diff --git a/README.md b/README.md index cf92513..cf2db8a 100644 --- a/README.md +++ b/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! @@ -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)!
diff --git a/test_singles.py b/test_singles.py index 6f72476..8d8b0b4 100644 --- a/test_singles.py +++ b/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()