def genmod_bivvcspread(cmddir, age, mass, vvcmu1, vvcsig1, vvcmu2, vvcsig2, vvclim):
vvcs = np.arange(0.0, vvclim, 0.1)
vvcweights = gen_bigaussweights(vvcs, vvcmu1, vvcsig1, vvcmu2, vvcsig2)
photbase = cmddir.split('/')[1]
bf, av, agebin, logz, dmod = fio.parse_fname(photbase, mode="str")
hess_arr = []
for vvc in vvcs:
a_cmd = cmd.CMD(fio.get_cmdf(cmddir, bf, age, logz, vvc, av, dmod))
mock_hess = a_cmd.cmd['Nsim']
mock_hess /= np.sum(mock_hess)
#if age == ages[0]:
# composite_hess = mock_hess
#else:
# composite_hess += mock_hess
hess_arr.append(mock_hess)
hess_arr = np.array(hess_arr)
#print(len(hess_arr))
#print(len(vvcweights))
composite_hess = np.sum(vvcweights*hess_arr.T, axis=1)
#print(len(composite_hess))
truths = {}
for i, vvc in enumerate(vvcs):
truths[vvc] = mass*vvcweights[i]
return mass*composite_hess, truths
def genmod_agespread(cmddir, mass, agemu, agesig):
ages = np.arange(8.50, 9.50, 0.02)
ageweights = gen_gaussweights(ages, agemu, agesig)
photbase = cmddir.split('/')[1]
bf, av, agebin, logz, dmod = fio.parse_fname(photbase, mode="str")
hess_arr = []
for age in ages:
a_cmd = cmd.CMD(fio.get_cmdf(cmddir, bf, age, logz, 0.0, av, dmod))
mock_hess = a_cmd.cmd['Nsim']
mock_hess /= np.sum(mock_hess)
#if age == ages[0]:
# composite_hess = mock_hess
#else:
# composite_hess += mock_hess
hess_arr.append(mock_hess)
hess_arr = np.array(hess_arr)
#print(len(hess_arr))
#print(len(ageweights))
composite_hess = np.sum(ageweights*hess_arr.T, axis=1)
#print(len(composite_hess))
truth = np.sum(composite_hess)
return mass*composite_hess
def genmod_agebivvcspread(cmddir, mass, agemu, agesig, vvcmu1, vvcsig1, vvcmu2, vvcsig2, vvclim):
ages = np.arange(8.50, 9.50, 0.02)
#mu, sig = 9.00, 0.3
ageweights = gen_gaussweights(ages, agemu, agesig)
vvcs = np.arange(0.0, vvclim, 0.1)
#mu, sig = 0.3, 0.2
vvcweights = gen_bigaussweights(vvcs, vvcmu1, vvcsig1, vvcmu2, vvcsig2)
photbase = cmddir.split('/')[1]
bf, av, agebin, logz, dmod = fio.parse_fname(photbase, mode="str")
composite_hess = cmd.CMD(fio.get_cmdf(cmddir, bf, 9.00, logz, 0.0, av, dmod))
composite_hess = np.zeros(len(composite_hess.cmd['Nsim']))
vvc_pts = []
for i, a_vvc in enumerate(vvcs):
# step through in age, adding an age vector at each point in v/vc space
age_vector = []
for j, an_age in enumerate(ages):
a_cmd = cmd.CMD(fio.get_cmdf(cmddir, bf, an_age, logz, a_vvc, av, dmod))
model_hess = a_cmd.cmd['Nsim']
model_hess /= np.sum(model_hess)
age_vector.append(model_hess)
vvc_pts.append(np.array(age_vector))
model = np.array(vvc_pts)
composite_hess += np.sum((vvcweights[:, np.newaxis])*np.sum(ageweights[:,np.newaxis]*model, axis=1), axis=0)
#print(len(hess_arr))
#print(len(vvcweights))
#composite_hess = np.sum(vvcweights*hess_arr.T, axis=1)
#print(len(composite_hess))
truths = {}
for i, vvc in enumerate(vvcs):
truths[vvc] = mass*vvcweights[i]
return mass*composite_hess, truths
def pgplot(obs, model, cmddir, bf, age, logz, av, dmod, vvclim, weights, filters, age_range, svname=None, log=False, svdir=None):
"""
Creates a MATCH pg style plot of data, model, data-model, and -2lnP map.
