diff --git a/Two-phase-Two-phase/two-patch/injection/TP-TP-2-patch-injection.py b/Two-phase-Two-phase/two-patch/injection/TP-TP-2-patch-injection.py
new file mode 100755
index 0000000000000000000000000000000000000000..6206cd934a9cc9d2930cc7c73069686509c99fe4
--- /dev/null
+++ b/Two-phase-Two-phase/two-patch/injection/TP-TP-2-patch-injection.py
@@ -0,0 +1,608 @@
+#!/usr/bin/python3
+import dolfin as df
+import mshr
+import numpy as np
+import sympy as sym
+import typing as tp
+import domainPatch as dp
+import LDDsimulation as ldd
+import functools as ft
+import helpers as hlp
+import datetime
+import os
+import pandas as pd
+
+date = datetime.datetime.now()
+datestr = date.strftime("%Y-%m-%d")
+#import ufl as ufl
+
+# init sympy session
+sym.init_printing()
+
+use_case = "TP-TP-2-patch-injection"
+# solver_tol = 5E-7
+max_iter_num = 1000
+FEM_Lagrange_degree = 1
+mesh_study = False
+resolutions = {
+ # 1: 1e-7, # h=2
+ # 2: 2e-5, # h=1.1180
+ # 4: 1e-6, # h=0.5590
+ # 8: 1e-6, # h=0.2814
+ # 16: 5e-7, # h=0.1412
+ 32: 2e-6,
+ # 64: 5e-7,
+ # 128: 5e-7
+ }
+
+
+############ GRID #######################
+# mesh_resolution = 20
+timestep_size = 0.000001
+number_of_timesteps = 13
+plot_timestep_every = 1
+# decide how many timesteps you want analysed. Analysed means, that we write out
+# subsequent errors of the L-iteration within the timestep.
+number_of_timesteps_to_analyse = 0
+starttime = 0.0
+
+Lw = 0.5 #/timestep_size
+Lnw=Lw
+
+lambda_w = 4000
+lambda_nw = 4000
+
+include_gravity = False
+debugflag = True
+analyse_condition = True
+
+if mesh_study:
+ output_string = "./output/{}-{}_timesteps{}_P{}".format(datestr, use_case, number_of_timesteps, FEM_Lagrange_degree)
+else:
+ for tol in resolutions.values():
+ solver_tol = tol
+ output_string = "./output/{}-{}_timesteps{}_P{}_solver_tol{}".format(datestr, use_case, number_of_timesteps, FEM_Lagrange_degree, solver_tol)
+
+
+# toggle what should be written to files
+if mesh_study:
+ write_to_file = {
+ 'space_errornorms': True,
+ 'meshes_and_markers': True,
+ 'L_iterations_per_timestep': False,
+ 'solutions': False,
+ 'absolute_differences': False,
+ 'condition_numbers': analyse_condition,
+ 'subsequent_errors': False
+ }
+else:
+ write_to_file = {
+ 'space_errornorms': True,
+ 'meshes_and_markers': True,
+ 'L_iterations_per_timestep': False,
+ 'solutions': True,
+ 'absolute_differences': True,
+ 'condition_numbers': analyse_condition,
+ 'subsequent_errors': True
+ }
+
+##### Domain and Interface ####
+# global simulation domain domain
+sub_domain0_vertices = [df.Point(-1.0,-1.0), #
+ df.Point(1.0,-1.0),#
+ df.Point(1.0,1.0),#
+ df.Point(-1.0,1.0)]
+# interface between subdomain1 and subdomain2
+interface12_vertices = [df.Point(-1.0, 0.9),
+ df.Point(1.0, 0.4) ]
+ # interface equation: y = -1/4*x + 13/20
+# subdomain1.
+sub_domain1_vertices = [interface12_vertices[0],
+ interface12_vertices[1],
+ sub_domain0_vertices[2],
+ sub_domain0_vertices[3] ]
+
+# vertex coordinates of the outer boundaries. If it can not be specified as a
+# polygon, use an entry per boundary polygon. This information is used for defining
+# the Dirichlet boundary conditions. If a domain is completely internal, the
+# dictionary entry should be 0: None
+subdomain1_outer_boundary_verts = {
+ 0: [interface12_vertices[1],
+ sub_domain0_vertices[2],
+ sub_domain0_vertices[3], #
+ interface12_vertices[0]]
+}
+# subdomain2
+sub_domain2_vertices = [sub_domain0_vertices[0],
+ sub_domain0_vertices[1],
+ interface12_vertices[1],
+ interface12_vertices[0] ]
+
+subdomain2_outer_boundary_verts = {
+ 0: [interface12_vertices[0], #
+ sub_domain0_vertices[0],
+ sub_domain0_vertices[1],
+ interface12_vertices[1]]
+}
+
+# list of subdomains given by the boundary polygon vertices.
