starting doc

docs/source/conf.py

@@ -15,13 +15,16 @@
 import sys
 import os
 import sphinx_rtd_theme
-
-# 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 pina
 
+# -- Project information -----------------------------------------------------
+
+project = pina.__project__
+copyright = pina.__copyright__
+author = pina.__author__
+version = release ='1.0.0' 
+
+
 sys.path.insert(0, os.path.abspath('../sphinx_extensions')) # extension to remove paramref link from lightinig
 
 # -- General configuration ------------------------------------------------
@@ -49,13 +52,30 @@ extensions = [
     'sphinx.ext.intersphinx',
     'paramref_extension',    # this extension is made to remove paramref links from lightining doc
     'sphinx_copybutton', 
+    'sphinx_design'
 ]
 
+# The root document.
+root_doc = 'index'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'docstrings', 'nextgen', 'Thumbs.db', '.DS_Store']
+
+# The reST default role (used for this markup: `text`) to use for all documents.
+#default_role = 'literal'
+
+# Generate the API documentation when building
+autosummary_generate = True
+numpydoc_show_class_members = False
+
 intersphinx_mapping = {
     'python': ('http://docs.python.org/3', None),
     # 'numpy': ('http://docs.scipy.org/doc/numpy/', None),
     # 'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
     'matplotlib': ('http://matplotlib.sourceforge.net/', None),
+    # 'matplotlib': ('https://matplotlib.org/stable', None),
     'torch': ('https://pytorch.org/docs/stable/', None), 
     'pytorch_lightning': ("https://lightning.ai/docs/pytorch/stable/", None),
     }
@@ -68,7 +88,6 @@ nitpick_ignore = [
     ('py:func', 'torch.inference_mode'),
     ('py:func', 'torch.no_grad'),
     ('py:class', 'torch.utils.data.DistributedSampler'),
-    ('py:class', 'CartesianDomain'), # TO FIX
     ('py:class', 'pina.model.layers.convolution.BaseContinuousConv'),
     ('py:class', 'Module'),
     ('py:class', 'torch.nn.modules.loss._Loss'), # TO FIX
@@ -162,7 +181,7 @@ viewcode_import = True
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
-html_theme = "sphinx_rtd_theme"
+html_theme = 'pydata_sphinx_theme'
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
@@ -181,12 +200,26 @@ html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 
 # The name of an image file (relative to this directory) to place at the top
 # of the sidebar.
-html_logo = "index_files/pina_logo.png"
+html_logo = "index_files/pina_logo_pina.png"
 html_theme_options = {
-    'logo_only': True,
-    'display_version': True,
-    'prev_next_buttons_location': 'bottom',
-
+    "icon_links": [
+        {
+            "name": "GitHub",
+            "url": "https://github.com/mathLab/PINA",
+            "icon": "fab fa-github",
+            "type": "fontawesome",    
+        },
+        {
+            "name": "Twitter",
+            "url": "http://twitter.com/rozzagroup",
+            "icon": "fab fa-twitter",
+            "type": "fontawesome",
+        },
+    ],
+    "show_prev_next": False,
+    "navbar_start": ["navbar-logo"],
+    "navbar_end": ["navbar-icon-links"],
+    "header_links_before_dropdown": 8,
 }
 
 # The name of an image file (within the static path) to use as favicon of the
@@ -195,16 +228,17 @@ html_theme_options = {
 # html_favicon = None
 
 # Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
+# relative to this directory. They are copied after the builtin static files,# so a file named "default.css" will overwrite the builtin "default.css".
 html_static_path = ['_static']
-
+html_css_files = [
+    '/css/custom.css',
+]
 # Add any extra paths that contain custom files (such as robots.txt or
 # .htaccess) here, relative to this directory. These files are copied
 # directly to the root of the documentation.
 # html_extra_path = ['_tutorial']
 
-# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# If not ''i, a 'Last updated on:' timestamp is inserted at every page bottom,
 # using the given strftime format.
 html_last_updated_fmt = '%b %d, %Y'
 
@@ -269,7 +303,7 @@ latex_elements = {
     'papersize': 'a4paper',
 
     # The font size ('10pt', '11pt' or '12pt').
-    'pointsize': '10pt',
+    'pointsize': '20pt',
 
     # Additional stuff for the LaTeX preamble.
     'preamble': '',

docs/source/index.rst

@@ -1,73 +1,82 @@
-Welcome to PINA's documentation!
-===================================================
+:html_theme.sidebar_secondary.remove:
 
-Physics Informed Neural network for Advanced modeling (**PINA**) is
-an open-source Python library providing an intuitive interface for
-solving differential equations using PINNs, NOs or both together.
-Based on `PyTorch <https://pytorch.org/>`_ and `PyTorchLightning <https://lightning.ai/docs/pytorch/stable/>`_,
-PINA offers a simple and intuitive way to formalize a specific (differential) problem
-and solve it using neural networks . The approximated solution of a differential equation
-can be implemented using PINA in a few lines of code thanks to the intuitive and user-friendly interface.
+Welcome to PINA’s documentation!
+=======================================
 
