def pseudocolor_vs_z(simgridIa=None, clobber=0):
""" Plot medium-broad pseudo-colors from a grid simulation as a function
of redshift.
:param simgridIa: SNANA Simulation Table, or None to make/load it anew
:param clobber: passed to snanasim.dosimGrid() to re-run the SNANA sims
:return: new or existing SNANA sim table (a stardust.SimTable object)
"""
import stardust
import snanasim
import mkplots
from matplotlib import pyplot as pl
sn = stardust.SuperNova('HST_CANDELS2_colfax.dat')
if simgridIa is None:
if clobber:
simgridIa = snanasim.dosimGrid(sn,
ngridz=20,
clobber=clobber,
x1range=[-2, 2],
crange=[-0.2, 0.5],
trestrange=[-5, 5])
else:
simgridIa = stardust.SimTable('sim_colfax_medbandGrid_Ia')
mkplots.pseudocolor_vs_z(simgridIa, medbands='OPQ', broadbands='JNH')
pl.suptitle('MED BAND GRID SIM FOR SN COLFAX @ z=2.1+- 0.2', fontsize=20)
return (simgridIa)
def mkcirclefigGrid(simdataGrid=None, clobber=0, snanadatfile=_SNANADATFILE):
""" Plot the results of a SNANA Grid simulation as a circle diagram.
"""
import snanasim
import mkplots
import stardust
import numpy as np
from matplotlib import pyplot as pl
sn = stardust.SuperNova(snanadatfile)
if simdataGrid is None:
if clobber:
simdataGrid = snanasim.dosimGrid(sn,
ngridz=20,
clobber=clobber,
x1range=[-2, 2],
crange=[-0.2, 0.5],
trestrange=[-5, 5])
else:
simdataGrid = stardust.SimTable('sim_colfax_medbandGrid_Ia')
mjdmedband = sn.MJD[np.where((sn.FLT == '7') | (sn.FLT == '8')
| (sn.FLT == 'P'))]
if len(mjdmedband) > 0:
mjdobs = np.median(mjdmedband)
else:
mjdobs = sn.pkmjd
fig = pl.gcf()
ax1 = fig.add_subplot(2, 2, 1)
mkplots.gridsim_circleplot(simdataGrid, 'O-J', 'P-N')
ax2 = fig.add_subplot(2, 2, 2)
mkplots.gridsim_circleplot(simdataGrid, 'Q-H', 'P-N')
ax2 = fig.add_subplot(2, 2, 3)
mkplots.gridsim_circleplot(simdataGrid, 'O-J', 'Q-H')
pl.draw()
return simdataGrid
def colorCheck(datfile, nrow, irow, ifiglist=[1, 2], clobber=False, verbose=1):
sn = stardust.SuperNova(datfile)
sn.getClassSim('HST_colormag',
Nsim=2000,
dustmodel='mid',
simpriors=True,
clobber=clobber,
verbose=verbose)
pkbands = np.unique([
sn.FLT[i] for i in range(len(sn.MJD))
if abs(sn.MJD[i] - sn.pkmjdobs) <= sn.pkmjdobserr
])
sn.ClassSim.Ia.samplephot(sn.pkmjdobs,
tmatch=sn.pkmjdobserr,
bandlist=pkbands)
sn.ClassSim.Ibc.samplephot(sn.pkmjdobs,
tmatch=sn.pkmjdobserr,
bandlist=pkbands)
sn.ClassSim.II.samplephot(sn.pkmjdobs,
tmatch=sn.pkmjdobserr,
bandlist=pkbands)
ipk = np.where(np.abs(sn.MJD - sn.pkmjdobs) < sn.pkmjdobserr)[0]
for ifig, redfilt in zip(ifiglist, ['H', 'J']):
if redfilt not in pkbands: continue
fig = pl.figure(ifig)
ax1 = fig.add_subplot(nrow, 4, 1)
RpkSimIa = sn.ClassSim.Ia.__dict__['%s%i' %
(redfilt, int(sn.pkmjdobs))]
RpkSimIbc = sn.ClassSim.Ibc.__dict__['%s%i' %
(redfilt, int(sn.pkmjdobs))]
RpkSimII = sn.ClassSim.II.__dict__['%s%i' %
(redfilt, int(sn.pkmjdobs))]
ipkR = np.where(sn.FLT[ipk] == redfilt)[0]
if not len(ipkR): continue
snR = sn.MAG[ipk][ipkR][0]
snRerr = sn.MAGERR[ipk][ipkR][0]
for icol, bluefilt in zip(range(4), ['W', 'V', 'I', 'Z']):
ax = fig.add_subplot(nrow, 4, irow * 4 + icol + 1, sharex=ax1)
if icol == 0: ax.set_ylabel(sn.nickname)
if irow == 0: ax.set_title('%s-%s' % (bluefilt, redfilt))
if bluefilt not in pkbands: continue
ipkB = np.where(sn.FLT[ipk] == bluefilt)[0]
if not len(ipkB): continue
snB = sn.MAG[ipk][ipkB][0]
snBerr = sn.MAGERR[ipk][ipkB][0]
