Merge branch 'master' into multirom

Author: ndem0

.github/workflows/testing_pr.yml

@@ -13,7 +13,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [windows-latest, macos-latest, ubuntu-latest]
-        python-version: [3.7, 3.8]
+        python-version: [3.8, 3.9, 3.10, 3.11]
 
     steps:
     - uses: actions/checkout@v2

.gitignore

@@ -66,3 +66,6 @@ target/
 
 #Ipython Notebook
 .ipynb_checkpoints
+
+#revieweing and package modernization
+venv_ezyrb/

docs/source/conf.py

@@ -18,12 +18,22 @@
 import sphinx
 from sphinx.errors import VersionRequirementError
 import sphinx_rtd_theme
+import time
+import importlib.metadata
+
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 sys.path.insert(0, os.path.abspath('../..'))
-import ezyrb.meta as meta
+
+
+# -- Project infirmation --------
+_DISTRIBUTION_METADATA = importlib.metadata.metadata("ezyrb")
+project = _DISTRIBUTION_METADATA["Name"]
+copyright = f'2016-{time.strftime("%Y")}, EZyRB contributors'
+author = _DISTRIBUTION_METADATA["Author"]
+
 
 # -- General configuration ------------------------------------------------
 
@@ -69,10 +79,6 @@
 # The master toctree document.
 master_doc = 'index'
 
-# General information about the project.
-project = meta.__project__
-copyright = meta.__copyright__
-author = meta.__author__
 
 # autoclass
 autoclass_content = 'both'
@@ -82,9 +88,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = meta.__version__
-# The full version, including alpha/beta/rc tags.
+version = _DISTRIBUTION_METADATA["Version"]
 release = version
+# The full version, including alpha/beta/rc tags.
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -287,7 +293,7 @@
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    (master_doc, meta.__title__, u'EZyRB Documentation',
+    (master_doc, 'ezyrb' , u'EZyRB Documentation',
      [author], 1)
 ]
 

ezyrb/__init__.py

@@ -7,7 +7,6 @@
     'MultiReducedOrderModel'
 ]
 
-from .meta import *
 from .database import Database
 from .snapshot import Snapshot
 from .parameter import Parameter

ezyrb/approximation/ann.py

@@ -60,6 +60,13 @@ def __init__(self, layers, function, stop_training, loss=None,
         if not isinstance(stop_training, list):
             stop_training = [stop_training]
 
+        if torch.cuda.is_available(): # Check if GPU is available
+            print("Using cuda device")
+            torch.cuda.empty_cache()
+            self.use_cuda = True
+        else:
+            self.use_cuda = False
+
         self.layers = layers
         self.function = function
         self.loss = loss
@@ -156,13 +163,19 @@ def fit(self, points, values):
         """
 
         self._build_model(points, values)
+
+        if self.use_cuda:
+            self.model = self.model.cuda()
+            points = self._convert_numpy_to_torch(points).cuda()
+            values = self._convert_numpy_to_torch(values).cuda()
+        else:
+            points = self._convert_numpy_to_torch(points)
+            values = self._convert_numpy_to_torch(values)
+
         optimizer = self.optimizer(
             self.model.parameters(),
             lr=self.lr, weight_decay=self.l2_regularization)
 
-        points = self._convert_numpy_to_torch(points)
-        values = self._convert_numpy_to_torch(values)
-
         n_epoch = 1
         flag = True
         while flag:
@@ -201,6 +214,12 @@ def predict(self, new_point):
         :return: the predicted values via the ANN.
         :rtype: numpy.ndarray
         """
-        new_point = self._convert_numpy_to_torch(np.array(new_point))
-        y_new = self.model(new_point)
-        return self._convert_torch_to_numpy(y_new)
+        if self.use_cuda :
+            new_point = self._convert_numpy_to_torch(new_point).cuda()
+            new_point = self._convert_numpy_to_torch(
+                np.array(new_point.cpu())).cuda()
+            y_new = self._convert_torch_to_numpy(self.model(new_point).cpu())
+        else:
+            new_point = self._convert_numpy_to_torch(np.array(new_point))
+            y_new = self._convert_torch_to_numpy(self.model(new_point))
+        return y_new

ezyrb/meta.py

@@ -3,6 +3,7 @@
 import numpy as np
 import torch
 
+from ezyrb import Database, Snapshot, Parameter
 from .plugin import Plugin
 
 
@@ -152,16 +153,25 @@ def fit_preprocessing(self, rom):
                 snap.values.reshape(-1, 1))
 
             snap.values = self.interpolator.predict(
-                reference_snapshot.space.reshape(-1, 1)).flatten()
+                reference_snapshot.space.reshape(-1, 1)).flatten()  # reconstructing shifted snapshots in physical space 
 
     def predict_postprocessing(self, rom):
 
         ref_space = self.reference_snapshot.space
+        db = Database()
 
         for param, snap in rom.predict_full_database._pairs:
             input_shift = np.hstack([
                 ref_space.reshape(-1, 1),
                 np.ones(shape=(ref_space.shape[0], 1))*param.values])
             shift = self.shift_network.predict(input_shift)
-            snap.space = ref_space + shift.flatten()
+            snap.space = ref_space + shift.flatten()    # shifted space transports to correct physical frame 
             snap.space = snap.space.flatten()
+
+            self.interpolator.fit(snap.space, snap.values.reshape(-1, 1))
+            snap.values = self.interpolator.predict(ref_space)  # reconstruct snapshot in physical space
+
+            snaps = Snapshot(values = snap.values, space = ref_space)
+            db.add(Parameter(param.values), snaps)
+
+        rom._full_database = db

ezyrb/plugin/automatic_shift.py

@@ -26,7 +26,7 @@ def __init__(self, scaler, mode, target) -> None:
         self.scaler = scaler
         self.mode = mode
         self.target = target
-
+        
     @property
     def target(self):
         """
@@ -141,5 +141,5 @@ def rom_postprocessing(self, rom):
                 db.parameters_matrix,
                 self.scaler.inverse_transform(self._select_matrix(db)),
             )
-
+            
         rom._reduced_database = new_db