From 1bed6cee413588715123c6b959d985a30fb7696a Mon Sep 17 00:00:00 2001 From: Lars Bilke Date: Tue, 28 Nov 2023 09:07:23 +0100 Subject: [PATCH 1/6] [py] ruff autofixes: `pipx run ruff . --fix`. --- MaterialLib/SolidModels/MFront/Lubby2.py | 3 +- .../MFront/ModCamClay_ShearTest.py | 13 +- .../MFront/ModCamClay_TriaxTest.py | 26 +- MaterialLib/SolidModels/MFront/pi-plane.py | 9 +- .../SolidModels/MFront/pi-plane_ortho.py | 9 +- .../MFront/three_ax_strain_ortho.py | 2 +- .../cube_1x1x1_SteadyStateDiffusion/cube.py | 3 +- .../bcs_laplace_eq.py | 4 +- .../sin_x_sin_y_source_term.py | 3 +- .../square_1e1_neumann-insitu.py | 2 +- .../GroundEquilibrium/pythonBCsOGS.py | 1 - .../flow_pressure_boundary/python_boundary.py | 4 +- .../python_boundary.py | 14 +- .../SeabedResponse/Stationary_waves.ipynb | 6 +- .../InjectionProduction1D/python_boundary.py | 10 +- .../python_boundary_staggered.py | 8 +- ..._Disc_with_hole_convergence_analysis.ipynb | 26 +- .../mesh_quarter_of_rectangle_with_hole.py | 2 +- .../PythonHertzContact/gen-unit-circle.py | 9 +- .../PythonHertzContact/hertz_contact_bc.py | 7 +- .../Linear/PythonHertzContact/post.py | 28 +- .../Mechanics/Linear/PythonPiston/post.py | 13 +- .../Mechanics/Linear/SimpleMechanics.ipynb | 4 +- .../Linear/test_ip_data/2D-clamped-gravity.py | 10 +- .../mtest/ModCamClay_TestIsotrop.ipynb | 5 +- Tests/Data/Mechanics/PLLC/PLLC.ipynb | 5 +- .../MixedElements/check_point_cloud.ipynb | 6 +- Tests/Data/Notebooks/testrunner.py | 24 +- .../Notebooks/thermo-osmosis.run-skip.ipynb | 7 +- .../DiffusionSorptionDecay.ipynb | 12 +- .../MultiLayerDiffusion.ipynb | 17 +- .../DecayChain/DecayChain.ipynb | 28 +- .../performance_measurements.ipynb | 14 +- .../RadionuclidesMigration.ipynb | 14 +- .../LiquidFlow/AxiSymTheis/axisym_theis.ipynb | 18 +- .../BlockingConductingFracture.ipynb | 12 +- Tests/Data/Parabolic/T/1D_neumann/plotLine.py | 20 +- .../T/1D_neumann/temperature_analytical.py | 4 +- .../Parabolic/T/3D_3BHEs_array/bcs_tespy.py | 3 +- .../T/3D_3BHEs_array/bcs_tespy_closedloop.py | 4 +- .../Parabolic/T/3D_3BHEs_array/pre/3bhes.py | 14 +- .../T/3D_3BHEs_array/pre/3bhes_closedloop.py | 15 +- .../bcs_tespy_and_serverCommunication.py | 3 +- .../pre/3bhes.py | 14 +- .../pre/3bhes_closedloop.py | 15 +- .../simulationX_test.py | 2 +- .../HeatPipe/heatpipe.ipynb | 27 +- .../Kregime_Propagating_jupyter.ipynb | 17 +- .../sen_shear.ipynb | 257 ++- .../beam_jupyter_notebook/beam.ipynb | 275 ++- .../Kregime_Static_jupyter.ipynb | 65 +- .../Data/PhaseField/surfing/Surfing_python.py | 2 +- .../surfing_pyvista.ipynb | 1614 ++++++++------- .../PhaseField/tpb_jupyter_notebook/TPB.ipynb | 245 ++- Tests/Data/TH2M/H/diffusion/diffusion.ipynb | 152 +- .../phase_appearance.ipynb | 103 +- Tests/Data/TH2M/H2/mcWhorter/mcWhorter.ipynb | 55 +- .../ogs-jupyter-lab-h2m-2d-liakopoulos.ipynb | 95 +- .../TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb | 125 +- .../confined_gas_compression.ipynb | 109 +- .../TH2/heatpipe/comparison.ci-skip.ipynb | 149 +- Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb | 313 +-- .../SaturatedPointheatsource.ipynb | 1809 +++++++++-------- .../Data/ThermoMechanics/BDT/generate_ref.py | 2 +- .../LinearMFront/generate_ref.py | 2 +- Tests/Python/__init__.py | 1 - Tests/Python/test_cli.py | 5 +- Tests/Python/test_matrix_debug_output.py | 4 +- Tests/Python/test_ogs_asm_threads.py | 4 +- Tests/Python/test_python_bc_simulation.py | 7 +- Tests/Python/test_simulator.py | 7 +- Tests/Python/test_simulator_mesh_interface.py | 14 +- Tests/Python/test_wrapped_cli_tools.py | 4 +- scripts/doc/append-xml-tags.py | 17 +- scripts/doc/check-project-params.py | 23 +- .../extract-media-properties-from-ctests.py | 1 + scripts/doc/linked-xml-file.py | 25 +- scripts/doc/normalize-param-cache.py | 16 +- scripts/snakemake/vtkdiff/wrapper.py | 1 + scripts/test/cppcheck_gen_hashes.py | 2 +- scripts/test/gmldiff.py | 2 +- .../bhe_array_analytical_solver.py | 23 +- .../advancing-glacier/glacierclass.py | 5 +- .../tutorials/advancing-glacier/mesh_basin.py | 4 +- 84 files changed, 3410 insertions(+), 2643 deletions(-) diff --git a/MaterialLib/SolidModels/MFront/Lubby2.py b/MaterialLib/SolidModels/MFront/Lubby2.py index f4beb9373cb..a618da10410 100644 --- a/MaterialLib/SolidModels/MFront/Lubby2.py +++ b/MaterialLib/SolidModels/MFront/Lubby2.py @@ -1,7 +1,6 @@ +import matplotlib.pyplot as plt import mtest import numpy as np -import matplotlib.pyplot as plt - GM0 = 9.54e3 KM0 = 2.78e4 diff --git a/MaterialLib/SolidModels/MFront/ModCamClay_ShearTest.py b/MaterialLib/SolidModels/MFront/ModCamClay_ShearTest.py index 450b32df374..c97d9db8e18 100644 --- a/MaterialLib/SolidModels/MFront/ModCamClay_ShearTest.py +++ b/MaterialLib/SolidModels/MFront/ModCamClay_ShearTest.py @@ -1,7 +1,6 @@ +import matplotlib.pyplot as plt import mtest import numpy as np -import matplotlib.pyplot as plt - # Material constants E = 150.0e3 # Young's modulus in MPa @@ -158,7 +157,7 @@ for k in range(runs): ax.plot(ltime, results[3][k], label=prelabel + "%.2f" % (valueList[k])) ax.set_xlabel("$t$ / s") -ax.set_ylabel("$\phi$") +ax.set_ylabel(r"$\phi$") ax.grid() ax.legend() fig.savefig("ModCamClay_ParamStudy_Porosity.pdf") @@ -167,8 +166,8 @@ # ax.set_title('Shear stress over shear strain') for k in range(runs): ax.plot(results[4][k], results[5][k], label=prelabel + "%.2f" % (valueList[k])) -ax.set_xlabel("$\epsilon_{xy}$") -ax.set_ylabel("$\sigma_{xy}$ / MPa") +ax.set_xlabel(r"$\epsilon_{xy}$") +ax.set_ylabel(r"$\sigma_{xy}$ / MPa") ax.grid() ax.legend() fig.savefig("ModCamClay_ParamStudy_ShearCurves.pdf") @@ -179,8 +178,8 @@ ax.plot( results[4][k], results[7][k] * 100, label=prelabel + "%.2f" % (valueList[k]) ) -ax.set_xlabel("$\epsilon_{xy}$") -ax.set_ylabel("${\epsilon}_p^V$ / %") +ax.set_xlabel(r"$\epsilon_{xy}$") +ax.set_ylabel(r"${\epsilon}_p^V$ / %") ax.grid() ax.legend() fig.savefig("ModCamClay_ParamStudy_eplVCurves.pdf") diff --git a/MaterialLib/SolidModels/MFront/ModCamClay_TriaxTest.py b/MaterialLib/SolidModels/MFront/ModCamClay_TriaxTest.py index 08bdd7aa834..70de9f7ea06 100644 --- a/MaterialLib/SolidModels/MFront/ModCamClay_TriaxTest.py +++ b/MaterialLib/SolidModels/MFront/ModCamClay_TriaxTest.py @@ -1,6 +1,6 @@ +import matplotlib.pyplot as plt import mtest as mtest import numpy as np -import matplotlib.pyplot as plt m = mtest.MTest() mtest.setVerboseMode(mtest.VerboseLevel.VERBOSE_QUIET) @@ -199,7 +199,7 @@ ax.set_title("Numerical solution versus analytical solution (Peric, 2006)") ax.plot(eQCurve, qCurve / 1e3, "+", markersize=14, markevery=4, label="numerical") ax.plot(v0xEpsQ / v0, qRangeAna / 1e3, linewidth=2, label="analytical") -ax.set_xlabel("$\epsilon_{q}$") +ax.set_xlabel(r"$\epsilon_{q}$") ax.set_ylabel("q / kPa") ax.grid() ax.legend() @@ -238,20 +238,20 @@ ax.legend() fig, ax = plt.subplots() -ax.plot(ltime, stresses[0][:], label="$\sigma_{rr}$ / MPa") -ax.plot(ltime, stresses[2][:], label="$\sigma_{\phi\phi}$ / MPa") -ax.plot(ltime, stresses[1][:], label="$\sigma_{zz}$ / MPa") -ax.plot(ltime, stresses[3][:], label="$\sigma_{rz}$ / MPa") +ax.plot(ltime, stresses[0][:], label=r"$\sigma_{rr}$ / MPa") +ax.plot(ltime, stresses[2][:], label=r"$\sigma_{\phi\phi}$ / MPa") +ax.plot(ltime, stresses[1][:], label=r"$\sigma_{zz}$ / MPa") +ax.plot(ltime, stresses[3][:], label=r"$\sigma_{rz}$ / MPa") ax.set_xlabel("$t$ / s") ax.set_ylabel("stress / MPa") ax.grid() ax.legend() fig, ax = plt.subplots() -ax.plot(ltime, strains[0][:], color="red", label="$\epsilon_{rr}$") -ax.plot(ltime, strains[2][:], "--", color="green", label="$\epsilon_{\phi\phi}$") -ax.plot(ltime, strains[1][:], label="$\epsilon_{zz}$") -ax.plot(ltime, strains[3][:], color="black", label="$\epsilon_{rz}$") +ax.plot(ltime, strains[0][:], color="red", label=r"$\epsilon_{rr}$") +ax.plot(ltime, strains[2][:], "--", color="green", label=r"$\epsilon_{\phi\phi}$") +ax.plot(ltime, strains[1][:], label=r"$\epsilon_{zz}$") +ax.plot(ltime, strains[3][:], color="black", label=r"$\epsilon_{rz}$") ax.set_xlabel("$t$ / s") ax.set_ylabel("strain") ax.grid() @@ -259,9 +259,9 @@ fig.savefig("ModCamClay_TriaxStudy_Strains.pdf") fig, ax = plt.subplots() -ax.plot(ltime, phiCurve - phi0, label="$\phi-\phi_0$") -ax.plot(ltime, eVCurve, label="$\epsilon_{V}$") -ax.plot(ltime, lpCurve, label="$\epsilon_{eq}$") +ax.plot(ltime, phiCurve - phi0, label=r"$\phi-\phi_0$") +ax.plot(ltime, eVCurve, label=r"$\epsilon_{V}$") +ax.plot(ltime, lpCurve, label=r"$\epsilon_{eq}$") ax.set_xlabel("$t$ / s") ax.set_ylabel(" ") ax.grid() diff --git a/MaterialLib/SolidModels/MFront/pi-plane.py b/MaterialLib/SolidModels/MFront/pi-plane.py index 4dc2f3cb51d..4a885f53906 100644 --- a/MaterialLib/SolidModels/MFront/pi-plane.py +++ b/MaterialLib/SolidModels/MFront/pi-plane.py @@ -1,10 +1,11 @@ -from math import pi, cos, sin, sqrt -from tfel.material import projectOnPiPlane +from math import cos, pi, sin + import mtest +from tfel.material import projectOnPiPlane divisions = 1000 for theta in [ - pi * (-1.0 + 2.0 * float(i) / (float(divisions) - 1.0)) for i in range(0, divisions) + pi * (-1.0 + 2.0 * float(i) / (float(divisions) - 1.0)) for i in range(divisions) ]: # for theta in [-1.3010636242139548]: em = 5.0e-3 @@ -35,7 +36,7 @@ m.completeInitialisation() m.initializeCurrentState(s) m.initializeWorkSpace(wk) - ltime = [float((tmax / (npas - 1))) * i for i in range(npas)] + ltime = [float(tmax / (npas - 1)) * i for i in range(npas)] plas = 0 plas_tol = 1e-10 p = s.getInternalStateVariableValue("EquivalentPlasticStrain") diff --git a/MaterialLib/SolidModels/MFront/pi-plane_ortho.py b/MaterialLib/SolidModels/MFront/pi-plane_ortho.py index 733e73ccf2a..e6998eedd15 100644 --- a/MaterialLib/SolidModels/MFront/pi-plane_ortho.py +++ b/MaterialLib/SolidModels/MFront/pi-plane_ortho.py @@ -1,10 +1,11 @@ -from math import pi, cos, sin, sqrt -from tfel.material import projectOnPiPlane +from math import cos, pi, sin + import mtest +from tfel.material import projectOnPiPlane divisions = 1000 for theta in [ - pi * (-1.0 + 2.0 * float(i) / (float(divisions) - 1.0)) for i in range(0, divisions) + pi * (-1.0 + 2.0 * float(i) / (float(divisions) - 1.0)) for i in range(divisions) ]: # for theta in [-1.3010636242139548]: em = 5.0e-3 @@ -42,7 +43,7 @@ m.completeInitialisation() m.initializeCurrentState(s) m.initializeWorkSpace(wk) - ltime = [float((tmax / (npas - 1))) * i for i in range(npas)] + ltime = [float(tmax / (npas - 1)) * i for i in range(npas)] plas = 0 plas_tol = 1e-10 for i in range(npas - 1): diff --git a/MaterialLib/SolidModels/MFront/three_ax_strain_ortho.py b/MaterialLib/SolidModels/MFront/three_ax_strain_ortho.py index 31010dd966a..4bdc9dd9931 100644 --- a/MaterialLib/SolidModels/MFront/three_ax_strain_ortho.py +++ b/MaterialLib/SolidModels/MFront/three_ax_strain_ortho.py @@ -30,7 +30,7 @@ m.completeInitialisation() m.initializeCurrentState(s) m.initializeWorkSpace(wk) -ltime = [float((tmax / (npas - 1))) * i for i in range(npas)] +ltime = [float(tmax / (npas - 1)) * i for i in range(npas)] for i in range(npas - 1): m.execute(s, wk, ltime[i], ltime[i + 1]) print( diff --git a/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py b/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py index cc00ae7edc0..983dc7302eb 100644 --- a/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py +++ b/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py @@ -1,6 +1,5 @@ -from paraview.simple import * from paraview import coprocessing - +from paraview.simple import * # -------------------------------------------------------------- # Code generated from cpstate.py to create the CoProcessor. diff --git a/Tests/Data/Elliptic/square_1x1_SteadyStateDiffusion_Python/bcs_laplace_eq.py b/Tests/Data/Elliptic/square_1x1_SteadyStateDiffusion_Python/bcs_laplace_eq.py index 89c5c4e8e5c..2dcca3dadf6 100644 --- a/Tests/Data/Elliptic/square_1x1_SteadyStateDiffusion_Python/bcs_laplace_eq.py +++ b/Tests/Data/Elliptic/square_1x1_SteadyStateDiffusion_Python/bcs_laplace_eq.py @@ -1,8 +1,10 @@ +from math import cos, cosh, pi, sin, sinh + import OpenGeoSys -from math import pi, sin, cos, sinh, cosh a = 2.0 * pi / 3.0 + # analytical solution used to set the Dirichlet BCs def solution(x, y): return sin(a * x) * sinh(a * y) diff --git a/Tests/Data/Elliptic/square_1x1_SteadyStateDiffusion_Python/sin_x_sin_y_source_term.py b/Tests/Data/Elliptic/square_1x1_SteadyStateDiffusion_Python/sin_x_sin_y_source_term.py index 310bb0ff18e..e441394b693 100644 --- a/Tests/Data/Elliptic/square_1x1_SteadyStateDiffusion_Python/sin_x_sin_y_source_term.py +++ b/Tests/Data/Elliptic/square_1x1_SteadyStateDiffusion_Python/sin_x_sin_y_source_term.py @@ -1,6 +1,7 @@ -import OpenGeoSys from math import pi, sin +import OpenGeoSys + a = 2.0 * pi b = 2.0 * pi diff --git a/Tests/Data/EllipticPETSc/square_1e1_neumann-insitu.py b/Tests/Data/EllipticPETSc/square_1e1_neumann-insitu.py index 7fb0cee4588..16116bd95d0 100644 --- a/Tests/Data/EllipticPETSc/square_1e1_neumann-insitu.py +++ b/Tests/Data/EllipticPETSc/square_1e1_neumann-insitu.py @@ -22,8 +22,8 @@ # paraview version 5.8.0 # -------------------------------------------------------------- -from paraview.simple import * from paraview import coprocessing +from paraview.simple import * # ----------------------- CoProcessor definition ----------------------- diff --git a/Tests/Data/HydroMechanics/GroundEquilibrium/pythonBCsOGS.py b/Tests/Data/HydroMechanics/GroundEquilibrium/pythonBCsOGS.py index baaffb4fa0f..6e2f6950d06 100644 --- a/Tests/Data/HydroMechanics/GroundEquilibrium/pythonBCsOGS.py +++ b/Tests/Data/HydroMechanics/GroundEquilibrium/pythonBCsOGS.py @@ -5,7 +5,6 @@ except ModuleNotFoundError: import OpenGeoSys -import numpy as np s_a = 365.25 * 24 * 3600 # =31557600 seconds per year diff --git a/Tests/Data/HydroMechanics/IdealGas/flow_pressure_boundary/python_boundary.py b/Tests/Data/HydroMechanics/IdealGas/flow_pressure_boundary/python_boundary.py index 8dc2f689258..dbff37df9ce 100644 --- a/Tests/Data/HydroMechanics/IdealGas/flow_pressure_boundary/python_boundary.py +++ b/Tests/Data/HydroMechanics/IdealGas/flow_pressure_boundary/python_boundary.py @@ -1,10 +1,9 @@ import OpenGeoSys - - p_flux_in = 1e-2 p_0 = 1e5 + # Source Terms ## Pressure class BC_p_D(OpenGeoSys.BoundaryCondition): @@ -17,5 +16,6 @@ def getFlux(self, t, coords, primary_vars): Jac = [0.0, 0.0, 0.0] return (True, p_flux_in, Jac) + bc_p_D = BC_p_D() bc_p_N = BC_p_N() diff --git a/Tests/Data/HydroMechanics/Linear/Unconfined_Compression_early/python_boundary.py b/Tests/Data/HydroMechanics/Linear/Unconfined_Compression_early/python_boundary.py index d02c58a99c5..8a484407730 100644 --- a/Tests/Data/HydroMechanics/Linear/Unconfined_Compression_early/python_boundary.py +++ b/Tests/Data/HydroMechanics/Linear/Unconfined_Compression_early/python_boundary.py @@ -1,25 +1,25 @@ import OpenGeoSys - dirichlet_displacement_top = -0.05 dirichlet_displacement_0 = 0 dirichlet_pressure_0 = 0 - # Boundary conditions ## Pressure -class BC_p_D_0(OpenGeoSys.BoundaryCondition): +class BC_p_D_0(OpenGeoSys.BoundaryCondition): def getDirichletBCValue(self, t, coords, node_id, primary_vars): return (True, dirichlet_pressure_0) - + + ## Displacement ### Dirichlet -class BC_u_D_0(OpenGeoSys.BoundaryCondition): +class BC_u_D_0(OpenGeoSys.BoundaryCondition): def getDirichletBCValue(self, t, coords, node_id, primary_vars): return (True, dirichlet_displacement_0) - -class BC_u_D_top(OpenGeoSys.BoundaryCondition): + + +class BC_u_D_top(OpenGeoSys.BoundaryCondition): def getDirichletBCValue(self, t, coords, node_id, primary_vars): return (True, dirichlet_displacement_top) diff --git a/Tests/Data/HydroMechanics/SeabedResponse/Stationary_waves.ipynb b/Tests/Data/HydroMechanics/SeabedResponse/Stationary_waves.ipynb index ded6fcfdbc6..8dc4ac63837 100644 --- a/Tests/Data/HydroMechanics/SeabedResponse/Stationary_waves.ipynb +++ b/Tests/Data/HydroMechanics/SeabedResponse/Stationary_waves.ipynb @@ -178,16 +178,14 @@ }, "outputs": [], "source": [ - "import numpy as np\n", - "\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "\n", "plt.rc(\"font\", size=8)\n", "plt.rc(\"axes\", titlesize=10)\n", "plt.rc(\"axes\", labelsize=10)\n", "\n", "import gmsh\n", - "\n", "import pyvista as pv\n", "\n", "pv.set_plot_theme(\"document\")\n", @@ -434,7 +432,7 @@ " if y == 0:\n", " ax[1].legend(loc=\"upper right\")\n", "\n", - "ax[1].set_ylabel(\"$\\sigma$'/$\\\\alpha\\\\tilde{p}$\")\n", + "ax[1].set_ylabel(\"$\\\\sigma$'/$\\\\alpha\\\\tilde{p}$\")\n", "\n", "\n", "ax[0].set_title(\"Pore pressure over time\")\n", diff --git a/Tests/Data/HydroMechanics/StaggeredScheme/InjectionProduction1D/python_boundary.py b/Tests/Data/HydroMechanics/StaggeredScheme/InjectionProduction1D/python_boundary.py index e1037fbd4d3..10a393606fa 100644 --- a/Tests/Data/HydroMechanics/StaggeredScheme/InjectionProduction1D/python_boundary.py +++ b/Tests/Data/HydroMechanics/StaggeredScheme/InjectionProduction1D/python_boundary.py @@ -1,12 +1,11 @@ import OpenGeoSys - - dirichlet_displacement_0 = 0 neumann_displacement_overburden = -2.125e6 source_term_injection = 1.16e-4 source_term_production = -1.16e-4 + # Source Terms ## Pressure class Source_p_injection(OpenGeoSys.SourceTerm): @@ -15,6 +14,7 @@ def getFlux(self, t, coords, primary_vars): Jac = [0.0, 0.0, 0.0] return (source_term_injection, Jac) + class Source_p_production(OpenGeoSys.SourceTerm): def getFlux(self, t, coords, primary_vars): # print("ST prim vars:", primary_vars) @@ -22,17 +22,18 @@ def getFlux(self, t, coords, primary_vars): return (source_term_production, Jac) - # Boundary conditions ## Pressure + ## Displacement ### Dirichlet -class BC_u_D(OpenGeoSys.BoundaryCondition): +class BC_u_D(OpenGeoSys.BoundaryCondition): def getDirichletBCValue(self, t, coords, node_id, primary_vars): # print(f"DBC xs={coords} pvs={primary_vars}") return (True, dirichlet_displacement_0) + class BC_u_N(OpenGeoSys.BoundaryCondition): def getFlux(self, t, coords, primary_vars): # print(f"NBC xs={coords} pvs={primary_vars}") @@ -40,7 +41,6 @@ def getFlux(self, t, coords, primary_vars): return (True, neumann_displacement_overburden, Jac) - injection = Source_p_injection() production = Source_p_production() bc_u_D = BC_u_D() diff --git a/Tests/Data/HydroMechanics/StaggeredScheme/InjectionProduction1D/python_boundary_staggered.py b/Tests/Data/HydroMechanics/StaggeredScheme/InjectionProduction1D/python_boundary_staggered.py index ddda21cf304..b4740b7686c 100644 --- a/Tests/Data/HydroMechanics/StaggeredScheme/InjectionProduction1D/python_boundary_staggered.py +++ b/Tests/Data/HydroMechanics/StaggeredScheme/InjectionProduction1D/python_boundary_staggered.py @@ -5,6 +5,7 @@ source_term_injection = 1.16e-4 source_term_production = -1.16e-4 + # Source Terms ## Pressure class Source_p_injection(OpenGeoSys.SourceTerm): @@ -12,25 +13,30 @@ def getFlux(self, t, coords, primary_vars): Jac = [0.0] return (source_term_injection, Jac) + class Source_p_production(OpenGeoSys.SourceTerm): def getFlux(self, t, coords, primary_vars): Jac = [0.0] return (source_term_production, Jac) + # Boundary conditions ## Pressure + ## Displacement ### Dirichlet -class BC_u_D(OpenGeoSys.BoundaryCondition): +class BC_u_D(OpenGeoSys.BoundaryCondition): def getDirichletBCValue(self, t, coords, node_id, primary_vars): return (True, dirichlet_displacement_0) + class BC_u_N(OpenGeoSys.BoundaryCondition): def getFlux(self, t, coords, primary_vars): Jac = [0.0, 0.0] return (True, neumann_displacement_overburden, Jac) + injection = Source_p_injection() production = Source_p_production() bc_u_D = BC_u_D() diff --git a/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis.ipynb b/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis.ipynb index 1a801d39c0e..d687ece1603 100644 --- a/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis.ipynb +++ b/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis.ipynb @@ -82,9 +82,8 @@ }, "outputs": [], "source": [ - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from matplotlib import cm\n", + "import numpy as np\n", "\n", "# Some plot settings\n", "plt.style.use(\"seaborn-v0_8-deep\")\n", @@ -679,8 +678,9 @@ }, "outputs": [], "source": [ - "from ogs6py import ogs\n", - "import shutil" + "import shutil\n", + "\n", + "from ogs6py import ogs" ] }, { @@ -1088,8 +1088,8 @@ " ax[i][j].grid(True)\n", " ax[i][j].set(xlim=(0, STUDY_mesh_size))\n", " ax[i][j].set_xlabel(\"$r$ / cm\")\n", - " ax[i][1].set_ylabel(\"$\\Delta\\\\sigma$ / kPa\")\n", - " ax[i][2].set_ylabel(\"$\\Delta\\\\sigma$ / $\\sigma_{\\mathrm{analytical}}$\")\n", + " ax[i][1].set_ylabel(\"$\\\\Delta\\\\sigma$ / kPa\")\n", + " ax[i][2].set_ylabel(\"$\\\\Delta\\\\sigma$ / $\\\\sigma_{\\\\mathrm{analytical}}$\")\n", "\n", " for iteration, idx in enumerate(STUDY_indices):\n", " # we use the line mesh we extracted before\n", @@ -1683,31 +1683,31 @@ " l2_rr.values(),\n", " color=\"firebrick\",\n", " linestyle=\":\",\n", - " label=\"$\\ell_{2, rr}$\",\n", + " label=r\"$\\ell_{2, rr}$\",\n", ")\n", "ax[0].plot(\n", " size.values(),\n", " l2_tt.values(),\n", " color=\"firebrick\",\n", " linestyle=\"--\",\n", - " label=\"$\\ell_{2, \\\\theta\\\\theta}$\",\n", + " label=\"$\\\\ell_{2, \\\\theta\\\\theta}$\",\n", ")\n", "ax[0].plot(\n", - " size.values(), l2_rt.values(), color=\"firebrick\", label=\"$\\ell_{2, r\\\\theta}$\"\n", + " size.values(), l2_rt.values(), color=\"firebrick\", label=\"$\\\\ell_{2, r\\\\theta}$\"\n", ")\n", "ax[0].plot(\n", " size.values(),\n", " l2_x.values(),\n", " color=\"royalblue\",\n", " linestyle=\"--\",\n", - " label=\"$\\ell_{2, x}$\",\n", + " label=r\"$\\ell_{2, x}$\",\n", ")\n", - "ax[0].plot(size.values(), l2_y.values(), color=\"royalblue\", label=\"$\\ell_{2, y}$\")\n", + "ax[0].plot(size.values(), l2_y.values(), color=\"royalblue\", label=r\"$\\ell_{2, y}$\")\n", "\n", "plot_slope_sketch(ax[0], 1.5e-1, 5e-2, [1, 2, 3], xmax=2.5e-1)\n", "\n", - "ax[0].set_title(\"$\\ell_2$ norms\")\n", - "ax[0].set_ylabel(\"$\\ell_2$ / kPa or cm\")\n", + "ax[0].set_title(r\"$\\ell_2$ norms\")\n", + "ax[0].set_ylabel(r\"$\\ell_2$ / kPa or cm\")\n", "\n", "ax[1].plot(\n", " size.values(), rms_rr.values(), color=\"firebrick\", linestyle=\":\", label=\"$RMS_{rr}$\"\n", diff --git a/Tests/Data/Mechanics/Linear/DiscWithHole/mesh_quarter_of_rectangle_with_hole.py b/Tests/Data/Mechanics/Linear/DiscWithHole/mesh_quarter_of_rectangle_with_hole.py index 17e964bd4c3..6203946cf97 100644 --- a/Tests/Data/Mechanics/Linear/DiscWithHole/mesh_quarter_of_rectangle_with_hole.py +++ b/Tests/Data/Mechanics/Linear/DiscWithHole/mesh_quarter_of_rectangle_with_hole.py @@ -1,8 +1,8 @@ # mesh quarter of rectangle with circular hole # meshing strategy as FEM example in "Hoehere TM" by Kreissig and Benedix # Dominik Kern -import numpy as np import gmsh +import numpy as np def run(output_file, a=3.0, b=4.0, r=1.0, R=2.0, lc=0.5, Nx=4, Ny=5, NR=5, Nr=5, P=1.3): diff --git a/Tests/Data/Mechanics/Linear/PythonHertzContact/gen-unit-circle.py b/Tests/Data/Mechanics/Linear/PythonHertzContact/gen-unit-circle.py index a4a20cdfbcc..0df4658ad4b 100755 --- a/Tests/Data/Mechanics/Linear/PythonHertzContact/gen-unit-circle.py +++ b/Tests/Data/Mechanics/Linear/PythonHertzContact/gen-unit-circle.py @@ -1,9 +1,10 @@ #!/usr/bin/vtkpython import sys + import numpy as np -from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk import vtk +from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy in_grid, out_grid, out_geom = sys.argv[1:] @@ -101,9 +102,7 @@ def distribute_points_evenly(c2): ) for i, (x, y, z) in enumerate(new_coords[idcs]): - fh.write( - ' \n'.format(i + 3, x, y, z) - ) + fh.write(f' \n') fh.write( """ @@ -122,7 +121,7 @@ def distribute_points_evenly(c2): ) for i in range(len(idcs)): - fh.write(" {}\n".format(i + 3)) + fh.write(f" {i + 3}\n") fh.write( """ diff --git a/Tests/Data/Mechanics/Linear/PythonHertzContact/hertz_contact_bc.py b/Tests/Data/Mechanics/Linear/PythonHertzContact/hertz_contact_bc.py index ba293a82b7b..2f0651b6bc0 100644 --- a/Tests/Data/Mechanics/Linear/PythonHertzContact/hertz_contact_bc.py +++ b/Tests/Data/Mechanics/Linear/PythonHertzContact/hertz_contact_bc.py @@ -1,8 +1,5 @@ -from __future__ import print_function - import OpenGeoSys - SPHERE_RADIUS = 1.0 START_TIME = 0.0 @@ -95,14 +92,14 @@ def getDirichletBCValue(self, t, coords, node_id, primary_vars): try: # check that we are at the outer boundary - assert abs(x ** 2 + y ** 2 + z ** 2 - SPHERE_RADIUS ** 2) < 1e-15 + assert abs(x**2 + y**2 + z**2 - SPHERE_RADIUS**2) < 1e-15 except: print( "assert abs(x**2 + y**2 + z**2 - 1.0) < 1e-15", x, y, z, - x ** 2 + y ** 2 + z ** 2 - SPHERE_RADIUS ** 2, + x**2 + y**2 + z**2 - SPHERE_RADIUS**2, ) raise diff --git a/Tests/Data/Mechanics/Linear/PythonHertzContact/post.py b/Tests/Data/Mechanics/Linear/PythonHertzContact/post.py index 1e4ffcd2d79..eb04e3f9299 100755 --- a/Tests/Data/Mechanics/Linear/PythonHertzContact/post.py +++ b/Tests/Data/Mechanics/Linear/PythonHertzContact/post.py @@ -1,12 +1,10 @@ #!/usr/bin/vtkpython -from __future__ import print_function -import vtk -from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk +import matplotlib.pyplot as plt import numpy as np +import vtk from scipy.interpolate import interp1d - -import matplotlib.pyplot as plt +from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy pvd_file = "out/hertz_pcs_0.pvd" @@ -29,7 +27,7 @@ def p_contact(r, r_contact): - return kappa * np.sqrt(r_contact ** 2 - r ** 2) + return kappa * np.sqrt(r_contact**2 - r**2) ### helpers ############################################## @@ -37,7 +35,7 @@ def p_contact(r, r_contact): import os try: - import xml.etree.cElementTree as ET + import xml.etree.ElementTree as ET except: import xml.etree.ElementTree as ET @@ -163,7 +161,7 @@ def strain_triangle_axi(cell, point_data, strain_data): for node in range(3): l1, l2 = T_inv * (cell_pts[node, :].T - cell_pts[2, :].T) - assert -1e-15 < l1 and 1 + 1e-15 > l1 and -1e-15 < l2 and 1 + 1e-15 > l2 + assert l1 > -1e-15 and 1 + 1e-15 > l1 and l2 > -1e-15 and 1 + 1e-15 > l2 grad = np.empty((2, 2)) for comp in range(2): @@ -237,9 +235,7 @@ def computeStrain(grid): grid.GetPointData().AddArray(stress_symm_tensor_vtk) writer.SetInputData(grid) - writer.SetFileName( - os.path.join(os.path.dirname(fn), "post_{:.0f}.vtu".format(t)) - ) + writer.SetFileName(os.path.join(os.path.dirname(fn), f"post_{t:.0f}.vtu")) writer.Write() return grid @@ -281,9 +277,9 @@ def average_stress(rs, stress): avg_stress = np.empty_like(rs_int) for i, r in enumerate(rs_int): - rho_max = np.sqrt(r_contact ** 2 - r ** 2) + rho_max = np.sqrt(r_contact**2 - r**2) rhos = np.linspace(0, rho_max, 100) - xis = np.sqrt(rhos ** 2 + r ** 2) + xis = np.sqrt(rhos**2 + r**2) try: assert max(xis) <= r_contact + 1e-8 except: @@ -345,7 +341,7 @@ def stress_at_contact_area(): avg_stress_yy, color=h.get_color(), ls="-", - label=r"$w_0 = {}$".format(w_0), + label=rf"$w_0 = {w_0}$", ) if False: @@ -357,7 +353,7 @@ def stress_at_contact_area(): rs, avg_stress_yy, color=h.get_color(), - label=r"$w_0 = {}$".format(2 * (1.0 - y_top)), + label=rf"$w_0 = {2 * (1.0 - y_top)}$", ) ax.scatter([r_contact], [0], color=h.get_color()) @@ -397,7 +393,7 @@ def stress_at_contact_area(): ax.scatter(rs_contact[1:], Fs, label="ogs") rs_ref = np.linspace(0, max(rs_contact), 200) -Fs_ref = 8.0 * rs_ref ** 3 * kappa / 3.0 +Fs_ref = 8.0 * rs_ref**3 * kappa / 3.0 ax.plot(rs_ref, Fs_ref, label="ref") diff --git a/Tests/Data/Mechanics/Linear/PythonPiston/post.py b/Tests/Data/Mechanics/Linear/PythonPiston/post.py index 73a63e93685..8a0cc5e795e 100755 --- a/Tests/Data/Mechanics/Linear/PythonPiston/post.py +++ b/Tests/Data/Mechanics/Linear/PythonPiston/post.py @@ -1,13 +1,10 @@ #!/usr/bin/vtkpython -from __future__ import print_function -import vtk -from vtk.util.numpy_support import vtk_to_numpy, numpy_to_vtk -import numpy as np -from scipy.interpolate import interp1d - -import matplotlib.pyplot as plt import chamber as ch +import matplotlib.pyplot as plt +import numpy as np +import vtk +from vtk.util.numpy_support import vtk_to_numpy pvd_file = "out/piston_pcs_0.pvd" @@ -17,7 +14,7 @@ import os try: - import xml.etree.cElementTree as ET + import xml.etree.ElementTree as ET except: import xml.etree.ElementTree as ET diff --git a/Tests/Data/Mechanics/Linear/SimpleMechanics.ipynb b/Tests/Data/Mechanics/Linear/SimpleMechanics.ipynb index c69a1a6da29..80fa266fdb5 100644 --- a/Tests/Data/Mechanics/Linear/SimpleMechanics.ipynb +++ b/Tests/Data/Mechanics/Linear/SimpleMechanics.ipynb @@ -60,8 +60,8 @@ "\n", "prj_name = \"SimpleMechanics\"\n", "model = ogs.OGS(PROJECT_FILE=os.path.join(out_dir, f\"{prj_name}.prj\"))\n", - "model.geo.add_geom(filename=f\"./square_1x1.gml\")\n", - "model.mesh.add_mesh(filename=f\"./square_1x1_quad_1e2.vtu\")\n", + "model.geo.add_geom(filename=\"./square_1x1.gml\")\n", + "model.mesh.add_mesh(filename=\"./square_1x1_quad_1e2.vtu\")\n", "model.processes.set_process(\n", " name=\"SD\",\n", " type=\"SMALL_DEFORMATION\",\n", diff --git a/Tests/Data/Mechanics/Linear/test_ip_data/2D-clamped-gravity.py b/Tests/Data/Mechanics/Linear/test_ip_data/2D-clamped-gravity.py index 941d6e1a6b8..1d3655fdf26 100644 --- a/Tests/Data/Mechanics/Linear/test_ip_data/2D-clamped-gravity.py +++ b/Tests/Data/Mechanics/Linear/test_ip_data/2D-clamped-gravity.py @@ -16,15 +16,15 @@ # # Deformation of a linear elastic Material due to its own gravity # %% -import pyvista as pv import numpy as np +import pyvista as pv pv.set_jupyter_backend("static") -import matplotlib.pyplot as plt -import subprocess import os -import sys +import subprocess + +import matplotlib.pyplot as plt # %% outdir = os.environ.get("OGS_TESTRUNNER_OUT_DIR", "_out") @@ -318,7 +318,7 @@ def add_vertex_cells(mesh): # # Checks (Assertions) # %% -from IPython.display import display, HTML +from IPython.display import HTML, display def allclose(x, y, abstol): diff --git a/Tests/Data/Mechanics/ModifiedCamClay/mtest/ModCamClay_TestIsotrop.ipynb b/Tests/Data/Mechanics/ModifiedCamClay/mtest/ModCamClay_TestIsotrop.ipynb index 8fba5c8f4d2..0b748496466 100644 --- a/Tests/Data/Mechanics/ModifiedCamClay/mtest/ModCamClay_TestIsotrop.ipynb +++ b/Tests/Data/Mechanics/ModifiedCamClay/mtest/ModCamClay_TestIsotrop.ipynb @@ -38,10 +38,11 @@ "outputs": [], "source": [ "# HIDDEN\n", - "import numpy as np\n", + "import site\n", + "\n", "import matplotlib.pyplot as plt\n", "import mtest as mtest\n", - "import site" + "import numpy as np" ] }, { diff --git a/Tests/Data/Mechanics/PLLC/PLLC.ipynb b/Tests/Data/Mechanics/PLLC/PLLC.ipynb index 5c5de5df16c..0aa3fa4409e 100644 --- a/Tests/Data/Mechanics/PLLC/PLLC.ipynb +++ b/Tests/Data/Mechanics/PLLC/PLLC.ipynb @@ -34,10 +34,11 @@ "outputs": [], "source": [ "import contextlib\n", - "import matplotlib.pyplot as plt\n", + "import os\n", + "\n", "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import os\n", "import vtuIO\n", "from ogs6py import ogs\n", "\n", diff --git a/Tests/Data/NodePartitionedMesh/WithIntegrationPointStress/MixedElements/check_point_cloud.ipynb b/Tests/Data/NodePartitionedMesh/WithIntegrationPointStress/MixedElements/check_point_cloud.ipynb index 049d410acf8..43bec5905ed 100644 --- a/Tests/Data/NodePartitionedMesh/WithIntegrationPointStress/MixedElements/check_point_cloud.ipynb +++ b/Tests/Data/NodePartitionedMesh/WithIntegrationPointStress/MixedElements/check_point_cloud.ipynb @@ -27,8 +27,8 @@ "metadata": {}, "outputs": [], "source": [ - "import pyvista as pv\n", - "import numpy as np" + "import numpy as np\n", + "import pyvista as pv" ] }, { @@ -236,7 +236,7 @@ "ax.plot(zs_ref, sigs_zz_ref, label=\"reference\")\n", "\n", "ax.legend()\n", - "ax.set_ylabel(\"$\\sigma_{zz}$\")\n", + "ax.set_ylabel(r\"$\\sigma_{zz}$\")\n", "ax.set_xlabel(\"$z$\")" ] }, diff --git a/Tests/Data/Notebooks/testrunner.py b/Tests/Data/Notebooks/testrunner.py index 41c8aba5051..1ab5704b8f5 100644 --- a/Tests/Data/Notebooks/testrunner.py +++ b/Tests/Data/Notebooks/testrunner.py @@ -1,17 +1,18 @@ -import nbformat -from nbconvert.preprocessors import ExecutePreprocessor, CellExecutionError -from nbclient.exceptions import DeadKernelError -from nbconvert import HTMLExporter import argparse import os import shutil +import subprocess import sys -from timeit import default_timer as timer from datetime import timedelta -import toml from pathlib import Path +from timeit import default_timer as timer + import jupytext -import subprocess +import nbformat +import toml +from nbclient.exceptions import DeadKernelError +from nbconvert import HTMLExporter +from nbconvert.preprocessors import CellExecutionError, ExecutePreprocessor def save_to_website(exec_notebook_file, web_path): @@ -51,7 +52,7 @@ def save_to_website(exec_notebook_file, web_path): check=True, ) - if not "Tests/Data" in exec_notebook_file: + if "Tests/Data" not in exec_notebook_file: return Path(output_path).mkdir(parents=True, exist_ok=True) @@ -104,7 +105,7 @@ def check_and_modify_frontmatter(): repo = os.environ["CI_MERGE_REQUEST_SOURCE_PROJECT_URL"] branch = os.environ["CI_MERGE_REQUEST_SOURCE_BRANCH_NAME"] binder_link = f"https://mybinder.org/v2/gh/bilke/binder-ogs-requirements/master?urlpath=git-pull%3Frepo={repo}%26urlpath=lab/tree/ogs/{notebook_file_path_relative}%26branch={branch}" - text = f""" + text = """

""" - text += f"""

\n\n""" + text += """

\n\n""" second_cell = nb["cells"][1] if second_cell.cell_type == "markdown": @@ -186,7 +187,7 @@ def check_and_modify_frontmatter(): convert_notebook_file = convert_notebook_file.replace("notebook-", "") jupytext.write(nb, convert_notebook_file) else: - with open(notebook_file_path, mode="r", encoding="utf-8") as f: + with open(notebook_file_path, encoding="utf-8") as f: nb = nbformat.read(f, as_version=4) ep = ExecutePreprocessor(kernel_name="python3") @@ -209,7 +210,6 @@ def check_and_modify_frontmatter(): except CellExecutionError: notebook_success = False success = False - pass end = timer() # Write new notebook diff --git a/Tests/Data/Notebooks/thermo-osmosis.run-skip.ipynb b/Tests/Data/Notebooks/thermo-osmosis.run-skip.ipynb index 2f57bd7e3ae..c6f7fa2ad8b 100644 --- a/Tests/Data/Notebooks/thermo-osmosis.run-skip.ipynb +++ b/Tests/Data/Notebooks/thermo-osmosis.run-skip.ipynb @@ -66,8 +66,9 @@ ], "source": [ "import os\n", - "import vtuIO\n", + "\n", "import numpy as np\n", + "import vtuIO\n", "\n", "filename = \"expected_Column_ts_68_t_7200000.000000.vtu\"\n", "data_dir = os.environ.get(\"OGS_DATA_DIR\", \"../../Data\")\n", @@ -237,7 +238,7 @@ " )\n", "plt.xlabel(\"$x$ / m\")\n", "plt.xlim([0, 20])\n", - "plt.ylabel(\"$\\Delta T$ / K\")\n", + "plt.ylabel(r\"$\\Delta T$ / K\")\n", "plt.legend()\n", "plt.title(\"temperature\")" ] @@ -267,7 +268,7 @@ "for i, model in enumerate(resp):\n", " plt.plot(x, resp[model][\"pressure\"] - aTO.p(x, t, 200), marker[i], label=model)\n", "plt.xlabel(\"$x$ / m\")\n", - "plt.ylabel(\"$\\Delta p$ / Pa\")\n", + "plt.ylabel(r\"$\\Delta p$ / Pa\")\n", "plt.xlim([0, 20])\n", "plt.legend()\n", "plt.title(\"pressure\")" diff --git a/Tests/Data/Parabolic/ComponentTransport/DiffusionSorptionDecay/DiffusionSorptionDecay.ipynb b/Tests/Data/Parabolic/ComponentTransport/DiffusionSorptionDecay/DiffusionSorptionDecay.ipynb index ad096f9e061..cabde8d7eca 100644 --- a/Tests/Data/Parabolic/ComponentTransport/DiffusionSorptionDecay/DiffusionSorptionDecay.ipynb +++ b/Tests/Data/Parabolic/ComponentTransport/DiffusionSorptionDecay/DiffusionSorptionDecay.ipynb @@ -129,12 +129,12 @@ "outputs": [], "source": [ "import os\n", - "import ogs6py\n", - "import vtuIO\n", - "import numpy as np\n", - "from scipy import special\n", + "\n", "import matplotlib.pyplot as plt\n", - "from matplotlib.pyplot import cm" + "import numpy as np\n", + "import vtuIO\n", + "from matplotlib.pyplot import cm\n", + "from scipy import special" ] }, { @@ -408,7 +408,7 @@ " ax.set_ylim((-4, 0))\n", "\n", " plt.xlabel(\"Time [year]\")\n", - " plt.ylabel(\"Log $||\\mathbf{c}-\\mathbf{c^{exact}}||_{2}$\")\n", + " plt.ylabel(r\"Log $||\\mathbf{c}-\\mathbf{c^{exact}}||_{2}$\")\n", "\n", " ax.plot(\n", " time, l2_norm_error, linestyle=\"-\", lw=1.5, marker=\"o\", zorder=10, clip_on=False\n", diff --git a/Tests/Data/Parabolic/ComponentTransport/MultiLayerDiffusion/MultiLayerDiffusion.ipynb b/Tests/Data/Parabolic/ComponentTransport/MultiLayerDiffusion/MultiLayerDiffusion.ipynb index ca2ddb03e1d..00af0e86a9d 100644 --- a/Tests/Data/Parabolic/ComponentTransport/MultiLayerDiffusion/MultiLayerDiffusion.ipynb +++ b/Tests/Data/Parabolic/ComponentTransport/MultiLayerDiffusion/MultiLayerDiffusion.ipynb @@ -105,14 +105,13 @@ "outputs": [], "source": [ "import os\n", - "import ogs6py\n", - "import vtuIO\n", - "import pandas as pd\n", - "import numpy as np\n", - "from scipy import special\n", + "\n", "import matplotlib.pyplot as plt\n", - "from matplotlib.pyplot import cm\n", - "from IPython.display import Image" + "import numpy as np\n", + "import pandas as pd\n", + "import vtuIO\n", + "from IPython.display import Image\n", + "from matplotlib.pyplot import cm" ] }, { @@ -351,9 +350,9 @@ } ], "source": [ - "from IPython.display import display, Image\n", + "from IPython.display import Image, display\n", "\n", - "display(Image(filename=f\"./sketch_molar_flux_calculation.jpg\", width=400))" + "display(Image(filename=\"./sketch_molar_flux_calculation.jpg\", width=400))" ] }, { diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/DecayChain.ipynb b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/DecayChain.ipynb index d0eedda04c9..543e558a3d6 100644 --- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/DecayChain.ipynb +++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/DecayChain.ipynb @@ -49,17 +49,13 @@ "import os\n", "import time\n", "\n", - "import ogs6py\n", - "import vtuIO\n", - "\n", - "import numpy as np\n", - "from scipy import special\n", - "\n", "import h5py\n", - "\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import vtuIO\n", + "from IPython.display import Image, display\n", "from matplotlib.pyplot import cm\n", - "from IPython.display import display, Image" + "from scipy import special" ] }, { @@ -86,7 +82,7 @@ } ], "source": [ - "display(Image(filename=f\"chains.png\", width=600))" + "display(Image(filename=\"chains.png\", width=600))" ] }, { @@ -202,12 +198,12 @@ "\n", "\n", "def computeInitialAuxiliaryVariable(c_inlet, k):\n", - " a_inlet = np.empty((0))\n", + " a_inlet = np.empty(0)\n", "\n", " for i in range(len(c_inlet)):\n", " value = c_inlet[i]\n", " if i > 0:\n", - " for j in range(0, i):\n", + " for j in range(i):\n", " value += computeProduct(j, i, k, c_inlet)\n", " a_inlet = np.append(a_inlet, value)\n", "\n", @@ -543,7 +539,7 @@ "# numerical solution by reference code\n", "# added once the bc value is double-checked\n", "porosity = 0.12\n", - "with h5py.File(f\"./solution_reference_code.hdf5\", \"r\") as f:\n", + "with h5py.File(\"./solution_reference_code.hdf5\", \"r\") as f:\n", " species_ = f[\"species\"][:]\n", " x_ = f[\"x\"][:]\n", " for s_, radionuclide, color in zip(species_, radionuclides, color_map):\n", @@ -610,7 +606,7 @@ ")\n", "\n", "# numerical solution by reference code\n", - "with h5py.File(f\"./solution_reference_code.hdf5\", \"r\") as f:\n", + "with h5py.File(\"./solution_reference_code.hdf5\", \"r\") as f:\n", " Ac_227_ = f[\"species\"][-1]\n", " x_ = f[\"x\"][:]\n", " Ac_227_ = f[Ac_227_][:]\n", @@ -828,7 +824,7 @@ "ax.plot(\n", " x[np.where(x < 0.6)],\n", " Ac_227_os[np.where(x < 0.6)],\n", - " label=\"Time step size $\\Delta$t = 100 years\",\n", + " label=r\"Time step size $\\Delta$t = 100 years\",\n", " color=color_map[1],\n", " linestyle=\"--\",\n", " zorder=10,\n", @@ -842,7 +838,7 @@ "ax.plot(\n", " x[np.where(x < 0.6)],\n", " Ac_227_os_small_ts[np.where(x < 0.6)],\n", - " label=\"Time step size $\\Delta$t = 5 years\",\n", + " label=r\"Time step size $\\Delta$t = 5 years\",\n", " color=color_map[-2],\n", " linestyle=\"-.\",\n", " zorder=10,\n", @@ -948,7 +944,7 @@ "\n", "ax.bar(list(runtime.keys())[:2], list(runtime.values())[:2], width=0.5, zorder=3)\n", "\n", - "for i in range(0, 2):\n", + "for i in range(2):\n", " ax.annotate(\n", " list(runtime.values())[i], (i, list(runtime.values())[i] + 50), ha=\"center\"\n", " )" diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb index 21f979062c4..b92488b148e 100644 --- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb +++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb @@ -29,19 +29,17 @@ }, "outputs": [], "source": [ - "from ogs6py.log_parser.log_parser import parse_file\n", + "import subprocess\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import pandas as pd\n", "from ogs6py.log_parser.common_ogs_analyses import (\n", - " fill_ogs_context,\n", " analysis_time_step,\n", - " analysis_convergence_newton_iteration,\n", - " analysis_convergence_coupling_iteration,\n", - " analysis_simulation_termination,\n", + " fill_ogs_context,\n", " time_step_vs_iterations,\n", ")\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import subprocess" + "from ogs6py.log_parser.log_parser import parse_file" ] }, { diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/RadionuclidesMigration/RadionuclidesMigration.ipynb b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/RadionuclidesMigration/RadionuclidesMigration.ipynb index d62c0fe8c23..fca0bf9e081 100644 --- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/RadionuclidesMigration/RadionuclidesMigration.ipynb +++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/RadionuclidesMigration/RadionuclidesMigration.ipynb @@ -155,12 +155,12 @@ "outputs": [], "source": [ "import os\n", - "import ogs6py\n", - "import vtuIO\n", - "import numpy as np\n", + "import time\n", + "\n", "import matplotlib.pyplot as plt\n", - "from matplotlib.pyplot import cm\n", - "import time" + "import numpy as np\n", + "import vtuIO\n", + "from matplotlib.pyplot import cm" ] }, { @@ -247,7 +247,7 @@ " y,\n", " color=c,\n", " marker=\".\",\n", - " label=\"{:.0e} yr\".format(round(t / 3600 / 24 / 365, 0)),\n", + " label=f\"{round(t / 3600 / 24 / 365, 0):.0e} yr\",\n", " )\n", "\n", "ax.set_xlim(0, 5)\n", @@ -380,7 +380,7 @@ " color=c,\n", " marker=\".\",\n", " markevery=5,\n", - " label=\"{:.0e} yr\".format(round(t / 3600 / 24 / 365, 0)),\n", + " label=f\"{round(t / 3600 / 24 / 365, 0):.0e} yr\",\n", " )\n", "\n", "ax.set_xlim(0, 40)\n", diff --git a/Tests/Data/Parabolic/LiquidFlow/AxiSymTheis/axisym_theis.ipynb b/Tests/Data/Parabolic/LiquidFlow/AxiSymTheis/axisym_theis.ipynb index 0c05c074c9e..8fdd054d353 100644 --- a/Tests/Data/Parabolic/LiquidFlow/AxiSymTheis/axisym_theis.ipynb +++ b/Tests/Data/Parabolic/LiquidFlow/AxiSymTheis/axisym_theis.ipynb @@ -33,16 +33,16 @@ "outputs": [], "source": [ "# modules\n", - "from IPython.display import Image\n", "import os\n", - "import pyvista as pv\n", - "import numpy as np\n", + "import time\n", + "\n", "import matplotlib.pyplot as plt\n", - "from scipy.special import exp1\n", - "import vtk\n", - "from vtk.util.numpy_support import vtk_to_numpy\n", "import matplotlib.tri as tri\n", - "import time" + "import numpy as np\n", + "import pyvista as pv\n", + "import vtk\n", + "from scipy.special import exp1\n", + "from vtk.util.numpy_support import vtk_to_numpy" ] }, { @@ -459,9 +459,9 @@ } ], "source": [ - "import vtuIO\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import vtuIO\n", "\n", "# Read simulation results\n", "pvdfile = vtuIO.PVDIO(f\"{out_dir}/{pvd_name}.pvd\", dim=2)\n", diff --git a/Tests/Data/Parabolic/LiquidFlow/BlockingConductingFracture/BlockingConductingFracture.ipynb b/Tests/Data/Parabolic/LiquidFlow/BlockingConductingFracture/BlockingConductingFracture.ipynb index b8e2a325342..0d3804426d9 100644 --- a/Tests/Data/Parabolic/LiquidFlow/BlockingConductingFracture/BlockingConductingFracture.ipynb +++ b/Tests/Data/Parabolic/LiquidFlow/BlockingConductingFracture/BlockingConductingFracture.ipynb @@ -68,13 +68,12 @@ "outputs": [], "source": [ "# HIDDEN\n", - "from ogs6py.ogs import OGS\n", - "import vtuIO\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", "import matplotlib.tri as tri\n", - "import plot_settings" + "import numpy as np\n", + "import seaborn as sns\n", + "import vtuIO\n", + "from ogs6py.ogs import OGS" ] }, { @@ -232,8 +231,8 @@ ], "source": [ "# Post-Processing\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "\n", "levels = np.linspace(np.min(field), np.max(field), 60)\n", "levelsf = np.linspace(np.min(field), np.max(field), 60)\n", @@ -334,7 +333,6 @@ "source": [ "import matplotlib.cm as cm\n", "import matplotlib.colors as mcolors\n", - "import matplotlib.colorbar as mcolorbar\n", "\n", "fig, ax = plt.subplots(ncols=2, figsize=(20, 8))\n", "\n", diff --git a/Tests/Data/Parabolic/T/1D_neumann/plotLine.py b/Tests/Data/Parabolic/T/1D_neumann/plotLine.py index 4eafb6cdab4..5751e663d7c 100755 --- a/Tests/Data/Parabolic/T/1D_neumann/plotLine.py +++ b/Tests/Data/Parabolic/T/1D_neumann/plotLine.py @@ -1,15 +1,13 @@ #!/usr/bin/env python + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd import temperature_analytical -from matplotlib.legend import Legend from vtk import * from vtk.numpy_interface import dataset_adapter as dsa from vtk.util.numpy_support import vtk_to_numpy -import matplotlib.pyplot as plt -import numpy as np -import os -import pandas as pd -import scipy.optimize as sp def probeFileAlongLine(filename): @@ -131,13 +129,13 @@ def plotErrors(ax, data, with_labels=True): "column": ("newton", "error"), "color": "green", "ls": "-", - "label": "$e_\mathrm{newton}$", + "label": r"$e_\mathrm{newton}$", }, { "column": ("newton_masslumping", "error"), "color": "blue", "ls": "-", - "label": "$e_\mathrm{newton}^\mathrm{ML}$", + "label": r"$e_\mathrm{newton}^\mathrm{ML}$", }, ] lines = [] @@ -167,7 +165,7 @@ def plotCases(data, output): fig.tight_layout() fig.savefig(output + ".png", dpi=150) plt.close("all") - return None + return def plotPicardTemperature(ax, data, with_labels=True): @@ -249,7 +247,7 @@ def plotNewtonVsPicard(data, output): lines += plotNewtonVsPicardErrors(axes[1], data, with_labels=True) axes[1].legend(loc="best", fontsize=12, ncol=1, bbox_to_anchor=(0.5, 0.5, 0.5, 0.5)) - axes[1].set_ylabel("$T_\mathrm{diff}$ (newton - picard) / K", fontsize=18) + axes[1].set_ylabel(r"$T_\mathrm{diff}$ (newton - picard) / K", fontsize=18) axes[1].set_xlabel("$x$ / m", fontsize=16) for ax in axes: @@ -259,7 +257,7 @@ def plotNewtonVsPicard(data, output): fig.tight_layout() fig.savefig(output + ".png", dpi=300) plt.close("all") - return None + return def singleTimeStep(ts): diff --git a/Tests/Data/Parabolic/T/1D_neumann/temperature_analytical.py b/Tests/Data/Parabolic/T/1D_neumann/temperature_analytical.py index 1aa3a3eabf6..aba814e7950 100755 --- a/Tests/Data/Parabolic/T/1D_neumann/temperature_analytical.py +++ b/Tests/Data/Parabolic/T/1D_neumann/temperature_analytical.py @@ -1,9 +1,9 @@ #!