WellMet is pure Python framework for spatial structural reliability analysis. Or, more specifically, for "failure probability estimation and detection of failure surfaces by adaptive sequential decomposition of the design domain".
List of commits:
| Subject | Hash | Author | Date (UTC) |
|---|---|---|---|
| replace flatten() by reshape(-1) everywhere. The former always allocates new array | 97b35c989a5ad8e459bb60133222d50c7f78e1f0 | I am | 2023-01-15 12:04:30 |
| qt_gui.qt_gui: explicitly export sample_box to console's namespace | dd2e50245157082ddb645b693a8a8c573215d50c | I am | 2023-01-15 11:42:01 |
| replace everywhere np.bool by just bool (it looks like newer numpy dropped it) | ac208f89bbf0ad76d38790686041ea778a9b6376 | I am | 2023-01-15 11:31:56 |
| simplex._Triangulation: implement averaged gradients() | f6608597cf490dd6cf1a6af3b7d8e6d0a72732d9 | I am | 2023-01-15 09:37:14 |
| add line and two lines problems for sensitivity analysis | 0c620f7960399ed8b34e02d663772d0eb34e66f1 | I am | 2023-01-14 08:30:56 |
| simplex.FullCubatureIntegration.get_sensitivities: do not normalize global gradient | c648c484a5599a00236ab70c36cdf407c24f5306 | I am | 2023-01-14 04:51:16 |
| simplex: implement sensitivities | 65056319c51fd93deb76ce14e52c08869dfc04a2 | I am | 2023-01-13 11:27:46 |
| mplot.maxes: prepare GRaph plot | 68774535915af3deaf6104a0bbe9b3c4d24c0cec | I am | 2023-01-12 02:43:20 |
| mplot.mgraph.tri_estimation_plot: use planar vertex estimator | 1d300fa56248be9cdab5a4e5b0d4372f68fd5255 | I am | 2023-01-11 15:48:28 |
| mplot.mart: implement lsf_boundary() contouring | 8642d2f0c043f4e71af1cc45b9e25da326afd3ca | I am | 2023-01-11 05:20:00 |
| mplot.mart.setup_labels: make labels more adaptive | 60cd0b34a67b38df1c730dddc9e0771139818f33 | I am | 2023-01-11 04:35:47 |
| simplex._SamplingTriangulation: self reference fix | 79204ff15cba7c5ca24bb5b48e2073058acf00ae | I am | 2023-01-11 03:21:34 |
| testcases.testcases_2D: add pf for rastrigin | 4f3b35fe18496fcb9f59860174c876cb7a82749a | I am | 2023-01-10 23:39:45 |
| dicebox.circumtri: fix initialization on ED loading. Use simple potential for space filling | c02656e588a164685c4a750ce865c4b50f716563 | I am | 2023-01-10 23:37:30 |
| simplex.FullCubatureIntegration.get_failure_moments: negative probabilities fix | 27dd6bb1e5bb0eb1e28eef88e28913d1eca461da | I am | 2023-01-10 20:16:09 |
| add more nD problems from literature | 2380cdbdcf17dd5e0af19506c6f7bb1e838de03b | I am | 2023-01-10 06:13:56 |
| f_models: implement Norm class | 698c3216b1c8a0e2d57bfb33340a7ea8758d4e63 | I am | 2023-01-09 23:13:48 |
| simplex.FullCabatureIntegration: implement get_failure_moments() | 0742ee3d3932d7c3c0b6bbe6e327c92ced04aedd | I am | 2023-01-09 19:53:55 |
| simplex._Triangulation: add get_failure_moments() method | 3e362970b67a50643c1e486729efbf554cacf03f | I am | 2023-01-08 03:26:02 |
| simplex: use advanced indexing instead of isin function | 568c8623ad4cfaa343df05ae880d099556037cf0 | I am | 2023-01-06 13:41:00 |
Commit 97b35c989a5ad8e459bb60133222d50c7f78e1f0
- replace flatten() by reshape(-1) everywhere. The former always allocates new array
Author: I am
Author date (UTC): 2023-01-15 12:04
Committer name: I am
Committer date (UTC): 2023-01-15 12:04
Parent(s): dd2e50245157082ddb645b693a8a8c573215d50c
Signer:
Signing key:
Signing status: N
Tree: 68c84c3435c7df611a304d87fafc84a12216808c
Author: I am
Author date (UTC): 2023-01-15 12:04
Committer name: I am
Committer date (UTC): 2023-01-15 12:04
Parent(s): dd2e50245157082ddb645b693a8a8c573215d50c
Signer:
Signing key:
Signing status: N
Tree: 68c84c3435c7df611a304d87fafc84a12216808c
| File | Lines added | Lines deleted |
|---|---|---|
| wellmet/convex_hull.py | 2 | 2 |
| wellmet/dicebox/__circumtri.py | 1 | 1 |
| wellmet/dicebox/_circumtri.py | 1 | 1 |
| wellmet/dicebox/censoring.py | 6 | 6 |
| wellmet/dicebox/circumtri.py | 2 | 2 |
| wellmet/dicebox/goal.py | 4 | 4 |
| wellmet/estimation.py | 5 | 5 |
| wellmet/f_models.py | 1 | 1 |
| wellmet/mplot/mart.py | 3 | 3 |
| wellmet/mplot/mart3d.py | 4 | 4 |
| wellmet/qt_gui/gl_plot.py | 1 | 1 |
| wellmet/qt_gui/qt_plot.py | 1 | 1 |
| wellmet/reader.py | 2 | 2 |
| wellmet/samplebox.py | 4 | 2 |
| wellmet/voronoi.py | 4 | 5 |
| File wellmet/convex_hull.py changed (mode: 100644) (index 0b596c2..4fd7d7e) | |||
| ... | ... | class DirectHull: | |
| 636 | 636 | # N=ns, E - number of hyperplane equations | # N=ns, E - number of hyperplane equations |
| 637 | 637 | ExN = A @ x.T + np.atleast_2d(b).T | ExN = A @ x.T + np.atleast_2d(b).T |
| 638 | 638 | i = np.argmax(ExN, axis=0) | i = np.argmax(ExN, axis=0) |
| 639 | d = np.take_along_axis(ExN, np.atleast_2d(i), axis=0).flatten() | ||
| 639 | d = np.take_along_axis(ExN, np.atleast_2d(i), axis=0).reshape(-1) | ||
| 640 | 640 | return d, i | return d, i |
| 641 | 641 | ||
| 642 | 642 | def is_inside(hull, nodes): | def is_inside(hull, nodes): |
| ... | ... | class QHull: | |
| 906 | 906 | # N=ns, E - number of hyperplane equations | # N=ns, E - number of hyperplane equations |
| 907 | 907 | ExN = A @ x.T + np.atleast_2d(b).T | ExN = A @ x.T + np.atleast_2d(b).T |
| 908 | 908 | i = np.argmax(ExN, axis=0) | i = np.argmax(ExN, axis=0) |
| 909 | d = np.take_along_axis(ExN, np.atleast_2d(i), axis=0).flatten() | ||
| 909 | d = np.take_along_axis(ExN, np.atleast_2d(i), axis=0).reshape(-1) | ||
| 910 | 910 | return d, i | return d, i |
| 911 | 911 | else: | else: |
| 912 | 912 | return DirectHull(self.sample, self.fallback_plan, self.space).query(nodes) | return DirectHull(self.sample, self.fallback_plan, self.space).query(nodes) |
| File wellmet/dicebox/__circumtri.py changed (mode: 100644) (index b4b44a3..4d820fc) | |||
| ... | ... | class CircumTri(_Exploration): | |
| 255 | 255 | #else: | #else: |
| 256 | 256 | ||
| 257 | 257 | nodes_model = getattr(nodes, bx.tri_space) | nodes_model = getattr(nodes, bx.tri_space) |
| 258 | dr = distance.cdist(nodes_model, [circum_center]).flatten() | ||
| 258 | dr = distance.cdist(nodes_model, [circum_center]).reshape(-1) | ||
| 259 | 259 | nodes_pdf = nodes.pdf(bx.tri_space) | nodes_pdf = nodes.pdf(bx.tri_space) |
| 260 | 260 | node_potentials = (r - dr) * nodes_pdf**(1/bx.nvar) | node_potentials = (r - dr) * nodes_pdf**(1/bx.nvar) |
| 261 | 261 | ||
| File wellmet/dicebox/_circumtri.py changed (mode: 100644) (index a11e0fb..5f94305) | |||
| ... | ... | class CircumTri(_Exploration): | |
| 235 | 235 | if np.any(mask): | if np.any(mask): |
| 236 | 236 | nodes = nodes[mask] | nodes = nodes[mask] |
| 237 | 237 | ||
| 238 | dr = distance.cdist(nodes.G, [circum_center]).flatten() | ||
| 238 | dr = distance.cdist(nodes.G, [circum_center]).reshape(-1) | ||
| 239 | 239 | nodes_pdf = nodes.pdf('G') | nodes_pdf = nodes.pdf('G') |
| 240 | 240 | node_potentials = (r - dr) * nodes_pdf**(1/bx.nvar) | node_potentials = (r - dr) * nodes_pdf**(1/bx.nvar) |
| 241 | 241 | ||
| File wellmet/dicebox/censoring.py changed (mode: 100644) (index 3d245b8..1df3764) | |||
| ... | ... | class BlackBox: | |
| 218 | 218 | ||
| 219 | 219 | LHS_node = np.empty(bx.nvar, dtype=float) | LHS_node = np.empty(bx.nvar, dtype=float) |
