mathLab
Fix Tutorial Export + Add Dynamic Tests
Description
This PR fixes #587 .
Detail description
-
Updated
deployer.yml
Export tutorials in HTML format. Each month, when the documentation is built, tutorials are automatically built alongside the docs. This removes the need to store tutorials in the repository, reducing repo size and speeding up clones. -
Updated
tester.yml
Added a step to dynamically generate placeholder tutorials for the documentation build.
This ensures that documentation can be successfully tested even when the actual tutorials haven’t been exported yet, while keeping tutorial execution checks separate. -
Added a monthly tutorial test workflow
Ensures that all tutorials run successfully, catching errors early. It also works on workflow_dispatch, so it can be used for PR. -
Added a comment-triggered tutorial export workflow
By commenting/tutorial-exporter [tutorial number]on a PR, the corresponding Jupyter notebook can be exported to a.pyfile. This is useful for exporting tutorials when creating a new PR.
Checklist
- [x] Code follows the project’s Code Style Guidelines
- [x] Tests have been added or updated
- [x] Documentation has been updated if necessary
- [x] Pull request is linked to an open issue
Code Coverage Summary
Filename Stmts Miss Cover Missing
---------------------------------------------------------------------- ------- ------ ------- -------------------------------------------------------------------------------------------------------
__init__.py 7 0 100.00%
graph.py 114 11 90.35% 99-100, 112, 124, 126, 142, 144, 166, 169, 182, 271
label_tensor.py 251 28 88.84% 81, 121, 144-148, 165, 177, 182, 188-193, 273, 280, 332, 334, 348, 444-447, 490, 537, 629, 664-673, 710
operator.py 72 2 97.22% 269, 465
trainer.py 75 5 93.33% 195-204, 293, 314, 318
type_checker.py 22 0 100.00%
utils.py 73 11 84.93% 59, 75, 141, 178, 181, 184, 220-223, 268
adaptive_function/__init__.py 3 0 100.00%
adaptive_function/adaptive_function.py 55 0 100.00%
adaptive_function/adaptive_function_interface.py 51 6 88.24% 98, 141, 148-151
callback/__init__.py 5 0 100.00%
callback/normalizer_data_callback.py 68 1 98.53% 141
callback/optimizer_callback.py 23 0 100.00%
callback/processing_callback.py 49 5 89.80% 42-43, 73, 168, 171
callback/refinement/__init__.py 3 0 100.00%
callback/refinement/r3_refinement.py 28 1 96.43% 88
callback/refinement/refinement_interface.py 50 5 90.00% 32, 59, 67, 72, 78
condition/__init__.py 7 0 100.00%
condition/condition.py 19 1 94.74% 141
condition/condition_interface.py 37 4 89.19% 32, 76, 95, 125
condition/data_condition.py 26 1 96.15% 78
condition/domain_equation_condition.py 19 0 100.00%
condition/input_equation_condition.py 43 1 97.67% 157
condition/input_target_condition.py 44 1 97.73% 172
data/__init__.py 3 0 100.00%
data/data_module.py 201 22 89.05% 41-52, 132, 172, 193, 232, 313-317, 323-327, 399, 466, 546, 637, 639
data/dataset.py 82 7 91.46% 42, 123-126, 256, 293
domain/__init__.py 10 0 100.00%
domain/cartesian.py 112 10 91.07% 37, 47, 75-76, 92, 97, 103, 246, 256, 264
domain/difference_domain.py 25 2 92.00% 54, 87
domain/domain_interface.py 20 5 75.00% 37-41
domain/ellipsoid.py 104 24 76.92% 52, 56, 127, 250-257, 269-282, 286-287, 290, 295
domain/exclusion_domain.py 28 1 96.43% 86
domain/intersection_domain.py 28 1 96.43% 85
domain/operation_interface.py 26 1 96.15% 88
domain/simplex.py 72 14 80.56% 62, 207-225, 246-247, 251, 256
domain/union_domain.py 25 1 96.00% 43
equation/__init__.py 4 0 100.00%
equation/equation.py 15 1 93.33% 56
equation/equation_factory.py 101 1 99.01% 181
equation/equation_interface.py 4 0 100.00%
equation/system_equation.py 20 0 100.00%
loss/__init__.py 9 0 100.00%
loss/linear_weighting.py 14 0 100.00%
loss/loss_interface.py 17 2 88.24% 45, 51
loss/lp_loss.py 15 0 100.00%
loss/ntk_weighting.py 18 0 100.00%
loss/power_loss.py 15 0 100.00%
loss/scalar_weighting.py 16 0 100.00%
loss/self_adaptive_weighting.py 12 0 100.00%
loss/weighting_interface.py 29 3 89.66% 35, 41-42
model/__init__.py 13 0 100.00%
model/average_neural_operator.py 31 2 93.55% 73, 82
model/deeponet.py 93 13 86.02% 187-190, 209, 240, 283, 293, 303, 313, 323, 333, 488, 498
model/equivariant_graph_neural_operator.py 51 1 98.04% 217
model/feed_forward.py 89 11 87.64% 58, 195, 200, 278-292
model/fourier_neural_operator.py 78 10 87.18% 96-100, 110, 155-159, 218, 220, 242, 342
model/graph_neural_operator.py 40 2 95.00% 58, 60
model/kernel_neural_operator.py 34 6 82.35% 83-84, 103-104, 123-124
model/low_rank_neural_operator.py 27 2 92.59% 89, 98
model/multi_feed_forward.py 12 5 58.33% 25-31
model/pirate_network.py 27 1 96.30% 118
model/sindy.py 21 0 100.00%
model/spline.py 89 37 58.43% 30, 41-66, 69, 128-132, 135, 159-177, 180
