From 9f28d15713c793f2a68749b91f12e3009cc9fdf9 Mon Sep 17 00:00:00 2001 From: Alessio Fumagalli Date: Fri, 31 May 2019 15:36:36 +0200 Subject: [PATCH 01/25] dfn transport modify post process files --- examples/papers/dfn_transport/example1/pot.py | 119 ++++++++++++++++-- examples/papers/dfn_transport/example2/pot.py | 2 +- .../dfn_transport/example3/post_process.py | 4 +- examples/papers/dfn_transport/example3/pot.py | 5 +- 4 files changed, 117 insertions(+), 13 deletions(-) diff --git a/examples/papers/dfn_transport/example1/pot.py b/examples/papers/dfn_transport/example1/pot.py index d27a2c277a..cfc5eab7be 100644 --- a/examples/papers/dfn_transport/example1/pot.py +++ b/examples/papers/dfn_transport/example1/pot.py @@ -9,12 +9,33 @@ plt.rc("font", size=15) -def plot_single(file_name, legend, title): +def plot_single(file_name, legend, title, **kwargs): data = np.loadtxt(file_name, delimiter=",") + reference = kwargs.get("reference", None) + + fig = plt.figure(0) + ax = fig.add_subplot(111) + + # if the data is a reference + if reference: + data_p = data[:, 1] + data[:, 1] * reference / 100 + plt.plot(data[:, 0], data_p, label=legend, linestyle="--", color="gray") + text = "ref + " + str(reference) + "\%" + pos = (np.median(data[:, 0]), np.median(data_p)) + pos_t = (pos[0], pos[1]+5*pos[1]/100) + ax.annotate(text, xy=pos, xytext=pos_t) + + data_m = data[:, 1] - data[:, 1] * reference / 100 + plt.plot(data[:, 0], data_m, label=legend, linestyle="--", color="gray") + text = "ref - " + str(reference) + "\%" + pos = (np.median(data[:, 0]), np.median(data_m)) + pos_t = (pos[0], pos[1]-5*pos[1]/100) + ax.annotate(text, xy=pos, xytext=pos_t) + + else: + plt.plot(data[:, 0], data[:, 1], label=legend) - plt.figure(0) - plt.plot(data[:, 0], data[:, 1], label=legend) plt.title(title) plt.xlabel("$t$") plt.ylabel("$\\theta$") @@ -25,14 +46,36 @@ def plot_single(file_name, legend, title): # ------------------------------------------------------------------------------# -def plot_multiple(file_name, legend, title, num_frac): +def plot_multiple(file_name, legend, title, num_frac, **kwargs): data = np.loadtxt(file_name, delimiter=",") frac_label = {0: "$\\Omega_l$", 1: "$\\Omega_m$", 2: "$\\Omega_r$"} + reference = kwargs.get("reference", None) + for frac_id in np.arange(num_frac): - plt.figure(frac_id) - plt.plot(data[:, 0], data[:, frac_id + 1], label=legend) + fig = plt.figure(frac_id) + ax = fig.add_subplot(111) + + # if the data is a reference + if reference: + data_p = data[:, frac_id + 1] + data[:, frac_id + 1] * reference / 100 + plt.plot(data[:, 0], data_p, label=legend, linestyle="--", color="gray") + text = "ref + " + str(reference) + "\%" + pos = (np.median(data[:, 0]), np.median(data_p)) + pos_t = (pos[0], pos[1]+5*pos[1]/100) + ax.annotate(text, xy=pos, xytext=pos_t) + + data_m = data[:, frac_id + 1] - data[:, frac_id + 1] * reference / 100 + plt.plot(data[:, 0], data_m, label=legend, linestyle="--", color="gray") + text = "ref - " + str(reference) + "\%" + pos = (np.median(data[:, 0]), np.median(data_m)) + pos_t = (pos[0], pos[1]-5*pos[1]/100) + ax.annotate(text, xy=pos, xytext=pos_t) + + else: + plt.plot(data[:, 0], data[:, frac_id + 1], label=legend) + plt_title = ( title[0] + " on " @@ -113,13 +156,17 @@ def main(): methods_alessio = ["MVEM_UPWIND", "Tpfa_UPWIND", "RT0_UPWIND"] methods_andrea = ["MVEM_VEMSUPG"] + method_reference = "GCmfem" + reference = {"grid_0": 10, "grid_1": 5, "grid_2": 3.5} + grids = { "grid_0": ("1k", "220", "1", "0.005"), - "grid_1": ("3k", "650", "3", "0.001"), - "grid_2": ("10k", "2100", "10", "0.0003"), + "grid_1": ("3k", "650", "3", "0.0015"), + "grid_2": ("10k", "2100", "10", "0.00045"), } grids_label = {"grid_0": "coarse", "grid_1": "medium", "grid_2": "fine"} + for grid_name, grid in grids.items(): grid_label = grids_label[grid_name] for simul in np.arange(num_simul): @@ -128,6 +175,20 @@ def main(): folder_out = folder_in + "img/" title = ["avg $\\theta$", grid_label, simul] + + # Reference + data = ( + folder_in + + method_reference + + "/" + + method_reference + + "_Cmean_" + + str(simul + 1) + + "_big" + + ".csv" + ) + plot_multiple(data, None, title, num_frac, reference=reference[grid_name]) + # Alessio for method in methods_alessio: data = ( @@ -192,6 +253,20 @@ def main(): ########### title = ["min $\\theta$", grid_label, simul] + + # Reference + data = ( + folder_in + + method_reference + + "/" + + method_reference + + "_Cmin_" + + str(simul + 1) + + "_big" + + ".csv" + ) + plot_multiple(data, None, title, num_frac, reference=reference[grid_name]) + # Alessio for method in methods_alessio: data = ( @@ -257,6 +332,20 @@ def main(): ########### title = ["max $\\theta$", grid_label, simul] + + # Reference + data = ( + folder_in + + method_reference + + "/" + + method_reference + + "_Cmax_" + + str(simul + 1) + + "_big" + + ".csv" + ) + plot_multiple(data, None, title, num_frac, reference=reference[grid_name]) + # Alessio for method in methods_alessio: data = ( @@ -321,6 +410,20 @@ def main(): ########### title = "production on " + grid_label + " - config " + str(simul) + + # Reference + data = ( + folder_in + + method_reference + + "/" + + method_reference + + "_production_" + + str(simul + 1) + + "_big" + + ".csv" + ) + plot_single(data, None, title, reference=reference[grid_name]) + # Alessio for method in methods_alessio: data = ( diff --git a/examples/papers/dfn_transport/example2/pot.py b/examples/papers/dfn_transport/example2/pot.py index 3d58a39e3d..704cb82998 100644 --- a/examples/papers/dfn_transport/example2/pot.py +++ b/examples/papers/dfn_transport/example2/pot.py @@ -82,7 +82,7 @@ def main(): methods_stefano_1 = ["OPTxfem", "OPTfem"] methods_stefano_2 = ["GCmfem"] methods_alessio = ["MVEM_UPWIND", "Tpfa_UPWIND", "RT0_UPWIND"] - methods_andrea = ["MVEM_VEMSUPG"] + methods_andrea = ["MVEM_VEMSUPG", "MVEM_VEMSUPG_POWERTAU"] grids = {"grid_0": ("3k", "200", "3", "9e-05"), "grid_1": ("40k", "2600", "40", "0.0015")} grids_label = {"grid_0": "coarse", "grid_1": "fine"} diff --git a/examples/papers/dfn_transport/example3/post_process.py b/examples/papers/dfn_transport/example3/post_process.py index 98aa4c737e..f9dc5d35bf 100644 --- a/examples/papers/dfn_transport/example3/post_process.py +++ b/examples/papers/dfn_transport/example3/post_process.py @@ -108,12 +108,12 @@ def main(): field = "scalar" n_step = 200 - time_step = 0.5*3.154e+7/200 + time_step = 3.154e+7/200 num_frac = 89 grids = ["different", "same"] - folder_master = "/home/elle/tmp/tipetut++/" + folder_master = "/home/elle/tmp/tipetut++/new/" #folder_master = "./" folder_master_out = "./CSV/" methods = ["MVEM", "Tpfa", "RT0"] diff --git a/examples/papers/dfn_transport/example3/pot.py b/examples/papers/dfn_transport/example3/pot.py index 220675d6f0..1138abedf3 100644 --- a/examples/papers/dfn_transport/example3/pot.py +++ b/examples/papers/dfn_transport/example3/pot.py @@ -79,12 +79,13 @@ def main(): master_folder = "/home/elle/Dropbox/Work/PresentazioniArticoli/2019/Articles/tipetut++/Results/example3/" - methods_stefano = [] #["OPTxfem", "OPTfem", "GCmfem"] + methods_stefano = ["OPTfem"] #["OPTxfem", , "GCmfem"] methods_alessio = ["MVEM_UPWIND", "Tpfa_UPWIND", "RT0_UPWIND"] methods_andrea = [] #["MVEM_VEMSUPG"] - cases = {"case_0": ("different", "200", "0.005"), "case_1": ("same", "2600", "0.001")} + cases = {"case_0": ("different", "different", "0.005"), "case_1": ("same", "2600", "0.001")} cases_label = {"case_0": "different", "case_1": "same"} + cases_label = {"case_0": "different"} for case_name, case in cases.items(): case_label = cases_label[case_name] From 778e4698b84ccc3298e6875b7a61e6cfc1834049 Mon Sep 17 00:00:00 2001 From: Alessio Fumagalli Date: Fri, 31 May 2019 15:37:23 +0200 Subject: [PATCH 02/25] with the coarsening add also the original grid to be able to revert the process, useful for post process purposes --- src/porepy/grids/coarsening.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/src/porepy/grids/coarsening.py b/src/porepy/grids/coarsening.py index d5ae10cd2a..cd25ef1207 100644 --- a/src/porepy/grids/coarsening.py +++ b/src/porepy/grids/coarsening.py @@ -97,7 +97,7 @@ def reorder_partition(subdiv): the subdivision written in a contiguous way """ if isinstance(subdiv, dict): - for _, partition in subdiv.items(): + for _, (_, partition) in subdiv.items(): old_ids = np.unique(partition) for new_id, old_id in enumerate(old_ids): partition[partition == old_id] = new_id @@ -239,7 +239,7 @@ def generate_coarse_grid_gb(gb, subdiv): g = gb.get_grids(lambda g: g.dim == gb.dim_max())[0] subdiv = {g: subdiv} - for g, partition in subdiv.items(): + for g, (_, partition) in subdiv.items(): # Construct the coarse grids face_map = generate_coarse_grid_single(g, partition, True) @@ -274,7 +274,6 @@ def generate_coarse_grid_gb(gb, subdiv): # update the map d["face_cells"] = face_cells.tocsc() - # ------------------------------------------------------------------------------# @@ -450,7 +449,7 @@ def create_aggregations(g, **kwargs): np.sum(has_not_coarse_id) ) - partition[g] = partition_local + partition[g] = (g.copy(), partition_local) return partition From 04574a22e0aabfa7dfb2d8bfefcba39d307c8c2c Mon Sep 17 00:00:00 2001 From: Alessio Fumagalli Date: Fri, 31 May 2019 15:38:24 +0200 Subject: [PATCH 03/25] add geometry example 3 --- .../geometries/example3_connected.fab | 1433 +++++++++++++++++ 1 file changed, 1433 insertions(+) create mode 100644 examples/papers/dfn_transport/geometries/example3_connected.fab diff --git a/examples/papers/dfn_transport/geometries/example3_connected.fab b/examples/papers/dfn_transport/geometries/example3_connected.fab new file mode 100644 index 0000000000..9dedadc7f2 --- /dev/null +++ b/examples/papers/dfn_transport/geometries/example3_connected.fab @@ -0,0 +1,1433 @@ +BEGIN FORMAT + Format = Ascii + XAxis = + Scale = + No_Fractures = 89 + No_TessFractures = 0 + No_Nodes = 1385 + No_Properties = 1 +END FORMAT + +BEGIN PROPERTIES + Prop1 = (Real*4) "Transmissivity" +END PROPERTIES + +BEGIN SETS + Set1 = "Single Fractures" +END SETS + +BEGIN FRACTURE + 1 7 1 + 1 141.152484947 258.04720157 -100 + 2 136.431890406 147.092029261 -32.4404832711 + 3 134.993041206 113.272611088 0 + 4 134.428357162 100 18.994906386 + 5 134.4283571618066 100.0000000000082 399.9999999999999 + 6 193.9914421507489 1500.00000000001 399.9999999999999 + 7 193.991442151 1500 -100 + 0 0 0 0 + 2 8 1 + 1 -257.896840181 548.783182031 -100 + 2 -261.934507322 526.651331103 -90.25956197799999 + 3 -281.356536645 420.192466939 0 + 4 -300.034755851 317.810683439 128.887240652 + 5 -319.2891195444703 212.2708526126899 400 + 6 -84.35975544627553 1499.999999999813 400 + 7 -84.3597554473 1500 -6.65313483202 + 8 -105.107952619 1386.27195091 -100 + 0 0 0 0 + 3 11 1 + 1 -143.227928058 1177.32317322 -1.98951966013e-13 + 2 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+ 15 186.67255004 746.689584915 84.9959437008 + 16 187.031517372 744.447377384 145.35816873 + 17 191.014804888 719.566680872 200.256610641 + 18 193.532848216 703.838297822 218.9382297 + 19 198.015993172 675.835355878 241.333479308 + 20 206.969214772 619.911100208 262.33519384 + 0 0 0 0 + 89 16 1 + 1 285.6790144596533 916.7535752001573 400.0000000000001 + 2 296.7658441 900.441659049 376.160283857 + 3 322.115376108 863.145204237 269.678823564 + 4 322.629343384 862.389010502 154.045395398 + 5 298.229499069 898.288201487 46.8641405784 + 6 276.783590896 929.84130254 0 + 7 252.630498276 965.37745079 -35.5475664499 + 8 192.774375519 1053.44302837 -80.6432903556 + 9 127.773682891 1149.07774836 -81.5576159677 + 10 67.5241866109 1237.72209157 -38.1513455001 + 11 41.1805795299 1276.48111681 0 + 12 21.1983263255 1305.8807603 42.9673098472 + 13 -4.15120568218 1343.17721512 149.44877014 + 14 -4.66517295805 1343.93340885 265.082198306 + 15 19.7346713565 1308.03421787 372.263453126 + 16 32.427434521294 1289.359511457029 400.0000000000001 + 0 0 0 0 +END FRACTURE + +BEGIN TESSFRACTURE +END TESSFRACTURE + From dfd1124f974570a491efc12cf18520dbd6faa570 Mon Sep 17 00:00:00 2001 From: Alessio Fumagalli Date: Fri, 7 Jun 2019 08:55:58 +0200 Subject: [PATCH 04/25] dfn_transport update the test cases --- examples/papers/dfn_transport/example3/main.py | 2 +- examples/papers/dfn_transport/example3/post_process.py | 7 ++++--- examples/papers/dfn_transport/example3/pot.py | 7 +++---- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/papers/dfn_transport/example3/main.py b/examples/papers/dfn_transport/example3/main.py index 57d8dcbdf7..d5651ece43 100644 --- a/examples/papers/dfn_transport/example3/main.py +++ b/examples/papers/dfn_transport/example3/main.py @@ -57,7 +57,7 @@ def bc_different(g, domain, tol): def main(): input_folder = "../geometries/" - file_name = input_folder + "example3.fab" + file_name = input_folder + "example3_connected.fab" # define the discretizations for the Darcy part discretizations = compute.get_discr() diff --git a/examples/papers/dfn_transport/example3/post_process.py b/examples/papers/dfn_transport/example3/post_process.py index f9dc5d35bf..4ef71f5aa4 100644 --- a/examples/papers/dfn_transport/example3/post_process.py +++ b/examples/papers/dfn_transport/example3/post_process.py @@ -100,7 +100,8 @@ def cot_domain(file_in, step, field, num_frac, padding=6): cot_min[i, frac_id] = np.amin(c[is_loc]) cot_max[i, frac_id] = np.amax(c[is_loc]) - return cot_avg, cot_min, cot_max + zero = 273.15 + return cot_avg + zero, cot_min + zero, cot_max + zero #------------------------------------------------------------------------------# @@ -109,11 +110,11 @@ def main(): field = "scalar" n_step = 200 time_step = 3.154e+7/200 - num_frac = 89 + num_frac = 89-7 grids = ["different", "same"] - folder_master = "/home/elle/tmp/tipetut++/new/" + folder_master = "/home/elle/tmp/tipetut++/example3/" #folder_master = "./" folder_master_out = "./CSV/" methods = ["MVEM", "Tpfa", "RT0"] diff --git a/examples/papers/dfn_transport/example3/pot.py b/examples/papers/dfn_transport/example3/pot.py index 1138abedf3..2da1f41248 100644 --- a/examples/papers/dfn_transport/example3/pot.py +++ b/examples/papers/dfn_transport/example3/pot.py @@ -75,17 +75,16 @@ def save_multiple(filename, num_frac, folder): def main(): - num_frac = 86 + num_frac = 86-7 master_folder = "/home/elle/Dropbox/Work/PresentazioniArticoli/2019/Articles/tipetut++/Results/example3/" - methods_stefano = ["OPTfem"] #["OPTxfem", , "GCmfem"] + methods_stefano = ["OPTfem", "OPTxfem", "GCmfem"] methods_alessio = ["MVEM_UPWIND", "Tpfa_UPWIND", "RT0_UPWIND"] methods_andrea = [] #["MVEM_VEMSUPG"] - cases = {"case_0": ("different", "different", "0.005"), "case_1": ("same", "2600", "0.001")} + cases = {"case_0": ("different", "different", "0.005"), "case_1": ("same", "same", "0.001")} cases_label = {"case_0": "different", "case_1": "same"} - cases_label = {"case_0": "different"} for case_name, case in cases.items(): case_label = cases_label[case_name] From e96fc5858ded0995a28588f69cccf0e77719a0ae Mon Sep 17 00:00:00 2001 From: Alessio Fumagalli Date: Fri, 7 Jun 2019 08:57:38 +0200 Subject: [PATCH 05/25] git add FVElliptic to the init --- src/porepy/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/porepy/__init__.py b/src/porepy/__init__.py index 5f5822fd98..ddd859dbcd 100644 --- a/src/porepy/__init__.py +++ b/src/porepy/__init__.py @@ -28,6 +28,7 @@ # Numerics # Control volume, elliptic from porepy.numerics.fv.mpsa import Mpsa, FracturedMpsa +from porepy.numerics.fv.fv_elliptic import FVElliptic from porepy.numerics.fv.tpfa import Tpfa from porepy.numerics.fv.mpfa import Mpfa from porepy.numerics.fv.biot import Biot, GradP, DivD, BiotStabilization From eccab2878ff00ddfee20e33ecd2e0c566598c35e Mon Sep 17 00:00:00 2001 From: Alessio Fumagalli Date: Fri, 7 Jun 2019 08:59:49 +0200 Subject: [PATCH 06/25] add the .eggs folder to gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 0cd186d4b1..031644bfbd 100644 --- a/.gitignore +++ b/.gitignore @@ -31,3 +31,4 @@ bin/* *.ipynb_checkpoints* *.eps *.pdf +.eggs/ From fb3a2c9dda58190f4dddcccbe7a10d9ae340522f Mon Sep 17 00:00:00 2001 From: Alessio Fumagalli Date: Fri, 7 Jun 2019 09:14:52 +0200 Subject: [PATCH 07/25] test_coarsening update function call in a test due to a change in the interface --- test/integration/test_coarsening.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/test/integration/test_coarsening.py b/test/integration/test_coarsening.py index 2a6cf2eb4c..1de017c07d 100644 --- a/test/integration/test_coarsening.py +++ b/test/integration/test_coarsening.py @@ -135,7 +135,7 @@ def test_coarse_grid_2d_1d(self): gb = pp.meshing.cart_grid([f], [4, 2]) gb.compute_geometry() - co.generate_coarse_grid(gb, part) + co.generate_coarse_grid(gb, (None, part)) # Test known = np.array([1, 5, 18, 19]) @@ -182,7 +182,7 @@ def test_coarse_grid_2d_1d_cross(self): ] ) - co.generate_coarse_grid(gb, part) + co.generate_coarse_grid(gb, (None, part)) # Test for e_d in gb.edges(): @@ -218,7 +218,7 @@ def test_coarse_grid_3d_2d(self): g = gb.get_grids(lambda g: g.dim == gb.dim_max())[0] part = np.zeros(g.num_cells) part[g.cell_centers[0, :] < 2.0] = 1 - co.generate_coarse_grid(gb, part) + co.generate_coarse_grid(gb, (None, part)) # Test known_indices = np.array([1, 3, 0, 2, 5, 7, 4, 6]) @@ -252,7 +252,7 @@ def test_coarse_grid_3d_2d_cross(self): part[np.logical_and(np.logical_not(p1), p2)] = 3 part[np.logical_and(np.logical_not(p1), np.logical_not(p2))] = 4 - co.generate_coarse_grid(gb, part) + co.generate_coarse_grid(gb, (None, part)) cell_centers_1 = np.array( [ @@ -755,7 +755,7 @@ def test_create_partition_2d_1d_test0(self): gb.compute_geometry() part = co.create_partition(co.tpfa_matrix(gb)) - co.generate_coarse_grid(gb, part) + co.generate_coarse_grid(gb, (None, part)) # Test known_indices = np.array([1, 0, 3, 2]) @@ -774,7 +774,7 @@ def test_create_partition_2d_1d_test1(self): gb.compute_geometry() part = co.create_partition(co.tpfa_matrix(gb)) - co.generate_coarse_grid(gb, part) + co.generate_coarse_grid(gb, (None, part)) # Test known_indices = np.array([0, 1]) @@ -797,7 +797,7 @@ def test_create_partition_2d_1d_test2(self): self.assertTrue(np.array_equal(seeds, known_seeds)) part = co.create_partition(co.tpfa_matrix(gb), seeds=seeds) - co.generate_coarse_grid(gb, part) + co.generate_coarse_grid(gb, (None, part)) # Test known_indices = np.array([0, 1]) @@ -816,7 +816,7 @@ def test_create_partition_2d_1d_test3(self): gb.compute_geometry() part = co.create_partition(co.tpfa_matrix(gb)) - co.generate_coarse_grid(gb, part) + co.generate_coarse_grid(gb, (None, part)) # Test known_indices = np.array([0, 1]) @@ -839,7 +839,7 @@ def test_create_partition_2d_1d_test4(self): self.assertTrue(np.array_equal(seeds, known_seeds)) part = co.create_partition(co.tpfa_matrix(gb), seeds=seeds) - co.generate_coarse_grid(gb, part) + co.generate_coarse_grid(gb, (None, part)) # Test known_indices = np.array([0, 1]) @@ -862,7 +862,7 @@ def test_create_partition_2d_1d_cross_test5(self): gb.compute_geometry() part = co.create_partition(co.tpfa_matrix(gb), cdepth=3) - co.generate_coarse_grid(gb, part) + co.generate_coarse_grid(gb, (None, part)) cell_centers_1 = np.array( [ @@ -925,7 +925,7 @@ def test_create_partition_2d_1d_cross_test6(self): self.assertTrue(np.array_equal(np.sort(seeds), np.sort(known_seeds))) part = co.create_partition(co.tpfa_matrix(gb), cdepth=3, seeds=seeds) - co.generate_coarse_grid(gb, part) + co.generate_coarse_grid(gb, (None, part)) cell_centers_1 = np.array( [ @@ -989,7 +989,7 @@ def test_create_partition_2d_1d_cross_test7(self): self.assertTrue(np.array_equal(np.sort(seeds), np.sort(known_seeds))) part = co.create_partition(co.tpfa_matrix(gb), cdepth=3, seeds=seeds) - co.generate_coarse_grid(gb, part) + co.generate_coarse_grid(gb, (None, part)) cell_centers_1 = np.array( [ From dbaabe2d256dd38e2782175b13afe37400de52f9 Mon Sep 17 00:00:00 2001 From: Runar Date: Thu, 13 Jun 2019 15:10:16 +0200 Subject: [PATCH 08/25] Upgraded mortars.match_grids_1d to also work for 1d grids that are split in two. This typically happens when two 1d grids intersect. --- src/porepy/fracs/mortars.py | 62 ++++++++------ src/porepy/geometry/intersections.py | 48 ++++++++++- test/unit/test_intersections.py | 26 ++++++ test/unit/test_mortars.py | 124 ++++++++++++++++----------- 4 files changed, 180 insertions(+), 80 deletions(-) diff --git a/src/porepy/fracs/mortars.py b/src/porepy/fracs/mortars.py index 89bf940488..69930da63e 100644 --- a/src/porepy/fracs/mortars.py +++ b/src/porepy/fracs/mortars.py @@ -135,6 +135,11 @@ def update_physical_high_grid(mg, g_new, g_old, tol): new_nodes = g_new.face_centers[:, new_faces] # we assume only one old node + for i in range(1, old_nodes.shape[1]): + is_same = pp.distances.point_pointset(old_nodes[:, 0], old_nodes[:, i]) < tol + if not is_same: + raise ValueError('0d->1d mappings must map to the same physical point') + old_nodes = old_nodes[:, 0] mask = pp.distances.point_pointset(old_nodes, new_nodes) < tol new_faces = new_faces[mask] @@ -233,35 +238,36 @@ def match_grids_1d(new_1d, old_1d, tol): grid. """ + cell_nodes1 = new_1d.cell_nodes() + cell_nodes2 = old_1d.cell_nodes() + nodes1 = pp.utils.mcolon.mcolon(cell_nodes1.indptr[0:-1], cell_nodes1.indptr[1:]) + nodes2 = pp.utils.mcolon.mcolon(cell_nodes2.indptr[0:-1], cell_nodes2.indptr[1:]) - # Create a grid that contains all nodes of both the old and new grids. - combined, _, new_ind, old_ind, _, _ = non_conforming.merge_1d_grids( - new_1d, old_1d, tol=tol - ) - combined.compute_geometry() - weights = combined.cell_volumes - - switch_new = new_ind[0] > new_ind[-1] - if switch_new: - new_ind = new_ind[::-1] - switch_old = old_ind[0] > old_ind[-1] - if switch_old: - old_ind = old_ind[::-1] - - diff_new = np.diff(new_ind) - diff_old = np.diff(old_ind) - new_in_full = rldecode(np.arange(diff_new.size), diff_new) - old_in_full = rldecode(np.arange(diff_old.size), diff_old) - - if switch_new: - new_in_full = new_in_full.max() - new_in_full - if switch_old: - old_in_full = old_in_full.max() - old_in_full - - old_1d.compute_geometry() - - weights /= old_1d.cell_volumes[old_in_full] - return weights, new_in_full, old_in_full + p1 = new_1d.nodes + p2 = old_1d.nodes + lines1 = cell_nodes1.indices[nodes1].reshape((2, -1), order='F') + lines2 = cell_nodes2.indices[nodes2].reshape((2, -1), order='F') + + intersect = pp.intersections.line_tesselation(p1, p2, lines1, lines2) + + num = len(intersect) + new_g_ind = np.zeros(num, dtype=np.int) + old_g_ind = np.zeros(num, dtype=np.int) + weights = np.zeros(num) + + for ind, i in enumerate(intersect): + new_g_ind[ind] = i[0] + old_g_ind[ind] = i[1] + weights[ind] = i[2] + weights /= old_1d.cell_volumes[old_g_ind] + + # Remove zero weight intersections + mask = weights > tol + new_g_ind = new_g_ind[mask] + old_g_ind = old_g_ind[mask] + weights = weights[mask] + + return weights, new_g_ind, old_g_ind # ------------------------------------------------------------------------------# diff --git a/src/porepy/geometry/intersections.py b/src/porepy/geometry/intersections.py index 1b8bbe3951..8a87b1c18c 100644 --- a/src/porepy/geometry/intersections.py +++ b/src/porepy/geometry/intersections.py @@ -32,7 +32,7 @@ def segments_2d(start_1, end_1, start_2, end_2, tol=1e-8): have been discovered so far. Implementation note: - This function can be replaced by a call to segments_intersect_3d. Todo. + This function can be replaced by a call to segments_3d. Todo. Example: >>> lines_intersect([0, 0], [1, 1], [0, 1], [1, 0]) @@ -1067,8 +1067,8 @@ def triangulations(p_1, p_2, t_1, t_2): p_2 (np.array, 2 x n_p2): Points in second tessalation. t_1 (np.array, 3 x n_tri_1): Triangles in first tessalation, referring to indices in p_1. - t_2 (np.array, 3 x n_tri_1): Triangles in first tessalation, referring - to indices in p_1. + t_2 (np.array, 3 x n_tri_1): Triangles in second tessalation, referring + to indices in p_2. Returns: list of tuples: Each representing an overlap. The tuple contains index @@ -1352,6 +1352,48 @@ def mod_sign(v, tol): return unique_all_pt, new_edge.astype(np.int) +def line_tesselation(p1, p2, l1, l2): + """ Compute intersection of two line segment tessalations of a line. + + The function will identify partly overlapping line segments between l1 and + l2, and compute their common length. If parts of domain 1 or 2 is covered by + one tessalation only, this will simply be ignored by the function. + + Parameters: + p1 (np.array, 3 x n_p1): Points in first tessalation. + p2 (np.array, 3 x n_p2): Points in second tessalation. + l1 (np.array, 2 x n_tri_1): Line segments in first tessalation, referring + to indices in p2. + l2 (np.array, 2 x n_tri_1): Line segments in second tessalation, referring + to indices in p2. + + Returns: + list of tuples: Each representing an overlap. The tuple contains index + of the overlapping line segments in the first and second tessalation, + and their common length. + + Raise: + AssertionError(): if pp.segments_3d returns out an unknown shape + + """ + intersections = [] + for i in range(l1.shape[1]): + start_1 = p1[:, l1[0, i]] + end_1 = p1[:, l1[1, i]] + for j in