+# Subdomains are given as a list of dolfin points forming
+# a closed polygon, such that mshr.Polygon(subdomain_def_points[i]) can be used
+# to create the subdomain. subdomain_def_points[0] contains the
+# vertices of the global simulation domain and subdomain_def_points[i] contains the
+# vertices of the subdomain i.
+subdomain_def_points = [sub_domain0_vertices,#
+ sub_domain1_vertices,#
+ sub_domain2_vertices]
+# in the below list, index 0 corresponds to the 12 interface which has index 1
+interface_def_points = [interface12_vertices]
+
+# if a subdomain has no outer boundary write None instead, i.e.
+# i: None
+# if i is the index of the inner subdomain.
+outer_boundary_def_points = {
+ # subdomain number
+ 1 : subdomain1_outer_boundary_verts,
+ 2 : subdomain2_outer_boundary_verts
+}
+
+# adjacent_subdomains[i] contains the indices of the subdomains sharing the
+# interface i (i.e. given by interface_def_points[i]).
+adjacent_subdomains = [[1,2]]
+isRichards = {
+ 1: False, #
+ 2: False
+ }
+
+
+viscosity = {#
+# subdom_num : viscosity
+ 1 : {'wetting' :1,
+ 'nonwetting': 1/50}, #
+ 2 : {'wetting' :1,
+ 'nonwetting': 1/50}
+}
+
+porosity = {#
+# subdom_num : porosity
+ 1 : 0.2,#
+ 2 : 0.002
+}
+
+# Dict of the form: { subdom_num : density }
+densities = {
+ 1: {'wetting': 997, #997,
+ 'nonwetting': 1225}, #1225},
+ 2: {'wetting': 997, #997,
+ 'nonwetting': 1225}, #1225},
+}
+
+gravity_acceleration = 9.81
+
+L = {#
+# subdom_num : subdomain L for L-scheme
+ 1 : {'wetting' :Lw,
+ 'nonwetting': Lnw},#
+ 2 : {'wetting' :Lw,
+ 'nonwetting': Lnw}
+}
+
+
+lambda_param = {#
+# subdom_num : lambda parameter for the L-scheme
+ 1 : {'wetting' :lambda_w,
+ 'nonwetting': lambda_nw},#
+ 2 : {'wetting' :lambda_w,
+ 'nonwetting': lambda_nw}
+}
+
+## relative permeabilty functions on subdomain 1
+def rel_perm1w(s):
+ # relative permeabilty wetting on subdomain1
+ return s**2
+
+def rel_perm1nw(s):
+ # relative permeabilty nonwetting on subdomain1
+ return (1-s)**2
+
+_rel_perm1w = ft.partial(rel_perm1w)
+_rel_perm1nw = ft.partial(rel_perm1nw)
+
+subdomain1_rel_perm = {
+ 'wetting': _rel_perm1w,#
+ 'nonwetting': _rel_perm1nw
+}
+## relative permeabilty functions on subdomain 2
+def rel_perm2w(s):
+ # relative permeabilty wetting on subdomain2
+ return s**3
+def rel_perm2nw(s):
+ # relative permeabilty nonwetting on subdosym.cos(0.8*t - (0.8*x + 1/7*y))main2
+ return (1-s)**3
+
+_rel_perm2w = ft.partial(rel_perm2w)
+_rel_perm2nw = ft.partial(rel_perm2nw)
+
+subdomain2_rel_perm = {
+ 'wetting': _rel_perm2w,#
+ 'nonwetting': _rel_perm2nw
+}
+
+## dictionary of relative permeabilties on all domains.
