{ "cells": [ { "cell_type": "markdown", "id": "d8f424ec", "metadata": {}, "source": [ "# Overview\n", "\n", "To facilitate rapid prototyping, the Dune Python bindings\n", "provide Python classes for all the core interfaces of Dune. This makes\n", "it easy to develop algorithms directly in Python which go beyond what is\n", "available through the Dune-Fem discretization module. This is especially\n", "of interest for pre- and postprocessing part of the overall simulation\n", "package. While most interface methods from the Dune-Fem package are\n", "high level, i.e., computationally expensive, many of the core Dune\n", "interface methods are not, i.e., the methods for computing the geometric\n", "representation of an element or the methods required to iterate over a\n", "grid. Consequently, depending on the requirements of the developed code\n", "parts, using the Dune core interfaces through Python could incur a too high\n", "hit on the performance of the code. Since the exported Python interfaces\n", "are very close to the original Dune C++ interfaces, transferring a Python\n", "prototype of the algorithm to C++ is mostly straightforward. A just in\n", "time compilation utility available in Dune-Python makes it then very easy\n", "to use these [C++ algorithms](cpp.rst) from within Python.\n", "\n", "In the following we briefly introduce the most important parts of the\n", "Dune core interface\n", "\n", ".. index::\n", " pair: Overview; Dense Vectors\n", "\n", ".. index::\n", " pair: Overview; Geometries\n", "\n", "# Dense Vectors and the Geometry Classes\n", "We start with a quick survey of Dune-Common and Dune-Geometry modules.\n", "The core module Dune-Common provides some classes for dense linear algebra." ] }, { "cell_type": "code", "execution_count": 1, "id": "8c1d6834", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:30:59.622600Z", "iopub.status.busy": "2023-10-22T10:30:59.622270Z", "iopub.status.idle": "2023-10-22T10:31:43.961400Z", "shell.execute_reply": "2023-10-22T10:31:43.960264Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.250000, 0.250000, 0.250000)\n", "[0.25 0.25 0.25]\n" ] } ], "source": [ "import time, numpy, sys\n", "import matplotlib.pyplot as pyplot\n", "import dune.fem\n", "\n", "from dune.common import FieldVector\n", "x = FieldVector([0.25,0.25,0.25])\n", "print(x)\n", "print(numpy.array(x))" ] }, { "cell_type": "markdown", "id": "de9320e1", "metadata": {}, "source": [ "The `FieldVector` and `FieldMatrix` classes are heavily used in the grid\n", "geometry realizations. The conceptional basis for these geometries is\n", "provided by Dune-Geometry, providing, for example, reference elements and\n", "quadrature rules." ] }, { "cell_type": "code", "execution_count": 2, "id": "39f438cb", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:43.970110Z", "iopub.status.busy": "2023-10-22T10:31:43.969424Z", "iopub.status.idle": "2023-10-22T10:31:44.012411Z", "shell.execute_reply": "2023-10-22T10:31:44.010982Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.000000, 0.000000)\t(1.000000, 0.000000)\t(0.000000, 1.000000)\n", "position: (0.333333, 0.333333) () weight: -0.28125 ()\n", "position: (0.600000, 0.200000) () weight: 0.2604166666666667 ()\n", "position: (0.200000, 0.600000) () weight: 0.2604166666666667 ()\n", "position: (0.200000, 0.200000) () weight: 0.2604166666666667 ()\n" ] } ], "source": [ "import dune.geometry\n", "geometryType = dune.geometry.simplex(2)\n", "referenceElement = dune.geometry.referenceElement(geometryType)\n", "print(\"\\t\".join(str(c) for c in referenceElement.corners))\n", "\n", "for p in dune.geometry.quadratureRule(geometryType, 3):\n", " print(\"position:\",p.position,f\"({type(p.position)})\", \"weight:\",p.weight,f\"({type(p.weight)})\")" ] }, { "cell_type": "markdown", "id": "3beb49f8", "metadata": {}, "source": [ "We can also obtain all weights and points stored in numpy arrays which\n", "can be useful to vectorize the computation of quadratures:" ] }, { "cell_type": "code", "execution_count": 3, "id": "5004b965", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:44.017857Z", "iopub.status.busy": "2023-10-22T10:31:44.017306Z", "iopub.status.idle": "2023-10-22T10:31:44.025402Z", "shell.execute_reply": "2023-10-22T10:31:44.024244Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "quadrature points: [[0.33333333 0.6 0.2 0.2 ]\n", " [0.33333333 0.2 0.6 0.2 ]] (,(2, 4))\n", "quadrature weights: [-0.28125 0.26041667 0.26041667 0.26041667] (,(4,))\n" ] } ], "source": [ "points,weights = dune.geometry.quadratureRule(geometryType, 3).get()\n", "print(\"quadrature points:\",points,f\"({type(points)},{points.shape})\")\n", "print(\"quadrature weights:\",weights,f\"({type(weights)},{weights.shape})\")" ] }, { "cell_type": "markdown", "id": "15877fa8", "metadata": {}, "source": [ ".. index::\n", " pair: Grid construction; Structured grids\n", "\n", "# Grid Construction and Basic Interface\n", "We now move on to the Dune-Grid module. First let us discuss different\n", "possibilities of constructing a grid.\n", "\n", "#### Structured grids\n", "We start with a structured grid. We saw in previous section the use of\n", "`dune.grid.structuredGrid` which is just a shortcut for setting up a\n", "Cartesian grid based on the `yaspGrid` grid manager which is part of\n", "`dune.grid`." ] }, { "cell_type": "code", "execution_count": 4, "id": "d887652c", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:44.030883Z", "iopub.status.busy": "2023-10-22T10:31:44.030568Z", "iopub.status.idle": "2023-10-22T10:31:44.172508Z", "shell.execute_reply": "2023-10-22T10:31:44.171409Z" } }, "outputs": [], "source": [ "from dune.grid import cartesianDomain, yaspGrid\n", "domain = cartesianDomain([0, 0], [1, 0.25], [15, 4])\n", "yaspView = yaspGrid(domain)" ] }, { "cell_type": "markdown", "id": "eed63ad5", "metadata": {}, "source": [ "Let's visualize the grid and then globally refine it once" ] }, { "cell_type": "code", "execution_count": 5, "id": "eee71b33", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:44.179009Z", "iopub.status.busy": "2023-10-22T10:31:44.178703Z", "iopub.status.idle": "2023-10-22T10:31:44.719057Z", "shell.execute_reply": "2023-10-22T10:31:44.718236Z" } }, "outputs": [ { "data": { "image/jpeg": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "yaspView.plot()\n", "yaspView.hierarchicalGrid.globalRefine()\n", "yaspView.plot()" ] }, { "cell_type": "markdown", "id": "e46dc961", "metadata": {}, "source": [ "The `dune.alugrid` module provides 2d and 3d unstructured grids (both\n", "triangle and cubes). We can construct a triangle grid, visualize, and\n", "refine it in exactly the same way as the structured grid used above.