Renaming and Back Compatibility

.github/workflows/black-formatter-test.yml

@@ -0,0 +1,19 @@
+name: Black Formatter (PR Check)
+
+on:
+  pull_request:
+    branches:
+      - "**"  # Run on pull requests for all branches
+
+jobs:
+  linter:
+    name: runner / black
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Run Black formatter (check mode)
+        uses: psf/black@stable
+        with:
+          options: "--check -l 80"
+          src: "./pina"

.github/workflows/black-formatter.yml

@@ -27,4 +27,4 @@ jobs:
             There appear to be some python formatting errors in ${{ github.sha }}. This pull request
             uses the [psf/black](https://github.com/psf/black) formatter to fix these issues.
           base: ${{ github.head_ref }} # Creates pull request onto pull request or commit branch
-          branch: actions/black
+          branch: actions/black

pina/__init__.py

@@ -1,18 +1,16 @@
 __all__ = [
-    "Trainer", "LabelTensor", "Plotter", "Condition",
-    "PinaDataModule", 'TorchOptimizer', 'Graph',
-    "RadiusGraph", "KNNGraph"
+    "Trainer",
+    "LabelTensor",
+    "Condition",
+    "PinaDataModule",
+    "Graph",
+    "SolverInterface",
+    "MultiSolverInterface",
 ]
 
-from .meta import *
 from .label_tensor import LabelTensor
-from .solvers.solver import SolverInterface
+from .graph import Graph
+from .solver import SolverInterface, MultiSolverInterface
 from .trainer import Trainer
-from .plotter import Plotter
 from .condition.condition import Condition
-
 from .data import PinaDataModule
-
-from .optim import TorchOptimizer
-from .optim import TorchScheduler
-from .graph import Graph, RadiusGraph, KNNGraph

pina/adaptive_function/__init__.py

@@ -0,0 +1,31 @@
+__all__ = [
+    "AdaptiveActivationFunctionInterface",
+    "AdaptiveReLU",
+    "AdaptiveSigmoid",
+    "AdaptiveTanh",
+    "AdaptiveSiLU",
+    "AdaptiveMish",
+    "AdaptiveELU",
+    "AdaptiveCELU",
+    "AdaptiveGELU",
+    "AdaptiveSoftmin",
+    "AdaptiveSoftmax",
+    "AdaptiveSIREN",
+    "AdaptiveExp",
+]
+
+from .adaptive_function import (
+    AdaptiveReLU,
+    AdaptiveSigmoid,
+    AdaptiveTanh,
+    AdaptiveSiLU,
+    AdaptiveMish,
+    AdaptiveELU,
+    AdaptiveCELU,
+    AdaptiveGELU,
+    AdaptiveSoftmin,
+    AdaptiveSoftmax,
+    AdaptiveSIREN,
+    AdaptiveExp,
+)
+from .adaptive_function_interface import AdaptiveActivationFunctionInterface

pina/adaptive_functions/adaptive_func.py → pina/adaptive_function/adaptive_function.py

@@ -1,8 +1,8 @@
-""" Module for adaptive functions. """
+"""Module for adaptive functions."""
 
 import torch
 from ..utils import check_consistency
-from .adaptive_func_interface import AdaptiveActivationFunctionInterface
+from .adaptive_function_interface import AdaptiveActivationFunctionInterface
 
 
 class AdaptiveReLU(AdaptiveActivationFunctionInterface):

pina/adaptive_functions/adaptive_func_interface.py → pina/adaptive_function/adaptive_function_interface.py

@@ -1,4 +1,4 @@
-""" Module for adaptive functions. """
+"""Module for adaptive functions."""
 
 import torch
 
@@ -9,7 +9,7 @@
 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/adaptive_functions/__init__.py