+relative_permeability = {#
+ 1: subdomain1_rel_perm,
+ 2: subdomain2_rel_perm
+}
+
+
+# definition of the derivatives of the relative permeabilities
+# relative permeabilty functions on subdomain 1
+def rel_perm1w_prime(s):
+ # relative permeabilty on subdomain1
+ return 2*s
+
+def rel_perm1nw_prime(s):
+ # relative permeabilty on subdomain1
+ return -2*(1-s)
+
+# # definition of the derivatives of the relative permeabilities
+# # relative permeabilty functions on subdomain 1
+def rel_perm2w_prime(s):
+ # relative permeabilty on subdomain1
+ return 3*s**2
+
+def rel_perm2nw_prime(s):
+ # relative permeabilty on subdomain1
+ return -3*(1-s)**2
+
+_rel_perm1w_prime = ft.partial(rel_perm1w_prime)
+_rel_perm1nw_prime = ft.partial(rel_perm1nw_prime)
+_rel_perm2w_prime = ft.partial(rel_perm2w_prime)
+_rel_perm2nw_prime = ft.partial(rel_perm2nw_prime)
+
+subdomain1_rel_perm_prime = {
+ 'wetting': _rel_perm1w_prime,
+ 'nonwetting': _rel_perm1nw_prime
+}
+
+
+subdomain2_rel_perm_prime = {
+ 'wetting': _rel_perm2w_prime,
+ 'nonwetting': _rel_perm2nw_prime
+}
+
+# dictionary of relative permeabilties on all domains.
+ka_prime = {
+ 1: subdomain1_rel_perm_prime,
+ 2: subdomain2_rel_perm_prime,
+}
+
+
+
+def saturation(pc, index):
+ # inverse capillary pressure-saturation-relationship
+ return df.conditional(pc > 0, 1/((1 + pc)**(1/(index + 1))), 1)
+
+
+def saturation_sym(pc, index):
+ # inverse capillary pressure-saturation-relationship
+ return 1/((1 + pc)**(1/(index + 1)))
+
+
+# derivative of S-pc relationship with respect to pc. This is needed for the
+# construction of a analytic solution.
+def saturation_sym_prime(pc, index):
+ # inverse capillary pressure-saturation-relationship
+ return -1/((index+1)*(1 + pc)**((index+2)/(index+1)))
+
+
+# note that the conditional definition of S-pc in the nonsymbolic part will be
+# incorporated in the construction of the exact solution below.
+S_pc_sym = {
+ 1: ft.partial(saturation_sym, index=3),
+ 2: ft.partial(saturation_sym, index=4),
+ # 3: ft.partial(saturation_sym, index=2),
+ # 4: ft.partial(saturation_sym, index=1)
+}
+
+S_pc_sym_prime = {
+ 1: ft.partial(saturation_sym_prime, index=3),
+ 2: ft.partial(saturation_sym_prime, index=4),
+ # 3: ft.partial(saturation_sym_prime, index=2),
+ # 4: ft.partial(saturation_sym_prime, index=1)
+}
+
+sat_pressure_relationship = {
+ 1: ft.partial(saturation, index=3),
+ 2: ft.partial(saturation, index=4),
+ # 3: ft.partial(saturation, index=2),
+ # 4: ft.partial(saturation, index=1)
+}
+
+#
+# def saturation(pc, n_index, alpha):
+# # inverse capillary pressure-saturation-relationship
+# return df.conditional(pc > 0, 1/((1 + (alpha*pc)**n_index)**((n_index - 1)/n_index)), 1)
+#
+# # S-pc-relation ship. We use the van Genuchten approach, i.e. pc = 1/alpha*(S^{-1/m} -1)^1/n, where
+# # we set alpha = 0, assume m = 1-1/n (see Helmig) and assume that residual saturation is Sw
+# def saturation_sym(pc, n_index, alpha):
+# # inverse capillary pressure-saturation-relationship
+# #df.conditional(pc > 0,
+# return 1/((1 + (alpha*pc)**n_index)**((n_index - 1)/n_index))
+#
+#
+# # derivative of S-pc relationship with respect to pc. This is needed for the
+# # construction of a analytic solution.
+# def saturation_sym_prime(pc, n_index, alpha):
+# # inverse capillary pressure-saturation-relationship
+# return -(alpha*(n_index - 1)*(alpha*pc)**(n_index - 1)) / ( (1 + (alpha*pc)**n_index)**((2*n_index - 1)/n_index) )
+#
+# # note that the conditional definition of S-pc in the nonsymbolic part will be
+# # incorporated in the construction of the exact solution below.
