FgAA7YSQBACtxKMbd6amtj6rt+zO208/Ju97+XEpLy8rehYAAO2IkAQArcBfl2/OZbfOyc59DfmvsyfkvCnDip4EAEA7JCQBQAv3g/mP533/vSCdOpRnRnVVXnzsgKInAQDQTglJANBClUqlfOM3y/LFny/Nkb26ZEZ1VU44ulfRswAAaMeEJABogQ40NuXD31uYO2evyQlH9cqM6qoc2btL0bMAAGjnhCQAaGG27z2QK26bmz8u25TTjxuQa982KT06+5YNAEDx3JUCQAvy+NY9qam9P0vX78z5zxuWT7x+XDpUlBc9CwAAkghJANBiLFyzLdNm1mfjjn354KvG5rLTRqWsrKzoWQAA8DdCEgC0AL9cvD5X3jEvTaVSrjt/Ul494aiiJwEAwFMISQBQsFvuW5mP37Mofbp1yo0XVWby8COKngQAAE9LSAKAgjQ2lfLZHz+Um/+4IqP6d09tTVWG9+te9CwAAPiHhCQAKMCe/Y15z6x5+dmi9akacURuuLAyR3TvVPQsAAD4p4QkADjMNu7Yl0tumZ0HHtua1590dD7/5hPTpWNF0bMAAOBfEpIA4DBatmFHqmvrs+bJPXnnGaMz/cxjU17uZDYAAFoHIQkADpP7Ht2cy2+dnV37G/O5N03IuVXDip4EAADPiJAEAIfB9+atyfvvWpAuHSpSV1OVU8cMKHoSAAA8Y0ISABxCpVIpX/vVsnz5l0tzdO8umVFTlbFH9ip6FgAAPCtCEgAcIvsbmvLBuxfmu3PXZPzgXrl5alUG9epS9CwAAHjWhCQAOAS27TmQt982J39+dHNeMnZgvn7exHTv7NsuAACtmztaAGhmj23ZnWl19Xlkw85c+Pzh+djrTkiHivKiZwEAwHMmJAFAM1qwZmum1c3O5l378p+vOT4Xv2hkysrKip4FAADNQkgCgGby80Xr8u7vzE9TqZRvnj8prxx/VNGTAACgWQlJANAMav+0Ip+8d3H6duuUm6ZWZuKwI4qeBAAAzU5IAoDnoLGplE//aHFq/7QyowZ0T131lAzr163oWQAAcEgISQDwLO3e35B3f2d+frF4fZ43sm+uv3By+nTrVPQsAAA4ZIQkAHgWNuzYm0tmzs6CNdty1sTBufpNE9K5Q0XRswAA4JASkgDgGXpk/Y5U19bn8a178q6XjM57zzzWyWwAALQLQhIAPAN/XrYpl982J3v2N+YLbz4x51QOLXoSAAAcNkISAByku+asyVXfXZCunSoyc9qUvHB0/6InAQDAYSUkAcC/UCqV8uVfPpKv/eqRDO7TNbU1VTl2UM+iZwEAwGEnJAHAP7G/oSlXfXdB7p73eCYM7p2bqyszsGeXomcBAEAhhCQA+Ae27T6Qy2+bnb8s35KXHT8wXztvYrp18q0TAID2y90wADyNx7bsTnXt/Xl0465Uv2BEPvLaE1JR7mQ2AADaNyEJAP6P+Y9tzSUz67N51/589LUnZNqLRhY9CQAAWgQhCQD+zk8fXJf3zJqXJPnWBZPzinFHFrwIAABaDiEJAPL/Tma7+Y8r8pkfP5R+3Tvl5qlVOWlon6JnAQBAiyIkAdDuNTaV8skfLsrM+1Zl9MAeqa2uytC+3YqeBQAALY6QBEC7tmtfQ951x7z8asmGnDKqX751weT07tax6FkAANAiCUkAtFsbtu/NtJn1efDx7Tl70uBcffaJ6dShvOhZAADQYglJALRLD6/bkWl19Xl8656852Vj8u6XjklZWVnRswAAoEUTkgBod/74yKa8/bY52dvQmC+dc1LeNHlI0ZMAAKBVEJIAaFfurH8sH/rewnTtVJGZ06bkBcf0L3oSAAC0GkISAO1CqVTKNb9Ymq//elmGHNE1dTVVGT2wZ9GzAACgVRGSAGjz9jU05v13LcgP5j+Rk4b0zk1TqzKgZ+eiZwEAQKsjJAHQpm3dvT+X3TIn96/ckpefMChffevEdO1UUfQsAABolYQkANqsVZt3paauPss37sq0F47Mh19zfCrKncwGAADPlpAEQJs0d/WTuWTm7GzdvT8ff90JqX7hyKInAQBAqyckAdDm/GTh2rxn1vyUl5Xl+gsrc+YJg4qeBAAAbYKQBECbUSqVcuMflue/frIk/Xt0zoypVZkwpHfRswAAoM0QkgBoExoam/LxHy7KbX9ZnWMH9ciM6qoMOaJb0bMAAKBNEZIAaPV27WvIO789N795eGNeOLpfrjt/cnp37Vj0LAAAaHOEJABatXXb9mZaXX0Wr92eN08eks+eNSGdOpQXPQsAANokIQmAVuuhtdszra4+a7ftzb+feWze+ZLRKSsrK3oWAAC0WUISAK3S75duzBW3z83+hqZ85dyT88aJg4ueBAAAbZ6QBECr8537V+fD338wPTp3yC0XT8nzR/UrehIAALQLQhIArUZTUylf/PnDue63j2Zo366prZ6S0QN7FD0LAADaDSEJgFZh74HG/Md/P5B7F6zNyUP75Kaplenfo3PRswAAoF0RkgBo8Z7ctT+X3jI7s1c9mVeOOzJfPvfkdO1UUfQsAABod4QkAFq0lZt2paauPis27cqlp47MB191fMrLncwGAABFEJIAaLHmrNqSS2bOzrY9B/KpN4zLhaeMKHoSAAC0a0ISAC3SvQueyPQ7H0iH8rLceFFlXnr8oKInAQBAuyckAdCilEqlXP/75bn6J0sysGfnzKiuyvjBvYueBQAAREgCoAVpaGzKR+9ZlG//dXWOG9QztTVVObpP16JnAQAA/0NIAqBF2LmvIe+4fW5+t3RjTh3TP984f1J6delY9CwAAODvCEkAFG7ttj2ZVjc7D63dnnMrh+bTZ41Px4ryomcBAAD/h5AEQKEWPbEt0+rqs377vrzvFcflitOPSVlZWdGzAACApyEkAVCY3zy8Ie+8fW4ONJby1beenDecPLjoSQAAwD8hJAFQiNv/uiof/cGi9OzSIbU1lZkysm/RkwAAgH9BSALgsGpqKuVzP1uS63+3PMP7dUttdVVGDehR9CwAAOAgCEkAHDZ7DzTm3+98ID9auDaThvXJjRdVpl+PzkXPAgAADpKQBMBhsXnnvlx6y+zMXb01r5lwVL70lpPSpWNF0bMAAIBnQEgC4JBbvnFnaurqs2rz7lz+4lH5wCvGprzcyWwAANDaCEkAHFL1K7fk0ltmZ/ueA/n0G8fngucPL3oSAADwLAlJABwy9zzwRP7jzgfSsaIsN1dX5YzjBhY9CQAAeA6EJACaXalUynW/fTRf+NnDGdSrc2ZUV2Xc0b2LngUAADxHQhIAzepAY1P+83sPZtbsxzL2yJ6pranKUb27Fj0LAABoBkISAM1mx94DueL2ufnDI5ty2rED8o23TUzPLh2LngUAADQTIQmAZvHE1j2ZVlefJet25LwpQ/PJN4xPx4ryomcBAADNSEgC4Dl78PFtmVZXnw079uUDrxybf3vxqJSVlRU9CwAAaGZCEgDPya+XrM87vz0vDU2lfP28iXndSUcXPQkAADhEhCQAnrVb71uZj92zKL27dswtF1WmckTfoicBAACHkJAEwDPW1FTK1T9dkht+vzwj+nVLbc2UjOzfvehZAADAISYkAfCM7D3QmPfOmp+fPLgulcOPyA0XVaZv905FzwIAAA4DIQmAg7Zp575cesvszFu9Na898ah88ZyT0qVjRdGzAACAw0RIAuCgPLpxZ6pr789jW/bk7acfk/e9/LiUlzuZDQAA2hMhCYB/6a/LN+eyW+dk576G/NfZE3LelGFFTwIAAAogJAHwT31/3uN5/10L0qlDeWZUV+XFxw4oehIAAFAQIQmAp1UqlXLtr5flS79YmqN6d8mM6qocf1SvomcBAAAFEpIAeIoDjU350N0L899z1uSEo3plRnVVjuzdpehZAABAwYQkAP6X7XsP5Irb5uaPyzbl9OMG5Nq3TUqPzr5dAAAAQhIAf2fNk7szra4+S9fvzPnPG5ZPvH5cOlSUFz0LAABoIYQkAJIkC9dsy7SZ9dm4Y18+9OqxufTUUSkrKyt6FgAA0IIISQDkl4vX58o75qWpVMp150/KqyccVfQkAACgBRKSANq5mX9emU/8cFH6dOuUGy+qzOThRxQ9CQAAaKGEJIB2qrGplM/86KHM+NOKjOrfPbU1VRner3vRswAAgBZMSAJoh/bsb8y7vzMvP1+8PlNG9M31F07OEd07FT0LAABo4YQkgHZm4459ueSW2Xngsa15/UlH5wvnnJjOHSqKngUAALQCQhJAO7Jsw45U19ZnzZN78s4zRmf6mcemvNzJbAAAwMERkgDaiT8/uin/duuc7NrfmM+9aULOrRpW9CQAAKCVEZIA2oG7567JB767IF06VKSupiqnjhlQ9CQAAKAVEpIA2rBSqZSv/WpZvvzLpTm6d5fMqKnK2CN7FT0LAABopYQkgDZqf0NTPnj3wnx37pqMH9wrN0+tyqBeXYqeBQAAtGJCEkAbtG3PgfzbrXNy3/LNecnYgfn6eRPTvbMv+QAAwHPjpwqANuaxLbtTU1efZRt25qJThuejrz0hHSrKi54FAAC0AUISQBvywGNbc/HM2dm8a1/+8zXH5+IXjUxZWVnRswAAgDZCSAJoI36+aF3e9Z15KZWSb54/Ka8cf1TRkwAAgDZGSAJoA2b8cUU+9aPF6dutU26aWpmJw44oehIAANAGCUkArVhjUymfundx6v68MqMGdE9d9ZQM69et6FkAAEAbJSQBtFK79zfkXXfMzy8fWp/njeyb6y+cnD7dOhU9CwAAaMOEJIBWaMOOvblk5uwsWLMtZ00cnKvfNCGdO1QUPQsAAGjjhCSAVmbp+h2pqa3P41v35F0vGZ33nnmsk9kAAIDDQkgCaEX+vGxTLr9tTvbsb8wX3nxizqkcWvQkAACgHRGSAFqJu+asyVXfXZCunSoyc9qUvHB0/6InAQAA7YyQBNDClUqlfPkXS/O1Xy/L4D5dU1tTlWMH9Sx6FgAA0A4JSQAt2L6Gxlz13YX53rzHM2Fw79xcXZmBPbsUPQsAAGinhCSAFmrb7gO57NbZ+euKLXnZ8QPztfMmplsnX7YBAIDi+IkEoAV6bMvuVNfen0c37kr1C0bkI689IRXlTmYDAACKJSQBtDDzVj+ZS2+Znc279uejrz0h0140suhJAAAASYQkgBblpw+uzbu/Mz9lZcm3LpicV4w7suhJAAAAfyMkAbQApVIpN/9xRT7z44fSr3un3Dy1KicN7VP0LAAAgP9FSAIoWENjUz557+Lcct+qjB7YI7XVVRnat1vRswAAAJ5CSAIo0K59DXnXHfPyqyUbcsqofvnWBZPTu1vHomcBAAA8LSEJoCDrt+/NtLr6LHpie86eNDhXn31iOnUoL3oWAADAPyQkARRgybrtmVZbnye27c17XjYm737pmJSVlRU9CwAA4J8SkgAOsz88sjFX3DY3exsa86VzTsqbJg8pehIAAMBBEZIADqM76x/Lh763MF07VWTmtCl5wTH9i54EAABw0IQkgMOgVCrlSz9fmmt/syxDjuiaupqqjB7Ys+hZAAAAz4iQBHCI7WtozPvvWpAfzH8iJw3pnZumVmVAz85FzwIAAHjGhCSAQ+jJXftz+a1zcv/KLXn5CYPy1bdOTNdOFUXPAgAAeFaEJIBDZNXmXamprc/yTbsy7YUj8+HXHJ+KciezAQAArZeQBHAIzFn1ZC69ZXa27t6fj7/uhFS/cGTRkwAAAJ4zIQmgmf144dq8d9b8lJeV5foLK3PmCYOKngQAANAshCSAZlIqlXLD75fnv36yJAN6ds6MqVWZMKR30bMAAACajZAE0AwaGpvysXsW5fa/rs6xg3pkRnVVhhzRrehZAAAAzUpIAniOdu5ryJXfnpvfPLwxLxzdL9edPzm9u3YsehYAAECzE5IAnoN12/ZmWl19Fq/dnnMmD8lnzpqQTh3Ki54FAABwSAhJAM/SQ2u3Z1pdfdZu25t/P/PYvPMlo1NWVlb0LAAAgENGSAJ4Fn63dGPecfvc7G9oylfOPTlvnDi46EkAAACHnJAE8Azdcf/q/Of3H0yPzh1yy8VT8vxR/YqeBAAAcFgISQAHqamplC/8/OF887ePZmjfrqmtnpLRA3sUPQsAAOCwEZIADsLeA435j/9+IPcuWJuTh/bJTVMr079H56JnAQAAHFZCEsC/sGXX/lx2y+zMXvVkXjnuyHzlrSenS8eKomcBAAAcdkISwD+xYtOu1NTen5Wbd+fSU0fmg686PuXlTmYDAADaJyEJ4B+YvXJLLr1ldrbtOZBPvWFcLjxlRNGTAAAACiUkATyNexc8kel3PpAO5WW5aWplXjJ2UNGTAAAACickAfydUqmUb/1ueT730yUZ2LNzZlRXZfzg3kXPAgAAeCkg3wAAIABJREFUaBGEJID/0dDYlI/8YFHuuH91xh7ZMzOqq3J0n65FzwIAAGgxhCSAJDv2Hsg7vj0vv1+6MaeO6Z/rzp+Unl06Fj0LAACgRRGSgHZv7bY9qamtz5J1O/LWqqH51BvHp2NFedGzAAAAWhwhCWjXFj2xLdPq6rN++7687xXH5YrTj0lZWVnRswAAAFokIQlot37z8Ia88/a5OdBYylffenLecPLgoicBAAC0aEIS0C7d/tdV+egPFqVnlw6pranMlJF9i54EAADQ4glJQLvS1FTK5362JNf/bnmG9+uW2uqqjBrQo+hZAAAArYKQBLQbew805t/vfCA/Wrg2k4b1yY0XVaZfj85FzwIAAGg1hCSgXdi8c18uvWV25q7emtdMOCpfestJ6dKxouhZAAAArYqQBLR5yzfuTE1dfVZt3p3LXzwqH3jF2JSXO5kNAADgmRKSgDbt/hVbctmts7Njb0M+c9b4nP+84UVPAgAAaLWEJKDN+sH8x/O+/16QjhVluWlqZc44bmDRkwAAAFo1IQloc0qlUq777aP5ws8ezpG9umRGdVVOOLpX0bMAAABaPSEJaFMONDblP7/3YGbNfixjj+yZ2pqqHNW7a9GzAAAA2gQhCWgztu89kHfcPjd/eGRTTjt2QL7xtonp2aVj0bMAAADaDCEJaBMe37on02rr8/D6HTlvyrB88g3j0rGivOhZAAAAbUqz/JTV1NSUK6+8Msccc0xGjx6da6+99h9e++Mf/ziTJk3KySefnPHjx2fmzJnNMQFoxx58fFvO+saf8vD6HbnqVWPz2bPGi0gAAACHQLO8Ium2227L4sWLs3Tp0mzbti0TJ07MGWeckXHjxv2v60qlUi644IL89re/zYknnpiVK1dm7NixOfvss9OzZ8/mmAK0M79esj7v/Pa8NDSVcu3bJua1Jx5d9CQAAIA2q1n+y37WrFm59NJLU1FRkb59++bcc8/NHXfc8bTXlpWVZevWrUmS7du3p1+/funcuXNzzADamVvvW5lLZs5O5w7l+fYlzxORAAAADrFmeUXS6tWrM3z48L89HjFiRP7yl7885bqysrLMmjUrZ599drp3754nn3wyd999dzp16tQcM4B2oqmplP/6yUO58Q8rMrJ/99RWV2VE/+5FzwIAAGjzDioknXLKKXnkkUee9mPz5s076CdraGjIpz/96dx999057bTTUl9fn9e//vVZuHBh+vfv/5Trr7nmmlxzzTV/e7xz586Dfi6gbdqzvzHvnTU/P120LpXDj8iNF1XmiO5iNAAAwOFwUL/adt9992XTpk1P+2fo0KEZNmxYVq1a9bfrV65cmWHDhj3l88yfPz9PPPFETjvttCRJVVVVhgwZ8g9j1PTp07NmzZq//enRo8ez+TcCbcSmnfty3o1/yU8XrcvrTjo6t13yPBEJAADgMGqW90g655xzcuONN6axsTFbtmzJrFmzcu655z7luqFDh2bt2rV56KGHkiTLli3Lo48+muOOO645ZgBt2LINO3PWdX/K/Me25orTj8lXzz05XTpWFD0LAACgXWmW90i68MILU19fnzFjxqSsrCzTp0/PhAkTkiT33HNP7rnnntx0000ZNGhQbrjhhrzlLW9JeXl5mpqacu211z7tq5cA/n9/Wb45l986Jzv3NeTqsyfkrVN8zQAAAChCWalUKhU94mANGTIka9asKXoGcBh9f97jed9dD6Rzh4p84/xJefGxA4qeBAAA0KY8k97SLK9IAmhupVIp1/56Wb70i6U5qneXzKiuyvFH9Sp6FgAAQLsmJAEtzv6Gpnzoewtz15w1OeGoXqmtqcqgXl2KngUAANDuCUlAi7Jtz4Fccfuc/GnZ5pxx3IB8/W2T0qOzL1UAAAAtgZ/OgBZjzZO7M62uPkvX78z5zxuWT7x+XDpUNMvhkgAAADQDIQloERas2ZqLZ87Oxh378qFXj82lp45KWVlZ0bMAAAD4O0ISULhfLl6fK++Yl6ZSKdedPymvnnBU0ZMAAAB4GkISUKi6P63IJ+9dnD7dOuWmqZWZNOyIoicBAADwDwhJQCEam0r5zI8eyow/rcio/t1TW1OV4f26Fz0LAACAf0JIAg67Pfsb8+7vzMvPF6/PlBF9c8NFk9OnW6eiZwEAAPAvCEnAYbVxx75cMrM+D6zZljecfHQ+/+YT07lDRdGzAAAAOAhCEnDYLNuwI9W19Vnz5J5c+ZLRmX7msU5mAwAAaEWEJOCw+POjm/Jvt87J7v2N+fybTsxbqoYWPQkAAIBnSEgCDrnvzlmTq+5ekC4dKlJXMyUvGtO/6EkAAAA8C0IScMiUSqV89VeP5Cu/fCRH9+6S2popOe7InkXPAgAA4FkSkoBDYn9DU666e0Hunvt4xg/ulRlTqzKwV5eiZwEAAPAcCElAs9u2+0D+7bY5uW/55rx07MB87byJ6d7ZlxsAAIDWzk92QLN6bMvu1NTVZ9mGnZl6yvB89HXjUlHuZDYAAIC2QEgCms38x7bmkpn12bxrfz7y2hMy7YUjUlYmIgEAALQVQhLQLH62aF3e/Z15SZJvnj85rxx/ZMGLAAAAaG5CEvCczfjjinzqR4vTr3un3DS1KicP7VP0JAAAAA4BIQl41hqbSvnUvYtT9+eVOWZA99TVTMnQvt2KngUAAMAhIiQBz8ru/Q151x3z8suHNuT5o/rm+gsq07tbx6JnAQAAcAgJScAztmHH3lxcNzsLH9+WsycOztVvOjGdOpQXPQv+v/buPMrrgt7/+GsYdhAQBFwGRFlcEFmH0vyV2e1mi1ouVy2Xgdyy5Zpdta6Vti+322aaK4O4lbmUZcutLNO0ZNhEXAB1BJRNkB0Ghvn+/vj9rud2M/2iMJ9ZHo9z+IPjR3mdo2858zx85wMAAOxiQhKwQ+YvX59JtdPz/JrN+dd3DM8F/zTcm9kAAADaCSEJKNufF76Y826akc3btudbJ43OieOrip4EAABAMxKSgLL8pG5xPnPX3HTrXJlpkyfm8GF7FD0JAACAZiYkAa+qVCrlO7+dn+/ftzD79OmWqZOqM3zgbkXPAgAAoABCEvAPNTRuz6fvnJu7Zz2fQ6t65/ozJ2TAbl2LngUAAEBBhCTgFa3ZtDXn3DQjjzy7Ou88eGC+d8qYdO/sfxkAAADtma8Kgb+zaNWm1Ex9JM+s3JhJbxmSz7734FR28GY2AACA9k5IAv7GrEUv5awb67J609ZcdszBmfSW/YqeBAAAQAshJAEv+9Xcpbngx7NTUZFcc9r4/PPIPYueBAAAQAsiJAEplUq54cFn85VfPpF+PbrkhjMnZPSgPkXPAgAAoIURkqCda9zelC/8/PHc9JfnMnxAz0ypqc6gvt2LngUAAEALJCRBO7axoTEfv21W7ntyRQ4f2i8/PG18enfrVPQsAAAAWighCdqp5eu2ZPLU6Zn3wrqcMK4qXzt+VDp37FD0LAAAAFowIQnaoSeXrcvk2ul5Ye2WXPjOEfn4UcNSUVFR9CwAAABaOCEJ2pkHFqzMR26emYbG7fnOyaPzgbFVRU8CAACglRCSoB358fRFufTux9K9c2WmTX5TDhvar+hJAAAAtCJCErQDTU2l/Odvn8qVf3g6Vbt3y9RJ1Rk2YLeiZwEAANDKCEnQxjU0bs9FP3k098x5IaMH9cn1Z0xI/926FD0LAACAVkhIgjbspY1bc+5NM/JI/eq8a+TAfPfksenWubLoWQAAALRSQhK0Uc+t2pia2ul59sWNOeuI/fKZ9xyUyg7ezAYAAMDrJyRBGzTjuZdy9rS6rNm0NV88bmTOOGxI0ZMAAABoA4QkaGPufXRpPnn77FRWVOS6MybkHQcNLHoSAAAAbYSQBG1EqVTKtX96Jl/71ZPpv1uXTDmzOqOqehc9CwAAgDZESII2oHF7Uy67Z15u+euijBjYM7WTJmafPt2KngUAAEAbIyRBK7ehoTEfu3Vm/vjUyhwxbI9cddq49OraqehZAAAAtEFCErRiy9ZuyaSp0/PE0nX5lwlV+coHRqVTZYeiZwEAANBGCUnQSj3+wrpMnjo9y9Ztyb/984h89O3DUlFRUfQsAAAA2jAhCVqh++evzPk3z8i27aV875QxOW7MPkVPAgAAoB0QkqCVufWvi/K5nz2Wnl06ZkrN