| 220 | 220 | for i in range(bx.nvar): | for i in range(bx.nvar): |
| 221 | if to_sample_node.U.flatten()[i] <= bx.sorted_plan_U[i][0]: | ||
| 221 | if to_sample_node.U.reshape(-1)[i] <= bx.sorted_plan_U[i][0]: | ||
| 222 | 222 | LHS_node[i] = (bx.sorted_plan_U[i][0] + bx.sorted_plan_U[i][1]) / 2 | LHS_node[i] = (bx.sorted_plan_U[i][0] + bx.sorted_plan_U[i][1]) / 2 |
| 223 | elif to_sample_node.U.flatten()[i] >= bx.sorted_plan_U[i][-1]: | ||
| 223 | elif to_sample_node.U.reshape(-1)[i] >= bx.sorted_plan_U[i][-1]: | ||
| 224 | 224 | LHS_node[i] = (bx.sorted_plan_U[i][-2] + bx.sorted_plan_U[i][-1]) / 2 | LHS_node[i] = (bx.sorted_plan_U[i][-2] + bx.sorted_plan_U[i][-1]) / 2 |
| 225 | 225 | else: | else: |
| 226 | plan_index = np.searchsorted(bx.sorted_plan_U[i], to_sample_node.U.flatten()[i]) | ||
| 226 | plan_index = np.searchsorted(bx.sorted_plan_U[i], to_sample_node.U.reshape(-1)[i]) | ||
| 227 | 227 | # vzdy | # vzdy |
| 228 | 228 | LHS_node[i] = (bx.sorted_plan_U[i][plan_index] + bx.sorted_plan_U[i][plan_index - 1]) / 2 | LHS_node[i] = (bx.sorted_plan_U[i][plan_index] + bx.sorted_plan_U[i][plan_index - 1]) / 2 |
| 229 | 229 | ||
| ... | ... | class MinEnergyCensoredSampling(Censoring): | |
| 885 | 885 | # zkontrolujeme co se změnilo | # zkontrolujeme co se změnilo |
| 886 | 886 | # předpokladám, že se počet simplexů přidaním bodů nezměnší | # předpokladám, že se počet simplexů přidaním bodů nezměnší |
| 887 | 887 | equal_mask = former_simplices == bx.tri.simplices[:len(former_simplices)] | equal_mask = former_simplices == bx.tri.simplices[:len(former_simplices)] |
| 888 | changed_simplices_ids = np.argwhere(~equal_mask.all(axis=1)).flatten() | ||
| 888 | changed_simplices_ids = np.argwhere(~equal_mask.all(axis=1)).reshape(-1) | ||
| 889 | 889 | ||
| 890 | 890 | # invalidirujeme jejich odhady | # invalidirujeme jejich odhady |
| 891 | 891 | for simplex_id in changed_simplices_ids: | for simplex_id in changed_simplices_ids: |
| ... | ... | class OptimizedCensoredSampling(MinEnergyCensoredSampling): | |
| 1295 | 1295 | # zkontrolujeme co se změnilo | # zkontrolujeme co se změnilo |
| 1296 | 1296 | # předpokladám, že se počet simplexů přidaním bodů nezměnší | # předpokladám, že se počet simplexů přidaním bodů nezměnší |
| 1297 | 1297 | equal_mask = former_simplices == bx.tri.simplices[:len(former_simplices)] | equal_mask = former_simplices == bx.tri.simplices[:len(former_simplices)] |
| 1298 | changed_simplices_ids = np.argwhere(~equal_mask.all(axis=1)).flatten() | ||
| 1298 | changed_simplices_ids = np.argwhere(~equal_mask.all(axis=1)).reshape(-1) | ||
| 1299 | 1299 | ||
| 1300 | 1300 | # invalidirujeme jejich odhady | # invalidirujeme jejich odhady |
| 1301 | 1301 | for simplex_id in changed_simplices_ids: | for simplex_id in changed_simplices_ids: |
| ... | ... | class BlackSimpleX(OptimizedCensoredSampling): | |
| 1505 | 1505 | # zkontrolujeme co se změnilo | # zkontrolujeme co se změnilo |
| 1506 | 1506 | # předpokladám, že se počet simplexů přidaním bodů nezměnší | # předpokladám, že se počet simplexů přidaním bodů nezměnší |
| 1507 | 1507 | equal_mask = former_simplices == bx.tri.simplices[:len(former_simplices)] | equal_mask = former_simplices == bx.tri.simplices[:len(former_simplices)] |
| 1508 | changed_simplices_ids = np.argwhere(~equal_mask.all(axis=1)).flatten() | ||
| 1508 | changed_simplices_ids = np.argwhere(~equal_mask.all(axis=1)).reshape(-1) | ||
| 1509 | 1509 | ||
| 1510 | 1510 | ||