\n", "We use `dune.alugrid.aluConformGrid` which uses newest vertex bisection\n", "to refine the grid. So one step of refinement only reduces the grid width\n", "by a factor of $\\sqrt2$. We we need two steps to half the grid width:" ] }, { "cell_type": "code", "execution_count": 6, "id": "bcb33232", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:44.723168Z", "iopub.status.busy": "2023-10-22T10:31:44.722500Z", "iopub.status.idle": "2023-10-22T10:31:45.294722Z", "shell.execute_reply": "2023-10-22T10:31:45.293749Z" } }, "outputs": [ { "data": { "image/jpeg": 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dune.alugrid import aluConformGrid\n", "aluView = aluConformGrid(domain)\n", "aluView.plot()\n", "aluView.hierarchicalGrid.globalRefine()\n", "aluView.plot()\n", "aluView.hierarchicalGrid.globalRefine()\n", "aluView.plot()" ] }, { "cell_type": "markdown", "id": "59413225", "metadata": {}, "source": [ "We can also provide the number of levels to refine to the `globalRefine`\n", "method:" ] }, { "cell_type": "code", "execution_count": 7, "id": "3ff20fc9", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:45.300511Z", "iopub.status.busy": "2023-10-22T10:31:45.300175Z", "iopub.status.idle": "2023-10-22T10:31:45.551779Z", "shell.execute_reply": "2023-10-22T10:31:45.550645Z" } }, "outputs": [ { "data": { "image/jpeg": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "domain = cartesianDomain([0, 0], [1, 0.25], [8, 2])\n", "yaspView = yaspGrid(domain)\n", "aluView = aluConformGrid(domain)\n", "\n", "yaspView.hierarchicalGrid.globalRefine(2)\n", "yaspView.plot()\n", "aluView.hierarchicalGrid.globalRefine(4)\n", "aluView.plot()" ] }, { "cell_type": "markdown", "id": "f92f98c4", "metadata": {}, "source": [ "The `dune.grid.yaspGrid` grid manager also provides the option to construct\n", "tensor product grid. For a 2d grid one needs to provide a vector\n", "of x-coordinates $x=(x_i)$ and y-coordinates $y=(y_j)$\n", "resulting in a grid with points $(x_i,y_j)$:" ] }, { "cell_type": "code", "execution_count": 8, "id": "f3aa55e6", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:45.557003Z", "iopub.status.busy": "2023-10-22T10:31:45.556629Z", "iopub.status.idle": "2023-10-22T10:31:45.763084Z", "shell.execute_reply": "2023-10-22T10:31:45.762447Z" } }, "outputs": [ { "data": { "image/jpeg": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dune.grid import tensorProductCoordinates\n", "cst = dune.grid.tensorProductCoordinates([[1,2,4,8,16],[6,8,10,12,14,16]])\n", "yaspView = dune.grid.yaspGrid(cst)\n", "yaspView.plot()" ] }, { "cell_type": "markdown", "id": "7a0e6ef6", "metadata": {}, "source": [ ".. index:: Grid construction; Dictionaries\n", "\n", "#### Unstructured grids\n", "General unstructured grids can be constructed by providing a dictionary containing vertex coordinate\n", "and element connectivity" ] }, { "cell_type": "code", "execution_count": 9, "id": "cabc4d85", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:45.766424Z", "iopub.status.busy": "2023-10-22T10:31:45.766192Z", "iopub.status.idle": "2023-10-22T10:31:46.054382Z", "shell.execute_reply": "2023-10-22T10:31:46.053125Z" } }, "outputs": [ { "data": { "image/jpeg": 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", 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vertices = [(0,0), (1,0), (1,0.6), (0,0.6), (-1,0.6), (-1,0), (-1,-0.6), (0,-0.6)]\n", "triangles = [(2,0,1), (0,2,3), (4,0,3), (0,4,5), (6,0,5), (0,6,7)]\n", "aluView = aluConformGrid({\"vertices\": vertices, \"simplices\": triangles})\n", "aluView.plot(figsize=(5,5))\n", "aluView.hierarchicalGrid.globalRefine(2)\n", "aluView.plot(figsize=(5,5))" ] }, { "cell_type": "markdown", "id": "47d76f73", "metadata": {}, "source": [ "\n", "An unstructured grid can also be constructed from a file.\n", "Currently [gmsh and DGF](othergrids_nb.ipynb) are supported." ] }, { "cell_type": "markdown", "id": "917d32f8", "metadata": {}, "source": [ ".. index:: Adaptation; h-refinement\n", "\n", "#### Local grid adaptivity\n", "In addition to global refinement we can also pre process the grid by\n", "marking a subset of elements for local refinement." ] }, { "cell_type": "code", "execution_count": 10, "id": "b4f811d9", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:46.061303Z", "iopub.status.busy": "2023-10-22T10:31:46.060426Z", "iopub.status.idle": "2023-10-22T10:31:47.117566Z", "shell.execute_reply": "2023-10-22T10:31:47.113300Z" } }, "outputs": [ { "data": { "image/jpeg": 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", 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dune.grid import Marker\n", "aluView.plot(figsize=(5,5))\n", "for i in range(1,4):\n", " def mark(e):\n", " x = e.geometry.center\n", " return Marker.refine if x.two_norm < 0.64**i else Marker.keep\n", " aluView.hierarchicalGrid.adapt(mark)\n", " aluView.plot(figsize=(5,5))\n", "\n", "from dune.alugrid import aluSimplexGrid\n", "vertices = aluView.coordinates()\n", "triangles = [aluView.indexSet.subIndices(e, 2) for e in aluView.elements]\n", "aluView = aluSimplexGrid({\"vertices\": vertices, \"simplices\": triangles})" ] }, { "cell_type": "markdown", "id": "c4cdcd47", "metadata": {}, "source": [ ".. tip:: If discrete functions based on the `dune.fem` spaces have been\n", " constructed before local or global grid refinement is performed, the\n", " degree of freedom (dof) vectors will be resized but the data will be\n", " lost. Use `dune.fem.adapt` and `dune.fem.globalRefine` if data is\n", " supposed to be retained during grid modification. This is described in\n", " detail in the section on\n", " [dynamic grid modification](gridviews.rst#Dynamic-Local-Grid-Refinement-and-Coarsening)." ] }, { "cell_type": "markdown", "id": "d897dfc3", "metadata": {}, "source": [ ".. index::\n", " pair: Overview; Iterators\n", "\n", "### Basic information and iterators\n", "We next discuss how to retrieve basic information from a constructed grid\n", "and iterate over its entities (i.e., elements, faces, edges, vertices, etc.)