Merge pull request #281 from lnccbrown/doc-design-improvement

Some cosmetic changes to the documentation
lnccbrown · Sep 19, 2023 · c0b1501 · c0b1501
2 parents 3ee1789 + 51f2a12
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Showing 5 changed files with 116 additions and 39 deletions.
diff --git a/docs/index.md b/docs/index.md
@@ -1,21 +1,24 @@
-<div style="position: relative; width: 100%;">
-  <img src="images/mainlogo.png" style="width: 250px;">
-  <a href="https://ccbs.carney.brown.edu/brainstorm" style="position: absolute; right: 0; top: 50%; transform: translateY(-50%);">
-    <img src="images/Brain-Bolt-%2B-Circuits.gif" style="width: 100px;">
-  </a>
+<div>
+    <a href="https://ccbs.carney.brown.edu/brainstorm" style="display: block; float: right; padding: 10px">
+        <img src="images/Brain-Bolt-%2B-Circuits.gif" style="width: 100px;">
+    </a>
+    <img src="images/mainlogo.png" style="width: 250px;">
 </div>
 
-HSSM is a Python toolbox that provides a seamless combination of state-of-the-art likelihood approximation methods with the wider ecosystem of probabilistic programming languages. It facilitates flexible hierarchical model building and inference via modern MCMC samplers. HSSM is user-friendly and provides the ability to rigorously estimate the impact of neural and other trial-by-trial covariates through parameter-wise mixed-effects models for a large variety of cognitive process models.
+![PyPI](https://img.shields.io/pypi/v/hssm)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/HSSM?link=https%3A%2F%2Fpypi.org%2Fproject%2Fhssm%2F)
+![PyPI - Python Version](https://img.shields.io/pypi/pyversions/hssm)
+![GitHub pull requests](https://img.shields.io/github/issues-pr/lnccbrown/HSSM)
+![GitHub Workflow Status (with event)](https://img.shields.io/github/actions/workflow/status/lnccbrown/HSSM/run_tests.yml)
+![GitHub Repo stars](https://img.shields.io/github/stars/lnccbrown/HSSM)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
 
-**Authors**: Alexander Fengler, Aisulu Omar, Paul Xu, Krishn Bera, Michael J. Frank
+**HSSM** (Hierarchical Sequential Sampling Modeling) is a modern Python toolbox that provides state-of-the-art likelihood approximation methods within the Python Bayesian ecosystem. It facilitates hierarchical model building and inference via fast and robust MCMC samplers. User-friendly, extensible, and flexible, HSSM can rigorously estimate the impact of neural and other trial-by-trial covariates through parameter-wise mixed-effects models for a large variety of cognitive process models.
 
-**Contacts**: [email protected]
 
-**GitHub**: https://github.com/lnccbrown/HSSM
+HSSM is a [BRAINSTORM](https://ccbs.carney.brown.edu/brainstorm) project in collaboration with the [Center for Computation and Visualization (CCV)](https://ccv.brown.edu/) and the [Center for Computational Brain Science](https://ccbs.carney.brown.edu/) within the [Carney Institute at Brown University](https://www.brown.edu/carney/).
 
-**Copyright**: This document has been placed in the public domain.
-
-**License**: HSSM is licensed under [Copyright 2023, Brown University, Providence, RI](../LICENSE)
+## Features
 
 - Allows approximate hierarchical Bayesian inference via various likelihood approximators.
 - Estimate impact of neural and other trial-by-trial covariates via native hierarchical mixed-regression support.
@@ -29,13 +32,13 @@ HSSM is a Python toolbox that provides a seamless combination of state-of-the-ar
 
 `hssm` is available through PyPI. You can install it with Pip via:
 
-```
+```bash
 pip install hssm
 ```
 
 You can also install the bleeding edge version of `hssm` directly from this repo:
 
-```
+```bash
 pip install git+https://github.com/lnccbrown/HSSM.git
 ```
 

diff --git a/docs/overrides/main.html b/docs/overrides/main.html
@@ -8,7 +8,7 @@
     Navigate the site here!
 </span>
 <span class="right-margin">
-    v0.1.4 is out!
+    v0.1.5 is released!
 </span>
 <span>
     <span class="twemoji">

diff --git a/docs/styles/extra.css b/docs/styles/extra.css
@@ -1,45 +1,99 @@
+[data-md-color-scheme="ocean"] {
+  --md-primary-fg-color: #245c81;
+  --md-accent-fg-color: #ec205b;
+  --md-typeset-a-color: #338fb8;
+}
+
+[data-md-color-scheme="slate"] {
+  --md-primary-fg-color: #245c81;
+  --md-accent-fg-color: #ec205b;
+  --md-typeset-a-color: #00b4b5;
+}
+
 .md-header-nav__title img {
-    height: 200px;  /* adjust size as needed */
-    width: 200px;  /* adjust size as needed */
+  height: 200px; /* adjust size as needed */
+  width: 200px; /* adjust size as needed */
+}
+
+.md-typeset h1 {
+  font-weight: 300;
+}
+
+h2 {
+  font-weight: 700;
+}
+
+.md-header-__title {
+  font-size: 1rem;
+}
+
+ul .md-nav__list {
+  margin-left: 0.2rem;
+}
+
+.md-nav__list label {
+  font-size: 0.7rem;
+  font-weight: 600;
+  margin-bottom: 0.8rem;
+  text-transform: uppercase;
+}
+
+.md-nav__title {
+  font-size: 0.7rem;
+  font-weight: 600;
+  margin-bottom: 0.8rem;
+  text-transform: uppercase;
+}
+
+div .md-sidebar__inner {
+  margin-left: 0.2rem;
+}
+
+div .md-sidebar__inner nav .md-nav__list {
+  margin-left: 0.2rem;
+}
+
+div .md-sidebar__inner {
+  margin-left: 0.2rem;
 }
 
 #wrapper {
-    display: grid;
-    grid-template-columns: 33.334% 66.667%;
+  display: grid;
+  grid-template-columns: 33.334% 66.667%;
 }
 
 #main-logo {
-    text-align: center;
+  text-align: center;
 }
 
 #main-title {
-    padding-top: 1.3em;
-    padding-left: 1.0em;
+  padding-top: 1.3em;
+  padding-left: 1em;
 }
 
 .right-margin {
-    margin-right: 10px;
+  margin-right: 10px;
 }
 
 @media screen and (min-width: 1220px) {
-    .show-on-small {
-        display: none
-    }
+  .show-on-small {
+    display: none;
+  }
 }
 
 .move-down {
-    transform: translate(0px, -5px);
-    animation: down_movement 1000ms infinite ease-out;
+  transform: translate(0px, -5px);
+  animation: down_movement 1000ms infinite ease-out;
 }
 
 @keyframes down_movement {
-    to {
-        transform: translate(0px, 5px);
-    }
+  to {
+    transform: translate(0px, 5px);
+  }
 }
 
 .question {
-    color: pink;
-    width: 35px;
-    height: 35px;
+  color: pink;
+  width: 35px;
+  height: 35px;
 }
diff --git a/mkdocs.yml b/mkdocs.yml
@@ -28,7 +28,7 @@ plugins:
   - search
   - autorefs
   - mkdocs-jupyter:
-      execute: True
+      execute: true
       execute_ignore:
         - getting_started/getting_started.ipynb
         - tutorials/main_tutorial.ipynb
@@ -52,13 +52,19 @@ plugins:
             docstring_section_style: "list"
             show_root_members_full_path: true
             show_object_full_path: false
-            show_category_heading: true
+            show_category_heading: false
             show_signature_annotations: true
             show_source: false
-            group_by_category: false
+            group_by_category: true
             signature_crossrefs: true
+            summary: true
+            filters:
+            - "!^_"
 theme:
   logo: images/navlogo.png
+  font:
+    text: Noto Sans
+    code: Roboto Mono
   name: material
   custom_dir: docs/overrides
   features:
@@ -76,20 +82,24 @@ theme:
   palette:
     # Palette toggle for automatic mode
     - media: "(prefers-color-scheme)"
+      scheme: ocean
+      primary: custom
       toggle:
         icon: material/brightness-auto
         name: Switch to light mode
 
     # Palette toggle for light mode
     - media: "(prefers-color-scheme: light)"
-      scheme: default
+      scheme: ocean
+      primary: custom
       toggle:
         icon: material/brightness-7
         name: Switch to dark mode
 
     # Palette toggle for dark mode
     - media: "(prefers-color-scheme: dark)"
       scheme: slate
+      primary: custom
       toggle:
         icon: material/brightness-4
         name: Switch to system preference
@@ -101,8 +111,11 @@ extra_css:
   - "styles/extra.css"
 
 markdown_extensions:
+  - toc:
+      permalink: true
   - admonition
   - pymdownx.highlight:
+      use_pygments: true
       anchor_linenums: true
       line_spans: __span
       pygments_lang_class: true

diff --git a/src/hssm/__init__.py b/src/hssm/__init__.py
@@ -1,4 +1,11 @@
-"""HSSM - Hierarchical Sequential Sampling Models."""
+"""Top-level entry to the HSSM package.
+
+The `hssm` module is the top-level entry to the HSSM package. It exports some of the
+most important classes, including the `HSSM` class that handles model creation and
+sampling. You will also find utility classes such as `hssm.Prior`, `hssm.ModelConfig`,
+and `hssm.Param`, with which users will often interface. Additionally, most frequently
+used utility functions can also be found.
+"""
 
 import importlib.metadata
 import logging