| 1511 | 1511 | #č popajem pouze mixy, ty musel jsem spočítat před aktualizací | #č popajem pouze mixy, ty musel jsem spočítat před aktualizací |
| File wellmet/dicebox/circumtri.py changed (mode: 100644) (index 55bd26d..e113932) | |||
| ... | ... | class FullCircumTri(_Exploration): | |
| 217 | 217 | ||
| 218 | 218 | # max_node_potential = -np.inf | # max_node_potential = -np.inf |
| 219 | 219 | # if not bx.circumcenters_only: | # if not bx.circumcenters_only: |
| 220 | # dr = distance.cdist(nodes.G, [circum_center]).flatten() | ||
| 220 | # dr = distance.cdist(nodes.G, [circum_center]).reshape(-1) | ||
| 221 | 221 | # nodes_pdf = nodes.pdf(bx.tri_space) | # nodes_pdf = nodes.pdf(bx.tri_space) |
| 222 | 222 | # node_potentials = (r - dr)**bx.nvar * nodes_pdf#**bx.psi_q * PDF[i]**(1-bx.psi_q) | # node_potentials = (r - dr)**bx.nvar * nodes_pdf#**bx.psi_q * PDF[i]**(1-bx.psi_q) |
| 223 | 223 | # | # |
| ... | ... | class FullCircumTri(_Exploration): | |
| 280 | 280 | if np.any(mask): | if np.any(mask): |
| 281 | 281 | nodes = nodes[mask] | nodes = nodes[mask] |
| 282 | 282 | ||
| 283 | dr = distance.cdist(nodes.G, [circum_center]).flatten() | ||
| 283 | dr = distance.cdist(nodes.G, [circum_center]).reshape(-1) | ||
| 284 | 284 | nodes_pdf = nodes.pdf(bx.tri_space) | nodes_pdf = nodes.pdf(bx.tri_space) |
| 285 | 285 | #č nejdřív rouška, pak slice. V tomto pořadí numpy to zvladné bez alokací | #č nejdřív rouška, pak slice. V tomto pořadí numpy to zvladné bez alokací |
| 286 | 286 | i = ii[mask][:,0] | i = ii[mask][:,0] |
| File wellmet/dicebox/goal.py changed (mode: 100644) (index b686600..fd1981f) | |||
| ... | ... | class DiceBox: | |
| 249 | 249 | ||
| 250 | 250 | LHS_node = np.empty(bx.nvar, dtype=float) | LHS_node = np.empty(bx.nvar, dtype=float) |
| 251 | 251 | for i in range(bx.nvar): | for i in range(bx.nvar): |
| 252 | if to_sample_node.U.flatten()[i] <= bx.sorted_plan_U[i][0]: | ||
| 252 | if to_sample_node.U.reshape(-1)[i] <= bx.sorted_plan_U[i][0]: | ||
| 253 | 253 | LHS_node[i] = (bx.sorted_plan_U[i][0] + bx.sorted_plan_U[i][1]) / 2 | LHS_node[i] = (bx.sorted_plan_U[i][0] + bx.sorted_plan_U[i][1]) / 2 |
| 254 | elif to_sample_node.U.flatten()[i] >= bx.sorted_plan_U[i][-1]: | ||
| 254 | elif to_sample_node.U.reshape(-1)[i] >= bx.sorted_plan_U[i][-1]: | ||
| 255 | 255 | LHS_node[i] = (bx.sorted_plan_U[i][-2] + bx.sorted_plan_U[i][-1]) / 2 | LHS_node[i] = (bx.sorted_plan_U[i][-2] + bx.sorted_plan_U[i][-1]) / 2 |
| 256 | 256 | else: | else: |
| 257 | plan_index = np.searchsorted(bx.sorted_plan_U[i], to_sample_node.U.flatten()[i]) | ||
| 257 | plan_index = np.searchsorted(bx.sorted_plan_U[i], to_sample_node.U.reshape(-1)[i]) | ||
| 258 | 258 | # vzdy | # vzdy |
| 259 | 259 | LHS_node[i] = (bx.sorted_plan_U[i][plan_index] + bx.sorted_plan_U[i][plan_index - 1]) / 2 | LHS_node[i] = (bx.sorted_plan_U[i][plan_index] + bx.sorted_plan_U[i][plan_index - 1]) / 2 |
| 260 | 260 | ||
| ... | ... | class DiceSimpleX: | |
| 1740 | 1740 | # zkontrolujeme co se změnilo | # zkontrolujeme co se změnilo |
| 1741 | 1741 | # předpokladám, že se počet simplexů přidaním bodů nezměnší | # předpokladám, že se počet simplexů přidaním bodů nezměnší |
| 1742 | 1742 | equal_mask = former_simplices == bx.tri.simplices[:len(former_simplices)] | equal_mask = former_simplices == bx.tri.simplices[:len(former_simplices)] |
| 1743 | changed_simplices_ids = np.argwhere(~equal_mask.all(axis=1)).flatten() | ||
| 1743 | changed_simplices_ids = np.argwhere(~equal_mask.all(axis=1)).reshape(-1) | ||
| 1744 | 1744 | ||
| 1745 | 1745 | ||
