Installation¶
zELDA, installation is divided in two blocks. First you will need to install the python package containing all the scritps. With this you can already use the Deep Neural Network methodologies to extract information from observed Lyman-alpha line profiles. The second block contains all the grids computed from LyaRT. These are necessary in order to compute line profiles and escape fractions for all the outflow geometries. As a consequence, the second block is mandatory to make MCMC analysis.
Python package¶
The simplest way of installing zELDA’s scripts is via pip:
$ pip install Lya_zelda
An alternative method to install zELDA’scripts is downloading the code from GitHub:
$ git clone https://github.com/sidgurun/Lya_zelda.git
$ cd Lya_zelda
$ pip install .
Remember that you can also add the tag --user , if necessary.
zELDA uses a specific version of numpy and sci-kit-learn. This means that most likely pip will try to change to those versions when you install zELDA. If you want to avoid this you can create a virtual environment, which is always useful to tests installations.
LyaRT data grids¶
Next, let’s download the data grids necessary for generating mock Lyman-alpha line profiles as escape fractions. The data is stored at https://zenodo.org/record/4733518#.YJjw_y_Wf0c . Download the Grids.zip file. You can do this in different ways. The recomended method is using the commamnd wget or curl, which should be more stable. For example, for downloawing it with curl, you can do:
$ curl --cookie zenodo-cookies.txt "https://zenodo.org/record/4733518/files/Grids.zip?download=1" --output Grids.zip
The download might take a while, as it is about 13Gb, so grab your fauvorite snack and be patient =D .
Other way of getting the data is going to the zenodo webpage and download it through your internet borwser. As this is a large file, if you brower is a little bit unstable the download might stop in halfway, causing you to restart the download again.
Once you have the Grids.zip file, unzip it in the place that you want to keep it.
In order to compute line profiles and escape fraction you will need to indicate zELDA the location of grids by doing
>>> import Lya_zelda as Lya
>>> your_grids_location = '/This/Folder/Contains/The/Grids/'
>>> Lya.funcs.Data_location = your_grids_location
where your_grids_location is a string with the place where you have stored the grids. If you run the ls command you should see something like this:
$ ls /This/Folder/Contains/The/Grids/
Dictonary_Bicone_X_Slab_Grid_Lines_In_Bicone_False.npy
.
.
.
GRID_data__V_29_logNH_19_logta_9_EW_20_Wi_31.npy
GRID_data__V_29_logNH_19_logta_9_EW_8_Wi_9.npy
GRID_info__V_29_logNH_19_logta_9_EW_20_Wi_31.npy
GRID_info__V_29_logNH_19_logta_9_EW_8_Wi_9.npy
.
.
.
finalized_model_wind_f_esc_Tree_f_esc.sav
You can check if you have set properly the directoy by loading a grid after setting Lya.funcs.Data_location, for example:
>>> print( Lya.Check_if_DATA_files_are_found() )
If the location has been properly set the command should return 1. If the data files are not found, then 0 is return. This function will also tell you the current value of Lya.funcs.Data_location. If the funtions returns 0 make sure than running ls gives you the expected output (see just above).
Partial installation for testing¶
This section is optional and not required for the full installation. If you have done the previous steps you don’t need to go through this.
The full zELDA (grids+code) is about 13GB of storage. There could be the case in which you might want to test the code but not install it completely. If this is the case, you can download a lighter version of the grid for the Thin Shell geoemtry used to fit observed data. Remember that once you have installed the scripts by pip (above), you can already make the neural network analysis of the line profiles, there is no need of the line profiles grids. However, if you want to plot the line profile given by the predicted outflow propeties you will need the grid of line profiles.
Go to the location where you want to store the test grids. You can download the lighter version of the grids with
$ curl -0 --output GRID_data__V_29_logNH_19_logta_9_EW_8_Wi_9.npy https://zenodo.org/record/4890276/files/GRID_data__V_29_logNH_19_logta_9_EW_8_Wi_9.npy
$ curl -0 --output GRID_info__V_29_logNH_19_logta_9_EW_8_Wi_9.npy https://zenodo.org/record/4890276/files/GRID_info__V_29_logNH_19_logta_9_EW_8_Wi_9.npy
Done! This files should be less than 2GB.
Let’s see how you can load them.
>>> import Lya_zelda as Lya
>>> your_grids_location = '/This/Folder/Contains/The/Grids/'
>>> Lya.funcs.Data_location = your_grids_location
where your_grids_location is a string with the place where you have stored the grids. If you run the ls command you should see something like this:
$ ls /This/Folder/Contains/The/Grids/
GRID_data__V_29_logNH_19_logta_9_EW_8_Wi_9.npy
GRID_info__V_29_logNH_19_logta_9_EW_8_Wi_9.npy
You can check if you have set properly the directoy by loading a grid after setting Lya.funcs.Data_location, for example:
>>> Geometry = 'Thin_Shell_Cont'
>>> LyaRT_Grid = Lya.load_Grid_Line( Geometry , MODE='LIGHT' )
If this last command worked, then the grids were found correctly and you can start using this line profile grid to test the creation of mock line profiles, for example. However, you won’t be able to compute escape fractions and the line profile for the other gas geometries until you install the full package. Also, the grid you have just downlaoded is less heavy because there are fewer bins, which means that the nodes are more spaced. This means that the line profiles computed from this grid will have in general a lower accuracy in comparison with using the full grid. Therefore, for science you sould use the full grid, not this one.