From 42cff1ad7dfe36a5bc368d5bc82328d7a7d159c6 Mon Sep 17 00:00:00 2001 From: Anushka Sigh Date: Mon, 10 Mar 2025 18:25:09 +0100 Subject: [PATCH 1/6] Add solution plots to poisson solver notebook --- chapter2/poisson.ipynb | 299 ++++++++++++++++++++++++++++++++--------- 1 file changed, 236 insertions(+), 63 deletions(-) diff --git a/chapter2/poisson.ipynb b/chapter2/poisson.ipynb index 76aad47..2c6e594 100644 --- a/chapter2/poisson.ipynb +++ b/chapter2/poisson.ipynb @@ -5,22 +5,22 @@ "id": "9f28f9af", "metadata": {}, "source": [ - "# The Poisson equation\n", + "# Your first code using SymPDE & PsyDAC\n", + "*Author: Ahmed Ratnani*\n", "\n", - "As a first example, we consider the Poisson equation\n", + "We start by writing our first example using SymPDE.\n", + "Let $\\Omega := (0,1)^2$. We consider the Poisson problem with homogeneous Dirichlet boundary conditions. \n", "\n", "$$\n", "\\begin{align}\n", - " - \\nabla^2 u = f \\quad &\\text{in $\\Omega$}, \\\\ \n", - " u = 0 \\quad &\\text{on $\\Gamma_0$}, \\\\\n", - " u = g_i \\quad &\\text{on $\\Gamma_I$}, \\\\\n", - " \\partial_n u = g_n \\quad &\\text{on $\\Gamma_N := \\partial \\Omega \\setminus \\left( \\Gamma_0 \\cup \\Gamma_I \\right)$}.\n", + " - \\nabla^2 u = f \\quad \\text{in $\\Omega$}, \\quad \\quad \n", + " u = 0 \\quad \\text{on $\\partial \\Omega$}. \n", "\\end{align}\n", "$$\n", "\n", "## Variational Formulation\n", "\n", - "An $H^1$-conforming variational formulation of reads\n", + "An $H^1$-conforming variational formulation of the previous problem reads\n", "\n", "$$\n", "\\begin{align}\n", @@ -30,9 +30,9 @@ "\n", "where \n", "\n", - "- $V \\subset H^1(\\Omega)$, \n", - "- $a(u,v) := \\int_{\\Omega} \\nabla u \\cdot \\nabla v ~ d\\Omega$,\n", - "- $l(v) := \\int_{\\Omega} f v ~ d\\Omega + \\int_{\\Gamma_N} g_n v ~ d\\Gamma$." + "- $V \\subset H^1_0(\\Omega)$, \n", + "- $a(u,v) := \\int_{\\Omega} \\nabla u \\cdot \\nabla v ~ d\\Omega$, \n", + "- $l(v) := \\int_{\\Omega} f v ~ d\\Omega$." ] }, { @@ -45,55 +45,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "id": "d742586c", "metadata": {}, "outputs": [], "source": [ - "from sympde.expr import BilinearForm, LinearForm, integral\n", + "from sympde.expr import BilinearForm, LinearForm, integral\n", "from sympde.expr import find, EssentialBC, Norm, SemiNorm\n", "from sympde.topology import ScalarFunctionSpace, Square, element_of\n", - "from sympde.calculus import grad, dot, laplace\n", - "from sympde.topology import NormalVector, Union\n", - "\n", - "from sympy import pi, sin\n", + "from sympde.calculus import grad, dot\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "\n", - "from psydac.api.discretization import discretize\n", + "from sympy import pi, sin, lambdify\n", "\n", - "domain = Square()\n", - "Gamma_0 = domain.get_boundary(axis=0, ext=-1)\n", - "Gamma_i = domain.get_boundary(axis=0, ext=1)\n", - "Gamma_n = domain.boundary.complement(Union(Gamma_0, Gamma_i))\n", - "nn = NormalVector('nn')\n", + "domain = Square() # defines unit sq of [0,1] x [0,1]\n", "\n", - "V = ScalarFunctionSpace('V', domain)\n", + "V = ScalarFunctionSpace('V', domain) # finite element space where galerkin method is applied,\n", + " # fxn space for scalar valued functions over the square domain\n", "\n", "x,y = domain.coordinates\n", "\n", - "u,v = [element_of(V, name=i) for i in ['u', 'v']]\n", + "u,v = [element_of(V, name=i) for i in ['u', 'v']] #trial and test functions \n", "\n", "# bilinear form\n", - "a = BilinearForm((u,v), integral(domain , dot(grad(v), grad(u))))\n", - "\n", - "# exact solution\n", - "ue = sin(pi*x) * (1+y*sin(pi*y/3))**2\n", - "L = lambda w: - laplace(w)\n", - "f = L(ue)\n", - "gi = ue\n", - "gn = ue\n", + "a = BilinearForm((u,v), integral(domain , dot(grad(v), grad(u)))) # derived from variational form of