Filters in GalSBI¶

Computing an apparent magnitude in a specific filter band in galsbi can be done in two ways. If you define magnitude_calculation="direct" in the configuration file, the apparent magnitude is calculated on the fly. For this you need to provide the template files and the filter file. The config file could contain the following lines:

import os
from cosmo_torrent import data_path

magnitude_calculation = "table"
filters_file_name = os.path.join(data_path("all_filters_v1"), "filters_collection.h5")
templates_file_name = os.path.join(
    data_path("template_BlantonRoweis07"), "template_spectra_BlantonRoweis07.h5"
)

If you define magnitude_calculation="table", the apparent magnitude is calculated using the precomputed tables. The config file could contain the following lines:

import os
from cosmo_torrent import data_path

magnitude_calculation = "table"
templates_int_tables_file_name = os.path.join(
    data_path("all_filters_v1"), "sed_integrals__template_spectra_BlantonRoweis07.h5"
)
templates_file_name = os.path.join(
    data_path("template_BlantonRoweis07"), "template_spectra_BlantonRoweis07.h5"
)

These files contain all the necessary information needed for the supported filters. Currently, galsbi supports the following filters:

DECamNoAtm_g, DECamNoAtm_r, DECamNoAtm_i, DECamNoAtm_z, DECamNoAtm_Y
DECam_u, DECam_g, DECam_r, DECam_i, DECam_z, DECam_Y
GALEX_FUV, GALEX_NUV
GenericBessel_U, GenericBessel_B, GenericBessel_V, GenericBessel_R, GenericBessel_I
GenericJohnson_U, GenericJohnson_B, GenericJohnson_V, GenericJohnson_R, GenericJohnson_I, GenericJohnson_J, GenericJohnson_M
HyperSuprimeCam_g, HyperSuprimeCam_r, HyperSuprimeCam_r2, HyperSuprimeCam_i, HyperSuprimeCam_i2, HyperSuprimeCam_z, HyperSuprimeCam_y
MegaPrime_u
SuprimeCam_B, SuprimeCam_V, SuprimeCam_r, SuprimeCam_ip, SuprimeCam_zp, SuprimeCam_zpp, SuprimeCam_IA484, SuprimeCam_IA527, SuprimeCam_IA624, SuprimeCam_IA679, SuprimeCam_IA738, SuprimeCam_IA767, SuprimeCam_IB427, SuprimeCam_IB464, SuprimeCam_IB505, SuprimeCam_IB574, SuprimeCam_IB709, SuprimeCam_IB827, SuprimeCam_NB711, SuprimeCam_NB816
VISTA_J, VISTA_H, VISTA_Ks
Vircam_Y, Vircam_J, Vircam_H, Vircam_Ks
irac_ch1, irac_ch2, irac_ch3, irac_ch4
mips_24
wircam_H, wircam_Ks

The raw filter files are stored at resources/filters in the galsbi package. Using a specific filter is as simple as defining the filter name in the configuration file. For example, if you want to use the DECam filters, you can define the following lines in the config file:

filters = ["u", "g", "r", "i", "z", "Y"]
filters_full_names = {
    "u": "DECam_u",
    "g": "DECam_g",
    "r": "DECam_r",
    "i": "DECam_i",
    "z": "DECam_z",
    "Y": "DECam_Y",
}

Adding a new filter¶

To add a new filter or to create a new filter file (e.g. with less filters to have a smaller file), you can use the run_template_filter_integrals app. This app was also used to create the sed_integrals__template_spectra_BlantonRoweis07.h5 and filters_collection.h5 files. We assume in the following that you have cloned the galsbi repository, installed the package from the source, and are in the galsbi root directory. You have to follow these steps:

Save the raw filter files in a folder, e.g. new_resources/filters, and the template file at new_resources/template/template_spectra_BlantonRoweis07.h5. You can use any templates but we recommend to use the Blanton & Roweis 2007 templates since the galsbi package was tested with these templates. Otherwise, the template coefficients would have to be constrained independently.
Edit the filter interface in galsbi/src/galsbi/ucat/filters_utils.py. If you add a new filter, make an additional nested function in create_filter_collection following the same structure as the other filters. If you want to create a new filter file, you will have to remove the code that processes the filters that are not in the new filter file. Add the new filter to the filter_collect dictionary with the following convention: filters_collect["InstrumentName_band"] = dict(amp=amplitude, lam=lambda).
Run the app with the following command:
```
esub src/galsbi/ucat/apps/run_template_filter_integrals.py --dirpath_res=new_resources/ --filename_sed_templates=template/template_spectra_BlantonRoweis07.h5 --function=preprocess --mode=run
```
This will create the new filters_collection.h5 files. If you want to use the direct calculation, you can stop here. If you want to use the table calculation, you have to continue with the next steps.
Run the app with the following command:
esub src/galsbi/ucat/apps/run_template_filter_integrals.py --dirpath_res=new_resources/ --filename_sed_templates=template/template_spectra_BlantonRoweis07.h5 --function=all --mode=jobarray --system=slurm
This will submit jobs for each filter and each template on a slurm cluster. If your cluster uses IBM’s LSF, you can remove the --system=slurm option. If you don’t want to use a cluster, you can change the --mode option to run. Then, the jobs will be run sequentially. This will create the new sed_integrals__template_spectra_BlantonRoweis07.h5 files.

You can now use the new files in the configuration file. You have to define the new filter in the config file together with the updated paths to the new files.

filter_file_name = "new_resources/filters/filters_collection.h5"
templates_file_name = "new_resources/template/template_spectra_BlantonRoweis07.h5"
templates_int_tables_file_name = "new_resources/sed_integrals__template_spectra_BlantonRoweis07.h5"
filters = ["g", "r", "i", "z", "y"]
filters_full_names = {
    "g": "NewFilter_g",
    "r": "NewFilter_r",
    "i": "NewFilter_i",
    "z": "NewFilter_z",
    "y": "NewFilter_y",
}

If you add a new filter, consider to submit a merge request to the galsbi repository such that the new filter can be used by other users as well.