| Name | Auriga JSON |
| Version |
1.1
JSON |
| download |
| home_page | https://github.com/mkounkel/Auriga |
| Summary | A neural network for structure parameter determination |
| upload_time | 2024-01-07 04:35:55 |
| maintainer | |
| docs_url | None |
| author | Marina Kounkel |
| requires_python | >=3.6 |
| license | |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Auriga
Auriga neural net predicts age, extinction, and distance to stellar populations
## Installation:
```pip install auriga```
(requires Python3)
## Keywords:
```
positional arguments:
tableIn Input table with Gaia DR2 source ids and cluster ids
optional arguments:
-h, --help show this help message and exit
--tutorial Use included test.fits or test.csv files as inputs
--tableOut TABLEOUT Prefix of the csv file into which the cluster properties should be written, default tableIn-out
--iters ITERS Number of iterations of each cluster is passed through Auriga to generate the errors, default 10
--localFlux Download necessary flux from Gaia archive for all source ids, default True
--saveFlux SAVEFLUX If downloading flux, prefix of file where to save it, default empty
--silent Suppress print statements, default False
--cluster CLUSTER Column with cluster membership
--source_id SOURCE_ID
Column with Gaia DR2 source id,
--gaiaFluxErrors If loading flux, whether uncertainties in Gaia bands have been converted from flux to magnitude, default True
--g G If loading flux, column for G magnitude
--bp BP If loading flux, column for BP magnitude
--rp RP If loading flux, column for RP magnitude
--j J If loading flux, column for J magnitude
--h H If loading flux, column for H magnitude
--k K If loading flux, column for K magnitude
--parallax PARALLAX If loading flux, column for parallax
--eg EG If loading flux, column for uncertainty in G magnitude
--ebp EBP If loading flux, column for uncertainty in BP magnitude
--erp ERP If loading flux, column for uncertainty in RP magnitude
--ej EJ If loading flux, column for uncertainty in J magnitude
--eh EH If loading flux, column for uncertainty in H magnitude
--ek EK If loading flux, column for uncertainty in K magnitude
--eparallax EPARALLAX
If loading flux, column for uncertainty in parallax
--gf GF If uncertainties have not been converted to magnitudes, column for G flux
--bpf BPF If uncertainties have not been converted to magnitudes, column for BP flux
--rpf RPF If uncertainties have not been converted to magnitudes, column for RP flux
--egf EGF If uncertainties have not been converted to magnitudes, column for uncertainty in G flux
--ebpf EBPF If uncertainties have not been converted to magnitudes, column for uncertainty in BP flux
--erpf ERPF If uncertainties have not been converted to magnitudes, column for uncertainty in RP flux
--memoryOnly Store table only in memory without saving to disk
--ver VER Version of Gaia data to download, default DR3
```
## Examples:
Downloading photometry from the Gaia Archive for the sources defined in the fits table, saving the fluxes, and generating the outputs
```
auriga test.fits --tableOut test-out --saveFlux test --tutorial
```
Using previously downloaded fluxes to generate predictions. 20 implementations of each cluster are generated instead of 10, to estimate the uncertainties in the cluster parameters
```
auriga test.csv --localFlux --iters=20 --tutorial
```
Using previously downloaded fluxes, defining all the necessary columns
```
auriga test.fits --localFlux --gaiaFluxErrors --g phot_g_mean_mag --bp phot_bp_mean_mag \
--rp phot_rp_mean_mag --j j_m --h h_m --k ks_m --ej j_msigcom --eh h_msigcom \
--ek ks_msigcom --eparallax parallax_error --tutorial --silent
```
Using from within a code, outside of a command line
```
from auriga.auriga import getClusterAge
t=Table.read('test.csv')
out=getClusterAge(t,localFlux=True)
out=getClusterAge('test.csv',tutorial=True,memoryOnly=True)
```
## Required packages:
* Astropy
* Astroquery
* Pytorch
* Pandas
Raw data
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