." ] }, { "cell_type": "code", "execution_count": 11, "id": "15d8dd08", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:47.128180Z", "iopub.status.busy": "2023-10-22T10:31:47.125929Z", "iopub.status.idle": "2023-10-22T10:31:47.146708Z", "shell.execute_reply": "2023-10-22T10:31:47.145781Z" }, "lines_to_next_cell": 1 }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 elements and 4 vertices\n", "(1.000000, 1.000000), (1.000000, 0.000000), (0.000000, 0.000000)\n", "(0.000000, 0.000000), (0.000000, 1.000000), (1.000000, 1.000000)\n", "(0.000000, 0.000000), (1.000000, 0.000000)\n", "(0.000000, 0.000000), (1.000000, 1.000000)\n", "(0.000000, 0.000000), (0.000000, 1.000000)\n", "(1.000000, 0.000000), (1.000000, 1.000000)\n", "(1.000000, 1.000000), (0.000000, 1.000000)\n", "(0.000000, 0.000000)\n", "(1.000000, 0.000000)\n", "(1.000000, 1.000000)\n", "(0.000000, 1.000000)\n", "(0.000000, 0.000000), (1.000000, 0.000000)\n", "(0.000000, 0.000000), (1.000000, 1.000000)\n", "(0.000000, 0.000000), (0.000000, 1.000000)\n", "(1.000000, 0.000000), (1.000000, 1.000000)\n", "(1.000000, 1.000000), (0.000000, 1.000000)\n" ] } ], "source": [ "vertices = [(0,0), (1,0), (1,1), (0,1)]\n", "triangles = [(2,0,1), (0,2,3)]\n", "unitSquare = aluSimplexGrid({\"vertices\": vertices, \"simplices\": triangles})\n", "print(unitSquare.size(0),\"elements and\",unitSquare.size(2),\"vertices\")\n", "\n", "for codim in range(0, unitSquare.dimension+1):\n", " for entity in unitSquare.entities(codim):\n", " print(\", \".join(str(c) for c in entity.geometry.corners))\n", "\n", "for edge in unitSquare.edges:\n", " print(\", \".join(str(c) for c in edge.geometry.corners))" ] }, { "cell_type": "markdown", "id": "ac2e464b", "metadata": {}, "source": [ "In the above we have used the geometry method on the entity which\n", "provides the geometric mapping between the reference element of the\n", "entity and it's position in physical space. We have used the corners\n", "method to retrieve corners of the entity. Other properties and methods\n", "are available to provide volume or the integration element needed to\n", "compute quadratures of a grid function over the element -\n", "which is discussed in the next section." ] }, { "cell_type": "markdown", "id": "65d2af0d", "metadata": {}, "source": [ "\n", ".. index::\n", " pair: Functions; Grid Functions\n", "\n", "# Using grid functions\n", "This is a fundamental concept in any grid based discretization package.\n", "These are functions that can be evaluated given an entity in the grid and\n", "a local coordinate within the reference element of that entity.\n", "\n", ".. tip:: in the following we will use the `gridFunction` decorator\n", " provided by `dune.fem`. A very similar decorator is also available in\n", " `dune.grid` but the latter does not provide the option to use the grid\n", " functions in ufl expressions." ] }, { "cell_type": "code", "execution_count": 12, "id": "118049ca", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:47.153779Z", "iopub.status.busy": "2023-10-22T10:31:47.152411Z", "iopub.status.idle": "2023-10-22T10:31:50.010874Z", "shell.execute_reply": "2023-10-22T10:31:50.009945Z" } }, "outputs": [ { "data": { "image/jpeg": 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "@dune.fem.function.gridFunction(aluView, name=\"cos\", order=3)\n", "def f(x):\n", " return numpy.cos(2.*numpy.pi/(0.3+abs(x[0]*x[1])))\n", "\n", "@dune.fem.function.gridFunction(aluView, name=\"hat0\", order=3)\n", "def hat0(element,hatx):\n", " return 1-hatx[0]-hatx[1]\n", "\n", "hatx = FieldVector([1./3., 1./3.])\n", "maxValue = max(f(e, hatx) for e in f.gridView.elements)\n", "\n", "maxValue = max(f(e.geometry.toGlobal(hatx)) for e in f.gridView.elements)\n", "\n", "hat0.plot()\n", "f.plot()\n", "f.plot(level=3,xlim=[0,0.4], ylim=[0,0.4])" ] }, { "cell_type": "markdown", "id": "0fabe73d", "metadata": { "lines_to_next_cell": 0 }, "source": [ ".. index::\n", " pair: Quadratures; Simple Quadrature Rules\n", "\n", "We can now use the quadrature rules discuss at the beginning of this\n", "chapter to compute the integral over each element of the grid function." ] }, { "cell_type": "code", "execution_count": 13, "id": "68c38ce8", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:50.016725Z", "iopub.status.busy": "2023-10-22T10:31:50.015364Z", "iopub.status.idle": "2023-10-22T10:31:50.052906Z", "shell.execute_reply": "2023-10-22T10:31:50.052087Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "integral of function 'f': 0.07942992585030194\n" ] } ], "source": [ "from dune.geometry import quadratureRules\n", "rules = quadratureRules(5)\n", "integral = 0\n", "for e in aluView.elements:\n", " geo = e.geometry\n", " for qp in rules(e.type):\n", " x,w = qp.position, qp.weight\n", " integral += f(e,x)*w*geo.integrationElement(x)\n", "print(\"integral of function 'f':\",integral)" ] }, { "cell_type": "markdown", "id": "5a30a722", "metadata": { "lines_to_next_cell": 0 }, "source": [ "There are some approach available to improve the efficiency of the\n", "computation of integrals since this is an important component of many\n", "grid based schemes. Especially, using vectorization can greatly reduce\n", "the complexity of the above loop:" ] }, { "cell_type": "code", "execution_count": 14, "id": "85b1cdd3", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:50.058698Z", "iopub.status.busy": "2023-10-22T10:31:50.057272Z", "iopub.status.idle": "2023-10-22T10:31:50.072572Z", "shell.execute_reply": "2023-10-22T10:31:50.071712Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "integral of function 'f': 0.07942992585030188\n" ] } ], "source": [ "integral = 0\n", "for e in aluView.elements:\n", " # obtain numpy arrays containing the points and weights and then call\n", " # required functions using these vectors with requiring a loop\n", " points, weights = rules(e.type).get()\n", " ies = e.geometry.integrationElement(points)\n", " values = f(e,points)\n", " integral += numpy.sum(values*ies*weights,axis=-1)\n", "print(\"integral of function 'f':\",integral)" ] }, { "cell_type": "markdown", "id": "0b6c54fa", "metadata": { "lines_to_next_cell": 0 }, "source": [ "Now a vector valued function" ] }, { "cell_type": "code", "execution_count": 15, "id": "9768021a", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:50.078186Z", "iopub.status.busy": "2023-10-22T10:31:50.076787Z", "iopub.status.idle": "2023-10-22T10:31:50.172829Z", "shell.execute_reply": "2023-10-22T10:31:50.171887Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "integral of function 'g': [0.07942993 1.8 ]\n" ] } ], "source": [ "# an easy case\n", "dimR = 2\n", "@dune.fem.function.gridFunction(aluView, name=\"g\", order=3)\n", "def g(x):\n", " return [numpy.cos(2.