+Lxp/35FTwIAAKCdEJKglWhqKuWbv3kqV9//dAb37Z7aSdUZ2r9n0bMAAABoR4QkaAW2bNueT/1kTu59dGnGDu6T68+YkH49uxQ9CwAAgHZGSIIWbvXGrTlnWl3qnnsp7z5kz3zn5DHp2qmy6FkAAAC0Q0IStGDPvrgxk2ofSf2qTTn3rfvnkqMPTIcO3swGAABAMYQkaKHq6lfn7Gl1Wbt5W770/kNy+pv3LXoSAAAA7ZyQBC3Qz+e8kE/9ZE46dqjIDWdW5+0HDih6EgAAAAhJ0JKUSqX88P6n881fP5WBvbrkhjOrc8g+vYueBQAAAEmEJGgxtm1vyud/9lhue2RxDtxzt9ROqs5evbsVPQsAAABeJiRBC7B+y7Z89NZZ+dP8lXnriP658oNjs1vXTkXPAgAAgL8hJEHBlq7dnEm10/PksvU5pXpQvvT+Q9KpskPRswAAAODvCElQoHkvrM3kqdOzfF1DLj76gHzkbUNTUVFR9CwAAAB4RUISFOQPT67Ix26dmW1NpVxx6tgcM3rvoicBAADAqxKSoAA3/+W5fP5nj6VXt06ZesaEVA/pW/QkAAAAeE1CEjSjpqZSvvHrJ3PNn57JkH7dUztpYvbbo0fRswAAAKAsQhI0ky3btufC22fnl3OXZfy+u+e6Myakb4/ORc8CAACAsglJ0AxWbWjIWdPqMmvRmrz30L3ynyeNTtdOlUXPAgAAgB0iJMEu9vTKDZlUOz2LVm/KeW8bmovfdUA6dPBmNgAAAFofIQl2oUeeXZ2zp9VlQ0Njvnb8qJw6cXDRkwAAAOB1E5JgF/nZ7Odz0U8eTafKikypqc7bRvQvehIAAAC8IUIS7GSlUilX/mFhvvVf87Nnr66ZUlOdg/fuVfQsAAAAeMOEJNiJtm1vyqV3z83tdUty0F69UltTnT17dy16FgAAAOwUQhLsJOu2bMtHb5mZBxa8mLeN6J8rPzQuPbs4MQAAANoOX+XCTvD8ms2ZXDs9Ty1fnw+9aXC+cOzIdKzsUPQsAAAA2KmEJHiDHnt+bSZNnZ6V6xvymXcfmHPeun8qKiqKngUAAAA7nZAEb8Dvn1iej982K41NpVz5wXF576F7FT0JAAAAdhkhCV6nmx6uz2X3zEuf7p1z3RnjM37fvkVPAgAAgF1KSIId1NRUyld/+USuf/DZ7LdHj0ydVJ19+/UoehYAAADsckIS7IDNW7fnkz+enV/PW5bqIbvn2tMnZPcenYueBQAAAM1CSIIyvbihIR++sS5zFq/JMaP3zn+ceGi6dqosehYAAAA0GyEJyrBwxYZMmvpIFq/enI++fWg+9c4D0qGDN7MBAADQvghJ8Br+8syqnDOtLhu3bs83ThiVk6sHFz0JAAAACiEkwau4e9aSXHzHo+nSsTK1NdV564j+RU8CAACAwghJ8ApKpVKuuG9hvv3b+dm7d9dMmVSdA/fsVfQsAAAAKJSQBP/L1sam/Pvdc3PHjCUZuXevTKmpzsBeXYueBQAAAIUTkuB/WLt5Wz5y84w89PSqHHXggFxx6tj06OJMAAAAIBGS4GVLXtqUSbXTs2DFhpz+5n1z2TEHp2Nlh6JnAQAAQIshJEGSR5esyeSpdVm1sSGffe9B+fAR+6WioqLoWQAAANCiCEm0e799fHk+cdusNJVKueqD4/LuUXsVPQkAAABaJCGJdq32z8/mi794PH27d851Z07IuMG7Fz0JAAAAWiwhiXZpe1MpX7738dT+uT779++RqTUTM7hf96JnAQAAQIsmJNHubNramH/90ez89vHlmbhf31x7+vj06d656FkAAADQ4glJtCsr1m/JWTfW5dEla/P+MXvnGycemi4dK4ueBQAAAK2CkES7sWD5+tTUTs/zazbnE0cNyyffOcKb2QAAAGAHCEm0Cw8tfDHn3jwjm7duzzdPPDT/MmFQ0ZMAAACg1RGSaPPumLEkn77z0XTrVJkbJ0/MW4btUfQkAAAAaJWEJNqsUqmU7/5uQb73+wXZp0+3TKmpzgF77lb0LAAAAGi1hCTapK2NTfn0nY/mrlnPZ9Q+vXPDmRMyoFfXomcBAABAqyYk0eas3bQt595cl788szr/dNCAfP/Usene2X/qAAAA8Eb56po2ZfHqTampfSRPr9yYmsOH5HPvOziVHbyZDQAAAHYGIYk2Y/biNTnrxulZtXFrPv++gzP5iP2KngQAAABtipBEm/Drx5blgh/PSpJcfdr4vGvkngUvAgAAgLZHSKJVK5VKmfLn+nz53sfTr0fnXH9mdcYM6lP0LAAAAGiThCRare1NpXzx5/Ny48PPZdiAnqmtqc6gvt2LngUAAABtlpBEq7Rpa2M+cdus/O6JFXnz/n1zzWkT0rt7p6JnAQAAQJsmJNHqrFi3JR++sS5zn1+b48fuk6+fcGg6d+xQ9CwAAABo84QkWpX5y9dnUu30PL9mcy74p+H513cMT0VFRdGzAAAAoF0Qkmg1HlzwYj5y84xsadyeb500OieOryp6EgAAALQrQhKtwu11i/Pvd81Nt86VuXHyxBw+dI+iJwEAAEC7IyTRopVKpXz7t/NzxX0Ls0+fbpk6qTrDB+5W9CwAAABol4QkWqyGxu255I5H89PZL2R0Ve9cd+aEDNita9GzAAAAoN0SkmiR1mzamnNumpFHnl2ddx48MN8/ZWy6da4sehYAAAC0a0ISLc6iVZtSM/WRPLNyYya/Zb9c+t6DUtnBm9kAAACgaEISLcrMRS/l7Bvr8tKmrbn8mINT85b9ip4EAAAA/H9CEi3Gr+YuzQU/np0OFRW55vQJeefBA4ueBAAAAPwPQhKFK5VKuf6BZ/PVXz2Rfj26ZErNhBxa1afoWQAAAMD/IiRRqMbtTbn85/Ny818WZcTAnplSU52q3bsXPQsAAAB4BUIShdnY0JiP3Tozf3hqZQ4f2i8/PG18enfrVPQsAAAA4B8QkijE8nVbMnnq9Mx7YV1OHF+Vr35gVDp37FD0LAAAAOBVCEk0uyeXrcuk2ulZunZLLnzniHz8qGGpqKgoehYAAADwGoQkmtWf5q/M+bfMTEPj9nzn5NH5wNiqoicBAAAAZRKSaDY/emRRLv3pY+nZpWNu+vCb8ub9+xU9CQAAANgBQhK7XFNTKd/6r6dy1R+fzqC+3VJbMzHDBvQsehYAAACwg4Qkdqkt27bnojsezc/nvJAxg/rk+jMnZI+eXYqeBQAAALwOQhK7zEsbt+acm+oyvf6lvGvkwHz35LHp1rmy6FkAAADA6yQksUvUv7gxk6ZOz7MvbsxZR+yXz7znoFR28GY2AAAAaM2EJHa6Gc+tzlk31mXt5m354nEjc8ZhQ4qeBAAAAOwEQhI71b2PLs0nb5+dyoqKXHfGhLzjoIFFTwIAAAB2EiGJnaJUKuWaPz2Tr//qyQzYrUum1FTnkH16Fz0LAAAA2ImEJN6wxu1N+fw983LrXxflgIG7Zcqk6uzTp1vRswAAAICdTEjiDdnQ0JiP3jIz989fmf8zfI9c+aFx6dW1U9GzAAAAgF1ASOJ1W7p2cyZPrcsTS9fl5AmD8uUPHJJOlR2KngUAAADsIkISr8vjL6zL5KnTs2zdllz0rgNy/pFDU1FRUfQsAAAAYBcSkthhf3hqRT52y8xs217K904Zk+PG7FP0JAAAAKAZCEnskFv/uiif+9lj2a1rx9ROmpCJ+/UtehIAAADQTIQkytLUVMo3fvNkrrn/mQzu2z21k6oztH/PomcBAAAAzUhI4jVt2bY9n7p9Tu6duzRjB/fJ9WdMSL+eXYqeBQAAADQzIYlXtXrj1pw9rS4znnsp7xm1Z779L2PStVNl0bMAAACAAghJ/EPPvrgxk2ofSf2qTTn3rfvnkqMPTIcO3swGAAAA7ZWQxCuaXr86Z0+ry7rN2/Ll9x+S0968b9GTAAAAgIIJSfyde+a8kH+7fU46VlbkhjOr8/YDBxQ9CQAAAGgBhCReViqVctUfn85//OapDOzVJTecWZ1D9uld9CwAAACghRCSSJJs296Uz/30sfxo+uIcuOduqZ1Unb16dyt6FgAAANCCCElk/ZZtOf+WmXlgwYt564j+ufKDY7Nb105FzwIAAABaGCGpnXthzeZMnjo9Ty5bn1MnDsoXjzsknSo7FD0LAAAAaIGEpHbssefXZvLU6VmxviGXHH1gznvb/qmoqCh6FgAAANBCCUnt1B+eXJGP3jozjU2lXHHq2Bwzeu+iJwEAAAAtnJDUDt30l+dy2c8eS+9unTLtjAmZMKRv0ZMAAACAVkBIakeamkr5+q+fzLV/eiZD+nVP7aSJ2W+PHkXPAgAAAFoJIamd2LJtez7549n51WPLMn7f3XPdGRPSt0fnomcBAAAArYiQ1A68uKEhZ0+ry6xFa/K+Q/fKt04ana6dKoueBQAAALQyQlIb9/TKDZlUOz2LVm/KR44cmov++YB06ODNbAAAAMCOE5LasL8+syrn3DQjGxoa87XjR+XUiYOLngQAAAC0YkJSG/Wz2c/nop88ms4dO2RKTXXeNqJ/0ZMAAACAVk5IamNKpVJ+cN/C/Odv52fPXl0zpaY6B+/dq+hZAAAAQBsgJLUh27Y35dK75+b2uiU5eK9emVJTnT17dy16FgAAANBGCEltxLot23L+zTPz4MIXc+QB/fODD45Lzy7+9QIAAAA7j9LQBix5aVMmT52e+cs35ENvGpwvHDsyHSs7FD0LAAAAaGOEpFZu7pK1mXzj9Kxc35DPvPvAnPPW/VNRUVH0LAAAAKANEpJasd89vjwfv21WtpdKufKD4/LeQ/cqehIAAADQhglJrdSND9XnCz+flz7dO+e6M8Zn/L59i54EAAAAtHFCUiuzvamUr/7yidzw4LPZf48eqZ1UnX379Sh6FgAAANAOCEmtyOat23PBj2flN/OWp3rI7rn29AnZvUfnomcBAAAA7YSQ1EqsXN+Qs6bVZc7iNTl29N755omHpmunyqJnAQAAAO3ITnlH/L333pvx48enS5cuueCCC1712QULFuTwww/PiBEjUl1dnXnz5u2MCW3awhXr84Gr/pw5i9fkY28flu+ePEZEAgAAAJrdTglJw4cPz5QpU3LRRRe95rPnnntuzjnnnMyfPz+XXHJJampqdsaENuvhp1fl+KseytK1W/KNE0bl3951QDp0qCh6FgAAANAO7ZSQNGLEiIwePTodO776J+VWrFiRurq6nHbaaUmSE044IYsXL87ChQt3xow2566ZS3LGlL+mVEqmTqrOydWDi54EAAAAtGM