| 1746 | 1746 | #č popajem pouze mixy, ty musel jsem spočítat před aktualizací | #č popajem pouze mixy, ty musel jsem spočítat před aktualizací |
| File wellmet/estimation.py changed (mode: 100644) (index 0f2452b..da765e2) | |||
| ... | ... | def Voronoi_tesselation(sample_box, model_space='Rn', sampling_space=None, p_nor | |
| 162 | 162 | ||
| 163 | 163 | # find distance to the nearest sampling point (from all points) | # find distance to the nearest sampling point (from all points) |
| 164 | 164 | dd2, ii2 = tree_sampling.query(sampled_plan_sing[failure_points], k=[2], p=p_norm) | dd2, ii2 = tree_sampling.query(sampled_plan_sing[failure_points], k=[2], p=p_norm) |
| 165 | mindist_sing = dd2.flatten() | ||
| 166 | #ii2 = ii2.flatten() | ||
| 165 | mindist_sing = dd2.reshape(-1) | ||
| 166 | #ii2 = ii2.reshape(-1) | ||
| 167 | 167 | ||
| 168 | 168 | ||
| 169 | 169 | ||
| ... | ... | def Voronoi_2_point_estimation(sample_box, model_space='Rn', sampling_space=None | |
| 268 | 268 | ||
| 269 | 269 | # find distance to the nearest sampling point (from all points) | # find distance to the nearest sampling point (from all points) |
| 270 | 270 | dd2, ii2 = tree_sampling.query(sampled_plan_sing, k=[2], p=p_norm) | dd2, ii2 = tree_sampling.query(sampled_plan_sing, k=[2], p=p_norm) |
| 271 | mindist_sing = dd2.flatten() | ||
| 271 | mindist_sing = dd2.reshape(-1) | ||
| 272 | 272 | ||
| 273 | 273 | # chcu, aby behem první iterace pulka (p_base) tečiček jistě dopadla do buňky | # chcu, aby behem první iterace pulka (p_base) tečiček jistě dopadla do buňky |
| 274 | 274 | base_r = sball.Sball(sample_box.nvar).get_r(1 - p_base) | base_r = sball.Sball(sample_box.nvar).get_r(1 - p_base) |
| ... | ... | def Voronoi_2_point_estimation(sample_box, model_space='Rn', sampling_space=None | |
| 411 | 411 | ||
| 412 | 412 | ||
| 413 | 413 | dd2, ii2 = tree.query(h_plan_model_ma, k=[2], p=p_norm) | dd2, ii2 = tree.query(h_plan_model_ma, k=[2], p=p_norm) |
| 414 | dd2 = dd2.flatten() | ||
| 415 | ii2 = ii2.flatten() | ||
| 414 | dd2 = dd2.reshape(-1) | ||
| 415 | ii2 = ii2.reshape(-1) | ||
| 416 | 416 | ||
| 417 | 417 | # tahle hračka s indexy je pro numpy poměrně drahá | # tahle hračka s indexy je pro numpy poměrně drahá |
| 418 | 418 | failsii_2 = failsi[ii2] | failsii_2 = failsi[ii2] |
| File wellmet/f_models.py changed (mode: 100644) (index 6f430b0..a255b14) | |||
| ... | ... | class WellMetError(ValueError): | |
| 19 | 19 | ||
| 20 | 20 | # alpha atribut beztak veřejný, ale tato funkce ještě provadí normalizaci a kontrolu | # alpha atribut beztak veřejný, ale tato funkce ještě provadí normalizaci a kontrolu |
| 21 | 21 | def set_alpha(f_model, input_alpha): | def set_alpha(f_model, input_alpha): |
| 22 | alpha = np.atleast_1d(input_alpha).flatten() | ||
| 22 | alpha = np.atleast_1d(input_alpha).reshape(-1) | ||
| 23 | 23 | if len(alpha) != f_model.nvar: | if len(alpha) != f_model.nvar: |
| 24 | 24 | raise WellMetError | raise WellMetError |
| 25 | 25 | else: | else: |
| File wellmet/mplot/mart.py changed (mode: 100644) (index 061ff90..a621ed0) | |||
| ... | ... | def rbf_density_colormesh(ax, ngrid=500, **kwargs): | |
| 380 | 380 | y = np.linspace(ymin, ymax, ngrid, endpoint=True) | y = np.linspace(ymin, ymax, ngrid, endpoint=True) |
| 381 | 381 | X, Y = np.meshgrid(x, y) | X, Y = np.meshgrid(x, y) |
| 382 | 382 | ||
| 383 | XY = np.vstack((X.flatten(), Y.flatten())).T | ||
| 383 | XY = np.vstack((X.reshape(-1), Y.reshape(-1))).T | ||
| 384 | 384 | z = ax.sample_box.sample_pdf(XY, ax.space) | z = ax.sample_box.sample_pdf(XY, ax.space) |