/usr/bin/env python # Solution of heatequation in a semi-infinite domain. -from vtk import * import numpy as np from scipy.special import erfc +from vtk import * r = vtkXMLUnstructuredGridReader() r.SetFileName("mesh.vtu") @@ -24,7 +24,7 @@ def temperature(x, t): return T_inf + 2 * q / lambda_coeff * ( - np.sqrt(alpha * t / np.pi) * np.exp(-(x ** 2) / (4 * alpha * t)) + np.sqrt(alpha * t / np.pi) * np.exp(-(x**2) / (4 * alpha * t)) - x / 2 * erfc(x / (2 * np.sqrt(alpha * t))) ) diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy.py b/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy.py index f1cf83110db..bacba3a7a6d 100644 --- a/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy.py +++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy.py @@ -9,9 +9,10 @@ print(sys.version) import os + import numpy as np -from pandas import read_csv import OpenGeoSys +from pandas import read_csv from tespy.networks import load_network # User setting ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy_closedloop.py b/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy_closedloop.py index c3d901101c9..a953ab4d00b 100644 --- a/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy_closedloop.py +++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy_closedloop.py @@ -9,9 +9,10 @@ print(sys.version) import os + import numpy as np -from pandas import read_csv import OpenGeoSys +from pandas import read_csv from tespy.networks import load_network # User setting ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ @@ -27,6 +28,7 @@ # give the consumer name defined by user in the network model consumer_name = "consumer" + # network status setting def network_status(t): nw_status = "on" diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/3bhes.py b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/3bhes.py index c63a38bbb9f..14e549fdf9b 100644 --- a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/3bhes.py +++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/3bhes.py @@ -6,14 +6,20 @@ ### # Execute this file to generate TESPy network csv files +import numpy as np +from tespy.components import ( + heat_exchanger_simple, + merge, + pump, + sink, + source, + splitter, +) +from tespy.connections import bus, connection, ref from tespy.networks import network -from tespy.components import sink, source, splitter, merge, pump, heat_exchanger_simple -from tespy.connections import connection, ref, bus from tespy.tools.characteristics import char_line from tespy.tools.data_containers import dc_cc -import numpy as np - # %% network btes = network( fluids=["water"], diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/3bhes_closedloop.py b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/3bhes_closedloop.py index 75b5c121031..3e66edcf095 100644 --- a/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/3bhes_closedloop.py +++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/pre/3bhes_closedloop.py @@ -6,20 +6,17 @@ ### # Execute this file to generate TESPy network csv files -from tespy.networks import network -from tespy.connections import connection, ref +import numpy as np from tespy.components import ( - source, - sink, + cycle_closer, + heat_exchanger_simple, + merge, pump, splitter, - merge, - heat_exchanger_simple, - cycle_closer, ) +from tespy.connections import connection +from tespy.networks import network from tespy.tools import char_line, dc_cc -import numpy as np - # %% network btes = network(fluids=["water"], T_unit="K", p_unit="bar", h_unit="kJ / kg") diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/bcs_tespy_and_serverCommunication.py b/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/bcs_tespy_and_serverCommunication.py index 16b769337c7..d91b2c87be7 100644 --- a/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/bcs_tespy_and_serverCommunication.py +++ b/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/bcs_tespy_and_serverCommunication.py @@ -9,9 +9,10 @@ print(sys.version) import os + import numpy as np -from pandas import read_csv import OpenGeoSys +from pandas import read_csv from tespy.networks import load_network # User setting ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/pre/3bhes.py b/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/pre/3bhes.py index c63a38bbb9f..14e549fdf9b 100644 --- a/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/pre/3bhes.py +++ b/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/pre/3bhes.py @@ -6,14 +6,20 @@ ### # Execute this file to generate TESPy network csv files +import numpy as np +from tespy.components import ( + heat_exchanger_simple, + merge, + pump, + sink, + source, + splitter, +) +from tespy.connections import bus, connection, ref from tespy.networks import network -from tespy.components import sink, source, splitter, merge, pump, heat_exchanger_simple -from tespy.connections import connection, ref, bus from tespy.tools.characteristics import char_line from tespy.tools.data_containers import dc_cc -import numpy as np - # %% network btes = network( fluids=["water"], diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/pre/3bhes_closedloop.py b/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/pre/3bhes_closedloop.py index 75b5c121031..3e66edcf095 100644 --- a/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/pre/3bhes_closedloop.py +++ b/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/pre/3bhes_closedloop.py @@ -6,20 +6,17 @@ ### # Execute this file to generate TESPy network csv files -from tespy.networks import network -from tespy.connections import connection, ref +import numpy as np from tespy.components import ( - source, - sink, + cycle_closer, + heat_exchanger_simple, + merge, pump, splitter, - merge, - heat_exchanger_simple, - cycle_closer, ) +from tespy.connections import connection +from tespy.networks import network from tespy.tools import char_line, dc_cc -import numpy as np - # %% network btes = network(fluids=["water"], T_unit="K", p_unit="bar", h_unit="kJ / kg") diff --git a/Tests/Data/Parabolic/T/3D_Beier_sandbox_python_interface/simulationX_test.py b/Tests/Data/Parabolic/T/3D_Beier_sandbox_python_interface/simulationX_test.py index 6a3f65af942..c366bd6346b 100644 --- a/Tests/Data/Parabolic/T/3D_Beier_sandbox_python_interface/simulationX_test.py +++ b/Tests/Data/Parabolic/T/3D_Beier_sandbox_python_interface/simulationX_test.py @@ -8,8 +8,8 @@ import sys print(sys.version) -from pandas import read_csv import OpenGeoSys +from pandas import read_csv df_server = read_csv( "initial.csv", delimiter=";", index_col=[0], dtype={"data_index": str} diff --git a/Tests/Data/Parabolic/ThermalTwoPhaseFlowPP/HeatPipe/heatpipe.ipynb b/Tests/Data/Parabolic/ThermalTwoPhaseFlowPP/HeatPipe/heatpipe.ipynb index 124c46ffb24..01e6e3c12fd 100644 --- a/Tests/Data/Parabolic/ThermalTwoPhaseFlowPP/HeatPipe/heatpipe.ipynb +++ b/Tests/Data/Parabolic/ThermalTwoPhaseFlowPP/HeatPipe/heatpipe.ipynb @@ -61,9 +61,9 @@ } ], "source": [ - "from IPython.display import display, Image\n", + "from IPython.display import Image, display\n", "\n", - "display(Image(filename=f\"./model_domain.jpg\", width=1000))" + "display(Image(filename=\"./model_domain.jpg\", width=1000))" ] }, { @@ -127,12 +127,11 @@ }, "outputs": [], "source": [ - "import os\n", - "import vtuIO\n", - "import pandas as pd\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from mpl_toolkits.axes_grid1.inset_locator import inset_axes, InsetPosition, mark_inset\n", + "import numpy as np\n", + "import pandas as pd\n", + "import vtuIO\n", + "from mpl_toolkits.axes_grid1.inset_locator import InsetPosition, mark_inset\n", "\n", "plt.rcParams[\"legend.fontsize\"] = 20\n", "plt.rcParams[\"font.size\"] = 20" @@ -248,7 +247,7 @@ }, "outputs": [], "source": [ - "result_file = f\"SemianalyticalSolutionResults.csv\"\n", + "result_file = \"SemianalyticalSolutionResults.csv\"\n", "soln = pd.read_csv(\n", " result_file,\n", " sep=\",\",\n", @@ -321,8 +320,8 @@ " ax[i].set_xlim([0, 2.4])\n", " ax[i].set_xlabel(\"$x$ / m\")\n", "ax[0].set_ylabel(\"$S_w$ / -\")\n", - "ax[1].set_ylabel(\"$\\Delta S_w$ / -\")\n", - "ax[2].set_ylabel(\"$\\Delta S_w/S_{w, analytical}$\")\n", + "ax[1].set_ylabel(r\"$\\Delta S_w$ / -\")\n", + "ax[2].set_ylabel(r\"$\\Delta S_w/S_{w, analytical}$\")\n", "ax[0].set_ylim([0, 1])\n", "ax[0].set_title(\"Saturation\")\n", "ax[1].set_title(\"Absolute error\")\n", @@ -394,8 +393,8 @@ " ax[i].set_xlim([0, 2.4])\n", " ax[i].set_xlabel(\"$x$ / m\")\n", "ax[0].set_ylabel(\"$T$ / K\")\n", - "ax[1].set_ylabel(\"$\\Delta T$ / K\")\n", - "ax[2].set_ylabel(\"$\\Delta T/T_{analytical}$\")\n", + "ax[1].set_ylabel(r\"$\\Delta T$ / K\")\n", + "ax[2].set_ylabel(r\"$\\Delta T/T_{analytical}$\")\n", "ax[0].set_title(\"Temperature\")\n", "ax[1].set_title(\"Absolute error\")\n", "ax[2].set_title(\"Relative error\")\n", @@ -443,8 +442,8 @@ " ax[i].set_xlim([0, 2.4])\n", " ax[i].set_xlabel(\"$x$ / m\")\n", "ax[0].set_ylabel(\"$P_g$ / Pa\")\n", - "ax[1].set_ylabel(\"$\\Delta P_g$ / Pa\")\n", - "ax[2].set_ylabel(\"$\\Delta P_g/P_{g, analytical}$\")\n", + "ax[1].set_ylabel(r\"$\\Delta P_g$ / Pa\")\n", + "ax[2].set_ylabel(r\"$\\Delta P_g/P_{g, analytical}$\")\n", "ax[0].set_title(\"Gas pressure\")\n", "ax[1].set_title(\"Absolute error\")\n", "ax[2].set_title(\"Relative error\")\n", diff --git a/Tests/Data/PhaseField/Kregime_Propagating_jupyter_notebook/Kregime_Propagating_jupyter.ipynb b/Tests/Data/PhaseField/Kregime_Propagating_jupyter_notebook/Kregime_Propagating_jupyter.ipynb index 362dd43b28d..01c27eaf16b 100644 --- a/Tests/Data/PhaseField/Kregime_Propagating_jupyter_notebook/Kregime_Propagating_jupyter.ipynb +++ b/Tests/Data/PhaseField/Kregime_Propagating_jupyter_notebook/Kregime_Propagating_jupyter.ipynb @@ -133,17 +133,17 @@ }, "outputs": [], "source": [ - "from ogs6py import ogs\n", - "import numpy as np\n", - "import ogs6py\n", - "import matplotlib.pyplot as plt\n", - "import time\n", + "import argparse\n", "import math\n", - "import gmsh\n", "import os\n", - "from ogstools.msh2vtu import run\n", - "import argparse\n", "import re\n", + "import time\n", + "\n", + "import gmsh\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from ogs6py import ogs\n", + "from ogstools.msh2vtu import run\n", "\n", "pi = math.pi\n", "plt.rcParams[\"text.usetex\"] = True" @@ -729,7 +729,6 @@ }, "outputs": [], "source": [ - "from IPython.display import Image\n", "import pyvista as pv\n", "\n", "pv.set_plot_theme(\"document\")\n", diff --git a/Tests/Data/PhaseField/PForthotropy_jupyter_notebook/sen_shear.ipynb b/Tests/Data/PhaseField/PForthotropy_jupyter_notebook/sen_shear.ipynb index 81ea7ba8d32..1e7b9da2337 100644 --- a/Tests/Data/PhaseField/PForthotropy_jupyter_notebook/sen_shear.ipynb +++ b/Tests/Data/PhaseField/PForthotropy_jupyter_notebook/sen_shear.ipynb @@ -48,16 +48,16 @@ "metadata": {}, "outputs": [], "source": [ - "from ogs6py import ogs\n", "import os\n", "import shutil\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pyvista as pv\n", "import time\n", - "import pandas as pd\n", + "from types import MethodType\n", "from xml.dom import minidom\n", - "from types import MethodType\n" + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pyvista as pv\n", + "from ogs6py import ogs" ] }, { @@ -67,26 +67,46 @@ "metadata": {}, "outputs": [], "source": [ - "data_dir = os.environ.get('OGS_DATA_DIR', '../../..')\n", - "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n", + "data_dir = os.environ.get(\"OGS_DATA_DIR\", \"../../..\")\n", + "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", "if not os.path.exists(out_dir):\n", - " os.makedirs(out_dir) \n", + " os.makedirs(out_dir)\n", "\n", - "output_dir= out_dir\n", + "output_dir = out_dir\n", "\n", "# define a method to replace a specific curve (analogue to replace_parameter method)\n", - "def replace_curve(self, name=None, value=None, coords=None, parametertype=None, valuetag=\"values\", coordstag=\"coords\"):\n", + "\n", + "\n", + "def replace_curve(\n", + " self,\n", + " name=None,\n", + " value=None,\n", + " coords=None,\n", + " parametertype=None,\n", + " valuetag=\"values\",\n", + " coordstag=\"coords\",\n", + "):\n", " root = self._get_root()\n", " parameterpath = \"./curves/curve\"\n", " parameterpointer = self._get_parameter_pointer(root, name, parameterpath)\n", " self._set_type_value(parameterpointer, value, parametertype, valuetag=valuetag)\n", " self._set_type_value(parameterpointer, coords, parametertype, valuetag=coordstag)\n", "\n", + "\n", "# define a method to change timstepping in project file\n", - "def set_timestepping(model,repeat_list, delta_t_list):\n", - " model.remove_element(xpath='./time_loop/processes/process/time_stepping/timesteps/pair')\n", + "\n", + "\n", + "def set_timestepping(model, repeat_list, delta_t_list):\n", + " model.remove_element(\n", + " xpath=\"./time_loop/processes/process/time_stepping/timesteps/pair\"\n", + " )\n", " for i in range(len(repeat_list)):\n", - " model.add_block(blocktag = 'pair',parent_xpath='./time_loop/processes/process/time_stepping/timesteps', taglist = ['repeat', 'delta_t'], textlist = [repeat_list[i], delta_t_list[i]])\n" + " model.add_block(\n", + " blocktag=\"pair\",\n", + " parent_xpath=\"./time_loop/processes/process/time_stepping/timesteps\",\n", + " taglist=[\"repeat\", \"delta_t\"],\n", + " textlist=[repeat_list[i], delta_t_list[i]],\n", + " )" ] }, { @@ -104,69 +124,92 @@ "metadata": {}, "outputs": [], "source": [ - "def ogs_ortho(phasefield_model, energy_split_model, length_scale = 1., bc_displacement = 1., ts_coords='0 1.0', values ='0 1.0', repeat_list=None, delta_t_list=None, hypre = True, MPI = True, ncores = 4):\n", + "def ogs_ortho(\n", + " phasefield_model,\n", + " energy_split_model,\n", + " length_scale=1.0,\n", + " bc_displacement=1.0,\n", + " ts_coords=\"0 1.0\",\n", + " values=\"0 1.0\",\n", + " repeat_list=None,\n", + " delta_t_list=None,\n", + " hypre=True,\n", + " MPI=True,\n", + " ncores=4,\n", + "):\n", + " without_hypre = \"-ksp_type cg -pc_type bjacobi -ksp_atol 1e-14 -ksp_rtol 1e-14\"\n", + " with_hypre = \"-ksp_type cg -pc_type hypre -pc_hypre_type boomeramg -pc_hypre_boomeramg_strong_threshold 0.7 -ksp_atol 1e-8 -ksp_rtol 1e-8\"\n", "\n", - " without_hypre='-ksp_type cg -pc_type bjacobi -ksp_atol 1e-14 -ksp_rtol 1e-14'\n", - " with_hypre='-ksp_type cg -pc_type hypre -pc_hypre_type boomeramg -pc_hypre_boomeramg_strong_threshold 0.7 -ksp_atol 1e-8 -ksp_rtol 1e-8'\n", - " \n", " prj_name = \"shear.prj\"\n", - " print(f\"> Running single edge notched shear test {phasefield_model} - {energy_split_model} ... <\")\n", + " print(\n", + " f\"> Running single edge notched shear test {phasefield_model} - {energy_split_model} ... <\"\n", + " )\n", " logfile = f\"{out_dir}/log_{phasefield_model}_{energy_split_model}.txt\"\n", " model = ogs.OGS(INPUT_FILE=prj_name, PROJECT_FILE=f\"{out_dir}/{prj_name}\", MKL=True)\n", - " \n", - " #generate prefix from properties\n", + "\n", + " # generate prefix from properties\n", " prefix = f\"{phasefield_model}\" + f\"_{energy_split_model}\"\n", "\n", " if MPI:\n", - " #partition mesh \n", + " # partition mesh\n", " ! NodeReordering -i shear.vtu -o {out_dir}/shear.vtu >> {logfile}\n", " ! constructMeshesFromGeometry -m {out_dir}/shear.vtu -g shear.gml >> {logfile}\n", - " shutil.move(\"shear_top.vtu\",f\"{out_dir}/shear_top.vtu\")\n", - " shutil.move(\"shear_bottom.vtu\",f\"{out_dir}/shear_bottom.vtu\")\n", - " shutil.move(\"shear_left.vtu\",f\"{out_dir}/shear_left.vtu\")\n", - " shutil.move(\"shear_right.vtu\",f\"{out_dir}/shear_right.vtu\")\n", - " \n", - " shutil.move(\"shear_p_0.vtu\",f\"{out_dir}/shear_p_0.vtu\")\n", - " shutil.move(\"shear_p_1.vtu\",f\"{out_dir}/shear_p_1.vtu\")\n", - " shutil.move(\"shear_p_2.vtu\",f\"{out_dir}/shear_p_2.vtu\")\n", - " shutil.move(\"shear_p_3.vtu\",f\"{out_dir}/shear_p_3.vtu\")\n", - " \n", + " shutil.move(\"shear_top.vtu\", f\"{out_dir}/shear_top.vtu\")\n", + " shutil.move(\"shear_bottom.vtu\", f\"{out_dir}/shear_bottom.vtu\")\n", + " shutil.move(\"shear_left.vtu\", f\"{out_dir}/shear_left.vtu\")\n", + " shutil.move(\"shear_right.vtu\", f\"{out_dir}/shear_right.vtu\")\n", + "\n", + " shutil.move(\"shear_p_0.vtu\", f\"{out_dir}/shear_p_0.vtu\")\n", + " shutil.move(\"shear_p_1.vtu\", f\"{out_dir}/shear_p_1.vtu\")\n", + " shutil.move(\"shear_p_2.vtu\", f\"{out_dir}/shear_p_2.vtu\")\n", + " shutil.move(\"shear_p_3.vtu\", f\"{out_dir}/shear_p_3.vtu\")\n", + "\n", " ! partmesh -s -o {out_dir} -i {out_dir}/shear.vtu >> {logfile}\n", " ! partmesh -m -n {ncores} -o {out_dir} -i {out_dir}/shear.vtu -- {out_dir}/shear_top.vtu {out_dir}/shear_bottom.vtu {out_dir}/shear_left.vtu {out_dir}/shear_right.vtu >> {logfile}\n", - " else :\n", + " else:\n", " ! NodeReordering -i shear.vtu -o {out_dir}/shear.vtu >> {logfile}\n", - " \n", - " #change some properties in prj file\n", + "\n", + " # change some properties in prj file\n", " model = ogs.OGS(INPUT_FILE=prj_name, PROJECT_FILE=f\"{out_dir}/{prj_name}\", MKL=True)\n", " model.replace_parameter_value(name=\"ls\", value=length_scale)\n", " model.replace_text(phasefield_model, xpath=\"./processes/process/phasefield_model\")\n", - " model.replace_text(energy_split_model, xpath=\"./processes/process/energy_split_model\")\n", + " model.replace_text(\n", + " energy_split_model, xpath=\"./processes/process/energy_split_model\"\n", + " )\n", " model.replace_text(prefix, xpath=\"./time_loop/output/prefix\")\n", - " \n", + "\n", " model.replace_parameter_value(name=\"dirichlet_top\", value=bc_displacement)\n", " model.replace_curve = MethodType(replace_curve, model)\n", " model.replace_curve(name=\"dirichlet_time\", value=values, coords=ts_coords)\n", "\n", - " if repeat_list != None and delta_t_list != None: \n", - " set_timestepping(model,repeat_list, delta_t_list)\n", + " if repeat_list != None and delta_t_list != None:\n", + " set_timestepping(model, repeat_list, delta_t_list)\n", " else:\n", - " set_timestepping(model,['1'], ['1e-2'])\n", + " set_timestepping(model, [\"1\"], [\"1e-2\"])\n", " if hypre == True:\n", - " model.replace_text(with_hypre, xpath='./linear_solvers/linear_solver/petsc/parameters',occurrence=1)\n", + " model.replace_text(\n", + " with_hypre,\n", + " xpath=\"./linear_solvers/linear_solver/petsc/parameters\",\n", + " occurrence=1,\n", + " )\n", " else:\n", - " model.replace_text(without_hypre, xpath='./linear_solvers/linear_solver/petsc/parameters', occurrence=1)\n", + " model.replace_text(\n", + " without_hypre,\n", + " xpath=\"./linear_solvers/linear_solver/petsc/parameters\",\n", + " occurrence=1,\n", + " )\n", " model.replace_text(\"./shear.gml\", xpath=\"./geometry\")\n", " model.write_input()\n", - " #run ogs\n", + " # run ogs\n", " t0 = time.time()\n", " if MPI:\n", " print(\" > OGS started execution with MPI - \" f\"{ncores} cores...\")\n", " ! mpirun -np {ncores} ogs {out_dir}/{prj_name} -o {output_dir} >> {logfile}\n", - " else :\n", + " else:\n", " print(\" > OGS started execution - \")\n", " ! ogs {out_dir}/{prj_name} -o {output_dir} >> {logfile}\n", " tf = time.time()\n", - " print(\" > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")\n" + " print(\" > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")" ] }, { @@ -215,16 +258,24 @@ "# phasefield_model = ['AT1', 'AT2']\n", "# energy_split_model = ['OrthoVolDev', 'OrthoMasonry']\n", "\n", - "disp = 1.e-6 # to change the intensity of the shear loading applied on the top edge\n", - "ls = 1.e-4 # regularization parameter to capture the convergence, though some references consider it as a material parameter (ls/h=4, h=2.5e-5)\n", + "disp = 1.0e-6 # to change the intensity of the shear loading applied on the top edge\n", + "ls = 1.0e-4 # regularization parameter to capture the convergence, though some references consider it as a material parameter (ls/h=4, h=2.5e-5)\n", "\n", - "mpi_cores = 4 # MPI cores\n", + "mpi_cores = 4 # MPI cores\n", "## Here we only run one selected case. Based on the user's local device, more/less cores can be added to speed up/save resources.\n", "\n", "# With the AT2 model, we are verifying two different anisotropic models, namely, orthotropic volumetric-deviatoric and orthotropic no-tension:\n", "# For more details of each model, please see the reference of Ziaei Rad et al., 2022.\n", "for b in [\"OrthoMasonry\", \"OrthoVolDev\"]:\n", - " ogs_ortho(\"AT2\", b, length_scale = ls, bc_displacement = disp, repeat_list=['1'], delta_t_list=['1.e-2'], ncores = mpi_cores)\n" + " ogs_ortho(\n", + " \"AT2\",\n", + " b,\n", + " length_scale=ls,\n", + " bc_displacement=disp,\n", + " repeat_list=[\"1\"],\n", + " delta_t_list=[\"1.e-2\"],\n", + " ncores=mpi_cores,\n", + " )" ] }, { @@ -255,8 +306,8 @@ "metadata": {}, "outputs": [], "source": [ - "from IPython.display import Image\n", "import pyvista as pv\n", + "\n", "reader = pv.get_reader(f\"{out_dir}/AT2_OrthoMasonry.pvd\")\n", "\n", "plotter = pv.Plotter()\n", @@ -266,23 +317,37 @@ "for time_value in reader.time_values:\n", " reader.set_active_time_value(time_value)\n", " mesh = reader.read()[0] # This dataset only has 1 block\n", - " \n", - " sargs=dict(title='Phase field', title_font_size=20, label_font_size=15, n_labels=5,\n", - " position_x=0.3, position_y=0.2, fmt=\"%.1f\", width=.5)\n", - " clim=[0, 1.]\n", + "\n", + " sargs = dict(\n", + " title=\"Phase field\",\n", + " title_font_size=20,\n", + " label_font_size=15,\n", + " n_labels=5,\n", + " position_x=0.3,\n", + " position_y=0.2,\n", + " fmt=\"%.1f\",\n", + " width=0.5,\n", + " )\n", + " clim = [0, 1.0]\n", " points = mesh.point_data[\"phasefield\"].shape[0]\n", - " xs = mesh.points[:,0]\n", - " ys = mesh.points[:,1]\n", + " xs = mesh.points[:, 0]\n", + " ys = mesh.points[:, 1]\n", " pf = mesh.point_data[\"phasefield\"]\n", " plotter.clear()\n", - " plotter.add_mesh(mesh, scalars=pf, show_scalar_bar=False, clim=clim, # colormap=\"coolwarm\"\n", - " scalar_bar_args=sargs, lighting=False)\n", + " plotter.add_mesh(\n", + " mesh,\n", + " scalars=pf,\n", + " show_scalar_bar=False,\n", + " clim=clim, # colormap=\"coolwarm\"\n", + " scalar_bar_args=sargs,\n", + " lighting=False,\n", + " )\n", " plotter.add_text(f\"Time: {time_value:.0f}\", color=\"black\")\n", "\n", " plotter.view_xy()\n", " plotter.write_frame()\n", "\n", - "plotter.close()\n" + "plotter.close()" ] }, { @@ -301,8 +366,6 @@ "metadata": {}, "outputs": [], "source": [ - "from matplotlib.colors import ListedColormap\n", - "\n", "reader = pv.get_reader(f\"{out_dir}/AT2_OrthoMasonry.pvd\")\n", "\n", "mesh = reader.read()[0]\n", @@ -318,9 +381,9 @@ ")\n", "\n", "p.view_xy()\n", - "p.camera.zoom(1.)\n", - "p.window_size = [800,400]\n", - "p.show()\n" + "p.camera.zoom(1.0)\n", + "p.window_size = [800, 400]\n", + "p.show()" ] }, { @@ -342,24 +405,36 @@ "# define function to obtain displacement applied on the top end of the square plate\n", "def displ_midpoint(filename):\n", " data = pv.read(filename)\n", - " max_y = max(data.points[:,1])\n", - " return np.mean(data.point_data[\"displacement\"][:,0], where= np.transpose(data.points[:,1]==max_y))\n", + " max_y = max(data.points[:, 1])\n", + " return np.mean(\n", + " data.point_data[\"displacement\"][:, 0],\n", + " where=np.transpose(data.points[:, 1] == max_y),\n", + " )\n", + "\n", "\n", "# define function to obtain force acting on the on the top end of the square plate from vtu file\n", + "\n", + "\n", "def force_midpoint(filename):\n", " data = pv.read(filename)\n", - " max_y = max(data.points[:,1])\n", - " return np.sum(data.point_data[\"NodalForces\"][:,0], where= np.transpose(data.points[:,1]==max_y))\n", + " max_y = max(data.points[:, 1])\n", + " return np.sum(\n", + " data.point_data[\"NodalForces\"][:, 0],\n", + " where=np.transpose(data.points[:, 1] == max_y),\n", + " )\n", "\n", - "# define function applying above-mentioned functions on all vtu files listed in the correspondent pvd file, \n", + "\n", + "# define function applying above-mentioned functions on all vtu files listed in the correspondent pvd file,\n", "# returning force-displacement curve\n", + "\n", + "\n", "def force_displ_from_pvd(pvd):\n", " doc = minidom.parse(pvd)\n", " DataSets = doc.getElementsByTagName(\"DataSet\")\n", " vtu_files = [x.getAttribute(\"file\") for x in DataSets]\n", " forces_sum = [force_midpoint(f\"{out_dir}/{x}\") for x in vtu_files]\n", " displs_mean = [displ_midpoint(f\"{out_dir}/{x}\") for x in vtu_files]\n", - " return [displs_mean, forces_sum]\n" + " return [displs_mean, forces_sum]" ] }, { @@ -370,28 +445,36 @@ "outputs": [], "source": [ "# AT2_OrthoVolDev.pvd\n", - "prefixes = ['AT2_OrthoVolDev', 'AT2_OrthoMasonry']\n", - "labels = [r'volumetric--deviatoric', r'no-tension']\n", - "ls=['-','--']\n", - "colors = ['#ffdf4d', '#006ddb']\n", + "prefixes = [\"AT2_OrthoVolDev\", \"AT2_OrthoMasonry\"]\n", + "labels = [r\"volumetric--deviatoric\", r\"no-tension\"]\n", + "ls = [\"-\", \"--\"]\n", + "colors = [\"#ffdf4d\", \"#006ddb\"]\n", "\n", "fig, ax = plt.subplots()\n", - "plt.rc('text', usetex=True)\n", + "plt.rc(\"text\", usetex=True)\n", "fig.set_size_inches(18.5, 10.5)\n", - "for i,pre in enumerate(prefixes):\n", + "for i, pre in enumerate(prefixes):\n", " pvd = f\"{out_dir}/{pre}.pvd\"\n", - " if os.path.isfile(pvd) :\n", + " if os.path.isfile(pvd):\n", " curve = force_displ_from_pvd(pvd)\n", - " ax.plot(curve[0],curve[1],ls[i%2], label = labels[i],linewidth=5, color = colors[i], alpha= 1)\n", - "\n", - "plt.rcParams['xtick.labelsize'] = 16 \n", - "plt.rcParams['ytick.labelsize'] = 16 \n", - "ax.grid(linestyle='dashed') \n", - "ax.set_xlabel('$\\Delta [m]$',fontsize =18)\n", - "ax.set_ylabel('$F_y [N]$',fontsize =18)\n", - "plt.legend(fontsize =18, ncol = 2)\n", - "ax.axhline(y = 0, color = 'black',linewidth=1) \n", - "ax.axvline(x = 0, color = 'black',linewidth=1)\n" + " ax.plot(\n", + " curve[0],\n", + " curve[1],\n", + " ls[i % 2],\n", + " label=labels[i],\n", + " linewidth=5,\n", + " color=colors[i],\n", + " alpha=1,\n", + " )\n", + "\n", + "plt.rcParams[\"xtick.labelsize\"] = 16\n", + "plt.rcParams[\"ytick.labelsize\"] = 16\n", + "ax.grid(linestyle=\"dashed\")\n", + "ax.set_xlabel(r\"$\\Delta [m]$\", fontsize=18)\n", + "ax.set_ylabel(\"$F_y [N]$\", fontsize=18)\n", + "plt.legend(fontsize=18, ncol=2)\n", + "ax.axhline(y=0, color=\"black\", linewidth=1)\n", + "ax.axvline(x=0, color=\"black\", linewidth=1)" ] }, { diff --git a/Tests/Data/PhaseField/beam_jupyter_notebook/beam.ipynb b/Tests/Data/PhaseField/beam_jupyter_notebook/beam.ipynb index a6eaa167e28..8c1b9b5afb9 100644 --- a/Tests/Data/PhaseField/beam_jupyter_notebook/beam.ipynb +++ b/Tests/Data/PhaseField/beam_jupyter_notebook/beam.ipynb @@ -52,15 +52,15 @@ "metadata": {}, "outputs": [], "source": [ - "from ogs6py import ogs\n", "import os\n", - "import ogs6py\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pyvista as pv\n", "import time\n", + "from types import MethodType\n", "from xml.dom import minidom\n", - "from types import MethodType\n" + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pyvista as pv\n", + "from ogs6py import ogs" ] }, { @@ -70,26 +70,47 @@ "metadata": {}, "outputs": [], "source": [ - "data_dir = os.environ.get('OGS_DATA_DIR', '../../..')\n", - "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n", + "data_dir = os.environ.get(\"OGS_DATA_DIR\", \"../../..\")\n", + "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", "if not os.path.exists(out_dir):\n", - " os.makedirs(out_dir) \n", + " os.makedirs(out_dir)\n", "\n", - "output_dir= out_dir\n", + "output_dir = out_dir\n", "\n", - "# define method to be assigned to model, to replace a specific curve, given by name \n", + "# define method to be assigned to model, to replace a specific curve, given by name\n", "# (analogue to replace_parameter method)\n", - "def replace_curve(self, name=None, value=None, coords=None, parametertype=None, valuetag=\"values\", coordstag=\"coords\"):\n", + "\n", + "\n", + "def replace_curve(\n", + " self,\n", + " name=None,\n", + " value=None,\n", + " coords=None,\n", + " parametertype=None,\n", + " valuetag=\"values\",\n", + " coordstag=\"coords\",\n", + "):\n", " root = self._get_root()\n", " parameterpath = \"./curves/curve\"\n", " parameterpointer = self._get_parameter_pointer(root, name, parameterpath)\n", " self._set_type_value(parameterpointer, value, parametertype, valuetag=valuetag)\n", " self._set_type_value(parameterpointer, coords, parametertype, valuetag=coordstag)\n", - "# define method to change timstepping in project file \n", - "def set_timestepping(model,repeat_list, delta_t_list):\n", - " model.remove_element(xpath='./time_loop/processes/process/time_stepping/timesteps/pair')\n", + "\n", + "\n", + "# define method to change timstepping in project file\n", + "\n", + "\n", + "def set_timestepping(model, repeat_list, delta_t_list):\n", + " model.remove_element(\n", + " xpath=\"./time_loop/processes/process/time_stepping/timesteps/pair\"\n", + " )\n", " for i in range(len(repeat_list)):\n", - " model.add_block(blocktag = 'pair',parent_xpath='./time_loop/processes/process/time_stepping/timesteps', taglist = ['repeat', 'delta_t'], textlist = [repeat_list[i], delta_t_list[i]])\n" + " model.add_block(\n", + " blocktag=\"pair\",\n", + " parent_xpath=\"./time_loop/processes/process/time_stepping/timesteps\",\n", + " taglist=[\"repeat\", \"delta_t\"],\n", + " textlist=[repeat_list[i], delta_t_list[i]],\n", + " )" ] }, { @@ -107,65 +128,91 @@ "metadata": {}, "outputs": [], "source": [ - "def ogs_beam(phasefield_model, energy_split_model, mesh_size = 0.01, length_scale = 0.02, bc_displacement = 5, ts_coords='0 0.05 1', values ='0 0.25 1', repeat_list=None, delta_t_list=None, hypre = False):\n", - " ##phasefield_model: 'AT1' or 'AT2' \n", + "def ogs_beam(\n", + " phasefield_model,\n", + " energy_split_model,\n", + " mesh_size=0.01,\n", + " length_scale=0.02,\n", + " bc_displacement=5,\n", + " ts_coords=\"0 0.05 1\",\n", + " values=\"0 0.25 1\",\n", + " repeat_list=None,\n", + " delta_t_list=None,\n", + " hypre=False,\n", + "):\n", + " ##phasefield_model: 'AT1' or 'AT2'\n", " ##energy_split_model: 'VolumetricDeviatoric' or 'Isotropic'\n", - " \n", - " without_hypre='-ksp_type cg -pc_type bjacobi -ksp_atol 1e-14 -ksp_rtol 1e-14'\n", - " with_hypre='-ksp_type cg -pc_type hypre -pc_hypre_type boomeramg -pc_hypre_boomeramg_strong_threshold 0.7 -ksp_atol 1e-8 -ksp_rtol 1e-8' \n", - " #file's name\n", + "\n", + " without_hypre = \"-ksp_type cg -pc_type bjacobi -ksp_atol 1e-14 -ksp_rtol 1e-14\"\n", + " with_hypre = \"-ksp_type cg -pc_type hypre -pc_hypre_type boomeramg -pc_hypre_boomeramg_strong_threshold 0.7 -ksp_atol 1e-8 -ksp_rtol 1e-8\"\n", + " # file's name\n", " prj_name = \"beam.prj\"\n", " print(f\"> Running beam model {phasefield_model} – {energy_split_model} ... <\")\n", " logfile = f\"{out_dir}/log_{phasefield_model}_{energy_split_model}.txt\"\n", - " #beam dimensions\n", + " # beam dimensions\n", " beam_height = 0.05\n", " beam_depth = beam_height\n", - " beam_length = 1.\n", - " #mesh properties\n", - " h = mesh_size # distance between nodes\n", + " beam_length = 1.0\n", + " # mesh properties\n", + " h = mesh_size # distance between nodes\n", " ls = length_scale\n", - " #generate prefix from properties\n", - " if energy_split_model == 'VolumetricDeviatoric':\n", - " prefix = phasefield_model + '_vd'\n", - " elif energy_split_model == 'Isotropic':\n", - " prefix = phasefield_model + '_iso'\n", - " else :\n", - " raise ValueError('\"'+energy_split_model+'\"'+' is no valid input for energy_split_model, choose between \"VolumetricDeviatoric\" and \"Isotropic\"') \n", + " # generate prefix from properties\n", + " if energy_split_model == \"VolumetricDeviatoric\":\n", + " prefix = phasefield_model + \"_vd\"\n", + " elif energy_split_model == \"Isotropic\":\n", + " prefix = phasefield_model + \"_iso\"\n", + " else:\n", + " raise ValueError(\n", + " '\"'\n", + " + energy_split_model\n", + " + '\"'\n", + " + ' is no valid input for energy_split_model, choose between \"VolumetricDeviatoric\" and \"Isotropic\"'\n", + " )\n", " if bc_displacement > 0:\n", - " prefix = prefix + '_tensile'\n", - " else :\n", - " prefix = prefix + '_compressive'\n", - " #generate mesh \n", + " prefix = prefix + \"_tensile\"\n", + " else:\n", + " prefix = prefix + \"_compressive\"\n", + " # generate mesh\n", " ! generateStructuredMesh -o {out_dir}/bar_.vtu -e hex --lx {beam_length} --nx {round(beam_length/h)} --ly {beam_height} --ny {round(beam_height/h)} --lz {beam_depth} --nz {round(beam_depth/h)} > {logfile}\n", " ! NodeReordering -i {out_dir}/bar_.vtu -o {out_dir}/bar.vtu >> {logfile}\n", " ! ExtractSurface -i {out_dir}/bar.vtu -o {out_dir}/bar_left.vtu -x 1 -y 0 -z 0 >> {logfile}\n", " ! ExtractSurface -i {out_dir}/bar.vtu -o {out_dir}/bar_right.vtu -x -1 -y 0 -z 0 >> {logfile}\n", " ! partmesh -s -o {out_dir} -i {out_dir}/bar.vtu >> {logfile}\n", " ! partmesh -m -n 3 -o {out_dir} -i {out_dir}/bar.vtu -- {out_dir}/bar_right.vtu {out_dir}/bar_left.vtu >> {logfile}\n", - " #change properties in prj file\n", + " # change properties in prj file\n", " model = ogs.OGS(INPUT_FILE=prj_name, PROJECT_FILE=f\"{out_dir}/{prj_name}\", MKL=True)\n", " model.replace_parameter_value(name=\"ls\", value=ls)\n", " model.replace_text(phasefield_model, xpath=\"./processes/process/phasefield_model\")\n", - " model.replace_text(energy_split_model, xpath=\"./processes/process/energy_split_model\")\n", + " model.replace_text(\n", + " energy_split_model, xpath=\"./processes/process/energy_split_model\"\n", + " )\n", " model.replace_text(prefix, xpath=\"./time_loop/output/prefix\")\n", " model.replace_parameter_value(name=\"dirichlet_right\", value=bc_displacement)\n", " model.replace_curve = MethodType(replace_curve, model)\n", - " model.replace_curve(name=\"dirichlet_time\",value=values, coords=ts_coords)\n", - " if repeat_list != None and delta_t_list != None: \n", - " set_timestepping(model,repeat_list, delta_t_list)\n", + " model.replace_curve(name=\"dirichlet_time\", value=values, coords=ts_coords)\n", + " if repeat_list != None and delta_t_list != None:\n", + " set_timestepping(model, repeat_list, delta_t_list)\n", " else:\n", - " set_timestepping(model,['1'], ['1e-2']) \n", + " set_timestepping(model, [\"1\"], [\"1e-2\"])\n", " if hypre == True:\n", - " model.replace_text(with_hypre, xpath='./linear_solvers/linear_solver/petsc/parameters',occurrence=1)\n", + " model.replace_text(\n", + " with_hypre,\n", + " xpath=\"./linear_solvers/linear_solver/petsc/parameters\",\n", + " occurrence=1,\n", + " )\n", " else:\n", - " model.replace_text(without_hypre, xpath='./linear_solvers/linear_solver/petsc/parameters', occurrence=1)\n", + " model.replace_text(\n", + " without_hypre,\n", + " xpath=\"./linear_solvers/linear_solver/petsc/parameters\",\n", + " occurrence=1,\n", + " )\n", " model.write_input()\n", - " #run ogs\n", + " # run ogs\n", " t0 = time.time()\n", " print(\" > OGS started execution ...\")\n", " ! mpirun -n 3 ogs {out_dir}/{prj_name} -o {output_dir} >> {logfile}\n", " tf = time.time()\n", - " print(\" > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")\n" + " print(\" > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")" ] }, { @@ -204,15 +251,15 @@ "metadata": {}, "outputs": [], "source": [ - "pf_ms = ['AT1', 'AT2']\n", - "es_ms = ['VolumetricDeviatoric','Isotropic']\n", - "displ = [4.,-4.]\n", - "'''\n", + "pf_ms = [\"AT1\", \"AT2\"]\n", + "es_ms = [\"VolumetricDeviatoric\", \"Isotropic\"]\n", + "displ = [4.0, -4.0]\n", + "\"\"\"\n", "for a in pf_ms:\n", " for c in displ:\n", " ogs_beam(a,es_ms[1],bc_displacement = c,mesh_size = 0.01, length_scale = 0.03)\n", "ogs_beam(pf_ms[1],es_ms[0],bc_displacement = 4,mesh_size = 0.01, length_scale = 0.03)\n", - " \n", + "\n", "# run AT1_vd_tensile with smaller timesteps in critical time range\n", "ogs_beam(pf_ms[0],es_ms[0],bc_displacement = 5,mesh_size = 0.01, length_scale = 0.03,repeat_list=['62','2','20','1'], delta_t_list=['1e-2','1e-3','1e-4','1e-2'])\n", "\n", @@ -229,18 +276,34 @@ "model = ogs.OGS(INPUT_FILE=prj_path+prj_name, PROJECT_FILE=prj_path+prj_name, MKL=True)\n", "model.replace_text('1e-14', xpath=\"./time_loop/processes/process/convergence_criterion/reltol\",occurrence=0)\n", "model.write_input()\n", - "''' \n", + "\"\"\"\n", "## run only cases easy to handle with coarse timestepping:\n", "for a in pf_ms:\n", " for b in es_ms:\n", " for c in displ:\n", - " if a == 'AT1' and b == 'VolumetricDeviatoric':\n", + " if a == \"AT1\" and b == \"VolumetricDeviatoric\":\n", " continue\n", - " if a == 'AT2' and b == 'VolumetricDeviatoric' and c < 0:\n", - " ogs_beam(a,b,bc_displacement = c,mesh_size = 0.01,length_scale = 0.03, hypre = True, repeat_list=['1'],delta_t_list=['1e-1'])\n", + " if a == \"AT2\" and b == \"VolumetricDeviatoric\" and c < 0:\n", + " ogs_beam(\n", + " a,\n", + " b,\n", + " bc_displacement=c,\n", + " mesh_size=0.01,\n", + " length_scale=0.03,\n", + " hypre=True,\n", + " repeat_list=[\"1\"],\n", + " delta_t_list=[\"1e-1\"],\n", + " )\n", " else:\n", - " ogs_beam(a,b,bc_displacement = c,mesh_size = 0.01,length_scale = 0.03, repeat_list=['1'],delta_t_list=['1e-1'])\n", - " " + " ogs_beam(\n", + " a,\n", + " b,\n", + " bc_displacement=c,\n", + " mesh_size=0.01,\n", + " length_scale=0.03,\n", + " repeat_list=[\"1\"],\n", + " delta_t_list=[\"1e-1\"],\n", + " )" ] }, { @@ -275,23 +338,36 @@ "def displ_right(filename):\n", " data = pv.read(filename)\n", " data.point_data[\"displacement\"]\n", - " max_x = max(data.points[:,0])\n", - " return np.mean(data.point_data[\"displacement\"][:,0], where= np.transpose(data.points[:,0]==max_x))\n", + " max_x = max(data.points[:, 0])\n", + " return np.mean(\n", + " data.point_data[\"displacement\"][:, 0],\n", + " where=np.transpose(data.points[:, 0] == max_x),\n", + " )\n", + "\n", + "\n", "# define fuction to obtain force acting on the right end of the beam from vtu file\n", + "\n", + "\n", "def force_right(filename):\n", " data = pv.read(filename)\n", " data.point_data[\"NodalForces\"]\n", - " max_x = max(data.points[:,0])\n", - " return np.sum(data.point_data[\"NodalForces\"][:,0], where= np.transpose(data.points[:,0]==max_x))\n", + " max_x = max(data.points[:, 0])\n", + " return np.sum(\n", + " data.point_data[\"NodalForces\"][:, 0],\n", + " where=np.transpose(data.points[:, 0] == max_x),\n", + " )\n", + "\n", + "\n", "# define function applying obove functions on all vtu file listet in pvd file, returning force-displacement curve\n", + "\n", + "\n", "def force_displ_from_pvd(pvd):\n", " doc = minidom.parse(pvd)\n", " DataSets = doc.getElementsByTagName(\"DataSet\")\n", " vtu_files = [x.getAttribute(\"file\") for x in DataSets]\n", " forces_right = [force_right(f\"{out_dir}/{x}\") for x in vtu_files]\n", " displs_right = [displ_right(f\"{out_dir}/{x}\") for x in vtu_files]\n", - " return [forces_right,displs_right]\n", - "\n" + " return [forces_right, displs_right]" ] }, { @@ -309,29 +385,64 @@ "metadata": {}, "outputs": [], "source": [ - "prefixes = ['AT1_vd_tensile','AT1_iso_tensile','AT2_vd_tensile','AT2_iso_tensile', 'AT1_vd_compressive', 'AT1_iso_compressive', 'AT2_vd_compressive', 'AT2_iso_compressive']\n", - "labels = [r'$\\texttt{AT}_1$ vol-dev tensile',r'$\\texttt{AT}_1$ iso tensile',r'$\\texttt{AT}_2$ vol-dev tensile',r'$\\texttt{AT}_2$ iso tensile', r'$\\texttt{AT}_1$ vol-dev compressive', r'$\\texttt{AT}_1$ iso compressive', r'$\\texttt{AT}_2$ vol-dev compressive', r'$\\texttt{AT}_2$ iso compressive']\n", - "ls=['-','--']\n", - "colors = ['#ffdf4d','#006ddb','#8f4e00','#ff6db6','#920000','#b66dff','#db6d00','#490092']\n", + "prefixes = [\n", + " \"AT1_vd_tensile\",\n", + " \"AT1_iso_tensile\",\n", + " \"AT2_vd_tensile\",\n", + " \"AT2_iso_tensile\",\n", + " \"AT1_vd_compressive\",\n", + " \"AT1_iso_compressive\",\n", + " \"AT2_vd_compressive\",\n", + " \"AT2_iso_compressive\",\n", + "]\n", + "labels = [\n", + " r\"$\\texttt{AT}_1$ vol-dev tensile\",\n", + " r\"$\\texttt{AT}_1$ iso tensile\",\n", + " r\"$\\texttt{AT}_2$ vol-dev tensile\",\n", + " r\"$\\texttt{AT}_2$ iso tensile\",\n", + " r\"$\\texttt{AT}_1$ vol-dev compressive\",\n", + " r\"$\\texttt{AT}_1$ iso compressive\",\n", + " r\"$\\texttt{AT}_2$ vol-dev compressive\",\n", + " r\"$\\texttt{AT}_2$ iso compressive\",\n", + "]\n", + "ls = [\"-\", \"--\"]\n", + "colors = [\n", + " \"#ffdf4d\",\n", + " \"#006ddb\",\n", + " \"#8f4e00\",\n", + " \"#ff6db6\",\n", + " \"#920000\",\n", + " \"#b66dff\",\n", + " \"#db6d00\",\n", + " \"#490092\",\n", + "]\n", "\n", "fig, ax = plt.subplots()\n", - "plt.rc('text', usetex=True)\n", + "plt.rc(\"text\", usetex=True)\n", "fig.set_size_inches(18.5, 10.5)\n", - "for i,pre in enumerate(prefixes):\n", + "for i, pre in enumerate(prefixes):\n", " pvd = f\"{output_dir}/{pre}.pvd\"\n", - " if os.path.isfile(pvd) :\n", + " if os.path.isfile(pvd):\n", " curve = force_displ_from_pvd(pvd)\n", - " ax.plot(curve[1],curve[0],ls[i%2], label = labels[i],linewidth=5, color = colors[i], alpha= 1)\n", - " \n", - "plt.rcParams['xtick.labelsize'] = 16 \n", - "plt.rcParams['ytick.labelsize'] = 16 \n", - "ax.grid(linestyle='dashed') \n", - "ax.set_xlabel('$\\Delta [m]$',fontsize =18)\n", - "ax.set_ylabel('$F_y [N]$',fontsize =18)\n", - "plt.legend(fontsize =18, ncol = 2)\n", - "ax.axhline(y = 0, color = 'black',linewidth=1) \n", - "ax.axvline(x = 0, color = 'black',linewidth=1) \n", - "ax.set_xlim(-4.5, 4.5);\n" + " ax.plot(\n", + " curve[1],\n", + " curve[0],\n", + " ls[i % 2],\n", + " label=labels[i],\n", + " linewidth=5,\n", + " color=colors[i],\n", + " alpha=1,\n", + " )\n", + "\n", + "plt.rcParams[\"xtick.labelsize\"] = 16\n", + "plt.rcParams[\"ytick.labelsize\"] = 16\n", + "ax.grid(linestyle=\"dashed\")\n", + "ax.set_xlabel(r\"$\\Delta [m]$\", fontsize=18)\n", + "ax.set_ylabel(\"$F_y [N]$\", fontsize=18)\n", + "plt.legend(fontsize=18, ncol=2)\n", + "ax.axhline(y=0, color=\"black\", linewidth=1)\n", + "ax.axvline(x=0, color=\"black\", linewidth=1)\n", + "ax.set_xlim(-4.5, 4.5)" ] }, { diff --git a/Tests/Data/PhaseField/kregime_jupyter_notebook/Kregime_Static_jupyter.ipynb b/Tests/Data/PhaseField/kregime_jupyter_notebook/Kregime_Static_jupyter.ipynb index 0b364fb9812..a1e79862fe5 100644 --- a/Tests/Data/PhaseField/kregime_jupyter_notebook/Kregime_Static_jupyter.ipynb +++ b/Tests/Data/PhaseField/kregime_jupyter_notebook/Kregime_Static_jupyter.ipynb @@ -80,17 +80,17 @@ "metadata": {}, "outputs": [], "source": [ - "from ogs6py import ogs\n", - "import numpy as np\n", - "import ogs6py\n", - "import matplotlib.pyplot as plt\n", - "import time\n", "import math\n", - "import gmsh\n", "import os\n", + "import time\n", + "\n", + "import gmsh\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from ogs6py import ogs\n", "\n", "pi = math.pi\n", - "plt.rcParams[\"text.usetex\"] = True\n" + "plt.rcParams[\"text.usetex\"] = True" ] }, { @@ -112,7 +112,7 @@ " 2 * (round(3.0 * a0 / h)) + 1\n", ") # number of slices for calcute width of fracture\n", "\n", - "phasefield_model = \"AT1\"\n" + "phasefield_model = \"AT1\"" ] }, { @@ -122,7 +122,7 @@ "outputs": [], "source": [ "h_list = [0.01] # list of mesh sizes (h)\n", - "# h_list =[0.01, 0.005, 0.0025] # list of mesh sizes (h), for mesh sensitivity\n" + "# h_list =[0.01, 0.005, 0.0025] # list of mesh sizes (h), for mesh sensitivity" ] }, { @@ -143,7 +143,7 @@ "meshname = \"mesh_full_pf\"\n", "\n", "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", - "os.makedirs(out_dir, exist_ok=True)\n" + "os.makedirs(out_dir, exist_ok=True)" ] }, { @@ -241,7 +241,7 @@ " output_file = f\"{out_dir}/\" + meshname + \".msh\"\n", " gmsh.model.mesh.generate(dim2)\n", " gmsh.write(output_file)\n", - " gmsh.finalize()\n" + " gmsh.finalize()" ] }, { @@ -272,7 +272,7 @@ " phase_field[node_id] = 0.0\n", "\n", " mesh.point_data[\"phase-field\"] = phase_field\n", - " mesh.save(f\"{out_dir}/mesh_full_pf_OGS_pf_ic.vtu\")\n" + " mesh.save(f\"{out_dir}/mesh_full_pf_OGS_pf_ic.vtu\")" ] }, { @@ -289,34 +289,41 @@ "outputs": [], "source": [ "import pyvista as pv\n", + "\n", "pv.set_plot_theme(\"document\")\n", "if \"PYVISTA_HEADLESS\" in os.environ:\n", " pv.start_xvfb()\n", "pv.set_jupyter_backend(\"static\")\n", "\n", + "\n", "def sneddon_numerical(h):\n", - " #mesh properties\n", - " ls = 2*h\n", - " #generate prefix from properties\n", - " filename = 'results_h_%0.4f'%h\n", + " # mesh properties\n", + " ls = 2 * h\n", + " # generate prefix from properties\n", + " filename = \"results_h_%0.4f\" % h\n", " mesh_generation(0.1, h)\n", " # Convert GMSH (.msh) meshes to VTU meshes appropriate for OGS simulation.\n", - " input_file = f\"{out_dir}/\"+meshname+\".msh\"\n", - " ! msh2vtu --ogs -o {out_dir}/{meshname} {input_file} \n", + " input_file = f\"{out_dir}/\" + meshname + \".msh\"\n", + " ! msh2vtu --ogs -o {out_dir}/{meshname} {input_file}\n", " # As a preprocessing step, define the initial phase-field (crack).\n", - " pre_processing(h,a0)\n", - " #change properties in prj file #For more information visit: https://github.com/joergbuchwald/ogs6py\n", - " model = ogs.OGS(INPUT_FILE=prj_name, PROJECT_FILE=f\"{out_dir}/{prj_name}\", MKL=True, args=f\"-o {out_dir}\")\n", + " pre_processing(h, a0)\n", + " # change properties in prj file #For more information visit: https://github.com/joergbuchwald/ogs6py\n", + " model = ogs.OGS(\n", + " INPUT_FILE=prj_name,\n", + " PROJECT_FILE=f\"{out_dir}/{prj_name}\",\n", + " MKL=True,\n", + " args=f\"-o {out_dir}\",\n", + " )\n", " model.replace_parameter_value(name=\"ls\", value=ls)\n", " model.replace_text(\"./Kregime_Static.gml\", xpath=\"./geometry\")\n", " model.replace_text(filename, xpath=\"./time_loop/output/prefix\")\n", - " model.write_input() \n", - " #run simulation with ogs\n", + " model.write_input()\n", + " # run simulation with ogs\n", " t0 = time.time()\n", " print(\">>> OGS started execution ... <<<\")\n", " !ogs {out_dir}/{prj_name} -o {out_dir} > {out_dir}/log.txt\n", " tf = time.time()\n", - " print(\">>> OGS terminated execution <<< Elapsed time: \", round(tf - t0, 2), \" s.\")\n" + " print(\">>> OGS terminated execution <<< Elapsed time: \", round(tf - t0, 2), \" s.\")" ] }, { @@ -342,7 +349,7 @@ ], "source": [ "for h_j in h_list:\n", - " sneddon_numerical(h=h_j)\n" + " sneddon_numerical(h=h_j)" ] }, { @@ -427,7 +434,7 @@ " (point[0] - point_a[0]) ** 2 + (point[1] - point_a[1]) ** 2\n", " ) ** 0.5 - dist_a_b / 2\n", "\n", - " return r_i, width_line\n" + " return r_i, width_line" ] }, { @@ -457,7 +464,7 @@ " 2 * a_eff * (1 - nu**2) * P / E * math.sqrt(1.0 - ((x[i]) / (a_eff)) ** 2)\n", " )\n", "\n", - " return x, uy, a_eff\n" + " return x, uy, a_eff" ] }, { @@ -495,7 +502,7 @@ "Label = [\"Closed form solution\", \"VPF-FEM\"]\n", "lineWIDTH = [1.5, 1.5, 1.5]\n", "\n", - "for (j, h_j) in enumerate(h_list):\n", + "for j, h_j in enumerate(h_list):\n", " r_i_num = []\n", " width_line_num = []\n", " filename = \"results_h_%0.4f\" % h_j\n", @@ -535,7 +542,7 @@ "plt.title(\"%s\" % phasefield_model)\n", "\n", "legend = plt.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n", - "plt.show()\n" + "plt.show()" ] }, { diff --git a/Tests/Data/PhaseField/surfing/Surfing_python.py b/Tests/Data/PhaseField/surfing/Surfing_python.py index 20acb99bc57..e99c3c562ad 100644 --- a/Tests/Data/PhaseField/surfing/Surfing_python.py +++ b/Tests/Data/PhaseField/surfing/Surfing_python.py @@ -2,7 +2,7 @@ import ogs.callbacks as OpenGeoSys except ModuleNotFoundError: import OpenGeoSys -from math import pi, sin, cos, atan2, sqrt +from math import atan2, cos, pi, sin, sqrt a = 2.0 # Length b = 1.0 # Height diff --git a/Tests/Data/PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb b/Tests/Data/PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb index e1da2db3749..0ce1825d0a9 100644 --- a/Tests/Data/PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb +++ b/Tests/Data/PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb @@ -1,755 +1,865 @@ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "+++\n", - "author = \"Mostafa Mollaali, Keita Yoshioka\"\n", - "date = \"2022-06-28\"\n", - "title = \"Surfing boundary\"\n", - "web_subsection = \"phase-field\"\n", - "+++\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Problem description\n", - "\n", - "Consider a plate, $\\Omega=[0,2]\\times [-0.5,0.5]$, with an explicit edge crack, $\\Gamma=[0,0.5]\\times \\{0\\}$; that is subjected to a time dependent crack opening displacement:\n", - "\n", - "\\begin{eqnarray}\n", - "\t\\label{eq:surfing_bc}\n", - "\t\\mathbf{u}(x,y,t)= \\mathbf{U}(x-\\text{v}t,y) \\quad \\text{on} \\quad \\partial\\Omega_D,\n", - "\\end{eqnarray}\n", - "where $\\text{v}$ is an imposed loading velocity; and $\\mathbf{U}$ is the asymptotic solution for the Mode-I crack opening displacement\n", - "\\begin{eqnarray}\n", - "\t\\label{eq:asymptotic}\n", - "\tU_x= \\dfrac{K_I}{2\\mu} \\sqrt{\\dfrac{r}{2\\pi}} (\\kappa-\\cos \\varphi) \\cos \\frac{\\varphi}{2}, \\nonumber\n", - "\t\\\\\n", - "\tU_y= \\dfrac{K_I}{2\\mu} \\sqrt{\\dfrac{r}{2\\pi}} (\\kappa-\\cos \\varphi) \\sin \\frac{\\varphi}{2},\n", - "\\end{eqnarray}\n", - "\n", - "\n", - "where $K_I$ is the stress intensity factor, $\\kappa=(3-\\nu)/(1+\\nu)$ and $\\mu=E / 2 (1 + \\nu) $; $(r,\\varphi)$ are the polar coordinate system, where the origin is crack tip.\n", - "Also, we used $G_\\mathrm{c}=K_{Ic}^2(1-\\nu^2)/E$ as the fracture surface energy under plane strain condition.\n", - "Table 1 lists the material properties and geometry of the numerical model.\n", - "\n", - "![Schematic view of surfing boundary condition benchmark](./figures/surfing_schematic.png#one-half \"Schematic view of surfing boundary condition benchmark.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Input Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "| **Name** | **Value** | **Unit** | **Symbol** |\n", - "|--------------------------------|--------------------|--------------|------------|\n", - "| _Young's modulus_ | 210x$10^3$ | MPa | $E$ |\n", - "| _Critical energy release rate_ | 2.7 | MPa$\\cdot$mm | $G_{c}$ |\n", - "| _Poisson's ratio_ | 0.3 | $-$ | $\\nu$ |\n", - "| _Regularization parameter_ | 2$h$ | mm | $\\ell_s$ |\n", - "| _Imposed loading velocity_ | 1.5 | mm/s | $\\text{v}$ |\n", - "| _Length_ | $2$ | mm | $L$ |\n", - "| _Height_ | $1$ | mm | $H$ |\n", - "| _Initial crack length_ | $0.5$ | mm | $a_0$ |" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "x_tip_Initial=0.5\n", - "y_tip_Initial=0.5\n", - "Height =1.\n", - "\n", - "Orientation=0\n", - "h = 0.05\n", - "G_i = 2.7\n", - "ls = 2*h\n", - "# We set ls=2h in our simulation\n", - "phasefield_model='AT1'# AT1 and AT2 \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Paths and project file name" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "# file's name\n", - "prj_name = \"surfing.prj\"\n", - "\n", - "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n", - "if not os.path.exists(out_dir):\n", - " os.makedirs(out_dir)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Mesh generation" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2022-10-25 10:09:52.705] [ogs] [\u001b[32minfo\u001b[m] Mesh created: 924 nodes, 861 elements.\n", - "[2022-10-25 10:09:52.988] [ogs] [\u001b[32minfo\u001b[m] Reordering nodes... \n", - "[2022-10-25 10:09:52.989] [ogs] [\u001b[32minfo\u001b[m] Corrected 0 elements.\n", - "[2022-10-25 10:09:52.991] [ogs] [\u001b[32minfo\u001b[m] VTU file written.\n" - ] - } - ], - "source": [ - "# https://www.opengeosys.org/docs/tools/meshing/structured-mesh-generation/\n", - "! generateStructuredMesh -o {out_dir}/surfing_quad_1x2.vtu -e quad --lx 2 --nx {round(2/h)+1} --ly 1 --ny {round(1/h)+1}\n", - "! NodeReordering -i {out_dir}/surfing_quad_1x2.vtu -o {out_dir}/surfing_quad_1x2_NR.vtu\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pre-processing \n", - "At fracture, we set the initial phase field to zero." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pyvista as pv\n", - "pv.set_plot_theme(\"document\")\n", - "pv.set_jupyter_backend(\"static\")\n", - "\n", - "import numpy as np\n", - "\n", - "mesh = pv.read(f\"{out_dir}/surfing_quad_1x2_NR.vtu\")\n", - "phase_field = np.ones((len(mesh.points),1))\n", - "\n", - "\n", - "for node_id, x in enumerate(mesh.points):\n", - " if x[0]< x_tip_Initial+h/10 and x[1] < Height/2+h and x[1] > Height/2-h:\n", - " phase_field[node_id] = 0.0\n", - " \n", - "mesh.point_data['pf-ic'] = phase_field\n", - "mesh.save(f\"{out_dir}/surfing_quad_1x2_NR_pf_ic.vtu\")\n", - "\n", - "pf_ic = mesh.point_data[\"pf-ic\"]\n", - "sargs=dict(title='pf-ic', title_font_size=20, label_font_size=15, n_labels=5,\n", - " position_x=0.24, position_y=0.0, fmt=\"%.1f\", width=.5)\n", - "clim=[0, 1.]\n", - "\n", - "p = pv.Plotter(shape=(1, 1), border=False)\n", - "p.add_mesh(mesh, scalars = pf_ic,\n", - " show_edges=True, show_scalar_bar=True,\n", - " colormap=\"coolwarm\", clim=clim,\n", - " scalar_bar_args=sargs)\n", - "\n", - "p.view_xy()\n", - "p.camera.zoom(1.5)\n", - "p.window_size = [800,400]\n", - "p.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Run the simulation " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>> OGS started execution ... <<<\n", - ">>> OGS terminated execution <<< Elapsed time: 11.77 s.\n" - ] - } - ], - "source": [ - "from ogs6py import ogs\n", - "#Change the length scale and phasefield model in project file\n", - "model = ogs.OGS(INPUT_FILE=prj_name, PROJECT_FILE=f\"{out_dir}/{prj_name}\", MKL=True, args=f\"-o {out_dir}\")\n", - "model.replace_parameter_value(name=\"ls\", value=2*h)\n", - "model.replace_text(phasefield_model, xpath=\"./processes/process/phasefield_model\")\n", - "model.replace_text(\"./surfing.gml\", xpath=\"./geometry\")\n", - "model.replace_text(\"./Surfing_python.py\", xpath=\"./python_script\")\n", - "model.write_input()\n", - "\n", - "import time\n", - "\n", - "t0 = time.time()\n", - "print(\">>> OGS started execution ... <<<\")\n", - "! ogs {out_dir}/{prj_name} -o {out_dir} > {out_dir}/log.txt\n", - "\n", - "tf = time.time()\n", - "print(\">>> OGS terminated execution <<< Elapsed time: \", round(tf - t0, 2), \" s.\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We computed the energy release rate using $G_{\\theta}$ method (Destuynder _et al._, 1983; Li _et al._, 2016) and plot the errors against the theoretical numerical toughness i.e. $(G_c^{\\text{eff}})_{\\texttt{num}}=G_c(1+\\frac{h}{2\\ell})$ for $\\texttt{AT}_2$,\n", - "and $(G_c^{\\text{eff}})_{\\texttt{num}}=G_c(1+\\frac{3h}{8\\ell})$ for $\\texttt{AT}_1$ (Bourdin _et al._, 2008).\n", - "\n", - "![Alt text](./figures/surfing_gtheta_schematic.png#one-half \"Phase field and $\\theta$ profile for the volumetric deviatoric $\\texttt{AT}_2$ models. We use virtual perturbation of $\\theta$ to compute energy release rate using $G_{\\theta}$ Dubois et al., 1998. The $\\theta$ value is 1 inside of $B_{r_{in}}(P)$, 0 outside, and a linear interpolation in between. We set $r_{in}=4\\ell$ and $r_{out}=2.5r_{in}$ (see Li et al., 2016).\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We computed the energy release rate using $G_{\\theta}$ method (Destuynder _et al._, 1983; Li _et al._, 2016) and plot the errors against the theoretical numerical toughness i.e. $(G_c^{\\text{eff}})_{\\texttt{num}}=G_c(1+\\frac{h}{2\\ell})$ for $\\texttt{AT}_2$,\n", - "and $(G_c^{\\text{eff}})_{\\texttt{num}}=G_c(1+\\frac{3h}{8\\ell})$ for $\\texttt{AT}_1$ (Bourdin _et al._, 2008).\n", - "\n", - "![Alt text](./figures/surfing_gtheta_schematic.png#one-half \"Phase field and $\\theta$ profile for the volumetric deviatoric $\\texttt{AT}_2$ models. We use virtual perturbation of $\\theta$ to compute energy release rate using $G_{\\theta}$ Dubois et al., 1998. The $\\theta$ value is 1 inside of $B_{r_{in}}(P)$, 0 outside, and a linear interpolation in between. We set $r_{in}=4\\ell$ and $r_{out}=2.5r_{in}$ (see Li et al., 2016).