7JSSVa/Hixdlrr71eDk4VFRUZPHhwFi1a9IrPf/vb305VVdXLPzZs2NCccwtTKpXyvd8tyIW3z0n/nl3yk48clv8zvH/RswAAAIB2rqxvtn3YYYdlwYIFr/jXZs2alUGDBu3UUf/twgsvzIUXXvjyz6uqqnbJr9OSbG1symfumps7Zy7JyL17ZUpNdQb26lr0LAAAAIDyQtLDDz+8U36xQYMGZenSpWlsbEzHjh1TKpWyaNGiDB7sI1tJsnbztpx304w8/MyqHHXggFxx6tj06OLFegAAAEDL0KwfbRswYEDGjRuXm2++OUly5513pqqqKsOGDWvOGS3S4tWbcuIPH8rDz6zK6W/eN9eePl5EAgAAAFqUilKpVHqj/5Df//73OfPMM7Nu3bqUSqX07t07V111VY499tjcc889ueeee3L99dcnSZ566qnU1NRk1apV6dWrV2prazNq1Kiyfp2qqqosWbLkjc5tceYsXpMP31iXVRsbcul7DsqHj9gvFRXezAYAAADsejvSW3ZKSGoubTEk/de8ZfnEj2alVEq+d8qYHH3IXkVPAgAAANqRHektPjtVoCkPPpsv3ft4+nbvnOvOnJBxg3cvehIAAADAPyQkFWB7Uylf+sXjmfpQffbv3yNTayZmcL/uRc8CAAAAeFVCUjPbtLUxn7htdn73xPK8ab++ueb08enTvXPRswAAAABek5DUzGY+tya/f3J53j9m73zjxEPTpWNl0ZMAAAAAyiIkNbMjhu+RO847POMG9/FmNgAAAKBVEZIKMH5f31QbAAAAaH06FD0AAAAAgNZBSAIAAACgLEISAAAAAGURkgAAAAAoi5AEAAAAQFmEJAAAAADKIiQBAAAAUBYhCQAAAICyCEkAAAAAlEVIAgAAAKAsQhIAAAAAZRGSAAAAACiLkAQAAABAWYQkAAAAAMoiJAEAAABQFiEJAAAAgLIISQAAAACURUgCAAAAoCxCEgAAAABlEZIAAAAAKIuQBAAAAEBZhCQAAAAAyiIkAQAAAFAWIQkAAACAsghJAAAAAJRFSAIAAACgLEISAAAAAGURkgAAAAAoi5AEAAAAQFmEJAAAAADKIiQBAAAAUBYhCQAAAICyCEkAAAAAlEVIAgAAAKAsQhIAAAAAZRGSAAAAACiLkAQAAABAWYQkAAAAAMoiJAEAAABQFiEJAAAAgLIISQAAAACUpaJUKpWKHlGuLl26pH///kXP2Ck2bNiQnj17Fj0DWjy3Aq/NnUB53AqUx61AedrSraxcuTINDQ1lPduqQlJbUlVVlSVLlhQ9A1o8twKvzZ1AedwKlMetQHna6634aBsAAAAAZRGSAAAAAChL5eWXX3550SPaq8MOO6zoCdAquBV4be4EyuNWoDxuBcrTHm/F90gCAAAAoCw+2gYAAABAWYQkAAAAAMoiJAEAAABQFiFpF1qwYEEOP/zwjBgxItXV1Zk3b94rPnfDDTdk+PDhGTp0aM4+++xs27atmZeNv6cMAAAFHUlEQVRCccq5k/r6+hx55JHp3bt3xowZU8BKKF45t3Lfffdl4sSJOfjggzNy5MhcfPHFaWpqKmAtFKecW3n44YczZsyYjBkzJiNHjsy5556bhoaGAtZCccr9WiVJSqVSjjrqqPTp06cZF0LLUM6t/PGPf0y3bt1e/r1lzJgx2bx5cwFrm4eQtAude+65OeecczJ//vxccsklqamp+btnnn322Xzuc5/LAw88kIULF2b58uW59tprm38sFKScO+nVq1e+/OUv59Zbb23+gdBClHMru+++e370ox/l8ccfz4wZM/LQQw9l2rRpzT8WClTOrYwePTrTp0/P7NmzM3fu3KxYsSJXXXVV84+FApVzK//tO9/5ToYOHdp846AFKfdWDjjggMyePfvlH926dWveoc1ISNpFVqxYkbq6upx22mlJkhNOOCGLFy/OwoUL/+a5O+64I8cee2z23HPPVFRU5Lzzzsttt91WxGRoduXeSd++fXPEEUekR48eRcyEwpV7K2PHjs3++++fJOnatWvGjBmT+vr65p4LhSn3Vrp3755OnTolSbZu3ZrNmzenoqKi2fdCUcq9lSSZN29efvrTn+bTn/50c8+Ewu3IrbQnQtIusnjx4uy1117p2LFjkqSioiKDBw/OokWL/ua5RYsWZd99933550OGDPm7Z6CtKvdOoL17PbeybNmy3HHHHXnf+97XXDOhcDtyK/X19Rk9enT22GOP9O7dO+eff35zz4XClHsr27Zty9lnn51rrrkmlZWVRUyFQu3I7ytPP/10xo0bl+rq6jb/p1yFJABoY9atW5djjjkmF198cSZMmFD0HGiRhgwZkjlz5mTZsmVpaGjIXXfdVfQkaHG+8IUv5Pjjj89BBx1U9BRo0caNG5clS5Zk5syZufvuu3P11Vfn9ttvL3rWLiMk7SKDBg3K0qVL09jYmOT/fYO6RYsWZfDgwX/z3ODBg/Pcc8+9/PP6+vq/ewbaqnLvBNq7HbmV9evX5+ijj85xxx2XCy+8sLmnQqFez+8rPXv2zCmnnJJbbrmluWZC4cq9lfvvvz9XXHFFhgwZkiOOOCLr1q3LkCFDsnLlyiJmQ7Mr91Z69eqV3r17J0mqqqpy6qmn5oEHHmj2vc1FSNpFBgwYkHHjxuXmm29Oktx5552pqqrKsGHD/ua5E044Iffcc0+WLVuWUqmUq6++OqecckoRk6HZlXsn0N6VeysbNmzI0UcfnaOPPjqf/exni5gKhSr3VhYuXPjyW3K3bt2au+++O4ceemiz74WilHsrDzzwQJ577rnU19fnwQcfTK9evVJfX5/+/fsXMRuaXbm3snTp0pfflLt+/fr84he/yNixY5t9b3OpKJVKpaJHtFVPPfVUampqsmrVqvTq1Su1tbUZNWpUzjrrrBx77LE59thjkyTXXXddvv71rydJjjzyyFx99dUvfwNIaOvKuZNNmzZlxIgRaWhoyNq1azNgwICcfvrp+drXvlb0fGg25dzKV77ylVx++eUZOXLky3/fSSedlEsvvbTA5dC8yrmVa6+9Nt///vdTWVmZxsbGvOMd78g3v/nNdO3atej50GzK/Vrlv9XX12fMmDFZs2ZNQYuhGOXcyg9+8IP88Ic/TMeOHdPY2JiTTjopl112WZt9kYOQBAAAAEBZfLQNAAAAgLIISQAAAACURUgCAAAAoCxCEgAAAABlEZIAAAAAKIuQBAAAAEBZhCQAAAAAyiIkAQAAAFCW/wuO6cvptCvRaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1280 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot solution\n",
    "plt.figure()\n",
    "bezier = domainExt1.sample('bezier', 9)\n",
    "x, uExt1 = bezier.eval([ns.x, ns.uExt1])\n",
    "#x, analytical = bezier.eval([ns.x, ns.analytical])\n",
    "fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')\n",
    "plt.plot(x, uExt1)\n",
    "#plt.plot(x, analytical, 'r')\n",
    "# plt.ylim(-2,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Internal node adjoint problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ns.x = geomInt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct residual\n",
    "# Element integrals\n",
    "# Diffusive part and RHS\n",
    "resInt = domainInt.integral('k uInt_,0 basisInt_n,0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part perturbation\n",
    "resInt += domainInt.integral('-v uInt_,0 basisInt_n' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part perturbation\n",
    "    #res += domain.integral('-v basis_n,0 u' @ ns, geometry=ns.x, degree=5)\n",
    "# Advective part primal basis\n",
    "resInt += domainInt.integral('-v basisTot1_1,0 basisInt_n' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part primal basis\n",
    "    #res += domain.integral('-v basis_n,0 u' @ ns, geometry=ns.x, degree=5)\n",
    "# SUPG stabilized part (will be added later)\n",
    "resInt -= domainInt.integral(' tau v basisInt_n,0 (k (uInt_,0)_,0 + v (uInt_,0 + basisTot1_1,0) )' @ ns, geometry=ns.x, degree=5)\n",
    "\n",
    "# Interface integrals\n",
    "# Diffusive part\n",
    "#    part one                      |\n",
    "#                                  v  minus might be necessary here because of jump definitions (likewise on second +)\n",
    "resInt += domainInt.interfaces.integral(' [uInt] n_0 k {basisInt_n,0} + beta k {uInt_,0} [basisInt_n] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #                                  v  minus might be necessary here because of jump definitions (likewise on second +)\n",
