def plot_patch_zpoffset(patchdat, maglim=None, etitle="", newfig=True, z_method=n.mean):
if 'x' not in list(patchdat.keys()):
patchdat['x'], patchdat['y'] = cpu.hammer_project_toxy(patchdat['ra']*deg2rad,
patchdat['dec']*deg2rad)
gridsize = cpu.calc_gridsize(patchdat, binsize='patch')
cpu.plot_density(patchdat['x'], patchdat['y'], patchdat['dmag'], z_method=z_method,
zlimits=maglim, gridsize=gridsize, radecgrid=True, newfig=newfig, cb_label='mag')
figtitle = "Zeropoint Offsets " + etitle
pyl.title(figtitle, fontsize='medium')
return
def plot_nstars_patch(patchdat, z_method=n.mean, zlimits=None, newfig=True):
"""Plot the n.min/mean/std number of stars ever seen in a particular patch."""
if 'x' not in list(patchdat.keys()):
patchdat['x'], patchdat['y'] = cpu.hammer_project_toxy(patchdat['ra']*deg2rad,
patchdat['dec']*deg2rad)
gridsize = cpu.calc_gridsize(patchdat, binsize='patch')
cpu.plot_density(patchdat['x'], patchdat['y'], z=patchdat['nstars'], z_method=z_method,
zlimits=zlimits, gridsize=gridsize, radecgrid=True, newfig=newfig)
pyl.title("Patch Minimum Nstars")
return
def plot_visits(visits, visitlim=None, etitle="", newfig=True):
"""Plot the location of the visits across the sky. Color code by number of visits."""
if 'x' not in list(visits.keys()):
visits['x'], visits['y'] = cpu.hammer_project_toxy(visits['ra']*deg2rad,
visits['dec']*deg2rad)
gridsize = cpu.calc_gridsize(visits,binsize='fov', rad_fov=1.8)
cpu.plot_density(visits['x'], visits['y'], gridsize=gridsize, zlimits=visitlim,
radecgrid=True, newfig=newfig)#, zscale='log')
pyl.title("Visit density " + etitle, fontsize='medium')
return
def plot_visits(visits, visitlim=None, etitle="", newfig=True):
"""Plot the location of the visits across the sky. Color code by number of visits."""
if 'x' not in list(visits.keys()):
visits['x'], visits['y'] = cpu.hammer_project_toxy(
visits['ra'] * deg2rad, visits['dec'] * deg2rad)
gridsize = cpu.calc_gridsize(visits, binsize='fov', rad_fov=1.8)
cpu.plot_density(visits['x'],
visits['y'],
gridsize=gridsize,
zlimits=visitlim,
radecgrid=True,
newfig=newfig) #, zscale='log')
pyl.title("Visit density " + etitle, fontsize='medium')
return
def plot_nstars_patch(patchdat, z_method=n.mean, zlimits=None, newfig=True):
"""Plot the n.min/mean/std number of stars ever seen in a particular patch."""
if 'x' not in list(patchdat.keys()):
patchdat['x'], patchdat['y'] = cpu.hammer_project_toxy(
patchdat['ra'] * deg2rad, patchdat['dec'] * deg2rad)
gridsize = cpu.calc_gridsize(patchdat, binsize='patch')
cpu.plot_density(patchdat['x'],
patchdat['y'],
z=patchdat['nstars'],
z_method=z_method,
zlimits=zlimits,
gridsize=gridsize,
radecgrid=True,
newfig=newfig)
pyl.title("Patch Minimum Nstars")
return
def plot_nvisits_patch(patchdat, subpatch=None, patchlim=None, newfig=True):
"""Plot the location of the (sub)patches across the sky. Color code by number of visits.
Also plot the number of stars observed in each patch. """
if 'x' not in list(patchdat.keys()):
patchdat['x'], patchdat['y'] = cpu.hammer_project_toxy(patchdat['ra']*deg2rad,
patchdat['dec']*deg2rad)
if subpatch != None:
condition = (patchdat['subpatch'] == subpatch)
x = patchdat['x'][condition]
y = patchdat['y'][condition]
else:
x = patchdat['x']
y = patchdat['y']
gridsize = cpu.calc_gridsize(patchdat, binsize='patch')
cpu.plot_density(x, y, gridsize=gridsize, zlimits=patchlim, radecgrid=True, newfig=newfig)
pyl.title("Patch Density", fontsize='medium')
return
def plot_patch_zpoffset(patchdat,
maglim=None,
etitle="",
newfig=True,
z_method=n.mean):
if 'x' not in list(patchdat.keys()):
patchdat['x'], patchdat['y'] = cpu.hammer_project_toxy(
patchdat['ra'] * deg2rad, patchdat['dec'] * deg2rad)
gridsize = cpu.calc_gridsize(patchdat, binsize='patch')
cpu.plot_density(patchdat['x'],
patchdat['y'],
patchdat['dmag'],
z_method=z_method,
zlimits=maglim,
gridsize=gridsize,
radecgrid=True,
newfig=newfig,
cb_label='mag')
figtitle = "Zeropoint Offsets " + etitle
pyl.title(figtitle, fontsize='medium')
return
def plot_nvisits_patch(patchdat, subpatch=None, patchlim=None, newfig=True):
"""Plot the location of the (sub)patches across the sky. Color code by number of visits.
Also plot the number of stars observed in each patch. """
if 'x' not in list(patchdat.keys()):
patchdat['x'], patchdat['y'] = cpu.hammer_project_toxy(
patchdat['ra'] * deg2rad, patchdat['dec'] * deg2rad)
if subpatch != None:
condition = (patchdat['subpatch'] == subpatch)
x = patchdat['x'][condition]
y = patchdat['y'][condition]
else:
x = patchdat['x']
y = patchdat['y']
gridsize = cpu.calc_gridsize(patchdat, binsize='patch')
cpu.plot_density(x,
y,
gridsize=gridsize,
zlimits=patchlim,
radecgrid=True,
newfig=newfig)
pyl.title("Patch Density", fontsize='medium')
return
def plot_patch_nstars(patchdat, maglim=None, etitle="", newfig=True,
patchcal=None, z_method=n.mean):
adjust_patchdat = False
if maglim != None:
maglim=n.array(maglim)
if 'dmagcal' not in list(patchdat.keys()):
if patchcal == None:
raise Exception("Must either pass in patchcal as well, or match_patch_calvstrue previously")
patchdat = match_calvstrue(patchdat, starcal)
adjust_patchdat = True
if 'magdiff' not in list(patchdat.keys()):
patchdat = adjust_calvstrue(patchdat)
adjust_patchdat = True
if 'x' not in list(patchdat.keys()):
patchdat['x'], patchdat['y'] = cpu.hammer_project_toxy(patchdat['ra']*deg2rad,
patchdat['dec']*deg2rad)
adjust_patchdat = True
gridsize = cpu.calc_gridsize(patchdat, binsize='patch')
cpu.plot_density(patchdat['x'], patchdat['y'], patchdat['nstars'], z_method=z_method,
zlimits=maglim, gridsize=gridsize, radecgrid=True, newfig=newfig, cb_label='')
figtitle = "N stars per patch" + etitle
pyl.title(figtitle, fontsize='medium')
return patchdat
def plot_stars_dmagcaltrue(stardat, maglim=None, etitle="", newfig=True,
starcal=None, z_method=n.mean):
adjust_stardat = False
if maglim !=0:
maglim=n.round(n.array(maglim)*1000, decimals=1)
if 'magcal' not in list(stardat.keys()):
if starcal == None:
raise Exception("Must either pass in starcal as well, or match_calvstrue previously")
stardat = match_calvstrue(stardat, starcal)
adjust_stardat = True
if 'magdiff' not in list(stardat.keys()):
stardat = adjust_calvstrue(stardat)
adjust_stardat = True
if 'x' not in list(stardat.keys()):
stardat['x'], stardat['y'] = cpu.hammer_project_toxy(stardat['ra']*deg2rad,
stardat['dec']*deg2rad)
adjust_stardat = True
gridsize = cpu.calc_gridsize(stardat, binsize='patch')
cpu.plot_density(stardat['x'], stardat['y'], stardat['magdiff']*1000, z_method=z_method,
zlimits=maglim, gridsize=gridsize, radecgrid=True, newfig=newfig, cb_label='mmag')
figtitle = "Stars dMag(true-bestfit) " + etitle
pyl.title(figtitle, fontsize='medium')
return stardat
def plot_stars_dmagcaltrue(stardat,
maglim=None,
etitle="",
newfig=True,
starcal=None,
z_method=n.mean):
adjust_stardat = False
if maglim != 0:
maglim = n.round(n.array(maglim) * 1000, decimals=1)
if 'magcal' not in list(stardat.keys()):
if starcal == None:
raise Exception(
"Must either pass in starcal as well, or match_calvstrue previously"
)
stardat = match_calvstrue(stardat, starcal)
adjust_stardat = True
if 'magdiff' not in list(stardat.keys()):
stardat = adjust_calvstrue(stardat)
adjust_stardat = True
if 'x' not in list(stardat.keys()):
stardat['x'], stardat['y'] = cpu.hammer_project_toxy(
stardat['ra'] * deg2rad, stardat['dec'] * deg2rad)
adjust_stardat = True
gridsize = cpu.calc_gridsize(stardat, binsize='patch')
cpu.plot_density(stardat['x'],
stardat['y'],
stardat['magdiff'] * 1000,
z_method=z_method,
zlimits=maglim,
gridsize=gridsize,
radecgrid=True,
newfig=newfig,
cb_label='mmag')
figtitle = "Stars dMag(true-bestfit) " + etitle
pyl.title(figtitle, fontsize='medium')
return stardat
def plot_patch_nstars(patchdat,
maglim=None,
etitle="",
newfig=True,
patchcal=None,
z_method=n.mean):
adjust_patchdat = False
if maglim != None:
maglim = n.array(maglim)
if 'dmagcal' not in list(patchdat.keys()):
if patchcal == None:
raise Exception(
"Must either pass in patchcal as well, or match_patch_calvstrue previously"
)
patchdat = match_calvstrue(patchdat, starcal)
adjust_patchdat = True
if 'magdiff' not in list(patchdat.keys()):
patchdat = adjust_calvstrue(patchdat)
adjust_patchdat = True
if 'x' not in list(patchdat.keys()):
patchdat['x'], patchdat['y'] = cpu.hammer_project_toxy(
patchdat['ra'] * deg2rad, patchdat['dec'] * deg2rad)
adjust_patchdat = True
gridsize = cpu.calc_gridsize(patchdat, binsize='patch')
cpu.plot_density(patchdat['x'],
patchdat['y'],
patchdat['nstars'],
z_method=z_method,
zlimits=maglim,
gridsize=gridsize,
radecgrid=True,
newfig=newfig,
cb_label='')
figtitle = "N stars per patch" + etitle
pyl.title(figtitle, fontsize='medium')
return patchdat
def starPlots(truestarfile = "stardata.dat", bestfitstarfile = "test_bestfit_Star.dat"):
#assume both the files are sorted by star id.
stardat = cp.read_true_stars(truestarfile) #keys are ('id', 'dbid', 'ra', 'dec', 'magtrue', 'color')
starcal = ui.readDatafile(bestfitstarfile, keys=('id','magcal','rms','iqr_sigma','nobs'))
HP = True
if np.size(starcal['id']) == 0:
starcal = ui.readDatafile(bestfitstarfile, keys=('id','magcal'))
HP = False
fitted_stars = np.in1d(stardat['id'], starcal['id'])
for key in stardat.keys():
stardat[key] = stardat[key][fitted_stars]
for key in starcal.keys():
stardat[key] = starcal[key]
stardat['magdiff'] = stardat['magcal'] - stardat['magtrue']
stardat = cp.adjust_stars_calvstrue(stardat)
starResults = {}
HPResults = {}
starResults['N stars Fit'] = np.size(stardat['magdiff'])
outlier_clip=np.where(np.abs(stardat['magdiff']) < .05)
rms = stardat['magdiff'][outlier_clip].std()
print 'true-bestfit RMS = %f, true-bestfit (clipped) = %f'%(stardat['magdiff'].std(), rms)
lim = rms*2.5
if lim < 0.001:
lim = round(lim * 10000) / 10000.0
elif lim < 0.01:
lim = round(lim * 1000) / 1000.0
print "Using magnitude limits for plots of %f, %f" %(-lim, lim)
maglim= [-lim, lim]
starResults['Residuals RMS (mmag)'] = stardat['magdiff'].std()*1e3
starResults['IQR_sigma (mmag)'] = so.robustSigma(stardat['magdiff'])*1e3
#star residual map
stardat['x'], stardat['y'] = cpu.hammer_project_toxy(stardat['ra']*deg2rad,
stardat['dec']*deg2rad)
gridsize = cpu.calc_gridsize(stardat, binsize='patch')
etitle = " RMS = %4.1f mmag"%(rms*1000)
cpu.plot_density(stardat['x'], stardat['y'], stardat['magdiff']*1000, z_method=np.mean,
zlimits=np.array(maglim)*1000, gridsize=gridsize, radecgrid=True, newfig=True, cb_label='mmag')
figtitle = "Stars dMag(true-bestfit)"
pyl.title(figtitle, fontsize='medium')
pyl.savefig('Sdmag.png',type='png')
maglim2 = [0, maglim[1]/2.]
cp.plot_stars_dmagcaltrue(stardat, maglim=maglim2, etitle=" RMS "+etitle, z_method=np.std)
pyl.savefig('Sdmag_rms.png', format='png')
histrange = [maglim[0]*2.5, maglim[1]*2.5]
nbins = 100
cp.histogram_stars_dmagcaltrue(stardat, nbins=nbins, range=histrange, etitle=etitle)
pyl.savefig('Sdamg_hist.png', format='png')
#color distribution of stars
pyl.figure()
pyl.hist(stardat['color'], bins=100)
pyl.xlabel(r'$g-i$')
pyl.ylabel('# of stars')
pyl.title('Color Distribution')
pyl.savefig('Scolordist.png',type='png')
#accuracy v color
pyl.figure()
#pyl.scatter(stardat['color'], stardat['magdiff']*1000., c=stardat['magtrue'], alpha=0.1,edgecolors=None)
pyl.hexbin(stardat['color'], stardat['magdiff']*1000., bins='log')
cb=pyl.colorbar()
cb.ax.set_ylabel(r'$\log{\rm{N}}$')
pyl.xlabel(r'$g-i$')
pyl.ylabel('Bestfit-True (mmag)')
pyl.savefig('Saccuracyvcolor.png',type='png')
#accuracy v mag
pyl.figure()
#pyl.scatter(stardat['magtrue'], stardat['magdiff']*1000., c=stardat['color'], alpha=0.1,edgecolors=None)
pyl.hexbin(stardat['magtrue'], stardat['magdiff']*1000., bins='log')
cb=pyl.colorbar()
cb.ax.set_ylabel(r'$\log{\rm{N}}$')
pyl.xlabel('True Mag')
pyl.ylabel('Bestfit-True (mmag)')
pyl.savefig('Saccuracyvmag.png',type='png')
if HP:
zlim = [np.min(stardat['nobs']), (np.median(stardat['nobs'])-np.min(stardat['nobs']))+np.median(stardat['nobs'])]
cpu.plot_density(stardat['x'], stardat['y'],stardat['nobs'], z_method=np.mean,
zlimits=zlim, gridsize=gridsize, radecgrid=True, newfig=True, cb_label='N observations')
pyl.title('Visit Density')
pyl.savefig('Snobs.png', format='png')
pyl.figure()
#I should just figure out from the data what nsides is!
hpid = hp.ang2pix(16, (stardat['dec']+90.)*deg2rad, stardat['ra']*deg2rad)
cpu.plot_density(stardat['x'], stardat['y'],np.mod(hpid,250), z_method=np.median,
gridsize=gridsize, radecgrid=True, newfig=True, cb_label='HEALpix ID mod 250')
pyl.title("HEALpix Map")
pyl.savefig('HPid.png')
HPResults['number of HEALpix'] = np.size(np.unique(hpid))
pyl.figure()
num, b, p = pyl.hist(stardat['nobs'], bins=100, range=zlim, histtype='bar')
pyl.xlabel("Number of Observations per Star")
pyl.title('Median = %d'%np.median(stardat['nobs']))
pyl.savefig('Snobs_hist.png',type='png')
starResults['median repeat obs'] = np.median(stardat['nobs'])
pyl.figure()
x = np.sort(stardat['iqr_sigma'])*1e3
y = np.arange(np.size(x), dtype='float')
y = y/np.max(y)
per50 = np.max(np.where(y <= .5))
per90 = np.max(np.where(y <= .9))
pyl.plot(x,y, 'k--', label='IQR, 50th,90th %2.1f, %2.1f'%(x[per50],x[per90]))
pyl.plot([0,x[per50]],[y[per50],y[per50]],'b--')
pyl.plot([x[per50],x[per50]],[0,y[per50]],'b--')
pyl.plot([0,x[per90]],[y[per90],y[per90]],'b--')
pyl.plot([x[per90],x[per90]],[0,y[per90]],'b--')
#pyl.title('Cumulative Distribution of Stellar Repeatability')
#pyl.xlabel('Repeatability IQR RMS (mmag)')
#pyl.ylabel('Cumulative Fraction')
#pyl.savefig('Srepeat_IQR_cumulative.png',type='png')
pyl.legend()
pyl.savefig('Srepeat_cumulative.png',type='png')
#repeatability from IQR
rs = so.robustSigma(stardat['iqr_sigma'])
med = np.median(stardat['iqr_sigma'])
maglim = np.around(np.array([med-3*rs, med+3.*rs])*1000, decimals=2)/1000
cpu.plot_density(stardat['x'], stardat['y'], stardat['iqr_sigma']*1000, z_method=np.mean,
zlimits=maglim*1000, gridsize=gridsize, radecgrid=True, newfig=True, cb_label='mmag')
pyl.title('Repeatability (IQR)')
pyl.savefig('Srepeat_IQR.png',type='png')
pyl.figure()
num, b, p = pyl.hist(stardat['iqr_sigma']*1000, bins=100, range=maglim*1000, histtype='bar')
pyl.xlabel('RMS per star (mmag)')
pyl.title('Repeatability IQR, Median = %4.2f mmag'%np.median(stardat['iqr_sigma']*1000))
pyl.savefig('Srepeat_IQR_hist.png',type='png')
starResults['Median Repeatability (IQR) (mmag)'] = np.median(med)*1e3
pyl.figure()
good = np.where(stardat['magtrue'] < 18)
num, b, p = pyl.hist(stardat['iqr_sigma'][good]*1000, bins=100, range=maglim*1000, histtype='bar')
pyl.xlabel('RMS per star, m < 18 (mmag)')
pyl.title('Repeatability IQR, Median = %4.2f mmag'%np.median(stardat['iqr_sigma'][good]*1000))
pyl.savefig('Srepeat_IQR_bright_hist.png',type='png')
starResults['Median Repeatability Bright (IQR) (mmag)'] = np.median(stardat['iqr_sigma'][good])*1e3
#repeatability v color
pyl.figure()
#pyl.plot(stardat['color'], stardat['rms']*1000., 'ko', alpha=.1)
pyl.hexbin(stardat['color'], stardat['iqr_sigma']*1000., bins='log')
cb = pyl.colorbar()
cb.set_label(r'$\log{\rm{N}}$')
pyl.ylim([0,40])
pyl.xlabel(r'$g-i$')
pyl.ylabel(r'Repeatability $\sigma_{\rm{IQR}}$ (mmag)')
pyl.savefig('Srepeatvcolor.png',type='png')
#repeatability v mag
pyl.figure()
#pyl.plot(stardat['magtrue'], stardat['rms']*1000., 'ko', alpha=.1)
pyl.hexbin(stardat['magtrue'], stardat['iqr_sigma']*1000.,bins='log')
cb = pyl.colorbar()
cb.set_label(r'$\log{\rm{N}}$')
pyl.ylim([0,40])
pyl.xlabel('True Mag')
pyl.ylabel(r'Repeatability $\sigma_{\rm{IQR}}$ (mmag)')
pyl.savefig('Srepeatvmag.png',type='png')
#repeatability
rs = so.robustSigma(stardat['rms'])
med = np.median(stardat['rms'])
maglim = np.around(np.array([med-3*rs, med+3.*rs])*1000, decimals=2)/1000
cpu.plot_density(stardat['x'], stardat['y'], stardat['rms']*1000, z_method=np.mean,
zlimits=maglim*1000, gridsize=gridsize, radecgrid=True, newfig=True, cb_label='mmag')
pyl.title('Repeatability')
pyl.savefig('Srepeat.png',type='png')
pyl.figure()
num, b, p = pyl.hist(stardat['rms']*1000, bins=100, range=maglim*1000, histtype='bar')
pyl.xlabel('RMS per star (mmag)')
pyl.title('Repeatability, Median = %4.2f mmag'%np.median(stardat['rms']*1000))
pyl.savefig('Srepeat_hist.png',type='png')
starResults['Median Repeatability (mmag)'] = np.median(med)*1e3
pyl.figure()
x = np.sort(stardat['rms'])*1e3
y = np.arange(np.size(x), dtype='float')
y = y/np.max(y)
per50 = np.max(np.where(y <= .5))
per90 = np.max(np.where(y <= .9))
pyl.plot(x,y, 'k', label='RMS, %2.1f, %2.1f'%(x[per50],x[per90]))
pyl.plot([0,x[per50]],[y[per50],y[per50]],'k')
pyl.plot([x[per50],x[per50]],[0,y[per50]],'k')
pyl.plot([0,x[per90]],[y[per90],y[per90]],'k')
pyl.plot([x[per90],x[per90]],[0,y[per90]],'k')
pyl.title('Cumulative Distribution of Stellar Repeatability')
pyl.xlabel('Repeatability RMS (mmag)')
pyl.ylabel('Cumulative Fraction')
pyl.savefig('Srepeat_cumulative.png',type='png')
#need to make a spatial uniformity plot since we can now have varying densities of stars.
nside = 64
stardat['hpid'] = hp.ang2pix(nside, (stardat['dec']+90.)*deg2rad, stardat['ra']*deg2rad)
uhpid = np.unique(stardat['hpid'])
hp_mean = uhpid*0.
hp_std = uhpid*0.
hp_dec, hp_ra = hp.pix2ang(nside,uhpid)
#hp_ra = hp_ra
hp_dec = hp_dec-90.*deg2rad
hp_x, hp_y = cpu.hammer_project_toxy(hp_ra, hp_dec)
stardat = dictsort(stardat,'hpid')
left = np.searchsorted(stardat['hpid'], uhpid)
right = np.searchsorted(stardat['hpid'], uhpid,side='right')
for i in np.arange(left.size):
hp_mean[i] = np.mean(stardat['magdiff'][left[i]:right[i]])
hp_std[i] = np.std(stardat['magdiff'][left[i]:right[i]])
cpu.plot_density(hp_x, hp_y, hp_mean*1000, z_method=np.mean,
zlimits=None, gridsize=gridsize, radecgrid=True, newfig=True, cb_label='mmag')
HPResults['Spatial Uniformity RMS (mmag)'] = np.std(hp_mean)*1e3
HPResults['Spatial Uniformity IQR RMS (mmag)'] = so.robustSigma(hp_mean)*1e3
pyl.title('mean(True-Bestfit), binned by HEALpix')
pyl.savefig('HPresid.png',type='png')
cpu.plot_density(hp_x, hp_y, hp_std*1000, z_method=np.mean,
zlimits=None, gridsize=gridsize, radecgrid=True, newfig=True, cb_label='mmag')
pyl.title('std(True-Bestfit), binned by HEALpix')
pyl.savefig('HPstd.png',type='png')
pyl.figure()
num, b, p = pyl.hist( hp_mean*1000, bins=100, range=None, histtype='bar')
pyl.xlabel("HEALpix(True-Bestfit) (mmag)")
pyl.title('Spatial Uniformity, RMS=%4.2f'%HPResults['Spatial Uniformity RMS (mmag)'])
pyl.savefig('HPmean_hist.png',type='png')
pyl.figure()
num, b, p = pyl.hist( hp_std*1000, bins=100, range=None, histtype='bar')
pyl.xlabel("std(True-Bestfit) per HEALpix (mmag)")
pyl.title('Variation within HEALpix, median=%4.2f mmag'%np.median(hp_std*1000))
pyl.savefig('HPstd_hist.png',type='png')
return starResults,HPResults
def starPlots(truestarfile="stardata.dat",
bestfitstarfile="test_bestfit_Star.dat"):
#assume both the files are sorted by star id.
stardat = cp.read_true_stars(
truestarfile
) #keys are ('id', 'dbid', 'ra', 'dec', 'magtrue', 'color')
starcal = ui.readDatafile(bestfitstarfile,
keys=('id', 'magcal', 'rms', 'iqr_sigma',
'nobs'))
HP = True
if np.size(starcal['id']) == 0:
starcal = ui.readDatafile(bestfitstarfile, keys=('id', 'magcal'))
HP = False
fitted_stars = np.in1d(stardat['id'], starcal['id'])
for key in list(stardat.keys()):
stardat[key] = stardat[key][fitted_stars]
for key in list(starcal.keys()):
stardat[key] = starcal[key]
stardat['magdiff'] = stardat['magcal'] - stardat['magtrue']
stardat = cp.adjust_stars_calvstrue(stardat)
starResults = {}
HPResults = {}
starResults['N stars Fit'] = np.size(stardat['magdiff'])
outlier_clip = np.where(np.abs(stardat['magdiff']) < .05)
rms = stardat['magdiff'][outlier_clip].std()
print('true-bestfit RMS = %f, true-bestfit (clipped) = %f' %
(stardat['magdiff'].std(), rms))
lim = rms * 2.5
if lim < 0.001:
lim = round(lim * 10000) / 10000.0
elif lim < 0.01:
lim = round(lim * 1000) / 1000.0
print("Using magnitude limits for plots of %f, %f" % (-lim, lim))
maglim = [-lim, lim]
starResults['Residuals RMS (mmag)'] = stardat['magdiff'].std() * 1e3
starResults['IQR_sigma (mmag)'] = so.robustSigma(stardat['magdiff']) * 1e3
#star residual map
stardat['x'], stardat['y'] = cpu.hammer_project_toxy(
stardat['ra'] * deg2rad, stardat['dec'] * deg2rad)
gridsize = cpu.calc_gridsize(stardat, binsize='patch')
etitle = " RMS = %4.1f mmag" % (rms * 1000)
cpu.plot_density(stardat['x'],
stardat['y'],
stardat['magdiff'] * 1000,
z_method=np.mean,
zlimits=np.array(maglim) * 1000,
gridsize=gridsize,
radecgrid=True,
newfig=True,
cb_label='mmag')
figtitle = "Stars dMag(true-bestfit)"
pyl.title(figtitle, fontsize='medium')
pyl.savefig('Sdmag.png', type='png')
maglim2 = [0, maglim[1] / 2.]
cp.plot_stars_dmagcaltrue(stardat,
maglim=maglim2,
etitle=" RMS " + etitle,
z_method=np.std)
pyl.savefig('Sdmag_rms.png', format='png')
histrange = [maglim[0] * 2.5, maglim[1] * 2.5]
nbins = 100
cp.histogram_stars_dmagcaltrue(stardat,
nbins=nbins,
range=histrange,
etitle=etitle)
pyl.savefig('Sdamg_hist.png', format='png')
#color distribution of stars
pyl.figure()
pyl.hist(stardat['color'], bins=100)
pyl.xlabel(r'$g-i$')
pyl.ylabel('# of stars')
pyl.title('Color Distribution')
pyl.savefig('Scolordist.png', type='png')
#accuracy v color
pyl.figure()
#pyl.scatter(stardat['color'], stardat['magdiff']*1000., c=stardat['magtrue'], alpha=0.1,edgecolors=None)
pyl.hexbin(stardat['color'], stardat['magdiff'] * 1000., bins='log')
cb = pyl.colorbar()
cb.ax.set_ylabel(r'$\log{\rm{N}}$')
pyl.xlabel(r'$g-i$')
pyl.ylabel('Bestfit-True (mmag)')
pyl.savefig('Saccuracyvcolor.png', type='png')
#accuracy v mag
pyl.figure()
#pyl.scatter(stardat['magtrue'], stardat['magdiff']*1000., c=stardat['color'], alpha=0.1,edgecolors=None)
pyl.hexbin(stardat['magtrue'], stardat['magdiff'] * 1000., bins='log')
cb = pyl.colorbar()
cb.ax.set_ylabel(r'$\log{\rm{N}}$')
pyl.xlabel('True Mag')
pyl.ylabel('Bestfit-True (mmag)')
pyl.savefig('Saccuracyvmag.png', type='png')
if HP:
zlim = [
np.min(stardat['nobs']),
(np.median(stardat['nobs']) - np.min(stardat['nobs'])) +
np.median(stardat['nobs'])
]
cpu.plot_density(stardat['x'],
stardat['y'],
stardat['nobs'],
z_method=np.mean,
zlimits=zlim,
gridsize=gridsize,
radecgrid=True,
newfig=True,
cb_label='N observations')
pyl.title('Visit Density')
pyl.savefig('Snobs.png', format='png')
pyl.figure()
#I should just figure out from the data what nsides is!
hpid = hp.ang2pix(16, (stardat['dec'] + 90.) * deg2rad,
stardat['ra'] * deg2rad)
cpu.plot_density(stardat['x'],
stardat['y'],
np.mod(hpid, 250),
z_method=np.median,
gridsize=gridsize,
radecgrid=True,
newfig=True,
cb_label='HEALpix ID mod 250')
pyl.title("HEALpix Map")
pyl.savefig('HPid.png')
HPResults['number of HEALpix'] = np.size(np.unique(hpid))
pyl.figure()
num, b, p = pyl.hist(stardat['nobs'],
bins=100,
range=zlim,
histtype='bar')
pyl.xlabel("Number of Observations per Star")
pyl.title('Median = %d' % np.median(stardat['nobs']))
pyl.savefig('Snobs_hist.png', type='png')
starResults['median repeat obs'] = np.median(stardat['nobs'])
pyl.figure()
x = np.sort(stardat['iqr_sigma']) * 1e3
y = np.arange(np.size(x), dtype='float')
y = y / np.max(y)
per50 = np.max(np.where(y <= .5))
per90 = np.max(np.where(y <= .9))
pyl.plot(x,
y,
'k--',
label='IQR, 50th,90th %2.1f, %2.1f' % (x[per50], x[per90]))
pyl.plot([0, x[per50]], [y[per50], y[per50]], 'b--')
pyl.plot([x[per50], x[per50]], [0, y[per50]], 'b--')
pyl.plot([0, x[per90]], [y[per90], y[per90]], 'b--')
pyl.plot([x[per90], x[per90]], [0, y[per90]], 'b--')
#pyl.title('Cumulative Distribution of Stellar Repeatability')
#pyl.xlabel('Repeatability IQR RMS (mmag)')
#pyl.ylabel('Cumulative Fraction')
#pyl.savefig('Srepeat_IQR_cumulative.png',type='png')
pyl.legend()
pyl.savefig('Srepeat_cumulative.png', type='png')
#repeatability from IQR
rs = so.robustSigma(stardat['iqr_sigma'])
med = np.median(stardat['iqr_sigma'])
maglim = np.around(np.array([med - 3 * rs, med + 3. * rs]) * 1000,
decimals=2) / 1000
cpu.plot_density(stardat['x'],
stardat['y'],
stardat['iqr_sigma'] * 1000,
z_method=np.mean,
zlimits=maglim * 1000,
gridsize=gridsize,
radecgrid=True,
newfig=True,
cb_label='mmag')
pyl.title('Repeatability (IQR)')
pyl.savefig('Srepeat_IQR.png', type='png')
pyl.figure()
num, b, p = pyl.hist(stardat['iqr_sigma'] * 1000,
bins=100,
range=maglim * 1000,
histtype='bar')
pyl.xlabel('RMS per star (mmag)')
pyl.title('Repeatability IQR, Median = %4.2f mmag' %
np.median(stardat['iqr_sigma'] * 1000))
pyl.savefig('Srepeat_IQR_hist.png', type='png')
starResults['Median Repeatability (IQR) (mmag)'] = np.median(med) * 1e3
pyl.figure()
good = np.where(stardat['magtrue'] < 18)
num, b, p = pyl.hist(stardat['iqr_sigma'][good] * 1000,
bins=100,
range=maglim * 1000,
histtype='bar')
pyl.xlabel('RMS per star, m < 18 (mmag)')
pyl.title('Repeatability IQR, Median = %4.2f mmag' %
np.median(stardat['iqr_sigma'][good] * 1000))
pyl.savefig('Srepeat_IQR_bright_hist.png', type='png')
starResults['Median Repeatability Bright (IQR) (mmag)'] = np.median(
stardat['iqr_sigma'][good]) * 1e3
#repeatability v color
pyl.figure()
#pyl.plot(stardat['color'], stardat['rms']*1000., 'ko', alpha=.1)
pyl.hexbin(stardat['color'], stardat['iqr_sigma'] * 1000., bins='log')
cb = pyl.colorbar()
cb.set_label(r'$\log{\rm{N}}$')
pyl.ylim([0, 40])
pyl.xlabel(r'$g-i$')
pyl.ylabel(r'Repeatability $\sigma_{\rm{IQR}}$ (mmag)')
pyl.savefig('Srepeatvcolor.png', type='png')
#repeatability v mag
pyl.figure()
#pyl.plot(stardat['magtrue'], stardat['rms']*1000., 'ko', alpha=.1)
pyl.hexbin(stardat['magtrue'],
stardat['iqr_sigma'] * 1000.,
bins='log')
cb = pyl.colorbar()
cb.set_label(r'$\log{\rm{N}}$')
pyl.ylim([0, 40])
pyl.xlabel('True Mag')
pyl.ylabel(r'Repeatability $\sigma_{\rm{IQR}}$ (mmag)')
pyl.savefig('Srepeatvmag.png', type='png')
#repeatability
rs = so.robustSigma(stardat['rms'])
med = np.median(stardat['rms'])
maglim = np.around(np.array([med - 3 * rs, med + 3. * rs]) * 1000,
decimals=2) / 1000
cpu.plot_density(stardat['x'],
stardat['y'],
stardat['rms'] * 1000,
z_method=np.mean,
zlimits=maglim * 1000,
gridsize=gridsize,
radecgrid=True,
newfig=True,
cb_label='mmag')
pyl.title('Repeatability')
pyl.savefig('Srepeat.png', type='png')
pyl.figure()
num, b, p = pyl.hist(stardat['rms'] * 1000,
bins=100,
range=maglim * 1000,
histtype='bar')
pyl.xlabel('RMS per star (mmag)')
pyl.title('Repeatability, Median = %4.2f mmag' %
np.median(stardat['rms'] * 1000))
pyl.savefig('Srepeat_hist.png', type='png')
starResults['Median Repeatability (mmag)'] = np.median(med) * 1e3
pyl.figure()
x = np.sort(stardat['rms']) * 1e3
y = np.arange(np.size(x), dtype='float')
y = y / np.max(y)
per50 = np.max(np.where(y <= .5))
per90 = np.max(np.where(y <= .9))
pyl.plot(x, y, 'k', label='RMS, %2.1f, %2.1f' % (x[per50], x[per90]))
pyl.plot([0, x[per50]], [y[per50], y[per50]], 'k')
pyl.plot([x[per50], x[per50]], [0, y[per50]], 'k')
pyl.plot([0, x[per90]], [y[per90], y[per90]], 'k')
pyl.plot([x[per90], x[per90]], [0, y[per90]], 'k')
pyl.title('Cumulative Distribution of Stellar Repeatability')
pyl.xlabel('Repeatability RMS (mmag)')
pyl.ylabel('Cumulative Fraction')
pyl.savefig('Srepeat_cumulative.png', type='png')
#need to make a spatial uniformity plot since we can now have varying densities of stars.
nside = 64
stardat['hpid'] = hp.ang2pix(nside, (stardat['dec'] + 90.) * deg2rad,
stardat['ra'] * deg2rad)
uhpid = np.unique(stardat['hpid'])
hp_mean = uhpid * 0.
hp_std = uhpid * 0.
hp_dec, hp_ra = hp.pix2ang(nside, uhpid)
#hp_ra = hp_ra
hp_dec = hp_dec - 90. * deg2rad
hp_x, hp_y = cpu.hammer_project_toxy(hp_ra, hp_dec)
stardat = dictsort(stardat, 'hpid')
left = np.searchsorted(stardat['hpid'], uhpid)
right = np.searchsorted(stardat['hpid'], uhpid, side='right')
for i in np.arange(left.size):
hp_mean[i] = np.mean(stardat['magdiff'][left[i]:right[i]])
hp_std[i] = np.std(stardat['magdiff'][left[i]:right[i]])
cpu.plot_density(hp_x,
hp_y,
hp_mean * 1000,
z_method=np.mean,
zlimits=None,
gridsize=gridsize,
radecgrid=True,
newfig=True,
cb_label='mmag')
HPResults['Spatial Uniformity RMS (mmag)'] = np.std(hp_mean) * 1e3
HPResults['Spatial Uniformity IQR RMS (mmag)'] = so.robustSigma(
hp_mean) * 1e3
pyl.title('mean(True-Bestfit), binned by HEALpix')
pyl.savefig('HPresid.png', type='png')
cpu.plot_density(hp_x,
hp_y,
hp_std * 1000,
z_method=np.mean,
zlimits=None,
gridsize=gridsize,
radecgrid=True,
newfig=True,
cb_label='mmag')
pyl.title('std(True-Bestfit), binned by HEALpix')
pyl.savefig('HPstd.png', type='png')
pyl.figure()
num, b, p = pyl.hist(hp_mean * 1000,
bins=100,
range=None,
histtype='bar')
pyl.xlabel("HEALpix(True-Bestfit) (mmag)")
pyl.title('Spatial Uniformity, RMS=%4.2f' %
HPResults['Spatial Uniformity RMS (mmag)'])
pyl.savefig('HPmean_hist.png', type='png')
pyl.figure()
num, b, p = pyl.hist(hp_std * 1000,
bins=100,
range=None,
histtype='bar')
pyl.xlabel("std(True-Bestfit) per HEALpix (mmag)")
pyl.title('Variation within HEALpix, median=%4.2f mmag' %
np.median(hp_std * 1000))
pyl.savefig('HPstd_hist.png', type='png')
return starResults, HPResults
def split_obs(nside,shift_size=5., splitNS=False, NorthOnly=False, SouthOnly=False, blocksize=1e6):
""" XXX kill this function XXX replaced by just including neighboring healpixels"""
"""nside gives the number of sides for healpixels. shift_size is in degrees and sets how """
#XXX-prob should put in a check so I don't sort repeatedly
#if not os.path.isfile('star_obs.dat.s'):
waitpop('sort -nk 1 star_obs.dat > star_obs.dat.s')
npix = hp.nside2npix(nside)
keys = ('visitid', 'patchid', 'subpatch', 'nstars', 'ra', 'dec')
patchfile='patchdata.dat'
patchdat = ui.readDatafile(patchfile, keys)
ord = np.argsort(patchdat['patchid'])
for key in list(patchdat.keys()): patchdat[key]=patchdat[key][ord]
shift_size = shift_size*np.pi/180. #1.*np.pi/180.
#compute galactic coords for healpixels and patches
heal={}
heal['ra'], heal['dec'] = hp.pix2ang(nside, np.arange(npix))
heal['dec'] = heal['dec']-90.*np.pi/180.
heal['b'] = galac_b(heal['ra'], heal['dec']) #np.arcsin(np.sin(heal['dec'])*np.cos(62.6*np.pi/180.)-np.cos(heal['dec'])*np.sin(heal['ra']-282.25*np.pi/180.)*np.sin(62.6*np.pi/180.))
patchdat['dec'] = patchdat['dec']*np.pi/180.
patchdat['ra'] = patchdat['ra']*np.pi/180.
patchdat['b'] = galac_b(patchdat['ra'], patchdat['dec'])
patchdat['l'] = galac_l(patchdat['ra'], patchdat['dec'])
patchdat['dec']=patchdat['dec']+90.*np.pi/180. #stupid healpix wants 0-180 I think...
patch_healpix = hp.ang2pix(nside, patchdat['dec'], patchdat['ra'])
pyl.figure()
cpu.plot_density(patchdat['ra'], patchdat['dec'], z=patch_healpix)
pyl.savefig('heal_check.png', type='png')
patch_healpix_shifted1 = hp.ang2pix(nside, patchdat['dec']+shift_size, patchdat['ra']+shift_size*2)
patch_healpix_shifted2 = hp.ang2pix(nside, patchdat['dec']-shift_size, patchdat['ra']+shift_size*2)
patch_healpix_shifted3 = hp.ang2pix(nside, patchdat['dec']+shift_size, patchdat['ra']-shift_size*2)
patch_healpix_shifted4 = hp.ang2pix(nside, patchdat['dec']-shift_size, patchdat['ra']-shift_size*2)
pyl.figure()
cpu.plot_density(patchdat['ra'], patchdat['dec'], z=patch_healpix_shifted1)
pyl.savefig('heal_check1.png', type='png')
#ok, just go through and set duplicates to -1. Also set any of the shifted ones that are not in patch_healpix to -1
uhp = np.unique(patch_healpix)
patch_healpix_shifted1[np.invert(np.in1d(patch_healpix_shifted1, uhp))] = -1
patch_healpix_shifted2[np.invert(np.in1d(patch_healpix_shifted2, uhp))] = -1
patch_healpix_shifted3[np.invert(np.in1d(patch_healpix_shifted3, uhp))] = -1
patch_healpix_shifted4[np.invert(np.in1d(patch_healpix_shifted4, uhp))] = -1
diff = patch_healpix_shifted1-patch_healpix
good = np.where(diff == 0)
patch_healpix_shifted1[good] = -1
diff1 = patch_healpix_shifted2-patch_healpix
diff2 = patch_healpix_shifted2-patch_healpix_shifted1
good = np.where( (diff1 == 0) | (diff2 == 0))
patch_healpix_shifted2[good] = -1
diff1 = patch_healpix_shifted3-patch_healpix
diff2 = patch_healpix_shifted3-patch_healpix_shifted1
diff3 = patch_healpix_shifted3-patch_healpix_shifted2
good = np.where( (diff1 == 0) | (diff2 == 0) | (diff3 == 0))
patch_healpix_shifted3[good] = -1
diff1 = patch_healpix_shifted4-patch_healpix
diff2 = patch_healpix_shifted4-patch_healpix_shifted1
diff3 = patch_healpix_shifted4-patch_healpix_shifted2
diff4 = patch_healpix_shifted4-patch_healpix_shifted3
good = np.where( (diff1 == 0) | (diff2 == 0) | (diff3 == 0) | (diff4 == 0))
patch_healpix_shifted4[good] = -1
pyl.figure()
cpu.plot_density(patchdat['ra'], patchdat['dec'], z=patch_healpix_shifted1)
pyl.savefig('heal_check1_post.png', type='png')
heal_pixels = np.unique(patch_healpix)
keys = [patch_healpix, patch_healpix_shifted1, patch_healpix_shifted2,patch_healpix_shifted3,patch_healpix_shifted4]
filenames = ['hp1.dat','hp2.dat','hp3.dat','hp4.dat','hp5.dat']
#ok, if a healpix stradles b =0, I want to split it in two
# I could also just loop through each HP. if the HP has patches w/ b> 0 and b< 0 and min l < xxx or max l < xxx, then break it apart.
if splitNS:
break_up = np.where( (patchdat['b'] < 0) & ( (patchdat['l'] < 120.*np.pi/180.) | (patchdat['l'] > 240)*np.pi/180.) & (np.abs(patchdat['b']) < 5*np.pi/180.))
for key in keys:
mask=np.where(key == -1)
key[break_up] = key[break_up]+max(heal_pixels)+1
key[mask] = -1
print('reading star_obs.dat.s')
star_obs = open('star_obs.dat.s', 'r')
#read in just the patch id's from star_obs
all_patch_ids = []
for line in star_obs:
if line.startswith('#'):
continue
linevalues = line.split()
all_patch_ids.append(linevalues[0])
all_patch_ids = np.array(all_patch_ids, dtype=np.int)
star_obs.close()
for j in np.arange(4):
filename = filenames[j]
patch_healpix = keys[j]
hpid = all_patch_ids*0
left = np.searchsorted(all_patch_ids, patchdat['patchid'])
right = np.searchsorted(all_patch_ids, patchdat['patchid'], side='right')
for i in np.arange(np.size(left)):
hpid[left[i]:right[i]] = patch_healpix[i]
print('left %i, hpid %i, all_patch_ids %i, patch_healpix %i '%(np.size(left), np.size(hpid), np.size(all_patch_ids), np.size( patch_healpix)))
file = open(filename,'w')
print("#", file=file)
for value in hpid: print(value, file=file)
file.close()
#print j, np.size(hpid), np.size(all_patch_ids)
waitpop('paste hp1.dat star_obs.dat.s > hp1_star_obs.dat')
waitpop('paste hp2.dat star_obs.dat.s > hp2_star_obs.dat')
waitpop("sed -i '/^-1/d' hp2_star_obs.dat" )
waitpop('paste hp3.dat star_obs.dat.s > hp3_star_obs.dat')
waitpop("sed -i '/^-1/d' hp3_star_obs.dat" )
waitpop('paste hp4.dat star_obs.dat.s > hp4_star_obs.dat')
waitpop("sed -i '/^-1/d' hp4_star_obs.dat" )
waitpop('paste hp5.dat star_obs.dat.s > hp5_star_obs.dat')
waitpop("sed -i '/^-1/d' hp5_star_obs.dat" )
#waitpop("mv temp.dat hp2_star_obs.dat")
waitpop('cat hp2_star_obs.dat >> hp1_star_obs.dat')
waitpop('cat hp3_star_obs.dat >> hp1_star_obs.dat')
waitpop('cat hp4_star_obs.dat >> hp1_star_obs.dat')
waitpop('cat hp5_star_obs.dat >> hp1_star_obs.dat')
waitpop(''' awk '{print $2" "$3" "$4" "$5 " "$6> ("h"$1"star_obs.dat")}' hp1_star_obs.dat''')
waitpop('rm h#star_obs.dat')
waitpop('mv hp1_star_obs.dat star_obs_hp1.dat')
waitpop('mv hp2_star_obs.dat star_obs_hp2.dat')
waitpop('mv hp3_star_obs.dat star_obs_hp3.dat')
waitpop('mv hp4_star_obs.dat star_obs_hp4.dat')
waitpop('mv hp5_star_obs.dat star_obs_hp5.dat')
histrange = [maglim[0] * 2, maglim[1] * 2] # or set range for histogram
nbins = 100 # or set number of bins for histogram
cp.histogram_stars_dmagcaltrue(stardat,
nbins=nbins,
range=histrange,
etitle=etitle)
if iteration_number > 0:
figname = "S%ddmag_hist." % (iteration_number)
else:
figname = "Sdmag_hist."
if savefig:
pyl.savefig(figname + figformat, format=figformat)
#just make a generic plot of the stellar density
cpu.plot_density(stardat['x'], stardat['y'], radecgrid=True, zlimits=[0, 30])
pyl.title('Stellar density')
if savefig:
pyl.savefig('Stardensity.png', format='png')
cpu.plot_density(stardat['x'],
stardat['y'],
z=stardat['magtrue'],
z_method=ninety,
radecgrid=True,
zlimits=[
n.min(stardat['magtrue']),
stats.scoreatpercentile(stardat['magtrue'], 98)
])
pyl.title('90th percentile magnitude')
if savefig:
if maglim == None:
histrange = None
else:
histrange = [maglim[0]*2, maglim[1]*2] # or set range for histogram
nbins = 100 # or set number of bins for histogram
cp.histogram_stars_dmagcaltrue(stardat, nbins=nbins, range=histrange, etitle=etitle)
if iteration_number > 0 :
figname = "S%ddmag_hist." %(iteration_number)
else:
figname = "Sdmag_hist."
if savefig:
pyl.savefig(figname + figformat, format=figformat)
#just make a generic plot of the stellar density
cpu.plot_density(stardat['x'],stardat['y'],radecgrid=True, zlimits=[0,30])
pyl.title('Stellar density')
if savefig:
pyl.savefig('Stardensity.png', format='png')
cpu.plot_density(stardat['x'],stardat['y'],z=stardat['magtrue'],z_method=ninety,radecgrid=True,
zlimits=[n.min(stardat['magtrue']), stats.scoreatpercentile(stardat['magtrue'], 98)])
pyl.title('90th percentile magnitude')
if savefig:
pyl.savefig('Starmag90.png', format='png')
cpu.plot_density(stardat['x'],stardat['y'],z=stardat['color'],radecgrid=True)
pyl.title('Average $g-i$')
def split_obs(nside,
shift_size=5.,
splitNS=False,
NorthOnly=False,
SouthOnly=False,
blocksize=1e6):
""" XXX kill this function XXX replaced by just including neighboring healpixels"""
"""nside gives the number of sides for healpixels. shift_size is in degrees and sets how """
#XXX-prob should put in a check so I don't sort repeatedly
#if not os.path.isfile('star_obs.dat.s'):
waitpop('sort -nk 1 star_obs.dat > star_obs.dat.s')
npix = hp.nside2npix(nside)
keys = ('visitid', 'patchid', 'subpatch', 'nstars', 'ra', 'dec')
patchfile = 'patchdata.dat'
patchdat = ui.readDatafile(patchfile, keys)
ord = np.argsort(patchdat['patchid'])
for key in list(patchdat.keys()):
patchdat[key] = patchdat[key][ord]
shift_size = shift_size * np.pi / 180. #1.*np.pi/180.
#compute galactic coords for healpixels and patches
heal = {}
heal['ra'], heal['dec'] = hp.pix2ang(nside, np.arange(npix))
heal['dec'] = heal['dec'] - 90. * np.pi / 180.
heal['b'] = galac_b(
heal['ra'], heal['dec']
) #np.arcsin(np.sin(heal['dec'])*np.cos(62.6*np.pi/180.)-np.cos(heal['dec'])*np.sin(heal['ra']-282.25*np.pi/180.)*np.sin(62.6*np.pi/180.))
patchdat['dec'] = patchdat['dec'] * np.pi / 180.
patchdat['ra'] = patchdat['ra'] * np.pi / 180.
patchdat['b'] = galac_b(patchdat['ra'], patchdat['dec'])
patchdat['l'] = galac_l(patchdat['ra'], patchdat['dec'])
patchdat['dec'] = patchdat[
'dec'] + 90. * np.pi / 180. #stupid healpix wants 0-180 I think...
patch_healpix = hp.ang2pix(nside, patchdat['dec'], patchdat['ra'])
pyl.figure()
cpu.plot_density(patchdat['ra'], patchdat['dec'], z=patch_healpix)
pyl.savefig('heal_check.png', type='png')
patch_healpix_shifted1 = hp.ang2pix(nside, patchdat['dec'] + shift_size,
patchdat['ra'] + shift_size * 2)
patch_healpix_shifted2 = hp.ang2pix(nside, patchdat['dec'] - shift_size,
patchdat['ra'] + shift_size * 2)
patch_healpix_shifted3 = hp.ang2pix(nside, patchdat['dec'] + shift_size,
patchdat['ra'] - shift_size * 2)
patch_healpix_shifted4 = hp.ang2pix(nside, patchdat['dec'] - shift_size,
patchdat['ra'] - shift_size * 2)
pyl.figure()
cpu.plot_density(patchdat['ra'], patchdat['dec'], z=patch_healpix_shifted1)
pyl.savefig('heal_check1.png', type='png')
#ok, just go through and set duplicates to -1. Also set any of the shifted ones that are not in patch_healpix to -1
uhp = np.unique(patch_healpix)
patch_healpix_shifted1[np.invert(np.in1d(patch_healpix_shifted1,
uhp))] = -1
patch_healpix_shifted2[np.invert(np.in1d(patch_healpix_shifted2,
uhp))] = -1
patch_healpix_shifted3[np.invert(np.in1d(patch_healpix_shifted3,
uhp))] = -1
patch_healpix_shifted4[np.invert(np.in1d(patch_healpix_shifted4,
uhp))] = -1
diff = patch_healpix_shifted1 - patch_healpix
good = np.where(diff == 0)
patch_healpix_shifted1[good] = -1
diff1 = patch_healpix_shifted2 - patch_healpix
diff2 = patch_healpix_shifted2 - patch_healpix_shifted1
good = np.where((diff1 == 0) | (diff2 == 0))
patch_healpix_shifted2[good] = -1
diff1 = patch_healpix_shifted3 - patch_healpix
diff2 = patch_healpix_shifted3 - patch_healpix_shifted1
diff3 = patch_healpix_shifted3 - patch_healpix_shifted2
good = np.where((diff1 == 0) | (diff2 == 0) | (diff3 == 0))
patch_healpix_shifted3[good] = -1
diff1 = patch_healpix_shifted4 - patch_healpix
diff2 = patch_healpix_shifted4 - patch_healpix_shifted1
diff3 = patch_healpix_shifted4 - patch_healpix_shifted2
diff4 = patch_healpix_shifted4 - patch_healpix_shifted3
good = np.where((diff1 == 0) | (diff2 == 0) | (diff3 == 0) | (diff4 == 0))
patch_healpix_shifted4[good] = -1
pyl.figure()
cpu.plot_density(patchdat['ra'], patchdat['dec'], z=patch_healpix_shifted1)
pyl.savefig('heal_check1_post.png', type='png')
heal_pixels = np.unique(patch_healpix)
keys = [
patch_healpix, patch_healpix_shifted1, patch_healpix_shifted2,
patch_healpix_shifted3, patch_healpix_shifted4
]
filenames = ['hp1.dat', 'hp2.dat', 'hp3.dat', 'hp4.dat', 'hp5.dat']
#ok, if a healpix stradles b =0, I want to split it in two
# I could also just loop through each HP. if the HP has patches w/ b> 0 and b< 0 and min l < xxx or max l < xxx, then break it apart.
if splitNS:
break_up = np.where((patchdat['b'] < 0)
& ((patchdat['l'] < 120. * np.pi / 180.)
| (patchdat['l'] > 240) * np.pi / 180.)
& (np.abs(patchdat['b']) < 5 * np.pi / 180.))
for key in keys:
mask = np.where(key == -1)
key[break_up] = key[break_up] + max(heal_pixels) + 1
key[mask] = -1
print('reading star_obs.dat.s')
star_obs = open('star_obs.dat.s', 'r')
#read in just the patch id's from star_obs
all_patch_ids = []
for line in star_obs:
if line.startswith('#'):
continue
linevalues = line.split()
all_patch_ids.append(linevalues[0])
all_patch_ids = np.array(all_patch_ids, dtype=np.int)
star_obs.close()
for j in np.arange(4):
filename = filenames[j]
patch_healpix = keys[j]
hpid = all_patch_ids * 0
left = np.searchsorted(all_patch_ids, patchdat['patchid'])
right = np.searchsorted(all_patch_ids,
patchdat['patchid'],
side='right')
for i in np.arange(np.size(left)):
hpid[left[i]:right[i]] = patch_healpix[i]
print('left %i, hpid %i, all_patch_ids %i, patch_healpix %i ' %
(np.size(left), np.size(hpid), np.size(all_patch_ids),
np.size(patch_healpix)))
file = open(filename, 'w')
print("#", file=file)
for value in hpid:
print(value, file=file)
file.close()
#print j, np.size(hpid), np.size(all_patch_ids)
waitpop('paste hp1.dat star_obs.dat.s > hp1_star_obs.dat')
waitpop('paste hp2.dat star_obs.dat.s > hp2_star_obs.dat')
waitpop("sed -i '/^-1/d' hp2_star_obs.dat")
waitpop('paste hp3.dat star_obs.dat.s > hp3_star_obs.dat')
waitpop("sed -i '/^-1/d' hp3_star_obs.dat")
waitpop('paste hp4.dat star_obs.dat.s > hp4_star_obs.dat')
waitpop("sed -i '/^-1/d' hp4_star_obs.dat")
waitpop('paste hp5.dat star_obs.dat.s > hp5_star_obs.dat')
waitpop("sed -i '/^-1/d' hp5_star_obs.dat")
#waitpop("mv temp.dat hp2_star_obs.dat")
waitpop('cat hp2_star_obs.dat >> hp1_star_obs.dat')
waitpop('cat hp3_star_obs.dat >> hp1_star_obs.dat')
waitpop('cat hp4_star_obs.dat >> hp1_star_obs.dat')
waitpop('cat hp5_star_obs.dat >> hp1_star_obs.dat')
waitpop(
''' awk '{print $2" "$3" "$4" "$5 " "$6> ("h"$1"star_obs.dat")}' hp1_star_obs.dat'''
)
waitpop('rm h#star_obs.dat')
waitpop('mv hp1_star_obs.dat star_obs_hp1.dat')
waitpop('mv hp2_star_obs.dat star_obs_hp2.dat')
waitpop('mv hp3_star_obs.dat star_obs_hp3.dat')
waitpop('mv hp4_star_obs.dat star_obs_hp4.dat')
waitpop('mv hp5_star_obs.dat star_obs_hp5.dat')