mms = [1e8, 1e9, 1e10, 1e11, 1e12]
if doexp:
mtpath = mtpath + '/Mexp/'
if mexp is not None:
mtpath = mtpath[:-1] + '%02d/'%(mexp*100)
try: os.makedirs(mtpath)
except: pass
###########################################
for M0 in mms:
print('For M0 = %0.2e'%M0)
fig, ax = plt.subplots(3, 3, figsize = (14, 12))
fit0 = dg.plot_noise(datas, predicts, M0=M0, binfit=bins, c='k', axin=ax, func=func, mbin=mbinsm, retfit=True, lsf='--')[0]
#fit0 = dg.plot_noise(datapR.value, predictR.value, M0=M0, binfit=bins, c='k', axin=ax, func=func, mbin=mbinsm, retfit=True, lsf='--')[0]
fits = []
for i, sg in enumerate(sgs):
#fitt = dg.plot_noise(datasgR.value, predictR.value, M0=M0, binfit=bins, c=colors[i], axin=ax, func=func, mbin=mbinsm, retfit=True)[0]
fitt = dg.plot_noise(datasgs[i], predicts, M0=M0, binfit=bins, c=colors[i], axin=ax, func=func, mbin=mbinsm, retfit=True)[0]
fits.append(fitt)
fig.savefig(mtpath + 'noisehist_M%02d_3seed.png'%(10*np.log10(M0)))
tosave = []
for i, res in enumerate(fit0):
#print(res)
tosave.append([msave[i], msave[i+1], res.x[1], res.x[2]])
fpath = mtpath + 'hist_M%02d_3seed.txt'%(10*np.log10(M0))
logl = logl + t
halomass2 = 10**logl
sort2 = np.argsort(halomass2)[::-1]
halomass2 = halomass2[sort2]
halopos2 = hpos[sort2]
return halomass2, halopos2
smin, smax = 0.1, 0.2
for M0 in [1e8, 1e9, 1e10, 1e11, 1e12]:
fig, ax = plt.subplots(3, 3, figsize=(14, 12))
fit0 = dg.plot_noise(datapR.value,
predictR.value,
M0=M0,
binfit=bins,
c='k',
axin=ax,
func=func,
mbin=mbinsm,
retfit=True,
lsf='--')[0]
fits = []
sgs = [0.2]
for i, sg in enumerate(sgs):
hmass, hpos = dg.scatter_catalog(hdictf['mass'],
hdictf['position'],
sigma=sg)
datasg = pm.paint(hpos[:num], hmass[:num])
datasgR = ft.smooth(datasg, 3, 'fingauss')
fitt = dg.plot_noise(datasgR.value,