+# S_pc_sym = {
+# 1: ft.partial(saturation_sym, n_index=3, alpha=0.001),
+# 2: ft.partial(saturation_sym, n_index=6, alpha=0.001),
+# # 3: ft.partial(saturation_sym, n_index=3, alpha=0.001),
+# # 4: ft.partial(saturation_sym, n_index=3, alpha=0.001),
+# # 5: ft.partial(saturation_sym, n_index=3, alpha=0.001),
+# # 6: ft.partial(saturation_sym, n_index=3, alpha=0.001)
+# }
+#
+# S_pc_sym_prime = {
+# 1: ft.partial(saturation_sym_prime, n_index=3, alpha=0.001),
+# 2: ft.partial(saturation_sym_prime, n_index=6, alpha=0.001),
+# # 3: ft.partial(saturation_sym_prime, n_index=3, alpha=0.001),
+# # 4: ft.partial(saturation_sym_prime, n_index=3, alpha=0.001),
+# # 5: ft.partial(saturation_sym_prime, n_index=3, alpha=0.001),
+# # 6: ft.partial(saturation_sym_prime, n_index=3, alpha=0.001)
+# }
+#
+# sat_pressure_relationship = {
+# 1: ft.partial(saturation, n_index=3, alpha=0.001),
+# 2: ft.partial(saturation, n_index=6, alpha=0.001),
+# # 3: ft.partial(saturation, n_index=3, alpha=0.001),
+# # 4: ft.partial(saturation, n_index=3, alpha=0.001),
+# # 5: ft.partial(saturation, n_index=3, alpha=0.001),
+# # 6: ft.partial(saturation, n_index=3, alpha=0.001)
+# }
+#
+
+
+#############################################
+# Manufacture source expressions with sympy #
+#############################################
+x, y = sym.symbols('x[0], x[1]') # needed by UFL
+t = sym.symbols('t', positive=True)
+
+initial_condition = {
+ 1: {'wetting': sym.printing.ccode(-6 - (1+t*t)*(1 + x*x + y*y)), #*cutoff,
+ 'nonwetting': sym.printing.ccode(-1 -t*(1.1+ y*y))}, #*(sym.sin((1+y)/2*sym.pi)*sym.sin((1+x)/2*sym.pi))**2},
+ 2: {'wetting': sym.printing.ccode(-6.0 - (1.0 + t*t)*(1.0 + x*x)), #*(sym.sin((1+y)/2*sym.pi)*sym.sin((1+x)/2*sym.pi))**2,
+ 'nonwetting': sym.printing.ccode(-1 -t*(1.1 + y*y) - sym.sin((x*y-0.5*t)*y**2)**2)}, #*(sym.sin((1+y)/2*sym.pi)*sym.sin((1+x)/2*sym.pi))**2},
+}
+
+### constructing source experessions.
+injection_coord = [-0.85, -0.8]
+extraction_coord = [0.75, 0.7]
+injection_radius = 0.075
+extraction_radius = 0.075
+# epsilon_y_inner = epsilon_x_inner
+# epsilon_y_outer = epsilon_x_outer
+#
+# def mollifier(x, epsilon):
+# """ one d mollifier """
+# out_expr = sym.exp(-1/(1-(x/epsilon)**2) + 1)
+# return out_expr
+#
+# mollifier_handle = ft.partial(mollifier, epsilon=epsilon_x_inner)
+#
+# pw_sym_x = sym.Piecewise(
+# (mollifier_handle(x), x**2 < epsilon_x_outer**2),
+# (0, True)
+# )
+# pw_sym_y = sym.Piecewise(
+# (mollifier_handle(y), y**2 < epsilon_y_outer**2),
+# (0, True)
+# )
+#
+def mollifier2d(x, y, epsilon):
+ """ one d mollifier """
+ out_expr = sym.exp(-1/(1-(x**2 + y**2)/epsilon**2))
+ return out_expr
+
+mollifier2d_handle_i = ft.partial(mollifier2d, epsilon=injection_radius)
+
+source_in = sym.Piecewise(
+ (0*t + mollifier2d_handle_i(x, y), (x-injection_coord[0])**2 + (y-injection_coord[1])**2 < injection_radius**2),
+ (0*t, True)
+)
+
+mollifier2d_handle_e = ft.partial(mollifier2d, epsilon=extraction_radius)
+
+source_ext = sym.Piecewise(
+ (0*t + mollifier2d_handle_e(x, y), (x-extraction_coord[0])**2 + (y-extraction_coord[1])**2 < extraction_radius**2),
+ (0*t, True)
+)
+
+extraction_water_ratio = 0.7
+injection_water_ratio = 0.7
+
+source_expression = {
+ 1: {"wetting": sym.printing.ccode(extraction_water_ratio*source_ext),
+ "nonwetting": sym.printing.ccode((1-extraction_water_ratio)*source_ext)},
+ 2: {"wetting": sym.printing.ccode(injection_water_ratio*source_in),
+ "nonwetting": sym.printing.ccode((1-injection_water_ratio)*source_in)}
+}
+
+exact_solution = None
+#
+# zero_on_epsilon_shrinking_of_subdomain = sym.Piecewise(
+# (mollifier_handle(sym.sqrt(x**2 + y**2)+2*epsilon_x_inner), ((-2*epsilon_x_inner<sym.sqrt(x**2 + y**2)) & (sym.sqrt(x**2 + y**2)<-epsilon_x_inner))),
+# (0, ((-epsilon_x_inner<=sym.sqrt(x**2 + y**2)) & (sym.sqrt(x**2 + y**2)<=epsilon_x_inner))),
+# (mollifier_handle(sym.sqrt(x**2 + y**2)-2*epsilon_x_inner), ((epsilon_x_inner<sym.sqrt(x**2 + y**2)) & (sym.sqrt(x**2 + y**2)<2*epsilon_x_inner))),
+# (1, True),
+# )
+#
+# zero_on_epsilon_shrinking_of_subdomain_x = sym.Piecewise(
+# (mollifier_handle(x+2*epsilon_x_inner), ((-2*epsilon_x_inner<x) & (x<-epsilon_x_inner))),
+# (0, ((-epsilon_x_inner<=x) & (x<=epsilon_x_inner))),
+# (mollifier_handle(x-2*epsilon_x_inner), ((epsilon_x_inner<x) & (x<2*epsilon_x_inner))),
+# (1, True),
+# )
+#
+# zero_on_epsilon_shrinking_of_subdomain_y = sym.Piecewise(
+# (1, y<=-2*epsilon_x_inner),
+# (mollifier_handle(y+2*epsilon_x_inner), ((-2*epsilon_x_inner<y) & (y<-epsilon_x_inner))),
+# (0, ((-epsilon_x_inner<=y) & (y<=epsilon_x_inner))),
+# (mollifier_handle(y-2*epsilon_x_inner), ((epsilon_x_inner<y) & (y<2*epsilon_x_inner))),
+# (1, True),
+# )
+#
+# zero_on_shrinking = zero_on_epsilon_shrinking_of_subdomain #zero_on_epsilon_shrinking_of_subdomain_x + zero_on_epsilon_shrinking_of_subdomain_y
+# gaussian = pw_sym2d_x# pw_sym_y*pw_sym_x
+# cutoff = gaussian/(gaussian + zero_on_shrinking)
+
+
+# pc_e_sym = dict()
+# for subdomain, isR in isRichards.items():
+# if isR:
+# pc_e_sym.update({subdomain: -p_e_sym[subdomain]['wetting'].copy()})
+# else:
+# pc_e_sym.update({subdomain: p_e_sym[subdomain]['nonwetting'].copy()
+# - p_e_sym[subdomain]['wetting'].copy()})
+
+
+symbols = {"x": x,
+ "y": y,
+ "t": t}
+# # turn above symbolic code into exact solution for dolphin and
+# # construct the rhs that matches the above exact solution.
+# exact_solution_example = hlp.generate_exact_solution_expressions(
+# symbols=symbols,
+# isRichards=isRichards,
+# symbolic_pressure=p_e_sym,
+# symbolic_capillary_pressure=pc_e_sym,
+# saturation_pressure_relationship=S_pc_sym,
+# saturation_pressure_relationship_prime=S_pc_sym_prime,
+# viscosity=viscosity,
+# porosity=porosity,
+# relative_permeability=relative_permeability,
+# relative_permeability_prime=ka_prime,
+# densities=densities,
+# gravity_acceleration=gravity_acceleration,
+# include_gravity=include_gravity,
+# )
+# source_expression = exact_solution_example['source']
+# exact_solution = exact_solution_example['exact_solution']
+# initial_condition = exact_solution_example['initial_condition']
+
+# Dictionary of dirichlet boundary conditions.
+dirichletBC = dict()
+# similarly to the outer boundary dictionary, if a patch has no outer boundary
+# None should be written instead of an expression.
+# This is a bit of a brainfuck:
+# dirichletBC[ind] gives a dictionary of the outer boundaries of subdomain ind.
+# Since a domain patch can have several disjoint outer boundary parts, the
+# expressions need to get an enumaration index which starts at 0.
+# So dirichletBC[ind][j] is the dictionary of outer dirichlet conditions of
+# subdomain ind and boundary part j.
+# Finally, dirichletBC[ind][j]['wetting'] and dirichletBC[ind][j]['nonwetting']
+# return the actual expression needed for the dirichlet condition for both
+# phases if present.
+
+# subdomain index: {outer boudary part index: {phase: expression}}
+for subdomain in isRichards.keys():
+ # if subdomain has no outer boundary, outer_boundary_def_points[subdomain] is None
+ if outer_boundary_def_points[subdomain] is None:
+ dirichletBC.update({subdomain: None})
+ else:
+ dirichletBC.update({subdomain: dict()})
+ # set the dirichlet conditions to be the same code as exact solution on
+ # the subdomain.
+ for outer_boundary_ind in outer_boundary_def_points[subdomain].keys():
+ dirichletBC[subdomain].update(
+ # {outer_boundary_ind: exact_solution[subdomain]}
+ {
+ outer_boundary_ind: {
+ "wetting": sym.printing.ccode(0*t),
+ "nonwetting": sym.printing.ccode(0*t)
+ }
+ }
+ )
+
+
+# def saturation(pressure, subdomain_index):
+# # inverse capillary pressure-saturation-relationship
+# return df.conditional(pressure < 0, 1/((1 - pressure)**(1/(subdomain_index + 1))), 1)
+#
+# sa
+
+for mesh_resolution, solver_tol in resolutions.items():
+ # initialise LDD simulation class
+ simulation = ldd.LDDsimulation(
+ tol=1E-14,
+ LDDsolver_tol=solver_tol,
+ debug=debugflag,
+ max_iter_num=max_iter_num,
+ FEM_Lagrange_degree=FEM_Lagrange_degree,
+ mesh_study=mesh_study
+ )
+
+ simulation.set_parameters(use_case=use_case,
+ output_dir=output_string,
+ subdomain_def_points=subdomain_def_points,
+ isRichards=isRichards,
+ interface_def_points=interface_def_points,
+ outer_boundary_def_points=outer_boundary_def_points,
+ adjacent_subdomains=adjacent_subdomains,
+ mesh_resolution=mesh_resolution,
+ viscosity=viscosity,
+ porosity=porosity,
+ L=L,
+ lambda_param=lambda_param,
+ relative_permeability=relative_permeability,
+ saturation=sat_pressure_relationship,
+ starttime=starttime,
+ number_of_timesteps=number_of_timesteps,
+ number_of_timesteps_to_analyse=number_of_timesteps_to_analyse,
+ plot_timestep_every=plot_timestep_every,
+ timestep_size=timestep_size,
+ sources=source_expression,
+ initial_conditions=initial_condition,
+ dirichletBC_expression_strings=dirichletBC,
+ exact_solution=exact_solution,
+ densities=densities,
+ include_gravity=include_gravity,
+ write2file=write_to_file,
+ )
+
+ simulation.initialise()
+ output_dir = simulation.output_dir
+ # simulation.write_exact_solution_to_xdmf()
+ output = simulation.run(analyse_condition=analyse_condition)
+ for subdomain_index, subdomain_output in output.items():
+ mesh_h = subdomain_output['mesh_size']
+ for phase, different_errornorms in subdomain_output['errornorm'].items():
+ filename = output_dir + "subdomain{}-space-time-errornorm-{}-phase.csv".format(subdomain_index, phase)
+ # for errortype, errornorm in different_errornorms.items():
+
+ # eocfile = open("eoc_filename", "a")
+ # eocfile.write( str(mesh_h) + " " + str(errornorm) + "\n" )
+ # eocfile.close()
+ # if subdomain.isRichards:mesh_h
+ data_dict = {
+ 'mesh_parameter': mesh_resolution,
+ 'mesh_h': mesh_h,
+ }
+ for error_type, errornorms in different_errornorms.items():
+ data_dict.update(
+ {error_type: errornorms}
+ )
+ errors = pd.DataFrame(data_dict, index=[mesh_resolution])
+ # check if file exists
+ if os.path.isfile(filename) == True:
+ with open(filename, 'a') as f:
+ errors.to_csv(f, header=False, sep='\t', encoding='utf-8', index=False)
+ else:
+ errors.to_csv(filename, sep='\t', encoding='utf-8', index=False)