Ultralytics Code Refactor https://ultralytics.com/actions

README.md

@@ -54,7 +54,7 @@ If this dataset and our analysis tools facilitate your research or pique your in
 
 ## 🤝 Contribute
 
-We welcome contributions from the community! Whether you're fixing bugs, adding new features, or improving documentation, your input is invaluable. Take a look at our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started. Also, we'd love to hear about your experience with Ultralytics products. Please consider filling out our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge 🙏 and thank you to all of our contributors!
+We welcome contributions from the community! Whether you're fixing bugs, adding new features, or improving documentation, your input is invaluable. Take a look at our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started. Also, we'd love to hear about your experience with Ultralytics products. Please consider filling out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge 🙏 and thank you to all of our contributors!
 
 <!-- Ultralytics contributors -->
 
@@ -65,12 +65,12 @@ We welcome contributions from the community! Whether you're fixing bugs, adding
 
 Ultralytics is excited to offer two different licensing options to meet your needs:
 
-- **AGPL-3.0 License**: Perfect for students and hobbyists, this [OSI-approved](https://opensource.org/licenses/) open-source license encourages collaborative learning and knowledge sharing. Please refer to the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for detailed terms.
-- **Enterprise License**: Ideal for commercial use, this license allows for the integration of Ultralytics software and AI models into commercial products without the open-source requirements of AGPL-3.0. For use cases that involve commercial applications, please contact us via [Ultralytics Licensing](https://ultralytics.com/license).
+- **AGPL-3.0 License**: Perfect for students and hobbyists, this [OSI-approved](https://opensource.org/license) open-source license encourages collaborative learning and knowledge sharing. Please refer to the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for detailed terms.
+- **Enterprise License**: Ideal for commercial use, this license allows for the integration of Ultralytics software and AI models into commercial products without the open-source requirements of AGPL-3.0. For use cases that involve commercial applications, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).
 
 ## 📬 Contact Us
 
-For bug reports, feature requests, and contributions, head to [GitHub Issues](https://github.com/ultralytics/velocity/issues). For questions and discussions about this project and other Ultralytics endeavors, join us on [Discord](https://ultralytics.com/discord)!
+For bug reports, feature requests, and contributions, head to [GitHub Issues](https://github.com/ultralytics/velocity/issues). For questions and discussions about this project and other Ultralytics endeavors, join us on [Discord](https://discord.com/invite/ultralytics)!
 
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test_singles.py

@@ -24,7 +24,7 @@ def calibrate_energy(x):
     # plt.plot(px, py, '.')
     # plt.plot(xspan, exp1(xspan, *fit1))
 
-    label = "%.3g * exp(%.3g * x) + %.3g" % tuple(fit1)
+    label = "{:.3g} * exp({:.3g} * x) + {:.3g}".format(*tuple(fit1))
     return exp1(x, *fit1).clip(max=2000), label
 
 
@@ -34,7 +34,7 @@ def test_singles(file="my_data_singles.txt"):
     """
     x = np.loadtxt(file)
     t, e, ch = x.transpose()  # time (ps), energy (samples), channel
-    print("Read %s, %g events recorded over %gs" % (file, x.shape[0], (t.max() - t.min()) / 1e12))
+    print(f"Read {file}, {x.shape[0]:g} events recorded over {(t.max() - t.min()) / 1e12:g}s")
 
     # calibrate energy
     e0 = copy(e)
@@ -95,7 +95,7 @@ def plots(x, e0, ecal, dt, efit_label):
         kde=False,
         fit=stats.norm,
         axlabel="time (ps)",
-        label="Normal fit $\mu=${0:.1f}, $\sigma=${1:.2f}".format(*stats.norm.fit(dt)),
+        label="Normal fit $\mu=${:.1f}, $\sigma=${:.2f}".format(*stats.norm.fit(dt)),
         ax=ax[2],
     )
     a.legend()