range(l2.shape[1]): + start_2 = p2[:, l2[0, j]] + end_2 = p2[:, l2[1, j]] + X = segments_3d(start_1, end_1, start_2, end_2) + if X is None: + continue + elif X.shape[1]==1: #Point intersection (zero measure) + intersections.append([i, j, 0]) + elif X.shape[1]==2: + intersections.append([i, j, np.sqrt(np.sum((X[:, 0] - X[:, 1])**2))]) + else: + raise AssertionError() + + return intersections def _axis_aligned_bounding_box_2d(p, e): """ For a set of lines in 2d, obtain the bounding box for each line. diff --git a/test/unit/test_intersections.py b/test/unit/test_intersections.py index a3c2e44019..aa8e489dd7 100644 --- a/test/unit/test_intersections.py +++ b/test/unit/test_intersections.py @@ -203,5 +203,31 @@ def test_meeting_in_point(self): self.assertTrue(pi[0, 0] == 1 and pi[1, 0] == 0) +class LineTesselation(unittest.TestCase): + def test_tesselation_do_not(self): + p1 = np.array([[0.3, 0.3, 0],[0.5, 0.5, 0], [0.9, 0.9, 0]]).T + p2 = np.array([[0.4, 0.4, 0.1], [1.0, 1.0, 0.1]]).T + l1 = np.array([[0, 1], [1, 2]]).T + l2 = np.array([[0, 1]]).T + intersect = pp.intersections.line_tesselation(p1, p2, l1, l2) + self.assertTrue(len(intersect) == 0) + + def test_tesselation_do(self): + p1 = np.array([[0.0, 0.0, 0],[0.5, 0.5, 0], [1.0, 1.0, 0]]).T + p2 = np.array([[0.25, 0.25, 0], [1.0, 1.0, 0]]).T + l1 = np.array([[0, 1], [1, 2]]).T + l2 = np.array([[0, 1]]).T + intersections = pp.intersections.line_tesselation(p1, p2, l1, l2) + for inter in intersections: + if inter[0]==0: + if inter[1]==0: + self.assertTrue(inter[2] == np.sqrt(0.25**2 + 0.25**2)) + continue + elif inter[0]==1: + if inter[1]==1: + self.assertTrue(inter[2] == np.sqrt(0.5**2 + 0.5**2)) + else: + self.assertFalse() + if __name__ == "__main__": unittest.main() diff --git a/test/unit/test_mortars.py b/test/unit/test_mortars.py index 1c6d58dfc0..dbbbd02f65 100644 --- a/test/unit/test_mortars.py +++ b/test/unit/test_mortars.py @@ -10,25 +10,26 @@ import unittest import scipy.sparse as sps -from porepy.grids.structured import TensorGrid -from porepy.grids.simplex import StructuredTriangleGrid -from porepy.fracs import mortars, meshing +import porepy as pp class TestGridMappings1d(unittest.TestCase): def test_merge_grids_all_common(self): - g = TensorGrid(np.arange(3)) - weights, new, old = mortars.match_grids_1d(g, g, tol=1e-4) + g = pp.TensorGrid(np.arange(3)) + g.compute_geometry() + weights, new, old = pp.mortars.match_grids_1d(g, g, tol=1e-4) self.assertTrue(np.allclose(weights, np.ones(2))) self.assertTrue(np.allclose(old, np.arange(2))) self.assertTrue(np.allclose(new, np.arange(2))) def test_merge_grids_non_matching(self): - g = TensorGrid(np.arange(3)) - h = TensorGrid(np.arange(3)) + g = pp.TensorGrid(np.arange(3)) + h = pp.TensorGrid(np.arange(3)) h.nodes[0, 1] = 0.5 - weights, new, old = mortars.match_grids_1d(g, h, tol=1e-4) + g.compute_geometry() + h.compute_geometry() + weights, new, old = pp.mortars.match_grids_1d(g, h, tol=1e-4) # Weights give mappings from h to g. The first cell in h is # fully within the first cell in g. The second in h is split 1/3 @@ -38,17 +39,42 @@ def test_merge_grids_non_matching(self): self.assertTrue(np.allclose(old, np.array([0, 1, 1]))) def test_merge_grids_reverse_order(self): - g = TensorGrid(np.arange(3)) - h = TensorGrid(np.arange(3)) + g = pp.TensorGrid(np.arange(3)) + h = pp.TensorGrid(np.arange(3)) h.nodes = h.nodes[:, ::-1] - weights, new, old = mortars.match_grids_1d(g, h, tol=1e-4) - + g.compute_geometry() + h.compute_geometry() + weights, new, old = pp.mortars.match_grids_1d(g, h, tol=1e-4) self.assertTrue(np.allclose(weights, np.array([1, 1]))) # In this case, we don't know which ordering the combined grid uses # Instead, make sure that the two mappings are ordered in separate # directions self.assertTrue(np.allclose(new[::-1], old)) + def test_merge_grids_split(self): + g1 = pp.TensorGrid(np.linspace(0, 2, 2)) + g2 = pp.TensorGrid(np.linspace(2, 4, 2)) + g_nodes = np.hstack((g1.nodes, g2.nodes)) + g_face_nodes = sps.block_diag((g1.face_nodes, g2.face_nodes), 'csc') + g_cell_faces = sps.block_diag((g1.cell_faces, g2.cell_faces), 'csc') + g = pp.Grid(1, g_nodes, g_face_nodes, g_cell_faces, 'pp.TensorGrid') + + h1 = pp.TensorGrid(np.linspace(0, 2, 3)) + h2 = pp.TensorGrid(np.linspace(2, 4, 3)) + h_nodes = np.hstack((h1.nodes, h2.nodes)) + h_face_nodes = sps.block_diag((h1.face_nodes, h2.face_nodes), 'csc') + h_cell_faces = sps.block_diag((h1.cell_faces, h2.cell_faces), 'csc') + h = pp.Grid(1, h_nodes, h_face_nodes, h_cell_faces, 'pp.TensorGrid') + + g.compute_geometry() + h.compute_geometry() + weights, new, old = pp.mortars.match_grids_1d(g, h, tol=1e-4) + + # Weights give mappings from h to g. All cells are split in two + self.assertTrue(np.allclose(weights, np.array([1.0, 1.0, 1.0, 1.0]))) + self.assertTrue(np.allclose(new, np.array([0, 0, 1, 1]))) + self.assertTrue(np.allclose(old, np.array([0, 1, 2, 3]))) + class TestReplaceHigherDimensionalGrid(unittest.TestCase): # Test functionality for replacing the higher dimensional grid in a bucket. @@ -62,7 +88,7 @@ def test_replace_by_same(self): f1 = np.array([[0, 1], [0.5, 0.5]]) N = [1, 2] - gb = meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) + gb = pp.meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) gb.compute_geometry() # Pick out mortar grid by a loop, there is only one edge in the bucket @@ -74,7 +100,7 @@ def test_replace_by_same(self): g_old = gb.grids_of_dimension(2)[0] g_new = g_old.copy() - mortars.replace_grids_in_bucket(gb, {g_old: g_new}) + pp.mortars.replace_grids_in_bucket(gb, {g_old: g_new}) # Get mortar grid again for e, d in gb.edges(): @@ -90,7 +116,7 @@ def test_refine_high_dim(self): f1 = np.array([[0, 1], [0.5, 0.5]]) N = [1, 2] - gb = meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) + gb = pp.meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) gb.compute_geometry() # Pick out mortar grid by a loop, there is only one edge in the bucket @@ -103,12 +129,12 @@ def test_refine_high_dim(self): # Create a new, finer 2d grid. This is the simplest # way to put the fracture in the right place is to create a new # bucket, and pick out the higher dimensional grid - gb_new = meshing.cart_grid([f1], [2, 2], **{"physdims": [1, 1]}) + gb_new = pp.meshing.cart_grid([f1], [2, 2], **{"physdims": [1, 1]}) gb_new.compute_geometry() g_new = gb_new.grids_of_dimension(2)[0] - mortars.replace_grids_in_bucket(gb, {g_old: g_new}) + pp.mortars.replace_grids_in_bucket(gb, {g_old: g_new}) # Get mortar grid again for e, d in gb.edges(): @@ -124,7 +150,7 @@ def test_refine_high_dim(self): fi = np.where(g_new.face_centers[1] == 0.5)[0] self.assertTrue(fi.size == 4) - # Hard coded test (based on knowledge of how the grids and meshing + # Hard coded test (based on knowledge of how the grids and pp.meshing # is implemented). Faces to the uppermost cell are always kept in # place, the lowermost are duplicated towards the end of the face # definition. @@ -136,7 +162,7 @@ def test_coarsen_high_dim(self): f1 = np.array([[0, 1], [0.5, 0.5]]) N = [2, 2] - gb = meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) + gb = pp.meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) gb.compute_geometry() # Pick out mortar grid by a loop, there is only one edge in the bucket @@ -149,12 +175,12 @@ def test_coarsen_high_dim(self): # Create a new, coarser 2d grid. This is the simplest # way to put the fracture in the right place is to create a new # bucket, and pick out the higher dimensional grid - gb_new = meshing.cart_grid([f1], [1, 2], **{"physdims": [1, 1]}) + gb_new = pp.meshing.cart_grid([f1], [1, 2], **{"physdims": [1, 1]}) gb_new.compute_geometry() g_new = gb_new.grids_of_dimension(2)[0] - mortars.replace_grids_in_bucket(gb, {g_old: g_new}) + pp.mortars.replace_grids_in_bucket(gb, {g_old: g_new}) # Get mortar grid again for e, d in gb.edges(): @@ -170,7 +196,7 @@ def test_coarsen_high_dim(self): fi = np.where(g_new.face_centers[1] == 0.5)[0] self.assertTrue(fi.size == 2) - # Hard coded test (based on knowledge of how the grids and meshing + # Hard coded test (based on knowledge of how the grids and pp.meshing # is implemented). Faces to the uppermost cell are always kept in # place, the lowermost are duplicated towards the end of the face # definition. @@ -185,7 +211,7 @@ def test_refine_distort_high_dim(self): f1 = np.array([[0, 1], [0.5, 0.5]]) N = [1, 2] - gb = meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) + gb = pp.meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) gb.compute_geometry() # Pick out mortar grid by a loop, there is only one edge in the bucket @@ -198,7 +224,7 @@ def test_refine_distort_high_dim(self): # Create a new, finer 2d grid. This is the simplest # way to put the fracture in the right place is to create a new # bucket, and pick out the higher dimensional grid - gb_new = meshing.cart_grid([f1], [2, 2], **{"physdims": [1, 1]}) + gb_new = pp.meshing.cart_grid([f1], [2, 2], **{"physdims": [1, 1]}) gb_new.compute_geometry() g_new = gb_new.grids_of_dimension(2)[0] @@ -211,7 +237,7 @@ def test_refine_distort_high_dim(self): g_new.nodes[0, 6] = 0.7 g_new.compute_geometry() - mortars.replace_grids_in_bucket(gb, {g_old: g_new}) + pp.mortars.replace_grids_in_bucket(gb, {g_old: g_new}) # Get mortar grid again for e, d in gb.edges(): @@ -227,7 +253,7 @@ def test_refine_distort_high_dim(self): fi = np.where(g_new.face_centers[1] == 0.5)[0] self.assertTrue(fi.size == 4) - # Hard coded test (based on knowledge of how the grids and meshing + # Hard coded test (based on knowledge of how the grids and pp.meshing # is implemented). Faces to the uppermost cell are always kept in # place, the lowermost are duplicated towards the end of the face # definition. @@ -242,7 +268,7 @@ def test_distort_high_dim(self): f1 = np.array([[0, 1], [0.5, 0.5]]) N = [2, 2] - gb = meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) + gb = pp.meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) gb.compute_geometry() # Pick out mortar grid by a loop, there is only one edge in the bucket @@ -255,7 +281,7 @@ def test_distort_high_dim(self): # Create a new, finer 2d grid. This is the simplest # way to put the fracture in the right place is to create a new # bucket, and pick out the higher dimensional grid - gb_new = meshing.cart_grid([f1], [2, 2], **{"physdims": [1, 1]}) + gb_new = pp.meshing.cart_grid([f1], [2, 2], **{"physdims": [1, 1]}) gb_new.compute_geometry() g_new = gb_new.grids_of_dimension(2)[0] @@ -268,7 +294,7 @@ def test_distort_high_dim(self): g_new.nodes[0, 6] = 0.7 g_new.compute_geometry() - mortars.replace_grids_in_bucket(gb, {g_old: g_new}) + pp.mortars.replace_grids_in_bucket(gb, {g_old: g_new}) # Get mortar grid again for e, d in gb.edges(): @@ -284,7 +310,7 @@ def test_distort_high_dim(self): fi = np.where(g_new.face_centers[1] == 0.5)[0] self.assertTrue(fi.size == 4) - # Hard coded test (based on knowledge of how the grids and meshing + # Hard coded test (based on knowledge of how the grids and pp.meshing # is implemented). Faces to the uppermost cell are always kept in # place, the lowermost are duplicated towards the end of the face # definition. @@ -306,7 +332,7 @@ def test_permute_nodes_in_replacement_grid(self): # 1d lines f1 = np.array([[0, 1], [0.5, 0.5]]) N = [2, 2] - gb = meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) + gb = pp.meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) gb.compute_geometry() # Pick out mortar grid by a loop, there is only one edge in the bucket @@ -319,7 +345,7 @@ def test_permute_nodes_in_replacement_grid(self): # Create a new, finer 2d grid. This is the simplest # way to put the fracture in the right place is to create a new # bucket, and pick out the higher dimensional grid - gb_new = meshing.cart_grid([f1], [2, 2], **{"physdims": [1, 1]}) + gb_new = pp.meshing.cart_grid([f1], [2, 2], **{"physdims": [1, 1]}) gb_new.compute_geometry() g_new = gb_new.grids_of_dimension(2)[0] @@ -346,7 +372,7 @@ def test_permute_nodes_in_replacement_grid(self): fn[:, 12] = np.array([7, 5]) fn[:, 13] = np.array([5, 3]) - mortars.replace_grids_in_bucket(gb, {g_old: g_new}) + pp.mortars.replace_grids_in_bucket(gb, {g_old: g_new}) # Get mortar grid again for e, d in gb.edges(): @@ -362,7 +388,7 @@ def test_permute_nodes_in_replacement_grid(self): fi = np.where(g_new.face_centers[1] == 0.5)[0] self.assertTrue(fi.size == 4) - # Hard coded test (based on knowledge of how the grids and meshing + # Hard coded test (based on knowledge of how the grids and pp.meshing # is implemented). Faces to the uppermost cell are always kept in # place, the lowermost are duplicated towards the end of the face # definition. @@ -379,7 +405,7 @@ def test_permute_perturb_nodes_in_replacement_grid(self): # 1d lines. Also perturb nodes along the segment. f1 = np.array([[0, 1], [0.5, 0.5]]) N = [2, 2] - gb = meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) + gb = pp.meshing.cart_grid([f1], N, **{"physdims": [1, 1]}) gb.compute_geometry() # Pick out mortar grid by a loop, there is only one edge in the bucket @@ -392,7 +418,7 @@ def test_permute_perturb_nodes_in_replacement_grid(self): # Create a new, finer 2d grid. This is the simplest # way to put the fracture in the right place is to create a new # bucket, and pick out the higher dimensional grid - gb_new = meshing.cart_grid([f1], [2, 2], **{"physdims": [1, 1]}) + gb_new = pp.meshing.cart_grid([f1], [2, 2], **{"physdims": [1, 1]}) gb_new.compute_geometry() g_new = gb_new.grids_of_dimension(2)[0] @@ -415,7 +441,7 @@ def test_permute_perturb_nodes_in_replacement_grid(self): fn[:, 12] = np.array([7, 5]) fn[:, 13] = np.array([5, 3]) - mortars.replace_grids_in_bucket(gb, {g_old: g_new}) + pp.mortars.replace_grids_in_bucket(gb, {g_old: g_new}) # Get mortar grid again for e, d in gb.edges(): @@ -431,7 +457,7 @@ def test_permute_perturb_nodes_in_replacement_grid(self): fi = np.where(g_new.face_centers[1] == 0.5)[0] self.assertTrue(fi.size == 4) - # Hard coded test (based on knowledge of how the grids and meshing + # Hard coded test (based on knowledge of how the grids and pp.meshing # is implemented). Faces to the uppermost cell are always kept in # place, the lowermost are duplicated towards the end of the face # definition. @@ -462,7 +488,7 @@ def __init__(self, nodes, fn, cf, cc, n, cv, dim, frac_face=None, glob_pi=None): self.dim = dim if self.dim == 1: - self.name = ["TensorGrid"] + self.name = ["pp.TensorGrid"] elif self.dim == 2: self.name = ["TriangleGrid"] elif self.dim == 3: @@ -827,7 +853,7 @@ def grid_2d_four_cells_no_1d(self, pert=False): def grid_1d(self, n_nodes=2): x = np.linspace(0, 1, n_nodes) - g = TensorGrid(x) + g = pp.TensorGrid(x) g.nodes = np.tile(x, (3, 1)) g.compute_geometry() g.global_point_ind = 1 + np.arange(n_nodes) @@ -840,7 +866,7 @@ def setup_bucket(self, pert=False, include_1d=True): g2 = self.grid_2d_two_cells(pert) g1 = self.grid_1d() - gb = meshing._assemble_in_bucket( + gb = pp.meshing._assemble_in_bucket( [[g3], [g2], [g1]], ensure_matching_face_cell=False ) @@ -861,7 +887,7 @@ def setup_bucket(self, pert=False, include_1d=True): else: g3 = self.grid_3d_no_1d(pert) g2 = self.grid_2d_two_cells_no_1d(pert) - gb = meshing._assemble_in_bucket( + gb = pp.meshing._assemble_in_bucket( [[g3], [g2]], ensure_matching_face_cell=False ) for e, d in gb.edges(): @@ -872,7 +898,7 @@ def setup_bucket(self, pert=False, include_1d=True): a[15, 1] = 1 d["face_cells"] = sps.csc_matrix(a.T) - meshing.create_mortar_grids(gb) + pp.meshing.create_mortar_grids(gb) return gb def _mortar_grids(self, gb): @@ -895,7 +921,7 @@ def test_replace_1d_with_identity(self): gn = self.grid_1d(2) go = gb.grids_of_dimension(1)[0] - mortars.replace_grids_in_bucket(gb, {go: gn}) + pp.mortars.replace_grids_in_bucket(gb, {go: gn}) mg1, mg2 = self._mortar_grids(gb) p1h = mg1.master_to_mortar_int().copy() @@ -913,7 +939,7 @@ def test_replace_2d_with_identity_no_1d(self): gn = self.grid_2d_two_cells() go = gb.grids_of_dimension(2)[0] - mortars.replace_grids_in_bucket(gb, {go: gn}) + pp.mortars.replace_grids_in_bucket(gb, {go: gn}) mg1, mg2 = self._mortar_grids(gb) p2h = mg2.master_to_mortar_int().copy() @@ -929,7 +955,7 @@ def test_replace_2d_with_finer_no_1d(self): gn = self.grid_2d_four_cells_no_1d() go = gb.grids_of_dimension(2)[0] - mortars.replace_grids_in_bucket(gb, {go: gn}) + pp.mortars.replace_grids_in_bucket(gb, {go: gn}) mg1, mg2 = self._mortar_grids(gb) p2h = mg2.master_to_mortar_int().copy() @@ -948,7 +974,7 @@ def test_replace_2d_with_finer_no_1d_pert(self): gn = self.grid_2d_four_cells_no_1d(pert=True) go = gb.grids_of_dimension(2)[0] - mortars.replace_grids_in_bucket(gb, {go: gn}) + pp.mortars.replace_grids_in_bucket(gb, {go: gn}) mg1, mg2 = self._mortar_grids(gb) p2h = mg2.master_to_mortar_int().copy() @@ -975,7 +1001,7 @@ def test_replace_2d_with_identity(self): gn = self.grid_2d_two_cells() go = gb.grids_of_dimension(2)[0] - mortars.replace_grids_in_bucket(gb, {go: gn}) + pp.mortars.replace_grids_in_bucket(gb, {go: gn}) mg1, mg2 = self._mortar_grids(gb) p1h = mg1.master_to_mortar_int().copy() @@ -998,7 +1024,7 @@ def test_replace_2d_with_finer_pert(self): gn = self.grid_2d_four_cells(pert=True) go = gb.grids_of_dimension(2)[0] - mortars.replace_grids_in_bucket(gb, {go: gn}) + pp.mortars.replace_grids_in_bucket(gb, {go: gn}) mg1, mg2 = self._mortar_grids(gb) p1h = mg1.master_to_mortar_int().copy() From a90ab6158159e4d22753946ba46c9993706f1ddf Mon Sep 17 00:00:00 2001 From: Runar Date: Thu, 13 Jun 2019 15:20:13 +0200 Subject: [PATCH 09/25] Fixed vtk unittest that were broke --- test/unit/test_vtk.py | 58 +++++++++++++++++++++---------------------- 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/test/unit/test_vtk.py b/test/unit/test_vtk.py index 29c166d1ac..5682e4e89c 100644 --- a/test/unit/test_vtk.py +++ b/test/unit/test_vtk.py @@ -2,11 +2,7 @@ import numpy as np import unittest -from porepy.grids import structured, simplex -from porepy.fracs import meshing -from porepy.grids import coarsening as co - -from porepy.viz.exporter import Exporter +import porepy as pp if_vtk = "vtk" in sys.modules if not if_vtk: @@ -25,7 +21,7 @@ def test_single_grid_1d(self): if not if_vtk: return - g = structured.CartGrid(3, 1) + g = pp.CartGrid(3, 1) g.compute_geometry() dummy_scalar = np.ones(g.num_cells) * g.dim @@ -33,7 +29,7 @@ def test_single_grid_1d(self): folder = "./test_vtk/" file_name = "grid" - save = Exporter(g, file_name, folder, binary=False) + save = pp.Exporter(g, file_name, folder, binary=False) save.write_vtk({"dummy_scalar": dummy_scalar, "dummy_vector": dummy_vector}) with open(folder + file_name + "_000000.vtu", "r") as content_file: @@ -47,7 +43,7 @@ def test_single_grid_2d_simplex(self): if not if_vtk: return - g = simplex.StructuredTriangleGrid([3] * 2, [1] * 2) + g = pp.StructuredTriangleGrid([3] * 2, [1] * 2) g.compute_geometry() dummy_scalar = np.ones(g.num_cells) * g.dim @@ -55,7 +51,7 @@ def test_single_grid_2d_simplex(self): folder = "./test_vtk/" file_name = "grid" - save = Exporter(g, file_name, folder, binary=False) + save = pp.Exporter(g, file_name, folder, binary=False) save.write_vtk({"dummy_scalar": dummy_scalar, "dummy_vector": dummy_vector}) with open(folder + file_name + "_000000.vtu", "r") as content_file: @@ -68,7 +64,7 @@ def test_single_grid_2d_cart(self): if not if_vtk: return - g = structured.CartGrid([4] * 2, [1] * 2) + g = pp.CartGrid([4] * 2, [1] * 2) g.compute_geometry() dummy_scalar = np.ones(g.num_cells) * g.dim @@ -76,7 +72,7 @@ def test_single_grid_2d_cart(self): folder = "./test_vtk/" file_name = "grid" - save = Exporter(g, file_name, folder, binary=False) + save = pp.Exporter(g, file_name, folder, binary=False) save.write_vtk({"dummy_scalar": dummy_scalar, "dummy_vector": dummy_vector}) with open(folder + file_name + "_000000.vtu", "r") as content_file: @@ -89,9 +85,9 @@ def test_single_grid_2d_polytop(self): if not if_vtk: return - g = structured.CartGrid([3, 2], [1] * 2) + g = pp.CartGrid([3, 2], [1] * 2) g.compute_geometry() - co.generate_coarse_grid(g, [0, 0, 1, 0, 1, 1]) + pp.coarsening.generate_coarse_grid(g, [0, 0, 1, 0, 1, 1]) g.compute_geometry() dummy_scalar = np.ones(g.num_cells) * g.dim @@ -99,7 +95,7 @@ def test_single_grid_2d_polytop(self): folder = "./test_vtk/" file_name = "grid" - save = Exporter(g, file_name, folder, binary=False) + save = pp.Exporter(g, file_name, folder, binary=False) save.write_vtk({"dummy_scalar": dummy_scalar, "dummy_vector": dummy_vector}) with open(folder + file_name + "_000000.vtu", "r") as content_file: @@ -112,7 +108,7 @@ def test_single_grid_3d_simplex(self): if not if_vtk: return - g = simplex.StructuredTetrahedralGrid([3] * 3, [1] * 3) + g = pp.StructuredTetrahedralGrid([3] * 3, [1] * 3) g.compute_geometry() dummy_scalar = np.ones(g.num_cells) * g.dim @@ -120,7 +116,7 @@ def test_single_grid_3d_simplex(self): folder = "./test_vtk/" file_name = "grid" - save = Exporter(g, file_name, folder, binary=False) + save = pp.Exporter(g, file_name, folder, binary=False) save.write_vtk({"dummy_scalar": dummy_scalar, "dummy_vector": dummy_vector}) with open(folder + file_name + "_000000.vtu", "r") as content_file: @@ -133,7 +129,7 @@ def test_single_grid_3d_cart(self): if not if_vtk: return - g = structured.CartGrid([4] * 3, [1] * 3) + g = pp.CartGrid([4] * 3, [1] * 3) g.compute_geometry() dummy_scalar = np.ones(g.num_cells) * g.dim @@ -141,7 +137,7 @@ def test_single_grid_3d_cart(self): folder = "./test_vtk/" file_name = "grid" - save = Exporter(g, file_name, folder, binary=False) + save = pp.Exporter(g, file_name, folder, binary=False) save.write_vtk({"dummy_scalar": dummy_scalar, "dummy_vector": dummy_vector}) with open(folder + file_name + "_000000.vtu", "r") as content_file: @@ -154,9 +150,9 @@ def test_single_grid_3d_polytop(self): if not if_vtk: return - g = structured.CartGrid([3, 2, 3], [1] * 3) + g = pp.CartGrid([3, 2, 3], [1] * 3) g.compute_geometry() - co.generate_coarse_grid( + pp.coarsening.generate_coarse_grid( g, [0, 0, 1, 0, 1, 1, 0, 2, 2, 3, 2, 2, 4, 4, 4, 4, 4, 4] ) g.compute_geometry() @@ -166,7 +162,7 @@ def test_single_grid_3d_polytop(self): folder = "./test_vtk/" file_name = "grid" - save = Exporter(g, file_name, folder, binary=False) + save = pp.Exporter(g, file_name, folder, binary=False) save.write_vtk({"dummy_scalar": dummy_scalar, "dummy_vector": dummy_vector}) with open(folder + file_name + "_000000.vtu", "r") as content_file: @@ -180,18 +176,20 @@ def test_gb_1(self): return f1 = np.array([[0, 1], [0.5, 0.5]]) - gb = meshing.cart_grid([f1], [4] * 2, **{"physdims": [1, 1]}) + gb = pp.meshing.cart_grid([f1], [4] * 2, **{"physdims": [1, 1]}) gb.compute_geometry() gb.add_node_props(["scalar_dummy", "dummy_vector"]) for g, d in gb: - d["dummy_scalar"] = np.ones(g.num_cells) * g.dim - d["dummy_vector"] = np.ones((3, g.num_cells)) * g.dim + d[pp.STATE] = { + "dummy_scalar": np.ones(g.num_cells) * g.dim, + "dummy_vector": np.ones((3, g.num_cells)) * g.dim, + } folder = "./test_vtk/" file_name = "grid" - save = Exporter(gb, file_name, folder, binary=False) + save = pp.Exporter(gb, file_name, folder, binary=False) save.write_vtk(["dummy_scalar", "dummy_vector"]) with open(folder + file_name + "_000000.pvd", "r") as content_file: @@ -218,18 +216,20 @@ def test_gb_2(self): f1 = np.array([[0, 1], [0.5, 0.5]]) f2 = np.array([[0.5, 0.5], [0.25, 0.75]]) - gb = meshing.cart_grid([f1, f2], [4] * 2, **{"physdims": [1, 1]}) + gb = pp.meshing.cart_grid([f1, f2], [4] * 2, **{"physdims": [1, 1]}) gb.compute_geometry() gb.add_node_props(["dummy_scalar", "dummy_vector"]) for g, d in gb: - d["dummy_scalar"] = np.ones(g.num_cells) * g.dim - d["dummy_vector"] = np.ones((3, g.num_cells)) * g.dim + d[pp.STATE] = { + "dummy_scalar": np.ones(g.num_cells) * g.dim, + "dummy_vector": np.ones((3, g.num_cells)) * g.dim, + } folder = "./test_vtk/" file_name = "grid" - save = Exporter(gb, file_name, folder, binary=False) + save = pp.Exporter(gb, file_name, folder, binary=False) save.write_vtk(["dummy_scalar", "dummy_vector"]) with open(folder + file_name + "_000000.pvd", "r") as content_file: From ef9ec6b5ac4f0a9df7f54a5b1e652001550630d2 Mon Sep 17 00:00:00 2001 From: Runar Date: Thu, 13 Jun 2019 15:20:33 +0200 Subject: [PATCH 10/25] Black --- src/porepy/fracs/mortars.py | 10 ++++++---- src/porepy/geometry/intersections.py | 8 +++++--- test/unit/test_intersections.py | 19 ++++++++++--------- test/unit/test_mortars.py | 12 ++++++------ test/unit/test_vtk.py | 4 ++-- 5 files changed, 29 insertions(+), 24 deletions(-) diff --git a/src/porepy/fracs/mortars.py b/src/porepy/fracs/mortars.py index 69930da63e..8b3d1241f2 100644 --- a/src/porepy/fracs/mortars.py +++ b/src/porepy/fracs/mortars.py @@ -136,9 +136,11 @@ def update_physical_high_grid(mg, g_new, g_old, tol): # we assume only one old node for i in range(1, old_nodes.shape[1]): - is_same = pp.distances.point_pointset(old_nodes[:, 0], old_nodes[:, i]) < tol + is_same = ( + pp.distances.point_pointset(old_nodes[:, 0], old_nodes[:, i]) < tol + ) if not is_same: - raise ValueError('0d->1d mappings must map to the same physical point') + raise