Installation¶

As of version 0.2.0, the deproject package can be installed directly from PyPI. This requires CIAO 4.11 or later (as installation with pip in earlier versions of CIAO is not well supported). The module can be used with the standalone release of Sherpa, but it is only useful if Sherpa has been built with XSPEC support which is trickier to achieve than we would like.

Requirements¶

The package uses Astropy and SciPy, for units support and cosmological-distance calculations. It is assumed that Matplotlib is available for plotting (which is included in CIAO 4.11).

Using pip¶

CIAO¶

Installation within CIAO requires:

having sourced the CIAO initialisation script (e.g. ciao.csh or ciao.bash);
and using a constraints file, to avoid updating NumPy in CIAO.

It should be as simple as:

echo "numpy==1.12.1" > constraints.txt
pip install -c constraints.txt 'astropy<3.1' deproject

Note

The constraints are for CIAO 4.11, please adjust accordingly if using a newer version of CIAO (the Astropy restriction is because version 3.1 requires NumPy version 1.13 or later but CIAO 4.11 is shipped with NumPy version 1.12).

Standalone¶

When using the standalone Sherpa intallation, the following should be all that is required:

pip install deproject

Manual installation¶

The source is available on github at https://github.com/sherpa-deproject/deproject, with releases available at https://github.com/sherpa-deproject/deproject/releases.

After downloading the source code (whether from a release or by cloning the repository) and moving into the directory (deproject-<version> or deproject), installation just requires:

python setup.py install

Note

This command should only be run after setting up CIAO or whatever Python environment contains your Standalone Sherpa installation.

Test¶

The source installation includes a basic test suite, which can be run with either

pytest

or

python setup.py test

Example data¶

The example data can be download from either http://cxc.cfa.harvard.edu/contrib/deproject/downloads/m87.tar.gz or from GitHub. The source distribution includes scripts - in the examples directory - that can be used to replicate both the basic M87 example and the follow-on example combining annuli.

As an example (from within an IPython session, such as the Sherpa shell in CIAO):

>>> %run fit_m87.py
...
... a lot of screen output will whizz by
...

The current density estimates can then be displayed with:

>>> dep.density_plot()

the reduced-statistic for the fit to each shell with:

>>> dep.fit_plot('rstat')

and the fit results for the first annulus can be displayed using the Sherpa functions:

>>> set_xlog()
>>> plot_fit_delchi(0)