diff --git a/docs/source/conf.py b/docs/source/conf.py
index 7efb647..b61041d 100644
--- a/docs/source/conf.py
+++ b/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': '',
@@ -342,4 +376,4 @@ texinfo_documents = [
# If true, do not generate a @detailmenu in the "Top" node's menu.
# texinfo_no_detailmenu = False
-autodoc_member_order = 'bysource'
\ No newline at end of file
+autodoc_member_order = 'bysource'
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 684867f..afdb471 100644
--- a/docs/source/index.rst
+++ b/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 `_ and `PyTorchLightning `_,
-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 `_ 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 `_ 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 `_ 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 `_ and `PyTorchLightning `_, **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.
+
+
+
+ Contact us by email for further information or questions about **PINA**, or suggest pull requests.
+
-.. toctree::
- :maxdepth: 1
- :caption: Package Documentation:
-
- API <_rst/_code>
+ .. grid-item-card:: Contents
+ :class-title: sd-fs-5
+ :class-body: sd-pl-4
-.. 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
-.. toctree::
- :maxdepth: 1
- :caption: Getting Started:
+ Installing <_rst/_installation>
+ Tutorial <_rst/_tutorial>
+ API <_rst/_code>
- Installation <_rst/_installation>
- Tutorials <_rst/_tutorial>
+.. .. grid-item-card:: Features
+.. :columns: 12 12 4 4
+.. :class-title: sd-fs-5
+.. :class-body: sd-pl-3
-.. toctree::
- :maxdepth: 1
- :caption: Community:
-
- Team & Fundings <_team.rst>
- Contributing <_rst/_contributing>
- License <_LICENSE.rst>
- Cite PINA <_cite.rst>
+.. * :bdg-secondary:`New` Objects: :ref:`API ` | :doc:`Tutorial `
+.. * Relational plots: :ref:`API ` | :doc:`Tutorial `
+.. * Distribution plots: :ref:`API ` | :doc:`Tutorial `
+.. * Categorical plots: :ref:`API ` | :doc:`Tutorial `
+.. * Regression plots: :ref:`API ` | :doc:`Tutorial `
+.. * Multi-plot grids: :ref:`API ` | :doc:`Tutorial `
+.. * Figure theming: :ref:`API ` | :doc:`Tutorial `
+.. * Color palettes: :ref:`API ` | :doc:`Tutorial `
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