\")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "R_inn=4*ls\n", - "R_out=2.5*R_inn\n", - "\n", - "if phasefield_model=='AT1':\n", - " G_eff=G_i*(1+3*h/(8*ls))\n", - "elif phasefield_model=='AT2':\n", - " G_eff= G_i*(1+h/(2*ls))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We run the simulation with a coarse mesh here to reduce computing time; however, a finer mesh would give a more accurate results. The energy release rate and its error for Models $\\texttt{AT}_1$ and $\\texttt{AT}_2$ with a mesh size of $h=0.005$ are shown below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Alt text](./figures/surfing_gtheta_ref.png#one-half)\n", - "![Alt text](./figures/surfing_gtheta_error_ref.png#one-half)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Post-processing" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.spatial import Delaunay\n", - "\n", - "reader = pv.get_reader(f\"{out_dir}/surfing.pvd\")\n", - "G_theta_time = np.zeros((len(reader.time_values),2))\n", - "\n", - "\n", - "for t, time_value in enumerate(reader.time_values):\n", - " reader.set_active_time_value(time_value)\n", - "\n", - " mesh = reader.read()[0] \n", - " points = mesh.point_data[\"phasefield\"].shape[0]\n", - " xs = mesh.points[:,0]\n", - " ys = mesh.points[:,1]\n", - " pf = mesh.point_data[\"phasefield\"]\n", - " sigma = mesh.point_data[\"sigma\"]\n", - " disp = mesh.point_data[\"displacement\"]\n", - "\n", - " num_points = disp.shape\n", - " theta = np.zeros(num_points) \n", - " \n", - " #--------------------------------------------------------------------------------\n", - " # find fracture tip\n", - " #-------------------------------------------------------------------------------- \n", - " min_pf=min(pf[:])\n", - " coord_pf_0p5=mesh.points[pf<0.5]\n", - " if min_pf <= 0.5:\n", - " coord_pf_0p5[np.argmax(coord_pf_0p5, axis=0)[0]][1]\n", - " x0=coord_pf_0p5[np.argmax(coord_pf_0p5, axis=0)[0]][0]\n", - " y0=coord_pf_0p5[np.argmax(coord_pf_0p5, axis=0)[0]][1]\n", - " else:\n", - " x0=x_tip_Initial \n", - " y0=y_tip_Initial\n", - " Crack_position = [x0,y0] \n", - " #--------------------------------------------------------------------------------\n", - " #define \\theta\n", - " #--------------------------------------------------------------------------------\n", - " for i, x in enumerate(mesh.points):\n", - " # distance from the crack tip\n", - " R = np.sqrt((x[0] - Crack_position[0])**2 + (x[1] - Crack_position[1])**2)\n", - " if R < R_inn:\n", - " theta_funct = 1.0\n", - " elif R > R_out:\n", - " theta_funct = 0.0\n", - " else:\n", - " theta_funct = (R-R_out)/(R_inn-R_out)\n", - " theta[i][0] = theta_funct * np.cos(Orientation)\n", - " theta[i][1] = theta_funct * np.sin(Orientation)\n", - "\n", - " mesh.point_data['theta'] = theta\n", - "\n", - " #--------------------------------------------------------------------------------\n", - " #define grad \\theta\n", - " #--------------------------------------------------------------------------------\n", - " mesh_theta = mesh.compute_derivative(scalars=\"theta\")\n", - " mesh_theta[\"gradient\"]\n", - " def gradients_to_dict(arr):\n", - " \"\"\"A helper method to label the gradients into a dictionary.\"\"\"\n", - " keys = np.array([\"thetax_x\", \"thetax_y\", \"thetax_z\", \"thetay_x\", \"thetay_y\", \"thetay_z\"])\n", - " keys = keys.reshape((2, 3))[:, : arr.shape[1]].ravel()\n", - " return dict(zip(keys, mesh_theta[\"gradient\"].T))\n", - "\n", - "\n", - " gradients_theta = gradients_to_dict(mesh_theta[\"gradient\"])\n", - " mesh.point_data.update(gradients_theta)\n", - " #--------------------------------------------------------------------------------\n", - " #define grad u\n", - " #--------------------------------------------------------------------------------\n", - " mesh_u = mesh.compute_derivative(scalars=\"displacement\")\n", - " mesh_u[\"gradient\"]\n", - " def gradients_to_dict(arr):\n", - " \"\"\"A helper method to label the gradients into a dictionary.\"\"\"\n", - " keys = np.array(\n", - " [\"Ux_x\", \"Ux_y\", \"Ux_z\", \"Uy_x\", \"Uy_y\", \"Uy_z\"])\n", - " keys = keys.reshape((2, 3))[:, : arr.shape[1]].ravel()\n", - " return dict(zip(keys, mesh_u[\"gradient\"].T))\n", - "\n", - " # a=np.array([1,2,3,4,5,6])\n", - " # np.reshape(a.ravel(), (2, 3))\n", - " gradients_u = gradients_to_dict(mesh_u[\"gradient\"])\n", - " # gradients\n", - " mesh.point_data.update(gradients_u)\n", - "\n", - " #--------------------------------------------------------------------------------\n", - " #define G_theta\n", - " #--------------------------------------------------------------------------------\n", - " G_theta_i = np.zeros(num_points[0])\n", - " sigma = mesh.point_data[\"sigma\"]\n", - " Ux_x = mesh.point_data[\"Ux_x\"]\n", - " Ux_y = mesh.point_data[\"Ux_y\"]\n", - " Uy_x = mesh.point_data[\"Uy_x\"]\n", - " Uy_y = mesh.point_data[\"Uy_y\"]\n", - "\n", - " thetax_x = mesh.point_data[\"thetax_x\"]\n", - " thetax_y = mesh.point_data[\"thetax_y\"]\n", - " thetay_x = mesh.point_data[\"thetay_x\"]\n", - " thetay_y = mesh.point_data[\"thetay_y\"]\n", - "\n", - " for i, x in enumerate(mesh.points):\n", - " #---------------------------------------------------------------------------\n", - " sigma_xx = sigma[i][0]\n", - " sigma_yy = sigma[i][1]\n", - " sigma_xy = sigma[i][3]\n", - "\n", - "\n", - " Ux_x_i = Ux_x[i]\n", - " Ux_y_i = Ux_y[i]\n", - " Uy_x_i = Uy_x[i]\n", - " Uy_y_i = Uy_y[i]\n", - "\n", - " thetax_x_i = thetax_x[i]\n", - " thetax_y_i = thetax_y[i]\n", - " thetay_x_i = thetay_x[i]\n", - " thetay_y_i = thetay_y[i]\n", - " #---------------------------------------------------------------------------\n", - " dUdTheta_11 = Ux_x_i*thetax_x_i + Ux_y_i*thetay_x_i\n", - " dUdTheta_12 = Ux_x_i*thetax_y_i + Ux_y_i*thetay_y_i\n", - " dUdTheta_21 = Uy_x_i*thetax_x_i + Uy_y_i*thetay_x_i\n", - " dUdTheta_22 = Uy_x_i*thetax_y_i + Uy_y_i*thetay_y_i\n", - " trace_sigma_grad_u_grad_theta = sigma_xx*dUdTheta_11 + sigma_xy*(dUdTheta_12 + dUdTheta_21) + sigma_yy*dUdTheta_22\n", - " trace_sigma_grad_u = sigma_xx*Ux_x_i + sigma_xy*(Uy_x_i + Ux_y_i) + sigma_yy*Uy_y_i\n", - " div_theta_i = thetax_x_i + thetay_y_i\n", - " G_theta_i[i] = trace_sigma_grad_u_grad_theta - 0.5*trace_sigma_grad_u*div_theta_i\n", - " mesh.point_data['G_theta_node'] = G_theta_i \n", - " #--------------------------------------------------------------------------------\n", - " #Integral G_theta\n", - " #-------------------------------------------------------------------------------- \n", - " X = mesh.points[:,0]\n", - " Y = mesh.points[:,1]\n", - " G_theta_i = mesh.point_data[\"G_theta_node\"]\n", - "\n", - " domain_points = np.array(list(zip(X,Y)))\n", - " tri = Delaunay(domain_points)\n", - "\n", - " def area_from_3_points(x, y, z):\n", - " return np.sqrt(np.sum(np.cross(x-y, x-z), axis=-1)**2)/2\n", - " \n", - " G_theta = 0\n", - " for vertices in tri.simplices:\n", - " mean_value = (G_theta_i[vertices[0]] + G_theta_i[vertices[1]] + G_theta_i[vertices[2]]) / 3\n", - " area = area_from_3_points(domain_points[vertices[0]], domain_points[vertices[1]], domain_points[vertices[2]])\n", - " G_theta += mean_value*area\n", - " G_theta_time[t][1]= G_theta\n", - " G_theta_time[t][0]= time_value\n", - "mesh.save(f\"{out_dir}/surfing_Post_Processing.vtu\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plots" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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t2hRt27YFcLcb8aeffqpSrK4Ct2PKioqKkJGRYbx/7tw5pKamol69eggNDcXkyZPx3nvvoVmzZggPD8ecOXMQHByMwYMHAwBatGiB/v3745lnnsE333wDrVaLl156CSNHjkRwcLCDrMwzZAjwyy/iLMw//hAv+fHjSty+LZYPGeLgAAmCIKxAoQA8Pat2bN++QOPGwKefijPRy6/9qdcDixYB4eHicVJ8Se3ZsycWLFiAevXqoVGjRsaJEwYqLrhqGHxuYNy4cbhx4wY+++wzhIWFwc3NDTExMcZxUCEhIUhLS8PWrVuRmJiISZMm4eOPP8bOnTtRVFQElUqFQ4cOVVqJvk6dOgBgMvavbt26VXIyrMu5dOlSY6Jl2JDc29u7SnVUlWXLluHEiRMmfze9Xo/vv/8eTz/9tE3P5Ui4Tcr+/vtvxMXFGe9PnToVgPjCX7FiBWbMmIHbt2/j2WefRV5eHrp27YrNmzcbBxECwI8//oiXXnoJDz/8MJRKJYYOHYrPP//c7i5VYcgQYNAg4McfdRg3To1atRjOnFFAze0rgCAInjD0GgwbJvYSvP460Lo1cPw4MH++2Gvwyy/S9Rp4enqiSZMmqFu3rlWrwe/ZsweLFy/GgAEDAAAXL15Ebm6uyTEeHh547LHH8Nhjj+HFF19EVFQUjh07hnbt2kEQBOTk5KBbt25m6zcM5q8OAQEBCA4OxtmzZzFmzBgAYqJUUFBgMbFr0aIFfv/9d5Oy8hOUzHHs2DH8/fff2LFjB+rVq2csv3nzJnr27InTp08jKiqq2vE7I9x+JPfs2bPStNvyKBQKvPvuu3j33XctHlOvXj2sWbNGivAkQaUChgxRYdw4oKxMgeJioIpfiFwStVqNAQMGVPpGKjfIU37w4mpvz/K9BrGxd8vDw6XvNVAoFKhbt67Ve0I2a9YMq1evRseOHVFQUIDXXnvNZHbjihUrIAgCOnfujNq1a+OHH36Ah4cHwsLCUL9+fYwZMwZjx47F//73P7Rr1w7Xr1/Htm3b8MADD2DgwIFWe82dOxevvPIKvL290b9/f9y5cwcHDx5EXl4epk2bVun4559/Hv/73//w2muvYeLEiTh06BBWrFhxz3MsW7YMnTp1Qvfu3Ss99uCDD2LZsmUm65ZZIisrCzdv3kRWVhYEQTC2DkZERBhbDKuKVK9ZGlPGGZ6egJeXmIxaWLlDVpSf5i1nyFN+8OJqb88hQ8QxtUlJwJo14s/0dPsM47hXQ8D9WLZsGW7duoX27dvjv//9L1555RWTZZl8fHywdOlSdOnSBQ888AC2bt2KP/74A/Xr1wcALF++HGPHjsW0adMQGRmJwYMH4+DBgwgNDa2R08SJE/Hdd99h+fLlaNOmDXr27ImVK1ciPDzc7PGhoaH49ddfsXHjRrRt2xbffPMNPvjgA4v1l5WV4YcffsDQoUPNPj506FCsWrWqStsevfXWW2jXrh3efvttFBUVoV27dmjXrh3+/vvvqsnaAQWryauEqBEFBQXw9vZGfn5+lfvwa4pWq0XjxqW4cqUOdu4EzHzxkA1arRbx8fEYMGCArDfIJU/5wYtrdT3v3LmDc+fOITw83GQoiStQvltPqs2snQE5ed7r9Xbjxg00aNDA5p/frv0XI6zC11dcqPDqVQcHQhAEQRCEEUrKOMTHR1x1mofuS4IgCIJwFSgp45B69cQp1DwkZXIfKG2APOUHL668eBJEVaCkjDM0Gg1iYsQBmHLvvtRoNBg4cKCsx+QA5ClHeHG11tMVh0IrlUr4+Pi4/Dir+yEnz3u9zqT633T9vxpRLfR6PTw9xc1e5d5SptfrkZOTA71ef/+DXRjylB+8uFbX0/BBWFxcLGVYksAYg1ardcmEsjrIydPwOjOXgEn1v0ntxpwhCAJu3DgBIEb2SZkgCEhJScGAAQNk8a3NEuQpP3hxra6nSqWCj4+Pcfuh2rVrW73ul70xrHRfp04dWV9TOXgyxlBcXIycnBz4+PhU2gUBEF+7UkBJGYf4+IizL+WelBEEIT8CAwMBwGRfSFeAMYaSkhJ4eHi4TCJpDXLy9PHxMb7e7AUlZRzi6yvOvszJAXQ60FZLBEG4DAqFAkFBQfD396/SgqHOglarRXJyMrp37y7rsYJy8dRoNGZbyKSGPo45Q6FQoGHDWlAqGfR6Ba5fB4KCHB2VNCgUCnh5ebn8t7X7QZ7ygxfXmniqVCqHfGhai1qthoeHBzw8PGQ945QXT6n+N2lFfwfiiBX9DQQFid2Xhw8D7drZ9dQEQRAE4dJI9fntmqPwCKvR6/W4cOECAgPlv/+lwZWHGWzkKS94ceXFE+DHlSdPKaCkjDMEQUBqaqoxKZPzWmUGV6lmyTgL5Ck/eHHlxRPgx5UnTymgpIxTAgLEn3JuKSMIgiAIV4KSMk4JCJB/9yVBEARBuBKUlHGGQqGAn58fDEuvyDkpM7jyMIONPOUFL668eAL8uPLkKUm9NPvScThy9uX69cDw4UDXrsCuXXY9NUEQBEG4NDT7krAJgiDg9OnT8PMTBynKuaXM4MrDgFPylBe8uPLiCfDjypOnFFBSxhl6vR5paWnw9xen88o5KTO48jA1mzzlBS+uvHgC/Ljy5CkFlJRximFMWVGReCMIgiAIwrFQUsYpXl6Ap6f4u5xbywiCIAjCVaCkjDOUSiVCQ0OhVCplPwOzvKucIU/5wYsrL54AP648eUoBzb50II6cfQmIMy/37BFnYg4bZvfTEwRBEIRLQrMvCZsgCAKOHDkCQRAQFCSWyXWrpfKucoY85Qcvrrx4Avy48uQpBZSUcYZer0dWVhb0er3suy/Lu8oZ8pQfvLjy4gnw48qTpxRQUsYxck/KCIIgCMKVoKSMYygpIwiCIAjngZIyzlAqlYiMjIRSqZT9mLLyrnKGPOUHL668eAL8uPLkKQU0+9KBOHr25eHDQIcOQFAQcOWK3U9PEARBEC4Jzb4kbIJOp8PevXuh0+mM3Zc5OYAcJ8qUd5Uz5Ck/eHHlxRPgx5UnTymgpIwzGGO4fv06GGPw9wcUCjEhy811dGS2p7yrnCFP+cGLKy+eAD+uPHlKASVlHKNWA35+4u802J8gCIIgHAslZZxDMzAJgiAIwjmgpIwzVCoVoqOjoVKpAMg7KavoKlfIU37w4sqLJ8CPK0+eUqCWpFbCaVEqlQgLCzPeNyRlclwWo6KrXCFP+cGLKy+eAD+uPHlKUq8ktRJOi06nw/bt240zRwxrlcmxpayiq1whT/nBiysvngA/rjx5SgElZfegsLAQkydPRlhYGDw8PBAbG4uDBw8aHx8/fjwUCoXJrX///g6M+P4wxlBYWGicOSLn7suKrnKFPOUHL668eAL8uPLkKQXUfXkPJk6ciOPHj2P16tUIDg7GDz/8gN69e+PkyZNo2LAhAKB///5Yvny58Tlubm6OCtcq5JyUEQRBEIQrQS1lFigpKcGvv/6KBQsWoHv37oiIiMA777yDiIgIfP3118bj3NzcEBgYaLz5+vo6MOrqI/etlgiCIAjCVaCWMgvodDoIggB3d3eTcg8PD+zevdt4f8eOHfD394evry969eqF9957D/Xr1zdbZ2lpKUpLS433CwoKAABarRZarRaAOHhQpVJBEATo9XrjsYZynU5n0myqUqmgVCotlhvqLV/PQw89BL1eD61WCzFUDa5dY2Cscj+5RqOBXq+HUG7Jf4VCAbVabbHcUuxSOanV4su4YuwVXeXgZK5cr9cjJiYGCoXCpB5XdgIqXye9Xo9OnTpBpVLJxql87JZc5eJUvtwQu16vx4MPPmh8XA5OFWO35CoHp4rlOp3O6GlwloOTudip+9LOeHl5ISYmBvPmzUOLFi0QEBCAtWvXIiUlBREREQDErsshQ4YgPDwcmZmZmD17Nh555BGkpKSYnS47f/58zJ07t1J5QkICateuDQAIDQ1Fu3btcPToUWRlZRmPiYyMRFRUFA4cOIDr168by6OjoxEWFobk5GQUFhYay2NiYuDv74+EhASTF3JcXBzq1auH+Ph4AMDt22oAA1FQoEB2dgH2708yHqtWqzFw4EDk5uYiJSXF5G/Tq1cvXLx4EampqcZyPz8/xMbGIj09HWlpacZyezh5eHgYnQwMGDAAHh4e2Lx5s6ycSkpKkJRU+Trl5OTIzsncdQoKCsKFCxdk5WTpOgUFBeH06dOycpLjdSIn/pzKn9OW0Ibk9yAzMxMTJkxAcnIyVCoV2rdvj+bNm+PQoUM4depUpePPnj2Lpk2bYuvWrXj44YcrPW6upSwkJAS5ubnGDU2l/hbMGENCQgJ69eoFjUYDxgBvbzXu3FEgI4MhNNR5vonU9NtVRVc5OJkr12q1SEpKQu/evU2mabuyE1D5Omm1Wmzfvh39+vUzxunqTuVjt+SqVCpl4VS+3BC7wbN3795wd3eXhVPF2C25ysGpYrlOp0NZWRm2b9+OXr16wd3dXRZO5mLPzs5GYGCgzTckp5aye9C0aVPs3LkTt2/fRkFBAYKCgjBixAg0adLE7PFNmjRBgwYNkJGRYTYpc3NzMzsRQKPRmCQNgHjhzbW2GV6cVS2vWK9Wq4UgCCbnDAoCzp0DsrMVaNpUU6kOpVJpdk0WS+WWYpfKyVK5Odf7xe7sTpbKdTodlEql2eNd1QmoHLvhTVZOTpbKDa5ycjJQPnZBEIy/y8XJUnl5V7k4lUetVoMxZnzfNdTp6k7msFReU2igfxXw9PREUFAQbt26hS1btmDQoEFmj7t06RJu3LiBIMPoeReBZmASBEEQhOOhlrJ7sGXLFjDGEBkZiYyMDLz22muIiorCU089haKiIsydOxdDhw5FYGAgMjMzMWPGDERERKBfv36ODr1aUFJGEARBEI6HWsruQX5+Pl588UVERUVh7Nix6Nq1K7Zs2QKNRgOVSoWjR4/i8ccfR/PmzfH000+jQ4cO2LVrl1OvVaZWqxEXF2fS9CrXrZbMucoR8pQfvLjy4gnw48qTpyT1SlKrTBg+fDiGDx9u9jEPDw9s2bLFzhHZBg8PD5P7ct5qqaKrXCFP+cGLKy+eAD+uvHhKAbWUcYZOp0N8fLzJLBa5dl+ac5Uj5Ck/eHHlxRPgx5UnTymgpIyQbVJGEARBEK4EJWUEbbVEEARBEE4AJWWEsaUsOxsot3YeQRAEQRB2hFb0dyAFBQXw9va2+YrA94IxBp1OB7VaDYVCAQAoKwMME0avXwcaNLBLKJJjzlWOkKf84MWVF0+AH1dePPPz8+Hj42Pzz29qKeOQkpISk/u1agGGPdTl1oVZ0VWukKf84MWVF0+AH1dePKWAkjLO0Ol0SEpKqjRzRI7LYlhylRvkKT94ceXFE+DHlSdPKaCkjABAMzAJgiAIwtFQUkYAoKSMIAiCIBwNJWUcYm57CDlvtcQD5Ck/eHHlxRPgx5UXTymg2ZcOxBGzLy2xcCEwbRowahSwZo1DQyEIgiAIp0aqz29qKeMMvV6PnJwc6CssSCbH7ktLrnKDPOUHL668eAL8uPLkKQWUlHGGIAhISUmBIAgm5XJMyiy5yg3ylB+8uPLiCfDjypOnFFBSRgCQ75gygiAIgnAVKCkjANxdpywvD7hzx6GhEARBEASXUFLGGQqFAl5eXpW2v/DxEVf2B8Q9MOWAJVe5QZ7ygxdXXjwBflx58pSkXpp96TicafYlAISFAVlZQEoK8NBDjo6GIAiCIJwTmn1J2AS9Xo8LFy6YnTkit62W7uUqJ8hTfvDiyosnwI8rT55SQEkZZwiCgNTUVLMzR+Q2A/NernKCPOUHL668eAL8uPLkKQWUlBFG5JaUEQRBEIQrQUkZYYSWxSAIgiAIx0FJGWcoFAr4+fmZnTkitzFl93KVE+QpP3hx5cUT4MeVJ09J6qXZl47D2WZf/t//AYMHA506Afv3OzoagiAIgnBOaPYlYRMEQcDp06e5GehvyVVOkKf84MWVF0+AH1eePKXAYUlZYmIivvjiC3z55ZfYtm2bo8LgDr1ej7S0NLPTecsnZXJoP72Xq5wgT/nBiysvngA/rjx5SoFaklrvQUZGBoYMGYLjx48byxQKBR544AH89ttvaNy4sb1DIv7FkJSVlQG3bgH16jk2HoIgCILgCbu3lD377LNQqVTYvXs3bt++jZs3b2LVqlXIzMzE888/b+9wiHK4uQG+vuLvcunCJAiCIAhXQbKkLCUlxWz57t27sWDBAsTGxsLDwwM+Pj4YM2YMxo8fj+TkZKnCIf5FqVQiNDQUSqX5Sy+ncWX3c5UL5Ck/eHHlxRPgx5UnT0nqlaRWAN26dcPLL7+MoqIik/L69evj77//NinT6/VITU1F/fr1pQqH+BeVSoV27dpBpVKZfdywLIYc1iq7n6tcIE/5wYsrL54AP648eUqBZElZfHw84uPj0bJlS2zatMlY/uqrr+LNN99E//79MWvWLEyZMgVt2rTB7t27MXnyZKnCIf5FEAQcOXLE4swRObWU3c9VLpCn/ODFlRdPgB9XnjylQLKkrG/fvjh+/DiGDRuGwYMHY+TIkbh+/TpmzZqFH374Afn5+ViyZAlWrlwJHx8frFu3DtOmTZMqHOJf9Ho9srKyLM4ckVNSdj9XuUCe8oMXV148AX5cefKUAklnX3p4eGDhwoUYNWoUJk6ciBYtWuCTTz7B+PHjMWrUKClPTVgJbbVEEARBEI7BLiPxHnzwQRw+fBhTp07FpEmT0Lt3b5w7d84epyaqidy2WiIIgiAIV8Fu0yNUKhVmz56N1NRU6HQ6tG7dGh9//LHsmzidDaVSicjISG5mX97LVS6Qp/zgxZUXT4AfV548pUDSvS8vX76MH3/8EVlZWQgNDcWYMWPQsGFDAMCSJUswc+ZMNGnSBN999x3atWsnVRhOi7PtfQkAx48DbdoA9esDubmOjoYgCIIgnA+X2/ty7969aNGiBWbPno1ffvkFs2fPRosWLbBv3z4A4iKyJ0+eRFhYGDp37owZM2bgzp07UoVD/ItOp8PevXuh0+nMPm5oKbtxQ1zZ35W5n6tcIE/5wYsrL54AP648eUqBZEnZ9OnT4efnh7Nnz+LatWs4e/Ys/Pz8MH36dOMxQUFB2LBhA9atW4cff/wRbdq0kSoc4l8YY7h+/TosNZDWqwdoNOLv2dl2DEwC7ucqF8hTfvDiyosnwI8rT55SIFlSdvz4cQwZMgShoaEAgNDQUAwZMgRHjx6tdOyQIUNw8uRJ9OrVS6pwrKKwsBCTJ09GWFgYPDw8EBsbi4MHDxofZ4zhrbfeQlBQEDw8PNC7d2+kp6c7MOKao1QCAQHi73IYV0YQBEEQroJkSVnDhg1x4MABk7IDBw4Yx5RVxNvbG99++61U4VjFxIkTkZiYiNWrV+PYsWPo27cvevfujcuXLwMAFixYgM8//xzffPMN9u/fD09PT/Tr18/lu2HlNNifIAiCIFwFyZKyqVOnYteuXWjZsiVGjx6NVq1aYdeuXZgyZYpUp7QpJSUl+PXXX7FgwQJ0794dEREReOeddxAREYGvv/4ajDEsWrQIb775JgYNGoQHHngAq1atwpUrV7Bx40ZHh28RlUqF6Ojoe24RIZe1yqriKgfIU37w4sqLJ8CPK0+eUiDZ4rHPPPMMfH198d133+Gff/5BSEgI5s6di2HDhkl1Spui0+kgCALc3d1Nyj08PLB7926cO3cO165dQ+/evY2PeXt7o3PnzkhJScHIkSMr1VlaWorS0lLj/YKCAgCAVquFVqsFIE6zValUEATBZLkQQ7lOpzPpy1apVFAqlRbLDfUaUKvVCA0NNfqVLzd4BwSoAChx+bIAQAW9Xm9yrEKhgFqttlhuKXYpnQyxV8VVo9G4tJO58rCwMOj1epN6XN3J3HVq2LAhlEqlrJwsXSeDq5yczF2n4OBgY1xycSofuyVXuTiVLzfEHhwcbKxPLk4VY3fJFf2HDRvmMklYRby8vBATE4N58+ahRYsWCAgIwNq1a5GSkoKIiAhc+7dvL8AwAOtfAgICjI9VZP78+Zg7d26l8oSEBNSuXRuAOPauXbt2OHr0KLKysozHREZGIioqCgcOHMD169eN5dHR0QgLC0NycjIKCwuN5TExMfD390dCQoLJC7l79+44fPhwpY3iBwwYgJKSEiQlJaGwMApAJP7++xKAMOTm5iIlJcXkb9OrVy9cvHgRqampxnI/Pz/ExsYiPT0daWlpxnKpneLi4uDh4YH4+HgTp759+2L37t0oLi42lqnVagwcONBlncpfp/JOHh4eaNGihcmQAVd3MnedlEolHnnkEVy+fFk2Tpauk0qlQv/+/ZGRkSEbJ0vXKSQkBO3bt5eVk6Xr1KxZM7Rs2VJWTnK8Tvdz2r17N6RA0nXKXJ3MzExMmDABycnJUKlUaN++PZo3b45Dhw5h2bJl6NKlC65cuYIgwzL4AIYPHw6FQoGffvqpUn3mWspCQkKQm5trXOdE6m/BjDH89ddf6NOnDzSGaZYw/SbyzTdKvPKKCo8/rsf//Z/lVglnb62w5Cq3ljKtVovExET079/fZEFDV3YCKl8ng+eAAQOMcbq6U/nYLblW/Fbuqk7lyw2xGzz79esHd3d3WThVjN2SqxycKpbrdDqUlZUhMTERffr0gbu7uyyczMWenZ2NwMBAm69TJmlLGQBcvHgRK1euxM6dO5Geno78/HwAYldfs2bN0LNnT/z3v/81ztJ0Jpo2bYqdO3fi9u3bKCgoQFBQEEaMGIEmTZog8N+BV9nZ2SZJWXZ2NqKjo83W5+bmBjc3t0rlGo3GJGkAxAtvrs/a8OKsannFeg3/BObOaShv1Ej8PSdH/IBXKpVmVy+2VG4pdqmcLJXfy9VVne5VrlQqLZa7spMcrxM5mcZu+F1OTpbKDb/LycmAWq02JjAajcZYp6s7mcNSeU2RdB+ETz/9FM2bN8dbb72Fbdu24ebNm/D09ISnpydu3ryJbdu2Yc6cOYiMjMSiRYukDKVGeHp6IigoCLdu3cKWLVswaNAghIeHIzAwENu2bTMeV1BQgP379yMmJsaB0dYcmn1JEARBEPZHsu7L9evXY8SIEWjevDlmz56Nfv36VRp/lZ2djc2bN+ODDz5ARkYGfvrpJ6cag7ZlyxYwxhAZGYmMjAy89tprcHd3x65du6DRaPDRRx/hww8/xMqVKxEeHo45c+bg6NGjOHnyZKUJAuZwxDZLer0eubm5aNCggcW9u86dA5o0AdzdgeJiQKGwS2g2pyqucoA85Qcvrrx4Avy48uKZl5cHX19fm39+S5aUxcTEIDs7G//88w+8vLzueWx+fj6io6MRGBhoMmDP0fz88894/fXXcenSJdSrVw9Dhw7F+++/D29vbwDimKW3334bS5YsQV5eHrp27YrFixejefPmVarfGfe+BICSEuDfeQe4dQvw8XFoOARBEAThVLjc3pfHjh3D0KFD75uQAeL4sqFDh+LYsWNShWMVw4cPR2ZmJkpLS3H16lV8+eWXxoQMEAcOvvvuu7h27Rru3LmDrVu3VjkhcxRarRabNm2qNMCyPB4egEHTlbswq+IqB8hTfvDiyosnwI8rT55SIFlSptFoTKbA3o/CwkKLg/sI21KVjVTlMq5M7pviGiBP+cGLKy+eAD+uvHhKgWRJWUxMDNatW1el1q9//vkH69atQ2xsrFThENVELkkZQRAEQbgKki2JMXfuXHTt2hWdO3fGmDFj0KdPHzRr1szY/Zefn4/09HQkJCRgzZo10Ov1ZhdWJRyDXLZaIgiCIAhXQdLFY5OSkvDMM8/g7NmzUFiYwscYQ5MmTfDdd9+hZ8+eUoXilDhioD9jDIWFhfDy8rJ4TQBgyhRg0SJgxgzgo4/sEprNqaqrq0Oe8oMXV148AX5cefHMz8+Hj4+Pay0eGxcXh7S0NGzfvh07duwwu3hsjx498PDDD8t+81JnwsPD477HyKX7siqucoA85Qcvrrx4Avy48uIpBZIvIqJSqdCnTx+8//77+Pnnn7FlyxZs2bIFP//8M95//3307duXEjI7otPpEB8ff9+BmHLovqyqq6tDnvKDF1dePAF+XHnylAL5ruxG1AjDzlGu3lJGEARBEK4CJWWEWeTSfUkQBEEQrgJtSE6YxZCU5eYCWi1AS8gRBEEQhLRIOvvy008/xezZs1FaWgoAqFOnjnGWQkFBAYqKigAAbm5umD9/PiZPnixVKE6Jo2Zf6nQ6qNXqe86M0euBWrUAQQAuXQIaNrRLeDalqq6uDnnKD15cefEE+HHlxVOq2ZeSdV+uX78e06ZNQ1hYGFasWIGrV6+ioKAAly5dwqVLl1BQUICrV69i+fLlCA0NxbRp0/DLL79IFQ5RjpKSkvseo1QChv3jXbkLsyqucoA85Qcvrrx4Avy48uIpBZIlZQsXLkTjxo1x8OBBjB07FgGGT/hyBAQEYNy4cThw4ABCQ0Pxv//9T6pwiH/R6XRISkriYqul6ri6MuQpP3hx5cUT4MeVJ08poA3JCYu4elJGEARBEK4EbUhOWEQOa5URBEEQhKtAG5JziFpdtUm3clirrKqurg55yg9eXHnxBPhx5cVTCiSbfXnw4EF07doVKpWqyhuS7969Gx07dpQiHKfEEbMvq8OXXwIvvwwMHQrQHAyCIAiCEJHq81uydPbBBx/E5s2b8cwzz2DZsmX4/vvvzR5XfkNynhIyR6HX65Gbm4sGDRpAqbx3Q6mrd19Wx9WVIU/5wYsrL54AP648eUqBpH8xw4bkW7ZswaxZszB06FD07t0bvXv3xtChQzFr1ixs3rwZaWlp6Nmzp5ShEP8iCAJSUlIgCMJ9j3X17svquLoy5Ck/eHHlxRPgx5UnTymQvOPXsCF5nz59pD4VYWPKz75kDJDxOoAEQRAE4XDk27ZI1BjD0nLFxcC/my8QBEEQBCERTpOULVu2DBMmTHB0GLJHoVDAy8urSttf1Kkj3gDXHFdWHVdXhjzlBy+uvHgC/Ljy5ClJvVLufVkdnnrqKaxatUr2/dDlcfbZlwDQvDmQng7s3Al07+7oaAiCIAjC8Uj1+e00LWWEfdDr9bhw4UKVZ4648qr+1XV1VchTfvDiyosnwI8rT55SINlA/1WrVlXr+IyMDIkiIcojCAJSU1MRHBxcpenKrpyUVdfVVSFP+cGLKy+eAD+uPHlKgWRJ2fjx46vV58oYk30ftCvi6muVEQRBEISrIFlSVqtWLQQHB+O5556r0vHr16/HkSNHpAqHsBJXX6uMIAiCIFwFyZKyNm3aICsrCzNnzqzS8adPn6akzA4oFAr4+flVuVXSlbsvq+vqqpCn/ODFlRdPgB9XnjylQLIO3w4dOiA3NxcXL16U6hSEFajVasTGxlZ5w1hX7r6srqurQp7ygxdXXjwBflx58pQCyZKybt26ITQ0FOnp6VU6vmvXrhg7dqxU4RD/IggCTp8+XeVBiq7cUlZdV1eFPOUHL668eAL8uPLkKQWSJWVjxozBuXPn0KtXryod//TTT2P58uVShUP8i16vR1paWpWn8xrGlF2/Drja/1h1XV0V8pQfvLjy4gnw48qTpxTId74qYRP8/AClEtDrxcSMIAiCIAhpoKSMuCcqlZiYAa45rowgCIIgXAVKyjhDqVQiNDS0Wov6ueqyGNa4uiLkKT94ceXFE+DHlSdPKXCavS95xBX2vgSARx4BNm8Gvv8eeOopR0dDEARBEI6F9r4kbIIgCDhy5Ei1Zo646gxMa1xdEfKUH7y48uIJ8OPKk6cUUFLGGXq9HllZWdWaOeKqa5VZ4+qKkKf84MWVF0+AH1eePKWAkjLivrjqmDKCIAiCcCUkT8q0Wi0WL158z2NycnKwZs0aqUOpFoIgYM6cOQgPD4eHhweaNm2KefPmofwQPMOm6+Vv/fv3d2DU0uCq3ZcEQRAE4UpIvg/Cl19+ienTp+Pvv//G999/X+nx7Oxs9OzZE5mZmejUqRMiIiKkDqlKfPTRR/j666+xcuVKtGrVCn///TeeeuopeHt745VXXjEe179/f5NFb93c3BwRbpVRKpWIjIys1swRV+2+tMbVFSFP+cGLKy+eAD+uPHlKgeRJ2auvvopDhw5hxYoVEAQBK1asMG7kee3aNWNCtmrVKqdJyABg7969GDRoEAYOHAgAaNy4MdauXYsDBw6YHOfm5oZAQ9biAqhUKkRFRVXrOa7aUmaNqytCnvKDF1dePAF+XHnylALJkzKlUonVq1dDoVBg9erVEAQBq1evxrVr1xAXF4fMzEz88MMPGDFihNShVIvY2FgsWbIEZ86cQfPmzfHPP/9g9+7dWLhwoclxO3bsgL+/P3x9fdGrVy+89957qF+/vtk6S0tLUVpaarxfUFAAQOzi1Wq1AMS/l0qlgiAIJgMJDeU6nc6kC1WlUkGpVFosN9RbngMHDqB9+/YmG6oaftfpdCbHajQaBAToAShRVATcuqWFl5cCarUaer3eZAaKQiGWW4pdKidLsVty1Wg0FmN3didz5TqdDkeOHEHHjh2NX3hc3QmofJ10Oh0OHz6Mzp07Q6lUysKpfOyWXBUKhSycypcbYtfpdDh06BAefPBBuLm5ycKpYuyWXOXgVLFcp9NBq9Xi0KFD6NChA9zc3GThZC72O3fuQArsso27QqHAqlWroFAo8MMPP6CkpAQnTpxw2oQMAGbNmoWCggJERUUZL9j777+PMWPGGI/p378/hgwZgvDwcGRmZmL27Nl45JFHkJKSYjaLnj9/PubOnVupPCEhAbVr1wYAhIaGol27djh69CiysrKMx0RGRiIqKgoHDhzA9XL7HUVHRyMsLAzJyckoLCw0lsfExMDf3x8JCQkmL+Ru3bohNzcXCQkJJjEMGDAAJSUlSEpKMpap1WoMHDgQd+7kws2tHkpL1fjppx1o3lyFXr164eLFi0hNTTUe7+fnh9jYWKSnpyMtLc1YLrVTXFwcPDw8EB8fb+LUp0+fSq4Gp9zcXKSkpBjLvby8XMLJ0nXS6XTIzc3FwYMHZeNk7joBAGNMVk6WrpPBNSMjQzZOlq7T8ePH0aFDB1k5WbpOGRkZaNWqlayczF2nhIQE2TkBd6/Tvn37IAV2XTyWMYZRo0bh559/NiZoo0aNstfpq8W6devw2muv4eOPP0arVq2QmpqKyZMnY+HChRg3bpzZ55w9exZNmzbF1q1b8fDDD1d63FxLWUhICHJzc42Lz0n9LZgxhr/++gt9+vSBRqMxlt/vm0izZgqcPatAUpIOXbvCJVpgLLnKraVMq9UiMTER/fv3Nxnn4MpOQOXrZPAcMGCAMU5XdyofuyVXpVIpC6fy5YbYDZ79+vWDu7u7LJwqxm7JVQ5OFct1Oh3KysqQmJiIPn36wN3dXRZO5mLPzs5GYGCgzRePtUtLmYHLly/j77//BiB+YP75558YOXKkSZeLs/Daa69h1qxZGDlyJACgTZs2uHDhAubPn28xKWvSpAkaNGiAjIwMs0mZm5ub2YkAGo3GJGkAxAtvrrWtfDdcVcor1mv4JzB3TnPHA+KLMygIOHsWyM1Vw3AqpVJpdrCjpdilcrJUfi9XS7E7u9O9ypVKpcVyV3aS43UiJ9PYDb/LyclSueF3OTkZUKvVxgRGo9EY63R1J3NYKq8pdkvKLl26hB49euDixYv44YcfsG3bNixfvhyMMfzwww9ON1OjuLi4UkwqleqeC8ZdunQJN27cQJBhYS8nRKVSITo6utqDFF1xsL+1rq4GecoPXlx58QT4ceXJUwrskpRdvHgRcXFxxoRs+PDhGD16NJRKJZYtWwYATpeYPfbYY3j//fcRGhqKVq1a4ciRI1i4cCEmTJgAACgqKsLcuXMxdOhQBAYGIjMzEzNmzEBERAT69evn4Ogto1QqERYWVu3nuWJSZq2rq0Ge8oMXV148AX5cefKUpF5Jai1HVlYWevbsiaysLKxbtw7Dhw83PrZ06VJMnDgR69atw5gxY5xqW4YvvvgCw4YNw6RJk9CiRQtMnz4dzz33HObNmwdAzJKPHj2Kxx9/HM2bN8fTTz+NDh06YNeuXU69VplOp8P27dvNzlS8F664Vpm1rq4GecoPXlx58QT4ceXJUwokbylLTk7GlStXsH79egwaNKjS40uWLIFSqcTGjRuRlZWFxo0bSx1SlfDy8sKiRYuwaNEis497eHhgy5Yt9g3KBjDGUFhYiOrO73DFrZasdXU1yFN+8OLKiyfAjytPnlIgeUvZk08+iYyMDLMJmYFvvvkGhw8fdpqEjKiMK3ZfEgRBEIQrYZdBXA0bNrzvMcHBwXaIhLAWSsoIgiAIQlrsuk4ZYUpBQQG8vb1tvs7JvdDr9cjNzUWDBg2qNVDx8mWgUSNApQJKS8Wfzo61rq4GecoPXlx58QT4ceXFMy8vD76+vjb//KakzIE4IimzFq0WcHMDGAOyswF/f0dHRBAEQRCOQarPb/mmsYRZtFotNm3aZHZPzHuh0QANGoi/u0oXprWurgZ5yg9eXHnxBPhx5clTCigp4xBrp/K66rIYPECe8oMXV148AX5cefGUAkrKiCpDg/0JgiAIQjooKSOqjCuuVUYQBEEQroJDkzK5743ljKjVasTFxVm1maqrtZTVxNWVIE/5wYsrL54AP648eUqBQ5MymvjpGDw8PKx6niuOKbPW1dUgT/nBiysvngA/rrx4SoFDkzKFQuHI03OJTqdDfHy8VQMxXa37siaurgR5yg9eXHnxBPhx5clTCqxqfwsPD7cqoZo8eTJeeeUVa05JOAGu1n1JEARBEK6EVUnZihUrrDoZ7W3p2lBSRhAEQRDSYVVS1qNHD1vHQbgAhqQsPx8oKQFo2ABBEARB2A6HbrOkUqkgCIKjTu9wHLHNEmMMOp0OarW62l3QjAG1awN37gBnzwLh4RIFaSNq4upKkKf84MWVF0+AH1dePPPz8+Hj42Pzz2+bz+ksLS3F77//jqysLJOEa8aMGbY+FWElJSUl8PLyqvbzFAqxtez8ebEL09mTMsB6V1eDPOUHL668eAL8uPLiKQU2n335yCOPYOvWrahVqxY8PT2NN8I50Ol0SEpK4mKrpZq6ugrkKT94ceXFE+DHlSdPKbB5S1leXh6+/fZbW1dLOAk02J8gCIIgpMHmSdnEiROxePFi9O3bF7Vq1TKWh4aG2vpUhANwtbXKCIIgCMJVsHlSdvPmTXz11VdYtWqVcZCfQqHA3r17bX0qwkpqsj2Eq7WUyX2rDwPkKT94ceXFE+DHlRdPKbD57MtmzZrh2LFjcHd3v++xNPvS/rMva8qSJcBzzwGPPgr88YejoyEIgiAI+yPV57fNB/q3b98eOTk5tq6WsBF6vR45OTnQ6/VWPd+Vui9r6uoqkKf84MWVF0+AH1eePKXA5klZeno6oqKi0LFjR8TGxiImJgaxsbG2Pg1hJYIgICUlxeoWSlfqvqypq6tAnvKDF1dePAF+XHnylAKbd/z+9ttvtq6ScCIMSVl2NqDXA0qHbmlPEARBEPLB5h+pCoXC7I2QBwEB4k+tFrh507GxEARBEIScsHlL2YgRI6BQKMAYQ2lpKU6dOoWWLVvi0KFDtj4VYQUKhQJeXl5WJ8q1agH16wM3bohdmA0a2DhAG1JTV1eBPOUHL668eAL8uPLkKUm9Uu99mZGRgTlz5mDt2rWVHqPZl643+xIAWrcGTpwAEhOB3r0dHQ1BEARB2BeXmX1ZkSZNmiA1NVXq0xBVRK/X48KFCzWaOeIqWy3ZwtUVIE/5wYsrL54AP648eUqBzbsvR40aZWzWEwQBJ0+eRN++fW19GsJKBEFAamoqgoODobRylL6rzMC0hasrQJ7ygxdXXjwBflx58pQCmydlzz//vPF3lUqF0NBQ2mJJZrjSWmUEQRAE4SrYPI3t0aMHmjRpArVaDb1ej/PnzyM5OdnWpyEciKu0lBEEQRCEK2HzlrJXX30VW7duRevWrY1NlwqFAt27d7f1qQgrUCgU8PPzq9HMEVcZU2YLV1eAPOUHL668eAL8uPLkKUm9tp59GRUVhRMnTkClUt332OrOviwtLcXvv/+OrKwsk+fNmDHDqlgdjavOvty2TZx12aIFcPKko6MhCIIgCPviMrMvO3TogKysLFtXCwB45JFHsHXrVtSqVQuenp7GG1F1BEHA6dOnazRI0VXGlNnC1RUgT/nBiysvngA/rjx5SoHNuy9Pnz6NVq1aoUWLFnBzcwNjDAqFAnv37q1x3Xl5efj2229tECW/6PV6pKWloWnTplVqzTSHofvy1i2gtBRwc7NhgDbEFq6uAHnKD15cefEE+HHlyVMKbJ6UbdiwwdZVGpk4cSIWL16Mvn37olatWsZymt1pX3x9xZX9y8rE1rKwMEdHRBAEQRCuj82TsjAJP6Fv3ryJr776CqtWrTIOsrNVKxxRdRQKsbUsK4uSMoIgCIKwFTYZUyYIAq5evYrMzEwUFBTYokqzrFy5EufOncO+ffuQkpKClJQUyRIyQRAwZ84chIeHw8PDA02bNsW8efNQfl4EYwxvvfUWgoKC4OHhgd69eyM9PV2SeGyFUqlEaGhojRf1c4VlMWzl6uyQp/zgxZUXT4AfV548JanX2ifevn0bS5cuxcMPP4y6deuiUaNGaN68OXx9fdG0aVO88MILNt+EvH379sjJybFpnZb46KOP8PXXX+PLL7/EqVOn8NFHH2HBggX44osvjMcsWLAAn3/+Ob755hvs378fnp6e6NevH+7cuWOXGK1BpVKhXbt2Ne7rd4VlMWzl6uyQp/zgxZUXT4AfV548pcCqpOyTTz5BWFgYVqxYgYcffhi//fYbUlNTkZaWhpSUFMyZMwdarRZ9+/ZFv379kJaWZpNg09PTERUVhY4dOyI2NhYxMTGIjY21Sd0V2bt3LwYNGoSBAweicePGGDZsGPr27YsDBw4AEFvJFi1ahDfffBODBg3CAw88gFWrVuHKlSvYuHGjJDHZAkEQcOTIkRrPHHGFljJbuTo75Ck/eHHlxRPgx5UnTymwakzZ8ePHsXfvXjRv3tzs4506dcL48ePx9ddfY/Xq1di/fz8iIyNrFCgA/PbbbzWuo6rExsZiyZIlOHPmDJo3b45//vkHu3fvxsKFCwEA586dw7Vr19C7d2/jc7y9vdG5c2ekpKRg5MiRleosLS1FaWmp8b6hq1er1UKr1QIQm0QN67eVn91hKNfpdCZdqCqVCkql0mK5oV4DjDFkZWUhMjISGo3GWK5Wiy8FnU5ncrxGo4Ferzd5ASoUCgQFicdfuSJAq9Uby9VqtcXYpXKyFLslV0tOhl0ozJU7i5O5cq1Wi6ysLLRs2dIkFld2AipfJ4Nn69atoVAoZOFUPnZLrgBk4VS+3BC7wbNFixaycaoYuyVXOThVLNfpdCgrKzO+77q7u8vCyVzsZWVlkIJqJWVffvklXnrpJTz11FMWE7LyaDQaTJgwwergKmLPFYJnzZqFgoICREVFGS/Y+++/jzFjxgAArv3bRBQQEGDyvICAAONjFZk/fz7mzp1bqTwhIQG1a9cGIM4kbdeuHY4ePWqy3ltkZCSioqJw4MABXL9+3VgeHR2NsLAwJCcno7Cw0FgeExMDf39/JCQkmLyQu3XrBgBITEw0iWHAgAEoKSlBUlKSsUytVmPgwIHIzc1FSkqKsdzLywuBgb0AAP/8k4P4eLH10M/PD7GxsUhPTzdpHZXaKS4uDh4eHoiPjzdx6tOnTyXXezn16tULFy9eRGpqqrHc2ZwsXScAuHHjBg4ePCgbJ3PXyYCcnCxdJwNycrJ0nU6cOIEOHTrIysnSdcrMzESrVq1k5WTuOiUmJsrOCbh7nfbv3w8pqNaK/m3btsU///yD9u3b4/DhwzU+eXVX9I+JiYFCoQBjDKWlpTh16hRatmxp87FrALBu3Tq89tpr+Pjjj9GqVSukpqZi8uTJWLhwIcaNG4e9e/eiS5cuuHLlCoIMq6kCGD58OBQKBX766adKdZprKQsJCUFubq5xRWB7tJT99ddf6NOnT41ayv78U43//Ad48EE99uwRjOXO1FphyVWOLWWJiYno37+/yeBTV3YCzLeUJSYmYsCAAZXeO1zVqXzsllyVSqUsnMqXl289SkxMRL9+/eDu7i4Lp4qxW3KVg1PFckNLWWJiIvr06SPrlrLs7GwEBgbafEX/arWUPfzwwwgODkZubi6Cg4ON5YYFYq9cuWKzwMxR8RtzRkYG5syZI8m5XnvtNcyaNcvYDdmmTRtcuHAB8+fPx7hx4xD476Cq7Oxsk6QsOzsb0dHRZut0c3ODm5mVVjUajUnSAIgX3txAQsOLs6rlFesVBAGRkZFwc3MzW3/F4wHxxVlxpolhTFl2thIajeljlmKXyslS+b1czTndq9xZnMyVK5VKREZGQq1Wm43FFZ0MlI/d4Gkok4OTpfLyrnJxKo8hdoOnoU45OFkqr+gqB6eKqNVqKBQK4/uuoU5XdzJH+bVSbUm1BvovXLgQV65cwZAhQ3DlyhXj7erVq5InZOZo0qSJ2eZ+W1BcXFzpgqpUKmMmHR4ejsDAQGzbts34eEFBAfbv34+YmBhJYrIFKpXK2CVbE8pvtWTb3VNth61cnR3ylB+8uPLiCfDjypOnFFQrKfvyyy8BAC+88IIkwdyPUaNGYfTo0Rg9ejRGjBiBtm3bom/fvpKc67HHHsP777+PTZs24fz58/jtt9+wcOFC/Oc//wEgNoVOnjwZ7733Hn7//XccO3YMY8eORXBwMAYPHixJTLZAp9Nh7969lZqBq4thKF1ZGZCXV/O4pMBWrs4OecoPXlx58QT4ceXJUwqq1X25dOlSvPTSS5gyZYpNxpRVl+eff974u0qlQmhoqGRbLH3xxReYM2cOJk2ahJycHAQHB+O5557DW2+9ZTxmxowZuH37Np599lnk5eWha9eu2Lx5M9zd3SWJyRYwxnD9+nVUYyihWdzdAR8fMSG7elXcesnZsJWrs0Oe8oMXV148AX5cefKUghqNKTOMJbPXmLIePXrg4sWLyMrKgiAIOH/+PM6fP4/u3bvb/FxeXl5YtGgRFi1aZPEYhUKBd999F++++67Nz+8KBAWJSdm1a0DLlo6OhiAIgiBcm2olZQsXLsTChQsxcuRIrFu3TqqYLPLqq69i69ataN26tXG8l0KhkCQpI+5PYCBw6pRzLyBLEARBEK6CVYvHrlu3DidOnEB6ejoGDx6MgoIClJSUVFqzy9Zs2bIFJ06ckP0AQilRqVSIjo62yd/Q2bdasqWrM0Oe8oMXV148AX5cefKUAqu2Wfroo48wbdo0zJgxAwCQl5eHJ554wuSYhQsXIq/cCPBbt27h008/tT5SAB06dDBZ3I2oPkqlEmFhYTbZTNXZt1qypaszQ57ygxdXXjwBflx58pSkXmuetHbtWvz111/w9PQEIK6Im5+fb3LM6tWr4ePjY7zv6+uLVatWWR8pgNOnT6NVq1bo0KGD5HtfyhWdToft27fbZOZI+WUxnBFbujoz5Ck/eHHlxRPgx5UnTymwqvuyVq1aUCgUxm2PKiZkgLhwZ0lJCTw8PAAAt2/frrHEhg0bavR8QpwxUlhYaJOZI87eUmZLV2eGPOUHL668eAL8uPLkKQVWJWXPPvssRo8ejRs3buDDDz/EunXrMH36dJNjXnjhBXTt2hUjRowAAPz88894+eWXaxRsWFhYjZ5P2BZnH1NGEARBEK6EVUnZxIkTERMTg6SkJDDG8OOPP6JVq1Ymx7zwwgvo0qULdu7cCQBYvnw52rRpU+1zCYKAnJwcFBcXw8/Pz6Z7TBE1w9lbygiCIAjClajWhuS2xtKG5Ldv38aaNWuwbt067Nu3D3fu3DE+1rhxY/Tt2xcTJ05Ehw4d7BmuzSkoKIC3t7fNNzS9F3q9Hrm5uWjQoEGNByrm5gJ+fuLvpaWARFuBWY0tXZ0Z8pQfvLjy4gnw48qLZ15eHnx9fW3++W31X2zXrl0YMmSI8f7KlSuxf/9+3L59u0YBffLJJwgLC8OKFSvw8MMP47fffkNqairS0tKQkpKCOXPmQKvVom/fvujXrx/S0tJqdD7eUCqV8Pf3t8k/S716gGGv1pycGldnc2zp6syQp/zgxZUXT4AfV548JanX2id+8cUXaNu2rfH+Sy+9hNjYWHh7eyMiIgLDhw9HaWlptes9fvw49u7diz179mD27Nno27cv2rRpg4iICHTq1Anjx4/Hd999h2vXrmHEiBHYv3+/tQpcotVqsWnTJmi12hrXpVTe3QPTGceV2dLVmSFP+cGLKy+eAD+uPHlKgdVJ2ZEjRzBo0CCTsh07diA+Ph6TJk3C4cOH8f3331epLsNG5zt37sSKFSvQvHnz+z5Ho9FgwoQJGDt2bPWD5xxbTuV19nFlcp+WbYA85Qcvrrx4Avy48uIpBVYnZZcuXYKfYUDRvzRs2BB9+/bF1KlTMXPmTKxfv75KdS1duhQAMGXKFGvDIRyEs69VRhAEQRCugtVJmbe3Ny5fvmy8f+zYMTRs2NB4PzY2FkePHq1SXYaNzo8fP47g4GDjLSgoCMHBwdaGSNgBZ28pIwiCIAhXwerZl8OHD0dAQAC++OILs49nZmaiTZs2KC4utlhHxdmXjtro3FE4YvalYWE/Ly8v4+K/NWHOHOC994AXXgAWL7ZBgDbE1q7OCnnKD15cefEE+HHlxTM/Px8+Pj7OM/tyxowZWLJkicWtk/bt24cmTZpUqS7DmLIXXnjB2nCIamDYZcEWOHv3pS1dnRnylB+8uPLiCfDjyounFFidlHXs2BFLly7FhAkTMGjQIGzatAk5OTm4desW/vjjD8ycORPDhw+vUl00psx+6HQ6xMfH22wgpjN3X9ra1VkhT/nBiysvngA/rjx5SoFVK/obGDt2LJo1a4apU6fiscceMzZVMsbQt29fzJw5s0r1GMaU5ebmIjg4GIwxKBQK488rV67UJExCQmirJYIgCIKwDTVKygAgJiYGKSkpyMzMxLFjx1BUVISWLVuiffv2Va5j4cKFWLhwIXdjyuRA+ZYyxgAZDyEgCIIgCEmxKinTarXQaDQmZU2bNkXTpk2rfLw51q1bhxMnTiA9PR2DBw9GQUEBSkpKEGBYoZRwOgxJ2Z07QEEB4O3t2HgIgiAIQkoEAdi9W5oWCKtmXzZu3BjTp0/HmDFj4Ovra/G4vXv3YtGiRWjXrh1ef/31So9XnH350UcfISkpCWfPnsWZM2eQlZWFJ598EsnJydUN0SVw1OxLnU4HtVpts5kx3t5iQnb6NBAZaZMqbYIUrs4IecoPXlx58QT4cZW754YNwLRpwPnzBQBs//lt1UD/P/74AwkJCWjYsCF69OiBKVOmYMGCBfjss88wd+5cjBo1CmFhYRg2bBi6dOmC6dOnV6netWvX4q+//oKnpycAIDQ0FPn5+SbHLFy4EHl5ecb7t27dwqeffmqNBreUlJTYtD5nHldma1dnhTzlBy+uvHgC/LjK1XPDBmDYMKBNGyAx0arVxO6LVUlZmzZt8Pvvv+PEiRMYOHAgzp49izVr1uDrr79GQkICfH198dlnnyErKwuvvvpqlbouAaBWrVpQKBTG7LpiQgYAq1evho+Pj/G+r6+vxWU5iMrodDokJSVxsdWSFK7OCHnKD15cefEE+HGVq6cgiC1kjz4KbNwItGvnhLMvw8PDMWPGDFvFgmeffRajR4/GjRs38OGHH2LdunWVWtkEQUBJSYlxHZTbt2/L7uK7Gs6+VhlBEARB1IRdu4Dz54G1awGlEvj1V2m6Zms8+9KWTJw4ETExMUhKSgJjDD/++CNatWplcswLL7yArl27YsSIEQCAn3/+GS+//LIjwiX+xVlbygiCIAjCFhiG57RuLe5e8+KLKknO41RJGQC0atWqUiJWnhdeeAFdunTBzp07AQDLly9HmzZt7BWeLFCrbXvZnXlMma1dnRXylB+8uPLiCfDjKkdPQ4/QjBnA118DgBPNvrQVFWdfGti1axc+/fRTbNiwAQCwcuVKREVFoXXr1sZJAHLAEbMvpWDlSmD8eKBvX2DLFkdHQxAEQRC2RacDGjQADEPdX3utAB9/7CSzL6Xmiy++QNu2bY33X3rpJcTGxsLb2xsREREYPnw4SktLHRih66LX65GTkwO9Xm+zOp21+1IKV2eEPOUHL668eAL8uMrRU68HXn75bkLWsiXQu7c0fjZNylQqFc6cOVPjeo4cOYJBgwaZlO3YsQPx8fGYNGkSDh8+jGXLltX4PDwiCAJSUlLMtlBai7N2X0rh6oyQp/zgxZUXT4AfV7l5arXAf/8LfPONuGPN888DxcVAv37StGnZtOPXVj2hly5dgp+fn0lZw4YN0aRJE/Tt2xdeXl5Yu3YtJk2aZJPzETXDkJTl5oov4CqugEIQBEEQTsudO8CIEcDvvwNqNbB6NTBypLg8xp9/6jB4sO3P6ZTdl97e3rh8+bLx/rFjx9CwYUPj/djYWBw9etQRoRFmaNAAUKnEvS+vX3d0NARBEARRMwoLgYEDxYTM3V1cm2zkSPExlQro2tWJFo+Vmu7du2P16tXG+40bN4abm5vxvru7O4qLix0RmsujUCjg5eVl0+0vVCrA31/83ZnGlUnh6oyQp/zgxZUXT4AfVzl43rwJ9OkDbN8O1KkD/PWXmKCVRyo/m86+VCqVOH36NJo3b16l4y3Nvvz777/RpUsXLF26FGPHjq30+I8//oj58+fj+PHjNY7Zkchl9iUAtG8PHDkC/Pln5RcvQRAEQbgC166JKwkcOwbUqwds3gw8+GDl46T6/HbKlrKOHTti6dKlmDBhAgYNGoRNmzYhJycHt27dwh9//IGZM2di+PDhjg7TJdHr9bhw4YLNZ8Y44wxMqVydDfKUH7y48uIJ8OPqyp4XLgDduokJWVAQsHOn+YQMgGR+TpmUAcDYsWOxa9cu5OTk4LHHHkNQUBAaNGiAQYMGoXXr1pg5c6ajQ3RJBEFAamqqzWfGOONWS1K5OhvkKT94ceXFE+DH1VU9T58GunYFMjKA8HBxW6XWrS0fL5WfUy+7GxMTg5SUFGRmZuLYsWMoKipCy5Yt0b59e0eHRlTAGVvKCIIgCOJ+HDkC9OsnTlRr0QJITATKzS20KzZNymbOnIn69evXqA6tVgtNhTUVmjZtiqZNm1b5eML+OOtaZQRBEARhiT17xHHQ+flAhw7iGLIGDRwXj027L+fPn1/jpKxZs2b48ssvcevWrXset3fvXgwfPhyffPJJjc7HGwqFAn5+fjafOeKM3ZdSuTob5Ck/eHHlxRPgx9WVPBMSxEH9+fniWLJt26qekLnE7MvqYm725bFjx/DGG29g69atePDBB9G+fXsEBQXBzc0NeXl5OH36NPbu3QutVouZM2di0qRJkrSUNW7cGBcuXKhUPmnSJHz11Vfo2bOncVN0A8899xy++eabKp9DTrMvd+8WX9RNm4p98gRBEAThrPz6KzBqlLjg+SOPAL/8AtSuXfXnczP7sk2bNvj9999x4sQJDBw4EGfPnsWaNWvw9ddfIyEhAb6+vvjss8+QlZWFV199VbKuy4MHD+Lq1avGW2JiIgDgiSeeMB7zzDPPmByzYMECSWKxJYIg4PTp0zYfpFi++9Jxab4pUrk6G+QpP3hx5cUT4MfVFTxXrgSGDxcTsieeEBeGrU5CBnA40D88PBwzZsxw2PkrbvP04YcfomnTpujRo4exrHbt2gg0ZCNVoLS01GQj9YKCAgDiuDitVgtAXOvN0IJYfsqtoVyn05lsZ6VSqaBUKi2WG+o1wBhDWloaQkNDTRJatVp8Keh0OpPjNRoN9Hq9yQtQoVBArVablIu91hoUFwP5+QI8PSvHLpWTpdgtuVbVqXy5pdjt7WSuXKvVIi0tDeHh4SaxuLITUPk6GTybNm0KhUIhC6fysVtyBSALp/LlhtgNno0bN5aNU8XYLbnKwaliuU6nQ1lZmfF9193d3emcFi3SY8oUsT3qqaf0+OYbhlq1qv/aKysrgxTYPCnLycnBpUuXoFAo0KhRo0rJjStSVlaGH374AVOnTjXpR/7xxx/xww8/IDAwEI899hjmzJmD2vdIt+fPn4+5c+dWKk9ISDA+LzQ0FO3atcPRo0eRlZVlPCYyMhJRUVE4cOAArpfbyyg6OhphYWFITk5GYWGhsTwmJgb+/v5ISEgweSF369YNAIwtfwYGDBiAkpISJCUlGcvUajUGDhyI3NxcpKSkGMu9vLzQq1cvXLx4EampqcZyD49HUVKiwr5951FaendhX6md4uLi4OHhgfj4eBOnPn36VHKtrpOfnx9iY2ORnp6OtLQ0hztZuk4AcOPGDRw8eFA2TuaukwE5OVm6Tgbk5GTpOp04cQIdOnSQlZOl65SZmYlWrVrJysncdUpMTHQqpwYN/LB9eyzeektMyB5/PAOPP34CJ05Y9/+0f/9+SIHNxpQdOXIE06dPh0qlwqFDh9C1a1dcvnwZtWvXxueff47o6OhKz7G0or+z8fPPP2P06NHIyspCcHAwAGDJkiUICwtDcHAwjh49ipkzZ6JTp07YsGGDxXrMtZSFhIQgNzfX2Cdtj5ayv/76C3369LFpSxkAtGypRkaGAklJArp0cfw3e0uucmwpS0xMRP/+/aFU3h2R4MpOgPmWssTERAwYMKDSe4erOpWP3ZKrUqmUhVP58vKtR4mJiejXrx/c3d1l4VQxdkuucnCqWG5oKUtMTESfPn2cpqWMMWDWLBU+/VR8f3zrLQFvvKGHQmH9/1N2djYCAwNtPyac2Yjo6Gh28eJF4+8G/vnnH9amTRuzz1EqlbY6vaT07duXPfroo/c8Ztu2bQwAy8jIqHK9+fn5DADLz8+vaYhVRqfTscOHDzOdTmfzurt2ZQxg7KefbF61VUjp6kyQp/zgxZUXT8b4cXU2T52OsYkTxc8mgLFFi2xT782bNyX5/LZZ9+WdO3cQ9O+6COW7+CIiInDnzh1bncbuXLhwAVu3br1nCxgAdO7cGQCQkZFhcU01Z0ClUqFdu3aS1O1sC8hK6epMkKf84MWVF0+AH1dn8iwrA8aOBX76CVAqge++A556yjZ1q1Qq21RUAZvNvpw5cyZ69eqFjz/+GDdu3MDnn3+OWbNmoUuXLnjrrbdsdRq7s3z5cvj7+2PgfXbZNvRdGxJTZ0UQBBw5ckSSbmNnW6tMSldngjzlBy+uvHgC/Lg6i2dJCfCf/4gJmUYj/rRVQgZIN/vSZknZ+PHj8ccffyAsLAyvvPIKtFot2rdvj+TkZDz55JO2Oo1d0ev1WL58OcaNG2fs0wbEgZrz5s3DoUOHcP78efz+++8YO3YsunfvjgceeMCBEd8fvV6PrKwsSTZTdbaWMildnQnylB+8uPLiCfDj6gyeBQVA//5AfDzg4QH8/jswbJhtzyGVn01nX9atWxfDhw+3ZZUOZevWrcjKysKECRNMymvVqoWtW7di0aJFuH37NkJCQjB06FC8+eabDorUOaCtlgiCIAh7Igji5uFXr4q9NS1aAI8+Cvz9N1C3LvDnn+LC5q6C1UnZrl278OmnnxrHWq1cuRJRUVFo3bo1PD09bRagI+nbt6/JrAsDISEhlVbzJ5yvpYwgCIKQLxs2ANOmAefP3y3TaMRFYRs0ALZsAdq3d1h4VmF19+UXX3yBtm3bGu+/9NJLiI2Nhbe3NyIiIjB8+HCT5R8I50CpVCIyMtJk6QRb4WxjyqR0dSbIU37w4sqLJ8CPq708N2wQuyTbtAFSUoCjR8XPIMMqG3PmSJuQSeVn9TplzZo1w/r1643rj3l5eSE+Ph4lJSU4fvw4Fi9ejGnTpuGFF16wWIerrFMmFXLa+xIQm4+Dg8VZLmVlgESTUwiCIAiOEQQgIkJMyDZuBE6fBvr0Aa5cAZo0AcLDgbNngfR06T6HnG7vy0uXLlVarb9hw4bo27cvpk6dipkzZ2L9+vU1DpCwLTqdDnv37q20CJ8t8PMDFApArwfKLYDsMKR0dSbIU37w4sqLJ8CPqz08d+0SuyxnzxY3Fo+NFROy1q2B3buBefOAc+fE46RCKj+rkzJvb29cvnzZeP/YsWNo2LCh8X5sbCyOHj1as+gIm8MYw/Xr182OlaspajXg7y/+7gxdmFK6OhPkKT94ceXFE+DH1R6ehslk334rbiyeny8mZjt3il2YrVubHicFUvlZnZR1794dq1evNt5v3Lgx3NzcjPfd3d1RXFxcs+gIl4MG+xMEQRBSYkgtVqwQe2dmzwZ27ADq1RPLj/+79bKTLxtqFquTshkzZmDJkiVYtWqV2cf37duHJk2aWB0Y4ZrQshgEQRCEFDAGfPUVMGmSeN/NDUhIAN5/X5x1CYjDZ+bPF8eVudJSGAasTso6duyIpUuXYsKECRg0aBA2bdqEnJwc3Lp1C3/88QdmzpwpqzXL5IJKpUJ0dLRkW0Q4U0uZ1K7OAnnKD15cefEE+HGVyvPGDWDwYOCll8SJZB06iD8//1ycfVlYKP4cPFhcm+yTT6SdbCbVdazR4rFjx45Fs2bNMHXqVDz22GPGPS8ZY+jbty9mzpxpkyAJ26FUKhEWFiZZ/c60LIbUrs4CecoPXlx58QT4cZXCc8cO4MkngcuXgVq1xITrpZeA334T1ymLjb17bHg48MsvwJAhNg2hElItiVHjWmNiYpCSkoIzZ87gl19+wcqVK/H3339j8+bNJmPMCOdAp9Nh+/btks0ccaaWMqldnQXylB+8uPLiCfDjaktPnU5cb6xXLzEhi4wE9u8HXn5ZHEs2ZAiQkQEkJQFr1og/09OlT8jE2KS5jjbbZikiIgIRERG2qo6QCMYYCgsLJZs54kxjyqR2dRbIU37w4sqLJ8CPq608L1wARo8G9u4V70+YIHZVVtwwSKUCevas0amsQqrraFVSFh4ebuyqrA6TJ0/GK6+8Ys0pCRfBmVrKCIIgCNfjl1+AZ54B8vLE/Su//RYYOdLRUdkHq5KyFStWWHWyxo0bW/U8wnVwpjFlBEEQhOtQXAxMmQIsWSLef+ghsVsyPNyxcdkTq7dZsgVKpRJ6vd5Rp3c4jthmSa/XIzc3Fw0aNJBkoGJBAeDtLf5eVFS5qdmeSO3qLJCn/ODFlRdPgB9Xaz2PHhVbw06dEseLvf468M47d5e6cDby8vLg6+tr889vhyZlvCO3vS8BcR0ZT0+gpEQcgNm0qaMjIgiCIJwVxoDFi8VZlKWlYm/L6tXAww87OrJ743R7XxKuiVarxaZNm6DVaiWpX6Fwni5MqV2dBfKUH7y48uIJ8ONaHc8bN4D//Edc3qK0FBg4EPjnH+dPyABIdh0pKeMQqadkO9Ngf7lPPzdAnvKDF1dePAF+XKviuXMn0LYt8H//J6499tlnwB9/AH5+dgjQiaGkjLA5zrQsBkEQBOE86HTAW28BcXHi2mPNmwP79gGvvCL2tPCOzdYpIwgDztRSRhAEQTgHFy4AY8YAe/aI9ydMEFvI6tRxbFzOBLWUcYZarUZcXBzUaunycWcZU2YPV2eAPOUHL668eAL8uFry/PVXIDpaTMjq1hWXuli2zHUTMqmuo7xfHYRZPDw8JK3fmVrKpHZ1FshTfvDiyosnIH9XQQCSk4FLl2qjUSOge3dxAH/5tcc6dxYTsiZNHBurs0ItZZyh0+kQHx8v6YBTZxlTZg9XZ4A85Qcvrrx4AvJ33bABiIgAevVSYOxYNXr1UiA0VNyvcsmSu2uP7dolj4TM6fe+JAgDztRSRhAEQUjLhg3AsGHAo48Cq1frcPHiZhw8+Ag++0wFvR7w8RG3TnKFpS4cDSVlhM0xjCnLzgb0ekDGi1cTBEFwjSCIC78++iiwcSOQk8MwbVoHHDigAgD4+wPu7o7ZNNwVoY9Lwub4+4s/BUFcHJAgCIKQJ7t2AefPA7NnAykpwIMPqnHgQBA0GoZPPwV++w3IyhKPI+4PbbPkQByxzRJjDDqdDmq1GgoJF4Xx8wNyc8XVmR94QLLT3BN7uToa8pQfvLjy4gnI13XtWmD0aOD998X1xwQBaNaMYe1aoEMHBQoL7862HDXK0dHajvz8fPj4+NA2S0TNKSkpkfwczjKuzB6uzgB5yg9eXHnxBOTpaljS4o03xIRs5EiGpKRCtG8vlh8/Lv40DGsh7g0lZZyh0+mQlJQk+QwgZ1irzF6ujoY85Qcvrrx4AvJ0/ftvcSV+QJxd+dVXwMqVOvz9t+ip1wPz5wPh4UC3bo6N1dZIdR0pKSMkwVmWxSAIgiBsC2PA4sVAly7ieDLDOOLNm4H9+xUoKVFj3z4FBg8G/vwT+OQTQKVyZMSuA82+JCTBWbovCYIgCNtRWAg88wzw00/i/cGDgeXLge3bxVmY3burAQwEILaQ/fILMGSIw8J1OSgp4xB7bPPhDN2XgH1cnQHylB+8uPLiCbi+69GjwBNPAGfOAGo1sGABMHmy2HU5ZAgwaBCQlKRDQsIx9O3bBnFxamohqyY0+9KBOGL2pb0wzMjp2RNISnJ0NARBEERNWL4cmDQJuHMHaNQI+PlnICbG0VE5Dqk+v2lMGWfo9Xrk5ORAr9dLeh5nGFNmL1dHQ57ygxdXXjwB13UtLgaeegqYMEFMyPr3B44csZyQuapndZHKj5IyzhAEASkpKRAEQdLzOMOYMnu5OhrylB+8uPLiCbim6+nT4gbiK1aIO7O8/z6waRPQoIHl57iipzVI5efaHdyE02IYU5afD5SUAB4ejo2HIAiCqDpr1wLPPgsUFYlfsteupa2S7AG1lBGS4O0NuLmJv2dnOzYWgiAIomrcuSOOHRs9WkzI4uLE7kpKyOwDJWWcoVAo4OXlJfk2HwqF48eV2cvV0ZCn/ODFlRdPwDVcz54V1x77+mvxPfzNN4HExLvv5VXBFTxtgVR+NPvSgch59iUAPPQQsH8/sGED8J//ODoagiAIwhK//SYO6M/PB+rXB374QRzUT5iHZl/amcaNG0OhUFS6vfjiiwCAO3fu4MUXX0T9+vVRp04dDB06FNku0E+n1+tx4cIFu8yMcfRaZfZ0dSTkKT94ceXFE3BeV61WXPR1yBAxIYuNFbsrrU3InNXT1tDsSztz8OBBXL161XhLTEwEADzxxBMAgClTpuCPP/7A+vXrsXPnTly5cgVDXGDZYkEQkJqaapeZMY7uvrSnqyMhT/nBiysvnoBzul68CPToASxcKN6fNg3YsQMICbG+Tmf0lAKafWln/Pz8TO5/+OGHaNq0KXr06IH8/HwsW7YMa9asQa9evQAAy5cvR4sWLbBv3z489NBDZussLS1FaWmp8X5BQQEAQKvVQqvVAgCUSiVUKhUEQTDJxA3lOp0O5XucVSoVlEqlxXJDvQYMx1QsN6w0XXGTVY1GA71eb/ICVCgUUKvVFssNsfv5KQGocPWqHoBSMidLsVtyrYlTxdjt7WSu3PBcvV5vUo8rOwGVr1P5+uTiVD52S65ycSpfbojdcG5BEKDRaGThVDF2S66Odtq8WYGnnlLhxg0FvL0Zli0T8Pjj7N/nWv//ZIhTq9W65HUy52TpOkkBJWVVoKysDD/88AOmTp0KhUKBQ4cOQavVonfv3sZjoqKiEBoaipSUFItJ2fz58zF37txK5QkJCahduzYAIDQ0FO3atcPRo0eRlZVlPCYyMhJRUVE4cOAArl+/biyPjo5GWFgYkpOTUVhYaCyPiYmBv78/EhISTF483bp1AwBjy5+BAQMGoKSkBEnllt9Xq9UYOHAgcnNzkZKSYiz38vJCr169cPHiRaSmphrL/fz8EBsbi/T0dKSlpSE3NwxANM6cKQTgLZlTXFwcPDw8EB8fb+LUp0+fSq41dTIg9XWy5GTpOgHAjRs3cPDgQdk4mbtOBuTkZOk6GZCTk6XrdOLECXTo0EFWTpauU2ZmJlq1auUwp92792HdukisXx8JAOjQAfj00yu4efNvGKqyxf9TYmKiS1+n+7329u/fDymggf5V4Oeff8bo0aORlZWF4OBgrFmzBk899ZRJqxcAdOrUCXFxcfjoo4/M1mOupSwkJAS5ubnGgYJSfwsGgAMHDqB9+/Ym+7BJ8U3kjz8UGDpUjY4dGQ4eVNj9W7AlV0d/u7L1N0adTocjR46gY8eOJjOCXNkJqHyddDodDh8+jM6dO0OpVMrCqXzsllwVCoUsnMqXG2LX6XQ4dOgQHnzwQbi5ucnCqWLsllyldiot1WH3bgWuXhXH98bFqXHtmh5jxgA7d4ojl55/Xo9Fi5TQaGz3HqHVanHo0CF06NABbm5uLnedzDmZiz0nJwcBAQE2H+hPSVkV6NevH2rVqoU//vgDAKxOyioi99mXBw6Iq0E3aiSOXSAIgiCkZ8MGcXzY+fN3ywIDxYW88/OBOnWApUuBkSMdFqLLQ7MvHcSFCxewdetWTJw40VgWGBiIsrIy5OXlmRybnZ2NwOos6OIABEHA6dOn7TrQPzsbcMREHHu6OhLylB+8uPLiCdjPdcMGYNgwoE0bICVFTMKefVacBZ+fD4SGAn//LV1Cxss1lcqPkrL7sHz5cvj7+2PgwIHGsg4dOkCj0WDbtm3GsrS0NGRlZSHG0i6tToJer0daWppdpisHBIg/tVrg1i3JT1cJe7o6EvKUH7y48uIJ2MdVEMQWskcfBTZuBCIigOHDgSVLxMdDQsQ9LCMiJAuBm2tKS2I4AL1ej+XLl2PcuHEmY5K8vb3x9NNPY+rUqUhKSsKhQ4fw1FNPISYmxuIgfx5xcwPq1RN/d+TG5ARBEDywa5fYZTluHDB/PtC2LbBli7j38PffAz/9JD6+a5ejIyUsQbMv78HWrVuRlZWFCRMmVHrs008/hVKpxNChQ1FaWop+/fph8eLFDojSuQkMBG7eFNcqa9XK0dEQBEHIk+vXgZUrxd+HDbtbHhkJrF8vdmcaJkA6au1I4v5QUnYP+vbtC0vzINzd3fHVV1/hq6++snNUNUOpVCI0NBRKpX0aSQMDgZMnHdNSZm9XR0Ge8oMXV148AWlci4qA//s/4McfgYQEsfsSEPetfPhhcVPxESOAf1dcwvHj4k/DbitSwMs1lcqPZl86ELnPvgSAJ58U3zA+/hiYPt3R0RAEQbg2Wq3YJblmjZiQFRfffaxDB+DcOfHn5s3i+DEDej0weLCYmKWnAyqV3UOXFTT7krAJgiDgyJEjdpsZ48itluzt6ijIU37w4sqLJ1AzV70e2L0bmDRJbOV67DFg7VoxIYuIAN5+Gzh9WpxVuXQpsHWrmIClpIhdlikp4v0//wQ++UTahIyXa0qzLwmboNfrkZWVZbeZMYakzBHdl/Z2dRTkKT94ceXFE7DO9fhxYPZsoEkToFs34OuvgRs3xJntr74qrgV55gzwzjvi2DFA3Fj8l1+AY8fEzcXr1hV/Hj8ulku9RTMv11QqPxpTRkiKI5MygiAIVyMrC1i3Thz2cfTo3XIvLzGhGjMGiIsD1Pf49B4yBBg0SJxlaVjRv1s36rJ0BSgpIyTFMKCUkjKCIHhFEICdOxVITm4IT08F4uJME6SbN8VWrB9/BJKT75ZrNMCAAWIi9uij4tIWVUWlAnr2tJkCYScoKeMMpVKJyMhIu86+BBwzpszero6CPOUHL648eN7d8kgNoCMWLgQaNwY++EAciL9mDfDXX+IAfgM9eoiJ2LBhgK+voyK3Dh6uKUCzL2UJD7Mvb9wAGjQQf79zR1xQliAIggcMWx49+qg4NiwqClixAvjf/4BLl0yPjY4Wl7AYOVJceZ9wbmj2JWETdDod9u7dC51OZ5fz1asnNsED4h6Y9sTero6CPOUHL65y9iy/5dHHHwM//qhH06ZlmDLlbkKmVgOzZgEnTgBHjgCvveb6CZmcr2l5pPKjpIwzGGO4fv26xUVxbY1CcXcPzJUrgR077i5wKDX2dnUU5Ck/eHGVs6dhy6OyMqBlS+DLL5W4ebMWGjRgmDQJ+PZbQKcD+vUTH5cLcr6m5ZHKj5IyQlI2bABycsTf33pLnDUUESGWEwRByJGjR8UWMEBc6FWvBwYM0OPNN1Nw4YIOX30FjBolPk5bHhHloaSMkAzDeArDQNVFi8RFDNu0EcspMSMIQk4cPgz85z/iRuD794tlPXuK5Rs3CujYMcc4nMMeWx4RrgclZZyhUqkQHR0NlcQL1pQfT/H442LZrVvAQw8BGzeK5dOnS9uVaS9XR0Oe8oMXV7l47t8vvqd16CC+vykUwBNPAMHB4vpibduauur1wPz5QHi4uH6YnJDLNb0fUvlRUsYZSqUSYWFhkk9XNoynmD0baNhQLPvwQ+C554CMDOD118U92nbtki4Ge7k6GvKUH7y4urrn7t3imLCHHgI2bRKXuHjySXHg/s8/A198IW5tNHgwsH+/EvXqhWH/fqXdtjxyBK5+TauKVH7y/qsRldDpdNi+fbvkM2MM4yRatwbGjQM6dwZKS4ElS8Rp4e+/b3qcFNjL1dGQp/zgxdUVPRkDkpLE8bHdugEJCeIsygkTgLQ0YPVqoEUL8VhHb3nkCFzxmloDzb4kbAJjDIWFhZLPjDGMkzh+XFwoMSUF2LlT3EiXMfFbJSAmZxs3igNhbY29XB0NecoPXlxdyZMxcdB+t25Ar17iTHKNRmz9T08Hli0TJzFVZMgQsXcgMVGHqVP/RmKiDunp8kzIANe6pjWBZl8SLkW3bndXrdbrxTEW3bsDv/8uJmqhoeJxJ06IA2NbtBBb0e7ccWjYBEEQJjAmdjU+9BDQvz+wZ4+4CPZLLwGZmcA334jvdfdCpQJ69GDo3v0yevRgsuuyJGwHJWWEJKhU4qrVhvEUKSlAYaH48/XXgYsXge++E3/38QHOnBG/cYaFAfPmiTsBEARBOAq9Xpwh3qGD2MJ/4IC49+SUKeJ42C++cP2FXgnng7ZZciCO2GZJr9cjNzcXDRo0sMtAzLv7vt0tCw8XB7gamu8LC8Wm/08/BbKyxLLatcUxGlOnisdbg71dHQV5yg9eXJ3RUxDE8V7vvXd32QpPT7FlbOpUwN/funqd0VUKePHMy8uDr6+vzT+/KSlzIDzsfQmIb3K7domD+oOCxK5Nc833Wi2wfr24JUlqqlimVIprmr32GtCxo13DJghCZtzrvUinA9atE8e5nj4tltWtC7zyCjB5MlC/vsPCJpwQ2vuSsAlarRabNm2CVqu12zlVKnEBxVGjxJ+WxlNoNOKGvIcPA1u3ilPN9XpxavmDD4qzneLjxTEeVcERro6APOUHL6729NywQRyIHxcnvs8Ydhf5+Wfg++/FWeH//a+YkPn6AnPnAhcuiMMpbJGQ0TWVF1L5UVLGIc4+VVmhAB5+GNi8GfjnH/GNUq0WZzsNHCjuCLBihbin3P1wdldbQZ7ygxdXe3gadhdp0+bu+NadO8WWsBEjgKefFgftN2ggTk46f17cFs7Hx7Zx0DUl7gclZYRT88ADwKpVwNmz4tg0Ly9xxuZTT4ljzT76CMjLq/w8QQB27lQgObkhdu5U2G0TdIIgnIvyu4v89ps4OH/FCmDMGHGPSkBsvV+wQEzGXn9dTNYIwhFQUka4BCEh4uSAixfFRCw4GLhyRdz0NzRUfNO9eFE81tBN0aePGgsXdkSfPmraBJ0gnAwpvzjdvg0cOSKOEXv6aTHZOnNGbPlq1Ah4+WXg0iXxfWTyZDGWBx8UB/QThCOhgf4OxBED/Q0L+3l5eUGhUNjlnFJQVgasWSMmaidOiGVqtbha9q5d4rfi119nCAsrxIULXpg/X4E//5TnKtpyuab3gxdPQP6u5mZlN24sLqNT1f9PQRC/iKWlVb5dumT5eSqVOH7sxRfFFnetVmwZW7NGHPcqFXK/pgZ48czPz4ePj4/NP7/VNquJcBk8PDwcHUKNqVULGD9e3MLpr7/EGZs7dgDJyeLjd+4ARUVAgwYeCAoSdw0YPFjcBH3QIPntNyeHa1oVePEE5OtqGN/16KPAmjUMUVE6nD6txvz5CgwbVvmLU16e+cQrPV3cus0SDRoAkZFiwvXXX2IL++OPA02aiO8fBo4cEX8adiGRErle04rw4ikF1FLmQBzRUqbVahEfH48BAwZAo9HY5Zz24ttvgeefFycKGF7VtWtr0bq1Ci1bKuHpCXz1FfDjj+LgXrkkZnK+puXhxROQr6sgiEML2rQRvygJgujZp88AZGVpMG6cuCXR4MFi0pWWBuTkWK6vVi2xvsjIyrd69cyfs/zSWXq9eK7jx8XzSfmeINdrWhFePG/cuIEGDRpQSxlBWMLwf3H0qLhl07JlDMXFGhw4IK7GbWDMGHGcSVSUuL1Ty5Z3f0ZEiEtzWENV12MjCF7ZtUvssvz2W3FNwj//VCEpqReys9UoP2Hv++9NnxcUZD7xCgsThy3cC8PuIsOGiQnY668DrVuLidj8+TAOa6D/VcIZoKSMkA2G7oeiIuDzz4H583X4/vtdaNCgO9LT1UhOBrZtE5OuO3fEBWoNi9QaUKuBZs3EJK18whYZKe4yYAlbjJEhCLnCGHDsmLhPJAA88ojYSiXONfMCIG5h1LSpmCz95z/A0KHi/13z5jWfDTlkiJh4TZsmjjs1EB4uz3GmhOtCSRkhG8pvgr5xo9i1ERpaiAEDxA2ABw8W34RPnxa3czp5Ejh1SrwZfi8qultWHoVCrLt8q5ohcdu27e4YmbVr734L/+ADmB0jQxA8UFgo/m/Ex4u3y5fvPqbXA61aAf36CahT5wDGju2I8HAN9u8Xk6ZXXhEXmrYlQ4aI40mpNZtwZmhMmQNx1OxLnU4HtVoty5kx5QcRz5p1dxDxhx/ef/YlY+KsrfJJ2smT4u3mTcvnVKnEFcBHjhQ/aFq3FtdXq1PHPuNV5H5NDfDiCbimK2PishOGJGznTnFmowEPD6BXL2DfPiA6GkhIABSKu56MKew2vssRuOI1tQZePGn2JWEzSkpK4OXl5egwJKF8N0WXLgoA4gCxqnRTKBTiemghIUDfvnfLGQOuXzdN1gy/X7kijiXLzQW+/NK0viZNxLrOnQM+/FDcmSAkRDyPrZHzNS0PL56Aa7iWlIizng2J2Nmzpo83aSLuwjFwINCjB+DufveL0+DB4jqDYWEluHDBCx9+KP/xXa5wTW0BL55SQC1lDoRmX0qHIABJSTr89VcqHnkkGnFxakne6JctAyZOBBYvFj+QTp4Ux84YFrKtiK8v0Lat2FJguLVoYTpFvzrYy9MZ4OW1C9jftTqTVM6fv5uEbd8uJmYGatUSk68BA8Rbs2bmv4SYG4MZHi6uOyjXrn5eXr+8eNLsS4KoBioV0KMHw+3bl9GjR1vJEpWmTcWf7doBL7xwt/zGDXHfzv/7P3HSQZMm4ji2W7fEloUdO+4eq9GIY9Sio+8mbG3b3p3Sb4m7H2xqAB2xcCFNLpAD5Ve69/RUIC5O2paj+01SKSsDdu++m4hVHG/ZqJGYgA0cKHZP1qlz/3Maxnfx8oWCIKoKJWUEUQMqTi4wrIFUv744UHnRIrEF4MwZQKcTP9AMsz7/+Uf8mZcn/v7PP6Z1h4SYtqi1bSvWpVSajp1bvVqHS5c2o1Gj/liwQE2TC1wYeyfa5V9H5SepvPWWWN6pk9j6W1h49zkqFdCly93WsNatreuSt9cXJ4JwJSgp4xD1/Rb2kRFSu1ZnDSSV6m6CZYAxsQWtfJKWmiqOQ7t4Ubz98cfd4728xEkER4+KP998E4iKYsjPV6BzZ2aXnQscuR6bnF+7FRPta9e2IjCwt80TbZ1O3BsyP1+c5di1K/Dqq+IirfPni61hhqVi9u8Xf/r7i8tYDBggjrf08al5HAbkfE0rwosrL55SQGPKHIgjxpQR0mDrMTL5+WLiVb5V7fhx89vKKJXiuQICgMBAMdH77TdgyhQxYTKUBwTUfMNlR63HJveFeS2tOs+YuEzLsGFii9XGjUBxsVhWWCj+tPS7pcfLjwGzhEIhLq586pS4ttgzz5iuhE8QvCPV5zclZQ7EEUmZXq9Hbm4uGjRoAKXM32Xt7Sp14qDVitvOfP21OLGgZ08xUcvNrXodnp5icma4GZK1irfAwMpjg8q35Myebboem5SbvTtyYV4pr6leL9Z77pzYOjV/vtgSVVQEnD/PcOMGQ3GxAoxJs6yAUinG0LChuDirl5c49nHAAKBfP3EJC6k36qb3I/nBi2deXh58fX1poL89uXz5MmbOnIm//voLxcXFiIiIwPLly9GxY0cAwPjx47Fy5UqT5/Tr1w+bN292RLhVQhAEpKSkYMCAAbL+hwHs76pS2X7By/JoNGIi9MQTYlI2fz7QuTNw4YIWa9bsQ5MmMbhxQ41Dh4Dly8VxPzodkJ0t3kpKxG6rs2crL11gjtq17yZp/v5AUpI4m65vX3Eh0NJS8YN73jyx3ilTxK6wOnXEpQ9s8Sd35Ng5WySDeXni3/rcOfFW/vfz5yu3fMbHG35T/HszxccH8PMTE6g6dcSb4ffqlu3dKw7M/+UX4KGHKseekiL+lHKjbno/kh88eUoBJWUWuHXrFrp06YK4uDj89ddf8PPzQ3p6Onx9fU2O69+/P5YvX2687+bmZu9QCc6oOLmgYUOgVaublXYu2LnzbquOoRssOxu4du1uolb+Vr68uFi8GRIIA4WFwMsvW44tIODu725uYnLm4XH3Vp37bm7Au++KY/DGjhVnrmZk+CA0FHjvvbuJYM+eYqJhy9n3lgbAV9yl4c4dMbky/J0qJl55efc+j0olTujw8RG7qZ97DujeHQgJ0eHEiR0YOLAH6tXTIDVVTHh/+812iX/37uYnqQBiC9r8+eLrqFs325yPIIj7Q0mZBT766COEhISYJFzh4eGVjnNzc0NgYGCV6iwtLUVpua/GBQUFAMR1XbT/Ln2tVCqhUqkgCAL04uZwJuU6nQ7le5xVKhWUSqXFcm35JbUB4zEVyw0DM3XldwUGoNFooNfrTb4VKBQKqNVqi+WWYpfKyVLsllxd2UmtVkOpBD76SMDIkSo8/jjDtGkCSkrU2LuX4eOP9YiPV2DdOgHiae46ubuLGzg3bnx/p4IC/b8JmgLXrysQH6/C8uXAU08JuHlTgexs4NYtBUpKFCgpYSguBm7fNm3ZKS0Vb/n5qBE3boitg+LbVVfMmWP6eP36hr8NULs2Q+3aYiufh4f409NTAQ8PBg8Pw2MMHh4K1KmjhLu78O9xDB4eQJ06Snh4KPHiiwxdujDMnSv+3Q4eVOHCBSXatBFw+LASo0eLS5ZcvXr/rkV/f4bGjRkaNwYiIpQIC9MjLEyPxo0ZQkKAWrUUUCjUiIhguHSJYehQAYKgxc2btyG+tQiYP1+B8HAFHnpIB0Gw3Wvvf/9TYtgwhscfZ5g5U49Wrdi/O2CIXdLr1gnQ6xn0emneIwyve0EQoNFoHPL/ZGunirFbcpWDU8VynU5njFOr1crGydJ1kgIaU2aBli1bol+/frh06RJ27tyJhg0bYtKkSXjmmWeMx4wfPx4bN25ErVq14Ovri169euG9995DfcOnRAXeeecdzJ07t1L5mjVrUPvf3a5DQ0PRrl07HDlyBFlZWcZjIiMjERUVhb179+L69evG8ujoaISFhWH79u0oLDdvPSYmBv7+/ti0aZPJi6d79+44fPgwioqKTGIYMGAASkpKkJSUZCxTq9UYOHAgcnJykGLoywDg5eWFXr164cKFC0gtt6O3n58fYmNjcfr0aaSlpRnLpXaKi4uDh4cH4u/2/QAA+vbti927d6O4uFg2TuWvU0pKEJYvb4WcnLuj9wMCbmP8+BOIiblqU6dr16IQFwd89FEyIiNvVXI6eFCNWbO6Y+7cPXjyyeaoU8cPmzZtQ0kJUFqqRFmZCq1adYBe74Y9ew5Dq1WhrEyJ0lIVmjRphaIiLdLSslBaqkJZmQo6nRq3bzfC/v1A69bXIQjisVqtCipVHRQV6VFYyFBa6vjvlXXqMNSvX4CAgGLjbcCAFggKuoPz53fAw0P8YLjfa+/bb3Pwwgt+6NjxGoYOTUd4+G0EBfXB22+XYMeOOpgx4yBiYq7a/LX3xhuH8N13USavo/BwhuHDxfMZkPI9IiQkBO3bt3fo/5OtnSxdp2bNmqFly5aycpLjdbqf0++//45BgwbRQH974e7uDgCYOnUqnnjiCRw8eBCvvvoqvvnmG4wbNw4AsG7dOtSuXRvh4eHIzMzE7NmzUadOHaSkpEBlZjSwuZaykJAQ5ObmGi+qI1tgzJU7+psIOd3bSRCA3bsVuH5djYAAPWJjBWOXpS2dABUiIhhatWL49VcBSuXd2MvKdBgyRImTJxU4eVKHWrVsc5327NEgLg7YtUuHzp1ZJac9e/To3l2N+HgdOnYEysrUKCwUUFSkR0mJAsXFQEmJAqWlKhQV6XH7Nvu3TCy/c0eJoiK9savWUH7pkgIXLgANGzLcuSN2UQYGAuHhCjRuLCAoCJg3T4V33hEwaZIS9esDgmCb194vv+gxc6YK58/fbYELD2f48EMB//kPu+91sva1Z3gdXb0KNGqkQrduAGP8/T+REzlV1enmzZuoX7++7SfqMcIsGo2GxcTEmJS9/PLL7KGHHrL4nMzMTAaAbd26tUrnyM/PZwBYfn5+jWKtDoIgsPPnzzNBEOx2TkfBi6u9PH/9lTGFgrHHHmNs717GCgrEn489Jpb/+qttz6fTMda4sVi/IJh6CoJYHh4uHmdLkpIYAxhLSTH/+N694uNJSbY9rwGdjrFt2wT22Wc5bNs2weZ+zgQv/6OM8ePKi+etW7ck+fyW79SIGhIUFISWLVualLVo0cKkebMiTZo0QYMGDZCRkSF1eFYjCAJSU1MlmzniTPDiai9Pw2bvx44BsbHicgmxseIAeClmQRoW5v3zT3Hywp49eqSkHMeePXoMHiyWf/KJ7dcrKz+RotwXZwD2GQCvUgHdugkIC9uLbt0Em/s5E7z8jwL8uPLkKQWOH5DhpHTp0sWk3xkAzpw5g7CwMIvPuXTpEm7cuIEgKeeQE4QDMexZaK+FXA2J4LRpQPfuagADAYhJkVTLYVRnlwaCIAhbQkmZBaZMmYLY2Fh88MEHGD58OA4cOIAlS5ZgyZIlAICioiLMnTsXQ4cORWBgIDIzMzFjxgxERESgX79+Do6eIKRD6vXYKuKIzavLJ4OxsXfLpUwGCYIgKCmzwIMPPojffvsNr7/+Ot59912Eh4dj0aJFGDNmDABxsN/Ro0excuVK5OXlITg4GH379sW8efOceq0yhUIBPz8/KKzZQdjF4MWVB09DIli7dhk6dbJPK5W9WwXLw8M1BfjxBPhx5clTknoZo9mXjoL2viQIgiAI10Oqz28a6M8ZgiDg9OnTsh+ECfDjSp7ygxdXXjwBflx58pQCSso4Q6/XIy0tzWQ9FrnCiyt5yg9eXHnxBPhx5clTCigpIwiCIAiCcAIoKSMIgiAIgnACKCnjDKVSidDQ0H+3zpE3vLiSp/zgxZUXT4AfV548pYBmXzoQmn1JEARBEK4Hzb4kbIIgCDhy5IjsZ8YA/LiSp/zgxZUXT4AfV548pYCSMs7Q6/XIysqS/cwYgB9X8pQfvLjy4gnw48qTpxRQUkYQBEEQBOEE0DZLDsQwnK+goMBu59RqtSguLkZBQQE0Go3dzusIeHElT/nBiysvngA/rrx4FhYWArj7OW4rKClzIIaLGhIS4uBICIIgCIKoLjdu3IC3t7fN6qPZlw5Er9fjypUr8PLystvmrQUFBQgJCcHFixdlP+OTF1fylB+8uPLiCfDjyotnfn4+QkNDcevWLfj4+NisXmopcyBKpRKNGjVyyLnr1q0r63+Y8vDiSp7ygxdXXjwBflx58bT1emU00J8gCIIgCMIJoKSMIAiCIAjCCaCkjDPc3Nzw9ttvw83NzdGhSA4vruQpP3hx5cUT4MeVPGsGDfQnCIIgCIJwAqiljCAIgiAIwgmgpIwgCIIgCMIJoKSMIAiCIAjCCaCkjCAIgiAIwgmgpEyGfPXVV2jcuDHc3d3RuXNnHDhw4J7Hr1+/HlFRUXB3d0ebNm0QHx9vp0hrTnVcT5w4gaFDh6Jx48ZQKBRYtGiR/QKtIdXxXLp0Kbp16wZfX1/4+vqid+/e930NOAvV8dywYQM6duwIHx8feHp6Ijo6GqtXr7ZjtDWjuv+nBtatWweFQoHBgwdLG6CNqI7nihUroFAoTG7u7u52jNZ6qns98/Ly8OKLLyIoKAhubm5o3ry5y7z3Vse1Z8+ela6pQqHAwIED7RixdVT3mi5atAiRkZHw8PBASEgIpkyZgjt37lTvpIyQFevWrWO1atVi33//PTtx4gR75plnmI+PD8vOzjZ7/J49e5hKpWILFixgJ0+eZG+++SbTaDTs2LFjdo68+lTX9cCBA2z69Ols7dq1LDAwkH366af2DdhKqus5evRo9tVXX7EjR46wU6dOsfHjxzNvb2926dIlO0deParrmZSUxDZs2MBOnjzJMjIy2KJFi5hKpWKbN2+2c+TVp7quBs6dO8caNmzIunXrxgYNGmSfYGtAdT2XL1/O6taty65evWq8Xbt2zc5RV5/qepaWlrKOHTuyAQMGsN27d7Nz586xHTt2sNTUVDtHXn2q63rjxg2T63n8+HGmUqnY8uXL7Rt4Namu548//sjc3NzYjz/+yM6dO8e2bNnCgoKC2JQpU6p1XkrKZEanTp3Yiy++aLwvCAILDg5m8+fPN3v88OHD2cCBA03KOnfuzJ577jlJ47QF1XUtT1hYmMskZTXxZIwxnU7HvLy82MqVK6UK0SbU1JMxxtq1a8fefPNNKcKzKda46nQ6Fhsby7777js2btw4l0jKquu5fPly5u3tbafobEd1Pb/++mvWpEkTVlZWZq8QbUZN/08//fRT5uXlxYqKiqQK0SZU1/PFF19kvXr1MimbOnUq69KlS7XOS92XMqKsrAyHDh1C7969jWVKpRK9e/dGSkqK2eekpKSYHA8A/fr1s3i8s2CNqytiC8/i4mJotVrUq1dPqjBrTE09GWPYtm0b0tLS0L17dylDrTHWur777rvw9/fH008/bY8wa4y1nkVFRQgLC0NISAgGDRqEEydO2CNcq7HG8/fff0dMTAxefPFFBAQEoHXr1vjggw8gCIK9wrYKW7wfLVu2DCNHjoSnp6dUYdYYazxjY2Nx6NAhYxfn2bNnER8fjwEDBlTr3LQhuYzIzc2FIAgICAgwKQ8ICMDp06fNPufatWtmj7927ZpkcdoCa1xdEVt4zpw5E8HBwZWSb2fCWs/8/Hw0bNgQpaWlUKlUWLx4Mfr06SN1uDXCGtfdu3dj2bJlSE1NtUOEtsEaz8jISHz//fd44IEHkJ+fj08++QSxsbE4ceIEGjVqZI+wq401nmfPnsX27dsxZswYxMfHIyMjA5MmTYJWq8Xbb79tj7CtoqbvRwcOHMDx48exbNkyqUK0CdZ4jh49Grm5uejatSsYY9DpdHj++ecxe/bsap2bkjKCkDEffvgh1q1bhx07drjMgOnq4OXlhdTUVBQVFWHbtm2YOnUqmjRpgp49ezo6NJtRWFiI//73v1i6dCkaNGjg6HAkJSYmBjExMcb7sbGxaNGiBb799lvMmzfPgZHZFr1eD39/fyxZsgQqlQodOnTA5cuX8fHHHzt1UlZTli1bhjZt2qBTp06ODsXm7NixAx988AEWL16Mzp07IyMjA6+++irmzZuHOXPmVLkeSspkRIMGDaBSqZCdnW1Snp2djcDAQLPPCQwMrNbxzoI1rq5ITTw/+eQTfPjhh9i6dSseeOABKcOsMdZ6KpVKREREAACio6Nx6tQpzJ8/36mTsuq6ZmZm4vz583jssceMZXq9HgCgVquRlpaGpk2bShu0Fdjif1Sj0aBdu3bIyMiQIkSbYI1nUFAQNBoNVCqVsaxFixa4du0aysrKUKtWLUljtpaaXNPbt29j3bp1ePfdd6UM0SZY4zlnzhz897//xcSJEwEAbdq0we3bt/Hss8/ijTfegFJZtdFiNKZMRtSqVQsdOnTAtm3bjGV6vR7btm0z+fZZnpiYGJPjASAxMdHi8c6CNa6uiLWeCxYswLx587B582Z07NjRHqHWCFtdT71ej9LSUilCtBnVdY2KisKxY8eQmppqvD3++OOIi4tDamoqQkJC7Bl+lbHFNRUEAceOHUNQUJBUYdYYazy7dOmCjIwMY3INAGfOnEFQUJDTJmRAza7p+vXrUVpaiieffFLqMGuMNZ7FxcWVEi9D0s2qs8V4NSckEE7OunXrmJubG1uxYgU7efIke/bZZ5mPj49xWvl///tfNmvWLOPxe/bsYWq1mn3yySfs1KlT7O2333apJTGq41paWsqOHDnCjhw5woKCgtj06dPZkSNHWHp6uqMUqkR1PT/88ENWq1Yt9ssvv5hMRS8sLHSUQpWorucHH3zAEhISWGZmJjt58iT75JNPmFqtZkuXLnWUQpWprmtFXGX2ZXU9586dy7Zs2cIyMzPZoUOH2MiRI5m7uzs7ceKEoxSqRHU9s7KymJeXF3vppZdYWloa+/PPP5m/vz977733HKVQZax97Xbt2pWNGDHC3uFaTXU93377bebl5cXWrl3Lzp49yxISEljTpk3Z8OHDq3VeSspkyBdffMFCQ0NZrVq1WKdOndi+ffuMj/Xo0YONGzfO5Piff/6ZNW/enNWqVYu1atWKbdq0yc4RW091XM+dO8cAVLr16NHD/oFXk+p4hoWFmfV8++237R94NamO5xtvvMEiIiKYu7s78/X1ZTExMWzdunUOiNo6qvt/Wh5XScoYq57n5MmTjccGBASwAQMGsMOHDzsg6upT3eu5d+9e1rlzZ+bm5saaNGnC3n//fabT6ewctXVU1/X06dMMAEtISLBzpDWjOp5arZa98847rGnTpszd3Z2FhISwSZMmsVu3blXrnArGqtOuRhAEQRAEQUgBjSkjCIIgCIJwAigpIwiCIAiCcAIoKSMIgiAIgnACKCkjCIIgCIJwAigpIwiCIAiCcAIoKSMIgiAIgnACKCkjCIIgCIJwAigpIwiCIAiCcAIoKSMIgiAIgnACKCkjCIKQgDfeeAMKhQJ79uxxdCgEQbgIlJQRBEFIwKFDh6BUKhEdHe3oUAiCcBFo70uCIAgJ8Pf3R7169XD69GlHh0IQhItALWUEQRA2ZPLkyVAoFLh+/TrS0tKgUCiMt1OnTjk6PIIgnBi1owMgCIKQE506dcKIESPw008/oX///ujcuTMAQKFQoHnz5g6OjiAIZ4aSMoIgCBsyevRoXL58GT/99BNeeuklDBw40NEhEQThIlD3JUEQhI05fPgwAKBdu3YOjoQgCFeCBvoTBEHYmKioKNy6dQvZ2dmODoUgCBeCWsoIgiBsyO3bt5Genk5LYRAEUW0oKSMIgrAhqamp0Ov11HVJEES1oaSMIAjChhw9ehQAqKWMIIhqQ0kZQRCEDblx4wYAwNfX18GREAThatCSGARBEDbE0G35yiuvYMiQIXBzc0NcXBx69Ojh4MgIgnB2aPYlQRCEjfnwww+xZMkSXLx4ETqdDmvWrMGoUaMcHRZBEE4OJWUEQRAEQRBOAI0pIwiCIAiCcAIoKSMIgiAIgnACKCkjCIIgCIJwAigpIwiCIAiCcAIoKSMIgiAIgnACKCkjCIIgCIJwAigpIwiCIAiCcAIoKSMIgiAIgnACKCkjCIIgCIJwAigpIwiCIAiCcAIoKSMIgiAIgnACKCkjCIIgCIJwAv4f/J53R73CU0cAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.xlabel('$t$',fontsize =14)\n", - "plt.ylabel(r'$\\frac{|{G}_\\mathrm{\\theta}-({G}_\\mathrm{c}^{\\mathrm{eff}})_\\mathrm{num}|}{({G}_\\mathrm{c}^{\\mathrm{eff}})_\\mathrm{num}}\\times 100\\%$',fontsize=14) \n", - "plt.plot(G_theta_time[:,0], abs(G_theta_time[:,1])/G_eff, '-ob',fillstyle='none' ,linewidth=1.5,label='Phase-field %s'%phasefield_model)\n", - "plt.plot(G_theta_time[:,0], np.append(0, np.ones(len(G_theta_time[:,0])-1)), '-k',fillstyle='none' ,linewidth=1.5,label='Closed form') \n", - "plt.grid(linestyle='dashed') \n", - "plt.xlim(-0.05,0.8)\n", - "legend = plt.legend(loc='lower right')\n", - "plt.show()\n", - "\n", - "plt.xlabel('$t$',fontsize =14)\n", - "plt.ylabel(r'$\\frac{|{G}_\\mathrm{\\theta}-({G}_\\mathrm{c}^{\\mathrm{eff}})_\\mathrm{num}|}{({G}_\\mathrm{c}^{\\mathrm{eff}})_\\mathrm{num}}\\times 100\\%$',fontsize=14) \n", - "plt.plot(G_theta_time[:,0], abs(G_theta_time[:,1]-G_eff)/G_eff*100, '-ob',fillstyle='none' ,linewidth=1.5,label='Phase-field %s'%phasefield_model )\n", - "plt.grid(linestyle='dashed') \n", - "plt.xlim(-0.05,0.8)\n", - "# plt.ylim(0,4)\n", - "legend = plt.legend(loc='upper right')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hint: Accurate results can be obtained by using the mesh size below 0.02." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Phase field profile " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fracture propagation animation" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/gif": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 40, - "metadata": { - "image/gif": { - "height": 400, - "width": 800 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "\n", - "plotter = pv.Plotter()\n", - "\n", - "plotter.open_gif(f\"figures/surfing.gif\")\n", - "pv.set_plot_theme(\"document\")\n", - "for time_value in reader.time_values:\n", - " reader.set_active_time_value(time_value)\n", - " mesh = reader.read()[0] # This dataset only has 1 block\n", - " \n", - " sargs=dict(title='Phase field', title_font_size=20, label_font_size=15, n_labels=5,\n", - " position_x=0.3, position_y=0.2, fmt=\"%.1f\", width=.5)\n", - " clim=[0, 1.]