diff --git a/.github/workflows/build-publish.yml b/.github/workflows/build-publish.yml
index 8596b9f9..d631ed1e 100644
--- a/.github/workflows/build-publish.yml
+++ b/.github/workflows/build-publish.yml
@@ -20,7 +20,7 @@ jobs:
build:
# The type of runner that the job will run on
runs-on: ubuntu-latest
- container: dokken92/dolfinx_custom:07012022
+ container: dokken92/dolfinx_custom:21012022
env:
HDF5_MPI: "ON"
@@ -84,8 +84,7 @@ jobs:
- name: Build the book
run: |
PYVISTA_JUPYTER_BACKEND=static PYVISTA_OFF_SCREEN=false jupyter-book build -W .
- # Add in -W once matplotlib is gone
- # Pusb book to HTML to github pages
+ # Push book to HTML to github pages
- name: GitHub Pages action
uses: peaceiris/actions-gh-pages@v3.5.9
with:
diff --git a/.github/workflows/docker-image.yml b/.github/workflows/docker-image.yml
index 7bed87aa..d2a51f16 100644
--- a/.github/workflows/docker-image.yml
+++ b/.github/workflows/docker-image.yml
@@ -37,4 +37,4 @@ jobs:
run: echo ${{ secrets.DOCKERHUB_TOKEN }} | docker login -u ${{ secrets.DOCKERHUB_USERNAME }} --password-stdin
- name: Push to the DockerHub registry
run: |
- docker push dokken92/dolfinx_custom:07012022
+ docker push dokken92/dolfinx_custom:21012022
diff --git a/.github/workflows/main-test.yml b/.github/workflows/main-test.yml
index 6330b15f..32ba9bcd 100644
--- a/.github/workflows/main-test.yml
+++ b/.github/workflows/main-test.yml
@@ -42,11 +42,14 @@ jobs:
apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
pip3 install --no-cache-dir tqdm pandas seaborn
- pip3 install notebook nbconvert jupyter-book myst_parser pyvista jupyterlab
+ pip3 install notebook nbconvert jupyter-book myst_parser pyvista jupyterlab jupyter
pip3 install ipygany pythreejs
pip3 install --no-cache-dir matplotlib setuptools --upgrade
jupyter nbextension enable --py --sys-prefix ipygany
rm -rf /usr/local/share/.cache/*
+ - name: Test building the book
+ run:
+ PYVISTA_JUPYTER_BACKEND=static PYVISTA_OFF_SCREEN=false jupyter-book build -W .
- name: Test notebooks in parallel
run: |
cd chapter1
diff --git a/Changelog.md b/Changelog.md
index ecf7f31e..3873eb31 100644
--- a/Changelog.md
+++ b/Changelog.md
@@ -1,8 +1,10 @@
# Changelog
## Dev
+- All `pyvista` plotting has been rewritten to use `ipygany` and `pythreejs` as well as using a cleaner interface.
+- `dolfinx.plot.create_vtk_topology` has been renamed to `dolfinx.plot.create_vtk_mesh` and can now be directly used as input to `pyvista.UnstructuredGrid`.
+- `dolfinx.fem.Function.compute_point_values` has been deprecated. Interpolation into a CG-1 is now the way of getting vertex values.
- API updates wrt. DOLFINx. `Form`->`form`, `DirichletBC`->`dirichletbc`.
-- Switch plotting backend to `ipygany` and `pythreejs`
- Updates on error computations in [Error control: Computing convergence rates](chapter4/convergence).
- Added tutorial on interpolation of `ufl.Expression` in [Deflection of a membrane](chapter1/membrane_code).
- Added tutorial on how to apply constant-valued Dirichet conditions in [Deflection of a membrane](chapter1/membrane_code).
diff --git a/Dockerfile b/Dockerfile
index 81f7d289..542149cf 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,4 +1,4 @@
-FROM dokken92/dolfinx_custom:07012022
+FROM dokken92/dolfinx_custom:21012022
# create user with a home directory
ARG NB_USER
diff --git a/chapter1/fundamentals_code.ipynb b/chapter1/fundamentals_code.ipynb
index 767363c0..50d2bf8c 100644
--- a/chapter1/fundamentals_code.ipynb
+++ b/chapter1/fundamentals_code.ipynb
@@ -335,7 +335,7 @@
"$ u = \\sum_{j=1}^N U_j\\phi_j.$\n",
"By writing `problem.solve()` we compute all the coefficients $U_1,\\dots, U_N$. These values are known as the _degrees of freedom_ (dofs). We can access the degrees of freedom by accessing the underlying vector in `uh`.\n",
"However, as a second order function space has more dofs than a linear function space, we cannot compare these arrays directly.\n",
- "Therefore we compute the values of both `uex` and `uD` at the mesh nodes (for a linear mesh this is the vertices)."
+ "As we allready have interpolated the exact solution into the first order space when creating the boundary condition, we can compare the maximum values at any degree of freedom of the approximation space."
]
},
{
@@ -353,9 +353,7 @@
}
],
"source": [
- "u_vertex_values = uh.compute_point_values()\n",
- "u_ex_vertex_values = uex.compute_point_values()\n",
- "error_max = numpy.max(numpy.abs(u_vertex_values - u_ex_vertex_values))\n",
+ "error_max = numpy.max(numpy.abs(uD.x.array-uh.x.array))\n",
"# Only print the error on one process\n",
"if mesh.comm.rank == 0:\n",
" print(f\"Error_L2 : {error_L2:.2e}\")\n",
@@ -366,10 +364,10 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Plotting the solution using pyvista\n",
- "Once the solution has been computed, we will visualize it using [pyvista](https://docs.pyvista.org/), an interface to the VTK toolkit.\n",
+ "## Plotting the mesh using pyvista\n",
+ "We will visualizing the mesh using [pyvista](https://docs.pyvista.org/), an interface to the VTK toolkit.\n",
"We start by converting the mesh to a format that can be used with `pyvista`.\n",
- "To do this we use the function `dolfinx.plot.create_vtk_topology`. The first step is to create an unstructured grid that can be used by `pyvista`."
+ "To do this we use the function `dolfinx.plot.create_vtk_mesh`. The first step is to create an unstructured grid that can be used by `pyvista`."
]
},
{
@@ -378,8 +376,10 @@
"metadata": {},
"outputs": [],
"source": [
- "from dolfinx.plot import create_vtk_topology\n",
- "topology, cell_types = create_vtk_topology(mesh, mesh.topology.dim)"
+ "from dolfinx.plot import create_vtk_mesh\n",
+ "import pyvista\n",
+ "topology, cell_types, geometry = create_vtk_mesh(mesh, mesh.topology.dim)\n",
+ "grid = pyvista.UnstructuredGrid(topology, cell_types, geometry)"
]
},
{
@@ -395,7 +395,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import pyvista\n",
"pyvista.set_jupyter_backend(\"pythreejs\")"
]
},
@@ -403,38 +402,74 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We start by creating a pyvista grid the `vtk_topology` and the `mesh.geometry`.\n",
- "Next, we attach data from our solution `uh` by computing the values of the function at each vertex."
+ "We can now use the `pyvista.Plotter` to visualize the mesh. We visualize it by showing it in 2D and warped in 3D.\n",
+ "In the jupyter notebook environment, we use the default setting of `pyvista.OFF_SCREEN=False`, which will render plots directly in the notebook."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "8153fbfcef014a3899925003792a4ba1",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Renderer(camera=PerspectiveCamera(aspect=1.3333333333333333, children=(DirectionalLight(color='#fefefe', inten…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x)\n",
- "grid.point_data[\"u\"] = uh.compute_point_values().real\n",
- "grid.set_active_scalars(\"u\")"
+ "plotter = pyvista.Plotter()\n",
+ "plotter.add_mesh(grid, show_edges=True)\n",
+ "plotter.view_xy()\n",
+ "if not pyvista.OFF_SCREEN:\n",
+ " plotter.show()\n",
+ "else:\n",
+ " pyvista.start_xvfb()\n",
+ " figure = plotter.screenshot(\"fundamentals_mesh.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We can now use the `pyvista.Plotter` to visualize the solution. We visualize it by showing it in 2D and warped in 3D.\n",
- "In the jupyter notebook environment, we use the default setting of `pyvista.OFF_SCREEN=False`, which will render plots directly in the notebook."
+ "## Plotting a function using pyvista\n",
+ "We want to plot the solution `uh`. As the function space used to defined the mesh is disconnected from the function space defining the mesh, we create a mesh based on the dof coordinates for the function space `V`. We use `dolfinx.plot.create_vtk_mesh` with the function space as input to create a mesh with mesh geometry based on the dof coordinates."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
+ "outputs": [],
+ "source": [
+ "u_topology, u_cell_types, u_geometry = create_vtk_mesh(V)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we create the `pyvista.UnstructuredGrid` and add the dof-values to the mesh."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "026dd4bdc8354af88ecd23be302ee96c",
+ "model_id": "54e3bc7b8f764a31836e14906e188aff",
"version_major": 2,
"version_minor": 0
},
@@ -447,14 +482,14 @@
}
],
"source": [
- "plotter = pyvista.Plotter()\n",
- "plotter.add_mesh(grid, show_edges=True)\n",
- "plotter.view_xy()\n",
+ "u_grid = pyvista.UnstructuredGrid(u_topology, u_cell_types, u_geometry)\n",
+ "u_grid.point_data[\"u\"] = uh.x.array.real\n",
+ "u_grid.set_active_scalars(\"u\")\n",
+ "u_plotter = pyvista.Plotter()\n",
+ "u_plotter.add_mesh(u_grid, show_edges=True)\n",
+ "u_plotter.view_xy()\n",
"if not pyvista.OFF_SCREEN:\n",
- " plotter.show()\n",
- "else:\n",
- " pyvista.start_xvfb()\n",
- " figure = plotter.screenshot(\"fundamentals.png\")"
+ " u_plotter.show()"
]
},
{
@@ -467,13 +502,13 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "2b14ac59f2cf4d129b2843ce038c4b90",
+ "model_id": "c03cdb732aa94579ab795f0188ce9572",
"version_major": 2,
"version_minor": 0
},
@@ -487,7 +522,7 @@
],
"source": [
"if not pyvista.OFF_SCREEN:\n",
- " warped = grid.warp_by_scalar()\n",
+ " warped = u_grid.warp_by_scalar()\n",
" plotter2 = pyvista.Plotter()\n",
" plotter2.add_mesh(warped, show_edges=True, show_scalar_bar=True)\n",
" plotter2.show(jupyter_backend=\"ipygany\")"
@@ -503,7 +538,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
@@ -523,13 +558,6 @@
" :filter: cited and ({\"chapter1/fundamentals_code\"} >= docnames)\n",
"```"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/chapter1/membrane_code.ipynb b/chapter1/membrane_code.ipynb
index 489c93e7..04a700f9 100644
--- a/chapter1/membrane_code.ipynb
+++ b/chapter1/membrane_code.ipynb
@@ -83,11 +83,11 @@
"text": [
"Info : Meshing 1D...\n",
"Info : Meshing curve 1 (Ellipse)\n",
- "Info : Done meshing 1D (Wall 0.000206132s, CPU 0.000536s)\n",
+ "Info : Done meshing 1D (Wall 0.000192024s, CPU 0.000347s)\n",
"Info : Meshing 2D...\n",
"Info : Meshing surface 1 (Plane, Frontal-Delaunay)\n",
- "Info : Done meshing 2D (Wall 0.0743901s, CPU 0.074966s)\n",
- "Info : 1549 nodes 3097 elements\n"
+ "Info : Done meshing 2D (Wall 0.0758838s, CPU 0.076262s)\n",
+ "Info : 1550 nodes 3099 elements\n"
]
}
],
@@ -106,7 +106,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
@@ -128,7 +128,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
@@ -155,7 +155,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
@@ -181,7 +181,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
@@ -199,7 +199,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
@@ -221,7 +221,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
@@ -239,7 +239,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
@@ -256,7 +256,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
@@ -273,7 +273,8 @@
"metadata": {},
"source": [
"## Interpolation of a `ufl`-expression\n",
- "As we previously defined the load `p` as a spatially varying function, we would like to interpolate this function into an appropriate function space for visualization. To do this we use the `dolfinx.Expression`. The expression takes in any `ufl`-expression, and a set of points on the reference element. We will use the interpolation points of the space we want to interpolate in to."
+ "As we previously defined the load `p` as a spatially varying function, we would like to interpolate this function into an appropriate function space for visualization. To do this we use the `dolfinx.Expression`. The expression takes in any `ufl`-expression, and a set of points on the reference element. We will use the interpolation points of the space we want to interpolate in to.\n",
+ "We choose a high order function space to represent the function `p`, as it is rapidly varying in space."
]
},
{
@@ -282,8 +283,9 @@
"metadata": {},
"outputs": [],
"source": [
- "expr = fem.Expression(p, V.element.interpolation_points)\n",
- "pressure = fem.Function(V)\n",
+ "Q = fem.FunctionSpace(mesh, (\"CG\", 5))\n",
+ "expr = fem.Expression(p, Q.element.interpolation_points)\n",
+ "pressure = fem.Function(Q)\n",
"pressure.interpolate(expr)"
]
},
@@ -297,7 +299,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 14,
"metadata": {
"scrolled": true
},
@@ -305,12 +307,12 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b20881a066eb4acbbca59f155880745b",
+ "model_id": "74efaadd973f4ed082d3277b9804de12",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "AppLayout(children=(VBox(children=(HTML(value='u '), Dropdown(description='Colormap:', options={'BrBG'…"
+ "Renderer(camera=PerspectiveCamera(aspect=1.3333333333333333, children=(DirectionalLight(color='#fefefe', inten…"
]
},
"metadata": {},
@@ -318,17 +320,16 @@
}
],
"source": [
- "from dolfinx.plot import create_vtk_topology\n",
+ "from dolfinx.plot import create_vtk_mesh\n",
"import pyvista\n",
- "pyvista.set_jupyter_backend(\"ipygany\")\n",
+ "pyvista.set_jupyter_backend(\"pythreejs\")\n",
"\n",
"# Extract topology from mesh and create pyvista mesh\n",
- "topology, cell_types = create_vtk_topology(mesh, mesh.topology.dim)\n",
- "grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x)\n",
- "\n",
+ "topology, cell_types, x = create_vtk_mesh(V)\n",
+ "grid = pyvista.UnstructuredGrid(topology, cell_types, x)\n",
"\n",
"# Set deflection values and add it to plotter\n",
- "grid.point_data[\"u\"] = uh.compute_point_values().real\n",
+ "grid.point_data[\"u\"] = uh.x.array\n",
"warped = grid.warp_by_scalar(\"u\", factor=25)\n",
"\n",
"plotter = pyvista.Plotter()\n",
@@ -349,13 +350,22 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 15,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:py.warnings:/usr/local/lib/python3.9/dist-packages/dolfinx/plot.py:104: UserWarning: Plotting of higher order functions is experimental.\n",
+ " warnings.warn(\"Plotting of higher order functions is experimental.\")\n",
+ "\n"
+ ]
+ },
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e02a1b16fe7e49c79bbb27530b9ec539",
+ "model_id": "b7718aa1e156481098d53a01dd1b5127",
"version_major": 2,
"version_minor": 0
},
@@ -368,11 +378,14 @@
}
],
"source": [
+ "pyvista.set_jupyter_backend(\"ipygany\")\n",
"load_plotter = pyvista.Plotter()\n",
- "grid.point_data[\"p\"] = pressure.compute_point_values().real\n",
- "warped_p = grid.warp_by_scalar(\"p\", factor=0.5)\n",
+ "p_grid = pyvista.UnstructuredGrid(*create_vtk_mesh(Q))\n",
+ "p_grid.point_data[\"p\"] = pressure.x.array.real\n",
+ "warped_p = p_grid.warp_by_scalar(\"p\", factor=0.5)\n",
"warped_p.set_active_scalars(\"p\")\n",
"load_plotter.add_mesh(warped_p, show_scalar_bar=True)\n",
+ "load_plotter.view_xy()\n",
"if not pyvista.OFF_SCREEN:\n",
" load_plotter.show()\n",
"else:\n",
@@ -391,7 +404,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -414,7 +427,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -436,7 +449,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
@@ -461,7 +474,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@@ -479,12 +492,12 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -517,7 +530,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
diff --git a/chapter1/nitsche.ipynb b/chapter1/nitsche.ipynb
index a0ed8131..da38e9a9 100644
--- a/chapter1/nitsche.ipynb
+++ b/chapter1/nitsche.ipynb
@@ -153,12 +153,12 @@
"metadata": {},
"source": [
"We observe that the $L^2$-error is of the same magnitude as in the first tutorial.\n",
- "As in the previous tutorial, we also compute the maximal error at the mesh vertices (over all processes)."
+ "As in the previous tutorial, we also compute the maximal error for all the degrees of freedom."
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 8,
"id": "b3413383",
"metadata": {},
"outputs": [
@@ -171,9 +171,7 @@
}
],
"source": [
- "u_vertex_values = uh.compute_point_values()\n",
- "u_ex_vertex_values = uD.compute_point_values()\n",
- "error_max = mesh.comm.allreduce(numpy.max(numpy.abs(u_vertex_values - u_ex_vertex_values)), op=MPI.MAX)\n",
+ "error_max = mesh.comm.allreduce(numpy.max(numpy.abs(uD.x.array-uh.x.array)), op=MPI.MAX)\n",
"if mesh.comm.rank == 0:\n",
" print(f\"Error_max : {error_max:.2e}\")"
]
@@ -188,14 +186,14 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 11,
"id": "b12d508e",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "395eff191c3f4db39e4cea8c67eb0cc9",
+ "model_id": "d9f26395c5034306abafca9a76a148ec",
"version_major": 2,
"version_minor": 0
},
@@ -208,13 +206,12 @@
}
],
"source": [
- "from dolfinx.plot import create_vtk_topology\n",
+ "from dolfinx.plot import create_vtk_mesh\n",
"import pyvista\n",
"pyvista.set_jupyter_backend(\"pythreejs\")\n",
"\n",
- "topology, cell_types = create_vtk_topology(mesh, mesh.topology.dim)\n",
- "grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x)\n",
- "grid.point_data[\"u\"] = u_vertex_values.real\n",
+ "grid = pyvista.UnstructuredGrid(*create_vtk_mesh(V))\n",
+ "grid.point_data[\"u\"] = uh.x.array.real\n",
"grid.set_active_scalars(\"u\")\n",
"plotter = pyvista.Plotter()\n",
"plotter.add_mesh(grid, show_edges=True, show_scalar_bar=True)\n",
diff --git a/chapter2/diffusion_code.ipynb b/chapter2/diffusion_code.ipynb
index cd93a4c3..a22c5bf7 100644
--- a/chapter2/diffusion_code.ipynb
+++ b/chapter2/diffusion_code.ipynb
@@ -129,7 +129,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -146,7 +146,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -165,7 +165,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -185,7 +185,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 10,
"metadata": {
"scrolled": true
},
@@ -193,7 +193,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b195d7e280844622a702db307c7abd28",
+ "model_id": "5240e87a2af74a98b0ed1229bfe73edb",
"version_major": 2,
"version_minor": 0
},
@@ -209,9 +209,8 @@
"import pyvista\n",
"pyvista.set_jupyter_backend(\"ipygany\")\n",
"\n",
- "from dolfinx.plot import create_vtk_topology\n",
- "topology, cell_types = create_vtk_topology(mesh, mesh.topology.dim)\n",
- "grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x)\n",
+ "from dolfinx.plot import create_vtk_mesh\n",
+ "grid = pyvista.UnstructuredGrid(*create_vtk_mesh(V))\n",
"\n",
"def plot_function(t, uh):\n",
" \"\"\"\n",
@@ -219,7 +218,7 @@
" \"\"\"\n",
" p = pyvista.Plotter()\n",
" # Update point values on pyvista grid\n",
- " grid.point_data[f\"u({t})\"] = uh.compute_point_values().real\n",
+ " grid.point_data[f\"u({t})\"] = uh.x.array.real\n",
" # Warp mesh by point values\n",
" warped = grid.warp_by_scalar(f\"u({t})\", factor=1.5)\n",
"\n",
@@ -250,18 +249,18 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e5e1a82cd26d426899e96956546c8349",
+ "model_id": "848763cda39d4d11b6ad4f866a4ac458",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "AppLayout(children=(VBox(children=(HTML(value='u(0.09836065573770492) '), Dropdown(description='Colorm…"
+ "AppLayout(children=(VBox(children=(HTML(value='u(2.032786885245903) '), Dropdown(description='Colormap…"
]
},
"metadata": {},
@@ -270,12 +269,12 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "43e3bf41a0f743de81ec6c1649cae17a",
+ "model_id": "8c662d03e3b14a3d8da17e2c7301afbb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "AppLayout(children=(VBox(children=(HTML(value='u(0.5901639344262295) '), Dropdown(description='Colorma…"
+ "AppLayout(children=(VBox(children=(HTML(value='u(2.524590163934428) '), Dropdown(description='Colormap…"
]
},
"metadata": {},
@@ -284,12 +283,12 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "62276d6f144f4361aa1c3512ca0e24e7",
+ "model_id": "0efc34292edd4ec6bfff96f1066489af",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "AppLayout(children=(VBox(children=(HTML(value='u(1.0819672131147546) '), Dropdown(description='Colorma…"
+ "AppLayout(children=(VBox(children=(HTML(value='u(3.0163934426229533) '), Dropdown(description='Colorma…"
]
},
"metadata": {},
@@ -298,12 +297,12 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "22ff9e3e94604cd9abbd935bd6368011",
+ "model_id": "c73b09fc0a5f485aac1122b24dfb3de5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "AppLayout(children=(VBox(children=(HTML(value='u(1.5737704918032798) '), Dropdown(description='Colorma…"
+ "AppLayout(children=(VBox(children=(HTML(value='u(3.5081967213114784) '), Dropdown(description='Colorma…"
]
},
"metadata": {},
@@ -312,12 +311,12 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3d98b89234b54958b23b541ae9d06d14",
+ "model_id": "ace95bf1eaeb4ce5b39046fc9d98b75e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "AppLayout(children=(VBox(children=(HTML(value='u(2.0655737704918047) '), Dropdown(description='Colorma…"
+ "AppLayout(children=(VBox(children=(HTML(value='u(4.0000000000000036) '), Dropdown(description='Colorma…"
]
},
"metadata": {},
diff --git a/chapter2/heat_code.ipynb b/chapter2/heat_code.ipynb
index ca44d533..e3ea980b 100644
--- a/chapter2/heat_code.ipynb
+++ b/chapter2/heat_code.ipynb
@@ -48,7 +48,7 @@
"source": [
"import numpy\n",
"from dolfinx import fem\n",
- "from dolfinx.mesh import CellType, create_unit_square, locate_entities_boundary\n",
+ "from dolfinx.mesh import CellType, create_unit_square, compute_boundary_facets\n",
"from mpi4py import MPI\n",
"from petsc4py import PETSc\n",
"\n",
@@ -98,8 +98,8 @@
"u_D = fem.Function(V)\n",
"u_D.interpolate(u_exact)\n",
"fdim = mesh.topology.dim - 1\n",
- "boundary_facets = locate_entities_boundary(\n",
- " mesh, fdim, lambda x: numpy.full(x.shape[1], True, dtype=bool))\n",
+ "mesh.topology.create_connectivity(mesh.topology.dim - 1, mesh.topology.dim)\n",
+ "boundary_facets = numpy.flatnonzero(compute_boundary_facets(mesh.topology))\n",
"bc = fem.dirichletbc(u_D, fem.locate_dofs_topological(V, fdim, boundary_facets))"
]
},
@@ -146,7 +146,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -167,7 +167,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -187,7 +187,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -212,7 +212,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -236,8 +236,7 @@
" uh.x.scatter_forward()\n",
"\n",
" # Update solution at previous time step (u_n)\n",
- " with uh.vector.localForm() as loc, u_n.vector.localForm() as loc_n:\n",
- " loc.copy(loc_n)"
+ " u_n.x.array[:] = uh.x.array"
]
},
{
@@ -251,7 +250,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -273,9 +272,7 @@
" print(f\"L2-error: {error_L2:.2e}\")\n",
"\n",
"# Compute values at mesh vertices\n",
- "u_vertex_values = uh.compute_point_values()\n",
- "u_ex_vertex_values = u_ex.compute_point_values()\n",
- "error_max = mesh.comm.allreduce(numpy.max(numpy.abs(u_vertex_values - u_ex_vertex_values)), op=MPI.MAX)\n",
+ "error_max = mesh.comm.allreduce(numpy.max(numpy.abs(uh.x.array-u_D.x.array)), op=MPI.MAX)\n",
"if mesh.comm.rank == 0:\n",
" print(f\"Error_max: {error_max:.2e}\")"
]
diff --git a/chapter2/hyperelasticity.ipynb b/chapter2/hyperelasticity.ipynb
index 21f73b4b..bee0854a 100644
--- a/chapter2/hyperelasticity.ipynb
+++ b/chapter2/hyperelasticity.ipynb
@@ -310,7 +310,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 16,
"id": "noble-perception",
"metadata": {},
"outputs": [
@@ -318,7 +318,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "WARNING:py.warnings:/usr/local/dolfinx-real/lib/python3.8/dist-packages/dolfinx/plot.py:120: UserWarning: Plotting of higher order functions is experimental.\n",
+ "WARNING:py.warnings:/usr/local/lib/python3.9/dist-packages/dolfinx/plot.py:104: UserWarning: Plotting of higher order functions is experimental.\n",
" warnings.warn(\"Plotting of higher order functions is experimental.\")\n",
"\n"
]
@@ -326,7 +326,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c3ae780b4a3546488543f096c7bef2d3",
+ "model_id": "5ea64edb4c6b4652a659996ae080f81b",
"version_major": 2,
"version_minor": 0
},
@@ -342,29 +342,20 @@
"import pyvista\n",
"pyvista.set_jupyter_backend(\"ipygany\")\n",
"\n",
- "from dolfinx.plot import create_vtk_topology\n",
- "topology, cell_types = create_vtk_topology(mesh, mesh.topology.dim)\n",
- "grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x)\n",
+ "from dolfinx.plot import create_vtk_mesh\n",
+ "grid = pyvista.UnstructuredGrid(*create_vtk_mesh(mesh, mesh.topology.dim))\n",
"\n",
"def plot_function(t, uh):\n",
" \"\"\"\n",
" Create a figure of the concentration uh warped visualized in 3D at timet step t.\n",
" \"\"\"\n",
" p = pyvista.Plotter()\n",
- " \n",
- " # Update point values on pyvista grid\n",
- "\n",
- " topology, cell_types = create_vtk_topology(V)\n",
- " # We create a geometry for our modified mesh using the dof coordinates\n",
- " geometry = V.tabulate_dof_coordinates()\n",
- " # As we are dealing with a vector field, we reshape the underlying dof array to accommedate for the three dimensional space\n",
- " num_dofs = V.dofmap.index_map.size_local + V.dofmap.index_map.num_ghosts\n",
- " values = np.zeros((num_dofs, 3), dtype=np.float64)\n",
- " values[:, :mesh.geometry.dim] = uh.x.array.real.reshape(num_dofs, V.dofmap.index_map_bs)\n",
- "\n",
- " # Create grid defined by the function space for visualization\n",
- " function_grid = pyvista.UnstructuredGrid(topology, cell_types, geometry)\n",
+ " # Create grid defined by the function space for visualization of the function\n",
+ " topology, cells, geometry = create_vtk_mesh(uh.function_space)\n",
+ " function_grid = pyvista.UnstructuredGrid(topology, cells, geometry)\n",
" var_name = f\"u({t})\"\n",
+ " values = np.zeros((geometry.shape[0], 3))\n",
+ " values[:, :len(uh)] = uh.x.array.reshape(geometry.shape[0], len(uh))\n",
" function_grid[var_name] = values\n",
" function_grid.set_active_vectors(var_name)\n",
" # Warp mesh by deformation\n",
@@ -394,7 +385,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 17,
"id": "vanilla-referral",
"metadata": {},
"outputs": [
@@ -402,20 +393,21 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:20.660 ( 1.136s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:21.501 ( 1.976s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:21.963 ( 2.438s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 22.2455 (tol = 1e-08) r (rel) = 0.134278(tol = 1e-08)\n",
- "2022-01-07 20:29:22.178 ( 2.654s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:22.640 ( 3.116s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 2.43261 (tol = 1e-08) r (rel) = 0.0146837(tol = 1e-08)\n",
- "2022-01-07 20:29:22.859 ( 3.335s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:23.306 ( 3.782s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 4.43158 (tol = 1e-08) r (rel) = 0.0267498(tol = 1e-08)\n",
- "2022-01-07 20:29:23.510 ( 3.986s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:23.966 ( 4.442s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.144189 (tol = 1e-08) r (rel) = 0.000870353(tol = 1e-08)\n",
- "2022-01-07 20:29:24.161 ( 4.637s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:24.562 ( 5.038s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 0.021424 (tol = 1e-08) r (rel) = 0.000129319(tol = 1e-08)\n",
- "2022-01-07 20:29:24.769 ( 5.245s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:25.168 ( 5.644s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 4.80068e-06 (tol = 1e-08) r (rel) = 2.89778e-08(tol = 1e-08)\n",
- "WARNING:py.warnings:/usr/local/dolfinx-real/lib/python3.8/dist-packages/dolfinx/plot.py:120: UserWarning: Plotting of higher order functions is experimental.\n",
+ "2022-01-20 22:09:47.988 ( 280.774s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:48.829 ( 281.615s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:49.291 ( 282.077s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 22.2455 (tol = 1e-08) r (rel) = 0.134278(tol = 1e-08)\n",
+ "2022-01-20 22:09:49.508 ( 282.294s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:49.968 ( 282.753s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 2.43261 (tol = 1e-08) r (rel) = 0.0146837(tol = 1e-08)\n",
+ "2022-01-20 22:09:50.188 ( 282.974s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:50.648 ( 283.434s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 4.43158 (tol = 1e-08) r (rel) = 0.0267498(tol = 1e-08)\n",
+ "2022-01-20 22:09:50.864 ( 283.650s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:51.323 ( 284.109s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.144189 (tol = 1e-08) r (rel) = 0.000870353(tol = 1e-08)\n",
+ "2022-01-20 22:09:51.539 ( 284.325s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:52.003 ( 284.789s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 0.021424 (tol = 1e-08) r (rel) = 0.000129319(tol = 1e-08)\n",
+ "2022-01-20 22:09:52.229 ( 285.014s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:52.688 ( 285.474s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 4.80068e-06 (tol = 1e-08) r (rel) = 2.89778e-08(tol = 1e-08)\n",
+ "2022-01-20 22:09:52.899 ( 285.684s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "WARNING:py.warnings:/usr/local/lib/python3.9/dist-packages/dolfinx/plot.py:104: UserWarning: Plotting of higher order functions is experimental.\n",
" warnings.warn(\"Plotting of higher order functions is experimental.\")\n",
"\n"
]
@@ -431,15 +423,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:25.361 ( 5.836s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:25.741 ( 6.216s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 8: r (abs) = 2.62988e-11 (tol = 1e-08) r (rel) = 1.58745e-13(tol = 1e-08)\n",
- "2022-01-07 20:29:25.741 ( 6.216s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 8 iterations and 8 linear solver iterations.\n"
+ "2022-01-20 22:09:53.379 ( 286.165s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 8: r (abs) = 2.62988e-11 (tol = 1e-08) r (rel) = 1.58745e-13(tol = 1e-08)\n",
+ "2022-01-20 22:09:53.379 ( 286.165s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 8 iterations and 8 linear solver iterations.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "4a83b8aebca94f1c85e23b17f206bcbc",
+ "model_id": "7243c376df964525896c71fc5720ac59",
"version_major": 2,
"version_minor": 0
},
@@ -454,20 +445,18 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:26.010 ( 6.486s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:26.603 ( 7.079s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:26.985 ( 7.460s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 17.3254 (tol = 1e-08) r (rel) = 0.117842(tol = 1e-08)\n",
- "2022-01-07 20:29:27.174 ( 7.650s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:27.556 ( 8.032s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 5.14882 (tol = 1e-08) r (rel) = 0.0350207(tol = 1e-08)\n",
- "2022-01-07 20:29:27.744 ( 8.220s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:28.125 ( 8.600s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 7.24003 (tol = 1e-08) r (rel) = 0.0492445(tol = 1e-08)\n",
- "2022-01-07 20:29:28.314 ( 8.790s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:28.695 ( 9.170s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.777889 (tol = 1e-08) r (rel) = 0.00529096(tol = 1e-08)\n",
- "2022-01-07 20:29:28.881 ( 9.357s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:29.259 ( 9.734s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 1.25525 (tol = 1e-08) r (rel) = 0.00853785(tol = 1e-08)\n",
- "2022-01-07 20:29:29.448 ( 9.923s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:29.834 ( 10.310s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 0.00849512 (tol = 1e-08) r (rel) = 5.77813e-05(tol = 1e-08)\n",
- "2022-01-07 20:29:30.025 ( 10.501s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
+ "2022-01-20 22:09:53.669 ( 286.455s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:54.344 ( 287.130s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:54.803 ( 287.589s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 17.3254 (tol = 1e-08) r (rel) = 0.117842(tol = 1e-08)\n",
+ "2022-01-20 22:09:55.020 ( 287.806s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:55.481 ( 288.267s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 5.14882 (tol = 1e-08) r (rel) = 0.0350207(tol = 1e-08)\n",
+ "2022-01-20 22:09:55.704 ( 288.489s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:56.179 ( 288.964s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 7.24003 (tol = 1e-08) r (rel) = 0.0492445(tol = 1e-08)\n",
+ "2022-01-20 22:09:56.391 ( 289.177s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:56.888 ( 289.674s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.777889 (tol = 1e-08) r (rel) = 0.00529096(tol = 1e-08)\n",
+ "2022-01-20 22:09:57.115 ( 289.901s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:57.621 ( 290.407s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 1.25525 (tol = 1e-08) r (rel) = 0.00853785(tol = 1e-08)\n",
+ "2022-01-20 22:09:57.847 ( 290.632s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
]
},
{
@@ -481,16 +470,18 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:30.410 ( 10.886s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 8: r (abs) = 0.000192107 (tol = 1e-08) r (rel) = 1.30665e-06(tol = 1e-08)\n",
- "2022-01-07 20:29:30.601 ( 11.076s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:30.995 ( 11.471s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 9: r (abs) = 1.70417e-10 (tol = 1e-08) r (rel) = 1.15913e-12(tol = 1e-08)\n",
- "2022-01-07 20:29:30.995 ( 11.471s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 9 iterations and 9 linear solver iterations.\n"
+ "2022-01-20 22:09:58.341 ( 291.127s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 0.00849512 (tol = 1e-08) r (rel) = 5.77813e-05(tol = 1e-08)\n",
+ "2022-01-20 22:09:58.569 ( 291.355s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:59.066 ( 291.852s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 8: r (abs) = 0.000192107 (tol = 1e-08) r (rel) = 1.30665e-06(tol = 1e-08)\n",
+ "2022-01-20 22:09:59.293 ( 292.079s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:09:59.795 ( 292.580s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 9: r (abs) = 1.70417e-10 (tol = 1e-08) r (rel) = 1.15913e-12(tol = 1e-08)\n",
+ "2022-01-20 22:09:59.795 ( 292.580s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 9 iterations and 9 linear solver iterations.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c2f91ab543f94e9598348473d183ab4a",
+ "model_id": "3c8e2930f6f4435895a0b5c4935ef103",
"version_major": 2,
"version_minor": 0
},
@@ -505,19 +496,22 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:31.254 ( 11.730s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:31.839 ( 12.315s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:32.258 ( 12.733s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 10.0011 (tol = 1e-08) r (rel) = 0.0887471(tol = 1e-08)\n",
- "2022-01-07 20:29:32.474 ( 12.950s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:32.956 ( 13.432s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 5.33026 (tol = 1e-08) r (rel) = 0.0472992(tol = 1e-08)\n",
- "2022-01-07 20:29:33.180 ( 13.656s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:33.574 ( 14.050s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 11.9901 (tol = 1e-08) r (rel) = 0.106397(tol = 1e-08)\n",
- "2022-01-07 20:29:33.764 ( 14.240s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:34.149 ( 14.625s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 2.29702 (tol = 1e-08) r (rel) = 0.0203831(tol = 1e-08)\n",
- "2022-01-07 20:29:34.341 ( 14.817s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:34.731 ( 15.206s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 3.90234 (tol = 1e-08) r (rel) = 0.0346282(tol = 1e-08)\n",
- "2022-01-07 20:29:34.919 ( 15.394s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:35.306 ( 15.781s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 0.236535 (tol = 1e-08) r (rel) = 0.00209895(tol = 1e-08)\n"
+ "2022-01-20 22:10:00.090 ( 292.876s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:00.813 ( 293.599s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:01.268 ( 294.054s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 10.0011 (tol = 1e-08) r (rel) = 0.0887471(tol = 1e-08)\n",
+ "2022-01-20 22:10:01.478 ( 294.264s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:01.943 ( 294.729s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 5.33026 (tol = 1e-08) r (rel) = 0.0472992(tol = 1e-08)\n",
+ "2022-01-20 22:10:02.154 ( 294.940s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:02.623 ( 295.409s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 11.9901 (tol = 1e-08) r (rel) = 0.106397(tol = 1e-08)\n",
+ "2022-01-20 22:10:02.832 ( 295.617s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:03.298 ( 296.083s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 2.29702 (tol = 1e-08) r (rel) = 0.0203831(tol = 1e-08)\n",
+ "2022-01-20 22:10:03.515 ( 296.301s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:03.979 ( 296.765s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 3.90234 (tol = 1e-08) r (rel) = 0.0346282(tol = 1e-08)\n",
+ "2022-01-20 22:10:04.191 ( 296.977s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:04.645 ( 297.430s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 0.236535 (tol = 1e-08) r (rel) = 0.00209895(tol = 1e-08)\n",
+ "2022-01-20 22:10:04.858 ( 297.644s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:05.318 ( 298.104s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 8: r (abs) = 0.0427142 (tol = 1e-08) r (rel) = 0.000379034(tol = 1e-08)\n",
+ "2022-01-20 22:10:05.527 ( 298.312s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
]
},
{
@@ -531,19 +525,16 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:35.499 ( 15.974s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:35.890 ( 16.365s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 8: r (abs) = 0.0427142 (tol = 1e-08) r (rel) = 0.000379034(tol = 1e-08)\n",
- "2022-01-07 20:29:36.081 ( 16.556s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:36.484 ( 16.960s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 9: r (abs) = 2.87798e-05 (tol = 1e-08) r (rel) = 2.55384e-07(tol = 1e-08)\n",
- "2022-01-07 20:29:36.692 ( 17.167s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:37.088 ( 17.564s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 10: r (abs) = 6.0895e-10 (tol = 1e-08) r (rel) = 5.40365e-12(tol = 1e-08)\n",
- "2022-01-07 20:29:37.088 ( 17.564s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 10 iterations and 10 linear solver iterations.\n"
+ "2022-01-20 22:10:06.008 ( 298.794s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 9: r (abs) = 2.87798e-05 (tol = 1e-08) r (rel) = 2.55384e-07(tol = 1e-08)\n",
+ "2022-01-20 22:10:06.229 ( 299.015s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:06.687 ( 299.473s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 10: r (abs) = 6.0895e-10 (tol = 1e-08) r (rel) = 5.40365e-12(tol = 1e-08)\n",
+ "2022-01-20 22:10:06.687 ( 299.473s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 10 iterations and 10 linear solver iterations.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "6ba1ce7132754f828f6a6a7c60b73be5",
+ "model_id": "a0912c108b4a42a595ad8e7619b53984",
"version_major": 2,
"version_minor": 0
},
@@ -558,18 +549,18 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:37.338 ( 17.814s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:37.902 ( 18.378s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:38.285 ( 18.760s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 5.50693 (tol = 1e-08) r (rel) = 0.0653918(tol = 1e-08)\n",
- "2022-01-07 20:29:38.472 ( 18.948s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:38.855 ( 19.331s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 26.2489 (tol = 1e-08) r (rel) = 0.311692(tol = 1e-08)\n",
- "2022-01-07 20:29:39.048 ( 19.523s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:39.427 ( 19.903s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 2.30927 (tol = 1e-08) r (rel) = 0.0274213(tol = 1e-08)\n",
- "2022-01-07 20:29:39.615 ( 20.091s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:40.048 ( 20.524s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 14.0562 (tol = 1e-08) r (rel) = 0.16691(tol = 1e-08)\n",
- "2022-01-07 20:29:40.236 ( 20.712s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:40.619 ( 21.094s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 0.222774 (tol = 1e-08) r (rel) = 0.00264532(tol = 1e-08)\n",
- "2022-01-07 20:29:40.808 ( 21.284s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
+ "2022-01-20 22:10:06.973 ( 299.759s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:07.659 ( 300.445s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:08.104 ( 300.890s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 5.50693 (tol = 1e-08) r (rel) = 0.0653918(tol = 1e-08)\n",
+ "2022-01-20 22:10:08.308 ( 301.093s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:08.767 ( 301.553s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 26.2489 (tol = 1e-08) r (rel) = 0.311692(tol = 1e-08)\n",
+ "2022-01-20 22:10:08.980 ( 301.766s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:09.443 ( 302.229s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 2.30927 (tol = 1e-08) r (rel) = 0.0274213(tol = 1e-08)\n",
+ "2022-01-20 22:10:09.661 ( 302.447s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:10.173 ( 302.959s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 14.0562 (tol = 1e-08) r (rel) = 0.16691(tol = 1e-08)\n",
+ "2022-01-20 22:10:10.387 ( 303.172s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:10.872 ( 303.658s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 0.222774 (tol = 1e-08) r (rel) = 0.00264532(tol = 1e-08)\n",
+ "2022-01-20 22:10:11.084 ( 303.870s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
]
},
{
@@ -583,18 +574,18 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:41.193 ( 21.669s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 0.286671 (tol = 1e-08) r (rel) = 0.00340406(tol = 1e-08)\n",
- "2022-01-07 20:29:41.385 ( 21.861s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:41.767 ( 22.242s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 8: r (abs) = 0.000321869 (tol = 1e-08) r (rel) = 3.82203e-06(tol = 1e-08)\n",
- "2022-01-07 20:29:41.957 ( 22.433s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:42.363 ( 22.839s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 9: r (abs) = 2.63797e-07 (tol = 1e-08) r (rel) = 3.13245e-09(tol = 1e-08)\n",
- "2022-01-07 20:29:42.363 ( 22.839s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 9 iterations and 9 linear solver iterations.\n"
+ "2022-01-20 22:10:11.561 ( 304.347s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 0.286671 (tol = 1e-08) r (rel) = 0.00340406(tol = 1e-08)\n",
+ "2022-01-20 22:10:11.771 ( 304.557s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:12.247 ( 305.033s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 8: r (abs) = 0.000321869 (tol = 1e-08) r (rel) = 3.82203e-06(tol = 1e-08)\n",
+ "2022-01-20 22:10:12.465 ( 305.251s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:12.941 ( 305.726s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 9: r (abs) = 2.63797e-07 (tol = 1e-08) r (rel) = 3.13245e-09(tol = 1e-08)\n",
+ "2022-01-20 22:10:12.941 ( 305.726s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 9 iterations and 9 linear solver iterations.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d3c0963ff0554592a3b7b10966901ec1",
+ "model_id": "e1e12954515e481daee3b63f8b214517",
"version_major": 2,
"version_minor": 0
},
@@ -609,18 +600,12 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:42.620 ( 23.096s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:43.199 ( 23.675s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:43.586 ( 24.061s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 3.19462 (tol = 1e-08) r (rel) = 0.0496479(tol = 1e-08)\n",
- "2022-01-07 20:29:43.777 ( 24.253s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:44.166 ( 24.642s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 7.71429 (tol = 1e-08) r (rel) = 0.119888(tol = 1e-08)\n",
- "2022-01-07 20:29:44.356 ( 24.832s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:44.741 ( 25.217s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 0.850873 (tol = 1e-08) r (rel) = 0.0132235(tol = 1e-08)\n",
- "2022-01-07 20:29:44.932 ( 25.407s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:45.320 ( 25.796s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.371434 (tol = 1e-08) r (rel) = 0.0057725(tol = 1e-08)\n",
- "2022-01-07 20:29:45.509 ( 25.985s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:45.895 ( 26.371s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 0.00215066 (tol = 1e-08) r (rel) = 3.34236e-05(tol = 1e-08)\n",
- "2022-01-07 20:29:46.085 ( 26.560s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
+ "2022-01-20 22:10:13.250 ( 306.036s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:13.997 ( 306.783s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:14.484 ( 307.270s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 3.19462 (tol = 1e-08) r (rel) = 0.0496479(tol = 1e-08)\n",
+ "2022-01-20 22:10:14.713 ( 307.499s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:15.200 ( 307.986s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 7.71429 (tol = 1e-08) r (rel) = 0.119888(tol = 1e-08)\n",
+ "2022-01-20 22:10:15.419 ( 308.205s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
]
},
{
@@ -634,16 +619,22 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:46.470 ( 26.946s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 2.54607e-06 (tol = 1e-08) r (rel) = 3.95687e-08(tol = 1e-08)\n",
- "2022-01-07 20:29:46.658 ( 27.134s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:47.049 ( 27.524s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 8: r (abs) = 3.33871e-13 (tol = 1e-08) r (rel) = 5.18872e-15(tol = 1e-08)\n",
- "2022-01-07 20:29:47.049 ( 27.524s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 8 iterations and 8 linear solver iterations.\n"
+ "2022-01-20 22:10:15.915 ( 308.701s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 0.850873 (tol = 1e-08) r (rel) = 0.0132235(tol = 1e-08)\n",
+ "2022-01-20 22:10:16.138 ( 308.924s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:16.621 ( 309.407s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.371434 (tol = 1e-08) r (rel) = 0.0057725(tol = 1e-08)\n",
+ "2022-01-20 22:10:16.837 ( 309.623s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:17.322 ( 310.108s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 0.00215066 (tol = 1e-08) r (rel) = 3.34236e-05(tol = 1e-08)\n",
+ "2022-01-20 22:10:17.549 ( 310.335s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:18.036 ( 310.821s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 2.54607e-06 (tol = 1e-08) r (rel) = 3.95687e-08(tol = 1e-08)\n",
+ "2022-01-20 22:10:18.259 ( 311.045s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:18.743 ( 311.529s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 8: r (abs) = 3.33871e-13 (tol = 1e-08) r (rel) = 5.18872e-15(tol = 1e-08)\n",
+ "2022-01-20 22:10:18.743 ( 311.529s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 8 iterations and 8 linear solver iterations.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ca74d752ba3d43fbb4dcb50d56822911",
+ "model_id": "4f49f4708cbd4179b3f4b3e7d82f76fa",
"version_major": 2,
"version_minor": 0
},
@@ -658,14 +649,13 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:47.302 ( 27.777s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:47.879 ( 28.355s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:48.266 ( 28.741s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 2.00649 (tol = 1e-08) r (rel) = 0.0395622(tol = 1e-08)\n",
- "2022-01-07 20:29:48.455 ( 28.931s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:48.847 ( 29.323s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 4.60977 (tol = 1e-08) r (rel) = 0.0908914(tol = 1e-08)\n",
- "2022-01-07 20:29:49.037 ( 29.513s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:49.419 ( 29.894s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 0.185372 (tol = 1e-08) r (rel) = 0.00365501(tol = 1e-08)\n",
- "2022-01-07 20:29:49.606 ( 30.082s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
+ "2022-01-20 22:10:19.053 ( 311.838s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:19.761 ( 312.547s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:20.249 ( 313.034s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 2.00649 (tol = 1e-08) r (rel) = 0.0395622(tol = 1e-08)\n",
+ "2022-01-20 22:10:20.475 ( 313.261s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:20.953 ( 313.739s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 4.60977 (tol = 1e-08) r (rel) = 0.0908914(tol = 1e-08)\n",
+ "2022-01-20 22:10:21.181 ( 313.967s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:21.666 ( 314.452s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 0.185372 (tol = 1e-08) r (rel) = 0.00365501(tol = 1e-08)\n"
]
},
{
@@ -679,18 +669,19 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:49.987 ( 30.462s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.024688 (tol = 1e-08) r (rel) = 0.000486777(tol = 1e-08)\n",
- "2022-01-07 20:29:50.177 ( 30.653s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:50.562 ( 31.037s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 5.69255e-06 (tol = 1e-08) r (rel) = 1.12241e-07(tol = 1e-08)\n",
- "2022-01-07 20:29:50.751 ( 31.227s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:51.138 ( 31.614s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 2.58918e-11 (tol = 1e-08) r (rel) = 5.10512e-13(tol = 1e-08)\n",
- "2022-01-07 20:29:51.138 ( 31.614s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 7 iterations and 7 linear solver iterations.\n"
+ "2022-01-20 22:10:21.888 ( 314.674s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:22.362 ( 315.148s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.024688 (tol = 1e-08) r (rel) = 0.000486777(tol = 1e-08)\n",
+ "2022-01-20 22:10:22.573 ( 315.359s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:23.049 ( 315.835s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 5.69255e-06 (tol = 1e-08) r (rel) = 1.12241e-07(tol = 1e-08)\n",
+ "2022-01-20 22:10:23.274 ( 316.060s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:23.763 ( 316.549s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 2.58918e-11 (tol = 1e-08) r (rel) = 5.10512e-13(tol = 1e-08)\n",
+ "2022-01-20 22:10:23.763 ( 316.549s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 7 iterations and 7 linear solver iterations.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f3b630e147254b8f85aed933a8bdfe00",
+ "model_id": "3724d87750034970908188c6fb648487",
"version_major": 2,
"version_minor": 0
},
@@ -705,14 +696,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:51.390 ( 31.866s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:51.959 ( 32.435s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:52.343 ( 32.818s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 1.38506 (tol = 1e-08) r (rel) = 0.0336622(tol = 1e-08)\n",
- "2022-01-07 20:29:52.531 ( 33.007s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:52.918 ( 33.394s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 3.03739 (tol = 1e-08) r (rel) = 0.07382(tol = 1e-08)\n",
- "2022-01-07 20:29:53.108 ( 33.584s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:53.490 ( 33.966s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 0.0412386 (tol = 1e-08) r (rel) = 0.00100225(tol = 1e-08)\n",
- "2022-01-07 20:29:53.682 ( 34.158s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
+ "2022-01-20 22:10:24.076 ( 316.862s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:24.813 ( 317.599s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:25.314 ( 318.100s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 1.38506 (tol = 1e-08) r (rel) = 0.0336622(tol = 1e-08)\n",
+ "2022-01-20 22:10:25.550 ( 318.336s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:26.084 ( 318.870s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 3.03739 (tol = 1e-08) r (rel) = 0.07382(tol = 1e-08)\n",
+ "2022-01-20 22:10:26.316 ( 319.102s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:26.800 ( 319.586s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 0.0412386 (tol = 1e-08) r (rel) = 0.00100225(tol = 1e-08)\n",
+ "2022-01-20 22:10:27.028 ( 319.814s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
]
},
{
@@ -726,16 +717,16 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:54.068 ( 34.544s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.00205057 (tol = 1e-08) r (rel) = 4.98364e-05(tol = 1e-08)\n",
- "2022-01-07 20:29:54.256 ( 34.732s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:54.640 ( 35.115s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 1.78867e-08 (tol = 1e-08) r (rel) = 4.34714e-10(tol = 1e-08)\n",
- "2022-01-07 20:29:54.640 ( 35.115s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 6 iterations and 6 linear solver iterations.\n"
+ "2022-01-20 22:10:27.522 ( 320.308s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.00205057 (tol = 1e-08) r (rel) = 4.98364e-05(tol = 1e-08)\n",
+ "2022-01-20 22:10:27.754 ( 320.540s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:28.262 ( 321.048s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 1.78867e-08 (tol = 1e-08) r (rel) = 4.34714e-10(tol = 1e-08)\n",
+ "2022-01-20 22:10:28.262 ( 321.048s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 6 iterations and 6 linear solver iterations.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "db9bf709f61b42a9a4e9c66b0f045139",
+ "model_id": "d5c53970f5ec4607a59303b4f029aa85",
"version_major": 2,
"version_minor": 0
},
@@ -750,12 +741,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:54.887 ( 35.363s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:55.457 ( 35.933s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:55.844 ( 36.320s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 1.06336 (tol = 1e-08) r (rel) = 0.031085(tol = 1e-08)\n",
- "2022-01-07 20:29:56.034 ( 36.509s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:56.416 ( 36.891s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 2.0477 (tol = 1e-08) r (rel) = 0.0598598(tol = 1e-08)\n",
- "2022-01-07 20:29:56.605 ( 37.081s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
+ "2022-01-20 22:10:28.562 ( 321.348s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:29.312 ( 322.098s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:29.830 ( 322.616s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 1.06336 (tol = 1e-08) r (rel) = 0.031085(tol = 1e-08)\n",
+ "2022-01-20 22:10:30.077 ( 322.863s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:30.549 ( 323.335s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 2.0477 (tol = 1e-08) r (rel) = 0.0598598(tol = 1e-08)\n",
+ "2022-01-20 22:10:30.794 ( 323.580s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:31.293 ( 324.079s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 0.00897719 (tol = 1e-08) r (rel) = 0.000262427(tol = 1e-08)\n",
+ "2022-01-20 22:10:31.530 ( 324.316s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
]
},
{
@@ -769,18 +762,16 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:56.989 ( 37.465s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 0.00897719 (tol = 1e-08) r (rel) = 0.000262427(tol = 1e-08)\n",
- "2022-01-07 20:29:57.178 ( 37.653s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:57.561 ( 38.036s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.000167422 (tol = 1e-08) r (rel) = 4.89419e-06(tol = 1e-08)\n",
- "2022-01-07 20:29:57.751 ( 38.226s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:58.134 ( 38.610s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 3.25874e-11 (tol = 1e-08) r (rel) = 9.52617e-13(tol = 1e-08)\n",
- "2022-01-07 20:29:58.134 ( 38.610s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 6 iterations and 6 linear solver iterations.\n"
+ "2022-01-20 22:10:32.017 ( 324.803s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.000167422 (tol = 1e-08) r (rel) = 4.89419e-06(tol = 1e-08)\n",
+ "2022-01-20 22:10:32.249 ( 325.035s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:32.747 ( 325.532s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 3.25874e-11 (tol = 1e-08) r (rel) = 9.52617e-13(tol = 1e-08)\n",
+ "2022-01-20 22:10:32.747 ( 325.532s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 6 iterations and 6 linear solver iterations.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "2543c604a99b42a0a54d5a90c1ad8949",
+ "model_id": "a8b896a5c2314ec3a8522d89acaca26f",
"version_major": 2,
"version_minor": 0
},
@@ -795,10 +786,18 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:29:58.381 ( 38.857s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:59.001 ( 39.476s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:29:59.399 ( 39.874s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 0.898789 (tol = 1e-08) r (rel) = 0.0309666(tol = 1e-08)\n",
- "2022-01-07 20:29:59.591 ( 40.067s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n"
+ "2022-01-20 22:10:33.054 ( 325.840s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:33.772 ( 326.558s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:34.252 ( 327.038s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 0.898789 (tol = 1e-08) r (rel) = 0.0309666(tol = 1e-08)\n",
+ "2022-01-20 22:10:34.477 ( 327.263s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:34.973 ( 327.759s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 1.38354 (tol = 1e-08) r (rel) = 0.0476679(tol = 1e-08)\n",
+ "2022-01-20 22:10:35.200 ( 327.986s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:35.692 ( 328.477s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 0.00185096 (tol = 1e-08) r (rel) = 6.37724e-05(tol = 1e-08)\n",
+ "2022-01-20 22:10:35.919 ( 328.705s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:36.411 ( 329.197s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 7.87183e-06 (tol = 1e-08) r (rel) = 2.71213e-07(tol = 1e-08)\n",
+ "2022-01-20 22:10:36.639 ( 329.425s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:10:37.131 ( 329.917s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 7.21799e-13 (tol = 1e-08) r (rel) = 2.48686e-14(tol = 1e-08)\n",
+ "2022-01-20 22:10:37.131 ( 329.917s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 6 iterations and 6 linear solver iterations.\n"
]
},
{
@@ -808,24 +807,10 @@
"Time step 9, Number of iterations 6, Load [ 0. 0. -13.5]\n"
]
},
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2022-01-07 20:29:59.974 ( 40.449s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 1.38354 (tol = 1e-08) r (rel) = 0.0476679(tol = 1e-08)\n",
- "2022-01-07 20:30:00.163 ( 40.638s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:30:00.542 ( 41.018s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 0.00185096 (tol = 1e-08) r (rel) = 6.37724e-05(tol = 1e-08)\n",
- "2022-01-07 20:30:00.728 ( 41.204s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:30:01.113 ( 41.589s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 7.87183e-06 (tol = 1e-08) r (rel) = 2.71213e-07(tol = 1e-08)\n",
- "2022-01-07 20:30:01.301 ( 41.777s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:30:01.682 ( 42.158s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 7.21799e-13 (tol = 1e-08) r (rel) = 2.48686e-14(tol = 1e-08)\n",
- "2022-01-07 20:30:01.682 ( 42.158s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 6 iterations and 6 linear solver iterations.\n"
- ]
- },
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3e78ae95978744c5b1c73f4ae88ea1de",
+ "model_id": "f0ed892ecba346cba1f3def02b623de9",
"version_major": 2,
"version_minor": 0
},
diff --git a/chapter2/linearelasticity_code.ipynb b/chapter2/linearelasticity_code.ipynb
index 43cc9967..83fdfe6a 100644
--- a/chapter2/linearelasticity_code.ipynb
+++ b/chapter2/linearelasticity_code.ipynb
@@ -189,13 +189,13 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "808fcc0d6cbb48718e14d9c91e0dbf97",
+ "model_id": "ed50ecef14af4bd29ce654b84a1e8fdd",
"version_major": 2,
"version_minor": 0
},
@@ -211,15 +211,15 @@
"import pyvista\n",
"pyvista.set_jupyter_backend(\"pythreejs\")\n",
"\n",
- "from dolfinx.plot import create_vtk_topology\n",
+ "from dolfinx.plot import create_vtk_mesh\n",
"\n",
"# Create plotter and pyvista grid\n",
"p = pyvista.Plotter()\n",
- "topology, cell_types = create_vtk_topology(mesh, mesh.topology.dim)\n",
+ "topology, cell_types, geometry = create_vtk_mesh(V)\n",
"grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x)\n",
"\n",
"# Attach vector values to grid and warp grid by vector\n",
- "grid[\"u\"] = uh.compute_point_values().real \n",
+ "grid[\"u\"] = uh.x.array.reshape((geometry.shape[0], 3))\n",
"actor_0 = p.add_mesh(grid, style=\"wireframe\", color=\"k\")\n",
"warped = grid.warp_by_vector(\"u\", factor=1.5)\n",
"actor_1 = p.add_mesh(warped, show_edges=True)\n",
@@ -242,7 +242,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -263,7 +263,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -280,7 +280,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -299,13 +299,13 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f1e07f4833ed48688571b12748ae6ac4",
+ "model_id": "83f716ed7d5a431caad49aa62ef893f1",
"version_major": 2,
"version_minor": 0
},
diff --git a/chapter2/nonlinpoisson_code.ipynb b/chapter2/nonlinpoisson_code.ipynb
index 9271b5e4..45036bf9 100644
--- a/chapter2/nonlinpoisson_code.ipynb
+++ b/chapter2/nonlinpoisson_code.ipynb
@@ -16,27 +16,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2022-01-07 20:35:20.186 ( 37.888s) [main ] utils.cpp:531 INFO| Compute partition of cells across ranks\n",
- "2022-01-07 20:35:20.186 ( 37.888s) [main ] graphbuild.cpp:549 INFO| Build mesh dual graph\n",
- "2022-01-07 20:35:20.186 ( 37.888s) [main ] graphbuild.cpp:307 INFO| Build local part of mesh dual graph\n",
- "2022-01-07 20:35:20.187 ( 37.889s) [main ] graphbuild.cpp:47 INFO| Build nonlocal part of mesh dual graph\n",
- "2022-01-07 20:35:20.187 ( 37.889s) [main ] graphbuild.cpp:561 INFO| Graph edges (local:560, non-local:0)\n",
- "2022-01-07 20:35:20.187 ( 37.889s) [main ] partitioners.cpp:273 INFO| Compute graph partition using PT-SCOTCH\n",
- "2022-01-07 20:35:20.187 ( 37.890s) [main ] graphbuild.cpp:307 INFO| Build local part of mesh dual graph\n",
- "2022-01-07 20:35:20.188 ( 37.891s) [main ] ordering.cpp:201 INFO| GPS pseudo-diameter:(38) 181-18\n",
- "\n",
- "2022-01-07 20:35:20.188 ( 37.891s) [main ] Topology.cpp:591 INFO| Create topology\n",
- "2022-01-07 20:35:20.188 ( 37.891s) [main ] partition.cpp:183 INFO| Compute ghost indices\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import ufl\n",
"from mpi4py import MPI\n",
@@ -68,26 +50,9 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2022-01-07 20:35:20.856 ( 38.558s) [main ]topologycomputation.cpp:616 INFO| Computing mesh entities of dimension 0\n",
- "2022-01-07 20:35:20.857 ( 38.560s) [main ]topologycomputation.cpp:616 INFO| Computing mesh entities of dimension 1\n",
- "2022-01-07 20:35:20.858 ( 38.561s) [main ]topologycomputation.cpp:656 INFO| Requesting connectivity 1 - 2\n",
- "2022-01-07 20:35:20.858 ( 38.561s) [main ]topologycomputation.cpp:520 INFO| Computing mesh connectivity 1 - 2 from transpose.\n",
- "2022-01-07 20:35:20.858 ( 38.561s) [main ]topologycomputation.cpp:656 INFO| Requesting connectivity 1 - 1\n",
- "2022-01-07 20:35:20.858 ( 38.561s) [main ]topologycomputation.cpp:656 INFO| Requesting connectivity 1 - 0\n",
- "2022-01-07 20:35:20.858 ( 38.561s) [main ]topologycomputation.cpp:656 INFO| Requesting connectivity 0 - 2\n",
- "2022-01-07 20:35:20.858 ( 38.561s) [main ]topologycomputation.cpp:520 INFO| Computing mesh connectivity 0 - 2 from transpose.\n",
- "2022-01-07 20:35:20.858 ( 38.561s) [main ]topologycomputation.cpp:656 INFO| Requesting connectivity 2 - 0\n",
- "2022-01-07 20:35:20.859 ( 38.562s) [main ]topologycomputation.cpp:656 INFO| Requesting connectivity 1 - 2\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from petsc4py import PETSc\n",
"import numpy\n",
@@ -110,7 +75,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -129,7 +94,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -145,18 +110,9 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 5,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "2022-01-07 20:35:22.474 ( 40.176s) [main ] SparsityPattern.cpp:381 INFO| Column ghost size increased from 0 to 0\n",
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"solver = NewtonSolver(MPI.COMM_WORLD, problem)\n",
"solver.convergence_criterion = \"incremental\"\n",
@@ -173,7 +129,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -195,7 +151,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -209,22 +165,22 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:35:23.672 ( 41.375s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:35:23.677 ( 41.379s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:35:23.680 ( 41.382s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 20.3794 (tol = 1e-10) r (rel) = 0.922544(tol = 1e-06)\n",
- "2022-01-07 20:35:23.680 ( 41.383s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:35:23.683 ( 41.386s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 6.95285 (tol = 1e-10) r (rel) = 0.314745(tol = 1e-06)\n",
- "2022-01-07 20:35:23.684 ( 41.386s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:35:23.685 ( 41.388s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 2.93575 (tol = 1e-10) r (rel) = 0.132897(tol = 1e-06)\n",
- "2022-01-07 20:35:23.685 ( 41.388s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:35:23.687 ( 41.389s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.700593 (tol = 1e-10) r (rel) = 0.0317148(tol = 1e-06)\n",
- "2022-01-07 20:35:23.687 ( 41.389s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:35:23.688 ( 41.390s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 0.0490796 (tol = 1e-10) r (rel) = 0.00222176(tol = 1e-06)\n",
- "2022-01-07 20:35:23.688 ( 41.390s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:35:23.690 ( 41.392s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 0.000299464 (tol = 1e-10) r (rel) = 1.35563e-05(tol = 1e-06)\n",
- "2022-01-07 20:35:23.690 ( 41.392s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
- "2022-01-07 20:35:23.691 ( 41.394s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 8: r (abs) = 1.52969e-08 (tol = 1e-10) r (rel) = 6.92466e-10(tol = 1e-06)\n",
- "2022-01-07 20:35:23.691 ( 41.394s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 8 iterations and 45 linear solver iterations.\n"
+ "2022-01-20 22:13:50.963 ( 10.145s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:13:50.965 ( 10.147s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:13:50.966 ( 10.148s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 2: r (abs) = 20.3794 (tol = 1e-10) r (rel) = 0.922544(tol = 1e-06)\n",
+ "2022-01-20 22:13:50.967 ( 10.148s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:13:50.968 ( 10.149s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 3: r (abs) = 6.95285 (tol = 1e-10) r (rel) = 0.314745(tol = 1e-06)\n",
+ "2022-01-20 22:13:50.968 ( 10.149s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:13:50.969 ( 10.150s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 4: r (abs) = 2.93575 (tol = 1e-10) r (rel) = 0.132897(tol = 1e-06)\n",
+ "2022-01-20 22:13:50.969 ( 10.150s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:13:50.971 ( 10.152s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 5: r (abs) = 0.700593 (tol = 1e-10) r (rel) = 0.0317148(tol = 1e-06)\n",
+ "2022-01-20 22:13:50.971 ( 10.152s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:13:50.972 ( 10.153s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 6: r (abs) = 0.0490796 (tol = 1e-10) r (rel) = 0.00222176(tol = 1e-06)\n",
+ "2022-01-20 22:13:50.972 ( 10.153s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:13:50.973 ( 10.154s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 7: r (abs) = 0.000299464 (tol = 1e-10) r (rel) = 1.35563e-05(tol = 1e-06)\n",
+ "2022-01-20 22:13:50.973 ( 10.155s) [main ] petsc.cpp:769 INFO| PETSc Krylov solver starting to solve system.\n",
+ "2022-01-20 22:13:50.974 ( 10.156s) [main ] NewtonSolver.cpp:34 INFO| Newton iteration 8: r (abs) = 1.52969e-08 (tol = 1e-10) r (rel) = 6.92466e-10(tol = 1e-06)\n",
+ "2022-01-20 22:13:50.974 ( 10.156s) [main ] NewtonSolver.cpp:250 INFO| Newton solver finished in 8 iterations and 45 linear solver iterations.\n"
]
}
],
@@ -246,7 +202,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -254,15 +210,15 @@
"output_type": "stream",
"text": [
"L2-error: 1.51e-15\n",
- "Error_max: 5.55e-15\n"
+ "Error_max: 5.77e-15\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "2022-01-07 20:35:25.667 ( 43.370s) [main ]topologycomputation.cpp:616 INFO| Computing mesh entities of dimension 0\n",
- "2022-01-07 20:35:25.667 ( 43.370s) [main ]topologycomputation.cpp:616 INFO| Computing mesh entities of dimension 1\n"
+ "2022-01-20 22:14:23.587 ( 42.769s) [main ]topologycomputation.cpp:616 INFO| Computing mesh entities of dimension 0\n",
+ "2022-01-20 22:14:23.587 ( 42.769s) [main ]topologycomputation.cpp:616 INFO| Computing mesh entities of dimension 1\n"
]
}
],
@@ -277,9 +233,7 @@
" print(f\"L2-error: {error_L2:.2e}\")\n",
"\n",
"# Compute values at mesh vertices\n",
- "u_vertex_values = uh.compute_point_values()\n",
- "u_ex_vertex_values = u_ex.compute_point_values()\n",
- "error_max = mesh.comm.allreduce(numpy.max(numpy.abs(u_vertex_values - u_ex_vertex_values)), op=MPI.MAX)\n",
+ "error_max = mesh.comm.allreduce(numpy.max(numpy.abs(uh.x.array -u_D.x.array)), op=MPI.MAX)\n",
"if mesh.comm.rank == 0:\n",
" print(f\"Error_max: {error_max:.2e}\")"
]
diff --git a/chapter2/ns_code1.ipynb b/chapter2/ns_code1.ipynb
index 067d06f4..b0bf2f87 100644
--- a/chapter2/ns_code1.ipynb
+++ b/chapter2/ns_code1.ipynb
@@ -138,7 +138,6 @@
"metadata": {},
"outputs": [],
"source": [
- "\n",
"def walls(x):\n",
" return np.logical_or(np.isclose(x[1],0), np.isclose(x[1],1))\n",
"wall_dofs = locate_dofs_geometrical(V, walls)\n",
@@ -222,7 +221,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -256,7 +255,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -274,7 +273,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -304,7 +303,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -341,7 +340,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -361,7 +360,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -385,7 +384,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 14,
"metadata": {
"tags": []
},
@@ -394,32 +393,32 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Time 0.04, L2-error 4.68e-01, Max error 2.35e-04\n",
- "Time 0.44, L2-error 8.91e-03, Max error 1.59e-04\n",
- "Time 0.84, L2-error 1.74e-04, Max error 1.38e-04\n",
- "Time 1.24, L2-error 1.92e-05, Max error 8.60e-05\n",
- "Time 1.64, L2-error 9.44e-06, Max error 5.43e-05\n",
- "Time 2.04, L2-error 5.95e-06, Max error 3.91e-05\n",
- "Time 2.44, L2-error 4.62e-06, Max error 3.06e-05\n",
- "Time 2.84, L2-error 4.05e-06, Max error 2.52e-05\n",
- "Time 3.24, L2-error 3.76e-06, Max error 2.15e-05\n",
- "Time 3.64, L2-error 3.59e-06, Max error 1.89e-05\n",
- "Time 4.04, L2-error 3.49e-06, Max error 1.70e-05\n",
- "Time 4.44, L2-error 3.43e-06, Max error 1.55e-05\n",
- "Time 4.84, L2-error 3.39e-06, Max error 1.44e-05\n",
- "Time 5.24, L2-error 3.37e-06, Max error 1.35e-05\n",
- "Time 5.64, L2-error 3.35e-06, Max error 1.29e-05\n",
- "Time 6.04, L2-error 3.34e-06, Max error 1.23e-05\n",
- "Time 6.44, L2-error 3.33e-06, Max error 1.19e-05\n",
- "Time 6.84, L2-error 3.32e-06, Max error 1.16e-05\n",
- "Time 7.24, L2-error 3.32e-06, Max error 1.13e-05\n",
- "Time 7.64, L2-error 3.32e-06, Max error 1.11e-05\n",
- "Time 8.04, L2-error 3.32e-06, Max error 1.09e-05\n",
- "Time 8.44, L2-error 3.32e-06, Max error 1.08e-05\n",
- "Time 8.84, L2-error 3.32e-06, Max error 1.07e-05\n",
- "Time 9.24, L2-error 3.31e-06, Max error 1.06e-05\n",
- "Time 9.64, L2-error 3.31e-06, Max error 1.05e-05\n",
- "Time 10.02, L2-error 3.31e-06, Max error 9.92e-06\n"
+ "Time 0.02, L2-error 5.88e-01, Max error 1.60e-01\n",
+ "Time 0.42, L2-error 1.09e-02, Max error 1.28e-04\n",
+ "Time 0.82, L2-error 2.11e-04, Max error 2.65e-04\n",
+ "Time 1.22, L2-error 1.98e-05, Max error 1.56e-04\n",
+ "Time 1.62, L2-error 9.53e-06, Max error 8.46e-05\n",
+ "Time 2.02, L2-error 5.89e-06, Max error 5.24e-05\n",
+ "Time 2.42, L2-error 4.55e-06, Max error 3.62e-05\n",
+ "Time 2.82, L2-error 3.99e-06, Max error 2.70e-05\n",
+ "Time 3.22, L2-error 3.71e-06, Max error 2.13e-05\n",
+ "Time 3.62, L2-error 3.55e-06, Max error 1.75e-05\n",
+ "Time 4.02, L2-error 3.46e-06, Max error 1.49e-05\n",
+ "Time 4.42, L2-error 3.41e-06, Max error 1.30e-05\n",
+ "Time 4.82, L2-error 3.37e-06, Max error 1.16e-05\n",
+ "Time 5.22, L2-error 3.35e-06, Max error 1.06e-05\n",
+ "Time 5.62, L2-error 3.34e-06, Max error 9.75e-06\n",
+ "Time 6.02, L2-error 3.33e-06, Max error 9.11e-06\n",
+ "Time 6.42, L2-error 3.32e-06, Max error 8.92e-06\n",
+ "Time 6.82, L2-error 3.32e-06, Max error 8.92e-06\n",
+ "Time 7.22, L2-error 3.32e-06, Max error 9.05e-06\n",
+ "Time 7.62, L2-error 3.31e-06, Max error 9.26e-06\n",
+ "Time 8.02, L2-error 3.31e-06, Max error 9.44e-06\n",
+ "Time 8.42, L2-error 3.31e-06, Max error 9.58e-06\n",
+ "Time 8.82, L2-error 3.31e-06, Max error 9.69e-06\n",
+ "Time 9.22, L2-error 3.31e-06, Max error 9.79e-06\n",
+ "Time 9.62, L2-error 3.31e-06, Max error 9.86e-06\n",
+ "Time 10.00, L2-error 3.31e-06, Max error 1.05e-05\n"
]
}
],
@@ -480,29 +479,19 @@
"## Verification\n",
"As for the previous problems we compute the error at each degree of freedom and the $L^2(\\Omega)$-error. We start with the initial condition $u=(0,0)$. We have not specified the initial condition explicitly, and FEniCSx will initialize all `Function`s including `u_n` and `u_` to zero. Since the exact solution is quadratic, we expect to reach machine precision within finite time. For our implementation, we observe that the error quickly approaches zero, and is of order $10^{-6}$ at $T=10\n",
"\n",
- "## Visualization of higher order functions\n",
- "As the velocity field $u$ is a second order Lagrangian space, it's degrees of freedom does not align with the mesh vertices.\n",
- "To be able to visualize this, we use the convenience function `dolfinx.plot.create_vtk_topology`. However, we will send in the function-space of the velocity field."
+ "## Visualization of vectors\n",
+ "We have already looked at how to plot higher order functions and vector functions. In this section we will look at how to visualize vector functions with glyphs, instead of warping the mesh."
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 24,
"metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING:py.warnings:/usr/local/dolfinx-real/lib/python3.8/dist-packages/dolfinx/plot.py:120: UserWarning: Plotting of higher order functions is experimental.\n",
- " warnings.warn(\"Plotting of higher order functions is experimental.\")\n",
- "\n"
- ]
- },
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e74777ba3f9c4468a860176cbcb21958",
+ "model_id": "6c1082dbfece46fe99cc308b93835b90",
"version_major": 2,
"version_minor": 0
},
@@ -518,16 +507,9 @@
"import dolfinx.plot\n",
"import pyvista\n",
"pyvista.set_jupyter_backend(\"pythreejs\")\n",
- "\n",
- "topology, cell_types = dolfinx.plot.create_vtk_topology(V)\n",
- "# To make this function work in parallel, we only consider ghosts owned by the current process\n",
- "num_dofs_local = V.dofmap.index_map.size_local\n",
- "# We create a geometry for our modified mesh using the dof coordinates\n",
- "geometry = V.tabulate_dof_coordinates()\n",
- "# As we are dealing with a vector field, we reshape the underlying dof array to accommedate for the three dimensional space\n",
- "num_dofs = V.dofmap.index_map.size_local + V.dofmap.index_map.num_ghosts\n",
- "values = np.zeros((num_dofs, 3), dtype=np.float64)\n",
- "values[:, :mesh.geometry.dim] = u_n.x.array.real.reshape(num_dofs, V.dofmap.index_map_bs)\n",
+ "topology, cell_types, geometry = dolfinx.plot.create_vtk_mesh(V)\n",
+ "values = np.zeros((geometry.shape[0], 3), dtype=np.float64)\n",
+ "values[:, :len(u_n)] = u_n.x.array.real.reshape((geometry.shape[0], len(u_n)))\n",
"\n",
"# Create a point cloud of glyphs\n",
"function_grid = pyvista.UnstructuredGrid(topology, cell_types, geometry)\n",
@@ -535,8 +517,7 @@
"glyphs = function_grid.glyph(orient=\"u\", factor=0.2)\n",
"\n",
"# Create a pyvista-grid for the mesh\n",
- "topology_m, cell_types_m = dolfinx.plot.create_vtk_topology(mesh, mesh.topology.dim)\n",
- "grid = pyvista.UnstructuredGrid(topology_m, cell_types_m, mesh.geometry.x)\n",
+ "grid = pyvista.UnstructuredGrid(*dolfinx.plot.create_vtk_mesh(mesh, mesh.topology.dim))\n",
"\n",
"# Create plotter\n",
"plotter = pyvista.Plotter()\n",
diff --git a/chapter2/ns_code2.ipynb b/chapter2/ns_code2.ipynb
index 34ca806a..4de51471 100644
--- a/chapter2/ns_code2.ipynb
+++ b/chapter2/ns_code2.ipynb
@@ -108,7 +108,7 @@
"inlet_marker, outlet_marker, wall_marker, obstacle_marker = 2, 3, 4, 5\n",
"inflow, outflow, walls, obstacle = [], [], [], []\n",
"if rank == 0:\n",
- " boundaries = gmsh.model.getBoundary(volumes)\n",
+ " boundaries = gmsh.model.getBoundary(volumes, oriented=False)\n",
" for boundary in boundaries:\n",
" center_of_mass = gmsh.model.occ.getCenterOfMass(boundary[0], boundary[1])\n",
" if np.allclose(center_of_mass, [0, H/2, 0]):\n",
@@ -172,7 +172,7 @@
"metadata": {},
"source": [
"## Generating the mesh\n",
- "We are now ready to generate the mesh. However, we have to decide if our mesh should consist of triangles or quadrilaterals. In this demo, to match the DFG 2D-3 benchmark, we use quadrilateral elements. This is done by recombining the mesh, setting two gmsh options. "
+ "We are now ready to generate the mesh. However, we have to decide if our mesh should consist of triangles or quadrilaterals. In this demo, to match the DFG 2D-3 benchmark, we use quadrilateral elements. This is done by recombining the mesh, setting three gmsh options. "
]
},
{
@@ -190,15 +190,25 @@
"Info : [ 40%] Meshing curve 7 (Line)\n",
"Info : [ 60%] Meshing curve 8 (Line)\n",
"Info : [ 80%] Meshing curve 9 (Line)\n",
- "Info : Done meshing 1D (Wall 0.0164382s, CPU 0.019196s)\n",
+ "Info : Done meshing 1D (Wall 0.00772085s, CPU 0.009064s)\n",
"Info : Meshing 2D...\n",
"Info : Meshing surface 1 (Plane, Frontal-Delaunay)\n",
- "Info : Simple recombination completed (Wall 0.00588923s, CPU 0.005468s): 307 quads, 90 triangles, 0 invalid quads, 0 quads with Q < 0.1, avg Q = 0.746284, min Q = 0.426971\n",
- "Info : Simple recombination completed (Wall 0.0131032s, CPU 0.013091s): 1498 quads, 0 triangles, 0 invalid quads, 0 quads with Q < 0.1, avg Q = 0.798747, min Q = 0.364541\n",
- "Info : Done meshing 2D (Wall 0.0328755s, CPU 0.033065s)\n",
- "Info : 1582 nodes 1671 elements\n",
+ "Info : Simple recombination completed (Wall 0.0244557s, CPU 0.024457s): 1180 quads, 354 triangles, 0 invalid quads, 0 quads with Q < 0.1, avg Q = 0.773076, min Q = 0.435836\n",
+ "Info : Done meshing 2D (Wall 0.065611s, CPU 0.066035s)\n",
+ "Info : Refining mesh...\n",
+ "Info : Meshing order 2 (curvilinear on)...\n",
+ "Info : [ 0%] Meshing curve 5 order 2\n",
+ "Info : [ 20%] Meshing curve 6 order 2\n",
+ "Info : [ 40%] Meshing curve 7 order 2\n",
+ "Info : [ 50%] Meshing curve 8 order 2\n",
+ "Info : [ 70%] Meshing curve 9 order 2\n",
+ "Info : [ 90%] Meshing surface 1 order 2\n",
+ "Info : Surface mesh: worst distortion = 0.845684 (0 elements in ]0, 0.2]); worst gamma = 0.667609\n",
+ "Info : Done meshing order 2 (Wall 0.00938187s, CPU 0.010146s)\n",
+ "Info : Done refining mesh (Wall 0.0110295s, CPU 0.012072s)\n",
+ "Info : 5948 nodes 6119 elements\n",
"Info : Optimizing mesh (Netgen)...\n",
- "Info : Done optimizing mesh (Wall 1.304e-06s, CPU 3e-06s)\n"
+ "Info : Done optimizing mesh (Wall 8.19e-07s, CPU 2e-06s)\n"
]
}
],
@@ -206,6 +216,7 @@
"if rank == 0:\n",
" gmsh.option.setNumber(\"Mesh.RecombinationAlgorithm\", 8)\n",
" gmsh.option.setNumber(\"Mesh.RecombineAll\", 2)\n",
+ " gmsh.option.setNumber(\"Mesh.SubdivisionAlgorithm\", 1)\n",
" gmsh.model.mesh.generate(gdim)\n",
" gmsh.model.mesh.optimize(\"Netgen\")"
]
@@ -232,6 +243,7 @@
"from dolfinx.mesh import create_meshtags, create_mesh \n",
"if MPI.COMM_WORLD.rank == 0:\n",
" # Get mesh geometry\n",
+ " \n",
" x = extract_gmsh_geometry(gmsh.model)\n",
"\n",
" # Get mesh topology for each element\n",
@@ -302,7 +314,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -335,7 +347,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -421,7 +433,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -448,7 +460,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -473,7 +485,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -493,7 +505,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -513,7 +525,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -559,7 +571,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -584,7 +596,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -612,7 +624,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 17,
"metadata": {
"tags": []
},
@@ -620,7 +632,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "158c43eb4e084fa1aa662cff88f9adf6",
+ "model_id": "31cffa8f3de644f0bf31a0b12385a915",
"version_major": 2,
"version_minor": 0
},
@@ -732,14 +744,14 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 18,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -751,7 +763,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -763,7 +775,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
""
]
diff --git a/chapter3/component_bc.ipynb b/chapter3/component_bc.ipynb
index aebcc380..4ff2e1a4 100644
--- a/chapter3/component_bc.ipynb
+++ b/chapter3/component_bc.ipynb
@@ -226,13 +226,13 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e495e93acf254071b4b7b934ac7ae47a",
+ "model_id": "80510912e3fc4bb6ba89a2d785933e66",
"version_major": 2,
"version_minor": 0
},
@@ -247,21 +247,21 @@
"source": [
"import pyvista\n",
"pyvista.set_jupyter_backend(\"pythreejs\")\n",
- "from dolfinx.plot import create_vtk_topology\n",
+ "from dolfinx.plot import create_vtk_mesh\n",
"\n",
"# Create plotter and pyvista grid\n",
"p = pyvista.Plotter()\n",
- "topology, cell_types = create_vtk_topology(mesh, mesh.topology.dim)\n",
- "grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x)\n",
+ "topology, cell_types, x = create_vtk_mesh(V)\n",
+ "grid = pyvista.UnstructuredGrid(topology, cell_types, x)\n",
"\n",
"# Attach vector values to grid and warp grid by vector\n",
- "vals_2D = uh.compute_point_values().real \n",
- "vals = np.zeros((vals_2D.shape[0], 3))\n",
- "vals[:,:2] = vals_2D\n",
+ "\n",
+ "vals = np.zeros((x.shape[0], 3))\n",
+ "vals[:,:len(uh)] = uh.x.array.reshape((x.shape[0], len(uh)))\n",
"grid[\"u\"] = vals\n",
"actor_0 = p.add_mesh(grid, style=\"wireframe\", color=\"k\")\n",
"warped = grid.warp_by_vector(\"u\", factor=1.5)\n",
- "actor_1 = p.add_mesh(warped, show_edges=True)\n",
+ "actor_1 = p.add_mesh(warped,opacity=0.8)\n",
"p.view_xy()\n",
"if not pyvista.OFF_SCREEN:\n",
" p.show()\n",
diff --git a/chapter3/em.ipynb b/chapter3/em.ipynb
index d02629b5..01204bbd 100644
--- a/chapter3/em.ipynb
+++ b/chapter3/em.ipynb
@@ -113,13 +113,13 @@
"Info : [ 90%] Meshing curve 17 (Ellipse)\n",
"Info : [ 90%] Meshing curve 18 (Ellipse)\n",
"Info : [100%] Meshing curve 19 (Ellipse)\n",
- "Info : Done meshing 1D (Wall 0.0254852s, CPU 0.030127s)\n",
+ "Info : Done meshing 1D (Wall 0.0204444s, CPU 0.019641s)\n",
"Info : Meshing 2D...\n",
"Info : [ 0%] Meshing surface 1 (Plane, Bamg)\n",
"Info : [ 0%] BAMG succeeded 794 vertices 1316 triangles\n",
"Info : [ 10%] Meshing surface 3 (Plane, Bamg)\n",
- "Info : [ 10%] BAMG succeeded 22 vertices 29 triangles\n",
- "Info : [ 10%] BAMG succeeded 22 vertices 29 triangles\n",
+ "Info : [ 10%] BAMG succeeded 21 vertices 27 triangles\n",
+ "Info : [ 10%] BAMG succeeded 21 vertices 27 triangles\n",
"Info : [ 20%] Meshing surface 4 (Plane, Bamg)\n",
"Info : [ 20%] BAMG succeeded 22 vertices 29 triangles\n",
"Info : [ 20%] BAMG succeeded 22 vertices 29 triangles\n",
@@ -157,8 +157,8 @@
"Info : [ 70%] BAMG succeeded 22 vertices 29 triangles\n",
"Info : [ 70%] BAMG succeeded 22 vertices 29 triangles\n",
"Info : [ 80%] Meshing surface 16 (Plane, Bamg)\n",
- "Info : [ 80%] BAMG succeeded 21 vertices 27 triangles\n",
- "Info : [ 80%] BAMG succeeded 21 vertices 27 triangles\n",
+ "Info : [ 80%] BAMG succeeded 22 vertices 29 triangles\n",
+ "Info : [ 80%] BAMG succeeded 22 vertices 29 triangles\n",
"Info : [ 80%] Meshing surface 17 (Plane, Bamg)\n",
"Info : [ 80%] BAMG succeeded 22 vertices 29 triangles\n",
"Info : [ 80%] BAMG succeeded 22 vertices 29 triangles\n",
@@ -166,14 +166,14 @@
"Info : [ 90%] BAMG succeeded 22 vertices 29 triangles\n",
"Info : [ 90%] BAMG succeeded 22 vertices 29 triangles\n",
"Info : [ 90%] Meshing surface 19 (Plane, Bamg)\n",
- "Info : [ 90%] BAMG succeeded 1810 vertices 3323 triangles\n",
- "Info : [ 90%] BAMG succeeded 1803 vertices 3309 triangles\n",
+ "Info : [ 90%] BAMG succeeded 1811 vertices 3325 triangles\n",
+ "Info : [ 90%] BAMG succeeded 1812 vertices 3327 triangles\n",
"Info : [100%] Meshing surface 20 (Plane, Bamg)\n",
- "Info : [100%] BAMG succeeded 1192 vertices 2168 triangles\n",
- "Info : [100%] BAMG succeeded 1173 vertices 2130 triangles\n",
- "Info : [100%] BAMG succeeded 1170 vertices 2124 triangles\n",
- "Info : Done meshing 2D (Wall 2.44564s, CPU 1.70276s)\n",
- "Info : 3635 nodes 7767 elements\n"
+ "Info : [100%] BAMG succeeded 1198 vertices 2180 triangles\n",
+ "Info : [100%] BAMG succeeded 1178 vertices 2140 triangles\n",
+ "Info : [100%] BAMG succeeded 1176 vertices 2136 triangles\n",
+ "Info : Done meshing 2D (Wall 2.25798s, CPU 1.53992s)\n",
+ "Info : 3650 nodes 7797 elements\n"
]
}
],
@@ -257,7 +257,7 @@
" # Create mesh resolution that is fine around the wires and\n",
" # iron cylinder, coarser the further away you get\n",
" gmsh.model.mesh.field.add(\"Distance\", 1)\n",
- " edges = gmsh.model.getBoundary(other_surfaces)\n",
+ " edges = gmsh.model.getBoundary(other_surfaces, oriented=False)\n",
" gmsh.model.mesh.field.setNumbers(1, \"EdgesList\", [e[1] for e in edges])\n",
" gmsh.model.mesh.field.add(\"Threshold\", 2)\n",
" gmsh.model.mesh.field.setNumber(2, \"IField\", 1)\n",
@@ -377,13 +377,13 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "09331cba68f6464bb1bd6401068a38d9",
+ "model_id": "990232533b8843fdaae230f20f34f9de",
"version_major": 2,
"version_minor": 0
},
@@ -398,15 +398,14 @@
"source": [
"import pyvista\n",
"pyvista.set_jupyter_backend(\"pythreejs\")\n",
- "from dolfinx.plot import create_vtk_topology\n",
+ "from dolfinx.plot import create_vtk_mesh\n",
"\n",
"plotter = pyvista.Plotter()\n",
- "topology, cell_types = create_vtk_topology(mesh, mesh.topology.dim)\n",
- "grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x)\n",
+ "grid = pyvista.UnstructuredGrid(*create_vtk_mesh(mesh, mesh.topology.dim))\n",
"num_local_cells = mesh.topology.index_map(mesh.topology.dim).size_local\n",
"grid.cell_data[\"Marker\"] = ct.values[ct.indicesN1curl\n",
" 1 \n",
" -O1\\n-march=native \n",
- " 0.017124 \n",
+ " 0.016676 \n",
" \n",
" \n",
" 1 \n",
" N1curl \n",
" 1 \n",
" -O2\\n-march=native \n",
- " 0.015475 \n",
+ " 0.016659 \n",
" \n",
" \n",
" 2 \n",
" N1curl \n",
" 1 \n",
" -O3\\n-march=native \n",
- " 0.014635 \n",
+ " 0.015443 \n",
" \n",
" \n",
" 3 \n",
" N1curl \n",
" 1 \n",
" -Ofast\\n-march=native \n",
- " 0.014883 \n",
+ " 0.015680 \n",
" \n",
" \n",
" 4 \n",
" N1curl \n",
" 1 \n",
" -O1 \n",
- " 0.015623 \n",
+ " 0.016467 \n",
" \n",
" \n",
" ... \n",
@@ -234,35 +234,35 @@
" RT \n",
" 3 \n",
" -Ofast\\n-march=native \n",
- " 0.334979 \n",
+ " 0.409227 \n",
" \n",
" \n",
" 68 \n",
" RT \n",
" 3 \n",
" -O1 \n",
- " 0.515080 \n",
+ " 0.645869 \n",
" \n",
" \n",
" 69 \n",
" RT \n",
" 3 \n",
" -O2 \n",
- " 0.461205 \n",
+ " 0.559253 \n",
" \n",
" \n",
" 70 \n",
" RT \n",
" 3 \n",
" -O3 \n",
- " 0.387479 \n",
+ " 0.476186 \n",
" \n",
" \n",
" 71 \n",
" RT \n",
" 3 \n",
" -Ofast \n",
- " 0.435881 \n",
+ " 0.477359 \n",
" \n",
" \n",
"\n",
@@ -271,17 +271,17 @@
],
"text/plain": [
" Space Degree Options Time\n",
- "0 N1curl 1 -O1\\n-march=native 0.017124\n",
- "1 N1curl 1 -O2\\n-march=native 0.015475\n",
- "2 N1curl 1 -O3\\n-march=native 0.014635\n",
- "3 N1curl 1 -Ofast\\n-march=native 0.014883\n",
- "4 N1curl 1 -O1 0.015623\n",
+ "0 N1curl 1 -O1\\n-march=native 0.016676\n",
+ "1 N1curl 1 -O2\\n-march=native 0.016659\n",
+ "2 N1curl 1 -O3\\n-march=native 0.015443\n",
+ "3 N1curl 1 -Ofast\\n-march=native 0.015680\n",
+ "4 N1curl 1 -O1 0.016467\n",
".. ... ... ... ...\n",
- "67 RT 3 -Ofast\\n-march=native 0.334979\n",
- "68 RT 3 -O1 0.515080\n",
- "69 RT 3 -O2 0.461205\n",
- "70 RT 3 -O3 0.387479\n",
- "71 RT 3 -Ofast 0.435881\n",
+ "67 RT 3 -Ofast\\n-march=native 0.409227\n",
+ "68 RT 3 -O1 0.645869\n",
+ "69 RT 3 -O2 0.559253\n",
+ "70 RT 3 -O3 0.476186\n",
+ "71 RT 3 -Ofast 0.477359\n",
"\n",
"[72 rows x 4 columns]"
]
@@ -313,7 +313,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -323,7 +323,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4sAAAEkCAYAAACGxqRxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAx2UlEQVR4nO3de1xUdf7H8TcoF4XBG2iB5q0UWbwgqOm6ectbpetmlmlYKSbrpTTXzbQ03HazNDO1UMP6SWqWhpn6S/1ltWZ5SaW8gJZKmngJUXEIBIHz+8OHU+NBRR1mRng9Hw8f65zz4Xu+Zz57PLw7Z854GIZhCAAAAACAP/B09QQAAAAAAO6HsAgAAAAAMCEsAgAAAABMCIsAAAAAABPCIgAAAADAhLAIAAAAADAhLAIAbmnjx4/XE0884eppAABQ5lR09QQAALiS8ePHa8WKFabllStXVnJysgtm5Bhdu3ZV7969NWrUKKds76efftL8+fO1ZcsWnTlzRkFBQQoLC1N0dLTuvvtuW93Bgwf1zjvvaOvWrcrIyFDVqlVVp04dPfDAA+rTp4/8/PyKHX/79u1auHChfvjhB509e1a33XabevXqpWHDhsnb29sp+wgAcDzCIgDArUVFRWnmzJl2yzw9uTGmpL7++muNGDFCERERevnll1W/fn3l5ORo48aNmjx5statW2erGzlypJo3b65Jkyapfv368vDw0I8//qjly5crICBAvXr1KnYbO3fuVJ06dTRo0CDddtttSk1N1eTJk3Xq1CnFxcU5c3cBAA7kYRiG4epJAABQnPHjx+vEiRP6n//5n+uqWbNmjebPn69Dhw4pKChIXbt21TPPPKPKlStLkqKjo1WnTh0FBQXpo48+0oULFzRw4EA988wzevvtt7VkyRIVFRXpkUce0ZgxY2zjXrhwQXPnztWKFSuUkZGhO+64Q9HR0erfv7+tpnHjxpo0aZK+//57ff7557JYLBo4cKCGDRtm2/a2bdvs9mHDhg2qXbu2A94xe7m5uerSpYvCwsKUkJBgWp+VlaUqVarY6po2bap58+YVO5ZhGPLw8Cjxtt977z3NnTtXW7duveH5AwBciyuLAIAyJSkpSa+88oomTpyoyMhInThxQlOmTNHp06c1bdo0W926devUv39/LVmyRDt27NDEiRO1d+9eNWrUSIsXL9b333+v8ePHq2XLlurQoYMk6cUXX9TevXs1ZcoU1a1bV7t379akSZNUoUIF9evXzzb2W2+9pdGjR2vUqFH6+uuvNWXKFDVr1kxt27bV7Nmz9eCDD6p79+4aPHiwJKl69erF7sunn36qyZMnX3V/e/XqpSlTphS77ptvvlFmZqZiY2OLXV+lShW7ukuBtjjXExQl6dy5c6pUqdJ1/QwAwL0QFgEAbm3btm2KiIiwW9amTRvNnTu32Po5c+bo2WefVZ8+fSRJderU0aRJk/TYY4/phRdesAWk2rVra9y4cZKk+vXr67333tPJkydtV+AuLduyZYs6dOigX375RZ988onWrFmjhg0b2sY+dOiQFi1aZBcW77vvPj388MOSpIEDB2rRokX69ttv1bZtW1WtWlUVKlRQ5cqVFRQUdNV979y5s5o3b37VGn9//yuuS0tLkyTdeeedVx3jUt2l/ZIkq9Wqe+65x/b6aqH0cgcPHtTChQv17LPPlqgeAOCeCIsAALfWrFkzvfrqq3bLfH19i609ffq00tPTNXXqVL322mu25Zc+cXH48GE1a9ZMkhQaGmr3s4GBgQoMDLRbFhQUpMzMTEnSnj17ZBiGHnroIbuagoICVahQwW7Z5WPXrFlTp06duup+Fsff3/+qYfBarveTJn+s9/Pz0yeffCJJeu6555Sfn1+iMX7++WcNHjxY999/vx577LHr2j4AwL0QFgEAbs3X11d169YtUW1RUZEkaeLEiWrTpo1p/W233Wb7e8WK9qdADw8PeXl5XXHMS0Hqgw8+MN1eefktmpeP4+Hhcd3BTbr521AbNGggSTpw4ICioqKuOEb9+vUlXbwiGBkZKeniQ4Quve9XCueX+/HHHzV48GB17tyZB9sAQBlAWAQAlBmBgYG6/fbblZaWZrsN1FH+9Kc/SZKOHz+uTp063dRYXl5eKiwsvGbdzd6G+uc//1k1atTQ3Llzr/qAm0t18fHxxdaVxK5duzR06FD16tVLEydOvO7POAIA3A9hEQDg1i5cuKCMjAzT8sDAwGIDyejRo/XCCy8oICBAXbp0UcWKFXXo0CFt3LixxJ+5K07dunXVt29fvfjii/rHP/6hiIgI5ebmas+ePTp9+rSeeuqpEo9Vu3Zt7dy5U8eOHZOvr6+qVq1a7NeB3OxtqJUqVdIrr7yikSNH6oknntCTTz6pBg0aKCcnR5s2bdJHH32kdevW2dVFR0dr8ODBql+/vgoLC7V7924dOHBAwcHBV9zOd999p2HDhql79+4aNmyY3S231/pcJgDAfREWAQBubfv27Wrfvr1p+ebNm4t9imifPn3k7++vd955R3PnzlWFChVUp04dde3a9abn8q9//Uvvvvuu5s6dq6NHj8rPz0933XWXBg4ceF3jjBo1SpMmTVKPHj2Ul5dXal+dIUkdOnTQ8uXLNX/+fE2cOFFnz55VYGCgwsPD9fLLL9vVJSUl6Z133tFLL72kzMxMeXt7684779Tjjz+uRx999Irb+Pjjj/Xbb78pKSlJSUlJduv2799fKvsFACh9fM8iAAAAAMDEfM8LAAAAAKDcIywCAAAAAEwIiwAAAAAAE8IiAAAAAMCEsAgAAAAAMOGrMyRlZmarqIiHwgIAAAAoX4KCLFdcx5VFAAAAAIAJYREAAAAAYEJYBAAAAACYEBYBAAAAACZODYtFRUWaMWOG2rVrp4iICA0ZMkTp6elXrE9JSVH//v3VvHlzdezYUYmJicXWFRQUqG/fvmrcuLGOHj1aWtMHAAAAgHLDqWExISFBq1ev1qJFi7Rp0yYFBwcrNjZWRUVFptrs7GzFxMSoffv22rZtm2bOnKk5c+Zo7dq1ptq5c+eqatWqTtgDAAAAACgfnBoWly5dqpiYGDVo0EB+fn4aN26c0tLStGPHDlPt+vXr5enpqeHDh8vHx0ctWrRQv379tGTJEru6vXv3auXKlRo3bpyzdgMAAAAAyjynhUWr1ar09HSFh4fblgUEBKhu3bpKTU011e/bt09hYWHy9Px9iuHh4dq3b5/tdX5+vsaPH6/JkyfL39+/dHcAAAAAAMqRis7aUHZ2tqSLAfGPLBaLbd3l9RaL/RdEBgQE2NXOnDlTzZo1U/v27W/qs4o1ahA0AQAAAOCPnBYWL135s1qtdsutVmuxVwX9/f2VmZlpt+zcuXO22p07d2rt2rX69NNPb3pumZnZKioybnocAABQflUJ8Ja3j4+rp1Fm5OflKetcvqunAZR5QUGWK65zWli0WCwKCQnRnj171LRpU0kXg+KRI0fUpEkTU31oaKg+++wzFRUV2W5F3bt3r0JDQyVJ33zzjU6dOqUuXbpIkgzjYth78MEHNWTIEA0bNswZuwUAACBJ8vbx0Yzn+f3DUZ59ZZ4kwiLgSk59wE3//v21YMECpaWlKScnR9OmTVO9evUUGRlpqu3WrZsKCwsVHx+v/Px87dq1S8uWLdOjjz4qSXryySe1fv16rVy5UitXrtT8+fMlSfPnz9fAgQOduVsAAAAAUOY47cqiJMXExMhqtWrAgAHKzc1VZGSk4uPj5enpqe3bt2vo0KFas2aNgoOD5e/vr4SEBMXFxWnevHmqVq2aRowYoZ49e0q6eJvqH29fLSgokCQFBgbysBsAAAAAuEkexqX7N8sxPrMIAABuVlCQhdtQHejZV+YpI8N67UIAN+Vqn1l06m2oAAAAAIBbA2ERAAAAAGBCWAQAAAAAmBAWAQAAAAAmhEUAAAAAgAlhEQAAAABg4tTvWQQAADemSkAleftw2naU/LwCZZ3LdfU0AMCtcdYBAOAW4O1TUf+ZuNzV0ygzJvz7IVdPAQDcHrehAgAAAABMuLJ4HSwBvvL18XL1NMqE83kXZD133tXTAAAAAHAFhMXr4OvjpQH/XOzqaZQJS14bKKsIiwAAAIC7IiyizKhWxVsVvX1cPY0yoyA/T2ey8l09DQAAALgIYRFlRkVvH+14LcbV0ygzIv+ZIImwCAAAUF7xgBsAAAAAgAlhEQAAAABgQlgEAAAAAJgQFgEAAAAAJoRFAAAAAIAJYREAAAAAYEJYBAAAAACY8D2LAAAAKPOqVamkit786usoBfkFOpOV6+ppoJRxxAAAAKDMq+hdUT+8/ZWrp1FmNB/e0dVTgBNwGyoAAAAAwISwCAAAAAAwISwCAAAAAEwIiwAAAAAAEx5wAwAAAMDlqlTxlbe3l6unUSbk519QVtb5mx6HsAgAAADA5by9vfT666+7ehplwtixYyXdfFjkNlQAAAAAgAlXFgE4RUAVH/l4e7t6GmVGXn6+zmXlOXTMqhZvefn6OHTM8uzC+Tydtea7ehoAANwwwiIAp/Dx9tYT7z3j6mmUGf/z5JuSHBsWvXx99L+DnnTomOXZfYnvSYRFAMAtjNtQAQAAAAAmhEUAAAAAgAlhEQAAAABgQlgEAAAAAJgQFgEAAAAAJoRFAAAAAIAJYREAAAAAYEJYBAAAAACYODUsFhUVacaMGWrXrp0iIiI0ZMgQpaenX7E+JSVF/fv3V/PmzdWxY0clJibarY+Li1OnTp3UsmVLtW3bVqNGjdLRo0dLezcAAAAAoMxzalhMSEjQ6tWrtWjRIm3atEnBwcGKjY1VUVGRqTY7O1sxMTFq3769tm3bppkzZ2rOnDlau3atrWbAgAFatWqVdu7cqQ0bNig4OFijR4924h4BAAAAQNnk1LC4dOlSxcTEqEGDBvLz89O4ceOUlpamHTt2mGrXr18vT09PDR8+XD4+PmrRooX69eunJUuW2Gruuusu+fv72157enoqLS3NKfsCAAAAAGWZ08Ki1WpVenq6wsPDbcsCAgJUt25dpaammur37dunsLAweXr+PsXw8HDt27fPrm7JkiWKjIxURESEEhMTNWrUqNLbCQAAAAAoJyo6a0PZ2dmSLgbEP7JYLLZ1l9dbLBa7ZQEBAabaAQMGaMCAATp58qQ+/vhjhYWFXffcatTwv3YRHC4oyHLtIrgUPXJv9Mf90SP3Rn/cHz1yb/THvTmiP04Li5duF7VarXbLrVar3a2kf6zPzMy0W3bu3LliayWpVq1aeuSRR3Tvvfdqw4YNql69eonnlpmZraIi45p1HBCOlZFhvXbRdaA/jufIHtEfx+MYcn8cQ+6NY8j9cQy5N44h91bS/lztfXfabagWi0UhISHas2ePbZnVatWRI0fUpEkTU31oaKhSUlLsHn6zd+9ehYaGXnEbBQUFysnJ0cmTJx07eQAAAAAoZ5z6gJv+/ftrwYIFSktLU05OjqZNm6Z69eopMjLSVNutWzcVFhYqPj5e+fn52rVrl5YtW6ZHH31UkpSVlaXly5frzJkzkqTjx4/rpZdeUkhIiBo2bOjM3QIAAACAMsepYTEmJkY9e/bUgAED1K5dO6Wnpys+Pl6enp7avn27IiIidOzYMUkXb0NNSEjQxo0bFRUVpVGjRmnEiBHq2bOnJMnDw0Nr1qxRjx491KJFCz3yyCOqXLmy3nvvPXl7eztztwAAAACgzHHaZxali19tMXbsWI0dO9a0LioqSsnJyXbLwsLC9OGHHxY7VkBAgN57771SmScAAAAAlHdOvbIIAAAAALg1EBYBAAAAACaERQAAAACACWERAAAAAGBCWAQAAAAAmBAWAQAAAAAmhEUAAAAAgAlhEQAAAABgQlgEAAAAAJgQFgEAAAAAJoRFAAAAAIAJYREAAAAAYEJYBAAAAACYEBYBAAAAACaERQAAAACACWERAAAAAGBCWAQAAAAAmBAWAQAAAAAmhEUAAAAAgAlhEQAAAABgQlgEAAAAAJgQFgEAAAAAJiUOi/n5+dqwYYPeffddWa1WSdLRo0d17ty5UpscAAAAAMA1Kpak6NixYxo8eLBOnDih/Px8de3aVRaLRQsXLlReXp6mTJlS2vMEAAAAADhRia4svvLKKwoNDdW2bdvk4+NjW96lSxdt3bq11CYHAAAAAHCNEl1Z3L59uxITE+Xt7W23vHbt2jp58mSpTAwAAAAA4DolurJ4/vx5eXl5mZafPn3a7kojAAAAAKBsKFFYbNmypVavXm1anpiYqKioKIdPCgAAAADgWiW6DXXMmDGKjo5WWlqaCgsLNX/+fO3fv18HDx7U0qVLS3uOAAAAAAAnK9GVxfDwcC1btkze3t664447tGPHDtWrV08fffSR7rrrrtKeIwAAAADAyUp0ZVGS7rzzTr3yyiulORcAAAAAgJsocViUpNzcXGVmZsowDLvlderUceikAAAAAACuVaKwePDgQU2YMEG7du2yW24Yhjw8PJSamloqkwMAAAAAuEaJwuL48ePl5eWl2bNnKzAwUB4eHqU9LwAAAACAC5UoLP70009KSkpSgwYNSns+AAAAAAA3UKKnoYaFhSkjI6O05wIAAAAAcBMlurI4ZcoUxcXFyWq1qlGjRqpY0f7HgoODS2VyAAAAAADXKPHTULOysjRy5Ei7zyvygBsAAAAAKJtKFBb/+c9/qnLlypo5cyYPuAEAAACAcqDEX52RlJSkhg0b3tTGioqKNHPmTC1fvly5ublq2bKlpkyZopCQkGLrU1JSNGXKFKWmpqpatWoaPHiwBg0aJEnKz8/Xyy+/rC1btigjI0NVqlRRjx49NHr0aPn6+t7UPAEAAACgvCvRA26aNGmizMzMm95YQkKCVq9erUWLFmnTpk0KDg5WbGysioqKTLXZ2dmKiYlR+/bttW3bNs2cOVNz5szR2rVrJUkFBQWqVq2a4uPjtX37dr3//vvasmWLpk2bdtPzBAAAAIDyrkRh8e9//7umTp2qL774Qunp6Tp58qTdn5JaunSpYmJi1KBBA/n5+WncuHFKS0vTjh07TLXr16+Xp6enhg8fLh8fH7Vo0UL9+vXTkiVLJEmVK1fWmDFj1LBhQ1WoUEF16tTRQw89pG3btpV4PgAAAACA4pXoNtRhw4ZJkoYPH37DD7ixWq1KT09XeHi4bVlAQIDq1q2r1NRUtWrVyq5+3759CgsLk6fn73k2PDxcy5Ytu+I2Nm/erNDQ0JLsEgAAAADgKkoUFhMTE296Q9nZ2ZIuBsQ/slgstnWX11ssFrtlAQEBxdZKF29x3blzpz7++OPrnluNGv7X/TO4eUFBlmsXwaXokXujP+6PHrk3+uP+6JF7oz/uzRH9KVFYbN269U1vyN//YiCzWq12y61Wq23d5fWXf07y3LlzxdYuWLBA7733nhYuXHhD3/mYmZmtoiLjmnUcEI6VkWG9dtF1oD+O58ge0R/H4xhyfxxD7o1jyP1xDLk3jiH3VtL+XO19v2JYPHnypGrVqmX7+9Vcqrsai8WikJAQ7dmzR02bNpV0MSgeOXJETZo0MdWHhobqs88+U1FRke1W1L1795puM509e7aWLVum999/Xw0aNLjmPAAAAAAA13bFsNixY0dt2rRJNWrUUIcOHYr9bsXr+cyiJPXv318LFizQ3XffrVq1amnatGmqV6+eIiMjTbXdunXT9OnTFR8fr6FDh2rfvn1atmyZXnrpJVvNq6++qnXr1mnx4sWqU6dOieYAAAAAALi2K4bFhQsXqkqVKpIc85lFSYqJiZHVatWAAQOUm5uryMhIxcfHy9PTU9u3b9fQoUO1Zs0aBQcHy9/fXwkJCYqLi9O8efNUrVo1jRgxQj179pQkpaen691335WXl5d69+5tt53k5GSHzBcAAAAAyqsrhsXWrVurS5cuWr58uUM+syhJnp6eGjt2rMaOHWtaFxUVZQp5YWFh+vDDD4sdKyQkRPv373fIvAAAAAAA9q76PYvp6ekqKipy1lwAAAAAAG7iqmERAAAAAFA+XfOrMzIyMlRQUHDVmpI8DRUAAAAAcOu4Zlj829/+dsV11/s0VAAAAADAreGaYXHWrFm2p6ICAAAAAMqHa4bFli1bqkaNGs6YCwAAAADATVz1ATceHh7OmgcAAAAAwI1cNSwahuGseQAAAAAA3MhVb0Pdt2+fs+YBAAAAAHAjfM8iAAAAAMCEsAgAAAAAMCEsAgAAAABMCIsAAAAAABPCIgAAAADAhLAIAAAAADAhLAIAAAAATAiLAAAAAAATwiIAAAAAwISwCAAAAAAwISwCAAAAAEwIiwAAAAAAE8IiAAAAAMCEsAgAAAAAMCEsAgAAAABMCIsAAAAAABPCIgAAAADAhLAIAAAAADAhLAIAAAAATAiLAAAAAAATwiIAAAAAwISwCAAAAAAwISwCAAAAAEwIiwAAAAAAE8IiAAAAAMCEsAgAAAAAMCEsAgAAAABMCIsAAAAAABPCIgAAAADAhLAIAAAAADBxalgsKirSjBkz1K5dO0VERGjIkCFKT0+/Yn1KSor69++v5s2bq2PHjkpMTLRbn5iYqH79+ql58+bq3LlzaU8fAAAAAMoNp4bFhIQErV69WosWLdKmTZsUHBys2NhYFRUVmWqzs7MVExOj9u3ba9u2bZo5c6bmzJmjtWvX2mpq1qypmJgYxcbGOnM3AAAAAKDMc2pYXLp0qWJiYtSgQQP5+flp3LhxSktL044dO0y169evl6enp4YPHy4fHx+1aNFC/fr105IlS2w1PXr0UPfu3VWrVi1n7gYAAAAAlHlOC4tWq1Xp6ekKDw+3LQsICFDdunWVmppqqt+3b5/CwsLk6fn7FMPDw7Vv3z6nzBcAAAAAyrOKztpQdna2pIsB8Y8sFott3eX1FovFbllAQECxtTerRg1/h4+JawsKsly7CC5Fj9wb/XF/9Mi90R/3R4/cG/1xb47oj9PCor//xUBmtVrtllutVtu6y+szMzPtlp07d67Y2puVmZmtoiLjmnUcEI6VkWG9dtF1oD+O58ge0R/H4xhyfxxD7o1jyP1xDLk3jiH3VtL+XO19d9ptqBaLRSEhIdqzZ49tmdVq1ZEjR9SkSRNTfWhoqFJSUuwefrN3716FhoY6Zb4AAAAAUJ459QE3/fv314IFC5SWlqacnBxNmzZN9erVU2RkpKm2W7duKiwsVHx8vPLz87Vr1y4tW7ZMjz76qK2moKBAeXl5KigokGEYysvLU15engzj2lcJAQAAAABX5rTbUCUpJiZGVqtVAwYMUG5uriIjIxUfHy9PT09t375dQ4cO1Zo1axQcHCx/f38lJCQoLi5O8+bNU7Vq1TRixAj17NnTNl58fLzmzJlje92sWTNJ0oYNG1S7dm1n7hoAAAAAlClODYuenp4aO3asxo4da1oXFRWl5ORku2VhYWH68MMPrzjeqFGjNGrUKIfPEwAAAADKO6fehgoAAAAAuDUQFgEAAAAAJoRFAAAAAIAJYREAAAAAYEJYBAAAAACYEBYBAAAAACaERQAAAACACWERAAAAAGBCWAQAAAAAmBAWAQAAAAAmhEUAAAAAgAlhEQAAAABgQlgEAAAAAJgQFgEAAAAAJoRFAAAAAIAJYREAAAAAYEJYBAAAAACYEBYBAAAAACaERQAAAACACWERAAAAAGBCWAQAAAAAmBAWAQAAAAAmhEUAAAAAgAlhEQAAAABgQlgEAAAAAJgQFgEAAAAAJoRFAAAAAIAJYREAAAAAYEJYBAAAAACYEBYBAAAAACaERQAAAACACWERAAAAAGBCWAQAAAAAmBAWAQAAAAAmhEUAAAAAgAlhEQAAAABgQlgEAAAAAJgQFgEAAAAAJoRFAAAAAICJU8NiUVGRZsyYoXbt2ikiIkJDhgxRenr6FetTUlLUv39/NW/eXB07dlRiYqLd+vPnz2vSpElq3bq1WrZsqdGjR+vs2bOlvBcAAAAAUPY5NSwmJCRo9erVWrRokTZt2qTg4GDFxsaqqKjIVJudna2YmBi1b99e27Zt08yZMzVnzhytXbvWVvOf//xHe/bs0apVq/Tll18qJydHzz33nDN3CQAAAADKJKeGxaVLlyomJkYNGjSQn5+fxo0bp7S0NO3YscNUu379enl6emr48OHy8fFRixYt1K9fPy1ZskTSxauKn3zyiZ555hnVqlVLVapU0XPPPaevvvpKx44dc+ZuAQAAAECZU9FZG7JarUpPT1d4eLhtWUBAgOrWravU1FS1atXKrn7fvn0KCwuTp+fveTY8PFzLli2TJP3888/Ky8tT06ZNbesbNmyoSpUqKTU1VcHBwSWem6enR4lrA6v5lbgWV3c973tJeQfUcPiY5ZmjexToX92h45V3pXEMVQrkGHIkR/eoStXKDh2vvCuNYyigKseQIzm6R14WX4eOV96VyjEUEODwMcsrR/THaWExOztbkvn/ABaLxbbu8nqLxWK3LCAgwFZ76X8vr7nSeFdT7ToC4Kzn+1zX2LiyGjX8HT5m09hXHT5meeboHk3vN9mh45V3pXEMdZox3eFjlmeO7tGIcfc5dLzyrjSOoZjn/uPwMcszR/coLPpuh45X3pXGMTR06FCHj1leOaI/TrsN1d//4mStVqvdcqvValt3ef3loe/cuXO22usdDwAAAABQck4LixaLRSEhIdqzZ49tmdVq1ZEjR9SkSRNTfWhoqFJSUuwefrN3716FhoZKkurVqycfHx+78Q4ePKjc3FxbDQAAAADgxjj1ATf9+/fXggULlJaWppycHE2bNk316tVTZGSkqbZbt24qLCxUfHy88vPztWvXLi1btkyPPvqoJMnX11d9+vTRrFmz9OuvvyorK0vTpk1Thw4dFBIS4szdAgAAAIAyx8MwDMNZGysqKtIbb7yh5cuXKzc3V5GRkYqLi1Pt2rW1fft2DR06VGvWrLE9nCYlJUVxcXFKTU1VtWrVNGTIEA0aNMg23vnz5/Xvf/9ba9euVWFhof7yl78oLi5OVatWddYuAQAAAECZ5NSwCAAAAAC4NTj1NlQAAAAAwK2BsAgAAAAAMCEsAgAAAABMCIsAAAAAABPCIgDAre3Zs0d//etfFRERofHjx7t6OgCAcqY8n4cqunoCuHGHDx/WrFmztHnzZmVnZysoKEjdu3fXiBEj5OfnZ6ubOHGivv/+e6Wlpal3796aOnWqC2ddfpSkP2lpaXrjjTeUnJys7Oxs3X777Xr88cf1yCOPuHj25UNJepSbm6unnnpKBw8e1Pnz52WxWNS9e3eNHTtWPj4+Lt6DW1tJ/w2bMWOGWrdurZUrV970NpOSkjRnzhx98cUXNz0WOA+5O85D7o/zkGtxHro2rizeon766Sf17dtXXl5eWrZsmZKTkzV79mx99913io6OVm5urq22cePGGj9+vDp37uzCGZcvJe3PuXPn1KZNGy1fvlw7d+5UXFycXnvtNa1bt87Fe1D2lbRHXl5emjhxor766ivt3LlTy5cvV0pKit58800X78Gt7Xr+Dfvll1/UpEkTF84WxeE85N44D7k/zkOuxXmoZPiexVvU4MGDdf78eS1ZssRu+enTp9WtWzc99dRTeuqpp+zWXbpszn/RLX030p9Lnn76adWsWVMvvPCCM6Zabt1ojzIyMvTss8/K399f8fHxzppumVOS93/IkCGKiopSbm6uvLy8VLFiRU2dOlX169fXyy+/rP3796uwsFBNmjTRhAkTbCfyY8eOafLkyfrhhx9UWFio22+/XS+99JIk6cknn9SFCxdUqVIlSdK0adN07733OnXfywrOQ+6N85D74zzkWpyHSoYri7eg8+fPa8uWLfrb3/5mWle9enV16NBBX375pQtmBunm+pOTk6MffvhBjRs3Lu1plms30qOxY8eqRYsWat++vfbv368hQ4Y4a7plTknf/woVKig5OVnBwcGKi4tTcnKyunfvLkn6+9//ro0bN2rjxo2qX7++RowYoQsXLkiSXn/9ddWqVUtff/21vvvuO82aNUu1atVSVFSU4uLiFBwcrOTkZCUnJ7v1CdqdcR5yb5yH3B/nIdfiPFRyhMVbUFZWlgoLC1WzZs1i19922206ffq0k2eFS260PwUFBfrHP/6hkJAQ9enTp5RnWb7dSI9ef/11JScna+XKlerfv7+Cg4OdMdUy6Wb/DWvUqJHatm0rHx8fVa5cWWPHjlV6erqOHDkiSfL29tapU6d05MgReXh4qEGDBqpTp06p7Et5xXnIvXEecn+ch1yL81DJERZvAZ9++qkiIiJsfwoKClShQgX9+uuvxdafOHFC1atXd/Isyy9H9Cc/P19jxozR6dOnNW/ePHl5eTlj6uWGo44hDw8PhYaGKjQ0VM8880xpT7vMcPS/YUePHtXTTz+tDh06qGXLlurSpYskKTMzU5L0z3/+U3fccYdGjhypdu3aacKECQSXm8R5yL1xHnJ/nIdci/PQjSMs3gJ69+5tu1SdnJyskJAQtWnTptgnMp05c0YbN25Ux44dnT/Rcupm+3P+/HkNHz5cZ8+e1YIFC2SxWJw4+/LB0cdQQUGB0tLSSnHGZYuj3/9JkybJy8tLK1as0M6dO7VhwwZJ0qWP4FerVk0TJkzQunXr9Mknn+iXX37Rq6++Kkny9OS0dyM4D7k3zkPuj/OQa3EeunG31mxhM378eKWmpmrChAk6fvy4CgsLlZqaqtjYWAUHBys6OtpWm5+fr7y8PBUWFqqwsFB5eXnKz8934ezLvpL2Jzs7W0OHDpVhGHrnnXfsHtOM0lXSHu3atUtff/21cnJyVFRUpD179uitt95Shw4dXLwHt7br+TfsclarVZUqVZLFYpHVatW0adPs1q9Zs0ZHjhxRUVGR/Pz85O3tbTs5BwYG6vTp08rKyirV/SsPOA+5N85D7o/zkGtxHioZnoZ6Czt06JDmzJmjb7/9Vjk5OQoMDLR9N4y/v7+tLjo6Wtu2bbP72ZCQkFvm+11uVSXpz4oVKzR+/Hj5+vra/ZemyMhIJSQkuGrq5UZJepScnKx///vfOnTokAzDUGBgoLp27Wr6DiZcv5L+G9a5c2eNHDlSDz74oCTp+++/1+TJk3X48GHVrFlTo0eP1pgxY5SYmKg2bdro9ddf1+rVq3XmzBn5+vqqbdu2evHFF1W9enUVFBRo7Nix2rJliwoLC/Xqq6/abh/C9eM85N44D7k/zkOuxXno2giLAAAAAAATbkMFAAAAAJgQFgEAAAAAJoRFAAAAAIAJYREAAAAAYEJYvAUkJSWpc+fOrp7GNUVERGj79u2unoZL0CP3Rn/cHz0CAMD9EBZx3bZu3arGjRublicnJysqKsoFM8Ll6JF7oz/ujx4BAEBYdAsXLlxw9RRwDfTIvdEf90ePAAC49RAWr6Fx48ZKTEzUQw89pBYtWujhhx/WsWPHlJiYqI4dO6p169aaPn26rT4vL09PP/202rdvr4iICD3wwAP63//9X9OYCxcuVL9+/dS8eXNt2rRJubm5ev3119W1a1dFRESoW7duWrdund3PffDBB+rUqZMiIyM1evRoZWdn39A+de7cWfHx8YqJibFt6/PPP7et//HHHzVo0CC1adNGUVFRio6OVmpqqiTp2LFjGjp0qKSLt2NFREQoMTHRtl9bt25VYWGh2rdvr9WrV9ttd9asWRo4cKDt9X//+1/169dPrVq1Urdu3WzjXC965N49oj/u3Z9L26VH7t0jAABcwsBVNWrUyOjbt69x7NgxIycnx4iOjja6detmzJgxw8jLyzP27t1r/OlPfzK2bt1qGIZh5ObmGklJSca5c+eMCxcuGElJSUZYWJhx4MABuzF79uxpHDhwwCgqKjJyc3ONMWPGGP369TMOHTpkGIZhHDt2zEhNTTUMwzA+/vhjIywszJg6daqRm5trnDhxwujSpYsxZ84c25hPPfWUERkZecU/8+bNs9V26tTJ6NSpk7F3716jsLDQeOedd4yWLVsa2dnZhmEYxv79+41vv/3WOH/+vPHbb78ZL774otGpUycjPz/fMAzD2LJli9GoUaNi36stW7YYhmEY06dPN5544gnbusLCQqNjx47GihUrDMMwjM2bNxtRUVHGt99+axQWFhr79+837rnnHmPlypX0qIz1iP64d3/o0a3RIwAAXIGweA2NGjUyVq1aZXu9aNEio3nz5kZBQYFtWZ8+fYwFCxZccYxevXoZixcvthvzo48+sr3OzMw0GjVqZKSkpBT78x9//LHRrFkz48KFC7ZlU6dONYYNG3ZD+9SpUycjPj7e9jo7O9to1KiRsXv37mLrz549azRq1Mj2i2BJfolKS0szQkNDjV9++cUwDMPYuHGjERkZaeTk5BiGYRjDhg0z3njjDbufnzdvnvH4449f9/7QI/fuEf1x7/5c2i49cu8eAQDgChVdfWXTnXz66aeaPHmy7fWaNWskSUFBQbZllSpVUvXq1VWhQgXbMl9fX/3222+SpPz8fE2fPl1ffvmlMjMz5enpqZycHJ0+fdpuW7Vr17b9/ejRo5Kk+vXrX3FuNWrUUMWKv7ercuXKtm3eiJo1a9r+7ufnJ0m28Y4eParXXntNP/zwg6xWqzw9L96tnJmZqYYNG5Zo/Hr16ikqKkpJSUl6+umntXz5ct1///2qVKmSJOnw4cPavHmzFi1aZPuZwsJCBQcHX3VceuTePaI/7t0fiR7dCj0CAMBdEBb/oHfv3urdu/dNjfHuu+9q06ZNSkhI0B133CEPDw/17t1bhmHY1V36xUT6/Reqn3/+WaGhoTe03ZiYGO3YseOK64cNG6bY2NgSjTVp0iRVq1ZNK1asUPXq1ZWVlaXWrVvb9uGPc7+avn376s0339Rjjz2mDRs26IMPPrCtCwwM1F//+tcSz+kSenSRu/aI/lzkrv2R6NEl7twjAADcBWHRwaxWq7y9vVWtWjUVFBRo2bJlOnDggLp27XrFn6levboeeOABvfTSS5o6darq1aunEydO6OzZsyX+pSohIcFRuyCr1arg4GBZLBZZrVZNmzbNbn1gYKAk6dChQ2rQoMEVx+nRo4defvllPf/882rYsKGaNm1qW/f4449r0qRJat68uVq1amUbLysry/a6tNCj37ljj+jP79yxPxI9+iN37REAAI7A01AdbPDgwapRo4Y6dOigTp066eTJk2rZsuU1f+5f//qXWrZsqSFDhigiIkKDBg3SkSNHnDBjs4kTJ2r37t1q1aqV+vbtq3bt2tmtr1+/vqKjozVgwABFRUXZ3WL1R76+vnrggQf01Vdf6aGHHrJbd++992rq1Kl688031a5dO7Vr104vvPCCzpw5U2r7dQk9+p079oj+/M4d+yPRoz9y1x4BAOAIHsbl9w0BAAAAAMo9riwCAAAAAEwIiwAAAAAAE8IiAAAAAMCEsAgAAAAAMCEsAgAAAABMCIsAAAAAABPCIgAALhIdHa2JEye6ehoAABSL71kEAOAKTp06pfj4eH355Zf69ddf5e/vr1atWmn48OFq0qRJicd5++23tXz5cn3xxRd2y8+ePauKFSvK39/f0VMHAOCmcWURAIBiHD9+XH379lVycrImT56s9evXa/78+fLy8tLDDz+sjRs33vQ2qlatSlAEALgtwiIAAMWIi4tTQUGBEhMT1aFDBwUHB6tZs2aaMWOG7r77bj3//PM6f/68Zs+era5du2rVqlXq0qWLmjZtqieffFJHjx6VJCUlJenNN99Uenq6GjdurMaNG2v27NmSzLehXrhwQdOnT9df/vIXhYeH67777tOqVavs5tW4cWMtXrxY48aNU0REhO655x7NmzfPrubzzz9Xnz591Lx5c0VFRemhhx5SSkpKKb9jAICyhrAIAMBlsrKy9N///lcDBw4s9srfsGHDdOrUKX3zzTeSpIyMDC1ZskQzZ87U4sWLlZ2drVGjRskwDN13330aOnSobrvtNm3atEmbNm3S4MGDi93ujBkztGzZMk2YMEGrVq1S7969NW7cOG3evNmu7q233lKrVq20cuVKDRs2TDNmzLDVZGRkaPTo0br//vu1evVqffjhh3r88cdVoUIFB79LAICyrqKrJwAAgLs5fPiwioqKdNdddxW7/s4775QkpaWlSZJyc3M1depU1a1bV5L02muvqUePHtqyZYvatm2rypUrq0KFCgoKCrriNnNzc/X+++/r+eefV8+ePSVJsbGx2r17t+Lj49W2bVtb7X333aeHH35YkjRw4EAtWrRI3377rdq2bauMjAxduHBBPXv2VO3atSVJDRs2vMl3BABQHnFlEQCAm1S9enVbUJSk+vXrq1q1avrpp59KPMbhw4d14cIFtWrVym55q1atdODAAbtloaGhdq9r1qypU6dOSbp4m2r79u3Vq1cvjRgxQgsXLtTx48evd5cAACAsAgBwuTvuuEMeHh768ccfi11/KbzVr1/fmdOy8fLysnvt4eGhSw83r1ChghISErRw4UI1bdpU69evV/fu3fXll1+6YqoAgFsYYREAgMtUrVpVHTp0sH3+8HLz5s1TYGCg/vznP0uSTp8+rSNHjtjWp6Wl6cyZM7bbVb28vFRYWHjVbdatW1fe3t767rvv7JZ/9913V7wd9ko8PDzUrFkzxcbGavHixWrVqpWSkpKuawwAAAiLAAAUY9KkSapQoYIGDRqkjRs36vjx49q1a5fGjh2rLVu26JVXXpGvr68kqVKlSnr++ee1e/du7d69W+PHj1eTJk1snzOsXbu2Tp06peTkZJ0+fVq5ubmm7VWqVEnR0dGaNWuWPvvsM6WlpWnu3LnasGGDYmNjSzzvnTt36q233tIPP/ygY8eOafPmzdq/fz+fWwQAXDcecAMAQDFCQkKUlJSkt99+W5MnT1ZGRob8/PzUunVrffjhhwoLC7PVBgUF6eGHH9YzzzyjjIwMtWzZUtOnT5eHh4ck6d5771WPHj00bNgwZWVlaeTIkRo1apRpm2PGjJGnp6f+85//6MyZM7rjjjs0bdo0u4fbXIvFYtH333+vJUuWKCsrS0FBQerVq5eGDx9+828KAKBc8TAufcgBAABct9mzZ+vTTz/V//3f/7l6KgAAOBS3oQIAAAAATAiLAAAAAAATbkMFAAAAAJhwZREAAAAAYEJYBAAAAACYEBYBAAAAACaERQAAAACACWERAAAAAGBCWAQAAAAAmPw/n5WULZ9KZoYAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
@@ -333,7 +333,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -343,7 +343,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -353,7 +353,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4oAAAEkCAYAAABpBM9PAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAuQElEQVR4nO3deVxV9b7/8TeTgIA5oBbOegI0xRgdricccizNk+k1iwbFIUuzOJhD6tEGTRRLLfWEnRup2dEoTc+pbmWZN7U0KgeyTJLELATTzWGG9fvDn7uWoG4Q9t7C6/l48Ii91nd/12ftT7B4u9Ze28UwDEMAAAAAAPx/ro4uAAAAAADgXAiKAAAAAAATgiIAAAAAwISgCAAAAAAwISgCAAAAAEwIigAAAAAAE4IiAMBpzJgxQw888ICjy8BV2rt3r4KCgnTq1ClHlwIAqCKCIgDALmbMmKGgoKByX6GhoY4u7ar0799fK1assMu2+vbtq6CgIH3yySem5Vu2bFFQUJD1cWFhoWbOnKnhw4erc+fO6t+/v13qq4zNmzcrJiZG3bp1U2hoqO68805t3brV0WUBAP4/d0cXAACoOyIiIvT888+blrm68m+WleHp6amEhAT16tVLbm5uFY4pLS2Vh4eHRo0apa+++kqpqal2q6+4uNimcXv27FG/fv0UHx+vhg0b6oMPPtATTzwhd3d3DRkypIarBABcCUdnAIDdeHh4qGnTpqavJk2aXPY527dv1x133KEuXbqob9++WrhwofLy8qzrY2JiNGvWLC1btkw9evRQRESEli1bprKyMq1cuVI9e/ZU9+7dtWzZMtO8xcXFWrFihfr27asuXbrotttu08aNG01jgoKCtH79esXHxys0NFS33HKL1qxZY9p2RkaGVq5caT1DeuLEiWp4pS5t5MiRyszM1JtvvnnJMfXr19eCBQs0ZswYtWrV6pLjMjIyNHXqVEVFRalr164aOnSoduzYIUlKSUlRp06dTONPnTqloKAg7d27V9Lvl5h+/PHHuvvuu9WlSxdt2rTJpv1YsmSJHnjgAYWEhKh169YaO3asoqOj9e9//9um5wMAahZnFAEATislJUULFy7U7NmzFR4erlOnTmnBggXKyclRQkKCddx7772n0aNHa8OGDdq/f79mz56tQ4cOKTAwUOvXr9dXX32lGTNmKCwsTNHR0ZKkOXPm6NChQ1qwYIHatGmjAwcOaO7cuXJzc9PIkSOtc7/44ouaNm2apkyZok8//VQLFixQSEiIevTooRUrVujOO+/UwIEDNXbsWElS48aNK9yXrVu3at68eZfd36FDh2rBggWXHdOkSRPFxsZq+fLluv3221W/fn2bXsuLZWVlafTo0QoMDNRLL72kZs2a6bvvvqvSGd5FixZp+vTpuvHGG+Xh4aHjx49XqSaLxaIWLVpU6bkAgOpFUAQA2M3nn39e7j2J3bp10+rVqyscv3LlSj3++OMaPny4JKlVq1aaO3eu7r33Xj355JO67rrrJEktW7ZUfHy8JKldu3b6xz/+oV9++UVJSUmmZXv27FF0dLR++uknvf3229q+fbs6dOhgnfvYsWNat26dKSgOGTJEo0aNkiTdc889WrdunT777DP16NFDDRs2lJubm+rXr6+mTZtedt/79u2rrl27XnaMr6/vZddfMHbsWG3cuFFJSUmaOnWqTc+52Pr16+Xi4qKXXnrJGjZbt25dpbkmTZqkvn37Wh9XJShu2bJFX3/9tWbNmlWlGgAA1YugCACwm5CQED333HOmZV5eXhWOzcnJUWZmphYtWqTFixdblxuGIel8GAkJCZEkBQcHm57r7+8vf39/07KmTZsqOztbknTw4EEZhqG77rrLNKakpKTc+/4unrtZs2Y6ffr0ZfezIr6+vjYHwSvx9vbWo48+qmeeeUajR4+u0hyHDh1SaGholc9I/tGFPlTVBx98oDlz5ujpp5/WTTfddNX1AACuHkERAGA3Xl5eatOmjU1jy8rKJEmzZ89Wt27dyq2//vrrrd+7u5sPZy4uLvLw8LjknBfC5uuvvy5vb+9yz/2ji+dxcXGxPr8yquvS0wvuvPNOJScn64UXXlBUVFSl67mSii5BvdSNaq4mbG7fvl0zZszQU089ZT1zDABwPIIiAMAp+fv764YbblB6err10s/qcuGs1c8//6w+ffpc1VweHh4qLS294rjqvPRUOh/kpk+frgkTJqhRo0Y2P++Cm266SZs2bVJeXl6FQa9x48YqLS3V6dOnrWdnDx8+XOntXM4///lPPfXUU3ruuee40ykAOBmCIgDAboqLi5WVlVVuub+/f7kzeZI0bdo0Pfnkk2rQoIH69esnd3d3HTt2TDt37rT5zFtF2rRpoxEjRmjOnDn661//qtDQUOXn5+vgwYPKycnRhAkTbJ6rZcuW+vLLL3Xy5El5eXmpYcOGFZ6Nq85LTy/o1auXevTooddee63cuqNHj1pf7+LiYqWlpUmSOnTooHr16mnMmDF64403NHnyZE2ZMkXNmjXT0aNH5erqqujoaIWEhMjHx0dLly7VxIkTlZGRoRdffLHaav+f//kfLV68WHPnzlVkZKT1/wsPDw81bNiw2rYDAKgagiIAwG727dunXr16lVu+e/fuCu8WOnz4cPn6+urll1/W6tWr5ebmplatWlXLB8g/9dRTeuWVV7R69WqdOHFCPj4+uvHGG3XPPfdUap4pU6Zo7ty5GjRokAoLC/Xhhx+qZcuWV12fraZPn17hJZsTJkxQZmam9fGFMRfqa9asmTZs2KAlS5ZowoQJKikpUZs2bRQXFydJatiwoRITE/Xcc89p2LBh6tSpk+Lj4xUbG1stdScnJ6u0tFTz5s0zXZIbFRVVYfAFANiXi1GVN1oAAAAAAGqtyn9YEgAAAACgViMoAgAAAABMCIoAAAAAABOCIgAAAADAhKAIAAAAADDh4zEkZWfnqqyMm78CAAAAqFuaNvWrcDlnFAEAAAAAJgRFAAAAAIAJQREAAAAAYEJQBAAAAACYEBQBAAAAACYERQAAAACACUERAAAAAGBCUAQAAAAAmBAUAQAAAAAm7o4u4Fri18BLXp4eji6jVigoLJblXIGjywAAAABQAYJiJXh5emjM9PWOLqNW2LD4HllEUAQAAACcEUERAACgGlzXoJ7qeXo6uoxaoaiwUGfPFTm6DKBOIygCAABUg3qenkqcOdHRZdQKjy9cI4mgCDgSQREAgGvAdQ28Vc+Tw3Z1KSos0dlz+Y4uAwCcFkccAACuAfU83fXs7M2OLqPWmPXMXY4uAQCcGh+PAQAAAAAwISgCAAAAAEwIigAAAAAAE4IiAAAAAMCEoAgAAAAAMCEoAgAAAABMCIoAAAAAABOCIgAAAADAhKAIAAAAADAhKAIAAAAATNwdXQBQXRpdV0/u9TwdXUatUVJUqDNnixxdBgAAAByAoIhaw72ep/YvjnV0GbVG+PQkSQRFAACAuohLTwEAAAAAJgRFAAAAAIAJQREAAAAAYEJQBAAAAACYEBQBAAAAACZ2DYplZWVKTExUz549FRoaqnHjxikzM7PCsQUFBZo6daoGDBig4OBgrVixotyYFStWqGPHjgoNDbV+Pf744zW9GwAAAABQq9k1KCYlJWnbtm1at26ddu3apYCAAE2aNEllZWXlxrq4uCgsLEwLFixQSEjIJeeMiIhQamqq9SsxMbEmdwEAAAAAaj27BsWNGzcqNjZW7du3l4+Pj+Lj45Wenq79+/eXG+vp6akHHnhA3bt3l6cnH6IOAAAAAPbibq8NWSwWZWZmqnPnztZlDRo0UJs2bZSWlqbIyMgqzXvw4EF1795d3t7eCgsL07Rp09SqVavqKhsAAAC1QKPrvOVez25/+tZ6JUUlOnM239FloAbZ7aclNzdX0vlw+Ed+fn7WdZU1cOBA3XnnnQoICNCvv/6qpUuX6sEHH9SWLVvk4+Nj8zxNmvhWafu4Ok2b+jm6BFwBPQJQm/E7zrnVRH++funjap+zruo6uXe196ikpETu7oT56lAdr6XdOuHrez6MWSwW03KLxWJdV1mBgYHW75s3b65nnnnG+p7FXr162TxPdnauysqMK47jgFK9srIsVx5UCfSn+lV3jwBUHb/jqh/HIedGf5xfTfRo6dKl1TpnXRUXF2dzfy71s2G39yj6+fmpRYsWOnjwoHWZxWJRRkaGOnbsWC3bcHFxkYuLiwzjyqEPAAAAAFAxu97MZvTo0Vq7dq3S09OVl5enhIQEtW3bVuHh4RWOLyoqUmFhocrKylRSUqLCwkIVFRVZ1//rX/9STk6OJCk7O1tz5sxR48aNFRoaapf9AQAAAIDayK5BMTY2VoMHD9aYMWPUs2dPZWZmatWqVXJ1ddW+ffsUGhqqkydPWscPGjRIISEh2rdvn1avXq2QkBCNGzfOun7r1q0aMmSIunbtquHDh6uwsFD/+Mc/qnwpKwAAAADAju9RlCRXV1fFxcUpLi6u3LoL7y38o48++uiy861evbpa6wMAAAAA2PmMIgAAAADA+REUAQAAAAAmBEUAAAAAgAlBEQAAAABgQlAEAAAAAJgQFAEAAAAAJgRFAAAAAIAJQREAAAAAYEJQBAAAAACYEBQBAAAAACYERQAAAACACUERAAAAAGDi7ugCANQdDa7zlGe9eo4uo1YoLCrSubOFji4DAADUUgRFAHbjWa+eHvjHo44uo1b4nwdfkERQBAAANYOgCACQJDX0qycPL09Hl1ErFBcU6jdLkaPLAACgygiKAABJkoeXp/5134OOLqNWGJL8D4mgCAC4hnEzGwAAAACACUERAAAAAGBCUAQAAAAAmBAUAQAAAAAmBEUAAAAAgAlBEQAAAABgQlAEAAAAAJgQFAEAAAAAJgRFAAAAAIAJQREAAAAAYEJQBAAAAACYEBQBAAAAACYERQAAAACACUERAAAAAGBCUAQAAAAAmBAUAQAAAAAmBEUAAAAAgAlBEQAAAABgQlAEAAAAAJgQFAEAAAAAJgRFAAAAAIAJQREAAAAAYEJQBAAAAACYEBQBAAAAACYERQAAAACACUERAAAAAGBCUAQAAAAAmNgcFIuKivThhx/qlVdekcVikSSdOHFC586dq7HiAAAAAAD2527LoJMnT2rs2LE6deqUioqK1L9/f/n5+enVV19VYWGhFixYUNN1AgAAAADsxKYzigsXLlRwcLA+//xzeXp6Wpf369dPe/furbHiAAAAAAD2Z1NQ3Ldvnx5++GHVq1fPtLxly5b65ZdfbN5YWVmZEhMT1bNnT4WGhmrcuHHKzMyscGxBQYGmTp2qAQMGKDg4WCtWrLiq+QAAAAAAtrEpKBYUFMjDw6Pc8pycHNMZxitJSkrStm3btG7dOu3atUsBAQGaNGmSysrKyo11cXFRWFiYFixYoJCQkKueDwAAAABgG5uCYlhYmLZt21ZueXJysiIiImze2MaNGxUbG6v27dvLx8dH8fHxSk9P1/79+8uN9fT01AMPPKDu3btfMoxWZj4AAAAAgG1supnNY489ppiYGKWnp6u0tFR///vfdeTIEf3www/auHGjTRuyWCzKzMxU586drcsaNGigNm3aKC0tTZGRkZUqvLrnAwAAAACcZ1NQ7Ny5szZt2qS1a9eqdevW2r9/vzp37qyFCxeqQ4cONm0oNzdX0vkw90d+fn7WdZVRnfM1aeJb6e3j6jVt6ufoEnAF9Mi50R/nRn+cHz1ybvTH+dEj53a1/bEpKErSn/70Jy1cuLDKG/L1PR/GLnwG4wUWi8W6zlHzZWfnqqzMuOI4fhiqV1aW5cqDKoH+VD965Nzoj3OjP86PHjk3+uP86JFzs7U/l3rdbQ6KkpSfn6/s7GwZhjlUtWrV6orP9fPzU4sWLXTw4EF16dJF0vlQl5GRoY4dO1amjBqZDwAAAABwnk1B8YcfftCsWbP0zTffmJYbhiEXFxelpaXZtLHRo0dr7dq16t69u5o3b66EhAS1bdtW4eHhFY4vKiqSYRgqKytTSUmJCgsL5eLiYv2YjsrOBwAAAAC4MpuC4owZM+Th4aEVK1bI399fLi4uVdpYbGysLBaLxowZo/z8fIWHh2vVqlVydXXVvn37NH78eG3fvl0BAQGSpEGDBlk/F3Hfvn1avXq1oqKi9Nprr11xPgAAAABA1dgUFL///nulpKSoffv2V7UxV1dXxcXFKS4urty6iIgIpaammpZ99NFHVZ4PAAAAAFA1Np1669Spk7Kysmq6FgAAAACAE7DpjOKCBQs0f/58WSwWBQYGyt3d/LQLl4oCAAAAAK59Nt/19OzZs3rkkUdM70+s7M1sAAAAAADOz6agOH36dNWvX1/PP//8Vd3MBgAAAADg/Gz+eIyUlBR16NChpusBAAAAADiYTTez6dixo7Kzs2u6FgAAAACAE7DpjOJDDz2kRYsW6ZFHHlFQUFC5m9k0b968RooDAAAAANifTUFx4sSJkqTJkydzMxsAAAAAqOVsCorJyck1XQcAAAAAwEnYFBSjoqJqug4AAAAAgJO4ZFD85ZdfrO89/OWXXy47Ce9RBAAAAIDa45JBsXfv3tq1a5eaNGmi6OjoCj87kfcoAgAAAEDtc8mg+Oqrr+q6666TxHsUAQAAAKAuuWRQjIqKUr9+/bR582beowgAAAAAdYjr5VZmZmaqrKzMXrUAAAAAAJzAZYMiAAAAAKDuueLHY2RlZamkpOSyY7jrKQAAAADUHlcMin/5y18uuY67ngIAAABA7XPFoLh8+XLr3U8BAAAAALXfFYNiWFiYmjRpYo9aAAAAAABO4LI3s3FxcbFXHQAAAAAAJ3HZoGgYhr3qAAAAAAA4icteevrtt9/aqw4AAAAAgJPgcxQBAAAAACYERQAAAACACUERAAAAAGBCUAQAAAAAmBAUAQAAAAAmBEUAAAAAgAlBEQAAAABgQlAEAAAAAJgQFAEAAAAAJgRFAAAAAIAJQREAAAAAYEJQBAAAAACYEBQBAAAAACYERQAAAACACUERAAAAAGBCUAQAAAAAmBAUAQAAAAAmBEUAAAAAgAlBEQAAAABgQlAEAAAAAJgQFAEAAAAAJgRFAAAAAIAJQREAAAAAYGLXoFhWVqbExET17NlToaGhGjdunDIzMy85/vDhwxo9erS6du2q3r17Kzk52bR+xowZuummmxQaGmr9SkhIqOndAAAAAIBaza5BMSkpSdu2bdO6deu0a9cuBQQEaNKkSSorKys3Njc3V7GxserVq5c+//xzPf/881q5cqXeffdd07ihQ4cqNTXV+hUfH2+v3QEAAACAWsmuQXHjxo2KjY1V+/bt5ePjo/j4eKWnp2v//v3lxr7//vtydXXV5MmT5enpqZtvvlkjR47Uhg0b7FkyAAAAANQ5dguKFotFmZmZ6ty5s3VZgwYN1KZNG6WlpZUb/+2336pTp05ydf29xM6dO+vbb781jfvggw/UrVs33XrrrZo7d65ycnJqbicAAAAAoA5wt9eGcnNzJZ0Ph3/k5+dnXXfxeD8/P9OyBg0amMbee++9iouLk7+/v3766SfNnz9fDz30kDZu3CgXFxeba2vSxLcyu4Jq0rSp35UHwaHokXOjP86N/jg/euTc6I/zo0fO7Wr7Y7eg6Ot7PoxZLBbTcovFYl138fjs7GzTsnPnzpnG/vHsZOvWrfXMM88oOjpaP/74o9q1a2dzbdnZuSorM644jh+G6pWVZbnyoEqgP9WPHjk3+uPc6I/zo0fOjf44P3rk3Gztz6Ved7tdeurn56cWLVro4MGD1mUWi0UZGRnq2LFjufHBwcE6fPiw6UY3hw4dUnBw8CW3ceEsomFcOfQBAAAAACpm15vZjB49WmvXrlV6erry8vKUkJCgtm3bKjw8vNzYAQMGqLS0VKtWrVJRUZG++eYbbdq0SXfffbckqbCwUO+++671DGVmZqbmzJmjm266SW3btrXnbgEAAABArWLXoBgbG6vBgwdrzJgx6tmzpzIzM7Vq1Sq5urpq3759Cg0N1cmTJyWdv/Q0KSlJO3fuVEREhKZMmaKHH35YgwcPlnT+MxmTk5PVr18/3Xzzzbr33nt1/fXXa82aNaYb4AAAAAAAKsdu71GUJFdXV8XFxSkuLq7cuoiICKWmppqWderUSW+88UaFc3l7e/NRGQAAAABQAzj1BgAAAAAwISgCAAAAAEwIigAAAAAAE4IiAAAAAMCEoAgAAAAAMCEoAgAAAABMCIoAAAAAABOCIgAAAADAhKAIAAAAADAhKAIAAAAATAiKAAAAAAATgiIAAAAAwISgCAAAAAAwISgCAAAAAEwIigAAAAAAE4IiAAAAAMCEoAgAAAAAMCEoAgAAAABMCIoAAAAAABOCIgAAAADAhKAIAAAAADAhKAIAAAAATAiKAAAAAAATgiIAAAAAwISgCAAAAAAwISgCAAAAAEwIigAAAAAAE4IiAAAAAMCEoAgAAAAAMCEoAgAAAABMCIoAAAAAABOCIgAAAADAhKAIAAAAADAhKAIAAAAATAiKAAAAAAATgiIAAAAAwISgCAAAAAAwISgCAAAAAEwIigAAAAAAE4IiAAAAAMCEoAgAAAAAMCEoAgAAAABMCIoAAAAAABOCIgAAAADAhKAIAAAAADAhKAIAAAAATOwaFMvKypSYmKiePXsqNDRU48aNU2Zm5iXHHz58WKNHj1bXrl3Vu3dvJScnm9YXFBRo7ty5ioqKUlhYmKZNm6bffvuthvcCAAAAAGo3uwbFpKQkbdu2TevWrdOuXbsUEBCgSZMmqaysrNzY3NxcxcbGqlevXvr888/1/PPPa+XKlXr33XetY5599lkdPHhQ77zzjnbs2KG8vDw98cQT9twlAAAAAKh17BoUN27cqNjYWLVv314+Pj6Kj49Xenq69u/fX27s+++/L1dXV02ePFmenp66+eabNXLkSG3YsEHS+bOJb7/9th599FE1b95c1113nZ544gl9/PHHOnnypD13CwAAAABqFXd7bchisSgzM1OdO3e2LmvQoIHatGmjtLQ0RUZGmsZ/++236tSpk1xdf8+ynTt31qZNmyRJP/74owoLC9WlSxfr+g4dOsjb21tpaWkKCAiwuTZXVxebx/o38rF5LC6vMq+7reo1aFLtc9ZlNdEjf9/G1T5nXVUT/fH252eoutREf65rWL/a56zLaqJHDRryM1RdaqI/Hn5e1T5nXVYjP0MNGlT7nHXV1fbHbkExNzdXUvnm+/n5WdddPN7Pz8+0rEGDBtaxF/578ZhLzXc5jSoR/pbPHF6puXFpTZr4VvucXSY9V+1z1mU10aMlI+dV+5x1VU30p0/ikmqfs66qif48HD+k2uesy2qiR7FPPFvtc9ZVNdGfTjHdq33OuqwmejR+/Phqn7Ouutr+2O3SU1/f84VaLBbTcovFYl138fiLA9+5c+esYys7HwAAAADANnYLin5+fmrRooUOHjxoXWaxWJSRkaGOHTuWGx8cHKzDhw+bbnRz6NAhBQcHS5Latm0rT09P03w//PCD8vPzrWMAAAAAAJVn15vZjB49WmvXrlV6erry8vKUkJCgtm3bKjw8vNzYAQMGqLS0VKtWrVJRUZG++eYbbdq0SXfffbckycvLS8OHD9fy5cv166+/6uzZs0pISFB0dLRatGhhz90CAAAAgFrFxTAMw14bKysr07Jly7R582bl5+crPDxc8+fPV8uWLbVv3z6NHz9e27dvt96I5vDhw5o/f77S0tLUqFEjjRs3Tvfdd591voKCAj3zzDN69913VVpaqj//+c+aP3++GjZsaK9dAgAAAIBax65BEQAAAADg/Ox66SkAAAAAwPkRFAEAAAAAJgRFAAAAAIAJQREAAAAAYEJQBAA4tYMHD+qOO+5QaGioZsyY4ehyAAB1TF09Drk7ugBU3fHjx7V8+XLt3r1bubm5atq0qQYOHKiHH35YPj4+1nGzZ8/WV199pfT0dA0bNkyLFi1yYNV1hy39SU9P17Jly5Samqrc3FzdcMMNuv/++/Xf//3fDq6+brClR/n5+ZowYYJ++OEHFRQUyM/PTwMHDlRcXJw8PT0dvAfXNlt/hyUmJioqKkpbtmy56m2mpKRo5cqV+uijj656LnAccnYch5wfxyHH4jh0eZxRvEZ9//33GjFihDw8PLRp0yalpqZqxYoV+uKLLxQTE6P8/Hzr2KCgIM2YMUN9+/Z1YMV1i639OXfunLp166bNmzfryy+/1Pz587V48WK99957Dt6D2s/WHnl4eGj27Nn6+OOP9eWXX2rz5s06fPiwXnjhBQfvwbWtMr/DfvrpJ3Xs2NGB1aIiHIecG8ch58dxyLE4Dl0Zn6N4jRo7dqwKCgq0YcMG0/KcnBwNGDBAEyZM0IQJE0zrLpwq519ya15V+nPB1KlT1axZMz355JP2KLXOqmqPsrKy9Pjjj8vX11erVq2yV7m1ji2v/7hx4xQREaH8/Hx5eHjI3d1dixYtUrt27fT000/ryJEjKi0tVceOHTVr1izrQfzkyZOaN2+evv76a5WWluqGG27Q3/72N0nSgw8+qOLiYnl7e0uSEhISdOutt9p132sLjkPOjeOQ8+M45Fgch66MM4rXoIKCAu3Zs0d/+ctfyq1r3LixoqOjtWPHDgdUBunq+pOXl6evv/5aQUFBNV1mnVaVHsXFxenmm29Wr169dOTIEY0bN85e5dY6tr7+bm5uSk1NVUBAgObPn6/U1FQNHDhQkvTQQw9p586d2rlzp9q1a6eHH35YxcXFkqSlS5eqefPm+vTTT/XFF19o+fLlat68uSIiIjR//nwFBAQoNTVVqampTntwdnYch5wbxyHnx3HIsTgO2YageA06e/asSktL1axZswrXX3/99crJybFzVbigqv0pKSnRX//6V7Vo0ULDhw+v4Srrtqr0aOnSpUpNTdWWLVs0evRoBQQE2KPUWulqf4cFBgaqR48e8vT0VP369RUXF6fMzExlZGRIkurVq6fTp08rIyNDLi4uat++vVq1alUj+1JXcRxybhyHnB/HIcfiOGQbguI1YOvWrQoNDbV+lZSUyM3NTb/++muF40+dOqXGjRvbucq6qzr6U1RUpMcee0w5OTlas2aNPDw87FF6nVFdP0MuLi4KDg5WcHCwHn300Zouu9ao7t9hJ06c0NSpUxUdHa2wsDD169dPkpSdnS1Jmj59ulq3bq1HHnlEPXv21KxZswgtV4njkHPjOOT8OA45FsehqiEoXgOGDRtmPT2dmpqqFi1aqFu3bhXeeenMmTPauXOnevfubf9C66ir7U9BQYEmT56s3377TWvXrpWfn58dq68bqvtnqKSkROnp6TVYce1S3a//3Llz5eHhobfeektffvmlPvzwQ0nShbfcN2rUSLNmzdJ7772nt99+Wz/99JOee+45SZKrK4e9quA45Nw4Djk/jkOOxXGoaq6dSmEyY8YMpaWladasWfr5559VWlqqtLQ0TZo0SQEBAYqJibGOLSoqUmFhoUpLS1VaWqrCwkIVFRU5sPraz9b+5Obmavz48TIMQy+//LLpVsyoWbb26JtvvtGnn36qvLw8lZWV6eDBg3rxxRcVHR3t4D24tlXmd9jFLBaLvL295efnJ4vFooSEBNP67du3KyMjQ2VlZfLx8VG9evWsB2Z/f3/l5OTo7NmzNbp/dQHHIefGccj5cRxyLI5DV8ZdT69hx44d08qVK/XZZ58pLy9P/v7+1s9+8fX1tY6LiYnR559/bnpuixYtronPb7mW2dKft956SzNmzJCXl5fpX5jCw8OVlJTkqNLrDFt6lJqaqmeeeUbHjh2TYRjy9/dX//79y33GEirP1t9hffv21SOPPKI777xTkvTVV19p3rx5On78uJo1a6Zp06bpscceU3Jysrp166alS5dq27ZtOnPmjLy8vNSjRw/NmTNHjRs3VklJieLi4rRnzx6Vlpbqueees14yhMrjOOTcOA45P45DjsVx6PIIigAAAAAAEy49BQAAAACYEBQBAAAAACYERQAAAACACUERAAAAAGBCULwGpKSkqG/fvo4u44pCQ0O1b98+R5fhEPTIudEf50ePAABwLgRFVNrevXsVFBRUbnlqaqoiIiIcUBEuRo+cG/1xfvQIAFDXERSdQHFxsaNLwBXQI+dGf5wfPQIA4NpCULyCoKAgJScn66677tLNN9+sUaNG6eTJk0pOTlbv3r0VFRWlJUuWWMcXFhZq6tSp6tWrl0JDQ3X77bfrX//6V7k5X331VY0cOVJdu3bVrl27lJ+fr6VLl6p///4KDQ3VgAED9N5775me9/rrr6tPnz4KDw/XtGnTlJubW6V96tu3r1atWqXY2Fjrtj744APr+u+++0733XefunXrpoiICMXExCgtLU2SdPLkSY0fP17S+UuwQkNDlZycbN2vvXv3qrS0VL169dK2bdtM212+fLnuuece6+NPPvlEI0eOVGRkpAYMGGCdp7LokXP3iP44d38ubJceOXePAACwOwOXFRgYaIwYMcI4efKkkZeXZ8TExBgDBgwwEhMTjcLCQuPQoUPGTTfdZOzdu9cwDMPIz883UlJSjHPnzhnFxcVGSkqK0alTJ+Po0aOmOQcPHmwcPXrUKCsrM/Lz843HHnvMGDlypHHs2DHDMAzj5MmTRlpammEYhvHmm28anTp1MhYtWmTk5+cbp06dMvr162esXLnSOueECROM8PDwS36tWbPGOrZPnz5Gnz59jEOHDhmlpaXGyy+/bISFhRm5ubmGYRjGkSNHjM8++8woKCgw/vOf/xhz5swx+vTpYxQVFRmGYRh79uwxAgMDK3yt9uzZYxiGYSxZssR44IEHrOtKS0uN3r17G2+99ZZhGIaxe/duIyIiwvjss8+M0tJS48iRI8Ytt9xibNmyhR7Vsh7RH+fuDz26NnoEAIC9ERSvIDAw0HjnnXesj9etW2d07drVKCkpsS4bPny4sXbt2kvOMXToUGP9+vWmOf/5z39aH2dnZxuBgYHG4cOHK3z+m2++aYSEhBjFxcXWZYsWLTImTpxYpX3q06ePsWrVKuvj3NxcIzAw0Dhw4ECF43/77TcjMDDQ+kegLX9ApaenG8HBwcZPP/1kGIZh7Ny50wgPDzfy8vIMwzCMiRMnGsuWLTM9f82aNcb9999f6f2hR87dI/rj3P25sF165Nw9AgDA3twdfUbTmWzdulXz5s2zPt6+fbskqWnTptZl3t7eaty4sdzc3KzLvLy89J///EeSVFRUpCVLlmjHjh3Kzs6Wq6ur8vLylJOTY9pWy5Ytrd+fOHFCktSuXbtL1takSRO5u//ervr161u3WRXNmjWzfu/j4yNJ1vlOnDihxYsX6+uvv5bFYpGr6/krlLOzs9WhQweb5m/btq0iIiKUkpKiqVOnavPmzbrtttvk7e0tSTp+/Lh2796tdevWWZ9TWlqqgICAy85Lj5y7R/THufsj0aNroUcAADgDguIfDBs2TMOGDbuqOV555RXt2rVLSUlJat26tVxcXDRs2DAZhmEad+GPEun3P6Z+/PFHBQcHV2m7sbGx2r9//yXXT5w4UZMmTbJprrlz56pRo0Z666231LhxY509e1ZRUVHWffhj7ZczYsQIvfDCC7r33nv14Ycf6vXXX7eu8/f31x133GFzTRfQo/OctUf05zxn7Y9Ejy5w5h4BAOAMCIrVzGKxqF69emrUqJFKSkq0adMmHT16VP3797/kcxo3bqzbb79df/vb37Ro0SK1bdtWp06d0m+//WbzH1RJSUnVtQuyWCwKCAiQn5+fLBaLEhISTOv9/f0lSceOHVP79u0vOc+gQYP09NNPa+bMmerQoYO6dOliXXf//fdr7ty56tq1qyIjI63znT171vq4ptCj3zljj+jP75yxPxI9+iNn7REAAFeLu55Ws7Fjx6pJkyaKjo5Wnz599MsvvygsLOyKz3vqqacUFhamcePGKTQ0VPfdd58yMjLsUHF5s2fP1oEDBxQZGakRI0aoZ8+epvXt2rVTTEyMxowZo4iICNNlVX/k5eWl22+/XR9//LHuuusu07pbb71VixYt0gsvvKCePXuqZ8+eevLJJ3XmzJka268L6NHvnLFH9Od3ztgfiR79kbP2CACAq+ViXHytEAAAAACgTuOMIgAAAADAhKAIAAAAADAhKAIAAAAATAiKAAAAAAATgiIAAAAAwISgCAAAAAAwISgCAOAgMTExmj17tqPLAACgHD5HEQCASzh9+rRWrVqlHTt26Ndff5Wvr68iIyM1efJkdezY0eZ5XnrpJW3evFkfffSRaflvv/0md3d3+fr6VnfpAABcFc4oAgBQgZ9//lkjRoxQamqq5s2bp/fff19///vf5eHhoVGjRmnnzp1XvY2GDRsSEgEATomgCABABebPn6+SkhIlJycrOjpaAQEBCgkJUWJiorp3766ZM2eqoKBAK1asUP/+/fXOO++oX79+6tKlix588EGdOHFCkpSSkqIXXnhBmZmZCgoKUlBQkFasWCGp/KWnxcXFWrJkif785z+rc+fOGjJkiN555x1TXUFBQVq/fr3i4+MVGhqqW265RWvWrDGN+eCDDzR8+HB17dpVERERuuuuu3T48OEafsUAALUJQREAgIucPXtWn3zyie65554Kz/hNnDhRp0+f1v/93/9JkrKysrRhwwY9//zzWr9+vXJzczVlyhQZhqEhQ4Zo/Pjxuv7667Vr1y7t2rVLY8eOrXC7iYmJ2rRpk2bNmqV33nlHw4YNU3x8vHbv3m0a9+KLLyoyMlJbtmzRxIkTlZiYaB2TlZWladOm6bbbbtO2bdv0xhtv6P7775ebm1s1v0oAgNrM3dEFAADgbI4fP66ysjLdeOONFa7/05/+JElKT0+XJOXn52vRokVq06aNJGnx4sUaNGiQ9uzZox49eqh+/fpyc3NT06ZNL7nN/Px8vfbaa5o5c6YGDx4sSZo0aZIOHDigVatWqUePHtaxQ4YM0ahRoyRJ99xzj9atW6fPPvtMPXr0UFZWloqLizV48GC1bNlSktShQ4erfEUAAHUNZxQBALhKjRs3toZESWrXrp0aNWqk77//3uY5jh8/ruLiYkVGRpqWR0ZG6ujRo6ZlwcHBpsfNmjXT6dOnJZ2/NLVXr14aOnSoHn74Yb366qv6+eefK7tLAIA6jqAIAMBFWrduLRcXF3333XcVrr8Q3Nq1a2fPsqw8PDxMj11cXHThJuZubm5KSkrSq6++qi5duuj999/XwIEDtWPHDkeUCgC4RhEUAQC4SMOGDRUdHW19v+HF1qxZI39/f/3Xf/2XJCknJ0cZGRnW9enp6Tpz5oz1ElUPDw+VlpZedptt2rRRvXr19MUXX5iWf/HFF5e8BPZSXFxcFBISokmTJmn9+vWKjIxUSkpKpeYAANRtBEUAACowd+5cubm56b777tPOnTv1888/65tvvlFcXJz27NmjhQsXysvLS5Lk7e2tmTNn6sCBAzpw4IBmzJihjh07Wt9X2LJlS50+fVqpqanKyclRfn5+ue15e3srJiZGy5cv17///W+lp6dr9erV+vDDDzVp0iSb6/7yyy/14osv6uuvv9bJkye1e/duHTlyhPcpAgAqhZvZAABQgRYtWiglJUUvvfSS5s2bp6ysLPn4+CgqKkpvvPGGOnXqZB3btGlTjRo1So8++qiysrIUFhamJUuWyMXFRZJ06623atCgQZo4caLOnj2rRx55RFOmTCm3zccee0yurq569tlndebMGbVu3VoJCQmmG9lciZ+fn7766itt2LBBZ8+eVdOmTTV06FBNnjz56l8UAECd4WJceFMDAACotBUrVmjr1q363//9X0eXAgBAteHSUwAAAACACUERAAAAAGDCpacAAAAAABPOKAIAAAAATAiKAAAAAAATgiIAAAAAwISgCAAAAAAwISgCAAAAAEwIigAAAAAAk/8HbA1F9rhEcJIAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
@@ -363,7 +363,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -373,7 +373,7 @@
},
{
"data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5MAAAEkCAYAAACyv2VtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAA70UlEQVR4nO3de1xUdf7H8TejXFQGr2iBF9JSYBFEUFfW8lJqZpmt0aJG7Spe0kyLdb2VpW7mipmpRRb2K9ZcS1NL2zW3slw3zTRKEbBSyoRKwsQhkOv5/eHDWUe8cASGCV7Px8OH+T2f+Z7v4bPj8b3nzBk3wzAMAQAAAABggqW2FwAAAAAA+PUhTAIAAAAATCNMAgAAAABMI0wCAAAAAEwjTAIAAAAATCNMAgAAAABMI0wCAOqFmTNn6o9//GNtLwMAgDqjYW0vAACAqpo5c6Y2bdpUYbxx48ZKSUmphRVVj4EDB2rYsGGaMmVKje9rwIABysrKkiR5eHjommuu0W233abJkydr69atmjVr1mVf/+CDD150nV999ZVWrFihjIwMHTt2TCNGjNCTTz5ZI8cAAHAuwiQAoE6IjIzUsmXLHMYsFm7AMWPcuHG6//77VVJSoi+++EJz5syRYRiaNGmSbrzxRnvdk08+qZycHIefd+PGjS86Z2Fhofz8/DRgwAC98sorNXwEAABnIkwCAOoEd3d3+fr6mnrNO++8oxdffFFHjx6Vr6+vBg4cqKlTp9qDUWxsrNq1aydfX1+98cYbKikp0ejRozV16lQ9//zzWrt2rcrLy/WHP/xBDz/8sH3ekpISvfDCC9q0aZNycnLUvn17xcbGKiYmxl7TpUsXzZ07V59//rnee+89Wa1WjR49WhMmTLDv+9ixY1q5cqVWrlwpSXr//ffVtm3bqv6oLqlx48b2n6Gfn5/++c9/ateuXXrkkUfk5eVlr/Py8qr0zzs0NFShoaGSpDfffLNmFg4AqBWESQBAvbRx40Y99dRTmjNnjiIiIvTDDz9o/vz5OnnypBISEux17777rmJiYrR27Vrt379fc+bM0aFDh9S5c2e99tpr+vzzzzVz5kx1795dffv2lSQ99thjOnTokObPn68OHTro4MGDmjt3rho0aKDo6Gj73M8995ymTZumKVOm6D//+Y/mz5+v0NBQ9e7dWytWrNDvf/97DR48WGPGjJEktWjR4qLH8vbbb+vxxx+/7PHecccdmj9/fqV/Pmlpadq/f7/atWtX6dcAAOoXwiQAoE7Yu3evwsPDHcZ69eqlF1544aL1K1eu1COPPKLhw4dLktq1a6e5c+fq3nvv1aOPPqqmTZtKktq2bavp06dLkq677jr93//9n3788UclJSU5jO3Zs0d9+/bVd999p82bN+udd95Rp06d7HMfPXpUa9ascQiTt912m+655x5J0ujRo7VmzRp9/PHH6t27t5o1a6YGDRo4XC28lAEDBigsLOyyNd7e3pfdLknPP/+8XnrpJZWUlKikpEQNGjQwFUABAPULYRIAUCeEhobqb3/7m8PY+bdmnu/kyZPKysrSokWLtHjxYvu4YRiSpG+//dZ+a2ZgYKDDa1u1aqVWrVo5jPn6+io3N1eSlJqaKsMwdPfddzvUlJaWqkGDBg5jF87dunVr/fTTT5c9zovx9vauVFi8ktGjR2vUqFHKy8vTCy+8oLZt2+qWW26p8rwAgLqJMAkAqBO8vLzUoUOHStWWl5dLkubMmaNevXpV2H7NNdfY/7thQ8dTpZubm9zd3S8557lA+o9//EONGjWq8NrzXTiPm5ub/fVmVNdtrk2bNrX/DJctW6YhQ4YoJCREw4YNM70mAEDdR5gEANQ7rVq10rXXXqvMzEz7babV5Te/+Y0k6fvvv1f//v2rNJe7u7vKysquWFddt7mez8PDQxMnTtTixYs1cODACsEYAADCJACgTigpKVFOTk6F8VatWlW4IihJ06ZN06OPPiofHx/dfPPNatiwoY4ePaqdO3dW6XOCHTp00IgRI/TYY4/pz3/+s8LDw1VYWKjU1FSdPHlS48ePr/Rcbdu21Weffabs7Gx5eXmpWbNmF/26k+q6zfVCd955p5599lklJyfbnzJrVnFxsY4cOSJJ+uWXX5SXl6f09HS5u7vr+uuvr87lAgCcjDAJAKgT9u3bpz59+lQY371790Wfgjp8+HB5e3vrpZde0gsvvKAGDRqoXbt2GjhwYJXXsmDBAr388st64YUXdPz4cTVp0kQ33HCDRo8ebWqeKVOmaO7cubr11ltVVFRU418NciEPDw/de++9SkpKUkxMjP2hRGacOHHC/pAjSTp06JD+/e9/y9/fXx988EE1rhYA4GxuxtV8OAMAAAAAUK9VvFcGAAAAAIArIEwCAAAAAEwjTAIAAAAATCNMAgAAAABMI0wCAAAAAEzjq0EqKTc3X+XlPPgWAAAAQP3h62u95DauTAIAAAAATCNMAgAAAABMI0wCAAAAAExzapgsLy/X0qVLFRUVpfDwcI0dO1ZZWVmXrE9LS1NMTIzCwsLUr18/JScnO2xPTk5WdHS0wsLCNGDAgAqv/+ijj3TfffepV69e6tGjh0aOHKm9e/dW+3EBAAAAQH3j1DCZlJSkrVu3as2aNdq1a5f8/Pw0ceJElZeXV6jNz89XXFyc+vTpo71792rZsmVauXKltm3bZq9p3bq14uLiNHHixIvuLy8vT7GxsXrvvfe0e/duDR06VOPHj9f3339fY8cIAAAAAPWBU8PkunXrFBcXp44dO6pJkyaaPn26MjMztX///gq127dvl8Vi0aRJk+Tp6alu3bopOjpaa9eutdfceuutGjx4sNq0aXPR/Q0bNkwDBw6U1WpVw4YNde+998rT01MHDx6ssWMEAAAAgPrAaWHSZrMpKytLISEh9jEfHx916NBB6enpFeozMjIUHBwsi+V/SwwJCVFGRsZVryE9PV02m02dO3e+6jkAAAAAAE78nsn8/HxJZwPk+axWq33bhfVWq+N3mvj4+Fy0tjJycnL00EMPacyYMQoICDD9+pYtva9qvwAAAABQFzktTHp7nw1jNpvNYdxms9m3XVifm5vrMHb69OmL1l7Jjz/+qPvvv1833nij4uPjTb9eknJz81VeblzVawEAAADg18jX13rJbU4Lk1arVf7+/kpNTVXXrl0lnQ2Sx44dU1BQUIX6wMBA/etf/1J5ebn9VtdDhw4pMDDQ1H6/++47/fGPf9SgQYM0Y8aMqh/IZVh9vOTl6V6j+6hPzhSVyHb6TG0vAwAAAMBFOC1MSlJMTIxWr16t3/72t2rTpo0SEhIUEBCgiIiICrWDBg3SkiVLlJiYqHHjxikjI0Pr16/XE088Ya8pLS1VWVmZSktLZRiGioqKJEkeHh5yc3PTkSNH9Kc//UkjRozQ1KlTa/z4vDzdNeovr9X4fuqLtYtHyybCJAAAAOCKnPo017i4OA0ZMkSjRo1SVFSUsrKylJiYKIvFon379ik8PFzZ2dmSzt7mmpSUpJ07dyoyMlJTpkzR5MmTNWTIEPt8iYmJCg0N1WOPPabs7GyFhoYqNDTU/t2VSUlJ+vHHH/XKK68oPDzc/uuFF15w5mEDAAAAQJ3jZhgGHwSshMp8ZtLX18qVyWq0dvFo5eTYrlwIAAAAoEZc7jOTTr0yCQAAAACoGwiTAAAAAADTCJMAAAAAANMIkwAAAAAA0wiTAAAAAADTCJMAAAAAANMIkwAAAAAA0xrW9gIAZ2re1EMNPTxrexl1QmlxkX7OK67tZQAAAKCWECZRrzT08NT+xXG1vYw6IeIvSZIIkwAAAPUVt7kCAAAAAEwjTAIAAAAATOM2VwAAACdo6uMhD08+t19diouKlHeaj1sAtYkwCQAA4AQenp5aOmtCbS+jznjkqVXis/tA7SJMAgAAAJKaN22khh7887g6lBaX6ue8wtpeBmoY7xYAAOqIpj6N5OHJqb26FBeVKu80/xiuTxp6NNQXz39Y28uoE8Im9avtJcAJOOMAcBk+TT3l6eFR28uoM4qKi3U6r6i2lwEn8vBsqIVzNtT2MuqM2U/eXdtLAACXRpgE4DI8PTz0x/+bWtvLqDNe+dOzkgiTAACgZvDVIAAAAAAA0wiTAAAAAADTuM0VAAAAgMtr2tRLHh7utb2MOqO4uER5eWeqNAdhEgAAAIDL8/Bw19NPP13by6gz4uPjJVUtTHKbKwAAAADANMIkAAAAAMA0p4bJ8vJyLV26VFFRUQoPD9fYsWOVlZV1yfq0tDTFxMQoLCxM/fr1U3JyssP25ORkRUdHKywsTAMGDLjoHB9//LGGDRumsLAwDR48WP/85z+r9ZgAAAAAoD5y6mcmk5KStHXrVq1Zs0Zt2rTRokWLNHHiRL311luyWBxzbX5+vuLi4jRq1Ci9+uqrSk9P1/jx49W6dWvdeuutkqTWrVsrLi5OR48e1fr16yvs7/jx43rggQf06KOP6s4779R///tfTZs2Tf7+/goLC3PKMQNAXdHM6iF3L8/aXkadUXKmSKdsxbW9DAAArppTw+S6desUFxenjh07SpKmT5+uqKgo7d+/Xz169HCo3b59uywWiyZNmiSLxaJu3bopOjpaa9eutYfJc79v3LjxovvbtGmTOnfurOjoaElS//791b9/f61bt44wCQAmuXt56p/3/am2l1Fn3Jb8fxJhEgDwK+a021xtNpuysrIUEhJiH/Px8VGHDh2Unp5eoT4jI0PBwcEOVyxDQkKUkZFR6X1mZGQ47O9q5gAAAAAAVOS0K5P5+fmSzgbI81mtVvu2C+utVqvDmI+Pz0VrL7fP66+/vkpznNOypbfp16DqfH2tVy5CraE/ro8euTb64/rokWujP66N/ri+qvbIaWHS2/tsGLPZbA7jNpvNvu3C+tzcXIex06dPX7T2cvu8cH9m5zgnNzdf5eXGZWt4w1S/nBzblYtMoEfVi/64vursEf2pfryHXB/vIdfGe8i10R/XV5keXe7n7rTbXK1Wq/z9/ZWammofs9lsOnbsmIKCgirUBwYGKi0tTeXl5faxQ4cOKTAwsNL7DAwMdNjf1cwBAAAAAKjIqV8NEhMTo9WrVyszM1MFBQVKSEhQQECAIiIiKtQOGjRIZWVlSkxMVHFxsQ4cOKD169dr5MiR9prS0lIVFRWptLRUhmGoqKhIRUVFMoyzVxCHDx+uw4cP680331RJSYk++ugj7dixQzExMU47ZgAAAACoi5waJuPi4jRkyBCNGjVKUVFRysrKUmJioiwWi/bt26fw8HBlZ2dLOnuLalJSknbu3KnIyEhNmTJFkydP1pAhQ+zzJSYmKjQ0VI899piys7MVGhqq0NBQ+3dXtmvXTomJiXrllVcUERGhJ598UgsXLuRJrgAAAABQRU79ahCLxaL4+HjFx8dX2BYZGamUlBSHseDgYL3++uuXnG/KlCmaMmXKZfcZFRWlLVu2XN2CAQAAAAAX5dQrkwAAAACAuoEwCQAAAAAwjTAJAAAAADCNMAkAAAAAMI0wCQAAAAAwjTAJAAAAADCNMAkAAAAAMI0wCQAAAAAwjTAJAAAAADCNMAkAAAAAMI0wCQAAAAAwjTAJAAAAADCNMAkAAAAAMI0wCQAAAAAwjTAJAAAAADCNMAkAAAAAMI0wCQAAAAAwjTAJAAAAADCNMAkAAAAAMI0wCQAAAAAwjTAJAAAAADCNMAkAAAAAMI0wCQAAAAAwzalhsry8XEuXLlVUVJTCw8M1duxYZWVlXbI+LS1NMTExCgsLU79+/ZScnOyw/cyZM5o7d6569uyp7t27a9q0aTp16pR9e1lZmZYuXar+/fsrPDxcgwcP1rp162rq8AAAAACg3nBqmExKStLWrVu1Zs0a7dq1S35+fpo4caLKy8sr1Obn5ysuLk59+vTR3r17tWzZMq1cuVLbtm2z1yxcuFCpqanasmWLduzYoYKCAs2YMcO+fe3atdqwYYNefPFFpaSkaMGCBVq4cKF2797tlOMFAAAAgLrKqWFy3bp1iouLU8eOHdWkSRNNnz5dmZmZ2r9/f4Xa7du3y2KxaNKkSfL09FS3bt0UHR2ttWvXSjp7VXLz5s2aOnWq2rRpo6ZNm2rGjBn68MMPlZ2dLUn69ttvFRkZqRtuuEGS1LNnT11//fVKT0933kEDAAAAQB3ktDBps9mUlZWlkJAQ+5iPj486dOhw0XCXkZGh4OBgWSz/W2JISIgyMjIkSd98842KiorUtWtX+/ZOnTqpUaNG9vn+8Ic/6OjRo8rIyFB5ebl2796t48eP66abbqqpwwQAAACAeqGhs3aUn58v6WyAPJ/VarVvu7DearU6jPn4+Nhrz/1+Yc3587Vt21a9e/fWXXfdJTc3N1ksFs2ZM0fXX3+96fW3bOlt+jWoOl9f65WLUGvoj+ujR66N/rg+euTa6I9roz+ur6o9clqY9PY+G8ZsNpvDuM1ms2+7sD43N9dh7PTp0/ba8+dr0aLFReebP3++vvrqK7377rtq166dDh8+rMmTJ6thw4aKjo42tf7c3HyVlxuXreENU/1ycmxXLjKBHlUv+uP6qrNH9Kf68R5yfbyHXBvvIddGf1xfZXp0uZ+7025ztVqt8vf3V2pqqn3MZrPp2LFjCgoKqlAfGBiotLQ0h4fzHDp0SIGBgZKkgIAAeXp6Osx35MgRFRYW2mtSU1N15513qn379nJzc1NgYKBuueUWvf/++zV1mAAAAABQLzj1ATwxMTFavXq1MjMzVVBQoISEBAUEBCgiIqJC7aBBg1RWVqbExEQVFxfrwIEDWr9+vUaOHClJ8vLy0vDhw7V8+XKdOHFCeXl5SkhIUN++feXv7y9JioiI0JYtW+xfP/LVV1/pvffe029+8xvnHTQAAAAA1EFODZNxcXEaMmSIRo0apaioKGVlZSkxMVEWi0X79u1TeHi4/Ums3t7eSkpK0s6dOxUZGakpU6Zo8uTJGjJkiH2+2bNnKygoSEOHDlX//v3l6empxYsX27dPnz5dISEhGjVqlMLDwzV+/HgNGjRIEyZMcOZhAwAAAECd47TPTEqSxWJRfHy84uPjK2yLjIxUSkqKw1hwcLBef/31S87n5eWlBQsWaMGCBRfd3qRJE82dO1dz586t2sIBAAAAAA6cemUSAAAAAFA3ECYBAAAAAKYRJgEAAAAAphEmAQAAAACmESYBAAAAAKYRJgEAAAAAplU6TBYXF+v999/Xyy+/LJvNJkk6fvy4Tp8+XWOLAwAAAAC4pkp9z2R2drbGjBmjH374QcXFxRo4cKCsVqteffVVFRUVaf78+TW9TgAAAACAC6nUlcmnnnpKgYGB2rt3rzw9Pe3jN998sz755JMaWxwAAAAAwDVV6srkvn37lJycLA8PD4fxtm3b6scff6yRhQEAAAAAXFelrkyeOXNG7u7uFcZPnjzpcKUSAAAAAFA/VCpMdu/eXVu3bq0wnpycrMjIyGpfFAAAAADAtVXqNteHH35YsbGxyszMVFlZmV588UUdPnxYR44c0bp162p6jQAAAAAAF1OpK5MhISFav369PDw81L59e+3fv18BAQF64403dMMNN9T0GgEAAAAALqZSVyYl6frrr9dTTz1Vk2sBAAAAAPxKVDpMSlJhYaFyc3NlGIbDeLt27ap1UQAAAAAA11apMHnkyBHNnj1bBw4ccBg3DENubm5KT0+vkcUBAAAAAFxTpcLkzJkz5e7urhUrVqhVq1Zyc3Or6XUBAAAAAFxYpcLkV199pY0bN6pjx441vR4AAAAAwK9ApZ7mGhwcrJycnJpeCwAAAADgV6JSVybnz5+vefPmyWazqXPnzmrY0PFlfn5+NbI4AAAAAIBrqvTTXPPy8vTggw86fF6SB/AAAAAAQP1UqTD5l7/8RY0bN9ayZct4AA8AAAAAoPJfDbJx40Z16tSpSjsrLy/XsmXLtGHDBhUWFqp79+6aP3++/P39L1qflpam+fPnKz09Xc2bN9eYMWN033332befOXNGCxcu1LZt21RaWqqbbrpJTzzxhJo1a2avOX78uBISEvTxxx+rrKxM7du316pVq9SmTZsqHQsAAAAA1GeVegBPUFCQcnNzq7yzpKQkbd26VWvWrNGuXbvk5+eniRMnqry8vEJtfn6+4uLi1KdPH+3du1fLli3TypUrtW3bNnvNwoULlZqaqi1btmjHjh0qKCjQjBkz7NtPnjypUaNGqV27dnrvvfe0b98+LV68WE2aNKnysQAAAABAfVapMPnAAw9o0aJF+uCDD5SVlaUff/zR4VdlrVu3TnFxcerYsaOaNGmi6dOnKzMzU/v3769Qu337dlksFk2aNEmenp7q1q2boqOjtXbtWklnr0pu3rxZU6dOVZs2bdS0aVPNmDFDH374obKzsyVJr7zyinx9ffXnP/9ZTZs2lcViUefOneXt7V3pNQMAAAAAKqrUba4TJkyQJE2aNOmqH8Bjs9mUlZWlkJAQ+5iPj486dOig9PR09ejRw6E+IyNDwcHBslj+l3dDQkK0fv16SdI333yjoqIide3a1b69U6dOatSokdLT0+Xn56c9e/aobdu2euCBB7R//361atVKI0eOVGxsbGUOGwAAAABwCZUKk8nJyVXeUX5+vqSzAfJ8VqvVvu3CeqvV6jDm4+Njrz33+4U158/3888/6+DBg1qyZImWL1+u1NRUjRs3Ts2bN9ftt99uav0tW3I1szb4+lqvXIRaQ39cHz1ybfTH9dEj10Z/XBv9cX1V7VGlwmTPnj2rtBNJ9ltLbTabw7jNZrvobafe3t4VPqd5+vRpe+3587Vo0eKi8zVp0kRhYWEaOnSoJCk8PFx33HGH3nvvPdNhMjc3X+XlxmVreMNUv5wc25WLTKBH1Yv+uL7q7BH9qX68h1wf7yHXxnvItdEf11eZHl3u537Jz0ye/1nICz8jeTWfmbRarfL391dqaqp9zGaz6dixYwoKCqpQHxgYqLS0NIeH8xw6dEiBgYGSpICAAHl6ejrMd+TIERUWFtprgoODK3yNCV9rAgAAAABVd8kw2a9fP/uVwb59+6pfv34Vfp0br6yYmBitXr1amZmZKigoUEJCggICAhQREVGhdtCgQSorK1NiYqKKi4t14MABrV+/XiNHjpQkeXl5afjw4Vq+fLlOnDihvLw8JSQkqG/fvvavGomJidHBgwf17rvvqry8XAcOHNDWrVs1ePBgMz8jAAAAAMAFLnmb66uvvqqmTZtKqp7PTEpSXFycbDabRo0apcLCQkVERCgxMVEWi0X79u3TuHHj9M4778jPz0/e3t5KSkrSvHnztGrVKjVv3lyTJ0/WkCFD7PPNnj1bTz75pIYOHaqysjLdeOONmjdvnn17aGionnnmGT3zzDOaMWOGrrnmGj3yyCMOcwAAAAAAzLtkmOzZs6duvvlmbdiwoVo+MylJFotF8fHxio+Pr7AtMjJSKSkpDmPBwcF6/fXXLzmfl5eXFixYoAULFlyyZuDAgRo4cODVLxoAAAAAUMFlv2cyKyvL4TOLAAAAAABIVwiTAAAAAABczBW/GiQnJ0elpaWXrWnTpk21LQgAAAAA4PquGCbvuuuuS24zDENubm5KT0+v1kUBAAAAAFzbFcPk8uXL7U91BQAAAABAqkSY7N69u1q2bOmMtQAAAAAAfiUu+wAeNzc3Z60DAAAAAPArctkwaRiGs9YBAAAAAPgVuextrhkZGc5aBwAAAADgV4TvmQQAAAAAmEaYBAAAAACYRpgEAAAAAJhGmAQAAAAAmEaYBAAAAACYRpgEAAAAAJhGmAQAAAAAmEaYBAAAAACYRpgEAAAAAJhGmAQAAAAAmEaYBAAAAACYRpgEAAAAAJhGmAQAAAAAmEaYBAAAAACY5tQwWV5erqVLlyoqKkrh4eEaO3assrKyLlmflpammJgYhYWFqV+/fkpOTnbYfubMGc2dO1c9e/ZU9+7dNW3aNJ06deqic7366qvq0qWLVqxYUZ2HBAAAAAD1klPDZFJSkrZu3ao1a9Zo165d8vPz08SJE1VeXl6hNj8/X3FxcerTp4/27t2rZcuWaeXKldq2bZu9ZuHChUpNTdWWLVu0Y8cOFRQUaMaMGRXmOnr0qJKTk9W5c+caPT4AAAAAqC+cGibXrVunuLg4dezYUU2aNNH06dOVmZmp/fv3V6jdvn27LBaLJk2aJE9PT3Xr1k3R0dFau3atpLNXJTdv3qypU6eqTZs2atq0qWbMmKEPP/xQ2dnZ9nnKyso0Y8YMzZo1S82aNXPWoQIAAABAnea0MGmz2ZSVlaWQkBD7mI+Pjzp06KD09PQK9RkZGQoODpbF8r8lhoSEKCMjQ5L0zTffqKioSF27drVv79Spkxo1auQw36pVq9S+fXvdcsstNXFYAAAAAFAvNXTWjvLz8yWdDZDns1qt9m0X1lutVocxHx8fe+253y+sOX++9PR0vfHGG9q0aVOV19+ypXeV54B5vr7WKxeh1tAf10ePXBv9cX30yLXRH9dGf1xfVXvktDDp7X02jNlsNodxm81m33ZhfW5ursPY6dOn7bXnz9eiRYsK85WUlOgvf/mLZs+erebNm1d5/bm5+SovNy5bwxum+uXk2K5cZAI9ql70x/VVZ4/oT/XjPeT6eA+5Nt5Dro3+uL7K9OhyP3en3eZqtVrl7++v1NRU+5jNZtOxY8cUFBRUoT4wMFBpaWkOD+c5dOiQAgMDJUkBAQHy9PR0mO/IkSMqLCxUYGCgfvzxR3355Zd67LHH1KtXL/Xq1UufffaZkpKSdMcdd9TgkQIAAABA3efUB/DExMRo9erVyszMVEFBgRISEhQQEKCIiIgKtYMGDVJZWZkSExNVXFysAwcOaP369Ro5cqQkycvLS8OHD9fy5ct14sQJ5eXlKSEhQX379pW/v7+uvfZaffTRR3rrrbfsv0JCQhQTE6OXX37ZmYcNAAAAAHWOU8NkXFychgwZolGjRikqKkpZWVlKTEyUxWLRvn37FB4ebn8Sq7e3t5KSkrRz505FRkZqypQpmjx5soYMGWKfb/bs2QoKCtLQoUPVv39/eXp6avHixZKkBg0a6JprrnH45eHhIW9vb/n6+jrzsAEAAACgznHaZyYlyWKxKD4+XvHx8RW2RUZGKiUlxWEsODhYr7/++iXn8/Ly0oIFC7RgwYJK7f/vf/+7uQUDAAAAAC7KqVcmAQAAAAB1A2ESAAAAAGAaYRIAAAAAYBphEgAAAABgGmESAAAAAGAaYRIAAAAAYBphEgAAAABgGmESAAAAAGAaYRIAAAAAYBphEgAAAABgGmESAAAAAGAaYRIAAAAAYBphEgAAAABgGmESAAAAAGAaYRIAAAAAYBphEgAAAABgGmESAAAAAGAaYRIAAAAAYBphEgAAAABgGmESAAAAAGAaYRIAAAAAYBphEgAAAABgmlPDZHl5uZYuXaqoqCiFh4dr7NixysrKumR9WlqaYmJiFBYWpn79+ik5Odlh+5kzZzR37lz17NlT3bt317Rp03Tq1Cn79s2bNysmJkY9evRQr169NHbsWGVkZNTU4QEAAABAveHUMJmUlKStW7dqzZo12rVrl/z8/DRx4kSVl5dXqM3Pz1dcXJz69OmjvXv3atmyZVq5cqW2bdtmr1m4cKFSU1O1ZcsW7dixQwUFBZoxY4Z9+y+//KIpU6boo48+0s6dOxUUFKSxY8eqsLDQKccLAAAAAHWVU8PkunXrFBcXp44dO6pJkyaaPn26MjMztX///gq127dvl8Vi0aRJk+Tp6alu3bopOjpaa9eulXT2quTmzZs1depUtWnTRk2bNtWMGTP04YcfKjs7W5I0evRo/e53v1Pjxo3l6empBx54QD/99JOOHj3qzMMGAAAAgDrHaWHSZrMpKytLISEh9jEfHx916NBB6enpFeozMjIUHBwsi+V/SwwJCbHfpvrNN9+oqKhIXbt2tW/v1KmTGjVqdNH5JGn37t1q1KiROnToUF2HBQAAAAD1UkNn7Sg/P1/S2QB5PqvVat92Yb3VanUY8/Hxsdee+/3CmkvNd+TIEc2ZM0czZsyQt7e36fW3bGn+Nag6X1/rlYtQa+iP66NHro3+uD565Nroj2ujP66vqj1yWpg8F+BsNpvDuM1mu2i48/b2Vm5ursPY6dOn7bXnz9eiRYvLzvfll19qzJgxiouL08iRI69q/bm5+SovNy5bwxum+uXk2K5cZAI9ql70x/VVZ4/oT/XjPeT6eA+5Nt5Dro3+uL7K9OhyP3en3eZqtVrl7++v1NRU+5jNZtOxY8cUFBRUoT4wMFBpaWkOD+c5dOiQAgMDJUkBAQHy9PR0mO/IkSMqLCy010jSwYMHdd9992nChAkaN25cTRwaAAAAANQ7Tn0AT0xMjFavXq3MzEwVFBQoISFBAQEBioiIqFA7aNAglZWVKTExUcXFxTpw4IDWr19vv7Lo5eWl4cOHa/ny5Tpx4oTy8vKUkJCgvn37yt/fX5K0b98+jRkzRvHx8YqNjXXmoQIAAABAnebUMBkXF6chQ4Zo1KhRioqKUlZWlhITE2WxWLRv3z6Fh4fbn8Tq7e2tpKQk7dy5U5GRkZoyZYomT56sIUOG2OebPXu2goKCNHToUPXv31+enp5avHixffuzzz4rm82mhQsXKjw83P7r7bffduZhAwAAAECd47TPTEqSxWJRfHy84uPjK2yLjIxUSkqKw1hwcLBef/31S87n5eWlBQsWaMGCBRfd/ve//71qCwYAAAAAXJRTr0wCAAAAAOoGwiQAAAAAwDTCJAAAAADANMIkAAAAAMA0wiQAAAAAwDTCJAAAAADANMIkAAAAAMA0wiQAAAAAwDTCJAAAAADANMIkAAAAAMA0wiQAAAAAwDTCJAAAAADANMIkAAAAAMA0wiQAAAAAwDTCJAAAAADANMIkAAAAAMA0wiQAAAAAwDTCJAAAAADANMIkAAAAAMA0wiQAAAAAwDTCJAAAAADANMIkAAAAAMA0wiQAAAAAwDSnhsny8nItXbpUUVFRCg8P19ixY5WVlXXJ+rS0NMXExCgsLEz9+vVTcnKyw/YzZ85o7ty56tmzp7p3765p06bp1KlTDjVbt27VoEGDFBoaqmHDhmn37t01cWgAAAAAUK84NUwmJSVp69atWrNmjXbt2iU/Pz9NnDhR5eXlFWrz8/MVFxenPn36aO/evVq2bJlWrlypbdu22WsWLlyo1NRUbdmyRTt27FBBQYFmzJhh3/7ZZ59p9uzZmjVrlvbt26fY2Fg98MADys7OdsrxAgAAAEBd5dQwuW7dOsXFxaljx45q0qSJpk+frszMTO3fv79C7fbt22WxWDRp0iR5enqqW7duio6O1tq1ayWdvSq5efNmTZ06VW3atFHTpk01Y8YMffjhh/aw+MYbb2jAgAHq37+/PDw8FB0drRtuuEEbN2505mEDAAAAQJ3T0Fk7stlsysrKUkhIiH3Mx8dHHTp0UHp6unr06OFQn5GRoeDgYFks/8u7ISEhWr9+vSTpm2++UVFRkbp27Wrf3qlTJzVq1Ejp6eny8/NTRkaGbr/9dod5Q0JClJGRYXr9FotbpepaNW9iem5cWmV/7mZ4+LSs9jnrq5roTyvvFtU+Z31W3T1q1Ir3T3WqifdQ02aNq33O+qy6e+TTjPdQdaqJ95C71ava56yvaqI/Pj4+1T5nfVbVHjktTObn50uq+D8Aq9Vq33ZhvdVqdRjz8fGx1577/cKa8+fLz8+vsD8fHx9lZmaaXn/zSobE5bOGm54bl9aypXe1z9l14t+qfc76qib6syT68Wqfsz6r7h71X7qkWuer72riPTR5+m3VPmd9Vt09ipuxsFrnq+9q4j0UHPvbap+zvqqJ/owbN67a56zPqtojp93m6u19dqE2m81h3Gaz2bddWH9hyDx9+rS9tjLzeXt7V9h+/hwAAAAAgKvjtDBptVrl7++v1NRU+5jNZtOxY8cUFBRUoT4wMFBpaWkOD+c5dOiQAgMDJUkBAQHy9PR0mO/IkSMqLCy01wQGBjpsv3AOAAAAAMDVceoDeGJiYrR69WplZmaqoKBACQkJCggIUERERIXaQYMGqaysTImJiSouLtaBAwe0fv16jRw5UpLk5eWl4cOHa/ny5Tpx4oTy8vKUkJCgvn37yt/fX5J0zz336IMPPtBHH32kkpISvfnmm/ryyy911113OfOwAQAAAKDOcTMMw3DWzsrLy/XMM89ow4YNKiwsVEREhObNm6e2bdtq3759GjdunN555x35+flJOvs9k/PmzVN6erqaN2+usWPH6r777rPPd+bMGT355JPatm2bysrKdOONN2revHlq1qyZvWbr1q1avny5fvjhB3Xo0EGzZ89W7969nXXIAAAAAFAnOTVMAgAAAADqBqfe5goAAAAAqBsIkwAAAAAA0wiTAAAAAADTCJMAAAAAANMIkwCAX73U1FTdeeedCg8P18yZM2t7OQCAeqQ+n4Ma1vYCUHO+/fZbLV++XLt371Z+fr58fX01ePBgTZ48WU2aNLHXzZkzR59//rkyMzM1bNgwLVq0qBZXXb9UpkeZmZl65plnlJKSovz8fF177bW6//779Yc//KGWV1/3VaY/hYWFGj9+vI4cOaIzZ87IarVq8ODBio+Pl6enZy0fwa9fZf8eW7p0qXr27Km33nqryvvcuHGjVq5cqQ8++KDKc9V3nIdcH+ch18Z5qHZxDroyrkzWUV999ZVGjBghd3d3rV+/XikpKVqxYoU+/fRTxcbGqrCw0F7bpUsXzZw5UwMGDKjFFdc/le3R6dOn1atXL23YsEGfffaZ5s2bp8WLF+vdd9+t5SOo2yrbH3d3d82ZM0cffvihPvvsM23YsEFpaWl69tlna/kIfv3M/D323XffKSgoqBZXiwtxHnJ9nIdcG+eh2sU5qHL4nsk6asyYMTpz5ozWrl3rMH7y5EkNGjRI48eP1/jx4x22nbssz/8j7BxX06NzHnroIbVu3VqPPvqoM5ZaL11tf3JycvTII4/I29tbiYmJzlpunVSZHowdO1aRkZEqLCyUu7u7GjZsqEWLFum6667TX//6Vx0+fFhlZWUKCgrS7Nmz7Sf77OxsPf744/riiy9UVlama6+9Vk888YQk6U9/+pNKSkrUqFEjSVJCQoJuueUWpx57XcB5yPVxHnJtnIdqF+egyuHKZB105swZ7dmzR3fddVeFbS1atFDfvn21Y8eOWlgZzqlKjwoKCvTFF1+oS5cuNb3Meutq+hMfH69u3bqpT58+Onz4sMaOHeus5dZJle1BgwYNlJKSIj8/P82bN08pKSkaPHiwJOmBBx7Qzp07tXPnTl133XWaPHmySkpKJElPP/202rRpo//85z/69NNPtXz5crVp00aRkZGaN2+e/Pz8lJKSopSUFJc+ibsqzkOuj/OQa+M8VLs4B1UeYbIOysvLU1lZmVq3bn3R7ddcc41Onjzp5FXhfFfbo9LSUv35z3+Wv7+/hg8fXsOrrL+upj9PP/20UlJS9NZbbykmJkZ+fn7OWGqdVdW/xzp37qzevXvL09NTjRs3Vnx8vLKysnTs2DFJkoeHh3766ScdO3ZMbm5u6tixo9q1a1cjx1IfcR5yfZyHXBvnodrFOajyCJN1wNtvv63w8HD7r9LSUjVo0EAnTpy4aP0PP/ygFi1aOHmV9Vt19Ki4uFgPP/ywTp48qVWrVsnd3d0ZS68Xqus95ObmpsDAQAUGBmrq1Kk1vew6pbr/Hjt+/Lgeeugh9e3bV927d9fNN98sScrNzZUk/eUvf1H79u314IMPKioqSrNnzybcVAHnIdfHeci1cR6qXZyDrh5hsg4YNmyY/VJ4SkqK/P391atXr4s+Uernn3/Wzp071a9fP+cvtB6rao/OnDmjSZMm6dSpU1q9erWsVqsTV1/3Vfd7qLS0VJmZmTW44rqnunswd+5cubu7a9OmTfrss8/0/vvvS5LOPSagefPmmj17tt59911t3rxZ3333nf72t79JkiwWTo1mcR5yfZyHXBvnodrFOejq/bpWi0qbOXOm0tPTNXv2bH3//fcqKytTenq6Jk6cKD8/P8XGxtpri4uLVVRUpLKyMpWVlamoqEjFxcW1uPr6obI9ys/P17hx42QYhl566SWHR1Gj5lS2PwcOHNB//vMfFRQUqLy8XKmpqXruuefUt2/fWj6CXz8zf49dyGazqVGjRrJarbLZbEpISHDY/s477+jYsWMqLy9XkyZN5OHhYT+Bt2rVSidPnlReXl6NHl9dx3nI9XEecm2ch2oX56DK4WmuddjRo0e1cuVKffzxxyooKFCrVq3s343j7e1tr4uNjdXevXsdXuvv7/+r+X6bX7PK9GjTpk2aOXOmvLy8HP7fqoiICCUlJdXW0uuFyvQnJSVFTz75pI4ePSrDMNSqVSsNHDiwwndQ4epU9u+xAQMG6MEHH9Tvf/97SdLnn3+uxx9/XN9++61at26tadOm6eGHH1ZycrJ69eqlp59+Wlu3btXPP/8sLy8v9e7dW4899phatGih0tJSxcfHa8+ePSorK9Pf/vY3+y1KMIfzkOvjPOTaOA/VLs5BV0aYBAAAAACYxm2uAAAAAADTCJMAAAAAANMIkwAAAAAA0wiTAAAAAADTCJN1xMaNGzVgwIDaXsYVhYeHa9++fbW9DKejP66PHrk2+gMAgOshTKJGfPLJJ+rSpUuF8ZSUFEVGRtbCinA++uP66JFroz8AABAmfzVKSkpqewm4DPrj+uiRa6M/AAD8+hAmq0GXLl2UnJysu+++W926ddM999yj7OxsJScnq1+/furZs6eWLFliry8qKtJDDz2kPn36KDw8XLfffrv++c9/Vpjz1VdfVXR0tMLCwrRr1y4VFhbq6aef1sCBAxUeHq5Bgwbp3XffdXjdP/7xD/Xv318RERGaNm2a8vPzr+qYBgwYoMTERMXFxdn39d5779m3f/nll7rvvvvUq1cvRUZGKjY2Vunp6ZKk7OxsjRs3TtLZW77Cw8OVnJxsP65PPvlEZWVl6tOnj7Zu3eqw3+XLl2v06NH2P3/00UeKjo5Wjx49NGjQIPs8ZtAf1+7Puf3SI9ftEf1x7f4AAFBrDFRZ586djREjRhjZ2dlGQUGBERsbawwaNMhYunSpUVRUZBw6dMj4zW9+Y3zyySeGYRhGYWGhsXHjRuP06dNGSUmJsXHjRiM4ONj4+uuvHeYcMmSI8fXXXxvl5eVGYWGh8fDDDxvR0dHG0aNHDcMwjOzsbCM9Pd0wDMN48803jeDgYGPRokVGYWGh8cMPPxg333yzsXLlSvuc48ePNyIiIi75a9WqVfba/v37G/379zcOHTpklJWVGS+99JLRvXt3Iz8/3zAMwzh8+LDx8ccfG2fOnDF++eUX47HHHjP69+9vFBcXG4ZhGHv27DE6d+580Z/Vnj17DMMwjCVLlhh//OMf7dvKysqMfv36GZs2bTIMwzB2795tREZGGh9//LFRVlZmHD582LjpppuMt956i/7Uof7QI9fvEf1x7f4AAFBbCJPVoHPnzsaWLVvsf16zZo0RFhZmlJaW2seGDx9urF69+pJz3HHHHcZrr73mMOcbb7xh/3Nubq7RuXNnIy0t7aKvf/PNN43Q0FCjpKTEPrZo0SJjwoQJV3VM/fv3NxITE+1/zs/PNzp37mwcPHjwovWnTp0yOnfubP/HYmX+oZWZmWkEBgYa3333nWEYhrFz504jIiLCKCgoMAzDMCZMmGA888wzDq9ftWqVcf/995s6Fvrj2v05t1965Lo9oj+u3R8AAGpLw9q+Mvpr8/bbb+vxxx+3//mdd96RJPn6+trHGjVqpBYtWqhBgwb2MS8vL/3yyy+SpOLiYi1ZskQ7duxQbm6uLBaLCgoKdPLkSYd9tW3b1v7fx48flyRdd911l1xby5Yt1bDh/1rauHFj+z6vRuvWre3/3aRJE0myz3f8+HEtXrxYX3zxhWw2myyWs3dM5+bmqlOnTpWaPyAgQJGRkdq4caMeeughbdiwQUOHDlWjRo0kSd9++612796tNWvW2F9TVlYmPz+/S85Jf1y7PxI9cvUe0R/X7g8AAK6EMGnSsGHDNGzYsCrN8fLLL2vXrl1KSkpS+/bt5ebmpmHDhskwDIe6c/94kf73j65vvvlGgYGBV7XfuLg47d+//5LbJ0yYoIkTJ1Zqrrlz56p58+batGmTWrRooby8PPXs2dN+DOev/XJGjBihZ599Vvfee6/ef/99/eMf/7Bva9Wqle68885Kr0miP+e4an8kenSOq/aI/pzlqv0BAMCVECZrgc1mk4eHh5o3b67S0lKtX79eX3/9tQYOHHjJ17Ro0UK33367nnjiCS1atEgBAQH64YcfdOrUqUr/wyspKam6DkE2m01+fn6yWq2y2WxKSEhw2N6qVStJ0tGjR9WxY8dLznPrrbfqr3/9q2bNmqVOnTqpa9eu9m3333+/5s6dq7CwMPXo0cM+X15env3PNYH+/I8r9keiR+dzxR7Rn/9xxf4AAFBdeJprLRgzZoxatmypvn37qn///vrxxx/VvXv3K75uwYIF6t69u8aOHavw8HDdd999OnbsmBNWXNGcOXN08OBB9ejRQyNGjFBUVJTD9uuuu06xsbEaNWqUIiMjHW7jOp+Xl5duv/12ffjhh7r77rsdtt1yyy1atGiRnn32WUVFRSkqKkqPPvqofv755xo7Lon+nM8V+yPRo/O5Yo/oz/+4Yn8AAKgubsaF9x0BAAAAAHAFXJkEAAAAAJhGmAQAAAAAmEaYBAAAAACYRpgEAAAAAJhGmAQAAAAAmEaYBAAAAACYRpgEAMBFxcbGas6cObW9DAAALorvmQQA4Cr99NNPSkxM1I4dO3TixAl5e3urR48emjRpkoKCgio9z/PPP68NGzbogw8+cBg/deqUGjZsKG9v7+peOgAAVcaVSQAArsL333+vESNGKCUlRY8//ri2b9+uF198Ue7u7rrnnnu0c+fOKu+jWbNmBEkAgMsiTAIAcBXmzZun0tJSJScnq2/fvvLz81NoaKiWLl2q3/72t5o1a5bOnDmjFStWaODAgdqyZYtuvvlmde3aVX/60590/PhxSdLGjRv17LPPKisrS126dFGXLl20YsUKSRVvcy0pKdGSJUt04403KiQkRLfddpu2bNnisK4uXbrotdde0/Tp0xUeHq6bbrpJq1atcqh57733NHz4cIWFhSkyMlJ333230tLSavgnBgCoawiTAACYlJeXp48++kijR4++6JXDCRMm6KefftJ///tfSVJOTo7Wrl2rZcuW6bXXXlN+fr6mTJkiwzB02223ady4cbrmmmu0a9cu7dq1S2PGjLnofpcuXar169dr9uzZ2rJli4YNG6bp06dr9+7dDnXPPfecevToobfeeksTJkzQ0qVL7TU5OTmaNm2ahg4dqq1bt+r111/X/fffrwYNGlTzTwkAUNc1rO0FAADwa/Ptt9+qvLxcN9xww0W3X3/99ZKkzMxMSVJhYaEWLVqkDh06SJIWL16sW2+9VXv27FHv3r3VuHFjNWjQQL6+vpfcZ2Fhof7+979r1qxZGjJkiCRp4sSJOnjwoBITE9W7d2977W233aZ77rlHkjR69GitWbNGH3/8sXr37q2cnByVlJRoyJAhatu2rSSpU6dOVfyJAADqI65MAgBQw1q0aGEPkpJ03XXXqXnz5vrqq68qPce3336rkpIS9ejRw2G8R48e+vrrrx3GAgMDHf7cunVr/fTTT5LO3gbbp08f3XHHHZo8ebJeffVVff/992YPCQAAwiQAAGa1b99ebm5u+vLLLy+6/Vy4u+6665y5LDt3d3eHP7u5uencw9sbNGigpKQkvfrqq+ratau2b9+uwYMHa8eOHbWxVADArxhhEgAAk5o1a6a+ffvaP/94oVWrVqlVq1b63e9+J0k6efKkjh07Zt+emZmpn3/+2X47rLu7u8rKyi67zw4dOsjDw0Offvqpw/inn356ydttL8XNzU2hoaGaOHGiXnvtNfXo0UMbN240NQcAAIRJAACuwty5c9WgQQPdd9992rlzp77//nsdOHBA8fHx2rNnj5566il5eXlJkho1aqRZs2bp4MGDOnjwoGbOnKmgoCD75xzbtm2rn376SSkpKTp58qQKCwsr7K9Ro0aKjY3V8uXL9a9//UuZmZl64YUX9P7772vixImVXvdnn32m5557Tl988YWys7O1e/duHT58mM9NAgBM4wE8AABcBX9/f23cuFHPP/+8Hn/8ceXk5KhJkybq2bOnXn/9dQUHB9trfX19dc8992jq1KnKyclR9+7dtWTJErm5uUmSbrnlFt16662aMGGC8vLy9OCDD2rKlCkV9vnwww/LYrFo4cKF+vnnn9W+fXslJCQ4PHznSqxWqz7//HOtXbtWeXl58vX11R133KFJkyZV/YcCAKhX3IxzH6IAAADVbsWKFXr77bf173//u7aXAgBAteI2VwAAAACAaYRJAAAAAIBp3OYKAAAAADCNK5MAAAAAANMIkwAAAAAA0wiTAAAAAADTCJMAAAAAANMIkwAAAAAA0wiTAAAAAADT/h+KSaX+0YY36AAAAABJRU5ErkJggg==\n",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -383,7 +383,7 @@
},
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/plain": [
""
]
@@ -393,7 +393,7 @@
},
{
"data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7gAAAEkCAYAAAAFAp1KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAvqElEQVR4nO3de1xUdf7H8Td3FPAKauAFdRUt07hp+nPDu2VltpZLGlmJypqaRu7PS2m622aipmlphbXxU7N0dSvc39avy2akZhKaKOtWsqJ4WRTDIe7M+f3ho0kCdVBmcA6v5+PR4+Gc8z3f8znzecwc3s2ZM26GYRgCAAAAAMDFudd3AQAAAAAA1AUCLgAAAADAFAi4AAAAAABTIOACAAAAAEyBgAsAAAAAMAUCLgAAAADAFAi4AABcZPbs2Xr44YfruwwAAHAVPOu7AAAAnGX27Nnatm1bteWNGzdWRkZGPVRUN4YOHaqRI0dq2rRpDt/XoEGDlJubK0ny9vZWmzZtNGLECD322GNKTU3VnDlzLrv91KlTa6zz888/1+rVq/Xvf/9bRUVFat26te6880499thj8vb2dsixAADMh4ALAGhQoqKitGLFiirL3N25oKk2Jk6cqPHjx6u8vFz79+/XvHnzZBiGpkyZol//+te2cc8++6zy8vKqPN+NGzeucU5/f3899NBD6tKli/z8/JSVlaWnn35axcXFmjt3rqMPCQBgEgRcAECD4uXlpaCgoFpts337dr366qs6cuSIgoKCNHToUD3++OO2sBYXF6d27dopKChI77zzjsrLyzVu3Dg9/vjjevnll7Vx40ZZrVb99re/1cyZM23zlpeXa+3atdq2bZvy8vLUvn17xcXFKTY21jYmLCxM8+fP1759+/TRRx8pICBA48aN0+TJk237zsnJ0erVq7V69WpJ0scff6y2bdte61N1SY0bN7Y9h8HBwfrb3/6mtLQ0PfHEE/L19bWN8/X1tfv5Dg8PV3h4uO1xSEiI9uzZoz179tT9AQAATIuACwDAZWzdulXPPfec5s2bp8jISJ06dUqLFi1Sfn6+kpKSbOM++OADxcbGauPGjUpPT9e8efN08OBBde3aVRs2bNC+ffs0e/ZsRUREKCYmRpL09NNP6+DBg1q0aJE6dOigAwcOaP78+fLw8ND9999vm/ull17SjBkzNG3aNH3++edatGiRevbsqb59+2rVqlX6zW9+o+HDh+vRRx+VJLVo0aLGY3nvvfe0YMGCyx7v3XffrUWLFtn9/Bw6dEjp6elq166d3dvY4/vvv9eOHTtszxUAAPYg4AIAGpQ9e/ZU+aRQkvr06aO1a9fWOH716tV64oknNGrUKElSu3btNH/+fD344IN66qmn1LRpU0lS27ZtNWvWLElSx44d9cYbb+j06dNKTk6usmz37t2KiYnRsWPH9Ne//lXbt29X586dbXMfOXJE69evrxJwR4wYoTFjxkiSxo0bp/Xr12vnzp3q27evmjVrJg8Pjyqfql7KoEGD1KtXr8uO8ff3v+x6SXr55Zf12muvqby8XOXl5fLw8KhVKL6c2267Tfn5+SovL1dsbKz++7//u07mBQA0DARcAECD0rNnTz3//PNVll18We3F8vPzlZubq8WLF2vJkiW25YZhSJKOHj2qnj17SpK6detWZdvAwEAFBgZWWRYUFKSzZ89KkjIzM2UYhu67774qYyoqKuTh4VFl2S/nbtWqlc6cOXPZ46yJv7+/XQH2SsaNG6exY8eqoKBAa9euVdu2bTVkyJBrnleSNmzYoJKSEh06dEhLly5Vy5YtNX369DqZGwBgfgRcAECD4uvrqw4dOtg11mq1SpLmzZunPn36VFvfpk0b2789PaueUt3c3OTl5XXJOX8KyW+99ZYaNWpUbduL/XIeNzc32/a1UVeXKDdt2tT2HK5YsUJ33HGHevTooZEjR9a6pl/66VLnLl26yMPDQ7NmzVJ8fPwlb04FAMDFCLgAAFxCYGCgbrjhBmVnZ9suEa4rN910kyTp5MmTGjhw4DXN5eXlpcrKyiuOq6tLlC/m7e2thIQELVmyREOHDq0W1q+F1WqV1WpVeXl5nc0JADA3Ai4AoEEpLy9XXl5eteWBgYHVPjmVpBkzZuipp55SkyZNNHjwYHl6eurIkSPasWPHNX3vtEOHDho9erSefvppPfnkkwoPD1dxcbEyMzOVn5+vSZMm2T1X27Zt9fXXX+vEiRPy9fVVs2bNavzpo7q6RPmX7rnnHq1cuVIpKSm2uzvX1uuvv65OnTopNDRUbm5uyszMVFJSkgYNGmT7njMAAFdCwAUANCh79+5V//79qy3ftWtXjXcfHjVqlPz9/fXaa69p7dq18vDwULt27TR06NBrruUPf/iDXn/9da1du1bHjx+Xn5+funTponHjxtVqnmnTpmn+/Pm6/fbbVVpa6vCfCfolb29vPfjgg0pOTlZsbOxVBdKKigolJSUpNzdXbm5uCgkJ0bhx4zR+/HgHVAwAMCs342q+xAMAAAAAwHWm+vVLAAAAAAC4IAIuAAAAAMAUCLgAAAAAAFMg4AIAAAAATIGACwAAAAAwhQbxM0FnzxbKauVm0QAAAABgBkFBATUu5xNcAAAAAIApEHABAAAAAKZAwAUAAAAAmAIBFwAAAABgCgRcAAAAAIApEHABAAAAAKZAwAUAAAAAmAIBFwAAAABgCgRcAAAAAIApeNZ3AdeTgCa+8vXxqu8yTK2ktFyW8yX1XQYAAAAAEyLgXsTXx0tjf7+hvsswtY1LxskiAi4AAACAusclygAAAAAAUyDgAgAAAABMgYALAAAAADAFAi4AAAAAwBQIuAAAAAAAUyDgAgAAAABMgYALAAAAADAFAi4AAAAAwBQIuAAAAAAAUyDgAgAAAABMgYALAAAAADAFAi4AAAAAwBQIuAAAAAAAUyDgAgAAAABMgYALAAAAADAFz/ouAAAAOF/TJo3k7cOfAY5UVlqhgvPF9V0GADQoTj2zWa1WrVixQlu2bFFxcbEiIiK0aNEihYSE1Dg+LCxMPj4+8vDwsC3btGmTwsLCnFUyAACm5O3jqT/N21LfZZja3Gfvq+8SAKDBceolysnJyUpNTdX69euVlpam4OBgJSQkyGq1XnKb1157TRkZGbb/CLcAAAAAgJo4NeBu2rRJ8fHx6tSpk/z8/DRr1ixlZ2crPT3dmWUAAAAAAEzIaZcoWywW5ebmqkePHrZlTZo0UYcOHZSVlaXo6Ogat0tMTFR5ebmCg4P1wAMPaMyYMc4qGS6keVNveXr71HcZplZRVqpzBWX1XQYAAABwSU4LuIWFhZIuhNqLBQQE2Nb90p///GeFh4fL3d1du3fv1pNPPqmKigqNHTu2Vvtu2dL/6oqGQwQFBThk3vQl8Q6ZFxdE/j5ZQUH8TwQAqA1HnfMAADVzWsD1978QMi0WS5XlFovFtu6X+vbta/v3bbfdpocffljvvfderQPu2bOFslqNK47jJOQceXmWKw+qJXrnHI7oHYD6wfumc/C+CQCOcanzmNO+gxsQEKCQkBBlZmballksFuXk5Kh79+52zeHu7i7DuHJQBQAAAAA0PE69yVRsbKzWrVun7OxsFRUVKSkpSaGhoYqMjKw29uDBgzpw4IDKyspUUVGhL774Qm+88YbuvPNOZ5YMAAAAAHARTv0d3Pj4eFksFo0dO1bFxcWKjIzUmjVr5O7urr1792rixInavn27goODdfr0aSUlJenUqVPy8PBQcHCwZsyYoQceeMCZJQMAAAAAXIRTA667u7sSExOVmJhYbV1UVJQyMjJsjwcNGqRBgwY5szwAAAAAgAtz6iXKAAAAAAA4CgEXAAAAAGAKBFwAAAAAgCkQcAEAAAAApkDABQAAAACYAgEXAAAAAGAKBFwAAAAAgCkQcAEAAAAApuBZ3wUAAADAfk2beMvbx6e+yzC1stJSFZwvq+8yAFwFAi4AAIAL8fbx0fI5k+u7DFN74rlXJBFwAVfEJcoAAAAAAFMg4AIAAAAATIGACwAAAAAwBQIuAAAAAMAUCLgAAAAAAFMg4AIAAAAATIGfCQIAAACAy2ja1Ffe3l71XYaplZWVq6Cg5JrnIeACAAAAwGV4e3tp2bJl9V2GqSUmJkq69oDLJcoAAAAAAFMg4AIAAAAATIGACwAAAAAwBQIuAAAAAMAUCLgAAAAAAFMg4AIAAAAATIGACwAAAAAwBacGXKvVquXLl6tfv34KDw/XhAkTlJube8XtMjMzddNNNykuLs4JVQIAAAAAXJFTA25ycrJSU1O1fv16paWlKTg4WAkJCbJarZfcprS0VHPmzFF0dLQTKwUAAAAAuBqnBtxNmzYpPj5enTp1kp+fn2bNmqXs7Gylp6dfcpsXXnhBt956qyIjI51YKQAAAADA1Tgt4FosFuXm5qpHjx62ZU2aNFGHDh2UlZVV4zZfffWVPv30Uz3xxBPOKhMAAAAA4KI8nbWjwsJCSRdC7cUCAgJs6y72448/au7cufrTn/6kRo0aXdO+W7b0v6btUbeCggLquwRcJXoHALXD+6broneA89XF685pAdff/0LItFgsVZZbLBbbuos9//zziomJqZPv3p49Wyir1bjiON7InCMvz3LlQbVE75zDEb0DUD9433QOznmui3MeLsbrzjlq87q7VE+cFnADAgIUEhKizMxM3XzzzZIuhNucnBx179692vi0tDSdP39e77//viSppKREFRUV6tOnj7Zs2aJ27do5q3QAAAAAgAtwWsCVpNjYWK1bt0633nqrWrduraSkJIWGhtZ4A6m3335blZWVtsdvvPGG9u3bp5UrVyooKMiZZQMAAAAAXIBTA258fLwsFovGjh2r4uJiRUZGas2aNXJ3d9fevXs1ceJEbd++XcHBwdVCrL+/v7y9vdWmTRtnlgwAAADUieZNG8nT26l/fjc4FWUVOldQXN9loB459RXm7u6uxMREJSYmVlsXFRWljIyMS247bdo0R5YGAAAAOJSnt6f2v/yP+i7D1HpNGVDfJaCeOfV3cAEAAAAAcBQCLgAAAADAFAi4AAAAAABTIOACAAAAAEyBgAsAAAAAMAUCLgAAAADAFAi4AAAAAABTIOACAAAAAEyBgAsAAAAAMAUCLgAAAADAFAi4AAAAAABTIOACAAAAAEyBgAsAAAAAMAW7A25ZWZk+/vhjvf7667JYLJKk48eP6/z58w4rDgAAAAAAe3naM+jEiRN69NFHderUKZWVlWno0KEKCAjQm2++qdLSUi1atMjRdQIAAAAAcFl2fYL73HPPqVu3btqzZ498fHxsywcPHqwvv/zSYcUBAAAAAGAvuz7B3bt3r1JSUuTt7V1ledu2bXX69GmHFAagYWjS1Ec+v3hvQd0qLSvT+YJSh8zdLMBbXr4+Vx6Iq1ZeUqofLGX1XQYAAC7BroBbUlIiLy+vasvz8/OrfKILALXl4+2th994vL7LMLU/P7JSkmMCrpevj/720CMOmRsXjEh5QyLgAgBgF7suUY6IiFBqamq15SkpKYqKiqrzogAAAAAAqC27PsGdOXOm4uLilJ2drcrKSr366qs6fPiwvv/+e23atMnRNQIAAAAAcEV2fYLbo0cPbd68Wd7e3mrfvr3S09MVGhqqd955R126dHF0jQAAAAAAXJFdn+BK0q9+9Ss999xzjqwFAAAAAICrZnfAlaTi4mKdPXtWhmFUWd6uXbs6LQoAAAAAgNqyK+B+//33mjt3rr755psqyw3DkJubm7KyshxSHAAAAAAA9rIr4M6ePVteXl5atWqVAgMD5ebmdlU7s1qtWrFihbZs2aLi4mJFRERo0aJFCgkJqTY2NzdXiYmJys7OVnl5uVq0aKF7771XU6ZMuer9AwAAAADMy66A++2332rr1q3q1KnTNe0sOTlZqampWr9+vVq3bq3FixcrISFB7777rtzdq97vqlmzZnr22WcVGhoqDw8PHTt2TJMmTVKzZs00bty4a6oDAAAAAGA+dt1F+cYbb1ReXt4172zTpk2Kj49Xp06d5Ofnp1mzZik7O1vp6enVxvr5+alz587y8PD4uVh3d2VnZ19zHQAAAAAA87HrE9xFixZp4cKFslgs6tq1qzw9q24WHBx8xTksFotyc3PVo0cP27ImTZqoQ4cOysrKUnR0dI3bjR07VpmZmSotLVWbNm30wAMP2FMyAAAAAKCBsfsuygUFBZo6dWqV77/W5iZThYWFki6E2osFBATY1tVk48aNqqys1P79+/XZZ5+pZcuW9pZs07Klf623geMEBQXUdwm4SvTOddE710b/XBe9c130znXRO9dVF72zK+D+/ve/V+PGjbVixYqrvsmUv/+FkGmxWKost1gstnWX4uHhoYiICKWnp+uZZ57RihUrarXvs2cLZbUaVxzHi8E58vIsVx5US/TOOeid63JE7yT65yy89lwXvXNd9M510TvXVZveXaondv9M0NatW9W5c2e7d/hLAQEBCgkJUWZmpm6++WZJF8JtTk6OunfvbtccFRUVfAcXAAAAAFAju24y1b17d509e/aadxYbG6t169YpOztbRUVFSkpKUmhoqCIjI6uN3blzp9LT01VaWqqKigrt3r1bKSkpiomJueY6AAAAAADmY9cnuL/73e+0ePFiTZ06VWFhYdVuMtW6dWu7dhYfHy+LxaKxY8equLhYkZGRWrNmjdzd3bV3715NnDhR27dvV3BwsIqKirR48WIdO3ZM7u7uat26tcaPH6+JEyfW/igBAAAAAKZnV8CdPHmyJGnKlClXfZMp6cLP/CQmJioxMbHauqioKGVkZNgeDxkyREOGDLFrXgAAAAAA7Aq4KSkpjq4DAAAAAIBrYlfA7d27t6PrAAAAAADgmlwy4J4+fdr23drTp09fdhJ7v4MLAAAAAICjXDLgDhgwQGlpaWrZsqViYmJq/O3b2n4HFwAAAAAAR7lkwH3zzTfVtGlTSXwHFwAAAABw/btkwO3du7cGDx6sLVu28B1cAAAAAMB1z/1yK3Nzc2W1Wp1VCwAAAAAAV+2yARcAAAAAAFdxxZ8JysvLU0VFxWXHcBdlAAAAAEB9u2LAvffeey+5jrsoAwAAAACuF1cMuC+++KLtbsoAAAAAAFyvrhhwIyIi1LJlS2fUAgAAAADAVbvsTabc3NycVQcAAAAAANfksgHXMAxn1QEAAAAAwDW57CXK//znP51VBwAAAAAA14TfwQUAAAAAmAIBFwAAAABgCgRcAAAAAIApEHABAAAAAKZAwAUAAAAAmAIBFwAAAABgCgRcAAAAAIApEHABAAAAAKZAwAUAAAAAmAIBFwAAAABgCk4NuFarVcuXL1e/fv0UHh6uCRMmKDc3t8ax+/bt06RJk9SvXz9FRETo3nvv1YcffujMcgEAAAAALsSpATc5OVmpqalav3690tLSFBwcrISEBFmt1mpjCwoKNGLECKWmpmrv3r1KSEhQYmKivvnmG2eWDAAAAABwEU4NuJs2bVJ8fLw6deokPz8/zZo1S9nZ2UpPT682NiYmRqNGjVKLFi3k7u6u4cOHq0uXLjWOBQAAAADAaQHXYrEoNzdXPXr0sC1r0qSJOnTooKysrCtuf/r0aR05ckTdunVzZJkAAAAAABfl6awdFRYWSroQai8WEBBgW3cpP/74o6ZNm6aBAweqb9++td53y5b+td4GjhMUFFDfJeAq0TvXRe9cG/1zXfTOddE710XvXFdd9M5pAdff/0LItFgsVZZbLBbbuppYLBZNmjRJQUFBev75569q32fPFspqNa44jheDc+TlWa48qJbonXPQO9fliN5J9M9ZeO25Lnrnuuid66J3rqs2vbtUT5x2iXJAQIBCQkKUmZlpW2axWJSTk6Pu3bvXuM25c+c0fvx43XDDDVq5cqW8vb2dVS4AAAAAwMU49SZTsbGxWrdunbKzs1VUVKSkpCSFhoYqMjKy2ti8vDzFxcUpLCxMS5culaen0z5sBgAAAAC4IKemxvj4eFksFo0dO1bFxcWKjIzUmjVr5O7urr1792rixInavn27goOD9fbbb+vbb7/V8ePH9fe//902x913361FixY5s2wAAAAAgAtwasB1d3dXYmKiEhMTq62LiopSRkaG7fHUqVM1depUZ5YHAAAAAHBhTr1EGQAAAAAARyHgAgAAAABMgYALAAAAADAFAi4AAAAAwBQIuAAAAAAAUyDgAgAAAABMgYALAAAAADAFAi4AAAAAwBQIuAAAAAAAUyDgAgAAAABMgYALAAAAADAFAi4AAAAAwBQIuAAAAAAAUyDgAgAAAABMgYALAAAAADAFAi4AAAAAwBQIuAAAAAAAUyDgAgAAAABMgYALAAAAADAFAi4AAAAAwBQIuAAAAAAAUyDgAgAAAABMgYALAAAAADAFAi4AAAAAwBQIuAAAAAAAU3BqwLVarVq+fLn69eun8PBwTZgwQbm5uTWOLSkp0fTp0zVs2DB169ZNq1atcmapAAAAAAAX49SAm5ycrNTUVK1fv15paWkKDg5WQkKCrFZrtbFubm6KiIjQokWL1LNnT2eWCQAAAABwQU4NuJs2bVJ8fLw6deokPz8/zZo1S9nZ2UpPT6821sfHRw8//LBuvfVW+fj4OLNMAAAAAIALclrAtVgsys3NVY8ePWzLmjRpog4dOigrK8tZZQAAAAAATMrTWTsqLCyUdCHUXiwgIMC2zlFatvR36PyonaCggPouAVeJ3rkueufa6J/roneui965Lnrnuuqid04LuP7+F0KmxWKpstxisdjWOcrZs4WyWo0rjuPF4Bx5eZYrD6oleucc9M51OaJ3Ev1zFl57roveuS5657roneuqTe8u1ROnXaIcEBCgkJAQZWZm2pZZLBbl5OSoe/fuzioDAAAAAGBSTr3JVGxsrNatW6fs7GwVFRUpKSlJoaGhioyMrHF8WVmZSktLZbVaVVFRodLSUpWVlTmzZAAAAACAi3BqwI2Pj9cdd9yhsWPHql+/fsrNzdWaNWvk7u6uvXv3Kjw8XCdOnLCNv/3229WzZ0/t3btXa9euVc+ePTVhwgRnlgwAAAAAcBFO+w6uJLm7uysxMVGJiYnV1kVFRSkjI6PKsk8++cRZpQEAAAAAXJxTP8EFAAAAAMBRCLgAAAAAAFMg4AIAAAAATIGACwAAAAAwBQIuAAAAAMAUCLgAAAAAAFMg4AIAAAAATIGACwAAAAAwBQIuAAAAAMAUCLgAAAAAAFMg4AIAAAAATIGACwAAAAAwBQIuAAAAAMAUCLgAAAAAAFMg4AIAAAAATIGACwAAAAAwBQIuAAAAAMAUCLgAAAAAAFMg4AIAAAAATIGACwAAAAAwBQIuAAAAAMAUCLgAAAAAAFMg4AIAAAAATIGACwAAAAAwBacGXKvVquXLl6tfv34KDw/XhAkTlJube8nxhw4dUmxsrHr16qUBAwYoJSXFidUCAAAAAFyJUwNucnKyUlNTtX79eqWlpSk4OFgJCQmyWq3VxhYWFio+Pl79+/fXnj17tGLFCq1evVp///vfnVkyAAAAAMBFODXgbtq0SfHx8erUqZP8/Pw0a9YsZWdnKz09vdrYDz/8UO7u7poyZYp8fHx0yy236P7779fGjRudWTIAAAAAwEV4OmtHFotFubm56tGjh21ZkyZN1KFDB2VlZSk6OrrK+H/+85+68cYb5e7+cwbv0aOHNm/eXOt9u7u72T02sLlfredH7dSmH7Xh3aSlQ+bFzxzVu0D/Fg6ZFz9zVO8kqVEgrz1Hc1T/mjZr7JB58TNH9a5JM153juao3nkF+DpkXvzMYa+7Jk0cMi9+Vhe9czMMw6iDWq7o5MmTGjBggD744AOFhobalsfGxuq2227TlClTqoyfO3euSktLtWzZMtuyL774QhMnTtShQ4ecUTIAAAAAwIU47RJlf39/SRc+yb2YxWKxrfvl+MLCwirLzp8/X+NYAAAAAACcFnADAgIUEhKizMxM2zKLxaKcnBx179692vhu3brp0KFDVW5AdfDgQXXr1s0p9QIAAAAAXItTbzIVGxurdevWKTs7W0VFRUpKSlJoaKgiIyOrjR02bJgqKyu1Zs0alZWV6ZtvvtHmzZv1wAMPOLNkAAAAAICLcNp3cKULv4P7wgsvaMuWLSouLlZkZKQWLlyotm3bau/evZo4caK2b9+u4OBgSRd+B3fhwoXKyspS8+bNNWHCBD300EPOKhcAAAAA4EKcGnABAAAAAHAUp16iDAAAAACAoxBwAQAAAACmQMAFAAAAAJgCARcAAAAAYAoEXACAaWVmZuqee+5ReHi4Zs+eXd/lAADgMJzzLvCs7wJgn6NHj+rFF1/Url27VFhYqKCgIA0fPlyPPfaY/Pz8bOPmzZunffv2KTs7WyNHjtTixYvrsWpI9vUuOztbL7zwgjIyMlRYWKgbbrhB48eP129/+9t6rr5hs6d3xcXFmjRpkr7//nuVlJQoICBAw4cPV2Jionx8fOr5CMzL3vfE5cuXq3fv3nr33XeveZ9bt27V6tWr9cknn1zzXLg8znmui3Oe6+Kcd/3inFc7fILrAr799luNHj1aXl5e2rx5szIyMrRq1Sp99dVXiouLU3FxsW1sWFiYZs+erUGDBtVjxfiJvb07f/68+vTpoy1btujrr7/WwoULtWTJEn3wwQf1fAQNl7298/Ly0rx58/SPf/xDX3/9tbZs2aJDhw5p5cqV9XwE5lWb98Rjx46pe/fu9VgtaotznuvinOe6OOddvzjn1R6/g+sCHn30UZWUlGjjxo1Vlufn52vYsGGaNGmSJk2aVGXdT5cl8H+z69fV9O4n06dPV6tWrfTUU085o1T8wtX2Li8vT0888YT8/f21Zs0aZ5XboNjTmwkTJigqKkrFxcXy8vKSp6enFi9erI4dO+qPf/yjDh8+rMrKSnXv3l1z5861/UFw4sQJLViwQPv371dlZaVuuOEGPfPMM5KkRx55ROXl5WrUqJEkKSkpSUOGDHHqsTcEnPNcF+c818U57/rFOa/2+AT3OldSUqLdu3fr3nvvrbauRYsWiomJ0aeffloPleFKrqV3RUVF2r9/v8LCwhxdJmpwNb1LTEzULbfcov79++vw4cOaMGGCs8ptUOztjYeHhzIyMhQcHKyFCxcqIyNDw4cPlyT97ne/044dO7Rjxw517NhRjz32mMrLyyVJy5YtU+vWrfX555/rq6++0osvvqjWrVsrKipKCxcuVHBwsDIyMpSRkeEyJ3pXwjnPdXHOc12c865fnPOuDgH3OldQUKDKykq1atWqxvVt2rRRfn6+k6uCPa62dxUVFXryyScVEhKiUaNGObhK1ORqerds2TJlZGTo3XffVWxsrIKDg51RaoNzre+JXbt2Vd++feXj46PGjRsrMTFRubm5ysnJkSR5e3vrzJkzysnJkZubmzp16qR27do55FhQHec818U5z3Vxzrt+cc67OgTc68x7772n8PBw238VFRXy8PDQf/7znxrHnzp1Si1atHBylahJXfSurKxMM2fOVH5+vl555RV5eXk5o/QGr65ed25uburWrZu6deumxx9/3NFlNwh1/Z54/PhxTZ8+XTExMYqIiNDgwYMlSWfPnpUk/f73v1f79u01depU9evXT3PnziVQORDnPNfFOc91cc67fnHOqxsE3OvMyJEjbZcCZGRkKCQkRH369Knxbmjnzp3Tjh07NGDAAOcXimqutXclJSWaMmWKfvjhB61bt04BAQFOrL5hq+vXXUVFhbKzsx1YccNR172ZP3++vLy8tG3bNn399df6+OOPJUk/3Y6iefPmmjt3rj744AP99a9/1bFjx/T8889LktzdOWXWNc55rotznuvinHf94pxXN1y38gZk9uzZysrK0ty5c3Xy5ElVVlYqKytLCQkJCg4OVlxcnG1sWVmZSktLVVlZqcrKSpWWlqqsrKweq2/Y7O1dYWGhJk6cKMMw9Nprr1W55Tvqh729++abb/T555+rqKhIVqtVmZmZeumllxQTE1PPR2BetXlP/CWLxaJGjRopICBAFotFSUlJVdZv375dOTk5slqt8vPzk7e3t+0kHxgYqPz8fBUUFDj0+Bo6znmui3Oe6+Kcd/3inFd73EXZRRw5ckSrV6/Wzp07VVRUpMDAQNvvX/n7+9vGxcXFac+ePVW2DQkJccnfsDILe3q3bds2zZ49W76+vlX+j1lkZKSSk5Prq/QGz57eZWRk6Nlnn9WRI0dkGIYCAwM1dOjQar9Nh7pl73vioEGDNHXqVP3mN7+RJO3bt08LFizQ0aNH1apVK82YMUMzZ85USkqK+vTpo2XLlik1NVXnzp2Tr6+v+vbtq6efflotWrRQRUWFEhMTtXv3blVWVur555+3Xe6FusU5z3VxznNdnPOuX5zzaoeACwAAAAAwBS5RBgAAAACYAgEXAAAAAGAKBFwAAAAAgCkQcAEAAAAApkDAvc5s3bpVgwYNqu8yrig8PFx79+6t7zKuK/TOtdE/10XvAADATwi4uKwvv/xSYWFh1ZZnZGQoKiqqHiqCveida6N/roveAQBQfwi4TlZeXl7fJeAq0TvXRv9cF70DAAD2IuBeJCwsTCkpKbrvvvt0yy23aMyYMTpx4oRSUlI0YMAA9e7dW0uXLrWNLy0t1fTp09W/f3+Fh4frrrvu0t/+9rdqc7755pu6//771atXL6Wlpam4uFjLli3T0KFDFR4ermHDhumDDz6ost1bb72lgQMHKjIyUjNmzFBhYeFVHdOgQYO0Zs0axcfH2/b10Ucf2db/61//0kMPPaQ+ffooKipKcXFxysrKkiSdOHFCEydOlHTh0rrw8HClpKTYjuvLL79UZWWl+vfvr9TU1Cr7ffHFFzVu3Djb488++0z333+/oqOjNWzYMNs8dYXeuW7vfqqJ/rlm/+id6/YOAABTMmDTtWtXY/To0caJEyeMoqIiIy4uzhg2bJixfPlyo7S01Dh48KBx0003GV9++aVhGIZRXFxsbN261Th//rxRXl5ubN261bjxxhuN7777rsqcd9xxh/Hdd98ZVqvVKC4uNmbOnGncf//9xpEjRwzDMIwTJ04YWVlZhmEYxl/+8hfjxhtvNBYvXmwUFxcbp06dMgYPHmysXr3aNuekSZOMyMjIS/73yiuv2MYOHDjQGDhwoHHw4EGjsrLSeO2114yIiAijsLDQMAzDOHz4sLFz506jpKTE+PHHH42nn37aGDhwoFFWVmYYhmHs3r3b6Nq1a43P1e7duw3DMIylS5caDz/8sG1dZWWlMWDAAGPbtm2GYRjGrl27jKioKGPnzp1GZWWlcfjwYeO2224z3n333Wvu2cX10DvX7N1PNdE/1+wfvXPd3gEAYEYE3It07drVeP/9922P169fb/Tq1cuoqKiwLRs1apSxbt26S85x9913Gxs2bKgy5zvvvGN7fPbsWaNr167GoUOHatz+L3/5i9GzZ0+jvLzctmzx4sXG5MmTr+qYBg4caKxZs8b2uLCw0Ojatatx4MCBGsf/8MMPRteuXW1/bNrzh1p2drbRrVs349ixY4ZhGMaOHTuMyMhIo6ioyDAMw5g8ebLxwgsvVNn+lVdeMcaPH39Vx1QTeue6vfupJvrnmv2jd67bOwAAzMizvj9Bri/vvfeeFixYYHu8fft2SVJQUJBtWaNGjdSiRQt5eHjYlvn6+urHH3+UJJWVlWnp0qX69NNPdfbsWbm7u6uoqEj5+flV9tW2bVvbv48fPy5J6tix4yVra9mypTw9f25N48aNbfu8Gq1atbL928/PT5Js8x0/flxLlizR/v37ZbFY5O5+4ar1s2fPqnPnznbNHxoaqqioKG3dulXTp0/Xli1bdOedd6pRo0aSpKNHj2rXrl1av369bZvKykoFBwdf1fHQO9ftnUT/XLl/9M51ewcAQEPRYAPuyJEjNXLkyGua4/XXX1daWpqSk5PVvn17ubm5aeTIkTIMo8q4n/74kX7+o+3f//63unXrdlX7jY+PV3p6+iXXT548WQkJCXbNNX/+fDVv3lzbtm1TixYtVFBQoN69e9uO4eLaL2f06NFauXKlHnzwQX388cd66623bOsCAwN1zz332F3TldC7C1yxdxL9+4kr9o/eXeCKvQMAoKFosAG3LlgsFnl7e6t58+aqqKjQ5s2b9d1332no0KGX3KZFixa666679Mwzz2jx4sUKDQ3VqVOn9MMPP9j9h1tycnJdHYIsFouCg4MVEBAgi8WipKSkKusDAwMlSUeOHFGnTp0uOc/tt9+uP/7xj5ozZ446d+6sm2++2bZu/Pjxmj9/vnr16qXo6GjbfAUFBbbHzkbvfuZqvZPo38VcrX/07meu1jsAAFwBd1G+Bo8++qhatmypmJgYDRw4UKdPn1ZERMQVt/vDH/6giIgITZgwQeHh4XrooYeUk5PjhIqrmzdvng4cOKDo6GiNHj1a/fr1q7K+Y8eOiouL09ixYxUVFVXlcrmL+fr66q677tI//vEP3XfffVXWDRkyRIsXL9bKlSvVr18/9evXT0899ZTOnTvnsOO6Enr3M1frnUT/LuZq/aN3P3O13gEA4ArcjF9eFwYAAAAAgAviE1wAAAAAgCkQcAEAAAAApkDABQAAAACYAgEXAAAAAGAKBFwAAAAAgCkQcAEAAAAApkDABQDABOLi4jRv3rz6LgMAgHrF7+ACAOAEZ86c0Zo1a/Tpp5/qP//5j/z9/RUdHa0pU6aoe/fuds/z8ssva8uWLfrkk0+qLP/hhx/k6ekpf3//ui4dAACXwSe4AAA42MmTJzV69GhlZGRowYIF+vDDD/Xqq6/Ky8tLY8aM0Y4dO655H82aNSPcAgAaPAIuAAAOtnDhQlVUVCglJUUxMTEKDg5Wz549tXz5ct16662aM2eOSkpKtGrVKg0dOlTvv/++Bg8erJtvvlmPPPKIjh8/LknaunWrVq5cqdzcXIWFhSksLEyrVq2SVP0S5fLyci1dulS//vWv1aNHD40YMULvv/9+lbrCwsK0YcMGzZo1S+Hh4brtttv0yiuvVBnz0UcfadSoUerVq5eioqJ033336dChQw5+xgAAuDoEXAAAHKigoECfffaZxo0bV+MnrJMnT9aZM2f0xRdfSJLy8vK0ceNGrVixQhs2bFBhYaGmTZsmwzA0YsQITZw4UW3atFFaWprS0tL06KOP1rjf5cuXa/PmzZo7d67ef/99jRw5UrNmzdKuXbuqjHvppZcUHR2td999V5MnT9by5cttY/Ly8jRjxgzdeeedSk1N1dtvv63x48fLw8Ojjp8lAADqhmd9FwAAgJkdPXpUVqtVXbp0qXH9r371K0lSdna2JKm4uFiLFy9Whw4dJElLlizR7bffrt27d6tv375q3LixPDw8FBQUdMl9FhcX63/+5380Z84c3XHHHZKkhIQEHThwQGvWrFHfvn1tY0eMGKExY8ZIksaNG6f169dr586d6tu3r/Ly8lReXq477rhDbdu2lSR17tz5Gp8RAAAch09wAQC4jrRo0cIWbiWpY8eOat68ub799lu75zh69KjKy8sVHR1dZXl0dLS+++67Ksu6detW5XGrVq105swZSRcuYe7fv7/uvvtuPfbYY3rzzTd18uTJ2h4SAABOQ8AFAMCB2rdvLzc3N/3rX/+qcf1PgbNjx47OLMvGy8urymM3Nzf99AMLHh4eSk5O1ptvvqmbb75ZH374oYYPH65PP/20PkoFAOCKCLgAADhQs2bNFBMTY/s+7S+98sorCgwM1H/9139JkvLz85WTk2Nbn52drXPnztkuZfby8lJlZeVl99mhQwd5e3vrq6++qrL8q6++uuSl0pfi5uamnj17KiEhQRs2bFB0dLS2bt1aqzkAAHAWAi4AAA42f/58eXh46KGHHtKOHTt08uRJffPNN0pMTNTu3bv13HPPydfXV5LUqFEjzZkzRwcOHNCBAwc0e/Zsde/e3fa92bZt2+rMmTPKyMhQfn6+iouLq+2vUaNGiouL04svvqj//d//VXZ2ttauXauPP/5YCQkJdtf99ddf66WXXtL+/ft14sQJ7dq1S4cPH+Z7uACA6xY3mQIAwMFCQkK0detWvfzyy1qwYIHy8vLk5+en3r176+2339aNN95oGxsUFKQxY8bo8ccfV15eniIiIrR06VK5ublJkoYMGaLbb79dkydPVkFBgaZOnapp06ZV2+fMmTPl7u6uP/3pTzp37pzat2+vpKSkKjeYupKAgADt27dPGzduVEFBgYKCgnT33XdrypQp1/6kAADgAG7GT1+0AQAA9WrVqlV677339H//93/1XQoAAC6JS5QBAAAAAKZAwAUAAAAAmAKXKAMAAAAATIFPcAEAAAAApkDABQAAAACYAgEXAAAAAGAKBFwAAAAAgCkQcAEAAAAApkDABQAAAACYwv8Dxla8niLbpogAAAAASUVORK5CYII=\n",
+ "image/png": "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\n",
"text/plain": [
""
]
diff --git a/chapter4/solvers.ipynb b/chapter4/solvers.ipynb
index 0f7ee037..bedcd901 100644
--- a/chapter4/solvers.ipynb
+++ b/chapter4/solvers.ipynb
@@ -131,7 +131,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "KSP Object: (dolfinx_solve_139997790839424) 1 MPI processes\n",
+ "KSP Object: (dolfinx_solve_140338950761200) 1 MPI processes\n",
"\n",
" type: preonly\n",
"\n",
@@ -143,7 +143,7 @@
"\n",
" using NONE norm type for convergence test\n",
"\n",
- "PC Object: (dolfinx_solve_139997790839424) 1 MPI processes\n",
+ "PC Object: (dolfinx_solve_140338950761200) 1 MPI processes\n",
"\n",
" type: lu\n",
"\n",
@@ -216,7 +216,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "KSP Object: (dolfinx_solve_139997790842112) 1 MPI processes\n",
+ "KSP Object: (dolfinx_solve_140338945323648) 1 MPI processes\n",
"\n",
" type: cg\n",
"\n",
@@ -228,7 +228,7 @@
"\n",
" using PRECONDITIONED norm type for convergence test\n",
"\n",
- "PC Object: (dolfinx_solve_139997790842112) 1 MPI processes\n",
+ "PC Object: (dolfinx_solve_140338945323648) 1 MPI processes\n",
"\n",
" type: ilu\n",
"\n",
@@ -301,7 +301,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "KSP Object: (dolfinx_solve_139997650749568) 1 MPI processes\n",
+ "KSP Object: (dolfinx_solve_140338950761680) 1 MPI processes\n",
"\n",
" type: gmres\n",
"\n",
@@ -317,7 +317,7 @@
"\n",
" using PRECONDITIONED norm type for convergence test\n",
"\n",
- "PC Object: (dolfinx_solve_139997650749568) 1 MPI processes\n",
+ "PC Object: (dolfinx_solve_140338950761680) 1 MPI processes\n",
"\n",
" type: none\n",
"\n",
diff --git a/local_dockerfile/Dockerfile b/local_dockerfile/Dockerfile
index c7930e70..748b557d 100644
--- a/local_dockerfile/Dockerfile
+++ b/local_dockerfile/Dockerfile
@@ -1,4 +1,30 @@
-FROM dolfinx/lab
+FROM dolfinx/dev-env
+
+RUN pip3 install --upgrade --no-cache-dir jupyter jupyterlab
+
+# pyvista dependencies from apt
+RUN apt-get -qq update && \
+ apt-get -y install libgl1-mesa-dev xvfb && \
+ apt-get clean && \
+ rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
+
+RUN git clone https://github.com/FEniCS/basix.git && \
+ cmake -G Ninja -B build-basix -DCMAKE_BUILD_TYPE="Release" -S ./basix/cpp/ && \
+ cmake --build build-basix --parallel 3 && \
+ cmake --install build-basix && \
+ BUILD_TYPE="Release" pip3 install ./basix/python && \
+ python3 -m pip install git+https://github.com/FEniCS/ufl.git && \
+ python3 -m pip install git+https://github.com/FEniCS/ffcx.git
+
+RUN git clone https://github.com/FEniCS/dolfinx.git && \
+ PETSC_ARCH=linux-gnu-real-32 cmake -G Ninja -DCMAKE_BUILD_TYPE="Release" -B build-dolfinx -S ./dolfinx/cpp/ && \
+ cmake --build build-dolfinx && \
+ cmake --install build-dolfinx && \
+ . /usr/local/lib/dolfinx/dolfinx.conf && \
+ PETSC_ARCH=linux-gnu-real-32 BUILD_TYPE="Release" python3 -m pip -v install ./dolfinx/python/
+
+# ------------------------------------
+# FROM dolfinx/lab
# Install h5py
RUN HDF5_MPI="ON" CC=mpicc HDF5_DIR="/usr/lib/x86_64-linux-gnu/hdf5/mpich/" pip3 install --no-cache-dir --no-binary=h5py h5py