Renaming

* solvers -> solver
* adaptive_functions -> adaptive_function
* callbacks -> callback
* operators -> operator
* pinns -> physics_informed_solver
* layers -> block

pina/__init__.py

@@ -10,6 +10,7 @@ __all__ = [
 
 from .label_tensor import LabelTensor
 from .graph import Graph
-from .solvers.solver import SolverInterface, MultiSolverInterface
+from .solver import SolverInterface, MultiSolverInterface
 from .trainer import Trainer
 from .condition.condition import Condition
+from .data import PinaDataModule

pina/adaptive_function/adaptive_func_interface.py

@@ -9,7 +9,7 @@ from abc import ABCMeta
 class AdaptiveActivationFunctionInterface(torch.nn.Module, metaclass=ABCMeta):
     r"""
     The
-    :class:`~pina.adaptive_functions.adaptive_func_interface.AdaptiveActivationFunctionInterface`
+    :class:`~pina.adaptive_function.adaptive_func_interface.AdaptiveActivationFunctionInterface`
     class makes a :class:`torch.nn.Module` activation function into an adaptive
     trainable activation function. If one wants to create an adpative activation
     function, this class must be use as base class.

pina/callback/__init__.py

@@ -0,0 +1,10 @@
+__all__ = [
+    "SwitchOptimizer",
+    "R3Refinement",
+    "MetricTracker",
+    "PINAProgressBar",
+]
+
+from .optimizer_callback import SwitchOptimizer
+from .adaptive_refinment_callback import R3Refinement
+from .processing_callback import MetricTracker, PINAProgressBar

pina/callback/optimizer_callback.py

@@ -19,7 +19,7 @@ class SwitchOptimizer(Callback):
 
         :param new_optimizers: The model optimizers to switch to. Can be a
             single :class:`torch.optim.Optimizer` or a list of them for multiple
-            model solvers.
+            model solver.
         :type new_optimizers: pina.optim.TorchOptimizer | list
         :param epoch_switch: The epoch at which to switch to the new optimizer.
         :type epoch_switch: int

pina/callback/processing_callback.py

@@ -87,12 +87,12 @@ class PINAProgressBar(TQDMProgressBar):
         :Keyword Arguments:
             The additional keyword arguments specify the progress bar
             and can be choosen from the `pytorch-lightning
-            TQDMProgressBar API <https://lightning.ai/docs/pytorch/stable/_modules/lightning/pytorch/callbacks/progress/tqdm_progress.html#TQDMProgressBar>`_
+            TQDMProgressBar API <https://lightning.ai/docs/pytorch/stable/_modules/lightning/pytorch/callback/progress/tqdm_progress.html#TQDMProgressBar>`_
 
         Example:
             >>> pbar = PINAProgressBar(['mean'])
             >>> # ... Perform training ...
-            >>> trainer = Trainer(solver, callbacks=[pbar])
+            >>> trainer = Trainer(solver, callback=[pbar])
         """
         super().__init__(**kwargs)
         # check consistency

pina/callbacks/__init__.py

@@ -1,10 +0,0 @@
-__all__ = [
-    "SwitchOptimizer",
-    "R3Refinement",
-    "MetricTracker",
-    "PINAProgressBar",
-]
-
-from .optimizer_callbacks import SwitchOptimizer
-from .adaptive_refinment_callbacks import R3Refinement
-from .processing_callbacks import MetricTracker, PINAProgressBar

pina/condition/input_equation_condition.py

@@ -27,7 +27,7 @@ class InputPointsEquationCondition(ConditionInterface):
         if key == 'input_points':
             check_consistency(
                 value, (LabelTensor)
-            )  # for now only labeltensors, we need labels for the operators!
+            )  # for now only labeltensors, we need labels for the operator!
             InputPointsEquationCondition.__dict__[key].__set__(self, value)
         elif key == 'equation':
             check_consistency(value, (EquationInterface))

pina/equation/equation_factory.py

@@ -1,7 +1,7 @@
 """ Module """
 
 from .equation import Equation
-from ..operators import grad, div, laplacian
+from ..operator import grad, div, laplacian
 
 
 class FixedValue(Equation):

pina/model/avno.py

@@ -2,9 +2,9 @@
 
 import torch
 from torch import nn, cat
-from .layers import AVNOBlock
+from .block import AVNOBlock
 from .base_no import KernelNeuralOperator
-from pina.utils import check_consistency
+from ..utils import check_consistency
 
 
 class AveragingNeuralOperator(KernelNeuralOperator):

pina/model/base_no.py

@@ -3,14 +3,14 @@ Kernel Neural Operator Module.
 """
 
 import torch
-from pina.utils import check_consistency
+from ..utils import check_consistency
 
 
 class KernelNeuralOperator(torch.nn.Module):
     r"""
     Base class for composing Neural Operators with integral kernels.
 