\n", - " points = mesh.point_data[\"phasefield\"].shape[0]\n", - " xs = mesh.points[:,0]\n", - " ys = mesh.points[:,1]\n", - " pf = mesh.point_data[\"phasefield\"]\n", - " plotter.clear()\n", - " plotter.add_mesh(mesh, scalars=pf, show_scalar_bar=False, colormap=\"coolwarm\", clim=clim,\n", - " scalar_bar_args=sargs, lighting=False)\n", - " plotter.add_text(f\"Time: {time_value:.0f}\", color=\"black\")\n", - "\n", - " plotter.view_xy()\n", - " plotter.write_frame()\n", - "\n", - "plotter.close()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](./figures/surfing.gif)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Phase field profile at last time step" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mesh = reader.read()[0]\n", - "\n", - "pv.set_jupyter_backend(\"static\")\n", - "p = pv.Plotter(shape=(1, 1), border=False)\n", - "p.add_mesh(\n", - " mesh,\n", - " scalars=pf,\n", - " show_edges=False,\n", - " show_scalar_bar=True,\n", - " colormap=\"coolwarm\",\n", - " clim=clim,\n", - " scalar_bar_args=sargs,\n", - ")\n", - "\n", - "p.view_xy()\n", - "p.camera.zoom(1.5)\n", - "p.window_size = [800,400]\n", - "p.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "[1] B. Bourdin, G.A. Francfort, and J.-J. Marigo, _The variational approach to fracture_, Journal of Elasticity **91** (2008), no. 1-3, 5–148.\n", - "\n", - "[2] Li, Tianyi, Jean-Jacques Marigo, Daniel Guilbaud, and Serguei Potapov. _Numerical investigation of dynamic brittle fracture via gradient damage models._ Advanced Modeling and Simulation in Engineering Sciences **3**, no. 1 (2016): 1-24.\n", - "\n", - "[3] Dubois, Frédéric and Chazal, Claude and\n", - "Petit, Christophe, _A Finite Element Analysis of Creep-Crack Growth in Viscoelastic Media_, Mechanics Time-Dependent Materials **2** (1998), no. 3, 269–286" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.2" - }, - "vscode": { - "interpreter": { - "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" - } - } + "cells": [ + { + "cell_type": "raw", + "id": "a4f6e19f-6b14-45eb-a3c2-98aacba77a61", + "metadata": {}, + "source": [ + "+++\n", + "author = \"Mostafa Mollaali, Keita Yoshioka\"\n", + "date = \"2022-06-28\"\n", + "title = \"Surfing boundary\"\n", + "web_subsection = \"phase-field\"\n", + "+++\n" + ] + }, + { + "cell_type": "markdown", + "id": "864bdd42-20d1-4e2a-80b4-5661a106394a", + "metadata": {}, + "source": [ + "## Problem description\n", + "\n", + "Consider a plate, $\\Omega=[0,2]\\times [-0.5,0.5]$, with an explicit edge crack, $\\Gamma=[0,0.5]\\times \\{0\\}$; that is subjected to a time dependent crack opening displacement:\n", + "\n", + "\\begin{eqnarray}\n", + "\t\\label{eq:surfing_bc}\n", + "\t\\mathbf{u}(x,y,t)= \\mathbf{U}(x-\\text{v}t,y) \\quad \\text{on} \\quad \\partial\\Omega_D,\n", + "\\end{eqnarray}\n", + "where $\\text{v}$ is an imposed loading velocity; and $\\mathbf{U}$ is the asymptotic solution for the Mode-I crack opening displacement\n", + "\\begin{eqnarray}\n", + "\t\\label{eq:asymptotic}\n", + "\tU_x= \\dfrac{K_I}{2\\mu} \\sqrt{\\dfrac{r}{2\\pi}} (\\kappa-\\cos \\varphi) \\cos \\frac{\\varphi}{2}, \\nonumber\n", + "\t\\\\\n", + "\tU_y= \\dfrac{K_I}{2\\mu} \\sqrt{\\dfrac{r}{2\\pi}} (\\kappa-\\cos \\varphi) \\sin \\frac{\\varphi}{2},\n", + "\\end{eqnarray}\n", + "\n", + "\n", + "where $K_I$ is the stress intensity factor, $\\kappa=(3-\\nu)/(1+\\nu)$ and $\\mu=E / 2 (1 + \\nu) $; $(r,\\varphi)$ are the polar coordinate system, where the origin is crack tip.\n", + "Also, we used $G_\\mathrm{c}=K_{Ic}^2(1-\\nu^2)/E$ as the fracture surface energy under plane strain condition.\n", + "Table 1 lists the material properties and geometry of the numerical model.\n", + "\n", + "![Schematic view of surfing boundary condition benchmark](./figures/surfing_schematic.png#one-half \"Schematic view of surfing boundary condition benchmark.\")" + ] + }, + { + "cell_type": "markdown", + "id": "3eebd8c0-31d8-4a65-9666-fcb9c1d6c1c3", + "metadata": {}, + "source": [ + "# Input Data" + ] + }, + { + "cell_type": "markdown", + "id": "0fbc3b47-f9c5-4d74-be98-502b7cd4ad68", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "| **Name** | **Value** | **Unit** | **Symbol** |\n", + "|--------------------------------|--------------------|--------------|------------|\n", + "| _Young's modulus_ | 210x$10^3$ | MPa | $E$ |\n", + "| _Critical energy release rate_ | 2.7 | MPa$\\cdot$mm | $G_{c}$ |\n", + "| _Poisson's ratio_ | 0.3 | $-$ | $\\nu$ |\n", + "| _Regularization parameter_ | 2$h$ | mm | $\\ell_s$ |\n", + "| _Imposed loading velocity_ | 1.5 | mm/s | $\\text{v}$ |\n", + "| _Length_ | $2$ | mm | $L$ |\n", + "| _Height_ | $1$ | mm | $H$ |\n", + "| _Initial crack length_ | $0.5$ | mm | $a_0$ |" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "0cce3ce8-4cf8-4a89-b2dd-62fce09b7ccf", + "metadata": {}, + "outputs": [], + "source": [ + "x_tip_Initial = 0.5\n", + "y_tip_Initial = 0.5\n", + "Height = 1.0\n", + "\n", + "Orientation = 0\n", + "h = 0.05\n", + "G_i = 2.7\n", + "ls = 2 * h\n", + "# We set ls=2h in our simulation\n", + "phasefield_model = \"AT1\" # AT1 and AT2" + ] + }, + { + "cell_type": "markdown", + "id": "92c190de-b503-49e6-9390-b537a3843018", + "metadata": {}, + "source": [ + "## Paths and project file name" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "fc69bd31-de9a-4cba-b7ba-fe114e854495", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# file's name\n", + "prj_name = \"surfing.prj\"\n", + "\n", + "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", + "if not os.path.exists(out_dir):\n", + " os.makedirs(out_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "4ca8ca18-1658-4063-852e-6aa2d82f86cf", + "metadata": {}, + "source": [ + "# Mesh generation" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "a64685e3-8def-42dc-bfe4-0ea48c6cea6b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2022-10-25 10:09:52.705] [ogs] [\u001b[32minfo\u001b[m] Mesh created: 924 nodes, 861 elements.\n", + "[2022-10-25 10:09:52.988] [ogs] [\u001b[32minfo\u001b[m] Reordering nodes... \n", + "[2022-10-25 10:09:52.989] [ogs] [\u001b[32minfo\u001b[m] Corrected 0 elements.\n", + "[2022-10-25 10:09:52.991] [ogs] [\u001b[32minfo\u001b[m] VTU file written.\n" + ] + } + ], + "source": [ + "# https://www.opengeosys.org/docs/tools/meshing/structured-mesh-generation/\n", + "! generateStructuredMesh -o {out_dir}/surfing_quad_1x2.vtu -e quad --lx 2 --nx {round(2/h)+1} --ly 1 --ny {round(1/h)+1}\n", + "! NodeReordering -i {out_dir}/surfing_quad_1x2.vtu -o {out_dir}/surfing_quad_1x2_NR.vtu" + ] + }, + { + "cell_type": "markdown", + "id": "573e53ec-64d1-4e76-a81e-e2197a815886", + "metadata": {}, + "source": [ + "# Pre-processing \n", + "At fracture, we set the initial phase field to zero." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "fcd3b715-7d76-45a6-91ea-363e882883c7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pyvista as pv\n", + "\n", + "pv.set_plot_theme(\"document\")\n", + "pv.set_jupyter_backend(\"static\")\n", + "\n", + "import numpy as np\n", + "\n", + "mesh = pv.read(f\"{out_dir}/surfing_quad_1x2_NR.vtu\")\n", + "phase_field = np.ones((len(mesh.points), 1))\n", + "\n", + "\n", + "for node_id, x in enumerate(mesh.points):\n", + " if (\n", + " x[0] < x_tip_Initial + h / 10\n", + " and x[1] < Height / 2 + h\n", + " and x[1] > Height / 2 - h\n", + " ):\n", + " phase_field[node_id] = 0.0\n", + "\n", + "mesh.point_data[\"pf-ic\"] = phase_field\n", + "mesh.save(f\"{out_dir}/surfing_quad_1x2_NR_pf_ic.vtu\")\n", + "\n", + "pf_ic = mesh.point_data[\"pf-ic\"]\n", + "sargs = dict(\n", + " title=\"pf-ic\",\n", + " title_font_size=20,\n", + " label_font_size=15,\n", + " n_labels=5,\n", + " position_x=0.24,\n", + " position_y=0.0,\n", + " fmt=\"%.1f\",\n", + " width=0.5,\n", + ")\n", + "clim = [0, 1.0]\n", + "\n", + "p = pv.Plotter(shape=(1, 1), border=False)\n", + "p.add_mesh(\n", + " mesh,\n", + " scalars=pf_ic,\n", + " show_edges=True,\n", + " show_scalar_bar=True,\n", + " colormap=\"coolwarm\",\n", + " clim=clim,\n", + " scalar_bar_args=sargs,\n", + ")\n", + "\n", + "p.view_xy()\n", + "p.camera.zoom(1.5)\n", + "p.window_size = [800, 400]\n", + "p.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1e78599c-c840-4880-923e-b4b9960cdae7", + "metadata": {}, + "source": [ + "# Run the simulation " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "fbc154a1-9f22-4a51-8678-a9457a3d33b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>> OGS started execution ... <<<\n", + ">>> OGS terminated execution <<< Elapsed time: 11.77 s.\n" + ] + } + ], + "source": [ + "from ogs6py import ogs\n", + "\n", + "# Change the length scale and phasefield model in project file\n", + "model = ogs.OGS(\n", + " INPUT_FILE=prj_name,\n", + " PROJECT_FILE=f\"{out_dir}/{prj_name}\",\n", + " MKL=True,\n", + " args=f\"-o {out_dir}\",\n", + ")\n", + "model.replace_parameter_value(name=\"ls\", value=2 * h)\n", + "model.replace_text(phasefield_model, xpath=\"./processes/process/phasefield_model\")\n", + "model.replace_text(\"./surfing.gml\", xpath=\"./geometry\")\n", + "model.replace_text(\"./Surfing_python.py\", xpath=\"./python_script\")\n", + "model.write_input()\n", + "\n", + "import time\n", + "\n", + "t0 = time.time()\n", + "print(\">>> OGS started execution ... <<<\")\n", + "! ogs {out_dir}/{prj_name} -o {out_dir} > {out_dir}/log.txt\n", + "\n", + "tf = time.time()\n", + "print(\">>> OGS terminated execution <<< Elapsed time: \", round(tf - t0, 2), \" s.\")" + ] + }, + { + "cell_type": "markdown", + "id": "0fac65ef-ee16-4070-918e-54d03ad341d7", + "metadata": {}, + "source": [ + "# Results" + ] + }, + { + "cell_type": "markdown", + "id": "eefd6208-8530-4dfa-9e15-a42d390e412a", + "metadata": {}, + "source": [ + "We computed the energy release rate using $G_{\\theta}$ method (Destuynder _et al._, 1983; Li _et al._, 2016) and plot the errors against the theoretical numerical toughness i.e. $(G_c^{\\text{eff}})_{\\texttt{num}}=G_c(1+\\frac{h}{2\\ell})$ for $\\texttt{AT}_2$,\n", + "and $(G_c^{\\text{eff}})_{\\texttt{num}}=G_c(1+\\frac{3h}{8\\ell})$ for $\\texttt{AT}_1$ (Bourdin _et al._, 2008).\n", + "\n", + "![Alt text](./figures/surfing_gtheta_schematic.png#one-half \"Phase field and $\\theta$ profile for the volumetric deviatoric $\\texttt{AT}_2$ models. We use virtual perturbation of $\\theta$ to compute energy release rate using $G_{\\theta}$ Dubois et al., 1998. The $\\theta$ value is 1 inside of $B_{r_{in}}(P)$, 0 outside, and a linear interpolation in between. We set $r_{in}=4\\ell$ and $r_{out}=2.5r_{in}$ (see Li et al., 2016).\")" + ] + }, + { + "cell_type": "markdown", + "id": "b006f9ac-527a-450f-8679-c51f8efbeaf0", + "metadata": {}, + "source": [ + "We computed the energy release rate using $G_{\\theta}$ method (Destuynder _et al._, 1983; Li _et al._, 2016) and plot the errors against the theoretical numerical toughness i.e. $(G_c^{\\text{eff}})_{\\texttt{num}}=G_c(1+\\frac{h}{2\\ell})$ for $\\texttt{AT}_2$,\n", + "and $(G_c^{\\text{eff}})_{\\texttt{num}}=G_c(1+\\frac{3h}{8\\ell})$ for $\\texttt{AT}_1$ (Bourdin _et al._, 2008).\n", + "\n", + "![Alt text](./figures/surfing_gtheta_schematic.png#one-half \"Phase field and $\\theta$ profile for the volumetric deviatoric $\\texttt{AT}_2$ models. We use virtual perturbation of $\\theta$ to compute energy release rate using $G_{\\theta}$ Dubois et al., 1998. The $\\theta$ value is 1 inside of $B_{r_{in}}(P)$, 0 outside, and a linear interpolation in between. We set $r_{in}=4\\ell$ and $r_{out}=2.5r_{in}$ (see Li et al., 2016).\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "17a47780-af70-4e84-87ec-8ddff89a2fb7", + "metadata": {}, + "outputs": [], + "source": [ + "R_inn = 4 * ls\n", + "R_out = 2.5 * R_inn\n", + "\n", + "if phasefield_model == \"AT1\":\n", + " G_eff = G_i * (1 + 3 * h / (8 * ls))\n", + "elif phasefield_model == \"AT2\":\n", + " G_eff = G_i * (1 + h / (2 * ls))" + ] + }, + { + "cell_type": "markdown", + "id": "c1b42e4a-d028-424d-bd0f-d2a62e5fe85e", + "metadata": {}, + "source": [ + "We run the simulation with a coarse mesh here to reduce computing time; however, a finer mesh would give a more accurate results. The energy release rate and its error for Models $\\texttt{AT}_1$ and $\\texttt{AT}_2$ with a mesh size of $h=0.005$ are shown below." + ] + }, + { + "cell_type": "markdown", + "id": "e1069645-0dd0-4b06-bd99-99f948f5a34d", + "metadata": {}, + "source": [ + "![Alt text](./figures/surfing_gtheta_ref.png#one-half)\n", + "![Alt text](./figures/surfing_gtheta_error_ref.png#one-half)" + ] + }, + { + "cell_type": "markdown", + "id": "7f8c21dd-c248-423c-afb5-0002c01271f5", + "metadata": {}, + "source": [ + "# Post-processing" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "66d63d54-68e0-4521-bc52-4189a0068672", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.spatial import Delaunay\n", + "\n", + "reader = pv.get_reader(f\"{out_dir}/surfing.pvd\")\n", + "G_theta_time = np.zeros((len(reader.time_values), 2))\n", + "\n", + "\n", + "for t, time_value in enumerate(reader.time_values):\n", + " reader.set_active_time_value(time_value)\n", + "\n", + " mesh = reader.read()[0]\n", + " points = mesh.point_data[\"phasefield\"].shape[0]\n", + " xs = mesh.points[:, 0]\n", + " ys = mesh.points[:, 1]\n", + " pf = mesh.point_data[\"phasefield\"]\n", + " sigma = mesh.point_data[\"sigma\"]\n", + " disp = mesh.point_data[\"displacement\"]\n", + "\n", + " num_points = disp.shape\n", + " theta = np.zeros(num_points)\n", + "\n", + " # --------------------------------------------------------------------------------\n", + " # find fracture tip\n", + " # --------------------------------------------------------------------------------\n", + " min_pf = min(pf[:])\n", + " coord_pf_0p5 = mesh.points[pf < 0.5]\n", + " if min_pf <= 0.5:\n", + " coord_pf_0p5[np.argmax(coord_pf_0p5, axis=0)[0]][1]\n", + " x0 = coord_pf_0p5[np.argmax(coord_pf_0p5, axis=0)[0]][0]\n", + " y0 = coord_pf_0p5[np.argmax(coord_pf_0p5, axis=0)[0]][1]\n", + " else:\n", + " x0 = x_tip_Initial\n", + " y0 = y_tip_Initial\n", + " Crack_position = [x0, y0]\n", + " # --------------------------------------------------------------------------------\n", + " # define \\theta\n", + " # --------------------------------------------------------------------------------\n", + " for i, x in enumerate(mesh.points):\n", + " # distance from the crack tip\n", + " R = np.sqrt((x[0] - Crack_position[0]) ** 2 + (x[1] - Crack_position[1]) ** 2)\n", + " if R_inn > R:\n", + " theta_funct = 1.0\n", + " elif R_out < R:\n", + " theta_funct = 0.0\n", + " else:\n", + " theta_funct = (R - R_out) / (R_inn - R_out)\n", + " theta[i][0] = theta_funct * np.cos(Orientation)\n", + " theta[i][1] = theta_funct * np.sin(Orientation)\n", + "\n", + " mesh.point_data[\"theta\"] = theta\n", + "\n", + " # --------------------------------------------------------------------------------\n", + " # define grad \\theta\n", + " # --------------------------------------------------------------------------------\n", + " mesh_theta = mesh.compute_derivative(scalars=\"theta\")\n", + " mesh_theta[\"gradient\"]\n", + "\n", + " def gradients_to_dict(arr):\n", + " \"\"\"A helper method to label the gradients into a dictionary.\"\"\"\n", + " keys = np.array(\n", + " [\"thetax_x\", \"thetax_y\", \"thetax_z\", \"thetay_x\", \"thetay_y\", \"thetay_z\"]\n", + " )\n", + " keys = keys.reshape((2, 3))[:, : arr.shape[1]].ravel()\n", + " return dict(zip(keys, mesh_theta[\"gradient\"].T))\n", + "\n", + " gradients_theta = gradients_to_dict(mesh_theta[\"gradient\"])\n", + " mesh.point_data.update(gradients_theta)\n", + " # --------------------------------------------------------------------------------\n", + " # define grad u\n", + " # --------------------------------------------------------------------------------\n", + " mesh_u = mesh.compute_derivative(scalars=\"displacement\")\n", + " mesh_u[\"gradient\"]\n", + "\n", + " def gradients_to_dict(arr):\n", + " \"\"\"A helper method to label the gradients into a dictionary.\"\"\"\n", + " keys = np.array([\"Ux_x\", \"Ux_y\", \"Ux_z\", \"Uy_x\", \"Uy_y\", \"Uy_z\"])\n", + " keys = keys.reshape((2, 3))[:, : arr.shape[1]].ravel()\n", + " return dict(zip(keys, mesh_u[\"gradient\"].T))\n", + "\n", + " # a=np.array([1,2,3,4,5,6])\n", + " # np.reshape(a.ravel(), (2, 3))\n", + " gradients_u = gradients_to_dict(mesh_u[\"gradient\"])\n", + " # gradients\n", + " mesh.point_data.update(gradients_u)\n", + "\n", + " # --------------------------------------------------------------------------------\n", + " # define G_theta\n", + " # --------------------------------------------------------------------------------\n", + " G_theta_i = np.zeros(num_points[0])\n", + " sigma = mesh.point_data[\"sigma\"]\n", + " Ux_x = mesh.point_data[\"Ux_x\"]\n", + " Ux_y = mesh.point_data[\"Ux_y\"]\n", + " Uy_x = mesh.point_data[\"Uy_x\"]\n", + " Uy_y = mesh.point_data[\"Uy_y\"]\n", + "\n", + " thetax_x = mesh.point_data[\"thetax_x\"]\n", + " thetax_y = mesh.point_data[\"thetax_y\"]\n", + " thetay_x = mesh.point_data[\"thetay_x\"]\n", + " thetay_y = mesh.point_data[\"thetay_y\"]\n", + "\n", + " for i, x in enumerate(mesh.points):\n", + " # ---------------------------------------------------------------------------\n", + " sigma_xx = sigma[i][0]\n", + " sigma_yy = sigma[i][1]\n", + " sigma_xy = sigma[i][3]\n", + "\n", + " Ux_x_i = Ux_x[i]\n", + " Ux_y_i = Ux_y[i]\n", + " Uy_x_i = Uy_x[i]\n", + " Uy_y_i = Uy_y[i]\n", + "\n", + " thetax_x_i = thetax_x[i]\n", + " thetax_y_i = thetax_y[i]\n", + " thetay_x_i = thetay_x[i]\n", + " thetay_y_i = thetay_y[i]\n", + " # ---------------------------------------------------------------------------\n", + " dUdTheta_11 = Ux_x_i * thetax_x_i + Ux_y_i * thetay_x_i\n", + " dUdTheta_12 = Ux_x_i * thetax_y_i + Ux_y_i * thetay_y_i\n", + " dUdTheta_21 = Uy_x_i * thetax_x_i + Uy_y_i * thetay_x_i\n", + " dUdTheta_22 = Uy_x_i * thetax_y_i + Uy_y_i * thetay_y_i\n", + " trace_sigma_grad_u_grad_theta = (\n", + " sigma_xx * dUdTheta_11\n", + " + sigma_xy * (dUdTheta_12 + dUdTheta_21)\n", + " + sigma_yy * dUdTheta_22\n", + " )\n", + " trace_sigma_grad_u = (\n", + " sigma_xx * Ux_x_i + sigma_xy * (Uy_x_i + Ux_y_i) + sigma_yy * Uy_y_i\n", + " )\n", + " div_theta_i = thetax_x_i + thetay_y_i\n", + " G_theta_i[i] = (\n", + " trace_sigma_grad_u_grad_theta - 0.5 * trace_sigma_grad_u * div_theta_i\n", + " )\n", + " mesh.point_data[\"G_theta_node\"] = G_theta_i\n", + " # --------------------------------------------------------------------------------\n", + " # Integral G_theta\n", + " # --------------------------------------------------------------------------------\n", + " X = mesh.points[:, 0]\n", + " Y = mesh.points[:, 1]\n", + " G_theta_i = mesh.point_data[\"G_theta_node\"]\n", + "\n", + " domain_points = np.array(list(zip(X, Y)))\n", + " tri = Delaunay(domain_points)\n", + "\n", + " def area_from_3_points(x, y, z):\n", + " return np.sqrt(np.sum(np.cross(x - y, x - z), axis=-1) ** 2) / 2\n", + "\n", + " G_theta = 0\n", + " for vertices in tri.simplices:\n", + " mean_value = (\n", + " G_theta_i[vertices[0]] + G_theta_i[vertices[1]] + G_theta_i[vertices[2]]\n", + " ) / 3\n", + " area = area_from_3_points(\n", + " domain_points[vertices[0]],\n", + " domain_points[vertices[1]],\n", + " domain_points[vertices[2]],\n", + " )\n", + " G_theta += mean_value * area\n", + " G_theta_time[t][1] = G_theta\n", + " G_theta_time[t][0] = time_value\n", + "mesh.save(f\"{out_dir}/surfing_Post_Processing.vtu\")" + ] + }, + { + "cell_type": "markdown", + "id": "177dcc3f-4968-474d-9ddd-23c97d4e1e40", + "metadata": {}, + "source": [ + "## Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "2bb1b244-ea34-4644-be4b-98932972c7d8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.xlabel(\"$t$\", fontsize=14)\n", + "plt.ylabel(\n", + " r\"$\\frac{|{G}_\\mathrm{\\theta}-({G}_\\mathrm{c}^{\\mathrm{eff}})_\\mathrm{num}|}{({G}_\\mathrm{c}^{\\mathrm{eff}})_\\mathrm{num}}\\times 100\\%$\",\n", + " fontsize=14,\n", + ")\n", + "plt.plot(\n", + " G_theta_time[:, 0],\n", + " abs(G_theta_time[:, 1]) / G_eff,\n", + " \"-ob\",\n", + " fillstyle=\"none\",\n", + " linewidth=1.5,\n", + " label=\"Phase-field %s\" % phasefield_model,\n", + ")\n", + "plt.plot(\n", + " G_theta_time[:, 0],\n", + " np.append(0, np.ones(len(G_theta_time[:, 0]) - 1)),\n", + " \"-k\",\n", + " fillstyle=\"none\",\n", + " linewidth=1.5,\n", + " label=\"Closed form\",\n", + ")\n", + "plt.grid(linestyle=\"dashed\")\n", + "plt.xlim(-0.05, 0.8)\n", + "legend = plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "\n", + "plt.xlabel(\"$t$\", fontsize=14)\n", + "plt.ylabel(\n", + " r\"$\\frac{|{G}_\\mathrm{\\theta}-({G}_\\mathrm{c}^{\\mathrm{eff}})_\\mathrm{num}|}{({G}_\\mathrm{c}^{\\mathrm{eff}})_\\mathrm{num}}\\times 100\\%$\",\n", + " fontsize=14,\n", + ")\n", + "plt.plot(\n", + " G_theta_time[:, 0],\n", + " abs(G_theta_time[:, 1] - G_eff) / G_eff * 100,\n", + " \"-ob\",\n", + " fillstyle=\"none\",\n", + " linewidth=1.5,\n", + " label=\"Phase-field %s\" % phasefield_model,\n", + ")\n", + "plt.grid(linestyle=\"dashed\")\n", + "plt.xlim(-0.05, 0.8)\n", + "# plt.ylim(0,4)\n", + "legend = plt.legend(loc=\"upper right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "34fc7c5a-8026-43d6-8fb5-6ce86a3e7f23", + "metadata": {}, + "source": [ + "Hint: Accurate results can be obtained by using the mesh size below 0.02." + ] + }, + { + "cell_type": "markdown", + "id": "d6cb3a55-1297-4773-bc0c-14656514b618", + "metadata": {}, + "source": [ + "## Phase field profile " + ] + }, + { + "cell_type": "markdown", + "id": "489fdf0f-ddae-495c-9fdd-30ceea465066", + "metadata": {}, + "source": [ + "### Fracture propagation animation" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "224ba5c9-a66d-4ceb-87d1-d3aaed9682af", + "metadata": {}, + "outputs": [ + { + "data": { + "image/gif": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": { + "image/gif": { + "height": 400, + "width": 800 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "plotter = pv.Plotter()\n", + "\n", + "plotter.open_gif(\"figures/surfing.gif\")\n", + "pv.set_plot_theme(\"document\")\n", + "for time_value in reader.time_values:\n", + " reader.set_active_time_value(time_value)\n", + " mesh = reader.read()[0] # This dataset only has 1 block\n", + "\n", + " sargs = dict(\n", + " title=\"Phase field\",\n", + " title_font_size=20,\n", + " label_font_size=15,\n", + " n_labels=5,\n", + " position_x=0.3,\n", + " position_y=0.2,\n", + " fmt=\"%.1f\",\n", + " width=0.5,\n", + " )\n", + " clim = [0, 1.0]\n", + " points = mesh.point_data[\"phasefield\"].shape[0]\n", + " xs = mesh.points[:, 0]\n", + " ys = mesh.points[:, 1]\n", + " pf = mesh.point_data[\"phasefield\"]\n", + " plotter.clear()\n", + " plotter.add_mesh(\n", + " mesh,\n", + " scalars=pf,\n", + " show_scalar_bar=False,\n", + " colormap=\"coolwarm\",\n", + " clim=clim,\n", + " scalar_bar_args=sargs,\n", + " lighting=False,\n", + " )\n", + " plotter.add_text(f\"Time: {time_value:.0f}\", color=\"black\")\n", + "\n", + " plotter.view_xy()\n", + " plotter.write_frame()\n", + "\n", + "plotter.close()" + ] + }, + { + "cell_type": "markdown", + "id": "47605061-083b-4468-808a-6ca328e34457", + "metadata": {}, + "source": [ + "![](./figures/surfing.gif)" + ] + }, + { + "cell_type": "markdown", + "id": "8eda0f1a-414d-49e7-9ffd-939572001f5e", + "metadata": {}, + "source": [ + "### Phase field profile at last time step" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "58ba2518-e866-46e2-8cae-6b9170ca9bab", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mesh = reader.read()[0]\n", + "\n", + "pv.set_jupyter_backend(\"static\")\n", + "p = pv.Plotter(shape=(1, 1), border=False)\n", + "p.add_mesh(\n", + " mesh,\n", + " scalars=pf,\n", + " show_edges=False,\n", + " show_scalar_bar=True,\n", + " colormap=\"coolwarm\",\n", + " clim=clim,\n", + " scalar_bar_args=sargs,\n", + ")\n", + "\n", + "p.view_xy()\n", + "p.camera.zoom(1.5)\n", + "p.window_size = [800, 400]\n", + "p.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2743cb08-fcdf-4c56-b0cf-ea59d5ad48b8", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[1] B. Bourdin, G.A. Francfort, and J.-J. Marigo, _The variational approach to fracture_, Journal of Elasticity **91** (2008), no. 1-3, 5–148.\n", + "\n", + "[2] Li, Tianyi, Jean-Jacques Marigo, Daniel Guilbaud, and Serguei Potapov. _Numerical investigation of dynamic brittle fracture via gradient damage models._ Advanced Modeling and Simulation in Engineering Sciences **3**, no. 1 (2016): 1-24.\n", + "\n", + "[3] Dubois, Frédéric and Chazal, Claude and\n", + "Petit, Christophe, _A Finite Element Analysis of Creep-Crack Growth in Viscoelastic Media_, Mechanics Time-Dependent Materials **2** (1998), no. 3, 269–286" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + }, + "vscode": { + "interpreter": { + "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb index d68470ca65a..2eb0ed3415f 100644 --- a/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb +++ b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb @@ -47,16 +47,17 @@ "metadata": {}, "outputs": [], "source": [ - "from ogs6py import ogs\n", "import os\n", "import shutil\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pyvista as pv\n", "import time\n", - "import pandas as pd\n", + "from types import MethodType\n", "from xml.dom import minidom\n", - "from types import MethodType\n" + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pyvista as pv\n", + "from ogs6py import ogs" ] }, { @@ -66,25 +67,46 @@ "metadata": {}, "outputs": [], "source": [ - "data_dir = os.environ.get('OGS_DATA_DIR', '../../..')\n", - "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n", + "data_dir = os.environ.get(\"OGS_DATA_DIR\", \"../../..\")\n", + "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", "if not os.path.exists(out_dir):\n", - " os.makedirs(out_dir) \n", + " os.makedirs(out_dir)\n", + "\n", + "output_dir = out_dir\n", "\n", - "output_dir= out_dir\n", + "# define function to replace a specific curve, given by name\n", "\n", - "# define function to replace a specific curve, given by name \n", - "def replace_curve(self, name=None, value=None, coords=None, parametertype=None, valuetag=\"values\", coordstag=\"coords\"):\n", + "\n", + "def replace_curve(\n", + " self,\n", + " name=None,\n", + " value=None,\n", + " coords=None,\n", + " parametertype=None,\n", + " valuetag=\"values\",\n", + " coordstag=\"coords\",\n", + "):\n", " root = self._get_root()\n", " parameterpath = \"./curves/curve\"\n", " parameterpointer = self._get_parameter_pointer(root, name, parameterpath)\n", " self._set_type_value(parameterpointer, value, parametertype, valuetag=valuetag)\n", " self._set_type_value(parameterpointer, coords, parametertype, valuetag=coordstag)\n", - "# define function to change time_stepping in project file \n", - "def set_timestepping(model,repeat_list, delta_t_list):\n", - " model.remove_element(xpath='./time_loop/processes/process/time_stepping/timesteps/pair')\n", + "\n", + "\n", + "# define function to change time_stepping in project file\n", + "\n", + "\n", + "def set_timestepping(model, repeat_list, delta_t_list):\n", + " model.remove_element(\n", + " xpath=\"./time_loop/processes/process/time_stepping/timesteps/pair\"\n", + " )\n", " for i in range(len(repeat_list)):\n", - " model.add_block(blocktag = 'pair',parent_xpath='./time_loop/processes/process/time_stepping/timesteps', taglist = ['repeat', 'delta_t'], textlist = [repeat_list[i], delta_t_list[i]])\n" + " model.add_block(\n", + " blocktag=\"pair\",\n", + " parent_xpath=\"./time_loop/processes/process/time_stepping/timesteps\",\n", + " taglist=[\"repeat\", \"delta_t\"],\n", + " textlist=[repeat_list[i], delta_t_list[i]],\n", + " )" ] }, { @@ -102,62 +124,89 @@ "metadata": {}, "outputs": [], "source": [ - "def ogs_TPB(phasefield_model, energy_split_model, softening_curve = \"Linear\", length_scale = 5., bc_displacement = -1., ts_coords='0 1.0', values ='0 1.0', repeat_list=None, delta_t_list=None, hypre = False, MPI = True, ncores = 4):\n", - " ## define input file\n", + "def ogs_TPB(\n", + " phasefield_model,\n", + " energy_split_model,\n", + " softening_curve=\"Linear\",\n", + " length_scale=5.0,\n", + " bc_displacement=-1.0,\n", + " ts_coords=\"0 1.0\",\n", + " values=\"0 1.0\",\n", + " repeat_list=None,\n", + " delta_t_list=None,\n", + " hypre=False,\n", + " MPI=True,\n", + " ncores=4,\n", + "):\n", + " ## define input file\n", "\n", - " without_hypre='-ksp_type cg -pc_type bjacobi -ksp_atol 1e-14 -ksp_rtol 1e-14'\n", - " with_hypre='-ksp_type cg -pc_type hypre -pc_hypre_type boomeramg -pc_hypre_boomeramg_strong_threshold 0.7 -ksp_atol 1e-8 -ksp_rtol 1e-8' \n", + " without_hypre = \"-ksp_type cg -pc_type bjacobi -ksp_atol 1e-14 -ksp_rtol 1e-14\"\n", + " with_hypre = \"-ksp_type cg -pc_type hypre -pc_hypre_type boomeramg -pc_hypre_boomeramg_strong_threshold 0.7 -ksp_atol 1e-8 -ksp_rtol 1e-8\"\n", "\n", " prj_name = \"TPB.prj\"\n", - " print(f\"> Running three point bending test {phasefield_model} - {energy_split_model} - {softening_curve} ... <\")\n", + " print(\n", + " f\"> Running three point bending test {phasefield_model} - {energy_split_model} - {softening_curve} ... <\"\n", + " )\n", " logfile = f\"{out_dir}/log_{phasefield_model}_{energy_split_model}.txt\"\n", " model = ogs.OGS(INPUT_FILE=prj_name, PROJECT_FILE=f\"{out_dir}/{prj_name}\", MKL=True)\n", - " #generate prefix from properties\n", + " # generate prefix from properties\n", " prefix = f\"{phasefield_model}\" + f\"_{energy_split_model}\"\n", - " if phasefield_model == 'COHESIVE':\n", - " prefix = f\"{phasefield_model}\" + f\"_{energy_split_model}\" + f\"_{softening_curve}\"\n", + " if phasefield_model == \"COHESIVE\":\n", + " prefix = (\n", + " f\"{phasefield_model}\" + f\"_{energy_split_model}\" + f\"_{softening_curve}\"\n", + " )\n", "\n", " if MPI:\n", - " #partition mesh \n", + " # partition mesh\n", " ! NodeReordering -i TPB.vtu -o {out_dir}/TPB.vtu >> {logfile}\n", " ! constructMeshesFromGeometry -m {out_dir}/TPB.vtu -g TPB.gml >> {logfile}\n", - " shutil.move(\"TPB_left.vtu\",f\"{out_dir}/TPB_left.vtu\")\n", - " shutil.move(\"TPB_right.vtu\",f\"{out_dir}/TPB_right.vtu\")\n", - " shutil.move(\"TPB_top.vtu\",f\"{out_dir}/TPB_top.vtu\")\n", + " shutil.move(\"TPB_left.vtu\", f\"{out_dir}/TPB_left.vtu\")\n", + " shutil.move(\"TPB_right.vtu\", f\"{out_dir}/TPB_right.vtu\")\n", + " shutil.move(\"TPB_top.vtu\", f\"{out_dir}/TPB_top.vtu\")\n", " ! partmesh -s -o {out_dir} -i {out_dir}/TPB.vtu >> {logfile}\n", " ! partmesh -m -n {ncores} -o {out_dir} -i {out_dir}/TPB.vtu -- {out_dir}/TPB_right.vtu {out_dir}/TPB_left.vtu {out_dir}/TPB_top.vtu >> {logfile}\n", - " else :\n", + " else:\n", " ! NodeReordering -i TPB.vtu -o {out_dir}/TPB.vtu >> {logfile}\n", - " #change properties in prj file\n", + " # change properties in prj file\n", " model = ogs.OGS(INPUT_FILE=prj_name, PROJECT_FILE=f\"{out_dir}/{prj_name}\", MKL=True)\n", " model.replace_parameter_value(name=\"ls\", value=length_scale)\n", " model.replace_text(phasefield_model, xpath=\"./processes/process/phasefield_model\")\n", - " model.replace_text(energy_split_model, xpath=\"./processes/process/energy_split_model\")\n", + " model.replace_text(\n", + " energy_split_model, xpath=\"./processes/process/energy_split_model\"\n", + " )\n", " model.replace_text(softening_curve, xpath=\"./processes/process/softening_curve\")\n", " model.replace_text(prefix, xpath=\"./time_loop/output/prefix\")\n", " model.replace_parameter_value(name=\"dirichlet_load\", value=bc_displacement)\n", " model.replace_curve = MethodType(replace_curve, model)\n", - " model.replace_curve(name=\"dirichlet_time\",value=values, coords=ts_coords)\n", - " if repeat_list != None and delta_t_list != None: \n", - " set_timestepping(model,repeat_list, delta_t_list)\n", + " model.replace_curve(name=\"dirichlet_time\", value=values, coords=ts_coords)\n", + " if repeat_list != None and delta_t_list != None:\n", + " set_timestepping(model, repeat_list, delta_t_list)\n", " else:\n", - " set_timestepping(model,['1'], ['1e-2']) \n", + " set_timestepping(model, [\"1\"], [\"1e-2\"])\n", " if hypre == True:\n", - " model.replace_text(with_hypre, xpath='./linear_solvers/linear_solver/petsc/parameters',occurrence=1)\n", + " model.replace_text(\n", + " with_hypre,\n", + " xpath=\"./linear_solvers/linear_solver/petsc/parameters\",\n", + " occurrence=1,\n", + " )\n", " else:\n", - " model.replace_text(without_hypre, xpath='./linear_solvers/linear_solver/petsc/parameters', occurrence=1)\n", + " model.replace_text(\n", + " without_hypre,\n", + " xpath=\"./linear_solvers/linear_solver/petsc/parameters\",\n", + " occurrence=1,\n", + " )\n", " model.replace_text(\"./TPB.gml\", xpath=\"./geometry\")\n", " model.write_input()\n", - " #run ogs\n", + " # run ogs\n", " t0 = time.time()\n", " if MPI:\n", " print(\" > OGS started execution with MPI - \" f\"{ncores} cores...\")\n", " ! mpirun -np {ncores} ogs {out_dir}/{prj_name} -o {output_dir} >> {logfile}\n", - " else :\n", + " else:\n", " print(\" > OGS started execution ...\")\n", " ! ogs {out_dir}/{prj_name} -o {output_dir} >> {logfile}\n", " tf = time.time()\n", - " print(\" > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")\n" + " print(\" > OGS terminated execution. Elapsed time: \", round(tf - t0, 2), \" s.\")" ] }, { @@ -225,18 +274,34 @@ "# energy_split_model = [\"EffectiveStress\",'VolumetricDeviatoric','Isotropic']\n", "# softening_curve = ['Line','Exponential']\n", "\n", - "disp = -1.\n", - "ls = 5.\n", - "mpi_cores = 4 # MPI cores\n", + "disp = -1.0\n", + "ls = 5.0\n", + "mpi_cores = 4 # MPI cores\n", "## only run selected cases\n", "# For the COHESIVE model\n", - "for a in [\"Linear\",\"Exponential\"]:\n", - " ogs_TPB(\"COHESIVE\", \"EffectiveStress\", a, length_scale = ls, bc_displacement = disp, repeat_list=['1'], delta_t_list=['1e-1'], ncores = mpi_cores)\n", + "for a in [\"Linear\", \"Exponential\"]:\n", + " ogs_TPB(\n", + " \"COHESIVE\",\n", + " \"EffectiveStress\",\n", + " a,\n", + " length_scale=ls,\n", + " bc_displacement=disp,\n", + " repeat_list=[\"1\"],\n", + " delta_t_list=[\"1e-1\"],\n", + " ncores=mpi_cores,\n", + " )\n", "\n", "# For AT1 and AT2 models with isotropic split\n", - "for b in [\"AT1\",\"AT2\"]:\n", - " ogs_TPB(b, \"Isotropic\", length_scale = ls, bc_displacement = disp, repeat_list=['1'], delta_t_list=['1e-1'], ncores = mpi_cores)\n", - " " + "for b in [\"AT1\", \"AT2\"]:\n", + " ogs_TPB(\n", + " b,\n", + " \"Isotropic\",\n", + " length_scale=ls,\n", + " bc_displacement=disp,\n", + " repeat_list=[\"1\"],\n", + " delta_t_list=[\"1e-1\"],\n", + " ncores=mpi_cores,\n", + " )" ] }, { @@ -270,20 +335,36 @@ "# define function to obtain displacement applied at the loading point from vtu file\n", "def displ_midpoint(filename):\n", " data = pv.read(filename)\n", - " return np.sum(data.point_data[\"displacement\"][:,1], where= (data.points[:,0]==225) * (data.points[:,1]==100))\n", + " return np.sum(\n", + " data.point_data[\"displacement\"][:, 1],\n", + " where=(data.points[:, 0] == 225) * (data.points[:, 1] == 100),\n", + " )\n", + "\n", + "\n", "# define function to obtain force at the loading point from vtu file\n", + "\n", + "\n", "def force_midpoint(filename):\n", " data = pv.read(filename)\n", - " return np.sum(data.point_data[\"NodalForces\"][:,1], where= (data.points[:,0]==225) * (data.points[:,1]==100)) / 10.0\n", + " return (\n", + " np.sum(\n", + " data.point_data[\"NodalForces\"][:, 1],\n", + " where=(data.points[:, 0] == 225) * (data.points[:, 1] == 100),\n", + " )\n", + " / 10.0\n", + " )\n", + "\n", + "\n", "# define function to apply the above functions on all vtu files listed in pvd file, returning force-displacement curves\n", + "\n", + "\n", "def force_displ_from_pvd(pvd):\n", " doc = minidom.parse(pvd)\n", " DataSets = doc.getElementsByTagName(\"DataSet\")\n", " vtu_files = [x.getAttribute(\"file\") for x in DataSets]\n", " forces_midpoint = [force_midpoint(f\"{out_dir}/{x}\") for x in vtu_files]\n", " displs_midpoint = [displ_midpoint(f\"{out_dir}/{x}\") for x in vtu_files]\n", - " return [forces_midpoint,displs_midpoint]\n", - "\n" + " return [forces_midpoint, displs_midpoint]" ] }, { @@ -302,8 +383,8 @@ "outputs": [], "source": [ "# Load experimental data\n", - "data_lower = pd.read_csv(f\"figures/experiment_data_lower_limit.csv\") \n", - "data_upper = pd.read_csv(f\"figures/experiment_data_upper_limit.csv\") \n" + "data_lower = pd.read_csv(\"figures/experiment_data_lower_limit.csv\")\n", + "data_upper = pd.read_csv(\"figures/experiment_data_upper_limit.csv\")" ] }, { @@ -334,30 +415,52 @@ } ], "source": [ - "prefixes = ['AT1_Isotropic','AT2_Isotropic','COHESIVE_EffectiveStress_Linear','COHESIVE_EffectiveStress_Exponential']\n", - "labels = [r'${AT}_1$ Isotropic',r'${AT}_2$ Isotropic',r'${COHESIVE}$ EffectiveStress Linear',r'${COHESIVE}$ EffectiveStress Exponential']\n", - "ls=['-.','--', '.', '-']\n", - "colors = ['#ffdf4d','#006ddb','#8f4e00','#ff6db6']\n", + "prefixes = [\n", + " \"AT1_Isotropic\",\n", + " \"AT2_Isotropic\",\n", + " \"COHESIVE_EffectiveStress_Linear\",\n", + " \"COHESIVE_EffectiveStress_Exponential\",\n", + "]\n", + "labels = [\n", + " r\"${AT}_1$ Isotropic\",\n", + " r\"${AT}_2$ Isotropic\",\n", + " r\"${COHESIVE}$ EffectiveStress Linear\",\n", + " r\"${COHESIVE}$ EffectiveStress Exponential\",\n", + "]\n", + "ls = [\"-.\", \"--\", \".\", \"-\"]\n", + "colors = [\"#ffdf4d\", \"#006ddb\", \"#8f4e00\", \"#ff6db6\"]\n", "\n", "fig, ax = plt.subplots()\n", - "for i,pre in enumerate(prefixes):\n", + "for i, pre in enumerate(prefixes):\n", " pvd = f\"{output_dir}/{pre}.pvd\"\n", - " if os.path.isfile(pvd) :\n", + " if os.path.isfile(pvd):\n", " curve = force_displ_from_pvd(pvd)\n", - " plt.plot(curve[1],curve[0],ls[i%2],label = labels[i], linewidth=2, color = colors[i], alpha= 1)\n", + " plt.plot(\n", + " curve[1],\n", + " curve[0],\n", + " ls[i % 2],\n", + " label=labels[i],\n", + " linewidth=2,\n", + " color=colors[i],\n", + " alpha=1,\n", + " )\n", "\n", - "plt.rcParams['xtick.labelsize'] = 12\n", - "plt.rcParams['ytick.labelsize'] = 12 \n", - "ax.grid(linestyle='dashed')\n", - "ax.set_xlabel('$u^{\\\\ast}$ [mm]',fontsize =12)\n", - "ax.set_ylabel('$F^{\\\\ast}$ [kN]',fontsize =12)\n", + "plt.rcParams[\"xtick.labelsize\"] = 12\n", + "plt.rcParams[\"ytick.labelsize\"] = 12\n", + "ax.grid(linestyle=\"dashed\")\n", + "ax.set_xlabel(\"$u^{\\\\ast}$ [mm]\", fontsize=12)\n", + "ax.set_ylabel(\"$F^{\\\\ast}$ [kN]\", fontsize=12)\n", "plt.xlim(plt.xlim()[::-1])\n", "plt.ylim(plt.ylim()[::-1])\n", - "plt.legend(ncol = 1)\n", - "ax.axhline(y = 0, color = 'black',linewidth=1)\n", - "ax.axvline(x = 0, color = 'black',linewidth=1)\n", - "plt.fill_between(data_upper.iloc[:,0],0,data_upper.iloc[:,1], facecolor='green', alpha=0.3)\n", - "plt.fill_between(data_lower.iloc[:,0],0,data_lower.iloc[:,1], facecolor='white', alpha=1)\n" + "plt.legend(ncol=1)\n", + "ax.axhline(y=0, color=\"black\", linewidth=1)\n", + "ax.axvline(x=0, color=\"black\", linewidth=1)\n", + "plt.fill_between(\n", + " data_upper.iloc[:, 0], 0, data_upper.iloc[:, 1], facecolor=\"green\", alpha=0.3\n", + ")\n", + "plt.fill_between(\n", + " data_lower.iloc[:, 0], 0, data_lower.iloc[:, 1], facecolor=\"white\", alpha=1\n", + ")" ] }, { diff --git a/Tests/Data/TH2M/H/diffusion/diffusion.ipynb b/Tests/Data/TH2M/H/diffusion/diffusion.ipynb index 53c065378e2..aad0a85eb10 100644 --- a/Tests/Data/TH2M/H/diffusion/diffusion.ipynb +++ b/Tests/Data/TH2M/H/diffusion/diffusion.ipynb @@ -51,23 +51,27 @@ "import numpy as np\n", "from scipy.special import erfc\n", "\n", + "\n", "# Analytical solution of the diffusion equation\n", - "def Diffusion(x,t):\n", + "def Diffusion(x, t):\n", " if type(t) != int and type(t) != np.float64 and type(t) != float:\n", - " # In order to avoid a division by zero, the time field is increased \n", - " # by a small time unit at the start time (t=0). This should have no \n", + " # In order to avoid a division by zero, the time field is increased\n", + " # by a small time unit at the start time (t=0). This should have no\n", " # effect on the result.\n", " tiny = np.finfo(np.float64).tiny\n", " t[t < tiny] = tiny\n", - " \n", - " d = np.sqrt(4*D*t)\n", - " e = (c_b - c_i)*erfc(x/d)+c_i\n", + "\n", + " d = np.sqrt(4 * D * t)\n", + " e = (c_b - c_i) * erfc(x / d) + c_i\n", " return e\n", "\n", + "\n", "# Utility-function transforming mass fraction into conctration\n", + "\n", + "\n", "def concentration(xm_WL):\n", - " xm_CL = 1. - xm_WL\n", - " return xm_CL / beta_c\n" + " xm_CL = 1.0 - xm_WL\n", + " return xm_CL / beta_c" ] }, { @@ -87,7 +91,7 @@ "source": [ "# Henry-coefficient and compressibility of solution\n", "H = 7.65e-6\n", - "beta_c = 2.e-6\n", + "beta_c = 2.0e-6\n", "\n", "# Diffusion coefficient\n", "D = 1.0e-9\n", @@ -97,8 +101,8 @@ "pGR_i = 1e5\n", "\n", "# Boundary and initial concentration\n", - "c_b = concentration(1. - (beta_c*H*pGR_b))\n", - "c_i = concentration(1. - (beta_c*H*pGR_i))\n" + "c_b = concentration(1.0 - (beta_c * H * pGR_b))\n", + "c_i = concentration(1.0 - (beta_c * H * pGR_i))" ] }, { @@ -118,9 +122,9 @@ "source": [ "import os\n", "\n", - "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n", + "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", "if not os.path.exists(out_dir):\n", - " os.makedirs(out_dir)\n" + " os.makedirs(out_dir)" ] }, { @@ -141,15 +145,17 @@ "source": [ "from ogs6py.ogs import OGS\n", "\n", - "model=OGS(INPUT_FILE=\"diffusion.prj\", PROJECT_FILE=f\"{out_dir}/modified.prj\")\n", + "model = OGS(INPUT_FILE=\"diffusion.prj\", PROJECT_FILE=f\"{out_dir}/modified.prj\")\n", "model.replace_text(1e7, xpath=\"./time_loop/processes/process/time_stepping/t_end\")\n", - "model.replace_text(5e4, xpath=\"./time_loop/processes/process/time_stepping/timesteps/pair/delta_t\")\n", + "model.replace_text(\n", + " 5e4, xpath=\"./time_loop/processes/process/time_stepping/timesteps/pair/delta_t\"\n", + ")\n", "# Write every timestep\n", "model.replace_text(1, xpath=\"./time_loop/output/timesteps/pair/each_steps\")\n", "model.write_input()\n", "\n", "# Run OGS\n", - "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m .\")\n" + "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m .\")" ] }, { @@ -159,9 +165,9 @@ "metadata": {}, "outputs": [], "source": [ - " #Colors\n", - "cls1 = ['#4a001e', '#731331', '#9f2945', '#cc415a', '#e06e85', '#ed9ab0']\n", - "cls2 = ['#0b194c', '#163670', '#265191', '#2f74b3', '#5d94cb', '#92b2de']\n" + "# Colors\n", + "cls1 = [\"#4a001e\", \"#731331\", \"#9f2945\", \"#cc415a\", \"#e06e85\", \"#ed9ab0\"]\n", + "cls2 = [\"#0b194c\", \"#163670\", \"#265191\", \"#2f74b3\", \"#5d94cb\", \"#92b2de\"]" ] }, { @@ -182,13 +188,13 @@ "time_steps = [1e6, 2e6, 4e6, 6e6, 8e6, 1e7]\n", "\n", "# 'Continuous' space axis for c vs. x plots for plotting\n", - "length = np.linspace(0,1.0,101)\n", + "length = np.linspace(0, 1.0, 101)\n", "\n", "# Draws a line through the domain for sampling results\n", - "x_axis=[(i,0,0) for i in length]\n", + "x_axis = [(i, 0, 0) for i in length]\n", "\n", "# Discrete locations for c vs. t plots\n", - "location = [0.01,0.05,0.1,0.2,0.5,1.0]\n" + "location = [0.01, 0.05, 0.1, 0.2, 0.5, 1.0]" ] }, { @@ -198,19 +204,23 @@ "metadata": {}, "outputs": [], "source": [ - " # The sample locations have to be converted into a 'dict' for vtuIO\n", - "observation_points = dict(('x='+str(x),(x,0.0,0.0)) for x in location)\n", + "# The sample locations have to be converted into a 'dict' for vtuIO\n", + "observation_points = dict((\"x=\" + str(x), (x, 0.0, 0.0)) for x in location)\n", "# Samples concentration field at the observation points for all timesteps\n", "\n", - "c_over_t_at_x = pvdfile.read_time_series('xmWL', observation_points)\n", + "c_over_t_at_x = pvdfile.read_time_series(\"xmWL\", observation_points)\n", "for key in c_over_t_at_x:\n", - " x = c_over_t_at_x[key];\n", + " x = c_over_t_at_x[key]\n", " c_over_t_at_x[key] = concentration(x)\n", - " \n", + "\n", "# Samples concentration field along the domain at certain timesteps\n", "c_over_x_at_t = []\n", - "for t in range(0,len(time_steps)):\n", - " c_over_x_at_t.append(concentration(pvdfile.read_set_data(time_steps[t], \"xmWL\", pointsetarray=x_axis)))\n" + "for t in range(len(time_steps)):\n", + " c_over_x_at_t.append(\n", + " concentration(\n", + " pvdfile.read_set_data(time_steps[t], \"xmWL\", pointsetarray=x_axis)\n", + " )\n", + " )" ] }, { @@ -246,7 +256,8 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "plt.rcParams['figure.figsize'] = (14, 4)\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (14, 4)\n", "\n", "# Plot of concentration vs. time at different locations\n", "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", @@ -255,36 +266,54 @@ "ax1.set_ylabel(\"$c$ / mol m$^{-3}$\", fontsize=12)\n", "\n", "ax2.set_xlabel(\"$t$ / s\", fontsize=12)\n", - "ax2.set_ylabel(\"$\\epsilon_\\mathrm{abs}$ / mol m$^{-3}$\", fontsize=12)\n", + "ax2.set_ylabel(r\"$\\epsilon_\\mathrm{abs}$ / mol m$^{-3}$\", fontsize=12)\n", "\n", "label_x = []\n", "for key, c in c_over_t_at_x.items():\n", " x = observation_points[key][0]\n", - " label_x.append(key+r\" m\")\n", + " label_x.append(key + r\" m\")\n", " # numerical solution\n", - " ax1.plot(time, c_over_t_at_x[key], color=cls1[location.index(x)], linewidth=3,\n", - " linestyle=\"--\")\n", + " ax1.plot(\n", + " time,\n", + " c_over_t_at_x[key],\n", + " color=cls1[location.index(x)],\n", + " linewidth=3,\n", + " linestyle=\"--\",\n", + " )\n", " # analytical solution\n", - " ax1.plot(time, Diffusion(x,time), color=cls1[location.index(x)], linewidth=2,\n", - " linestyle=\"-\")\n", + " ax1.plot(\n", + " time,\n", + " Diffusion(x, time),\n", + " color=cls1[location.index(x)],\n", + " linewidth=2,\n", + " linestyle=\"-\",\n", + " )\n", " # absolute error\n", - " err_abs = Diffusion(x,time) - c_over_t_at_x[key]\n", - " ax2.plot(time, err_abs, color=cls1[location.index(x)], linewidth=1, linestyle=\"-\", label=key+r\" m\")\n", + " err_abs = Diffusion(x, time) - c_over_t_at_x[key]\n", + " ax2.plot(\n", + " time,\n", + " err_abs,\n", + " color=cls1[location.index(x)],\n", + " linewidth=1,\n", + " linestyle=\"-\",\n", + " label=key + r\" m\",\n", + " )\n", "\n", "\n", "# Hack to force a custom legend:\n", "from matplotlib.lines import Line2D\n", + "\n", "custom_lines = []\n", "\n", - "for i in range(0,6):\n", + "for i in range(6):\n", " custom_lines.append(Line2D([0], [0], color=cls1[i], lw=4))\n", "\n", - "custom_lines.append(Line2D([0], [0], color='black', lw=3, linestyle=\"--\"))\n", - "custom_lines.append(Line2D([0], [0], color='black', lw=2, linestyle=\"-\"))\n", - "label_x.append('OGS-TH2M')\n", - "label_x.append('analytical')\n", + "custom_lines.append(Line2D([0], [0], color=\"black\", lw=3, linestyle=\"--\"))\n", + "custom_lines.append(Line2D([0], [0], color=\"black\", lw=2, linestyle=\"-\"))\n", + "label_x.append(\"OGS-TH2M\")\n", + "label_x.append(\"analytical\")\n", "\n", - "ax1.legend(custom_lines, label_x, loc='right')\n", + "ax1.legend(custom_lines, label_x, loc=\"right\")\n", "ax2.legend()\n", "fig1.savefig(f\"{out_dir}/diffusion_c_vs_t.pdf\")\n", "\n", @@ -294,41 +323,46 @@ "\n", "ax1.set_xlabel(\"$x$ / m\", fontsize=12)\n", "ax1.set_ylabel(\"$c$ / mol m$^{-3}$\", fontsize=12)\n", - "ax1.set_xlim(0,0.4)\n", + "ax1.set_xlim(0, 0.4)\n", "\n", "ax2.set_xlabel(\"$x$ / m\", fontsize=12)\n", - "ax2.set_ylabel(\"$\\epsilon$ / mol $m^{-3}$\", fontsize=12)\n", - "ax2.set_xlim(0,0.4)\n", + "ax2.set_ylabel(r\"$\\epsilon$ / mol $m^{-3}$\", fontsize=12)\n", + "ax2.set_xlim(0, 0.4)\n", "\n", "\n", "# Plot concentration over domain at five moments\n", "label_t = []\n", - "for t in range(0,len(time_steps)):\n", - " s = r\"$t=$\"+str(time_steps[t]/1e6)+\"$\\,$Ms\"\n", + "for t in range(len(time_steps)):\n", + " s = r\"$t=$\" + str(time_steps[t] / 1e6) + r\"$\\,$Ms\"\n", " label_t.append(s)\n", " # numerical solution\n", " ax1.plot(length, c_over_x_at_t[t], color=cls2[t], linewidth=3, linestyle=\"--\")\n", " # analytical solution\n", - " ax1.plot(length, Diffusion(length,time_steps[t]),color=cls2[t], linewidth=2, \\\n", - " linestyle=\"-\")\n", + " ax1.plot(\n", + " length,\n", + " Diffusion(length, time_steps[t]),\n", + " color=cls2[t],\n", + " linewidth=2,\n", + " linestyle=\"-\",\n", + " )\n", " # absolute error\n", - " err_abs = Diffusion(length,time_steps[t]) - c_over_x_at_t[t]\n", + " err_abs = Diffusion(length, time_steps[t]) - c_over_x_at_t[t]\n", " ax2.plot(length, err_abs, color=cls2[t], linewidth=1, linestyle=\"-\", label=s)\n", "\n", "custom_lines = []\n", "\n", - "for i in range(0,6):\n", + "for i in range(6):\n", " custom_lines.append(Line2D([0], [0], color=cls2[i], lw=4))\n", "\n", - "custom_lines.append(Line2D([0], [0], color='black', lw=3, linestyle=\"--\"))\n", - "custom_lines.append(Line2D([0], [0], color='black', lw=2, linestyle=\"-\"))\n", - "label_t.append('OGS-TH2M')\n", - "label_t.append('analytical')\n", + "custom_lines.append(Line2D([0], [0], color=\"black\", lw=3, linestyle=\"--\"))\n", + "custom_lines.append(Line2D([0], [0], color=\"black\", lw=2, linestyle=\"-\"))\n", + "label_t.append(\"OGS-TH2M\")\n", + "label_t.append(\"analytical\")\n", "\n", - "ax1.legend(custom_lines, label_t, loc='right')\n", + "ax1.legend(custom_lines, label_t, loc=\"right\")\n", "ax2.legend()\n", "\n", - "fig1.savefig(f\"{out_dir}/diffusion_c_vs_x.pdf\")\n" + "fig1.savefig(f\"{out_dir}/diffusion_c_vs_x.pdf\")" ] }, { diff --git a/Tests/Data/TH2M/H2/dissolution_diffusion/phase_appearance.ipynb b/Tests/Data/TH2M/H2/dissolution_diffusion/phase_appearance.ipynb index cc0dc6c45ac..7a887306880 100644 --- a/Tests/Data/TH2M/H2/dissolution_diffusion/phase_appearance.ipynb +++ b/Tests/Data/TH2M/H2/dissolution_diffusion/phase_appearance.ipynb @@ -81,9 +81,9 @@ "source": [ "import os\n", "\n", - "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n", + "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", "if not os.path.exists(out_dir):\n", - " os.makedirs(out_dir)\n" + " os.makedirs(out_dir)" ] }, { @@ -102,10 +102,10 @@ } ], "source": [ - "from ogs6py.ogs import OGS\n", "import numpy as np\n", + "from ogs6py.ogs import OGS\n", "\n", - "model=OGS(INPUT_FILE=\"bourgeat.prj\", PROJECT_FILE=f\"{out_dir}/modified.prj\")\n", + "model = OGS(INPUT_FILE=\"bourgeat.prj\", PROJECT_FILE=f\"{out_dir}/modified.prj\")\n", "# This Jupyter notebook version of this test runs not as far as its cTest-counterpart,\n", "# it'll stop after approx. 800 ka\n", "model.replace_text(2.5e13, xpath=\"./time_loop/processes/process/time_stepping/t_end\")\n", @@ -113,24 +113,26 @@ "\n", "# The cTest version shows only a few output-timesteps while this version is supposed to\n", "# output much higher resolution time steps to be able to compare the results. Thus, every\n", - "# timestep will be written and the maximum timestep-size of the adaptive time stepping \n", + "# timestep will be written and the maximum timestep-size of the adaptive time stepping\n", "# method will be reduced drastically\n", "time_end = 1e11\n", - "model.replace_text(time_end, xpath=\"./time_loop/processes/process/time_stepping/maximum_dt\")\n", + "model.replace_text(\n", + " time_end, xpath=\"./time_loop/processes/process/time_stepping/maximum_dt\"\n", + ")\n", "\n", - "# The following for loop generates a text with output times,which is then replaced by \n", + "# The following for loop generates a text with output times,which is then replaced by\n", "# the ogs6py API in the project file.