-`PyTorchLightning <https://lightning.ai/docs/pytorch/stable/>`_ as backhand is done to offer
-professional AI researchers and machine learning engineers the possibility of using advancement
-training strategies provided by the library, such as multiple device training, modern model compression techniques,
-gradient accumulation, and so on. In addition, it provides the possibility to add arbitrary
-self-contained routines (callbacks) to the training for easy extensions without the need to touch the
-underlying code.
+.. grid:: 6
+  :gutter: 1
 
-The high-level structure of the package is depicted in our API. The pipeline to solve differential equations
-with PINA follows just five steps: problem definition, model selection, data generation, solver selection, and training.
+  .. grid-item::
 
-.. figure:: index_files/API_color.png
-    :alt: PINA application program interface
-    :align: center
-    :width: 600
+      .. image:: index_files/tutorial_13_3.png
+        :target: _rst/tutorials/tutorial2/tutorial.html
 
-|
+  .. grid-item::
 
-Physics-informed neural network
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+      .. image:: index_files/tutorial_32_0.png
+        :target: _rst/tutorials/tutorial4/tutorial.html
 
-`PINN <https://www.sciencedirect.com/science/article/abs/pii/S0021999118307125>`_ is a novel approach that
-involves neural networks to solve differential equations in an unsupervised manner, while respecting
-any given law of physics described by general differential equations. Proposed in "*Physics-informed neural
-networks: A deep learning framework for solving forward and inverse problems
-involving nonlinear partial differential equations*", such framework aims to
-solve problems in a continuous and nonlinear settings. 
+  .. grid-item::
 
-Neural operator learning
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+      .. image:: index_files/tutorial_13_01.png
+        :target: _rst/tutorials/tutorial9/tutorial.html
 
-`Neural Operators <https://www.jmlr.org/papers/v24/21-1524.html>`_ is a novel approach involving neural networks
-to learn differential operators using supervised learning strategies. By learning the differential operator, the
-neural network is able to generalize across different instances of the differential equations (e.g. different forcing
-terms), without the need of re-training. 
+  .. grid-item::
+
+      .. image:: index_files/tutorial_5_0.png
+        :target: _rst/tutorials/tutorial10/tutorial.html
+
+  .. grid-item::
+
+      .. image:: index_files/tutorial_36_0.png
+        :target: _rst/tutorials/tutorial6/tutorial.html
+
+  .. grid-item::
+
+      .. image:: index_files/tutorial_15_0.png
+        :target: _rst/tutorials/tutorial13/tutorial.html
+
+.. grid:: 1 1 3 3
+
+  .. grid-item::
+    :columns: 12 12 6 6
+
+    Physics Informed Neural network for Advanced modeling (**PINA**) is
+    an open-source Python library providing an intuitive interface for
+    solving differential equations using PINNs, NOs or both together.
+
+    Based on `PyTorch <https://pytorch.org/>`_ and `PyTorchLightning <https://lightning.ai/docs/pytorch/stable/>`_, **PINA** offers a simple and intuitive way to formalize a specific (differential) problem
+    and solve it using neural networks . The approximated solution of a differential equation
+    can be implemented using PINA in a few lines of code thanks to the intuitive and user-friendly  interface.        
 
  
 
-.. toctree::
-   :maxdepth: 1
-   :caption: Package Documentation:
+    Contact us by email for further information or questions about **PINA**, or suggest pull requests. 
 
-   API <_rst/_code>
 
-.. the following is demo content intended to showcase some of the features you can invoke in reStructuredText
-.. this can be safely deleted or commented out
-.. ........................................................................................
     
-.. toctree::
-    :maxdepth: 1
-    :caption: Getting Started:
 
-    Installation <_rst/_installation>
-    Tutorials <_rst/_tutorial>
+  .. grid-item-card:: Contents
+    :class-title: sd-fs-5
+    :class-body: sd-pl-4
 
-.. toctree::
-    :maxdepth: 1
-    :caption: Community:
+    .. toctree::
+      :maxdepth: 1
 
-    Team & Fundings <_team.rst>
-    Contributing <_rst/_contributing>
-    License <_LICENSE.rst>
-    Cite PINA <_cite.rst>
+      Installing <_rst/_installation>
+      Tutorial <_rst/_tutorial>
+      API <_rst/_code>
+
+..   .. grid-item-card:: Features
+..     :columns: 12 12 4 4
+..     :class-title: sd-fs-5
+..     :class-body: sd-pl-3
+
+..     * :bdg-secondary:`New` Objects: :ref:`API <objects_api>` | :doc:`Tutorial <tutorial/objects_interface>`
+..     * Relational plots: :ref:`API <relational_api>` | :doc:`Tutorial <tutorial/relational>`
+..     * Distribution plots: :ref:`API <distribution_api>` | :doc:`Tutorial <tutorial/distributions>`
+..     * Categorical plots: :ref:`API <categorical_api>` | :doc:`Tutorial <tutorial/categorical>`
+..     * Regression plots: :ref:`API <regression_api>` | :doc:`Tutorial <tutorial/regression>`
+..     * Multi-plot grids: :ref:`API <grid_api>` | :doc:`Tutorial <tutorial/axis_grids>`
+..     * Figure theming: :ref:`API <style_api>` | :doc:`Tutorial <tutorial/aesthetics>`
+..     * Color palettes: :ref:`API <palette_api>` | :doc:`Tutorial <tutorial/color_palettes>`