-    This is a base class for composing neural operators with multiple
+    This is a base class for composing neural operator with multiple
     integral kernels. All neural operator models defined in PINA inherit
     from this class. The structure is inspired by the work of Kovachki, N.
     et al. see Figure 2 of the reference for extra details. The Neural

pina/model/block/__init__.py

@@ -0,0 +1,35 @@
+__all__ = [
+    "ContinuousConvBlock",
+    "ResidualBlock",
+    "EnhancedLinear",
+    "SpectralConvBlock1D",
+    "SpectralConvBlock2D",
+    "SpectralConvBlock3D",
+    "FourierBlock1D",
+    "FourierBlock2D",
+    "FourierBlock3D",
+    "PODBlock",
+    "OrthogonalBlock",
+    "PeriodicBoundaryEmbedding",
+    "FourierFeatureEmbedding",
+    "AVNOBlock",
+    "LowRankBlock",
+    "RBFBlock",
+    "GNOBlock"
+]
+
+from .convolution_2d import ContinuousConvBlock
+from .residual import ResidualBlock, EnhancedLinear
+from .spectral import (
+    SpectralConvBlock1D,
+    SpectralConvBlock2D,
+    SpectralConvBlock3D,
+)
+from .fourier import FourierBlock1D, FourierBlock2D, FourierBlock3D
+from .pod import PODBlock
+from .orthogonal import OrthogonalBlock
+from .embedding import PeriodicBoundaryEmbedding, FourierFeatureEmbedding
+from .avno_layer import AVNOBlock
+from .lowrank_layer import LowRankBlock
+from .rbf_layer import RBFBlock
+from .gno_block import GNOBlock

pina/model/block/fourier.py

@@ -2,7 +2,7 @@ import torch
 import torch.nn as nn
 from ...utils import check_consistency
 
-from pina.model.layers import (
+from pina.model.block import (
     SpectralConvBlock1D,
     SpectralConvBlock2D,
     SpectralConvBlock3D,

pina/model/deeponet.py

@@ -18,7 +18,7 @@ class MIONet(torch.nn.Module):
     .. seealso::
 
         **Original reference**: Jin, Pengzhan, Shuai Meng, and Lu Lu.
-        *MIONet: Learning multiple-input operators via tensor product.*
+        *MIONet: Learning multiple-input operator via tensor product.*
         SIAM Journal on Scientific Computing 44.6 (2022): A3490-A351
         DOI: `10.1137/22M1477751
         <https://doi.org/10.1137/22M1477751>`_
@@ -289,8 +289,8 @@ class DeepONet(MIONet):
     .. seealso::
 
         **Original reference**: Lu, L., Jin, P., Pang, G. et al. *Learning
-        nonlinear operators via DeepONet based on the universal approximation
-        theorem of operators*. Nat Mach Intell 3, 218–229 (2021).
+        nonlinear operator via DeepONet based on the universal approximation
+        theorem of operator*. Nat Mach Intell 3, 218–229 (2021).
         DOI: `10.1038/s42256-021-00302-5
         <https://doi.org/10.1038/s42256-021-00302-5>`_
 

pina/model/feed_forward.py

@@ -3,7 +3,7 @@
 import torch
 import torch.nn as nn
 from ..utils import check_consistency
-from .layers.residual import EnhancedLinear
+from .block.residual import EnhancedLinear
 
 
 class FeedForward(torch.nn.Module):

pina/model/fno.py

@@ -7,7 +7,7 @@ import torch.nn as nn
 from ..label_tensor import LabelTensor
 import warnings
 from ..utils import check_consistency
-from .layers.fourier import FourierBlock1D, FourierBlock2D, FourierBlock3D
+from .block.fourier import FourierBlock1D, FourierBlock2D, FourierBlock3D
 from .base_no import KernelNeuralOperator
 
 

pina/model/gno.py

@@ -1,6 +1,6 @@
 import torch
 from torch.nn import Tanh
-from .layers import GNOBlock
+from .block import GNOBlock
 from .base_no import KernelNeuralOperator
 
