Welcome to Naima

Naima is a Python package for computation of non-thermal radiation from relativistic particle populations. It includes tools to perform MCMC fitting of radiative models to X-ray, GeV, and TeV spectra using emcee, an affine-invariant ensemble sampler for Markov Chain Monte Carlo. Naima is an Astropy affiliated package.

There are two main components of the package: a set of nonthermal Radiative Models, and a set of utility functions that make it easier to fit a given model to observed spectral data (see Model fitting).

Nonthermal radiative models are available for Synchrotron, inverse Compton, Bremsstrahlung, and neutral pion decay processes. All of the models allow the use of an arbitrary shape of the particle energy distribution, and several functional models are also available to be used as particle distribution functions. See Radiative Models for a detailed explanation of these.

Use the sidebar on the left to access the documentation.

License & Attribution

Naima is released under a 3-clause BSD style license - see the LICENSE.rst for details.

If you find Naima useful in your research, you can cite Zabalza (2015) (arXiv, ADS) to acknowledge its use. The BibTeX entry for the paper is:

@ARTICLE{naima,
   author = {{Zabalza}, V.},
    title = {naima: a Python package for inference of relativistic particle
             energy distributions from observed nonthermal spectra},
     year = 2015,
  journal = {Proc.~of International Cosmic Ray Conference 2015},
    pages = "922",
   eprint = {1509.03319},
   adsurl = {http://adsabs.harvard.edu/abs/2015arXiv150903319Z},
}

Contributing

Please report any issues with the package here.

All development of Naima is done through the github repository, and contributions to the code are welcome. The development model is similar to that of astropy, so you can check the astropy Developer Documentation if you need information on how to make a code contribution.

Changelog

0.10.2 (2025-01-17)

  • Fix bug when Eemin and Eemax where set too close. Fixes #225.

0.10.1 (2025-01-17)

  • Dropped support for Python 3.9, updated to numpy v2, updated packaging.

0.10.0 (2021-11-02)

  • Updated emcee dependency to version 3. The interface to access the chain, log-probability, and blobs has changed accordingly, see emcee’s documentation.

0.9.1 (2020-01-31)

  • Fix exceptions thrown by np.array_equal for some combinations of model and blob outputs.

0.9.0 (2019-11-08)

  • Dropped Python 2 support. If you need to use naima in Python 2, please use version 0.8.4.

0.8.4 (2019-08-27)

  • Updated deprecated uses of numpy and astropy.Quantity logic

  • Updated tests for latest versions of dependencies

0.8.3 (2018-11-27)

Bug fixes

  • Fixed plotting scalar blobs with units.

  • Fixed plotting vector blobs with same length as data but incompatible units.

0.8.2 (2018-11-26)

  • Formatted source code with black.

Bug fixes

  • Fixed deprecated use of np.all and normed argument to matplotlib’s hist.

0.8.1 (2017-09-27)

Bug fixes

  • Fixed deprecated negative signs on numpy booleans.

  • Fixed wrong sign in delta functional approximation of Kelner PionDecay.

0.8 (2016-12-21)

  • Added a threads parameter to plot_fit and plot_samples that set the number of cores to use in computing model samples for plotting.

  • Added a new model for EBL absorption based on the tables of Dominguez et al. 2011.

Bug fixes

  • Updated to use new emcee autocorrelation API in version 2.2.

  • Fixed sherpa models string representation.

0.7.1 (2016-02-04)

  • Packaging bugfix

0.7 (2016-02-04)

  • The InverseCompton class can now compute IC on arbitrary seed photon fields passed as arrays.

  • plot_fit and plot_data have new options (errorbar_opts and ulim_opts) to control the properties of spectral flux points and upper-limits.

  • There is a new table model class in naima.models.TableModel.

  • Added corner (former triangle_plot) as a dependency.

0.6.1 (2015-10-29)

  • Performance improvements to the memoize logic that result in 10% or higher improvement in model execution time.

0.6 (2015-09-10)

  • Medians and associated errors are now shown with a precision corresponding to a single significant digit in the errors (except when the leading digit is 1, when two significant digits are shown). Note that they are still saved with full precision to the result tables.

  • There is a new GUI tool for interactive model fitting: InteractiveModelFitter, which can be accessed directly or through the interactive argument of get_sampler prior to a sampling run.

  • Sampling run results can be saved and retrieved for later analysis or archival with the new functions naima.save_run and naima.read_run.

  • The individual contributions to the total Inverse Compton spectrum of the different seed photon fields can now be accessed through the seed argument of the InverseCompton.flux and InverseCompton.sed functions.

Bug Fixes

  • save_diagnostic_plots now turns matplotlib interactivity off, so the plots are only saved and not shown.

  • The group column is now preserved if a data table is validated more than once.

0.5 (2015-08-05)

  • save_results_table now saves the maximum log likelihood and ML parameters of the sample.

  • Update astropy_helpers to 1.0.2 and require astropy >= 1.0.2 to ensure pickleable Tables.

  • Internal data is a QTable instead of a dict.

  • When multiple input spectra are used, they are now plotted with different colors and markers.

  • Now doing the prefit with a minimizer that allows for relative tolerance termination: in general prefit will be faster.

  • Add e_range and e_npoints parameters to plot_fit to allow computing the model samples for a wider energy range that the observed spectrum (or at energies between data sets, such as X-ray and gamma-ray)

  • Added plot_corner as a thin wrapper around triangle.corner with ML parameter plotting.

0.4 (2015-03-19)

  • All models have a cache of length 10 so that the output is not computed when the parameters have not changed. It can be turned off through the _memoize attribute of the models.

Bug Fixes

  • Fixed concatenation of UL and CL in validate_data_table.

0.3 (2015-02-19)

  • Added an option to save the distribution properties of scalar blobs when saving results table: option include_blobs of save_results_table.

  • A new method for radiative classes allows to renormalize the particle distributions to a given energy content in particles. See set_We and set_Wp in leptonic and hadronic classes, respectively.

  • The default FIR and NIR photon fields for naima.InverseCompton have been set to the GALPROP values at a galactic radius of 6.5 kpc.

  • Require astropy >= 1.0.

Bug Fixes

  • Fixed sed conversion of residuals [#69]

  • Fixed plot_data so it can take lists of data_tables.

API Changes

  • The name of the table_format parameter of save_results_table has been changed to format for consistency with astropy.io.ascii.

0.2 (2015-02-10)

  • Refactored sherpa models to use parent SherpaModelECPL class [#62]

  • Added a data_sed flag to get_sampler to select whether to convert all data tables to SED or choose format of first data tables when providing multiple data tables.

  • Added support for a flux_ul column in input data tables.

  • Added a method to estimate magnetic field: naima.estimate_B.

  • Added the option to perform an optimization of the parameters before the MCMC run: see option prefit in naima.get_sampler.

  • Convert between SED and differential fluxes automatically if the model and data physical types do not match.

  • Add blob_labels parameter to save_diagnostic_plots.

Bug Fixes

  • Fix sherpa models guess() for integrated datasets.

  • Only complain about CL when there are ULs at a different CL.

  • Fix parsing of string upper limit columns in Python 3.

  • Use old energy unit when plotting a new data set onto a figure [#64]

  • Show ordinate units when plotting blobs without spectral data.

API Changes

  • module sherpamod is now sherpa_modules.

0.1 (2015-02-02)

  • Initial release