def map_of_errors():
t = atpy.Table(workDir + '20.KS2_PMA/wd1_catalog.fits')
xbins = np.arange(0, 4251, 250)
ybins = np.arange(0, 4200, 250)
xb2d, yb2d = np.meshgrid(xbins, ybins)
xe_mean = np.zeros(xb2d.shape, dtype=float)
ye_mean = np.zeros(yb2d.shape, dtype=float)
me_mean = np.zeros(yb2d.shape, dtype=float)
xe_std = np.zeros(xb2d.shape, dtype=float)
ye_std = np.zeros(yb2d.shape, dtype=float)
me_std = np.zeros(yb2d.shape, dtype=float)
for xx in range(len(xbins)-1):
for yy in range(len(ybins)-1):
idx = np.where((t.x_160 > xbins[xx]) & (t.x_160 <= xbins[xx+1]) &
(t.y_160 > ybins[yy]) & (t.y_160 <= ybins[yy+1]) &
(t.xe_160 < 0.2) & (t.ye_160 < 0.2))[0]
if len(idx) > 0:
xe_mean[yy, xx] = statsIter.mean(t.xe_160[idx], hsigma=3, iter=5)
ye_mean[yy, xx] = statsIter.mean(t.ye_160[idx], hsigma=3, iter=5)
me_mean[yy, xx] = statsIter.mean(t.me_160[idx], hsigma=3, iter=5)
xe_std[yy, xx] = statsIter.std(t.xe_160[idx], hsigma=3, iter=5)
ye_std[yy, xx] = statsIter.std(t.ye_160[idx], hsigma=3, iter=5)
me_std[yy, xx] = statsIter.std(t.me_160[idx], hsigma=3, iter=5)
py.close('all')
py.figure(1, figsize=(12, 6))
py.clf()
py.subplots_adjust(left=0.05, bottom=0.05)
py.subplot(1, 2, 1)
py.imshow(xe_mean, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]],
vmin=0.01, vmax=0.07)
py.colorbar(orientation='horizontal')
py.title('X Error Mean')
py.subplot(1, 2, 2)
py.imshow(xe_std, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]],
vmin=0.01, vmax=0.07)
py.colorbar(orientation='horizontal')
py.title('X Error Std')
py.figure(2, figsize=(12, 6))
py.clf()
py.subplots_adjust(left=0.05, bottom=0.05)
py.subplot(1, 2, 1)
py.imshow(ye_mean, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]],
vmin=0.01, vmax=0.07)
py.colorbar(orientation='horizontal')
py.title('Y Error Mean')
py.subplot(1, 2, 2)
py.imshow(ye_std, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]],
vmin=0.01, vmax=0.07)
py.colorbar(orientation='horizontal')
py.title('Y Error Std')
py.figure(3, figsize=(12, 6))
py.clf()
py.subplots_adjust(left=0.05, bottom=0.05)
py.subplot(1, 2, 1)
py.imshow(me_mean, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]],
vmin=0.01, vmax=0.07)
py.colorbar(orientation='horizontal')
py.title('M Error Mean')
py.subplot(1, 2, 2)
py.imshow(me_std, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]],
vmin=0.01, vmax=0.07)
py.colorbar(orientation='horizontal')
py.title('M Error Std')
def map_of_errors():
t = atpy.Table(workDir + '20.KS2_PMA/wd1_catalog.fits')
xbins = np.arange(0, 4251, 250)
ybins = np.arange(0, 4200, 250)
xb2d, yb2d = np.meshgrid(xbins, ybins)
xe_mean = np.zeros(xb2d.shape, dtype=float)
ye_mean = np.zeros(yb2d.shape, dtype=float)
me_mean = np.zeros(yb2d.shape, dtype=float)
xe_std = np.zeros(xb2d.shape, dtype=float)
ye_std = np.zeros(yb2d.shape, dtype=float)
me_std = np.zeros(yb2d.shape, dtype=float)
for xx in range(len(xbins)-1):
for yy in range(len(ybins)-1):
idx = np.where((t.x_160 > xbins[xx]) & (t.x_160 <= xbins[xx+1]) &
(t.y_160 > ybins[yy]) & (t.y_160 <= ybins[yy+1]) &
(t.xe_160 < 0.2) & (t.ye_160 < 0.2))[0]
if len(idx) > 0:
xe_mean[yy, xx] = statsIter.mean(t.xe_160[idx], hsigma=3, iter=5)
ye_mean[yy, xx] = statsIter.mean(t.ye_160[idx], hsigma=3, iter=5)
me_mean[yy, xx] = statsIter.mean(t.me_160[idx], hsigma=3, iter=5)
xe_std[yy, xx] = statsIter.std(t.xe_160[idx], hsigma=3, iter=5)
ye_std[yy, xx] = statsIter.std(t.ye_160[idx], hsigma=3, iter=5)
me_std[yy, xx] = statsIter.std(t.me_160[idx], hsigma=3, iter=5)
py.close('all')
py.figure(1, figsize=(12, 6))
py.clf()
py.subplots_adjust(left=0.05, bottom=0.05)
py.subplot(1, 2, 1)
py.imshow(xe_mean, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]],
vmin=0.01, vmax=0.07)
py.colorbar(orientation='horizontal')
py.title('X Error Mean')
py.subplot(1, 2, 2)
py.imshow(xe_std, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]],
vmin=0.01, vmax=0.07)
py.colorbar(orientation='horizontal')
py.title('X Error Std')
py.figure(2, figsize=(12, 6))
py.clf()
py.subplots_adjust(left=0.05, bottom=0.05)
py.subplot(1, 2, 1)
py.imshow(ye_mean, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]],
vmin=0.01, vmax=0.07)
py.colorbar(orientation='horizontal')
py.title('Y Error Mean')
py.subplot(1, 2, 2)
py.imshow(ye_std, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]],
vmin=0.01, vmax=0.07)
py.colorbar(orientation='horizontal')
py.title('Y Error Std')
py.figure(3, figsize=(12, 6))
py.clf()
py.subplots_adjust(left=0.05, bottom=0.05)
py.subplot(1, 2, 1)
py.imshow(me_mean, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]],
vmin=0.01, vmax=0.07)
py.colorbar(orientation='horizontal')
py.title('M Error Mean')
py.subplot(1, 2, 2)
py.imshow(me_std, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]],
vmin=0.01, vmax=0.07)
py.colorbar(orientation='horizontal')
py.title('M Error Std')
def plot_final_synthesis():
data = atpy.Table(workDir + 'gal1_master.txt', type='ascii')
img = pyfits.getdata(workDir + 'gal1_img.fits')
imgBkg = statsIter.mean(img.flatten(), hsigma=5, iter=1, verbose=True)
imgStd = statsIter.std(img.flatten(), hsigma=5, iter=1, verbose=True)
print 'Image mean = %5.1f std = %5.1f' % (imgBkg, imgStd)
fluxBins = np.arange(0, 500, 8)
velBins = np.arange(-500, 801, 50)
n_bins = np.arange(10, 60, 2)
npix, fluxEdges = np.histogram(img.flatten(), bins=fluxBins)
nstars, binEdges = np.histogram(data.vel, bins=velBins)
n, b = np.histogram(nstars, bins=n_bins)
bkg = np.mean(nstars)
noise = np.std(nstars)
print 'First Iteration: mean = %5.1f, std = %5.1f' % (bkg, noise)
# Do one round of rejection
idx = np.where(nstars < (bkg + (3.0 * noise)))[0]
bkg = np.mean(nstars[idx])
noise = np.std(nstars[idx])
print 'Second Iteration: mean = %5.1f, std = %5.1f' % (bkg, noise)
gaussianCDF = scipy.stats.norm.cdf(b, loc=bkg, scale=noise)
gaussianPDF = np.diff(gaussianCDF) * n.sum()
gaussianBinCenters = n_bins[0:-1] + np.diff(n_bins) / 2.0
fluxBinsModel = np.arange(0, 500, 1)
fluxNormCDF = scipy.stats.norm.cdf(fluxBinsModel, loc=imgBkg, scale=imgStd)
fluxNormPDF = np.diff(fluxNormCDF)
fluxNormPDF *= npix.max() / fluxNormPDF.max()
fluxBinCenters = fluxBinsModel[0:-1] + np.diff(fluxBinsModel) / 2.0
# Images
py.figure(1)
py.clf()
py.subplots_adjust(left=0.22)
py.subplot(2, 1, 1)
py.hist(img.flatten(), histtype='step', bins=fluxBins)
py.plot(fluxBinCenters, fluxNormPDF, 'g-')
py.title('Field #1')
py.xlim(0, 200)
py.subplot(2, 1, 2)
py.hist(img.flatten(), histtype='step', bins=fluxBins)
py.plot(fluxBinCenters, fluxNormPDF, 'g-')
py.ylim(0, 1000)
py.xlim(0, 200)
py.xlabel('Flux')
py.savefig(workDir + 'final_syn_gal1_flux_hist.png')
py.figure(1)
py.subplots_adjust(left=0.15)
py.clf()
py.hist(data.vel, bins=velBins, histtype='step')
py.xlim(-500, 800)
py.xlabel('Velocity (km/s)')
py.ylabel('Number of Stars')
py.savefig(workDir + 'final_syn_gal1_vel_hist_no_lines.png')
py.axhline(bkg, linestyle='-', linewidth=3, color='black')
py.axhline(bkg + noise, linestyle='--', linewidth=3, color='black')
py.axhline(bkg - noise, linestyle='--', linewidth=3, color='black')
py.savefig(workDir + 'final_syn_gal1_vel_hist.png')
py.figure(2)
py.clf()
py.subplots_adjust(left=0.15)
(n, b, p) = py.hist(nstars, histtype='step', bins=n_bins, label='Data')
py.ylim(0, n.max() * 1.3)
py.xlim(b.min(), b.max())
py.xlabel('Number of Stars per Velocity Bin')
py.ylabel('Number of Velocity Bins')
print gaussianPDF.max(), n.max()
py.plot(gaussianBinCenters, gaussianPDF, label='Gaussian Fit')
axLim = py.axis()
py.axvline(bkg, linestyle='-', color='black', linewidth=3)
py.arrow(bkg + 1 * noise,
axLim[3],
0,
-0.5,
color='black',
linewidth=3,
head_width=1,
head_length=0.1)
py.arrow(bkg + 2 * noise,
axLim[3],
0,
-0.5,
color='black',
linewidth=3,
head_width=1,
head_length=0.1)
py.arrow(bkg + 3 * noise,
axLim[3],
0,
-0.5,
color='black',
linewidth=3,
head_width=1,
head_length=0.1)
py.text(bkg + 1 * noise,
axLim[3] + 0.15,
r'$1\sigma$',
horizontalalignment='center',
verticalalignment='bottom')
py.text(bkg + 2 * noise,
axLim[3] + 0.15,
r'$2\sigma$',
horizontalalignment='center',
verticalalignment='bottom')
py.text(bkg + 3 * noise,
axLim[3] + 0.15,
r'$3\sigma$',
horizontalalignment='center',
verticalalignment='bottom')
py.savefig(workDir + 'final_syn_gal1_vel_hist_hist.png')
def cmd():
# Load up observed data:
wd1_data = '/u/jlu/data/Wd1/hst/from_jay/EXPORT_WEST1.2012.02.04/wd1_catalog.fits'
data = atpy.Table(wd1_data)
# Load up model isochrone
iso = load_isochrone()
# Select a region in NIR CMD for defining cluster center and
# velocity dispersion.
magLo = 15.0
magHi = 16.5
colorLo = 0.8
colorHi = 0.88
# Pull out a few key masses along the isochrone
idxM1 = np.abs(iso.M - 1.0).argmin()
idxM01 = np.abs(iso.M - 0.1).argmin()
colM1 = iso.mag125w[idxM1] - iso.mag160w[idxM1]
magM1 = iso.mag125w[idxM1]
colM01 = iso.mag125w[idxM01] - iso.mag160w[idxM01]
magM01 = iso.mag125w[idxM01]
# Plot up the complete near-infrared color magnitude diagram, select out
# a color region to show cluster members in the VPD.
py.clf()
py.subplots_adjust(left=0.11)
py.plot(data.mag125 - data.mag160, data.mag125, 'k.', ms=2)
py.plot(iso.mag125w - iso.mag160w, iso.mag125w, 'r-', ms=2,
color='red', linewidth=2)
py.plot([colM1], [magM1], 'r*', ms=25)
py.plot([colM01], [magM01],'r*', ms=25)
py.text(colM1+0.1, magM1, r'1 M$_\odot$', color='red',
fontweight='normal', fontsize=28)
py.text(colM01+0.1, magM01, r'0.1 M$_\odot$', color='red',
fontweight='normal', fontsize=28)
py.gca().invert_yaxis()
py.plot([colorLo, colorHi, colorHi, colorLo, colorLo,],
[magLo, magLo, magHi, magHi, magLo],
'k-', color='lightgreen', linewidth=4)
py.xlim(0, 2)
py.ylim(23, 12)
py.xlabel('F125W - F160W')
py.ylabel('F125W')
py.savefig(plotDir + 'cmd_infrared_vpd_box.png')
# Plot up the VPD with the CMD selected highlighted.
# Also trim out anything without good astrometric and
# photometric errors.
idx = np.where((data.x2005_e*scale < 2) & (data.y2005_e*scale < 2) &
(data.x2010_e*scale < 2) & (data.y2010_e*scale < 2) &
(data.mag814_e < 0.04) & (data.mag125_e < 0.04))[0]
mag = data.mag125[idx]
color = data.mag125[idx] - data.mag160[idx]
tdx = np.where((mag > magLo) & (mag < magHi) &
(color > colorLo) & (color < colorHi))[0]
dx_mean = statsIter.mean(data.dx[idx[tdx]], hsigma=5, lsigma=5, iter=5)
dy_mean = statsIter.mean(data.dy[idx[tdx]], hsigma=5, lsigma=5, iter=5)
dx_std = statsIter.std(data.dx[idx[tdx]], hsigma=5, lsigma=5, iter=5)
dy_std = statsIter.std(data.dy[idx[tdx]], hsigma=5, lsigma=5, iter=5)
print 'Cluster Center: '
print ' x = %5.1f +/- %5.1f mas' % (dx_mean, dx_std)
print ' y = %5.1f +/- %5.1f mas' % (dy_mean, dy_std)
# Select out cluster members
dr = np.hypot(data.dx[idx] - dx_mean, data.dy[idx] - dy_mean)
dr_cluster = np.max([dx_std, dy_std])
inCluster = np.where(dr <= dr_cluster)[0]
dt = 2010.6521 - 2005.4846
py.clf()
py.subplots_adjust(left=0.13)
py.plot(data.dx[idx]/dt, data.dy[idx]/dt, 'k.', ms=4)
py.plot(data.dx[idx[tdx]]/dt, data.dy[idx[tdx]]/dt,
'b.', color='lightgreen', ms=10)
circ = py.Circle([dx_mean/dt, dy_mean/dt], radius=dr_cluster/dt,
fc='none', ec='yellow', linewidth=4, zorder=10)
py.gca().add_patch(circ)
lim = 20
py.axis([-lim/dt, lim/dt, -lim/dt, lim/dt])
py.xlabel('X Proper Motion (mas/yr)')
py.ylabel('Y Proper Motion (mas/yr)')
py.title('2010 F814W - 2005 F125W')
py.savefig(plotDir + 'vpd_cmd_selected.png')
# Plot up the complete near-infrared color magnitude diagram,
# and overplot cluster members as determined from proper motions
# from optical/IR data set.
py.clf()
py.subplots_adjust(left=0.11)
py.plot(data.mag125 - data.mag160, data.mag125, 'k.', ms=2)
py.plot(data.mag125[idx[inCluster]] - data.mag160[idx[inCluster]],
data.mag125[idx[inCluster]], 'y.', ms=5)
py.plot(iso.mag125w - iso.mag160w, iso.mag125w, 'r-', ms=2,
linewidth=2)
py.plot([colM1], [magM1], 'r*', ms=25)
py.plot([colM01], [magM01],'r*', ms=25)
py.text(colM1+0.1, magM1, r'1 M$_\odot$', color='red',
fontweight='normal', fontsize=28)
py.text(colM01+0.1, magM01, r'0.1 M$_\odot$', color='red',
fontweight='normal', fontsize=28)
py.gca().invert_yaxis()
py.xlim(0, 2)
py.ylim(23, 12)
py.xlabel('F125W - F160W')
py.ylabel('F125W')
py.savefig(plotDir + 'cmd_infrared_members.png')
def plot_comparison(tableSuffix,
posLim=0.15,
magLimits=[-13, -1],
errLim=0.04,
quiverScale=2):
"""
Load up starlists produced by matchup_ks2_pass1() and plot some parameters
of interest. Just pass in the filter suffix (e.g. "_f0"). Plots include:
delta-x vs. m
delta-y vs. m
delta-m vs. m
uncertainties vs. m for both lists
me vs. m for stars in ks2 vs. stars not in ks2
"""
stars = atpy.Table('stars_ks2_pass1%s.fits' % tableSuffix)
other = atpy.Table('stars_pass1_only%s.fits' % tableSuffix)
print '%6d stars in ks2' % len(stars)
print '%6d stars not found in ks2' % len(other)
#####
# Plot delta-x vs. m
#####
dx = stars.x_ks2 - stars.x_pass1
dxe = stars.xe_ks2
#py.close('all')
py.figure(1)
py.clf()
ax1 = py.subplot(2, 1, 1)
py.plot(stars.m_pass1, dx, 'k.', ms=2)
py.ylabel('dx (pix)')
py.ylim(-posLim, posLim)
py.axhline(y=0, color='r', linewidth=2)
py.title('KS2 - One Pass')
ax2 = py.subplot(2, 1, 2, sharex=ax1)
py.plot(stars.m_pass1, dx / dxe, 'k.', ms=2)
py.ylabel('dx/dxe (sigma)')
py.ylim(-3, 3)
py.axhline(y=0, color='r', linewidth=2)
py.xlabel('Magnitude')
py.xlim(magLimits[0], magLimits[1])
py.savefig('plot_dx_m%s.png' % tableSuffix)
#####
# Plot delta-y vs. m
#####
dy = stars.y_ks2 - stars.y_pass1
dye = stars.ye_ks2
py.figure(2)
py.clf()
ax1 = py.subplot(2, 1, 1)
py.plot(stars.m_pass1, dy, 'k.', ms=2)
py.ylabel('dy (pix)')
py.ylim(-posLim, posLim)
py.axhline(y=0, color='r', linewidth=2)
py.title('KS2 - One Pass')
ax2 = py.subplot(2, 1, 2, sharex=ax1)
py.plot(stars.m_pass1, dy / dye, 'k.', ms=2)
py.ylabel('dy/dye (sigma)')
py.ylim(-3, 3)
py.axhline(y=0, color='r', linewidth=2)
py.xlabel('Magnitude')
py.xlim(magLimits[0], magLimits[1])
py.savefig('plot_dy_m%s.png' % tableSuffix)
#####
# Plot delta-m vs. m
#####
dm = stars.m_ks2 - stars.m_pass1
dme = stars.me_ks2
py.figure(3)
py.clf()
ax1 = py.subplot(2, 1, 1)
py.plot(stars.m_pass1, dm, 'k.', ms=2)
py.ylabel('dm (mag)')
py.ylim(-0.1, 0.1)
py.axhline(y=0, color='r', linewidth=2)
py.title('KS2 - One Pass')
ax2 = py.subplot(2, 1, 2, sharex=ax1)
py.plot(stars.m_pass1, dm / dme, 'k.', ms=2)
py.ylabel('dm/dme (sigma)')
py.ylim(-3, 3)
py.axhline(y=0, color='r', linewidth=2)
py.xlabel('Magnitude')
py.xlim(magLimits[0], magLimits[1])
py.savefig('plot_dm_m%s.png' % tableSuffix)
#####
# Plot differences in errors vs. m
#####
dxerr = stars.xe_ks2 - stars.xe_pass1
dyerr = stars.ye_ks2 - stars.ye_pass1
dmerr = stars.me_ks2 - stars.me_pass1
py.close(4)
py.figure(4, figsize=(9, 12))
py.clf()
py.subplots_adjust(left=0.12, bottom=0.07, top=0.95)
ax1 = py.subplot(3, 1, 1)
py.plot(stars.m_ks2, dxerr, 'k.', ms=2)
py.ylabel('xe diff')
py.ylim(-0.05, 0.05)
py.axhline(y=0, color='r', linewidth=2)
py.title('KS2 - One Pass')
ax2 = py.subplot(3, 1, 2, sharex=ax1)
py.plot(stars.m_ks2, dyerr, 'k.', ms=2)
py.ylabel('ye diff')
py.ylim(-0.05, 0.05)
py.axhline(y=0, color='r', linewidth=2)
ax3 = py.subplot(3, 1, 3, sharex=ax1)
py.plot(stars.m_ks2, dmerr, 'k.', ms=2)
py.ylabel('me diff')
py.ylim(-0.05, 0.05)
py.axhline(y=0, color='r', linewidth=2)
py.xlabel('Magnitude')
py.xlim(magLimits[0], magLimits[1])
py.savefig('plot_compare_errors%s.png' % tableSuffix)
#####
# Plot stars that are NOT in KS2 vs. those that are.
#####
py.figure(5)
py.clf()
py.semilogy(stars.m_pass1, stars.me_pass1, 'k.', label='In KS2', ms=2)
py.semilogy(other.m, other.me, 'r.', label='Not In KS2', ms=3)
py.legend(numpoints=1, loc='upper left')
py.xlabel('Magnitude')
py.xlim(-23, -1)
py.ylabel('Magnitude Error')
py.ylim(0.004, 0.1)
py.savefig('plot_m_me_others%s.png' % tableSuffix)
#####
# Plot vector point diagram of positional differences
#####
idx = np.where((stars.m_ks2 < -5) & (np.abs(dx) < posLim)
& (np.abs(dy) < posLim) & (stars.xe_pass1 < errLim)
& (stars.ye_pass1 < errLim))[0]
py.figure(6)
py.clf()
py.plot(dx[idx], dy[idx], 'k.', ms=2)
py.xlabel('X KS2 - One Pass')
py.ylabel('Y KS2 - One Pass')
py.axis([-posLim, posLim, -posLim, posLim])
py.savefig('plot_dxdy_vpd%s.png' % tableSuffix)
#####
# Plot vector field of positional differences
#####
py.figure(7)
py.clf()
q = py.quiver(stars.x_pass1[idx],
stars.y_pass1[idx],
dx[idx],
dy[idx],
scale=quiverScale)
py.quiverkey(q, 0.5, 0.95, 0.05, '0.05 pix', color='red')
py.title('KS2 - One Pass')
py.xlabel('X (pixels)')
py.xlabel('Y (pixels)')
py.savefig('plot_dxdy_quiver%s.png' % tableSuffix)
#####
# Print out some statistics
#####
idx = np.where(stars.m_ks2 < -5)[0]
print 'Mean dx = %7.3f +/- %7.3f (pix)' % \
(statsIter.mean(dx[idx], lsigma=4, hsigma=4, iter=5),
statsIter.std(dx[idx], lsigma=4, hsigma=4, iter=5))
print 'Mean dy = %7.3f +/- %7.3f (pix)' % \
(statsIter.mean(dy[idx], lsigma=4, hsigma=4, iter=5),
statsIter.std(dy[idx], lsigma=4, hsigma=4, iter=5))
print 'Mean dxerr = %7.3f +/- %7.3f (pix)' % \
(statsIter.mean(dxerr[idx], lsigma=4, hsigma=4, iter=5),
statsIter.std(dxerr[idx], lsigma=4, hsigma=4, iter=5))
print 'Mean dyerr = %7.3f +/- %7.3f (pix)' % \
(statsIter.mean(dyerr[idx], lsigma=4, hsigma=4, iter=5),
statsIter.std(dyerr[idx], lsigma=4, hsigma=4, iter=5))
def plot_vector_diff(undersample):
"""
Make a vector plot of the differences between the input positions
and the output positions after running starfinder.
"""
# Dimensions of new grid
npsf_side = get_npsf_side(undersample)
suffix = '%d_%d' % (npsf_side, npsf_side)
s = starset.StarSet('grid_' + suffix + '/align_' + suffix)
cnt = s.getArray('velCnt')
mag = s.getArray('mag')
idx = np.where((cnt == 2) & (mag < 15))[0]
print 'Using {0} stars out of {1}'.format(len(idx), len(cnt))
newStars = [s.stars[ii] for ii in idx]
s.stars = newStars
x0 = s.getArrayFromEpoch(0, 'xorig')
y0 = s.getArrayFromEpoch(0, 'yorig')
x1 = s.getArrayFromEpoch(1, 'xorig')
y1 = s.getArrayFromEpoch(1, 'yorig')
dx = x1 - x0
dy = y1 - y0
dr = np.hypot(dx, dy)
# Boundaries
t = atpy.Table('svpar_' + suffix + '.txt', type='ascii')
lx = t.col1
ux = t.col2
ly = t.col3
uy = t.col4
xedges = np.unique(np.append(lx, ux))
yedges = np.unique(np.append(ly, uy))
py.clf()
q = py.quiver(x0, y0, dx, dy, scale=0.2)
py.xlim(0, 1200)
py.ylim(0, 1200)
py.quiverkey(q,
0.5,
0.97,
0.02,
'0.02 pixel (0.2 mas)',
color='red',
coordinates='axes')
for xx in xedges:
py.axvline(xx, linestyle='--')
for yy in yedges:
py.axhline(yy, linestyle='--')
py.savefig('plots/vec_diff_' + suffix + '.png')
xmean = statsIter.mean(dx, hsigma=5, lsigma=5, iter=5)
ymean = statsIter.mean(dy, hsigma=5, lsigma=5, iter=5)
rmean = statsIter.mean(dr, hsigma=5, lsigma=5, iter=5)
xstd = statsIter.std(dx, hsigma=5, lsigma=5, iter=5)
ystd = statsIter.std(dy, hsigma=5, lsigma=5, iter=5)
rstd = statsIter.std(dr, hsigma=5, lsigma=5, iter=5)
print('Mean Delta-X: {0:9.5f} +/- {1:9.5f} pixels'.format(xmean, xstd))
print('Mean Delta-Y: {0:9.5f} +/- {1:9.5f} pixels'.format(ymean, ystd))
print('Mean Delta-R: {0:9.5f} +/- {1:9.5f} pixels'.format(rmean, rstd))
f_name = 'diff_stats_' + suffix + '.txt'
f_stat = open(f_name, 'w')
hdr = '#{0:>5s} {1:>10s} {2:>10s} {3:>10s} {4:>10s} {5:>10s} {6:>10s}\n'
fmt = '{0:6d} {1:10.5f} {2:10.5f} {3:10.5f} {4:10.5f} {5:10.5f} {6:10.5f}\n'
f_stat.write(
hdr.format('Npsf', 'Xmean', 'Ymean', 'Rmean', 'Xstd', 'Ystd', 'Rstd'))
f_stat.write(fmt.format(npsf_side, xmean, ymean, rmean, xstd, ystd, rstd))
f_stat.close()
return f_name
def plot_spie_figure3():
"""
Pass in a starlist (stack of starlists) and calculate the
astrometric and photometric errors per star. Then plot them.
"""
align_dir = '/g/lu/data/gsaoi/commission/reduce/ngc1851/align_2013_03_04_G2/'
plot_dir = '/u/jlu/doc/papers/proceed_2014_spie/'
from jlu.gsaoi import stars as stars_obj
stars2 = stars_obj.Starlist(align_dir + 'align_a2')
stars3 = stars_obj.Starlist(align_dir + 'align_a3')
stars4 = stars_obj.Starlist(align_dir + 'align_a4')
stars_all = [stars2, stars3, stars4]
py.close('all')
f, ax_all = py.subplots(1, 3, sharex=True, sharey=True, figsize=(12, 4))
f.subplots_adjust(left=0.10, bottom=0.18, wspace=0.01)
for ii in range(len(stars_all)):
stars = stars_all[ii]
ax = ax_all[ii]
Nstars = stars.dxm.shape[1]
Nimages = stars.dxm.shape[0]
mavg = stars.mm.mean(axis=0)
# Error on the mean
xerr = stars.dxm.std(axis=0) / math.sqrt(Nimages)
yerr = stars.dym.std(axis=0) / math.sqrt(Nimages)
merr = stars.mm.std(axis=0) / math.sqrt(Nimages)
# Convert to mas
xerr *= 1e3
yerr *= 1e3
rerr = np.hypot(xerr, yerr)
xerr_med = np.median(xerr)
yerr_med = np.median(yerr)
err_med = np.median([xerr_med, yerr_med])
rerr_med = np.median(np.hypot(xerr, yerr))
# Make a curve of the median error
magBinCent = np.arange(10, 13.5, 0.05)
magBinSize = 0.5
rerrMed = np.zeros(len(magBinCent), dtype=float)
rerrAvg = np.zeros(len(magBinCent), dtype=float)
rerrStd = np.zeros(len(magBinCent), dtype=float)
for ii in range(len(magBinCent)):
magLo = magBinCent[ii] - (magBinSize / 2.0)
magHi = magBinCent[ii] + (magBinSize / 2.0)
idx = np.where((mavg > magLo) & (mavg <= magHi))[0]
rerrAvg[ii] = statsIter.mean(rerr[idx], hsigma=2, lsigma=5, iter=10)
rerrStd[ii] = statsIter.std(rerr[idx], hsigma=2, lsigma=5, iter=10)
rerrMed[ii] = np.median(rerr[idx])
# Smooth the average curve
n_window = 20
window = np.hanning(n_window)
s = np.r_[rerrAvg[n_window-1:0:-1], rerrAvg, rerrAvg[-1:-n_window:-1]]
rerrAvg_smooth = np.convolve(window/window.sum(), s, mode='valid')
rerrAvg_smooth = rerrAvg_smooth[(n_window/2)-1:-(n_window/2)]
ax.plot(mavg, rerr, 'k.', alpha=0.4)
#ax.plot(magBinCent, rerrMed, 'k-')
#ax.plot(magBinCent, rerrAvg, 'r-', linewidth=2)
ax.plot(magBinCent, rerrAvg_smooth, 'r-', linewidth=2)
# ax.plot(magBinCent, rerrAvg+rerrStd, 'r--')
# ax.plot(magBinCent, rerrAvg-rerrStd, 'r--')
ax.set_xlabel('K Magnitude')
ax.set_ylim(0, 1)
ax.set_xlim(9.1, 14)
ax_all[0].set_ylabel('Astrometric Error (mas)')
py.savefig(plot_dir + 'gems_ngc1851_pos_err.png')
def plot_comparison(tableSuffix, posLim=0.15, magLimits=[-13,-1], errLim=0.04,
quiverScale=2):
"""
Load up starlists produced by matchup_ks2_pass1() and plot some parameters
of interest. Just pass in the filter suffix (e.g. "_f0"). Plots include:
delta-x vs. m
delta-y vs. m
delta-m vs. m
uncertainties vs. m for both lists
me vs. m for stars in ks2 vs. stars not in ks2
"""
stars = atpy.Table('stars_ks2_pass1%s.fits' % tableSuffix)
other = atpy.Table('stars_pass1_only%s.fits' % tableSuffix)
print '%6d stars in ks2' % len(stars)
print '%6d stars not found in ks2' % len(other)
#####
# Plot delta-x vs. m
#####
dx = stars.x_ks2 - stars.x_pass1
dxe = stars.xe_ks2
#py.close('all')
py.figure(1)
py.clf()
ax1 = py.subplot(2, 1, 1)
py.plot(stars.m_pass1, dx, 'k.', ms=2)
py.ylabel('dx (pix)')
py.ylim(-posLim, posLim)
py.axhline(y=0, color='r', linewidth=2)
py.title('KS2 - One Pass')
ax2 = py.subplot(2, 1, 2, sharex=ax1)
py.plot(stars.m_pass1, dx/dxe, 'k.', ms=2)
py.ylabel('dx/dxe (sigma)')
py.ylim(-3, 3)
py.axhline(y=0, color='r', linewidth=2)
py.xlabel('Magnitude')
py.xlim(magLimits[0], magLimits[1])
py.savefig('plot_dx_m%s.png' % tableSuffix)
#####
# Plot delta-y vs. m
#####
dy = stars.y_ks2 - stars.y_pass1
dye = stars.ye_ks2
py.figure(2)
py.clf()
ax1 = py.subplot(2, 1, 1)
py.plot(stars.m_pass1, dy, 'k.', ms=2)
py.ylabel('dy (pix)')
py.ylim(-posLim, posLim)
py.axhline(y=0, color='r', linewidth=2)
py.title('KS2 - One Pass')
ax2 = py.subplot(2, 1, 2, sharex=ax1)
py.plot(stars.m_pass1, dy/dye, 'k.', ms=2)
py.ylabel('dy/dye (sigma)')
py.ylim(-3, 3)
py.axhline(y=0, color='r', linewidth=2)
py.xlabel('Magnitude')
py.xlim(magLimits[0], magLimits[1])
py.savefig('plot_dy_m%s.png' % tableSuffix)
#####
# Plot delta-m vs. m
#####
dm = stars.m_ks2 - stars.m_pass1
dme = stars.me_ks2
py.figure(3)
py.clf()
ax1 = py.subplot(2, 1, 1)
py.plot(stars.m_pass1, dm, 'k.', ms=2)
py.ylabel('dm (mag)')
py.ylim(-0.1, 0.1)
py.axhline(y=0, color='r', linewidth=2)
py.title('KS2 - One Pass')
ax2 = py.subplot(2, 1, 2, sharex=ax1)
py.plot(stars.m_pass1, dm/dme, 'k.', ms=2)
py.ylabel('dm/dme (sigma)')
py.ylim(-3, 3)
py.axhline(y=0, color='r', linewidth=2)
py.xlabel('Magnitude')
py.xlim(magLimits[0], magLimits[1])
py.savefig('plot_dm_m%s.png' % tableSuffix)
#####
# Plot differences in errors vs. m
#####
dxerr = stars.xe_ks2 - stars.xe_pass1
dyerr = stars.ye_ks2 - stars.ye_pass1
dmerr = stars.me_ks2 - stars.me_pass1
py.close(4)
py.figure(4, figsize=(9,12))
py.clf()
py.subplots_adjust(left=0.12, bottom=0.07, top=0.95)
ax1 = py.subplot(3, 1, 1)
py.plot(stars.m_ks2, dxerr, 'k.', ms=2)
py.ylabel('xe diff')
py.ylim(-0.05, 0.05)
py.axhline(y=0, color='r', linewidth=2)
py.title('KS2 - One Pass')
ax2 = py.subplot(3, 1, 2, sharex=ax1)
py.plot(stars.m_ks2, dyerr, 'k.', ms=2)
py.ylabel('ye diff')
py.ylim(-0.05, 0.05)
py.axhline(y=0, color='r', linewidth=2)
ax3 = py.subplot(3, 1, 3, sharex=ax1)
py.plot(stars.m_ks2, dmerr, 'k.', ms=2)
py.ylabel('me diff')
py.ylim(-0.05, 0.05)
py.axhline(y=0, color='r', linewidth=2)
py.xlabel('Magnitude')
py.xlim(magLimits[0], magLimits[1])
py.savefig('plot_compare_errors%s.png' % tableSuffix)
#####
# Plot stars that are NOT in KS2 vs. those that are.
#####
py.figure(5)
py.clf()
py.semilogy(stars.m_pass1, stars.me_pass1, 'k.', label='In KS2', ms=2)
py.semilogy(other.m, other.me, 'r.', label='Not In KS2', ms=3)
py.legend(numpoints=1, loc='upper left')
py.xlabel('Magnitude')
py.xlim(-23, -1)
py.ylabel('Magnitude Error')
py.ylim(0.004, 0.1)
py.savefig('plot_m_me_others%s.png' % tableSuffix)
#####
# Plot vector point diagram of positional differences
#####
idx = np.where((stars.m_ks2 < -5) & (np.abs(dx) < posLim) & (np.abs(dy) < posLim) &
(stars.xe_pass1 < errLim) & (stars.ye_pass1 < errLim))[0]
py.figure(6)
py.clf()
py.plot(dx[idx], dy[idx], 'k.', ms=2)
py.xlabel('X KS2 - One Pass')
py.xlabel('Y KS2 - One Pass')
py.axis([-posLim, posLim, -posLim, posLim])
py.savefig('plot_dxdy_vpd%s.png' % tableSuffix)
#####
# Plot vector field of positional differences
#####
py.figure(7)
py.clf()
q = py.quiver(stars.x_pass1[idx], stars.y_pass1[idx], dx[idx], dy[idx], scale=quiverScale)
py.quiverkey(q, 0.5, 0.95, 0.05, '0.05 pix', color='red')
py.title('KS2 - One Pass')
py.xlabel('X (pixels)')
py.xlabel('Y (pixels)')
py.savefig('plot_dxdy_quiver%s.png' % tableSuffix)
#####
# Print out some statistics
#####
idx = np.where(stars.m_ks2 < -5)[0]
print 'Mean dx = %7.3f +/- %7.3f (pix)' % \
(statsIter.mean(dx[idx], lsigma=4, hsigma=4, iter=5),
statsIter.std(dx[idx], lsigma=4, hsigma=4, iter=5))
print 'Mean dy = %7.3f +/- %7.3f (pix)' % \
(statsIter.mean(dy[idx], lsigma=4, hsigma=4, iter=5),
statsIter.std(dy[idx], lsigma=4, hsigma=4, iter=5))
print 'Mean dxerr = %7.3f +/- %7.3f (pix)' % \
(statsIter.mean(dxerr[idx], lsigma=4, hsigma=4, iter=5),
statsIter.std(dxerr[idx], lsigma=4, hsigma=4, iter=5))
print 'Mean dyerr = %7.3f +/- %7.3f (pix)' % \
(statsIter.mean(dyerr[idx], lsigma=4, hsigma=4, iter=5),
statsIter.std(dyerr[idx], lsigma=4, hsigma=4, iter=5))
def image_noise_properites():
print 'READ NOISE:'
hdr = pyfits.getheader(workDir + 'mag10maylgs_kp.fits')
itime = hdr['ITIME']
coadds = hdr['COADDS']
nsamp = hdr['MULTISAM']
gain = float(hdr['GAIN'])
readnoise = (16.0 / math.sqrt(nsamp)) * math.sqrt(coadds) # in DN
readnoise *= gain # convert to e-
print ' MCDS with %d Fowler samples and %d coadds' % (nsamp, coadds)
print ' noise = %.1f DN (%.1f e-)' % (readnoise/gain, readnoise)
print ''
# Figure out the dark current in a 2.8 sec x 10 coadd image.
darkImg = pyfits.getdata(workDir + 'dark_2.8s_10ca.fits')
darkImgFlat = darkImg.flatten()
darkCurrent = istats.mean(darkImg, lsigma=5, hsigma=5, iter=10)*gain
darkNoise = istats.std(darkImg, lsigma=5, hsigma=5, iter=10)*gain
print 'DARK:'
print ' Dark Current = %.1f DN (%.1f e-)' % \
(darkCurrent/gain, darkCurrent)
print ' Dark Noise = %.1f DN (%.1f e-)' % \
(darkNoise/gain, darkNoise)
print ' !! Dark noise does not match expected readnoise.'
print ''
py.clf()
bins = np.arange(-100, 100, 1)
fit = stats.norm.pdf(bins, loc=darkCurrent/gain, scale=darkNoise/gain)
loCut = (darkCurrent - 3.0 * darkNoise) / gain
hiCut = (darkCurrent + 3.0 * darkNoise) / gain
idx = np.where((darkImgFlat > loCut) & (darkImgFlat < hiCut))[0]
fit *= len(idx)
py.hist(darkImgFlat, bins=bins, histtype='step', label='Observed')
py.plot(bins, fit, 'k-', linewidth=2, label='Gaussian with Mean/STD')
py.xlabel('Counts from Dark Image (DN)')
py.ylabel('Number of Pixels')
py.legend()
py.title('Mean Current = %.1f DN, Stddev = %.1f DN' % \
(darkCurrent/gain, darkNoise/gain))
# Reset so that readnoise = darkNoise
# print 'SETTING READ NOISE = DARK NOISE'
# readnoise = darkNoise
# Sky background flux
skyImg = pyfits.getdata(workDir + 'mag10maylgs_sky_kp.fits')
skyCount = 10.0
skyLevel = istats.mean(skyImg, lsigma=5, hsigma=5, iter=5) * gain
skyStd = istats.std(skyImg, lsigma=5, hsigma=5, iter=5) * gain
skyNoise = math.sqrt((skyLevel + readnoise**2) / skyCount)
print 'SKY:'
print ' Sky Level = %.1f DN (%.1f e-)' % (skyLevel/gain, skyLevel)
print ' Sky Stddev = %.1f DN (%.1f e-)' % (skyStd/gain, skyStd)
print ' Assuming %d sky exposures' % skyCount
print ' Sky Noise = %.1f DN (%.1f e-)' % (skyNoise/gain, skyNoise)
print ''
# GC background flux
backLevelDC = 90.0 * gain # Subtracted DC offset
backNoiseDC = math.sqrt(backLevelDC)
backImg = pyfits.getdata(workDir + 'mag10maylgs_kp_back.fits')
backLevel = istats.mean(backImg, lsigma=5, hsigma=5, iter=5) * gain
backNoise = math.sqrt(backLevel)
backStd = istats.std(backImg, lsigma=5, hsigma=5, iter=5) * gain
print 'GC BACKGROUND:'
print ' GC Background subtracted off is 90 DN (360 e-)'
print ' Background Level = %.1f DN (%.1f e-)' % \
(backLevel/gain, backLevel)
print ' Background Stddev = %.1f DN (%.1f e-)' % \
(backStd/gain, backStd)
print ' Background Photon Noise = %.1f DN (%.1f e-)' % \
(backNoise/gain, backNoise)
print ' Actual Photon Noise = %.1f DN (%.1f e-)' % \
(backNoiseDC/gain, backNoiseDC)
print ''
# Final Noise Estimate
skyFact = 1.0 + (1.0 / skyCount)
gcCount = 150
gcNoise = skyFact * (skyLevel + readnoise**2)
gcNoise += backLevelDC
gcNoise /= gcCount
gcNoise = math.sqrt(gcNoise)
print 'FINAL GC NOISE (excluding photon noise from stars):'
print ' %.1f DN (%.1f e-)' % (gcNoise/gain, gcNoise)
print ''
gcNoise = math.sqrt((skyNoise**2 + readnoise**2) / gcCount)
print 'GC NOISE added to sky/DC/star photon noise (from sky subtraction and readnoise):'
print ' %.1f DN (%.1f e-)' % (gcNoise/gain, gcNoise)
print ''
return skyLevel, backLevelDC, gcNoise, gain
def plot_final_synthesis():
data = atpy.Table(workDir + 'gal1_master.txt', type='ascii')
img = pyfits.getdata(workDir + 'gal1_img.fits')
imgBkg = statsIter.mean(img.flatten(), hsigma=5, iter=1, verbose=True)
imgStd = statsIter.std(img.flatten(), hsigma=5, iter=1, verbose=True)
print 'Image mean = %5.1f std = %5.1f' % (imgBkg, imgStd)
fluxBins = np.arange(0, 500, 8)
velBins = np.arange(-500, 801, 50)
n_bins = np.arange(10, 60, 2)
npix, fluxEdges = np.histogram(img.flatten(), bins=fluxBins)
nstars, binEdges = np.histogram(data.vel, bins=velBins)
n, b = np.histogram(nstars, bins=n_bins)
bkg = np.mean(nstars)
noise = np.std(nstars)
print 'First Iteration: mean = %5.1f, std = %5.1f' % (bkg, noise)
# Do one round of rejection
idx = np.where(nstars < (bkg + (3.0 * noise)))[0]
bkg = np.mean(nstars[idx])
noise = np.std(nstars[idx])
print 'Second Iteration: mean = %5.1f, std = %5.1f' % (bkg, noise)
gaussianCDF = scipy.stats.norm.cdf(b, loc=bkg, scale=noise)
gaussianPDF = np.diff(gaussianCDF) * n.sum()
gaussianBinCenters = n_bins[0:-1] + np.diff(n_bins) / 2.0
fluxBinsModel = np.arange(0, 500, 1)
fluxNormCDF = scipy.stats.norm.cdf(fluxBinsModel, loc=imgBkg, scale=imgStd)
fluxNormPDF = np.diff(fluxNormCDF)
fluxNormPDF *= npix.max() / fluxNormPDF.max()
fluxBinCenters = fluxBinsModel[0:-1] + np.diff(fluxBinsModel) / 2.0
# Images
py.figure(1)
py.clf()
py.subplots_adjust(left=0.22)
py.subplot(2, 1, 1)
py.hist(img.flatten(), histtype='step', bins=fluxBins)
py.plot(fluxBinCenters, fluxNormPDF, 'g-')
py.title('Field #1')
py.xlim(0, 200)
py.subplot(2, 1, 2)
py.hist(img.flatten(), histtype='step', bins=fluxBins)
py.plot(fluxBinCenters, fluxNormPDF, 'g-')
py.ylim(0, 1000)
py.xlim(0, 200)
py.xlabel('Flux')
py.savefig(workDir + 'final_syn_gal1_flux_hist.png')
py.figure(1)
py.subplots_adjust(left=0.15)
py.clf()
py.hist(data.vel, bins=velBins, histtype='step')
py.xlim(-500, 800)
py.xlabel('Velocity (km/s)')
py.ylabel('Number of Stars')
py.savefig(workDir + 'final_syn_gal1_vel_hist_no_lines.png')
py.axhline(bkg, linestyle='-', linewidth=3, color='black')
py.axhline(bkg+noise, linestyle='--', linewidth=3, color='black')
py.axhline(bkg-noise, linestyle='--', linewidth=3, color='black')
py.savefig(workDir + 'final_syn_gal1_vel_hist.png')
py.figure(2)
py.clf()
py.subplots_adjust(left=0.15)
(n, b, p) = py.hist(nstars, histtype='step', bins=n_bins, label='Data')
py.ylim(0, n.max()*1.3)
py.xlim(b.min(), b.max())
py.xlabel('Number of Stars per Velocity Bin')
py.ylabel('Number of Velocity Bins')
print gaussianPDF.max(), n.max()
py.plot(gaussianBinCenters, gaussianPDF, label='Gaussian Fit')
axLim = py.axis()
py.axvline(bkg, linestyle='-', color='black', linewidth=3)
py.arrow(bkg+1*noise, axLim[3], 0, -0.5,
color='black', linewidth=3, head_width=1, head_length=0.1)
py.arrow(bkg+2*noise, axLim[3], 0, -0.5,
color='black', linewidth=3, head_width=1, head_length=0.1)
py.arrow(bkg+3*noise, axLim[3], 0, -0.5,
color='black', linewidth=3, head_width=1, head_length=0.1)
py.text(bkg+1*noise, axLim[3]+0.15, r'$1\sigma$',
horizontalalignment='center', verticalalignment='bottom')
py.text(bkg+2*noise, axLim[3]+0.15, r'$2\sigma$',
horizontalalignment='center', verticalalignment='bottom')
py.text(bkg+3*noise, axLim[3]+0.15, r'$3\sigma$',
horizontalalignment='center', verticalalignment='bottom')
py.savefig(workDir + 'final_syn_gal1_vel_hist_hist.png')
def plot_spie_figure3():
"""
Pass in a starlist (stack of starlists) and calculate the
astrometric and photometric errors per star. Then plot them.
"""
align_dir = '/g/lu/data/gsaoi/commission/reduce/ngc1851/align_2013_03_04_G2/'
plot_dir = '/u/jlu/doc/papers/proceed_2014_spie/'
from jlu.gsaoi import stars as stars_obj
stars2 = stars_obj.Starlist(align_dir + 'align_a2')
stars3 = stars_obj.Starlist(align_dir + 'align_a3')
stars4 = stars_obj.Starlist(align_dir + 'align_a4')
stars_all = [stars2, stars3, stars4]
py.close('all')
f, ax_all = py.subplots(1, 3, sharex=True, sharey=True, figsize=(12, 4))
f.subplots_adjust(left=0.10, bottom=0.18, wspace=0.01)
for ii in range(len(stars_all)):
stars = stars_all[ii]
ax = ax_all[ii]
Nstars = stars.dxm.shape[1]
Nimages = stars.dxm.shape[0]
mavg = stars.mm.mean(axis=0)
# Error on the mean
xerr = stars.dxm.std(axis=0) / math.sqrt(Nimages)
yerr = stars.dym.std(axis=0) / math.sqrt(Nimages)
merr = stars.mm.std(axis=0) / math.sqrt(Nimages)
# Convert to mas
xerr *= 1e3
yerr *= 1e3
rerr = np.hypot(xerr, yerr)
xerr_med = np.median(xerr)
yerr_med = np.median(yerr)
err_med = np.median([xerr_med, yerr_med])
rerr_med = np.median(np.hypot(xerr, yerr))
# Make a curve of the median error
magBinCent = np.arange(10, 13.5, 0.05)
magBinSize = 0.5
rerrMed = np.zeros(len(magBinCent), dtype=float)
rerrAvg = np.zeros(len(magBinCent), dtype=float)
rerrStd = np.zeros(len(magBinCent), dtype=float)
for ii in range(len(magBinCent)):
magLo = magBinCent[ii] - (magBinSize / 2.0)
magHi = magBinCent[ii] + (magBinSize / 2.0)
idx = np.where((mavg > magLo) & (mavg <= magHi))[0]
rerrAvg[ii] = statsIter.mean(rerr[idx],
hsigma=2,
lsigma=5,
iter=10)
rerrStd[ii] = statsIter.std(rerr[idx], hsigma=2, lsigma=5, iter=10)
rerrMed[ii] = np.median(rerr[idx])
# Smooth the average curve
n_window = 20
window = np.hanning(n_window)
s = np.r_[rerrAvg[n_window - 1:0:-1], rerrAvg,
rerrAvg[-1:-n_window:-1]]
rerrAvg_smooth = np.convolve(window / window.sum(), s, mode='valid')
rerrAvg_smooth = rerrAvg_smooth[(n_window / 2) - 1:-(n_window / 2)]
ax.plot(mavg, rerr, 'k.', alpha=0.4)
#ax.plot(magBinCent, rerrMed, 'k-')
#ax.plot(magBinCent, rerrAvg, 'r-', linewidth=2)
ax.plot(magBinCent, rerrAvg_smooth, 'r-', linewidth=2)
# ax.plot(magBinCent, rerrAvg+rerrStd, 'r--')
# ax.plot(magBinCent, rerrAvg-rerrStd, 'r--')
ax.set_xlabel('K Magnitude')
ax.set_ylim(0, 1)
ax.set_xlim(9.1, 14)
ax_all[0].set_ylabel('Astrometric Error (mas)')
py.savefig(plot_dir + 'gems_ngc1851_pos_err.png')
def image_noise_properites():
print 'READ NOISE:'
hdr = pyfits.getheader(workDir + 'mag10maylgs_kp.fits')
itime = hdr['ITIME']
coadds = hdr['COADDS']
nsamp = hdr['MULTISAM']
gain = float(hdr['GAIN'])
readnoise = (16.0 / math.sqrt(nsamp)) * math.sqrt(coadds) # in DN
readnoise *= gain # convert to e-
print ' MCDS with %d Fowler samples and %d coadds' % (nsamp, coadds)
print ' noise = %.1f DN (%.1f e-)' % (readnoise / gain, readnoise)
print ''
# Figure out the dark current in a 2.8 sec x 10 coadd image.
darkImg = pyfits.getdata(workDir + 'dark_2.8s_10ca.fits')
darkImgFlat = darkImg.flatten()
darkCurrent = istats.mean(darkImg, lsigma=5, hsigma=5, iter=10) * gain
darkNoise = istats.std(darkImg, lsigma=5, hsigma=5, iter=10) * gain
print 'DARK:'
print ' Dark Current = %.1f DN (%.1f e-)' % \
(darkCurrent/gain, darkCurrent)
print ' Dark Noise = %.1f DN (%.1f e-)' % \
(darkNoise/gain, darkNoise)
print ' !! Dark noise does not match expected readnoise.'
print ''
py.clf()
bins = np.arange(-100, 100, 1)
fit = stats.norm.pdf(bins, loc=darkCurrent / gain, scale=darkNoise / gain)
loCut = (darkCurrent - 3.0 * darkNoise) / gain
hiCut = (darkCurrent + 3.0 * darkNoise) / gain
idx = np.where((darkImgFlat > loCut) & (darkImgFlat < hiCut))[0]
fit *= len(idx)
py.hist(darkImgFlat, bins=bins, histtype='step', label='Observed')
py.plot(bins, fit, 'k-', linewidth=2, label='Gaussian with Mean/STD')
py.xlabel('Counts from Dark Image (DN)')
py.ylabel('Number of Pixels')
py.legend()
py.title('Mean Current = %.1f DN, Stddev = %.1f DN' % \
(darkCurrent/gain, darkNoise/gain))
# Reset so that readnoise = darkNoise
# print 'SETTING READ NOISE = DARK NOISE'
# readnoise = darkNoise
# Sky background flux
skyImg = pyfits.getdata(workDir + 'mag10maylgs_sky_kp.fits')
skyCount = 10.0
skyLevel = istats.mean(skyImg, lsigma=5, hsigma=5, iter=5) * gain
skyStd = istats.std(skyImg, lsigma=5, hsigma=5, iter=5) * gain
skyNoise = math.sqrt((skyLevel + readnoise**2) / skyCount)
print 'SKY:'
print ' Sky Level = %.1f DN (%.1f e-)' % (skyLevel / gain, skyLevel)
print ' Sky Stddev = %.1f DN (%.1f e-)' % (skyStd / gain, skyStd)
print ' Assuming %d sky exposures' % skyCount
print ' Sky Noise = %.1f DN (%.1f e-)' % (skyNoise / gain, skyNoise)
print ''
# GC background flux
backLevelDC = 90.0 * gain # Subtracted DC offset
backNoiseDC = math.sqrt(backLevelDC)
backImg = pyfits.getdata(workDir + 'mag10maylgs_kp_back.fits')
backLevel = istats.mean(backImg, lsigma=5, hsigma=5, iter=5) * gain
backNoise = math.sqrt(backLevel)
backStd = istats.std(backImg, lsigma=5, hsigma=5, iter=5) * gain
print 'GC BACKGROUND:'
print ' GC Background subtracted off is 90 DN (360 e-)'
print ' Background Level = %.1f DN (%.1f e-)' % \
(backLevel/gain, backLevel)
print ' Background Stddev = %.1f DN (%.1f e-)' % \
(backStd/gain, backStd)
print ' Background Photon Noise = %.1f DN (%.1f e-)' % \
(backNoise/gain, backNoise)
print ' Actual Photon Noise = %.1f DN (%.1f e-)' % \
(backNoiseDC/gain, backNoiseDC)
print ''
# Final Noise Estimate
skyFact = 1.0 + (1.0 / skyCount)
gcCount = 150
gcNoise = skyFact * (skyLevel + readnoise**2)
gcNoise += backLevelDC
gcNoise /= gcCount
gcNoise = math.sqrt(gcNoise)
print 'FINAL GC NOISE (excluding photon noise from stars):'
print ' %.1f DN (%.1f e-)' % (gcNoise / gain, gcNoise)
print ''
gcNoise = math.sqrt((skyNoise**2 + readnoise**2) / gcCount)
print 'GC NOISE added to sky/DC/star photon noise (from sky subtraction and readnoise):'
print ' %.1f DN (%.1f e-)' % (gcNoise / gain, gcNoise)
print ''
return skyLevel, backLevelDC, gcNoise, gain
def plot_vector_diff(undersample):
"""
Make a vector plot of the differences between the input positions
and the output positions after running starfinder.
"""
# Dimensions of new grid
npsf_side = get_npsf_side(undersample)
suffix = '%d_%d' % (npsf_side, npsf_side)
s = starset.StarSet('grid_' + suffix + '/align_'+suffix)
cnt = s.getArray('velCnt')
mag = s.getArray('mag')
idx = np.where((cnt == 2) & (mag < 15))[0]
print 'Using {0} stars out of {1}'.format(len(idx), len(cnt))
newStars = [s.stars[ii] for ii in idx]
s.stars = newStars
x0 = s.getArrayFromEpoch(0, 'xorig')
y0 = s.getArrayFromEpoch(0, 'yorig')
x1 = s.getArrayFromEpoch(1, 'xorig')
y1 = s.getArrayFromEpoch(1, 'yorig')
dx = x1 - x0
dy = y1 - y0
dr = np.hypot(dx, dy)
# Boundaries
t = atpy.Table('svpar_' + suffix + '.txt', type='ascii')
lx = t.col1
ux = t.col2
ly = t.col3
uy = t.col4
xedges = np.unique( np.append(lx, ux) )
yedges = np.unique( np.append(ly, uy) )
py.clf()
q = py.quiver(x0, y0, dx, dy, scale=0.2)
py.xlim(0, 1200)
py.ylim(0, 1200)
py.quiverkey(q, 0.5, 0.97, 0.02, '0.02 pixel (0.2 mas)', color='red', coordinates='axes')
for xx in xedges:
py.axvline(xx, linestyle='--')
for yy in yedges:
py.axhline(yy, linestyle='--')
py.savefig('plots/vec_diff_'+suffix+'.png')
xmean = statsIter.mean(dx, hsigma=5, lsigma=5, iter=5)
ymean = statsIter.mean(dy, hsigma=5, lsigma=5, iter=5)
rmean = statsIter.mean(dr, hsigma=5, lsigma=5, iter=5)
xstd = statsIter.std(dx, hsigma=5, lsigma=5, iter=5)
ystd = statsIter.std(dy, hsigma=5, lsigma=5, iter=5)
rstd = statsIter.std(dr, hsigma=5, lsigma=5, iter=5)
print('Mean Delta-X: {0:9.5f} +/- {1:9.5f} pixels'.format(xmean, xstd))
print('Mean Delta-Y: {0:9.5f} +/- {1:9.5f} pixels'.format(ymean, ystd))
print('Mean Delta-R: {0:9.5f} +/- {1:9.5f} pixels'.format(rmean, rstd))
f_name = 'diff_stats_' + suffix + '.txt'
f_stat = open(f_name, 'w')
hdr = '#{0:>5s} {1:>10s} {2:>10s} {3:>10s} {4:>10s} {5:>10s} {6:>10s}\n'
fmt = '{0:6d} {1:10.5f} {2:10.5f} {3:10.5f} {4:10.5f} {5:10.5f} {6:10.5f}\n'
f_stat.write(hdr.format('Npsf', 'Xmean', 'Ymean', 'Rmean', 'Xstd', 'Ystd', 'Rstd'))
f_stat.write(fmt.format(npsf_side, xmean, ymean, rmean, xstd, ystd, rstd))
f_stat.close()
return f_name