ValueError("0d->1d mappings must map to the same physical point") old_nodes = old_nodes[:, 0] mask = pp.distances.point_pointset(old_nodes, new_nodes) < tol new_faces = new_faces[mask] @@ -245,8 +247,8 @@ def match_grids_1d(new_1d, old_1d, tol): p1 = new_1d.nodes p2 = old_1d.nodes - lines1 = cell_nodes1.indices[nodes1].reshape((2, -1), order='F') - lines2 = cell_nodes2.indices[nodes2].reshape((2, -1), order='F') + lines1 = cell_nodes1.indices[nodes1].reshape((2, -1), order="F") + lines2 = cell_nodes2.indices[nodes2].reshape((2, -1), order="F") intersect = pp.intersections.line_tesselation(p1, p2, lines1, lines2) diff --git a/src/porepy/geometry/intersections.py b/src/porepy/geometry/intersections.py index 8a87b1c18c..161bc5e9c0 100644 --- a/src/porepy/geometry/intersections.py +++ b/src/porepy/geometry/intersections.py @@ -1352,6 +1352,7 @@ def mod_sign(v, tol): return unique_all_pt, new_edge.astype(np.int) + def line_tesselation(p1, p2, l1, l2): """ Compute intersection of two line segment tessalations of a line. @@ -1386,15 +1387,16 @@ def line_tesselation(p1, p2, l1, l2): X = segments_3d(start_1, end_1, start_2, end_2) if X is None: continue - elif X.shape[1]==1: #Point intersection (zero measure) + elif X.shape[1] == 1: # Point intersection (zero measure) intersections.append([i, j, 0]) - elif X.shape[1]==2: - intersections.append([i, j, np.sqrt(np.sum((X[:, 0] - X[:, 1])**2))]) + elif X.shape[1] == 2: + intersections.append([i, j, np.sqrt(np.sum((X[:, 0] - X[:, 1]) ** 2))]) else: raise AssertionError() return intersections + def _axis_aligned_bounding_box_2d(p, e): """ For a set of lines in 2d, obtain the bounding box for each line. diff --git a/test/unit/test_intersections.py b/test/unit/test_intersections.py index aa8e489dd7..e861f16a69 100644 --- a/test/unit/test_intersections.py +++ b/test/unit/test_intersections.py @@ -205,7 +205,7 @@ def test_meeting_in_point(self): class LineTesselation(unittest.TestCase): def test_tesselation_do_not(self): - p1 = np.array([[0.3, 0.3, 0],[0.5, 0.5, 0], [0.9, 0.9, 0]]).T + p1 = np.array([[0.3, 0.3, 0], [0.5, 0.5, 0], [0.9, 0.9, 0]]).T p2 = np.array([[0.4, 0.4, 0.1], [1.0, 1.0, 0.1]]).T l1 = np.array([[0, 1], [1, 2]]).T l2 = np.array([[0, 1]]).T @@ -213,21 +213,22 @@ def test_tesselation_do_not(self): self.assertTrue(len(intersect) == 0) def test_tesselation_do(self): - p1 = np.array([[0.0, 0.0, 0],[0.5, 0.5, 0], [1.0, 1.0, 0]]).T + p1 = np.array([[0.0, 0.0, 0], [0.5, 0.5, 0], [1.0, 1.0, 0]]).T p2 = np.array([[0.25, 0.25, 0], [1.0, 1.0, 0]]).T l1 = np.array([[0, 1], [1, 2]]).T l2 = np.array([[0, 1]]).T intersections = pp.intersections.line_tesselation(p1, p2, l1, l2) for inter in intersections: - if inter[0]==0: - if inter[1]==0: - self.assertTrue(inter[2] == np.sqrt(0.25**2 + 0.25**2)) + if inter[0] == 0: + if inter[1] == 0: + self.assertTrue(inter[2] == np.sqrt(0.25 ** 2 + 0.25 ** 2)) continue - elif inter[0]==1: - if inter[1]==1: - self.assertTrue(inter[2] == np.sqrt(0.5**2 + 0.5**2)) + elif inter[0] == 1: + if inter[1] == 1: + self.assertTrue(inter[2] == np.sqrt(0.5 ** 2 + 0.5 ** 2)) else: - self.assertFalse() + self.assertFalse() + if __name__ == "__main__": unittest.main() diff --git a/test/unit/test_mortars.py b/test/unit/test_mortars.py index dbbbd02f65..d7d04d66fb 100644 --- a/test/unit/test_mortars.py +++ b/test/unit/test_mortars.py @@ -55,16 +55,16 @@ def test_merge_grids_split(self): g1 = pp.TensorGrid(np.linspace(0, 2, 2)) g2 = pp.TensorGrid(np.linspace(2, 4, 2)) g_nodes = np.hstack((g1.nodes, g2.nodes)) - g_face_nodes = sps.block_diag((g1.face_nodes, g2.face_nodes), 'csc') - g_cell_faces = sps.block_diag((g1.cell_faces, g2.cell_faces), 'csc') - g = pp.Grid(1, g_nodes, g_face_nodes, g_cell_faces, 'pp.TensorGrid') + g_face_nodes = sps.block_diag((g1.face_nodes, g2.face_nodes), "csc") + g_cell_faces = sps.block_diag((g1.cell_faces, g2.cell_faces), "csc") + g = pp.Grid(1, g_nodes, g_face_nodes, g_cell_faces, "pp.TensorGrid") h1 = pp.TensorGrid(np.linspace(0, 2, 3)) h2 = pp.TensorGrid(np.linspace(2, 4, 3)) h_nodes = np.hstack((h1.nodes, h2.nodes)) - h_face_nodes = sps.block_diag((h1.face_nodes, h2.face_nodes), 'csc') - h_cell_faces = sps.block_diag((h1.cell_faces, h2.cell_faces), 'csc') - h = pp.Grid(1, h_nodes, h_face_nodes, h_cell_faces, 'pp.TensorGrid') + h_face_nodes = sps.block_diag((h1.face_nodes, h2.face_nodes), "csc") + h_cell_faces = sps.block_diag((h1.cell_faces, h2.cell_faces), "csc") + h = pp.Grid(1, h_nodes, h_face_nodes, h_cell_faces, "pp.TensorGrid") g.compute_geometry() h.compute_geometry() diff --git a/test/unit/test_vtk.py b/test/unit/test_vtk.py index 5682e4e89c..42f38c44f3 100644 --- a/test/unit/test_vtk.py +++ b/test/unit/test_vtk.py @@ -185,7 +185,7 @@ def test_gb_1(self): d[pp.STATE] = { "dummy_scalar": np.ones(g.num_cells) * g.dim, "dummy_vector": np.ones((3, g.num_cells)) * g.dim, - } + } folder = "./test_vtk/" file_name = "grid" @@ -225,7 +225,7 @@ def test_gb_2(self): d[pp.STATE] = { "dummy_scalar": np.ones(g.num_cells) * g.dim, "dummy_vector": np.ones((3, g.num_cells)) * g.dim, - } + } folder = "./test_vtk/" file_name = "grid" From 848cfc03715a41bc00282911b88c4936e75ce907 Mon Sep 17 00:00:00 2001 From: Runar Date: Thu, 13 Jun 2019 15:28:28 +0200 Subject: [PATCH 11/25] Fixed bug in test --- test/unit/test_intersections.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/unit/test_intersections.py b/test/unit/test_intersections.py index e861f16a69..d49904726d 100644 --- a/test/unit/test_intersections.py +++ b/test/unit/test_intersections.py @@ -227,7 +227,7 @@ def test_tesselation_do(self): if inter[1] == 1: self.assertTrue(inter[2] == np.sqrt(0.5 ** 2 + 0.5 ** 2)) else: - self.assertFalse() + self.assertTrue(False) if __name__ == "__main__": From 951007697382866ac784888c6ab09b2cb28da99e Mon Sep 17 00:00:00 2001 From: Alessio Fumagalli Date: Fri, 14 Jun 2019 08:45:17 +0200 Subject: [PATCH 12/25] Update Readme.md --- examples/papers/Readme.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/papers/Readme.md b/examples/papers/Readme.md index 7121e5932d..d1e6a01ffe 100644 --- a/examples/papers/Readme.md +++ b/examples/papers/Readme.md @@ -13,5 +13,8 @@ We try to keep the examples updated as the code changes, but may not always succ * [arXiv_1803_01732](./arXiv_1803_01732) paper "*Conforming, non-conforming and non-matching discretization couplings in discrete fracture network simulations*" by Alessio Fumagalli, Eirik Keilegavlen, and Stefano Scialo'. See [arXiv pre-print](https://arxiv.org/abs/1803.01732) or [journal article](https://www.sciencedirect.com/science/article/pii/S0021999118306508). * [arXiv_1802_05961](./arXiv_1802_05961) paper "*Unified approach to discretization of flow in fractured porous media*" by Jan M. Nordbotten, Wietse M. Boon, Alessio Fumagalli, and Eirik Keilegavlen. See [arXiv pre-print](https://arxiv.org/abs/1802.05961) or [journal article](https://link.springer.com/article/10.1007/s10596-018-9778-9). * [arXiv_1809_06926](./arXiv_1809_06926) paper "*Call for participation: Verification benchmarks for single-phase flow in three-dimensional fractured porous media*" by Inga Berre, Wietse Boon, Bernd Flemisch, Alessio Fumagalli, Dennis Glaser, Eirik Keilegavlen, Anna Scotti, Ivar Stefansson, Alexandru Tatomir. See [arXiv pre-print](https://arxiv.org/abs/1809.06926). -* [arXiv_1810_12761](./arXiv_1810_12761) paper "*A multiscale flux basis for mortar mixed discretizations of reduced Darcy-Forchheimer fracture models*" by Elyes Ahmed, Alessio Fumagalli, Ana Budisa. See [arXiv pre-print](https://arxiv.org/abs/1810.12761). +* [arXiv_1810_12761](./arXiv_1810_12761) paper "*A multiscale flux basis for mortar mixed discretizations of reduced Darcy-Forchheimer fracture models*" by Elyes Ahmed, Alessio Fumagalli, Ana Budiša. See [arXiv pre-print](https://arxiv.org/abs/1810.12761) or [journal article](https://www.sciencedirect.com/science/article/pii/S0045782519303044). * [arXiv_1903_01117](./arXiv_1903_01117) paper "*A multi-layer reduced model for flow in porous media with a fault and surrounding damage zones*" by Alessio Fumagalli, Anna Scotti. See [arXiv pre-print](https://arxiv.org/abs/1903.01117). + +# Separate repository +* [repo](https://github.com/alessiofumagalli/multiscale_timedependent) paper "*Robust linear domain decomposition schemes for reduced non-linear fracture flow models*" by Elyes Ahmed, Alessio Fumagalli, Ana Budiša, Eirik Keilegavlen, Jan M. Nordbotten, A. Radu Forin. See [arXiv pre-print](https://arxiv.org/abs/1906.05831). From 6efe9a1ebe846e537370a6ca311d1fe5c943bb7b Mon Sep 17 00:00:00 2001 From: Alessio Fumagalli Date: Fri, 14 Jun 2019 08:50:55 +0200 Subject: [PATCH 13/25] update the scripts according to the new version of assembler --- .../papers/dfn_transport/discretization.py | 57 ++++++++++--------- .../papers/dfn_transport/example1/main.py | 2 +- .../papers/dfn_transport/example2/main.py | 5 +- .../papers/dfn_transport/example3/main.py | 5 +- 4 files changed, 36 insertions(+), 33 deletions(-) diff --git a/examples/papers/dfn_transport/discretization.py b/examples/papers/dfn_transport/discretization.py index d34468ad15..af52c20ec8 100644 --- a/examples/papers/dfn_transport/discretization.py +++ b/examples/papers/dfn_transport/discretization.py @@ -158,10 +158,10 @@ def flow(gb, discr, param, bc_flag): } # solution of the darcy problem - assembler = pp.Assembler() + assembler = pp.Assembler(gb) logger.info("Assemble the flow problem") - A, b, block_dof, full_dof = assembler.assemble_matrix_rhs(gb) + A, b = assembler.assemble_matrix_rhs() logger.info("done") logger.info("Solve the linear system") @@ -169,7 +169,7 @@ def flow(gb, discr, param, bc_flag): logger.info("done") logger.info("Variable post-process") - assembler.distribute_variable(gb, x, block_dof, full_dof) + assembler.distribute_variable(x) # extract the pressure from the solution for g, d in gb: @@ -317,17 +317,22 @@ def advdiff(gb, discr, param, bc_flag): model_data_diff, discr_diff, discr_diff_interface ) + # mass term + mass_id = "mass" + discr_mass = pp.MassMatrix(model_data_adv) + discr_mass_interface = pp.CellDofFaceDofMap(model_data_adv) + discr_src = pp.ScalarSource(model_data_src) for g, d in gb: d[pp.PRIMARY_VARIABLES] = {variable: {"cells": 1}} if g.dim == gb.dim_max(): d[pp.DISCRETIZATION] = { - variable: {adv_id: discr_adv, diff_id: discr_diff, src_id: discr_src} + variable: {adv_id: discr_adv, diff_id: discr_diff, mass_id: discr_mass, src_id: discr_src} } else: d[pp.DISCRETIZATION] = { - variable: {adv_id: discr_adv_interface, diff_id: discr_diff_interface} + variable: {adv_id: discr_adv_interface, diff_id: discr_diff_interface, mass_id: discr_mass_interface} } for e, d in gb.edges(): @@ -348,34 +353,30 @@ def advdiff(gb, discr, param, bc_flag): } # setup the advection-diffusion problem - assembler = pp.Assembler() + assembler = pp.Assembler(gb, active_variables=[variable, mortar_diff, mortar_adv]) logger.info("Assemble the advective and diffusive terms of the transport problem") - A, b, block_dof, full_dof = assembler.assemble_matrix_rhs(gb) + block_A, block_b = assembler.assemble_matrix_rhs(add_matrices=False) logger.info("done") - # mass term - mass_id = "mass" - discr_mass = pp.MassMatrix(model_data_adv) - discr_mass_interface = pp.CellDofFaceDofMap(model_data_adv) - - for g, d in gb: - d[pp.PRIMARY_VARIABLES] = {variable: {"cells": 1}} - if g.dim == gb.dim_max(): - d[pp.DISCRETIZATION] = {variable: {mass_id: discr_mass}} - else: - d[pp.DISCRETIZATION] = {variable: {mass_id: discr_mass_interface}} + # unpack the matrices just computed + diff_name = diff_id + "_" + variable + adv_name = adv_id + "_" + variable + mass_name = mass_id + "_" + variable + source_name = src_id + "_" + variable - gb.remove_edge_props(pp.COUPLING_DISCRETIZATION) + diff_coupling_name = diff_id + "_" + mortar_diff + "_" + variable + "_" + variable + adv_coupling_name = adv_id + "_" + mortar_adv + "_" + variable + "_" + variable - for e, d in gb.edges(): - g_slave, g_master = gb.nodes_of_edge(e) - d[pp.PRIMARY_VARIABLES] = {mortar_adv: {"cells": 1}, mortar_diff: {"cells": 1}} + # need a sign for the convention of the conservation equation + M = block_A[mass_name] + A = block_A[diff_name] + block_A[diff_coupling_name] + \ + block_A[adv_name] + block_A[adv_coupling_name] + b = block_b[diff_name] + block_b[diff_coupling_name] + \ + block_b[adv_name] + block_b[adv_coupling_name] + \ + block_b[source_name] - logger.info("Assemble the mass term of the transport problem") - M, _, _, _ = assembler.assemble_matrix_rhs(gb) M_t = M.copy() / param["time_step"] * param.get("mass_weight", 1) M_r = M.copy() * param.get("reaction", 0) - logger.info("done") # Perform an LU factorization to speedup the solver IE_solver = sps.linalg.factorized((M_t + A + M_r).tocsc()) @@ -391,12 +392,12 @@ def advdiff(gb, discr, param, bc_flag): # assign the initial condition x = np.zeros(A.shape[0]) - assembler.distribute_variable(gb, x, block_dof, full_dof) + assembler.distribute_variable(x) for g, d in gb: if g.dim == gb.dim_max(): d[variable] = param.get("init_trans", 0) * np.ones(g.num_cells) - x = assembler.merge_variable(gb, variable, block_dof, full_dof) + x = assembler.merge_variable(variable) outflow = np.zeros(param["n_steps"]) @@ -407,7 +408,7 @@ def advdiff(gb, discr, param, bc_flag): logger.info("done") logger.info("Variable post-process") - assembler.distribute_variable(gb, x, block_dof, full_dof) + assembler.distribute_variable(x) logger.info("done") logger.info("Export variable") diff --git a/examples/papers/dfn_transport/example1/main.py b/examples/papers/dfn_transport/example1/main.py index a4871d684c..7d62832dff 100644 --- a/examples/papers/dfn_transport/example1/main.py +++ b/examples/papers/dfn_transport/example1/main.py @@ -23,7 +23,7 @@ def bc_flag(g, domain, tol): in_flow_end = np.array([0, 1, 0.3]) # detect all the points aligned with the segment - dist, _ = pp.cg.dist_points_segments(b_face_centers, in_flow_start, in_flow_end) + dist, _ = pp.distances.points_segments(b_face_centers, in_flow_start, in_flow_end) dist = dist.flatten() in_flow = np.logical_and(dist < tol, dist >= -tol) diff --git a/examples/papers/dfn_transport/example2/main.py b/examples/papers/dfn_transport/example2/main.py index 3978248e89..b7bf429121 100644 --- a/examples/papers/dfn_transport/example2/main.py +++ b/examples/papers/dfn_transport/example2/main.py @@ -17,7 +17,8 @@ def bc_flag(g, domain, tol): out_flow_end = np.array([1.012528, 0.190858, 0.886822]) # detect all the points aligned with the segment - dist, _ = pp.cg.dist_points_segments(b_face_centers, out_flow_start, out_flow_end) + dist, _ = pp.distances.points_segments(b_face_centers, out_flow_start, out_flow_end) + dist = dist.flatten() out_flow = np.logical_and(dist < tol, dist >= -tol) @@ -26,7 +27,7 @@ def bc_flag(g, domain, tol): in_flow_end = np.array([0.181980, 0.813947, 0.478618]) # detect all the points aligned with the segment - dist, _ = pp.cg.dist_points_segments(b_face_centers, in_flow_start, in_flow_end) + dist, _ = pp.distances.points_segments(b_face_centers, in_flow_start, in_flow_end) dist = dist.flatten() in_flow = np.logical_and(dist < tol, dist >= -tol) diff --git a/examples/papers/dfn_transport/example3/main.py b/examples/papers/dfn_transport/example3/main.py index 985e69750a..8c450410f2 100644 --- a/examples/papers/dfn_transport/example3/main.py +++ b/examples/papers/dfn_transport/example3/main.py @@ -13,12 +13,13 @@ def bc_flag(g, domain, out_flow_start, out_flow_end, in_flow_start, in_flow_end, b_face_centers = g.face_centers[:, b_faces] # detect all the points aligned with the segment - dist, _ = pp.cg.dist_points_segments(b_face_centers, out_flow_start, out_flow_end) + dist, _ = pp.distances.points_segments(b_face_centers, out_flow_start, out_flow_end) dist = dist.flatten() out_flow = np.logical_and(dist < tol, dist >= -tol) # detect all the points aligned with the segment - dist, _ = pp.cg.dist_points_segments(b_face_centers, in_flow_start, in_flow_end) + dist, _ = pp.distances.points_segments(b_face_centers, in_flow_start, in_flow_end) + dist = dist.flatten() in_flow = np.logical_and(dist < tol, dist >= -tol) From 161445508e3d6fa27f1708687699448ddeea5df1 Mon Sep 17 00:00:00 2001 From: Eirik Keilegavlen Date: Fri, 14 Jun 2019 08:56:04 +0200 Subject: [PATCH 14/25] Minor change to intersections module --- src/porepy/geometry/intersections.py | 88 ++++++++++++++-------------- 1 file changed, 44 insertions(+), 44 deletions(-) diff --git a/src/porepy/geometry/intersections.py b/src/porepy/geometry/intersections.py index 161bc5e9c0..bb0867fcfc 100644 --- a/src/porepy/geometry/intersections.py +++ b/src/porepy/geometry/intersections.py @@ -1136,6 +1136,50 @@ def triangulations(p_1, p_2, t_1, t_2): return intersections +def line_tesselation(p1, p2, l1, l2): + """ Compute intersection of two line segment tessalations of a line. + + The function will identify partly overlapping line segments between l1 and + l2, and compute their common length. If parts of domain 1 or 2 is covered by + one tessalation only, this will simply be ignored by the function. + + Parameters: + p1 (np.array, 3 x n_p1): Points in first tessalation. + p2 (np.array, 3 x n_p2): Points in second tessalation. + l1 (np.array, 2 x n_tri_1): Line segments in first tessalation, referring + to indices in p2. + l2 (np.array, 2 x n_tri_1): Line segments in second tessalation, referring + to indices in p2. + + Returns: + list of tuples: Each representing an overlap. The tuple contains index + of the overlapping line segments in the first and second tessalation, + and their common length. + + Raise: + AssertionError(): if pp.segments_3d returns out an unknown shape + + """ + intersections = [] + for i in range(l1.shape[1]): + start_1 = p1[:, l1[0, i]] + end_1 = p1[:, l1[1, i]] + for j in range(l2.shape[1]): + start_2 = p2[:, l2[0, j]] + end_2 = p2[:, l2[1, j]] + X = segments_3d(start_1, end_1, start_2, end_2) + if X is None: + continue + elif X.shape[1] == 1: # Point intersection (zero measure) + intersections.append([i, j, 0]) + elif X.shape[1] == 2: + intersections.append([i, j, np.sqrt(np.sum((X[:, 0] - X[:, 1]) ** 2))]) + else: + raise AssertionError() + + return intersections + + def split_intersecting_segments_2d(p, e, tol=1e-4): """ Process a set of points and connections between them so that the result is an extended point set and new connections that do not intersect. @@ -1353,50 +1397,6 @@ def mod_sign(v, tol): return unique_all_pt, new_edge.astype(np.int) -def line_tesselation(p1, p2, l1, l2): - """ Compute intersection of two line segment tessalations of a line. - - The function will identify partly overlapping line segments between l1 and - l2, and compute their common length. If parts of domain 1 or 2 is covered by - one tessalation only, this will simply be ignored by the function. - - Parameters: - p1 (np.array, 3 x n_p1): Points in first tessalation. - p2 (np.array, 3 x n_p2): Points in second tessalation. - l1 (np.array, 2 x n_tri_1): Line segments in first tessalation, referring - to indices in p2. - l2 (np.array, 2 x n_tri_1): Line segments in second tessalation, referring - to indices in p2. - - Returns: - list of tuples: Each representing an overlap. The tuple contains index - of the overlapping line segments in the first and second tessalation, - and their common length. - - Raise: - AssertionError(): if pp.segments_3d returns out an unknown shape - - """ - intersections = [] - for i in range(l1.shape[1]): - start_1 = p1[:, l1[0, i]] - end_1 = p1[:, l1[1, i]] - for j in range(l2.shape[1]): - start_2 = p2[:, l2[0, j]] - end_2 = p2[:, l2[1, j]] - X = segments_3d(start_1, end_1, start_2, end_2) - if X is None: - continue - elif X.shape[1] == 1: # Point intersection (zero measure) - intersections.append([i, j, 0]) - elif X.shape[1] == 2: - intersections.append([i, j, np.sqrt(np.sum((X[:, 0] - X[:, 1]) ** 2))]) - else: - raise AssertionError() - - return intersections - - def _axis_aligned_bounding_box_2d(p, e): """ For a set of lines in 2d, obtain the bounding box for each line. From f394bc907de01952136a0e1d503faeeb6cdcd8e4 Mon Sep 17 00:00:00 2001 From: Eirik Keilegavlen Date: Fri, 14 Jun 2019 10:53:29 +0200 Subject: [PATCH 15/25] Comments in mortar and intersection modules --- src/porepy/fracs/mortars.py | 12 ++++++++++-- src/porepy/geometry/intersections.py | 5 +++-- 2 files changed, 13 insertions(+), 4 deletions(-) diff --git a/src/porepy/fracs/mortars.py b/src/porepy/fracs/mortars.py index 8b3d1241f2..2023811aaa 100644 --- a/src/porepy/fracs/mortars.py +++ b/src/porepy/fracs/mortars.py @@ -231,6 +231,8 @@ def match_grids_1d(new_1d, old_1d, tol): Parameters: new_1d (grid): First grid to be matched old_1d (grid): Second grid to be matched. + tol (double): Tolerance used to filter away false overlaps caused by + numerical errors. Should be scaled relative to the cell size. Returns: np.array: Ratio of cell volume in the common grid and the original grid. @@ -240,16 +242,22 @@ def match_grids_1d(new_1d, old_1d, tol): grid. """ + # Cell-node relation between grids - we know there are two nodes per cell cell_nodes1 = new_1d.cell_nodes() cell_nodes2 = old_1d.cell_nodes() nodes1 = pp.utils.mcolon.mcolon(cell_nodes1.indptr[0:-1], cell_nodes1.indptr[1:]) nodes2 = pp.utils.mcolon.mcolon(cell_nodes2.indptr[0:-1], cell_nodes2.indptr[1:]) - p1 = new_1d.nodes - p2 = old_1d.nodes + # Reshape so that the nodes of cells are stored columnwise lines1 = cell_nodes1.indices[nodes1].reshape((2, -1), order="F") lines2 = cell_nodes2.indices[nodes2].reshape((2, -1), order="F") + p1 = new_1d.nodes + p2 = old_1d.nodes + + # Compute the intersection between the two tessalations. + # intersect is a list, every list member is a tuple with overlapping + # cells in grid 1 and 2, and their common area. intersect = pp.intersections.line_tesselation(p1, p2, lines1, lines2) num = len(intersect) diff --git a/src/porepy/geometry/intersections.py b/src/porepy/geometry/intersections.py index bb0867fcfc..01e600c908 100644 --- a/src/porepy/geometry/intersections.py +++ b/src/porepy/geometry/intersections.py @@ -1140,8 +1140,7 @@ def line_tesselation(p1, p2, l1, l2): """ Compute intersection of two line segment tessalations of a line. The function will identify partly overlapping line segments between l1 and - l2, and compute their common length. If parts of domain 1 or 2 is covered by - one tessalation only, this will simply be ignored by the function. + l2, and compute their common length. Parameters: p1 (np.array, 3 x n_p1): Points in first tessalation. @@ -1160,6 +1159,8 @@ def line_tesselation(p1, p2, l1, l2): AssertionError(): if pp.segments_3d returns out an unknown shape """ + # Loop over both set of lines, use segment intersection method to compute + # common segments, thus areas. intersections = [] for i in range(l1.shape[1]): start_1 = p1[:, l1[0, i]] From 367c31373df6029d1bc5f19691f77d944e869400 Mon Sep 17 00:00:00 2001 From: Runar Lie Berge Date: Mon, 24 Jun 2019 09:30:29 +0200 Subject: [PATCH 16/25] updated dockerfile and readme to include vtk from conda-forge (#280) --- Readme.md | 2 +- dockerfiles/Dockerfile | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/Readme.md b/Readme.md index 9805d0930f..fc06a136a3 100644 --- a/Readme.md +++ b/Readme.md @@ -63,7 +63,7 @@ For the moment, Docker support should be considered experimental. To function optimally, PorePy should have access to the pypi packages: * `pymetis` (for mesh partitioning). Will be installed on Linux (not so on Windows, to avoid installation issues for the core package in the case where no C compiler is available). * Some computationally expensive methods can be accelerated with `Cython` or `Numba`. Cython is automatically installed on many Linux systems, if not, use pip or conda. Numba can be installed using `conda`. -* Visualization by either matplotlib or (preferrable for larger problems) vtk/paraview. To dump data to paraview, a vtk filter must be available; the only solution we have found is from the 'conda' repositories, e.g. 'conda install -c clinicalgraphics vtk=7.1.0' (note that vtk should be version 7.0.0 or later, hence not the official channel) +* Visualization by either matplotlib or (preferrable for larger problems) vtk/paraview. To dump data to paraview, a vtk filter must be available; the only solution we have found is from the 'conda' repositories, e.g. 'RUN conda install -c conda-forge vtk' * We use `shapely` for certain geometry-operations. * Meshing: currently by [gmsh](http://gmsh.info/doc/texinfo/gmsh.html). For its configuration see [Install](https://github.com/pmgbergen/porepy/blob/develop/Install.md). diff --git a/dockerfiles/Dockerfile b/dockerfiles/Dockerfile index 08783796f7..2299540f2a 100644 --- a/dockerfiles/Dockerfile +++ b/dockerfiles/Dockerfile @@ -72,7 +72,9 @@ RUN git clone https://github.com/pmgbergen/porepy.git pp # python setup.py install WORKDIR /home/porepy/pp -RUN conda install numpy=1.16.3 scipy=1.2.1 networkx=2.3 sympy=1.4 cython=0.29.7 numba=0.43.1 matplotlib=3.0.3 pytest=4.5.0 pytest-cov=2.6.1 pytest-runner=4.4 vtk=8.2.0 jupyter=1.0.0 +RUN conda install numpy=1.16.3 scipy=1.2.1 networkx=2.3 sympy=1.4 cython=0.29.7 numba=0.43.1 matplotlib=3.0.3 pytest=4.5.0 pytest-cov=2.6.1 pytest-runner=4.4 jupyter=1.0.0 +# Vtk should be install from conda-forged (not all dependencies are installed otherwise): +RUN conda install -c conda-forge vtk RUN pip install meshio==2.3.8 shapely==1.6.4.post2 shapely[vectorized]==1.6.4.post2 RUN python setup.py install From 2f92514063c49eee282edaa61a6a2de46be22cee Mon Sep 17 00:00:00 2001 From: Alessio Fumagalli Date: Mon, 24 Jun 2019 09:31:36 +0200 Subject: [PATCH 17/25] remove dfn transport folder, now it's in a separate repository (#278) --- .../papers/dfn_transport/discretization.py | 458 ----- .../papers/dfn_transport/example1/docopy.py | 65 - 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- -def setup_custom_logger(): - formatter = logging.Formatter( - fmt="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S" - ) - handler = logging.FileHandler("log.txt", mode="w") - handler.setFormatter(formatter) - screen_handler = logging.StreamHandler(stream=sys.stdout) - screen_handler.setFormatter(formatter) - - logger = logging.getLogger() - logger.setLevel(logging.DEBUG) - - logger.addHandler(handler) - logger.addHandler(screen_handler) - return logger - - -logger = setup_custom_logger() - -# ------------------------------------------------------------------------------# - - -def get_discr(): - return { - "MVEM": {"scheme": pp.MVEM, "dof": {"cells": 1, "faces": 1}}, - "RT0": {"scheme": pp.RT0, "dof": {"cells": 1, "faces": 1}}, - "Tpfa": {"scheme": pp.Tpfa, "dof": {"cells": 1}}, - } - - -# ------------------------------------------------------------------------------# - - -def data_flow(gb, discr, model, data, bc_flag): - tol = data["tol"] - - model_data = model + "_data" - - for g, d in gb: - param = {} - - unity = np.ones(g.num_cells) - zeros = np.zeros(g.num_cells) - empty = np.empty(0) - - d["frac_num"] = (g.frac_num if g.dim == 2 else -1) * unity - d["cell_volumes"] = g.cell_volumes - d["is_tangential"] = True - d["Aavatsmark_transmissibilities"] = True - d["tol"] = tol - - # assign permeability - kxx = data["k"] * unity - if discr["scheme"] is pp.MVEM or discr["scheme"] is pp.RT0: - perm = pp.SecondOrderTensor(2, kxx=kxx, kyy=kxx, kzz=1) - - elif discr["scheme"] is pp.Tpfa: - perm = pp.SecondOrderTensor(3, kxx=kxx, kyy=kxx, kzz=kxx) - - else: - raise ValueError - - param["second_order_tensor"] = perm - - # assign aperture - param["aperture"] = unity - - # source - param["source"] = zeros - - # Boundaries - b_faces = g.tags["domain_boundary_faces"].nonzero()[0] - bc_val = np.zeros(g.num_faces) - if b_faces.size: - in_flow, out_flow = bc_flag(g, data["domain"], tol) - - labels = np.array(["neu"] * b_faces.size) - labels[in_flow + out_flow] = "dir" - param["bc"] = pp.BoundaryCondition(g, b_faces, labels) - - bc_val = np.zeros(g.num_faces) - bc_val[b_faces[in_flow]] = data.get("bc_flow", 1) - - # save the tags outflow and inflow - g.tags["bc_flow_id"] = np.zeros(g.num_faces) - g.tags["bc_flow_id"][b_faces[in_flow]] = 1 # it's just a flag - g.tags["bc_flow_id"][b_faces[out_flow]] = 2 - - else: - param["bc"] = pp.BoundaryCondition(g, empty, empty) - - param["bc_values"] = bc_val - - d[pp.PARAMETERS] = pp.Parameters(g, model_data, param) - d[pp.DISCRETIZATION_MATRICES] = {model_data: {}} - - for _, d in gb.edges(): - d[pp.DISCRETIZATION_MATRICES] = {model_data: {}} - - return model_data - - -# ------------------------------------------------------------------------------# - - -def flow(gb, discr, param, bc_flag): - - model = "flow" - - model_data = data_flow(gb, discr, model, param, bc_flag) - - # discretization operator name - flux_id = "flux" - - # master variable name - variable = "flow_variable" - mortar = "lambda_" + variable - - # post process variables - pressure = "pressure" - flux = "darcy_flux" # it has to be this one - - # save variable name for the advection-diffusion problem - param["pressure"] = pressure - param["flux"] = flux - param["mortar_flux"] = mortar - - discr_scheme = discr["scheme"](model_data) - discr_interface = pp.CellDofFaceDofMap(model_data) - - coupling = pp.FluxPressureContinuity(model_data, discr_scheme, discr_interface) - - # define the dof and discretization for the grids - for g, d in gb: - if g.dim == gb.dim_max(): - d[pp.PRIMARY_VARIABLES] = {variable: discr["dof"]} - d[pp.DISCRETIZATION] = {variable: {flux_id: discr_scheme}} - else: - d[pp.PRIMARY_VARIABLES] = {variable: {"cells": 1}} - d[pp.DISCRETIZATION] = {variable: {flux_id: discr_interface}} - - # define the interface terms to couple the grids - for e, d in gb.edges(): - g_slave, g_master = gb.nodes_of_edge(e) - d[pp.PRIMARY_VARIABLES] = {mortar: {"cells": 1}} - d[pp.COUPLING_DISCRETIZATION] = { - flux: { - g_slave: (variable, flux_id), - g_master: (variable, flux_id), - e: (mortar, coupling), - } - } - - # solution of the darcy problem - assembler = pp.Assembler(gb) - - logger.info("Assemble the flow problem") - A, b = assembler.assemble_matrix_rhs() - logger.info("done") - - logger.info("Solve the linear system") - x = sps.linalg.spsolve(A, b) - logger.info("done") - - logger.info("Variable post-process") - assembler.distribute_variable(x) - - # extract the pressure from the solution - for g, d in gb: - if g.dim == 2: - d[pressure] = discr_scheme.extract_pressure(g, d[variable], d) - d[flux] = discr_scheme.extract_flux(g, d[variable], d) - else: - d[pressure] = np.zeros(g.num_cells) - d[flux] = np.zeros(g.num_faces) - - # export the P0 flux reconstruction only for some scheme - if discr["scheme"] is pp.MVEM or discr["scheme"] is pp.RT0: - P0_flux = "P0_flux" - param["P0_flux"] = P0_flux - pp.project_flux(gb, discr_scheme, flux, P0_flux, mortar) - - logger.info("done") - - -# ------------------------------------------------------------------------------# - - -def data_advdiff(gb, model, data, bc_flag): - tol = data["tol"] - - model_data_adv = model + "_data_adv" - model_data_diff = model + "_data_diff" - model_data_src = model + "_data_src" - - flux_discharge_name = data["flux"] - flux_mortar_name = data["mortar_flux"] - - for g, d in gb: - param_adv = {} - param_diff = {} - param_src = {} - - d["Aavatsmark_transmissibilities"] = True - unity = np.ones(g.num_cells) - - # weight for the mass matrix - param_adv["mass_weight"] = unity - - # diffusion term - kxx = data["diff"] * unity - param_diff["second_order_tensor"] = pp.SecondOrderTensor(3, kxx) - - # Assign apertures - param_diff["aperture"] = unity - param_adv["aperture"] = unity - - # Flux - param_adv[flux_discharge_name] = ( - data.get("flux_weight", 1) * d[flux_discharge_name] - ) - - # Source - param_src["source"] = data.get("src", 0) * g.cell_volumes - - # Boundaries - b_faces = g.tags["domain_boundary_faces"].nonzero()[0] - bc_val = np.zeros(g.num_faces) - if b_faces.size: - in_flow, out_flow = bc_flag(g, data["domain"], tol) - - labels_adv = np.array(["neu"] * b_faces.size) - labels_adv[in_flow + out_flow] = ["dir"] - - labels_diff = np.array(["neu"] * b_faces.size) - labels_diff[in_flow] = ["dir"] - - param_adv["bc"] = pp.BoundaryCondition(g, b_faces, labels_adv) - param_diff["bc"] = pp.BoundaryCondition(g, b_faces, labels_diff) - - bc_val = np.zeros(g.num_faces) - bc_val[b_faces[in_flow]] = data.get("bc_trans", 1) - else: - param_adv["bc"] = pp.BoundaryCondition(g, np.empty(0), np.empty(0)) - param_diff["bc"] = pp.BoundaryCondition(g, np.empty(0), np.empty(0)) - - param_adv["bc_values"] = bc_val - param_diff["bc_values"] = bc_val - - param = pp.Parameters( - g, - [model_data_adv, model_data_diff, model_data_src], - [param_adv, param_diff, param_src], - ) - d[pp.PARAMETERS] = param - d[pp.DISCRETIZATION_MATRICES] = { - model_data_adv: {}, - model_data_diff: {}, - model_data_src: {}, - } - - for e, d in gb.edges(): - param_adv = {} - param_diff = {} - - param_adv[flux_discharge_name] = ( - data.get("flux_weight", 1) * d[flux_mortar_name] - ) - - param = pp.Parameters( - e, [model_data_adv, model_data_diff], [param_adv, param_diff] - ) - d[pp.PARAMETERS] = param - d[pp.DISCRETIZATION_MATRICES] = {model_data_adv: {}, model_data_diff: {}} - - return model_data_adv, model_data_diff, model_data_src - - -# ------------------------------------------------------------------------------# - - -def advdiff(gb, discr, param, bc_flag): - - model = "transport" - - model_data_adv, model_data_diff, model_data_src = data_advdiff( - gb, model, param, bc_flag - ) - - # discretization operator names - adv_id = "advection" - diff_id = "diffusion" - src_id = "source" - - # variable names - variable = "scalar" - mortar_adv = "lambda_" + variable + "_" + adv_id - mortar_diff = "lambda_" + variable + "_" + diff_id - - # save variable name for the post-process - param["scalar"] = variable - - discr_adv = pp.Upwind(model_data_adv) - discr_adv_interface = pp.CellDofFaceDofMap(model_data_adv) - - discr_diff = pp.Tpfa(model_data_diff) - discr_diff_interface = pp.CellDofFaceDofMap(model_data_diff) - - coupling_adv = pp.UpwindCoupling(model_data_adv) - coupling_diff = pp.FluxPressureContinuity( - model_data_diff, discr_diff, discr_diff_interface - ) - - # mass term - mass_id = "mass" - discr_mass = pp.MassMatrix(model_data_adv) - discr_mass_interface = pp.CellDofFaceDofMap(model_data_adv) - - discr_src = pp.ScalarSource(model_data_src) - - for g, d in gb: - d[pp.PRIMARY_VARIABLES] = {variable: {"cells": 1}} - if g.dim == gb.dim_max(): - d[pp.DISCRETIZATION] = { - variable: {adv_id: discr_adv, diff_id: discr_diff, mass_id: discr_mass, src_id: discr_src} - } - else: - d[pp.DISCRETIZATION] = { - variable: {adv_id: discr_adv_interface, diff_id: discr_diff_interface, mass_id: discr_mass_interface} - } - - for e, d in gb.edges(): - g_slave, g_master = gb.nodes_of_edge(e) - d[pp.PRIMARY_VARIABLES] = {mortar_adv: {"cells": 1}, mortar_diff: {"cells": 1}} - - d[pp.COUPLING_DISCRETIZATION] = { - adv_id: { - g_slave: (variable, adv_id), - g_master: (variable, adv_id), - e: (mortar_adv, coupling_adv), - }, - diff_id: { - g_slave: (variable, diff_id), - g_master: (variable, diff_id), - e: (mortar_diff, coupling_diff), - }, - } - - # setup the advection-diffusion problem - assembler = pp.Assembler(gb, active_variables=[variable, mortar_diff, mortar_adv]) - logger.info("Assemble the advective and diffusive terms of the transport problem") - block_A, block_b = assembler.assemble_matrix_rhs(add_matrices=False) - logger.info("done") - - # unpack the matrices just computed - diff_name = diff_id + "_" + variable - adv_name = adv_id + "_" + variable - mass_name = mass_id + "_" + variable - source_name = src_id + "_" + variable - - diff_coupling_name = diff_id + "_" + mortar_diff + "_" + variable + "_" + variable - adv_coupling_name = adv_id + "_" + mortar_adv + "_" + variable + "_" + variable - - # need a sign for the convention of the conservation equation - M = block_A[mass_name] - A = block_A[diff_name] + block_A[diff_coupling_name] + \ - block_A[adv_name] + block_A[adv_coupling_name] - b = block_b[diff_name] + block_b[diff_coupling_name] + \ - block_b[adv_name] + block_b[adv_coupling_name] + \ - block_b[source_name] - - M_t = M.copy() / param["time_step"] * param.get("mass_weight", 1) - M_r = M.copy() * param.get("reaction", 0) - - # Perform an LU factorization to speedup the solver - IE_solver = sps.linalg.factorized((M_t + A + M_r).tocsc()) - - # time loop - logger.info("Prepare the exporting") - save = pp.Exporter(gb, "solution", folder=param["folder"]) - logger.info("done") - - variables = [variable, param["pressure"], "frac_num", "cell_volumes"] - if discr["scheme"] is pp.MVEM or discr["scheme"] is pp.RT0: - variables.append(param["P0_flux"]) - - # assign the initial condition - x = np.zeros(A.shape[0]) - assembler.distribute_variable(x) - for g, d in gb: - if g.dim == gb.dim_max(): - d[variable] = param.get("init_trans", 0) * np.ones(g.num_cells) - - x = assembler.merge_variable(variable) - - outflow = np.zeros(param["n_steps"]) - - logger.info("Start the time loop with " + str(param["n_steps"]) + " steps") - for i in np.arange(param["n_steps"]): - logger.info("Solve the linear system for time step " + str(i)) - x = IE_solver(b + M_t.dot(x)) - logger.info("done") - - logger.info("Variable post-process") - assembler.distribute_variable(x) - logger.info("done") - - logger.info("Export variable") - save.write_vtk(variables, time_step=i) - logger.info("done") - - logger.info("Compute the production") - outflow[i] = compute_outflow(gb, param) - logger.info("done") - - time = np.arange(param["n_steps"]) * param["time_step"] - save.write_pvd(time) - - logger.info("Save outflow on file") - file_out = param["folder"] + "/outflow.csv" - data = np.vstack((time, outflow)).T - np.savetxt(file_out, data, delimiter=",") - logger.info("done") - - -# ------------------------------------------------------------------------------# - - -def compute_outflow(gb, param): - outflow = 0.0 - for g, d in gb: - if g.dim < 2: - continue - faces, cells, sign = sps.find(g.cell_faces) - index = np.argsort(cells) - faces, sign = faces[index], sign[index] - - flux = d[param["flux"]].copy() - scalar = d[param["scalar"]] - - flux[faces] *= sign - flux[g.get_internal_faces()] = 0 - flux[flux < 0] = 0 - # outflow += np.dot(flux, np.abs(g.cell_faces).dot(scalar)) - - flux[flux != 0] = 1 - outflow += np.dot(flux * g.face_areas, np.abs(g.cell_faces).dot(scalar)) - - return outflow - - -# ------------------------------------------------------------------------------# diff --git a/examples/papers/dfn_transport/example1/docopy.py b/examples/papers/dfn_transport/example1/docopy.py deleted file mode 100644 index c74ce297fd..0000000000 --- a/examples/papers/dfn_transport/example1/docopy.py +++ /dev/null @@ -1,65 +0,0 @@ -from shutil import copyfile - -folder_src = "/home/elle/Dropbox/Work/PresentazioniArticoli/2019/Articles/tipetut++/Results/example1/img/" -folder_dist = "/home/elle/Dropbox/Work/PresentazioniArticoli/2019/Articles/tipetut++/Article/Parts/Images/" - -# the number of cells -grid = [0, 1, 2] -grids_label = {0: "coarse", 1: "medium", 2: "fine"} - -for g in grid: - name_src = grids_label[g] + "_num_cells.png" - name_dist = "example1_" + name_src - copyfile(folder_src + name_src, folder_dist + name_dist) - -# outflow -grid = [0, 1, 2] -config = [0, 10, 20] - -for g in grid: - for c in config: - name_src = grids_label[g] + "_outflow_" + str(c) + ".png" - name_dist = "example1_" + name_src - copyfile(folder_src + name_src, folder_dist + name_dist) - -# avgerage temperature -grid = [0, 1, 2] -config = [0, 10, 20] -frac = [0] - -for g in grid: - for c in config: - for f in frac: - name_src = ( - grids_label[g] + "_cot_avg_" + str(c) + "_frac_" + str(f) + ".png" - ) - name_dist = "example1_" + name_src - copyfile(folder_src + name_src, folder_dist + name_dist) - -# minimum temperature -grid = [0, 1, 2] -config = [0, 10, 20] -frac = [0] - -for g in grid: - for c in config: - for f in frac: - name_src = ( - grids_label[g] + "_cot_min_" + str(c) + "_frac_" + str(f) + ".png" - ) - name_dist = "example1_" + name_src - copyfile(folder_src + name_src, folder_dist + name_dist) - -# maximum temperature -grid = [0, 1, 2] -config = [0, 10, 20] -frac = [0] - -for g in grid: - for c in config: - for f in frac: - name_src = ( - grids_label[g] + "_cot_max_" + str(c) + "_frac_" + str(f) + ".png" - ) - name_dist = "example1_" + name_src - copyfile(folder_src + name_src, folder_dist + name_dist) diff --git a/examples/papers/dfn_transport/example1/main.py b/examples/papers/dfn_transport/example1/main.py deleted file mode 100644 index 7d62832dff..0000000000 --- a/examples/papers/dfn_transport/example1/main.py +++ /dev/null @@ -1,109 +0,0 @@ -import numpy as np -import porepy as pp - -import examples.papers.dfn_transport.discretization as compute - -# from examples.papers.dfn_transport.grid_export import grid_export - -# from examples.papers.dfn_transport.flux_trace import jump_flux - - -def bc_flag(g, domain, tol): - b_faces = g.tags["domain_boundary_faces"].nonzero()[0] - b_face_centers = g.face_centers[:, b_faces] - - # define inflow type boundary conditions - out_flow_z = b_face_centers[2] > 0.4 - out_flow_x = b_face_centers[0] < tol - - out_flow = np.logical_and(out_flow_x, out_flow_z) - - # define outflow type boundary conditions - in_flow_start = np.array([0, 0, 0.3]) - in_flow_end = np.array([0, 1, 0.3]) - - # detect all the points aligned with the segment - dist, _ = pp.distances.points_segments(b_face_centers, in_flow_start, in_flow_end) - dist = dist.flatten() - in_flow = np.logical_and(dist < tol, dist >= -tol) - - return in_flow, out_flow - - -def main(): - - input_folder = "../geometries/example1/" - - # define the discretizations for the Darcy part - discretizations = compute.get_discr() - - # geometric tolerance - tol = 1e-8 - - # define the mesh sizes - mesh_sizes = { - "1k": 0.095, # for 1k triangles - "3k": 0.8375 * np.power(2.0, -4), # for 3k triangles - "10k": 0.91 * np.power(2.0, -5), # for 10k triangles - } - - for mesh_size_key in mesh_sizes.keys(): - - for discr_key, discr in discretizations.items(): - - if discr_key == "MVEM": - if mesh_size_key == "1k": - mesh_size = 1 / 16 - elif mesh_size_key == "3k": - mesh_size = 0.9 * np.power(2.0, -4) / 1.55 - elif mesh_size_key == "10k": - mesh_size = 0.875 * np.power(2.0, -5) / 1.4 - else: - mesh_size = mesh_sizes[mesh_size_key] - - mesh_kwargs = {"mesh_size_frac": mesh_size, "mesh_size_min": mesh_size / 20} - - num_simul = 21 - for simul in np.arange(1, num_simul + 1): - - file_name = input_folder + "DFN_" + str(simul) + ".fab" - folder = ( - "solution_" + discr_key + "_" + mesh_size_key + "_" + str(simul) - ) - - network = pp.fracture_importer.network_3d_from_fab(file_name, tol=tol) - gb = network.mesh(mesh_kwargs, dfn=True) - - gb.remove_nodes(lambda g: g.dim == 0) - gb.compute_geometry() - gb.assign_node_ordering() - - if discr_key == "MVEM": - pp.coarsening.coarsen(gb, "by_volume") - - domain = gb.bounding_box(as_dict=True) - - param = { - "domain": domain, - "tol": tol, - "k": 1, - "diff": 1e-4, - "time_step": 0.05, - "n_steps": 300, - "folder": folder, - } - - # the flow problem - compute.flow(gb, discr, param, bc_flag) - - # if discr_key == "Tpfa": - # grid_export(gb, None, "grid_" + mesh_size_key + "_" + str(simul) + "/") - - # jump_flux(gb, param["mortar_flux"]) - - # the advection-diffusion problem - compute.advdiff(gb, discr, param, bc_flag) - - -if __name__ == "__main__": - main() diff --git a/examples/papers/dfn_transport/example1/post_process.py b/examples/papers/dfn_transport/example1/post_process.py deleted file mode 100644 index 642249d4e2..0000000000 --- a/examples/papers/dfn_transport/example1/post_process.py +++ /dev/null @@ -1,190 +0,0 @@ -import paraview.simple as pv - -import vtk -from vtk.util.numpy_support import vtk_to_numpy - -import csv -import os -import shutil - -import numpy as np - -#------------------------------------------------------------------------------# - -def read_file(file_in): - vtk_reader = vtk.vtkXMLUnstructuredGridReader() - vtk_reader.SetFileName(file_in) - vtk_reader.Update() - return vtk_reader - -#------------------------------------------------------------------------------# - -def read_data(vtk_reader, field): - data = vtk_reader.GetOutput().GetCellData().GetArray(field) - return vtk_to_numpy(data) - -#------------------------------------------------------------------------------# - -def plot_over_line(file_in, file_out, pts, resolution=2000): - - if file_in.lower().endswith('.pvd'): - # create a new 'PVD Reader' - sol = pv.PVDReader(FileName=file_in) - elif file_in.lower().endswith('.vtu'): - # create a new 'XML Unstructured Grid Reader' - sol = pv.XMLUnstructuredGridReader(FileName=file_in) - else: - raise ValueError, "file format not yet supported" - - # create a new 'Plot Over Line' - pol = pv.PlotOverLine(Input=sol, Source='High Resolution Line Source') - - # Properties modified on plotOverLine1.Source - pol.Source.Point1 = pts[0] - pol.Source.Point2 = pts[1] - pol.Source.Resolution = resolution - - # save data - pv.SaveData(file_out, proxy=pol, Precision=15) - -#------------------------------------------------------------------------------# - -def read_csv(file_in, fields=None): - - # post-process the file by selecting only few columns - if fields is not None: - data = list(list() for _ in fields) - with open(file_in, 'r') as csvfile: - reader = csv.DictReader(csvfile) - [d.append(row[f]) for row in reader for f, d in zip(fields, data)] - else: - with open(file_in, 'r') as csvfile: - reader = csv.reader(csvfile) - data = list(reader) - - return data - -#------------------------------------------------------------------------------# - -def write_csv(file_out, fields, data): - with open(file_out, 'w') as csvfile: - writer = csv.DictWriter(csvfile, fieldnames=fields) - #writer.writeheader() - for dd in zip(*data): - if np.isnan(np.array(dd)).any(): - print(dd) - writer.writerow({f: d for f, d in zip(fields, dd)}) - -#------------------------------------------------------------------------------# - -def cot_domain(file_in, step, field, num_frac, padding=6): - - cot_avg = np.zeros((step, num_frac)) - cot_min = np.zeros((step, num_frac)) - cot_max = np.zeros((step, num_frac)) - - for i in np.arange(step): - - ifile = file_in+str(i).zfill(padding)+".vtu" - vtk_reader = read_file(ifile) - - weight = read_data(vtk_reader, "cell_volumes") - frac_num = read_data(vtk_reader, "frac_num") - c = read_data(vtk_reader, field) - - for frac_id in np.arange(num_frac): - is_loc = frac_num == frac_id - weight_loc = weight[is_loc] - - cot_avg[i, frac_id] = np.sum(c[is_loc]*weight_loc)/np.sum(weight_loc) - cot_min[i, frac_id] = np.amin(c[is_loc]) - cot_max[i, frac_id] = np.amax(c[is_loc]) - - return cot_avg, cot_min, cot_max - -#------------------------------------------------------------------------------# - -def num_cells(file_in, num_frac, padding=6): - - i = 0 - - ifile = file_in+str(i).zfill(padding)+".vtu" - vtk_reader = read_file(ifile) - - frac_num = read_data(vtk_reader, "frac_num") - num = np.zeros(num_frac+1, dtype=np.int) - - for frac_id in np.arange(num_frac): - num[frac_id] = np.sum(frac_num == frac_id) - - num[-1] = np.sum(num[:-1]) - return num - -#------------------------------------------------------------------------------# - -def main(): - - num_simul = 21 - - field = "scalar" - n_step = 300 - time_step = 0.05 - num_frac = 3 - - grids = ["1k", "3k", "10k"] - - #folder_master = "/home/elle/simul/example1/" - folder_master = "./" - folder_master_out = folder_master + "CSV/" - methods = ["MVEM", "Tpfa", "RT0"] - - for method in methods: - for grid in grids: - # store the number of cells - num = np.zeros((num_simul, num_frac+1), dtype=np.int) - for simul in np.arange(num_simul): - - folder_in = folder_master + "solution_" + method + "_" + grid + "_" + str(simul+1) + "/" - folder_out = folder_master_out + method + "/" - - # in this file the constant data are saved - file_in = folder_in + "solution_2_" - - cot_avg, cot_min, cot_max = cot_domain(file_in, n_step, field, num_frac) - - times = np.arange(n_step) * time_step - labels = np.arange(num_frac).astype(np.str) - labels = np.core.defchararray.add("cot_", labels) - labels = np.insert(labels, 0, 'time') - - if not os.path.exists(folder_out): - os.makedirs(folder_out) - - # create the output files - file_out = folder_out + "Cmin_" + str(simul+1) + "_" + grid + ".csv" - data = np.insert(cot_min, 0, times, axis=1).T - write_csv(file_out, labels, data) - - # create the output files - file_out = folder_out + "Cmax_" + str(simul+1) + "_" + grid + ".csv" - data = np.insert(cot_max, 0, times, axis=1).T - write_csv(file_out, labels, data) - - # create the output files - file_out = folder_out + "Cmean_" + str(simul+1) + "_" + grid + ".csv" - data = np.insert(cot_avg, 0, times, axis=1).T - write_csv(file_out, labels, data) - - # count number of cells - num[simul, :] = num_cells(file_in, num_frac) - - # copy outflow file - file_in = folder_in + "outflow.csv" - file_out = folder_out + "production_" + str(simul+1) + "_" + grid + ".csv" - shutil.copy(file_in, file_out) - - file_out = folder_out + "num_cells_" + grid + ".csv" - np.savetxt(file_out, np.atleast_2d(num), delimiter=',', fmt="%d") - -if __name__ == "__main__": - main() diff --git a/examples/papers/dfn_transport/example1/pot.py b/examples/papers/dfn_transport/example1/pot.py deleted file mode 100644 index cfc5eab7be..0000000000 --- a/examples/papers/dfn_transport/example1/pot.py +++ /dev/null @@ -1,517 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import os - -# ------------------------------------------------------------------------------# - -plt.rc("text", usetex=True) -plt.rc("font", family="serif") -plt.rc("font", size=15) - - -def plot_single(file_name, legend, title, **kwargs): - - data = np.loadtxt(file_name, delimiter=",") - reference = kwargs.get("reference", None) - - fig = plt.figure(0) - ax = fig.add_subplot(111) - - # if the data is a reference - if reference: - data_p = data[:, 1] + data[:, 1] * reference / 100 - plt.plot(data[:, 0], data_p, label=legend, linestyle="--", color="gray") - text = "ref + " + str(reference) + "\%" - pos = (np.median(data[:, 0]), np.median(data_p)) - pos_t = (pos[0], pos[1]+5*pos[1]/100) - ax.annotate(text, xy=pos, xytext=pos_t) - - data_m = data[:, 1] - data[:, 1] * reference / 100 - plt.plot(data[:, 0], data_m, label=legend, linestyle="--", color="gray") - text = "ref - " + str(reference) + "\%" - pos = (np.median(data[:, 0]), np.median(data_m)) - pos_t = (pos[0], pos[1]-5*pos[1]/100) - ax.annotate(text, xy=pos, xytext=pos_t) - - else: - plt.plot(data[:, 0], data[:, 1], label=legend) - - plt.title(title) - plt.xlabel("$t$") - plt.ylabel("$\\theta$") - plt.grid(True) - plt.legend() - - -# ------------------------------------------------------------------------------# - - -def plot_multiple(file_name, legend, title, num_frac, **kwargs): - - data = np.loadtxt(file_name, delimiter=",") - frac_label = {0: "$\\Omega_l$", 1: "$\\Omega_m$", 2: "$\\Omega_r$"} - - reference = kwargs.get("reference", None) - - for frac_id in np.arange(num_frac): - fig = plt.figure(frac_id) - ax = fig.add_subplot(111) - - # if the data is a reference - if reference: - data_p = data[:, frac_id + 1] + data[:, frac_id + 1] * reference / 100 - plt.plot(data[:, 0], data_p, label=legend, linestyle="--", color="gray") - text = "ref + " + str(reference) + "\%" - pos = (np.median(data[:, 0]), np.median(data_p)) - pos_t = (pos[0], pos[1]+5*pos[1]/100) - ax.annotate(text, xy=pos, xytext=pos_t) - - data_m = data[:, frac_id + 1] - data[:, frac_id + 1] * reference / 100 - plt.plot(data[:, 0], data_m, label=legend, linestyle="--", color="gray") - text = "ref - " + str(reference) + "\%" - pos = (np.median(data[:, 0]), np.median(data_m)) - pos_t = (pos[0], pos[1]-5*pos[1]/100) - ax.annotate(text, xy=pos, xytext=pos_t) - - else: - plt.plot(data[:, 0], data[:, frac_id + 1], label=legend) - - plt_title = ( - title[0] - + " on " - + frac_label[frac_id] - + " " - + title[1] - + " - " - + " config " - + str(title[2]) - ) - plt.title(plt_title) - plt.xlabel("$t$") - plt.ylabel("$\\theta$") - plt.grid(True) - plt.legend() - - -# ------------------------------------------------------------------------------# - - -def plot_num_cells(data, legend, title): - - data = np.loadtxt(data, delimiter=",") - data = np.atleast_2d(data) - - plt.figure(0) - plt.plot(np.arange(data.shape[0]), data[:, -1], label=legend) - plt.title(title) - plt.xlabel("config.") - plt.ylabel("num. cells") - plt.grid(True) - plt.legend() - # useful to plot the legend as flat - # ncol = 5 # number of methods - # plt.legend(bbox_to_anchor=(1, -0.2), ncol=5) - - -# ------------------------------------------------------------------------------# - - -def save_single(filename, folder, figure_id=0): - - if not os.path.exists(folder): - os.makedirs(folder) - - plt.figure(figure_id) - plt.savefig(folder + filename, bbox_inches="tight") - plt.gcf().clear() - - -# ------------------------------------------------------------------------------# - - -def save_multiple(filename, num_frac, folder): - - if not os.path.exists(folder): - os.makedirs(folder) - - for frac_id in np.arange(num_frac): - plt.figure(frac_id) - name = filename + "_frac_" + str(frac_id) - plt.savefig(folder + name, bbox_inches="tight") - plt.gcf().clear() - - -# ------------------------------------------------------------------------------# - - -def main(): - - num_simul = 21 - num_frac = 3 - - master_folder = "/home/elle/Dropbox/Work/PresentazioniArticoli/2019/Articles/tipetut++/Results/example1/" - - methods_stefano_1 = ["OPTxfem", "OPTfem"] - methods_stefano_2 = ["GCmfem"] - methods_alessio = ["MVEM_UPWIND", "Tpfa_UPWIND", "RT0_UPWIND"] - methods_andrea = ["MVEM_VEMSUPG"] - - method_reference = "GCmfem" - reference = {"grid_0": 10, "grid_1": 5, "grid_2": 3.5} - - grids = { - "grid_0": ("1k", "220", "1", "0.005"), - "grid_1": ("3k", "650", "3", "0.0015"), - "grid_2": ("10k", "2100", "10", "0.00045"), - } - grids_label = {"grid_0": "coarse", "grid_1": "medium", "grid_2": "fine"} - - - for grid_name, grid in grids.items(): - grid_label = grids_label[grid_name] - for simul in np.arange(num_simul): - - folder_in = master_folder - folder_out = folder_in + "img/" - - title = ["avg $\\theta$", grid_label, simul] - - # Reference - data = ( - folder_in - + method_reference - + "/" - + method_reference - + "_Cmean_" - + str(simul + 1) - + "_big" - + ".csv" - ) - plot_multiple(data, None, title, num_frac, reference=reference[grid_name]) - - # Alessio - for method in methods_alessio: - data = ( - folder_in - + method - + "/" - + "Cmean_" - + str(simul + 1) - + "_" - + grid[0] - + ".csv" - ) - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # Stefano - for method in methods_stefano_1: - data = ( - folder_in - + method - + "/" - + method - + "_Cmean_" - + str(simul + 1) - + "_" - + grid[1] - + ".csv" - ) - plot_multiple(data, method, title, num_frac) - - for method in methods_stefano_2: - data = ( - folder_in - + method - + "/" - + method - + "_Cmean_" - + str(simul + 1) - + "_" - + grid[2] - + ".csv" - ) - plot_multiple(data, method, title, num_frac) - - # Andrea - for method in methods_andrea: - data = ( - folder_in - + method - + "/" - + "Cmean_" - + str(simul + 1) - + "_" - + grid[3] - + ".csv" - ) - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # save - name = grid_label + "_cot_avg_" + str(simul) - save_multiple(name, num_frac, folder_out) - - ########### - - title = ["min $\\theta$", grid_label, simul] - - # Reference - data = ( - folder_in - + method_reference - + "/" - + method_reference - + "_Cmin_" - + str(simul + 1) - + "_big" - + ".csv" - ) - plot_multiple(data, None, title, num_frac, reference=reference[grid_name]) - - # Alessio - for method in methods_alessio: - data = ( - folder_in - + method - + "/" - + "Cmin_" - + str(simul + 1) - + "_" - + grid[0] - + ".csv" - ) - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # Stefano - for method in methods_stefano_1: - data = ( - folder_in - + method - + "/" - + method - + "_Cmin_" - + str(simul + 1) - + "_" - + grid[1] - + ".csv" - ) - plot_multiple(data, method, title, num_frac) - - # Stefano - for method in methods_stefano_2: - data = ( - folder_in - + method - + "/" - + method - + "_Cmin_" - + str(simul + 1) - + "_" - + grid[2] - + ".csv" - ) - plot_multiple(data, method, title, num_frac) - - # Andrea - for method in methods_andrea: - data = ( - folder_in - + method - + "/" - + "Cmin_" - + str(simul + 1) - + "_" - + grid[3] - + ".csv" - ) - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # save - name = grid_label + "_cot_min_" + str(simul) - save_multiple(name, num_frac, folder_out) - - ########### - - title = ["max $\\theta$", grid_label, simul] - - # Reference - data = ( - folder_in - + method_reference - + "/" - + method_reference - + "_Cmax_" - + str(simul + 1) - + "_big" - + ".csv" - ) - plot_multiple(data, None, title, num_frac, reference=reference[grid_name]) - - # Alessio - for method in methods_alessio: - data = ( - folder_in - + method - + "/" - + "Cmax_" - + str(simul + 1) - + "_" - + grid[0] - + ".csv" - ) - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # Stefano - for method in methods_stefano_1: - data = ( - folder_in - + method - + "/" - + method - + "_Cmax_" - + str(simul + 1) - + "_" - + grid[1] - + ".csv" - ) - plot_multiple(data, method, title, num_frac) - - for method in methods_stefano_2: - data = ( - folder_in - + method - + "/" - + method - + "_Cmax_" - + str(simul + 1) - + "_" - + grid[2] - + ".csv" - ) - plot_multiple(data, method, title, num_frac) - - # Andrea - for method in methods_andrea: - data = ( - folder_in - + method - + "/" - + "Cmax_" - + str(simul + 1) - + "_" - + grid[3] - + ".csv" - ) - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # save - name = grid_label + "_cot_max_" + str(simul) - save_multiple(name, num_frac, folder_out) - - ########### - - title = "production on " + grid_label + " - config " + str(simul) - - # Reference - data = ( - folder_in - + method_reference - + "/" - + method_reference - + "_production_" - + str(simul + 1) - + "_big" - + ".csv" - ) - plot_single(data, None, title, reference=reference[grid_name]) - - # Alessio - for method in methods_alessio: - data = ( - folder_in - + method - + "/" - + "production_" - + str(simul + 1) - + "_" - + grid[0] - + ".csv" - ) - plot_single(data, method.replace("_", " "), title) - - # Stefano - for method in methods_stefano_1: - data = ( - folder_in - + method - + "/" - + method - + "_production_" - + str(simul + 1) - + "_" - + grid[1] - + ".csv" - ) - plot_single(data, method, title) - - for method in methods_stefano_2: - data = ( - folder_in - + method - + "/" - + method - + "_production_" - + str(simul + 1) - + "_" - + grid[2] - + ".csv" - ) - plot_single(data, method, title) - - # Andrea - for method in methods_andrea: - data = ( - folder_in - + method - + "/" - + "production_" - + str(simul + 1) - + "_" - + grid[3] - + ".csv" - ) - plot_single(data, method.replace("_", " "), title) - - # save - name = grid_label + "_outflow_" + str(simul) - save_single(name, folder_out) - - ######## - - title = "number of cells - " + grid_label - # Alessio - for method in methods_alessio: - data = folder_in + method + "/" + "num_cells_" + grid[0] + ".csv" - plot_num_cells(data, method.replace("_", " "), title) - - # Stefano - for method in methods_stefano_1: - data = folder_in + method + "/" + "num_cells_" + grid[1] + ".csv" - plot_num_cells(data, method.replace("_", " "), title) - - for method in methods_stefano_2: - data = folder_in + method + "/" + method + "_cells_" + grid[2] + ".csv" - plot_num_cells(data, method.replace("_", " "), title) - - # Andrea - for method in methods_andrea: - data = folder_in + method + "/" + "num_cells_" + grid[3] + ".csv" - plot_num_cells(data, method.replace("_", " "), title) - - name = grid_label + "_num_cells" - save_single(name, folder_out) - - -# ------------------------------------------------------------------------------# - -if __name__ == "__main__": - main() diff --git a/examples/papers/dfn_transport/example2/main.py b/examples/papers/dfn_transport/example2/main.py deleted file mode 100644 index b7bf429121..0000000000 --- a/examples/papers/dfn_transport/example2/main.py +++ /dev/null @@ -1,105 +0,0 @@ -import numpy as np -import porepy as pp - -import examples.papers.dfn_transport.discretization as compute - -# from examples.papers.dfn_transport.grid_export import grid_export - -# from examples.papers.dfn_transport.flux_trace import jump_flux - - -def bc_flag(g, domain, tol): - b_faces = g.tags["domain_boundary_faces"].nonzero()[0] - b_face_centers = g.face_centers[:, b_faces] - - # define inflow type boundary conditions - out_flow_start = np.array([1.011125, 0.249154, 0.598708]) - out_flow_end = np.array([1.012528, 0.190858, 0.886822]) - - # detect all the points aligned with the segment - dist, _ = pp.distances.points_segments(b_face_centers, out_flow_start, out_flow_end) - - dist = dist.flatten() - out_flow = np.logical_and(dist < tol, dist >= -tol) - - # define outflow type boundary conditions - in_flow_start = np.array([0.206507, 0.896131, 0.183632]) - in_flow_end = np.array([0.181980, 0.813947, 0.478618]) - - # detect all the points aligned with the segment - dist, _ = pp.distances.points_segments(b_face_centers, in_flow_start, in_flow_end) - dist = dist.flatten() - in_flow = np.logical_and(dist < tol, dist >= -tol) - - return in_flow, out_flow - - -def main(): - - input_folder = "../geometries/" - file_name = input_folder + "example2.fab" - - # define the discretizations for the Darcy part - discretizations = compute.get_discr() - - # geometric tolerance - tol = 1e-5 - - # define the mesh sizes - mesh_sizes = { - "3k": 0.9 * np.power(2.0, -4), # for 3k triangles - "40k": 0.49 * 0.875 * np.power(2.0, -5), # for 40k triangles - } - - for mesh_size_key in mesh_sizes.keys(): - - for discr_key, discr in discretizations.items(): - - if discr_key == "MVEM": - if mesh_size_key == "3k": - mesh_size = 0.9 * np.power(2.0, -4) * 0.675 - elif mesh_size_key == "40k": - mesh_size = 0.49 * 0.875 * np.power(2.0, -5) * 0.7 - else: - mesh_size = mesh_sizes[mesh_size_key] - - mesh_kwargs = {"mesh_size_frac": mesh_size, "mesh_size_min": mesh_size / 20} - - folder = "solution_" + discr_key + "_" + mesh_size_key - - network = pp.fracture_importer.network_3d_from_fab(file_name, tol=tol) - gb = network.mesh(mesh_kwargs, dfn=True) - - gb.remove_nodes(lambda g: g.dim == 0) - gb.compute_geometry() - gb.assign_node_ordering() - - if discr_key == "MVEM": - pp.coarsening.coarsen(gb, "by_volume") - - domain = gb.bounding_box(as_dict=True) - - param = { - "domain": domain, - "tol": tol, - "k": 1, - "diff": 1e-4, - "time_step": 0.05, - "n_steps": 500, - "folder": folder, - } - - # the flow problem - compute.flow(gb, discr, param, bc_flag) - - # if discr_key == "Tpfa": - # grid_export(gb, None, "grid_" + mesh_size_key + "/") - - # jump_flux(gb, param["mortar_flux"]) - - # the advection-diffusion problem - compute.advdiff(gb, discr, param, bc_flag) - - -if __name__ == "__main__": - main() diff --git a/examples/papers/dfn_transport/example2/post_process.py b/examples/papers/dfn_transport/example2/post_process.py deleted file mode 100644 index 320a1a10f0..0000000000 --- a/examples/papers/dfn_transport/example2/post_process.py +++ /dev/null @@ -1,161 +0,0 @@ -import paraview.simple as pv - -import vtk -from vtk.util.numpy_support import vtk_to_numpy - -import csv -import os -import shutil - -import numpy as np - -#------------------------------------------------------------------------------# - -def read_file(file_in): - vtk_reader = vtk.vtkXMLUnstructuredGridReader() - vtk_reader.SetFileName(file_in) - vtk_reader.Update() - return vtk_reader - -#------------------------------------------------------------------------------# - -def read_data(vtk_reader, field): - data = vtk_reader.GetOutput().GetCellData().GetArray(field) - return vtk_to_numpy(data) - -#------------------------------------------------------------------------------# - -def plot_over_line(file_in, file_out, pts, resolution=2000): - - if file_in.lower().endswith('.pvd'): - # create a new 'PVD Reader' - sol = pv.PVDReader(FileName=file_in) - elif file_in.lower().endswith('.vtu'): - # create a new 'XML Unstructured Grid Reader' - sol = pv.XMLUnstructuredGridReader(FileName=file_in) - else: - raise ValueError, "file format not yet supported" - - # create a new 'Plot Over Line' - pol = pv.PlotOverLine(Input=sol, Source='High Resolution Line Source') - - # Properties modified on plotOverLine1.Source - pol.Source.Point1 = pts[0] - pol.Source.Point2 = pts[1] - pol.Source.Resolution = resolution - - # save data - pv.SaveData(file_out, proxy=pol, Precision=15) - -#------------------------------------------------------------------------------# - -def read_csv(file_in, fields=None): - - # post-process the file by selecting only few columns - if fields is not None: - data = list(list() for _ in fields) - with open(file_in, 'r') as csvfile: - reader = csv.DictReader(csvfile) - [d.append(row[f]) for row in reader for f, d in zip(fields, data)] - else: - with open(file_in, 'r') as csvfile: - reader = csv.reader(csvfile) - data = list(reader) - - return data - -#------------------------------------------------------------------------------# - -def write_csv(file_out, fields, data): - with open(file_out, 'w') as csvfile: - writer = csv.DictWriter(csvfile, fieldnames=fields) - #writer.writeheader() - for dd in zip(*data): - if np.isnan(np.array(dd)).any(): - print(dd) - writer.writerow({f: d for f, d in zip(fields, dd)}) - -#------------------------------------------------------------------------------# - -def cot_domain(file_in, step, field, num_frac, padding=6): - - cot_avg = np.zeros((step, num_frac)) - cot_min = np.zeros((step, num_frac)) - cot_max = np.zeros((step, num_frac)) - - for i in np.arange(step): - - ifile = file_in+str(i).zfill(padding)+".vtu" - vtk_reader = read_file(ifile) - - weight = read_data(vtk_reader, "cell_volumes") - frac_num = read_data(vtk_reader, "frac_num") - c = read_data(vtk_reader, field) - - for frac_id in np.arange(num_frac): - is_loc = frac_num == frac_id - weight_loc = weight[is_loc] - - cot_avg[i, frac_id] = np.sum(c[is_loc]*weight_loc)/np.sum(weight_loc) - cot_min[i, frac_id] = np.amin(c[is_loc]) - cot_max[i, frac_id] = np.amax(c[is_loc]) - - return cot_avg, cot_min, cot_max - -#------------------------------------------------------------------------------# - -def main(): - - field = "scalar" - n_step = 500 - time_step = 0.05 - num_frac = 10 - - grids = ["3k", "40k"] - - #folder_master = "/home/elle/simul/example1/" - folder_master = "./" - folder_master_out = "./CSV/" - methods = ["MVEM", "Tpfa", "RT0"] - - for method in methods: - for grid in grids: - - folder_in = folder_master + "solution_" + method + "_" + grid + "/" - folder_out = folder_master_out + method + "/" - - # in this file the constant data are saved - file_in = folder_in + "solution_2_" - - cot_avg, cot_min, cot_max = cot_domain(file_in, n_step, field, num_frac) - - times = np.arange(n_step) * time_step - labels = np.arange(num_frac).astype(np.str) - labels = np.core.defchararray.add("cot_", labels) - labels = np.insert(labels, 0, 'time') - - if not os.path.exists(folder_out): - os.makedirs(folder_out) - - # create the output files - file_out = folder_out + "Cmin_" + grid + ".csv" - data = np.insert(cot_min, 0, times, axis=1).T - write_csv(file_out, labels, data) - - # create the output files - file_out = folder_out + "Cmax_" + grid + ".csv" - data = np.insert(cot_max, 0, times, axis=1).T - write_csv(file_out, labels, data) - - # create the output files - file_out = folder_out + "Cmean_" + grid + ".csv" - data = np.insert(cot_avg, 0, times, axis=1).T - write_csv(file_out, labels, data) - - # copy outflow file - file_in = folder_in + "outflow.csv" - file_out = folder_out + "production_" + grid + ".csv" - shutil.copy(file_in, file_out) - -if __name__ == "__main__": - main() diff --git a/examples/papers/dfn_transport/example2/pot.py b/examples/papers/dfn_transport/example2/pot.py deleted file mode 100644 index d61c11687c..0000000000 --- a/examples/papers/dfn_transport/example2/pot.py +++ /dev/null @@ -1,205 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import os - -# ------------------------------------------------------------------------------# - -plt.rc("text", usetex=True) -plt.rc("font", family="serif") -plt.rc("font", size=15) - - -def plot_single(file_name, legend, title): - - data = np.loadtxt(file_name, delimiter=",") - - plt.figure(0) - plt.plot(data[:, 0], data[:, 1], label=legend) - plt.title(title) - plt.xlabel("$t$") - plt.ylabel("$\\theta$") - plt.grid(True) - plt.legend() - - -# ------------------------------------------------------------------------------# - - -def plot_multiple(file_name, legend, title, num_frac): - - data = np.loadtxt(file_name, delimiter=",") - - for frac_id in np.arange(num_frac): - plt.figure(frac_id) - plt.plot(data[:, 0], data[:, frac_id + 1], label=legend) - plt_title = ( - title[0] + " on " + "$\\Omega_{" + str(frac_id) + "}$" + " " + title[1] - ) - plt.title(plt_title) - plt.xlabel("$t$") - plt.ylabel("$\\theta$") - plt.grid(True) - plt.legend() - - -# ------------------------------------------------------------------------------# - - -def save_single(filename, folder, figure_id=0): - - if not os.path.exists(folder): - os.makedirs(folder) - - plt.figure(figure_id) - plt.savefig(folder + filename, bbox_inches="tight") - plt.gcf().clear() - - -# ------------------------------------------------------------------------------# - - -def save_multiple(filename, num_frac, folder): - - if not os.path.exists(folder): - os.makedirs(folder) - - for frac_id in np.arange(num_frac): - plt.figure(frac_id) - name = filename + "_frac_" + str(frac_id) - plt.savefig(folder + name, bbox_inches="tight") - plt.gcf().clear() - - -# ------------------------------------------------------------------------------# - - -def main(): - - num_frac = 10 - - master_folder = "/home/elle/Dropbox/Work/PresentazioniArticoli/2019/Articles/tipetut++/Results/example2/" - - methods_stefano_1 = ["OPTxfem", "OPTfem"] - methods_stefano_2 = ["GCmfem"] - methods_alessio = ["MVEM_UPWIND", "Tpfa_UPWIND", "RT0_UPWIND"] - methods_andrea = ["MVEM_VEMSUPG", "MVEM_VEMSUPG_POWERTAU"] - - grids = { - "grid_0": ("3k", "200", "3", "9e-05"), - "grid_1": ("40k", "2600", "40", "0.0015"), - } - grids_label = {"grid_0": "coarse", "grid_1": "fine"} - - for grid_name, grid in grids.items(): - grid_label = grids_label[grid_name] - - folder_in = master_folder - folder_out = folder_in + "img/" - - title = ["avg $\\theta$", grid_label] - # Alessio - for method in methods_alessio: - data = folder_in + method + "/" + "Cmean_" + grid[0] + ".csv" - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # Stefano - for method in methods_stefano_1: - data = folder_in + method + "/" + method + "_Cmean_" + grid[1] + ".csv" - plot_multiple(data, method, title, num_frac) - - for method in methods_stefano_2: - data = folder_in + method + "/" + method + "_Cmean_" + grid[2] + ".csv" - plot_multiple(data, method, title, num_frac) - - # Andrea - for method in methods_andrea: - data = folder_in + method + "/" + "Cmean_" + grid[3] + ".csv" - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # save - name = grid_label + "_cot_avg" - save_multiple(name, num_frac, folder_out) - - ########### - - title = ["min $\\theta$", grid_label] - # Alessio - for method in methods_alessio: - data = folder_in + method + "/" + "Cmin_" + grid[0] + ".csv" - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # Stefano - for method in methods_stefano_1: - data = folder_in + method + "/" + method + "_Cmin_" + grid[1] + ".csv" - plot_multiple(data, method, title, num_frac) - - for method in methods_stefano_2: - data = folder_in + method + "/" + method + "_Cmin_" + grid[2] + ".csv" - plot_multiple(data, method, title, num_frac) - - # Andrea - for method in methods_andrea: - data = folder_in + method + "/" + "Cmin_" + grid[3] + ".csv" - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # save - name = grid_label + "_cot_min" - save_multiple(name, num_frac, folder_out) - - ########### - - title = ["max $\\theta$", grid_label] - # Alessio - for method in methods_alessio: - data = folder_in + method + "/" + "Cmax_" + grid[0] + ".csv" - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # Stefano - for method in methods_stefano_1: - data = folder_in + method + "/" + method + "_Cmax_" + grid[1] + ".csv" - plot_multiple(data, method, title, num_frac) - - for method in methods_stefano_2: - data = folder_in + method + "/" + method + "_Cmax_" + grid[2] + ".csv" - plot_multiple(data, method, title, num_frac) - - # Andrea - for method in methods_andrea: - data = folder_in + method + "/" + "Cmax_" + grid[3] + ".csv" - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # save - name = grid_label + "_cot_max" - save_multiple(name, num_frac, folder_out) - - ########### - - title = "production on " + grid_label - # Alessio - for method in methods_alessio: - data = folder_in + method + "/" + "production_" + grid[0] + ".csv" - plot_single(data, method.replace("_", " "), title) - - # Stefano - for method in methods_stefano_1: - data = folder_in + method + "/" + method + "_production_" + grid[1] + ".csv" - plot_single(data, method, title) - - for method in methods_stefano_2: - data = folder_in + method + "/" + method + "_production_" + grid[2] + ".csv" - plot_single(data, method, title) - - # Andrea - for method in methods_andrea: - data = folder_in + method + "/" + "production_" + grid[3] + ".csv" - plot_single(data, method.replace("_", " "), title) - - # save - name = grid_label + "_outflow" - save_single(name, folder_out) - - -# ------------------------------------------------------------------------------# - -if __name__ == "__main__": - main() diff --git a/examples/papers/dfn_transport/example3/data.py b/examples/papers/dfn_transport/example3/data.py deleted file mode 100644 index cd281089f4..0000000000 --- a/examples/papers/dfn_transport/example3/data.py +++ /dev/null @@ -1,101 +0,0 @@ -import numpy as np -import porepy as pp - - -def flow(gb, data, tol): - physics = data["physics"] - - for g, d in gb: - - unity = np.ones(g.num_cells) - empty = np.empty(0) - - d["frac_num"] = (g.frac_num if g.dim == 2 else -1) * unity - d["cell_volumes"] = g.cell_volumes - d["is_tangential"] = True - d["tol"] = tol - - param = pp.Parameters(g) - - kxx = data["k"] * unity - perm = pp.SecondOrderTensor(2, kxx=kxx, kyy=kxx, kzz=1) - param.set_tensor(physics, perm) - - # Boundaries - b_faces = g.tags["domain_boundary_faces"].nonzero()[0] - if b_faces.size: - in_flow, out_flow = bc_flag(g, data["domain"], tol) - - labels = np.array(["neu"] * b_faces.size) - labels[in_flow + out_flow] = "dir" - param.set_bc(physics, pp.BoundaryCondition(g, b_faces, labels)) - - bc_val = np.zeros(g.num_faces) - bc_val[b_faces[in_flow]] = 1 - param.set_bc_val(physics, bc_val) - else: - param.set_bc(physics, pp.BoundaryCondition(g, empty, empty)) - - d["param"] = param - - -# ------------------------------------------------------------------------------# - - -def advdiff(gb, data, tol): - physics = data["physics"] - - for g, d in gb: - param = d["param"] - d["deltaT"] = data["deltaT"] - - kxx = data["diff"] * np.ones(g.num_cells) - perm = pp.SecondOrderTensor(3, kxx) - param.set_tensor(physics, perm) - - # Boundaries - b_faces = g.tags["domain_boundary_faces"].nonzero()[0] - if b_faces.size: - in_flow, out_flow = bc_flag(g, data["domain"], tol) - - labels = np.array(["neu"] * b_faces.size) - labels[in_flow + out_flow] = ["dir"] - param.set_bc(physics, pp.BoundaryCondition(g, b_faces, labels)) - - bc_val = np.zeros(g.num_faces) - bc_val[b_faces[in_flow]] = 1 - param.set_bc_val(physics, bc_val) - else: - param.set_bc(physics, pp.BoundaryCondition(g, np.empty(0), np.empty(0))) - - lambda_flux = "lambda_" + data["flux"] - for _, d in gb.edges(): - d["flux_field"] = d[lambda_flux] - - -# ------------------------------------------------------------------------------# - - -def bc_flag(g, domain, tol): - b_faces = g.tags["domain_boundary_faces"].nonzero()[0] - b_face_centers = g.face_centers[:, b_faces] - - # define inflow type boundary conditions - out_flow_start = np.array([1.011125, 0.249154, 0.598708]) - out_flow_end = np.array([1.012528, 0.190858, 0.886822]) - - # detect all the points aligned with the segment - dist, _ = pp.cg.dist_points_segments(b_face_centers, out_flow_start, out_flow_end) - dist = dist.flatten() - out_flow = np.logical_and(dist < tol, dist >= -tol) - - # define outflow type boundary conditions - in_flow_start = np.array([0.206507, 0.896131, 0.183632]) - in_flow_end = np.array([0.181980, 0.813947, 0.478618]) - - # detect all the points aligned with the segment - dist, _ = pp.cg.dist_points_segments(b_face_centers, in_flow_start, in_flow_end) - dist = dist.flatten() - in_flow = np.logical_and(dist < tol, dist >= -tol) - - return in_flow, out_flow diff --git a/examples/papers/dfn_transport/example3/main.py b/examples/papers/dfn_transport/example3/main.py deleted file mode 100644 index 8c450410f2..0000000000 --- a/examples/papers/dfn_transport/example3/main.py +++ /dev/null @@ -1,141 +0,0 @@ -import numpy as np -import porepy as pp - -import examples.papers.dfn_transport.discretization as compute - -# from examples.papers.dfn_transport.grid_export import grid_export - -# from examples.papers.dfn_transport.flux_trace import jump_flux - - -def bc_flag(g, domain, out_flow_start, out_flow_end, in_flow_start, in_flow_end, tol): - b_faces = g.tags["domain_boundary_faces"].nonzero()[0] - b_face_centers = g.face_centers[:, b_faces] - - # detect all the points aligned with the segment - dist, _ = pp.distances.points_segments(b_face_centers, out_flow_start, out_flow_end) - dist = dist.flatten() - out_flow = np.logical_and(dist < tol, dist >= -tol) - - # detect all the points aligned with the segment - dist, _ = pp.distances.points_segments(b_face_centers, in_flow_start, in_flow_end) - - dist = dist.flatten() - in_flow = np.logical_and(dist < tol, dist >= -tol) - - return in_flow, out_flow - - -def bc_same(g, domain, tol): - - # define outflow type boundary conditions - out_flow_start = np.array([-319.289, 212.271, 400]) - out_flow_end = np.array([-300.035, 317.811, 128.887]) - - # define inflow type boundary conditions - in_flow_start = np.array([-84.3598, 1500, -6.65313]) - in_flow_end = np.array([-84.3598, 1500, 400]) - - return bc_flag( - g, domain, out_flow_start, out_flow_end, in_flow_start, in_flow_end, tol - ) - - -def bc_different(g, domain, tol): - - # define outflow type boundary conditions - out_flow_start = np.array([134.428, 100, 18.9949]) - out_flow_end = np.array([134.429, 100, 400]) - - # define inflow type boundary conditions - in_flow_start = np.array([-84.3598, 1500, -6.65313]) - in_flow_end = np.array([-84.3598, 1500, 400]) - - return bc_flag( - g, domain, out_flow_start, out_flow_end, in_flow_start, in_flow_end, tol - ) - - -def main(): - - input_folder = "../geometries/" - file_name = input_folder + "example3_connected.fab" - - # define the discretizations for the Darcy part - discretizations = compute.get_discr() - - # geometric tolerance - tol = 1e-3 - - # initial condition and type of fluid/rock - theta = 80 * pp.CELSIUS - - # reaction coefficient \gamma * (T - T_rock) - gamma = 2.44e-9 * 0.125 - theta_rock = theta - - # boundary conditions - bc_flow = 2500 * pp.METER / 5 - bc_trans = 30 * pp.CELSIUS - - end_time = 3.154e7 - n_steps = 200 - time_step = end_time / n_steps - - bc_types = {"same": bc_same, "different": bc_different} - for bc_type_key, bc_type in bc_types.items(): - - for discr_key, discr in discretizations.items(): - - if discr_key == "MVEM": - mesh_size = 0.17 * 1e2 - else: - mesh_size = 1e2 # np.power(2., -4) - - mesh_kwargs = {"mesh_size_frac": mesh_size, "mesh_size_min": mesh_size / 20} - - folder = "solution_" + discr_key + "_" + bc_type_key - - network = pp.fracture_importer.network_3d_from_fab(file_name, tol=tol) - gb = network.mesh(mesh_kwargs, dfn=True) - - gb.remove_nodes(lambda g: g.dim == 0) - gb.compute_geometry() - gb.assign_node_ordering() - - if discr_key == "MVEM": - pp.coarsening.coarsen(gb, "by_volume") - - domain = gb.bounding_box(as_dict=True) - - param = { - "domain": domain, - "tol": tol, - "k": 1.84e-6, - "bc_flow": bc_flow, - "diff": 0.35e-9, - "mass_weight": 1.95e-3, - "src": gamma * theta_rock, - "reaction": gamma, - "flux_weight": 1.0, - "bc_trans": bc_trans, - "init_trans": theta, - "time_step": time_step, - "n_steps": n_steps, - "folder": folder, - } - - # the flow problem - compute.flow(gb, discr, param, bc_type) - - # if discr_key == "Tpfa": - # grid_export(gb, None, folder + "/grid/") - - # jump_flux(gb, param["mortar_flux"]) - - # the advection-diffusion problem - compute.advdiff(gb, discr, param, bc_type) - - -if __name__ == "__main__": - main() diff --git a/examples/papers/dfn_transport/example3/post_process.py b/examples/papers/dfn_transport/example3/post_process.py deleted file mode 100644 index 4ef71f5aa4..0000000000 --- a/examples/papers/dfn_transport/example3/post_process.py +++ /dev/null @@ -1,162 +0,0 @@ -import paraview.simple as pv - -import vtk -from vtk.util.numpy_support import vtk_to_numpy - -import csv -import os -import shutil - -import numpy as np - -#------------------------------------------------------------------------------# - -def read_file(file_in): - vtk_reader = vtk.vtkXMLUnstructuredGridReader() - vtk_reader.SetFileName(file_in) - vtk_reader.Update() - return vtk_reader - -#------------------------------------------------------------------------------# - -def read_data(vtk_reader, field): - data = vtk_reader.GetOutput().GetCellData().GetArray(field) - return vtk_to_numpy(data) - -#------------------------------------------------------------------------------# - -def plot_over_line(file_in, file_out, pts, resolution=2000): - - if file_in.lower().endswith('.pvd'): - # create a new 'PVD Reader' - sol = pv.PVDReader(FileName=file_in) - elif file_in.lower().endswith('.vtu'): - # create a new 'XML Unstructured Grid Reader' - sol = pv.XMLUnstructuredGridReader(FileName=file_in) - else: - raise ValueError, "file format not yet supported" - - # create a new 'Plot Over Line' - pol = pv.PlotOverLine(Input=sol, Source='High Resolution Line Source') - - # Properties modified on plotOverLine1.Source - pol.Source.Point1 = pts[0] - pol.Source.Point2 = pts[1] - pol.Source.Resolution = resolution - - # save data - pv.SaveData(file_out, proxy=pol, Precision=15) - -#------------------------------------------------------------------------------# - -def read_csv(file_in, fields=None): - - # post-process the file by selecting only few columns - if fields is not None: - data = list(list() for _ in fields) - with open(file_in, 'r') as csvfile: - reader = csv.DictReader(csvfile) - [d.append(row[f]) for row in reader for f, d in zip(fields, data)] - else: - with open(file_in, 'r') as csvfile: - reader = csv.reader(csvfile) - data = list(reader) - - return data - -#------------------------------------------------------------------------------# - -def write_csv(file_out, fields, data): - with open(file_out, 'w') as csvfile: - writer = csv.DictWriter(csvfile, fieldnames=fields) - #writer.writeheader() - for dd in zip(*data): - if np.isnan(np.array(dd)).any(): - print(dd) - writer.writerow({f: d for f, d in zip(fields, dd)}) - -#------------------------------------------------------------------------------# - -def cot_domain(file_in, step, field, num_frac, padding=6): - - cot_avg = np.zeros((step, num_frac)) - cot_min = np.zeros((step, num_frac)) - cot_max = np.zeros((step, num_frac)) - - for i in np.arange(step): - - ifile = file_in+str(i).zfill(padding)+".vtu" - vtk_reader = read_file(ifile) - - weight = read_data(vtk_reader, "cell_volumes") - frac_num = read_data(vtk_reader, "frac_num") - c = read_data(vtk_reader, field) - - for frac_id in np.arange(num_frac): - is_loc = frac_num == frac_id - weight_loc = weight[is_loc] - - cot_avg[i, frac_id] = np.sum(c[is_loc]*weight_loc)/np.sum(weight_loc) - cot_min[i, frac_id] = np.amin(c[is_loc]) - cot_max[i, frac_id] = np.amax(c[is_loc]) - - zero = 273.15 - return cot_avg + zero, cot_min + zero, cot_max + zero - -#------------------------------------------------------------------------------# - -def main(): - - field = "scalar" - n_step = 200 - time_step = 3.154e+7/200 - num_frac = 89-7 - - grids = ["different", "same"] - - folder_master = "/home/elle/tmp/tipetut++/example3/" - #folder_master = "./" - folder_master_out = "./CSV/" - methods = ["MVEM", "Tpfa", "RT0"] - - for method in methods: - for grid in grids: - - folder_in = folder_master + "solution_" + method + "_" + grid + "/" - folder_out = folder_master_out + method + "_UPWIND/" - - # in this file the constant data are saved - file_in = folder_in + "solution_2_" - - cot_avg, cot_min, cot_max = cot_domain(file_in, n_step, field, num_frac) - - times = np.arange(n_step) * time_step - labels = np.arange(num_frac).astype(np.str) - labels = np.core.defchararray.add("cot_", labels) - labels = np.insert(labels, 0, 'time') - - if not os.path.exists(folder_out): - os.makedirs(folder_out) - - # create the output files - file_out = folder_out + "Cmin_" + grid + ".csv" - data = np.insert(cot_min, 0, times, axis=1).T - write_csv(file_out, labels, data) - - # create the output files - file_out = folder_out + "Cmax_" + grid + ".csv" - data = np.insert(cot_max, 0, times, axis=1).T - write_csv(file_out, labels, data) - - # create the output files - file_out = folder_out + "Cmean_" + grid + ".csv" - data = np.insert(cot_avg, 0, times, axis=1).T - write_csv(file_out, labels, data) - - # copy outflow file - file_in = folder_in + "outflow.csv" - file_out = folder_out + "production_" + grid + ".csv" - shutil.copy(file_in, file_out) - -if __name__ == "__main__": - main() diff --git a/examples/papers/dfn_transport/example3/pot.py b/examples/papers/dfn_transport/example3/pot.py deleted file mode 100644 index 297e1d3f69..0000000000 --- a/examples/papers/dfn_transport/example3/pot.py +++ /dev/null @@ -1,185 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import os - -# ------------------------------------------------------------------------------# - -plt.rc("text", usetex=True) -plt.rc("font", family="serif") -plt.rc("font", size=15) - - -def plot_single(file_name, legend, title): - - data = np.loadtxt(file_name, delimiter=",") - - plt.figure(0) - plt.plot(data[:, 0], data[:, 1], label=legend) - plt.title(title) - plt.xlabel("$t$") - plt.ylabel("$\\theta$") - plt.grid(True) - plt.legend() - - -# ------------------------------------------------------------------------------# - - -def plot_multiple(file_name, legend, title, num_frac): - - data = np.loadtxt(file_name, delimiter=",") - - for frac_id in np.arange(num_frac): - plt.figure(frac_id) - plt.plot(data[:, 0], data[:, frac_id + 1], label=legend) - plt_title = ( - title[0] + " on " + "$\\Omega_{" + str(frac_id) + "}$" + " " + title[1] - ) - plt.title(plt_title) - plt.xlabel("$t$") - plt.ylabel("$\\theta$") - plt.grid(True) - plt.legend() - - -# ------------------------------------------------------------------------------# - - -def save_single(filename, folder, figure_id=0): - - if not os.path.exists(folder): - os.makedirs(folder) - - plt.figure(figure_id) - plt.savefig(folder + filename, bbox_inches="tight") - plt.gcf().clear() - - -# ------------------------------------------------------------------------------# - - -def save_multiple(filename, num_frac, folder): - - if not os.path.exists(folder): - os.makedirs(folder) - - for frac_id in np.arange(num_frac): - plt.figure(frac_id) - name = filename + "_frac_" + str(frac_id) - plt.savefig(folder + name, bbox_inches="tight") - plt.gcf().clear() - - -# ------------------------------------------------------------------------------# - - -def main(): - - num_frac = 86-7 - - master_folder = "/home/elle/Dropbox/Work/PresentazioniArticoli/2019/Articles/tipetut++/Results/example3/" - - methods_stefano = ["OPTfem", "OPTxfem", "GCmfem"] - methods_alessio = ["MVEM_UPWIND", "Tpfa_UPWIND", "RT0_UPWIND"] - methods_andrea = [] # ["MVEM_VEMSUPG"] - - cases = {"case_0": ("different", "different", "0.005"), "case_1": ("same", "same", "0.001")} - cases_label = {"case_0": "different", "case_1": "same"} - - for case_name, case in cases.items(): - case_label = cases_label[case_name] - - folder_in = master_folder - folder_out = folder_in + "img/" - - title = ["avg $\\theta$", case_label] - # Alessio - for method in methods_alessio: - data = folder_in + method + "/" + "Cmean_" + case[0] + ".csv" - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # Stefano - for method in methods_stefano: - data = folder_in + method + "/" + method + "_Cmean_" + case[1] + ".csv" - plot_multiple(data, method, title, num_frac) - - # Andrea - for method in methods_andrea: - data = folder_in + method + "/" + "Cmean_" + case[2] + ".csv" - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # save - name = case_label + "_cot_avg" - save_multiple(name, num_frac, folder_out) - - ########### - - title = ["min $\\theta$", case_label] - # Alessio - for method in methods_alessio: - data = folder_in + method + "/" + "Cmin_" + case[0] + ".csv" - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # Stefano - for method in methods_stefano: - data = folder_in + method + "/" + method + "_Cmin_" + case[1] + ".csv" - plot_multiple(data, method, title, num_frac) - - # Andrea - for method in methods_andrea: - data = folder_in + method + "/" + "Cmin_" + case[2] + ".csv" - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # save - name = case_label + "_cot_min" - save_multiple(name, num_frac, folder_out) - - ########### - - title = ["max $\\theta$", case_label] - # Alessio - for method in methods_alessio: - data = folder_in + method + "/" + "Cmax_" + case[0] + ".csv" - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # Stefano - for method in methods_stefano: - data = folder_in + method + "/" + method + "_Cmax_" + case[1] + ".csv" - plot_multiple(data, method, title, num_frac) - - # Andrea - for method in methods_andrea: - data = folder_in + method + "/" + "Cmax_" + case[2] + ".csv" - plot_multiple(data, method.replace("_", " "), title, num_frac) - - # save - name = case_label + "_cot_max" - save_multiple(name, num_frac, folder_out) - - ########### - - title = "production on " + case_label - # Alessio - for method in methods_alessio: - data = folder_in + method + "/" + "production_" + case[0] + ".csv" - plot_single(data, method.replace("_", " "), title) - - # Stefano - for method in methods_stefano: - data = folder_in + method + "/" + method + "_production_" + case[1] + ".csv" - plot_single(data, method, title) - - # Andrea - for method in methods_andrea: - data = folder_in + method + "/" + "production_" + case[2] + ".csv" - plot_single(data, method.replace("_", " "), title) - - # save - name = case_label + "_outflow" - save_single(name, folder_out) - - -# ------------------------------------------------------------------------------# - -if __name__ == "__main__": - main() diff --git a/examples/papers/dfn_transport/flux_trace.py b/examples/papers/dfn_transport/flux_trace.py deleted file mode 100644 index c710f78251..0000000000 --- a/examples/papers/dfn_transport/flux_trace.py +++ /dev/null @@ -1,20 +0,0 @@ -def jump_flux(gb, flux_mortar): - - # loop on the lagrange multiplier nodes - for g in gb.grids_of_dimension(1): - - # loop on the associated edges - for _, d_e in gb.edges_of_node(g): - - # get the projector from the mortar grid to the slave - proj = d_e["mortar_grid"].mortar_to_slave_int() - - # project the mortar variable from the mortar grid to the 1d - # grid by thus doing the sum on the same 1d-cell - # the variable now contains the jump of the flux from the current 2d - # fracture to thourgh the 1d object. - jump_flux = proj.dot(d_e[flux_mortar]) - - print(jump_flux) - - print("----") diff --git a/examples/papers/dfn_transport/geometries/example1/DFN_1.fab b/examples/papers/dfn_transport/geometries/example1/DFN_1.fab deleted file mode 100644 index d110a5e7b6..0000000000 --- a/examples/papers/dfn_transport/geometries/example1/DFN_1.fab +++ /dev/null @@ -1,40 +0,0 @@ -BEGIN FORMAT - Format = Ascii - XAxis = East - Scale = 100.0 - No_Fractures = 3 - No_TessFractures = 0 - No_Nodes = 12 - No_Properties = 1 -END FORMAT - -BEGIN PROPERTIES - Prop1 = (Real*4) "Transmissivity" -END PROPERTIES - -BEGIN SETS -END SETS - -BEGIN FRACTURE - 1 4 1 - 1 0 0 0.3 - 2 1 0 0.3 - 3 1 1 0.3 - 4 0 1 0.3 - 0 -1 -1 -1 -2 4 1 -1 0.000000 0.000000 0.800000 -2 1.000000 0.000000 0.800000 -3 1.000000 1.000000 0.800000 -4 0.000000 1.000000 0.800000 -0 -1 -1 -1 -3 4 1 -1 0.500000 0.500000 0.000000 -2 0.500000 1.207107 0.707107 -3 0.500000 0.500000 1.414214 -4 0.500000 -0.207107 0.707107 -0 -1 -1 -1 -END FRACTURE - -BEGIN TESSFRACTURE -END TESSFRACTURE diff --git a/examples/papers/dfn_transport/geometries/example1/DFN_10.fab b/examples/papers/dfn_transport/geometries/example1/DFN_10.fab deleted file mode 100644 index ec6da816dd..0000000000 --- a/examples/papers/dfn_transport/geometries/example1/DFN_10.fab +++ /dev/null @@ -1,40 +0,0 @@ -BEGIN FORMAT - Format = Ascii - XAxis = East - Scale = 100.0 - No_Fractures = 3 - No_TessFractures = 0 - No_Nodes = 12 - No_Properties = 1 -END FORMAT - -BEGIN PROPERTIES - Prop1 = (Real*4) "Transmissivity" -END PROPERTIES - -BEGIN SETS -END SETS - -BEGIN FRACTURE - 1 4 1 - 1 0 0 0.3 - 2 1 0 0.3 - 3 1 1 0.3 - 4 0 1 0.3 - 0 -1 -1 -1 -2 4 1 -1 0.000000 0.000000 0.932750 -2 1.000000 0.000000 0.932750 -3 1.000000 1.000000 0.932750 -4 0.000000 1.000000 0.932750 -0 -1 -1 -1 -3 4 1 -1 0.500000 0.500000 0.132750 -2 0.500000 1.207107 0.839857 -3 0.500000 0.500000 1.546964 -4 0.500000 -0.207107 0.839857 -0 -1 -1 -1 -END FRACTURE - -BEGIN TESSFRACTURE -END TESSFRACTURE diff --git a/examples/papers/dfn_transport/geometries/example1/DFN_11.fab b/examples/papers/dfn_transport/geometries/example1/DFN_11.fab deleted file mode 100644 index 492d6507c7..0000000000 --- a/examples/papers/dfn_transport/geometries/example1/DFN_11.fab +++ /dev/null @@ -1,40 +0,0 @@ -BEGIN FORMAT - Format = Ascii - XAxis = East - Scale = 100.0 - No_Fractures = 3 - No_TessFractures = 0 - No_Nodes = 12 - No_Properties = 1 -END FORMAT - -BEGIN PROPERTIES - Prop1 = (Real*4) "Transmissivity" -END PROPERTIES - -BEGIN SETS -END SETS - -BEGIN FRACTURE - 1 4 1 - 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13 -4.15120568218 1343.17721512 149.44877014 - 14 -4.66517295805 1343.93340885 265.082198306 - 15 19.7346713565 1308.03421787 372.263453126 - 16 32.427434521294 1289.359511457029 400.0000000000001 - 0 0 0 0 -END FRACTURE - -BEGIN TESSFRACTURE -END TESSFRACTURE - diff --git a/examples/papers/dfn_transport/grid_export.py b/examples/papers/dfn_transport/grid_export.py deleted file mode 100644 index 800580b19a..0000000000 --- a/examples/papers/dfn_transport/grid_export.py +++ /dev/null @@ -1,155 +0,0 @@ -import numpy as np -import os -import porepy as pp - - -def grid_export(gb, P0_flux, folder): - - if not os.path.exists(folder): - os.makedirs(folder) - - # export the grids - for g, d in gb: - - # only 2d fractures - if g.dim != 2: - continue - - # extract the id of the fracture - frac_num = int(d["frac_num"][0]) - - # extract the cell faces in I, J format - cell_faces_I = [] - cell_faces_J = [] - # loop on all the cells and get the faces (from the indices map) - indices = g.cell_faces.indices - indptr = g.cell_faces.indptr - face_cells = -np.ones((g.num_faces, 2), dtype=np.int) - for cell in np.arange(g.num_cells): - faces = indices[indptr[cell] : indptr[cell + 1]] - cell_faces_I.append([cell] * faces.size) - cell_faces_J.append(faces.tolist()) - for face in faces: - idx = np.where(face_cells[face, :] == -1)[0] - face_cells[face, idx[0]] = cell - - # convert to numpy array - cell_faces_I = np.hstack(cell_faces_I).astype(np.int) - cell_faces_J = np.hstack(cell_faces_J).astype(np.int) - - # save to file the map - fname = "g_" + str(frac_num) + "_cell_faces.txt" - cell_faces = np.vstack((cell_faces_I, cell_faces_J)).T - np.savetxt(folder + fname, cell_faces, fmt="%d", delimiter=",") - - # save to file the map - fname = "g_" + str(frac_num) + "_face_cells.txt" - np.savetxt(folder + fname, face_cells, fmt="%d", delimiter=",") - - # extract the face nodes in I, J format - face_nodes_I = [] - face_nodes_J = [] - # loop on all the faces and get the nodes (from the indices map) - indices = g.face_nodes.indices - indptr = g.face_nodes.indptr - for face in np.arange(g.num_faces): - nodes = indices[indptr[face] : indptr[face + 1]] - face_nodes_I.append([face] * nodes.size) - face_nodes_J.append(nodes.tolist()) - - # convert to numpy array - face_nodes_I = np.hstack(face_nodes_I) - face_nodes_J = np.hstack(face_nodes_J) - - # save to file the map - fname = "g_" + str(frac_num) + "_face_nodes.txt" - face_nodes = np.vstack((face_nodes_I, face_nodes_J)).T - np.savetxt(folder + fname, face_nodes, fmt="%d", delimiter=",") - - # save to file the points - fname = "g_" + str(frac_num) + "_nodes.txt" - np.savetxt(folder + fname, g.nodes.T, fmt="%10.14f", delimiter=",") - - # save the cell neighbors - cell_cell_map = np.zeros((g.num_cells, 3)) - cell_connection = g.cell_connection_map() - indices = cell_connection.indices - indptr = cell_connection.indptr - # NOTE: I'm assuming a simplicial grid - for cell in np.arange(g.num_cells): - cells = np.sort(indices[indptr[cell] : indptr[cell + 1]]) - # do not save the current cell - cells = np.setdiff1d(cells, cell, assume_unique=True) - # in case of boundary cell put -1 as flag - if cells.size < 3: - cells = np.append(cells, [-1] * (3 - cells.size)) - cell_cell_map[cell, :] = cells.copy() - - fname = "g_" + str(frac_num) + "_cell_cells.txt" - np.savetxt(folder + fname, cell_cell_map, fmt="%d", delimiter=",") - - # save to file the cell centroids and volume - fname = "g_" + str(frac_num) + "_cell_data.txt" - cell_data = np.vstack((g.cell_volumes, g.cell_centers)).T - np.savetxt(folder + fname, cell_data, fmt="%10.14f", delimiter=",") - - # save to file the bc type - fname = "g_" + str(frac_num) + "_face_data.txt" - bc = d[pp.PARAMETERS]["flow_data"]["bc"] - bc_tag = bc.is_dir.astype(np.int) + 2 * bc.is_neu.astype(np.int) - bc_tags = np.vstack((bc_tag, g.tags["bc_flow_id"])).T - np.savetxt(folder + fname, bc_tags, fmt="%d", delimiter=",") - - # export the connectivity maps - for g, d in gb: - - # only 1d grid - if g.dim != 1: - continue - - # we assume only two intersecting fractures - faces = np.empty((g.num_cells, 4), dtype=np.int) - frac_num = np.empty(2, dtype=np.int) - - frac_id = 0 - for e, d_e in gb.edges_of_node(g): - g_h = gb.nodes_of_edge(e)[1] - frac_num[frac_id] = gb.node_props(g_h, "frac_num")[0] - - face_cells = d_e["face_cells"].astype(np.int).tocsr() - # loop on all the cells and get the faces (from the indices map) - for cell in np.arange(g.num_cells): - indices = face_cells.indices - indptr = face_cells.indptr - faces[cell, (2 * frac_id) : (2 * frac_id + 2)] = indices[ - indptr[cell] : indptr[cell + 1] - ] - - frac_id += 1 - - faces_sorted = np.empty((g.num_cells, 4), dtype=np.int) - sort = np.argsort(frac_num) - for idx, idx_sorted in enumerate(sort): - faces_sorted[:, (2 * idx) : (2 * idx + 2)] = faces[ - :, (2 * idx_sorted) : (2 * idx_sorted + 2) - ] - - # save to file - trace_id = "_".join([str(f) for f in frac_num[sort]]) - fname = "t_" + trace_id + "_faces.txt" - np.savetxt(folder + fname, faces_sorted, fmt="%d", delimiter=",") - - # export the flux - if P0_flux is not None: - for g, d in gb: - - # only 2d grid - if g.dim != 2: - continue - - # extract the id of the fracture - frac_num = int(d["frac_num"][0]) - - # save to file the points - fname = "g_" + str(frac_num) + "_P0_flux.txt" - np.savetxt(folder + fname, d[P0_flux].T, fmt="%10.14f", delimiter=",") From 67be8f63ea98e4146c9e6bf1ff3cd8bb8f1a7d77 Mon Sep 17 00:00:00 2001 From: Alessio Fumagalli Date: Thu, 27 Jun 2019 12:54:44 +0200 Subject: [PATCH 18/25] Update Readme.md fix Florin name --- examples/papers/Readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/papers/Readme.md b/examples/papers/Readme.md index d1e6a01ffe..2165dc7672 100644 --- a/examples/papers/Readme.md +++ b/examples/papers/Readme.md @@ -17,4 +17,4 @@ We try to keep the examples updated as the code changes, but may not always succ * [arXiv_1903_01117](./arXiv_1903_01117) paper "*A multi-layer reduced model for flow in porous media with a fault and surrounding damage zones*" by Alessio Fumagalli, Anna Scotti. See [arXiv pre-print](https://arxiv.org/abs/1903.01117). # Separate repository -* [repo](https://github.com/alessiofumagalli/multiscale_timedependent) paper "*Robust linear domain decomposition schemes for reduced non-linear fracture flow models*" by Elyes Ahmed, Alessio Fumagalli, Ana Budiša, Eirik Keilegavlen, Jan M. Nordbotten, A. Radu Forin. See [arXiv pre-print](https://arxiv.org/abs/1906.05831). +* [repo](https://github.com/alessiofumagalli/multiscale_timedependent) paper "*Robust linear domain decomposition schemes for reduced non-linear fracture flow models*" by Elyes Ahmed, Alessio Fumagalli, Ana Budiša, Eirik Keilegavlen, Jan M. Nordbotten, A. Radu Florin. See [arXiv pre-print](https://arxiv.org/abs/1906.05831). From 0c9f7e2b07a6cdcb4d7919839ca503b48a6feb45 Mon Sep 17 00:00:00 2001 From: Runar Date: Mon, 1 Jul 2019 10:35:03 +0200 Subject: [PATCH 19/25] Moved compresssible flow tutorial from examples to tutorials --- .../compressible_flow_with_automatic_differentiation.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {examples/example10 => tutorials}/compressible_flow_with_automatic_differentiation.ipynb (100%) diff --git a/examples/example10/compressible_flow_with_automatic_differentiation.ipynb b/tutorials/compressible_flow_with_automatic_differentiation.ipynb similarity index 100% rename from examples/example10/compressible_flow_with_automatic_differentiation.ipynb rename to tutorials/compressible_flow_with_automatic_differentiation.ipynb From 43956ec2f78bc397786257668e8e22c315755416 Mon Sep 17 00:00:00 2001 From: Runar Date: Mon, 1 Jul 2019 10:36:17 +0200 Subject: [PATCH 20/25] Updated compressible flow tutorial --- ..._flow_with_automatic_differentiation.ipynb | 244 +++++++----------- 1 file changed, 90 insertions(+), 154 deletions(-) diff --git a/tutorials/compressible_flow_with_automatic_differentiation.ipynb b/tutorials/compressible_flow_with_automatic_differentiation.ipynb index 0dd8d754ac..64d06395fa 100644 --- a/tutorials/compressible_flow_with_automatic_differentiation.ipynb +++ b/tutorials/compressible_flow_with_automatic_differentiation.ipynb @@ -5,11 +5,7 @@ "metadata": {}, "source": [ "# Introduction\n", - "This notebook gives suggests how to solve the problem of non-linear compressible flow using the automatic differentiation library included in PorePy. \n", - "\n", - "Similar tutorials include:\n", - "- Solving same non-linear compressible flow using third party AD-software please see tutorial: \"AutomaticDifferentiationWithThirdPartySoftware\".\n", - "- Solving a linear slightly compressible flow problem see tutorial: slightly_compressible_flow\n" + "This notebook gives suggests how to solve the problem of non-linear compressible flow using the automatic differentiation library included in PorePy. \n" ] }, { @@ -19,7 +15,7 @@ "# Model\n", "As an example, we will set up a non-linear problem for compressible flow. As usuall, we assume Darcy's law is valid:\n", "$$\n", - "\\vec u = \\mathcal K \\nabla p,\n", + "\\vec u = -\\mathcal K \\nabla p,\n", "$$\n", "where $\\vec u$ is the flux, $\\mathcal K$ the permeability tensor and $p$ the fulid pressure. Further, the conservation of mass gives\n", "$$\n", @@ -44,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -53,12 +49,8 @@ "import matplotlib.pyplot as plt\n", "\n", "# Porepy modules\n", - "from porepy.grids import structured\n", - "from porepy.numerics.fv import tpfa\n", - "from porepy.params.data import Parameters\n", - "from porepy.viz.plot_grid import plot_grid\n", - "from porepy.ad.forward_mode import Ad_array\n", - "import porepy.ad.functions as af" + "import porepy as pp\n", + "import porepy.ad as ad" ] }, { @@ -68,28 +60,17 @@ "## Define constitutive laws and constants" ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Create grid\n", - "gb = structured.CartGrid([11,11])\n", - "gb.compute_geometry()" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "We set the porosity to 0.2 and let set the permeability to the default value (i.e. $\\mathcal K = 1$).\n", - "We define the depenecy of $\\rho$ on $p$ as a function" + "We define the depenecy of $\\rho$ on $p$ as a function. Note that we have to use the exponent function ad.exp (and not np.exp)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -98,15 +79,11 @@ "phi = 0.2 # Porosity \n", "c = 1e-1 # Compressibility\n", "\n", - "# Set data\n", - "data = {'param': Parameters(gb)}\n", - "data['param'].set_porosity(phi)\n", - "\n", "# Constitutive law\n", "def rho(p):\n", " rho0 = 1\n", " p_ref = 1\n", - " return rho0 * af.exp(c * (p - p_ref))" + " return rho0 * ad.exp(c * (p - p_ref))" ] }, { @@ -114,51 +91,90 @@ "metadata": {}, "source": [ "## Discretization\n", - "We create discretized versions of the operators div. div is a mapping from faces to cells, and the divergence at a cell is the sum of the values on the faces (possibly multiplied with -1 if the normal vector of the face points into the cell).\n", "\n", - "The density is defined at the cell centers, but in the divergence term we need to evaluate it at the faces. To do so, we will simply take the average of the two neighbooring cells." + "We use a finite-volume method to discretize the model equation. As a first step we create a partition of the domain into grid cells:\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "cell_faces_T = gb.cell_faces.T\n", - "def div(x):\n", - " \"\"\"\n", - " Discrete divergence\n", - " \"\"\"\n", - " return cell_faces_T * x\n", - "\n", - "def avg(x):\n", - " \"\"\"\n", - " Averageing. Note that this is not strictly correct for the boundary faces since\n", - " these only have 1 cell neighboor, but we have zero flux condition on these, so \n", - " this is not a problem.\n", - " \"\"\"\n", - " return 0.5 * np.abs(gb.cell_faces) * x" + "# Create grid\n", + "g = pp.CartGrid([11,11])\n", + "g.compute_geometry()\n", + "pp.plot_grid(g, plot_2d=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, the model equation is integrated over each controll volume (i.e., each cell of the grid), and the divergence theorem is applied to the flux term:\n", + "$$\n", + "\\int_\\Omega \\phi \\frac{\\partial \\rho}{\\partial t} dV - \\int_{\\partial\\Omega}\\vec n\\cdot(\\rho\\vec u)dS - \\int_\\Omega q dV= 0\n", + "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Flux discretization\n", - "To discretize the flux operator $-\\mathcal K \\nabla$ we use the two point flux apporoximation. This is implemented as the class Tpfa in PorePy. When we call Tpfa.discretize(...) we will store the dizcretisation as a scipy matrix in the data dictionary with a keyword 'flux'" + "The key-point of the finite-volume discretization is how the flux-term $\\vec u$ is approximated. We do not cover that in this tutorial(see e.g., I. Aavatsmark. An introduction to multipoint flux approximations for quadrilateral grids. Comput. Geosci., Vol. 6, No. 3, pp. 405–432, 2002. DOI: 10.1023/A:1021291114475).\n", + "However, the main idea is that the fluid flux $\\vec u$ across a face is expressed as a linear combination of the cell-centered pressures $\\vec u = \\text{flux}\\ \\vec p$. Here, $\\text{flux}$ is the discretization matrix and $\\vec p$ is the vector of all cell-centered pressures.\n", + "\n", + "In porepy we can obtain the discretization matrix with, e.g, the two-point flux approximation:\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "# Initialize default data (i.e., unit parameters)\n", + "data = pp.initialize_default_data(g, {}, 'flow')\n", "# Define flux discretization:\n", - "flx_disc = tpfa.Tpfa('flow')\n", + "flx_disc = pp.Tpfa('flow')\n", "# Discretize\n", - "flx_disc.discretize(gb, data)" + "flx_disc.discretize(g, data)\n", + "# The flux discretization can now be found in the dictionary as:\n", + "flux = data[pp.DISCRETIZATION_MATRICES]['flow']['flux']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the negative sign in front of the surface-integral is included into the flux discretization matrix.\n", + "\n", + "The density is defined at the cell centers, but in the flux term we need to evaluate it at the faces. To do so, we simply take the average of the two neighbooring cells (note that other alternatives, such as upstream weighting, are commonly used).\n", + "\n", + "We also create discretized versions of the divergence operator div. The discrete divergence operator sums the fluxes in and out of each grid cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cell_faces_T = g.cell_faces.T\n", + "def div(x):\n", + " \"\"\"\n", + " Discrete divergence\n", + " \"\"\"\n", + " return cell_faces_T * x\n", + "\n", + "def avg(x):\n", + " \"\"\"\n", + " Averageing. Note that this is not strictly correct for the boundary faces since\n", + " these only have 1 cell neighboor, but we have zero flux condition on these, so \n", + " this is not a problem.\n", + " \"\"\"\n", + " return 0.5 * np.abs(g.cell_faces) * x" ] }, { @@ -166,31 +182,37 @@ "metadata": {}, "source": [ "## Residual function\n", - "We insert Darcy's law into the mass conservation, and write this on residual form. We use backward Euler to discretize in time. This gives us the residual\n", + "To discretize the time-deriveative, we use backward Euler. Further, we assume that the densities are constant over each cell so we can take them out of the integral:\n", "$$\n", - "\\phi \\frac{\\rho^k - \\rho^{k-1}}{\\Delta t} - \\text{div}(\\text{avg}(\\rho^k)\\ u^k) - q^k = 0\n", + "\\int_\\Omega \\phi \\frac{\\rho^k - \\rho^{k-1}}{\\Delta t} dV =\\phi \\frac{\\rho^k - \\rho^{k-1}}{\\Delta t} \\int_\\Omega dV = \\phi \\frac{\\rho^k - \\rho^{k-1}}{\\Delta t}V.\n", + "$$\n", + "The same is also done for the source term.\n", + "\n", + "This gives us the residual\n", + "$$\n", + "\\phi \\frac{\\rho^k - \\rho^{k-1}}{\\Delta t} V + \\text{div}(\\text{avg}(\\rho^k)\\text{flux } p^k) - q^k V= 0\n", "$$" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def f(p, p0):\n", " # darcy:\n", - " u = data['flux'] * p\n", + " u = flux * p\n", "\n", " # Source:\n", - " src = np.zeros(gb.num_cells)\n", + " src = np.zeros(g.num_cells)\n", " src[60] = 1\n", "\n", " # Define residual function\n", - " time = phi * (rho(p) - rho(p0)) / dt\n", - " flux = div(avg(rho(p)) * u)\n", - " lhs = time + flux\n", - " rhs = src\n", + " time = phi * (rho(p) - rho(p0)) / dt * g.cell_volumes\n", + " advection = div(avg(rho(p)) * u)\n", + " lhs = time + advection\n", + " rhs = src * g.cell_volumes\n", "\n", " return lhs - rhs" ] @@ -205,13 +227,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set initial condition\n", - "p0 = np.ones(gb.num_cells)\n", - "p = Ad_array(p0, sps.diags(np.ones(p0.shape)))\n", + "p0 = np.zeros(g.num_cells)\n", + "p = ad.Ad_array(p0, sps.diags(np.ones(p0.shape)))\n", "\n" ] }, @@ -225,95 +247,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Solving time step: 1\n" - ] - }, - { - "data": { - "image/png": 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TKSMjg3fffddSTqTS0lJmzJjBzp07McbwwgsvMGzYMIttbbn0HqhSym+zZs1i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\nQ0VFBffdd18IWx1ktXPhfVl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PytLHQslPP/0EHx+Z/ExFc7iFJ8XF0W74ldRMJhN2796NlJQU7N69G8yMXr16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\n2H9JmyKgAU3XAgWaphUKAD4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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Solving time step: 5\n" - ] - }, - { - "data": { - "image/png": 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btw/MjB49euDIkSNgZjAzOnToAIvFgl59ersgE0l9No7ajB+dDjCpzCrSaQGT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\nFhiNRsTFxeHUqVPo27evXU9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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# define iteration parameters\n", "newton_tol = 1e-6\n", @@ -336,7 +272,7 @@ " eq = f(p, p0) \n", " p = p - sps.linalg.spsolve(eq.jac, eq.val)\n", " err = np.sqrt(np.sum(eq.val**2))\n", - " plot_grid(gb, p.val,color_map = [1., 1.8])" + " pp.plot_grid(g, p.val,color_map = [0, 1])" ] } ], From 7ef6dbe5189990537aec5d5371d3e75db6f9a84f Mon Sep 17 00:00:00 2001 From: Runar Date: Mon, 1 Jul 2019 11:19:48 +0200 Subject: [PATCH 21/25] Updated plot_grid and compute_discharges to work with pp.STATE keyword --- src/porepy/numerics/fv/fvutils.py | 8 ++++---- src/porepy/viz/plot_grid.py | 6 +++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/src/porepy/numerics/fv/fvutils.py b/src/porepy/numerics/fv/fvutils.py index b2dd082a14..05e1e0861c 100644 --- a/src/porepy/numerics/fv/fvutils.py +++ b/src/porepy/numerics/fv/fvutils.py @@ -1358,7 +1358,7 @@ def compute_darcy_flux( matrix_dictionary = data[pp.DISCRETIZATION_MATRICES][keyword] if "flux" in matrix_dictionary: dis = ( - matrix_dictionary["flux"] * data[p_name] + matrix_dictionary["flux"] * data[pp.STATE][p_name] + matrix_dictionary["bound_flux"] * parameter_dictionary["bc_values"] ) else: @@ -1377,7 +1377,7 @@ def compute_darcy_flux( matrix_dictionary = d[pp.DISCRETIZATION_MATRICES][keyword] if "flux" in matrix_dictionary: dis = ( - matrix_dictionary["flux"] * d[p_name] + matrix_dictionary["flux"] * d[pp.STATE][p_name] + matrix_dictionary["bound_flux"] * parameter_dictionary["bc_values"] ) @@ -1402,12 +1402,12 @@ def compute_darcy_flux( bound_flux = d_h[pp.DISCRETIZATION_MATRICES][keyword]["bound_flux"] induced_flux = ( - bound_flux * d["mortar_grid"].mortar_to_master_int() * d[lam_name] + bound_flux * d["mortar_grid"].mortar_to_master_int() * d[pp.STATE][lam_name] ) # Remove contribution directly on the boundary faces. induced_flux[g_h.tags["fracture_faces"]] = 0 d_h[pp.PARAMETERS][keyword_store][d_name] += induced_flux - d[pp.PARAMETERS][keyword_store][d_name] = d[lam_name].copy() + d[pp.PARAMETERS][keyword_store][d_name] = d[pp.STATE][lam_name].copy() def boundary_to_sub_boundary(bound, subcell_topology): diff --git a/src/porepy/viz/plot_grid.py b/src/porepy/viz/plot_grid.py index dc5b4113dd..da6134a1bf 100644 --- a/src/porepy/viz/plot_grid.py +++ b/src/porepy/viz/plot_grid.py @@ -273,14 +273,14 @@ def plot_gb(gb, cell_value, vector_value, info, **kwargs): else: extr_value = np.array([np.inf, -np.inf]) for _, d in gb: - extr_value[0] = min(np.amin(d[cell_value]), extr_value[0]) - extr_value[1] = max(np.amax(d[cell_value]), extr_value[1]) + extr_value[0] = min(np.amin(d[pp.STATE][cell_value]), extr_value[0]) + extr_value[1] = max(np.amax(d[pp.STATE][cell_value]), extr_value[1]) kwargs["color_map"] = color_map(extr_value) gb.assign_node_ordering() for g, d in gb: kwargs["rgb"] = np.divide(kwargs.get("rgb", [1, 0, 0]), d["node_number"] + 1) - plot_grid_xd(g, d.get(cell_value), d.get(vector_value), ax, **kwargs) + plot_grid_xd(g, d[pp.STATE].get(cell_value), d[pp.STATE].get(vector_value), ax, **kwargs) val = np.array([lim(g.nodes) for g, _ in gb]) From 4e100566d5202d83f3e304fca62dcc150421a4a6 Mon Sep 17 00:00:00 2001 From: Runar Date: Mon, 1 Jul 2019 11:20:10 +0200 Subject: [PATCH 22/25] Updated tutorials for pp.STATE keyword --- tutorials/biot.ipynb | 34 ++--- ..._flow_with_automatic_differentiation.ipynb | 134 ++++++++++++++++-- tutorials/tracer_transport.ipynb | 59 ++++---- 3 files changed, 168 insertions(+), 59 deletions(-) diff --git a/tutorials/biot.ipynb b/tutorials/biot.ipynb index dd3fa1c0bd..294be0d21c 100644 --- a/tutorials/biot.ipynb +++ b/tutorials/biot.ipynb @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -267,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -281,8 +281,8 @@ " # Distribute the variables (pressure and displacement) on the data\n", " # dictionaries of the grid bucket\n", " assembler.distribute_variable(x)\n", - " displacement = data[variable_m]\n", - " pressure = data[variable_f]\n", + " displacement = data[pp.STATE][variable_m]\n", + " pressure = data[pp.STATE][variable_f]\n", " pp.set_state(data, {variable_m: displacement, variable_f: pressure})\n", " stored_pressures.append(pressure)\n", " stored_displacements.append(displacement)" @@ -298,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "scrolled": true }, @@ -307,7 +307,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/ivar/anaconda3/envs/porepy/lib/python3.6/site-packages/mpl_toolkits/mplot3d/axes3d.py:744: UserWarning: Attempting to set identical bottom==top results\n", + "/home/rbe051/anaconda3/envs/porepy/lib/python3.6/site-packages/mpl_toolkits/mplot3d/axes3d.py:738: UserWarning: Attempting to set identical bottom==top results\n", "in singular transformations; automatically expanding.\n", "bottom=0.0, top=0.0\n", " 'bottom=%s, top=%s') % (bottom, top))\n" @@ -315,9 +315,9 @@ }, { "data": { - "image/png": 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99NHA7bvuuovHHnsshBE5Q+2y/ZZuoaKEJiJha+bMmTz77LMA+P1+VqxYwbe/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\nbjeLFi1i3rx57Nmzh1tvvZX+/fvz6KO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0pd+T54ZWr15NRkZGW7zkQMzPPvss1lo2btxIYmIiPXv2bHW/Bw4cCMyz5Obm4vf7m/wh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\nbjeLFi1i3rx57Nmzh1tvvZX+/fvz6KO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oEZcG7Dw8ias6aZ1MYtWPtZ7XTnC4NIbUJFRKEobUZPdjSmJ1qdH4P3H5X3l5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\nQeAB0AWBB0AXBB4AXRB4AHThb1C2srTKmOl1AAAAAElFTkSuQmCC\n", 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oEZcG7Dw8ias6aZ1MYtWPtZ7XTnC4NIbUJFRKEobUZPdjSmJ1qdH4P3H5X3l5\nOWazGWOzqkGL5kASnh8ppTCbzSQlJVFUVITVakVrzcaNG7HZbBiNxoD+p15+AnpEw9Q27r655mbh\nm2XMvn8/aRmm6uTlTma9f2PmilHVya1DBObY0Ly81LGjgLKhY3AdPHzaxGVISUKlJhNxbkdUSmKt\n19wLMeZ6fzRZ/vEBMDgAZ7EYsOCusRmqH431PG/MOmzcuJF7772XgoICpk+fzu9+9zuZHzNIpIYn\nzprBYCAtLY3o6GjPti5durBu3TpsNhtOpzNg/6GntwvIYfzmugmJ5N6ZiMHQPL8Ajed2JHn1QgwJ\n8adNXKGvF1CBu0/OVf1Y37qtAfucug7jxo3jl19+wWq1cvfdd9OrVy9+85vfBOzshJs0aYomEx8f\nT2RkpOe5zKLRMEqpZj1KVCmFsV2bYIfho85+LHs6ERERpKWl8dvf/pZHH32Udu2a+a+0ZkqmFhN+\n0zx/6QvR9PLz89m7dy9//OMfufXWW+nRowfZ2dm8+OKLdfZdsWIFiYmJ9O3bl759+3pNLC1EfaSG\nJ4QIOSaTieeff55+/fpx4sQJLrjgAoYPH06PHj289hs8eDBLliwJUpThTS5LEEKIAGjXrp2nOTM+\nPp6srCyKi4vrJDzhH+HahydNmkKIkFZQUMD69esZOHBgnddWrlxJnz59uOqqq9i8eXMQogtPcsdz\nIYQIsPLycm688UZeeOEFEhK8p2rr168fe/bsIS4ujqVLl3L99dezffv2IEUafmTQihBCBIjdbufG\nG29k7Nix3HBD3cmmExISiItzz4gzcuRI7HY7hw4dCnSYohmRGp4QIuRorZk4cSJZWVk89NBD9e6z\nf/9+2rRpg1KKNWvW4HK5SE1NDXCk4UnLXJpCCBEY3333HW+//Ta9evWib9++ADzzzDPs3bsXgHvu\nuYcFCxbw8ssvYzKZMJvN5OXlySU+TSRcB61IwhNChJyLL774jBMyTJ48mcmTJwcoopZHEp4QQoiw\nJzOtCCGEEM2Y1PCEEEJ4kUErQgghWgzpwxNCCBH2wnWUpvThCSGECDil1Ail1C9KqR1KqUfref0h\npdQWpdSPSqnlSqmOvh5TEp4QQggv/p5LUyllBF4CrgJ6AGOUUqfODL4eyNFa9wYWAM/5el7SpCmE\nEKIOP1+WMADYobXeBaCUygOuA7bU7KC1/qrW/quA23w9qNTwhBBha9q0abzwwgue548//ni9N5MV\n3mpGaTZ2aYB0oLDW86LqbaczEfjUh1MCJOEJIcLYhAkTmD9/PgAul4u8vDxuu83nikLYa4ImzVZK\nqfxay12NjUUpdRuQA/zZ1/M7tL5RAAAgAElEQVSSJk0hRNjq1KkTqamprF+/ntLSUs4//3yZYDow\nDmmtc37l9WKgQ63nGdXbvCilLgceB4Zora2+BiUJz89sNhvff/895eXlnm2nPgeIUKA2BDq6pmM0\nQT+1NdhhNJ7JyAHfB4EFkQGYHuwgfGQgJyeHVq1a8frrrzNu3DhKS0tRSnHXXXcxZcoUr7211kyZ\nMoWlS5cSExPD3Llz6devX51SJ02axNy5c9m/fz8TJkwI1Mk0e36+LGEt0FUp1Rl3ohsN3Fp7B6XU\n+cCrwAit9YGmOKgkPD+LjIzkwgsv5Pvvv/dsO/U5gF3D/kAH14TaOmCXbhfsMBqtiyqho26+CXuP\nygKeDXYYPppKfn4+ACUlJTz//PP069ePEydOcMEFFzB8+HB69Dg5kO/TTz9l+/btbN++ndWrV3Pv\nvfeyevXqOqX+9re/Zdq0adjtdv75z38G7GyaM3/Ppam1diilJgOfAUbgH1rrzUqpmUC+1nox7ibM\nOODD6rtg7NVa5/pyXEl4QoiQ065dO9q1c/+Aio+PJysri+LiYq+Et2jRIsaNG4dSikGDBlFWVkZJ\nSYnn72pERkZy2WWXkZSUhNEYfhdT+0MgphbTWi8Flp6ybVqt9cub+piS8IQQIa2goID169czcOBA\nr+3FxcV06HCyGygjI4Pi4uI6Cc/lcrFq1So+/PDDgMQbLmSmFSGECKDy8nJuvPFGXnjhBRISEs76\n77ds2UJmZibDhg2ja9eufohQNCdSwxNChCS73c6NN97I2LFjueGGG+q8np6eTmHhyUu5ioqKSE/3\nvpSrR48e7Nq1y++xhhuZS1MIIQJEa83EiRPJysrioYceqnef3Nxc5s+fj9aaVatWkZiYWKc5UzSe\nP6cWCxap4QkhQs53333H22+/Ta9evejbty8AzzzzDHv37gXgnnvuYeTIkSxdupTMzExiYmJ46623\nghlyWAnXO55LwhNChJyLL74YrfWv7qOU4qWXXgpQRCIcSMITQgjhRe54LoQQosUI5b64xpKEJ5qU\n1prqWRGEEM2UjNIU4gx+WGkjy7yfC1rv59bLDgc7HCFEI9UMWmnsEqqkhid8tqP6cfp/HcNmBbtN\n062nfLSEEKFFvpXEWXMBG3HfjfFT4ET19odnJfDR3EoGDolkzF2xQYsvXGmrDVdlFcbkxGCHIloA\nGbQiWiw7sBL3TK+f4Z7C/CrgRaAv0B4YPDyKwcOjghZjuNJao09UcOJ/36Ps8ReIPD+LxGn3EZM7\nNNihiTAVrn14kvDEaVUAX+GuxS0HuuBOch8AMith49UkMOfBI7gOHsF54Ih7veaxelvt11RkBCom\nGlwubOs2U/XZfyThCb+RhCdahEPA/wHLgO+BHOBK4L8BmbSpflprdHnlaZNVvQnMZMKYloKhdQrG\n1skY0lIxtk7GlN4GQ9/zMLZO8WwztE7BYI7Gvr2Aow//meRn/0hE987BPm0R5iThibC0B3eC+xTY\nDAwBrgf+H9BSe4tcFZW1ktRhXAePnnw8JYG5Dh4BgwFDWgrGUxKYsV1rInt399pmaJ2CIcZ81jFF\ndO1E2sJwmFnkCLAJ6A+c/fsgRGNJwmuBNO7E9inuRLcfdy3ufmAwEB280PymssLFkYMuDh90cfhA\n9Xr145GDLgBK+t/sSWpo7U5gaakYWidjTEt117rapBLRs6v3ttbJGGJjgnyG/qQBJ+6eXCfg+JWl\nIa+XANtw9wgPAOreCeGXX36hQ4cOxMSE8/saumQuTeEXTqcTp9MZkGP9CCzAnegU7v64p3H/zm5u\nH+2qSs2Rg856E1h925xOTWqagdTWBlLTjKS0NpCaZiCltYHMHiY+mldFyktPeJoYVWxMUC+g1y4X\n2OxoixVttZ1cLLXXrVC97pbPyaRiP2W9IYnodPs5cX9CTNWLEYg4ZZvpV1431loigJokFgO0wp0A\n3f8Hvv76a2w2G+PHj+fo0aNcd911zJs3j6goGQwVSDK1mPCZy+XC6XTy888/U1FRgdYao9GI0RiY\ndPMR7ibKeUAW7qTXnHySV8Wf/3SCIwddOOzak7BqElhNEuvc3USrNINXUouNU7+awKZOOEbUgN5+\njd9RWMKRe6bjqrR4JSuvJFb9HLsdIiNQ0VGoqEj3Eh15cj0qEmq95raT+pNPNA1LVLX/5tTnNXNU\nfAAcx31xSk1ytAOWWs9rv+asXhTeyS8GiAJWA+uo+cl1zTXXEBUVRVlZGU6nkw8//JC//OUvZGRk\neL2XK1as4LrrrqNzZ3df5g033MC0adMa/48j6pA+PHFWtNZYrVbWr19PeXk5BoMBo9FIq1atOHz4\ncMBrEDMCerSmd3luNL1yIkhNMxAX/+sJLBQZ26QS/+A4MBprJbJTklpUJCo6yp3szuL89qgsYJT/\ngvfoD9g4mbhOrcEZ63nNQMMmdZrKiRPuqzp/97vf0alTJ/75z3/WSXY1Bg8ezJIlS3w6G9GySMLz\nI6UUBoOBbt26UVVV5dneqlUrtm3bFsTImidzjKJTZvP9yKrISMzDLwp2GD4KzOjQ119/nYKCAt57\n772AHE94C9fLEmQuTT+LiIggNlZmHRGiqa1cuZI+ffpw1VVXsXnz5mCHE1ZkLk0hhAgR/fr1Y8+e\nPcTFxbF06VKuv/56tm/fHuywwko4DlqRGp4QotlJSEggLi4OgJEjR2K32zl06FCQowofNU2ajV1C\nlSQ8IUSzs3//frTWAKxZswaXy0VqamqQoxKhLvzqrEKIZm/MmDGsWLGCQ4cOkZGRwYwZM7Db7QDc\nc889LFiwgJdffhmTyYTZbCYvL6/ZjdoNZeE6aEUSnhAi5JxpdObkyZOZPHlygKJpmUJ58EljScIT\nQgjhRWZaEUII0SKEa5OmDFoRQgjRIkgNTwghRB3hWMOThCeEEMJLuDZpSsITQgjhRROeozSlD08I\nIUSLIDU8IYQQp5DLEoQQQrQA0ocnhBCixZCEJ4QQIuzV3A8v3MiglRDgcDiCHYIQQoQ9SXhBorXG\nZrNRXl7umQVeiJYuJyeHESNGADBhwgTS0tLo2bNnvftqrXnggQfIzMykd+/e/PDDD4EMNazVzKXZ\n2CVUScILMJfLxS+//EJFRQVaa2JjYzGbzcEOS4iQkJ+fz7JlywC44447POv1+fTTT9m+fTvbt2/n\ntdde49577w1UmC1CON4ANnRTcZhxOp1YrVa01l53a65hAtoGJ7QmYTJBF1US7DAaz2Rkj8oKdhQ+\nMABTgx2Ej7y/KC+55BIKCgpOu/eiRYsYN24cSikGDRpEWVkZJSUltGvXzs9xhj8ZpSnOmtYau93O\n6tWrsVqtREVFYTQaadeuHbt37/ba1wFMD0qUTWO6Az7Q1wY7jEa7RX1Cmt4T7DAa7YDqCBQGOwwf\ndTirvYuLi+nQ4eTfZGRkUFxcLAmvCWgUTlf4JTxp0vQjrTUul4vevXsTExOD0Rh+HyAhhGgupIbn\nRwaDgaioKOmjE8IP0tPTKSw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ZmYnJZGLFihVMmzaNsWPHBuldEf4mNTwhRMAlJSXRpk0b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\nQeAB0AWBB0AXBB4AXRB4AHThb1C2srTKmOl1AAAAAElFTkSuQmCC\n", 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DE5ourA4zYww9e/bkb3/7GwcOHCAhIQFjTOiX6amnnsrUqVMZMmQIgUCAm2++mdzcXCZM\nmEB+fj4FBQWMGTOGG264gZycHDIyMigsLDzmeT0ezzHr/cMf/sCcOXPweDxkZGQwffr0ev9dI0eO\nZMGCBezcuZPs7GwmTpyIz+cD4LbbbmP48OHMmzePnJwcEhMTmTZtWpPUO2vWLJ599lk8Hg8JCQkU\nFhbW+0vws88+4+WXX+aMM84gL69mO97HHnuMLVu2NDru+tTd0Ni3bdvG6NGjCQQCBINBrr76ai69\n9NJGf0bqU29jPiNH0tiYpZk49MJqbfAZRn6/n08//ZRzzjmHzz//nOrqanw+H+effz5FRUVAzaSD\nU089tc7A+dChQ+t0/zQV1a26o7nuE0x4Nvg8ydii649d7oeYJ469wacxZijwNDWp80Vr7ZTDnr8N\nuBMIAOXArdbar45Wp7ocm1FsbCxxcXEUFRWFLrguLS1lw4YNdS7APt6xleOhulV3NNctLZ8xxg08\nAwwDegAjjTE9Div2mrX2DGttHvA48OSx6lWXYzPzeDzk5uayePFiEhIScLvdlJaWcuZZfajYv7dW\n2XCOJzR53a4YCPrCU/chwle3x6FxH6zbA/jDVHd4HF53Sko6+/btDtv55DiEf3HivsB6a+1GAGNM\nIXA5EGqBWWv3HVI+iXr0ECqhRUBqaiqJiYkcOHCA+Ph4PB5PTTJ70ME9uv9tYKSD43/dQIqD499v\ngJWRjqJR9u/vFekQ5FCNG0NrbYwpOuT+89ba5w+5nwUcelFjKVDnIkhjzJ3APUAsMPBYJ1VCixCX\ny0VSUhIHDhyo13qPIiLNpvEttJ3HGkOrD2vtM8AzxphrgV8DR70QUmNoEWSMITExEb+/6buKRERa\nsDKg4yH3s79/7IcUAsdciUAJLcIOJjURgfz8fIYOHRrpMCT8F1YvBboYY04xxsQCI4A5tUIwpssh\ndy8B1h2rUnU5ikiLcfByloMCgQD5+flkZWUxd+7cWs95vV5GjRrFsmXLyMzM5I033qBTp07NGG0U\nC/OkEGut3xgzFnifmtG6P1trVxljJlGzUv8cYKwx5iLAB3zHMbobCW/IIiKN8/TTT9O9e3f27dtX\n57k//elPpKens379egoLC7nvvvt44403IhBllArzhdXW2nnAvMMem3DI7XHHW6e6HEWkRdL2MxGk\ntRxFRJqOtp+R46WEJiItjrafiTC10EREmoa2n2kBtGO1iEjjafuZCHNoC02zHEXEMbT9TDMJ/1qO\nYeHAkEXkRHLBBRdwwQUXADBp0qTQ4/Hx8cycOTNCUUlLpIQmIiK1OXSDTyU0ERGpTV2OIiISNRyY\nHTTLMcy0NYyISPNwYA52Dr/fz4EDByguLo50KCIi9efQLke10MLI4/GQlJREdXU1FRUVkWut7d0C\nL18Ez3aPzPmbytI74P/Ohjmnws6FkY6mYXx/Bt/bENgQ6UhEftjBSSEOu7DagTnYWYwxdO3ale3b\nt1NZWYnH42nexFa+HaaeCkE/nHJx8503HL79O+z7CtxJkNQ50tEcP7sPqu4CKmruJ64Bd9eIhlR/\nvwQ6AMOAHhGORcJOLTQ5GrfbTVJSEgBLliwhEAg0z4mT28Elz4MrBroMa55zhkuvyTXJbPA/IKFd\npKM5fvYbMCcDboh700HJDOBLYAYwEngtwrFIs9BKIXIscXFx5ObmsmjRIjweD3FxceE/ad5N0Hkw\nxKeH/1zhlF0AV31Xk5ydyNUVEueB/Q7ceZGO5jh1AuKA3wLdIhuKyA9QQouAlJQUkpKS8Hq9VFRU\nEB8fH/6TtsoO/zmag1OT2UGuk4GTIx1FA/weiEVfGScIXVgtxysuLo6YmBgqKysjHYrIMSRGOgBp\nThpDk4ZwuVyhsTWRE11+fj5Dhw6NdBji0NX2ldBEpMUoKipi/vz5VFVV0bdvX3r16kVubi4PPfRQ\nnbLTp0+nTZs25OXlkZeXx4svvhiBiKOYpu2LiDReXFwcH3/8McnJyfh8Pn784x8zbNgw+vfvX6vc\nNddcw9SpUyMUpbQ0Smgi0uIYY0hOTgbA5/Ph8/m0eWdz0hiaiEjTCQQC5OXl0bZtWwYPHky/fv3q\nlHnrrbfo2bMnV111FSUlJRGIMkppDE1EpOm43W5WrFhBaWkpS5Ys4csvv6z1/GWXXUZxcTH/+te/\nGDx4MKNHj45QpFFICU1EpOmlpaVx4YUXMn/+/FqPZ2ZmhhYmuOWWW1i2bFkkwpMWRAlNRFqcHTt2\nsGfPHgAqKyv54IMPOO2002qV2bZtW+j2nDlz6N7d4YtvtzSa5Sgi0njbtm1j9OjRBAIBgsEgV199\nNZdeeikTJkwgPz+fgoIC/vCHPzBnzhw8Hg8ZGRlMnz490mFHD4dOCnFgyCIS7Xr27Mny5cvrPD5p\n0qTQ7cmTJzN58uTmDOvEoYQmIiJRw4FrOWoMTUREooJaaCIiUpu6HEVEJCoooYmISFRQQpMjsdZG\nOgQRkeNmNSlEDhUIBKioqGDDhg1KbCIiYaYWWhi53W6SkpKIjY2loqICt9sdWqpHRKSlsgYCDswO\nDgzZWYwxdOzYkZKSEvx+P1VVVRQVFeH3+/F49PaLSAukhCbH4vF48Hg8nHbaaSxevJiqqipiY2OJ\niYkBVwz8t4P3ezIeeN3B8eOB/U6O3w30inQQjaSvo5bCGvC7GzMiFWyyWI6HPkERkJycTEJCAtZa\nqqurqaiogKCPbLsu0qE1WKnpAisdPE7Yy8ApDo5/kwEejnQUjfRwpAMQh1NCiyBjDHFxcRpXE/le\nfn4+rVu3rrNVjDQvawyBRg2JVDdZLMdDCU1EWoyioiIAqqqqGDBgAF6vF7/fz1VXXcXEiRNrlfV6\nvYwaNYply5aRmZnJG2+8QadOnSIQdXQKuJ03b1/T9kWkxYmLi+Pjjz9m5cqVrFixgvnz57No0aJa\nZf70pz+Rnp7O+vXr+cUvfsF9990XoWijj8UQwN3gI1KU0ESkxTHGkJycDIDP58Pn82FM7Uk7s2fP\nZvTo0QBcddVVfPTRR7res4lYDH7cDT4iRQlNRFqkQCBAXl4ebdu2ZfDgwfTr16/W82VlZXTs2BGo\nmUGcmprKrl27IhGqtBBKaCLSIrndblasWEFpaSlLlizhyy+/jHRIJ5QAngYfkaKEJiItWlpaGhde\neGGdmY9ZWVmUlJQA4Pf72bt3L5mZmZEIMepoDE1EpIns2LGDPXv2AFBZWckHH3zAaaedVqtMQUEB\nL730EgCzZs1i4MCBdcbZpGGcmtA0bV9EWpxt27YxevRoAoEAwWCQq6++mksvvZQJEyaQn59PQUEB\nY8aM4YYbbiAnJ4eMjAwKCwsjHXZUiWRiaiglNBFpcXr27Mny5cvrPD5p0qTQ7fj4eGbOnNmcYUkL\np4QmIiK1HJy27zRKaCIiUkvNGJrz0oPzIhY5muI1sO872LMTzh0GDly+R6QlcOIYmmY5nmACu75j\n1y0PRO+KCtf3h1HnwM8vgw1fRToaEWlGSmgnkIpps9iW9WMq/zQTgpHZryhsNq2G+0aC3wfWwthH\noesZkY5KxJGcOm1fCe0EUvXh5+CtxmSkYqKlK27Lehg/Cm4aAF17wl83wK+mwi2/inRkDbf7ASjp\nDFsHQMWcSEcjJyALjlzLUWNoJwhrLcEdu0m4voCY3K6RDqfxyorh+f+GBbNh5F0wdz0kt6p5bsSd\nEQ2tQYKV4P0HVH4A+1+B4DbwF0NgdKQjkxOSJoVIC3Zg2iyCu/fSet6LmEZt3Bdh35TAC4/Ch7Pg\n6jvg3XXQKj3SUR0/a8H3BVT+X00Sq/ocYntCwmDIfBJ2/xLavQ1xfSMd6WEsoNU4ot3BLkencfA3\nW8tnraWiooK1a9fi9/txu90RW5rHldaKjNeedHYye/n38MIjcOWtMHsNpDls3T7rhfI3vk9gH4BJ\nqUlgKbdD2zfBlfrvsskjIhfnUT1FzUhFJ2A4EBPRaEQO5eBvt5bPGENCQgJpaWmUlpbi9Xqx1rJy\n5Uqqq6txu924m2ksK+GKIc1ynrDqOxCGXweZbSMdScP41kLlexB/IaRPgphTIh3RcbLAfiD4/b8X\nAKlHe4E4mFpoUofL5aJt27bEx8eHHuvcuTPLli2jurqaQCCgBVXrq1uvSEfQOLFnQNvXIx1FIxig\nH1BJTessNrLhSNioy1HqLSUlhdjYf38ZRO01YRKFwtvSz8/Pp3Xr1nW2ipHm1RxLXxljhgJPA27g\nRWvtlMOevwe4BfADO4CbrbWbj1anpu23AGqhidQoKipi/vz5lJSUcOGFF9KjRw9yc3N5+umn65Rd\nsGABqamp5OXlkZeXV2vhYmnZjDFu4BlgGNADGGmM6XFYseVAvrW2JzALePxY9aqFJiItjsfj4Ykn\nnqB3797s37+fs846i8GDB9OjR+3vvPPOO4+5c+dGKMroFuZp+32B9dbajQDGmELgciC0vI+19m+H\nlF8EXH+sSpXQRKTFad++Pe3btwdquui7d+9OWVlZnYQm4dEMY2hZQMkh90upGaD9IWOA945Vqboc\nRaRFKy4uZvny5fTrV/f7buHChfTq1Ythw4axatWqCEQXnZpg6avWxpiiQ45bGxqLMeZ6IB/47bHK\nqoUmIi1WeXk5V155JU899RStWrWq9Vzv3r3ZvHkzycnJzJs3j5/85CesW7cuQpFGn0ZOCtlprc0/\nyvNlQMdD7md//1gtxpiLgPHA+dZa77FOqhaaiLRIPp+PK6+8kuuuu44rrriizvOtWrUiOTkZgOHD\nh+Pz+di5c2dzhykNsxToYow5xRgTC4wAai1caow5E3gOKLDWflufStVCE5EWx1rLmDFj6N69O/fc\nc88Ry3zzzTe0a9cOYwxLliwhGAySmemw1WNaqHBv8Gmt9RtjxgLvUzNt/8/W2lXGmElAkbV2DjVd\njMnAzO9ngm+x1hYcrV4lNBFpcT777DNefvllzjjjDPLy8gB47LHH2LJlCwC33XYbs2bN4tlnn8Xj\n8ZCQkEBhYaEugWkizXFhtbV2HjDvsMcmHHL7ouOtUwlNRFqcH//4x8dccGDs2LGMHTu2mSI68Wil\nEBERcbzmWCkkHDQpREREooJaaCIiUku4J4WEi/MiFhGRsNMYmoiIOJ5Tt4/RGJqIiEQFtdBERKQW\np7bQlNBERKQOTdsXEWlBJkyYwFNPPRW6P378+CNuFiq1HZzl2NAjUpTQRCRq3XzzzcyYMQOAYDBI\nYWEh119/zH0iT3hNsH1MRKjLUUSiVqdOncjMzGT58uVs376dM888UwsYRzEltDCrrq7m888/p7y8\nPPTY4fcBTIyHUtOlucNrOm4P9HLywrAe2OTk+F3Aw5EOopFc5Ofn07p1a1544QVGjRrF9u3bMcZw\n6623Mm7cuFqlrbWMGzeOefPmkZiYyPTp0+ndu3edWm+55RamT5/ON998w80339xcf4zjaVKI1BEb\nG8s555zD559/Hnrs8PsA1ufncfvz5g6vydxr/oeudmWkw2iwtaYXnH30xXBbtIUGeCTSUTTSrykq\nKgJg27ZtPPHEE/Tu3Zv9+/dz1llnMXjwYHr06BEq/d5777Fu3TrWrVvH4sWLuf3221m8eHGdWn/6\n058yYcIEfD4fr732WrP9NU7m1LUcldBEpMVp37497du3ByAlJYXu3btTVlZWK6HNnj2bUaNGYYyh\nf//+7Nmzh23btoVed1BsbCwXXnghaWlpuN3O+5KOBC19JSISBsXFxSxfvpx+/frVerysrIyOHTuG\n7mdnZ1NWVlYnoQWDQRYtWsTMmTObJd5o4cQuR81yFJEWq7y8nCuvvJKnnnqKVq1aHffrv/rqK3Jy\nchg0aBBdujh4jFrqRS00EWmRfD4fV155Jddddx1XXHFFneezsrIoKSkJ3S8tLSUrK6tWmR49erBx\n48awxxptnLpSiFpoItLiWGsZM2YM3bt355577jlimYKCAmbMmIG1lkWLFpGamlqnu1EaTtehiYg0\ngc8++4yXX36ZM844g7y8PAAee+wxtmzZAsBtt93G8OHDmTdvHjk5OSQmJjJt2rRIhhxVNMtRRKSJ\n/PjHP8bao19GYYzhmWeeaaaIxAmU0EREpBZN2xcRkaihSSHiKPu3H+C5QW+z8s11kQ5FRFoQLU4s\njvKvWet5/dr3CfiC9PuP3EiHIyItiFMnhaiFdoJKzUoiGKwZdD/57JMiHI2ISOOphXYCstbyyZMr\n6Hfr6Zzcvx1pP0qJdEhhVTqgz0tBAAAfsElEQVT4P/Fv/RYbDNLhnaeI63ZKpEMSafE0KUQcoWja\n1+xY8x0jXh5MTHz0fgSC5QfY/8Z8Khd/gd1fgSu9Fe622gtL5Fi0Uog4wo51e5h332dc+9qQqExm\n1loql3zB9lsnsbHjxZS/+wnpv7wBz4/a02nVX/CkH/96gCInGk0KkRbPXx3g9WvfZ/DD/Tjp9Ohq\nqQR272XfK3PZ++Lb2ANVpI75KZ2+ehtP+zYAZE64DWMcuIHnrrehag2kDoGkXmD0G1SahxNbaEpo\nJ5APHlpMcrtEzr7jjEiH0iRsMEjl34vY++LbVPz1U5IuOY+2T/0XCRf0wbhqf/E7KplZC96NUL4M\ntj4OFcuAX0G7sdD5fyIdnUiLpYR2gtiwoJSil1bzixUjnPXlfgT+bTvY99Ic9r74NiYhjtT/uIK2\n/3M/7ozUSId2/KwFb3FN0iov+v7fZeBOgqR8iD+15rGTxkGnJyMd7fd2A2loxCJ6OXXavhJahAUC\nAQKBQNjPs7esgmtfu5jktolhP1e4VC37il2PPE/lgiKSrxrMSa9NJr7P6c5K0N7NhySu7/818ZCc\nD0lnQftf1Pwb266mfNALWfdD0pmRjTtkD/Ak4AbaAHcCDnr/pV609JUcUzAYJBAIsHr1aioqKrDW\n4na7m2Vb+N7XdQv7OcLNV7yV5EsG0H7Go7hSkiIdzvE7sAq+Hg5JPWtaX+3v+j55HWXLE1dcC0pm\nFphGTQILAq1o6mSWn59P69at6dChA3PnzqVt27Z8+eWXdcotWLCAyy+/nFNOqbkE44orrmDChAlN\nGsuJTmNoUou1Fq/Xy/LlyykvL8flcuF2u2ndujW7du1yVsuiBUi58qJIh9A4iblw1uZIR9EIBvgF\n8BHQHujR5GcoKioC4JNPPmHs2LGMGjXqB8ued955zJ07t8ljEOdSQgsjYwwul4uuXbtSWVkZerx1\n69asXbs2gpGJNMagsJ9hwIABFBcXh/08cmS6Dk2OKCYmhqQkB3aPibRwCxcupFevXgwbNoxVq1ZF\nOpyocnBSSEOPSFELTUQcp3fv3mzevJnk5GTmzZvHT37yE9at064RTcmJk0LUQhMRx2nVqhXJyckA\nDB8+HJ/Px86dOyMcVfRw6kohSmgi4jjffPMN1tbsFrFkyRKCwSCZmdG1+o0cP+e1KUUk6o0cOZIF\nCxawc+dOsrOzmThxIj6fD4DbbruNWbNm8eyzz+LxeEhISKCwsFCzhpuQUyeFKKGJSIvz+uuvH/X5\nsWPHMnbs2GaK5sSklUJERMTxtFKIiIhEBad2OWpSiIiIRAW10EREpA4nttCU0EREpBandjkqoYmI\nSC0WZ85y1BiaiIhEBbXQRETkMJq2LyIiUUBjaCIiEjWU0ERExPEO7ofmNJoU0gL4/f5IhyAi4nhK\naBFiraW6upry8vLQKuIiJ7r8/HyGDh0KwM0330zbtm05/fTTj1jWWstdd91FTk4OPXv25J///Gdz\nhhrVDq7l2NAjUpTQmlkwGGTNmjVUVFRgrSUpKYmEhIRIhyXSIhQVFTF//nwAbrzxxtDtI3nvvfdY\nt24d69at4/nnn+f2229vrjBPCE7c4FNjaM0kEAjg9Xqx1tbabfcgt8fFveZ/IhRdE/C4WWt6RTqK\nhjMeWOjk/bRcwK8jHUQj1f4iHDBgAMXFxT9Yevbs2YwaNQpjDP3792fPnj1s27aN9u3bhznO6KdZ\njlKHtRafz8fixYvxer3ExcXhdrtp3749mzZtqlU24A/ycGTCbBIP+wO0txsjHUaDbTOdId9GOoyG\nKzLAskhH0UhnHVfpsrIyOnbsGLqfnZ1NWVmZEloTsBgCQeclNHU5hpG1lmAwSM+ePUlMTMTtdt4H\nRETEKdRCCyOXy0VcXJzGyETCICsri5KSktD90tJSsrKyIhhRFLHg9zvvB7haaCLiSAUFBcyYMQNr\nLYsWLSI1NVXdjU3EWkPA72nwESlqoYlIizRy5EgWLFjAzp07yc7OZuLEiaFLXG677TaGDx/OvHnz\nyMnJITExkWnTpkU44uhRk9DC20IzxgwFnqZmNtCL1tophz0/AHgK6AmMsNbOOladSmgi0iK9/vrr\nR33eGMMzzzzTTNFIUzLGuIFngMFAKbDUGDPHWvvVIcW2ADcC/6++9SqhiYhIbZZwt9D6AuutrZka\nbYwpBC4HQgnNWlv8/XPB+laqhCYiIrVYa/D7GpXQWhtjig65/7y19vlD7mcBJYfcLwX6NeaEoIQm\nIiJ1GIKBRqWHndba/KaKpr6U0EREpDYLhLfLsQzoeMj97O8faxRN2xcRkea2FOhijDnFGBMLjADm\nNLZSJTQREanNmpoWWkOPY1VvrR8YC7wPfA28aa1dZYyZZIwpADDG9DHGlAI/A54zxqw6Vr3qchQR\nkdos4A/vYt3W2nnAvMMem3DI7aXUdEXWmxKaiIjU5cB9h5XQTmBBaubKfgPsBoZGNhwRkUZRQjuB\n7QD+/P3tNJTQROR7FrXQxDmqgIVADOADro1sOGHj37gFAgE8XU6JdCgizuHQhKZZji1AIBBo1vNt\nAP6XmhVBfw5cB7Rt1giaR3D3Hvb+53h2dBvEtsQeVPzvy5EOScQZLDW/dBt6RIgSWoR5vV6qqqqa\n51zAXGA2UABcBrQCujTL2cPHBgL412yk8s2/sm/879h96Ri2dzyXbzsNwL++GABXZhqxF/SPbKBH\nYwPg3xvpKCIuPz+foUNrOr/nz59Pt27dyMnJYcqUKXXKTp8+nTZt2pCXl0deXh4vvvhic4cbvSwQ\naMQRIepyjJBAIEBVVRUej4ekpKSwn68aeI6aS/NvB5y65Whw3378/1qNb+XX+Fauxr/ya/yr1uFq\nm4mnV3diep1G4pir8fTqjrtTNoHiUipff5fke2/FxMREOvy6rIVgOXw3B4pHQVJ/aH8/pF0W6cgO\nEQBeAgYAOWE9U1FRzfJ/gUCAO++8kw8++IDs7Gz69OlDQUEBPXr0qFX+mmuuYerUqWGNSZxDCa2Z\nWWtZu3YtVVVVJCQk4HI1TyM5BrgGaNcsZwuPiudeY/8vH8OT2+XfyWvUT/H0PA1Xq5QjvsbT+Uek\njL+zmSP9Ad4tsPlW8O+CwHcQ2AOBvWDiwCQCQaj4HL59poUlNC81O308R83AynTgjLCeccmSJeTk\n5NC5c2cARowYwezZs+skNAkjB46hKaE1o0AgQGVlJVlZWc3SKjuUwdnJDCDx1pEk3nINxu28reEB\niGkHJ/0/cKWAJx3c6eBOBVcsVG+FTddDh/+GlHMjHelhEqgZcU2kZi7saWE/Y1lZGR07/nupv+zs\nbBYvXlyn3FtvvcUnn3xC165d+f3vf1/rNdIIDp0UooTWDA52LwYCARITE+nUqRNbt26NdFiOY4wB\npyYzAFcctLroyM/FdoBuHzdvPPVmqNlYuCfQvD/Ejuayyy5j5MiRxMXF8dxzzzF69Gg+/rilvocO\n49CEpkkhYeb3+1m0aBEul4ukpKRm62IUaVpn05zJLCsri5KSf2+XVVpaSlZWVq0ymZmZxMXFAXDL\nLbewbNmyZosv6h1MaA09IkTfrmHk9/uprq6md+/exMbGRjocEcfo06cP69atY9OmTVRXV1NYWEhB\nQUGtMtu2bQvdnjNnDt27d2/uMKWFUZdjGHk8HhITE0lIcOqcQpHI8Hg8TJ06lSFDhhAIBLj55pvJ\nzc1lwoQJ5OfnU1BQwB/+8AfmzJmDx+MhIyOD6dOnRzrs6OHQLkclNBFpkYYPH87w4cNrPTZp0qTQ\n7cmTJzN58uTmDuvEoYQmIiKOd3ClEIfRGJqIiEQFtdBERKS2g0tfOYwSmoiI1KZJISIiEhWU0ERE\nJCo4NKFpUoiIiEQFtdBERKQuB7bQlNBERKQ2h3Y5KqGJiEhtSmgiIhIVtFKINFQwGIx0CCIijqeE\nFmGBQIADBw5EOgyRFiE/P5+hQ4cCMH/+fLp160ZOTg5TpkypU9br9XLNNdeQk5NDv379KC4ubuZo\no9jBlUIaekSIuhwjyO/3U1VVRWJiYqRDEWkRioqKgJofenfeeScffPAB2dnZ9OnTh4KCAnr06BEq\n+6c//Yn09HTWr19PYWEh9913H2+88UakQo8+DhxDUwstQqqrq/F6vdrFWuQIlixZQk5ODp07dyY2\nNpYRI0Ywe/bsWmVmz57N6NGjAbjqqqv46KOPsNZGItzoox2rpb7WrFmD3+8nKSkJY0ykwxFpMQ52\nOZaVldGxY8fQ49nZ2ZSVldUqe2gZj8dDamoqu3btatZ4pWVRl2Mzq6ysBKjVzehyuUhOSODh759z\nIuPxsM10jnQYDWdioMjJPy48wFmRDqJRjPEQCATYuXMnjzzyCPn5+ZEO6cSlaftyNNZaDhw4QExM\nDN26dePzzz8HapJZIBDgnXffpbKyEo/HQ2xsbJOfPxAIhMbrwtEq9Hq9GGPCEjtARUUFSUlJYak7\n3PUfOHCAhISEsLzvgUCA6upqEhISmrxuqIk9NjYWj6fpvyr8fj9er5eEhARcLhe//OUvKS0tpUOH\nDlRXV/POO+/w4osvAlBaWkpWVlat12dlZVFSUkJ2djZ+v5+9e/eSmZnZ5HGekBw6bV8JrRlUVlZS\nUVFBfHx8rS8Gay29e/fG7/fzr3/9i27dunHSSSc1+fl9Ph/Lly+nT58+YfnSrq6uZsWKFeTn54dl\nPLCiooKNGzdyxhlnNHndBy1dupQ+ffqEpe6NGzeSnJxM27Ztw1L/ypUrOeWUU2jVqlWT133ws5Ob\nmxuWyUt79uxh7dq1nH766XzyySd89NFHPPzww7zzzjsMGzaMTZs2kZWVRWFhIa+99lqt1xYUFPDS\nSy9x9tlnM2vWLAYOHKgu/Kbi0P3QzHEOomrE9Tj4/X4WLFiAy+UiGAzidrsB6N+/PwsXLsTtduP3\n+zlw4ABxcXFh+RUM4f2VDVBVVYXb7SYmJiYs9VdXVwOErfUH4W2hBQIBfD4f8fHxYavf6/WGbbZs\nuFv3B+tPSEjggQceYM+ePZSUlNCmTRu+/fZbXC4X48ePZ/z48UyYMIH8/HwKCgqoqqrihhtuYPny\n5WRkZFBYWEjnzg7u9m6YsGRw0y7fck1Rwyv4H7PMWtvsfcZqoYWRz+ejqqqKc889lxUrVoQe9/v9\nuN1ufD4flZWVYU1mXq8Xt9sdtvqDwSCBQCBsX9ZQ84UXFxcXtvrD7WC3cri43W6MMfj9/rD8d3a7\n3cTGxoaSTjjqj4+Pp7KykkcffZTx48djreW7777j5JNPJiUlhU8//RSASZMmhV4XHx/PzJkzmzwe\ncS7NcgyjmJgYEhMTQ7+crbW4XC6WLl3aLMnM7/eHPRlUV1eHtX5rLcFg0NGXNhhjMMaEdUWYuLi4\nUEs2HGJiYjDGhO0cbrebhIQEqqqqePTRR0lPTycjI4Pdu3ezZ88edu7cGbrgWpqBQ6ftq4UWZsYY\nrLVYa3G73QQCAYLBIJWVlcTHx4e6IZtaMBgMazfUwXMEg8GwJeSD5wjXe9ScPJ6aGXzhSswulwuX\nyxW2VhrUJM3KykpcLldYzuFyuUhISKCyspJHHnkEj8fDvffey5YtW/D7a74l8/Pzad26NfPnz2/y\n88shHDopxLk/ex3CWksgEAgls0AgEPZkZq0NnSOcg+Rerzes41rw7+5Zpzs4XhpOsbGxeL3esNVv\njCE+Ph6v1xu21ubBpOb1evH7/Tz++OOcfPLJAOzYsQNArbXm4NClrzQpJIwCgQCffvpprfGTqqoq\nYmNjw9qF5vf7sdaGbZLGQV6vN+xjW9XV1aHurnA6cOBA2Jcga673y+PxhPXzdfCHWTh/zFhra43Z\n3X///aGE1rp169C/aqmFaVJIZr7lkkZMCnlZk0Kijtvt5rzzzot0GFIPmzdvDrUEpOVZtGgR1lrK\ny8tJS0uLdDjSQimhhVk0dJedCE7A6d6OpGTWjBy4UojG0ESk2e3evZvBgwfTpUsXBg8ezHfffXfE\nci+99BJdunShS5cuvPTSS6HHly1bxhlnnEFOTg533XVXaFHimTNnkpubi8vlCq3cf9DkyZPJycmh\nW7duvP/++6HHj7VNzQnp4KSQhh4RojE0EWl29957L/Hx8SxcuJDly5eTnJzM8uXLSU9PD5XZvXs3\n+fn5/PKXv+TJJ59ky5YtPP3009xxxx307duXO+64gyeeeIKNGzdy0UUX8c4777B69Wr27dvH0KFD\nSUlJoVu3brz55pts27aNQYMG0bZtW3w+H+vXrycQCPDNN9/Qv39/vF4vaWlpbNiwgVNPPZUvv/wy\ngu/OcQnPGFp6vuXCRoyhvR2ZMTS10ESk2c2ePZudO3cyaNAgvvjiCyoqKuq0jt5//33OO+88nnji\nCZYuXcp1113Hww8/zNdff82+ffv43//9X1544QVeeOEFli9fzvz58+nevTtvvfUW6enp/OUvf2HQ\noEFMmTKF2bNnc9ddd7Fy5Uq++uorcnNzycvLY/369eTk5ODxeFiwYAETJkzguuuui9C7Io2lFpqI\nNLu0tDTatWvH22+/zbhx4/joo49ISEigtLQ01Er73e9+x5IlS0hPT+ecc87hnnvuoaKigksuuYRv\nv/2WHTt28Oqrr/Kzn/2MsrIyunTpwhdffMFpp51Gq1atcLlc7Nixg+3btzNy5EguuOACsrOzufzy\ny6mqqiIlJYVBgwaRkpLChx9+yG9+8xvuvvtuDhw4wK9+9Svuv//+CL9L9RKeFlpavmVAI1po76qF\nJiJR5KKLLuL000+vcxzcqHP79u1Mnz6dQYMGkZycTGVlJZ07d641prZv3z4yMzOZOHEit956K6ec\ncgrvvPMOS5cuxe12c/vtt3PvvffStWtXVq9eTVZWFsXFxWzbto0+ffqwdOlSKisreeWVV5gyZQr7\n9+/nnHPOIRAIkJCQwMKFC3n33Xeprq5m1KhRuN1uvF4vEydO5LTTTjtxt7Bx6EohSmgiEhYffvjh\nEXePGD9+PElJSVhrmT17NikpKVRUVGCtxePxhLoJs7Ky2LNnD2vWrMEYwzPPPMPatWvp2rUrSUlJ\nbNiwgQ0bNvDwww/z1Vdf0a5dOy699FKCwSDbt2/nnXfeIScnB5fLRUpKCtnZ2Tz//PN88sknWGvp\n3Lkzr7zyCllZWSQmJpKens6BAwfwer0MGzaM0aNH15lYcsLQpBARkfr5r//6L6ZPn47X6yUmJobK\nykp8Ph9+v5//+I//4O9//zsLFy6kW7duJCQkkJGRwddffx1aPiwtLY19+/YB0L59e7Zs2YK1lvz8\nfLZs2cL27dt58MEHmTJlCj6fj5iYGHJyciguLqaqqiqUPHv06MF3333Hjh07aNOmDWVlZaHnzjzz\nTBYvXhzhd+qYwtPl2Crfkt+IZP43dTmKSBQ6Utfj3LlzAdi/f39oqbabbrqJmJgYXnnlFbZu3UpG\nRgYPPvggJSUlrF69mg4dOpCcnAxAamoq1dXVeL1evvvuO9LS0jj55JNZsWIFu3fvBuDRRx/F7/dz\n7rnn4vf7GTRoEJWVlVhreeutt5g8eTKrVq0KbSj67bffcuqppxITE4Pf72fjxo08//zzEXvf5Pgp\noYlIWH344Yd8+eWXtbof3W53aAmriooKjDHcfffdJCcnEwgEKC8vZ8qUKdx1112kp6fj9/vZvXs3\n559/PtZaNmzYEFpGzFob2qvt4NgY1Kxt6Xa76dKlCwCvvvoqANdccw1XXHEFixcvJhAIsHTp0tBC\n29u2bWPcuHHExsbi8/kYN25caNfsE4pDx9C0UoiINIsPP/ywzmMFBQWsWrWKnTt3MmPGDLxeLyef\nfDIbNmzgoYceYvv27bRr1479+/czfvx4/vKXv+D3+0lISAhtZfOzn/2Mjz76KLQp6N69ewHIzs7G\nGMObb75Jenp6qHz79u2BmuXODpb79ttvqa6upqqqijfffBOv14vL5SIxMZE77riDrVu3MmHChOZ4\nm1qGgwnNYTSGJiIRM3/+fH72s59RXV1NmzZtiI2NpaysjKysLNLT09m6dSudO3dm7dq1xMfHU1pa\nisfjCe0kUV5eTlxcHD6fL7Qre9u2bSkpKQklvqqqKuLi4ujatSsrV67E5XLxox/9iE2bNpGcnMz+\n/fsZMmQIpaWlfPvtt1RVVXHgwAHi4+Pp1asXW7ZsYenSpaFE2MKEZwwtKd/SoxFjaEUaQxORE8xF\nF11ESkoKvXv3JjU1leLiYowxXHfddQwcOJDdu3ezZMkSqqurQy2tYDBIRUUFv/jFL7DW4vV6Q9P+\nKysr6dmzJ8FgEGstcXFxWGuprq5m//79oWvcSkpKAKisrCQhIYF//OMffP311yQkJHDgwIHQRJXz\nzz8fYwx9+/alZ8+e/POf/4zk29V8HLp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sWUIwGCQz02Grx7RS4d7g01rrN8aMBd6ldtr+n621q4wxk4Bia+0carsYk4GZ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\n5l//+lck366W49DtY5TQRCRiPB4Pf/z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v89qaNWvo3bs3I0eOZOvWrUGIzpiMNgWYJDohRMgqKyvjlltu4ZlnnqFVq1Y+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\nQXlJSQnTp08nPDwch8PB9OnTvauctyg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bu3Ytffr0YdSoUWzdujUI0RmT0aYAk0QnhAhZ5eXl3HrrrTzzzDPExcX5PNe3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\nrkJggg==\n", "text/plain": [ - "
" + "" ] }, "metadata": {}, diff --git a/tutorials/compressible_flow_with_automatic_differentiation.ipynb b/tutorials/compressible_flow_with_automatic_differentiation.ipynb index 64d06395fa..08e70f4015 100644 --- a/tutorials/compressible_flow_with_automatic_differentiation.ipynb +++ b/tutorials/compressible_flow_with_automatic_differentiation.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -98,9 +98,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Create grid\n", "g = pp.CartGrid([11,11])\n", @@ -130,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -157,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -184,9 +195,9 @@ "## Residual function\n", "To discretize the time-deriveative, we use backward Euler. Further, we assume that the densities are constant over each cell so we can take them out of the integral:\n", "$$\n", - "\\int_\\Omega \\phi \\frac{\\rho^k - \\rho^{k-1}}{\\Delta t} dV =\\phi \\frac{\\rho^k - \\rho^{k-1}}{\\Delta t} \\int_\\Omega dV = \\phi \\frac{\\rho^k - \\rho^{k-1}}{\\Delta t}V.\n", + "\\int_\\Omega \\phi \\frac{\\rho^k - \\rho^{k-1}}{\\Delta t} dV =\\phi \\frac{\\rho^k - \\rho^{k-1}}{\\Delta t} \\int_\\Omega dV = \\phi \\frac{\\rho^k - \\rho^{k-1}}{\\Delta t}V,\n", "$$\n", - "The same is also done for the source term.\n", + "where $V$ is the volume of the cell. The same is also done for the source term.\n", "\n", "This gives us the residual\n", "$$\n", @@ -196,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -227,14 +238,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Set initial condition\n", "p0 = np.zeros(g.num_cells)\n", - "p = ad.Ad_array(p0, sps.diags(np.ones(p0.shape)))\n", - "\n" + "p = ad.Ad_array(p0, sps.diags(np.ones(p0.shape)))" ] }, { @@ -247,9 +257,105 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving time step: 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rbe051/anaconda3/envs/porepy/lib/python3.6/site-packages/mpl_toolkits/mplot3d/axes3d.py:738: UserWarning: Attempting to set identical bottom==top results\n", + "in singular transformations; automatically expanding.\n", + "bottom=0.0, top=0.0\n", + " 'bottom=%s, top=%s') % (bottom, top))\n" + ] + }, + { + "data": { + "image/png": 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J3R7ZLbjW6F14pZQlo0aNIj8/P+h0Xq+Xbdu28cc//rEdatVx6qc1jmQaQIPm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\nH39MTU2Nfyi+6OhoduzYwYI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F9OnTO7KbHSrMs3J2CB2BKhWlfv3rXzN//nzq6+sZOnQozz77bFDlYmJiuPzy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\nCgoKAsuRI0eora0NXD13uVz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kJJdeeikdO3bEbg/vHlxT9Cq8UsqSIUOGkJ2dHfB2Xq+XLVu28Le//a0FWtV6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\n7+q50+mkffv2QPUoVf/xH//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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving time step: 4\n" + ] + }, + { + "data": { + "image/png": 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HFEAB5s+fz0svvcTBgweZN29eW1azTYVyVc62oi1QpSJQcnIy6enpfPPNNwBs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\nOHLkCElJSQCUlZWRn58P1M6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Solving time step: 5\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# define iteration parameters\n", "newton_tol = 1e-6\n", diff --git a/tutorials/tracer_transport.ipynb b/tutorials/tracer_transport.ipynb index 9fe8424790..fc7d4ddb73 100644 --- a/tutorials/tracer_transport.ipynb +++ b/tutorials/tracer_transport.ipynb @@ -74,7 +74,7 @@ " pp.initialize_default_data(g, d, parameter_keyword, specified_parameters)\n", " \n", " # Store the dimension in the dictionary for visualization purposes\n", - " d[\"dimension\"] = g.dim * np.ones(g.num_cells)\n", + " d[pp.STATE] = {\"dimension\": g.dim * np.ones(g.num_cells)}\n", " \n", " for e, d in gb.edges():\n", " d[pp.PARAMETERS].update_dictionaries(parameter_keyword, {})\n", @@ -94,24 +94,14 @@ "execution_count": 3, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/eke001/Dropbox/workspace/python/ppdir/src/porepy/utils/comp_geom.py:1168: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", - " all_pt = np.hstack((p, np.vstack((i for i in new_pts)).T))\n" - ] - }, { "data": { - "image/png": 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\n", 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RXFNwIXuF5oTC7n2Av1rLzgKNYxXwlXNcUKnAuBNQtf9Se+DnzrENmGgMG/M3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MXtCHgK37nYBTzhHF9EhhMHFfDYwewRRHH0jAQC+yvW0B12LtIJ4iIQwFxr7sGsVq+JUViA7UJc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" + "" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -179,16 +169,22 @@ "execution_count": 5, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rbe051/anaconda3/envs/porepy/lib/python3.6/site-packages/vtk/util/numpy_support.py:134: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n", + " assert not numpy.issubdtype(z.dtype, complex), \\\n" + ] + }, { "data": { - "image/png": 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\n", 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rr79O1apVWbduHSNGjGDUqFEh/bpKE1+uR7t27UhOTuann35i4MCB3HfffRGqNjx8uSYA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\nNGlSZOSmXr16NGjQgKeeegqA6dOnn9kWFBTEv//975r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" + "" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -285,7 +281,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/eke001/anaconda3/envs/pp/lib/python3.6/site-packages/scipy/sparse/linalg/dsolve/linsolve.py:296: SparseEfficiencyWarning: splu requires CSC matrix format\n", + "/home/rbe051/anaconda3/envs/porepy/lib/python3.6/site-packages/scipy/sparse/linalg/dsolve/linsolve.py:295: SparseEfficiencyWarning: splu requires CSC matrix format\n", " warn('splu requires CSC matrix format', SparseEfficiencyWarning)\n" ] } @@ -353,7 +349,16 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rbe051/anaconda3/envs/porepy/lib/python3.6/site-packages/vtk/util/numpy_support.py:134: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n", + " assert not numpy.issubdtype(z.dtype, complex), \\\n" + ] + } + ], "source": [ "for i in range(n_steps):\n", " \n", @@ -390,14 +395,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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6BAIBfvjhBzzPK3ObcEPEN998Q4cOHWjfvj0pKSn87ne/Y9y4cXtsM27cOK688koALrzw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3BVIYNJDmeCywVRhaxDujaEr24SBtybigBjQiQitm3JYWDbNNqcW4kbXm+A1oV7nsXGxlGlivVv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\n87w+wbQ+HQuup9qEy1AgQ2vWVnLcF1LSP0LRh5J4gFFK0R54WAj2lHgsB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VYAACAASURB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\n4QSEwxElXwB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SRwXg9DRlOvAb4E+tnHcYDa+nVbv9fBWwOkbO28eN0+35uzTlQyFwRwi0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\nYhO1JEmSJLXJBEKSJElS3ixhkiRJkjrEJmp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" + "" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -413,7 +416,7 @@ "source": [ "g = gb.grids_of_dimension(2)[0]\n", "data = gb.node_props(g)\n", - "assert np.allclose(data['tracer'][-1], 0.99089862)" + "assert np.allclose(data[pp.STATE]['tracer'][-1], 0.99089862)" ] } ], @@ -433,7 +436,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.3" } }, "nbformat": 4, From 379b7f9e3ed8b7a74181c1e7d37a104cd89946ce Mon Sep 17 00:00:00 2001 From: Runar Date: Mon, 1 Jul 2019 11:39:19 +0200 Subject: [PATCH 23/25] Black --- src/porepy/grids/coarsening.py | 1 + src/porepy/viz/plot_grid.py | 4 +++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/src/porepy/grids/coarsening.py b/src/porepy/grids/coarsening.py index cd25ef1207..1311c136a7 100644 --- a/src/porepy/grids/coarsening.py +++ b/src/porepy/grids/coarsening.py @@ -274,6 +274,7 @@ def generate_coarse_grid_gb(gb, subdiv): # update the map d["face_cells"] = face_cells.tocsc() + # ------------------------------------------------------------------------------# diff --git a/src/porepy/viz/plot_grid.py b/src/porepy/viz/plot_grid.py index da6134a1bf..c8a1c2d3ca 100644 --- a/src/porepy/viz/plot_grid.py +++ b/src/porepy/viz/plot_grid.py @@ -280,7 +280,9 @@ def plot_gb(gb, cell_value, vector_value, info, **kwargs): gb.assign_node_ordering() for g, d in gb: kwargs["rgb"] = np.divide(kwargs.get("rgb", [1, 0, 0]), d["node_number"] + 1) - plot_grid_xd(g, d[pp.STATE].get(cell_value), d[pp.STATE].get(vector_value), ax, **kwargs) + plot_grid_xd( + g, d[pp.STATE].get(cell_value), d[pp.STATE].get(vector_value), ax, **kwargs + ) val = np.array([lim(g.nodes) for g, _ in gb]) From b14e5908df7f47206b5fe059c41cb60bb39b46eb Mon Sep 17 00:00:00 2001 From: Runar Date: Tue, 2 Jul 2019 11:43:25 +0200 Subject: [PATCH 24/25] Fixed bug in matching of 2d grids. The new grid and old grid were rotated to the 2d-plane. However, the rotation was only defined up to the sign of the normal vector, as the normal vector was calculated seperatly for each grids. This resulted in some cases in grids that were matched "upside down" --- src/porepy/fracs/mortars.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/src/porepy/fracs/mortars.py b/src/porepy/fracs/mortars.py index 2023811aaa..1587a66607 100644 --- a/src/porepy/fracs/mortars.py +++ b/src/porepy/fracs/mortars.py @@ -300,8 +300,9 @@ def match_grids_2d(new_g, old_g, tol): """ - def proj_pts(p, cc): - rot = pp.map_geometry.project_plane_matrix(p - cc) + def proj_pts(p, cc, normal): + """ Project points to the 2d plane defined by normal and center them around cc""" + rot = pp.map_geometry.project_plane_matrix(p - cc, normal) return rot.dot(p - cc)[:2] shape = (new_g.dim + 1, new_g.num_cells) @@ -309,9 +310,18 @@ def proj_pts(p, cc): shape = (old_g.dim + 1, old_g.num_cells) cn_old_g = old_g.cell_nodes().indices.reshape(shape, order="F") + + # Center points around mean cc = np.mean(new_g.nodes, axis=1).reshape((3, 1)) + # Calculate common normal for both grids + n = pp.map_geometry.compute_normal(new_g.nodes - cc) + n_old = pp.map_geometry.compute_normal(old_g.nodes - cc) + if not (np.allclose(n, n_old) or np.allclose(n, -n_old)): + raise ValueError("The new and old grid must lie in the same plane") + + # Calculate intersection isect = pp.intersections.triangulations( - proj_pts(new_g.nodes, cc), proj_pts(old_g.nodes, cc), cn_new_g, cn_old_g + proj_pts(new_g.nodes, cc, n), proj_pts(old_g.nodes, cc, n), cn_new_g, cn_old_g ) num = len(isect) From 078fe92fac5f8836be3af2c2bf80be4fdc24ba22 Mon Sep 17 00:00:00 2001 From: Runar Date: Tue, 2 Jul 2019 12:37:21 +0200 Subject: [PATCH 25/25] Fixed test_fvutils after STATE was introduced --- test/integration/test_fvutils.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/test/integration/test_fvutils.py b/test/integration/test_fvutils.py index 757596e5f8..e715b5bf52 100644 --- a/test/integration/test_fvutils.py +++ b/test/integration/test_fvutils.py @@ -141,15 +141,16 @@ def test_compute_darcy_flux_mono_grid(self): bc_val = np.array([1, 2, 3, 4]) specified_parameters = {"bc_values": bc_val} data = pp.initialize_default_data(g, {}, "flow", specified_parameters) + data[pp.STATE] = {} matrix_dictionary = data[pp.DISCRETIZATION_MATRICES]["flow"] matrix_dictionary["flux"] = flux matrix_dictionary["bound_flux"] = bound_flux - data["pressure"] = np.array([3.14]) + data[pp.STATE]["pressure"] = np.array([3.14]) fvutils.compute_darcy_flux(g, data=data) dis = data[pp.PARAMETERS]["flow"]["darcy_flux"] - dis_true = flux * data["pressure"] + bound_flux * bc_val + dis_true = flux * data[pp.STATE]["pressure"] + bound_flux * bc_val self.assertTrue(np.allclose(dis, dis_true))