def __init__(self, hdr, bandname=None, scale=1.):
import numpy as np
from tractor.brightness import NanoMaggies
if hdr is not None:
self.exptime = hdr['EXPTIME']
self.phot_c = hdr['PHOT_C']
self.phot_k = hdr['PHOT_K']
self.airmass = hdr['AIRMASS']
print('CFHT photometry:', self.exptime, self.phot_c, self.phot_k,
self.airmass)
zpt = (2.5 * np.log10(self.exptime) + self.phot_c + self.phot_k *
(self.airmass - 1))
print('-> zeropoint', zpt)
scale = NanoMaggies.zeropointToScale(zpt)
print('-> scale', scale)
super(CfhtLinearPhotoCal, self).__init__(scale, band=bandname)
def main():
global survey
init()
ps = PlotSequence('sky')
# export LEGACY_SURVEY_DIR=/scratch1/scratchdirs/desiproc/DRs/dr4-bootes/legacypipe-dir/
#survey = LegacySurveyData()
survey = get_survey('dr4v2')
ccds = survey.get_ccds_readonly()
print(len(ccds), 'CCDs')
ccds = ccds[ccds.camera == 'mosaic']
print(len(ccds), 'Mosaic CCDs')
# plt.clf()
# plt.hist(ccds.mjd_obs % 1.0, bins=50)
# plt.xlabel('MJD mod 1')
# ps.savefig()
ccds.imjd = np.floor(ccds.mjd_obs).astype(int)
mjds = np.unique(ccds.imjd)
print(len(mjds), 'unique MJDs')
mp = multiproc(nthreads=8, init=init)
allvals = []
medmjds = []
args = []
for kk, mjd in enumerate(mjds):
I = np.flatnonzero(ccds.imjd == mjd)
print('MJD', mjd, ' (%i of %i):' % (kk + 1, len(mjds)), len(I), 'CCDs')
if len(I) == 0:
continue
# pick one near the middle
#i = I[len(I)/2]
for i in [I[len(I) / 4], I[len(I) / 2], I[3 * len(I) / 4]]:
ccd = ccds[i]
key = dict(expnum=ccd.expnum, ccdname=ccd.ccdname)
oldvals = key
vals = cache.find(key)
print('Got', vals.count(), 'cache hits for', key)
gotone = False
for val in vals:
#if 'median_adu' in val:
if 'mode_adu' in val:
print('cache hit:', val)
allvals.append(val)
medmjds.append(mjd)
gotone = True
break
else:
print('partial cache hit:', val)
oldvals = val
###!
cache.delete_one(val)
if gotone:
continue
print('args: key', key, 'oldvals', oldvals)
args.append((mjd, key, oldvals, ccd))
# plt.clf()
# plt.hist(tim.getImage().ravel(), range=(-0.1, 0.1), bins=50,
# histtype='step', color='b')
# plt.axvline(med, color='b', alpha=0.3, lw=2)
# plt.axvline(0., color='k', alpha=0.3, lw=2)
# plt.xlabel('Sky-subtracted pixel values')
# plt.title('Date ' + ccd.date_obs + ': ' + str(im))
# ps.savefig()
if len(args):
meds = mp.map(read_sky_val, args)
print('Medians:', meds)
for (mjd, key, oldvals, ccd), val in zip(args, meds):
if val is None:
continue
allvals.append(val)
medmjds.append(mjd)
medians = []
median_adus = []
mode_adus = []
skyadus = []
keepmjds = []
for mjd, val in zip(medmjds, allvals):
madu = val['median_adu']
if madu is None:
continue
if 'median' in val:
medians.append(val['median'])
else:
from tractor.brightness import NanoMaggies
zpscale = NanoMaggies.zeropointToScale(val['ccdzpt'])
med = (val['median_adu'] - val['skyadu']) / zpscale
print('Computed median diff:', med, 'nmgy')
medians.append(med)
keepmjds.append(mjd)
median_adus.append(val['median_adu'])
mode_adus.append(val['mode_adu'])
skyadus.append(val['skyadu'])
medmjds = keepmjds
median_adus = np.array(median_adus)
mode_adus = np.array(mode_adus)
skyadus = np.array(skyadus)
medmjds = np.array(medmjds)
medians = np.array(medians)
plt.clf()
plt.plot(medmjds, median_adus - skyadus, 'b.')