@@ -1,31 +1,14 @@
-__all__ = [
-    "AdaptiveActivationFunctionInterface",
-    "AdaptiveReLU",
-    "AdaptiveSigmoid",
-    "AdaptiveTanh",
-    "AdaptiveSiLU",
-    "AdaptiveMish",
-    "AdaptiveELU",
-    "AdaptiveCELU",
-    "AdaptiveGELU",
-    "AdaptiveSoftmin",
-    "AdaptiveSoftmax",
-    "AdaptiveSIREN",
-    "AdaptiveExp",
-]
+import warnings
 
-from .adaptive_func import (
-    AdaptiveReLU,
-    AdaptiveSigmoid,
-    AdaptiveTanh,
-    AdaptiveSiLU,
-    AdaptiveMish,
-    AdaptiveELU,
-    AdaptiveCELU,
-    AdaptiveGELU,
-    AdaptiveSoftmin,
-    AdaptiveSoftmax,
-    AdaptiveSIREN,
-    AdaptiveExp,
+from ..adaptive_function import *
+from ..utils import custom_warning_format
+
+# back-compatibility 0.1
+# Set the custom format for warnings
+warnings.formatwarning = custom_warning_format
+warnings.filterwarnings("always", category=DeprecationWarning)
+warnings.warn(
+    f"'pina.adaptive_functions' is deprecated and will be removed "
+    f"in future versions. Please use 'pina.adaptive_function' instead.",
+    DeprecationWarning,
 )
-from .adaptive_func_interface import AdaptiveActivationFunctionInterface

pina/callback/__init__.py

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

pina/callbacks/adaptive_refinment_callbacks.py → pina/callback/adaptive_refinement_callback.py

@@ -67,7 +67,7 @@ def _compute_residual(self, trainer):
         # compute residual
         res_loss = {}
         tot_loss = []
-        for location in self._sampling_locations: #TODO fix for new collector
+        for location in self._sampling_locations:  # TODO fix for new collector
             condition = solver.problem.conditions[location]
             pts = solver.problem.input_pts[location]
             # send points to correct device

pina/callbacks/optimizer_callbacks.py → pina/callback/optimizer_callback.py

@@ -19,7 +19,7 @@ def __init__(self, new_optimizers, epoch_switch):
 
         :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/callbacks/processing_callbacks.py → pina/callback/processing_callback.py

@@ -26,7 +26,7 @@ def __init__(self, metrics_to_track=None):
         super().__init__()
         self._collection = []
         # Default to tracking 'train_loss' and 'val_loss' if not specified
-        self.metrics_to_track = metrics_to_track or ['train_loss', 'val_loss']
+        self.metrics_to_track = metrics_to_track or ["train_loss", "val_loss"]
 
     def on_train_epoch_end(self, trainer, pl_module):
         """
@@ -40,7 +40,8 @@ def on_train_epoch_end(self, trainer, pl_module):
         if trainer.current_epoch > 0:
             # Append only the tracked metrics to avoid unnecessary data
             tracked_metrics = {
-                k: v for k, v in trainer.logged_metrics.items() 
+                k: v
+                for k, v in trainer.logged_metrics.items()
                 if k in self.metrics_to_track
             }
             self._collection.append(copy.deepcopy(tracked_metrics))
@@ -57,16 +58,18 @@ def metrics(self):
             return {}
 
         # Get intersection of keys across all collected dictionaries
-        common_keys = set(self._collection[0]).intersection(*self._collection[1:])
-
+        common_keys = set(self._collection[0]).intersection(
+            *self._collection[1:]
+        )
+
         # Stack the metric values for common keys and return
         return {
             k: torch.stack([dic[k] for dic in self._collection])
-            for k in common_keys if k in self.metrics_to_track
+            for k in common_keys
+            if k in self.metrics_to_track
         }
 
 
-
 class PINAProgressBar(TQDMProgressBar):
 
     BAR_FORMAT = "{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_noinv_fmt}{postfix}]"
@@ -87,7 +90,7 @@ def __init__(self, metrics="val", **kwargs):
         :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'])
@@ -142,7 +145,8 @@ def on_fit_start(self, trainer, pl_module):
         for key in self._sorted_metrics:
             if (
                 key not in trainer.solver.problem.conditions.keys()
-                and key != "train" and key != "val"
+                and key != "train"
+                and key != "val"
             ):
                 raise KeyError(f"Key '{key}' is not present in the dictionary")
         # add the loss pedix

pina/callbacks/__init__.py

@@ -1,10 +1,14 @@
-__all__ = [
-    "SwitchOptimizer",
-    "R3Refinement",
-    "MetricTracker",
-    "PINAProgressBar",
-]
+import warnings
 
-from .optimizer_callbacks import SwitchOptimizer
-from .adaptive_refinment_callbacks import R3Refinement
-from .processing_callbacks import MetricTracker, PINAProgressBar
+from ..callback import *
+from ..utils import custom_warning_format
+
+# back-compatibility 0.1
+# Set the custom format for warnings
+warnings.formatwarning = custom_warning_format
+warnings.filterwarnings("always", category=DeprecationWarning)
+warnings.warn(
+    f"'pina.callbacks' is deprecated and will be removed "
+    f"in future versions. Please use 'pina.callback' instead.",
+    DeprecationWarning,
+)

pina/collector.py

@@ -1,6 +1,7 @@
 """
 # TODO
 """
+
 from .graph import Graph
 from .utils import check_consistency
 
@@ -14,14 +15,12 @@ def __init__(self, problem):
         # those variables are used for the dataloading
         self._data_collections = {name: {} for name in self.problem.conditions}
         self.conditions_name = {
-            i: name
-            for i, name in enumerate(self.problem.conditions)
+            i: name for i, name in enumerate(self.problem.conditions)
         }
 
         # variables used to check that all conditions are sampled
         self._is_conditions_ready = {
-            name: False
-            for name in self.problem.conditions
+            name: False for name in self.problem.conditions
         }
         self.full = False
 
@@ -51,13 +50,16 @@ def store_fixed_data(self):
         for condition_name, condition in self.problem.conditions.items():
             # if the condition is not ready and domain is not attribute
             # of condition, we get and store the data
-            if (not self._is_conditions_ready[condition_name]) and (not hasattr(
-                    condition, "domain")):
+            if (not self._is_conditions_ready[condition_name]) and (
+                not hasattr(condition, "domain")
+            ):
                 # get data
                 keys = condition.__slots__
                 values = [getattr(condition, name) for name in keys]
-                values = [value.data if isinstance(
-                    value, Graph) else value for value in values]
+                values = [
+                    value.data if isinstance(value, Graph) else value
+                    for value in values
+                ]
                 self.data_collections[condition_name] = dict(zip(keys, values))
                 # condition now is ready
                 self._is_conditions_ready[condition_name] = True
@@ -74,6 +76,6 @@ def store_sample_domains(self):
             samples = self.problem.discretised_domains[condition.domain]
 
             self.data_collections[condition_name] = {
-                'input_points': samples,
-                'equation': condition.equation
+                "input_points": samples,
+                "equation": condition.equation,
             }

pina/condition/__init__.py

@@ -1,12 +1,12 @@
 __all__ = [
-    'Condition',
-    'ConditionInterface',
-    'DomainEquationCondition',
-    'InputPointsEquationCondition',
-    'InputOutputPointsCondition',
+    "Condition",
+    "ConditionInterface",
+    "DomainEquationCondition",
+    "InputPointsEquationCondition",
+    "InputOutputPointsCondition",
 ]
 
 from .condition_interface import ConditionInterface
 from .domain_equation_condition import DomainEquationCondition
 from .input_equation_condition import InputPointsEquationCondition
-from .input_output_condition import InputOutputPointsCondition
+from .input_output_condition import InputOutputPointsCondition