def test_query_region_box_async(coordinates, patch_get):
response = Irsa.query_region_async(
coordinates, catalog='fp_psc', spatial='Box',
width=2 * u.arcmin, get_query_payload=True)
assert response['size'] == 120
response = Irsa.query_region_async(
coordinates, catalog='fp_psc', spatial='Box', width=2 * u.arcmin)
assert response is not None
def test_query_region_cone_async(coordinates, patch_get):
response = Irsa.query_region_async(
coordinates, catalog='fp_psc', spatial='Cone',
radius=2 * u.arcmin, get_query_payload=True)
assert response['radius'] == 2
assert response['radunits'] == 'arcmin'
response = Irsa.query_region_async(
coordinates, catalog='fp_psc', spatial='Cone', radius=2 * u.arcmin)
assert response is not None
def GetPositionsAndEpochs(ra, dec, Epochs, radius=6):
t1 = Irsa.query_region(coords.SkyCoord(ra,
dec,
unit=(u.deg, u.deg),
frame='icrs'),
catalog="allsky_4band_p1bs_psd",
spatial="Cone",
radius=radius * u.arcsec)
t2 = Irsa.query_region(coords.SkyCoord(ra,
dec,
unit=(u.deg, u.deg),
frame='icrs'),
catalog="allsky_3band_p1bs_psd",
spatial="Cone",
radius=radius * u.arcsec)
if len(t2) == 0:
t2 = Irsa.query_region(coords.SkyCoord(ra,
dec,
unit=(u.deg, u.deg),
frame='icrs'),
catalog="allsky_2band_p1bs_psd",
spatial="Cone",
radius=radius * u.arcsec)
t3 = Irsa.query_region(coords.SkyCoord(ra,
dec,
unit=(u.deg, u.deg),
frame='icrs'),
catalog="neowiser_p1bs_psd",
spatial="Cone",
radius=radius * u.arcsec)
t00 = vstack([t1, t2], join_type='inner')
t0 = vstack([t00, t3], join_type='inner')
t = t0
#### Find the epoch clusters
Groups = []
for epoch in Epochs:
group = np.where(t['mjd'] == epoch)
if len(group[0]) != 0:
Groups.append(group[0][0])
#print(Epochs)
#print(Groups)
#print(len(Epochs),len(Groups))
if len(Groups) >= 0.5 * len(Epochs):
#print('Good')
#print(t['ra'][Groups].data, t['dec'][Groups].data, t['mjd'][Groups].data, t['w1mpro'][Groups].data )
return t['ra'][Groups].data, t['dec'][Groups].data, t['mjd'][
Groups].data, t['w1mpro'][Groups].data
else:
return [-9999], [-9999], [-9999], [-9999]
def get_nir_cat(self,clobber=False,use_twomass=True):
"""
Get the NIR catalog
Catalog (necessary for zero-point determination) is saved
into self.data_dir as
self.name+"_"+self.nir_survey+"cat.fits"
"""
print("Fetching NIR catalog from server...")
if use_twomass:
if (not os.path.isfile(self.nir_cal_cat)) or clobber:
from astroquery.irsa import Irsa
Irsa.ROW_LIMIT = 2000.
table = Irsa.query_region(coordinates.Galactic(l=self.glon,
b=self.glat, unit=(u.deg, u.deg)),
catalog="fp_psc", spatial="Box",
width=self.nir_im_size)
#print(table)
#IPAC table does not take overwrite? But FITS does? So inconsistent and bad
table.write(self.nir_cal_cat,format='votable',overwrite=clobber)
else:
print("NIR catalog already downloaded. Use clobber=True to fetch new versions.")
else:
if (not os.path.isfile(self.nir_cat)) or clobber:
if self.nir_survey == "VISTA":
from astroquery.vista import Vista as NIR
if self.nir_survey == "UKIDSS":
from astroquery.ukidss import Ukidss as NIR
table = NIR.query_region(coordinates.Galactic(l=self.glon,
b=self.glat, unit=(u.deg, u.deg)), radius=self.nir_im_size)
table.write(self.nir_cat,format="fits",overwrite=clobber)
else:
print("NIR catalog already downloaded. Use clobber=True to fetch new versions.")
def search_2MASS():
'''
Faz busca no catálogo 2MASS a partir das coordenadas celestes
'''
w = WCS(fits.getheader(UPLOAD_FOLDER+'/'+session['name']))
r = session['r']
o = SkyCoord(w.wcs_pix2world([(0,0)],1), unit='deg')
opr = SkyCoord(w.wcs_pix2world([(r,r)],1), unit='deg')
rw = o.separation(opr)[0]
print('Separação',rw)
req = request.get_json()
data = pd.DataFrame(req)
src = SkyCoord(ra=data['ra'], dec=data['dec'], unit='deg', frame='icrs')
crval = SkyCoord(ra=np.mean(data['ra']), dec=np.mean(data['dec']), unit='deg', frame='icrs')
r = 1.1*crval.separation(src).max()
Q = Irsa.query_region(crval,catalog='fp_psc',spatial='Cone', radius=r,selcols=['ra','dec','j_m','k_m']).to_pandas()
print(Q)
m = SkyCoord(ra=Q['ra'],dec=Q['dec'], unit=('deg','deg'), frame='icrs')
idx, d2, _ = match_coordinates_sky(src,m)
Q.loc[idx[d2>=rw]] = None # retira estrela que não conseguiu chegar perto
data[['j','k']] = Q[['j_m','k_m']].loc[idx].values
print(data)
res = make_response(data.to_json(), 200)
return res
def download_ptf(coords,name=None,directory=None):
"""Download PTF light curve data.
Keyword arguments:
coords -- astropy.coordinates.SkyCoord object
name -- string for filename (default None, i.e. PTF oid)
directory -- location to save data (default './')
"""
#Download the PTF data
if directory is None:
directory = datadir
table = Irsa.query_region(coordinates=coords,catalog='ptf_lightcurves',radius=5*u.arcsec)
table = table.filled(-99)
#Don't only use the nearest!
nearest = np.where(table['dist'] == np.min(table['dist']))
if name is None:
name = str(table["oid"][0])
nearestcoords = SkyCoord(table["ra"][nearest][0],table["dec"][nearest][0],unit="deg")
matchedinds = []
for i in range(len(table)):
if nearestcoords.separation(SkyCoord(table["ra"][i],table["dec"][i],unit="deg")) < 3*u.arcsec:
matchedinds.append(i)
fname = directory+name+'.xml'
table[matchedinds].write(fname, format='votable', overwrite=True)
print(str(len(matchedinds))+" data points saved to "+fname)
#add to target menu and display
targets[name] = fname
target_select.options.append(name)
target_select.value = target_select.options[-1]
def cross_match():
"""
Nothing but test query data and cross match.
"""
twomass = Irsa.query_region(coord.SkyCoord(28.2,
-0.049,
unit=(u.deg, u.deg),
frame='galactic'),
catalog='fp_psc',
radius='1d0m0s')
v = Vizier(columns=["**", "RAJ2000", "DEJ2000"])
v.ROW_LIMIT = 9000000
result = v.query_region(coord.SkyCoord(ra=280.7421273, dec=-4.2326516,\
unit=(u.deg, u.deg), frame='icrs'),radius=0.05*u.deg, catalog=["GAIA DR2"])
gaia_data = result[4]
coo_wise = coord.SkyCoord(twomass['ra'], twomass['dec'])
coo_twomass = coord.SkyCoord(gaia_data['RAJ2000'], gaia_data['DEJ2000'])
idx_twomass, d2d_twomass, d3d_twomass = coo_wise.match_to_catalog_sky(
coo_twomass)
YSO = gaia_data[:0].copy()
for k in idx_twomass:
YSO.add_row(gaia_data[k])
return YSO
def test_query_region_box_async(self):
response = Irsa.query_region_async("00h42m44.330s +41d16m07.50s",
catalog='fp_psc',
spatial='Box',
width=2 * u.arcmin,
cache=False)
assert response is not None
def test_query_region_box(self):
result = Irsa.query_region("00h42m44.330s +41d16m07.50s",
catalog='fp_psc',
spatial='Box',
width=2 * u.arcmin,
cache=False)
assert isinstance(result, Table)
def test_query_region_cone_async(self):
response = Irsa.query_region_async('m31',
catalog='fp_psc',
spatial='Cone',
radius=2 * u.arcmin,
cache=False)
assert response is not None
def test_query_region_cone(self):
result = Irsa.query_region('m31',
catalog='fp_psc',
spatial='Cone',
radius=2 * u.arcmin,
cache=False)
assert isinstance(result, Table)
def query_survey(dataframe, idx, features, survey):
series = dataframe.loc[idx]
coord = SkyCoord(ra=series.RA, dec=series.Dec, unit=(u.deg, u.deg))
try:
if survey == '2MASS':
temp = Irsa.query_region(coord,
radius=search_radius,
catalog='fp_psc').to_pandas()
elif survey == 'GAIA':
temp = Gaia.query_object(coordinate=coord,
width=search_radius,
height=search_radius).to_pandas()
else:
print('Invalid Survey')
catalog = SkyCoord(ra=temp.ra, dec=temp.dec, unit=(u.deg, u.deg))
i, _, _ = match_coordinates_sky(coord, catalog)
for feature in features:
dataframe.at[idx, feature] = temp.at[int(i), feature]
except Exception as e:
print(e)
def test_query_region_async_polygon(polygon, patch_get):
response = Irsa.query_region_async(
"m31", catalog="fp_psc", spatial="Polygon",
polygon=polygon, get_query_payload=True)
for a, b in zip(re.split("[ ,]", response["polygon"]),
re.split("[ ,]", "10.1 +10.1,10.0 +10.1,10.0 +10.0")):
for a1, b1 in zip(a.split(), b.split()):
a1 = float(a1)
b1 = float(b1)
np.testing.assert_almost_equal(a1, b1)
response = Irsa.query_region_async(
"m31", catalog="fp_psc", spatial="Polygon", polygon=polygon)
assert response is not None
def galaxies_moc(field, moc_field, radius, moc_order=15):
"""
MOC with the intersection of field with galaxies
included in the 2MASS Large Galaxy Atlas.
"""
galaxies = Irsa.query_region(field,
catalog="lga_v2",
spatial="Cone",
radius=2 * u.deg)
moc_galaxies = MOC()
if galaxies:
w = obsid_wcs(field)
field_reg = CircleSkyRegion(center=field, radius=radius)
moc_galaxies = MOC()
for g in galaxies:
gcoords = SkyCoord(ra=g['ra'], dec=g['dec'], unit=u.deg)
amajor = 1.5 * 2 * g['r_ext'] * u.arcsec
galaxy_reg = EllipseSkyRegion(center=gcoords,
width=amajor,
height=amajor * g['sup_ba'],
angle=(90 + g['sup_pa']) * u.deg)
region = field_reg.intersection(galaxy_reg)
moc_galaxies += reg2moc(region, moc_field, w, moc_order)
return moc_galaxies
def test_query_region_polygon(self):
polygon = [(10.1, 10.1), (10.0, 10.1), (10.0, 10.0)]
result = Irsa.query_region("m31",
catalog="fp_psc",
spatial="Polygon",
polygon=polygon,
cache=False)
assert isinstance(result, Table)
def query(self, **kwargs):
# check for data release to use
if "coordinates" in kwargs:
self.__coordinates = kwargs["coordinates"]
elif "ra" in kwargs and "dec" in kwargs:
self.__coordinates = str(kwargs["ra"]) + "," + str(kwargs["dec"])
else:
raise ValueError(
"Not valid coordinates found. Used coordinates key or ra dec keys"
)
self.__coordinates = CoordinateParser.validateCoordinates(
self.__coordinates)
radius = 1 # arcmin
if "radius" in kwargs:
radius = kwargs["radius"]
params = {
"bgApp": "/FinderChart/nph-finder",
"romeserver": "ROMEDEV",
"srchsize": 12.0,
"outsize": 200,
"colortbl": 1,
"nthread": 10,
"markercolor": "red",
"markersize": 10,
"mode": "cgi",
"outtype": "single",
"locstr": "20.48371,0.4223",
"subsetsize": 5.0,
"survey": "sdss",
"survey": "dss",
"survey": "2mass",
"markervis_shrunk": "true"
}
print(Irsa.list_catalogs())
r = Irsa.query_region(self.__coordinates,
catalog='fp_psc',
radius=radius * u.arcmin)
return r
def test_query_region_async_polygon(self):
polygon = [
SkyCoord(ra=10.1, dec=10.1, unit=(u.deg, u.deg)),
SkyCoord(ra=10.0, dec=10.1, unit=(u.deg, u.deg)),
SkyCoord(ra=10.0, dec=10.0, unit=(u.deg, u.deg))
]
response = Irsa.query_region_async("m31",
catalog="fp_psc",
spatial="Polygon",
polygon=polygon,
cache=False)
assert response is not None
def query2mass(ra, dec):
tmass = Irsa.query_region(
SkyCoord(df['RA'][i], df['DEC'][i], unit=(u.deg, u.deg), frame='fk5'),
catalog='fp_psc',
radius='0d0m2s',
selcols='ra,dec,j_m,j_cmsig,h_m,h_cmsig,k_m,k_cmsig')
if tmass:
return tmass['ra'].data[0], tmass['dec'].data[0], tmass['j_m'].data[
0], tmass['j_cmsig'].data[0], tmass['h_m'].data[0], tmass[
'h_cmsig'].data[0], tmass['k_m'].data[0], tmass[
'k_cmsig'].data[0]
else:
return ra, dec, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0, -9999.0
def get_cat(method):
cwd = os.getcwd()
try:
os.mkdir(method)
except OSError:
pass
if method == 'wise':
from astroquery.irsa import Irsa
Irsa.ROW_LIMIT = 1000000
ra_factor, pos = tile(cwd + '/image_ampphase1.app.restored.fits')
print 'Downloading catalogues for', len(pos), 'sky positions'
for i, p in enumerate(pos):
outfile = method + '/' + method + '-' + str(i) + '.vo'
if os.path.isfile(outfile):
print 'Catalogue at position', p, 'already present'
continue
print 'Downloading at position', p
if method == 'panstarrs':
while True:
try:
r = requests.post(
'http://archive.stsci.edu/panstarrs/search.php',
data={
'ra': p[0],
'dec': p[1],
'SR': CSIZE,
'max_records': 100000,
'nDetections': ">+5",
'action': 'Search',
'selectedColumnsCsv': 'objid,ramean,decmean'
},
timeout=300)
except requests.exceptions.Timeout:
print 'Timeout, retrying!'
else:
break
f = open(outfile, 'w')
f.writelines(r.text)
f.close()
elif method == 'wise':
t = Irsa.query_region(coord.SkyCoord(p[0],
p[1],
unit=(u.deg, u.deg)),
catalog='allwise_p3as_psd',
radius='0d30m0s')
t.write(outfile, format='votable')
else:
raise NotImplementedError('Method ' + method)
def queryWISE(ra, dec):
wise = Irsa.query_region(
SkyCoord(ra, dec, unit=(u.deg, u.deg), frame='fk5'),
catalog='allwise_p3as_psd',
radius='0d0m2s',
selcols=
'ra,dec,w1mpro,w1sigmpro,w2mpro,w2sigmpro,w3mpro,w3sigmpro,w4mpro,w4sigmpro'
)
#print wise['ra'].data[0]
return (wise['ra'].data[0], wise['dec'].data[0], wise['w1mpro'].data[0],
wise['w1sigmpro'].data[0], wise['w2mpro'].data[0],
wise['w2sigmpro'].data[0], wise['w3mpro'].data[0],
wise['w3sigmpro'].data[0], wise['w4mpro'].data[0],
wise['w4sigmpro'].data[0])
def do_ppmxl(catalog):
task_str = catalog.get_current_task_str()
keys = list(catalog.entries.keys())
warnings.filterwarnings("ignore")
for oname in pbar(keys, task_str):
# Some events may be merged in cleanup process, skip them if
# non-existent.
try:
name = catalog.add_entry(oname)
except Exception:
catalog.log.warning(
'"{}" was not found, suggests merge occurred in cleanup '
'process.'.format(oname))
continue
if (FASTSTARS.RA not in catalog.entries[name] or
FASTSTARS.DEC not in catalog.entries[name]):
continue
else:
radec= str(catalog.entries[name][FASTSTARS.RA][0]['value'])+str(catalog.entries[name][FASTSTARS.DEC][0]['value'])
c=coord(radec,unit=(un.hourangle, un.deg),frame='icrs')
cnttry = 0
foundstar = False
while foundstar == False and cnttry < 1:
try:
cnttry += 1
time.sleep(0.1)
result = Irsa.query_region(c,catalog='ppmxl',radius='0d0m10s')
except TypeError:
#print(radec,cnttry)
continue
if len(result) > 1:
foundstar = True
if foundstar == True:
source = (catalog.entries[name]
.add_source(name='The PPMXL Catalog',
bibcode="2010AJ....139.2440R",
url="https://irsa.ipac.caltech.edu/Missions/ppmxl.html",
secondary=True))
catalog.entries[name].add_quantity(FASTSTARS.PROPER_MOTION_RA, str(result['pmra'][0]*degperyrtomasperyear), source, e_value=str(result['e_pmra'][0]*degperyrtomasperyear), u_value='mas/yr')
catalog.entries[name].add_quantity(FASTSTARS.PROPER_MOTION_DEC, str(result['pmde'][0]*degperyrtomasperyear), source, e_value=str(result['e_pmde'][0]*degperyrtomasperyear), u_value='mas/yr')
catalog.journal_entries()
return
def surveyFieldConverter(self,ra,dec,margin,need_clean=False,cat = 'fp_xsc'):
'''
for 2MASS return the designation for each detected source in search field
'''
pos = FK5(ra*u.degree, dec*u.degree)
#pos = coordinates.SkyFrame(ra*u.deg,dec*u,deg, frame='fk5')
#pos =SkyCoord(ra* u.deg,dec* u.deg, frame='fk5')
tbl = Irsa.query_region(pos,catalog=cat, spatial='Box',width=2*margin*u.deg)
lst=[]
if (need_clean and len(tbl)>0):
for i in range(len(tbl['designation'])):
if (tbl[0]['cc_flg']=='0'):
lst.append(tbl[i]['designation'])
return lst
else:
return list(tbl['designation'])
def get_nir_cat(self, clobber=False, use_twomass=True):
"""
Get the NIR catalog
Catalog (necessary for zero-point determination) is saved
into self.data_dir as
self.name+"_"+self.nir_survey+"cat.fits"
"""
print("Fetching NIR catalog from server...")
if use_twomass:
if (not os.path.isfile(self.nir_cal_cat)) or clobber:
from astroquery.irsa import Irsa
Irsa.ROW_LIMIT = 2000.
table = Irsa.query_region(coordinates.Galactic(l=self.glon,
b=self.glat,
unit=(u.deg,
u.deg)),
catalog="fp_psc",
spatial="Box",
width=self.nir_im_size)
#print(table)
#IPAC table does not take overwrite? But FITS does? So inconsistent and bad
table.write(self.nir_cal_cat,
format='votable',
overwrite=clobber)
else:
print(
"NIR catalog already downloaded. Use clobber=True to fetch new versions."
)
else:
if (not os.path.isfile(self.nir_cat)) or clobber:
if self.nir_survey == "VISTA":
from astroquery.vista import Vista as NIR
if self.nir_survey == "UKIDSS":
from astroquery.ukidss import Ukidss as NIR
table = NIR.query_region(coordinates.Galactic(l=self.glon,
b=self.glat,
unit=(u.deg,
u.deg)),
radius=self.nir_im_size)
table.write(self.nir_cat, format="fits", overwrite=clobber)
else:
print(
"NIR catalog already downloaded. Use clobber=True to fetch new versions."
)
def query_cat(coords,catalog,radius=0.5*u.arcsec,cols=None,fill_val=-99.99,full=False,IRSA=False):
#one-to-one query
if IRSA:
results = Irsa.query_region(coords,catalog=catalog,radius=radius)
else:
results = Vizier.query_region(coords,catalog=catalog,radius=radius)
if len(results) == 0:
return None
if full:
return results
results = results[0]
if cols is not None:
# if dict, remap colnames
if isinstance(cols,dict):
for k,v in cols.iteritems():
results.rename_column(k,v)
names = cols.values()
else:
names = cols
else:
names = results.colnames
# make new columns one-to-one with coords
newtable = Table(masked=True)
for col in names:
oldcol = results[col]
newcol = MaskedColumn(data=np.zeros(len(coords),dtype=oldcol.dtype),unit=oldcol.unit,name=col,mask=np.ones(len(coords),dtype=bool),fill_value=fill_val)
# copy data from results
for row in results:
if not row[col]:
continue
# _q IS 1-BASED INDEXING?!
newcol[row['_q']-1] = row[col]
newcol.mask[row['_q']-1] = False
newtable.add_column(newcol)
return newtable
def irsa_search(folder_path, input_file, output_file, catalog, radius,
row_range, input_cols, output_cols):
"""
Perform an IRSA catalog search on an excel spreadsheet with RA-DEC coords
given in degrees for the closest matches distance-wise within the catalog,
and output the name, distance, RA-DEC coords and catalog into specified
columns. Note: convert HMS and DMS coords into degrees beforehand.
"""
from astroquery.irsa import Irsa
from astropy.coordinates import SkyCoord
import astropy.units as u
from openpyxl import load_workbook
import numpy as np
wb = load_workbook(folder_path + input_file)
sheet = wb.active
for i in range(row_range[0], row_range[1]):
ra = sheet[input_cols[0] + str(i)].value
dec = sheet[input_cols[1] + str(i)].value
Q = Irsa.query_region(SkyCoord(ra=ra, dec=dec, unit=(u.deg, u.deg)),
catalog=catalog,
radius=radius * u.arcmin,
selcols="object,ra,dec").as_array()
if len(Q) < 1:
continue
sources_cat = []
for source in Q:
dist_cat = np.sqrt((ra - source[1])**2 + (dec - source[2])**2)
sources_cat.append([
dist_cat, source[1], source[2], catalog,
str(source[0])[2:-1]
])
sorted_sources_cat = sorted(sources_cat, key=lambda x: x[0])
sheet[output_cols[0] + str(i)] = sorted_sources_cat[0][4]
sheet[output_cols[1] + str(i)] = sorted_sources_cat[0][0]
sheet[output_cols[2] + str(i)] = sorted_sources_cat[0][1]
sheet[output_cols[3] + str(i)] = sorted_sources_cat[0][2]
sheet[output_cols[4] + str(i)] = sorted_sources_cat[0][3]
wb.save(folder_path + output_file)
return
def query_2MASS(ra,dec):
'''
Busca no 2MASS dado um campo com o raio definido pelo usuário
'''
w = WCS(fits.getheader(session['wcs']))
r = session['r']
orim = SkyCoord(w.wcs_pix2world([(0,0)],1), unit='deg')
opr = SkyCoord(w.wcs_pix2world([(r,0)],1), unit='deg')
rw = orim.separation(opr)[0]
print('Separação',rw)
crval = SkyCoord(ra=ra, dec=dec, unit='deg', frame='icrs')
Q = Irsa.query_region(crval,catalog='fp_psc',spatial='Cone', radius=rw,\
selcols=['ra','dec','j_m','k_m']).to_pandas()
print(Q)
m = SkyCoord(ra=Q['ra'],dec=Q['dec'], unit=('deg','deg'), frame='icrs')
idx, _, _ = match_coordinates_sky(crval,m)
j , k = Q.loc[idx][['j_m','k_m']]
return j, k
def cmd_setup(ra_min, ra_max, dec_min, dec_max, GridSize=100, height=8, width=20):
"""Prepares a 2d histogram of HST data and scatter plot of unWISE data from given RA and DEC"""
Irsa.ROW_LIMIT = 100000
data_table = Irsa.query_region("m33", catalog="unwise_2019", spatial="Polygon",
polygon=[SkyCoord(ra=ra_min,dec=dec_min,unit=(u.deg,u.deg),frame='icrs'),
SkyCoord(ra=ra_max,dec=dec_min,unit=(u.deg,u.deg),frame='icrs'),
SkyCoord(ra=ra_max,dec=dec_max,unit=(u.deg,u.deg),frame='icrs'),
SkyCoord(ra=ra_min,dec=dec_max,unit=(u.deg,u.deg),frame='icrs')])
data_table['W1'] = f_to_mag(data_table['flux_1'])
data_table['W2'] = f_to_mag(data_table['flux_2'])
data_table['W2 - W1'] = data_table['W2'] - data_table['W1']
fig, (ax1, ax2) = plt.subplots(1,2)
fig.set_figheight(height)
fig.set_figwidth(width)
ax1.set_xlabel('W2 - W1', fontsize=20)
ax1.set_ylabel('W2', fontsize=20)
ax1.scatter(data_table['W2 - W1'],data_table['W2'], s=10,c='black')
ax1.set_ylim(ax1.get_ylim()[::-1])
ax1.set_title('unWISE', size=20)
"""HST sources 2d histogram"""
hst_table = pd.read_csv('./small_data.csv')
selection = hst_table.where((hst_table['RA'] > ra_min) & (hst_table['RA'] < ra_max)
& (hst_table['DEC'] > dec_min) & (hst_table['DEC'] < dec_max))
color = selection['F110W_VEGA'] - selection['F160W_VEGA']
mag = selection['F160W_VEGA']
ax2.hexbin(color, mag, gridsize=GridSize, bins='log')
ax2.invert_yaxis()
ax2.invert_xaxis()
ax2.set_xlabel('F110W - F160W', fontsize=20)
ax2.set_ylabel('F160W', fontsize=20)
ax2.set_title('HST', fontsize=20)
sigma_clip=True,
sigma_clip_func=np.ma.median)
sci_med.write(red_path + sci + '.fits', overwrite=True)
with fits.open(red_path + sci + '.fits', mode='update') as hdr:
hdr[0].header['RDNOISE'] = header['RDNOISE'] / len(sci_list[sci])
hdr[0].header['NFILES'] = len(sci_list[sci])
for k, n in enumerate(sci_list[sci]):
hdr[0].header['FILE' +
str(k + 1)] = (os.path.basename(n),
'Name of file used in median.')
if args.wcs == 'True' or args.wcs == 'yes':
fig, ax = plt.subplots(figsize=(7, 7))
ax = plt.subplot(projection=wcs_object)
gaia = Irsa.query_region(SkyCoord(hdr[0].header['CRVAL1'] * u.deg,
hdr[0].header['CRVAL2'] * u.deg,
frame='fk5'),
catalog="gaia_dr2_source",
spatial="Cone",
radius=3 * u.arcmin)
if len(gaia) == 0:
log.info('No GAIA stars found within 3 arcmin for starlist.')
plt.close()
else:
ax.imshow(sci_med,
cmap='gray',
norm=ImageNormalize(sci_med, interval=ZScaleInterval()))
_, median, std = sigma_clipped_stats(sci_med, sigma=3.0)
daofind = DAOStarFinder(fwhm=7.0, threshold=5. * std)
sources = daofind(np.asarray(sci_med))
for l, m in enumerate(gaia['source_id']):
x, y = (wcs.WCS(hdr[0].header)).all_world2pix(
gaia['ra'][l], gaia['dec'][l], 1)
def WISE_LC(obj,
alldata=False,
interac=False,
clobber=False): # moreplots=False,
# return 1 if successfully finished
# return 0 if doesnt download
retval = 0
# create directories that we'll need
if not os.path.exists('data'):
os.makedirs('data')
if not os.path.exists('img'):
os.makedirs('img')
# the WISE tables to search
cats = [
'neowiser_p1bs_psd', 'allsky_4band_p1bs_psd', 'allsky_3band_p1bs_psd',
'allsky_2band_p1bs_psd'
]
# if clobber=False (i.e. don't overwrite),
# double-check the data/ dir to see if this star has already been run!
doobj = True
if clobber is False:
#booleans of if each output file prev. exists.
t1 = os.path.isfile('data/' + obj + cats[0] + '.csv')
t2 = os.path.isfile('data/' + obj + cats[1] + '.csv')
t3 = os.path.isfile('data/' + obj + cats[2] + '.csv')
t4 = os.path.isfile('data/' + obj + cats[3] + '.csv')
# if ANY of these files exists, then DONT do object again
doobj = not (t1 | t2 | t3 | t4)
if doobj == False:
print("\x1b[31mWISE_LC: Data already pulled\x1b[0m")
# should we actually Do this Object? False if prev found & clobber=False
if doobj:
colors = ['#1f77b4', '#ff7f0e', '#c5b0d5', '#d62728']
plt.figure(figsize=(13, 8))
totvisits = 0
for k in range(len(cats)):
try:
table1 = Irsa.query_region(obj,
catalog=cats[k],
spatial='Cone',
radius=3 * u.arcsec)
# table2 = Irsa.query_region(obj, catalog=cats[1], spatial='Cone', radius=3 * u.arcsec)
# table3 = Irsa.query_region(obj, catalog=cats[2], spatial='Cone', radius=3 * u.arcsec)
# table4 = Irsa.query_region(obj, catalog=cats[3], spatial='Cone', radius=3 * u.arcsec)
table1.sort('mjd')
# table2.sort('mjd')
# table3.sort('mjd')
# table4.sort('mjd')
df1 = table1.to_pandas()
# df2 = table2.to_pandas()
# df3 = table3.to_pandas()
# df4 = table4.to_pandas()
totvisits = totvisits + len(
df1) #+ len(df2) + len(df3) + len(df4)
# manually fix the Python3 str=>bytestr problem... boo
df1['ph_qual'] = df1['ph_qual'].str.decode('ascii')
# df2['ph_qual'] = df2['ph_qual'].str.decode('ascii')
# df3['ph_qual'] = df3['ph_qual'].str.decode('ascii')
# df4['ph_qual'] = df4['ph_qual'].str.decode('ascii')
df1['cc_flags'] = df1['cc_flags'].str.decode('ascii')
# df2['cc_flags'] = df2['cc_flags'].str.decode('ascii')
# df3['cc_flags'] = df3['cc_flags'].str.decode('ascii')
# df4['cc_flags'] = df4['cc_flags'].str.decode('ascii')
df1.to_csv('data/' + obj + cats[k] + '.csv')
# df2.to_csv('data/' + obj + cats[1] + '.csv')
# df3.to_csv('data/' + obj + cats[2] + '.csv')
# df4.to_csv('data/' + obj + cats[3] + '.csv')
#### QUALITY CUTS
# can't add this to the latter 3 surveys... (df1['qual_frame'] > 8)
if k == 0:
ok1 = (df1['ph_qual'].str[0] == 'A') & (df1['nb'] == 1) & (
df1['cc_flags'].str[0:2] == '00') & (
df1['w1rchi2'] < 5) & (df1['qual_frame'] > 8)
else:
ok1 = (df1['ph_qual'].str[0] == 'A') & (
df1['nb'] == 1) & (df1['cc_flags'].str[0:2]
== '00') & (df1['w1rchi2'] < 5)
# ok3 = (df3['ph_qual'].str[0] == 'A') & (df3['nb'] == 1) & (df3['cc_flags'].str[0:2] == '00') & (df3['w1rchi2'] < 5)
# ok4 = (df4['ph_qual'].str[0] == 'A') & (df4['nb'] == 1) & (df4['cc_flags'].str[0:2] == '00') & (df4['w1rchi2'] < 5)
if alldata:
plt.scatter(df1['mjd'],
df1['w1mpro'],
c='k',
s=8,
alpha=0.25)
# plt.scatter(df2['mjd'], df2['w1mpro'], c='k', s=8, alpha=0.25)
# plt.scatter(df3['mjd'], df3['w1mpro'], c='k', s=8, alpha=0.25)
# plt.scatter(df4['mjd'], df4['w1mpro'], c='k', s=8, alpha=0.25)
plt.errorbar(df1['mjd'][ok1],
df1['w1mpro'][ok1],
yerr=df1['w1sigmpro'][ok1],
marker='o',
linestyle='none',
alpha=0.25,
color=colors[k])
# plt.errorbar(df2['mjd'][ok2], df2['w1mpro'][ok2], yerr=df2['w1sigmpro'][ok2],
# marker='o', linestyle='none', alpha=0.25, color=colors[1])
# plt.errorbar(df3['mjd'][ok3], df3['w1mpro'][ok3], yerr=df3['w1sigmpro'][ok3],
# marker='o', linestyle='none', alpha=0.25, color=colors[2])
# plt.errorbar(df4['mjd'][ok4], df4['w1mpro'][ok4], yerr=df4['w1sigmpro'][ok4],
# marker='o', linestyle='none', alpha=0.25, color=colors[3])
except Exception as eek:
print(
"\x1b[31mWISE_LC: ' + str(eek) + ' encountered. Huh.\x1b[0m"
)
plt.ylabel('W1 (mag)')
plt.xlabel('MJD (days)')
plt.gca().invert_yaxis()
plt.title(obj + ', N=' + str(totvisits))
plt.savefig('img/' + obj + '_W1.png',
dpi=150,
bbox_inches='tight',
pad_inches=0.25)
if interac:
plt.show()
else:
plt.close()
retval = 1 # a value to return, assuming the code makes it this far!
# # 2) W1-W2 color light curve
# if moreplots:
# plt.figure(figsize=(13,8))
# if alldata:
# plt.scatter(df1['mjd'], df1['w1mpro']-df1['w2mpro'], c='k', s=8, alpha=0.25)
# plt.scatter(df2['mjd'], df2['w1mpro']-df2['w2mpro'], c='k', s=8, alpha=0.25)
# plt.scatter(df3['mjd'], df3['w1mpro']-df3['w2mpro'], c='k', s=8, alpha=0.25)
# plt.scatter(df4['mjd'], df4['w1mpro']-df4['w2mpro'], c='k', s=8, alpha=0.25)
#
# plt.errorbar(df1['mjd'][ok1], df1['w1mpro'][ok1] - df1['w2mpro'][ok1],
# yerr=np.sqrt(df1['w1sigmpro'][ok1]**2 + df1['w2sigmpro'][ok1]**2),
# marker='o', linestyle='none', alpha=0.25, color=colors[0])
# plt.errorbar(df2['mjd'][ok2], df2['w1mpro'][ok2] - df2['w2mpro'][ok2],
# yerr=np.sqrt(df2['w1sigmpro'][ok2]**2 + df2['w2sigmpro'][ok2]**2),
# marker='o', linestyle='none', alpha=0.25, color=colors[1])
# plt.errorbar(df3['mjd'][ok3], df3['w1mpro'][ok3] - df3['w2mpro'][ok3],
# yerr=np.sqrt(df3['w1sigmpro'][ok3]**2 + df3['w2sigmpro'][ok3]**2),
# marker='o', linestyle='none', alpha=0.25, color=colors[2])
# plt.errorbar(df4['mjd'][ok4], df4['w1mpro'][ok4] - df4['w2mpro'][ok4],
# yerr=np.sqrt(df4['w1sigmpro'][ok4]**2 + df4['w2sigmpro'][ok4]**2),
# marker='o', linestyle='none', alpha=0.25, color=colors[3])
# plt.xlabel('MJD (days)')
# plt.ylabel('W1-W2 (mag)')
# plt.title(obj + ', N=' + str(totvisits))
# plt.savefig('img/'+obj + '_W1W2.png', dpi=150, bbox_inches='tight', pad_inches=0.25)
# # plt.show()
# plt.close()
# 3) CMD
# plt.figure(figsize=(8,8))
# plt.errorbar(df1['w1mpro'] - df1['w2mpro'], df1['w1mpro'],
# xerr=np.sqrt(df1['w1sigmpro']**2 + df1['w2sigmpro']**2), yerr=df1['w1sigmpro'],
# marker='o', linestyle='none', alpha=0.25, color='#1f77b4')
# plt.errorbar(df2['w1mpro_ep'] - df2['w2mpro_ep'], df2['w1mpro_ep'],
# xerr=np.sqrt(df2['w1sigmpro_ep']**2 + df2['w2sigmpro_ep']**2 ), yerr=df2['w1sigmpro_ep'],
# marker='o', linestyle='none', alpha=0.25, color='#ff7f0e')
#
# plt.ylabel('W1 (mag)')
# plt.xlabel('W1-W2 (mag)')
# plt.gca().invert_yaxis()
# plt.savefig('img/'+obj + '_cmd.png', dpi=150, bbox_inches='tight', pad_inches=0.25)
# plt.close()
# bonus: RA,Dec to make sure not a blend, etc
# plt.figure(figsize=(8, 8))
# plt.scatter(df1['ra'], df1['dec'],
# marker='o', alpha=0.25, color='#1f77b4')
# plt.scatter(df2['ra'], df2['dec'],
# marker='o', alpha=0.25, color='#ff7f0e')
# plt.xlabel('RA (deg)')
# plt.ylabel('Dec (deg)')
# plt.savefig('img/' + obj + '_radec.png', dpi=150, bbox_inches='tight', pad_inches=0.25)
# plt.close()
return retval
def contamVerify(RA, DEC, INSTRUMENT, APAlist, binComp=[], PDF='', web=False):
""" Generates a PDF file of figures displaying a simulation
of the science image for any given observation using the parameters provided.
Parameter(s)
------------
RA : str
The Right Ascension of your target in HH:MM:SS
DEC : str
The Declination of your target in DD:MM:SS
INSTRUMENT : str
The instrument you are observing with (case-sensitive).
The software currently supports:
'MIRI', 'NIRISS', 'NIRCam F322W2', 'NIRCam F444W'
APAlist : list
A list of Aperture Position Angle(s). Element(s) must be in integers.
Example 1:
[1, 25, 181, 205]
Example 2:
[25]
binComp : list
A list containing parameters of a missing companion that is not
in the 2MASS IRSA point-source catalog. The format is:
[RA (arcseconds), DEC (arcseconds), J mag, H mag, K mag]
[string, string, integer, integer, integer]
PDF : string
The path to where the PDF file will be saved. If left blank, the PDF
file will be saved in your current working directory.
Example:
'path/to/my/file.pdf'
web : boolean
Makes it easier to integrate it onto the website. Leave this as false,
unless you're running this in app_exoctk.py
Returns
-------
A .PDF file containing a simulation of the FOV of your target in the
science coordinate system. Some things to consider when reading the figures:
1. The target is circled in red
2. Stellar temperatures of all sources are plotted by color
3. The gray region oulined in blue represents the aperture for the given
instrument.
4. The blue square represents the readout region, or the "origin"
"""
print('Generating FOV...')
# Decimal degrees --> HMSDMS for Irsa.query_region()
targetcrd = crd.SkyCoord(ra=RA, dec=DEC, unit='deg').to_string('hmsdms')
targetRA, targetDEC = RA, DEC
# Querying for neighbors with 2MASS IRSA's fp_psc (point-source catalog)
rad = 2.5
print('Querying for point-sources within {} arcminutes...'.format(
str(rad)))
info = Irsa.query_region(targetcrd,
catalog='fp_psc',
spatial='Cone',
radius=rad * u.arcmin)
# Coordinates of all stars in FOV, including target
allRA = info['ra'].data.data
allDEC = info['dec'].data.data
# Initiating a dictionary to hold all relevant star information
stars = {}
stars['RA'], stars['DEC'] = allRA, allDEC
print('Total point-sources found in region: {}'.format(len(stars['RA'])))
# Finding the target using relative distances
sindRA = (targetRA - stars['RA']) * np.cos(targetDEC)
cosdRA = targetDEC - stars['DEC']
distance = np.sqrt(sindRA**2 + cosdRA**2)
targetIndex = np.argmin(distance)
# Appending missing companion to the above lists (if any)
if binComp != []:
print('Adding missing companion...')
bb = binComp[0] / 3600 / np.cos(allDEC[targetIndex] * deg2rad)
allRA = np.append(allRA, (allRA[targetIndex] + bb))
allDEC = np.append(allDEC, (allDEC[targetIndex] + binComp[1] / 3600))
Jmag = np.append(Jmag, binComp[2])
Hmag = np.append(Kmag, binComp[3])
Kmag = np.append(Kmag, binComp[4])
J_Hobs = Jmag - Hmag
H_Kobs = Hmag - Kmag
# Restoring model parameters
modelParam = readsav(os.path.join(TRACES_PATH, 'NIRISS', 'modelsInfo.sav'),
verbose=False)
models = modelParam['models']
modelPadX = modelParam['modelpadx']
modelPadY = modelParam['modelpady']
dimXmod = modelParam['dimxmod']
dimYmod = modelParam['dimymod']
jhMod = modelParam['jhmod']
hkMod = modelParam['hkmod']
teffMod = modelParam['teffmod']
# JHK bands of all stars in FOV, including target
Jmag = info['j_m'].data.data
Hmag = info['h_m'].data.data
Kmag = info['k_m'].data.data
# J-H band, H-K band. This will be used to derive the stellar Temps later
J_Hobs = Jmag - Hmag
H_Kobs = Hmag - Kmag
# Number of stars
nStars = stars['RA'].size
# Find/assign Teff of each star
print('Calculating effective temperatures...')
starsT = np.empty(nStars)
for j in range(nStars):
color_separation = (J_Hobs[j] - jhMod)**2 + (H_Kobs[j] - hkMod)**2
min_separation_ind = np.argmin(color_separation)
starsT[j] = teffMod[min_separation_ind]
# Record keeping
stars['Temp'] = starsT
# Initiating a dictionary for customizability
apertures = {}
apertures['NIRISS'] = ['NIS_SOSSFULL', 'NIS_SOSSFULL']
apertures['NIRCam F444W'] = ['NRCA5_GRISM256_F444W', 'NRCA5_FULL']
apertures['NIRCam F322W2'] = ['NRCA5_GRISM256_F322W2', 'NRCA5_FULL']
apertures['MIRI'] = ['MIRIM_SLITLESSPRISM', 'MIRIM_FULL']
# Instantiate SIAF object
siaf = pysiaf.Siaf(INSTRUMENT.split(' ')[0])
aper = siaf.apertures[apertures[INSTRUMENT][0]]
full = siaf.apertures[apertures[INSTRUMENT][1]]
# DET_targ -> TEL_targ -> get attitude matrix for target
# -> TEL_neighbor -> DET_neighbor -> SCI_neighbor
print('Converting Sky --> Science coordinates...')
xSweet, ySweet = aper.reference_point('det')
v2targ, v3targ = aper.det_to_tel(xSweet, ySweet)
contam = {}
if not web:
filename = 'contam_{}_{}_{}.pdf'.format(RA, DEC, INSTRUMENT)
defaultPDF = os.path.join(os.getcwd(), filename).replace(' ', '_')
PDF = defaultPDF if PDF == '' else PDF
elif web:
filename = 'contam_{}_{}_{}.pdf'.format(RA, DEC, INSTRUMENT)
PDF = os.path.join(TRACES_PATH, filename)
print('Saving figures to: {}'.format(PDF))
print('This will take a second...')
pdfobj = PdfPages(PDF)
for APA in APAlist:
attitude = pysiaf.utils.rotations.attitude_matrix(
v2targ, v3targ, targetRA, targetDEC, APA)
xdet, ydet = [], []
xsci, ysci = [], []
for starRA, starDEC in zip(stars['RA'], stars['DEC']):
# Get the TEL coordinates of each star using the attitude
# matrix of the target
V2, V3 = pysiaf.utils.rotations.sky_to_tel(attitude, starRA,
starDEC)
# Convert to arcsec and turn to a float
V2, V3 = V2.to(u.arcsec).value, V3.to(u.arcsec).value
XDET, YDET = aper.tel_to_det(V2, V3)
XSCI, YSCI = aper.det_to_sci(XDET, YDET)
xdet.append(XDET)
ydet.append(YDET)
xsci.append(XSCI)
ysci.append(YSCI)
XDET, YDET = np.array(xdet), np.array(ydet)
XSCI, YSCI = np.array(xsci), np.array(ysci)
starsAPA = {'xdet': XDET, 'ydet': YDET, 'xsci': XSCI, 'ysci': YSCI}
# Finding indexes of neighbor sources that land on detector
rows, cols = full.corners('det')
minrow, maxrow = rows.min(), rows.max()
mincol, maxcol = cols.min(), cols.max()
inFOV = []
for star in range(0, nStars):
x, y = starsAPA['xdet'][star], starsAPA['ydet'][star]
if (mincol < x) & (x < maxcol) & (minrow < y) & (y < maxrow):
inFOV.append(star)
inFOV = np.array(inFOV)
# Making final plot
fig = plt.figure(figsize=(15, 15))
aper.plot(frame='sci', fill_color='gray', color='blue')
plt.scatter(XSCI[targetIndex],
YSCI[targetIndex],
s=400,
lw=1.5,
facecolor='gray',
edgecolor='red')
plotTemps(starsT[inFOV], XSCI[inFOV], YSCI[inFOV])
aper.plot_frame_origin(frame='sci', which='sci')
# Fine-tune trace lengths
start, stop = traceLength(INSTRUMENT)
# Plotting the trace footprints
for x, y in zip(XSCI[inFOV], YSCI[inFOV]):
if 'F322W2' in INSTRUMENT:
plt.plot([x - stop, x + start], [y, y],
lw=40,
color='white',
alpha=0.2)
plt.plot([x - stop, x + start], [y, y], lw=2., color='white')
elif 'F444W' in INSTRUMENT:
plt.plot([x - start, x + stop], [y, y],
lw=40,
color='white',
alpha=0.2)
plt.plot([x - start, x + stop], [y, y], lw=2., color='white')
else:
plt.plot([x, x], [y - stop, y + start],
lw=40,
color='white',
alpha=0.2)
plt.plot([x, x], [y - stop, y + start], lw=2., color='white')
# Labeling
aperstr = str(aper.AperName.replace('_', ' '))
tx, ty = str(round(XSCI[targetIndex])), str(round(YSCI[targetIndex]))
plt.title(
'The FOV in SCIENCE coordinates at APA {}$^o$'.format(str(APA)) +
'\n' + '{}'.format(aperstr) + '\n' +
'Target (X,Y): {}, {}'.format(tx, ty),
fontsize=20)
# Adding to PDF
pdfobj.savefig(fig, bbox_inches='tight')
pdfobj.close()
if web:
return PDF
def getWISE(entry):
'''
get IR data from AllWISE Source Catalog
attempts to query Irsa 5 times; if they keep failing, abort
returns updated entry
'''
ir_pos = coord.SkyCoord(entry['consensus']['ir_ra'],
entry['consensus']['ir_dec'],
unit=(u.deg, u.deg),
frame='icrs')
tryCount = 0
while (
True
): #in case of error, wait 10 sec and try again; give up after 5 tries
tryCount += 1
try:
table = Irsa.query_region(ir_pos,
catalog='allwise_p3as_psd',
radius=3. * u.arcsec)
break
except (astroquery.exceptions.TimeoutError,
astroquery.exceptions.TableParseError) as e:
if tryCount > 5:
message = 'Unable to connect to IRSA; trying again in 10 min'
logging.exception(message)
print message
raise fn.DataAccessError(message)
logging.exception(e)
time.sleep(10)
except Exception as e:
if 'Query failed' in str(e) or 'timed out' in str(e):
if tryCount > 5:
message = 'Unable to connect to IRSA; trying again in 10 min'
logging.exception(message)
print message
raise fn.DataAccessError(message)
logging.exception(e)
time.sleep(10)
else:
raise
if len(table):
number_matches = 0
if table[0]['w1snr'] > 5:
match = table[0]
dist = match['dist']
number_matches += 1
else:
match = None
dist = np.inf
if len(table) > 1:
for row in table:
if row['dist'] < dist and row['w1snr'] > 5:
match = row
dist = match['dist']
number_matches += 1
if match:
wise_match = {'designation':'WISEA'+match['designation'], 'ra':match['ra'], 'dec':match['dec'], \
'number_matches':np.int16(number_matches), \
'w1mpro':match['w1mpro'], 'w1sigmpro':match['w1sigmpro'], 'w1snr':match['w1snr'], \
'w2mpro':match['w2mpro'], 'w2sigmpro':match['w2sigmpro'], 'w2snr':match['w2snr'], \
'w3mpro':match['w3mpro'], 'w3sigmpro':match['w3sigmpro'], 'w3snr':match['w3snr'], \
'w4mpro':match['w4mpro'], 'w4sigmpro':match['w4sigmpro'], 'w4snr':match['w4snr']}
else:
wise_match = None
else:
wise_match = None
if wise_match:
logging.info('AllWISE match found')
for key in wise_match.keys():
if wise_match[key] is np.ma.masked:
wise_match.pop(key)
elif wise_match[key] and type(wise_match[key]) is not str:
wise_match[key] = wise_match[key].item()
elif wise_match[key] == 0:
wise_match[key] = 0
else:
logging.info('No AllWISE match found')
return wise_match
def get_cat(method, retries=100):
cwd = os.getcwd()
try:
os.mkdir(method)
except OSError:
pass
if method == 'pslocal':
hplist = []
if method == 'wise':
from astroquery.irsa import Irsa
Irsa.ROW_LIMIT = 1000000
ra_factor, pos = tile(find_fullres_image())
print 'Downloading catalogues for', len(pos), 'sky positions'
for i, p in enumerate(pos):
outfile = method + '/' + method + '-' + str(i) + '.vo'
if os.path.isfile(outfile):
print 'Catalogue at position', p, 'already present'
continue
print 'Downloading at position', p
if method == 'panstarrs':
count = 0
while True:
try:
r = requests.post(
'http://archive.stsci.edu/panstarrs/search.php',
data={
'ra': p[0],
'dec': p[1],
'SR': CSIZE,
'max_records': 100000,
'nDetections': ">+5",
'action': 'Search',
'selectedColumnsCsv': 'objid,ramean,decmean'
},
timeout=300)
except requests.exceptions.Timeout:
print 'Timeout, retrying!'
else:
if 'Warning' not in r.text and 'Please' not in r.text:
break
else:
# will go round the loop again
print 'Bad response, retry download (%i)' % count
sleep(5 + count * 15)
count += 1
if count >= retries:
raise RuntimeError(
'Number of retries exceeded for download')
f = open(outfile, 'w')
f.writelines(r.text)
f.close()
elif method == 'wise':
t = Irsa.query_region(coord.SkyCoord(p[0],
p[1],
unit=(u.deg, u.deg)),
catalog='allwise_p3as_psd',
radius='0d30m0s')
t.write(outfile, format='votable')
elif method == 'pslocal':
from astropy_healpix import HEALPix
hp = HEALPix(nside=64)
cs = hp.cone_search_lonlat(p[0] * u.deg,
p[1] * u.deg,
radius=CSIZE * u.deg)
hplist += list(cs)
if not os.path.isdir(PSBASE):
# we don't have a local PS database, so download
for pix in cs:
outfile = method + '/' + str(pix)
if not os.path.isfile(outfile):
print 'Downloading healpix pixel', pix
download_file(
'http://uhhpc.herts.ac.uk/panstarrs-healpix/' +
str(pix), outfile)
else:
raise NotImplementedError('Method ' + method)
if method == 'pslocal':
hplist = list(set(hplist))
print 'Found', len(hplist), 'unique healpix pixels'
outname = method + '/' + method + '.txt'
with open(outname, 'w') as outfile:
outfile.write('# RA DEC ObjID\n')
for pixel in hplist:
print 'Appending pixel', pixel
if os.path.isdir(PSBASE):
pixelfile = PSBASE + '/' + str(pixel)
else:
pixelfile = method + '/' + str(pixel)
if not os.path.isfile(pixelfile):
raise RuntimeError('Pixel file ' + pixelfile +
'does not exist')
os.system('cat ' + pixelfile + ' >> ' + outname)
def get_cat(method,retries=100):
cwd=os.getcwd()
try:
os.mkdir(method)
except OSError:
pass
if method=='pslocal':
hplist=[]
if method=='wise':
from astroquery.irsa import Irsa
Irsa.ROW_LIMIT=1000000
ra_factor,pos=tile(cwd+'/image_ampphase1.app.restored.fits')
print 'Downloading catalogues for',len(pos),'sky positions'
for i,p in enumerate(pos):
outfile=method+'/'+method+'-'+str(i)+'.vo'
if os.path.isfile(outfile):
print 'Catalogue at position',p,'already present'
continue
print 'Downloading at position',p
if method=='panstarrs':
count=0
while True:
try:
r = requests.post('http://archive.stsci.edu/panstarrs/search.php', data = {'ra':p[0],'dec':p[1],'SR':CSIZE,'max_records':100000,'nDetections':">+5",'action':'Search','selectedColumnsCsv':'objid,ramean,decmean'},timeout=300)
except requests.exceptions.Timeout:
print 'Timeout, retrying!'
else:
if 'Warning' not in r.text and 'Please' not in r.text:
break
else:
# will go round the loop again
print 'Bad response, retry download (%i)' % count
sleep(5+count*15)
count+=1
if count>=retries:
raise RuntimeError('Number of retries exceeded for download')
f=open(outfile,'w')
f.writelines(r.text)
f.close()
elif method=='wise':
t=Irsa.query_region(coord.SkyCoord(p[0],p[1],unit=(u.deg,u.deg)), catalog='allwise_p3as_psd', radius='0d30m0s')
t.write(outfile,format='votable')
elif method=='pslocal':
from astropy_healpix import HEALPix
hp = HEALPix(nside=64)
cs = hp.cone_search_lonlat(p[0]*u.deg, p[1]*u.deg, radius=CSIZE*u.deg)
hplist += list(cs)
if not os.path.isdir(PSBASE):
# we don't have a local PS database, so download
for pix in cs:
outfile=method+'/'+str(pix)
if not os.path.isfile(outfile):
print 'Downloading healpix pixel',pix
download_file('http://uhhpc.herts.ac.uk/panstarrs-healpix/'+str(pix),outfile)
else:
raise NotImplementedError('Method '+method)
if method=='pslocal':
hplist=list(set(hplist))
print 'Found',len(hplist),'unique healpix pixels'
outname=method+'/'+method+'.txt'
with open(outname,'w') as outfile:
outfile.write('# RA DEC ObjID\n')
for pixel in hplist:
print 'Appending pixel',pixel
if os.path.isdir(PSBASE):
pixelfile=PSBASE+'/'+str(pixel)
else:
pixelfile=method+'/'+str(pixel)
if not os.path.isfile(pixelfile):
raise RuntimeError('Pixel file '+pixelfile+'does not exist')
os.system('cat '+pixelfile+' >> '+outname)
from astroquery.irsa import Irsa print Irsa.list_catalogs()
from astropy import units as u
from astropy import coordinates
from astroquery.irsa import Irsa
from common_constants import distance
IRAS = Irsa.query_region(coordinates.SkyCoord.from_name("W51"), catalog="iraspsc", radius=1 * u.arcmin)
Akari = Irsa.query_region(coordinates.SkyCoord.from_name("W51"), catalog="akari_fis", radius=1 * u.arcmin)
# formulae from http://marc.sauvage.free.fr/astro_book/IRAS_pages/IRAS.html
fir_lum_iras = 3.96e5 * (2.58 * IRAS["fnu_60"][0] + IRAS["fnu_100"][0]) * (distance.to(u.Mpc).value) ** 2 * u.L_sun
mir_lum_iras = 1.611e6 * (2.61 * IRAS["fnu_12"][0] + IRAS["fnu_25"][0]) * (distance.to(u.Mpc).value) ** 2 * u.L_sun
print("IRAS FIR luminosity: {0}".format(fir_lum_iras))
print("IRAS MIR luminosity: {0}".format(mir_lum_iras))
print("Harvey 1986 luminosity: {0}".format(1e7 * u.L_sun))
print("Sievers 1991 luminosity: {0}".format(1.8e7 * u.L_sun * (7.5 * u.kpc / distance) ** -2))
# Harvey 1986a 1986ApJ...300..737H: 10^7 Lsun
import pylab as pl
iras_wl = [12, 25, 60, 100]
akari_wl = [65, 90, 140, 160]
pl.figure(1).clf()
pl.plot(iras_wl, [IRAS["fnu_{0}".format(wl)] for wl in iras_wl], "s")
pl.plot(akari_wl, [Akari["flux{0}".format(wl)] for wl in akari_wl], "o")
def sossFieldSim(ra, dec, binComp='', dimX=256):
# binComp: [deltaRA,deltaDEC,J,H,K]
# stars in large field around target
targetcrd = crd.SkyCoord(ra = ra, dec = dec, unit=(u.hour, u.deg))
targetRA = targetcrd.ra.value
targetDEC = targetcrd.dec.value
info = Irsa.query_region(targetcrd, catalog = 'fp_psc', spatial = 'Cone', radius = 2.5*u.arcmin)
# coordinates of all stars in FOV, including target
allRA = info['ra'].data.data
allDEC = info['dec'].data.data
Jmag = info['j_m'].data.data
Hmag = info['h_m'].data.data
Kmag = info['k_m'].data.data
J_Hobs = Jmag-Hmag
H_Kobs = Hmag-Kmag
# target coords
distance = np.sqrt( ((targetRA-allRA)*np.cos(targetDEC))**2 + (targetDEC-allDEC)**2 )
targetIndex = np.argmin(distance) # the target
# add any missing companion
cubeNameSuf=''
if binComp!='':
deg2rad = np.pi/180
allRA = np.append(allRA, (allRA[targetIndex] + binComp[0]/3600/np.cos(allDEC[targetIndex]*deg2rad)))
allDEC = np.append(allDEC, (allDEC[targetIndex] + binComp[1]/3600))
Jmag = np.append(Jmag,binComp[2])
Hmag = np.append(Kmag,binComp[3])
Kmag = np.append(Kmag,binComp[4])
J_Hobs = Jmag-Hmag
H_Kobs = Hmag-Kmag
cubeNameSuf ='_custom'
#number of stars
nStars=allRA.size
cooTar=crd.SkyCoord(ra=allRA[targetIndex],dec=allDEC[targetIndex], unit=(u.deg, u.deg))
#Restoring model parameters
modelParam = readsav(os.path.join(idlsave_path,'modelsInfo.sav'),verbose=False)
models = modelParam['models']
modelPadX = modelParam['modelpadx']
modelPadY = modelParam['modelpady']
dimXmod = modelParam['dimxmod']
dimYmod = modelParam['dimymod']
jhMod = modelParam['jhmod']
hkMod = modelParam['hkmod']
teffMod = modelParam['teffmod']
# find/assign Teff of each star
starsT=np.empty(nStars)
for j in range(nStars):
color_separation = (J_Hobs[j]-jhMod)**2+(H_Kobs[j]-hkMod)**2
min_separation_ind = np.argmin(color_separation)
starsT[j]=teffMod[min_separation_ind]
radeg = 180/np.pi
niriss_pixel_scale = 0.065 # arcsec
sweetSpot = dict(x=856,y=107,RA=allRA[targetIndex],DEC=allDEC[targetIndex],jmag=Jmag[targetIndex])
#offset between all stars and target
dRA=(allRA - sweetSpot['RA'])*np.cos(sweetSpot['DEC']/radeg)*3600
dDEC=(allDEC - sweetSpot['DEC'])*3600
# Put field stars positions and magnitudes in structured array
_ = dict(RA=allRA, DEC=allDEC, dRA=dRA, dDEC=dDEC, jmag=Jmag, T=starsT,
x=np.empty(nStars), y=np.empty(nStars), dx=np.empty(nStars), dy=np.empty(nStars))
stars=np.empty(nStars,dtype=[(key,val.dtype) for key,val in _.items()])
for key,val in _.items(): stars[key]=val
# Initialize final fits cube that contains the modelled traces with contamination
PAmin = 0 #instrument PA, degrees
PAmax = 360
dPA = 1 # degrees
# Set of IPA values to cover
PAtab = np.arange(PAmin, PAmax, dPA) # degrees
nPA = len(PAtab)
# dimX=256 #2048 #########now as argument, with default to 256
dimY=2048
simuCube=np.zeros([nPA+2,dimY, dimX]) # cube of trace simulation at every degree of field rotation, +target at O1 and O2
# saveFiles = glob.glob('idlSaveFiles/*.sav')[:-1]
saveFiles = glob.glob(os.path.join(idlsave_path,'*.sav'))[:-1]
#pdb.set_trace()
# Big loop to generate a simulation at each instrument PA
for kPA in range(PAtab.size):
APA= PAtab[kPA]
V3PA=APA+0.57 #from APT
stars['dx']= (np.cos(np.pi/2+APA/radeg)*stars['dRA']-np.sin(np.pi/2+APA/radeg)*stars['dDEC'])/niriss_pixel_scale
stars['dy']= (np.sin(np.pi/2+APA/radeg)*stars['dRA']+np.cos(np.pi/2+APA/radeg)*stars['dDEC'])/niriss_pixel_scale
stars['x'] = stars['dx']+sweetSpot['x']
stars['y'] = stars['dy']+sweetSpot['y']
# Display the star field (blue), target (red), subarray (green), full array (blue), and axes
if (kPA==0 and nStars > 1) and False:
print(kPA)
plt.plot([0,2047,2047,0,0],[0,0,2047,2047,0], 'b')
plt.plot([0,255,255,0,0],[0,0,2047,2047,0], 'g')
#the order 1 & 2 traces
t1=np.loadtxt('/Users/david/Documents/work/jwst/niriss/soss/data/trace_order1.txt',unpack=True)
plt.plot(t1[0],t1[1],'r')
t2=np.loadtxt('/Users/david/Documents/work/jwst/niriss/soss/data/trace_order2.txt',unpack=True)
plt.plot(t2[0],t2[1],'r')
plt.plot(stars['x'], stars['y'], 'b*')
plt.plot(sweetSpot['x'], sweetSpot['y'], 'r*')
plt.title("APA= {} (V3PA={})".format(APA,V3PA))
ax=plt.gca()
#add V2 & V3 axes
l,hw,hl=250,50,50
adx,ady=-l*np.cos(-0.57/radeg),-l*np.sin(-0.57/radeg)
ax.arrow(2500, 1800, adx,ady, head_width=hw, head_length=hl, length_includes_head=True, fc='k') #V3
plt.text(2500+1.4*adx,1800+1.4*ady,"V3",va='center',ha='center')
adx,ady=-l*np.cos((-0.57-90)/radeg),-l*np.sin((-0.57-90)/radeg)
ax.arrow(2500, 1800, adx, ady, head_width=hw, head_length=hl, length_includes_head=True, fc='k') #V2
plt.text(2500+1.4*adx,1800+1.4*ady,"V2",va='center',ha='center')
#add North and East
adx,ady=-l*np.cos(APA/radeg),-l*np.sin(APA/radeg)
ax.arrow(2500, 1300, adx, ady, head_width=hw, head_length=hl, length_includes_head=True, fc='k') #N
plt.text(2500+1.4*adx,1300+1.4*ady,"N",va='center',ha='center')
adx,ady=-l*np.cos((APA-90)/radeg),-l*np.sin((APA-90)/radeg)
ax.arrow(2500, 1300, adx, ady, head_width=hw, head_length=hl, length_includes_head=True, fc='k') #E
plt.text(2500+1.4*adx,1300+1.4*ady,"E",va='center',ha='center')
ax.set_xlim(-400,2047+800)
ax.set_ylim(-400,2047+400)
ax.set_aspect('equal')
plt.show()
# Retain stars that are within the Direct Image NIRISS POM FOV
ind, = np.where((stars['x'] >= -162) & (stars['x'] <= 2047+185) & (stars['y'] >= -154) & (stars['y'] <= 2047+174))
starsInFOV=stars[ind]
for i in range(len(ind)):
intx = round(starsInFOV['dx'][i])
inty = round(starsInFOV['dy'][i])
# print(intx,inty)
k=np.where(teffMod == starsInFOV['T'][i])[0][0]
fluxscale = 10.0**(-0.4*(starsInFOV['jmag'][i] - sweetSpot['jmag']))
#deal with subection sizes
mx0=int(modelPadX-intx)
mx1=int(modelPadX-intx+dimX)
my0=int(modelPadY-inty)
my1=int(modelPadY-inty+dimY)
if (mx0 > dimXmod) or (my0 > dimYmod):
continue
if (mx1 < 0) or (my1 < 0):
continue
x0 =(mx0<0)*(-mx0)
y0 =(my0<0)*(-my0)
mx0 *=(mx0 >= 0)
mx1 = dimXmod if mx1>dimXmod else mx1
my0 *=(my0 >= 0)
my1 =dimYmod if my1>dimYmod else my1
# if target and first kPA, add target traces of order 1 and 2 in output cube
if (intx == 0) & (inty == 0) & (kPA == 0):
fNameModO12 = saveFiles[k]
modelO12 = readsav(fNameModO12,verbose=False)['modelo12']
simuCube[0, y0:y0+my1-my0, x0:x0+mx1-mx0] = modelO12[0, my0:my1, mx0:mx1] * fluxscale # order 1
simuCube[1, y0:y0+my1-my0, x0:x0+mx1-mx0] = modelO12[1, my0:my1, mx0:mx1] * fluxscale # order 2
if (intx != 0) or (inty != 0): #field star
simuCube[kPA+2, y0:y0+my1-my0, x0:x0+mx1-mx0] += models[k, my0:my1, mx0:mx1] * fluxscale
# fits.writeto(cubeName, simuCube, clobber = True)
# print(cubeName)
return simuCube