BpkSimIa = sn.ClassSim.Ia.__dict__['%s%i' %
(bluefilt, int(sn.pkmjdobs))]
BpkSimIbc = sn.ClassSim.Ibc.__dict__['%s%i' %
(bluefilt, int(sn.pkmjdobs))]
BpkSimII = sn.ClassSim.II.__dict__['%s%i' %
(bluefilt, int(sn.pkmjdobs))]
CpkSimIa = BpkSimIa - RpkSimIa
CpkSimIbc = BpkSimIbc - RpkSimIbc
CpkSimII = BpkSimII - RpkSimII
CIa, cbins = np.histogram(CpkSimIa, bins=np.arange(-5, 12, 0.2))
CIbc, cbins = np.histogram(CpkSimIbc, bins=np.arange(-5, 12, 0.2))
CII, cbins = np.histogram(CpkSimII, bins=np.arange(-5, 12, 0.2))
ax.plot(cbins[:-1], CIa, 'r-', drawstyle='steps-mid')
ax.plot(cbins[:-1], CIbc, 'g-', drawstyle='steps-mid')
ax.plot(cbins[:-1], CII, 'b-', drawstyle='steps-mid')
snC = snB - snR
snCerr = np.sqrt(snBerr**2 + snRerr**2)
snCmin = snC - snCerr
snCmax = snC + snCerr
if snBerr < 0: snCmin = snC
if snRerr < 0: snCmax = snC
ymin, ymax = ax.get_ylim()
snCbar = patches.Rectangle([snCmin, 0.0],
snCmax - snCmin,
ymax,
color='0.5',
alpha=0.5,
zorder=-100)
ax.add_patch(snCbar)
ax.set_xlim([-2, 6])
fig.suptitle('(W,V,I,Z)-%s band color distributions' % redfilt)
def doOneSN(datfile,
useLuminosityPrior=False,
modelerror=[0.05, 0.07, 0.07],
clobber=False,
testrun=False,
returnsn=False,
debug=False):
if debug:
import pdb
pdb.set_trace()
import stardust
start = time.time()
try:
sn = stardust.SuperNova(datfile)
sn.PEAKMJD = sn.MJD[sn.signoise.argmax()]
thistypeindex = int(sn.SIM_NON1a.split()[0])
thistype = modelIndexDict[thistypeindex][0]
thismodel = sn.SIM_COMMENT.split('=')[-1].split('.')[0].strip()
if thistype == 'Ia': omittemp = None
else: omittemp = thismodel
if testrun:
pIa, pIbc, pII = 0, 0, 0
chi2Ia, chi2Ibc, chi2II = 0, 0, 0
Ndof = 0
bestIbc = 'None'
bestII = 'None'
else:
sn.doGridClassify(clobber=clobber,
useLuminosityPrior=useLuminosityPrior,
kcorfile='DES/kcor_DES_grizY.fits',
modelerror=modelerror,
omitTemplateIbc=omittemp,
omitTemplateII=omittemp,
nlogz=1,
nlumipar=20,
ncolorpar=20,
ncolorlaw=1,
npkmjd=20)
pIa, pIbc, pII = sn.PIa, sn.PIbc, sn.PII
chi2Ia, chi2Ibc, chi2II = min(sn.chi2Ia) / sn.Ndof, min(
sn.chi2Ibc) / sn.Ndof, min(sn.chi2II) / sn.Ndof
Ndof = sn.Ndof
bestIbc = stardust.constants.IBCMODELS[
'%03i' % sn.maxLikeIbcModel.LUMIPAR][1]
bestII = stardust.constants.IIMODELS['%03i' %
sn.maxLikeIIModel.LUMIPAR][1]
end = time.time()
outstr = '%15s %4s %5.3f %12s %6.3f %6.3f %6.3f %7.3f %7.3f %7.3f %3i %5i %12s %12s' % (
os.path.basename(datfile), thistype, sn.z,
thismodel, pIa, pIbc, pII, chi2Ia, chi2Ibc, chi2II, Ndof,
int(end - start), bestIbc, bestII)
except Exception as exc:
print('classify error : %s' % exc)
end = time.time()
bestIbc, bestII = 'None', 'None'
outstr = '%15s %4s %5.3f %12s %6.3f %6.3f %6.3f %7.3f %7.3f %7.3f %3i %5i %12s %12s %s' % (
os.path.basename(datfile), thistype, sn.z, thismodel, -9, -9, -9,
-9, -9, -9, -9, int(end - start), bestIbc, bestII, exc)
if returnsn:
print(outstr)
return (sn)
return (outstr)
def mkDemoFig(simdata,
linelevels=[0, 0.82],
plotstyle='contourf',
Nbins=80,
showsn=False):
""" construct the medium-band demo figure for the FrontierSN proposal:
filter bandpasses on the left for three redshifts, two color-color plots on the
right with SN observation points overlaid """
import stardust
import snanasim
w1a, f1a = stardust.snsed.getsed(
sedfile='/usr/local/SNDATA_ROOT/snsed/Hsiao07.dat', day=0)
# first the band-pass plots on the left
z = 1.8
w1az = w1a * (1 + z)
f1az = f1a / f1a.max() / 2.