 

pina/model/layers/__init__.py

@@ -1,35 +0,0 @@
-__all__ = [
-    "ContinuousConvBlock",
-    "ResidualBlock",
-    "EnhancedLinear",
-    "SpectralConvBlock1D",
-    "SpectralConvBlock2D",
-    "SpectralConvBlock3D",
-    "FourierBlock1D",
-    "FourierBlock2D",
-    "FourierBlock3D",
-    "PODBlock",
-    "OrthogonalBlock",
-    "PeriodicBoundaryEmbedding",
-    "FourierFeatureEmbedding",
-    "AVNOBlock",
-    "LowRankBlock",
-    "RBFBlock",
-    "GNOBlock"
-]
-
-from .convolution_2d import ContinuousConvBlock
-from .residual import ResidualBlock, EnhancedLinear
-from .spectral import (
-    SpectralConvBlock1D,
-    SpectralConvBlock2D,
-    SpectralConvBlock3D,
-)
-from .fourier import FourierBlock1D, FourierBlock2D, FourierBlock3D
-from .pod import PODBlock
-from .orthogonal import OrthogonalBlock
-from .embedding import PeriodicBoundaryEmbedding, FourierFeatureEmbedding
-from .avno_layer import AVNOBlock
-from .lowrank_layer import LowRankBlock
-from .rbf_layer import RBFBlock
-from .gno_block import GNOBlock

pina/model/lno.py

@@ -3,10 +3,10 @@
 import torch
 from torch import nn, cat
 
-from pina.utils import check_consistency
+from ..utils import check_consistency
 
 from .base_no import KernelNeuralOperator
-from .layers.lowrank_layer import LowRankBlock
+from .block.lowrank_layer import LowRankBlock
 
 
 class LowRankNeuralOperator(KernelNeuralOperator):
@@ -19,7 +19,7 @@ class LowRankNeuralOperator(KernelNeuralOperator):
     to other functions. It can be trained with Supervised or PINN based
     learning strategies.
     LowRankNeuralOperator does convolution by performing a low rank
-    approximation, see :class:`~pina.model.layers.lowrank_layer.LowRankBlock`.
+    approximation, see :class:`~pina.model.block.lowrank_layer.LowRankBlock`.
 
     .. seealso::
 

pina/model/spline.py

@@ -1,7 +1,6 @@
 """Module for Spline model"""
 
 import torch
-import torch.nn as nn
 from ..utils import check_consistency
 
 

pina/operator.py

@@ -1,7 +1,7 @@
 """
-Module for operators vectorize implementation. Differential operators are used to write any differential problem.
-These operators are implemented to work on different accellerators: CPU, GPU, TPU or MPS.
-All operators take as input a tensor onto which computing the operator, a tensor with respect
+Module for operator vectorize implementation. Differential operator are used to write any differential problem.
+These operator are implemented to work on different accellerators: CPU, GPU, TPU or MPS.
+All operator take as input a tensor onto which computing the operator, a tensor with respect
 to which computing the operator, the name of the output variables to calculate the operator
 for (in case of multidimensional functions), and the variables name on which the operator is calculated.
 """

pina/problem/inverse_problem.py

@@ -16,7 +16,7 @@ class InverseProblem(AbstractProblem):
 
     :Example:
         >>> from pina.problem import SpatialProblem, InverseProblem
-        >>> from pina.operators import grad
+        >>> from pina.operator import grad
         >>> from pina.equation import ParametricEquation, FixedValue
         >>> from pina import Condition
         >>> from pina.geometry import CartesianDomain

pina/problem/parametric_problem.py

@@ -16,7 +16,7 @@ class ParametricProblem(AbstractProblem):
 
     :Example:
         >>> from pina.problem import SpatialProblem, ParametricProblem
-        >>> from pina.operators import grad
+        >>> from pina.operator import grad
         >>> from pina.equations import Equation, FixedValue
         >>> from pina import Condition
         >>> from pina.geometry import CartesianDomain

pina/problem/spatial_problem.py

@@ -14,7 +14,7 @@ class SpatialProblem(AbstractProblem):
 
     :Example:
         >>> from pina.problem import SpatialProblem
-        >>> from pina.operators import grad
+        >>> from pina.operator import grad
         >>> from pina.equation import Equation, FixedValue
         >>> from pina import Condition
         >>> from pina.geometry import CartesianDomain

pina/problem/timedep_problem.py

@@ -14,7 +14,7 @@ class TimeDependentProblem(AbstractProblem):
 
