Installation¶
Note
Requires: Python >=3.8
Tested on:
macOS Monterey (12.6.4) - Ventura (13.2.1)
How to Install¶
The GDT Core package can be installed from PyPI using:
pip install astro-gdt
gdt-data init
The gdt-data init
is required to initialize the library after installation.
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 Fermi GBM test/tutorial data only, for example:
gdt-data download fermi-gbm
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 Fermi GBM test data directory:
>>> from gdt.core import data_path
>>> gbm_path = data_path.joinpath('fermi-gbm')
Once you are done using the data, you can delete the data files with the following command:
gdt-data clean fermi-gbm
or delete all of the data with:
gdt-data clean --all
Quickstart¶
To load the GDT Core package within your python environment, simply:
>>> import gdt.core
How to Uninstall¶
To uninstall:
gdt-data clean --all
pip uninstall astro-gdt
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 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.
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 havetkinter
installed. You don’t need to installtkinter
if you don’t want to; instead, you can create a file named matplotlibrc in your working directory that contains the following:backend : Agg