    "    #res += domain.interfaces.integral(' [basis_n] n_0 k {u_,0} + beta k {basis_n,0} [u] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#    part two (stabilized)\n",
    "resInt += domainInt.interfaces.integral(' tauA [uInt] n_0 [basisInt_n] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #    part two (stabilized)\n",
    "    #res += domain.interfaces.integral(' tauA [basis_n] n_0 [u] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part perturbation\n",
    "resInt += domainInt.interfaces.integral('- v [uInt] n_0 ({basisInt_n} - .5 C [basisInt_n] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part perturbation\n",
    "    #res += domain.interfaces.integral('- v [basis_n] n_0 ({u} - .5 C [u] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "# Advective part primal basis\n",
    "resInt += domainInt.interfaces.integral('- v [basisTot1_1] n_0 ({basisInt_n} - .5 C [basisInt_n] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part primal basis\n",
    "    #res += domain.interfaces.integral('- v [basis_n] n_0 ({u} - .5 C [u] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "\n",
    "# Boundary integrals LHS\n",
    "#    part one\n",
    "resInt += domainInt.boundary.integral('- k uInt basisInt_n,0 + beta k uInt_,0 basisInt_n ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #    part one\n",
    "    #res += domain.boundary.integral('- k basis_n u_,0 + beta k basis_n,0 u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#    part two (stabilized)\n",
    "resInt += domainInt.boundary['left'].integral(' tauZero uInt basisInt_n ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "resInt += domainInt.boundary['right'].integral(' tauN uInt basisInt_n ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #    part two (stabilized)\n",
    "    #res += domain.boundary['left'].integral(' tauZero basis_n u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res += domain.boundary['right'].integral(' tauN basis_n u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "\n",
    "# Boundary integrals RHS (weakly imposed boundary conditions) (including stabilization)\n",
    "# Diffusive part (keep an eye on the signs)\n",
    "resInt += domainInt.boundary['left'].integral(' ( k basisInt_n,0 - tauZero basisInt_n) glInt ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "resInt += domainInt.boundary['right'].integral(' ( k basisInt_n,0 - tauN basisInt_n) glInt ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    # Diffusive part\n",
    "    #res -= domain.boundary['left'].integral(' ( beta k basis_n,0 + tauZero basis_n) gl ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res -= domain.boundary['right'].integral(' ( beta k basis_n,0 + tauN basis_n) gr ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part perturbation\n",
    "resInt += domainInt.boundary['left'].integral('v basisInt_n n_0 (0.5 (glInt + uInt) + .5 C (glInt - uInt) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "resInt += domainInt.boundary['right'].integral('v basisInt_n n_0 (0.5 (grInt + uInt) + .5 C (grInt - uInt) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    # Advective part perturbation\n",
    "    #res += domain.boundary['left'].integral('v basis_n n_0 (0.5 (gl + u) - .5 C (gl - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res += domain.boundary['right'].integral('v basis_n n_0 (0.5 (gr + u) - .5 C (gr - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part primal basis\n",
    "resInt += domainInt.boundary['left'].integral('v basisInt_n n_0 (0.5 (glInt + basisTot1_1) + .5 C (glInt - basisTot1_1) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "resInt += domainInt.boundary['right'].integral('v basisInt_n n_0 (0.5 (grInt + basisTot1_1) + .5 C (grInt - basisTot1_1) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    # Advective part primal basis\n",
    "    #res += domain.boundary['left'].integral('v basis_n n_0 (0.5 (gl + u) - .5 C (gl - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res += domain.boundary['right'].integral('v basis_n n_0 (0.5 (gr + u) - .5 C (gr - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here comes the Jacobian\n",
      "(4, 4)\n",
      "[[ -1.98960000e+04  -1.00000000e+00  -1.00000000e+00   0.00000000e+00]\n",
      " [  1.95000000e+02   2.01020000e+04  -2.02980000e+04  -1.00000000e+00]\n",
      " [ -1.00000000e+00  -2.00980000e+04   2.01020000e+04  -1.00000000e+00]\n",
      " [  0.00000000e+00  -1.00000000e+00   1.95000000e+02  -1.98960000e+04]]\n",
      "[  -2. -198.    2.  198.]\n",
      "solve > solver returned with residual 4e-12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/slukic/Coding/Python/nutils/nutils/warnings.py:30: NutilsWarning: using explicit inflation; this is usually a bug.\n",
      "  warnings.warn(message, category, stacklevel=stacklevel)\n"
     ]
    }
   ],
   "source": [
    "# find lhs such that res == 0 and substitute this lhs in the namespace\n",
    "# lhs = solver.solve_linear('lhs', res, constrain=cons)\n",
    "lhsInt = solver.solve_linear('lhsInt', resInt)\n",
    "ns = ns(lhsInt=lhsInt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f24ea1e2e48>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x1280 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot solution\n",