| 385 | 385 | Z = z.reshape(ngrid, ngrid) | Z = z.reshape(ngrid, ngrid) |
| 386 | 386 | ||
| ... | ... | def lsf_boundary(ax, ngrid=200, limits=None, **kwargs): | |
| 472 | 472 | x = np.linspace(xmin, xmax, ngrid) | x = np.linspace(xmin, xmax, ngrid) |
| 473 | 473 | y = np.linspace(ymin, ymax, ngrid) | y = np.linspace(ymin, ymax, ngrid) |
| 474 | 474 | X, Y = np.meshgrid(x, y) | X, Y = np.meshgrid(x, y) |
| 475 | XY = np.vstack((X.flatten(), Y.flatten())).T | ||
| 475 | XY = np.vstack((X.reshape(-1), Y.reshape(-1))).T | ||
| 476 | 476 | sb = ax.sample_box.gm(ax.sample_box.f_model.new_sample(XY, space=ax.space)) | sb = ax.sample_box.gm(ax.sample_box.f_model.new_sample(XY, space=ax.space)) |
| 477 | 477 | Z = sb.g_values | Z = sb.g_values |
| 478 | 478 | return ax.contour(X, Y, Z.reshape(ngrid, ngrid), np.zeros(1), **kwargs) | return ax.contour(X, Y, Z.reshape(ngrid, ngrid), np.zeros(1), **kwargs) |
| ... | ... | def isocurves(ax, ngrid=200, limits=None, ncurves=5, **kwargs): | |
| 496 | 496 | x = np.linspace(xmin, xmax, ngrid) | x = np.linspace(xmin, xmax, ngrid) |
| 497 | 497 | y = np.linspace(ymin, ymax, ngrid) | y = np.linspace(ymin, ymax, ngrid) |
| 498 | 498 | X, Y = np.meshgrid(x, y) | X, Y = np.meshgrid(x, y) |
| 499 | XY = np.vstack((X.flatten(), Y.flatten())).T | ||
| 499 | XY = np.vstack((X.reshape(-1), Y.reshape(-1))).T | ||
| 500 | 500 | Z = ax.sample_box.f_model.sample_pdf(XY, ax.space) | Z = ax.sample_box.f_model.sample_pdf(XY, ax.space) |
| 501 | 501 | ||
| 502 | 502 | ||
| File wellmet/mplot/mart3d.py changed (mode: 100644) (index 10c45eb..56de7bf) | |||
| ... | ... | def get_g_model_wireframe_data(shape_share, limits, space='R', ngrid=50): | |
| 35 | 35 | x = np.linspace(xmin, xmax, ngrid) | x = np.linspace(xmin, xmax, ngrid) |
| 36 | 36 | y = np.linspace(ymin, ymax, ngrid) | y = np.linspace(ymin, ymax, ngrid) |
| 37 | 37 | X, Y = np.meshgrid(x, y) | X, Y = np.meshgrid(x, y) |
| 38 | XY = np.vstack((X.flatten(), Y.flatten())).T | ||
| 38 | XY = np.vstack((X.reshape(-1), Y.reshape(-1))).T | ||
| 39 | 39 | ||
| 40 | 40 | if space == 'R': | if space == 'R': |
| 41 | 41 | g_values = shape_share.gm(XY).g_values | g_values = shape_share.gm(XY).g_values |
| ... | ... | def density_surface(ax3d, **kwargs): | |
| 277 | 277 | x = np.linspace(xmin, xmax, ngrid) | x = np.linspace(xmin, xmax, ngrid) |
| 278 | 278 | y = np.linspace(ymin, ymax, ngrid) | y = np.linspace(ymin, ymax, ngrid) |
| 279 | 279 | X, Y = np.meshgrid(x, y) | X, Y = np.meshgrid(x, y) |
| 280 | XY = np.vstack((X.flatten(), Y.flatten())).T | ||
| 280 | XY = np.vstack((X.reshape(-1), Y.reshape(-1))).T | ||
| 281 | 281 | z = ax3d.sample_box.sample_pdf(XY, ax3d.space) | z = ax3d.sample_box.sample_pdf(XY, ax3d.space) |
| 282 | 282 | Z = z.reshape(ngrid, ngrid) | Z = z.reshape(ngrid, ngrid) |
| 283 | 283 | ||
| ... | ... | def density_colored_surface(ax3d, hull, colors=('#7735C2', '#808080'), **kwargs) | |
| 298 | 298 | x = np.linspace(xmin, xmax, ngrid) | x = np.linspace(xmin, xmax, ngrid) |
| 299 | 299 | y = np.linspace(ymin, ymax, ngrid) | y = np.linspace(ymin, ymax, ngrid) |
| 300 | 300 | X, Y = np.meshgrid(x, y) | X, Y = np.meshgrid(x, y) |
| 301 | XY = np.vstack((X.flatten(), Y.flatten())).T | ||
| 301 | XY = np.vstack((X.reshape(-1), Y.reshape(-1))).T | ||
| 302 | 302 | z = ax3d.sample_box.sample_pdf(XY, ax3d.space) | z = ax3d.sample_box.sample_pdf(XY, ax3d.space) |
| 303 | 303 | Z = z.reshape(ngrid, ngrid) | Z = z.reshape(ngrid, ngrid) |
| 304 | 304 | ||
