def extract_standard(name, filter):
images = glob.glob(dataDir + name + '_' + filter + '/c????.fits')
imageRoots = [ii.replace('.fits', '') for ii in images]
if filter == 'h':
aperture = 160
skyannul = 200
skywidth = 50
if filter == 'kp':
aperture = 130
skyannul = 170
skywidth = 50
if filter == 'lp2':
aperture = 20
skyannul = 100
skywidth = 200
for image in imageRoots:
(r, f, m, me) = run_phot(image,
silent=True,
apertures=[aperture],
sky_annulus=skyannul,
sky_dannulus=skywidth)
def extract_standard(name, filter):
images = glob.glob(dataDir + name + '_' + filter + '/c????.fits')
imageRoots = [ii.replace('.fits', '') for ii in images]
aperture = findap.standard_aperture[filter]
skyannul = findap.sky_annulus_start[filter]
skywidth = findap.sky_annulus_width[filter]
for image in imageRoots:
(r, f, m, me) = run_phot(image, silent=False,
apertures=[aperture],
sky_annulus=skyannul,
sky_dannulus=skywidth)
def extract_standard(name, filter):
images = glob.glob(dataDir + name + '_' + filter + '/c????.fits')
imageRoots = [ii.replace('.fits', '') for ii in images]
aperture = findap.standard_aperture[filter]
skyannul = findap.sky_annulus_start[filter]
skywidth = findap.sky_annulus_width[filter]
for image in imageRoots:
(r, f, m, me) = run_phot(image,
silent=False,
apertures=[aperture],
sky_annulus=skyannul,
sky_dannulus=skywidth)
def extract_standard(name, filter):
images = glob.glob(dataDir + name + '_' + filter + '/c????.fits')
imageRoots = [ii.replace('.fits', '') for ii in images]
if filter == 'h':
aperture = 160
skyannul = 200
skywidth = 50
if filter == 'kp':
aperture = 130
skyannul = 170
skywidth = 50
if filter == 'lp2':
aperture = 20
skyannul = 100
skywidth = 200
for image in imageRoots:
(r, f, m, me) = run_phot(image, silent=True,
apertures=[aperture],
sky_annulus=skyannul,
sky_dannulus=skywidth)
def test_sky(suffix):
dataDir = '/u/jlu/data/w51/10aug14/clean/' + suffix + '/'
images = glob.glob(dataDir + 'c????.fits')
imageRoots = [ii.replace('.fits', '') for ii in images]
# Use a fixed aperture and vary the sky annuli
aperture = range(10, 201, 5)
sky_a = range(150, 251, 20)
sky_da = range(25, 101, 25)
mag = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=float)
merr1 = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=float)
merr2 = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=float)
flux = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=float)
ferr = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=float)
aper = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=int)
skya = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=int)
skyda = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=int)
imcnt = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=int)
for sa in range(len(sky_a)):
for sd in range(len(sky_da)):
print 'Sky Annulus = %d + %d' % (sky_a[sa], sky_da[sd])
iflux = np.zeros((len(imageRoots), len(aperture)), dtype=float)
imag = np.zeros((len(imageRoots), len(aperture)), dtype=float)
imerr = np.zeros((len(imageRoots), len(aperture)), dtype=float)
# Loop through all the images and get the mean magnitude
# and the standard deviation of the magnitude.
for ii in range(len(imageRoots)):
(r, f, m, me) = run_phot(imageRoots[ii],
silent=True,
apertures=aperture,
sky_annulus=sky_a[sa],
sky_dannulus=sky_da[sd])
iflux[ii] = f
imag[ii] = m
imerr[ii] = me
for aa in range(len(aperture)):
idx = np.where(iflux[:, aa] != 0)[0]
flux[aa, sa, sd] = iflux[idx, aa].mean()
ferr[aa, sa, sd] = iflux[idx, aa].std()
mag[aa, sa, sd] = imag[idx, aa].mean()
merr1[aa, sa, sd] = imerr[idx, aa].mean()
merr2[aa, sa,
sd] = 1.0857 * ferr[aa, sa, sd] / flux[aa, sa, sd]
aper[aa, sa, sd] = aperture[aa]
skya[aa, sa, sd] = sky_a[sa]
skyda[aa, sa, sd] = sky_da[sd]
imcnt[aa, sa, sd] = len(imageRoots)
pfile = open('test_sky_' + suffix + '.dat', 'w')
pickle.dump(flux, pfile)
pickle.dump(ferr, pfile)
pickle.dump(mag, pfile)
pickle.dump(merr1, pfile)
pickle.dump(merr2, pfile)
pickle.dump(aper, pfile)
pickle.dump(skya, pfile)
pickle.dump(skyda, pfile)
pickle.dump(imcnt, pfile)
def curve_of_growth(star, filter):
"""
Make a plot of the radius of growth for each of the stars.
Plot all the associated images for that star on top of each other.
"""
dataDir = '/u/jlu/data/w51/10aug14/clean/'
dir = star + '_' + filter
images = glob.glob(dataDir + dir + '/c????.fits')
imageRoots = [ii.replace('.fits', '') for ii in images]
aperture = range(6, 201, 2)
sky_a = sky_annulus_start[filter]
sky_da = sky_annulus_width[filter]
mag = np.zeros((len(aperture), len(imageRoots)), dtype=float)
merr = np.zeros((len(aperture), len(imageRoots)), dtype=float)
diff = np.zeros((len(aperture), len(imageRoots)), dtype=float)
for ii in range(len(imageRoots)):
(r, f, m, me) = run_phot(imageRoots[ii],
silent=True,
apertures=aperture,
sky_annulus=sky_a,
sky_dannulus=sky_da)
mag[:, ii] = m
merr[:, ii] = me
diff[1:, ii] = mag[1:, ii] - mag[:-1, ii]
# Clean up arrays
idx = np.where(mag == 0)
mag[idx] = np.nan
merr[idx] = np.nan
diff[idx] = np.nan
py.figure(2, figsize=(6, 8))
py.clf()
py.subplots_adjust(left=0.15, bottom=0.08, top=0.95, right=0.95)
py.subplot(2, 1, 1)
for ii in range(len(imageRoots)):
py.plot(aperture, mag[:, ii], '--')
py.plot(aperture, mag.mean(axis=1), 'k-', linewidth=2)
py.xlabel('Aperture (pix)')
py.ylabel('Magnitude')
py.title('Star = %s, Filter = %s, Sky = %d + %d pix' %
(star, filter, sky_a, sky_da))
py.xlim(aperture[0], aperture[-1])
py.subplot(2, 1, 2)
for ii in range(len(imageRoots)):
py.plot(aperture[1:], diff[1:, ii], '--')
py.plot(aperture[1:], diff[1:, :].mean(axis=1), 'k-', linewidth=2)
lims = py.axis()
py.plot([lims[0], lims[1]], [0, 0], 'b-')
py.xlabel('Aperture (pix)')
py.ylabel('Delta-Magnitude')
py.xlim(aperture[0], aperture[-1])
py.ylim(-0.1, 0.05)
py.savefig('curve_of_growth_' + star + '_' + filter + '.png')
def calc_aperture_corrections():
"""
Calculate aperture correction curves from all the standard
stars. Store the curves (mean + errors) so that they can be
read in later for getting the specific aperture correction for
a given inner and outer aperture.
"""
stars = ['fs140', 'fs147', 'fs148']
filters = ['h', 'kp', 'lp2']
sky_ann = sky_annulus_start
sky_da = sky_annulus_width
big_ap = standard_aperture
# Calculate Curve of Growth for each filter
for filter in filters:
skya = sky_ann[filter]
skyda = sky_da[filter]
#outer = big_ap[filter]
outer = sky_ann[filter]
aperture = range(5, outer)
growthCurves = []
# Loop through all the stars and gather up the growth-curves
for star in stars:
dataDir = '/u/jlu/data/w51/10aug14/clean/'
dir = star + '_' + filter
images = glob.glob(dataDir + dir + '/c????.fits')
imageRoots = [ii.replace('.fits', '') for ii in images]
apCnt = len(aperture)
imCnt = len(imageRoots)
mag = np.zeros((apCnt, imCnt), dtype=float)
merr = np.zeros((apCnt, imCnt), dtype=float)
diff = np.zeros((apCnt, imCnt), dtype=float)
for ii in range(len(imageRoots)):
# run_phot can only handle 100 apertures at a time
for aa in range(int(math.ceil(apCnt / 100.))):
apStart = aa * 100
apStop = (aa + 1) * 100
if (apStop > apCnt):
apStop = apCnt
(r, f, m,
me) = run_phot(imageRoots[ii],
silent=True,
apertures=aperture[apStart:apStop],
sky_annulus=skya,
sky_dannulus=skyda)
mag[apStart:apStop, ii] = m
merr[apStart:apStop, ii] = me
diff[1:, ii] = mag[1:, ii] - mag[:-1, ii]
# Cut it out if it is really really discrepant.
# In our case, discrepant means a delta-mag in the first
# differenced bin of < -0.3
if (diff[:, ii].min() < -0.25):
continue
# Clean up arrays
idx = np.where(m == 0)
mag[idx, ii] = np.nan
merr[idx, ii] = np.nan
diff[idx, ii] = np.nan
growthCurves.append(diff[:, ii])
####################
# All the growth curves for this filter (combine the stars)
####################
growthCurves = np.array(growthCurves)
aperture = np.array(aperture)
py.clf()
for ii in range(len(growthCurves)):
py.plot(aperture[1:], growthCurves[ii, 1:], '--')
py.plot(aperture[1:],
growthCurves[:, 1:].mean(axis=0),
'k-',
linewidth=2)
lims = py.axis()
py.plot([lims[0], lims[1]], [0, 0], 'b-')
py.xlabel('Aperture (pix)')
py.ylabel('Delta-Magnitude')
py.xlim(aperture[0], aperture[-1])
py.ylim(-0.25, 0.01)
py.savefig('curve_of_growth_' + filter + '.png')
##########
# Save off to a file for later retrieval
##########
pfile = open('curve_of_growth_' + filter + '.dat', 'w')
pickle.dump(aperture, pfile)
pickle.dump(growthCurves, pfile)
pfile.close()
def test_sky(suffix):
dataDir = '/u/jlu/data/w51/10aug14/clean/' + suffix + '/'
images = glob.glob(dataDir + 'c????.fits')
imageRoots = [ii.replace('.fits', '') for ii in images]
# Use a fixed aperture and vary the sky annuli
aperture = range(10, 201, 5)
sky_a = range(150, 251, 20)
sky_da = range(25, 101, 25)
mag = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=float)
merr1 = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=float)
merr2 = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=float)
flux = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=float)
ferr = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=float)
aper = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=int)
skya = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=int)
skyda = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=int)
imcnt = np.zeros((len(aperture), len(sky_a), len(sky_da)), dtype=int)
for sa in range(len(sky_a)):
for sd in range(len(sky_da)):
print 'Sky Annulus = %d + %d' % (sky_a[sa], sky_da[sd])
iflux = np.zeros((len(imageRoots), len(aperture)), dtype=float)
imag = np.zeros((len(imageRoots), len(aperture)), dtype=float)
imerr = np.zeros((len(imageRoots), len(aperture)), dtype=float)
# Loop through all the images and get the mean magnitude
# and the standard deviation of the magnitude.
for ii in range(len(imageRoots)):
(r, f, m, me) = run_phot(imageRoots[ii], silent=True,
apertures=aperture,
sky_annulus=sky_a[sa],
sky_dannulus=sky_da[sd])
iflux[ii] = f
imag[ii] = m
imerr[ii] = me
for aa in range(len(aperture)):
idx = np.where(iflux[:,aa] != 0)[0]
flux[aa, sa, sd] = iflux[idx,aa].mean()
ferr[aa, sa, sd] = iflux[idx,aa].std()
mag[aa, sa, sd] = imag[idx,aa].mean()
merr1[aa, sa, sd] = imerr[idx,aa].mean()
merr2[aa, sa, sd] = 1.0857 * ferr[aa,sa,sd] / flux[aa,sa,sd]
aper[aa, sa, sd] = aperture[aa]
skya[aa, sa, sd] = sky_a[sa]
skyda[aa, sa, sd] = sky_da[sd]
imcnt[aa, sa, sd] = len(imageRoots)
pfile = open('test_sky_' + suffix + '.dat', 'w')
pickle.dump(flux, pfile)
pickle.dump(ferr, pfile)
pickle.dump(mag, pfile)
pickle.dump(merr1, pfile)
pickle.dump(merr2, pfile)
pickle.dump(aper, pfile)
pickle.dump(skya, pfile)
pickle.dump(skyda, pfile)
pickle.dump(imcnt, pfile)
def curve_of_growth(star, filter):
"""
Make a plot of the radius of growth for each of the stars.
Plot all the associated images for that star on top of each other.
"""
dataDir = '/u/jlu/data/w51/10aug14/clean/'
dir = star + '_' + filter
images = glob.glob(dataDir + dir + '/c????.fits')
imageRoots = [ii.replace('.fits', '') for ii in images]
aperture = range(6, 201, 2)
sky_a = sky_annulus_start[filter]
sky_da = sky_annulus_width[filter]
mag = np.zeros((len(aperture), len(imageRoots)), dtype=float)
merr = np.zeros((len(aperture), len(imageRoots)), dtype=float)
diff = np.zeros((len(aperture), len(imageRoots)), dtype=float)
for ii in range(len(imageRoots)):
(r, f, m, me) = run_phot(imageRoots[ii], silent=True,
apertures=aperture,
sky_annulus=sky_a, sky_dannulus=sky_da)
mag[:,ii] = m
merr[:,ii] = me
diff[1:,ii] = mag[1:,ii] - mag[:-1,ii]
# Clean up arrays
idx = np.where(mag == 0)
mag[idx] = np.nan
merr[idx] = np.nan
diff[idx] = np.nan
py.figure(2, figsize=(6,8))
py.clf()
py.subplots_adjust(left=0.15, bottom=0.08, top=0.95, right=0.95)
py.subplot(2, 1, 1)
for ii in range(len(imageRoots)):
py.plot(aperture, mag[:,ii], '--')
py.plot(aperture, mag.mean(axis=1), 'k-', linewidth=2)
py.xlabel('Aperture (pix)')
py.ylabel('Magnitude')
py.title('Star = %s, Filter = %s, Sky = %d + %d pix' %
(star, filter, sky_a, sky_da))
py.xlim(aperture[0], aperture[-1])
py.subplot(2, 1, 2)
for ii in range(len(imageRoots)):
py.plot(aperture[1:], diff[1:,ii], '--')
py.plot(aperture[1:], diff[1:,:].mean(axis=1), 'k-', linewidth=2)
lims = py.axis()
py.plot([lims[0], lims[1]], [0, 0], 'b-')
py.xlabel('Aperture (pix)')
py.ylabel('Delta-Magnitude')
py.xlim(aperture[0], aperture[-1])
py.ylim(-0.1, 0.05)
py.savefig('curve_of_growth_' + star + '_' + filter + '.png')
def calc_aperture_corrections():
"""
Calculate aperture correction curves from all the standard
stars. Store the curves (mean + errors) so that they can be
read in later for getting the specific aperture correction for
a given inner and outer aperture.
"""
stars = ['fs140', 'fs147', 'fs148']
filters = ['h', 'kp', 'lp2']
sky_ann = sky_annulus_start
sky_da = sky_annulus_width
big_ap = standard_aperture
# Calculate Curve of Growth for each filter
for filter in filters:
skya = sky_ann[filter]
skyda = sky_da[filter]
#outer = big_ap[filter]
outer = sky_ann[filter]
aperture = range(5, outer)
growthCurves = []
# Loop through all the stars and gather up the growth-curves
for star in stars:
dataDir = '/u/jlu/data/w51/10aug14/clean/'
dir = star + '_' + filter
images = glob.glob(dataDir + dir + '/c????.fits')
imageRoots = [ii.replace('.fits', '') for ii in images]
apCnt = len(aperture)
imCnt = len(imageRoots)
mag = np.zeros((apCnt, imCnt), dtype=float)
merr = np.zeros((apCnt, imCnt), dtype=float)
diff = np.zeros((apCnt, imCnt), dtype=float)
for ii in range(len(imageRoots)):
# run_phot can only handle 100 apertures at a time
for aa in range(int(math.ceil(apCnt / 100.))):
apStart = aa*100
apStop = (aa+1)*100
if (apStop > apCnt):
apStop = apCnt
(r, f, m, me) = run_phot(imageRoots[ii], silent=True,
apertures=aperture[apStart:apStop],
sky_annulus=skya,
sky_dannulus=skyda)
mag[apStart:apStop,ii] = m
merr[apStart:apStop,ii] = me
diff[1:,ii] = mag[1:,ii] - mag[:-1,ii]
# Cut it out if it is really really discrepant.
# In our case, discrepant means a delta-mag in the first
# differenced bin of < -0.3
if (diff[:,ii].min() < -0.25):
continue
# Clean up arrays
idx = np.where(m == 0)
mag[idx,ii] = np.nan
merr[idx,ii] = np.nan
diff[idx,ii] = np.nan
growthCurves.append(diff[:,ii])
####################
# All the growth curves for this filter (combine the stars)
####################
growthCurves = np.array(growthCurves)
aperture = np.array(aperture)
py.clf()
for ii in range(len(growthCurves)):
py.plot(aperture[1:], growthCurves[ii,1:] , '--')
py.plot(aperture[1:], growthCurves[:,1:].mean(axis=0), 'k-',
linewidth=2)
lims = py.axis()
py.plot([lims[0], lims[1]], [0, 0], 'b-')
py.xlabel('Aperture (pix)')
py.ylabel('Delta-Magnitude')
py.xlim(aperture[0], aperture[-1])
py.ylim(-0.25, 0.01)
py.savefig('curve_of_growth_' + filter + '.png')
##########
# Save off to a file for later retrieval
##########
pfile = open('curve_of_growth_' + filter + '.dat', 'w')
pickle.dump(aperture, pfile)
pickle.dump(growthCurves, pfile)
pfile.close()