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astrodatadownloader: use SECTOR for TESS and TTABLEID for Kepler/K2 as extra information

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SGCMarkus
2024-03-08 11:37:02 +01:00
parent 8f7fb78c8f
commit 2d4133617f
1 changed files with 11 additions and 13 deletions
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main/astrodatadownloader/astrodatadownloader.py
+11 -13
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@@ -1,5 +1,6 @@
from astroquery.mast import Observations from astroquery.mast import Observations
from astropy import table from astropy import table
from astropy.io import fits
import numpy as np import numpy as np
from enum import Enum from enum import Enum
from copy import deepcopy from copy import deepcopy
@@ -26,25 +27,22 @@ def parse_manifest(manifest):
exts = [] exts = []
for i,f in enumerate(manifest['Local Path']): for i,f in enumerate(manifest['Local Path']):
file_parts = np.array(np.unique(f.split(sep = '-'))) file_parts = np.array(np.unique(f.split(sep = '-')))
sectors = list( map ( lambda x: x[0:2] == 's0', file_parts)) try:
if(len(file_parts[sectors]) > 0): with fits.open(f, mode="readonly") as hdu:
s1 = file_parts[sectors][0] if("SECTOR" in hdu[0].header):
try: sector_range.append(f"{hdu[0].header['SECTOR']}")
s2 = file_parts[sectors][1] elif("TTABLEID" in hdu[0].header):
except: sector_range.append(f"{hdu[0].header['TTABLEID']}")
s2 = s1 hdu.close()
if(s1 == s2): except:
sector_range.append("%s" % s1)
else:
sector_range.append("%s-%s" % (s1,s2))
else:
sector_range.append("-") sector_range.append("-")
path_parts = np.array(f.split(sep = '/')) path_parts = np.array(f.split(sep = '/'))
filenames.append(path_parts[-1]) filenames.append(path_parts[-1])
exts.append(path_parts[-1][-8:]) exts.append(path_parts[-1][-8:])
results.add_column(table.Column(name = "filename", data = filenames)) results.add_column(table.Column(name = "filename", data = filenames))
results.add_column(table.Column(name = "sectors", data = sector_range)) results.add_column(table.Column(name = "sector", data = sector_range))
results.add_column(table.Column(name = "fileType", data = exts)) results.add_column(table.Column(name = "fileType", data = exts))
results.add_column(table.Column(name = "index", data = np.arange(0,len(manifest)))) results.add_column(table.Column(name = "index", data = np.arange(0,len(manifest))))