\n", - "new_line = u\"\\u000A\"\n", - "timesteps = str(0.4*1e6*86400*365.25)+new_line\n", - "for t in np.arange(0.6,1.1,0.1):\n", - " timesteps += str(t*1e6*86400*365.25)+new_line\n", + "new_line = \"\\u000A\"\n", + "timesteps = str(0.4 * 1e6 * 86400 * 365.25) + new_line\n", + "for t in np.arange(0.6, 1.1, 0.1):\n", + " timesteps += str(t * 1e6 * 86400 * 365.25) + new_line\n", "\n", "model.replace_text(1, xpath=\"./time_loop/output/timesteps/pair/each_steps\")\n", - "model.replace_text(timesteps,xpath=\"./time_loop/output/fixed_output_times\")\n", + "model.replace_text(timesteps, xpath=\"./time_loop/output/fixed_output_times\")\n", "model.write_input()\n", "\n", "# Run OGS\n", - "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m .\")\n" + "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m .\")" ] }, { @@ -140,8 +142,8 @@ "metadata": {}, "outputs": [], "source": [ - " #Colors\n", - "cls=['#e6191d','#337fb8','#4eae4c','#984ea3','#984ea3','#feff32']\n" + "# Colors\n", + "cls = [\"#e6191d\", \"#337fb8\", \"#4eae4c\", \"#984ea3\", \"#984ea3\", \"#feff32\"]" ] }, { @@ -152,18 +154,19 @@ "outputs": [], "source": [ "import vtuIO\n", + "\n", "# Read PVD-output\n", "pvdfile = vtuIO.PVDIO(f\"{out_dir}/result_bourgeat.pvd\", dim=2)\n", - "point={'A': (0.0,0.0,0.0)}\n", + "point = {\"A\": (0.0, 0.0, 0.0)}\n", "time = pvdfile.timesteps\n", "\n", "saturation = pvdfile.read_time_series(\"saturation\", point)\n", "gas_pressure = pvdfile.read_time_series(\"gas_pressure\", point)\n", "liquid_pressure = pvdfile.read_time_series(\"liquid_pressure_interpolated\", point)\n", "\n", - "num_results= [1.-saturation['A'], gas_pressure['A'], liquid_pressure['A']]\n", + "num_results = [1.0 - saturation[\"A\"], gas_pressure[\"A\"], liquid_pressure[\"A\"]]\n", "\n", - "time_years = time / 365.2425 / 86400\n" + "time_years = time / 365.2425 / 86400" ] }, { @@ -176,11 +179,13 @@ "import pandas as pd\n", "\n", "# Read the reference data from CSV files\n", - "refs = [pd.read_csv(f\"references/bourgeat_sG.csv\"),\n", - " pd.read_csv(f\"references/bourgeat_pGR.csv\"),\n", - " pd.read_csv(f\"references/bourgeat_pLR.csv\")]\n", + "refs = [\n", + " pd.read_csv(\"references/bourgeat_sG.csv\"),\n", + " pd.read_csv(\"references/bourgeat_pGR.csv\"),\n", + " pd.read_csv(\"references/bourgeat_pLR.csv\"),\n", + "]\n", "\n", - "header = list(refs[0].keys())\n" + "header = list(refs[0].keys())" ] }, { @@ -190,8 +195,8 @@ "metadata": {}, "outputs": [], "source": [ - "indices = {\"Gas saturation\" : 0,\"Gas pressure\" : 1,\"Liquid pressure\" : 2}\n", - "labels = [\"$s_{G}$\", '$p_{GR}$', '$p_{LR}$']\n" + "indices = {\"Gas saturation\": 0, \"Gas pressure\": 1, \"Liquid pressure\": 2}\n", + "labels = [\"$s_{G}$\", \"$p_{GR}$\", \"$p_{LR}$\"]" ] }, { @@ -240,35 +245,61 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "plt.rcParams['figure.figsize'] = (12, 4)\n", + "plt.rcParams[\"figure.figsize\"] = (12, 4)\n", "\n", "# Loop over gas_saturation, gas_pressure, and liquid_pressure\n", "for i in indices:\n", " index = indices[i]\n", - " ref_time = refs[index]['time']\n", - " \n", + " ref_time = refs[index][\"time\"]\n", + "\n", " fig1, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n", - " fig1.suptitle(i+r\" vs. time at $x=0$m\")\n", + " fig1.suptitle(i + r\" vs. time at $x=0$m\")\n", "\n", " ax1.set_xscale(\"log\")\n", " ax1.set_xlabel(\"time / a\", fontsize=12)\n", " ax1.set_ylabel(labels[index], fontsize=12)\n", "\n", - " for r in range(1,len(refs[index].columns)):\n", - " ax1.plot(ref_time, refs[index][refs[index].keys()[r]] , linewidth=1, linestyle=\"-\", label=refs[index].keys()[r])\n", - "\n", - " ax1.plot(time_years, num_results[index], color='black', linewidth=2, linestyle=\"--\", label='OGS-TH$^2$M')\n", + " for r in range(1, len(refs[index].columns)):\n", + " ax1.plot(\n", + " ref_time,\n", + " refs[index][refs[index].keys()[r]],\n", + " linewidth=1,\n", + " linestyle=\"-\",\n", + " label=refs[index].keys()[r],\n", + " )\n", + "\n", + " ax1.plot(\n", + " time_years,\n", + " num_results[index],\n", + " color=\"black\",\n", + " linewidth=2,\n", + " linestyle=\"--\",\n", + " label=\"OGS-TH$^2$M\",\n", + " )\n", "\n", " ax2.set_xlabel(\"time / a\", fontsize=12)\n", "\n", - " for r in range(1,len(refs[index].columns)):\n", - " ax2.plot(ref_time, refs[index][refs[index].keys()[r]] , linewidth=1, linestyle=\"-\", label=refs[index].keys()[r])\n", - " ax2.plot(time_years, num_results[index], color='black', linewidth=2, linestyle=\"--\", label='OGS-TH$^2$M')\n", + " for r in range(1, len(refs[index].columns)):\n", + " ax2.plot(\n", + " ref_time,\n", + " refs[index][refs[index].keys()[r]],\n", + " linewidth=1,\n", + " linestyle=\"-\",\n", + " label=refs[index].keys()[r],\n", + " )\n", + " ax2.plot(\n", + " time_years,\n", + " num_results[index],\n", + " color=\"black\",\n", + " linewidth=2,\n", + " linestyle=\"--\",\n", + " label=\"OGS-TH$^2$M\",\n", + " )\n", "\n", " ax1.legend()\n", "\n", "\n", - "fig1.savefig('results_sG_pGR_pLR.pdf')\n" + "fig1.savefig(\"results_sG_pGR_pLR.pdf\")" ] }, { diff --git a/Tests/Data/TH2M/H2/mcWhorter/mcWhorter.ipynb b/Tests/Data/TH2M/H2/mcWhorter/mcWhorter.ipynb index f6082c759a6..09cf5c81b07 100644 --- a/Tests/Data/TH2M/H2/mcWhorter/mcWhorter.ipynb +++ b/Tests/Data/TH2M/H2/mcWhorter/mcWhorter.ipynb @@ -83,9 +83,10 @@ "outputs": [], "source": [ "import numpy as np\n", + "\n", "# Import analytical solution from a CSV file\n", - "exact = np.loadtxt('data/ref_solution_saturation.csv', delimiter=\",\")\n", - "# Zeroth column is location, first column is saturation\n" + "exact = np.loadtxt(\"data/ref_solution_saturation.csv\", delimiter=\",\")\n", + "# Zeroth column is location, first column is saturation" ] }, { @@ -105,9 +106,9 @@ "source": [ "import os\n", "\n", - "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n", + "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", "if not os.path.exists(out_dir):\n", - " os.makedirs(out_dir)\n" + " os.makedirs(out_dir)" ] }, { @@ -129,8 +130,8 @@ "from ogs6py.ogs import OGS\n", "\n", "# run OGS\n", - "model=OGS(PROJECT_FILE=\"mcWhorter_h2.prj\")\n", - "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir}\")\n" + "model = OGS(PROJECT_FILE=\"mcWhorter_h2.prj\")\n", + "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir}\")" ] }, { @@ -141,8 +142,11 @@ "outputs": [], "source": [ "import vtuIO\n", + "\n", "# read OGS results from PVD file\n", - "pvdfile = vtuIO.PVDIO(f\"{out_dir}/result_McWhorter_H2.pvd\", dim=2, interpolation_backend=\"vtk\")\n" + "pvdfile = vtuIO.PVDIO(\n", + " f\"{out_dir}/result_McWhorter_H2.pvd\", dim=2, interpolation_backend=\"vtk\"\n", + ")" ] }, { @@ -155,18 +159,18 @@ "import numpy as np\n", "\n", "# The sampling routine requires a line of points in space\n", - "x_axis=[(i,0,0) for i in exact[:,0]]\n", + "x_axis = [(i, 0, 0) for i in exact[:, 0]]\n", "\n", "# Only the last timestep is written (together with the initial condition).\n", "# Thus the second element of the time vector (index = 1) is the to be sampled\n", "time = pvdfile.timesteps[1]\n", "\n", "# The numerical solution is sampled at the same supporting points as the analytical solution\n", - "sL_num = pvdfile.read_set_data(time, \"saturation\", pointsetarray=x_axis);\n", + "sL_num = pvdfile.read_set_data(time, \"saturation\", pointsetarray=x_axis)\n", "\n", "# Absolute and relative errors\n", - "err_abs = exact[:,1] - sL_num\n", - "err_rel = err_abs / exact[:,1]\n" + "err_abs = exact[:, 1] - sL_num\n", + "err_rel = err_abs / exact[:, 1]" ] }, { @@ -190,37 +194,40 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "plt.rcParams['figure.figsize'] = (14, 4)\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (14, 4)\n", "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", "\n", - "fig1.suptitle(r\"Liquid saturation and errors at t=\"+str(time)+\" seconds\")\n", + "fig1.suptitle(r\"Liquid saturation and errors at t=\" + str(time) + \" seconds\")\n", "\n", "# Saturation vs. time\n", "ax1.set_xlabel(\"$x$ / m\", fontsize=12)\n", - "ax1.set_ylabel(\"$s_\\mathrm{L}$\", fontsize=12)\n", + "ax1.set_ylabel(r\"$s_\\mathrm{L}$\", fontsize=12)\n", "\n", - "ax1.plot(exact[:,0], sL_num,'b', label=r\"$s_\\mathrm{L}$ numerical\")\n", - "ax1.plot(exact[:,0], exact[:,1],'g', label=r\"$s_\\mathrm{L}$ exact\")\n", + "ax1.plot(exact[:, 0], sL_num, \"b\", label=r\"$s_\\mathrm{L}$ numerical\")\n", + "ax1.plot(exact[:, 0], exact[:, 1], \"g\", label=r\"$s_\\mathrm{L}$ exact\")\n", "\n", - "lns2 = ax2.plot(exact[:,0], err_abs,'b', linewidth=2,\n", - " linestyle=\"-\", label=r\"absolute error\")\n", + "lns2 = ax2.plot(\n", + " exact[:, 0], err_abs, \"b\", linewidth=2, linestyle=\"-\", label=r\"absolute error\"\n", + ")\n", "\n", "ax2.set_xlabel(\"$x$ / m\", fontsize=12)\n", - "ax2.set_ylabel(\"$\\epsilon_\\mathrm{abs}$\", fontsize=12)\n", + "ax2.set_ylabel(r\"$\\epsilon_\\mathrm{abs}$\", fontsize=12)\n", "\n", "ax3 = ax2.twinx()\n", - "lns3 = ax3.plot(exact[:,0], err_rel,'g', linewidth=2,\n", - " linestyle=\"-\", label=r\"relative error\")\n", - "ax3.set_ylabel(\"$\\epsilon_\\mathrm{rel}$\", fontsize=12)\n", + "lns3 = ax3.plot(\n", + " exact[:, 0], err_rel, \"g\", linewidth=2, linestyle=\"-\", label=r\"relative error\"\n", + ")\n", + "ax3.set_ylabel(r\"$\\epsilon_\\mathrm{rel}$\", fontsize=12)\n", "\n", "ax1.legend()\n", "\n", - "lns = lns2+lns3\n", + "lns = lns2 + lns3\n", "labs = [l.get_label() for l in lns]\n", "ax2.legend(lns, labs, loc=0)\n", "\n", "\n", - "fig1.savefig(f\"{out_dir}/mcWhorter.pdf\")\n" + "fig1.savefig(f\"{out_dir}/mcWhorter.pdf\")" ] }, { diff --git a/Tests/Data/TH2M/H2M/Liakopoulos/ogs-jupyter-lab-h2m-2d-liakopoulos.ipynb b/Tests/Data/TH2M/H2M/Liakopoulos/ogs-jupyter-lab-h2m-2d-liakopoulos.ipynb index 7805e383240..9007d0b34b2 100644 --- a/Tests/Data/TH2M/H2M/Liakopoulos/ogs-jupyter-lab-h2m-2d-liakopoulos.ipynb +++ b/Tests/Data/TH2M/H2M/Liakopoulos/ogs-jupyter-lab-h2m-2d-liakopoulos.ipynb @@ -31,18 +31,18 @@ "metadata": {}, "outputs": [], "source": [ - "#1-modules\n", - "from IPython.display import Image\n", - "from scipy.special import exp1\n", - "from vtk.util.numpy_support import vtk_to_numpy\n", + "# 1-modules\n", "import os\n", + "\n", "import pyvista as pv\n", + "from IPython.display import Image\n", + "\n", "pv.set_jupyter_backend(\"static\")\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "#import vtk\n", + "\n", + "# import vtk\n", "import matplotlib.tri as tri\n", - "import vtuIO\n" + "import vtuIO" ] }, { @@ -52,16 +52,16 @@ "metadata": {}, "outputs": [], "source": [ - "#2-settings (file handling, title, figures)\n", + "# 2-settings (file handling, title, figures)\n", "fig_dir = \"./figures/\"\n", "\n", - "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n", + "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", "if not os.path.exists(out_dir):\n", " os.makedirs(out_dir)\n", "\n", "prj_file_test = \"liakopoulos_TH2M.prj\"\n", "pvd_file_test = f\"{out_dir}/result_liakopoulos.pvd\"\n", - "vtu_mesh_file = \"domain.vtu\"\n" + "vtu_mesh_file = \"domain.vtu\"" ] }, { @@ -90,7 +90,7 @@ } ], "source": [ - "Image(filename = fig_dir + \"ogs-jupyter-lab.png\", width=150, height=100)\n" + "Image(filename=fig_dir + \"ogs-jupyter-lab.png\", width=150, height=100)" ] }, { @@ -117,7 +117,7 @@ } ], "source": [ - "Image(filename = fig_dir + \"h2m-tet.png\", width=150, height=100)\n" + "Image(filename=fig_dir + \"h2m-tet.png\", width=150, height=100)" ] }, { @@ -221,7 +221,7 @@ "source": [ "mesh = pv.read(vtu_mesh_file)\n", "print(\"inspecting vtu_mesh_file\")\n", - "mesh\n" + "mesh" ] }, { @@ -259,7 +259,7 @@ "plotter.view_xy()\n", "plotter.add_axes()\n", "plotter.show_bounds(mesh, xlabel=\"x\", ylabel=\"y\")\n", - "plotter.show()\n" + "plotter.show()" ] }, { @@ -286,13 +286,14 @@ } ], "source": [ - "#run ogs\n", + "# run ogs\n", "import time\n", + "\n", "t0 = time.time()\n", "print(f\"ogs -o {out_dir} {prj_file_test} > {out_dir}/log.txt\")\n", "! ogs -o {out_dir} {prj_file_test} > {out_dir}/log.txt\n", "tf = time.time()\n", - "print(\"computation time: \", round(tf - t0, 2), \" s.\")\n" + "print(\"computation time: \", round(tf - t0, 2), \" s.\")" ] }, { @@ -310,16 +311,16 @@ "metadata": {}, "outputs": [], "source": [ - "#alternative way\n", + "# alternative way\n", "pv.set_plot_theme(\"document\")\n", "pv.set_jupyter_backend(\"static\")\n", - "pt1 = (0,0,0)\n", - "pt2 = (0,1,0)\n", - "yaxis = pv.Line(pt1,pt2,resolution=2)\n", - "#print(yaxis)\n", + "pt1 = (0, 0, 0)\n", + "pt2 = (0, 1, 0)\n", + "yaxis = pv.Line(pt1, pt2, resolution=2)\n", + "# print(yaxis)\n", "line_mesh = mesh.slice_along_line(yaxis)\n", - "y_num = line_mesh.points[:,1]\n", - "reader = pv.get_reader(pvd_file_test)\n" + "y_num = line_mesh.points[:, 1]\n", + "reader = pv.get_reader(pvd_file_test)" ] }, { @@ -329,7 +330,7 @@ "metadata": {}, "outputs": [], "source": [ - "reader.set_active_time_value(0.)\n", + "reader.set_active_time_value(0.0)\n", "mesh = reader.read()[0]\n", "line_mesh = mesh.slice_along_line(yaxis)\n", "p_gas0 = line_mesh.point_data[\"gas_pressure\"]\n", @@ -337,7 +338,7 @@ "p_cap0 = line_mesh.point_data[\"capillary_pressure\"]\n", "u_y0 = line_mesh.point_data[\"displacement\"].T[1]\n", "\n", - "reader.set_active_time_value(120.)\n", + "reader.set_active_time_value(120.0)\n", "mesh = reader.read()[0]\n", "line_mesh = mesh.slice_along_line(yaxis)\n", "p_gas120 = line_mesh.point_data[\"gas_pressure\"]\n", @@ -345,7 +346,7 @@ "p_cap120 = line_mesh.point_data[\"capillary_pressure\"]\n", "u_y120 = line_mesh.point_data[\"displacement\"].T[1]\n", "\n", - "reader.set_active_time_value(300.)\n", + "reader.set_active_time_value(300.0)\n", "mesh = reader.read()[0]\n", "line_mesh = mesh.slice_along_line(yaxis)\n", "p_gas300 = line_mesh.point_data[\"gas_pressure\"]\n", @@ -353,7 +354,7 @@ "p_cap300 = line_mesh.point_data[\"capillary_pressure\"]\n", "u_y300 = line_mesh.point_data[\"displacement\"].T[1]\n", "\n", - "reader.set_active_time_value(4800.)\n", + "reader.set_active_time_value(4800.0)\n", "mesh = reader.read()[0]\n", "line_mesh = mesh.slice_along_line(yaxis)\n", "p_gas4800 = line_mesh.point_data[\"gas_pressure\"]\n", @@ -361,13 +362,13 @@ "p_cap4800 = line_mesh.point_data[\"capillary_pressure\"]\n", "u_y4800 = line_mesh.point_data[\"displacement\"].T[1]\n", "\n", - "reader.set_active_time_value(7200.)\n", + "reader.set_active_time_value(7200.0)\n", "mesh = reader.read()[0]\n", "line_mesh = mesh.slice_along_line(yaxis)\n", "p_gas7200 = line_mesh.point_data[\"gas_pressure\"]\n", "s_wat7200 = line_mesh.point_data[\"saturation\"]\n", "p_cap7200 = line_mesh.point_data[\"capillary_pressure\"]\n", - "u_y7200 = line_mesh.point_data[\"displacement\"].T[1]\n" + "u_y7200 = line_mesh.point_data[\"displacement\"].T[1]" ] }, { @@ -388,10 +389,10 @@ } ], "source": [ - "plt.rcParams['figure.figsize'] = (10,6)\n", - "plt.rcParams['lines.color'] = 'red'\n", - "plt.rcParams['legend.fontsize'] = 7\n", - "fig1, (ax1, ax2) = plt.subplots(2,2)\n", + "plt.rcParams[\"figure.figsize\"] = (10, 6)\n", + "plt.rcParams[\"lines.color\"] = \"red\"\n", + "plt.rcParams[\"legend.fontsize\"] = 7\n", + "fig1, (ax1, ax2) = plt.subplots(2, 2)\n", "ax1[0].set_ylabel(r\"$p_g$ / Pa\")\n", "ax1[1].set_ylabel(r\"$p_c$ / Pa\")\n", "ax1[1].yaxis.set_label_position(\"right\")\n", @@ -402,7 +403,7 @@ "ax2[1].yaxis.tick_right()\n", "ax2[0].set_xlabel(r\"$y$ / m\")\n", "ax2[1].set_xlabel(r\"$y$ / m\")\n", - "#gas pressure\n", + "# gas pressure\n", "ax1[0].plot(y_num, p_gas0, label=r\"$p_g$ t=0\")\n", "ax1[0].plot(y_num, p_gas120, label=r\"$p_g$ t=120\")\n", "ax1[0].plot(y_num, p_gas300, label=r\"$p_g$ t=300\")\n", @@ -410,7 +411,7 @@ "ax1[0].plot(y_num, p_gas7200, label=r\"$p_g$ t=7200\")\n", "ax1[0].legend()\n", "ax1[0].grid()\n", - "#capillary pressure\n", + "# capillary pressure\n", "ax1[1].plot(y_num, p_cap0, label=r\"$p_c$ t=0\")\n", "ax1[1].plot(y_num, p_cap120, label=r\"$p_c$ t=120\")\n", "ax1[1].plot(y_num, p_cap300, label=r\"$p_c$ t=300\")\n", @@ -418,7 +419,7 @@ "ax1[1].plot(y_num, p_cap7200, label=r\"$p_c$ t=7200\")\n", "ax1[1].legend()\n", "ax1[1].grid()\n", - "#liquid saturation\n", + "# liquid saturation\n", "ax2[0].plot(y_num, s_wat0, label=r\"$s_l$ t=0\")\n", "ax2[0].plot(y_num, s_wat120, label=r\"$s_l$ t=120\")\n", "ax2[0].plot(y_num, s_wat300, label=r\"$s_l$ t=300\")\n", @@ -426,14 +427,14 @@ "ax2[0].plot(y_num, s_wat7200, label=r\"$s_l$ t=7200\")\n", "ax2[0].legend()\n", "ax2[0].grid()\n", - "#vertical displacement\n", + "# vertical displacement\n", "ax2[1].plot(y_num, u_y0, label=r\"$u_y$ t=0\")\n", "ax2[1].plot(y_num, u_y120, label=r\"$u_y$ t=120\")\n", "ax2[1].plot(y_num, u_y300, label=r\"$u_y$ t=300\")\n", "ax2[1].plot(y_num, u_y4800, label=r\"$u_y$ t=4800\")\n", "ax2[1].plot(y_num, u_y7200, label=r\"$u_y$ t=7200\")\n", "ax2[1].legend()\n", - "ax2[1].grid()\n" + "ax2[1].grid()" ] }, { @@ -474,22 +475,22 @@ "theme = \"Vertical cross-section\"\n", "print(theme)\n", "file_vtu = f\"{out_dir}/result_liakopoulos_t_7200.vtu\"\n", - "m_plot=vtuIO.VTUIO(file_vtu, dim=2)\n", - "triang=tri.Triangulation(m_plot.points[:,0],m_plot.points[:,1])\n", + "m_plot = vtuIO.VTUIO(file_vtu, dim=2)\n", + "triang = tri.Triangulation(m_plot.points[:, 0], m_plot.points[:, 1])\n", "p_plot = m_plot.get_point_field(\"gas_pressure\")\n", "s_plot = m_plot.get_point_field(\"saturation\")\n", "u_plot = m_plot.get_point_field(\"displacement\").T[1]\n", - "fig, ax = plt.subplots(ncols=3, figsize=(5,10))\n", + "fig, ax = plt.subplots(ncols=3, figsize=(5, 10))\n", "plt.subplots_adjust(wspace=2)\n", - "#fig.tight_layout()\n", - "#plt.subplot_tool()\n", + "# fig.tight_layout()\n", + "# plt.subplot_tool()\n", "contour_left = ax[0].tricontourf(triang, p_plot)\n", "contour_mid = ax[1].tricontourf(triang, s_plot)\n", "contour_right = ax[2].tricontourf(triang, u_plot)\n", - "fig.colorbar(contour_left,ax=ax[0],label='$p$ / [MPa]')\n", - "fig.colorbar(contour_mid,ax=ax[1],label='$S$ / [-]')\n", - "fig.colorbar(contour_right,ax=ax[2],label='$u$ / [m]')\n", - "plt.show()\n" + "fig.colorbar(contour_left, ax=ax[0], label=\"$p$ / [MPa]\")\n", + "fig.colorbar(contour_mid, ax=ax[1], label=\"$S$ / [-]\")\n", + "fig.colorbar(contour_right, ax=ax[2], label=\"$u$ / [m]\")\n", + "plt.show()" ] }, { diff --git a/Tests/Data/TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb b/Tests/Data/TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb index 36c2fe0415b..fa2aa00986f 100644 --- a/Tests/Data/TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb +++ b/Tests/Data/TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb @@ -98,7 +98,7 @@ "lambda_eff = 2.2\n", "\n", "# Thermal diffusivity\n", - "alpha = lambda_eff / (rho_eff * cp_eff)\n" + "alpha = lambda_eff / (rho_eff * cp_eff)" ] }, { @@ -124,22 +124,26 @@ "# Time - domain\n", "\n", "# Helper function to simplify conversions from seconds to days and back\n", + "\n", + "\n", "def day(value):\n", " # Converts seconds to days\n", " return value / 86400\n", "\n", + "\n", "def second(value):\n", " # Converts days to seconds\n", " return value * 86400\n", "\n", + "\n", "# Time discretisation\n", "delta_time = second(0.5)\n", "max_time = second(500)\n", "\n", - "domain_size = 50 # metre\n", + "domain_size = 50 # metre\n", "\n", "# Groundwater velocity\n", - "v_x = 1.5e-6\n" + "v_x = 1.5e-6" ] }, { @@ -160,20 +164,21 @@ "import numpy as np\n", "from scipy.special import erfc\n", "\n", + "\n", "def OgataBanks(t, x):\n", " if type(t) != int and type(t) != np.float64:\n", - " # In order to avoid a division by zero, the time field is increased \n", - " # by a small time unit at the start time (t=0). This should have no \n", + " # In order to avoid a division by zero, the time field is increased\n", + " # by a small time unit at the start time (t=0). This should have no\n", " # effect on the result.\n", " tiny = np.finfo(np.float64).tiny\n", " t[t < tiny] = tiny\n", - " \n", - " d = np.sqrt(4.*alpha*t)\n", - " a1 = np.divide((x-v_x*t),d,where=t!=0)\n", - " a2 = np.divide((x+v_x*t),d,where=t!=0)\n", - " \n", - " result = (T_0-T_i) / 2. * (erfc(a1)+np.exp(v_x*x/alpha)*erfc(a2)) + T_i\n", - " return result\n" + "\n", + " d = np.sqrt(4.0 * alpha * t)\n", + " a1 = np.divide((x - v_x * t), d, where=t != 0)\n", + " a2 = np.divide((x + v_x * t), d, where=t != 0)\n", + "\n", + " result = (T_0 - T_i) / 2.0 * (erfc(a1) + np.exp(v_x * x / alpha) * erfc(a2)) + T_i\n", + " return result" ] }, { @@ -193,9 +198,9 @@ "source": [ "import os\n", "\n", - "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n", + "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", "if not os.path.exists(out_dir):\n", - " os.makedirs(out_dir)\n" + " os.makedirs(out_dir)" ] }, { @@ -219,12 +224,15 @@ "from ogs6py.ogs import OGS\n", "\n", "# Modifies the project file of the original cTest so that the simulation runs for a longer time.\n", - "model=OGS(INPUT_FILE=\"ogata-banks.prj\", PROJECT_FILE=f\"{out_dir}/modified.prj\")\n", + "model = OGS(INPUT_FILE=\"ogata-banks.prj\", PROJECT_FILE=f\"{out_dir}/modified.prj\")\n", "model.replace_text(max_time, xpath=\"./time_loop/processes/process/time_stepping/t_end\")\n", - "model.replace_text(delta_time, xpath=\"./time_loop/processes/process/time_stepping/timesteps/pair/delta_t\")\n", - "# Output every timestep \n", + "model.replace_text(\n", + " delta_time,\n", + " xpath=\"./time_loop/processes/process/time_stepping/timesteps/pair/delta_t\",\n", + ")\n", + "# Output every timestep\n", "model.replace_text(1, xpath=\"./time_loop/output/timesteps/pair/each_steps\")\n", - "model.write_input()\n" + "model.write_input()" ] }, { @@ -244,7 +252,7 @@ ], "source": [ "# Run OGS\n", - "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m . -s .\")\n" + "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir} -m . -s .\")" ] }, { @@ -254,9 +262,9 @@ "metadata": {}, "outputs": [], "source": [ - "#Colors\n", - "cls1 = ['#4a001e', '#731331', '#9f2945', '#cc415a', '#e06e85']\n", - "cls2 = ['#0b194c', '#163670', '#265191', '#2f74b3', '#5d94cb']\n" + "# Colors\n", + "cls1 = [\"#4a001e\", \"#731331\", \"#9f2945\", \"#cc415a\", \"#e06e85\"]\n", + "cls2 = [\"#0b194c\", \"#163670\", \"#265191\", \"#2f74b3\", \"#5d94cb\"]" ] }, { @@ -274,16 +282,16 @@ "time = pvdfile.timesteps\n", "\n", "# Select individual timesteps for T vs. x plots for plotting\n", - "time_steps = [second(10),second(100),second(200),second(300), second(500)]\n", + "time_steps = [second(10), second(100), second(200), second(300), second(500)]\n", "\n", "# 'Continuous' space axis for T vs. x plots for plotting\n", - "length = np.linspace(0,domain_size,101)\n", + "length = np.linspace(0, domain_size, 101)\n", "\n", "# Draws a line through the domain for sampling results\n", - "x_axis=[(i,0,0) for i in length]\n", + "x_axis = [(i, 0, 0) for i in length]\n", "\n", "# Discrete locations for T vs. t plots\n", - "location = [1.,5.,10.,20.,50.]\n" + "location = [1.0, 5.0, 10.0, 20.0, 50.0]" ] }, { @@ -294,9 +302,9 @@ "outputs": [], "source": [ "# The sample locations have to be converted into a 'dict' for vtuIO\n", - "observation_points = dict(('x='+str(x),(x,0.0,0.0)) for x in location)\n", + "observation_points = dict((\"x=\" + str(x), (x, 0.0, 0.0)) for x in location)\n", "# Samples temperature field at the observation points for all timesteps\n", - "T_over_t_at_x = pvdfile.read_time_series('temperature_interpolated', observation_points)\n" + "T_over_t_at_x = pvdfile.read_time_series(\"temperature_interpolated\", observation_points)" ] }, { @@ -308,8 +316,10 @@ "source": [ "# Samples temperature field along the domain at certain timesteps\n", "T_over_x_at_t = []\n", - "for t in range(0,len(time_steps)):\n", - " T_over_x_at_t.append(pvdfile.read_set_data(time_steps[t], \"temperature\", pointsetarray=x_axis))\n" + "for t in range(len(time_steps)):\n", + " T_over_x_at_t.append(\n", + " pvdfile.read_set_data(time_steps[t], \"temperature\", pointsetarray=x_axis)\n", + " )" ] }, { @@ -333,7 +343,8 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "plt.rcParams['figure.figsize'] = (14, 6)\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (14, 6)\n", "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", "\n", "ax1.set_xlabel(\"$t$ / d\", fontsize=12)\n", @@ -343,25 +354,43 @@ "for key, T in T_over_t_at_x.items():\n", " x = observation_points[key][0]\n", " # Plot numerical solution\n", - " ax1.plot(day(time), T_over_t_at_x[key], color=cls1[location.index(x)], linewidth=3,\n", - " linestyle=\"--\", label=key+r\" m\")\n", + " ax1.plot(\n", + " day(time),\n", + " T_over_t_at_x[key],\n", + " color=cls1[location.index(x)],\n", + " linewidth=3,\n", + " linestyle=\"--\",\n", + " label=key + r\" m\",\n", + " )\n", " # Plot analytical solution\n", - " ax1.plot(day(time), OgataBanks(time,x), color=cls1[location.index(x)], linewidth=2,\n", - " linestyle=\"-\")\n", + " ax1.plot(\n", + " day(time),\n", + " OgataBanks(time, x),\n", + " color=cls1[location.index(x)],\n", + " linewidth=2,\n", + " linestyle=\"-\",\n", + " )\n", "\n", "ax2.set_xlabel(\"$x$ / m\", fontsize=12)\n", "ax2.set_ylabel(\"$T$ / K\", fontsize=12)\n", "\n", "# Plot Temperature over domain at five moments\n", - "for t in range(0,len(time_steps)):\n", - " s = r\"$t=$\"+str(day(time_steps[t]))+\"$\\,$d\"\n", - " ax2.plot(length, T_over_x_at_t[t], color=cls2[t], linewidth=3, linestyle=\"--\", label=s)\n", - " ax2.plot(length, OgataBanks(time_steps[t],length),color=cls2[t], linewidth=2, \\\n", - " linestyle=\"-\")\n", + "for t in range(len(time_steps)):\n", + " s = r\"$t=$\" + str(day(time_steps[t])) + r\"$\\,$d\"\n", + " ax2.plot(\n", + " length, T_over_x_at_t[t], color=cls2[t], linewidth=3, linestyle=\"--\", label=s\n", + " )\n", + " ax2.plot(\n", + " length,\n", + " OgataBanks(time_steps[t], length),\n", + " color=cls2[t],\n", + " linewidth=2,\n", + " linestyle=\"-\",\n", + " )\n", "\n", "ax1.legend()\n", "ax2.legend()\n", - "fig1.savefig(f\"{out_dir}/ogata_banks.pdf\")\n" + "fig1.savefig(f\"{out_dir}/ogata_banks.pdf\")" ] }, { @@ -392,11 +421,11 @@ ], "source": [ "# Spatial discretizations at the left boundary (smallest element)\n", - "dx = 0.17 #m\n", + "dx = 0.17 # m\n", "\n", "# von-Neumann-Stability-Criterion\n", - "Ne = alpha * delta_time / (dx*dx)\n", - "print (Ne)\n" + "Ne = alpha * delta_time / (dx * dx)\n", + "print(Ne)" ] }, { @@ -422,8 +451,8 @@ } ], "source": [ - "dt = 0.5*(dx*dx)/alpha\n", - "print(\"Smallest timestep should not exceed\",dt, \"seconds.\")\n" + "dt = 0.5 * (dx * dx) / alpha\n", + "print(\"Smallest timestep should not exceed\", dt, \"seconds.\")" ] }, { @@ -452,8 +481,8 @@ } ], "source": [ - "dx = np.sqrt(2*alpha*delta_time)\n", - "print(\"Minimum element size should be\",dx,\" metre.\")\n" + "dx = np.sqrt(2 * alpha * delta_time)\n", + "print(\"Minimum element size should be\", dx, \" metre.\")" ] }, { diff --git a/Tests/Data/TH2M/TH/idealGasLaw/confined_gas_compression.ipynb b/Tests/Data/TH2M/TH/idealGasLaw/confined_gas_compression.ipynb index 31199770888..ea530f5488c 100644 --- a/Tests/Data/TH2M/TH/idealGasLaw/confined_gas_compression.ipynb +++ b/Tests/Data/TH2M/TH/idealGasLaw/confined_gas_compression.ipynb @@ -74,8 +74,8 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n" + "import matplotlib.pyplot as plt\n", + "import numpy as np" ] }, { @@ -89,7 +89,7 @@ "t = np.linspace(0, 10, 11)\n", "\n", "# volume strain is a function of time\n", - "e = -t/100\n", + "e = -t / 100\n", "\n", "# initial state\n", "p_0 = 1e6\n", @@ -98,12 +98,12 @@ "M = 0.01\n", "c_p = 1000\n", "\n", - "c_v = c_p-R/M\n", - "rho_0 = p_0*M/R/T_0\n", - "kappa=c_p/c_v\n", + "c_v = c_p - R / M\n", + "rho_0 = p_0 * M / R / T_0\n", + "kappa = c_p / c_v\n", "\n", "# density\n", - "rho_GR = rho_0*np.exp(-e)\n" + "rho_GR = rho_0 * np.exp(-e)" ] }, { @@ -161,7 +161,7 @@ "outputs": [], "source": [ "# gas pressure\n", - "p_GR=p_0*np.exp(-kappa*e)\n" + "p_GR = p_0 * np.exp(-kappa * e)" ] }, { @@ -185,7 +185,7 @@ "outputs": [], "source": [ "# temperature\n", - "T = p_GR*M/R/rho_GR\n" + "T = p_GR * M / R / rho_GR" ] }, { @@ -203,8 +203,8 @@ "metadata": {}, "outputs": [], "source": [ - "from ogs6py.ogs import OGS\n", - "import vtuIO\n" + "import vtuIO\n", + "from ogs6py.ogs import OGS" ] }, { @@ -216,9 +216,9 @@ "source": [ "import os\n", "\n", - "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n", + "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", "if not os.path.exists(out_dir):\n", - " os.makedirs(out_dir)\n" + " os.makedirs(out_dir)" ] }, { @@ -238,8 +238,8 @@ ], "source": [ "# run OGS\n", - "cube_compression=OGS(PROJECT_FILE=\"compression_gas.prj\")\n", - "cube_compression.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir}\")\n" + "cube_compression = OGS(PROJECT_FILE=\"compression_gas.prj\")\n", + "cube_compression.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir}\")" ] }, { @@ -252,7 +252,7 @@ "# read PVD file\n", "pvdfile = vtuIO.PVDIO(f\"{out_dir}/result_compression_gas.pvd\", dim=2)\n", "# get all timesteps\n", - "time = pvdfile.timesteps\n" + "time = pvdfile.timesteps" ] }, { @@ -337,16 +337,16 @@ "source": [ "# read pressure, temperature and density from pvd result file\n", "# at point\n", - "point={'pt0': (0.0,1.0,0.0)}\n", + "point = {\"pt0\": (0.0, 1.0, 0.0)}\n", "\n", "pressure = pvdfile.read_time_series(\"gas_pressure_interpolated\", point)\n", - "p_GR_num=pressure['pt0']\n", + "p_GR_num = pressure[\"pt0\"]\n", "\n", "temperature = pvdfile.read_time_series(\"temperature_interpolated\", point)\n", - "T_num=temperature['pt0']\n", + "T_num = temperature[\"pt0\"]\n", "\n", "density = pvdfile.read_time_series(\"gas_density\", point)\n", - "rho_GR_num=density['pt0']\n" + "rho_GR_num = density[\"pt0\"]" ] }, { @@ -370,34 +370,35 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "plt.rcParams['figure.figsize'] = (10, 4)\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (10, 4)\n", "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", "fig1.suptitle(r\"Density evolution, relative and absolute errors\")\n", "\n", - "ax1.plot(time,rho_GR_num,'kx', label=r\"$\\rho_\\mathrm{GR}$ numerical\")\n", - "ax1.plot(t, rho_GR, 'b', label=r\"$\\rho_\\mathrm{GR}$ analytical\")\n", + "ax1.plot(time, rho_GR_num, \"kx\", label=r\"$\\rho_\\mathrm{GR}$ numerical\")\n", + "ax1.plot(t, rho_GR, \"b\", label=r\"$\\rho_\\mathrm{GR}$ analytical\")\n", "ax1.set_xlabel(r\"$t$ / s\")\n", "ax1.set_ylabel(r\"$\\rho_\\mathrm{GR}$ / kgs$^{-1}$\")\n", "ax1.legend()\n", "ax1.grid(True)\n", - "#ax1.set_xlim(0,1)\n", - "#ax1.set_ylim(0,1)\n", + "# ax1.set_xlim(0,1)\n", + "# ax1.set_ylim(0,1)\n", "\n", "err_rho_abs = rho_GR - rho_GR_num\n", "err_rho_rel = err_rho_abs / rho_GR\n", "\n", - "ax2.plot(t,err_rho_abs,'b', label=r\"absolute\")\n", - "ax2.plot(t,err_rho_rel, 'g', label=r\"relative\")\n", + "ax2.plot(t, err_rho_abs, \"b\", label=r\"absolute\")\n", + "ax2.plot(t, err_rho_rel, \"g\", label=r\"relative\")\n", "\n", "ax2.set_xlabel(r\"$t$ / s\")\n", "ax2.set_ylabel(r\"$\\epsilon_\\mathrm{rel}$ / - and $\\epsilon_\\mathrm{abs}$ / kgs$^{-1}$\")\n", "ax2.legend()\n", "ax2.grid(True)\n", - "#ax2.set_xlim(0,1)\n", - "#ax2.set_ylim(-0.001,0.02)\n", + "# ax2.set_xlim(0,1)\n", + "# ax2.set_ylim(-0.001,0.02)\n", "\n", "fig1.tight_layout()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -420,29 +421,29 @@ } ], "source": [ - "plt.rcParams['figure.figsize'] = (10, 4)\n", + "plt.rcParams[\"figure.figsize\"] = (10, 4)\n", "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", "fig1.suptitle(r\"Gas pressure evolution, relative and absolute errors\")\n", "\n", "\n", - "ax1.plot(time,p_GR_num,'kx', label=r\"$p_\\mathrm{GR}$ numerical\")\n", - "ax1.plot(t, p_GR, 'b', label=r\"$p_\\mathrm{GR}$ analytical\")\n", + "ax1.plot(time, p_GR_num, \"kx\", label=r\"$p_\\mathrm{GR}$ numerical\")\n", + "ax1.plot(t, p_GR, \"b\", label=r\"$p_\\mathrm{GR}$ analytical\")\n", "ax1.set_xlabel(r\"$t$ / s\")\n", "ax1.set_ylabel(r\"$p_\\mathrm{GR}$ / Pa\")\n", "ax1.legend()\n", "ax1.grid(True)\n", - "#ax1.set_xlim(0,1)\n", - "#ax1.set_ylim(0,1)\n", + "# ax1.set_xlim(0,1)\n", + "# ax1.set_ylim(0,1)\n", "\n", "err_p_abs = p_GR - p_GR_num\n", "err_p_rel = err_p_abs / p_GR\n", "\n", - "lns1 = ax2.plot(t,err_p_abs,'b', label=r\"absolute\")\n", + "lns1 = ax2.plot(t, err_p_abs, \"b\", label=r\"absolute\")\n", "ax3 = ax2.twinx()\n", - "lns2 = ax3.plot(t,err_p_rel, 'g', label=r\"relative\")\n", + "lns2 = ax3.plot(t, err_p_rel, \"g\", label=r\"relative\")\n", "\n", "# added these three lines\n", - "lns = lns1+lns2\n", + "lns = lns1 + lns2\n", "labs = [l.get_label() for l in lns]\n", "ax2.legend(lns, labs, loc=0)\n", "ax2.grid(True)\n", @@ -451,12 +452,12 @@ "ax2.set_ylabel(r\"$\\epsilon_\\mathrm{abs}$ / Pa\")\n", "ax3.set_ylabel(r\"$\\epsilon_\\mathrm{rel}$ / -\")\n", "\n", - "#ax2.set_xlim(0,1)\n", - "ax2.set_ylim(-3500,200)\n", - "ax3.set_ylim(-0.0035,0.0002)\n", + "# ax2.set_xlim(0,1)\n", + "ax2.set_ylim(-3500, 200)\n", + "ax3.set_ylim(-0.0035, 0.0002)\n", "\n", "fig1.tight_layout()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -479,30 +480,30 @@ } ], "source": [ - "plt.rcParams['figure.figsize'] = (10, 4)\n", + "plt.rcParams[\"figure.figsize\"] = (10, 4)\n", "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", "fig1.suptitle(r\"Temperature evolution, relative and absolute errors\")\n", "\n", "\n", - "ax1.plot(time,T_num,'kx', label=r\"$T$ numerical\")\n", - "ax1.plot(t, T, 'b', label=r\"$T$ analytical\")\n", + "ax1.plot(time, T_num, \"kx\", label=r\"$T$ numerical\")\n", + "ax1.plot(t, T, \"b\", label=r\"$T$ analytical\")\n", "ax1.set_xlabel(r\"$t$ / s\")\n", "ax1.set_ylabel(r\"$T$ / K\")\n", "ax1.legend()\n", "ax1.grid(True)\n", - "#ax1.set_xlim(0,1)\n", - "#ax1.set_ylim(0,1)\n", + "# ax1.set_xlim(0,1)\n", + "# ax1.set_ylim(0,1)\n", "\n", "err_T_abs = T - T_num\n", "err_T_rel = err_T_abs / T\n", "\n", "\n", - "lns1 = ax2.plot(t,err_T_abs,'b', label=r\"absolute\")\n", + "lns1 = ax2.plot(t, err_T_abs, \"b\", label=r\"absolute\")\n", "ax3 = ax2.twinx()\n", - "lns2 = ax3.plot(t,err_T_rel, 'g', label=r\"relative\")\n", + "lns2 = ax3.plot(t, err_T_rel, \"g\", label=r\"relative\")\n", "\n", "# added these three lines\n", - "lns = lns1+lns2\n", + "lns = lns1 + lns2\n", "labs = [l.get_label() for l in lns]\n", "ax2.legend(lns, labs, loc=0)\n", "ax2.grid(True)\n", @@ -511,12 +512,12 @@ "ax2.set_ylabel(r\"$\\epsilon_\\mathrm{abs}$ / K\")\n", "ax3.set_ylabel(r\"$\\epsilon_\\mathrm{rel}$ / -\")\n", "\n", - "#ax2.set_xlim(0,1)\n", - "ax2.set_ylim(-0.8,0.05)\n", - "ax3.set_ylim(-0.002,0.000125)\n", + "# ax2.set_xlim(0,1)\n", + "ax2.set_ylim(-0.8, 0.05)\n", + "ax3.set_ylim(-0.002, 0.000125)\n", "\n", "fig1.tight_layout()\n", - "plt.show()\n" + "plt.show()" ] } ], diff --git a/Tests/Data/TH2M/TH2/heatpipe/comparison.ci-skip.ipynb b/Tests/Data/TH2M/TH2/heatpipe/comparison.ci-skip.ipynb index 99ee6f22b3d..ff1283848b0 100644 --- a/Tests/Data/TH2M/TH2/heatpipe/comparison.ci-skip.ipynb +++ b/Tests/Data/TH2M/TH2/heatpipe/comparison.ci-skip.ipynb @@ -1,30 +1,4 @@ { - "metadata": { - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10-final" - }, - "orig_nbformat": 2, - "kernelspec": { - "name": "python3", - "display_name": "Python 3.8.10 64-bit", - "metadata": { - "interpreter": { - "hash": "5b3ded1ccb95c1d9bd405e7b823d9e85424cde40fbb5985eb47e999ef50e15b4" - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 2, "cells": [ { "cell_type": "code", @@ -32,9 +6,8 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", "import vtuIO" ] }, @@ -44,15 +17,15 @@ "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "results_heatpipe_rough.pvd\n" ] } ], "source": [ - "analytical_solution = pd.read_csv('analytical.csv')\n", + "analytical_solution = pd.read_csv(\"analytical.csv\")\n", "numerical_solution = vtuIO.PVDIO(\"results_heatpipe_rough.pvd\", dim=2)" ] }, @@ -63,14 +36,18 @@ "outputs": [], "source": [ "time = 1e7\n", - "x = np.linspace(0.0,1.0,201)\n", - "line=[(i, 0.0025 ,0) for i in x]\n", + "x = np.linspace(0.0, 1.0, 201)\n", + "line = [(i, 0.0025, 0) for i in x]\n", "\n", - "T_num = numerical_solution.read_point_set_data(time,'temperature', pointsetarray=line)\n", - "pGR_num = numerical_solution.read_point_set_data(time,'gas_pressure', pointsetarray=line)\n", - "pCap_num = numerical_solution.read_point_set_data(time,'capillary_pressure', pointsetarray=line)\n", - "xnCG_num = numerical_solution.read_point_set_data(time,'xnCG', pointsetarray=line)\n", - "sL_num = numerical_solution.read_point_set_data(time,'saturation', pointsetarray=line)" + "T_num = numerical_solution.read_point_set_data(time, \"temperature\", pointsetarray=line)\n", + "pGR_num = numerical_solution.read_point_set_data(\n", + " time, \"gas_pressure\", pointsetarray=line\n", + ")\n", + "pCap_num = numerical_solution.read_point_set_data(\n", + " time, \"capillary_pressure\", pointsetarray=line\n", + ")\n", + "xnCG_num = numerical_solution.read_point_set_data(time, \"xnCG\", pointsetarray=line)\n", + "sL_num = numerical_solution.read_point_set_data(time, \"saturation\", pointsetarray=line)" ] }, { @@ -79,8 +56,8 @@ "metadata": {}, "outputs": [ { - 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" + ] }, + "execution_count": 82, "metadata": {}, - "execution_count": 82 + "output_type": "execute_result" } ], "source": [ @@ -126,12 +103,12 @@ "metadata": {}, "outputs": [], "source": [ - "z_a = analytical_solution['z'].to_numpy()\n", - "T_a = analytical_solution['T'].to_numpy()\n", - "pGR_a = analytical_solution['pGR'].to_numpy()\n", - "pCap_a = analytical_solution['pCap'].to_numpy()\n", - "xnCG_a = analytical_solution['xnCG'].to_numpy()\n", - "sL_a = analytical_solution['sL'].to_numpy()" + "z_a = analytical_solution[\"z\"].to_numpy()\n", + "T_a = analytical_solution[\"T\"].to_numpy()\n", + "pGR_a = analytical_solution[\"pGR\"].to_numpy()\n", + "pCap_a = analytical_solution[\"pCap\"].to_numpy()\n", + "xnCG_a = analytical_solution[\"xnCG\"].to_numpy()\n", + "sL_a = analytical_solution[\"sL\"].to_numpy()" ] }, { @@ -140,25 +117,25 @@ "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "
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UdqtL4Xd4KIbdYeGlMNzVOWBbY5pD5DhIFY2xkkNUcTDmaAo5Cplq9uQrOFCo4DDFoHsw5mhqX6+kNVVFZfUItjUG9uQraA7FNiFTQUuZDyEwyEqSdGyGZgE9u1mts5vQ/uvx5/jzWceRKxzgoaefYRSHqOEQNaGRGhpLy8OMCIeo4XCH7YcZUQrDnU3VlY8pDlLFQao4FHM0kuNgzNFIFYfIcSDmaE5VESuG09CcZV+hkkaqaIw59sccrZlqqoePYv3eSGu6mgP5NL39CNW+nJPVICtJUv8wNA9yXQ3Dr3Wz2t/dvox7nljLx88aTnV+H0vWPMOocJDjOcBx4QDHcYCR4RAjwyFGUFyO5FCnQxhaY5oDVHEgVnOAag5Szf5YxaFQTT47gobWCva0VXGwtG9ffGl9b6Ga1uwwRtaM5IU9h1/XrAZ9OYWWwVaSpMHB0DwIvNYwiaOF4Y+9eSL/8O7xfPuu3/PYynWMZS9jwj7GhH3tQfj4cIBRHCwuw6FjfvbhWMFehnMoVcPuQjHM7mcY++Iw9sZh7Kf42hOH0ZSuoWL4cazdm6I5PZx9+Qyvt1f39QZeQ60kSeoKQ/MA0dVgvOBPJrPwtt+wbOUKJoSdjA+7qA17GcM+xoa9jA37OJ79ZELhVZ/RFLPspoY9sYbdsYZ9oYbm7ChebK2moW04e6lhb6xhV6xhHzXsjMMpZKpoTfDBCAZeSZLUHwzNA0TH8cILLpzGP99yH2uefoIpYSunprYyJWxlUmh4Va9wS0zTwCh2xZE0lF57UqM4XDGWF5qH8WJ+BLvDSHYURtKSGUZLvvjn2lc3oRlyJUnSQGRoLmP1+5t4y/9+gBjhlLCNC1JP8NbUKuak1lPdYbzw1jiaZwvj2RLGsbfiRDY0H8cL+ePZxli2x5FkM5lOZ27o7s1qBmBJkjSUGJrL2N/dvowDS/6Lz+Xu47TCcwCsi5NYkTmTJ1pOZk1+PBsZz/5YRUUmddRhEl0JwwZgSZKk12ZoLjNHepfPZg1fz17PKakdrC1M4mdt53Jv29nsCKO7/NAJw7AkSVLvMDSXmatvf5rxT/4Tn8/cxQtxHAtaL+E38U1MOn44C943g1+u3GEwliRJ6mc9Dc2ZvihmKJt69SJa8nn+IfNDPpZ5gP/Mz+Vr+Us5TI4Q4O1TxvD2KWN5+5SxLJg/o/24juuSJEkqL4bmXrb4irksv+FvOH/3A1ybfy//1PZhTho9rL13ueFAU9IlSpIkqZsMzb2sdvtDnL/7Zn7aNpd/4aNE4st6lyVJkjTwGJp7Sf3+Jr5842/53v7Ps7XiDaw9/W+545xT28cqS5IkaeAyNPeShQ9s4Nxt3yem9zHp8/dyTe00wLHKkiRJg4Gh+XWaevUimvMFTg1b+PuKB/hJ/nyu+dZzVGY2sm7BvKTLkyRJUi9IJV3AQLf4irlcWDeeKypu4RBV/Hv4ABfVjWfxlXOTLk2SJEm9xND8OtWOyHEy2zmPJfwkXsCO/HBqKjPU1uSSLk2SJEm9xOEZvWDWtv+ikErzR5d8lW0rm7zxT5IkaZAxNL9eh/fyrsZfwVkXM3XKFBZMSbogSZIk9TaHZ7xeK26B1kPwlr9MuhJJkiT1EUPz61C/v4l1v/4P8mNOh/Gzki5HkiRJfcTQ/Dr85N4Hmdq6ht9k3pl0KZIkSepDjmnugSNzM1+evgOy8Pcbz+Cyq+6hMpNybmZJkqRByJ7mHlh8xVwunHki8zMP81jhdHZlxzk3syRJ0iBmaO6B2hE5TuFFpoSt/DK+leZ8wbmZJUmSBjGHZ/TQSQ0PAvCRSz9L6+q8czNLkiQNYobmHvqz4Suh4kxOO+10FpyWdDWSJEnqSw7P6IlDu2DzYzD1T5KuRJIkSf3A0NwTG34FsQCnXZB0JZIkSeoHhuaeWL8Ihp8AJ9YlXYkkSZL6gaG5uwpt8PxDMOU8SPnrkyRJGgpMfd21/Wk4vAdO8SmAkiRJQ4WhuZsOrnkAgJ215yRciSRJkvqLobmbti27j/WFCfzLo/uTLkWSJEn9xHmau2jq1YuI+WaWVz7FTwtzufGxTdz42CYqMynWLZiXdHmSJEnqQ/Y0d9HiK+Zy+am7qQotPFyYTi6b4qK68Sy+cm7SpUmSJKmPGZq7qHZEjhkty2mLgWWp6TTnC9RUZqitySVdmiRJkvqYwzO6YcK+ZWyvPo0fX/pubn58Ew0HmpIuSZIkSf3A0NxV+Ram5tfBnD9nwvgRLJg/I+mKJEmS1E8cntFV256CfBOc5FRzkiRJQ42huas2PVJcnvTWZOuQJElSvzM0d9ULj8DoKTC8NulKJEmS1M8MzV1RKBR7mu1lliRJGpIMzV2xcx007TU0S5IkDVGG5q44Mp75ZEOzJEnSUGRo7orNT8CwsXDcKUlXIkmSpAQYmjtRv7+JrSsfovmE2RBC0uVIkiQpAYbmTnzvvqVMaNvC7w6dnHQpkiRJSkinTwQMIeSAh4DKUvtbY4z/M4SwGKgpNasFHo8xzg8hXAJcCQTgAPC5GOPyPqm+D029ehHN+QLvTC2HCvjhpjFcdtU9VGZSrFswL+nyJEmS1I+68hjtZuBdMcaDIYQs8PsQwqIY4zuONAgh3AbcWXr7PPDOGOOeEMI84HrgLb1deF9bfMVcFty7htNW30ZbDKxPT+Gis8bz1fdMS7o0SZIk9bNOQ3OMMQIHS2+zpVc8sj+EMAJ4F/CpUvuHOxz+KDCxt4rtT7UjctRUZjiTDWxgErvzldRUZqitySVdmiRJkvpZl8Y0hxDSIYSngHrg1zHGxzrsng88EGPcf5RD/wJYdIxzXhZCWBJCWNLQ0NC9qvvJrgOHOTv7POOmvYNL3nIyDQebky5JkiRJCejK8AxijG1AXQhhFHBHCGFGjHFlafdHgO+/8pgQwlyKofntxzjn9RSHbjBnzpx4tDZJ+/d5o+C7B6ia+gcsmDUj6XIkSZKUkG7NnhFj3Av8FrgAIIQwBjgbuKdjuxDCWRSD9EUxxl29UmkStjxRXE58c7J1SJIkKVGdhuYQwthSDzMhhCrgfGBtafcHgLtjjE0d2p8E3A5cGmNc3+sV96etS6ByBIw+NelKJEmSlKCuDM84EfhRCCFNMWT/LMZ4d2nfh4Gvv6L93wGjgX8LxYeB5GOMc3qp3v619UkYXwcpp7OWJEkayroye8bTwKxj7Dv3KNs+DXz6dVeWtHwz7FgFb/180pVIkiQpYXahHsuOlVBohfFvSroSSZIkJczQfCwvLisuxx+1k12SJElDiKH5WLYug+rRMOqkpCuRJElSwgzNx/LismIvc/FmRkmSJA1hhuajaTkEDWsczyxJkiTA0HxUe55dCrHA3uOmJ12KJEmSyoCh+SgeXvxrAK7bMDLhSiRJklQOuvJwkyFj6tWLaM4X+OfsUranjuPaJxu59sl7qMykWLdgXtLlSZIkKSH2NHew+Iq5XFg3nrNSz7OicAq5bIqL6saz+Mq5SZcmSZKkBBmaO6gdkeP4TCunsI214RSa8wVqKjPU1uSSLk2SJEkJcnjGK1TtWUMqRC7643nsqD+ZhgNNSZckSZKkhBmaX+HKmS2wFU464xwW/MGkpMuRJElSGXB4xittfxqqjoORE5OuRJIkSWXC0PxK256GE87ySYCSJElqZ2juqK0V6lfDiWclXYkkSZLKiKG5o4a10NYCJ8xMuhJJkiSVEUNzR9ueLi7taZYkSVIHhuaOtj8N2WoYPSXpSiRJklRGDM0dbXsaxs2AVDrpSiRJklRGDM1HFAqwfYVDMyRJkvQqhuYj9jwPLQeK081JkiRJHRiaj9juTYCSJEk6OkPzEduWQyoDtWckXYkkSZLKjKG5pHnzU2xMTaL+cEy6FEmSJJUZQzNAjLRufYonmiex8P4NSVcjSZKkMpNJuoCkTb16ESPzu3g8t4dVhcnc+NgmbnxsE5WZFOsWzEu6PEmSJJWBId/TvPiKufz5lP0ArCpMJpdNcVHdeBZfOTfhyiRJklQuhnxorh2R4435ZwF4Nj2Z5nyBmsoMtTW5hCuTJElSuRjywzMARh9Yx86Kidx42fnc/PgmGg40JV2SJEmSyoihGXhTdhOcPIcx40ewYP6MpMuRJElSmRnywzM4vAf2vuBDTSRJknRMhubtK4rLE2YmW4ckSZLKlqH5SGi2p1mSJEnHYGjesQqG1cLw2qQrkSRJUpkyNG9fASd4858kSZKObWiH5rY8NKyFcdOTrkSSJEllbGiH5l0boK0Fxp2ZdCWSJEkqY0M7NO9YVVza0yxJkqTXMLRD8/YVkMrCmNOSrkSSJEllbGiH5h2rYOxUyFQkXYkkSZLK2BAPzSthnDNnSJIk6bUN2dDcsGMrHNjGgVFTky5FkiRJZW7Ihua7fnU/ALdtGZlwJZIkSSp3maQL6G9Tr15Ec77An6efgCx8Z3UV11x1D5WZFOsWzEu6PEmSJJWhIdfTvPiKuVxYN57p6c00xBEczB7HRXXjWXzl3KRLkyRJUpkacqG5dkSOmsoMp7GRdfFkmvMFaioz1Nbkki5NkiRJZWrIDc8A2H2gkdPTL7L/zE9ySepkGg40JV2SJEmSytiQDM3XXjAS/q2F0W+czYKZTjknSZKk1zbkhmcAxfmZwcdnS5IkqUuGaGheBakMjHGOZkmSJHVuiIbmlcXA7OOzJUmS1AVDNDSvghMcyyxJkqSuGXqhuXE37N/qeGZJkiR12dALzTtWFZfj7GmWJElS1wzB0Hxk5gxDsyRJkrqm09AcQsiFEB4PISwPIawKIfx9afviEMJTpdeLIYSfl7aHEMLCEMIzIYSnQwhv6uOfoXt2rITqMTC8NulKJEmSNEB05eEmzcC7YowHQwhZ4PchhEUxxnccaRBCuA24s/R2HnBq6fUW4NrSsjxsX1m8CTCEpCuRJEnSANFpT3MsOlh6my294pH9IYQRwLuAn5c2XQT8uHTco8CoEMKJvVp1T7XloWGtQzMkSZLULV0a0xxCSIcQngLqgV/HGB/rsHs+8ECMcX/p/QRgc4f9W0rbkrf7Ocg3GZolSZLULV0KzTHGthhjHTARODuE0DF1fgT4z+5+cAjhshDCkhDCkoaGhu4e3iP7Nj4JwO6aU/vl8yRJkjQ4dGv2jBjjXuC3wAUAIYQxwNnAPR2abQUmdXg/sbTtlee6PsY4J8Y4Z+zYsd0su2eWPfHf5GOKf33K8cySJEnquq7MnjE2hDCqtF4FnA+sLe3+AHB3jLGpwyF3AR8vzaJxDrAvxritd8vunqlXL2LyVffQum0Vz8UT+dET25l81T1MvXpRkmVJkiRpgOhKT/OJwG9DCE8DT1Ac03x3ad+HefXQjHuB54BngO8Bn++lWnts8RVzubBuPFNTm1kfJ5HLpriobjyLr5ybdGmSJEkaADqdci7G+DQw6xj7zj3Ktgj81euurBfVjsgxOtPCSaGe2zmX5nyBmsoMtTW5pEuTJEnSANCVeZoHhYo9GwD4s3nns7P+ZBoONHVyhCRJklQ0ZELzl2cXYCtMOn0OC/7gDUmXI0mSpAGkW7NnDGj1ayBbDaMmJ12JJEmSBpihE5p3rIKxp0Nq6PzIkiRJ6h1DJ0HWr4baM5KuQpIkSQPQ0AjNBxvgUAOMMzRLkiSp+4ZGaK5fXVza0yxJkqQeGCKheU1xaWiWJElSDwyR0LwKqkfD8NqkK5EkSdIANERC85piL3MISVciSZKkAWjwh+ZC4aXQLEmSJPXA4A/N+zZDy0GonZZ0JZIkSRqgBn9oPjJzxrjpydYhSZKkAWvohOaxpydbhyRJkgasQR+am7asoD5dS31rRdKlSJIkaYAa9KF57wvLWdEygYX3b0i6FEmSJA1QmaQL6CtTr15EId/C6soXWBffw42PbeLGxzZRmUmxbsG8pMuTJEnSADJoe5oXXzGXP5+WJxvaWFuYRC6b4qK68Sy+cm7SpUmSJGmAGbShuXZEjlPaNgGwMXUSzfkCNZUZamtyCVcmSZKkgWbQDs8AGLF/PW2k+fpffoCbl26n4UBT0iVJkiRpABrUoflPxu2F7KmcMWkMCyaNSbocSZIkDVCDdngGADtW+fhsSZIkvW6DNzQ3H4S9LxiaJUmS9LoN3tDcsLa4HGdoliRJ0uszeEPzkcdn105Ltg5JkiQNeIM3NO9YDdlhMGpy0pVIkiRpgBu8obl+FdSeDqnB+yNKkiSpfwzeRFm/xqEZkiRJ6hWDMzQfbIBDDVA7PelKJEmSNAgMztDsTYCSJEnqRYM7NI+zp1mSJEmv3+ANzdWjYdjYpCuRJEnSIDA4Q/OO1cUnAYaQdCWSJEkaBAZfaC4Uik8D9PHZkiRJ6iWDLjTv3LoBWg6yf+SpSZciSZKkQWLQheZFD/wGgP98fnjClUiSJGmwyCRdQG+ZevUimvMFPp9+ErKwcGWW/33VPVRmUqxbMC/p8iRJkjSADZqe5sVXzOXCuvGckd7C5sJY2rLDuKhuPIuvnJt0aZIkSRrgBk1orh2Ro6YywxQ2s4FJNOcL1FRmqK3JJV2aJEmSBrhBMzwDYPeBQ0xJb2PMzAu5hJNpONCUdEmSJEkaBAZVaL72gpHwb3nGvKGOBWfNSLocSZIkDRKDZngGAPVrisuxpydbhyRJkgaVwRWaG9ZCSMGY05KuRJIkSYPI4ArN9Wvg+DdA1pv/JEmS1HsGX2h2aIYkSZJ62eAJzflm2P0c1E5LuhJJkiQNMoMnNO/cALHNnmZJkiT1usETmo/MnFF7RrJ1SJIkadAZPKG5YQ2kMjB6StKVSJIkaZAZPKG5fi0c/0bIVCRdiSRJkgaZwROaG9ZAreOZJUmS1PsGR2huaYTdzzueWZIkSX1icITmneuB6MwZkiRJ6hODIjTv37QCgF3D3pBwJZIkSRqMBkVofmrpI7TENP/6ZFvSpUiSJGkQyiRdwOsx9epFNOcL/Ed2Nc+G8fz48Rf58eMvUplJsW7BvKTLkyRJ0iDRaU9zCCEXQng8hLA8hLAqhPD3pe0hhPCPIYT1IYQ1IYS/Lm0fGUL4RYf2n+qr4hdfMZcL68YzNbWFDXEiuWyKi+rGs/jKuX31kZIkSRqCutLT3Ay8K8Z4MISQBX4fQlgETAMmAafHGAshhNpS+78CVscY3xtCGAusCyHcFGNs6e3ia0fkGJ1pYWJo4Fbm0pwvUFOZobYm19sfJUmSpCGs09AcY4zAwdLbbOkVgc8BH40xFkrt6o8cAtSEEAIwHNgN5Hu57nbZPRsA+LML/oidDSfTcKCprz5KkiRJQ1SXxjSHENLAUmAK8N0Y42MhhDcCHwohvA9oAP46xrgB+A5wF/AiUAN86EiwfsU5LwMuAzjppJN6/AN8ZQ6wFSadPocFb3tjj88jSZIkHUuXZs+IMbbFGOuAicDZIYQZQCXQFGOcA3wP+EGp+R8DTwHjgTrgOyGEEUc55/Uxxjkxxjljx47t+U9QvwYyOThucs/PIUmSJL2Gbk05F2PcC/wWuADYAtxe2nUHcFZp/VPA7bHoGeB5oO+eOtKwFsacCql0n32EJEmShrauzJ4xNoQwqrReBZwPrAV+DhyZpuKdwPrS+ibgvFL7ccBU4LneLPpl6tfC2Gl9dnpJkiSpK2OaTwR+VBrXnAJ+FmO8O4Twe+CmEMLfULxR8NOl9v8A3BBCWAEE4MoY484+qB2a9sH+LVBraJYkSVLf6crsGU8Ds46yfS/wnqNsfxF4d28U16mGdcWloVmSJEl9aGA/Rrt+TXE5tu+GTEuSJEkDOzQ3rIVsNYw6OelKJEmSNIgN7NBcvxrGToXUwP4xJEmSVN4Gdtp05gxJkiT1g4Ebmg/vgYPbodbxzJIkSepbAzc0168tLu1pliRJUh8bwKF5dXHpdHOSJEnqYwM3NDeshYoaGDkx6UokSZI0yA3Y0NyybRXr4wTqDzYnXYokSZIGuQEcmlezrOkEFt6/IelSJEmSNMh1+hjtcjP16kUMy+/lydwe1hcmcuNjm7jxsU1UZlKsWzAv6fIkSZI0CA24nubFV8zlk6ceBmB9nEgum+KiuvEsvnJuwpVJkiRpsBpwobl2RI7JhU0AbEydRHO+QE1lhtqaXMKVSZIkabAacMMzAEYceJbDqeFc97k/5eYnNtNwoCnpkiRJEtDa2sqWLVtoavLf5iTkcjkmTpxINptNupRBZ0CG5nNH7YSRMzhjwkgWTBiZdDmSJKlky5Yt1NTUMHnyZEIISZczpMQY2bVrF1u2bOGUU05JupxBZ8ANzyBGaFjj47MlSSpDTU1NjB492sCcgBACo0ePtpe/jwy80HywHg7v8fHZkiSVKQNzcvzd952BF5ob1hSX9jRLkiSpnwy80Fx/JDSfkWwdkiSpV9Tvb+KD1z1CfS/c2L9r1y7q6uqoq6vjhBNOYMKECe3vW1paeqHa3nXXXXfx9a9/vUfHTp48mZ07d/ZyRTqWgXcjYP0aqDoeho1NuhJJktQLFj6wgSc27mbh/RtY8L4zX9e5Ro8ezVNPPQXANddcw/Dhw/nSl77UC1X2vnw+z4UXXsiFF16YdCnqgoHX09ywFmqngWN2JEka0KZevYjJV93DjY9tIka48bFNTL7qHqZevahfPn/jxo1MmzaNz3zmM0yfPp13v/vdHD5cfIDaueeey5IlSwDYuXMnkydPBuCGG25g/vz5nH/++UyePJnvfOc7fOtb32LWrFmcc8457N69G4Bnn32WCy64gNmzZ/OOd7yDtWvXAvDJT36Sz372s7zlLW/hiiuu4IYbbuDyyy8HYMeOHbzvfe9j5syZzJw5k4cffhiA+fPnM3v2bKZPn87111/fL78bvdrACs0xQv1aGOt4ZkmSBrrFV8zlwrrx5LLFOJLEU343bNjAX/3VX7Fq1SpGjRrFbbfd1ukxK1eu5Pbbb+eJJ57gq1/9KtXV1Sxbtoy3vvWt/PjHPwbgsssu49vf/jZLly7lm9/8Jp///Ofbj9+yZQsPP/ww3/rWt1523r/+67/mne98J8uXL+fJJ59k+vTpAPzgBz9g6dKlLFmyhIULF7Jr165e/A2oqwbW8Iz9L0LzvmJPsyRJGtBqR+SoqczQnC9QmUkl8pTfU045hbq6OgBmz57Nxo0bOz1m7ty51NTUUFNTw8iRI3nve98LwJlnnsnTTz/NwYMHefjhh7n44ovbj2lubm5fv/jii0mn0686729+85v20J1Opxk5svgsioULF3LHHXcAsHnzZjZs2MDo0aN79POq5wZWaG6fOcPQLEnSYLDzYDOXvOVkPnr2Sdz8+KZ+f8pvZWVl+3o6nW4fnpHJZCgUCgCvmve44zGpVKr9fSqVIp/PUygUGDVqVPvY6lcaNmxYl+t78MEHuf/++3nkkUeorq7m3HPPdR7mhAys4Rn1xfFAztEsSdLgcN2lc1gwfwZnjB/BgvkzuO7SOUmXBBRnpli6dCkAt956a7eOHTFiBKeccgq33HILUHxS3/Llyzs97rzzzuPaa68FoK2tjX379rFv3z6OO+44qqurWbt2LY8++mg3fxL1loEVmhvWFGfNGOZXEpIkqe986Utf4tprr2XWrFk9mtbtpptu4j/+4z+YOXMm06dP58477+z0mH/913/lt7/9LWeeeSazZ89m9erVXHDBBeTzeaZNm8ZVV13FOeec05MfR70gxBiTroE5c+bEI3eovqbvvQsqhsEnftH3RUmSpG5bs2YN06b5jXCS/DN4bSGEpTHGbn+lMXB6mmOEhnUOzZAkSVK/GzA3Au7c+gxjWg6yf8QURiRdjCRJGjB27drFeeed96rtDzzwgLNQqMsGTGi+94Hf8HHgv56v5jNvT7oaSZI0UHR8SqDUU2UfmqdevYjmfIG/TC+DLHx7VZZ/vOoeKjMp1i2Yl3R5kiRJGgLKfkzzkacFTUtvZXs8jpbsiH5/WpAkSZKGtrIPzUeeFvQGNvNMnJjI04IkSZI0tJX98AyAXQcOc3r6RQ5M/xiXZE7u96cFSZIkaWgr+55mgH//0zFUxGZGv6GurJ4WJEmSysuuXbuoq6ujrq6OE044gQkTJrS/b2lp6bPPveGGG7j88ss7bfPiiy+2v//0pz/N6tWru/1ZDz74IH/6p3/a7eP0+gyInmYfny1Jkrqi40wZ11xzDcOHD+dLX/pSskWV3HDDDcyYMYPx48cD8P3vfz/hitQdAyM0N6wpLsdOTbYOSZLUdYuugu0revecJ5wJ877ea6ebP38+mzdvpqmpiS9+8YtcdtllAAwfPpwvfvGL3H333VRVVXHnnXcybtw4fvGLX7BgwQJaWloYPXo0N910E+PGjWs/34EDBzjrrLNYv3492WyW/fv3M3PmTL7xjW+wZMkSLrnkEqqqqnjkkUeYN28e3/zmN5kzZw6//OUv+cpXvkJbWxtjxozhgQce4PHHH+eLX/wiTU1NVFVV8cMf/pCpU81CSRkQwzOoXwMjJkLOx5pIkqTe84Mf/IClS5eyZMkSFi5cyK5duwA4dOgQ55xzDsuXL+cP//AP+d73vgfA29/+dh599FGWLVvGhz/8Yb7xjW+87Hw1NTWce+653HPPPQD89Kc/5f3vfz8XX3wxc+bM4aabbuKpp56iqqqq/ZiGhgY+85nPcNttt7F8+XJuueUWAE4//XQWL17MsmXL+NrXvsZXvvKV/viV6BgGRk9z/VqodWiGJEkDSi/2CPeVhQsXcscddwCwefNmNmzYwOjRo6moqGgfNzx79mx+/etfA7BlyxY+9KEPsW3bNlpaWjjllFNedc5Pf/rTfOMb32D+/Pn88Ic/bA/cx/Loo4/yh3/4h+3nOv744wHYt28fn/jEJ9iwYQMhBFpbW3vt51b3lX9Pc6ENdq6H2tOTrkSSJA0iDz74IPfffz+PPPIIy5cvZ9asWTQ1FWfoymazhBAASKfT5PN5AL7whS9w+eWXs2LFCq677rr29h297W1vY+PGjTz44IO0tbUxY8aMHtX3t3/7t8ydO5eVK1fyi1/84qifpf5T/qF59/PQ1uxNgJIkqVft27eP4447jurqatauXcujjz7apWMmTJgAwI9+9KNjtvv4xz/ORz/6UT71qU+1b6upqeHAgQOvanvOOefw0EMP8fzzzwOwe/fuV33WDTfc0OWfS32j/ENzfWkqFnuaJUlSL7rgggvI5/NMmzaNq666inPOOafTY6655houvvhiZs+ezZgxY47Z7pJLLmHPnj185CMfad/2yU9+ks9+9rPU1dVx+PDh9u1jx47l+uuv5/3vfz8zZ87kQx/6EABXXHEFX/7yl5k1a1Z7T7eSE2KMSdfAnDlz4pIlS46+83ffgN/+I3x5K1QO79/CJElSt6xZs4Zp0/x2+NZbb+XOO+/kJz/5Sb9/tn8Gry2EsDTG2O2HfpT/jYD1a2DUyQZmSZI0IHzhC19g0aJF3HvvvUmXol5U/qG5wZkzJElSz+3atYvzzjvvVdsfeOABRo8e3euf9+1vf7vXz6nklXdobmuFnRvg1HcnXYkkSRqgOj4lUOqp8r4RcNezUGi1p1mSpAGkHO6XGqr83fed8g7N7Y/PduYMSZIGglwux65duwxvCYgxsmvXLnK5XNKlDEplPTzj4JZVVBPYmTuJ2qSLkSRJnZo4cSJbtmyhoaEh6VKGpFwux8SJE5MuY1Aq69D8/Ool1MRavv+7rSx43/FJlyNJkjqRzWaP+mhpaaAry9A89epFNOcL3FexgQ1xAjc+tokbH9tEZSbFugXzki5PkiRJQ0xZjmlefMVc5s+s5ZSwjQ1xIrlsiovqxrP4yrlJlyZJkqQhqCxDc+2IHJPZRkVo4/kwieZ8gZrKDLU1DmyXJElS/yvL4RkAVXs3APC5i99D5XMjaTjQlHBFkiRJGqrKNjT/5bRW2BF4w+mzWHBWddLlSJIkaQjrdHhGCCEXQng8hLA8hLAqhPD3pe0hhPCPIYT1IYQ1IYS/7nDMuSGEp0rtf9ejyhrWwnEnQ4WBWZIkScnqSk9zM/CuGOPBEEIW+H0IYREwDZgEnB5jLIQQagFCCKOAfwMuiDFuOrK92+rXwlifBChJkqTkddrTHIsOlt5mS68IfA74WoyxUGpXX2rzUeD2GOOmV2zvurZW2PUMjJ3a7UMlSZKk3tal2TNCCOkQwlNAPfDrGONjwBuBD4UQloQQFoUQTi01Pw04LoTwYAhhaQjh492uavdzUGiFWnuaJUmSlLwuheYYY1uMsQ6YCJwdQpgBVAJNMcY5wPeAH5SaZ4DZwHuAPwb+NoRw2ivPGUK4rBS4l7zqUZv1a4rLsad3/yeSJEmSelm35mmOMe4FfgtcAGwBbi/tugM4q7S+BbgvxngoxrgTeAiYeZRzXR9jnBNjnDN27NiX72xYBwQY86qsLUmSJPW7rsyeMbZ0cx8hhCrgfGAt8HPgyCP63gmsL63fCbw9hJAJIVQDbwHWdKuqhjXOnCFJkqSy0ZXZM04EfhRCSFMM2T+LMd4dQvg9cFMI4W+Ag8CnAWKMa0IIvwSeBgrA92OMK7tVVcM6h2ZIkiSpbHQammOMTwOzjrJ9L8Vxy0c75p+Af+pRRW2tsHMDnPruHh0uSZIk9bZujWnuF86cIUmSpDJTfqG5YW1x6RzNkiRJKhPlF5rr11KcOcPQLEmSpPJQfqG5YS2MOsmZMyRJklQ2yi4057evZunhE6g/0JR0KZIkSRJQbqG5rRV2P8Pjh2pZeP+GpKuRJEmSgK7N09wvpl69iIltm3mgMs+GwgRuf2wTNz62icpMinUL5iVdniRJkoawsulpXnzFXD56SiMA6+NEctkUF9WNZ/GVczs5UpIkSepbZROaa0fkOLmwGYAt6Yk05wvUVGaorcklXJkkSZKGurIZngEw8uAz7K4Yz82XncfNj2+iwZsBJUmSVAbKKjS/uboexp/F8eNHsGD+jKTLkSRJkoAyGp5BWx52bvBJgJIkSSo75ROadz8HhVYYOy3pSiRJkqSXKZ/Q3LC2uLSnWZIkSWXG0CxJkiR1onxCc/0aGHUyVAxLuhJJkiTpZconNDesg7GnJ12FJEmS9CplEpoj7NoAtYZmSZIklZ/yCM35FmhrsadZkiRJZalMQvPh4tLQLEmSpDJUHqG5tfS4bGfOkCRJUhkqj9Ccb4JRJzlzhiRJkspS+YRmnwQoSZKkMlUmobnZoRmSJEkqW+URmmOB/SOmJF2FJEmSdFTlEZqBG5+tTroESZIk6ajKJjR/Z0WKyVfdw9SrFyVdiiRJkvQymaQLANjPMFLZai6afgJffY83BEqSJKm8lEVP86Y4juZ8gZrKDLU1uaTLkSRJkl6mLHqap9QOZ/5bTqbhQFPSpUiSJEmvUhahOZdNs2D+jKTLkCRJko6qLIZnSJIkSeXM0CxJkiR1wtAsSZIkdcLQLEmSJHXC0CxJkiR1wtAsSZIkdcLQLEmSJHXC0CxJkiR1wtAsSZIkdcLQLEmSJHXC0CxJkiR1IsQYk66BEMIBYF3SdajsjAF2Jl2Eyo7XhY7G60JH43Who5kaY6zp7kGZvqikB9bFGOckXYTKSwhhideFXsnrQkfjdaGj8brQ0YQQlvTkOIdnSJIkSZ0wNEuSJEmdKJfQfH3SBagseV3oaLwudDReFzoarwsdTY+ui7K4EVCSJEkqZ+XS0yxJkiSVrX4NzSGEC0II60IIz4QQrjrK/soQwn+V9j8WQpjcn/UpGV24Lv5HCGF1COHpEMIDIYSTk6hT/auz66JDuz8LIcQQgnfIDwFduS5CCB8s/Z2xKoRwc3/XqP7XhX9HTgoh/DaEsKz0b8mfJFGn+k8I4QchhPoQwspj7A8hhIWla+bpEMKbOjtnv4XmEEIa+C4wDzgD+EgI4YxXNPsLYE+McQrwz8D/6a/6lIwuXhfLgDkxxrOAW4Fv9G+V6m9dvC4IIdQAXwQe698KlYSuXBchhFOBLwNvizFOB/6f/q5T/auLf19cDfwsxjgL+DDwb/1bpRJwA3DBa+yfB5xael0GXNvZCfuzp/ls4JkY43Mxxhbgp8BFr2hzEfCj0vqtwHkhhNCPNar/dXpdxBh/G2NsLL19FJjYzzWq/3Xl7wuAf6D4n+um/ixOienKdfEZ4Lsxxj0AMcb6fq5R/a8r10UERpTWRwIv9mN9SkCM8SFg92s0uQj4cSx6FBgVQjjxtc7Zn6F5ArC5w/stpW1HbRNjzAP7gNH9Up2S0pXroqO/ABb1aUUqB51eF6Wv0ibFGO/pz8KUqK78fXEacFoI4b9DCI+GEF6rp0mDQ1eui2uAj4UQtgD3Al/on9JUxrqbP8rmiYBSp0IIHwPmAO9MuhYlK4SQAr4FfDLhUlR+MhS/bj2X4rdSD4UQzowx7k2yKCXuI8ANMcb/G0J4K/CTEMKMGGMh6cI0cPRnT/NWYFKH9xNL247aJoSQofgVyq5+qU5J6cp1QQjhj4CvAhfGGJv7qTYlp7ProgaYATwYQtgInAPc5c2Ag15X/r7YAtwVY2yNMT4PrKcYojV4deW6+AvgZwAxxkeAHDCmX6pTuepS/uioP0PzE8CpIYRTQggVFAfi3/WKNncBnyitfwD4TXQi6cGu0+sihDALuI5iYHZ84tDwmtdFjHFfjHFMjHFyjHEyxbHuF8YYlyRTrvpJV/4d+TnFXmZCCGMoDtd4rh9rVP/rynWxCTgPIIQwjWJobujXKlVu7gI+XppF4xxgX4xx22sd0G/DM2KM+RDC5cB9QBr4QYxxVQjha8CSGONdwH9Q/MrkGYqDtz/cX/UpGV28Lv4JGA7cUrovdFOM8cLEilaf6+J1oSGmi9fFfcC7QwirgTbg/4sx+o3lINbF6+L/Bb4XQvgbijcFftJOucEthPCfFP8DPaY0lv1/AlmAGOO/Uxzb/ifAM0Aj8KlOz+k1I0mSJL02nwgoSZIkdcLQLEmSJHXC0CxJkiR1wtAsSZIkdcLQLEmSJHXC0CxJkiR1wtAsSZIkdcLQLEmSJHXi/wdHEPCUomQU8wAAAABJRU5ErkJggg==\n", 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\n" + "text/plain": "
