Sherpa is a powerful modeling and fitting application designed for Python, enabling users to merge simple models into complex expressions for data fitting using multiple statistical and optimization methods. With extensive user-friendliness, it serves both as a high-level interface and a library component, allowing users to interact seamlessly with Jupyter notebooks and other applications.

What Can You Do with Sherpa?

Sherpa offers a plethora of capabilities that include:

• Fitting 1D data such as spectra, surface brightness profiles, light curves, and general ASCII arrays.
• Fitting 2D images in Poisson-Gaussian regimes.
• Building complex model expressions tailored to specific needs.
• Importing and utilizing your own models.
• Employing appropriate statistics for modeling Poisson or Gaussian data.
• Importing new statistics with required priors.
• Visualizing parameter space through simulations and 1D or 2D cuts.
• Calculating confidence levels on the best-fit model parameters.
• Selecting a robust optimization method for fitting models such as Levenberg-Marquardt, Nelder-Mead Simplex, or Monte Carlo Differential Evolution.

Documentation is available at Read The Docs and also for Sherpa in CIAO.

How To Install Sherpa

Using Conda

To install Sherpa using the Conda package manager, ensure you have Conda installed and execute the following command:

conda install -c https://cxc.cfa.harvard.edu/conda/sherpa -c conda-forge sherpa

Using pip

If you prefer using pip, which requires that the NumPy package is already installed, you can run the following command:

pip install sherpa

Building from Source

For platforms incompatible with binary builds, or for users who need custom build options, building from source is also an option. Detailed steps for this installation method can be found in the building from source documentation.

Troubleshooting Installation Issues

If you encounter installation issues, consider the following troubleshooting steps:

• Ensure that you are using the correct Python version. Sherpa is tested against Python versions 3.10, 3.11, and has experimental support for 3.12.
• Check your network connection if you encounter issues downloading packages.
• Make sure that your virtual environment is activated before you attempt the installation.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.