     :Example:
         >>> from pina.problem import SpatialProblem, TimeDependentProblem
-        >>> from pina.operators import grad, laplacian
+        >>> from pina.operator import grad, laplacian
         >>> from pina.equation import Equation, FixedValue
         >>> from pina import Condition
         >>> from pina.geometry import CartesianDomain

pina/problem/zoo/diffusion_reaction.py

@@ -5,7 +5,7 @@ from pina import Condition
 from pina.problem import SpatialProblem, TimeDependentProblem
 from pina.equation.equation import Equation
 from pina.domain import CartesianDomain
-from pina.operators import grad
+from pina.operator import grad
 
 def diffusion_reaction(input_, output_):
     """

pina/problem/zoo/inverse_diffusion_reaction.py

@@ -5,7 +5,7 @@ from pina import Condition, LabelTensor
 from pina.problem import SpatialProblem, TimeDependentProblem, InverseProblem
 from pina.equation.equation import Equation
 from pina.domain import CartesianDomain
-from pina.operators import grad
+from pina.operator import grad
 
 def diffusion_reaction(input_, output_):
     """

pina/problem/zoo/inverse_poisson_2d_square.py

@@ -3,7 +3,7 @@
 import torch
 from pina import Condition, LabelTensor
 from pina.problem import SpatialProblem, InverseProblem
-from pina.operators import laplacian
+from pina.operator import laplacian
 from pina.domain import CartesianDomain
 from pina.equation.equation import Equation
 from pina.equation.equation_factory import FixedValue

pina/problem/zoo/poisson_2d_square.py

@@ -1,7 +1,7 @@
 """ Definition of the Poisson problem on a square domain."""
 
 from pina.problem import SpatialProblem
-from pina.operators import laplacian
+from pina.operator import laplacian
 from pina import Condition
 from pina.domain import CartesianDomain
 from pina.equation.equation import Equation

pina/solver/__init__.py

@@ -15,7 +15,7 @@ __all__ = [
 ]
 
 from .solver import SolverInterface, SingleSolverInterface, MultiSolverInterface
-from .pinns import *
+from .physic_informed_solver import *
 from .supervised import SupervisedSolver
 from .rom import ReducedOrderModelSolver
 from .garom import GAROM

pina/solver/physic_informed_solver/causal_pinn.py

@@ -11,7 +11,7 @@ class CausalPINN(PINN):
     r"""
     Causal Physics Informed Neural Network (CausalPINN) solver class.
     This class implements Causal Physics Informed Neural
-    Network solvers, using a user specified ``model`` to solve a specific
+    Network solver, using a user specified ``model`` to solve a specific
     ``problem``. It can be used for solving both forward and inverse problems.
 
     The Causal Physics Informed Network aims to find

pina/solver/physic_informed_solver/competitive_pinn.py

@@ -12,7 +12,7 @@ class CompetitivePINN(PINNInterface, MultiSolverInterface):
     r"""
     Competitive Physics Informed Neural Network (PINN) solver class.
     This class implements Competitive Physics Informed Neural
-    Network solvers, using a user specified ``model`` to solve a specific
+    Network solver, using a user specified ``model`` to solve a specific
     ``problem``. It can be used for solving both forward and inverse problems.
 
     The Competitive Physics Informed Network aims to find

pina/solver/physic_informed_solver/gradient_pinn.py

@@ -3,7 +3,7 @@
 import torch
 
 from .pinn import PINN
-from pina.operators import grad
+from pina.operator import grad
 from pina.problem import SpatialProblem
 
 
@@ -11,7 +11,7 @@ class GradientPINN(PINN):
     r"""
     Gradient Physics Informed Neural Network (GradientPINN) solver class.
     This class implements Gradient Physics Informed Neural
-    Network solvers, using a user specified ``model`` to solve a specific
+    Network solver, using a user specified ``model`` to solve a specific
     ``problem``. It can be used for solving both forward and inverse problems.
 
     The Gradient Physics Informed Network aims to find

pina/solver/physic_informed_solver/pinn.py

@@ -11,7 +11,7 @@ class PINN(PINNInterface, SingleSolverInterface):
     r"""
     Physics Informed Neural Network (PINN) solver class.
     This class implements Physics Informed Neural
-    Network solvers, using a user specified ``model`` to solve a specific
+    Network solver, using a user specified ``model`` to solve a specific
     ``problem``. It can be used for solving both forward and inverse problems.
 
     The Physics Informed Network aims to find

pina/solver/physic_informed_solver/pinn_interface.py

@@ -18,7 +18,7 @@ from ...condition import (
 class PINNInterface(SolverInterface, metaclass=ABCMeta):
     """
     Base PINN solver class. This class implements the Solver Interface
-    for Physics Informed Neural Network solvers.
+    for Physics Informed Neural Network solver.
 
     This class can be used to define PINNs with multiple ``optimizers``, 
     and/or ``models``.

pina/solver/physic_informed_solver/rba_pinn.py

@@ -11,7 +11,7 @@ class RBAPINN(PINN):
     r"""
     Residual-based Attention PINN (RBAPINN) solver class.
     This class implements Residual-based Attention Physics Informed Neural
-    Network solvers, using a user specified ``model`` to solve a specific
+    Network solver, using a user specified ``model`` to solve a specific
     ``problem``. It can be used for solving both forward and inverse problems.
 