" }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12, 6))\n", "\n", - "ax.plot(x, T_num, '*', label='T_numerical')\n", - "ax.plot(x_a, T_a, '-', label='T_analytical')\n", + "ax.plot(x, T_num, \"*\", label=\"T_numerical\")\n", + "ax.plot(x_a, T_a, \"-\", label=\"T_analytical\")\n", "\n", "plt.xlim([0, 1])\n", - "plt.legend(loc='upper right', bbox_to_anchor=(0.9, 0.3))\n", + "plt.legend(loc=\"upper right\", bbox_to_anchor=(0.9, 0.3))\n", "plt.show()" ] }, @@ -168,28 +145,28 @@ "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "
", + "image/png": 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\n" + "text/plain": "
" }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12, 6))\n", "\n", - "ax.plot(x, sL_num, '*', label='sL_numerical')\n", - "ax.plot(x_a, sL_a, '-', label='sL_analytical')\n", + "ax.plot(x, sL_num, \"*\", label=\"sL_numerical\")\n", + "ax.plot(x_a, sL_a, \"-\", label=\"sL_analytical\")\n", "\n", - "ax.plot(x, xnCG_num, '*', label='xnCG_numerical')\n", - "ax.plot(x_a, xnCG_a, '-', label='xnCG_analytical')\n", + "ax.plot(x, xnCG_num, \"*\", label=\"xnCG_numerical\")\n", + "ax.plot(x_a, xnCG_a, \"-\", label=\"xnCG_analytical\")\n", "\n", "plt.xlim([0, 1])\n", - "plt.legend(loc='upper right', bbox_to_anchor=(0.9, 0.8))\n", + "plt.legend(loc=\"upper right\", bbox_to_anchor=(0.9, 0.8))\n", "plt.show()" ] }, @@ -199,30 +176,30 @@ "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": "
", + "image/png": 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\n" + "text/plain": "
" }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12, 6))\n", "\n", - "ax.plot(x, pGR_num, '*', label='pGR_numerical')\n", - "ax.plot(x_a, pGR_a, '-', label='pGR_analytical')\n", + "ax.plot(x, pGR_num, \"*\", label=\"pGR_numerical\")\n", + "ax.plot(x_a, pGR_a, \"-\", label=\"pGR_analytical\")\n", "\n", - "ax.plot(x, pCap_num, '*', label='pCap_numerical')\n", - "ax.plot(x_a, pCap_a, '-', label='pCap_analytical')\n", + "ax.plot(x, pCap_num, \"*\", label=\"pCap_numerical\")\n", + "ax.plot(x_a, pCap_a, \"-\", label=\"pCap_analytical\")\n", "\n", "plt.xlim([0, 1])\n", "plt.ylim([0, 180000])\n", "\n", - "plt.legend(loc='upper right', bbox_to_anchor=(0.9, 0.8))\n", + "plt.legend(loc=\"upper right\", bbox_to_anchor=(0.9, 0.8))\n", "plt.show()" ] }, @@ -233,5 +210,31 @@ "outputs": [], "source": [] } - ] + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.10 64-bit", + "metadata": { + "interpreter": { + "hash": "5b3ded1ccb95c1d9bd405e7b823d9e85424cde40fbb5985eb47e999ef50e15b4" + } + }, + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10-final" + }, + "orig_nbformat": 2 + }, + "nbformat": 4, + "nbformat_minor": 2 } \ No newline at end of file diff --git a/Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb b/Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb index d0b163f3547..b1589d76812 100644 --- a/Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb +++ b/Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb @@ -90,38 +90,39 @@ "outputs": [], "source": [ "import math\n", + "\n", "import numpy as np\n", "\n", - "q = -100.0 # heat injection [W/m²]\n", + "q = -100.0 # heat injection [W/m²]\n", "\n", - "K = 1e-12 # permeability [m²]\n", - "phi = 0.4 # porosity [-]\n", + "K = 1e-12 # permeability [m²]\n", + "phi = 0.4 # porosity [-]\n", "\n", - "p_ref = 101325 # reference pressure [Pa]\n", - "T_ref = 373.15 # reference temperature [K]\n", + "p_ref = 101325 # reference pressure [Pa]\n", + "T_ref = 373.15 # reference temperature [K]\n", "\n", - "lambda_G = 0.2 # thermal conductivity of gas phase [W/mK]\n", - "lambda_L = 0.5 # thermal conductivity of liquid phase [w/mK]\n", - "lambda_S = 1.0 # thermal conductivity of solid matrix [w/mK]\n", - "dh_evap = 2258000 # latent heat of evaporation [J/kg]\n", + "lambda_G = 0.2 # thermal conductivity of gas phase [W/mK]\n", + "lambda_L = 0.5 # thermal conductivity of liquid phase [w/mK]\n", + "lambda_S = 1.0 # thermal conductivity of solid matrix [w/mK]\n", + "dh_evap = 2258000 # latent heat of evaporation [J/kg]\n", "\n", - "D_pm = 2.6e-6 # binary diffusion coefficient [m²/s]\n", - "rho_L = 1000.0 # density of liquid phase [kg/m³]\n", - "MW = 0.018016 # molecular weight of water component [kg/mol]\n", - "MC = 0.028949 # molecular weight of air component [kg/mol]\n", - "R = 8.3144621 # universal gas constant\n", + "D_pm = 2.6e-6 # binary diffusion coefficient [m²/s]\n", + "rho_L = 1000.0 # density of liquid phase [kg/m³]\n", + "MW = 0.018016 # molecular weight of water component [kg/mol]\n", + "MC = 0.028949 # molecular weight of air component [kg/mol]\n", + "R = 8.3144621 # universal gas constant\n", "\n", - "mu_L = 2.938e-4 # dynamic viscosity of liquid phase [Pa.s]\n", - "muA_G = 2.194e-5 # dynamic viscosity of air component in gas phase [Pa.s]\n", - "muW_G =1.227e-5 # dynamic viscosity of water component in gas phase [Pa.s]\n", + "mu_L = 2.938e-4 # dynamic viscosity of liquid phase [Pa.s]\n", + "muA_G = 2.194e-5 # dynamic viscosity of air component in gas phase [Pa.s]\n", + "muW_G = 1.227e-5 # dynamic viscosity of water component in gas phase [Pa.s]\n", "\n", - "s_LRes = 0.0 # residual saturation of liquid phase [-]\n", - "s_GRes = 0.0 # residual saturation of gas phase [-]\n", + "s_LRes = 0.0 # residual saturation of liquid phase [-]\n", + "s_GRes = 0.0 # residual saturation of gas phase [-]\n", "\n", - "k_rG_min = 1e-5 # used for normalization of BC model\n", - "k_rL_min = 1e-5 # used for normalization of BC model\n", - "p_thr_BC = 5.0e3 # entry pressure for Brooks-Corey model [Pa]\n", - "exp_BC = 3.0 # Corey exponent for Brooks-Corey model [-]\n" + "k_rG_min = 1e-5 # used for normalization of BC model\n", + "k_rL_min = 1e-5 # used for normalization of BC model\n", + "p_thr_BC = 5.0e3 # entry pressure for Brooks-Corey model [Pa]\n", + "exp_BC = 3.0 # Corey exponent for Brooks-Corey model [-]" ] }, { @@ -146,19 +147,25 @@ "outputs": [], "source": [ "def capillary_pressure(sL_eff):\n", - " return p_thr_BC * (sL_eff ** (-1./exp_BC))\n", + " return p_thr_BC * (sL_eff ** (-1.0 / exp_BC))\n", + "\n", "\n", "def capillary_pressure_derivative(sL_eff):\n", - " return -p_thr_BC / exp_BC * (sL_eff ** (-(exp_BC+1.)/exp_BC))\n", + " return -p_thr_BC / exp_BC * (sL_eff ** (-(exp_BC + 1.0) / exp_BC))\n", + "\n", "\n", "def saturation_effective(p_c):\n", - " return (p_c/p_thr_BC) ** (-exp_BC)\n", + " return (p_c / p_thr_BC) ** (-exp_BC)\n", + "\n", "\n", "def relative_permeability_gas(sL_eff):\n", - " return max(k_rG_min, ((1.-sL_eff) ** 2) * (1-(sL_eff ** ((2.+exp_BC)/exp_BC))) )\n", + " return max(\n", + " k_rG_min, ((1.0 - sL_eff) ** 2) * (1 - (sL_eff ** ((2.0 + exp_BC) / exp_BC)))\n", + " )\n", + "\n", "\n", "def relative_permeability_liquid(sL_eff):\n", - " return max(k_rL_min, sL_eff ** ((2.+3*exp_BC)/exp_BC))\n" + " return max(k_rL_min, sL_eff ** ((2.0 + 3 * exp_BC) / exp_BC))" ] }, { @@ -179,14 +186,16 @@ "outputs": [], "source": [ "def vapour_pressure(p_sat, p_G, p_c, xA_G, T):\n", - " return p_sat * math.exp(-(p_c - xA_G*p_G) * MW / rho_L / R / T)\n", + " return p_sat * math.exp(-(p_c - xA_G * p_G) * MW / rho_L / R / T)\n", + "\n", "\n", "def saturation_vapour_pressure(T):\n", - " return p_ref * math.exp((1./T_ref - 1./T) * dh_evap * MW / R)\n", + " return p_ref * math.exp((1.0 / T_ref - 1.0 / T) * dh_evap * MW / R)\n", + "\n", "\n", "def partial_pressure_vapour(p_G, p_c, xA_G, T):\n", " p_sat = saturation_vapour_pressure(T)\n", - " return vapour_pressure(p_sat, p_G, p_c, xA_G, T)\n" + " return vapour_pressure(p_sat, p_G, p_c, xA_G, T)" ] }, { @@ -209,17 +218,20 @@ "def molar_mass_gas_phase(xA_G):\n", " return xA_G * MC + (1 - xA_G) * MW\n", "\n", + "\n", "def density_gas_phase(p_G, xA_G, T):\n", " M = molar_mass_gas_phase(xA_G)\n", " return p_G * M / (R * T)\n", "\n", + "\n", "def viscosity_gas_phase(xA_G):\n", - " return xA_G * muA_G + (1. - xA_G) * muW_G\n", + " return xA_G * muA_G + (1.0 - xA_G) * muW_G\n", + "\n", "\n", "def kinematic_viscosity_gas_phase(p_G, xA_G, T):\n", " mu_G = viscosity_gas_phase(xA_G)\n", " rho_G = density_gas_phase(p_G, xA_G, T)\n", - " return mu_G / rho_G\n" + " return mu_G / rho_G" ] }, { @@ -238,7 +250,7 @@ "outputs": [], "source": [ "def diffusivity(sL_eff):\n", - " return phi * (1. - sL_eff) * D_pm\n" + " return phi * (1.0 - sL_eff) * D_pm" ] }, { @@ -257,11 +269,11 @@ "outputs": [], "source": [ "def thermal_conductivity(sL_eff):\n", - " sL = sL_eff * (1. - s_GRes - s_LRes) + s_LRes\n", - " phi_G = (1. - sL) * phi\n", + " sL = sL_eff * (1.0 - s_GRes - s_LRes) + s_LRes\n", + " phi_G = (1.0 - sL) * phi\n", " phi_L = sL * phi\n", - " phi_S = 1. - phi\n", - " return lambda_G * phi_G + lambda_L * phi_L + lambda_S * phi_S\n" + " phi_S = 1.0 - phi\n", + " return lambda_G * phi_G + lambda_L * phi_L + lambda_S * phi_S" ] }, { @@ -383,45 +395,55 @@ "# Parameter grouping\n", "def alpha_(sL_eff, p_G, xA_G):\n", " p_c = capillary_pressure(sL_eff)\n", - " return 1. + ((p_c - xA_G * p_G) / (dh_evap * rho_L))\n", + " return 1.0 + ((p_c - xA_G * p_G) / (dh_evap * rho_L))\n", + "\n", "\n", "def delta_(sL_eff, p_G, xA_G, T):\n", " alpha = alpha_(sL_eff, p_G, xA_G)\n", " nu_G = kinematic_viscosity_gas_phase(p_G, xA_G, T)\n", " th_cond = thermal_conductivity(sL_eff)\n", - " return (rho_L * (dh_evap ** 2) * K * alpha) / (th_cond * nu_G * T)\n", + " return (rho_L * (dh_evap**2) * K * alpha) / (th_cond * nu_G * T)\n", + "\n", "\n", "def xi_(sL_eff, p_G, xA_G, T):\n", " k_rG = relative_permeability_gas(sL_eff)\n", " k_rL = relative_permeability_liquid(sL_eff)\n", " nu_G = kinematic_viscosity_gas_phase(p_G, xA_G, T)\n", " nu_L = mu_L / rho_L\n", - " result = ((1. + ((rho_L * R * T)/(p_G * MW * (1. - xA_G))) ) / k_rG) + (nu_L / nu_G) / k_rL\n", + " result = ((1.0 + ((rho_L * R * T) / (p_G * MW * (1.0 - xA_G)))) / k_rG) + (\n", + " nu_L / nu_G\n", + " ) / k_rL\n", " return result\n", "\n", + "\n", "def zeta_(sL_eff, p_G, xA_G, T):\n", " D = diffusivity(sL_eff)\n", " mu_G = viscosity_gas_phase(xA_G)\n", " a = K * rho_L * R * T * xA_G\n", - " b = mu_G * D * MW * (1. - xA_G)\n", - " c = p_G * MW / (rho_L * R * T) + 1. / (1. - xA_G)\n", + " b = mu_G * D * MW * (1.0 - xA_G)\n", + " c = p_G * MW / (rho_L * R * T) + 1.0 / (1.0 - xA_G)\n", " return (a / b) * c\n", "\n", + "\n", "def eta_(sL_eff, p_G, xA_G, T):\n", " delta = delta_(sL_eff, p_G, xA_G, T)\n", " xi = xi_(sL_eff, p_G, xA_G, T)\n", " zeta = zeta_(sL_eff, p_G, xA_G, T)\n", " return delta / (delta + xi + zeta)\n", "\n", + "\n", "def gamma_(sL_eff, p_G, xA_G, T):\n", " k_rG = relative_permeability_gas(sL_eff)\n", " k_rL = relative_permeability_liquid(sL_eff)\n", " nu_G = kinematic_viscosity_gas_phase(p_G, xA_G, T)\n", " nu_L = mu_L / rho_L\n", - " return 1. / k_rG * ((1. / (1. - xA_G)) + (nu_L / nu_G) * (k_rG / k_rL))\n", + " return 1.0 / k_rG * ((1.0 / (1.0 - xA_G)) + (nu_L / nu_G) * (k_rG / k_rL))\n", + "\n", "\n", "# Differential equations\n", "# Spatial variable (1D) derivative\n", + "\n", + "\n", "def dz_dsL_eff(sL_eff, p_G, xA_G, T):\n", " dpC_dsL_eff = capillary_pressure_derivative(sL_eff)\n", " eta = eta_(sL_eff, p_G, xA_G, T)\n", @@ -430,29 +452,38 @@ " omega = (q * nu_G) / (dh_evap * K)\n", " return -dpC_dsL_eff / (eta * omega * gamma)\n", "\n", + "\n", "# Gas-phase pressure derivative\n", + "\n", + "\n", "def dp_G_dsL_eff(sL_eff, p_G, xA_G, T):\n", " dpC_dsL_eff = capillary_pressure_derivative(sL_eff)\n", " gamma = gamma_(sL_eff, p_G, xA_G, T)\n", " k_rG = relative_permeability_gas(sL_eff)\n", - " return dpC_dsL_eff / (gamma * k_rG * (1. - xA_G))\n", + " return dpC_dsL_eff / (gamma * k_rG * (1.0 - xA_G))\n", + "\n", "\n", "# Mole fraction of air component in gas phase derivative\n", + "\n", + "\n", "def dxA_G_dsL_eff(sL_eff, p_G, xA_G, T):\n", " dpC_dsL_eff = capillary_pressure_derivative(sL_eff)\n", " gamma = gamma_(sL_eff, p_G, xA_G, T)\n", " mu_G = viscosity_gas_phase(xA_G)\n", " D = diffusivity(sL_eff)\n", - " return -dpC_dsL_eff * K / (mu_G * D) * xA_G / (1. - xA_G) / gamma\n", + " return -dpC_dsL_eff * K / (mu_G * D) * xA_G / (1.0 - xA_G) / gamma\n", + "\n", "\n", "# Temperature derivative\n", + "\n", + "\n", "def dT_dsL_eff(sL_eff, p_G, xA_G, T):\n", " dpC_dsL_eff = capillary_pressure_derivative(sL_eff)\n", " eta = eta_(sL_eff, p_G, xA_G, T)\n", " gamma = gamma_(sL_eff, p_G, xA_G, T)\n", " nu_G = kinematic_viscosity_gas_phase(p_G, xA_G, T)\n", " th_cond = thermal_conductivity(sL_eff)\n", - " return dpC_dsL_eff * (1. - eta) / eta * dh_evap / (nu_G * th_cond) * K / gamma\n" + " return dpC_dsL_eff * (1.0 - eta) / eta * dh_evap / (nu_G * th_cond) * K / gamma" ] }, { @@ -485,30 +516,37 @@ " dydsL[0] = dz_dsL_eff(sL_eff, p_G, xA_G, T)\n", " dydsL[1] = dp_G_dsL_eff(sL_eff, p_G, xA_G, T)\n", " dydsL[2] = dxA_G_dsL_eff(sL_eff, p_G, xA_G, T)\n", - " dydsL[3] = dT_dsL_eff(sL_eff, p_G, xA_G, T) \n", + " dydsL[3] = dT_dsL_eff(sL_eff, p_G, xA_G, T)\n", " return dydsL\n", "\n", + "\n", "# Numerical integration - Forward Euler method\n", "# to estimate the integrals of the coupled equation system\n", + "\n", + "\n", "def step_Euler(y, sL_eff, dsL_eff, p_G, xA_G, T):\n", " next_y = y + dsL_eff * dy_dsL_eff(y, sL_eff, p_G, xA_G, T)\n", " return next_y\n", "\n", + "\n", "def full_Euler(dsL_eff, y0, sL_eff_low, sL_eff_high):\n", - " max_steps = int(abs((sL_eff_low - sL_eff_high)/dsL_eff))\n", - " sL_eff_list = np.linspace(sL_eff_low, sL_eff_high, max_steps+1)\n", - " M = np.zeros((4, max_steps+1)) # Solution matrix containing the 4 primary variable\n", - " M[:,0] = y0\n", - " for i in range(0, max_steps):\n", - " p_G = M[1,i]\n", - " xA_G = M[2,i]\n", - " T = M[3,i]\n", - " if (dz_dsL_eff(sL_eff_list[i], p_G, xA_G, T)* dsL_eff) < 1.0:\n", - " M[:,i+1] = step_Euler(M[:,i], sL_eff_list[i], dsL_eff, p_G, xA_G, T) \n", + " max_steps = int(abs((sL_eff_low - sL_eff_high) / dsL_eff))\n", + " sL_eff_list = np.linspace(sL_eff_low, sL_eff_high, max_steps + 1)\n", + " M = np.zeros(\n", + " (4, max_steps + 1)\n", + " ) # Solution matrix containing the 4 primary variable\n", + " M[:, 0] = y0\n", + " for i in range(max_steps):\n", + " p_G = M[1, i]\n", + " xA_G = M[2, i]\n", + " T = M[3, i]\n", + " if (dz_dsL_eff(sL_eff_list[i], p_G, xA_G, T) * dsL_eff) < 1.0:\n", + " M[:, i + 1] = step_Euler(M[:, i], sL_eff_list[i], dsL_eff, p_G, xA_G, T)\n", " else:\n", - " M[:,i+1] = np.nan\n", + " M[:, i + 1] = np.nan\n", " return M, sL_eff_list\n", "\n", + "\n", "# initial condition\n", "z_0 = 0\n", "p_G0 = 101325\n", @@ -523,11 +561,11 @@ "# integration boundaries and saturation step size\n", "sL_eff_low = sL_eff_0\n", "sL_eff_high = 10e-16\n", - "n_dsL_eff = 10 ** 2\n", + "n_dsL_eff = 10**2\n", "dsL_eff = (sL_eff_high - sL_eff_low) / n_dsL_eff\n", "\n", "# execute analytical solution\n", - "M, sL_eff_list = full_Euler(dsL_eff, y0, sL_eff_low, sL_eff_high)\n" + "M, sL_eff_list = full_Euler(dsL_eff, y0, sL_eff_low, sL_eff_high)" ] }, { @@ -563,15 +601,16 @@ ], "source": [ "import os\n", + "\n", "from ogs6py import ogs\n", "\n", - "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n", + "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", "if not os.path.exists(out_dir):\n", " os.makedirs(out_dir)\n", "\n", "prj_file = \"heat_pipe_rough.prj\"\n", - "model=ogs.OGS(INPUT_FILE=prj_file, PROJECT_FILE=prj_file)\n", - "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir}\")\n" + "model = ogs.OGS(INPUT_FILE=prj_file, PROJECT_FILE=prj_file)\n", + "model.run_model(logfile=f\"{out_dir}/out.txt\", args=f\"-o {out_dir}\")" ] }, { @@ -593,31 +632,32 @@ "source": [ "# Import OGS simulation results\n", "import pyvista as pv\n", + "\n", "pv.set_plot_theme(\"document\")\n", "pv.set_jupyter_backend(\"static\")\n", "\n", "pvd_file = f\"{out_dir}/results_heatpipe_rough.pvd\"\n", "reader = pv.get_reader(pvd_file)\n", - "reader.set_active_time_value(1.0e7) # set reader to simulation end-time\n", + "reader.set_active_time_value(1.0e7) # set reader to simulation end-time\n", "mesh = reader.read()[0]\n", "\n", "# Define line along mesh and extract data along line for plotting\n", - "pt1 = (0,0.0025,0)\n", - "pt2 = (1,0.0025,0)\n", + "pt1 = (0, 0.0025, 0)\n", + "pt2 = (1, 0.0025, 0)\n", "xaxis = pv.Line(pt1, pt2, resolution=2)\n", - "line_mesh= mesh.slice_along_line(xaxis)\n", + "line_mesh = mesh.slice_along_line(xaxis)\n", "\n", - "x_num = line_mesh.points[:,0] # x coordinates of each point\n", + "x_num = line_mesh.points[:, 0] # x coordinates of each point\n", "S_num = line_mesh.point_data[\"saturation\"]\n", "xA_G_num = line_mesh.point_data[\"xnCG\"]\n", "p_G_num = line_mesh.point_data[\"gas_pressure\"]\n", "T_num = line_mesh.point_data[\"temperature\"]\n", "\n", "# Resampling dataset via linear interpolation for error calculation\n", - "S_num_interp = np.interp(M[0,:], x_num, S_num)\n", - "xA_G_num_interp = np.interp(M[0,:], x_num, xA_G_num)\n", - "p_G_num_interp = np.interp(M[0,:], x_num, p_G_num)\n", - "T_num_interp = np.interp(M[0,:], x_num, T_num)\n" + "S_num_interp = np.interp(M[0, :], x_num, S_num)\n", + "xA_G_num_interp = np.interp(M[0, :], x_num, xA_G_num)\n", + "p_G_num_interp = np.interp(M[0, :], x_num, p_G_num)\n", + "T_num_interp = np.interp(M[0, :], x_num, T_num)" ] }, { @@ -673,112 +713,123 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "plt.rcParams['lines.linewidth']= 2.0\n", - "plt.rcParams['lines.color']= 'black'\n", - "plt.rcParams['legend.frameon']=True\n", - "plt.rcParams['font.family'] = 'serif'\n", - "plt.rcParams['legend.fontsize']=14\n", - "plt.rcParams['font.size'] = 14\n", - "plt.rcParams['axes.axisbelow'] = True\n", - "plt.rcParams['figure.figsize'] = (16, 6)\n", + "\n", + "plt.rcParams[\"lines.linewidth\"] = 2.0\n", + "plt.rcParams[\"lines.color\"] = \"black\"\n", + "plt.rcParams[\"legend.frameon\"] = True\n", + "plt.rcParams[\"font.family\"] = \"serif\"\n", + "plt.rcParams[\"legend.fontsize\"] = 14\n", + "plt.rcParams[\"font.size\"] = 14\n", + "plt.rcParams[\"axes.axisbelow\"] = True\n", + "plt.rcParams[\"figure.figsize\"] = (16, 6)\n", "\n", "fig1, (ax1, ax2, ax3) = plt.subplots(1, 3)\n", - "ax1.plot(M[0,:], sL_eff_list,'kx', label=r\"$S_{L,eff}$ analytical\")\n", - "ax1.plot(M[0,:], M[2,:], 'kx', label=r\"$x_G^a$ analytical\")\n", - "ax1.plot(x_num, S_num, 'b', label=r\"$S_{L,eff}$ numerical\")\n", - "ax1.plot(x_num, xA_G_num, 'g', label=r\"$x_G^a$ numerical\")\n", + "ax1.plot(M[0, :], sL_eff_list, \"kx\", label=r\"$S_{L,eff}$ analytical\")\n", + "ax1.plot(M[0, :], M[2, :], \"kx\", label=r\"$x_G^a$ analytical\")\n", + "ax1.plot(x_num, S_num, \"b\", label=r\"$S_{L,eff}$ numerical\")\n", + "ax1.plot(x_num, xA_G_num, \"g\", label=r\"$x_G^a$ numerical\")\n", "ax1.set_xlabel(r\"$z$ / m\")\n", "ax1.set_ylabel(r\"$S_{L,eff}$ and $x_G^a$ / -\")\n", "ax1.legend()\n", "ax1.grid(True)\n", - "ax1.set_xlim(0,1)\n", - "ax1.set_ylim(0,1)\n", + "ax1.set_xlim(0, 1)\n", + "ax1.set_ylim(0, 1)\n", "\n", - "ax2.plot(M[0,:], S_num_interp-sL_eff_list,'b', label=r\"$\\Delta S_{L,eff}$\")\n", - "ax2.plot(M[0,:], xA_G_num_interp-M[2,:], 'g', label=r\"$\\Delta x_G^a$\")\n", + "ax2.plot(M[0, :], S_num_interp - sL_eff_list, \"b\", label=r\"$\\Delta S_{L,eff}$\")\n", + "ax2.plot(M[0, :], xA_G_num_interp - M[2, :], \"g\", label=r\"$\\Delta x_G^a$\")\n", "ax2.set_xlabel(r\"$z$ / m\")\n", "ax2.set_ylabel(r\"Absolute error / -\")\n", "ax2.legend()\n", "ax2.grid(True)\n", - "ax2.set_xlim(0,1)\n", - "ax2.set_ylim(-0.001,0.02)\n", - "\n", - "relError_S_w = np.zeros(len(M[0,:]))\n", - "relError_xA_G = np.zeros(len(M[0,:]))\n", - "for i in range(0, len(M[0,:])):\n", - " if (sL_eff_list[i]) >= 0.001:\n", - " relError_S_w[i] = (S_num_interp[i]-sL_eff_list[i])/sL_eff_list[i] \n", - " else:\n", - " relError_S_w[i] = np.nan\n", - " if (M[2,i]) >= 0.01:\n", - " relError_xA_G[i] = (xA_G_num_interp[i]-M[2,i])/M[2,i]\n", - " else:\n", - " relError_xA_G[i] = np.nan\n", - "ax3.plot(M[0,:], relError_S_w, 'b', label=r\"$\\Delta S_{L,eff}/S_{L,eff-analytical}$\")\n", - "ax3.plot(M[0,:], relError_xA_G, 'g', label=r\"$\\Delta x_G^a/x_{G-analytical}^a$\")\n", + "ax2.set_xlim(0, 1)\n", + "ax2.set_ylim(-0.001, 0.02)\n", + "\n", + "relError_S_w = np.zeros(len(M[0, :]))\n", + "relError_xA_G = np.zeros(len(M[0, :]))\n", + "for i in range(len(M[0, :])):\n", + " if (sL_eff_list[i]) >= 0.001:\n", + " relError_S_w[i] = (S_num_interp[i] - sL_eff_list[i]) / sL_eff_list[i]\n", + " else:\n", + " relError_S_w[i] = np.nan\n", + " if (M[2, i]) >= 0.01:\n", + " relError_xA_G[i] = (xA_G_num_interp[i] - M[2, i]) / M[2, i]\n", + " else:\n", + " relError_xA_G[i] = np.nan\n", + "ax3.plot(M[0, :], relError_S_w, \"b\", label=r\"$\\Delta S_{L,eff}/S_{L,eff-analytical}$\")\n", + "ax3.plot(M[0, :], relError_xA_G, \"g\", label=r\"$\\Delta x_G^a/x_{G-analytical}^a$\")\n", "ax3.set_xlabel(r\"$z$ / m\")\n", "ax3.set_ylabel(r\"Relative error / -\")\n", - "ax3.set_xlim(0,1)\n", + "ax3.set_xlim(0, 1)\n", "ax3.legend()\n", "ax3.grid(True)\n", "fig1.tight_layout()\n", "plt.show()\n", "\n", - "fig2, (ax1, ax2, ax3)=plt.subplots(1,3)\n", - "ax1.plot(M[0,:], M[3,:], 'kx', label=r\"$T$ analytical\")\n", - "ax1.plot(x_num, T_num, 'r', label=r\"$T$ numerical\")\n", + "fig2, (ax1, ax2, ax3) = plt.subplots(1, 3)\n", + "ax1.plot(M[0, :], M[3, :], \"kx\", label=r\"$T$ analytical\")\n", + "ax1.plot(x_num, T_num, \"r\", label=r\"$T$ numerical\")\n", "ax1.set_xlabel(r\"$z$ / m\")\n", "ax1.set_ylabel(r\"$T$ / K\")\n", "ax1.legend()\n", "ax1.grid(True)\n", - "ax1.set_xlim(0,1)\n", - "ax1.set_ylim(365,375)\n", + "ax1.set_xlim(0, 1)\n", + "ax1.set_ylim(365, 375)\n", "\n", - "ax2.plot(M[0,:], -T_num_interp+M[3,:],'r', label=r\"$\\Delta T$\")\n", + "ax2.plot(M[0, :], -T_num_interp + M[3, :], \"r\", label=r\"$\\Delta T$\")\n", "ax2.set_xlabel(r\"$z$ / m\")\n", "ax2.set_ylabel(r\"Absolute error / K\")\n", "ax2.legend()\n", "ax2.grid(True)\n", - "ax2.set_xlim(0,1)\n", - "ax2.set_ylim(0,0.12)\n", - "\n", - "ax3.plot(M[0,:], (-T_num_interp+M[3,:])/M[3,:], 'r', label=r\"$\\Delta T/T_{analytical}$\")\n", + "ax2.set_xlim(0, 1)\n", + "ax2.set_ylim(0, 0.12)\n", + "\n", + "ax3.plot(\n", + " M[0, :],\n", + " (-T_num_interp + M[3, :]) / M[3, :],\n", + " \"r\",\n", + " label=r\"$\\Delta T/T_{analytical}$\",\n", + ")\n", "ax3.set_xlabel(r\"$z$ / m\")\n", "ax3.set_ylabel(r\"Relative error / -\")\n", - "ax3.set_xlim(0,1)\n", - "ax3.set_ylim(0,0.0003)\n", + "ax3.set_xlim(0, 1)\n", + "ax3.set_ylim(0, 0.0003)\n", "ax3.legend()\n", "ax3.grid(True)\n", "fig2.tight_layout()\n", "plt.show()\n", "\n", - "fig3, (ax1, ax2, ax3)=plt.subplots(1,3)\n", - "ax1.plot(M[0,:], M[1,:]/1000, 'kx', label=r\"$p_G$ analytical\")\n", - "ax1.plot(x_num, p_G_num/1000, 'c', label=r\"$p_G$ numerical\")\n", + "fig3, (ax1, ax2, ax3) = plt.subplots(1, 3)\n", + "ax1.plot(M[0, :], M[1, :] / 1000, \"kx\", label=r\"$p_G$ analytical\")\n", + "ax1.plot(x_num, p_G_num / 1000, \"c\", label=r\"$p_G$ numerical\")\n", "ax1.set_xlabel(r\"$z$ / m\")\n", "ax1.set_ylabel(r\"$p_G$ / kPa\")\n", "ax1.legend()\n", "ax1.grid(True)\n", - "ax1.set_xlim(0,1)\n", - "ax1.set_ylim(100,106)\n", + "ax1.set_xlim(0, 1)\n", + "ax1.set_ylim(100, 106)\n", "\n", - "ax2.plot(M[0,:], -p_G_num_interp+M[1,:],'c', label=r\"$\\Delta p_G$\")\n", + "ax2.plot(M[0, :], -p_G_num_interp + M[1, :], \"c\", label=r\"$\\Delta p_G$\")\n", "ax2.set_xlabel(r\"$z$ / m\")\n", "ax2.set_ylabel(r\"Absolute error / Pa\")\n", "ax2.legend()\n", "ax2.grid(True)\n", - "ax2.set_xlim(0,1)\n", - "ax2.set_ylim(0,30)\n", - "\n", - "ax3.plot(M[0,:], (-p_G_num_interp+M[1,:])/M[1,:], 'c', label=r\"$\\Delta p_G/p_{G-analytical}$\")\n", + "ax2.set_xlim(0, 1)\n", + "ax2.set_ylim(0, 30)\n", + "\n", + "ax3.plot(\n", + " M[0, :],\n", + " (-p_G_num_interp + M[1, :]) / M[1, :],\n", + " \"c\",\n", + " label=r\"$\\Delta p_G/p_{G-analytical}$\",\n", + ")\n", "ax3.set_xlabel(r\"$z$ / m\")\n", "ax3.set_ylabel(r\"Relative error / -\")\n", - "ax3.set_xlim(0,1)\n", - "ax3.set_ylim(0,0.0004)\n", + "ax3.set_xlim(0, 1)\n", + "ax3.set_ylim(0, 0.0004)\n", "ax3.legend()\n", "ax3.grid(True)\n", "fig3.tight_layout()\n", - "plt.show()\n" + "plt.show()" ] }, { diff --git a/Tests/Data/ThermoHydroMechanics/Linear/Point_injection/SaturatedPointheatsource.ipynb b/Tests/Data/ThermoHydroMechanics/Linear/Point_injection/SaturatedPointheatsource.ipynb index 80751ba8b26..49655448027 100644 --- a/Tests/Data/ThermoHydroMechanics/Linear/Point_injection/SaturatedPointheatsource.ipynb +++ b/Tests/Data/ThermoHydroMechanics/Linear/Point_injection/SaturatedPointheatsource.ipynb @@ -1,801 +1,1012 @@ { - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "+++\n", - "author = \"Jörg Buchwald and Kata Kurgyis\"\n", - "date = \"2022-11-02\"\n", - "title = \"Point-Heatsource Problem\"\n", - "weight = 8\n", - "image = \"figures/placeholder_pointheatsource.png\"\n", - "web_subsection = \"th2m\"\n", - "coupling = \"thm\"\n", - "+++\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Problem description\n", - "\n", - "The problem describes a heat source embedded in a fully fluid-saturated porous medium.\n", - "The spherical symmetry is modeled using a 10 m x 10 m disc with a point heat source ($Q=150\\;\\mathrm{W}$) placed at one corner ($r=0$) and a curved boundary at $r=10\\;\\mathrm{m}$. Applying rotational axial symmetry at one of the linear boundaries, the model region transforms into a half-space configuration of the spherical symmetrical problemcorresponding to the analytical solution.\n", - "The initial temperature and the excess pore pressure are 273.15 K and 0 Pa, respectively.\n", - "The axis-normal displacements along the symmetry (inner) boundaries were set to zero, whereas the excess pore pressure, as well as the temperature, are set to their initial values along the outer (curved) boundary.\n", - "The heat coming from the point source is propagated through the medium, causing the fluid and the solid to expand at different rates. The resulting pore pressure (gradient) is triggering a thermally driven consolidation process caused by the fluid flowing away from the heat source until equilibrium is reached.\n", - "\n", - "![PointHeatSourceSchematic.png](figures/PointHeatSourceSchematic.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Governing equations\n", - "\n", - "For this problem we consider the following assumptions:\n", - "\n", - "* No thermal adverction is considered: $\\rho_\\text{w}c_\\text{w}T_{,i} v_i = 0$.\n", - "\n", - "* Gravitational forces are neglected: $\\rho g = 0$.\n", - "\n", - "* Both fluid and solid phases are intrinsically incompressible: $\\alpha_B = 1$; $\\beta = 0$.\n", - "\n", - "* No external fluid sink or source term: $q_H = 0$.\n", - "\n", - "* The porous medium is isotropic and homogeneous.\n", - "\n", - "These assumptions lead to the following set of governing equation describing the system behavior:\n", - "\n", - "**Energy balance**\n", - "\n", - "$$\n", - "\\begin{gather}\n", - " m \\dot T - (K T_{,i})_{,i} = q_T \n", - "%\n", - "\\\\\n", - "%\n", - " \\text{where}\\nonumber\n", - "%\n", - "\\\\\n", - "%\n", - " m = \\phi \\rho_w c_w + (1-\\phi) \\rho_s c_s\n", - "%\n", - "\\\\\n", - "%\n", - " K = \\phi K_w + (1 - \\phi) K_s\n", - "%\n", - "\\\\\n", - "%\n", - " v_i = -\\dfrac{k_s}{\\eta} (p_{,i})\n", - "\\end{gather}\n", - "$$\n", - "\n", - "**Mass balance**\n", - "\n", - "$$\n", - "\\begin{gather}\n", - " - a_u \\dot T+ \\dot u_{i,i} + v_{i,i} = 0\n", - "%\n", - "\\\\\n", - "%\n", - " \\text{where}\\nonumber\n", - "%\n", - "\\\\\n", - "%\n", - " a_u = \\phi a_w + (1-\\phi) a_s\n", - "\\end{gather}\n", - "$$\n", - "\n", - "**Momentum balance**\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " \\sigma_{ij} = \\sigma^\\prime_{ij} - p \\delta_{ij} = 0\n", - "\\end{equation}\n", - "$$\n", - "\n", - "A detailed description about the problem formulation and equation derivation can be found in the original work of Booker and Savvidou (1985) or Chaudhry et al. (2019).\n", - "\n", - "## Input parameters\n", - "\n", - "We considered the following set of values as input parameters:\n", - "\n", - "![PointHeatSourceInput.png](figures/PointHeatSourceInput.png)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# The analytical solution\n", - "\n", - "\n", - "The analytical solution of the coupled THM consolidation problem is derived in the original work of Booker and Savvidou (1985). In Chaudhry et al. (2019), a corrected solution is given for the effective stress term.\n", - "\n", - "For clarification, the equations below are based on the solid mechanics sign convention (tensile stress is positive). Furthermore, temporal partial derivative is indicated by the dot convention, while spatial partial derivatives are expressed by the comma convention, i.e. $(\\cdot)_{,i}=\\partial (\\cdot)/\\partial x_i$.\n", - "\n", - "The analytical solution for the three primary variables are expressed as:\n", - "\n", - "**Temperature**\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " \\Delta T = \\dfrac{Q}{4 \\pi K r} f^{\\kappa}\n", - "\\end{equation}\n", - "$$\n", - "\n", - "**Pore pressure**\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " p = \\dfrac{X Q}{(1 - \\frac{c}{\\kappa}) 4 \\pi K r} (f^{\\kappa}-f^{c})\n", - "\\end{equation}\n", - "$$\n", - "\n", - "**Displacement of the solid skeleton**\n", - "\n", - "$$\n", - "\\begin{equation}\n", - " u_{i} = \\dfrac{Q a_u x_i}{4 \\pi K r} g^{\\ast}\n", - "\\end{equation}\n", - "$$\n", - "\n", - "In the above equations, the following derived parameters are used:\n", - "\n", - "$$\n", - "\\begin{align}\n", - " \\kappa &= \\dfrac{K}{m}\n", - "%\n", - "\\\\\n", - "%\n", - " c &= \\dfrac{k_s}{\\eta}(\\lambda + 2G)\n", - "%\n", - "\\\\\n", - "%\n", - " r &= \\sqrt{x_{1}^{2}+x_{2}^{2}+x_{3}^{2}}\n", - "%\n", - "\\\\\n", - "%\n", - " X &= a_\\text{u}\\left(\\lambda+2G\\right)-b^{\\prime}\n", - "%\n", - "\\\\\n", - "%\n", - " Y &= \\dfrac{1}{\\lambda+2G}\\left(\\dfrac{X}{\\left(1-\\dfrac{c}{\\kappa}\\right)a_\\text{u}}+\\dfrac{b^{\\prime}}{a_\\text{u}}\\right)\n", - "%\n", - "\\\\\n", - "%\n", - " Z &= \\dfrac{1}{\\lambda+2G}\\left(\\dfrac{X}{\\left(1-\\dfrac{c}{\\kappa}\\right)a_\\text{u}}\\right)\n", - "%\n", - "\\\\\n", - "%\n", - " f^{A} &= \\text{erfc}\\left(\\dfrac{r}{2\\sqrt{At}}\\right),\\quad A=\\kappa,c\n", - "%\n", - "\\\\\n", - "%\n", - " g^{A} &= \\dfrac{At}{r^{2}}+\\left(\\frac{1}{2}-\\dfrac{At}{r^{2}}\\right)f^{A}-\\sqrt{\\dfrac{At}{\\pi r^{2}}} \\exp\\left(-\\dfrac{r^{2}}{4At}\\right)\n", - "%\n", - "\\\\\n", - "%\n", - " g^{\\ast} &= Yg^{\\kappa}-Zg^{c}\n", - "%\n", - "\\\\\n", - "%\n", - " g^{A}_{,i} &= \\frac{2x_{i}At}{r^{4}}\\left(f^{A}-1+\\frac{r}{\\sqrt{\\pi At}}\\exp\\left(-\\frac{r^{2}}{4At}\\right)\\right),\\quad i=1,2,3\n", - "%\n", - "\\\\\n", - "%\n", - " g^{\\ast}_{,i} &= Yg^{\\kappa}_{,i}-Zg^{c}_{,i}\n", - "\\end{align}\n", - "$$\n", - "\n", - "The corrected form of the effective stress:\n", - "\n", - "$$\n", - "\\begin{align}\n", - " \\sigma^{\\prime}_{ij|j=i} &= \\frac{Q a_\\text{u}}{4\\pi Kr}\\left( 2G\\left[g^{\\ast}\\left(1-\\frac{x^{2}_{i}}{r^{2}}\\right)+x_{i}g^{\\ast}_{,i}\\right]+\\lambda \\left[x_{i}g^{\\ast}_{,i}+2g^{\\ast}\\right]\\right)-b^{\\prime}\\Delta T\n", - "%\n", - "\\\\\n", - "%\n", - " \\sigma^\\prime_{ij|j \\neq i} &= \\frac{Q a_\\text{u}}{4\\pi Kr}\\left( G\\left[x_{i}g^{\\ast}_{,j}+x_{j}g^{\\ast}_{,i}-2g^{\\ast}\\dfrac{x_{i}x_{j}}{r^{2}}\\right]\\right)\n", - "\\end{align}\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from scipy import special as sp\n", - "import matplotlib.pyplot as plt\n", - "\n", - "class ANASOL(object):\n", - " def __init__(self):\n", - " # material parameters\n", - " self.phi = 0.16 # porosity of soil\n", - " self.k = 2e-20 # coefficient of permeability\n", - " self.eta = 1e-3 # viscosity water at 20 deg\n", - " self.E = 5.e9 # Youngs modulus\n", - " self.nu = 0.3 # Poisson ratio \n", - " self.rho_w = 999.1 # density of pore water\n", - " self.c_w = 4280 # specific heat of pore water\n", - " self.K_w = 0.6 # thermal conductivity of pore water\n", - " self.rho_s = 2290.0 # density of solid matrix\n", - " self.c_s = 917.654 # specific heat capacity of solid matrix\n", - " self.K_s = 1.838 # themal conductivity of solid matrix\n", - " self.a_s = 3*1.5e-5 # volumetric expansivity of matrix - value conversion from linear to volumetric expansivity\n", - " self.a_w = 4.0e-4 # coefficient of volume expansion of pore water (beta_w)\n", - " \n", - " # initial and boundary condition\n", - " self.Q = 2 * 150 # [Q]=W strength of the heat source - value corrected to account for domain size\n", - " self.T0 = 273.15 # initial temperature\n", - " \n", - " self.Init()\n", - " \n", - " # derived parameters\n", - " def f(self, ka, R, t):\n", - " return sp.erfc(R/(2*np.sqrt(ka*t)))\n", - "\n", - " def g(self, ka, R, t):\n", - " return (ka*t/R**2+(1/2-ka*t/R**2)*sp.erfc(R/(2*np.sqrt(ka*t)))-np.sqrt(ka*t/(np.pi*R**2))*np.exp(-R**2/(4*ka*t)))\n", - "\n", - " def gstar(self,R,t):\n", - " return (self.Y*self.g(self.kappa,R,t)-self.Z*self.g(self.c,R,t))\n", - " \n", - " def R(self,x,y,z):\n", - " return np.sqrt(x**2+y**2+z**2)\n", - " \n", - " def dg_dR(self,ka,i,R,t):\n", - " return ((2*i/R**3)*np.sqrt(ka*t/np.pi)*np.exp(-R*R/(4*ka*t))+(2*i*ka*t/R**4)*(self.f(ka,R,t)-1))\n", - "\n", - " def dgstar_dR(self,i,R,t): # Subscript R means derivative w.r.t R\n", - " return (self.Y*self.dg_dR(self.kappa,i,R,t)-self.Z*self.dg_dR(self.c,i,R,t))\n", - " \n", - " # corrected form of effective stress\n", - " def sigma_ii(self,x,y,z,t,ii): # for normal components\n", - " R = self.R(x, y, z)\n", - " index = {\"xx\": x, \"yy\": y, \"zz\": z}\n", - " return ((self.Q*self.a_u/(4*np.pi*self.K*R))*(2*self.G*(self.gstar(R,t)*(1-index[ii]**2/R**2)+index[ii]*self.dgstar_dR(index[ii],R,t))\n", - " +self.lambd*(x*self.dgstar_dR(x,R,t)+y*self.dgstar_dR(y,R,t)+z*self.dgstar_dR(z,R,t)+2*self.gstar(R,t)))\n", - " -self.bprime*(self.temperature(x,y,z,t)-self.T0))\n", - "\n", - " def sigma_ij(self,x,y,z,t,i,j): # for shear components\n", - " R = self.R(x, y, z)\n", - " index = {\"x\": x, \"y\": y, \"z\": z}\n", - " return ((self.Q*self.a_u/(4*np.pi*self.K*R))*(2*self.G*\n", - " (index[i]*self.dgstar_dR(index[j],R,t)/2+index[j]*self.dgstar_dR(index[i],R,t)/2-index[i]*index[j]*self.gstar(R,t)/R**2)))\n", - "\n", - " # primary variables\n", - " def temperature(self,x,y,z,t):\n", - " R = self.R(x, y, z)\n", - " return (self.Q/(4*np.pi*self.K*R)*self.f(self.kappa,R,t)+self.T0)\n", - "\n", - " def porepressure(self,x,y,z,t):\n", - " R = self.R(x, y, z)\n", - " return (self.X/(1-self.c/self.kappa)*self.Q/(4*np.pi*self.K*R)*(self.f(self.kappa,R,t)-self.f(self.c,R,t)))\n", - "\n", - " def u_i(self,x,y,z,t,i):\n", - " R = self.R(x, y, z)\n", - " index = {\"x\": x, \"y\": y, \"z\": z}\n", - " return self.a_u*index[i]*self.Q/(4*np.pi*self.K*R)*self.gstar(R,t)\n", - " \n", - " def Init(self):\n", - " # derived constants\n", - " self.lambd = self.E*self.nu/((1+self.nu)*(1-2*self.nu)) # Lame constant\n", - " self.G = self.E/(2*(1+self.nu)) # shear constant\n", - " \n", - " self.K = self.phi*self.K_w+(1-self.phi)*self.K_s # average thermal conductivity \n", - " self.m = self.phi*self.rho_w*self.c_w+(1-self.phi)*self.rho_s*self.c_s\n", - " self.kappa = self.K/self.m # scaled heat conductivity\n", - " self.c = self.k/self.eta*(self.lambd+2*self.G)\n", - " \n", - " self.aprime = self.a_s\n", - " self.a_u = self.a_s*(1-self.phi)+self.a_w*self.phi \n", - " self.bprime =(self.lambd+2*self.G/3)*self.aprime\n", - " \n", - " self.X = self.a_u*(self.lambd+2*self.G)-self.bprime\n", - " self.Y = 1/(self.lambd+2*self.G) * (self.X/((1-self.c/self.kappa)*self.a_u)+self.bprime/self.a_u)\n", - " self.Z = 1/(self.lambd+2*self.G) * (self.X/((1-self.c/self.kappa)*self.a_u))\n", - "\n", - "ana_model = ANASOL()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## The numerical solutions\n", - "\n", - "For the numerical solution we compare the Thermal-Hydro-Mechanical (THM - linear and quadratic mesh), Thermal-2-Phase-Hydro-Mechanical (TH2M) and Thermal-Richard-Mechanical (TRM - quadratic mesh) formulation of OGS. \n", - "\n", - "The TH2M and TRM formulation methods have essential differences when applied to an unsaturated media where a gas phase is also present along side the aqueous phase. The difference originates from the way how the two mobile phases are treated specifically in the equation system: in the TH2M formulation, both the gas phase and the liquid phase is explicitely present and each phase is comprised of the two distinct component of aqueous component and non-aqueous component. In this case, the gas phase has a variable pressure solved explicitely in the governing equations. On the other hand, the TRM model assumes that the gas phase mobility is high and fast enough that gas drainage can occur significantly faster than the other processes in the system and hence, gas pressure doesn't build up. This leads to the simplification, that no gas pressure is calculated in the TRM model explicitely.\n", - "\n", - "The THM model is a simplified form of the general TH2M model, where there is no gas phase, only the aqueous phase is present in the equation system.\n", - "\n", - "In addition to the different formulation, we also compare the performance of the THM formulation with a linear and a quadratic mesh as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from ogs6py import ogs\n", - "\n", - "data_dir = os.environ.get('OGS_DATA_DIR', '../../..')\n", - "out_dir = os.environ.get('OGS_TESTRUNNER_OUT_DIR', '_out')\n", - "if not os.path.exists(out_dir):\n", - " os.makedirs(out_dir) \n", - " \n", - "# THM formulation (current working dir)\n", - "prj_file_lin = \"pointheatsource_linear-mesh.prj\"\n", - "prj_file_quad = \"pointheatsource_quadratic-mesh.prj\"\n", - "ogs_model_lin = ogs.OGS(INPUT_FILE=prj_file_lin, PROJECT_FILE=f\"{out_dir}/{prj_file_lin}\")\n", - "ogs_model_quad = ogs.OGS(INPUT_FILE=prj_file_quad, PROJECT_FILE=f\"{out_dir}/{prj_file_quad}\")\n", - "\n", - "# TH2M formulation\n", - "prj_file_th2m = \"point_heatsource.prj\"\n", - "path_th2m = f\"{data_dir}/TH2M/THM/sphere\"\n", - "prj_filepath_th2m = f\"{path_th2m}/{prj_file_th2m}\"\n", - "ogs_model_th2m = ogs.OGS(INPUT_FILE=prj_filepath_th2m, PROJECT_FILE=f\"{out_dir}/pointheatsource_th2m.prj\")\n", - "\n", - "# TRM formulation\n", - "prj_file_trm = \"point_heat_source_2D.prj\"\n", - "path_trm = f\"{data_dir}/ThermoRichardsMechanics/PointHeatSource\"\n", - "prj_filepath_trm = f\"{path_trm}/{prj_file_trm}\"\n", - "ogs_model_trm = ogs.OGS(INPUT_FILE=prj_filepath_trm, PROJECT_FILE=f\"{out_dir}/pointheatsource_trm.prj\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Simulation time\n", - "t_end = 2e6 # <= was originally 5e6\n", - "ogs_model_lin.set(t_end=t_end)\n", - "ogs_model_quad.set(t_end=t_end)\n", - "ogs_model_th2m.set(t_end=t_end)\n", - "ogs_model_trm.set(t_end=t_end)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ogs_model_lin.set(output_prefix=\"pointheatsource_lin\")\n", - "ogs_model_quad.set(output_prefix=\"pointheatsource_quad\")\n", - "ogs_model_th2m.set(output_prefix=\"pointheatsource_th2m\")\n", - "ogs_model_th2m.replace_text(\"150\", xpath=\"./parameters/parameter[name='temperature_source_term']/value\")\n", - "ogs_model_trm.set(output_prefix=\"pointheatsource_trm\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ogs_model_lin.write_input()\n", - "ogs_model_quad.write_input()\n", - "ogs_model_th2m.write_input()\n", - "ogs_model_trm.write_input()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import concurrent.futures\n", - "from timeit import default_timer as timer\n", - "\n", - "# Run models in parallel via concurrent.futures\n", - "ogs_models = []\n", - "ogs_models.append({\"model\": ogs_model_lin.prjfile, \"logfile\": f\"{out_dir}/lin-out.txt\", \"args\": f\"-o {out_dir} -m . -s .\"})\n", - "ogs_models.append({\"model\": ogs_model_quad.prjfile, \"logfile\": f\"{out_dir}/quad-out.txt\", \"args\": f\"-o {out_dir} -m . -s .\"})\n", - "ogs_models.append({\"model\": ogs_model_th2m.prjfile, \"logfile\": f\"{out_dir}/th2m-out.txt\", \"args\": f\"-o {out_dir} -m {path_th2m} -s {path_th2m}\"})\n", - "ogs_models.append({\"model\": ogs_model_trm.prjfile, \"logfile\": f\"{out_dir}/trm-out.txt\", \"args\": f\"-o {out_dir} -m {path_trm} -s {path_trm}\"})\n", - "\n", - "def run_ogs(model):\n", - " prj = model[\"model\"]\n", - " print(f\"Starting {prj} ...\\n\")\n", - " start_sim = timer()\n", - " # Starting via ogs6py does not work (\"cannot pickle lxml\"), at least on mac.\n", - " ! ogs {prj} {model[\"args\"]} > {model[\"logfile\"]}\n", - " assert _exit_code == 0\n", - " runtime = timer() - start_sim\n", - " return [f\"Finished {prj} in {runtime} s\", runtime]\n", - "\n", - "import platform\n", - "if platform.system() == \"Darwin\":\n", - " import multiprocessing as mp\n", - " mp.set_start_method(\"fork\")\n", - "\n", - "runtimes = []\n", - "start = timer()\n", - "with concurrent.futures.ProcessPoolExecutor() as executor:\n", - " results = executor.map(run_ogs, ogs_models)\n", - " for result in results:\n", - " print(result[0])\n", - " runtimes.append(result[1])\n", - "print(f\"Elapsed time for all simulations: {timer() - start} s\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluation and Results\n", - "\n", - "The analytical expressions together with the numerical model can now be evaluated at different points as a function of time (time series) or for a given time as a function of their spatial coordinates (along radial axis)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import vtuIO\n", - "\n", - "# Point of interest\n", - "pts = {\"pt0\": (0.5,0.5,0.0)}\n", - "\n", - "# Time axis for analytical solution\n", - "t = np.linspace(1,50000*200,num=201, endpoint=True)\n", - "\n", - "projects = [\"pointheatsource_lin\", \"pointheatsource_quad\", \"pointheatsource_th2m\", \"pointheatsource_trm\"]\n", - "\n", - "pvds = []\n", - "for i, prj in enumerate(projects):\n", - " pvds.append(vtuIO.PVDIO(f\"{out_dir}/{prj}.pvd\", dim=2))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Time series plots for temperature, pressure and displacement\n", - "\n", - "Comparison between the analytical solution and the numerical solution shows very good agreement, as displayed below in the figures." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.rcParams['lines.linewidth']= 2.0\n", - "plt.rcParams['lines.color']= 'black'\n", - "plt.rcParams['legend.frameon']=True\n", - "plt.rcParams['font.family'] = 'serif'\n", - "plt.rcParams['legend.fontsize']=14\n", - "plt.rcParams['font.size'] = 14\n", - "plt.rcParams['axes.axisbelow'] = True\n", - "plt.rcParams['figure.figsize'] = (16, 6)\n", - "\n", - "output = {\"T\": (\"temperature\", \"temperature_interpolated\", \"temperature_interpolated\", \"temperature_interpolated\"),\n", - " \"p\": (\"pressure\", \"pressure_interpolated\", \"gas_pressure_interpolated\", \"pressure_interpolated\"),\n", - " \"u\": (\"displacement\", \"displacement\", \"displacement\", \"displacement\"),\n", - " \"color\": ('r+', 'rx', 'b+', 'g+'),\n", - " \"label\": (\"ogs6 thm lin\", \"ogs6 thm quad\", \"ogs6 th2m\", \"ogs6 trm\")}\n", - "\n", - "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", - "\n", - "ax1.plot(t, ana_model.temperature(pts[\"pt0\"][0],pts[\"pt0\"][1],pts[\"pt0\"][2],t), \"k\", label=\"analytical\")\n", - "for i, pvd in enumerate(pvds):\n", - " ax1.plot(pvd.timesteps, pvd.read_time_series(output[\"T\"][i], pts=pts)[\"pt0\"], output[\"color\"][i], label=output[\"label\"][i])\n", - "ax1.set_xscale(\"log\")\n", - "ax1.set_xlabel(\"t / s\")\n", - "ax1.set_ylabel(\"T / K\")\n", - "ax1.set_xlim(1.e4, 2.e7)\n", - "ax1.set_ylim(270.0, 292.0)\n", - "ax1.legend(loc='lower right')\n", - "ax1.set_title(\"Temperature\")\n", - "\n", - "ax2.set_xscale(\"log\")\n", - "ax2.set_xlabel(\"t / s\")\n", - "ax2.set_ylabel(\"error / K\")\n", - "ax2.set_xlim(1.e4, 2.e7)\n", - "ax2.set_title(\"Temperature error / numerical - analytical\")\n", - "\n", - "for i, pvd in enumerate(pvds):\n", - " interp_ana_model = np.interp(pvd.timesteps, t, ana_model.temperature(pts[\"pt0\"][0],pts[\"pt0\"][1],pts[\"pt0\"][2],t))\n", - " error = pvd.read_time_series(output[\"T\"][i], pts=pts)[\"pt0\"] - interp_ana_model\n", - " ax2.plot(pvd.timesteps, error, output[\"color\"][i], label=output[\"label\"][i])\n", - " assert np.all(error < 0.2)\n", - " assert np.all(error > -0.06)\n", - " \n", - "ax2.legend(loc='upper right') \n", - "\n", - "fig1.tight_layout()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig1, (ax1, ax2) = plt.subplots(1, 2) \n", - "\n", - "ax1.plot(t, ana_model.porepressure(pts[\"pt0\"][0],pts[\"pt0\"][1],pts[\"pt0\"][2],t) / 1.e6, \"k\", label=\"analytical\")\n", - "for i, pvd in enumerate(pvds):\n", - " ax1.plot(pvd.timesteps, pvd.read_time_series(output[\"p\"][i], pts=pts)[\"pt0\"] / 1.e6, output[\"color\"][i], label=output[\"label\"][i])\n", - "ax1.set_xscale(\"log\")\n", - "ax1.set_xlabel(\"t / s\")\n", - "ax1.set_ylabel(\"p / MPa\")\n", - "ax1.set_xlim(1.e4, 2.e7)\n", - "ax1.legend(loc='lower right')\n", - "ax1.set_title(\"Pressure\")\n", - "\n", - "ax2.set_xscale(\"log\")\n", - "ax2.set_xlabel(\"t / s\")\n", - "ax2.set_ylabel(\"error / MPa\")\n", - "ax2.set_xlim(1.e4, 2.e7)\n", - "ax2.set_title(\"Pressure error / numerical - analytical\") \n", - "\n", - "for i, pvd in enumerate(pvds):\n", - " interp_ana_model = np.interp(pvd.timesteps, t, ana_model.porepressure(pts[\"pt0\"][0],pts[\"pt0\"][1],pts[\"pt0\"][2],t))\n", - " error = pvd.read_time_series(output[\"p\"][i], pts=pts)[\"pt0\"] - interp_ana_model\n", - " ax2.plot(pvd.timesteps, error / 1.e6, output[\"color\"][i], label=output[\"label\"][i])\n", - " assert np.all(error < 0.1 * 1e6)\n", - " assert np.all(error > -0.06 * 1e6)\n", - "\n", - "ax2.legend(loc='upper right')\n", - "\n", - "fig1.tight_layout()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", - "\n", - "ax1.plot(t, ana_model.u_i(pts[\"pt0\"][0],pts[\"pt0\"][1],pts[\"pt0\"][2],t, \"x\") * 1000, \"k\", label=\"analytical\")\n", - "for i, pvd in enumerate(pvds):\n", - " ax1.plot(pvd.timesteps, pvd.read_time_series(output[\"u\"][i], pts=pts)[\"pt0\"][:,0] * 1000, output[\"color\"][i], label=output[\"label\"][i])\n", - "ax1.set_xscale(\"log\")\n", - "ax1.set_xlabel(\"t / s\")\n", - "ax1.set_ylabel(\"$u_x$ / $10^{-3}$ m\")\n", - "ax1.set_xlim(1.e4, 2.e7)\n", - "ax1.legend(loc='lower right')\n", - "ax1.set_title(\"Displacement\")\n", - "\n", - "ax2.set_xscale(\"log\")\n", - "ax2.set_xlabel(\"t / s\")\n", - "ax2.set_ylabel(\"error / $10^{-3}$ m\")\n", - "ax2.set_xlim(1.e4, 2.e7)\n", - "ax2.set_title(\"Displacement error / numerical - analytical\") \n", - "\n", - "for i, pvd in enumerate(pvds):\n", - " interp_ana_model = np.interp(pvd.timesteps, t, ana_model.u_i(pts[\"pt0\"][0],pts[\"pt0\"][1],pts[\"pt0\"][2],t, \"x\"))\n", - " error = pvd.read_time_series(output[\"u\"][i], pts=pts)[\"pt0\"][:,0] - interp_ana_model\n", - " ax2.plot(pvd.timesteps, error * 1000, output[\"color\"][i], label=output[\"label\"][i])\n", - " assert np.all(error < 0.0005)\n", - " assert np.all(error > -0.0035) \n", - "\n", - "ax2.legend(loc='lower right') \n", - "\n", - "fig1.tight_layout()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plots for temperature, pressure and displacement along the radial axis\n", - "\n", - "The comparison between the analytical and the numerical results along the radial axis generally shows good agreement. The differences observed can be primarily explained by mesh discretization and finite size effects. This is particularly the case for the th2m simulation results, where the differences are slightly more emphasized which is the results of larger time steps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Time stamp for the results along the radial axis\n", - "t_i = 1.0e5\n", - "\n", - "# Radial coordinates for plotting\n", - "x = np.linspace(start=0.0001, stop=10.0, num=100)\n", - "r = [(i,0,0) for i in x]\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", - "\n", - "ax1.plot(x, ana_model.temperature(x,0,0,t_i), \"k\", label=\"analytical\")\n", - "for i, pvd in enumerate(pvds):\n", - " ax1.plot(x, pvd.read_set_data(t_i, output[\"T\"][i], pointsetarray=r, data_type=\"point\"), output[\"color\"][i], label=output[\"label\"][i])\n", - "\n", - "ax1.set_xlim(0, 2.0)\n", - "ax1.set_ylim(250.0, 400.0)\n", - "ax1.set_xlabel(\"r / m\")\n", - "ax1.set_ylabel(\"T / K\")\n", - "ax1.legend()\n", - "ax1.set_title(\"Temperature\")\n", - "\n", - "ax2.set_xlim(0, 2.0)\n", - "ax2.set_ylim(-3,1)\n", - "ax2.set_xlabel(\"r / m\")\n", - "ax2.set_ylabel(\"error / K\")\n", - "ax2.set_title(\"Temperature error / numerical - analytical\")\n", - "\n", - "for i, pvd in enumerate(pvds):\n", - " error = pvd.read_set_data(t_i, output[\"T\"][i], pointsetarray=r, data_type=\"point\")-ana_model.temperature(x,0,0,t_i)\n", - " ax2.plot(x, error, output[\"color\"][i], label=output[\"label\"][i])\n", - " assert np.all(error[1:] < 0.5) # do not check first entry, which corresponds to the origin\n", - " assert np.all(error[1:] > -2.5) # do not check first entry, which corresponds to the origin\n", - " \n", - "ax2.legend()\n", - "\n", - "fig1.tight_layout()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", - "\n", - "ax1.plot(x, ana_model.porepressure(x,0,0,t_i) / 1e6, \"k\", label=\"analytical\")\n", - "for i, pvd in enumerate(pvds):\n", - " ax1.plot(x, pvd.read_set_data(t_i, output[\"p\"][i], pointsetarray=r, data_type=\"point\") / 1.e6, output[\"color\"][i], label=output[\"label\"][i])\n", - "\n", - "ax1.set_xlim(0, 2.0)\n", - "ax1.set_ylim(0, 35.0)\n", - "ax1.set_xlabel(\"r / m\")\n", - "ax1.set_ylabel(\"p / MPa\")\n", - "ax1.legend()\n", - "ax1.set_title(\"Pressure\")\n", - "\n", - "ax2.set_xlim(0, 2.0)\n", - "ax2.set_xlabel(\"r / m\")\n", - "ax2.set_ylabel(\"error / MPa\")\n", - "ax2.set_title(\"Pressure error / numerical - analytical\")\n", - "\n", - "for i, pvd in enumerate(pvds):\n", - " error = (pvd.read_set_data(t_i, output[\"p\"][i], pointsetarray=r, data_type=\"point\")-ana_model.porepressure(x,0,0,t_i)) / 1.e6\n", - " ax2.plot(x, error, output[\"color\"][i], label=output[\"label\"][i])\n", - " assert np.all(error < 2.5)\n", - " assert np.all(error > -1.0)\n", - "\n", - "ax2.legend()\n", - "\n", - "fig1.tight_layout()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", - "\n", - "ax1.plot(x, ana_model.u_i(x,0,0,t_i, \"x\") * 1000, \"k\", label=\"analytical\")\n", - "for i, pvd in enumerate(pvds):\n", - " ax1.plot(x, pvd.read_set_data(t_i, output[\"u\"][i], pointsetarray=r, data_type=\"point\")[:,0] * 1000, output[\"color\"][i], label=output[\"label\"][i])\n", - "\n", - "ax1.set_xlim(0, 2.0)\n", - "ax1.set_xlabel(\"r / m\")\n", - "ax1.set_ylabel(\"$u_r$ / $10^{-3}$ m\")\n", - "ax1.legend()\n", - "ax1.set_title(\"Displacement\")\n", - "\n", - "ax2.set_xlim(0, 2.0)\n", - "ax2.set_ylim(-0.025, 0.025)\n", - "ax2.set_xlabel(\"r / m\")\n", - "ax2.set_ylabel(\"error / $10^{-3}$ m\")\n", - "ax2.set_title(\"Displacement error / numerical - analytical\")\n", - "\n", - "for i, pvd in enumerate(pvds):\n", - " error = (pvd.read_set_data(t_i, output[\"u\"][i], pointsetarray=r, data_type=\"point\")[:,0]-ana_model.u_i(x,0,0,t_i, \"x\")) * 1000\n", - " ax2.plot(x, error, output[\"color\"][i], label=output[\"label\"][i])\n", - " assert np.all(error[1:] < 0.01)\n", - " assert np.all(error[1:] > -0.015)\n", - "\n", - "ax2.legend()\n", - "\n", - "fig1.tight_layout()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Execution times\n", - "\n", - "To compare the performance of the different numerical solutions implemented in OGS6, we compare the execution time of the simulations. The linear thm and trm solutions perform best, while the quadratic thm and th2m solutions take significantly longer time to run. It is also important to mention here, that the time step size selected for the th2m solution are twice as big as the other 3 implementation, yet simulation time still takes longer than any of the other solution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure()\n", - "ax = fig.add_axes([0,0,1,1])\n", - "mesh = ['thm linear', 'thm quadratic', 'th2m', 'trm']\n", - "ax.bar(mesh,runtimes)\n", - "plt.ylabel(\"exec. time / s\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "[1] Booker, J. R.; Savvidou, C. (1985), Consolidation around a point heat source. International Journal for Numerical and Analytical Methods in Geomechanics, 1985, 9. Jg., Nr. 2, S. 173-184.