ax18 = pl.subplot(3, 2, 1)
ax18.plot(w1az, f1az, ls='-', lw=0.7, color='0.5', label='_nolegend_')
ax18.plot(w127, f127, ls='-', color='DarkOrchid', label='F127M')
ax18.plot(w139, f139, ls='-', color='Teal', label='F139M')
ax18.plot(w153, f153, ls='-', color='Maroon', label='F153M')
ax18.fill_between(w1az,
f1az,
where=((w1az > 12400) & (w1az < 13120)),
color='DarkOrchid',
alpha=0.3)
ax18.fill_between(w1az,
f1az,
where=((w1az > 13500) & (w1az < 14150)),
color='teal',
alpha=0.3)
ax18.fill_between(w1az,
f1az,
where=((w1az > 15000) & (w1az < 15700)),
color='Maroon',
alpha=0.3)
ax18.text(0.95,
0.4,
'SNIa\n@ z=%.1f' % (z),
color='k',
ha='right',
va='bottom',
fontweight='bold',
transform=ax18.transAxes,
fontsize='large')
pl.setp(ax18.get_xticklabels(), visible=False)
pl.setp(ax18.get_yticklabels(), visible=False)
ax18.text(12700,
0.65,
'F127M',
ha='right',
va='center',
color='DarkOrchid',
fontweight='bold')
ax18.text(13900,
0.65,
'F139M',
ha='center',
va='center',
color='Teal',
fontweight='bold')
ax18.text(15300,
0.65,
'F153M',
ha='left',
va='center',
color='Maroon',
fontweight='bold')
z = 2.0
w1az = w1a * (1 + z)
f1az = f1a / f1a.max() / 2.
ax20 = pl.subplot(3, 2, 3, sharex=ax18)
ax20.plot(w127, f127, ls='-', color='DarkOrchid', label='F127M')
ax20.plot(w139, f139, ls='-', color='Teal', label='F139M')
ax20.plot(w153, f153, ls='-', color='Maroon', label='F153M')
ax20.plot(w1az, f1az, ls='-', lw=0.7, color='0.5', label='_nolegend_')
ax20.fill_between(w1az,
f1az,
where=((w1az > 12400) & (w1az < 13120)),
color='DarkOrchid',
alpha=0.3)
ax20.fill_between(w1az,
f1az,
where=((w1az > 13500) & (w1az < 14150)),
color='Teal',
alpha=0.3)
ax20.fill_between(w1az,
f1az,
where=((w1az > 15000) & (w1az < 15700)),
color='Maroon',
alpha=0.3)
ax20.text(0.95,
0.4,
'z=%.1f' % (z),
color='k',
ha='right',
va='bottom',
fontweight='bold',
transform=ax20.transAxes,
fontsize='large')
pl.setp(ax20.get_xticklabels(), visible=False)
pl.setp(ax20.get_yticklabels(), visible=False)
z = 2.2
w1az = w1a * (1 + z)
f1az = f1a / f1a.max() / 2.
ax22 = pl.subplot(3, 2, 5, sharex=ax18)
ax22.plot(w127, f127, ls='-', color='DarkOrchid', label='F127M')
ax22.plot(w139, f139, ls='-', color='Teal', label='F139M')
ax22.plot(w153, f153, ls='-', color='Maroon', label='F153M')
ax22.plot(w1az, f1az, ls='-', lw=0.7, color='0.5', label='_nolegend_')
ax22.fill_between(w1az,
f1az,
where=((w1az > 12400) & (w1az < 13120)),
color='DarkOrchid',
alpha=0.3)
ax22.fill_between(w1az,
f1az,
where=((w1az > 13500) & (w1az < 14150)),
color='Teal',
alpha=0.3)
ax22.fill_between(w1az,
f1az,
where=((w1az > 15000) & (w1az < 15700)),
color='Maroon',
alpha=0.3)
ax22.text(0.95,
0.4,
'z=%.1f' % (z),
color='k',
ha='right',
va='bottom',
fontweight='bold',
transform=ax22.transAxes,
fontsize='large')
pl.setp(ax22.get_yticklabels(), visible=False)
ax22.set_xlabel('Observed Wavelength [\AA]')
ax18.set_xlim(9000, 19900)
# ------------------------------------------------------------
# Now the color-color plots on the right
if type(simdata) == str: simdata = snanasim.readSimDataMC(simdata)
simIa, simIbc, simII = simdata
if showsn:
sn = stardust.SuperNova(_medbanddat)
H, dH = sn.MAG[np.where(sn.FLT == 'H')[0][0]], sn.MAGERR[np.where(
sn.FLT == 'H')[0][0]]
J, dJ = sn.MAG[np.where(sn.FLT == 'J')[0][0]], sn.MAGERR[np.where(
sn.FLT == 'J')[0][0]]
N, dN = sn.MAG[np.where(sn.FLT == 'N')[0][0]], sn.MAGERR[np.where(
sn.FLT == 'N')[0][0]]
Y, dY = sn.MAG[np.where(sn.FLT == 'Y')[0][0]], sn.MAGERR[np.where(
sn.FLT == 'Y')[0][0]]
L, dL = sn.MAG[np.where(sn.FLT == 'L')[0][0]], sn.MAGERR[np.where(
sn.FLT == 'L')[0][0]]
O, dO = sn.MAG[np.where(sn.FLT == 'O')[0][0]], sn.MAGERR[np.where(
sn.FLT == 'O')[0][0]]
P, dP = sn.MAG[np.where(sn.FLT == 'P')[0][0]], sn.MAGERR[np.where(
sn.FLT == 'P')[0][0]]
Q, dQ = sn.MAG[np.where(sn.FLT == 'Q')[0][0]], sn.MAGERR[np.where(
sn.FLT == 'Q')[0][0]]
ax1 = pl.subplot(2, 2, 2)
stardust.simplot.plotColorColor(simII,
'Q-H',
'O-J',
plotstyle=plotstyle,
linelevels=linelevels,
Nbins=Nbins,
sidehist=False)
stardust.simplot.plotColorColor(simIbc,
'Q-H',
'O-J',
plotstyle=plotstyle,
linelevels=linelevels,
Nbins=Nbins,
sidehist=False)
stardust.simplot.plotColorColor(simIa,
'Q-H',
'O-J',
plotstyle=plotstyle,
linelevels=linelevels,
Nbins=Nbins,
sidehist=False)
if showsn:
ax1.errorbar(Q - H,
O - J,
np.sqrt(dO**2 + dJ**2),
np.sqrt(dQ**2 + dH**2),
color='k',
mfc='w',
mec='k',
mew=2,
elinewidth=2,
marker='D')
ax1.xaxis.set_ticks_position('top')
ax1.xaxis.set_ticks_position('both')
ax1.xaxis.set_label_position('top')
ax1.yaxis.set_ticks_position('right')
ax1.yaxis.set_ticks_position('both')
ax1.yaxis.set_label_position('right')
ax2 = pl.subplot(2, 2, 4, sharex=ax1)
stardust.simplot.plotColorColor(simII,
'Q-H',
'P-N',
plotstyle=plotstyle,
linelevels=linelevels,
Nbins=Nbins,
sidehist=False)
stardust.simplot.plotColorColor(simIbc,
'Q-H',
'P-N',
plotstyle=plotstyle,
linelevels=linelevels,
Nbins=Nbins,
sidehist=False)
stardust.simplot.plotColorColor(simIa,
'Q-H',
'P-N',
plotstyle=plotstyle,
linelevels=linelevels,
Nbins=Nbins,
sidehist=False)
if showsn:
ax2.errorbar(Q - H,
P - N,
np.sqrt(dP**2 + dN**2),
np.sqrt(dQ**2 + dH**2),
color='k',
mfc='w',
mec='k',
mew=2,
elinewidth=2,
marker='D')
ax2.yaxis.set_ticks_position('right')
ax2.yaxis.set_ticks_position('both')
ax2.yaxis.set_label_position('right')
ax2.text(0.2,
-0.18,
'Ia',
ha='left',
va='top',
color='DarkRed',
fontsize='large',
fontweight='bold')
ax2.text(-0.2,
0.25,
'Ib/c',
ha='left',
va='top',
color='DarkGreen',
fontsize='large',
fontweight='bold')
ax2.text(-0.05,
0.21,
'II',
ha='left',
va='bottom',
color='DarkBlue',
fontsize='large',
fontweight='bold')
ax2.set_ylabel(ax2.get_ylabel(), rotation=-90, color='teal')
ax2.set_xlabel(ax2.get_xlabel(), color='maroon')
ax1.set_ylabel(ax1.get_ylabel(), rotation=-90, color='DarkOrchid')
ax1.set_xlabel(ax1.get_xlabel(), color='maroon')
fig = pl.gcf()
fig.subplots_adjust(left=0.03,
right=0.90,
top=0.90,
bottom=0.12,
hspace=0,
wspace=0.08)
ax1.set_xlim(-0.28, 0.38)
ax1.set_ylim(-0.54, 0.2)
ax2.set_ylim(-0.3, 0.32)
ax1.text(0.25,
-0.19,
'z=1.8',
color='k',
ha='left',
va='bottom',
fontweight='bold',
fontsize='large')
ax1.text(-0.15,
-0.32,
'z=2.0',
color='k',
ha='right',
va='bottom',
fontweight='bold',
fontsize='large')
ax1.text(0.29,
-0.51,
'z=2.2',
color='k',
ha='right',
va='bottom',
fontweight='bold',
fontsize='large')
ax1.plot([0.1, 0.24], [-0.2, -0.175],
ls='-',
marker=' ',
color='k',
lw=0.8)
ax1.plot([-0.14, -0.07], [-0.28, -0.185],
ls='-',
marker=' ',
color='k',
lw=0.8)
ax2.text(0.2,
0.21,
'1.8',
color='k',
ha='left',
va='bottom',
fontweight='bold',
fontsize='large')
ax2.text(-0.18,
-0.27,
'2.0',
color='k',
ha='right',
va='bottom',
fontweight='bold',
fontsize='large')
ax2.text(0.3,
0.01,
'2.2',
color='k',
ha='left',
va='bottom',
fontweight='bold',
fontsize='large')
def mkcirclefigMC(simdataMC=None,
linelevels=[0, 0.82],
plotstyle='contourf',
Nbins=80,
Nsim=2000,
clobber=0,
verbose=1,
snanadatfile=_SNANADATFILE):
""" Plot the results of a SNANA monte carlo simulation as a circle diagram.
:param simdataMC:
:param linelevels:
:param plotstyle:
:param Nbins:
:param nsim:
:param clobber:
:param verbose:
:param snanadatfile:
:return:
"""
import snanasim
sn = stardust.SuperNova(snanadatfile)
if simdataMC is None:
simdataMC = snanasim.dosimMC(sn,
Nsim=Nsim,
bands='XI78YJNHLOPQ',
clobber=clobber,
verbose=verbose)
simIa, simIbc, simII = simdataMC
mjdmedband = sn.MJD[np.where((sn.FLT == '7') | (sn.FLT == '8')
| (sn.FLT == 'P'))]
mjdobs = np.median(mjdmedband)
print('Binning up MC sim for color-color diagram...')