| ... | ... | def density_colored_surface(ax3d, hull, colors=('#7735C2', '#808080'), **kwargs) | |
| 306 | 306 | _x = np.linspace(xmin, xmax, ngrid-1, endpoint=False) + (xmax-xmin)/(ngrid-1)/2 | _x = np.linspace(xmin, xmax, ngrid-1, endpoint=False) + (xmax-xmin)/(ngrid-1)/2 |
| 307 | 307 | _y = np.linspace(ymin, ymax, ngrid-1, endpoint=False) + (ymax-ymin)/(ngrid-1)/2 | _y = np.linspace(ymin, ymax, ngrid-1, endpoint=False) + (ymax-ymin)/(ngrid-1)/2 |
| 308 | 308 | _X, _Y = np.meshgrid(_x, _y) | _X, _Y = np.meshgrid(_x, _y) |
| 309 | _XY = np.vstack((_X.flatten(), _Y.flatten())).T | ||
| 309 | _XY = np.vstack((_X.reshape(-1), _Y.reshape(-1))).T | ||
| 310 | 310 | facecolors = np.full(len(_XY), inside_color) | facecolors = np.full(len(_XY), inside_color) |
| 311 | 311 | mask = hull.is_outside(hull.sample.f_model.new_sample(_XY, space=ax3d.space)) | mask = hull.is_outside(hull.sample.f_model.new_sample(_XY, space=ax3d.space)) |
| 312 | 312 | facecolors[mask] = outside_color | facecolors[mask] = outside_color |
| File wellmet/qt_gui/gl_plot.py changed (mode: 100644) (index fd6775f..1d492ac) | |||
| ... | ... | class ConvexHull(gl.GLMeshItem): | |
| 343 | 343 | ## dd, ii = tree.query(self.points, k=1, p=2) | ## dd, ii = tree.query(self.points, k=1, p=2) |
| 344 | 344 | ## | ## |
| 345 | 345 | ## self.simplices = self.w.sample_box.convex_hull.simplices | ## self.simplices = self.w.sample_box.convex_hull.simplices |
| 346 | ## box_facets = ii[self.simplices.flatten()].reshape(-1, 3) | ||
| 346 | ## box_facets = ii[self.simplices.reshape(-1)].reshape(-1, 3) | ||
| 347 | 347 | ## self.events = self.w.sample_box.get_events(box_facets) | ## self.events = self.w.sample_box.get_events(box_facets) |
| 348 | 348 | ## | ## |
| 349 | 349 | ## | ## |
| File wellmet/qt_gui/qt_plot.py changed (mode: 100644) (index ba85bfd..2c2c2fa) | |||
| ... | ... | class Triangulation: | |
| 802 | 802 | else: | else: |
| 803 | 803 | #č zkontrolujeme co se změnilo | #č zkontrolujeme co se změnilo |
| 804 | 804 | equal_mask = former_simplices == self.simplices[:len(former_simplices)] | equal_mask = former_simplices == self.simplices[:len(former_simplices)] |
| 805 | changed_simplices_ids = np.argwhere(~equal_mask.all(axis=1)).flatten() | ||
| 805 | changed_simplices_ids = np.argwhere(~equal_mask.all(axis=1)).reshape(-1) | ||
| 806 | 806 | self.draw_simplices(changed_simplices_ids) | self.draw_simplices(changed_simplices_ids) |
| 807 | 807 | ||
| 808 | 808 | #č teď nové simplexy | #č teď nové simplexy |
| File wellmet/reader.py changed (mode: 100644) (index eba49da..f1379a8) | |||
| ... | ... | def reader(filename, f_model=None): | |
| 237 | 237 | ||
| 238 | 238 | if f_model is None: | if f_model is None: |
| 239 | 239 | # veškeré datové řadky, sloupy - od (včetně) do (nezahrnuje) | # veškeré datové řadky, sloupy - od (včetně) do (nezahrnuje) |
| 240 | return SampleBox(Ingot(data[:,:-2]), data[:,-2:-1].flatten(), rows[0][0]) | ||
| 240 | return SampleBox(Ingot(data[:,:-2]), data[:,-2:-1].reshape(-1), rows[0][0]) | ||
| 241 | 241 | else: | else: |
| 242 | 242 | sample = f_model() | sample = f_model() |
| 243 | 243 | sample.add_sample(data[:,:-2]) | sample.add_sample(data[:,:-2]) |
| 244 | return SampleBox(sample, data[:,-2:-1].flatten(), rows[0][0]) | ||
| 244 | return SampleBox(sample, data[:,-2:-1].reshape(-1), rows[0][0]) | ||
| 245 | 245 | ||
| 246 | 246 | ||
| 247 | 247 | ||
| File wellmet/samplebox.py changed (mode: 100644) (index a0e0b4e..e6e7459) | |||
| ... | ... | class SampleBox: | |
| 73 | 73 | # ~(g_values>0) to handle nan | # ~(g_values>0) to handle nan |
