This my version of a data analysis tool for the code AENUS-ALCAR. It is a easy-to-use package built entirely in Python. It's use is mainly to extract thermodynamics and hydrodynamics data from the simulations and use them to compute crucial core-collapse supernova analysis quantities such as radii (shock, neutrinos, PNS, ...), gravitational waves, energies and much more. All features are implemented, tested and work fairly well for 1D and 2D simulations, while I did not test them in 3D. However, most of them should work also in 3D.
Please bear in mind that this package will be updated fairly often, so refer always to the latest version.
Any tips or suggestion are more than welcome!

This being a Python package, it requires a version of Python3 installed on your computer. Besides, it makes heavy use of five Python libraries

• NumPy
• SciPy
• h5Py
• Pandas
• f90nml

To install their latest version just run

pip install -r requirements.txt

inside the package folder.

To install this package just clone it

git clone https://github.com/MarcoCusinato/scidata.git

then navigate to the package folder and run install it with pip

cd scidata
pip install .

Congrats you have successfully installed scidata!
The first time you will import it, it will create a local storage folder called Simulation_analysis_quantities, if it does not already exist, in your home directory (Linux) or Desktop (Windows) to store some quantities that are numerically expensive to calculate. Additionally, inside the scidata package will be created a dictionary to store the simulation path, and a symlink to the package will be added to the Desktop or home directory depending on the platform you are working on.

First of all let's include some paths to search for simulations. Navigate to

scidata/paths

and run add_paths.py with one or both of the following options:

• --add-paths-file, followed by one or more paths to files containing search paths
• --add-paths, followed by one or more search paths. A path file can be every text file or a .bz2 or .gz archive containing a text file. Moreover, it should look like a list of paths.
  Let's name foo.txt a file with a list of path that looks like:

/first/path/to/search
/second/path/to/search
...

Then call

python3 add_paths.py --add-paths-file /path/to/foo.txt
>Paths to search: [/first/path/to/search, /second/path/to/search]
>Path: /first/path/to/search
>Checking...
>   /subpath/one
>   /subpath/two
>   ...
>Added n simulations

This process can take from few seconds to several minutes depending on the number of simulations present in the chosen paths.
In case you are working on windows, the first time it will ask you where your Linu server is mounted. If you are working with simulations downloaded in your current machine just leave it blank.
PLEASE REMEMBER: every time you add a simulation to a folder you have to re-run the script.

Please before starting the data analysis bear in mind that this script will create a folder named Simulations_analysis_quantities in your home directory if you are working on Linux or Desktop on Windows to store some quantities that are numerically expensive to calculate.
To start the analysis, first of all, load the main script of the module

from scidata.quantities.quantities import SimulationAnalysis

then load a simulation (i. e. s20-064)

sim = SimulationAnalysis('s20-064')

If there are two simulations with different dimension (i. e. one is 1D and the other 3D) it will ask you to specify the requested one.
However, if you have not added any simulation path, you can supply it directly as

sim = SimulationAnalysis('sim_name', simulation_folder_path = '/path/to/sim/')

Now you have access to every SimulationAnalys method to get the most common quantities present in a simulation. Let's try, for example, to calculate the PNS radius for all simulation timestep. To do this just run:

sim.get_PNS_radius()
> PNS radius file not found. Creating one with default settings.
> If different settings are needed please refer to the "PNS_radius(...)"

With default settings this method would not return nothing but will create a file named PNS_radius.h5 inside the local storage folder in your home or desktop directory.
Now re-run the same method with some options enabled to get the averaged PNS radius,

averaged_pns_radius = sim.get_PNS_radius(PNS_radius = False, indices = False, min_max_average = True,
                                        ret_time = False, ghost_cells = False, tob_corrected = True)

By plotting it you will get something like this plot

Similarly you can get other quantities and derive many useful stuff by using other SimulationAnalysis methods. Please refer to its methods description (when avaiable) to find the ones you need.

Probably what I will need to use at the moment :)