pde\n", "\n", "# linear form\n", - "l = LinearForm(v, integral(domain, f*v))\n", - "\n", - "# Boundary term for the Neumann BC\n", - "ln = LinearForm(v, integral(Gamma_n, v * dot(grad(gn), nn)))\n", + "f = 2*pi**2*sin(pi*x)*sin(pi*y) #rhs function\n", + "l = LinearForm(v, integral(domain, f*v)) # from variational form of pde\n", "\n", "# Dirichlet boundary conditions\n", - "bc = [EssentialBC(u, 0, Gamma_0)]\n", - "bc += [EssentialBC(u, gi, Gamma_i)]\n", + "bc = [EssentialBC(u, 0, domain.boundary)]\n", "\n", "# Variational problem\n", - "equation = find(u, forall=v, lhs=a(u, v), rhs=l(v)+ln(v), bc=bc)" + "equation = find(u, forall=v, lhs=a(u, v), rhs=l(v), bc=bc)" + ] + }, + { + "cell_type": "markdown", + "id": "62ac1fd4", + "metadata": {}, + "source": [ + "\n", + "This very simple Python code reflects well the abstract mathematical framework needed for variational formulations.\n", + "The structure of the code is as follows,\n", + "\n", + "1. Create a domain.\n", + "2. Create a space of *scalar* functions over the domain.\n", + "3. Create elements from this function space. These elements will denote the test and trial functions.\n", + "4. Create the Bilinear and Linear forms, $a$ and $l$ respectively.\n", + "5. Create Essential Boundary Conditions.\n", + "6. Create the variational problem.\n", + "\n", + "Most of the time, you will need to follow the same steps, with some minor variants depending on the problem you're considering." ] }, { @@ -104,20 +109,40 @@ "## Discretization" ] }, + { + "cell_type": "markdown", + "id": "51095918", + "metadata": {}, + "source": [ + "We shall need the **discretize** function from **PsyDAC**." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, + "id": "a2a0a2a1", + "metadata": {}, + "outputs": [], + "source": [ + "from psydac.api.discretization import discretize" + ] + }, + { + "cell_type": "code", + "execution_count": 26, "id": "00e54163", "metadata": {}, "outputs": [], "source": [ - "degree = [2,2]\n", - "ncells = [8,8]" + "degree = [3,3]\n", + "p1, p2 = (3,3)\n", + "ncells = [16,16]\n", + "ne1, ne2 = (16,16)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 102, "id": "5999c62b", "metadata": {}, "outputs": [], @@ -142,21 +167,13 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "004dfdb3", - "metadata": {}, - "outputs": [], - "source": [ - "equation_h.set_solver('gmres', info=False, tol=1e-8)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 101, "id": "541192ee", "metadata": {}, "outputs": [], "source": [ + "# FEM func represented in terms of B spline basis functions\n", + "# uh.coeffs -> numerical coefficients of the solution in the b spline space (Vh)(finite subspace of V) \n", "uh = equation_h.solve()" ] }, @@ -186,15 +203,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "id": "5925c6cd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Norm(Integral(Square, (u - sin(pi*x1)*sin(pi*x2))**2))\n", + "9.49756716724664e-07\n" + ] + } + ], "source": [ - "u = element_of(V, name='u')\n", + "ue = sin(pi*x)*sin(pi*y)\n", "\n", + "u = element_of(V, name='u')\n", + "phi_exact = lambdify((x,y),ue,'numpy')\n", + "print(phi_exact)\n", "# create the formal Norm object\n", "l2norm = Norm(u - ue, domain, kind='l2')\n", + "print(l2norm)\n", "\n", "# discretize the norm\n", "l2norm_h = discretize(l2norm, domain_h, Vh)\n", @@ -216,10 +247,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "e5c1a8b8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.768702410721585e-05\n" + ] + } + ], "source": [ "# create the formal Norm object\n", "h1norm = SemiNorm(u - ue, domain, kind='h1')\n", @@ -244,10 +283,18 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "d829e410", + "execution_count": 8, + "id": "827c3e69-77ac-4312-a4dd-a1c26a40b27c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9.769164089397408e-05\n" + ] + } + ], "source": [ "# create the formal Norm object\n", "h1norm = Norm(u - ue, domain, kind='h1')\n", @@ -261,9 +308,135 @@ "# print the result\n", "print(h1_error)" ] + }, + { + "cell_type": "markdown", + "id": "14d92a71-8c0f-4a91-8f56-c405f3fc76d1", + "metadata": {}, + "source": [ + "### Visualization\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "1f3630d6-439a-496f-a005-c40352ff0c25", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_4048/1801382318.py:64: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", + "\n", + "N =10\n", + "\n", + "import numpy as np\n", + "\n", + "def refine_array_1d(breaks, N):\n", + " \"\"\"Refine a 1D array by inserting N points between each pair of original values.\"\"\"\n", + " refined = np.concatenate([np.linspace(breaks[i], breaks[i+1], N+1)[:-1] for i in range(len(breaks)-1)])\n", + " return np.append(refined, breaks[-1]) # Add the last point\n", + "\n", + "# Generate refined grid for visualization\n", + "eta1 = refine_array_1d(Vh.spaces[0].breaks, N)\n", + "eta2 = refine_array_1d(Vh.spaces[1].breaks, N)\n", + "\n", + "# Evaluate numerical solution on the refined grid\n", + "num = np.array([[uh(e1, e2) for e2 in eta2] for e1 in eta1])\n", + "\n", + "# Compute exact solution (assuming `phi_exact` is defined)\n", + "ex = np.array([[phi_exact(e1, e2) for e2 in eta2] for e1 in eta1])\n", + "err = num - ex\n", + "\n", + "# Generate physical grid coordinates (since no mapping, they are just `eta1` and `eta2`)\n", + "xx, yy = np.meshgrid(eta1, eta2, indexing='ij')\n", + "\n", + "# Create figure with 3 subplots:\n", + "fig, axes = plt.subplots(1, 3, figsize=(12.8, 4.8))\n", + "\n", + "def add_colorbar(im, ax):\n", + " divider = make_axes_locatable(ax)\n", + " cax = divider.append_axes(\"right\", size=0.2, pad=0.2)\n", + " cbar = ax.get_figure().colorbar(im, cax=cax)\n", + " return cbar\n", + "\n", + "# Plot exact solution\n", + "ax = axes[0]\n", + "im = ax.contourf(xx, yy, ex, 40, cmap='jet')\n", + "add_colorbar(im, ax)\n", + "ax.set_xlabel(r'$x$', rotation='horizontal')\n", + "ax.set_ylabel(r'$y$', rotation='horizontal')\n", + "ax.set_title (r'$\\phi_{exact}(x,y)$')\n", + "ax.set_aspect('equal')\n", + "\n", + "# Plot numerical solution\n", + "ax = axes[1]\n", + "im = ax.contourf(xx, yy, num, 40, cmap='jet')\n", + "add_colorbar(im, ax)\n", + "ax.set_xlabel(r'$x$', rotation='horizontal')\n", + "ax.set_ylabel(r'$y$', rotation='horizontal')\n", + "ax.set_title (r'$\\phi(x,y)$')\n", + "ax.set_aspect('equal')\n", + "\n", + "# Plot numerical error\n", + "ax = axes[2]\n", + "im = ax.contourf(xx, yy, err, 40, cmap='jet')\n", + "add_colorbar(im, ax)\n", + "ax.set_xlabel(r'$x$', rotation='horizontal')\n", + "ax.set_ylabel(r'$y$', rotation='horizontal')\n", + "ax.set_title (r'$\\phi(x,y) - \\phi_{exact}(x,y)$')\n", + "ax.set_aspect('equal')\n", + "\n", + "# Show figure\n", + "plt.tight_layout()\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f034ea6c-885e-4871-9312-49f98ce45f37", + "metadata": {}, + "outputs": [], + "source": [] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, "nbformat": 4, "nbformat_minor": 5 } From 92be763111685bf40ef6804fe6e98a157caf6e7b Mon Sep 17 00:00:00 2001 From: Anushka Sigh Date: Tue, 11 Mar 2025 15:31:51 +0530 Subject: [PATCH 2/6] Add solution plotting function --- chapter1/poisson.ipynb | 127 +++++++++++++++++++++++++++++++++++++++-- 1 file changed, 121 insertions(+), 6 deletions(-) diff --git a/chapter1/poisson.ipynb b/chapter1/poisson.ipynb index c8ec097..281da08 100644 --- a/chapter1/poisson.ipynb +++ b/chapter1/poisson.ipynb @@ -55,12 +55,16 @@ "from sympde.topology import ScalarFunctionSpace, Square, element_of\n", "from sympde.calculus import grad, dot\n", "\n", - "from sympy import pi, sin\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "\n", - "domain = Square()\n", + "from sympy import pi, sin, lambdify\n", "\n", - "V = ScalarFunctionSpace('V', domain)\n", + "domain = Square()\n", "\n", + "V = ScalarFunctionSpace('V', domain) \n", + " \n", "x,y = domain.coordinates\n", "\n", "u,v = [element_of(V, name=i) for i in ['u', 'v']]\n", @@ -202,11 +206,11 @@ "outputs": [], "source": [ "ue = sin(pi*x)*sin(pi*y)\n", - "\n", "u = element_of(V, name='u')\n", "\n", "# create the formal Norm object\n", "l2norm = Norm(u - ue, domain, kind='l2')\n", + "print(l2norm)\n", "\n", "# discretize the norm\n", "l2norm_h = discretize(l2norm, domain_h, Vh)\n", @@ -257,7 +261,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d829e410", + "id": "827c3e69-77ac-4312-a4dd-a1c26a40b27c", "metadata": {}, "outputs": [], "source": [ @@ -273,9 +277,120 @@ "# print the result\n", "print(h1_error)" ] + }, + { + "cell_type": "markdown", + "id": "14d92a71-8c0f-4a91-8f56-c405f3fc76d1", + "metadata": {}, + "source": [ + "### Visualization\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "968a2165-ab75-4bf0-b943-daab3bd9fa2d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", + "\n", + "def refine_array_1d(breaks, N):\n", + " \"\"\"Refine a 1D array by inserting N points between each pair of original values.\"\"\"\n", + " refined = np.concatenate([np.linspace(breaks[i], breaks[i+1], N+1)[:-1] for i in range(len(breaks)-1)])\n", + " return np.append(refined, breaks[-1]) # Add the last point\n", + "\n", + "def plot_solutions(Vh, uh, ue, N=10):\n", + " \"\"\"\n", + " Refine the grid, evaluate solutions, compute errors, and plot results.\n", + "\n", + " Parameters:\n", + " Vh: The finite element space (must have `spaces` attribute with `breaks`).\n", + " uh: The numerical solution function.\n", + " ue: The exact solution function.\n", + " N: Number of points to insert between breaks (default: 10).\n", + " \"\"\"\n", + " # Generate refined grid for visualization\n", + " eta1 = refine_array_1d(Vh.spaces[0].breaks, N)\n", + " eta2 = refine_array_1d(Vh.spaces[1].breaks, N)\n", + "\n", + " # Evaluate numerical solution on the refined grid\n", + " num = np.array([[uh(e1, e2) for e2 in eta2] for e1 in eta1])\n", + "\n", + " # Compute exact solution\n", + " ex = np.array([[phi_exact(e1, e2) for e2 in eta2] for e1 in eta1])\n", + " err = num - ex\n", + "\n", + " # Generate physical grid coordinates\n", + " xx, yy = np.meshgrid(eta1, eta2, indexing='ij')\n", + "\n", + " # Create figure with 3 subplots\n", + " fig, axes = plt.subplots(1, 3, figsize=(12.8, 4.8))\n", + "\n", + " def add_colorbar(im, ax):\n", + " \"\"\"Add a colorbar to the given axis.\"\"\"\n", + " divider = make_axes_locatable(ax)\n", + " cax = divider.append_axes(\"right\", size=0.2, pad=0.2)\n", + " cbar = ax.get_figure().colorbar(im, cax=cax)\n", + " return cbar\n", + "\n", + " # Plot exact solution\n", + " ax = axes[0]\n", + " im = ax.contourf(xx, yy, ex, 40, cmap='jet')\n", + " add_colorbar(im, ax)\n", + " ax.set_xlabel(r'$x$', rotation='horizontal')\n", + " ax.set_ylabel(r'$y$', rotation='horizontal')\n", + " ax.set_title(r'$\\phi_{exact}(x,y)$')\n", + " ax.set_aspect('equal')\n", + "\n", + " # Plot numerical solution\n", + " ax = axes[1]\n", + " im = ax.contourf(xx, yy, num, 40, cmap='jet')\n", + " add_colorbar(im, ax)\n", + " ax.set_xlabel(r'$x$', rotation='horizontal')\n", + " ax.set_ylabel(r'$y$', rotation='horizontal')\n", + " ax.set_title(r'$\\phi(x,y)$')\n", + " ax.set_aspect('equal')\n", + "\n", + " # Plot numerical error\n", + " ax = axes[2]\n", + " im = ax.contourf(xx, yy, err, 40, cmap='jet')\n", + " add_colorbar(im, ax)\n", + " ax.set_xlabel(r'$x$', rotation='horizontal')\n", + " ax.set_ylabel(r'$y$', rotation='horizontal')\n", + " ax.set_title(r'$\\phi(x,y) - \\phi_{exact}(x,y)$')\n", + " ax.set_aspect('equal')\n", + "\n", + " # Show