def plotCls(self,saveFile,keys=None,xlimits=None,ylimits=None,transform=True,showBinnedTheory=False,scaleX='linear',scaleY='linear'):
nsigma = 2.
binCenters = self.binner.getBinCenters()
if transform:
ylab = "$\ell C_{\ell}$"
mult = binCenters
multTh = 1.#binCenters*0.+1.
else:
ylab = "$C_{\ell}$"
mult = binCenters*0.+1.
multTh = 0.#binCenters*0.
pl = Plotter(labelX="$\ell$",labelY=ylab,scaleX=scaleX,scaleY=scaleY)
if keys is None: keys = list(self.datas.keys())
for key in keys:
dat = self.datas[key]
if dat['covmat'] is None:
#This is a theory curve
ells = np.array(list(range(len(dat['unbinned']))))
if dat['isFit']:
ls="--"
lw=1
else:
ls="-"
lw=2
base_line, = pl.add(ells,(multTh*(ells-1)+1.)*dat['unbinned'],label=dat['label'],lw=lw,ls=ls)
if dat['isFit']:
pl._ax.fill_between(ells,(multTh*(ells-1)+1.)*dat['unbinned']*(1.-nsigma*dat['amp'][1]/dat['amp'][0]),(multTh*(ells-1)+1.)*dat['unbinned']*(1.+nsigma*dat['amp'][1]/dat['amp'][0]),alpha=0.3, facecolor=base_line.get_color())
if showBinnedTheory:
pl.add(binCenters[:len(dat['binned'])],mult[:len(dat['binned'])]*dat['binned'],
ls='none',marker='x',mew=2,markersize=10,label=dat['label']+' binned')
else:
errs = np.sqrt(np.diagonal(dat['covmat']))
print((dat['label']))
pl.addErr(binCenters[:len(dat['binned'])],mult[:len(dat['binned'])]*dat['binned'],mult[:len(dat['binned'])]*errs,label=dat['label'],marker='o',elinewidth=2,markersize=10,mew=2,)
[i.set_linewidth(2.0) for i in list(pl._ax.spines.values())]
pl._ax.tick_params(which='major',width=2)
pl._ax.tick_params(which='minor',width=2)
pl._ax.axhline(y=0.,ls='--')
if not(xlimits is None):
pl._ax.set_xlim(*xlimits)
else:
pl._ax.set_xlim(self.binner.bin_edges[0],self.binner.bin_edges[-1])
if not(ylimits is None): pl._ax.set_ylim(*ylimits)
pl.legendOn(loc='lower left',labsize=10)
pl.done(saveFile)
def stat_analysis(cutouts, binsize, arcmax, cents, modRMaps):
profiles = []
for i in range(0, len(cutouts)):
thetaRange = np.arange(0., arcmax, binsize)
breali = bin2D(modRMaps[i] * 180. * 60. / np.pi, thetaRange)
a = breali.bin(cutouts[i])[1]
profiles.append(a)
statistics = stats.getStats(profiles)
mean = statistics['mean']
error = statistics['errmean']
covmat = statistics['cov']
corrcoef = stats.cov2corr(covmat)
io.quickPlot2d(corrcoef, 'corrcoef.png')
pl = Plotter(labelX='Distance from Center (arcminutes)',
labelY='Temperature Fluctuation ($\mu K$)',
ftsize=10)
pl.add(cents, mean)
pl.addErr(cents, mean, yerr=error)
pl._ax.axhline(y=0., ls="--", alpha=0.5)
pl.done(out_dir + "error.png")
listAllReconPower[polComb] = np.vstack(
(listAllReconPower[polComb], rcvInputPowerMat))
statsCross = {}
statsRecon = {}
pl = Plotter(scaleY='log')
pl.add(ellkk, Clkk, color='black', lw=2)
for polComb, col in zip(polCombList, colorList):
statsCross[polComb] = getStats(listAllCrossPower[polComb])
pl.addErr(centers,
statsCross[polComb]['mean'],
yerr=statsCross[polComb]['errmean'],
ls="none",
marker="o",
markersize=8,
label="recon x input " + polComb,
color=col,
mew=2,
elinewidth=2)
statsRecon[polComb] = getStats(listAllReconPower[polComb])
fp = interp1d(centers,
statsRecon[polComb]['mean'],
fill_value='extrapolate')
pl.add(ellkk, (fp(ellkk)) - Clkk, color=col, lw=2)
Nlkk2d = qest.N.Nlkk[polComb]
ncents, npow = stats.binInAnnuli(Nlkk2d, p2d.modLMap, bin_edges)
pl.add(ncents, npow, color=col, lw=2, ls="--")
def stack_on_map(lite_map,
width_stamp_arcminute,
pix_scale,
ra_range,
dec_range,
catalog=None,
n_random_points=None):
width_stamp_degrees = width_stamp_arcminute / 60.
Np = np.int(width_stamp_arcminute / pix_scale + 0.5)
pad = np.int(Np / 2 + 0.5)
print("Expected width in pixels = ", Np)
lmap = lite_map
stack = 0
N = 0
if catalog is not None:
looprange = range(0, len(catalog))
assert n_random_points is None
random = False
else:
assert n_random_points is not None
assert len(ra_range) == 2
assert len(dec_range) == 2
looprange = range(0, n_random_points)
random = True
print(looprange)
for i in looprange:
banana = True
mass = catalog[i][10]
if random:
ra = np.random.uniform(*ra_range)
dec = np.random.uniform(*dec_range)
if random == False:
ra = catalog[i][1] #1 for ACT catalog 2 for SDSS
dec = catalog[i][2] #2 for ACT catalog 3 for SDSS
for j in range(0, 2130):
distance = np.sqrt((ra - RAps[j])**2 + (dec - DECps[j])**2)
crit = 0.25
if distance < crit:
banana = False
print('too close')
ix, iy = lmap.skyToPix(ra, dec)
if ix >= pad and ix < lmap.Nx - pad and iy >= pad and iy < lmap.Ny - pad and banana == True and mass > 8:
print(i)
#print(ra,dec)
smap = lmap.selectSubMap(ra - width_stamp_degrees / 2.,
ra + width_stamp_degrees / 2.,
dec - width_stamp_degrees / 2.,
dec + width_stamp_degrees / 2.)
#print (smap.data.shape)
#cutout = zoom(smap.data.copy(),zoom=(float(Np)/smap.data.shape[0],float(Np)/smap.data.shape[1])
cutout = resize(smap.data.copy(),
output_shape=(Np, Np)) - randomstack
xMap, yMap, modRMap, xx, yy = fmaps.getRealAttributes(smap)
dt = pix_scale
arcmax = 20.
thetaRange = np.arange(0., arcmax, dt)
breali = bin2D(modRMap * 180. * 60. / np.pi, thetaRange)
a = breali.bin(cutout)[1]
profiles.append(a)
io.quickPlot2d(cutout, str(i) + "cutout.png")
#print (cutout.shape)
stack = stack + cutout
N = N + 1
else:
print("skip")
stack = stack / N #-randomstack
#print(stack.shape())
#print(smap.data.shape)
# print(stack)
print(N)
statistics = stats.getStats(profiles)
mean = statistics['mean']
error = statistics['errmean']
corrcoef = statistics['corr']
covmat = statistics['covmat']
print(mean / error)
np.save('statistics', statistics)
#np.save('newrandomstamp',stack)
# io.quickPlot2d(stack,out_dir+"newACTstack.png")
dt = pix_scale
arcmax = 20.
thetaRange = np.arange(0., arcmax, dt)
breal = bin2D(modRMap * 180. * 60. / np.pi, thetaRange)
cents, recons = breal.bin(stack)
pl = Plotter(labelX='Distance from Center (arcminutes)',
labelY='Temperature Fluctuation ($\mu K$)',
ftsize=10)
pl.add(cents, mean)
pl.addErr(cents, mean, yerr=error)
pl._ax.axhline(y=0., ls="--", alpha=0.5)
pl.done(out_dir + "error.png")
print(covmat)
io.quickPlot2d(covmat, 'covmat.png')
return (stack, cents, recons)
".png")
ellfac = el_ilc * (el_ilc + 1.) / (2. * np.pi) * 1e12 * constDict['TCMB']**2
ellfac2 = eln * (eln + 1.) / (2. * np.pi) * 1e12 * constDict['TCMB']**2
pl = Plotter(labelX="$\ell$",
labelY="$C_\ell \, (1 + \ell) \ell / 2\pi \, [\mu \mathrm{K}]$",
ftsize=12,
figsize=(8, 6),
scaleY='log')
pl._ax.set_ylim(1, 10000)
pl._ax.set_xlim(100., 5000.)
pl.add(el_ilc, cls_ilc * ellfac, color='black')
pl.add(eln, nl * ellfac2, label="$N_\ell$ CCATP")
pl.add(eln2, nl2 * ellfac2, label="$N_\ell$ SO")
pl.addErr(el_ilc, cls_ilc * ellfac, err_ilc * ellfac, label="CCATP")
pl.addErr(el_ilc2 + 10, cls_ilc2 * ellfac, err_ilc2 * ellfac, label="SO")
#pl.legend(loc='upper right',labsize=10)
pl.done(outDir + experimentName + "_cmb_cls" + constraint_tag[cf] + ".png")
ls = np.arange(2, 8000, 10)
pl = Plotter(labelX="$\ell$",
labelY="$C_\ell \, (1 + \ell) \ell / 2\pi \, [\mu \mathrm{K}]$",
ftsize=12,
figsize=(8, 6),
scaleY='log')
pl._ax.set_ylim(0.1, 1000)
pl._ax.set_xlim(100., 8000.)
pl.add(el_il, cls_il * ellfac, color='black')
pl.add(elnc, nlc * ellfac2, label="$N_\ell$ CCATP")
pl.add(elnc2, nlc2 * ellfac2, label="$N_\ell$ SO")
xint.generateCls(frange)
Clgg = xint.getCl("cmass", "cmass")
Clkg = xint.getCl("cmb", "cmass")
b = coreBinner(snrange)
cents, clkgbinned = b.binned(frange, Clkg)
from scipy.interpolate import interp1d
clkgint = interp1d(frange, Clkg)
print(("kg S/N ", sn))
pl = Plotter(labelX="$L$", labelY="$LC_L$")
pl.add(frange, frange * Clkg)
pl.addErr(cents,
clkgint(cents) * cents,
yerr=np.array(errs) * cents,
marker="o")
#pl.legendOn(loc='lower right',labsize=12)
pl._ax.set_xlim(0, 2000)
#pl._ax.set_ylim(-0.2e-7,1.6e-7)
pl._ax.axhline(y=0., ls="--", alpha=0.5)
pl.done("output/errs.png")
zcents, dndz = np.loadtxt("data/hscd6.csv", unpack=True)
xint.addNz("hsc", zcents, dndz[1:])
xint.generateCls(snrange)
Clss = xint.getCl("hsc", "hsc")
Clks = xint.getCl("cmb", "hsc")
ngal = 12.0