Download IGRINS data from Google Drive

IGRINS stores all of its data on Google Drive. Here we show how to programmatically fetch that data with Python.

from muler.igrins import IGRINSSpectrum
import requests
%matplotlib inline
%config InlineBackend.figure_format='retina'

For now, you need the filename and Google Drive ID for a spectrum. Eventually this information may come from an observation Log provided by the IGRINS team. At the moment, I just retrieved this information by navigating to the Google Drive website.

These data are already public on the muler_example_date repo, but I uploaded them to a public Google Drive for the purpose of this demo. Eventually the entire IGRINS archive will be in a public Google Drive like this.

download_dictionary = {'SDCH_20201202_0059.spec_a0v.fits':'1tBY0NCcTnnCkvXXvFNOiqd4e10S6W2RB',
                       'SDCH_20201202_0059.sn.fits':'1NlIUWPxiN_nkZ83JsTzXby_5ihSSZ34o'}

We need two files because the IGRINS pipeline houses the uncertainty values in a separate file from the flux values.

def download_file(filename, file_id):
    """Download the file from Google Drive"""
    URL = "https://docs.google.com/uc?export=download"
    session = requests.Session()
    response = session.get(URL, params = { 'id' : file_id }, stream = True)
    if response.status_code == 200: # Successfully found the file on Google Drive
        CHUNK_SIZE = 32768

        with open(filename, "wb") as f:
            for chunk in response.iter_content(CHUNK_SIZE):
                if chunk: # filter out keep-alive new chunks
                    f.write(chunk)
        print("Downloaded {}".format(filename))

for key, value in download_dictionary.items():
    download_file(key, value)

Downloaded SDCH_20201202_0059.spec_a0v.fits
Downloaded SDCH_20201202_0059.sn.fits

Hooray, we downloaded the files! They are saved in the local directory.

We can simply read them in by handing-in the filename:

spectrum = IGRINSSpectrum(file='SDCH_20201202_0059.spec_a0v.fits', order=11).normalize()

spectrum = spectrum.trim_edges().remove_nans()
spectrum.plot();

Great, we fetched the spectrum from Google Drive, did some light post-processing, and plotted it!