Metadata-Version: 2.1
Name: lsqfitgp
Version: 0.3
Summary: Gaussian processes in nonlinear least-squares fits
Home-page: https://github.com/Gattocrucco/lsqfitgp
Author: Giacomo Petrillo
Author-email: info@giacomopetrillo.com
License: UNKNOWN
Description: [![Documentation Status](https://readthedocs.org/projects/lsqfitgp/badge/?version=latest)](https://lsqfitgp.readthedocs.io/en/latest/?badge=latest)
        
        # lsqfitgp
        
        Module for manipulating gaussian processes. Features:
        
          * Use [gvar](https://github.com/gplepage/gvar) to keep track transparently of
            correlations between prior, data and posterior.
        
          * Fit a latent gaussian process in a nonlinear model with
            [lsqfit](https://github.com/gplepage/lsqfit).
            
          * [autograd](https://github.com/HIPS/autograd)-friendly.
          
          * Supports multidimensional structured non-numerical input with named
            dimensions.
            
          * Apply arbitrary linear transformations to the process.
          
          * Use dictionaries to manipulate hyperparameters and hyperpriors. Use
            `gvar.BufferDict` to transparently apply transformations.
            
          * Get a covariance matrix for the optimized hyperparameters.
          
        ## Installation
        
        ```
        pip install lsqfitgp
        ```
        
        ## Documentation
        
        The manual is available on
        [readthedocs](https://lsqfitgp.readthedocs.io/en/latest/index.html). All the
        code is documented with docstrings, so you can also use the Python help system
        directly from the shell:
        
        ```python
        >>> import lsqfitgp as lgp
        >>> help(lgp)
        >>> help(lgp.something)
        ```
        
        or, in an IPython shell/Jupyter notebook/Spyder IDE, use the question mark
        shortcut:
        
        ```
        In [1]: lgp?
        
        In [2]: lgp.something?
        ```
        
        ### Building the manual from source
        
        ```sh
        pip install sphinx<2
        cd docs
        make html
        ```
        
        If you add kernels, run `kernelsref.py` to regenerate `kernelsref.rst`.
        
        If you add a documentation page with code examples, use `runcode.py` to run
        all the code found in code sections in the rst file.
        
        ## Examples
        
        In the directory `examples` there are various scripts named with single letters
        (sorry for this nonsense notation). In an IPython shell, you can run
        `examples/RUNALL.ipy` to run all the examples and save the figures on files.
        
        ## Tests
        
        The test code is in `tests`. Launch `pytest` in the repository to run all the
        tests. `pytest` can be installed with `pip install pytest`.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Requires-Python: >=3.1
Description-Content-Type: text/markdown