model/block/__init__.py 13 0 100.00%
model/block/average_neural_operator_block.py 12 0 100.00%
model/block/convolution.py 64 13 79.69% 77, 81, 85, 91, 97, 111, 114, 151, 161, 171, 181, 191, 201
model/block/convolution_2d.py 146 27 81.51% 155, 162, 282, 314, 379-433, 456
model/block/embedding.py 48 7 85.42% 93, 143-146, 155, 168
model/block/fourier_block.py 31 0 100.00%
model/block/gno_block.py 22 4 81.82% 73-77, 87
model/block/integral.py 18 4 77.78% 22-25, 71
model/block/low_rank_block.py 24 0 100.00%
model/block/orthogonal.py 37 0 100.00%
model/block/pirate_network_block.py 25 1 96.00% 89
model/block/pod_block.py 75 10 86.67% 56-59, 71, 84, 114, 151-156, 191, 216
model/block/rbf_block.py 179 25 86.03% 18, 42, 53, 64, 75, 86, 97, 223, 280, 282, 298, 301, 329, 335, 363, 367, 511-524
model/block/residual.py 46 0 100.00%
model/block/spectral.py 83 4 95.18% 132, 140, 262, 270
model/block/stride.py 28 7 75.00% 55, 58, 61, 67, 72-74
model/block/utils_convolution.py 22 3 86.36% 58-60
model/block/message_passing/__init__.py 6 0 100.00%
model/block/message_passing/deep_tensor_network_block.py 21 0 100.00%
model/block/message_passing/en_equivariant_network_block.py 47 1 97.87% 164
model/block/message_passing/equivariant_graph_neural_operator_block.py 36 0 100.00%
model/block/message_passing/interaction_network_block.py 23 0 100.00%
model/block/message_passing/radial_field_network_block.py 20 0 100.00%
optim/__init__.py 5 0 100.00%
optim/optimizer_interface.py 7 0 100.00%
optim/scheduler_interface.py 7 0 100.00%
optim/torch_optimizer.py 14 0 100.00%
optim/torch_scheduler.py 19 2 89.47% 5-6
problem/__init__.py 6 0 100.00%
problem/abstract_problem.py 117 12 89.74% 39-40, 59-70, 149, 161, 179, 253, 257, 286
problem/inverse_problem.py 22 0 100.00%
problem/parametric_problem.py 8 1 87.50% 29
problem/spatial_problem.py 8 0 100.00%
problem/time_dependent_problem.py 8 0 100.00%
problem/zoo/__init__.py 8 0 100.00%
problem/zoo/advection.py 23 4 82.61% 20, 73-75
problem/zoo/allen_cahn.py 23 3 86.96% 20-22
problem/zoo/diffusion_reaction.py 32 5 84.38% 103-113
problem/zoo/helmholtz.py 22 4 81.82% 59, 78-82
problem/zoo/inverse_poisson_2d_square.py 48 3 93.75% 44-50
problem/zoo/poisson_2d_square.py 16 3 81.25% 61-66
problem/zoo/supervised_problem.py 11 0 100.00%
solver/__init__.py 6 0 100.00%
solver/garom.py 107 2 98.13% 129-130
solver/solver.py 188 10 94.68% 195, 218, 290, 293-294, 353, 435, 518, 559, 565
solver/ensemble_solver/__init__.py 4 0 100.00%
solver/ensemble_solver/ensemble_pinn.py 23 1 95.65% 104
solver/ensemble_solver/ensemble_solver_interface.py 27 0 100.00%
solver/ensemble_solver/ensemble_supervised.py 9 0 100.00%
solver/physics_informed_solver/__init__.py 8 0 100.00%
solver/physics_informed_solver/causal_pinn.py 47 3 93.62% 157, 166-167
solver/physics_informed_solver/competitive_pinn.py 58 0 100.00%
solver/physics_informed_solver/gradient_pinn.py 17 0 100.00%
solver/physics_informed_solver/pinn.py 18 0 100.00%
solver/physics_informed_solver/pinn_interface.py 54 3 94.44% 75, 166, 222
solver/physics_informed_solver/rba_pinn.py 74 1 98.65% 324
solver/physics_informed_solver/self_adaptive_pinn.py 104 1 99.04% 392
solver/supervised_solver/__init__.py 4 0 100.00%
solver/supervised_solver/reduced_order_model.py 24 1 95.83% 137
solver/supervised_solver/supervised.py 7 0 100.00%
solver/supervised_solver/supervised_solver_interface.py 25 1 96.00% 90
TOTAL 5008 434 91.33%
Results for commit: 9d465fc6672a8cf95c82295effe7e97e833db284
Minimum allowed coverage is 80.123%
:recycle: This comment has been updated with latest results
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Thank you @dario-coscia for the PR.
Everything looks good to me. I just have one point regarding reducing the ?epository size. I believe that although we are removing the HTML files, they still remain in the Git history, which means the repository size will not actually decrease. I’m not entirely sure, but it’s worth checking.
Perhaps @ndem0 can provide more insight on this.
Ok I will check this more in details, why do you say so?
Thank you @dario-coscia for the PR. Everything looks good to me. I just have one point regarding reducing the ?epository size. I believe that although we are removing the HTML files, they still remain in the Git history, which means the repository size will not actually decrease. I’m not entirely sure, but it’s worth checking. Perhaps @ndem0 can provide more insight on this.
Ok I will check this more in details, why do you say so?
It happened to me on a different repository, solved via git filter-repo. To be used carefully.
I think this PR was closed by mistake, reopen it now. @ndem0 waiting for your review