    "plt.figure()\n",
    "bezier = domainInt.sample('bezier', 9)\n",
    "x, uInt = bezier.eval([ns.x, ns.uInt])\n",
    "#x, analytical = bezier.eval([ns.x, ns.analytical])\n",
    "fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')\n",
    "plt.plot(x, uInt)\n",
    "#plt.plot(x, analytical, 'r')\n",
    "# plt.ylim(-2,2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Right exterior node adjoint problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ns.x = geomExt2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct residual\n",
    "# Element integrals\n",
    "# Diffusive part and RHS\n",
    "resExt2 = domainExt2.integral('k uExt2_,0 basisExt2_n,0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part perturbation\n",
    "resExt2 += domainExt2.integral('-v uExt2_,0 basisExt2_n' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part perturbation\n",
    "    #res += domain.integral('-v basis_n,0 u' @ ns, geometry=ns.x, degree=5)\n",
    "# Advective part primal basis\n",
    "resExt2 += domainExt2.integral('- v basisTot2_1,0 basisExt2_n' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part primal basis\n",
    "    #res += domain.integral('-v basis_n,0 u' @ ns, geometry=ns.x, degree=5)\n",
    "# SUPG stabilized part (will be added later)\n",
    "resExt2 -= domainExt2.integral(' tau v basisExt2_n,0 (k (uExt2_,0)_,0 + v (uExt2_,0 + basisTot2_1,0) )' @ ns, geometry=ns.x, degree=5)\n",
    "\n",
    "# Interface integrals\n",
    "# Diffusive part\n",
    "#    part one                      |\n",
    "#                                  v  minus might be necessary here because of jump definitions (likewise on second +)\n",
    "#resExt1 += domainExt1.interfaces.integral(' [uExt1] n_0 k {basisExt1_n,0} + beta k {uExt1_,0} [basisExt1_n] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #                                  v  minus might be necessary here because of jump definitions (likewise on second +)\n",
    "    #res += domain.interfaces.integral(' [basis_n] n_0 k {u_,0} + beta k {basis_n,0} [u] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#    part two (stabilized)\n",
    "#resExt1 += domainExt1.interfaces.integral(' tauA [uExt1] n_0 [basisExt1_n] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #    part two (stabilized)\n",
    "    #res += domain.interfaces.integral(' tauA [basis_n] n_0 [u] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part perturbation\n",
    "#resExt1 += domainExt1.interfaces.integral('- v [uExt1] n_0 ({basisExt1_n} - .5 C [basisExt1_n] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part perturbation\n",
    "    #res += domain.interfaces.integral('- v [basis_n] n_0 ({u} - .5 C [u] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "# Advective part primal basis\n",
    "#resExt1 += domainExt1.interfaces.integral('- v [basisTot_0] n_0 ({basisExt1_n} - .5 C [basisExt1_n] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "    # Advective part primal basis\n",
    "    #res += domain.interfaces.integral('- v [basis_n] n_0 ({u} - .5 C [u] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "\n",
    "# Boundary integrals LHS\n",
    "#    part one\n",
    "resExt2 += domainExt2.boundary.integral('- k uExt2 basisExt2_n,0 + beta k uExt2_,0 basisExt2_n ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #    part one\n",
    "    #res += domain.boundary.integral('- k basis_n u_,0 + beta k basis_n,0 u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#    part two (stabilized)\n",
    "resExt2 += domainExt2.boundary['left'].integral(' tauZero uExt2 basisExt2_n ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "resExt2 += domainExt2.boundary['right'].integral(' tauN uExt2 basisExt2_n ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #    part two (stabilized)\n",
    "    #res += domain.boundary['left'].integral(' tauZero basis_n u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res += domain.boundary['right'].integral(' tauN basis_n u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "\n",
    "# Boundary integrals RHS (weakly imposed boundary conditions) (including stabilization)\n",
    "# Diffusive part (keep an eye on the signs)\n",
    "resExt2 += domainExt2.boundary['left'].integral(' ( k basisExt2_n,0 - tauZero basisExt2_n) glExt2 ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "resExt2 += domainExt2.boundary['right'].integral(' ( k basisExt2_n,0 - tauN basisExt2_n) glExt2 ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    # Diffusive part\n",
    "    #res -= domain.boundary['left'].integral(' ( beta k basis_n,0 + tauZero basis_n) gl ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res -= domain.boundary['right'].integral(' ( beta k basis_n,0 + tauN basis_n) gr ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part perturbation\n",
    "resExt2 += domainExt2.boundary['left'].integral('v basisExt2_n n_0 (0.5 (glExt2 + uExt2) + .5 C (glExt2 - uExt2) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "resExt2 += domainExt2.boundary['right'].integral('v basisExt2_n n_0 (0.5 (grExt2 + uExt2) + .5 C (grExt2 - uExt2) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    # Advective part perturbation\n",
