213 clean up documentation

romtools/__init__.py

@@ -72,23 +72,21 @@
   - ROM/FOM exploitation via sampling
   - ROM/FOM exploitation via Dakota-driven sampling
 
-# Demos/HOWTOs
+# Demos/tutorials
 
-In this section, we present some demos/examples showing how to use `romtools` in practice.
-
-TBD
+Please see https://pressio.github.io/rom-tools-and-workflows-demos for demos and tutorials
 
 # License
 ```plaintext
 .. include:: ../LICENSE
 ```
 '''
 
-__all__ = ['vector_space', 'workflows', 'hyper_reduction']
+__all__ = ['vector_space', 'workflows', 'hyper_reduction','composite_vector_space']
 
 __docformat__ = "restructuredtext" # required to generate the license
 
 from romtools.vector_space import *
 from romtools.hyper_reduction import *
 from romtools.workflows import *
-from romtools.composite_vector_space import *
+from romtools.composite_vector_space import *

romtools/hyper_reduction/__init__.py

@@ -47,7 +47,7 @@
 The hyper-reduction library comprises a set of routines for hyper-reduction. Presently, we support algorithms for
 
 - The discrete empirical interpolation method (DEIM)
-
+- The energy conserving sample mesh and weighting method (ECSW)
 There are a number of approaches to sample mesh degrees of freedom and computed weight matrices for hyper-reduction approaches
 The hyper_reduction module provides these functionalities. Note that certain aspects of hyper-reduction are inherently problem-dependent.
 Implementations of these aspects, including the generation of residual snapshots and the construction of a sample mesh, are left to the user. 

romtools/workflows/model_builders.py

@@ -9,6 +9,11 @@
 from romtools.workflows.models import *
 
 class ModelBuilder(Protocol):
+    '''
+    Main protocol for a ModelBuilder.
+
+    Methods:
+    '''
     def __init__(self):
         pass
 
@@ -17,6 +22,12 @@ def build_from_training_dirs(self,offline_data_dir: str, training_data_dirs: Lis
 
 
 class QoiModelBuilder(Protocol):
+    '''
+    Main protocol for a QoiModelBuilder.
+
+    Methods:
+    '''
+
     def __init__(self):
         pass
 
@@ -25,6 +36,12 @@ def build_from_training_dirs(self,offline_data_dir: str, training_data_dirs: Lis
 
 
 class QoiModelWithErrorEstimateBuilder(Protocol):
+    '''
+    Main protocol for a QoiModelWithErrorEstimateBuilder.
+
+    Methods:
+    '''
+
     def __init__(self):
         pass
 

romtools/workflows/parameter_spaces.py

@@ -76,13 +76,10 @@ def get_dimensionality(self) -> int:
     @abc.abstractmethod
     def generate_samples(self, number_of_samples: int, seed=None) -> np.array:
         """
-        Generates samples from the parameter space given a set of samples
-        from a uniform distribution on (0,1)
+        Generates samples from the parameter space
 
-        uniform_dist_samples should be of shape
+        Returns np.array of shape
         (number_of_samples, self.get_dimensionality())
-
-        Returns np.array of the same shape
         """