plt.xlabel('MJD')
#plt.ylabel('Image median after sky subtraction (nmgy)')
plt.ylabel('Image median - SKYADU (ADU)')
#plt.ylim(-0.03, 0.03)
#plt.ylim(-10, 10)
plt.ylim(-1, 1)
plt.axhline(0, color='k', lw=2, alpha=0.5)
ps.savefig()
plt.clf()
plt.plot(medmjds, mode_adus - skyadus, 'b.')
plt.xlabel('MJD')
plt.ylabel('Image mode - SKYADU (ADU)')
plt.ylim(-1, 1)
plt.axhline(0, color='k', lw=2, alpha=0.5)
ps.savefig()
print('Median pcts:', np.percentile(medians, [0, 2, 50, 98, 100]))
mlo, mhi = np.percentile(medians, [2, 98])
I = np.flatnonzero((medians > mlo) * (medians < mhi))
d0 = np.mean(medmjds)
dmjd = medmjds[I] - d0
A = np.zeros((len(dmjd), 2))
A[:, 0] = 1.
A[:, 1] = dmjd
b = np.linalg.lstsq(A, medians[I])[0]
print('Lstsq:', b)
offset = b[0]
slope = b[1]
xx = np.array([dmjd.min(), dmjd.max()])
print('Offset', offset)
print('Slope', slope)
plt.clf()
plt.plot(medmjds, medians, 'b.')
ax = plt.axis()
plt.plot(xx + d0, offset + slope * xx, 'b-')
plt.axis(ax)
plt.xlabel('MJD')
plt.ylabel('Image median after sky subtraction (nmgy)')
plt.ylim(-0.03, 0.03)
plt.axhline(0, color='k', lw=2, alpha=0.5)
ps.savefig()
plt.clf()
plt.plot(median_adus, skyadus, 'b.')
ax = plt.axis()
lo = min(ax[0], ax[2])
hi = max(ax[1], ax[3])
plt.plot([lo, hi], [lo, hi], 'k-', alpha=0.5)
plt.xlabel('Median image ADU')
plt.ylabel('SKYADU')
plt.axis(ax)
ps.savefig()
plt.clf()
plt.plot(medmjds, skyadus / median_adus, 'b.')
plt.xlabel('MJD')
plt.ylim(0.98, 1.02)
plt.axhline(1.0, color='k', alpha=0.5)
plt.ylabel('SKYADU / median image ADU')
ps.savefig()
import argparse
parser = argparse.ArgumentParser(description='Produce annotated CCDs file by reading CCDs file + calibration products')
parser.add_argument('--part', action='append', help='CCDs file to read, survey-ccds-X.fits.gz, default: ["decals","nondecals","extra"]. Can be repeated.', default=[])
parser.add_argument('--threads', type=int, help='Run multi-threaded', default=4)
opt = parser.parse_args()
if False:
#### FIX mistake in DR3 annotated CCDs: sig1
for part in ['decals', 'nondecals', 'extra']:
from tractor.brightness import NanoMaggies
fn = '/global/cscratch1/sd/desiproc/dr3/ccds-annotated-%s.fits.gz' % part
T = fits_table(fn)
zpt = T.ccdzpt + 2.5 * np.log10(T.exptime)
print('Median zpt:', np.median(zpt))
zpscale = NanoMaggies.zeropointToScale(zpt)
print('Median zpscale:', np.median(zpscale))
print('Zpscale limits:', zpscale.min(), zpscale.max())
print('Non-finite:', np.sum(np.logical_not(np.isfinite(zpscale))))
T.sig1 /= zpscale
T.sig1[np.logical_not(np.isfinite(T.sig1))] = 0.
print('Median sig1:', np.median(T.sig1))
print('Before:')
for col in ['psfdepth', 'galdepth', 'gausspsfdepth', 'gaussgaldepth']:
c = T.get(col)
for band in 'grz':
I = np.flatnonzero((T.filter == band) * (T.sig1 > 0))
print(col, band, 'median', np.median(c[I]))