    "    #res += domain.boundary['left'].integral('v basis_n n_0 (0.5 (gl + u) - .5 C (gl - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res += domain.boundary['right'].integral('v basis_n n_0 (0.5 (gr + u) - .5 C (gr - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part primal basis\n",
    "resExt2 += domainExt2.boundary['left'].integral('v basisExt2_n n_0 (0.5 (glExt2 + basisTot2_1) + .5 C (glExt2 - basisTot2_1) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "resExt2 += domainExt2.boundary['right'].integral('v basisExt2_n n_0 (0.5 (grExt2 + basisTot2_1) + .5 C (grExt2 - basisTot2_1) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    # Advective part primal basis\n",
    "    #res += domain.boundary['left'].integral('v basis_n n_0 (0.5 (gl + u) - .5 C (gl - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "    #res += domain.boundary['right'].integral('v basis_n n_0 (0.5 (gr + u) - .5 C (gr - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here comes the Jacobian\n",
      "(2, 2)\n",
      "[[ -1.98960000e+04   1.11022302e-14]\n",
      " [  1.92000000e+02  -1.98960000e+04]]\n",
      "[ -2.00000000e+00  -2.00980000e+04]\n",
      "solve > solver returned with residual 2e-16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/slukic/Coding/Python/nutils/nutils/warnings.py:30: NutilsWarning: using explicit inflation; this is usually a bug.\n",
      "  warnings.warn(message, category, stacklevel=stacklevel)\n"
     ]
    }
   ],
   "source": [
    "# find lhs such that res == 0 and substitute this lhs in the namespace\n",
    "# lhs = solver.solve_linear('lhs', res, constrain=cons)\n",
    "lhsExt2 = solver.solve_linear('lhsExt2', resExt2)\n",
    "ns = ns(lhsExt2=lhsExt2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f24ea0743c8>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1280 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot solution\n",
    "plt.figure()\n",
    "bezier = domainExt2.sample('bezier', 9)\n",
    "x, uExt2 = bezier.eval([ns.x, ns.uExt2])\n",
    "#x, analytical = bezier.eval([ns.x, ns.analytical])\n",
    "fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')\n",
    "plt.plot(x, uExt2)\n",
    "#plt.plot(x, analytical, 'r')\n",
    "# plt.ylim(-2,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solve primal problem\n",
    "\n",
    "Build enriched basis at first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array<3>\n",
      "<class 'nutils.function.Inflate'>\n"
     ]
    }
   ],
   "source": [
    "ns.basisPertTot  = domainTot.basis('discont', degree=degree)\n",
    "perturbVect = []\n",
    "ns.tempExt1 = ns.basisPertTot[:2]\n",
    "perturbVect.append([ns.tempExt1[k] * lhsExt1[k] for k in range(0, len(ns.tempExt1))])\n",
    "ns.tempInt = ns.basisPertTot[:]\n",
    "perturbVect.append([ns.tempInt[k] * lhsInt[k] for k in range(0, len(ns.tempInt))])\n",
    "ns.tempExt2 = ns.basisPertTot[2:]\n",
    "perturbVect.append([ns.tempExt2[k] * lhsExt2[k] for k in range(0, len(ns.tempExt2))])\n",
    "\n",
    "#pertBasis = []\n",
    "#pertBasis.append([perturbVect[i][j] for i in range(0, len(perturbVect)) for j in range(0, len(perturbVect[i]))])\n",
    "\n",
    "print(ns.basisTot)\n",
    "print(type(ns.basisTot))\n",
    "basisEnriched = [0, 0, 0]\n",
    "testVect = []\n",
    "for x in range(3):\n",
    "    for turb in perturbVect[x]:\n",
    "        basisEnriched[x] += turb\n",
    "    testVect.append(basisEnriched[x])\n",
    "    basisEnriched[x] = basisEnriched[x] + ns.basisTot[x]\n",
    "ns.basisEnriched = basisEnriched\n",
    "ns.testVect = testVect\n",
    "\n",
    "\n",
    "ns.testVect = perturbVect[2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ns.x = geomTot\n",
    "\n",
    "ns.bla0 = ns.basisEnriched[0]\n",
    "ns.bla1 = ns.basisEnriched[1]\n",
    "ns.bla2 = ns.basisEnriched[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/slukic/Coding/Python/nutils/nutils/warnings.py:30: NutilsWarning: using explicit inflation; this is usually a bug.\n",
      "  warnings.warn(message, category, stacklevel=stacklevel)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f24ea062f28>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1280 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# plot solution\n",
    "plt.figure()\n",
    "bezier = domainTot.sample('bezier', 9)\n",
    "x, bla0 = bezier.eval([ns.x, ns.bla0])\n",
    "#x, analytical = bezier.eval([ns.x, ns.analytical])\n",
    "fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')\n",
    "plt.plot(x, bla0)\n",
    "#plt.plot(x, analytical, 'r')\n",
    "# plt.ylim(-2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/slukic/Coding/Python/nutils/nutils/warnings.py:30: NutilsWarning: using explicit inflation; this is usually a bug.\n",
      "  warnings.warn(message, category, stacklevel=stacklevel)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f24e9fc27b8>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1280 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot solution\n",
    "plt.figure()\n",
    "bezier = domainTot.sample('bezier', 9)\n",
    "x, bla1 = bezier.eval([ns.x, ns.bla1])\n",
    "#x, analytical = bezier.eval([ns.x, ns.analytical])\n",
    "fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')\n",
    "plt.plot(x, bla1)\n",
    "#plt.plot(x, analytical, 'r')\n",
    "# plt.ylim(-2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/slukic/Coding/Python/nutils/nutils/warnings.py:30: NutilsWarning: using explicit inflation; this is usually a bug.\n",
      "  warnings.warn(message, category, stacklevel=stacklevel)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f24e9f9bac8>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1280 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot solution\n",
    "plt.figure()\n",
    "bezier = domainTot.sample('bezier', 9)\n",
    "x, bla2 = bezier.eval([ns.x, ns.bla2])\n",
    "#x, analytical = bezier.eval([ns.x, ns.analytical])\n",
    "fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')\n",
    "plt.plot(x, bla2)\n",
    "#plt.plot(x, analytical, 'r')\n",
    "# plt.ylim(-2,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solve primal problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ns.x = geomTot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct residual\n",
    "# Element integrals\n",
    "# Diffusive part and RHS\n",
    "res = domainTot.integral('k basisEnriched_n,0 u_,0 - basisEnriched_n f' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part\n",
    "res += domainTot.integral('-v basisEnriched_n,0 u' @ ns, geometry=ns.x, degree=5)\n",
    "# SUPG stabilized part\n",
    "#res += domain.integral(' tau v basis_n,0 (v u_,0 - k (u_,0)_,0 - f )' @ ns, geometry=ns.x, degree=5)\n",
    "\n",
    "# Interface integrals\n",
    "# Diffusive part\n",
    "#    part one                      |\n",
    "#                                  v  minus might be necessary here because of jump definitions (likewise on second +)\n",
    "#res += domainTot.interfaces.integral(' [basisEnriched_n] n_0 k {u_,0} + beta k {basisEnriched_n,0} [u] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#    part two (stabilized)\n",
    "#res += domainTot.interfaces.integral(' tauA [basisEnriched_n] n_0 [u] n_0' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part\n",
    "#res += domainTot.interfaces.integral('- v [basisEnriched_n] n_0 ({u} - .5 C [u] n_0)' @ ns, geometry=ns.x, degree=5)\n",
    "\n",
    "# Boundary integrals LHS\n",
    "#    part one\n",
    "#res += domainTot.boundary.integral('- k basisEnriched_n u_,0 + beta k basisEnriched_n,0 u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#    part two (stabilized)\n",
    "#res += domainTot.boundary['left'].integral(' tauZero basisEnriched_n u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#res += domainTot.boundary['right'].integral(' tauN basisEnriched_n u ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "\n",
    "# Boundary integrals RHS (weakly imposed boundary conditions) (including stabilization)\n",
    "# Diffusive part\n",
    "#res -= domainTot.boundary['left'].integral(' ( beta k basisEnriched_n,0 + tauZero basisEnriched_n) gl ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#res -= domainTot.boundary['right'].integral(' ( beta k basisEnriched_n,0 + tauN basisEnriched_n) gr ' @ ns, geometry=ns.x, degree=degree*2)\n",
    "# Advective part\n",
    "#res += domainTot.boundary['left'].integral('v basisEnriched_n n_0 (0.5 (gl + u) - .5 C (gl - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)\n",
    "#res += domainTot.boundary['right'].integral('v basisEnriched_n n_0 (0.5 (gr + u) - .5 C (gr - u) n_0)' @ ns, geometry=ns.x, degree=degree*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "optimize > iter 1 > solve > solver returned with residual 0e+00\n",
      "optimize > constrained 2/3 dofs\n"
     ]
    }
   ],
   "source": [
    "sqr = domainTot.boundary['left'].integral('(u - gl)^2' @ ns, geometry=ns.x, degree=degree*2)\n",
    "sqr += domainTot.boundary['right'].integral('(u - gr)^2' @ ns, geometry=ns.x, degree=degree*2)\n",
    "cons = solver.optimize('lhs', sqr, droptol=1e-15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here comes the Jacobian\n",
      "(3, 3)\n",
      "[[ 2. -2.  0.]\n",
      " [-2.  4. -2.]\n",
      " [ 0. -2.  2.]]\n",
      "[ 0.  0.  0.]\n",
      "solve > solver returned with residual 0e+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/slukic/Coding/Python/nutils/nutils/warnings.py:30: NutilsWarning: using explicit inflation; this is usually a bug.\n",
      "  warnings.warn(message, category, stacklevel=stacklevel)\n"
     ]
    }
   ],
   "source": [
    "# find lhs such that res == 0 and substitute this lhs in the namespace\n",
    "lhs = solver.solve_linear('lhs', res, constrain=cons)\n",
    "#lhs = solver.solve_linear('lhs', res)\n",
    "ns = ns(lhs=lhs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fad38e2cdd8>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1280 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot solution\n",
    "plt.figure()\n",
    "bezier = domainTot.sample('bezier', 9)\n",
    "x, u = bezier.eval([ns.x, ns.u])\n",
    "#x, analytical = bezier.eval([ns.x, ns.analytical])\n",
    "fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')\n",
    "plt.plot(x, u)\n",
    "#plt.plot(x, analytical, 'r')\n",
    "# plt.ylim(-2,2)"
   ]
  },
  {
   "cell_type": "code",
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