"""
composite_cmd = cmd.CMD(fio.get_cmdf(cmddir, bf, age, logz, 0.0, av, dmod))#, ymag='I')
composite_cmd.cmd['Nsim'] = np.zeros(len(composite_cmd.cmd['Nsim']))
vvc_range = np.arange(0.0, vvclim, 0.1)
Nrot = len(vvc_range)
#age_range = np.arange(8.5, 9.5, 0.02)
mu = weights[Nrot]
try:
sigma = weights[Nrot+1]
except IndexError:
sigma = 0.0
ageweights = gen_gaussweights(age_range, mu, sigma)
#for i, avvc in enumerate(vvc_range):
# for j, anage in enumerate(age_range):
# a_cmd = cmd.CMD(fio.get_cmdf(cmddir, bf, anage, logz, avvc, av, dmod))#, ymag='I')
# 1 * (jth age weight, added i times) * ith rotation rate.
# a_cmd.cmd['Nsim'] = (a_cmd.cmd['Nsim'] / np.sum(a_cmd.cmd['Nsim'])) * (best_gweights[j]) * (10**weights[i])
# add each cmd (re-weighted by solutions) to the composite CMD model.
# composite_cmd.cmd['Nsim'] += a_cmd.cmd['Nsim']
vvcweights = weights[:Nrot]
composite_cmd.cmd['Nsim'] += np.sum((10**vvcweights[:, np.newaxis])*np.sum(ageweights[:,np.newaxis]*model, axis=1), axis=0)
composite_cmd.cmd['Nobs'] = obs
# arbitrary vvc used -- just need a file name to break up.
#fn_base = (fio.get_cmdf(cmddir, bf, age, logz, 0.0, av, dmod).split('/')[-1]).split('.out.cmd')[0]
#fehval = float((fn_base.split('logz')[-1]).split('_')[0])
#lageval = float((fn_base.split('_t')[-1]).split('_')[0])
#avval = float((fn_base.split('_av')[-1]).split('_')[0])
#dmodval = float((fn_base.split('_dmod')[-1]).split('_')[0])
print(max(composite_cmd.cmd['Nsim']))
filters, photstrs = match_to_mist(filters)
photstr = photstrs[0]
print(filters)
redmag_name = filters[1]
bluemag_name = filters[0]
color_name = "{:s}-{:s}".format(bluemag_name, redmag_name)
# iso00 = rmm.ISOCMD(round(float(logz), 2), min(vvc_range), ebv= round(float(av), 2)/3.1, photstr=photstr, exttag='TP')
# iso00.set_isodata(round(float(mu), 2), color_name, bluemag_name, dmod=round(float(dmod), 2))
# this try except is fudgy -- onl needed cause v/vc = 0.6 models aren't available on my local machine.
# try:
# iso06 = rmm.ISOCMD(round(float(logz), 2), max(vvc_range), ebv= round(float(av), 2)/3.1, photstr=photstr, exttag='TP')
# except Exception as e:
# iso06 = rmm.ISOCMD(round(float(logz), 2), 0.6, ebv= round(float(av), 2)/3.1, photstr=photstr, exttag='TP')
#iso06.set_isodata(round(float(mu), 2), color_name, bluemag_name, dmod=round(float(dmod), 2))
# (x, y), i.e., (color, red mag) points of each isochrone in a list:
#mist_pts = [
# isoget_colmags(iso00, [color_name, bluemag_name], lage=round(float(mu), 2), dmod=round(float(dmod), 2)),
# isoget_colmags(iso06, [color_name, bluemag_name], lage=round(float(mu), 2), dmod=round(float(dmod), 2))
# ]
# recalculate the d-m and signifigance hesses using the new hesses.
composite_cmd.recalc()
# create a MATCH pg style plot using the .cmd file:
pgcmd_kwargs = {}
#pgcmd_kwargs['mist_pts'] = mist_pts
if svname == None:
if svdir == None:
pgcmd_kwargs['figname'] = os.path.join(cmddir, 'match_pgplot.png')
else:
pgcmd_kwargs['figname'] = os.path.join(cmddir, svdir, 'match_pgplot.png')
else:
if svdir == None:
pgcmd_kwargs['figname'] = os.path.join(cmddir, svname)
else:
pgcmd_kwargs['figname'] = os.path.join(cmddir, svdir, svname)
# four panel plot:
if log:
pgcmd_kwargs['logcounts'] = True
composite_cmd.pgcmd(**pgcmd_kwargs)
else:
composite_cmd.pgcmd(**pgcmd_kwargs)
return
vvclim = round(Nrot / 10.0, 1)
vvc_range = np.arange(0.0, vvclim, 0.1)
age_range = np.arange(7.9, 9.5, 0.02) # lower lim was 8.5
# default, dummy truths; will be reassigned:
truths = {rot: 1e-11 for rot in vvc_range}
mass = 5E4 # mock cluster "mass" or total counts; 1e6 was default
# Generates mock data from the model library to use instead of observed data if told to:
if "mock" in mode:
# case of no rotation distribution:
if mockd_vvcsig1 == 0.0:
# and no age distribution (would be an SSP):
if mockd_agesig == 0.0:
obscmd = cmd.CMD(
fio.get_cmdf(cmddir, bf, mockd_agemu, logz, mockd_vvcmu1,
av, dmod))
obs = obscmd.cmd['Nobs']
# age distribution, no rotation distribution, P(sigtau); in practice will trigger so long as mode is mock-sigtau and agesig !=0.0:
else:
print("GENERATING MOCK DATA WITH AGE SPREAD...")
obs = pr.genmod_agespread(cmddir,
mass=mass,
agemu=mockd_agemu,
agesig=mockd_agesig)
obsweight = np.sum(obs)
# using lower limit of weight search prior for truths of "zero" components.
truths = {
rot: max([0.0, np.log10(obsweight * 1e-4)])
for rot in vvc_range
}
#ax6 = plt.subplot(gs[1, 0])
#ax7 = plt.subplot(gs[1, -1])
#ax8 = plt.subplot(gs[2, 0])
#ax9 = plt.subplot(gs[2, -1])
#ax10 = plt.subplot(gs[-1, 0])
#ax11 = plt.subplot(gs[-1, 1])
#ax12 = plt.subplot(gs[-1, 2])
#ax13 = plt.subplot(gs[-1, 3])
#ax14 = plt.subplot(gs[-1, 4])
#axa = [ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8,ax9,ax10,ax11,ax12,ax13,ax14]
bf, age, logz, av, dmod = "0.00", 9.00, '-0.30', '0.0', '0.00'
cmddir = 'output/bf0.00_av0.0_SFR0.01_t9.00_9.02_logZ-0.30_vvc0.4_Tycho_BTycho_V_ex/'
# empty out a hess to use as a composite model later:
composite_cmd = cmd.CMD(fio.get_cmdf(cmddir, bf, age, logz, 0.0, av,
dmod)) #, ymag='I')
composite_cmd.cmd['Nsim'] = np.zeros(len(composite_cmd.cmd['Nsim']))
obsweight = np.sum(composite_cmd.cmd['Nobs'])
# need random weights and mu, sigma
filters = ['Tycho_B', 'Tycho_V']
vvc_range = np.arange(0.0, 1.0, 0.2)
Nrot = len(vvc_range)
age_range = np.arange(8.5, 9.5, 0.2)
mu = np.random.uniform(8.5, 9.3)
sigma = 10**np.random.uniform(-2, 0)
# try:
# sigma = weights[Nrot+1]
# except IndexError:
# sigma = 0.0