figure\n", + " plt.tight_layout()\n", + " fig.show()\n", + "\n", + "ue = lambdify((x, y), u, 'numpy') # convert sympy function to a callable function \n", + "plot_solutions(Vh, uh, ue, N=10)" + ] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, "nbformat": 4, "nbformat_minor": 5 } From b5792dfec3fc78a62eb94bbdc56e7986dd89a06b Mon Sep 17 00:00:00 2001 From: Anushka Singh Date: Tue, 11 Mar 2025 12:08:09 +0100 Subject: [PATCH 3/6] Reverting incorrect changes --- chapter2/poisson.ipynb | 299 +++++++++-------------------------------- 1 file changed, 63 insertions(+), 236 deletions(-) diff --git a/chapter2/poisson.ipynb b/chapter2/poisson.ipynb index 2c6e594..76aad47 100644 --- a/chapter2/poisson.ipynb +++ b/chapter2/poisson.ipynb @@ -5,22 +5,22 @@ "id": "9f28f9af", "metadata": {}, "source": [ - "# Your first code using SymPDE & PsyDAC\n", - "*Author: Ahmed Ratnani*\n", + "# The Poisson equation\n", "\n", - "We start by writing our first example using SymPDE.\n", - "Let $\\Omega := (0,1)^2$. We consider the Poisson problem with homogeneous Dirichlet boundary conditions. \n", + "As a first example, we consider the Poisson equation\n", "\n", "$$\n", "\\begin{align}\n", - " - \\nabla^2 u = f \\quad \\text{in $\\Omega$}, \\quad \\quad \n", - " u = 0 \\quad \\text{on $\\partial \\Omega$}. \n", + " - \\nabla^2 u = f \\quad &\\text{in $\\Omega$}, \\\\ \n", + " u = 0 \\quad &\\text{on $\\Gamma_0$}, \\\\\n", + " u = g_i \\quad &\\text{on $\\Gamma_I$}, \\\\\n", + " \\partial_n u = g_n \\quad &\\text{on $\\Gamma_N := \\partial \\Omega \\setminus \\left( \\Gamma_0 \\cup \\Gamma_I \\right)$}.\n", "\\end{align}\n", "$$\n", "\n", "## Variational Formulation\n", "\n", - "An $H^1$-conforming variational formulation of the previous problem reads\n", + "An $H^1$-conforming variational formulation of reads\n", "\n", "$$\n", "\\begin{align}\n", @@ -30,9 +30,9 @@ "\n", "where \n", "\n", - "- $V \\subset H^1_0(\\Omega)$, \n", - "- $a(u,v) := \\int_{\\Omega} \\nabla u \\cdot \\nabla v ~ d\\Omega$, \n", - "- $l(v) := \\int_{\\Omega} f v ~ d\\Omega$." + "- $V \\subset H^1(\\Omega)$, \n", + "- $a(u,v) := \\int_{\\Omega} \\nabla u \\cdot \\nabla v ~ d\\Omega$,\n", + "- $l(v) := \\int_{\\Omega} f v ~ d\\Omega + \\int_{\\Gamma_N} g_n v ~ d\\Gamma$." ] }, { @@ -45,60 +45,55 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "id": "d742586c", "metadata": {}, "outputs": [], "source": [ - "from sympde.expr import BilinearForm, LinearForm, integral\n", + "from sympde.expr import BilinearForm, LinearForm, integral\n", "from sympde.expr import find, EssentialBC, Norm, SemiNorm\n", "from sympde.topology import ScalarFunctionSpace, Square, element_of\n", - "from sympde.calculus import grad, dot\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", + "from sympde.calculus import grad, dot, laplace\n", + "from sympde.topology import NormalVector, Union\n", + "\n", + "from sympy import pi, sin\n", "\n", - "from sympy import pi, sin, lambdify\n", + "from psydac.api.discretization import discretize\n", "\n", - "domain = Square() # defines unit sq of [0,1] x [0,1]\n", + "domain = Square()\n", + "Gamma_0 = domain.get_boundary(axis=0, ext=-1)\n", + "Gamma_i = domain.get_boundary(axis=0, ext=1)\n", + "Gamma_n = domain.boundary.complement(Union(Gamma_0, Gamma_i))\n", + "nn = NormalVector('nn')\n", "\n", - "V = ScalarFunctionSpace('V', domain) # finite element space where galerkin method is applied,\n", - " # fxn space for scalar valued functions over the square domain\n", + "V = ScalarFunctionSpace('V', domain)\n", "\n", "x,y = domain.coordinates\n", "\n", - "u,v = [element_of(V, name=i) for i in ['u', 'v']] #trial and test functions \n", + "u,v = [element_of(V, name=i) for i in ['u', 'v']]\n", "\n", "# bilinear form\n", - "a = BilinearForm((u,v), integral(domain , dot(grad(v), grad(u)))) # derived