def plotdist_1d_hist(x, probs, mode=None, ranges=None):
if mode is None or ranges is None:
mode, ranges = isg.getdist_1d_hist(x, probs, [0.68, 0.95])
fig = pylab.figure()
ax = fig.add_axes([0.12, 0.12, 0.95 - 0.12, 0.95 - 0.12])
ax.plot(x, probs, 'b-')
print '!!!!!!!!!'
print ranges
ax.axvline(mode, c='r', linewidth=1.5)
s68, s95 = ranges
if isinstance(s68[0], float):
s68 = [s68]
if isinstance(s95[0], float):
s95 = [s95]
for range in s68:
ax.axvline(range[0], c='m', linewidth=1.2, linestyle='--')
ax.axvline(range[1], c='m', linewidth=1.2, linestyle='--')
for range in s95:
ax.axvline(range[0], c='c', linewidth=1.2, linestyle=':')
ax.axvline(range[1], c='c', linewidth=1.2, linestyle=':')
return fig
def fitAltOffsetScript(data=None):
if data is None:
data = {}
worklist = readtxtfile('worklist')
clusters = [x[0] for x in worklist]
workdir = '/u/ki/dapple/nfs12/cosmos/simulations/clusters_2012-05-17-highdensity/'
subdirs = ['%sBVRIZ' % x for x in ['', 'contam0p10/', 'contam0p20/']]
concentration = 4.
mradius = 1.5
redshifts = cm.readClusterRedshifts()
figs = []
for subdir in subdirs:
if subdir not in data:
data[subdir] = {}
curdata = data[subdir]
if 'masses' not in curdata:
curdata['masses'], errs, massgrid, curdata[
'scale_radii'] = ss.readMLMasses(workdir, subdir, clusters)
masses = curdata['masses']
scale_radii = curdata['scale_radii']
if 'grid' not in curdata:
refmasses = {}
for cluster in clusters:
refmasses[cluster] = nfwutils.massInsideR(
scale_radii[cluster], concentration, redshifts[cluster],
mradius) * np.ones_like(masses[cluster])
curdata['grid'], curdata['means'], curdata[
'scatters'] = isg.intrinsicScatter(
refmasses,
masses,
means=1. + np.arange(-0.08, 0.08, 0.0001),
scatters=np.arange(0.005, 0.05, 0.0025))
means = curdata['means']
scatters = curdata['scatters']
mode, (r68, r95) = isg.getdist_1d_hist(means[0],
means[1],
levels=[0.68, 0.95])
curdata['meandist'] = (mode, r68, r95)
mode, (r68, r95) = isg.getdist_1d_hist(scatters[0],
scatters[1],
levels=[0.68, 0.95])
curdata['scatterdist'] = (mode, r68, r95)
for varname in 'mean scatter'.split():
mode, r68, r95 = curdata['%sdist' % varname]
print mode, r68, r95
print '%s\t%2.4f\t+%2.4f\t-%2.4f\t+%2.4f\t-%2.4f' % (
varname, mode, r68[0][1] - mode, mode - r68[0][0],
r95[0][1] - mode, mode - r95[0][0])
x, prob = curdata['%ss' % varname]
fig = isgp.plotdist_1d_hist(x, prob, mode, [r68[0], r95[0]])
ax = fig.axes[0]
ax.set_title('%s %s' % (subdir, varname))
figs.append(fig)
fig.show()
return figs, data
def measureMLCcBootIS(sample='worklist', data=None, diffR=None):
if data is None:
data = {}
if 'items' not in data:
items = readtxtfile(sample)
clusters = [x[0] for x in items]
redshifts = readClusterRedshifts()
properredshifts = np.array([redshifts[x] for x in clusters])
data['items'] = items
data['clusters'] = clusters
data['redshifts'] = redshifts
data['properredshifts'] = properredshifts
else:
items = data['items']
clusters = data['clusters']
redshifts = data['redshifts']
properredshifts = data['properredshifts']
if 'MLmasses' not in data or 'CCmasses' not in data:
if diffR is None:
if 'MLmasses' not in data:
MLmasses, MLmask = readMLBootstraps(
'/u/ki/dapple/ki06/bootstrap_2012-05-17', items,
np.arange(0, 200))
data['MLmasses'] = MLmasses
data['MLmask'] = MLmask
else:
MLmasses, MLmask = data['MLmasses'], data['MLmask']
if 'CCmasses' not in data:
CCmasses, CCmask = readCCSummary(
'/u/ki/dapple/ki06/bootstrap_2012-05-17', clusters,
np.arange(0, 200))
data['CCmasses'] = CCmasses
data['CCmask'] = CCmask
else:
CCmasses, CCmask = data['CCmasses'], data['CCmask']
else:
print 'Using Alternative diffR'
if 'MLmasses' not in data:
MLmasses, MLmask = readMLBootstraps_diffR(
'/u/ki/dapple/ki06/bootstrap_2012-05-17', items,
np.arange(0, 200), diffR, redshifts)
data['MLmasses'] = MLmasses
data['MLmask'] = MLmask
else:
MLmasses, MLmask = data['MLmasses'], data['MLmask']
if 'CCmasses' not in data:
CCmasses, CCmask = readCCSummary_diffR(
'/u/ki/dapple/ki06/bootstrap_2012-05-17', clusters,
np.arange(0, 200), diffR, redshifts)
data['CCmasses'] = CCmasses
data['CCmask'] = CCmask
else:
CCmasses, CCmask = data['CCmasses'], data['CCmask']
MLreduced = {}
CCreduced = {}
for key in MLmasses.keys():
totalmask = np.logical_and(MLmask[key], CCmask[key])
MLreduced[key] = MLmasses[key][totalmask]
CCreduced[key] = CCmasses[key][totalmask]
data['MLreduced'] = MLreduced
data['CCreduced'] = CCreduced
else:
MLreduced, CCreduced = data['MLreduced'], data['CCreduced']
grid, means, scatters = isg.intrinsicScatter(
MLreduced,
CCreduced,
means=np.arange(0.5, 1.5, 0.002),
scatters=np.arange(0.02, 0.2, 0.005))
data['grid'] = grid
data['means'] = means
data['scatters'] = scatters
figs = []
print
print
print '-----'
print 'var\tmode\t68%% +\t-\t95%% +\t-'
print '-----'
if 'meandist' not in data:
means = data['means']
scatters = data['scatters']
mode, (r68, r95) = isg.getdist_1d_hist(means[0],
means[1],
levels=[0.68, 0.95])
data['meandist'] = (mode, r68, r95)
mode, (r68, r95) = isg.getdist_1d_hist(scatters[0],
scatters[1],
levels=[0.68, 0.95])
data['scatterdist'] = (mode, r68, r95)
for varname in 'mean scatter'.split():
mode, r68, r95 = data['%sdist' % varname]
print mode, r68, r95
print '%s\t%2.4f\t+%2.4f\t-%2.4f\t+%2.4f\t-%2.4f' % (
varname, mode, r68[0][1] - mode, mode - r68[0][0],
r95[0][1] - mode, mode - r95[0][0])
x, prob = data['%ss' % varname]
fig = isgp.plotdist_1d_hist(x, prob, mode, [r68[0], r95[0]])
ax = fig.axes[0]
ax.set_title(varname)
figs.append(fig)
fig.show()
return figs, data
def profileScript(items, curdata = None):
clusters = [x[0] for x in items]
if curdata is None:
curdata = {}
if 'norm' not in curdata and 'cut' not in curdata:
normMass, normMask = cm.readCCSummary('/u/ki/dapple/ki06/bootstrap_5mpc_2012-02-14/',
clusters,np.arange(50),
'_rm1.5_ri0.75_ro3.0.out')
cutMass, cutMask = cm.readCCSummary('/u/ki/dapple/ki06/bootstrap_5mpc_2012-02-14/',
clusters, np.arange(50),
'_bmo2_t2p0_rm1.5_ri0.75_ro3.0.out')
reducedNorm = {}
reducedCut = {}
for cluster in normMass.keys():
totalmask = np.logical_and(normMask[cluster], cutMask[cluster])
reducedNorm[cluster] = normMass[cluster][totalmask]
reducedCut[cluster] = cutMass[cluster][totalmask]
curdata['norm'] = reducedNorm
curdata['cut'] = reducedCut
if 'grid' not in curdata:
grid, means, scatters = isg.intrinsicScatter(curdata['norm'], curdata['cut'], scatters=np.arange(0.005, 0.05, 0.01))
curdata['grid'] = grid
curdata['means'] = means
curdata['scatters'] = scatters
print
print
print '-----'
print 'var\tmode\t68%% +\t-\t95%% +\t-'
print '-----'
if 'meandist' not in curdata or 'scatterdist' not in curdata:
means = curdata['means']
scatters = curdata['scatters']
mode, (r68, r95) = isg.getdist_1d_hist(means[0], means[1], levels = [0.68, 0.95])
curdata['meandist'] = (mode, r68, r95)
mode, (r68, r95) = isg.getdist_1d_hist(scatters[0], scatters[1], levels = [0.68, 0.95])
curdata['scatterdist'] = (mode, r68, r95)
figs = []
for varname in 'mean scatter'.split():
mode, r68, r95 = curdata['%sdist' % varname]
print mode, r68, r95
print '%s\t%2.4f\t+%2.4f\t-%2.4f\t+%2.4f\t-%2.4f' % (varname, mode,
r68[0][1] - mode, mode - r68[0][0],
r95[0][1] - mode, mode - r95[0][0])
x, prob = curdata['%ss' % varname]
fig = isgp.plotdist_1d_hist(x, prob, mode, [r68[0], r95[0]])
ax = fig.axes[0]
ax.set_title(varname)
figs.append(fig)
fig.show()
return figs, data
def radialScript(items, curdata = None):
clusters = [x[0] for x in items]
if curdata is None:
curdata = {}
if 'ir' not in curdata:
curdata['ir'] = cm.readCCSummary('/u/ki/dapple/ki06/bootstrap_5mpc_2012-02-14/',
clusters,np.arange(50), '_rm1.5_ri0.75_ro3.0.out')
if 'or' not in curdata:
curdata['or'] = cm.readCCSummary('/u/ki/dapple/ki06/bootstrap_5mpc_2012-02-14/',
clusters,np.arange(50), '_rm1.5_ri0.75_ro5.0.out')
if 'reducedIR' not in curdata or 'reducedOR' not in curdata:
irMass, irMask = curdata['ir']
orMass, orMask = curdata['or']
reducedIR = {}
reducedOR = {}
for cluster in clusters:
totalmask = np.logical_and(irMask[cluster], orMask[cluster])
reducedIR[cluster] = irMass[cluster][totalmask]
reducedOR[cluster] = orMass[cluster][totalmask]
reducedIR[cluster][reducedIR[cluster] == 0.] = 1e13
reducedOR[cluster][reducedOR[cluster] == 0.] = 1e13
curdata['reducedIR'] = reducedIR
curdata['reducedOR'] = reducedOR
if 'grid' not in curdata:
grid, means, scatters = isg.intrinsicScatter(curdata['reducedIR'], curdata['reducedOR'])
curdata['grid'] = grid
curdata['means'] = means
curdata['scatters'] = scatters
print
print
print '-----'
print 'var\tmode\t68%% +\t-\t95%% +\t-'
print '-----'
if 'meandist' not in curdata:
means = curdata['means']
scatters = curdata['scatters']
mode, (r68, r95) = isg.getdist_1d_hist(means[0], means[1], levels = [0.68, 0.95])
curdata['meandist'] = (mode, r68, r95)
mode, (r68, r95) = isg.getdist_1d_hist(scatters[0], scatters[1], levels = [0.68, 0.95])
curdata['scatterdist'] = (mode, r68, r95)
for varname in 'mean scatter'.split():
mode, r68, r95 = curdata['%sdist' % varname]
print mode, r68, r95
print '%s\t%2.4f\t+%2.4f\t-%2.4f\t+%2.4f\t-%2.4f' % (varname, mode,
r68[0][1] - mode, mode - r68[0][0],
r95[0][1] - mode, mode - r95[0][0])
x, prob = curdata['%ss' % varname]
fig = isgp.plotdist_1d_hist(x, prob, mode, [r68[0], r95[0]])
ax = fig.axes[0]
ax.set_title(varname)
figs.append(fig)
fig.show()
return figs, data
def magSplit_script(category, data = None):
figs = []
if data is None:
data = {}
if 'items' not in data:
data['items'] = readtxtfile('worklist')
# try:
loc1 = '%s/%s/low' % (trialsdir, category)
loc2 = '%s/%s/high' % (trialsdir, category)
if category not in data:
masses = loadData(loc1, loc2,
data['items'])
data[category] = {}
data[category]['masses'] = masses
curdata = data[category]
if 'grid' not in curdata:
grid, means, scatters = isg.intrinsicScatter(curdata['masses'][0], curdata['masses'][1], means = np.arange(0.6, 1.4, 0.002), scatters = np.arange(0.02, 0.2, 0.01))
curdata['grid'] = grid
curdata['means'] = means
curdata['scatters'] = scatters
else:
grid = curdata['grid']
means = curdata['means']
scatters = curdata['scatters']
print
print
print category
print '-----'
print 'var\tmode\t68%% +\t-\t95%% +\t-'
print '-----'
if 'meandist' not in curdata:
mode, (r68, r95) = isg.getdist_1d_hist(means[0], means[1], levels = [0.68, 0.95])
curdata['meandist'] = (mode, r68, r95)
mode, (r68, r95) = isg.getdist_1d_hist(scatters[0], scatters[1], levels = [0.68, 0.95])
curdata['scatterdist'] = (mode, r68, r95)
for varname in 'mean scatter'.split():
mode, r68, r95 = curdata['%sdist' % varname]
print mode, r68, r95
print '%s\t%2.4f\t+%2.4f\t-%2.4f\t+%2.4f\t-%2.4f' % (varname, mode,
r68[0][1] - mode, mode - r68[0][0],
r95[0][1] - mode, mode - r95[0][0])
x, prob = curdata['%ss' % varname]
fig = isgp.plotdist_1d_hist(x, prob, mode, [r68[0], r95[0]])
ax = fig.axes[0]
ax.set_title(varname)
figs.append(fig)
fig.show()
xlabel, ylabel = label_conv[category]
fig, xmasses, ymasses = cm.publicationPlotMassMass(loc1, loc2, xlabel, ylabel)
ax = fig.axes[0]
mode, r68, r95 = curdata['meandist']
# ax.axhline(mode, c='r', ls='--', linewidth=1.5)
ax.fill_between([0.16, 0.72], r95[0][0], r95[0][1], facecolor=(1, 0.642, 0.610), zorder=-1)
ax.fill_between([0.16, 0.72], r68[0][0], r68[0][1], facecolor='#CC0000', zorder=-1)
ax.set_xlim(0.16, 0.72)
ax.set_ylim(0.1, 10)
fig.show()
fig.savefig(filename_conv[category])
figs.append(fig)
# finally:
return figs, data