Update branding

README.md

@@ -1,6 +1,6 @@
 <div align="center">
 
-# SCEPTR
+<img src="https://raw.githubusercontent.com/yutanagano/tidytcells/main/sceptr.svg" width=700>
 
 [![Latest release](https://img.shields.io/pypi/v/sceptr)](https://pypi.org/p/sceptr)
 ![Tests](https://github.com/yutanagano/sceptr/actions/workflows/tests.yaml/badge.svg)

docs/_static/css/colours.css

@@ -0,0 +1,9 @@
+html[data-theme="light"] {
+   --pst-color-primary: #7950f2;
+   --pst-color-secondary: #fd7e14;
+}
+
+html[data-theme="dark"] {
+   --pst-color-primary: #9775fa;
+   --pst-color-secondary: #ff922b;
+}

docs/conf.py

@@ -26,6 +26,11 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
 html_theme = "sphinx_book_theme"
+html_logo = "../sceptr.svg"
+html_static_path = ['_static']
+html_css_files = [
+    'css/colours.css',
+]
 html_theme_options = {
     "repository_url": "https://github.com/yutanagano/sceptr",
     "path_to_docs": "docs",

docs/index.rst

@@ -1,8 +1,8 @@
 SCEPTR
 ======
 
-**SCEPTR** (\ **S**\ imple **C**\ ontrastive **E**\ mbedding of the **P**\ rimary sequence of **T** cell **R**\ eceptors) is a small, fast, and performant TCR representation model that can be used for alignment-free downstream TCR and TCR repertoire analysis such as TCR clustering or classification.
-Our `preprint <https://arxiv.org/abs/2406.06397>`_ demonstrates SCEPTR's state-of-the-art performance (as of 2024) on downstream TCR specificity prediction.
+**SCEPTR** (\ **S**\ imple **C**\ ontrastive **E**\ mbedding of the **P**\ rimary sequence of **T** cell **R**\ eceptors) is a small, fast, and accurate TCR representation model that can be used for alignment-free TCR analysis, including for TCR-pMHC interaction prediction and TCR clustering (metaclonotype discovery).
+Our `preprint <https://arxiv.org/abs/2406.06397>`_ demonstrates that SCEPTR can be used for few-shot TCR specificity prediction with improved accuracy over previous methods.
 
 SCEPTR is a BERT-like transformer-based neural network implemented in `Pytorch <https://pytorch.org>`_.
 With the default model providing best-in-class performance with only 153,108 parameters (typical protein language models have tens or hundreds of millions), SCEPTR runs fast- even on a CPU!