from variational form of pde\n", + "a = BilinearForm((u,v), integral(domain , dot(grad(v), grad(u))))\n", + "\n", + "# exact solution\n", + "ue = sin(pi*x) * (1+y*sin(pi*y/3))**2\n", + "L = lambda w: - laplace(w)\n", + "f = L(ue)\n", + "gi = ue\n", + "gn = ue\n", "\n", "# linear form\n", - "f = 2*pi**2*sin(pi*x)*sin(pi*y) #rhs function\n", - "l = LinearForm(v, integral(domain, f*v)) # from variational form of pde\n", + "l = LinearForm(v, integral(domain, f*v))\n", + "\n", + "# Boundary term for the Neumann BC\n", + "ln = LinearForm(v, integral(Gamma_n, v * dot(grad(gn), nn)))\n", "\n", "# Dirichlet boundary conditions\n", - "bc = [EssentialBC(u, 0, domain.boundary)]\n", + "bc = [EssentialBC(u, 0, Gamma_0)]\n", + "bc += [EssentialBC(u, gi, Gamma_i)]\n", "\n", "# Variational problem\n", - "equation = find(u, forall=v, lhs=a(u, v), rhs=l(v), bc=bc)" - ] - }, - { - "cell_type": "markdown", - "id": "62ac1fd4", - "metadata": {}, - "source": [ - "\n", - "This very simple Python code reflects well the abstract mathematical framework needed for variational formulations.\n", - "The structure of the code is as follows,\n", - "\n", - "1. Create a domain.\n", - "2. Create a space of *scalar* functions over the domain.\n", - "3. Create elements from this function space. These elements will denote the test and trial functions.\n", - "4. Create the Bilinear and Linear forms, $a$ and $l$ respectively.\n", - "5. Create Essential Boundary Conditions.\n", - "6. Create the variational problem.\n", - "\n", - "Most of the time, you will need to follow the same steps, with some minor variants depending on the problem you're considering." + "equation = find(u, forall=v, lhs=a(u, v), rhs=l(v)+ln(v), bc=bc)" ] }, { @@ -109,40 +104,20 @@ "## Discretization" ] }, - { - "cell_type": "markdown", - "id": "51095918", - "metadata": {}, - "source": [ - "We shall need the **discretize** function from **PsyDAC**." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "a2a0a2a1", - "metadata": {}, - "outputs": [], - "source": [ - "from psydac.api.discretization import discretize" - ] - }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "00e54163", "metadata": {}, "outputs": [], "source": [ - "degree = [3,3]\n", - "p1, p2 = (3,3)\n", - "ncells = [16,16]\n", - "ne1, ne2 = (16,16)\n" + "degree = [2,2]\n", + "ncells = [8,8]" ] }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "id": "5999c62b", "metadata": {}, "outputs": [], @@ -167,13 +142,21 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, + "id": "004dfdb3", + "metadata": {}, + "outputs": [], + "source": [ + "equation_h.set_solver('gmres', info=False, tol=1e-8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "541192ee", "metadata": {}, "outputs": [], "source": [ - "# FEM func represented in terms of B spline basis functions\n", - "# uh.coeffs -> numerical coefficients of the solution in the b spline space (Vh)(finite subspace of V) \n", "uh = equation_h.solve()" ] }, @@ -203,29 +186,15 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "id": "5925c6cd", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Norm(Integral(Square, (u - sin(pi*x1)*sin(pi*x2))**2))\n", - "9.49756716724664e-07\n" - ] - } - ], + "outputs": [], "source": [ - "ue = sin(pi*x)*sin(pi*y)\n", - "\n", "u = element_of(V, name='u')\n", - "phi_exact = lambdify((x,y),ue,'numpy')\n", - "print(phi_exact)\n", + "\n", "# create the formal Norm object\n", "l2norm = Norm(u - ue, domain, kind='l2')\n", - "print(l2norm)\n", "\n", "# discretize the norm\n", "l2norm_h = discretize(l2norm, domain_h, Vh)\n", @@ -247,18 +216,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "e5c1a8b8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9.768702410721585e-05\n" - ] - } - ], + "outputs": [], "source": [ "# create the formal Norm object\n", "h1norm = SemiNorm(u - ue, domain, kind='h1')\n", @@ -283,18 +244,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "827c3e69-77ac-4312-a4dd-a1c26a40b27c", + "execution_count": null, + "id": "d829e410", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9.769164089397408e-05\n" - ] - } - ], + "outputs": [], "source": [ "# create the formal Norm object\n", "h1norm = Norm(u - ue, domain, kind='h1')\n", @@ -308,135 +261,9 @@ "# print the result\n", "print(h1_error)" ] - }, - { - "cell_type": "markdown", - "id": "14d92a71-8c0f-4a91-8f56-c405f3fc76d1", - "metadata": {}, - "source": [ - "### Visualization\n" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "1f3630d6-439a-496f-a005-c40352ff0c25", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_4048/1801382318.py:64: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", - " fig.show()\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - "\n", - "N =10\n", - "\n", - "import numpy as np\n", - "\n", - "def refine_array_1d(breaks, N):\n", - " \"\"\"Refine a 1D array by inserting N points between each pair of original values.\"\"\"\n", - " refined = np.concatenate([np.linspace(breaks[i], breaks[i+1], N+1)[:-1] for i in range(len(breaks)-1)])\n", - " return np.append(refined, breaks[-1]) # Add the last point\n", - "\n", - "# Generate refined grid for visualization\n", - "eta1 = refine_array_1d(Vh.spaces[0].breaks, N)\n", - "eta2 = refine_array_1d(Vh.spaces[1].breaks, N)\n", - "\n", - "# Evaluate numerical solution on the refined grid\n", - "num = np.array([[uh(e1, e2) for e2 in eta2] for e1 in eta1])\n", - "\n", - "# Compute exact solution (assuming `phi_exact` is defined)\n", - "ex = np.array([[phi_exact(e1, e2) for e2 in eta2] for e1 in eta1])\n", - "err = num - ex\n", - "\n", - "# Generate physical grid coordinates (since no mapping, they are just `eta1` and `eta2`)\n", - "xx, yy = np.meshgrid(eta1, eta2, indexing='ij')\n", - "\n", - "# Create figure with 3 subplots:\n", - "fig, axes = plt.subplots(1, 3, figsize=(12.8, 4.8))\n", - "\n", - "def add_colorbar(im, ax):\n", - " divider = make_axes_locatable(ax)\n", - " cax = divider.append_axes(\"right\", size=0.2, pad=0.2)\n", - " cbar = ax.get_figure().colorbar(im, cax=cax)\n", - " return cbar\n", - "\n", - "# Plot exact solution\n", - "ax = axes[0]\n", - "im = ax.contourf(xx, yy, ex, 40, cmap='jet')\n", - "add_colorbar(im, ax)\n", - "ax.set_xlabel(r'$x$', rotation='horizontal')\n", - "ax.set_ylabel(r'$y$', rotation='horizontal')\n", - "ax.set_title (r'$\\phi_{exact}(x,y)$')\n", - "ax.set_aspect('equal')\n", - "\n", - "# Plot numerical solution\n", - "ax = axes[1]\n", - "im = ax.contourf(xx, yy, num, 40, cmap='jet')\n", - "add_colorbar(im, ax)\n", - "ax.set_xlabel(r'$x$', rotation='horizontal')\n", - "ax.set_ylabel(r'$y$', rotation='horizontal')\n", - "ax.set_title (r'$\\phi(x,y)$')\n", - "ax.set_aspect('equal')\n", - "\n", - "# Plot numerical error\n", - "ax = axes[2]\n", - "im = ax.contourf(xx, yy, err, 40, cmap='jet')\n", - "add_colorbar(im, ax)\n", - "ax.set_xlabel(r'$x$', rotation='horizontal')\n", - "ax.set_ylabel(r'$y$', rotation='horizontal')\n", - "ax.set_title (r'$\\phi(x,y) - \\phi_{exact}(x,y)$')\n", - "ax.set_aspect('equal')\n", - "\n", - "# Show figure\n", - "plt.tight_layout()\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f034ea6c-885e-4871-9312-49f98ce45f37", - "metadata": {}, - "outputs": [], - "source": [] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } From 169331dde084a0e0f4cc79cdd1e14394ccb9ed71 Mon Sep 17 00:00:00 2001 From: Anushka Singh Date: Wed, 12 Mar 2025 17:42:32 +0100 Subject: [PATCH 4/6] cleaned nb --- chapter1/poisson.ipynb | 45 +++++++++++++----------------------------- 1 file changed, 14 insertions(+), 31 deletions(-) diff --git a/chapter1/poisson.ipynb b/chapter1/poisson.ipynb index 281da08..d6479b6 100644 --- a/chapter1/poisson.ipynb +++ b/chapter1/poisson.ipynb @@ -55,16 +55,12 @@ "from sympde.topology import ScalarFunctionSpace, Square, element_of\n", "from sympde.calculus import grad, dot\n", "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - "\n", - "from sympy import pi, sin, lambdify\n", + "from sympy import pi, sin\n", "\n", "domain = Square()\n", "\n", "V = ScalarFunctionSpace('V', domain) \n", - " \n", + "\n", "x,y = domain.coordinates\n", "\n", "u,v = [element_of(V, name=i) for i in ['u', 'v']]\n", @@ -206,11 +202,11 @@ "outputs": [], "source": [ "ue = sin(pi*x)*sin(pi*y)\n", + "\n", "u = element_of(V, name='u')\n", "\n", "# create the formal Norm object\n", "l2norm = Norm(u - ue, domain, kind='l2')\n", - "print(l2norm)\n", "\n", "# discretize the norm\n", "l2norm_h = discretize(l2norm, domain_h, Vh)\n", @@ -289,27 +285,28 @@ { "cell_type": "code", "execution_count": null, - "id": "968a2165-ab75-4bf0-b943-daab3bd9fa2d", + "id": "2a33eab4-f4b8-428d-ae35-2d086d5645c3", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", + "from sympy import lambdify\n", "\n", "def refine_array_1d(breaks, N):\n", " \"\"\"Refine a 1D array by inserting N points between each pair of original values.\"\"\"\n", " refined = np.concatenate([np.linspace(breaks[i], breaks[i+1], N+1)[:-1] for i in range(len(breaks)-1)])\n", " return np.append(refined, breaks[-1]) # Add the last point\n", "\n", - "def plot_solutions(Vh, uh, ue, N=10):\n", + "def plot_solutions(Vh, uh, phi_exact, N=10):\n", " \"\"\"\n", " Refine the grid, evaluate solutions, compute errors, and plot results.\n", "\n", " Parameters:\n", " Vh: The finite element space (must have `spaces` attribute with `breaks`).\n", " uh: The numerical solution function.\n", - " ue: The exact solution function.\n", + " phi_exact: The exact solution function (callable).\n", " N: Number of points to insert between breaks (default: 10).\n", " \"\"\"\n", " # Generate refined grid for visualization\n", @@ -367,30 +364,16 @@ " plt.tight_layout()\n", " fig.show()\n", "\n", - "ue = lambdify((x, y), u, 'numpy') # convert sympy function to a callable function \n", - "plot_solutions(Vh, uh, ue, N=10)" + "# Define the exact solution as a symbolic expression (example: phi_exact = x^2 + y^2)\n", + "ue = sin(np.pi*x)*sin(np.pi*y)\n", + "\n", + "# Convert the symbolic expression to a callable function using lambdify\n", + "phi_exact = lambdify((x, y), ue, 'numpy')\n", + "plot_solutions(Vh, uh, phi_exact, N=10)" ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } From 2698e293ef375b6bda76bff43e6e2d34e65877a4 Mon Sep 17 00:00:00 2001 From: Anushka Singh Date: Thu, 13 Mar 2025 18:11:24 +0100 Subject: [PATCH 5/6] nan --- chapter1/poisson.ipynb | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/chapter1/poisson.ipynb b/chapter1/poisson.ipynb index d6479b6..a0c0130 100644 --- a/chapter1/poisson.ipynb +++ b/chapter1/poisson.ipynb @@ -59,7 +59,7 @@ "\n", "domain = Square()\n", "\n", - "V = ScalarFunctionSpace('V', domain) \n", + "V = ScalarFunctionSpace('V', domain)\n", "\n", "x,y = domain.coordinates\n", "\n", @@ -373,7 +373,25 @@ ] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "iga-python-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, "nbformat": 4, "nbformat_minor": 5 } From 464d966897ff098e21c4369ee55bc526c31c2caa Mon Sep 17 00:00:00 2001 From: Anushka Singh Date: Thu, 13 Mar 2025 18:13:59 +0100 Subject: [PATCH 6/6] nan --- chapter1/poisson.ipynb | 20 +------------------- 1 file changed, 1 insertion(+), 19 deletions(-) diff --git a/chapter1/poisson.ipynb b/chapter1/poisson.ipynb index a0c0130..4e39175 100644 --- a/chapter1/poisson.ipynb +++ b/chapter1/poisson.ipynb @@ -373,25 +373,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "iga-python-env", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }