def plot_bliss_nightsum(fields,nitestr):
plot_bliss_coverage(fields)
plt.savefig('nightsum_coverage_%s.png'%nitestr)
new = (np.array(map(utc2nite,fields['DATE'])) == nitestr)
new_fields = fields[new]
old_fields = fields[~new]
db = Database()
db.connect()
query = """select id, qc_fwhm as psf, qc_teff as teff from exposure
where exptime = 90 and delivered = True and propid = '%s'
and qc_teff is not NULL and qc_fwhm is not NULL
and to_timestamp(utc_beg) %s '%s'
"""
new = db.query2recarray(query%(fields.PROPID,'>',datestr(date)))
try:
old = db.query2recarray(query%(fields.PROPID,'<',date))
except ValueError as e:
print(e)
old = np.recarray(0,dtype=new.dtype)
nbins = 35
kwargs = dict(normed=True)
step_kwargs = dict(kwargs,histtype='step',lw=3.5)
fill_kwargs = dict(kwargs,histtype='stepfilled',lw=1.0,alpha=0.7)
plt.figure()
step_kwargs['bins'] = np.linspace(0.5,2.5,nbins)
fill_kwargs['bins'] = np.linspace(0.5,2.5,nbins)
plt.hist(new['psf'],color='green',zorder=10, label='Observed tonight', **fill_kwargs)
plt.hist(new['psf'],color='green',zorder=10, **step_kwargs)
plt.hist(old['psf'],color='0.5', label='Observed previously', **fill_kwargs)
plt.hist(old['psf'],color='0.5', **step_kwargs)
plt.axvline(1.20,ls='--',lw=2,color='gray')
plt.legend()
plt.title('Seeing (%s)'%nitestr)
plt.xlabel('FWHM (arcsec)')
plt.ylabel('Normalized Number of Exposures')
plt.savefig('nightsum_psf_%s.png'%nitestr,bbox_inches='tight')
plt.figure()
step_kwargs['bins'] = np.linspace(0,1.5,nbins)
fill_kwargs['bins'] = np.linspace(0,1.5,nbins)
plt.hist(new['teff'],color='green',zorder=10,label='Observed tonight', **fill_kwargs)
plt.hist(new['teff'],color='green',zorder=10, **step_kwargs)
plt.hist(old['teff'],color='0.5',label='Observed previously', **fill_kwargs)
plt.hist(old['teff'],color='0.5', **step_kwargs)
plt.axvline(0.25,ls='--',lw=2,color='gray')
plt.legend()
plt.title('Effective Depth (%s)'%nitestr)
plt.xlabel('Teff')
plt.ylabel('Normalized Number of Exposures')
plt.savefig('nightsum_teff_%s.png'%nitestr,bbox_inches='tight')
from obztak.utils.database import Database
from obztak.utils.ortho import DECamBasemap, DECamMcBride
db = Database()
db.connect()
query ="""
SELECT id as expnum, telra as ra, teldec as dec, expTime, filter,
COALESCE(qc_teff,'NaN') as teff
FROM exposure where exptime >= 30 and discard = False and delivered = True
and flavor = 'object' and telra between 0 and 360 and teldec between -90 and 90
and filter in ('u','g','r','i','z','Y') and propid NOT LIKE '%-9999'
ORDER BY id;
"""
data = db.query2recarray(query)
exposures = odict([
('u',data[data['filter'] =='u']),
('g',data[data['filter'] =='g']),
('r',data[data['filter'] =='r']),
('i',data[data['filter'] =='i']),
('z',data[data['filter'] =='z']),
('Y',data[data['filter'] =='Y']),
])
for b,exp in exposures.items():
nan = np.isnan(exp['teff'])
median = np.median(exp[~nan]['teff'])
print "Median teff for %s-band: %s"%(b,median)
return vec
def ang2disc(nside, lon, lat, radius, inclusive=False, fact=4, nest=False):
"""
Wrap `query_disc` to use lon, lat, and radius in degrees.
"""
vec = ang2vec(lon, lat)
return hp.query_disc(nside, vec, radius, inclusive, fact, nest)
args.db = True
if args.db:
db = Database()
db.connect()
data = db.query2recarray(QUERY)
args.delve = True
if args.delve:
print("Reading DELVE QA values...")
delve = pd.read_csv('data/delve-qa-20201024.csv.gz')
df = pd.DataFrame(data)
print("Merging %i DELVE QA values..." % (len(delve)))
x = df.merge(delve, left_on='expnum', right_on='expnum', how='left')
sel = np.isnan(data['teff'])
data['teff'][sel] = x[sel]['teff_y']
if os.path.exists(args.outfile): os.remove(args.outfile)
print("Writing %s..." % args.outfile)
fitsio.write(args.outfile, data)
def plot_nightsum(fields,nitestr):
""" Plot the night summary for MagLiteS.
Parameters:
-----------
fields: the fields observed tonight
nitestr: the nite in strig format
Returns:
--------
None
"""
import pylab as plt
from obztak.utils.database import Database
from obztak.utils.ortho import makePlot
#fields = FieldArray.load_database()
#new = np.char.startswith(fields['DATE'],date)
date = nite2utc(nitestr)
new = (np.array(map(utc2nite,fields['DATE'])) == nitestr)
new_fields = fields[new]
old_fields = fields[~new]
kwargs = dict(edgecolor='none', s=50, vmin=0, vmax=4)
fig,basemap = makePlot(date=nitestr,name='nightsum',moon=False,airmass=False,center=(0,-90),bliss=False)
plt.title('Coverage (%s)'%nitestr)
kwargs['cmap'] = 'gray_r'
proj = basemap.proj(old_fields['RA'], old_fields['DEC'])
basemap.scatter(*proj, c=old_fields['TILING'],**kwargs)
kwargs['cmap'] = 'summer_r'
proj = basemap.proj(new_fields['RA'], new_fields['DEC'])
basemap.scatter(*proj, c=new_fields['TILING'], **kwargs)
colorbar = plt.colorbar()
colorbar.set_label('Tiling')
plt.plot(np.nan, np.nan,'o',color='green',mec='green',label='Observed tonight')
plt.plot(np.nan, np.nan,'o',color='0.7',mec='0.7',label='Observed previously')
plt.legend(fontsize=10,loc='lower left',scatterpoints=1)
plt.savefig('nightsum_coverage_%s.png'%nitestr,bbox_inches='tight')
db = Database()
db.connect()
query = """
select id, qc_fwhm as psf, qc_teff as teff from exposure
where exptime = 90 and delivered = True
and propid = '%s'
and qc_teff is not NULL and qc_fwhm is not NULL
and to_timestamp(utc_beg) %s '%s'
"""
new = db.query2recarray(query%(PROPID,'>',date))
old = db.query2recarray(query%(PROPID,'<',date))
nbins = 35
kwargs = dict(normed=True)
step_kwargs = dict(kwargs,histtype='step',lw=3.5)
fill_kwargs = dict(kwargs,histtype='stepfilled',lw=1.0,alpha=0.7)
plt.figure()
step_kwargs['bins'] = np.linspace(0.5,2.5,nbins)
fill_kwargs['bins'] = np.linspace(0.5,2.5,nbins)
plt.hist(new['psf'],color='green',zorder=10, label='Observed tonight', **fill_kwargs)
plt.hist(new['psf'],color='green',zorder=10, **step_kwargs)
plt.hist(old['psf'],color='0.5', label='Observed previously', **fill_kwargs)
plt.hist(old['psf'],color='0.5', **step_kwargs)
plt.axvline(1.20,ls='--',lw=2,color='gray')
plt.legend()
plt.title('Seeing (%s)'%nitestr)
plt.xlabel('FWHM (arcsec)')
plt.ylabel('Normalized Number of Exposures')
plt.savefig('nightsum_psf_%s.png'%nitestr,bbox_inches='tight')
plt.figure()
step_kwargs['bins'] = np.linspace(0,1.5,nbins)
fill_kwargs['bins'] = np.linspace(0,1.5,nbins)
plt.hist(new['teff'],color='green',zorder=10,label='Observed tonight', **fill_kwargs)
plt.hist(new['teff'],color='green',zorder=10, **step_kwargs)
plt.hist(old['teff'],color='0.5',label='Observed previously', **fill_kwargs)
plt.hist(old['teff'],color='0.5', **step_kwargs)
plt.axvline(0.25,ls='--',lw=2,color='gray')
plt.legend()
plt.title('Effective Depth (%s)'%nitestr)
plt.xlabel('Teff')
plt.ylabel('Normalized Number of Exposures')
plt.savefig('nightsum_teff_%s.png'%nitestr,bbox_inches='tight')
def plot_nightsum(fields,nitestr,date):
""" Plot the bliss night summary.
Parameters:
-----------
fields: the fields observed tonight
nitestr: the nite in strig format
Returns:
--------
None
"""
from obztak.utils.database import Database
plt.ioff()
# Select the fields from the database
db = Database()
db.connect()
query = """
select id, qc_fwhm as psf, qc_teff as teff, filter from exposure
where delivered = True and propid = '%s'
and flavor = 'object'
and qc_teff is not NULL
and qc_fwhm is not NULL
and to_timestamp(utc_beg) %s
"""
#new = db.query2recarray(query%(fields.PROPID,'>',datestr(date)))
d = datestr(date)
q = query%(fields.PROPID,"between (timestamp '%s') AND (timestamp '%s' + interval '12 hours')"%(d,d))
logging.debug(q)
new = db.query2recarray(q)
try:
q = query%(fields.PROPID,"< (timestamp '%s')"%d)
logging.debug(q)
old = db.query2recarray(q)
except ValueError as e:
print(e)
old = np.recarray(0,dtype=new.dtype)
for b in ['u','g','r','i','z','Y']:
f = new[new['filter'] == b]
print ' %s-band:'%b, len(f)
if not len(new):
logging.warn("No new exposures...")
return
##########################
plot_coverage(fields,nitestr)
##########################
fig,axes = plt.subplots(1,2,figsize=(12,5))
for i,d in enumerate(['2017/02/08 07:00:00','2017/02/08 19:00:00']):
plt.sca(axes[i])
bmap = DECamOrtho(date=d)
for b in np.unique(fields['FILTER']):
f = fields[fields['FILTER']==b]
bmap.draw_focal_planes(f['RA'],f['DEC'],color=COLORS[b],alpha=0.3)
bmap.draw_bliss()
bmap.draw_galaxy()
bmap.draw_des()
plt.suptitle('Coverage (%s)'%nitestr,fontsize=16)
plt.savefig('nightsum_summary_%s.png'%nitestr)
new_sel = (np.array(map(utc2nite,fields['DATE'])) == nitestr)
new_fields = fields[new_sel]
old_fields = fields[~new_sel]
##########################
fig,axes = plt.subplots(1,2,figsize=(12,5))
plt.sca(axes[0])
plt.plot(np.nan,np.nan,'-w',label='all')
plot_psf(new,old)
plt.title('Seeing (%s)'%nitestr)
plt.sca(axes[1])
plt.plot(np.nan,np.nan,'-w',label='all')
plot_teff(new,old)
plt.title('Effective Depth (%s)'%nitestr)
plt.savefig('nightsum_psf_teff_%s.png'%nitestr,bbox_inches='tight')
fig,axes = plt.subplots(2,2,figsize=(14,10))
axes = axes.flatten()
for i,b in enumerate(['g','r','i','z']):
plt.sca(axes[i])
plt.plot(np.nan,np.nan,'-w',label='%s-band'%b)
plot_psf(new[new['filter'] == b],old[old['filter'] == b])
plt.savefig('nightsum_psf_%s.png'%nitestr,bbox_inches='tight')
fig,axes = plt.subplots(2,2,figsize=(14,10))
axes = axes.flatten()
for i,b in enumerate(['g','r','i','z']):
plt.sca(axes[i])
plt.plot(np.nan,np.nan,'-w',label='%s-band'%b)
plot_teff(new[new['filter'] == b],old[old['filter'] == b])
plt.savefig('nightsum_teff_%s.png'%nitestr,bbox_inches='tight')