def _test():
pixels = np.vectorize(pixelsImpacted)
mag = np.arange(0, 19., 1) + 0.4
rs = pixels(mag)
for m, val in zip(mag, rs):
print m, val
print '\n\n\n0 mag'
n = stellarNumberCounts.bahcallSoneira(1, 0, 20, stellarNumberCounts.integratedCountsVband())
print 'objects perCCD areaLoss'
print n, n * 49.6 / 3600 , n * pixelsImpacted(1) * 49.6 / 3600 / (4096 * 4132.) * 100.
print '\n10 mag'
n = stellarNumberCounts.bahcallSoneira(10, 0, 20, stellarNumberCounts.integratedCountsVband())
print 'objects perCCD areaLoss'
print n, n * 49.6 / 3600 , n * pixelsImpacted(10) * 49.6 / 3600 / (4096 * 4132.) * 100.
print '\n18 mag'
n = stellarNumberCounts.bahcallSoneira(18, 0, 20, stellarNumberCounts.integratedCountsVband())
print 'objects perCCD areaLoss'
print n, n * 49.6 / 3600 , n * pixelsImpacted(18) * 49.6 / 3600 / (4096 * 4132.) * 100.
for m in mag:
n = stellarNumberCounts.bahcallSoneira(m, 0, 20, stellarNumberCounts.integratedCountsVband())
print m, n * 49.6 / 3600
def _test():
pixels = np.vectorize(pixelsImpacted)
mag = np.arange(0, 19., 1) + 0.4
rs = pixels(mag)
for m, val in zip(mag, rs):
print m, val
print '\n\n\n0 mag'
n = stellarNumberCounts.bahcallSoneira(
1, 0, 20, stellarNumberCounts.integratedCountsVband())
print 'objects perCCD areaLoss'
print n, n * 49.6 / 3600, n * pixelsImpacted(1) * 49.6 / 3600 / (
4096 * 4132.) * 100.
print '\n10 mag'
n = stellarNumberCounts.bahcallSoneira(
10, 0, 20, stellarNumberCounts.integratedCountsVband())
print 'objects perCCD areaLoss'
print n, n * 49.6 / 3600, n * pixelsImpacted(10) * 49.6 / 3600 / (
4096 * 4132.) * 100.
print '\n18 mag'
n = stellarNumberCounts.bahcallSoneira(
18, 0, 20, stellarNumberCounts.integratedCountsVband())
print 'objects perCCD areaLoss'
print n, n * 49.6 / 3600, n * pixelsImpacted(18) * 49.6 / 3600 / (
4096 * 4132.) * 100.
for m in mag:
n = stellarNumberCounts.bahcallSoneira(
m, 0, 20, stellarNumberCounts.integratedCountsVband())
print m, n * 49.6 / 3600
def areaImpacted(magnitudes=np.arange(0, 20., 1.), offset=0.5, star=False):
"""
"""
s = 0.1
Nvconst = stellarNumberCounts.integratedCountsVband()
print '\n mag b l stars CCD area'
for b in [20, 25, 30, 50, 90]:
for l in [0, 90, 180]:
area = 0
prev = 0
for ml in magnitudes:
m = s * math.ceil(float(ml + offset) / s)
n = stellarNumberCounts.bahcallSoneira(m, l, b, Nvconst)
n -= prev #subtract the number of stars in the previous bin
ccd = n * 49.6 / 3600
covering = pixelsImpacted(m, star=star)
area += (ccd * covering) / (4096 * 4132.) * 100.
prev = n #store the number of stars in the current bin
if area > 100:
area = 100.
txt = '%.1f %2d %3d %.2f %.1f %.3f' % (
m, b, l, n, ccd, area)
print txt
if b == 90:
#no need to do more than once... l is irrelevant
break
print '\n\n\nIntegrated Area Loss:'
blow = 20
bhigh = 90
llow = 0
lhigh = 180
bnum = 71
lnum = 181
prev = 0
for i, ml in enumerate(magnitudes):
m = s * math.ceil(float(ml + offset) / s)
l, b, counts = stellarNumberCounts.skyNumbers(m, blow, bhigh, llow,
lhigh, bnum, lnum)
counts -= prev
prev = counts.copy()
#average
stars = np.mean(counts)
ccd = stars * 49.6 / 3600
covering = pixelsImpacted(m, star=star)
area = (ccd * covering) / (4096 * 4132.) * 100.
if area > 100:
area = 100.
print 'magnitude = %.1f, average = %.5f, max = %.5f' % (ml, stars,
np.max(counts))
print '%i stars per square degree will mean %i stars per CCD and thus an area loss of %.4f per cent' % \
(stars, ccd, area)
if i < 1:
z = counts * covering / (
4096 * 4132. * (4096 * 0.1 * 4132 * 0.1 / 60. / 60.)) * 100.
else:
z += counts * covering / (
4096 * 4132. * (4096 * 0.1 * 4132 * 0.1 / 60. / 60.)) * 100.
msk = z > 100
z[msk] = 100.
_areaLossPlot(m, b, l, z, blow, bhigh, llow, lhigh, bnum, lnum,
'Masking of Saturated Pixels From All Stars', 'AreaLoss')
def _numberOfStarsAreaLoss(log,
magnitudelimits,
title,
offset=0.5,
covering=2550):
"""
Calculates the area loss for given magnitude limit at a few galactic coordinates and
integrated over an observing region.
"""
Nvconst = stellarNumberCounts.integratedCountsVband()
txt = '\n\n\nNumber of stars:'
print txt
log.info(txt)
s = 0.1
for ml in magnitudelimits:
print '\nmag b l stars CCD area'
for b in [20, 25, 30, 50, 90]:
for l in [0, 90, 180]:
m = s * math.ceil(float(ml + offset) / s)
n = stellarNumberCounts.bahcallSoneira(
m, l, b, Nvconst) * 1.15 #15 error in the counts
ccd = n * 49.6 / 3600
area = (ccd * covering) / (4096 * 4132.) * 100.
if area > 100:
area = 100.
#print 'At l=%i, b=%i, there are about %i stars in a square degree brighter than %.1f' % (l, b, n, m)
#print '%i stars per square degree will mean %i stars per CCD and thus an area loss of %.2f per cent' % \
# (n, ccd, area)
txt = '%.1f %2d %3d %.1f %.1f %.2f' % (m, b, l, n,
ccd, area)
print txt
log.info(txt)
if b == 90:
#no need to do more than once... l is irrelevant
break
txt = '\n\n\nIntegrated Area Loss:'
print txt
log.info(txt)
blow = 20
bhigh = 90
llow = 0
lhigh = 180
bnum = 71
lnum = 181
for ml in magnitudelimits:
m = s * math.ceil(float(ml + offset) / s)
l, b, counts = stellarNumberCounts.skyNumbers(m, blow, bhigh, llow,
lhigh, bnum, lnum)
counts *= 1.15 #15 error in the counts
stars = np.mean(counts)
ccd = stars * 49.6 / 3600
area = (ccd * covering) / (4096 * 4132.) * 100.
if area > 100:
area = 100.
txt = '%i stars per square degree will mean %i stars per CCD and thus an area loss of %.2f per cent' % \
(stars, ccd, area)
print txt
log.info(txt)
z = counts * covering / (4096 * 4132. *
(4096 * 0.1 * 4132 * 0.1 / 60. / 60.)) * 100.
msk = z > 100
z[msk] = 100.
_areaLossPlot(m, b, l, z, blow, bhigh, llow, lhigh, bnum, lnum, title,
'AreaLoss')
def CatalogueBachallSoneira(magnitudeLimit=28, b=25, l=0,
sqdeg=0.496, xmax=26000, ymax=29000,
types=np.arange(17, 103), output='catalogue.dat'):
"""
Generate an object catalogue with random positions using the Bachall and Soneira stellar model.
Includes also galaxies.
:param magnitudeLimit: limiting magnitude in V-band
:type magnitudeLimit: int
:param b: galactic longitude
:type b: int
:param l: galactic latitude
:type l: int
:param sqdeg: number of square degrees to cover
:type sqdeg: float
:param xmax: highest pixel value to use for the random positions in x
:type xmax: int
:param ymax: highest pixel value to use for the random positions in y
:type ymax: int
:param types: a list of galaxy types corresponding to the postage stamp images
:type types: list
:param output: name of the output file
:type output: str
:return: None
"""
#stars
Nvconst = stellarNumberCounts.integratedCountsVband()
n = stellarNumberCounts.bahcallSoneira(magnitudeLimit, l, b, Nvconst) #per square degree
nstars = int(n * sqdeg)
print '%i stars brighter than %i mag_V in %f square degrees at b=%i and l=%i' % (nstars, magnitudeLimit, sqdeg, b, l)
xcoords = np.random.random(nstars) * xmax
ycoords = np.random.random(nstars) * ymax
stcounts = []
stmags = np.linspace(3.5, 30, num=15)
for m in stmags:
tmp = stellarNumberCounts.bahcallSoneira(m, l, b, Nvconst)
stcounts.append(tmp)
stcounts = np.asarray(stcounts)
#fit a function and generate finer sample
z = np.polyfit(stmags, np.log10(stcounts), 4)
p = np.poly1d(z)
starmags = np.arange(1, 30.2, 0.2)
starcounts = 10**p(starmags)
cpdf = (starcounts - np.min(starcounts))/ (np.max(starcounts) - np.min(starcounts))
mag = drawFromCumulativeDistributionFunction(cpdf, starmags, nstars)
fh = open(output, 'w')
fh.write('# 1 X Object position along x [pixel]\n')
fh.write('# 2 Y Object position along y [pixel]\n')
fh.write('# 3 MAG Object magnitude [AB]\n')
fh.write('# 4 TYPE Object type [0=star, others=FITS]\n')
fh.write('# 5 ORIENTATION Objects orientation [deg]\n')
for x, y, m in zip(xcoords, ycoords, mag):
fh.write('%f %f %f %i %f \n' % (x, y, m, 0, 0.0))
#galaxies
d = np.loadtxt('data/cdf_galaxies.dat', usecols=(0, 1))
gmags = d[:, 0]
gcounts = d[:, 1]
nums = int(np.max(gcounts) * sqdeg)
print '%i galaxies' % nums
z = np.polyfit(gmags, np.log10(gcounts), 4)
p = np.poly1d(z)
galaxymags = np.arange(10.0, 30.2, 0.2)
galaxycounts = 10**p(galaxymags)
plotDistributionFunction(gmags, gcounts, galaxymags, galaxycounts, 'GalaxyDist.pdf')
cumulative = (galaxycounts - np.min(galaxycounts))/ (np.max(galaxycounts) - np.min(galaxycounts))
xc = np.random.random(nums) * xmax
yc = np.random.random(nums) * ymax
theta = np.random.random(nums) * 360.0
typ = np.random.random_integers(low=np.min(types), high=np.max(types), size=nums)
mag = drawFromCumulativeDistributionFunction(cumulative, galaxymags, nums)
for x, y, m, t, ang in zip(xc, yc, mag, typ, theta):
if t < 1:
fh.write('%f %f %f %i %f \n' % (x, y, m, t, 0.0))
else:
fh.write('%f %f %f %i %f \n' % (x, y, m, t, ang))
fh.close()
plotCatalog(output)
def starCatalogueBachallSoneira(magnitudeLimit=28, b=30, l=0, sqdeg=0.496,
xmax=26000, ymax=29000, output='starsOnly.dat'):
"""
Generate an object catalogue with random positions using the Bachall and Soneira stellar model.
For a full focal plane the default values should be fine. A full focal plane covers about 0.496 square degrees
and in x and y pixels:
5*(4096 + (1.643*1000/12.)) + 4096 and
5*(4132 + (8.116*1000/12.)) + 4132, respectively.
:param magnitudeLimit: limiting magnitude in V-band
:type magnitudeLimit: int
:param b: galactic longitude
:type b: int
:param l: galactic latitude
:type l: int
:param sqdeg: number of square degrees to cover
:type sqdeg: float
:param xmax: highest pixel value to use for the random positions in x
:type xmax: int
:param ymax: highest pixel value to use for the random positions in y
:type ymax: int
:return: None
"""
Nvconst = stellarNumberCounts.integratedCountsVband()
n = stellarNumberCounts.bahcallSoneira(magnitudeLimit, l, b, Nvconst) #per square degree
nstars = int(n * sqdeg)
print '%i stars brighter than %i mag_V in %f square degrees at b=%i and l=%i' % (nstars, magnitudeLimit, sqdeg, b, l)
xcoords = np.random.random(nstars) * xmax
ycoords = np.random.random(nstars) * ymax
stcounts = []
stmags = np.linspace(3.5, 30, num=15)
for m in stmags:
tmp = stellarNumberCounts.bahcallSoneira(m, l, b, Nvconst)
stcounts.append(tmp)
stcounts = np.asarray(stcounts)
#fit a function and generate finer sample
z = np.polyfit(stmags, np.log10(stcounts), 4)
p = np.poly1d(z)
starmags = np.arange(1, 30.2, 0.2)
starcounts = 10**p(starmags)
cpdf = (starcounts - np.min(starcounts))/ (np.max(starcounts) - np.min(starcounts))
mag = drawFromCumulativeDistributionFunction(cpdf, starmags, nstars)
fh = open(output, 'w')
fh.write('# 1 X Object position along x [pixel]\n')
fh.write('# 2 Y Object position along y [pixel]\n')
fh.write('# 3 MAG Object magnitude [AB]\n')
fh.write('# 4 TYPE Object type [0=star, others=FITS]\n')
fh.write('# 5 ORIENTATION Objects orientation [deg]\n')
for x, y, m in zip(xcoords, ycoords, mag):
fh.write('%f %f %f %i %f \n' % (x, y, m, 0, 0.0))
fh.close()
plotCatalog(output)
def areaImpacted(magnitudes=np.arange(0, 20., 1.), offset=0.5, star=False):
"""
"""
s = 0.1
Nvconst = stellarNumberCounts.integratedCountsVband()
print '\n mag b l stars CCD area'
for b in [20, 25, 30, 50, 90]:
for l in [0, 90, 180]:
area = 0
prev = 0
for ml in magnitudes:
m = s * math.ceil(float(ml + offset) / s)
n = stellarNumberCounts.bahcallSoneira(m, l, b, Nvconst)
n -= prev #subtract the number of stars in the previous bin
ccd = n * 49.6 / 3600
covering = pixelsImpacted(m, star=star)
area += (ccd * covering) / (4096 * 4132.) * 100.
prev = n #store the number of stars in the current bin
if area > 100:
area = 100.
txt = '%.1f %2d %3d %.2f %.1f %.3f' % (m, b, l, n, ccd, area)
print txt
if b == 90:
#no need to do more than once... l is irrelevant
break
print '\n\n\nIntegrated Area Loss:'
blow=20
bhigh=90
llow=0
lhigh=180
bnum=71
lnum=181
prev = 0
for i, ml in enumerate(magnitudes):
m = s * math.ceil(float(ml + offset) / s)
l, b, counts = stellarNumberCounts.skyNumbers(m, blow, bhigh, llow, lhigh, bnum, lnum)
counts -= prev
prev = counts.copy()
#average
stars = np.mean(counts)
ccd = stars * 49.6 / 3600
covering = pixelsImpacted(m, star=star)
area = (ccd * covering) / (4096 * 4132.) * 100.
if area > 100:
area = 100.
print 'magnitude = %.1f, average = %.5f, max = %.5f' % (ml, stars, np.max(counts))
print '%i stars per square degree will mean %i stars per CCD and thus an area loss of %.4f per cent' % \
(stars, ccd, area)
if i < 1:
z = counts * covering / (4096 * 4132. * (4096 * 0.1 * 4132 * 0.1 / 60. / 60.)) * 100.
else:
z += counts * covering / (4096 * 4132. * (4096 * 0.1 * 4132 * 0.1 / 60. / 60.)) * 100.
msk = z > 100
z[msk] = 100.
_areaLossPlot(m, b, l, z, blow, bhigh, llow, lhigh, bnum, lnum,
'Masking of Saturated Pixels From All Stars', 'AreaLoss')
def _numberOfStarsAreaLoss(log, magnitudelimits, title, offset=0.5, covering=2550):
"""
Calculates the area loss for given magnitude limit at a few galactic coordinates and
integrated over an observing region.
"""
Nvconst = stellarNumberCounts.integratedCountsVband()
txt = '\n\n\nNumber of stars:'
print txt
log.info(txt)
s = 0.1
for ml in magnitudelimits:
print '\nmag b l stars CCD area'
for b in [20, 25, 30, 50, 90]:
for l in [0, 90, 180]:
m = s * math.ceil(float(ml + offset) / s)
n = stellarNumberCounts.bahcallSoneira(m, l, b, Nvconst) * 1.15 #15 error in the counts
ccd = n * 49.6 / 3600
area = (ccd * covering) / (4096 * 4132.) * 100.
if area > 100:
area = 100.
#print 'At l=%i, b=%i, there are about %i stars in a square degree brighter than %.1f' % (l, b, n, m)
#print '%i stars per square degree will mean %i stars per CCD and thus an area loss of %.2f per cent' % \
# (n, ccd, area)
txt = '%.1f %2d %3d %.1f %.1f %.2f' % (m, b, l, n, ccd, area)
print txt
log.info(txt)
if b == 90:
#no need to do more than once... l is irrelevant
break
txt = '\n\n\nIntegrated Area Loss:'
print txt
log.info(txt)
blow=20
bhigh=90
llow=0
lhigh=180
bnum=71
lnum=181
for ml in magnitudelimits:
m = s * math.ceil(float(ml + offset) / s)
l, b, counts = stellarNumberCounts.skyNumbers(m, blow, bhigh, llow, lhigh, bnum, lnum)
counts *= 1.15 #15 error in the counts
stars = np.mean(counts)
ccd = stars * 49.6 / 3600
area = (ccd * covering) / (4096 * 4132.) * 100.
if area > 100:
area = 100.
txt = '%i stars per square degree will mean %i stars per CCD and thus an area loss of %.2f per cent' % \
(stars, ccd, area)
print txt
log.info(txt)
z = counts * covering / (4096 * 4132. * (4096 * 0.1 * 4132 * 0.1 / 60. / 60.)) * 100.
msk = z > 100
z[msk] = 100.
_areaLossPlot(m, b, l, z, blow, bhigh, llow, lhigh, bnum, lnum, title, 'AreaLoss')