*numpy.pi/(0.3+abs(x[0]*x[1])))]*dimR\n", "# bit more complex to code\n", "@dune.fem.function.gridFunction(aluView, name=\"g\", order=3)\n", "def g(x):\n", " if type(x[0]) is float:\n", " return [numpy.cos(2.*numpy.pi/(0.3+abs(x[0]*x[1]))),1]\n", " else:\n", " return [numpy.cos(2.*numpy.pi/(0.3+abs(x[0]*x[1]))),[1]*len(x[0])]\n", "integral = g.dimRange*[0]\n", "for e in aluView.elements:\n", " # obtain numpy arrays containing the points and weights and then call\n", " # required functions using these vectors with requiring a loop\n", " points, weights = rules(e.type).get()\n", " ies = e.geometry.integrationElement(points)\n", " values = g(e,points)\n", " integral += numpy.sum(values*ies*weights,axis=-1)\n", "print(\"integral of function 'g':\",integral)" ] }, { "cell_type": "markdown", "id": "aee23110", "metadata": {}, "source": [ ".. index::\n", " triple: Algorithm; C++; User defined code\n", "\n", "# Importing user defined C++ code\n", "To achieve close to native efficiency the Python prototype\n", "can be easily reimplemented as a C++ function and the `algorithm` module\n", "used for just in time compilation. This is discussed in a later section\n", "based on [some examples](cpp.rst)." ] }, { "cell_type": "markdown", "id": "cb28714a", "metadata": {}, "source": [ ".. index:: Grids; Attaching data\n", "\n", ".. index::\n", " pair: Overview; Dof Mappers\n", "\n", "# Attaching Data to the Grid\n", "To attach data to the entities in a grid, each Dune grid has an\n", "`IndexSet` which provides a consecutive, zero starting integer for the\n", "set of entities of a given geometry type, i.e., entities sharing the same\n", "reference element, like all triangles or all cubes. This can be used to attach\n", "data to these entities stored in random access containers like\n", "`numpy` arrays. To simplify the process further a `mapper` can be used\n", "which is initialized with the data layout, i.e., the number of degrees of\n", "freedom that is supposed to be attached to every entity with a given\n", "geometry type.\n", "\n", "In the following we first construct a mapper for a 2d cube grid\n", "attaching 2 dofs to each\n", "element (codimension 0), 4 to each edge (codimension 1), and 3 to each\n", "vertex (codimension 2):" ] }, { "cell_type": "code", "execution_count": 16, "id": "e4457b02", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:50.178833Z", "iopub.status.busy": "2023-10-22T10:31:50.177421Z", "iopub.status.idle": "2023-10-22T10:31:50.250031Z", "shell.execute_reply": "2023-10-22T10:31:50.249061Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36 36\n" ] } ], "source": [ "layout = [2,4,3]\n", "mapper = unitSquare.mapper([2, 4, 3])\n", "print( mapper.size, sum([layout[c]*unitSquare.size(c) for c in range(3)]) )" ] }, { "cell_type": "markdown", "id": "175acbfc", "metadata": {}, "source": [ "We can also use the reference element types from `dune.geometry` to\n", "define the layout" ] }, { "cell_type": "code", "execution_count": 17, "id": "c5eb6986", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:50.254575Z", "iopub.status.busy": "2023-10-22T10:31:50.254199Z", "iopub.status.idle": "2023-10-22T10:31:50.260816Z", "shell.execute_reply": "2023-10-22T10:31:50.260042Z" }, "lines_to_next_cell": 1 }, "outputs": [], "source": [ "layout = {dune.geometry.quadrilateral: 4, dune.geometry.triangle: 1}\n", "mapper = unitSquare.mapper(layout)" ] }, { "cell_type": "markdown", "id": "683d412b", "metadata": {}, "source": [ "Finally here is an example of using the mapper to attach one degree of\n", "freedom per vertex and using that to define a piecewise linear Lagrange\n", "interpolation:" ] }, { "cell_type": "code", "execution_count": 18, "id": "8e9c077b", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:50.264626Z", "iopub.status.busy": "2023-10-22T10:31:50.264299Z", "iopub.status.idle": "2023-10-22T10:31:50.275565Z", "shell.execute_reply": "2023-10-22T10:31:50.274714Z" }, "lines_to_next_cell": 1 }, "outputs": [], "source": [ "def interpolate(grid):\n", " mapper = grid.mapper({dune.geometry.vertex: 1})\n", " data = numpy.zeros(mapper.size)\n", " for v in grid.vertices:\n", " data[mapper.index(v)] = f(v.geometry.center)\n", " return mapper, data\n", "\n", "mapper, data = interpolate(aluView)\n", "@dune.fem.function.gridFunction(aluView, name=\"p12d\", order=1)\n", "def p12dEvaluate(e, x):\n", " bary = 1-x[0]-x[1], x[0], x[1]\n", " idx = mapper.subIndices(e, 2)\n", " return sum(b * data[i] for b, i in zip(bary, idx))" ] }, { "cell_type": "markdown", "id": "5a473436", "metadata": {}, "source": [ "We can use a grid function to compute the error of the Lagrange\n", "interpolation at the barycenter of each triangle:" ] }, { "cell_type": "code", "execution_count": 19, "id": "92bec5c4", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:50.279625Z", "iopub.status.busy": "2023-10-22T10:31:50.279298Z", "iopub.status.idle": "2023-10-22T10:31:50.302799Z", "shell.execute_reply": "2023-10-22T10:31:50.301998Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.6026566867981322\n" ] } ], "source": [ "@dune.fem.function.gridFunction(aluView, name=\"error\", order=3)\n", "def error(e, x):\n", " return abs(p12dEvaluate(e, x)-f(e, x))\n", "hatx = FieldVector([1./3., 1./3.])\n", "print(max(error(e, hatx) for e in aluView.elements))" ] }, { "cell_type": "markdown", "id": "c615e05f", "metadata": {}, "source": [ ".. tip:: the mapper describe here provides a slightly extended interface\n", " compared to the [mappers available from discrete spaces](concepts_nb.ipynb#Discrete-Spaces)\n", " in `dune.fem`. While the mappers from the grid provide methods like `subIndices` and\n", " similar the mapper from the space only provide a method to obtain all\n", " indices attached to a given (codimension 0) entity. This method\n", " is also available in the mappers from the grid." ] }, { "cell_type": "code", "execution_count": 20, "id": "b37c2656", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:50.306599Z", "iopub.status.busy": "2023-10-22T10:31:50.306294Z", "iopub.status.idle": "2023-10-22T10:31:50.311846Z", "shell.execute_reply": "2023-10-22T10:31:50.311089Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2]\n", "[0 3 1]\n", "[3 4 1]\n", "[5 4 3]\n", "[2 6 0]\n" ] } ], "source": [ "for i,e in enumerate(aluView.elements):\n", " print(mapper(e))\n", " if i==4: break" ] }, { "cell_type": "markdown", "id": "5a8868a1", "metadata": {}, "source": [ ".. index::\n", " pair: Grids; Plotting\n", "\n", ".. index::\n", " pair: I/O; Plotting\n", "\n", "# Output of Grid and Visualization of Data\n", "We already used `matplotlib` to plot the grid and grid functions. For more\n", "flexible plotting Dune relies on `vtk` and it easy to write suitable files." ] }, { "cell_type": "markdown", "id": "8aad8a7a", "metadata": {}, "source": [ "#### Matplotlib\n", "We have seen many examples already of using matplotlib to show the grid\n", "and grid functions. The most straightforward way is to use the `plot`\n", "method. We first show three plots using matplotlib\n", "\n", ".. tip:: the `plot` method will only work on 1d or 2d grids." ] }, { "cell_type": "code", "execution_count": 21, "id": "42e2a22f", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:50.315858Z", "iopub.status.busy": "2023-10-22T10:31:50.315566Z", "iopub.status.idle": "2023-10-22T10:31:53.806830Z", "shell.execute_reply": "2023-10-22T10:31:53.805868Z" } }, "outputs": [ { "data": { "image/jpeg": 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kxyelfPuXPLmkfW4TDRpq8jPnleQkLzUK2LSHLc1uxaeF68VZWCNO1aqtbSCO2WPp0lZGNDpv+zV6LTgOoxV3cq9AKY0tVGjWqHmV83XRglvGnan5VegFQNLUbS+9dtHKpS3PGr5u+5ZaWtbw9/yLWlf9ecP/AKAK5tpa6Tw9/wAi1pX/AF5w/wDoAr7LIsH9WjPTe36mWDxf1hy8rfqaVFFFe+dwVnato0GsRKk0s0WFeMmIgFkcYZTkHgjHTB44IrRooAy4tAtItYj1Xzbl7xYmjdmmO2TIQFmQYXOI16AD26Y5fXY4bfxhcmKNI2lsoHcqoBZt83J9TXeV5j44u/s3jQjPXT4D/wCRJq6MLQ9vVVPueNn7ksBJx30/NF1ZakWX3rnYdUz/ABZ+tXor9G74rTEZI+x8HDG1IfEbKy1IsvvWYlwrdGqVZa8HEZN5HpUM18zTWWpFlrNWWpFlrw8Rk3kexQzbzNISA0pCP1FUVl96kWWvEr5Q1sj2aGbeZaVNp+U8elOZciq6y+9SiWvNlg6tJ3R6P12FZWkRulV3SrpIaoXSvUwWLlF2Z5eLwykroz3SqskeQQa0pEqq6V9rl2N21PkcfhDnr6068Vzd7aZzxXdTQh1IIrEvbLrxX3uAxqkkrnjUqkqMrM89vbPrxXO3tn14r0K9s+vFc9e2fXivqMLiT6TB4w4aWIo2DUe2ty8s+elVPsftXrKUJas9+GIi1czttAGK0fsftR9j9qa9mivbRKSfeFaEHamfYyO1TRxMh5FE5JrQyqTUloaEHappulQwdqmm6VxP4jz5fEZs/es2frWnMpY8CqrWpY8iuunJLc7qUlHcziM0m2tH7H7UfY/arbg9zf20TO20BOa0fsftT47P5xxU/u1qDrxI7S1LEEiugsrPpxSWVn04rorKz6cVwYnEnjYzFjrKz6cV0djadOKjsrPpxXQ2tqIwCRzXzWNxiim2fMYnEObsiS2gEa9OauxpTUSrUaV8LmWO3dzqwOEbd2KiVZRKREqdVA618NjsY29D7DB4RLcciU5gSMDgU0yAVG0teE8PVryuz24YinQWm48RovXk0u8DpVdpajaX3rto5TKW5yV828yy0tRtLVVpajaX3r2sPk3keNXzbzLLS+9RtLVVpaiaYDqa9zD5N5Hj1828y00vvUbS1RkvEX+Kqc2pgdCBXvYbJX2PLqZhOexqtN712Xh7/kWtK/684f8A0AV5RPqnX5q9X8Pf8i1pX/XnD/6AK6MXgvqqj53/AEPpeFpTl7Vz/u/qaVFFFcJ9aFFFFABXivxVuGg8dRAd9Nh/9GTV7VXh/wAXP+R7h/7BsP8A6Nmr18jSeNin5/kefmiTw0k/L8znob9/etGC/fjrWFD0rRg7V9hVpx7HxFalDsbsF+/HWtCHUH96w4O1aEHWvOrUYPdHlVacVsbcV3u4INWlkNZcP3q0V6CvBxVCmnsYwqST0ZYWQ1IshqAVIteDiKMOx6lCrPuTrIalWQ1XWpVrw8RQp9j2aFafcnWQ1IHzUAOKXfXjVcNFvRHrU8Q0tWSlN/SoJIG9KnifmppGITcAD6iuKeOrYKdrXR2U8DTxq3szKeBvSqk1qXXBFa4u7djtf5W96f5UUg+RlNe3g+K/Ztc6seZjeE6m6ONutJkfO0A1gXuiz8/u69Mez9qrSWXtX2WB4ypStc8GeU4zDP3dTxy50O4ZwoTkmgeEtQIyIsivWH0yMuCYxnPpV+CyGANvFeziuNqOHpqS1NcP9fqy5IxseMf8IjqP/PGj/hEdR/5417TMlnb/AOtcCoPtOmf891ryF4mUntBntRyTOZK6S+48d/4RHUf+eNH/AAiOo/8APGvYvtOmf891qykFvJD5kfK9uKa8SqTaXI9SauT5vSi5zSSPFD4W1CJS7RhQOpNRQ6FdXufJCttOCAa9R18AWTgDArmfCn/Hy/8AvGvssLm9XEYR4m1vI+flja0VNu14s5n/AIRHUf8AnjR/wiOo/wDPGvaLa3jlX5hUckunxOUeUKw6givkq/iLChUdKcNUe1hcuzPFw56Nmjxv/hEdR/540f8ACI6j/wA8a9i+06Z/z3WnJLp0jbUmBP0rH/iJdL+R/czq/sLOl9lfceN/8IjqP/PGo28OXdvIPMjGfSvcHs0K5TBHqKzrjTUkcFkBIr0cBx5RxfSyPJxdPMMM7TSZ5tZaLPx+7robTRphjKAfWusisVXooH4VaSz9q5sdxjRjexxxwONxL1VjFtrAx4yATV9IG9K0ltVUZbA+tI01tD1cE+1fGY7i9VG+VXPXwfClWTuyukDelWEhI5NPhnEx+RML6mpJWwK+cqZvWxU+VKx7v9jwwcbzepHnbTWkNM30hOa6YYbW8kcc8RpaLBpDUTSGnNUTV62HoU+x5letPuI0hqJpDTjURr3cPRh2PGr1p9xGkNQS3Gzsakaqlx1r3cNQpt7Hk1as29yGa/YZwMVnT37+9Sz96z569+hRprZBSipbkU1+/vWdNfvz1qWbpWdN0NenSpx7Hp0aUOxFNfvz1r6J8Pf8i1pX/XnD/wCgCvmqbvX0r4e/5FrSv+vOH/0AV4fEkUlSt/e/Q+uyOKip28v1NKiiivlz3goorC8TaNcazbRxweQSqyLiYkBGZcLKuAfmU8jp1PIoA3a8P+Ln/I9w/wDYNh/9GzV6pDpt2/iZdTns7CJFt9m+CUmVnIG7cdg3KMALz7kZIC+T/FiNk8eIWmeTdp8TAMB8g8yXgYA4+uTz1r2Mi/36Pz/I4Mz/AN2l8vzOWh6VowdqzoelaMHavsqp8VWNGDtWhB1rPg7VoQda8+qeVWNGH71aK9BWdD96tFegrw8XuccdyUVItRing4rwa6b2PSotLclWnhsVBvo31508K5bnfHEKOxY30b6r76N9R9R8i/rnmXIn5q6hDLg1lRPzV+J6+azrBWufQZRjNUZup22MkVz8mo3Fk/UlRXZ3cQlizjmuS1S1+9xXz2Fkm+SZ+m5fWhVilLUmtfFIPDP+BrXg123mA3Y/CvMNQieJiyEgisxPEE1rJtkzx3r01lkai5qTsetVyOhXjzI9uF1aOu/eBjmsnUvEUVuhWMgV53F4sHkt+87etc1qviWSZiqMaqllVarLlqNtI48Pw9Qw83UnZI6vWvFn3v3nP1ri7nxHeSSnyJGHsKzkiub+Tndg11Gj+F2cqWTNe1GhhsJD3j24JKPuLlj3e5Z8NXV1c3Cm5dmOeM17JYf8gxa5XRvC4UKSmAO9dekSW1sIg2cV8/ia0cRiY+zWzPleJsZRnRcIvU53xB/x5yVzHhT/AI+X/wB410+v/wDHnJXMeFP+Pl/941+3ZV/yKn6H4PV2q+qPR7HpXDeMWZWcqSGB4IruLNgoGTWZq+greBpB8+a/F8wn7LMHOS0P1rg3FUqMI87PCpdb1KGUhpX25rc0fxUyFQ74PrmtfWvCnLHZz64rhr7R57OQsoIx6V7NOWGxULWsfpXNzrmh70X06o9j0fxSGCgvkeldVDfWtzHv3AEda+cbLWJ7NwrEjFdjp/izFucyYOO5ry8TlE6cuei7Hj4vJcNjFeG/bqety6rawdME1l3PidEBCsBXmF14qZ2wjEn2pLWee8YM5IB7VgsqlbmqsvD8O0KSu7aHdSeIZbl9sZJ96v2EckzBpCSawNLtenFdppltgA4rixPJSVoIwxjp0ItQRpQRiKEetQzPU8rYGKozPXXlGEc5czPzfOMZuN30b6r76N9fa/UfI+P+ueZPvpCc1Dvo31UcI47EvFJ7jjURp+7NMNdtGLjucdWSexG1VLjrVtqqXHWvcwu6PLqbmbP3rPnrQn71nz179E2omdN0rOm6GtGbpWdN0NejSPVomdN3r6V8Pf8AItaV/wBecP8A6AK+apu9fSvh7/kWtK/684f/AEAV4XEu1L/t79D6zJdp/L9TSooor5U9wKKKKACvD/i5/wAj3D/2DYf/AEbNXuFeH/Fz/ke4f+wbD/6Nmr2Mi/36Pz/I4Mz/AN2l8vzOSh6VowdqzoelaMHavsqp8VWNGDtWhB1rPg7VoQkDrXn1Tyqxow/erQUgAc1kLcKvQ077X/tV5dbDyqM47NM1949aPM96yPtf+1R9r/2q5/7PL5pmv5nvR5nvWR9r/wBqj7X/ALVH9nhzTNfzPejzPesj7X/tUfa/9qj+zw5pm5E/NX4XrCsbjzBjNasL18xneBtdHtZXinGVmaqEMuDWNqlt14rThelu4hLFmvy/F0nQrXP1HJ8XsjzbVLX73FcTqtrjJx0r1PUbJpGIVSTWDL4bkmbLqT7Yr18HjIwScmfoeDxlNQtNnlTxTbiEzg1c07TFmlG/Oe9ei/8ACJ8/6v8ASrMXhUgghMfhXozzany2TKVXCRlzN3M/RdDt/l+X9K9B0rSLeOMOV6Vk6fpM1swypI+ldZCvl24FfN4zESrTUU9z5/OMfLlbjIZJJ5a7UAAHpWbcXDAE1bnbrWResfKavuOHMupNpuJ+NZ7j6rbXMc7rmrybGTA2+lYWh6i9vM2xQCWqxrH8VZGmf60/Wv17D4enHDuKWh5VGlF0HfqenWF800YY8GtiC4auV0hjjHbFdDA3SvzniPLqLvaKNcpxdSnV5VIuXNlDdxkso3VxWs6JB83y/pXeQnIxWNq1m8zMEXNflqlLD13FOyP2PI8fNpXkeMazo0MZYqMY9q5s280bEITtr2O58NPKxLqTVF/CnP8Aq/0r6KhmsIxtJ3PsXiMNVScnZ90edWFuXkAIPvXaaXa/d4rQXwqVOQmD9K0bPSpbdgGQ49cVjisfCovdZdTF0lS5YM09Ltfu8V1dvGIoR61m6ZbYAOK1JGwMV867163KfEZvi7JkMz1Qmep5nrPuJAqk+lfoOR4LbQ/LM3xe4hfnrSeZ71km7+Y/N3pPtf8AtV90svPl+eZr+Z70eZ71kfa/9qj7X/tUf2eHNM1/M96PM96yPtf+1R9r/wBqj+zw5pmsWB7iq1xVL7X/ALVIboHqa1p4SUHclqT3I5+9Z89XpXVwcGqM9enSN6JnTdKzpuhrRm6VnTdDXo0j1aJnTd6+lfD3/ItaV/15w/8AoAr5qm719K+Hv+Ra0r/rzh/9AFeFxLtS/wC3v0PrMl2n8v1NKiiivlT3AooooAK8P+Ln/I9w/wDYNh/9GzV7hXh/xc/5HuH/ALBsP/o2avYyL/fo/P8AI4Mz/wB2l8vzOSh6VowVmxsFHNTi5AHBr7ScWz4ypFy2NiOVUHWpPtfvWH9q96PtXvWDoXOd4a+5ufa/ej7X71h/avej7V70fVxfVTc+1+9H2v3rD+1e9H2r3o+rh9VNz7X70