     The Residual-based Attention Physics Informed Neural Network aims to find

pina/solver/physic_informed_solver/self_adaptive_pinn.py

@@ -39,7 +39,7 @@ class SelfAdaptivePINN(PINNInterface, MultiSolverInterface):
     r"""
     Self Adaptive Physics Informed Neural Network (SelfAdaptivePINN)
     solver class. This class implements Self-Adaptive Physics Informed Neural
-    Network solvers, using a user specified ``model`` to solve a specific
+    Network solver, using a user specified ``model`` to solve a specific
     ``problem``. It can be used for solving both forward and inverse problems.
 
     The Self Adapive Physics Informed Neural Network aims to find

pina/solver/rom.py

@@ -2,7 +2,7 @@
 
 import torch
 
-from pina.solvers import SupervisedSolver
+from pina.solver import SupervisedSolver
 
 
 class ReducedOrderModelSolver(SupervisedSolver):

pina/solver/supervised.py

@@ -15,7 +15,7 @@ class SupervisedSolver(SingleSolverInterface):
     The  Supervised Solver class aims to find
     a map between the input :math:`\mathbf{s}:\Omega\rightarrow\mathbb{R}^m`
     and the output :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m`. The input
-    can be discretised in space (as in :obj:`~pina.solvers.rom.ROMe2eSolver`),
+    can be discretised in space (as in :obj:`~pina.solver.rom.ROMe2eSolver`),
     or not (e.g. when training Neural Operators).
 
     Given a model :math:`\mathcal{M}`, the following loss function is

pina/trainer.py

@@ -4,7 +4,7 @@ import torch
 import lightning
 from .utils import check_consistency
 from .data import PinaDataModule
-from .solvers import SolverInterface, PINNInterface
+from .solver import SolverInterface, PINNInterface
 
 
 class Trainer(lightning.pytorch.Trainer):

pina/utils.py

@@ -7,6 +7,10 @@ from functools import reduce
 from .label_tensor import LabelTensor
 
 
+def custom_warning_format(
+        message, category, filename, lineno, file=None, line=None
+        ):
+    return f"{filename}: {category.__name__}: {message}\n"
 
 def check_consistency(object, object_instance, subclass=False):
     """Helper function to check object inheritance consistency.

tests/test_adaptive_function.py

@@ -1,19 +1,19 @@
 import torch
 import pytest
 
-from pina.adaptive_functions import (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
+from pina.adaptive_function import (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
                             AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
                             AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
                             AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
 
 
-adaptive_functions = (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
+adaptive_function = (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
                       AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
                       AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
                       AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
 x = torch.rand(10, requires_grad=True)
 
-@pytest.mark.parametrize("Func", adaptive_functions)
+@pytest.mark.parametrize("Func", adaptive_function)
 def test_constructor(Func):
     if Func.__name__ == 'AdaptiveExp':
         # simple
@@ -50,12 +50,12 @@ def test_constructor(Func):
         Func(alpha='s')
         Func(alpha=1)
 
-@pytest.mark.parametrize("Func", adaptive_functions)      
+@pytest.mark.parametrize("Func", adaptive_function)      
 def test_forward(Func):
     af = Func()
     af(x)
 
-@pytest.mark.parametrize("Func", adaptive_functions)
+@pytest.mark.parametrize("Func", adaptive_function)
 def test_backward(Func):
     af = Func()
     y = af(x)

tests/test_blocks/test_conv.py

@@ -1,4 +1,4 @@
-from pina.model.layers import ContinuousConvBlock
+from pina.model.block import ContinuousConvBlock
 import torch
 
 

tests/test_blocks/test_embedding.py

@@ -1,7 +1,7 @@
 import torch
 import pytest
 
-from pina.model.layers import PeriodicBoundaryEmbedding, FourierFeatureEmbedding
+from pina.model.block import PeriodicBoundaryEmbedding, FourierFeatureEmbedding
 
 # test tolerance
 tol = 1e-6

tests/test_blocks/test_fourier.py

@@ -1,4 +1,4 @@
-from pina.model.layers import FourierBlock1D, FourierBlock2D, FourierBlock3D
+from pina.model.block import FourierBlock1D, FourierBlock2D, FourierBlock3D
 import torch
 
 input_numb_fields = 3

tests/test_blocks/test_lnolayer.py

@@ -1,7 +1,7 @@
 import torch
 import pytest
 
-from pina.model.layers import LowRankBlock
+from pina.model.block import LowRankBlock
 from pina import LabelTensor
 
 

tests/test_blocks/test_orthogonal.py

@@ -1,6 +1,6 @@
 import torch
 import pytest
-from pina.model.layers import OrthogonalBlock
+from pina.model.block import OrthogonalBlock
 
 torch.manual_seed(111)
 

tests/test_blocks/test_pod.py

@@ -1,7 +1,7 @@
 import torch
 import pytest
 
-from pina.model.layers.pod import PODBlock
+from pina.model.block.pod import PODBlock
 
 x = torch.linspace(-1, 1, 100)
 toy_snapshots = torch.vstack([torch.exp(-x**2)*c for c in torch.linspace(0, 1, 10)])

tests/test_blocks/test_rbf.py

@@ -2,7 +2,7 @@ import torch
 import pytest
 import math
 
-from pina.model.layers.rbf_layer import RBFBlock
+from pina.model.block.rbf_layer import RBFBlock
 
 x = torch.linspace(-1, 1, 100)
 toy_params = torch.linspace(0, 1, 10).unsqueeze(1)

tests/test_blocks/test_residual.py

@@ -1,4 +1,4 @@
-from pina.model.layers import ResidualBlock, EnhancedLinear
+from pina.model.block import ResidualBlock, EnhancedLinear
 import torch
 import torch.nn as nn
 

tests/test_blocks/test_spectral_conv.py

@@ -1,4 +1,4 @@
-from pina.model.layers import SpectralConvBlock1D, SpectralConvBlock2D, SpectralConvBlock3D
+from pina.model.block import SpectralConvBlock1D, SpectralConvBlock2D, SpectralConvBlock3D
 import torch
 
 input_numb_fields = 3

tests/test_callback/test_adaptive_refinment_callback.py

@@ -1,8 +1,8 @@
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import Poisson2DSquareProblem as Poisson
-from pina.callbacks import R3Refinement
+from pina.callback import R3Refinement
 
 
 # make the problem
@@ -25,7 +25,7 @@ solver = PINN(problem=poisson_problem, model=model)
 # def test_r3refinment_routine():
 #     # make the trainer
 #     trainer = Trainer(solver=solver,
-#                       callbacks=[R3Refinement(sample_every=1)],
+#                       callback=[R3Refinement(sample_every=1)],
 #                       accelerator='cpu',
 #                       max_epochs=5)
 #     trainer.train()
@@ -35,7 +35,7 @@ solver = PINN(problem=poisson_problem, model=model)
 #                     len(poisson_problem.output_variables))
 #     solver = PINN(problem=poisson_problem, model=model)
 #     trainer = Trainer(solver=solver,
-#                       callbacks=[R3Refinement(sample_every=1)],
+#                       callback=[R3Refinement(sample_every=1)],
 #                       accelerator='cpu',
 #                       max_epochs=5)
 #     before_n_points = {loc : len(pts) for loc, pts in trainer.solver.problem.input_pts.items()}

tests/test_callback/test_metric_tracker.py

@@ -1,7 +1,7 @@
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
-from pina.callbacks import MetricTracker
+from pina.callback import MetricTracker
 from pina.problem.zoo import Poisson2DSquareProblem as Poisson
 
 
@@ -24,14 +24,14 @@ def test_metric_tracker_constructor():
 # def test_metric_tracker_routine(): #TODO revert
 #     # make the trainer
 #     trainer = Trainer(solver=solver,
-#                       callbacks=[
+#                       callback=[
 #                           MetricTracker()
 #                       ],
 #                       accelerator='cpu',
 #                       max_epochs=5)
 #     trainer.train()
 #     # get the tracked metrics
-#     metrics = trainer.callbacks[0].metrics
+#     metrics = trainer.callback[0].metrics
 #     # assert the logged metrics are correct
 #     logged_metrics = sorted(list(metrics.keys()))
 #     assert logged_metrics == ['train_loss_epoch', 'train_loss_step', 'val_loss']

tests/test_callback/test_optimizer_callback.py

@@ -1,8 +1,8 @@
-from pina.callbacks import SwitchOptimizer
+from pina.callback import SwitchOptimizer
 import torch
 import pytest
 
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import Poisson2DSquareProblem as Poisson
@@ -31,7 +31,7 @@ def test_switch_optimizer_constructor():
 #     # make the trainer
 #     switch_opt_callback = SwitchOptimizer(lbfgs_optimizer, epoch_switch=3)
 #     trainer = Trainer(solver=solver,
-#                       callbacks=[switch_opt_callback],
+#                       callback=[switch_opt_callback],
 #                       accelerator='cpu',
 #                       max_epochs=5)
 #     trainer.train()

tests/test_callback/test_progress_bar.py

@@ -1,7 +1,7 @@
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
-from pina.callbacks.processing_callbacks import PINAProgressBar
+from pina.callback.processing_callback import PINAProgressBar
 from pina.problem.zoo import Poisson2DSquareProblem as Poisson
 
 
@@ -24,7 +24,7 @@ from pina.problem.zoo import Poisson2DSquareProblem as Poisson
 # def test_progress_bar_routine():
 #     # make the trainer
 #     trainer = Trainer(solver=solver,
-#                       callbacks=[
+#                       callback=[
 #                           PINAProgressBar(['mean', 'laplace_D'])
 #                       ],
 #                       accelerator='cpu',

tests/test_collector.py

@@ -7,7 +7,7 @@ from pina.problem import AbstractProblem, SpatialProblem
 from pina.domain import CartesianDomain
 from pina.equation.equation import Equation
 from pina.equation.equation_factory import FixedValue
-from pina.operators import laplacian
+from pina.operator import laplacian
 from pina.collector import Collector
 
 

tests/test_data/test_datamodule.py

@@ -6,7 +6,7 @@ from pina.problem.zoo import SupervisedProblem
 from pina.graph import RadiusGraph
 from pina.data.data_module import DummyDataloader
 from pina import Trainer
-from pina.solvers import SupervisedSolver
+from pina.solver import SupervisedSolver
 from torch_geometric.data import Batch
 from torch.utils.data import DataLoader
 

tests/test_equations/test_equation.py

@@ -1,5 +1,5 @@
 from pina.equation import Equation
-from pina.operators import grad, laplacian
+from pina.operator import grad, laplacian
 from pina import LabelTensor
 import torch
 import pytest

tests/test_equations/test_systemequation.py

@@ -1,5 +1,5 @@
 from pina.equation import SystemEquation
-from pina.operators import grad, laplacian
+from pina.operator import grad, laplacian
 from pina import LabelTensor
 import torch
 import pytest

tests/test_operator.py

@@ -2,7 +2,7 @@ import torch
 import pytest
 
 from pina import LabelTensor
-from pina.operators import grad, div, laplacian
+from pina.operator import grad, div, laplacian
 
 
 def func_vector(x):

tests/test_solver/test_causal_pinn.py

@@ -3,7 +3,7 @@ import pytest
 
 from pina import LabelTensor, Condition
 from pina.problem import SpatialProblem
-from pina.solvers import CausalPINN
+from pina.solver import CausalPINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import (
@@ -118,7 +118,7 @@ def test_solver_test(problem, batch_size, compile):
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
 def test_train_load_restore(problem):
-    dir = "tests/test_solvers/tmp"
+    dir = "tests/test_solver/tmp"
     problem = problem
     solver = CausalPINN(model=model, problem=problem)
     trainer = Trainer(solver=solver,
@@ -153,4 +153,4 @@ def test_train_load_restore(problem):
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_competitive_pinn.py

@@ -2,7 +2,7 @@ import torch
 import pytest 
 
 from pina import LabelTensor, Condition
-from pina.solvers import CompetitivePINN as CompPINN
+from pina.solver import CompetitivePINN as CompPINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import (
@@ -107,7 +107,7 @@ def test_solver_test(problem, batch_size, compile):
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
 def test_train_load_restore(problem):
-    dir = "tests/test_solvers/tmp"
+    dir = "tests/test_solver/tmp"
     problem = problem
     solver = CompPINN(problem=problem, model=model)
     trainer = Trainer(solver=solver,
@@ -142,4 +142,4 @@ def test_train_load_restore(problem):
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_garom.py

@@ -3,7 +3,7 @@ import torch.nn as nn
 
 import pytest
 from pina import Condition, LabelTensor
-from pina.solvers import GAROM
+from pina.solver import GAROM
 from pina.condition import InputOutputPointsCondition
 from pina.problem import AbstractProblem
 from pina.model import FeedForward
@@ -141,7 +141,7 @@ def test_solver_test(batch_size, compile):
 
 
 def test_train_load_restore():
-    dir = "tests/test_solvers/tmp/"
+    dir = "tests/test_solver/tmp/"
     problem = TensorProblem()
     solver = GAROM(problem=TensorProblem(),
                    generator=Generator(),
@@ -174,4 +174,4 @@ def test_train_load_restore():
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_gradient_pinn.py

@@ -3,7 +3,7 @@ import torch
 
 from pina import LabelTensor, Condition
 from pina.problem import TimeDependentProblem
-from pina.solvers import GradientPINN
+from pina.solver import GradientPINN
 from pina.model import FeedForward
 from pina.trainer import Trainer
 from pina.problem.zoo import (
@@ -117,7 +117,7 @@ def test_solver_test(problem, batch_size, compile):
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
 def test_train_load_restore(problem):
-    dir = "tests/test_solvers/tmp"
+    dir = "tests/test_solver/tmp"
     problem = problem
     solver = GradientPINN(model=model, problem=problem)
     trainer = Trainer(solver=solver,
@@ -152,4 +152,4 @@ def test_train_load_restore(problem):
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_pinn.py

@@ -4,7 +4,7 @@ import torch
 from pina import LabelTensor, Condition
 from pina.model import FeedForward
 from pina.trainer import Trainer
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.condition import (
     InputOutputPointsCondition,
     InputPointsEquationCondition,
@@ -98,7 +98,7 @@ def test_solver_test(problem, batch_size, compile):
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
 def test_train_load_restore(problem):
-    dir = "tests/test_solvers/tmp"
+    dir = "tests/test_solver/tmp"
     problem = problem
     solver = PINN(model=model, problem=problem)
     trainer = Trainer(solver=solver,
@@ -131,4 +131,4 @@ def test_train_load_restore(problem):
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_rba_pinn.py

@@ -4,7 +4,7 @@ import torch
 from pina import LabelTensor, Condition
 from pina.model import FeedForward
 from pina.trainer import Trainer
-from pina.solvers import RBAPINN
+from pina.solver import RBAPINN
 from pina.condition import (
     InputOutputPointsCondition,
     InputPointsEquationCondition,
@@ -119,7 +119,7 @@ def test_solver_test(problem, compile):
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
 def test_train_load_restore(problem):
-    dir = "tests/test_solvers/tmp"
+    dir = "tests/test_solver/tmp"
     problem = problem
     solver = RBAPINN(model=model, problem=problem)
     trainer = Trainer(solver=solver,
@@ -154,4 +154,4 @@ def test_train_load_restore(problem):
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_rom_solver.py

@@ -4,7 +4,7 @@ import pytest
 from pina import Condition, LabelTensor
 from pina.problem import AbstractProblem
 from pina.condition import InputOutputPointsCondition
-from pina.solvers import ReducedOrderModelSolver
+from pina.solver import ReducedOrderModelSolver
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import Poisson2DSquareProblem
@@ -149,7 +149,7 @@ def test_solver_test(use_lt, compile):
 
 
 def test_train_load_restore():
-    dir = "tests/test_solvers/tmp/"
+    dir = "tests/test_solver/tmp/"
     problem = LabelTensorProblem()
     solver = ReducedOrderModelSolver(problem=problem,
 
@@ -184,4 +184,4 @@ def test_train_load_restore():
         solver.forward(test_pts))
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_self_adaptive_pinn.py

@@ -2,7 +2,7 @@ import torch
 import pytest
 
 from pina import LabelTensor, Condition
-from pina.solvers import SelfAdaptivePINN as SAPINN
+from pina.solver import SelfAdaptivePINN as SAPINN
 from pina.trainer import Trainer
 from pina.model import FeedForward
 from pina.problem.zoo import (
@@ -122,7 +122,7 @@ def test_solver_test(problem, compile):
 
 @pytest.mark.parametrize("problem", [problem, inverse_problem])
 def test_train_load_restore(problem):
-    dir = "tests/test_solvers/tmp"
+    dir = "tests/test_solver/tmp"
     problem = problem
     solver = SAPINN(model=model, problem=problem)
     trainer = Trainer(solver=solver,
@@ -156,4 +156,4 @@ def test_train_load_restore(problem):
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_solver/test_supervised_solver.py

@@ -3,7 +3,7 @@ import pytest
 from pina import Condition, LabelTensor
 from pina.condition import InputOutputPointsCondition
 from pina.problem import AbstractProblem
-from pina.solvers import SupervisedSolver
+from pina.solver import SupervisedSolver
 from pina.model import FeedForward
 from pina.trainer import Trainer
 from torch._dynamo.eval_frame import OptimizedModule
@@ -97,7 +97,7 @@ def test_solver_test(use_lt, compile):
 
 
 def test_train_load_restore():
-    dir = "tests/test_solvers/tmp/"
+    dir = "tests/test_solver/tmp/"
     problem = LabelTensorProblem()
     solver = SupervisedSolver(problem=problem, model=model)
     trainer = Trainer(solver=solver,
@@ -130,4 +130,4 @@ def test_train_load_restore():
 
     # rm directories
     import shutil
-    shutil.rmtree('tests/test_solvers/tmp')
+    shutil.rmtree('tests/test_solver/tmp')

tests/test_weighting/test_standard_weighting.py

@@ -2,7 +2,7 @@ import pytest
 import torch
 
 from pina import Trainer
-from pina.solvers import PINN
+from pina.solver import PINN
 from pina.model import FeedForward
 from pina.problem.zoo import Poisson2DSquareProblem
 from pina.loss import ScalarWeighting