\n", - "\n", - "[2] Chaudhry, A. A.; Buchwald, J.; Kolditz, O. and Nagel, T. (2019), Consolidation around a point heatsource (correction & verification). International Journal for Numerical and Analytical Methods in Geomechanics, 2019, ." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv-with-ogs", - "language": "python", - "name": "venv-with-ogs" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - }, - "vscode": { - "interpreter": { - "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "cells": [ + { + "cell_type": "raw", + "id": "29c811dd-80b4-4862-87cb-bedd5e9d27eb", + "metadata": {}, + "source": [ + "+++\n", + "author = \"Jörg Buchwald and Kata Kurgyis\"\n", + "date = \"2022-11-02\"\n", + "title = \"Point-Heatsource Problem\"\n", + "weight = 8\n", + "image = \"figures/placeholder_pointheatsource.png\"\n", + "web_subsection = \"th2m\"\n", + "coupling = \"thm\"\n", + "+++\n" + ] + }, + { + "cell_type": "markdown", + "id": "66328d44-327f-4944-9708-542eb3ffead2", + "metadata": { + "tags": [] + }, + "source": [ + "## Problem description\n", + "\n", + "The problem describes a heat source embedded in a fully fluid-saturated porous medium.\n", + "The spherical symmetry is modeled using a 10 m x 10 m disc with a point heat source ($Q=150\\;\\mathrm{W}$) placed at one corner ($r=0$) and a curved boundary at $r=10\\;\\mathrm{m}$. Applying rotational axial symmetry at one of the linear boundaries, the model region transforms into a half-space configuration of the spherical symmetrical problemcorresponding to the analytical solution.\n", + "The initial temperature and the excess pore pressure are 273.15 K and 0 Pa, respectively.\n", + "The axis-normal displacements along the symmetry (inner) boundaries were set to zero, whereas the excess pore pressure, as well as the temperature, are set to their initial values along the outer (curved) boundary.\n", + "The heat coming from the point source is propagated through the medium, causing the fluid and the solid to expand at different rates. The resulting pore pressure (gradient) is triggering a thermally driven consolidation process caused by the fluid flowing away from the heat source until equilibrium is reached.\n", + "\n", + "![PointHeatSourceSchematic.png](figures/PointHeatSourceSchematic.png)" + ] + }, + { + "cell_type": "markdown", + "id": "abac9b6b-055f-4912-a72b-db6fc6e4af24", + "metadata": {}, + "source": [ + "# Governing equations\n", + "\n", + "For this problem we consider the following assumptions:\n", + "\n", + "* No thermal adverction is considered: $\\rho_\\text{w}c_\\text{w}T_{,i} v_i = 0$.\n", + "\n", + "* Gravitational forces are neglected: $\\rho g = 0$.\n", + "\n", + "* Both fluid and solid phases are intrinsically incompressible: $\\alpha_B = 1$; $\\beta = 0$.\n", + "\n", + "* No external fluid sink or source term: $q_H = 0$.\n", + "\n", + "* The porous medium is isotropic and homogeneous.\n", + "\n", + "These assumptions lead to the following set of governing equation describing the system behavior:\n", + "\n", + "**Energy balance**\n", + "\n", + "$$\n", + "\\begin{gather}\n", + " m \\dot T - (K T_{,i})_{,i} = q_T \n", + "%\n", + "\\\\\n", + "%\n", + " \\text{where}\\nonumber\n", + "%\n", + "\\\\\n", + "%\n", + " m = \\phi \\rho_w c_w + (1-\\phi) \\rho_s c_s\n", + "%\n", + "\\\\\n", + "%\n", + " K = \\phi K_w + (1 - \\phi) K_s\n", + "%\n", + "\\\\\n", + "%\n", + " v_i = -\\dfrac{k_s}{\\eta} (p_{,i})\n", + "\\end{gather}\n", + "$$\n", + "\n", + "**Mass balance**\n", + "\n", + "$$\n", + "\\begin{gather}\n", + " - a_u \\dot T+ \\dot u_{i,i} + v_{i,i} = 0\n", + "%\n", + "\\\\\n", + "%\n", + " \\text{where}\\nonumber\n", + "%\n", + "\\\\\n", + "%\n", + " a_u = \\phi a_w + (1-\\phi) a_s\n", + "\\end{gather}\n", + "$$\n", + "\n", + "**Momentum balance**\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\sigma_{ij} = \\sigma^\\prime_{ij} - p \\delta_{ij} = 0\n", + "\\end{equation}\n", + "$$\n", + "\n", + "A detailed description about the problem formulation and equation derivation can be found in the original work of Booker and Savvidou (1985) or Chaudhry et al. (2019).\n", + "\n", + "## Input parameters\n", + "\n", + "We considered the following set of values as input parameters:\n", + "\n", + "![PointHeatSourceInput.png](figures/PointHeatSourceInput.png)\n" + ] + }, + { + "cell_type": "markdown", + "id": "28a4808d-5e03-4f0c-9ee2-53e8f6de9aee", + "metadata": {}, + "source": [ + "# The analytical solution\n", + "\n", + "\n", + "The analytical solution of the coupled THM consolidation problem is derived in the original work of Booker and Savvidou (1985). In Chaudhry et al. (2019), a corrected solution is given for the effective stress term.\n", + "\n", + "For clarification, the equations below are based on the solid mechanics sign convention (tensile stress is positive). Furthermore, temporal partial derivative is indicated by the dot convention, while spatial partial derivatives are expressed by the comma convention, i.e. $(\\cdot)_{,i}=\\partial (\\cdot)/\\partial x_i$.\n", + "\n", + "The analytical solution for the three primary variables are expressed as:\n", + "\n", + "**Temperature**\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\Delta T = \\dfrac{Q}{4 \\pi K r} f^{\\kappa}\n", + "\\end{equation}\n", + "$$\n", + "\n", + "**Pore pressure**\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " p = \\dfrac{X Q}{(1 - \\frac{c}{\\kappa}) 4 \\pi K r} (f^{\\kappa}-f^{c})\n", + "\\end{equation}\n", + "$$\n", + "\n", + "**Displacement of the solid skeleton**\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " u_{i} = \\dfrac{Q a_u x_i}{4 \\pi K r} g^{\\ast}\n", + "\\end{equation}\n", + "$$\n", + "\n", + "In the above equations, the following derived parameters are used:\n", + "\n", + "$$\n", + "\\begin{align}\n", + " \\kappa &= \\dfrac{K}{m}\n", + "%\n", + "\\\\\n", + "%\n", + " c &= \\dfrac{k_s}{\\eta}(\\lambda + 2G)\n", + "%\n", + "\\\\\n", + "%\n", + " r &= \\sqrt{x_{1}^{2}+x_{2}^{2}+x_{3}^{2}}\n", + "%\n", + "\\\\\n", + "%\n", + " X &= a_\\text{u}\\left(\\lambda+2G\\right)-b^{\\prime}\n", + "%\n", + "\\\\\n", + "%\n", + " Y &= \\dfrac{1}{\\lambda+2G}\\left(\\dfrac{X}{\\left(1-\\dfrac{c}{\\kappa}\\right)a_\\text{u}}+\\dfrac{b^{\\prime}}{a_\\text{u}}\\right)\n", + "%\n", + "\\\\\n", + "%\n", + " Z &= \\dfrac{1}{\\lambda+2G}\\left(\\dfrac{X}{\\left(1-\\dfrac{c}{\\kappa}\\right)a_\\text{u}}\\right)\n", + "%\n", + "\\\\\n", + "%\n", + " f^{A} &= \\text{erfc}\\left(\\dfrac{r}{2\\sqrt{At}}\\right),\\quad A=\\kappa,c\n", + "%\n", + "\\\\\n", + "%\n", + " g^{A} &= \\dfrac{At}{r^{2}}+\\left(\\frac{1}{2}-\\dfrac{At}{r^{2}}\\right)f^{A}-\\sqrt{\\dfrac{At}{\\pi r^{2}}} \\exp\\left(-\\dfrac{r^{2}}{4At}\\right)\n", + "%\n", + "\\\\\n", + "%\n", + " g^{\\ast} &= Yg^{\\kappa}-Zg^{c}\n", + "%\n", + "\\\\\n", + "%\n", + " g^{A}_{,i} &= \\frac{2x_{i}At}{r^{4}}\\left(f^{A}-1+\\frac{r}{\\sqrt{\\pi At}}\\exp\\left(-\\frac{r^{2}}{4At}\\right)\\right),\\quad i=1,2,3\n", + "%\n", + "\\\\\n", + "%\n", + " g^{\\ast}_{,i} &= Yg^{\\kappa}_{,i}-Zg^{c}_{,i}\n", + "\\end{align}\n", + "$$\n", + "\n", + "The corrected form of the effective stress:\n", + "\n", + "$$\n", + "\\begin{align}\n", + " \\sigma^{\\prime}_{ij|j=i} &= \\frac{Q a_\\text{u}}{4\\pi Kr}\\left( 2G\\left[g^{\\ast}\\left(1-\\frac{x^{2}_{i}}{r^{2}}\\right)+x_{i}g^{\\ast}_{,i}\\right]+\\lambda \\left[x_{i}g^{\\ast}_{,i}+2g^{\\ast}\\right]\\right)-b^{\\prime}\\Delta T\n", + "%\n", + "\\\\\n", + "%\n", + " \\sigma^\\prime_{ij|j \\neq i} &= \\frac{Q a_\\text{u}}{4\\pi Kr}\\left( G\\left[x_{i}g^{\\ast}_{,j}+x_{j}g^{\\ast}_{,i}-2g^{\\ast}\\dfrac{x_{i}x_{j}}{r^{2}}\\right]\\right)\n", + "\\end{align}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a18b24e-7473-4f28-b429-f164d87d6d5f", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy import special as sp\n", + "\n", + "\n", + "class ANASOL:\n", + " def __init__(self):\n", + " # material parameters\n", + " self.phi = 0.16 # porosity of soil\n", + " self.k = 2e-20 # coefficient of permeability\n", + " self.eta = 1e-3 # viscosity water at 20 deg\n", + " self.E = 5.0e9 # Youngs modulus\n", + " self.nu = 0.3 # Poisson ratio\n", + " self.rho_w = 999.1 # density of pore water\n", + " self.c_w = 4280 # specific heat of pore water\n", + " self.K_w = 0.6 # thermal conductivity of pore water\n", + " self.rho_s = 2290.0 # density of solid matrix\n", + " self.c_s = 917.654 # specific heat capacity of solid matrix\n", + " self.K_s = 1.838 # themal conductivity of solid matrix\n", + " self.a_s = (\n", + " 3 * 1.5e-5\n", + " ) # volumetric expansivity of matrix - value conversion from linear to volumetric expansivity\n", + " self.a_w = 4.0e-4 # coefficient of volume expansion of pore water (beta_w)\n", + "\n", + " # initial and boundary condition\n", + " self.Q = (\n", + " 2 * 150\n", + " ) # [Q]=W strength of the heat source - value corrected to account for domain size\n", + " self.T0 = 273.15 # initial temperature\n", + "\n", + " self.Init()\n", + "\n", + " # derived parameters\n", + " def f(self, ka, R, t):\n", + " return sp.erfc(R / (2 * np.sqrt(ka * t)))\n", + "\n", + " def g(self, ka, R, t):\n", + " return (\n", + " ka * t / R**2\n", + " + (1 / 2 - ka * t / R**2) * sp.erfc(R / (2 * np.sqrt(ka * t)))\n", + " - np.sqrt(ka * t / (np.pi * R**2)) * np.exp(-(R**2) / (4 * ka * t))\n", + " )\n", + "\n", + " def gstar(self, R, t):\n", + " return self.Y * self.g(self.kappa, R, t) - self.Z * self.g(self.c, R, t)\n", + "\n", + " def R(self, x, y, z):\n", + " return np.sqrt(x**2 + y**2 + z**2)\n", + "\n", + " def dg_dR(self, ka, i, R, t):\n", + " return (2 * i / R**3) * np.sqrt(ka * t / np.pi) * np.exp(\n", + " -R * R / (4 * ka * t)\n", + " ) + (2 * i * ka * t / R**4) * (self.f(ka, R, t) - 1)\n", + "\n", + " def dgstar_dR(self, i, R, t): # Subscript R means derivative w.r.t R\n", + " return self.Y * self.dg_dR(self.kappa, i, R, t) - self.Z * self.dg_dR(\n", + " self.c, i, R, t\n", + " )\n", + "\n", + " # corrected form of effective stress\n", + " def sigma_ii(self, x, y, z, t, ii): # for normal components\n", + " R = self.R(x, y, z)\n", + " index = {\"xx\": x, \"yy\": y, \"zz\": z}\n", + " return (self.Q * self.a_u / (4 * np.pi * self.K * R)) * (\n", + " 2\n", + " * self.G\n", + " * (\n", + " self.gstar(R, t) * (1 - index[ii] ** 2 / R**2)\n", + " + index[ii] * self.dgstar_dR(index[ii], R, t)\n", + " )\n", + " + self.lambd\n", + " * (\n", + " x * self.dgstar_dR(x, R, t)\n", + " + y * self.dgstar_dR(y, R, t)\n", + " + z * self.dgstar_dR(z, R, t)\n", + " + 2 * self.gstar(R, t)\n", + " )\n", + " ) - self.bprime * (self.temperature(x, y, z, t) - self.T0)\n", + "\n", + " def sigma_ij(self, x, y, z, t, i, j): # for shear components\n", + " R = self.R(x, y, z)\n", + " index = {\"x\": x, \"y\": y, \"z\": z}\n", + " return (self.Q * self.a_u / (4 * np.pi * self.K * R)) * (\n", + " 2\n", + " * self.G\n", + " * (\n", + " index[i] * self.dgstar_dR(index[j], R, t) / 2\n", + " + index[j] * self.dgstar_dR(index[i], R, t) / 2\n", + " - index[i] * index[j] * self.gstar(R, t) / R**2\n", + " )\n", + " )\n", + "\n", + " # primary variables\n", + " def temperature(self, x, y, z, t):\n", + " R = self.R(x, y, z)\n", + " return self.Q / (4 * np.pi * self.K * R) * self.f(self.kappa, R, t) + self.T0\n", + "\n", + " def porepressure(self, x, y, z, t):\n", + " R = self.R(x, y, z)\n", + " return (\n", + " self.X\n", + " / (1 - self.c / self.kappa)\n", + " * self.Q\n", + " / (4 * np.pi * self.K * R)\n", + " * (self.f(self.kappa, R, t) - self.f(self.c, R, t))\n", + " )\n", + "\n", + " def u_i(self, x, y, z, t, i):\n", + " R = self.R(x, y, z)\n", + " index = {\"x\": x, \"y\": y, \"z\": z}\n", + " return (\n", + " self.a_u * index[i] * self.Q / (4 * np.pi * self.K * R) * self.gstar(R, t)\n", + " )\n", + "\n", + " def Init(self):\n", + " # derived constants\n", + " self.lambd = (\n", + " self.E * self.nu / ((1 + self.nu) * (1 - 2 * self.nu))\n", + " ) # Lame constant\n", + " self.G = self.E / (2 * (1 + self.nu)) # shear constant\n", + "\n", + " self.K = (\n", + " self.phi * self.K_w + (1 - self.phi) * self.K_s\n", + " ) # average thermal conductivity\n", + " self.m = (\n", + " self.phi * self.rho_w * self.c_w + (1 - self.phi) * self.rho_s * self.c_s\n", + " )\n", + " self.kappa = self.K / self.m # scaled heat conductivity\n", + " self.c = self.k / self.eta * (self.lambd + 2 * self.G)\n", + "\n", + " self.aprime = self.a_s\n", + " self.a_u = self.a_s * (1 - self.phi) + self.a_w * self.phi\n", + " self.bprime = (self.lambd + 2 * self.G / 3) * self.aprime\n", + "\n", + " self.X = self.a_u * (self.lambd + 2 * self.G) - self.bprime\n", + " self.Y = (\n", + " 1\n", + " / (self.lambd + 2 * self.G)\n", + " * (self.X / ((1 - self.c / self.kappa) * self.a_u) + self.bprime / self.a_u)\n", + " )\n", + " self.Z = (\n", + " 1\n", + " / (self.lambd + 2 * self.G)\n", + " * (self.X / ((1 - self.c / self.kappa) * self.a_u))\n", + " )\n", + "\n", + "\n", + "ana_model = ANASOL()" + ] + }, + { + "cell_type": "markdown", + "id": "f0085f93-7652-4f01-9c49-b640166ec293", + "metadata": {}, + "source": [ + "\n", + "## The numerical solutions\n", + "\n", + "For the numerical solution we compare the Thermal-Hydro-Mechanical (THM - linear and quadratic mesh), Thermal-2-Phase-Hydro-Mechanical (TH2M) and Thermal-Richard-Mechanical (TRM - quadratic mesh) formulation of OGS. \n", + "\n", + "The TH2M and TRM formulation methods have essential differences when applied to an unsaturated media where a gas phase is also present along side the aqueous phase. The difference originates from the way how the two mobile phases are treated specifically in the equation system: in the TH2M formulation, both the gas phase and the liquid phase is explicitely present and each phase is comprised of the two distinct component of aqueous component and non-aqueous component. In this case, the gas phase has a variable pressure solved explicitely in the governing equations. On the other hand, the TRM model assumes that the gas phase mobility is high and fast enough that gas drainage can occur significantly faster than the other processes in the system and hence, gas pressure doesn't build up. This leads to the simplification, that no gas pressure is calculated in the TRM model explicitely.\n", + "\n", + "The THM model is a simplified form of the general TH2M model, where there is no gas phase, only the aqueous phase is present in the equation system.\n", + "\n", + "In addition to the different formulation, we also compare the performance of the THM formulation with a linear and a quadratic mesh as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b55bd789-bf43-4e71-8606-b7b7c8b45e2d", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from ogs6py import ogs\n", + "\n", + "data_dir = os.environ.get(\"OGS_DATA_DIR\", \"../../..\")\n", + "out_dir = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n", + "if not os.path.exists(out_dir):\n", + " os.makedirs(out_dir)\n", + "\n", + "# THM formulation (current working dir)\n", + "prj_file_lin = \"pointheatsource_linear-mesh.prj\"\n", + "prj_file_quad = \"pointheatsource_quadratic-mesh.prj\"\n", + "ogs_model_lin = ogs.OGS(\n", + " INPUT_FILE=prj_file_lin, PROJECT_FILE=f\"{out_dir}/{prj_file_lin}\"\n", + ")\n", + "ogs_model_quad = ogs.OGS(\n", + " INPUT_FILE=prj_file_quad, PROJECT_FILE=f\"{out_dir}/{prj_file_quad}\"\n", + ")\n", + "\n", + "# TH2M formulation\n", + "prj_file_th2m = \"point_heatsource.prj\"\n", + "path_th2m = f\"{data_dir}/TH2M/THM/sphere\"\n", + "prj_filepath_th2m = f\"{path_th2m}/{prj_file_th2m}\"\n", + "ogs_model_th2m = ogs.OGS(\n", + " INPUT_FILE=prj_filepath_th2m, PROJECT_FILE=f\"{out_dir}/pointheatsource_th2m.prj\"\n", + ")\n", + "\n", + "# TRM formulation\n", + "prj_file_trm = \"point_heat_source_2D.prj\"\n", + "path_trm = f\"{data_dir}/ThermoRichardsMechanics/PointHeatSource\"\n", + "prj_filepath_trm = f\"{path_trm}/{prj_file_trm}\"\n", + "ogs_model_trm = ogs.OGS(\n", + " INPUT_FILE=prj_filepath_trm, PROJECT_FILE=f\"{out_dir}/pointheatsource_trm.prj\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "594713b0-e0c7-4132-af4b-22429953d292", + "metadata": {}, + "outputs": [], + "source": [ + "# Simulation time\n", + "t_end = 2e6 # <= was originally 5e6\n", + "ogs_model_lin.set(t_end=t_end)\n", + "ogs_model_quad.set(t_end=t_end)\n", + "ogs_model_th2m.set(t_end=t_end)\n", + "ogs_model_trm.set(t_end=t_end)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b64222d9-f5b1-46b7-a9cf-5458a570a91c", + "metadata": {}, + "outputs": [], + "source": [ + "ogs_model_lin.set(output_prefix=\"pointheatsource_lin\")\n", + "ogs_model_quad.set(output_prefix=\"pointheatsource_quad\")\n", + "ogs_model_th2m.set(output_prefix=\"pointheatsource_th2m\")\n", + "ogs_model_th2m.replace_text(\n", + " \"150\", xpath=\"./parameters/parameter[name='temperature_source_term']/value\"\n", + ")\n", + "ogs_model_trm.set(output_prefix=\"pointheatsource_trm\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a37f6676-b842-443a-ac3d-710762d67a35", + "metadata": {}, + "outputs": [], + "source": [ + "ogs_model_lin.write_input()\n", + "ogs_model_quad.write_input()\n", + "ogs_model_th2m.write_input()\n", + "ogs_model_trm.write_input()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d45317d-2403-40f6-b089-009519bfc3e4", + "metadata": {}, + "outputs": [], + "source": [ + "import concurrent.futures\n", + "from timeit import default_timer as timer\n", + "\n", + "# Run models in parallel via concurrent.futures\n", + "ogs_models = []\n", + "ogs_models.append(\n", + " {\n", + " \"model\": ogs_model_lin.prjfile,\n", + " \"logfile\": f\"{out_dir}/lin-out.txt\",\n", + " \"args\": f\"-o {out_dir} -m . -s .\",\n", + " }\n", + ")\n", + "ogs_models.append(\n", + " {\n", + " \"model\": ogs_model_quad.prjfile,\n", + " \"logfile\": f\"{out_dir}/quad-out.txt\",\n", + " \"args\": f\"-o {out_dir} -m . -s .\",\n", + " }\n", + ")\n", + "ogs_models.append(\n", + " {\n", + " \"model\": ogs_model_th2m.prjfile,\n", + " \"logfile\": f\"{out_dir}/th2m-out.txt\",\n", + " \"args\": f\"-o {out_dir} -m {path_th2m} -s {path_th2m}\",\n", + " }\n", + ")\n", + "ogs_models.append(\n", + " {\n", + " \"model\": ogs_model_trm.prjfile,\n", + " \"logfile\": f\"{out_dir}/trm-out.txt\",\n", + " \"args\": f\"-o {out_dir} -m {path_trm} -s {path_trm}\",\n", + " }\n", + ")\n", + "\n", + "\n", + "def run_ogs(model):\n", + " prj = model[\"model\"]\n", + " print(f\"Starting {prj} ...\\n\")\n", + " start_sim = timer()\n", + " # Starting via ogs6py does not work (\"cannot pickle lxml\"), at least on mac.\n", + " ! ogs {prj} {model[\"args\"]} > {model[\"logfile\"]}\n", + " assert _exit_code == 0\n", + " runtime = timer() - start_sim\n", + " return [f\"Finished {prj} in {runtime} s\", runtime]\n", + "\n", + "\n", + "import platform\n", + "\n", + "if platform.system() == \"Darwin\":\n", + " import multiprocessing as mp\n", + "\n", + " mp.set_start_method(\"fork\")\n", + "\n", + "runtimes = []\n", + "start = timer()\n", + "with concurrent.futures.ProcessPoolExecutor() as executor:\n", + " results = executor.map(run_ogs, ogs_models)\n", + " for result in results:\n", + " print(result[0])\n", + " runtimes.append(result[1])\n", + "print(f\"Elapsed time for all simulations: {timer() - start} s\")" + ] + }, + { + "cell_type": "markdown", + "id": "c13229f5-e43b-4b43-b509-713c1301b8b1", + "metadata": {}, + "source": [ + "## Evaluation and Results\n", + "\n", + "The analytical expressions together with the numerical model can now be evaluated at different points as a function of time (time series) or for a given time as a function of their spatial coordinates (along radial axis)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcb4fb45-f204-4f39-9f74-65fce7287b7c", + "metadata": {}, + "outputs": [], + "source": [ + "import vtuIO\n", + "\n", + "# Point of interest\n", + "pts = {\"pt0\": (0.5, 0.5, 0.0)}\n", + "\n", + "# Time axis for analytical solution\n", + "t = np.linspace(1, 50000 * 200, num=201, endpoint=True)\n", + "\n", + "projects = [\n", + " \"pointheatsource_lin\",\n", + " \"pointheatsource_quad\",\n", + " \"pointheatsource_th2m\",\n", + " \"pointheatsource_trm\",\n", + "]\n", + "\n", + "pvds = []\n", + "for i, prj in enumerate(projects):\n", + " pvds.append(vtuIO.PVDIO(f\"{out_dir}/{prj}.pvd\", dim=2))" + ] + }, + { + "cell_type": "markdown", + "id": "4ad0e771-7ee3-4995-84cc-0cb3ac02a5b8", + "metadata": {}, + "source": [ + "### Time series plots for temperature, pressure and displacement\n", + "\n", + "Comparison between the analytical solution and the numerical solution shows very good agreement, as displayed below in the figures." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d975df4-2024-427f-b766-92650c7d8e90", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.rcParams[\"lines.linewidth\"] = 2.0\n", + "plt.rcParams[\"lines.color\"] = \"black\"\n", + "plt.rcParams[\"legend.frameon\"] = True\n", + "plt.rcParams[\"font.family\"] = \"serif\"\n", + "plt.rcParams[\"legend.fontsize\"] = 14\n", + "plt.rcParams[\"font.size\"] = 14\n", + "plt.rcParams[\"axes.axisbelow\"] = True\n", + "plt.rcParams[\"figure.figsize\"] = (16, 6)\n", + "\n", + "output = {\n", + " \"T\": (\n", + " \"temperature\",\n", + " \"temperature_interpolated\",\n", + " \"temperature_interpolated\",\n", + " \"temperature_interpolated\",\n", + " ),\n", + " \"p\": (\n", + " \"pressure\",\n", + " \"pressure_interpolated\",\n", + " \"gas_pressure_interpolated\",\n", + " \"pressure_interpolated\",\n", + " ),\n", + " \"u\": (\"displacement\", \"displacement\", \"displacement\", \"displacement\"),\n", + " \"color\": (\"r+\", \"rx\", \"b+\", \"g+\"),\n", + " \"label\": (\"ogs6 thm lin\", \"ogs6 thm quad\", \"ogs6 th2m\", \"ogs6 trm\"),\n", + "}\n", + "\n", + "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", + "\n", + "ax1.plot(\n", + " t,\n", + " ana_model.temperature(pts[\"pt0\"][0], pts[\"pt0\"][1], pts[\"pt0\"][2], t),\n", + " \"k\",\n", + " label=\"analytical\",\n", + ")\n", + "for i, pvd in enumerate(pvds):\n", + " ax1.plot(\n", + " pvd.timesteps,\n", + " pvd.read_time_series(output[\"T\"][i], pts=pts)[\"pt0\"],\n", + " output[\"color\"][i],\n", + " label=output[\"label\"][i],\n", + " )\n", + "ax1.set_xscale(\"log\")\n", + "ax1.set_xlabel(\"t / s\")\n", + "ax1.set_ylabel(\"T / K\")\n", + "ax1.set_xlim(1.0e4, 2.0e7)\n", + "ax1.set_ylim(270.0, 292.0)\n", + "ax1.legend(loc=\"lower right\")\n", + "ax1.set_title(\"Temperature\")\n", + "\n", + "ax2.set_xscale(\"log\")\n", + "ax2.set_xlabel(\"t / s\")\n", + "ax2.set_ylabel(\"error / K\")\n", + "ax2.set_xlim(1.0e4, 2.0e7)\n", + "ax2.set_title(\"Temperature error / numerical - analytical\")\n", + "\n", + "for i, pvd in enumerate(pvds):\n", + " interp_ana_model = np.interp(\n", + " pvd.timesteps,\n", + " t,\n", + " ana_model.temperature(pts[\"pt0\"][0], pts[\"pt0\"][1], pts[\"pt0\"][2], t),\n", + " )\n", + " error = pvd.read_time_series(output[\"T\"][i], pts=pts)[\"pt0\"] - interp_ana_model\n", + " ax2.plot(pvd.timesteps, error, output[\"color\"][i], label=output[\"label\"][i])\n", + " assert np.all(error < 0.2)\n", + " assert np.all(error > -0.06)\n", + "\n", + "ax2.legend(loc=\"upper right\")\n", + "\n", + "fig1.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61dd4513-d2d8-4742-bad5-2a0522f454a3", + "metadata": {}, + "outputs": [], + "source": [ + "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", + "\n", + "ax1.plot(\n", + " t,\n", + " ana_model.porepressure(pts[\"pt0\"][0], pts[\"pt0\"][1], pts[\"pt0\"][2], t) / 1.0e6,\n", + " \"k\",\n", + " label=\"analytical\",\n", + ")\n", + "for i, pvd in enumerate(pvds):\n", + " ax1.plot(\n", + " pvd.timesteps,\n", + " pvd.read_time_series(output[\"p\"][i], pts=pts)[\"pt0\"] / 1.0e6,\n", + " output[\"color\"][i],\n", + " label=output[\"label\"][i],\n", + " )\n", + "ax1.set_xscale(\"log\")\n", + "ax1.set_xlabel(\"t / s\")\n", + "ax1.set_ylabel(\"p / MPa\")\n", + "ax1.set_xlim(1.0e4, 2.0e7)\n", + "ax1.legend(loc=\"lower right\")\n", + "ax1.set_title(\"Pressure\")\n", + "\n", + "ax2.set_xscale(\"log\")\n", + "ax2.set_xlabel(\"t / s\")\n", + "ax2.set_ylabel(\"error / MPa\")\n", + "ax2.set_xlim(1.0e4, 2.0e7)\n", + "ax2.set_title(\"Pressure error / numerical - analytical\")\n", + "\n", + "for i, pvd in enumerate(pvds):\n", + " interp_ana_model = np.interp(\n", + " pvd.timesteps,\n", + " t,\n", + " ana_model.porepressure(pts[\"pt0\"][0], pts[\"pt0\"][1], pts[\"pt0\"][2], t),\n", + " )\n", + " error = pvd.read_time_series(output[\"p\"][i], pts=pts)[\"pt0\"] - interp_ana_model\n", + " ax2.plot(pvd.timesteps, error / 1.0e6, output[\"color\"][i], label=output[\"label\"][i])\n", + " assert np.all(error < 0.1 * 1e6)\n", + " assert np.all(error > -0.06 * 1e6)\n", + "\n", + "ax2.legend(loc=\"upper right\")\n", + "\n", + "fig1.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f14c814c-5f20-4ee7-b1e3-a48000de9274", + "metadata": {}, + "outputs": [], + "source": [ + "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", + "\n", + "ax1.plot(\n", + " t,\n", + " ana_model.u_i(pts[\"pt0\"][0], pts[\"pt0\"][1], pts[\"pt0\"][2], t, \"x\") * 1000,\n", + " \"k\",\n", + " label=\"analytical\",\n", + ")\n", + "for i, pvd in enumerate(pvds):\n", + " ax1.plot(\n", + " pvd.timesteps,\n", + " pvd.read_time_series(output[\"u\"][i], pts=pts)[\"pt0\"][:, 0] * 1000,\n", + " output[\"color\"][i],\n", + " label=output[\"label\"][i],\n", + " )\n", + "ax1.set_xscale(\"log\")\n", + "ax1.set_xlabel(\"t / s\")\n", + "ax1.set_ylabel(\"$u_x$ / $10^{-3}$ m\")\n", + "ax1.set_xlim(1.0e4, 2.0e7)\n", + "ax1.legend(loc=\"lower right\")\n", + "ax1.set_title(\"Displacement\")\n", + "\n", + "ax2.set_xscale(\"log\")\n", + "ax2.set_xlabel(\"t / s\")\n", + "ax2.set_ylabel(\"error / $10^{-3}$ m\")\n", + "ax2.set_xlim(1.0e4, 2.0e7)\n", + "ax2.set_title(\"Displacement error / numerical - analytical\")\n", + "\n", + "for i, pvd in enumerate(pvds):\n", + " interp_ana_model = np.interp(\n", + " pvd.timesteps,\n", + " t,\n", + " ana_model.u_i(pts[\"pt0\"][0], pts[\"pt0\"][1], pts[\"pt0\"][2], t, \"x\"),\n", + " )\n", + " error = (\n", + " pvd.read_time_series(output[\"u\"][i], pts=pts)[\"pt0\"][:, 0] - interp_ana_model\n", + " )\n", + " ax2.plot(pvd.timesteps, error * 1000, output[\"color\"][i], label=output[\"label\"][i])\n", + " assert np.all(error < 0.0005)\n", + " assert np.all(error > -0.0035)\n", + "\n", + "ax2.legend(loc=\"lower right\")\n", + "\n", + "fig1.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "8bf37faa-b835-412b-bf8a-54f5d17a4c43", + "metadata": {}, + "source": [ + "### Plots for temperature, pressure and displacement along the radial axis\n", + "\n", + "The comparison between the analytical and the numerical results along the radial axis generally shows good agreement. The differences observed can be primarily explained by mesh discretization and finite size effects. This is particularly the case for the th2m simulation results, where the differences are slightly more emphasized which is the results of larger time steps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8686c6fe-cf0c-4f20-b6b2-d52af5c51fc6", + "metadata": {}, + "outputs": [], + "source": [ + "# Time stamp for the results along the radial axis\n", + "t_i = 1.0e5\n", + "\n", + "# Radial coordinates for plotting\n", + "x = np.linspace(start=0.0001, stop=10.0, num=100)\n", + "r = [(i, 0, 0) for i in x]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0c2e185-3c90-49ba-808f-dfce8fe287ea", + "metadata": {}, + "outputs": [], + "source": [ + "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", + "\n", + "ax1.plot(x, ana_model.temperature(x, 0, 0, t_i), \"k\", label=\"analytical\")\n", + "for i, pvd in enumerate(pvds):\n", + " ax1.plot(\n", + " x,\n", + " pvd.read_set_data(t_i, output[\"T\"][i], pointsetarray=r, data_type=\"point\"),\n", + " output[\"color\"][i],\n", + " label=output[\"label\"][i],\n", + " )\n", + "\n", + "ax1.set_xlim(0, 2.0)\n", + "ax1.set_ylim(250.0, 400.0)\n", + "ax1.set_xlabel(\"r / m\")\n", + "ax1.set_ylabel(\"T / K\")\n", + "ax1.legend()\n", + "ax1.set_title(\"Temperature\")\n", + "\n", + "ax2.set_xlim(0, 2.0)\n", + "ax2.set_ylim(-3, 1)\n", + "ax2.set_xlabel(\"r / m\")\n", + "ax2.set_ylabel(\"error / K\")\n", + "ax2.set_title(\"Temperature error / numerical - analytical\")\n", + "\n", + "for i, pvd in enumerate(pvds):\n", + " error = pvd.read_set_data(\n", + " t_i, output[\"T\"][i], pointsetarray=r, data_type=\"point\"\n", + " ) - ana_model.temperature(x, 0, 0, t_i)\n", + " ax2.plot(x, error, output[\"color\"][i], label=output[\"label\"][i])\n", + " assert np.all(\n", + " error[1:] < 0.5\n", + " ) # do not check first entry, which corresponds to the origin\n", + " assert np.all(\n", + " error[1:] > -2.5\n", + " ) # do not check first entry, which corresponds to the origin\n", + "\n", + "ax2.legend()\n", + "\n", + "fig1.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "131056c0-aa72-4129-bf91-f1c94458890a", + "metadata": {}, + "outputs": [], + "source": [ + "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", + "\n", + "ax1.plot(x, ana_model.porepressure(x, 0, 0, t_i) / 1e6, \"k\", label=\"analytical\")\n", + "for i, pvd in enumerate(pvds):\n", + " ax1.plot(\n", + " x,\n", + " pvd.read_set_data(t_i, output[\"p\"][i], pointsetarray=r, data_type=\"point\")\n", + " / 1.0e6,\n", + " output[\"color\"][i],\n", + " label=output[\"label\"][i],\n", + " )\n", + "\n", + "ax1.set_xlim(0, 2.0)\n", + "ax1.set_ylim(0, 35.0)\n", + "ax1.set_xlabel(\"r / m\")\n", + "ax1.set_ylabel(\"p / MPa\")\n", + "ax1.legend()\n", + "ax1.set_title(\"Pressure\")\n", + "\n", + "ax2.set_xlim(0, 2.0)\n", + "ax2.set_xlabel(\"r / m\")\n", + "ax2.set_ylabel(\"error / MPa\")\n", + "ax2.set_title(\"Pressure error / numerical - analytical\")\n", + "\n", + "for i, pvd in enumerate(pvds):\n", + " error = (\n", + " pvd.read_set_data(t_i, output[\"p\"][i], pointsetarray=r, data_type=\"point\")\n", + " - ana_model.porepressure(x, 0, 0, t_i)\n", + " ) / 1.0e6\n", + " ax2.plot(x, error, output[\"color\"][i], label=output[\"label\"][i])\n", + " assert np.all(error < 2.5)\n", + " assert np.all(error > -1.0)\n", + "\n", + "ax2.legend()\n", + "\n", + "fig1.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0969741-b613-44a5-b2d9-7fc623f2dcb4", + "metadata": {}, + "outputs": [], + "source": [ + "fig1, (ax1, ax2) = plt.subplots(1, 2)\n", + "\n", + "ax1.plot(x, ana_model.u_i(x, 0, 0, t_i, \"x\") * 1000, \"k\", label=\"analytical\")\n", + "for i, pvd in enumerate(pvds):\n", + " ax1.plot(\n", + " x,\n", + " pvd.read_set_data(t_i, output[\"u\"][i], pointsetarray=r, data_type=\"point\")[:, 0]\n", + " * 1000,\n", + " output[\"color\"][i],\n", + " label=output[\"label\"][i],\n", + " )\n", + "\n", + "ax1.set_xlim(0, 2.0)\n", + "ax1.set_xlabel(\"r / m\")\n", + "ax1.set_ylabel(\"$u_r$ / $10^{-3}$ m\")\n", + "ax1.legend()\n", + "ax1.set_title(\"Displacement\")\n", + "\n", + "ax2.set_xlim(0, 2.0)\n", + "ax2.set_ylim(-0.025, 0.025)\n", + "ax2.set_xlabel(\"r / m\")\n", + "ax2.set_ylabel(\"error / $10^{-3}$ m\")\n", + "ax2.set_title(\"Displacement error / numerical - analytical\")\n", + "\n", + "for i, pvd in enumerate(pvds):\n", + " error = (\n", + " pvd.read_set_data(t_i, output[\"u\"][i], pointsetarray=r, data_type=\"point\")[:, 0]\n", + " - ana_model.u_i(x, 0, 0, t_i, \"x\")\n", + " ) * 1000\n", + " ax2.plot(x, error, output[\"color\"][i], label=output[\"label\"][i])\n", + " assert np.all(error[1:] < 0.01)\n", + " assert np.all(error[1:] > -0.015)\n", + "\n", + "ax2.legend()\n", + "\n", + "fig1.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "234ec1ce-da7d-4eb9-a1d3-a0ca40da61e0", + "metadata": {}, + "source": [ + "## Execution times\n", + "\n", + "To compare the performance of the different numerical solutions implemented in OGS6, we compare the execution time of the simulations. The linear thm and trm solutions perform best, while the quadratic thm and th2m solutions take significantly longer time to run. It is also important to mention here, that the time step size selected for the th2m solution are twice as big as the other 3 implementation, yet simulation time still takes longer than any of the other solution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9bfe7c47-b3e9-490c-8f97-589bb0d2c16b", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_axes([0, 0, 1, 1])\n", + "mesh = [\"thm linear\", \"thm quadratic\", \"th2m\", \"trm\"]\n", + "ax.bar(mesh, runtimes)\n", + "plt.ylabel(\"exec. time / s\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6ebedf0d-face-4bce-ae11-18a3c5023201", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[1] Booker, J. R.; Savvidou, C. (1985), Consolidation around a point heat source. International Journal for Numerical and Analytical Methods in Geomechanics, 1985, 9. Jg., Nr. 2, S. 173-184.\n", + "\n", + "[2] Chaudhry, A. A.; Buchwald, J.; Kolditz, O. and Nagel, T. (2019), Consolidation around a point heatsource (correction & verification). International Journal for Numerical and Analytical Methods in Geomechanics, 2019, ." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ddbd9e0-fbf2-4da6-a35c-ae1baa91fe32", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv-with-ogs", + "language": "python", + "name": "venv-with-ogs" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + }, + "vscode": { + "interpreter": { + "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/Tests/Data/ThermoMechanics/BDT/generate_ref.py b/Tests/Data/ThermoMechanics/BDT/generate_ref.py index b94584435dc..0666129d778 100755 --- a/Tests/Data/ThermoMechanics/BDT/generate_ref.py +++ b/Tests/Data/ThermoMechanics/BDT/generate_ref.py @@ -1,8 +1,8 @@ #!/usr/bin/python +import numpy as np import vtk from vtk.util.numpy_support import numpy_to_vtk -import numpy as np reader = vtk.vtkXMLUnstructuredGridReader() reader.SetFileName("cube_1x1x1_hex_1e0.vtu") diff --git a/Tests/Data/ThermoMechanics/LinearMFront/generate_ref.py b/Tests/Data/ThermoMechanics/LinearMFront/generate_ref.py index 159a69c4baf..c15a025d8ed 100755 --- a/Tests/Data/ThermoMechanics/LinearMFront/generate_ref.py +++ b/Tests/Data/ThermoMechanics/LinearMFront/generate_ref.py @@ -1,8 +1,8 @@ #!/usr/bin/python +import numpy as np import vtk from vtk.util.numpy_support import numpy_to_vtk -import numpy as np reader = vtk.vtkXMLUnstructuredGridReader() reader.SetFileName("cube_1x1x1_hex_1e0.vtu") diff --git a/Tests/Python/__init__.py b/Tests/Python/__init__.py index fd1659acb19..ce2b4275313 100644 --- a/Tests/Python/__init__.py +++ b/Tests/Python/__init__.py @@ -1,5 +1,4 @@ import sys - from contextlib import contextmanager diff --git a/Tests/Python/test_cli.py b/Tests/Python/test_cli.py index 7c7f6dc1104..1e13dcb9bc7 100644 --- a/Tests/Python/test_cli.py +++ b/Tests/Python/test_cli.py @@ -1,6 +1,5 @@ -import pytest - import ogs._internal.provide_ogs_cli_tools_via_wheel as ogs_cli_wheel +import pytest from . import push_argv @@ -10,7 +9,7 @@ def _run(program, args): args = ["%s.py" % program] + args with push_argv(args), pytest.raises(SystemExit) as excinfo: func() - assert 0 == excinfo.value.code + assert excinfo.value.code == 0 def test_binaries(): diff --git a/Tests/Python/test_matrix_debug_output.py b/Tests/Python/test_matrix_debug_output.py index 7b5165161c3..11e3ade32d4 100644 --- a/Tests/Python/test_matrix_debug_output.py +++ b/Tests/Python/test_matrix_debug_output.py @@ -1,9 +1,9 @@ -import tempfile import os import platform +import tempfile -import pytest import ogs.simulator as sim +import pytest def run(prjpath, outdir, expect_successful): diff --git a/Tests/Python/test_ogs_asm_threads.py b/Tests/Python/test_ogs_asm_threads.py index 253902d6aca..791cd617d12 100644 --- a/Tests/Python/test_ogs_asm_threads.py +++ b/Tests/Python/test_ogs_asm_threads.py @@ -1,9 +1,9 @@ -import tempfile import os import platform +import tempfile -import pytest import ogs.simulator as sim +import pytest def run(prjpath, outdir, expect_successful): diff --git a/Tests/Python/test_python_bc_simulation.py b/Tests/Python/test_python_bc_simulation.py index 1b772315356..026f62c906d 100644 --- a/Tests/Python/test_python_bc_simulation.py +++ b/Tests/Python/test_python_bc_simulation.py @@ -1,7 +1,6 @@ -import tempfile import os +import tempfile -import pytest import ogs.simulator as sim @@ -17,9 +16,9 @@ def test_HM_ground_equil_TaylorHood_Python(): try: print("Python OpenGeoSys.init ...") - assert 0 == sim.initialize(arguments) + assert sim.initialize(arguments) == 0 print("Python OpenGeoSys.executeSimulation ...") - assert 0 == sim.executeSimulation() + assert sim.executeSimulation() == 0 finally: print("Python OpenGeoSys.finalize() ...") sim.finalize() diff --git a/Tests/Python/test_simulator.py b/Tests/Python/test_simulator.py index 44c53e6c786..8bfe3db3986 100644 --- a/Tests/Python/test_simulator.py +++ b/Tests/Python/test_simulator.py @@ -1,7 +1,6 @@ -import tempfile import os +import tempfile -import pytest import ogs.simulator as sim @@ -14,9 +13,9 @@ def test_simulator(): try: print("Python OpenGeoSys.init ...") - assert 0 == sim.initialize(arguments) + assert sim.initialize(arguments) == 0 print("Python OpenGeoSys.executeSimulation ...") - assert 0 == sim.executeSimulation() + assert sim.executeSimulation() == 0 finally: print("Python OpenGeoSys.finalize() ...") sim.finalize() diff --git a/Tests/Python/test_simulator_mesh_interface.py b/Tests/Python/test_simulator_mesh_interface.py index c284e7f2f3d..0a05caa1830 100644 --- a/Tests/Python/test_simulator_mesh_interface.py +++ b/Tests/Python/test_simulator_mesh_interface.py @@ -1,12 +1,8 @@ -import tempfile import os import sys +import tempfile import numpy as np - -import pytest - -import ogs.mesh as mesh from ogs import simulator @@ -45,7 +41,7 @@ def comparePointCoordinates(points): if ( float(x) != points[cnt, 0] or float(y) != points[cnt, 1] - or 1.0 != points[cnt, 2] + or points[cnt, 2] != 1.0 ): print( "Python: error: expected point [[" @@ -65,7 +61,7 @@ def checkCells(cells, celltypes, points): print("Python: error: cell isn't a quad") # structured mesh with equal size cells - for c in range(0, len(celltypes)): + for c in range(len(celltypes)): area = computeQuadArea( points[cells[5 * c + 1]], points[cells[5 * c + 2]], @@ -92,7 +88,7 @@ def test_simulator(): try: print("Python: OpenGeoSys.init ...") - assert 0 == simulator.initialize(arguments) + assert simulator.initialize(arguments) == 0 top_boundary_grid = simulator.getMesh("cuboid_1x1x1_hex_27_top_boundary") # compare grid point coordinates with expected point coordinates @@ -107,7 +103,7 @@ def test_simulator(): ) print("Python: OpenGeoSys.executeSimulation ...") - assert 0 == simulator.executeTimeStep() + assert simulator.executeTimeStep() == 0 print("Python: simulator.executeTimeStep() done") top_boundary_grid = simulator.getMesh("cuboid_1x1x1_hex_27_top_boundary") diff --git a/Tests/Python/test_wrapped_cli_tools.py b/Tests/Python/test_wrapped_cli_tools.py index b53e1d6c10c..1f017c02694 100644 --- a/Tests/Python/test_wrapped_cli_tools.py +++ b/Tests/Python/test_wrapped_cli_tools.py @@ -1,5 +1,3 @@ -import pytest - import os import tempfile @@ -21,7 +19,7 @@ def test_generate_structured_mesh(): outfile = os.path.join(tmpdirname, "test.vtu") assert not os.path.exists(outfile) - assert 0 == ogs.cli.generateStructuredMesh(e="line", lx=1, nx=10, o=outfile) + assert ogs.cli.generateStructuredMesh(e="line", lx=1, nx=10, o=outfile) == 0 assert os.path.exists(outfile) diff --git a/scripts/doc/append-xml-tags.py b/scripts/doc/append-xml-tags.py index 2e593c1be7b..7546f6c055a 100755 --- a/scripts/doc/append-xml-tags.py +++ b/scripts/doc/append-xml-tags.py @@ -7,24 +7,23 @@ # linked-xml-file.py # prevent broken pipe error -from signal import signal, SIGPIPE, SIG_DFL +from signal import SIG_DFL, SIGPIPE, signal signal(SIGPIPE, SIG_DFL) +import json import os import sys -import xml.etree.cElementTree as ET -import json -from print23 import print_ import pandas as pd +from print23 import print_ github_src_url = "https://gitlab.opengeosys.org/ogs/ogs/-/tree/master" github_data_url = "https://gitlab.opengeosys.org/ogs/ogs/-/tree/master/Tests/Data" if len(sys.argv) != 4: print_("Usage:") - print_("{0} EXT DATADIR DOCAUXDIR".format(sys.argv[0])) + print_(f"{sys.argv[0]} EXT DATADIR DOCAUXDIR") sys.exit(1) ext = sys.argv[1] @@ -54,7 +53,7 @@ def write_parameter_type_info(fh, tagpath, tagpath_expanded, dict_tag_info): for info in dict_tag_info[tagpath]: path = info[1] line = info[2] - fh.write(("\n## From {0} line {1}\n\n").format(path, line)) + fh.write(f"\n## From {path} line {line}\n\n") method = info[6] @@ -87,9 +86,9 @@ def write_parameter_type_info(fh, tagpath, tagpath_expanded, dict_tag_info): datatype = info[5] if datatype: - fh.write("- Data type: {0}\n".format(datatype)) + fh.write(f"- Data type: {datatype}\n") - fh.write("- Expanded tag path: {0}\n".format(tagpath_expanded)) + fh.write(f"- Expanded tag path: {tagpath_expanded}\n") fh.write( "- Go to source code: [→ ogs/ogs/master]({2}/{0}#L{1})\n".format( @@ -217,7 +216,7 @@ def dict_of_list_append(dict_, key, value): df_n_t_p_pcst = pd.read_json(fh, orient="records") # traverse dox file hierarchy -for (dirpath, _, filenames) in os.walk(docdir): +for dirpath, _, filenames in os.walk(docdir): reldirpath = dirpath[len(docdir) + 1 :] istag = True diff --git a/scripts/doc/check-project-params.py b/scripts/doc/check-project-params.py index 9e8abbbd34d..d57c3a0f433 100755 --- a/scripts/doc/check-project-params.py +++ b/scripts/doc/check-project-params.py @@ -3,11 +3,12 @@ # This script actually generates the QA page. # For its usage see generate-project-file-doc-qa.sh -from print23 import print_ -import sys -import re -import os.path import json +import os.path +import re +import sys + +from print23 import print_ github_src_url = "https://gitlab.opengeosys.org/ogs/ogs/-/tree/master" @@ -17,12 +18,12 @@ def debug(msg): if len(sys.argv) != 3: - print_("USAGE: {0} DOCAUXDIR SRCDIR".format(sys.argv[0])) + print_(f"USAGE: {sys.argv[0]} DOCAUXDIR SRCDIR") sys.exit(1) docauxdir = sys.argv[1] if not os.path.isdir(docauxdir): - print_("error: `{0}' is not a directory".format(docauxdir)) + print_(f"error: `{docauxdir}' is not a directory") sys.exit(1) doxdir = os.path.join(docauxdir, "dox", "ProjectFile") @@ -75,12 +76,12 @@ def debug(msg): debug("SPECIAL: " + " ".join(inline[1:])) # TODO implement proper handling # unneeded.append(inline[1:]) else: - debug("ERROR: unrecognized status {0}".format(status)) + debug(f"ERROR: unrecognized status {status}") wrong_status = True # traverse dox file hierarchy srcdocdir = os.path.join(srcdir, "Documentation", "ProjectFile") -for (dirpath, _, filenames) in os.walk(srcdocdir): +for dirpath, _, filenames in os.walk(srcdocdir): reldirpath = dirpath[len(srcdocdir) + 1 :] for f in filenames: @@ -97,7 +98,7 @@ def debug(msg): tagpath = os.path.join(reldirpath, f[2 : -len(".md")]) tag_or_attr = "attr" else: - debug("ERROR: Found md file with unrecognized name: {0}".format(filepath)) + debug(f"ERROR: Found md file with unrecognized name: {filepath}") continue tagpath = tagpath.replace(os.sep, ".") @@ -216,7 +217,7 @@ def debug(msg): print_("# Tags that do not occur in any CTest project file") for utag in sorted(utags): pagename = "ogs_file_param__" + utag.replace(".", "__") - print_(r'- \ref {0} "{1}"'.format(pagename, utag)) + print_(rf'- \ref {pagename} "{utag}"') uattrs = [ ua @@ -229,7 +230,7 @@ def debug(msg): print_("# Attributes that do not occur in any CTest project file") for uattr in sorted(uattrs): pagename = "ogs_file_attr__" + uattr.replace(".", "__") - print_(r'- \ref {0} "{1}"'.format(pagename, uattr)) + print_(rf'- \ref {pagename} "{uattr}"') # exit with error status if something was not documented/tested if qa_status_succeeded: diff --git a/scripts/doc/extract-media-properties-from-ctests.py b/scripts/doc/extract-media-properties-from-ctests.py index 1ea00684b57..8673824f3d6 100755 --- a/scripts/doc/extract-media-properties-from-ctests.py +++ b/scripts/doc/extract-media-properties-from-ctests.py @@ -2,6 +2,7 @@ import os import xml.etree.ElementTree as ET + import pandas as pd diff --git a/scripts/doc/linked-xml-file.py b/scripts/doc/linked-xml-file.py index 40f52775a0e..2c01d5d4e0f 100755 --- a/scripts/doc/linked-xml-file.py +++ b/scripts/doc/linked-xml-file.py @@ -12,19 +12,20 @@ # attributes are untested. # prevent broken pipe error -from signal import signal, SIGPIPE, SIG_DFL +from signal import SIG_DFL, SIGPIPE, signal signal(SIGPIPE, SIG_DFL) -from print23 import print_ +import json import os import sys -import xml.etree.cElementTree as ET -import json +import xml.etree.ElementTree as ET + +from print23 import print_ if len(sys.argv) != 3: sys.stderr.write("Usage:\n") - sys.stderr.write("{0} DATADIR DOCAUXDIR\n".format(sys.argv[0])) + sys.stderr.write(f"{sys.argv[0]} DATADIR DOCAUXDIR\n") sys.exit(1) datadir = sys.argv[1] @@ -47,7 +48,7 @@ def format_if_documented(is_doc, fmt, fullpagename, tag_attr, *args): if is_doc: - tag_attr_formatted = r'\ref {0} "{1}"'.format(fullpagename, tag_attr) + tag_attr_formatted = rf'\ref {fullpagename} "{tag_attr}"' else: tag_attr_formatted = ( r'{1}'.format( @@ -60,7 +61,7 @@ def format_if_documented(is_doc, fmt, fullpagename, tag_attr, *args): def format_if_documented_nowarn(is_doc, fmt, fullpagename, tag_attr, *args): if is_doc: - tag_attr_formatted = r'\ref {0} "{1}"'.format(fullpagename, tag_attr) + tag_attr_formatted = rf'\ref {fullpagename} "{tag_attr}"' else: tag_attr_formatted = tag_attr @@ -241,7 +242,7 @@ def dict_of_set_add(dos, key, value): dos[key].add(value) -for (dirpath, dirnames, filenames) in os.walk(datadir, topdown=False): +for dirpath, dirnames, filenames in os.walk(datadir, topdown=False): reldirpath = os.path.relpath(dirpath, datadir) outdirpath = os.path.join(outdir, reldirpath) print_(">", reldirpath) @@ -308,15 +309,13 @@ def dict_of_set_add(dos, key, value): with open(os.path.join(outdirpath, "index.dox"), "w") as fh: fh.write( - """/*! \page {0} {1} + rf"""/*! \page {pagename} {pagetitle} -""".format( - pagename, pagetitle - ) +""" ) for sp in sorted(subpages): - fh.write("- \\subpage {0}\n".format(sp)) + fh.write(f"- \\subpage {sp}\n") fh.write( """ diff --git a/scripts/doc/normalize-param-cache.py b/scripts/doc/normalize-param-cache.py index 59af0d8727a..edecbc11418 100755 --- a/scripts/doc/normalize-param-cache.py +++ b/scripts/doc/normalize-param-cache.py @@ -4,10 +4,10 @@ # and transforms it into a tabular representation for further # processing. -from print23 import print_ -import sys import re -import os.path +import sys + +from print23 import print_ def debug(msg): @@ -67,7 +67,7 @@ def merge_lines(it): msg = fn + m.group(2) + str(lno) + m.group(4) + line # remove non-doxygen comments - line = re.sub("/\*[^!*].*\*/|/\*\*/", "", line) + line = re.sub(r"/\*[^!*].*\*/|/\*\*/", "", line) line = re.sub("//[^!*].*|//$", "", line, 1) if buf_fn: @@ -127,7 +127,7 @@ def merge_lines(it): param_or_attr_comment = m.group(1) tag_path_comment = m.group(2).replace("__", ".") - debug(" {0:>5} //! {1}".format(lineno, tag_path_comment)) + debug(f" {lineno:>5} //! {tag_path_comment}") tag_name_comment = tag_path_comment.split(".")