Installation

Note

Requires: Python >=3.8

Tested on:

  • macOS Monterey (12.6.4) - Ventura (13.2.1)

How to Install

The GDT-CGRO package can be installed from PyPI using:

pip install astro-gdt-cgro
gdt-data init

Downloading Test/Tutorial Data

To download the data files used in the documentation and for testing, you need to run the gdt-data script after installation. The downloader script is designed so that you can download data from specific missions, or download all of the test/tutorial data. To see the list of available missions

gdt-data --help

If you want to download the BATSE test/tutorial data only, for example:

gdt-data download cgro-batse

Or to download all of the data:

gdt-data download --all

The data are downloaded to a default directory. To access the data from the GDT, there is a variable at the main level that stores the path dictionary for each mission. To access the BATSE test data directory:

>>> from gdt.core import data_path
>>> batse_path = data_path.joinpath('cgro-batse')

Once you are done using the data, you can delete the data files with the following command:

gdt-data clean cgro-batse

or delete all of the data with:

gdt-data clean --all

Quickstart

To load the GDT-CGRO package within your python environment, simply:

>>> import gdt.missions.cgro as cgro

How to Uninstall

To uninstall:

gdt-data clean gdt-cgro
pip uninstall astro-gdt-cgro

There are also a number of files for the tools that are copied into your $HOME/.gammaray_data_tools directory. You can delete these files if you wish.

Known Issues

  • There appears to be some differences arising between installations on Mac ARM processors (M1 and M2 chips) and other Mac or Linux processors. As of now, this only shows up when using some of the minimizers provided through scipy.optimize.minimize for spectral fitting. Users can test for the presence of these differences by running the gdt-core unit tests. The known failures on Mac ARM processors are:

    • test_fitting.py::TestSpectralFitterOne::test_hessian

    • test_fitting.py::TestSpectralFitterOne::test_jacobian

    • test_fitting.py::TestSpectralFitterOne::test_residuals

    The current understanding is that differences arise in spectral fit values above machine precision, but represent < 1% relative errors on the fit values themselves. The exact origin of these differences is unclear, but may be related to the underlying C or FORTRAN libraries and compilers that are used to compile scipy. Further investigation is ongoing.

  • The virtual environment is using your system ipython (or other package) install. This can sometimes happen if you didn’t install ipython (or other package) in the virtual environment. Try installing ipython (or other package) and restart your virtual environment. Sometimes you must deactivate the virtual environment and reactivate it after installing a package.

  • You observe the following error:

    ImportError: No module named '_tkinter'

    This is a situation where Matplotlib is using the tkinter backend for plotting. You would see this error if you don’t have tkinter installed. You don’t need to install tkinter if you don’t want to; instead, you can create a file named matplotlibrc in your working directory that contains the following:

    backend : Agg