| 74 | 74 | return ~(sb.g_values>0) | return ~(sb.g_values>0) |
| 75 | 75 | elif attr == 'success_points': | elif attr == 'success_points': |
| 76 | return np.argwhere(sb.g_values>0).flatten() | ||
| 76 | return np.argwhere(sb.g_values>0).reshape(-1) | ||
| 77 | 77 | elif attr == 'failure_points': | elif attr == 'failure_points': |
| 78 | return np.argwhere(~(sb.g_values>0)).flatten() | ||
| 78 | return np.argwhere(~(sb.g_values>0)).reshape(-1) | ||
| 79 | 79 | elif attr == 'failure_samples': | elif attr == 'failure_samples': |
| 80 | 80 | return sb[~(sb.g_values>0)] | return sb[~(sb.g_values>0)] |
| 81 | 81 | elif attr == 'success_samples': | elif attr == 'success_samples': |
| ... | ... | class SampleBox: | |
| 108 | 108 | return sb.consistency_check() | return sb.consistency_check() |
| 109 | 109 | else: | else: |
| 110 | 110 | #raise ValueError("Merge sa nám nějak nepovedol") | #raise ValueError("Merge sa nám nějak nepovedol") |
| 111 | print(sb.gm_signature, input_sb.gm_signature) | ||
| 112 | print(type(sb.gm_signature), type(input_sb.gm_signature)) | ||
| 111 | 113 | raise ValueError("gm_signatures are unequal. You are probably trying to merge data from different sources") | raise ValueError("gm_signatures are unequal. You are probably trying to merge data from different sources") |
| 112 | 114 | ||
| 113 | 115 | def new_sample(sb, input_sb): | def new_sample(sb, input_sb): |
| File wellmet/voronoi.py changed (mode: 100644) (index 9a12bdd..655f090) | |||
| ... | ... | from . import IS_stat | |
| 12 | 12 | from .candynodes import CandyNodes | from .candynodes import CandyNodes |
| 13 | 13 | from . import sball | from . import sball |
| 14 | 14 | ||
| 15 | ##č (jednotka dočasnosti - jeden furt) | ||
| 15 | |||
| 16 | 16 | ||
| 17 | 17 | ||
| 18 | 18 | ||
| ... | ... | class ContactVoronoi: | |
| 510 | 510 | č 3. sqrt(sum((w_i - w_mean)**2)) / n Směrodatná odchylka odhadu w_mean | č 3. sqrt(sum((w_i - w_mean)**2)) / n Směrodatná odchylka odhadu w_mean |
| 511 | 511 | """ | """ |
| 512 | 512 | def __init__(self, sample_box, hull, model_space='G', \ | def __init__(self, sample_box, hull, model_space='G', \ |
| 513 | p_norm=2, ns=1000, p_base=0.5, auto_update=True, workers=1,\ | ||
| 513 | ns=1000, p_base=0.5, auto_update=True, workers=1,\ | ||
| 514 | 514 | on_add_mixed=None, on_update_mixed=None, on_delete_mixed=None): | on_add_mixed=None, on_update_mixed=None, on_delete_mixed=None): |
| 515 | 515 | self.sample_box = sample_box | self.sample_box = sample_box |
| 516 | 516 | self.hull = hull | self.hull = hull |
| 517 | 517 | self.model_space = model_space | self.model_space = model_space |
| 518 | self.p_norm = p_norm | ||
| 519 | 518 | self.ns = ns | self.ns = ns |
| 520 | 519 | ||
| 521 | 520 | self.auto_update = auto_update | self.auto_update = auto_update |
| ... | ... | class ContactVoronoi: | |
| 1084 | 1083 | imask = nodes.imask | imask = nodes.imask |
| 1085 | 1084 | d2 = nodes.d2 | d2 = nodes.d2 |
| 1086 | 1085 | ||
| 1087 | di1 = cdist(nodes_model[imask], np.atleast_2d(self.points[i])).flatten() | ||
| 1088 | dj1 = cdist(nodes_model[~imask], np.atleast_2d(self.points[j])).flatten() | ||
| 1086 | di1 = cdist(nodes_model[imask], np.atleast_2d(self.points[i])).reshape(-1) | ||
| 1087 | dj1 = cdist(nodes_model[~imask], np.atleast_2d(self.points[j])).reshape(-1) | ||
| 1089 | 1088 | ||
| 1090 | 1089 | d1 = np.empty(len(imask)) | d1 = np.empty(len(imask)) |
| 1091 | 1090 | d1[imask] = di1 | d1[imask] = di1 |
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