[-1] continue @@ -175,7 +175,7 @@ def merge_lines(it): default_value, ) else: - debug(" {0:>5} {1} {2} ".format(lineno, param, paramtype)) + debug(f" {lineno:>5} {param} {paramtype} ") if param != tag_name_comment: debug( @@ -199,7 +199,7 @@ def merge_lines(it): elif lineno != oldlineno + 1: debug( "error: the associated comment is not on the line preceding this one." - + " line numbers {0} vs. {1}".format(oldlineno, lineno) + + f" line numbers {oldlineno} vs. {lineno}" ) write_out( "NODOC", @@ -287,7 +287,7 @@ def merge_lines(it): if lineno != oldlineno + 1: debug( "error: the associated comment is not on the line preceding this one." - + " line numbers {0} vs. {1}".format(oldlineno, lineno) + + f" line numbers {oldlineno} vs. {lineno}" ) write_out( "NODOC", path, lineno, "UNKNOWN", "UNKNOWN", paramtype, method diff --git a/scripts/snakemake/vtkdiff/wrapper.py b/scripts/snakemake/vtkdiff/wrapper.py index 5260a376b7e..cd140f6b5f3 100644 --- a/scripts/snakemake/vtkdiff/wrapper.py +++ b/scripts/snakemake/vtkdiff/wrapper.py @@ -5,6 +5,7 @@ __license__ = "BSD" import os + from snakemake.shell import shell if os.path.exists(snakemake.output[0]): diff --git a/scripts/test/cppcheck_gen_hashes.py b/scripts/test/cppcheck_gen_hashes.py index 189fb42955d..729ba53e6e0 100644 --- a/scripts/test/cppcheck_gen_hashes.py +++ b/scripts/test/cppcheck_gen_hashes.py @@ -16,4 +16,4 @@ with open(sys.argv[1], "w") as outfile: json.dump(data, outfile) -print("Added cppcheck fingerprints to {}.".format(sys.argv[1])) +print(f"Added cppcheck fingerprints to {sys.argv[1]}.") diff --git a/scripts/test/gmldiff.py b/scripts/test/gmldiff.py index e9729607d84..8d2d4e9abd0 100755 --- a/scripts/test/gmldiff.py +++ b/scripts/test/gmldiff.py @@ -5,9 +5,9 @@ # See accompanying file LICENSE.txt or # http://www.opengeosys.org/project/license -from xml.dom import minidom import argparse import math +from xml.dom import minidom parser = argparse.ArgumentParser(description="Diff OpenGeoSys GML files.") parser.add_argument( diff --git a/web/content/docs/benchmarks/heatconduction/BHE_array_benchmark/bhe_array_analytical_solver.py b/web/content/docs/benchmarks/heatconduction/BHE_array_benchmark/bhe_array_analytical_solver.py index b3602fa076d..89cd7a4cad1 100644 --- a/web/content/docs/benchmarks/heatconduction/BHE_array_benchmark/bhe_array_analytical_solver.py +++ b/web/content/docs/benchmarks/heatconduction/BHE_array_benchmark/bhe_array_analytical_solver.py @@ -19,12 +19,13 @@ Author: Shuang Chen """ +import math + import matplotlib.pyplot as plt import numpy as np from scipy import special as sp -import math -#%% input parameters +# %% input parameters # source term coordinates po_x = np.array( [ @@ -174,15 +175,15 @@ coeff_all = np.zeros([numtemppoints, numtimesteps]) -for currstep in range(0, numtimesteps): +for currstep in range(numtimesteps): Temp_po_to_referencepo = np.zeros([numtemppoints, numbhe]) po_dist_to_referencepo = np.zeros([numtemppoints, numbhe]) localcoeff_all = np.zeros([numtemppoints, 1]) localcoeff = np.zeros([numtemppoints, numbhe]) localcoeff1 = np.zeros([numtemppoints, numbhe]) - for i in range(0, numbhe): + for i in range(numbhe): if time_trans * (currstep + 1) - time_trans * 0 > 0: - for j in range(0, numtemppoints): + for j in range(numtemppoints): po_dist_to_referencepo[j, i] = ( abs(po_x[i] - point_x[j]) ** 2 + abs(po_y[i] - point_y[j]) ** 2 ) @@ -192,7 +193,7 @@ n = sp.exp1(exp) localcoeff[j, i] = 1 / (4 * math.pi * lamda_sp) * n if time_trans * (currstep + 1) - time_trans * 1 > 0: - for j in range(0, numtemppoints): + for j in range(numtemppoints): po_dist_to_referencepo[j, i] = ( abs(po_x[i] - point_x[j]) ** 2 + abs(po_y[i] - point_y[j]) ** 2 ) @@ -206,7 +207,7 @@ coeff_all[:, 1:] = coeff_all[:, : numtimesteps - 1] coeff_all[:, :1] = localcoeff_all -for currstep in range(0, numtimesteps): +for currstep in range(numtimesteps): T2[:, currstep] = ( np.sum( coeff_all[:, numtimesteps - 1 - currstep :] * qq_all[:, : currstep + 1], @@ -220,7 +221,7 @@ T2_trans = T2 -#%% plotting +# %% plotting png_num = 1 for i in range(png_num): @@ -228,10 +229,10 @@ plt.plot(point_x, T2[:, 4], "b", label="Analytical") plt.xlim([0, 100]) plt.ylim([-10, 20]) - plt.ylabel("Temperature [$^\circ$C]") + plt.ylabel(r"Temperature [$^\circ$C]") plt.xlabel("x [m]") plt.legend(loc="best", fontsize=8) plt.title( - f"Soil temperature distribution on A-A'section after 4 months", fontsize=12 + "Soil temperature distribution on A-A'section after 4 months", fontsize=12 ) - plt.savefig("pngfile{}.png".format(i), dpi=300, transparent=False) + plt.savefig(f"pngfile{i}.png", dpi=300, transparent=False) diff --git a/web/content/docs/tutorials/advancing-glacier/glacierclass.py b/web/content/docs/tutorials/advancing-glacier/glacierclass.py index d71dd06d15f..82f9cde739c 100644 --- a/web/content/docs/tutorials/advancing-glacier/glacierclass.py +++ b/web/content/docs/tutorials/advancing-glacier/glacierclass.py @@ -1,11 +1,10 @@ # model of the evolving glacier extensions (length and height) # parameterized, independent of concrete geometry -import numpy as np -import matplotlib.pyplot as plt import os -from math import pi, sin, cos, sinh, cosh, sqrt +import matplotlib.pyplot as plt +import numpy as np gravity = 9.81 # m/s² diff --git a/web/content/docs/tutorials/advancing-glacier/mesh_basin.py b/web/content/docs/tutorials/advancing-glacier/mesh_basin.py index 7ffc7eee34e..f3f3f50868c 100644 --- a/web/content/docs/tutorials/advancing-glacier/mesh_basin.py +++ b/web/content/docs/tutorials/advancing-glacier/mesh_basin.py @@ -8,10 +8,10 @@ # The Python API is entirely defined in the `gmsh.py' module (which contains the # full documentation of all the functions in the API): -import numpy -import gmsh import os +import gmsh + # Before using any functions in the Python API, Gmsh must be initialized: gmsh.initialize() From 9a15572474c70c8ba9286fb932ce21ce4f2dd7ef Mon Sep 17 00:00:00 2001 From: Lars Bilke Date: Tue, 28 Nov 2023 09:14:05 +0100 Subject: [PATCH 2/6] [py] ruff unsafe fixes: `pipx run ruff . --unsafe-fixes --fix`. --- MaterialLib/SolidModels/MFront/Lubby2.py | 9 ++-- .../MFront/ModCamClay_TriaxTest.py | 2 +- .../cube_1x1x1_SteadyStateDiffusion/cube.py | 2 +- .../bcs_laplace_eq.py | 12 +++-- .../GroundEquilibrium/pythonBCsOGS.py | 4 +- .../SeabedResponse/Stationary_waves.ipynb | 6 +-- ..._Disc_with_hole_convergence_analysis.ipynb | 7 +-- .../PythonHertzContact/gen-unit-circle.py | 5 +- .../PythonHertzContact/hertz_contact_bc.py | 4 +- .../Linear/PythonHertzContact/post.py | 9 ++-- .../mtest/ModCamClay_TestIsotrop.ipynb | 2 +- Tests/Data/Mechanics/PLLC/PLLC.ipynb | 33 ++++++++----- .../MixedElements/check_point_cloud.ipynb | 3 +- .../Notebooks/FailingNotebook.ci-skip.ipynb | 2 +- .../DecayChain/DecayChain.ipynb | 21 +++----- .../performance_measurements.ipynb | 14 +++--- .../LiquidFlow/AxiSymTheis/axisym_theis.ipynb | 3 +- .../Parabolic/T/3D_3BHEs_array/bcs_tespy.py | 3 +- .../T/3D_3BHEs_array/bcs_tespy_closedloop.py | 3 +- .../bcs_tespy_and_serverCommunication.py | 3 +- .../Kregime_Propagating_jupyter.ipynb | 48 +++++++++---------- .../sen_shear.ipynb | 24 +++++----- .../beam_jupyter_notebook/beam.ipynb | 4 +- .../Kregime_Static_jupyter.ipynb | 2 +- .../surfing_pyvista.ipynb | 40 ++++++++-------- .../PhaseField/tpb_jupyter_notebook/TPB.ipynb | 4 +- Tests/Data/TH2M/H/diffusion/diffusion.ipynb | 7 ++- .../TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb | 7 ++- Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb | 6 +-- Tests/Python/test_matrix_debug_output.py | 10 ++-- Tests/Python/test_ogs_asm_threads.py | 2 +- Tests/Python/test_simulator_mesh_interface.py | 6 +-- scripts/doc/append-xml-tags.py | 8 ++-- scripts/doc/check-project-params.py | 4 +- .../extract-media-properties-from-ctests.py | 19 ++++---- scripts/doc/linked-xml-file.py | 14 ++---- .../advancing-glacier/timeBCs_glacier.py | 2 +- 37 files changed, 166 insertions(+), 188 deletions(-) diff --git a/MaterialLib/SolidModels/MFront/Lubby2.py b/MaterialLib/SolidModels/MFront/Lubby2.py index a618da10410..ff297f08375 100644 --- a/MaterialLib/SolidModels/MFront/Lubby2.py +++ b/MaterialLib/SolidModels/MFront/Lubby2.py @@ -48,13 +48,12 @@ etaM = etaM0 * np.exp(mvM * sig_eff) -eps_xy = ( - lambda t: ( +def eps_xy(t): + return ( (1.0 / GM0 + t / etaM) * sig_xy + 1.0 / GK * (1.0 - np.exp(-GK / etaK * t)) * sig_xy - ) - / 2.0 -) + ) / 2.0 + s = mtest.MTestCurrentState() wk = mtest.MTestWorkSpace() diff --git a/MaterialLib/SolidModels/MFront/ModCamClay_TriaxTest.py b/MaterialLib/SolidModels/MFront/ModCamClay_TriaxTest.py index 70de9f7ea06..0112983f481 100644 --- a/MaterialLib/SolidModels/MFront/ModCamClay_TriaxTest.py +++ b/MaterialLib/SolidModels/MFront/ModCamClay_TriaxTest.py @@ -1,5 +1,5 @@ import matplotlib.pyplot as plt -import mtest as mtest +import mtest import numpy as np m = mtest.MTest() diff --git a/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py b/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py index 983dc7302eb..0526784fdb6 100644 --- a/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py +++ b/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py @@ -93,7 +93,7 @@ def CreatePipeline(self, datadescription): def RequestDataDescription(datadescription): "Callback to populate the request for current timestep" global coprocessor - if datadescription.GetForceOutput() == True: + if datadescription.GetForceOutput() is True: # We are just going to request all fields and meshes from the simulation # code/adaptor. for i in range(datadescription.GetNumberOfInputDescriptions()): diff --git a/Tests/Data/Elliptic/square_1x1_SteadyStateDiffusion_Python/bcs_laplace_eq.py b/Tests/Data/Elliptic/square_1x1_SteadyStateDiffusion_Python/bcs_laplace_eq.py index 2dcca3dadf6..ca7c2f7211c 100644 --- a/Tests/Data/Elliptic/square_1x1_SteadyStateDiffusion_Python/bcs_laplace_eq.py +++ b/Tests/Data/Elliptic/square_1x1_SteadyStateDiffusion_Python/bcs_laplace_eq.py @@ -19,7 +19,8 @@ def grad_solution(x, y): class BCTop(OpenGeoSys.BoundaryCondition): def getDirichletBCValue(self, t, coords, node_id, primary_vars): x, y, z = coords - assert y == 1.0 and z == 0.0 + assert y == 1.0 + assert z == 0.0 value = solution(x, y) return (True, value) @@ -27,7 +28,8 @@ def getDirichletBCValue(self, t, coords, node_id, primary_vars): class BCLeft(OpenGeoSys.BoundaryCondition): def getDirichletBCValue(self, t, coords, node_id, primary_vars): x, y, z = coords - assert x == 0.0 and z == 0.0 + assert x == 0.0 + assert z == 0.0 value = solution(x, y) return (True, value) @@ -35,7 +37,8 @@ def getDirichletBCValue(self, t, coords, node_id, primary_vars): class BCBottom(OpenGeoSys.BoundaryCondition): def getDirichletBCValue(self, t, coords, node_id, primary_vars): x, y, z = coords - assert y == 0.0 and z == 0.0 + assert y == 0.0 + assert z == 0.0 value = solution(x, y) return (True, value) @@ -44,7 +47,8 @@ def getDirichletBCValue(self, t, coords, node_id, primary_vars): class BCRight(OpenGeoSys.BoundaryCondition): def getFlux(self, t, coords, primary_vars): x, y, z = coords - assert x == 1.0 and z == 0.0 + assert x == 1.0 + assert z == 0.0 value = grad_solution(x, y)[0] Jac = [0.0] # value does not depend on primary variable return (True, value, Jac) diff --git a/Tests/Data/HydroMechanics/GroundEquilibrium/pythonBCsOGS.py b/Tests/Data/HydroMechanics/GroundEquilibrium/pythonBCsOGS.py index 6e2f6950d06..8a9cc1d1e1f 100644 --- a/Tests/Data/HydroMechanics/GroundEquilibrium/pythonBCsOGS.py +++ b/Tests/Data/HydroMechanics/GroundEquilibrium/pythonBCsOGS.py @@ -30,9 +30,7 @@ def ExternalDisplacement(x, t): - uy = u_max * (t / T) * (x / Lx) ** 2 - - return uy + return u_max * (t / T) * (x / Lx) ** 2 # Hydraulic BCs diff --git a/Tests/Data/HydroMechanics/SeabedResponse/Stationary_waves.ipynb b/Tests/Data/HydroMechanics/SeabedResponse/Stationary_waves.ipynb index 8dc4ac63837..0c4dfe199c8 100644 --- a/Tests/Data/HydroMechanics/SeabedResponse/Stationary_waves.ipynb +++ b/Tests/Data/HydroMechanics/SeabedResponse/Stationary_waves.ipynb @@ -872,8 +872,7 @@ "def read_timestep_mesh(a, time):\n", " reader = pv.PVDReader(f\"{out_dir}/square_{a}x100.pvd\")\n", " reader.set_active_time_point(int(time * 4)) # time [s], delta t = 0.25 s\n", - " mesh = reader.read()[0]\n", - " return mesh\n", + " return reader.read()[0]\n", "\n", "\n", "def slice_along_line(mesh, start_point, end_point):\n", @@ -885,8 +884,7 @@ " pressure = mesh.point_data[\"pressure_interpolated\"]\n", " depth = mesh.points[:, 1]\n", " indices_sorted = np.argsort(depth)\n", - " pressure_sorted = pressure[indices_sorted]\n", - " return pressure_sorted\n", + " return pressure[indices_sorted]\n", "\n", "\n", "def get_stresses_sorted(mesh):\n", diff --git a/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis.ipynb b/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis.ipynb index d687ece1603..e6e9b6e1a49 100644 --- a/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis.ipynb +++ b/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis.ipynb @@ -163,9 +163,7 @@ " reader = pv.PVDReader(f\"{d}/disc_with_hole_idx_is_{study_idx}.pvd\")\n", " reader.set_active_time_point(-1) # go to last timestep\n", "\n", - " mesh = reader.read()[0]\n", - "\n", - " return mesh\n", + " return reader.read()[0]\n", "\n", "\n", "def slice_along_line(mesh, start_point, end_point):\n", @@ -268,8 +266,7 @@ " number = (\n", " line_mesh.points.shape[0] - 1\n", " ) # number of cells along the right edge of the plate\n", - " size = STUDY_mesh_size / number # height of plate divided by number of cells\n", - " return size\n", + " return STUDY_mesh_size / number # height of plate divided by number of cells\n", "\n", "\n", "def resample_mesh_to_240_resolution(idx):\n", diff --git a/Tests/Data/Mechanics/Linear/PythonHertzContact/gen-unit-circle.py b/Tests/Data/Mechanics/Linear/PythonHertzContact/gen-unit-circle.py index 0df4658ad4b..df9a177feec 100755 --- a/Tests/Data/Mechanics/Linear/PythonHertzContact/gen-unit-circle.py +++ b/Tests/Data/Mechanics/Linear/PythonHertzContact/gen-unit-circle.py @@ -30,10 +30,7 @@ def distribute_points_evenly(c2): for node, r in enumerate(r2): b = nbin[node] i = bins[b].index(node) - if len(bins[b]) == 1: - phi = 0.0 - else: - phi = np.pi * 0.5 / (len(bins[b]) - 1) * i + phi = 0.0 if len(bins[b]) == 1 else np.pi * 0.5 / (len(bins[b]) - 1) * i c3[node, 0] = r * np.cos(phi) c3[node, 1] = r * np.sin(phi) diff --git a/Tests/Data/Mechanics/Linear/PythonHertzContact/hertz_contact_bc.py b/Tests/Data/Mechanics/Linear/PythonHertzContact/hertz_contact_bc.py index 2f0651b6bc0..6dddb584465 100644 --- a/Tests/Data/Mechanics/Linear/PythonHertzContact/hertz_contact_bc.py +++ b/Tests/Data/Mechanics/Linear/PythonHertzContact/hertz_contact_bc.py @@ -6,7 +6,7 @@ class HertzContactBC(OpenGeoSys.BoundaryCondition): def __init__(self): - super(HertzContactBC, self).__init__() + super().__init__() self._first_node = None # ID of the first node of this BC's geometry self._t_old = START_TIME - 1.0 # time of previous invocation of this BC @@ -128,7 +128,7 @@ def getDirichletBCValue(self, t, coords, node_id, primary_vars): # set change for changing primary variables. if x <= self._a_prev: - assert False # this case shouldn't happen + raise AssertionError() # this case shouldn't happen res = (True, y_top - y) elif self._boundary_x_coords_are_initialized(t): idx = self._boundary_x_coords.index(x) diff --git a/Tests/Data/Mechanics/Linear/PythonHertzContact/post.py b/Tests/Data/Mechanics/Linear/PythonHertzContact/post.py index eb04e3f9299..e979ec90445 100755 --- a/Tests/Data/Mechanics/Linear/PythonHertzContact/post.py +++ b/Tests/Data/Mechanics/Linear/PythonHertzContact/post.py @@ -161,7 +161,10 @@ def strain_triangle_axi(cell, point_data, strain_data): for node in range(3): l1, l2 = T_inv * (cell_pts[node, :].T - cell_pts[2, :].T) - assert l1 > -1e-15 and 1 + 1e-15 > l1 and l2 > -1e-15 and 1 + 1e-15 > l2 + assert l1 > -1e-15 + assert 1 + 1e-15 > l1 + assert l2 > -1e-15 + assert 1 + 1e-15 > l2 grad = np.empty((2, 2)) for comp in range(2): @@ -295,9 +298,7 @@ def total_force(rs, stress): rs_int = np.linspace(min(rs), max(rs), max(len(rs), 200)) stress_int = interp1d(rs, stress, bounds_error=False, fill_value=0.0) - F = 2.0 * np.pi * np.trapz(x=rs_int, y=rs_int * stress_int(rs_int)) - - return F + return 2.0 * np.pi * np.trapz(x=rs_int, y=rs_int * stress_int(rs_int)) def stress_at_contact_area(): global add_leg diff --git a/Tests/Data/Mechanics/ModifiedCamClay/mtest/ModCamClay_TestIsotrop.ipynb b/Tests/Data/Mechanics/ModifiedCamClay/mtest/ModCamClay_TestIsotrop.ipynb index 0b748496466..1d2c238b785 100644 --- a/Tests/Data/Mechanics/ModifiedCamClay/mtest/ModCamClay_TestIsotrop.ipynb +++ b/Tests/Data/Mechanics/ModifiedCamClay/mtest/ModCamClay_TestIsotrop.ipynb @@ -41,7 +41,7 @@ "import site\n", "\n", "import matplotlib.pyplot as plt\n", - "import mtest as mtest\n", + "import mtest\n", "import numpy as np" ] }, diff --git a/Tests/Data/Mechanics/PLLC/PLLC.ipynb b/Tests/Data/Mechanics/PLLC/PLLC.ipynb index 0aa3fa4409e..c4bc39d05a5 100644 --- a/Tests/Data/Mechanics/PLLC/PLLC.ipynb +++ b/Tests/Data/Mechanics/PLLC/PLLC.ipynb @@ -217,16 +217,20 @@ "Q2 = 24.5e3 # kJ / mol\n", "dGrain = 5e-2 # m\n", "sref = 1.0 # MPa\n", - "BGRa = (\n", - " lambda sig, T: A1\n", - " * np.exp(-Q1 / (8.3145 * (273.15 + T)))\n", - " * np.power(sig / sref, 5.0)\n", - ")\n", - "PLLC = lambda sig, T: A1 * np.exp(-Q1 / (8.3145 * (273.15 + T))) * np.power(\n", - " sig / sref, 5.0\n", - ") + A2 * np.exp(-Q2 / (8.3145 * (273.15 + T))) * sig / sref / np.power(dGrain, 3) / (\n", - " 273.15 + T\n", - ")" + "\n", + "\n", + "def BGRa(sig, T):\n", + " return A1 * np.exp(-Q1 / (8.3145 * (273.15 + T))) * np.power(sig / sref, 5.0)\n", + "\n", + "\n", + "def PLLC(sig, T):\n", + " return A1 * np.exp(-Q1 / (8.3145 * (273.15 + T))) * np.power(\n", + " sig / sref, 5.0\n", + " ) + A2 * np.exp(-Q2 / (8.3145 * (273.15 + T))) * sig / sref / np.power(\n", + " dGrain, 3\n", + " ) / (\n", + " 273.15 + T\n", + " )" ] }, { @@ -294,7 +298,7 @@ " ax.loglog(1e-6 * stresses, eps_dot, \"o\", c=col, markeredgecolor=\"k\")\n", "\n", "# plot experimental data points\n", - "for Ex, (temp, m, Data) in ExData.items():\n", + "for _Ex, (temp, m, Data) in ExData.items():\n", " stresses, eps_dot = np.array(Data).T\n", " ax.loglog(stresses, eps_dot, m, c=Exps[temp][0])\n", "\n", @@ -304,7 +308,12 @@ " for temp, (col, _) in Exps.items()\n", " if temp >= 25\n", "][::-1]\n", - "addLeg = lambda **args: patches.append(mpl.lines.Line2D([], [], **args))\n", + "\n", + "\n", + "def addLeg(**args):\n", + " return patches.append(mpl.lines.Line2D([], [], **args))\n", + "\n", + "\n", "addLeg(c=\"k\", label=\"PLLC\")\n", "addLeg(c=\"k\", ls=\"--\", label=\"BGRa\")\n", "addLeg(c=\"w\", ls=\"None\", marker=\"o\", mec=\"k\", label=\"OGS\")\n", diff --git a/Tests/Data/NodePartitionedMesh/WithIntegrationPointStress/MixedElements/check_point_cloud.ipynb b/Tests/Data/NodePartitionedMesh/WithIntegrationPointStress/MixedElements/check_point_cloud.ipynb index 43bec5905ed..321dccafde4 100644 --- a/Tests/Data/NodePartitionedMesh/WithIntegrationPointStress/MixedElements/check_point_cloud.ipynb +++ b/Tests/Data/NodePartitionedMesh/WithIntegrationPointStress/MixedElements/check_point_cloud.ipynb @@ -63,7 +63,8 @@ "def check_all_points_inside_bbox_2D(mesh, point_cloud):\n", " xmin, xmax, ymin, ymax, zmin, zmax = mesh.bounds\n", "\n", - " assert zmin == 0.0 and zmax == 0.0 # make sure we are in 2D\n", + " assert zmin == 0.0\n", + " assert zmax == 0.0\n", "\n", " assert np.all(point_cloud.points[:, 0] > xmin)\n", " assert np.all(point_cloud.points[:, 0] < xmax)\n", diff --git a/Tests/Data/Notebooks/FailingNotebook.ci-skip.ipynb b/Tests/Data/Notebooks/FailingNotebook.ci-skip.ipynb index 8ae8c9b1a65..31c3372b3d9 100644 --- a/Tests/Data/Notebooks/FailingNotebook.ci-skip.ipynb +++ b/Tests/Data/Notebooks/FailingNotebook.ci-skip.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "assert False\n", + "raise AssertionError()\n", "\n", "# This line will not be reached but would also work:\n", "raise SystemExit()" diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/DecayChain.ipynb b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/DecayChain.ipynb index 543e558a3d6..c6d0c031cb4 100644 --- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/DecayChain.ipynb +++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/DecayChain.ipynb @@ -214,7 +214,7 @@ " t *= 3.1536e7 # unit conversion from year to second\n", "\n", " beta = (v**2 / 4 / D**2 + k / D) ** 0.5\n", - " c_t = (\n", + " return (\n", " c_0\n", " / 2\n", " * np.exp(v * x / 2 / D)\n", @@ -226,8 +226,6 @@ " )\n", " )\n", "\n", - " return c_t\n", - "\n", "\n", "###Model parameters###\n", "# Diffusion coefficient [m2/s]\n", @@ -240,22 +238,15 @@ ")\n", "half_lifes = np.array([1.56e7, 7.37e3, 2.41e4, 7.04e8, 3.28e4, 21.773])\n", "# First-order decay constant [1/s]\n", - "k = dict(\n", - " [\n", - " (radionuclide, np.log(2) / half_life / 3.1536e7)\n", - " for radionuclide, half_life in zip(radionuclides, half_lifes)\n", - " ]\n", - ")\n", + "k = {\n", + " radionuclide: np.log(2) / half_life / 3.1536e7\n", + " for radionuclide, half_life in zip(radionuclides, half_lifes)\n", + "}\n", "\n", "###Initial and boundary conditions###\n", "c_inlet = np.ones(6)\n", "a_inlet = dict(\n", - " [\n", - " (radionuclide, a)\n", - " for radionuclide, a in zip(\n", - " radionuclides, computeInitialAuxiliaryVariable(c_inlet, list(k.values()))\n", - " )\n", - " ]\n", + " zip(radionuclides, computeInitialAuxiliaryVariable(c_inlet, list(k.values())))\n", ")\n", "\n", "###Spatial and temporal discretization###\n", diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb index b92488b148e..e7280929cf4 100644 --- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb +++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb @@ -361,7 +361,7 @@ " dfd = analysis_time_step(dfa)\n", " dfd = dfd.droplevel(\"mpi_process\")\n", " dfe = dfd.join(dfc)\n", - " dfe.drop(0, inplace=True) # remove timestep 0 (only output)\n", + " dfe = dfe.drop(0) # remove timestep 0 (only output)\n", "\n", " exec_times.append(dfa[\"execution_time\"].max())\n", "\n", @@ -390,8 +390,8 @@ " tmp[\"case_name\"] = name\n", " df_stats = pd.concat([df_stats, tmp])\n", "\n", - "df_stats.reset_index(inplace=True)\n", - "df_stats.set_index([\"case_name\", \"time_step\"], inplace=True)" + "df_stats = df_stats.reset_index()\n", + "df_stats = df_stats.set_index([\"case_name\", \"time_step\"])" ] }, { @@ -577,8 +577,8 @@ "source": [ "fig, ax = plt.subplots()\n", "\n", - "ax.bar([i for i in range(len(cases))], height=exec_times)\n", - "ax.set_xticks([i for i in range(len(cases))])\n", + "ax.bar(list(range(len(cases))), height=exec_times)\n", + "ax.set_xticks(list(range(len(cases))))\n", "ax.set_xticklabels([name for name, case in cases], rotation=15, ha=\"right\")\n", "ax.set_ylabel(\"execution time / s\")\n", "ax.grid(axis=\"y\", ls=\":\")\n", @@ -1137,7 +1137,7 @@ " ts = reader.time_values\n", " meshes = []\n", "\n", - " for ti, t in enumerate(ts):\n", + " for ti, _t in enumerate(ts):\n", " reader.set_active_time_point(ti)\n", " meshes.append(reader.read()[0])\n", "\n", @@ -1162,7 +1162,7 @@ " checked_cell_data = set()\n", " checked_field_data = set()\n", "\n", - " for name, case in cases[1:3]:\n", + " for name, _case in cases[1:3]:\n", " print(f\"checking case '{name}'\")\n", " var_ts = simulation_results[name][\"t\"]\n", " var_meshes = simulation_results[name][\"meshes\"]\n", diff --git a/Tests/Data/Parabolic/LiquidFlow/AxiSymTheis/axisym_theis.ipynb b/Tests/Data/Parabolic/LiquidFlow/AxiSymTheis/axisym_theis.ipynb index 8fdd054d353..993e1557c4a 100644 --- a/Tests/Data/Parabolic/LiquidFlow/AxiSymTheis/axisym_theis.ipynb +++ b/Tests/Data/Parabolic/LiquidFlow/AxiSymTheis/axisym_theis.ipynb @@ -171,8 +171,7 @@ " \"\"\"\n", "\n", " u = calc_u(r, S, T, t)\n", - " s_theis = Q / 4 / np.pi / T * exp1(u)\n", - " return s_theis" + " return Q / 4 / np.pi / T * exp1(u)" ] }, { diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy.py b/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy.py index bacba3a7a6d..f9af119c09d 100644 --- a/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy.py +++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy.py @@ -84,10 +84,9 @@ def dyn_frate(t): # create network dataframe def create_dataframe(): # return dataframe - df_nw = read_csv( + return read_csv( "./pre/bhe_network.csv", delimiter=";", index_col=[0], dtype={"data_index": str} ) - return df_nw # TESPy calculation process diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy_closedloop.py b/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy_closedloop.py index a953ab4d00b..9af36c6c511 100644 --- a/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy_closedloop.py +++ b/Tests/Data/Parabolic/T/3D_3BHEs_array/bcs_tespy_closedloop.py @@ -87,10 +87,9 @@ def dyn_frate(t): # create network dataframe def create_dataframe(): # return dataframe - df_nw = read_csv( + return read_csv( "./pre/bhe_network.csv", delimiter=";", index_col=[0], dtype={"data_index": str} ) - return df_nw # TESPy calculation process diff --git a/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/bcs_tespy_and_serverCommunication.py b/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/bcs_tespy_and_serverCommunication.py index d91b2c87be7..520254b3b12 100644 --- a/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/bcs_tespy_and_serverCommunication.py +++ b/Tests/Data/Parabolic/T/3D_3BHEs_array_python_interface/bcs_tespy_and_serverCommunication.py @@ -84,10 +84,9 @@ def dyn_frate(t): # create network dataframe def create_dataframe(): # return dataframe - df_nw = read_csv( + return read_csv( "./pre/bhe_network.csv", delimiter=";", index_col=[0], dtype={"data_index": str} ) - return df_nw # TESPy calculation process diff --git a/Tests/Data/PhaseField/Kregime_Propagating_jupyter_notebook/Kregime_Propagating_jupyter.ipynb b/Tests/Data/PhaseField/Kregime_Propagating_jupyter_notebook/Kregime_Propagating_jupyter.ipynb index 01c27eaf16b..1ff1f2395d1 100644 --- a/Tests/Data/PhaseField/Kregime_Propagating_jupyter_notebook/Kregime_Propagating_jupyter.ipynb +++ b/Tests/Data/PhaseField/Kregime_Propagating_jupyter_notebook/Kregime_Propagating_jupyter.ipynb @@ -356,7 +356,7 @@ " # mesh properties\n", " ls = 2 * h\n", " # generate prefix from properties\n", - " filename = \"results_h_%0.4f_%s\" % (h, phasefield_model)\n", + " filename = f\"results_h_{h:0.4f}_{phasefield_model}\"\n", " mesh_generation(0.1, h)\n", " # Convert GMSH (.msh) meshes to VTU meshes appropriate for OGS simulation.\n", " input_file = f\"{out_dir}/\" + meshname + \".msh\"\n", @@ -584,7 +584,7 @@ "# Open the file for reading\n", "with open(f\"{out_dir}/log.txt\") as fd:\n", " # Iterate over the lines\n", - " for i, line in enumerate(fd):\n", + " for _i, line in enumerate(fd):\n", " match_surface_energy = re.search(\n", " r\"\"\"Surface energy: (\\d+\\.\\d*) Pressure work: (\\d+\\.\\d*) at time: (\\d+\\.\\d*)\"\"\",\n", " line,\n", @@ -612,7 +612,7 @@ "# Open the file for reading\n", "with open(f\"{out_dir}/log.txt\") as fd:\n", " # Iterate over the lines\n", - " for i, line in enumerate(fd):\n", + " for _i, line in enumerate(fd):\n", " match_pressure = re.search(\n", " r\"\"\"Pressure: (\\d+\\.\\d*) at time: (\\d+\\.\\d*)\"\"\", line\n", " )\n", @@ -743,7 +743,7 @@ }, "outputs": [], "source": [ - "filename = \"results_h_%0.4f_%s\" % (h, phasefield_model)\n", + "filename = f\"results_h_{h:0.4f}_{phasefield_model}\"\n", "reader = pv.get_reader(f\"{out_dir}/\" + filename + \".pvd\")\n", "\n", "plotter = pv.Plotter(shape=(1, 2), border=False)\n", @@ -751,16 +751,16 @@ "for time_value in reader.time_values:\n", " reader.set_active_time_value(time_value)\n", " mesh = reader.read()[0]\n", - " sargs = dict(\n", - " title=\"Phase field\",\n", - " title_font_size=16,\n", - " label_font_size=12,\n", - " n_labels=5,\n", - " position_x=0.25,\n", - " position_y=0.15,\n", - " fmt=\"%.1f\",\n", - " width=0.5,\n", - " )\n", + " sargs = {\n", + " \"title\": \"Phase field\",\n", + " \"title_font_size\": 16,\n", + " \"label_font_size\": 12,\n", + " \"n_labels\": 5,\n", + " \"position_x\": 0.25,\n", + " \"position_y\": 0.15,\n", + " \"fmt\": \"%.1f\",\n", + " \"width\": 0.5,\n", + " }\n", " p = pv.Plotter(shape=(1, 2), border=False)\n", " clim = [0, 1.0]\n", " points = mesh.point_data[\"phasefield\"].shape[0]\n", @@ -819,16 +819,16 @@ "pf = mesh.point_data[\"phasefield\"]\n", "\n", "clim = [0, 1.0]\n", - "sargs = dict(\n", - " title=\"Phase field\",\n", - " title_font_size=16,\n", - " label_font_size=12,\n", - " n_labels=5,\n", - " position_x=0.25,\n", - " position_y=0.0,\n", - " fmt=\"%.1f\",\n", - " width=0.5,\n", - ")\n", + "sargs = {\n", + " \"title\": \"Phase field\",\n", + " \"title_font_size\": 16,\n", + " \"label_font_size\": 12,\n", + " \"n_labels\": 5,\n", + " \"position_x\": 0.25,\n", + " \"position_y\": 0.0,\n", + " \"fmt\": \"%.1f\",\n", + " \"width\": 0.5,\n", + "}\n", "plotter = pv.Plotter(shape=(1, 2), border=False)\n", "plotter.add_mesh(\n", " mesh,\n", diff --git a/Tests/Data/PhaseField/PForthotropy_jupyter_notebook/sen_shear.ipynb b/Tests/Data/PhaseField/PForthotropy_jupyter_notebook/sen_shear.ipynb index 1e7b9da2337..4d1294a6c6d 100644 --- a/Tests/Data/PhaseField/PForthotropy_jupyter_notebook/sen_shear.ipynb +++ b/Tests/Data/PhaseField/PForthotropy_jupyter_notebook/sen_shear.ipynb @@ -182,11 +182,11 @@ " model.replace_curve = MethodType(replace_curve, model)\n", " model.replace_curve(name=\"dirichlet_time\", value=values, coords=ts_coords)\n", "\n", - " if repeat_list != None and delta_t_list != None:\n", + " if repeat_list is not None and delta_t_list is not None:\n", " set_timestepping(model, repeat_list, delta_t_list)\n", " else:\n", " set_timestepping(model, [\"1\"], [\"1e-2\"])\n", - " if hypre == True:\n", + " if hypre is True:\n", " model.replace_text(\n", " with_hypre,\n", " xpath=\"./linear_solvers/linear_solver/petsc/parameters\",\n", @@ -318,16 +318,16 @@ " reader.set_active_time_value(time_value)\n", " mesh = reader.read()[0] # This dataset only has 1 block\n", "\n", - " sargs = dict(\n", - " title=\"Phase field\",\n", - " title_font_size=20,\n", - " label_font_size=15,\n", - " n_labels=5,\n", - " position_x=0.3,\n", - " position_y=0.2,\n", - " fmt=\"%.1f\",\n", - " width=0.5,\n", - " )\n", + " sargs = {\n", + " \"title\": \"Phase field\",\n", + " \"title_font_size\": 20,\n", + " \"label_font_size\": 15,\n", + " \"n_labels\": 5,\n", + " \"position_x\": 0.3,\n", + " \"position_y\": 0.2,\n", + " \"fmt\": \"%.1f\",\n", + " \"width\": 0.5,\n", + " }\n", " clim = [0, 1.0]\n", " points = mesh.point_data[\"phasefield\"].shape[0]\n", " xs = mesh.points[:, 0]\n", diff --git a/Tests/Data/PhaseField/beam_jupyter_notebook/beam.ipynb b/Tests/Data/PhaseField/beam_jupyter_notebook/beam.ipynb index 8c1b9b5afb9..88b72f8f61e 100644 --- a/Tests/Data/PhaseField/beam_jupyter_notebook/beam.ipynb +++ b/Tests/Data/PhaseField/beam_jupyter_notebook/beam.ipynb @@ -190,11 +190,11 @@ " model.replace_parameter_value(name=\"dirichlet_right\", value=bc_displacement)\n", " model.replace_curve = MethodType(replace_curve, model)\n", " model.replace_curve(name=\"dirichlet_time\", value=values, coords=ts_coords)\n", - " if repeat_list != None and delta_t_list != None:\n", + " if repeat_list is not None and delta_t_list is not None:\n", " set_timestepping(model, repeat_list, delta_t_list)\n", " else:\n", " set_timestepping(model, [\"1\"], [\"1e-2\"])\n", - " if hypre == True:\n", + " if hypre is True:\n", " model.replace_text(\n", " with_hypre,\n", " xpath=\"./linear_solvers/linear_solver/petsc/parameters\",\n", diff --git a/Tests/Data/PhaseField/kregime_jupyter_notebook/Kregime_Static_jupyter.ipynb b/Tests/Data/PhaseField/kregime_jupyter_notebook/Kregime_Static_jupyter.ipynb index a1e79862fe5..949d36441fe 100644 --- a/Tests/Data/PhaseField/kregime_jupyter_notebook/Kregime_Static_jupyter.ipynb +++ b/Tests/Data/PhaseField/kregime_jupyter_notebook/Kregime_Static_jupyter.ipynb @@ -397,7 +397,7 @@ " grad_dy = mesh.point_data[\"grad_dy\"]\n", " num_points = disp.shape\n", " Wnode = np.zeros(num_points[0])\n", - " for i, x in enumerate(mesh.points):\n", + " for i, _x in enumerate(mesh.points):\n", " u_x = disp[i][0]\n", " u_y = disp[i][1]\n", " gd_x = grad_dx[i]\n", diff --git a/Tests/Data/PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb b/Tests/Data/PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb index 0ce1825d0a9..5e063860469 100644 --- a/Tests/Data/PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb +++ b/Tests/Data/PhaseField/surfing_jupyter_notebook/surfing_pyvista.ipynb @@ -276,16 +276,16 @@ "mesh.save(f\"{out_dir}/surfing_quad_1x2_NR_pf_ic.vtu\")\n", "\n", "pf_ic = mesh.point_data[\"pf-ic\"]\n", - "sargs = dict(\n", - " title=\"pf-ic\",\n", - " title_font_size=20,\n", - " label_font_size=15,\n", - " n_labels=5,\n", - " position_x=0.24,\n", - " position_y=0.0,\n", - " fmt=\"%.1f\",\n", - " width=0.5,\n", - ")\n", + "sargs = {\n", + " \"title\": \"pf-ic\",\n", + " \"title_font_size\": 20,\n", + " \"label_font_size\": 15,\n", + " \"n_labels\": 5,\n", + " \"position_x\": 0.24,\n", + " \"position_y\": 0.0,\n", + " \"fmt\": \"%.1f\",\n", + " \"width\": 0.5,\n", + "}\n", "clim = [0, 1.0]\n", "\n", "p = pv.Plotter(shape=(1, 1), border=False)\n", @@ -733,16 +733,16 @@ " reader.set_active_time_value(time_value)\n", " mesh = reader.read()[0] # This dataset only has 1 block\n", "\n", - " sargs = dict(\n", - " title=\"Phase field\",\n", - " title_font_size=20,\n", - " label_font_size=15,\n", - " n_labels=5,\n", - " position_x=0.3,\n", - " position_y=0.2,\n", - " fmt=\"%.1f\",\n", - " width=0.5,\n", - " )\n", + " sargs = {\n", + " \"title\": \"Phase field\",\n", + " \"title_font_size\": 20,\n", + " \"label_font_size\": 15,\n", + " \"n_labels\": 5,\n", + " \"position_x\": 0.3,\n", + " \"position_y\": 0.2,\n", + " \"fmt\": \"%.1f\",\n", + " \"width\": 0.5,\n", + " }\n", " clim = [0, 1.0]\n", " points = mesh.point_data[\"phasefield\"].shape[0]\n", " xs = mesh.points[:, 0]\n", diff --git a/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb index 2eb0ed3415f..0145ba38cf3 100644 --- a/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb +++ b/Tests/Data/PhaseField/tpb_jupyter_notebook/TPB.ipynb @@ -179,11 +179,11 @@ " model.replace_parameter_value(name=\"dirichlet_load\", value=bc_displacement)\n", " model.replace_curve = MethodType(replace_curve, model)\n", " model.replace_curve(name=\"dirichlet_time\", value=values, coords=ts_coords)\n", - " if repeat_list != None and delta_t_list != None:\n", + " if repeat_list is not None and delta_t_list is not None:\n", " set_timestepping(model, repeat_list, delta_t_list)\n", " else:\n", " set_timestepping(model, [\"1\"], [\"1e-2\"])\n", - " if hypre == True:\n", + " if hypre is True:\n", " model.replace_text(\n", " with_hypre,\n", " xpath=\"./linear_solvers/linear_solver/petsc/parameters\",\n", diff --git a/Tests/Data/TH2M/H/diffusion/diffusion.ipynb b/Tests/Data/TH2M/H/diffusion/diffusion.ipynb index aad0a85eb10..dea27904363 100644 --- a/Tests/Data/TH2M/H/diffusion/diffusion.ipynb +++ b/Tests/Data/TH2M/H/diffusion/diffusion.ipynb @@ -62,8 +62,7 @@ " t[t < tiny] = tiny\n", "\n", " d = np.sqrt(4 * D * t)\n", - " e = (c_b - c_i) * erfc(x / d) + c_i\n", - " return e\n", + " return (c_b - c_i) * erfc(x / d) + c_i\n", "\n", "\n", "# Utility-function transforming mass fraction into conctration\n", @@ -205,7 +204,7 @@ "outputs": [], "source": [ "# The sample locations have to be converted into a 'dict' for vtuIO\n", - "observation_points = dict((\"x=\" + str(x), (x, 0.0, 0.0)) for x in location)\n", + "observation_points = {\"x=\" + str(x): (x, 0.0, 0.0) for x in location}\n", "# Samples concentration field at the observation points for all timesteps\n", "\n", "c_over_t_at_x = pvdfile.read_time_series(\"xmWL\", observation_points)\n", @@ -269,7 +268,7 @@ "ax2.set_ylabel(r\"$\\epsilon_\\mathrm{abs}$ / mol m$^{-3}$\", fontsize=12)\n", "\n", "label_x = []\n", - "for key, c in c_over_t_at_x.items():\n", + "for key, _c in c_over_t_at_x.items():\n", " x = observation_points[key][0]\n", " label_x.append(key + r\" m\")\n", " # numerical solution\n", diff --git a/Tests/Data/TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb b/Tests/Data/TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb index fa2aa00986f..c957ffa7cca 100644 --- a/Tests/Data/TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb +++ b/Tests/Data/TH2M/TH/Ogata-Banks/Ogata-Banks.ipynb @@ -177,8 +177,7 @@ " a1 = np.divide((x - v_x * t), d, where=t != 0)\n", " a2 = np.divide((x + v_x * t), d, where=t != 0)\n", "\n", - " result = (T_0 - T_i) / 2.0 * (erfc(a1) + np.exp(v_x * x / alpha) * erfc(a2)) + T_i\n", - " return result" + " return (T_0 - T_i) / 2.0 * (erfc(a1) + np.exp(v_x * x / alpha) * erfc(a2)) + T_i" ] }, { @@ -302,7 +301,7 @@ "outputs": [], "source": [ "# The sample locations have to be converted into a 'dict' for vtuIO\n", - "observation_points = dict((\"x=\" + str(x), (x, 0.0, 0.0)) for x in location)\n", + "observation_points = {\"x=\" + str(x): (x, 0.0, 0.0) for x in location}\n", "# Samples temperature field at the observation points for all timesteps\n", "T_over_t_at_x = pvdfile.read_time_series(\"temperature_interpolated\", observation_points)" ] @@ -351,7 +350,7 @@ "ax1.set_ylabel(\"$T$ / K\", fontsize=12)\n", "\n", "# Plot Temperature over time at five locations\n", - "for key, T in T_over_t_at_x.items():\n", + "for key, _T in T_over_t_at_x.items():\n", " x = observation_points[key][0]\n", " # Plot numerical solution\n", " ax1.plot(\n", diff --git a/Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb b/Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb index b1589d76812..b52a01c3f1b 100644 --- a/Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb +++ b/Tests/Data/TH2M/TH2/heatpipe/heatpipe.ipynb @@ -410,10 +410,9 @@ " k_rL = relative_permeability_liquid(sL_eff)\n", " nu_G = kinematic_viscosity_gas_phase(p_G, xA_G, T)\n", " nu_L = mu_L / rho_L\n", - " result = ((1.0 + ((rho_L * R * T) / (p_G * MW * (1.0 - xA_G)))) / k_rG) + (\n", + " return ((1.0 + ((rho_L * R * T) / (p_G * MW * (1.0 - xA_G)))) / k_rG) + (\n", " nu_L / nu_G\n", " ) / k_rL\n", - " return result\n", "\n", "\n", "def zeta_(sL_eff, p_G, xA_G, T):\n", @@ -525,8 +524,7 @@ "\n", "\n", "def step_Euler(y, sL_eff, dsL_eff, p_G, xA_G, T):\n", - " next_y = y + dsL_eff * dy_dsL_eff(y, sL_eff, p_G, xA_G, T)\n", - " return next_y\n", + " return y + dsL_eff * dy_dsL_eff(y, sL_eff, p_G, xA_G, T)\n", "\n", "\n", "def full_Euler(dsL_eff, y0, sL_eff_low, sL_eff_high):\n", diff --git a/Tests/Python/test_matrix_debug_output.py b/Tests/Python/test_matrix_debug_output.py index 11e3ade32d4..6aad9c108f7 100644 --- a/Tests/Python/test_matrix_debug_output.py +++ b/Tests/Python/test_matrix_debug_output.py @@ -139,16 +139,16 @@ def test_local_matrix_debug_output(monkeypatch, prefix_parameter, elements_param # https://docs.pytest.org/en/6.2.x/reference.html#pytest.MonkeyPatch with monkeypatch.context() as ctx: # prepare environment - if prefix_setting == False: + if prefix_setting is False: pass - elif prefix_setting == True: + elif prefix_setting is True: ctx.setenv("OGS_LOCAL_MAT_OUT_PREFIX", tmpdirname + os.sep) elif prefix_setting == "" or prefix_setting.startswith("."): ctx.setenv("OGS_LOCAL_MAT_OUT_PREFIX", prefix_setting) # change to the temporary directory such that log files will be written there. ctx.chdir(tmpdirname) - if elements_setting != False: + if elements_setting is not False: ctx.setenv("OGS_LOCAL_MAT_OUT_ELEMENTS", elements_setting) # run and test @@ -200,9 +200,9 @@ def test_global_matrix_debug_output(monkeypatch, prefix_parameter): # https://docs.pytest.org/en/6.2.x/reference.html#pytest.MonkeyPatch with monkeypatch.context() as ctx: # prepare environment - if prefix_setting == False: + if prefix_setting is False: pass - elif prefix_setting == True: + elif prefix_setting is True: ctx.setenv("OGS_GLOBAL_MAT_OUT_PREFIX", tmpdirname + os.sep) elif prefix_setting == "" or prefix_setting.startswith("."): ctx.setenv("OGS_GLOBAL_MAT_OUT_PREFIX", prefix_setting) diff --git a/Tests/Python/test_ogs_asm_threads.py b/Tests/Python/test_ogs_asm_threads.py index 791cd617d12..b0f1d350fcf 100644 --- a/Tests/Python/test_ogs_asm_threads.py +++ b/Tests/Python/test_ogs_asm_threads.py @@ -76,7 +76,7 @@ def test_ogs_asm_threads_env_var(monkeypatch, asm_threads_parameter): # https://docs.pytest.org/en/6.2.x/reference.html#pytest.MonkeyPatch with monkeypatch.context() as ctx: # prepare environment - if asm_threads_setting != False: + if asm_threads_setting is not False: ctx.setenv("OGS_ASM_THREADS", asm_threads_setting) ctx.chdir(tmpdirname) diff --git a/Tests/Python/test_simulator_mesh_interface.py b/Tests/Python/test_simulator_mesh_interface.py index 0a05caa1830..92536219b1b 100644 --- a/Tests/Python/test_simulator_mesh_interface.py +++ b/Tests/Python/test_simulator_mesh_interface.py @@ -7,19 +7,17 @@ def crossProduct(v, w): - u = np.array( + return np.array( [ v[1] * w[2] - v[2] * w[1], v[2] * w[0] - v[0] * w[2], v[0] * w[1] - v[1] * w[0], ] ) - return u def computeVectorFromPoints(a, b): - v = np.array([b[0] - a[0], b[1] - a[1], b[2] - a[2]]) - return v + return np.array([b[0] - a[0], b[1] - a[1], b[2] - a[2]]) def computeTriArea(a, b, c): diff --git a/scripts/doc/append-xml-tags.py b/scripts/doc/append-xml-tags.py index 7546f6c055a..a95532a8b58 100755 --- a/scripts/doc/append-xml-tags.py +++ b/scripts/doc/append-xml-tags.py @@ -91,8 +91,8 @@ def write_parameter_type_info(fh, tagpath, tagpath_expanded, dict_tag_info): fh.write(f"- Expanded tag path: {tagpath_expanded}\n") fh.write( - "- Go to source code: [→ ogs/ogs/master]({2}/{0}#L{1})\n".format( - path, line, github_src_url + "- Go to source code: [→ ogs/ogs/master]({}/{}#L{})\n".format( + github_src_url, path, line ) ) else: @@ -199,7 +199,7 @@ def dict_of_list_append(dict_, key, value): # maps tags to additional parameter info obtained prior to this script -dict_tag_info = dict() +dict_tag_info = {} with open(os.path.join(docauxdir, "tested-parameters-cache.json")) as fh: tested_tags_attrs = json.load(fh) @@ -224,7 +224,7 @@ def dict_of_list_append(dict_, key, value): if not f.endswith(".dox"): continue - if f.startswith("i_") or f.startswith("c_"): + if f.startswith(("i_", "c_")): tagpath = reldirpath elif f.startswith("t_"): tagpath = os.path.join(reldirpath, f[2 : -len(".dox")]) diff --git a/scripts/doc/check-project-params.py b/scripts/doc/check-project-params.py index d57c3a0f433..90b5d7f5f42 100755 --- a/scripts/doc/check-project-params.py +++ b/scripts/doc/check-project-params.py @@ -33,7 +33,7 @@ def debug(msg): unneeded_comments = [] wrong_input = [] no_doc_page = [] -unneeded_md_files = dict() +unneeded_md_files = {} good_tagpaths = set() wrong_status = False @@ -90,7 +90,7 @@ def debug(msg): filepath = os.path.join(reldirpath, f) tag_or_attr = "param" - if f.startswith("i_") or f.startswith("c_"): + if f.startswith(("i_", "c_")): tagpath = reldirpath elif f.startswith("t_"): tagpath = os.path.join(reldirpath, f[2 : -len(".md")]) diff --git a/scripts/doc/extract-media-properties-from-ctests.py b/scripts/doc/extract-media-properties-from-ctests.py index 8673824f3d6..d4341a901df 100755 --- a/scripts/doc/extract-media-properties-from-ctests.py +++ b/scripts/doc/extract-media-properties-from-ctests.py @@ -25,16 +25,14 @@ def parse_and_filter_prj_file(file_path): elif p == "OpenGeoSysProject/processes/process": yield obj # this is interesting, yield it elif p.startswith("OpenGeoSysProject/media/"): - if elem.tag in set(("type", "name")): + if elem.tag in {"type", "name"}: parent = objs[-1] parent[elem.tag] = elem.text - elif elem.tag in set( - ( - "phase", - # "component", - "property", - ) - ): + elif elem.tag in { + "phase", + # "component", + "property", + }: yield obj # this is interesting, yield it @@ -42,7 +40,7 @@ def parse_all_prj_files(datadir): records = [] map_file_path_to_pcs_type = {} - for i, (root, dirs, files) in enumerate(os.walk(datadir)): + for _i, (root, _dirs, files) in enumerate(os.walk(datadir)): for f in files: if not f.endswith(".prj"): continue @@ -75,7 +73,7 @@ def parse_all_prj_files(datadir): df_pcst = pd.DataFrame.from_dict( map_file_path_to_pcs_type, orient="index", columns=["pcs_type"] ) - df_n_t_p_pcst = ( + return ( df_n_t_p.join(df_pcst) .drop_duplicates() .sort_values(["pcs_type", "path", "name"]) @@ -83,7 +81,6 @@ def parse_all_prj_files(datadir): ) # columns: Name, Type, ~xml xPath, ProCeSs Type - return df_n_t_p_pcst def main(datadir, docauxdir): diff --git a/scripts/doc/linked-xml-file.py b/scripts/doc/linked-xml-file.py index 2c01d5d4e0f..a232b712ccd 100755 --- a/scripts/doc/linked-xml-file.py +++ b/scripts/doc/linked-xml-file.py @@ -51,7 +51,7 @@ def format_if_documented(is_doc, fmt, fullpagename, tag_attr, *args): tag_attr_formatted = rf'\ref {fullpagename} "{tag_attr}"' else: tag_attr_formatted = ( - r'{1}'.format( + r'{}'.format( fullpagename, tag_attr ) ) @@ -223,17 +223,14 @@ def print_tags(node, level, pagename, fh, typetag, typetag_levels_up, relfilepat def has_prj_file_in_subdirs(reldirpath): - for dn in dirs_with_prj_files: - if dn.startswith(reldirpath): - return True - return False + return any(dn.startswith(reldirpath) for dn in dirs_with_prj_files) dirs_with_prj_files = set() # maps tags/attributes to the set of prj files they appear in -map_tag_to_prj_files = dict() -map_attr_to_prj_files = dict() +map_tag_to_prj_files = {} +map_attr_to_prj_files = {} def dict_of_set_add(dos, key, value): @@ -265,12 +262,11 @@ def dict_of_set_add(dos, key, value): with open(outdoxfile, "w") as fh: fh.write( - r"""/*! \page %s %s + rf"""/*! \page {pagename} {fn} \parblock """ - % (pagename, fn) ) try: diff --git a/web/content/docs/tutorials/advancing-glacier/timeBCs_glacier.py b/web/content/docs/tutorials/advancing-glacier/timeBCs_glacier.py index a1e364140fb..3860321dec9 100644 --- a/web/content/docs/tutorials/advancing-glacier/timeBCs_glacier.py +++ b/web/content/docs/tutorials/advancing-glacier/timeBCs_glacier.py @@ -15,7 +15,7 @@ class BC_Y(OpenGeoSys.BoundaryCondition): def __init__(self, L_dom, L_max, H_max, x_0, t_0, t_1): - super(BC_Y, self).__init__() + super().__init__() # instantiate the glacier member object self.glacier = glc.glacier(L_dom, L_max, H_max, x_0, t_0, t_1) self.glacier.printMaxLoads() From 175fe5abcd83e4a1254a557444ba7b1e75cc01ac Mon Sep 17 00:00:00 2001 From: Lars Bilke Date: Tue, 28 Nov 2023 10:01:47 +0100 Subject: [PATCH 3/6] [py] Ignore some errors. --- Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py | 1 + Tests/Data/EllipticPETSc/square_1e1_neumann-insitu.py | 1 + scripts/python/scikit-build-plugins/scripts/__init__.py | 5 ++++- 3 files changed, 6 insertions(+), 1 deletion(-) diff --git a/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py b/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py index 0526784fdb6..77b94ac4f95 100644 --- a/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py +++ b/Tests/Data/Elliptic/cube_1x1x1_SteadyStateDiffusion/cube.py @@ -1,3 +1,4 @@ +# ruff: noqa from paraview import coprocessing from paraview.simple import * diff --git a/Tests/Data/EllipticPETSc/square_1e1_neumann-insitu.py b/Tests/Data/EllipticPETSc/square_1e1_neumann-insitu.py index 16116bd95d0..a7fb15ed885 100644 --- a/Tests/Data/EllipticPETSc/square_1e1_neumann-insitu.py +++ b/Tests/Data/EllipticPETSc/square_1e1_neumann-insitu.py @@ -22,6 +22,7 @@ # paraview version 5.8.0 # -------------------------------------------------------------- +# ruff: noqa from paraview import coprocessing from paraview.simple import * diff --git a/scripts/python/scikit-build-plugins/scripts/__init__.py b/scripts/python/scikit-build-plugins/scripts/__init__.py index 73780553db2..f32578847ec 100644 --- a/scripts/python/scikit-build-plugins/scripts/__init__.py +++ b/scripts/python/scikit-build-plugins/scripts/__init__.py @@ -4,7 +4,10 @@ from pathlib import Path sys.path.append(str(Path("Applications").joinpath("Python").absolute())) -from ogs._internal.provide_ogs_cli_tools_via_wheel import pyproject_get_binaries +# ruff: noqa: E402 +from ogs._internal.provide_ogs_cli_tools_via_wheel import ( + pyproject_get_binaries, +) __all__ = ["dynamic_metadata"] From fd23e4016792f0e1e4f51af37b418ba3e8f022c7 Mon Sep 17 00:00:00 2001 From: Lars Bilke Date: Tue, 28 Nov 2023 10:03:08 +0100 Subject: [PATCH 4/6] [py] ruff: manual fixes for notebook testrunner. --- ThirdParty/container-maker | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ThirdParty/container-maker b/ThirdParty/container-maker index ca8cbad7276..9e105b36f7e 160000 --- a/ThirdParty/container-maker +++ b/ThirdParty/container-maker @@ -1 +1 @@ -Subproject commit ca8cbad727676b562bc91d7f096741fc1d40399a +Subproject commit 9e105b36f7e0500fdbd3f07a6d51d140988d839d From 1318f54d29d4e5063db74e5669f696a0c0099e08 Mon Sep 17 00:00:00 2001 From: Lars Bilke Date: Tue, 28 Nov 2023 10:04:07 +0100 Subject: [PATCH 5/6] [py] ruff: manual fixes for glacier tutorial. --- .../tutorials/advancing-glacier/glacierclass.py | 14 +++++++------- .../docs/tutorials/advancing-glacier/mesh_basin.py | 4 ++-- .../tutorials/advancing-glacier/timeBCs_glacier.py | 2 +- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/web/content/docs/tutorials/advancing-glacier/glacierclass.py b/web/content/docs/tutorials/advancing-glacier/glacierclass.py index 82f9cde739c..ccd75f95929 100644 --- a/web/content/docs/tutorials/advancing-glacier/glacierclass.py +++ b/web/content/docs/tutorials/advancing-glacier/glacierclass.py @@ -24,14 +24,14 @@ def normalstress(self, x, t): # analytical function for the glacier's shape def local_height(self, x, t): - l = self.length(t) - if l == 0: + length = self.length(t) + if length == 0: return 0 * x - else: - xi = (x - self.x_0) / l - xi = np.array(xi) - xi[xi > 1] = 1.0 - return self.height(t) * np.sqrt(1 - xi**1) + + xi = (x - self.x_0) / length + xi = np.array(xi) + xi[xi > 1] = 1.0 + return self.height(t) * np.sqrt(1 - xi**1) def height(self, t): return self.H_max * (t - self.t_0) / self.t_1 diff --git a/web/content/docs/tutorials/advancing-glacier/mesh_basin.py b/web/content/docs/tutorials/advancing-glacier/mesh_basin.py index f3f3f50868c..5f3b22da946 100644 --- a/web/content/docs/tutorials/advancing-glacier/mesh_basin.py +++ b/web/content/docs/tutorials/advancing-glacier/mesh_basin.py @@ -118,8 +118,8 @@ # loop defines the exterior contour; additional curve loop define holes. # (only one here, representing the external contour, since there are no holes # --see `t4.py' for an example of a surface with a hole): -for l in range(1, 5): - gmsh.model.geo.addPlaneSurface([l], l) +for L in range(1, 5): + gmsh.model.geo.addPlaneSurface([L], L) # At this level, Gmsh knows everything to display the surfaces and # to mesh it. An optional step is needed if we want to group elementary diff --git a/web/content/docs/tutorials/advancing-glacier/timeBCs_glacier.py b/web/content/docs/tutorials/advancing-glacier/timeBCs_glacier.py index 3860321dec9..a35006ce43a 100644 --- a/web/content/docs/tutorials/advancing-glacier/timeBCs_glacier.py +++ b/web/content/docs/tutorials/advancing-glacier/timeBCs_glacier.py @@ -22,7 +22,7 @@ def __init__(self, L_dom, L_max, H_max, x_0, t_0, t_1): self.glacier.plotEvolution() def getFlux( - self, t, coords, primary_vars + self, t, coords, _primary_vars ): # here Neumann BC: flux of linear momentum x, y, z = coords From 5f6fc125308fee229c1808d4895413b6f989d061 Mon Sep 17 00:00:00 2001 From: Lars Bilke Date: Tue, 28 Nov 2023 10:04:36 +0100 Subject: [PATCH 6/6] [py] ruff: misc manual fixes. --- CMakePresets.json | 1 + Tests/Python/test_simulator_mesh_interface.py | 5 +++-- scripts/snakemake/vtkdiff/wrapper.py | 8 +++++--- scripts/test/cppcheck_gen_hashes.py | 5 +++-- scripts/test/generate_coverage_vis_data.in.py | 3 ++- .../BHE_array_benchmark/bhe_array_analytical_solver.py | 2 +- 6 files changed, 15 insertions(+), 9 deletions(-) diff --git a/CMakePresets.json b/CMakePresets.json index e983c4b7cd1..9c804dcec73 100644 --- a/CMakePresets.json +++ b/CMakePresets.json @@ -213,6 +213,7 @@ "OGS_INSTALL_DEPENDENCIES": "OFF", "OGS_USE_PIP": "OFF", "OGS_USE_MFRONT": "ON", + "DOGS_BUILD_PROCESSES": "SteadyStateDiffusion", "BUILD_SHARED_LIBS": "ON" } } diff --git a/Tests/Python/test_simulator_mesh_interface.py b/Tests/Python/test_simulator_mesh_interface.py index 92536219b1b..2221300c65a 100644 --- a/Tests/Python/test_simulator_mesh_interface.py +++ b/Tests/Python/test_simulator_mesh_interface.py @@ -3,6 +3,7 @@ import tempfile import numpy as np +import ogs.mesh as mesh # noqa: F401 from ogs import simulator @@ -120,9 +121,9 @@ def test_simulator(): if len(read_back_bc_values) != len(bc_values_for_second_time_step): print( "Python: error: data array size mismatch: got " - + str(len(new_bc_values)) + + str(len(read_back_bc_values)) + ", expected " - + str(len(bc_values)) + + str(len(bc_values_for_second_time_step)) ) comparison = read_back_bc_values == bc_values_for_second_time_step if not comparison.all(): diff --git a/scripts/snakemake/vtkdiff/wrapper.py b/scripts/snakemake/vtkdiff/wrapper.py index cd140f6b5f3..78d3902da57 100644 --- a/scripts/snakemake/vtkdiff/wrapper.py +++ b/scripts/snakemake/vtkdiff/wrapper.py @@ -4,12 +4,14 @@ __copyright__ = "Copyright 2020, OpenGeoSys Community" __license__ = "BSD" -import os +from pathlib import Path from snakemake.shell import shell -if os.path.exists(snakemake.output[0]): - os.remove(snakemake.output[0]) +# ruff: noqa: F821 +output = Path(snakemake.output[0]) +if output.exists(): + output.unlink() if snakemake.params.check_mesh: shell("vtkdiff {snakemake.input.a} {snakemake.input.b} -m > {snakemake.output[0]}") diff --git a/scripts/test/cppcheck_gen_hashes.py b/scripts/test/cppcheck_gen_hashes.py index 729ba53e6e0..5779fdc9484 100644 --- a/scripts/test/cppcheck_gen_hashes.py +++ b/scripts/test/cppcheck_gen_hashes.py @@ -2,9 +2,10 @@ import hashlib import json import sys +from pathlib import Path data = None -with open(sys.argv[1]) as json_file: +with Path(sys.argv[1]).open() as json_file: data = json.load(json_file) for entry in data: @@ -13,7 +14,7 @@ hash = hashlib.sha256((desc + path).encode("utf-8")).hexdigest() entry["fingerprint"] = hash -with open(sys.argv[1], "w") as outfile: +with Path(sys.argv[1]).open("w") as outfile: json.dump(data, outfile) print(f"Added cppcheck fingerprints to {sys.argv[1]}.") diff --git a/scripts/test/generate_coverage_vis_data.in.py b/scripts/test/generate_coverage_vis_data.in.py index 1944db4f297..61b40be83cd 100755 --- a/scripts/test/generate_coverage_vis_data.in.py +++ b/scripts/test/generate_coverage_vis_data.in.py @@ -1,13 +1,14 @@ #!/usr/bin/env python import os +from pathlib import Path # need to increase OGS_CTEST_MAX_RUNTIME to enable these: # ctests = ["SurfaceComplexation", "EquilibriumPhase", "KineticReactant"] ctests = ["SteadyState", "ComponentTransport", "ThermoHydroMechanics"] report_path = "./coverage_reports" -os.makedirs(report_path, exist_ok=True) +Path(report_path).mkdir(parents=True, exist_ok=True) for t in ctests: os.system("cmake --build . -t clean_coverage") diff --git a/web/content/docs/benchmarks/heatconduction/BHE_array_benchmark/bhe_array_analytical_solver.py b/web/content/docs/benchmarks/heatconduction/BHE_array_benchmark/bhe_array_analytical_solver.py index 89cd7a4cad1..b74b703802b 100644 --- a/web/content/docs/benchmarks/heatconduction/BHE_array_benchmark/bhe_array_analytical_solver.py +++ b/web/content/docs/benchmarks/heatconduction/BHE_array_benchmark/bhe_array_analytical_solver.py @@ -13,7 +13,7 @@ Bayer, P., de Paly, M., & Beck, M. (2014). Strategic optimization of borehole heat exchanger field for seasonal geothermal heating and cooling. -Applied Energy, 136, 445–453. +Applied Energy, 136, 445-453. https://doi.org/10.1016/j.apenergy.2014.09.029 Author: Shuang Chen