fa/esP7V70favej6uH1U3PtfvR9r96w/tXvR9q96Pq4fVTrdMu/n6966aCTIBFee6ZdfP17111rqMMcQDuM14Ob4F1F7q1OOadCtdHSQvVoTLtwea5oazAP46X+2YP+elfAYrhOtiJXkj38Nn/sFaKZ0OYP+ea0Zg/55rXPf2zB/wA9KP7Zg/56Vyf6kVOzOz/W2r5nQ5g/55rR5sS9EX8q53+2YP8AnpUT67AP4s1pDgebeqJlxXWa0udObpR0xTGuwRjNcuddh9f1pP7dh9f1rtp8E8uvKcdTiPET0aZ0ErBuhrMvf9S1Uv7dh9f1qObV4JUKk4z3r6DLsor4WSutDxcZiXiNeV3Oa1j+KsjTP9afrWtqzBgxU5FZOmf60/WvuaX8FnZQ/gM7vSOn4VvxcVy9jew2y/MwJ9Kvf25D6/rXyeaYCtiZNQWh59Co6VRyaOmS4VO9Si7B64rlP7dh9f1o/t2H1/WvlqvBrqO8lqe3T4gr01aKZ1n2hD1Vfyo3QHqi1yy69D3bH41MutQEffrgqcDyWyO2HFVaO9zo8wf881ozB/zzWue/tmD/AJ6Uf2zB/wA9Kx/1Iqdmaf621fM6NZIk+6oH0qOWQHoawP7Zg/56Uf2zB/z0rajwdWpS5oowrcSOsrTTNGZ+tY2pXOxCM1K+q27D7/Nc/qt4CGIbNfaZRlsqTXOrHzleo8RV02KzXfzHnvSfa/esQ3XzHnvSfavevrfq51/VTc+1+9H2v3rD+1e9H2r3p/Vw+qm59r96PtfvWH9q96PtXvR9XD6qbn2v3o+1+9Yf2r3o+1e9H1cPqpufa/eka5DDk1ifavej7V70ewD6qaMpBHBrPm6Gk+1e9MeVWFawg0b06biUpu9fSvh7/kWtK/684f8A0AV81Td6+lfD3/ItaV/15w/+gCvn+JdqX/b36H1GS7T+X6mlRRRXyp7gVga5DqjajbT2UdzLFHsYLBOEAIkUyBgWAbcgKjOQD6ZzW/RQBhaZbanHrdxJc+f5B87czzbo5MyAxbFydu1MhuFyT/F1ryj4srMPHaeZIjA6fEU2oRhfMl4PJyevPH0r3SvD/i5/yPcP/YNh/wDRs1exkX+/R+f5HBmX+7S+X5nD+XJ60eXJ61bh6VbSAOOK+4lUsfJSrcu5k+XJ60eXJ61s/ZPaj7J7VPt0R9YRjeXJ60eXJ61s/ZPaj7J7Ue3QfWEY3lyetHlyetbP2T2o+ye1Ht0H1hGN5cnrR5cnrWz9k9qPsntR7dB9YRjeXJ60eXJ61s/ZPaj7J7Ue3QfWEVLGGZ+Efaa0RpuoEZEjVa0y0+fp3rsbO3VYgCAa8vHZgsP71jz6+Kl7TlgkcL/Zmof89Go/szUP+ejV6OtojdFH5U77CP7o/KvBlxXQi7NG0aeLkrqKPNv7M1D/AJ6NR/Zmof8APRq9J+wj+6Pyo+wj+6Pypf624fyH7HGfyI81Omah/wA9GqJtOvgeXavT/sI/uj8qY2noesY/KqhxZhm+gezxkfsI8y/s+9/vmj+z73++a9IOmx/88xSf2dH/AM8xXSuJcO9jJzxK3gecf2fe/wB80f2fe/3zXo/9nxD+AVWubeKOJiqAH1rejn1OtLlgjOeJrU1eUUeeT2txChMkhx6V33g/4faVrXhi01K4ur6OacvuWJ0C8OyjGVJ6D1rk9Y/ir1j4cf8AIhab9Zf/AEa9bZriqtPCKdN2bktvRn0ORwhiJv2qurf5FD/hVejf8/8Aqn/f2P8A+Io/4VXo3/P/AKp/39j/APiK7mivmv7Txf8Az8Z9J/Z+F/kRw3/Cq9G/5/8AVP8Av7H/APEUf8Kr0b/n/wBU/wC/sf8A8RXc0Uf2ni/+fjD+z8L/ACI4b/hVejf8/wDqn/f2P/4ilHwt0cdNQ1T/AL+x/wDxFdxRR/aeL/nYf2fhf5EcR/wq7SP+ghqn/f2P/wCIo/4VdpH/AEENU/7+x/8AxFdvRR/aWL/nYv7Owv8Az7RxH/CrtI/6CGqf9/Y//iKP+FXaR/0ENU/7+x//ABFdvRR/aWL/AJ2H9nYX/n2jiP8AhV2kf9BDVP8Av7H/APEUh+FujMMG/wBTI/66x/8AxFdxRR/aWL/nYf2dhf8An2jgD8IdAJz9t1P/AL+x/wDxFJ/wqDQP+f3VP+/sf/xFegUVX9q4z/n4y/qWH/kR5/8A8Kg0D/n91T/v7H/8RR/wqDQP+f3VP+/sf/xFegUUf2rjf+fjD6lh/wCRHn//AAqDQP8An91T/v7H/wDEUf8ACoNA/wCf3VP+/sf/AMRXoFFH9q43/n4w+pYf+RHn/wDwqDQP+f3VP+/sf/xFH/CoNA/5/dU/7+x//EV6BRR/auN/5+MPqWH/AJEef/8ACoNA/wCf3VP+/sf/AMRR/wAKg0D/AJ/dU/7+x/8AxFegUUf2rjf+fjD6lh/5Eef/APCoNA/5/dU/7+x//EUf8Kg0D/n91T/v7H/8RXoFFH9q43/n4w+pYf8AkR5+fhBoB/5fdT/7+x//ABFdd4e/5FrSv+vOH/0AVpVm+Hv+Ra0r/rzh/wDQBXPXxdbEW9rK9jWlQp0r8itc0qKKK5zUKKKKACvD/i5/yPcP/YNh/wDRs1e4V4f8XP8Ake4f+wbD/wCjZq9jIv8Afo/P8jgzP/dpfL8zkoelaMHas6HpWjB2r7KqfFVjQhUMMGrAtg3SoIO1aEHWuCcmtjzKsmtiv9k9qPsntWvHGH4qb7H7VyyxSi7M5/byML7J7UfZPat37GfSj7GfSp+ux7h7eRhfZPaj7J7Vu/Yz6UfYz6UfXY9w9vIwvsntR9k9q3fsZ9KPsZ9KPrse4e3kU9MtfnziuhhTpVS1g8se9aUKV8xneO31O3AUXVqczLEKVawqpuIpkSVFfziOPaDX5PmWKlUqcsWfpeUYJOyaI31K3jbDDBpv9rWn+TXKapdfe5rlLzWZbYk7iRSoYOdVaSZ9vh8go1ldI9W/ta0/yakF/aP3xXi//CV4P3jVy18TPOwVGJNdDyuvFX5maS4YovZI9fE9q3RxUhiQpuHIrh9Ju3kZWdsmu3tX8y2HtXFKrWoTScm0fO5lk1GhF8q1KkwxWVe/6lq2Z161k3ibo2FfpHDtZS5bn5TnVJxkcNrH8VesfDj/AJELTfrL/wCjXryjWVI3Zr1f4cf8iFpv1l/9GvX3Oc/7jH/EvyZ6vDfxv0/VHVUUUV8ofXhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFZvh7/kWtK/684f/QBWlWb4e/5FrSv+vOH/ANAFAGlRRRQAUVHMZVhcwIjygfIruVUn0JAOPyNc9cXniAanbpLbiBS8QWO0zcRSAyASeY5jUptTkdMnuelAHS14f8XP+R7h/wCwbD/6Nmr1S3vri48TGOJ78WYt97LPaFI9xClQjFAcgZ3AtwTjGQdvk/xYMp8eJ5qIoGnxBNrlsr5kvJ4GD145+texkX+/R+f5HBmf+7S+X5nLQ9K0YO1Z0PStGDtX2VU+KrGjB2rQg61nwdq0IOtefVPKrGjD96tFegrOh+9WivQV4eL3OOO48Lml2UoqQV41StKB3U6UZEWyjZU+2jZWH1w2+qEGyjZU+yjZS+u+Y/qgyJOavwpVeJOavwpXzWdY3R6n0GUYTVEgxHGWNc/ql197mte/nEce0GuO1S6+9zXxdCLq1OZn6llOEskYuqXX3ua4rVLgu5Uc109xbXF45CKQPWpLXwqWbcyZJ7kV9JQq0qCvJn2lOdKjTtJ2Z541jcSguFYAe1FtezWUg3DGO+K9ei8KfuG/d9vSuW1nwoQWITB+ldVLNKNV8ktjmp1KM5fuZ2l+ZHo3icAqGIBr0PR/EyMoUkEGvD7rTbixkJUEAVb07XprVwrkjHrWeLyunXjzUycRh6VdclZcr/Bn0VHLDeJmNhn0qtcWnXivPdA8SvMyqjEmvSbWZp7EO5yTXm4bF4rLq0YX0bPzziPhqlTg5yOP1zS4RA8hzn0rufh2AvgXTgOgM3/o165bxB/x5yV1Xw9/5EfT/rN/6Nev1udedbK4Tm7vmX5M+F4cXLiqkFsl+qOnoooryD7IKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArN8Pf8i1pX/XnD/6AK0qzfD3/ACLWlf8AXnD/AOgCgDSooooAKKKKACvD/i5/yPcP/YNh/wDRs1e4V4f8XP8Ake4f+wbD/wCjZq9jIv8Afo/P8jgzP/dpfL8zkoelaMHas6HpWjB2r7KqfFVjRg7VoQdaz4O1aEHWvPqnlVjRh+9WivQVnQ/erRXoK8PF7nHHclFSLUYqRa8DEHp0CRakAzUa1KteJiG1sexQV9w2Uuyninhc15s8VKO56EcPGWw2JOatjCIWNRxLinyFCMMePSvnsxqzrz5YK572XUqdFKVR2Ri3zSXDFYwSapx+HpJ23Siuh82KP7qqKie896MNl+MmrQjY9erxNQw8bU2VYNCt4QN2Pwq6kFrCPlQH61TkvcZyapTapFHndIK9zC8KYmu7zuz5rGcYt6RZt/aEXgAYqvdadbXqHgBjXNyeIYFlVd3U+taMOt2wUEzqPbNejjOCq1OmpUk7+Rw4LiyrTq807pGHrPhTduxHnPtXG3XgK5mkJSMqPpXqv/CQW3/PZPzo/wCEgtf+eyfnXHQybPKKtGB9lS8Q8NGHJPU870XwvfaXMpZGZM+leo2Axpqg9apf8JBa/wDPZPzoOv2pGDMmPrWdXIM3rVY1KlLZnDmnGuDx9F03oZ/iD/jzkrqvh7/yI+n/AFm/9GvXG6ze29xZuIpVY+ma7L4e/wDIj6f9Zv8A0a9fozpzp5VCM1Z8y/JnyPDrUsXVa2t+qOnoooryz7IKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArN8Pf8i1pX/XnD/wCgCtKs3w9/yLWlf9ecP/oAoA0qKKKACiiigArxz4nWn2nxwhxnGnQj/wAiTV7HXnPi+0+0+MnOOmnwf+jJq9DK6qpYlTfn+R5Ge1HTwMpLy/M4CHS+ny1oQ6X/ALNdFFp6L1GatpbKvRa9qvm8V1Pz1161TYw4NL/2avxacq9a01iqRYvavExGdvoy4YSrU3KaWqL0Wp1i9qtLFUixV4WIzrzPQo5U30KyxVIsVWli9qkWKvDxGc+Z7FDKfIrLF7VIsXtVkRU8IAK8OvnDeiZ7FHKktWiusVO2hakZsdKgd6ypTq13rsa1I06K0Ed8VWeQ+tOkeqsj19Rl2BTtdHzmOxluojye9Ubm58sHnmnXNwI1965+9vOvNfd5dly0bR8xUqTrSsgvb5ufnP51zt7fNz85/OlvbzrzXO3t515r6/C4Vdj1MHg/IW7v2DZDkfjVb+0pP+erf99Gsa7vCW4NVvtTV7kMKuU+ip4Ncux0X9pSf89W/wC+jR/aUn/PVv8Avo1zv2pqPtTVX1VF/U12Oi/tKT/nq3/fRo/tKT/nq3/fRrnhcsTirUJJIyaTw8UTLCxjujaS8mc/6x/++jX0T8NCW+H2lkkknzeT/wBdXr5ug7V9I/DP/knul/8AbX/0a9fKcTpLCxt/MvyZvlsUq7t2/VHWUUUV8Me6FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVm+Hv+Ra0r/rzh/8AQBWlWb4e/wCRa0r/AK84f/QBQBpUUUUARzTLBC8rhyqDcQiF2/BVBJ+gFYj+J0a8SK3s5jEGiWZ7iOS3dfNkEabUdAW56ngAep4rfqGWztZ54p5raGSaHPlSOgLJnrtJ5H4UAZi+Ird/Ff8AYKeWZFgeV3MoDBl2HYE6n5ZAc+3fnHP626z+L7oKHBjs4FO5GXJ3zdMjke44rtI7W3i8ry4Ik8lPLj2oBsTj5R6D5V4HoPSuW1dN3iy4/wCvGD/0OaufFV/YUnU7Hn5pR9thnD0/MoLFUixVaWKpFir5nEZz5niUMp8issXtUixe1WRHilJROprxK+bt7M9mhlPkQrFUoiqJ7tE6YqpLqXoa82eLrVNj2aGUd0aJ2L1NRtcoOFGTWWJ5J24zir9tb9Ca5puX2meh9TpUVeRYQs/LcCnM2BQxAGBUDvXRhMM5u7PJxuLSVkI71Xd6V3qs719jgMDtofI43GCO9VJ5Qikk0+STAJNY19d9ea+4y3A3sfKYmu6kuVFe+u+TzXN31315qxfXfXmubvrvrzX22Ew1rI7MFhSve3nXmuevLskkA1Le3XXmseRySSa+ho04048zPq8LhlFXYO+Tk0zdUbvTQc1yVMd79keko6E26nA5qJakWumjUlN6iaJU+8K0IO1Z6feFaEHauqpsc1bY04O1fSPwz/5J7pf/AG1/9GvXzdB2r6R+Gf8AyT3S/wDtr/6NevjeKP8AdY/4l+TJy7+O/T9UdZRRRXwp7YUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWb4e/5FrSv+vOH/ANAFaVZvh7/kWtK/684f/QBQBpUUUUAFFFFABXMagoPiq6J/58rf/wBDmrp64vX7r7P4qnGcZsYP/Q5q8XiByWAny76fmjWhR9tNQLpaNOpqGS9RelYM2p/7VUJtS6/NX52sPUn8TPdoZWl0Ohl1L0NUJtS6/NXPS6iWOAcmiLzJjyeK6Y4RRV2epDAxgrs1GvXkbC5NWLeFpCC3NQ2lr04rdtLbGOKyqzjBWRhXqRpq0R9rbYAOKvEhRgUYCLgVE71lh6Eqsrs+Xx2M3Ed6ru9K71Wd6+uwGC20PkMbjBHeq7vSu9Z93chQQDX2uX4G9tD5PF4lydkR3t0AMA1zd9d9easXt315rm7676819tgsKopJCwmHcndle+u+vNc3fXfXmrF9d9ea5+4mMjH0r6XDULK7PrMHhrakU0pds5qsxzUhBNJsrSvGdTRbHsRsiErmgLiptlGyuL6i73L5hi1ItJtpwGK7KNKUHqS2SJ94VoQdqz0+8K0IO1dVTY5q2xpwdq+kfhn/AMk90v8A7a/+jXr5ug7V9I/DP/knul/9tf8A0a9fG8Uf7rH/ABL8mTl38d+n6o6yiiivhT2wooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACs3w9/yLWlf9ecP/oArSrN8Pf8i1pX/XnD/wCgCgDSooooAKKjmnitoXmnlSKJBud3YKqj1JPSskeKdLluEitJlvULIkk1rIkiQl22IGIbOSxxwDjqcDmgDarzDx3dfZ/GDDPXT4P/AEZNXeJrtrJrsmkqk3mx8NKVAj37Q+zOeW2sGwB0ryj4n3Qk8cbLd1kKWEKybDna3mS8HHQ8jivPzSk6uGlFeX5nrZJGMsbFSdlr+RnTal/tVXE8k54ziqEERY5c/nWvarCuN0sY+rCvlJUHTWkX9x9vOrh6S0kvvRYtbYkjNbtpa9OKqWstmuN11APrIK2bW805cZv7UfWZf8a8zEKs9ov7mePisbB7Mv2lr04rUVRGuBVGPVtKjXjUrPP/AF3X/Gmya1pgBJ1K0wO/nr/jXDTwderK7g/uZ8vjcZfRFx3qs71VfWNOP/L/AGv/AH+X/GoH1awP/L9bf9/V/wAa+nwOXyVro+Txleb2TLLvVd3qu+qWJ/5fbf8A7+r/AI1Un1S0CnbdQk+0gr7LA4NK1z5TFyrS2i/uZLdXARSAeawby6680XeoRtnEqH6NWJd3LNnaCfpX2WDpUoLdfeclDBVpSvKL+4hvbvrzXN31315q9cmeUkJG59flNZ7afO5y0Uh/4Ca+goToQ3kvvR9FhsK4K7TMO4LzE4yBVb7Kfeuj/syX/njJ/wB8mj+zJf8AnjJ/3ya9BY+ilZSX3o9ONWUVZI5z7Kfej7Kfeuj/ALMl/wCeMn/fJo/syX/njJ/3yaf9oUf5196K9vPsznPsp96Psp966M6bKOsLj/gJo/syX/njJ/3yaP7Qo/zr70Ht59mc59lPvR9lPvXR/wBmS/8APGT/AL5NH9mS/wDPGT/vk0f2hR/nX3oPbz7M50WzA55q1CCCMitj+zJf+eMn/fJo/syX/njJ/wB8mk8fRf2l96JlVlLdMhg7V9I/DP8A5J7pf/bX/wBGvXzwljOh4hk/75Ne9fDzV9Ms/A2nW11qNpBPH5u+KWdVZcyMRkE5HBB/GvluJa1OeFioST95fkzbL4v27bXT9UdzRWb/AMJDon/QY0//AMCU/wAaP+Eh0T/oMaf/AOBKf418Se0aVFZv/CQ6J/0GNP8A/AlP8aP+Eh0T/oMaf/4Ep/jQBpUVm/8ACQ6J/wBBjT//AAJT/Gg+ItEGM6zp/PT/AEpP8aANKis3/hIdE/6DGn/+BKf40f8ACQ6J/wBBjT//AAJT/GgDSorN/wCEh0T/AKDGn/8AgSn+NH/CQ6J/0GNP/wDAlP8AGgDSorN/4SHRP+gxp/8A4Ep/jR/wkOif9BjT/wDwJT/GgDSorNHiLRD01nTz/wBvSf40f8JDon/QY0//AMCU/wAaANKis3/hIdE/6DGn/wDgSn+NH/CQ6J/0GNP/APAlP8aANKis3/hIdE/6DGn/APgSn+NH/CQ6J/0GNP8A/AlP8aANKis0+ItEGM6zp/PT/Sk/xo/4SHRP+gxp/wD4Ep/jQBpUVm/8JDon/QY0/wD8CU/xo/4SHRP+gxp//gSn+NAGlRWb/wAJDon/AEGNP/8AAlP8aP8AhIdE/wCgxp//AIEp/jQBpVm+Hv8AkWtK/wCvOH/0AUf8JDon/QY0/wD8CU/xpPDrBvDGkspBBs4SCO/yCgDTooooAKp3Ol2d3dxXM0RaWLG0h2AODuXcAcNg8jIODyKuUUAc+66daak5trO8k1CNfkaZLnyWYJjcZCpTdtAXfye2e1SSartsdP1G3so2u9SRFVHm2KAI3lwzbTwAH7dT27bM0MVxC8M0aSRONro6gqw9CD1qr/Y2l/Zhbf2bZ/Zwqp5XkLt2qSVGMYwCxI9Mn1oAz9J8RjVr5IktTHBLE0kUjSfOdojJDLj5f9aMcnp2yK3agSytYrp7qO2hS4kAV5VjAdgMYBPU9B+VT0AFFFFAEN1dR2du00qzMgwCIYXlb/vlASfyrGXXYdSvPsBsnNlPIbRmnDxOXMTSEGNlB27QRnPU9Mc1v1Wl06ynmaaazt5JWjMTO8Slih6qSR05PHSgDKutZ1aO61C3s9GivHtREUCXm0vvYgg7kAUhRuIyeo9a1rG7S/0+2vIv9XcRLKvXowBHUD1pJ9PsrmOWOezt5UmYPIskSsHYAAEgjk4AGfYVYRFjRURQqqMBQMACgBaKKKACsW68Qok8draWtxJdSS+UguIJoIsgEn94yY6KcYzn9a2qiubW3vIGguoIp4WxmOVAynHI4NAGJJrT29lYapa6XvtNQ8ua6mMwUwBggDFcEscEDj+7VrR9abVZpka2EIWNJoyJN25HZ1GRgbW/dnI5xkc1bfS9PkkSR7C1Z43DozQqSrAAAg44OFUZ/wBkelSwWdravK1vbQwtK2+QxoFLt6nHU0ATUUUUAFVr2+hsI1kmS4ZWO0CC3kmP4hFJH1qzRQBhWWsx6xei2ez22775YHckOTDIgO9CoKHcykDJPHOOlQT+JL+C0muv7Jilgtp2juJorvKRxquWkBKAttOQQB1B5yCK2/7OsvPkn+x2/nSlWkk8pdzlSCpJxzggEemBUEeg6PFEkUek2KRo+9EW2QBW9QMcHgc0AaFFFFABRRRQBkvrTJrqaeLYGJpBCZfMwwcxtJ93HK7U656np3rHXxWYomni0uMS3Ma3gH2jAaIxOwLHbw+2HG3kdOetdLLp1lPcrczWdvJcJgLK8Slhg5GCRng8/Wozo+mNE8badaGN5PNdTAuGf+8Rjk+9AFtHEkauM4YAjIp1FFABRRRQBU1K9/s/T5boR+YyYCpu2gsSAMnsMkZPasubV5JLOyuzYQySi8eDyjcHcJFdoiY/k+fgOedvGScc43JYo54niljWSN1KujjIYHqCO4qn/Yekfu/+JVY/um3x/wCjp8jYAyOODhVH4D0oAi0vVLm8vb60vLD7HNbsGRfOEnmRMzBJOBgZ2HjqO/vqVBbWNpZmQ2trBAZW3SGKMLvPqcdTU9ABRRRQAVkvrTJrqaeLYGJpBCZfMwwcxtJ93HK7U656np3rWqtLp1lPcrczWdvJcJgLK8Slhg5GCRng8/WgDm/+EnuNPtriObRUtrqF5ZntzdAjy9rSF9wUjLENhfrkiusByAfWqQ0bShAYRplmIjJ5pTyF278Y3Yx1xxmr1ABRRRQAVS1O+ksoYfJhWaaaVYo1d9i5OTy2DgYB7HsKu1FcW0F5A0FzBHPC2N0cqBlODkZB96AMY6s92dAnisIpEvtsqF5yHhzEzFtoUggKSM5HLAd6taFqlzqlpK19YfYLyCTyprbzhLsbarj5gADw4PFXY7G0hCCK1gTYGVdsYG0McsB6ZIBPqRRa2drYw+TZ20NvFnOyGMIufXAoAnooooAKKKKAMm41prSfVluLYeXYWi3YaOTc0iHzOMEDB/dnueorNj8RvZPHp/8AZsUZtmWGVY7glY1zEF2HaN/Ey8YHQj0zu/2VpxvmvvsFr9rYYafyV8w8EctjPQkfQmkTSNNjWBU0+0VYGLQhYVAjYnJK8cHIHSgC5RRRQAUUUUAFFFFABXwcigqOB09K+8a+D0+6PpXuZIk5zv5GVQcFX+6Pyp4Rf7o/Kminivr6NOHYwbFCJ/dX8qeET+4v5Ugpwr1KNKH8qIbYvlp/cX8qljjj/uL+VMqWKu10qdvhX3Gcm7FmOGL/AJ5p/wB8ircVvD/zxj/75FV46uxVyzpQ/lRxVJPuTxW0H/PCP/vgVcitLYj/AI94v++BUEVXIulcc6cOyOCrKXcmjs7X/n2h/wC/Yq3HY2nH+iwf9+xUcdXIu1cc6cOxwVJy7kkWn2X/AD6Qf9+x/hVyLTrH/nyt/wDv0v8AhUcVXYq45wj2OCpUn3Y6PTLDI/0G2/79L/hVyPS9P4/0C1/78r/hTYuoq7H2rjqRj2PPqVZ92EOk6af+Yfaf9+V/wq2NI0z/AKB1p/34X/Clhq4OlePiErmUas7/ABMrDR9M/wCgdaf9+F/wp40fS/8AoG2f/fhf8KtCnivJrHdSqT7sqDRtL/6Btn/34X/CnjRtL/6Btn/34X/CrYp4rzKzZ6NKcu5UGi6V/wBAyz/78L/hUg0XSv8AoGWX/fhf8KtCpBXlVpPuelRlLuVBoulf9Ayy/wC/C/4U8aJpP/QMsv8AwHT/AAq2KeK8utOXc9GlJ9yoNE0n/oF2X/gOn+FPGh6T/wBAuy/8B0/wq4KeK8ytUn3PSpNlMaHpH/QLsf8AwHT/AApw0PSP+gVY/wDgOn+FXRThXl1as+7PRpGa+h6R/wBAux/8B0/wqtJomkjP/Essv/AdP8K2XqrL3rnjVqfzP7z1aKVjFk0bS/8AoG2f/fhf8KqyaPpn/QOtP+/C/wCFbElVJK64VZ92d9OK7GPJpOm/9A+0/wC/K/4VVk0rTv8Anwtf+/K/4VryVTk6muuFSfdndThHsZMml6f/AM+Nt/35X/Cqkmm2P/Plbf8Afpf8K1ZaqSV1wnLud9OEexlyafZD/lzt/wDv0v8AhVSSws/+fSD/AL9itSWqctdcJy7ndTpw7IzJLG0/59YP+/YqpJZ2uP8Aj2h/74FaUlVJehrrhKXc7qdKH8qMua1txnEEX/fAqm9vB/zxj/75FaM/eqb12wk+5VSlC3wr7io1vD/zxj/75FQPBF/zyT/vkVbeq710RbPMrU4dkVXhi/55p/3yK9X+ASqvibVdqgZsx0H+2K8revVfgH/yM2q/9eY/9DFddBvnR87m0YrDSsu35o9+ooor0D5IKKKKACiiigAooqnex6lIU+wXdpABneJ7Zpc+mMSLj9aALleH2/7OlibaIza7dRylAXQQqwVscjOea9Hs7bXIbHUI5I7tp5bfy0ZrlT+/2yFpFJY7EJ8sAY4P8IGTWdBp3idZtM3fbPLiuC0ubnqnmRn5gZmONofgtLkZ+7kY1pV6lF3puwmk9zj/APhnTTf+hgu//Adf8aX/AIZ207/oYLr/AMB1/wAa9L8N2ep2Zu1v5Z3iLKIftD7nJAO5j87gZ44BAyDhVzW9XUszxa2qMXJHseL/APDO+nf9DBdf+A6/40v/AAzzp/8A0MF1/wCA6/417Mc4OCAexIzXMatZeJJYwBdJcHY4j+wo1qY5SPkd90p3oDnK/o3a1nGPW1Vi9nHseer+z5afaHU65cCIIpV/JXJbJyMZ7AL+ftUq/s+2C9Nfuf8AwHX/ABr0HUrfWp9egubUXK2yKFSLzlSPIdw7SAHJyuwpgHB6gcisrTrDxBFZWkF3BqTzfbFdpxej91GFj3ZUzHfuYMMEsOWOBkKb/tzMP+frF7KHY5gfASzXpr0//gOP/iqlX4F2q9Nem/8AAYf/ABVet0VLznHv/l6yXh6T3ieUL8Erdemuy/8AgMP/AIqlk+DQjjBh1lnbeoIa3A+UsNx+92GT+Feh6pBrUqS/2bfWcAMZCrLas7bsHncJAB2/hOPfpWfaQaymlXVvDFPFJK+YDeXQdok2xhgXBc7ifNZT8wHGccCpebY1/wDLxkPB0HvFHKr8IEXprbf+Av8A9nUi/CYL01o/+Av/ANnW5baRqstjpwvX1CKaHSzHcm31Bi0k5QIAMttLD5zuI+8VOTjNbWgQXdtpCQ3pnMiu+03EgklKbjt3sCQWxjocVDzPFv8A5eMh5fhXvBHHL8LSvTWR/wCAv/2dSr8NJF6awv8A4Cf/AGdd/UN0ty9uy2c0MU3GHmiMij1+UMp/WpeYYp/bZDyvBvemjil+Hcy9NYj/APAQ/wDxdLb+BL3YTLqcKNvYAC1J+UMdp+/3GD+NaqW+uw60txcGS6VGLM1u3lQvF5TARiJpDh/M2nce38QHFVb2y1m5n1E28ep2sDzxqFS7VnljDEs8ZaQiMnI+X5flHqcCXjcQ95sh5PgXvSQxfBN2vTVof/AM/wDxypR4QvR/zFbf/wAAz/8AHKXR7PxBH4gjnv8Azvs/2dVcefuj3+VGDgbz/GH/AIM9TvOdtdZWUq9SW7F/Y2A/59I5P/hEr4f8xW3/APANv/jlKPCd8P8AmK2//gG3/wAcrq6wNTtdck2k3CXFoJcvb2UbW87JzgCUy44OCfu5wfoc229y1lWCW1NFE+F9TFwirqNqYijFn+yNkNkYGPM7gt+XvUv/AAi9/wD9BS2/8A2/+OUl5a69LY6fGBcm5ht/LlkjnVAbjbGVlbDDdGD5gKkc/wB0jBqp9h8Qx2mp+YuoXEzzJJDsu1XJErk7PnAEezZ8pwTzkGs3Ti90Wsuwq2gi6PDOoD/mKW3/AIBt/wDHKX/hG9RH/MUtf/ANv/jldKpJUEqVJHQ9RS1m8NSe8S1gqC2ijmv+Ec1H/oJ2v/gE3/x2mz6BqyW8rQahZyShCURrRgGbHAJ8zjmtrV47qXSriOzLCcr8oRtrEZ5CtxgkZAORgnqKxBFqy6dYj7JqLTQ6gWI+1rnyC5I3nzPnAQhSDk5U9eCc3gcM94I0WHpLaJN/YGpf9BO0/wDAJv8A47S/2Fqf/QTtP/AJv/jtZd7p3iia6vC8s4t5ZY3/ANBvBvKASjbGH2hMEw7uecHrmuvtBOLOAXIUT+WvmbCSN2OcZ5xms3lmDe9NFqnFbIxf7E1T/oJ2f/gE3/x2lGi6oP8AmJWf/gE3/wAdrfoqHk+Ae9JGidtjnzomqH/mJ2f/AIBN/wDHagXQNWd5hJqFmqh8RkWjHcu0cn95xzkfhUsEGqxa/cSTQ3ktrIsysy3C7CDsMWxS42kBXUkAcsDyORnwabrYawkY6oskN4zBDdjyjCzx7vNBkZmIUPtAZuTzgHaJ/sXL/wDn0jRVqi2Zabwvft11S2/8A2/+OVGfCN63XVbf/wAA2/8Ajlafh2K8jgunu7e7tvMnLRwXNwJyiYAHzb264yRkAE4HTJ2apZPgV/y6RaxVZbSOPbwXdt11aD/wDP8A8cqJvAly3XVov/AQ/wDxyu1rC8TW2p3NtGNN88sFkAEM3lFZCv7t2ORlQeo5zkcHFUsqwa/5dopY7ELabOfm8AXm+IJqkLKz4kJtSNq7TyPn55wPxoPw4lbrrCf+Ah/+LrUvrDULrU554hrUUFzaMkqJdxqIyVGPJAf5ZAe5464PTMVpp2vJeaU7TXgWMlZhNMGAjDyY34k+aRkKA/KwyBgjFUstwi/5dotZjiltNmW3wxduusr/AOAv/wBnUbfCst11r/yV/wDs69GoqlgMMvsItZrjFtUZ5q3wkVuutH/wF/8As6jb4PRt11tv/AX/AOyr0m6WZrSZbdgk5RhGzdA2OCfxrmIrHVpdA1CzaLUIZnZPsryX2Xj3IqsS6yEkKwdyM8ggDngWsHQX2UWs4x62qs5OL4MLLbxtcay0crIC6LbghWxyAd3PNIfgfbH/AJjsv/gMP/iq6e70vxC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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p12dEvaluate.plot(figsize=(9,9), gridLines=None, colorbar=\"horizontal\")\n", "f.plot(level=2, figsize=(9,9), gridLines=None, colorbar=\"horizontal\")\n", "error.plot(level=2, figsize=(9,9), gridLines=None, colorbar=\"horizontal\")" ] }, { "cell_type": "markdown", "id": "4b9c0a4a", "metadata": {}, "source": [ "#### Paraview (vtu/vtk files)\n", "Now we write out vtu files and use ``pvpython`` to prduce similar but 3d\n", "plots of the same functions" ] }, { "cell_type": "code", "execution_count": 22, "id": "421e9c93", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:53.810772Z", "iopub.status.busy": "2023-10-22T10:31:53.810070Z", "iopub.status.idle": "2023-10-22T10:31:53.948595Z", "shell.execute_reply": "2023-10-22T10:31:53.947649Z" } }, "outputs": [], "source": [ "pd = {\"exact\": f, \"discrete\": p12dEvaluate, \"error\": error}\n", "aluView.writeVTK(\"interpolation\", pointdata=pd)\n", "\n", "aluView.writeVTK(\"interpolation_subsampled\", subsampling=2, pointdata=pd)" ] }, { "cell_type": "markdown", "id": "cfab351d", "metadata": {}, "source": [ "Here are pngs generated with paraview from the above output:\n", "The discrete interpolation\n", "![interpolation](figures/interpolation_discrete.png)\n", "A zoom in\n", "![exact solution](figures/interpolation_exact.png)\n", "The interpolation error on the subsampled grid\n", "![interpolation error](figures/interpolation_error.png)" ] }, { "cell_type": "markdown", "id": "e49b86a7", "metadata": { "lines_to_next_cell": 0 }, "source": [ "#### Mayavi\n", "Mayavi can also be used to plot grid function in Python. This approach\n", "relies on methods to extract `numpy` representations of the grid data\n", "structure and values of a given grid function." ] }, { "cell_type": "code", "execution_count": 23, "id": "298efb70", "metadata": { "execution": { "iopub.execute_input": "2023-10-22T10:31:53.952966Z", "iopub.status.busy": "2023-10-22T10:31:53.952446Z", "iopub.status.idle": "2023-10-22T10:31:53.991285Z", "shell.execute_reply": "2023-10-22T10:31:53.990484Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mayavi module not found so not rendering plot - ignored\n" ] } ], "source": [ "level = 3\n", "triangulation = f.gridView.triangulation(level)\n", "z = f.pointData(level)[:,0]\n", "try:\n", " from mayavi import mlab\n", " from mayavi.tools.notebook import display\n", " mlab.init_notebook(\"png\")\n", " mlab.figure(bgcolor = (1,1,1))\n", " s = mlab.triangular_mesh(triangulation.x, triangulation.y, z*0.5,\n", " triangulation.triangles)\n", " display( s )\n", " # mlab.savefig(\"mayavi.png\", size=(400,300))\n", " mlab.close(all=True)\n", "except ImportError:\n", " print(\"mayavi module not found so not rendering plot - ignored\")\n", " pass" ] }, { "cell_type": "markdown", "id": "c36940b2", "metadata": {}, "source": [ "#### Low level output\n", "\n", ".. todo:: add some information on the ``tessellate`` and related methods, e.g.,\n", " ``coordinates`` , ``polygons`` , ``tessellate`` , ``triangulation``\n", "\n", "```\n", "gridView.triangulation(level=0, *, partition=...) (for 2d grids)\n", " Returns: matplotlib.tri.Triangulation structure\n", "```\n", "More general methods\n", "```\n", "coordinates(...)\n", " Returns: `numpy` array with the coordinates of all vertices in the grid in\n", " the format `[ [x_1,y_1], [x_2,y_2], ..., [x_N,y_N] ]` for example\n", " in 2d (will be filled up by zeros if dimworld is larger\n", " than the world dimension of the view.\n", "polygons(...) Store the grid in numpy arrays.\n", " Returns: coordinate array storing the vertex coordinate of each polygon\n", " in the grid.\n", "tessellate(level=0,dimworld=gv.dimensionworld) or tessellate(level=0,*,partition,dimworld=gv.dimensionworld)\n", " Generated a possibly refined tessellation using only simplices.\n", " Args: level: virtual refinement level to use to generate the tessellation\n", " Returns: (coordinates,simplices) where coordinates is a `numpy` array\n", " of the vertex coordinates (padded by to `dimworld` is needed)\n", " (e.g. in 2d `[ [x_1,y_1,0], [x_2,y_2,0], ..., [x_N,y_N],0 ]`\n", " if `dimworld` is set to 3 - default is no padding,\n", " `dimworld` less than actual world dimension of grid is ignored)\n", " and simplices is a `numpy` array of the vertices of the simplices\n", " (e.g. in 2d `[s_11,s_12,s_13], [s_21,s_22,s_23], ..., [s_N1,s_N2,s_N3] ]` )\n", "```\n", "Partner methods on grid function:\n", "```\n", "gridFunction.cellData, gridFunction.pointData, gridFunction.polygonData\n", "```\n", "\n", ".. todo:: add something on ``intersections``\n" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all" }, "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }