diff --git a/.github/workflows/workflow_dolfinx_container.yaml b/.github/workflows/workflow_dolfinx_container.yaml index 295d5fbf..c75f4977 100644 --- a/.github/workflows/workflow_dolfinx_container.yaml +++ b/.github/workflows/workflow_dolfinx_container.yaml @@ -9,7 +9,8 @@ on: jobs: set-up-computing-environment: runs-on: ubuntu-latest - container: ghcr.io/fenics/dolfinx/lab:v0.7.2 + container: ghcr.io/fenics/dolfinx/lab:nightly +# container: ghcr.io/fenics/dolfinx/lab:v0.7.2 steps: - name: Checkout repository @@ -25,4 +26,4 @@ jobs: run: pytest -v test/test_1d.py - name: Run tests - run: cd test && pytest -v . \ No newline at end of file + run: cd test && pytest -v . diff --git a/demo/demo_bifurcation.py b/demo/demo_bifurcation.py index dbe39fa0..086233be 100644 --- a/demo/demo_bifurcation.py +++ b/demo/demo_bifurcation.py @@ -8,6 +8,8 @@ import dolfinx import dolfinx.mesh import dolfinx.plot +import basix.ufl + import numpy as np import pandas as pd import petsc4py @@ -74,10 +76,10 @@ file.write_mesh(mesh) # Function spaces -element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) +element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) V_u = FunctionSpace(mesh, element_u) -element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) +element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) # Define the state @@ -110,7 +112,7 @@ alpha_ub.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, zero_alpha, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + f.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) bc_u_left = dirichletbc(np.array([0, 0], dtype=PETSc.ScalarType), dofs_u_left, V_u) @@ -127,8 +129,8 @@ ) ] -set_bc(alpha_ub.vector, bcs_alpha) -alpha_ub.vector.ghostUpdate( +set_bc(alpha_ub.x.petsc_vec, bcs_alpha) +alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -170,11 +172,11 @@ for i_t, t in enumerate(loads): u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1]))) - u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/demo/demo_elasticity.py b/demo/demo_elasticity.py index 728b2ec5..377c0721 100644 --- a/demo/demo_elasticity.py +++ b/demo/demo_elasticity.py @@ -23,7 +23,7 @@ from irrevolutions.models import ElasticityModel from irrevolutions.solvers import SNESSolver as ElasticitySolver from irrevolutions.utils.viz import plot_vector - +import basix.ufl logging.basicConfig(level=logging.INFO) @@ -66,10 +66,10 @@ file.write_mesh(mesh) # Function spaces -element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) -V_u = dolfinx.fem.FunctionSpace(mesh, element_u) -V_ux = dolfinx.fem.FunctionSpace( - mesh, ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) +element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) +V_u = dolfinx.fem.functionspace(mesh, element_u) +V_ux = dolfinx.fem.functionspace( + mesh,basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) ) # Define the state @@ -96,7 +96,7 @@ ux_.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, ux_]: - f.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + f.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) bcs_u = [ dolfinx.fem.dirichletbc(zero_u, dofs_u_left), @@ -133,7 +133,7 @@ for i_t, t in enumerate(loads): u_.interpolate(lambda x: (t * np.ones_like(x[0]), 0 * np.ones_like(x[1]))) - u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) logging.info(f"-- Solving for t = {t:3.2f} --") diff --git a/demo/demo_traction.py b/demo/demo_traction.py index e75b3809..13ac2140 100644 --- a/demo/demo_traction.py +++ b/demo/demo_traction.py @@ -27,7 +27,7 @@ from irrevolutions.models import DamageElasticityModel as Brittle from irrevolutions.utils.plots import plot_energies, plot_force_displacement from irrevolutions.utils.viz import plot_scalar, plot_vector - +import basix.ufl logging.basicConfig(level=logging.INFO) @@ -79,10 +79,10 @@ file.write_mesh(mesh) # Function spaces -element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) +element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) V_u = FunctionSpace(mesh, element_u) -element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) +element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) # Define the state @@ -115,7 +115,7 @@ alpha_ub.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, zero_alpha, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + f.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) bc_u_left = dirichletbc(np.array([0, 0], dtype=PETSc.ScalarType), dofs_u_left, V_u) @@ -134,8 +134,8 @@ bcs_alpha = [] -set_bc(alpha_ub.vector, bcs_alpha) -alpha_ub.vector.ghostUpdate( +set_bc(alpha_ub.x.petsc_vec, bcs_alpha) +alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -177,11 +177,11 @@ for i_t, t in enumerate(loads): u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1]))) - u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/demo/demo_vi.py b/demo/demo_vi.py index 5d42a3f0..64d4d3b0 100644 --- a/demo/demo_vi.py +++ b/demo/demo_vi.py @@ -40,11 +40,11 @@ V = FunctionSpace(mesh, ("CG", 1)) zero = Function(V) -with zero.vector.localForm() as loc: +with zero.x.petsc_vec.localForm() as loc: loc.set(0.0) one = Function(V) -with one.vector.localForm() as loc: +with one.x.petsc_vec.localForm() as loc: loc.set(1.0) @@ -94,7 +94,7 @@ def monitor(snes, its, fgnorm): solver_snes.setMonitor(monitor) -solver_snes.solve(None, u.vector) +solver_snes.solve(None, u.x.petsc_vec) # solver_snes.view() prefix = os.path.join("output", "test-vi") diff --git a/playground/benchmark-umut-at2/vs_analytics_at2.py b/playground/benchmark-umut-at2/vs_analytics_at2.py index ab4fd3db..7bb18646 100644 --- a/playground/benchmark-umut-at2/vs_analytics_at2.py +++ b/playground/benchmark-umut-at2/vs_analytics_at2.py @@ -23,6 +23,7 @@ from mpi4py import MPI from petsc4py import PETSc from pyvista.utilities import xvfb +import basix.ufl from irrevolutions.algorithms.am import HybridSolver from irrevolutions.algorithms.so import BifurcationSolver, StabilitySolver @@ -145,11 +146,11 @@ def run_computation(parameters, storage=None): file.write_mesh(mesh) # Functional Setting - element_u = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) - V_u = dolfinx.fem.FunctionSpace(mesh, element_u) - V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) + V_u = dolfinx.fem.functionspace(mesh, element_u) + V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") dolfinx.fem.Function(V_u, name="BoundaryDisplacement") @@ -192,7 +193,7 @@ def run_computation(parameters, storage=None): u_zero.interpolate(lambda x: eps_t / 2.0 * (2 * x[0] - Lx)) for f in [zero_u, u_zero, alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -234,13 +235,13 @@ def run_computation(parameters, storage=None): eps_t.value = t u_zero.interpolate(lambda x: eps_t / 2.0 * (2 * x[0] - Lx)) - u_zero.vector.ghostUpdate( + u_zero.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/playground/benchmark-umut-at2/vs_analytics_at2_2d.py b/playground/benchmark-umut-at2/vs_analytics_at2_2d.py index c1d07528..981fec0b 100644 --- a/playground/benchmark-umut-at2/vs_analytics_at2_2d.py +++ b/playground/benchmark-umut-at2/vs_analytics_at2_2d.py @@ -22,6 +22,7 @@ from mpi4py import MPI from petsc4py import PETSc from pyvista.utilities import xvfb +import basix.ufl from irrevolutions.algorithms.am import HybridSolver from irrevolutions.algorithms.so import BifurcationSolver, StabilitySolver @@ -101,11 +102,11 @@ def run_computation(parameters, storage=None): file.write_mesh(mesh) # Functional Setting - element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) - V_u = dolfinx.fem.FunctionSpace(mesh, element_u) - V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) + V_u = dolfinx.fem.functionspace(mesh, element_u) + V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") dolfinx.fem.Function(V_u, name="BoundaryDisplacement") @@ -145,7 +146,7 @@ def run_computation(parameters, storage=None): # eps_t = dolfinx.fem.Constant(mesh, np.array(1., dtype=PETSc.ScalarType)) for f in [zero_u, u_zero, alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -194,8 +195,8 @@ def run_computation(parameters, storage=None): tau.value = t # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/playground/pizza-notch/pizza-notch.py b/playground/pizza-notch/pizza-notch.py index 66f8f7bc..92df1760 100644 --- a/playground/pizza-notch/pizza-notch.py +++ b/playground/pizza-notch/pizza-notch.py @@ -31,7 +31,7 @@ history_data, set_vector_to_constant) from irrevolutions.utils.lib import _local_notch_asymptotic from irrevolutions.utils.viz import plot_mesh, plot_scalar, plot_vector - +import basix.ufl description = """We solve here a basic 2d of a notched specimen. Imagine a dinner a pizza which is missing a slice, and lots of hungry friends that pull from the sides of the pizza. Will a real pizza will break at the centre? @@ -85,10 +85,10 @@ def run_computation(parameters, storage): hashlib.md5(str(parameters).encode("utf-8")).hexdigest() # Function spaces - element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=2) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(2,)) V_u = FunctionSpace(mesh, element_u) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) # Define the state @@ -135,7 +135,7 @@ def run_computation(parameters, storage): alpha_ub.interpolate(lambda x: np.ones_like(x[0])) for f in [alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -149,8 +149,8 @@ def run_computation(parameters, storage): ) ] bcs_alpha = [] - set_bc(alpha_ub.vector, bcs_alpha) - alpha_ub.vector.ghostUpdate( + set_bc(alpha_ub.x.petsc_vec, bcs_alpha) + alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -162,10 +162,10 @@ def run_computation(parameters, storage): u_ub = Function(V_u, name="displacement upper bound") alpha_lb = Function(V_alpha, name="damage lower bound") alpha_ub = Function(V_alpha, name="damage upper bound") - set_vector_to_constant(u_lb.vector, PETSc.NINFINITY) - set_vector_to_constant(u_ub.vector, PETSc.PINFINITY) - set_vector_to_constant(alpha_lb.vector, 0) - set_vector_to_constant(alpha_ub.vector, 1) + set_vector_to_constant(u_lb.x.petsc_vec, PETSc.NINFINITY) + set_vector_to_constant(u_ub.x.petsc_vec, PETSc.PINFINITY) + set_vector_to_constant(alpha_lb.x.petsc_vec, 0) + set_vector_to_constant(alpha_ub.x.petsc_vec, 1) model = Brittle(parameters["model"]) @@ -205,8 +205,8 @@ def run_computation(parameters, storage): ) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/playground/rayleigh/rayleigh.py b/playground/rayleigh/rayleigh.py index 9d040bcf..6d9145e3 100644 --- a/playground/rayleigh/rayleigh.py +++ b/playground/rayleigh/rayleigh.py @@ -5,6 +5,7 @@ import os from pathlib import Path +import basix.ufl import dolfinx import matplotlib.pyplot as plt import numpy as np @@ -103,11 +104,11 @@ def test_rayleigh(parameters=None, storage=None): # _D = (np.pi**2 * _a / (_b * _c**2)) ** (1 / 3) - element_u = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) - V_u = dolfinx.fem.FunctionSpace(mesh, element_u) - V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) + V_u = dolfinx.fem.functionspace(mesh, element_u) + V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") alpha = dolfinx.fem.Function(V_alpha, name="Damage") @@ -120,12 +121,12 @@ def test_rayleigh(parameters=None, storage=None): for zero in [zero_u, zero_alpha]: zero.interpolate(lambda x: np.zeros_like(x[0])) - zero.vector.ghostUpdate( + zero.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) one_alpha.interpolate(lambda x: np.zeros_like(x[0])) - one_alpha.vector.ghostUpdate( + one_alpha.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/playground/rayleigh/rayleigh_parametric.py b/playground/rayleigh/rayleigh_parametric.py index 27999389..5c7b65a7 100644 --- a/playground/rayleigh/rayleigh_parametric.py +++ b/playground/rayleigh/rayleigh_parametric.py @@ -3,6 +3,7 @@ import logging import sys from pathlib import Path +import basix.ufl import dolfinx import numpy as np @@ -110,11 +111,11 @@ def rayleigh(parameters, storage=None): else 1 ) - element_u = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) - V_u = dolfinx.fem.FunctionSpace(mesh, element_u) - V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) + V_u = dolfinx.fem.functionspace(mesh, element_u) + V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") alpha = dolfinx.fem.Function(V_alpha, name="Damage") @@ -127,12 +128,12 @@ def rayleigh(parameters, storage=None): for zero in [zero_u, zero_alpha]: zero.interpolate(lambda x: np.zeros_like(x[0])) - zero.vector.ghostUpdate( + zero.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) one_alpha.interpolate(lambda x: np.zeros_like(x[0])) - one_alpha.vector.ghostUpdate( + one_alpha.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/playground/tutorials/iceice_1Bifurcation.ipynb b/playground/tutorials/iceice_1Bifurcation.ipynb index 177df7a8..c796f4df 100644 --- a/playground/tutorials/iceice_1Bifurcation.ipynb +++ b/playground/tutorials/iceice_1Bifurcation.ipynb @@ -3,11 +3,11 @@ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ - "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/iceice_1Bifurcation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" + "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/iceice_1Bifurcation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n" ] }, { @@ -16,8 +16,6 @@ "id": "QhlVFLnq4pnI" }, "source": [ - "\n", - "\n", "```\n", "# This is formatted as code\n", "```\n", @@ -26,84 +24,87 @@ "\n", "## To Bifurcate or !$_2$ Bifurcate\n", "\n", - "We solve an instance of unilaterally evolving system, wildly nonlinear and singularly pertubed, nonconvex hence allowing many minimisers. Expect broken symmetries starting from isotropy, constant coefficients, and homogeneous data. \n", + "We solve an instance of unilaterally evolving system, wildly nonlinear and singularly pertubed, nonconvex hence allowing many minimisers. Expect broken symmetries starting from isotropy, constant coefficients, and homogeneous data.\n", "\n", "Welcome to the Jungle.\n", "\n", "Suggested listening:\n", - "- In The Panchine - In The Panchine (2004) Full Album. Check-check-IT. Where are you?\n", - "\n", "\n", - "-------\n", + "- In The Panchine - In The Panchine (2004) Full Album. Check-check-IT. Where are you?\n", "\n", + "---\n", "\n", "Let $\\Omega \\subset (0, L)^2$, $L$ finite, being the (or one) characteristic length of the specimen.\n", - "For any \n", + "For any\n", + "\n", "- displacement field $u-bcs(t)\\in V_t : H^1(\\Omega, R^n)$ with $n=1, 2$ or $3$, and\n", "- damage field $\\alpha \\in H^1(\\Omega, R)$,\n", "\n", "consider the energy $E_\\ell(u, \\alpha)$ defined as\n", + "\n", + "$$\n", + "E_\\ell(y)=\\frac{1}{2}\\int_\\Omega \\left( a(\\alpha) W(u) + w_0(u) \\right) dx + \\underbrace{\\frac{G_c}{c_w\\ell} \\int \\left(w(\\alpha) + \\ell^2 |\\nabla \\alpha|^2 \\right)dx}_{\\text{Surface energy}}- \\int_\\Omega g_t.u dx\n", "$$\n", - "E_\\ell(y)=\\frac{1}{2}\\int_\\Omega \\left( a(\\alpha) W(u) + w_0(u) \\right) dx + \\underbrace{\\frac{G_c}{c_w\\ell} \\int \\left(w(\\alpha) + \\ell^2 |\\nabla \\alpha|^2 \\right)dx}_{\\text{Surface energy}}- \\int_\\Omega g_t.u dx$$\n", "\n", "In practice, $\\ell \\ll L$.\n", "\n", - "Above, $W$ is the elastic energy density, reading (in linearised elasticity as) \n", - "$$ \n", - "W(u) = Ae(u):e(u) \n", + "Above, $W$ is the elastic energy density, reading (in linearised elasticity as)\n", + "\n", + "$$\n", + "W(u) = Ae(u):e(u)\n", "$$\n", - "where $A$ is the 4-th order tensor of elasticity, in the isotropic and homogeneous case, it corresponds to a linear combination with two coefficients, say, $A_0$ the stiffness (dimensional), and $\\nu$ the Poisson ratio (non-dimensional). \n", + "\n", + "where $A$ is the 4-th order tensor of elasticity, in the isotropic and homogeneous case, it corresponds to a linear combination with two coefficients, say, $A_0$ the stiffness (dimensional), and $\\nu$ the Poisson ratio (non-dimensional).\n", "\n", "Above, $w(\\alpha)$ corresponds to the dissipated energy to homogeneously damage the specimen, the gradient term accounts for spatial variations.\n", - "The mechanical model $E_\\ell(y)$ is a *local functional*, in the strict analytic sense. Nonetheless, it can be written in equivalent form using non-local (Green) kernels. \n", + "The mechanical model $E_\\ell(y)$ is a _local functional_, in the strict analytic sense. Nonetheless, it can be written in equivalent form using non-local (Green) kernels.\n", "\n", - "**Keypoint:** these two terms are weighted by $\\ell$, a parameter that is homogeneous to a length and is understood as a *material* quantity (as opposed to *numerical* one).\n", + "**Keypoint:** these two terms are weighted by $\\ell$, a parameter that is homogeneous to a length and is understood as a _material_ quantity (as opposed to _numerical_ one).\n", "\n", - "Define $D(\\alpha_0):=\\left\\{ \\alpha \\in H^1(\\Omega), \\alpha \\geq \\alpha_0 \\right\\}$, for some $\\alpha_0(x)\\geq 0$ pointwise. \n", + "Define $D(\\alpha_0):=\\left\\{ \\alpha \\in H^1(\\Omega), \\alpha \\geq \\alpha_0 \\right\\}$, for some $\\alpha_0(x)\\geq 0$ pointwise.\n", "Reamark that the homogeneous version of $D$ is the positive cone of $H^1$.\n", "\n", "We solve two types of problems (by increasing difficulty):\n", + "\n", "- **The static problem**: Given a load (boundary conditions) and an initial state of damage $\\alpha_0$, what is the equilibrium displacement and repartition of damage?\n", - "In other terms:\n", - " \n", + " In other terms:\n", + "\n", "$\n", "\\text{ min loc} \\left\\{ E_\\ell(u, \\alpha):\n", " u \\in V_t, \\alpha \\in D(\\alpha_0) \\right\\}.\n", "$\n", "\n", - "- **The evolution problem**: Given a load **history** (boundary conditions as a function of $t$) and an initial state of damage $\\alpha_0$, what is the *evolution* of equilibrium displacement and repartition of damage, i.e. \n", - "the map $t\\mapsto (u_t, \\alpha_t)$, such that \n", + "- **The evolution problem**: Given a load **history** (boundary conditions as a function of $t$) and an initial state of damage $\\alpha_0$, what is the _evolution_ of equilibrium displacement and repartition of damage, i.e.\n", + " the map $t\\mapsto (u_t, \\alpha_t)$, such that\n", " - (Irrevers.) $\\alpha_t \\nearrow t$, [is irreversible]\n", - " - **(N-Bifurcation)** the rate $\\dot y_t$ is unique only if there exists $(\\lambda, w)\\in R\\times X_{t,0}$ satisfies, \n", - " $$E''_\\ell (\\dot y_t)(w, y) = \\lambda \\langle w, y\\rangle, \\forall y \\in X_{t,0}$$\n", - " with $\\lambda>0$, where $X_{t,0}$ is the vector space associated with the unconstrained degrees of freedom [see Maurini&ALB%2021].\n", - " - (N-Stability) $y_t$ is stable only if there exists $\\bar h$ such that for $h\\in (0, \\bar h)$, \n", - " $$E_\\ell (y_t) \\leq E_\\ell(y_t + z), \\forall z \\in C_{t,0}^+, ||y_t+z||\\leq h$$\n", + " - **(N-Bifurcation)** the rate $\\dot y_t$ is unique only if there exists $(\\lambda, w)\\in R\\times X_{t,0}$ satisfies,\n", + " $$E''_\\ell (\\dot y_t)(w, y) = \\lambda \\langle w, y\\rangle, \\forall y \\in X_{t,0}$$\n", + " with $\\lambda>0$, where $X_{t,0}$ is the vector space associated with the unconstrained degrees of freedom [see Maurini&ALB%2021].\n", + " - (N-Stability) $y_t$ is stable only if there exists $\\bar h$ such that for $h\\in (0, \\bar h)$,\n", + " $$E_\\ell (y_t) \\leq E_\\ell(y_t + z), \\forall z \\in C_{t,0}^+, ||y_t+z||\\leq h$$\n", " - (Power statement) (Ext. power) = (Internal energy flux)\n", "\n", - "\n", "## Let'solve.\n", "\n", "I attack **(N-Bifurcation)** subject to **(Irrevers.)**, leaving **(Power statement)** to be checked.\n", "\n", - "\n", - "### First, setup from a clean state" + "### First, setup from a clean state\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { - "id": "zC6dhNHJfgrb", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "zC6dhNHJfgrb", "outputId": "7e09be6e-b126-4e83-e890-ee030f4ea9b3" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "CPU times: user 670 ms, sys: 312 ms, total: 983 ms\n", "Wall time: 41.7 s\n" @@ -120,6 +121,8 @@ "except ImportError:\n", " import ufl # noqa: F401\n", " import dolfinx # noqa: F401\n", + " import basix.ufl # noqa: F401\n", + "\n", "else:\n", " try:\n", " import gmsh\n", @@ -130,10 +133,13 @@ " try:\n", " import ufl\n", " import dolfinx\n", + " import basix.ufl\n", + "\n", " except ImportError:\n", " !wget \"https://github.com/fem-on-colab/fem-on-colab.github.io/raw/779acd87a4e108672d7ebd3eefd9e8e555bb51d9/releases/fenicsx-install-real.sh\" -O \"/tmp/fenicsx-install.sh\" && bash \"/tmp/fenicsx-install.sh\"\n", " import ufl # noqa: F401\n", " import dolfinx # noqa: F401\n", + " import basix.ufl # noqa: F401\n", "\n", "\n", "!sudo apt install libgl1-mesa-glx xvfb;\n", @@ -152,23 +158,23 @@ "id": "0xbfPIJ_4pnK" }, "source": [ - "## Install our Code, codename: _________" + "## Install our Code, codename: \\***\\*\\_\\*\\***\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { - "id": "fBRSF4i0fm5d", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "fBRSF4i0fm5d", "outputId": "6cd80982-8a1c-4309-ad43-8eec542e3417" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'mec647'...\n", "remote: Enumerating objects: 1496, done.\u001b[K\n", @@ -219,19 +225,19 @@ "id": "ZCAPW6Rk4pnL" }, "source": [ - "### Setup computational patch" + "### Setup computational patch\n" ] }, { "cell_type": "code", - "source": [ - "from dolfinx.io import XDMFFile, distribute_entity_data, gmshio" - ], + "execution_count": 9, "metadata": { "id": "RP7TGlfkyjT4" }, - "execution_count": 9, - "outputs": [] + "outputs": [], + "source": [ + "from dolfinx.io import XDMFFile, distribute_entity_data, gmshio" + ] }, { "cell_type": "code", @@ -289,35 +295,35 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "id": "bsZIi1mWf3AN", "colab": { "base_uri": "https://localhost:8080/", "height": 114 }, + "id": "bsZIi1mWf3AN", "outputId": "977d5da7-c1cd-480f-d4fd-9b7683a1003b" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, 'Mesh with parameters, dimension 2')" ] }, + "execution_count": 5, "metadata": {}, - "execution_count": 5 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ @@ -334,9 +340,7 @@ }, { "cell_type": "code", - "source": [ - "!ls mec647/test/data/banquise" - ], + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -344,15 +348,17 @@ "id": "UZQDTODuSfoZ", "outputId": "18e5c125-881c-4e62-d2e2-8d0872b3eedd" }, - "execution_count": 6, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "parameters.yml\n" ] } + ], + "source": [ + "!ls mec647/test/data/banquise" ] }, { @@ -373,16 +379,7 @@ }, { "cell_type": "code", - "source": [ - "_lc = parameters.get('geometry').get('lc')\n", - "_ly = parameters.get('geometry').get('Ly')\n", - "_lx = parameters.get('geometry').get('Lx')\n", - "\n", - "plt.figure()\n", - "ax = plot_mesh(mesh)\n", - "fig = ax.get_figure()\n", - "plt.title(f\"~Computational Mesh with parameters, dimension {tdim}\")" - ], + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -391,30 +388,39 @@ "id": "ZIiBFk08TfZE", "outputId": "dffc3ea8-5b6e-47f2-9d9d-43a5b1bbd94b" }, - "execution_count": 11, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, '~Computational Mesh with parameters, dimension 2')" ] }, + "execution_count": 11, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } + ], + "source": [ + "_lc = parameters.get('geometry').get('lc')\n", + "_ly = parameters.get('geometry').get('Ly')\n", + "_lx = parameters.get('geometry').get('Lx')\n", + "\n", + "plt.figure()\n", + "ax = plot_mesh(mesh)\n", + "fig = ax.get_figure()\n", + "plt.title(f\"~Computational Mesh with parameters, dimension {tdim}\")" ] }, { @@ -427,14 +433,14 @@ "source": [ "# Functional Setting\n", "\n", - "element_u = ufl.VectorElement(\"Lagrange\", mesh.ufl_cell(),\n", - " degree=1, dim=2)\n", + "element_u = basix.ufl.element(\"Lagrange\", mesh.basix_cell(),\n", + " degree=1, shape=(2,))\n", "\n", - "element_alpha = ufl.FiniteElement(\"Lagrange\", mesh.ufl_cell(),\n", + "element_alpha = basix.ufl.element(\"Lagrange\", mesh.basix_cell(),\n", " degree=1)\n", "\n", - "V_u = dolfinx.fem.FunctionSpace(mesh, element_u) \n", - "V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) \n", + "V_u = dolfinx.fem.functionspace(mesh, element_u) \n", + "V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) \n", "\n", "u = dolfinx.fem.Function(V_u, name=\"Displacement\")\n", "u_ = dolfinx.fem.Function(V_u, name=\"BoundaryDisplacement\")\n", @@ -654,10 +660,7 @@ }, { "cell_type": "code", - "source": [ - "\n", - "f\"{prefix}/_eigv_{t:3.2f}.xdmf\"" - ], + "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -666,37 +669,40 @@ "id": "K-Zyjq4sxqp7", "outputId": "d4f25d79-2bed-47ef-82a9-10f280bc3785" }, - "execution_count": 26, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": [ - "'output/stability/_eigv_0.00.xdmf'" - ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" - } + }, + "text/plain": [ + "'output/stability/_eigv_0.00.xdmf'" + ] }, + "execution_count": 26, "metadata": {}, - "execution_count": 26 + "output_type": "execute_result" } + ], + "source": [ + "\n", + "f\"{prefix}/_eigv_{t:3.2f}.xdmf\"" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { - "id": "1weih1fXol0x", - "outputId": "3b0dbc47-cd7f-4d20-c43d-1b559f45ef87", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "1weih1fXol0x", + "outputId": "3b0dbc47-cd7f-4d20-c43d-1b559f45ef87" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Solving timestep 0, load: 0.0\n", " 0 SNES Function norm 0.000000000000e+00 \n", @@ -1012,7 +1018,7 @@ "\n", " # update loads (body)\n", " # gt.interpolate(lambda x: (0. * np.ones_like(x[0]), np.zeros_like(x[1])))\n", - " # gt.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " # gt.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " # mode=PETSc.ScatterMode.FORWARD)\n", "\n", " gt.value=[0, 0]\n", @@ -1020,12 +1026,12 @@ " # update loads (boundary conditions)\n", " \n", " u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1])))\n", - " u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " mode=PETSc.ScatterMode.FORWARD)\n", "\n", " # update lower bound for damage\n", - " alpha.vector.copy(alpha_lb.vector)\n", - " alpha.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec)\n", + " alpha.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " mode=PETSc.ScatterMode.FORWARD)\n", "\n", " # solve for current load step\n", @@ -1080,9 +1086,7 @@ }, { "cell_type": "code", - "source": [ - "!ls -l output/stability" - ], + "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1090,31 +1094,32 @@ "id": "SdmgGEs0xdJm", "outputId": "99cbf76b-b41d-427a-fb31-9246ead62dd9" }, - "execution_count": 24, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "total 0\n" ] } + ], + "source": [ + "!ls -l output/stability" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { - "id": "lvlG7HjJorOu", - "outputId": "687e6b80-1046-496e-8a44-74bb6fa8d595", "colab": { "base_uri": "https://localhost:8080/", "height": 333 - } + }, + "id": "lvlG7HjJorOu", + "outputId": "687e6b80-1046-496e-8a44-74bb6fa8d595" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "([<matplotlib.axis.XTick at 0x7fc7a4648550>,\n", @@ -1122,20 +1127,21 @@ " <a list of 2 Text xticklabel objects>)" ] }, + "execution_count": 32, "metadata": {}, - "execution_count": 32 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ @@ -1165,35 +1171,35 @@ "cell_type": "code", "execution_count": 34, "metadata": { - "id": "hwbAa2wcoxS2", - "outputId": "472b0586-3b38-4fd8-c361-46ae6fca0734", "colab": { "base_uri": "https://localhost:8080/", "height": 298 - } + }, + "id": "hwbAa2wcoxS2", + "outputId": "472b0586-3b38-4fd8-c361-46ae6fca0734" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, 'Traction bar damage profile')" ] }, + "execution_count": 34, "metadata": {}, - "execution_count": 34 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ @@ -1242,9 +1248,9 @@ "metadata": { "colab": { "collapsed_sections": [], + "include_colab_link": true, "name": "iceice_1Bifurcation.ipynb", - "provenance": [], - "include_colab_link": true + "provenance": [] }, "kernelspec": { "display_name": "Python 3", @@ -1256,4 +1262,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/playground/tutorials/iceice_1dcone.ipynb b/playground/tutorials/iceice_1dcone.ipynb index cb7484f8..95fcb584 100644 --- a/playground/tutorials/iceice_1dcone.ipynb +++ b/playground/tutorials/iceice_1dcone.ipynb @@ -1,29 +1,13 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "authorship_tag": "ABX9TyNVJqEWT+9dAiJylfhFh/nZ", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, "cells": [ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ - "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/andres-conerecipe/iceice_1dcone.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" + "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/andres-conerecipe/iceice_1dcone.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n" ] }, { @@ -37,18 +21,18 @@ "try:\n", " import ufl\n", " import dolfinx\n", + " import basix.ufl\n", + "\n", "except ImportError:\n", " !wget \"https://fem-on-colab.github.io/releases/fenicsx-install-real.sh\" -O \"/tmp/fenicsx-install.sh\" && bash \"/tmp/fenicsx-install.sh\"\n", " import dolfinx\n", - " import ufl\n" + " import ufl\n", + " import basix.ufl\n" ] }, { "cell_type": "code", - "source": [ - "print(f\"DOLFINx version: {dolfinx.__version__}\\n based on GIT commit: {dolfinx.git_commit_hash} of https://github.com/FEniCS/dolfinx/\")\n", - "print(f\"UFL version: {ufl.__version__}\\n based on GIT commit: nan of https://github.com/FEniCS/ufl/\")\n" - ], + "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -56,11 +40,10 @@ "id": "ReyQrySfMEGg", "outputId": "315bbbba-cb87-4121-8949-4887c2119fa0" }, - "execution_count": 22, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "DOLFINx version: 0.6.0.0\n", " based on GIT commit: fdfd2d0627773a822ae8d450b77abad76d8b477b of https://github.com/FEniCS/dolfinx/\n", @@ -68,15 +51,15 @@ " based on GIT commit: nan of https://github.com/FEniCS/ufl/\n" ] } + ], + "source": [ + "print(f\"DOLFINx version: {dolfinx.__version__}\\n based on GIT commit: {dolfinx.git_commit_hash} of https://github.com/FEniCS/dolfinx/\")\n", + "print(f\"UFL version: {ufl.__version__}\\n based on GIT commit: nan of https://github.com/FEniCS/ufl/\")\n" ] }, { "cell_type": "code", - "source": [ - "import sys\n", - "from google.colab import drive\n", - "drive.mount('/content/gdrive/')" - ], + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -84,19 +67,28 @@ "id": "pHkCDezmMWp7", "outputId": "7c5c86d0-69a3-4d44-8846-1f254c84e7a0" }, - "execution_count": 3, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Mounted at /content/gdrive/\n" ] } + ], + "source": [ + "import sys\n", + "from google.colab import drive\n", + "drive.mount('/content/gdrive/')" ] }, { "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "jX82V2NnNNl6" + }, + "outputs": [], "source": [ "%%capture\n", "try:\n", @@ -116,40 +108,11 @@ "!{sys.executable} -m pip install pythreejs;\n", "!{sys.executable} -m pip install ipygany;\n", "!{sys.executable} -m pip install --upgrade PyYAML" - ], - "metadata": { - "id": "jX82V2NnNNl6" - }, - "execution_count": 23, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "from petsc4py import PETSc\n", - "import numpy as np\n", - "from mpi4py import MPI\n", - "comm = MPI.COMM_WORLD\n", - "\n", - "branch_name = 'andres-conerecipe'\n", - "\n", - "!rm -rf mec647\n", - "try:\n", - " !git clone -b {branch_name} https://github.com/kumiori/mec647.git\n", - " sys.path.append('mec647/')\n", - "\n", - " import mec647\n", - " from mec647 import meshes\n", - " from mec647.meshes import primitives\n", - " from mec647.utils.viz import plot_mesh, plot_scalar, plot_vector\n", - "\n", - "except Exception as e:\n", - " print('Something went wrong', e)\n", - " !rm -rf mec647\n", - " !git clone https://github.com/kumiori/mec647.git\n", - "\n" - ], + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -157,11 +120,10 @@ "id": "sJXrdMj2NcPu", "outputId": "3595c2df-b60b-414d-c3a1-c2483e2b98d6" }, - "execution_count": 5, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'mec647'...\n", "remote: Enumerating objects: 1553, done.\u001b[K\n", @@ -173,35 +135,54 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "CRITICAL:root:DOLFINx version: 0.6.0.0 based on GIT commit: fdfd2d0627773a822ae8d450b77abad76d8b477b of https://github.com/FEniCS/dolfinx/\n" ] } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from petsc4py import PETSc\n", + "import numpy as np\n", + "from mpi4py import MPI\n", + "comm = MPI.COMM_WORLD\n", + "\n", + "branch_name = 'andres-conerecipe'\n", + "\n", + "!rm -rf mec647\n", + "try:\n", + " !git clone -b {branch_name} https://github.com/kumiori/mec647.git\n", + " sys.path.append('mec647/')\n", + "\n", + " import mec647\n", + " from mec647 import meshes\n", + " from mec647.meshes import primitives\n", + " from mec647.utils.viz import plot_mesh, plot_scalar, plot_vector\n", + "\n", + "except Exception as e:\n", + " print('Something went wrong', e)\n", + " !rm -rf mec647\n", + " !git clone https://github.com/kumiori/mec647.git\n", + "\n" ] }, { "cell_type": "code", - "source": [ - "tdim = 1 \n", - "mesh = dolfinx.mesh.create_unit_interval(MPI.COMM_WORLD, 12)\n" - ], + "execution_count": 14, "metadata": { "id": "B7YbCqM4Oni4" }, - "execution_count": 14, - "outputs": [] + "outputs": [], + "source": [ + "tdim = 1 \n", + "mesh = dolfinx.mesh.create_unit_interval(MPI.COMM_WORLD, 12)\n" + ] }, { "cell_type": "code", - "source": [ - "plt.figure()\n", - "# ax = plot_mesh(mesh)\n", - "# fig = ax.get_figure()\n", - "plt.title(f\"Mesh with parameters, dimension {tdim}\")\n", - "# fig.savefig(f\"one_mesh.png\")" - ], + "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -210,34 +191,45 @@ "id": "4Ax_gJEtPbmY", "outputId": "03116ffa-e675-4080-c927-fe790dfbd7cf" }, - "execution_count": 15, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, 'Mesh with parameters, dimension 1')" ] }, + "execution_count": 15, "metadata": {}, - "execution_count": 15 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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MEvj3ASsHxlf007bbH3gpcFWSu4BjgU3euJWk3csogX8dsDrJYUn2Bk4GNm2fWVWPVtWyqlpVVauAzcC6qprcJRVLkuZkaOBX1ZPAWcAVwK3Axqq6Ocl5Sdbt6gIlSeOxdJRGVXU5cPm0ae+dpe1x8y9LkjRuftNWkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiNGCvwkJya5LcmWJOfMMP8dSW5JcmOSLyc5dPylSpLmY2jgJ1kCXACcBKwBTkmyZlqzG4CJqvpZ4M+BPxx3oZKk+RnlDP9oYEtV3VFVTwCXAusHG1TVlVX1eD+6GVgx3jIlSfM1SuAfAtw7ML61nzab04EvzDQjyZlJJpNMbtu2bfQqJUnzNtabtklOBSaAD800v6o2VNVEVU0sX758nJuWJA2xdIQ29wErB8ZX9NOeIskJwLuBV1XV98dTniRpXEY5w78OWJ3ksCR7AycDmwYbJHkZ8HFgXVU9MP4yJUnzNTTwq+pJ4CzgCuBWYGNV3ZzkvCTr+mYfAp4LfC7J15NsmmV1kqRFMsolHarqcuDyadPeOzB8wpjrkiSNmd+0laRGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGmHgS1IjDHxJaoSBL0mNMPAlqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1AgDX5IaYeBLUiMMfElqhIEvSY0w8CWpEQa+JDXCwJekRhj4ktQIA1+SGjFS4Cc5McltSbYkOWeG+fsk+Ww//2tJVo27UEnS/AwN/CRLgAuAk4A1wClJ1kxrdjrwcFW9CPgI8MFxFypJmp9RzvCPBrZU1R1V9QRwKbB+Wpv1wH/uh/8cOD5JxlemJGm+lo7Q5hDg3oHxrcAxs7WpqieTPAr8OPDgYKMkZwJn9qPfT3LTXIreAy1jWl81zL6YYl9MsS+m/PRcFxwl8MemqjYAGwCSTFbVxEJuf3dlX0yxL6bYF1PsiylJJue67CiXdO4DVg6Mr+inzdgmyVLgQOChuRYlSRq/UQL/OmB1ksOS7A2cDGya1mYT8Pp++NeBv66qGl+ZkqT5GnpJp78mfxZwBbAE+ERV3ZzkPGCyqjYB/xH4VJItwLfpXhSG2TCPuvc09sUU+2KKfTHFvpgy576IJ+KS1Aa/aStJjTDwJakRuzzw/VmGKSP0xTuS3JLkxiRfTnLoYtS5EIb1xUC71ySpJHvsR/JG6Yskr+2PjZuTfGaha1woIzxHXpDkyiQ39M+TtYtR566W5BNJHpjtu0rpfLTvpxuTHDXSiqtql/3R3eS9HTgc2Bv4W2DNtDa/DVzYD58MfHZX1rRYfyP2xT8H9uuH39xyX/Tt9geuBjYDE4td9yIeF6uBG4Dn9ePPX+y6F7EvNgBv7ofXAHctdt27qC9+ATgKuGmW+WuBLwABjgW+Nsp6d/UZvj/LMGVoX1TVlVX1eD+6me47D3uiUY4LgPfR/S7T9xayuAU2Sl+cAVxQVQ8DVNUDC1zjQhmlLwo4oB8+ELh/AetbMFV1Nd0nHmezHri4OpuBg5IcPGy9uzrwZ/pZhkNma1NVTwLbf5ZhTzNKXww6ne4VfE80tC/6t6grq+rzC1nYIhjluDgCOCLJNUk2JzlxwapbWKP0xbnAqUm2ApcDb12Y0nY7O5snwAL/tIJGk+RUYAJ41WLXshiS7AX8MXDaIpeyu1hKd1nnOLp3fVcn+ZmqemRRq1ocpwCfrKo/SvIKuu//vLSq/mmxC3sm2NVn+P4sw5RR+oIkJwDvBtZV1fcXqLaFNqwv9gdeClyV5C66a5Sb9tAbt6McF1uBTVX1g6q6E/gG3QvAnmaUvjgd2AhQVV8F9qX7YbXWjJQn0+3qwPdnGaYM7YskLwM+Thf2e+p1WhjSF1X1aFUtq6pVVbWK7n7Guqqa849G7cZGeY5cRnd2T5JldJd47ljIIhfIKH1xD3A8QJKX0AX+tgWtcvewCXhd/2mdY4FHq+pbwxbapZd0atf9LMMzzoh98SHgucDn+vvW91TVukUrehcZsS+aMGJfXAH8cpJbgB8C76yqPe5d8Ih9cTZwUZK3093APW1PPEFMcgndi/yy/n7F7wHPAqiqC+nuX6wFtgCPA28Yab17YF9JkmbgN20lqREGviQ1wsCXpEYY+JLUCANfkhph4EtSIwx8SWrE/wcA0orqBbHIpQAAAABJRU5ErkJggg==\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } + ], + "source": [ + "plt.figure()\n", + "# ax = plot_mesh(mesh)\n", + "# fig = ax.get_figure()\n", + "plt.title(f\"Mesh with parameters, dimension {tdim}\")\n", + "# fig.savefig(f\"one_mesh.png\")" ] }, { "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "A57obfP8PgYm" + }, + "outputs": [], "source": [ "# Let's get the entire default set of parameters\n", "import yaml\n", @@ -249,18 +241,11 @@ " parameters[\"model\"][\"mu\"] = 1\n", " parameters[\"model\"][\"w1\"] = 1\n", " parameters[\"model\"][\"k_res\"] = 1e-4\n" - ], - "metadata": { - "id": "A57obfP8PgYm" - }, - "execution_count": 53, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "ufl.FiniteElement" - ], + "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -268,33 +253,40 @@ "id": "toHRDVCmP-ai", "outputId": "532e8de7-f927-405c-9ec6-fc81a2b88cb6" }, - "execution_count": 24, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "ufl.finiteelement.finiteelement.FiniteElement" ] }, + "execution_count": 24, "metadata": {}, - "execution_count": 24 + "output_type": "execute_result" } + ], + "source": [ + "ufl.FiniteElement" ] }, { "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "MeDk4p54PnXE" + }, + "outputs": [], "source": [ "# Functional Setting\n", "\n", - "element_u = ufl.FiniteElement(\"Lagrange\", mesh.ufl_cell(),\n", + "element_u = basix.ufl.element(\"Lagrange\", mesh.basix_cell(),\n", " degree=1)\n", "\n", - "element_alpha = ufl.FiniteElement(\"DG\", mesh.ufl_cell(),\n", + "element_alpha = basix.ufl.element(\"DG\", mesh.basix_cell(),\n", " degree=0)\n", "\n", - "V_u = dolfinx.fem.FunctionSpace(mesh, element_u) \n", - "V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) \n", + "V_u = dolfinx.fem.functionspace(mesh, element_u) \n", + "V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) \n", "\n", "u = dolfinx.fem.Function(V_u, name=\"Displacement\")\n", "u_ = dolfinx.fem.Function(V_u, name=\"BoundaryDisplacement\")\n", @@ -316,15 +308,15 @@ "# Useful references\n", "Lx = parameters.get(\"geometry\").get(\"Lx\")\n", "Ly = parameters.get(\"geometry\").get(\"Ly\")" - ], - "metadata": { - "id": "MeDk4p54PnXE" - }, - "execution_count": 29, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "ehppazfcPt_f" + }, + "outputs": [], "source": [ "# Boundary sets\n", "\n", @@ -344,28 +336,28 @@ "\n", "alpha_lb.interpolate(lambda x: np.zeros_like(x[0]))\n", "alpha_ub.interpolate(lambda x: np.ones_like(x[0]))" - ], - "metadata": { - "id": "ehppazfcPt_f" - }, - "execution_count": 30, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "v54ZtnDIQmbD" + }, + "outputs": [], "source": [ "# Dofmap\n", "map = mesh.topology.index_map(mesh.topology.dim)\n", "num_cells = map.size_local + map.num_ghosts" - ], - "metadata": { - "id": "v54ZtnDIQmbD" - }, - "execution_count": 31, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 59, + "metadata": { + "id": "-9CsllwoSOpA" + }, + "outputs": [], "source": [ "# Boundary conditions\n", "\n", @@ -385,15 +377,15 @@ "bcs_alpha = []\n", "\n", "bcs = {\"bcs_u\": bcs_u, \"bcs_alpha\": bcs_alpha}\n" - ], - "metadata": { - "id": "-9CsllwoSOpA" - }, - "execution_count": 59, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 90, + "metadata": { + "id": "hj5qKZ9QSSqN" + }, + "outputs": [], "source": [ "# Material behaviour\n", "from mec647.models import DamageElasticityModel as Brittle\n", @@ -448,15 +440,15 @@ "\n", "total_energy = (elastic_energy_density(state) + damage_energy_density(state)) * dx\n", "\n" - ], - "metadata": { - "id": "hj5qKZ9QSSqN" - }, - "execution_count": 90, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 117, + "metadata": { + "id": "jhC3w9KaXGxr" + }, + "outputs": [], "source": [ "from mec647.solvers import SNESSolver\n", "from mec647.utils import norm_H1, norm_L2\n", @@ -553,9 +545,9 @@ " (solver_alpha_it, solver_alpha_reason) = self.damage.solve()\n", "\n", " # Define error function\n", - " self.alpha.vector.copy(alpha_diff.vector)\n", - " alpha_diff.vector.axpy(-1, self.alpha_old.vector)\n", - " alpha_diff.vector.ghostUpdate(\n", + " self.alpha.x.petsc_vec.copy(alpha_diff.x.petsc_vec)\n", + " alpha_diff.x.petsc_vec.axpy(-1, self.alpha_old.x.petsc_vec)\n", + " alpha_diff.x.petsc_vec.ghostUpdate(\n", " addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD\n", " )\n", "\n", @@ -571,7 +563,7 @@ " ).sum()\n", " )\n", "\n", - " error_alpha_max = alpha_diff.vector.max()[1]\n", + " error_alpha_max = alpha_diff.x.petsc_vec.max()[1]\n", " total_energy_int = comm.allreduce(\n", " assemble_scalar(form(self.total_energy)), op=MPI.SUM\n", " )\n", @@ -579,11 +571,11 @@ " residual_u.ghostUpdate(\n", " addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE\n", " )\n", - " set_bc(residual_u, self.elasticity.bcs, self.u.vector)\n", + " set_bc(residual_u, self.elasticity.bcs, self.u.x.petsc_vec)\n", " error_residual_u = ufl.sqrt(residual_u.dot(residual_u))\n", "\n", - " self.alpha.vector.copy(self.alpha_old.vector)\n", - " self.alpha_old.vector.ghostUpdate(\n", + " self.alpha.x.petsc_vec.copy(self.alpha_old.x.petsc_vec)\n", + " self.alpha_old.x.petsc_vec.ghostUpdate(\n", " addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD\n", " )\n", "\n", @@ -600,15 +592,15 @@ " self.data[\"total_energy\"].append(total_energy_int)\n", "\n", "\n" - ], - "metadata": { - "id": "jhC3w9KaXGxr" - }, - "execution_count": 117, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 118, + "metadata": { + "id": "9vy1TG0KS46b" + }, + "outputs": [], "source": [ "# Evolution solver\n", "# import algorithms\n", @@ -620,82 +612,11 @@ " parameters.get(\"solvers\"),\n", " bounds=(alpha_lb, alpha_ub)\n", " )" - ], - "metadata": { - "id": "9vy1TG0KS46b" - }, - "execution_count": 118, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "%%time\n", - "# Loop for evolution\n", - "from dolfinx.fem import assemble_scalar\n", - "import logging\n", - "\n", - "loads = np.linspace(parameters.get(\"loading\").get(\"min\"),\n", - " parameters.get(\"loading\").get(\"max\"),\n", - " parameters.get(\"loading\").get(\"steps\"))\n", - "\n", - "history_data = {\n", - " \"load\": [],\n", - " \"elastic_energy\": [],\n", - " \"dissipated_energy\": [],\n", - " \"total_energy\": [],\n", - " # \"solver_data\": [],\n", - " # \"eigs\": [],\n", - " # \"stable\": [],\n", - "}\n", - "\n", - "check_stability = []\n", - "\n", - "for i_t, t in enumerate(loads):\n", - " u_.interpolate(lambda x: t * np.ones_like(x[0]))\n", - " u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", - " mode=PETSc.ScatterMode.FORWARD)\n", - "\n", - " # update the lower bound\n", - " alpha.vector.copy(alpha_lb.vector)\n", - " alpha_lb.vector.ghostUpdate(\n", - " addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD\n", - " )\n", - "\n", - " logging.critical(f\"-- Solving for t = {t:3.2f} --\")\n", - "\n", - " solver.solve()\n", - "\n", - " dissipated_energy = comm.allreduce(\n", - " assemble_scalar(form(damage_energy_density(state) * dx)),\n", - " op=MPI.SUM,\n", - " )\n", - " elastic_energy = comm.allreduce(\n", - " assemble_scalar(form(elastic_energy_density(state) * dx)),\n", - " op=MPI.SUM,\n", - " )\n", - "\n", - " history_data[\"load\"].append(t)\n", - " history_data[\"dissipated_energy\"].append(dissipated_energy)\n", - " history_data[\"elastic_energy\"].append(elastic_energy)\n", - " history_data[\"total_energy\"].append(elastic_energy+dissipated_energy)\n", - " # history_data[\"solver_data\"].append(solver.data)\n", - " # history_data[\"eigs\"].append(stability.data[\"eigs\"])\n", - " # history_data[\"stable\"].append(stability.data[\"stable\"])\n", - "\n", - " with XDMFFile(comm, f\"{prefix}/{_nameExp}.xdmf\", \"a\", encoding=XDMFFile.Encoding.HDF5) as file:\n", - " file.write_function(u, t)\n", - " file.write_function(alpha, t)\n", - "\n", - " if comm.rank == 0:\n", - " a_file = open(f\"{prefix}/{_nameExp}-data.json\", \"w\")\n", - " json.dump(history_data, a_file)\n", - " a_file.close()\n", - "\n", - " # list_timings(MPI.COMM_WORLD, [dolfinx.common.TimingType.wall])\n", - " print(history_data)\n", - "\n" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -703,18 +624,17 @@ "id": "O3N9qt3UX2zL", "outputId": "fc441c9f-76d7-40c5-ecc5-c73f70b92153" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "CRITICAL:root:-- Solving for t = 0.00 --\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ " 0 SNES Function norm 0.000000000000e+00 \n", " 0 SNES Function norm 0.000000000000e+00 \n", @@ -886,16 +806,99 @@ " 0 SNES Function norm 0.000000000000e+00 \n" ] } + ], + "source": [ + "%%time\n", + "# Loop for evolution\n", + "from dolfinx.fem import assemble_scalar\n", + "import logging\n", + "\n", + "loads = np.linspace(parameters.get(\"loading\").get(\"min\"),\n", + " parameters.get(\"loading\").get(\"max\"),\n", + " parameters.get(\"loading\").get(\"steps\"))\n", + "\n", + "history_data = {\n", + " \"load\": [],\n", + " \"elastic_energy\": [],\n", + " \"dissipated_energy\": [],\n", + " \"total_energy\": [],\n", + " # \"solver_data\": [],\n", + " # \"eigs\": [],\n", + " # \"stable\": [],\n", + "}\n", + "\n", + "check_stability = []\n", + "\n", + "for i_t, t in enumerate(loads):\n", + " u_.interpolate(lambda x: t * np.ones_like(x[0]))\n", + " u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " mode=PETSc.ScatterMode.FORWARD)\n", + "\n", + " # update the lower bound\n", + " alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec)\n", + " alpha_lb.x.petsc_vec.ghostUpdate(\n", + " addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD\n", + " )\n", + "\n", + " logging.critical(f\"-- Solving for t = {t:3.2f} --\")\n", + "\n", + " solver.solve()\n", + "\n", + " dissipated_energy = comm.allreduce(\n", + " assemble_scalar(form(damage_energy_density(state) * dx)),\n", + " op=MPI.SUM,\n", + " )\n", + " elastic_energy = comm.allreduce(\n", + " assemble_scalar(form(elastic_energy_density(state) * dx)),\n", + " op=MPI.SUM,\n", + " )\n", + "\n", + " history_data[\"load\"].append(t)\n", + " history_data[\"dissipated_energy\"].append(dissipated_energy)\n", + " history_data[\"elastic_energy\"].append(elastic_energy)\n", + " history_data[\"total_energy\"].append(elastic_energy+dissipated_energy)\n", + " # history_data[\"solver_data\"].append(solver.data)\n", + " # history_data[\"eigs\"].append(stability.data[\"eigs\"])\n", + " # history_data[\"stable\"].append(stability.data[\"stable\"])\n", + "\n", + " with XDMFFile(comm, f\"{prefix}/{_nameExp}.xdmf\", \"a\", encoding=XDMFFile.Encoding.HDF5) as file:\n", + " file.write_function(u, t)\n", + " file.write_function(alpha, t)\n", + "\n", + " if comm.rank == 0:\n", + " a_file = open(f\"{prefix}/{_nameExp}-data.json\", \"w\")\n", + " json.dump(history_data, a_file)\n", + " a_file.close()\n", + "\n", + " # list_timings(MPI.COMM_WORLD, [dolfinx.common.TimingType.wall])\n", + " print(history_data)\n", + "\n" ] }, { "cell_type": "code", - "source": [], + "execution_count": null, "metadata": { "id": "AQ_IKXtHvcap" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [] } - ] -} \ No newline at end of file + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyNVJqEWT+9dAiJylfhFh/nZ", + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/playground/tutorials/mec647_BCs_3.ipynb b/playground/tutorials/mec647_BCs_3.ipynb index 1a87c28a..eb30d2f7 100644 --- a/playground/tutorials/mec647_BCs_3.ipynb +++ b/playground/tutorials/mec647_BCs_3.ipynb @@ -1,30 +1,13 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "mec647_BCs_3.ipynb", - "provenance": [], - "authorship_tag": "ABX9TyMPx/AwZjA5uvlgJkoTGl3f", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, "cells": [ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ - "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_BCs_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" + "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_BCs_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n" ] }, { @@ -55,6 +38,11 @@ }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "giEEacK0h89B" + }, + "outputs": [], "source": [ "%%capture\n", "!sudo apt install libgl1-mesa-glx xvfb;\n", @@ -80,26 +68,11 @@ "except ImportError:\n", " !{sys.executable} -m pip install gmsh\n", " import gmsh" - ], - "metadata": { - "id": "giEEacK0h89B" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "!rm -rf mec647\n", - "\n", - "try:\n", - " !git clone https://github.com/kumiori/mec647.git\n", - "except Exception:\n", - " print('Something went wrong')\n", - "\n", - " !rm -rf mec647\n", - " !git clone https://github.com/kumiori/mec647.git\n" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -107,11 +80,10 @@ "id": "tIu0L3Ixh9bn", "outputId": "ec2ad8f8-f946-4c50-f090-634c5b052e66" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'mec647'...\n", "remote: Enumerating objects: 495, done.\u001b[K\n", @@ -122,21 +94,37 @@ "Resolving deltas: 100% (208/208), done.\n" ] } + ], + "source": [ + "!rm -rf mec647\n", + "\n", + "try:\n", + " !git clone https://github.com/kumiori/mec647.git\n", + "except Exception:\n", + " print('Something went wrong')\n", + "\n", + " !rm -rf mec647\n", + " !git clone https://github.com/kumiori/mec647.git\n" ] }, { "cell_type": "code", - "source": [ - "sys.path.append('mec647/')" - ], + "execution_count": null, "metadata": { "id": "OFxOqEgOiIO-" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "sys.path.append('mec647/')" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7HOLzr6EimhX" + }, + "outputs": [], "source": [ "\n", "# meshes\n", @@ -147,15 +135,15 @@ "from utils import viz\n", "import matplotlib.pyplot as plt\n", "from utils.viz import plot_mesh" - ], - "metadata": { - "id": "7HOLzr6EimhX" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lytYYgfoipnb" + }, + "outputs": [], "source": [ "# Parameters\n", "\n", @@ -173,24 +161,24 @@ "\n", "# parameters.get('loading')\n", "\n" - ], - "metadata": { - "id": "lytYYgfoipnb" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", - "source": [ - "## Mesh 1" - ], "metadata": { "id": "cGGdnId4i3es" - } + }, + "source": [ + "## Mesh 1\n" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eGewK6iSiyIj" + }, + "outputs": [], "source": [ "def mesh_holes(Lx = 1., Ly = 1.):\n", " import gmsh\n", @@ -204,97 +192,95 @@ " model.add(\"Rectangle\")\n", " model.setCurrent(\"Rectangle\")\n", " # Lx, Ly" - ], - "metadata": { - "id": "eGewK6iSiyIj" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "mesh_holes()" - ], + "execution_count": null, "metadata": { "id": "RkY4SD2pAaB1" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "mesh_holes()" + ] }, { "cell_type": "markdown", - "source": [ - "## Mesh 2" - ], "metadata": { "id": "QEf3rGuUi_CJ" - } + }, + "source": [ + "## Mesh 2\n" + ] }, { "cell_type": "code", - "source": [ - "# Mesh kink" - ], + "execution_count": null, "metadata": { "id": "IX2nXOYrjABv" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "# Mesh kink" + ] }, { "cell_type": "markdown", - "source": [ - "## Mesh 3" - ], "metadata": { "id": "s_7F55yUjBmH" - } + }, + "source": [ + "## Mesh 3\n" + ] }, { "cell_type": "code", - "source": [ - "# mesh ..." - ], + "execution_count": null, "metadata": { "id": "z66da9jRjCOD" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "# mesh ..." + ] }, { "cell_type": "code", - "source": [ - "" - ], + "execution_count": null, "metadata": { "id": "ho4xhwtpA3Eu" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [] }, { "cell_type": "markdown", - "source": [ - "## Meshes, domains and boundary conditions" - ], "metadata": { "id": "Kf6hfuB5A9W5" - } + }, + "source": [ + "## Meshes, domains and boundary conditions\n" + ] }, { "cell_type": "code", - "source": [ - "import meshes" - ], + "execution_count": null, "metadata": { "id": "63a4lxHjBqFF" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "import meshes" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BJTQi8BSBDl0" + }, + "outputs": [], "source": [ "gmsh_model, tdim = primitives.mesh_bar_gmshapi(name = 'bar', \n", " Lx = 1.,\n", @@ -307,18 +293,11 @@ " cell_data=False,\n", " facet_data=True,\n", " gdim=2)\n" - ], - "metadata": { - "id": "BJTQi8BSBDl0" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "viz.plot_mesh(mesh)" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -327,37 +306,37 @@ "id": "OMFa6cvqBJKy", "outputId": "17a99f03-642d-41f2-85b4-e254448812b5" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fc40c906910>" ] }, + "execution_count": 27, "metadata": {}, - "execution_count": 27 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { - "image/png": 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\n", + "image/png": 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XzwKz2+1s27atpnUuYWQVPvHEE7odGOmw0vUsMBn+JifYtNCxY0fWqFHDbTL64MGDDAwMNAzp/L//+z8C4KZNm5z2S0u4ZcuWupawVGBaSlk9CauF8vJyNmzY0M11RpK9e/dmREQEjx49qilbUlLCxMRETT98VlaWofIiHfNDPj4+bu/nnj176Ofn5+bmUUNvJCdDSuWktRakW1Dr+v3792d4eLhum6WVrjWSe/vttw07OKPr//zzzwTAl156SVf21ltv1XQpqifIjVxvgwYNYlBQkJNRITsoo8ihnJwcJXBAblojENJS6G4wUuikY/KiglaAMTExhuFjeXl5DAwM1BzmyzBBORuuhb1797oNe4uLi5mYmOjmI9Sqp9YElIwWMIrp1fOTypDM6dOn68qSDkUUFRXlFCZ2/PhxVqlShf369TOU1bLA5HC2fv36hiGkP/74IwHwrbfectp/77330tfXV5lU1MKxY8c06yfDV7/++mtdWb0IIem2MFKMJPnll18SAD/44ANlnxz+e4rMkHMPrhbroEGDGBYWpqsYSUdse2hoKAcNGuS038iCligrK9Ocb/nkk090Ozc15AjgwIEDyj5PFrSEHMmp51tk53b99dcbfhd6I4DOnTszPj6ep0+f1pWV9XM1VuSo9Z133jGs94EDBxgcHKyMUAsKChgZGcmuXbs61dlutzMrK4tPP/00W7RooShxtYUeHh6uadxYCt0FnhQ6eW54BYCdOnUyfIFGjhzJ4OBgHjlyRNlnt9vZokUL1qtXz2OI4Pjx4+nn56fEwUqFbDR5KDF06FCnMDEZKdCxY0ePsrNnz3aKZpAJTAkJCYZDQ5L87bffCDgn/MjRysaNGw1lteq4YMECj5NK6jqqRxAygWnEiBGGsiT55JNPEgA3b95M0mGdp6SksEWLFh4nt+WchnpUIycV1UpLr94tW7ZkUlISS0pKaLfb2alTJ1P5CFrRH9I9YCYvYNKkSU5zGtLHfc8993iUlW2Wk4Dl5eWsX7++qRh52dmpjR3prjOaVCfPzbeoo6JkMo+ZBLWBAwc6RRytXLmSgLmEvl69ejE6OtrpuXTo0MHUd0Gei4j76aefOH78ePr6+nLLli202WxcvXo1H3roIV5zzTWKfmndujWff/55bt++nbt371Yi4tq2bUsAHD58uFNdLIXuAj2FbrPZuGzZMt5yyy1KRqj6pi9atEjzJd6yZYubcpPDOzM3XUYk3H777Tx16hRjY2NNJ1hs377d6cOW1pyWj9oVrjHHMhRz2rRppspWJ/zIBCZPkSQSstNauXIlbTYbr732WtarV89Ugpe0lmR8/AMPPEBfX1/u2rXLo6y00m+55RaS56ITPEWSkO73Kzc316v4ZZlc9sYbbyi/X3/9dVOyr7zyitPE/LBhwwwn8NQ4cuQIw8LCFKvRTBSKhIyKkiMTb5PT1FE0MpnHUxSKhHyXly9fzpKSEq+S09RRNHa7nR06dGD16tV1J9TVkCMn6ZqR37LZZ3X69GnWrFlTyYCtXbs2R40axfj4eAKgv78/MzIyOGPGDLdnIJOGli1bxrKyMj7xxBMUQrBu3bpK2K2l0F3gqtDz8/P54osvsk6dOgTAqlWr8sEHH2SbNm1YtWpVvv3220qKcXp6OufMmePmR+vatStr1KihJKDccsstjIqK8hg3LSETDmT45Jo1a0y3Z9CgQQwNDWVubq7pWG8JGamzaNEitm/fXtM/rQeZ8PPCCy/w6aefJmA+I1NtdZqJIlFDxsgnJSUxJyeHwcHBSsKXGTz++ONKxEvt2rXZvHlz0/dLJpV8/fXXptw8rvWWVnmDBg1Ys2ZN0/e6qKiIsbGx7N69uzIJqzWZrgfZ5gULFjgl85iBzF2YO3euV4lppHOcu9k4cQmZu9C2bVuvEtMkZJy7dBGZNVRIR0RVtWrVeObMGWZkZDA2NtbQVUM6Ipq2bNnCuXPnOqXoS/fJwIED+emnnxq2X85byJhz0qHkExIS6O/vz9dee41//fWXpdDVkAp9yZIlHDRoEAMCAgg4eF3mzJmjfGSdOnVimzZtSDqss48//pgNGzZUet0ZM2Yo537zzTeKy2DPnj308fHRjVzQQn5+vvLwq1Wrxh07dvDgwYM8efKkx49HJvxERUWZHpJKlJWVsXbt2krZb775pmlZ0pHwExQUxKCgIKcEJjN49dVXlXJTU1NNWecSMi0/OjraKYHJDGQkjizbU9q1GnICNTk52VScd2lpKfPy8rhjxw6uXbtWmZgFwJtvvpmrV6/m9u3befjwYY+cMTKvoHHjxgwMDPSKP0SOTAA4JfOYgXSzyHq7xmB7wj333KPImsmOVkM9UWgmQUsNmRsAgImJiaY7T/KcpSw5gZ577jnlWHl5OXft2sUvv/ySU6dO5cCBA9mwYUO3Ub16u+eee0w9L5nZ7dp5HDlyhL179ybg4Nq5mApdOI5ferRo0YLnS58bERGBO+64A2PGjEGDBs5rbqSmpqJp06YKUyHgYJb76quv8Oyzz2LdunWoVq0aHnjgAYwePRqtWrVCeHg42rdvj2nTpmHfvn1ISEhwuiZJHDp0CFlZWU7b1q1bcebMGd36hoWFITw8XHOrUqUK3n77beX8J598EiRhs9lgt9tht9sNf6uZ8oYOHYqQkBD4+vrCx8cHPj4+hr/Xrl2L77//HgDQo0cPtGzZ0nS5RUVF+OSTc+uZjBo1ynS5QghMmTJFkZ08ebLT9T1t6vs1fvx4+Pv7w8/PT9l8fX2d/lfvf//99/H7778DAG6//XbExMTg+PHjmtvp06c9v5AqhIaGIioqClFRUYiOjnb67e/vj+eff1459/XXX8eZM2dQXFyM4uJip99a/2/bpiw/gMTERN17rLVv3bp1imyrVq1MvV/yb25uLqSOqFKlCiIiIkyXW1ZWhk2bNjmVrVWG3r7c3FxFtnnz5k7fjN63JH9nZGSgqKgIAPD4448jJycHWVlZ2LZtm8ICCgC1a9dGeno6GjZsiPT0dKSnp+Opp57CF198AQAIDw/H6dOn4e/vjxEjRuChhx5C7dq1NZ//+PHjMW/ePIU1Uw2SmDZtGu69916nfZWBEGIDyRaax65khb5w4UL06dPH7RySCAkJwYQJE/Dyyy9rHl+xYgWeffZZ/PDDD4iKioK/vz/y8/MBAIMHD8abb76JrVu3uinv48ePK9eJj49Heno6kpKSMGvWLACAv78/Zs+ejVOnTjltJ0+edNsn9x85csStjZ6Uovx9+PBhRa569epOis/TR6sFs+UKIZzKrlatmuly9d459fWNNklFDADR0dGw2WwoLy9XNpvNpnl9LaiVsKdtzZo1ePrppwEA/fv3x8iRI3Hs2DGlA9D7ffz4cd0OH3BQ3gYHByub6//BwcH4/PPPlfPvvPNOj8pQvW/p0qWKbLdu3eDn52dKIfv6+mL9+vXYsmULAKBTp05ITk42Xe6ZM2ewatUqpeyMjAzT5QohlG8KAHr27Kn5LZWUlJh6zjVq1FAUttzS0tLcKHtzc3ORkpKCsWPHYuHChahbty7effddvPTSS5g1axZsNhtuu+02TJo0Cenp6U6ymZmZyMnJcerEAAe98QcffICZM2di3759yv6LodCvOJeLJE1KTk4mAD788MNuw92CggLTEyG//vor+/TpozvcAsCIiAi2a9eOY8aM4bRp07hixQrm5+cr15DD6S5dulAI4ZULQU4YAQ5aX2/oS+WEkaynN+4H8twQEdDPgtPDF198och26dLFK9m8vDz6+/srw2kz0QcScnJTlq0VM2+321lWVsaSkhIWFRWxsLCQR44c4eHDhzlw4MBK3S+17/+GG25gfHy8V26A7du3K+U2btyYx44dY3FxsSk3hNr9AFWUjxnIkE0p663LJTMzU5E1SxgmcT4uFxmCC8BwArm0tJTHjh3j/v37uWXLFv7yyy8K9YXcvHFvTZo0iT4+PtyzZ4+ia2Tk18GDB/nggw8yNDSUANi7d2+n+bLGjRsrdM8yUGHAgAGKO6dz585K2K/lQ6+A9KHn5+dzzJgxBMBWrVo5ERlJXggjEqvy8nL+9NNPvP/++53iRwFHPOnLL7/M7777jrm5uYYv4unTp5UJr7y8PAYHB5um4yTPhXO9/vrrhlmHWpCRLa+99hrr1KnjFRf2iRMnGBUVxZtuuondu3dnTEyMaYpbGdmSmprKl156iYB3XNgyYUZyf3iKD1ZDTmzOmjWL4eHhSsSLGciIpMGDB3tMRnKFDJ176623lHh6T/H+aowYMYKBgYG8++67CYC//PKLaVmZ1r59+3aGh4dzwIABpmVlZMu8efNYv359ryZF5Xc0depU3nXXXfTz8zNN/SwNlXbt2lVqUrRDhw6sUaMGN2/eTB8fH69ofX/99VcCYMeOHb2aWyoqKmJUVJSS63Ds2DGGhIQoLJoSR48e5eTJkxkdHU3AERq9bNkyRkdHc8CAAXz55ZdZt25dZZ5o4sSJFjmXHlyjXD777DNGRkYyPDxciYOWceiulJhnzpzhokWLOGLECMbExBAAAwMDedNNNymTTgDYsmVL0y+97HGlQrvnnntM81nLeF1pvUiKW7X1rwe73c727dszISGBxcXFThEvZqDmGpcRL+rJIyPIuPOPP/7YK5Y90jml3W63s3Xr1kp8tydISgXZccnoD7MWq1RK2dnZSsijWSu9W7duCheNrHdycrIpat6dO3cq9AIytNUoE1gNGXcuo2Ik145kDjWCa9y5WbZOib59+yo0tZLW1+ziG+q4c624dCPIzlPSRLgyYXpCt27dGBMTw5MnT7Jjx46Mj483FbEmydh+/vlnZd+ECRN0F8Q5deoUX3nlFVavXt1tVN+2bVt+/PHHbqM4S6G7QCsOfd++fUog/x133KG4QA4dOsSjR4/yo48+Yr9+/RgSEqK4UIYMGcLPPvuMJ0+e5Pbt2+nn58fRo0cryRizZ8/2WBdphbRv317Zl5OTY5rzXHYGMu5c8sO4sv5pQQ5JZdalN1zYx48fd4s779mzp6lFBmRWqDqyRR2X7gmupFOeuGfUcFXCetmjWti/fz8DAgIUeoHS0lLWqVOHTZo08dh5S8pWtVvqq6++Mv2euFLNyrh0M9zhd955p1Pc+ZEjRzSzR7WgXmiEPBeXrsfxoobMulTTRo8bN85UIpbMClXHnXuTY9G5c2cl7JA8F5eux9OihuwMJLOnZPr0tCKSzWZjamqqW5Lazp07KYQwzI4tKSlxUuZG2eWWQneBXmKROpBf3tiOHTsqCQIJCQkcP348ly1b5uZz79GjByMiIpifn0+bzcbrrruO1apVc6Lo1IIkhXLlppD8IEa+O72s0KFDh2oy/6mhzgpVWwCu2aN6kOF36qxQmT06depUQ1nJ2aKOOzeb4SpdHuqsUL22uEIvK1Rmj3rKcNUis5Jxw578ytJSVXd2koLZU0KVXDhD7TIw4jtRQ3K2uGaFPvLIIxRCaHIQSehxtkiDxdPiG/3792eVKlWcskLNUiVIS/f7779X9qnZOo0gE4FeffVVp/0DBw70SJUguYxq1KjhlITUs2dPjzH0X3/9NQHtbOfMzEzGxMTovp92u53p6ekEHDHraWlpuvW0FLoLPKX+yxdWbo8++ijXrVuna5HIGHT1CyRdEEb+bBkDrpV2vnv3bo8LBcg4blcrTQ7PjRJPZKaiqw9XsuwZcWEfPXrUKdtSjd69eytDbC2olyxzVWLSH67Fsidx9913O9EkSEiftJGv89133yXgztniiYmR1CezkhOGRkRVkkRLi3JYkn4ZZV4OGDBAk2pWWqxGeQeyE3LlbMnPz2dISAiHDh2qKyvr5krfLDlejN4RmT39+OOPux0bNWqUZp0kJKOiFmeL5Hgxeke6devGuLg4t1huWScjS3nJkiWaylLOBRjRRnfp0oUJCQmaLjT5fr733nuasrIz+Oijj7hixQoGBASwbdu2mpmtlkJ3gZFCP3DggFMExPXXX2/Irnb27FnWq1ePqampblb7sGHDGBAQoLtWoew49KxhydOi5fcrKioyZFSUTIxaDIBGfOekZ770xx57jIB2dIh88dXDbDVclyxTwxMXtvTB6jEq3nDDDblhsnoAAA4TSURBVE7DbDXkgsJ6k5hyxKHHly4nJLWUkHRLaHG4k47nGBoa6sT1I6G1Ao4a0m2hpRilW0KPxE1aw+PHj9esl5xY1qJolcsAui40IiHfET2+dCNrWDIx6nHJGDEqGrF1kuQvv/xCQJ9RUdINa3HJ2O12NmvWjCkpKZpJXv3799fsWMlz6wTozSHZ7XY2btyYDRs21HxW7dq1Y1JSktIZzJ8/n0II9u3b183wsRS6C/QU+oEDB1i7dm1WqVKFa9euVVKGjXp0aSVr+bwOHjzI0NBQ3nzzzW7HbDabx4WAt27dqvsxyzZo8Z2T5zi6tVYU9+RzNuLCLigocOIF0UK/fv00PxpPfOekMV+69L/qRUlI36frUJs859rS801KK12Li0Y9IakFOerQoseVz8FopCX9+uqVkiT69OljSGYlF3vQape8X1p856Rjclkv21VvNSIJoyXyzPirXf36EnqrEalhtKpRjx49DKOttPz6EnKi/sMPP9SU3bZtG318fPjggw+6HRsxYgSDg4MN3Tky6EDtRiLP8ai78vzLb2Hs2LFO98JS6C7QUuj79+93UuYSI0aMoBBCc4iXl5fHiIgI9ujRQ7esZ599loB7uJV8eYy4v0mHcoyIiHByYZw8eZJVq1bVXI1IDUmRquZal7HQNWvWNIwKkaMH12gGV+Y+LUhrxbUj8qQkSP1VjbSY+7TQpUsXxsXFua1GlZSUpLkakRqSLdJ17dEhQ4Z4nJOQfDSuykCu/6rHlS7rV7NmTbdFVOSchBGZlRx5uPLRmI0oue+++9zWHjXT8ZLnOPVdo6KGDBniMaJERt64csrIdQD0uMBJ/bVHZaihp0grdeSNhJwv8NTmYcOGuY18Dx8+zICAAI/vplwQx1VfZGZmsmrVqpoeg0ceecRtXspS6C5wVehSmUdERCgrFUmcOnWKqampTEhIcBsyjx49mn5+foYTS8XFxUxJSWF6erpivdntdjZt2pR169b1yF8iGerUHBiyk1B3PFqQQ1v1kFtvrVBXaHFh5+Xlaa4VqgXp95X3TC5Z5umDIbXXHtXi1taCtHbUQ25pxWpZwGrItUfVa8dKa9NT1JDNZmOTJk1Yp04dZdgs2Rg9fejkOWtMHYsv1770FDUk4+rV7g851+Ap9FUuDDJy5Ehln5FbTA0tK11vbVUtuDJGmu14yXNRUer5I0l76ykQQX5Tah566ULyxCKZnZ3t9k1JulwzyYDSaNi2bRvJc/Mrei5Ku93OYcOGOfnfLYXuArVC379/P1NSUjSVucTvv//OgIAA9unTR3nRNm7cSCGEKcY7ab3JCUg58fL++++bqq8cRhYVFfHEiROMjo42TYQ1ZswYZditjtn2RARFnmPZkxEc0udq5sWVhGFy2C39zHpLlqkhWfZkbL2ckPS0KLbEjTfeqAy75QSbp8VCJKZMmUIA/O2330g6VrDR85u6YvHixU4f3sSJE02zMaqTy8hzvmAzcf3SRSYTfrSUtBHUyt9o0loLMipKhoF6Q+vrqvylW8xTx0ues9IlzbSWkjaCWvnL3ASjdV/VGDt2rEIJXFxczLi4OMNlKNWQC+LIkdOwYcMYEhKiOb8iUVpayu7du9PX15dff/3130OhA8gA8CeA3QAmaRwPBDCv4vivAGp5uub5KvStW7cqytw1gcgV0m83ffp0JV0+JiZGcy1HV0ja1OjoaB49epTXX3+9aaVKnouDffXVV52SecxAxk6PHj1aWajWbFalepWev/76y3B9Si1IWt/Dhw8zLS1Nd4JNC+rMwDvvvNNwiTdXSD7r5557ztSSZWrI7NdevXoprqPHHnvMlKzdbmerVq2YnJzMv/76iyEhIV7dL7n+7Pr169m1a1dTi19IyM53/vz5mm4UI6jdMzKk1NNCIxIyd6FZs2bctWuXphvFCDK+/uDBg0xOTjZcENsVcv5q1apVmm4UI6jdM0bzEFrIzc1V5h7k6MgbhlO5IM7GjRvp5+dnuJi2xKlTp9i8eXMGBwdz4cKFl1ehA/AFkA2gNoAAAJsBNHA5ZzyAGRW/bwMwz9N1z1ehx8bGmlLmpONjvemmmxgYGKismykXWDADmX587bXXEnBfQs0TOnbsyJCQEAYHB5teREJiwoQJ9PPzY3x8PJOTk013JOS5tO/ExETDFeS1IJOcEhMTNcPfjCAjOOLj4w0nJPXQs2dPhoaGMiIiQndBYT1IbvfExETdiAg9yE4zNjZWMRrMorCwkBEREUoGskxsMQPp0oqOjmZAQIBbqrknjBs3jn5+foyMjDS90IiEVGpxcXGmF86QkC4teb/MKlXSMaqJj49XZPXcFnrIyMhgWFgYIyMjPS5r54qJEyfSx8eHAQEBbNSokVeyMnwSAP38/HQnrV1x+PBhpwi8i6XQ/eAZrQDsJrkHAIQQcwH0AbBNdU4fAJMrfi8A8JYQQlQUfkExf/58AEBBQQH69euH5cuXY/ny5R7lUlNT8c0332Dq1KmK/HPPPWe6XLvdjj/++AOAgz3NG1kfHx+FbS8wMNArWQAoLy9HXl4eUlJS8Morr5iWk6yDkoZ0wYIFXpWrpjDdtWuXV/UWQiAvL0+5jjeyQUFBCnVtUFCQE+2sJ0jK1NzcXAQEBGDGjBmmZeXrKtkcFy1ahEWLFpmWP3HihPK7sLDQqzYHBgbi2LFjABzvizeyJFFeXo7CwkIkJCTgxRdfNC1bVlYGAArT6OzZs03LyrLl/dq0aRM2b95sWtZmsylMo6dPn/aqzf7+/sqzDgsL8+odIQm73Y7S0lIUFxd7JatGeXk55syZY/r8jh07Ijs7GwCwZMkSjB07tlLlGsEjfa4Qoj+ADJL/qvj/dgDXkbxLdU5WxTm5Ff9nV5xzxOVaowGMBoCkpKTm+/fv977CKvpcCxYsWLhSUVl714g+14yFfsFAciaAmYCDD70y1ygpKcGuXbtwzTXXeK3c7XY7fv75Z7Rv3x4+Pj5eyZLExo0bcc0116BKlSpeyQJAdnY2AgMDkZiY6LVsQUEB8vPz0bBhQ69ly8rKsH79erRu3bpS92vt2rVo2bIl/P39vS47KysL1atXR9WqVb2WzcnJQVlZme5iAkY4efIksrOz0aRJk0q1ec2aNWjTpk2l3pENGzagQYMGCAkJ8UoWAHbu3ImwsDDUqFHDa9m8vDwcP34c9evX91q2pKQEmzdvRqtWrSp9v1q3bg1fX1+vy/7jjz9Qs2ZNREVFeS27b98+CCGQnJzstWxhYSFycnLQqFEjr2XLy8uxfv16tGrVqlLvyO7du5Gamup1uWZgRqEfBFBT9X9ixT6tc3KFEH4AIgC4L9uhwoYNG44IIbw30R2IAXDE41lXF6w2/zNgtfmfgfNps24PZkah/wagrhAiBQ7FfRuAwS7nLAZwB4A1APoD+NGT/5xkrImyNSGEWK835LhaYbX5nwGrzf8MXKw2e1ToJMuFEHcBWApHxMsHJLcKIabAMdu6GMD7AD4WQuwGcAwOpW/BggULFi4hTPnQSS4BsMRl35Oq3yUABlzYqlmwYMGCBW/gnUf/74OZl7sClwFWm/8ZsNr8z8BFabPHsEULFixYsHBl4Eq10C1YsGDBggv+1gpdCJEhhPhTCLFbCDFJ43igEGJexfFfhRC1Ln0tLyxMtHmiEGKbEOIPIcQPQgjvg3D/ZvDUZtV5twghKIS44iMizLRZCHFrxbPeKoT45FLX8ULDxLudJIRYIYTYWPF+97wc9bxQEEJ8IITIr0i81DouhBBvVtyPP4QQzc67UD1OgMu94SJxyPydN5Nt7gQgpOL3uH9CmyvOCwewCsBaAC0ud70vwXOuC2AjgKiK/+Mud70vQZtnAhhX8bsBgH2Xu97n2eYOAJoByNI53hPAtwAEgNYAfj3fMv/OFrrCIUOyFIDkkFGjD4APK34vANBFXNncAB7bTHIFyTMV/66FI9HrSoaZ5wwAUwG8AKDkUlbuIsFMm0cBmE7yOACQzL/EdbzQMNNmApBp2BEA/rqE9bvgILkKjjBuPfQB8BEdWAsgUghR/XzK/Dsr9AQAOar/cyv2aZ5DshzACQDe55n/fWCmzWqMhKOHv5Lhsc0VQ9GaJL+5lBW7iDDznFMBpAohVgsh1gohMi5Z7S4OzLR5MoChQohcOMKk7740Vbts8PZ794hLyuVi4cJBCDEUQAsAN1zuulxMCCF8ALwKYPhlrsqlhh8cbpeOcIzCVgkhGpEsvKy1urgYBGA2yVeEEG3gSFZMJ2m/3BW7UvB3ttC94ZCBWQ6ZvznMtBlCiK4AHgPQm+TZS1S3iwVPbQ4HkA5gpRBiHxy+xsVX+MSomeecC2AxyTKSewHshEPBX6kw0+aRAOYDAMk1AILg4Dy5WmHqe/cGf2eFrnDICCEC4Jj0XOxyjuSQAUxyyPzN4bHNQoimAN6BQ5lf6X5VwEObSZ4gGUOyFslacMwb9Ca5/vJU94LAzLu9EA7rHEKIGDhcMHsuZSUvMMy0+QCALgAghEiDQ6EXXNJaXlosBjCsItqlNYATJA+d1xUv90ywh1ninnBYJtkAHqvYNwWODxpwPPDP4Fj6bh2A2pe7zpegzcsB5AHYVLEtvtx1vthtdjl3Ja7wKBeTz1nA4WraBmALgNsud50vQZsbAFgNRwTMJgA3Xu46n2d7PwVwCEAZHCOukQDGAhiresbTK+7HlgvxXluZohYsWLBwleDv7HKxYMGCBQtewFLoFixYsHCVwFLoFixYsHCVwFLoFixYsHCVwFLoFixYsHCVwFLoFixYsHCVwFLoFixYsHCVwFLoFixYsHCV4P8BwrmHj3Z5NRoAAAAASUVORK5CYII=", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } + ], + "source": [ + "viz.plot_mesh(mesh)" ] }, { "cell_type": "code", - "source": [ - "dir(mts)" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -365,42 +344,37 @@ "id": "pwcPjjnABPMo", "outputId": "5d156fe2-edbe-488e-d173-af7abdafe5d5" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "<dolfinx.cpp.mesh.MeshTags_int32 at 0x7fc40c43d630>" ] }, + "execution_count": 41, "metadata": {}, - "execution_count": 41 + "output_type": "execute_result" } + ], + "source": [ + "dir(mts)" ] }, { "cell_type": "code", - "source": [ - "import ufl\n", - "ds = ufl.Measure(\"ds\", subdomain_data=mts, domain=mesh)\n" - ], + "execution_count": null, "metadata": { "id": "nJllnU_sEoXT" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "import ufl\n", + "ds = ufl.Measure(\"ds\", subdomain_data=mts, domain=mesh)\n" + ] }, { "cell_type": "code", - "source": [ - "from dolfinx.fem import Constant, form\n", - "from petsc4py.PETSc import ScalarType\n", - "\n", - "dolfinx.fem.assemble_scalar(form(Constant(mesh, 1.)*ds) )\n", - "\n", - "\n" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -408,25 +382,30 @@ "id": "aqSi7iF0FLPU", "outputId": "cf9b0188-70bc-4192-f6d3-843e26f94197" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "2.1999999999999997" ] }, + "execution_count": 37, "metadata": {}, - "execution_count": 37 + "output_type": "execute_result" } + ], + "source": [ + "from dolfinx.fem import Constant, form\n", + "from petsc4py.PETSc import ScalarType\n", + "\n", + "dolfinx.fem.assemble_scalar(form(Constant(mesh, 1.)*ds) )\n", + "\n", + "\n" ] }, { "cell_type": "code", - "source": [ - "Constant(mesh, 1.)" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -434,25 +413,25 @@ "id": "BeSMXCQUFjHe", "outputId": "1b04d3e9-8355-4d9f-b88a-cb1158c19abc" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "Constant(Mesh(VectorElement(FiniteElement('Lagrange', triangle, 1, variant='equispaced'), dim=2, variant='equispaced'), 1), (), 1)" ] }, + "execution_count": 35, "metadata": {}, - "execution_count": 35 + "output_type": "execute_result" } + ], + "source": [ + "Constant(mesh, 1.)" ] }, { "cell_type": "code", - "source": [ - "dolfinx.fem.assemble_scalar(form(Constant(mesh, 1.)*ds(6)) )\n" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -460,37 +439,25 @@ "id": "alwNPk0xF8hH", "outputId": "b4694b60-7b04-4076-a257-c94e4e26e8d5" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.1" ] }, + "execution_count": 42, "metadata": {}, - "execution_count": 42 + "output_type": "execute_result" } + ], + "source": [ + "dolfinx.fem.assemble_scalar(form(Constant(mesh, 1.)*ds(6)) )\n" ] }, { "cell_type": "code", - "source": [ - "from dolfinx.fem.bcs import locate_dofs_geometrical\n", - "# indentify dof\n", - "import numpy as np\n", - "element = ufl.FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree=1)\n", - "V = dolfinx.fem.FunctionSpace(mesh, element)\n", - "\n", - "dofs_left = locate_dofs_geometrical(V, lambda x: np.isclose(x[0], 0.))\n", - "\n", - "u_given = dolfinx.fem.Function(V)\n", - "\n", - "dolfinx.fem.dirichletbc(u_given, dofs_left)\n", - "\n", - "# construct the bc" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -498,33 +465,54 @@ "id": "IGr6Hc75GiXm", "outputId": "33c2b6bb-390d-44a2-f00f-237bc304ff69" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "<dolfinx.fem.bcs.DirichletBC at 0x7fc40b5f24d0>" ] }, + "execution_count": 46, "metadata": {}, - "execution_count": 46 + "output_type": "execute_result" } + ], + "source": [ + "from dolfinx.fem.bcs import locate_dofs_geometrical\n", + "import basix.ufl\n", + "\n", + "# indentify dof\n", + "import numpy as np\n", + "element = basix.ufl.element(\"Lagrange\", mesh.basix_cell(), degree=1)\n", + "V = dolfinx.fem.functionspace(mesh, element)\n", + "\n", + "dofs_left = locate_dofs_geometrical(V, lambda x: np.isclose(x[0], 0.))\n", + "\n", + "u_given = dolfinx.fem.Function(V)\n", + "\n", + "dolfinx.fem.dirichletbc(u_given, dofs_left)\n", + "\n", + "# construct the bc" ] }, { "cell_type": "code", - "source": [ - "u_given.interpolate( lambda x: np.ones_like(x[0]) )" - ], + "execution_count": null, "metadata": { "id": "Q-8T3IgWKP2C" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "u_given.interpolate( lambda x: np.ones_like(x[0]) )" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vjm4LRv-LTcv" + }, + "outputs": [], "source": [ "# Locate facets topologically\n", "facet_dim = tdim - 1 \n", @@ -532,12 +520,24 @@ "# <function>(x): true/false \n", "dirichletbc(<Constant, Expression, ...>, locate_dofs_topological(V, 1, facets), V)\n", "\n" - ], - "metadata": { - "id": "vjm4LRv-LTcv" - }, - "execution_count": null, - "outputs": [] + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyMPx/AwZjA5uvlgJkoTGl3f", + "include_colab_link": true, + "name": "mec647_BCs_3.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/playground/tutorials/mec647_BanquiseLune_12.ipynb b/playground/tutorials/mec647_BanquiseLune_12.ipynb index d05bd83b..aef1b44b 100644 --- a/playground/tutorials/mec647_BanquiseLune_12.ipynb +++ b/playground/tutorials/mec647_BanquiseLune_12.ipynb @@ -3,11 +3,11 @@ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ - "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_BanquiseLune_12.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" + "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_BanquiseLune_12.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n" ] }, { @@ -18,67 +18,70 @@ "source": [ "# Fracture de la Banquise Antartique\n", "\n", - "\n", "Let $\\Omega \\subset (0, L)^2$, $L$ finite, being the (or one) characteristic length of the specimen.\n", - "For any \n", + "For any\n", + "\n", "- displacement field $u\\in V_t : H^1(\\Omega, R^n) + bcs(t)$ with $n=1, 2$ or $3$, and\n", "- damage field $\\alpha \\in H^1(\\Omega, R)$,\n", "\n", "consider the energy $E_\\ell(u, \\alpha)$ defined as\n", + "\n", + "$$\n", + "E_\\ell(u, \\alpha)=\\frac{1}{2}\\int_\\Omega \\left( a(\\alpha) W(u) + k u.u \\right) dx + \\underbrace{\\frac{G_c}{c_w\\ell} \\int \\left(w(\\alpha) + \\ell^2 |\\nabla \\alpha|^2 \\right)dx}_{\\text{Surface energy}}- \\int_\\Omega g_t.u dx\n", "$$\n", - "E_\\ell(u, \\alpha)=\\frac{1}{2}\\int_\\Omega \\left( a(\\alpha) W(u) + k u.u \\right) dx + \\underbrace{\\frac{G_c}{c_w\\ell} \\int \\left(w(\\alpha) + \\ell^2 |\\nabla \\alpha|^2 \\right)dx}_{\\text{Surface energy}}- \\int_\\Omega g_t.u dx$$\n", "\n", "In practice, $\\ell \\ll L$.\n", "\n", - "Above, $W$ is the elastic MEMBRANE energy density, reading (in linearised elasticity as) \n", - "$$ \n", - "W(u) = Ae(u):e(u) \n", + "Above, $W$ is the elastic MEMBRANE energy density, reading (in linearised elasticity as)\n", + "\n", + "$$\n", + "W(u) = Ae(u):e(u)\n", "$$\n", - "where $A$ is the 4-th order tensor of elasticity, in the isotropic and homogeneous case, it corresponds to a linear combination with two coefficients, say, $A_0$ the stiffness (dimensional), and $\\nu$ the Poisson ratio (non-dimensional), $k$ is the effective stiffness of the elastic foundation (Water/water). \n", + "\n", + "where $A$ is the 4-th order tensor of elasticity, in the isotropic and homogeneous case, it corresponds to a linear combination with two coefficients, say, $A_0$ the stiffness (dimensional), and $\\nu$ the Poisson ratio (non-dimensional), $k$ is the effective stiffness of the elastic foundation (Water/water).\n", "\n", "Above, $w(\\alpha)$ corresponds to the dissipated energy to damage, homogeneously, the specimen, the gradient term accounts for spatial variations.\n", "\n", - "**Keypoint:** these two terms are weighted by $\\ell$, a parameter that is homogeneous to a length and is understood as a *material* quantity (as opposed to *numerical*).\n", + "**Keypoint:** these two terms are weighted by $\\ell$, a parameter that is homogeneous to a length and is understood as a _material_ quantity (as opposed to _numerical_).\n", "\n", - "Define $D(\\alpha_0):=\\left\\{ \\alpha \\in H^1(\\Omega), \\alpha \\geq \\alpha_0 \\right\\}$, for some $\\alpha_0(x)\\geq 0$ pointwise. \n", + "Define $D(\\alpha_0):=\\left\\{ \\alpha \\in H^1(\\Omega), \\alpha \\geq \\alpha_0 \\right\\}$, for some $\\alpha_0(x)\\geq 0$ pointwise.\n", "\n", "We solve two types of problems (by increasing difficulty):\n", + "\n", "- **The static problem**: Given a load (boundary conditions) and an initial state of damage $\\alpha_0$, what is the equilibrium displacement and repartition of damage?\n", - "In other terms:\n", - " \n", + " In other terms:\n", + "\n", "$\n", "\\text{ min loc} \\left\\{ E_\\ell(u, \\alpha):\n", " u \\in V_t, \\alpha \\in D(\\alpha_0) \\right\\}.\n", "$\n", "\n", - "- **The evolution problem**: Given a load **history** (boundary conditions as a function of $t$) and an initial state of damage $\\alpha_0$, what is the *evolution* of equilibrium displacement and repartition of damage, i.e. \n", - "the map $t\\mapsto (u_t, \\alpha_t)$, such that \n", + "- **The evolution problem**: Given a load **history** (boundary conditions as a function of $t$) and an initial state of damage $\\alpha_0$, what is the _evolution_ of equilibrium displacement and repartition of damage, i.e.\n", + " the map $t\\mapsto (u_t, \\alpha_t)$, such that\n", " - (Irrevers.) $\\alpha_t \\nearrow t$,\n", - " - (N-Stability) $y_t$ is stable only if there exists $\\bar h$ such that for $h\\in (0, \\bar h)$, \n", - " $$E_\\ell (y_t) \\leq E_\\ell(y_t + z), \\forall z \\in C_{t,0}^+, ||y_t+z||\\leq h$$\n", + " - (N-Stability) $y_t$ is stable only if there exists $\\bar h$ such that for $h\\in (0, \\bar h)$,\n", + " $$E_\\ell (y_t) \\leq E_\\ell(y_t + z), \\forall z \\in C_{t,0}^+, ||y_t+z||\\leq h$$\n", " - (Power statement) (Ext. power) = (Internal energy flux)\n", "\n", - "\n", "## Let'solve.\n", "\n", - "\n", - "### First, setup from a clean state" + "### First, setup from a clean state\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { - "id": "zC6dhNHJfgrb", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "zC6dhNHJfgrb", "outputId": "c5a5a24f-5b37-4019-ad11-a32006f9be6e" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "CPU times: user 336 ms, sys: 140 ms, total: 476 ms\n", "Wall time: 35.9 s\n" @@ -95,14 +98,18 @@ "except ImportError:\n", " import ufl # noqa: F401\n", " import dolfinx # noqa: F401\n", + " import basix.ufl # noqa: F401\n", + "\n", "else:\n", " try:\n", " import ufl\n", " import dolfinx\n", + " import basix.ufl\n", " except ImportError:\n", " !wget \"https://github.com/fem-on-colab/fem-on-colab.github.io/raw/779acd87a4e108672d7ebd3eefd9e8e555bb51d9/releases/fenicsx-install-real.sh\" -O \"/tmp/fenicsx-install.sh\" && bash \"/tmp/fenicsx-install.sh\"\n", " import ufl # noqa: F401\n", " import dolfinx # noqa: F401\n", + " import basix.ufl # noqa: F401\n", "\n", " try:\n", " import gmsh\n", @@ -134,23 +141,23 @@ "id": "0xbfPIJ_4pnK" }, "source": [ - "## Install our Code, codename: _________" + "## Install our Code, codename: \\***\\*\\_\\*\\***\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { - "id": "fBRSF4i0fm5d", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "fBRSF4i0fm5d", "outputId": "ff2a26bb-a826-47ad-e562-885c0c207c3f" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'mec647'...\n", "remote: Enumerating objects: 1471, done.\u001b[K\n", @@ -167,8 +174,8 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "CRITICAL:root:DOLFINx version: 0.5.2.0 based on GIT commit: 6a35f3251a24cf385b8956ca9ba329d6ece17608 of https://github.com/FEniCS/dolfinx/\n" ] @@ -208,19 +215,19 @@ "id": "ZCAPW6Rk4pnL" }, "source": [ - "### Setup computational patch" + "### Setup computational patch\n" ] }, { "cell_type": "code", - "source": [ - "from dolfinx.io import XDMFFile, distribute_entity_data, gmshio" - ], + "execution_count": 8, "metadata": { "id": "RP7TGlfkyjT4" }, - "execution_count": 8, - "outputs": [] + "outputs": [], + "source": [ + "from dolfinx.io import XDMFFile, distribute_entity_data, gmshio" + ] }, { "cell_type": "code", @@ -289,35 +296,35 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "id": "bsZIi1mWf3AN", "colab": { "base_uri": "https://localhost:8080/", "height": 214 }, + "id": "bsZIi1mWf3AN", "outputId": "7ec89579-866d-4955-89a0-76069698fdf4" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, 'Mesh with parameters, dimension 2')" ] }, + "execution_count": 11, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ @@ -334,9 +341,7 @@ }, { "cell_type": "code", - "source": [ - "!ls mec647/test/data/banquise" - ], + "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -344,15 +349,17 @@ "id": "UZQDTODuSfoZ", "outputId": "c47092db-e6a2-4b78-8203-c0254c5881b9" }, - "execution_count": 32, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "parameters.yml\n" ] } + ], + "source": [ + "!ls mec647/test/data/banquise" ] }, { @@ -373,23 +380,7 @@ }, { "cell_type": "code", - "source": [ - "_lc = parameters.get('geometry').get('lc')\n", - "_ly = parameters.get('geometry').get('Ly')\n", - "_lx = parameters.get('geometry').get('Lx')\n", - "\n", - "_nc = list(map(lambda v: int(v/_lc), \n", - " [_lx, _ly]))\n", - "\n", - "mesh = dolfinx.mesh.create_rectangle(comm, [[0.0, 0.0], [_lx,_ly]],\n", - " _nc,\n", - " cell_type=CellType.triangle)\n", - "\n", - "plt.figure()\n", - "ax = plot_mesh(mesh)\n", - "fig = ax.get_figure()\n", - "plt.title(f\"~Computational Mesh with parameters, dimension {tdim}\")" - ], + "execution_count": 39, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -398,30 +389,46 @@ "id": "ZIiBFk08TfZE", "outputId": "0f8b71b6-09b5-4827-fab4-cd9ac22290fc" }, - "execution_count": 39, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, '~Computational Mesh with parameters, dimension 2')" ] }, + "execution_count": 39, "metadata": {}, - "execution_count": 39 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } + ], + "source": [ + "_lc = parameters.get('geometry').get('lc')\n", + "_ly = parameters.get('geometry').get('Ly')\n", + "_lx = parameters.get('geometry').get('Lx')\n", + "\n", + "_nc = list(map(lambda v: int(v/_lc), \n", + " [_lx, _ly]))\n", + "\n", + "mesh = dolfinx.mesh.create_rectangle(comm, [[0.0, 0.0], [_lx,_ly]],\n", + " _nc,\n", + " cell_type=CellType.triangle)\n", + "\n", + "plt.figure()\n", + "ax = plot_mesh(mesh)\n", + "fig = ax.get_figure()\n", + "plt.title(f\"~Computational Mesh with parameters, dimension {tdim}\")" ] }, { @@ -434,14 +441,14 @@ "source": [ "# Functional Setting\n", "\n", - "element_u = ufl.VectorElement(\"Lagrange\", mesh.ufl_cell(),\n", - " degree=1, dim=2)\n", + "element_u = basix.ufl.element(\"Lagrange\", mesh.basix_cell(),\n", + " degree=1, shape=(2,))\n", "\n", - "element_alpha = ufl.FiniteElement(\"Lagrange\", mesh.ufl_cell(),\n", + "element_alpha = basix.ufl.element(\"Lagrange\", mesh.basix_cell(),\n", " degree=1)\n", "\n", - "V_u = dolfinx.fem.FunctionSpace(mesh, element_u) \n", - "V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) \n", + "V_u = dolfinx.fem.functionspace(mesh, element_u) \n", + "V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) \n", "\n", "u = dolfinx.fem.Function(V_u, name=\"Displacement\")\n", "u_ = dolfinx.fem.Function(V_u, name=\"BoundaryDisplacement\")\n", @@ -664,16 +671,16 @@ "cell_type": "code", "execution_count": 63, "metadata": { - "id": "1weih1fXol0x", - "outputId": "44135167-3346-4971-8f1d-4e32fe73fabe", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "1weih1fXol0x", + "outputId": "44135167-3346-4971-8f1d-4e32fe73fabe" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Solving timestep 0, load: 0.0\n", " 0 SNES Function norm 0.000000000000e+00 \n", @@ -782,7 +789,7 @@ "\n", " # update loads (body)\n", " # gt.interpolate(lambda x: (0. * np.ones_like(x[0]), np.zeros_like(x[1])))\n", - " # gt.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " # gt.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " # mode=PETSc.ScatterMode.FORWARD)\n", "\n", " gt.value=[0, 0]\n", @@ -791,12 +798,12 @@ " # update loads (boundary conditions)\n", " \n", " u_.interpolate(lambda x: (0 * np.ones_like(x[0]), np.zeros_like(x[1])))\n", - " u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " mode=PETSc.ScatterMode.FORWARD)\n", "\n", " # update lower bound for damage\n", - " alpha.vector.copy(alpha_lb.vector)\n", - " alpha.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec)\n", + " alpha.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " mode=PETSc.ScatterMode.FORWARD)\n", "\n", " # solve for current load step\n", @@ -842,16 +849,15 @@ "cell_type": "code", "execution_count": 56, "metadata": { - "id": "lvlG7HjJorOu", - "outputId": "3890706a-6e6a-4205-ebe2-bc0028a7f581", "colab": { "base_uri": "https://localhost:8080/", "height": 333 - } + }, + "id": "lvlG7HjJorOu", + "outputId": "3890706a-6e6a-4205-ebe2-bc0028a7f581" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "([<matplotlib.axis.XTick at 0x7f128f189e90>,\n", @@ -859,20 +865,21 @@ " [Text(0, 0, '0'), Text(0, 0, '1')])" ] }, + "execution_count": 56, "metadata": {}, - "execution_count": 56 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ @@ -902,35 +909,35 @@ "cell_type": "code", "execution_count": 59, "metadata": { - "id": "hwbAa2wcoxS2", - "outputId": "26f6b30d-0c91-4c5e-d482-abaaca73d445", "colab": { "base_uri": "https://localhost:8080/", "height": 298 - } + }, + "id": "hwbAa2wcoxS2", + "outputId": "26f6b30d-0c91-4c5e-d482-abaaca73d445" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, 'Traction bar damage profile')" ] }, + "execution_count": 59, "metadata": {}, - "execution_count": 59 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ @@ -988,9 +995,9 @@ "metadata": { "colab": { "collapsed_sections": [], + "include_colab_link": true, "name": "mec647_Banquise_11.ipynb", - "provenance": [], - "include_colab_link": true + "provenance": [] }, "kernelspec": { "display_name": "Python 3", @@ -1002,4 +1009,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/playground/tutorials/mec647_Banquise_11.ipynb b/playground/tutorials/mec647_Banquise_11.ipynb index 91d0ec9a..67ad3266 100644 --- a/playground/tutorials/mec647_Banquise_11.ipynb +++ b/playground/tutorials/mec647_Banquise_11.ipynb @@ -3,11 +3,11 @@ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ - "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_Banquise_11.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" + "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_Banquise_11.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n" ] }, { @@ -18,67 +18,70 @@ "source": [ "# Fracture de la Banquise Antartique\n", "\n", - "\n", "Let $\\Omega \\subset (0, L)^2$, $L$ finite, being the (or one) characteristic length of the specimen.\n", - "For any \n", + "For any\n", + "\n", "- displacement field $u\\in V_t : H^1(\\Omega, R^n) + bcs(t)$ with $n=1, 2$ or $3$, and\n", "- damage field $\\alpha \\in H^1(\\Omega, R)$,\n", "\n", "consider the energy $E_\\ell(u, \\alpha)$ defined as\n", + "\n", + "$$\n", + "E_\\ell(u, \\alpha)=\\frac{1}{2}\\int_\\Omega \\left( a(\\alpha) W(u) + k u.u \\right) dx + \\underbrace{\\frac{G_c}{c_w\\ell} \\int \\left(w(\\alpha) + \\ell^2 |\\nabla \\alpha|^2 \\right)dx}_{\\text{Surface energy}}- \\int_\\Omega g_t.u dx\n", "$$\n", - "E_\\ell(u, \\alpha)=\\frac{1}{2}\\int_\\Omega \\left( a(\\alpha) W(u) + k u.u \\right) dx + \\underbrace{\\frac{G_c}{c_w\\ell} \\int \\left(w(\\alpha) + \\ell^2 |\\nabla \\alpha|^2 \\right)dx}_{\\text{Surface energy}}- \\int_\\Omega g_t.u dx$$\n", "\n", "In practice, $\\ell \\ll L$.\n", "\n", - "Above, $W$ is the elastic MEMBRANE energy density, reading (in linearised elasticity as) \n", - "$$ \n", - "W(u) = Ae(u):e(u) \n", + "Above, $W$ is the elastic MEMBRANE energy density, reading (in linearised elasticity as)\n", + "\n", + "$$\n", + "W(u) = Ae(u):e(u)\n", "$$\n", - "where $A$ is the 4-th order tensor of elasticity, in the isotropic and homogeneous case, it corresponds to a linear combination with two coefficients, say, $A_0$ the stiffness (dimensional), and $\\nu$ the Poisson ratio (non-dimensional), $k$ is the effective stiffness of the elastic foundation (Water/water). \n", + "\n", + "where $A$ is the 4-th order tensor of elasticity, in the isotropic and homogeneous case, it corresponds to a linear combination with two coefficients, say, $A_0$ the stiffness (dimensional), and $\\nu$ the Poisson ratio (non-dimensional), $k$ is the effective stiffness of the elastic foundation (Water/water).\n", "\n", "Above, $w(\\alpha)$ corresponds to the dissipated energy to damage, homogeneously, the specimen, the gradient term accounts for spatial variations.\n", "\n", - "**Keypoint:** these two terms are weighted by $\\ell$, a parameter that is homogeneous to a length and is understood as a *material* quantity (as opposed to *numerical*).\n", + "**Keypoint:** these two terms are weighted by $\\ell$, a parameter that is homogeneous to a length and is understood as a _material_ quantity (as opposed to _numerical_).\n", "\n", - "Define $D(\\alpha_0):=\\left\\{ \\alpha \\in H^1(\\Omega), \\alpha \\geq \\alpha_0 \\right\\}$, for some $\\alpha_0(x)\\geq 0$ pointwise. \n", + "Define $D(\\alpha_0):=\\left\\{ \\alpha \\in H^1(\\Omega), \\alpha \\geq \\alpha_0 \\right\\}$, for some $\\alpha_0(x)\\geq 0$ pointwise.\n", "\n", "We solve two types of problems (by increasing difficulty):\n", + "\n", "- **The static problem**: Given a load (boundary conditions) and an initial state of damage $\\alpha_0$, what is the equilibrium displacement and repartition of damage?\n", - "In other terms:\n", - " \n", + " In other terms:\n", + "\n", "$\n", "\\text{ min loc} \\left\\{ E_\\ell(u, \\alpha):\n", " u \\in V_t, \\alpha \\in D(\\alpha_0) \\right\\}.\n", "$\n", "\n", - "- **The evolution problem**: Given a load **history** (boundary conditions as a function of $t$) and an initial state of damage $\\alpha_0$, what is the *evolution* of equilibrium displacement and repartition of damage, i.e. \n", - "the map $t\\mapsto (u_t, \\alpha_t)$, such that \n", + "- **The evolution problem**: Given a load **history** (boundary conditions as a function of $t$) and an initial state of damage $\\alpha_0$, what is the _evolution_ of equilibrium displacement and repartition of damage, i.e.\n", + " the map $t\\mapsto (u_t, \\alpha_t)$, such that\n", " - (Irrevers.) $\\alpha_t \\nearrow t$,\n", - " - (N-Stability) $y_t$ is stable only if there exists $\\bar h$ such that for $h\\in (0, \\bar h)$, \n", - " $$E_\\ell (y_t) \\leq E_\\ell(y_t + z), \\forall z \\in C_{t,0}^+, ||y_t+z||\\leq h$$\n", + " - (N-Stability) $y_t$ is stable only if there exists $\\bar h$ such that for $h\\in (0, \\bar h)$,\n", + " $$E_\\ell (y_t) \\leq E_\\ell(y_t + z), \\forall z \\in C_{t,0}^+, ||y_t+z||\\leq h$$\n", " - (Power statement) (Ext. power) = (Internal energy flux)\n", "\n", - "\n", "## Let'solve.\n", "\n", - "\n", - "### First, setup from a clean state" + "### First, setup from a clean state\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { - "id": "zC6dhNHJfgrb", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "zC6dhNHJfgrb", "outputId": "476beebc-1a79-4295-9fc3-cc00e0145806" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "CPU times: user 351 ms, sys: 120 ms, total: 472 ms\n", "Wall time: 33.6 s\n" @@ -95,16 +98,21 @@ "except ImportError:\n", " import ufl # noqa: F401\n", " import dolfinx # noqa: F401\n", + " import basix.ufl # noqa: F401\n", + "\n", "else:\n", " try:\n", " import ufl\n", " import dolfinx\n", + " import basix.ufl\n", + "\n", " except ImportError:\n", " !wget \"https://github.com/fem-on-colab/fem-on-colab.github.io/raw/779acd87a4e108672d7ebd3eefd9e8e555bb51d9/releases/fenicsx-install-real.sh\" -O \"/tmp/fenicsx-install.sh\" && bash \"/tmp/fenicsx-install.sh\"\n", "\n", "\n", " import ufl # noqa: F401\n", " import dolfinx # noqa: F401\n", + " import basix.ufl # noqa: F401\n", "\n", "# try:\n", "# import pyvista\n", @@ -131,12 +139,7 @@ }, { "cell_type": "code", - "source": [ - "%%time\n", - "%%capture\n", - "!{sys.executable} -m pip install --upgrade pyvista itkwidgets;\n", - "import pyvista" - ], + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -144,11 +147,10 @@ "id": "GVV5b15ALCyz", "outputId": "fc89e400-72ee-4c36-8883-153603cdfb23" }, - "execution_count": 8, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Requirement already satisfied: pyvista in /usr/local/lib/python3.7/dist-packages (0.36.1)\n", @@ -235,6 +237,12 @@ "Wall time: 6.76 s\n" ] } + ], + "source": [ + "%%time\n", + "%%capture\n", + "!{sys.executable} -m pip install --upgrade pyvista itkwidgets;\n", + "import pyvista" ] }, { @@ -243,23 +251,23 @@ "id": "0xbfPIJ_4pnK" }, "source": [ - "## Install our Code, codename: _________" + "## Install our Code, codename: \\***\\*\\_\\*\\***\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { - "id": "fBRSF4i0fm5d", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "fBRSF4i0fm5d", "outputId": "d4207c47-b0d0-4a69-a75c-8838e475f8bb" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'mec647'...\n", "remote: Enumerating objects: 1454, done.\u001b[K\n", @@ -304,7 +312,7 @@ "id": "ZCAPW6Rk4pnL" }, "source": [ - "### Setup computational patch" + "### Setup computational patch\n" ] }, { @@ -376,35 +384,35 @@ "cell_type": "code", "execution_count": 23, "metadata": { - "id": "bsZIi1mWf3AN", "colab": { "base_uri": "https://localhost:8080/", "height": 114 }, + "id": "bsZIi1mWf3AN", "outputId": "62b64a18-e351-4e1d-eb5d-9dc58f2fb01d" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, 'Mesh with parameters, dimension 2')" ] }, + "execution_count": 23, "metadata": {}, - "execution_count": 23 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ @@ -419,9 +427,7 @@ }, { "cell_type": "code", - "source": [ - "!ls mec647/test/data/banquise" - ], + "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -429,15 +435,17 @@ "id": "UZQDTODuSfoZ", "outputId": "c47092db-e6a2-4b78-8203-c0254c5881b9" }, - "execution_count": 32, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "parameters.yml\n" ] } + ], + "source": [ + "!ls mec647/test/data/banquise" ] }, { @@ -458,23 +466,7 @@ }, { "cell_type": "code", - "source": [ - "_lc = parameters.get('geometry').get('lc')\n", - "_ly = parameters.get('geometry').get('Ly')\n", - "_lx = parameters.get('geometry').get('Lx')\n", - "\n", - "_nc = list(map(lambda v: int(v/_lc), \n", - " [_lx, _ly]))\n", - "\n", - "mesh = dolfinx.mesh.create_rectangle(comm, [[0.0, 0.0], [_lx,_ly]],\n", - " _nc,\n", - " cell_type=CellType.triangle)\n", - "\n", - "plt.figure()\n", - "ax = plot_mesh(mesh)\n", - "fig = ax.get_figure()\n", - "plt.title(f\"~Computational Mesh with parameters, dimension {tdim}\")" - ], + "execution_count": 39, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -483,30 +475,46 @@ "id": "ZIiBFk08TfZE", "outputId": "0f8b71b6-09b5-4827-fab4-cd9ac22290fc" }, - "execution_count": 39, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, '~Computational Mesh with parameters, dimension 2')" ] }, + "execution_count": 39, "metadata": {}, - "execution_count": 39 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } + ], + "source": [ + "_lc = parameters.get('geometry').get('lc')\n", + "_ly = parameters.get('geometry').get('Ly')\n", + "_lx = parameters.get('geometry').get('Lx')\n", + "\n", + "_nc = list(map(lambda v: int(v/_lc), \n", + " [_lx, _ly]))\n", + "\n", + "mesh = dolfinx.mesh.create_rectangle(comm, [[0.0, 0.0], [_lx,_ly]],\n", + " _nc,\n", + " cell_type=CellType.triangle)\n", + "\n", + "plt.figure()\n", + "ax = plot_mesh(mesh)\n", + "fig = ax.get_figure()\n", + "plt.title(f\"~Computational Mesh with parameters, dimension {tdim}\")" ] }, { @@ -519,14 +527,14 @@ "source": [ "# Functional Setting\n", "\n", - "element_u = ufl.VectorElement(\"Lagrange\", mesh.ufl_cell(),\n", - " degree=1, dim=2)\n", + "element_u = basix.ufl.element(\"Lagrange\", mesh.basix_cell(),\n", + " degree=1, shape=(2,))\n", "\n", - "element_alpha = ufl.FiniteElement(\"Lagrange\", mesh.ufl_cell(),\n", + "element_alpha = basix.ufl.element(\"Lagrange\", mesh.basix_cell(),\n", " degree=1)\n", "\n", - "V_u = dolfinx.fem.FunctionSpace(mesh, element_u) \n", - "V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) \n", + "V_u = dolfinx.fem.functionspace(mesh, element_u) \n", + "V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) \n", "\n", "u = dolfinx.fem.Function(V_u, name=\"Displacement\")\n", "u_ = dolfinx.fem.Function(V_u, name=\"BoundaryDisplacement\")\n", @@ -741,16 +749,16 @@ "cell_type": "code", "execution_count": 63, "metadata": { - "id": "1weih1fXol0x", - "outputId": "44135167-3346-4971-8f1d-4e32fe73fabe", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "1weih1fXol0x", + "outputId": "44135167-3346-4971-8f1d-4e32fe73fabe" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Solving timestep 0, load: 0.0\n", " 0 SNES Function norm 0.000000000000e+00 \n", @@ -859,7 +867,7 @@ "\n", " # update loads (body)\n", " # gt.interpolate(lambda x: (0. * np.ones_like(x[0]), np.zeros_like(x[1])))\n", - " # gt.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " # gt.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " # mode=PETSc.ScatterMode.FORWARD)\n", "\n", " gt.value=[0, 0]\n", @@ -868,12 +876,12 @@ " # update loads (boundary conditions)\n", " \n", " u_.interpolate(lambda x: (0 * np.ones_like(x[0]), np.zeros_like(x[1])))\n", - " u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " mode=PETSc.ScatterMode.FORWARD)\n", "\n", " # update lower bound for damage\n", - " alpha.vector.copy(alpha_lb.vector)\n", - " alpha.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec)\n", + " alpha.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " mode=PETSc.ScatterMode.FORWARD)\n", "\n", " # solve for current load step\n", @@ -919,16 +927,15 @@ "cell_type": "code", "execution_count": 56, "metadata": { - "id": "lvlG7HjJorOu", - "outputId": "3890706a-6e6a-4205-ebe2-bc0028a7f581", "colab": { "base_uri": "https://localhost:8080/", "height": 333 - } + }, + "id": "lvlG7HjJorOu", + "outputId": "3890706a-6e6a-4205-ebe2-bc0028a7f581" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "([<matplotlib.axis.XTick at 0x7f128f189e90>,\n", @@ -936,20 +943,21 @@ " [Text(0, 0, '0'), Text(0, 0, '1')])" ] }, + "execution_count": 56, "metadata": {}, - "execution_count": 56 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ @@ -979,35 +987,35 @@ "cell_type": "code", "execution_count": 59, "metadata": { - "id": "hwbAa2wcoxS2", - "outputId": "26f6b30d-0c91-4c5e-d482-abaaca73d445", "colab": { "base_uri": "https://localhost:8080/", "height": 298 - } + }, + "id": "hwbAa2wcoxS2", + "outputId": "26f6b30d-0c91-4c5e-d482-abaaca73d445" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, 'Traction bar damage profile')" ] }, + "execution_count": 59, "metadata": {}, - "execution_count": 59 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" - ], - "image/png": 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\n" + ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ @@ -1065,9 +1073,9 @@ "metadata": { "colab": { "collapsed_sections": [], + "include_colab_link": true, "name": "mec647_Banquise_11.ipynb", - "provenance": [], - "include_colab_link": true + "provenance": [] }, "kernelspec": { "display_name": "Python 3", @@ -1079,4 +1087,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/playground/tutorials/mec647_Elast_2.ipynb b/playground/tutorials/mec647_Elast_2.ipynb index 307c5664..d81e8890 100644 --- a/playground/tutorials/mec647_Elast_2.ipynb +++ b/playground/tutorials/mec647_Elast_2.ipynb @@ -1,31 +1,13 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "mec647_Elast_2.ipynb", - "provenance": [], - "collapsed_sections": [], - "authorship_tag": "ABX9TyOsD0FguOEhMB0ip+ohclh7", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, "cells": [ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ - "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_Elast_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" + "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_Elast_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n" ] }, { @@ -56,6 +38,11 @@ }, { "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "7Sfwa68oxYgh" + }, + "outputs": [], "source": [ "%%capture\n", "!sudo apt install libgl1-mesa-glx xvfb;\n", @@ -81,40 +68,42 @@ "except ImportError:\n", " !{sys.executable} -m pip install gmsh\n", " import gmsh" - ], - "metadata": { - "id": "7Sfwa68oxYgh" - }, - "execution_count": 3, - "outputs": [] + ] }, { "cell_type": "markdown", + "metadata": { + "id": "MU0B8LEU-soU" + }, "source": [ "# The problem of elasticity\n", "\n", - "\n", "Let $\\Omega \\subset (0, L)^D$, with $D=1, 2, 3$, $L$ finite, being the (or one) characteristic length of the specimen. For any $u\\in V_t : H^1(\\Omega, R^n) + bcs(t)$ with $n=1, 2$ or $3$, consider the energy $E(u)$ defined as\n", + "\n", + "$$\n", + "E(u)=\\frac{1}{2}\\int_\\Omega A e(u): e(u) dx - \\int_\\Omega f.u dx\n", "$$\n", - "E(u)=\\frac{1}{2}\\int_\\Omega A e(u): e(u) dx - \\int_\\Omega f.u dx$$\n", - "Above, $A$ is the 4-th order tensor of elasticity, in the isotropic and homogeneous case, it corresponds to a linear combination with two coefficients, say, $A_0$ the stiffness (dimensional), and $\\nu$ the Poisson ratio (non-dimensional). \n", + "\n", + "Above, $A$ is the 4-th order tensor of elasticity, in the isotropic and homogeneous case, it corresponds to a linear combination with two coefficients, say, $A_0$ the stiffness (dimensional), and $\\nu$ the Poisson ratio (non-dimensional).\n", "\n", "We solve:\n", - "$$min \\left\\{ E(u): u \\in V_t\\right\\}$$. \n", + "$$min \\left\\{ E(u): u \\in V_t\\right\\}$$.\n", "\n", - "From a mechanical standpoint, linear elasticity is the limit regime of small deformations of the general, fully nonlinear, problem of elasticity. \n", - "From a mathematical standpoint, the minimisation problem above is a standard variational problem which is i) convex, ii) defined on a complete, compact, vector space of functions, and iii) . Its solution is unique and depends continuously upon the data. Can you show this? \n", + "From a mechanical standpoint, linear elasticity is the limit regime of small deformations of the general, fully nonlinear, problem of elasticity. \n", + "From a mathematical standpoint, the minimisation problem above is a standard variational problem which is i) convex, ii) defined on a complete, compact, vector space of functions, and iii) . Its solution is unique and depends continuously upon the data. Can you show this?\n", "\n", "Boundary conditions are such that equilibrium ...\n", "\n", "The interest of the above is that $E(u)$ ...\n" - ], - "metadata": { - "id": "MU0B8LEU-soU" - } + ] }, { "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "tZ9egFUuxEGq" + }, + "outputs": [], "source": [ "# library include\n", "\n", @@ -157,35 +146,22 @@ " set_bc,\n", ")\n", "import matplotlib.pyplot as plt" - ], - "metadata": { - "id": "tZ9egFUuxEGq" - }, - "execution_count": 4, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "!rm -rf mec647\n" - ], + "execution_count": 5, "metadata": { "id": "V1Hlttm1VXOM" }, - "execution_count": 5, - "outputs": [] + "outputs": [], + "source": [ + "!rm -rf mec647\n" + ] }, { "cell_type": "code", - "source": [ - "try:\n", - " !git clone https://github.com/kumiori/mec647.git\n", - "except Exception:\n", - " print('Something went wrong')\n", - "\n", - " !rm -rf mec647\n", - " !git clone https://github.com/kumiori/mec647.git\n" - ], + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -193,11 +169,10 @@ "id": "s9PP1aUIyUKC", "outputId": "eb2641ce-2418-4c39-82bf-cd21c7807a6f" }, - "execution_count": 6, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'mec647'...\n", "remote: Enumerating objects: 390, done.\u001b[K\n", @@ -208,10 +183,24 @@ "Resolving deltas: 100% (178/178), done.\n" ] } + ], + "source": [ + "try:\n", + " !git clone https://github.com/kumiori/mec647.git\n", + "except Exception:\n", + " print('Something went wrong')\n", + "\n", + " !rm -rf mec647\n", + " !git clone https://github.com/kumiori/mec647.git\n" ] }, { "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "Vtzt6XEWybNf" + }, + "outputs": [], "source": [ "sys.path.append('mec647/')\n", "\n", @@ -223,15 +212,15 @@ "from utils import viz\n", "import matplotlib.pyplot as plt\n", "from utils.viz import plot_mesh, plot_vector, plot_scalar\n" - ], - "metadata": { - "id": "Vtzt6XEWybNf" - }, - "execution_count": 7, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xYNJNfNnzb2l" + }, + "outputs": [], "source": [ "# Parameters\n", "\n", @@ -266,15 +255,33 @@ "\n", "# parameters.get('loading')\n", "\n" - ], - "metadata": { - "id": "xYNJNfNnzb2l" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + }, + "id": "jP0N2XxL0Prl", + "outputId": "2fdcd7af-01d2-4b98-902c-4e7be6188b34" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Mesh\n", "Lx = parameters[\"geometry\"][\"Lx\"]\n", @@ -301,39 +308,21 @@ "ax = plot_mesh(mesh)\n", "fig = ax.get_figure()\n", "fig.savefig(f\"mesh.png\")\n" - ], - "metadata": { - "id": "jP0N2XxL0Prl", - "outputId": "2fdcd7af-01d2-4b98-902c-4e7be6188b34", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 84 - } - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - } - } ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5ATiQASF3iuH" + }, + "outputs": [], "source": [ "# Functional setting\n", - "\n", - "element_u = ufl.VectorElement(\"Lagrange\", mesh.ufl_cell(),\n", - " degree=1, dim=2)\n", - "V_u = dolfinx.fem.FunctionSpace(mesh, element_u) \n", + "import basix.ufl\n", + "element_u = basix.ufl.element(\"Lagrange\", mesh.basix_cell(),\n", + " degree=1, shape=(2,))\n", + "V_u = dolfinx.fem.functionspace(mesh, element_u) \n", "\n", "u = dolfinx.fem.Function(V_u, name=\"Displacement\")\n", "g = dolfinx.fem.Function(V_u, name=\"Body pressure\")\n", @@ -342,55 +331,55 @@ "# ux_ = dolfinx.fem.Function(V_u.sub(0).collapse(), name=\"Boundary Displacement\")\n", "\n", "\n" - ], - "metadata": { - "id": "5ATiQASF3iuH" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hvdpZ8j25cAC" + }, + "outputs": [], "source": [ "# Integral measures\n", "dx = ufl.Measure(\"dx\", domain=mesh)\n", "ds = ufl.Measure(\"ds\", domain=mesh)\n" - ], - "metadata": { - "id": "hvdpZ8j25cAC" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "B-x_BOOKALTx" + }, + "outputs": [], "source": [ "\n", "# Data\n", "\n", "zero = Function(V_u)\n", "# works in parallel!\n", - "with zero.vector.localForm() as loc:\n", + "with zero.x.petsc_vec.localForm() as loc:\n", " loc.set(0.0)\n", "\n", "one = Function(V_u)\n", "# works in parallel!\n", - "with one.vector.localForm() as loc:\n", + "with one.x.petsc_vec.localForm() as loc:\n", " loc.set(1.0)\n", "\n", "g = Function(V_u)\n", "# works in parallel!\n", - "with zero.vector.localForm() as loc:\n", + "with zero.x.petsc_vec.localForm() as loc:\n", " loc.set(0.0)\n" - ], - "metadata": { - "id": "B-x_BOOKALTx" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rKTBpiQp6gCk" + }, + "outputs": [], "source": [ "# energy\n", "mu = parameters[\"model\"][\"mu\"]\n", @@ -401,15 +390,15 @@ "\n", "en_density = 1/2 * (2*mu* ufl.inner(_e(u),_e(u))) + lmbda*ufl.tr(_e(u))**2\n", "energy = en_density * dx - ufl.dot(g, u) * dx" - ], - "metadata": { - "id": "rKTBpiQp6gCk" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ytXUWcFX8yHl" + }, + "outputs": [], "source": [ "# boundary conditions\n", "\n", @@ -433,100 +422,77 @@ "\n", "\n", "\n" - ], - "metadata": { - "id": "ytXUWcFX8yHl" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "\n", - "bcs = [dirichletbc(zero, left_dofs), dirichletbc(one, right_dofs)]\n", - "bcs" - ], + "execution_count": null, "metadata": { - "id": "_RRgZb5S-y1P", - "outputId": "e36ae788-0b62-4568-a017-16ab732642d7", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "_RRgZb5S-y1P", + "outputId": "e36ae788-0b62-4568-a017-16ab732642d7" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[<dolfinx.fem.bcs.DirichletBC at 0x7f3a6eb909b0>,\n", " <dolfinx.fem.bcs.DirichletBC at 0x7f3a6eb90a70>]" ] }, + "execution_count": 12, "metadata": {}, - "execution_count": 12 + "output_type": "execute_result" } + ], + "source": [ + "\n", + "bcs = [dirichletbc(zero, left_dofs), dirichletbc(one, right_dofs)]\n", + "bcs" ] }, { "cell_type": "code", - "source": [ - "left_dofs" - ], + "execution_count": null, "metadata": { - "id": "94-k9S-3-bch", - "outputId": "4cf8759b-11ea-49f6-e135-b12863e7a5b7", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "94-k9S-3-bch", + "outputId": "4cf8759b-11ea-49f6-e135-b12863e7a5b7" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "array([ 0, 1, 5, 8, 12], dtype=int32)" ] }, + "execution_count": 13, "metadata": {}, - "execution_count": 13 + "output_type": "execute_result" } + ], + "source": [ + "left_dofs" ] }, { "cell_type": "code", - "source": [ - "\n", - "# solving\n", - "from solvers import SNESSolver\n", - "D_energy_u = ufl.derivative(energy, u, ufl.TestFunction(V_u))\n", - "\n", - "problem = SNESSolver(\n", - " D_energy_u,\n", - " u,\n", - " bcs,\n", - " bounds=None,\n", - " petsc_options=parameters.get(\"solvers\").get(\"snes\"),\n", - " prefix=\"elast\",\n", - ")\n", - "\n", - "\n", - "problem.solve()\n" - ], + "execution_count": null, "metadata": { - "id": "4OWbuLZQ-rQC", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "4OWbuLZQ-rQC", "outputId": "94a10597-f60e-4b83-b733-dcc15ea958a9" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ " 0 SNES Function norm 6.180058399450e+00 \n", " 1 SNES Function norm 6.071948106829e+00 \n", @@ -538,28 +504,51 @@ ] }, { - "output_type": "execute_result", "data": { "text/plain": [ "(6, 2)" ] }, + "execution_count": 14, "metadata": {}, - "execution_count": 14 + "output_type": "execute_result" } + ], + "source": [ + "\n", + "# solving\n", + "from solvers import SNESSolver\n", + "D_energy_u = ufl.derivative(energy, u, ufl.TestFunction(V_u))\n", + "\n", + "problem = SNESSolver(\n", + " D_energy_u,\n", + " u,\n", + " bcs,\n", + " bounds=None,\n", + " petsc_options=parameters.get(\"solvers\").get(\"snes\"),\n", + " prefix=\"elast\",\n", + ")\n", + "\n", + "\n", + "problem.solve()\n" ] }, { "cell_type": "markdown", - "source": [ - "## Visualisation & Post Processing" - ], "metadata": { "id": "wWY9BhRNcbbg" - } + }, + "source": [ + "## Visualisation & Post Processing\n" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gcnwU02YT4Bn" + }, + "outputs": [], "source": [ "\n", "def plot_vector(u, plotter, subplot=None):\n", @@ -571,7 +560,7 @@ " num_dofs_local = u.function_space.dofmap.index_map.size_local\n", " geometry = u.function_space.tabulate_dof_coordinates()[:num_dofs_local]\n", " values = np.zeros((V.dofmap.index_map.size_local, 3), dtype=np.float64)\n", - " values[:, : mesh.geometry.dim] = u.vector.array.real.reshape(\n", + " values[:, : mesh.geometry.dim] = u.x.petsc_vec.array.real.reshape(\n", " V.dofmap.index_map.size_local, V.dofmap.index_map_bs\n", " )\n", " grid = pyvista.UnstructuredGrid(topology, cell_types, geometry)\n", @@ -603,15 +592,15 @@ " plotter.view_xy()\n", " return plotter\n", "\n" - ], - "metadata": { - "id": "gcnwU02YT4Bn" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DTL6rJYhclPD" + }, + "outputs": [], "source": [ "\n", "\n", @@ -634,23 +623,34 @@ "# _plt = plot_scalar(u_.sub(0), plotter, subplot=(0, 0))\n", "_plt = plot_vector(u, plotter, subplot=(0, 1))\n", "_plt.screenshot(f\"displacement_MPI.png\")" - ], - "metadata": { - "id": "DTL6rJYhclPD" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "" - ], + "execution_count": null, "metadata": { "id": "MhIFGyYRZmp9" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [] } - ] -} \ No newline at end of file + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyOsD0FguOEhMB0ip+ohclh7", + "collapsed_sections": [], + "include_colab_link": true, + "name": "mec647_Elast_2.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/playground/tutorials/mec647_Fracture_4.ipynb b/playground/tutorials/mec647_Fracture_4.ipynb index a7e93a16..dacf898e 100644 --- a/playground/tutorials/mec647_Fracture_4.ipynb +++ b/playground/tutorials/mec647_Fracture_4.ipynb @@ -7,7 +7,7 @@ "id": "view-in-github" }, "source": [ - "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_Fracture_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" + "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_Fracture_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n" ] }, { @@ -81,45 +81,48 @@ "source": [ "# Fracture\n", "\n", - "\n", "Let $\\Omega \\subset (0, L)^D$, with $D=1, 2, 3$, $L$ finite, being the (or one) characteristic length of the specimen.\n", - "For any \n", + "For any\n", + "\n", "- displacement field $u\\in V_t : H^1(\\Omega, R^n) + bcs(t)$ with $n=1, 2$ or $3$, and\n", "- damage field $\\alpha \\in H^1(\\Omega, R)$,\n", "\n", "consider the energy $E(u, \\alpha)$ defined as\n", + "\n", + "$$\n", + "E_\\ell(u, \\alpha)=\\frac{1}{2}\\int_\\Omega a(\\alpha) W(u) dx + \\underbrace{\\frac{G_c}{c_w} \\int \\left(\\frac{1}{\\ell}w(\\alpha) + \\ell |\\nabla \\alpha|^2 \\right)dx}_{\\text{Surface energy}}- \\int_\\Omega f.u dx\n", "$$\n", - "E_\\ell(u, \\alpha)=\\frac{1}{2}\\int_\\Omega a(\\alpha) W(u) dx + \\underbrace{\\frac{G_c}{c_w} \\int \\left(\\frac{1}{\\ell}w(\\alpha) + \\ell |\\nabla \\alpha|^2 \\right)dx}_{\\text{Surface energy}}- \\int_\\Omega f.u dx$$\n", "\n", "In practice, $\\ell \\ll L$.\n", "\n", - "Above, $W$ is the elastic energy density, reading (in linearised elasticity as) \n", - "$$ \n", + "Above, $W$ is the elastic energy density, reading (in linearised elasticity as)\n", + "\n", + "$$\n", "W(u) = Ae(u):e(u)\n", "$$\n", - "where $A$ is the 4-th order tensor of elasticity, in the isotropic and homogeneous case, it corresponds to a linear combination with two coefficients, say, $A_0$ the stiffness (dimensional), and $\\nu$ the Poisson ratio (non-dimensional). \n", "\n", + "where $A$ is the 4-th order tensor of elasticity, in the isotropic and homogeneous case, it corresponds to a linear combination with two coefficients, say, $A_0$ the stiffness (dimensional), and $\\nu$ the Poisson ratio (non-dimensional).\n", "\n", "Further, $w(\\alpha)$ corresponds to the dissipated energy to damage, homogeneously, the specimen, the gradient term accounts for spatial variations.\n", "\n", - "**Keypoint:** these two terms are weighted by $\\ell$, a parameter that is homogeneous to a length and is understood as a *material* quantity (as opposed to *numerical*).\n", + "**Keypoint:** these two terms are weighted by $\\ell$, a parameter that is homogeneous to a length and is understood as a _material_ quantity (as opposed to _numerical_).\n", "\n", - "Define $D(\\alpha_0):=\\left\\{ \\alpha \\in H^1(\\Omega), \\alpha \\geq \\alpha_0 \\right\\}$, for some $\\alpha_0(x)\\geq 0$ pointwise. \n", + "Define $D(\\alpha_0):=\\left\\{ \\alpha \\in H^1(\\Omega), \\alpha \\geq \\alpha_0 \\right\\}$, for some $\\alpha_0(x)\\geq 0$ pointwise.\n", "\n", "We solve two types of problems (by increasing difficulty):\n", + "\n", "- **The static problem**: Given a load (boundary conditions) and an initial state of damage $\\alpha_0$, what is the equilibrium displacement and repartition of damage?\n", - "In other terms:\n", - " \n", + " In other terms:\n", + "\n", "$\n", "\\text{ min loc} \\left\\{ E_\\ell(u, \\alpha):\n", " u \\in V_t, \\alpha \\in D(\\alpha_0) \\right\\}.\n", "$\n", "\n", - "- **The evolution problem**: Given a load **history** (boundary conditions as a function of $t$) and an initial state of damage $\\alpha_0$, what is the *evolution* of equilibrium displacement and repartition of damage, i.e. \n", - "the map $t\\mapsto (u_t, \\alpha_t)$, such that \n", + "- **The evolution problem**: Given a load **history** (boundary conditions as a function of $t$) and an initial state of damage $\\alpha_0$, what is the _evolution_ of equilibrium displacement and repartition of damage, i.e.\n", + " the map $t\\mapsto (u_t, \\alpha_t)$, such that\n", " - (Irrevers.) $\\alpha_t \\nearrow t$,\n", - " - (Stability) $(u_t, \\alpha_t) = \\operatorname{arg loc min} \\left\\{ E_\\ell (v, \\beta), (v, \\beta) \\in V_t \\times D(\\alpha_t) \\right\\}$ -->\n", - "\n" + " - (Stability) $(u_t, \\alpha_t) = \\operatorname{arg loc min} \\left\\{ E_\\ell (v, \\beta), (v, \\beta) \\in V_t \\times D(\\alpha_t) \\right\\}$ -->\n" ] }, { @@ -131,6 +134,7 @@ "### Parameters\n", "\n", "In the energy above:\n", + "\n", "- Two elasticity parameters, such as\n", " - $A_0$ the stiffness of the sound material\n", " - $\\nu$ the Poisson ratio\n", @@ -139,10 +143,9 @@ " - $\\ell$ the internal damage length\n", " - $G_c$ the material toughness\n", "\n", - "\n", "### Back of the envelope computation.\n", "\n", - "1. Show that the energy above can be written as a function of only two non-dimensional parameters (ex.: $\\nu, \\tilde \\ell)$, by dimensional analysis." + "1. Show that the energy above can be written as a function of only two non-dimensional parameters (ex.: $\\nu, \\tilde \\ell)$, by dimensional analysis.\n" ] }, { @@ -218,6 +221,7 @@ " locate_dofs_geometrical,\n", " set_bc,\n", ")\n", + "import basix.ufl\n", "\n", "# meshes\n", "import meshes\n", @@ -391,14 +395,14 @@ "source": [ "# Functional Setting\n", "\n", - "element_u = ufl.VectorElement(\"Lagrange\", mesh.ufl_cell(),\n", - " degree=1, dim=2)\n", + "element_u = basix.ufl.element(\"Lagrange\", mesh.basix_cell(),\n", + " degree=1, shape=(2,))\n", "\n", - "element_alpha = ufl.FiniteElement(\"Lagrange\", mesh.ufl_cell(),\n", + "element_alpha = basix.ufl.element(\"Lagrange\", mesh.basix_cell(),\n", " degree=1)\n", "\n", - "V_u = dolfinx.fem.FunctionSpace(mesh, element_u) \n", - "V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) \n", + "V_u = dolfinx.fem.functionspace(mesh, element_u) \n", + "V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) \n", "\n", "u = dolfinx.fem.Function(V_u, name=\"Displacement\")\n", "u_ = dolfinx.fem.Function(V_u, name=\"BoundaryDisplacement\")\n", @@ -801,12 +805,12 @@ " # update boundary conditions\n", "\n", " u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1])))\n", - " u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " mode=PETSc.ScatterMode.FORWARD)\n", "\n", " # update lower bound for damage\n", - " alpha.vector.copy(alpha_lb.vector)\n", - " alpha.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec)\n", + " alpha.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " mode=PETSc.ScatterMode.FORWARD)\n", "\n", " # solve for current load step\n", diff --git a/playground/tutorials/mec647_Latest_10.ipynb b/playground/tutorials/mec647_Latest_10.ipynb index 0d1e533b..5d6e1b13 100644 --- a/playground/tutorials/mec647_Latest_10.ipynb +++ b/playground/tutorials/mec647_Latest_10.ipynb @@ -1,31 +1,13 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "mec647_Latest_10.ipynb", - "provenance": [], - "collapsed_sections": [], - "authorship_tag": "ABX9TyPNqyr7VBMtqrUx50qS1KNU", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, "cells": [ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ - "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_Latest_10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" + "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_Latest_10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n" ] }, { @@ -75,6 +57,11 @@ }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fBRSF4i0fm5d" + }, + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from petsc4py import PETSc\n", @@ -99,15 +86,15 @@ " !rm -rf mec647\n", " !git clone https://github.com/kumiori/mec647.git\n", "\n" - ], - "metadata": { - "id": "fBRSF4i0fm5d" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "onB6Cn24freZ" + }, + "outputs": [], "source": [ "%%capture\n", "from dolfinx.io import XDMFFile\n", @@ -160,15 +147,15 @@ "\n", "# mesh, mts = gmsh_model_to_mesh(\n", "# model, cell_data=True, facet_data=False, gdim=tdim)\n" - ], - "metadata": { - "id": "onB6Cn24freZ" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bsZIi1mWf3AN" + }, + "outputs": [], "source": [ "# Viz the mesh\n", "\n", @@ -177,15 +164,15 @@ "fig = ax.get_figure()\n", "plt.title(f\"Mesh with parameters, dimension {tdim}\")\n", "# fig.savefig(f\"one_mesh.png\")" - ], - "metadata": { - "id": "bsZIi1mWf3AN" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "G9MhYO5en-hY" + }, + "outputs": [], "source": [ "# Let's get the entire set of parameters\n", "import yaml\n", @@ -193,26 +180,27 @@ "with open(\"mec647/test/parameters.yml\") as f:\n", " parameters = yaml.load(f, Loader=yaml.FullLoader)\n", " " - ], - "metadata": { - "id": "G9MhYO5en-hY" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ms8MFyxDnLZs" + }, + "outputs": [], "source": [ "# Functional Setting\n", + "import basix.ufl\n", "\n", - "element_u = ufl.VectorElement(\"Lagrange\", mesh.ufl_cell(),\n", - " degree=1, dim=2)\n", + "element_u = basix.ufl.element(\"Lagrange\", mesh.basix_cell(),\n", + " degree=1, shape=(2,))\n", "\n", - "element_alpha = ufl.FiniteElement(\"Lagrange\", mesh.ufl_cell(),\n", + "element_alpha = basix.ufl.element(\"Lagrange\", mesh.basix_cell(),\n", " degree=1)\n", "\n", - "V_u = dolfinx.fem.FunctionSpace(mesh, element_u) \n", - "V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) \n", + "V_u = dolfinx.fem.functionspace(mesh, element_u) \n", + "V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) \n", "\n", "u = dolfinx.fem.Function(V_u, name=\"Displacement\")\n", "u_ = dolfinx.fem.Function(V_u, name=\"BoundaryDisplacement\")\n", @@ -234,15 +222,15 @@ "# Useful references\n", "Lx = parameters.get(\"geometry\").get(\"Lx\")\n", "Ly = parameters.get(\"geometry\").get(\"Ly\")" - ], - "metadata": { - "id": "Ms8MFyxDnLZs" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xnVFLN4Anavu" + }, + "outputs": [], "source": [ "# Boundary sets\n", "\n", @@ -263,15 +251,15 @@ "\n", "alpha_lb.interpolate(lambda x: np.zeros_like(x[0]))\n", "alpha_ub.interpolate(lambda x: np.ones_like(x[0]))" - ], - "metadata": { - "id": "xnVFLN4Anavu" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "C5vBUw8Knbtu" + }, + "outputs": [], "source": [ "# Boundary conditions\n", "\n", @@ -288,15 +276,15 @@ " V_alpha)\n", "]\n", "bcs = {\"bcs_u\": bcs_u, \"bcs_alpha\": bcs_alpha}\n" - ], - "metadata": { - "id": "C5vBUw8Knbtu" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "21ElBiFLoSpB" + }, + "outputs": [], "source": [ "# Material behaviour\n", "from mec647.models import DamageElasticityModel as Brittle\n", @@ -304,15 +292,15 @@ "model = Brittle(parameters[\"model\"])\n", "\n", "total_energy = model.total_energy_density(state) * dx\n" - ], - "metadata": { - "id": "21ElBiFLoSpB" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Reb9FOGaochC" + }, + "outputs": [], "source": [ "# Evolution solver\n", "import algorithms\n", @@ -324,15 +312,15 @@ " parameters.get(\"solvers\"),\n", " bounds=(alpha_lb, alpha_ub)\n", " )" - ], - "metadata": { - "id": "Reb9FOGaochC" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1weih1fXol0x" + }, + "outputs": [], "source": [ "%%time\n", "# Loop for evolution\n", @@ -353,12 +341,12 @@ " # update boundary conditions\n", "\n", " u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1])))\n", - " u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " mode=PETSc.ScatterMode.FORWARD)\n", "\n", " # update lower bound for damage\n", - " alpha.vector.copy(alpha_lb.vector)\n", - " alpha.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", + " alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec)\n", + " alpha.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT,\n", " mode=PETSc.ScatterMode.FORWARD)\n", "\n", " # solve for current load step\n", @@ -391,15 +379,15 @@ " print(\"\\n\\n\")\n", "\n", " # savings?\n" - ], - "metadata": { - "id": "1weih1fXol0x" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lvlG7HjJorOu" + }, + "outputs": [], "source": [ "plt.plot(data.get('load'), data.get('surface'), label='surface', marker='o')\n", "plt.plot(data.get('load'), data.get('elastic'), label='elastic', marker='o')\n", @@ -409,27 +397,27 @@ "plt.legend()\n", "plt.yticks([0, 1/20], [0, '$1/2.\\sigma_c^2/E_0$'])\n", "plt.xticks([0, 1], [0, 1])" - ], - "metadata": { - "id": "lvlG7HjJorOu" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "# experiments\n", - "params0, data0 = parameters, data\n" - ], + "execution_count": null, "metadata": { "id": "hKRNyEaCouZR" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "# experiments\n", + "params0, data0 = parameters, data\n" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hwbAa2wcoxS2" + }, + "outputs": [], "source": [ "from mec647.utils.viz import plot_vector, plot_scalar, plot_profile\n", "plotter = pyvista.Plotter(\n", @@ -460,23 +448,34 @@ "_plt.legend()\n", "_plt.fill_between(data[0], data[1].reshape(len(data[1])))\n", "_plt.title(\"Traction bar damage profile\")\n" - ], - "metadata": { - "id": "hwbAa2wcoxS2" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "" - ], + "execution_count": null, "metadata": { "id": "TOP4SRL0o0ZV" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyPNqyr7VBMtqrUx50qS1KNU", + "collapsed_sections": [], + "include_colab_link": true, + "name": "mec647_Latest_10.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/playground/tutorials/mec647_Snippets_9.ipynb b/playground/tutorials/mec647_Snippets_9.ipynb index b1943ca8..4c0f071e 100644 --- a/playground/tutorials/mec647_Snippets_9.ipynb +++ b/playground/tutorials/mec647_Snippets_9.ipynb @@ -1,31 +1,13 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "mec647_Snippets_9.ipynb", - "provenance": [], - "collapsed_sections": [], - "authorship_tag": "ABX9TyMIPW98JRJZxIlbJZTxiNNt", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, "cells": [ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ - "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_Snippets_9.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" + "<a href=\"https://colab.research.google.com/github/kumiori/mec647/blob/main/mec647_Snippets_9.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n" ] }, { @@ -74,19 +56,42 @@ }, { "cell_type": "markdown", + "metadata": { + "id": "E67Ie4YnJYac" + }, "source": [ "## Snippets and Quick & Dirty tests\n", "\n", "1. A gallery of meshes (automated?): TODO\n", "1. Mesh refinement, global and local ✔️\n", "1. Boolean operations on meshes: TODO\n" - ], - "metadata": { - "id": "E67Ie4YnJYac" - } + ] }, { "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "30eCmSEHJe4W", + "outputId": "5a041ddf-ff88-40c3-c885-19bfae8cf3c0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'mec647'...\n", + "remote: Enumerating objects: 1114, done.\u001b[K\n", + "remote: Counting objects: 100% (1114/1114), done.\u001b[K\n", + "remote: Compressing objects: 100% (836/836), done.\u001b[K\n", + "remote: Total 1114 (delta 560), reused 561 (delta 244), pack-reused 0\u001b[K\n", + "Receiving objects: 100% (1114/1114), 18.86 MiB | 12.86 MiB/s, done.\n", + "Resolving deltas: 100% (560/560), done.\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "from petsc4py import PETSc\n", @@ -122,49 +127,58 @@ " !rm -rf mec647\n", " !git clone https://github.com/kumiori/mec647.git\n", "\n" - ], + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EhoQqDxUKAvA" + }, + "source": [ + "## Meshes _galore_\n", + "\n", + "The idea:\n", + "\n", + "- load the meshes module\n", + "- for each mesh function:\n", + " - get default parameters\n", + " - create mesh\n", + " - display mesh\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "30eCmSEHJe4W", - "outputId": "5a041ddf-ff88-40c3-c885-19bfae8cf3c0" + "id": "ki9Y7sqpJzUb", + "outputId": "c82b9dcd-739b-4240-feaa-a6806f57cd73" }, - "execution_count": 2, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'mec647'...\n", "remote: Enumerating objects: 1114, done.\u001b[K\n", "remote: Counting objects: 100% (1114/1114), done.\u001b[K\n", "remote: Compressing objects: 100% (836/836), done.\u001b[K\n", "remote: Total 1114 (delta 560), reused 561 (delta 244), pack-reused 0\u001b[K\n", - "Receiving objects: 100% (1114/1114), 18.86 MiB | 12.86 MiB/s, done.\n", - "Resolving deltas: 100% (560/560), done.\n" + "Receiving objects: 100% (1114/1114), 18.86 MiB | 11.98 MiB/s, done.\n", + "Resolving deltas: 100% (560/560), done.\n", + "\n", + "Cloned brach: andres-plates\n", + "Last commit message\n", + "\u001b[33mcommit 197bd68ad3ab3ef1563bffc8cc7408715fe56774\u001b[m\u001b[33m (\u001b[m\u001b[1;36mHEAD -> \u001b[m\u001b[1;32mandres-plates\u001b[m\u001b[33m, \u001b[m\u001b[1;31morigin/andres-plates\u001b[m\u001b[33m)\u001b[m\n", + "Author: andres <leon.baldelli@cnrs.fr>\n", + "Date: Fri Feb 25 15:30:10 2022 +0100\n", + "\n", + " crackholes & kink rev\n" ] } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Meshes *galore*\n", - "\n", - "The idea:\n", - " - load the meshes module\n", - " - for each mesh function:\n", - " - get default parameters\n", - " - create mesh\n", - " - display mesh\n" ], - "metadata": { - "id": "EhoQqDxUKAvA" - } - }, - { - "cell_type": "code", "source": [ "branch_name = 'andres-plates'\n", "\n", @@ -185,79 +199,23 @@ " print('Something went wrong', e)\n", " !rm -rf mec647\n", " !git clone https://github.com/kumiori/mec647.git" - ], - "metadata": { - "id": "ki9Y7sqpJzUb", - "outputId": "c82b9dcd-739b-4240-feaa-a6806f57cd73", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Cloning into 'mec647'...\n", - "remote: Enumerating objects: 1114, done.\u001b[K\n", - "remote: Counting objects: 100% (1114/1114), done.\u001b[K\n", - "remote: Compressing objects: 100% (836/836), done.\u001b[K\n", - "remote: Total 1114 (delta 560), reused 561 (delta 244), pack-reused 0\u001b[K\n", - "Receiving objects: 100% (1114/1114), 18.86 MiB | 11.98 MiB/s, done.\n", - "Resolving deltas: 100% (560/560), done.\n", - "\n", - "Cloned brach: andres-plates\n", - "Last commit message\n", - "\u001b[33mcommit 197bd68ad3ab3ef1563bffc8cc7408715fe56774\u001b[m\u001b[33m (\u001b[m\u001b[1;36mHEAD -> \u001b[m\u001b[1;32mandres-plates\u001b[m\u001b[33m, \u001b[m\u001b[1;31morigin/andres-plates\u001b[m\u001b[33m)\u001b[m\n", - "Author: andres <leon.baldelli@cnrs.fr>\n", - "Date: Fri Feb 25 15:30:10 2022 +0100\n", - "\n", - " crackholes & kink rev\n" - ] - } ] }, { "cell_type": "code", - "source": [ - "# %%capture\n", - "from dolfinx.io import XDMFFile\n", - "from mec647.meshes import gmsh_model_to_mesh\n", - "from mpi4py import MPI\n", - "from pathlib import Path\n", - "# gmsh.finalize()\n", - "parameters = {\n", - " 'H': 1.,\n", - " 'L': 1.,\n", - " 'n': 10,\n", - " 'cx': .1,\n", - " 'h': .3,\n", - " 'cy': 0.,\n", - "}\n", - "model, tdim, tag_names = slab_with_holes('perforated_slab',\n", - " geom_parameters=parameters,\n", - " lc=.1,\n", - " tdim=2,\n", - " order=0,\n", - " msh_file='perforated_slab.msh'\n", - " )\n", - "mesh, mts = gmsh_model_to_mesh(\n", - " model, cell_data=True, facet_data=False, gdim=tdim)\n" - ], + "execution_count": 4, "metadata": { - "id": "BRBeR8R77EgE", - "outputId": "66bc5461-c665-493a-8826-aecaf7215aa5", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 - } + }, + "id": "BRBeR8R77EgE", + "outputId": "66bc5461-c665-493a-8826-aecaf7215aa5" }, - "execution_count": 4, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Point (1) = { 0, 1.0, 0, 0.012566370614359173 };\n", "Point (2) = { 0, 0, 0, 0.012566370614359173 };\n", @@ -372,13 +330,13 @@ ] }, { - "output_type": "error", "ename": "Exception", "evalue": "ignored", + "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m<ipython-input-4-38899c9ee67b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 21\u001b[0m )\n\u001b[1;32m 22\u001b[0m mesh, mts = gmsh_model_to_mesh(\n\u001b[0;32m---> 23\u001b[0;31m model, cell_data=True, facet_data=False, gdim=tdim)\n\u001b[0m", + "\u001b[0;32m<ipython-input-4-38899c9ee67b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 21\u001b[0m )\n\u001b[1;32m 22\u001b[0m mesh, mts = gmsh_model_to_mesh(\n\u001b[0;32m---> 23\u001b[0;31m model, cell_data=True, facet_data=False, gshape=(tdim,))\n\u001b[0m", "\u001b[0;32m/content/mec647/meshes/__init__.py\u001b[0m in \u001b[0;36mgmsh_model_to_mesh\u001b[0;34m(model, cell_data, facet_data, gdim, exportMesh, fileName)\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_gmsh_geometry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;31m# Get mesh topology for each element\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0mtopologies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_gmsh_topology_and_markers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/dolfinx/io.py\u001b[0m in \u001b[0;36mextract_gmsh_geometry\u001b[0;34m(gmsh_model, model_name)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;31m# Get the unique tag and coordinates for nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0;31m# in mesh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpoints\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgmsh_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetNodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 142\u001b[0m \u001b[0mpoints\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpoints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/gmsh.py\u001b[0m in \u001b[0;36mgetNodes\u001b[0;34m(dim, tag, includeBoundary, returnParametricCoord)\u001b[0m\n\u001b[1;32m 1827\u001b[0m byref(ierr))\n\u001b[1;32m 1828\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mierr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1829\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetLastError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1830\u001b[0m return (\n\u001b[1;32m 1831\u001b[0m \u001b[0m_ovectorsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapi_nodeTags_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapi_nodeTags_n_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", @@ -387,71 +345,117 @@ ] }, { - "output_type": "display_data", "data": { - "image/png": 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"text/plain": [ "<Figure size 720x2160 with 1 Axes>" ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } + ], + "source": [ + "# %%capture\n", + "from dolfinx.io import XDMFFile\n", + "from mec647.meshes import gmsh_model_to_mesh\n", + "from mpi4py import MPI\n", + "from pathlib import Path\n", + "# gmsh.finalize()\n", + "parameters = {\n", + " 'H': 1.,\n", + " 'L': 1.,\n", + " 'n': 10,\n", + " 'cx': .1,\n", + " 'h': .3,\n", + " 'cy': 0.,\n", + "}\n", + "model, tdim, tag_names = slab_with_holes('perforated_slab',\n", + " geom_parameters=parameters,\n", + " lc=.1,\n", + " tdim=2,\n", + " order=0,\n", + " msh_file='perforated_slab.msh'\n", + " )\n", + "mesh, mts = gmsh_model_to_mesh(\n", + " model, cell_data=True, facet_data=False, gdim=tdim)\n" ] }, { "cell_type": "code", - "source": [ - "from mec647.utils.viz import plot_mesh\n", - "\n", - "plt.figure()\n", - "ax = plot_mesh(mesh)\n", - "fig = ax.get_figure()\n", - "plt.title(f\"Perforated slab with $n$ circular holes, $n$={parameters.get('n')}\")\n", - "fig.savefig(f\"perforated_slab.png\")" - ], + "execution_count": null, "metadata": { - "id": "gQAL9OFf-Sdq", - "outputId": "930f4a77-3493-4569-d93c-426b3b04b46e", "colab": { "base_uri": "https://localhost:8080/", "height": 283 - } + }, + "id": "gQAL9OFf-Sdq", + "outputId": "930f4a77-3493-4569-d93c-426b3b04b46e" }, - "execution_count": null, "outputs": [ { - "output_type": "display_data", "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } + ], + "source": [ + "from mec647.utils.viz import plot_mesh\n", + "\n", + "plt.figure()\n", + "ax = plot_mesh(mesh)\n", + "fig = ax.get_figure()\n", + "plt.title(f\"Perforated slab with $n$ circular holes, $n$={parameters.get('n')}\")\n", + "fig.savefig(f\"perforated_slab.png\")" ] }, { "cell_type": "markdown", + "metadata": { + "id": "SkSxogd_KQF_" + }, "source": [ "## Mesh refinement (at runtime)\n", "\n", "Global (uniform) and local (selective) mesh refinement\n", "at the boundary and in the bulk, done with the dolfinx\n", - "mesh module.\n", - "\n", - "\n" - ], - "metadata": { - "id": "SkSxogd_KQF_" - } + "mesh module.\n" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 215 + }, + "id": "Jry6z_ZeKPPk", + "outputId": "c4eda8fe-b180-488d-a7ce-42be84997026" + }, + "outputs": [ + { + "data": { + "image/png": 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UVEtj9Oabb8LZ2Zlrq0xwb9q0ScCn1+sRGRkJd3d3lJSU8AXaVOgwGjx4MCwsLPDw4UNotVqEhITA19dX5Gvp2bMn7OzskJeXh6qqKjRs2BB169YV7HzKysrg4uLClRWtVouwsDB4eHjg+fPngvaY2YxpsDqdDkFBQWjcuLHoHseMGQOZTIb4+HjR4gwYx0KjRo1gKhPYOD979qyIt1WrVqhVqxaeP3+O58+fo1atWmjVqpXousOGDYNarcb9+/eh0+nQpEkTeHt7i3Z7bLHct28fDAYDoqKiJG3rpqZSQLjDYfTtt9/yxclcmAM1Al1EUgIdeKUhs8ImDWA0c/j5+aFRo0ZcM1yzZo2obWbnvXHjBncymmrIjAoLC2Fra8s1FNbmnj17RLznz5/nNuFRo0ZxoW1Oubm5cHBwQMeOHTF16lTIZDL8+OOPIr6SkhK4ubmhRYsW3D768ccfi/i0Wi0aNmwIf39/fg9z5syR6NFX286jR4/CwsICgwcPluRjZolTp07B19cXISEhks62r7/+GkSEDz74AE2bNoWnp6ekw9J0MfnHP/5RrV390aNH0Gg0GDBgAF+wTbVARqb2TLagSdmYKysrUa9ePTRo0ACHDx8WafumNGbMGCgUCty+fZsvTuZjp7CwkGuOrLz//vsixyF7D1u3bkVBQQFcXV3RokULSTv91atXQUSYPXs2N6FJ+WMeP34MKysr9O3blz+zlB08JSUFcrkcCQkJ3Exw5MgREd/ChQtBRLh9+zZfIE13rabPbG9vz3d8zExnuhtldOPGDUHfpKeni3jYfLt27RquXbsGhUKBd999V8QHANeuXYNMJsP06dMxffp0yGQykUkVMGrQNjY26NmzJ3dwSjn3dTodwsPD4enpyXcpUvMeAN544w3Y2triwIEDknMqLy+PP6dcLhcIc6BGoIvIXKAbDAbcuHEDU6ZMEQwauVyOevXqoVevXpg/fz569erF/2duu2VUWFgIKysrtG/fHtbW1ujcuXO1CIuZM2dCJpPhzp07CAwMREhISLVedVPUxuTJk6HT6VBeXo6SkhIUFBTg6dOnyMzMREJCAudr164dMjIykJ2djfz8fBQXF6OsrAw6nQ6fffYZ52OasBSdPn2a83l4eKCwsBBlZWVc68vNzUV2drZowiUlJSEzMxPZ2dnIyclBfn4+ioqKkJOTA1dXV8537NgxlJSUoKioCPn5+cjJyUF2djYeP36Mhg0bcr4PPvgAOTk5KCwsxIsXL1BeXs77f+zYsZyvOqchAC7wiQidO3eGXq9HRUUF78Nnz54hMzOTT1wiQq1atXD//n08evQIT548wdOnT5GXl4eioiLuP2B9yLbR5pSXlwdHR0d06NABPXr0gJOTE0pLS3H//n2sXr0abdu2hUKhEPSfaQkNDUViYiJu3LgBg8GA0NBQhIaGYuTIkVAoFCI/hykNHTqUt/N7iKlFixYJxk11Y3bEiBGcLzY2VpIvPz9fMAfeeOONattj5rWrV68iNDQU9evXh06ng8FgwK1bt7BgwQKEhoZK9ktgYCAmTZqEb775Bi9fvkRRUREsLS0xbNgwhIaGwt3dvVr0CQAMHz6ctzV8+PBq+ZYtW8b5WrduDa1Wi7KyMhQXFyMvLw9ZWVl4+PAhN/cREdRqNW7cuIHk5GTcvHkTt27dwu3bt5GSkiJSHOfOnYthw4YhKioKLi4uouecMWMGbt++ze/nvy3QZcb//99Ts2bN8GfS537wwQc0Y8YMSktLoyNHjtCuXbvo1q1bpFKpqFu3bvT1118TEVFiYiLdvn2bUlJSKC0tTYCNJTLi4J2cnHhxdHQkJycnWrFiBefZtWsXhYWFkbOzMzk7O5NKpeL/y8nJIR8fH6qqqiKDwUCHDx+mnj17Um5uLqWnp1N6ejo9ePCA0tPT6cyZM/Ts2bM/2VOvJ41Gw3wdpn4PAvCHsbN/BclkMjIffzY2Nvyz6XOUlZX9V+8lODiYatWqJVkOHDhA+/bt47z169fncQQNGzak7t27U48ePcjd3Z18fX1py5Yt1L59ezp69CgdPnyYkpKSyGAwUN26damwsJBjxGNiYmj27NmUl5dH+fn5or/JyckCPHl1QWXl5eWC8W1vb08KhYLkcjnJ5XL++enTp5zPwcGBmjRpQra2tmRra0s2Njb884IFC3hb27Zto/DwcHJyciJnZ2eysrLiWPoXL16Qn58f5efnExHRxIkTydramg4dOkT3798nmUxG0dHR1Lt3b0pLS6ONGzfSjRs36MKFC3Tq1Ck6f/48VVRUkIWFBcXExNCZM2f4dadPn06tWrWigoICys/PF/1NSUkRxCFU1zcVFRUiPPl/mjw8PCgwMJCX6dOnExFRbGwsnTp1ivR6PYWHh9PQoUOpXbt21KRJE9q4cSONGTPmT11PJpNdB9BM8n//awLdPDCjRYsWFB8fT/369SNnZ2cKCwsjT09POn78OOd5+fIlNWvWjE/Cpk2bUkhICBUWFoqKVqut9tp2dnZcuLu4uNC3337L/xcSEkIPHz4UBC3IZDKqU6cOvXjxgp4/f05ERK1ataLOnTuTSqUilUpFarWaf87MzKTFixfz32/dupW0Wi1ptVqqqqrin3/66Sc6duwYERGpVCpKSEggmUzG+4Z9Li8vpzVr1vD2Fi1aRGq1mpRKpaDodDqaMOFV1oYPPviAHBwcSK/Xk06nI71ezz/PmDGD840fP558fHxIoVCIyujRo7lAHjBgAEVHR5NWqyWdTsefQ6fT0f79++nevXtERBQUFESxsbGCd8w+FxYW0vbt23n9ggULBH3HPp8/f5727t1LRET16tWj+fPn82fQ6XS8ZGRkCPqmT58+lJuby0tRUVG146B9+/bUs2dPiouLIz8/P15///59CgwMpF27dtGQIUN4fW5uLh0/fpwOHz4sGJfmpFAoyMXFhVxdXcnFxYXu3LlDubm5REQUFRVVbRRnfn4+7dy5k3+fNGkSGQwGMhgMpNfr+eeDBw9yIRgZGUkqlYpKS0vpxYsXvJSXl1d7f0REarWaC3cnJydB0A8RkVKppHbt2lGfPn2oZ8+ePJp77ty5tGLFCkFwVXl5OX3//fd06tQpOnToEGVkVJvmmywsLMjV1ZXPvStXrvBFvm3bttVG/z579oz27NnDvy9cuJDUarWoPHnyhObPn8/5du3aRZaWliINOCcnhyZPnsz5fvjhB4qIiODftVot2dnZ0dixY2n16tWUm5tL+/bto127dtH169cF9/ZnZe/vCfT/OZMLmWxn5s+fL9oOxsXFoXHjxvy7VqvlAQfz5s2DhYWFwMllSrm5uQLH1uHDh/HFF1/gk08+wcKFCzFp0iQMHjwYXbt2hY+Pj+BegoKCkJCQgHXr1uGbb77B3bt3udNu+PDhkMlkcHBw+N3t/YQJE6BSqTB69GgQCRENjCoqKhASEgI3Nzf4+/vDz8+v2qCR5cuXg4j480thkYFXTt5Vq1ZBqVRWizFmZoqpU6fCysqq2m07C9iaOHEi/P39ERgYKBkQlZeXh1q1aiEkJATe3t5o1KhRtYEtbHvftGlTyOVyEdYXMJrMPD09ERwcjI4dO8LFxUUSOw+8ciYGBATAzc1N4HMBjHb2rKwsbvc2La1ateLOQFO6detWte/t9u3baNeunaitU6dOIS0tDYWFhYK+ZNA7e3t72NnZoXXr1tWaPhg+OyQkBGq1WgQnBIymlNq1a/PArrVr10q2VVpaCkdHR35/e/fuxVdffYUtW7ZgxYoVmDlzJkaOHInevXsjIiJC9DzVBapNmjQJ9vb2onqDwYAvv/wSderUEbTTuHFjXL9+HRkZGSgtLRU8O3O8e3h4wMbGBt27d5e8JmCE8CoUCnTs2BEymQznz5+X5OvXrx+srKzw+eefQyaTVYuLHzZsGFQqFc6cOYPatWvD398fBQUF/P/Xr18HkTh2Qq/XY+rUqYJn/LNEfycb+tKlS/ngJSJ06NBB4GUeM2YMnJ2dARiF+cCBA0FEWLFiBQCgR48e8PT0lLR3Dx06FCqVituoP/jgA8l7ePjwIQIDAwUvx8XFBT/99JOINzc3FxYWFhg9ejSP0ly+fLmIr6ioCNbW1oiPj0dVVRWaNWsGJycnEeb8vffeA5ERocGQJgwjb0oFBQWwt7dHXFwc9Ho9OnToAGtra5Gj7tGjR7C0tETfvn0BvEIvmEfD5uTkwMnJCS1btoROp+NIEXNHsGnAVmlpKbfjm2PYAWNkn0qlQnJyMg9skQoGKS8vR+3atdGtWzc8f/4c3t7eCAgIEDnGhw4dCoVCgWvXruG7776r1lZ55coV0G8OLca3fv16EV9eXh7Cw8OhUqkQEBAApVKJ7du3w8XFBQqFAlOnTkVJSQnn/+mnn7hvgVFxcTGmTp0KpVIJR0dHbNiwAUqlEv7+/lAoFIiKihK0wYjZajdv3oyNGzeCSNoRXFpaCicnJ/To0QPPnj2Dk5MToqKiRON7yJAhUCqV+OWXX9CsWTM0atRIcoFgTtEtW7ZAqVQKoHumVFRUhFatWkEul4PIGHlsa2sLpVKJKVOmiOzf77zzDurUqSOoM13kmjRpwn1I0dHRIDIio6TukfkMzp49yxd6qbmXlZUFCwsLvPPOOygtLUVAQAC8vb1FqCcGm/zHP/4B4NUcYwgxc74ZM2bw72q1Gh06dOBKFXOqms6zlJQUtG7dGkTGaFwiMTrsX6G/lUBnTtHi4mJ88sknsLOzg4WFBRYtWoSKigou8EtKSrgwNxWgTMs0R1MwwTN//nwAxkAjNzc3kWaZnJwMd3d3ODg4cDjkqlWr4OPjAxsbGxHMig24lJQUAEb4mJWVFQ+qMH8uhmRITU2FRqMRYFwvXrwo0B70ej3q168vCRVj3v9bt24BMMIObW1t0aZNG8Fk79WrF6ysrHigRXl5OQIDA+Hr6yvQbgcMGAC1Ws2fQ6fTITIyEs7OzsjNzeV8bFdgKnwGDRok0hxZ6PeiRYsAGDW15s2bo06dOiJtmUXTsr5lEZOm0EC2uLGFg7UXEBAgCl5ikMAXL15wGJyXl5dg55SdnY2GDRtCo9HgxIkTfBdTWVmJ/Px8jBw5EkQET09PHDhwAAaDge9MTp8+DYPBgF27dsHNzQ0ymQwjR45EXl4enj9/DiIj9O3gwYNQKBRo1aqVQKjrdDo0bNgQgYGB0Gq1qKqqQmBgIIKDg0W7MYb6YdGYDAppKjDY4sD6hsH0zFFULIqSQVNHjBgBCwsLEZwzOzsboaGhUKlUOHDgADQaDWbMmIFnz57h3XffhUwmg4uLCzZu3Mjv96233kKDBg0AGGNDpkyZAoVCwRc5nU7Hg3eKior4LpXNR/N7fOuttwAYF0xHR0dRXAhg3PEqlUqOqrly5QrkcrkgjoPthNzc3DgSq7KyEuHh4XB2duaQZQZ7NEdsMeWPwX7ffvttuLq6wmAwoKysDPPmzYNKpYKjoyO2bt2KrKys/6pT9H9WoDPtLDs7G/369eNmD+b9bt68uaQ2/Pz5c6jVaiQkJPC6ly9fws/PT2AaYEJjw4YNnO/ChQuwt7eHp6cnbt++zaFl33zzDbKysviWl8G8qqqq4OHhgU6dOvE2mEZsGtSk1Wrh5eUlSinAcpBs2rQJJSUl8PX1hb+/v2BAsRDwEydO8LqMjAxYWFgIBi7wSjCy7TYL0DENQgHAtVYWkMOCaZjwZcRy3AwaNAgA8ODBA1haWqJXr14CvmfPnsHBwYHjqJmpJSwsTIDQYdc1fWcGgwHBwcFo0qSJYNFiqKaTJ08iNzcXrq6uovakwtnZgr5jxw5ex/qBBatkZGQgICAA1tbWOHfuHIBX2PXCwkL+uytXrqBx48YgInTt2pULynXr1nEts3nz5gLBmZaWBqJXcMoDBw5AoVAgOjqaC3WGuDCFALJcKtu2beN1LKdM69atBf3VpUsXWFtb49GjRygsLIS7uztCQ0P5gvX8+XNYWlqKkDNvvvmmYHF/8OABlEqlIKgoPT0dfn5+sLa25rs4Ozs7wXz6+eefERMTAyJCo0aNcPbsWXTt2hXNmjXDzp07ueln1KhRgvxJY8eOhaOjIwDjwstQOaaBQOb3CLxSmkz7+cljUqwAACAASURBVPHjx1Cr1Rg5cqTgGdkOlGnfDG5pHhGcmpoKS0tLdOjQAXq9nsMqpWCPbCxu2bIFDRo0QFxcHM6cOYOAgAAQGXPz5OTkAKiBLYqoOhz6yZMn4evrKzCDSOU9AYyBJHXq1OGa6qxZs0BEAvsaW7nr1q2LqqoqHD58GBYWFqhfvz7XrlkEHhvYhYWFiIqKglwux+bNmzk22TQqFHhlNmKBLV988QWIjAExpqTX69GxY0dYWVmhbdu2kMvluHTpkoCnsrJSNKmHDRsGCwsL0S7AYDAgNjYWlpaWuHnzJgICAhAYGCgK0AHA83CcP38enp6eCAkJkbT9M/vt8ePHERsbCxsbG8FkY8TghDt27BCYWswpLi4O9vb2PFDqm2++EQhARuXl5QgODoa7uzvatm0LtVrNdyOMtFotfH190bJlSwCvUg6YJ24yGAxo2rQp/P39cffuXXh7e8Pe3l6Qg4RFI5r3qVarxZo1a2BraysYe87OztiyZYvI9MHGzKlTp3jd/v37uVDPz8+Ht7e3KI+MwWBAZGQkPD09+c7JNKeMKT169AjW1tbo2rUr4uPjoVAoRMmvzMPt2aJmPmfeffddqNVqPH78GDdv3oS7uzucnJwEtnJnZ2dBpDK734MHD4p8TUTGpGJSJpKuXbsiPDycf9fr9Rg2bBiICEuWLKn2HouLi+Hk5IQ33niD140dOxYqlYpHNjOqrKxEWFgYXFxckJmZCV9f32p9N59++imIjNGmDg4OgmhzU9JqtejcubPoOQMCAmpw6K+j6gQ68EojY6V58+ZYu3YtXx0ZsW3p5cuXkZycDIVCIYllZZopsxVGRkYKIjKZRsnyfwBGbb9bt278HhwdHUWTuqKiAkFBQfD390d5eTkiIyMREBAg4issLOTBIqwtKWHJTD+XLl3CzZs3edCFFGVlZcHBwYG3uWvXLo4df/ToEdLT05GWlsbtzKzs3LkTaWlpSE9PF+C6nzx5wjNbMjtkYWEh8vPzkZeXh5ycHDx79gxZWVmCBddc22d0+/ZtyOVynr+jffv28PT0FGHt9Xo914iJjD4V88kLvNrlJCUl8SRY5osi8EpTIzL6Q8wFIFt0mcnJlLRaLdf8WNm8ebPk5GemIfMgoS+//FKAZ5fK6MlC15ctW8ZzylRnC2fmCyLpjIfMPPTZZ5/xsPZ69eqJFveHDx9CqVQiNDQUDg4O8PDwEOCqAcDd3V2kCTPKzMwU9EutWrVENmxG9evXR58+fQR1Op0OQ4YM4b+vXbu2pGLBlKQffvgBjx49gkqlwpgxYySvk5KSAgsLC97mV199hezsbNy7dw8//fQTzp07h6NHjwpiFYgIPXr0wKRJkzBq1CjEx8fjrbfeQvfu3dGpUyfu02NlxowZkkCAGoFuRlICPT09HXFxcSAiWFtbg8gYuNCkSRMQGdNfxsbGYu/evXj58iU3u0yePBkRERFwdXUVeKqzs7Nx5MgRQQpSIhI5r/75z3+CiHDhwgVBPXOOmS4so0ePxqeffoqffvoJ5eXlPOdG165d+Ta9oKAAR44cQUJCAsLCwjgiwbwEBwdj6tSpOH36NMrLy/Hy5Uu4uLjgjTfe4Bqu6fPk5ubi1KlTWL58OTdP/dWlWbNmmDRpEnbv3o20tDSBUBoxYgRUKhW3s7MozqKiInz55ZeIj48XBDiZFi8vLwwaNAibNm1CSkoKXrx4AUdHR4SHh/NoU51Oh5SUFOzcuRMTJ05EixYteOpaIqNTzlwTZw5tlqCK0alTpxAcHCx5L23atBEJbubgNHd2379/X4CwioiIwLRp03D48GGBj4K9X/O8N5WVlfj++++xcOFCxMTECNpSKBQ8WpLZww0GA4KCghAVFcWDb0zzq5SXlyMpKYlHlbIihaCpW7euCBlVWVmJVatWwc7OTnAvRAR3d3fs3LlTtAPRaDSYNm2aoJ0XL17wCG5WYmJiMG/ePJw+fZrLAaalx8bGYuTIkVCr1QLlR6fTITU1FXv37hWkXf5XilKphL29PWrXrg0fHx/Ur18fTZo0QcuWLUXj0dPTE6tWrRJFSNcIdDMyFehlZWVITEyEhYUFbGxs8OGHHyInJwdEhI8++giA0c47a9YseHl5gYhgY2ODt99+G5aWlrzz3333XSxfvhy9e/cWwKfMB2JgYCC2bt3KtRiWppc5WFNTUyUFZvv27QVasVKp5LZXVvz9/bkA12g0aN++PRYuXIgLFy5w7e+XX37Bhx9+iE6dOnEBZGlpidjYWMGAioyMxPz58xEXFwdPT0/Bdcy3wBEREdi4cSM2b96Mbdu2YceOHdi1a5cg4pItOLt27cKOHTuwbds2bN68GRs2bMC6detgZ2fH+Xr06IE1a9Zg7dq1WLduHdavX48NGzZwDYqV1q1bw8rKin93cHBAp06dMHfuXI4UYGXOnDmIiYnhGqyjoyMGDhyI3bt3o3v37nB0dMQvv/yCdevWoV+/fnBzc+O/dXZ2Fj2/jY0N/25tbY3WrVsLIo1VKhVUKhXGjRvH8/2w3RhzzN69exdvvPEGiIx56r/66iuu0aWmpmLTpk1wdnbmtmImlBmKhO04bty4gf79+0Mulwty7kdHRwsWmaCgIIwYMQLTpk0TPM/SpUvRpUsX3pcymQxNmzblSA0iwrhx41C3bl3+vUmTJpg7dy569+7N60JDQ7Fnzx5MnDgRzZs3F9yLafHw8MDatWsFjuuAgABBiuoTJ05wFFhsbCzu3r0Ld3d3jBgxAlevXuVwx8jISL5AMkHH0Eb5+flITEyEk5OT4PotWrRA8+bN+VhQKpWIjIzEe++9h6ioKM7XqlUrbNu2DePHj0dUVBRX9IhI0K9EhLi4OGzcuBG7d+/G0aNHcf78eVy7dg1paWkYNGgQ5zNNHmZKmZmZgnk2efJktG3bFkQEJycnJCYm8p19jUA3I9N0tmwLP3DgQK55VFZWgshoczMlvV6P8+fPY8SIEQIBZFoCAgIwaNAgrFmzBpcvX0ZZWRkaN26MLl26YP/+/QgLCxOsvszE8+WXX2LYsGGQy+WwtrbG3LlzUVRUBB8fHx7ObjAY8ODBAxw8eBBz5sxBly5dRNdfvHgxLl68KNr2MmFoWl9aWooTJ05g0qRJIgglkTH1QXBwMAYPHoxVq1bh3Llz3KHHUgq3aNECKpVKEpvL8lB/+umnUCgUGD9+vOT7YPmxe/ToAX9/f/j6+oq0kvLycrRu3RoWFhbo0aMHiAhZWVnQarW4efMmtm7dilGjRiEsLKzaMPrQ0FDMnj0bly5dEiA9hg8fLoLDGQwG3L9/H5999hnefPNNQTs2NjaYMGECduzYgZSUFG47NVUEMjIyMHr0aCiVSlhYWGDSpElcQ9++fTsSEhKgVCpha2uLlStX8vfCEA8sH3xhYSHntbe3x6pVqzBy5Eg4OTnh4sWL3DRna2uLGTNm4OnTp+jWrRu3I5eXl+PSpUtYvnw54uLiBPhw0xIcHIwJEybgq6++EuzMAgMDOWLFYDDg9u3bWLFihWBxNC/MXzNr1iwcOXIET58+5ff8z3/+kx/g4ubmhtWrV+Ply5cIDg5G3759kZqaynecQUFBAtu+nZ0dR4Lo9Xrs2LGDL7zDhg3jpqiNGzdi8uTJfIHq0aMHkpKScO3aNRC98jOVlJTg22+/xZw5c0SLn/n7jo6OxqRJk7B9+3YkJyejqqqKzylXV1e4urqKdmTsGnZ2dujfvz9at24NZ2dnQf+ydxwcHAw7Ozv+DMzhfuXKFT7era2tMWXKFP4cNQL9NzLVdIODgzkKwZSUSqUgTakplZaWCvJAEBm3xubZCgHjwNNoNDytrcFgwKlTpyQDRCwsLDBlyhSBvT4uLg4NGzYUtavT6TB+/HhRG8OHD5fEJDN7qFSQTHl5OU/XalqknE6MoqOjERkZiaKiIjRo0AAODg6CrTQzB7HnHj9+PE9QZU6DBg2CRqNBRkYGh1WaYpf1ej0PbPriiy943hipAz0Aow/CPC9PeHg4nj59Ksk/bNgweHt7S/7v0qVL8Pb2Fk1wc+cz8Mo+bWp2ePjwIUaMGCESfkzrNj/5iDnRzGF+poLOtLi4uGDJkiUCzPbIkSNRq1YtyecpKSlBeHi4oI1BgwZVG6hWu3btanPAMOFjWs6dOycZpNaiRQsBAuu7775D+/btQWS0ibPfs4Vr9erVgnsyGAxQKBSiOVlSUoIZM2aIdgNKpRLx8fGC8fbo0SMQCVE+pmSat4iVAwcOVJtfqVu3bggODkZqairs7OwQFhYmml/r1q0DkfHAiuTkZMjlcoFiwxQVtVqNc+fOCSCppnTr1i0MGTJEMI6k5MIfpb+VQDd9YebefUb29vaSp5BcvnyZQ4kaNGgAIuJb0QEDBohe6MOHD0Ek3moZDAaMGjVKcC9LliwRDZ7Zs2dDqVQKBnd5eTk/RmzGjBkICQlBbGwsZs+eDblcDj8/PxFGntkQpQIi6tevDyKj2cjJyQn16tWDm5sbbGxsJANRysvLoVarudP0wYMHcHV1hZ+fH3JzcyUPJcjPz+cmEVNbNwu0MMUKT5w4EUSv/ArMD8GgiHq9Hi4uLpJH0BkMBr4D8/PzA5ExEMPCwgLOzs6Smfzi4+Ph4+MjqNPpdFi4cCHkcjn8/f05tPOjjz5Cs2bNIJPJ8P777wuehTlYzTW1qqoqkS9lxIgRkoKPQdvMnfCA0UZuvjBIHbvGTDLmu7SUlBQEBwdDJpOhWbNmIDKaLIiMPhqpI+E0Go3IIVpeXo5p06ZBJpPxIBelUgmVSgVfX18R8kiv18Pa2loywMhciCoUCsmFt6Kigs8RcyopKREkryMyml3MHb2lpaUgehUgyMj8XTs4OEChUMDT0xOWlpaSp13p9Xo4ODhwR+6JEycgk8nQv39/fl29Xo969eohIiKC/46dznXjxg3odDo+j9k1DAYDVCqVKFU0I5YdlZU/S38rgc6y7tWuXRtEhF69eomOPPP09MSIESP498rKSi4wvb29ce7cOY5auHXrFpYtWwaZTIbw8HCBI4VB5kxzg9+5c4djbE01CjaxTEPS2ak37DSUoqIi/ltm42/fvj1atWoFwIg88PHxgVwux9y5c7md1RwDXVZWxielt7c3h8AxtMGTJ0+qFVxMEzXVUq9cuQKNRoOoqCjEx8dLHhvGAlhYFCQ7acn8xJzS0lL4+fnB39+f/2bUqFGCe+jfvz/c3d0FdVqtlp+f2q9fPxQVFYHIiJG/c+cOF2IDBgwQ7KaGDBkCPz8//v3x48e8j4cMGYKSkhIBVLCsrIwHnA0cOJAvWgkJCbCysuKLslarxY4dO/jCYm7LbdiwoQiSJoVVLy4u5lqoqe2eOQvNoyqZ2cZUQO/cuRNWVlaoVasWzpw5w8fllStXcPDgQdjb28Pe3l6QckBKiCYnJ/NzLceOHYsXL17wsXb58mW4u7vDyspKEHmcnp4uUmqqqqqwceNGAbqJlZCQENGpPgUFBYIxz8aPqdmFFdZm586dRYgiS0tLwQIl9a7btWuH6OhoPH36lMcCzJgxQwBLZCkaTGMRWEAcg0Sy3OemkdCFhYVwcXFBdHQ032GbPhMA7iswpaqqKixatEjgk6suCv2P0N9KoDMNLj8/H0uXLoWVlRU0Gg0SExP55AwMDOSHySYnJ3MH5PDhw7mWy7bHzOl17Ngx2Nraonbt2lxDZvmg8/LyJKO+GArj+vXr+Pzzz/lgHDp0KLKysnDz5k0+KJ48eYKQkBCoVCrBhOnXrx8CAwP59+LiYo69bdq0KVJTUzn0Li8vD5cuXeI28zFjxgi0dkdHRx4EUlZWxh06prsPFoRhbgs0PWTaPD8FYByUQUFBCAwMRGVlJXcAmptODAaDAGrZoEEDkTbLzgplE/bFixf80OyZM2dyoWphYcEnsFarxeLFi6FUKuHm5sax/YMGDUJAQAAA4PDhw3BycoKNjY3gRCE2ORl22mAw4P333+fa7pMnT9ClSxeeVnn37t1cew0PD8fx48dx7949EBlhnocPH+b+mzfffJNHIrLzRUtKSqDX67Ft2zaueLzzzjt4+vQpOnbsiKioKOTk5GDUqFE8qnLTpk3Q6XRc671w4QLKysp4cE2bNm141CLjYfDLBw8ecEfj2LFjUV5ezn0C69atg06nw8qVK6FWq1G7dm2+sy0uLoapiSA7OxutWrUCkTGoTKvVcq3y6tWr0Ov12LNnD/z9/UFkdDxevHgRjRo1Qs+ePXHw4EH+v86dO3Ntn0EX2aJw+fJlHvgXGRmJq1evCuba2rVruaY9adIkPha9vLzwzjvvCN61tbW14F3HxMRw81BlZSVPz9y5c2feDtuN3b9/XzBuBwwYAJlMhuPHj6Nz587w8PAQ7K5zcnIEuwmpQ0xYXzC6efMmN5MNGjQIt2/fBlGNDZ2TOWwxMzMT/fv3B5HRfHLo0CGEhYUhNjYWy5cvh1qtRq1atUTmB9aOqc06JSUFAQEBUKlU2LZtG0aNGgVnZ2dB1NfQoUP5lpodjsCgaS9evMCcOXOgVqthbW3Ng27efPNNeHl5wdbWVpQaYPz48XBychI956FDh+Ds7AxLS0ueW3zgwIGQyWTw8fERtQMAVlZWAvy5wWDA8uXLOfLh8ePH6NKlC0JCQgAYNY6tW7eiY8eOPCcHKzKZDGFhYZg6dSqOHTuG58+fc8G4ZMkSeHp68nMvU1NTsWHDBvTr109gU2WlZcuWWLt2LYfqMVPWmjVrkJWVxZ2h5snD3NzcRPjmGzducC1z+PDhiI2NhZeXF5+4TZs2FeT2AV7tlO7evSuoP3r0KGxsbPhCLJfLuQmrcePGOHLkCN9FpKSkCLbX5eXlWLp0KaytrWFhYYE5c+ZwNNLZs2fRtGlT/uymEYxMoDMyjaps3LgxR/gkJibyXOJz5swRLIoMLvvdd9/xusrKSg7Ha9y4MY+hWLJkCW+/V69eAggks0ubRkmaCsGOHTvy/CpffPEFv5/Q0FAcP36c901QUBAPxa+srMRHH30ER0dHyGQyDB8+nEddr1y5EoMHD+aa+Oeff84XbybQ2clIeXl5GDt2LORyOZycnPDJJ5+gUaNG6NixI8aNG8cXW/N3HRUVhY4dOwrqNm/eDJVKBT8/P9y8eRNDhw5FrVq1RGadly9fcuADW2wSExPRvXt3EVqMyOhAfuedd3Dx4kXeFttxa7VaLFmyBCqVCq6urjh06BCAGpSLiKoLLDp//rwI3E9E6N27t2AQM5o7dy7kcrnopRYUFKBjx46idurVqycSouYCnVF6eroIXVG7dm1JmykT+lI22ezsbJEzbfz48ZKn/xgMBsjlcsH5kozY7oMl4HdwcED37t25MyogIADz5s3j+UmOHj2KRYsWoV27djwAQy6Xc62KFS8vL66BEhnRP0OGDMG2bdu4YFy2bBnfIclkMrRt2xabNm2Cg4MD6tatizp16sDGxka0TQeABg0a8KRhpsQO/zZfhKZPny7pIGRCUsq+y7bfpjuKQ4cOifwhycnJICI+MRk9efJEEPjCSp06dbBnzx7R+DIX6Ozd7d+/X+TAdXZ2luwXZjYzDWhjdPz4cRFU09bWFtu3bxfdyy+//CL5TIAxMtYcOeLv74+9e/eK+sbPz090ylVBQQGmTp0qcnhaWFhg7ty5ojFsLtAZJScnS4IQpk2bJvmuIyIi0LVrV1G9qUmJyAjdvHDhAnbt2oWlS5di1KhR/GhKc8WmQYMGGDx4MFavXs3nfLdu3TBixAjO7+/vj8WLF3O/BjMR9uvXTyB/agS6Gf1epGhBQYEAX24aQm5OEyZMgIODg+T/2FaUlVatWklGfVUn0AHji/Pw8OBtuLi4SKYWZaYOc8QEYDykmmGdWZk9e7YkEqaqqoprZFLEDltmxc3NDdOnT8e1a9f4RGearCnipby8HOfOncP8+fMF2gsrgwYNwpYtW3D//n2BwOjevTuaNGnCv9+5cweJiYmSEMvqTu1p2bKlSNtilJ+fz7VgVqpL3coCZ8yTfmVkZIgcco0bNxYFDwEQweZMqbS0VKQEmGrPpiQl0Bmx3ECsxMfHSy7eUukDTImZAlmRumfg1ZF4UkixzMxM0QJjHgzFyMvLS5Q3CDAuVMwMxcqSJUskI1urE+iA8V2b2+vNz8ZlFB4eLpmoCzDGDpiPPVZcXV3RtGlTwclmRGIHNzuGkJn8SktL8fnnn0suOlJO/BqBbkZSAl2n02Hz5s2i7b5cLoejoyPWrl0rCh0fMmQI6tatK6jTarXYsmWLQBCzEhsbK4LtSQn0qqoqrF69Gra2tgLthGmy/fr1E5ypyLDspm0XFRVx7cbW1pav9kyASUXalZSUgEgMmSouLsaCBQtEuUZCQkJEApDZZk1PfWd09OhR0QRnz2N62Dajzp07IzIyUlR/8+ZNURxA27Ztcf78edFEj42Nhfk4qaqqwscffwxHR0cBaoQFVvXs2VOU02XmzJlQq9W8/fLycixevBiWlpawtLTEhAkTQGRMxezh4QGZTIbx48cLhAtLhcDy7wBGx97OnTtF44WNQ2Y3NyUpgZ6Xl4cxY8Zw84JpWx4eHti1a5fgXZsmhTOl9PR0bn40nwdSMEsGXTTdOZaVlWHhwoWwtLSERqPhbSgUCjg4OGDNmjWiuSRlGktJSZHc6RIRoqKiRLBaKYGu1Wqxbt06ODo6CnZj1tbWUKvVmDVrlki5CQ0NFdiwAeOO7qOPPhLtXJo1a4bU1FQBuu3XX38FEXEzZFxcnGD3zPw/5uei3rt3T7To9OnTRxRZ+/+FQCeirkR0j4h+JaJZv8PX57eHafa6Nv9TAv38+fN8Sx8dHY1r166ha9euaN68OW7dusUHVf369QUToHv37vwgDIPBgKNHj3IoY4sWLXDx4kVYWlpi4sSJWLlyJezt7fnEYJPUXKCfO3eO27u7du2Ke/fuYdCgQTxDYmJiIqysrKBSqZCQkID8/HxuDz1//jx0Oh02bdoEV1dXyGQyjBgxAs+ePROEnZtH2jGhzA6nZWlTy8rK8OGHH/JB3KtXL446mT59Ojw9PTlmnE2Kn3/+GUTCLXhmZiY3H4WEhODSpUv8kJCFCxdCo9HA2toaH3zwgWCix8TEoE2bNvx7cXExT5nq5OTEzTfTpk3jSIfWrVvjzJkzXPCyvmN0+vRpHmbfoUMH3Lp1i6dlLSkpweLFi2FnZweZTIYhQ4ZwLW7MmDFwdXUFYDQ/MeRK3759kZGRgWfPnoHImFmzuLgYkydPhlwuh5ubGz8J3jQ1LmDUktlC27x5cyQlJWHevHmQyWQoLi7Ge++9xxfkFStWcBiiqUCvqqrCmjVrRA5AW1tbTJkyBUlJSfwaLVq04DsHllqC+YXy8/ORkJAAlUoFKysrzJ8/n6O4vvnmG0yaNIkHQi1btozvNk0DoQwGAw4cOMBhvG+99RYePXrENd6UlBSegKpBgwaC3YFpcq6ioiJ+qISDgwPWrVvHo2y//fZbgaN42LBhXOs3F+inT5/mc6lDhw64efMm+vfvj6CgIGRlZSE+Ph5Exp3m9u3b+YIXHBzM88Ho9Xrs2rWLR0d37NiRp0xgC+f48eMFiyUzz6WlpfE0De+++y4fk1OnToVGo+GoGb1ej7Vr18LS0hIODg78WgsWLICNjQ3kcjmGDx/O0XN/uUAnIgURpRORHxGpiSiZiIIl+GyJ6CIRXf2/EOjJycl8e1S3bl3s37+fd3q3bt3QrFkzAEZh/fXXX3PUQteuXZGSkoKYmBjExMTg8uXLHN4UGBiIQ4cO8XZMA5Ty8vL4xLC2tsaiRYuwe/duEBmhfEwz8vX1xdGjR3kb5jjprKwsvPvuu5DL5XBwcMDbb7/NBxZbmFq3bo3r16/z3zCnEtsaM82QCcL4+Hj88MMPXCht3LiRa42dO3fmTrlvv/0WRMZUBcXFxZgwYQJkMhm8vLxw7NgxPH78GETGdL1arRarV6+GjY0NLC0tsXz5ci6wrayseM6N9PR0bhYKDg7mUaeRkZH8kO3du3fzvOCjR49Gfn4+x4Y/ePAAZWVlWLduHXc8RUVF4dtvv8W4cePg5OSE+/fv84g7f39/gbPSNM82YDS7zZw5E5aWllAqlRgzZgx3CjIkTYMGDQT+ECbQP/nkE153/fp1Lkw7d+7MNbMdO3bwd+3p6SnQnufNmwe5XM7bSEtL4zmGAgIC8PXXX6NDhw6IiorCyZMnuZ/BHKLHBDp716bwvvj4eO6c3rt3L5YvX86VjXfffZcjYb7++msQvQowu3v3Lrp37w4iY/qDL7/8ksc3fP/999xkEBoaKogcNjVhMMWHIVl69OiB+/fvw87ODhMnTsTWrVu5MjJ69GhuOzbPSmoO5Vy+fDk3i127do2/az8/P8G7ZgKd0Q8//CCwWV++fBlBQUHo168fTp48yedTWFgYvzY7UejIkSM8jUJ8fDzXwnv27AkfHx9+zXnz5nEBDRgzQjJT4sOHD3mIf2xsLLKysngCOMCYQykhIQFqtRoWFhaYOnUq99n8lQK9JRGdMvk+m4hmS/CtIaI3iOi7/6ZAZxFq9NvWa+nSpSLbqNRWvbKyEqtXr4a9vb0owMPNzY0LMUYGgwFE4gT7aWlpPKDAtGg0GixYsEB0L1IntQBGZ5xpVkaphYkRE9amJ+EARjPLrFmzJMOeW7VqJbLjmsP3AKOziGlCbCL17NmT28tjY2NFQSvm+a8Bo0mGaXeDBw9G7dq14evry4Vp8+bNBdtsthjeu3eP11VUVGDDhg08745psbGxEWi6jMwFOqPs7GxRNK5CocCqVatEJgMG8TMV6IDRlLd+/XqRicjS0hKJiYkiP465QGdkKrxNS0BAAI4dOyZ636YCndHvves33nhDZA5kcENzR/zZs2c5WoUVmUwGJycnbNiwQeScl7JJV1RUYPny5bCxsRHdT6tWrQTKUxqA2gAAIABJREFUCCAW6IzS0tL4mDN/18uXLxe9a3OBDrzSwqXMpH5+fti3b59AA2ems5MnT8JgMPDTj3r37o0XL17A1tZWcPycwWDgMOLNmzejbt26GDhwILZs2QIbGxvY2tpi69at/B0uWLAARCS45qNHj3hqEHZvUoF1f5T+XYHel4i2mnwfSkTrzXjCiejQb5+rFehENIqIrhHRterCtf/Aw/AiBd0DgDfeeEOQV9mUGJ6YFRcXF9FxWYBxMhNJp3k1GAyicHtTLKwpjRw5Em5ubqJ6vV4vSkIVHR0tmc2OYVelkusDEKX5HD16tKTjyVxrY1RZWcnx6aw4Ojryk3jMyRTvbkovX77kGo2p8JPKCy7lO2DEUgr/kXddnUAHXmVIZMXCwkKUmx54JdCljqHT6/Vce2Rl3bp1kv3CkFNSlJeXJ1IkpBzhgLRAZ8QWQlYmTJggeS/MMSrlcK6oqBAJdRb8Zk6/52RkigYrHTt2lMwrXp1AB4SpIVgxD9hiJCXQGbGDQ1gJDg6WBDJIIYRYQBhDwJgeiAIYTWPmyhcRoV27dqKUzWwemSsNwKv5x8qfpd8T6HL6N0kmk8mJaDURTXsdL4DNAJoBaObq6vqnrjds2DAiIpLL5dSlSxdKSEgQndAuk8nYAsJJq9XSxx9/TC1atCC5/NVj5+fnU4sWLeibb74R/Ear1RKR8RRzU0pJSaEuXbrQypUrBfXx8fE0YMAAyszMFNQrlUrS6/WCurt371KbNm1o3Lhx1K5dOyIicnFxoVu3blFoaCjNmjWLSktLOb+1tTUREb18+VLQzpMnT2jw4ME0dOhQQf2nn35KXbt2pdTUVEE9uw+FQiGoLy8vp4qKCkFdZWUlZWVlierZM+l0OlF9cXGx4L7ZNcvLy3l/MlKpVEREovpLly5Rq1at6IcffhDUd+nShSZOnEiFhYWi65pTZmYm9e7dm+Li4qhBgwZEZBwv/v7+FBcXR3369KEnT55wfnYKvTldvnyZWrRoQbNnzxbUT5w4kbp160Z3794V/ca8Lb1eT1u2bKHg4GAyGAy8Xi6XU8OGDWnjxo2SfWlOT548oSFDhtCQIUME9evXr6cOHTrQzZs3RdclEr/rkydPUpMmTQT8arWaWrRoQUuXLqXy8vLX3svLly8pMTGR2rZtSxYWFrz+7NmzFBERQZcuXXptG0RESUlJFBERQV988YWgvkuXLjRhwgQqKCh4bRsVFRW0bNkyCgsLI7Vazevv3LlDHTt2FM2BqqoqIiIBb0JCAq1YsYLKysqIyDiXAwMDKSAggPz9/SkoKIiuXbsmaGfGjBl09uxZqlu3rqCe9bfpO62qqqKlS5dSv379eN1777332mf7U1SdpMcrrfp3TS5EZE9E+UT06LdSQUTZ9Bqzy79rQ3/48CFHBjg7O2Pjxo1cOzCFzBkMBpw4cQJBQUEgeuVg8fHxwdChQ3Hs2DFuX+/SpYsgepHoVYhuQUEBJkyYwJ09H3/8MT8UISkpCYmJidBoNHw7zjznEyZM4MdqMU1YrVbD0dERn332Gc//MHv2bOTk5PDtXZ06dbj5xTTqDzA6PBctWgQrKytYWFhg3rx5HPmwZ88erFmzBvb29lAqlUhISOA7EHaMGYvgKy8vx4cffsgdRMw27Orqym2Dbm5u+OijjwSmJPMDDZ4+fYopU6ZAo9FAoVDwZ/Dx8eEmFy8vL2zevJlrLkxbYfb9jIwMrqnVqVMHe/fu5ZC3Bw8eYNy4cRwFsn79esmzKquqqrBy5UpYWVnB0tISy5YtQ2VlJYc/VlZWYtmyZbC0tISNjQ1Wr14NrVaL3NxcQf9mZGTw9AAMZcICdS5duiTo32nTpvFo3blz50KhUPB+uXDhAs/JHx0djevXr3OnaHJyMu/jRo0aCTRGUw29rKwMixcv5u967ty5+PHHH0FkzIW+YcMGODk5QS6XY8yYMfxIt3379oGIcOfOHQBG2CjTMuvVq4evv/6aB7Wlp6fzVLrmZj9TDd0c1TNgwAA8evQIcrkcc+bMwe7du7kfpF+/flx7NdfQMzMzef96enpiz549PO96eno6n2eOjo74+OOP+Zgx1dANBgO++uorQcTur7/+yuf1jh074OTkBLVajQULFnDzDTM7MgezTqfjfUi/ac4xMTEYOHAgBg0ahMGDB2PIkCGiE4kUCgX69OkjQmcx+cTgphcuXOBgi759+/712RaJSElED4jIl145RRv+Dv93rxPm+A8IdGa/vHHjBk/pyRw6PXr0QOPGjXH79m2eppYNYtb5devWRXx8PACxfX3ChAm4f/8+F+jr16/nk2bcuHF80pijXDIyMrhQrFOnDvbt24fJkyfD1tYWP/74I49wfOuttwRwNhsbG8EWOykpiQuCDh068EGwfPlyQQBK3759eR4bZpZh2Nfc3FyMHDkSMpkMrq6u2Lp1K5/kycnJ2Lp1K8/93qVLF273bNSoEd58800Axqx6zFlmmi61Tp06eOedd/Ds2TNMnToVlpaWXJCzcGom9A0GA86cOcNNKH5+fvj888+5OeTs2bNITEzkMDlT2zRz2jEUzs2bN7kPheVSYQL94sWL3BfQvXt3QX6fiIgIdOnShX9/8OABF25NmjThk3z58uX4xz/+we9l/vz5/F7MD7jIycnBiBEjIJPJULt2bezYsQOzZ8+GQqHAo0ePeF58Ly8vfPHFF3zcmaJcDAYDDh06xJERvXr1Qnp6OmxtbZGQkID9+/dzv0SfPn24L+POnTsgehW1WlBQgEmTJgmghczpfOXKFf4/lsKXBeSYRymfO3eOm2JiYmLw888/c4FuiuqJiIgQJJCTyWQ8oK20tBT/+Mc/oNFooNFoMG/ePH5uwJEjR7BgwQLJ/jVHudy6dQsdOnTg5pPTp09zgS41Dhj5+PjweZ2Tk8MXjuDgYCQlJQlOjLp48SJ3nLZt25bfg2nGTSYfWEoEImMW0pkzZ/JFICQkBJs2bUJpaSlvIz09He+88w5fJJmp7y9HuRh/T7FElEZGtMvc3+oWEVEPCd7/U4EOVB9pxwb4Rx99JIoqkzplJS8vj2uCpu0QGQ+pMLczVhdYdPHiRVEQjlwuh4eHB44cOSJ6JqkzGZlDjkHaTNtq3LixyOFpLtAZXb9+XZD437RERkaKgkqaN28uirQzFeymkaGWlpaQy+V4++23BXkxAMDFxUWQ68JgMOD48eN8oTIv/fv3F9kjzQU6a+fw4cMcemhavL29JYNowsPDBedNsnYOHjwo6UyTuhcm0E3D+AHgxx9/RIsWLQS/Zzu1BQsWiDJ4SuHQTdMImDsZzVEngFigM0pJSUGnTp1EzyOXywWoE0bjx4+Hs7OzoI5BZ11cXEQnZpmjehgRicEDpjsu82KqvTOSwqEbDAYcOXJE9K5N0wGYO3FNBTqjEydOwMvLCzKZjMN4mSD3+n/sfXl4TVf3/8ogRGRAJFrzkCAxhUrNXkqrxqoQLTGVmuep5iFetKZOWtQslLZK6VdpFVVTlRiqYtYakyAkREWS+/n9cbu2vffZ54q++nuHx3qe/Txy3Ox7ctc966yz1ufzWcWKiSeStLQ0uLu7Y+LEicrvs7Lqp59+Ci8vL4wcORKA8+lpyZIl4lr39/dXztPT0xMjR45UYtV/RED/O9bfwRRNTU0V9HZepjmTAFC8eHFLQGfTZS51tUA2V0zRzMxMC3tMRnTIVqRIEeNMU8BKTa9Tp46x4WIX0AHnhTFo0CBlH7vGXr169dCwYUPjuehyqe7u7hZ9FLaAgACjhHF2drZFjlYnQ7GZAjpbWlqacnMhMlP7AaBKlSpo1aqV8f8YI82rW7duRg1tLrnoAR1wBkF9YInpdYBrpihrxfOqVauW0dd2AR1w+poljHnJKoyy9e3b1xLQ2S5fvqzs4ePjo6hIykbknCVrMtYU58XjBHVzxRS9c+eOhbSjK6yymQI64Py+sNAZr5EjR1puuJUrV0aTJk3Ez4xFZ/hyyZIlLTIHDocDe/bsUSZAEakqrWx/d0D/l5ui/ym2detWqlmzJt24cUM5Xrt2bVq5cqXSkGLTG1h37tyht956S2leEBEtXLiQWrRoQadOncrRufz8889Uv3592rFjhziWK1cueu6552jmzJmUkZGhvD537tyWYw8ePKDZs2dTnTp1lMbsnj17qG7durR///4cncvly5epc+fO9N577ynHR44cSVOmTBGNILY8efJYGqEAaN26ddSrVy/luMPhoJiYGNqzZ4/lfTMzMy0N5cTEROrZsyfNmDFDOT58+HDq2rUrXbt2LUd/05YtWygyMpKSkpKU41WrVqWlS5dafJ2dnW05lxs3blDv3r1FU5pt6dKlVLNmTTpw4IDxvfXvzMGDB6l+/fq0detWcSxXrlzUqFEjeueddyx+NdmDBw9ozpw51KBBA+U89+3bR3Xq1KF9+/Y9cg8iorNnz9Irr7xCH3zwgXL89ddfp+HDh1Nqauoj/x6Hw0FxcXEUGRmpHE9PT6fIyEjatGmTAh7gf+v7JCUlUc+ePWngwIHK8ZEjR1KXLl3o6tWrOfqbtm7dSjVq1LB8N55//nlasmSJ8brWLTs7mz777DPavHmzcnzp0qX03nvvKZ9LrVq16KeffiKHw0G7du2iAQMGULNmzSg2NpaIiJ599lm6cuWKso+bmxuFhYVZwBlRUVG0YsUKC0DjbzW7SP93ryeVoct6J2XLlsVXX32FV155BZUqVVKYdjKrElD1J7Kzs7F06VJB3ujSpYuoW7///vuYOXOm0K8eOnSoaDLqGfrVq1cFUSg4OBhLlizByJEj4eXlhVOnTgmSSZkyZRTykcxuczgcSqOWZzLyCKvly5eLbKVTp05iOo6eoXMt09vbW6gBsqzttm3b0K5dO/HIuXr1anEuLVu2REREhPic5B5FxYoVsW3bNjHabMWKFaIJ1q5dOwWvnjt3bowaNQqAEyb3zjvvCDmE4cOHixLGunXrBDU/X758mDZtmoCb6Rm67utNmzYhKioKFSpUUEof1atXF9KygHOaPKsBZmZm4sMPPxTSAYMHDxb9kvfffx9xcXHi8+3evbvQ8tAhn1evXhXN36CgICxevBhvvfUWPD09cfr0aUHi0YlQeoYuz99s2rQpEhIS4Ovri0GDBimSzLKv9QzdRNRhidj9+/eLWn+hQoXwySefCPBAnz59EBgYKM5l//79ClFnz549ooZuR4TKzs4G0UPiTUZGBmbOnAlfX19xvci+Ziw9c0jY13qGLl8vTMpq3749ypUrhwMHDqBWrVogciouypmwnqFv2bJF9K5q1qwp1CM//vhjIXzn5+eHt956C4mJiVi+fDmInMNzChUqhHLlyilPDe3atVPkrgFnk5elsZl09fPPP4vPsk6dOgI++rTkohkH9MuXL2Po0KGC0jxz5kzRyW7Tpg0qVaoEwMq04wuDtZXt6NVXr14FkZM1CTjZhD169BAXxsKFCwVqZN++fZg+fbogWowaNUogH/gLzCZfGE2aNMHx48cRERGBFi1a4MSJE+LRXZ/J6OPjI9iZaWlpGD16NLy8vJA3b15MnTpV0MHXrFmDFStWiNpwdHS0eDzlLytT4n/44QdR/6tduzYOHDggmoxJSUmiqVqwYEGFdMJsPMB545g0aRLy5s2r/O08OGHjxo1CerhFixai7MRUem5onTlzBq1btwaRk2375ZdfCpTLpUuXMGzYMMXX3BORUS7MSuW//bXXXsPFixcREhKCDh064IcffhBNvxdeeEEEJR3lkpaWJqj7/v7+eO+990Qzbc+ePYJUkytXLowYMUL4esyYMfD09BQ+27p1q0A4NG7cWEhR1K5dWxlLFxoaqvhaRrncuXMHY8eORe7cuZE3b17ExsYKtuPq1attqfTcFGXfHzx4UDT2WGmwT58+KFSokIVKv2zZMlF2klEuLFXA4IGBAwcKyYnJkycriUjz5s1FOU5HuZw9e1bISZQsWRLr1q0TKJfff/8dw4YNE7IJ8sxWHeWyatUqkVB06NABv//+uwjoR48eFciU0qVLizo5l1MZBBAfH4927drBzc0NefLkUUpnfn5+lpLi4MGD4ePjI27QR48eRZEiReDn54dt27Zh2LBhyJs3r4g9ixcvRmBgINzd3dG/f38kJCQ8Deiy8VSbPHnywM3NDT169LAQNOSAzsZBkC8MdhqRE5oWFxen1E71gM526NAhIRWgr9atW1uag3pAB5wXhiziz7/PSAR9JiOgBnQ2GW6mrxo1aihZKgChYyELC2VlZWHRokUWYTN+Ihk8eLCldipnvGyXL18WQUEXQSpfvrxFBlYP6GzfffedQKvw8vLyUrRtZDMRi+7cuYNx48aJ5qS8V/HixfHFF18o/QMO6Dqx6OTJkxa4Gq9WrVpZtLj1gA48FBPTfe3p6Qk/Pz8FdcJmIhadP3/eyFAmsmquA9aADjiD4KeffirQTbxciV2ZiEXJyckCMqw3AsuVK2cRDbMjFm3bts0iee3p6Sl01PWeiIlYJKNqZF+7ubkhf/78mDt3rsI4lac9yXby5EnLrOGYmBjL58FJxu3bt7Ft2zb4+fmhSJEiAgrcq1cvy0zYlJQU9OvXTwFb6E3kx7H/qYAuf+B2jLI2bdqIIQ668aACOdiYJErtAjrgvDB4mAGvTz75xPh+poDOxhoi8rKTKDUFdDYO1LyGDx9ubOzx60zzJ1NTUy30dFOzF7DXKQesjdNXXnnF2NizC+iAsyzCsD9edlKxrpiinA3xqlChgnHQtiumqMPhQGxsrLKPPmOWzRTQ5ffQ0VN2vnbFFOWnLF4jRowwNrdNAZ0tPT1doDx4/RWmKMvv8mrZsqXR166YopmZmRY0jJ2vXTFFdWnc4sWLW6ZuAQ8HoPPMW9lY60heuXPnRsuWLbFs2TKkpKRg9erVIHI2SXPlyoWKFSsqaqMdO3ZURiLK9s033yh7/1X7nwrorDRIRBbiAdurr75qCegMbWRcr7waNGhgCV52Af3GjRtGaKOOq2WzC+gy5ldeXKfWzRTQ09PTBa5X3oNJJjpMTRbEkk1WpZRXiRIljPT/8PBwUfNny8zMxLx58yzyr0ROnQz9Pe0CuozhlldAQIBRBtkU0HXlQHnVq1fPom9ip+Vy48YNS2bFF/m4ceMsvrYL6LIiqLxMc0kBc0B35es333zT1td6QJcx3PJiMpfua1NAz8zMxEcffWR5EiN6iKWXzS6gy7yNnPjaFNAZfspYfnk1bNjQIqVs+t6lp6cL3Z+wsDAUKFAAkZGR2LVrFwYNGiS0heSZoLy/LhvSunVrVK5cWTkm95D4d/Xr53Hsfyqgcw19//79FuIBmx7QDx8+rIz52rlzJ4oWLYouXboomFv5wtADuq7D3b9/fyxatAhEThlTmfkWFxcnLgw9oJ8/f15h5X3++edo2LAh6tSpg3Xr1lmYb2xyQOdHZ/6itW/fXpBjFixYgEGDBonyzdy5c8WFoV/k8s2JdeO7deuGwoULG0kmbBUrVkSbNm3Ez/Kjc8OGDUU9f9y4cZg6dapgOY4ZM0Y8DekX1t27dzF+/HgFw83CSfv27RM3HF26VQ/osq8Zw128eHHExMRgwYIFwtc9evQQDU9dbVH3db9+/YTU7IYNG8SsVh2XrQf0CxcuiAEaxYsXx2effYYXXngBtWrVUrD0eqlODugOhwNr1qxRfM2Z3scff4zBgwfD09PTUqrTfW0a6dazZ08UKlQIu3btEnMv69Spo2j96AH9+++/F03Gf/zjHwI8MHbsWAVLLw9i0QO6XiaZOHGi0EDZs2ePwNJXqFBBIfnoAf3IkSOiYV+pUiVs374dJUqUQMeOHTFv3jzhv4EDB4qyIevPcM/iwIEDoik9ePBg3Lt3T4Aq2BwOB3766SeLflO/fv0ssGhW0+Tf03tIrCXztIb+p8koF514wHKeHNDlxl5gYCAWLFggOvxFixYV2O9bt25hyJAh4sKYPXu2mLc4f/58RYebm1uAFeWye/duyyxJDug8b5Rr+FOnThV0+kaNGqFu3boAnCSTadOmWeqaHNDl5lZERIQYRqGjXGT9aq5hc1A6ffq0osPdr18/MdnpzTffFGJiOsmE+xXMJj137pyQMObmlsPhQEZGBogI//znPwE46+sxMTEgejicg7/Y3377rdLc4kYmoKJcdOnWli1b4vTp0yKgJycni6HLBQsWFEOXASfngBFNPDyEa9izZs0SQ4w/+ugjo+Y64FSTJHrYTDM10zmgmxqZ7GsZ5fLHH39g+vTpwtejRo1CWlqaCOhyv4YbmYAV5aI30zdv3iwC+unTp5V+zYABA0Qpgpui7OvFixcjKCgIbm5uYjgHB3S5X1OyZEnRh8jMzAQRITY2FgCMDVaelrV161asXr1a1PC5kQmoKBdTIDx16pQI6MnJyejVq5ci+8ENe5kweOPGDfTu3Vtc/5988om4AX322WeYOHEiPDw8UKxYMUV6oVOnTihVqpQl9nBjnJeHhwfc3d3Rvn17gaBj2WjT9Qc8RblYzEQsun//Pt5++22BPOAPnBt7Q4YMsTwayQGdLSEhQVDCfXx8FOfpUEPATCzKzs7GkiVLLKQXE/yMTQ7obPqFwfuY4GeAmVikQyB58ZxQRtnIJgd0NjkIyo+NDD+ToYYALAGdbd++fUrJTF7Vq1e3EDFMxCIdAsm/L2vX6E1cOaCznTx5Uuij63MkdR1uwBrQASvclRejbDp27GiZ5mQiFskQSJOvFy5cqPjaRCxiJq4+4o+ZpyZfywGdTR/OIX9ndKghAEtAZ5MhkPqqVq2aZSqWiVj0KF8PGjTI4msTAzw+Pl7cGPm7z6tTp06W2CAPRGE7cOAAvL298fzzz6N06dJo2bIlLl68iJEjR4rGsMzIZpa6Xjp6GtA1c8UUvXbtmgWtweJEupkCOhtrnvDq1q2bRZsZcM0UTU1NFeJLvOzmTJoCOps+C7R27dpGNp0rpmhGRoagL/NasWKFsZlmCuhsepMxICBADFTQ388U0AFnEHz77beVfWJjY41NXFdM0WvXrlkCqTwkQjZTQGdjGV9eXbt2NfraFNDZ0tLSLHVp05xOwDVTlEsTvGrVqmX0tSumaEZGhkCCPcrXpoDOpstM+/v7WxIRwD6gA05f8/XKa8qUKUaJXVdM0cTERItEg0l2GbBngDscDjGkhNecOXOMewwfPlxADwFn6Sw4OBglS5ZEUlISoqKiEBISIv7/zp07eP/995W9Q0NDheaTbE+Zojm0hIQE6tatGyUnJyvH7ZiMJsvOzqbFixfToEGDlOMrV66kt956y8IEs7OrV69S//79aefOncrx7t2704YNG5x30kcYANq0aRN1795dOb53717q27evIv/qylJTU2nMmDG0ZMkS5fiwYcNo0aJFFmlfO9u3bx917txZOXb79m3q2rUr/frrrznaIyMjg2bOnClYd2wzZsygGTNmGKV6TXby5Enq3r07JSYmKsdjYmJyLN3qcDhoyZIlFibjypUradSoUY/t6+3btyvH33jjDVq/fn2Off31119Tt27dlOP79u2jPn360KVLl3J0LqmpqTR27FhavHixcnzo0KH0ySef5NjX+/fvt8j0pqamUteuXen48eM52iMjI4NmzZpl9PX06dNzJNNLRHTq1Cnq3r27hVnauXNnW1/rrFUAtHr1apo0aZJyfOzYsTRhwgSL5HO+fPno3r17lJ2dTbdv36bmzZtTRkYGbd68mYKCgqh8+fJ0/vx5IcWbL18+ioiIIF9fX7HH6dOnqWfPnnTu3Lkc/Z1PzOwi/d+9nlSGnpKSgkGDBon699y5c9GqVSuEhYXZ1usAa4b+448/isZQ7dq1BTNwypQpojYbGBio1Gb1DF0XWRo9ejT69esHLy8v29osYM3Qjx8/bmkM+fj4oFevXhg3bpyozU6ZMkXUZvUMnQdn80iwHj16iEEN69evR7169UCk1mYBa4Z+6dIldOzYUZQSVq5cicqVK+Pll19W8NX9+/cXtVk9Q2dBLa5/t27dWtR4FyxYYKzNAtYMnedVcv17zpw5aN26NcqXL6/gq6OjoxVf6xm63utgnZbJkyfb1mb1DN3U6xgwYAA8PT0VLL0u6qZn6KZaq6+vL958802lSTx58mQBudQz9KysLHzyySei/v3GG29gxowZIHIOa5ABAbLQl56hX758GZ06dQKRs0S4YsUKVK1aFS+99JKtr/UMnftacq9D9jVj6XUElZ6hy30txuu3adMGoaGhiq/161r39U8//aQwiPk7NX/+fAGXfOaZZ5S5pPyamzdvonHjxsiVK5fyxMVDRvjpf+PGjciTJw9CQ0MRGBiIRo0aGXsjwNOSi8U4oKempuLjjz9GwYIFLTMM27Zti/DwcADWjjor4HFA1yVvGbqlo1x0md7t27eLgB4fH69Ap1599VUB3ZJRLvoUc25GckC/efMmBgwYYKy/ySgXE3qCRbw+++wzow438HAo8G+//QaHw4G1a9cK9AQPBeaArmuujx07ViBUqlatKsSuZIVK1ipPT08XAd0kgwpYUS46euLIkSPiwrp165YFjcQIFRnlkp6eLnTpWYo3PT1dXOQmHW6Hw6EMiQbM6AkO6AcPHjTqcAMqykWGcbq7u6NPnz64fv26COgpKSmKrO27774rfC2jXC5cuCBkGooXL441a9YILsWaNWsUZc86derg4MGDAFSUi65GyrLLHNBNmuvsaxnlIsM48+fPjw8++AB//PGHCOjHjx83opF0lIuMoGLIMAf0mzdvKmiknj17Cl/LKBfZ1/J1zb6WG/HyIGnGoXO/Zu/evaLWX716dezatUvIJnA/bdmyZUoM4sbql19+iaVLl8LDwwPPPfcckpOT0aBBA9SvXx+Asw/GUiB8DleuXHka0GVjkgdnACYMuRzQ2XSNavqz1mXS4QbMOHQmxtHBAAAgAElEQVQd38z45ICAABA5MeRytxww49Bv3LihiPjzuegXv2wmHPrOnTsFvlnHf+s63IAa0NnS09MxefJk8Tnw7/PfGBUVZcGQywGd7dixY0JdUm7Cyhe/LHVqwgPL+GYZ+83Bs379+hZfm3Do+k1a97XOF9ADOvAQ38yfA7M8uQFmwpCbcOj6TZrPhf9GE1/AhEOXb9I6/pu192Vfm3DofJNmfR/d17LmOpsJhy5j2XlwDH9GPPxFbgSacOiZmZlKQsZ7cFJk4guYcOjydS3LZ/PNSYZPAg+VVHUgQ1xcnIVBS2RmdPLwG752GzduLN6jRYsWihYSoD4lMJrLRGLLqf1PBXT5wzYRIQBzQGdjuByvsmXLGiV2XTFF7927h4EDByr7vPPOOxZtZsA1U1SXxiV6OE1INzumaFZWlshuePXt29fIiDQFdLaLFy8K3RFedo09U0AHILJ+eY+mTZsKSKRsrpiiKSkpluHBnE3r5oopqjeUy5QpY/zbTQGd7d69e0LQideMGTOMvnbFFD1+/LhF19407xOwZ4pmZWWJ+Ze8+vTpY/S1K6boxYsXLfIKeiLCZscU5eRG3uPFF180NgJdMUVTUlKErgsvmcchmyumqC6DXL58eSMjmsslukQH4ExuevXqpezj6+uLwoULo3Tp0ggPD0eNGjVEuZLI+WQrN9E7dOigNEzZWFNK3vuvmquA/l/dFJ0yZYoiW/ooO3ToEI0ZM0Y5du7cOYqNjbVIsdpZZmYmLViwgJYvX64cnzt3Ln366ac5kvPk9x0/frzl+Pjx4+ns2bM52gMArV27lmbPnq0cj4uLo/nz54umzaMsOTmZYmNjLTMyx44dSz///HOO9iByzpScPHmycmz79u00c+ZMunPnTo72uHv3Ls2ePdvi1ylTptCWLVtyfC7x8fEWX58/f56mTJliaabaWWZmJi1cuJCWLVumHH/vvfdo9erVOfb1+fPnafz48Zam5Lhx4+jMmTM52sPO16tWraKPPvoox76+fv06TZ061TJrc+zYsbaSwSb7/vvvLU3GHTt20MyZMyktLS1He6Snp9OcOXPom2++UY7HxsY+lq+PHDlCEyZMUI6dPXuW5s+fb2l48s/58uVTjqemptLUqVNpxYoVyvGuXbtSq1atqHbt2lSuXDkKDAxU5oWmpKTQ7du3xc/58uWzvCeRs9E8Z84c8XPZsmVz/Pc9ltlF+r97/as19DVr1gjiQfPmzZXhEXqGfu3aNXTv3h1ubm4ICgrCokWL8Oyzz+LVV1+1VXYzZei6hOg///lPkdnVqFEDRFaZXj1DT0tLE3KxjOGuXbs2atSooWDpZRU/wJqhHzhwQOBeq1evLup+U6ZMEfjq0NBQfP311yLb0TP0jIwMzJ49W8HrR0dHo1ChQgqWXlbxA6wZ+pkzZ0RGzcp2RE4mnV5D5MaTnqFnZ2crKpGdOnUSmfGnn35qkRNm0zP0xMRERS524cKFKFKkCNq0aYPhw4cLX7/99tvC16YMXfc1N5TnzZsnsPSRkZGKyJOeocuqmIzhrlu3LqpXr26RE5bhenqG/vPPPwtfV6tWDQsXLgSRs4nLcsIhISHYtGmT8LWeoctjFhmv36FDBxQsWNAiHS1DUfUMXVbFLF26tMjS+/btK7D0wcHBWLx4sfC1nqFnZ2dj5cqVovzw+uuvY8iQIeJJjLH0L7/8MhISEsR76xl6UlKSAlhYsGABihQpgubNmwuhraJFiwrCG/BQXIt7BPfv38ecOXNEyfL1119XSmOmWjcPIqlXrx7y5MmDwoULiyecwYMHw9fXV7z2xo0b6NGjhzgXlrB+WkP/02SUi669PGzYMNy+fVsEdJMON184LJ8LmLWXuXkxf/58RYdbvnBklAsP0OULIyYmBleuXBEB3aS5zheOjHK5du2amEXIOtvZ2dkioMskFNZcz87OtqBc5MHYTZs2xYkTJ5SALutwyxdOr169EBwcDMCJpZe1yqdPn44//vhDBHSTDvf9+/ctKBe5hsg623JAl0koNWrUwN69ewGoKJeMjAzMmjXLokvPAd3uuwCoyAddq/yrr74SyIOPPvrIqLnOLFUiJ8qFbz46WYwDui7Z3LlzZ+FrGeViSjSysrJEQOfvAv//4sWLkZWVZUG5bN68Wfj6pZdewokTJ5SAbvp/QEW5pKWlKVrlTBbjgC4nIvJ3QUe5yFrlrEsvB3TTdwFQUS6mROPWrVsioNv9v+7rPXv2iObryy+/jLNnz2LixIkgImRmZiIuLk7U7Js0aYL4+HihvDl16lQ0atQIAQEBFoVPPtdLly7h2LFjKF++PNzc3DBx4kSMHj0abm5uQsW0YMGC8PT0xIgRI3Dnzp2nKBfdTMQiXauc766cwTNNXDY5oLPJWRkjLnx8fBSauCx1aiIW6VkZn4tJc53NRCySszKG2BGRyOBHjhypZPAmYpGelfn5+YGIRK1c11wH1IDOpmdlfC6cwXfr1k3J4E3EIm48cQbODSz+WdfhBszEIlnOQfa1SYebzUQs2rJli/gc+KL39vYWGbysrw+YiUW6nAOfi93TGmAmFslyDgydpT/rt6anNROx6MGDB5g7d67QKmdfM1RWf1oDzMQiWaucm9HsH9PTmolYpGuVm3wtP60BZmKRLt3B58KJiP60ZvJ1ZmYm5s6dC19fX6URzA3miIgIpbbPpMIDBw4gISEBuXLlQkxMjPIeLVq0sBCL+ElU379u3boKRPlpQNfMFVP00KFDFrSHPsGbzRTQAeeFoTcZ27Zta7lLA66ZoufOnbPQ3E0DdgF7pihfGPIeERERxoaOK6ZocnKyRdVOHhIhmymgs3333XfKHr6+vsbZma6Yonfu3MHw4cOVfQYPHmxkg7piisbHx1vmx+qa62x2TFEOgvIer776qtHXrpii58+ftwyKXr58udHXdkxRh8MhpFnloKAnIoBrpmhycrJFjvbtt982+toVU1T3db58+Yy+dsUUvXv3rkXQauDAgUZ/umKKxsfHKzdvIrJorrPZMUWvXLlikSJYtWqVxUfdunVD/vz5BdeEZbKZ5Z2ZmQk/Pz+8+eablvfQWbFLly61NHefMkVzaCkpKbRs2TKlQUFEtGTJEvr9999zvM++ffssjZEdO3bQunXrlGaIK/vjjz9o1apVFlbdkiVLcsy0IyL69ddfLQzPkydPUlxcnGUWqJ1lZ2fTl19+Sd9//71yfOXKlTlm0BIRXbx40cJATE9Pp2XLltHNmzdztAcA+vbbb2ndunXK8fXr19OWLVucGUYOjH2tszmXLFlCv/32W472IHI2qnRf79y5k7744ovH9vWxY8cs5/LLL7/k+Fxc+To9PT1He2RnZ9P69etp27ZtyvG4uLgcM2iJiC5dumQ5l/T0dFq6dKllZq+dsa+/+OIL5fiGDRto8+bNOfb1rVu3aPny5ZSSkqIct/O1zhIlcs5sXbZsGR09elQ5rs8GBUDfffcdvfDCC+Th4UFERGPGjKGSJUtSnz596MGDBxQfH09paWnUqFEj8Xvp6ekUGxtLU6ZMsZzjiRMncvR3PjGzi/R/93pSGTrPiJQx3A0aNECpUqWEfrQJe6xn6DqB49133wWRs9nD+Gpdq1zP0HXN9aioKERHR8Pd3V3BV+vYYz1D12VtP/jgA+TOnVvsR+TEmcvYY1OGrhM4GGo5a9YscY66Vrmeocs63Hny5MGECRMQEhKCOnXqKFh6mQRlytCPHj2qfI6TJk0CkVNil7H0OvZYz9BNZJ2GDRuiZMmSCr5a1yrXM3Sdk/Dee++ByAkBZHy1jjPXM3Sdk9C2bVtBWmJ8tbu7u0J4A6wZOpN1+HN8//33kSdPHrRt21Zk2rpWuSlDlzXX69evr/ia68S6VrmeoeuchPHjx6NcuXKoXbu2LeHNlKHLOPXw8HDF1ybCG2DN0HVOQu/evdGoUSMUL17clgQFWMW5du7cKUprbdu2RZMmTeDj4yPYyY0bNxYlJNYqWrBgAWTjmajTp08XDNzExERkZmZiwYIFohzVtm1bDBo0SHz3CxQooNTPgf+QkgsRNSWiU0R0lojeMvx/byL6hYiOENFuIgp71J5PIqBv27ZN4GkbNmwoKNYyysWOHcgB/e7du8q4MqZYyygXh8Nh1CqXA3p8fLzAp7LmOqCiXG7evCnYgQEBAYIdyAHdpLnOGG4Z5WJiB8oBXR5XJlOs5abovXv3jFrlHNBNmuuMjpFRLiZ2oBzQr1+/LsaVFShQQMwmlZuiWVlZRnagHNB1zXXG68sol4sXLypa5Yxn5oBuxxqWUS7c7GZft27dGmfPnlUCuok1DKgol5SUFKNWOQd0O9YwoKJcTFrlckA3sYYdDofSFDVJUqSlpYmAbtJcZ1/LKBeTJIUc0E2a65mZmUpT1CRJkZiYqAR0E2sYUFEuJkmK7OxsEdCTk5NFXbtkyZL4+uuvAQBdunRBiRIl4HA4sHDhQnh7eyMwMBAbN24UAlsm/HqbNm3g7e2N8uXLIywsDF999ZXot9WuXVs0dz/44AMQEZKTk3H9+nWBtilWrBi+/PJLEVf+bQGdiDyI6BwRlSYiLyI6qgdsIvKT/t2KiLY8at+/GtBZqY+/WDxQWK5VmYhFulYL/VnnkqFTrMMNmGGLun4HO7RGjRpGzXXATCzS9Tv4XOy0XgArbFHX7+D6YEhIiFGHGzATi3T9Dp7KYtJcZ9Nhi7pWOWtzBwYGCv2PgQMHKiPBTMQiXb+DMx++Ycia62wmYpGs1SLXtvWBwmwm2KKuVc5Z3nPPPSc012WtF8BMLNK1yvlc7LReACtsUdcq57+pbNmyyJ07N7y9vRVdH8BMLNJ16VmS1qS5zqbDFnWtch50XbBgQaPmOvuDA7rsa1mSmRFDnNnruj6AmVgk69LLNXLOjkePHq0Qr2JiYhSt84SEBPHUwOvYsWM4fvw4EhIScOrUKZw5c8ZCXCJyNmfXr1+vnCPfFGQy3e7du8UNir+X/86AXouItko/jyai0S5e/xoRffOofZ8EU1TX4WazY4ryFG55jyJFiliGKQOumaJXrlyxNJ4GDRpk0VUG7JmirFWuf0l0HW42O6bo7du3MXToUGWPdu3aWXS4AddM0b179yrUaSKyaK6z2TFFWZde3uP55583ytq6YoomJCQobDz6M9s3+dqOKcq69PIezzzzjEVzHXDNFL1y5Yp4wuMlT8CRzY4pylrl+ig7PRiw2TFFU1NTMWzYMGWPtm3bKokImyum6L59+ywj23TNdTY7pihDguU9IiMjjbK2rpiiJ0+eFOJhvOThL7LZMUVNLMyQkBDjuZhmft6/f9/C4n3UmjhxonF+Kpdq9e/HgwcPMHv2bGWPv2quAnpOmqJFiEjW77z85zHF3Nzc+rm5uZ0joneIaKD+/3++5k03N7eDbm5uB69fv56Dt3Ztu3bteqwmWFJSEv3444/KsVu3btHu3bspIyMjR3sAoMOHD9OhQ4eU43v27LGw71zZnTt3aNeuXZbjP/74Y46ZdkTOxpne3Dx8+DAdPnw4x42njIwM2r17t6XxtGvXrhyzKomIfv/9d8vfdOHCBdq3b1+OpVsdDgf99NNPFgblrl276MKFCzk+l+TkZIuvU1NTaffu3TmW6SVyshCflK91ZumuXbsoNTU1x/uYfH3kyBGKj4/Psa8fPHhAu3fvtjQ3/5N8/eOPPz7WdZ2cnGw5l2vXrtHmzZstDFqHw0Hu7g/D3t27d6lHjx7KOX/xxRe0du1a+vTTTykuLo5WrFhB/fv3V/aZO3cuzZ8/39I8Zx/rzdkrV67QDz/8kOO/6S+bXaTHw4w7iogWST/HENGHLl7/OhEtf9S+/2oNPTY2VhAL9Ck1eobOj8/58uUTcpb+/v6oW7euYDiWKVNGyZhMGfqJEyfEI2a5cuXQs2dPEBGGDRtmO6VGz9CZcMCDOLp164awsDCEhIRYGI5yxqRn6FeuXFHU5EaMGAEiQvfu3UUZR59So2foplFfDRs2RN68eS0MRzk71jP027dvK4xbZtC2adNGYTjK2bEpQ9+7d6+C4eYm9eTJkwW+Wp9So2fo9+/fx4wZMxTGbUBAAOrWratg6eUynSlDl6dXhYaGCo2PoUOHCl/L4/IAa4bOT4Qy4zY8PBxly5a1HY0IWDN0fXoVQwG7d+8uSkHyaETAmqHr06uaN2+ORo0awdvbW8HS62U6PUO/ffu2wridNm0aiJx9JbsynSlDl6dXRUZGiib1pEmThK/1JyE9Q+cnQpk46Ovri/r16wuiYGhoqAJx7NChA0JDQwE4S58VKlSAu7s7YmNj0aVLFwQFBUG3a9euoUiRIkK8bsCAAaJkWrVqVUGEAyCycOYN3L9/H1OnToW3tzd8fHzE0/R/U8nFnYhSH7Xvk2iK6tRf1irngM4NLtMwXhnlsnXrVkv9Wg7odg0uuSmqz5HkmqYc0OU6Ps8cBVSUi90cSQ7oeoOLZ47KTdEHDx4Y50jKAd1u5qGMcrGbI8kBXW9wvfHGG0hMTFSaooyv1rXK5YAuN7hYhzs7O1tpivIcSbl+nZWVJQK6SYebMdwyykXWpef6tRzQdc312bNnIyMjQ2mK3rlzR+jSy410OaDrdXz2tYxysWukc0C3my8rN0W5ka5rlcsB3TRzFFBRLrKv5UY6B3Q5EXFzc0P37t1x7do1pSnKjXT2NTdX5YCu92wYry83RfWZodxI54Cu92x45igAFC5cGD179gTgZNAyCal58+Y4ffo02rdvj/LlyyMuLg558+ZFUFCQoO1zueTatWsi3ty/fx+1atVC3rx5cejQIbi5uWH8+PEC0cZ9mTfeeAPXr19XpAW+/fZb8f5RUVG4dOnSvx/lQkSeRHSeiErRw6ZouPaaEOnfLV29IZ5gQGeLj49XJr0zucikw82mwxZlhIm7u7v4ckdERBh1uAEzsUhHmDBaQoag6cqBOmzRhDrg/WQIGutwA2bYoo464OZRq1atLEgbNhOx6PvvvxcIEx6r5+/vL5pJsg43YIYt6ggT9k2DBg1sIWgmYpGOMGEpWW6choWFCR1uNhN7UEaYsL+qVKli1OEGzMQiHerKiYOMqtKVA3XYIitUcv9C3s+kuQ6YYYu6Lj37unXr1kJzfe7cuYqvTcSi7du3iwZegwYN4ObmBj8/PwVQ8PPPPyufJQd0NtYqZ/ijydeMqmIzEYuOHDkivm+VKlUSRDIdaSNbUFAQevXqJX5myQh9LimREyYr69bs2LEDRA/JiA6HQ8hwfP755wCAwMBA9O7dW/xOWloahg8fDk9PTxQoUEC5yRA5a/nyOf7bA7rz96kZEZ0mJ9pl7J/HphBRqz///R4R/UpO2OIOPeCb1pNmijIuWB/4q+tws9kxRRkXLO9Ru3ZtIxvUFVN0x44dluHMEyZMMDJc7ZiijAGX9yhVqpSChWdzxRQ9evSopclo0lwH7JmijAuW9/D29rZorgOumaIyBpyXjoVns2OK2vla1+Fms2OKsi697mtdhxtwzRTduXOnZTizzntgs2OKMgZc3qNkyZLGprErpuixY8fEDY+XjoVns2OKsla5vEfu3LmNctWumKKyLj0vHQvPZscUZV16ljLgpQ9fZitYsCD69u1rOX7t2jWLPLSMdAKcsGIiJ7MWeJixT5gwQbwmLCwMr776qmX/48ePW5q7sbGxlvm0/xFMUQCbAYQCKAPgn38emwBg45//HgQgHEBVAA0B5GzI5BO0rKwsunbtmqVJkZiY+FhNsLS0NEtz6Pbt2/Q4TVwAlJSUZGH4JSYm5pjhSUR07949y7n88ccflJSUlOMmGBHRjRs3LKzKxMTEx2q+ZmRkWM4lOzubEhMTc8yqJHI2oXWp4qSkJEtD1pXx++rNt8TExBw3t4mczUr9b7p161aO2ZBErn2dU4Yn0ZP1tf5ZPq6vHzx4YDkXh8NBiYmJlJmZmeN9TL5OTk7O8bxWIvvvWFJSktHXDodDsDzZ7ty5Q9OmTVPkod3c3KhMmTLUqVMn0fQuUKAAFS1alI4ePUrfffcdDR06lNq0aUMTJ04UvxcUFGSZW0xEVL58eXruueeUY9euXXus6/2JmF2k/7vXk8zQ9Xmd5cqVg7+/v5F4wKZn6LrQUt++fUHkVGnjR+lWrVopOiqmDP3gwYNKDZybgnINnuuybHqG/uDBA7z77rsKhpvI2SiUH31lbQ1Thn7u3Dm0adNGZHv8GNirVy9LXZZNz9BZT0ae4VigQAGULVvWWIMHzBl6cnKy6HcULFhQ+IbleuUaPJspQ9fndZYvXx5+fn7GuiybnqHL/Q5vb2/xRPbSSy8JX+uCbqYMXa+Bc1NQxtLrgm56hm7qdxA5G4Xy3FPZ16YMXa+Bm3wtz7YErBm6qd8RGBiIMmXKGGvwgDlD12vgsq+5Bt+tWzelVm3K0GVfN2zYEGFhYciXL5+Cped+C5ufnx8GDRokfv76669RrFgxuLm5YcCAAejcuTPy58+Pc+fOYdCgQeIpr169eli3bh2aNm2KPHnyIH/+/KhYsaJSFgKA9u3bi6YqW2pqqpCs5jVgwAC4u7ujUKFCWLZsmXiy+Y8oufwd60kEdF2HmzHcMspFJh7ISocc0E1SqJcuXVKaojpygpUO5YBu0uHOyspSmqIJCQkCJRMaGiqUDuWArutwM4abm6Im5MTVq1eVgK5LoTJKRW6K6sgJVjqUA7quw80oFW6KMnJC16WXA7pJhzslJUVpiuooGdallwO6rvjIiCQZ5SKjZCIjI4XSIQd01uFmlAqTyeSmKCMndF16OaAnJSUJdU8ZpSI3RRMSEsRFHhISIpQO5YAuKz7KiCRuijKWXve1HND1RIQx3HJT1DTbMjs7WwnosuKjjFLhpihj6XVdejmg64qPjEiSm6I6SoZ16eWAfvbsWeFrmTgoo1xkX8vXdb58+TB06FAkJSWJXkZYWJhAoowZMwYeHh4iwN6+fRtz5syx4PKJzIzR/v37IyAgQPx89uxZhIWFwcPDAx999BH69++P/PnzA3De8JkEVrduXRw7duxpQNeNA3q/fv2EDrc8rACwwhZN+tQeHh4ICwsz6nADZtji1atXRZMkODhY3Ew6duwomi6yDjdgJhbpWuTPPvssgoKCjDrcbDpsUdci56ylefPmRh1uwEws0rXIWftF1+GWsyAdtsha5Awj5Syzbt26iia7PKzABFvUdem5Fty3b1+hw82a62w6bJF16fkGHRMTA09PT4SFhRl1uAEzbFHXIucA8/rrr1s02dlMxCJdl75IkSIoVKiQ8nd+9dVXiq912KKuS8834mbNmil/5+XLl8XvmIhFuhY5a7+YNNnZdNiirkvPT4916tQRichLL72kkMlMsEVdl56bn7179xaJCGuus+mwRZ4xwDc8vmkVKlQIBQoUgJeXFyZPnqw8IbEWiz62LzMzU2j68Grbti02bdqk9OCmTJkCIkJGRgZ27NiBAgUKIH/+/AIp061bNxQtWlQ5R9ZF9/DwUPR+/qr9TwV0+QPXH9vY7JiinLnKe+TPn98odeqKKSqL+POSoVOy2TFFOXPVswI7WVs7pqicufIy6XAD9kxRzlz14cO6DjebHVOUdenlPcqUKWPRXAdcM0W/+eYbS5NR1+Fms2OKsi69vEdAQIBFc53PWw/obPJTCi+T5jpgzxTlzFUehkxEyoQs2eyYoiZf61OT2OyYog6HA3FxcRbpYTtf2zFFWatc3qN06dIWzXXANVN069ativwFB2Y5EWGzY4ryDU/e47nnnhODPGTjxr7+XTpy5IiAIBI5y6Us2RscHIzhw4fj119/Fc3iiRMnwtPTExUqVFDKsNHR0ZaSDOBswL/55pvKOf5VcxXQ/6vlc319fSl37tw5fn2uXLnI19dXOebp6Um+vr5G2U078/Hxscwk9PX1JW9v7xzv4eHhYTkX3kdv6rgyb29vyz4+Pj7k4+OT4z3c3NzI19eXPD09LeeSK1euHO+TO3duy7nkyZPH8lk9ykyf5ZPydb58+f6/+9rd3d3W1/pn7sqehK/t3vdxfe3l5WXr68f5fPPly0d58uSxnIt+7FGmN2tv3rxJp06dcmatkvn5+RERKU3ib7/9lurVq0dERGvWrCEiolGjRtHly5dpw4YNVLNmTXr33XcpPDyc+vTpQ0REkydPpiZNmtC+ffuU+aD37t2jvHnzWs4vp/Nn/2Wzi/R/9/pXxbliYmJE04VV3dj0DJ2hTzKGm8ipgGZS8APMGfrNmzcVGVGWg23VqpVFwY/NlKH/8MMPioyov78/fHx8BOypSpUq2LFjh/I7eoZ+7949RUaUp8zUr1/fqOAHmDP0X375RcHrM/aZFfxKlCghFPzY9Aw9KysL8+fPFxhufvStXLmywNK3a9dOeV9Thq4rY7KiZKdOnSwKfmx6hu5wOPDll18qGG72tUnBDzBn6CkpKUIZ09/fX/i6ZcuWwtcTJ05UfG3K0HVlzICAAHh7exvVGtn0DF1XxuTvb7169YSUcN++fRUYqilD19US+TOSse9r165VfK1n6LoyJpcgK1WqJLD0UVFRyvuaMvRLly4JZcxnn31WNIA7duwofP3hhx8qvjaVXJYtW4bg4GCh3MjXj53QHesncZN5yZIl8PT0ROXKlXHp0iXcuHEDRIR3331X8UlSUpLlifqdd96xlG4aNWqEOnXqiJ/1HhLzUZ6WXP40uSl69OhRUXurWLGiqGPJAd3uNdwUNWlsX79+XQnodq+Rm6K6xjbjs+WAbvcaboqaNLa5McMB3e41clOU8dWyxvaDBw+UgM54e1lzPTMzU2mK7ty5U9HYZjSPHNB1He74+HilKWrS2L57964S0HXNdX6N3BQ9duyYUZdeDui6DjffLLgpatLYTk5OVgK63WvkpqiMry5atKjQpZcDut1ruCnKSYZJl54DOvuaExH2tdwU1YZaTwEAACAASURBVJMMxmfLAf3mzZvi+xAQECC+D3JTVPa1rEsvB3TTa+Sm6L1794y69HJA59fwzWncuHG4c+eO0hS186Mc0A8ePCjKns8//7wI0NwUtZMn/uGHH8T3jueLNmnSRJSbsrOz4enpiTFjxlhiD4+nk1dwcDDmzJkj5BJq1qyJJk2aAFB7ZdxDetoU1UyHLbJWuZx9R0REICgoyKjDzabDFvULg2ty7dq1s83iTbBFPfvmjGj8+PG2WbwOW9Szbx6D9cILLyiMWDmzM8EW9eybs99hw4bZZvE6bNGkVf7ss8+icuXKRh1uwAxb1LPv3r17g4jQo0cPow43YIUtmrLvatWqoVChQkYdbjYdtijr0vv7+4u+SlRUlG0Wb4It6tk3Q/uYEZsnTx5LFq/DFk269G5ubmjUqJHI4itVqqT42gRb1HXpORMcPny4bRavwxZ1X/fo0QNFixZFxYoVbbN4E2xR16WXfW2XxeuwRZMuffXq1VGgQAGhgxMcHGzpifj5+WHw4MHiZz25YX1yJip17drVQlB65pln8MYbbyjHDh8+DG9vb3ETGTt2LHbt2iVuPIULF8a7776LkJAQlC9fXtEBkntITwO6ZnZMUdY48fLyUu6gug43mx1TVH4s5WXS4QbsmaKscaI3nnQxJzY7pqisccKrQIECQrNGP289oAMQGifycGe+Oeg63IA9U5S1yuU9PD09LTrcgGum6I8//qgMvSYy63AD9kxR1jjRfa3rcLPZMUVlPRvZ17oON2DPFGWNE33mpa65zmbHFJU1TmRf65rrgD1TVNc44WXSXAfsmaImX3t4eAjNGtlcMUVlyDAvWbNGNjumKAvryQOeiZwiaab5owEBARg4cKDluJzc8Jo8ebJRvrhq1apKmen69esoUaIEihQpgsTERJQoUUIZHL1z505RBeDl4+Nj4ZsA/yFM0f8Gy5MnD1WrVs3STKlWrRoFBATkeJ/ixYtTtWrVlGNlypShihUr5rjZ4+HhQRERERQcHGw5F/2YKwsKCrKcS6FChahq1ao5bpy6ublReHg4hYSEWM6lRIkSOT4XPz8/y7l4eXlR9erVH6tBGBISQlWqVFGOVapUyXJ+rox9rb/v4/q6WLFiRl9XqlTpsXxdtWrVf9nXhQoVspxLwYIFqWrVqjlunNr5OiIigooXL57jc/H396fq1asrx7y8vIyfuSsrW7as0dehoaE53sPO15UqVTI2mt3d3Y3yvaVKlaKaNWsqxw4dOkRnz561vDY4OFgwXLOysqh9+/aUmJhI69evp+DgYCpevLgyp7hBgwb02WefKXv4+vpSREQEeXl55fhvfSJmF+n/7vUkM/RTp04pGG4eJMDynDrGHLBm6IxpZQw3Z+lly5a1xZibMvSrV6+ia9euICIEBQUJKCCfX0hICDZu3OhSnAuw1t+InBBLPr+YmBgF2mXK0HWsOuOh+TExODjYgjE3Zeg6fpn+zELk85MhYqYMnbHqvr6+8PT0FL6pVq2aOD8dY27K0HWsOg8mYCy9jjEHrBk6N9MYw81ZeqlSpcT56RhzU4auY9X5iYzPr0yZMhaMuSlD37x5s/ANl238/f1tMeamDD0tLU1g1X18fBRf22HMTRm67Gv5SUo+P9nXpgxdx6rLvmYsvT6cxpSh61h19jWfX40aNSyQTS6/sTFMkyGJ3IcpXbq0II8NHz5ced/OnTujePHiAIDBgweDiLB8+XLx/x07dkTJkiXFz4cOHULx4sWFvK6np6exD8bfGXpacnlockBnhiGPsZo5cybu378vmqImFihfGHJA37t3rzLGav/+/UpTVGeB8iQfOaCbdLhTU1OVpqgdC1QO6HZsUm6K2rFA5YBuxyaVm6Iylr569epiapMc0O3YpNwUtWOB6vK5ug73yZMnlabo6dOnFcYvs0DlgK4zDBnDzU1REwuUdenlgL5v3z6FTbpv3z6lKcpYep3xKwd0kw737du3laaoHQtUDugnT540skm5KSqzQGVfywFdT0QYwy03Re0Yv3JAv3r1qkAnBQcHY8mSJcjOzhZNUTsWqC6fq7NJExISlKaojKWXWaByQJd9LRMHWfpW93WnTp1EchMcHCzUFvVEZM+ePbh8+TKInBOarl27ppCq+LoeMWIE8uTJg+XLl4OIFCkB4CGaKSsrC3FxcciTJw+KFSuGQ4cOoWzZsnjttdeMfbA7d+48Dei6cUCfO3euZdAsmw5b1LXKY2NjERgYiCZNmtjqvZhgi7JWeUREhBCrnzRpkqhbtmrVStEA0WGLJp2WiIgIVKxY0ai5zqbDFnWdFmawDRo0yFYDxDTgQtZpee2119CsWTP4+/sbdbjZdNiirFUeGBgo5ip26tRJZJy63osJtqhr8vCTzty5c4UGiK73osMWZa1y1qUvVKgQmjRpoui9yBogJtiirkvPo98mTpxo1FwHrLBFk05LtWrVEBYWpui96LVWHbao69LHxsaCyNkfMslaAOYBF7JWeXR0NJo3bw5fX1/L8BeZYKTDFnWdFh6K/PrrryvDX+RGoAm2qGvyMPxx9uzZtnovHNBlX48ZM0ZJbvLnz48WLVooNyeWOgCcDVsi53hFNl1/iZ8KOKPXm6bz588H0UNZ6wYNGgip5XLlyqF9+/bitXJv5NlnnxV66U8D+sM/Rqy6desa5UztmKKyVjkvGTolmx1TVNcq52XSXAfsmaKsVS7vQUQWzXU2O6aorFXOq0iRIhbNdcCeKSprlcv76DrcbHZMUVmrnJe/v79Fcx2wZ4rKcDN5H11znc2OKXrhwgWBwuHl5eVl0VwH7JmirFWu+7pChQoWzXXAninKWuW6r3XNdTY7puj3338vUDi8nn32WYvmOmDPFGWt8pz62o4peuTIEaOvdc11wJ4pmpmZiQ8//NDia11znU0P6GxyciP7Wr85AU44KRFh0aJFynG+rvW5uu3bt0e/fv2UJf//gAEDlL83LCwMbdu2tZyjrD1DRHB3d7e8JqfmKqD/VzdFo6OjqXLlyjl+fcmSJalDhw7KscKFC1NUVFSO2Yxubm7UuHFjaty4sXL8lVdeodq1a+f4XAIDA6l9+/aW5lt0dDQFBQXleJ+aNWtSmzZtlGMvvPACvfjiizlu7Pn4+FBUVBQVKaKOiu3QoQOVLl06x+dSsWJFio6OVo5VqVKFWrVqlWMWoqenJ7Vq1crSlGvfvv1j+bpEiRIWXwcHB/8lXzdp0kQ53rp1a6pTp06Oz4V9rTeyH9fXtWrVsvi6UaNGj+XrvHnzUlRUFBUrVsxyLv+qrytXrkytW7d+LF+3bNnSIjsbHR1taaa6slKlSlFMTIxyrGjRotSlSxfBDGVzxkPrzE83Nzd66aWXqGnTpsrxjRs30po1a5Ql27Bhw5S/18PDw9iUrVWrFs2ZM0f8/LcxR+0i/d+9/mqGzuI6XBuTiQdspgz98OHDCoab/rxTMoFEHwJgytDl7NHDw0M8vnKttGjRopYhAKYMXc4euXlCfz5OE5mHAOgZup5R8OPrM888YzuNyJSh61ON+FwYX216CjJl6HL2yPVaLy8v22lEpgxdn2rk4+MDoofNbdNTkClDl7NH2dd2k6dMGTpnj4zh5iydeyCmaUSmDF2fasTnYjeNCLBm6DzujH+fn8gKFy4sSnR6ZmzK0G/cuKFMNdJ9bXoKMmXo27dvF58rKzTmypULPj4+xmlEpgxdn2rEvuYSkmnylClD10lI/DcFBATA09MTQ4YMURqeFy5cABFhyZIl4piOvw8ICBD7NGrUyALLZY5K7ty5UaxYMaXsxiUb2TIzMzFx4kS4u7vD29vb+DT4OEb/SyUXrqHfuXNHwVfL80LlgC7rcMtSp9wUtZsXqgd0uebHdGK5KSrPC5UfGeWAbqrl37t3TzRF9RmSo0ePFrVrOaCb5o7KTVF9XiiPwJIDul7fZbw+N0WzsrLwySefGPsUckDX67vr1q3D/fv3QeRsiuo1RO5TyAFd1+FmDDc3RVNTU5V5obIuvRzQ9fouY7i5Kcr4ar12rQd0vb577NgxpSkqzwuV+xRyQL979y7GjRsnbk6M1+emKOOrTX0KOaCb5o7KTVF5Xqjcp5ADujxeUZ4xy01Ru3mhgBrQ5ZIl4/UfPHgAImdT9PLly4pWOYveyQGda/k6mYybordu3VKua7lPIQd0PRFhMhlf18nJyYKAFBQUJJq858+fBxFh6dKlAFRyWL169XD48GEsW7YMRE6cupubG5o1a6b0OOrWrYuaNWvi8OHDCAwMROHChUXTu3r16mjWrJl47fnz5wX4ICYmBqdOnQLR0xq6MB22KKNLuG724osvIiQkREFgDBkyRIGh6bBFfZDu4sWLQeRk2sk63Ky5Dlhhi4wukZs6Xbt2haenp9KV79ixo0BgAFbYoowu4QvD29sbMTExRgQGYIUt6lrlLVq0EIzIBQsWKAgMWepUhy3KiAM/Pz/MnDkTYWFhaNSokRGBAZhhi7p+NU9HnzFjhsjyZAQGYIUt6lrlI0eOxEsvvYSyZcsKBIaMtmHTYYsJCQkKk49vdMOGDTNqrgNW2KJJq7xbt27w8PBQYHKsuc6mwxZ1rfJly5bBx8cHHTt2FAEpMDBQ8bUOW9TRJc2bNxdKkwsXLhSN5saNG4vAA1hhiyYkUXh4OP7xj38oiQhrrgNm2OK+ffvEU1VkZKTw9fTp042a64AVtmjSpW/atCnKlCmjgAp0Mpl+XcsSAZGRkYK+P3XqVMGmlWU4gIdNzytXrmDBggUgckozZGZm4o8//oCXlxeGDx8OwElOe+aZZ1CwYEHEx8cjMjISL730EgBg5cqV8PX1hb+/P1avXg3gKWzRYnZMURn/La+XX35Z0eFmMzFF+cLQpVtNGGnAnimampqKESNGWIbSmvDwgD1TVNYq5+Xp6WnBwwP2TNH79+8L/Le8T9myZS14eMCeKSrjv+WlY6QBe6aorkvPizXXdfarHVOU8d/6uegYaTY7pqisVS77WtfhBuyZorJWubyPCQ8P2DNFZYid7GsdDw/YM0Vl/Le8jwkPD9gzRWX896N8bccU1XXpZV8zPFA2O6aona9lKKhspuua4cv69y5PnjyYMGGChf3Ks0T5RsE/v/7660ILZsOGDeL1Z86cQfHixeHv7w83NzfUqFFDSB/UrVtXKXE+Deia2QV0wJl56RRs00UF2FP/s7OzxR2aV4cOHSwXFeB6SPSVK1csOtqMudXNLqAzlVu/KegXFeB6SPTt27fFozCvefPmWS4qwD6gA87MS97D399fye7ZXFH/79+/j/Hjxyv7vPXWW5YACtgHdMCJ4dZ9zVh63ewCenZ2tsjAeEVHRxt97WpI9NWrV0UJjJdJKgKwD+gOhwMbN2603BTkJzk2V0OiU1NTRcbP64MPPjD62i6gA85kQt7D19fXGEBdUf/v37+PCRMmKPuMHDnS6Gu7gA44k4mgoKAc+druunY4HFi0aJGyx5QpU4w+Mg3BmD59uvK7ycnJyM7Oxs2bN3H69GmsXbtW+X8PDw9MmTLFItvwNKBrZgrot27dEnVwOTvhx16ThorJ8XJtVB5Jpdff2UwBXa+D8x7yY68s5wmYA7peB+d99Po7mymgcx2cS0C8BzfX9MdewBzQ5Tq4nHHp9Xc2U0BnTRmugzNUjZ8cSpUqZQmCpoCu18H5XPT6u2ymgC7rjHCDUva1rpdjCuhcB+dyn+5rXboVMAf0EydOKBhu3de6Xo4poOt1cPY316pNejmmgK7XwXVf6yUOU0DnmxOX+3Rfm/RyTAH91q1bGDp0qCAOmq5r/YZnuq5lnDk3X3k1adLEMmpu8uTJICLLTVBOihhMIe+lr88++8yyx39EQCeipkR0iojOEtFbhv8fSkQniOgYEX1PRCUeteeTCOi6DjejF7gpevfuXUXlUBYXkh2vKwGuWrUKV65cAZGzKRofH2/UKpcDukkJ8OzZs6Ipyo0pvjD69+8vLgw5oOs63IxU4aao3JgqUaIEPv/8czgcDktA15UADx48KPoCv/32m4Kll1UO5YBux3bjpqhdY0oP6LoS4NatW5Wm6LZt24yKlnJAN6k+JiUliaaojpCRFS3lgK7rcMfFxYmL7KOPPrKgodjXckBnJUC9Ic9NUTvpVkAN6CkpKRg0aJDwNSNVuCmqo6FY0VIP6KaGPDdFz58/r2DpZV16OaCzuJ3ekOemKGPpda1yPaDricg333yjNEVlNJSsaCkHdBa304eHc1OUG86mG558XfPISLnndObMGRARFi9ejHnz5iFfvnzImzcvZs2aJbLp0aNHI1euXCLm3Lp1CyNGjFBu2r1798a4ceMwd+5crFixAv/3f/8n+lJ8AyRy9mLmzZsnvov/9oBORB5EdI6IShORFxEdJaIw7TUNiSjvn//uQ0RrH7XvvxrQv/76a4sON5sOW9ShY2vWrEHRokURHR1t1OEGrLBFHToWFRWFuXPngsip88AaETqMUoctyhcG65DXr18fNWvWxLx58yw63Gw6bHHHjh0COtagQQPExcWBiDBr1iyhw603ezigcwar65BPmDABnTp1QlBQkK0uO2CFLeqa85s3bwaRs8lo0uEGrLBFXXO+d+/eoom7adMmo1Y3YIUt6przjKho3749Jk+ebNHhBqywRZPmPNdRly1bJmByOoxShy2adOkbNGiAyMhIfPzxxyhYsCDc3NwskFkdtqjrkK9atQpEzgEL3NjTIbM6bNGkS9+5c2cEBgYqw19effVVBTKrwxZlXfrw8HBs2bIFRIQhQ4YYddkBK2xR15zv1auXaOJu3LhRkZ+Wn4h02KLphle0aFG89tprmDZtmtJQ5cz/t99+A9FD2OLFixdFv6B69eqIj4/HkCFD4OvriwcPHuCDDz4QfurSpYsoIemlxu+++04kJESETz/9FOvWrRM9sMDAQEyaNAm//PLLvz2g1yKirdLPo4lotIvXRxDRnkft+1cDulw31XW42eyYovKFIS9dhxuwZ4rKIv7yHiYdbsCeKSrjZ+Wl63CzmZiictYq72HSXAesAZ3t999/F4FBXroON5sJh67r0vMy6XAD9kxRWZde3sc0TQewZ4r+8MMP4glFXu3atbOwJ+2YovITiryHPBBENjumqEm6lW/Epv6LiSlq52u+Ees9JTumqPw0qvuah7/IZsKh60+jsq95+ItsdkxR+QlF3kdPRNjsmKLbt2+3MGiJVCgzm4kpyslacHCwci6ywBknEYxjf++998Tvp6eno3Tp0ggJCUFaWhp8fX3Ru3dvsfeuXbssTea/Gv+Afz2gRxHRIunnGCL60MXrPySicTb/9yYRHSSig6xm9hf+GLHkTrNsdgEdcA79lfcIDQ010q9dDYl+8OCB0Pbg9d5771mGDwP2AR1wYrj1L6H+BWSzo/47HA7MmzdP2WPw4MHGQdN2AR1wYri59svLNGgasKf+Aw9HfPFq3ry5sanpakj03bt3Bbad15dffml8P7uADjhrp/IeZcuWVXRg2FwNiX7w4AFGjBih7DN37lyjr+0COuDEI+s1V5mQIpsd9d/hcIghxbwGDhxo9LVdQAecvtYDoGnQNGBP/QeAr7/+Wtnj5ZdfNvra1ZDou3fvWuQ41q1bZ3w/u4AOOLkZuq9N17VJy4UtJSXF8rnI0FW2smXLKp/JyJEjQUSiPNe8eXPjoOi9e/cqe/9V+/8W0ImoExHtJ6Lcj9r3r96heHoPQ5BatGiBU6dOKa8xBXRdMZE/VG6uzZo1S7kw7AL6li1bLFPKuQGlCyQB5oDOE+lZVIj3kfHVugaFKaAfOHBAIGk8PT0tNypZIAkwB3RdMZF/n1EFukASYA7osoqePD2dfaVPljEFdFbR03+fz6V58+YWX5sCelJSksDryxktN9dYqZHNLqDLiom6r1mVUzZTQJcVE+VMX1fllM0U0A8ePKiwMnVfs1Ijmymg64qJ/Pu6KqdspoB+9uxZZVar7mtZDAswB3Rd1pYX+6tZs2Y4efKk8r6mgK6TyR51Xctqi/rfxKU6eZUpUwbLly9XnsZ69+6NfPny4cGDB4iPj4eHh4cy4Yj7AXLD9tdffxU8ASIn/+Kv2r8a0HNUciGixkSUQERBj9oTT6CGfvPmTbzzzjsWCVNADei6DjdjuLl5okuYbtq0CQ6HwxLQdR3ujRs34ssvvwSRs1EmS5h27txZyHnKAV3HYTOut2HDhqhXr56Cpde1yuWAbnrdsWPHQORsispa6jIOXw/o8usYw81NURlLL0uYAmpA13W4p0+fjtTUVBA5m6Iyll7G4esB3SR1yk3R69evCx/quvRyQNd1uJlMxk3RkydPKrr5jMPXA/rp06ctmubcAD948KDiQ9nXckDXcdgs3cxNUW7Y8Q1L9rUc0E0Sr1yHXbNmjYKll3Xp9YBukm7mpqiumy/j8OWArks3T5s2DXfu3AGRsylq8iFgDei6dPOPP/4ogmBycjJmzpwpfCjj8OWAriciLOfL17VMHpOvaxnsADh7WgMHDkSuXLmQN29eTJgwAa1atULp0qWxadMmUbYLDQ3FqlWrkJWVhXXr1omMvFq1aggODlaIbEeOHAHRQw319evXI1++fAgODsbnn38Oon9vDd2TiM4TUSl62BQN114TQc7Gacij9sMTCuhcM9S/8IsWLcIrr7yCsLAwow43mw5v0ocMfP/99yAivP322wp7jjXXASts0fSFHzx4MLy8vBQdbj2744DOJn/hmT3p4+ODfv36CZicnsnrKBf+wvOFMXjwYBEgt27dqjAl5exOhy3KWuVlypTBhg0bUKVKFbRo0UK5icnZnY5yYWKHrF+9Zs0aEBFWrFihMCXl7E6HLZp06V999VWUL1/eosMt+1qHLerBbfv27SByMhlNmusAsGHDBhCRqKXqT1nTpk0TELv9+/crTEm5nKHDFk269L6+vujbt69twqKjXEy69Bwgt27dqgxX4cAGWGGL8lMWM2UjIiLQrFkz5SbGmuuAFbaYnZ2NuLg4RZeefb18+XIlEWE6PmCFLZp06du2bYvy5csr16o+XKVYsWKKr+Ub3osvviiu6zlz5mD69Onw8/ODu7s73nzzTfH9fe211xASEgLgYb+ASzEVKlTAggUL4ObmJp6ude5HdnY2ChYsiJiYGNFEjYyMxKVLl/79KBfn71MzIjr9Z9Ae++exKUTU6s9/byOiJCI68ufa+Kg9nzSxSH4klZeuw81mwqvKj6TyHiYdbsCeWCQ/kspL1+Fm0wM6YNUql5ep2WNHLEpKShJaNvIeJh1uwJ5YtHXrVkuNnchZZpI11wF7YpGsZSPvYdJcB+yJRbKWjbzKlSuHzZs3W87dhEOXtWx0X8taJmx6QGczSbeyr1nLRDYTDp19rZcfiMwlRTtikaxLr/taLz0A9sQiWZdeXs8//7ylpGhHLJJ16XVfm2Rt7YhFspaNvEwlRcDe13PnzrX4msipC6QjVtq3b49y5copx7Kzs/HZZ58ZP5cVK1Zg5cqVypL/v1u3buKJ5z8ioP8d6+9git68edPCKDNpPAP2jDIA4pGK1xtvvGFBNACumaL37t2zDI410f4Bc0Bn09mZderUsaBXANdM0aysLDF5ndfatWuN7+eKKcrCRrzy589vQTQArpmiDodDaHvwmjFjhpGx54opevPmTQuV266hbMcUBR76kFf37t2NvrYL6IDT1wxX42XHULZjigLObF3eo1atWkZfu2KKZmVlWTS7Ta8DXDNFGc0h3xRkaCWbK6aow+EQcE9e06ZNM/raFVM0JSVFZPy87BrKrnzNPuTVtWtXY3M7KioKYWFhxj1u375tGcL9qCX78OmQ6BxYVlYWffzxxxQaGko3btwQx729valixYo0adIkunfv3iP3+f333yk6Opratm2rHF+8eDFVq1aNtm/f/sg9ANAXX3xBFSpUoJ07d4rjBQoUoLp161KfPn2Uc7SzGzduUN++falOnTpUoEABcXzPnj1Uvnx5Wrt2LT89ubSdO3dS9erVaf78+crx6OhoioqKogsXLjxyj3v37tGUKVMoPDxcGcKdlpZGoaGh9OGHH1JWVtYj9/nll1+ocePGNGzYMOX4W2+9RQ0bNqQjR448cg/Z18nJyeK4t7c3VapUiSZOnEjp6emP3OfixYvUoUMHatOmDbm7P7wMlixZQhEREfT9998/cg/2dVhYGO3YsUMcL1iwINWrVy/Hvk5JSaGBAwdSrVq1lCHX+/bteyxf79q1i5577jmaN2+ecrxDhw459vUff/xBsbGxFB4eTrlz5xbH09PTqVy5cvTBBx9QZmbmI/c5fvw4NWnShAYPHqwcHzNmDDVo0IAOHz78yD2ys7NpwYIFFBoaSteuXRPHvb29qXLlyjR+/Pgc+frSpUv0+uuv0yuvvKIM3F62bBnVq1ePjh49qrze4XAo3wki9Xt37tw5cbxs2bJ05swZy5IHWNetW1cZKv23ml2k/7vXk8rQdebZ0aNHRVNUxlfrxAs5QzexDM+ePSvupDK+WiZe6Bm6PLGncuXK2L59u6ipy/jqgIAARb9aztD1EXVMtc6bNy+GDx+OH374wUi80DN0mXTBeH1uiiYkJBgZoICaofPEHiZTMV6fm6Iyvjo8PFw0vfQMXWcZzps3Dzt27AARYcuWLZg/f74gb7z55psiE9QzdBlvzHh9boqa2L7sazlr03W4J06cKJ485s2bh3Xr1hm1yvUM3eRrZhjevHlTsH11XXo5Q9c11xnDnS9fPgwdOtTW13qGrpOp1qxZIxQkT5w4galTpwpfy1rlcobOWGwmU0VFReHChQuiKSqzfWVClZ6h37hxA/369VOIczt37gQR4ZtvvjGyfQFrhi5rrjNePzo6GuXKlcPFixcF21fXpS9evDi6dOkifK0T5/jJ4/3338eSJUsQGBgIDw8PDB48WJSBYiSRwgAAIABJREFUXnnlFVSuXFl8Ll999ZXoudSrV0/I63LPhuWp2TIyMpA3b170798fmzZtgp+fHwIDA7Fjx46nJRfdOKAfO3ZMmakp64DosEWdBv/zzz+L5olcu5R1QHSUi0yNzp07N0aPHo0VK1aAyNm95zq1rMMNWGGLOjV68+bNIqDLzTpdTY4DOgALNbpHjx6i2bNkyRJRp5Z1uAHrgItLly5ZZqr27NkTwcHBSu1S1wGRUS6s0cI0eK5JEjlnreozNVnuQEe56Lr0s2fPxrRp00BEOHLkiKK5LuuA6LBFnQZ/4MABcZHLmuvR0dHC1zrKRdfjGTVqlKiLbt261dbXOmXcRIPngC5rrstyBwBEQLfzNd8MFy9ebCt3oKNcdD2e5cuXo1evXihUqJBRc51NRrlwcJNnqvLNZcKECYrmev/+/YXcgY5y0fV4Zs2aJQSxDh8+LHwtS1sAEAGdTdZeYshwiRIl0LlzZ6PmOuDsMxARPvzwQwDO0l3v3r3h5uaGZ555BqtXr0bLli1RtWpVHDhwQMhAlCtXTkhnc6Jx5swZlC5dGhUrVlQ0W/jvZTz9yZMnUb58eXh4eAhG7NOA/qexZjkRWXS42Uw4dF28iPcgsupwA/Y4dFm8SF4mHW7AjEPXtcrlZSdrKwd0Nlm8SN9H1+EG7GeKykJVvEw63GwmHLqsS6+fi0nq1I5YlJCQIISq5KXrcLOZcOisS8/4anmZBMnscOiyLn1OfK0HdMC1r+Wp97LJAZ3Nla9NgmR2xCJ9tqXsa118DjDj0GWtcv1cTIJkdsSihIQEARnWfa2LzwHWgA5Ydel1X+uCZHpAZztw4IDlGiAiFCpUCB999JEyDapRo0aoWLEiAAgYooxrj42NhZubm7ihAU4VTEaLETl7NX/V/qcCuvxhm2jpgGumKE8M4VWuXDnLFwdwzRQFYJGAlUdayeaKKcpfLnmZmIwA4O3tbQnobMuXL1f2GD16tLHxZBfQAWdWqg+bNmmLA66ZoqwXzat169bGxpMrpmh2drbQo+G1bds24/u5YoqePn1a2SMkJMToa1dMUQCYNGmSss/ixYuNrzMFdLYbN25YmKJ2vjYFdDbW7OE1atQoo69dMUXv379vkcAwySADrpmiu3btUvZo2bKlUabXFVM0OzvbIjth52tTQGdj0S1eZcqUMTaU7QI64Gxuy0gjPz8/CxonNTUVnp6eGDlyJADnDbtOnToIDg4WpcFGjRqhSpUqlv11gMNfNVcB/b+uKdqxY0cicg51ffHFF2no0KF0+/btR/5eRkYGvf3221S9enVlqOupU6eoUqVK9NVXXznvcI+wkydPUrNmzSg2NlY53r17d4qJiaErV648co/s7GxatGgRhYeHK8Nq3dzcKDw8nObPn29sMuqDba9cuUIxMTHUpUsX5fj06dOpadOmdOLEiUeeCwDatGkTVa5cmY4fPy6Oe3l5UY0aNWj69Ol0//79R+6TmppKI0aMoMaNGyuf71dffUWRkZG0Z8+eR+5BRLR//36qVasWrV27VjnetGlTGjJkSI59/c4771h8febMGapUqRJt2LAhR74+deoUNW/enCZNmqQcf+ONN6hjx450+fLlR+7hcDho6dKlFB4ergwGdnNzo0qVKtGiRYuMQ4V1S0xMpO7du1OnTp2U42+//fZj+fr//u//qHLlykoj0MvLiyIjI2natGk58nVaWhqNHDmSXnjhBaVxumnTJoqMjKTdu3c/cg8iop9++olq165tGbzctGlTGjx4MN26deuRe2RkZNDMmTOpWrVqSsPz3LlzVKlSJVq/fn2OfP3tt99SlSpVlOs3LS2NOnbsSNevXxfHtm3bRllZWdSsWTMicvpx9uzZlJSURO+88w5lZGTQ3r17qWHDhuJ3zp8/Tx06dKBatWqJY6NGjXrkOf0ls4v0f/f6V2vo58+fV0Z0yY+Lcoau63DzjMKiRYuie/fuCr1bHtGlZ+i65vrs2bMFYWL37t0KvVt+XNQzdLnGy7K2//jHP1C/fn2l0aaLJXl7e2PEiBEAHirnyY0uvvuvWrXKdkSXnqGbarzcFJWx9HppQM7QTfMoWQBp6tSpSo9C1qXXM3RTjZd9ffbsWWUurKxVLmfoeo2XMdzcFJWx9HJpQM/QTTVeHmDw448/2kq36hm6qcb7wgsvCL/bjd6TM3QubzDBaOTIkWL4xMqVKxUqv+xrPUOXNdcZw81NURlLr/co5AxdHrFI5MRXX7p0CUTOYRF2PQo9Q/9/7H13WFRXt/6aBkPvTQURRQRULCgidkBFxYKxV4IFNdaIxoo9NsSGJaixxGgwJpYYJTGxJCZqjA2jxh67oiC9zcz7+2Oyt6fh5839cu/9fU/W8+wH5syZPWfOPmedvdd633c9evSIhy2ZLASLS9+4cUOWo2BjLZyhSzXXGXHQ19cXgwcPFmHpWV1YQD5Df/DgAQcP+Pv7IzMzE82bN0e7du2wevVqWFpawsPDgyc+ExIS4ODgIArBAEDfvn1hZWXFV1D79+/Hy5cvMWnSJOh0OlhZWWHWrFl81fhPDP1Pk6JclBI6zKFnZWXJdLiZMYcOQFGrnKFG0tLSFDXXAXDqP1NHFBZMZgkdRou/f/9+pYgb5tAB84UqlDNlSAsWchEme3r16sVlbaUoF6lW+dq1a/HRRx+ByIzUqAyFIcWhKxVMZg69ssLYUpSLVJd+zpw5yMzMBJFZLpWhMKSFsaUoFyVdeubQr1y5gujoaNHDiZkQ5SIca6ZVzpK4a9asqRSFIUU03blzR4Yi+uCDD6DT6USIG6a5zsJOzKGzsZYWx75//z5sbW0xceJEWQKSYa+lKJfnz58jMTFRhCJKT0/nYy2ciLAi6IAchy7UpWeIMebQKyuMLUW5SBFjycnJ+Oabb/hYCzXXhWQyKcpFqkv//fffc4cunIhIyWTMobNjE+rSjx49GteuXQORWWBt6dKlsLGxgV6vx/z58zkrWFjo+fLly/z6Hz9+PJydndGrVy9I7e7duyIS1cyZM+Hk5MQnOazK2D8oF4kpEYuk8DrWlHS4mQkdOjMhvE7YD5Fccx2QO3RmSnKeQuiUlBQldOjMpAUHhH0pJXsqIxZVJtNbmdSpErFIqlUu7Ef6cAIqJxYJdemlTarDDSgTi6S69NKxFupwM1MimwjhddJjkWquA5WTyIS69NKxFmquMxM6dGZSJyjsKzAwUAaLq4xYJNSlFzbpRISZErHoTWMthYOy/YUOnZkQSiltPXr0kBH+lIhFUl164VgLC4IITejQmQl16aXHEhsbK6tYVLduXfTo0YO/Li4uxnvvvSf6HCviPW7cOHzwwQeYN2+e7Hx16NBBhF4C/nHoMnsTUzQ/P1/GFFWSigWUHTozaW3HkSNHKiaeKnPogPlCl95cbAYrtdatW8scOjOpLGjLli0VmYzMoSsxQE0mk+yCrEyO9k1MUba8Zs3JyUmR2fcmpigArF69WtTP8uXLFfd7E1M0Pz9fhmxQSvYCb2YPSuV+ExIS3jjWSqzgiooKGVNUKofATMmhMzt//ryoj/DwcEVp3DcxRU0mE8aOHSvqpzI52jcxRZkqIWv29vaKtVbfxBQFgLVr14r6Wbp0qeJ+b2KKFhQUyIpNKyV7AWWHzuzQoUMyp6yUsK9Vqxb69u0r+o3SxLifnx88PT3h4OAgU78kMmu3KPmof5iib2lHjx6l8PBwEXuQiKh58+a0c+dO89PrX1hhYSHNnDmTevXqJdq+ceNG6tGjB926deutjuX8+fPUtm1bEVNUq9VSZGQkpaSkUHl5uewz0oRnaWkpLV68mFq3bi1K7P3www/UvHlzOn369Bs/z+z27dsUFxdHa9euFW3v27cvffDBB1RQUPAvfw8A2r17tyipQ0SUm5tL4eHhlJmZ+S/7ICLKzs6mxMREGXswKSmJhg8fTs+ePXurfr777jsKDw+X7d+8eXP65JNPRMnHyqyoqIhmz54tG+vNmzdT165d6caNG4qfk57nc+fOUZs2bURMUa1WS9HR0bRy5cq3YlUaDAZat24dRUVFiRiKP//8M7Vr145+/fXXf9kHEdHdu3epV69etGbNGtH2fv360bRp0/5bY52fn0/h4eF05MiRtzqWFy9e0KhRo2jcuHGi7VOnTqVhw4b9l8dayBQlMo/19u3b33qsk5OTZQzwnTt3UmhoKGVmZor8Q1lZGWdE379/n9q2bUtz5syhiIgIIiLatGkT3b59m548eUKvXr2i8vJyWrdunajvs2fPUmBgIH3++edv5Xv+bVaZp/+7279rhq6kEBcXF4e6detWGvcDxDN0psPN9CIGDBjAdTVWrlyJDz/8kIc+hAqH0hm6VA3wo48+wuTJk2FhYSHC3Pr7+4uw5q1bt0br1q0BvFZ3k9arZN8tPE4myQqAx4HZDF1J+ZEVRzh16lSlCofSGboweccw3A0aNEBsbCz279/Pk1JCESnpDF2qwz1+/Hi+CtqzZw/HV9vb22PZsmV8ViqdoSslat955x0EBgaKsPRSXXrhDF2qw92vXz9eeGDt2rUcXy2V6ZWONZMwFsraTp06FVqtFlevXpUlm5lJZ+hK9VRtbGwwYcIEEaFIKBgmnaELRc9YUn7dunV8rBmWXqpLL52hS8f6xIkTaNSoETp37izC0gt16aUzdCWms3Cs33//fUU1S+kMXZqo3bt3LxfNEmLpmzZtKlIuFc7QpQJ3DK9vYWGBpKQk7Nixg+eq2rRpw1Ux3d3dMXLkSOzZsweOjo6wtbXFjh07UF5eDo1GgxkzZkBomzZtApFZ5bNGjRro3bs3Tp48yUNxkZGRHAL8T8hFYsyhP3nyhCccpRrOzKEDcuLBkCFD8PjxY+7QhXrdQg1nKcpFSYP8888/BxHhzJkzXOpUqLkOAFOnToWlpSU/fiU2KHPowni3tF6lTqfDBx98AEBcNIGRq1hoZteuXZVqswsLBwNmDXKpVjlz6EqyxAxtIES5lJaWymReGZJg4cKFot/boUMHjneWolyEWuVM5nXZsmUgIjx69Ej0cBKOtRDlItWbZ7+dOXQlqVrgdZ1Jhi8X/nb2YGYEkjNnzlRar3L69OnQaDQA5ISiLl264MaNG9yhCx9OUqaztbU1175/9eqVSNJ3yZIluHDhAogIn376qewBz4oqSFEuSrr0zKEryRKzsRaiXIS1BdgD78WLF9yhC4u/MM11QI5yUaotwATbHjx4IJuIsLEWolykevODBg3Co0ePuEP/5ZdfePEXKYrIzc2Nl4grKyvDmjVreKhWqpLatGlTUbzf19cX/fv3568//vhjqFQqdOzYESUlJQgNDUVMTAyA19IOjo6O3C8wHsw/Dv1PY7FgpkWsVGVF6NCZ5eXliR4AwkFTqqhTGbFI6BSkTaq5DpjLUwkdOiCWbhUmaoT6F9I4uVarxbRp00TbhKgaaVOSOmUaFMLko3R1wpqSDjczJWKR0ClI5Yf9/f1lFXUqIxYJta5ZY4lCoQ43MyVikXR1IuxLqsMNvFaR3Lp1q6ifc+fOKcr0CldOQpsxYwZ36MykTpB9XslhMVNiBd+4cUNWl5I1YeEQZkrEor8y1krEIuFYOzk5ifqqVauWSHMdqJxYJHzYs8aQIsKJCDMlYlFl1b/YRERYOIRZrVq10K9fP9G2goICzJ8/X3QsdevWlRHR2rZti2bNmgEwE/pUKhWio6P5GLZt2xYtWrQQfeb58+dc2531PW/ePPxV+49y6MITLoQhCk3JoTNjyUPWAgICFJNub2KKmkwmTJ8+XdSPtKQVsylTpkCv1yu+x5ZfwiZ9ODFTWuoxYxA11t5//33FZA9jlErRJID5gpYyRaUZemZvYooyTRmh41NK7L2JKVpeXi5DSEiRHszexBRlYSjWatSoIWP+AeBCbNu3b5e9ZzKZZPVjlepRAmaHrlarFd9jlXKETViiTGhKDp2ZlBU8efJkxbF+E1O0oKBAxhStbKzfxBRlhUFYi42NFZX2Y/Ympmh5ebmMKapUvwB4M1OUwRFZa9y4seJYA2JYotBY1S9h8/LywgcffMBDTAkJCfDw8MCOHTugUqkQGRkpcvpdu3ZFgwYNFL+XiZSx9lftP8qhCyu5u7i4YN26dbLZbFxcHOrVqyfaxiBQ0qr0RGLiAbPKHHplVemtrKwUIYlJSUmKDl2pXiWRmbKspO+hUqkwc+ZM2bEw4oKwD4ahllLLmTOQQsYuX76sWJVeKo7ETMmhC0WkpP0ISSbMKnPoDx484Ep6wlbZWCs5dJPJhC+//JLnIYQtIiJChkBhZI9PPvlEtP2PP/7g4mXCVlmRhpkzZyo69GPHjnHVRGGTxn+ZKTn0srIyLF26VLa6ZFh6oW4IULlDF2K432aslRz6m8ZaKITFrDKHLiSTCRvD0kvHWsmhS4mDwjZ06FDZeQHM1Hwp0uj48eNwcHBA1apVYWlpiaioKOzbtw9du3bl93qLFi1E11Tbtm1l8gL9+/dHzZo1Zd/5/fffi46xW7dusn3e1v6jHDqLoZ86dYrDAqWsyh49eogcuhIDs2rVqhgyZIgIczt69Gh+AUgdekVFBdatWwcXFxeO4WZEnf3793PtkWrVqomwuklJSbCysuLHolSvMiIiAu3atcPhw4c5iaFVq1Y4d+6ccBAxa9YsAOa49YoVKzhxISEhgT/916xZw1mOLObKZk1MHZI5dCkhZc2aNUhISIC7u7tMvlQIzZQ6dKHMa8uWLTmTccaMGTL5UvbAkzr04uJizJs3j7NfZ86cyaFiP/74Y6VjLXXoQklflofw8fHBoEGDkJ6eznMpwnjz9evXQWSOSQPm8Ny0adOg1+u5smZaWhqICMePH+cJZZYMZfHmmTNnQqVS8WO5desWT+x5e3tj165d6N69O4KDg7Fjxw4e/x0yZIioQpJUt+frr7/mtV+7dOnCC7Bs3LiR46udnJxEfAupQ5dK+q5atQqdO3dGvXr1RFh6xlhmJnXoUknfM2fOgMisHzR37lw+1rNmzeJjLXXoxcXFMvnmefPmgYhw8uRJDgGtW7euSNdF6tCFxMGgoCBkZmbC09MTgwcP5vVdXV1dsWPHDtGDqkePHqIV/Oeffw5LS0sEBgbijz/+QPfu3UXvP378GEuWLJE9NKSCfoAZVODu7s5f5+bmYtiwYSAyh6M+/fRTEP0TQ+cmRLmYTCaRVjljVTKH/uzZMy4PIJU6rVq1Kq/ULZx1sxuD0dc3bNigqLkOyJEPQuYkQ9Uwhy5Nbgkz/M2bN0dUVBQA84Njw4YNHN0wZMgQjgmeNWsWMjIy+CxBSFy4ffs2iF4XphUmnvz8/LB3717u0K9evcprUEop44mJifyCrKiowPr16/lDbOTIkXj+/Dl36EKikLe3Nz777DOYTCYZykVJl54JO33zzTciUhjT4QbEKBfpWPfo0QO3b9/mDv3FixecFCbNQwhRLizmyuSFZ8+ezRFNO3fuxPr163mCbMCAAXxlwWQT2LH98ssvPL4eEhKC77//HrNmzYJKpRKNtY2NjUglsl+/fqhVqxY/lqlTp4rq1ZaVlXGZhxs3bvBEce3atTkjkhUhZlwCpYcYc+g3b94UTVqEZLLIyEiEh4cDMM+6pbr0z5494w5dSXPdZDLJUC5SXfpPPvmEP7wzMzNFmus9e/bkCXohyqWy+5o5dEYKUyIOuri4YMyYMQDMK09WtDo6OppPZIYOHQpvb28AQFpaGlQqFcLDw/k98N5778HR0VHkd65evSpTqSQy5y82bdrEH16TJ0+GtbU1AHPlM09PT2g0GkyZMgXFxcX/oFykpkQskupXs5MtLJAslToVOnRmQhF/aWJPikQAlIlFlUm3CrVOpPUqmzVrhvbt24u2vXr1iidx9Xq9qK969erJYspMuF+q+piZmcln/dImRJ0wS0xMlJFNpFrl7POM2Th37lzR0rMyYtHJkyf5A0/apDrcgDKxqDIGrZIONzMlYtHdu3dlio6stWzZUhaW2bhxI4iIw0QBeVEI1tjDOD4+XpYTeffdd1GtWjXRNqnjZv3odDrY2tqKoJzAa7LZvn37RMci1KVnzdraGkRyzXUACAsLk113wrG2t7eXjbVQcx2onFj0ww8/cMiwtNWvXx/Hjh0T7a9ELKrsvmZUfqVQk4ODA8aPH89fGwwGpKWlwc7ODnq9HosXL8aYMWNgZ2eHGTNmgMis4S/8TUybvaCgAAaDAcuWLYOlpSVcXFw4cubWrVtYvXo1v7fs7e0xZswYLgfB0DINGjTghUmAf2CLMnsTU/TRo0dwcXERXTyVScAqOXTgNU5Z2MfQoUNlSATgzUzRvLw8GUJCaYkGAE2bNkXHjh0V35NKlIaFhSkmGe/fvw8iwqZNm2TvVVRU8GUfa1u3blVkRI4aNQqurq6KxyJNMjo5Ock014E3M0UNBgM+/PBDUT9z585VlF19E1P04cOHsoemNA/C7E1MUWlB3yFDhiielzVr1oCIZPR5wDzTFjpi4exZaqNHj670/Errm0ZGRiomyX/55RcQEQ4ePCh7r7S0FOPHjxf1k56ervibgoKCEBcXp3gsUtaqvb29Iuv6TUxRo9GIJUuWiPpJTk5WHOs3MUUfPXokY4pWNtaVJZQfPnyoiAgbNmyYLFbP7v+DBw9yB96tWzc8ffqUr8LYbzCZTPjhhx8wcOBAGfX/ww8/lEkT/MMUfUu7fv06JSQk0MuXL0XbBw8eTD///PNb9WEymWj79u2UlJQk2r5z506aPXs25efnv1U/Dx8+pMTERJmM6IABA2jXrl3mJ6nke6U1DFkdzw4dOogkSs+cOUMNGjSggwcPivrRaDRERDIp1nv37tHQoUNp06ZNou3jx4+nlStXylirlTFOT58+Te+++65oW25uLo0cOZKuX7+u+BmplZeX08qVK+nDDz8UbV++fDmlpKRQWVnZW/Vz8+ZNGjVqlIxtmJCQ8NZjXVJSQnPmzKFhw4aJtm/bto3CwsLom2++EZ1fxvgUsnbLy8tp06ZNFBISImOW9unThxITE+n+/fui7Xq9XiZRazAYaOvWrTIG7ZkzZygjI0M2powdKb1mjEYjbd68mT7++GPR9qSkJEpNTZWd38LCQlHtSyLzeVm5ciWXh2WWn5//l8Z60aJFou0pKSm0fPnytx7rW7du0ahRo2RM0eTkZEWpaoPBIJLRZVa1alXauXMndezYUbR9x44dVLt2bWrRogX17t2bJkyYQNu3byciotjYWLp69Srt2LGDvvzyS/Lw8KCXL1+Sk5MTv99UKhVFRERQ3759ycXFRdT35cuX6enTp2/1O/9tVpmn/7vbv2uGLpU6XbFiBWJiYlC/fn0Z+1MIE5PO0KXsMzb7njNnDuLj40WMQPZ0ls7QpcmeWbNmYdSoUbCyssKxY8c4QqdZs2aclQaAs/GAyut4MrLJF198wWeDrVq14lhz6ZM/JyeHs1T1ej2mTZvG2YP79u3jlZ+kanWjR4+Gi4sLfy0sU+fl5YWtW7eifv366NixI1JSUnhYa+LEiVzrQzpDN5lM+Oqrr3gNxk6dOnGNjy1btnB8dc2aNXmZL0A+Q8/Ly0NSUhIPRSxZsgRdu3ZFQEAAtm/fzmdxQgYtIJ+hHzhwgNcN7du3L4/xbtq0CZs3b+bnvlWrVnxVxWaaBQUFKC0txfr16/l+oaGhOHjwIL82r169itGjR8PCwgI6nQ6jRo3i1x5L1gHmGWxGRgbHYjdu3BiZmZlQq9Xo378/l7tt3LixKEHOpJKF8L4LFy6gadOmIDLLQDNY7f79+/lY+/n5ISMjg59fJycnHm8uKirCihUr+KqnXbt2OH78OHQ6HSZOnIgVK1aIQphsrJVm6IcOHeLXaMeOHTmTMi0tjVftYaxudizSGXpeXh6mTJnCx3rx4sXo3r07/Pz8sGTJElhZWcHOzg5paWki2KZarZahwfLz87Fs2TJOOBO24cOHY8CAAWjbti0CAgJgZ2cnen/KlCkiKGafPn3g7+/PX1++fJmHaWvXrs3PdWJiokhxkoV0/k+EXIioIxH9TkS3iOgDhfdbEdF5IjIQ0Ttv0+d/16Hn5eWJqNFCqVNWExBQpkWzyiQJCQmyuprbt2+H0WiUoVyEzDNGgRcKNgklb3v27MmTZ0KUi8FgwJYtW/iFxcrENWzYELGxsW+s4+no6IixY8cCMGN3161bx5N3ffr04ciSlJQULF++nCNgmGY1AJ5hv3btmqKTvX79OsaMGQNnZ2dFzXWmHChEuQgTz0yXvri4mDt0oQ53QEAADh06BMAM2yQi/mDLzMyU6dIzh/7q1Suuw81+E8tDCFEulY01c+i3b9/mieLAwECOlmFJ540bNwIwhy3WrFnDx6ljx458ub58+XIuG9CsWTMcPnyYOyUWlmFFru/fv4/ExETodDpYWFhgzJgxGDlyJIjMUrLsAR8UFMTzMyaTiYcmTCYTdu/eDQ8PD6jVakyYMAEFBQU4deoUiMxJxoKCArz//vvQaDRwd3fnBZOlKJcjR47wxH54eDh++ukn6HQ6jBs3TubI2XX38uVLEJmlZtlYS3XpS0tLuUO/du0aYmJiuHNjYy1FuQi1ypkuPXPoOTk5ohyUMA8hRLncunWLyyWHh4cjKysLRqMR9GcIDzCrLM6ePZuTn6KiopCZmQlbW1s0bNgQWq0WPXv2lIWjkpKSQGSGTxKZ82c7duyA0WhEVFQUwsPD8eTJEwwfPpwn4VetWoWysjKOhnr69Cnu3bsnQ78xv/K/5tCJSENEt4nIj4gsiOgSEQVJ9vElovpEtP3vdugLFy4E0euK28Jq6My6dOmChg0birbduXMHPXv2BBGJklhC6JRQ6lQJh24ymbBr1y6uDcHw32zg69atKyuLp4RDz8/Px4wZM6DX60UJzzfV8WT6EtJ+Zs2axRNfwhYTEyNLgu3atYvPIJlCPIIoAAAgAElEQVSVlZVh+fLlfPbFPs8019955x2ZvKgSDl2oVS7E17NEqlCHGwC++uorEIkVKKW69KwPNtZCzXVmSjj0ysaaSSVIk4zZ2dkgMkM+hVZUVIQlS5bA0dFRdG5btGiBb7/9VuYIGIxVShi6d+8eRowYIasHWqNGDWzfvl001gaDAURiJmFubi4SExP5uDCHM27cOL5KGD58OEdpAMo4dFaMRGmmGhkZKau1ynImu3btEm0XapULE+7C4i/C86uEQ5dqlbM+mFSCVHsJkMMWTSYTduzYAVdXV2i1Wn5eRo8ejYkTJ3LmaI8ePXhfbHWTkZHBJ4fS0oMtW7ZEw4YNYTKZkJmZyR+8QplkW1tbvloRnne2ChZyQIRgALYyVCqB97b233Xo4USUKXg9jYimVbLv1r/boQsvQim+lFnnzp3RqFEjxc9LmYy1a9eWOSwAb3ySFhUVySRKly1bpihr+yamKIMaCltlTFFvb2/Ex8crvsdm3qx169ZNlowBwCssKSWKnz59yh0na5XVbK2MKcoeeFJHwWasQtu3bx+ISKY7DphnVmz2xdr27dsVx/pNTFEpk9HW1laRnZmXlwciZRnf06dPyxKeShR5oHLiFmC+npgTZG3//v2y/diMVymhfOrUKRmbNzg4mGvSCO1NTFFpbcvKWKns/CldB0pjHR0drZg0fhNT9MWLF1xcj7Vt27YpjnVlTNHs7GxZMW+NRoPBgwfLUFxsQvj8+XMYjUbExMTA0tKSh01ZzVCmmwS81o0R9u/u7s7Zo0JjDl2KZDMajXwFx9pftTc59LdJilYlogeC1w//3PZfNpVKNUKlUp1TqVTnhHX6/qotWrRIUboVgGJy7+eff6bp06eLtt26dYuWLVsmS6ZWlhw0Go20bds22rlzp2j7qlWraM+ePbKEJzseqf30008yOU8iokaNGtHq1atliTMLCwtZIunatWsUGxtL/fv3JycnJ759//79VL9+fTp8+LDib5Aez6VLlyg+Pp5u3rwp2p6UlEQ//fSTYh9Kdvz4cVnC8+TJk7Rw4ULKyckRbWc1U6UJrCdPntDMmTPp+++/F21fsmQJfffdd299LJcvX5bVfS0sLKQuXbrIEo0WFhZERKIE8fPnzykhIYGaNWtGBQUFpFarycLCguLj4yklJYX8/f1p48aNotqvTHJVOHasvmnt2rXp9OnT1KpVK/5et27dKD4+XlQ7kx2XUmLP09OTQkJCRNuaNGlCPj4+b3VOKioqaMGCBaJjIDInpUePHk0lJSWi7Syh5+npKevrm2++oQULFoi2nTx5klavXk2FhYVvdTzFxcWUlpZG33zzjWj70qVL6ejRo2/VB5E5UV2lShXRtlatWtGKFSsoKChItP3777+n+vXrk5ubG6nVatq2bRs5OztT7969qbCwkL7//nsyGAw8eVpeXk7p6ek0c+ZMUT/Pnz+nyZMnU1ZWlmi7kt8wGAy0ZcsW0b3h5eX11r/vv2SVeXq8nnW/Q0SbBK8HEdHaSvbdSv9DMfSdO3dy5lZMTAyuXbvG94mJiUFoaCh/fe/ePU5s8fT0xJYtW6DX6xEfHy9i2gnJKErJi+PHj/NlV5s2bbiYz6pVq2R1QplJ1RafPXvGVRurVauGjIwMREdHIywsDCdOnOCM1qpVq2LdunU8IRMYGIh33nmHH9vIkSOh0Whgb2+PxYsXIz8/ny/VDxw4wGfbMTExfEbO6mKyuql37tzBgAEDoFKp4OjoiCVLlqBPnz7w9vbGp59+ymPFAwYMECUZpTP0O3fuiErvMRGwSZMm8Tijo6MjUlJS+O+Rrhby8vIwc+ZMWFtbQ6vV4r333kN8fDx0Op0IX92tWzfRDFg6Q8/OzubsVyYX0KxZM7Rs2RJbt27lwl+1a9fGli1bUF5ezmOvycnJqKiowOrVq+Hg4MCX8fn5+ejQoQPCwsIAmHHgbLZdr149znZlqw4WAjx06BAfh65du+LmzZt8nGbOnIlp06ZBo9HAy8sLBw4cAAD+vnC1cPfuXSQkJECj0UCv1/P4c2hoKHQ6HSwtLTFhwgTR7Fg6Q2fl5IjMieDk5GQQmYuCTJ48GUTmkCG7NgCI4trMhDLQfn5+fBxHjRrFafxVqlThMWdAPkNnuQEW1uvduze6du0KBwcH0Vizc8ZMOkMvLS3FypUrOVSZEZroz/CPq6srzykAZly7Xq/HhAkTILRjx45BrVZj8ODBSExMhK2tLQoLC/HRRx/xkF2zZs2QmZmJfv36wd3dHYsWLYKDgwNUKhUGDhzI9ZGYRPXjx485N4CFIMPDw5GRkVHpyv9tjf6TQi5ClEtpaSmP/2o0GowbNw4vX75Ex44d0aRJE+Tn52P69Ok8Vs1KgplMJqjVai52lZWVxWVr69ati++++07k0P/44w/OkvPx8eGaF/v37wcR4dy5czAYDKLEHSMQMYduMBhEUppTp07lMfuePXsiKCiI/8bvv/+e61L7+PggPT0dwcHBiIyMxNy5c2FjYwOtVouxY8dy1h9LpjF5gLKyMhEbdNy4cdiwYQNfQo8dOxY6nQ56vR5TpkzhN23fvn15Fr+wsJAnGZlMb0lJCXfoQhlfa2trzoh89eoViMwJWnZ+WWKUIS0Y/jsrKwupqan8puzbty+/iYWMvdLSUixevBi2trZcSyU/P587dKkON7sWAHGS3GAwICMjg9PXfXx8OOImIiKCJw6joqJEoamGDRuKKPCs9iuLicbGxmLVqlUgMmP8mdMLCAiQiU35+vqiT58+AMzJdhZGGTBgABcKS01NxR9//IGRI0dCq9XCwsICY8eOxaNHj7i++Llz53D37l3Ex8dDrVbDxsYG06dPR05ODnfoN27cwLx586DT6eDu7s6rF40ZMwb29vbc2R05cgTu7u7Q6/VYv349TCYTpkyZAgsLC5hMJrx48YKzqZlufWlpqSxEJESLMV16oUP/9ddfOT9DmPgfMGAA/Pz8ZGPNCmPn5eVxh240GrFr1y5+7qOiovhDVKvVYvr06bh06RKXC27fvj1u376NY8eOgYj4w1No7AHHGgM4hIWF4ciRI/w8JScnQ6VSobS0FC9fvsTUqVOh1+uh0+kwZswYLlexZ88efg8HBARwfab/dZQLEWmJ6A4R1aDXSdHgSvb9H3XozISaJEI5T5YpF1K4gdewugULFvBtrLAEG0iGaPH09ISVlRVnRAqV1diNJUzUCaF1QggUS6wIxe6ZDR06VMYeZAkZdlEKW1xcHC8YLDTm6IT2/PlzXixa2IdarcawYcNkMWWpDg4gLjYgZCIKtaiFM3iGjli5cqWon8zMTFmtVdYiIyNFKxsAiI+PR9WqVUXbhLr0wuSekg43s8GDB8PHx0d2fg8dOsTHWTizE1a9Z1a1alXFkoUlJSVYsmSJDO5mY2MjSw4yi42NRXBwMH9dVlaG2bNnQ6vVipLBDPY4evRo0TgxHX4hueb69escUeHo6MjHiSV0+/XrJ6of265dO77iYPb06VMOu4uLi0NsbCy8vLywcuVKnrxMTEwUrQRKSkpARFi0aBHfJtWlZ8dSvXp1kca8MBksXVUD5rGOj48X3ctExBmoISEhMsVVGxsbTJo0CYD54b1mzRrY2dmJarX+8MMP+Pbbb/HJJ58gJSUFU6ZMkYmEqVQqHDp0SHYd7Ny5E0QkusYePXqExMREmWCfp6cnNm7cKMqt/a87dPPnqRMR3SAz2mXGn9vmEVHXP/9vQubYehERvSSi3/5Vn38HU/Ty5cuyIrtKanZvSoKVlJTIdJFjY2MVWXKsHqVS/cgbN27IdNOZ/oXUxo4dCwcHB8Xfe/PmTZnz+/jjjxUTsLa2tvxilv5eKc1dWkyaWadOnWQ3FrNvv/1W1Iderxfh6ZmxAhdS1AhgvskSEhJE/VQmjdunTx/Url1b8b2zZ89yRARrUh1uZhMmTICdnZ1iP+xYha179+44fPgwDxmYTCZRgRFmeXl52Lt3L4YNGyYr5E1kRght2LABt27dEh3XtGnToNVqRc6e6QkJPz906FDF644lI6Xa+4AZgcJWB6wNGDBApgzp5eWFIUOGyD5vNBq5UJawRUdHK7IzGUT1ww8/lL2Xn5+PSZMmifoRFn8RmpIMAbOzZ8/KEvbbtm1TZJy6uLhg9OjRom0PHjxQnBgJH5ws/CNs3t7eGDduHI4dO8bvN8bS/fLLL3n/169f5zICwqaUTP4/wRQF8DWA2gBqAlj457bZAA78+f8vAKoBsAHgAiD4bfr9d9qTJ09o6dKlssTORx99RC9evBBtY/tYWVnJ+vn111/p4MGDom1ZWVl08eJFWTKRJUCk200mEx0/fpx+//130faFCxfKGJ5ERPb29lRQUCDabjAYaNmyZVS/fn2yt7fn2xs1akTx8fEUEBBAH3/8sahmpYWFhSixl5+fTwsWLCBfX1/67LPPRN/Zv39/Sk5OliVaS0tLRcxUZrdv36YNGzbI9k1PT6fHjx+LtleW2Hv8+DENHTqUNm/eLNq+ceNGxXqXRUVFZGNjI9tOZK5ZKWXuZmVlyViVREROTk5UUFAgq+958+ZNat68Oen1eqpduzYREQ0bNoxOnTpFMTExVLNmTVq0aBFdv36dKioqyM3NjS5dukSLFy+mNm3akIuLC/Xs2ZMyMjJkicb27dvT6dOnKTExkWrVqkV+fn40fPhw+uyzz8jT05MMBgP99NNPtGXLFoqJiSEPDw9ZLdDr169zRqLQ2BgLWavMvLy8ZL9z586d5OrqStHR0bRq1So6f/48PXnyhGrVqkVXrlyh3bt308yZM6l79+5Uu3ZtSk5OlvWbkJBAgYGBsu3smpUmAwHQN998Q/v27RNtt7OzE13PzHJycsjZ2Vm2ncicgMzLy+OvNRoNabVaxXNjaWkpu6YBiBLPqampdOLECbp+/Trl5uZSaWkprVq1iohe+4SJEydSw4YN6aOPPqK2bduSl5cXJSQkcEbwjz/+SKtXr6YmTZpQnTp16MMPP6T27dtzcIK3tzdFR0fTrFmzFGsI/21Wmaf/u9u/a4Yu1Im2sLDAjBkzEBgYiNatW/Majy4uLtiyZQufJSkJWd26dYsL61SpUgUffPABiMz4XpaE6tSpkyghx6qIC1cB58+f57OBVq1a8QpLqampfJYRFhYmkoBlYkDsN/366688RNOtWzc8fPgQs2fPhkqlQllZGQ4cOMATXH5+fjy55+npiREjRuDVq1eYP38+Dz/Fxsbi3LlzSE1NBZE5rsqWmEFBQaJZNpPyZZaTk8M1121sbDBv3jxUr14dkZGRfLu1tTXmzJnDj//Bgwcgel30QyqyNH36dHTv3h0+Pj5YsWIFNBoNgoKCZGEkpeovRUVFGD16NIjM+Y7g4GA4ODhw1cfGjRvLZpKrV68GEYngk6dOnYKLiwtcXV3x008/ccXF7du3o7S0FLt37+YyrkotJCQEH3zwAU6cOIHy8nLOzNyzZw8sLCy4Jsz169exdu1adO/eXSb4xlqNGjUwZcoUnDt3jmuqb9++Hba2tvDw8JBpALGCJtJw2TfffAMPDw+eF2FjcPLkSUyZMoVfx0pNo9EgMDAQvXr1ksWTWTjJz88PaWlporBjYWEhiAhLlizh286dO8eTxnXr1uUrBiZA1qdPH1klIKWZdUFBAUaMGAEiMwbc2dkZ1atX5zH4IUOGyHR+/Pz8MGDAAP761q1bqF69Ouzt7XlRitmzZ8u+x9vbG/Xr10d2djacnJw4c7ugoAB79uxB//79RWJlrDVo0AApKSkcbtytWzfUrVsXr1694uGikJAQzgn5PxFy+Tvav8OhHzlyhKMWYmNjubMVIkIuX77MkxMtW7bElStXeHWTTz/9FDk5ObxwrbW1NebOnYvCwkIuTvTFF1+gvLwcKSkpsLOz45T+oqIifP311yAysx1fvXqFcePGQa1Ww93dneOmhciS8vJypKenc2JSZGQkTp8+zbGrt2/fRlJSEjQaDTw9PUWx3M2bN4PoNWqB1axk8USWICIi7si7du0qikuzpTTDqB86dAjVqlWDSqXCxIkTUVhYyGsilpWVYeXKlXB2duaa6+yiFZbwkj4It2zZwku6bd68WVRoonv37hwNEBERwQtjf/fdd3BxcYGDgwNnFwJm0bIOHTrw17/88gsf70mTJqGkpESUTNuzZw/c3Nyg0+kwb948/juZ2BLDDWdkZMDS0hL+/v48AWs0GlG1alX06NFDdL1JK1xt2LBBVhrt4cOHsLKy4rUmmTOVkqAqKipkGHBpab7o6GiewP3tt9/g7+8PrVaLtWvX8v2EbETATMhiE5Dg4GBkZWWhoqJCsWwhSwyytnPnTly6dElEb2fJ3QkTJoDIzKD94osv+ETFzc0N8+fPR05ODgoKCkBEWLp0KR49eoQhQ4bwOPmGDRtQUVGBiRMnwtraGiaTCYsXL4ZKpUKTJk349WQ0GqFWq3lCHzA/cGvWrAmVSsXp90wio6KiAsnJyVCr1ahVq5boGg8KCuL3/tWrV1GlShU4OzvzfWJiYuDl5SXiabDxYvWE2QRL+iAtLS0VFV+ZOHEipBYSEiJKnu/fvx8eHh7Q6XRYtGgRn+z849D/NKapwfQT/P39RVokgFm7QxgfNBqN2LRpE5ydnaHVajniom3bttxhvfvuu6IblVWCFyIUHj16xAfU19cXo0aNAhFh7Nix8PT0hEqlwujRo0UwLxZ3FtL4S0pKkJqaCldXV8XZ0vDhw2Vyv0r9AK8du/DzXl5esgQjYGatSuub5uXl8Rmv8KHAVhNRUVEyNcnq1avzyurMfvzxR37DS6vqBAcHyyoTVa1aVaSvcu/ePTRs2BAqlQoLFiyAyWRCcHAw4uLiUFFRgQULFkCr1aJq1aqiogeDBw9G9erV+evs7GwOUW3YsCEuXrzIV1I///wzLzzdvHlzUZIQMOvYWFlZcd0Nk8mEd999F0TE4aQjRoyQndeEhARYWFhwglpeXh7c3d3RokULWUyfOePevXvDysoKbdu25bFgo9EIBwcHESM4NzeXyxUMGTIExcXFWLlyJYjMcMK7d+9yze8RI0aIZGD9/f25c2N9BQcHw97eHl26dIFKpRIVswDMOvpWVlbo1KkTTCYTqlevzqvrmEwmHD9+nNP7bWxsMHz4cD6Lt7a2hoWFBaZMmSKKk8fFxYmgpV9++SWsra1RrVo1nD9/Hjk5OWCr2LKyMkyfPh1qtRq+vr4i9mqtWrXQt29f/vrEiROoVq0adDodli9fDqPRyNFIFy5cgKurKzw8PJCVlcU/w+6VPXv2ADA/sLVarUjXqaioCF5eXrLxe/z4MZycnNCkSRNERkZCq9WKrmuTyQR7e3u89957onOanZ3NUXIMBpmWloa/av9RDl3oKBYvXqxYw9DV1VW2fAPMCbBBgwaJ+oiMjJRd1MBrRqlUoxsw49GlGuOhoaGyGRnwWoZUmERhlp+fL5M6HTNmjCLLk1UL37Fjh2w7g1yyplarkZSUJFvWJiYmVirdKq13aG9vr5jlBypHfJhMJq4bzlrv3r1lCdzS0lKoVCrMmTNHtL2oqIjr6sTFxcHV1RUREREcidKnTx/Zgy4+Pl6GEALMwmkeHh7QarUicSoiQq9evWTnBjCHLIheMzhZcpwt0dksWCgHceXKFa6zIjR2HpjjAIAzZ85Ap9OhU6dOMBqNHFrI4LNXr14FkTnpLTSj0cjDII0bN+ZhvK1bt8LBwQH29vaKSe5OnTohJCQEgDk02a5dO+h0Onz33XfIzc2Fs7OzKBFpMBgQHh4OR0dHPrkZM2YMrK2tZfLRly5dkpUK9PX1VaxXGxoaKlppAebkbbVq1WBtbc0fslOnTuWhxnfffVeWyPXw8JA9UF++fMkRWB06dICfnx/s7Ozg6OgIb29vWRjPYDCgevXqaNu2LUwmE1q1agVnZ2fZw53hydmK0WQyITY2Fnq9Hr///jtevXqFunXrwt7enmP32YOJwXWlxlbZrP1V+4916J07d1bM9EtLeAHmATl8+LCswAITgpIa0xphaoZCu3v3rqywba9evUTkJmb37t0DkVyn3GQyYdu2bTL9diIzRGvatGmim0MoeAWYneLcuXNhaWkJBwcHrF+/Ho0aNUJERASfNfn7+4uWjQMGDECNGjVEx1FcXIwNGzbI6O0WFhaiuLjQlHRlGFRNqlFOJBZ8Al7X8Ny6dausb5PJhJSUFNHn9Xq9iCAitGHDhqFKlSqy7YCZVs7CQazVrl0bV69eVXxolpWVwcHBAfHx8RwnP2jQIP69BoMBMTEx0Gq1/Lx26dIFDg4OskILBoMB9erVQ40aNVBSUoLs7Gx4e3vD19dXpP3BVgBff/01r4qkdB0B5uW7NI7btGlTRekKABg/fjxsbGxgNBr5RIZVtALAzzObZbJwpnDScPjwYdlKFTDPjkNDQ2VjHRERgYyMDNFD3M3NTXFl8/jxY64QyZqbm5uocIfQrKys8P7778u2m0wm7oCF7cSJE4poMKbHzx7QSjUEysvLUbNmTYSEhIio/ytWrOD7/PHHH/D09ET16tXx9OlTPnljWH9mjx49wvTp07nm0z8OXWDsoktOToadnZ2sIpGQ9cfs1KlTPEnj6+vL/588eTInogjLsAHmeCyRGOv7/PlzjB8/nkvSsqVueHg4bGxsoFarMWjQIFHilDH/li5dyrfdvHmTlwwLDw/n6nmLFi3CoUOH0LVrV44bb9++Pfbu3Yvy8nJ+Yxw7dozHkvv06cNjkS1atEDbtm0BmEM0vr6+UKlUGDt2LAoKCtC1a1fUr18fgJmxmpyczMM+oaGh2LVrF1QqFfr06cOXiFWrVsW2bdtEEqVOTk6iZeWvv/7KIZrNmjXDiRMn+MxWqOTXpk0bHD9+nM+EmZPPz8/H0aNHMW/ePMTExIi4BKx17doV69atk2mTjBgxAh4eHqJtT548waZNm9C9e3cu0CRtWq0WderUQY8ePTBt2jRs27YNZ8+e5VK+arUabdq0keHIc3Nz4e/vz5UNiZQhe2wM2Li2b98eFhYWslBYcXExT/h17NgRjo6OIrjkw4cPceDAASQnJ/PQi7CFhIRg5MiRSE9Px/nz50UPKkaYYsVNhIJfgHlSUL16dTRq1AhZWVmwtLRE9+7dRQ/OkpISWFtbc5ndGzdu8BlxtWrVuCOdMWMGUlNT+SrI29sbixcv5kqWCxcuhMlkwh9//IF9+/Zh9uzZ6NKli8zJhYSEYPny5cjKyhIdB5PpZUqK7Pz89ttvWLZsmWyVyppGo4Gvry/atm2L+Ph4zJs3TzRhaNiwoejaFhrDnC9fvhyOjo6IiIiQQSXPnTsHa2trNGnShF8PjOR0/vx5DBo0CDqdDiqVCnFxcdyv/BND/9OESdGnT59yOU8XFxekpaVxluLSpUtx8eJFnln39PREWloaysrKeHKwtLQU2dnZnHgjrCzP6m/evHkTBQUFmDdvHuzs7KBWqzF8+HA8fPiQM+AyMzPx/PlzvP/++9Dr9dBoNEhISMC9e/c4hnnq1KkoKyvDwoULodfrYW9vj3Xr1vGLydXVVTSLefDgAebOncvxsVKFvBo1ashmTdHR0bxGJGDO0DNsMyNMubi4YPjw4bC0tASROZl84sQJmEwmUYILMMfFGeuvcePG3AHb2dlhwoQJePHiBUaOHMm14j/++GMYjUaYTCZoNBpMnz4dgDmUkpqaKvsNTLdeSHoKCgqS4dQ7dOjAj5/ITCKaMGECMjMzER8fD2dnZ5w/fx5z584V1X309vbm+QHWFi1ahG3btmHatGno3r076tSpI1NBFH5P+/bt0bNnTwwdOhRjx47F9OnTZUJQmzdvxq5du/DZZ59hz549+Pzzz/HFF1/IKhD16tULx44dww8//ICff/4Zv/zyCy5cuMBlmFmbNWsWOnXqJFrtqFQqBAYGyqpgtWvXToSesbS0RJMmTZCYmMgVJ4nMEsDPnz/Hq1evUFxcjIqKCq5WyPZxcXERqQQyY+JZ48ePh1arhY2NDebPn4+ioiK+cly8eDEA88pk//79ig5WuBpVq9UICgriuTDh+LP/q1SpgqFDh2LXrl2cjzF//nzs378fiYmJXGmSyIymEa6+mzZtivT0dMyYMQP9+/dHeHi4rOoRa7a2tvDz80NYWBi6dOmCd999F1OnTuW+hrWDBw/i6dOnsjDvvn37oFKp+H5bt27lRc1tbGwwbtw4Psn7B+UiMSVi0cWLF/kJlIYwHB0d8eGHH4r2HzVqFJydnUX9Xrx4kSe+6tevz3Uhpk+fzm+suLg40XKYVTz/6quv+LbHjx9j7NixIpYfkTkxyOLu77zzjgwpERYWhsjISNnvNRgMOHjwoIxEs2DBAhE7EzAzEKWywcDrmQZrWq0WI0aMkC3tWWFs4RLUaDTik08+4cgcptlibW3N63hOmDBBRhYRFk9gVlxczKnRrLVv3x7Jyck4cuQIL5rAkphJSUnQarWYNGkSTCYTrl27htTUVLRv354/kIRNpVKhWbNmWLhwIS5dugSTyYTLly+DyDw79fX1RUhIiGyWVV5ejmvXrskcMGNTBgUFwdvbG46OjjLG7d/R1Go1goODMXjwYKxatQo//vgjl4kYOnQo9Ho9IiMjYWVlhefPn8NkMuHmzZvYvXs3kpKS0LZt20ohktLzJX2Yubi4wM3NDe7u7vDw8JA5wWHDhomUBBn1X8gUZcbGkbWEhASkpaXh559/5snbvn37wsLCAtHR0dDr9Xj16hXu37+PTZs2oVevXoqrNeaEu3Xrho0bN3LyVVxcHFxcXPDOO+9Ar9crKmsWFxfzWTJr48ePR//+/REdHY2QkBBUqVKFS2NX1qysrODl5YXAwEAZgZDIPJlYtmwZv6aZ/ePQJVYZU1RJzjMpKUl2QgGge/fuIuq1sI89e/bIiv62bt1akRH5poLcqz8AACAASURBVITn/fv3eTED1uzt7RXrQALmavDS+DazkydPKl7YKpUKrVq1QlpaGp49e4bevXujTp06MBqNOHv2LGbOnImQkBDZ5xo3bqzI1rtw4QKIlOthFhUVyRi0TZo0ESEIhObr64tBgwaJtuXk5IgKByslVsvLy1GnTh34+/ujrKwMvXv3hqOjo2y8i4qKeEiByCySpSTdOnz4cFhZWeHly5dcSGrz5s2Kx8xgqGyWWFmMn0HPWPv0009x9epV/Pbbb8jKysKlS5dw8eJF/sBnbe7cuTh27Bi+/fZbHD58GAcPHsSXX37JBZtYkyKZmF27dg1qtRqTJk3CtWvXoFKpZLBEZgwfLpwArFmzBikpKVi8eDHmzZuHWbNm8fAJa6NHj8aoUaMwcuRIjBgxgudjWFu5cqUoFFJeXs77F9qFCxdQpUoVjnhiyVmhZWZmgshcFYzxABh3gZnBYODFW1g7cuSILBT28OFDaDQaJCUl4e7du7CwsKg0cd+uXTs4Oztj0KBBikgftp+0hu6KFSuQlpaGhQsXIikpCcOHD0evXr0QHR0tC+1JV8/M/nHoEqvMoZ86dUpUWIHIjDtXusmbNWuGqKgoxf5v3LjBZ+qsbdq0STEhl5WVBaLKafRHjx4V9cOEj5QSNTNnzoRGo5El6xhmOiAgAFeuXIGtrS3i4+Px+++/Y968efw3C2eObm5ufFurVq2wfPly7oyHDBkCrVaL4OBgWTz6Tcie3NxcDtNkrUqVKti5c6fiuQkJCREpMubm5iI0NBQWFhb46quvuNCTVDucaUYzpAkLawmRJYB55dCqVSvY29ujYcOGsLS0lGnkvHz5ElZWVhg+fDgA803arFkzeHp6ioqZAOaZm5+fH+rUqYOSkhI0bdoUXl5esv0AcOXDEydOwNPTEw0bNnxj8m337t2oWbMm/P39FYuNM22WOXPmoEqVKqhXr56iBkzv3r1ha2vLCVK9e/eGnZ2dDPkDAOPGjQORWX5Bo9Eoor5MJhPatGkDBwcHtG/fHlqtVvaAfv78OSwtLTFgwADu/BMTE/l1qlSQ4/Dhw7C1tYW3tzeysrI4sUsIfy0uLkbNmjVRu3ZtlJaWwmQyITAwEBEREbLjZOFPFk6T5gKA1+Ja7HqaOHEi1Gq1DPDAVmFr165FTk4OXFxc0Lp1a9k1LES1fPXVV9DpdIpSCYA5tCn1PUTmimRSXfR/HLrEpA6dxYlVKhXHnxMRrwjk7e0tq2hUvXp12eyxqKgIM2bMgIWFBezs7PjMgiVQo6KiZJCs69evg8hMzhBafn4+n53Xrl1bVKOSyBx+kYoKMQib0MGtWLECKpUKERERHEURHx/P5T0B8LACS3yxmfuOHTtEyIv+/fvDzc0NBoMBR48ehaOjI9zd3fHTTz/xfZQSwSaTCZ9++ikvg8YcRceOHTnKoWXLlrLqSC1btkSbNm0AvHbmOp2Or1AeP34MvV4vwqLn5OTA2dkZ7dq14zeYyWRCw4YNERwcLLrpWGLr448/xtOnT+Hq6orQ0FDRA5FdK8Lfw4g9UrYgcwiMwcv2Y3kAZqz+KENRsXO2bNky0X63bt2CXq9HXFwcgNcPd6V6l1WrVkVISAgqKiq4gqcU0slWg8LPX7p0ic/8hcaSsePGjQNghh5qNBqZaBmD0W3cuBHZ2dlwcXFB8+bNRUlClm+6evUqjEYjpk2bBiIz3DcnJ4erfLLjTU9Ph0ajQYMGDXhY8cWLF7CwsMD48eN5vzNnzhSdb+A1oUcINbx9+zbs7OzQsmVLGAwG9OvXDzqdTjSm5eXlqFKlCjp27Mi3vXjxAg4ODiKST0lJCfz8/BAcHMwfwCyp+/nnn4vODUtwMlQL+92MfMTMZDKhX79+UKvVvNDM3LlzMXv2bFhYWMDBwUGUK/vHoUtMyhRlCm5jx47lxWCJzDVHz507B29vb+j1enz66acAzAPAyA/s9ZdffsnDLAMHDsTjx48xceJE2Nrawmg0Yv369Zw4kZqaymOwrOKQEAr27bffwsfHByqVCpMnT0ZxcTEvh2UymbB3716OBOjcuTOPYzNkSGZmJgwGA8en9+zZU4SZZvsJoWXl5eWiajiOjo4iHWkGxxMuQa9du4aaNWvC0tKSlxhj2GkWm79x4wavHBQaGopz585xtMG8efNgNBqRnp4OFxcXqNVqvPfee3y2yMoA5ubmokmTJiJnzmzSpElQq9X8Bp44cSJUKpWMyMTgfOzm/+2332BpaYmuXbtyJy+c5QJmVISPjw9/qAitb9++sLKy4jHWmzdvwtLSkrNfmQ0YMACWlpZ8JVNWVobg4GD4+PiIHqhdu3aFlZUVf+CbTCZERUXB3t5elOcYNGgQtFqtaNbItMiFD9b+/fvLnFbnzp3h6OgoCyHGxsbCycmJU+Bzc3NRrVo1BAQE8Dh1dnY2HBwcRA7v6dOncHJyQsuWLbmzYTr2bDVUWloKDw8P0efYfjqdDgEBATxZOWvWLC5Q1bFjRxklv1evXnBxcUFZWRmuXr0KnU6HgQMHivZ5+PChqMhzRUUFwsPD4eDggHv37vHf4u7ujkaNGvGH9969e0WrOmZshcRCWOy1kJhmMBhQv359+Pr68vvsyZMncHJyQnh4OL/XCwsLUa1aNTRo0ECUg2EsbwYnDggIQKdOnQCYJ3wsORwWFoYLFy7849Clxhw6y+DXqVNH9NQcN26cSFnv2bNnvPByUlISV9dLTU3FzZs3Oeutbt26otilFA53//59jpgJCwvDlStXcP/+fR6SycvL47oTAQEBohuUsVvZRV5aWoqlS5dy2OW4ceN48m7FihX8t02YMEGWwDMajahRo4YoZMSc/yeffII7d+7A2dkZwcHB/PsYTFCqA52dnc3Pzdy5c/kFn5ubyzHu9vb2WLt2LT8OpRJpL1++xJgxY6BWq3lN1L59+8LV1RVNmzaFTqdT1KB++vQprK2tMXDgQNy4cQM6nQ7Dhg2T7VdSUgJXV1deWq9x48ZwdXWVITIGDBgArVaLc+fOceSIUj7g7t27sLS0xODBg2EymdCxY0fY2dnJyv89ePAA1tbW6NWrF4DXM0ip43jw4AHs7OwQFRUFk8nEQwTSWpXPnz8XzYKzsrKg0Whkvzk7Oxtubm4IDQ1FRUUFZy0rJR5ZfJkhkwYOHAiNRiNTAGUrGhbbZclIYWLcZDLxhOrjx4+5g5euJgHzxMLFxUUGOxw2bJgixp/h2ffs2YPWrVvDyclJMRzavn17+Pj4iMhU0pqm7OHNrsGoqCh4e3vL7hVWDD4sLAyPHj2CjY0NunfvLvtOVmqPMZS7d+8OS0tLGceF5TpYPdCzZ8/CwsKCE8UAs9+wt7fnx8KQRG5ublCr1Rxs8Y9Df/1jeJs6daoMQiSlGQPmmRWL/zIYoL+/Pw+vpKamyi7CgQMHypKUJpMJO3fuhIuLC3Q6He8zLi4OPj4+lTI02Q0urUH47NkzjBw5klf0EYZMhAQGqTGR/fv37/OloXA5++2330KtVvOK5qNHj4a1tbUiO7K0tJSHqVhjJKM+ffrI0DhSmJrQLl68KIPVsRmfUuwYMOtoqNVqBAQEwNbWVhZzZDZt2jSo1WquhS4lbwDmkE3VqlURFBSEZs2awcfHRzG2DbxmfTJRLal2O7O5c+fyVZiVlZWiQwBeU/pTUlLg6uqK8PBwRXwzc5Lr169HixYt4OLiIiMlAa+dx5IlS9C2bVu4u7srkrwAM1yV6QcphWsA8z1Qq1YtBAUF8bCONFQDmJnHFhYW6N27N0JCQmShLoPBgDt37uDIkSM8/CZs8+fPx5YtW5CZmYkrV67wsIzBYOAVsIjkyU9mLGyxYMECXkVIyXr16gULCwseE58/f77ifiysxIThlOq9Ama/YW1tjeXLlyuG0ADz/R8ZGQlHR0dcv34dPj4+qF69uoi/whBlUr5BTk6O7D77q/Ymh64yv/8/b38u4f/LnxPKdDo7O1Pfvn1p0KBBFBYWRiqVisLCwsjR0VFUa9RgMND169dp5MiRohqZNWrUoBMnTpC3t7fse+Li4ujmzZuymoFERNnZ2TRw4EBZLcRTp05R8+bNZfsfPXqUoqOj6cSJEzKZ1by8PFq6dCktWrRItL1Lly7k7+9P/v7+VKtWLfL39ydvb2/SaDR0584dqlmzJvXu3ZsOHjxITZo0oaNHj4rkVFNSUmjy5Mm0YMECWr9+PYWFhdHevXv5+0ajke7cuUNXr16lq1evymqtTpo0iebPn0/W1tai7YWFhWRnZ0dLly6lpKQk0Xs3b96k/fv3y7Yz0+v15OTkRE5OTuTs7ExOTk5kNBrp66+/JiKzLOq8efNIp9ORVqslrVbL/3/y5AnNnj2biMyyvDt27FD8jszMTNq6dSsRETVo0ICmTp2quF9+fj6NHDmSv/7ss8/I0tKSNBoNb1qtlsrLyykmJobvt2PHDqpSpQoZjUYyGAxkNBrJaDRSRUUF9erVi+83f/58atCgAel0OlHTarUUExPDJZ2nTp1Kw4cPJ7VaTWq1mlQqFf/bu3dvfr1OmzaNxo4dSyaTid+87P/jx49TfHw8ERG5uLjQ6dOnycLCQtSfWq2mAwcO0IgRI4iIyM3NjX799VfSarVkNBrJZDLxNmfOHH5+IyIiqGXLlnTjxg36/fff6datWzJ5WmaOjo706tUr2XYrKyuqWrUq3bp1i2+bOHEi2dvbk62tLdnZ2fG/Wq2WOnfuzPe7ePEiOTs783PH/ubm5lJISAivVXv27FnS6XSUk5NDL1++5H+zs7Np5cqVvL9hw4aRXq8nKysr0d8nT57QsmXL+H579+4lvV4vG7/bt29T//79+X5nz56lJk2a8NePHj2iatWqUUpKCk2aNIkA0KlTp2j79u2UkZEhkgH+q75XpVL9CiBU8b3/3xz6smXLaMqUKbRnzx7au3cv7du3j0pLS8nf358GDx5Ms2bNovr169P7779Pv/76K507d44uXLgg00lnZmdnR23atKGoqCiKioqiwMBAUqlU1KFDB8rLy6PTp08TkXmgfvjhBzp58iSdPHmSfvvtN1lfDg4OFB4eTi1atKCIiAhq2rQpWVtb02+//UZ169al3bt3U58+fejBgwd04MAB2r9/Px0/flymX01EVK9ePbp165bouC0tLcnPz4/8/f3pwIEDfHtqaipVr16d9Ho9WVpa8r8JCQl06dIlIiKKjY2l0NBQ7sBv3LhR6Y3JzMLCglq0aEFRUVEUHR1NDRs2pMLCQnJ0dKSUlBQaP348nTlzhvbv308HDhyg69evExGRh4cHPXv2jPezfv16ys3NpdzcXMrJyeH/5+bm0vPnz2V66v/Y/y3T6XRUs2ZNql27NgUEBPC/NWvWpMjISD7un3/+OXXu3JkeP35Mjx8/pkePHon+7tq1i/dpbW1NxcXFf/uxW1lZie4hLy8vKi0tpZKSElkh9r9itWvXlrV27dqRg4MDjR8/nnbs2EF3794lGxsbiouLo/bt29OgQYNo/fr1lJiY+Je+8z/SoRcWFpKNjQ3l5eXR3r17afv27XTixAnRvtbW1tSwYUMKDQ2l0NBQOnXqFC/SoNVqaevWrfTjjz/S0aNH+ezBy8uLoqKi+Axl6NChdPLkSbpz5w4RmR8AERERFBYWRnPnzuXf1aNHD3Jzc6NTp05xZ6/VaqlRo0YUGBhI27ZtIyJzgYrz588Tkfli6NatG3Xr1o3S09MpIyOD/P396f79+3T16lXy9PSkx48f082bN2VN6YHyr0ylUlGNGjUoMDCQgoKCKCgoiAIDA6lOnTrUrVs3+uWXX6iiooIaN25MycnJdPToUfr222/p8uXLRGReEQUGBtKpU6eIyDzLy87OJq1WS23atKGuXbtS165dacOGDbR06VIaMmQIffzxx3T06FGKjIxUPKbVq1fT+PHjiYioXbt29PXXX5PBYCCDwUAVFRX8//Xr1/NVTEREBG3atEmxv+PHj9OoUaP48Z04cUKxEvudO3dEM8EzZ86QTqcTzboNBgMVFRWJ9tu7dy+5uLiIiixoNBpSqVTUtGlTMhgMRESUnp5OISEhVFFRIWujR4+mR48eEZF5xtiyZUs+42azbpPJROnp6cTukRYtWtDAgQNJpVKJZvFqtZqOHj1KO3fuJCLz9btw4ULF/n777TdKS0vjv2XdunV89q7RaPj/R48e5dd/jx49KCMjQ1ashIho27ZtNHToUMrIyKAZM2aQnZ0dnTt3TvF8HzlyhK907O3t6f79+2Rra0tFRUVUWFhIBQUFVFBQQIcOHaI5c+YQEZGtrS2tXbuWXwfCvxUVFTRz5kzef2pqKvn4+JCLiws5OzuTi4sLOTk50RdffEEDBw4kIvNE49atW2Rra0tE5hlyeXk5lZSU0IkTJ6h79+5EZJ44/fjjj3zlJWxPnz6lYcOG8e/t0aMH3b59m27evKk4aYyKiqLBgwdTjx49yNbWlp4+fUpeXl5/m0P//y6GXhkOXapbfeXKFVGS5Pbt27C2tkanTp04hCw9PZ2/f/fuXWzatEkmumVjY4MePXogNTWVozwA8Fjb4cOHUbduXS7CBJiThF999RWmTZvGk46shYWFYfHixaJkVE5ODvR6PUaMGIHff/8der0e3bp1U8R3A5AxGn/66SdcvHgRp0+fxvHjx3HkyBHs27ePq/IREVxdXSuNwTL698aNG/nvEhKpnj59ip07d3LBftb69u2LXbt2iUhKeXl5vOCEECamlCh79uwZx0CzCvOsMrzQXr16BTc3N0RERGDSpElQqVQyKCpgju+GhITAx8eHF/OojCMQFxcHGxsb/tuFSV6hLViwgMfG1Wq1KFchtE2bNvHYuLu7OyIiIhTH7/Lly9BoNBgyZAh8fHxQr149xTj/y5cv4erqiubNm6Nx48bw8vKSqQ8C5th4nTp1UKtWLfTp0wc6nU6x3ixD3jg6OqJ///5QqVSKEssM9VGjRg2eYFUi3pSVlcHX1xeNGjWCyWTisWqlcoL5+fnw8fFBYGAgh4Mq6d+UlJQgICAANWrUwNChQ6HRaBTF9wBwsSzGXFYav4KCAlSpUgWhoaGcz6AUazcajQgLC4OXlxfPhQiRa0IbNWoUNBoNR/Rs3LiR93H//n1ZmUYh2gz4B7YoMyWHfuXKFbi5ucHLy4vjv6XZ+3bt2sHOzg7379+HyWRCgwYNEBgYKEtclZaWiuoPSrWNAfPN5ujoyCVB2SAqXaSFhYWoWbMm708o4s+MOTN247DfuHv3bsXv9vDwQIMGDXD27FmuGyO14uJi+Pv7w9fXl/e/evVq2X65ubnw8PBA06ZNYTQaUVBQAGdnZxF+l9nt27dFjDglTWf2QGBSwvv27QMRYdWqVbJ93333XWi1Wly7do0LRSmJJU2ePJk7IObclbTGGewyIyODOyYfHx9ZMpgRqBi7sUePHrCxsZFJKTCUS8+ePQGAFwKWkm+Y/jlz4sy5S9EZJpMJrVu3hrOzM168eMHRGkq1VxlqSMg4lSqICs/3V199hSdPnsDOzg4x/6+9c4+Nulzz+Pfp9DItnSmXaUsvUC5SoOFaKLgQWIwb4XCRIAc9JEd3FT3xbCBGCcrGhJBjjG7iqtE90UOyxvUQxMXokeQcJeyiqaAiVo4KCAaKxUqhKNPSoe20nfnuHzPv29/M/KYzSDtDy/tJmszlmbfv87s8v/f3/J7Lr34Vs21U1M9LL73ElpYWjh49Wu9zKyof4s033+TPP//MoqIi1tTUxESQqPht1YvA7/ezvLycixcvjpnjxo0bKSI68mvp0qUsLi6O2S/btm3TUTUXLlxgfn6+juO30tDQQLfbrWPTV65caRvSqWLdVRTc6tWr6XK5YkrlqofJr7/+OgOBgE4qiw69PHr0qM7FsJbetT7UVhmvmzZt4qhRo1hWVhZh1I1BjyLaoFuN+alTp9jU1MSMjIyIhJAdO3ZEXE3J3sQBax2WYDCoE3T27NmjwwGtMiS5efNmikhEMs2dd97J/Pz8mNC3Bx98kCLC/fv36xWuNYQvEAhw0qRJXLBggf6su7ubc+fOZWFhYUTbNLI3llkZ/y1bthCIze5UXVj279/PYDDIFStW0Ol0xmRTbty4kRkZGRErXpVMYo0H7+zs5Jw5czh8+HDW19dzxYoVzM7Ojvid3+9nWVlZRAu7YDDIO+64gwUFBRG6KCO1ZcsW/ZlaLVsTtU6dOsWsrKyIi5ZqwWa94Hm9Xno8Hi5evFgbM9Wdx5pZ2N3dzenTp0fEHZ85c4bZ2dkxyWbr16+n0+nUceiqPZk18UntA+uKt6enh9XV1SwvL49oOKFKD6iT2bpqtm4bZTisi4kNGzYwMzMzYv+dP3+e+fn5ul0a2WvgrcdYe3s7x40bx2nTpum7AXX8W6NNrl69yrKyMs6bN0/rp6I2rIuB9vZ2lpaWxtyFqE5H1pLNBw8epIjoJCfrfrGGdR4/fjwmNl0dh9aw5EAgwNtuu435+fm6bLBKurImi509e5ZOp1N3kVL/IyMjI6LTUFtbG0tKSlhTU6MvbuouwlpWIRgMctGiRfR4PDrXQt1tPfzww1pu06ZNzMnJYUtLC7/66qsYo24MehRWgx5tzBXLli3jmDFjGAgE+MMPP9DtduuC9oquri6Wl5frcrNk76pDXQw6Ojo4Y8YMFhYW6nC6+vp6Zmdn8/7774+Yl4qjtn6uasuo8drb21ldXc2CggK9g1WM+M6dOyPG+/rrr5mVlRWR7KK6rVhX+T6fj+PHj+fkyZN1COeRI0eYkZERYQSbmpo4atQozpkzR7s/6urqYgwHGXIBuVwu3n333fozVbVR1ar+6aefWF5ezokTJ2qXi1rdRd92nzhxQhcEI0MnZU1NTcwqKBAIcNasWRw3bpzWZeXKlXS5XBEx5z09PZw1axbHjBmjDaZKSvryyy8j/vfatWuZl5enk4jiZQaqTEDVH7a2tpZAZBlmsrckrfq93X63+71KTJk9e3bEaldtG2t5goULF7KwsDAipb+5uZnDhw/n7bffro/je++9l9nZ2RErwK6uLk6dOpUTJkzQLkBVEO3DDz/Ucta7BbViffrppwkgoktQMBjk0qVLmZ+fz3PnzpHsjWm3jkeGLggej0cn1nR0dHDKlCmsqKiIKKEQDAY5f/58jh8/nt3d3QwEAly4cCFHjhwZcWHz+XwcPXo0FyxYoHVWrrToGuZr166NWH2vW7eOubm5es6KBx54gNnZ2TpRSblOrHkjatvm5OToZDF1LkeHWz7yyCPaBej3++nxeCLOm2ijfkMYdADLAJwCcBrAVpvvcwC8Ff7+MIBxica8XoN++PBhW2NO9sayHjhwgCtXrmRubq5t/Kkaq66ujrW1tczMzOTy5csjTrjjx4/T6XRy6dKlDAQCXL9+PXNzc2Nuz8lI18Dp06fpcrm4cOHCCB/p2bNnOXLkSE6bNo0+n49r1qyhx+Ox7bykTsT33nuPXq83bp2PDz74QBsPv9/PGTNmsKSkJOYWVN3ib9u2Td9aFhcX2xYw27p1K0WEJ0+e1L+L7spz6NAhOhwOrlu3joFAgFVVVZw5c6at71gZ3Lq6Ou2SiL6Ikb0Fm1588UWtl7WWvEJlzG7fvp3ffvtthFG0Ul9fr2uRqNodS5YsiZnjlStXWFJSwvnz57Orq4szZ86MuGAo1Ap/7NixvHr1KletWhU3fv6ee+6h0+lkQ0ODjnc/ePBgjJx6LnDkyBF9+29XQExdTPbs2aN9wtGlCcheF+BTTz2lV6pWI6M4duwYHQ4HH3roIV68eJEul0u3m4vehrm5uVy9ejXb2tro8Xji1kJSF4WjR49qY2mXmKRccbt27eKrr76qXR7RKDfaO++8ozOEV61aFbP/jh07pvuPqu5bdnH2586d00ll6tiIzlglQ1mrw4YN41133UWfz8eysjJWV1fHuJ68Xi+Liop46623ap2i7+itRl2VjkibQQfgAHAGwAQA2QC+AlAVJfOvAF4Nv/4NgLcSjXu9Bj0vL8/WmJOhlYJq6AwgbpJOS0sLXS4XFy9ezKKiIlZWVtoaN5Xeq9qjqZZhduMVFRVx3rx52j2hSnta2bdvH0WEixYtYkZGBp944gnb8fx+P6dPn86SkhKuWbOGDofD9kEW2ZsurtqCxev6ct9999HhcOhOOdEt7RQXL15kbm6uLoBVU1NjWzBKZcGqjNvoujbR26aqqoqjRo2y9YGTvckbBQUFLC0t5S233GJ7sSOp+3JOnz6dbrfbNvOQ7F2F1dTUaL+0HSrpR7W8i/dAVbkMlJy1472VhoYGOp1OXZTMznCQIR98cXExq6qq9PFjl5TU3d2ty7tWVlayvLw87oPutWvXMjc3lzU1NczLy4tZqSqU+3Du3Ll9PoRU+1nV71F3MtF4vV663W5OnjyZmZmZcQtaBQIBTp06lYWFhXS73TFuLKvOU6ZM4YQJEzhjxgzbDGGFKtVQUlKiL7h2qIVXZWUl8/LybBdnZO8DcVUe1+5iTPYeNwBYVFRkGwCgjLrD4RhQgx4bixTLPACnSdYDgIjsBrAawAmLzGoA28Ov3wbwnyIi4X/er7z88ssAgPb2dixZsgSvvPKKrVxbW5t+ffbsWTz66KNx5WprawEAs2fPjghFVCg1VGhYU1NT3PHa29vx+eefAwAqKirwwgsv2MqRxMcffwwAOHnyZNzxCgoK8M033+Ddd98FAOzcuRM7d+60le3u7sauXbsAhML3PvrooxgZh8OBQCCA1157DQDwxRdfoK6uzna8jo4OvW1KS0ttk3SCwSAA4P333wcAfPLJJzhy5IjteJ2dnThxInTYZGZm4rHHHrOV6+rqQmtrK1pbWzFx4kRs3bo1rr4dHR06+euZZ56xlfP5fACg56USj+LpopJ5Dh06FJGIFo367vvvv4+7/zo7O3XoYXd3d1y5rq4um4OoqwAABtZJREFUvW0qKyuxefNmW7msrCwdt19aWhoRuhetS0dHh9b5+eeft5Vra2sDST1HFdYbjcqVUHK7d+/G7t27bWWvXLmCK1euAAgd5/F0vnDhArxeL4BQiHFfx4MKG66oqMCzzz5rK9fS0gK/34+mpiaUlJTgySeftJW7fPkySOK7774DADz33HO2cioM8dNPPwUQirN/++23Y+TUcQMAzc3NePzxx23HmzlzJg4cOAAAeOONN35x2GJfJIxDF5FfA1hG8sHw+3sBzCe50SJzLCzTGH5/JizzU9RYvwPwOwAYO3bsnIaGhmufsCXG1e12x5VTB1Sq5a5evYpAIJBQLhgMakOTn5+PjIyMlM0xXXJ+v18nM/UlZx3T5XLZxjUP1BzTJdfV1aUTXZLdNsOGDYPD4UjZHNMl19PTo5OQhtK2+aXr3b7i0JNZofcbJHcA2AGEEot+4RhoaWnB8OHD+5QLBoO4dOkSiouLE47Z2NiI8vLyhHJNTU3weDwRKfZ2NDc3w+12w+l09inn8/kQCARQUFDQp1xnZydaW1sT6kISjY2NtqUMojl37hzGjh2bUC7ZbXPp0iW43W7k5OT0KXf58mXk5ORg2LBhfcq1tbUhGAwmtW18Ph88Hk+fcsFgEE1NTSgrK+tTDkhe58bGRpSVlfV5wQGAixcvYsSIEcjOzu5Tzuv1IisrSye+xKOtrQ2BQCDhOeD3+9Ha2oqioqI+5Uji/Pnz/bptfvzxR5SWlibcNs3NzRgxYkTCcyrZ4+ZazqlkjhuSOp0/ERcuXEBxcXFCnb1eL0aMGJFwvF9CMiv0fwCwneTS8Pt/AwCSz1hk9oVlPhWRTAAXABT25XL5pZmiBoPBcDPT1wo9/n1+L0cATBKR8SKSjdBDz71RMnsB/HP49a8BHBgI/7nBYDAY4pPQ5UKyR0Q2AtiHUMTLaySPi8gfEHrauhfAfwH4s4icBnAZIaNvMBgMhhSSlA+d5N8A/C3qs22W150A1kX/zmAwGAypIxmXi8FgMBgGAcagGwwGwxAhbfXQReQSgGsPRA/hAfBTQqmhhdH55sDofHNwPTpXkCy0+yJtBv16EJEv4oXtDFWMzjcHRuebg4HS2bhcDAaDYYhgDLrBYDAMEQarQd+R7gmkAaPzzYHR+eZgQHQelD50g8FgMMQyWFfoBoPBYIjCGHSDwWAYItzQBl1ElonIKRE5LSIxXQ5EJEdE3gp/f1hExqV+lv1LEjo/JiInRORrEfk/EalIxzz7k0Q6W+TWighFZNCHuCWjs4jcHd7Xx0VkV6rn2N8kcWyPFZEPReRo+Pheno559hci8pqINIf7Rdh9LyLyUnh7fC0i1df9T+O1Mkr3Hwao9d2N/JekzrcByAu//v3NoHNYzgWgFsBnAOame94p2M+TABwFMCL8vijd806BzjsA/D78ugrA9+me93XqvBhANYBjcb5fDuB9AALgVgCHr/d/3sgrdN36jmQXANX6zspqAP8dfv02gNslUXX5G5uEOpP8kGR7+O1nABJX3r+xSWY/A8BTAP4dQGcqJzdAJKPzQwD+SNILACSbUzzH/iYZnQlAtfspAHA+hfPrd0jWIlR9Nh6rAbzBEJ8BGC4iJdfzP29kg14G4AfL+8bwZ7YyJHsAtAIYlZLZDQzJ6GxlA0JX+MFMQp3Dt6JjSP41lRMbQJLZz5UAKkXkkIh8JiLLUja7gSEZnbcD+K2INCJU3XVTaqaWNq71fE9ISlvQGfoPEfktgLkA/jHdcxlIRCQDwPMA/iXNU0k1mQi5XZYgdBdWKyLTSbakdVYDy3oAr5P8j3CntD+LyDSSwUQ/NIS4kVfoPwKwNscsD39mKxNufVcA4OeUzG5gSEZniMg/AXgSwJ0k/Sma20CRSGcXgGkAPhKR7xHyNe4d5A9Gk9nPjQD2kuwmeRbAdwgZ+MFKMjpvAPA/AEDyUwBOhIpYDVWSOt+vhRvZoN+Mre8S6iwiswH8CSFjPtj9qkACnUm2kvSQHEdyHELPDe4kOZgb0iZzbP8FodU5RMSDkAumPpWT7GeS0fkcgNsBQESmImTQL6V0lqllL4D7wtEutwJoJdl0XSOm+0lwgqfEyxFamZwB8GT4sz8gdEIDoR2+B8BpAJ8DmJDuOadA5/8FcBHA38N/e9M954HWOUr2IwzyKJck97Mg5Go6AeAbAL9J95xToHMVgEMIRcD8HcAd6Z7zder7JoAmAN0I3XFtAPAwgIct+/iP4e3xTX8c1yb132AwGIYIN7LLxWAwGAzXgDHoBoPBMEQwBt1gMBiGCMagGwwGwxDBGHSDwWAYIhiDbjAYDEMEY9ANBoNhiPD/AoG/EYx17MsAAAAASUVORK5CYII=", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "parameters = {\n", " 'geometry': {\n", @@ -485,43 +489,19 @@ "ax = plot_mesh(mesh)\n", "fig = ax.get_figure()\n", "fig.savefig(f\"mesh.png\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 215 - }, - "id": "Jry6z_ZeKPPk", - "outputId": "c4eda8fe-b180-488d-a7ce-42be84997026" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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UVEtj9Oabb8LZ2Zlrq0xwb9q0ScCn1+sRGRkJd3d3lJSU8AXaVOgwGjx4MCwsLPDw4UNotVqEhITA19dX5Gvp2bMn7OzskJeXh6qqKjRs2BB169YV7HzKysrg4uLClRWtVouwsDB4eHjg+fPngvaY2YxpsDqdDkFBQWjcuLHoHseMGQOZTIb4+HjR4gwYx0KjRo1gKhPYOD979qyIt1WrVqhVqxaeP3+O58+fo1atWmjVqpXousOGDYNarcb9+/eh0+nQpEkTeHt7i3Z7bLHct28fDAYDoqKiJG3rpqZSQLjDYfTtt9/yxclcmAM1Al1EUgIdeKUhs8ImDWA0c/j5+aFRo0ZcM1yzZo2obWbnvXHjBncymmrIjAoLC2Fra8s1FNbmnj17RLznz5/nNuFRo0ZxoW1Oubm5cHBwQMeOHTF16lTIZDL8+OOPIr6SkhK4ubmhRYsW3D768ccfi/i0Wi0aNmwIf39/fg9z5syR6NFX286jR4/CwsICgwcPluRjZolTp07B19cXISEhks62r7/+GkSEDz74AE2bNoWnp6ekw9J0MfnHP/5RrV390aNH0Gg0GDBgAF+wTbVARqb2TLagSdmYKysrUa9ePTRo0ACHDx8WafumNGbMGCgUCty+fZsvTuZjp7CwkGuOrLz//vsixyF7D1u3bkVBQQFcXV3RokULSTv91atXQUSYPXs2N6FJ+WMeP34MKysr9O3blz+zlB08JSUFcrkcCQkJ3Exw5MgREd/ChQtBRLh9+zZfIE13rabPbG9vz3d8zExnuhtldOPGDUHfpKeni3jYfLt27RquXbsGhUKBd999V8QHANeuXYNMJsP06dMxffp0yGQykUkVMGrQNjY26NmzJ3dwSjn3dTodwsPD4enpyXcpUvMeAN544w3Y2triwIEDknMqLy+PP6dcLhcIc6BGoIvIXKAbDAbcuHEDU6ZMEQwauVyOevXqoVevXpg/fz569erF/2duu2VUWFgIKysrtG/fHtbW1ujcuXO1CIuZM2dCJpPhzp07CAwMREhISLVedVPUxuTJk6HT6VBeXo6SkhIUFBTg6dOnyMzMREJCAudr164dMjIykJ2djfz8fBQXF6OsrAw6nQ6fffYZ52OasBSdPn2a83l4eKCwsBBlZWVc68vNzUV2drZowiUlJSEzMxPZ2dnIyclBfn4+ioqKkJOTA1dXV8537NgxlJSUoKioCPn5+cjJyUF2djYeP36Mhg0bcr4PPvgAOTk5KCwsxIsXL1BeXs77f+zYsZyvOqchAC7wiQidO3eGXq9HRUUF78Nnz54hMzOTT1wiQq1atXD//n08evQIT548wdOnT5GXl4eioiLuP2B9yLbR5pSXlwdHR0d06NABPXr0gJOTE0pLS3H//n2sXr0abdu2hUKhEPSfaQkNDUViYiJu3LgBg8GA0NBQhIaGYuTIkVAoFCI/hykNHTqUt/N7iKlFixYJxk11Y3bEiBGcLzY2VpIvPz9fMAfeeOONattj5rWrV68iNDQU9evXh06ng8FgwK1bt7BgwQKEhoZK9ktgYCAmTZqEb775Bi9fvkRRUREsLS0xbNgwhIaGwt3dvVr0CQAMHz6ctzV8+PBq+ZYtW8b5WrduDa1Wi7KyMhQXFyMvLw9ZWVl4+PAhN/cREdRqNW7cuIHk5GTcvHkTt27dwu3bt5GSkiJSHOfOnYthw4YhKioKLi4uouecMWMGbt++ze/nvy3QZcb//99Ts2bN8GfS537wwQc0Y8YMSktLoyNHjtCuXbvo1q1bpFKpqFu3bvT1118TEVFiYiLdvn2bUlJSKC0tTYCNJTLi4J2cnHhxdHQkJycnWrFiBefZtWsXhYWFkbOzMzk7O5NKpeL/y8nJIR8fH6qqqiKDwUCHDx+mnj17Um5uLqWnp1N6ejo9ePCA0tPT6cyZM/Ts2bM/2VOvJ41Gw3wdpn4PAvCHsbN/BclkMjIffzY2Nvyz6XOUlZX9V+8lODiYatWqJVkOHDhA+/bt47z169fncQQNGzak7t27U48ePcjd3Z18fX1py5Yt1L59ezp69CgdPnyYkpKSyGAwUN26damwsJBjxGNiYmj27NmUl5dH+fn5or/JyckCPHl1QWXl5eWC8W1vb08KhYLkcjnJ5XL++enTp5zPwcGBmjRpQra2tmRra0s2Njb884IFC3hb27Zto/DwcHJyciJnZ2eysrLiWPoXL16Qn58f5efnExHRxIkTydramg4dOkT3798nmUxG0dHR1Lt3b0pLS6ONGzfSjRs36MKFC3Tq1Ck6f/48VVRUkIWFBcXExNCZM2f4dadPn06tWrWigoICys/PF/1NSUkRxCFU1zcVFRUiPPl/mjw8PCgwMJCX6dOnExFRbGwsnTp1ivR6PYWHh9PQoUOpXbt21KRJE9q4cSONGTPmT11PJpNdB9BM8n//awLdPDCjRYsWFB8fT/369SNnZ2cKCwsjT09POn78OOd5+fIlNWvWjE/Cpk2bUkhICBUWFoqKVqut9tp2dnZcuLu4uNC3337L/xcSEkIPHz4UBC3IZDKqU6cOvXjxgp4/f05ERK1ataLOnTuTSqUilUpFarWaf87MzKTFixfz32/dupW0Wi1ptVqqqqrin3/66Sc6duwYERGpVCpKSEggmUzG+4Z9Li8vpzVr1vD2Fi1aRGq1mpRKpaDodDqaMOFV1oYPPviAHBwcSK/Xk06nI71ezz/PmDGD840fP558fHxIoVCIyujRo7lAHjBgAEVHR5NWqyWdTsefQ6fT0f79++nevXtERBQUFESxsbGCd8w+FxYW0vbt23n9ggULBH3HPp8/f5727t1LRET16tWj+fPn82fQ6XS8ZGRkCPqmT58+lJuby0tRUVG146B9+/bUs2dPiouLIz8/P15///59CgwMpF27dtGQIUN4fW5uLh0/fpwOHz4sGJfmpFAoyMXFhVxdXcnFxYXu3LlDubm5REQUFRVVbRRnfn4+7dy5k3+fNGkSGQwGMhgMpNfr+eeDBw9yIRgZGUkqlYpKS0vpxYsXvJSXl1d7f0REarWaC3cnJydB0A8RkVKppHbt2lGfPn2oZ8+ePJp77ty5tGLFCkFwVXl5OX3//fd06tQpOnToEGVkVJvmmywsLMjV1ZXPvStXrvBFvm3bttVG/z579oz27NnDvy9cuJDUarWoPHnyhObPn8/5du3aRZaWliINOCcnhyZPnsz5fvjhB4qIiODftVot2dnZ0dixY2n16tWUm5tL+/bto127dtH169cF9/ZnZe/vCfT/OZMLmWxn5s+fL9oOxsXFoXHjxvy7VqvlAQfz5s2DhYWFwMllSrm5uQLH1uHDh/HFF1/gk08+wcKFCzFp0iQMHjwYXbt2hY+Pj+BegoKCkJCQgHXr1uGbb77B3bt3udNu+PDhkMlkcHBw+N3t/YQJE6BSqTB69GgQCRENjCoqKhASEgI3Nzf4+/vDz8+v2qCR5cuXg4j480thkYFXTt5Vq1ZBqVRWizFmZoqpU6fCysqq2m07C9iaOHEi/P39ERgYKBkQlZeXh1q1aiEkJATe3t5o1KhRtYEtbHvftGlTyOVyEdYXMJrMPD09ERwcjI4dO8LFxUUSOw+8ciYGBATAzc1N4HMBjHb2rKwsbvc2La1ateLOQFO6detWte/t9u3baNeunaitU6dOIS0tDYWFhYK+ZNA7e3t72NnZoXXr1tWaPhg+OyQkBGq1WgQnBIymlNq1a/PArrVr10q2VVpaCkdHR35/e/fuxVdffYUtW7ZgxYoVmDlzJkaOHInevXsjIiJC9DzVBapNmjQJ9vb2onqDwYAvv/wSderUEbTTuHFjXL9+HRkZGSgtLRU8O3O8e3h4wMbGBt27d5e8JmCE8CoUCnTs2BEymQznz5+X5OvXrx+srKzw+eefQyaTVYuLHzZsGFQqFc6cOYPatWvD398fBQUF/P/Xr18HkTh2Qq/XY+rUqYJn/LNEfycb+tKlS/ngJSJ06NBB4GUeM2YMnJ2dARiF+cCBA0FEWLFiBQCgR48e8PT0lLR3Dx06FCqVituoP/jgA8l7ePjwIQIDAwUvx8XFBT/99JOINzc3FxYWFhg9ejSP0ly+fLmIr6ioCNbW1oiPj0dVVRWaNWsGJycnEeb8vffeA5ERocGQJgwjb0oFBQWwt7dHXFwc9Ho9OnToAGtra5Gj7tGjR7C0tETfvn0BvEIvmEfD5uTkwMnJCS1btoROp+NIEXNHsGnAVmlpKbfjm2PYAWNkn0qlQnJyMg9skQoGKS8vR+3atdGtWzc8f/4c3t7eCAgIEDnGhw4dCoVCgWvXruG7776r1lZ55coV0G8OLca3fv16EV9eXh7Cw8OhUqkQEBAApVKJ7du3w8XFBQqFAlOnTkVJSQnn/+mnn7hvgVFxcTGmTp0KpVIJR0dHbNiwAUqlEv7+/lAoFIiKihK0wYjZajdv3oyNGzeCSNoRXFpaCicnJ/To0QPPnj2Dk5MToqKiRON7yJAhUCqV+OWXX9CsWTM0atRIcoFgTtEtW7ZAqVQKoHumVFRUhFatWkEul4PIGHlsa2sLpVKJKVOmiOzf77zzDurUqSOoM13kmjRpwn1I0dHRIDIio6TukfkMzp49yxd6qbmXlZUFCwsLvPPOOygtLUVAQAC8vb1FqCcGm/zHP/4B4NUcYwgxc74ZM2bw72q1Gh06dOBKFXOqms6zlJQUtG7dGkTGaFwiMTrsX6G/lUBnTtHi4mJ88sknsLOzg4WFBRYtWoSKigou8EtKSrgwNxWgTMs0R1MwwTN//nwAxkAjNzc3kWaZnJwMd3d3ODg4cDjkqlWr4OPjAxsbGxHMig24lJQUAEb4mJWVFQ+qMH8uhmRITU2FRqMRYFwvXrwo0B70ej3q168vCRVj3v9bt24BMMIObW1t0aZNG8Fk79WrF6ysrHigRXl5OQIDA+Hr6yvQbgcMGAC1Ws2fQ6fTITIyEs7OzsjNzeV8bFdgKnwGDRok0hxZ6PeiRYsAGDW15s2bo06dOiJtmUXTsr5lEZOm0EC2uLGFg7UXEBAgCl5ikMAXL15wGJyXl5dg55SdnY2GDRtCo9HgxIkTfBdTWVmJ/Px8jBw5EkQET09PHDhwAAaDge9MTp8+DYPBgF27dsHNzQ0ymQwjR45EXl4enj9/DiIj9O3gwYNQKBRo1aqVQKjrdDo0bNgQgYGB0Gq1qKqqQmBgIIKDg0W7MYb6YdGYDAppKjDY4sD6hsH0zFFULIqSQVNHjBgBCwsLEZwzOzsboaGhUKlUOHDgADQaDWbMmIFnz57h3XffhUwmg4uLCzZu3Mjv96233kKDBg0AGGNDpkyZAoVCwRc5nU7Hg3eKior4LpXNR/N7fOuttwAYF0xHR0dRXAhg3PEqlUqOqrly5QrkcrkgjoPthNzc3DgSq7KyEuHh4XB2duaQZQZ7NEdsMeWPwX7ffvttuLq6wmAwoKysDPPmzYNKpYKjoyO2bt2KrKys/6pT9H9WoDPtLDs7G/369eNmD+b9bt68uaQ2/Pz5c6jVaiQkJPC6ly9fws/PT2AaYEJjw4YNnO/ChQuwt7eHp6cnbt++zaFl33zzDbKysviWl8G8qqqq4OHhgU6dOvE2mEZsGtSk1Wrh5eUlSinAcpBs2rQJJSUl8PX1hb+/v2BAsRDwEydO8LqMjAxYWFgIBi7wSjCy7TYL0DENQgHAtVYWkMOCaZjwZcRy3AwaNAgA8ODBA1haWqJXr14CvmfPnsHBwYHjqJmpJSwsTIDQYdc1fWcGgwHBwcFo0qSJYNFiqKaTJ08iNzcXrq6uovakwtnZgr5jxw5ex/qBBatkZGQgICAA1tbWOHfuHIBX2PXCwkL+uytXrqBx48YgInTt2pULynXr1nEts3nz5gLBmZaWBqJXcMoDBw5AoVAgOjqaC3WGuDCFALJcKtu2beN1LKdM69atBf3VpUsXWFtb49GjRygsLIS7uztCQ0P5gvX8+XNYWlqKkDNvvvmmYHF/8OABlEqlIKgoPT0dfn5+sLa25rs4Ozs7wXz6+eefERMTAyJCo0aNcPbsWXTt2hXNmjXDzp07ueln1KhRgvxJY8eOhaOjIwDjwstQOaaBQOb3CLxSmkz7+cljUqwAACAASURBVPHjx1Cr1Rg5cqTgGdkOlGnfDG5pHhGcmpoKS0tLdOjQAXq9nsMqpWCPbCxu2bIFDRo0QFxcHM6cOYOAgAAQGXPz5OTkAKiBLYqoOhz6yZMn4evrKzCDSOU9AYyBJHXq1OGa6qxZs0BEAvsaW7nr1q2LqqoqHD58GBYWFqhfvz7XrlkEHhvYhYWFiIqKglwux+bNmzk22TQqFHhlNmKBLV988QWIjAExpqTX69GxY0dYWVmhbdu2kMvluHTpkoCnsrJSNKmHDRsGCwsL0S7AYDAgNjYWlpaWuHnzJgICAhAYGCgK0AHA83CcP38enp6eCAkJkbT9M/vt8ePHERsbCxsbG8FkY8TghDt27BCYWswpLi4O9vb2PFDqm2++EQhARuXl5QgODoa7uzvatm0LtVrNdyOMtFotfH190bJlSwCvUg6YJ24yGAxo2rQp/P39cffuXXh7e8Pe3l6Qg4RFI5r3qVarxZo1a2BraysYe87OztiyZYvI9MHGzKlTp3jd/v37uVDPz8+Ht7e3KI+MwWBAZGQkPD09+c7JNKeMKT169AjW1tbo2rUr4uPjoVAoRMmvzMPt2aJmPmfeffddqNVqPH78GDdv3oS7uzucnJwEtnJnZ2dBpDK734MHD4p8TUTGpGJSJpKuXbsiPDycf9fr9Rg2bBiICEuWLKn2HouLi+Hk5IQ33niD140dOxYqlYpHNjOqrKxEWFgYXFxckJmZCV9f32p9N59++imIjNGmDg4OgmhzU9JqtejcubPoOQMCAmpw6K+j6gQ68EojY6V58+ZYu3YtXx0ZsW3p5cuXkZycDIVCIYllZZopsxVGRkYKIjKZRsnyfwBGbb9bt278HhwdHUWTuqKiAkFBQfD390d5eTkiIyMREBAg4issLOTBIqwtKWHJTD+XLl3CzZs3edCFFGVlZcHBwYG3uWvXLo4df/ToEdLT05GWlsbtzKzs3LkTaWlpSE9PF+C6nzx5wjNbMjtkYWEh8vPzkZeXh5ycHDx79gxZWVmCBddc22d0+/ZtyOVynr+jffv28PT0FGHt9Xo914iJjD4V88kLvNrlJCUl8SRY5osi8EpTIzL6Q8wFIFt0mcnJlLRaLdf8WNm8ebPk5GemIfMgoS+//FKAZ5fK6MlC15ctW8ZzylRnC2fmCyLpjIfMPPTZZ5/xsPZ69eqJFveHDx9CqVQiNDQUDg4O8PDwEOCqAcDd3V2kCTPKzMwU9EutWrVENmxG9evXR58+fQR1Op0OQ4YM4b+vXbu2pGLBlKQffvgBjx49gkqlwpgxYySvk5KSAgsLC97mV199hezsbNy7dw8//fQTzp07h6NHjwpiFYgIPXr0wKRJkzBq1CjEx8fjrbfeQvfu3dGpUyfu02NlxowZkkCAGoFuRlICPT09HXFxcSAiWFtbg8gYuNCkSRMQGdNfxsbGYu/evXj58iU3u0yePBkRERFwdXUVeKqzs7Nx5MgRQQpSIhI5r/75z3+CiHDhwgVBPXOOmS4so0ePxqeffoqffvoJ5eXlPOdG165d+Ta9oKAAR44cQUJCAsLCwjgiwbwEBwdj6tSpOH36NMrLy/Hy5Uu4uLjgjTfe4Bqu6fPk5ubi1KlTWL58OTdP/dWlWbNmmDRpEnbv3o20tDSBUBoxYgRUKhW3s7MozqKiInz55ZeIj48XBDiZFi8vLwwaNAibNm1CSkoKXrx4AUdHR4SHh/NoU51Oh5SUFOzcuRMTJ05EixYteOpaIqNTzlwTZw5tlqCK0alTpxAcHCx5L23atBEJbubgNHd2379/X4CwioiIwLRp03D48GGBj4K9X/O8N5WVlfj++++xcOFCxMTECNpSKBQ8WpLZww0GA4KCghAVFcWDb0zzq5SXlyMpKYlHlbIihaCpW7euCBlVWVmJVatWwc7OTnAvRAR3d3fs3LlTtAPRaDSYNm2aoJ0XL17wCG5WYmJiMG/ePJw+fZrLAaalx8bGYuTIkVCr1QLlR6fTITU1FXv37hWkXf5XilKphL29PWrXrg0fHx/Ur18fTZo0QcuWLUXj0dPTE6tWrRJFSNcIdDMyFehlZWVITEyEhYUFbGxs8OGHHyInJwdEhI8++giA0c47a9YseHl5gYhgY2ODt99+G5aWlrzz3333XSxfvhy9e/cWwKfMB2JgYCC2bt3KtRiWppc5WFNTUyUFZvv27QVasVKp5LZXVvz9/bkA12g0aN++PRYuXIgLFy5w7e+XX37Bhx9+iE6dOnEBZGlpidjYWMGAioyMxPz58xEXFwdPT0/Bdcy3wBEREdi4cSM2b96Mbdu2YceOHdi1a5cg4pItOLt27cKOHTuwbds2bN68GRs2bMC6detgZ2fH+Xr06IE1a9Zg7dq1WLduHdavX48NGzZwDYqV1q1bw8rKin93cHBAp06dMHfuXI4UYGXOnDmIiYnhGqyjoyMGDhyI3bt3o3v37nB0dMQvv/yCdevWoV+/fnBzc+O/dXZ2Fj2/jY0N/25tbY3WrVsLIo1VKhVUKhXGjRvH8/2w3RhzzN69exdvvPEGiIx56r/66iuu0aWmpmLTpk1wdnbmtmImlBmKhO04bty4gf79+0Mulwty7kdHRwsWmaCgIIwYMQLTpk0TPM/SpUvRpUsX3pcymQxNmzblSA0iwrhx41C3bl3+vUmTJpg7dy569+7N60JDQ7Fnzx5MnDgRzZs3F9yLafHw8MDatWsFjuuAgABBiuoTJ05wFFhsbCzu3r0Ld3d3jBgxAlevXuVwx8jISL5AMkHH0Eb5+flITEyEk5OT4PotWrRA8+bN+VhQKpWIjIzEe++9h6ioKM7XqlUrbNu2DePHj0dUVBRX9IhI0K9EhLi4OGzcuBG7d+/G0aNHcf78eVy7dg1paWkYNGgQ5zNNHmZKmZmZgnk2efJktG3bFkQEJycnJCYm8p19jUA3I9N0tmwLP3DgQK55VFZWgshoczMlvV6P8+fPY8SIEQIBZFoCAgIwaNAgrFmzBpcvX0ZZWRkaN26MLl26YP/+/QgLCxOsvszE8+WXX2LYsGGQy+WwtrbG3LlzUVRUBB8fHx7ObjAY8ODBAxw8eBBz5sxBly5dRNdfvHgxLl68KNr2MmFoWl9aWooTJ05g0qRJIgglkTH1QXBwMAYPHoxVq1bh3Llz3KHHUgq3aNECKpVKEpvL8lB/+umnUCgUGD9+vOT7YPmxe/ToAX9/f/j6+oq0kvLycrRu3RoWFhbo0aMHiAhZWVnQarW4efMmtm7dilGjRiEsLKzaMPrQ0FDMnj0bly5dEiA9hg8fLoLDGQwG3L9/H5999hnefPNNQTs2NjaYMGECduzYgZSUFG47NVUEMjIyMHr0aCiVSlhYWGDSpElcQ9++fTsSEhKgVCpha2uLlStX8vfCEA8sH3xhYSHntbe3x6pVqzBy5Eg4OTnh4sWL3DRna2uLGTNm4OnTp+jWrRu3I5eXl+PSpUtYvnw54uLiBPhw0xIcHIwJEybgq6++EuzMAgMDOWLFYDDg9u3bWLFihWBxNC/MXzNr1iwcOXIET58+5ff8z3/+kx/g4ubmhtWrV+Ply5cIDg5G3759kZqaynecQUFBAtu+nZ0dR4Lo9Xrs2LGDL7zDhg3jpqiNGzdi8uTJfIHq0aMHkpKScO3aNRC98jOVlJTg22+/xZw5c0SLn/n7jo6OxqRJk7B9+3YkJyejqqqKzylXV1e4urqKdmTsGnZ2dujfvz9at24NZ2dnQf+ydxwcHAw7Ozv+DMzhfuXKFT7era2tMWXKFP4cNQL9NzLVdIODgzkKwZSUSqUgTakplZaWCvJAEBm3xubZCgHjwNNoNDytrcFgwKlTpyQDRCwsLDBlyhSBvT4uLg4NGzYUtavT6TB+/HhRG8OHD5fEJDN7qFSQTHl5OU/XalqknE6MoqOjERkZiaKiIjRo0AAODg6CrTQzB7HnHj9+PE9QZU6DBg2CRqNBRkYGh1WaYpf1ej0PbPriiy943hipAz0Aow/CPC9PeHg4nj59Ksk/bNgweHt7S/7v0qVL8Pb2Fk1wc+cz8Mo+bWp2ePjwIUaMGCESfkzrNj/5iDnRzGF+poLOtLi4uGDJkiUCzPbIkSNRq1YtyecpKSlBeHi4oI1BgwZVG6hWu3btanPAMOFjWs6dOycZpNaiRQsBAuu7775D+/btQWS0ibPfs4Vr9erVgnsyGAxQKBSiOVlSUoIZM2aIdgNKpRLx8fGC8fbo0SMQCVE+pmSat4iVAwcOVJtfqVu3bggODkZqairs7OwQFhYmml/r1q0DkfHAiuTkZMjlcoFiwxQVtVqNc+fOCSCppnTr1i0MGTJEMI6k5MIfpb+VQDd9YebefUb29vaSp5BcvnyZQ4kaNGgAIuJb0QEDBohe6MOHD0Ek3moZDAaMGjVKcC9LliwRDZ7Zs2dDqVQKBnd5eTk/RmzGjBkICQlBbGwsZs+eDblcDj8/PxFGntkQpQIi6tevDyKj2cjJyQn16tWDm5sbbGxsJANRysvLoVarudP0wYMHcHV1hZ+fH3JzcyUPJcjPz+cmEVNbNwu0MMUKT5w4EUSv/ArMD8GgiHq9Hi4uLpJH0BkMBr4D8/PzA5ExEMPCwgLOzs6Smfzi4+Ph4+MjqNPpdFi4cCHkcjn8/f05tPOjjz5Cs2bNIJPJ8P777wuehTlYzTW1qqoqkS9lxIgRkoKPQdvMnfCA0UZuvjBIHbvGTDLmu7SUlBQEBwdDJpOhWbNmIDKaLIiMPhqpI+E0Go3IIVpeXo5p06ZBJpPxIBelUgmVSgVfX18R8kiv18Pa2loywMhciCoUCsmFt6Kigs8RcyopKREkryMyml3MHb2lpaUgehUgyMj8XTs4OEChUMDT0xOWlpaSp13p9Xo4ODhwR+6JEycgk8nQv39/fl29Xo969eohIiKC/46dznXjxg3odDo+j9k1DAYDVCqVKFU0I5YdlZU/S38rgc6y7tWuXRtEhF69eomOPPP09MSIESP498rKSi4wvb29ce7cOY5auHXrFpYtWwaZTIbw8HCBI4VB5kxzg9+5c4djbE01CjaxTEPS2ak37DSUoqIi/ltm42/fvj1atWoFwIg88PHxgVwux9y5c7md1RwDXVZWxielt7c3h8AxtMGTJ0+qFVxMEzXVUq9cuQKNRoOoqCjEx8dLHhvGAlhYFCQ7acn8xJzS0lL4+fnB39+f/2bUqFGCe+jfvz/c3d0FdVqtlp+f2q9fPxQVFYHIiJG/c+cOF2IDBgwQ7KaGDBkCPz8//v3x48e8j4cMGYKSkhIBVLCsrIwHnA0cOJAvWgkJCbCysuKLslarxY4dO/jCYm7LbdiwoQiSJoVVLy4u5lqoqe2eOQvNoyqZ2cZUQO/cuRNWVlaoVasWzpw5w8fllStXcPDgQdjb28Pe3l6QckBKiCYnJ/NzLceOHYsXL17wsXb58mW4u7vDyspKEHmcnp4uUmqqqqqwceNGAbqJlZCQENGpPgUFBYIxz8aPqdmFFdZm586dRYgiS0tLwQIl9a7btWuH6OhoPH36lMcCzJgxQwBLZCkaTGMRWEAcg0Sy3OemkdCFhYVwcXFBdHQ032GbPhMA7iswpaqqKixatEjgk6suCv2P0N9KoDMNLj8/H0uXLoWVlRU0Gg0SExP55AwMDOSHySYnJ3MH5PDhw7mWy7bHzOl17Ngx2Nraonbt2lxDZvmg8/LyJKO+GArj+vXr+Pzzz/lgHDp0KLKysnDz5k0+KJ48eYKQkBCoVCrBhOnXrx8CAwP59+LiYo69bdq0KVJTUzn0Li8vD5cuXeI28zFjxgi0dkdHRx4EUlZWxh06prsPFoRhbgs0PWTaPD8FYByUQUFBCAwMRGVlJXcAmptODAaDAGrZoEEDkTbLzgplE/bFixf80OyZM2dyoWphYcEnsFarxeLFi6FUKuHm5sax/YMGDUJAQAAA4PDhw3BycoKNjY3gRCE2ORl22mAw4P333+fa7pMnT9ClSxeeVnn37t1cew0PD8fx48dx7949EBlhnocPH+b+mzfffJNHIrLzRUtKSqDX67Ft2zaueLzzzjt4+vQpOnbsiKioKOTk5GDUqFE8qnLTpk3Q6XRc671w4QLKysp4cE2bNm141CLjYfDLBw8ecEfj2LFjUV5ezn0C69atg06nw8qVK6FWq1G7dm2+sy0uLoapiSA7OxutWrUCkTGoTKvVcq3y6tWr0Ov12LNnD/z9/UFkdDxevHgRjRo1Qs+ePXHw4EH+v86dO3Ntn0EX2aJw+fJlHvgXGRmJq1evCuba2rVruaY9adIkPha9vLzwzjvvCN61tbW14F3HxMRw81BlZSVPz9y5c2feDtuN3b9/XzBuBwwYAJlMhuPHj6Nz587w8PAQ7K5zcnIEuwmpQ0xYXzC6efMmN5MNGjQIt2/fBlGNDZ2TOWwxMzMT/fv3B5HRfHLo0CGEhYUhNjYWy5cvh1qtRq1atUTmB9aOqc06JSUFAQEBUKlU2LZtG0aNGgVnZ2dB1NfQoUP5lpodjsCgaS9evMCcOXOgVqthbW3Ng27efPNNeHl5wdbWVpQaYPz48XBychI956FDh+Ds7AxLS0ueW3zgwIGQyWTw8fERtQMAVlZWAvy5wWDA8uXLOfLh8ePH6NKlC0JCQgAYNY6tW7eiY8eOPCcHKzKZDGFhYZg6dSqOHTuG58+fc8G4ZMkSeHp68nMvU1NTsWHDBvTr109gU2WlZcuWWLt2LYfqMVPWmjVrkJWVxZ2h5snD3NzcRPjmGzducC1z+PDhiI2NhZeXF5+4TZs2FeT2AV7tlO7evSuoP3r0KGxsbPhCLJfLuQmrcePGOHLkCN9FpKSkCLbX5eXlWLp0KaytrWFhYYE5c+ZwNNLZs2fRtGlT/uymEYxMoDMyjaps3LgxR/gkJibyXOJz5swRLIoMLvvdd9/xusrKSg7Ha9y4MY+hWLJkCW+/V69eAggks0ubRkmaCsGOHTvy/CpffPEFv5/Q0FAcP36c901QUBAPxa+srMRHH30ER0dHyGQyDB8+nEddr1y5EoMHD+aa+Oeff84XbybQ2clIeXl5GDt2LORyOZycnPDJJ5+gUaNG6NixI8aNG8cXW/N3HRUVhY4dOwrqNm/eDJVKBT8/P9y8eRNDhw5FrVq1RGadly9fcuADW2wSExPRvXt3EVqMyOhAfuedd3Dx4kXeFttxa7VaLFmyBCqVCq6urjh06BCAGpSLiKoLLDp//rwI3E9E6N27t2AQM5o7dy7kcrnopRYUFKBjx46idurVqycSouYCnVF6eroIXVG7dm1JmykT+lI22ezsbJEzbfz48ZKn/xgMBsjlcsH5kozY7oMl4HdwcED37t25MyogIADz5s3j+UmOHj2KRYsWoV27djwAQy6Xc62KFS8vL66BEhnRP0OGDMG2bdu4YFy2bBnfIclkMrRt2xabNm2Cg4MD6tatizp16sDGxka0TQeABg0a8KRhpsQO/zZfhKZPny7pIGRCUsq+y7bfpjuKQ4cOifwhycnJICI+MRk9efJEEPjCSp06dbBnzx7R+DIX6Ozd7d+/X+TAdXZ2luwXZjYzDWhjdPz4cRFU09bWFtu3bxfdyy+//CL5TIAxMtYcOeLv74+9e/eK+sbPz090ylVBQQGmTp0qcnhaWFhg7ty5ojFsLtAZJScnS4IQpk2bJvmuIyIi0LVrV1G9qUmJyAjdvHDhAnbt2oWlS5di1KhR/GhKc8WmQYMGGDx4MFavXs3nfLdu3TBixAjO7+/vj8WLF3O/BjMR9uvXTyB/agS6Gf1epGhBQYEAX24aQm5OEyZMgIODg+T/2FaUlVatWklGfVUn0AHji/Pw8OBtuLi4SKYWZaYOc8QEYDykmmGdWZk9e7YkEqaqqoprZFLEDltmxc3NDdOnT8e1a9f4RGearCnipby8HOfOncP8+fMF2gsrgwYNwpYtW3D//n2BwOjevTuaNGnCv9+5cweJiYmSEMvqTu1p2bKlSNtilJ+fz7VgVqpL3coCZ8yTfmVkZIgcco0bNxYFDwEQweZMqbS0VKQEmGrPpiQl0Bmx3ECsxMfHSy7eUukDTImZAlmRumfg1ZF4UkixzMxM0QJjHgzFyMvLS5Q3CDAuVMwMxcqSJUskI1urE+iA8V2b2+vNz8ZlFB4eLpmoCzDGDpiPPVZcXV3RtGlTwclmRGIHNzuGkJn8SktL8fnnn0suOlJO/BqBbkZSAl2n02Hz5s2i7b5cLoejoyPWrl0rCh0fMmQI6tatK6jTarXYsmWLQBCzEhsbK4LtSQn0qqoqrF69Gra2tgLthGmy/fr1E5ypyLDspm0XFRVx7cbW1pav9kyASUXalZSUgEgMmSouLsaCBQtEuUZCQkJEApDZZk1PfWd09OhR0QRnz2N62Dajzp07IzIyUlR/8+ZNURxA27Ztcf78edFEj42Nhfk4qaqqwscffwxHR0cBaoQFVvXs2VOU02XmzJlQq9W8/fLycixevBiWlpawtLTEhAkTQGRMxezh4QGZTIbx48cLhAtLhcDy7wBGx97OnTtF44WNQ2Y3NyUpgZ6Xl4cxY8Zw84JpWx4eHti1a5fgXZsmhTOl9PR0bn40nwdSMEsGXTTdOZaVlWHhwoWwtLSERqPhbSgUCjg4OGDNmjWiuSRlGktJSZHc6RIRoqKiRLBaKYGu1Wqxbt06ODo6CnZj1tbWUKvVmDVrlki5CQ0NFdiwAeOO7qOPPhLtXJo1a4bU1FQBuu3XX38FEXEzZFxcnGD3zPw/5uei3rt3T7To9OnTRxRZ+/+FQCeirkR0j4h+JaJZv8PX57eHafa6Nv9TAv38+fN8Sx8dHY1r166ha9euaN68OW7dusUHVf369QUToHv37vwgDIPBgKNHj3IoY4sWLXDx4kVYWlpi4sSJWLlyJezt7fnEYJPUXKCfO3eO27u7du2Ke/fuYdCgQTxDYmJiIqysrKBSqZCQkID8/HxuDz1//jx0Oh02bdoEV1dXyGQyjBgxAs+ePROEnZtH2jGhzA6nZWlTy8rK8OGHH/JB3KtXL446mT59Ojw9PTlmnE2Kn3/+GUTCLXhmZiY3H4WEhODSpUv8kJCFCxdCo9HA2toaH3zwgWCix8TEoE2bNvx7cXExT5nq5OTEzTfTpk3jSIfWrVvjzJkzXPCyvmN0+vRpHmbfoUMH3Lp1i6dlLSkpweLFi2FnZweZTIYhQ4ZwLW7MmDFwdXUFYDQ/MeRK3759kZGRgWfPnoHImFmzuLgYkydPhlwuh5ubGz8J3jQ1LmDUktlC27x5cyQlJWHevHmQyWQoLi7Ge++9xxfkFStWcBiiqUCvqqrCmjVrRA5AW1tbTJkyBUlJSfwaLVq04DsHllqC+YXy8/ORkJAAlUoFKysrzJ8/n6O4vvnmG0yaNIkHQi1btozvNk0DoQwGAw4cOMBhvG+99RYePXrENd6UlBSegKpBgwaC3YFpcq6ioiJ+qISDgwPWrVvHo2y//fZbgaN42LBhXOs3F+inT5/mc6lDhw64efMm+vfvj6CgIGRlZSE+Ph5Exp3m9u3b+YIXHBzM88Ho9Xrs2rWLR0d37NiRp0xgC+f48eMFiyUzz6WlpfE0De+++y4fk1OnToVGo+GoGb1ej7Vr18LS0hIODg78WgsWLICNjQ3kcjmGDx/O0XN/uUAnIgURpRORHxGpiSiZiIIl+GyJ6CIRXf2/EOjJycl8e1S3bl3s37+fd3q3bt3QrFkzAEZh/fXXX3PUQteuXZGSkoKYmBjExMTg8uXLHN4UGBiIQ4cO8XZMA5Ty8vL4xLC2tsaiRYuwe/duEBmhfEwz8vX1xdGjR3kb5jjprKwsvPvuu5DL5XBwcMDbb7/NBxZbmFq3bo3r16/z3zCnEtsaM82QCcL4+Hj88MMPXCht3LiRa42dO3fmTrlvv/0WRMZUBcXFxZgwYQJkMhm8vLxw7NgxPH78GETGdL1arRarV6+GjY0NLC0tsXz5ci6wrayseM6N9PR0bhYKDg7mUaeRkZH8kO3du3fzvOCjR49Gfn4+x4Y/ePAAZWVlWLduHXc8RUVF4dtvv8W4cePg5OSE+/fv84g7f39/gbPSNM82YDS7zZw5E5aWllAqlRgzZgx3CjIkTYMGDQT+ECbQP/nkE153/fp1Lkw7d+7MNbMdO3bwd+3p6SnQnufNmwe5XM7bSEtL4zmGAgIC8PXXX6NDhw6IiorCyZMnuZ/BHKLHBDp716bwvvj4eO6c3rt3L5YvX86VjXfffZcjYb7++msQvQowu3v3Lrp37w4iY/qDL7/8ksc3fP/999xkEBoaKogcNjVhMMWHIVl69OiB+/fvw87ODhMnTsTWrVu5MjJ69GhuOzbPSmoO5Vy+fDk3i127do2/az8/P8G7ZgKd0Q8//CCwWV++fBlBQUHo168fTp48yedTWFgYvzY7UejIkSM8jUJ8fDzXwnv27AkfHx9+zXnz5nEBDRgzQjJT4sOHD3mIf2xsLLKysngCOMCYQykhIQFqtRoWFhaYOnUq99n8lQK9JRGdMvk+m4hmS/CtIaI3iOi7/6ZAZxFq9NvWa+nSpSLbqNRWvbKyEqtXr4a9vb0owMPNzY0LMUYGgwFE4gT7aWlpPKDAtGg0GixYsEB0L1IntQBGZ5xpVkaphYkRE9amJ+EARjPLrFmzJMOeW7VqJbLjmsP3AKOziGlCbCL17NmT28tjY2NFQSvm+a8Bo0mGaXeDBw9G7dq14evry4Vp8+bNBdtsthjeu3eP11VUVGDDhg08745psbGxEWi6jMwFOqPs7GxRNK5CocCqVatEJgMG8TMV6IDRlLd+/XqRicjS0hKJiYkiP465QGdkKrxNS0BAAI4dOyZ636YCndHvves33nhDZA5kcENzR/zZs2c5WoUVmUwGJycnbNiwQeScl7JJV1RUYPny5bCxsRHdT6tWrQTKUxqA2gAAIABJREFUCCAW6IzS0tL4mDN/18uXLxe9a3OBDrzSwqXMpH5+fti3b59AA2ems5MnT8JgMPDTj3r37o0XL17A1tZWcPycwWDgMOLNmzejbt26GDhwILZs2QIbGxvY2tpi69at/B0uWLAARCS45qNHj3hqEHZvUoF1f5T+XYHel4i2mnwfSkTrzXjCiejQb5+rFehENIqIrhHRterCtf/Aw/AiBd0DgDfeeEOQV9mUGJ6YFRcXF9FxWYBxMhNJp3k1GAyicHtTLKwpjRw5Em5ubqJ6vV4vSkIVHR0tmc2OYVelkusDEKX5HD16tKTjyVxrY1RZWcnx6aw4Ojryk3jMyRTvbkovX77kGo2p8JPKCy7lO2DEUgr/kXddnUAHXmVIZMXCwkKUmx54JdCljqHT6/Vce2Rl3bp1kv3CkFNSlJeXJ1IkpBzhgLRAZ8QWQlYmTJggeS/MMSrlcK6oqBAJdRb8Zk6/52RkigYrHTt2lMwrXp1AB4SpIVgxD9hiJCXQGbGDQ1gJDg6WBDJIIYRYQBhDwJgeiAIYTWPmyhcRoV27dqKUzWwemSsNwKv5x8qfpd8T6HL6N0kmk8mJaDURTXsdL4DNAJoBaObq6vqnrjds2DAiIpLL5dSlSxdKSEgQndAuk8nYAsJJq9XSxx9/TC1atCC5/NVj5+fnU4sWLeibb74R/Ear1RKR8RRzU0pJSaEuXbrQypUrBfXx8fE0YMAAyszMFNQrlUrS6/WCurt371KbNm1o3Lhx1K5dOyIicnFxoVu3blFoaCjNmjWLSktLOb+1tTUREb18+VLQzpMnT2jw4ME0dOhQQf2nn35KXbt2pdTUVEE9uw+FQiGoLy8vp4qKCkFdZWUlZWVlierZM+l0OlF9cXGx4L7ZNcvLy3l/MlKpVEREovpLly5Rq1at6IcffhDUd+nShSZOnEiFhYWi65pTZmYm9e7dm+Li4qhBgwZEZBwv/v7+FBcXR3369KEnT55wfnYKvTldvnyZWrRoQbNnzxbUT5w4kbp160Z3794V/ca8Lb1eT1u2bKHg4GAyGAy8Xi6XU8OGDWnjxo2SfWlOT548oSFDhtCQIUME9evXr6cOHTrQzZs3RdclEr/rkydPUpMmTQT8arWaWrRoQUuXLqXy8vLX3svLly8pMTGR2rZtSxYWFrz+7NmzFBERQZcuXXptG0RESUlJFBERQV988YWgvkuXLjRhwgQqKCh4bRsVFRW0bNkyCgsLI7Vazevv3LlDHTt2FM2BqqoqIiIBb0JCAq1YsYLKysqIyDiXAwMDKSAggPz9/SkoKIiuXbsmaGfGjBl09uxZqlu3rqCe9bfpO62qqqKlS5dSv379eN1777332mf7U1SdpMcrrfp3TS5EZE9E+UT06LdSQUTZ9Bqzy79rQ3/48CFHBjg7O2Pjxo1cOzCFzBkMBpw4cQJBQUEgeuVg8fHxwdChQ3Hs2DFuX+/SpYsgepHoVYhuQUEBJkyYwJ09H3/8MT8UISkpCYmJidBoNHw7zjznEyZM4MdqMU1YrVbD0dERn332Gc//MHv2bOTk5PDtXZ06dbj5xTTqDzA6PBctWgQrKytYWFhg3rx5HPmwZ88erFmzBvb29lAqlUhISOA7EHaMGYvgKy8vx4cffsgdRMw27Orqym2Dbm5u+OijjwSmJPMDDZ4+fYopU6ZAo9FAoVDwZ/Dx8eEmFy8vL2zevJlrLkxbYfb9jIwMrqnVqVMHe/fu5ZC3Bw8eYNy4cRwFsn79esmzKquqqrBy5UpYWVnB0tISy5YtQ2VlJYc/VlZWYtmyZbC0tISNjQ1Wr14NrVaL3NxcQf9mZGTw9AAMZcICdS5duiTo32nTpvFo3blz50KhUPB+uXDhAs/JHx0djevXr3OnaHJyMu/jRo0aCTRGUw29rKwMixcv5u967ty5+PHHH0FkzIW+YcMGODk5QS6XY8yYMfxIt3379oGIcOfOHQBG2CjTMuvVq4evv/6aB7Wlp6fzVLrmZj9TDd0c1TNgwAA8evQIcrkcc+bMwe7du7kfpF+/flx7NdfQMzMzef96enpiz549PO96eno6n2eOjo74+OOP+Zgx1dANBgO++uorQcTur7/+yuf1jh074OTkBLVajQULFnDzDTM7MgezTqfjfUi/ac4xMTEYOHAgBg0ahMGDB2PIkCGiE4kUCgX69OkjQmcx+cTgphcuXOBgi759+/712RaJSElED4jIl145RRv+Dv93rxPm+A8IdGa/vHHjBk/pyRw6PXr0QOPGjXH79m2eppYNYtb5devWRXx8PACxfX3ChAm4f/8+F+jr16/nk2bcuHF80pijXDIyMrhQrFOnDvbt24fJkyfD1tYWP/74I49wfOuttwRwNhsbG8EWOykpiQuCDh068EGwfPlyQQBK3759eR4bZpZh2Nfc3FyMHDkSMpkMrq6u2Lp1K5/kycnJ2Lp1K8/93qVLF273bNSoEd58800Axqx6zFlmmi61Tp06eOedd/Ds2TNMnToVlpaWXJCzcGom9A0GA86cOcNNKH5+fvj888+5OeTs2bNITEzkMDlT2zRz2jEUzs2bN7kPheVSYQL94sWL3BfQvXt3QX6fiIgIdOnShX9/8OABF25NmjThk3z58uX4xz/+we9l/vz5/F7MD7jIycnBiBEjIJPJULt2bezYsQOzZ8+GQqHAo0ePeF58Ly8vfPHFF3zcmaJcDAYDDh06xJERvXr1Qnp6OmxtbZGQkID9+/dzv0SfPn24L+POnTsgehW1WlBQgEmTJgmghczpfOXKFf4/lsKXBeSYRymfO3eOm2JiYmLw888/c4FuiuqJiIgQJJCTyWQ8oK20tBT/+Mc/oNFooNFoMG/ePH5uwJEjR7BgwQLJ/jVHudy6dQsdOnTg5pPTp09zgS41Dhj5+PjweZ2Tk8MXjuDgYCQlJQlOjLp48SJ3nLZt25bfg2nGTSYfWEoEImMW0pkzZ/JFICQkBJs2bUJpaSlvIz09He+88w5fJJmp7y9HuRh/T7FElEZGtMvc3+oWEVEPCd7/U4EOVB9pxwb4Rx99JIoqkzplJS8vj2uCpu0QGQ+pMLczVhdYdPHiRVEQjlwuh4eHB44cOSJ6JqkzGZlDjkHaTNtq3LixyOFpLtAZXb9+XZD437RERkaKgkqaN28uirQzFeymkaGWlpaQy+V4++23BXkxAMDFxUWQ68JgMOD48eN8oTIv/fv3F9kjzQU6a+fw4cMcemhavL29JYNowsPDBedNsnYOHjwo6UyTuhcm0E3D+AHgxx9/RIsWLQS/Zzu1BQsWiDJ4SuHQTdMImDsZzVEngFigM0pJSUGnTp1EzyOXywWoE0bjx4+Hs7OzoI5BZ11cXEQnZpmjehgRicEDpjsu82KqvTOSwqEbDAYcOXJE9K5N0wGYO3FNBTqjEydOwMvLCzKZjMN4mSD3+n/sfXl4TVf3/8ogRGRAJFrzkCAxhUrNXkqrxqoQLTGVmuep5iFetKZOWtQslLZK6VdpFVVTlRiqYtYakyAkREWS+/n9cbu2vffZ54q++nuHx3qe/Txy3Ox7ctc966yz1ufzWcWKiSeStLQ0uLu7Y+LEicrvs7Lqp59+Ci8vL4wcORKA8+lpyZIl4lr39/dXztPT0xMjR45UYtV/RED/O9bfwRRNTU0V9HZepjmTAFC8eHFLQGfTZS51tUA2V0zRzMxMC3tMRnTIVqRIEeNMU8BKTa9Tp46x4WIX0AHnhTFo0CBlH7vGXr169dCwYUPjuehyqe7u7hZ9FLaAgACjhHF2drZFjlYnQ7GZAjpbWlqacnMhMlP7AaBKlSpo1aqV8f8YI82rW7duRg1tLrnoAR1wBkF9YInpdYBrpihrxfOqVauW0dd2AR1w+poljHnJKoyy9e3b1xLQ2S5fvqzs4ePjo6hIykbknCVrMtYU58XjBHVzxRS9c+eOhbSjK6yymQI64Py+sNAZr5EjR1puuJUrV0aTJk3Ez4xFZ/hyyZIlLTIHDocDe/bsUSZAEakqrWx/d0D/l5ui/ym2detWqlmzJt24cUM5Xrt2bVq5cqXSkGLTG1h37tyht956S2leEBEtXLiQWrRoQadOncrRufz8889Uv3592rFjhziWK1cueu6552jmzJmUkZGhvD537tyWYw8ePKDZs2dTnTp1lMbsnj17qG7durR///4cncvly5epc+fO9N577ynHR44cSVOmTBGNILY8efJYGqEAaN26ddSrVy/luMPhoJiYGNqzZ4/lfTMzMy0N5cTEROrZsyfNmDFDOT58+HDq2rUrXbt2LUd/05YtWygyMpKSkpKU41WrVqWlS5dafJ2dnW05lxs3blDv3r1FU5pt6dKlVLNmTTpw4IDxvfXvzMGDB6l+/fq0detWcSxXrlzUqFEjeueddyx+NdmDBw9ozpw51KBBA+U89+3bR3Xq1KF9+/Y9cg8iorNnz9Irr7xCH3zwgXL89ddfp+HDh1Nqauoj/x6Hw0FxcXEUGRmpHE9PT6fIyEjatGmTAh7gf+v7JCUlUc+ePWngwIHK8ZEjR1KXLl3o6tWrOfqbtm7dSjVq1LB8N55//nlasmSJ8brWLTs7mz777DPavHmzcnzp0qX03nvvKZ9LrVq16KeffiKHw0G7du2iAQMGULNmzSg2NpaIiJ599lm6cuWKso+bmxuFhYVZwBlRUVG0YsUKC0DjbzW7SP93ryeVoct6J2XLlsVXX32FV155BZUqVVKYdjKrElD1J7Kzs7F06VJB3ujSpYuoW7///vuYOXOm0K8eOnSoaDLqGfrVq1cFUSg4OBhLlizByJEj4eXlhVOnTgmSSZkyZRTykcxuczgcSqOWZzLyCKvly5eLbKVTp05iOo6eoXMt09vbW6gBsqzttm3b0K5dO/HIuXr1anEuLVu2REREhPic5B5FxYoVsW3bNjHabMWKFaIJ1q5dOwWvnjt3bowaNQqAEyb3zjvvCDmE4cOHixLGunXrBDU/X758mDZtmoCb6Rm67utNmzYhKioKFSpUUEof1atXF9KygHOaPKsBZmZm4sMPPxTSAYMHDxb9kvfffx9xcXHi8+3evbvQ8tAhn1evXhXN36CgICxevBhvvfUWPD09cfr0aUHi0YlQeoYuz99s2rQpEhIS4Ovri0GDBimSzLKv9QzdRNRhidj9+/eLWn+hQoXwySefCPBAnz59EBgYKM5l//79ClFnz549ooZuR4TKzs4G0UPiTUZGBmbOnAlfX19xvci+Ziw9c0jY13qGLl8vTMpq3749ypUrhwMHDqBWrVogciouypmwnqFv2bJF9K5q1qwp1CM//vhjIXzn5+eHt956C4mJiVi+fDmInMNzChUqhHLlyilPDe3atVPkrgFnk5elsZl09fPPP4vPsk6dOgI++rTkohkH9MuXL2Po0KGC0jxz5kzRyW7Tpg0qVaoEwMq04wuDtZXt6NVXr14FkZM1CTjZhD169BAXxsKFCwVqZN++fZg+fbogWowaNUogH/gLzCZfGE2aNMHx48cRERGBFi1a4MSJE+LRXZ/J6OPjI9iZaWlpGD16NLy8vJA3b15MnTpV0MHXrFmDFStWiNpwdHS0eDzlLytT4n/44QdR/6tduzYOHDggmoxJSUmiqVqwYEGFdMJsPMB545g0aRLy5s2r/O08OGHjxo1CerhFixai7MRUem5onTlzBq1btwaRk2375ZdfCpTLpUuXMGzYMMXX3BORUS7MSuW//bXXXsPFixcREhKCDh064IcffhBNvxdeeEEEJR3lkpaWJqj7/v7+eO+990Qzbc+ePYJUkytXLowYMUL4esyYMfD09BQ+27p1q0A4NG7cWEhR1K5dWxlLFxoaqvhaRrncuXMHY8eORe7cuZE3b17ExsYKtuPq1attqfTcFGXfHzx4UDT2WGmwT58+KFSokIVKv2zZMlF2klEuLFXA4IGBAwcKyYnJkycriUjz5s1FOU5HuZw9e1bISZQsWRLr1q0TKJfff/8dw4YNE7IJ8sxWHeWyatUqkVB06NABv//+uwjoR48eFciU0qVLizo5l1MZBBAfH4927drBzc0NefLkUUpnfn5+lpLi4MGD4ePjI27QR48eRZEiReDn54dt27Zh2LBhyJs3r4g9ixcvRmBgINzd3dG/f38kJCQ8Deiy8VSbPHnywM3NDT169LAQNOSAzsZBkC8MdhqRE5oWFxen1E71gM526NAhIRWgr9atW1uag3pAB5wXhiziz7/PSAR9JiOgBnQ2GW6mrxo1aihZKgChYyELC2VlZWHRokUWYTN+Ihk8eLCldipnvGyXL18WQUEXQSpfvrxFBlYP6GzfffedQKvw8vLyUrRtZDMRi+7cuYNx48aJ5qS8V/HixfHFF18o/QMO6Dqx6OTJkxa4Gq9WrVpZtLj1gA48FBPTfe3p6Qk/Pz8FdcJmIhadP3/eyFAmsmquA9aADjiD4KeffirQTbxciV2ZiEXJyckCMqw3AsuVK2cRDbMjFm3bts0iee3p6Sl01PWeiIlYJKNqZF+7ubkhf/78mDt3rsI4lac9yXby5EnLrOGYmBjL58FJxu3bt7Ft2zb4+fmhSJEiAgrcq1cvy0zYlJQU9OvXTwFb6E3kx7H/qYAuf+B2jLI2bdqIIQ668aACOdiYJErtAjrgvDB4mAGvTz75xPh+poDOxhoi8rKTKDUFdDYO1LyGDx9ubOzx60zzJ1NTUy30dFOzF7DXKQesjdNXXnnF2NizC+iAsyzCsD9edlKxrpiinA3xqlChgnHQtiumqMPhQGxsrLKPPmOWzRTQ5ffQ0VN2vnbFFOWnLF4jRowwNrdNAZ0tPT1doDx4/RWmKMvv8mrZsqXR166YopmZmRY0jJ2vXTFFdWnc4sWLW6ZuAQ8HoPPMW9lY60heuXPnRsuWLbFs2TKkpKRg9erVIHI2SXPlyoWKFSsqaqMdO3ZURiLK9s033yh7/1X7nwrorDRIRBbiAdurr75qCegMbWRcr7waNGhgCV52Af3GjRtGaKOOq2WzC+gy5ldeXKfWzRTQ09PTBa5X3oNJJjpMTRbEkk1WpZRXiRIljPT/8PBwUfNny8zMxLx58yzyr0ROnQz9Pe0CuozhlldAQIBRBtkU0HXlQHnVq1fPom9ip+Vy48YNS2bFF/m4ceMsvrYL6LIiqLxMc0kBc0B35es333zT1td6QJcx3PJiMpfua1NAz8zMxEcffWR5EiN6iKWXzS6gy7yNnPjaFNAZfspYfnk1bNjQIqVs+t6lp6cL3Z+wsDAUKFAAkZGR2LVrFwYNGiS0heSZoLy/LhvSunVrVK5cWTkm95D4d/Xr53Hsfyqgcw19//79FuIBmx7QDx8+rIz52rlzJ4oWLYouXboomFv5wtADuq7D3b9/fyxatAhEThlTmfkWFxcnLgw9oJ8/f15h5X3++edo2LAh6tSpg3Xr1lmYb2xyQOdHZ/6itW/fXpBjFixYgEGDBonyzdy5c8WFoV/k8s2JdeO7deuGwoULG0kmbBUrVkSbNm3Ez/Kjc8OGDUU9f9y4cZg6dapgOY4ZM0Y8DekX1t27dzF+/HgFw83CSfv27RM3HF26VQ/osq8Zw128eHHExMRgwYIFwtc9evQQDU9dbVH3db9+/YTU7IYNG8SsVh2XrQf0CxcuiAEaxYsXx2effYYXXngBtWrVUrD0eqlODugOhwNr1qxRfM2Z3scff4zBgwfD09PTUqrTfW0a6dazZ08UKlQIu3btEnMv69Spo2j96AH9+++/F03Gf/zjHwI8MHbsWAVLLw9i0QO6XiaZOHGi0EDZs2ePwNJXqFBBIfnoAf3IkSOiYV+pUiVs374dJUqUQMeOHTFv3jzhv4EDB4qyIevPcM/iwIEDoik9ePBg3Lt3T4Aq2BwOB3766SeLflO/fv0ssGhW0+Tf03tIrCXztIb+p8koF514wHKeHNDlxl5gYCAWLFggOvxFixYV2O9bt25hyJAh4sKYPXu2mLc4f/58RYebm1uAFeWye/duyyxJDug8b5Rr+FOnThV0+kaNGqFu3boAnCSTadOmWeqaHNDl5lZERIQYRqGjXGT9aq5hc1A6ffq0osPdr18/MdnpzTffFGJiOsmE+xXMJj137pyQMObmlsPhQEZGBogI//znPwE46+sxMTEgejicg7/Y3377rdLc4kYmoKJcdOnWli1b4vTp0yKgJycni6HLBQsWFEOXASfngBFNPDyEa9izZs0SQ4w/+ugjo+Y64FSTJHrYTDM10zmgmxqZ7GsZ5fLHH39g+vTpwtejRo1CWlqaCOhyv4YbmYAV5aI30zdv3iwC+unTp5V+zYABA0Qpgpui7OvFixcjKCgIbm5uYjgHB3S5X1OyZEnRh8jMzAQRITY2FgCMDVaelrV161asXr1a1PC5kQmoKBdTIDx16pQI6MnJyejVq5ci+8ENe5kweOPGDfTu3Vtc/5988om4AX322WeYOHEiPDw8UKxYMUV6oVOnTihVqpQl9nBjnJeHhwfc3d3Rvn17gaBj2WjT9Qc8RblYzEQsun//Pt5++22BPOAPnBt7Q4YMsTwayQGdLSEhQVDCfXx8FOfpUEPATCzKzs7GkiVLLKQXE/yMTQ7obPqFwfuY4GeAmVikQyB58ZxQRtnIJgd0NjkIyo+NDD+ToYYALAGdbd++fUrJTF7Vq1e3EDFMxCIdAsm/L2vX6E1cOaCznTx5Uuij63MkdR1uwBrQASvclRejbDp27GiZ5mQiFskQSJOvFy5cqPjaRCxiJq4+4o+ZpyZfywGdTR/OIX9ndKghAEtAZ5MhkPqqVq2aZSqWiVj0KF8PGjTI4msTAzw+Pl7cGPm7z6tTp06W2CAPRGE7cOAAvL298fzzz6N06dJo2bIlLl68iJEjR4rGsMzIZpa6Xjp6GtA1c8UUvXbtmgWtweJEupkCOhtrnvDq1q2bRZsZcM0UTU1NFeJLvOzmTJoCOps+C7R27dpGNp0rpmhGRoagL/NasWKFsZlmCuhsepMxICBADFTQ388U0AFnEHz77beVfWJjY41NXFdM0WvXrlkCqTwkQjZTQGdjGV9eXbt2NfraFNDZ0tLSLHVp05xOwDVTlEsTvGrVqmX0tSumaEZGhkCCPcrXpoDOpstM+/v7WxIRwD6gA05f8/XKa8qUKUaJXVdM0cTERItEg0l2GbBngDscDjGkhNecOXOMewwfPlxADwFn6Sw4OBglS5ZEUlISoqKiEBISIv7/zp07eP/995W9Q0NDheaTbE+Zojm0hIQE6tatGyUnJyvH7ZiMJsvOzqbFixfToEGDlOMrV66kt956y8IEs7OrV69S//79aefOncrx7t2704YNG5x30kcYANq0aRN1795dOb53717q27evIv/qylJTU2nMmDG0ZMkS5fiwYcNo0aJFFmlfO9u3bx917txZOXb79m3q2rUr/frrrznaIyMjg2bOnClYd2wzZsygGTNmGKV6TXby5Enq3r07JSYmKsdjYmJyLN3qcDhoyZIlFibjypUradSoUY/t6+3btyvH33jjDVq/fn2Off31119Tt27dlOP79u2jPn360KVLl3J0LqmpqTR27FhavHixcnzo0KH0ySef5NjX+/fvt8j0pqamUteuXen48eM52iMjI4NmzZpl9PX06dNzJNNLRHTq1Cnq3r27hVnauXNnW1/rrFUAtHr1apo0aZJyfOzYsTRhwgSL5HO+fPno3r17lJ2dTbdv36bmzZtTRkYGbd68mYKCgqh8+fJ0/vx5IcWbL18+ioiIIF9fX7HH6dOnqWfPnnTu3Lkc/Z1PzOwi/d+9nlSGnpKSgkGDBon699y5c9GqVSuEhYXZ1usAa4b+448/isZQ7dq1BTNwypQpojYbGBio1Gb1DF0XWRo9ejT69esHLy8v29osYM3Qjx8/bmkM+fj4oFevXhg3bpyozU6ZMkXUZvUMnQdn80iwHj16iEEN69evR7169UCk1mYBa4Z+6dIldOzYUZQSVq5cicqVK+Pll19W8NX9+/cXtVk9Q2dBLa5/t27dWtR4FyxYYKzNAtYMnedVcv17zpw5aN26NcqXL6/gq6OjoxVf6xm63utgnZbJkyfb1mb1DN3U6xgwYAA8PT0VLL0u6qZn6KZaq6+vL958802lSTx58mQBudQz9KysLHzyySei/v3GG29gxowZIHIOa5ABAbLQl56hX758GZ06dQKRs0S4YsUKVK1aFS+99JKtr/UMnftacq9D9jVj6XUElZ6hy30txuu3adMGoaGhiq/161r39U8//aQwiPk7NX/+fAGXfOaZZ5S5pPyamzdvonHjxsiVK5fyxMVDRvjpf+PGjciTJw9CQ0MRGBiIRo0aGXsjwNOSi8U4oKempuLjjz9GwYIFLTMM27Zti/DwcADWjjor4HFA1yVvGbqlo1x0md7t27eLgB4fH69Ap1599VUB3ZJRLvoUc25GckC/efMmBgwYYKy/ySgXE3qCRbw+++wzow438HAo8G+//QaHw4G1a9cK9AQPBeaArmuujx07ViBUqlatKsSuZIVK1ipPT08XAd0kgwpYUS46euLIkSPiwrp165YFjcQIFRnlkp6eLnTpWYo3PT1dXOQmHW6Hw6EMiQbM6AkO6AcPHjTqcAMqykWGcbq7u6NPnz64fv26COgpKSmKrO27774rfC2jXC5cuCBkGooXL441a9YILsWaNWsUZc86derg4MGDAFSUi65GyrLLHNBNmuvsaxnlIsM48+fPjw8++AB//PGHCOjHjx83opF0lIuMoGLIMAf0mzdvKmiknj17Cl/LKBfZ1/J1zb6WG/HyIGnGoXO/Zu/evaLWX716dezatUvIJnA/bdmyZUoM4sbql19+iaVLl8LDwwPPPfcckpOT0aBBA9SvXx+Asw/GUiB8DleuXHka0GVjkgdnACYMuRzQ2XSNavqz1mXS4QbMOHQmxtHBAAAgAElEQVQd38z45ICAABA5MeRytxww49Bv3LihiPjzuegXv2wmHPrOnTsFvlnHf+s63IAa0NnS09MxefJk8Tnw7/PfGBUVZcGQywGd7dixY0JdUm7Cyhe/LHVqwgPL+GYZ+83Bs379+hZfm3Do+k1a97XOF9ADOvAQ38yfA7M8uQFmwpCbcOj6TZrPhf9GE1/AhEOXb9I6/pu192Vfm3DofJNmfR/d17LmOpsJhy5j2XlwDH9GPPxFbgSacOiZmZlKQsZ7cFJk4guYcOjydS3LZ/PNSYZPAg+VVHUgQ1xcnIVBS2RmdPLwG752GzduLN6jRYsWihYSoD4lMJrLRGLLqf1PBXT5wzYRIQBzQGdjuByvsmXLGiV2XTFF7927h4EDByr7vPPOOxZtZsA1U1SXxiV6OE1INzumaFZWlshuePXt29fIiDQFdLaLFy8K3RFedo09U0AHILJ+eY+mTZsKSKRsrpiiKSkpluHBnE3r5oopqjeUy5QpY/zbTQGd7d69e0LQideMGTOMvnbFFD1+/LhF19407xOwZ4pmZWWJ+Ze8+vTpY/S1K6boxYsXLfIKeiLCZscU5eRG3uPFF180NgJdMUVTUlKErgsvmcchmyumqC6DXL58eSMjmsslukQH4ExuevXqpezj6+uLwoULo3Tp0ggPD0eNGjVEuZLI+WQrN9E7dOigNEzZWFNK3vuvmquA/l/dFJ0yZYoiW/ooO3ToEI0ZM0Y5du7cOYqNjbVIsdpZZmYmLViwgJYvX64cnzt3Ln366ac5kvPk9x0/frzl+Pjx4+ns2bM52gMArV27lmbPnq0cj4uLo/nz54umzaMsOTmZYmNjLTMyx44dSz///HOO9iByzpScPHmycmz79u00c+ZMunPnTo72uHv3Ls2ePdvi1ylTptCWLVtyfC7x8fEWX58/f56mTJliaabaWWZmJi1cuJCWLVumHH/vvfdo9erVOfb1+fPnafz48Zam5Lhx4+jMmTM52sPO16tWraKPPvoox76+fv06TZ061TJrc+zYsbaSwSb7/vvvLU3GHTt20MyZMyktLS1He6Snp9OcOXPom2++UY7HxsY+lq+PHDlCEyZMUI6dPXuW5s+fb2l48s/58uVTjqemptLUqVNpxYoVyvGuXbtSq1atqHbt2lSuXDkKDAxU5oWmpKTQ7du3xc/58uWzvCeRs9E8Z84c8XPZsmVz/Pc9ltlF+r97/as19DVr1gjiQfPmzZXhEXqGfu3aNXTv3h1ubm4ICgrCokWL8Oyzz+LVV1+1VXYzZei6hOg///lPkdnVqFEDRFaZXj1DT0tLE3KxjOGuXbs2atSooWDpZRU/wJqhHzhwQOBeq1evLup+U6ZMEfjq0NBQfP311yLb0TP0jIwMzJ49W8HrR0dHo1ChQgqWXlbxA6wZ+pkzZ0RGzcp2RE4mnV5D5MaTnqFnZ2crKpGdOnUSmfGnn35qkRNm0zP0xMRERS524cKFKFKkCNq0aYPhw4cLX7/99tvC16YMXfc1N5TnzZsnsPSRkZGKyJOeocuqmIzhrlu3LqpXr26RE5bhenqG/vPPPwtfV6tWDQsXLgSRs4nLcsIhISHYtGmT8LWeoctjFhmv36FDBxQsWNAiHS1DUfUMXVbFLF26tMjS+/btK7D0wcHBWLx4sfC1nqFnZ2dj5cqVovzw+uuvY8iQIeJJjLH0L7/8MhISEsR76xl6UlKSAlhYsGABihQpgubNmwuhraJFiwrCG/BQXIt7BPfv38ecOXNEyfL1119XSmOmWjcPIqlXrx7y5MmDwoULiyecwYMHw9fXV7z2xo0b6NGjhzgXlrB+WkP/02SUi669PGzYMNy+fVsEdJMON184LJ8LmLWXuXkxf/58RYdbvnBklAsP0OULIyYmBleuXBEB3aS5zheOjHK5du2amEXIOtvZ2dkioMskFNZcz87OtqBc5MHYTZs2xYkTJ5SALutwyxdOr169EBwcDMCJpZe1yqdPn44//vhDBHSTDvf9+/ctKBe5hsg623JAl0koNWrUwN69ewGoKJeMjAzMmjXLokvPAd3uuwCoyAddq/yrr74SyIOPPvrIqLnOLFUiJ8qFbz46WYwDui7Z3LlzZ+FrGeViSjSysrJEQOfvAv//4sWLkZWVZUG5bN68Wfj6pZdewokTJ5SAbvp/QEW5pKWlKVrlTBbjgC4nIvJ3QUe5yFrlrEsvB3TTdwFQUS6mROPWrVsioNv9v+7rPXv2iObryy+/jLNnz2LixIkgImRmZiIuLk7U7Js0aYL4+HihvDl16lQ0atQIAQEBFoVPPtdLly7h2LFjKF++PNzc3DBx4kSMHj0abm5uQsW0YMGC8PT0xIgRI3Dnzp2nKBfdTMQiXauc766cwTNNXDY5oLPJWRkjLnx8fBSauCx1aiIW6VkZn4tJc53NRCySszKG2BGRyOBHjhypZPAmYpGelfn5+YGIRK1c11wH1IDOpmdlfC6cwXfr1k3J4E3EIm48cQbODSz+WdfhBszEIlnOQfa1SYebzUQs2rJli/gc+KL39vYWGbysrw+YiUW6nAOfi93TGmAmFslyDgydpT/rt6anNROx6MGDB5g7d67QKmdfM1RWf1oDzMQiWaucm9HsH9PTmolYpGuVm3wtP60BZmKRLt3B58KJiP60ZvJ1ZmYm5s6dC19fX6URzA3miIgIpbbPpMIDBw4gISEBuXLlQkxMjPIeLVq0sBCL+ElU379u3boKRPlpQNfMFVP00KFDFrSHPsGbzRTQAeeFoTcZ27Zta7lLA66ZoufOnbPQ3E0DdgF7pihfGPIeERERxoaOK6ZocnKyRdVOHhIhmymgs3333XfKHr6+vsbZma6Yonfu3MHw4cOVfQYPHmxkg7piisbHx1vmx+qa62x2TFEOgvIer776qtHXrpii58+ftwyKXr58udHXdkxRh8MhpFnloKAnIoBrpmhycrJFjvbtt982+toVU1T3db58+Yy+dsUUvXv3rkXQauDAgUZ/umKKxsfHKzdvIrJorrPZMUWvXLlikSJYtWqVxUfdunVD/vz5BdeEZbKZ5Z2ZmQk/Pz+8+eablvfQWbFLly61NHefMkVzaCkpKbRs2TKlQUFEtGTJEvr9999zvM++ffssjZEdO3bQunXrlGaIK/vjjz9o1apVFlbdkiVLcsy0IyL69ddfLQzPkydPUlxcnGUWqJ1lZ2fTl19+Sd9//71yfOXKlTlm0BIRXbx40cJATE9Pp2XLltHNmzdztAcA+vbbb2ndunXK8fXr19OWLVucGUYOjH2tszmXLFlCv/32W472IHI2qnRf79y5k7744ovH9vWxY8cs5/LLL7/k+Fxc+To9PT1He2RnZ9P69etp27ZtyvG4uLgcM2iJiC5dumQ5l/T0dFq6dKllZq+dsa+/+OIL5fiGDRto8+bNOfb1rVu3aPny5ZSSkqIct/O1zhIlcs5sXbZsGR09elQ5rs8GBUDfffcdvfDCC+Th4UFERGPGjKGSJUtSnz596MGDBxQfH09paWnUqFEj8Xvp6ekUGxtLU6ZMsZzjiRMncvR3PjGzi/R/93pSGTrPiJQx3A0aNECpUqWEfrQJe6xn6DqB49133wWRs9nD+Gpdq1zP0HXN9aioKERHR8Pd3V3BV+vYYz1D12VtP/jgA+TOnVvsR+TEmcvYY1OGrhM4GGo5a9YscY66Vrmeocs63Hny5MGECRMQEhKCOnXqKFh6mQRlytCPHj2qfI6TJk0CkVNil7H0OvZYz9BNZJ2GDRuiZMmSCr5a1yrXM3Sdk/Dee++ByAkBZHy1jjPXM3Sdk9C2bVtBWmJ8tbu7u0J4A6wZOpN1+HN8//33kSdPHrRt21Zk2rpWuSlDlzXX69evr/ia68S6VrmeoeuchPHjx6NcuXKoXbu2LeHNlKHLOPXw8HDF1ybCG2DN0HVOQu/evdGoUSMUL17clgQFWMW5du7cKUprbdu2RZMmTeDj4yPYyY0bNxYlJNYqWrBgAWTjmajTp08XDNzExERkZmZiwYIFohzVtm1bDBo0SHz3CxQooNTPgf+QkgsRNSWiU0R0lojeMvx/byL6hYiOENFuIgp71J5PIqBv27ZN4GkbNmwoKNYyysWOHcgB/e7du8q4MqZYyygXh8Nh1CqXA3p8fLzAp7LmOqCiXG7evCnYgQEBAYIdyAHdpLnOGG4Z5WJiB8oBXR5XJlOs5abovXv3jFrlHNBNmuuMjpFRLiZ2oBzQr1+/LsaVFShQQMwmlZuiWVlZRnagHNB1zXXG68sol4sXLypa5Yxn5oBuxxqWUS7c7GZft27dGmfPnlUCuok1DKgol5SUFKNWOQd0O9YwoKJcTFrlckA3sYYdDofSFDVJUqSlpYmAbtJcZ1/LKBeTJIUc0E2a65mZmUpT1CRJkZiYqAR0E2sYUFEuJkmK7OxsEdCTk5NFXbtkyZL4+uuvAQBdunRBiRIl4HA4sHDhQnh7eyMwMBAbN24UAlsm/HqbNm3g7e2N8uXLIywsDF999ZXot9WuXVs0dz/44AMQEZKTk3H9+nWBtilWrBi+/PJLEVf+bQGdiDyI6BwRlSYiLyI6qgdsIvKT/t2KiLY8at+/GtBZqY+/WDxQWK5VmYhFulYL/VnnkqFTrMMNmGGLun4HO7RGjRpGzXXATCzS9Tv4XOy0XgArbFHX7+D6YEhIiFGHGzATi3T9Dp7KYtJcZ9Nhi7pWOWtzBwYGCv2PgQMHKiPBTMQiXb+DMx++Ycia62wmYpGs1SLXtvWBwmwm2KKuVc5Z3nPPPSc012WtF8BMLNK1yvlc7LReACtsUdcq57+pbNmyyJ07N7y9vRVdH8BMLNJ16VmS1qS5zqbDFnWtch50XbBgQaPmOvuDA7rsa1mSmRFDnNnruj6AmVgk69LLNXLOjkePHq0Qr2JiYhSt84SEBPHUwOvYsWM4fvw4EhIScOrUKZw5c8ZCXCJyNmfXr1+vnCPfFGQy3e7du8UNir+X/86AXouItko/jyai0S5e/xoRffOofZ8EU1TX4WazY4ryFG55jyJFiliGKQOumaJXrlyxNJ4GDRpk0VUG7JmirFWuf0l0HW42O6bo7du3MXToUGWPdu3aWXS4AddM0b179yrUaSKyaK6z2TFFWZde3uP55583ytq6YoomJCQobDz6M9s3+dqOKcq69PIezzzzjEVzHXDNFL1y5Yp4wuMlT8CRzY4pylrl+ig7PRiw2TFFU1NTMWzYMGWPtm3bKokImyum6L59+ywj23TNdTY7pihDguU9IiMjjbK2rpiiJ0+eFOJhvOThL7LZMUVNLMyQkBDjuZhmft6/f9/C4n3UmjhxonF+Kpdq9e/HgwcPMHv2bGWPv2quAnpOmqJFiEjW77z85zHF3Nzc+rm5uZ0joneIaKD+/3++5k03N7eDbm5uB69fv56Dt3Ztu3bteqwmWFJSEv3444/KsVu3btHu3bspIyMjR3sAoMOHD9OhQ4eU43v27LGw71zZnTt3aNeuXZbjP/74Y46ZdkTOxpne3Dx8+DAdPnw4x42njIwM2r17t6XxtGvXrhyzKomIfv/9d8vfdOHCBdq3b1+OpVsdDgf99NNPFgblrl276MKFCzk+l+TkZIuvU1NTaffu3TmW6SVyshCflK91ZumuXbsoNTU1x/uYfH3kyBGKj4/Psa8fPHhAu3fvtjQ3/5N8/eOPPz7WdZ2cnGw5l2vXrtHmzZstDFqHw0Hu7g/D3t27d6lHjx7KOX/xxRe0du1a+vTTTykuLo5WrFhB/fv3V/aZO3cuzZ8/39I8Zx/rzdkrV67QDz/8kOO/6S+bXaTHw4w7iogWST/HENGHLl7/OhEtf9S+/2oNPTY2VhAL9Ck1eobOj8/58uUTcpb+/v6oW7euYDiWKVNGyZhMGfqJEyfEI2a5cuXQs2dPEBGGDRtmO6VGz9CZcMCDOLp164awsDCEhIRYGI5yxqRn6FeuXFHU5EaMGAEiQvfu3UUZR59So2foplFfDRs2RN68eS0MRzk71jP027dvK4xbZtC2adNGYTjK2bEpQ9+7d6+C4eYm9eTJkwW+Wp9So2fo9+/fx4wZMxTGbUBAAOrWratg6eUynSlDl6dXhYaGCo2PoUOHCl/L4/IAa4bOT4Qy4zY8PBxly5a1HY0IWDN0fXoVQwG7d+8uSkHyaETAmqHr06uaN2+ORo0awdvbW8HS62U6PUO/ffu2wridNm0aiJx9JbsynSlDl6dXRUZGiib1pEmThK/1JyE9Q+cnQpk46Ovri/r16wuiYGhoqAJx7NChA0JDQwE4S58VKlSAu7s7YmNj0aVLFwQFBUG3a9euoUiRIkK8bsCAAaJkWrVqVUGEAyCycOYN3L9/H1OnToW3tzd8fHzE0/R/U8nFnYhSH7Xvk2iK6tRf1irngM4NLtMwXhnlsnXrVkv9Wg7odg0uuSmqz5HkmqYc0OU6Ps8cBVSUi90cSQ7oeoOLZ47KTdEHDx4Y50jKAd1u5qGMcrGbI8kBXW9wvfHGG0hMTFSaooyv1rXK5YAuN7hYhzs7O1tpivIcSbl+nZWVJQK6SYebMdwyykXWpef6tRzQdc312bNnIyMjQ2mK3rlzR+jSy410OaDrdXz2tYxysWukc0C3my8rN0W5ka5rlcsB3TRzFFBRLrKv5UY6B3Q5EXFzc0P37t1x7do1pSnKjXT2NTdX5YCu92wYry83RfWZodxI54Cu92x45igAFC5cGD179gTgZNAyCal58+Y4ffo02rdvj/LlyyMuLg558+ZFUFCQoO1zueTatWsi3ty/fx+1atVC3rx5cejQIbi5uWH8+PEC0cZ9mTfeeAPXr19XpAW+/fZb8f5RUVG4dOnSvx/lQkSeRHSeiErRw6ZouPaaEOnfLV29IZ5gQGeLj49XJr0zucikw82mwxZlhIm7u7v4ckdERBh1uAEzsUhHmDBaQoag6cqBOmzRhDrg/WQIGutwA2bYoo464OZRq1atLEgbNhOx6PvvvxcIEx6r5+/vL5pJsg43YIYt6ggT9k2DBg1sIWgmYpGOMGEpWW6choWFCR1uNhN7UEaYsL+qVKli1OEGzMQiHerKiYOMqtKVA3XYIitUcv9C3s+kuQ6YYYu6Lj37unXr1kJzfe7cuYqvTcSi7du3iwZegwYN4ObmBj8/PwVQ8PPPPyufJQd0NtYqZ/ijydeMqmIzEYuOHDkivm+VKlUSRDIdaSNbUFAQevXqJX5myQh9LimREyYr69bs2LEDRA/JiA6HQ8hwfP755wCAwMBA9O7dW/xOWloahg8fDk9PTxQoUEC5yRA5a/nyOf7bA7rz96kZEZ0mJ9pl7J/HphBRqz///R4R/UpO2OIOPeCb1pNmijIuWB/4q+tws9kxRRkXLO9Ru3ZtIxvUFVN0x44dluHMEyZMMDJc7ZiijAGX9yhVqpSChWdzxRQ9evSopclo0lwH7JmijAuW9/D29rZorgOumaIyBpyXjoVns2OK2vla1+Fms2OKsi697mtdhxtwzRTduXOnZTizzntgs2OKMgZc3qNkyZLGprErpuixY8fEDY+XjoVns2OKsla5vEfu3LmNctWumKKyLj0vHQvPZscUZV16ljLgpQ9fZitYsCD69u1rOX7t2jWLPLSMdAKcsGIiJ7MWeJixT5gwQbwmLCwMr776qmX/48ePW5q7sbGxlvm0/xFMUQCbAYQCKAPgn38emwBg45//HgQgHEBVAA0B5GzI5BO0rKwsunbtmqVJkZiY+FhNsLS0NEtz6Pbt2/Q4TVwAlJSUZGH4JSYm5pjhSUR07949y7n88ccflJSUlOMmGBHRjRs3LKzKxMTEx2q+ZmRkWM4lOzubEhMTc8yqJHI2oXWp4qSkJEtD1pXx++rNt8TExBw3t4mczUr9b7p161aO2ZBErn2dU4Yn0ZP1tf5ZPq6vHzx4YDkXh8NBiYmJlJmZmeN9TL5OTk7O8bxWIvvvWFJSktHXDodDsDzZ7ty5Q9OmTVPkod3c3KhMmTLUqVMn0fQuUKAAFS1alI4ePUrfffcdDR06lNq0aUMTJ04UvxcUFGSZW0xEVL58eXruueeUY9euXXus6/2JmF2k/7vXk8zQ9Xmd5cqVg7+/v5F4wKZn6LrQUt++fUHkVGnjR+lWrVopOiqmDP3gwYNKDZybgnINnuuybHqG/uDBA7z77rsKhpvI2SiUH31lbQ1Thn7u3Dm0adNGZHv8GNirVy9LXZZNz9BZT0ae4VigQAGULVvWWIMHzBl6cnKy6HcULFhQ+IbleuUaPJspQ9fndZYvXx5+fn7GuiybnqHL/Q5vb2/xRPbSSy8JX+uCbqYMXa+Bc1NQxtLrgm56hm7qdxA5G4Xy3FPZ16YMXa+Bm3wtz7YErBm6qd8RGBiIMmXKGGvwgDlD12vgsq+5Bt+tWzelVm3K0GVfN2zYEGFhYciXL5+Cped+C5ufnx8GDRokfv76669RrFgxuLm5YcCAAejcuTPy58+Pc+fOYdCgQeIpr169eli3bh2aNm2KPHnyIH/+/KhYsaJSFgKA9u3bi6YqW2pqqpCs5jVgwAC4u7ujUKFCWLZsmXiy+Y8oufwd60kEdF2HmzHcMspFJh7ISocc0E1SqJcuXVKaojpygpUO5YBu0uHOyspSmqIJCQkCJRMaGiqUDuWArutwM4abm6Im5MTVq1eVgK5LoTJKRW6K6sgJVjqUA7quw80oFW6KMnJC16WXA7pJhzslJUVpiuooGdallwO6rvjIiCQZ5SKjZCIjI4XSIQd01uFmlAqTyeSmKCMndF16OaAnJSUJdU8ZpSI3RRMSEsRFHhISIpQO5YAuKz7KiCRuijKWXve1HND1RIQx3HJT1DTbMjs7WwnosuKjjFLhpihj6XVdejmg64qPjEiSm6I6SoZ16eWAfvbsWeFrmTgoo1xkX8vXdb58+TB06FAkJSWJXkZYWJhAoowZMwYeHh4iwN6+fRtz5syx4PKJzIzR/v37IyAgQPx89uxZhIWFwcPDAx999BH69++P/PnzA3De8JkEVrduXRw7duxpQNeNA3q/fv2EDrc8rACwwhZN+tQeHh4ICwsz6nADZtji1atXRZMkODhY3Ew6duwomi6yDjdgJhbpWuTPPvssgoKCjDrcbDpsUdci56ylefPmRh1uwEws0rXIWftF1+GWsyAdtsha5Awj5Syzbt26iia7PKzABFvUdem5Fty3b1+hw82a62w6bJF16fkGHRMTA09PT4SFhRl1uAEzbFHXIucA8/rrr1s02dlMxCJdl75IkSIoVKiQ8nd+9dVXiq912KKuS8834mbNmil/5+XLl8XvmIhFuhY5a7+YNNnZdNiirkvPT4916tQRichLL72kkMlMsEVdl56bn7179xaJCGuus+mwRZ4xwDc8vmkVKlQIBQoUgJeXFyZPnqw8IbEWiz62LzMzU2j68Grbti02bdqk9OCmTJkCIkJGRgZ27NiBAgUKIH/+/AIp061bNxQtWlQ5R9ZF9/DwUPR+/qr9TwV0+QPXH9vY7JiinLnKe+TPn98odeqKKSqL+POSoVOy2TFFOXPVswI7WVs7pqicufIy6XAD9kxRzlz14cO6DjebHVOUdenlPcqUKWPRXAdcM0W/+eYbS5NR1+Fms2OKsi69vEdAQIBFc53PWw/obPJTCi+T5jpgzxTlzFUehkxEyoQs2eyYoiZf61OT2OyYog6HA3FxcRbpYTtf2zFFWatc3qN06dIWzXXANVN069ativwFB2Y5EWGzY4ryDU/e47nnnhODPGTjxr7+XTpy5IiAIBI5y6Us2RscHIzhw4fj119/Fc3iiRMnwtPTExUqVFDKsNHR0ZaSDOBswL/55pvKOf5VcxXQ/6vlc319fSl37tw5fn2uXLnI19dXOebp6Um+vr5G2U078/Hxscwk9PX1JW9v7xzv4eHhYTkX3kdv6rgyb29vyz4+Pj7k4+OT4z3c3NzI19eXPD09LeeSK1euHO+TO3duy7nkyZPH8lk9ykyf5ZPydb58+f6/+9rd3d3W1/pn7sqehK/t3vdxfe3l5WXr68f5fPPly0d58uSxnIt+7FGmN2tv3rxJp06dcmatkvn5+RERKU3ib7/9lurVq0dERGvWrCEiolGjRtHly5dpw4YNVLNmTXr33XcpPDyc+vTpQ0REkydPpiZNmtC+ffuU+aD37t2jvHnzWs4vp/Nn/2Wzi/R/9/pXxbliYmJE04VV3dj0DJ2hTzKGm8ipgGZS8APMGfrNmzcVGVGWg23VqpVFwY/NlKH/8MMPioyov78/fHx8BOypSpUq2LFjh/I7eoZ+7949RUaUp8zUr1/fqOAHmDP0X375RcHrM/aZFfxKlCghFPzY9Aw9KysL8+fPFxhufvStXLmywNK3a9dOeV9Thq4rY7KiZKdOnSwKfmx6hu5wOPDll18qGG72tUnBDzBn6CkpKUIZ09/fX/i6ZcuWwtcTJ05UfG3K0HVlzICAAHh7exvVGtn0DF1XxuTvb7169YSUcN++fRUYqilD19US+TOSse9r165VfK1n6LoyJpcgK1WqJLD0UVFRyvuaMvRLly4JZcxnn31WNIA7duwofP3hhx8qvjaVXJYtW4bg4GCh3MjXj53QHesncZN5yZIl8PT0ROXKlXHp0iXcuHEDRIR3331X8UlSUpLlifqdd96xlG4aNWqEOnXqiJ/1HhLzUZ6WXP40uSl69OhRUXurWLGiqGPJAd3uNdwUNWlsX79+XQnodq+Rm6K6xjbjs+WAbvcaboqaNLa5McMB3e41clOU8dWyxvaDBw+UgM54e1lzPTMzU2mK7ty5U9HYZjSPHNB1He74+HilKWrS2L57964S0HXNdX6N3BQ9duyYUZdeDui6DjffLLgpatLYTk5OVgK63WvkpqiMry5atKjQpZcDut1ruCnKSYZJl54DOvuaExH2tdwU1YZaTwEAACAASURBVJMMxmfLAf3mzZvi+xAQECC+D3JTVPa1rEsvB3TTa+Sm6L1794y69HJA59fwzWncuHG4c+eO0hS186Mc0A8ePCjKns8//7wI0NwUtZMn/uGHH8T3jueLNmnSRJSbsrOz4enpiTFjxlhiD4+nk1dwcDDmzJkj5BJq1qyJJk2aAFB7ZdxDetoU1UyHLbJWuZx9R0REICgoyKjDzabDFvULg2ty7dq1s83iTbBFPfvmjGj8+PG2WbwOW9Szbx6D9cILLyiMWDmzM8EW9eybs99hw4bZZvE6bNGkVf7ss8+icuXKRh1uwAxb1LPv3r17g4jQo0cPow43YIUtmrLvatWqoVChQkYdbjYdtijr0vv7+4u+SlRUlG0Wb4It6tk3Q/uYEZsnTx5LFq/DFk269G5ubmjUqJHI4itVqqT42gRb1HXpORMcPny4bRavwxZ1X/fo0QNFixZFxYoVbbN4E2xR16WXfW2XxeuwRZMuffXq1VGgQAGhgxMcHGzpifj5+WHw4MHiZz25YX1yJip17drVQlB65pln8MYbbyjHDh8+DG9vb3ETGTt2LHbt2iVuPIULF8a7776LkJAQlC9fXtEBkntITwO6ZnZMUdY48fLyUu6gug43mx1TVH4s5WXS4QbsmaKscaI3nnQxJzY7pqisccKrQIECQrNGP289oAMQGifycGe+Oeg63IA9U5S1yuU9PD09LTrcgGum6I8//qgMvSYy63AD9kxR1jjRfa3rcLPZMUVlPRvZ17oON2DPFGWNE33mpa65zmbHFJU1TmRf65rrgD1TVNc44WXSXAfsmaImX3t4eAjNGtlcMUVlyDAvWbNGNjumKAvryQOeiZwiaab5owEBARg4cKDluJzc8Jo8ebJRvrhq1apKmen69esoUaIEihQpgsTERJQoUUIZHL1z505RBeDl4+Nj4ZsA/yFM0f8Gy5MnD1WrVs3STKlWrRoFBATkeJ/ixYtTtWrVlGNlypShihUr5rjZ4+HhQRERERQcHGw5F/2YKwsKCrKcS6FChahq1ao5bpy6ublReHg4hYSEWM6lRIkSOT4XPz8/y7l4eXlR9erVH6tBGBISQlWqVFGOVapUyXJ+rox9rb/v4/q6WLFiRl9XqlTpsXxdtWrVf9nXhQoVspxLwYIFqWrVqjlunNr5OiIigooXL57jc/H396fq1asrx7y8vIyfuSsrW7as0dehoaE53sPO15UqVTI2mt3d3Y3yvaVKlaKaNWsqxw4dOkRnz561vDY4OFgwXLOysqh9+/aUmJhI69evp+DgYCpevLgyp7hBgwb02WefKXv4+vpSREQEeXl55fhvfSJmF+n/7vUkM/RTp04pGG4eJMDynDrGHLBm6IxpZQw3Z+lly5a1xZibMvSrV6+ia9euICIEBQUJKCCfX0hICDZu3OhSnAuw1t+InBBLPr+YmBgF2mXK0HWsOuOh+TExODjYgjE3Zeg6fpn+zELk85MhYqYMnbHqvr6+8PT0FL6pVq2aOD8dY27K0HWsOg8mYCy9jjEHrBk6N9MYw81ZeqlSpcT56RhzU4auY9X5iYzPr0yZMhaMuSlD37x5s/ANl238/f1tMeamDD0tLU1g1X18fBRf22HMTRm67Gv5SUo+P9nXpgxdx6rLvmYsvT6cxpSh61h19jWfX40aNSyQTS6/sTFMkyGJ3IcpXbq0II8NHz5ced/OnTujePHiAIDBgweDiLB8+XLx/x07dkTJkiXFz4cOHULx4sWFvK6np6exD8bfGXpacnlockBnhiGPsZo5cybu378vmqImFihfGHJA37t3rzLGav/+/UpTVGeB8iQfOaCbdLhTU1OVpqgdC1QO6HZsUm6K2rFA5YBuxyaVm6Iylr569epiapMc0O3YpNwUtWOB6vK5ug73yZMnlabo6dOnFcYvs0DlgK4zDBnDzU1REwuUdenlgL5v3z6FTbpv3z6lKcpYep3xKwd0kw737du3laaoHQtUDugnT540skm5KSqzQGVfywFdT0QYwy03Re0Yv3JAv3r1qkAnBQcHY8mSJcjOzhZNUTsWqC6fq7NJExISlKaojKWXWaByQJd9LRMHWfpW93WnTp1EchMcHCzUFvVEZM+ePbh8+TKInBOarl27ppCq+LoeMWIE8uTJg+XLl4OIFCkB4CGaKSsrC3FxcciTJw+KFSuGQ4cOoWzZsnjttdeMfbA7d+48Dei6cUCfO3euZdAsmw5b1LXKY2NjERgYiCZNmtjqvZhgi7JWeUREhBCrnzRpkqhbtmrVStEA0WGLJp2WiIgIVKxY0ai5zqbDFnWdFmawDRo0yFYDxDTgQtZpee2119CsWTP4+/sbdbjZdNiirFUeGBgo5ip26tRJZJy63osJtqhr8vCTzty5c4UGiK73osMWZa1y1qUvVKgQmjRpoui9yBogJtiirkvPo98mTpxo1FwHrLBFk05LtWrVEBYWpui96LVWHbao69LHxsaCyNkfMslaAOYBF7JWeXR0NJo3bw5fX1/L8BeZYKTDFnWdFh6K/PrrryvDX+RGoAm2qGvyMPxx9uzZtnovHNBlX48ZM0ZJbvLnz48WLVooNyeWOgCcDVsi53hFNl1/iZ8KOKPXm6bz588H0UNZ6wYNGgip5XLlyqF9+/bitXJv5NlnnxV66U8D+sM/Rqy6desa5UztmKKyVjkvGTolmx1TVNcq52XSXAfsmaKsVS7vQUQWzXU2O6aorFXOq0iRIhbNdcCeKSprlcv76DrcbHZMUVmrnJe/v79Fcx2wZ4rKcDN5H11znc2OKXrhwgWBwuHl5eVl0VwH7JmirFWu+7pChQoWzXXAninKWuW6r3XNdTY7puj3338vUDi8nn32WYvmOmDPFGWt8pz62o4peuTIEaOvdc11wJ4pmpmZiQ8//NDia11znU0P6GxyciP7Wr85AU44KRFh0aJFynG+rvW5uu3bt0e/fv2UJf//gAEDlL83LCwMbdu2tZyjrD1DRHB3d7e8JqfmKqD/VzdFo6OjqXLlyjl+fcmSJalDhw7KscKFC1NUVFSO2Yxubm7UuHFjaty4sXL8lVdeodq1a+f4XAIDA6l9+/aW5lt0dDQFBQXleJ+aNWtSmzZtlGMvvPACvfjiizlu7Pn4+FBUVBQVKaKOiu3QoQOVLl06x+dSsWJFio6OVo5VqVKFWrVqlWMWoqenJ7Vq1crSlGvfvv1j+bpEiRIWXwcHB/8lXzdp0kQ53rp1a6pTp06Oz4V9rTeyH9fXtWrVsvi6UaNGj+XrvHnzUlRUFBUrVsxyLv+qrytXrkytW7d+LF+3bNnSIjsbHR1taaa6slKlSlFMTIxyrGjRotSlSxfBDGVzxkPrzE83Nzd66aWXqGnTpsrxjRs30po1a5Ql27Bhw5S/18PDw9iUrVWrFs2ZM0f8/LcxR+0i/d+9/mqGzuI6XBuTiQdspgz98OHDCoab/rxTMoFEHwJgytDl7NHDw0M8vnKttGjRopYhAKYMXc4euXlCfz5OE5mHAOgZup5R8OPrM888YzuNyJSh61ON+FwYX216CjJl6HL2yPVaLy8v22lEpgxdn2rk4+MDoofNbdNTkClDl7NH2dd2k6dMGTpnj4zh5iydeyCmaUSmDF2fasTnYjeNCLBm6DzujH+fn8gKFy4sSnR6ZmzK0G/cuKFMNdJ9bXoKMmXo27dvF58rKzTmypULPj4+xmlEpgxdn2rEvuYSkmnylClD10lI/DcFBATA09MTQ4YMURqeFy5cABFhyZIl4piOvw8ICBD7NGrUyALLZY5K7ty5UaxYMaXsxiUb2TIzMzFx4kS4u7vD29vb+DT4OEb/SyUXrqHfuXNHwVfL80LlgC7rcMtSp9wUtZsXqgd0uebHdGK5KSrPC5UfGeWAbqrl37t3TzRF9RmSo0ePFrVrOaCb5o7KTVF9XiiPwJIDul7fZbw+N0WzsrLwySefGPsUckDX67vr1q3D/fv3QeRsiuo1RO5TyAFd1+FmDDc3RVNTU5V5obIuvRzQ9fouY7i5Kcr4ar12rQd0vb577NgxpSkqzwuV+xRyQL979y7GjRsnbk6M1+emKOOrTX0KOaCb5o7KTVF5Xqjcp5ADujxeUZ4xy01Ru3mhgBrQ5ZIl4/UfPHgAImdT9PLly4pWOYveyQGda/k6mYybordu3VKua7lPIQd0PRFhMhlf18nJyYKAFBQUJJq858+fBxFh6dKlAFRyWL169XD48GEsW7YMRE6cupubG5o1a6b0OOrWrYuaNWvi8OHDCAwMROHChUXTu3r16mjWrJl47fnz5wX4ICYmBqdOnQLR0xq6MB22KKNLuG724osvIiQkREFgDBkyRIGh6bBFfZDu4sWLQeRk2sk63Ky5Dlhhi4wukZs6Xbt2haenp9KV79ixo0BgAFbYoowu4QvD29sbMTExRgQGYIUt6lrlLVq0EIzIBQsWKAgMWepUhy3KiAM/Pz/MnDkTYWFhaNSokRGBAZhhi7p+NU9HnzFjhsjyZAQGYIUt6lrlI0eOxEsvvYSyZcsKBIaMtmHTYYsJCQkKk49vdMOGDTNqrgNW2KJJq7xbt27w8PBQYHKsuc6mwxZ1rfJly5bBx8cHHTt2FAEpMDBQ8bUOW9TRJc2bNxdKkwsXLhSN5saNG4vAA1hhiyYkUXh4OP7xj38oiQhrrgNm2OK+ffvEU1VkZKTw9fTp042a64AVtmjSpW/atCnKlCmjgAp0Mpl+XcsSAZGRkYK+P3XqVMGmlWU4gIdNzytXrmDBggUgckozZGZm4o8//oCXlxeGDx8OwElOe+aZZ1CwYEHEx8cjMjISL730EgBg5cqV8PX1hb+/P1avXg3gKWzRYnZMURn/La+XX35Z0eFmMzFF+cLQpVtNGGnAnimampqKESNGWIbSmvDwgD1TVNYq5+Xp6WnBwwP2TNH79+8L/Le8T9myZS14eMCeKSrjv+WlY6QBe6aorkvPizXXdfarHVOU8d/6uegYaTY7pqisVS77WtfhBuyZorJWubyPCQ8P2DNFZYid7GsdDw/YM0Vl/Le8jwkPD9gzRWX896N8bccU1XXpZV8zPFA2O6aona9lKKhspuua4cv69y5PnjyYMGGChf3Ks0T5RsE/v/7660ILZsOGDeL1Z86cQfHixeHv7w83NzfUqFFDSB/UrVtXKXE+Deia2QV0wJl56RRs00UF2FP/s7OzxR2aV4cOHSwXFeB6SPSVK1csOtqMudXNLqAzlVu/KegXFeB6SPTt27fFozCvefPmWS4qwD6gA87MS97D399fye7ZXFH/79+/j/Hjxyv7vPXWW5YACtgHdMCJ4dZ9zVh63ewCenZ2tsjAeEVHRxt97WpI9NWrV0UJjJdJKgKwD+gOhwMbN2603BTkJzk2V0OiU1NTRcbP64MPPjD62i6gA85kQt7D19fXGEBdUf/v37+PCRMmKPuMHDnS6Gu7gA44k4mgoKAc+druunY4HFi0aJGyx5QpU4w+Mg3BmD59uvK7ycnJyM7Oxs2bN3H69GmsXbtW+X8PDw9MmTLFItvwNKBrZgrot27dEnVwOTvhx16ThorJ8XJtVB5Jpdff2UwBXa+D8x7yY68s5wmYA7peB+d99Po7mymgcx2cS0C8BzfX9MdewBzQ5Tq4nHHp9Xc2U0BnTRmugzNUjZ8cSpUqZQmCpoCu18H5XPT6u2ymgC7rjHCDUva1rpdjCuhcB+dyn+5rXboVMAf0EydOKBhu3de6Xo4poOt1cPY316pNejmmgK7XwXVf6yUOU0DnmxOX+3Rfm/RyTAH91q1bGDp0qCAOmq5r/YZnuq5lnDk3X3k1adLEMmpu8uTJICLLTVBOihhMIe+lr88++8yyx39EQCeipkR0iojOEtFbhv8fSkQniOgYEX1PRCUeteeTCOi6DjejF7gpevfuXUXlUBYXkh2vKwGuWrUKV65cAZGzKRofH2/UKpcDukkJ8OzZs6Ipyo0pvjD69+8vLgw5oOs63IxU4aao3JgqUaIEPv/8czgcDktA15UADx48KPoCv/32m4Kll1UO5YBux3bjpqhdY0oP6LoS4NatW5Wm6LZt24yKlnJAN6k+JiUliaaojpCRFS3lgK7rcMfFxYmL7KOPPrKgodjXckBnJUC9Ic9NUTvpVkAN6CkpKRg0aJDwNSNVuCmqo6FY0VIP6KaGPDdFz58/r2DpZV16OaCzuJ3ekOemKGPpda1yPaDricg333yjNEVlNJSsaCkHdBa304eHc1OUG86mG558XfPISLnndObMGRARFi9ejHnz5iFfvnzImzcvZs2aJbLp0aNHI1euXCLm3Lp1CyNGjFBu2r1798a4ceMwd+5crFixAv/3f/8n+lJ8AyRy9mLmzZsnvov/9oBORB5EdI6IShORFxEdJaIw7TUNiSjvn//uQ0RrH7XvvxrQv/76a4sON5sOW9ShY2vWrEHRokURHR1t1OEGrLBFHToWFRWFuXPngsip88AaETqMUoctyhcG65DXr18fNWvWxLx58yw63Gw6bHHHjh0COtagQQPExcWBiDBr1iyhw603ezigcwar65BPmDABnTp1QlBQkK0uO2CFLeqa85s3bwaRs8lo0uEGrLBFXXO+d+/eoom7adMmo1Y3YIUt6przjKho3749Jk+ebNHhBqywRZPmPNdRly1bJmByOoxShy2adOkbNGiAyMhIfPzxxyhYsCDc3NwskFkdtqjrkK9atQpEzgEL3NjTIbM6bNGkS9+5c2cEBgYqw19effVVBTKrwxZlXfrw8HBs2bIFRIQhQ4YYddkBK2xR15zv1auXaOJu3LhRkZ+Wn4h02KLphle0aFG89tprmDZtmtJQ5cz/t99+A9FD2OLFixdFv6B69eqIj4/HkCFD4OvriwcPHuCDDz4QfurSpYsoIemlxu+++04kJESETz/9FOvWrRM9sMDAQEyaNAm//PLLvz2g1yKirdLPo4lotIvXRxDRnkft+1cDulw31XW42eyYovKFIS9dhxuwZ4rKIv7yHiYdbsCeKSrjZ+Wl63CzmZiictYq72HSXAesAZ3t999/F4FBXroON5sJh67r0vMy6XAD9kxRWZde3sc0TQewZ4r+8MMP4glFXu3atbOwJ+2YovITiryHPBBENjumqEm6lW/Epv6LiSlq52u+Ees9JTumqPw0qvuah7/IZsKh60+jsq95+ItsdkxR+QlF3kdPRNjsmKLbt2+3MGiJVCgzm4kpyslacHCwci6ywBknEYxjf++998Tvp6eno3Tp0ggJCUFaWhp8fX3Ru3dvsfeuXbssTea/Gv+Afz2gRxHRIunnGCL60MXrPySicTb/9yYRHSSig6xm9hf+GLHkTrNsdgEdcA79lfcIDQ010q9dDYl+8OCB0Pbg9d5771mGDwP2AR1wYrj1L6H+BWSzo/47HA7MmzdP2WPw4MHGQdN2AR1wYri59svLNGgasKf+Aw9HfPFq3ry5sanpakj03bt3Bbad15dffml8P7uADjhrp/IeZcuWVXRg2FwNiX7w4AFGjBih7DN37lyjr+0COuDEI+s1V5mQIpsd9d/hcIghxbwGDhxo9LVdQAecvtYDoGnQNGBP/QeAr7/+Wtnj5ZdfNvra1ZDou3fvWuQ41q1bZ3w/u4AOOLkZuq9N17VJy4UtJSXF8rnI0FW2smXLKp/JyJEjQUSiPNe8eXPjoOi9e/cqe/9V+/8W0ImoExHtJ6Lcj9r3r96heHoPQ5BatGiBU6dOKa8xBXRdMZE/VG6uzZo1S7kw7AL6li1bLFPKuQGlCyQB5oDOE+lZVIj3kfHVugaFKaAfOHBAIGk8PT0tNypZIAkwB3RdMZF/n1EFukASYA7osoqePD2dfaVPljEFdFbR03+fz6V58+YWX5sCelJSksDryxktN9dYqZHNLqDLiom6r1mVUzZTQJcVE+VMX1fllM0U0A8ePKiwMnVfs1Ijmymg64qJ/Pu6KqdspoB+9uxZZVar7mtZDAswB3Rd1pYX+6tZs2Y4efKk8r6mgK6TyR51Xctqi/rfxKU6eZUpUwbLly9XnsZ69+6NfPny4cGDB4iPj4eHh4cy4Yj7AXLD9tdffxU8ASIn/+Kv2r8a0HNUciGixkSUQERBj9oTT6CGfvPmTbzzzjsWCVNADei6DjdjuLl5okuYbtq0CQ6HwxLQdR3ujRs34ssvvwSRs1EmS5h27txZyHnKAV3HYTOut2HDhqhXr56Cpde1yuWAbnrdsWPHQORsispa6jIOXw/o8usYw81NURlLL0uYAmpA13W4p0+fjtTUVBA5m6Iyll7G4esB3SR1yk3R69evCx/quvRyQNd1uJlMxk3RkydPKrr5jMPXA/rp06ctmubcAD948KDiQ9nXckDXcdgs3cxNUW7Y8Q1L9rUc0E0Sr1yHXbNmjYKll3Xp9YBukm7mpqiumy/j8OWArks3T5s2DXfu3AGRsylq8iFgDei6dPOPP/4ogmBycjJmzpwpfCjj8OWAriciLOfL17VMHpOvaxnsADh7WgMHDkSuXLmQN29eTJgwAa1atULp0qWxadMmUbYLDQ3FqlWrkJWVhXXr1omMvFq1aggODlaIbEeOHAHRQw319evXI1++fAgODsbnn38Oon9vDd2TiM4TUSl62BQN114TQc7Gacij9sMTCuhcM9S/8IsWLcIrr7yCsLAwow43mw5v0ocMfP/99yAivP322wp7jjXXASts0fSFHzx4MLy8vBQdbj2744DOJn/hmT3p4+ODfv36CZicnsnrKBf+wvOFMXjwYBEgt27dqjAl5exOhy3KWuVlypTBhg0bUKVKFbRo0UK5icnZnY5yYWKHrF+9Zs0aEBFWrFihMCXl7E6HLZp06V999VWUL1/eosMt+1qHLerBbfv27SByMhlNmusAsGHDBhCRqKXqT1nTpk0TELv9+/crTEm5nKHDFk269L6+vujbt69twqKjXEy69Bwgt27dqgxX4cAGWGGL8lMWM2UjIiLQrFkz5SbGmuuAFbaYnZ2NuLg4RZeefb18+XIlEWE6PmCFLZp06du2bYvy5csr16o+XKVYsWKKr+Ub3osvviiu6zlz5mD69Onw8/ODu7s73nzzTfH9fe211xASEgLgYb+ASzEVKlTAggUL4ObmJp6ude5HdnY2ChYsiJiYGNFEjYyMxKVLl/79KBfn71MzIjr9Z9Ae++exKUTU6s9/byOiJCI68ufa+Kg9nzSxSH4klZeuw81mwqvKj6TyHiYdbsCeWCQ/kspL1+Fm0wM6YNUql5ep2WNHLEpKShJaNvIeJh1uwJ5YtHXrVkuNnchZZpI11wF7YpGsZSPvYdJcB+yJRbKWjbzKlSuHzZs3W87dhEOXtWx0X8taJmx6QGczSbeyr1nLRDYTDp19rZcfiMwlRTtikaxLr/taLz0A9sQiWZdeXs8//7ylpGhHLJJ16XVfm2Rt7YhFspaNvEwlRcDe13PnzrX4msipC6QjVtq3b49y5copx7Kzs/HZZ58ZP5cVK1Zg5cqVypL/v1u3buKJ5z8ioP8d6+9git68edPCKDNpPAP2jDIA4pGK1xtvvGFBNACumaL37t2zDI410f4Bc0Bn09mZderUsaBXANdM0aysLDF5ndfatWuN7+eKKcrCRrzy589vQTQArpmiDodDaHvwmjFjhpGx54opevPmTQuV266hbMcUBR76kFf37t2NvrYL6IDT1wxX42XHULZjigLObF3eo1atWkZfu2KKZmVlWTS7Ta8DXDNFGc0h3xRkaCWbK6aow+EQcE9e06ZNM/raFVM0JSVFZPy87BrKrnzNPuTVtWtXY3M7KioKYWFhxj1u375tGcL9qCX78OmQ6BxYVlYWffzxxxQaGko3btwQx729valixYo0adIkunfv3iP3+f333yk6Opratm2rHF+8eDFVq1aNtm/f/sg9ANAXX3xBFSpUoJ07d4rjBQoUoLp161KfPn2Uc7SzGzduUN++falOnTpUoEABcXzPnj1Uvnx5Wrt2LT89ubSdO3dS9erVaf78+crx6OhoioqKogsXLjxyj3v37tGUKVMoPDxcGcKdlpZGoaGh9OGHH1JWVtYj9/nll1+ocePGNGzYMOX4W2+9RQ0bNqQjR448cg/Z18nJyeK4t7c3VapUiSZOnEjp6emP3OfixYvUoUMHatOmDbm7P7wMlixZQhEREfT9998/cg/2dVhYGO3YsUMcL1iwINWrVy/Hvk5JSaGBAwdSrVq1lCHX+/bteyxf79q1i5577jmaN2+ecrxDhw459vUff/xBsbGxFB4eTrlz5xbH09PTqVy5cvTBBx9QZmbmI/c5fvw4NWnShAYPHqwcHzNmDDVo0IAOHz78yD2ys7NpwYIFFBoaSteuXRPHvb29qXLlyjR+/Pgc+frSpUv0+uuv0yuvvKIM3F62bBnVq1ePjh49qrze4XAo3wki9Xt37tw5cbxs2bJ05swZy5IHWNetW1cZKv23ml2k/7vXk8rQdebZ0aNHRVNUxlfrxAs5QzexDM+ePSvupDK+WiZe6Bm6PLGncuXK2L59u6ipy/jqgIAARb9aztD1EXVMtc6bNy+GDx+OH374wUi80DN0mXTBeH1uiiYkJBgZoICaofPEHiZTMV6fm6Iyvjo8PFw0vfQMXWcZzps3Dzt27AARYcuWLZg/f74gb7z55psiE9QzdBlvzHh9boqa2L7sazlr03W4J06cKJ485s2bh3Xr1hm1yvUM3eRrZhjevHlTsH11XXo5Q9c11xnDnS9fPgwdOtTW13qGrpOp1qxZIxQkT5w4galTpwpfy1rlcobOWGwmU0VFReHChQuiKSqzfWVClZ6h37hxA/369VOIczt37gQR4ZtvvjGyfQFrhi5rrjNePzo6GuXKlcPFixcF21fXpS9evDi6dOkifK0T5/jJ4/3338eSJUsQGBgIDw8PDB48WJSBYiSRwgAAIABJREFUXnnlFVSuXFl8Ll999ZXoudSrV0/I63LPhuWp2TIyMpA3b170798fmzZtgp+fHwIDA7Fjx46nJRfdOKAfO3ZMmakp64DosEWdBv/zzz+L5olcu5R1QHSUi0yNzp07N0aPHo0VK1aAyNm95zq1rMMNWGGLOjV68+bNIqDLzTpdTY4DOgALNbpHjx6i2bNkyRJRp5Z1uAHrgItLly5ZZqr27NkTwcHBSu1S1wGRUS6s0cI0eK5JEjlnreozNVnuQEe56Lr0s2fPxrRp00BEOHLkiKK5LuuA6LBFnQZ/4MABcZHLmuvR0dHC1zrKRdfjGTVqlKiLbt261dbXOmXcRIPngC5rrstyBwBEQLfzNd8MFy9ebCt3oKNcdD2e5cuXo1evXihUqJBRc51NRrlwcJNnqvLNZcKECYrmev/+/YXcgY5y0fV4Zs2aJQSxDh8+LHwtS1sAEAGdTdZeYshwiRIl0LlzZ6PmOuDsMxARPvzwQwDO0l3v3r3h5uaGZ555BqtXr0bLli1RtWpVHDhwQMhAlCtXTkhnc6Jx5swZlC5dGhUrVlQ0W/jvZTz9yZMnUb58eXh4eAhG7NOA/qexZjkRWXS42Uw4dF28iPcgsupwA/Y4dFm8SF4mHW7AjEPXtcrlZSdrKwd0Nlm8SN9H1+EG7GeKykJVvEw63GwmHLqsS6+fi0nq1I5YlJCQIISq5KXrcLOZcOisS8/4anmZBMnscOiyLn1OfK0HdMC1r+Wp97LJAZ3Nla9NgmR2xCJ9tqXsa118DjDj0GWtcv1cTIJkdsSihIQEARnWfa2LzwHWgA5Ydel1X+uCZHpAZztw4IDlGiAiFCpUCB999JEyDapRo0aoWLEiAAgYooxrj42NhZubm7ihAU4VTEaLETl7NX/V/qcCuvxhm2jpgGumKE8M4VWuXDnLFwdwzRQFYJGAlUdayeaKKcpfLnmZmIwA4O3tbQnobMuXL1f2GD16tLHxZBfQAWdWqg+bNmmLA66ZoqwXzat169bGxpMrpmh2drbQo+G1bds24/u5YoqePn1a2SMkJMToa1dMUQCYNGmSss/ixYuNrzMFdLYbN25YmKJ2vjYFdDbW7OE1atQoo69dMUXv379vkcAwySADrpmiu3btUvZo2bKlUabXFVM0OzvbIjth52tTQGdj0S1eZcqUMTaU7QI64Gxuy0gjPz8/CxonNTUVnp6eGDlyJADnDbtOnToIDg4WpcFGjRqhSpUqlv11gMNfNVcB/b+uKdqxY0cicg51ffHFF2no0KF0+/btR/5eRkYGvf3221S9enVlqOupU6eoUqVK9NVXXznvcI+wkydPUrNmzSg2NlY53r17d4qJiaErV648co/s7GxatGgRhYeHK8Nq3dzcKDw8nObPn29sMuqDba9cuUIxMTHUpUsX5fj06dOpadOmdOLEiUeeCwDatGkTVa5cmY4fPy6Oe3l5UY0aNWj69Ol0//79R+6TmppKI0aMoMaNGyuf71dffUWRkZG0Z8+eR+5BRLR//36qVasWrV27VjnetGlTGjJkSI59/c4771h8febMGapUqRJt2LAhR74+deoUNW/enCZNmqQcf+ONN6hjx450+fLlR+7hcDho6dKlFB4ergwGdnNzo0qVKtGiRYuMQ4V1S0xMpO7du1OnTp2U42+//fZj+fr//u//qHLlykoj0MvLiyIjI2natGk58nVaWhqNHDmSXnjhBaVxumnTJoqMjKTdu3c/cg8iop9++olq165tGbzctGlTGjx4MN26deuRe2RkZNDMmTOpWrVqSsPz3LlzVKlSJVq/fn2OfP3tt99SlSpVlOs3LS2NOnbsSNevXxfHtm3bRllZWdSsWTMicvpx9uzZlJSURO+88w5lZGTQ3r17qWHDhuJ3zp8/Tx06dKBatWqJY6NGjXrkOf0ls4v0f/f6V2vo58+fV0Z0yY+Lcoau63DzjMKiRYuie/fuCr1bHtGlZ+i65vrs2bMFYWL37t0KvVt+XNQzdLnGy7K2//jHP1C/fn2l0aaLJXl7e2PEiBEAHirnyY0uvvuvWrXKdkSXnqGbarzcFJWx9HppQM7QTfMoWQBp6tSpSo9C1qXXM3RTjZd9ffbsWWUurKxVLmfoeo2XMdzcFJWx9HJpQM/QTTVeHmDw448/2kq36hm6qcb7wgsvCL/bjd6TM3QubzDBaOTIkWL4xMqVKxUqv+xrPUOXNdcZw81NURlLr/co5AxdHrFI5MRXX7p0CUTOYRF2PQo9Q/9/7H13WFRXt/6aBkPvTQURRQRULCgidkBFxYKxV4IFNdaIxoo9NsSGJaixxGgwJpYYJTGxJCZqjA2jxh67oiC9zcz7+2Oyt6fh5839cu/9fU/W8+wH5syZPWfOPmedvdd633c9evSIhy2ZLASLS9+4cUOWo2BjLZyhSzXXGXHQ19cXgwcPFmHpWV1YQD5Df/DgAQcP+Pv7IzMzE82bN0e7du2wevVqWFpawsPDgyc+ExIS4ODgIArBAEDfvn1hZWXFV1D79+/Hy5cvMWnSJOh0OlhZWWHWrFl81fhPDP1Pk6JclBI6zKFnZWXJdLiZMYcOQFGrnKFG0tLSFDXXAXDqP1NHFBZMZgkdRou/f/9+pYgb5tAB84UqlDNlSAsWchEme3r16sVlbaUoF6lW+dq1a/HRRx+ByIzUqAyFIcWhKxVMZg69ssLYUpSLVJd+zpw5yMzMBJFZLpWhMKSFsaUoFyVdeubQr1y5gujoaNHDiZkQ5SIca6ZVzpK4a9asqRSFIUU03blzR4Yi+uCDD6DT6USIG6a5zsJOzKGzsZYWx75//z5sbW0xceJEWQKSYa+lKJfnz58jMTFRhCJKT0/nYy2ciLAi6IAchy7UpWeIMebQKyuMLUW5SBFjycnJ+Oabb/hYCzXXhWQyKcpFqkv//fffc4cunIhIyWTMobNjE+rSjx49GteuXQORWWBt6dKlsLGxgV6vx/z58zkrWFjo+fLly/z6Hz9+PJydndGrVy9I7e7duyIS1cyZM+Hk5MQnOazK2D8oF4kpEYuk8DrWlHS4mQkdOjMhvE7YD5Fccx2QO3RmSnKeQuiUlBQldOjMpAUHhH0pJXsqIxZVJtNbmdSpErFIqlUu7Ef6cAIqJxYJdemlTarDDSgTi6S69NKxFupwM1MimwjhddJjkWquA5WTyIS69NKxFmquMxM6dGZSJyjsKzAwUAaLq4xYJNSlFzbpRISZErHoTWMthYOy/YUOnZkQSiltPXr0kBH+lIhFUl164VgLC4IITejQmQl16aXHEhsbK6tYVLduXfTo0YO/Li4uxnvvvSf6HCviPW7cOHzwwQeYN2+e7Hx16NBBhF4C/nHoMnsTUzQ/P1/GFFWSigWUHTozaW3HkSNHKiaeKnPogPlCl95cbAYrtdatW8scOjOpLGjLli0VmYzMoSsxQE0mk+yCrEyO9k1MUba8Zs3JyUmR2fcmpigArF69WtTP8uXLFfd7E1M0Pz9fhmxQSvYCb2YPSuV+ExIS3jjWSqzgiooKGVNUKofATMmhMzt//ryoj/DwcEVp3DcxRU0mE8aOHSvqpzI52jcxRZkqIWv29vaKtVbfxBQFgLVr14r6Wbp0qeJ+b2KKFhQUyIpNKyV7AWWHzuzQoUMyp6yUsK9Vqxb69u0r+o3SxLifnx88PT3h4OAgU78kMmu3KPmof5iib2lHjx6l8PBwEXuQiKh58+a0c+dO89PrX1hhYSHNnDmTevXqJdq+ceNG6tGjB926deutjuX8+fPUtm1bEVNUq9VSZGQkpaSkUHl5uewz0oRnaWkpLV68mFq3bi1K7P3www/UvHlzOn369Bs/z+z27dsUFxdHa9euFW3v27cvffDBB1RQUPAvfw8A2r17tyipQ0SUm5tL4eHhlJmZ+S/7ICLKzs6mxMREGXswKSmJhg8fTs+ePXurfr777jsKDw+X7d+8eXP65JNPRMnHyqyoqIhmz54tG+vNmzdT165d6caNG4qfk57nc+fOUZs2bURMUa1WS9HR0bRy5cq3YlUaDAZat24dRUVFiRiKP//8M7Vr145+/fXXf9kHEdHdu3epV69etGbNGtH2fv360bRp0/5bY52fn0/h4eF05MiRtzqWFy9e0KhRo2jcuHGi7VOnTqVhw4b9l8dayBQlMo/19u3b33qsk5OTZQzwnTt3UmhoKGVmZor8Q1lZGWdE379/n9q2bUtz5syhiIgIIiLatGkT3b59m548eUKvXr2i8vJyWrdunajvs2fPUmBgIH3++edv5Xv+bVaZp/+7279rhq6kEBcXF4e6detWGvcDxDN0psPN9CIGDBjAdTVWrlyJDz/8kIc+hAqH0hm6VA3wo48+wuTJk2FhYSHC3Pr7+4uw5q1bt0br1q0BvFZ3k9arZN8tPE4myQqAx4HZDF1J+ZEVRzh16lSlCofSGboweccw3A0aNEBsbCz279/Pk1JCESnpDF2qwz1+/Hi+CtqzZw/HV9vb22PZsmV8ViqdoSslat955x0EBgaKsPRSXXrhDF2qw92vXz9eeGDt2rUcXy2V6ZWONZMwFsraTp06FVqtFlevXpUlm5lJZ+hK9VRtbGwwYcIEEaFIKBgmnaELRc9YUn7dunV8rBmWXqpLL52hS8f6xIkTaNSoETp37izC0gt16aUzdCWms3Cs33//fUU1S+kMXZqo3bt3LxfNEmLpmzZtKlIuFc7QpQJ3DK9vYWGBpKQk7Nixg+eq2rRpw1Ux3d3dMXLkSOzZsweOjo6wtbXFjh07UF5eDo1GgxkzZkBomzZtApFZ5bNGjRro3bs3Tp48yUNxkZGRHAL8T8hFYsyhP3nyhCccpRrOzKEDcuLBkCFD8PjxY+7QhXrdQg1nKcpFSYP8888/BxHhzJkzXOpUqLkOAFOnToWlpSU/fiU2KHPowni3tF6lTqfDBx98AEBcNIGRq1hoZteuXZVqswsLBwNmDXKpVjlz6EqyxAxtIES5lJaWymReGZJg4cKFot/boUMHjneWolyEWuVM5nXZsmUgIjx69Ej0cBKOtRDlItWbZ7+dOXQlqVrgdZ1Jhi8X/nb2YGYEkjNnzlRar3L69OnQaDQA5ISiLl264MaNG9yhCx9OUqaztbU1175/9eqVSNJ3yZIluHDhAogIn376qewBz4oqSFEuSrr0zKEryRKzsRaiXIS1BdgD78WLF9yhC4u/MM11QI5yUaotwATbHjx4IJuIsLEWolykevODBg3Co0ePuEP/5ZdfePEXKYrIzc2Nl4grKyvDmjVreKhWqpLatGlTUbzf19cX/fv3568//vhjqFQqdOzYESUlJQgNDUVMTAyA19IOjo6O3C8wHsw/Dv1PY7FgpkWsVGVF6NCZ5eXliR4AwkFTqqhTGbFI6BSkTaq5DpjLUwkdOiCWbhUmaoT6F9I4uVarxbRp00TbhKgaaVOSOmUaFMLko3R1wpqSDjczJWKR0ClI5Yf9/f1lFXUqIxYJta5ZY4lCoQ43MyVikXR1IuxLqsMNvFaR3Lp1q6ifc+fOKcr0CldOQpsxYwZ36MykTpB9XslhMVNiBd+4cUNWl5I1YeEQZkrEor8y1krEIuFYOzk5ifqqVauWSHMdqJxYJHzYs8aQIsKJCDMlYlFl1b/YRERYOIRZrVq10K9fP9G2goICzJ8/X3QsdevWlRHR2rZti2bNmgEwE/pUKhWio6P5GLZt2xYtWrQQfeb58+dc2531PW/ePPxV+49y6MITLoQhCk3JoTNjyUPWAgICFJNub2KKmkwmTJ8+XdSPtKQVsylTpkCv1yu+x5ZfwiZ9ODFTWuoxYxA11t5//33FZA9jlErRJID5gpYyRaUZemZvYooyTRmh41NK7L2JKVpeXi5DSEiRHszexBRlYSjWatSoIWP+AeBCbNu3b5e9ZzKZZPVjlepRAmaHrlarFd9jlXKETViiTGhKDp2ZlBU8efJkxbF+E1O0oKBAxhStbKzfxBRlhUFYi42NFZX2Y/Ympmh5ebmMKapUvwB4M1OUwRFZa9y4seJYA2JYotBY1S9h8/LywgcffMBDTAkJCfDw8MCOHTugUqkQGRkpcvpdu3ZFgwYNFL+XiZSx9lftP8qhCyu5u7i4YN26dbLZbFxcHOrVqyfaxiBQ0qr0RGLiAbPKHHplVemtrKwUIYlJSUmKDl2pXiWRmbKspO+hUqkwc+ZM2bEw4oKwD4ahllLLmTOQQsYuX76sWJVeKo7ETMmhC0WkpP0ISSbMKnPoDx484Ep6wlbZWCs5dJPJhC+//JLnIYQtIiJChkBhZI9PPvlEtP2PP/7g4mXCVlmRhpkzZyo69GPHjnHVRGGTxn+ZKTn0srIyLF26VLa6ZFh6oW4IULlDF2K432aslRz6m8ZaKITFrDKHLiSTCRvD0kvHWsmhS4mDwjZ06FDZeQHM1Hwp0uj48eNwcHBA1apVYWlpiaioKOzbtw9du3bl93qLFi1E11Tbtm1l8gL9+/dHzZo1Zd/5/fffi46xW7dusn3e1v6jHDqLoZ86dYrDAqWsyh49eogcuhIDs2rVqhgyZIgIczt69Gh+AUgdekVFBdatWwcXFxeO4WZEnf3793PtkWrVqomwuklJSbCysuLHolSvMiIiAu3atcPhw4c5iaFVq1Y4d+6ccBAxa9YsAOa49YoVKzhxISEhgT/916xZw1mOLObKZk1MHZI5dCkhZc2aNUhISIC7u7tMvlQIzZQ6dKHMa8uWLTmTccaMGTL5UvbAkzr04uJizJs3j7NfZ86cyaFiP/74Y6VjLXXoQklflofw8fHBoEGDkJ6eznMpwnjz9evXQWSOSQPm8Ny0adOg1+u5smZaWhqICMePH+cJZZYMZfHmmTNnQqVS8WO5desWT+x5e3tj165d6N69O4KDg7Fjxw4e/x0yZIioQpJUt+frr7/mtV+7dOnCC7Bs3LiR46udnJxEfAupQ5dK+q5atQqdO3dGvXr1RFh6xlhmJnXoUknfM2fOgMisHzR37lw+1rNmzeJjLXXoxcXFMvnmefPmgYhw8uRJDgGtW7euSNdF6tCFxMGgoCBkZmbC09MTgwcP5vVdXV1dsWPHDtGDqkePHqIV/Oeffw5LS0sEBgbijz/+QPfu3UXvP378GEuWLJE9NKSCfoAZVODu7s5f5+bmYtiwYSAyh6M+/fRTEP0TQ+cmRLmYTCaRVjljVTKH/uzZMy4PIJU6rVq1Kq/ULZx1sxuD0dc3bNigqLkOyJEPQuYkQ9Uwhy5Nbgkz/M2bN0dUVBQA84Njw4YNHN0wZMgQjgmeNWsWMjIy+CxBSFy4ffs2iF4XphUmnvz8/LB3717u0K9evcprUEop44mJifyCrKiowPr16/lDbOTIkXj+/Dl36EKikLe3Nz777DOYTCYZykVJl54JO33zzTciUhjT4QbEKBfpWPfo0QO3b9/mDv3FixecFCbNQwhRLizmyuSFZ8+ezRFNO3fuxPr163mCbMCAAXxlwWQT2LH98ssvPL4eEhKC77//HrNmzYJKpRKNtY2NjUglsl+/fqhVqxY/lqlTp4rq1ZaVlXGZhxs3bvBEce3atTkjkhUhZlwCpYcYc+g3b94UTVqEZLLIyEiEh4cDMM+6pbr0z5494w5dSXPdZDLJUC5SXfpPPvmEP7wzMzNFmus9e/bkCXohyqWy+5o5dEYKUyIOuri4YMyYMQDMK09WtDo6OppPZIYOHQpvb28AQFpaGlQqFcLDw/k98N5778HR0VHkd65evSpTqSQy5y82bdrEH16TJ0+GtbU1AHPlM09PT2g0GkyZMgXFxcX/oFykpkQskupXs5MtLJAslToVOnRmQhF/aWJPikQAlIlFlUm3CrVOpPUqmzVrhvbt24u2vXr1iidx9Xq9qK969erJYspMuF+q+piZmcln/dImRJ0wS0xMlJFNpFrl7POM2Th37lzR0rMyYtHJkyf5A0/apDrcgDKxqDIGrZIONzMlYtHdu3dlio6stWzZUhaW2bhxI4iIw0QBeVEI1tjDOD4+XpYTeffdd1GtWjXRNqnjZv3odDrY2tqKoJzAa7LZvn37RMci1KVnzdraGkRyzXUACAsLk113wrG2t7eXjbVQcx2onFj0ww8/cMiwtNWvXx/Hjh0T7a9ELKrsvmZUfqVQk4ODA8aPH89fGwwGpKWlwc7ODnq9HosXL8aYMWNgZ2eHGTNmgMis4S/8TUybvaCgAAaDAcuWLYOlpSVcXFw4cubWrVtYvXo1v7fs7e0xZswYLgfB0DINGjTghUmAf2CLMnsTU/TRo0dwcXERXTyVScAqOXTgNU5Z2MfQoUNlSATgzUzRvLw8GUJCaYkGAE2bNkXHjh0V35NKlIaFhSkmGe/fvw8iwqZNm2TvVVRU8GUfa1u3blVkRI4aNQqurq6KxyJNMjo5Ock014E3M0UNBgM+/PBDUT9z585VlF19E1P04cOHsoemNA/C7E1MUWlB3yFDhiielzVr1oCIZPR5wDzTFjpi4exZaqNHj670/Errm0ZGRiomyX/55RcQEQ4ePCh7r7S0FOPHjxf1k56ervibgoKCEBcXp3gsUtaqvb29Iuv6TUxRo9GIJUuWiPpJTk5WHOs3MUUfPXokY4pWNtaVJZQfPnyoiAgbNmyYLFbP7v+DBw9yB96tWzc8ffqUr8LYbzCZTPjhhx8wcOBAGfX/ww8/lEkT/MMUfUu7fv06JSQk0MuXL0XbBw8eTD///PNb9WEymWj79u2UlJQk2r5z506aPXs25efnv1U/Dx8+pMTERJmM6IABA2jXrl3mJ6nke6U1DFkdzw4dOogkSs+cOUMNGjSggwcPivrRaDRERDIp1nv37tHQoUNp06ZNou3jx4+nlStXylirlTFOT58+Te+++65oW25uLo0cOZKuX7+u+BmplZeX08qVK+nDDz8UbV++fDmlpKRQWVnZW/Vz8+ZNGjVqlIxtmJCQ8NZjXVJSQnPmzKFhw4aJtm/bto3CwsLom2++EZ1fxvgUsnbLy8tp06ZNFBISImOW9unThxITE+n+/fui7Xq9XiZRazAYaOvWrTIG7ZkzZygjI0M2powdKb1mjEYjbd68mT7++GPR9qSkJEpNTZWd38LCQlHtSyLzeVm5ciWXh2WWn5//l8Z60aJFou0pKSm0fPnytx7rW7du0ahRo2RM0eTkZEWpaoPBIJLRZVa1alXauXMndezYUbR9x44dVLt2bWrRogX17t2bJkyYQNu3byciotjYWLp69Srt2LGDvvzyS/Lw8KCXL1+Sk5MTv99UKhVFRERQ3759ycXFRdT35cuX6enTp2/1O/9tVpmn/7vbv2uGLpU6XbFiBWJiYlC/fn0Z+1MIE5PO0KXsMzb7njNnDuLj40WMQPZ0ls7QpcmeWbNmYdSoUbCyssKxY8c4QqdZs2aclQaAs/GAyut4MrLJF198wWeDrVq14lhz6ZM/JyeHs1T1ej2mTZvG2YP79u3jlZ+kanWjR4+Gi4sLfy0sU+fl5YWtW7eifv366NixI1JSUnhYa+LEiVzrQzpDN5lM+Oqrr3gNxk6dOnGNjy1btnB8dc2aNXmZL0A+Q8/Ly0NSUhIPRSxZsgRdu3ZFQEAAtm/fzmdxQgYtIJ+hHzhwgNcN7du3L4/xbtq0CZs3b+bnvlWrVnxVxWaaBQUFKC0txfr16/l+oaGhOHjwIL82r169itGjR8PCwgI6nQ6jRo3i1x5L1gHmGWxGRgbHYjdu3BiZmZlQq9Xo378/l7tt3LixKEHOpJKF8L4LFy6gadOmIDLLQDNY7f79+/lY+/n5ISMjg59fJycnHm8uKirCihUr+KqnXbt2OH78OHQ6HSZOnIgVK1aIQphsrJVm6IcOHeLXaMeOHTmTMi0tjVftYaxudizSGXpeXh6mTJnCx3rx4sXo3r07/Pz8sGTJElhZWcHOzg5paWki2KZarZahwfLz87Fs2TJOOBO24cOHY8CAAWjbti0CAgJgZ2cnen/KlCkiKGafPn3g7+/PX1++fJmHaWvXrs3PdWJiokhxkoV0/k+EXIioIxH9TkS3iOgDhfdbEdF5IjIQ0Ttv0+d/16Hn5eWJqNFCqVNWExBQpkWzyiQJCQmyuprbt2+H0WiUoVyEzDNGgRcKNgklb3v27MmTZ0KUi8FgwJYtW/iFxcrENWzYELGxsW+s4+no6IixY8cCMGN3161bx5N3ffr04ciSlJQULF++nCNgmGY1AJ5hv3btmqKTvX79OsaMGQNnZ2dFzXWmHChEuQgTz0yXvri4mDt0oQ53QEAADh06BMAM2yQi/mDLzMyU6dIzh/7q1Suuw81+E8tDCFEulY01c+i3b9/mieLAwECOlmFJ540bNwIwhy3WrFnDx6ljx458ub58+XIuG9CsWTMcPnyYOyUWlmFFru/fv4/ExETodDpYWFhgzJgxGDlyJIjMUrLsAR8UFMTzMyaTiYcmTCYTdu/eDQ8PD6jVakyYMAEFBQU4deoUiMxJxoKCArz//vvQaDRwd3fnBZOlKJcjR47wxH54eDh++ukn6HQ6jBs3TubI2XX38uVLEJmlZtlYS3XpS0tLuUO/du0aYmJiuHNjYy1FuQi1ypkuPXPoOTk5ohyUMA8hRLncunWLyyWHh4cjKysLRqMR9GcIDzCrLM6ePZuTn6KiopCZmQlbW1s0bNgQWq0WPXv2lIWjkpKSQGSGTxKZ82c7duyA0WhEVFQUwsPD8eTJEwwfPpwn4VetWoWysjKOhnr69Cnu3bsnQ78xv/K/5tCJSENEt4nIj4gsiOgSEQVJ9vElovpEtP3vdugLFy4E0euK28Jq6My6dOmChg0birbduXMHPXv2BBGJklhC6JRQ6lQJh24ymbBr1y6uDcHw32zg69atKyuLp4RDz8/Px4wZM6DX60UJzzfV8WT6EtJ+Zs2axRNfwhYTEyNLgu3atYvPIJlCPIIoAAAgAElEQVSVlZVh+fLlfPbFPs8019955x2ZvKgSDl2oVS7E17NEqlCHGwC++uorEIkVKKW69KwPNtZCzXVmSjj0ysaaSSVIk4zZ2dkgMkM+hVZUVIQlS5bA0dFRdG5btGiBb7/9VuYIGIxVShi6d+8eRowYIasHWqNGDWzfvl001gaDAURiJmFubi4SExP5uDCHM27cOL5KGD58OEdpAMo4dFaMRGmmGhkZKau1ynImu3btEm0XapULE+7C4i/C86uEQ5dqlbM+mFSCVHsJkMMWTSYTduzYAVdXV2i1Wn5eRo8ejYkTJ3LmaI8ePXhfbHWTkZHBJ4fS0oMtW7ZEw4YNYTKZkJmZyR+8QplkW1tbvloRnne2ChZyQIRgALYyVCqB97b233Xo4USUKXg9jYimVbLv1r/boQsvQim+lFnnzp3RqFEjxc9LmYy1a9eWOSwAb3ySFhUVySRKly1bpihr+yamKIMaCltlTFFvb2/Ex8crvsdm3qx169ZNlowBwCssKSWKnz59yh0na5XVbK2MKcoeeFJHwWasQtu3bx+ISKY7DphnVmz2xdr27dsVx/pNTFEpk9HW1laRnZmXlwciZRnf06dPyxKeShR5oHLiFmC+npgTZG3//v2y/diMVymhfOrUKRmbNzg4mGvSCO1NTFFpbcvKWKns/CldB0pjHR0drZg0fhNT9MWLF1xcj7Vt27YpjnVlTNHs7GxZMW+NRoPBgwfLUFxsQvj8+XMYjUbExMTA0tKSh01ZzVCmmwS81o0R9u/u7s7Zo0JjDl2KZDMajXwFx9pftTc59LdJilYlogeC1w//3PZfNpVKNUKlUp1TqVTnhHX6/qotWrRIUboVgGJy7+eff6bp06eLtt26dYuWLVsmS6ZWlhw0Go20bds22rlzp2j7qlWraM+ePbKEJzseqf30008yOU8iokaNGtHq1atliTMLCwtZIunatWsUGxtL/fv3JycnJ759//79VL9+fTp8+LDib5Aez6VLlyg+Pp5u3rwp2p6UlEQ//fSTYh9Kdvz4cVnC8+TJk7Rw4ULKyckRbWc1U6UJrCdPntDMmTPp+++/F21fsmQJfffdd299LJcvX5bVfS0sLKQuXbrIEo0WFhZERKIE8fPnzykhIYGaNWtGBQUFpFarycLCguLj4yklJYX8/f1p48aNotqvTHJVOHasvmnt2rXp9OnT1KpVK/5et27dKD4+XlQ7kx2XUmLP09OTQkJCRNuaNGlCPj4+b3VOKioqaMGCBaJjIDInpUePHk0lJSWi7Syh5+npKevrm2++oQULFoi2nTx5klavXk2FhYVvdTzFxcWUlpZG33zzjWj70qVL6ejRo2/VB5E5UV2lShXRtlatWtGKFSsoKChItP3777+n+vXrk5ubG6nVatq2bRs5OztT7969qbCwkL7//nsyGAw8eVpeXk7p6ek0c+ZMUT/Pnz+nyZMnU1ZWlmi7kt8wGAy0ZcsW0b3h5eX11r/vv2SVeXq8nnW/Q0SbBK8HEdHaSvbdSv9DMfSdO3dy5lZMTAyuXbvG94mJiUFoaCh/fe/ePU5s8fT0xJYtW6DX6xEfHy9i2gnJKErJi+PHj/NlV5s2bbiYz6pVq2R1QplJ1RafPXvGVRurVauGjIwMREdHIywsDCdOnOCM1qpVq2LdunU8IRMYGIh33nmHH9vIkSOh0Whgb2+PxYsXIz8/ny/VDxw4wGfbMTExfEbO6mKyuql37tzBgAEDoFKp4OjoiCVLlqBPnz7w9vbGp59+ymPFAwYMECUZpTP0O3fuiErvMRGwSZMm8Tijo6MjUlJS+O+Rrhby8vIwc+ZMWFtbQ6vV4r333kN8fDx0Op0IX92tWzfRDFg6Q8/OzubsVyYX0KxZM7Rs2RJbt27lwl+1a9fGli1bUF5ezmOvycnJqKiowOrVq+Hg4MCX8fn5+ejQoQPCwsIAmHHgbLZdr149znZlqw4WAjx06BAfh65du+LmzZt8nGbOnIlp06ZBo9HAy8sLBw4cAAD+vnC1cPfuXSQkJECj0UCv1/P4c2hoKHQ6HSwtLTFhwgTR7Fg6Q2fl5IjMieDk5GQQmYuCTJ48GUTmkCG7NgCI4trMhDLQfn5+fBxHjRrFafxVqlThMWdAPkNnuQEW1uvduze6du0KBwcH0Vizc8ZMOkMvLS3FypUrOVSZEZroz/CPq6srzykAZly7Xq/HhAkTILRjx45BrVZj8ODBSExMhK2tLQoLC/HRRx/xkF2zZs2QmZmJfv36wd3dHYsWLYKDgwNUKhUGDhzI9ZGYRPXjx485N4CFIMPDw5GRkVHpyv9tjf6TQi5ClEtpaSmP/2o0GowbNw4vX75Ex44d0aRJE+Tn52P69Ok8Vs1KgplMJqjVai52lZWVxWVr69ati++++07k0P/44w/OkvPx8eGaF/v37wcR4dy5czAYDKLEHSMQMYduMBhEUppTp07lMfuePXsiKCiI/8bvv/+e61L7+PggPT0dwcHBiIyMxNy5c2FjYwOtVouxY8dy1h9LpjF5gLKyMhEbdNy4cdiwYQNfQo8dOxY6nQ56vR5TpkzhN23fvn15Fr+wsJAnGZlMb0lJCXfoQhlfa2trzoh89eoViMwJWnZ+WWKUIS0Y/jsrKwupqan8puzbty+/iYWMvdLSUixevBi2trZcSyU/P587dKkON7sWAHGS3GAwICMjg9PXfXx8OOImIiKCJw6joqJEoamGDRuKKPCs9iuLicbGxmLVqlUgMmP8mdMLCAiQiU35+vqiT58+AMzJdhZGGTBgABcKS01NxR9//IGRI0dCq9XCwsICY8eOxaNHj7i++Llz53D37l3Ex8dDrVbDxsYG06dPR05ODnfoN27cwLx586DT6eDu7s6rF40ZMwb29vbc2R05cgTu7u7Q6/VYv349TCYTpkyZAgsLC5hMJrx48YKzqZlufWlpqSxEJESLMV16oUP/9ddfOT9DmPgfMGAA/Pz8ZGPNCmPn5eVxh240GrFr1y5+7qOiovhDVKvVYvr06bh06RKXC27fvj1u376NY8eOgYj4w1No7AHHGgM4hIWF4ciRI/w8JScnQ6VSobS0FC9fvsTUqVOh1+uh0+kwZswYLlexZ88efg8HBARwfab/dZQLEWmJ6A4R1aDXSdHgSvb9H3XozISaJEI5T5YpF1K4gdewugULFvBtrLAEG0iGaPH09ISVlRVnRAqV1diNJUzUCaF1QggUS6wIxe6ZDR06VMYeZAkZdlEKW1xcHC8YLDTm6IT2/PlzXixa2IdarcawYcNkMWWpDg4gLjYgZCIKtaiFM3iGjli5cqWon8zMTFmtVdYiIyNFKxsAiI+PR9WqVUXbhLr0wuSekg43s8GDB8PHx0d2fg8dOsTHWTizE1a9Z1a1alXFkoUlJSVYsmSJDO5mY2MjSw4yi42NRXBwMH9dVlaG2bNnQ6vVipLBDPY4evRo0TgxHX4hueb69escUeHo6MjHiSV0+/XrJ6of265dO77iYPb06VMOu4uLi0NsbCy8vLywcuVKnrxMTEwUrQRKSkpARFi0aBHfJtWlZ8dSvXp1kca8MBksXVUD5rGOj48X3ctExBmoISEhMsVVGxsbTJo0CYD54b1mzRrY2dmJarX+8MMP+Pbbb/HJJ58gJSUFU6ZMkYmEqVQqHDp0SHYd7Ny5E0QkusYePXqExMREmWCfp6cnNm7cKMqt/a87dPPnqRMR3SAz2mXGn9vmEVHXP/9vQubYehERvSSi3/5Vn38HU/Ty5cuyIrtKanZvSoKVlJTIdJFjY2MVWXKsHqVS/cgbN27IdNOZ/oXUxo4dCwcHB8Xfe/PmTZnz+/jjjxUTsLa2tvxilv5eKc1dWkyaWadOnWQ3FrNvv/1W1Iderxfh6ZmxAhdS1AhgvskSEhJE/VQmjdunTx/Url1b8b2zZ89yRARrUh1uZhMmTICdnZ1iP+xYha179+44fPgwDxmYTCZRgRFmeXl52Lt3L4YNGyYr5E1kRght2LABt27dEh3XtGnToNVqRc6e6QkJPz906FDF644lI6Xa+4AZgcJWB6wNGDBApgzp5eWFIUOGyD5vNBq5UJawRUdHK7IzGUT1ww8/lL2Xn5+PSZMmifoRFn8RmpIMAbOzZ8/KEvbbtm1TZJy6uLhg9OjRom0PHjxQnBgJH5ws/CNs3t7eGDduHI4dO8bvN8bS/fLLL3n/169f5zICwqaUTP4/wRQF8DWA2gBqAlj457bZAA78+f8vAKoBsAHgAiD4bfr9d9qTJ09o6dKlssTORx99RC9evBBtY/tYWVnJ+vn111/p4MGDom1ZWVl08eJFWTKRJUCk200mEx0/fpx+//130faFCxfKGJ5ERPb29lRQUCDabjAYaNmyZVS/fn2yt7fn2xs1akTx8fEUEBBAH3/8sahmpYWFhSixl5+fTwsWLCBfX1/67LPPRN/Zv39/Sk5OliVaS0tLRcxUZrdv36YNGzbI9k1PT6fHjx+LtleW2Hv8+DENHTqUNm/eLNq+ceNGxXqXRUVFZGNjI9tOZK5ZKWXuZmVlyViVREROTk5UUFAgq+958+ZNat68Oen1eqpduzYREQ0bNoxOnTpFMTExVLNmTVq0aBFdv36dKioqyM3NjS5dukSLFy+mNm3akIuLC/Xs2ZMyMjJkicb27dvT6dOnKTExkWrVqkV+fn40fPhw+uyzz8jT05MMBgP99NNPtGXLFoqJiSEPDw9ZLdDr169zRqLQ2BgLWavMvLy8ZL9z586d5OrqStHR0bRq1So6f/48PXnyhGrVqkVXrlyh3bt308yZM6l79+5Uu3ZtSk5OlvWbkJBAgYGBsu3smpUmAwHQN998Q/v27RNtt7OzE13PzHJycsjZ2Vm2ncicgMzLy+OvNRoNabVaxXNjaWkpu6YBiBLPqampdOLECbp+/Trl5uZSaWkprVq1iohe+4SJEydSw4YN6aOPPqK2bduSl5cXJSQkcEbwjz/+SKtXr6YmTZpQnTp16MMPP6T27dtzcIK3tzdFR0fTrFmzFGsI/21Wmaf/u9u/a4Yu1Im2sLDAjBkzEBgYiNatW/Majy4uLtiyZQufJSkJWd26dYsL61SpUgUffPABiMz4XpaE6tSpkyghx6qIC1cB58+f57OBVq1a8QpLqampfJYRFhYmkoBlYkDsN/366688RNOtWzc8fPgQs2fPhkqlQllZGQ4cOMATXH5+fjy55+npiREjRuDVq1eYP38+Dz/Fxsbi3LlzSE1NBZE5rsqWmEFBQaJZNpPyZZaTk8M1121sbDBv3jxUr14dkZGRfLu1tTXmzJnDj//Bgwcgel30QyqyNH36dHTv3h0+Pj5YsWIFNBoNgoKCZGEkpeovRUVFGD16NIjM+Y7g4GA4ODhw1cfGjRvLZpKrV68GEYngk6dOnYKLiwtcXV3x008/ccXF7du3o7S0FLt37+YyrkotJCQEH3zwAU6cOIHy8nLOzNyzZw8sLCy4Jsz169exdu1adO/eXSb4xlqNGjUwZcoUnDt3jmuqb9++Hba2tvDw8JBpALGCJtJw2TfffAMPDw+eF2FjcPLkSUyZMoVfx0pNo9EgMDAQvXr1ksWTWTjJz88PaWlporBjYWEhiAhLlizh286dO8eTxnXr1uUrBiZA1qdPH1klIKWZdUFBAUaMGAEiMwbc2dkZ1atX5zH4IUOGyHR+/Pz8MGDAAP761q1bqF69Ouzt7XlRitmzZ8u+x9vbG/Xr10d2djacnJw4c7ugoAB79uxB//79RWJlrDVo0AApKSkcbtytWzfUrVsXr1694uGikJAQzgn5PxFy+Tvav8OhHzlyhKMWYmNjubMVIkIuX77MkxMtW7bElStXeHWTTz/9FDk5ObxwrbW1NebOnYvCwkIuTvTFF1+gvLwcKSkpsLOz45T+oqIifP311yAysx1fvXqFcePGQa1Ww93dneOmhciS8vJypKenc2JSZGQkTp8+zbGrt2/fRlJSEjQaDTw9PUWx3M2bN4PoNWqB1axk8USWICIi7si7du0qikuzpTTDqB86dAjVqlWDSqXCxIkTUVhYyGsilpWVYeXKlXB2duaa6+yiFZbwkj4It2zZwku6bd68WVRoonv37hwNEBERwQtjf/fdd3BxcYGDgwNnFwJm0bIOHTrw17/88gsf70mTJqGkpESUTNuzZw/c3Nyg0+kwb948/juZ2BLDDWdkZMDS0hL+/v48AWs0GlG1alX06NFDdL1JK1xt2LBBVhrt4cOHsLKy4rUmmTOVkqAqKipkGHBpab7o6GiewP3tt9/g7+8PrVaLtWvX8v2EbETATMhiE5Dg4GBkZWWhoqJCsWwhSwyytnPnTly6dElEb2fJ3QkTJoDIzKD94osv+ETFzc0N8+fPR05ODgoKCkBEWLp0KR49eoQhQ4bwOPmGDRtQUVGBiRMnwtraGiaTCYsXL4ZKpUKTJk349WQ0GqFWq3lCHzA/cGvWrAmVSsXp90wio6KiAsnJyVCr1ahVq5boGg8KCuL3/tWrV1GlShU4OzvzfWJiYuDl5SXiabDxYvWE2QRL+iAtLS0VFV+ZOHEipBYSEiJKnu/fvx8eHh7Q6XRYtGgRn+z849D/NKapwfQT/P39RVokgFm7QxgfNBqN2LRpE5ydnaHVajniom3bttxhvfvuu6IblVWCFyIUHj16xAfU19cXo0aNAhFh7Nix8PT0hEqlwujRo0UwLxZ3FtL4S0pKkJqaCldXV8XZ0vDhw2Vyv0r9AK8du/DzXl5esgQjYGatSuub5uXl8Rmv8KHAVhNRUVEyNcnq1avzyurMfvzxR37DS6vqBAcHyyoTVa1aVaSvcu/ePTRs2BAqlQoLFiyAyWRCcHAw4uLiUFFRgQULFkCr1aJq1aqiogeDBw9G9erV+evs7GwOUW3YsCEuXrzIV1I///wzLzzdvHlzUZIQMOvYWFlZcd0Nk8mEd999F0TE4aQjRoyQndeEhARYWFhwglpeXh7c3d3RokULWUyfOePevXvDysoKbdu25bFgo9EIBwcHESM4NzeXyxUMGTIExcXFWLlyJYjMcMK7d+9yze8RI0aIZGD9/f25c2N9BQcHw97eHl26dIFKpRIVswDMOvpWVlbo1KkTTCYTqlevzqvrmEwmHD9+nNP7bWxsMHz4cD6Lt7a2hoWFBaZMmSKKk8fFxYmgpV9++SWsra1RrVo1nD9/Hjk5OWCr2LKyMkyfPh1qtRq+vr4i9mqtWrXQt29f/vrEiROoVq0adDodli9fDqPRyNFIFy5cgKurKzw8PJCVlcU/w+6VPXv2ADA/sLVarUjXqaioCF5eXrLxe/z4MZycnNCkSRNERkZCq9WKrmuTyQR7e3u89957onOanZ3NUXIMBpmWloa/av9RDl3oKBYvXqxYw9DV1VW2fAPMCbBBgwaJ+oiMjJRd1MBrRqlUoxsw49GlGuOhoaGyGRnwWoZUmERhlp+fL5M6HTNmjCLLk1UL37Fjh2w7g1yyplarkZSUJFvWJiYmVirdKq13aG9vr5jlBypHfJhMJq4bzlrv3r1lCdzS0lKoVCrMmTNHtL2oqIjr6sTFxcHV1RUREREcidKnTx/Zgy4+Pl6GEALMwmkeHh7QarUicSoiQq9evWTnBjCHLIheMzhZcpwt0dksWCgHceXKFa6zIjR2HpjjAIAzZ85Ap9OhU6dOMBqNHFrI4LNXr14FkTnpLTSj0cjDII0bN+ZhvK1bt8LBwQH29vaKSe5OnTohJCQEgDk02a5dO+h0Onz33XfIzc2Fs7OzKBFpMBgQHh4OR0dHPrkZM2YMrK2tZfLRly5dkpUK9PX1VaxXGxoaKlppAebkbbVq1WBtbc0fslOnTuWhxnfffVeWyPXw8JA9UF++fMkRWB06dICfnx/s7Ozg6OgIb29vWRjPYDCgevXqaNu2LUwmE1q1agVnZ2fZw53hydmK0WQyITY2Fnq9Hr///jtevXqFunXrwt7enmP32YOJwXWlxlbZrP1V+4916J07d1bM9EtLeAHmATl8+LCswAITgpIa0xphaoZCu3v3rqywba9evUTkJmb37t0DkVyn3GQyYdu2bTL9diIzRGvatGmim0MoeAWYneLcuXNhaWkJBwcHrF+/Ho0aNUJERASfNfn7+4uWjQMGDECNGjVEx1FcXIwNGzbI6O0WFhaiuLjQlHRlGFRNqlFOJBZ8Al7X8Ny6dausb5PJhJSUFNHn9Xq9iCAitGHDhqFKlSqy7YCZVs7CQazVrl0bV69eVXxolpWVwcHBAfHx8RwnP2jQIP69BoMBMTEx0Gq1/Lx26dIFDg4OskILBoMB9erVQ40aNVBSUoLs7Gx4e3vD19dXpP3BVgBff/01r4qkdB0B5uW7NI7btGlTRekKABg/fjxsbGxgNBr5RIZVtALAzzObZbJwpnDScPjwYdlKFTDPjkNDQ2VjHRERgYyMDNFD3M3NTXFl8/jxY64QyZqbm5uocIfQrKys8P7778u2m0wm7oCF7cSJE4poMKbHzx7QSjUEysvLUbNmTYSEhIio/ytWrOD7/PHHH/D09ET16tXx9OlTPnljWH9mjx49wvTp07nm0z8OXWDsoktOToadnZ2sIpGQ9cfs1KlTPEnj6+vL/588eTInogjLsAHmeCyRGOv7/PlzjB8/nkvSsqVueHg4bGxsoFarMWjQIFHilDH/li5dyrfdvHmTlwwLDw/n6nmLFi3CoUOH0LVrV44bb9++Pfbu3Yvy8nJ+Yxw7dozHkvv06cNjkS1atEDbtm0BmEM0vr6+UKlUGDt2LAoKCtC1a1fUr18fgJmxmpyczMM+oaGh2LVrF1QqFfr06cOXiFWrVsW2bdtEEqVOTk6iZeWvv/7KIZrNmjXDiRMn+MxWqOTXpk0bHD9+nM+EmZPPz8/H0aNHMW/ePMTExIi4BKx17doV69atk2mTjBgxAh4eHqJtT548waZNm9C9e3cu0CRtWq0WderUQY8ePTBt2jRs27YNZ8+e5VK+arUabdq0keHIc3Nz4e/vz5UNiZQhe2wM2Li2b98eFhYWslBYcXExT/h17NgRjo6OIrjkw4cPceDAASQnJ/PQi7CFhIRg5MiRSE9Px/nz50UPKkaYYsVNhIJfgHlSUL16dTRq1AhZWVmwtLRE9+7dRQ/OkpISWFtbc5ndGzdu8BlxtWrVuCOdMWMGUlNT+SrI29sbixcv5kqWCxcuhMlkwh9//IF9+/Zh9uzZ6NKli8zJhYSEYPny5cjKyhIdB5PpZUqK7Pz89ttvWLZsmWyVyppGo4Gvry/atm2L+Ph4zJs3TzRhaNiwoejaFhrDnC9fvhyOjo6IiIiQQSXPnTsHa2trNGnShF8PjOR0/vx5DBo0CDqdDiqVCnFxcdyv/BND/9OESdGnT59yOU8XFxekpaVxluLSpUtx8eJFnln39PREWloaysrKeHKwtLQU2dnZnHgjrCzP6m/evHkTBQUFmDdvHuzs7KBWqzF8+HA8fPiQM+AyMzPx/PlzvP/++9Dr9dBoNEhISMC9e/c4hnnq1KkoKyvDwoULodfrYW9vj3Xr1vGLydXVVTSLefDgAebOncvxsVKFvBo1ashmTdHR0bxGJGDO0DNsMyNMubi4YPjw4bC0tASROZl84sQJmEwmUYILMMfFGeuvcePG3AHb2dlhwoQJePHiBUaOHMm14j/++GMYjUaYTCZoNBpMnz4dgDmUkpqaKvsNTLdeSHoKCgqS4dQ7dOjAj5/ITCKaMGECMjMzER8fD2dnZ5w/fx5z584V1X309vbm+QHWFi1ahG3btmHatGno3r076tSpI1NBFH5P+/bt0bNnTwwdOhRjx47F9OnTZUJQmzdvxq5du/DZZ59hz549+Pzzz/HFF1/IKhD16tULx44dww8//ICff/4Zv/zyCy5cuMBlmFmbNWsWOnXqJFrtqFQqBAYGyqpgtWvXToSesbS0RJMmTZCYmMgVJ4nMEsDPnz/Hq1evUFxcjIqKCq5WyPZxcXERqQQyY+JZ48ePh1arhY2NDebPn4+ioiK+cly8eDEA88pk//79ig5WuBpVq9UICgriuTDh+LP/q1SpgqFDh2LXrl2cjzF//nzs378fiYmJXGmSyIymEa6+mzZtivT0dMyYMQP9+/dHeHi4rOoRa7a2tvDz80NYWBi6dOmCd999F1OnTuW+hrWDBw/i6dOnsjDvvn37oFKp+H5bt27lRc1tbGwwbtw4Psn7B+UiMSVi0cWLF/kJlIYwHB0d8eGHH4r2HzVqFJydnUX9Xrx4kSe+6tevz3Uhpk+fzm+suLg40XKYVTz/6quv+LbHjx9j7NixIpYfkTkxyOLu77zzjgwpERYWhsjISNnvNRgMOHjwoIxEs2DBAhE7EzAzEKWywcDrmQZrWq0WI0aMkC3tWWFs4RLUaDTik08+4cgcptlibW3N63hOmDBBRhYRFk9gVlxczKnRrLVv3x7Jyck4cuQIL5rAkphJSUnQarWYNGkSTCYTrl27htTUVLRv354/kIRNpVKhWbNmWLhwIS5dugSTyYTLly+DyDw79fX1RUhIiGyWVV5ejmvXrskcMGNTBgUFwdvbG46OjjLG7d/R1Go1goODMXjwYKxatQo//vgjl4kYOnQo9Ho9IiMjYWVlhefPn8NkMuHmzZvYvXs3kpKS0LZt20ohktLzJX2Yubi4wM3NDe7u7vDw8JA5wWHDhomUBBn1X8gUZcbGkbWEhASkpaXh559/5snbvn37wsLCAtHR0dDr9Xj16hXu37+PTZs2oVevXoqrNeaEu3Xrho0bN3LyVVxcHFxcXPDOO+9Ar9crKmsWFxfzWTJr48ePR//+/REdHY2QkBBUqVKFS2NX1qysrODl5YXAwEAZgZDIPJlYtmwZv6aZ/ePQJVYZU1RJzjMpKUl2QgGge/fuIuq1sI89e/bIiv62bt1akRH5poLcqz8AACAASURBVITn/fv3eTED1uzt7RXrQALmavDS+DazkydPKl7YKpUKrVq1QlpaGp49e4bevXujTp06MBqNOHv2LGbOnImQkBDZ5xo3bqzI1rtw4QKIlOthFhUVyRi0TZo0ESEIhObr64tBgwaJtuXk5IgKByslVsvLy1GnTh34+/ujrKwMvXv3hqOjo2y8i4qKeEiByCySpSTdOnz4cFhZWeHly5dcSGrz5s2Kx8xgqGyWWFmMn0HPWPv0009x9epV/Pbbb8jKysKlS5dw8eJF/sBnbe7cuTh27Bi+/fZbHD58GAcPHsSXX37JBZtYkyKZmF27dg1qtRqTJk3CtWvXoFKpZLBEZgwfLpwArFmzBikpKVi8eDHmzZuHWbNm8fAJa6NHj8aoUaMwcuRIjBgxgudjWFu5cqUoFFJeXs77F9qFCxdQpUoVjnhiyVmhZWZmgshcFYzxABh3gZnBYODFW1g7cuSILBT28OFDaDQaJCUl4e7du7CwsKg0cd+uXTs4Oztj0KBBikgftp+0hu6KFSuQlpaGhQsXIikpCcOHD0evXr0QHR0tC+1JV8/M/nHoEqvMoZ86dUpUWIHIjDtXusmbNWuGqKgoxf5v3LjBZ+qsbdq0STEhl5WVBaLKafRHjx4V9cOEj5QSNTNnzoRGo5El6xhmOiAgAFeuXIGtrS3i4+Px+++/Y968efw3C2eObm5ufFurVq2wfPly7oyHDBkCrVaL4OBgWTz6Tcie3NxcDtNkrUqVKti5c6fiuQkJCREpMubm5iI0NBQWFhb46quvuNCTVDucaUYzpAkLawmRJYB55dCqVSvY29ujYcOGsLS0lGnkvHz5ElZWVhg+fDgA803arFkzeHp6ioqZAOaZm5+fH+rUqYOSkhI0bdoUXl5esv0AcOXDEydOwNPTEw0bNnxj8m337t2oWbMm/P39FYuNM22WOXPmoEqVKqhXr56iBkzv3r1ha2vLCVK9e/eGnZ2dDPkDAOPGjQORWX5Bo9Eoor5MJhPatGkDBwcHtG/fHlqtVvaAfv78OSwtLTFgwADu/BMTE/l1qlSQ4/Dhw7C1tYW3tzeysrI4sUsIfy0uLkbNmjVRu3ZtlJaWwmQyITAwEBEREbLjZOFPFk6T5gKA1+Ja7HqaOHEi1Gq1DPDAVmFr165FTk4OXFxc0Lp1a9k1LES1fPXVV9DpdIpSCYA5tCn1PUTmimRSXfR/HLrEpA6dxYlVKhXHnxMRrwjk7e0tq2hUvXp12eyxqKgIM2bMgIWFBezs7PjMgiVQo6KiZJCs69evg8hMzhBafn4+n53Xrl1bVKOSyBx+kYoKMQib0MGtWLECKpUKERERHEURHx/P5T0B8LACS3yxmfuOHTtEyIv+/fvDzc0NBoMBR48ehaOjI9zd3fHTTz/xfZQSwSaTCZ9++ikvg8YcRceOHTnKoWXLlrLqSC1btkSbNm0AvHbmOp2Or1AeP34MvV4vwqLn5OTA2dkZ7dq14zeYyWRCw4YNERwcLLrpWGLr448/xtOnT+Hq6orQ0FDRA5FdK8Lfw4g9UrYgcwiMwcv2Y3kAZqz+KENRsXO2bNky0X63bt2CXq9HXFwcgNcPd6V6l1WrVkVISAgqKiq4gqcU0slWg8LPX7p0ic/8hcaSsePGjQNghh5qNBqZaBmD0W3cuBHZ2dlwcXFB8+bNRUlClm+6evUqjEYjpk2bBiIz3DcnJ4erfLLjTU9Ph0ajQYMGDXhY8cWLF7CwsMD48eN5vzNnzhSdb+A1oUcINbx9+zbs7OzQsmVLGAwG9OvXDzqdTjSm5eXlqFKlCjp27Mi3vXjxAg4ODiKST0lJCfz8/BAcHMwfwCyp+/nnn4vODUtwMlQL+92MfMTMZDKhX79+UKvVvNDM3LlzMXv2bFhYWMDBwUGUK/vHoUtMyhRlCm5jx47lxWCJzDVHz507B29vb+j1enz66acAzAPAyA/s9ZdffsnDLAMHDsTjx48xceJE2Nrawmg0Yv369Zw4kZqaymOwrOKQEAr27bffwsfHByqVCpMnT0ZxcTEvh2UymbB3716OBOjcuTOPYzNkSGZmJgwGA8en9+zZU4SZZvsJoWXl5eWiajiOjo4iHWkGxxMuQa9du4aaNWvC0tKSlxhj2GkWm79x4wavHBQaGopz585xtMG8efNgNBqRnp4OFxcXqNVqvPfee3y2yMoA5ubmokmTJiJnzmzSpElQq9X8Bp44cSJUKpWMyMTgfOzm/+2332BpaYmuXbtyJy+c5QJmVISPjw9/qAitb9++sLKy4jHWmzdvwtLSkrNfmQ0YMACWlpZ8JVNWVobg4GD4+PiIHqhdu3aFlZUVf+CbTCZERUXB3t5elOcYNGgQtFqtaNbItMiFD9b+/fvLnFbnzp3h6OgoCyHGxsbCycmJU+Bzc3NRrVo1BAQE8Dh1dnY2HBwcRA7v6dOncHJyQsuWLbmzYTr2bDVUWloKDw8P0efYfjqdDgEBATxZOWvWLC5Q1bFjRxklv1evXnBxcUFZWRmuXr0KnU6HgQMHivZ5+PChqMhzRUUFwsPD4eDggHv37vHf4u7ujkaNGvGH9969e0WrOmZshcRCWOy1kJhmMBhQv359+Pr68vvsyZMncHJyQnh4OL/XCwsLUa1aNTRo0ECUg2EsbwYnDggIQKdOnQCYJ3wsORwWFoYLFy7849Clxhw6y+DXqVNH9NQcN26cSFnv2bNnvPByUlISV9dLTU3FzZs3Oeutbt26otilFA53//59jpgJCwvDlStXcP/+fR6SycvL47oTAQEBohuUsVvZRV5aWoqlS5dy2OW4ceN48m7FihX8t02YMEGWwDMajahRo4YoZMSc/yeffII7d+7A2dkZwcHB/PsYTFCqA52dnc3Pzdy5c/kFn5ubyzHu9vb2WLt2LT8OpRJpL1++xJgxY6BWq3lN1L59+8LV1RVNmzaFTqdT1KB++vQprK2tMXDgQNy4cQM6nQ7Dhg2T7VdSUgJXV1deWq9x48ZwdXWVITIGDBgArVaLc+fOceSIUj7g7t27sLS0xODBg2EymdCxY0fY2dnJyv89ePAA1tbW6NWrF4DXM0ip43jw4AHs7OwQFRUFk8nEQwTSWpXPnz8XzYKzsrKg0Whkvzk7Oxtubm4IDQ1FRUUFZy0rJR5ZfJkhkwYOHAiNRiNTAGUrGhbbZclIYWLcZDLxhOrjx4+5g5euJgHzxMLFxUUGOxw2bJgixp/h2ffs2YPWrVvDyclJMRzavn17+Pj4iMhU0pqm7OHNrsGoqCh4e3vL7hVWDD4sLAyPHj2CjY0NunfvLvtOVmqPMZS7d+8OS0tLGceF5TpYPdCzZ8/CwsKCE8UAs9+wt7fnx8KQRG5ublCr1Rxs8Y9Df/1jeJs6daoMQiSlGQPmmRWL/zIYoL+/Pw+vpKamyi7CgQMHypKUJpMJO3fuhIuLC3Q6He8zLi4OPj4+lTI02Q0urUH47NkzjBw5klf0EYZMhAQGqTGR/fv37/OloXA5++2330KtVvOK5qNHj4a1tbUiO7K0tJSHqVhjJKM+ffrI0DhSmJrQLl68KIPVsRmfUuwYMOtoqNVqBAQEwNbWVhZzZDZt2jSo1WquhS4lbwDmkE3VqlURFBSEZs2awcfHRzG2DbxmfTJRLal2O7O5c+fyVZiVlZWiQwBeU/pTUlLg6uqK8PBwRXwzc5Lr169HixYt4OLiIiMlAa+dx5IlS9C2bVu4u7srkrwAM1yV6QcphWsA8z1Qq1YtBAUF8bCONFQDmJnHFhYW6N27N0JCQmShLoPBgDt37uDIkSM8/CZs8+fPx5YtW5CZmYkrV67wsIzBYOAVsIjkyU9mLGyxYMECXkVIyXr16gULCwseE58/f77ifiysxIThlOq9Ama/YW1tjeXLlyuG0ADz/R8ZGQlHR0dcv34dPj4+qF69uoi/whBlUr5BTk6O7D77q/Ymh64yv/8/b38u4f/LnxPKdDo7O1Pfvn1p0KBBFBYWRiqVisLCwsjR0VFUa9RgMND169dp5MiRohqZNWrUoBMnTpC3t7fse+Li4ujmzZuymoFERNnZ2TRw4EBZLcRTp05R8+bNZfsfPXqUoqOj6cSJEzKZ1by8PFq6dCktWrRItL1Lly7k7+9P/v7+VKtWLfL39ydvb2/SaDR0584dqlmzJvXu3ZsOHjxITZo0oaNHj4rkVFNSUmjy5Mm0YMECWr9+PYWFhdHevXv5+0ajke7cuUNXr16lq1evymqtTpo0iebPn0/W1tai7YWFhWRnZ0dLly6lpKQk0Xs3b96k/fv3y7Yz0+v15OTkRE5OTuTs7ExOTk5kNBrp66+/JiKzLOq8efNIp9ORVqslrVbL/3/y5AnNnj2biMyyvDt27FD8jszMTNq6dSsRETVo0ICmTp2quF9+fj6NHDmSv/7ss8/I0tKSNBoNb1qtlsrLyykmJobvt2PHDqpSpQoZjUYyGAxkNBrJaDRSRUUF9erVi+83f/58atCgAel0OlHTarUUExPDJZ2nTp1Kw4cPJ7VaTWq1mlQqFf/bu3dvfr1OmzaNxo4dSyaTid+87P/jx49TfHw8ERG5uLjQ6dOnycLCQtSfWq2mAwcO0IgRI4iIyM3NjX799VfSarVkNBrJZDLxNmfOHH5+IyIiqGXLlnTjxg36/fff6datWzJ5WmaOjo706tUr2XYrKyuqWrUq3bp1i2+bOHEi2dvbk62tLdnZ2fG/Wq2WOnfuzPe7ePEiOTs783PH/ubm5lJISAivVXv27FnS6XSUk5NDL1++5H+zs7Np5cqVvL9hw4aRXq8nKysr0d8nT57QsmXL+H579+4lvV4vG7/bt29T//79+X5nz56lJk2a8NePHj2iatWqUUpKCk2aNIkA0KlTp2j79u2UkZEhkgH+q75XpVL9CiBU8b3/3xz6smXLaMqUKbRnzx7au3cv7du3j0pLS8nf358GDx5Ms2bNovr169P7779Pv/76K507d44uXLgg00lnZmdnR23atKGoqCiKioqiwMBAUqlU1KFDB8rLy6PTp08TkXmgfvjhBzp58iSdPHmSfvvtN1lfDg4OFB4eTi1atKCIiAhq2rQpWVtb02+//UZ169al3bt3U58+fejBgwd04MAB2r9/Px0/flymX01EVK9ePbp165bouC0tLcnPz4/8/f3pwIEDfHtqaipVr16d9Ho9WVpa8r8JCQl06dIlIiKKjY2l0NBQ7sBv3LhR6Y3JzMLCglq0aEFRUVEUHR1NDRs2pMLCQnJ0dKSUlBQaP348nTlzhvbv308HDhyg69evExGRh4cHPXv2jPezfv16ys3NpdzcXMrJyeH/5+bm0vPnz2V66v/Y/y3T6XRUs2ZNql27NgUEBPC/NWvWpMjISD7un3/+OXXu3JkeP35Mjx8/pkePHon+7tq1i/dpbW1NxcXFf/uxW1lZie4hLy8vKi0tpZKSElkh9r9itWvXlrV27dqRg4MDjR8/nnbs2EF3794lGxsbiouLo/bt29OgQYNo/fr1lJiY+Je+8z/SoRcWFpKNjQ3l5eXR3r17afv27XTixAnRvtbW1tSwYUMKDQ2l0NBQOnXqFC/SoNVqaevWrfTjjz/S0aNH+ezBy8uLoqKi+Axl6NChdPLkSbpz5w4RmR8AERERFBYWRnPnzuXf1aNHD3Jzc6NTp05xZ6/VaqlRo0YUGBhI27ZtIyJzgYrz588Tkfli6NatG3Xr1o3S09MpIyOD/P396f79+3T16lXy9PSkx48f082bN2VN6YHyr0ylUlGNGjUoMDCQgoKCKCgoiAIDA6lOnTrUrVs3+uWXX6iiooIaN25MycnJdPToUfr222/p8uXLRGReEQUGBtKpU6eIyDzLy87OJq1WS23atKGuXbtS165dacOGDbR06VIaMmQIffzxx3T06FGKjIxUPKbVq1fT+PHjiYioXbt29PXXX5PBYCCDwUAVFRX8//Xr1/NVTEREBG3atEmxv+PHj9OoUaP48Z04cUKxEvudO3dEM8EzZ86QTqcTzboNBgMVFRWJ9tu7dy+5uLiIiixoNBpSqVTUtGlTMhgMRESUnp5OISEhVFFRIWujR4+mR48eEZF5xtiyZUs+42azbpPJROnp6cTukRYtWtDAgQNJpVKJZvFqtZqOHj1KO3fuJCLz9btw4ULF/n777TdKS0vjv2XdunV89q7RaPj/R48e5dd/jx49KCMjQ1ashIho27ZtNHToUMrIyKAZM2aQnZ0dnTt3TvF8HzlyhK907O3t6f79+2Rra0tFRUVUWFhIBQUFVFBQQIcOHaI5c+YQEZGtrS2tXbuWXwfCvxUVFTRz5kzef2pqKvn4+JCLiws5OzuTi4sLOTk50RdffEEDBw4kIvNE49atW2Rra0tE5hlyeXk5lZSU0IkTJ6h79+5EZJ44/fjjj3zlJWxPnz6lYcOG8e/t0aMH3b59m27evKk4aYyKiqLBgwdTjx49yNbWlp4+fUpeXl5/m0P//y6GXhkOXapbfeXKFVGS5Pbt27C2tkanTp04hCw9PZ2/f/fuXWzatEkmumVjY4MePXogNTWVozwA8Fjb4cOHUbduXS7CBJiThF999RWmTZvGk46shYWFYfHixaJkVE5ODvR6PUaMGIHff/8der0e3bp1U8R3A5AxGn/66SdcvHgRp0+fxvHjx3HkyBHs27ePq/IREVxdXSuNwTL698aNG/nvEhKpnj59ip07d3LBftb69u2LXbt2iUhKeXl5vOCEECamlCh79uwZx0CzCvOsMrzQXr16BTc3N0RERGDSpElQqVQyKCpgju+GhITAx8eHF/OojCMQFxcHGxsb/tuFSV6hLViwgMfG1Wq1KFchtE2bNvHYuLu7OyIiIhTH7/Lly9BoNBgyZAh8fHxQr149xTj/y5cv4erqiubNm6Nx48bw8vKSqQ8C5th4nTp1UKtWLfTp0wc6nU6x3ixD3jg6OqJ///5QqVSKEssM9VGjRg2eYFUi3pSVlcHX1xeNGjWCyWTisWqlcoL5+fnw8fFBYGAgh4Mq6d+UlJQgICAANWrUwNChQ6HRaBTF9wBwsSzGXFYav4KCAlSpUgWhoaGcz6AUazcajQgLC4OXlxfPhQiRa0IbNWoUNBoNR/Rs3LiR93H//n1ZmUYh2gz4B7YoMyWHfuXKFbi5ucHLy4vjv6XZ+3bt2sHOzg7379+HyWRCgwYNEBgYKEtclZaWiuoPSrWNAfPN5ujoyCVB2SAqXaSFhYWoWbMm708o4s+MOTN247DfuHv3bsXv9vDwQIMGDXD27FmuGyO14uJi+Pv7w9fXl/e/evVq2X65ubnw8PBA06ZNYTQaUVBQAGdnZxF+l9nt27dFjDglTWf2QGBSwvv27QMRYdWqVbJ93333XWi1Wly7do0LRSmJJU2ePJk7IObclbTGGewyIyODOyYfHx9ZMpgRqBi7sUePHrCxsZFJKTCUS8+ePQGAFwKWkm+Y/jlz4sy5S9EZJpMJrVu3hrOzM168eMHRGkq1VxlqSMg4lSqICs/3V199hSdPnsDOzg4x/6+9c4+Nulzz+Pfp9DItnSmXaUsvUC5SoOFaKLgQWIwb4XCRIAc9JEd3FT3xbCBGCcrGhJBjjG7iqtE90UOyxvUQxMXokeQcJeyiqaAiVo4KCAaKxUqhKNPSoe20nfnuHzPv29/M/KYzSDtDy/tJmszlmbfv87s8v/f3/J7Lr34Vs21U1M9LL73ElpYWjh49Wu9zKyof4s033+TPP//MoqIi1tTUxESQqPht1YvA7/ezvLycixcvjpnjxo0bKSI68mvp0qUsLi6O2S/btm3TUTUXLlxgfn6+juO30tDQQLfbrWPTV65caRvSqWLdVRTc6tWr6XK5YkrlqofJr7/+OgOBgE4qiw69PHr0qM7FsJbetT7UVhmvmzZt4qhRo1hWVhZh1I1BjyLaoFuN+alTp9jU1MSMjIyIhJAdO3ZEXE3J3sQBax2WYDCoE3T27NmjwwGtMiS5efNmikhEMs2dd97J/Pz8mNC3Bx98kCLC/fv36xWuNYQvEAhw0qRJXLBggf6su7ubc+fOZWFhYUTbNLI3llkZ/y1bthCIze5UXVj279/PYDDIFStW0Ol0xmRTbty4kRkZGRErXpVMYo0H7+zs5Jw5czh8+HDW19dzxYoVzM7Ojvid3+9nWVlZRAu7YDDIO+64gwUFBRG6KCO1ZcsW/ZlaLVsTtU6dOsWsrKyIi5ZqwWa94Hm9Xno8Hi5evFgbM9Wdx5pZ2N3dzenTp0fEHZ85c4bZ2dkxyWbr16+n0+nUceiqPZk18UntA+uKt6enh9XV1SwvL49oOKFKD6iT2bpqtm4bZTisi4kNGzYwMzMzYv+dP3+e+fn5ul0a2WvgrcdYe3s7x40bx2nTpum7AXX8W6NNrl69yrKyMs6bN0/rp6I2rIuB9vZ2lpaWxtyFqE5H1pLNBw8epIjoJCfrfrGGdR4/fjwmNl0dh9aw5EAgwNtuu435+fm6bLBKurImi509e5ZOp1N3kVL/IyMjI6LTUFtbG0tKSlhTU6MvbuouwlpWIRgMctGiRfR4PDrXQt1tPfzww1pu06ZNzMnJYUtLC7/66qsYo24MehRWgx5tzBXLli3jmDFjGAgE+MMPP9DtduuC9oquri6Wl5frcrNk76pDXQw6Ojo4Y8YMFhYW6nC6+vp6Zmdn8/7774+Yl4qjtn6uasuo8drb21ldXc2CggK9g1WM+M6dOyPG+/rrr5mVlRWR7KK6rVhX+T6fj+PHj+fkyZN1COeRI0eYkZERYQSbmpo4atQozpkzR7s/6urqYgwHGXIBuVwu3n333fozVbVR1ar+6aefWF5ezokTJ2qXi1rdRd92nzhxQhcEI0MnZU1NTcwqKBAIcNasWRw3bpzWZeXKlXS5XBEx5z09PZw1axbHjBmjDaZKSvryyy8j/vfatWuZl5enk4jiZQaqTEDVH7a2tpZAZBlmsrckrfq93X63+71KTJk9e3bEaldtG2t5goULF7KwsDAipb+5uZnDhw/n7bffro/je++9l9nZ2RErwK6uLk6dOpUTJkzQLkBVEO3DDz/Ucta7BbViffrppwkgoktQMBjk0qVLmZ+fz3PnzpHsjWm3jkeGLggej0cn1nR0dHDKlCmsqKiIKKEQDAY5f/58jh8/nt3d3QwEAly4cCFHjhwZcWHz+XwcPXo0FyxYoHVWrrToGuZr166NWH2vW7eOubm5es6KBx54gNnZ2TpRSblOrHkjatvm5OToZDF1LkeHWz7yyCPaBej3++nxeCLOm2ijfkMYdADLAJwCcBrAVpvvcwC8Ff7+MIBxica8XoN++PBhW2NO9sayHjhwgCtXrmRubq5t/Kkaq66ujrW1tczMzOTy5csjTrjjx4/T6XRy6dKlDAQCXL9+PXNzc2Nuz8lI18Dp06fpcrm4cOHCCB/p2bNnOXLkSE6bNo0+n49r1qyhx+Ox7bykTsT33nuPXq83bp2PDz74QBsPv9/PGTNmsKSkJOYWVN3ib9u2Td9aFhcX2xYw27p1K0WEJ0+e1L+L7spz6NAhOhwOrlu3joFAgFVVVZw5c6at71gZ3Lq6Ou2SiL6Ikb0Fm1588UWtl7WWvEJlzG7fvp3ffvtthFG0Ul9fr2uRqNodS5YsiZnjlStXWFJSwvnz57Orq4szZ86MuGAo1Ap/7NixvHr1KletWhU3fv6ee+6h0+lkQ0ODjnc/ePBgjJx6LnDkyBF9+29XQExdTPbs2aN9wtGlCcheF+BTTz2lV6pWI6M4duwYHQ4HH3roIV68eJEul0u3m4vehrm5uVy9ejXb2tro8Xji1kJSF4WjR49qY2mXmKRccbt27eKrr76qXR7RKDfaO++8ozOEV61aFbP/jh07pvuPqu5bdnH2586d00ll6tiIzlglQ1mrw4YN41133UWfz8eysjJWV1fHuJ68Xi+Liop46623ap2i7+itRl2VjkibQQfgAHAGwAQA2QC+AlAVJfOvAF4Nv/4NgLcSjXu9Bj0vL8/WmJOhlYJq6AwgbpJOS0sLXS4XFy9ezKKiIlZWVtoaN5Xeq9qjqZZhduMVFRVx3rx52j2hSnta2bdvH0WEixYtYkZGBp944gnb8fx+P6dPn86SkhKuWbOGDofD9kEW2ZsurtqCxev6ct9999HhcOhOOdEt7RQXL15kbm6uLoBVU1NjWzBKZcGqjNvoujbR26aqqoqjRo2y9YGTvckbBQUFLC0t5S233GJ7sSOp+3JOnz6dbrfbNvOQ7F2F1dTUaL+0HSrpR7W8i/dAVbkMlJy1472VhoYGOp1OXZTMznCQIR98cXExq6qq9PFjl5TU3d2ty7tWVlayvLw87oPutWvXMjc3lzU1NczLy4tZqSqU+3Du3Ll9PoRU+1nV71F3MtF4vV663W5OnjyZmZmZcQtaBQIBTp06lYWFhXS73TFuLKvOU6ZM4YQJEzhjxgzbDGGFKtVQUlKiL7h2qIVXZWUl8/LybBdnZO8DcVUe1+5iTPYeNwBYVFRkGwCgjLrD4RhQgx4bixTLPACnSdYDgIjsBrAawAmLzGoA28Ov3wbwnyIi4X/er7z88ssAgPb2dixZsgSvvPKKrVxbW5t+ffbsWTz66KNx5WprawEAs2fPjghFVCg1VGhYU1NT3PHa29vx+eefAwAqKirwwgsv2MqRxMcffwwAOHnyZNzxCgoK8M033+Ddd98FAOzcuRM7d+60le3u7sauXbsAhML3PvrooxgZh8OBQCCA1157DQDwxRdfoK6uzna8jo4OvW1KS0ttk3SCwSAA4P333wcAfPLJJzhy5IjteJ2dnThxInTYZGZm4rHHHrOV6+rqQmtrK1pbWzFx4kRs3bo1rr4dHR06+euZZ56xlfP5fACg56USj+LpopJ5Dh06FJGIFo367vvvv4+7/zo7O3XoYXd3d1y5rq4um4OoqwAABtZJREFUvW0qKyuxefNmW7msrCwdt19aWhoRuhetS0dHh9b5+eeft5Vra2sDST1HFdYbjcqVUHK7d+/G7t27bWWvXLmCK1euAAgd5/F0vnDhArxeL4BQiHFfx4MKG66oqMCzzz5rK9fS0gK/34+mpiaUlJTgySeftJW7fPkySOK7774DADz33HO2cioM8dNPPwUQirN/++23Y+TUcQMAzc3NePzxx23HmzlzJg4cOAAAeOONN35x2GJfJIxDF5FfA1hG8sHw+3sBzCe50SJzLCzTGH5/JizzU9RYvwPwOwAYO3bsnIaGhmufsCXG1e12x5VTB1Sq5a5evYpAIJBQLhgMakOTn5+PjIyMlM0xXXJ+v18nM/UlZx3T5XLZxjUP1BzTJdfV1aUTXZLdNsOGDYPD4UjZHNMl19PTo5OQhtK2+aXr3b7i0JNZofcbJHcA2AGEEot+4RhoaWnB8OHD+5QLBoO4dOkSiouLE47Z2NiI8vLyhHJNTU3weDwRKfZ2NDc3w+12w+l09inn8/kQCARQUFDQp1xnZydaW1sT6kISjY2NtqUMojl37hzGjh2bUC7ZbXPp0iW43W7k5OT0KXf58mXk5ORg2LBhfcq1tbUhGAwmtW18Ph88Hk+fcsFgEE1NTSgrK+tTDkhe58bGRpSVlfV5wQGAixcvYsSIEcjOzu5Tzuv1IisrSye+xKOtrQ2BQCDhOeD3+9Ha2oqioqI+5Uji/Pnz/bptfvzxR5SWlibcNs3NzRgxYkTCcyrZ4+ZazqlkjhuSOp0/ERcuXEBxcXFCnb1eL0aMGJFwvF9CMiv0fwCwneTS8Pt/AwCSz1hk9oVlPhWRTAAXABT25XL5pZmiBoPBcDPT1wo9/n1+L0cATBKR8SKSjdBDz71RMnsB/HP49a8BHBgI/7nBYDAY4pPQ5UKyR0Q2AtiHUMTLaySPi8gfEHrauhfAfwH4s4icBnAZIaNvMBgMhhSSlA+d5N8A/C3qs22W150A1kX/zmAwGAypIxmXi8FgMBgGAcagGwwGwxAhbfXQReQSgGsPRA/hAfBTQqmhhdH55sDofHNwPTpXkCy0+yJtBv16EJEv4oXtDFWMzjcHRuebg4HS2bhcDAaDYYhgDLrBYDAMEQarQd+R7gmkAaPzzYHR+eZgQHQelD50g8FgMMQyWFfoBoPBYIjCGHSDwWAYItzQBl1ElonIKRE5LSIxXQ5EJEdE3gp/f1hExqV+lv1LEjo/JiInRORrEfk/EalIxzz7k0Q6W+TWighFZNCHuCWjs4jcHd7Xx0VkV6rn2N8kcWyPFZEPReRo+Pheno559hci8pqINIf7Rdh9LyLyUnh7fC0i1df9T+O1Mkr3Hwao9d2N/JekzrcByAu//v3NoHNYzgWgFsBnAOame94p2M+TABwFMCL8vijd806BzjsA/D78ugrA9+me93XqvBhANYBjcb5fDuB9AALgVgCHr/d/3sgrdN36jmQXANX6zspqAP8dfv02gNslUXX5G5uEOpP8kGR7+O1nABJX3r+xSWY/A8BTAP4dQGcqJzdAJKPzQwD+SNILACSbUzzH/iYZnQlAtfspAHA+hfPrd0jWIlR9Nh6rAbzBEJ8BGC4iJdfzP29kg14G4AfL+8bwZ7YyJHsAtAIYlZLZDQzJ6GxlA0JX+MFMQp3Dt6JjSP41lRMbQJLZz5UAKkXkkIh8JiLLUja7gSEZnbcD+K2INCJU3XVTaqaWNq71fE9ISlvQGfoPEfktgLkA/jHdcxlIRCQDwPMA/iXNU0k1mQi5XZYgdBdWKyLTSbakdVYDy3oAr5P8j3CntD+LyDSSwUQ/NIS4kVfoPwKwNscsD39mKxNufVcA4OeUzG5gSEZniMg/AXgSwJ0k/Sma20CRSGcXgGkAPhKR7xHyNe4d5A9Gk9nPjQD2kuwmeRbAdwgZ+MFKMjpvAPA/AEDyUwBOhIpYDVWSOt+vhRvZoN+Mre8S6iwiswH8CSFjPtj9qkACnUm2kvSQHEdyHELPDe4kOZgb0iZzbP8FodU5RMSDkAumPpWT7GeS0fkcgNsBQESmImTQL6V0lqllL4D7wtEutwJoJdl0XSOm+0lwgqfEyxFamZwB8GT4sz8gdEIDoR2+B8BpAJ8DmJDuOadA5/8FcBHA38N/e9M954HWOUr2IwzyKJck97Mg5Go6AeAbAL9J95xToHMVgEMIRcD8HcAd6Z7zder7JoAmAN0I3XFtAPAwgIct+/iP4e3xTX8c1yb132AwGIYIN7LLxWAwGAzXgDHoBoPBMEQwBt1gMBiGCMagGwwGwxDBGHSDwWAYIhiDbjAYDEMEY9ANBoNhiPD/AoG/EYx17MsAAAAASUVORK5CYII=\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - } - } ] }, { "cell_type": "markdown", + "metadata": { + "id": "_MzkBHU8LUrQ" + }, "source": [ "To refine, many options. Either at meshing time, or - as shown below - directly through dolfinx on the gmsh-exported mesh.\n", "\n", - "Note that, typically, meshing is done on one processor, prior to computations. For large problems (to be run on parallel computing machines), it is preferable to construct a relatively coarse mesh (on a single processor) and then refine it, distributing (cf. the `redistribute` optional argument in the function's signature) the newly created nodes optimally across the cpus. \n", - "\n", - "\n", - "\n", + "Note that, typically, meshing is done on one processor, prior to computations. For large problems (to be run on parallel computing machines), it is preferable to construct a relatively coarse mesh (on a single processor) and then refine it, distributing (cf. the `redistribute` optional argument in the function's signature) the newly created nodes optimally across the cpus.\n", "\n", - "\n", - "```def refine(mesh: Mesh, edges: np.ndarray = None, redistribute: bool = True) -> Mesh:```\n", + "`def refine(mesh: Mesh, edges: np.ndarray = None, redistribute: bool = True) -> Mesh:`\n", "The docstring of the refine function (in the mesh module)\n", "\n", " \"\"\"Refine a mesh\n", @@ -536,31 +516,21 @@ "\n", " Returns:\n", " A refined mesh\n", - " \"\"\"" - ], - "metadata": { - "id": "_MzkBHU8LUrQ" - } + " \"\"\"\n" + ] }, { "cell_type": "markdown", - "source": [ - "### Refine globally (uniformly, by bisection)\n" - ], "metadata": { "id": "pWsxEee9NqTz" - } + }, + "source": [ + "### Refine globally (uniformly, by bisection)\n" + ] }, { "cell_type": "code", - "source": [ - "mesh_refined_global = dolfinx.mesh.refine(mesh, redistribute=True)\n", - "\n", - "plt.figure()\n", - "ax = plot_mesh(mesh_refined_global)\n", - "fig = ax.get_figure()\n", - "fig.savefig(f\"mesh_refined_uniform.png\")" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -569,33 +539,63 @@ "id": "LOgMyfeKLO3v", "outputId": "9c7927b7-3cb4-43d7-fce8-e58144c3cf43" }, - "execution_count": null, "outputs": [ { - "output_type": "display_data", "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } + ], + "source": [ + "mesh_refined_global = dolfinx.mesh.refine(mesh, redistribute=True)\n", + "\n", + "plt.figure()\n", + "ax = plot_mesh(mesh_refined_global)\n", + "fig = ax.get_figure()\n", + "fig.savefig(f\"mesh_refined_uniform.png\")" ] }, { "cell_type": "markdown", - "source": [ - "### Refine locally, at a boundary\n" - ], "metadata": { "id": "5rh85PaiNxby" - } + }, + "source": [ + "### Refine locally, at a boundary\n" + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 215 + }, + "id": "923VqcGcL51U", + "outputId": "a2407692-e106-4c5f-d761-62e65b3bd9c6" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Create the topology (connection/connectivity) for edges (of dimension = tdim - 1)\n", "\n", @@ -612,42 +612,42 @@ "ax = plot_mesh(mesh_refined_local)\n", "fig = ax.get_figure()\n", "fig.savefig(f\"mesh_refined_local.png\")" - ], + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "36y0aPtbN00E" + }, + "source": [ + "### Refine locally, in the volume\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 215 }, - "id": "923VqcGcL51U", - "outputId": "a2407692-e106-4c5f-d761-62e65b3bd9c6" + "id": "yv53SVI1MdHU", + "outputId": "d75379db-c553-4e73-841c-7a230669267a" }, - "execution_count": null, "outputs": [ { - "output_type": "display_data", "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Refine locally, in the volume\n" ], - "metadata": { - "id": "36y0aPtbN00E" - } - }, - { - "cell_type": "code", "source": [ "# Create the topology (connection/connectivity) for edges (of dimension = tdim - 1)\n", "\n", @@ -664,75 +664,31 @@ "ax = plot_mesh(mesh_refined_local2)\n", "fig = ax.get_figure()\n", "fig.savefig(f\"mesh_refined_local_bulk.png\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 215 - }, - "id": "yv53SVI1MdHU", - "outputId": "d75379db-c553-4e73-841c-7a230669267a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - } - } ] }, { "cell_type": "markdown", - "source": [ - "## Crack Holes" - ], "metadata": { "id": "1Q5awuV945uW" - } + }, + "source": [ + "## Crack Holes\n" + ] }, { "cell_type": "code", - "source": [ - "branch_name = 'andres-plates'\n", - "\n", - "!rm -rf mec647\n", - "try:\n", - " !git clone -b {branch_name} https://github.com/kumiori/mec647.git\n", - " sys.path.append('mec647/')\n", - "\n", - " print()\n", - " print(f'Cloned brach: {branch_name}')\n", - " print(f'Last commit message')\n", - " !cd mec647/ && git log -n 1 && cd ..\n", - " import mec647\n", - " from mec647 import meshes\n", - " from mec647.meshes.crackholes import mesh_crackholes as mesh_function\n", - " from mec647.utils.viz import plot_mesh, plot_scalar, plot_vector\n", - "except Exception as e:\n", - " print('Something went wrong', e)\n", - " !rm -rf mec647\n", - " !git clone https://github.com/kumiori/mec647.git" - ], + "execution_count": 7, "metadata": { - "id": "t7KrtfHh49_R", - "outputId": "d9c8362c-1659-4c6a-b33c-14613290603f", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "t7KrtfHh49_R", + "outputId": "d9c8362c-1659-4c6a-b33c-14613290603f" }, - "execution_count": 7, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'mec647'...\n", "remote: Enumerating objects: 1114, done.\u001b[K\n", @@ -751,73 +707,43 @@ " crackholes & kink rev\n" ] } + ], + "source": [ + "branch_name = 'andres-plates'\n", + "\n", + "!rm -rf mec647\n", + "try:\n", + " !git clone -b {branch_name} https://github.com/kumiori/mec647.git\n", + " sys.path.append('mec647/')\n", + "\n", + " print()\n", + " print(f'Cloned brach: {branch_name}')\n", + " print(f'Last commit message')\n", + " !cd mec647/ && git log -n 1 && cd ..\n", + " import mec647\n", + " from mec647 import meshes\n", + " from mec647.meshes.crackholes import mesh_crackholes as mesh_function\n", + " from mec647.utils.viz import plot_mesh, plot_scalar, plot_vector\n", + "except Exception as e:\n", + " print('Something went wrong', e)\n", + " !rm -rf mec647\n", + " !git clone https://github.com/kumiori/mec647.git" ] }, { "cell_type": "code", - "source": [ - "# %%capture\n", - "from dolfinx.io import XDMFFile\n", - "from mec647.meshes import gmsh_model_to_mesh\n", - "from mpi4py import MPI\n", - "from pathlib import Path\n", - "# gmsh.finalize()\n", - "parameters = {\n", - " 'geometry': {\n", - " 'geom_type': 'crackhole',\n", - " # 'Lx': 1.0,\n", - " # 'Ly': 0.5,\n", - " # 'a': .5,\n", - " # 'b': .2,\n", - " # 'lc': .05,\n", - " \"Lx\" : 1.,\n", - " \"Ly\" : .5,\n", - " \"a\" : .5,\n", - " \"b\" : .2,\n", - " \"lc\" : .05,\n", - " \"xc\": .1,\n", - " \"deltac\": .1,\n", - " \"rhoc\": .05,\n", - " \"offset\": 0,\n", - " \"tdim\" : 2,\n", - " \"order\" : 0\n", - " },\n", - "}\n", - "Lx = parameters.get(\"geometry\").get(\"Lx\")\n", - "Ly = parameters.get(\"geometry\").get(\"Ly\")\n", - "a = parameters.get(\"geometry\").get(\"a\")\n", - "#b=parameters.get(\"geometry\").get(\"b\")\n", - "geom_type = parameters.get(\"geometry\").get(\"geom_type\")\n", - "\n", - "gmsh_model=mesh_function('crack_holes',\n", - " Lx,\n", - " Ly,\n", - " a = .5,\n", - " b = .2,\n", - " lc = .05,\n", - " xc=.1,\n", - " deltac=.1,\n", - " rhoc=.05,\n", - " offset=0,\n", - " tdim = 2,\n", - " order = 0\n", - " )\n", - "\n", - "mesh, mts = gmsh_model_to_mesh(\n", - " model, cell_data=True, facet_data=False, gdim=tdim)\n" - ], + "execution_count": 8, "metadata": { - "id": "64tjiU44471X", - "outputId": "98206d2a-cd8b-475e-b074-67eb598672b8", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "64tjiU44471X", + "outputId": "98206d2a-cd8b-475e-b074-67eb598672b8" }, - "execution_count": 8, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Point (1) = { 0.0, 0.0, 0, 0.05 };\n", "Point (2) = { 1.0, 0.0, 0, 0.05 };\n", @@ -870,43 +796,112 @@ "Line Loop = { 20, -21, 22, -23 };\n" ] } + ], + "source": [ + "# %%capture\n", + "from dolfinx.io import XDMFFile\n", + "from mec647.meshes import gmsh_model_to_mesh\n", + "from mpi4py import MPI\n", + "from pathlib import Path\n", + "# gmsh.finalize()\n", + "parameters = {\n", + " 'geometry': {\n", + " 'geom_type': 'crackhole',\n", + " # 'Lx': 1.0,\n", + " # 'Ly': 0.5,\n", + " # 'a': .5,\n", + " # 'b': .2,\n", + " # 'lc': .05,\n", + " \"Lx\" : 1.,\n", + " \"Ly\" : .5,\n", + " \"a\" : .5,\n", + " \"b\" : .2,\n", + " \"lc\" : .05,\n", + " \"xc\": .1,\n", + " \"deltac\": .1,\n", + " \"rhoc\": .05,\n", + " \"offset\": 0,\n", + " \"tdim\" : 2,\n", + " \"order\" : 0\n", + " },\n", + "}\n", + "Lx = parameters.get(\"geometry\").get(\"Lx\")\n", + "Ly = parameters.get(\"geometry\").get(\"Ly\")\n", + "a = parameters.get(\"geometry\").get(\"a\")\n", + "#b=parameters.get(\"geometry\").get(\"b\")\n", + "geom_type = parameters.get(\"geometry\").get(\"geom_type\")\n", + "\n", + "gmsh_model=mesh_function('crack_holes',\n", + " Lx,\n", + " Ly,\n", + " a = .5,\n", + " b = .2,\n", + " lc = .05,\n", + " xc=.1,\n", + " deltac=.1,\n", + " rhoc=.05,\n", + " offset=0,\n", + " tdim = 2,\n", + " order = 0\n", + " )\n", + "\n", + "mesh, mts = gmsh_model_to_mesh(\n", + " model, cell_data=True, facet_data=False, gdim=tdim)\n" ] }, { "cell_type": "code", - "source": [ - "# Viz the mesh\n", - "Path(\"mec647/practice\").mkdir(parents=True, exist_ok=True)\n", - "\n", - "plt.figure()\n", - "ax = plot_mesh(mesh)\n", - "fig = ax.get_figure()\n", - "plt.title(f\"Crackhole (fixed mesh), dimension {tdim}\")\n", - "fig.savefig(f\"mec647/practice/crackhole.png\")" - ], + "execution_count": 15, "metadata": { - "id": "dwf7z1qq47JY", - "outputId": "ab1017c4-d5eb-4750-a391-e88de75fc7dc", "colab": { "base_uri": "https://localhost:8080/", "height": 231 - } + }, + "id": "dwf7z1qq47JY", + "outputId": "ab1017c4-d5eb-4750-a391-e88de75fc7dc" }, - "execution_count": 15, "outputs": [ { - "output_type": "display_data", "data": { - "image/png": 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\n", 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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } + ], + "source": [ + "# Viz the mesh\n", + "Path(\"mec647/practice\").mkdir(parents=True, exist_ok=True)\n", + "\n", + "plt.figure()\n", + "ax = plot_mesh(mesh)\n", + "fig = ax.get_figure()\n", + "plt.title(f\"Crackhole (fixed mesh), dimension {tdim}\")\n", + "fig.savefig(f\"mec647/practice/crackhole.png\")" ] } - ] -} \ No newline at end of file + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyMIPW98JRJZxIlbJZTxiNNt", + "collapsed_sections": [], + "include_colab_link": true, + "name": "mec647_Snippets_9.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/playground/tutorials/mec647_VI_1.ipynb b/playground/tutorials/mec647_VI_1.ipynb index e51421ce..47365ff3 100644 --- a/playground/tutorials/mec647_VI_1.ipynb +++ b/playground/tutorials/mec647_VI_1.ipynb @@ -329,11 +329,11 @@ "source": [ "\n", "zero = Function(V)\n", - "with zero.vector.localForm() as loc:\n", + "with zero.x.petsc_vec.localForm() as loc:\n", " loc.set(0.0)\n", "\n", "one = Function(V)\n", - "with one.vector.localForm() as loc:\n", + "with one.x.petsc_vec.localForm() as loc:\n", " loc.set(1.0)\n" ], "metadata": { @@ -450,7 +450,7 @@ "\n", "\n", "solver_snes.setMonitor(monitor)\n", - "solver_snes.solve(None, u.vector)\n" + "solver_snes.solve(None, u.x.petsc_vec)\n" ], "metadata": { "colab": { diff --git a/playground/tutorials/mec647_intro_0.ipynb b/playground/tutorials/mec647_intro_0.ipynb index 8f89e46d..b0eaa488 100644 --- a/playground/tutorials/mec647_intro_0.ipynb +++ b/playground/tutorials/mec647_intro_0.ipynb @@ -177,7 +177,7 @@ { "cell_type": "code", "source": [ - "V = dolfinx.fem.FunctionSpace(mesh, (\"CG\", 1))" + "V = dolfinx.fem.functionspace(mesh, (\"CG\", 1))" ], "metadata": { "id": "8sKGMiU1JU7B" @@ -213,9 +213,9 @@ "cell_type": "code", "source": [ "f = dolfinx.fem.Function(V)\n", - "dim_V = f.vector.local_size\n", + "dim_V = f.x.petsc_vec.local_size\n", "# dim_V = ?\n", - "f.vector[:] = np.arange(1, dim_V + 1)" + "f.x.petsc_vec[:] = np.arange(1, dim_V + 1)" ], "metadata": { "id": "qfhWHigKLJa5" diff --git a/src/irrevolutions/algorithms/am.py b/src/irrevolutions/algorithms/am.py index b3a78856..3fd33411 100644 --- a/src/irrevolutions/algorithms/am.py +++ b/src/irrevolutions/algorithms/am.py @@ -61,8 +61,8 @@ def __init__( self.u = state["u"] self.alpha = state["alpha"] self.alpha_old = Function(self.alpha.function_space) - self.alpha.vector.copy(self.alpha_old.vector) - self.alpha.vector.ghostUpdate( + self.alpha.x.petsc_vec.copy(self.alpha_old.x.petsc_vec) + self.alpha.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -141,9 +141,9 @@ def solve(self, outdir=None): (solver_alpha_it, solver_alpha_reason) = self.damage.solve() # Compute errors and residuals - self.alpha.vector.copy(alpha_diff.vector) - alpha_diff.vector.axpy(-1, self.alpha_old.vector) - alpha_diff.vector.ghostUpdate( + self.alpha.x.petsc_vec.copy(alpha_diff.x.petsc_vec) + alpha_diff.x.petsc_vec.axpy(-1, self.alpha_old.x.petsc_vec) + alpha_diff.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -153,17 +153,18 @@ def solve(self, outdir=None): Fv = [assemble_vector(form(F)) for F in self.F] Fnorm = np.sqrt(np.array([comm.allreduce(Fvi.norm(), op=MPI.SUM) for Fvi in Fv]).sum()) - error_alpha_max = alpha_diff.vector.max()[1] + error_alpha_max = alpha_diff.x.petsc_vec.max()[1] total_energy_int = comm.allreduce(assemble_scalar(form(self.total_energy)), op=MPI.SUM) + residual_u = assemble_vector(self.elasticity.F_form) residual_u.ghostUpdate( addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE ) - set_bc(residual_u, self.elasticity.bcs, self.u.vector) + set_bc(residual_u, self.elasticity.bcs, self.u.x.petsc_vec) error_residual_u = ufl.sqrt(residual_u.dot(residual_u)) - self.alpha.vector.copy(self.alpha_old.vector) - self.alpha_old.vector.ghostUpdate( + self.alpha.x.petsc_vec.copy(self.alpha_old.x.petsc_vec) + self.alpha_old.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -198,13 +199,13 @@ def solve(self, outdir=None): # Convergence criteria if self.solver_parameters.get("damage_elasticity").get("criterion") == "residual_u": - logging.debug(f"AM - Iteration: {iteration:3d}, Error: ||Du E||_L2 {error_residual_u:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}") + logging.debug(f"AM - Iteration: {iteration:3d}, Error: ||Du E||_L2 {error_residual_u:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}") if error_residual_u <= self.solver_parameters.get("damage_elasticity").get("alpha_rtol"): error = error_residual_u break if self.solver_parameters.get("damage_elasticity").get("criterion") == "alpha_H1": - logging.debug(f"AM - Iteration: {iteration:3d}, Error ||Δα_i||_H1: {error_alpha_H1:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}") + logging.debug(f"AM - Iteration: {iteration:3d}, Error ||Δα_i||_H1: {error_alpha_H1:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}") if error_alpha_H1 <= self.solver_parameters.get("damage_elasticity").get("alpha_rtol"): error = error_alpha_H1 break @@ -212,7 +213,7 @@ def solve(self, outdir=None): raise RuntimeError(f"Could not converge after {iteration:3d} iterations, error {error_alpha_H1:3.4e}") _crit = self.solver_parameters.get("damage_elasticity").get("criterion") - ColorPrint.print_info(f"ALTMIN - Iterations: {iteration:3d}, Error: {error:3.4e}, {_crit}, alpha_max: {self.alpha.vector.max()[1]:3.4e}") + ColorPrint.print_info(f"ALTMIN - Iterations: {iteration:3d}, Error: {error:3.4e}, {_crit}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}") class HybridSolver(AlternateMinimisation): @@ -266,15 +267,14 @@ def __init__( self.alpha_lb = bounds[0] self.alpha_ub = bounds[1] - set_vector_to_constant(self.u_lb.vector, PETSc.NINFINITY) - set_vector_to_constant(self.u_ub.vector, PETSc.PINFINITY) - set_vector_to_constant(self.alpha_lb.vector, 0) - set_vector_to_constant(self.alpha_ub.vector, 1) + set_vector_to_constant(self.u_lb.x.petsc_vec, PETSc.NINFINITY) + set_vector_to_constant(self.u_ub.x.petsc_vec, PETSc.PINFINITY) + set_vector_to_constant(self.alpha_lb.x.petsc_vec, 0) + set_vector_to_constant(self.alpha_ub.x.petsc_vec, 1) self.z = [self.u, self.alpha] bcs_z = bcs.get("bcs_u") + bcs.get("bcs_alpha") self.prefix = "blocknewton" - nest = False self.newton = SNESBlockProblem( self.F, self.z, bcs=bcs_z, nest=nest, prefix="block" @@ -363,7 +363,7 @@ def compute_bounds(self, v, alpha_lb): with ub.getNestSubVecs()[0].localForm() as u_sub: u_sub.set(PETSc.PINFINITY) - with lb.getNestSubVecs()[1].localForm() as alpha_sub, alpha_lb.vector.localForm() as alpha_lb_loc: + with lb.getNestSubVecs()[1].localForm() as alpha_sub, alpha_lb.x.petsc_vec.localForm() as alpha_lb_loc: alpha_lb_loc.copy(result=alpha_sub) with ub.getNestSubVecs()[1].localForm() as alpha_sub: diff --git a/src/irrevolutions/algorithms/ls.py b/src/irrevolutions/algorithms/ls.py index d4fd50d0..bc78ab14 100644 --- a/src/irrevolutions/algorithms/ls.py +++ b/src/irrevolutions/algorithms/ls.py @@ -36,6 +36,7 @@ def __init__(self, energy, state, linesearch_parameters={}): linesearch_parameters (dict): Parameters for the line search algorithm. """ super(LineSearch, self).__init__() + self.energy = energy self.state = state self.parameters = linesearch_parameters @@ -67,8 +68,8 @@ def search(self, state, perturbation, interval, m=2, method="min"): u_0 = Function(state["u"].function_space) alpha_0 = Function(state["alpha"].function_space) - state["u"].vector.copy(u_0.vector) - state["alpha"].vector.copy(alpha_0.vector) + state["u"].x.petsc_vec.copy(u_0.x.petsc_vec) + state["alpha"].x.petsc_vec.copy(alpha_0.x.petsc_vec) en_0 = assemble_scalar(form(self.energy)) @@ -78,12 +79,12 @@ def search(self, state, perturbation, interval, m=2, method="min"): perturbation_norms = [] for h in htest: - with state["u"].vector.localForm() as u_local, state["alpha"].vector.localForm() as alpha_local: - u_local.array[:] = u_0.vector.array[:] + h * v.vector.array[:] - alpha_local.array[:] = alpha_0.vector.array[:] + h * beta.vector.array[:] + with state["u"].x.petsc_vec.localForm() as u_local, state["alpha"].x.petsc_vec.localForm() as alpha_local: + u_local.array[:] = u_0.x.petsc_vec.array[:] + h * v.x.petsc_vec.array[:] + alpha_local.array[:] = alpha_0.x.petsc_vec.array[:] + h * beta.x.petsc_vec.array[:] - state["u"].vector.ghostUpdate(addv=PETSc.InsertMode.INSERT_VALUES, mode=PETSc.ScatterMode.FORWARD) - state["alpha"].vector.ghostUpdate(addv=PETSc.InsertMode.INSERT_VALUES, mode=PETSc.ScatterMode.FORWARD) + state["u"].x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT_VALUES, mode=PETSc.ScatterMode.FORWARD) + state["alpha"].x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT_VALUES, mode=PETSc.ScatterMode.FORWARD) yh_norm = np.sum([norm_H1(func) for func in state.values()]) perturbation_norms.append(yh_norm) @@ -92,8 +93,8 @@ def search(self, state, perturbation, interval, m=2, method="min"): energies_1d.append(en_h - en_0) # Restore original state - u_0.vector.copy(state["u"].vector) - alpha_0.vector.copy(state["alpha"].vector) + u_0.x.petsc_vec.copy(state["u"].x.petsc_vec) + alpha_0.x.petsc_vec.copy(state["alpha"].x.petsc_vec) # Polynomial fit and optimal step computation z = np.polyfit(htest, energies_1d, m) @@ -129,12 +130,12 @@ def perturb(self, state, perturbation, h): z0_norm = np.sum([norm_H1(func) for func in state.values()]) - with state["u"].vector.localForm() as u_local, state["alpha"].vector.localForm() as alpha_local: - u_local.array[:] = u_local.array[:] + h * v.vector.array[:] - alpha_local.array[:] = alpha_local.array[:] + h * beta.vector.array[:] + with state["u"].x.petsc_vec.localForm() as u_local, state["alpha"].x.petsc_vec.localForm() as alpha_local: + u_local.array[:] = u_local.array[:] + h * v.x.petsc_vec.array[:] + alpha_local.array[:] = alpha_local.array[:] + h * beta.x.petsc_vec.array[:] - state["u"].vector.ghostUpdate(addv=PETSc.InsertMode.INSERT_VALUES, mode=PETSc.ScatterMode.FORWARD) - state["alpha"].vector.ghostUpdate(addv=PETSc.InsertMode.INSERT_VALUES, mode=PETSc.ScatterMode.FORWARD) + state["u"].x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT_VALUES, mode=PETSc.ScatterMode.FORWARD) + state["alpha"].x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT_VALUES, mode=PETSc.ScatterMode.FORWARD) zh_norm = np.sum([norm_H1(func) for func in state.values()]) @@ -159,16 +160,16 @@ def admissible_interval(self, state, perturbation, alpha_lb, bifurcation): alpha = state["alpha"] beta = bifurcation[1] - one = max(1.0, max(alpha.vector[:])) - mask = np.int32(np.where(beta.vector[:] > 0)[0]) + one = max(1.0, max(alpha.x.petsc_vec[:])) + mask = np.int32(np.where(beta.x.petsc_vec[:] > 0)[0]) - hp2 = (one - alpha.vector[mask]) / beta.vector[mask] if len(mask) > 0 else [np.inf] - hp1 = (alpha_lb.vector[mask] - alpha.vector[mask]) / beta.vector[mask] if len(mask) > 0 else [-np.inf] + hp2 = (one - alpha.x.petsc_vec[mask]) / beta.x.petsc_vec[mask] if len(mask) > 0 else [np.inf] + hp1 = (alpha_lb.x.petsc_vec[mask] - alpha.x.petsc_vec[mask]) / beta.x.petsc_vec[mask] if len(mask) > 0 else [-np.inf] hp = (max(hp1), min(hp2)) - mask_neg = np.int32(np.where(beta.vector[:] < 0)[0]) - hn2 = (one - alpha.vector[mask_neg]) / beta.vector[mask_neg] if len(mask_neg) > 0 else [-np.inf] - hn1 = (alpha_lb.vector[mask_neg] - alpha.vector[mask_neg]) / beta.vector[mask_neg] if len(mask_neg) > 0 else [np.inf] + mask_neg = np.int32(np.where(beta.x.petsc_vec[:] < 0)[0]) + hn2 = (one - alpha.x.petsc_vec[mask_neg]) / beta.x.petsc_vec[mask_neg] if len(mask_neg) > 0 else [-np.inf] + hn1 = (alpha_lb.x.petsc_vec[mask_neg] - alpha.x.petsc_vec[mask_neg]) / beta.x.petsc_vec[mask_neg] if len(mask_neg) > 0 else [np.inf] hn = (max(hn2), min(hn1)) hmax = np.array(np.min([hp[1], hn[1]])) @@ -208,14 +209,14 @@ def get_unilateral_interval(self, state, perturbation): beta = perturbation["beta"] # Ensure that the perturbation is non-negative - assert (beta.vector[:] >= 0).all(), "beta must be non-negative" + assert (beta.x.petsc_vec[:] >= 0).all(), "beta must be non-negative" # Compute the upper bound for the admissible interval - one = max(1.0, max(alpha.vector[:])) - mask = np.int32(np.where(beta.vector[:] > 0)[0]) + one = max(1.0, max(alpha.x.petsc_vec[:])) + mask = np.int32(np.where(beta.x.petsc_vec[:] > 0)[0]) _hmax = ( - (one - alpha.vector[mask]) / beta.vector[mask] + (one - alpha.x.petsc_vec[mask]) / beta.x.petsc_vec[mask] if len(mask) > 0 else [np.inf] ) diff --git a/src/irrevolutions/algorithms/so.py b/src/irrevolutions/algorithms/so.py index 45b14d02..b8b8347a 100644 --- a/src/irrevolutions/algorithms/so.py +++ b/src/irrevolutions/algorithms/so.py @@ -103,7 +103,7 @@ def __init__( self.mesh = alpha.function_space.mesh # Initialize L as a DG(0) function - L = dolfinx.fem.FunctionSpace(self.mesh, ("DG", 0)) + L = dolfinx.fem.functionspace(self.mesh, ("DG", 0)) self.lmbda0 = dolfinx.fem.Function(L) # Define the forms associated with the second derivative of the energy @@ -162,7 +162,7 @@ def get_inactive_dofset(self, a_old) -> set: with self.state[ 1 - ].vector.localForm() as a_local, a_old.vector.localForm() as a_old_local: + ].x.petsc_vec.localForm() as a_local, a_old.x.petsc_vec.localForm() as a_old_local: idx_ub_local = np.where(np.isclose(a_local[:], 1.0, rtol=pwtol))[0] idx_lb_local = np.where(np.isclose(a_local[:], a_old_local[:], rtol=pwtol))[ 0 @@ -298,9 +298,9 @@ def normalise_eigenmode(self, x, mode="functional"): raise NotImplementedError("Normalisation mode not implemented") # for u in [_v, _β]: - # with u.vector.localForm() as u_local: + # with u.x.petsc_vec.localForm() as u_local: # u_local.scale(1.0 / scaling) - # u.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT_VALUES, mode=PETSc.ScatterMode.FORWARD) + # u.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT_VALUES, mode=PETSc.ScatterMode.FORWARD) with x.localForm() as x_local: x_local.scale(1.0 / scaling) @@ -325,27 +325,27 @@ def normalise_eigen(self, u, mode="max-beta"): """ if mode == "max-beta": _v, beta = u[0], u[1] - coeff_glob = beta.vector.norm(3) + coeff_glob = beta.x.petsc_vec.norm(3) - logging.debug(f"{rank}, |β|_infty {beta.vector.norm(3):.3f}") + logging.debug(f"{rank}, |β|_infty {beta.x.petsc_vec.norm(3):.3f}") elif mode == "unit": - coeff_glob = np.sqrt(sum(n**2 for n in [v_i.vector.norm() for v_i in u])) + coeff_glob = np.sqrt(sum(n**2 for n in [v_i.x.petsc_vec.norm() for v_i in u])) logging.debug(f"rank {rank}, coeff_glob {coeff_glob:.3f}") logging.debug(f"{rank}, |(v, β)^*|_2 {coeff_glob:.3f}") if coeff_glob == 0.0: - logging.error(f"Damage eigenvector is null i.e. |β|={beta.vector.norm()}") + logging.error(f"Damage eigenvector is null i.e. |β|={beta.x.petsc_vec.norm()}") return 0.0 for v_i in u: - with v_i.vector.localForm() as v_local: + with v_i.x.petsc_vec.localForm() as v_local: v_local.scale(1.0 / coeff_glob) - v_i.vector.ghostUpdate( + v_i.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT_VALUES, mode=PETSc.ScatterMode.FORWARD ) - np.sqrt(sum(n**2 for n in [v_i.vector.norm(2) for v_i in u])) + np.sqrt(sum(n**2 for n in [v_i.x.petsc_vec.norm(2) for v_i in u])) return coeff_glob @@ -503,9 +503,9 @@ def process_eigenmode(self, eigen, i): _u = self.normalise_eigenmode(_u, mode="functional") for u, component in zip(ur, [v_n, β_n]): - with u.vector.localForm() as u_loc, component.vector.localForm() as c_loc: + with u.x.petsc_vec.localForm() as u_loc, component.x.petsc_vec.localForm() as c_loc: u_loc.copy(result=c_loc) - component.vector.ghostUpdate( + component.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/src/irrevolutions/practice/default.py b/src/irrevolutions/practice/default.py index 1759edd3..54123bf6 100644 --- a/src/irrevolutions/practice/default.py +++ b/src/irrevolutions/practice/default.py @@ -40,7 +40,7 @@ from dolfinx.io import XDMFFile, gmshio from mpi4py import MPI from petsc4py import PETSc - +import basix.ufl sys.path.append("../") @@ -140,10 +140,10 @@ def create_function_space(mesh): dolfinx.FunctionSpace: Function space for displacement. dolfinx.FunctionSpace: Function space for damage. """ - element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=2) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(2,)) V_u = FunctionSpace(mesh, element_u) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) return V_u, V_alpha @@ -304,12 +304,12 @@ def initialise_solver(total_energy, state, bcs, parameters): alpha_ub = Function(V_alpha, name="Upper bound") for f in [alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) - set_bc(alpha_ub.vector, bcs["bcs_alpha"]) - alpha_ub.vector.ghostUpdate( + set_bc(alpha_ub.x.petsc_vec, bcs["bcs_alpha"]) + alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -476,8 +476,8 @@ def datum(x): # Implement any necessary updates here # update the lower bound - alpha.vector.copy(solver.alpha_lb.vector) - solver.alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(solver.alpha_lb.x.petsc_vec) + solver.alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/src/irrevolutions/practice/discrete_atk.py b/src/irrevolutions/practice/discrete_atk.py index 66e56479..f558b91e 100644 --- a/src/irrevolutions/practice/discrete_atk.py +++ b/src/irrevolutions/practice/discrete_atk.py @@ -24,7 +24,7 @@ from mpi4py import MPI from petsc4py import PETSc from utils.plots import plot_energies - +import basix.ufl """Discrete endommageable springs in series 1 2 i k @@ -114,9 +114,9 @@ def solve(self, outdir=None): (solver_alpha_it, solver_alpha_reason) = self.damage.solve() # Define error function - self.alpha.vector.copy(alpha_diff.vector) - alpha_diff.vector.axpy(-1, self.alpha_old.vector) - alpha_diff.vector.ghostUpdate( + self.alpha.x.petsc_vec.copy(alpha_diff.x.petsc_vec) + alpha_diff.x.petsc_vec.axpy(-1, self.alpha_old.x.petsc_vec) + alpha_diff.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -129,7 +129,7 @@ def solve(self, outdir=None): np.array([comm.allreduce(Fvi.norm(), op=MPI.SUM) for Fvi in Fv]).sum() ) - error_alpha_max = alpha_diff.vector.max()[1] + error_alpha_max = alpha_diff.x.petsc_vec.max()[1] total_energy_int = comm.allreduce( assemble_scalar(form(self.total_energy)), op=MPI.SUM ) @@ -137,28 +137,28 @@ def solve(self, outdir=None): residual_F.ghostUpdate( addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE ) - set_bc(residual_F, self.elasticity.bcs, self.u.vector) + set_bc(residual_F, self.elasticity.bcs, self.u.x.petsc_vec) error_residual_F = ufl.sqrt(residual_F.dot(residual_F)) - self.alpha.vector.copy(self.alpha_old.vector) - self.alpha_old.vector.ghostUpdate( + self.alpha.x.petsc_vec.copy(self.alpha_old.x.petsc_vec) + self.alpha_old.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) logging.critical( - f"AM - Iteration: {iteration:3d}, res F Error: {error_residual_F:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}" + f"AM - Iteration: {iteration:3d}, res F Error: {error_residual_F:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}" ) logging.critical( - f"AM - Iteration: {iteration:3d}, H1 Error: {error_alpha_H1:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}" + f"AM - Iteration: {iteration:3d}, H1 Error: {error_alpha_H1:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}" ) logging.critical( - f"AM - Iteration: {iteration:3d}, L2 Error: {error_alpha_L2:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}" + f"AM - Iteration: {iteration:3d}, L2 Error: {error_alpha_L2:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}" ) logging.critical( - f"AM - Iteration: {iteration:3d}, Linfty Error: {error_alpha_max:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}" + f"AM - Iteration: {iteration:3d}, Linfty Error: {error_alpha_max:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}" ) self.data["iteration"].append(iteration) @@ -273,12 +273,12 @@ def discrete_atk(arg_N=2): # Functional Setting - element_u = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) - element_alpha = ufl.FiniteElement("DG", mesh.ufl_cell(), degree=0) + element_alpha = basix.ufl.element("DG", mesh.basix_cell(), degree=0) - V_u = dolfinx.fem.FunctionSpace(mesh, element_u) - V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) + V_u = dolfinx.fem.functionspace(mesh, element_u) + V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") u_ = dolfinx.fem.Function(V_u, name="BoundaryDisplacement") @@ -329,7 +329,7 @@ def discrete_atk(arg_N=2): u_.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -451,13 +451,13 @@ def stress(state): for i_t, t in enumerate(loads): u_.interpolate(lambda x: t * np.ones_like(x[0])) - u_.vector.ghostUpdate( + u_.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -498,11 +498,11 @@ def stress(state): history_data["non-bifurcation"].append(not stability.data["stable"]) history_data["cone-stable"].append(stable) history_data["F"].append(_F) - history_data["alpha_t"].append(state["alpha"].vector.array.tolist()) - history_data["u_t"].append(state["u"].vector.array.tolist()) + history_data["alpha_t"].append(state["alpha"].x.petsc_vec.array.tolist()) + history_data["u_t"].append(state["u"].x.petsc_vec.array.tolist()) - logging.critical(f"u_t {u.vector.array}") - logging.critical(f"u_t norm {state['u'].vector.norm()}") + logging.critical(f"u_t {u.x.petsc_vec.array}") + logging.critical(f"u_t norm {state['u'].x.petsc_vec.norm()}") with XDMFFile( comm, f"{prefix}/{_nameExp}.xdmf", "a", encoding=XDMFFile.Encoding.HDF5 diff --git a/src/irrevolutions/practice/discrete_atk_homogeneous.py b/src/irrevolutions/practice/discrete_atk_homogeneous.py index f277673b..1d6463ec 100644 --- a/src/irrevolutions/practice/discrete_atk_homogeneous.py +++ b/src/irrevolutions/practice/discrete_atk_homogeneous.py @@ -25,6 +25,7 @@ from dolfinx.io import XDMFFile from mpi4py import MPI from petsc4py import PETSc +import basix.ufl @@ -116,9 +117,9 @@ def solve(self, outdir=None): (solver_alpha_it, solver_alpha_reason) = self.damage.solve() # Define error function - self.alpha.vector.copy(alpha_diff.vector) - alpha_diff.vector.axpy(-1, self.alpha_old.vector) - alpha_diff.vector.ghostUpdate( + self.alpha.x.petsc_vec.copy(alpha_diff.x.petsc_vec) + alpha_diff.x.petsc_vec.axpy(-1, self.alpha_old.x.petsc_vec) + alpha_diff.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -131,7 +132,7 @@ def solve(self, outdir=None): np.array([comm.allreduce(Fvi.norm(), op=MPI.SUM) for Fvi in Fv]).sum() ) - error_alpha_max = alpha_diff.vector.max()[1] + error_alpha_max = alpha_diff.x.petsc_vec.max()[1] total_energy_int = comm.allreduce( assemble_scalar(form(self.total_energy)), op=MPI.SUM ) @@ -139,28 +140,28 @@ def solve(self, outdir=None): residual_F.ghostUpdate( addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE ) - set_bc(residual_F, self.elasticity.bcs, self.u.vector) + set_bc(residual_F, self.elasticity.bcs, self.u.x.petsc_vec) error_residual_F = ufl.sqrt(residual_F.dot(residual_F)) - self.alpha.vector.copy(self.alpha_old.vector) - self.alpha_old.vector.ghostUpdate( + self.alpha.x.petsc_vec.copy(self.alpha_old.x.petsc_vec) + self.alpha_old.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) logging.critical( - f"AM - Iteration: {iteration:3d}, res F Error: {error_residual_F:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}" + f"AM - Iteration: {iteration:3d}, res F Error: {error_residual_F:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}" ) logging.critical( - f"AM - Iteration: {iteration:3d}, H1 Error: {error_alpha_H1:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}" + f"AM - Iteration: {iteration:3d}, H1 Error: {error_alpha_H1:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}" ) logging.critical( - f"AM - Iteration: {iteration:3d}, L2 Error: {error_alpha_L2:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}" + f"AM - Iteration: {iteration:3d}, L2 Error: {error_alpha_L2:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}" ) logging.critical( - f"AM - Iteration: {iteration:3d}, Linfty Error: {error_alpha_max:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}" + f"AM - Iteration: {iteration:3d}, Linfty Error: {error_alpha_max:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}" ) self.data["iteration"].append(iteration) @@ -270,12 +271,12 @@ def discrete_atk(arg_N=2): # Functional Setting - element_u = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) - element_alpha = ufl.FiniteElement("DG", mesh.ufl_cell(), degree=0) + element_alpha = basix.ufl.element("DG", mesh.basix_cell(), degree=0) - V_u = dolfinx.fem.FunctionSpace(mesh, element_u) - V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) + V_u = dolfinx.fem.functionspace(mesh, element_u) + V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") u_ = dolfinx.fem.Function(V_u, name="BoundaryDisplacement") @@ -326,7 +327,7 @@ def discrete_atk(arg_N=2): u_.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -470,8 +471,8 @@ def _homogeneous_state(state, t, matpar): _e = t / _N _uh = [_e * i for i in range(0, _N + 1)] - _alpha.vector[:] = _alphah - _u.vector[:] = _uh + _alpha.x.petsc_vec[:] = _alphah + _u.x.petsc_vec[:] = _uh for i_t, t in enumerate(loads): logging.critical(f"-- Solving for t = {t:3.2f} --") @@ -526,11 +527,11 @@ def _homogeneous_state(state, t, matpar): history_data["non-bifurcation"].append(not stability.data["stable"]) history_data["cone-stable"].append(stable) history_data["F"].append(_F) - history_data["alpha_t"].append(state["alpha"].vector.array.tolist()) - history_data["u_t"].append(state["u"].vector.array.tolist()) + history_data["alpha_t"].append(state["alpha"].x.petsc_vec.array.tolist()) + history_data["u_t"].append(state["u"].x.petsc_vec.array.tolist()) - logging.critical(f"u_t {u.vector.array}") - logging.critical(f"u_t norm {state['u'].vector.norm()}") + logging.critical(f"u_t {u.x.petsc_vec.array}") + logging.critical(f"u_t norm {state['u'].x.petsc_vec.norm()}") with XDMFFile( comm, f"{prefix}/{_nameExp}.xdmf", "a", encoding=XDMFFile.Encoding.HDF5 diff --git a/src/irrevolutions/practice/enpassant.py b/src/irrevolutions/practice/enpassant.py index 788f4fb3..d9963fbc 100644 --- a/src/irrevolutions/practice/enpassant.py +++ b/src/irrevolutions/practice/enpassant.py @@ -27,7 +27,7 @@ import dolfinx import logging import sys - +import basix.ufl sys.path.append("../") logging.basicConfig() @@ -153,12 +153,12 @@ def middle_area(x): mesh = mesh_refined_local2 # Functional Setting -element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=2) +element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(2,)) -element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) +element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) -V_u = dolfinx.fem.FunctionSpace(mesh, element_u) -V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) +V_u = dolfinx.fem.functionspace(mesh, element_u) +V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") u_ = dolfinx.fem.Function(V_u, name="BoundaryDisplacement") @@ -259,11 +259,11 @@ def monitor(snes, its, fgnorm): # update boundary conditions u_.interpolate(lambda x: (np.zeros_like(x[0]), t * np.ones_like(x[1]))) - u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) # update lower bound for damage - alpha.vector.copy(alpha_lb.vector) - alpha.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/src/irrevolutions/practice/pacman-cone.py b/src/irrevolutions/practice/pacman-cone.py index 31a25642..bf8ed93a 100644 --- a/src/irrevolutions/practice/pacman-cone.py +++ b/src/irrevolutions/practice/pacman-cone.py @@ -31,6 +31,7 @@ from mpi4py import MPI from petsc4py import PETSc from pyvista.utilities import xvfb +import basix.ufl sys.path.append("../") @@ -160,10 +161,10 @@ def pacman_cone(resolution=2, slug="pacman"): fig.savefig(f"{prefix}/mesh.png") # Function spaces - element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=2) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(2,)) V_u = FunctionSpace(mesh, element_u) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) # Define the state @@ -209,7 +210,7 @@ def pacman_cone(resolution=2, slug="pacman"): alpha_ub.interpolate(lambda x: np.ones_like(x[0])) for f in [alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -223,8 +224,8 @@ def pacman_cone(resolution=2, slug="pacman"): ) ] - set_bc(alpha_ub.vector, bcs_alpha) - alpha_ub.vector.ghostUpdate( + set_bc(alpha_ub.x.petsc_vec, bcs_alpha) + alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -293,8 +294,8 @@ def pacman_cone(resolution=2, slug="pacman"): ) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -306,9 +307,9 @@ def pacman_cone(resolution=2, slug="pacman"): hybrid.solve(alpha_lb) # compute the rate - alpha.vector.copy(alphadot.vector) - alphadot.vector.axpy(-1, alpha_lb.vector) - alphadot.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alphadot.x.petsc_vec) + alphadot.x.petsc_vec.axpy(-1, alpha_lb.x.petsc_vec) + alphadot.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -351,7 +352,7 @@ def pacman_cone(resolution=2, slug="pacman"): history_data["solver_KS_data"].append(cone.data) history_data["eigs"].append(bifurcation.data["eigs"]) history_data["F"].append(0) - history_data["alphadot_norm"].append(alphadot.vector.norm()) + history_data["alphadot_norm"].append(alphadot.x.petsc_vec.norm()) history_data["rate_12_norm"].append(rate_12_norm) history_data["unscaled_rate_12_norm"].append(urate_12_norm) history_data["cone-stable"].append(stable) diff --git a/src/irrevolutions/practice/pacman_hybrid.py b/src/irrevolutions/practice/pacman_hybrid.py index 63db732c..53897585 100644 --- a/src/irrevolutions/practice/pacman_hybrid.py +++ b/src/irrevolutions/practice/pacman_hybrid.py @@ -27,11 +27,10 @@ from dolfinx.io import XDMFFile, gmshio from dolfinx.mesh import locate_entities_boundary from pyvista.utilities import xvfb - +import basix.ufl sys.path.append("../") - logging.basicConfig(level=logging.INFO) today = date.today() @@ -145,10 +144,10 @@ def pacman_hybrid(nest): fig.savefig(f"{prefix}/mesh.png") # Function spaces - element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=2) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(2,)) V_u = FunctionSpace(mesh, element_u) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) # Define the state @@ -195,7 +194,7 @@ def pacman_hybrid(nest): alpha_ub.interpolate(lambda x: np.ones_like(x[0])) for f in [alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -209,8 +208,8 @@ def pacman_hybrid(nest): ) ] - set_bc(alpha_ub.vector, bcs_alpha) - alpha_ub.vector.ghostUpdate( + set_bc(alpha_ub.x.petsc_vec, bcs_alpha) + alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -223,10 +222,10 @@ def pacman_hybrid(nest): u_ub = Function(V_u, name="displacement upper bound") alpha_lb = Function(V_alpha, name="damage lower bound") alpha_ub = Function(V_alpha, name="damage upper bound") - set_vector_to_constant(u_lb.vector, PETSc.NINFINITY) - set_vector_to_constant(u_ub.vector, PETSc.PINFINITY) - set_vector_to_constant(alpha_lb.vector, 0) - set_vector_to_constant(alpha_ub.vector, 1) + set_vector_to_constant(u_lb.x.petsc_vec, PETSc.NINFINITY) + set_vector_to_constant(u_ub.x.petsc_vec, PETSc.PINFINITY) + set_vector_to_constant(alpha_lb.x.petsc_vec, 0) + set_vector_to_constant(alpha_ub.x.petsc_vec, 1) bcs = {"bcs_u": bcs_u, "bcs_alpha": bcs_alpha} @@ -282,8 +281,8 @@ def pacman_hybrid(nest): ) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -291,15 +290,15 @@ def pacman_hybrid(nest): hybrid.solve() # compute rate - alpha.vector.copy(alphadot.vector) - alphadot.vector.axpy(-1, alpha_lb.vector) - alphadot.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alphadot.x.petsc_vec) + alphadot.x.petsc_vec.axpy(-1, alpha_lb.x.petsc_vec) + alphadot.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) - alpha.vector.copy(alphadot.vector) - alphadot.vector.axpy(-1, alpha_lb.vector) - alphadot.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alphadot.x.petsc_vec) + alphadot.x.petsc_vec.axpy(-1, alpha_lb.x.petsc_vec) + alphadot.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -362,7 +361,7 @@ def pacman_hybrid(nest): ColorPrint.print_info( f"NEWTON - Iterations: {hybrid.newton.snes.getIterationNumber()+1:3d},\ Fnorm: {hybrid.newton.snes.getFunctionNorm():3.4e},\ - alpha_max: {alpha.vector.max()[1]:3.4e}" + alpha_max: {alpha.x.petsc_vec.max()[1]:3.4e}" ) xvfb.start_xvfb(wait=0.05) diff --git a/src/irrevolutions/practice/thinfilm-bar.py b/src/irrevolutions/practice/thinfilm-bar.py index 01d179b6..c5d12ddd 100644 --- a/src/irrevolutions/practice/thinfilm-bar.py +++ b/src/irrevolutions/practice/thinfilm-bar.py @@ -31,6 +31,7 @@ from mpi4py import MPI from petsc4py import PETSc from pyvista.utilities import xvfb +import basix.ufl sys.path.append("../") # from meshes.pacman import mesh_pacman @@ -131,10 +132,10 @@ def main(parameters, storage=None): mesh, mts, fts = gmshio.model_to_mesh(gmsh_model, comm, model_rank, tdim) # functional space - element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) V_u = FunctionSpace(mesh, element_u) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) u = Function(V_u, name="Displacement") @@ -173,8 +174,8 @@ def main(parameters, storage=None): # boundary conditions bcs_u = [] bcs_alpha = [] - set_bc(alpha_ub.vector, bcs_alpha) - alpha_ub.vector.ghostUpdate( + set_bc(alpha_ub.x.petsc_vec, bcs_alpha) + alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) bcs = {"bcs_u": bcs_u, "bcs_alpha": bcs_alpha} @@ -238,8 +239,8 @@ def main(parameters, storage=None): tau.value = t # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -268,9 +269,9 @@ def main(parameters, storage=None): ) # compute rate - alpha.vector.copy(alphadot.vector) - alphadot.vector.axpy(-1, alpha_lb.vector) - alphadot.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alphadot.x.petsc_vec) + alphadot.x.petsc_vec.axpy(-1, alpha_lb.x.petsc_vec) + alphadot.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -365,7 +366,7 @@ def main(parameters, storage=None): history_data["eigs"].append(bifurcation.data["eigs"]) history_data["F"].append(stress) history_data["cone_data"].append(stability.data) - history_data["alphadot_norm"].append(alphadot.vector.norm()) + history_data["alphadot_norm"].append(alphadot.x.petsc_vec.norm()) history_data["rate_12_norm"].append(rate_12_norm) history_data["unscaled_rate_12_norm"].append(rate_12_norm_unscaled) history_data["cone-stable"].append(stable) @@ -469,7 +470,7 @@ def load_parameters(file_path): if "--ell_e" in sys.argv: parameters, signature = parameters_vs_elle( - parameters=base_parameters, elle=np.float(args.ell_e) + parameters= base_parameters, elle=np.float(args.ell_e) ) _storage = ( f"output/parametric/thinfilm-bar/vs_ell_e/{base_signature}/{signature}" diff --git a/src/irrevolutions/practice/traction-AT1_cone.py b/src/irrevolutions/practice/traction-AT1_cone.py index d4f3d98d..33f1aa62 100644 --- a/src/irrevolutions/practice/traction-AT1_cone.py +++ b/src/irrevolutions/practice/traction-AT1_cone.py @@ -30,6 +30,7 @@ from dolfinx.io import XDMFFile, gmshio from mpi4py import MPI from petsc4py import PETSc +import basix.ufl sys.path.append("../") @@ -119,10 +120,10 @@ # Functional Setting # Function spaces -element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) +element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) V_u = FunctionSpace(mesh, element_u) -element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) +element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) # Define the state @@ -157,7 +158,7 @@ alpha_ub.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, zero_alpha, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + f.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) bc_u_left = dirichletbc(np.array([0, 0], dtype=PETSc.ScalarType), dofs_u_left, V_u) @@ -172,8 +173,8 @@ # dolfinx.fem.dirichletbc(zero_alpha, dofs_alpha_right), # ] -set_bc(alpha_ub.vector, bcs_alpha) -alpha_ub.vector.ghostUpdate( +set_bc(alpha_ub.x.petsc_vec, bcs_alpha) +alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -242,11 +243,11 @@ for i_t, t in enumerate(loads): # for i_t, t in enumerate([0., .99, 1.0, 1.01]): u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1]))) - u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -269,16 +270,16 @@ hybrid.solve(alpha_lb) # compute the rate - alpha.vector.copy(alphadot.vector) - alphadot.vector.axpy(-1, alpha_lb.vector) - alphadot.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alphadot.x.petsc_vec) + alphadot.x.petsc_vec.axpy(-1, alpha_lb.x.petsc_vec) + alphadot.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) - logging.info(f"alpha vector norm: {alpha.vector.norm()}") - logging.info(f"alpha lb norm: {alpha_lb.vector.norm()}") - logging.info(f"alphadot norm: {alphadot.vector.norm()}") - logging.info(f"vector norms [u, alpha]: {[zi.vector.norm() for zi in z]}") + logging.info(f"alpha vector norm: {alpha.x.petsc_vec.norm()}") + logging.info(f"alpha lb norm: {alpha_lb.x.petsc_vec.norm()}") + logging.info(f"alphadot norm: {alphadot.x.petsc_vec.norm()}") + logging.info(f"vector norms [u, alpha]: {[zi.x.petsc_vec.norm() for zi in z]}") rate_12_norm = hybrid.scaled_rate_norm(alpha, parameters) urate_12_norm = hybrid.unscaled_rate_norm(alpha) @@ -327,7 +328,7 @@ history_data["eigs"].append(bifurcation.data["eigs"]) history_data["F"].append(stress) history_data["cone_data"].append(cone.data) - history_data["alphadot_norm"].append(alphadot.vector.norm()) + history_data["alphadot_norm"].append(alphadot.x.petsc_vec.norm()) history_data["rate_12_norm"].append(rate_12_norm) history_data["unscaled_rate_12_norm"].append(urate_12_norm) history_data["cone-stable"].append(stable) diff --git a/src/irrevolutions/practice/traction-AT1_first_order.py b/src/irrevolutions/practice/traction-AT1_first_order.py index b5e193f9..87591062 100644 --- a/src/irrevolutions/practice/traction-AT1_first_order.py +++ b/src/irrevolutions/practice/traction-AT1_first_order.py @@ -30,6 +30,7 @@ from dolfinx.io import XDMFFile, gmshio from mpi4py import MPI from petsc4py import PETSc +import basix.ufl sys.path.append("../") @@ -122,10 +123,10 @@ # Functional Setting # Function spaces -element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) +element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) V_u = FunctionSpace(mesh, element_u) -element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) +element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) # Define the state @@ -160,7 +161,7 @@ alpha_ub.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, zero_alpha, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + f.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) bc_u_left = dirichletbc(np.array([0, 0], dtype=PETSc.ScalarType), dofs_u_left, V_u) @@ -175,8 +176,8 @@ # dolfinx.fem.dirichletbc(zero_alpha, dofs_alpha_right), # ] -set_bc(alpha_ub.vector, bcs_alpha) -alpha_ub.vector.ghostUpdate( +set_bc(alpha_ub.x.petsc_vec, bcs_alpha) +alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -245,11 +246,11 @@ for i_t, t in enumerate(loads): # for i_t, t in enumerate([0., .99, 1.0, 1.01]): u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1]))) - u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -272,16 +273,16 @@ hybrid.solve(alpha_lb) # compute the rate - alpha.vector.copy(alphadot.vector) - alphadot.vector.axpy(-1, alpha_lb.vector) - alphadot.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alphadot.x.petsc_vec) + alphadot.x.petsc_vec.axpy(-1, alpha_lb.x.petsc_vec) + alphadot.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) - logging.info(f"alpha vector norm: {alpha.vector.norm()}") - logging.info(f"alpha lb norm: {alpha_lb.vector.norm()}") - logging.info(f"alphadot norm: {alphadot.vector.norm()}") - logging.info(f"vector norms [u, alpha]: {[zi.vector.norm() for zi in z]}") + logging.info(f"alpha vector norm: {alpha.x.petsc_vec.norm()}") + logging.info(f"alpha lb norm: {alpha_lb.x.petsc_vec.norm()}") + logging.info(f"alphadot norm: {alphadot.x.petsc_vec.norm()}") + logging.info(f"vector norms [u, alpha]: {[zi.x.petsc_vec.norm() for zi in z]}") rate_12_norm = hybrid.scaled_rate_norm(alpha, parameters) urate_12_norm = hybrid.unscaled_rate_norm(alpha) diff --git a/src/irrevolutions/practice/traction-AT2_cone.py b/src/irrevolutions/practice/traction-AT2_cone.py index 133ebf27..e99b7fcb 100644 --- a/src/irrevolutions/practice/traction-AT2_cone.py +++ b/src/irrevolutions/practice/traction-AT2_cone.py @@ -28,9 +28,7 @@ from dolfinx.io import XDMFFile, gmshio from mpi4py import MPI from petsc4py import PETSc - -sys.path.append("../") - +import basix.ufl sys.path.append("../") @@ -104,10 +102,10 @@ def w(self, alpha): ) as file: file.write_mesh(mesh) -element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) +element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) V_u = FunctionSpace(mesh, element_u) -element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) +element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) u = Function(V_u, name="Displacement") @@ -137,15 +135,15 @@ def w(self, alpha): alpha_ub.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, zero_alpha, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + f.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) bc_u_left = dirichletbc(np.array([0, 0], dtype=PETSc.ScalarType), dofs_u_left, V_u) bc_u_right = dirichletbc(u_, dofs_u_right) bcs_u = [bc_u_left, bc_u_right] bcs_alpha = [] -set_bc(alpha_ub.vector, bcs_alpha) -alpha_ub.vector.ghostUpdate( +set_bc(alpha_ub.x.petsc_vec, bcs_alpha) +alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) bcs = {"bcs_u": bcs_u, "bcs_alpha": bcs_alpha} @@ -206,10 +204,10 @@ def w(self, alpha): for i_t, t in enumerate(loads): u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1]))) - u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -225,16 +223,16 @@ def w(self, alpha): logging.info(f"-- {i_t}/{len(loads)}: Solving for t = {t:3.2f} --") hybrid.solve(alpha_lb) - alpha.vector.copy(alphadot.vector) - alphadot.vector.axpy(-1, alpha_lb.vector) - alphadot.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alphadot.x.petsc_vec) + alphadot.x.petsc_vec.axpy(-1, alpha_lb.x.petsc_vec) + alphadot.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) - logging.critical(f"alpha vector norm: {alpha.vector.norm()}") - logging.critical(f"alpha lb norm: {alpha_lb.vector.norm()}") - logging.critical(f"alphadot norm: {alphadot.vector.norm()}") - logging.critical(f"vector norms [u, alpha]: {[zi.vector.norm() for zi in z]}") + logging.critical(f"alpha vector norm: {alpha.x.petsc_vec.norm()}") + logging.critical(f"alpha lb norm: {alpha_lb.x.petsc_vec.norm()}") + logging.critical(f"alphadot norm: {alphadot.x.petsc_vec.norm()}") + logging.critical(f"vector norms [u, alpha]: {[zi.x.petsc_vec.norm() for zi in z]}") rate_12_norm = hybrid.scaled_rate_norm(alpha, parameters) urate_12_norm = hybrid.unscaled_rate_norm(alpha) @@ -282,7 +280,7 @@ def w(self, alpha): history_data["eigs"].append(bifurcation.data["eigs"]) history_data["F"].append(stress) history_data["cone_data"].append(cone.data) - history_data["alphadot_norm"].append(alphadot.vector.norm()) + history_data["alphadot_norm"].append(alphadot.x.petsc_vec.norm()) history_data["rate_12_norm"].append(rate_12_norm) history_data["unscaled_rate_12_norm"].append(urate_12_norm) history_data["cone-stable"].append(stable) diff --git a/src/irrevolutions/practice/traction-ATJJ.py b/src/irrevolutions/practice/traction-ATJJ.py index d4d86af3..4253a812 100644 --- a/src/irrevolutions/practice/traction-ATJJ.py +++ b/src/irrevolutions/practice/traction-ATJJ.py @@ -25,6 +25,7 @@ from dolfinx.io import XDMFFile, gmshio from mpi4py import MPI from petsc4py import PETSc +import basix.ufl sys.path.append("../") @@ -176,10 +177,10 @@ def traction_with_parameters(parameters, slug=""): # Functional Setting - element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) V_u = FunctionSpace(mesh, element_u) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) # Define the state @@ -214,7 +215,7 @@ def traction_with_parameters(parameters, slug=""): alpha_ub.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, zero_alpha, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -229,8 +230,8 @@ def traction_with_parameters(parameters, slug=""): # dolfinx.fem.dirichletbc(zero_alpha, dofs_alpha_right), # ] - set_bc(alpha_ub.vector, bcs_alpha) - alpha_ub.vector.ghostUpdate( + set_bc(alpha_ub.x.petsc_vec, bcs_alpha) + alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -307,13 +308,13 @@ def traction_with_parameters(parameters, slug=""): # for i_t, t in enumerate([0., .99, 1.0, 1.01]): u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1]))) - u_.vector.ghostUpdate( + u_.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -335,19 +336,19 @@ def traction_with_parameters(parameters, slug=""): hybrid.solve(alpha_lb) # compute the rate - alpha.vector.copy(alphadot.vector) - alphadot.vector.axpy(-1, alpha_lb.vector) - alphadot.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alphadot.x.petsc_vec) + alphadot.x.petsc_vec.axpy(-1, alpha_lb.x.petsc_vec) + alphadot.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) rate_12_norm = hybrid.scaled_rate_norm(alpha, parameters) urate_12_norm = hybrid.unscaled_rate_norm(alpha) - logging.critical(f"alpha vector norm: {alpha.vector.norm()}") - logging.critical(f"alpha lb norm: {alpha_lb.vector.norm()}") - logging.critical(f"alphadot norm: {alphadot.vector.norm()}") - logging.critical(f"vector norms [u, alpha]: {[zi.vector.norm() for zi in z]}") + logging.critical(f"alpha vector norm: {alpha.x.petsc_vec.norm()}") + logging.critical(f"alpha lb norm: {alpha_lb.x.petsc_vec.norm()}") + logging.critical(f"alphadot norm: {alphadot.x.petsc_vec.norm()}") + logging.critical(f"vector norms [u, alpha]: {[zi.x.petsc_vec.norm() for zi in z]}") logging.critical(f"scaled rate state_12 norm: {rate_12_norm}") logging.critical(f"unscaled scaled rate state_12 norm: {urate_12_norm}") @@ -395,7 +396,7 @@ def traction_with_parameters(parameters, slug=""): history_data["solver_KS_data"].append(cone.data) history_data["eigs"].append(bifurcation.data["eigs"]) history_data["F"].append(stress) - history_data["alphadot_norm"].append(alphadot.vector.norm()) + history_data["alphadot_norm"].append(alphadot.x.petsc_vec.norm()) history_data["rate_12_norm"].append(rate_12_norm) history_data["unscaled_rate_12_norm"].append(urate_12_norm) history_data["cone-stable"].append(stable) @@ -557,7 +558,7 @@ def _plot_bif_spectrum_profile( _axes = axes[row] if n > 1 else axes # __import__('pdb').set_trace() # if label == '': - label = f"$\lambda_{i}$ = {spectrum[i].get('lambda'):.1e}, |$\\beta$|={u.vector.norm():.2f}" + label = f"$\lambda_{i}$ = {spectrum[i].get('lambda'):.1e}, |$\\beta$|={u.x.petsc_vec.norm():.2f}" _plt, data = plot_profile( u, @@ -635,7 +636,7 @@ def _plot_bif_spectrum_profile_fullvec( # if label == '': label = ( - f"mode {i} $\lambda_{i}$ = {field.get('lambda'):.2e}, ||={u.vector.norm()}" + f"mode {i} $\lambda_{i}$ = {field.get('lambda'):.2e}, ||={u.x.petsc_vec.norm()}" ) print(label) diff --git a/src/irrevolutions/practice/traction-bar-clean.py b/src/irrevolutions/practice/traction-bar-clean.py index 410fe56d..d7e9a690 100644 --- a/src/irrevolutions/practice/traction-bar-clean.py +++ b/src/irrevolutions/practice/traction-bar-clean.py @@ -28,6 +28,7 @@ from dolfinx.io import XDMFFile, gmshio from mpi4py import MPI from petsc4py import PETSc +import basix.ufl sys.path.append("../") @@ -165,10 +166,10 @@ def main(parameters, model="at2", storage=None): ) as file: file.write_mesh(mesh) - element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) V_u = FunctionSpace(mesh, element_u) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) u = Function(V_u, name="Displacement") @@ -202,7 +203,7 @@ def main(parameters, model="at2", storage=None): v = Function(V_u, name="DisplacementPerturbation") for f in [zero_u, zero_alpha, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -218,8 +219,8 @@ def main(parameters, model="at2", storage=None): ) ] - set_bc(alpha_ub.vector, bcs_alpha) - alpha_ub.vector.ghostUpdate( + set_bc(alpha_ub.x.petsc_vec, bcs_alpha) + alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) bcs = {"bcs_u": bcs_u, "bcs_alpha": bcs_alpha} @@ -285,12 +286,12 @@ def main(parameters, model="at2", storage=None): for i_t, t in enumerate(loads): u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1]))) - u_.vector.ghostUpdate( + u_.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -306,16 +307,16 @@ def main(parameters, model="at2", storage=None): logging.info(f"-- {i_t}/{len(loads)}: Solving for t = {t:3.2f} --") hybrid.solve(alpha_lb) - alpha.vector.copy(alphadot.vector) - alphadot.vector.axpy(-1, alpha_lb.vector) - alphadot.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alphadot.x.petsc_vec) + alphadot.x.petsc_vec.axpy(-1, alpha_lb.x.petsc_vec) + alphadot.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) - logging.critical(f"alpha vector norm: {alpha.vector.norm()}") - logging.critical(f"alpha lb norm: {alpha_lb.vector.norm()}") - logging.critical(f"alphadot norm: {alphadot.vector.norm()}") - logging.critical(f"vector norms [u, alpha]: {[zi.vector.norm() for zi in z]}") + logging.critical(f"alpha vector norm: {alpha.x.petsc_vec.norm()}") + logging.critical(f"alpha lb norm: {alpha_lb.x.petsc_vec.norm()}") + logging.critical(f"alphadot norm: {alphadot.x.petsc_vec.norm()}") + logging.critical(f"vector norms [u, alpha]: {[zi.x.petsc_vec.norm() for zi in z]}") rate_12_norm = hybrid.scaled_rate_norm(alpha, parameters) urate_12_norm = hybrid.unscaled_rate_norm(alpha) @@ -368,7 +369,7 @@ def main(parameters, model="at2", storage=None): history_data["eigs"].append(bifurcation.data["eigs"]) history_data["F"].append(stress) history_data["cone_data"].append(stability.data) - history_data["alphadot_norm"].append(alphadot.vector.norm()) + history_data["alphadot_norm"].append(alphadot.x.petsc_vec.norm()) history_data["rate_12_norm"].append(rate_12_norm) history_data["unscaled_rate_12_norm"].append(urate_12_norm) history_data["cone-stable"].append(stable) diff --git a/src/irrevolutions/practice/traction-cone.py b/src/irrevolutions/practice/traction-cone.py index c3c1c02b..89211186 100644 --- a/src/irrevolutions/practice/traction-cone.py +++ b/src/irrevolutions/practice/traction-cone.py @@ -25,9 +25,9 @@ from mpi4py import MPI from petsc4py import PETSc from irrevolutions.utils.parametric import (parameters_vs_ell, parameters_vs_SPA_scaling) +import basix.ufl sys.path.append("../") - logging.getLogger().setLevel(logging.ERROR) @@ -107,10 +107,10 @@ def traction_with_parameters(parameters, slug=""): # Functional Setting - element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) V_u = FunctionSpace(mesh, element_u) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) # Define the state @@ -145,7 +145,7 @@ def traction_with_parameters(parameters, slug=""): alpha_ub.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, zero_alpha, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -160,8 +160,8 @@ def traction_with_parameters(parameters, slug=""): # dolfinx.fem.dirichletbc(zeroKalpha, dofs_alpha_right), # ] - set_bc(alpha_ub.vector, bcs_alpha) - alpha_ub.vector.ghostUpdate( + set_bc(alpha_ub.x.petsc_vec, bcs_alpha) + alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -236,13 +236,13 @@ def traction_with_parameters(parameters, slug=""): for i_t, t in enumerate(loads): # for i_t, t in enumerate([0., .99, 1.0, 1.01]): u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1]))) - u_.vector.ghostUpdate( + u_.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -264,19 +264,19 @@ def traction_with_parameters(parameters, slug=""): hybrid.solve(alpha_lb) # compute the rate - alpha.vector.copy(alphadot.vector) - alphadot.vector.axpy(-1, alpha_lb.vector) - alphadot.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alphadot.x.petsc_vec) + alphadot.x.petsc_vec.axpy(-1, alpha_lb.x.petsc_vec) + alphadot.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) rate_12_norm = hybrid.scaled_rate_norm(alpha, parameters) urate_12_norm = hybrid.unscaled_rate_norm(alpha) - logging.critical(f"alpha vector norm: {alpha.vector.norm()}") - logging.critical(f"alpha lb norm: {alpha_lb.vector.norm()}") - logging.critical(f"alphadot norm: {alphadot.vector.norm()}") - logging.critical(f"vector norms [u, alpha]: {[zi.vector.norm() for zi in z]}") + logging.critical(f"alpha vector norm: {alpha.x.petsc_vec.norm()}") + logging.critical(f"alpha lb norm: {alpha_lb.x.petsc_vec.norm()}") + logging.critical(f"alphadot norm: {alphadot.x.petsc_vec.norm()}") + logging.critical(f"vector norms [u, alpha]: {[zi.x.petsc_vec.norm() for zi in z]}") logging.critical(f"scaled rate state_12 norm: {rate_12_norm}") logging.critical(f"unscaled scaled rate state_12 norm: {urate_12_norm}") @@ -324,7 +324,7 @@ def traction_with_parameters(parameters, slug=""): history_data["solver_KS_data"].append(cone.data) history_data["eigs"].append(bifurcation.data["eigs"]) history_data["F"].append(stress) - history_data["alphadot_norm"].append(alphadot.vector.norm()) + history_data["alphadot_norm"].append(alphadot.x.petsc_vec.norm()) history_data["rate_12_norm"].append(rate_12_norm) history_data["unscaled_rate_12_norm"].append(urate_12_norm) history_data["cone-stable"].append(stable) diff --git a/src/irrevolutions/practice/traction-parametric.py b/src/irrevolutions/practice/traction-parametric.py index f0971287..650a9d77 100644 --- a/src/irrevolutions/practice/traction-parametric.py +++ b/src/irrevolutions/practice/traction-parametric.py @@ -28,6 +28,7 @@ from dolfinx.io import gmshio from mpi4py import MPI from petsc4py import PETSc +import basix.ufl sys.path.append("../") @@ -172,10 +173,10 @@ def main(parameters, model="at2", storage=None): # with XDMFFile(comm, f"{prefix}/{_nameExp}.xdmf", "w", encoding=XDMFFile.Encoding.HDF5) as file: # file.write_mesh(mesh) - element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) V_u = FunctionSpace(mesh, element_u) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) V_alpha = FunctionSpace(mesh, element_alpha) u = Function(V_u, name="Displacement") @@ -205,7 +206,7 @@ def main(parameters, model="at2", storage=None): alpha_ub.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, zero_alpha, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -214,8 +215,8 @@ def main(parameters, model="at2", storage=None): bcs_u = [bc_u_left, bc_u_right] bcs_alpha = [] - set_bc(alpha_ub.vector, bcs_alpha) - alpha_ub.vector.ghostUpdate( + set_bc(alpha_ub.x.petsc_vec, bcs_alpha) + alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) bcs = {"bcs_u": bcs_u, "bcs_alpha": bcs_alpha} @@ -280,12 +281,12 @@ def main(parameters, model="at2", storage=None): logging.getLogger().setLevel(logging.WARNING) u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1]))) - u_.vector.ghostUpdate( + u_.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -301,17 +302,17 @@ def main(parameters, model="at2", storage=None): logging.info(f"-- {i_t}/{len(loads)}: Solving for t = {t:3.2f} --") hybrid.solve(alpha_lb) - alpha.vector.copy(alphadot.vector) - alphadot.vector.axpy(-1, alpha_lb.vector) - alphadot.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alphadot.x.petsc_vec) + alphadot.x.petsc_vec.axpy(-1, alpha_lb.x.petsc_vec) + alphadot.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) - logging.critical(f"alpha vector norm: {alpha.vector.norm()}") - logging.critical(f"alpha lb norm: {alpha_lb.vector.norm()}") - logging.critical(f"alphadot norm: {alphadot.vector.norm()}") + logging.critical(f"alpha vector norm: {alpha.x.petsc_vec.norm()}") + logging.critical(f"alpha lb norm: {alpha_lb.x.petsc_vec.norm()}") + logging.critical(f"alphadot norm: {alphadot.x.petsc_vec.norm()}") # logging.critical( - # f"vector norms [u, alpha]: {[zi.vector.norm() for zi in z]}") + # f"vector norms [u, alpha]: {[zi.x.petsc_vec.norm() for zi in z]}") rate_12_norm = hybrid.scaled_rate_norm(alpha, parameters) urate_12_norm = hybrid.unscaled_rate_norm(alpha) @@ -359,7 +360,7 @@ def main(parameters, model="at2", storage=None): history_data["eigs"].append(bifurcation.data["eigs"]) history_data["F"].append(stress) history_data["cone_data"].append(cone.data) - history_data["alphadot_norm"].append(alphadot.vector.norm()) + history_data["alphadot_norm"].append(alphadot.x.petsc_vec.norm()) history_data["rate_12_norm"].append(rate_12_norm) history_data["unscaled_rate_12_norm"].append(urate_12_norm) history_data["cone-stable"].append(stable) diff --git a/src/irrevolutions/practice/unstabinst.py b/src/irrevolutions/practice/unstabinst.py index 778cf3e2..003ed220 100644 --- a/src/irrevolutions/practice/unstabinst.py +++ b/src/irrevolutions/practice/unstabinst.py @@ -17,7 +17,7 @@ import dolfinx import logging import sys - +import basix.ufl sys.path.append("../") @@ -216,10 +216,10 @@ def mesh_V( # Functional setting -element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=2) -element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) -V_u = dolfinx.fem.FunctionSpace(mesh, element_u) -V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) +element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(2,)) +element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) +V_u = dolfinx.fem.functionspace(mesh, element_u) +V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") # the displacement @@ -250,7 +250,7 @@ def mesh_V( u_corner.interpolate(lambda x: (np.zeros_like(x[0]), np.zeros_like(x[1]))) for u in (u_corner,): - u.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + u.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) # total_energy = model.total_energy_density( # state) * dx - ufl.dot(force, u)*ds(107) - ufl.dot(force, u)*ds(108) @@ -297,8 +297,8 @@ def _corners(x): bcs = {"bcs_u": bcs_u, "bcs_alpha": bcs_alpha} # Update the bounds -set_bc(alpha_ub.vector, bcs_alpha) -set_bc(alpha_lb.vector, bcs_alpha) +set_bc(alpha_ub.x.petsc_vec, bcs_alpha) +set_bc(alpha_lb.x.petsc_vec, bcs_alpha) model = Brittle(parameters["model"]) @@ -327,11 +327,11 @@ def _corners(x): # update boundary conditions u_.interpolate(lambda x: (np.zeros_like(x[0]), t * np.ones_like(x[1]))) - u_.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + u_.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) # update lower bound for damage - alpha.vector.copy(alpha_lb.vector) - alpha.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/src/irrevolutions/solvers/__init__.py b/src/irrevolutions/solvers/__init__.py index 969ad05c..16742c58 100644 --- a/src/irrevolutions/solvers/__init__.py +++ b/src/irrevolutions/solvers/__init__.py @@ -1,15 +1,16 @@ +from mpi4py import MPI +import sys +import petsc4py +petsc4py.init(sys.argv) +from petsc4py import PETSc + from dolfinx.fem.petsc import (apply_lifting, assemble_matrix, assemble_vector, create_matrix, create_vector, set_bc) from dolfinx.cpp.log import LogLevel, log -import sys import dolfinx -import petsc4py import ufl -from mpi4py import MPI -from petsc4py import PETSc -petsc4py.init(sys.argv) # from damage.utils import ColorPrint @@ -18,7 +19,6 @@ comm = MPI.COMM_WORLD - class SNESSolver: """ Problem class for elasticity, compatible with PETSC.SNES solvers. @@ -46,7 +46,6 @@ def __init__( prefix = "snes_{}".format(str(id(self))[0:4]) self.prefix = prefix - if self.bounds is not None: self.lb = bounds[0] self.ub = bounds[1] @@ -62,7 +61,9 @@ def __init__( self.petsc_options = petsc_options - self.b = create_vector(self.F_form) + assert len(self.F_form.function_spaces) == 1, "F is not a linear form" + assert self.F_form.function_spaces[0] == V._cpp_object + self.b = dolfinx.fem.Function(V) self.a = create_matrix(self.J_form) self.monitor = monitor @@ -74,7 +75,8 @@ def set_petsc_options(self, debug=False): opts.prefixPush(self.prefix) if debug is True: print(self.petsc_options) - + if self.petsc_options.get("snes_type") == "newtontr": + self.petsc_options["snes_type"] = "newtonls" for k, v in self.petsc_options.items(): opts[k] = v @@ -87,7 +89,7 @@ def solver_setup(self): # Set options snes.setOptionsPrefix(self.prefix) self.set_petsc_options() - snes.setFunction(self.F, self.b) + snes.setFunction(self.F, self.b.x.petsc_vec) snes.setJacobian(self.J, self.a) # We set the bound (Note: they are passed as reference and not as values) @@ -96,7 +98,7 @@ def solver_setup(self): snes.setMonitor(self.monitor) if self.bounds is not None: - snes.setVariableBounds(self.lb.vector, self.ub.vector) + snes.setVariableBounds(self.lb.x.petsc_vec, self.ub.x.petsc_vec) snes.setFromOptions() @@ -112,10 +114,9 @@ def F(self, snes: PETSc.SNES, x: PETSc.Vec, b: PETSc.Vec): b: Vector to assemble the residual into. """ # We need to assign the vector to the function - x.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) - x.copy(self.u.vector) - self.u.vector.ghostUpdate( + x.copy(self.u.x.petsc_vec) + self.u.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -145,7 +146,7 @@ def solve(self): log(LogLevel.INFO, f"Solving {self.prefix}") try: - self.solver.solve(None, self.u.vector) + self.solver.solve(None, self.u.x.petsc_vec) # print( # f"{self.prefix} SNES solver converged in", # self.solver.getIterationNumber(), @@ -153,7 +154,7 @@ def solve(self): # "with converged reason", # self.solver.getConvergedReason(), # ) - self.u.vector.ghostUpdate( + self.u.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) return (self.solver.getIterationNumber(), self.solver.getConvergedReason()) diff --git a/src/irrevolutions/solvers/function.py b/src/irrevolutions/solvers/function.py index 453ac2ea..8a5a4d23 100644 --- a/src/irrevolutions/solvers/function.py +++ b/src/irrevolutions/solvers/function.py @@ -77,16 +77,16 @@ def functions_to_vec(u: typing.List[Function], x): """Copies functions into block vector.""" if x.getType() == "nest": for i, subvec in enumerate(x.getNestSubVecs()): - u[i].vector.copy(subvec) + u[i].x.petsc_vec.copy(subvec) subvec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) else: offset = 0 for i in range(len(u)): - size_local = u[i].vector.getLocalSize() + size_local = u[i].x.petsc_vec.getLocalSize() with x.localForm() as loc: - loc.array[offset : offset + size_local] = u[i].vector.array_r + loc.array[offset : offset + size_local] = u[i].x.petsc_vec.array_r offset += size_local x.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) @@ -96,17 +96,17 @@ def vec_to_functions(x, u: typing.List[dolfinx.fem.Function]): if x.getType() == "nest": for i, subvec in enumerate(x.getNestSubVecs()): - subvec.copy(u[i].vector) - u[i].vector.ghostUpdate( + subvec.copy(u[i].x.petsc_vec) + u[i].x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) else: offset = 0 for i in range(len(u)): - size_local = u[i].vector.getLocalSize() - u[i].vector.array[:] = x.array_r[offset : offset + size_local] + size_local = u[i].x.petsc_vec.getLocalSize() + u[i].x.petsc_vec.array[:] = x.array_r[offset : offset + size_local] offset += size_local - u[i].vector.ghostUpdate( + u[i].x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/src/irrevolutions/solvers/restriction.py b/src/irrevolutions/solvers/restriction.py index 7c0386e3..07512fd6 100644 --- a/src/irrevolutions/solvers/restriction.py +++ b/src/irrevolutions/solvers/restriction.py @@ -153,11 +153,11 @@ def update_functions(self, f: typing.List[dolfinx.fem.Function], rx: PETSc.Vec): for i, fi in enumerate(f): num_rdofs = self.bglobal_dofs_vec[i].shape[0] - fi.vector.array[self.bglobal_dofs_vec[i] - self.boffsets_vec[i]] = ( + fi.x.petsc_vec.array[self.bglobal_dofs_vec[i] - self.boffsets_vec[i]] = ( rx.array_r[rdof_offset : (rdof_offset + num_rdofs)] ) - fi.vector.ghostUpdate( + fi.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) rdof_offset += num_rdofs \ No newline at end of file diff --git a/src/irrevolutions/solvers/snesblockproblem.py b/src/irrevolutions/solvers/snesblockproblem.py index 58ca232a..4e7fb5cf 100644 --- a/src/irrevolutions/solvers/snesblockproblem.py +++ b/src/irrevolutions/solvers/snesblockproblem.py @@ -129,7 +129,7 @@ def __init__( self.snes = PETSc.SNES().create(self.comm) if bounds: - self.snes.setVariableBounds(self.lb.vector, self.ub.vector) + self.snes.setVariableBounds(self.lb.x.petsc_vec, self.ub.x.petsc_vec) # Set up solver for nested or block matrix form if nest: @@ -138,7 +138,7 @@ def __init__( self.J = dolfinx.fem.petsc.create_matrix_nest(self.J_form) self.F = dolfinx.fem.petsc.create_vector_nest(self.F_form) - self.x = self.F.copy() + self.x = dolfinx.fem.petsc.create_vector_nest(self.F_form) self.snes.setFunction(self._F_nest, self.F) self.snes.setJacobian(self._J_nest, self.J) @@ -147,8 +147,7 @@ def __init__( else: self.J = dolfinx.fem.petsc.create_matrix_block(self.J_form) self.F = dolfinx.fem.petsc.create_vector_block(self.F_form) - self.x = self.F.copy() - + self.x = dolfinx.fem.petsc.create_vector_block(self.F_form) if restriction is not None: self._J = dolfinx.fem.petsc.create_matrix_block(self.J_form) self._J.assemble() @@ -200,7 +199,7 @@ def _F_block(self, snes, x, F): self.update_functions(x) dolfinx.fem.petsc.assemble_vector_block( - self.F, self.F_form, self.J_form, self.bcs, x0=self.x, scale=-1.0 + self.F, self.F_form, self.J_form, self.bcs, x0=self.x, alpha=-1.0 ) if self.restriction is not None: @@ -223,7 +222,7 @@ def _F_nest(self, snes, x, F): with F_sub.localForm() as F_sub_local: F_sub_local.set(0.0) dolfinx.fem.assemble_vector(F_sub, L) - dolfinx.fem.apply_lifting(F_sub, a, bc, x0=x, scale=-1.0) + dolfinx.fem.apply_lifting(F_sub, a, bc, x0=x, alpha=-1.0) F_sub.ghostUpdate(addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE) F.assemble() @@ -357,7 +356,7 @@ def compute_norms_block(self, snes): if self.restriction is not None: size_local = self.restriction.bglobal_dofs_vec[i].shape[0] else: - size_local = ui.vector.getLocalSize() + size_local = ui.x.petsc_vec.getLocalSize() subvec_r = r[offset : offset + size_local] subvec_dx = dx[offset : offset + size_local] diff --git a/src/irrevolutions/utils/__init__.py b/src/irrevolutions/utils/__init__.py index 4bfb03dd..f27c9ba5 100644 --- a/src/irrevolutions/utils/__init__.py +++ b/src/irrevolutions/utils/__init__.py @@ -17,6 +17,7 @@ from dolfinx.fem import assemble_scalar, form from mpi4py import MPI from petsc4py import PETSc +import basix.ufl comm = MPI.COMM_WORLD @@ -474,10 +475,10 @@ def indicator_function(v): # Create the indicator function w = dolfinx.fem.Function(v.function_space) - with w.vector.localForm() as w_loc, v.vector.localForm() as v_loc: + with w.x.petsc_vec.localForm() as w_loc, v.x.petsc_vec.localForm() as v_loc: w_loc[:] = np.where(v_loc[:] > 0, 1.0, 0.0) - w.vector.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) + w.x.petsc_vec.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) return w @@ -613,11 +614,11 @@ def sample_data(N, positive=True): comm = MPI.COMM_WORLD comm.Get_rank() - element_u = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) - V_u = dolfinx.fem.FunctionSpace(mesh, element_u) - V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) + V_u = dolfinx.fem.functionspace(mesh, element_u) + V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") alpha = dolfinx.fem.Function(V_alpha, name="Damage") dx = ufl.Measure("dx", alpha.function_space.mesh) diff --git a/src/irrevolutions/utils/viz.py b/src/irrevolutions/utils/viz.py index 2a65d25f..3ab6dba5 100644 --- a/src/irrevolutions/utils/viz.py +++ b/src/irrevolutions/utils/viz.py @@ -50,7 +50,7 @@ def plot_vector(u, plotter, subplot=None, scale=1.0): num_dofs_local = u.function_space.dofmap.index_map.size_local geometry = u.function_space.tabulate_dof_coordinates()[:num_dofs_local] values = np.zeros((V.dofmap.index_map.size_local, 3), dtype=np.float64) - values[:, : mesh.geometry.dim] = u.vector.array.real.reshape( + values[:, : mesh.geometry.dim] = u.x.petsc_vec.array.real.reshape( V.dofmap.index_map.size_local, V.dofmap.index_map_bs ) grid = pyvista.UnstructuredGrid(topology, cell_types, geometry) @@ -89,7 +89,7 @@ def plot_scalar(u, plotter, subplot=None, lineproperties={}): topology, cell_types, _ = ret grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x) plotter.subplot(0, 0) - values = u.vector.array.real.reshape( + values = u.x.petsc_vec.array.real.reshape( V.dofmap.index_map.size_local, V.dofmap.index_map_bs ) grid.point_data["u"] = values diff --git a/test/test_1d.py b/test/test_1d.py index f97fff5c..7cadf8da 100644 --- a/test/test_1d.py +++ b/test/test_1d.py @@ -3,6 +3,8 @@ import logging import os import sys +import basix.ufl + from pathlib import Path import dolfinx @@ -118,9 +120,9 @@ def solve(self, outdir=None): (solver_alpha_it, solver_alpha_reason) = self.damage.solve() # Define error function - self.alpha.vector.copy(alpha_diff.vector) - alpha_diff.vector.axpy(-1, self.alpha_old.vector) - alpha_diff.vector.ghostUpdate( + self.alpha.x.petsc_vec.copy(alpha_diff.x.petsc_vec) + alpha_diff.x.petsc_vec.axpy(-1, self.alpha_old.x.petsc_vec) + alpha_diff.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -133,7 +135,7 @@ def solve(self, outdir=None): np.array([comm.allreduce(Fvi.norm(), op=MPI.SUM) for Fvi in Fv]).sum() ) - error_alpha_max = alpha_diff.vector.max()[1] + error_alpha_max = alpha_diff.x.petsc_vec.max()[1] total_energy_int = comm.allreduce( assemble_scalar(form(self.total_energy)), op=MPI.SUM ) @@ -141,28 +143,28 @@ def solve(self, outdir=None): residual_F.ghostUpdate( addv=PETSc.InsertMode.ADD, mode=PETSc.ScatterMode.REVERSE ) - set_bc(residual_F, self.elasticity.bcs, self.u.vector) + set_bc(residual_F, self.elasticity.bcs, self.u.x.petsc_vec) error_residual_F = ufl.sqrt(residual_F.dot(residual_F)) - self.alpha.vector.copy(self.alpha_old.vector) - self.alpha_old.vector.ghostUpdate( + self.alpha.x.petsc_vec.copy(self.alpha_old.x.petsc_vec) + self.alpha_old.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) logging.critical( - f"AM - Iteration: {iteration:3d}, res F Error: {error_residual_F:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}" + f"AM - Iteration: {iteration:3d}, res F Error: {error_residual_F:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}" ) logging.critical( - f"AM - Iteration: {iteration:3d}, H1 Error: {error_alpha_H1:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}" + f"AM - Iteration: {iteration:3d}, H1 Error: {error_alpha_H1:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}" ) logging.critical( - f"AM - Iteration: {iteration:3d}, L2 Error: {error_alpha_L2:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}" + f"AM - Iteration: {iteration:3d}, L2 Error: {error_alpha_L2:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}" ) logging.critical( - f"AM - Iteration: {iteration:3d}, Linfty Error: {error_alpha_max:3.4e}, alpha_max: {self.alpha.vector.max()[1]:3.4e}" + f"AM - Iteration: {iteration:3d}, Linfty Error: {error_alpha_max:3.4e}, alpha_max: {self.alpha.x.petsc_vec.max()[1]:3.4e}" ) self.data["iteration"].append(iteration) @@ -245,12 +247,12 @@ def run_computation(parameters, storage=None): file.write_mesh(mesh) # Functional Setting - element_u = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) - V_u = dolfinx.fem.FunctionSpace(mesh, element_u) - V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) + V_u = dolfinx.fem.functionspace(mesh, element_u) + V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") u_ = dolfinx.fem.Function(V_u, name="BoundaryDisplacement") @@ -303,7 +305,7 @@ def run_computation(parameters, storage=None): u_.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -433,13 +435,13 @@ def stress(state): for i_t, t in enumerate(loads): u_.interpolate(lambda x: t * np.ones_like(x[0])) - u_.vector.ghostUpdate( + u_.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) diff --git a/test/test_cone_convergence.py b/test/test_cone_convergence.py index 9a25013e..e053ba39 100644 --- a/test/test_cone_convergence.py +++ b/test/test_cone_convergence.py @@ -1,6 +1,7 @@ import logging import os import pickle +import basix.ufl import dolfinx import numpy as np @@ -79,11 +80,11 @@ def load_minimal_constraints(filename, spaces): ) as file: mesh = file.read_mesh(name="mesh") -element_u = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) -element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) +element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) +element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) -V_u = dolfinx.fem.FunctionSpace(mesh, element_u) -V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) +V_u = dolfinx.fem.functionspace(mesh, element_u) +V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") alpha = dolfinx.fem.Function(V_alpha, name="Damage") dx = ufl.Measure("dx", alpha.function_space.mesh) diff --git a/test/test_linsearch.py b/test/test_linsearch.py index 446d5a92..6baa10de 100644 --- a/test/test_linsearch.py +++ b/test/test_linsearch.py @@ -5,6 +5,7 @@ import os import sys from pathlib import Path +import basix.ufl import dolfinx import dolfinx.mesh @@ -17,12 +18,13 @@ import ufl import yaml from dolfinx.common import list_timings -from dolfinx.fem import (Constant, Function, FunctionSpace, assemble_scalar, +from dolfinx.fem import (Constant, Function, functionspace, assemble_scalar, dirichletbc, form, locate_dofs_geometrical, set_bc) from dolfinx.io import XDMFFile, gmshio from mpi4py import MPI from petsc4py import PETSc from pyvista.utilities import xvfb +import basix.ufl from irrevolutions.algorithms.am import AlternateMinimisation, HybridSolver from irrevolutions.algorithms.ls import LineSearch @@ -86,11 +88,11 @@ def test_linsearch(): file.write_mesh(mesh) # Function spaces - element_u = ufl.VectorElement("Lagrange", mesh.ufl_cell(), degree=1, dim=tdim) - V_u = FunctionSpace(mesh, element_u) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1, shape=(tdim,)) + V_u = functionspace(mesh, element_u) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) - V_alpha = FunctionSpace(mesh, element_alpha) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) + V_alpha = functionspace(mesh, element_alpha) # Define the state u = Function(V_u, name="Displacement") @@ -126,7 +128,7 @@ def test_linsearch(): alpha_ub.interpolate(lambda x: np.ones_like(x[0])) for f in [zero_u, zero_alpha, u_, alpha_lb, alpha_ub]: - f.vector.ghostUpdate( + f.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -144,8 +146,8 @@ def test_linsearch(): ] bcs_alpha = [] - set_bc(alpha_ub.vector, bcs_alpha) - alpha_ub.vector.ghostUpdate( + set_bc(alpha_ub.x.petsc_vec, bcs_alpha) + alpha_ub.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -190,13 +192,13 @@ def test_linsearch(): for i_t, t in enumerate(loads): u_.interpolate(lambda x: (t * np.ones_like(x[0]), np.zeros_like(x[1]))) - u_.vector.ghostUpdate( + u_.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) # update the lower bound - alpha.vector.copy(alpha_lb.vector) - alpha_lb.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alpha_lb.x.petsc_vec) + alpha_lb.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -214,9 +216,9 @@ def test_linsearch(): hybrid.solve(alpha_lb) # compute the rate - alpha.vector.copy(alphadot.vector) - alphadot.vector.axpy(-1, alpha_lb.vector) - alphadot.vector.ghostUpdate( + alpha.x.petsc_vec.copy(alphadot.x.petsc_vec) + alphadot.x.petsc_vec.axpy(-1, alpha_lb.x.petsc_vec) + alphadot.x.petsc_vec.ghostUpdate( addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD ) @@ -351,10 +353,10 @@ def test_linsearch(): ColorPrint.print_bold(f"State is elastic: {is_elastic}") ColorPrint.print_bold(f"Evolution is unique: {is_unique}") ColorPrint.print_bold(f"State's inertia: {inertia}") - logging.critical(f"alpha vector norm: {alpha.vector.norm()}") - logging.critical(f"alpha lb norm: {alpha_lb.vector.norm()}") - logging.critical(f"alphadot norm: {alphadot.vector.norm()}") - logging.critical(f"vector norms [u, alpha]: {[zi.vector.norm() for zi in z]}") + logging.critical(f"alpha vector norm: {alpha.x.petsc_vec.norm()}") + logging.critical(f"alpha lb norm: {alpha_lb.x.petsc_vec.norm()}") + logging.critical(f"alphadot norm: {alphadot.x.petsc_vec.norm()}") + logging.critical(f"vector norms [u, alpha]: {[zi.x.petsc_vec.norm() for zi in z]}") logging.critical(f"scaled rate state_12 norm: {rate_12_norm}") logging.critical(f"unscaled scaled rate state_12 norm: {urate_12_norm}") diff --git a/test/test_sample_data.py b/test/test_sample_data.py index 1c3a7239..f38a6266 100644 --- a/test/test_sample_data.py +++ b/test/test_sample_data.py @@ -7,6 +7,7 @@ from dolfinx.cpp.la.petsc import get_local_vectors, scatter_local_vectors from mpi4py import MPI from petsc4py import PETSc +import basix.ufl from irrevolutions.utils import _logger @@ -23,11 +24,11 @@ def init_data(N, positive=True): comm = MPI.COMM_WORLD comm.Get_rank() - element_u = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) - V_u = dolfinx.fem.FunctionSpace(mesh, element_u) - V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) + V_u = dolfinx.fem.functionspace(mesh, element_u) + V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") alpha = dolfinx.fem.Function(V_alpha, name="Damage") dx = ufl.Measure("dx", alpha.function_space.mesh) diff --git a/test/test_scatter.py b/test/test_scatter.py index 333769cb..635788e3 100644 --- a/test/test_scatter.py +++ b/test/test_scatter.py @@ -2,6 +2,8 @@ import random import sys +import basix.ufl + import dolfinx import numpy as np import petsc4py @@ -34,12 +36,12 @@ outdir = os.path.join(os.path.dirname(__file__), "output") prefix = os.path.join(outdir, "test_cone") -element_u = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) +element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) -element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) +element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) -V_u = dolfinx.fem.FunctionSpace(mesh, element_u) -V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) +V_u = dolfinx.fem.functionspace(mesh, element_u) +V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) u = dolfinx.fem.Function(V_u, name="Displacement") alpha = dolfinx.fem.Function(V_alpha, name="Damage") @@ -85,7 +87,7 @@ def get_inactive_dofset(): v = dolfinx.fem.petsc.create_vector_block(F) x = dolfinx.fem.petsc.create_vector_block(F) -# scatter_local_vectors(x, [u.vector.array_r, p.vector.array_r], +# scatter_local_vectors(x, [u.x.petsc_vec.array_r, p.x.petsc_vec.array_r], # [(u.function_space.dofmap.index_map, u.function_space.dofmap.index_map_bs), # (p.function_space.dofmap.index_map, p.function_space.dofmap.index_map_bs)]) # x.ghostUpdate(addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD) @@ -180,7 +182,7 @@ def converged(x): # update xold # x.copy(_xold) - # x.vector.ghostUpdate( + # x.x.petsc_vec.ghostUpdate( # addv=PETSc.InsertMode.INSERT, mode=PETSc.ScatterMode.FORWARD # ) diff --git a/test/test_spa.py b/test/test_spa.py index b8f16b15..0f487127 100644 --- a/test/test_spa.py +++ b/test/test_spa.py @@ -3,6 +3,8 @@ import pickle import sys +import basix.ufl + import dolfinx import numpy as np import ufl @@ -162,11 +164,11 @@ def _convergenceTest(x, xold, y=None): ) as file: mesh = file.read_mesh(name="mesh") - element_u = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) - element_alpha = ufl.FiniteElement("Lagrange", mesh.ufl_cell(), degree=1) + element_u = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) + element_alpha = basix.ufl.element("Lagrange", mesh.basix_cell(), degree=1) - V_u = dolfinx.fem.FunctionSpace(mesh, element_u) - V_alpha = dolfinx.fem.FunctionSpace(mesh, element_alpha) + V_u = dolfinx.fem.functionspace(mesh, element_u) + V_alpha = dolfinx.fem.functionspace(mesh, element_alpha) # u = dolfinx.fem.Function(V_u, name="Displacement") alpha = dolfinx.fem.Function(V_alpha, name="Damage") ufl.Measure("dx", alpha.function_space.mesh)