90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
from astroquery.mast import Observations
|
|
from astropy import table
|
|
import numpy as np
|
|
from enum import Enum
|
|
from copy import deepcopy
|
|
|
|
class DataType(Enum):
|
|
DataValidation = 1
|
|
LightCurve = 2
|
|
TargetPixel = 3
|
|
|
|
class ObservationSource(Enum):
|
|
KEPLER = 1
|
|
K2 = 2
|
|
TESS = 3
|
|
|
|
# base from https://spacetelescope.github.io/mast_notebooks/notebooks/TESS/beginner_astroquery_dv/beginner_astroquery_dv.html
|
|
# modified for sector span
|
|
def parse_manifest(manifest):
|
|
"""
|
|
Parse manifest and add back columns that are useful for TESS DV exploration.
|
|
"""
|
|
results = deepcopy(manifest)
|
|
filenames = []
|
|
sector_range = []
|
|
exts = []
|
|
for i,f in enumerate(manifest['Local Path']):
|
|
file_parts = np.array(np.unique(f.split(sep = '-')))
|
|
sectors = list( map ( lambda x: x[0:2] == 's0', file_parts))
|
|
s1 = file_parts[sectors][0]
|
|
try:
|
|
s2 = file_parts[sectors][1]
|
|
except:
|
|
s2 = s1
|
|
if(s1 == s2):
|
|
sector_range.append("%s" % s1)
|
|
else:
|
|
sector_range.append("%s-%s" % (s1,s2))
|
|
path_parts = np.array(f.split(sep = '/'))
|
|
filenames.append(path_parts[-1])
|
|
exts.append(path_parts[-1][-8:])
|
|
|
|
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 = "fileType", data = exts))
|
|
results.add_column(table.Column(name = "index", data = np.arange(0,len(manifest))))
|
|
|
|
return results
|
|
|
|
def getStarObservations(starName: str, sources: list[ObservationSource], sequences: list[list[int]] = []) -> str:
|
|
obs = Observations.query_object(objectname=starName, radius="0 deg")
|
|
|
|
obsWantedFilter = obs["dataproduct_type"] == "timeseries"
|
|
|
|
if(len(sequences) > 1):
|
|
obsWantedFilter &= ((obs['sequence_number'] >= max(sequences)) &
|
|
(obs['sequence_number'] <= min(sequences)))
|
|
elif(len(sequences) == 1):
|
|
obsWantedFilter &= (obs['sequence_number'] == sequences[0])
|
|
|
|
obsSourceFilter = np.ma.MaskedArray(data=np.full(obsWantedFilter.shape, False),
|
|
mask=False, fill_value=True)
|
|
if(ObservationSource.KEPLER in sources):
|
|
obsSourceFilter |= (obs['obs_collection'] == "Kepler")
|
|
if(ObservationSource.K2 in sources):
|
|
obsSourceFilter |= (obs['obs_collection'] == "K2")
|
|
if(ObservationSource.TESS in sources):
|
|
obsSourceFilter |= (obs['obs_collection'] == "TESS")
|
|
|
|
obsWantedFilter &= obsSourceFilter
|
|
|
|
return obs[obsWantedFilter]
|
|
|
|
def downloadStarProducts(obs_wanted):
|
|
dataProducts = Observations.get_product_list(obs_wanted)
|
|
|
|
# LC: TESS lightcurve
|
|
# SLC: Kepler short cadence lightcurve
|
|
# LLC: Kepler long cadence lightcurve
|
|
productSubGroups = ["LC", "SLC"]
|
|
|
|
productsWanted = Observations.filter_products(dataProducts,
|
|
productSubGroupDescription=productSubGroups)
|
|
|
|
productsWanted = productsWanted.group_by("productFilename")
|
|
|
|
filenames = Observations.download_products(productsWanted)
|
|
return parse_manifest(filenames)
|
|
|