Refactor code for speed and clarity

.github/workflows/format.yml

@@ -18,12 +18,11 @@ jobs:
       - name: Run Ultralytics Formatting
         uses: ultralytics/actions@main
         with:
-          token: ${{ secrets.PERSONAL_ACCESS_TOKEN || secrets.GITHUB_TOKEN }} # note GITHUB_TOKEN automatically generated
+          token: ${{ secrets._GITHUB_TOKEN }} # note GITHUB_TOKEN automatically generated
           labels: true # autolabel issues and PRs
           python: true # format Python code and docstrings
           prettier: true # format YAML, JSON, Markdown and CSS
           spelling: true # check spelling
           links: false # check broken links
           summary: true # print PR summary with GPT4o (requires 'openai_api_key')
-          openai_azure_api_key: ${{ secrets.OPENAI_AZURE_API_KEY }}
-          openai_azure_endpoint: ${{ secrets.OPENAI_AZURE_ENDPOINT }}
+          openai_api_key: ${{ secrets.OPENAI_API_KEY }}

README.md

@@ -1,13 +1,13 @@
 <br>
-<a href="https://ultralytics.com" target="_blank"><img src="https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg" width="320" alt="Ultralytics logo"></a>
+<a href="https://www.ultralytics.com/" target="_blank"><img src="https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg" width="320" alt="Ultralytics logo"></a>
 
 # 🌊 Introduction
 
 Welcome to the [Ultralytics WAVE repository](https://github.com/ultralytics/wave) – the cutting-edge solution for the machine learning driven analysis and interpretation of waveform data in particle physics! 🎉
 
 Here, we introduce **WA**veform **V**ector **E**xploitation (WAVE), a novel approach that uses Deep Learning to readout and reconstruct signals from particle physics detectors. This repository contains our open-source codebase and aims to foster collaboration and innovation in this exciting intersection of ML and physics.
 
-[![Ultralytics Actions](https://github.com/ultralytics/wave/actions/workflows/format.yml/badge.svg)](https://github.com/ultralytics/wave/actions/workflows/format.yml) <a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>
+[![Ultralytics Actions](https://github.com/ultralytics/wave/actions/workflows/format.yml/badge.svg)](https://github.com/ultralytics/wave/actions/workflows/format.yml) <a href="https://discord.com/invite/ultralytics"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>
 
 ## 🚀 Project Objectives
 
@@ -51,9 +51,9 @@ If you use this code in your research or wish to refer to the WAVE methodology,
 
 # 🤝 Contribute
 
-We value each contribution and invite you to participate in developing this pioneering ML approach for physics! Whether you're sharpening bugs, proposing new features, or enriching our documentation, find out how to contribute through our [Contributing Guide](https://docs.ultralytics.com/help/contributing). Also, let us know your thoughts by completing our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A massive thank you 🙏 to everyone involved!
+We value each contribution and invite you to participate in developing this pioneering ML approach for physics! Whether you're sharpening bugs, proposing new features, or enriching our documentation, find out how to contribute through our [Contributing Guide](https://docs.ultralytics.com/help/contributing/). Also, let us know your thoughts by completing our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A massive thank you 🙏 to everyone involved!
 
-![Ultralytics Open-Source Contributors](https://github.com/ultralytics/assets/raw/main/im/image-contributors.png)
+![Ultralytics Open-Source Contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)
 
 # 📄 License
 
@@ -80,5 +80,5 @@ For bug reports, feature requests, and contributions, head to [GitHub Issues](ht
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
   <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics BiliBili"></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
+  <a href="https://discord.com/invite/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
 </div>

gcp/wave_pytorch_gcp.py

@@ -28,11 +28,17 @@ def runexample(H, model, str, lr=0.001, amsgrad=False):
 
     cuda = torch.cuda.is_available()
     os.makedirs(f"{pathr}models", exist_ok=True)
-    name = (data[:-4] + f"{H[:]}{lr:g}lr{str}").replace(", ", ".").replace("[", "_").replace("]", "_")
+    name = (
+        f"{data[:-4]}{H[:]}{lr:g}lr{str}".replace(", ", ".")
+        .replace("[", "_")
+        .replace("]", "_")
+    )
 
     tica = time.time()
     device = torch.device("cuda:0" if cuda else "cpu")
-    print("Running {} on {}\n{}".format(name, device.type, torch.cuda.get_device_properties(0) if cuda else ""))
+    print(
+        f'Running {name} on {device.type}\n{torch.cuda.get_device_properties(0) if cuda else ""}'
+    )
 
     if not os.path.isfile(pathd + data):
         os.system(f"wget -P data/ https://storage.googleapis.com/ultralytics/{data}")

train.py

@@ -23,7 +23,11 @@ def train(H, model, str, lr=0.001):
 
     cuda = torch.cuda.is_available()
     os.makedirs(f"{pathr}models", exist_ok=True)
-    name = (data[:-4] + f"{H[:]}{lr:g}lr{str}").replace(", ", ".").replace("[", "_").replace("]", "_")
+    name = (
+        f"{data[:-4]}{H[:]}{lr:g}lr{str}".replace(", ", ".")
+        .replace("[", "_")
+        .replace("]", "_")
+    )
     print(f"Running {name}")
 
     device = select_device()

train_tf.py

@@ -25,7 +25,11 @@ def runexample(H, model, str):
     tf.set_random_seed(1)
     path = "data/"
     os.makedirs(f"{path}models", exist_ok=True)
-    name = (data[:-4] + f"{H[:]}{lr:g}lr{eps:g}eps{str}").replace(", ", "_").replace("[", "_").replace("]", "_")
+    name = (
+        f"{data[:-4]}{H[:]}{lr:g}lr{eps:g}eps{str}".replace(", ", "_")
+        .replace("[", "_")
+        .replace("]", "_")
+    )
 
     tica = time.time()
     device = "/gpu:0" if cuda else "/cpu:0"