pl.clf()
ax1 = pl.subplot(2, 2, 1)
stardust.simplot.plotColorColor(simIa,
'7-I',
'P-N',
binrange=[[-0.2, 0.8], [-0.5, 0.5]],
mjdrange=[mjdobs, mjdobs],
tsample=1,
plotstyle=plotstyle,
linelevels=linelevels,
Nbins=Nbins,
sidehist=False)
#stardust.simplot._plot_colorcolor_singlesim( simIbc, '7-I','P-N', binrange=[[-0.2,0.8],[-0.5,0.5]], mjdrange=[mjdobs,mjdobs], tsample=1, plotstyle=plotstyle, linelevels=linelevels, Nbins=Nbins, sidehist=False )
#stardust.simplot._plot_colorcolor_singlesim( simII, '7-I','P-N', binrange=[[-0.2,0.8],[-0.5,0.5]], mjdrange=[mjdobs,mjdobs], tsample=1, plotstyle=plotstyle, linelevels=linelevels, Nbins=Nbins, sidehist=False )
ax2 = pl.subplot(2, 2, 2)
stardust.simplot.plotColorColor(simIa,
'8-I',
'P-N',
binrange=[[-0.6, 0.4], [-0.5, 0.5]],
mjdrange=[mjdobs, mjdobs],
tsample=1,
plotstyle=plotstyle,
linelevels=linelevels,
Nbins=Nbins,
sidehist=False)
#stardust.simplot._plot_colorcolor_singlesim( simIbc, '8-I','P-N', binrange=[[-0.6,0.4],[-0.5,0.5]], mjdrange=[mjdobs,mjdobs], tsample=1, plotstyle=plotstyle, linelevels=linelevels, Nbins=Nbins, sidehist=False )
#stardust.simplot._plot_colorcolor_singlesim( simII, '8-I','P-N', binrange=[[-0.6,0.4],[-0.5,0.5]], mjdrange=[mjdobs,mjdobs], tsample=1, plotstyle=plotstyle, linelevels=linelevels, Nbins=Nbins, sidehist=False )
ax3 = pl.subplot(2, 2, 3)
stardust.simplot.plotColorColor(simIa,
'7-I',
'8-I',
binrange=[[-0.2, 0.8], [-0.6, 0.4]],
mjdrange=[mjdobs, mjdobs],
tsample=1,
plotstyle=plotstyle,
linelevels=linelevels,
Nbins=Nbins,
sidehist=False)
#stardust.simplot._plot_colorcolor_singlesim( simIbc, '7-I','8-I', binrange=[[-0.2,0.8],[-0.6,0.4]], mjdrange=[mjdobs,mjdobs], tsample=1, plotstyle=plotstyle, linelevels=linelevels, Nbins=Nbins, sidehist=False )
#stardust.simplot._plot_colorcolor_singlesim( simII, '7-I','8-I', binrange=[[-0.2,0.8],[-0.6,0.4]], mjdrange=[mjdobs,mjdobs], tsample=1, plotstyle=plotstyle, linelevels=linelevels, Nbins=Nbins, sidehist=False )
pl.draw()
return simdataMC
def mkfig3d(
simdata,
dz=0.05,
snanadatfile=_SNANADATFILE,
):
""" Plot the results of a SNANA monte carlo simulation as a 3-D circle
diagram.
:param simdata:
:param dz:
:param snanadatfile:
:return:
"""
from mpl_toolkits.mplot3d.axes3d import Axes3D
from matplotlib import cm
import mpltools
from pytools import plotsetup
fig = plotsetup.fullpaperfig(1, [10, 10])
sn = stardust.SuperNova(snanadatfile)
simIa, simIbc, simII = simdata
ax1 = fig.add_axes([0.12, 0.12, 0.85, 0.85], projection='3d')
c7I, c8I, cPN = get_colors(simII)
ax1.plot3D(c7I,
c8I,
cPN,
marker='o',
color='k',
alpha=1,
ls=' ',
mew=0,
label='Type II')
c7I, c8I, cPN = get_colors(simIbc)
ax1.plot3D(c7I,
c8I,
cPN,
marker='o',
color='sienna',
alpha=1,
ls=' ',
mew=0,
label='Type Ibc')
zbins, c7I, c8I, cPN = get_colors_binned_by_z(sn, simIa, dz=dz)
mpltools.color.cycle_cmap(len(zbins), cmap=cm.gist_rainbow_r, ax=ax1)
for iz in range(len(zbins) - 1):
z0, z1 = zbins[iz], zbins[iz + 1]
#if dz>=0.1 : zmid = round(np.mean([z0,z1]),1)
#elif dz>=0.01 : zmid = round(np.mean([z0,z1]),2)
ax1.plot3D(c7I[iz],
c8I[iz],
cPN[iz],
marker='o',
alpha=1,
ls=' ',
mew=0,
label='%.2f-%.2f' % (z0, z1))
ax1.set_xlim(-0.1, 0.5)
ax1.set_ylim(-0.4, 0.2)
ax1.set_zlim(-0.3, 0.3)
ax1.legend(loc='upper left',
ncol=2,
numpoints=1,
frameon=False,
handletextpad=0.3,
handlelength=0.2)
ax1.set_xlabel('F763M-F814W')
ax1.set_ylabel('F845M-F140W')
ax1.set_zlabel('F139M-F140W')