def configPlot(scale=1e4, verbose=False):
prefix = '/data/user/fmcnally/anisotropy/maps/merged/'
fileList = np.array(glob.glob(prefix + 'IC?*_24H_sid.fits'))
alpha = 1/20.
deg = 180./np.pi
# Sort fileList according to configs
configs = np.array([os.path.basename(f).split('_')[0] for f in fileList])
csort = configs.argsort()
configs, fileList = configs[csort], fileList[csort]
dipoleParams = np.zeros((len(fileList), 6))
for i, f in enumerate(fileList):
data = mapFunctions.getMap(f, mapName='data')
bg = mapFunctions.getMap(f, mapName='bg')
p = mapFunctions.multifit(1, data, bg, alpha, params=True)
if verbose:
print configs[i]
ptable = [[key, p[key], p['d'+key]] for key in sorted(p.keys())]
print tabulate(ptable, headers=['Ylm','Value','Error'])
dipoleParams[i] = getDipole(p)
p0, dp0, theta, dTheta, phi, dPhi = dipoleParams.T
if verbose:
for i, config in enumerate(configs):
print config, ':'
print 'Theta: %.3f +/- %.3f' % (theta[i]*deg, dTheta[i]*deg)
print 'Phi: %.3f +/- %.3f' % (phi[i]*deg, dPhi[i]*deg)
fig = plt.figure(figsize=(17,6))
x = range(len(configs))
pltParams = {'fmt':'.','lw':2,'ms':14}
ax = fig.add_subplot(121)
ax.set_title('Dipole Strength vs Energy')
ax.set_xlabel(r'log$_{10}$(E/GeV)')
ax.set_ylabel('Dipole Strength (x %s)' % scale)
ax.errorbar(x, p0*scale, yerr=dp0*scale, **pltParams)
ax.set_xlim(-1, x[-1]+1)
ax.set_xticks(x)
ax.set_xticklabels(configs)
ax = fig.add_subplot(122)
ax.set_title('Dipole Phase vs Energy')
ax.set_xlabel(r'log$_{10}$(E/GeV)')
ax.set_ylabel(r'Dipole Phase ($\phi^{\circ}$)')
phi, dPhi = phi*deg, dPhi*deg
phi[phi>180] -= 360
ax.errorbar(x, phi, yerr=dPhi, **pltParams)
ax.set_xlim(-1, x[-1]+1)
ax.set_xticks(x)
ax.set_xticklabels(configs)
plt.show()
def getRelInt(file, **opts):
# Get background and data maps
bgmap = getMap(file, mapName='bg', **opts)
datamap = getMap(file, mapName='data', **opts)
# They'll be used as weights, so set unseen values to 0
bgmap[bgmap==hp.UNSEEN] = 0
datamap[datamap==hp.UNSEEN] = 0
vals = returnRI(bgmap, datamap, **opts)
return vals
def __init__(self, infile, **kwargs):
# Apply default values if arguments not in input dictionary
defaults = {'multi': False, 'smooth': 5,
'stype': 'tophat', 'swindow': 3,
'verbose': False, 'mask': False, 'decmin': -90.,
'decmax': 90., 'ramin':0., 'ramax':360.,
'nRAbins':24, 'fix_multi': False}
opts = {k: kwargs[k] for k in kwargs if k in defaults}
opts.update({k: defaults[k] for k in defaults if k not in opts})
self.Opts = opts
self.MapFile = infile
self.RelintMap = getMap(*[infile], mapName='relint', **self.Opts)
self.RelerrMap = getMap(*[infile], mapName='relerr', **self.Opts)
self.NPix = len(self.RelintMap)
self.NSide = hp.npix2nside(self.NPix)
def minimumPlot(offset=180, out=False, batch=False, verbose=False):
prefix = '/data/user/fmcnally/anisotropy/maps/merged'
alpha = 1/20.
deg = 180./np.pi
colorDict = {'IC':'b','IT':'r'}
# Create dictionaries to store IceCube and IceTop data separately
files, meds, sigL, sigR = {},{},{},{}
thetas, phis, dThetas, dPhis = {},{},{},{}
# IceCube information
meds['IC'], sigL['IC'], sigR['IC'] = [],[],[]
ebins = getEbins()
files['IC'] = getEnergyMaps('IC')
for i in range(len(ebins)-1):
distInfo = readDist_IC('IC', ebins[i], ebins[i+1])
meds['IC'] += [distInfo[0]]
sigL['IC'] += [distInfo[1]]
sigR['IC'] += [distInfo[2]]
# IceTop information
files['IT'], meds['IT'], sigL['IT'], sigR['IT'] = [],[],[],[]
files['IT'] += [['%s/IT_24H_sid_STA8.fits' % (prefix)]]
distInfo = readDist_IT('IT', 8, 100)
meds['IT'] += [distInfo[0]]
sigL['IT'] += [distInfo[1]]
sigR['IT'] += [distInfo[2]]
for detector in files.keys():
fileList = files[detector]
theta, phi = np.zeros((2, len(fileList)))
mapParams = {'mask':True,'smooth':20}
for i, f in enumerate(fileList):
sig = mapFunctions.getMap(*f, mapName='signal', **mapParams)
ri = mapFunctions.getMap(*f, mapName='relint', **mapParams)
rierr = mapFunctions.getMap(*f, mapName='relerr', **mapParams)
c0 = (sig==hp.UNSEEN)
sig[c0] = 0
nside = hp.npix2nside(len(sig))
minPix = sig.argmin()
theta[i], phi[i] = hp.pix2ang(nside, minPix)
if verbose:
for i, median in enumerate(medians):
print 'Median energy : %.3f' % median
print 'Theta: %.3f' % (theta[i]*deg)
print 'Phi: %.3f' % (phi[i]*deg)
#print 'Theta: %.3f +/- %.3f' % (theta[i]*deg, dTheta[i]*deg)
#print 'Phi: %.3f +/- %.3f' % (phi[i]*deg, dPhi[i]*deg)
thetas[detector] = theta
#dThetas[detector] = dTheta
phis[detector] = phi
#dPhis[detector] = dPhi
#fig = plt.figure(figsize=(17,6))
fig = plt.figure()
#pltParams = {'fmt':'.','lw':2,'ms':14}
pltParams = {'lw':2,'ms':14}
#ax = fig.add_subplot(121)
#ax.set_title('Dipole Strength vs Energy')
#ax.set_xlabel(r'log$_{10}$(E/GeV)')
#ax.set_ylabel('Dipole Strength (x %s)' % scale)
#ax.errorbar(x, p0*scale, yerr=dp0*scale, **pltParams)
#ax = fig.add_subplot(122)
ax = fig.add_subplot(111)
#ax.set_title('Dipole Phase vs Energy')
ax.set_xlabel(r'Reconstructed Energy (log$_{10}$(E/GeV))')
ax.set_ylabel(r'Dipole Phase ($\phi^{\circ}$)')
for detector in files.keys():
x = meds[detector]
xerr = [sigL[detector], sigR[detector]]
phi = phis[detector]*deg
#phi, dPhi = phis[detector]*deg, dPhis[detector]*deg
# Phi offset (requires renaming ylabels)
pcut = phi > offset
phi[pcut] -= offset
phi[np.logical_not(pcut)] += (360-offset)
ax.errorbar(x, phi, xerr=xerr,
fmt=colorDict[detector]+'.', **pltParams)
#ax.errorbar(x, phi, xerr=xerr, yerr=dPhi,
# fmt=colorDict[detector]+'.', **pltParams)
ax.set_ylim(-10,370)
ax.set_yticks(range(0, 361, 45))
ylabels = [str(i) for i in range(offset, 360-1, 45)]
ylabels += [str(i) for i in range(0, offset+1, 45)]
ax.set_yticklabels(ylabels)
plt.axhline(360-offset, linewidth=1.5, linestyle='--', color='black')
if out:
plt.savefig(out, dpi=300, bbox_inches='tight')
if not batch:
plt.show()
def energyPlot(scale=1e4,offset=180, out=False, batch=False, verbose=False):
prefix = '/data/user/fmcnally/anisotropy/maps/merged'
alpha = 1/20.
deg = 180./np.pi
colorDict = {0:'b'}
# IceCube information
files, meds, sigL, sigR = [],[],[],[]
ebins = getEbins()
files = getEnergyMaps('IC')
for i in range(len(ebins)-1):
distInfo = readDist_IC('IC', ebins[i], ebins[i+1])
meds += [distInfo[0]]
sigL += [distInfo[1]]
sigR += [distInfo[2]]
# IceTop information
idx0 = len(files)
colorDict[idx0] = 'r'
files += [['%s/IT_24H_sid_STA8.fits' % (prefix)]]
distInfo = readDist_IT('IT', 8, 100)
meds += [distInfo[0]]
sigL += [distInfo[1]]
sigR += [distInfo[2]]
dipoleParams = np.zeros((len(files), 6))
for i, files_i in enumerate(files):
data = mapFunctions.getMap(*files_i, mapName='data')
bg = mapFunctions.getMap(*files_i, mapName='bg')
p = mapFunctions.multifit(1, data, bg, alpha, params=True)
if verbose:
print meds[i]
print('key = {}'.format(sorted(p.keys())))
#ptable = [[key, p[key], p['d'+key]] for key in sorted(p.keys())]
ptable = [[key, p[key]] for key in sorted(p.keys())]
#print tabulate(ptable, headers=['Ylm','Value','Error'])
#print tabulate(ptable, headers=['Ylm','Value'])
print('files_i = {}'.format(files_i))
for key in sorted(p.keys()):
print('p[{}] = {}'.format(key,p[key]))
print('p[Y(1,1)]+p[Y(1,-1)] = {}'.format(np.sqrt(3.0/(4.0*np.pi))*np.power(p['Y(1,1)'],2.0)+np.power(p['Y(1,-1)'],2.0)))
dipoleParams[i] = getDipole(p)
p0, dp0, thetas, dThetas, phis, dPhis = dipoleParams.T
if verbose:
for i, median in enumerate(meds):
print 'Median energy : %.3f' % median
print 'Theta: %.3f +/- %.3f' % (thetas[i]*deg, dThetas[i]*deg)
print 'Phi: %.3f +/- %.3f' % (phis[i]*deg, dPhis[i]*deg)
#fig = plt.figure(figsize=(17,6))
fig = plt.figure()
#pltParams = {'fmt':'.','lw':2,'ms':14}
pltParams = {'lw':2,'ms':14}
#ax = fig.add_subplot(121)
#ax.set_title('Dipole Strength vs Energy')
#ax.set_xlabel(r'log$_{10}$(E/GeV)')
#ax.set_ylabel('Dipole Strength (x %s)' % scale)
#ax.errorbar(x, p0*scale, yerr=dp0*scale, **pltParams)
#ax = fig.add_subplot(122)
ax = fig.add_subplot(111)
ax.set_title('Dipole Phase vs Energy')
ax.set_xlabel(r'Reconstructed Energy (log$_{10}$(E/GeV))')
ax.set_ylabel(r'Dipole Phase ($\phi^{\circ}$)')
for i in range(0, len(meds), idx0):
x = meds[i:i+idx0]
xerr = [sigL[i:i+idx0], sigR[i:i+idx0]]
phi, dPhi = phis[i:i+idx0]*deg, dPhis[i:i+idx0]*deg
# Phi offset (requires renaming ylabels)
pcut = phi > offset
phi[pcut] -= offset
phi[np.logical_not(pcut)] += (360-offset)
ax.errorbar(x, phi, xerr=xerr, yerr=dPhi,
fmt=colorDict[i]+'.', **pltParams)
ax.set_ylim(-10,370)
ax.set_yticks(range(0, 361, 45))
ylabels = [str(i) for i in range(offset, 360-1, 45)]
ylabels += [str(i) for i in range(0, offset+1, 45)]
ax.set_yticklabels(ylabels)
plt.axhline(360-offset, linewidth=1.5, linestyle='--', color='black')
if out:
plt.savefig(out, dpi=300, bbox_inches='tight')
if not batch:
plt.show()
def getRelInt_new(file, **opts):
# Get relative intensity map
relint = getMap(file, mapName='relint', **opts)
relerr = getMap(file, mapName='relerr', **opts)
vals = returnRI_new(relint, relerr, **opts)
return vals
nfiles = len(files)
lmax = args.nbins / 2 - 1
if lmax > 15:
lmax = 15
opts = {"mask": True, "ramin": 0.0, "ramax": 360.0, "nbins": args.nbins}
amp, phase, amp_err, phase_err = np.zeros((4, nfiles))
chi2array = np.zeros((nfiles, lmax - 1))
# Calculate best harmonic function fit value based on chi2
# Fill chi-squared array
for i, file in enumerate(files):
if type(file) != list:
file = [file]
# Get relint and relerr maps
relint = getMap(*file, mapName="relint", **opts)
relerr = getMap(*file, mapName="relerr", **opts)
ra, ri, ra_err, ri_err = getRIRAProj(relint, relerr, **opts)
for l in range(1, lmax):
popt, perr, chi2 = getHarmonicFitParams(ra, ri, l, ri_err)
chi2array[i][l - 1] = chi2
if i == args.solo:
if l == 1:
ax.errorbar(ra, ri, ri_err, fmt=".", label=r"Data")
fullra = range(0, 360, 5)
fit = [cosFit(j, *popt) for j in fullra]
ax.plot(fullra, fit, label=r"$l_{} = {}$ Fit".format("{max}", l))
ax.set_xlim(0.0, 360.0)
ax.invert_xaxis()
if args.chi2:
